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Studies have shown that regular physical activity can contribute to longer life and less risk for serious health problems, such as heart disease, type 2 diabetes, obesity and some cancers. The Centers for Disease Control (CDC) continues to partner with national groups, states and communities to provide quality education around the physical activity.
An analysis, Adult Physical Inactivity Prevalence Maps by Race/Ethnicity, published on the CDC web site in January 2020 demonstrated that “all states and territories had more than 15 percent of adults who were physically inactive.” The analysis included state maps that used combined data from 2015 through 2018 with “noticeable differences in the prevalence of physical inactivity by race/ethnicity.” Physical inactivity is reported as “no leisure-time physical activity.”
Here are findings from their analysis:
The South (28.0%) had the highest prevalence of physical inactivity, followed by the Northeast (25.6%), Midwest (25.0%), and the West (20.5%).
In 7 states (Tennessee, Oklahoma, Louisiana, Alabama, Kentucky, Arkansas, and Mississippi), and 2 US territories (Puerto Rico, and Guam), 30% or more of adults were physically inactive.
In 4 states (Colorado, Washington, Utah, and Oregon) and the District of Columbia, 15% to less than 20% of adults were physically inactive.
In 24 states, 20% to less than 25% of adults were physically inactive.
In 15 states, 25% to less than 30% of adults were physically inactive.
More analysis showed:
Hispanics (31.7%) had the highest prevalence of physical inactivity, followed by non-Hispanic blacks (30.3%) and non-Hispanic whites (23.4%).
In the majority of states, non-Hispanic blacks and Hispanics had a significantly higher prevalence of inactivity than non-Hispanic whites.
5 states and Puerto Rico had a physical inactivity prevalence of 30% or higher among non-Hispanic white adults.
22 states and Puerto Rico had a physical inactivity prevalence of 30% or higher among Hispanic adults.
Artificial Intelligence and Cardiovascular Disease
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.
The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.
Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.
In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.
One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.
Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.
In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.
The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.
Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.
@Cleveland Clinic – Serial measurements of high-sensitivity C-reactive protein (hsCRP) post acute coronary syndrome (ACS) may help identify patients at higher risk for morbidity and mortality
Reporter: Aviva Lev-Ari, PhD, RN
Original Investigation
March 6, 2019
Association of Initial and Serial C-Reactive Protein Levels With Adverse Cardiovascular Events and Death After Acute Coronary Syndrome, A Secondary Analysis of the VISTA-16 Trial
Question Are initial and serial increases in high-sensitivity C-reactive protein levels after acute coronary syndrome in medically optimized patients associated with increased risk of a major cardiac event, cardiovascular death, and all-cause death?
Findings In this secondary analysis of the VISTA-16 randomized clinical trial that included 5145 patients, baseline and longitudinal high-sensitivity C-reactive protein levels were independently associated with increased risk of a major adverse cardiac event, cardiovascular death, and all-cause death during the 16-week follow-up.
Meaning Monitoring high-sensitivity C-reactive protein levels in patients after acute coronary syndrome may help better identify patients at greater risk for recurrent cardiovascular events or death.
Abstract
Importance Higher baseline high-sensitivity C-reactive protein (hsCRP) levels after an acute coronary syndrome (ACS) are associated with adverse cardiovascular outcomes. The usefulness of serial hsCRP measurements for risk stratifying patients after ACS is not well characterized.
Objective To assess whether longitudinal increases in hsCRP measurements during the 16 weeks after ACS are independently associated with a greater risk of a major adverse cardiac event (MACE), all-cause death, and cardiovascular death.
Results Among 4257 patients in this study, 3141 (73.8%) were men and the mean age was 60.3 years (interquartile range [IQR], 53.5-67.8 years). The median 16-week low-density lipoprotein cholesterol level was 64.9 mg/dL (IQR, 50.3-82.3 mg/dL), and the median hsCRP level was 2.4 mg/L (IQR, 1.1-5.2 mg/L). On multivariable analysis, higher baseline hsCRP level (hazard ratio [HR], 1.36 [95% CI, 1.13-1.63]; P = .001) and higher longitudinal hsCRP level (HR, 1.15 [95% CI, 1.09-1.21]; P < .001) were independently associated with MACE. Similar significant and independent associations were shown between baseline and longitudinal hsCRP levels and cardiovascular death (baseline: HR, 1.61 per SD [95% CI, 1.07-2.41], P = .02; longitudinal: HR, 1.26 per SD [95% CI, 1.19-1.34], P < .001) and between baseline and longitudinal hsCRP levels and all-cause death (baseline: HR, 1.58 per SD [95% CI, 1.07-2.35], P = .02; longitudinal: HR, 1.25 per SD [95% CI, 1.18-1.32], P < .001).
