Archive for the ‘Lipid metabolism’ Category

Reversing Heart Disease: Combination of PCSK9 Inhibitors and Statins – Opinion by Steven Nissen, MD, Chairman of Cardiovascular Medicine at Cleveland Clinic

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 2/25/2019

While nearly 10% of middle-age adults in China have high risk for cardiovascular disease, only 0.6% of these high-risk individuals use statins and 2.4% take aspirin, a national screening project reported in the Annals of Internal Medicine.

UPDATED on 5/5/2017

Europeans Mull PCSK9i Post-FOURIER Fallout on Clinical Practice

Patrice Wendling, May 04, 2017

But it was panelist Dr Stephen Nicholls (University of Adelaide, Australia) who took aim at the elephant in the packed auditorium. At an annual cost of about $14,100 for evolocumab and $14,600 for alirocumab (Praluent, Sanofi/Regeneron), the important question facing cardiologists is who will be eligible for these drugs “in a world where we can’t just write a scrip for every FOURIER-type patient; we won’t be allowed to.”

He suggested initially this will include patients with familial hypercholesterolemia and only those with established atherosclerotic CVD whose LDL-C remains unacceptably high despite therapy. Future FOURIER subanalyses may define other eligible high-risk groups.




UPDATED on 3/14/2017

PCSK9 Inhibitor Access Snarled in Red Tape, Rejections

Patrice Wendling, March 21, 2017

To determine whether this experience is happening nationwide, Navar and colleagues examined first PCSK9 prescriptions in 45,029 patients (median age 66 years; 51% female) between August 1, 2015 and July 31, 2016 in the Symphony Health Solutions database, which covers 90% of retail, 70% of specialty, and 60% of mail-order pharmacies in the US.

Nearly half (48%) of prescribers were cardiologists, and 37% were general practitioners. Most patients (52%) had government insurance, typically Medicare, and 40% had commercial insurance.

In the first 24 hours after being submitted to the pharmacy, 79.2% of prescriptions were rejected. Ultimately, 52.8% of all PCSK9 prescriptions were rejected.

Of special note, 34.7% of prescriptions for the pricy lipid-lowering drugs were abandoned at the pharmacy.



How 2 Drugs Lower Cholesterol Remarkably — and Reverse Heart Disease

Study shows promise for combination of newer drug and statins

How 2 Drugs Lower Cholesterol Remarkably --- and Reverse Heart Disease

Newer cholesterol-lowering drugs combined with more conventional medicine reduces bad cholesterol to incredibly low levels, a new study shows. Perhaps even more important, the combination also reduces the heart-attack-inducing plaque that forms inside the arteries, the study says.

The study was led by cardiologist Steven Nissen, MD, Chairman of Cardiovascular Medicine at Cleveland Clinic. Results appeared recently in the Journal of the American Medical Association (JAMA).

The study looked at the use of a drug called evolocumab by people who took statins to lower the amount of LDL, or bad, cholesterol in their blood. Evolocumab is a drug called a PCSK9 inhibitor. This is a newer kind of medicine that can make LDL cholesterol levels plummet.

The people who took statins and evolocumab had greater reductions in atherosclerosis compared with people who took statins and a placebo.  Atherosclerosis is  a disease in which plaque builds up inside your arteries.  The condition can lead to serious problems, including heart attack, stroke, or even death.

The results are an intriguing indicator — rather than definite proof — that evolocumab may have benefit for patients taking statins, Dr. Nissen says. Researchers are still awaiting the results of large clinical trials investigating whether evolocumab is safe and will prevent heart attack, stroke or death. The first results of these studies are expected in April 2017.

Special ultrasound

In the study, researchers treated for 18 months 968 high-risk people who had extremely high levels of blood cholesterol.

Participants were randomly assigned to take either a statin and a placebo, or a statin and evolocumab.

Researchers monitored the participants’ cholesterol levels. They also used a special ultrasound probe to measure the amount of plaque in their arteries at the beginning and the end of the study. 

“We were able to show that getting the bad cholesterol levels down to really low levels, down to the 20s and 30s, can actually remove plaque from the coronary arteries,” Dr. Nissen says. “This going to levels that we’ve never been able to achieve before.”           

Low cholesterol, less plaque

Results show the group treated with statins and a placebo reduced their LDL cholesterol levels to 93 on average. At the same time, the group treated with the combination of the statin plus evolocumab got down to an average bad cholesterol level of 36.6.

“No one’s ever reached levels that low in a clinical trial,” Dr. Nissen says.

Participants who took evolocumab also had less plaque in their arteries at the end of the study — essentially reversing their heart disease.

“We, for the first time now, have shown that this new class of drugs, the PCSK9 inhibitors, has a favorable effect on the development of plaques on the coronary artery and can actually regress those plaques,” Dr. Nissen says. “And it turns out about two-thirds of patients actually had less plaque at the end of 18 months than they started with.” 

PCSK9 inhibitors, which are expensive, are not for everybody, Dr. Nissen says. Currently, the drug is used in addition to statins for the highest-risk patients with particularly high cholesterol.


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LIVE 9/21 8AM to 2:40PM Targeting Cardio-Metabolic Diseases: A focus on Liver Fibrosis and NASH Targets at CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston






Nonalcoholic Steatohepatitis (NASH)


Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a

Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN




Wednesday, September 21

7:30 am Registration Open and Morning Coffee

8:00 Chairperson’s Opening Remarks

Rebecca Taub, M.D., Ph.D., CEO, Madrigal Pharmaceuticals

  • Epidemic of NASH,
  • approaches to treating NASH – Fibrosis
  • NASH is a metabolic Disease of the Liver
  • Treating the HCV will treat the Fibrosis

8:10 FEATURED PRESENTATION: The Epidemic of Fatty Liver Disease: Silent, Serious and Still Growing?

Lee Kaplan, M.D., Ph.D., Director, Obesity, Metabolism and Nutrition Institute, Massachusetts General Hospital, Harvard Medical School

  • Silent, Serious and Growing
  • Obesity the Disease = BMI>30: Medical Complicastions for BMI >%) – On ANti-Obisity and Bariatric SUrgery, Type 2 Diabetis .. NAFLD .. NASH .. Cirrhosis .. HCC
  • Parkinson’s Disease
  1. Medical Complications of Obisity =197 :
  2. NAFLD – Nonalcoholic Fatty Liver Disease >>> Liver transplantation replacing HCV
  3. Associated with obesity and type 2 diabetes
  4. NAFLD is UP 90% wiht Severe Obesity
  5. Viral hepatitis and Hemochromatosis
  6. NAFLD: Steatosis, Inflamamtion, Hepatocellular Necrosis, Fibrosis, Cirrhosis
  7. NASH: insulin resistence .. metabolic syndrom .. interaction
  8. Alternative Model: Metabolis Syndrom.. Steatosis .. NASH … FIbrosis
  9. Genetics of Liver DIsease
  10. PNPLA3 Associated with NAFLD – Not Weight Gain
  11. Other genes: A Partial List:
  12. Diagnosis of NASH: Liver biopsy macrovescicular fatty change: InflammationMollery bodies
  13. 75% Patients with Cirrhousis have obisity
  14. Alcoholoc hepatisis >> Progression to Cirrhousis
  15. Macrovesicular Steatosis
  16. NASH – inflammation
  17. Sinusoidal Pericellular Fibrosis –
  18. LAB Features of NAFLD
  • Transaminase elevation
  • Akaline phosphate
  1. Biomarkers – NASH – associated cirrhousis with lower rate 30% of elevation
  2. Fibrosure
  • Clinical Features of NASH: none presentation, Bright, Echo Fibroscan FibroscanScreen for HCC, Varices if Gray zone: Biopsy
  • Treatment of NASH
  • Treat liver disease: Treat steatosis then Inflamamtion and fibrosis
  1. NAFLD Treatment Strategy: Stepwise Approach
  • Treat the steatosis Piodlitazone
  • PPARalpha, delta,
  • Treat Inflammation: ANtioxidant
  • CCR2/CCR% inhibitors
  • Metabolic SUrgery
  • Weigh-independent for bariatric
  • Bariatic: improvrment of steatosis,effect on inflammationless clear
  • dramatic on weigh loss
  • NO clear is surgery improved cirhousis
  • If NASH developed >>>> progression s the rule
  • No great treatment of NASH

Medication-assciated NASH: Glucocorti


8:40 Non-Alcoholic Steatohepatitis and Cardiovascular Disease: Modulation by Novel PPAR Agonists

Bart Staels, Ph.D., Professor, INSERM, University of Lille, Pasteur Institute

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear receptors which regulate lipid and glucose metabolism as well as inflammation. In this presentation, we will review recent findings on the pathophysiological role of PPARs in the different stages of non-alcoholic fatty liver disease (NAFLD), from steatosis development to steatohepatitis and fibrosis, as well as the preclinical and clinical evidences for potential therapeutical use of PPAR agonists in the treatment of NAFLD. PPARs play a role in modulating hepatic triglyceride accumulation, a hallmark of the development of NAFLD. Moreover, PPARs may also influence the evolution of reversible steatosis towards irreversible, more advanced lesions. Large controlled trials of long duration to assess the long-term clinical benefits of PPAR agonists in humans are ongoing.

Non-Alcoholic Steatohepatisis and CVD – Meta inflammatory disease

  • NAFL — abnormal Lipid accumulation
  • NASH >> Balooning, FIbrosis inflamation
  • Resolution of NASH is associated with reduction of Fibrosis (Golden – 505 trial)

CVD is linked to NAFLD: Lipids elevated and therosclerosis

  • TG – elevated, APO B elevated, VLDL – elevated HDL decrease
  • PPAR Alpha
  • Gamma
  • PPAR Beta/Delta agonist: GENFIT – Elafibranor

Trans-activation: Lipid and Glucose homeostasis: Trans-repression – anti-inflammatory properties

  • Hapatic mitochondrial activity deseases upon progression from NAFL to NASH: Obese NAFL and NASH
  1. Upregulated hepatic respiratory in obese humans with or without NAFL
  2. Impaired
  3. Hepatic PPARalpha Expression Decreases upon Progression of Nash and Fibrosis
  4. hepatic PPARalpha expression – target genes increase in patients with improved NASH histology after 1 year
  5. Metabolic Regualtion by thehepatic JNK Signaling Pathway
  6. Target gene transcription – miR-21 expression increases in human
  7. PPAR Delta: Elafibbranor: – effect on plasma lipids: A Dual PPAR alpha/Delts (GFT505): 80mg vs placebo and 120mg vs placebo, improves plasma apolipolipids and glucose HbA1C – insulin sensitivity
  8. efficacy in NASH acting on: Steatosis, fibrosis and cirrhosis
  9. inflammatory markers: RESOLVE-IT Phase 3 Study Desing: NASH ressolution without adverse on FIbrosis and Cirrhosis

GOLDEN505 Trial: Improves plasma lipid levels: Triglycerides

Inclusion Criteria:


Improve atherogenic dyslipidemia

  • APOC3 – associated with CVD

9:10 PANEL DISCUSSION: Liver Fibrosis and NASH Targets

Moderator: H. James Harwood, Ph.D., Delphi BioMedical Consultants, LLC


Lee Kaplan, M.D., Ph.D., Director, Obesity, Metabolism and Nutrition Institute, Massachusetts General Hospital, Harvard Medical School

Bart Staels, Ph.D., Professor, INSERM, University of Lille, Pasteur Institute

Rebecca Taub, M.D., Ph.D., CEO, Madrigal Pharmaceuticals

Weilin Xie, Ph.D., Senior Principal Scientist, Biotherapeutics, Celgene Corp.

  • FDA’s view on surrogate endpoints
  • Biomarkers of NASH
  • Regulatory challenges
  1. Liver biopsy: gold standard, invasive direct measure of endpoints pros/cons
  2. non-invasive functional tests – plasma bioamrkers
  3. non-invasisve liver imaging techniques: MRI to assess hepatic fat content MRE to assess hepatic fibrosis, Fibroscan,
  4. Endpoints acceptable by FDA: Current vs Future
  • Pre clinical Translational animal models

Discussion by Panel members

Progression from NAFLD to NASH: Oxidative stress and toxic lipids

NASH and Steatosis are different populations

Alcoholoc Steatosis vs Non-Alcoholic Steatosis

  • Obesity cause of Fatty liver
  • NASH in Diabetes
  • NASH progresses
  • Steatosis is associated with NASH
  • Different types of NASH: HTN, Dislipedemia,
  • GENETICS underlining factors, more genes are discovered
  • Limitations of Animal Studies for inference on Humans – careful in over generalizing results
  • Metabolic SYndrom -not all progresses to NASH
  • Nonalcoholic Steatohepatitis (NASH) depend on Steatosis


9:40 Coffee Break in the Exhibit Hall with Poster Viewing

10:25 Targeting Fibroblast Activation Protein (FAP) and FGF21 to Treat Fatty Liver Disease

Diana Ronai Dunshee, Ph.D., Department of Molecular Biology, Senior Scientific Researcher, Genentech, Inc.

FGF21 is a hormone with anti-obesity and hepatoprotective properties. However, the beneficial effects of FGF21 are limited by a relatively short half-life in circulation. We discovered that fibroblast activation protein (FAP), an endopeptidase overexpressed in liver with cirrhosis, cleaves and inactivates FGF21. Pharmacological inhibition of FAP increases endogenous levels of active FGF21, thus making FAP a promising target for the treatment of non-alcoholic-steatohepatitis (NASH).

  • Medical complications of obisity: NASH and DM-2
  • energy consumption
  • white adipose tissue – energy storage
  • brown adipose tissue matochondia’s energy
  • FGF21 – Human activation of protein cleavage: A Homone beneficial on metabolic health circulation, weigh loss
  • it suppreses hepatic Steatohepatitis
  • One singleinjection in mice — leads to energy expenditure induced weigh loss and metabolic improvement in Obese Humans
  • Negative FGF21 is Rapidly Eliminated from the body – renal degradation and Inactivation of FGF21 Endopeptidase Cleavage Site – Fibroblast Activation Protein Matched FAP Endopeptidease Specificity
  • Closest relative of DPP4 upregulted during tissue injury in NASH
  • FAP is SUfficient to Cleave FGF21
  • Recombinant FGF21 with Recombinant FAP in Serum or Plasma
  • FAP Protease – Serum Immunodepleted Ablates FGF21 Cleavage Activity: Peptide IgG vs anti-FAP
  • FAP Cleavage Inactivates Human FGF21 dependent on KLB-FGFR1c placed on the site
  • hFGF21 in Not Cleaved in FAP KO Mice
  • Fc-hFGF21 is more stable in FAP KO mice
  • FAR cleaves Endogenously Produced FGF21 In Vivo in monkeys and in dogs
  • The FAP Cleavage Consensus GLY-Pro is COnserved in most mammalian FGF21
  • FAP Does not Cleave the C-Terminal Residues of Mouse FGF21
  • Human: FAP, DPPIV
  • Mouse: FAP, DPP4
  • FAP INhibition
  • FAP is Overexpressed in Liver with Steatohepatitis: Early NASH vs Late NASH
  • Proposal: FAP Inhibition for FGF21 Stabilization in NASH
  1. Fatty hepatocytes – e.g. NASH
  2. Activated stellate cells, e.g. NASH


10:55 Thyroid Hormone Receptor Beta (THR-ß) Agonist for NASH: Correcting a Primary Deficiency in NASH Livers

Rebecca Taub, M.D., Ph.D., CEO, Madrigal Pharmaceuticals

NASH patients typically have metabolic syndrome including diabetes, dyslipidemia, obesity, and primarily die of cardiovascular disease. Hypothyroidism at the level of the thyroid gland and liver-specific hypothyroidism are common in NASH. Based on clinical and preclinical data, Thyroid receptor beta agonists decrease insulin resistance, reduce LDL-C, triglycerides fatty liver, inflammation and fibrosis in NASH. The target will also provide CV benefit to patients with NASH. MGL-3196 is a highly THR-ß selective liver-directed once daily oral medication that has shown excellent safety and lipid-lowering efficacy in humans; unlike prior thyroid receptor agonist(s), no cartilage findings in chronic toxicology or ALT increases in human studies. MGL-3196 is being advanced in Phase II studies in patients with genetic dyslipidemia or NASH.

Madrigal Portfolio of drugs:

  • MGL-3196: First-in-Class THR-Beta Agonist – discovered first at ROCHE – THR-beta selective targeted to the Liver – regulated by THR-Alpha  – in Phase II – no side effects on bone
  • Large & underserved Markets in NASH
  • Phase 2 HeFH Patients
  1. Hypothyroidism common in NASH patients
  2. Liver-specific Hypothyroidism present in human NASH degradation of thyroid hormone increases deiodised 9DIO) 3 produced by Stelllate cells in NASH liver
  3. Treating NASHrather than fibrosis is key in addressing the disease – approvable endpoint
  4. THR – Thyroid hormone reduces Cholesterol
  5. Thyroid hormone T3 thyroxine – treatment amy cause osteoporosis
  6. MGL 0 3196: Liver size, Live Triglycerides, Improve Insulin tolerance, decrease ALT
  7. Reduction of key NASH, Fibrosis Pathway Genes at Human Comparable Drug levels
  8. THR-beta: Decreased Liver Fibrosis, Apoptosis in mice:


  • Single ascending dose study
  • Multiple – ascending studies: LDL and TG decrease
  • decrease Non-HDL CHolesterol
  • Decrease Apolipoprotein B
  • Pleiotropic Pioglitazone Effect in NASH at 6 month treatment and biopsy of liver – dramatic effect in NASH – ten years ago study
  • PPAR gamma agonist – NEGATIVE SIDE EFFECTS: weight gain, CHF, Bone osteoporosis
  • Anti-inflammatory: well tolerated

No Single NASH Therapeutics – Conbination agents

MGL – 3196 Phase 2 – Study: Proposed Phase 2 Proof of COncepts NASH Protocol

  • Unmet needs in FH, a severeGenetic Dyslipedemia
  • Weight loss in 6 weeksreduction in cholesterol and TG
  • Likelihood of Success
  • second study after 9 months
  • is different on NASH Patients in 12 weeks using MRI on Liver
  • prevalence
  • HeFH, PCSK9 inhibitors plus standard care
  • Unique and Complementary Lipid Lowering Profile
  1. Lowers Lp(a) and severely atherogenic practice
  2. Proposed Phase 2 HeFH Patients


11:25 Enjoy Lunch on Your Own


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LIVE 9/20 8AM to noon GENE THERAPIES BREAKTHROUGHS at CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston






Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a

Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN



COMMENTS BY Stephen J Williams, PhD

Gene Therapy Breakthroughs

New Strategies for Better Specificity and Delivery


2:05  Chairman’s Remarks

Joseph Gold, Ph.D. Director Manufacturing Center for Biomedicine and Genetics, Beckman Research Institute City of Hope


  • CBG (center for biomedicine and Genetics) 20000 sq feet
  • CTPC (center therapy production) mainly CART
  • CBG 16 years operation do all stem cells and >400 products
  • New stem cell Beta cell progenitor
  • Do oncolytic VSV
  • CTPC is investigator driven CART islet cells,
  • Like to do novel work so work with CIRM
  • Banking of modified stem cells
  • Adherent scale out limitations: cost,inefficient; solution can be suspension
  • Establish hESC; plate on CELLstart > Accutase>StemPRO SFM>differentiation process; defined reagents — they use this for cardiomyocyte differentiation: they are functional (inotropy, chronotropy response to isoproterenol) can freeze back cells
  • Create a bank of intermediate cells and when you need it for surgery they will put on their matrix, enrich, expand and ship out
  • Allogeneic cells: project where take allogeneic neural stem cells to deliver a chemotherapy payload as they like to migrate to brain tumors
  • Allogeneic cells: for ALS modified to express GDNF
  • HIV resistance with engineered CCR5 negative blood stem cells
  • Release assay considerations: viability, sterility, if cryopreserved then can determine identity, viral insertions, VSV-G copy number, endotoxin and potency (FDA is wanting phase I potency assays) for CART potency is % transduced
  • Good in vivo activity of the neural stem cells loaded with chemotherapeutic



  • If deliver GDNF to muscle  using genetically modified myoblasts
  • Best to use fetal stem cells – less issues


Canavan disease: progressive fatal neurologic disorder that begins in infancy and don’t make it past teenage years

  • Rossbach is taking autologous cells reprogramming generating iPS cells and then modifying by CRISPR but the CRISPR issues of off target effects persist as well the time required for process and verification; also don’t want to use a selectable marker and put in patients; so you can differentiate the cells and hit them with a lentiviral vector system


They have been named a PACT Center Production Assistance for Cell Therapy where you can apply for a project grant.  Applicable for startups up to larger mature companies



They do a standard panel of tests for viral infections.

They work with investigators or companies at all stages of manufacturing processes.





2:15 Large-Scale Production of Cell Therapies for Regenerative Medicine

Joseph Gold, Ph.D. Director Manufacturing Center for Biomedicine and Genetics, Beckman Research Institute


2:45  Directed Evolution of New Viruses for Therapeutic Gene Delivery

David Schaffer, Ph.D.  Professor of Chemical and Biomolecular Engineering, BioEngineering, Molecular and Cell Biology and Neuroscience;


AAV is very safe as many people already infected with it


  • Spark (Leber’s cogenital anaurosis
  • Hemophilia B
  • Lipoprotein lipase deficiency
  • Spinal muscular atrophy
  • Challenges: are we just getting the ‘low hanging fruit’ eg Spark therapy must be injected after retinal therapy, hemo B needs to be given at high doses
  • Their theory was AAV had been evolving for its own purposes so hence the limitations of AAV;
  • Utilized 25 different techniques to generate variants of AAV in a library then packaged (each will have its own barcode)
  • Broad platform technology: retina, lung, brain and spinal cord

Retinal:  AAV may be too large to get through layers of the eye, problems; subretinal injections and damage or retinal detachment.  Then they used their whole library in an in-vivo screen (as hard to recapitulate the multi cell layers of the retina).  


Cystic Fibrosis

  • AAV2H22 variant worked very well to supply the CFTR gene in pig model of cystic fibrosis and increases chloride transport and reduce bacterial load
  • Then found pig variant AAV did not work well on human tissue so designed a human variant and worked well in human tissues
  • The variant AAV2.5T surrounds sialic acid binding pockets and increases binding and endocytosis


Brain and Spinal Cord:  Sanfilippo B trial  8 holes drilled into skull followed by 16 AAV injections


  • Injected a generated AAV variant (by evolution process) : engineered AAV2 is 100 fold better getting through blood brain barrier… novel variant undergoes retrograde transport to cortex ; made a cas9 to remove a tdTomato gene overexpressed in mouse and found 90% knockdown
  • Also interesting point: the porcine variant did not work in human and the human variant did not work in porcine.  Implication for FDA safety and efficacy testing must do in monkeys

They started a spinout 4D Molecular Therapeutics


4:25 Lentiviral Vectors for Gene Therapy


Munapaty Swani, Ph.D. Texas Tech Health Science Center


  • Can express multiple shRNA under a separate promoter but toxic so if expressed in miRNA backbone could be safer under a pol II
  • How much of flanking sequence is needed?
  • 30 nt flanking sequence is enough for Drosha processing
  • Constructed 1 to 7 shRNA-miR targeting CCR5 and 6 viral genes; all constructs were functional
  • Problem with pol ii promoter
  • These 7 shRNA miRNA protect against HIV entry if against CCR5 and the 7 viral elements
  • Used the non-integrating lentivirus for transient to see if infect T cells or not versus integrating lentivirus ; results non-integrating lentivirus did not infect t cells so safer to use
  • CCR5 disruption reduced HIV infection in T cells in vitro;
  • ZFN treatment of HIV+ PBMC prevents activation of HIV
  • Encapsulted CAS9 within LV; cas9 protein is incorporated within LV and is functional
  • First transduce then come in with the Cas9 so made all in one lentivirus with Cas9 and an sgRNA expression vector *******
  • This shows that it is possible to put all in a nanoparticle based lentivirus and an all in one may make it easier and safer (supposedly)


4:55 AAV Capsid Design

Miguel Seria Esteves, PhD Associate Professor Neurology, Gene Therapy Center University of Massachusetts Medical School


-AAV replication dependent no known human disease with native AAV

  •  Multiple barriers to get across blood brain barrier
  • AAV9 preferentially target neonatal neurons and adult astrocytes
  • Multiple capsids can be used for AAV9 infection in brain but not complete
  • Can we design better capsids to give it better tropic properties and better penetration to blood brain barrier
  • Using a polyalanine in the 5’ end of the caspid was most efficeint
  • Increases gene transfer efficiency especially IN SELECT CELL TYPES; Glial transduction and increased in striatum: increase is structure specific so little in thalamus but good in cerebellum and spinal cord
  • AAV9 tranduces also in peripheral tissues with or without modified capsid


Huntington’s Disease

  • Polyglutamate disease polyy glu on huntingtin protein
  • They get a 40 to 50% reduction of huntingtin but not significant between capsid design
  • They did a directed evolution of AAV capsid and generated capsid gene delivery diversity: DNA shuffling and in vivo selection
  • AAV-B1 is a new tropic capsid showing transduction of different structures
  • Five fold reduction in tropism to the liver but massive increases in muscle and beta exocrine cells and lung
  • Presence of neutralizing antibodies is a problem with AAV therapy
  • In conclusion unknown mechanisms by whivh a highly hydrophobic string of 19 alanines modifies the CHS tropism of AAV9 kvariants
  • Chimeric capsids identified from in vivo screen can reveal interesting patterns of tropism

8:20 AAV for Gene Therapy and Genome Editing

James Wilson, M.D., Ph.D., Professor, Department of Pathology and Laboratory Medicine, Perelman School of Medicine; Director, Orphan Disease Center and Director, Gene Therapy Program, University of Pennsylvania

AAV delivery for CNS can be direct into brain or into CSF but AAV are big and vectors usually don’t cross BBB

Mucopolysaccharidoses a lysosomal storage disease including Hunter, Morquio

  • There are canine models for these diseases
  • Data show that intrathecal delivery has good distribution to the CSF not the serum
  • With IV you only get spinal distribution but IT you get good distribution to cerebellum and frontal cortex
  • Intrathecal superior for clearing lesions in cortex of dogs
  • In the dog the disease is more skeletal and less neurologic so with IT dogs were better than control but still some problems
  • Avaxis: AAV9 gene therapy for SMA and ALS but trials are very small but seems to be dose dependent effect ; phase I/II done; Pfizer, Esteves and REGENEXBIO have trials
  • Liver transduction of AAV has always been a success; early vectors were not efficient for uptake but AAV9 hemophelia B (factor 9) was
  • OCTD disease of urea acid cycle (neonatal versus late onset); proteins altered metabolism
  • Have to create a metabolic sink to detoxify metabolites; not lie a gene replacement therapy
  • They got stable expression in mouse adult  model of OCTD with AAV and had rapid onset of expression; but when done in newborn mice they saw transient expression
  • Newborn liver is proliferating so gene vector may be diluted out versus the adult liver
  • So turned to gene editing (ZFN:NEHJ, TALEN:HR gene correction, CRSPR:transgene addition by HR
  • Staph aureus cas9 is smaller than most and can fit in a 4.79kb vector*****
  • Put in 2.6 kb doner OCTD gRNA
  • With a CRSPR-Cas9 mediated deliverycould maintain the expression of OCTD for over a week in newborn mice
  • BUT in adults the gene corrected animals started to die; they were losing their ability to break down protein
  • In newborns you got 10% gene editing with 30% indels but with adults 30% resulted only in 1% gene editing
  • There is a propensity to create large (>50bp) indels in the adult
  • NGS was needed to fully detect the target transgene integration, PCR is not good enough
  • Says that large animal models are needed for safety/efficacy studies
  • Problem with Rhesus monkey: started with a humanized mouse in Rag mice because did not want to do monkey studies; but did not get good expression in the monkeys (it was not the vector which was the problem)
  • AAV may be good enough of a donor to cause the HR recombination


Summary:  AAV vectors combined with a CRSPR CAS9 system is effective in neonatal delivery however 1) AAV by itself may be a good delivery system by itself but need the crsipir guide RNA to make the break to promote HR and get the best efficiency of integration 2) use of CRSPR CAS9 may direct the proper integration you want to deliver the OCTD exons to correct gene loci


Talk specific @ and #




9:20 Using CRISPR/Cas9 to Target and Destroy Viral DNA Genomes; Inactivating HBV


Bryan R. Cullen, Ph.D., James B. Duke Professor of Molecular Genetics and Microbiology and Director, Center for Virology, Duke University


The CRISPR array is an evolutionary record of the bacteriophage that the bacteria have encountered.

  • Incredible that a bacteria that never encountered a chromosomal structure would scan the genome with these gRNAs and then initiate HR and NEHJ (error prone in mammalian cells usually 3 nt
  • HBV is a very confused retrovirus because it first goes into the nucleus then becomes a template for RNA synthesis to make the particles that reinfect the cell and invisible to immune response
  • Although they do not produce infectious virus it still remains in genome
  • sgRNA screening: use a luciferase based assay to correct or knock out luc so looking for decrease of luciferase activity
  • In a model of HBV infection where they have an inducible HBV cccDNA in a single integrated copy the cas9 reduced the DNA but protein was not decreased that much (if you hit episomal DNA you see loss and the disintegration of cut out DNA but if you hit integrated DNA you see repair
  • In their case the integrated DNA was just mutated (A or AA insertion) so in essence a frameshift mutation
  • Future strategies: use two guide RNAs to permit deletions or allow use of nickase. Currently these gRNA use polIII which is very large
  • They are editing the VEGF loci using Sau Cas9 and two sgRNAs
  • In mice with HBV integrated in their genome get some cutting but they need to go to higher doses of Cas9 system
  • Potential future success if reduce viral load as HBV continually release antigen which results in T cell anergy and if reduce the viral load may help to reduce anergy and wake up the immune system


Meeting specific # and @






10:35 Targeted Endonucleases as Antiviral Agents: Promises and Pitfalls

Keith R. Jerome, M.D., Ph.D., Member, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Professor and Head, Virology Division, Department of Laboratory Medicine, University of Washington


  • Can cure Hepatititis C because can stop replication
  • HSV in neurons and live for life: long lived form like HBV
  • Herpes simplex (HSV) establishes in dorsal root ganglion: acyclovir might be useful but came off patent
  • He reached out to advocacy groups to get data on need for HSV cure to convince funding agencies this is important
  • They found it is important to people; 90% want a cure if 5 years away
  • Homing endonucleases: small 800bp high specificity easy to put in vectors more difficult retargeting to other DNA targets
  • www.ltk.uzh.ch/de/dyn
  • HSV homing endonuclease from Cellectis AG targets a 24bp sequence in Ul19; introduces a DSB at target site with 4 bp 3’ overhang;
  • AAV targeted endonuclease delivery; nonimmunogenic; persists in episomal state
  • Exposure to HSV specific HE decreases virus production from neuronal cultures at all stages of replication cycle; if infect and let go for a month but the HE disrupts HSV in acute late-acute and late cycle
  • Developed a mouse model of HSV infection and AAV delivery in vivo system; so the AAV was accessing the nerve endings and going down to the trigenital dorsal ganglia; transport is independent of AAV serotype but transgene expression is HIGHLY dependent on AAV serotype
  • The in vivo HE treatment appears well tolerated however just a casual observation
  • Used NGS with bioinformatic approach to determine off target sites for the most likely mouse loci
  • HE suppresses viral reactivation (used PCR based reactivation assay)


Talk Specific @ and #








12:05 pm CRISPR/Cas9 for the Screening of the Human Kinome – A Pilot Study in an Aggressive Pediatric Cancer Cell Line

Simone T. Sredni, M.D., Ph.D., Research Assistant Professor, Neurological Surgery, Northwestern University Feinberg School of Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago


LentiArray CRISPR Kinase Array

  • Malignant rhabdoid tumors (MRT); among most aggressive of pediatric tumors rare but lethal
  • Inactivating mutations in SMARCB1 (INI1 gene)
  • Component of swi/snf chromatin remodeling complex
  • Can originate anywhere in kidney brain and spine
  • Kinase inhibitors may be effective (PLK1, ERBB2, AURAKA) : AURKA inhibitor in phase 2 and giving promising response
  • Used LentiArray viral vector system and stable expressed Cas9
  • Tested 160 kinases in screen; high transfection efficiency
  • PIMs; protooncogenes (proviral common integrations sites in Maloney leukemia; overexpressed in prostate  and hematologic; effects cell cycle cdc25a is a target and cell survival targets as well; PIM3 high copy number in MRT as well as PIM2
  • KO limits proliferation increases senescence and confirmed by MTT with pan PIM inhibitor CS6258 (Cyclene Pharmaceuticals)
  • PLK4 overexpression in peds haploinsufficient mice do make tumors; is it mutated? Yes inactivating mutations
  • PLK4 expression higher through cell cycle – dependent?
  • KI67 was down with Knockdown
  • Decrease in clonogenic assay on plastic not soft agar
  • CFI-400945 is PLK4 inhibitor is ovarian trials
  • Is effective in MRT and comparable to their cas9 knockdown










Tuesday, September 20

7:00 am Registration Open and Morning Coffee



8:05 Chairperson’s Opening Remarks

Bryan R. Cullen, Ph.D., James B. Duke Professor of Molecular Genetics and Microbiology and Director, Center for Virology, Duke University

8:20 AAV for Gene Therapy and Genome Editing

James Wilson, M.D., Ph.D., Professor, Department of Pathology and Laboratory Medicine, Perelman School of Medicine; Director, Orphan Disease Center and Director, Gene Therapy Program, University of Pennsylvania

In vivo delivery of nucleic acid therapeutics remains the primary barrier to success. My lab has focused on the use of vectors based on adeno-associated virus (AAV) for achieving success in pre-clinical and clinical applications of gene replacement therapy. Most of the current academic and commercial applications of in vivo gene replacement therapy are based on endogenous AAVs we discovered as latent viral genomes in primates. These vectors are reasonably safe and efficient for application of gene replacement therapy. The emergence of genome editing methods has suggested more precise and effective methods to treat inherited diseases in which genes are silenced or mutations are corrected. AAV vectors have been the most efficient platform for achieving genome editing in vivo. We will review our attempts to achieve therapeutic genome editing in animal models of liver disease using AAV.

