Systematic Review and Mendelian Randomization of Plasma Biomarkers to Predict Their Causal Role in Peripheral Artery Disease Pathophysiology

Open AccessPublished:June 10, 2022DOI:


      Observational analyses have described hundreds of biomarkers for peripheral artery disease (PAD). These studies can be limited by sample size, lack of replication, residual confounding, and reverse causality. To assess this, we performed a systematic review of the literature and leveraged genetic approaches to causal inference.


      We performed a systematic literature review for terms related to PAD and/or biomarkers using PubMed, the Cochrane database, and Embase, followed by manual review to extract biomarkers and their direction of effect. To test for evidence of causality we used two-sample Mendelian randomization. We developed genetic instruments for the biomarkers by mapping them to genome-wide association studies (GWAS) of circulating biomolecules agglomerated in the IEU Open GWAS project. We tested the association of the genetic instruments with PAD using summary statistics from a GWAS of 31,307 individuals with and 211,753 individuals without PAD in the VA Million Veteran Program. We used the Wald ratio or inverse variance weighted Mendelian randomization; weighted median and weighted mode methods were applied as sensitivity analyses.


      After manual review, we identified 159 unique papers mentioning 268 unique PAD biomarkers. We mapped 76 biomarkers to genetic data, 19 of which were nominally associated with PAD (P < .05). After accounting for multiple testing (false discovery rate of <0.05), 12 remained significant, of which only 7 had concordant directions of effect with published reports: ApoB, ApoA1, high-density lipoprotein-associated cholesterol, triglycerides, Von Willebrand factor, cadherin-5, and b2-microglobulin.


      This systematic review paired with genetic causal inference illuminates key biomarkers causally relevant to PAD, and highlights discrepancies between observational and genetic findings. This highlights the importance of rigorous analysis of observational biomarker data and the opportunity to leverage human genetics to inform these studies.