Conclusions and Relevance Initial and subsequent increases in hsCRP levels during 16 weeks after ACS were associated with a greater risk of the combined MACE end point, cardiovascular death, and all-cause death despite established background therapies. Serial measurements of hsCRP during clinical follow-up after ACS may help to identify patients at higher risk for mortality and morbidity.
Residual risk of cardiovascular events or death remains high following ACS, despite coronary revascularization and optimal guideline-directed treatment with antiplatelet and LDL cholesterol-lowering agents. Inflammation is thought to drive this risk, but no effective treatment for such inflammation is commercially available. The secretory phospholipase A2 inhibitor varespladib was developed to meet this need, and it was evaluated in VISTA-16.
VISTA-16 was an international, multicenter clinical trial that randomized 5,145 patients in a double-blind manner to varespladib or placebo on a background of atorvastatin treatment within 96 hours of presentation with ACS. The trial was terminated early due to futility and likely harm from the drug, which was subsequently pulled from development.
Implications for practice
The association of increasing CRP levels with residual cardiovascular risk may prompt more intensive treatment to lower this risk. In particular, a secondary analysis showed that use of antiplatelet agents (clopidogrel, ticlopidine and prasugrel) was associated with stable or decreasing hsCRP levels.
“Monitoring not only lipids but also hsCRP after ACS may help us better identify patients at increased risk for recurrent cardiovascular events or death,” notes Dr. Puri. “High or increasing CRP levels could be an indication to optimize dual antiplatelet therapy post-ACS, along with high-intensity statin therapy (and possibly PCSK9 inhibitors) and antihypertensive therapy, in addition to instituting measures that are globally beneficial, such as dietary modifications and cardiac rehabilitation/exercise.”
Acute Coronary Syndrome (ACS): Strategies in Anticoagulant Selection: Diagnostics Approaches – Genetic Testing Aids vs. Biomarkers (Troponin types and BNP)
In Europe, BigData@Heart aim to improve patient outcomes and reduce societal burden of atrial fibrillation (AF), heart failure (HF) and acute coronary syndrome (ACS).
Lesson 8 Cell Signaling and Motility: Lesson and Supplemental Information on Cell Junctions and ECM: #TUBiol3373
Curator: Stephen J. Williams, Ph.D.
Please click on the following link for the PowerPoint Presentation for Lecture 8 on Cell Junctions and the Extracellular Matrix: (this is same lesson from 2018 so don’t worry that file says 2018)
(for this lesson pay attention to the part that shows how Receptor Tyrosine Kinase activation (RTK) can lead to signaling to an integrin and also how the thrombin receptor leads to cellular signals both to GPCR (G-protein coupled receptors like the thrombin receptor, the ADP receptor; but also the signaling cascades that lead to integrin activation of integrins leading to adhesion to insoluble fibrin mesh of the newly formed clot and subsequent adhesion of platelets, forming the platelet plug during thrombosis.)
Today’s lesson 3 explains how extracellular signals are transduced (transmitted) into the cell through receptors to produce an agonist-driven event (effect). This lesson focused on signal transduction from agonist through G proteins (GTPases), and eventually to the effectors of the signal transduction process. Agonists such as small molecules like neurotransmitters, hormones, nitric oxide were discussed however later lectures will discuss more in detail the large growth factor signalings which occur through receptor tyrosine kinases and the Ras family of G proteins as well as mechanosignaling through Rho and Rac family of G proteins.
Transducers: The Heterotrimeric G Proteins (GTPases)
An excellent review of heterotrimeric G Proteins found in the brain is given by
Cyclic AMP is an important second messenger. It forms, as shown, when the membrane enzyme adenylyl cyclase is activated (as indicated, by the alpha subunit of a G protein).
The cyclic AMP then goes on the activate specific proteins. Some ion channels, for example, are gated by cyclic AMP. But an especially important protein activated by cyclic AMP is protein kinase A, which goes on the phosphorylate certain cellular proteins. The scheme below shows how cyclic AMP activates protein kinase A.
Updated 7/15/2019
Additional New Studies on Regulation of the Beta 2 Adrenergic Receptor
We had discussed regulation of the G protein coupled beta 2 adrenergic receptor by the B-AR receptor kinase (BARK)/B arrestin system which uncouples and desensitizes the receptor from its G protein system. In an article by Xiangyu Liu in Science in 2019, the authors describe another type of allosteric modulation (this time a POSITIVE allosteric modulation) in the intracellular loop 2. See below:
Mechanism of β2AR regulation by an intracellular positive allosteric modulator
Xiangyu Liu1,*, Ali Masoudi2,*, Alem W. Kahsai2,*, Li-Yin Huang2, Biswaranjan Pani2, Dean P. Staus2, Paul J. Shim2, Kunio Hirata3,4, Rishabh K. Simhal2, Allison M. Schwalb2, Paula K. Rambarat2, Seungkirl Ahn2, Robert J. Lefkowitz2,5,6,†, Brian Kobilka1
Positive reinforcement in a GPCR
Many drug discovery efforts focus on G protein–coupled receptors (GPCRs), a class of receptors that regulate many physiological processes. An exemplar is the β2-adrenergic receptor (β2AR), which is targeted by both blockers and agonists to treat cardiovascular and respiratory diseases. Most GPCR drugs target the primary (orthosteric) ligand binding site, but binding at allosteric sites can modulate activation. Because such allosteric sites are less conserved, they could possibly be targeted more specifically. Liu et al. report the crystal structure of β2AR bound to both an orthosteric agonist and a positive allosteric modulator that increases receptor activity. The structure suggests why the modulator compound is selective for β2AR over the closely related β1AR. Furthermore, the structure reveals that the modulator acts by enhancing orthosteric agonist binding and stabilizing the active conformation of the receptor.
Abstract
Drugs targeting the orthosteric, primary binding site of G protein–coupled receptors are the most common therapeutics. Allosteric binding sites, elsewhere on the receptors, are less well-defined, and so less exploited clinically. We report the crystal structure of the prototypic β2-adrenergic receptor in complex with an orthosteric agonist and compound-6FA, a positive allosteric modulator of this receptor. It binds on the receptor’s inner surface in a pocket created by intracellular loop 2 and transmembrane segments 3 and 4, stabilizing the loop in an α-helical conformation required to engage the G protein. Structural comparison explains the selectivity of the compound for β2– over the β1-adrenergic receptor. Diversity in location, mechanism, and selectivity of allosteric ligands provides potential to expand the range of receptor drugs.
Recent structures of GPCRs bound to allosteric modulators have revealed that receptor surfaces are decorated with diverse cavities and crevices that may serve as allosteric modulatory sites (1). This substantiates the notion that GPCRs are structurally plastic and can be modulated by a variety of allosteric ligands through distinct mechanisms (2-7). Most of these structures have been solved with negative allosteric modulators (NAMs), which stabilize receptors in their inactive states (1). To date, only a single structure of an active GPCR bound to a small-molecule positive allosteric modulator (PAM) has been reported, namely, the M2 muscarinic acetylcholine receptor with LY2119620 (8). Thus, mechanisms of PAMs and their potential binding sites remain largely unexplored.
Fig 1. Structure of the active state T4L-B2AR in complex with the orthosteric agonist BI-167107, nanobody 689, and compound 6FA. (A) The chemical structure of compound-6FA (Cmpd-6FA). (B) Isoproterenol (ISO) competition binding with 125I-cyanopindolol (CYP) to the β2AR reconstituted in nanodisks in the presence of vehicle (0.32% dimethylsulfoxide; DMSO), Cmpd-6, or Cmpd-6FA at 32 μM. Values were normalized to percentages of the maximal 125I-CYP binding level obtained from a one-site competition binding–log IC50 (median inhibitory concentration) curve fit. Binding curves were generated by GraphPad Prism. Points on curves represent mean ± SEM obtained from five independent experiments performed in duplicate. (C) Analysis of Cmpd-6FA interaction with the BI-167107–bound β2AR by ITC. Representative thermogram (inset) and binding isotherm, of three independent experiments, with the best titration curve fit are shown. Summary of thermodynamic parameters obtained by ITC: binding affinity (KD = 1.2 ± 0.1 μM), stoichiometry (N = 0.9 ± 0.1 sites), enthalpy (ΔH = 5.0 ± 1.2 kcal mol−1), and entropy (ΔS =13 ± 2.0 cal mol−1 deg−1). (D) Side view of T4L-β2AR bound to the orthosteric agonist BI-167107, nanobody 6B9 (Nb6B9), and Cmpd-6FA. The gray box indicates the membrane layer as defined by the OPM database. (E) Close-up view of Cmpd-6FA binding site. Covering Cmpd-6FA is 2Fo– Fc electron density contoured at 1.0 σ (green mesh).From Science 28 Jun 2019:
Vol. 364, Issue 6447, pp. 1283-1287
Fig 3. Fig. 3Mechanism of allosteric activation of the β2AR by Cmpd-6FA.