  • AAV for delivery of vector to CNS
  • Novel AAV Platform – capsid platform – distributed by PENN Vector
  • direct
  • IV
  • into CNS: Head and Spinal MPS – Mucopolysaccharidosis – Class of lyposomal Storage Disorder
  1. AAV9: Binds Glycans with Terminal Gal – BBB

CNS gene transfer in canine model of MPS VII following IV and intrathecal AA( administration

  • Serum
  • CSF: Frontal cortex, Cerebelum, Spinal Cord
  • Intravenous (IV) and Intracisternal (IC) – AA9

Gene Therapy for Motor Neuron Disease: SMA and ALS: PhaseI/II clinical trial of AAV9-SMN IV in infants with SMA1 – Nationwide Children’s

  • High dose +23.3 Month survival


  • Avexis
  • Abeona
  • Pfizer
  • Nationwide
  • Esteves

Liver Transduction Following IV Adm of AAV8 Vectors

  • Mouth liver Day 3 vs Day 90
  • Hemophilia B: Therapeutic protein

Urea Cycle Disorder

  • newborn OTCD infant in HA crisis

Goals: efficient but Transient vorrection following AAV* Gene Therapy in Newborn

transfer of caspid – promoter vector  to correct diffect

  • Survival: neonatal gene therapy – two doses of vector, three injections – immunogenic
  • Liver transplantation of neonatal before 1 year of age

Gene Editing

  • gene targeting by ZFNs, TALENs or CRISPR/Cas9
  • WT donor DNA
  • Transgene donor DNA
  • Gene disruptions

In Vivo correction – liver mouse by AAV. CRISPR-SaCas9

  • OTC donor template
  • efficient restoration of OTC Expression in the liver  – mice treated at Neonatal Stage by AAV8.CRISPR-SaCas9 – Vector administration
  • Cas9 – Kinetics of Cas9 – Week 1,3,8 – dilutes with time
  • Should work in Adult mice vs Neonatal mice: Low dose vs High dose
  • Ureogensis increased
  • On-target Deep Sequensing and their Distribution in Neonatal-treated and Adult-treated Animals

In vivo gene editing: mixed results: Site-directed Insertion of hOTCco Gene Cassette in the OTC gene : Controls: WT and spf(ash)

Western Blot: NGS analysis demonstrates on target transgene integration 20 to 32%

High Protein DIet to Evaluate the Efficacy

  • NEW BORN – Gene Targeting in FIX-KO Mouse by CRISPR/Cas9: Infron1, Exon 2 Infron2 Exon 3
  • ADULTS – Gene Targeting in FIX-KO Mouse by CRISPR/Cas9:

Gene therapy must accumulate experience in Animal models: Safety and Efficacy

NEGATIVE RESULTS IN MONKEYS: Analysis of Liver Tissue for Editing and SaCas9

  • CRISPR/Cas9 -mediated Gene knock down of rhPCSK9 in Monkeys (Rhesus Macaque) in LIVER
  • In vitro sgRNAs in monkeys and human cells
  • EGFP sgRNA vs hrPCSK
  • In vivo does not infer In vitro
  • Gene Editing in Human — we are not yet there

9:20 Using CRISPR/Cas to Target and Destroy Viral DNA Genomes

Bryan R. Cullen, Ph.D., James B. Duke Professor of Molecular Genetics and Microbiology and Director, Center for Virology, Duke University

A number of pathogenic human DNA viruses, including HBV, HIV-1 and HSV1, cause chronic diseases in humans that remain refractory to cure, though these diseases can be controlled by antivirals. In addition the DNA virus HPV causes tumors that depend on the continued expression of viral genes. Here, I will present data demonstrating that several of these viruses can be efficiently cleaved and destroyed using viral vectors that express Cas9 and virus-specific guide RNAs, thus providing a potential novel approach to treatment.

  • HBV – as target intervention
  • CRISPR/Cas9 RNA Guided Nuclease System (RGN)
  • NGG- PAM >> dsDNA cleavage >> NHEJ Repair >> Prfect repair >> Completion Repair Cycle
  • vs Mutagenesis – cycle exit
  1. HBV – Inactivation with CRISPR/Cas9: HBV lifecycle of the virus — reverse transcripatse (RT) of caspid and envelope – release antigens in blood invisible to Immune response, SUrfacce Antigen,COre Antigen, X-protein,
  2. Inhibitors of HBV  – cccDNA is stable for decades – virus in blood do not create new virus
  3. tetracycline represses HBV expression
  4. HBsAG va HBeAG: on core, surface, RT and N.S. sgRNA

Vector Delivery Strategies to the Liver

  • Strep pyogenes Cas9 – a diffrence PAM (5′ – NNGRRT-3′) – too large – ~4.8kb, including th ITRs.
  • Can we identify smaller Promoters
  • Packaging Sau Cas9 and two sgRNAs into AAV


  • using HBV-infected hepatocytes of a humanized liver or transgenic mouse – HBV Dual Target

Using HBV with RT and CRISPR Cas9 — viral load reduced to awake the immune response – allergy stage  – potential for future therapeutics



9:50 Grand Opening Coffee Break in the Exhibit Hall with Poster Viewing

10:35 Targeted Endonucleases as Antiviral Agents: Promises and Pitfalls

Keith R. Jerome, M.D., Ph.D., Member, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Professor and Head, Virology Division, Department of Laboratory Medicine, University of Washington

Genome editing offers the prospect of cure for infections such as HIV, hepatitis B virus, herpes simplex, and human papillomavirus, by disruption of essential viral nucleic acids or the human genes encoding receptors needed for viral entry. This talk will highlight the most recent laboratory data and the challenges still ahead in bringing this technology to the clinic.

  • Anti HSV – episonal DNA in Neuron, Acylovar – willingness to Participate in studies
  • Anti-HBV –
  • cART – HIV –

Viral replication returns if medication is taken away in two weeks

Herpes Simplex Virus HSV -1 – 50% HSV-2 16%

  1. CRISPR/Cas – difficult vectorization
  2. Targeted endonucleases
  3. Rare-cutting endonucleases in Gene Therapy – target distruction
  4. Derived from Crel enzyme by CELLECTIS AG (Paris) – co-expressed with Trex2 to remove 3″ hangover
  5. AAV as a targeted endonuclease delivery vector – mediated delivery to primary neuronal cultures
  6. Exposure to HSV-specific HE decreases virus production from neuronal cultures – virus production in treated cells
  • HSV-specific HE can disrupt HSV at all stages of the replication cycle – established in vivo
  1. cell planting
  2. AAV-mediated transgene delivery to the mouse TG in vivo – injection whiskerpad – eye scarified vs Eye not scarified
  3. AAV serotype: Transgene expression in TG is highly dependent on AAV serotype
  4. Dose dependence – Trigeminal ganglion (TG) – AAV-mediated transgene delivery to all branches ot TG
  5. In vivo mutugenesis of latent HSV
  6. Specificity – NGS analysis of Off target activity of NV1: Insertion vs Deletion vs NGS analysis of On and Off target activity of HSV1m8
  7. Endonuclease therapy suppresses viral reactivation
  8. Viral eradication: critical determinant: 3 of doses before cure occurs


Delivery system is the most important factor


11:05 Nucleic Acid Delivery Systems for RNA Therapy and Gene Editing

Daniel Anderson, Ph.D., Professor, Department of Chemical Engineering, Institute for Medical Engineering & Science, Harvard-MIT Division of Health Sciences & Technology and David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology

High throughput, combinatorial approaches have revolutionized small molecule drug discovery. Here we describe our high throughput methods for developing and characterizing RNA delivery and gene editing systems. Libraries of degradable polymers and lipid-like materials have been synthesized, formulated and screened for their ability to deliver RNA, both in vitro and in vivo. A number of delivery formulations have been developed with in vivo efficacy, and show potential applications for the treatment of genetic diseases, viral infections and cancers.

  1. Nanoparticulate approaches – repair your DNA while you still use it
  2. Barriers to Intracellular Delivery – What organs are most amenable to Targeting
  3. Liver, spleen, bone marrow, kidney
  4. Intracellular Drug Delivery:
  • Modular Pharmaci=ology with siRNA siRNA silences mRNA
  • Turning Nuclaic Acids into Drugs: Sequence Selection,  Mechanical modification (ligand conjugation), Encapsulation

Materials used for RNA Delivery – increase diversity of materials

  • Liquid light material: Combinatorial synthesis of lipid-like materials
  • RNA NAnoparticles – Lipid -siRNA-Nanoformulations targeting TTR in the liver of Primates
  • Mechanism of ApoE mediated iLNP Delivery [Phil Sharp/Alnylam]
  • si delivery to ENdothelium
  • Lipid modified Polymers: Short amino polymers – Total Dose 5 siRNA
  • Nanoformulation Chemistry: Endothelium in many organs(preferred) vs Hepatocytes
  • Immune cells as a target: CD45 or control
  • In vivo mediated Homologous Recombination Gene repair:  Nanoformulation deliver sgRNA
  1. In vivo delivery of sgRNA to endothelium wiht nanoparticle: mediated guide RNA delivery to endothilium: DNA Repair and Protein delivery
  2. Can CRISPR be used to repair a disease gene in vivo – PLASMID encoding Cas9 and GuideRNA + 199nt ss DNA repair template
  3. in vivo CRISPR rescue repairs defect, restores body weight and stops
  4. mRNA with nanoparticles: In vivo delivery- EPO Protein: Mean Human EPO
  • AAV Only
  • AAV +CAs9 – nano – 6% repain is therapeutic
  • In vivo mediated Gene Knockout without the Virus
  1. PCSK( Blood Cholesterol Liver PCSK9 Analysis – 60% gene mutated 65% reduction in CHolesterol — Synthetic system to do gene knockout


11:35 PANEL DISCUSSION: CRISPR/Cas: A Realistic and Practical Look at What the Future Could Hold

Moderator: Bryan R. Cullen, Ph.D., James B. Duke Professor of Molecular Genetics and Microbiology and Director, Center for Virology, Duke University

Participants: Session Speakers

Each speaker will spend a few minutes sharing their viewpoints and experiences on where things stand with using the CRISPR/Cas system for in vivo applications. Attendees will have an opportunity to ask questions and share their opinions.


  • Ex vivo delivery to Immune cell rather than to the tumor itself
  • solid cells therapy needs to reach each cell alternatives are needed

12:05 pm CRISPR/Cas9 for the Screening of the Human Kinome – A Pilot Study in an Aggressive Pediatric Cancer Cell Line

Simone T. Sredni, M.D., Ph.D., Research Associate Professor, Neurological Surgery, Northwestern University Feinberg School of Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago

The CRISPR-Cas9 system for genome editing is a powerful tool to identify genes involved in vital biological processes. A systematic functional screening of the human kinome has the potential to reveal molecules that are essential for tumor survival, growth, and migration. We will describe our experience using the Invitrogen LentiArray™CRISPR library to mutate 160kinases in a highly malignant pediatric tumor cell line. We will discuss our approach for screening, monitoring of cells lines, and validation.

  • LentiArray CRISPR Kinase Library – Bet Test 160 Kinase inhibitors
  • MRT – Malignant Rhabdoid Tumors – Children lexx 3 Years old
  • Genetic landmark, histology anatomical location: Kidney, Brain, Spine
  • Kinase Inhibitors and MRT
  • Finding Novel Targets
  1. Invitrogen – lentiArray CRISPR Library – edit 160 kinase genes – Viral Vector design
  • Cas9
  • gRNA-Kinase
  • Positive Control
  • Negative Control

2.  Kinome Screening – Impact on Proliferation 160 – only EIGHT were tested – significal=nly impaired cell profiferation

Retransaction and Confirmation of the identified  targets

PIM – Leukemia virus induce lymphomas: Proviral – PIM-1,2,3 KO

  • Verification of Genome Editing
  • PIMs – Proliferation
  • PIMs – Senescence – Adult hematological diseases and refractory solid tumors
  • PLK4 – Direct Mitosis Regulator – Activated Protein (mRNA) – expression in cytoplasm
  • PLK-4 and Cancer: Over-expression vs Deregulation – Colony Formation – colonygenic
  • Gene expression – Frozen Tumors – abnormality in children and in adults
  • Verification of Gene Editing: Cleavage and deletion
  • PLK-4 as a Cancer Drug Target – inhibitor enzymatic  – PLK-4 Inhibitor Xenografs





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Coronary Heart Disease Research: Sugar Industry influenced national conversation on heart disease – Adoption of Low Fat Diet vs Low Carbohydrates Diet

Reporter: Aviva Lev-Ari, PhD, RN

Public Health Outcome:

  • Uncontrolled consumption of sugar prevailed 1965 – 2005 – role of sugar in CVD was played down


  • Consumption of fat become the diet factor to be control and monitored in the Medical community – role of Fat was the main focus and its management by Statins


  • FDA Food Pyramid evolution

USDA Food Pyramid History

In January 1977, after listening to the testimony of Ancel Keys and other doctors and scientists intent on promoting the unsupported Dietary Fat-Heart hypothesis, the Committee published the “Dietary Goals for the United States” recommending that all Americans reduce their fat, saturated fat and cholesterol consumption, and increase their carbohydrate consumption to 55-60% of daily calories.


Concerns that were raised with the first dietary recommendations 30 y ago have yet to be adequately addressed. The initial Dietary Goals for Americans (1977) proposed increases in carbohydrate intake and decreases in fat, saturated fat, cholesterol, and salt consumption that are carried further in the 2010 Dietary Guidelines Advisory Committee (DGAC) Report. Important aspects of these recommendations remain unproven, yet a dietary shift in this direction has already taken place even as overweight/obesity and diabetes have increased. Although appealing to an evidence-based methodology, the DGAC Report demonstrates several critical weaknesses, including use of an incomplete body of relevant science; inaccurately representing, interpreting, or summarizing the literature; and drawing conclusions and/or making recommendations that do not reflect the limitations or controversies in the science. An objective assessment of evidence in the DGAC Report does not suggest a conclusive proscription against low-carbohydrate diets. The DGAC Report does not provide sufficient evidence to conclude that increases in whole grain and fiber and decreases in dietary saturated fat, salt, and animal protein will lead to positive health outcomes. Lack of supporting evidence limits the value of the proposed recommendations as guidance for consumers or as the basis for public health policy. It is time to reexamine how US dietary guidelines are created and ask whether the current process is still appropriate for our needs.



Curator: Aviva Lev-Ari, PhD, RN


UCSF reveals how sugar industry influenced national conversation on heart disease


Special Communication |

Sugar Industry and Coronary Heart Disease Research – A Historical Analysis of Internal Industry Documents

Cristin E. Kearns, DDS, MBA1,2; Laura A. Schmidt, PhD, MSW, MPH1,3,4; Stanton A. Glantz, PhD1,5,6,7,8
JAMA Intern Med. Published online September 12, 2016. doi:10.1001/jamainternmed.2016.5394

Corresponding Author: Stanton A. Glantz, PhD, UCSF Center for Tobacco Control Research and Education, 530 Parnassus Ave, Ste 366, San Francisco, CA 94143-1390 (glantz@medicine.ucsf.edu).

Accepted for Publication: July 2, 2016.

Published Online: September 12, 2016. doi:10.1001/jamainternmed.2016.5394

Author Contributions: Drs Kearns and Glantz had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of data analysis.

Early warning signals of the coronary heart disease (CHD) risk of sugar (sucrose) emerged in the 1950s. We examined Sugar Research Foundation (SRF) internal documents, historical reports, and statements relevant to early debates about the dietary causes of CHD and assembled findings chronologically into a narrative case study. The SRF sponsored its first CHD research project in 1965, a literature review published in the New England Journal of Medicine, which singled out fat and cholesterol as the dietary causes of CHD and downplayed evidence that sucrose consumption was also a risk factor. The SRF set the review’s objective, contributed articles for inclusion, and received drafts. The SRF’s funding and role was not disclosed. Together with other recent analyses of sugar industry documents, our findings suggest the industry sponsored a research program in the 1960s and 1970s that successfully cast doubt about the hazards of sucrose while promoting fat as the dietary culprit in CHD. Policymaking committees should consider giving less weight to food industry–funded studies and include mechanistic and animal studies as well as studies appraising the effect of added sugars on multiple CHD biomarkers and disease development.

These internal documents show that the SRF initiated CHD research in 1965 to protect market share and that its first project, a literature review, was published in NEJM in 1967 without disclosure of the sugar industry’s funding or role. The NEJM review served the sugar industry’s interests by arguing that epidemiologic, animal, and mechanistic studies associating sucrose with CHD were limited, implying they should not be included in an evidentiary assessment of the CHD risks of sucrose. Instead, the review argued that the only evidence modality needed to yield a definitive answer to the question of how to modify the American diet to prevent CHD was RCTs that exclusively used serum cholesterol level as a CHD biomarker. Randomized clinical trials using serum cholesterol level as the CHD biomarker made the high sucrose content of the American diet seem less hazardous than if the entire body of evidence had been considered.

Following the NEJM review, the sugar industry continued to fund research on CHD and other chronic diseases “as a main prop of the industry’s defense.”51 For example, in 1971, it influenced the National Institute of Dental Research’s National Caries Program to shift its emphasis to dental caries interventions other than restricting sucrose.8 The industry commissioned a review, “Sugar in the Diet of Man,” which it credited with, among other industry tactics, favorably influencing the 1976 US Food and Drug Administration evaluation of the safety of sugar.51 These findings, our analysis, and current Sugar Association criticisms of evidence linking sucrose to cardiovascular disease6,7 suggest the industry may have a long history of influencing federal policy.

This historical account of industry efforts demonstrates the importance of having reviews written by people without conflicts of interest and the need for financial disclosure. Scientific reviews shape policy debates, subsequent investigations, and the funding priorities of federal agencies.52 The NEJM has required authors to disclose all conflicts of interest since 1984,53 and conflict of interest disclosure policies have been widely implemented since the sugar industry launched its CHD research program. Whether current conflict of interest policies are adequate to withstand the economic interests of industry remains unclear.54

Many industries sponsor research to influence assessments of the risks and benefits of their products.55– 57The influence of industry sponsorship on nutrition research is receiving increased scrutiny.58 Access to documents not meant for public consumption has provided the public health community unprecedented insight into industry motives, strategies, tactics, and data designed to protect companies from litigation and regulation.59 This insight has been a major factor behind successful global tobacco control policies.60 Our analysis suggests that research using sugar industry documents has the potential to inform the health community about how to counter this industry’s strategies and tactics to control information on the adverse health effects of sucrose.

Study Limitations

The Roger Adams papers and other documents used in this research provide a narrow window into the activities of 1 sugar industry trade association; therefore, it is difficult to validate that the documents gathered are representative of the entirety of SRF internal materials related to Project 226 from the 1950s and 1960s or that the proper weight was given to each data source. There is no direct evidence that the sugar industry wrote or changed the NEJM review manuscript; the evidence that the industry shaped the review’s conclusions is circumstantial. We did not analyze the role of other organizations, nutrition leaders, or food industries that advocated that saturated fat and dietary cholesterol were the main dietary cause of CHD. We could not interview key actors involved in this historical episode because they have died.

This study suggests that the sugar industry sponsored its first CHD research project in 1965 to downplay early warning signals that sucrose consumption was a risk factor in CHD. As of 2016, sugar control policies are being promulgated in international,61 federal,62,63 state, and local venues.64 Yet CHD risk is inconsistently cited as a health consequence of added sugars consumption. Because CHD is the leading cause of death globally, the health community should ensure that CHD risk is evaluated in future risk assessments of added sugars. Policymaking committees should consider giving less weight to food industry–funded studies, and include mechanistic and animal studies as well as studies appraising the effect of added sugars on multiple CHD biomarkers and disease development.65



Council on Foods and Nutrition (American Medical Association).  The regulation of dietary fat: a report of the council. JAMA. 1962;181(5):411-429.
Link to Article

Yudkin  J. Pure, White and Deadly: The Problem of Sugar. London, England: Davis-Poynter Ltd; 1972.

Yudkin  J.  Diet and coronary thrombosis hypothesis and fact. Lancet. 1957;273(6987):155-162.
PubMed   |  Link to Article

Yudkin  J.  Dietary fat and dietary sugar in relation to ischaemic heart-disease and diabetes. Lancet. 1964;2(7349):4-5.
PubMed   |  Link to Article

Technical Group of Committee on Lipoproteins and Atherosclerosis and Committee on Lipoproteins and Atherosclerosis of National Advisory Heart Council.  Evaluation of serum lipoprotein and cholesterol measurements as predictors of clinical complications of atherosclerosis: report of a cooperative study of lipoproteins and atherosclerosisCirculation. 1956;14(4, pt 2):691-742.

Albrink  MJ.  Carbohydrate metabolism in cardiovascular disease. Ann Intern Med. 1965;62(6):1330-1333.
PubMed   |  Link to Article

Taubes  G, Couzens  CK. Big sugar’s sweet little lies: how the industry kept scientists from asking, does sugar kill? 2012. http://www.motherjones.com/environment/2012/10/sugar-industry-lies-campaign Accessed October 17, 2014.

Bero  L.  Implications of the tobacco industry documents for public health and policy. Annu Rev Public Health. 2003;24:267-288.
PubMed   |  Link to Article

US Department of Health and Human Services and US Department of Agriculture. 2015-2020 Dietary Guidelines for Americans. 8th ed. Washington, DC: U.S. Government Printing Office; 2016.

US Food and Drug Administration. Changes to the nutrition facts label. 2016.http://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/LabelingNutrition/ucm385663.htm. Accessed June 7, 2016.

Miller  M, Stone  NJ, Ballantyne  C,  et al; American Heart Association Clinical Lipidology, Thrombosis, and Prevention Committee of the Council on Nutrition, Physical Activity, and Metabolism; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Nursing; Council on the Kidney in Cardiovascular Disease.  Triglycerides and cardiovascular disease: a scientific statement from the American Heart AssociationCirculation. 2011;123(20):2292-2333.
PubMed   |  Link to Article

Teicholz  N. The Big Fat Surprise: Why Butter, Meat, and Cheese Belong in a Healthy Diet. New York, NY: Simon and Schuster; 2014.


Other related articles published in this Open Access Online Scientific Journal include the following:  


Metabolomics, Metabonomics and Functional Nutrition: The Next Step in Nutritional Metabolism and Biotherapeutics

Larry H. Bernstein, MD, FCAP  and Aviva Lev-Ari, PhD, RN

Reference Genes in the Human Gut Microbiome: The BGI Catalogue

Aviva Lev-Ari, PhD, RN

Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

Aviva Lev-Ari, PhD, RN

HDL-C: Target of Therapy – Steven E. Nissen, MD, MACC, Cleveland Clinic vs Peter Libby, MD, BWH

Aviva Lev-Ari, PhD, RN


The following articles in

Series A: e-Books on Cardiovascular Diseases

Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC


Etiologies of Cardiovascular Diseases:

Epigenetics, Genetics and Genomics




Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator


Aviva Lev-Ari, PhD, RN, Editor and Curator


2.2.2: Endothelium, Angiogenesis, and Disordered Coagulation What is the Role of Plasma Viscosity in Hemostasis and Vascular Disease Risk? 

Larry H Bernstein, MD, FACP and Aviva Lev-Ari, PhD, RN Special Considerations in Blood Lipoproteins, Viscosity, Assessment and Treatment 

Larry H Bernstein, MD, FACP  and Aviva Lev-Ari, PhD, RN Biomarkers and risk factors for cardiovascular events, endothelial dysfunction, and thromboembolic complication

Larry H Bernstein, MD, FCAP A future for plasma metabolomics in cardiovascular disease assessment  

Larry H Bernstein, MD, FCAP Nitric Oxide Function in Coagulation – Part II

Larry H Bernstein, MD, FACP Nitric Oxide, Platelets, Endothelium and Hemostasis (Coagulation Part II)

Larry H Bernstein, MD, FACP Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes 

Aviva Lev-Ari, PhD, RN

Endothelium Inflammatory Biomarkers Cardiovascular Risk: C-Reactive Protein BioMarker and Plasma Fibrinogen

Aviva Lev-Ari, PhD, RN Cardiovascular Risk Inflammatory Marker: Risk Assessment for Coronary Heart Disease and Ischemic Stroke ­ – Atherosclerosis

Aviva Lev-Ari, PhD, RN Importance of high sensitivity C-reactive protein (hs-CRP)

Larry H Bernstein, MD, FCAP


See also our Series A: Cardiovascular Diseases





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Metabolic Genomics & Pharmaceutics




Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence


Chapter 1: Metabolic Pathways

1.1            Carbohydrate Metabolism

1.2            Studies of Respiration Lead to Acetyl CoA

1.3            Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief

1.4            The Multi-step Transfer of Phosphate Bond and Hydrogen Exchange Energy

1.5            Diabetes Mellitus

1.6            Glycosaminoglycans, Mucopolysaccharides, L-iduronidase, Enzyme Therapy

Chapter 2: Lipid Metabolism

2.1            Lipid Classification System

2.2            Essential Fatty Acids

2.3            Lipid Oxidation and Synthesis of Fatty Acids

2.4            Cholesterol and Regulation of Liver Synthetic Pathways

2.5            Sex hormones, Adrenal cortisol, Prostaglandins

2.6            Cytoskeleton and Cell Membrane Physiology

2.7            Pharmacological Action of Steroid hormone

Chapter 3: Cell Signaling

3.1            Signaling and Signaling Pathways

3.2            Signaling Transduction Tutorial

3.3            Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence

3.4            Integrins, Cadherins, Signaling and the Cytoskeleton

3.5            Complex Models of Signaling: Therapeutic Implications

3.6            Functional Correlates of Signaling Pathways

Chapter 4: Protein Synthesis and Degradation

4.1            The Role and Importance of Transcription Factors

4.2            RNA and the Transcription of the Genetic Code

4.3            9:30 – 10:00, 6/13/2014, David Bartel “MicroRNAs, Poly(A) tails and Post-transcriptional Gene Regulation

4.4            Transcriptional Silencing and Longevity Protein Sir2

4.5            Ca2+ Signaling: Transcriptional Control

4.6            Long Noncoding RNA Network regulates PTEN Transcription

4.7            Zinc-Finger Nucleases (ZFNs) and Transcription Activator–Like Effector Nucleases (TALENs)

4.8            Cardiac Ca2+ Signaling: Transcriptional Control

4.9            Transcription Factor Lyl-1 Critical in Producing Early T-Cell Progenitors

4.10            Human Frontal Lobe Brain: Specific Transcriptional Networks

4.11            Somatic, Germ-cell, and Whole Sequence DNA in Cell Lineage and Disease

Chapter 5:  Sub-cellular Structure

5.1            Mitochondria: Origin from Oxygen free environment, Role in Aerobic Glycolysis and Metabolic Adaptation

5.2            Mitochondrial Metabolism and Cardiac Function

5.3            Mitochondria: More than just the “Powerhouse of the Cell”

5.4            Mitochondrial Fission and Fusion: Potential Therapeutic Targets?

5.5            Mitochondrial Mutation Analysis might be “1-step” Away

5.6            Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

5.7            Chromatophagy, A New Cancer Therapy: Starve The Diseased Cell Until It Eats Its Own DNA

5.8           A Curated Census of Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy

5.9           Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility

Chapter 6: Proteomics

6.1            Proteomics, Metabolomics, Signaling Pathways, and Cell Regulation: a Compilation of Articles in the Journal http://pharmaceuticalintelligence.com

6.2            A Brief Curation of Proteomics, Metabolomics, and Metabolism

6.3            Using RNA-seq and Targeted Nucleases to Identify Mechanisms of Drug Resistance in Acute Myeloid Leukemia, SK Rathe in Nature, 2014

6.4            Proteomics – The Pathway to Understanding and Decision-making in Medicine

6.5            Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

6.6           Expanding the Genetic Alphabet and Linking the Genome to the Metabolome

6.7            Genomics, Proteomics and Standards

6.8            Proteins and Cellular Adaptation to Stress

6.9            Genes, Proteomes, and their Interaction

6.10           Regulation of Somatic Stem Cell Function

6.11           Scientists discover that Pluripotency factor NANOG is also active in Adult Organism

Chapter 7: Metabolomics

7.1            Extracellular Evaluation of Intracellular Flux in Yeast Cells

7.2            Metabolomic Analysis of Two Leukemia Cell Lines Part I

7.3            Metabolomic Analysis of Two Leukemia Cell Lines Part II

7.4            Buffering of Genetic Modules involved in Tricarboxylic Acid Cycle Metabolism provides Homeomeostatic Regulation

7.5            Metabolomics, Metabonomics and Functional Nutrition: The Next Step in Nutritional Metabolism and Biotherapeutics

7.6            Isoenzymes in Cell Metabolic Pathways

7.7            A Brief Curation of Proteomics, Metabolomics, and Metabolism

7.8            Metabolomics is about Metabolic Systems Integration

7.9             Mechanisms of Drug Resistance

7.10           Development Of Super-Resolved Fluorescence Microscopy

7.11            Metabolic Reactions Need Just Enough

Chapter 8.  Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and CAncer

8.1           Omega3 Fatty Acids, Depleting the Source, and Protein Insufficiency in Renal Disease

8.2             Liver Endoplasmic Reticulum Stress and Hepatosteatosis

8.3            How Methionine Imbalance with Sulfur Insufficiency Leads to Hyperhomocysteinemia

8.4            AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth InVivo

8.5           A Second Look at the Transthyretin Nutrition Inflammatory Conundrum

8.6            Mitochondrial Damage and Repair under Oxidative Stress

8.7            Metformin, Thyroid Pituitary Axis, Diabetes Mellitus, and Metabolism

8.8            Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

8.9            Social Behavior Traits Embedded in Gene Expression

8.10          A Future for Plasma Metabolomics in Cardiovascular Disease Assessment

Chapter 9: Genomic Expression in Health and Disease 

9.1            Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

9.2            BRCA1 a Tumour Suppressor in Breast and Ovarian Cancer – Functions in Transcription, Ubiquitination and DNA Repair

9.3            Metabolic Drivers in Aggressive Brain Tumors

9.4            Modified Yeast Produces a Range of Opiates for the First time

9.5            Parasitic Plant Strangleweed Injects Host With Over 9,000 RNA Transcripts

9.6            Plant-based Nutrition, Neutraceuticals and Alternative Medicine: Article Compilation the Journal

9.7            Reference Genes in the Human Gut Microbiome: The BGI Catalogue

9.8            Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

9.9            HDL-C: Target of Therapy – Steven E. Nissen, MD, MACC, Cleveland Clinic vs Peter Libby, MD, BWH



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PCSK9 inhibitors: Reducing annual drug prices from more than $14 000 to $4536 would be necessary to meet a $100 000 per QALY threshold per JAMA

Curator: Aviva Lev-Ari, PhD, RN


UPDATED on 6/21/2017

When Cholesterol Drugs Cost $14,000, an Insurance Tug-of-War – PCSK9 inhibitors



Our team has researched PCSK9 inhibitors as a class of drugs in the following articles:


Efficacy and Tolerability of PCSK9 Inhibitors by Patients with Muscle-related Statin Intolerance – New Cleveland Clinic study published in JAMA 4/2016

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN



FDA ask Regeneron and Sanofi to assess potential Neurocognitive Side Effects of Alirocumab, PCSK9 inhibitors Class Designed to Block a Protein causing Persistence of “bad” LDL Cholesterol in the Bloodstream

Reporter & Curator: Aviva Lev-Ari, PhD, RN



SNPs in apoE are found to influence statin response significantly. Less frequent variants in PCSK9 and smaller effect sizes in SNPs in HMGCR

Reporter: Aviva Lev-Ari, PhD, RN



Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

Reporter: Aviva Lev-Ari, PhD, RN



Targeting Cardio-Metabolic Diseases and Metabolomics in Drug Discovery – CHI’s 14th Annual Discovery On Target September 19-22, 2016 | Westin Boston Waterfront — Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN


Triglycerides: Is it a Risk Factor or a Risk Marker for Atherosclerosis and Cardiovascular Disease ? The Impact of Genetic Mutations on (ANGPTL4) Gene, encoder of (angiopoietin-like 4) Protein, inhibitor of Lipoprotein Lipase

Reporters, Curators and Authors: Aviva Lev-Ari, PhD, RN and Larry H. Bernstein, MD, FCAP



New study in JAMA demonstrates lack of cost effectiveness and extra burden on Health Care Costs associated with PCSK9 Inhibitor Therapy


Original Investigation |

Cost-effectiveness of PCSK9 Inhibitor Therapy in Patients With Heterozygous Familial Hypercholesterolemia or Atherosclerotic Cardiovascular Disease

Dhruv S. Kazi, MD, MSc, MS1,2,3,4,5; Andrew E. Moran, MD, MPH6,7; Pamela G. Coxson, PhD1,2,8; Joanne Penko, MS, MPH1,2; Daniel A. Ollendorf, PhD9; Steven D. Pearson, MD, MSc9; Jeffrey A. Tice, MD2; David Guzman, MSPH1; Kirsten Bibbins-Domingo, PhD, MD, MAS1,2,3,8

Importance  Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors were recently approved for lowering low-density lipoprotein cholesterol in heterozygous familial hypercholesterolemia (FH) or atherosclerotic cardiovascular disease (ASCVD) and have potential for broad ASCVD prevention. Their long-term cost-effectiveness and effect on total health care spending are uncertain.

Objective  To estimate the cost-effectiveness of PCSK9 inhibitors and their potential effect on US health care spending.

Design, Setting, and Participants  The Cardiovascular Disease Policy Model, a simulation model of US adults aged 35 to 94 years, was used to evaluate cost-effectiveness of PCSK9 inhibitors or ezetimibe in heterozygous FH or ASCVD. The model incorporated 2015 annual PCSK9 inhibitor costs of $14 350 (based on mean wholesale acquisition costs of evolocumab and alirocumab); adopted a health-system perspective, lifetime horizon; and included probabilistic sensitivity analyses to explore uncertainty.

Exposures  Statin therapy compared with addition of ezetimibe or PCSK9 inhibitors.

Main Outcomes and Measures  Lifetime major adverse cardiovascular events (MACE: cardiovascular death, nonfatal myocardial infarction, or stroke), incremental cost per quality-adjusted life-year (QALY), and total effect on US health care spending over 5 years.

Results  Adding PCSK9 inhibitors to statins in heterozygous FH was estimated to prevent 316 300 MACE at a cost of $503 000 per QALY gained compared with adding ezetimibe to statins (80% uncertainty interval [UI], $493 000-$1 737 000). In ASCVD, adding PCSK9 inhibitors to statins was estimated to prevent 4.3 million MACE compared with adding ezetimibe at $414 000 per QALY (80% UI, $277 000-$1 539 000). Reducing annual drug costs to $4536 per patient or less would be needed for PCSK9 inhibitors to be cost-effective at less than $100 000 per QALY. At 2015 prices, PCSK9 inhibitor use in all eligible patients was estimated to reduce cardiovascular care costs by $29 billion over 5 years, but drug costs increased by an estimated $592 billion (a 38% increase over 2015 prescription drug expenditures). In contrast, initiating statins in these high-risk populations in all statin-tolerant individuals who are not currently using statins was estimated to save $12 billion.

Conclusions and Relevance  Assuming 2015 prices, PCSK9 inhibitor use in patients with heterozygous FH or ASCVD did not meet generally acceptable incremental cost-effectiveness thresholds and was estimated to increase US health care costs substantially. Reducing annual drug prices from more than $14 000 to $4536 would be necessary to meet a $100 000 per QALY threshold.

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Targeting Cardio-Metabolic Diseases and Metabolomics in Drug Discovery – CHI’s 14th Annual Discovery On Target September 19-22, 2016 | Westin Boston Waterfront — Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN


14th Annual Discovery On Target
September 19-22, 2016 | Westin Boston Waterfront — Boston, MA

2016 Prospectus Download | Current Sponsors | Current Exhibitors & Floorplan

Sponsorship Opportunities | 2015 Attendee List

Hello Aviva,

I wanted to inform you of the opportunity to meet with thought leaders at the 14th Annual Discovery On Target event, taking place September 19-22, 2016 in Boston attending the Targeting Cardio-Metabolic Diseases and Metabolomics in Drug Discovery conference programs. As a sponsor and/or exhibitor of this meeting, you have the opportunity to speak and network with 1,100+ attendees from 20+ countries composed of scientists, executives, directors, and managers from large biotech and pharmaceutical companies.

Delivering a sponsored presentation during the conference program is the most effective way to access even the hardest-to-reach decision makers from within your target market. This will increase your scientific presence and drive more qualified leads to your booth space, maximizing your ROI.

Please see the session topics, below:

This conference focuses on new cardiometabolic drug targets, mostly PCSK9 and the connections between cardiometabolic disease and liver metabolism, especially as manifested in a disease of the fatty liver, NASH (Non-Alcoholic SteatoHepatitis). 

Topics include:

  • New Cardiometabolic Drug Targets and PCSK9
  • NASH: Non-Alcohlic Steatohepatitis and Cardiometabolism

This conference will emphasize presentations that analyze metabolomics data in a larger context of cellular functioning or disease states. A few introductory type presentations will highlight the current state of the field and its major technologies.

Topics include:

  • Metabolomics Overview and Technologies
  • Disease-Focused Research Stemming from the Metabolomic Analysis
  • Cancer Metabolism

Opportunities are available for sponsored presentations during the conference agenda, One-on-One Meetings, and exhibit opportunities. Act now, as priority placement is given to companies who sign on early. We can customize a sponsorship package to meet your company’s needs and reach your target audience. Thank you for your time and I look forward to hearing from you!

Kind regards,

Jon Stroup

Senior Manager, Business Development
T: 781.972.5483
F: 781.972.5452
E: jstroup@cambridgeinnovationinstitute.com
W: DiscoveryOnTarget.com

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2016 Prospectus Now Available!



From: Jon Stroup <sales2@healthtech.com>

Date: Monday, June 13, 2016 at 2:40 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Targeting Cardio-Metabolic Diseases & Metabolomics in Drug Discovery

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A Concise Review of Cardiovascular Biomarkers of Hypertension

Curator: Larry H. Bernstein, MD, FCAP


Revised 5/25/2016



While a large body of work had been done on cholesterol synthesis, HDL and LDL cholesterol, triglycerides, and lipoproteins for a quarter century, and the concept of metabolic syndrome was emerging, there was neither a unifying concept nor a sufficient multivariable approach to apply the use of laboratory markers to clinical practice.  The mathematical foundation for such an evaluation of the biological markers and the computational tools were maturing at the turn of the 20th century, and the interest in outcomes research for improved healthcare practice was maturing. In addition, there was now heavy investment in health information systems that would support emerging health networks of a rapidly consolidating patient base.  This has become important for the pharmaceutical industry and for allied health sciences to enable a suitable method of measuring the effectiveness of drug and of lifestyle changes to improve the population health.

The importance of finding biomarkers for hypertension is significant as stated above. I refer to observations in a lecture by Teresa Seeman, Ph.D., Professor, UCLA Geffen School of Medicine (1).
The missed cased of hypertension in the U.S. alone has been examined by the NHANES studies. Table  I
shows the poor identification of this serious chronic condition. The next table (Table II)*, also from NHANES  (Seeman study) looks at Allostatic Load for biomarkers using component biomarker measurement criterion cutpoints.  Table III* gives the odds ratios for mortality by Allostatic Load Score.

An explanatory problem for our difficulty with diagnosis of a number of hypertension disease “subsets” is that there is peripheral hypertension that might be idiopathic, or it might be related to coexisting diseases with both inflammatory and vascular structural dynamics nature.  In addition, this may be concurrent with pulmonary hypertension, systemic hypertension, and progressive renal disease.  This discussion is reserved for later.  As stated, the late or missed diagnosis of systemic or essential idiopathic hypertension is illustrated in the three Seeman tables (1).


Table 1

Table 2

Table 3





Table 1*. Missed cases by “self report”


vs undiagnosed

study NHANES 88-94 NHANES 99-2004 NHANES 2005-08
Hypertension %unaware  BP > 140/90 42.7 43.5 39.06











High cholesterol Chol > 220 g/dl 55.93 49.3 47.05
SR- high
Diabetes HgA1C > 6.4%      

SR- high









*modified from Seeman










Table II* USHANES: Allostatic Load – component cutpoints

Biomarker Total N High Risk Percent (%) Cutpoint
DBP (mm Hg) 15,489 1,180   7.62    90
SBP (mm Hg) 15,491 3,461 22.34  140
Pulse Rate 15,117 1,009   6.67    90
HgA1C (%) 15,441 1,482   9.60    6.4
WHR 14,824 6,778 45.72    0.94
HDL Cholesterol (mg/dl) 15,187 3,440 22.65     40
Total Cholesterol


15,293 3,196  20.90    240

*From  T. Seaman, UCLA Geffen SOM


Table III*. Odds of mortality by Allostatic Load Score.

ALS Odds Ratio
7-8 5
6 2.6
5 2.3
4 2.1
3 1.8
2 1.5
1 1.4


*From  T. Seaman, UCLA Geffen SOM


I refer to cardiovascular diseases in reference to an aggregate of diseases affecting the heart, the circulatory system from large artery to the capillary, the lungs and kidneys, excluding the lymphatics.
These major disease entities are both separate and interrelated, not necessarily found in the same combinations. However, they account for a growing proportion of illness, apart from cancers, that affect the aging population of western societies. In the discussion that follows, I shall construct a picture of the pathophysiology of cardiovascular diseases, describe the major biomarkers for the assessment of these, point out the relationship of these to hypertension, and try to develop a more targeted approach to the assessment of hypertension and related disorders.

Chronic kidney disease (CKD) is defined as persistent kidney damage accompanied by a reduction in the glomerular filtration rate (GFR) and the presence of albuminuria. The rise in incidence of CKD is attributed to an aging populace and increases in hypertension (HTN), diabetes, and obesity within the U.S. population. CKD is associated with a host of complications including electrolyte imbalances, mineral and bone disorders, anemia, dyslipidemia, and HTN. It is well known that CKD is a risk factor for cardiovascular disease (CVD), and that a reduced GFR and albuminuria are independently associated with an increase in cardiovascular and all-cause mortality.

The relationship between CKD and HTN is cyclic, as CKD can contribute to or cause HTN (3). Elevated BP leads to damage of blood vessels within the kidney, as well as throughout the body. This damage impairs the kidney’s ability to filter fluid and waste from the blood, leading to an increase of fluid volume in the blood—thus causing an increase in BP.


A cursory description of the blood circulation

The full circulation involves the heart as a pump, and the arteries and veins, comprising small and large vessels, and capillaries at the point of delivery of oxygen and capture of carbon dioxide, and of transfer of substrates to tissues.  The brain, liver, pancreas and spleen, and endocrines are not further considered here, except for a consideration on neuro-humoral peptides that have emerged in the regulation of blood pressure and are essential to the stress response. The lung and the liver are both important with respect to the exchange of air and metabolites, and both have secondary circulations, the pulmonary and the portal vascular circulations.  In the case of the lungs, the vena cava flows into the right atrium, which delivers unoxygenated blood to the lungs via the right ventricle and right pulmonary artery, which returns to the left atrium by way of the right pulmonary vein.  The blood from the left atrium that flows into the left ventricle is ejected into the aorta.  The coronary arteries that nourish the heart are at the base of the aorta.  The heart muscle is a syncytium, unlike striated muscle, and it is densely packed with mitochondria, suitable for continuous contraction under vasovagal control. This is the anatomical construct, but the physiology is still being clarified because normal function and disease are both a matter of regulatory control.

In order to understand hypertension, we have to view the heart functioning over a long period of time.
In a still frame picture, we envision the left ventricle contracts emptying the oxygenated blood into the circulation. The ejection of blood into the aorta is called systole, by which the blood is delivered by the force of contraction into the circulation.  The filling pressure is called diastole.  So we have a filling and an emptying, and heard by the stethoscope is a lub-dub, synchronously repeated.   A normal systolic blood pressure is below 120. A systolic blood pressure of 120 to 139 means you have prehypertension, or borderline high blood pressure. Even people with prehypertension are at a higher risk of developing heart disease. A systolic blood pressure number of 140 or higher is considered to be hypertension, or high blood pressure. The diastolic blood pressure number or the bottom number indicates the pressure in the arteries when the heart rests between beats. A normal diastolic blood pressure number is less than 80. A diastolic blood pressure between 80 and 89 indicates prehypertension. A diastolic blood pressure number of 90 or higher is considered to be hypertension or high blood pressure. So now we have identified a systolic and a diastolic high blood pressure. Systolic pressure increases with vigorous activity, and becomes normal when the activity resides.  The systolic blood pressure increases with age. Over time, consistently high blood pressure weakens and damages the blood vessels so affected. Moreover, changes in the body’s normal functions may cause high blood pressure, including changes to kidney fluid and salt balances, the renin-angiotensin-aldosterone system, sympathetic nervous system activity, and blood vessel structure and function.


Starling’s Law of the Heart

Two principal intrinsic mechanisms, namely the Frank-Starling mechanism and rate induced regulation, enable the myocardium to adapt to changes in hemodynamic conditions. The Frank-Starling mechanism (also referred to as Starling’s law of the heart), is invoked in response to changes in the resting length of the myocardial fibers. Rate-induced regulation is invoked in response to changes in the frequency of the heartbeat.  (3-9).

Frank and Starling (3, 4) showed that an increase in diastolic volume caused an increase in systolic performance. The stretch effect persists across a range of myocardial contractile states, but during exercise it plays only a lesser role augmenting ventricular function maximal exercise. This is because in healthy human subjects adrenergic reflex mechanisms modulate myocardial performance, heart rate, vascular impedance and coronary flow during exercise and changes in these variables can overshadow the effect of fiber stretch or even prevent an increase in end-diastolic volume during stress (5). (See you- tube (6).

According to Lakatta muscle length modulates the extent of myofilament calcium ion (Ca2+) activation (7-9).   Similarly, the fiber length during a contraction, which is determined in part by the load encountered during shortening, also determines the extent of myofilament Ca2+ activation. Therefore, the terms preload, afterload and myocardial contractile state lose part of their significance in light of current knowledge.


Biology and High Blood Pressure

Researchers continue to study how various changes in normal body functions cause high blood pressure. The key functions affected in high blood pressure include (10):

Kidney Fluid and Salt Balances

The kidneys normally regulate the body’s salt balance by retaining sodium and water and excreting potassium. Imbalances in this kidney function can expand blood volumes, which can cause high blood pressure.

Renin-Angiotensin-Aldosterone System

The renin-angiotensin-aldosterone system makes angiotensin and aldosterone hormones. Angiotensin narrows or constricts blood vessels, which can lead to an increase in blood pressure. Aldosterone controls how the kidneys balance fluid and salt levels. Increased aldosterone levels or activity may change this kidney function, leading to increased blood volumes and high blood pressure.

Sympathetic Nervous System Activity

The sympathetic nervous system has important functions in blood pressure regulation, including heart rate, blood pressure, and breathing rate. Researchers are investigating whether imbalances in this system cause high blood pressure.

Blood Vessel Structure and Function

Changes in the structure and function of small and large arteries may contribute to high blood pressure. The angiotensin pathway and the immune system may stiffen small and large arteries, which can affect blood pressure.

Two or more types of hypertension

Systemic hypertension

Idiopathic hypertension

Hypertension from chronic renal disease

Pulmonary artery hypertension

Hypertension associated with systemic chronic inflammatory disease (rheumatoid arthritis and other collagen vascular diseases)

Genetic Causes of High Blood Pressure

Much of the understanding of the body systems involved in high blood pressure has come from genetic studies. High blood pressure often runs in families. Years of research have identified many genes and other mutations associated with high blood pressure, some in the renal salt regulatory and renin-angiotensin-aldosterone pathways. However, these known genetic factors only account for 2 to 3 percent of all cases. Emerging research suggests that certain DNA changes during fetal development also may cause the development of high blood pressure later in life.

Environmental Causes of High Blood Pressure

Environmental causes of high blood pressure include unhealthy lifestyle habits, being overweight or obese, and medicines.

Other medical causes of high blood pressure include other medical conditions such as chronic kidney disease, sleep apnea, thyroid problems, or certain tumors.

The common complications of hypertension and their signs and symptoms include:



Pulse Pressure and Stroke Volume

The  pulse pressure is the difference between systolic (the upper number) and diastolic (the lower number) (11).

Systemic pulse pressure = Psystolic – Pdiastolic

The pulse pressure is 40 mmHg for a typical blood pressure reading of 120/80 mmHg.

Pulse pressure (PP) is proportional to stroke volume (SV), the amount of blood pumped from the heart in one beat, and inversely proportional to the compliance or flexibility of the blood vessels, mainly the aorta.

A low (also called narrow) pulse pressure means that not much blood is being expelled from the heart, and can be caused by a number of factors, including severe blood loss due to trauma, congestive heart failure, shock, a narrowing of the valve leading from the heart to the aorta (stenosis), and fluid accumulating around the heart (tamponade).

High (or wide) pulse pressures occur during exercise, as stroke volume increases and the overall resistance to blood flow decreases. It can also occur for many reasons, such as hardening of the arteries (which can have numerous causes), various deficiencies in the aorta (mainly) or other arteries, including leaksfistulas, and a usually-congenital condition known as AVM, pain/anxiety, fever, anemia, pregnancy, and more. Certain medications for high blood pressure can widen pulse pressure, while others narrow it. A chronic increase in pulse pressure is a risk factor for heart disease, and can lead to the type of arrhythmia called atrial fibrillation or A-Fib.


Hypertension Background and Definition

The prevalence of CKD has steadily increased over the past two decades, and was reported to affect over 13% of the U.S. population in 2004.  In 2009, more than 570,000 people in the United States were classified as having end-stage renal disease (ESRD), including nearly 400,000 dialysis patients and over 17,000 transplant recipients.  A patient is determined to have ESRD when he or she requires replacement therapy, including dialysis or kidney transplantation. A National Health Examination Survey (NHANES) spanning 2005-2006 showed that 29% of US adults 18 years of age and older were hypertensive, and of those with high blood pressure (BP), 78% were aware they were hypertensive, 68% were being treated with antihypertensive agents, and only 64% of treated individuals had controlled hypertension (12, 13). In addition, data from NHANES 1999-2006 estimated that 30% of adults 20 years of age and older have prehypertension, defined as an untreated SBP of 120-139 mm Hg or untreated DBP of 80-89 mmHg (12, 13).

Hypertension is the most important modifiable risk factor for coronary heart disease (the leading cause of death in North America), stroke (the third leading cause), congestive heart failure, end-stage renal disease, and peripheral vascular disease. The 2010 Institute for Clinical Systems Improvement (ICSI) guideline (14) on the diagnosis and treatment of hypertension indicates that systolic blood pressure (SBP) should be the major factor to detect, evaluate, and treat hypertension In adults aged 50 years and older. The 2013 joint European Society of Hypertension (ESH) (15) and the European Society of Cardiology (ESC) (16) guidelines recommend that ambulatory blood-pressure monitoring (ABPM) be incorporated into the assessment of cardiovascular risk factors and hypertension.

The JNC 7 (17) identifies the following as major cardiovascular risk factors:

  • Hypertension: component of metabolic syndrome
  • Tobacco use, particularly cigarettes, including chewing tobacco
  • Elevated LDL cholesterol (or total cholesterol ≥240 mg/dL) or low HDL cholesterol: component of metabolic syndrome
  • Diabetes mellitus: component of metabolic syndrome
  • Obesity (BMI ≥30 kg/m 2): component of metabolic syndrome
  • Age greater than 55 years for men or greater than 65 years for women: increased risk begins at the respective ages; the Adult Treatment Panel III used earlier age cut points to suggest the need for earlier action
  • Estimated glomerular filtration rate less than 60 mL/min
  • Microalbuminuria
  • Family history of premature cardiovascular disease (men < 55 years; women < 65 years)
  • Lack of exercise

The Eighth Report of the JNC (JNC 8), released in December 2013 no longer recommends just thiazide-type diuretics as initial therapy in most patients. In essence, the JNC 8 recommends treating to 150/90 mm Hg in patients over age 60 years; for everybody else, the goal BP is 140/90 (18).

Biomarkers Associated with Hypertension

The biomarkers associated with hypertension are for the most part derived from features that characterize the disordered physiology. We might first consider the measurement of blood pressure. Then it becomes necessary to analyze the physiological elements that largely contribute to blood pressure. Finally, there are several biomarkers that have loomed large as measures are myocardial function or myocardial cell death, and are also not independent of renal function, that are indicators of short term and long term cardiovascular status. Having already indicated the importance of measurement of pulse, diastolic and systolic blood pressure in the routine examination of physical status, which is related to cardiac output we shall pay attention to the pulse pressure and pulse wave velocity.    These were defined in the preceding discussion.  They are critically related to the development of hypertension and in the long term, they emerge significantly earlier than either congestive heart failure, chronic kidney disease, acute coronary syndrome, stroke, or cardio-renal syndrome.

Even though cardiovascular disease (CVD), the leading cause of death in developed countries, is not predicted by classic risk factors, there are elements of the risk factor association that need further exploration and will be dissected, such as activity level, obesity, lipids, diabetes mellitus, family history and stress.  Further analysis will point to endocrine and/or metabolic factors that drive cardiovascular risk.

In taking into account the blood pressure measurements, we consider the pulse pressure (PP) and the pulse wave velocity (PWV).  If we refer back to the stroke volume and the Law of the Heart, the systolic blood pressure (SBP) is increased with increased left ventricular output that raises the left ventricular (LV) afterload. This coincides with a decrease in diastolic pressure (DBP) that accompanies a change in coronary artery perfusion (CAP).  Thus, many studies point to increased SBP as a strong risk factor for stroke and CVD.  However, there are sufficient studies that indicate the brachial artery pulse pressure (PP) is a strong determinant of CVD and stroke, and these two elements, SBP and brachial artery PP, may be an indicator of increased arterial stiffness in hypertensive patients and the general population. Brachial PP is also a determinant of recurrent events after acute coronary syndrome (ACS) or with left ventricular hypertrophy (LVH), or the risk of CHF in the aging population, and of all-cause-mortality in the general population.  In addition, the aortic PWV calculated from the Framingham equations was a suitable predictor of CVD risk. In a classic study of arterial stiffness and of CVD and all-cause mortality in an essential hypertension cohort at the Broussais Hospital between 1980 and 1996 (19), the carotid-femoral PWV was measured as an indicator of aortic stiffness, and it was found to be significantly associated with all-cause and CVD mortality independent of previous CVD, age, and diabetes. They tested the hypothesis that aortic stiffness is a predictor of cardiovascular and all-cause mortality in hypertensive patients based on the consideration that the elastic properties of the aorta and central arteries are the major determinants of systemic arterial impedance, and the PWV measured along the aortic and aorto-iliac pathway is the most clinically relevant. They assessed arterial stiffness by measuring the PWV using  the Moens-Korteweg equation based on the increase of the square root of the elasticity modulus in stiffer arteries (20).

PWV as a Diagnostic Test

To assess the performance of PWV considered as a diagnostic test, with the use of receiver operating characteristic (ROC) curves, they calculated sensitivities, specificities, positive predictive values, and negative predictive values of PWV at different cutoff values, first to detect the presence of AA in the overall population and second to detect patients with high 10-year cardiovascular mortality risk in the subgroup of 462 patients without AA with age range from 30 to 74 years. Optimal cutoff values of PWV were defined as the maximization of the sum of sensitivity and specificity.

The main finding of the study was that PWV was a strong predictor of cardiovascular risks as determined by the Framingham equations in a population of treated or untreated subjects with essential hypertension (21). They measured the PWV from foot-to-foot transit time in the aorta for a noninvasive evaluation of regional aortic stiffness, which allows an estimate of the distance traveled by the pulse. The presence of a PWV > 13 m/s, taken alone, appeared as a strong predictor of cardiovascular mortality with high performance values (21). Their work and other studies (22, 23) established increased pulse pressure, the major hemodynamic consequence of increased aortic PWV, as a strong independent predictor of cardiac mortality, mainly MI, in populations of normotensive and hypertensive subjects.

In addition to the findings above, the PWV was found to be an independent predictor of future increase in SBP and of incident hypertension in the Baltimore study (21). The authors reported that in a subset of 306 subjects who were normotensive at baseline, hypertension developed in 105 (34%) during a median follow-up of 4.3 years (range 2 to 12 years). PWV was also an independent predictor of incident hypertension (hazard ratio 1.10 per 1 m/s increase in PWV, 95% confidence interval 1.00 to 1.30, p = 0.03) in individuals with a follow-up duration greater than the median. The authors (21) concluded that carotid-femoral PWV measured using nondirectional transcutaneous Doppler probes (model 810A, 9 to 10-Mhz probes, Parks Medical Electronics, Inc., Aloha, Oregon) could be done to identify normotensive individuals who should be targeted for the implementation of interventions aimed at preventing or delaying the progression of subclinical arterial stiffening and the onset of hypertension.  They reported that age, BMI, and MAP were independently associated with higher SBP on the last visit (Table IV); in addition, PWV was also independently associated with higher SBP on the last visit, and explained 4% of its variance. As shown in Table V, age, BMI, and MAP (p = 0.09, p = 0.009, p < 0.0001 respectively for the interaction terms with time) were predictors of the longitudinal changes in SBP. In addition, PWV was also an independent predictor of the longitudinal increase in SBP (p = 0.003 for the interaction term with time).

In addition, they report that in the group with follow-up duration greater than the median (in which all subjects remained normotensive for the first 4.3 years), beyond age (hazard ratio [HR] 1.02 per 1 year, 95% confidence interval [CI] 0.99 to 1.04, p = 0.2) and SBP (HR 1.05 per 1 mm Hg, 95% CI 1.01 to 1.09, p = 0.006), both HDL (HR 0.96 per 1 mg/dl, 95% CI 0.93 to 0.99, p = 0.02) and PWV (HR 1.10 per 1 m/s, 95% CI 1.00 to 1.30, p = 0.03) (Fig. 1) were independent predictors of incident HTN.

Their findings in a longitudinal projection indicate that PWV, a marker of central arterial stiffening, is an independent determinant of longitudinal SBP increase in healthy BLSA volunteers, and an independent risk factor for incident hypertension among normotensive subjects followed up for longer than 4 years. The study was accompanied by a commentary in the same journal that states: “Pulse wave velocity (PWV) is a simple measure of the time taken by the pressure wave to travel over a specific distance. By virtue of its intrinsic relation to the mechanical properties of the artery by the Moens–Kortweg formula (PWV=√(Eh/2)Rρ; where E is the Young’s Modulus of the arterial wall, h the wall thickness, R the end- diastolic radius and ρ is the density of blood)(20), and buoyed a number of longitudinal studies that reported on the independent predictive value of PWV measurement for cardiovascular events and mortality in various populations, PWV is now widely accepted as the ‘gold standard’ measure of arterial stiffness.




Table IV Multiple Regression Analysis Evaluating the Predictors of Last Visit SBP 21

Variable Parameter
p Value
Age (yrs) 0.32 0.06 <0.0001
Gender (men) 0.65 1.78 0.71
Race (white) −1.22 2.00 0.54
Smoking (ever) 2.48 1.61 0.12
BMI (kg/m2)* 0.61 0.22 0.006
MAP (mm Hg)* 0.60 0.08 <0.0001
PWV (m/s)* 1.56 0.38 <0.0001
Heart rate (beats/min) 0.08 0.06 0.20
Total cholesterol (mg/dl) −0.005 0.02 0.83
Triglycerides (mg/dl) −0.009 0.01 0.50
HDL cholesterol (mg/dl) −0.001 0.07 0.98
Glucose (mg/dl) −0.02 0.06 0.75









Table V Predictors of Longitudinal SBP Derived From a Linear Mixed-Effects Regression Model 21

Variable Coefficient Standardized


95% Confidence


p Value
Time (yrs) 3.14 0.14 0.61 to 5.66 0.02
Age (yrs) −0.37 0.25 −0.68 to −0.06 0.02
Age2 (yrs2)* 0.006 0.08 0.002 to 0.008 <0.0001
Gender (men) 0.61 0.03 −1.26 to 2.47 0.52
BMI (kg/m2)* 0.25 0.11 −0.01 to 0.50 0.06
MAP (mmHg)* 1.03 0.47 0.93 to 1.12 <0.0001
PWV (m/s) 0.29 0.12 −0.16 to 0.74 0.21
Time × age* 0.02 0.04 −0.002 to 0.038 0.09
Time × BMI* 0.10 0.06 0.02 to 0.183 0.009
Time × MAP* −0.08 −0.12 −0.11 to −0.05 <0.0001
Time × PWV* 0.22 0.08 0.07 to 0.36 0.003



Figure 1 21


Figure 2.21


The interest in this physiological measure is illustrated by the increasing number and diversity of research publications in this arena related to human hypertension, relating PWV to pathophysiological processes (for example, homocysteine, inflammation and extracellular matrix turnover and disorders related to hypertension, such as sleep apnea). The epidemiology, genetic associations and prognostic implications of PWV (and arterial stiffness) have also been reported as has the relationship to hemodynamics, cardiac structure and function.” (24) Furthermore, arterial stiffening may be “characterized by an increase in (central) PP and changes in the morphology of the arterial waveform, both of which can now be measured non-invasively using tonometers from commercially available devices. Wave reflection is typically characterized by aortic pressure augmentation (ΔP) and the augmentation index (ΔP/PP) (Figure 3)(24). Higher augmented pressure, as an index of wave reflection, has been linked to adverse clinical outcomes in different populations.

Figure 3.24

Analysis of the pressure waveform. The initial systolic pressure is labelled as P1 and augmented pressure ( P) is typically measured as the difference between peak pressure (P2) and P1. Augmentation index is  P/PP. PP, pulse pressure.    http://www.nature.com/jhh/journal/v22/n10/images/jhh200847f1.gif 24

A review by Payne et al. (25) states that aortic stiffness and arterial pulse wave reflections determine elevated central systolic pressure and are associated with risk of adverse cardiovascular outcomes. This is because an impaired compensatory mechanism through matrix metalloproteinases of remodeling to compensate for changes in wall stress, possibly related to angiotensin II and inhibition of the vascular adhesion protein semicarbazide-sensitive amine oxidase, related to reduced elastin fiber cross-linking. This has implications for pharmacological agents that target age-related advanced glycation end-product cross-links. This also brings into consideration NO playing a considerable role. But they caution that the endogenous NO synthase inhibitors asymmetric dimethylarginine and L-NG-monomethyl arginine associated with clinical atherosclerosis don’t appear to be associated with arterial stiffening. The matter leaves much to be explained.  The mechanisms underlying arterial stiffness could well require insights into inflammation, calcification, vascular growth and remodeling, and endothelial dysfunction. Nevertheless, arterial stiffness is independently associated with cardiovascular outcome in most of the situations where it has been examined.  Given this train of thinking, O’Rourke (26) considers a progressive arterial dilatation with repeated cycles of stress that leads to degeneration of the arterial wall and increases the pressure wave impulse and wave velocity, augmenting the pressure in late systole. Drugs may reduce wave reflection, but have no direct effect on arterial stiffness.  However, reduction in wave reflection decreases aortic systolic pressure augmentation.  DK Arnett (26) depicts the effect of persistently elevated blood pressure in the following diagram (Figure 4).


Figure 4.26  Both transient and sustained stiffening of the artery are likely to be present in hypertension.

An initial elevation in blood pressure may establish a positive feedback in which hypertension biomechanically increases arterial stiffness without any structural change. This elevated blood pressure   might later lead to additional vascular hypertrophy and hyperplasia, collagen deposition, and atherosclerosis, and fixed elevations in arterial stiffness.  As to a genetic factor, she refers to a gene contributing to pulse pressure on chromosome 8 located at 32 cM, which also contains the lipoprotein lipase (LPL) gene which has been associated with hypertension. LPL may be an important candidate gene for pulse pressure.  She specifically identifies a relationship between genetic regions contributing to aortic compliance in African American sibships ascertained for hypertension in Figure 5 (27).  These results suggest there may be influential genetic regions contributing to aortic compliance in African American sibships ascertained for hypertension (27). Collectively, these two studies, the first to our knowledge, indicate the presence of genetic factors influencing hypertension.

Other authors state that PWV has a direct relationship to intrinsic elasticity of the arterial wall, and it is an independent predictor of CVD related morbidity and mortality, but it is not associated with classical risk factors for atherosclerosis (28).  They point out that PWV doesn’t increase during early stages of atherosclerosis, as measured by intima-media thickness and non-calcified atheroma, but it does increase in the presence of aortic calcification that occurs with advanced atherosclerotic plaque. Age-related
PWV measurement. Carotid-to-femoral PWV is calculated by dividing the distance (d) between the two arterial sites by the difference in time of pressure wave arrival between the carotid (t1) and femoral artery (t2) referenced to the R wave of the electrocardiogram.

