Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases

Abstract

Background: We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. Methods: This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5–24.9 kg/m2), overweight (OW, metBMI of 25–29.9 kg/m2), obesity (OB, metBMI ≥ 30 kg/m2), overestimated (OE, metBMI-actBMI > 5 kg/m2), and underestimated (UE, metBMI-actBMI < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02–3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach. Conclusions: Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of ‘healthily obese’ and ‘unhealthily lean’ individuals.