Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score <0.3 (2) minor allele frequency <1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p < 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur