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Posts Tagged ‘Coagulation’


Warfarin and Dabigatran, Similarities and Differences

Author and Curator: Danut Dragoi, PhD

 

What anticoagulants do?

An anticoagulant helps your body control how fast your blood clots; therefore, it prevents clots from forming inside your arteries, veins or heart during certain medical conditions.

If you have a blood clot, an anticoagulant may prevent the clot from getting larger. It also may prevent a piece of the clot from breaking off and traveling to your lungs, brain or heart. The anticoagulant medication does not dissolve the blood clot. With time, however, this clot may dissolve on its own.

Blood tests you will need

The blood tests for clotting time are called prothrombin time (Protime, PT) and international normalized ratio (INR). These tests help determine if your medication is working. The tests are performed at a laboratory, usually once a week to once a month, as directed by your doctor. Your doctor will help you decide which laboratory you will go to for these tests.

The test results help the doctor decide the dose of warfarin (Coumadin) that you should take to keep a balance between clotting and bleeding.

Important things to keep in mind regarding blood tests include:

  • Have your INR checked when scheduled.
  • Go to the same laboratory each time. (There can be a difference in results between laboratories).
  • If you are planning a trip, talk with your doctor about using another laboratory while traveling.
Dosage

The dose of medication usually ranges from 1 mg to 10 mg once daily. The doctor will prescribe one strength and change the dose as needed (your dose may be adjusted with each INR).

The tablet is scored and breaks in half easily. For example: if your doctor prescribes a 5 mg tablet and then changes the dose to 2.5 mg (2½ mg), which is half the strength, you should break one of the 5 mg tablets in half and take the half-tablet. If you have any questions about your dose, talk with your doctor or pharmacist.

What warfarin (Coumadin) tablets look like

Warfarin is made by several different drug manufacturers and is available in many different shapes. Each color represents a different strength, measured in milligrams (mg). Each tablet has the strength imprinted on one side, and is scored so you can break it in half easily to adjust your dose as your doctor instructed.

https://my.clevelandclinic.org/health/drugs_devices_supplements/hic_Understanding_Coumadin

Today, on the basis of 4 clinical trials involving over 9,000 patients, PRADAXA is approved to treat blood clots in the veins of your legs(deep vein thrombosis, or DVT) or lungs (pulmonary embolism, or PE)in patients who have been treated with blood thinner injections, and to reduce the risk of them occurring again.

In these trials, PRADAXA was compared to warfarin or to placebo (sugar pills) for the treatment of DVT and PE patients.

https://www.pradaxa.com/pradaxa-vs-warfarin?gclid=CMaRq7al9ssCFUxZhgodZuoC5w

Warfarin (NB-which goes by the brand name Coumadin, see link in here) reduces the risk of stroke in patients with atrial fibrillation (NB- atrial fibrillation (also called AFib or AF) is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure and other heart-related complications. Some people refer to AF as a quivering heart, see link here) but increases the risk of hemorrhage and is difficult to use.

Dabigatran is a new oral direct thrombin inhibitor (NB-direct thrombin inhibitors are a class of medication that act as anticoagulants by directly inhibiting the enzyme thrombin). Some are in clinical use, while others are undergoing clinical development), see link in here.

Some international large clinical trials, see link in here,  show results for patients with atrial fibrillation, dabigatran given at a dose of 110 mg was associated with rates of stroke and systemic embolism that were similar to those associated with warfarin, as well as lower rates of major hemorrhage. Dabigatran administered at a dose of 150 mg, as compared with warfarin, was associated with lower rates of stroke and systemic embolism but similar rates of major hemorrhage.

Picture below shows a deep vein thrombosis which is a blood clot that forms inside a vein, usually deep within the leg. About half a million Americans every year get one, and up to 100,000 die because of it. The danger is that part of the clot can break off and travel through your bloodstream. It could get stuck in your lungs and block blood flow, causing organ damage or death, see link in here.

Blod Clot

Image SOURCE: http://www.webmd.com/heart-disease/guide/warfarin-other-blood-thinners

The behaviour of blood thinning drugs is dependent on their physico-chemical properties and since a significant proportion of drugs contain ionisable centers a knowledge of their pKa (NB-pKa was introduced as an index to express the acidity of weak acids, where pKa is defined as follows. For example, the Ka constant for acetic acid (CH3C00H) is 0.0000158 (= 10-4.8), but the pKa constant is 4.8, which is a simpler expression. In addition, the smaller the pKa value, the stronger the acid, see link in here ) is essential, see link in here. The pKa is defined as the negative log of the dissociation constant, see link in here:

pka=-log10(Ka)              (1)

where the dissociation constant is defined thus:

Ka=[A][H+]/[AH]

Most drugs have pKa in the range 0-12, and whilst it is possible to calculate pKa it is desirable to experimentally measure the value for representative examples. There are a number of instruments that are capable of measuring pKa utilising Sirius T3 instrument, see link in here .

Table 1 below shows the pka values for warfarin, see link in here  and dabigatran, see link in here.

Table 1

==========================

Anticoagulant           pka          

warfarin                     4.99

dabigatran                 4.24        11.51*

==========================

* dabigatran possess both acidic and basic functionality.

Both groups are at ionized at blood pH and exist as zwitterionic

structures, see link in here.

Adding physico-chemical features of anticoagulants utilized in “dissolving” blood clots is important for better understanding the de-blocking process within the veins utilizing anticoagulants.

SOURCE

http://theochem.chem.rug.nl/publications/PDF/ft683.pdf

http://www.rsc.org/chemical-sciences-repository/articles/article/dr000000003197?doi=10.1039/c5ra04680g

http://pubs.rsc.org/en/content/articlelanding/2015/ra/c5ra11623f#!divAbstract

http://www.cambridgemedchemconsulting.com/resources/physiochem/pka.html

http://www.webmd.com/heart-disease/guide/warfarin-other-blood-thinners

https://www.google.com/#q=define+atrial+fibrillation

https://www.researchgate.net/profile/Lars_Wallentin/publication/26777612_Dabigatran_versus_Warfarin_in_Patients_with_Atrial_Fibrillation/links/02bfe50c8c2fa639c0000000.pdf

http://www.webmd.com/heart-disease/guide/warfarin-other-blood-thinners

 

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

Coagulation N=69

https://pharmaceuticalintelligence.com/?s=Coagulation

Peripheral Arterial Disease N=43

https://pharmaceuticalintelligence.com/?s=Peripheral

Antiarrhythmic drugs

https://pharmaceuticalintelligence.com/?s=Antiarrhythmic+drugs

A-Fib

https://pharmaceuticalintelligence.com/?s=a-fib

Electrophysiology N = 80

https://pharmaceuticalintelligence.com/?s=Electrophysiology

 

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The Relation between Coagulation and Cancer affects Supportive Treatments

Demet Sag, PhD

 

Coagulation and Cancer

There are several supportive therapies for cancer patients. One of the most important one is controlling the blood intake. This is sometimes observe keeping the blood cell count at certain levels, or providing safe blood/blood products to avoid any contaminations or infections,

The relation between cancer and coagulation was known for a long time but it was becoming clear recently.  Having coagulapathies also reduce the survival of patients since they can’t response to given treatments. Thus, it is necessary to give supportive therapies to control the coagulation. Problems in coagulation may develop from inherited (genetics), or acquired due to given therapies that cause varying abnormalities towards bleeding or thrombose at many levels.  The thrombotic events are important since they are the second leading cause of death in cancer patients (after cancer itself).  The presence of these coagulopathies determines the survival rate, length of survival either short-term or long-term, as well as relapses.

Cancer and Coagulation from start to finish:

Thrombotic risk factors in cancer patients

  1. Patient related
  2. Cancer related
  3. Treatment related

.

  1. Patient Related:
  • Older age
  • Bed rest
  • Obesity
  • Previous thrombosis
  • Prothrombotic mutations
  • High leukocyte and platelet counts
  • Comorbidities
  1. Cancer related:

a. Site of cancer:

  • brain,
  • pancreas,
  • kidney,
  • stomach,
  • lung,
  • bladder,
  • gynecologic,
  • hematologic malignancies

b. Stage of cancer:

  • advanced stage and
  • initial period after diagnosis
  1. Treatments:
  • Hospitalization
  • Surgery
  • Chemo- and
  • hormonal therapy
  • Anti-angiogenic therapy
  • Erythropoiesis stimulating agents
  • Blood transfusions
  • CVC, central venous catheters
  • Radiations

Thromboembolic events can be venous or arterial.

Venous events include

  • deep vein thrombosis (DVT),
  • pulmonary embolism (PE)

together categorized as venous thromboembolism (VTE).

Arterial events, include

  • stroke, myocardial infarction and
  • arterial embolism.

increase in the rate of VTEIncrease in the rate of venous thromboembolism (VTE) over time. Results are presented as annual rates of deep venous thrombosis (DVT), pulmonary embolism (PE) without deep venous thrombosis, and both between 1995 and 2003. Significant trends for increasing rates were observed for all 3 diagnoses (P < .0001). The rate of increase was found to be greater in the subgroup of patients who received chemotherapy. Error bars represent 95% confidence intervals.

There is an increase in both venous and arterial eventsrecently with “unacceptably high” event rates documented in the most contemporary studies:

There are significant consequences to the occurrence of thromboembolism in this setting:

  • requirement for long-term anticoagulation,
  • a 12% annual risk of bleeding complications,
  • an up to 21% annual risk of recurrent VTEand
  • potential impact on chemotherapy delivery and patient quality of life.

 

Therapeutic interventions enhance the risk of VTE in cancer.

  • Cancer patients undergoing surgery have a two-fold increased risk of postoperative VTE as compared to non-cancer patients, and this elevation in risk can persist for a period up to 7 weeks
  • Hospitalization also substantially increases the risk of developing VTE in cancer patients (OR 2.34, 95% CI 1.63 – 3.36)
  • The use of systemic chemotherapy is associated with a 2-to 6-fold increased risk of VTE compared to the general population.
  • Anti-angiogenic agents, particularly thalidomide and lenalidomide, have been associated with high rates of VTE when given in combination with dexamethasone or chemotherapy.
  • Bevacizumab-containing regimens have been associated with increased risk for an arterial thromboembolic event (hazard ratio [HR] 2.0, 95% CI 1.05- 3.75) but the data for risk of VTE are conflicting
  • Sunitinib and sorafenib, agents targeting the angiogenesis pathway, have also similarly been associated with elevated risk for arterial (but not venous) events [RR 3.03 (95% CI, 1.25 to 7.37)]

Anticoagulants and Cancer Coagulopathies

There are many studies on coagulation and use of anti-coagulants yet the same patient may also thrombose at any given time so the coagulant therapies should be under close surveillance.  The study (PMID:111278600) by Palereti et all in 2000 to many  compared this issue.

fig1_10.1002_cncr.23062

Palereti et al. showed that:

“The outcome of anticoagulation courses in 95 patients with malignancy with those of 733 patients without malignancy. All patients were participants in a large, nation-wide population study and were prospectively followed from the initiation of their oral anticoagulant therapy.

Based on 744 patient-years of treatment and follow-up, the rates of major (5.4% vs 0.9%), minor (16.2% vs 3.6%) and total (21.6% vs 4.5%) bleeding were statistically significantly higher in cancer patients compared with patients without cancer.

Bleeding was also a more frequent cause of early anticoagulation withdrawal in patients with malignancy (4.2% vs. 0.7%; p <0.01; RR 6.2 (95% CI 1.95-19.4). There was a trend towards a higher rate of thrombotic complications in cancer patients (6.8% vs. 2.5%; p = 0.058; RR 2.5 [CI 0.96-6.5]) but this did not achieve statistical significance”.

They concluded that “patients with malignancy treated with oral anticoagulants have a higher rate of bleeding and possibly an increased risk of recurrent thrombosis compared with patients without malignancy.”

http://www.cancernetwork.com/sites/default/files/figures_diagrams/1502FeinsteinFigure.png

1502FeinsteinFigure

Cancer and Coagulation in more detail at Molecular Level:

Cancer is a complex disease from its initiation to its treatment. In the body the response to drugs generates side effects for being foreign (immune responses and inflammation), toxic, or disturbing the hemostasis of the coagulation system. In addition, activation of oncogenic pathways cab also be activated that may not only effect the development of the cancer but also may induce oncogenes to activate dormant cancer cells. In the coagulation system the balance is important to keep anti-coagulant state, with oversimplification, such as having certain number of tissue factor (TF) that is a receptor determines the anticoagulant state. However, certain pro-oncogenic genes like RAS, EGFR, HER2, MET, SHH and loss of tumor suppressors (PTEN, TP53) change the gene regulation so they alter the expression, activity and vesicular release of coagulation effectors, as exemplified by tissue factor (TF). As a result, there is a bridge between the coagulation-related genes (coagulome) and specific cancer coagulapathies, such as in glioblastoma multiforme (GBM), medulloblastoma (MB), etc. Therefore, these coagulome can be a great target not only to inhibit angiogenesis and tumor growth but also prevent any coagulopathies, use in single genomics/circulating cancer cells as well as grading the level of cancer specifically.

Here in this figures Tumor-hemostatic system interactions http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f1.gif?v=1&t=ifxvwlxk&s=62da078fc1c8d85d58c256e83954181a16f7463b

and Microparticle (MP) production and activities in cancer are well summarized http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f2.gif?v=1&t=ifxvwlzv&s=13f9b775d7417f12e3ae5f879c09ac8825918d61

coagulation and cancer

 

 

http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f1.gif?v=1&t=ifxvwlxk&s=62da078fc1c8d85d58c256e83954181a16f7463b

Tumor-hemostatic system interactions. Tumor cells activate the hemostatic system in multiple ways. Tumor cells may release procoagulant tissue factor, cancer procoagulant and microparticles (MP) that can directly activate the coagulation cascade. Tumor cells may also activate the host’s hemostatic cells (endothelial cells and platelets), by either release of soluble factors or by direct adhesive contact, thus further enhancing clotting activation.

 

 tumor and coagulation cascade

 

http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f2.gif?v=1&t=ifxvwlzv&s=13f9b775d7417f12e3ae5f879c09ac8825918d61

Microparticle (MP) production and activities in cancer. Tumor cells actively release MP but also promote MP formation by platelets. Tissue factor (TF) and phosphatidylserine (PS) expression on the surfaces of both platelet- and tumor-derived MP are involved in blood clotting activation and thrombus formation. On the other hand, the elevated content of proangiogenic factors in platelet-derived MP (VEGF, vascular endothelial growth factor, FGF, fibroblast growth factor, PDGF, platelet-derived growth factor), render these elements also important mediators of the neangiogenesis process. Finally, intracellular transfer of MP may occur between cancer cells, leading to a horizontal propagation of oncogenes and amplification of their angiogenic phenotype.

 

Immune Response and Cancer with Coagulopathies:

  1. I. Goufman et al also suggested that plasma level of IgG autoantibodies to plasminogen changes during cancer coagulopathies.

Their data based on ELISA measurements of their patients:

  • with benign prostatic hyperplasia (n=25),
  • prostatic cancer (n=17),
  • lung cancer (n=15), and
  • healthy volunteers (n=44).

High levels of IgG to plasminogen were found

  • in 2 (12%) of 17 healthy women, in 1 (3.6%) of 27 specimens in a healthy man,
  • in 17 (68%) of 25 specimens in prostatic cancer,
  • in 10 (59%) of 17 specimens in lung cancer,
  • in 5 (30%) of 15 specimens in benign prostatic hyperplasia.

Comparison of plasma levels of anti-plasminogen IgG by affinity chromatography showed 3-fold higher levels in patients with prostatic cancer vs. healthy men.

Structure and function of platelet receptors initiating blood clotting.

There is a missed or overlooked concept about coagulation and cancer. In their article they mainly focused on the structure and function of key platelet receptors taking role in the thorombus formation and coagulation.

At the clinical level, recent studies reveal the link between coagulation and other pathophysiological processes, including platelet activation, inflammation, cancer, the immune response, and/or infectious diseases. These links are likely to underpin the coagulopathy associated with risk factors for venous thromboembolic (VTE) and deep vein thrombosis (DVT). At the molecular level, the interactions between platelet-specific receptors and coagulation factors could help explain coagulopathy associated with aberrant platelet function, as well as revealing new approaches targeting platelet receptors in diagnosis or treatment of VTE or DVT. Glycoprotein (GP)Ibα, the major ligand-binding subunit of the platelet GPIb-IX-V complex, that binds the adhesive ligand, von Willebrand factor (VWF), is co-associated with the platelet-specific collagen receptor, GPVI. The GPIb-IX-V/GPVI adheso-signaling complex not only initiates platelet activation and aggregation (thrombus formation) in response to vascular injury or disease but GPIbα also regulates coagulation through a specific interaction with thrombin and other coagulation factors.

Clinical Data and Some Samples of Biomarkers:

Development of biomarkers and management of cancer coagulapathies are underway since there are times this coagulapathies may be as deadly as the cancer itself.

The sample study and data from Reference: Alok A. Khorana, M.D. Cancer and Coagulation. Am J Hematol. 2012 May; 87(Suppl 1): S82–S87. Published online 2012 Mar 3. doi:  10.1002/ajh.23143 PMCID: PMC3495606. NIHMSID: NIHMS386379

Resource: PMC full text: Am J Hematol. Author manuscript; available in PMC 2013 May 1.

Published in final edited form as:

Am J Hematol. 2012 May; 87(Suppl 1): S82–S87.

Published online 2012 Mar 3. doi:  10.1002/ajh.23143

Copyright/License ►Request permission to reuse

Table 1

Selected Clinical Risk Factors and Biomarkers for Cancer-associated Thrombosis

Patient-associated risk factors
 Older age
 Race
 Gender
 Medical comorbidities
 Obesity
 Prior history of thrombosis
Cancer-associated risk factors
 Primary site
 Stage
 Cancer histology (higher for adenocarcinoma than squamous cell)
 Time after initial diagnosis (highest in first 3-6 months)
Treatment-associated risk factors
 Chemotherapy
 Anti-angiogenic agents
 Hormonal therapy
 Erythropoiesis-stimulating agents
 Transfusions
 Indwelling venous access devices
 Radiation
 Surgery
Biomarkers
Currently widely available
 Platelet count (≥350,000/mm3)23
 Leukocyte count (> 11,000/mm3)23
 Hemoglobin (< 10 g/dL)23
 D-dimer25,26
Investigational and/or not widely available
 Tissue factor (antigen expression, circulating microparticles, antigen or activity)3133

 

 

Table 2

Predictive Model for chemotherapy-associated VTE23

Patient Characteristics Risk Score
Site of cancer
 Very high risk (stomach, pancreas) 2
 High risk (lung, lymphoma, gynecologic, bladder, testicular) 1
Prechemotherapy platelet count 350000/mm3 or more 1
Hemoglobin level less than 10g/dl or use of red cell growth factors 1
Prechemotherapy leukocyte count more than 11000/mm3 1
Body mass index 35kg/m2 or more 1

High-risk score ≥ 3

Intermediate risk score =1-2

Low-risk score =0

 

 

Rates of VTE According to Risk Score

Study Type, f/u N Low-risk (score=0) Intermediate–risk (score =1-2) High-risk (score≥3)
Khorana et al23, 2008 Development cohort, 2.5 mos 2701 0.8% 1.8% 7.1%
Khorana et al23, 2008 Validation cohort, 2.5 mos 1365 0.3% 2% 6.7%
Kearney et al67, 2009 Retrospective, 2 yrs 112 5% 15.9% 41.4%
Price et al68, 2010 Retrospective, pancreatic, NA 108 – * 14% 27%
Ay et al36, 2010 Prospective, 643 days 819 1.5% 9.6% (score= 2) 17.7%
3.8% (score=1)
Khorana et al69, 2010 Prospective**, 3 mos 30 – *** 27%
Moore et al2, 2011 Retrospective, cisplatin-based chemotherapy only 932 13% 17.1% 28.2%
Mandala et al37, 2011 Retrospective, phase I patients only, 2 months 1,415 1.5% 4.8% 12.9%

NA=not available

*Pancreatic cancer patients are assigned a score of 2 based on site of cancer and therefore there were no patients in the low-risk category

**included 4-weekly screening ultrasonography

***enrolled only high-risk patients

Table 4

ASCO and NCCN Recommendations for Treatment of VTE in Cancer

ASCO NCCN
Initial treatment
LMWH is the preferred approach for the initial 5-10 days LMWH, UFH or factor Xa antagonists according to patient’s characteristics and clinical situation
Long term treatment
LMWH for at least 6 months is preferred. LMWH is preferred
VKA are acceptable when LMWH is not available. Indefinite anticoagulation in patients with active cancer or persistent risk factors
Indefinite anticoagulation in patients with active cancer.
Thrombolytic therapy in initial treatment
Restricted to patients with life- or limb-threatening thrombotic events Restricted to massive or submassive PE with moderate or severe right ventricular enlargement or dysfunction
Inferior vena cava filters
Restricted to patients with contraindications to anticoagulation or recurrent VTE despite adequate long-term LMWH Restricted to patients with contraindications to or failure of anticoagulation, cardiac or pulmonary dysfunction severe enough to make any new PE life-threatening or multiple PE with chronic pulmonary hypertension
Treatment of catheter-related thrombosis
NA LMWH or VKA for as long as catheter is in place or for 1 to 3 months after catheter removal
 Soluble P-selectin (> 53.1 ng/mL)65
 Factor VIII66
 Prothrombin fragment F 1+2 (>358 pmol/L) 26

 

 

 

Genome Analysis at the crossroads of Coagulation and Cancer

, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disordersGenome Medicine, 2015, 7,1

Phenotype similarity clustering of cases according to HPO terms. Heat map showing pairwise phenotypic similarity among affected members of pedigrees, cases with classical syndromes and cases with variants in ACTN1. The groups are ordered through complete-linkage hierarchical clustering within each class and P values of phenotypic similarity are shown in a scatterplot superimposed over a histogram showing the distribution of P values.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

Phenotype clusters 18 and 29. Illustrative subgraphs of the HPO showing terms for the phenotype clusters 18 (15 cases) and 29 (16 cases). Arrows indicate direct (solid) or indirect (dashed) is a relations between terms in the ontology. DMPV: decreased mean platelet volume; PA: phenotypic abnormality; Plt-agg: platelet aggregation abnormality.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

s13073-015-0151-5-5 s13073-015-0151-5-6

Rare variants identified inACTN1
Case Transcript variant ENST00000394419 Protein variant ENSP00000377941.4 HGMD variant Classification PLT, ×109/L MPV, fL, and/or presence of macrothrombocytes Bleeding phenotype
B200726 14:69392385 A/C F37C No LPV 57 18.1, macrothrombocytes None
B200207 14:69392358 C/T R46Q Yes PV 53 >13, macrothrombocytes None
B200209 PV 76 >13, macrothrombocytes Mild
B200212 PV 98 >13, macrothrombocytes None
B200254 PV 34 >13, macrothrombocytes None
B200735 PV 52 12.0, macrothrombocytes None
B200746 14:69392359 G/A R46W No LPV 96 15.2, macrothrombocytes None
B200197 14:69392344 G/C Q51E No LPV 113 >13, macrothrombocytes Mild
B200836 14:69387750 C/T V105I Yes PV 53 NA, macrothrombocytes None
B200837a PV 75 NA, macrothrombocytes None
B200671 14:69371375 C/T E225K Yes PV 97 13.7, macrothrombocytes Mild
B200716 PV 82 15.0, macrothrombocytes None
B200398 14:69369274 C/T V228I No LPV 31 15.4, macrothrombocytes Mild
B200280 14:69358897 C/T R320Q No LPV 108 15.1, macrothrombocytes Mild
B200281a LPV 111 13.9, macrothrombocytes None
B200835 14:69352254 C/T A425T No VUS 50 10.0, no macrothrombocytes Mild
B200283 14:69349768 A/G L547P No LPV 91 13.3, macrothrombocytes Mild
B200048 14:69349648 G/A A587V No VUS 390 NA, no macrothrombocytes Mild
B200284 14:69346749 G/T T737N No LPV 60 16.1, macrothrombocytes Mild
B200285a LPV 48 16.8, macrothrombocytes Mild
B200741 14:69346747 G/A R738W Yes PV 94 12.9, macrothrombocytes None
B200745 PV 70 14.5, macrothrombocytes None
B200750 14:69346746 C/T R738Q No LPV 106 14.0, macrothrombocytes None
B200414 14:69346704 C/G R752P No LPV 121 11.4, macrothrombocytes Mild

aAffected family member.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Rare variants identified inMYH9and validated by Sanger sequencing
Case Transcript variant ENST00000216181 Protein variant ENSP00000216181 HGMD variant Classification PLT, ×109/L MPV, fL and/or presence of macrothrombocytes OtherMYH9-RD characteristics
B200760 22:36744995 G/A S96L Yes PV 180 Macrothrombocytes None
B200771 22:36705438 C/A D578Y No VUS 184 10.1 None
B200423 22:36696237 G/A A971V No VUS 262 10.2 None
B200024 22:36691696 A/G S1114P Yes VUS 164 NA None
B200245 VUS 53 11.1, Macrothrombocytes None
B200243 22:36691115 G/A R1165C Yes PV 22 Macrothrombocytes None
B200594 PV 46 Macrothrombocytes None
B200595a PV 61 Macrothrombocytes None
B200614 22:36688151 C/T D1409N No VUS 319 9.8 None
B200752 VUS 149 10.1, Macrothrombocytes None
B200855 VUS 95 16.8, Macrothrombocytes None
B200208 22:36688106 C/T D1424N Yes PV 99 13.6 None
B200010 22:36685249 G/C S1480W No VUS 244 NA None
B200244 22:36678800 G/A R1933X Yes PV 26 Macrothrombocytes Döhle inclusions

Other MYH9-RD characteristics sought were the presence of Döhle inclusions, cataract, deafness or renal pathology.

