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Lead: Mika Ala-Korpela
Key collaborators: Michael Holmes (University of Oxford, UK), Adam Butterworth and John Danesh (University of Cambridge, UK), George Davey Smith (University of Bristol, UK)

Definitive evidence on the causal role of low-density lipoproteins (LDL) in cardiovascular disease comes from trials of LDL cholesterol lowering compounds, which have shown beneficial effects on risk of coronary heart disease and stroke. Consistent effects have been seen for drugs acting on related pathways, such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibitors (i.e. statins) and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors, both of which upregulate hepatic LDL receptor expression, and for drugs acting on other pathways, such as ezetimibe, which inhibits intestinal absorption of cholesterol. However, trials of drugs primarily designed to alter concentrations of lipids other than LDL cholesterol have had mixed results. One such example is the class of drugs designed to inhibit cholesteryl ester transfer protein (CETP), a lipid transport protein responsible for the exchange of triglycerides and cholesteryl esters between apolipoprotein B-containing particles and high-density lipoprotein (HDL) particles.

We recently introduced and validated a new concept enabling assessment of systemic drug effects solely based on large-scale genetic and metabolic data (1). We demonstrated that the correspondence of the metabolic effects of genetic variation in HMGCR — mimicking a very small dose of statin allocated to rs12196-T carriers via Mendelian randomisation — with the metabolic changes observed longitudinally in people being on statin treatment was excellent. These findings demonstrated the generalisability of this concept and JACC also published an editorial entitled Pharmacometabolomics meets genetics: a “natural” clinical trial of statin effects (2).

This new concept relies on the applications of Mendelian randomisation (MR) analysis which is a powerful modern genetic epidemiology tool that exploits genetic information to inform on the likely causal relevance of an exposure to an outcome, that should be free from reverse causality and minimises confounding. MR imitates the ‘gold standard’ randomised, controlled trials for determining causality; instead of drug or placebo, it compares carriers of certain genetic variants to non-carriers. While these analyses with drug targets are rather straightforward (1), MR analyses with complex genetic instruments and disease outcomes need to be performed with caution to ensure correct interpretations; we have recently written a review-opinion to discuss these issues in more detail: Mendelian randomization in cardiometabolic disease: challenges in evaluating causality (3).

In this project we will apply Mendelian randomisation and this new concept and study the on-target effects of existing and emerging lipid-lowering drugs on multiple systemic metabolic measures, including detailed lipoprotein subclass profiling and also mass spectrometry lipidomics data on individual lipid molecules, and various other cardiometabolic biomarkers. With comprehensive molecular data on systemic metabolism it will be possible to improve the understanding of the metabolic effects of interventions and drug therapies.

Results are already available from our recent studies on APOC3 (4), PCSK9 (5), and CETP (6). We have already shown that, in contrast to genetic inhibition of HMG-CoA (proxying statin therapy), genetic inhibition of CETP does not alter circulating size-specific LDL cholesterol concentrations. This is masked by using conventional, non-specific assays for LDL cholesterol. Our findings also suggest potential additional mechanisms by which CETP inhibition could prevent coronary heart disease through reductions in the triglyceride composition of HDL particles. Our findings thus call attention to the need for metabolic precision in measurements of lipoprotein lipids and in assessing the role of lipoprotein metabolism in cardiovascular disease in relation to on-going treatment trials of novel lipid-altering therapies.

We anticipate that this new concept will become a routine tool in the pharmaceutical industry to elucidate molecular mechanisms, clarify pleiotropic effects and eventually help in preventing late-stage failures in phase III clinical drug trials. This concept therefore is likely to save millions of dollars and years of wasted research in drug development. Knowing the causal biomarkers of disease is fundamental for the development of efficacious interventions.

Relevant reading

  1. Metabolomic profiling of statin use and genetic inhibition of HMG-CoA reductase
    J Am Coll Cardiol 2016 Mar 15;67(10):1200–1210
  2. Pharmacometabolomics meets genetics: a “natural” clinical trial of statin effects
    J Am Coll Cardiol 2016 Mar 15;67(10):1211–1213
  3. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
    Nat Rev Cardiol 2017 Oct;14(10):577-590
  4. Metabolic characterization of a rare genetic variation within APOC3 and its lipoprotein lipase-independent effects
    Circ Cardiovasc Genet 2016 Jun;9(3):231–9
  5. Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment
    bioRxiv 278861
  6. Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition
    bioRxiv 295394

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