Characterization of metabolically healthy and unhealthy obesity through circulating proteins and metabolites

BackgroundIndividuals affected by obesity present different health trajectories and do not suffer from cardiometabolic complications all in the same way. There is a need to better understand obesity subtypes and to develop approaches for stratification. In this study we investigated both metabolomic and proteomic signatures in serum and blood plasma samples discriminating metabolically healthy from unhealthy obesity. MethodsWe investigated cross-sectional metabolomic and proteomic data from participants of the Cooperative Health Research in South Tyrol (CHRIS) study. Participants were grouped into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) based on available health data in the study. A total of 461 individuals were included in the analysis, with n=130 MHO and n=331 MUO. Random forest (RF) classifiers were used to discriminate metabolically healthy from unhealthy obesity and to identify molecular features characteristic of MHO/MUO. Linear regression models were used to assess associations between each relevant metabolite/protein and MHO/MUO phenotypes independently of age, sex and body composition. ResultsThe MHO/MUO RF classifier achieved a performance of AUC = 0.709, 95% CI = (0.698,0.721). Three plasma proteins and 12 circulating metabolites were identified as relevant predictors of MHO/MUO phenotypes. Linear regression models confirmed the Apolipoprotein C-III (APOC3) association to be independent of age, visceral fat composition, medication or serum triglyceride levels. ConclusionAPOC3 was identified as a novel predictor for obesity stratification, highlighting the importance of circulating triglyceride levels in relation to metabolic health.