aFather of B200594.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Pathogenic and likely pathogenic variants identified in genes associated with autosomal recessive and X-linked recessive bleeding and platelet disorders
Case Position Gene Ref Alt Genotype HGMD Effecta Haematological HPO terms Other HPO terms Classification:
Variant Phenotype
B200286 3:148881737 HPS3 G C C|C Yes Abnormal splicing Bleeding with minor or no trauma, subcutaneous haemorrhage, menorrhagia, postpartum haemorrhage, impaired ADP-induced platelet aggregation, impaired epinephrine-induced platelet aggregation, epistaxis, prolonged bleeding after surgery, prolonged bleeding after dental extraction, increased mean platelet volume. Hypothyroidism, visual impairment, nystagmus, albinism. PV Explained
B200412 3:148858819 HPS3 T TA T|TA No Frameshift Impaired epinephrine-induced platelet aggregation, bleeding with minor or no trauma, subcutaneous haemorrhage, epistaxis, menorrhagia, prolonged bleeding after surgery, abnormal dense granules. Ocular albinism. LPV Possibly explained
3:148876539 HPS3 G A G|A No W593a LPV
B200068 10:103827041 HPS6 C G C|G No L604V Increased mean platelet volume. Congenital cataract, strabismus, maternal diabetes. LPV Possibly explained
10:103827554 HPS6 C G C|G No L775V LPV
B200196 X:48542673 WAS C T T Yes T45M Thrombocytopenia, abnormal bleeding, decreased mean platelet volume, abnormal platelet shape. Recurrent infections. PV Explained
B200725 X:48544145 WAS T C C Yes F128S Monocytosis, neutrophilia, thrombocytopenia, leukocytosis, subcutaneous haemorrhage, gastrointestinal haemorrhage. PV Explained
B200443 X:138633272 F9 G A A Yes R191H Reduced factor IX activity, impaired ADP-induced platelet aggregation, bleeding with minor or no trauma, spontaneous haematomas, abnormal number of dense granules. PV Partially explained
B200452 X:154124407 F8 C G G Yes S2125T Reduced factor VIII activity, persistent bleeding after trauma, prolonged bleeding after surgery, prolonged bleeding after dental extraction, bleeding requiring red cell transfusion, impaired collagen-induced platelet aggregation, bleeding with minor or no trauma, joint haemorrhage, abnormal platelet shape, abnormal number of dense granules. PV Partially explained
B200772 X:154176011 F8 A G G No F692S Reduced factor VIII activity, bruising susceptibility, impaired ADP-induced platelet aggregation, impaired collagen-induced platelet aggregation, impaired thromboxane A2 agonist-induced platelet aggregation, impaired ristocetin-induced platelet aggregation, impaired arachidonic acid-induced platelet aggregation, impaired thrombin-induced platelet aggregation, abnormal platelet granules, bleeding with minor or no trauma. LPV Possibly partially explained

Alt: alternative; Ref: reference.

aEffect considered relative to the Consensus Coding Sequence (CCDS) for each gene.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Table 2

TFPI and TF tumor mRNA expression across clinicopathological breast cancer subtypes

  mRNA expression (tumor) Protein levels (plasma)
Characteristic Groups Total TFPI (α + β) P TFPIα P TFPIβ P TF P Total TFPI P Free TFPI P TF P
T-status T1 −0.146 0.054 −0.135 0.257 −0.084 0.201 −0.023 0.652 72.01 0.013 10.82 0.997 4.14 0.125
T2-T3 0.085 0.018 0.060 0.054 65.02 10.82 4.66
Grade G1-G2 −0.022 0.850 −0.005 0.424 −0.033 0.743 0.271 0.003 71.04 0.082 10.66 0.682 4.63 0.557
G3 −0.045 −0.113 0.004 −0.229 66.12 10.97 4.14
N-status Negative −0.109 0.091 −0.136 0.127 −0.082 0.104 0.005 0.881 69.93 0.183 10.77 0.869 4.95 0.282
Positive 0.104 0.078 0.110 0.032 66.00 10.90 4.14
ER status Positive −0.067 0.317 −0.082 0.557 −0.056 0.183 0.001 0.784 69.42 0.240 10.91 0.671 4.42 0.409
PR status Negative 0.076 0.011 0.123 0.057 65.44 10.52 5.28
Positive −0.131 0.021 −0.145 0.075 −0.112 0.014 0.085 0.244 69.81 0.195 11.19 0.175 4.32 0.246
HER2-status Negative 0.161 0.108 0.182 −0.127 65.92 10.08 5.04
Negative −0.072 0.054 −0.101 0.073 −0.041 0.154 0.004 0.731 68.45 0.893 10.68 0.287 4.47 0.428
Positive 0.313 0.301 0.228 0.103 69.09 12.05 4.78
HR status Yes 0.076 0.326 0.007 0.587 0.114 0.221 0.016 0.991 64.78 0.161 10.41 0.568 5.26 0.470
No −0.066 −0.080 −0.052 0.014 69.57 10.94 4.47
Triple-negative status Yes −0.051 0.886 −0.110 0.718 0.041 0.635 −0.158 0.326 63.21 0.072 10.06 0.345 5.23 0.969
No −0.029 −0.048 −0.027 0.055 69.73 10.99 4.57

Median values for TFPI and TF mRNA expression in tumors and protein levels in plasma according to clinically defined groups. Corresponding P-values (unadjusted) are shown. Significant P-values in bold. TFPI, tissue factor pathway inhibitor; TF, tissue factor; HER2, human epidermal growth factor receptor 2.Abbreviations: T, tumor; G, grade; N, node; ER, estrogen receptor; PR, progesterone receptor; HR, hormone receptor.

Table 3

Significant association between TFPI single nucleotide polymorphisms (SNPs) and clinicopathological characteristics and molecular subtypes

Characteristic SNP Risk allele Odds ratio 95% CI P False discovery rate
T status
T1 Reference Reference Reference Reference
T2 to T3 rs10153820 A 3.14 1.44, 6.86 0.004 0.056
TN status (ER-/PR-/HER2-negative)
No Reference Reference Reference Reference
Yes rs8176541a G 2.62 1.11, 5.35 0.026 0.092
rs3213739a G 2.58 1.34, 4.99 0.005 0.033
rs8176479a C 3.10 1.24, 7.72 0.015 0.071
rs2192824a T 2.44 1.39, 4.93 0.002 0.033
N status
Positive Reference Reference Reference Reference
Negative rs10179730 G 3.34 1.42, 7.89 0.006 0.083
Basal tumor subtype
Non-basal Reference Reference Reference Reference
Basal rs3213739a G 2.23 1.15, 4.34 0.018 0.107
rs8176479a C 2.79 1.12, 6.96 0.028 0.107
rs2192824a T 2.41 1.24, 4.65 0.009 0.107
rs10187622a C 5.20 1.17, 23.20 0.031 0.107
Luminal B tumor subtype
Non-luminal B Reference Reference Reference Reference
Luminal B rs16829086a T 2.09 1.03, 4.25 0.041 0.191
rs10179730a G 3.53 1.47, 8.46 0.005 0.066
rs10187622a T 2.73 1.24, 6.03 0.013 0.091
Normal-like tumor subtype
Non-normal-like Reference Reference Reference Reference
Normal-like rs5940 T 22.17 4.43, 110.8 0.0002 0.003

aSNPs representing a haplotype effect. SNPs are listed by ascending chromosome positions. TFPI, tissue factor pathway inhibitor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor 2.

Table 4

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and TFPI mRNA expression in breast tumors

Probe SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
TFPIα rs2192824b Intronic C:T 0.490 −0.209 −0.180 0.029 0.200
TFPIα rs7594359b Intronic C:T 0.483 −0.219 −0.184 0.025 0.200
TFPIβ rs3213739b Intronic G:T 0.417 0.187 0.213 0.010 0.032
TFPIβ rs8176479b Intronic C:A 0.238 0.184 0.192 0.021 0.049
TFPIβ rs2192824b Intronic C:T 0.490 −0.267 −0.273 0.001 0.011
TFPIβ rs12613071b Intronic T:C 0.158 0.284 0.208 0.011 0.032
TFPIβ rs2192825b Intronic T:C 0.466 −0.251 −0.249 0.002 0.012
TFPIβ rs7594359b Intronic C:T 0.483 −0.248 −0.247 0.002 0.012
TFPIα + β rs2192824b Intronic C:T 0.490 −0.168 −0.161 0.050 0.187
TFPIα + β rs12613071b Intronic T:C 0.158 0.238 0.164 0.048 0.187
TFPIα + β rs7594359b Intronic C:T 0.483 −0.190 −0.178 0.030 0.187

aMajor:minor. bSNPs representing a haplotype effect. mRNA expression was assayed by the Agilent Human V2 Gene Expression 8x60k array, and probes for tissue factor pathway inhibitor (TFPI)α, TFPIβ and total TFPI (TFPIα + β) mRNA were analyzed. Alleles for the positive DNA strand (UCSC annotated) are shown, and SNPs are listed by ascending chromosome positions.

“Eight TFPI SNPs were found to be correlated to total TFPI protein levels in patient plasma (Table 5). The A-T-A-C-T-A-C-G haplotype composed of these eight SNPs (rs8176541-rs3213739-rs8176479-rs2192824-rs2192825-rs16829088-rs7594359-rs10153820) represented a common haplotype (frequency 0.19) with quite strong correlation to total TFPI protein; r = 0.481 (B = 14.62, P = 6.35 × 10−10). No correlation between TFPI SNPs and free TFPI protein, or between TF SNPs and TF protein in plasma was observed (P >0.05, data not shown). Adjusting for age had no effect on the correlation (data not shown).”

Table 5

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and total TFPI protein levels in plasma

Protein SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
Total TFPI rs8176541b Intronic G:A 0.283 15.64 0.571 7.69 × 10−14 1.08 × 10−12
Total TFPI rs3213739b Intronic G:T 0.417 11.35 0.488 5.38 × 10−10 3.77 × 10−9
Total TFPI rs8176479b Intronic C:A 0.238 12.22 0.480 1.20 × 10−9 5.62 × 10−9
Total TFPI rs2192824b Intronic C:T 0.490 −9.88 −0.404 3.81 × 10−7 1.07 × 106
Total TFPI rs2192825b Intronic T:C 0.466 −7.55 −0.301 2.40 × 10−4 5.30 × 10−4
Total TFPI rs16829088b Intronic G:A 0.250 11.23 0.424 1.00 × 10−7 3.51 × 10−7
Total TFPI rs7594359b Intronic C:T 0.483 −6.90 −0.275 6.90 × 10−4 0.001
Total TFPI rs10153820b Near 5UTR G:A 0.125 −7.79 −0.215 0.009 0.016

aMajor:minor. bSNPs representing a haplotype effect for total tissue factor pathway inhibitor (TFPI). Alleles for the positive DNA strand (UCSC annotated) are shown.

In sum, combination of molecular physiology and genomics will improve the conditions of the patients not only to diagnose early or to monitor the disease but also to streamline the current drugs to be more efficient and therapeutic.

References:

·         PMID: 25480646, Gardiner EE1, Andrews RK. Structure and function of platelet receptors initiating blood clotting. Adv Exp Med Biol. 2014;844:263-75. doi: 10.1007/978-1-4939-2095-2_13.

 

Further Reading:

Mari Tinholt, Hans Kristian Moen Vollan, Kristine Kleivi Sahlberg, Sandra Jernström, Fatemeh Kaveh, Ole Christian Lingjærde,Rolf Kåresen, Torill Sauer, Vessela Kristensen, Anne-Lise Børresen-Dale, Per Morten Sandset, Nina Iversen, Tumor expression, plasma levels and genetic polymorphisms of the coagulation inhibitor TFPI are associated with clinicopathological parameters and survival in breast cancer, in contrast to the coagulation initiator TFBreast Cancer Research, 2015, 17, 1

 Chaabane, L. Tei, L. Miragoli, L. Lattuada, M. von Wronski, F. Uggeri, V. Lorusso, S. Aime, In Vivo MR Imaging of Fibrin in a Neuroblastoma Tumor Model by Means of a Targeting Gd-Containing PeptideMolecular Imaging and Biology, 2015,

Daniela Bianconi, Alexandra Schuler, Clemens Pausz, Angelika Geroldinger, Alexandra Kaider, Heinz-Josef Lenz, Gabriela Kornek, Werner Scheithauer, Christoph C. Zielinski, Ingrid Pabinger, Cihan Ay, Gerald W. Prager, Integrin beta-3 genetic variants and risk of venous thromboembolism in colorectal cancer patients, Thrombosis Research, 2015,

Olumide B Gbolahan, Trista J Stankowski-Drengler, Abiola Ibraheem, Jessica M Engel, Adedayo A Onitilo, Management of chemotherapy-induced thromboembolism in breast cancerBreast Cancer Management, 2015, 4, 4, 187

Ami Schattner, Meital Adi, Mobile menace- floating aortic arch thrombusThe American Journal of Medicine, 2015,

Chuang-Chi Liaw, Hung Chang, Tsai-Sheng Yang, Ming-Sheng Wen, Pulmonary Venous Obstruction in Cancer Patients,Journal of Oncology, 2015, 2015, 1

Esther Rabizadeh, Izhack Cherny, Doron Lederfein, Shany Sherman, Natalia Binkovsky, Yevgenia Rosenblat, Aida Inbal, The cell-membrane prothrombinase, fibrinogen-like protein 2, promotes angiogenesis and tumor developmentThrombosis Research, 2015, 136, 1, 118

Anna Falanga, Marina Marchetti, Laura Russo, The mechanisms of cancer-associated thrombosis, Thrombosis Research,2015, 135, S8

I. Goufman, V. N. Yakovlev, N. B. Tikhonova, R. B. Aisina, K. N. Yarygin, L. I. Mukhametova, K. B. Gershkovich, D. A. Gulin,Autoantibodies to Plasminogen and Their Role in Tumor DiseasesBulletin of Experimental Biology and Medicine, 2015, 158,4, 493

Trisha A. Rettig, Julie N. Harbin, Adelaide Harrington, Leonie Dohmen, Sherry D. Fleming, Evasion and interactions of the humoral innate immune response in pathogen invasion, autoimmune disease, and cancerClinical Immunology, 2015, 160, 2,244

Sarah K Westbury, Ernest Turro, Daniel Greene, Claire Lentaigne, Anne M Kelly, Tadbir K Bariana, Ilenia Simeoni, Xavier Pillois, Antony Attwood, Steve Austin, Sjoert BG Jansen, Tamam Bakchoul, Abi Crisp-Hihn, Wendy N Erber, Rémi Favier,Nicola Foad, Michael Gattens, Jennifer D Jolley, Ri Liesner, Stuart Meacham, Carolyn M Millar, Alan T Nurden, Kathelijne Peerlinck, David J Perry, Pawan Poudel, Sol Schulman, Harald Schulze, Jonathan C Stephens, Bruce Furie, Peter N Robinson, Chris van Geet, Augusto Rendon, Keith Gomez, Michael A Laffan, Michele P Lambert, Paquita Nurden, Willem H Ouwehand, Sylvia Richardson, Andrew D Mumford, Kathleen Freson, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disordersGenome Medicine, 2015, 7,1

Ades, S. Kumar, M. Alam, A. Goodwin, D. Weckstein, M. Dugan, T. Ashikaga, M. Evans, C. Verschraegen, C. E. Holmes,Tumor oncogene (KRAS) status and risk of venous thrombosis in patients with metastatic colorectal cancer,Journal of Thrombosis and Haemostasis, 2015, 13, 6

Marcel Levi, Cancer-related coagulopathiesThrombosis Research, 2014, 133, S70

Axel C. Matzdorff, David Green, Management of venous thromboembolism in cancer patientsReviews in Vascular Medicine,2014, 2, 1, 24

Claude Bachmeyer, Milène Buffo, Bérénice Soyez, No Evidence Not to Prescribe Thromboprophylaxis in Hospitalized Medical Patients with Cancer, The American Journal of Medicine, 2014, 127, 7, e33

Nathalie Magnus, Esterina D’Asti, Brian Meehan, Delphine Garnier, Janusz Rak, Oncogenes and the coagulation system – forces that modulate dormant and aggressive states in cancer, Thrombosis Research, 2014, 133, S1

Maria Sofra, Anna Antenucci, Michele Gallucci, Chiara Mandoj, Rocco Papalia, Claudia Claroni, Ilaria Monteferrante, Giulia Torregiani, Valeria Gianaroli, Isabella Sperduti, Luigi Tomao, Ester Forastiere, Perioperative changes in pro and anticoagulant factors in prostate cancer patients undergoing laparoscopic and robotic radical prostatectomy with different anaesthetic techniquesJournal of Experimental & Clinical Cancer Research, 2014, 33, 1, 63

Taslim A. Al-Hilal, Farzana Alam, Jin Woo Park, Kwangmeyung Kim, Ick Chan Kwon, Gyu Ha Ryu, Youngro Byun, Prevention effect of orally active heparin conjugate on cancer-associated thrombosisJournal of Controlled Release, 2014, 195, 155

Samridhi Sharma, Sandipan Ray, Aliasgar Moiyadi, Epari Sridhar, Sanjeeva Srivastava, Quantitative Proteomic Analysis of Meningiomas for the Identification of Surrogate Protein Markers, Scientific Reports, 2014, 4, 7140

W. Yau, P. Liao, J. C. Fredenburgh, A. R. Stafford, A. S. Revenko, B. P. Monia, J. I. Weitz, Selective depletion of factor XI or factor XII with antisense oligonucleotides attenuates catheter thrombosis in rabbits,Blood, 2014, 123, 13, 2102

Anna Falanga, Laura Russo, Viola Milesi, The coagulopathy of cancerCurrent Opinion in Hematology, 2014, 21, 5, 423

Sarah J. Barsam, Raj Patel, Roopen Arya, Anticoagulation for prevention and treatment of cancer-related venous thromboembolismBritish Journal of Haematology, 2013, 161, 6

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Compilation of References in Leaders in Pharmaceutical Intelligence about proteomics, metabolomics, signaling pathways, and cell regulation


Compilation of References in Leaders in Pharmaceutical Intelligence about
proteomics, metabolomics, signaling pathways, and cell regulation

Curator: Larry H. Bernstein, MD, FCAP

 

Proteomics

  1. The Human Proteome Map Completed
    Reporter and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/28/the-human-proteome-map-completed/
  1. Proteomics – The Pathway to Understanding and Decision-making in Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/06/24/proteomics-the-pathway-to-understanding-and-decision-making-in-medicine/
  1. Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/
  1. Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/
  1. Synthesizing Synthetic Biology: PLOS Collections
    Reporter: Aviva Lev-Ari
    https://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/

 

Metabolomics

  1. Extracellular evaluation of intracellular flux in yeast cells
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/ 
  2. Metabolomic analysis of two leukemia cell lines. I.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/ 
  3. Metabolomic analysis of two leukemia cell lines. II.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/ 
  4. Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics
    Reviewer and Curator, Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/ 
  5. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    https://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

 

Metabolic Pathways

  1. Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
    Reviewer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/21/pentose-shunt-electron-transfer-galactose-more-lipids-in-brief/
  2. Mitochondria: More than just the “powerhouse of the cell”
    Reviewer and Curator: Ritu Saxena
    https://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/
  3. Mitochondrial fission and fusion: potential therapeutic targets?
    Reviewer and Curator: Ritu saxena
    https://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/ 
  4. Mitochondrial mutation analysis might be “1-step” away
    Reviewer and Curator: Ritu Saxena
    https://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/
  5. Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com
    Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/
  6. Metabolic drivers in aggressive brain tumors
    Prabodh Kandal, PhD
    https://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/ 
  7. Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
    Author and Curator: Aviva Lev-Ari, PhD, RD
    https://pharmaceuticalintelligence.com/2012/10/22/metabolite-identification-combining-genetic-and-metabolic-information-genetic-association-links-unknown-metabolites-to-functionally-related-genes/
  8. Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation
    Author and curator:Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/
  9. Therapeutic Targets for Diabetes and Related Metabolic Disorders
    Reporter, Aviva Lev-Ari, PhD, RD
    https://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
  10. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    https://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/
  11. The multi-step transfer of phosphate bond and hydrogen exchange energy
    Curator:Larry H. Bernstein, MD, FCAP,
    https://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-bond-and-hydrogen-exchange-energy/
  12. Studies of Respiration Lead to Acetyl CoA
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
  13. Lipid Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/15/lipid-metabolism/
  14. Carbohydrate Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
  15. Prologue to Cancer – e-book Volume One – Where are we in this journey?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/04/13/prologue-to-cancer-ebook-4-where-are-we-in-this-journey/
  16. Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/
  17. Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/11/01/inhibition-of-the-cardiomyocyte-specific-kinase-tnni3k/
  18. The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/05/15/the-binding-of-oligonucleotides-in-dna-and-3-d-lattice-structures/
  19. Mitochondrial Metabolism and Cardiac Function
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
  20. How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
    Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
  21. AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
    Author and Curator: SJ. Williams
    https://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/
  22. A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
  23. Overview of Posttranslational Modification (PTM)
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/
  24. Malnutrition in India, high newborn death rate and stunting of children age under five years
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/15/malnutrition-in-india-high-newborn-death-rate-and-stunting-of-children-age-under-five-years/
  25. Update on mitochondrial function, respiration, and associated disorders
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/
  26. Omega-3 fatty acids, depleting the source, and protein insufficiency in renal disease
    Larry H. Bernstein, MD, FCAP, Curator
    https://pharmaceuticalintelligence.com/2014/07/06/omega-3-fatty-acids-depleting-the-source-and-protein-insufficiency-in-renal-disease/ 
  27. Late Onset of Alzheimer’s Disease and One-carbon Metabolism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    https://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
  28. Problems of vegetarianism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    https://pharmaceuticalintelligence.com/2013/04/22/problems-of-vegetarianism/

 

Signaling Pathways

  1. Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine
    Larry H. Bernstein, MD, FCAP, writer, and Aviva Lev- Ari, PhD, RN  https://pharmaceuticalintelligence.com/2014/04/27/larryhbernintroduction_to_cardiovascular_diseases-translational_medicine-part_2/
  2. Epilogue: Envisioning New Insights in Cancer Translational Biology
    Series C: e-Books on Cancer & Oncology
    Author & Curator: Larry H. Bernstein, MD, FCAP, Series C Content Consultant
    https://pharmaceuticalintelligence.com/2014/03/29/epilogue-envisioning-new-insights/
  3. Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter  Writer and Curator: Larry H Bernstein, MD, FCAP and Curator and Content Editor: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocy
  4. Cardiac Contractility & Myocardial Performance: Therapeutic Implications of Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
    Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
    Author and Curator: Larry H Bernstein, MD, FCAP and Article Curator: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/
  5. Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
    Author and Curator: Larry H Bernstein, MD, FCAP Author: Stephen Williams, PhD, and Curator: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/
  6. Identification of Biomarkers that are Related to the Actin Cytoskeleton
    Larry H Bernstein, MD, FCAP, Author and Curator
    https://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/
  7. Advanced Topics in Sepsis and the Cardiovascular System at its End Stage
    Author and Curator: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/
  8. The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology
    Demet Sag, PhD, Author and Curator
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  9. IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase
    Demet Sag, PhD, Author and Curator
    https://pharmaceuticalintelligence.com/2013/08/04/ido-for-commitment-of-a-life-time-the-origins-and-mechanisms-of-ido-indolamine-2-3-dioxygenase/
  10. Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad
    Author and Curator: Demet Sag, PhD, CRA, GCP
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
  11. Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/06/26/signaling-pathway-that-makes-young-neurons-connect-was-discovered-scripps-research-institute/
  12. Naked Mole Rats Cancer-Free
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
  13. Amyloidosis with Cardiomyopathy
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/03/31/amyloidosis-with-cardiomyopathy/
  14. Liver endoplasmic reticulum stress and hepatosteatosis
    Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2013/03/10/liver-endoplasmic-reticulum-stress-and-hepatosteatosis/
  15. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
  16. Nitric Oxide Function in Coagulation – Part II
    Curator and Author: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-function-in-coagulation/
  17. Nitric Oxide, Platelets, Endothelium and Hemostasis
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/
  18. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/09/14/interaction-of-nitric-oxide-and-prostacyclin-in-vascular-endothelium/
  19. Nitric Oxide and Immune Responses: Part 1
    Curator and Author:  Aviral Vatsa PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/18/nitric-oxide-and-immune-responses-part-1/
  20. Nitric Oxide and Immune Responses: Part 2
    Curator and Author:  Aviral Vatsa PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  21. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/
  22. New Insights on Nitric Oxide donors – Part IV
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/
  23. Crucial role of Nitric Oxide in Cancer
    Curator and Author: Ritu Saxena, Ph.D.
    https://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
  24. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/
  25. Nitric Oxide and Immune Responses: Part 2
    Author and Curator: Aviral Vatsa, PhD, MBBS
    https://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  26. Mitochondrial Damage and Repair under Oxidative Stress
    Author and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/
  27. Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/
  28. Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
    Curator and Author: Ziv Raviv, PhD, RN 04/06/2013
    https://pharmaceuticalintelligence.com/2013/04/06/targeting-mitochondrial-bound-hexokinase-for-cancer-therapy/
  29. Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/
  30. Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/
  31. Biochemistry of the Coagulation Cascade and Platelet Aggregation – Part I
    Curator and Author: Larry H Bernstein, MD, FACP
    https://pharmaceuticalintelligence.com/2012/11/26/biochemistry-of-the-coagulation-cascade-and-platelet-aggregation/

 

Genomics, Transcriptomics, and Epigenetics

  1. What is the meaning of so many RNAs?
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/06/what-is-the-meaning-of-so-many-rnas/
  2. RNA and the transcription the genetic code
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  3. A Primer on DNA and DNA Replication
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/29/a_primer_on_dna_and_dna_replication/
  4. Pathology Emergence in the 21st Century
    Author and Curator: Larry Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  5. RNA and the transcription the genetic code
    Writer and Curator, Larry H. Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  6. Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease: Views by Larry H Bernstein, MD, FCAP
    Author: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/07/16/commentary-on-biomarkers-for-genetics-and-genomics-of-cardiovascular-disease-views-by-larry-h-bernstein-md-fcap/
  7. Observations on Finding the Genetic Links in Common Disease: Whole Genomic Sequencing Studies
    Author an Curator: Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2013/05/18/observations-on-finding-the-genetic-links/
  8. Silencing Cancers with Synthetic siRNAs
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
  9. Cardiometabolic Syndrome and the Genetics of Hypertension: The Neuroendocrine Transcriptome Control Points
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/12/12/cardiometabolic-syndrome-and-the-genetics-of-hypertension-the-neuroendocrine-transcriptome-control-points/
  10. Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    https://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/
  11. CT Angiography & TrueVision™ Metabolomics (Genomic Phenotyping) for new Therapeutic Targets to Atherosclerosis
    Reporter: Aviva Lev-Ari, PhD, RN
    https://pharmaceuticalintelligence.com/2013/11/15/ct-angiography-truevision-metabolomics-genomic-phenotyping-for-new-therapeutic-targets-to-atherosclerosis/
  12. CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
    Genomics Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/
  13. Big Data in Genomic Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/
  14.  From Genomics of Microorganisms to Translational Medicine
    Author and Curator: Demet Sag, PhD
    https://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/
  15.  Summary of Genomics and Medicine: Role in Cardiovascular Diseases
    Author and Curator, Larry H Bernstein, MD, FCAP
    https://pharmaceuticalintelligence.com/2014/01/06/summary-of-genomics-and-medicine-role-in-cardiovascular-diseases/

Read Full Post »


Anticoagulation Genotype guided Dosing

Author and Curator: Larry H. Bernstein, MD, FCAP 

 

A common complication of patients on Warfarin (coumadin) is massive hematoma associated with a fall.  Warfarin is an inhibitor of the liver production of prothrombin.  Inadequate Warfarin dosing results in a risk of thromboembolism to the lungs or the brain, depending on where the clot is initiated.  However, there is a pharmacogenomic problem in that there are individuals who have a genetic polymorphism (CYP2C9 and VKORC1) that affects the coagulation effect of the coumadin.  This introduces the question of whether such individuals should be identified and dosed according to pharmacogenomic testing.  The usual measurement of the effect of the drug is the International Normalized Ratio (INR) from the Prothrombin Time (PT).  The existence of chronic liver disease and the use of coumadin are perhaps almost the exclusive value for the PT.
A similar question arises for the genotyping of clopidogrel for antiplatelet guidance. Does it reduce risk in stenting?

A Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing

Stephen E. Kimmel, M.D., Benjamin French, Ph.D., Scott E. Kasner, M.D., Julie A. Johnson, Pharm.D., and others

Center for Therapeutic Effectiveness Research,  Philadelphia, PA 19104-6021, or at stevek@mail.med.upenn.edu.

A complete list of investigators and committees in the Clarification of Optimal Anticoagulation through Genetics (COAG) trial is provided in the Supplementary Appendix, available at NEJM.org.

November 19, 2013 | NEJM
http://dx.doi.org/10.1056/NEJMoa1310669

Comparison of  genotype-guided dosing algorithm with the clinically guided dosing algorithm for Warfarin dosing

Abstract

nejmoa1310669.pdf  

Background

The clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results.
Methods
We randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy.

Results

At 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], −0.2; 95% confidence interval, −3.4 to 3.1; P=0.91). There also was no significant between-group difference among patients with a predicted dose difference between the two algorithms of 1 mg per day or more. There was, however, a significant interaction between dosing strategy and race (P=0.003). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group. The rates of the combined outcome of any INR of 4 or more, major bleeding, or thromboembolism did not differ significantly according to dosing strategy.

Conclusions

Genotype-guided dosing of warfarin did not improve anticoagulation control during the first 4 weeks of therapy. (Funded by the National Heart, Lung, and Blood Institute and others; COAG ClinicalTrials.gov number, NCT00839657.)
Figure 1 Distribution of Time in the Therapeutic Range.
nejmoa1310669_f1 Distribution of Time in the Therapeutic Range.
Figure 2 Range of INRs during the 4-Week Study.
nejmoa1310669_f2 Range of INRs during the 4-Week Study.

Introduction

The need for clinical trials before widespread adoption of genotype-guided drug dosing and selection remains widely debated.1-4 Warfarin therapy has served as a model for the potential for pharmacogenetics to improve patient care.1 Observational studies have identified two genes, CYP2C9 and VKORC1, that are associated with variation in warfarin maintenance doses. However, the clinical utility of starting warfarin at the maintenance dose predicted by genotype-guided algorithms has been tested only in small trials, none of which were definitive.5-8 In contrast, observational studies have suggested potential benefits from genotype-guided dosing.9,10 In addition, previous clinical trials could not determine the usefulness of current dosing algorithms among black patients, for whom genotype-guided algorithms perform less well than for other populations.11-13
On the basis of available data, the Food and Drug Administration (FDA) has updated the label for warfarin twice, suggesting that variants in CYP2C9 and VKORC1 may be taken into consideration when choosing the initial warfarin dose. However, the Centers for Medicare and Medicaid Services did not find sufficient evidence to cover the cost of genotyping for warfarin dosing.14 Our study, called the Clarification of Optimal Anticoagulation through Genetics (COAG) trial, was designed to test the effect of genotype-guided dosing on anticoagulation control.

Methods

Study Design and Oversight

The COAG trial was a multicenter, double-blind, randomized, controlled trial that compared a genotype-guided warfarin-dosing strategy with a clinically based dosing strategy during the first 5 days of therapy among patients initiating warfarin treatment.15-17 The study was designed by the authors and approved by the institutional review board at the University of Pennsylvania and at each participating clinical center. The data were collected, analyzed, and interpreted by the authors. A steering committee provided oversight of the trial (for details, see the Supplementary Appendix, available with the full text of this article at NEJM.org). An independent data and safety monitoring board monitored the trial and made recommendations to the National Heart, Lung, and Blood Institute. The first two authors wrote the first draft of the manuscript, which was edited and approved by all the authors. The National Heart, Lung, and Blood Institute supported this study. Bristol-Myers Squibb donated Coumadin (warfarin). GenMark Diagnostics and AutoGenomics loaned genotyping platforms to the clinical centers. None of the companies supporting the trial had any role in the design of the protocol or in the collection, analysis, or interpretation of the data. The authors vouch for the data and the analyses, and for the fidelity of this report to the trial protocol, which is available at NEJM.org.

Study Patients and Randomization

From September 2009 through April 2013, we enrolled both inpatients and outpatients at 18 clinical centers in the United States. All the patients were adults initiating warfarin therapy with a target international normalized ratio (INR) of 2 to 3. Detailed inclusion and exclusion criteria are provided in the Supplementary Appendix. All patients provided written informed consent.
Patients were randomly assigned, in a 1:1 ratio, to the use of a dosing algorithm that included both clinical variables and genotype data or to a clinically guided dosing strategy. Randomization was stratified according to clinical center and self-reported race (black vs. nonblack).
Genotyping for CYP2C9 and VKORC1 at each clinical center was performed with the use of one of two FDA-approved platforms, the GenMark Dx eSensor XT-8 or the AutoGenomics INFINITI Analyzer. Per protocol, genotyping was performed in all patients immediately after blood-sample collection to maintain blinding to the treatment assignment. Genotyping was repeated at the central laboratory with the use of either pyrosequencing or real-time polymerase-chain-reaction assay to measure the accuracy at clinical centers.

Study Intervention and Follow-up

The study intervention period was the first 5 days of warfarin therapy. During this period, the prespecified algorithms were used to determine the warfarin dose. For each dosing strategy, a dose-initiation algorithm was used during the first 3 days of therapy, and a dose-revision algorithm was used on day 4, 5, or both. The algorithms for the genotype-guided dosing strategy12,18 included clinical variables and genotype data for CYP2C9*2, CYP2C9*3, and VKORC1. The algorithms for the clinically based dosing strategy included clinical variables only. The dosing algorithms are provided in the Supplementary Appendix. If genotype information was not available for a patient in the genotype-guided dosing group before the administration of warfarin on any given day in the first 5 days, the clinical algorithm was used on that day.
During the first 4 weeks of therapy, patients and clinicians were unaware of the actual dose of warfarin that was administered, because the pills were encapsulated to prevent identification of the dose (see the Supplementary Appendix). After the 5-day initiation period, we adjusted the dose during the first 4 weeks using standardized dose-adjustment techniques,5,10 starting with the doses predicted by the algorithms and making the same relative adjustments on the basis of the INR in the two study groups. Clinicians were informed of the relative dose change (e.g., a 10% dose increase) at each INR measurement but not the actual dose of warfarin. Clinicians could contact the medical monitor (who was aware of the study-group assignments) to request an override of these relative dose changes without being informed of the actual dose. All patients were to be followed for a total of 6 months.

Study Outcomes

The primary outcome was the percentage of time in the therapeutic range (INR, 2 to 3) from the completion of the intervention period (day 4 or 5) through day 28 of therapy. We calculated the percentage of time in the therapeutic range using a standard linear interpolation method between successive INR values,19 as detailed in the Supplementary Appendix. Each clinical center measured INRs with the use of instruments certified by the Clinical Laboratory Improvement Amendments and following strict quality assurance.
Secondary outcomes included a composite outcome of any INR of 4 or more, major bleeding, or thromboembolism in the first 4 weeks (principal secondary outcome); the time to the first therapeutic INR; the time to the determination of a maintenance dose (which was defined as the time to the first of two consecutive INR measurements, measured at least 1 week apart, that were in the therapeutic range without a dose change); and the time to an adverse event (death from any cause, major bleeding, thromboembolism, or any clinically relevant nonmajor bleeding event20,21) in the first 4 weeks. Two physicians who were unaware of the study-group assignments adjudicated major bleeding and thromboembolic serious adverse events. The definitions of major bleeding,22 clinically relevant nonmajor bleeding, and thromboembolism are provided in the Supplementary Appendix.

Statistical Analysis

We analyzed the primary outcome in the modified intention-to-treat population, which included all patients who underwent randomization with the exception of patients for whom INR data were not available (Fig. S1 in the Supplementary Appendix). Safety outcomes were analyzed in the entire cohort, regardless of whether patients received the study drug. We used regression models to analyze the primary and secondary outcomes, using linear regression for the percentage of time in the therapeutic range and Cox regression for time-to-event outcomes. The protocol specified that we conduct coprimary analyses in which we evaluated the primary outcome in all patients and in a primary subgroup, which comprised patients who had an absolute difference of 1.0 mg or more in the predicted initial daily dose between the genotype-guided dosing algorithm and the clinical dosing algorithm. We used an alpha allocation approach, which formally allows for the evaluation of the treatment benefit in an enriched subgroup as a coprimary end point. In this approach, the overall type I error rate of 0.05 for the primary outcome was split between the analyses performed among all patients and among those in the primary subgroup.17 All models were adjusted for the stratification variables (center and race). Additional subgroups, which were prespecified, were race (black vs. nonblack), sex, and the total number of allelic variants (1 variant vs. 0 or >1 variant in either CYP2C9 or VKORC1 5). All statistical tests were two-sided. All analyses were performed with the use of the R statistical package, version 3.0.1 (R Development Core Team).
We specified a minimum detectable difference of 5.5% in the mean percentage of time in the therapeutic range between the genotype-guided group and the clinically guided group in the entire study population.16 We assumed a standard deviation for the percentage of time in therapeutic range of 25% and a potential dropout rate of 10%. On the basis of recruitment rates,15 the initial sample size of 1238 patients was revised to 1022 patients on September 16, 2012 (with the approval of the data and safety monitoring board). The revised sample size provided a power of at least 80% to detect a between-group difference of 5.5% at a type I error rate of 0.04 among all patients and a 9.0% difference at a type I error rate of 0.01 among patients in the coprimary analysis.

Results

Patients, Genotyping, and Follow-up

A total of 1015 patients were enrolled and randomly assigned to either the genotype-guided dosing algorithm or the clinically guided dosing algorithm (Fig.S1 in the Supplementary Appendix). There were no significant between-group differences at baseline (Table 1Table 1Characteristics of the Patients at Baseline.). The characteristics of the patients according to self-reported race are provided in Table S1 in the Supplementary Appendix. A total of 60 participants (30 in each group) withdrew before completing the intervention period and did not have an available percentage of time in the therapeutic range, resulting in an analytic sample size of 955. A median of six INRs were measured during the first 4 weeks in each of the two study groups. Dispensed doses during the intervention period are summarized in Table S2 in the Supplementary Appendix.
Genotype data were available in the genotype-guided group for 45% of the patients before the first warfarin dose, for 94% before the second warfarin dose, and for 99% before the application of the dose-revision algorithm on day 4 or 5. The mean (±SD) difference between the dose calculated for patients without genotype data on day 1, as compared with the dose they would have received if genotype data had been available, was −0.1±0.4 mg per day during the first 3 days. The central laboratory confirmed 99.8% of all genotyping results from the clinical centers. All genotype distributions were in Hardy–Weinberg equilibrium (P>0.20 for all comparisons).

Primary Outcome

At 4 weeks, there was no significant between-group difference in the mean percentage of time in the therapeutic range: 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference [genotype-guided group minus clinically guided group], −0.2%; 95% confidence interval, −3.4 to 3.1; P=0.91) (Table 2Table 2Percentage of Time in the Therapeutic INR Range through Week 4 of Therapy, According to Subgroup. and Figure 1Figure 1Distribution of Time in the Therapeutic Range.). There was also no significant between-group difference in the percentage of time in the therapeutic range among patients in the coprimary analysis (Table 2). When the 4-week trial was divided into two 2-week intervals, there was also no significant difference between the groups in either interval (Table 2).
However, there was a significant interaction between race and dosing strategy (P=0.003) (Table 2). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group (35.2% vs. 43.5%; adjusted mean difference, −8.3%; P=0.01). Among nonblack patients, the mean percentage of time in the therapeutic range was slightly higher in the genotype-guided group than in the clinically guided group (48.8% vs. 46.1%; adjusted mean difference, 2.8%; P=0.15). There were no significant differences in the percentage of time in the therapeutic range according to sex or the total number of genetic variants (Table 2).

Anticoagulation Control and Dose Prediction

There were no significant between-group differences in the mean percentage of time above the therapeutic range (INR, >3) or below the therapeutic range (INR, <2) (Figure 2Figure 2Range of INRs during the 4-Week Study., and Table S3 in the Supplementary Appendix). However, black patients in the genotype-guided group were more likely to have INRs above the therapeutic range than were those in the clinically guided group (Fig. S2 and Table S3 in the Supplementary Appendix).
There was no overall between-group difference in the time to the first INR in the therapeutic range (Table S4 in the Supplementary Appendix). However, black patients in the genotype-guided group took longer on average to reach the first therapeutic INR than did those in the clinically guided group (Table S4 and Fig. S3 in the Supplementary Appendix). The time to the determination of the maintenance dose did not differ significantly between the two groups overall or according to the primary subgroup, race, or total number of genetic variants (Table S5 in the Supplementary Appendix).
The performance characteristics of the dosing algorithms with respect to the maintenance dose that was determined are shown in Table S6 (which includes the accuracy of a hypothetical, empirical dosing strategy of 5 mg per day) and in Fig. S4, both in the Supplementary Appendix. The genotype-guided algorithms performed better at predicting the maintenance dose among nonblack patients than among black patients. Dose overrides during the first 4 weeks were rare, occurring in only 3.9% of doses in the genotype-guided group and 3.6% of those in the clinically guided group; rates of overrides did not differ according to race.

Adverse Events

At 4 weeks, there were no significant between-group differences in the principal secondary outcome (the time to any INR of ≥4, major bleeding, or thromboembolism) or any other adverse events (Table 3Table 3Adverse Events through Day 28 of Warfarin Therapy., and Table S7 in the Supplementary Appendix). Safety data for the entire duration of follow-up (i.e., past the primary outcome duration) are provided in Table S8 in the Supplementary Appendix.

Discussion

In our study, we found no benefit of genotype-guided dosing of warfarin with respect to the primary outcome of the percentage of time in the therapeutic INR range, either overall or among patients with a predicted dose difference between the genotype-guided algorithm and the clinically guided algorithm of at least 1 mg per day. Our findings exclude a meaningful effect of genotype-guided dosing on the percentage of time in the therapeutic range during the first month of warfarin treatment. However, there was a significant difference in the effects of the algorithms in the prespecified subgroup of black patients, as compared with nonblack patients. Although the interaction between race and dosing strategy with respect to the primary outcome could be due to chance, the analysis was prespecified and was consistent with our a priori hypothesis that there would be race-based differences.
The dosing algorithms that we used in the trial have been validated and account for race (specifically black vs. nonblack).11-13,18 The genotype-guided algorithm performed as well as anticipated on the basis of previous studies,5,8,10-12,18,23 with an R2 of 0.48 and a mean absolute error of 1.3 mg per day for the dose-initiation algorithm and an R2 of 0.69 and a mean absolute error of 1.0 mg per day for the dose-revision algorithm. Despite this accuracy in predicting maintenance doses, there was no benefit of genotype-guided dosing with respect to anticoagulation control.
Observational studies have shown an association between the use of genetic algorithms and improved outcomes, but because of limitations in the study design, they were unable to assess whether the observed associations were causal.1,9,10 Previous clinical trials have produced equivocal results,5-8 but these trials were limited by a small size and lack of blinding to the warfarin dose. The two trials that suggested possible benefit also were limited by large numbers of dropouts6 and a comparison with nonalgorithm-based dosing.8 Previous studies also enrolled either no black patients6-8 or a minimal number of black patients5 (a total of 3) (Anderson J: personal communication).
The average percentage of time in the therapeutic range of 45% in our study is similar to that in other trials, taking into account the range of INRs used for the calculation and the timing and duration of therapy (Tables S9A and S9B in the Supplementary Appendix).5,10,24,25 Unlike previous trials that used only a baseline genotype-guided algorithm, our study used both a dose-initiation and a dose-revision algorithm. A recent study comparing a similar initiation algorithm with a combined initiation and revision algorithm showed no effect on the percentage of time in the therapeutic range with the addition of the revision algorithm.10
There are several questions that our study was not designed to answer. First, the trial did not compare genotype-based dosing with usual care or a fixed initial dose (e.g., 5 mg per day). However, such a comparison could not have discerned whether differences in outcomes were due to the marginal benefit of genetic information or to the use of the clinical information that is included in all genotype-guided dosing algorithms. Second, our study does not address the question of whether a longer duration of genotype-guided dosing would have improved INR control,26 an issue that is being addressed in another trial.27 Third, the dosing algorithms that we used included the three single-nucleotide polymorphisms among the two genes that are most likely to influence warfarin dosing. Although other genes may contribute to warfarin dosing, it is unlikely that they have a substantial effect, particularly in white populations.28 Fourth, although there were no significant between-group differences in the rates of bleeding or thromboembolic events during the primary follow-up period of 4 weeks, the trial was not powered for these outcomes. Fifth, the first dose of warfarin was not informed by genotyping in 55% of the patients; whether this influenced the results is unknown. However, the effect of missing genetics data on day 1 on the dose administered during the first 3 days of therapy was trivial.
In conclusion, our findings do not support the hypothesis that initiating warfarin therapy at a genotype-guided maintenance dose for the first 5 days, as compared with initiating warfarin at a clinically predicted maintenance dose, improves anticoagulation control during the first 4 weeks of therapy. Our results emphasize the importance of performing randomized trials for pharmacogenetics, particularly for complex regimens such as warfarin.

References

1 Ginsburg GS, Voora D. The long and winding road to warfarin pharmacogenetic testing. J Am Coll Cardiol 2010;55:2813-2815
2 Burke W, Laberge AM, Press N. Debating clinical utility. Public Health Genomics 2010;13:215-223
3 Ashley EA, Hershberger RE, Caleshu C, et al. Genetics and cardiovascular disease: a policy statement from the American Heart Association. Circulation 2012;126:142-157
4 Woodcock J, Lesko LJ. Pharmacogenetics — tailoring treatment for the outliers. N Engl J Med 2009;360:811-813
5 Anderson JL, Horne BD, Stevens SM, et al. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 2007;116:2563-2570
6 Caraco Y, Blotnick S, Muszkat M. CYP2C9 genotype-guided warfarin prescribing enhances the efficacy and safety of anticoagulation: a prospective randomized controlled study. Clin Pharmacol Ther 2008;83:460-470
A complete list of investigators and committees in the Clarification of Optimal Anticoagulation through Genetics (COAG) trial is provided in the Supplementary Appendix, available at NEJM.org.

Editorial

Do Pharmacogenetics Have a Role in the Dosing of Vitamin K Antagonists?

Bruce Furie, M.D.
Nov 19, 2013    http://dx.doi.org/10.1056/NEJMe1313682

 

Article

Vitamin K plays a single role in human biology — as a cofactor for the synthesis of γ-carboxyglutamic acid. This amino acid is a component of at least 14 proteins, including 4 blood-coagulation proteins (factor IX, factor VII, factor X, and prothrombin) and 2 regulatory proteins (protein C and protein S), and it is critical for the physiologic function of these proteins. Humans do not synthesize vitamin K. Rather, we ingest it in our diet. The vitamin K quinone is reduced to the semiquinone, and this reduced vitamin K is a cofactor that is required for the conversion of specific glutamic-acid residues on the vitamin K–dependent proteins to γ-carboxyglutamic acid by the vitamin K–dependent carboxylase. Vitamin K epoxide, a product of this reaction, is converted back to the vitamin K quinone by the vitamin K epoxide reductase, otherwise known as VKOR. This vitamin K cycle can be broken, and a state of vitamin K deficiency at the carboxylase level effected, by the inhibition of VKOR by vitamin K antagonists, including warfarin.
Warfarin and its analogues have been used as oral anticoagulant agents for more than 50 years. By targeting VKOR, the post-translational modification of the vitamin K–dependent blood-coagulation proteins is impaired. A reduced functional level of factor IX, factor VII, factor X, and prothrombin leads to delayed blood coagulation. This inhibition is monitored in the clinical laboratory with the use of the prothrombin time and is corrected for the varied potencies of tissue factor used in the assay by means of a calibration factor, yielding the international normalized ratio (INR). The intensity of therapy with vitamin K antagonists varies according to the indication for anticoagulation, and the INR is adjusted by varying the dose of the vitamin K antagonist.
The goal of therapy is to keep the INR in the therapeutic range, since patients with an INR that is subtherapeutic are at increased risk for thrombosis and patients with an INR that is supratherapeutic are at increased risk for bleeding. Keeping the INR within the therapeutic range can be challenging. Warfarin binds to albumin, and only about 3% is free and pharmacologically active. A number of medications can displace warfarin, leading to its increased activity and subsequent increased rate of degradation. Diet, specifically the intake of foods containing vitamin K, can offset the effect of the daily dose of the vitamin K antagonist. Age, weight, and sex are other factors that influence the dose.
In addition, the catabolic rate of the vitamin K antagonists appears to have a genetic basis. Genetic polymorphisms in the cytochrome P-450 enzyme CYP2C9 include two variants, C144R in CYP2C9*2 and I359L in CYP2C9*3. These variants have substantially reduced activity, as compared with CYP2C9*1, and are associated with reduced clearance and thus a decrease in the warfarin-dose requirement.1 Similarly, mutations in VKOR, the target of the vitamin K antagonists, lead to various degrees of warfarin resistance. Polymorphisms in VKORC1, the gene encoding this protein, lead to variability in the sensitivity to vitamin K antagonists.2
Might genotyping of CYP2C9 and VKORC1 in patients initiating anticoagulant therapy with vitamin K antagonists lead to more precise dosing and, by extrapolation, reduce the risk of thrombotic and bleeding complications? Numerous anecdotal, observational, and small clinical trials have been published on the use of this information, with many authorities promoting this approach.
The results of three large, randomized clinical trials that test this hypothesis have now been published in the Journal.3-5 Although they vary in organization and structure (duration of study, vitamin K antagonist used, double-blind vs. single-blind design, racial characteristics of the study group, and method for dosing in the control group), they are also similar (large, multicenter, randomized studies; primary end point of the time in the therapeutic range; genotyping of CYP2C9 and VKORC1; and the use of the INR target as a biomarker for the risk of bleeding and thrombosis). Importantly, these trials all examine the initiation of therapy with vitamin K antagonists and use as a primary end point the percentage of time that a patient is within the therapeutic range during the initial phase of treatment. The more important end point, the rate of bleeding and thrombotic complications, was beyond the power design of these trials.
Despite design differences, the conclusions of the three studies are similar. In an initial period of 4 weeks of anticoagulation with warfarin, the randomized, double-blind study by Kimmel et al.3 showed results in the study group that included pharmacogenetic information to supplement clinically guided dosing that were nearly identical to the results in the group that used clinically guided dosing alone (percentage of time in the therapeutic INR range, 45.2% vs. 45.4%). In the 12 weeks after the initiation of anticoagulation with acenocoumarol and phenprocoumon, Verhoef et al.4 used a new point-of-care device and found that a genotype-guided algorithm that included clinical variables yielded results that were similar to those achieved with an algorithm based on clinical variables (61.6% vs. 60.2% at 12 weeks). Pirmohamed et al.5 compared genotype-guided dosing of warfarin, also using a point-of-care device, with standard dosing methods used in clinical practice. The results at 12 weeks were 67.4% and 60.3%, which are significantly different albeit similar, indicating a modest improvement.
What can we conclude from these trials? First, we must recall that these trials address the process of the initiation of anticoagulant therapy — during the very first week — and not an approach to intermediate or long-term anticoagulation. Second, it would appear that, despite the variation in trial design, these trials indicate that this pharmacogenetic testing has either no usefulness in the initial dosing of vitamin K antagonists or, at best, marginal usefulness, given the cost and effort required to perform this testing.
Improved safety with the use of vitamin K antagonists is nonetheless an important goal, and it remains so, despite the introduction of new oral anticoagulants. Perhaps we should concentrate on improvements in the infrastructure for INR testing, including better communication among the laboratory, the physician, and the patient (e.g., through social media); in the use of formal algorithms for dosing, without concern for genotype; in patient adherence to therapy and possibly more responsibility for dosing being assumed by the patient; and in increased diligence by medical and paramedical personnel in testing, monitoring, and dosing on the basis of the INR, given the high percentage of medical mismanagement associated with these anticoagulant agents.  http://dx.doi.org/nejm1313682.pdf

References

1 Aithal GP, Day CP, Kesteven PJ, Daly AK. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet 1999;353:717-719
2 D’Andrea G, D’Ambrosio RL, Di Perna P, et al. A polymorphism in the VKORC1 gene is associated with an interindividual variability in the dose-anticoagulant effect of warfarin. Blood 2005;105:645-649
3 Kimmel SE, French B, Kasner SE, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med 2013. DOI: 10.1056/NEJMoa1310669.
4 Verhoef TI, Ragia G, de Boer A, et al. A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon. N Engl J Med 2013. DOI: 10.1056/NEJMoa1311388.
5 Pirmohamed M, Burnside G, Eriksson N, et al. A randomized trial of genotype-guided dosing of warfarin. N Engl J Med 2013. DOI: 10.1056/NEJMoa1311386.

