Cardiovascular disease (CVD) was linked to an estimated 17.7 million deaths in 2015 and remains the world’s overall leading cause of death globally.1 Despite the widespread use of prescribed cholesterol-lowering drugs (e.g. statins) and significant advances in interventional surgeries, CVD-linked mortality and cardiovascular incidents remain a significant health problem.
With public trust in statins at an all-time low, many have suggested now is the time for us to rethink our approach towards managing and treating CVD.2 Perhaps the answer lies in harnessing a new emergent power in medicine – omics technologies.
Chronic diseases such as CVD are a considerable burden on healthcare resources. Heart disease and stroke treatments in the US alone are thought to cost approximately $1 billion daily.3 By developing more effective intervention strategies and diagnostic techniques, it will soon be possible for clinicians to predict and prevent CVD before it develops.
Predictive medicine has the potential to transform global healthcare and dramatically lower the number of cardiovascular incidents that occur. Spearheading this mission are cardiologists and lipidologists, working to unravel the complex molecular interactions that are the underlying causes of heart disease.
CVD is an umbrella term for a wide range of conditions with varying symptoms. The general medical consensus is that CVD is caused by the interaction of two conditions; thrombosis, proposed by Dr. James Bryan Herrick as part of his landmark discovery of myocardial infractions (heart attacks), and atherosclerosis. Thrombosis is the process of blood clots forming inside blood vessels as a response to injury. Atherosclerosis, on the other hand, is characterized by atheroma plaques (fatty deposits and scar tissue) developing in the lining of artery walls, causing them to narrow and harden.4 It’s thought that the majority of coronary heart disease (CHD) cases are caused by atherosclerosis developing into a thrombosis, resulting in a heart attack.5
Lipid imbalance (dyslipidemia) has been singled out as the primary cause of atherosclerosis. Whereas statins, a group of cholesterol-lowering drugs, are an effective preventative treatment for patients either diagnosed with a CVD-related condition or identified as belonging to a high-risk group.
The basic concept of how lipid imbalance causes atherosclerosis, states that increased levels of low-density lipoproteins (LDL) act to increase the probability of cholesterol (transported by LDLs) becoming embedded in the arterial wall. Accumulated LDLs undergoes chemical modification, leading to a cascade of cellular and inflammatory effects that result in white blood cells taking up modified lipids to form characteristic atheroma plaques.
It has been suggested that High-density lipoproteins (HDL) may lower the atherogenic potential of LDLs (i.e. how likely they are to cause atherosclerosis), and are often dubbed “good cholesterol” in the media; although this is a poor definition for a number of reasons (e.g. HDLs transport cholesterol and are not technically cholesterol themselves).6
If we know that statins are effective at reducing LDLs, then why haven’t we “solved” heart disease yet? Part of the public confusion surrounding statins may lie in the fact that many researchers and clinicians have trouble fully explaining how they work. Recent studies have indicated that any positive role HDLs may play in reducing CVD risk is unclear, with results observed in animals not replicated in human studies.7 There is however, a considerable body of evidence to suggest that statins are effective, with their well-publicised side-effects less likely to be as common as reported in the media.8
Whilst the majority of clinicians and researchers are in agreement that statins are helping to improve the outcomes of patients with a high-risk of CVD, it is hoped that other drug classes that lower LDL-C (cholesterol transported by LDLs) can also be developed. This is particularly important as there is a small subset of population that are statin resistant, providing researchers with an incentive to investigate alternatives. Cholesteryl ester transfer protein (CETP) inhibitors are one such example.
The hypothesis of how CETP inhibitors work is based on them increasing levels of HDLs in the body, however studies have shown that the resulting increases in HDL levels didn’t always produce clear benefits for the patients tested. Complicating things further is a recent drug trial that found CETP inhibitors did lower mortality and overall demonstrated their efficacy. The researchers involved suggested that this was due to the CETP inhibitors lowering LDL levels, rather than increasing HDLs.9
The advent of omics, new fields in biology that use biological information measured from population-sized groups, has provided researchers with new methodologies to uncover complex molecular relationships (in this case the effect of CETP inhibitors on lipid levels). Datasets with information on genetics (genomics), metabolites (metabolomics) and proteins (proteomics) can all be analysed to identify previously hidden associations. Mendelian Randomization is one example of how we can harness this data and use it to identify new causal relationships e.g. using data to identify genetic variants that mimic certain drug targets.