(A) Superposition of the inactive β2AR bound to the antagonist carazolol (PDB code: 2RH1) and the active β2AR bound to the agonist BI-167107, Cmpd-6FA, and Nb6B9. Close-up view of the Cmpd-6FA binding site is shown. The residues of the inactive (yellow) and active (blue) β2AR are depicted, and the hydrogen bond formed between Asp1303.49and Tyr141ICL2 in the active state is indicated by a black dashed line. (B) Topography of Cmpd-6FA binding surface on the active β2AR (left, blue) and the corresponding surface of the inactive β2AR (right, yellow) with Cmpd-6FA (orange sticks) docked on top. Molecular surfaces are of only those residues involved in interaction with Cmpd-6FA. Steric clash between Cmpd-6FA and the surface of inactive β2AR is represented by a purple asterisk. (C) Overlay of the β2AR bound to BI-167107, Nb6B9, and Cmpd-6FA with the β2AR–Gscomplex (PDB code: 3SN6). The inset shows the position of Phe139ICL2 relative to the α subunit of Gs. (D) Superposition of the active β2AR bound to the agonist BI-167107, Nb6B9, and Cmpd-6FA (blue) with the inactive β2AR bound to carazolol (yellow) (PDB code: 2RH1) as viewed from the cytoplasm. For clarity, Nb6B9 and the orthosteric ligands are omitted. The arrows indicate shifts in the intracellular ends of the TM helices 3, 5, and 6 upon activation and their relative distances.
Allosteric sites may not face the same evolutionary pressure as do orthosteric sites, and thus are more divergent across subtypes within a receptor family (24–26). Therefore, allosteric sites may provide a greater source of specificity for targeting GPCRs.
D. M. Thal, A. Glukhova, P. M. Sexton, A. Christopoulos, Structural insights into G-protein-coupled receptor allostery. Nature 559, 45–53 (2018). doi:10.1038/s41586-018-0259-zpmid:29973731CrossRefPubMedGoogle Scholar
D. Wacker, R. C. Stevens, B. L. Roth, How Ligands Illuminate GPCR Molecular Pharmacology. Cell 170, 414–427 (2017).
D. P. Staus, R. T. Strachan, A. Manglik, B. Pani, A. W. Kahsai, T. H. Kim, L. M. Wingler, S. Ahn, A. Chatterjee, A. Masoudi, A. C. Kruse, E. Pardon, J. Steyaert, W. I. Weis, R. S. Prosser, B. K. Kobilka, T. Costa, R. J. Lefkowitz, Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation. Nature 535, 448–452 (2016). doi:10.1038/nature18636pmid:27409812CrossRefPubMedGoogle Scholar
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Additional information on Nitric Oxide as a Cellular Signal
Nitric oxide is actually a free radical and can react with other free radicals, resulting in a very short half life (only a few seconds) and so in the body is produced locally to its site of action (i.e. in endothelial cells surrounding the vascular smooth muscle, in nerve cells). In the late 1970s, Dr. Robert Furchgott observed that acetylcholine released a substance that produced vascular relaxation, but only when the endothelium was intact. This observation opened this field of research and eventually led to his receiving a Nobel prize. Initially, Furchgott called this substance endothelium-derived relaxing factor (EDRF), but by the mid-1980s he and others identified this substance as being NO.
Nitric oxide is implicated in many pathologic processes as well. Nitric oxide post translational modifications have been attributed to nitric oxide’s role in pathology however, although the general mechanism by which nitric oxide exerts its physiological effects is by stimulation of soluble guanylate cyclase to produce cGMP, these post translational modifications can act as a cellular signal as well. For more information of NO pathologic effects and how NO induced post translational modifications can act as a cellular signal see the following:
Hypertriglyceridemia: Evaluation and Treatment Guideline
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Severe and very severe hypertriglyceridemia increase the risk for pancreatitis, whereas mild or moderate hypertriglyceridemia may be a risk factor for cardiovascular disease. Individuals found to have any elevation of fasting triglycerides should be evaluated for secondary causes of hyperlipidemia including endocrine conditions and medications. Patients with primary hypertriglyceridemia must be assessed for other cardiovascular risk factors, such as central obesity, hypertension, abnormalities of glucose metabolism, and liver dysfunction. The aim of this study was to develop clinical practice guidelines on hypertriglyceridemia.
