Analyses of plasma protein levels in relation to body composition in the UK Biobank

Background Excess fat mass is causally linked to multiple diseases, but the use of existing therapies, such as glucagon-like peptide-1 receptor agonists, is limited by varying effects and common side effects, including loss of fat-free mass. We aimed to identify proteins associated with fat mass, but ideally not also associated with fat-free mass, as potential drug targets for alternative therapeutic strategies. Methods We used UK Biobank data on plasma levels of 3K proteins ( n = 35K) and fat and fat-free mass ( n = 368K). We applied a machine learning procedure to select proteins for downstream cis Mendelian randomisation analyses, which estimated associations of genetically predicted protein levels with fat mass. These were followed by colocalisation analyses to assess shared causal genetic variants and further cis Mendelian randomisation analyses to assess associations with fat-free mass. Results Here we present evidence on the associations of genetically predicted levels of 21 proteins with fat mass, including five which also colocalise with the trait (AGER, CRIM1, DNER, NEFL, and PRRT3). Seven of the 21 proteins have no clear association with fat-free mass, and ADM has an association with fat-free mass opposite in direction to its association with fat mass, suggesting that modulating these proteins may be less likely to also lower fat-free mass. Two proteins, AGER and ADM, have been investigated in advanced-stage clinical trials for indications unrelated to adiposity. Conclusions These findings support prioritising several proteins for further research as potential drug targets for fat mass reduction. They highlight proteins that may help diversify therapy options beyond glucagon-like peptide-1 receptor agonists and suggest possible repurposing opportunities.