Gender-Dependent Associations of Metabolite Profiles and Body Fat Distribution in a Healthy Population with Central Obesity: Towards Metabolomics Diagnostics
Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics Szymanska, E.; Bouwman, J.; Strassburg, K.; Vervoort, J.; Kangas, A.J.; Soininen, P.; Ala-Korpela, M.; Westerhuis, J.A.; van Duynhoven, J.P.M.; Mela, D.J.; Macdonald, I.A.; Vreeken, R.J.; Smilde, A.K.; Jacobs, D.M. . (2012). Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central
... ation with central obesity: towards metabolomics diagnostics. Omics, 16(12), 652-667. Abstract Obesity is a risk factor for cardiovascular diseases and type 2 diabetes especially when the fat is accumulated to central depots. Novel biomarkers are crucial to develop diagnostics for obesity and related metabolic disorders. We evaluated the associations between metabolite profiles (136 lipid components, 12 lipoprotein subclasses, 17 low-molecular-weight metabolites, 12 clinical markers) and 28 phenotype parameters (including different body fat distribution parameters such as android (A), gynoid (G), abdominal visceral (VAT), subcutaneous (SAT) fat) in 215 plasma/serum samples from healthy overweight men (n = 32) and women (n = 83) with central obesity. (Partial) correlation analysis and partial least squares (PLS) regression analysis showed that only specific metabolites were associated to A:G ratio, VAT, and SAT, respectively. These association patterns were gender dependent. For example, insulin, cholesterol, VLDL, and certain triacylglycerols (TG 54:1-3) correlated to VAT in women, while in men VAT was associated with TG 50:1-5, TG 55:1, phosphatidylcholine (PC 32:0), and VLDL ((X)L). Moreover, multiple regression analysis revealed that waist circumference and total fat were sufficient to predict VAT and SAT in women. In contrast, only VAT but not SAT could be predicted in men and only when plasma metabolites were included, with PC 32:0 being most strongly associated with VAT. These findings collectively highlight the potential of metabolomics in obesity and that gender differences need to be taken into account for novel biomarker and diagnostic discovery for obesity and metabolic disorders.