Figure 5. Linkage of arterial compliance on chromosome 2: HyperGEN27

Widening of the pulse pressure is the major cause of age-related increase in prevalence of hypertension and is related to arterial stiffening. (28)  Commonly used points for measuring the PWV are the carotid and femoral artery because they are superficial and easy to access. Arterial distensibility is measured by the Bramwell and Hill equation (29): PWV = √(V × ΔP/ρ × ΔV), where ρ is blood density. This is shown in Figure 6.


Figure 6 28


View larger version:


Furthermore, these authors (28) report arterial stiffness increases with age by approximately 0.1 m/s/y in East Asian populations with low prevalences of atherosclerosis, but some authors have found accelerated stiffening between 50 and 60 years of age. In contrast, stiffness of peripheral arteries increases less or not at all with increasing age. Again, ageing of the arterial media is associated with increased expression of matrix metalloproteinases (MMP), which are members of the zinc-dependent endopeptidase family and are involved in degradation of vascular elastin and collagen fibers. Several different types of MMP exist in the vascular wall, but in relation to arterial stiffness, much interest has focused on MMP-2 and MMP-9.  This concludes the discussion of PP and PWV in the evolution of hypertension.


Diagnostic Biomarkers of essential hypertension.

Ioannidis and Tzoulaki (30) reviewed the literature on 10 popular ‘‘new’’ biomarkers and found that each one had accrued more than 6000 publications.1 The predictive effects of these popular blood biomarkers for coronary heart disease in the general population are listed in Table VI (31).


Table VI.* Predictive Value of New Biomarkers 30,31

Biomarker Adjusted Relative Risk (95% C.I.)
Triglycerides 0.99 (0.94–1.05)
C-reactive protein 1.39 (1.32–1.47)
Fibrinogen 1.45 (1.34–1.57)
Interleukin 6 1.27 (1.19–1.35)
BNP or NT-proBNP 1.42 (1.24–1.63)
Serum albumin 1.2 (1.1–1.3)
ICAM-1 (0.75–1.64)
Homocysteine 1.05 (1.03–1.07)
Uric acid 1.09 (1.03–1.16)

*Ionnidis and Tzoulaki from Giles
The majority of these biomarkers show small effects, if any, even in combination.  Giles (31) points out that an elevated homocysteine level might be of great importance to a young person with a myocardial infarction and a positive family history of similar occurrences. Emerging biomarkers, eg, asymmetric and symmetric dimethylarginine and galectin-3, are promising more specific biomarkers based on pathophysiologies for cardiovascular disease. Even then, blood pressure remains the biomarker par excellence for hypertension and for many other cardiovascular entities.

The importance of blood pressure was highlighted by the report of the cardiovascular lifetime risk pooling project.(10) Starting at 55 years of age, 61,585 men and women were followed over an average of 14 years, ie, 700,000 person-years. Individuals who maintained or decreased their blood pressure to normal levels had the lowest remaining lifetime risk for cardiovascular disease (22–41%) compared with individuals who had or developed hypertension by 55 years of age (42–69%). The study indicated that efforts should continue to emphasize the importance of lowering blood pressure and avoiding or delaying the incidence of hypertension to reduce the lifetime risk for cardiovascular disease

A small study involving 120 hypertensive patients with or without heart failure tried to establish a multi-biomarker approach to heart failure (HF) in hypertensive patients using N-terminal pro BNP (32). The following biomarkers were included in the study: Collagen III N-terminal propeptide (PIIINP), cystatin C (CysC), lipocalin-2/NGAL, syndecan-4, tumor necrosis factor-α (TNF-α), interleukin 1 receptor type I (IL1R1), galectin-3, cardiotrophin-1 (CT-1), transforming growth factor β (TGF-β) and N-terminal pro-brain natriuretic peptide (NT-proBNP). The highest discriminative value for HF was observed for NT-proBNP (area under the receiver operating characteristic curve (AUC) = 0.873) and TGF-β (AUC = 0.878). On the basis of ROC curve analysis they found that CT-1 > 152 pg/mL, TGF-β < 7.7 ng/mL, syndecan > 2.3 ng/mL, NT-proBNP > 332.5 pg/mL, CysC > 1 mg/L and NGAL > 39.9 ng/mL were significant predictors of overt HF. There was only a small improvement in predictive ability of the multi-biomarker panel including the four biomarkers with the best performance in the detection of HF (NT-proBNP, TGF-β, CT-1, CysC) compared to the panel with NT-proBNP, TGF-β and CT-1 (absent  CysC). The biomarkers with different pathophysiological backgrounds (NT-proBNP, TGF-β, CT-1) give additive prognostic value for incident compared to NT-proBNP alone.

Inflammation has been associated with pathophysiology of hypertension and vascular damage. Resistant hypertensive patients (RHTN) have unfavorable prognosis due to poor blood pressure control and higher prevalence of target organ damage. Endothelial dysfunction and arterial stiffness are involved in such condition. Previous studies showed that RHTN patients have higher arterial stiffness and endothelial dysfunction than controlled hypertensive and normotensive subjects. The relationship between high blood pressure levels and arterial stiffness may be explained in part, by inflammatory pathways. Previous studies also found that hypertensive subjects have higher levels of inflammatory cytokines including TNF-α, IL-10, IL-1β and CRP. Moreover, IL-1β correlates with arterial stiffness and levels of blood pressure, which are particularly high in patients with resistant hypertension. Increased inflammatory cytokines levels might be related to the development of vascular damage and to the higher cardiovascular risk of resistant hypertensive patients. Elevated BP may cause cardiovascular structural and functional alterations leading to organ damage such as left ventricular hypertrophy, arterial and renal dysfunction. TNF-α inhibition reduced systolic BP and endothelial inflammation in SHR [33]. They also found that IL-1β correlates with arterial stiffness and levels of blood pressure, even after adjust for age and glucose [33]. These investigators then demonstrated that isoprostane levels, an oxidative stress marker, were associated with endothelial dysfunction in these patients [33].

Chao et al. carried out studies of kallistatin (34-36). Kallistatin is an endogenous protein in human plasma as a tissue Kallikrein-Binding Protein (KBP). Tissue kallikrein is a serine protease that releases vasodilating kinin peptides from kininogen substrate. The tissue kallikrein-kinin system is involved in mediating beneficial effects in hypertension as well as cardiac, cerebral and renal injury. KBP was later identified as a serine protease inhibitor (serpin) because of its ability to inhibit tissue kallikrein activity, and was subsequently named “kallistatin”. Kallistatin is mainly expressed in the liver, but is also present in the heart, kidney and blood vessel. Kallistatin protein contains two structural elements: an active site and a heparin-binding domain. The active site of kallistatin is crucial for complex formation with tissue kallikrein, and thus tissue kallikrein inhibition.

Kallistatin is expressed in tissues relevant to cardiovascular function, and has consequently been shown to have vasodilating properties.  Kallistatin has pleiotropic effects in vasodilation and inhibition of inflammation, angiogenesis, oxidative stress, fibrosis, and cancer progression. Injection of a neutralizing Kallistatin antibody into hypertensive rats aggravates cardiovascular and renal injury in association with increased inflammation, oxidative stress and tissue remodeling.  Neither the blood pressure-lowering effect nor the vasorelaxation ability of kallistatin is abolished by icatibant (Hoe140, a kinin B2 receptor antagonist), indicating that kallistatin-mediated vasodilation is unrelated to the tissue kallikrein-kinin system.

The findings reported indicate that kallistatin exerts beneficial effects against hypertension and organ damage. Kallistatin levels in circulation, body fluids or tissues were lower in patients with liver disease, septic syndrome, diabetic retinopathy, severe pneumonia, inflammatory bowel disease, and cancer of the colon and prostate. In addition, reduced plasma kallistatin levels are associated with adiposity and metabolic risk in apparently healthy African American youths. Considered a negative acute-phase protein, circulating kallistatin levels as well as hepatic expression are rapidly reduced within 24 hours after Lipopolysaccharide (LPS) induced endotoxemia in mice. Similarly, circulating kallistatin levels are markedly decreased in patients with septic syndrome and liver disease. Taking together, the studies indicate that kallistatin exhibits potent anti-inflammatory activity.

The pathogenesis of hypertension and cardiovascular and renal diseases is tightly linked to increased oxidative stress and reduced NO bioavailability (37-39). Time-dependent elevation of circulating oxygen species are associated with reduced kallistatin levels in animal models of hypertension and cardiovascular and renal injury. Stimulation of NO formation by kallistatin may lead to inhibition of oxidative stress and thus multi-organ damage. On the other hand, endogenous kallistatin depletion by neutralizing antibody increased oxidative stress and aggravated cardiovascular and renal damage.

A human kallistatin gene polymorphism has been shown to correlate with a decreased risk of developing acute kidney injury during septic shock. Kallistatin levels are markedly reduced in both humans and mice with sepsis syndrome. However, kallistatin administration protects against lethality and organ injury in animal models of toxic septic shock. Moreover, kallistatin levels are decreased in patients with liver disease, septic shock, inflammatory bowel disease, severe pneumonia and acute respiratory distress syndrome. Taken together, the results indicate that kallistatin has the potential to be a molecular biomarker for patients with sepsis, cardiovascular and metabolic disorders.

Pulmonary hypertension (PH) is defined as a mean pulmonary artery pressure of .25 mmHg at rest or .30 mmHg with exercise. Right heart catheterization is required for the definitive diagnosis. Subsequent investigations are instituted to further characterize the disease. The 6-min walk test (6MWT), a measure of exercise capacity, and the New York Heart Association (NYHA)/World Health Organization (WHO) functional classification, a measure of severity, are used to follow the clinical course while receiving treatment, and these both correlate with disease severity and prognosis (43).

Pulmonary arterial hypertension (PAH) is a progressive disease of the pulmonary vasculature that leads to exercise limitation, right heart failure, and death. There is a need for biomarkers that can aid in early detection, disease surveillance, and treatment monitoring in PAH. Several potential molecules have been investigated; however, only brain natriuretic peptide is currently recommended at diagnosis and for follow-up of PAH patients.

ANP is released from storage granules in atrial tissue, while BNP is secreted from ventricular tissue in a constitutive fashion. ANP secretion is stimulated by atrial stretch caused by atrial volume overload; BNP is released in response to ventricular stretch. Natriuretic peptides act on the kidney, causing natriuresis and diuresis, and relax vascular smooth muscle, causing arterial and venous dilatation, leading to reduced blood pressure and ventricular preload. ANP and BNP are released as prohormones and then cleaved into the active peptide and an inactive N-terminal fragment (43).

Natriuretic peptide precursors are released in response to atrial and ventricular stretch, cleaved into active molecules and inactive precursors and convert guanosine 59-triphosphate (GTP) to cyclic guanosine monophosphate (cGMP), leading to their various physiological actions.

There are a number of confounding factors in the interpretation of natriuretic peptide levels, including left heart disease, sex, age and renal dysfunction. Since most studies exclude patients with left heart disease and renal dysfunction, it becomes problematic extrapolating these results to an unselected population (43).

Endothelin-1 (ET-1) is a peptide found in abundance in the human lung and, through action of endothelin receptors (ETA and ETB) on vascular smooth muscle cells, is implicated in the pathogenesis of PAH. Endothelin receptor antagonists are approved for the treatment of PAH. Levels of circulating ET-1 and related molecules are logical biomarkers of interest in PAH. ET-1 is elevated in PAH compared to controls, and correlates with pulmonary hemodynamic parameters. In addition, higher ET-1 levels are associated with increased mortality in patients treated for PAH. ET-1’s precursor, big-ET-1, has a longer half-life and hence is more stable than ET-1.

Endothelin-1 ET-1 is a potent endogenous vasoconstrictor and proliferative cytokine. The ET-1 gene is translated to prepro-ET-1 which is then cleaved, by the action of an intracellular endopeptidase, to form the biologically inactive big ET-1. ET-converting enzymes further cleave this to form functional ET-1 . There are two ET receptor isoforms, termed type A (ETA), located predominantly on vascular smooth muscle cells, and type B (ETB), predominantly expressed on vascular endothelial cells but also on arterial smooth muscle. Activation of both receptor subtypes, when located on vascular smooth muscle, results in vasoconstriction and cell proliferation. In addition, the endothelial ETB receptor mediates vasodilatation and clearance of ET-1 (43).

Prepro-ET-1 is cleaved to inactive big ET-1 and then further cleaved to form active ET-1. This acts on vascular smooth muscle via the ETA and ETB receptors, causing vasoconstriction and cell proliferation, and on endothelial cells via ETB receptors, releasing nitric oxide (NO) and prostacyclin (PGI2), causing vasorelaxation.

As a biomarker, ADMA has been evaluated in several different classes of PH (43, 44). In IPAH, plasma levels are significantly higher than in healthy, matched controls. In such patients, plasma ADMA correlates positively with right atrial pressure, and negatively with mixed venous oxygen saturation, stroke volume, cardiac index and survival. On stepwise multiple regression analysis, ADMA is an independent predictor of mortality and, using Kaplan–Meier survival curves, patients with supramedian ADMA levels have significantly worse survival than those with inframedian levels.

Patients with idiopathic PAH, plasma levels of Ang-1 and Ang-2 were higher in PAH patients as compared to healthy controls.  Moreover, higher plasma levels of Ang-2 were associated with lower CI and mixed venous oxygen saturation (SvO2) and higher PVR, and, with therapy initiation, changes in Ang-2 correlated with changes in hemodynamics (45, 46).

Endostatin is an antiangiogenic peptide. It is synthesized by myocardium, is detectable in the peripheral circulation of patients with decompensated heart failure, and predicts mortality.48 In PAH, reduced RV myocardial oxygen delivery is felt to contribute to a transition from RV adaptation to failure (46).

Cyclic guanosine monophosphate (cGMP) is an intracellular second messenger of nitric oxide and an indirect marker of natriuretic peptide production (46).

Human pentraxin 3 (PTX3) is a protein synthesized by vascular cells that regulates angiogenesis, inflammation, and cell proliferation (46).

N-terminal propeptide of procollagen III (PIIINP), carboxy-terminal telopeptide of collagen I (CITP), matrix metalloproteinase-9 (MMP-9), and tissue inhibitor of metalloproteinase I (TIMP-1)(46).

Osteopontin (OPN) is a matricellular protein that mediates cell migration, adhesion, remodeling, and survival of the vascular and inflammatory cells (46).

F2-isoprostane is a marker of lipid peroxidation of arachidonic acid, which stimulates endothelial cell proliferation and ET-1 synthesis and may play a role in the pathogenesis of PAH (46).

Circulating fibrocytes are bone marrow-derived cells (CD45 /collagen I ) that contribute to organ fibrosis and extracellular matrix deposition (46).

Circulating miRs (46)

Despite many other substances being investigated as potential biomarkers in PAH, more research is needed to validate the results of small studies and assess their clinical utility. Widespread clinical use of current investigational biomarkers will require validated clinical laboratory techniques and increased knowledge of levels in the healthy population as well as other disease states.

Here are important tests in clinical practice (47):


6-min walk distance

Cardiac index



Higher tertiles associated with worse disease

worse renal function

higher right atrial pressure (RAP)

CITP – vascular remodeling


Recent guidelines (17, 18) encourage the use of screening examinations, such as an echocardiogram (UCG), in high-risk populations for the early detection of PAH . To detect PAH in patients with connective tissue disease (CTD), the obvious screening tests are an UCG and spirometry, including assessment of the diffusing capacity of the lung for carbon monoxide (DLCO). Previous studies have suggested that B-type natriuretic peptide (BNP) and its N-terminal prohormone (NT-proBNP) are potential biomarkers for PAH. However, neither BNP nor NT-pro BNP are specific biomarkers of the degeneration of the pulmonary artery; rather, they are biomarkers of cardiac burden resulting from right heart failure.

Human pentraxin 3 (PTX3) is a specific biomarker for PAH, reflecting pulmonary vascular proteins. They are divided into short and long pentraxins on the basis of their primary structure.
C-Reactive protein (CRP) and serum amyloid P are the classic short pentraxins that are produced in the liver in response to systemic inflammatory cytokines (48). In contrast, PTX3 is one of the long pentraxins. It is synthesized by local vascular cells, such as smooth muscle cells, endothelial cells and fibroblasts, as well as innate immunity cells at sites of inflammation. PTX3 plays a key role in the regulation of cell proliferation and angiogenesis (49).

Increased plasma PTX3 levels have been reported in patients with acute myocardial injury in the
24 h after admission to hospital, with levels returning to normal after 3 days. Similarly, PTX3 levels are higher in patients with unstable angina pectoris, with the changes in PTX3 levels found to be independent of other coronary risk factors, such as obesity and diabetes mellitus. Finally, high serum PTX3 levels have been reported in patents with vasculitis, such as small-vessel vasculitis  and Takayasu aortitis.

Mean plasma PTX3 concentrations in the CTD-PAH and CTD patients were 5.02+0.69 ng/mL (range 1.82–12.94 ng/mL) and 2.40+0.14 ng/mL (range 0.70–4.29 ng/mL), respectively (Table 2). Log transformation of the data revealed significantly higher PTX3 levels in CTD-PAH than in CTD patients (1.49+0.12 vs. 0.82+0.06 log ng/mL, respectively; P = 0.001).(not shown)(50)

Figure 1. Serum pentraxin 3 (PTX3) concentrations in 50 patients with pulmonary arterial hypertension (PAH) and 100 healthy controls, and their correlation with serum concentrations of other biomarkers. A: Comparison of PTX3 concentrations in PAH patients and healthy controls. Mean plasma PTX3 concentrations were 4.4060.37 and 1.94+0.09 ng/mL in the controls and PAH patients, respectively. B: Distribution of log-transformed PTX3 concentrations in PAH patients and healthy controls. C: Log-transformed PTX3 concentrations were significantly higher in patients with PAH than in healthy controls (1.34+0.07 vs. 0.55+0.05 log ng/mL, respectively; P,0.001). D, E: There was no correlation between plasma concentrations of PTX3 and either B-type natriuretic peptide (BNP; r=0.33, P=0.02) or C-reactive protein (CRP; r=0.21, P=0.14) in PAH patients. (not shown) (50)


Table 2. Clinical characteristics and biomarkers in patients with connective tissue disease, with or without pulmonary arterial hypertension.

CTD-PAH ( n =17)                CTD alone ( n =34)       P -value

Age (years)                                 56.3+4.6                                 56.3+2.7               0.990

No. women (%)                         15 (88)                                      31(91)                  0.745

No. with SSc (%)                       10 (59)                                      20 (59)                    1

No. with heart failure (%)          1 (6)                                         0                            –

No. being treated for PAH (%)   17 (100)                                  0                           –

Serum PTX3 (mg/dL)                   5.02+0.69                          2.40+0.14             0.001

Serum CRP (mg/dL)                   0.24+0.09                            0.22+0.04             0.936

Serum BNP (pg/mL)                 189.3+74.                            4 49.3+12.1            0.014

…..  CTD, connective tissue disease; PAH, pulmonary arterial hypertension; SSc, scleroderma;

Figure 3. Receiver operating characteristic (ROC) curves for pentraxin 3 (PTX3) and other biomarkers in patients with connective tissue disease (CTD). The areas under the ROC curve (AUCROC) for PTX3 was 0.866 (95% confidence interval (CI) 0.757–0.974). The star indicates the threshold concentration of 2.85 ng/mL PTX3 that maximized true-positive and false-negative results (sensitivity 94.1%, specificity 73.5%). The AUCROC for C-reactive protein (CRP) was 0.518 (95% CI 0.333–0.704), whereas that for B-type natriuretic peptide (BNP) was 0.670 (95% CI 0.497–0.842). (50)  http://dx.doi.org:/10.1371/journal.pone.0045834.g003

This study was to determine whether PTX3, the regulation of which is independent of that of the systemic inflammatory marker CRP, is a useful biomarker for diagnosing PAH. The investigators found that PTX3 may be a more sensitive biomarker for PAH than BNP, which is, to date, the most established biomarker for PAH, especially in patients with CTD-PAH. Their findings suggest that PTX3 does not reflect the cardiac burden due to the pulmonary hypertension, but rather the activity of pulmonary vascular degeneration because PTX3 levels were significantly decreased after active treatment specifically for PAH (50). PLoS ONE 7(9): e45834. http://dx.doi.org:/10.1371/journal.pone.0045834.

Pharmacologic treatment for pulmonary arterial hypertension (PAH) remains suboptimal and mortality rates are still high, even with pulmonary vasodilator therapy. In addition, we have only an incomplete understanding of the pathobiology of PAH, which is characterized at the tissue level by fibrosis, hypertrophy and plexiform remodeling of the distal pulmonary arterioles. Novel therapeutic approaches that might target pulmonary vascular remodeling, rather than pulmonary vaso-reactivity, require precise patient phenotyping both in terms of clinical status and disease subtype. However, current risk stratification models are cumbersome and not precise enough for choosing or assessing the results of therapeutic intervention. Biomarkers used in patients with left heart failure, such as troponin-T and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are elevated in PAH patients but tend to simply reflect increased circulating plasma volumes and elevated right heart pressure, rather than conveying information about disease mechanism.

In this issue of Heart, Calvier and colleagues (see page 390) (51)propose galectin-3 as a useful biomarker in PAH. The rationale for this hypothesis is that elevated aldosterone levels induce an increase in serum levels of galectin-3, a β-galactoside-binding lectin expressed by circulating myocytes, endothelial cells and other cardiovascular cell types. Among other effects, activation of the aldosterone/galactin-3 pathway promotes fibrosis (51), suggesting that elevated levels will correlate with the severity of PAH due to increased pulmonary arteriolar remodeling. To test this hypothesis, serum levels were measured in a total of 57 patients – 41 with idiopathic PAH (iPAH) and 16 with PAH associated with a connective tissue disorder (CTD). The magnitude of elevation in serum levels of aldosterone, galectin-3 and NT-proBNP each correlated with the severity of PAH. However, as shown in figure 1, although serum levels of galectin-3 were elevated in both iPAH and PAH-CTD patients, aldosterone was elevated only in those with iPAH.

In addition, elevated vascular cell adhesion molecule 1 (VCAM-1) and proinflammatory, anti-angiogenic interleukin 12 (IL-12) in were elevated only in PAH-CTD patients, not in those in iPAH. These data suggest that aldosterone and galectin-3 can be used as biomarkers “in tandem” that reflect both the severity and cause of PAH (52).

In the accompanying editorial, Maron (see page 335) summarizes the knowledge gaps in PAH and concludes: “Taken together, Calvier and colleagues provide a key contribution to an underdeveloped area of pulmonary vascular medicine and in doing so identify galectin-3/aldosterone as promising biomarker(s) for informing both disease pathobiology and clinical status in PAH. The rationale of this pursuit in PAH was based, in part, on lessons earned from left heart failure in which the importance of systemically circulating vasoactive factors to clinical trajectory is well established. In this regard, the current work not only develops a novel scientific avenue worthy of further investigation, but also adds to the evolving body of evidence implicating a role for neurohumoral activation in the pathophysiology of PAH”.

Rheumatoid arthritis (RA) affects about 1% of the population and is known to be a significant risk factor for cardiovascular disease, with a 3-fold increased risk of myocardial infarction, a 2-fold increased risk of sudden death and a 50% increase in cardiovascular mortality rates. However, outcomes after PCI in RA patients have not been well characterized and there is little data on the possible effects of disease modifying therapy for RA on risk of restenosis after percutaneous coronary intervention (PCI). In a single center retrospective cohort study, Sintek and colleagues (53)(see page 363) compared the primary endpoint of repeat target vessel revascularization (TVR) in 143 RA patients matched to 541 other.

Pathophysiological targets of differing imaging modalities, demonstrate targets for tracers/contrast agents/pharmacotherapy used in SPECT, PET, MRI and echocardiography to assess myocardial viability.  (Not shown. Adapted from Schuster et al., J Am Coll Cardiol 2012; 59:359–70.)

Ischemic cardiomyopathy implies significant left ventricular systolic dysfunction with an underlying pathophysiology that includes myocardial scarring, hibernation and stunning, or a combination of these disease states. The role of imaging in assessment of myocardial viability is emphasized (not shown) (54) with brief summaries of the role of echocardiography, single photon emission computed tomography (SPECT), positron emission tomography (PET), and magnetic resonance imaging (MRI). The effects of revascularization in patients with ischemic cardiomyopathy remain controversial. Instead, the key elements of evidence based therapy for ischemic cardiomyopathy are standard medical therapy for heart failure combined with implantable cardiac defibrillation (ICD) and/or biventricular pacing device therapy in appropriate patients.

The relationship between the heart and the kidney in hypertension and heart failure

Hypertension is undoubtedly a factor in the treatment of chronic kidney disease because of the relationship between kidney function and BP components that have been studied in people with CKD, diabetes, and hypertension.  Cystatin C was used to evaluate the association between kidney function and both SBP and DBP and 24-h creatinine clearance (CrCl) among 906 participants in the Heart and Soul Study.  (56).  The study investigators hypothesized that although both creatinine and cystatin C are freely filtered at the glomerulus, a major difference between them is that creatinine is secreted by renal tubules, whereas cystatin C is metabolized by the proximal tubule and only a small fraction appears in the urine. In addition, Cystatin C has also been shown to be a stronger predictor of adverse outcomes than serum creatinine. Based on the more linear relationship of cystatin C with GFR, they hypothesized that cystatin C would have a stronger association with SBP than conventional measures of kidney function. Their results found that SBP was linearly associated with cystatin C concentrations (1.19 ± 0.55 mm Hg increase per 0.4 mg/L cystatin C, P = .03) across the range of kidney functions, but only in subjects with CrCl <60 mL/min (6.4 ± 2.13 mm Hg increase per 28 mL/min, P = .003), not >60 mL/min. Further, the DBP was not associated with cystatin C or CrCl. However, PP was linearly associated with both cystatin C (1.28 ± 0.55 mm Hg per 0.4 mg/L cystatin, P = .02) and CrCl <60 mL/min (7.27 ± 2.16 mm Hg per 28 mL/min, P = .001). The relationship between SBP and cystatin C by decile is shown in Figure 7 and Table 3.

Figure 7.

Mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) by decile of kidney function measured as cystatin C. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2771570/bin/nihms-153474-f0001.jpg



Table 3

Linear regression of systolic blood pressure by kidney function (N = 906)

Age-adjusted Multivariable adjusted*
Measure N β coefficient P β coefficient P
Cystatin-C (per 0.4 mg/L [SD] increase) 1.75 ± 0.72 .01 1.19 ± 0.55 .03
    >1.0 551 2.23 ± 0.07 .03 1.23 ± 0.03 .04
    <1.0 355 1.59 ± 0.04 .71 0.54 ± 0.01 .87
Spline P value for difference in slopes .85
24-h CrCl (per 28 mL/min [SD] decrease)
    Overall 1.96 ± 0.76 .01 0.91 ± 0.61 .14
    <60 222 11.20 ± 2.74 <.001 6.40 ± 2.13 .003
    >60 684 0.31 ± 0.99 .42 0.36 ± 0.77 .64
    Spline P-value for difference in slopes .01

The results for both Cystatin C and for eGFR are in agreement with incidence rates for heart failure (57)categorized by ejection fraction (EF) and kidney function over 1992−2000 in the Cardiovascular Health Study. Estimated glomerular filtration rate (mL/min per 1.73 m2) is labeled as “eGFR”. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2258307/bin/nihms-39968-f0002.jpg).

The association of cystatin C with risk for SHF appeared linear across quartiles of cystatin C (57) and slightly stronger at the highest categories of cystatin C, whereas the lower three quartiles of cystatin C had similar risks for DHF. Participants with an estimated GFR ≥ 60 mL/min per 1.73 m2 had an equal likelihood of developing DHF or SHF, whereas participants with an estimated GFR < 60 mL/min per 1.73 m2 had a greater likelihood of developing SHF.

When an interaction term for HF type (SHF or DHF) was inserted into a fully adjusted standard Cox proportional hazards model with HF with either type of EF as the outcome, the association of continuous cystatin C with SHF was significantly greater than the association of cystatin C with DHF ( P value for interaction < 0.001). The association of estimated GFR and SHF compared with DHF was weaker (P value for interaction = 0.06 for the fully adjusted model).

Ascending quartiles of cystatin C were associated with increasing adjusted risk for the development of “unclassified” HF, defined by the absence of a point-of-care EF measurement. The magnitude of the fully adjusted hazard ratios for the association between cystatin C and risk of unclassified HF were intermediate between those described for DHF and SHF [hazard ratios (95% confidence intervals) for each higher quartile of cystatin C 1.00 (reference), 1.12 (0.80−1.57), 1.84 (1.34−2.51), 2.18 (1.58−3.00)]. The authors state that increased left atrial filling pressures trigger the release of atrial natriuretic peptide and inhibition of vasopressin, which leads to decreased renal sympathetic tone and diuresis early in the pathogenesis of HF (57).  They suggest that even relatively small decrements in k58idney function contribute to the risk of SHF.

Aldosterone plays a key role in homeostatic control and maintenance of blood pressure (BP) by regulation of extracellular volume, vascular tone, and cardiac output. Taking this assumption further, a study unrelated to that above explored the magnitude of the effect of relative aldosterone excess in predicting peripheral as well as aortic blood pressure in a cohort of patients undergoing coronary angiography.  (58) They found that mean peripheral systolic blood pressure (SBP) and diastolic blood pressure (DBP) of the entire cohort were 141 ± 24 mm Hg and 81 ± 11 mm Hg, respectively. Median SBP and aortic SBP increased steadily and significantly from aldosterone/renin ratio (ARR), respectively; p < 0.0001 for both) after multivariate adjustment for parameters potentially influencing BP. ARR emerged as the second most significant independent predictor (after age) of mean SBP and as the most important predictor of mean DBP in this patient cohort.  The authors stress the importance of the ARR in modulating BP over a much wider range than is currently appreciated, as it was already known that the ARR was positively associated with pulse wave velocity in young normotensive healthy adults, indicating that relative aldosterone excess might affect arterial remodeling and precede BP rise as a result of increased vascular stiffness. In this study the ARR was calculated as the PAC/PRC ratio (pg/ml/pg/ml). An ARR >50 pg/ml had a sensitivity and specificity of ARR of 89% and 96%, respectively, for primary aldosteronism. The ARR was modeled as a continuous ratio (with log-transformed values).  The study carried out a multivariate stepwise regression analysis for predictors of BP (not shown). They illustrate (not shown) that marked increases in PRC are a major characteristic of lower ARR categories, and that  across a broad range of ARR values, inappropriately elevated aldosterone levels exert a strong effect on BP values and constitute the most important and second-most important predictor of DBP and SBP, respectively.

Cystatin C may be ordered when a health practitioner is not satisfied with the results of other tests, such as a creatinine or creatinine clearance, or wants to check for early kidney dysfunction, particularly in the elderly, and/or wants to monitor known impairment over time. In diverse populations it has been found to improve the estimate of GFR when combined in an equation with blood creatinine. A high level in the blood corresponds to a decreased glomerular filtration rate (GFR) and hence to kidney dysfunction. Since cystatin C is produced throughout the body at a constant rate and removed and broken down by the kidneys, it should remain at a steady level in the blood if the kidneys are working efficiently and the GFR is normal.

Chronic kidney disease (CKD) is defined as the presence of: persistent and usually progressive reduction in GFR (GFR <60 mL/min/1.73 m2) and/or albuminuria (>30 mg of urinary albumin per gram of urinary creatinine), regardless of GFR. Cystatin C is an index of GFR, especially in patients where serum creatinine may be misleading (eg, very obese, elderly, or malnourished patients); for such patients, use of CKD-EPI cystatin C equation is recommended to estimate GFR. Cystatin C eGFR may have advantages over creatinine eGFR in certain patient groups in whom muscle mass is abnormally high or low (for example quadriplegics, very elderly, or malnourished individuals). Blood levels of cystatin C also equilibrate more quickly than creatinine, and therefore, serum cystatin C may be more accurate than serum creatinine when kidney function is rapidly changing (59) (for example amongst hospitalized individuals).

It is a low molecular weight (13,250 kD) cysteine proteinase inhibitor that is produced by all nucleated cells and found in body fluids, including serum. Since it is formed at a constant rate and freely filtered by the kidneys, its serum concentration is inversely correlated with the glomerular filtration rate (GFR); that is, high values indicate low GFRs while lower values indicate higher GFRs, similar to creatinine. While both cystatin C and creatinine are freely filtered by glomeruli, cystatin C is reabsorbed and metabolized by proximal renal tubules. Thus, under normal conditions, cystatin C does not enter the final excreted urine to any significant degree, and the serum concentration is unaffected by infections, inflammatory or neoplastic states, or by body mass, diet, or drugs.  GFR can be estimated (eGFR) from serum cystatin C utilizing an equation which includes the age and gender of the patient (CKD-EPI cystatin C equation, developed by Inker et al. (59) It demonstrated good correlation with measured iothalamate clearance in patients with all common causes of kidney disease, including kidney transplant recipients.

