Abstract Body: Background: Obesity is a critical modifiable risk factor for adverse cardiovascular events, yet traditional measures like Body Mass Index (BMI) have limitations in capturing the complexities of adiposity and its associated cardiovascular disease risk. Proteomics may improve the understanding of the molecular underpinnings of obesity and its implications for cardiovascular health.

Methods: Proteomic profiling was performed using the Olink Explore 3072 platform on blood plasma samples from a subset of the UK Biobank participants. The relative protein abundance was normalized using rank-based inverse normalization. A total of 15,652 healthy participants without prevalent or incident diabetes, cardiovascular disease, renal disease, and cancer were included for training and testing of the protein-predicted obesity phenotypes, including BMI, body fat percentage (BFP), and waist-hip ratio (WHR). An additional 24,999 participants without prevalent stroke or coronary artery disease were included in predicting major adverse cardiovascular events (MACE). Protein-predicted scores of BMI, BFP, and WHR were generated using the least absolute shrinkage and selection operator (LASSO) algorithm with cross-validation among the healthy participants. Associations between these protein-predicted scores and MACE were evaluated using Fine and Gray's competing risk model accounting for all-cause death as competing risk, adjusting for lipids, blood pressure, estimated glomerular filtration rate, diabetes, smoking, blood pressure-lowering medication, cholesterol-lowering medication, and the measured obesity-related phenotypes.

Results: Strong correlations were observed between protein-predicted obesity phenotypes and their measured counterparts (R²: BMI = 0.78, BFP = 0.85, WHR = 0.63). A standard deviation of elevated protein-predicted scores for BFP and WHR, but not for BMI, was significantly associated with an increased risk of MACE (Hazard Ratio [HR] 1.25, 95% CI 1.14 - 1.38, p <0.0001; HR 1.15, 95% CI 1.06 - 1.24, p = 0.001, respectively), independent of established cardiovascular risk factors and outperformed the measured obesity-related phenotypes.

Conclusion: Proteomic markers can improve the assessment of obesity-related risks for cardiovascular outcomes. Integrating proteomic data into clinical practice complements current metrics for adiposity and has the potential to facilitate personalized preventive strategies for adverse cardiovascular outcomes.