Rapid Warfarin Reversal With 4-Factor Prothrombin Complex Concentrate

Samuel Z. Goldhaber, MD    Nov 07, 2013     Clotblog at theheart.org

Hello. This is Dr. Sam Goldhaber from the Clotblog at theheart.org, speaking to you from Brigham and Women’s Hospital and Harvard Medical School on the important topic of 4-factor prothrombin complex concentrate (4F-PCC), which is the optimal approach to urgent warfarin reversal.
The US Food and Drug Administration only recently approved 4F-PCC to reverse excessive bleeding from warfarin. We have had 3-factor PCC around for a long time but it doesn’t have much factor XII in it. Our European colleagues have had 4F-PCC for the past few years.
I was very pleased recently to have had the opportunity to use 4F-PCC to reverse an intracranial hemorrhage in a patient who had an international normalized ratio (INR) of 2.8 and a spontaneous head bleed. She was receiving warfarin for anticoagulation, and within about 15 minutes the 4F-PCC, along with 10 mg of intravenous vitamin K, returned her INR to normal. Of greater importance, the head bleeding stopped and she regained virtually all of her neurologic function. It seemed miraculous to me and it happened so quickly that it was very gratifying.
Almost simultaneously with my clinical experience, we published an observational study in Circulation [1] with about 300 patients who bled on warfarin. Approximately 80% of these patients were on warfarin because of permanent atrial fibrillation, and they had an average age in the 70s. Initially patients received fresh frozen plasma (before 4F-PCC became available in Canada) and their outcomes with fresh frozen plasma were tracked very carefully. When 4F-PCC came along and practice switched to that therapy, those outcomes were tracked as well.

Clopidogrel Genotyping for Antiplatelet Guidance in MI Stenting: Maybe Reduced Ischemic Risk

Steve Stiles    Nov 06, 2013

SAN FRANCISCO, CA — A novel study of genotype-guided antiplatelet therapy in patients who received a stent for acute MI saw a sharp drop in ischemic events over one year among those who tested positive for a clopidogrel loss-of-function (LOF) gene pattern and had their originally prescribed antiplatelet therapy altered based on the assay results.
In the prospective GIANT trial with 1445 patients, reported here at TCT 2013 , it was discretionary whether clinicians raised the clopidogrel dosage or switched thienopyridine agents based on the assay results, which they had in hand within 48 hours after stenting. Such changes were made in 86% of the 316 who tested positive for the LOF genotype, a group known to be at increased ischemic risk on standard clopidogrel-containing antiplatelet therapy after stenting.
Among those 272 patients with assay-guided antiplatelet changes, the one-year composite risk of death, MI, or stent thrombosis closely matched that of patients lacking the high-risk genotype, according to co–principal investigator Dr Bernard R Chevalier (L’Institut CardioVasculaire Paris-Sud, Massy, France), who presented the study.
Dr Bernard R Chevalier
Of note, the composite end point was about five times higher for the remaining 14% of LOF-genotype patients whose antiplatelet therapy wasn’t changed based the assay.
“These are really the first clinical-trial data in the genotype space compared with the phenotype space, and I think it’s long overdue,” according to Dr Daniel I Simon (University Hospitals Case Medical Center, Cleveland, OH), speaking from the panel charged with critiquing GIANT after its formal presentation. As did many throughout TCT 2013, Simon was weighing two different approaches to sharpening thienopyridine selection for dual-agent antiplatelet therapy after coronary interventions, specifically those focusing on genotyping for the CYP2C19 clopidogrel loss-of-function variant vs platelet-function assays like VerifyNow (Accumetrics).
Such platelet-function testing with coronary stenting has its supporters and detractors but hasn’t found a consistent role in managing patients undergoing PCI, even for acute coronary syndromes, as heartwire has long reported.
One-Year Rates (%) of Primary End Point (Death, MI, or Stent Thrombosis) by Clopidogrel LOF Genotype Status
End point Normal
LOF, treatment is adjusted LOF, treatment is not adjusted
                              n=1118           n=272                     n=55
Primary              3.04                 3.3*                        15.6
 *p=0.83 vs normal; p<0.0001 for noninferiority; p=0.04 vs LOF-treatment-is-not-adjusted
“I think these are amazing results,” Dr Cindy L Grines (Detroit Medical Center Cardiovascular Institute, MI) said at a press briefing on GIANT, referring to both the high event rate in LOF-genotype patients whose treatment wasn’t changed and similarly lower event rates in the other two groups. “Both of those [findings] are a little bit unexpected, I’d guess?” She asked Chevalier why clinicians did not modify antiplatelet therapy in 14% of patients positive for the LOF genotype.
In GIANT, said Chevalier in his presentation, investigators were “strongly recommended” to give such patients prasugrel (Effient, Lilly/Daiichi-Sanyo) or, if they had contraindications to prasugrel, to double their clopidogrel dosage.
But prasugrel, a more potent antiplatelet than clopidogrel, had already been chosen for initial antiplatelet therapy in more than half of patients in the study. Perhaps clinicians believed such patients would benefit from it regardless of their ultimate genotype status. Indeed, some patients later found not to have the clopidogrel LOF genotype were switched from prasugrel to clopidogrel, perhaps satisfied by the assay that the latter drug would be adequate after all.
Chevalier speculated that clinicians also may not have boosted antiplatelet therapy in some LOF-genotype patients if it was considered too risky, such as for those with bleeding risk factors. The high event rate in patients with the LOF genotype whose antiplatelet therapy wasn’t adjusted, therefore, may be more related to how sick the patient was, rather than any cues from genotyping. He said his group is currently looking for the answer in further analyses.
Prevalence of Thienopyridine Use and Dosages, Before and After Genotyping, by Assay Outcome
Thienopyridine and dosage by timing
n=1118                                     treatment is  adjusted,   (%)                 n=272 (%)
                                                                           Normal,     LOF          p
Clopidogrel 75 mg/d (preassay)           35.6           34.7      NS
Clopidogrel 75 mg/d (postassay)        44.5             0       <0.001
Clopidogrel 150 mg/d (preassay)        10               9.1         NS
Clopidogrel 150 mg/d (postassay)    8.9             16.8   <0.05
Prasugrel 10 mg/d (preassay)            53.3           55.5       NS
Prasugrel 10 mg/d (postassay)         46.1            83.1    <0.001
NS=nonsignificant
GIANT was funded by Biotronik. Chevalier discloses consulting for or receiving research grants or speaker fees from Abbott Vascular, Asahi, Astra Zeneca, AVI, Boston Scientific, Biotronik, Colibri, Cook, Cordis, Daiichi Sankyo, Eli-Lilly, Iroko, Medtronic, and Terumo and being general director of and owning equity interest in the European Cardiovascular Research Center.

 Bivalirudin Started during Emergency Transport for Primary PCI

Philippe Gabriel Steg, M.D., Arnoud van ‘t Hof, M.D., Ph.D., Christian W. Hamm, M.D., Peter Clemmensen, M.D., Ph.D., Frédéric Lapostolle, M.D., Ph.D., Pierre Coste, M.D., Jurrien Ten Berg, M.D., Ph.D., Pierre Van Grunsven, M.D., Gerrit Jan Eggink, M.D., Lutz Nibbe, M.D., Uwe Zeymer, M.D., Marco Campo dell’ Orto, M.D., Holger Nef, M.D., Jacob Steinmetz, M.D., Ph.D., Louis Soulat, M.D., Kurt Huber, M.D., Efthymios N. Deliargyris, M.D., Debra Bernstein, Ph.D., Diana Schuette, Ph.D., Jayne Prats, Ph.D., Tim Clayton, M.Sc., Stuart Pocock, Ph.D., Martial Hamon, M.D., and Patrick Goldstein, M.D. for the EUROMAX Investigators

N Engl J Med 2013; 369:2207-2217December 5, 2013DOI: 10.1056/NEJMoa1311096

 

 Background

Bivalirudin, as compared with heparin and glycoprotein IIb/IIIa inhibitors, has been shown to reduce rates of bleeding and death in patients undergoing primary percutaneous coronary intervention (PCI). Whether these benefits persist in contemporary practice characterized by prehospital initiation of treatment, optional use of glycoprotein IIb/IIIa inhibitors and novel P2Y12 inhibitors, and radial-artery PCI access use is unknown.

 

 Methods

We randomly assigned 2218 patients with ST-segment elevation myocardial infarction (STEMI) who were being transported for primary PCI to receive either bivalirudin or unfractionated or low-molecular-weight heparin with optional glycoprotein IIb/IIIa inhibitors (control group). The primary outcome at 30 days was a composite of death or major bleeding not associated with coronary-artery bypass grafting (CABG), and the principal secondary outcome was a composite of death, reinfarction, or non-CABG major bleeding.

 Results

Bivalirudin, as compared with the control intervention, reduced the risk of the primary outcome (5.1% vs. 8.5%; relative risk, 0.60; 95% confidence interval [CI], 0.43 to 0.82; P=0.001) and the principal secondary outcome (6.6% vs. 9.2%; relative risk, 0.72; 95% CI, 0.54 to 0.96; P=0.02). Bivalirudin also reduced the risk of major bleeding (2.6% vs. 6.0%; relative risk, 0.43; 95% CI, 0.28 to 0.66; P<0.001). The risk of acute stent thrombosis was higher with bivalirudin (1.1% vs. 0.2%; relative risk, 6.11; 95% CI, 1.37 to 27.24; P=0.007). There was no significant difference in rates of death (2.9% vs. 3.1%) or reinfarction (1.7% vs. 0.9%). Results were consistent across subgroups of patients.

 Conclusions

Bivalirudin, started during transport for primary PCI, improved 30-day clinical outcomes with a reduction in major bleeding but with an increase in acute stent thrombosis. (Funded by the Medicines Company; EUROMAX ClinicalTrials.gov number, NCT01087723.)

  Source Information

The authors’ affiliations are listed in the Appendix.
Dr. Steg at Cardiologie, Département Hospitalo-Universitaire FIRE, Hôpital Bichat, Assistance Publique–Hôpitaux de Paris,  Paris, France, or at gabriel.steg@bch.aphp.fr.
A complete list of the European Ambulance Acute Coronary Syndrome Angiography (EUROMAX) investigators is provided in the Supplementary Appendix, available at NEJM.org.

 

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The Effects of Bovine Thrombin on HUVEC and AoSMC

Curators: Demet Sağ, 1,* and Jeffrey Harold Lawson 1,2

From the Department of Surgery1 and PathologyDuke University Medical Center Durham, NC-USA

Running Foot:

Thrombin induces vascular cell proliferation

 

crystal structure of thrombin.

crystal structure of thrombin. (Photo credit: Wikipedia)

Review Profs and correspondence should be addressed to:

Dr. Jeffrey Lawson

Duke University Medical Center

Room 481 MSRB/ Boxes 2622

Research Drive

Durham, NC 27710

Phone (919) 681-6432

Fax      (919) 681-1094

Email: lawso717@duke.edu, demet.sag@gmail.com

*Current Address:  TransGenomics Consulting, Principal, 3830 Valley Center Drive, Suite 705-223 San Diego, CA 92130

 

Abstract: 

Thrombin is a serine protease with multiple cellular functions that acts through protease activated receptor kinases (PARs) and responds to trauma at the endothelial cells of vein resulting in coagulation.  In this study, we had analyzed the activity of thrombin on the vein by using human umbilical vein endothelial (HUVEC) and human aorta smooth muscle (AoSMC) cells.  Ectopic thrombin increases the expression of PARs, cAMP concentration, and Gi signaling as a result the proliferation events in the smooth muscle cells achieved by the elevation of activated ERK leading to gene activation through c-AMP binding elements responsive transcription factors such as CREB, NFkB50, c-fos, ATF-2.  We had observed activation of p38 as well as JNK but they were related to stress and inflammation. In the nucleus, ATF-2 activity is the start point of IL-2 proliferation through T cell activation creating APC and B-cell memory leading to autoimmune reaction as a result of ectopic thrombin.  These changes in the gene activation increased connective tissue growth factor as well as cysteine rich protein expression at the mRNA level, which proven to involve in vascularization and angiogenesis in several studies.  Consequently, when ectopic thrombin used during the graft transplant surgeries, it causes occlusion of the veins so that transplant needs to be replaced within six months due to thrombin’s proliferative function as mitogen in the smooth muscle cells.

WORD COUNT OF ABSTRACT: 221

 

  

The Effect of Thrombin(s) on Smooth Muscle and Endothelial Cells

Thrombin is a multifunctional serine protease that plays a major role in the highly regulated series of biochemical reactions leading to the formation of fibrin (1, 2).  Thrombin has been shown to affect a vast number of cell types, including platelets, endothelial cells, smooth muscle cells, cardiomyocytes, fibroblasts, mast cells, neurons, keratinocytes, monocytes, macrophages and a variety of lymphocytes, including B-cells and T-cells, and stimulate smooth muscle and endothelial cell proliferation (3-13).

Induction of thrombin results in cells response as immune response and proliferation by affecting transcriptional control of gene expression through series of signaling mechanisms (14).  First, protease activated receptor kinases (PAR), which are seven membrane spanning receptors called G protein coupled receptors (GPCR) are initiate the line of mechanism by thrombin resulting in variety of cellular responses. These receptorsare activated by a unique mechanism in which the protease createsa new extracellular amino-terminus functioning as a tetheredligand, results in intermolecular activation.  PARs are ‘single-use’ receptors: activation is irreversible and the cleaved receptors are degraded in lysosomes, as they play important roles in ’emergency situations’, such as trauma and inflammation.  Protease activated receptor 1 (PAR1) is the prototype of this family and is activated when thrombin cleaves its amino-terminal extracellular domain.  PAR1, PAR3, and PAR4 are activated by thrombin. Whereas PAR2 is activated by trypsin, factor VIIa, tissue factor, factor Xa, thrombin cleaved PAR1.

Second, the activated PAR by the thrombin stimulates downstream signaling events by G protein dependent or independent pathways.  Although each of the PAR respond to thrombin undoubtedly mediates different thrombin responses, most of what is known about thrombin signaling downstream of the receptors themselves has derived from studies of PAR1.  PAR couples with at least three G protein families Gq, Gi, and G12/13.  With G protein activation: Gi/q leads InsP3 induced Ca release and/or Rac induced membrane ruffling.  Gi dependent signaling activates Ras, p42/44, Src/Fak, p42.  Rho related proteins and phospholipase C results in mitogenesis and actin cytoskeletal rearrangements. G protein independent activation happens either through tyrosine kinase trans-activation results in mitogenesis and stress-fibre formation, neurite retraction by Rho path, or activation of choline for Rap association with newly systhesized actin.  These events are tightly regulated to support diverse cellular responses of thrombin. (15-17).

Treatment of veins with topical bovine thrombin showed early occlusion of the veins result in proliferation of smooth muscle cells (18-24) due to change of gene expression transcription.  The change of Ca++ and cAMP concentrations influence cAMP response element binding protein (25-30) carrying transcription factors such as CREB, ATF-2, c-jun, c-fos, c-Rel.  Activation of angiogenesis and vascularization affects cysteine rich gene family (CCN) genes such as connective tissue factor (CTGF) and cysteine rich gene (Cyr61) according to performed studies and microarray analysis by (31-36).   Currently the most common topical products approved by FDA are bovine originated.   Although bovine thrombin is very similar to human (37, 38), it has a species specific activity, shown to cause autoimmune-response (39-42), which results in repeated surgeries (40, 43, 44), and renal failures that cost to health of individuals as well as to the economy.

In this report we had evaluated the effect of topically applied bovine thrombin to human umbilical endothelial cells (HUVECs) and human aorta smooth muscle cells (AoSMCs).  We had showed that use of bovine thrombin cause adverse affects on the cellular physiology of human vein towards proliferation of smooth muscle tissue.   Collectively, thrombin usage should be assessed before and after surgery because it is a very potent substance.

MATERIALS AND METHODS:

Thrombins:  Bovine thrombin and human thrombin ((Haematologic Technologies Inc, VT); topical bovine thrombin (JMI, King’s Pharmaceutical, KS); topical human thrombin (Baxter, NC human thrombin sealant).

Cell Culture:  The pooled cells were received from Clonetics. Human endothelial cells  (HUVEC) were grown in EGM-2MV bullet kit (refinements to basal medium CCMD130 and the growth factors, 5% FBS, 0.04% hydrocortisone, 2.5% hFGF, 0.1% of each VEGF, IGF-1, Ascorbic acid, hEGF, GA-1000) and human aorta smooth muscle cells (AoSMC) were grown in SmGM-2 medium (5% FBS, 0.1% Insulin, 1.25% hFGF, 0.1% GA-1000, and 0.1% hEGF).     The cells were grown to confluence (2-3 days for HUVEC and 4-5 days for HOSMC) before splitted, and only used from passage 3 to 5.  Before stimulating the confluent cells, they had been starved with starvation media containing 0.1% bovine serum albumin (BSA) EGM-2 or SmBM basal media.

RNA isolation and RT-PCR:  The total RNA was isolated by RNeasy mini kit (Qiagen, Cat#74104) fibrous animal tissue protocol.  The two-step protocol had been applied to amplify cDNA by Prostar Ultra HF RT PCR kit (Stratagene Cat# 600166).  At first step, cDNA from the total RNA had been synthesized. After denaturing the RNA at 65 oC for 5 min, the Pfu Turbo added at room temperature to the reaction with random primers, then incubated at 42oC for 15min for cDNA amplification.   At the second step, hot start PCR reaction had been designed by use of gene specific primers for PAR1, PAR2, PAR3, and PAR4 to amplify DNA with robotic arm PCR. The reaction conditions were one cycle at 95oC for 1 min, 40 cycles for denatured at 95oC for 1 min, annealed at 50 oC 1min, amplified at 68 oC for 3min, finally one cycle of extension at 68 oC for 10 min.  The cDNA products were then usedas PCR templates for the amplification of a 614 bp PAR-1 fragment(PAR-1 sense: 5′-CTGACGCTCTTCATCCCCTCCGTG, PAR-1 antisense:5′-GACAGGAACAAAGCCCGCGACTTC), a 599 bp PAR-2 fragment (PAR-2sense: 5′-GGTCTTTCTTCCGGTCGTCTACAT, PAR-2 antisense: 5′-GCAGTTATGCAGTCAGGC),a 601 bp PAR-3 fragment (PAR-3 sense: 5′-GAGTCCCTGCCCACACAGTC,PAR-3 antisense: 5′-TCGCCAAATACCCAGTTGTT), a 492 bp PAR-4 fragment(PAR-4 sense: 5′-GAGCCGAAGTCCTCAGACAA, PAR-4 antisense: 5′-AGGCCACCAAACAGAGTCCA). The PCR consistedof 25 to 40 cycles between 95°C (15 seconds) and 55°C(45 seconds). Controls included reactions without template,without reverse transcriptase, and water alone. Primers forglyceraldehydes phosphate dehydrogenase (GAPDH; sense: 5′-GACCCCTTCATTGACCTCAAC,antisense: 5′-CTTCTCCATGGTGGTGAAGA) were used as controls. Reactionproducts were resolved on a 1.2% agarose gel and visualizedusing ethidium bromide.

The primers CTGF-(forward) 5′- GGAGCGAGACACCAACC -3′ and CTGF-(reverse) CCAGTCATAATCAAAGAAGCAGC ; Cyr61- (forward)  GGAAGCCTTGCT CATTCTTGA  and Cyr61- (reverse) TCC AAT CGT GGC TGC ATT AGT were used for RT-PCR.  The conditions were hot start at 95C for 1 min, fourty cycles of denaturing for 45 sec at 95C, annealing for 45 sec at 55C and amplifying for 2min at 68C, followed by 10 minutes at 68C extension.

 

Cell Proliferation Assay with WST-1—Cell proliferation assays were performed using the cell proliferation reagent 3-(4,5 dimethylthiazaol-2-y1)-2,5-dimethyltetrazolium bromide (WST-1, Roche Cat# 1-644-807) via indirect mechanism.   This non-radioactive colorimetric assay is based on the cleavage of the tetrazolium salt WST-1 by mitocondrial dehydrogenases in viable cells forming colored reaction product.   HUVECs were grown in 96 well plates (starting from 250, 500, and 1000 cells/well) for 1 day and then incubated the medium without FBS and growth factors for 24 h.  The cells were then treated with WST-1 and four types of thrombins, 100 units of each BIIa, HIIa, TBIIa, and THIIa.  The reaction was stopped by H2SO4 and absorbance (450 nm) of the formazan product was measured as an index of cell proliferation. The standard error of mean had been calculated.

BrDu incorporation:  This method being chosen to determine the cellular proliferation with a direct non-radioactive measurement of DNA synthesis based on the incorporation of the pyridine analogous 5 bromo-2’-deoxyuridine (BrDu) instead of thymidine into the DNA of proliferating cells. The antibody conjugate reacts with BrDu and with BrDu incorporated into DNA.  The antibody does not cross-react with endogenous cellular components such as thymidine, uridine, or DNA.  The cells were seeded, next day starved for 24h, and were stimulated at time intervals 3h, 24h, and 72h with 100 units of each BIIa, HIIa, TBIIa, and THIIa, and BrDu (Roche).  Cells were fixed for 15 min with fixation-denature solution and incubated with primary antibody (anti-BrDu) prior to incubation with the secondary antibody.  The cells were then fixed in 3.7% formaldehyde for 10 min at room temperature, rinsed in PBS and the chromatin was rendered accessible by a 10 min treatment with HCI (2 M), then measured the activity at A450nm.

Nuclear Extract Preparation:  The nuclear extracts were prepared by the protocol suggested in the ELISA inflammation kit (BD).   For each treatment one 100mm plate were used per cell line.

EMSA:  The 96 well-plates were blocked at room temperature before incubating with the 50 ul of prepared nuclear extracts from each treated cell line were placed for one hour at 25C.  The washed plates were incubated with primary antibodies of each transcription factors for another hour at 25C and repeat the wash step with transfactor/blocking buffer prior to secondary antibody addition for 30 min at 25C, wash again with transfactor buffer, which was followed by development of the blue color for ten minutes and the reaction was stopped with 1M sulfuric acid, and the absorbance readings were taking at 450nm by multiple well plate reader.

Immunoblotting:  The activated level of pERK, Gi, Gq, and PAR1 had been immunoblotted to observe the mitogenic effect of bovine thrombin on both HUVEC and AoSMCs.   The cells were lysed in sample buffer (0.25M Tris-HCl, pH 6.8, 10% glycerol, 5%SDS, 5% b-mercaptoethanol, 0.02%bromophenol blue).  The samples were run on the 16% SDS-PAGE for 1 hour at 30mA per gel. Following the completion of transfer onto 0.45micro molar nitrocellulose membrane for 1 hour at 250mA, the membranes were blocked in 5% skim milk phosphate buffered saline at 4C for 4 hours. The membranes were washed three times for 10 minutes each in 0.1% Tween-20 in PBS after both primary and secondary antibody incubations.  The pERK (42/44 kD), Gi (40kDa), Gq (40kDa) and PAR1 (55kDa) visualized with the polyclonal antibody raised against each in rabbit (1:5000 dilution from g/ml, Cell Signaling) and chemiluminescent detection of anti-rabbit IgG 1/200 conjugated with horseradish peroxidase (ECL, Amersham Corp).

RESULTS:

The expression of PARs differs for the types  of  vascular cells. 

Figure 1 shows PAR 1 and PAR3 expression on HUVECs and AoSMCs. The expression was evaluated consisted with prior work PAR1 and PAR3 express on AoSMC but PAR2 and PAR4 are not.  The level of PAR1 expression is significantly greater on AoSMC (3:1) then HUVECs.  We determine the PAR2 in vitro in HUVECs or AoSMCs, PAR2, does not respond to thrombin however according to reports, has function in inflammation. PAR4 is not detected in either cell types. However, PAR3 responding to thrombin at low concentration showed minute amount in AoSMC compare to weak presence in HUVECs. The origin of the thrombin may influence the difference in expression of PAR4 in HUVECs, since BIIa caused higher PAR4 expression than HIIA, but THIIa had almost none (not shown).

The expression of the PARs, G proteins, and pERK use different signaling dynamics. The application of thrombin triggers the extracellular signaling mechanism through the PARs on the membrane; next, the signal travels through cytoplasm by Gi and Gq to MAPKs. Gi was activated   more on AoSMC than HUVECs (Figure 2 and Figure 3).

In Figure 2 demonstrates the expression of Gi on HUVEC starts at 20minutes and continues to be expressed until 5.5h time interval, but Gq/11 expression is almost same between non-stimulated and stimulated samples from 20min to 5.5 h period.  The difference of expression between the two kinds of G proteins is subtle, Gi is at least five fold more than Gi expression on AoSMC. 

In Figure 3, there is a difference between Gi and Gq/11 expression on HUVEC. The linear  increase from 0 to 30 minutes was detected, at 1hour the expression decreased by 50%, then the expression became un-detectable.   Both Gi and Gq/11 showed the same pattern of expression but only Gi had again showed five times stronger signal than Gq/11.  This brings the possibility that Gi had been activated due to thrombin and this signal pass onto AoSMC and remain there long period of time.

Next, the proliferation through MAPK signaling had been tested by ERK activation.  Figure 4 represents this activation data that both HUVECs and AoSMCs express activated ERK, but the activity dynamics is different as expected from G protein signaling pattern.   Both AoSMC and HUVECs starts to express the activated ERK around 20min time and reach to the plato at 3.5hr.  AoSMCs get phosphorylated at least 5 times more than HUVECs.   This might be related to dynamics of each PARs as it had been suggested previously (by Coughlin group PAR1 vs. PAR4).

Activation of DNA synthesis in AoSMCs.  As it had been shown the serine proteases, thrombin and trypsin are among many factors that malignant cells secrete into the extracellular space to mediate metastatic processes such as cellular invasion, extracellular matrix degradation, angiogenesis, and tissue remodeling. We want to examine whether the types of thrombin had any specificity on proliferation on either cell types. Moreover, if there was a correlation between the number of cells and origin of thrombin, it can be use as reference to predict the response from the patient that may be valuable in patient’s recovery. As a result, we had investigated the proliferation of HUVECs and AoSMCs by WST-1 and BrDu.

DNA synthesis experiments for HUVECs with WST-1and BrDu showed no mitogenic response to thrombins we used with WST-1 or BrDu.   All together, in our data showed that there is no significant proliferation in HUVECs due to thrombins we used (data not shown).

DNA synthesis for AoSMCs With WST-1: After the starvation of the cells hours by depleting the cells were treated with WST-1 and readings were collected at time intervals of 0, 3.5, 25, and 45hours.  The measured WST-1 reaction increased 20% between each time points from 0 to 25 h and stop at 45 h except THIIa continue 20% increase (not shown). 

DNA synthesis at AoSMCs With BrDu: We had observed 2.5 fold increase of DNA synthesis of AoSMC after 72 hr in response to thrombin treatments, that resulted in cell proliferation according to Figure 5.  The plates were seeded with 500 cells and the proliferation was measured at time intervals 3h, 24h, and 72h.  At 3h time interval no difference between non-stimulated and  stimulated by topical bovine thrombin AoSMC.  At 24h the cells proliferate 20% by favor of treated cells, finally at 72h the ectopical bovine thrombin cause 253% more cell proliferationthan baseline. On the same token, TBIIa had 100% more mitogenic than THIIa but there was almost no difference between the HIIa and BIIa on proliferation (not shown).  This predicts that as well as the origin of the product the purity of the preparation is important.

Effects of thrombin and TRAPS (thrombin receptor activated peptides) on the HUVECs

Figure 6A (Figure 6) presents how TRAP stimulated cells change their transcription factor expression.  PAR1 effects CREB and c-Rel, but PAR3 affects ATF-2 and c-Rel. The proliferation signals eventually affect the gene expression and activation of downstream genes.  HUVECs were treated all four known TRAPs directly, before treating them with types of ectopical thrombins.  As a result, it is important to find how direct application of specific peptides for each PAR receptor will change the gene expression in the nucleus of ECs as well as their phenotype to activate SMCs.  PAR1 caused 175% increase on 200% on c-rel, 175% CREB, 90% on ATF2, 80% on c-fos, 70% on NfkB 50 and 60% on NFkB65. On the other hand, PAR3 affected the ATF2 by 200%.  PAR3 increased the c-Rel by 160%, and NfkB50, NFkB65, and c-fos by 60%.  These factors have CREs (cAMP response elements) in their transcriptional sequence and they bind to p300/CREB either creating homodimers or heterodimers to trigger transcriptional control mechanism of a cell, e.g. T cell activation by IL2 proliferation activated by ATF dimers or choosing between controlled versus un-controlled cellular proliferation. These decisions determine what downstream genes are going to be on and when.  This data confirms the increased of activated ERK, p38 and JNK protein expression in vivo study (Sag et al., 2013)

The effects of thrombins on the transcription factors.  Figure 7 demonstrates the comparison between HUVECs and AoSMC after topical bovine thrombin (JMI) stimulation to detect a difference on transcription activation. First, Figure 7A shows in HUVECs  topical bovine thrombin causes elevation of ATF2 activation by  50% and c-Rel by 30%.  Figure 7B represents in AoSMC thrombin affects CREB specifically since no change on HUVECs.  As a result, the transcription factors are activated differently, therefore, CREB 40%, ATF2 80%, and c-Rel 10% elevated by TBII treatment compare to baseline.

Gene Interaction changes after the thrombin treatment both in vivo and in vitro:  Figure 8 shows RT-PCR for two of the cysteine rich family proteins in vitro (this study) as well as in vivo (Sag et al manuscript 2006).  These genes have a  predicted function in angiogenesis, connective tissue growth factor (CTGF) and cystein rich protein 61 (Cyr61).  In our in vivo study, CTGF was only expressed if the veins are treated with thrombin and Cys61 expression is also elevated but both controls and bovine thrombin treated veins showed expression.  The total RNA from the cells was purified and testes against controls, the negative controls by water or by no reverse transcriptase and positive controls by internal gene, expression of beta actin.  The expression of beta actin is  at least two-three times abundant in HUVECs than that of AoSMC.  The CTGF is higher in AoSMCs  than HUVEC.  Simply the fact that the concentration of RNA is lower along with low internal expression positive control gene, but the CTGF expression was even 1 fold higher than HUVEC.  In perfect picture this theoretically adds up to 4 times difference between the cell types in favor of AoSMCs.  However, the Cyr61 expression adds up to the equal level of cDNA expression.

Consequently, the overall use of topical thrombins changed the fate of the cells plus when they were in their very fragile state under the surgical trauma and inflammation caused by the operation.  As a result, the cells may not be able make cohesive decision to avoid these extra signals, depending on the age and types of operations but eventually they lead to complications.

DISCUSSION:

In this study, we had shown the molecular pathway(s) affected by using ectopic thrombin during/after surgery on pig animal model that causing differentiation in the gene interactions for proliferation. In our study the mechanism for ectopic thrombins to investigate whether there was a difference in cell stimulation and gene interactions. Starting from the cell surface to the nucleus we had tested the mechanisms for thrombin affect on cells.  We had found that there were differences between endothelial cells and smooth muscle cell responses depending on the type of thrombin origin.  For example, PAR1 expressed heavily on HUVECs, but PAR1 and PAR3 on the AoSMCs.   Activated PARs couples to signaling cascades affect cell shape, secretion, integrin activation, metabolic responses, transcriptional responses and cell motility. Moreover, according to the literature these diverse functions differ depending on the cell type and time that adds another dimension.

Presence of PARs on different cell types have been studied by many groups for different reasons development, coagulation, inflammation and immune response. For example, PAR1 is the predominant thrombin receptor expressed in HUVECs and cleavage of PAR1 is required for EC responses to thrombin.  As a result, PAR2 may activate PAR1 for action in addition to transactivation between PAR3 and PAR4 observed. PAR4 is not expressed on HUVEC; and transactivation of PAR2 by cleaved PAR1 can contribute to endothelial cell responses to thrombin, particularly when signaling through PAR1 is blocked.

Next, the measurement of G protein expression shows that Gi and Gq have function at both cell types in terms of ectopical response to cAMP; therefore, Gi was heavily expressed. However Gi was stated to be function in development and growth therefore activates MAPKs most.  As it was expected from previous studies and our hands in vivo, observation of elevated ERK phosphorylation in vitro at time intervals relay us to determine simply what molecular genetics and development players cause the thickening in the vessel.  Analysis between the cell types resulted in proliferation of AoSMC, which was enough to occlude a vessel.

The ability of the immune system to distinguish between benignand harmful antigens is central to maintaining the overall healthof an organism. Fields and Shoenecker (2003) from our lab showed that proteases, namely those that can activate the PAR-2 transmembraneprotein, can up-regulate costimulatory molecules on DC and initiatean immune response (45).  Once activated, PAR-2 initiates a numberof intracellular events, including G and Gß signaling. Here, we show the PAR protein expression for PAR1 and PAR3 but not for PAR2.  Yet we had seen mRNA expression of PAR2 in vitro. We had also detected Gi and Gq but no expression of Ga or Gbg.   However, we did detect the difference of transcription factor activation by EMSA that correlates well with danger signal creation by thrombin.  In this report with the highlights of our data it seems that it is possibly an indirect response.

The bovine thrombin also affected the gene activation, measured by EMSA ELISA by direct treatment of the cells with thrombin response activation peptides (TRAPs) for PAR1, PAR2, PAR3, PAR4 on HUVECs since the endothelial cells directly exposed to ectopical thrombin treatment on vascular system and smooth muscle cells are inside of the vein.  Therefore, plausibly ECs transfer the signals received from their surface to the smooth muscle cells.  Second, we applied ectopical thrombins on AoSMCs as well as HUVECs by the same technique for the analysis of change same transcription factors previously with HUVEC for response to TRAPs.  These factors were ATF-2, CREB, c-rel, NFkB p50, NFkB p65, and c-fos.   In HUVECs, NFkB 50 increased the most by PAR2 oligo and PAR4 oligo, CREB as inflammatory response by PAR1 oligo, and ATF2 for PAR3 and PAR4 oligos, and c-fos with PAR4 oligo  The cellular response for thrombin in AoSMC differs from HUVEC since the at AoSMC not only proliferation by CREB  but also T cell activation by ATF-2 observed.

CREB (CRE-binding protein, Cyclic AMP Responsive DNA Binding Protein) protein has been shown to function as calcium regulated transcription factor as well as a substrate for depolarization-activated calcium calmodulin-dependent protein kinases II and I.   Some growth control genes, such as FOS have CRE, in their transcriptional regulatory region and their expression is induced by increase in the intracellular cAMP levels. This data goes very well with our finding of highly elevated Gi expression compare to Gq/11.  The CREB, or ATF (activating transcription factor, CRBP1, cAMP response element-binding protein 2, formerly; (CREB2) are also interacting with p300/CBP.  Transcriptional activation of CREB is controlled through phosphorylation at Ser133 by p90Rsk and the p44/42 MAP kinase (pERK, phosphorylated ERK). The transcriptional activity of the proto-oncogene c-Fos has been implicated in cell growth, differentiation, and development. Like CREB, c-Fos is regulated by p90Rsk.   NFKB has been detected in numerous cell types that express cytokines, chemokines, growth factors, cell adhesion molecules, and some acute phase proteins in health and in various disease states. In sum, our data is coherent from cellular membrane to nucleus as well as from nucleus to cellular membrane.

The origin of the thrombin is proven to be important, and required to be used very defined and clear concentrations.  It is not an old dog trick since ectopical thrombins have been used to control bleeding very widely without much required regulations not only in the surgeries but also in many other common applications.

In our experiments we observe MAPKs activities showed that pERK is active in AoSMCs more than HUVECs. The underlying mechanism how MAPKs connects to the cell cycle agree with our data that the mitogen-dependent induction of cyclin D1 expression, one of the earliest cell cycle-related events to occur during the G0/G1 to S-phase transition, is a potential target of MAPK regulation.  Activation of this signaling pathway by thrombin cause similar affects as expression of a constitutively active MKK1 mutant (46) does which results in dramatically increased cyclin D1 promoter activity and cyclin D1 protein expression.  In marked contrast, the p38 (MAPK) cascade showed an opposite effect on the regulation of cyclin D1 expression, which means that using unconcerned use of ectopic bovine thrombin will lead to more catastrophic affects then it was thought.  Since the p38 also is responsible for immune response mechanism, the system will be alarmed by the danger signal created by bovine thrombin.  The minute amount of well balanced mechanism will start against itself as it was observed previously (39-43, 47).

Finally, according to the lead from the literature tested the cysteine rich gene expression of CTGF and Cyr61 showing elevation of CTGF in AoSMCs also  make our argument stronger that the use of bovine thrombin does affect the cells beyond the proliferation but as system.

All together, both in vivo and in vitro studies confirms that choosing the right kind of ectopic product for the proper “hemostasis” to be resumed at an unexpected situation in the operation room is critical, therefore, this decision should require careful considiration to avoid long term health problems.

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Figure Legends:

Figure 1: PAR signaling in HUVEC AND AoSMC by western blotting. Figure 1

Figure 2: The Effects of TBIIa on G Protein signaling of AoSMCs. (a) Gi (B) Gq/11 Figure 2

Figure 3:  The Effects of TBIIa on G Protein signaling of HUVECs (a) Gi (B) Gq/11  Figure 3

Figure 4:  The effects of TBIIa on AoSMC and HUVEC ERK activation. Figure 4

Figure 5:  AoSMC proliferation after BrDu treatment. Figure 5

Figure 6:  Affects of TRAPs, thrombin responsive activation peptides, for the transcription factors on HUVEC Figure 6

Figure 7:  The ectopical thrombin effects the transcription factors differently on HUVECs and AoSMCs.  Figure 7

Figure 8:  Gene interactions differ after ectopic IIa. (A) in the AoSMC,  (B) In the HUVEC. Figure 8

 

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aprotinin-sequence.Par.0001.Image.260

aprotinin-sequence.Par.0001.Image.260 (Photo credit: redondoself)

English: Protein folding: amino-acid sequence ...

Protein folding: amino-acid sequence of bovine BPTI (basic pancreatic trypsin inhibitor) in one-letter code, with its folded 3D structure represented by a stick model of the mainchain and sidechains (in gray), and the backbone and secondary structure by a ribbon colored blue to red from N- to C-terminus. 3D structure from PDB file 1BPI, visualized in Mage and rendered in Raster3D. (Photo credit: Wikipedia)

 

 

 

 

 

 

 

 

 

 

 

 

The Effects of Aprotinin on Endothelial Cell Coagulant Biology

Demet Sag, PhD*†, Kamran Baig, MBBS, MRCS; James Jaggers, MD, Jeffrey H. Lawson, MD, PhD

Departments of Surgery and Pathology (J.H.L.) Duke University Medical Center Durham, NC  27710

Correspondence and Reprints:

                             Jeffrey H. Lawson, M.D., Ph.D.

                              Departments of Surgery & Pathology

                              DUMC Box 2622

                              Durham, NC  27710

                              (919) 681-6432 – voice

                              (919) 681-1094 – fax

                              lawso006@mc.duke.edu

*Current Address: Demet SAG, PhD

                          3830 Valley Centre Drive Suite 705-223, San Diego, CA 92130

Support:

Word Count: 4101 Journal Subject Heads:  CV surgery, endothelial cell activationAprotinin, Protease activated receptors,

Potential Conflict of Interest:         None

Abstract

Introduction:  Cardiopulmonary bypass is associated with a systemic inflammatory response syndrome, which is responsible for excessive bleeding and multisystem dysfunction. Endothelial cell activation is a key pathophysiological process that underlies this response. Aprotinin, a serine protease inhibitor has been shown to be anti-inflammatory and also have significant hemostatic effects in patients undergoing CPB. We sought to investigate the effects of aprotinin at the endothelial cell level in terms of cytokine release (IL-6), tPA release, tissue factor expression, PAR1 + PAR2 expression and calcium mobilization. Methods:  Cultured Human Umbilical Vein Endothelial Cells (HUVECS) were stimulated with TNFa for 24 hours and treated with and without aprotinin (200KIU/ml + 1600KIU/ml). IL-6 and tPA production was measured using ELISA. Cellular expression of Tissue Factor, PAR1 and PAR2 was measured using flow cytometry. Intracellular calcium mobilization following stimulation with PAR specific peptides and agonists (trypsin, thrombin, Human Factor VIIa, factor Xa) was measured using fluorometry with Fluo-3AM. Results: Aprotinin at the high dose (1600kIU/mL), 183.95 ± 13.06mg/mL but not low dose (200kIU/mL) significantly reduced IL-6 production from TNFa stimulated HUVECS (p=0.043). Aprotinin treatment of TNFa activated endothelial cells significantly reduce the amount of tPA released in a dose dependent manner (A200 p=0.0018, A1600 p=0.033). Aprotinin resulted in a significant downregulation of TF expression to baseline levels. At 24 hours, we found that aprotinin treatment of TNFa stimulated cells resulted in a significant downregulation of PAR-1 expression. Aprotinin significantly inhibited the effects of the protease thrombin upon PAR1 mediated calcium release. The effects of PAR2 stimulatory proteases such as human factor Xa, human factor VIIa and trypsin on calcium release was also inhibited by aprotinin. Conclusion:  We have shown that aprotinin has direct anti-inflammatory effects on endothelial cell activation and these effects may be mediated through inhibition of proteolytic activation of PAR1 and PAR2. Abstract word count: 297

INTRODUCTION   Each year it is estimated that 350,000 patients in the United States, and 650,000 worldwide undergo cardiopulmonary bypass (CPB). Despite advances in surgical techniques and perioperative management the morbidity and mortality of cardiac surgery related to the systemic inflammatory response syndrome(SIRS), especially in neonates is devastatingly significant. Cardiopulmonary bypass exerts an extreme challenge upon the haemostatic system as part of the systemic inflammatory syndrome predisposing to excessive bleeding as well as other multisystem dysfunction (1). Over the past decade major strides have been made in the understanding of the pathophysiology of the inflammatory response following CPB and the role of the vascular endothelium has emerged as critical in maintaining cardiovascular homeostasis (2).

CPB results in endothelial cell activation and initiation of coagulation via the Tissue Factor dependent pathway and consumption of important clotting factors. The major stimulus for thrombin generation during CPB has been shown to be through the tissue factor dependent pathway. As well as its effects on the fibrin and platelets thrombin has been found to play a role in a host of inflammatory responses in the vascular endothelium. The recent discovery of the Protease-Activated Receptors (PAR), one of which through which thrombin acts (PAR-1) has stimulated interest that they may provide a vital link between inflammation and coagulation (3).

Aprotinin is a nonspecific serine protease inhibitor that has been used for its ability to reduce blood loss and preserve platelet function during cardiac surgery procedures requiring cardiopulmonary bypass and thus the need for subsequent blood and blood product transfusions. However there have been concerns that aprotinin may be pro-thrombotic, especially in the context of coronary artery bypass grafting, which has limited its clinical use. These reservations are underlined by the fact that the mechanism of action of aprotinin has not been fully understood. Recently aprotinin has been shown to exert anti-thrombotic effects mediated by blocking the PAR-1 (4). Much less is known about its effects on endothelial cell activation, especially in terms of Tissue Factor but it has been proposed that aprotinin may also exert protective effects at the endothelial level via protease-activated receptors (PAR1 and PAR2). In this study we simulated in vitro the effects of endothelial cell activation during CPB by stimulating Human Umbilical Vein Endothelial Cells (HUVECs) with a proinflammatory cytokine released during CPB, Tumor Necrosis Factor (TNF-a) and characterize the effects of aprotinin treatment on TF expression, PAR1 and PAR2 expression, cytokine release IL-6 and tPA secretion.  In order to investigate the mechanism of action of aprotinin we studied its effects on PAR activation by various agonists and ligands.

These experiments provide insight into the effects of aprotinin on endothelial related coagulation mechanisms in terms of Tissue Factor expression and indicate it effects are mediated through Protease-Activated Receptors (PAR), which are seven membrane spanning proteins called G-protein coupled receptors (GPCR), that link coagulant and inflammatory pathways. Therefore, in this study we examine the effects of aprotinin on the human endothelial cell coagulation biology by different-dose aprotinin, 200 and 1600units.  The data demonstrates that aprotinin appears to directly alter endothelial expression of inflammatory cytokines, tPA and PAR receptor expression following treatment with TNF.  The direct mechanism of action is unknown but may act via local protease inhibition directly on endothelial cells.  It is hoped that with improved understanding of the mechanisms of action of aprotinin, especially an antithrombotic effect at the endothelial level the fears of prothrombotic tendency may be lessened and its use will become more routine.  

METHODS Human Umbilical Vein Endothelial Cells (HUVECS) used as our model to study the effects of endothelial cell activation on coagulant biology. In order to simulate the effects of cardiopulmonary bypass at the endothelial cell interface we stimulated the cells with the proinflammatory cytokine TNFa. In the study group the HUVECs were pretreated with low (200kIU/mL) and high (1600kIU/mL) dosages of aprotinin prior to stimulation with TNFa and complement activation fragments. The effects of TNFa stimulation upon endothelial Tissue Factor expression, PAR1 and PAR2 expression, and tPA and IL6 secretion were determined and compared between control and aprotinin treated cells. In order to delineate whether aprotinin blocks PAR activation via its protease inhibition properties we directly activated PAR1 and PAR2 using specific agonist ligands such thrombin (PAR1), trypsin, Factor VIIa, Factor Xa (PAR2) in the absence and presence of aprotinin.

Endothelial Cell Culture HUVECs were supplied from Clonetics. The cells were grown in EBM-2 containing 2MV bullet kit, including 5% FBS, 100-IU/ml penicillin, 0.1mg/mL streptomycin, 2mmol/L L-glutamine, 10 U/ml heparin, 30µg/mL EC growth supplement (ECGS). Before the stimulation cells were starved in 0.1%BSA depleted with FBS and growth factors for 24 hours. Cells were sedimented at 210g for 10 minutes at 4C and then resuspended in culture media. The HUVECs to be used will be between 3 and 5 passages.

Assay of IL-6 and tPA production Levels of IL-6 were measured with an ELISA based kit (RDI, MN) according to the manufacturers instructions. tPA was measured using a similar kit (American Diagnostica).

  Flow Cytometry The expression of transmembrane proteins PAR1, PAR2 and tissue factor were measured by single color assay as FITC labeling agent. Prepared suspension of cells disassociated trypsin free cell disassociation solution (Gibco) to be labeled. First well washed, and resuspended into “labeling buffer”, phosphate buffered saline (PBS) containing 0.5% BSA plus 0.1% NaN3, and 5% fetal bovine serum to block Fc and non-specific Ig binding sites. Followed by addition of 5mcl of antibody to approx. 1 million cells in 100µl labeling buffer and incubate at 4C for 1 hour. After washing the cells with 200µl with wash buffer, PBS + 0.1% BSA + 0.1% NaN3, the cells were pelletted at 1000rpm for 2 mins. Since the PAR1 and PAR2 were directly labeled with FITC these cells were fixed for later analysis by flow cytometry in 500µl PBS containing 1%BSA + 0.1% NaN3, then add equal volume of 4% formalin in PBS. For tissue factor raised in mouse as monoclonal primary antibody, the pellet resuspended and washed twice more as before, and incubated at 4C for 1 hour addition of 5µl donkey anti-mouse conjugated with FITC secondary antibody directly to the cell pellets at appropriate dilution in labeling buffer. After the final wash three times, the cell pellets were resuspended thoroughly in fixing solution. These fixed and labeled cells were then stored in the dark at 4C until there were analyzed. On analysis, scatter gating was used to avoid collecting data from debris and any dead cells. Logarithmic amplifiers for the fluorescence signal were used as this minimizes the effects of different sensitivities between machines for this type of data collection.  

Intracellular Calcium Measurement

Measured the intracellular calcium mobilization by Fluo-3AM. HUVECs were grown in calcium and phenol free EBM basal media containing 2MV bullet kit. Then the cell cultures were starved with the same media by 0.1% BSA without FBS for 24 hour with or without TNFa stimulation presence or absence of aprotinin (200 and 1600KIU/ml). Next the cells were loaded with Fluo-3AM 5µg/ml containing agonists, PAR1 specific peptide SFLLRN-PAR1 inhibitor, PAR2 specific peptide SLIGKV-PAR2 inhibitor, human alpha thrombin, trypsin, factor VIIa, factor Xa for an hour at 37C in the incubation chamber. Finally the media was replaced by Flou-3AM free media and incubated for another 30 minutes in the incubation chamber. The readings were taken at fluoromatic bioplate reader. For comparison purposes readings were taken before and during Fluo-3AM loading as well.  

RESULTS Aprotinin reduces IL-6 production from activated/stimulated HUVECS The effects of aprotinin analyzed on HUVEC for the anti-inflammatory effects of aprotinin at cultured HUVECS with high and low doses.  Figure 1 shows that TNF-a stimulated a considerable increase in IL-6 production, 370.95 ± 109.9 mg/mL.   If the drug is used alone the decrease of IL-6 at the low dose is 50% that is 183.95 ng/ml and with the high dose of 20% that is 338.92 from 370.95ng/ml being compared value.  TNFa-aprotinin results in reduction of the IL-6 expression from 370.95ng/ml to 58.6 (6.4fold) fro A200 and 75.85 (4.9 fold) ng/ml, for A1600.  After the treatment the cells reach to the below baseline limit of IL-6 expression. Aprotinin at the high dose (1600kIU/mL), 183.95 ± 13.06mg/mL but not low dose (200kIU/mL) significantly reduced IL-6 production from TNF-a stimulated HUVECS (p=0.043).  Therefore, the aprotinin prevents inflammation as well as loss of blood.  

Aprotinin reduces tPA production from stimulated HUVECS Whether aprotinin exerted part of its fibrinolytic effects through inhibition of tPA mediated plasmin generation examined by the effects on TNFa stimulated HUVECS. Figure 2 also demonstrates that the amount of tPA released from HUVECS under resting, non-stimulated conditions incubated with aprotinin are significantly different. Figure 2 represents that the resting level of tPA released from non-stimulated cells significantly, by 100%, increase following TNF-a stimulation for 24 hours.  After application of aprotinin alone at two doses the tPA level goes down 25% of TNFa stimulated cells.  However, aprotinin treatment of TNF-a activated endothelial cells significantly lower the amount of tPA release in a dose dependent manner that is low dose decreased 25 but high dose causes 50% decrease of tPA expression (A200 p=0.0018, A1600 p=0.033) This finding suggests that aprotinin exerts a direct inhibitory effect on endothelial cell tPA production.

Aprotinin and receptor expression on activated HUVECS

TF is expressed when the cell in under stress such as TNFa treatments. The stimulated HUVECs with TNF-a tested for the expression of PAR1, PAR2, and tissue factor by single color flow cytometry through FITC labeled detection antibodies at 1, 3, and 24hs.

 

Tissue Factor expression is reduced:

Figure 3 demonstrates that there is a fluctuation of TF expression from 1 h to 24h that the TF decreases at first hour after aprotinin application 50% and 25%, A1600 and A200 respectively.  Then at 3 h the expression come back up 50% more than the baseline.  Finally, at 24h the expression of TF becomes almost as same as baseline.  Moreover, TNFa stimulated cells remains 45% higher than baseline after at 3h as well as at 24h.

PAR1 decreased:
Figure 4 demonstrates that aprotinin reduces the PAR1 expression 80% at 24h but there is no affect at 1 and 3 h intervals for both doses.

During the treatment with aprotinin only high dose at 1 hour time interval decreases the PAR1 expression on the cells. This data explains that ECCB is affected due to the expression of PAR1 is lowered by the high dose of aprotinin.

PAR2 is decreased by aprotinin:

  Figure 5 shows the high dose of aprotinin reduces the PAR2 expression close to 25% at 1h, 50% at 3h and none at 24h.  This pattern is exact opposite of PAR1 expression.  Figure 5 demonstrates the 50% decrease at 3h interval only.  Does that mean aprotinin affecting the inflammation first and then coagulation?

This suggests that aprotinin may affect the PAR2 expression at early and switched to PAR1 reduction later time intervals.  This fluctuation can be normal because aprotinin is not a specific inhibitor for proteases.  This approach make the aprotinin work better the control bleeding and preventing the inflammation causing cytokine such as IL-6.

Aprotinin inhibits Calcium fluxes induced by PAR1/2 specific agonists

  The specificity of aprotinin’s actions upon PAR studied the effects of the agent on calcium release following proteolytic and non-proteolytic stimulation of PAR1 and PAR2. Figure 6A (Figure 6) shows the stimulation of the cells with the PAR1 specific peptide (SFLLRN) results in release of calcium from the cells. Pretreatment of the cells with aprotinin has no significant effect on PAR1 peptide stimulated calcium release. This suggests that aprotinin has no effect upon the non-proteolytic direct activation of the PAR 1 receptor. Yet, Figure 6B (Figure 6) demonstrates human alpha thrombin does interact with the drug as a result the calcium release drops below base line after high dose (A1600) aprotinin used to zero but low dose does not show significant effect on calcium influx. Figure 7 demonstrates the direct PAR2 and indirect PAR2 stimulation by hFVIIa, hFXa, and trypsin of cells.  Similarly, at Figure 7A aprotinin has no effect upon PAR2 peptide stimulated calcium release, however, at figures 7B, C, and D shows that PAR2 stimulatory proteases Human Factor Xa, Human Factor VIIa and Trypsin decreases calcium release. These findings indicate that aprotinin’s mechanism of action is directed towards inhibiting proteolytic cleavage and hence subsequent activation of the PAR1 and PAR2 receptor complexes.  The binding site of the aprotinin on thrombin possibly is not the peptide sequence interacting with receptors.

Measurement of calcium concentration is essential to understand the mechanism of aprotinin on endothelial cell coagulation and inflammation because these mechanisms are tightly controlled by presence of calcium.  For example, activation of PAR receptors cause activation of G protein q subunit that leads to phosphoinositol to secrete calcium from endoplasmic reticulum into cytoplasm or activation of DAG to affect Phospho Lipase C (PLC). In turn, certain calcium concentration will start the serial formation of chain reaction for coagulation.  Therefore, treatment of the cells with specific factors, thrombin receptor activating peptides (TRAPs), human alpha thrombin, trypsin, human factor VIIa, and human factor Xa, would shed light into the effect of aprotinin on the formation of complexes for pro-coagulant activity.    DISCUSSION   There are two fold of outcomes to be overcome during cardiopulmonary bypass (CPB):  mechanical stress and the contact of blood with artificial surfaces results in the activation of pro- and anticoagulant systems as well as the immune response leading to inflammation and systemic organ failure.  This phenomenon causes the “postperfusion-syndrome”, with leukocytosis, increased capillary permeability, accumulation of interstitial fluid, and organ dysfunction.  CPB is also associated with a significant inflammatory reaction, which has been related to complement activation, and release of various inflammatory mediators and proteolytic enzymes. CPB induces an inflammatory state characterized by tumor necrosis factor-alpha release. Aprotinin, a low molecular-weight peptide inhibitor of trypsin, kallikrein and plasmin has been proposed to influence whole body inflammatory response inhibiting kallikrein formation, complement activation and neutrophil activation (5, 6). But shown that aprotinin has no significant influence on the inflammatory reaction to CPB in men.  Understanding the endothelial cell responses to injury is therefore central to appreciating the role that dysfunction plays in the preoperative, operative, and postoperative course of nearly all cardiovascular surgery patients.  Whether aprotinin increases the risk of thrombotic complications remains controversial.   The anti-inflammatory properties of aprotinin in attenuating the clinical manifestations of the systemic inflammatory response following cardiopulmonary bypass are well known(15) 16)  However its mechanisms and targets of action are not fully understood. In this study we have investigated the actions of aprotinin at the endothelial cell level. Our experiments showed that aprotinin reduced TNF-a induced IL-6 release from cultured HUVECS. Thrombin mediates its effects through PAR-1 receptor and we found that aprotinin reduced the expression of PAR-1 on the surface of HUVECS after 24 hours incubation. We then demonstrated that aprotinin inhibited endothelial cell PAR proteolytic activation by thrombin (PAR-1), trypsin, factor VII and factor X (PAR-2) in terms of less release of Ca preventing the activation of coagulation.  So aprotinin made cells produce less receptor, PAR1, PAR2, and TF as a result there would be less Ca++ release.    Our findings provide evidence for anti-inflammatory as well as anti-coagulant properties of aprotinin at the endothelial cell level, which may be mediated through its inhibitory effects on proteolytic activation of PARs.   IL6   Elevated levels of IL-6 have been shown to correlate with adverse outcomes following cardiac surgery in terms of cardiac dysfunction and impaired lung function(Hennein et al 1992). Cardiopulmonary bypass is associated with the release of the pro-inflammatory cytokines IL-6, IL-8 and TNF-a.  IL-6 is produced by T-cells, endothelial cells as a result monocytes and plasma levels of this cytokine tend to increase during CPB (21, 22). In some studies aprotinin has been shown to reduce levels of IL-6 post CPB(23) Hill(5). Others have failed to demonstrate an inhibitory effect of aprotinin upon pro-inflammatory cytokines following CPB(24) (25).  Our experiments showed that aprotinin significantly reduced the release of IL-6 from TNF-a stimulated endothelial cells, which may represent an important target of its anti-inflammatory properties. Its has been shown recently that activation of HUVEC by PAR-1 and PAR-2 agonists stimulates the production of IL-6(26). Hence it is possible that the effects of aprotinin in reducing IL-6 may be through targeting activation of such receptors.   TPA   Tissue Plasminogen activator is stored, ready made, in endothelial cells and it is released at its highest levels just after commencing CPB and again after protamine administration. The increased fibrinolytic activity associated with the release of tPA can be correlated to the excessive bleeding postoperatively. Thrombin is thought to be the major stimulus for release of t-PA from endothelial cells. Aprotinin’s haemostatic properties are due to direct inhibition of plasmin, thereby reducing fibrinolytic activity as well as inhibiting fibrin degradation.  Aprotinin has not been shown to have any significant effect upon t-PA levels in patients post CPB(27), which would suggest that aprotinin reduced fibrinolytic effects are not the result of inhibition of t-PA mediated plasmin generation. Our study, however demonstrates that aprotinin inhibits the release of t-PA from activated endothelial cells, which may represent a further haemostatic mechanism at the endothelial cell level.   TF   Resting endothelial cells do not normally express tissue factor on their cell surface. Inflammatory mediators released during CPB such as complement (C5a), lipopolysaccharide, IL-6, IL-1, TNF-a, mitogens, adhesion molecules and hypoxia may induce the expression of tissue factor on endothelial cells and monocytes. The expression of TF on activated endothelial cells activates the extrinsic pathway of coagulation, ultimately resulting in the generation of thrombin and fibrin. Aprotinin has been shown to reduce the expression of TF on monocytes in a simulated cardiopulmonary bypass circuit (28).

We found that treatment of activated endothelial cells with aprotinin significantly reduced the expression of TF after 24 hours. This would be expected to result in reduced thrombin generation and represent an additional possible anticoagulant effect of aprotinin. In a previous study from our laboratory we demonstrated that there were two peaks of inducible TF activity on endothelial cells, one immediately post CPB and the second at 24 hours (29). The latter peak is thought to be responsible for a shift from the initial fibrinolytic state into a procoagulant state.  In addition to its established early haemostatic and coagulant effect, aprotinin may also have a delayed anti-coagulant effect through its inhibition of TF mediated coagulation pathway. Hence its effects may counterbalance the haemostatic derangements, i.e. first bleeding then thrombosis caused by CPB. The anti-inflammatory effects of aprotinin may also be related to inhibition of TF and thrombin generation. PARs  

It has been suggested that aprotinin may target PAR on other cells types, especially endothelial cells. We investigated the role of PARs in endothelial cell activation and whether they can be the targets for aprotinin.  In recent study by Day group(30) demonstrated that endothelial cell activation by thrombin and downstream inflammatory responses can be inhibited by aprotinin in vitro through blockade of protease-activated receptor 1. Our results provide a new molecular basis to help explain the anti-inflammatory properties of aprotinin reported clinically.    The finding that PAR-2 can also be activated by the coagulation enzymes factor VII and factor X indicates that PAR may represent the link between inflammation and coagulation.  PAR-2 is believed to play an important role in inflammatory response. PAR-2 are widely expressed in the gastrointestinal tract, pancreas, kidney, liver, airway, prostrate, ovary, eye of endothelial, epithelial, smooth muscle cells, T-cells and neutrophils. Activation of PAR-2 in vivo has been shown to be involved in early inflammatory processes of leucocyte recruitment, rolling, and adherence, possibly through a mechanism involving platelet-activating factor (PAF)   We investigated the effects of TNFa stimulation on PAR-1 and PAR-2 expression on endothelial cells. Through functional analysis of PAR-1 and PAR-2 by measuring intracellular calcium influx we have demonstrated that aprotinin blocks proteolytic cleavage of PAR-1 by thrombin and activation of PAR-2 by the proteases trypsin, factor VII and factor X.  This confirms the previous findings on platelets of an endothelial anti-thrombotic effect through inhibition of proteolysis of PAR-1. In addition, part of aprotinin’s anti-inflammatory effects may be mediated by the inhibition of serine proteases that activate PAR-2. There have been conflicting reports regarding the regulation of PAR-1 expression by inflammatory mediators in cultured human endothelial cells. Poullis et al first showed that thrombin induced platelet aggregation was mediated by via the PAR-1(4) and demonstrated that aprotinin inhibited the serine protease thrombin and trypsin induced platelet aggregation. Aprotinin did not block PAR-1 activation by the non-proteolytic agonist peptide, SFLLRN indicating that the mechanism of action was directed towards inhibiting proteolytic cleavage of the receptor. Nysted et al showed that TNF did not affect mRNA and cell surface protein expression of PAR-1 (35), whereas Yan et al showed downregulation of PAR-1 mRNA levels (36). Once activated PAR1 and PAR2 are rapidly internalized and then transferred to lysosomes for degradation.

Endothelial cells contain large intracellular pools of preformed receptors that can replace the cleaved receptors over a period of approximately 2 hours, thus restoring the capacity of the cells to respond to thrombin. In this study we found that after 1-hour stimulation with TNF there was a significant upregulation in PAR-1 expression. However after 3 hours and 24 hours there was no significant change in PAR-1 expression suggesting that cleaved receptors had been internalized and replenished. Aprotinin was interestingly shown to downregulate PAR-1 expression on endothelial cells at 1 hour and increasingly more so after 24 hours TNF stimulation. These findings may suggest an effect of aprotinin on inhibiting intracellular cycling and synthesis of PAR-1.    

Conclusions   Our study has identified the anti-inflammatory and coagulant effects of aprotinin at the endothelial cell level. All together aprotinin affects the ECCB by reducing the t-PA, IL-6, PAR1, PAR 2, TF expressions. Our data correlates with the previous foundlings in production of tPA (7, (8) 9) 10), and  decreased IL-6 levels (11) during coronary artery bypass graft surgery (12-14). We have importantly demonstrated that aprotinin may target proteolytic activation of endothelial cell associated PAR-1 to exert a possible anti-inflammatory effect. This evidence should lessen the concerns of a possible prothrombotic effect and increased incidence of graft occlusion in coronary artery bypass patients treated with aprotinin. Aprotinin may also inhibit PAR-2 proteolytic activation, which may represent a key mechanism for attenuating the inflammatory response at the critical endothelial cell level. Although aprotinin has always been known as a non-specific protease inhibitor we would suggest that there is growing evidence for a PAR-ticular mechanism of action.  

REFERENCES

1.         Levy, J. H., and Tanaka, K. A. Inflammatory response to cardiopulmonary bypass. Ann Thorac Surg. 75: S715-720, 2003.

2.         Verrier, E. D., and Morgan, E. N. Endothelial response to cardiopulmonary bypass surgery. Ann Thorac Surg. 66: S17-19; discussion S25-18, 1998.

3.         Cirino, G., Napoli, C., Bucci, M., and Cicala, C. Inflammation-coagulation network: are serine protease receptors the knot? Trends Pharmacol Sci. 21: 170-172, 2000. 4.         Poullis, M., Manning, R., Laffan, M., Haskard, D. O., Taylor, K. M., and Landis, R. C. The antithrombotic effect of aprotinin: actions mediated via the proteaseactivated receptor 1. J Thorac Cardiovasc Surg. 120: 370-378, 2000.

5.         Hill, G. E., Alonso, A., Spurzem, J. R., Stammers, A. H., and Robbins, R. A. Aprotinin and methylprednisolone equally blunt cardiopulmonary bypass-induced inflammation in humans. J Thorac Cardiovasc Surg. 110: 1658-1662, 1995.

6.         Hill, G. E., Pohorecki, R., Alonso, A., Rennard, S. I., and Robbins, R. A. Aprotinin reduces interleukin-8 production and lung neutrophil accumulation after cardiopulmonary bypass. Anesth Analg. 83: 696-700, 1996. 7.         Lu, H., Du Buit, C., Soria, J., Touchot, B., Chollet, B., Commin, P. L., Conseiller, C., Echter, E., and Soria, C. Postoperative hemostasis and fibrinolysis in patients undergoing cardiopulmonary bypass with or without aprotinin therapy. Thromb Haemost. 72: 438-443, 1994.

8.         de Haan, J., and van Oeveren, W. Platelets and soluble fibrin promote plasminogen activation causing downregulation of platelet glycoprotein Ib/IX complexes: protection by aprotinin. Thromb Res. 92: 171-179, 1998.

9.         Erhardtsen, E., Bregengaard, C., Hedner, U., Diness, V., Halkjaer, E., and Petersen, L. C. The effect of recombinant aprotinin on t-PA-induced bleeding in rats. Blood Coagul Fibrinolysis. 5: 707-712, 1994.

10.       Orchard, M. A., Goodchild, C. S., Prentice, C. R., Davies, J. A., Benoit, S. E., Creighton-Kemsford, L. J., Gaffney, P. J., and Michelson, A. D. Aprotinin reduces cardiopulmonary bypass-induced blood loss and inhibits fibrinolysis without influencing platelets. Br J Haematol. 85: 533-541, 1993.

11.       Tassani, P., Augustin, N., Barankay, A., Braun, S. L., Zaccaria, F., and Richter, J. A. High-dose aprotinin modulates the balance between proinflammatory and anti-inflammatory responses during coronary artery bypass graft surgery. J Cardiothorac Vasc Anesth.14: 682-686, 2000.

12.       Asehnoune, K., Dehoux, M., Lecon-Malas, V., Toueg, M. L., Gonieaux, M. H., Omnes, L., Desmonts, J. M., Durand, G., and Philip, I. Differential effects of aprotinin and tranexamic acid on endotoxin desensitization of blood cells induced by circulation through an isolated extracorporeal circuit. J Cardiothorac Vasc Anesth. 16: 447-451, 2002.

13.       Dehoux, M. S., Hernot, S., Asehnoune, K., Boutten, A., Paquin, S., Lecon-Malas, V., Toueg, M. L., Desmonts, J. M., Durand, G., and Philip, I. Cardiopulmonary bypass decreases cytokine production in lipopolysaccharide-stimulated whole blood cells: roles of interleukin-10 and the extracorporeal circuit. Crit Care Med. 28: 1721-1727, 2000.

14.       Greilich, P. E., Brouse, C. F., Rinder, C. S., Smith, B. R., Sandoval, B. A., Rinder, H. M., Eberhart, R. C., and Jessen, M. E. Effects of epsilon-aminocaproic acid and aprotinin on leukocyte-platelet adhesion in patients undergoing cardiac surgery. Anesthesiology. 100: 225-233, 2004.

15.       Mojcik, C. F., and Levy, J. H. Aprotinin and the systemic inflammatory response after cardiopulmonary bypass. Ann Thorac Surg. 71: 745-754, 2001.

16.       Landis, R. C., Asimakopoulos, G., Poullis, M., Haskard, D. O., and Taylor, K. M. The antithrombotic and antiinflammatory mechanisms of action of aprotinin. Ann Thorac Surg. 72: 2169-2175, 2001.

17.       Asimakopoulos, G., Kohn, A., Stefanou, D. C., Haskard, D. O., Landis, R. C., and Taylor, K. M. Leukocyte integrin expression in patients undergoing cardiopulmonary bypass. Ann Thorac Surg. 69: 1192-1197, 2000.

18.       Landis, R. C., Asimakopoulos, G., Poullis, M., Thompson, R., Nourshargh, S., Haskard, D. O., and Taylor, K. M. Effect of aprotinin (trasylol) on the inflammatory and thrombotic complications of conventional cardiopulmonary bypass surgery. Heart Surg Forum. 4 Suppl 1: S35-39, 2001.

19.       Asimakopoulos, G., Thompson, R., Nourshargh, S., Lidington, E. A., Mason, J. C., Ratnatunga, C. P., Haskard, D. O., Taylor, K. M., and Landis, R. C. An anti-inflammatory property of aprotinin detected at the level of leukocyte extravasation. J Thorac Cardiovasc Surg. 120: 361-369, 2000.

20.       Asimakopoulos, G., Lidington, E. A., Mason, J., Haskard, D. O., Taylor, K. M., and Landis, R. C. Effect of aprotinin on endothelial cell activation. J Thorac Cardiovasc Surg. 122: 123-128, 2001.

21.       Butler, J., Chong, G. L., Baigrie, R. J., Pillai, R., Westaby, S., and Rocker, G. M. Cytokine responses to cardiopulmonary bypass with membrane and bubble oxygenation. Ann Thorac Surg. 53: 833-838, 1992.

22.       Hennein, H. A., Ebba, H., Rodriguez, J. L., Merrick, S. H., Keith, F. M., Bronstein, M. H., Leung, J. M., Mangano, D. T., Greenfield, L. J., and Rankin, J. S. Relationship of the proinflammatory cytokines to myocardial ischemia and dysfunction after uncomplicated coronary revascularization. J Thorac Cardiovasc Surg. 108: 626-635, 1994.

23.       Diego, R. P., Mihalakakos, P. J., Hexum, T. D., and Hill, G. E. Methylprednisolone and full-dose aprotinin reduce reperfusion injury after cardiopulmonary bypass. J Cardiothorac Vasc Anesth. 11: 29-31, 1997.

24.       Ashraf, S., Tian, Y., Cowan, D., Nair, U., Chatrath, R., Saunders, N. R., Watterson, K. G., and Martin, P. G. “Low-dose” aprotinin modifies hemostasis but not proinflammatory cytokine release. Ann Thorac Surg. 63: 68-73, 1997.

25.       Schmartz, D., Tabardel, Y., Preiser, J. C., Barvais, L., d’Hollander, A., Duchateau, J., and Vincent, J. L. Does aprotinin influence the inflammatory response to cardiopulmonary bypass in patients? J Thorac Cardiovasc Surg. 125: 184-190, 2003.

26.       Chi, L., Li, Y., Stehno-Bittel, L., Gao, J., Morrison, D. C., Stechschulte, D. J., and Dileepan, K. N. Interleukin-6 production by endothelial cells via stimulation of protease-activated receptors is amplified by endotoxin and tumor necrosis factor-alpha. J Interferon Cytokine Res. 21: 231-240, 2001.

27.       Ray, M. J., and Marsh, N. A. Aprotinin reduces blood loss after cardiopulmonary bypass by direct inhibition of plasmin. Thromb Haemost. 78: 1021-1026, 1997.

28.       Khan, M. M., Gikakis, N., Miyamoto, S., Rao, A. K., Cooper, S. L., Edmunds, L. H., Jr., and Colman, R. W. Aprotinin inhibits thrombin formation and monocyte tissue factor in simulated cardiopulmonary bypass. Ann Thorac Surg. 68: 473-478, 1999.

29.       Jaggers, J. J., Neal, M. C., Smith, P. K., Ungerleider, R. M., and Lawson, J. H. Infant cardiopulmonary bypass: a procoagulant state. Ann Thorac Surg. 68: 513-520, 1999.

30.       Day, J. R., Taylor, K. M., Lidington, E. A., Mason, J. C., Haskard, D. O., Randi, A. M., and Landis, R. C. Aprotinin inhibits proinflammatory activation of endothelial cells by thrombin through the protease-activated receptor 1. J Thorac Cardiovasc Surg. 131: 21-27, 2006.

31.       Vergnolle, N. Proteinase-activated receptor-2-activating peptides induce leukocyte rolling, adhesion, and extravasation in vivo. J Immunol. 163: 5064-5069, 1999.

32.       Vergnolle, N., Hollenberg, M. D., Sharkey, K. A., and Wallace, J. L. Characterization of the inflammatory response to proteinase-activated receptor-2 (PAR2)-activating peptides in the rat paw. Br J Pharmacol. 127: 1083-1090, 1999.

33.       McLean, P. G., Aston, D., Sarkar, D., and Ahluwalia, A. Protease-activated receptor-2 activation causes EDHF-like coronary vasodilation: selective preservation in ischemia/reperfusion injury: involvement of lipoxygenase products, VR1 receptors, and C-fibers. Circ Res. 90: 465-472, 2002.

34.       Maree, A., and Fitzgerald, D. PAR2 is partout and now in the heart. Circ Res. 90: 366-368, 2002.

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36.       Yan, W., Tiruppathi, C., Lum, H., Qiao, R., and Malik, A. B. Protein kinase C beta regulates heterologous desensitization of thrombin receptor (PAR-1) in endothelial cells. Am J Physiol. 274: C387-395, 1998.

37.       Shinohara, T., Suzuki, K., Takada, K., Okada, M., and Ohsuzu, F. Regulation of proteinase-activated receptor 1 by inflammatory mediators in human vascular endothelial cells. Cytokine. 19: 66-75, 2002.

FIGURES

Figure 1: IL-6 production following TNF-a stimulation Figure 1

Figure 2:  tPA production following TNF-a stimulation Figure 2

Figure 3:  Tissue Factor Expression on TNF-a stimulated HUVECS Figure 3

Figure 4:  PAR-1 Expression on TNF-a stimulated HUVECS Figure 4

Figure 5:  PAR-2 Expression on TNF-a stimulated HUVECS Figure 5

Figure 6:  Calcium Fluxes following PAR1 Activation Figure 6

Figure 7:  Calcium Fluxes following PAR2 Activation Figure 7

 

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Special Considerations in Blood Lipoproteins, Viscosity, Assessment and Treatment


Special Considerations in Blood Lipoproteins, Viscosity, Assessment and Treatment

Author: Larry H. Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN

This is the second of a two part discussion of viscosity, hemostasis, and vascular risk

This is Part II of a series on blood flow and shear stress effects on hemostasis and vascular disease.

See Part I on viscosity, triglycerides and LDL, and thrombotic risk.

 

Hemostatic Factors in Thrombophilia

Objectives.—To review the state of the art relating to elevated hemostatic factor levels as a potential risk factor for thrombosis, as reflected by the medical literature and the consensus opinion of recognized experts in the field, and to make recommendations for the use of specific measurements of hemostatic factor levels in the assessment of thrombotic risk in individual patients.

Data Sources.—Review of the medical literature, primarily from the last 10 years.

Data Extraction and Synthesis.—After an initial assessment of the literature, key points were identified. Experts were assigned to do an in-depth review of the literature and to prepare a summary of their findings and recommendations.

A draft manuscript was prepared and circulated to every participant in the College of American Pathologists Conference XXXVI: Diagnostic Issues in Thrombophilia prior to the conference. Each of the key points and associated recommendations was then presented for discussion at the conference. Recommendations were accepted if a consensus of the 27 experts attending the conference was reached. The results of the discussion were used to revise the manuscript into its final form.

Consensus was reached on 8 recommendations concerning the use of hemostatic factor levels in the assessment of thrombotic risk in individual patients.

The underlying premise for measuring elevated coagulation factor levels is that if the average level of the factor is increased in the patient long-term, then the patient may be at increased risk of thrombosis long-term. Both risk of thrombosis and certain factors increase with age (eg, fibrinogen, factor VII, factor VIII, factor IX, and von Willebrand factor). Are these effects linked or do we need age specific ranges? Do acquired effects like other diseases or medications affect factor levels, and do the same risk thresholds apply in these instances? How do we assure that the level we are measuring is a true indication of the patient’s average baseline level and not a transient change? Fibrinogen, factor VIII, and von Willebrand factor are all strong acute-phase reactants.

