Abstract P273: Sex-Stratified Machine Learning Analysis of Proteomics, Phenotypic, and Genomic Influences on Visceral Adipose Tissue Volume in the UK Biobank

• DOI: 10.1161/circ.149.suppl_1.p273
• Publication Date: 2024-05-16T14:05:30Z
• Authors: Zahra Azizi; Trisha Gupte; Pik Fang Kho; Kevin Nzenkue; Jiayan Zhou; Rodrigo Guarischi...; Daniel Panyard; Ming-Li Chen; Fahim Abbasi; Shoa L CLARKE; Philip S Tsao; Themistocles L As...
• Keywords: 03 medical and health sciences; 0302 clinical medicine

Introduction: Adiposity distribution plays an important role in insulin resistance (IR); sex differences body composition can influence the risk of cardiometabolic disease (CMD). We aimed to assess intersection proteomic, genomic and phenotypic factors related visceral adipose tissue (VAT) IR. Methods: examined UK biobank participants with proteomic data generated using Olink proximity extension antibody assay (PEA). Association between proteomics, genomics phenotypes VAT volume was evaluated. To complex relationships features, a multistep approach employed. Following evaluation each feature types, combined effects were assessed. Then, top 30 features from final model (Figure). R2 tested as indicator variance explained by SHAP values reported. Results: analyzed total 5342 (52.2% female, mean age 54.8±7.9 years). Our analysis revealed significant variation plasma proteins when added both sexes. Polygenic scores, factors, 4%, 55%, 57%, 60% women, respectively. In men, these lower at 2%, 49%, 51%, 56%, While there common men several sex-specific noted women (e.g., FGF21, LDLR, INHBC, CES1, WFIKKN2, CLEC4A, CDHR2, PRSS8, TNXB, ERBB2, F9, THBS2, CCL16) CA14, ADM, PON3, STC1, MEGF10, ASGR1, REN, FURIN, OXT, PSPN, HSP90B1, CTSD, SERPINB5, CNTN3, CTSB, WFDC12, ADGRG1). Conclusion: study demonstrated presence proteomics associated VAT. These findings may provide insights into protein expression CMD enable individualized measurement adiposity one means better understanding biology