According to the National Kidney Foundation Kidney Disease Outcome Quality Initiative (K/DOQI) classification, among patients with CKD, irrespective of diagnosis, the stage of disease should be assigned based on the level of kidney function:

Table 4

Stage Description GFR mL/min/BSA
1 Kidney damage with normal or  increased GFR 90
2 Kidney damage with mild decrease in  GFR 60-89
3 Moderate decrease in GFR 30-59
4 Severe decrease in GFR 15-29
5 Kidney failure <15 (or dialysis)


In a study to evaluate cystatin C as a measure of renal function in comparison to serum creatinine, 500 patients had cystatin C measured by nephelometry and glomerular filtration rate (GFR) measured by nonradiolabeled iothalamate clearance (59). In addition, serum creatinine was measured and the patients’ medical records reviewed. The correlation of 1/cystatin C with GFR (r=0.90) was significantly superior than 1/creatinine (r=0.82, p<0.05) with GFR. The superior correlation of 1/cystatin C with GFR was observed in the various clinical subgroups of patients studied (ie, subjects with no suspected renal disease, renal transplant patients, recipients of some other transplant, patients with glomerular disease, and patients with non-glomerular renal disease). The findings indicated that cystatin C may be superior to serum creatinine for the assessment of GFR in a wide spectrum of patients (59). Others have similarly found that cystatin C correlates better than serum creatinine for assessment of GFR. (60)

Patients were screened for 3 chronic kidney disease (CKD) studies in the United States (n = 2,980) and a clinical population in Paris, France (n = 438)(61).   GFR was measured by using urinary clearance of iodine125-iothalamate in the US studies and chromium51-EDTA in the Paris study. GFR was calculated using the 4 new equations based on serum cystatin C alone, serum cystatin C, serum creatinine, or both with age, sex, and race. New equations were developed by using linear regression with log GFR as the outcome in two thirds of data from US studies. Internal validation was performed in the remaining one third of data from US CKD studies; external validation was performed in the Paris study.

Mean mGFR, serum creatinine, and serum cystatin C values were 48 mL/min/1.73 m2 (5th to 95th percentile, 15 to 95), 2.1 mg/dL, and 1.8 mg/L, respectively. For the new equations, coefficients for age, sex, and race were significant in the equation with serum cystatin C, but 2- to 4-fold smaller than in the equation with serum creatinine (62, 63). Measures of performance in new equations were consistent across the development and internal and external validation data sets. Percentages of estimated GFR within 30% of mGFR for equations based on serum cystatin C alone, serum cystatin C, serum creatinine, or both levels with age, sex, and race were 81%, 83%, 85%, and 89%, respectively. The equation using serum cystatin C level alone yields estimates with small biases in age, sex, and race subgroups, which are improved in equations including these variables. It is concluded that Serum cystatin C level alone provides GFR estimates not linked to muscle mass, and that an equation including serum cystatin C level in combination with serum creatinine level, age, sex, and race provides the most accurate estimates.
The authors report that absence of urinary excretion has made it difficult to rigorously evaluate cystatin C as a filtration marker and to examine its non-GFR determinants. They also point out that a high level of variation in the cystatin C assay (64, 65), and standardization and calibration of clinical laboratories will be important to obtain accurate GFR estimation using cystatin C, as has been shown for creatinine.

The study reported above was followed by a major study by Inker LA, et al. (59). Their findings are summarized as follows. Mean measured GFRs were 68 and 70 ml per minute per 1.73 m2 of body-surface area in the development and validation data sets, respectively. In the validation data set, the creatinine–cystatin C equation performed better than equations that used creatinine or cystatin C alone. Bias was similar among the three equations, with a median difference between measured and estimated GFR of 3.9 ml per minute per 1.73 m2 with the combined equation, as compared with 3.7 and 3.4 ml per minute per 1.73 m2 with the creatinine equation and the cystatin C equation (P=0.07 and P=0.05), respectively. Precision was improved with the combined equation (interquartile range of the difference, 13.4 vs. 15.4 and 16.4 ml per minute per 1.73 m2, respectively [P=0.001 and P<0.001]), and the results were more accurate (percentage of estimates that were >30% of measured GFR, 8.5 vs. 12.8 and 14.1, respectively [P<0.001 for both comparisons]). In participants whose estimated GFR based on creatinine was 45 to 74 ml per minute per 1.73 m2, the combined equation improved the classification of measured GFR as either less than 60 ml per minute per 1.73 m2 or greater than or equal to 60 ml per minute per 1.73 m2 (net reclassification index, 19.4% [P<0.001]) and correctly reclassified 16.9% of those with an estimated GFR of 45 to 59 ml per minute per 1.73 m2 as having a GFR of 60 ml or higher per minute per 1.73 m2.

Other studies have established the importance of cystatin C levels(66, 67) and the factors influencing cystatin C levels on renal function measurement (68), including an implication that cystatin C, an alternative measure of kidney function, was a stronger predictor of the risk of cardiovascular events and death than either creatinine or the estimated GFR (69). This includes the Dallas Heart Study (30) finding that cystatin C was independently associated with a specific cardiac phenotype of concentric hypertrophy, including increased LV mass, concentricity, and wall thickness, but it was not associated with LV systolic function or volume. This association was particularly robust in hypertensives and blacks. The Cystatin C concentrations within stages of CKD are shown in Table 5 (70).

Table 5

      Cystatin C level
Stage a Description GFR range a (ml/min/1.73 m2) Native kidney disease b Transplant recipient c
1 Normal or increased GFR 90 0.80 0.87
2 Mildly decreased GFR 60 to 89 0.80 to 1.09 0.87 to 1.23
3 Moderately decreased GFR 30 to 59 1.10 to 1.86 1.24 to 2.24
4 Severely decreased GFR 15 to 29 1.87 to 3.17 2.25 to 4.10
5 Kidney Failure <15 >3.17 >4.10

a GFR estimates and CKD stage will be inaccurate if there is a calibration difference with the Dade-Behring BN II Nephelometer assay used in this study.

b Using the prediction equation: GFR=66.8 (cystatin C)-1.30.

c Using the prediction equation: GFR=76.6 (cystatin C)-1.16.


Copeptin, a novel marker

Urinary albumin excretion is a powerful predictor of progressive cardiovascular and renal disease. Copeptin is the inactive C-terminal fragment of the vasopressin precursor. It is a reliable marker of vasopressin secretion serves as a useful substitute for circulating vasopressin concentration. This allows  for the indirect measurement of vasopressin in epidemiological studies. Moreover, it has been shown that copeptin is a candidate biomarker for pneumonia 32), a predictor of outcome in heart failure, and is a powerful predictor of renal disease associated with albumin excretion (71).  Figure 8 shows the association between copeptin and 24-hour urinary volume, 24-h urinary osmolality and osmolality (71).


Figure 8


Association between quintiles of copeptin and median 24-h UAE (upper panel) and prevalence of microalbuminuria (lower panel) for males and females. Differences between the quintiles were tested by Kruskal–Wallis test. UAE, urinary albumin excretion.



Table 6 shows the association between copeptin concentration and urinary albumin excretion (UAE) in a log-log plot (71).


Model Corrected for β 95% CI for β P
 1 − (Crude) 0.25 0.20–0.30 <0.001
 2 As 1+age 0.21 0.16–0.26 <0.001
 3 As 2+MAP, BMI, smoking, glucose, cholesterol, CRP, and eGFR 0.10 0.05–0.16 <0.001
 4 As 3+diuretics and ACEi/ARB. 0.09 0.04–0.15 0.001
 1 − (Crude) 0.19 0.15–0.23 <0.001
 2 As 1+age 0.17 0.14–0.22 <0.001
 3 As 2+MAP, BMI, smoking, glucose, cholesterol, CRP, and eGFR 0.16 0.11–0.21 <0.001
 4 As 3+diuretics and ACEi/ARB. 0.17 0.12–0.21 <0.001

ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin-II-receptor blocker; BMI, body mass index; CHD, coronary heart disease; CI, confidence interval; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; MAP, mean arterial pressure.

Log copeptin concentration was entered in the regression analyses as independent and log UAE as the dependent variable. Copeptin was associated with UAE in all age groups, but this association is the strongest when subjects are older. Twenty-four-hour urinary volume and 24-h urinary osmolarity were significantly different, with 24-h urinary volume being higher and 24-h urinary osmolarity being lower in the oldest age group when compared with the youngest age group. In both males and females, high copeptin concentration (a surrogate for vasopressin) is associated with low 24-h urinary volume and high 24-h urinary osmolarity. However, urinary osmolarity was independently associated with UAE, but it was weaker than that between copeptin and UAE.  This might indicate that induction of specific glomerular hyperfiltration or decreased tubular albumin reabsorption are associated with this relationship. In addition, subjects with higher levels of copeptin had lower renal function.  These investigators concluded that copeptin (a reliable substitute for vasopressin) is associated with UAE and microalbuminuria, consistent with the hypothesis that vasopressin induces UAE (72).  Other studies indicated that copeptin levels are increased in patients with pulmonary artery hypertension (73), and
higher serum copeptin levels, a surrogate for arginine vasopressin (AVP) release, are associated not only with systolic and diastolic blood pressure but also with several components of metabolic syndrome (74) including obesity, elevated concentration of triglycerides, albuminuria, and serum uric acid level.



Natriuretic peptides in the evaluation of heart failure

The brain type natriuretic peptide (BNP) and the N-terminal pro B-type natriuretic peptide (NT proBNP), but not yet the atrial natriuretic peptide have gained prominence in the evaluation of patients with CHF, which may be with or without preserved ejection fraction . Richards et al. (75)  make the following points.


  • Threshold values of B-type natriuretic peptide (BNP) and N-terminal prohormone B-type natriuretic peptide (NT-proBNP) validated for diagnosis of undifferentiated acutely decompensated heart failure (ADHF) remain useful in patients with heart failure with preserved ejection fraction (HFPEF), with minor loss of diagnostic performance.


  • BNP and NT-proBNP measured on admission with ADHF are powerfully predictive of in-hospital mortality in both HFPEF and heart failure with reduced EF (HFREF), with similar or greater risk in HFPEF as in HFREF associated with any given level of either peptide.


  • In stable treated heart failure, plasma natriuretic peptide concentrations often fall below cut-point values used for the diagnosis of ADHF in the emergency department; in HFPEF, levels average approximately half those in HFREF.


  • BNP and NT-proBNP are powerful independent prognostic markers in both chronic HFREF and chronic HFPEF, and the risk of important clinical adverse outcomes for a given peptide level is similar regardless of left ventricular ejection fraction.


  • Serial measurement of BNP or NT-proBNP to monitor status and guide treatment in chronic heart failure may be more applicable in HFREF than in HFPEF.


In addition, they point out the following:


BNP and NT-proBNP fall below ADHF thresholds in stable HFREF in approximately 50% and 20% of cases, respectively. Levels in stable HFPEF are even lower, approximately half those in HFREF.


Whereas BNPs have 90% sensitivity for asymptomatic LVEF of less than 40% in the community (a precursor state for HFREF), they offer no clear guide to the presence of early community based HFPEF.


Guidelines recommend BNP and NT-proBNP as adjuncts to the diagnosis of acute and chronic HF and for risk stratification. Refinements for application to HFPEF are needed.


The prognostic power of NPs is similar in HFREF and HFPEF. Defined levels of BNP and NT-proBNP correlate with similar short-term and long-term risks of important clinical adverse outcomes in both HFREF and HFPEF.


They provide a diagnostic algorithm for suspected heart failure (75)(Figure 9).


Figure 9

Diagnostic algorithm for suspected heart failure presenting either acutely or nonacutely



Diagnostic algorithm for suspected heart failure presenting either acutely or nonacutely. a In the acute setting, mid-regional pro–atrial natriuretic peptide may also be used (cutoff point 120 pmol/L; ie, <120 pmol/L 5 heart failure unlikely). b Other causes of elevated natriuretic peptide levels in the acute setting are an acute coronary syndrome, atrial or ventricular arrhythmias, pulmonary embolism, and severe chronic obstructive pulmonary disease with elevated right heart pressures, renal failure, and sepsis. Other causes of an elevated natriuretic level in the nonacute setting are old age (>75 years), atrial arrhythmias, left ventricular hypertrophy, chronic obstructive pulmonary disease, and chronic kidney disease. c Exclusion cutoff points for natriuretic peptides are chosen to minimize the false-negative rate while reducing unnecessary referrals for echocardiography. Treatment may reduce natriuretic peptide concentration, and natriuretic peptide concentrations may not be markedly elevated in patients with heart failure with preserved ejection fraction.


Patients with acute pulmonary symptoms and with acute myocardial infarct present with dyspnea to the Emergency Department.  The evaluation is made particularly difficult in a patient for whom there is no prior history. Maisel et al. (76) presented the utility of the midregion proadrenomedullin (MR-proADM) in all patients presenting with acute shortness of breath.  They found that MR-proADM was superior to BNP or troponin for predicting 90-day all-cause mortality in patients presenting with acute dyspnea (c index = 0.755, p < 0.0001). Furthermore, MR-proADM added significantly to all clinical variables (all adjusted hazard ratios: HR=3.28), and it was also superior to all other biomarkers.


There is a large body of recent work that has enlarged our view of hypertension, kidney disease, cardiovascular disease, including heart failure with (HFpEF) or without preserved ejection fraction. I shall here refer to my review in Leaders in Pharmaceutical Innovation  (78).  The piece contains a study that I published  (79) with collaborators in Brooklyn, Bridgeport and Philadelphia that is no longer available from the publisher.


The natriuretic peptides, B-type natriuretic peptide (BNP) and NT-proBNP that have emerged as tools for diagnosing congestive heart failure (CHF) are affected by age and renal insufficiency (RI).  NTproBNP is used in rejecting CHF and as a marker of risk for patients with acute coronary syndromes. This observational study was undertaken to evaluate the reference value for interpreting NT-proBNP concentrations. The hypothesis is that increasing concentrations of NT-proBNP are associated with the effects of multiple co-morbidities, not merely CHF,

resulting in altered volume status or myocardial filling pressures.


NT-proBNP was measured in a population with normal trans-thoracic echocardiograms
(TTE) and free of anemia or renal impairment. Exclusion conditions were the following



  • anemia as defined by WHO,
  • atrial fibrillation (AF),
  • elevated troponin T exceeding 0.070 mg/dl,
  • systolic or diastolic blood pressure exceeding 140 and 90 respectively,
  • ejection fraction less than 45%,
  • left ventricular hypertrophy (LVH),
  • left ventricular wall relaxation impairment, and
  • renal insufficiency (RI) defined by creatinine clearance < 60ml/min using
    the MDRD formula .

Study participants were seen in acute care for symptoms of shortness of breath suspicious for CHF requiring evaluation with cardiac NTproBNP assay. The median NT-proBNP for patients under 50 years is 60.5 pg/ml with an upper limit of 462 pg/ml, and for patients over 50 years the median was 272.8 pg/ml with an upper limit of 998.2 pg/ml.

We suggested that NT-proBNP levels can be more accurately interpreted only after removal of the major co-morbidities that affect an increase in this  peptide in serum. The PRIDE study guidelines (http://www.pridestudy.org/)  should be applied until presence or absence of comorbidities is diagnosed. With no comorbidities, the reference range for normal over 50 years of age remains steady at ~1000 pg/ml. The effect shown in previous papers likely is due to increasing concurrent comorbidity with age.

We observed the following changes with respect to NTproBNP and age:

(i) Sharp increase in NT-proBNP at over age 50

(ii) Increase in NT-proBNP at 7% per decade over 50

(iii) Decrease in eGFR at 4% per decade over 50

(iv) Slope of NT-proBNP increase with age is related to proportion of patients with eGFR less than 90

(v) NT-proBNP increase can be delayed or accelerated based on disease comorbidities

The mean and 95% CI of NTproBNP (CHF removed) by the National Kidney Foundation staging for eGFR interval (eGFR scale: 0, > 120; 1, 90 to 119;2, 60 to 89; 3, 40 to 59; 4, 15 to 39; 5, under 15 ml/min). We created a new variable to minimize the effects of age and eGFR variability by correcting these large effects in the whole sample population.

Adjustment of the NT-proBNP for  both eGFR and for age over 50 differences. We have carried out a normalization to adjust for both eGFR and for age over 50:

(i) Take Log of NT-proBNP and multiply by 1000
(ii) Divide the result by eGFR (using MDRD9 or Cockroft Gault10)
(iii) Compare results for age under 50, 50-70, and over 70 years
(iv) Adjust to age under 50 years by multiplying by 0.66 and 0.56.

Figure 10



NKF staging by GFRe interval and NT-proBNP (CHF removed).



The equation does not require weight because the results are reported normalized

to 1.73 m2 body surface area, which is an accepted average adult surface area.


This is illustrated in Figure 11.

Figure 11


Plot of 1000*log (NT-proBNP)/GFR vs age at  eGFR over 90  and 60 ml/min

Figure 12 compares the reference ranges for NTproBNP before and after adjustment.

  • before adjustment; b) after adjustment. c) the scatterplot for 1000xlog(NT proBNP) versus 1000xlog(NT-proBNP/eGFR). Superimposed scatterplot and regression line with centroid and

confidence interval for 1000*log(NT-proBNP)/eGFR vs age (anemia removed)

at eGFR over 40 and 90 ml/min. (Black: eGFR > 90, Blue:  eGFR > 40)


More recent work is enlightening.  Hijazi et al. (80) studied the incremental value of measuring N-terminal pro–B-type natriuretic peptide (NT-proBNP) levels in addition to established risk factors (including the CHA2DS2VASc [heart failure, hypertension, age 75 years and older, diabetes, and previous stroke or transient ischemic attack, vascular disease, age 65 to 74 years, and sex category) for the prediction of cardiovascular and bleeding events. They concluded that NT-proBNP levels are often elevated in atrial fibrillation (AF) and it is independently associated with an increased risk for stroke and mortality. NT-proBNP improves risk stratification beyond the CHA2DS2VASc score and might be a novel tool for improved stroke prediction in AF. The

efficacy of apixaban compared with warfarin was independent of the NT-proBNP level. Moreover, natriuretic peptides are regulatory hormones associated with cardiac remodeling, namely, left ventricular hypertrophy and systolic/diastolic dysfunction. Another study reported that the risk of death of patients with plasma NT-proBNP 133 pg/mL (third tertile of the distribution) was 3.3 times that of patients with values 50.8 pg/mL (first tertile; hazard ratio: 3.30 [95% CI: 0.90 to 12.29]). This predictive value was independent of, and superior to, that of 2 ECG indexes of left ventricular hypertrophy, the Sokolov-Lyon index and the amplitude of the R wave in lead aVL and it persisted in patients without ECG left ventricular hypertrophy (81).
Many patients presenting with acute dyspnea (including those with ADHF) have multiple coexisting medical disorders that may complicate their diagnosis and management. These patients presenting with acute dyspnea may have longer hospital length of stay and are at high risk for repeat hospitalization or death. In this presentation testing for brain natriuretic peptide (BNP) or NT-proBNP has been shown to be valuable for an accurate and efficient diagnosis and prognostication of HF (82).


The biological activity of BNP, the product of an intracellular peptide (proBNP108) that is converted to NT-proBNP, includes stimulation of natriuresis and vasorelaxation; inhibition of renin, aldosterone, and sympathetic nervous activity; inhibition of fibrosis; and improvement in myocardial relaxation.


Figure 13


Biology of the natriuretic peptide system. BNP indicates brain natriuretic peptide; NT-proBNP, amino-terminal pro-B-type natriuretic peptide; and DPP-IV, dipeptidyl peptidase-4.

The authors remind us that approximately 20% of patients with acute dyspnea have BNP or NT-proBNP levels that are above the cutoff point to exclude HF but too low to definitively identify it (82). Knowledge of the differential diagnosis of non-HF elevation of NP, as well as interpretation of the BNP or NT-proBNP value in the context of a clinical assessment is essential.  Across all stages of HF, elevated BNP or NT-proBNP concentrations are at least comparable prognostic predictors of mortality and cardiovascular events relative to traditional predictors of outcome in this setting, with increasing NP concentrations predicting worse prognosis in a linear fashion. This prognostic value may be used to stratify patients at the highest risk of adverse outcomes (see Figure 2 In this page). Age-adjusted Kaplan-Meier survival curve of mortality at 1 year associated with an elevated amino-terminal pro-B-type natriuretic peptide    (NT-proBNP) concentration at emergency department presentation with dyspnea in those with acutely decompensated heart failure. Reproduced from Januzzi et al22. (82)

The importance of determining diastolic and systolic function and for measurement of pulmonary artery pressure by echocardiography is clear, as NT-proBNP levels may be increased with increase in pulmonary pressure as well as conditions that increase cardiac output. Although Hijazi et al. used the Cockcroft-Gault (CG) equation to determine the glomerular filtration rate (GFR) the CG equation may find higher eGFR in older individuals (80). In addition, elevated NT-proBNP independently predicts all-cause mortality and morbidity of patients with AF. A prominent disease with elevated NT-proBNP is a respiratory system disease, such as chronic obstructive pulmonary disease, pulmonary embolism, and interstitial lung disease, in which B-type natriuretic peptide levels are elevated in response to the pressure of the right side of the heart. The authors conclude that one should keep in mind that NT-proBNP alone may be inadequate.

NT-proBNP level is used for the detection of acute CHF and as a predictor of survival. However, a number of factors, including renal function, may affect the NT-proBNP levels. This study aims to provide a more precise way of interpreting NT-proBNP levels based on GFR, independent of age. This study includes 247 pts in whom CHF and known confounders of elevated NT-proBNP were excluded, to show the relationship of GFR in association with age. The effect of eGFR on NT-proBNP level was adjusted by dividing 1000 x log(NT-proBNP) by eGFR then further adjusting for age in order to determine a normalized NT-proBNP value. The normalized NT-proBNP levels were affected by eGFR independent of the age of the patient. A normalizing function based on eGFR eliminates the need for an age-based reference ranges for NT-proBNP (79).

The routine use of natiuretic peptides in severely dyspneic patients has recently been called into question. We hypothesized that the diagnostic utility of Amino Terminal pro Brain Natiuretic Peptide (NT-proBNP) is diminished in a complex elderly population (83)

We studied 502 consecutive patients in whom NT-proBNP values were obtained to evaluate severe dyspnea in the emergency department (84). The diagnostic utility of NT-proBNP for the diagnosis of congestive heart failure (CHF) was assessed utilizing several published guidelines, as well as the manufacturer’s suggested age dependent cut-off points. The area under the receiver operator curve (AUC) for NT-proBNP was 0.70. Using age-related cut points, the diagnostic accuracy of NT-proBNP for the diagnosis of CHF was below prior reports (70% vs. 83%). Age and estimated creatinine clearance correlated directly with NT-proBNP levels, while hematocrit correlated inversely. Both age > 50 years and to a lesser extent hematocrit < 30% affected the diagnostic accuracy of NT-proBNP, while renal function had no effect. In multivariate analysis, a prior history of CHF was the best predictor of current CHF, odds ratio (OR) = 45; CI: 23-88.

The diagnostic accuracy of NT-proBNP for the evaluation of CHF appears less robust in an elderly population with a high prevalence of prior CHF. Age and hematocrit levels, may adversely affect the diagnostic accuracy off NT-proBNP (85).

Obesity and hypertension.

Obesity is associated with an increased risk of hypertension. In the past 5 years there have been dramatic advances into the genetic and neurobiological mechanisms of obesity with the discovery of leptin and novel neuropeptide pathways regulating appetite and metabolism. In this brief review, we argue that these mounting advances into the neurobiology of obesity have and will continue to provide new insights into the regulation of arterial pressure in obesity. We focus our comments on the sympathetic, vascular, and renal mechanisms of leptin and melanocortin receptor agonists and on the regulation of arterial pressure in rodent models of genetic obesity. Three concepts are proposed (86).

First, the effect of obesity on blood pressure may depend critically on the genetic-neurobiological mechanisms underlying the obesity. Second, obesity is not consistently associated with increased blood pressure, at least in rodent models. Third, the blood pressure response to obesity may be critically influenced by modifying alleles in the genetic background.

Leptin plays an important role in regulation of body weight through regulation of food intake and sympathetically mediated thermogenesis. The hypothalamic melanocortin system, via activation of the melanocortin-4 receptor (MC4-R), decreases appetite and weight, but its effects on sympathetic nerve activity (SNA) are unknown. In addition, it is not known whether sympathoactivation to leptin is mediated by the melanocortin system.

The following study (87) tested the interactions between these systems in regulation of brown adipose tissue (BAT) and renal and lumbar SNA in anesthetized Sprague-Dawley rats. Intracerebroventricular administration of the MC4-R agonist MT-II (200 to 600 pmol) produced a dose-dependent sympathoexcitation affecting BAT and renal and lumbar beds. This response was completely blocked by the MC4-R antagonist SHU9119 (30 pmol ICV). Administration of leptin (1000 m g/kg IV) slowly increased BAT SNA (baseline, 4166 spikes/s; 6 hours, 196628 spikes/s; P50.001) and renal SNA (baseline, 116616 spikes/s; 6 hours, 169626 spikes/s; P50.014).

Intracerebroventricular administration of SHU9119 did not inhibit leptin-induced BAT sympathoexcitation (baseline, 3567 spikes/s; 6 hours, 158634 spikes/s; P50.71 versus leptin alone). However, renal sympathoexcitation to leptin was completely blocked by SHU9119 (baseline, 142617 spikes/s; 6 hours, 146625 spikes/s; P50.007 versus leptin alone). The study (87) demonstrates that the hypothalamic melanocortin system can act to increase sympathetic nerve traffic to thermogenic BAT and other tissues. Our data also suggest that leptin increases renal SNA through activation of hypothalamic melanocortin receptors. In contrast, sympathoactivation to thermogenic BAT by leptin appears to be independent of the melanocortin system.


The introduction of the first generation troponins T and I was an important event leading to the declining use of creatine kinase isoenzyme MB because of the short half-life in the circulation of CKMB and the possibility of missing a late presenting ACS. The situation then would call for the measurement of lactate dehydrogenase isoenzyme 1 (H-type), which had a decline in use.  The troponins T and I are proteins associated with the muscle contractile element with high specificity for the cardiomyocyte apparatus, which increased rapidly after ACS and which had estimated diagnostic cutoffs of 0.08 mg/dl and 1 mg/dl respectively.  The choice of marker was largely dependent of the instrument platform.  These biomarkers went through several generations of improvement to improve the diagnostic sensitivity to a cutoff at 2 SD of the lower limit of detection, magnifying confusion in interpretation that had always existed. These cardiospecific markers are elevated in patients with hypertension and specifically, long term CKD. This was clarified by introducing the terms Type 1 and Type 2 myocardial infarct, designating the classic ACS due to plaque rupture as Type 1.  However, the type 2 class might well be non-homogeneous. In any case, these are the best we have in detecting myocardial ischemic damage with biomarker release.



This discussion has covered a large body of research involving hypertension, the kidney, and cardiovascular humoral mechanisms of control with a broad brush.  The work that has been done is far more than is cited.  There are several biomarkers that we have considered. They are not only laboratory based measurements.  They are: PWV, cystatin C, eGFR, copeptin, BNP or NT-BNP, Midregional prohormone adrenomedullin (MR-ADM), urinary albumin excretion, and the aldosterone/renin ratio.

The preceding discussion reminds us of the story of the blind men palpating an elephant, set in a poem by John Godfrey Saxe. These blind men were asked to tell of their experiences palpating different parts of an elephant, without seeing the entire animal Figure 1. Each of the blind men was able to palpate one part of the elephant, and thus was able to describe it in terms that were “partly in the right.” However, because none of them was able to encompass the entire elephant in their hands, they were also “in the wrong,” in that they failed to identify the whole elephant (88).
The blind men and the elephant. Poem by John Godfrey Saxe (Cartoon originally copyrighted by the authors (88); G. Renee Guzlas, artist). http://www.nature.com/ki/journal/v62/n5/thumbs/4493262f1bth.gif

These authors advanced the “elephant” as the increased oxidative burden in the uremic milieu of patients with chronic kidney disease. I introduce the concept in the diagnostic dilemma about what biomarkers are diagnostically informative in hypertension and ischemic CVD poses a conundrum. In reviewing the full gamut of biomarkers, we have a replay of the Lone Ranger and the silver bullet.  The problem is that there is no “silver” bullet.  We are accustomed to rely on clinical observations that are themselves weak covariates in actual experience.  The studies that have been done to validate the effectiveness of key biomarkers are well designed and show relevance in the populations studied.  However, they are insufficient by themselves in the emergent care population.

Impediments to a solution to the problem

Tests are ordered by physicians based on the findings in a clinical history and physical examination. Test that are ordered are reimbursed by insurance carriers, Medicare and Medicaid based on a provisional diagnosis.  The provisional diagnosis generates an ICD10 code, which has been most recently revised with a weighted input from the insurers that is not in favor of considered clinical evidence.  Moreover, the provider of care is graded based on the number of patients seen and the tests performed on a daily basis over any period.  Given this situation, and in addition, the requirement to interact with an outmoded information system that is more helpful to the insurer and less helpful to the provider, it is not surprising that there is a large burnout of the nursing and physician practitioner workforce.  If the diagnosis is inconclusive at the time of patient examination, then the work is not reimbursable based on ICD10 coding requirements that are disease specific.   This problem breaks down into a workload and a reimbursement inconsistency, neither of which makes sense in terms of the original studies on Diagnosis Related Groups (89) at Yale by Robert Fetter’s group.  The problem is made worse by the design and selection of healthcare information systems.

Many have pointed out the flaws in current EHR design that impede the optimum use of data and hinder workflow. Researchers have suggested that EHRs can be part of a learning health system to better capture and use data to improve clinical practice, create new evidence, educate, and support research efforts. The health care system suffers from both inefficient and ineffective use of data. Data are suboptimally displayed to users, undernetworked, underutilized, and wasted. Errors, inefficiencies, and increased costs occur on the basis of unavailable data in a system that does not coordinate the exchange of information, or adequately support its use (90). Clinicians’ schedules are stretched to the limit and yet the system in which they work exerts little effort to streamline and support carefully engineered care processes. Information for decision-making is difficult to access in the context of hurried real-time workflows(91)



The solution to the problem

The current design of the Electronic Medical Record (EMR) is a linear presentation of portions of the record by services, by diagnostic method, and by date, to cite examples.  This allows perusal through a graphical user interface (GUI) that partitions the information or necessary reports in a workstation entered by keying to icons.  This requires that the medical practitioner finds the history, medications, laboratory reports, cardiac imaging and EKGs, and radiology in different workspaces.  The introduction of a DASHBOARD has allowed a presentation of drug reactions, allergies, primary and secondary diagnoses, and critical information about any patient the care giver needing access to the record.  The advantage of this innovation is obvious.  The startup problem is what information is presented and how it is displayed, which is a source of variability and a key to its success.

Gil David and Larry Bernstein have developed, in consultation with Prof. Ronald Coifman, in the Yale University Applied Mathematics Program, a software system that is the equivalent of an intelligent Electronic Health Records Dashboard (92)( that provides empirical medical reference and suggests quantitative diagnostics options.

The most commonly ordered test used for managing patients worldwide is the hemogram that often incorporates the review of a peripheral smear.  While the hemogram has undergone progressive modification of the measured features over time the subsequent expansion of the panel of tests has provided a window into the cellular changes in the production, release or suppression of the formed elements from the blood-forming organ to the circulation.  In the hemogram one can view data reflecting the characteristics of a broad spectrum of medical conditions.

How we frame our expectations is so important that it determines the data we collect to examine the process.   In the absence of data to support an assumed benefit, there is no proof of validity at whatever cost.   This has meaning for hospital operations, for nonhospital laboratory operations, for companies in the diagnostic business, and for planning of health systems.

In 1983, a vision for creating the EMR was introduced by Lawrence Weed, expressed by McGowan and Winstead-Fry (93)

The data presented has to be comprehended in context with vital signs, key symptoms, and an accurate medical history.  Consequently, the limits of memory and cognition are tested in medical practice on a daily basis.  We deal with problems in the interpretation of data presented to the physician, and how through better design of the software that presents this data the situation could be improved.  The computer architecture that the physician uses to view the results is more often than not presented as the designer would prefer, and not as the end-user would like.

Eugene Rypka contributed greatly to clarifying the extraction of features (94) in a series of articles, which set the groundwork for the methods used today in clinical microbiology.  The method he describes is termed S-clustering, and will have a significant bearing on how we can view hematology data.  He describes S-clustering as extracting features from endogenous data that amplify or maximize structural information to create distinctive classes.  The method classifies by taking the number of features with sufficient variety to map into a theoretic standard. The mapping is done by a truth table, and each variable is scaled to assign values for each: message choice.  The number of messages and the number of choices forms an N-by N table.  He points out that the message choice in an antibody titer would be converted from 0 + ++ +++ to 0 1 2 3.

Bernstein and colleagues had a series of studies using Kullback-Liebler Distance  (effective information) for clustering to examine the latent structure of the elements commonly used for diagnosis of myocardial infarction (95-97)(CK-MB, LD and the isoenzyme-1 of LD),  protein-energy malnutrition (serum albumin, serum transthyretin, condition associated with protein malnutrition (see Jeejeebhoy and subjective global assessment), prolonged period with no oral intake), prediction of respiratory distress syndrome of the newborn (RDS), and prediction of lymph nodal involvement of prostate cancer, among other studies.   The exploration of syndromic classification has made a substantial contribution to the diagnostic literature, but has only been made useful through publication on the web of calculators and nomograms (such as Epocrates and Medcalc) accessible to physicians through an iPhone.  These are not an integral part of the EMR, and the applications require an anticipation of the need for such processing.

Gil David et al. (90, 92) introduced an AUTOMATED processing of the data available to the ordering physician and can anticipate an enormous impact in diagnosis and treatment of perhaps half of the top 20 most common causes of hospital admission that carry a high cost and morbidity.  For example: anemias (iron deficiency, vitamin B12 and folate deficiency, and hemolytic anemia or myelodysplastic syndrome); pneumonia; systemic inflammatory response syndrome (SIRS) with or without bacteremia; multiple organ failure and hemodynamic shock; electrolyte/acid base balance disorders; acute and chronic liver disease; acute and chronic renal disease; diabetes mellitus; protein-energy malnutrition; acute respiratory distress of the newborn; acute coronary syndrome; congestive heart failure; disordered bone mineral metabolism; hemostatic disorders; leukemia and lymphoma; malabsorption syndromes; and cancer(s)[breast, prostate, colorectal, pancreas, stomach, liver, esophagus, thyroid, and parathyroid]. The same approach has also been applied to the problem of hospital malnutrition, but it has not been sufficiently applied to hypertension, cardiovascular diseases, acute coronary syndrome, chronic renal failure.

We have developed (David G, Bernstein L, and Coifman) (92) a software system that is the equivalent of an intelligent Electronic Health Records Dashboard that provides empirical medical reference and suggests quantitative diagnostics options. The primary purpose is to gather medical information, generate metrics, analyze them in realtime and provide a differential diagnosis, meeting the highest standard of accuracy. The system builds its unique characterization and provides a list of other patients that share this unique profile, therefore utilizing the vast aggregated knowledge (diagnosis, analysis, treatment, etc.) of the medical community. The main mathematical breakthroughs are provided by accurate patient profiling and inference methodologies in which anomalous subprofiles are extracted and compared to potentially relevant cases. As the model grows and its knowledge database is extended, the diagnostic and the prognostic become more accurate and precise. We anticipate that the effect of implementing this diagnostic amplifier would result in higher physician productivity at a time of great human resource limitations, safer prescribing practices, rapid identification of unusual patients, better assignment of patients to observation, inpatient beds, intensive care, or referral to clinic, shortened length of patients ICU and bed days.

The main benefit is a real time assessment as well as diagnostic options based on comparable cases, flags for risk and potential problems as illustrated in the following case acquired on 04/21/10. The patient was diagnosed by our system with severe SIRS at a grade of 0.61 .

Method for data organization and classification via characterization metrics.

The database is organized to enable linking a given profile to known profiles. This is achieved by associating a patient to a peer group of patients having an overall similar profile, where the similar profile is obtained through a randomized search for an appropriate weighting of variables. Given the selection of a patients’ peer group, we build a metric that measures the dissimilarity of the patient from its group. This is achieved through a local iterated statistical analysis in the peer group.

This characteristic metric is used to locate other patients with similar unique profiles, for each of whom we repeat the procedure described above. This leads to a network of patients with similar risk condition. Then, the classification of the patient is inferred from the medical known condition of some of the patients in the linked network.

How do we organize the data and linkages provided in the first place?

Predictors: PWV, cystatin C, creatinine, urea, eGFR, copeptin, BNP or NT-BNP, TnI or TnT, Midregional prohormone adrenomedullin (MR-ADM), urinary albumin excretion, and the aldosterone/renin ratio, homocysteine, transthyretin, glucose, albumin, chol/LDL, LD, Na+, K+,  Cl, HCO3, pH.

Conditions: AMI, CRF, ARF, hypertension, HFpEF, HFcEF, ADHF, obesity, PHT, RVHF, pulmonary edema, PEM

Other variables: sex (M,F), age, BMI. …

Conditioning data: take log transform for large ascending values, OR take deciles of variables, if necessary.  This could apply to NT-proBNP, BNP, TnI, TnT, CK and LD.

Arrange predictor variables in columns and patient-sequence in rows.  This is a bidimentional table.  The problem is to assign diagnoses to each patient-in sequence. There can be more than one diagnosis.

In reality the patient-sequence or identifier is not relevant. Only the condition assignment is.  The condition assignments are made in a column adjacent to the patient, and they fall into rows.
The construct appears to be a 2×2, but it is actually an n-dimensional  matrix.  Each patient position has one or more diagnoses.

Multivariate statistical analysis is used to extend this analysis to two or more predictors.   In this case a multiple linear regression or a linear discriminant function would be used to predict a dependent variable from two or more independent variables.   If there is linear association dependency of the variables is assumed and the test of hypotheses requires that the variances of the predictors are normally distributed.  A method using a log-linear model circumvents the problem of the distributional dependency in a method called ordinal regression.    There is also a relationship of analysis of variance, a method of examining differences between the means of  two or more groups.  Then there is linear discriminant analysis, a method by which we examine the linear separation between groups rather than the linear association between groups.  Finally, the neural network is a nonlinear, nonparametric model for classifying data with several variables into distinct classes. In this case we might imagine a curved line drawn around the groups to divide the classes. The focus of this discussion will be the use of linear regression  and explore other methods for classification purposes (98).

The real issue is how a combination of variables falls into a table with meaningful information.  We are concerned with accurate assignment into uniquely variable groups by information in test relationships. One determines the effectiveness of each variable by its contribution to information gain in the system.  The reference or null set is the class having no information.  Uncertainty in assigning to a classification is only relieved by providing sufficient information.  One determines the effectiveness of each variable by its contribution to information gain in the system.  The possibility for realizing a good model for approximating the effects of factors supported by data used for inference owes much to the discovery of Kullback-Liebler distance or “information” (99), and Akaike (100) found a simple relationship between K-L information and Fisher’s maximized log-likelihood function. A solid foundation in this work was elaborated by Eugene Rypka (101).  Of course, this was made far less complicated by the genetic complement that defines its function, which made more accessible the study of biochemical pathways.  In addition, the genetic relationships in plant genetics were accessible to Ronald Fisher for the application of the linear discriminant function.    In the last 60 years the application of entropy comparable to the entropy of physics, information, noise, and signal processing, has been fully developed by Shannon, Kullback, and others,  and has been integrated with modern statistics, as a result of the seminal work of Akaike, Leo Goodman, Magidson and Vermunt, and unrelated work by Coifman. Dr. Magidson writes about Latent Class Model evolution:

The recent increase in interest in latent class models is due to the development of extended algorithms which allow today’s computers to perform LC analyses on data containing more than just a few variables, and the recent realization that the use of such models can yield powerful improvements over traditional approaches to segmentation, as well as to cluster, factor, regression and other kinds of analysis.

Perhaps the application to medical diagnostics had been slowed by limitations of data capture and computer architecture as well as lack of clarity in definition of what are the most distinguishing features needed for diagnostic clarification.  Bernstein and colleagues (102-104) had a series of studies using Kullback-Liebler Distance  (effective information) for clustering to examine the latent structure of the elements commonly used for diagnosis of myocardial infarction (CK-MB, LD and the isoenzyme-1 of LD),  protein-energy malnutrition (serum albumin, serum transthyretin, condition associated with protein malnutrition (see Jeejeebhoy and subjective global assessment), prolonged period with no oral intake), prediction of respiratory distress syndrome of the newborn (RDS), and prediction of lymph nodal involvement of prostate cancer, among other studies.   The exploration of syndromic classification has made a substantial contribution to the diagnostic literature, but has only been made useful through publication on the web of calculators and nomograms (such as Epocrates and Medcalc) accessible to physicians through an iPhone.  These are not an integral part of the EMR, and the applications require an anticipation of the need for such processing.

Gil David et al. introduced an AUTOMATED processing of the data (104) available to the ordering physician and can anticipate an enormous impact in diagnosis and treatment of perhaps half of the top 20 most common causes of hospital admission that carry a high cost and morbidity.  For example: anemias (iron deficiency, vitamin B12 and folate deficiency, and hemolytic anemia or myelodysplastic syndrome); pneumonia; systemic inflammatory response syndrome (SIRS) with or without bacteremia; multiple organ failure and hemodynamic shock; electrolyte/acid base balance disorders; acute and chronic liver disease; acute and chronic renal disease; diabetes mellitus; protein-energy malnutrition; acute respiratory distress of the newborn; acute coronary syndrome; congestive heart failure; disordered bone mineral metabolism; hemostatic disorders; leukemia and lymphoma; malabsorption syndromes; and cancer(s)[breast, prostate, colorectal, pancreas, stomach, liver, esophagus, thyroid, and parathyroid].

Our database organized to enable linking a given profile to known profiles(102-104). This is achieved by associating a patient to a peer group of patients having an overall similar profile, where the similar profile is obtained through a randomized search for an appropriate weighting of variables. Given the selection of a patients’ peer group, we build a metric that measures the dissimilarity of the patient from its group. This is achieved through a local iterated statistical analysis in the peer group.

We then use this characteristic metric to locate other patients with similar unique profiles, for each of whom we repeat the procedure described above. This leads to a network of patients with similar risk condition. Then, the classification of the patient is inferred from the medical known condition of some of the patients in the linked network. Given a set of points (the database) and a newly arrived sample (point), we characterize the behavior of the newly arrived sample, according to the database. Then, we detect other points in the database that match this unique characterization. This collection of detected points defines the characteristic neighborhood of the newly arrived sample. We use the characteristic neighborhood in order to classify the newly arrived sample. This process of differential diagnosis is repeated for every newly arrived point.   The medical colossus we have today has become a system out of control and beset by the elephant in the room – an uncharted complexity.



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  19. Richards M, Januzzi JL, and Troughton RW. Natriuretic Peptides in Heart Failure with Preserved Ejection Fraction.  Heart Failure Clin 2014; 10:453–470. http://dx.doi.org/10.1016/j.hfc.2014.04.006
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  21. Bernstein LH. Heart-Lung-Kidney: Essential Ties. Leaders in Pharmaceutical Innovation. http://pharmaceuticalinnovations.com
  22. Bernstein LH, Zions MY, Alam ME, et al.  What is the best approximation of reference normal for NT-proBNP? Clinical levels for enhanced assessment of NT-proBNP (CLEAN). J Med Lab and Diag 04/2011; 2:16-21. http://www.academicjournals.org/jmld
  23. Hijazi  Z., Wallentin  L., Siegbahn  A., et al; N-terminal pro-B-type natriuretic peptide for risk assessment in patients with atrial fibrillation: insights from the ARISTOTLE trial (Apixaban for the Prevention of Stroke in Subjects With Atrial Fibrillation. J Am Coll Cardiol. 2013; 61:2274-2284
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  25. Kim Han-Naand  Januzzi JL.  Natriuretic Peptide Testing in Heart Failure. Circulation 2011;  123: 2015-2019. http://dx.doi.org:/10.1161/CIRCULATIONAHA.110.979500
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  27. Bernstein LH1, Zions MY, Haq SA, et al. Effect of renal function loss on NT-proBNP level variations. Clin Biochem. 2009 Jul; 42(10-11): 1091-8. http://dx.doi.org:/10.1016/j.clinbiochem.2009.02.027
  28. Afaq MA, Shoraki A, Oleg I, Bernstein L, and Stuart W. Zarich.  Validity of Amino Terminal pro-Brain Natiuretic Peptide in a Medically Complex Elderly Population. J Clin Med Res. 2011 Aug; 3(4): 156–163.   doi:  10.4021/jocmr606w
  29. Mark AL, Correia M, MorganDA, et al. New Concepts From the Emerging Biology of Obesity. Hypertension. 1999; 33[part II]:537-541.
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  31. The blind men and the elephant. Poem by John Godfrey Saxe (Cartoon originally copyrighted by the authors; G. Renee Guzlas, artist). http://www.nature.com/ki/journal/v62/n5/thumbs/4493262f1bth.gif
  32. Fetter RB. Diagnosis Related Groups: Understanding Hospital Performance. Interfaces Jan. – Feb., 1991; 21(1), Franz Edelman Award Papers: 6-26
  33. Bernstein LH. Inadequacy of EHRs. Pharmaceutical Intelligence. https://pharmaceuticalintelligence.com/2015/11/05/inadequacy-of-ehrs/
  34. Celi LA,  Marshall JD, Lai Y, Stone DJ. Disrupting Electronic Health Records Systems: The Next Generation.  JMIR  Med Inform 2015 (23.10.15);  3(4) :e34
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  38. Rudolph, R. A., Bernstein, L. H. and Babb, J. Information induction for predicting acute myocardial infarction. Clinical Chemistry 1988; 34: 2031-2038.
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  41. Bernstein LH. Regression: A richly textured method for comparison and classification of predictor variables. https://pharmaceuticalintelligence.com/2012/08/14/regression-a-richly-textured-method-for-comparison-and-classification-of-predictor-variables/
  42. Posada D and Buckley TR. Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches over Likelihood Ratio Tests. Syst. Biol. 200; 53(5):793–808. http://dx.doi.org:/10.1080/10635150490522304
  1. Kullback S. and Leibler R. On Information and Sufficiency. Ann Math Statistics. Mar 1951; 22(1):79-86. http://www.csee.wvu.edu/~xinl/library/papers/math/statistics/Kullback_Leibler_1951.pdf
  2. Bernstein LH, David G, Rucinski J, Coifman RR. Converting Hematology Based Data Into an Inferential Interpretation. In INTECH Open Access Publisher, 2012. https://books.google.com/books/about/Converting_Hematology_Based_Data_Into_an.html
  3. Bernstein LH, David G, Coifman RR. Generating Evidence Based Interpretation of Hematology Screens via Anomaly Characterization. Open Clin Chem J 2011; 4:10-16
  4. Bernstein LH. Automated Inferential Diagnosis of SIRS, sepsis, septic shock. Medical Informatics View. https://pharmaceuticalintelligence.com/2012/08/01/automated-inferential-diagnosis-of-sirs-sepsis-septic-shock/
  5. Bernstein LH, David G, Coifman RR. The Automated Nutritional Assessment. Nutrition  2013; 29: 113-121


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Insight into Blood Brain Barrier

Larry H. Bernstein, MD, FCAP, Curator




Gateway to The Brain

This image shows the structural model of critical transporter, Mfsd2a. Source: Duke-NUS Medical School
This image shows the structural model of critical transporter, Mfsd2a. Source: Duke-NUS Medical School.  http://www.dddmag.com/sites/dddmag.com/files/rd1604_brain.jpg

Scientists from Duke-NUS Medical School (Duke-NUS) have derived a structural model of a transporter at the blood-brain barrier called Mfsd2a. This is the first molecular model of this critical transporter, and could prove important for the development of therapeutic agents that need to be delivered to the brain — across the blood-brain barrier. In future, this could help treat neurological disorders such as glioblastoma.

Currently, there are limitations to drug delivery to the brain as it is tightly protected by the blood-brain barrier. The blood-brain barrier is a protective barrier that separates the circulating blood from the central nervous system which can prevent the entry of certain toxins and drugs to the brain. This restricts the treatment of many brain diseases. However, as a transporter at the blood-brain barrier, Mfsd2a is a potential conduit for drug delivery directly to the brain, thus bypassing the barrier.

In this study, recently published in the Journal of Biological Chemistry, first author Duke-NUS MD/PhD student Debra Quek and senior author Professor David Silver used molecular modeling and biochemical analyses of altered Mfsd2a transporters to derive a structural model of human Mfsd2a. Importantly, the work identifies new binding features of the transporter, providing insight into the transport mechanism of Mfsd2a.

“Our study provides the first glimpse into what Mfsd2a looks like and how it might transport essential lipids across the blood-brain barrier,” said Ms Quek. “It also facilitates a structure-guided search and design of scaffolds for drug delivery to the brain via Mfsd2a, or of drugs that can be directly transported by Mfsd2a.”

Currently this information is being used by Duke-NUS researchers to design novel therapeutic agents for direct drug delivery across the blood brain barrier for the treatment of neurological diseases. This initiative by the Centre for Technology and Development (CTeD) at Duke-NUS, is one of many collaborative research efforts aimed at translating Duke-NUS’ research findings into tangible commercial and therapeutic applications for patients.

Ms Quek plans to further validate her findings by purifying the Mfsd2a protein in order to further dissect how it functions as a transporter.


J Biol Chem. 2016 Mar 4. pii: jbc.M116.721035. [Epub ahead of print]
Structural insights into the transport mechanism of the human sodium-dependent lysophosphatidylcholine transporter Mfsd2a.

Major Facilitator Superfamily Domain containing 2A (Mfsd2a) was recently characterized as a sodium-dependent lysophosphatidylcholine (LPC) transporter expressed at the blood-brain barrier endothelium. It is the primary route for importation of docosohexaenoic acid and other long-chain fatty acids into foetal and adult brain, and is essential for mouse and human brain growth and function. Remarkably, Mfsd2a is the first identified MFS family member that uniquely transports lipids, implying that Mfsd2a harbours unique structural features and transport mechanism. Here, we present three 3D structural models of human Mfsd2a derived by homology modelling using MelB- and LacY-based crystal structures, and refined by biochemical analysis. All models revealed 12 transmembrane helices and connecting loops, and represented the partially outward-open, outward-partially occluded, and inward-open states of the transport cycle. In addition to a conserved sodium-binding site, three unique structural features were identified: A phosphate headgroup binding site, a hydrophobic cleft to accommodate a hydrophobic hydrocarbon tail, and three sets of ionic locks that stabilize the outward-open conformation. Ligand docking studies and biochemical assays identified Lys436 as a key residue for transport. It is seen forming a salt bridge with the negative charge on the phosphate headgroup. Importantly, Mfsd2a transported structurally related acylcarnitines but not a lysolipid without a negative charge, demonstrating the necessity of a negative charged headgroup interaction with Lys436 for transport. These findings support a novel transport mechanism by which LPCs are flipped within the transporter cavity by pivoting about Lys436 leading to net transport from the outer to the inner leaflet of the plasma membrane.


Brain and eye contain membrane phospholipids that are enriched in the omega-3 fatty acid docosohexaenoic acid (DHA). It is widely accepted that DHA is important for brain and eye function and brain development (1,2), although mechanisms for DHA function in these tissues are not well defined.   The mechanism by which DHA and other conditionally essential and essential fatty acids cross the blood-brain barrier (BBB) has been a long-standing mystery. Recently, we identified Major Facilitator Superfamily Domain containing 2a (Mfsd2a, aka NLS1) as the primary transporter by which the brain obtains DHA. Importantly, Mfsd2a does not transport unesterified DHA, but transports DHA in the chemical form of lysophosphatidylcholine (LPC) that are synthesized by the liver and circulate largely on albumin (3). This is consistent with biochemical evidence that the brain does not transport unesterified fatty acids (4) and that LPC is the preferred carrier of DHA to the brain (5,6).   Mfsd2a is a sodium-dependent transporter that is part of the Major Facilitator Superfamily (MFS) of proteins. Members of this family with elucidated structures have 12 transmembrane domains composed of two evolutionarily duplicated 6 transmembrane units (7). Transporting an LPC is a unique feature of Mfsd2a, since most members of this family transport water-soluble and minimally polar substrates such as sugars (GLUT, MelB, LacY), and amino acids (TAT1). Mfsd2a transport is not limited to LPCs containing DHA, as it can transport LPCs containing a variety of fatty acyl chains, with higher specificity for LPCs with unsaturated fatty acyl chains with a minimum chain length of 14 carbons (6,8). Crystal structures have been solved for more than a dozen members of the MFS family, with more than 19 structures, including that of Melibiose permease (MelB) of S. typhimurium (9), Lactose permease (LacY) of Escherichia coli (10), glycerol-3-phosphate transporter of E. coli (11) and the mammalian glucose transporters 1, 3, and 5 (GLUT1, GLUT3, GLUT5) (12-14). A common transport mechanism has emerged from both biochemical and structural analyses of MFSs, in which they transport via a rocker-switch, alternating access mechanism (7,15). In the rocker-switch model, rigid-body relative motion of the N- and C-termini domains renders the substrate-binding site alternatively accessible from either side of the membrane.

Mfsd2a is highly expressed at the bloodbrain barrier in both mouse and human (6,16). Mfsd2a deficient mice (KO) have significantly reduced brain DHA as a result of a 90% reduction in brain uptake of LPC containing DHA as well as other LPCs. The most prominent phenotype of Mfsd2a KO mice is microcephaly, and KO mice additionally exhibit motor dysfunction, and behavioral disorders including anxiety and memory and learning deficits (6). In line with the mouse KO phenotypes, human patients with partially or completely inactivating mutations in Mfsd2a presented with severe microcephaly, intellectual disability, and motor dysfunction (8,16). Plasma LPCs are significantly elevated in both KO mice and human patients with Mfsd2a mutations, consistent with reduced uptake at the blood-brain barrier. Taken together, these findings demonstrate that LPCs are essential for normal brain development and function in mouse and humans.

The fact that Mfsd2a transports a lysolipid, a non-canonical substrate for an MFS protein, might indicate unique structure features and a novel transport mechanism. However, no structural information or mechanism of transport of Mfsd2a is known. Human Mfsd2a is composed of 530 amino acids, with two glycosylation sites at Asn217 and Asn227. Mfsd2a is evolutionarily conserved from teleost fish to humans. Although not a functional ortholog of bacterial MFS transporters, Mfsd2a shares 25% and 26% amino acid sequence identity with S. typhimurium MelB (9,17), and LacY from E. coli (10), respectively. Given the high conservation of the MFS fold, the use of homology modeling to gain insight into the structure of S. typhimurium MelB, for example, has proven to be highly accurate and largely consistent with subsequent X-ray crystal data (9,18). Here, we take advantage of two recently derived high resolution X-ray crystal structures of S. typhimurium MelB (9), and a high resolution X-ray crystal structure of LacY (10) to generate three predictive structural models of human Mfsd2a. These models reveal three unique regions critical for function – an LPC headgroup binding site, a hydrophobic cleft occupied by the LPC fatty acyl tail, and three sets of ionic locks. These structural features indicate a novel mechanism of transport for LPCs.

Mfsd2a is a sodium-dependent lysophosphatidylcholine transporter essential for human brain growth and function (40). Mfsd2a is the only known MFS member or secondary transporter that transports a lipid. In line with its unique function, the current study has identified three unique structural features based on a combination of homology structural modeling and biochemical analysis – (1) a unique headgroup binding site and (2) a hydrophobic cleft for acyl chain binding, and (4) 3 sets of ionic locks that stabilize the outward open conformation. Drawing together these findings with studies of the mechanism of transport of other MFS family members, we propose the following alternatingaccess mechanism for LPC transport (Fig. 6). In the first steps, LPC inserts itself into the outer leaflet of the membrane and diffuses laterally into the transporter’s hydrophobic cleft. As Mfsd2a undergoes conformational changes from the outward open to the inward open conformation, the zwitterionic headgroup is inverted from the outer membrane leaflet to the inner membrane leaflet along a translocation pathway within the transporter, interacting with specific polar and charged residues lining the path. Since LPCs are hydrophobic phospholipids, it is unlikely that they will partition out of the transporter into the aqueous environment of the cytoplasm. We propose that the “flipped” LPC exits the transporter laterally into the membrane environment of the inner leaflet. This model of LPC flipping requires further biochemical proof. Of particular interest is the visualization of the interaction of the negatively charged phosphate headgroup of LPC with Lys436 that is maintained in both outward and inward open conformations. The sidechain of Lys436 is seen to be pointing in the upward direction in the outward open conformation, but pointing downward into the translocation cleft in the inward open conformation. These findings suggest that the Lys436 acts as a tether to push or pivot the headgroup down into the translocation cavity while the N- and C-termini of Mfsd2a rock and switch from outward to inward open.

Interestingly, Lys436 is orthologous to the residue Lys377 in the melibiose transporter of S. typhimurium. Based on the S. typhimurium MelB crystal structure, Lys377 has been predicted to be involved in binding melibiose, and in forming a hydrogen bond with Tyr120, likely separating the sodium binding site from the central hydrophilic cavity (9). In a recent molecular dynamic simulation of E. coli MelB, Lys377 was noted to interact differently with residues involved in the sodium binding site (Asp55, Asp59, and Asp124) in the presence or absence of a sodium ion, and thought to be critical for the spatial organization of the sodium binding site (41). Similarly, in our refined models of Mfsd2a, Lys436 is localized in close proximity to the sodium-binding site residue, Asp93, and the central translocation pathway where it has been identified by docking studies to interact with the charged headgroup of LPC. We hypothesize that Lys436 may shuttle between the two binding sites, communicating and coordinating the occupancy status of the two sites. Interestingly, there is a distinct mobility shift in Mfsd2a bands on SDS-PAGE between wild-type Mfsd2a and the L-3 mutant (R498E, R499E, R500E, K503E, K504E) (Fig. 5I) that is not seen when each of the residues are mutated individually (Fig. S1). These findings are consistent with a conformational change in the L-3 mutant. Given that the L-3 ionic lock is visualized in the outward partially occluded model, we hypothesize that the loss of the L-3 ionic lock results in Mfsd2a being trapped in an energetically more favorable inward open conformation, resulting in the loss of transport function (Fig. 5H).

Patients with the partially inactivating mutation p.(S399L) exhibited significant increases specifically in plasma LPCs having monounsaturated (18:1 – 92%, p=0.004) and polyunsaturated LPCs (18:2, 20:4, 20:3 – 254%, p=0.002; 117%, p=0.007, and 238%, p=0.002), but not in the most abundant LPCs – saturated LPCs (C16:0, C18:0) (8). This is consistent with a greater specificity of Mfsd2a for LPCs with unsaturated fatty acyl chains (6)…A possible explanation for this acyl chain specificity is related to the mobility of the acyl tail in the membrane. It is known that phospholipids with unsaturated fatty acyl chains disrupt the packing of the bilayer, resulting in greater lateral membrane fluidity (42). Therefore, one possible mechanism for LPC specificity is that LPCs with unsaturated fatty acyl chains have greater lateral mobility in the membrane, increasing the Ka for interacting with the transport cleft of Mfsd2a.

Another important structural feature of the physiological ligand, LPC, is a minimum acyl chain length of 14 carbons is required for transport by Mfsd2a. A possible explanation for this requirement is that the hydrocarbon chain must extend beyond the cleft, protruding into the hydrophobic milieu of the phospholipid bilayer core. This interaction of the fatty acyl tail with the acyl chains of the membrane bilayer may provide a hydrophobic force strong enough to pull the molecule through and out of the transporter as the LPC headgroup partitions into the inner leaflet of the membrane. A similar scenario is seen in the Sec translocon where a hydrophobic transmembrane domain of a protein partitions laterally from the Sec61p complex channel into the lipid bilayer (43,44). This proposal that the omega carbon of the fatty acyl chain sticks out of the Mfsd2a pocket is consistent with the observation that Mfsd2a can transport nitrobenzoxadiazole (NBD) or Topfluor when these moieties are attached to the omega carbon of the LPC fatty acyl tail [1].

Other known transmembrane phospholipid transporters include flippases, floppases, and scramblases. Flippases and floppases utilize ATP to drive the uphill transport of aminophospholipids from the outer to the inner leaflet, and specific substrates from the inner to the outer leaflet, respectively (45-47). Scramblases are less well understood, facilitating transport of substrates in either direction down concentration gradients upon activation. While the substrates are similar, several differences make comparisons between Mfsd2a and phospholipid transporters of limited relevance. First, the shapes of the substrates differ in shape and size – lysophospholipids are smaller and conical while phospholipids are cylindrical. Second, unlike flippases and floppases, Mfsd2a is a secondary transporter, utilizing a sodium electrochemical gradient to drive the transport of lysophospholipids from one leaflet to the other. Third, the overall structure of MFS members is different from P4- ATPases and ABC transporters. Consequently, the mechanism of action between Mfsd2a and flippases such as P4-ATPases and ABC transporters, or floppases is expected to differ.

Being expressed at the blood-brain barrier, Mfsd2a is a potential conduit for drug delivery to the brain. The blood-brain barrier is highly impermeable, protecting the brain from bloodderived molecules, pathogens, and toxins. However, its impermeability poses a challenge for pharmacological treatment of brain diseases. It has been predicted that 98% of small molecule drugs are excluded from the brain by the blood-brain barrier (48). Currently, most drugs used to treat brain diseases are lipid soluble small molecules with a molecular weight of less than 400 Da (49). A small number of drugs traverse the blood-brain barrier by carrier-mediated transport. An example of this is Levodopa, a treatment for Parkinson’s Disease, which is a precursor of the neurotransmitter dopamine. Levodopa is transported across the blood-brain barrier by the large neutral amino acid transporter, LAT1 (50). Our findings here provide a further refinement of understanding of the structure-activity relationship of LPCs to their transport, and educates the search and design of drugs that can be transported by Mfsd2a. Candidates for transport, whether as a drug itself or as a LPC scaffold, must have a zwitterionic headgroup, but not necessarily a phosphate, and a minimal threshold of hydrophobic character. As the binding pocket is several times larger than LPC, it is sterically feasible to attach a small molecule drug onto LPC or LPC-like scaffolds for delivery across the blood-brain barrier.

In summary, these studies represent a first structural model of human Mfsd2a based on homology modeling and biochemical interrogation. We expect that this model will serve as a foundation for the future development of X-ray crystal structures of the protein, which would provide further insight into the structure and function of this physiologically important transporter required for human brain growth and function.


1. Salem, N., Jr., Litman, B., Kim, H. Y., and Gawrisch, K. (2001) Mechanisms of action of docosahexaenoic acid in the nervous system. Lipids 36, 945-959

2. Bazan, N. G. (2009) Neuroprotectin D1-mediated anti-inflammatory and survival signaling in stroke, retinal degenerations, and Alzheimer’s disease. Journal of lipid research 50 Suppl, S400- 405

3. Baisted, D. J., Robinson, B. S., and Vance, D. E. (1988) Albumin stimulates the release of lysophosphatidylcholine from cultured rat hepatocytes. The Biochemical journal 253, 693-701

4. Edmond, J., Higa, T. A., Korsak, R. A., Bergner, E. A., and Lee, W. N. (1998) Fatty acid transport and utilization for the developing brain. Journal of neurochemistry 70, 1227-1234

5. Lagarde, M., Bernoud, N., Brossard, N., Lemaitre-Delaunay, D., Thies, F., Croset, M., and Lecerf, J. (2001) Lysophosphatidylcholine as a preferred carrier form of docosahexaenoic acid to the brain. Journal of molecular neuroscience : MN 16, 201-204; discussion 215-221

6. Nguyen, L. N., Ma, D., Shui, G., Wong, P., Cazenave-Gassiot, A., Zhang, X., Wenk, M. R., Goh, E. L., and Silver, D. L. (2014) Mfsd2a is a transporter for the essential omega-3 fatty acid docosahexaenoic acid. Nature 509, 503-506

7. Law, C. J., Maloney, P. C., and Wang, D. N. (2008) Ins and outs of major facilitator superfamily antiporters. Annual review of microbiology 62, 289-305

8. Alakbarzade, V., Hameed, A., Quek, D. Q. Y., Chioza, B. A., Baple, E. L., Cazenave-Gassiot, A., Nguyen, L. N., Wenk, M. R., Ahmad, A. Q., Sreekantan-Nair, A., Weedon, M. N., Rich, P., Patton, M. A., Warner, T. T., Silver, D. L., and Crosby, A. H. (2015) A partially inactivating mutation in the sodium-dependent lysophosphatidylcholine transporter MFSD2A causes a non-lethal microcephaly syndrome. Nat Genet 47, 814-817

9. Ethayathulla, A. S., Yousef, M. S., Amin, A., Leblanc, G., Kaback, H. R., and Guan, L. (2014) Structure-based mechanism for Na(+)/melibiose symport by MelB. Nature communications 5, 3009

10. Guan, L., Mirza, O., Verner, G., Iwata, S., and Kaback, H. R. (2007) Structural determination of wild-type lactose permease. Proceedings of the National Academy of Sciences of the United States of America 104, 15294-15298

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Lipids link to breast cancer

Larry H. Bernstein, MD, FCAP, Curator



Lipids Found Critical to Breast Cancer Cell Proliferation




Scientists in Spain report finding that breast cancer cells need to take up lipids from the extracellular environment so that they can continue to proliferate. The main protein involved in this process is LIPG, an enzyme found in the cell membrane and without which tumor cell growth is arrested. Analyses of more than 500 clinical samples from patients with various kinds of breast tumors reveal that 85% have high levels of LIPG expression.

The research (“FoxA and LIPG Endothelial Lipase Control the Uptake of Extracellular Lipids for Breast Cancer Growth”) is published in Nature Communications.

In Spain, breast cancer is the most common tumor in women and the fourth most common type in both sexes (data from the Spanish Society of Medical Oncology, 2012), registering more than 25,000 new diagnoses each year. According to figures from the World Health Organization, every year 1.38 million new cases of breast cancer are diagnosed and 458,000 people die from this disease (International Agency for Research on Cancer Globocan, 2008).

It was already known that cancer cells require extracellular glucose to grow and that they reprogram their internal machinery to produce greater amounts of lipids. The relevance of this study is that it reveals for the first time that tumor cells must import extracellular lipids to grow.