Risk of bleeding associated with coagulation factor levels increases with roughly log decreases in factor levels. Compared to normal (100%), 60% to 90% decreases in a coagulation factor may be associated with excess bleeding with major trauma, 95% to 98% decreases with minor trauma, and .99% decrease with spontaneous hemorrhage. In contrast, the difference between low risk and high risk for thrombosis may be separated by as little as 15% above normal.

It may be possible to define relative cutoffs for specific factors, for example, 50% above the mean level determined locally in healthy subjects for a certain factor. Before coagulation factor levels can be routinely used to assess individual risk, work must be done to better standardize and calibrate the assays used.

Detailed discussion of the rationale for each of these recommendations is presented in the article. This is an evolving area of research. While routine use of factor level measurements is not recommended, improvements in assay methodology and further clinical studies may change these recommendations in the future.

Chandler WL, Rodgers GM, Sprouse JT, Thompson AR.  Elevated Hemostatic Factor Levels as Potential Risk Factors for Thrombosis.  Arch Pathol Lab Med. 2002;126:1405–1414

Model System for Hemostatic Behavior

This study explores the behavior of a model system in response to perturbations in

  • tissue factor
  • thrombomodulin surface densities
  • tissue factor site dimensions
  • wall shear rate.

The classic time course is characterized by

  • initiation and
  • amplification of thrombin generation
  • the existence of threshold-like responses

This author defines a new parameter, the „effective prothrombotic zone‟,  and its dependence on model parameters. It was found that prothrombotic effects may extend significantly beyond the dimensions of the spatially discrete site of tissue factor expression in both axial and radial directions. Furthermore, he takes advantage of the finite element modeling approach to explore the behavior of systems containing multiple spatially distinct sites of TF expression in a physiologic model. The computational model is applied to assess individualized thrombotic risk from clinical data of plasma coagulation factor levels. He proposes a systems-based parameter with deep venous thrombosis using computational methods in combination with biochemical panels to predict hypercoagulability for high risk populations.

 

The Vascular Surface

The ‘resting’ endothelium synthesizes and presents a number of antithrombogenic molecules including

  • heparan sulfate proteoglycans
  • ecto-adenosine diphosphatase
  • prostacyclin
  • nitric oxide
  • thrombomodulin.

In response to various stimuli

  • inflammatory mediators
  • hypoxia
  • oxidative stress
  • fluid shear stress

the cell surface becomes ‘activated’ and serves to organize membrane-associated enzyme complexes of coagulation.

Fluid Phase Models of Coagulation

Leipold et al. developed a model of the tissue factor pathway as a design aid for the development of exogenous serine protease inhibitors. In contrast, Guo et al. focused on the reactions of the contact, or intrinsic pathway, to study parameters relevant to material-induced thrombosis, including procoagulant surface area.

Alternative approaches to modeling the coagulation cascade have been pursued including the use of stochastic activity networks to represent the intrinsic, extrinsic, and common pathways through fibrin formation and a kinetic Monte Carlo simulation of TF-initiated thrombin generation. Generally, fluid phase models of the kinetics of coagulation are both computationally and experimentally less complex. As such, the computational models are able to incorporate a large number of species and their reactions, and empirical data is often available for regression analysis and model validation. The range of complexity and motivations for these models is wide, and the models have been used to describe various phenomena including the ‘all-or-none’ threshold behavior of thrombin generation. However, the role of blood flow in coagulation is well recognized in promoting the delivery of substrates to the vessel wall and in regulating the thrombin response by removing activated clotting factors.

Flow Based Models of Coagulation

In 1990, Basmadjian presented a mathematical analysis of the effect of flow and mass transport on a single reactive event at the vessel wall and consequently laid the foundation for the first flow-based models of coagulation. It was proposed that for vessels greater than 0.1 mm in diameter, reactive events at the vessel wall could be adequately described by the assumption of a concentration boundary layer very close to the reactive surface, within which the majority of concentration changes took place. The height of the boundary layer and the mass transfer coefficient that described transport to and from the vessel wall were shown to stabilize on a time scale much shorter than the time scale over which concentration changes were empirically observed. Thus, the vascular space could be divided into two compartments, a boundary volume and a bulk volume, and furthermore, changes within the bulk phase could be considered negligible, thereby reducing the previously intractable problem to a pseudo-one compartment model described by a system of ordinary differential equations.

Basmadjian et al. subsequently published a limited model of six reactions, including two positive feedback reactions and two inhibitory reactions, of the common pathway of coagulation triggered by exogenous factor IXa under flow. As a consequence of the definition of the mass transfer coefficient, the kinetic parameters were dependent on the boundary layer height. Furthermore, the model did not explicitly account for intrinsic tenase or prothrombinase formation, but rather derived a rate expression for reaction in the presence of a cofactor. The major finding of the study was the predicted effect of increased mass transport to enhance thrombin generation by decreasing the induction time up to a critical mass transfer rate, beyond which transport significantly decreased peak thrombin levels thereby reducing overall thrombin production.

Kuharsky and Fogelson formulated a more comprehensive, pseudo-one compartment model of tissue factor-initiated coagulation under flow, which included the description of 59 distinct fluid- and surface-bound species. In contrast to the Baldwin-Basmadjian model, which defined a mass transfer coefficient as a rate of transport to the vessel surface, the Kuharsky-Fogelson model defined the mass transfer coefficient as a rate of transport into the boundary volume, thus eliminating the dependence of kinetic parameters on transport parameters. The computational study focused on the threshold response of thrombin generation to the availability of membrane binding sites. Additionally, the model suggested that adhered platelets may play a role in blocking the activity of the TF/ VIIa complex. Fogelson and Tania later expanded the model to include the protein C and TFPI pathways.

Modeling surface-associated reactions under flow uses finite element method (FEM), which is a technique for solving partial differential equations by dividing the vascular space into a finite number of discrete elements. Hall et al. used FEM to simulate factor X activation over a surface presenting TF in a parallel plate flow reactor. The steady state model was defined by the convection-diffusion equation and Michaelis-Menten reaction kinetics at the surface. The computational results were compared to experimental data for the generation of factor Xa by cultured rat vascular smooth muscle cells expressing TF.

Based on discrepancies between numerical and experimental studies, the catalytic activity of the TF/ VIIa complex may be shear-dependent. Towards the overall objective of developing an antithrombogenic biomaterial, Tummala and Hall studied the kinetics of factor Xa inhibition by surface-immobilized recombinant TFPI under unsteady flow conditions. Similarly, Byun et al. investigated the association and dissociation kinetics of ATIII inactivation of thrombin accelerated by surface-immobilized heparin under steady flow conditions. To date, finite element models that detail surface-bound reactions under flow have been restricted to no more than a single reaction catalyzed by a single surface-immobilized species.

 

Models of Coagulation Incorporating Spatial Parameter

Major findings include the roles of these specific coagulation pathways in the

  • initiation
  • amplification
  • termination phases of coagulation.

Coagulation near the activating surface was determined by TF/VIIa catalyzed factor Xa production, which was rapidly inhibited close to the wall. In contrast, factor IXa diffused farther from the surface, and thus factor Xa generation and clot formation away from the reactive wall was dependent on intrinsic tenase (IXa/ VIIIa) activity. Additionally, the concentration wave of thrombin propagated away from the activation zone at a rate which was dependent on the efficiency of inhibitory mechanisms.

Experimental and ‘virtual’ addition of plasma-phase thrombomodulin resulted in dose-dependent termination of thrombin generation and provided evidence of spatial localization of clot formation by TM with final clot lengths of 0.2-2 mm under diffusive conditions.

These studies provide an interesting analysis of the roles of specific factors in relation to space due to diffusive effects, but neglect the essential role of blood flow in the transport analysis. Additionally, the spatial dynamics of clot localization by thrombomodulin would likely be affected by restricting the inhibitor to its physiologic site on the vessel surface.

Finite Element Modeling

Finite element method (FEM) is a numerical technique for solving partial differential equations. Originally proposed in the 1940s to approach structural analysis problems in civil engineering, FEM now finds application in a wide variety of disciplines. The computational method relies on mesh discretization of a continuous domain which subdivides the space into a finite number of ‘elements’. The physics of each element are defined by its own set of physical properties and boundary conditions, and the simultaneous solution of the equations describing the individual elements approximate the behavior of the overall domain.

Sumanas W. Jordan, PhD Thesis. A Mathematical Model of Tissue Factor-Induced Blood Coagulation: Discrete Sites of Initiation and Regulation under Conditions of Flow.

Doctor of Philosophy in Biomedical Engineering. Emory University, Georgia Institute of Technology. May 2010.  Under supervision of: Dr. Elliot L. Chaikof, Departments of Surgery and Biomedical Engineering.

Blood Coagulation (Thrombin) and Protein C Pat...

Blood Coagulation (Thrombin) and Protein C Pathways (Blood_Coagulation_and_Protein_C_Pathways.jpg) (Photo credit: Wikipedia)

Coagulation cascade

Coagulation cascade (Photo credit: Wikipedia)

 

Cardiovascular Physiology: Modeling, Estimation and Signal Processing

With cardiovascular diseases being among the main causes of death in the world, quantitative modeling, assessment and monitoring of cardiovascular dynamics, and functioning play a critical role in bringing important breakthroughs to cardiovascular care. Quantification of cardiovascular physiology and its control mechanisms from physiological recordings, by use of mathematical models and algorithms, has been proved to be of important value in understanding the causes of cardiovascular diseases and assisting the diagnostic and prognostic process. This E-Book is derived from the Frontiers in Computational Physiology and Medicine Research Topic entitled “Engineering Approaches to Study Cardiovascular Physiology: Modeling, Estimation and Signal Processing.”

There are two review articles. The first review article by Chen et al. (2012) presents a unified point process probabilistic framework to assess heart beat dynamics and autonomic cardiovascular control. Using clinical recordings of healthy subjects during Propofol anesthesia, the authors demonstrate the effectiveness of their approach by applying the proposed paradigm to estimate

  • instantaneous heart rate (HR),
  • heart rate variability (HRV),
  • respiratory sinus arrhythmia (RSA)
  • baroreflex sensitivity (BRS).

The second review article, contributed by Zhang et al. (2011), provides a comprehensive overview of tube-load model parameter estimation for monitoring arterial hemodynamics.

The remaining eight original research articles can be mainly classified into two categories. The two articles from the first category emphasize modeling and estimation methods. In particular, the paper “Modeling the autonomic and metabolic effects of obstructive sleep apnea: a simulation study” by Cheng and Khoo (2012), combines computational modeling and simulations to study the autonomic and metabolic effects of obstructive sleep apnea (OSA).

The second paper, “Estimation of cardiac output and peripheral resistance using square-wave-approximated aortic flow signal” by Fazeli and Hahn (2012), presents a model-based approach to estimate cardiac output (CO) and total peripheral resistance (TPR), and validates the proposed approach via in vivo experimental data from animal subjects.

The six articles in the second category focus on application of signal processing techniques and statistical tools to analyze cardiovascular or physiological signals in practical applications. the paper “Modulation of the sympatho-vagal balance during sleep: frequency domain study of heart rate variability and respiration” by Cabiddu et al. (2012), uses spectral and cross-spectral analysis of heartbeat and respiration signals to assess autonomic cardiac regulation and cardiopulmonary coupling variations during different sleep stages in healthy subjects.

The paper “increased non-gaussianity of heart rate variability predicts cardiac mortality after an acute myocardial infarction” by Hayano et al. (2011) uses a new non-gaussian index to assess the HRV of cardiac mortality using 670 post-acute myocardial infarction (AMI) patients. the paper “non-gaussianity of low frequency heart rate variability and sympathetic activation: lack of increases in multiple system atrophy and parkinson disease” by Kiyono et al. (2012), applies a non-gaussian index to assess HRV in patients with multiple system atrophy (MSA) and parkinson diseases and reports the relation between the non-gaussian intermittency of the heartbeat and increased sympathetic activity. The paper “Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings” by Faes et al. (2011), proposes an information domain approach to evaluate nonlinear causality among heartbeat, arterial pressure, and respiration measures during tilt testing and paced breathing protocols. The paper “integrated central-autonomic multifractal complexity in the heart rate variability of healthy humans” by Lin and Sharif (2012), uses a relative multifractal complexity measure to assess HRV in healthy humans and discusses the related implications in central autonomic interactions. Lastly, the paper “Time scales of autonomic information flow in near-term fetal sheep” by Frasch et al. (2012), analyzes the autonomic information flow (AIF) with kullback–leibler entropy in fetal sheep as a function of vagal and sympathetic modulation of fetal HRV during atropine and propranolol blockade.

In summary, this Research Topic attempts to give a general panorama of the possible state-of-the-art modeling methodologies, practical tools in signal processing and estimation, as well as several important clinical applications, which can altogether help deepen our understanding about heart physiology and pathology and further lead to new scientific findings. We hope that the readership of Frontiers will appreciate this collected volume and enjoy reading the presented contributions. Finally, we are grateful to all contributed authors, reviewers, and editorial staffs who had all put tremendous effort to make this E-Book a reality.

Cabiddu, R., Cerutti, S., Viardot, G., Werner, S., and Bianchi, A. M. (2012). Modulation of the sympatho-vagal balance during sleep: frequency domain study of heart rate variability and respiration. Front. Physio. 3:45. doi: 10.3389/fphys.2012.00045

Chen, Z., Purdon, P. L., Brown, E. N., and Barbieri, R. (2012). A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control. Front. Physio. 3:4. doi: 10.3389/fphys.2012.00004

Cheng, L., and Khoo, M. C. K. (2012). Modeling the autonomic and metabolic effects of obstructive sleep apnea: a simulation study. Front. Physio. 2:111. doi: 10.3389/fphys.2011.00111

Faes, L., Nollo, G., and Porta, A. (2011). Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings. Front. Physio. 2:80. doi: 10.3389/fphys.2011.00080

Fazeli, N., and Hahn, J.-O. (2012). Estimation of cardiac output and peripheral resistance using square-wave-approximated aortic flow signal. Front. Physio. 3:298. doi: 10.3389/fphys.2012.00298

Frasch, M. G., Frank, B., Last, M., and Müller, T. (2012). Time scales of autonomic information flow in near-term fetal sheep. Front. Physio. 3:378. doi: 10.3389/fphys.2012.00378

Hayano, J., Kiyono, K., Struzik, Z. R., Yamamoto, Y., Watanabe, E., Stein, P. K., et al. (2011). Increased non-gaussianity of heart rate variability predicts cardiac mortality after an acute myocardial infarction. Front. Physio. 2:65. doi: 10.3389/fphys.2011.00065

Kiyono, K., Hayano, J., Kwak, S., Watanabe, E., and Yamamoto, Y. (2012). Non-Gaussianity of low frequency heart rate variability and sympathetic activation: lack of increases in multiple system atrophy and Parkinson disease. Front. Physio. 3:34. doi: 10.3389/fphys.2012.00034

Lin, D. C., and Sharif, A. (2012). Integrated central-autonomic multifractal complexity in the heart rate variability of healthy humans. Front. Physio. 2:123. doi: 10.3389/fphys.2011.00123

Zhang, G., Hahn, J., and Mukkamala, R. (2011). Tube-load model parameter estimation for monitoring arterial hemodynamics. Front. Physio. 2:72. doi: 10.3389/fphys.2011.00072

Citation: Chen Z and Barbieri R (2012) Editorial: engineering approaches to study cardiovascular physiology: modeling, estimation, and signal processing. Front. Physio. 3:425. doi: 10.3389/fphys.2012.00425

fluctuations of cerebral blood flow and metabolic demand following hypoxia in neonatal brain

Most of the research investigating the pathogenesis of perinatal brain injury following hypoxia-ischemia has focused on excitotoxicity, oxidative stress and an inflammatory response, with the response of the developing cerebrovasculature receiving less attention. This is surprising as the presentation of devastating and permanent injury such as germinal matrix-intraventricular haemorrhage (GM-IVH) and perinatal stroke are of vascular origin, and the origin of periventricular leukomalacia (PVL) may also arise from poor perfusion of the white matter. This highlights that cerebrovasculature injury following hypoxia could primarily be responsible for the injury seen in the brain of many infants diagnosed with hypoxic-ischemic encephalopathy (HIE).

The highly dynamic nature of the cerebral blood vessels in the fetus, and the fluctuations of cerebral blood flow and metabolic demand that occur following hypoxia suggest that the response of blood vessels could explain both regional protection and vulnerability in the developing brain.

This review discusses the current concepts on the pathogenesis of perinatal brain injury, the development of the fetal cerebrovasculature and the blood brain barrier (BBB), and key mediators involved with the response of cerebral blood vessels to hypoxia.

Baburamani AA, Ek CJ, Walker DW and Castillo-Melendez M. Vulnerability of the developing brain to hypoxic-ischemic damage: contribution of the cerebral vasculature to injury and repair? Front. Physio. 2012;  3:424. doi: 10.3389/fphys.2012.00424

remodeling of coronary and cerebral arteries and arterioles 

Effects of hypertension on arteries and arterioles often manifest first as a thickened wall, with associated changes in passive material properties (e.g., stiffness) or function (e.g., cellular phenotype, synthesis and removal rates, and vasomotor responsiveness). Less is known, however, regarding the relative evolution of such changes in vessels from different vascular beds.

We used an aortic coarctation model of hypertension in the mini-pig to elucidate spatiotemporal changes in geometry and wall composition (including layer-specific thicknesses as well as presence of collagen, elastin, smooth muscle, endothelial, macrophage, and hematopoietic cells) in three different arterial beds, specifically aortic, cerebral, and coronary, and vasodilator function in two different arteriolar beds, the cerebral and coronary.

Marked geometric and structural changes occurred in the thoracic aorta and left anterior descending coronary artery within 2 weeks of the establishment of hypertension and continued to increase over the 8-week study period. In contrast, no significant changes were observed in the middle cerebral arteries from the same animals. Consistent with these differential findings at the arterial level, we also found a diminished nitric oxide-mediated dilation to adenosine at 8 weeks of hypertension in coronary arterioles, but not cerebral arterioles.

These findings, coupled with the observation that temporal changes in wall constituents and the presence of macrophages differed significantly between the thoracic aorta and coronary arteries, confirm a strong differential progressive remodeling within different vascular beds.

These results suggest a spatiotemporal progression of vascular remodeling, beginning first in large elastic arteries and delayed in distal vessels.

Hayenga HN, Hu J-J, Meyer CA, Wilson E, Hein TW, Kuo L and Humphrey JD  Differential progressive remodeling of coronary and cerebral arteries and arterioles in an aortic coarctation model of hypertension. Front. Physio. 2012; 3:420. doi: 10.3389/fphys.2012.00420

C-reactive protein oxidant-mediated release of pro-thrombotic  factor

Inflammation and the generation of reactive oxygen species (ROS) have been implicated in the initiation and progression of atherosclerosis. Although C-reactive protein (CRP) has traditionally been considered to be a biomarker of inflammation, recent in vitro and in vivo studies have provided evidence that CRP, itself, exerts pro-thrombotic effects on vascular cells and may thus play a critical role in the development of atherothrombosis. Of particular importance is that CRP interacts with Fcγ receptors on cells of the vascular wall giving rise to the release of pro-thrombotic factors. The present review focuses on distinct sources of CRP-mediated ROS generation as well as the pivotal role of ROS in CRP-induced tissue factor expression. These studies provide considerable insight into the role of the oxidative mechanisms in CRP-mediated stimulation of pro-thrombotic factors and activation of platelets. Collectively, the available data provide strong support for ROS playing an important intermediary role in the relationship between CRP and atherothrombosis.

Zhang Z, Yang Y, Hill MA and Wu J.  Does C-reactive protein contribute to atherothrombosis via oxidant-mediated release of pro-thrombotic factors and activation of platelets? Front. Physio.  2012; 3:433. doi: 10.3389/fphys.2012.00433

CRP association with Peripheral Vascular Disease

To determine whether the increase in plasma levels of C-Reactive Protein (CRP), a non-specifi c reactant in the acute-phase of systemic infl ammation, is associated with clinical severity of peripheral arterial disease (PAD).

This is a cross-sectional study at a referral hospital center of institutional practice in Madrid, Spain.  These investigators took a stratifi ed random sampling of 3370 patients with symptomatic PAD from the outpatient vascular laboratory database in 2007 in the order of their clinical severity:

  • the fi rst group of patients with mild chronological clinical severity who did not require surgical revascularization,
  • the second group consisted of patients with moderate clinical severity who had only undergone only one surgical revascularization procedure and
  • the third group consisted of patients who were severely affected and had undergone two or more surgical revascularization procedures of the lower extremities in different areas or needed late re-interventions.

The Neyman affi xation was used to calculate the sample size with a fi xed relative error of 0.1.

A homogeneity analysis between groups and a unifactorial analysis of comparison of medians for CRP was done.

The groups were homogeneous for

  • age
  • smoking status
  • Arterial Hypertension
  • diabetes mellitus
  • dyslipemia
  • homocysteinemia and
  • specifi c markers of infl ammation.

In the unifactorial analysis of multiple comparisons of medians according to Scheffé, it was observed that

the median values of CRP plasma levels were increased in association with higher clinical severity of PAD

  • 3.81 mg/L [2.14–5.48] vs.
  • 8.33 [4.38–9.19] vs.
  • 12.83 [9.5–14.16]; p  0.05

as a unique factor of tested ones.

Plasma levels of CRP are associated with not only the presence of atherosclerosis but also with its chronological clinical severity.

De Haro J, Acin F, Medina FJ, Lopez-Quintana A, and  March JR.  Relationship Between the Plasma Concentration of C-Reactive Protein and Severity of Peripheral Arterial Disease.
Clinical Medicine: Cardiology 2009;3: 1–7

Hemostasis induced by hyperhomocysteinemia

Elevated concentration of homocysteine (Hcy) in human tissues, defined as hyperhomocysteinemia has been correlated with some diseases, such as

  • cardiovascular
  • neurodegenerative
  • kidney disorders

L-Homocysteine (Hcy) is an endogenous amino acid, containing a free thiol group, which in healthy cells is involved in methionine and cysteine synthesis/resynthesis. Indirectly, Hcy participates in methyl, folate, and cellular thiol metabolism. Approximately 80% of total plasma Hcy is protein-bound, and only a small amount exists as a free reduced Hcy (about 0.1 μM). The majority of the unbound fraction of Hcy is oxidized, and forms dimers (homocystine) or mixed disulphides consisting of cysteine and Hcy.

Two main pathways of Hcy biotoxicity are discussed:

  1. Hcy-dependent oxidative stress – generated during oxidation of the free thiol group of Hcy. Hcy binds via a disulphide bridge with

—     plasma proteins

—     or with other low-molecular plasma  thiols

—     or with a second Hcy molecule.

Accumulation of oxidized biomolecules alters the biological functions of many cellular pathways.

  1. Hcy-induced protein structure modifications, named homocysteinylation.

Two main types of homocysteinylation exist: S-homocysteinylation and N-homocysteinylation; both considered as posttranslational protein modifications.

a)      S-homocysteinylation occurs when Hcy reacts, by its free thiol group, with another free thiol derived from a cysteine residue in a protein molecule.

These changes can alter the thiol-dependent redox status of proteins.

b)      N-homocysteinylation takes place after acylation of the free ε-amino lysine groups of proteins by the most reactive form of Hcy — its cyclic thioester (Hcy thiolactone — HTL), representing up to 0.29% of total plasma Hcy.

Homocysteine occurs in human blood plasma in several forms, including the most reactive one, the homocysteine thiolactone (HTL) — a cyclic thioester, which represents up to 0.29% of total plasma Hcy. In human blood, N-homocysteinylated (N-Hcy-protein) and S-homocysteinylated proteins (S-Hcy-protein) such as NHcy-hemoglobin, N-(Hcy-S-S-Cys)-albumin, and S-Hcyalbumin are known. Other pathways of Hcy biotoxicity might be apoptosis and excitotoxicity mediated through glutamate receptors. The relationship between homocysteine and risk appears to hold for total plasma concentrations of homocysteine between 10 and 30 μM.

Different forms of homocysteine present in human blood.

*Total level of homocysteine — the term “total homocysteine” describes the pool of homocysteine released by reduction of all disulphide bonds in the sample (Perla-Kajan et al., 2007; Zimny, 2008; Manolescu et al., 2010, modified).

The form of Hcy The concentration in human blood
Homocysteine thiolactone (HTL) 0–35 nM
Protein N-linked homocysteine:
N-Hcy-hemoglobin, N-(Hcy-S-S-Cys)-albumin
about 15.5 μM: 12.7 μM, 2.8 μM
Protein S-linked homocysteine — S-Hcy-albumin about 7.3 μM*
Homocystine (Hcy-S-S-Hcy) and combined with cysteine to from mixed disulphides (Hcy-S-S-Cys) about 2 μM*
Free reduced Hcy about 0.1 μM*

As early as in the 1960s it was noted that the risk of atherosclerosis is markedly increased in patients with homocystinuria, an inherited disease resulting from homozygous CBS deficiency and characterized by episodes of

—     thromboembolism

—     mental retardation

—     lens dislocation

—     hepatic steatosis

—     osteoporosis.

—     very high concentrations of plasma homocysteine and methionine.

Patients with homocystinuria have very severe hyperhomocysteinemia, with plasma homocysteine concentration reaching even 400 μM, and represent a very small proportion of the population (approximately 1 in 200,000 individuals). Heterozygous lack of CBS, CBS mutations and polymorphism of the methylenetetrahydrofolate reductase gene are considered to be the most probable causes of hyperhomocysteinemia.

The effects of hyperhomocysteinemia include the complex process of hemostasis, which regulates the properties of blood flow. Interactions of homocysteine and its different derivatives, including homocysteine thiolactone, with the major components of hemostasis are:

  • endothelial cells
  • platelets
  • fibrinogen
  • plasminogen

Elevated plasma Hcy (>15 μM; Hcy) is associated with an increased risk of cardiovascular diseases

  • thrombosis
  • thrombosis related diseases
  • ischemic brain stroke (independent of other, conventional risk factors of this disease)

Every increase of 2.5 μM in plasma Hcy may be associated with an increase of stroke risk of about 20%.  Total plasma Hcy level above 20 μM are associated with a nine-fold increase of the myocardial infarction and stroke risk, in comparison to the concentrations below 9 μM. The increase of Hcy concentration has been also found in other human pathologies, including neurodegenerative diseases

Modifications of hemostatic proteins (N-homocysteinylation or S-homocysteinylation) induced by Hcy or its thiolactone seem to be the main cause of homocysteine biotoxicity in hemostatic abnormalities.

