Characterization of metabolically healthy and unhealthy obesity by circulating omics

Individuals 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 metabolomic and proteomic signatures in serum and blood plasma samples discriminating metabolically healthy from unhealthy obesity.

We investigated 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. A total of 461 individuals were included in the analysis, with n = 130 MHO and n = 331 MUO. Random forest (RF) classifiers and linear regression models were used to discriminate metabolically healthy from unhealthy obesity and to identify molecular features characteristic of MHO/MUO. Regression models were used to assess associations between each relevant metabolite/protein and MHO/MUO phenotypes. Three plasma proteins and 12 circulating metabolites were identified as relevant predictors of MHO/MUO phenotypes. Among them, Apolipoprotein C-III (APOC3) was the most important predictor in the RF models. Causal mediation analysis confirmed a direct effect of APOC3 on the MHO/MUO phenotypes, and identified triglyceride levels as a mediator of APOC3.

APOC3 was identified as a novel predictor for obesity stratification, highlighting the importance of this apolipoprotein in relation to metabolic health in obesity.