Metabolomic signatures of lipid-modifying therapies using drug target Mendelian randomization

Abstract

Background: Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. Methods: We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9 & NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4 & LPL). We then used Mendelian randomization to evaluate the effect of each score on coronary artery disease (CAD) risk, and to systematically estimate their effects on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Results: There was strong evidence of an effect of drug-based genetic scores on CAD risk with the exception of ANGPTL3. Genetically predicted effects on the blood metabolome were generally consistent amongst 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 (e.g. HMGCR vs all 4 triglyceride targets had r2<0.02), despite largely consistent effects on risk of CAD. 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 (e.g. PCSK9: Beta=0.01, 95 CI%=-0.06 to 0.08, P=0.78). In contrast, all of the triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker (e.g. LPL: Beta=-0.43, 95 CI%=-0.37 to -0.48, P=9x10-50). Conclusions: Multiple lipid-modifying drug targets have therapeutically beneficial effects on CAD risk. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, with potential implications for biomarkers in clinical development and measuring treatment response.

Novel genetic insights into the roles of amino acids in metabolic dysfunction-associated steatotic liver disease

“…The MR analyses were predicated on three assumptions: (1) the genetic variants were associated with exposure; (2) the genetic instruments were not associated with the outcomes through confounding factors; and (3) the genetic instruments did not have direct effects on the outcome, but potentially only through exposure (Figure 1). We used data from the largest available genome-wide association study (GWAS) for amino acids from the UK Biobank, which included 115,082 European participants [15]. The GWAS of MASLD was obtained from the study by Ghodsian et al, which included 8,434 MASLD patients and 770,180 controls [16].…”

“…For the nine amino acids that had robust significant associations with MASLD, we performed two-sample bi-directional MR based on the previously published GWAS of MASLD and the GWAS of the UK Biobank [15][16] (Figure 2). The number of genetic loci selected in the instrumental variables for amino acids ranged from seven for isoleucine and phenylalanine to as many as 35 for glycine, with F statistics ranging from 62.2 for leucine to 603.9 for glycine.…”