The diagnosis of hypertriglyceridemia should be based on fasting levels, that mild and moderate hypertriglyceridemia (triglycerides of 150–999 mg/dl) be diagnosed to aid in the evaluation of cardiovascular risk, and that severe and very severe hypertriglyceridemia (triglycerides of >1000 mg/dl) be considered a risk for pancreatitis. The patients with hypertriglyceridemia must be evaluated for secondary causes of hyperlipidemia and that subjects with primary hypertriglyceridemia be evaluated for family history of dyslipidemia and cardiovascular disease.
The treatment goal in patients with moderate hypertriglyceridemia should be a non-high-density lipoprotein cholesterol level in agreement with National Cholesterol Education Program Adult Treatment Panel guidelines. The initial treatment should be lifestyle therapy; a combination of diet modification, physical activity and drug therapy may also be considered. In patients with severe or very severe hypertriglyceridemia, a fibrate can be used as a first-line agent for reduction of triglycerides in patients at risk for triglyceride-induced pancreatitis.
Three drug classes (fibrates, niacin, n-3 fatty acids) alone or in combination with statins may be considered as treatment options in patients with moderate to severe triglyceride levels. Statins are not be used as monotherapy for severe or very severe hypertriglyceridemia. However, statins may be useful for the treatment of moderate hypertriglyceridemia when indicated to modify cardiovascular risk.
Changes in Levels of Sex Hormones and N-Terminal Pro–B-Type Natriuretic Peptide as Biomarker for Cardiovascular Diseases
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Considerable differences exist in the prevalence and manifestation of atherosclerotic cardiovascular disease (CVD) and heart failure (HF) between men and women. Premenopausal women have a lower risk of CVD and HF compared with men; however, this risk increases after menopause. Sex hormones, particularly androgens, are associated with CVD risk factors and events and have been postulated to mediate the observed sex differences in CVD.
B-type natriuretic peptides (BNPs) are secreted from cardiomyocytes in response to myocardial wall stress. BNP plays an important role in cardiovascular remodelling and volume homeostasis. It exerts numerous cardioprotective effects by promoting vasodilation, natriuresis, and ventricular relaxation and by antagonizing fibrosis and the effects of the renin-angiotensin-aldosterone system. Although the physiological role of BNP is cardioprotective, pathologically elevated N-terminal pro–BNP (NT-proBNP) levels are used clinically to indicate left ventricular hypertrophy, dysfunction, and myocardial ischemia. Higher NT-proBNP levels among individuals free of clinical CVD are associated with an increased risk of incident CVD, HF, and cardiovascular mortality.
BNP and NT-proBNP levels are higher in women than men in the general population. Several studies have proposed the use of sex- and age-specific reference ranges for BNP and NT-proBNP levels, in which reference limits are higher for women and older individuals. The etiology behind this sex difference has not been fully elucidated, but prior studies have demonstrated an association between sex hormones and NT-proBNP levels. Recent studies measuring endogenous sex hormones have suggested that androgens may play a larger role in BNP regulation by inhibiting its production.
Data were collected from a large, multiethnic community-based cohort of individuals free of CVD and HF at baseline to analyze both the cross-sectional and longitudinal associations between sex hormones [total testosterone (T), bioavailable T, freeT, dehydroepiandrosterone (DHEA), SHBG, and estradiol] and NT-proBNP, separately for women and men. It was found that a more androgenic pattern of sex hormones was independently associated with lower NT-proBNP levels in cross-sectional analyses in men and postmenopausal women.
This association may help explain sex differences in the distribution of NT-proBNP and may contribute to the NP deficiency in men relative to women. In longitudinal analyses, a more androgenic pattern of sex hormones was associated with a greater increase in NT-proBNP levels in both sexes, with a more robust association among women. This relationship may reflect a mechanism for the increased risk of CVD and HF seen in women after menopause.
Additional research is needed to further explore whether longitudinal changes in NT-proBNP levels seen in our study are correlated with longitudinal changes in sex hormones. The impact of menopause on changes in NT-proBNP levels over time should also be explored. Furthermore, future studies should aim to determine whether sex hormones directly play a role in biological pathways of BNP synthesis and clearance in a causal fashion. Lastly, the dual role of NTproBNP as both
a cardioprotective hormone and
a biomarker of CVD and HF, as well as
the role of sex hormones in delineating these processes,
should be further explored. This would provide a step toward improved clinical CVD risk stratification and prognostication based on
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