“This new knowledge related to metabolism could be the Achilles heel of breast cancer,” explains ICREA researcher and Institute for Research in Biomedicine–Barcelona group leader Roger Gomis, Ph.D., co-leader of the study together with Joan J. Guinovart, Ph.D., director of IRB Barcelona and professor at the University of Barcelona. Using animal models and cancer cell cultures, the scientists have demonstrated that blocking of LIPG activity arrests tumor growth.

“What is promising about this new therapeutic target is that LIPG function does not appear to be indispensable for life, so its inhibition may have fewer side effects than other treatments,” explains the first author of the study, Felipe Slebe, a Ph.D. Fellow at IRB Barcelona.

According to Dr. Guinovart, “because LIPG is a membrane protein, it is potentially easier to design a pharmacological agent to block its activity.”

“If a drug were found to block its activity, it could be used to develop more efficient chemotherapy treatments that are less toxic than those currently available,” adds Dr. Gomis.

The scientists are now looking into international collaborations for developing LIPG inhibitors.

FoxA and LIPG endothelial lipase control the uptake of extracellular lipids for breast cancer growth

Felipe SlebeFederico RojoMaria Vinaixa,…Joan AlbanellJoan J. Guinovart & Roger R. Gomis

Nature Communications7,Article number:11199      http://dx.doi.org:/10.1038/ncomms11199

The mechanisms that allow breast cancer (BCa) cells to metabolically sustain rapid growth are poorly understood. Here we report that BCa cells are dependent on a mechanism to supply precursors for intracellular lipid production derived from extracellular sources and that the endothelial lipase (LIPG) fulfils this function. LIPG expression allows the import of lipid precursors, thereby contributing to BCa proliferation. LIPG stands out as an essential component of the lipid metabolic adaptations that BCa cells, and not normal tissue, must undergo to support high proliferation rates. LIPG is ubiquitously and highly expressed under the control of FoxA1 or FoxA2 in all BCa subtypes. The downregulation of either LIPG or FoxA in transformed cells results in decreased proliferation and impaired synthesis of intracellular lipids.

FoxA1 and FoxA2 in BCa growth

The importance of FoxA1 in BCa cells differentiation and its contribution to controlling the expression of metabolic genes in several other tissues makes this transcription factor a highly attractive target to explain the metabolic alterations reported in BCa. For these reason, we decided to ascertain the metabolic processes controlled by FoxA1 in BCa. We first confirmed the association between high FoxA1 expression (mRNA and protein) and luminal subtype (Fig. 1a). To this end, we used two cohorts of primary breast tumours with annotated clinical features and follow-up. The MSKCC/EMC BCa data set is based on gene expression profiles from an original series of 560 cases10, whereas the Spanish BCa data set (n=439) is a tissue microarray of formalin-fixed paraffin-embedded stage I–III breast tumour specimens11 (details provided in Methods Section). High FoxA1 gene expression significantly correlated with high expression of well-established luminal markers, such as GATA3 and ESR1, in primary tumours (Supplementary Fig. 1a). Next we explored FoxA1 expression beyond the luminal subtype. Lower FoxA1 expression was observed in non-luminal tumours (Fig. 1a,b); however, a subset also expressed higher FoxA1 levels (Supplementary Fig. 1b and Supplementary Table 1). Given that FoxA2, in conjunction with FoxA1, is also involved in the regulation of several metabolic pathways, we determined the expression of this factor in BCa samples. Unfortunately, no FoxA2 probes in the Affymetrix platform used in the MSKCC/EMC data set provided a reliable interpretation. To overcome this limitation, we used tissue arrays of early BCa samples (Spanish BCa set). Histological examination of FoxA2-stained tissue microarray slides from the Spanish BCa set revealed the expression of this factor in six non-luminal samples, which were scored as FoxA1 (examples in Fig. 1b and summarized inSupplementary Table 1). Collectively, the number of FoxA+ BCa samples detected by immunohistochemistry accounted for 81.3% of all samples in the Spanish BCa set (Supplementary Table 1), which represent a significant proportion of BCa and point to the participation of FoxA in this disease, beyond to its involvement in differentiation and control of hormonal responses.

Figure 1: FoxA1 and FoxA2 in BCa growth.


(a, top) FoxA1 mRNA expression in the MSKCC/EMC set. BCa samples were stratified in Luminal A, Luminal B, Her2, triple negative and unknown subgroups. The unknown group represents specimens that were not classified in any group. (bottom) FoxA1 protein levels by IHC staining in Luminal, Her2 and triple negative samples in the Spanish BCa set (cohort of 439 BCa patients). Data is average±s.d. (b) FoxA1 and FoxA2 IHC staining in FFPE human specimens representative of the different BCa subtypes. Six independent cases are depicted. FoxA1 and FoxA2 are expressed mainly in the nuclei of tumour cells. Scale bar, 50μm. (c) FoxA1 and FoxA2 mRNA expression analysis by qRT-PCR and protein expression by western blot in human BCa cell lines compared with HMECs. T-test was used. Data are average±s.e.m.; n= 3. Of note, MDA435 are of melanoma origin. (d) FoxA1 and FoxA2 expression in MCF7, MDA231 and their derivatives cells by qRT-PCR and western blot. FoxA1 and FoxA2 depletion was achieved with a doxycycline-inducible short hairpin vector. FoxA-depleted cells were rescued by expression of FoxA2 in MCF7 cells or FoxA1 in MDA231 cells. Cell populations were cultured in the presence or absence of doxycycline for 6 days. P value is the result of T-test. Data are average±s.e.m.;n=3. *P≤0.05, ***P≤0.001 (e, left) Schematic representation of MDA231 and MCF7 cells grown without doxycycline and inoculated in Balb/c nude mice treated with or without doxycycline to induce the expression of the indicated FoxA short hairpins. All tumour cell lines have GFP constitutive expression, and tRFP concomitantly with the short hairpin were expressed in doxycycline treated tumours. (right) Tumour growth of the indicated cell populations inoculated in Balb/c nude mice are determined at the indicated time points. P value is the result of T-test. Data are average±s.e.m.; n= 5–8 tumours. *P≤0.05,**P≤0.01, ***P≤0.001. FFPE, formalin-fixed paraffin-embedded.

Next, we extended our analysis to BCa cell lines for further mechanistic studies. We compared FoxA1 and FoxA2 mRNA expression in four estrogen receptor positive (ER+) (MCF7, T47D, BT474 and ZR75) and four estrogen receptor negative (ER−) (SKBR3, MDA468, BT20 and MDA231) BCa cell lines, a cell line of melanoma origin (MDA435), and human mammary epithelial cells (HMECs). Of note, two of the BCa lines tested were HER2+ (BT474 and SKBR3) (Fig. 1c). All ER+ BCa cells (MCF7, T47D, BT474 and ZR75), the ER−/HER2+ SKBR3 and both triple negative-like MDA468 and BT20 cell lines expressed FoxA1. Interestingly, MDA231 triple negative-like cells expressed high levels of FoxA2 but not FoxA1, and the non-tumour HMECs did not express these factors (Fig. 1c). No BCa cells co-expressed these two proteins (Fig. 1c). Our results suggest that the expression of FoxA transcription factors is a common feature of breast tumours, as well as of BCa cell lines. This notion implies that FoxA factors play a major role in BCa growth, independently of luminal fate specification.

To examine the molecular basis of the contribution of FoxA1 and FoxA2 to BCa growth, we engineered constitutive GFP-luciferase-expressing MCF7 and MDA231 cells with a doxycycline-inducible short-hairpin RNA (sh-RNA) vector targeting either FoxA1 or FoxA2. Doxycycline addition to the cell culture media decreased FoxA expression in both cell lines compared with control cells (ShControl (Dox+) and Sh FoxA1 or Sh FoxA2 (Dox−))(Fig. 1d), with the concomitant expression of tRFP (Supplementary Fig. 1c). Of note, there was no gain of expression of FoxA2 in FoxA1-depleted cells or vice versa (Fig. 1d). Interestingly, cancer cell proliferation was impaired in vitroupon depletion of either FoxA1 or FoxA2 in MCF7 and MDA231 cells, respectively (Supplementary Fig. 1d,e). Similarly, when Balb/c nude mice implanted with xenograft tumours from the above described cellular populations were treated with doxycycline and the short hairpins were induced, striking differences in tumour growth were observed. FoxA1-depleted MCF7 and FoxA2-depleted MDA231 tumour growth was blunted (Fig. 1e and additional controls in Supplementary Fig. 1f. Experimental details in the Supplementary Methods Section). Collectively, these observations confirm that FoxA1 or FoxA2 expression is required for BCa growth.

Previous studies indicate that FoxA1 and FoxA2 transcriptionally regulate common genes in the liver and pancreas that are central to development and metabolism. We therefore hypothesized that crossed expression of FoxA factors could rescue tumour growth by restoring the expression of essential metabolic genes. To this end, we engineered doxycycline-driven shFoxA1 MCF7 cells to express exogenous FoxA2 and doxycycline-driven shFoxA2 MDA231 cells to express exogenous FoxA1 (Fig. 1d). Interestingly, when these BCa modified cells were implanted in Balb/c nude mice and FoxA depletion was induced with doxycycline, the sustained expression of another FoxA factor (FoxA2 in MCF7 and FoxA1 in MDA231 cells) was sufficient for tumours to continuously grow (Fig. 1e and additional controls in Supplementary Fig. 1f). Quantitative real-time PCR (qRT-PCR) analysis confirmed FoxA expression in the distinct tumour populations ex-vivo (Supplementary Fig. 1g). These results showed that retention of minimal levels of FoxA1 or FoxA2 expression is necessary for BCa cell growth.

FoxA1- and FoxA2-regulated transcripts for BCa growth

Figure 2: A genomic approach to identify FoxA1- and FoxA2-regulated transcripts in MCF7 and MDA231 cells.


(a) FACS profiling of MCF7 and MDA231 cells derived from tumours isolated from mice on the basis of the expression of GFP+ and RFP− (control group) or GFP+ and tRFP+ (knockdown and rescue groups). (b) Representation of the transcripts up- and downregulated by FoxA in MCF7 and MDA231 cells isolated from tumours. Up- and downregulated transcripts present a Bayesian false discovery rate below 5% and fold change >2.5. (c) LIPG, Bcl2 and Cdh11OB mRNA levels of the indicated genetically modified MCF7 and MDA231 tumour xenografts analysed by qRT-PCR. P value is the result of T-test. Data are average±s.e.m.; n= 5–8 tumours. *P≤0.05, ***P≤0.001. (d) LIPG protein expression in constitutive shFoxA1 MCF7 or shFoxA2 MDA231 cells. (e) Promoter reporter assay in HEK 293 cells. Cells were transfected with LIPG promoter reporter and FoxA1 or FoxA2 expressing vectors when indicated. P value is the result of T-test. Data are average±s.e.m.; n=3. ****P≤0.0001.

LIPG expression in BCa

Next, we showed that LIPG expression in primary tumours was specific to BCa tumour cells and not to other stroma cellular entities (Fig. 3a). Subsequently, we tested LIPG expression in normal breast epithelia and interrogated 20 samples from mammoplasty reductions. Normal breast epithelial cells showed a lower expression of LIPG than cells from tumour specimens (Fig. 3b). Similar results were obtained for LIPG protein levels in a panel from BCa lines compared with HMEC cells. Of the cellular populations tested, the eight BCa cell lines expressing FoxA1 or FoxA2 had very high levels of LIPG protein compared with the melanoma MDA435 cell line and the human epithelial cell (Fig. 3c). Consistent with this observation, 83.8% of BCa samples in the Spanish tumour cohort were LIPG+ (Fig. 3d and Supplementary Table 3), and LIPG expression correlated with FoxA expression (Spearman correlation; r=0.477, P=0.000001; Fig. 3e). Further analysis showed that LIPG expression levels in primary tumours do not have the capacity to stratify patients for differential risk of overall or disease-free survival (Supplementary Fig. 2a) and are not dependent on estrogen signalling (Supplementary Fig. 2b), thus reinforcing the notion that LIPG is essential for BCa growth.

Figure 3: LIPG contributes to BCa growth.


a) Representative LIPG IHC staining on primary BCa tissues (cohort of 439 BCa patients). LIPG is expressed in the cytoplasm of tumour cells. Faint staining is also detected in the extracellular area. Scale bar, 50μm. (b) Representative LIPG IHC staining in normal breast tissue from mammoplasty reductions. Weak LIPG expression occurs in epithelial cells from ducts and lobuli. Scale bar, 50μm. (c) LIPG protein expression in human cancer cell lines compared with HMECs. Actin was used as loading control.*Unspecific band. Of note, MDA435 are of melanoma origin. (d) LIPG protein levels by IHC staining in Luminal, Her2, and triple negative samples in the Spanish BCa set (cohort of 439 BCa patients). Data is average±s.d. (e) Spearman correlation (P=0.000001) between FoxA and LIPG IHC staining intensities in Spanish BCa set (cohort of 439 BCa patients). (f) Left panel, in vitro proliferation curves of MCF7 and MDA231 cells transduced with a control or a LIPG short hairpin. Data are average±s.e.m.; n=3. (right) LIPG protein expression in shLIPG MCF7 and shLIPG MDA231 cells. The blot shown is representative of three independent experiments. P value is the result of T-test.**P≤0.01, ***P≤0.001. (g) Tumour growth of the indicated cell populations inoculated in Balb/c nude mice are determined at the indicated time points.P value is the result of T-test. Data are average±s.e.m.; n= 6–8 tumours. *P≤0.05.

LIPG is a phospholipase located in the cytosol and cellular membrane and has been shown to hydrolyse extracellular phospholipids from high-density lipoprotein that are afterwards incorporated into intracellular lipid species thus providing lipid precursors of cell metabolism17, 18. Thus we questioned whether LIPG regulates essential lipid intake in BCa and whether it is necessary for proliferation. To validate this hypothesis, we genetically downregulated the expression of this protein in MCF7 and MDA231 cells by means of sh-RNA (Fig. 3f and Supplementary Fig. 2c). LIPG depletion blunted BCa cell capacity to proliferate in vitro (Fig. 3f), as previously observed in FoxA-depleted cells (Supplementary Fig. 1d,e), and caused a reduction in invasion and self-renewal properties (Supplementary Fig. 3a–d). Similarly, LIPG-depleted cells were unable to grow tumours in vivo (Fig. 3g).

LIPG induces BCa cells lipid metabolic reprograming

Figure 4: LIPG regulates the uptake of lipids in BCa cells inducing a lipid metabolic reprograming.

LIPG regulates the uptake of lipids in BCa cells inducing a lipid metabolic reprograming.


(a) Schematic representation of LIPG action. (b) Heat map representation of the downregulated (blue) lipids identified by MS/MS in the cell homogenates of MCF7 or MDA231 LIPG-depleted cells compared with shControl cells. Depicted lipids have a fold change >1.5 and P value<0.05 using the Welch’s t-testn=5. (c) Downregulated lipid species (previously identified in b) that are common to LIPG-depleted MCF7 and LIPG-depleted MDA231 cells. ShControl cells (red box), and shLIPG (blue box). P values are <0.05 and calculated using Welch’s t-test, n=5. Whiskers extend to a maximum of 1.5 × IQR beyond the box. (d) Heat map representation of the upregulated (red) lipids identified by MS/MS in the media of MCF7 or MDA231 LIPG-depleted cells compared with the corresponding shControl cells. Characterized lipids have a fold change >1.5 and P value<0.05 using the Welch’s t-test n=5. (e) Upregulated lipid species in the media (previously identified in d) that are common to LIPG-depleted MCF7 and LIPG-depleted MDA231 cells. ShControl cells (red box), and shLIPG cells (blue box). P values are <0.05 and calculated using Welch’s t-test, n=5. Whiskers extend to a maximum of 1.5 × IQR beyond the box. (f) Heat map representation of the MS/MS downregulated (blue) lipids in the cell media of MCF7/MDA231 LIPG-depleted or shControl cells (as described in d) compared with fresh medium (without cell incubation). Depicted lipid species have a log2 fold change>1.5 and P value<0.05 using the Welch’s t-test n=5. (g) MDA231 and MCF7 cell growth for 48h in complete medium: medium containing 10% FBS 10%); lipoprotein-free medium: medium containing 10% free lipoprotein FBS; and LPC (18:0): medium containing 10% free lipoprotein FBS and 20μM of LPC (18:0). P value is the result of T-test. Data are average±s.e.m.; n=3. **P≤0.01, ***P≤0.001, ****P≤0.0001. (h) Above, schematic representation of the experimental protocol used. (bottom) Tumour growth of the indicated cell populations inoculated in Balb/c nude mice treated with high-fat diet (HFD) are determined at the indicated time points. P value is the result of T-test. Data are average±s.e.m.; n= 6–8 tumours. *P≤0.05, **P≤0.01. Inside graph, plasma cholesterol levels of animals treated with standard diet (SD) or HFD. P value is the result of T-test. Data are average±s.e.m.; n= 4 animals per group. **P≤0.01, ***P≤0.001.

LIPG location has been shown to be functional on the outer face of the cellular membrane (Fig. 4a)18, thus we postulated the possibility that BCa cells are dependent on LIPG function to access extracellular lipids to support their growth needs. To test this notion, we profiled the media of control and LIPG-depleted MCF7 and MDA231 cells following the same liquid chromatography-mass spectrometry-based untargeted lipidomic approach as for cell homogenates. LIPG depletion prevented the absorption of particular lipids from the media (Supplementary Fig. 4a). The structural identification of the lipids by MS/MS confirms the absence of degradation of glycerophospholipids belonging to the LPC class in both MCF7 and MDA231 cells, which is depicted by higher levels in the media of these species in LIPG-depleted when compared with control cells (Fig. 4d,e). Interestingly when we analysed the LPCs species in the media of control and LIPG-depleted cells and compared with fresh media (without cells), all LPC species from control cell media were decreased. This reduction was weaker in the media of Sh LIPG cells, indicating that LIPG-depleted cells have a defect in processing and importing of pre-existing lipid species from the medium (Fig. 4f).

Finally, we evaluated which of the commonly identified potential substrates of LIPG sustains BCa cell proliferation. Initially, we confirmed that the growth of MCF7 and MDA231 cells is impaired when grown in vitro in lipoprotein-depleted media (Fig. 4g). Next we tested the capacity of LPC (18:0) to rescue BCa cell growth in the absence of lipoproteins and confirmed that this lysophosphatidylcholine was able to restore the cells’ capacity to proliferate (Fig. 4g). In accordance, this process was dependent on LIPG expression (Fig. 4g). Similarly, LIPG-depleted cells were not able to grow in vivo in animals fed with high-fat diet (Fig. 4h) indicating that LIPG is indispensable to process the extracellular lipids and mediate their uptake by the cells, irrespectively of the concentration of lipid substrates in circulation, a phenotype also observed in FoxA-depleted cells (Fig. 4h).

LIPG activity supports BCa growth

Figure 5: LIPG activity is essential for BCa growth.

LIPG activity is essential for BCa growth.


(a, top) Homology 3D structural model of LIPG (backbone coloured according to the QMEANlocal parameter values; red residues with low error). The heavy atoms of the three catalytic residues are shown explicitly and the residue mutated in this study is shown in green (Asp 193). (b) FoxA1, FoxA2 and LIPG protein expression in MCF7, MDA231 and their derivative cells determine by western blot. FoxA1 and FoxA2 depletion was achieved with a doxycycline-inducible short hairpin vector. FoxA-depleted cells were rescued by expression of a WT or Inactive LIPG. Cell populations were cultured in the presence or absence of doxycycline for 6 days. *blots represent different exposition times. (c) Tumour growth of the indicated cell populations inoculated in Balb/c nude mice are determined at the indicated time points. Pvalue is the result of T-test. Data are average±s.e.m.; n=5–8 tumours. *P≤0.05, **P≤0.01. (d) MDA231 and MCF7 cell growth for 48h treated with DMSO (control), FAS inhibitor (C75) and/or lipase inhibitor (Orlistat). For MDA231 cells C75 was used at a final concentration of 10μgml−1 and for MCF7 cells 8μgml−1. Orlistat was used at a final concentration of 30 or 10μgml−1 in MCF7 or MD231 respectively. Pvalue is the result of T-test. Data are average±s.e.m.; n=3.*P≤0.05, **P≤0.01, ***P≤0.001. (e) Forty-eight hours cell growth of MDA231 or MCF7 cells overexpressing exogenous WT or Inactive LIPG. Cells were treated with DMSO (control) and FAS inhibitor (C75) at a final concentration of 20μgml−1. P value is the result of T-test. Data are average±s.e.m.; n=3.***P≤0.001, ****P≤0.0001 (f) Schematic representation showing how FoxA controls LIPG and lipid metabolism to support tumour growth.

As previous reports showed that de novo lipid metabolism is necessary for BCa growth3, 22, we next questioned whether this lipid synthesis was sufficient or, instead, whether exogenous sources are also required to support BCa cell growth and proliferation, as suggested by our experimental data. To this end, we inhibited the activity of fatty acid synthase (FAS) in BCa cells by means of the chemical inhibitor C75 (ref. 23). FAS activity is crucial for de novo lipid synthesis in cancer cells3,22. To test the complementarity of both de novo and/or exogenous lipid supplies, we used a C75 concentration causing a 50% reduction in BCa cell growth in vitro 48h post incubation (Fig. 5d andSupplementary Fig. 5d). Similarly, we tested the contribution of LIPG inhibition by means of treatment with a lipase inhibitor, Orlistat21. A specific dose causing a 50% reduction in the growth of each BCa cell line was further used (Fig. 5d and Supplementary Fig. 5d). Interestingly, concomitant treatment with FAS and LIPG inhibitors caused an additive effect, blunting BCa cell growth (Fig. 5d). Next, we evaluated whether LIPG activity was sufficient to rescue the chemical inhibition of FAS. To this end, we overexpressed WT and inactive LIPG and grew MCF7 and MDA231 cells in the presence or absence of a high dose of C75 (20mgml−1), which blocks cell growth (Supplementary Fig. 5d). Complete blockade of FAS was not rescued by LIPG (Fig. 5e). Collectively, our results suggest that both exogenous lipid precursors provided by means of LIPG activity and de novo lipid synthesis mediated by FAS are necessary for BCa cell growth.


Here we reveal that FoxA factors provide a central metabolic growth function by specifically regulating LIPG expression, thereby allowing the acquisition of indispensable extracellular lipids for BCa tumour proliferation. FoxA family of transcription factors are expressed in the vast majority of BCa and FoxA1 is expressed across various BCa subtypes. Moreover we show that, in some cases, its absence is associated with the expression of FoxA2. Interestingly, in addition of FoxA1 contribution to luminal commitment24, 25, 26, 27 the factor may drive BCa growth by specifically regulating LIPG levels.

The catalytic activity of LIPG generates extracellular lipid precursors that are imported to fulfill the intracellular production of lipid species (Fig. 5f). LIPG downregulation blocks BCa cell growth, thereby indicating that the import of extracellular lipid precursors is important for the proliferation of these cells. This is a striking observation given that it is generally believed that de novo fatty acid synthesis is the main driver of tumour growth22. Indeed, our experimental data with LIPG-depleted BCa cells revealed a massive decrease of most intracellular glycerolipid intermediates in the synthesis of TG (PC, PE, PG and DG) and their derivatives (LPC and LPE). Accordingly, certain lipid species (LPC) in the media were not decreased in LIPG-depleted cells as much as in control cells, thus indicating that extracellular lipids are the substrates for intracellular lipid production. In particular, we demonstrate the relevance of extracellular LPC (18:0) for BCa cell proliferation in a lipoprotein-depleted medium, a process dependent on LIPG. In this context, a high-fat diet was shown to rescue the absence of a critical intracellular lipase, Monoacylglycerol lipase, for cancer pathogenesis given cancer cells ability to uptake lipids from the extracellular compartment was functional19. Herein, we showed that this rescue mechanism is not functional in BCa cells in the absence of FoxA2 or LIPG. In support of this notion, it is worth noting that extracellular LIPG activity releases fatty acids from high-density lipoprotein phospholipids and these acids are further employed for intracellular lipid production in the human hepatic cell line HepG2 (refs 28, 29).

In conclusion, BCa cells are dependent on a mechanism to supply precursors derived from extracellular sources for intracellular lipid production, and LIPG fulfills this function. Therefore, LIPG stands out as an important component of the lipid metabolic adaptations that BCa cells, and not normal tissue, must undergo to support high proliferation rates. Our results also suggest thatde novo lipid synthesis is necessary but not sufficient to support lipid production for BCa tumour growth. Accordingly, recent clinical studies demonstrate the association between lipids and lipoproteins in circulation and risk of BCa in women with extensive mammographic density. This observation implies that interventions aimed to reduce them may have effect on BCa risk30. All together, these observations make LIPG activity an Achilles heel of luminal and, more importantly, of triple negative/basal-like breast tumours, for which limited therapeutic options are currently available.

In normal cells, the glucose carbon flow is directed into a de novo lipogenic pathway that is regulated, in part, via phosphoinositide-3 kinase (PI-3K)-dependent activation of ATP citrate lyase (ACL), a key rate-limiting, enzyme in de novo lipogenesis. ACL is a cytosolic enzyme that catalyzes the generation of acetyl CoA from citrate. Inhibition of ACL results in a loss of B-cell growth and cell viability [10] .
The plasma membrane and its constituent phosphoinositides form the basis of the phosphatidylinositol 3-kinase (PI3-K) signaling pathway, which is crucial for cell proliferation and survival. Phosphatase and tensin-homolog deleted on chromosome 10 (PTEN) is a tumor-suppressor protein that regulates phosphatidylinositol 3-kinase (PI3-K) signaling by binding to the plasma membrane and hydrolyzing the 3′ phosphate from phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P3) to form phosphatidylinositol (4,5)-bisphosphate (PI(4,5)P2). Several loss-of-function mutations in PTEN that impair lipid phosphatase activity and membrane binding are oncogenic, leading to the development of a variety of cancers. Of these three residues, R335 was observed to interact with the membrane to the greatest extent across all of the simulations. R335L, in common with several other germline mutations, has been associated with the inherited cancer [11] .
ACLY is up-regulated or activated in several types of cancers, and its inhibition is known to induce proliferation arrest in cancer cells both in vitro and in vivo. The last studies were showed that BCR-mediated signaling is regulated in part by the amount of membrane cholesterol. It was observed that statins (Lovostatin), the pharmacological inhibitors of cholesterol synthesis, induce apoptosis of CLL cells in vitro and in vivo. Also the ectopic expression of CD5 in a B-cell line stimulates the transcription of genes involved in the synthesis of cholesterol [12] .

[10] Zaidi N, Swinnen JV, Smans K. ATP-citrate lyase: a key player in cancer metabolism Cancer Res; 2012 (11): 3709-14.

[11] Craig N, Mark S.P. Sansom. Defining the Membrane-Associated State of the PTEN Tumor Suppressor Protein. Biophys J 2013; 5; 104(3: 613–21.

[12] Tomowiak C, Kennel A, Gary-Gouy, Hadife N. High Membrane Cholesterol in CLL B-Cells and Differential Expression of Cholesterol Synthesis Genes in IG GENE Unmutated vs Mutated Cells. British Journal of Medicine & Medical Research 2012; 2(3): 313-26.


Cancer’s Vanguard

Exosomes are emerging as key players in metastasis.

By Catherine Offord | April 1, 2016   http://www.the-scientist.com/?articles.view/articleNo/45577/title/Cancer-s-Vanguard/


PREPARING THE TURF: Before tumor cells arrive at their metastatic destination, part of the site is readied for them. One recent study of liver metastasis in mice found that resident macrophages called Kupffer cells take up exosomes from the original tumor (1). Additionally, macrophages from the bone marrow show up upon the release of fibronectin by other liver cells called stellate cells (2). A current proposal for additional steps in metastatic niche development includes the recruitment of epithelial cells and fibroblasts, which contribute to angiogenesis, and, finally, the arrival of tumor cells themselves (3).© IKUMI KAYAMA/STUDIO KAYAMA

In 2005, David Lyden noticed something unexpected. He and his colleagues at Weill Cornell Medical College had been researching metastasis—the spread of cancer from one part of the body to another. The team had shown that bone marrow–derived cells (BMDCs) were recruited to future metastatic sites before the arrival of tumor cells, confirming that metastasis occurred after a habitable microenvironment, or “premetastatic niche,” had been prepared.1

But carefully studying images of this microenvironment in the lung tissue of mice, Lyden saw something else. Amongst the BMDCs, the micrographs showed tiny specks, far too small to be cells, gathering at the future site of metastasis. “I said, ‘What are these viruses doing here?’” recalls Lyden. “I had no idea about exosomes, microvesicles, and microparticles.”

Those specks, Lyden would come to realize, were in fact primary tumor–derived exosomes. These membrane-enclosed vesicles packed full of molecules are now attracting growing attention as important mediators of intercellular communication, particularly when it comes to cancer’s insidious capacity to spread from one organ to another.

Preparing the ground

Tumors require a community of support cells, including fibroblasts, BMDCs, and endothelial cells, to provide functional and structural assistance and to modulate immune system behavior. Bringing together the first members of this community before the arrival of tumor cells is all part of cancer’s survival strategy, says Joshua Hood, a cancer researcher at the University of Louisville.

“It wouldn’t be efficient for tumor cells to strike out on their own, and just say, ‘Oh, here we are!’” he says. “They would run the risk of being destroyed.” Preparing a “nest” in advance makes the process much safer. “Then the tumor can just efficiently come along and set up shop without ever having to fight much of a battle with the immune system.”

But although Lyden’s group had shown that this preparation was taking place, it remained unclear how such a process might be regulated. For the next few years, many cancer researchers believed that tumor cells must communicate with the premetastatic niche primarily through tumor-secreted signaling molecules such as cytokines.

Meanwhile, research into extracellular vesicles, previously considered biological garbage bags, was revealing new modes of intercellular communication. In 2007, a group of scientists in Sweden discovered that exosomes, tiny vesicles measuring just 30 nanometers to 100 nanometers across, transport mRNA and microRNAs intercellularly, with the potential to effect changes in protein synthesis in recipient cells.2 A new means for tumors to regulate distant cellular environments came into focus, and research on exosomes exploded. In 2011, Hood and his colleagues showed that exosomes facilitate melanoma metastasis through the lymphatic system.3 The following year, Lyden’s group demonstrated that tumor-derived exosomes can direct BMDCs to one of melanoma’s most common sites of metastasis, the lung.4 Exosomes, it seemed, had been underestimated.

Tiny terraformers

Armed with the knowledge that exosomes are involved in multiple stages of melanoma metastasis, Lyden’s lab went searching for the vesicles’ potential role in the metastasis of other cancers. Turning to pancreatic ductal adenocarcinoma (PDAC)—one of the most lethal cancers in humans—postdoctoral researcher Bruno Costa-Silva led a series of exhaustive in vitro and in vivo experiments in mouse models to detail the process of premetastatic niche formation in the liver, PDAC’s most common destination. The team’s results, published last May, reveal an intricate series of sequential steps—mediated by PDAC-derived exosomes (Nature Cell Biol, 17:816-26, 2015).

Using fluorescence labeling, Lyden’s group observed that PDAC-derived exosomes are taken up by Kupffer cells, specialized macrophages lining the outer walls of blood vessels in the liver. There, the exosomes trigger the cells’ secretion of transforming growth factor β (a type of cytokine involved in cell proliferation), plus the production of fibronectin by neighboring hepatic stellate cells, and the recruitment of BMDCs.

The researchers also showed that this cascade of events could be inhibited by depleting exosomal macrophage migratory inhibitory factor (MIF), an abundant protein in PDAC exosomes. “If you target the specific proteins of exosomes, you can reduce metastasis,” explains coauthor Héctor Peinado, leader of the microenvironment and metastasis group at the Spanish National Cancer Research Center.

For Hood, the findings add to a developing picture of exosomes’ vital role as “vanguard” in the progression of cancer. “It’s like the colonization of a new planet,” he says. “They’re terraforming the environment to make it hospitable.”



  • B. Costa-Silva et al., “Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver,”Nature Cell Biol, 17:816-26, 2015.
  • A. Hoshino et al., “Tumour exosome integrins determine organotropic metastasis,” Nature, 527:329-35, 2015.
  • L. Zhang et al., “Microenvironment-induced PTEN loss by exosomal microRNA primes brain metastasis outgrowth,”Nature, 527:100-04, 2015.

Internal mail

Although research was revealing the steps involved in forming premetastatic sites, it was less clear how these sites were being selected. “This has always been a great mystery in cancer,” says Ayuko Hoshino, a research associate in Lyden’s lab. “Why do certain cancers metastasize to certain organs?”

One theory, proposed in 1928 by pathologist James Ewing, suggested that anatomical and mechanical factors explained organ specificity in metastasis. The premetastatic niche, then, might form wherever exosomes are likely to land. But this couldn’t be the whole story, says Hoshino. “For instance, there’s eye melanoma. Thinking about that site, you could imagine it metastasizing to the brain. But actually, it almost only metastasizes to the liver.”

