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Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality

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posted on 14.03.2022, 22:57 authored by Filip Ottosson, Einar Smith, Ulrika Ericson, Louise Brunkwall, Marju Orho-Melander, Salvatore Di Somma, Paola Antonini, Peter M Nilsson, Céline Fernandez, Olle Melander
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 if 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 7663 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 more than 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, 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 to lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals, to initiate lifestyle and pharmacological interventions.

Funding

This work was supported by the Swedish Foundation for Strategic Research (IRC LUDC), Swedish Research Council (SFO-EXODIAB), AIR Lund (Artificially Intelligent use of Registers at Lund University) research environment (VR; Grant No. 2019-61406), Lund University Infrastructure Grants for population-based cohorts and metabolomics platforms (STYR 2019/2046), European Research Council AdG 2019-885003, Novo Nordisk Foundation NNF200C0063465, Swedish Research Council grant Dnr 2018-02760, Swedish Heart and Lung Foundation grant Dnr 20180278, Ernhold Lundstrom Research Foundation, Hulda and E Conrad Mossfelts Foundation and the Albert Pahlsson Foundation.

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