In a new study published in the Journal of the American Medical Association (JAMA), aiming to clear up the confusion about CETP inhibitors, Ference and colleagues used Mendelian Randomization to study the effects of genetic variants on two biomarkers that can indicate CVD risk: apolipoprotein B (apoB) and LDL-C. The group used genetic variants in both CETP and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) genes that mimicked the action of CETP inhibitors and statins. By comparing the metabolic profiles of these genetic variants, it was possible for the researchers to observe changes in LDL-C and apoB levels to see if they matched predictions based on existing theories.
The results showed that a reduction in the risk of cardiovascular events was proportional to a corresponding reduction in apoB levels, rather than LDL-C. The association was also found to be weaker for changes in LDL-C, indicating that the clinical benefit of reducing LDL-C levels may depend on a similar reduction in the amount of apoB-containing lipoprotein particles.
Statins are effective because they lower both apoB and LDL-C levels; if combined with CETP inhibitors, levels of both LDL-C and apoB go down. However, adding CETP inhibitors to statin therapies does not increase the overall lowering effect on apoB levels, suggesting that combination therapies may not be effective. This study is one example of Nightingale NMR metabolomics platform being used to discover more about drug target mechanism of action.10
Metabolomics tools are used to monitor metabolites in the body. Nightingale’s technology uses NMR spectroscopy to measure levels of a wide range of metabolites present in circulating blood. Whilst Nightingale’s platform was only used to obtain measurements of LDL-C and apoB in Ference’s study, the platform measures over 220 metabolites simultaneously in a single experiment. Serum or plasma samples are processed by an automated system, with proprietary software used to produce measurements of metabolites (such as fatty acids, glucose, lipoproteins and amino acids) in absolute concentrations. As the platform is a high-throughput technique, it is fully-scalable to accommodate large numbers of samples.
The potential for omics technologies is great. For example, if researchers want to further validate the association between apoB and CVD risk, large-scale populations could be metabolically profiled, providing more data and increasing the statistical power of any findings made.
CVD is a major health issue but by using methods like Mendelian Randomization and tools such as blood analysis platforms, we can make fast progress in discovering which drugs are the most effective and, crucially, why they work. The benefits of restoring public trust and reducing mortality could have profound implications for medicine.
1. World Health Organization. Cardiovascular diseases (CVDs) – Factsheet (2017).
2. Elias, J. et al. Frontiers Physiology (2016). https://doi.org/10.3389/fphys.2016.00002
3. Stinson, C. Heart Disease And Stroke Cost America Nearly $1 Billion A Day In Medical Costs, Lost Productivity. CDC Foundation (2015). https://www.cdcfoundation.org/pr/2015/heart-disease-and-stroke-cost-america-nearly-1-billion-day-medical-costs-lost-productivity
4. Badimon, L. et al. European Heart Journal Acute Cardiovascular Care. (1): 60–74 (2012). https://dx.doi.org/10.1177%2F2048872612441582
5. Otsuka, F. et al. Cardiovascular Diagnosis & Therapy. 6(4): 396–408 (2016). https://dx.doi.org/10.21037%2Fcdt.2016.06.01
6. Sigurdsson, A et al. Low-Density Lipoprotein (LDL) in Atherosclerosis and Heart Disease (2016). https://www.docsopinion.com/2016/01/25/low-density-lipoprotein-in-atherosclerosis-and-heart-disease/
7. Westerterp, M. et al. Circulation Research. 119:13-15 (2016). https://doi.org/10.1161/CIRCRESAHA.116.309116
8. Collins, R. et al. The Lancet. 388(10059):2532-2561 (2016). https://www.ncbi.nlm.nih.gov/pubmed/27616593
9. Sniderman, A. et al. JAMA. Published online (2017). http://jamanetwork.com/journals/jama/fullarticle/2650885
10. Ference, B. et al. JAMA. Published online (2017).
https://www.ncbi.nlm.nih.gov/pubmed/28846118