Because exosomes arrive at metastatic sites before tumor cells, the team reasoned, perhaps the exosomes themselves were organotropic (i.e., attracted to particular organs or tissues). Sure enough, Lyden says, when Hoshino and Costa-Silva began injecting tumor-derived exosomes into mice, “their preliminary findings were that wherever they injected the exosomes, the pancreatic cancer ones were ending up in the liver and the breast metastasis exosomes would end up in the lung.”

Using mass spectrometry, the researchers analyzed the protein content of exosomes from lung-tropic, liver-tropic, and brain-tropic tumors. They found that the composition of exosomes’ integrins—membrane proteins involved in cell adhesion—was destination-specific (Nature, 527:329-35, 2015). Exosomes bearing integrin α6β4, for example, were directed to the lung, where they could prepare a premetastatic niche potent enough even for normally bone-tropic tumor cells to colonize. Integrin αvβ5, meanwhile, directed metastasis to the liver.

The researchers also showed that exosomal integrins didn’t necessarily correspond to the parent-cell proteins, making exosomes potentially better indicators of where a cancer will spread than the tumor cells themselves. “We can show that an integrin that’s high in the tumor cell might be completely absent in the tumor exosome or vice versa,” says Lyden, adding that, taken together, the results point to a role for exosomes in “dictating the future sites of metastasis.”

“It’s a beautiful story,” says Dihua Yu, a molecular and cellular oncologist at the University of Texas MD Anderson Cancer Center. “This is a very novel finding that gives really good indicators for potential strategies to intervene in metastasis.”

Metastatic crosstalk

In the same month that Lyden’s group published its work on organotropism, Yu’s own lab published a different exosome study—one that told another side of the story.

Yu and her colleagues had found that when tumor cells in mice metastasized to the brain, they downregulated expression of a tumor suppressor gene called PTEN, and became primed for growth at the metastatic site. When the tumor cells were taken out of the microenvironment and put in culture, however, they restored normal PTEN expression.

The researchers demonstrated that a microRNA from astrocytes—star-shape glial cells in the brain—reversibly downregulated the levels of PTEN transcripts in the tumor cells, but they couldn’t figure out how the microRNA was getting into the tumor. Blocking “obvious signaling pathways,” such as gap junctions, failed to have an effect, Yu says.

Scrutinizing astrocyte-conditioned media using electron microscopy, the researchers identified spherical vesicles between 30 nanometers and 100 nanometers in diameter—the defining size of exosomes. Exposing mouse tumor cells to these vesicles increased cell microRNA content and reduced PTENexpression (Nature, 527:100-04, 2015). The study revealed yet another role for exosomes in the communication between tumors and their microenvironment.

The findings were a surprise, says Yu, not least because they showed a different perspective from the bulk of recent research. “We’re talking about astrocytes in the brain secreting exosomes to give welcome help to the cancer cells,” she says.

“I find it an extremely interesting paper because it shows that the astrocytes can change the whole phenotype of the tumor in the brain,” says Lyden. He adds that the results underline the importance of studying the mutational status of tumors at various sites. “All this work in exosomes, it adds to the complexity,” he says. “We can’t just target tumor cells at the primary site. We’ll have to understand all the details of metastasis if we’re really going to tackle it.”

What’s next?

The discovery of multiple roles for exosomes in metastasis has generated excitement about the potential for their use in diagnostics and treatment. As protective containers of tumor-derived genetic material, exosomes could provide information about the status of cancer progression. And as mediators of premetastatic niche formation, they make obvious targets for inhibition. (See “Banking on Blood Tests,”here.)

Exosomes might even be useful as vehicles to deliver drugs because they’re patient-matched and “naturally designed to function in a biocompatible way with living systems,” says Hood. “You could take them out of people, and at some point down the road try to have patients be their own nanofactory, using their own particles for treatment purposes.”

Pancreatic cancer exosomes initiate pre-metastatic nihe formation in the liver

Bruno Costa-SilvaNicole M. AielloAllyson J. Ocean, et al.   Nature Cell Biology 2015; 17,816–826   http://dx.doi.org:/10.1038/ncb3169

Pancreatic ductal adenocarcinomas (PDACs) are highly metastatic with poor prognosis, mainly due to delayed detection. We hypothesized that intercellular communication is critical for metastatic progression. Here, we show that PDAC-derived exosomes induce liver pre-metastatic niche formation in naive mice and consequently increase liver metastatic burden. Uptake of PDAC-derived exosomes by Kupffer cells caused transforming growth factor β secretion and upregulation of fibronectin production by hepatic stellate cells. This fibrotic microenvironment enhanced recruitment of bone marrow-derived macrophages. We found that macrophage migration inhibitory factor (MIF) was highly expressed in PDAC-derived exosomes, and its blockade prevented liver pre-metastatic niche formation and metastasis. Compared with patients whose pancreatic tumours did not progress, MIF was markedly higher in exosomes from stage I PDAC patients who later developed liver metastasis. These findings suggest that exosomal MIF primes the liver for metastasis and may be a prognostic marker for the development of PDAC liver metastasis.

Ayuko HoshinoBruno Costa-SilvaTang-Long ShenGoncalo RodriguesAyako HashimotoMilica Tesic Mark, et al. Nature Nov 2015; 527,329–335  http://dx.doi.org:/10.1038/nature15756

Ever since Stephen Paget’s 1889 hypothesis, metastatic organotropism has remained one of cancer’s greatest mysteries. Here we demonstrate that exosomes from mouse and human lung-, liver- and brain-tropic tumour cells fuse preferentially with resident cells at their predicted destination, namely lung fibroblasts and epithelial cells, liver Kupffer cells and brain endothelial cells. We show that tumour-derived exosomes uptaken by organ-specific cells prepare the pre-metastatic niche. Treatment with exosomes from lung-tropic models redirected the metastasis of bone-tropic tumour cells. Exosome proteomics revealed distinct integrin expression patterns, in which the exosomal integrins α6β4 and α6β1 were associated with lung metastasis, while exosomal integrin αvβ5 was linked to liver metastasis. Targeting the integrins α6β4 and αvβ5 decreased exosome uptake, as well as lung and liver metastasis, respectively. We demonstrate that exosome integrin uptake by resident cells activates Src phosphorylation and pro-inflammatory S100 gene expression. Finally, our clinical data indicate that exosomal integrins could be used to predict organ-specific metastasis.

  1. Paget, S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev. 8, 98101 (1989)
  2. Hart, I. R. & Fidler, I. J. Role of organ selectivity in the determination of metastatic patterns of B16 melanoma. Cancer Res. 40, 22812287 (1980)
  3. Müller, A. et al. Involvement of chemokine receptors in breast cancer metastasis. Nature410, 5056 (2001)
  4. Weilbaecher, K. N., Guise, T. A. & McCauley, L. K. Cancer to bone: a fatal attraction. Nature Rev. Cancer 11, 411425 (2011)
  5. Zhou, W. et al. Cancer-secreted miR-105 destroys vascular endothelial barriers to promote metastasis. Cancer Cell 25, 501515 (2014)
  6. Chang, Q. et al. The IL-6/JAK/Stat3 feed-forward loop drives tumorigenesis and metastasis.Neoplasia 15, 848862 (2013)
  7. Lu, X. & Kang, Y. Organotropism of breast cancer metastasis. J. Mammary Gland Biol. Neoplasia 12, 153162 (2007)


Microenvironment-induced PTEN loss by exosomal microRNA primes brain metastasis outgrowth

Lin ZhangSiyuan ZhangJun YaoFrank J. LoweryQingling ZhangWen-Chien Huang, et al.  Nature  Nov 2015; 527,100–104   http://dx.doi.org:/10.1038/nature15376

The development of life-threatening cancer metastases at distant organs requires disseminated tumour cells’ adaptation to, and co-evolution with, the drastically different microenvironments of metastatic sites1. Cancer cells of common origin manifest distinct gene expression patterns after metastasizing to different organs2. Clearly, the dynamic interaction between metastatic tumour cells and extrinsic signals at individual metastatic organ sites critically effects the subsequent metastatic outgrowth3, 4. Yet, it is unclear when and how disseminated tumour cells acquire the essential traits from the microenvironment of metastatic organs that prime their subsequent outgrowth. Here we show that both human and mouse tumour cells with normal expression of PTEN, an important tumour suppressor, lose PTEN expression after dissemination to the brain, but not to other organs. The PTEN level in PTEN-loss brain metastatic tumour cells is restored after leaving the brain microenvironment. This brain microenvironment-dependent, reversible PTEN messenger RNA and protein downregulation is epigenetically regulated by microRNAs from brain astrocytes. Mechanistically, astrocyte-derived exosomes mediate an intercellular transfer of PTEN-targeting microRNAs to metastatic tumour cells, while astrocyte-specific depletion of PTEN-targeting microRNAs or blockade of astrocyte exosome secretion rescues the PTEN loss and suppresses brain metastasis in vivo. Furthermore, this adaptive PTEN loss in brain metastatic tumour cells leads to an increased secretion of the chemokine CCL2, which recruits IBA1-expressing myeloid cells that reciprocally enhance the outgrowth of brain metastatic tumour cells via enhanced proliferation and reduced apoptosis. Our findings demonstrate a remarkable plasticity of PTEN expression in metastatic tumour cells in response to different organ microenvironments, underpinning an essential role of co-evolution between the metastatic cells and their microenvironment during the adaptive metastatic outgrowth. Our findings signify the dynamic and reciprocal cross-talk between tumour cells and the metastatic niche; importantly, they provide new opportunities for effective anti-metastasis therapies, especially of consequence for brain metastasis patients.

  1. Quail, D. F. & Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nature Med. 19, 14231437 (2013)
  2. Park, E. S. et al. Cross-species hybridization of microarrays for studying tumor transcriptome of brain metastasis. Proc. Natl Acad. Sci. USA 108, 1745617461 (2011)
  3. Joyce, J. A. & Pollard, J. W. Microenvironmental regulation of metastasis. Nature Rev. Cancer 9, 239252 (2009)
  4. Vanharanta, S. & Massagué, J. Origins of metastatic traits. Cancer Cell 24, 410421 (2013)
  5. Gray, J. Cancer: genomics of metastasis. Nature 464, 989990 (2010)
  6. Friedl, P. & Alexander, S. Cancer invasion and the microenvironment: plasticity and reciprocity. Cell 147, 9921009 (2011)

Banking on Blood Tests

How close are liquid biopsies to replacing current diagnostics?

By Jyoti Madhusoodanan | April 1, 2016  http://www.the-scientist.com/?articles.view/articleNo/45584/title/Banking-on-Blood-Tests/

No matter where a tumor lurks in the body, its secrets circulate in the blood. Stray tumor cells begin metastatic migrations by slipping into the vasculature. Vesicles secreted by cancer cells and free-floating DNA are also released into the bloodstream. Because these bits of cellular debris are a grab-bag of biomarkers that could both signal a cancer’s presence and predict its progression and response to treatment, the use of blood-based tests, or liquid biopsies, to detect and evaluate them is now drawing significant commercial interest.

Last year, San Diego–based Pathway Genomics began advertising a screen “for the early detection of up to 10 different cancer types in high-risk populations.” But the screen had only been tested in already-diagnosed patients, not in at-risk individuals, and within weeks of making it commercially available, the company received an FDA notice to provide more information about their promotional claims before further marketing. “We . . . have not found any published evidence that this test or any similar test has been clinically validated as a screening tool for early detection of cancer in high risk individuals,” the agency wrote.

The Forces of Cancer

A tumor’s physical environment fuels its growth and causes treatment resistance.

By Lance L. Munn and Rakesh K. Jain | April 1, 2016   http://www.the-scientist.com/?articles.view/articleNo/45603/title/The-Forces-of-Cancer/

Ahelium balloon tugs gently at the end of its string. The tension in the string resists the buoyant force of the helium, and the elastic nature of the balloon’s rubber contains the helium gas as it tries   to expand. Cutting the string or poking the rubber with a pin reveals the precarious balance between the forces, upsets the equilibrium, and sets the system into motion.

Some biological tissues also exist in such a state of offsetting forces. The most familiar example is the balance between blood pressure and the elastic tension in the cardiovascular system that contains and conveys blood without bursting or collapsing. And in tumors, both solid and fluid forces are generated that make the cancerous tissue a lot like that helium balloon: cut a tumor with a scalpel and it rapidly swells and deforms as pent-up forces break free from structural elements that are severed.1

One force that is notably higher in tumors than in healthy tissues is fluid pressure, resulting from hyperpermeable, leaky blood vessels and a dearth of draining lymphatic vessels. Researchers have known since the 1950s that tumors exhibit elevated fluid pressure, but the implications for tumor progression and drug delivery were not realized until the late 1980s. That was when we (R.K.J. and colleagues) used a mathematical model to predict—and subsequently validate in animal and human tumors—that a precipitous drop in fluid pressure at the tumor–normal tissue interface causes interstitial fluid to ooze out of the tumor.2 This seeping fluid pushes drugs, growth factors, and cancer cells into the surrounding tissue and lymphatics, reducing drug delivery and facilitating local tumor invasion and distant metastasis.

Based on this insight, we suggested in 2001 that anti-angiogenic drugs could be used to lower a tumor’s fluid pressure and improve treatment outcome.3 This hypothesis changed the thinking about how existing anti-angiogenesis therapies actually work and spurred research into other physical forces acting in cancer.4 In the last 15 years, researchers have identified diverse sources of increased pressure in tumors, which may serve as possible targets for cancer therapy.5 For example, solid forces exerted by the extracellular matrix can be reduced by treatment with drugs approved by the US Food and Drug Administration (FDA) for controlling hypertension (angiotensin blockers) or diabetes (metformin). Retrospective clinical studies have found improved survival in cancer patients who were treated with these agents, which are now being tested in prospective trials for a variety of solid tumors.6,7

Tumors under pressure

In vitro experiments showing that cancer cells actively migrate in response to fluid flow have supported the hypothesis that fluid escaping from the boundary of a tumor may guide the invasive migration of cancer cells toward lymphatic or blood vessels, potentially encouraging metastasis. There remains controversy over how the fluid forces induce the migration; the cells may respond to chemical gradients created by the cells and distorted by the flowing fluid,8 or the fluid may activate cell mechanosensors.9 Because of the potential for new therapeutic interventions, the transduction of mechanical fluid forces into biochemical signals by cell mechanosensors is an active area of investigation. In a more direct manner, the fluid flow can physically carry cancer cells to lymph nodes.

Fluid forces may also promote tumor progression by recruiting blood vessels into the cancerous mass.10 Because tumor blood vessels are leaky, plasma can pass freely between vessels that have different pressures. When this happens at the periphery of a tumor, where angiogenic growth factors are prevalent, there can be synergistic induction of new vessel sprouts.


And fluid pressure is just one of the many forces in a tumor that can influence its development and progression. Tumors also develop increased solid pressure, as compared with normal tissue, stemming from the uncontrolled division of cancer cells and from the infiltration and proliferation of stromal and immune cells from the surrounding tissue and circulation. High-molecular-weight polysaccharides known as hydrogels found in the extracellular matrix (ECM) also add pressure on a tumor. The most well-studied of these hydrogels is hyaluronan; when the polysaccharide absorbs water, it swells, pressing on surrounding cells and structural elements of the tissue.

The ECM contains a highly interconnected network of collagen and other fibers and is normally very good at resisting and containing such tension. It also has support from infiltrating myofibroblasts, which detect areas where the ECM density or tension is not normal and initiate actomyosin-based contraction of collagen and elastin matrix structures to restore tensional homeostasis. But while this repair effort is typically effective in healthy tissues, uncooperative tumor cells interfere with these efforts, both by themselves generating pressure and by hyperactivating cancer-associated fibroblasts to produce more ECM and thus produce even more force.11

Because cell growth and ECM composition are not spatially uniform in cancer, tumors are subjected to multiple, dispersed sources of pressure associated with matrix “containers” of various sizes. This solid pressure from within the tumor deforms the surrounding normal tissue, potentially facilitating the metastatic escape of cancer cells. The physical forces also compress blood vessels and lymphatic vessels in the tumor and adjacent normal tissue,12 increasing the fluid pressure in the tumor13  and interrupting the delivery of nutrients, removal of waste, and entry of tumor-targeted drugs via the blood.4 Insufficient blood flow also results in poor oxygenation, which has been linked to immunosuppression, inflammation, invasion, and metastasis, as well as lowered efficacy of chemo-, radio-, and immunotherapies.4 These are all indirect consequences of solid stresses in and on tumors.

Such forces can also have direct effects on cancer cells, and may serve as independent triggers for tumor invasion. Mechanical forces are central to many of our sense systems, such as hearing, touch, and pain, and to tissue maintenance programs, such as bone regeneration and blood vessel remodeling. In these systems, mechanical forces are transduced by mechanosensors to activate downstream biochemical and genetic pathways. (See “Full Speed Ahead,” The Scientist, December 2009.) Cancer cells may similarly be able to sense and respond to dynamic forces in tumors. We have shown, for example, that metastatic cancer cells exposed to compressive stresses in a culture dish undergo a phenotypic transformation to become more invasive,14 and others have shown that compressive forces applied in vivo can also induce oncogenes in normal epithelium of the mouse colon.15

It is thus becoming quite clear that the physical environment can influence a tumor’s development and spread, and it may even be possible for physical forces to kick-start cancerous growth.


Full Speed Ahead

Physical forces acting in and around cells are fast—and making waves in the world of molecular biology.

By Jef Akst | December 1, 2009    http://www.the-scientist.com/?articles.view/articleNo/27816/title/Full-Speed-Ahead/

When it comes to survival, few things are more important than being able to respond quickly to a change of circumstances. And when it comes to fast-acting indicators, it turns out that signals induced by physical forces acting in and around cells, appropriately dubbed biomechanical signals, are the champions of the cellular world.

“If you look at this mechanical signaling, it’s about 30 meters per second—that’s very fast,” says bioengineer Ning Wang of the University of Illinois at Urbana-Champaign. That’s faster than most family-owned speedboats, and second only to electrical (e.g., nerve) impulses in biological signaling. By comparison, small chemicals moving by diffusion average a mere 2 micrometers per second—a speed even the slowest row boater could easily top.

Indeed, when the two signal types were pitted against each other in a cellular race last year, the mechanical signals left chemical signals in their wake, activating proteins at distant sites in the cytoplasm in just a fraction of a second, at least 40 times faster than their growth factor opponent.1 Mechanical signals are so fast, Wang adds, they are “beyond our resolution,” meaning that current imaging techniques cannot capture the very first cellular changes that result from mechanical stress, which occur within nanoseconds.

For centuries, scientists have scrutinized the molecular inner workings of the body, with little or no regard to the physical environment in which these biological reactions take place. But the growing realization that physical forces have a pervasive presence in physiology (operating in a variety of bodily systems in thebone, blood, kidney, and ear, for instance), and act with astonishing speed, has caused many to consider the possibility that mechanical signaling may be just as important as chemical communication in the life of a cell.

“Biologists have traditionally ignored the role of mechanics in biology,” says biomechanical engineer Mohammad Mofrad of the University of California, Berkley, “[but] biomechanics is becoming increasingly accepted, and people are recognizing its role in development, in disease, and in general cellular and tissue function.”

The wave within: Mechanical forces acting inside the cell

Once believed to be little more than sacks of chemically active goop, cells didn’t seem capable of transmitting physical forces into their depths, and researchers largely limited their search for molecules or structures that respond to physical forces, or mechanosensors, to the plasma membrane.

Mechanical signaling may be just as important as chemical communication in the life of a cell.

In the late 1990s, however, closer examination revealed that the cell’s interior is in fact a highly structured environment, composed of a network of filaments.2 Pull on one side of the cell, and these filaments will transmit the force all the way to other side, tugging on and bumping into a variety of cellular structures along the way—similar to how a boat’s wake sends a series of small waves lapping up on a distant and otherwise peaceful shoreline. Scientists are now realizing the potential of such intracellular jostling to induce molecular changes throughout the cell, and the search for mechanosensing molecules has escalated dramatically in scope, including, for example, several proteins of the nucleus.

It’s a search that will likely last a while, predicts cell biologist Donald Ingber, director of the Wyss Institute for Biologically Inspired Engineering at Harvard University. “To try to find out what’s the mechanosensor is kind of crazy at this point,” he says. As scientists are now learning, “the whole cell is the mechanosensor.”

A key player, most agree, is the cytoskeleton, which is comprised of a variety of microfilaments, including rigid actin filaments and active myosin motors—the two principle components of muscle. Activation of the so-called nonmuscle myosins causes the cytoskeleton to contract, much like an arm muscle does when it lifts a heavy object.

The first intimation that the cytoskeleton could go beyond its established inner-cell duties (molecule transport and cell movement and division) came in 1997, when Ingber did the logical (in hindsight, at least) experiment of pulling on the cells to see what happened inside.2 Using a tiny glass micropipette coated in ligands, Ingber and his team gently probed the surface proteins known as integrins, which secure the cell to the extracellular matrix. When they quickly pulled the micropipette away, they saw an immediate cellular makeover: cytoskeletal elements turned 90 degrees, the nucleus distorted, and the nucleolus—a small, dense structure within the nucleus that functions primarily in ribosome assembly—aligned itself with the direction of the applied force.

“That kind of blew people away,” Ingber recalls. “It revealed that cells have incredible levels of structure not only in the cytoplasm but in the nucleus as well.”

Wang (once a postdoc in Ingber’s lab at the Harvard School of Public Health) and other collaborators combined a similar technique with fluorescent imaging technology to visualize how these forces were channeled within the cell’s interior. Upping the resolution and further refining these techniques, Wang began mapping these intracellular forces as they made their way through the cell. In 2005, the maps confirmed the physical connection between the cell-surface integrins and the nucleus, and showed that these external forces follow a nonrandom path dictated by the tension of the cytoskeletal elements.3

“Biomechanics is becoming increasingly accepted, and people are recognizing its role in development, in disease, and in general cellular and tissue function.”
–Mohammad Mofrad

The end point of these mechanical pathways is likely a mechanosensitive protein, which changes shape in response to the force, thereby exposing new binding areas or otherwise changing the protein’s function. In mitochondria, for example, mechanical forces may trigger the release of reactive oxygen species and activation of signaling molecules that contribute to inflammation and atherosclerosis.

Similarly, proteins on the nuclear membrane may pass mechanical signals into the nucleus by way of a specialized structure known as LINC (linker of nucleoskeleton and cytoskeleton), which physically links the actin cytoskeleton to proteins important in nuclear organization and gene function. To determine if mechanical forces directly affect gene expression, last year scientists began exploiting the increasingly popular fluorescence resonance energy transfer (FRET) technology,1 in which energy emitted by one fluorescent molecule can stimulate another, resulting in a visible energy transfer that can track enzymatic activities in live cells. By combining FRET technology with the techniques that apply physical forces to specific cell membrane proteins, scientists can visualize entire mechanochemical transduction pathways, Wang says.

“The big issue right now in the field of mechanotransduction is whether the genes in the nucleus can be directly activated by forces applied to the cell surface,” Wang explains. While the physical maps of the cytoskeleton tentatively sketch out a path that supports this possibility, confirmatory data is lacking. This combination of new technologies will be “tremendously” helpful in answering that question, he says, and “push the field” towards a more complete understanding of how mechanical forces can influence cellular life.

An early start: Mechanical forces in development

In the world of developmental biology, the cytoskeleton’s role in biomechanics really comes into its own. As the embryo develops, the cells themselves are the force generators, and by contracting at critical times, the cytoskeleton can initiate many key developmental steps, from invagination and gastrulation to proliferation and differentiation, and overall cellular organization.

The idea that physical forces play a role in development is not a new one. In the early 20th century, back when Albert Einstein was first developing the molecular basis of viscosity and scientists were realizing molecules are distinct particles, biologist and mathematician D’Arcy Thompson of the University of Dundee in Scotland suggested that mechanical strain is a key player in morphogenesis. Now, nearly a century later, biologists are finally beginning to agree.

Because Thompson “couldn’t measure [the forces] at that time, that kind of thinking got pushed to the wayside as genetic thinking took over biology,” says bioengineer Christopher Chen of the University of Pennsylvania. That is, until 2003, when Emmanuel Farge of the Curie Institute in France squeezedDrosophila embryos to mimic the compression experienced during early development and activated twist—a critical gene in the formation of the digestive tract.4 These results gave weight to Thompson’s idea that stress in the embryo stimulates development and growth, and inspired developmental scientists to begin considering mechanical effects, Chen says. “Now we’re at the stage where there’s a lot of interest and willingness to consider the fact that mechanical forces are not only shaping the embryo, but are linked to the differentiation programs that are going on.”

Again, the cytoskeleton is a key player in this process. In fruit flies and frogs, for example, nonmuscle myosins contract the actin filaments to generate the compressive forces necessary for successful gastrulation—the first major shape-changing event of development. Myosins similarly influence proliferation in the development of the Drosophila egg chamber, with increased myosin activity resulting in increased cell division.

Cytoskeleton contractility also appears to direct stem cell differentiation. In 2006, Dennis Discher of the University of Pennsylvania demonstrated that the tension of the substrate on which cells are grown in culture is important for determining what type of tissue the cells will form.5 Cells grown on soft matrices that mimic brain tissue tended to grow into neural cells, while cells grown on stiffer matrices grew into muscle cell precursors, and hard matrices yielded bone. In this case, it seems that stiffer substrates increased the expression of nonmuscle myosin, generating greater tension in the actin cytoskeleton and affecting differentiation. (Altering or inhibiting myosin contraction can also affect differentiation.)

“To try to find out what’s the mechanosensor is kind of crazy at this point. As scientists are now learning, the whole cell is the mechanosensor.”
–Donald Ingber
Shaping a tumor

In addition to the influence of physical forces on cancer growth and invasion, forces can alter a tumor’s mechanical properties, and vice versa. Tumors are more rigid, or stiffer, than surrounding tissues, usually because they contain excess collagen in the ECM,5 and this can contain and amplify local forces produced by proliferating cancer cells. On the other hand, tumor rigidity can be further enhanced if the cells exert tension on ECM collagen fibers by pulling on them, or by stretching them, as occurs when tumors grow uncontrollably. Fluid forces can also influence the assembly of collagen fibers within and around tumors,8potentially increasing stiffness.

Importantly, tumor stiffness tends to be associated with poor prognoses, though the reasons for this are not fully understood. Cells are known to differentiate into different lineages depending on the local rigidity;16 for example, stem cells differentiate into bone on stiff substrates, but make adipose (fat) cells on softer substrates. Similar mechanisms are thought to affect tumor progression when the ECM changes rigidity, inducing cancer cells to become more invasive as well as more likely to metastasize. Indeed, longer collagen fibers in the matrix are associated with increased invasion and metastasis, as well as reduced survival, in mice.17

In addition, the abnormal ECM in tumors can affect cancer progression by activating normal stromal cells, such as macrophages and fibroblasts, that accelerate tumor growth and treatment resistance. These activated stromal cells further strengthen and stretch the ECM, causing a snowball effect.

The biochemical composition and organization of the ECM also influences tumor biology. Dysregulation of normal matrix signals can lead to tumor progression, characterized by excessive cell proliferation, immortality, enhanced migration, changes in metabolism, and evasion of the immune response. More research is needed to dissect the relationships between the ECM’s mechanical properties, forces, and cell signaling pathways.

Targeting the ECM

Because unchecked proliferation of cancer cells increases solid stress in the tumor, anticancer therapies should decrease the compressive forces in tumors and reopen collapsed blood and lymphatic vessels.11 This is exactly what happens when tumors are treated with certain doses of paclitaxel or docetaxel, two widely used cancer drugs. Shrinking tumors increases blood flow and allows more efficient fluid movement through the extravascular space, lowering the tumor interstitial fluid pressure in mouse models and in patients with breast cancer.5 However, cancer cells invariably develop resistance to treatment and begin to regrow, increasing solid stress again. As a result, other targets for reducing solid stresses are needed.

Because of its role in containing and concentrating the forces in a tumor, the collagen matrix within and around the tumor is another potential target for relieving tumor-related stresses. Indeed, solid stress in tumors can be reduced by drugs that selectively reprogram activated fibroblasts or modify the assembly of matrix components such as collagen and hyaluronan. In rodent studies, targeting these force-altering components in the tumor microenvironment has been shown to decrease solid stress, improve blood perfusion and drug delivery, and improve tumor response to chemotherapy and animal survival.6 We have found, for example, that injecting tumors with a collagen-digesting enzyme increases the diffusion of antibodies and viral particles and improves drug penetration in the tumor. Similarly, treatments that target transforming growth factor–beta (TGF-β), which controls the production of collagen by myofibroblasts, increase perfusion, improve the delivery of drugs of all sizes in mammary tumors, and improve treatment outcomes in mice.5

A class of drugs that is widely used to control blood pressure in hypertensive patients also blocks the TGF-β pathway. These drugs, known as angiotensin receptor 1 blockers, can reduce collagen production in and around the tumor by reducing the activity of TGF-β, as well as by blocking the function of connective tissue growth factor (CTGF), which is involved in stabilizing collagen and inducing resistance to chemotherapy.6Losartan and other angiotensin inhibitors reduce levels of collagen in various experimental models of fibrosis, and decrease renal and cardiac fibrosis in hypertensive patients. When given to mice with one of four different types of tumors characterized by high levels of cancer-associated fibroblasts (CAFs) and excess extracellular matrix—pancreatic ductal adenocarcinoma, breast cancer, sarcoma, and melanoma—losartan treatment caused a decrease in collagen content in a dose-dependent manner, enhanced penetration of nanoparticles into the tumor, and improved efficacy of diverse anticancer drugs. This is supported by a number of retrospective studies in patients with pancreatic, lung, and kidney cancers.6Researchers at Massachusetts General Hospital are now running a Phase 1/2 clinical trial to test losartan in pancreatic cancer patients.


THE TUMOR ENVIRONMENT: The extracellular matrix and stromal cells within a tumor’s microenvironment influence the physical forces a tumor experiences. Left: The immunofluorescent image shows stromal cells (red and green) surrounding tumor cells (red cluster with blue nuclei); the cells were isolated from a mouse model of lung adenocarcinoma. Right: In this immunofluorescent image of triple-negative breast cancer, tumor cells (blue) are in close contact with matrix collagen (purple). Immune cells are labeled in red and green.VASILENA GOCHEVA, JACKS LAB, KOCH INSTITUTE AT MIT; DONGMEI ZUO, LABORATORY DR. MORAG PARK

Another potential cancer treatment target is hyaluronan, which is abundant in 20 percent to 30 percent of human tumors, most notably breast, colon, and prostate cancers. In addition to its role as a pressure-creating gel, hyaluronan can sequester growth factors and inhibit interstitial fluid movement within the tumor. Hyaluronidase, an enzyme that digests hyaluronan, reduces mechanical stress in tumors grown in mice.1 And San Diego–based Halozyme Therapeutics’s PEGPH20, a formulation of hyaluronidase coated with polyethylene glycol to enhance bioavailability, can decompress blood vessels and improve treatment outcome in genetically engineered mouse models of pancreatic ductal adenocarcinoma. Based on these studies, Halozyme researchers are now testing PEGPH20 in a randomized clinical trial of pancreatic cancer patients. Another matrix-altering drug is the widely-prescribed antidiabetic drug metformin, which has been shown to decrease collagen and hyaluronan levels in pancreatic tumors in obese mice and patients.7 Metformin is currently being tested in more than 200 clinical trials worldwide as a treatment for different types of cancer.

Clearly, tumors should be studied not only in light of their biochemical processes and genetic underpinnings, but also for the specific physical forces and mechanical properties that may influence progression. Understanding the physical microenvironment of tumors, as well as its interplay with the biochemical environment, is necessary to improve cancer detection, prevention, and treatment.

  1. T. Stylianopoulos et al., “Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors,” PNAS, 109:15101-08, 2012.
  2. R.K. Jain, L.T. Baxter, “Mechanisms of heterogeneous distribution of monoclonal antibodies and other macromolecules in tumors: Significance of elevated interstitial pressure,” Cancer Res, 48:7022-32, 1988.
  3. R.K. Jain, “Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy,”Science, 307:58-62, 2005.
  4. R.K. Jain, “Antiangiogenesis strategies revisited: From starving tumors to alleviating hypoxia,”Cancer Cell, 26:605-22, 2014.
  5. R.K. Jain et al., “The role of mechanical forces in tumor growth and therapy,” Annu Rev Biomed Eng, 16:321-46, 2014.
  6. V.P. Chauhan et al., “Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels,” Nat Commun, 4:2516, 2013.
  7. J. Incio et al., “Metformin reduces desmoplasia in pancreatic cancer by reprogramming stellate cells and tumor-associated macrophages,” PLOS ONE, 10:e0141392, 2015.
  8. M.A. Swartz, A.W. Lund, “Lymphatic and interstitial flow in the tumour microenvironment: linking mechanobiology with immunity,” Nat Rev Cancer, 12:210-19, 2012.
  9. H. Qazi et al., “Cancer cell glycocalyx mediates mechanotransduction and flow-regulated invasion,”Integr Biol, 5:1334-43, 2013.
  10. J.W. Song, L.L. Munn, “Fluid forces control endothelial sprouting,” PNAS, 108:15342-47, 2011.

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