OBJECTIVE: Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes. RESEARCH DESIGN AND METHODS: Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality. RESULTS: The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters. CONCLUSIONS: We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.
OBJECTIVE: Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes. RESEARCH DESIGN AND METHODS: Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality. RESULTS: The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters. CONCLUSIONS: We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.
Authors: Céline Fernandez; Michal A Surma; Christian Klose; Mathias J Gerl; Filip Ottosson; Ulrika Ericson; Nikolay Oskolkov; Marju Ohro-Melander; Kai Simons; Olle Melander Journal: Diabetes Care Date: 2019-12-08 Impact factor: 19.112
Authors: Johan Korduner; Erasmus Bachus; Amra Jujic; Martin Magnusson; Peter M Nilsson Journal: Obes Res Clin Pract Date: 2019-11-08 Impact factor: 2.288
Authors: Peter Würtz; Aki S Havulinna; Pasi Soininen; Tuulia Tynkkynen; David Prieto-Merino; Therese Tillin; Anahita Ghorbani; Anna Artati; Qin Wang; Mika Tiainen; Antti J Kangas; Johannes Kettunen; Jari Kaikkonen; Vera Mikkilä; Antti Jula; Mika Kähönen; Terho Lehtimäki; Debbie A Lawlor; Tom R Gaunt; Alun D Hughes; Naveed Sattar; Thomas Illig; Jerzy Adamski; Thomas J Wang; Markus Perola; Samuli Ripatti; Ramachandran S Vasan; Olli T Raitakari; Robert E Gerszten; Juan-Pablo Casas; Nish Chaturvedi; Mika Ala-Korpela; Veikko Salomaa Journal: Circulation Date: 2015-01-08 Impact factor: 29.690
Authors: Yan Zheng; Frank B Hu; Miguel Ruiz-Canela; Clary B Clish; Courtney Dennis; Jordi Salas-Salvado; Adela Hruby; Liming Liang; Estefania Toledo; Dolores Corella; Emilio Ros; Montserrat Fitó; Enrique Gómez-Gracia; Fernando Arós; Miquel Fiol; José Lapetra; Lluis Serra-Majem; Ramón Estruch; Miguel A Martínez-González Journal: J Am Heart Assoc Date: 2016-09-15 Impact factor: 5.501
Authors: Olle Melander; Paola Antonini; Filip Ottosson; Louise Brunkwall; Widet Gallo; Peter M Nilsson; Marju Orho-Melander; Gaetano Pacente; Giovanni D'Arena; Salvatore Di Somma Journal: Intern Emerg Med Date: 2021-01-30 Impact factor: 3.397