Body Mass Index and Facial Aging: Mendelian Randomization and Exploratory Target Prioritization

Abstract

Background Facial aging reflects genetic, metabolic, and environmental influences. Although obesity has been associated with older perceived facial age, the shared genetic basis and direction of the BMI-facial aging relationship remain uncertain. Objective To evaluate genetic correlations between facial aging and 15 metabolic traits, assess bidirectional associations by Mendelian randomization (MR), and conduct exploratory locus, gene, and compound prioritization. Methods Public genome-wide association study (GWAS) summary statistics were analyzed using linkage disequilibrium score regression (LDSC) and bidirectional MR with inverse-variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO methods. Because BMI showed the most consistent signal, related loci were evaluated by fine-mapping, colocalization, ANNOVAR, MAGMA, and GCTA-fastBAT. DGIdb screening and molecular docking were used for hypothesis generation. Results BMI showed the strongest genetic correlation with facial aging (rg = 0.215; FDR-adjusted P = 3.94×10−33). After MR-PRESSO outlier removal, higher genetically predicted BMI was associated with greater odds of appearing older (IVW OR = 1.053, 95% CI 1.044–1.063; P = 1.80×10−28), although heterogeneity remained and reverse-direction estimates were less robust. Of four retained variants, three met the prespecified colocalization threshold. JAZF1, RAD52, and PPARG were prioritized by positional and gene-based analyses. Docking of five natural compounds did not establish target engagement or efficacy. Limitations The facial-aging phenotype was perception-based, the GWAS datasets were predominantly of European ancestry and may have partially overlapping samples, and all downstream analyses were computational. Conclusion Higher BMI may contribute to perceived facial aging, but these findings do not show that BMI reduction or any candidate compound improves facial aging. Metabolic health is the more clinically actionable implication, whereas the prioritized genes and compounds remain exploratory and require functional validation.