Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

Abstract

Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.

Fig 1. A schematic representation of the drug-target MR approach undertaken in this study using, for instance, HMGCR variants to proxy for HMG-CoA reductase inhibition (the mechanism of action of statin therapy) to estimate its genetically predicted effect on CAD.

Genetic variants robustly associated with a lipoprotein lipid trait (for instance, LDL cholesterol) based on P < 1 × 10⁻⁶ within 100 kbs of encoding genes were identified as genetic proxies for perturbing therapeutic targets. A sensitivity analysis restricted to 50 kbs on either side of encoding genes was also undertaken in this study. CAD, coronary artery disease; kbs, kilobases; LDL, low-density lipoprotein; MR, mendelian randomisation.

Fig 2. A forest plot visualising the genetically predicted effects of lipid-modifying drug targets on risk of CAD and T2D using MR.

Estimates are colour coded based on the lipoprotein lipid trait estimates used to derive genetic scores. Each genetic score was oriented to mimic the putative effects of drug targets on lipoprotein traits, meaning that effect estimates correspond to relative odds of disease per 1 SD change in either lower LDL cholesterol, higher HDL cholesterol, or lower triglyceride levels via each specific drug target. Note that in the case of CETP, we are not ascribing causal effects to HDL cholesterol—rather, we are orientating CAD/T2D effect estimates corresponding to a genetically predicted increase in HDL cholesterol arising from pharmacological inhibition of CETP. The data underlying this figure can be found in S2 Table. CAD, coronary artery disease; CETP, cholesteryl ester transfer protein; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MR, mendelian randomisation; SD, standard deviation; T2D, type 2 diabetes.

Fig 3. Forest plots illustrating the genetically predicted effects of lipid-modifying drug targets on measures of circulating metabolite concentrations using NMR in the UKB study.

Effect estimates are based on an SD change in the genetically predicted drug target scores oriented to reflect therapeutic intervention (i.e., lower LDL cholesterol, lower triglycerides, and higher HDL cholesterol). Scores were derived using genetic variants robustly associated which lipoprotein lipid traits (as indicated in each target’s legend) at each encoding gene’s region. The data underlying this figure can be found in S7–S14 Tables. CETP, cholesteryl ester transfer protein; HDL, high-density lipoprotein; IDL, intermediate density lipoprotein; LDL, low-density lipoprotein; NMR, nuclear magnetic resonance; SD, standard deviation; UKB, UK Biobank; VLDL, very low-density lipoprotein.

Fig 4

A comparison of distributions between genetically predicted drug target effects (A) PCSK9, (B) CETP, and (C) LPL on metabolic traits using NMR in the UKB study. In each figure estimates are compared with the results from the HMGCR score. Effect estimates were scaled in accordance with the corresponding effects of these genetically predicted drugs targets on risk of CAD, which is why axes vary between plots. Points are coloured based on subcategories of metabolic traits indicated in the figure legends. The data underlying this figure can be found in S7, S8, S10, and S14 Tables. CAD, coronary artery disease; CETP, cholesteryl ester transfer protein; HDL, high-density lipoprotein; IDL, intermediate density lipoprotein; LDL, low-density lipoprotein; NMR, nuclear magnetic resonance; SD, standard deviation; UKB, UK Biobank; VLDL, very low-density lipoprotein.