Hcy and HTL may act as oxidants, but various polyphenolic antioxidants are able to inhibit the oxidative damage induced by Hcy or HTL. Therefore, we have to consider the role of phenolic antioxidants in hyperhomocysteinemia –induced changes in hemostasis.

The synthesis of homocysteine thiolactone is associated with the activation of the amino acid by aminoacyl-tRNA synthetase (AARS). Hcy may also undergo erroneous activation, e.g. by methionyl-t-RNA synthetase (MetRS). In the first step of conversion of Hcy to HTL, MetRS misactivates Hcy giving rise to homocysteinyl-adenylate. In the next phase, the homocysteine side chain thiol group reacts with the activated carboxyl group and HTL is produced. The level of HTL synthesis in cultured cells depends on Hcy and Met levels.

Hyperhomocysteinemia and Changes in Fibrinolysis and Coagulation Process

The fibrinolytic activity of blood is regulated by specific inhibitors; the inhibition of fibrinolysis takes place at the level of plasminogen activation (by PA-inhibitors: plasminogen activator inhibitor type-1, -2; PAI-1 or PAI-2) or at the level of plasmin activity (mainly by α2-antiplasmin). Hyperhomocysteinemia disturbs hemostasis and shifts the hemostatic mechanisms in favor of thrombosis. The recent reports indicate that the prothrombotic state observed in hyperhomocysteinemia may arise not only due to endothelium dysfunction or blood platelet and coagulation activation, but also due to impaired fibrinolysis. Hcy-modified fibrinogen is more resistant to the fibrinolytic action. Oral methionine load increases total Hcy, but may diminish the fibrinolytic activity of the euglobulin plasma fraction. Homocysteine-lowering therapies may increase fibrinolytic activity, thereby, prevent atherothrombotic events in patients with cardiovascular diseases after the first myocardial infarction.

Homocysteine — Fibronectin Interaction and its Consequences

Fibronectin (Fn) plays key roles in

  • cell adhesion
  • migration
  • embryogenesis
  • differentiation
  • hemostasis
  • thrombosis
  • wound healing
  • tissue remodeling

Interaction of FN with fibrin, mediated by factor XIII transglutaminase, is thought to be important for cell adhesion or cell migration into fibrin clots. After tissue injury, a blood clot formation serves the dual role of restoring vascular integrity and serving as a temporary scaffold for the wound healing process. Fibrin and plasma FN, the major protein components of blood clots, are essential to perform these functions. In the blood clotting process, after fibrin deposition, plasma FN-fibrin matrix is covalently crosslinked, and it then promotes fibroblast adhesion, spreading, and migration into the clot.

Homocysteine binds to several human plasma proteins, including fibronectin. If homocysteine binds to fibronectin via a disulphide linkage, this binding results in a functional change, namely, the inhibition of fibrin binding by fibronectin. This inhibition may lead to a prolonged recovery from a thrombotic event and contribute to vascular occlusion.

Grape seeds are one of the richest plant sources of phenolic substances, and grape seed extract reduces the toxic effect of Hcys and HTL on fibrinolysis. The grape seed extract (12.5–50 μg/ml) supported plasminogen to plasmin conversion inhibited by Hcys or HTL. In vitro experiments showed in the presence of grape seed extract (at the highest tested concentration — 50 μg/ml) the increase of about 78% (for human plasminogen-treated with Hcys) and 56% (for human plasma-treated with Hcys). Thus, in the in vitro model system, that the grape seed extract (12.5–50 μg/ml) diminished the reduction of thiol groups and of lysine ε-amino groups in plasma proteins treated with Hcys (0.1 mM) or HTL (1 μM). In the presence of the grape seed extract at the concentration of 50 μg/ml, the level of reduction of thiol groups reached about 45% (for plasma treated with Hcys) and about 15% (for plasma treated with HTL).

In the presence of the grape seed extract at the concentration of 50 μg/ml, the level of reduction of thiol groups reached about 45% (for plasma treated with Hcys) and about 15% (for plasma treated with HTL).Very similar protective effects of the grape seed extract were observed in the measurements of lysine ε-amino groups in plasma proteins treated with Hcys or HTL. These results indicated that the extract from berries of Aronia melanocarpa (a rich source of phenolic substances) reduces the toxic effects of Hcy and HTL on the hemostatic properties of fibrinogen and plasma. These findings indicate a possible protective action of the A. melanocarpa extract in hyperhomocysteinemia-induced cardiovascular disorders. Moreover, the extract from berries of A. melanocarpa, due to its antioxidant action, significantly attenuated the oxidative stress (assessed by measuring of the total antioxidant status — TAS) in plasma in a model of hyperhomocysteinemia.

Proposed model for the protective role of phenolic antioxidants on selected elements of hemostasis during hyperhomocysteinemia.

various antioxidants (present in human diet), including phenolic compounds, may reduce the toxic effects of Hcy or its derivatives on hemostasis. These findings give hope for the develop development of dietary supplements, which will be capable of preventing thrombosis which occurs under pathological conditions, observed also in hyperhomocysteinemia, such as plasma procoagulant activity and oxidative stress.

Malinowska J,  Kolodziejczyk J and Olas B. The disturbance of hemostasis induced by hyper-homocysteinemia; the role of antioxidants. Acta Biochimica Polonica 2012; 59(2): 185–194.

Lipoprotein (a)

Lipoprotein (a) (Lp(a)), for the first time described in 1963 by Berg belongs to the lipoproteins with the strongest atherogenic effect. Its importance for the development of various atherosclerotic vasculopathies (coronary heart disease, ischemic stroke, peripheral vasculopathy, abdominal aneurysm) was recognized considerably later.

Lipoprotein(a) (Lp(a)), an established risk marker of cardiovascular diseases, is independent from other risk markers. The main difference of Lp(a) compared to low density lipoprotein (LDL) is the apo(a) residue, covalently bound to apoB is covalently by a disulfide-bridge. Apo(a) synthesis is performed in the liver, probably followed by extracellular assembly to the apoB location of the LDL.

 

ApoB-100_______LDL¬¬___ S-S –    9

Apo(a) has been detected bound to triglyceride-rich lipoproteins (Very Low Density Lipoproteins; VLDL). Corresponding to the structural similarity to LDL, both particles are very similar to each other with regard to their composition. It is a glycoprotein which underlies a large genetic polymorphism caused by a variation of the kringle-IV-type-2 repeats of the protein, characterized by a structural homology to plasminogen. Apo(a)’s structural homology to plasminogen, shares the gene localization on chromosome 6. The kringle repeats present a particularly characteristic structure, which have a high similarity to kringle IV (K IV) of plasminogen. Apo(a) also has a kringle V structure of plasminogen and also a protease domain, which cannot be activated, as opposed to the one of plasminogen. At least 30 genetically determined apo(a) isoforms were identified in man.

Features:

  • Non covalent binding of kringle -4 types 7 and 8 of apo (a) to apo B
  • Disulfide bond at Cys4326 of ApoB (near its receptor binding domain ) and the only free cysteine group in K –IV type 9 (Cys4057) of apo(a )
  • Binding to fibrin and cell membranes
  • Enhancement by small isoforms ; high concentrations compared to plasminogen and homocysteine
  • Binding to different lysine rich components of the coagulation system (e. g. TFPI)
  • Intense homology to plasminogen but no protease activity
ApoB-100_______LDL¬¬___ S-S – 9

The synthesis of Lp(a), which thus occurs as part of an assembly, is a two-step process.

  • In a first step, which can be competitively inhibited by lysine analogues, the free sulfhydryl groups of apo(a) and apoB are brought close together.
  • The binding of apo(a) then occurs near the apoB domain which binds to the LDL receptor, resulting in a reduced affinity of Lp(a) to the LDL-receptor.

Particles that show a reduced affinity to the LDL receptor are not able to form stable compounds with apo(a). Thus the largest part of apo(a) is present as apo(a) bound to LDL. Only a small, quantitatively variable part of apo(a) remains as free apo(a) and probably plays an important role in the metabolism and physiological function of Lp(a).

The Lp(a) plasma concentration in the population is highly skewed and determined to more than 90 % by genetic factors. In healthy subjects the Lp(a)-concentration is correlated with its synthesis.

It is assumed that the kidney has a specific function in Lp(a) catabolizm, since nephrotic syndrome and terminal kidney failure are associated with an elevation of the Lp(a) plasma concentration. One consequence of the poor knowledge of the metabolic path of Lp(a) is the fact that so far pharmaceutical science has failed to develop drugs that are able to reduce elevated Lp(a) plasma concentrations to a desirable level.

Plasma concentrations of Lp(a) are affected by different diseases (e.g. diseases of liver and kidney), hormonal factors (e.g. sexual steroids, glucocorticoids, thyroid hormones), individual and environmental factors (e.g. age, cigarette smoking) as well as pharmaceuticals (e.g. derivatives of nicotinic acid) and therapeutic procedures (lipid apheresis). This review describes the physiological regulation of Lp(a) as well as factors influencing its plasma concentration.

Apart from its significance as an important agent in the development of atherosclerosis, Lp(a) has even more physiological functions, e.g. in

  • wound healing
  • angiogenesis
  • hemostasis

However, in the meaning of a pleiotropic mechanism the favorable action mechanisms are opposed by pathogenic mechanisms, whereby the importance of Lp(a) in atherogenesis is stressed.

Lp(a) in Atherosclerosis

In transgenic, hyperlipidemic and Lp(a) expressing Watanabe rabbits, Lp(a) leads to enhanced atherosclerosis. Under the influence of Lp(a), the binding of Lp(a) to glycoproteins, e.g. laminin, results – via its apo(a)-part – both in

  • an increased invasion of inflammatory cells and in
  • an activation of smooth vascular muscle cells

with subsequent calcifications in the vascular wall.

The inhibition of transforming growth factor-β1 (TGF-β1) activation is another mechanism via which Lp(a) contributes to the development of atherosclerotic vasculopathies. TGF-β1 is subject to proteolytic activation by plasmin and its active form leads to an inhibition of the proliferation and migration of smooth muscle cells, which play a central role in the formation and progression of atherosclerotic vascular diseases.

In man, Lp(a) is an important risk marker which is independent of other risk markers. Its importance, partly also under consideration of the molecular weight and other genetic polymorphisms, could be demonstrated by a high number of epidemiological and clinical studies investigating the formation and progression of atherosclerosis, myocardial infarction, and stroke.

Lp(a) in Hemostasis

Lp(a) is able to competitively inhibit the binding of plasminogen to fibrinogen and fibrin, and to inhibit the fibrin-dependent activation of plasminogen to plasmin via the tissue plasminogen activator, whereby apo(a) isoforms of low molecular weight have a higher affinity to fibrin than apo(a) isoforms of higher molecular weight. Like other compounds containing sulfhydryl groups, homocysteine enhances the binding of Lp(a) to fibrin.

Pleiotropic effect of Lp(a).

Prothrombotic :

  • Binding to fibrin
  • Competitive inhibition of plasminogen
  • Stimulation of plasminogen activator inhibitor I and II (PAI -I, PAI -II)
  • Inactivation of tissue factor pathway inhibitor (TFPI)

Antithrombotic :

  • Inhibition of platelet activating factor acetylhydrolase (PAF -AH)
  • Inhibition of platelet activating factor
  • Inhibition of collagen dependent platelet aggregation
  • Inhibition of secretion of serotonin und thromboxane

Lp(a) in Angiogenesis

Lp(a) is also important for the process of angiogenesis and the sprouting of new vessels.

  • angiogenesis starts with the remodelling of matrix proteins and
  • activation of matrix metalloproteinases (MMP).

The latter ones are usually synthesised as

  • inactive zymogens and
  • require activation by proteases,

Recall that Apo(a) is not activated by proteases. The angiogenesis is also accomplished by plasminogen. Lp(a) and apo(a) and its fragments has an antiangiogenetic and metastasis inhibiting effect related to the structural homology with plasminogen without the protease activity.

Siekmeier R, Scharnagl H, Kostner GM, T. Grammer T, Stojakovic T and März W.  Variation of Lp(a) Plasma Concentrations in Health and Disease.  The Open Clinical Chemistry Journal, 2010; 3: 72-89.

LDL-Apheresis

In 1985, Brown and Goldstein were awarded the Nobel Prize for medicine for their work on the regulation of cholesterol metabolism. On the basis of numerous studies, they were able to demonstrate that circulating low-density lipoprotein (LDL) is absorbed into the cell through receptor linked endocytosis. The absorption of LDL into the cell is specific and is mediated by a LDL receptor. In patients with familial hypercholesterolemia, this receptor is changed, and the LDL particles can no longer be recognized. Their absorption can thus no longer be mediated, leading to an accumulation of LDL in blood.

Furthermore, an excess supply of cholesterol also blocks the 3-hydrox-3 methylglutaryl-Co enzyme A (HMG CoA), reductase enzyme, which otherwise inhibits the cholesterol synthesis rate. Brown and Goldstein also determined the structure of the LDL receptor. They discovered structural defects in this receptor in many patients with familial hypercholesterolemia. Thus, familial hypercholesterolemia was the first metabolic disease that could be tracked back to the mutation of a receptor gene.

Dyslipoproteinemia in combination with diabetes mellitus causes a cumulative insult to the vasculature resulting in more severe disease which occurs at an earlier age in large and small vessels as well as capillaries. The most common clinical conditions resulting from this combination are myocardial infarction and lower extremity vascular disease. Ceriello et al. show an independent and cumulative effect of postprandial hypertriglyceridemia and hyperglycemia on endothelial function, suggesting oxidative stress as common mediator of such effect. The combination produces greater morbidity and mortality than either alone.

As an antiatherogenic factor, HDL cholesterol correlates inversely to the extent of postprandial lipemia. A high concentration of HDL is a sign that triglyceride-rich particles are quickly decomposed in the postprandial phase of lipemia. Conversely, with a low HDL concentration this decomposition is delayed. Thus, excessively high triglyceride concentrations are accompanied by very low HDL counts. This combination has also been associated with an increased risk of pancreatitis.

The importance of lipoprotein (a) (Lp(a)) as an atherogenic substance has also been recognized in recent years. Lp(a) is very similar to LDL. But it also contains Apo(a), which is very similar to plasminogen, enabling Lp(a) to bind to fibrin clots. Binding of plasminogen is prevented and fibrinolysis obstructed. Thrombi are integrated into the walls of the arteries and become plaque components.

Another strong risk factor for accelerated atherogenesis, which must be mentioned here, are the widespread high homocysteine levels found in dialysis patients. This risk factor is independent of classic risk factors such as high cholesterol and LDL levels, smoking, hypertension, and obesity, and much more predictive of coronary events in dialysis patients than are these better-known factors. Homocysteine is a sulfur aminoacid produced in the metabolism of methionine. Under normal conditions, about 50 percent of homocysteine is remethylated to methionine and the remaining via the transsulfuration pathway.

Defining hyperhomocysteinemia as levels greater than the 90th percentile of controls and elevated Lp(a) level as greater than 30mg/dL, the frequency of the combination increased with declining renal function. Fifty-eight percent of patients with a GFR less than 10mL/min had both hyperhomocysteinemia and elevated Lp(a) levels, and even in patients with mild renal impairment, 20 percent of patients had both risk factors present.

The prognosis of patients suffering from severe hyperlipidemia, sometimes combined with elevated lipoprotein (a) levels, and coronary heart disease refractory to diet and lipid-lowering drugs is poor. For such patients, regular treatment with low-density lipoprotein (LDL) apheresis is the therapeutic option. Today, there are five different LDL-apheresis systems available: cascade filtration or lipid filtration, immunoadsorption, heparin-induced LDL precipitation, dextran sulfate LDL adsorption, and the LDL hemoperfusion. The requirement that the original level of cholesterol is to be reduced by at least 60 percent is fulfilled by all these systems.

There is a strong correlation between hyperlipidemia and atherosclerosis. Besides the elimination of other risk factors, in severe hyperlipidemia therapeutic strategies should focus on a drastic reduction of serum lipoproteins. Despite maximum conventional therapy with a combination of different kinds of lipid-lowering drugs, sometimes the goal of therapy cannot be reached. Hence, in such patients, treatment with LDL-apheresis is indicated. Technical and clinical aspects of these five different LDL-apheresis methods are depicted. There were no significant differences with respect to or concerning all cholesterols, or triglycerides observed.

High plasma levels of Lp(a) are associated with an increased risk for atherosclerotic coronary heart       disease
(CHD) by a mechanism yet to be determined. Because of its structural properties, Lp(a) can have both atherogenic and thrombogenic potentials. The means for correcting the high plasma levels of Lp(a) are still limited in effectiveness. All drug therapies tried thus far have failed. The most effective therapeutic methods in lowering Lp(a) are the LDL-apheresismethods. Since 1993, special immunoadsorption polyclonal antibody columns (Pocard, Moscow, Russia) containing sepharose bound anti-Lp(a) have been available for the treatment of patients with elevated Lp(a) serum concentrations.

With respect to elevated lipoprotein (a) levels, however, the immunoadsorption method seems to be most effective. The different published data clearly demonstrate that treatment with LDL-apheresis in patients suffering from severe hyperlipidemia refractory to maximum conservative therapy is effective and safe in long-term application.

LDL-apheresis decreases not only LDL mass but also improves the patient’s life expectancy. LDL-apheresis performed with different techniques decreases the susceptibility of LDL to oxidation. This decrease may be related to a temporary mass imbalance between freshly produced and older LDL particles. Furthermore, the baseline fatty acid pattern influences pretreatment and postreatment susceptibility to oxidation.

Bambauer R, Bambauer C, Lehmann B, Latza R, and Ralf Schiel R. LDL-Apheresis: Technical and Clinical Aspects. The Scientific World Journal 2012; Article ID 314283, pp 1-19. doi:10.1100/2012/314283

Summary:  This discussion is a two part sequence that first establishes the known strong relationship between blood flow viscosity, shear stress, and plasma triglycerides (VLDL) as risk factors for hemostatic disorders leading to thromboembolic disease, and the association with atherosclerotic disease affecting the heart, the brain (via carotid blood flow), peripheral circulation,the kidneys, and retinopathy as well.

The second part discusses the modeling of hemostasis and takes into account the effects of plasma proteins involved with red cell and endothelial interaction, which is related to part I.  The current laboratory assessment of thrombophilias is taken from a consensus document of the American Society for Clinical Pathology.  The problems encountered are sufficient for the most common problems of coagulation testing and monitoring, but don’t address the large number of patients who are at risk for complications of accelerated vasoconstrictive systemic disease that precede serious hemostatic problems.  Special attention is given to Lp(a) and to homocysteine.  Lp(a) is a protein that has both prothrombotic and antithrombotic characteristics, and is a homologue of plasminogen and is composed of an apo(a) bound to LDL.  Unlike plasminogen, it has no protease activity.   Homocysteine elevation is a known risk factor for downstream myocardial infarct.  Homocysteine is a mirror into sulfur metabolism, so an increase is an independent predictor of risk, not fully discussed here.  The modification of risk is discussed by diet modification.  In the most serious cases of lipoprotein disorders, often including Lp(a) the long term use of LDL-apheresis is described.

see Relevent article that appears in NEJM from American College of Cardiology

Apolipoprotein(a) Genetic Sequence Variants Associated With Systemic Atherosclerosis and Coronary Atherosclerotic Burden but Not With Venous Thromboembolism

Helgadottir A, Gretarsdottir S, Thorleifsson G, et al

J Am Coll Cardiol. 2012;60:722-729

Study Summary

The LPA gene codes for apolipoprotein(a), which, when linked with low-density lipoprotein particles, forms lipoprotein(a) [Lp(a)] — a well-studied molecule associated with coronary artery disease (CAD). The Lp(a) molecule has both atherogenic and thrombogenic effects in vitro , but the extent to which these translate to differences in how atherothrombotic disease presents is unknown.

LPA contains many single-nucleotide polymorphisms, and 2 have been identified by previous groups as being strongly associated with levels of Lp(a) and, as a consequence, strongly associated with CAD. However, because atherosclerosis is thought to be a systemic disease, it is unclear to what extent Lp(a) leads to atherosclerosis in other arterial beds (eg, carotid, abdominal aorta, and lower extremity), as well as to other thrombotic disorders (eg, ischemic/cardioembolic stroke and venous thromboembolism). Such distinctions are important, because therapies that might lower Lp(a) could potentially reduce forms of atherosclerosis beyond the coronary tree.

To answer this question, Helgadottir and colleagues compiled clinical and genetic data on the LPA gene from thousands of previous participants in genetic research studies from across the world. They did not have access to Lp(a) levels, but by knowing the genotypes for 2 LPA variants, they inferred the levels of Lp(a) on the basis of prior associations between these variants and Lp(a) levels. [1] Their studies included not only individuals of white European descent but also a significant proportion of black persons, in order to widen the generalizability of their results.

Their main findings are that LPA variants (and, by proxy, Lp(a) levels) are associated with CAD,  peripheral arterial disease, abdominal aortic aneurysm, number of CAD vessels, age at onset of CAD diagnosis, and large-artery atherosclerosis-type stroke. They did not find an association with cardioembolic or small-vessel disease-type stroke; intracranial aneurysm; venous thrombosis; carotid intima thickness; or, in a small subset of individuals, myocardial infarction.

Viewpoint

The main conclusion to draw from this work is that Lp(a) is probably a strong causal factor in not only CAD, but also the development of atherosclerosis in other arterial trees. Although there is no evidence from this study that Lp(a) levels contribute to venous thrombosis, the investigators do not exclude a role for Lp(a) in arterial thrombosis.

Large-artery atherosclerosis stroke is thought to involve some element of arterial thrombosis or thromboembolism, [2] and genetic substudies of randomized trials of aspirin demonstrate that individuals with LPA variants predicted to have elevated levels of Lp(a) benefit the most from antiplatelet therapy. [3] Together, these data suggest that Lp(a) probably has clinically relevant effects on the development of atherosclerosis and arterial thrombosis.

Of  note, the investigators found no association between Lp(a) and carotid intima thickness, suggesting that either intima thickness is a poor surrogate for the clinical manifestations of atherosclerosis or that Lp(a) affects a distinct step in the atherosclerotic disease process that is not demonstrable in the carotid arteries.

Although Lp(a) testing is available, these studies do not provide any evidence that testing for Lp(a) is of clinical benefit, or that screening for atherosclerosis should go beyond well-described clinical risk factors, such as low-density lipoprotein cholesterol levels, high-density lipoprotein levels, hypertension, diabetes, smoking, and family history. Until evidence demonstrates that adding information on Lp(a) levels to routine clinical practice improves the ability of physicians to identify those at highest risk for atherosclerosis, Lp(a) testing should remain a research tool. Nevertheless, these findings do suggest that therapies to lower Lp(a) may have benefits that extend to forms of atherothrombosis beyond the coronary tree.

The finding of this study is interesting:

[1] It consistent with Dr. William LaFramboise..   examination specifically at APO B100, which is part of Lp(a) with some 14 candidate predictors for a more accurate exclusion of patients who don’t need intervention.          Apo B100 was not one of 5 top candidates.

William LaFramboise • Our study (http://www.ncbi.nlm.nih.gov/pubmed/23216991) comprised discovery research using targeted immunochemical screening of retrospective patient samples using both Luminex and Aushon platforms as opposed to shotgun proteomics. Hence the costs constrained sample numbers. Nevertheless, our ability to predict outcome substantially exceeded available methods:

The Framingham CHD scores were statistically different between groups (P <0.001, unpaired Student’s t test) but they classified only 16% of the subjects without significant CAD (10 of 63) at a 95% sensitivity for patients with CAD. In contrast, our algorithm incorporating serum values for OPN, RES, CRP, MMP7 and IFNγ identified 63% of the subjects without significant CAD (40 of 63) at 95% sensitivity for patients with CAD. Thus, our multiplex serum protein classifier correctly identified four times as many patients as the Framingham index.

This study is consistent with the concept of CAD, PVD, and atheromatous disease is a systemic vascular disease, but the point that is made is that it appears to have no relationship to venous thrombosis. The importance for predicting thrombotic events is considered serious.   The venous flow does not have the turbulence of large arteries, so the conclusion is no surprise.  The flow in capillary beds is a linear cell passage with minimal viscosity or turbulence.  The finding of no association with carotid artery disease  is interpreted to mean that the Lp(a) might be an earlier finding than carotid intimal thickness.  It is reassuring to find a recommendation for antiplatelet therapy for individuals with LPA variants based on randomized trials of aspirin substudies.

If that is the conclusion from the studies, and based on the strong association between the prothrombotic (pleiotropic) effect and the association with hyperhomocysteinemia, my own impression is that the recommendation is short-sighted.

[2]  Lp(a) is able to competitively inhibit the binding of plasminogen to fibrinogen and fibrin, and to inhibit the fibrin-dependent activation of plasminogen to plasmin via the tissue plasminogen activator, whereby apo(a) isoforms of low molecular weight have a higher affinity to fibrin than apo(a) isoforms of higher molecular weight. Like other compounds containing sulfhydryl groups, homocysteine enhances the binding of Lp(a) to fibrin.

Prothrombotic :

  • Binding to fibrin
  • Competitive inhibition of plasminogen
  • Stimulation of plasminogen activator inhibitor I and II (PAI -I, PAI -II)
  • Inactivation of tissue factor pathway inhibitor (TFPI)

Source for Lp(a)

Artherogenesis: Predictor of CVD – the Smaller and Denser LDL Particles

https://pharmaceuticalintelligence.com/2012/11/15/artherogenesis-predictor-of-cvd-the-smaller-and-denser-ldl-particles/

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