Research highlight: Using metabolomics to compare PCSK9 and statins

Sliz et al. Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment. Circulation 2018; DOI:10.1161/CIRCULATIONAHA.118.034942

Statins are the most widely prescribed drug class in the world used to reduce the risk of cardiovascular disease and mortality. It is thought they achieve a reduction in disease risk by lowering circulating low-density lipoprotein cholesterol (LDL-C) levels, a pivotal risk factor for atherosclerosis and heart disease. Detailed metabolic profiling has previously highlighted many other effects of statins on circulating metabolites (Würtz et al, JACC 2016), making it a valuable tool for studying detailed molecular effects.(1) PCSK9 inhibitors have emerged as a new drug class to lower LDL-C and have recently been shown in two outcome trials to reduce the risk of major cardiovascular events when added to statin therapy.(2) Prior studies have reported that PCSK9 inhibitors can be highly effective at lowering LDL-C levels, resulting in total reductions that range 45-60% and down to below 1 mmol.(3) However, recent contradictory findings have suggested that PCSK9 inhibitors may be slightly less effective than statins at reducing cardiovascular disease events (for a given LDL-C reduction). In order to study the detailed molecular effects of PCSK9 inhibition (in comparison to statins), Sliz and colleagues used detailed metabolic profiling to compare the impact of statin use and genetic inhibition of PCSK9 on circulating metabolites.

Two study designs (a large randomized statin trial and meta-analysis of 8 cohorts) were profiled using Nightingale’s NMR-metabolomics platform, quantifying 228 circulating metabolic measures, including lipoprotein subclass concentrations and their lipid compositions. The statin trial consisted of 5,359 participants (2,659 on 40mg/day pravastatin treatment) at 6-month post-randomization. The meta-analysis of 72,185 people was drawn from eight population cohorts: INTERVAL, ALSPAC mothers and offspring, FINRISK-1997, FINRISK-2007, Northern Finland Birth Cohort studies 1966 and 1986 and the China Kadoorie Biobank. Due to a lack of metabolomics data from a large randomized trial of PCSK9 inhibitor therapy, the metabolic effects of PCSK9 inhibition were assessed using Mendelian randomization with PCSK9 rs11591147-T (R46L); a loss-of-function allele associated with lower LDL-C and decreased cardiovascular risk, which acted as an unconfounded proxy to mimic the therapeutic effects of PCSK9 inhibitors. While prior studies (including smaller studies with Nightingale platform) have examined the metabolic signature of PCSK9 variants, the direct comparison to the statin trial effects serves as a reference point with which to better understand the detailed effects of PCSK9. 

Comparison of the metabolic effects of statin therapy and genetic inhibition of PCSK9 (when scaled to an equivalent lowering of LDL-C) displayed similar effects between most alterations in lipoprotein lipid composition and fatty acid balance across the study populations. However, the analyses also identified a number of discrepancies related to very-low-density lipoprotein (VLDL) lipid measures. Genetic inhibition of PCSK9 had a weaker effect on lowering VLDL-cholesterol when compared with statins (54% vs. 77%) and a smaller extent of lowering small, medium-sized and large VLDL particle concentrations. PCSK9 rs11591147 also had a weaker effect on lowering total plasma triglycerides, total fatty acids and the ratio of apolipoprotein B to A-I. Whilst genetic inhibition of PCSK9 showed no effect on GlycA (a marker of inflammation), statin treatment was observed to slightly lower GlycA levels. A substantial difference between genetic inhibition of PCSK9 and statins was also observed for the overall degree of fatty acid unsaturation (16%LDL-C reduction for PCSK9 vs 26%LDL-C increase for statin). Further analyses were conducted to ensure the subtle differences in metabolic effects were not due to chance. Metabolic profiling results of statins observed in the PROSPER trial were further replicated using results from the PREVEND-IT trial, which also showed stronger VLDL lipid effects as observed for genetic inhibition of PCSK9. Further, the metabolic effects of PCSK9 rs11591147 were also found to be broadly similar compared to those caused by rs12916 in the HMGCR gene (encoding the target for statin therapy) within the same study population of 72,185 individuals, hereby using the genetic variants to effectively act as two naturally occurring trials in the same study population

In conclusion, these findings provide evidence that PCSK9 inhibition results in subtly different metabolic alterations than those resulting from statin medications. PCSK9 inhibition has a weaker effect on reducing VLDL particles and their cholesterol concentrations, when scaled to an equivalent lowering of LDL-C. This suggests that PCSK9 inhibitors are less effective at clearing triglyceride-rich lipoproteins via upregulation of LDL receptors on cell surfaces. If VLDL lipids have independent causal effects on cardiovascular disease risk, the observed discrepancy on VLDL lipid lowering could contribute to differences in cardiovascular risk reductions between statins and PCSK9 inhibitors for an equivalent reduction in LDL-C. This is of clinical relevance since VLDL lipids are thought to have independent causal effects on cardiovascular risk (possibly due to VLDL-C underpinning the link between triglycerides and cardiovascular disease risk). Hence, statins would result in a greater risk reduction compared to PCSK9 inhibitors for the same reduction in LDL-C levels. 

This study is pioneering for combining large randomized trial data with Mendelian randomization analyses (using genetic variants as naturally-occurring trials). Incorporating analyses of the full metabolite from the large INTERVAL trial and other cohort studies also demonstrates the benefits of comprehensively metabolic profiling large cohorts and biobanks. Further metabolic profiling of large biobanks with genetic information, such as UK Biobank, will allow to further extend these applications for additional known and new drug targets.

In this study, Nightingale’s blood biomarker analysis service was used to quantify 228 lipid and metabolite measures for 5,359 participants in the PROSPER trial at 6-months post-randomization and 72,185 individuals across eight population cohorts. Nightingale’s NMR-based metabolomics platform has been successfully used in a wide range of research applications and has featured in over 150 peer-reviewed studies. 

Further reading

Access the full paper here.


1. Wurtz et al. Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase. JACC 2016;67(10):1200-10 

2. Cholesterol Treatment Trialists' (CTT) Collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012;380:581- 90. 

3. Sabatine et al. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. N Engl J Med 2015;372:1500-9.