Association and Age Heterogeneity of Systemic Metabolomic Signatures With Age-Related Cataract: A Mendelian Randomization and Heterogeneity Analysis

Purpose: To evaluate systemic metabolomic signatures associated with age-related cataract and prioritize signals with age-stable or age-heterogeneous patterns. Methods: We performed two-sample Mendelian randomization (MR) analyses of 249 Nightingale Health metabolomic traits against age-related cataract in FinnGen 12. Higher-confidence MR candidates were defined by inverse-variance weighted (IVW) false discovery rate (FDR) < 0.05, complete directional concordance across seven complementary MR and sensitivity analyses, and nominal support in at least five analyses. MR-prioritized candidates were then triangulated against UK Biobank incident cataract associations. Age-stratum heterogeneity was assessed using tertile-specific UK Biobank estimates and Cochran Q statistics. Results: Among 249 metabolites, 71 met the IVW FDR threshold, and 22 remained after higher-confidence MR filtering. Of these, 13 showed MR-concordant associations with incident cataract in the UK Biobank 500,000 summary layer. Age-stratum assessment prioritized eight metabolites, comprised of five age-stable fatty-acid/lipoprotein-related signals and three age-heterogeneous signals involving glycoprotein acetyls, phenylalanine, and total fatty acids. Conclusions: Using an MR-guided triangulation framework, we prioritized eight systemic metabolomic signals associated with age-related cataract. These signals were comprised of an age-stable fatty-acid/lipoprotein axis and age-heterogeneous inflammatory, amino-acid, and fatty-acid traits, highlighting distinct systemic metabolic patterns that may inform future cataract biomarker and mechanistic studies. Translational Relevance: This study provides a focused set of systemic metabolic candidates for future cataract biomarker validation and prospective risk-stratification research. The integration of MR prioritization, UK Biobank triangulation, and age-stratum heterogeneity assessment offers a transferable framework for studying metabolic signatures in age-related ocular disease.