Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts

Habtamu B. Beyene, Corey Giles, Kevin Huynh, Tingting Wang, Michelle Cinel, Natalie A. Mellett, Gavriel Olshansky, Thomas G. Meikle, Gerald F. Watts, Joseph Hung, Jennie Hui, Gemma Cadby, John Beilby, John Blangero, Eric K. Moses, Jonathan E. Shaw, Dianna J. Magliano () and Peter J. Meikle ()
Additional contact information
Habtamu B. Beyene: Baker Heart and Diabetes Institute
Corey Giles: Baker Heart and Diabetes Institute
Kevin Huynh: Baker Heart and Diabetes Institute
Tingting Wang: Baker Heart and Diabetes Institute
Michelle Cinel: Baker Heart and Diabetes Institute
Natalie A. Mellett: Baker Heart and Diabetes Institute
Gavriel Olshansky: Baker Heart and Diabetes Institute
Thomas G. Meikle: Baker Heart and Diabetes Institute
Gerald F. Watts: University of Western Australia
Joseph Hung: University of Western Australia
Jennie Hui: PathWest Laboratory Medicine of Western Australia
Gemma Cadby: University of Western Australia
John Beilby: University of Western Australia
John Blangero: The University of Texas Rio Grande Valley
Eric K. Moses: University of Western Australia
Jonathan E. Shaw: Baker Heart and Diabetes Institute
Dianna J. Magliano: Baker Heart and Diabetes Institute
Peter J. Meikle: Baker Heart and Diabetes Institute

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: Abstract Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of “metabolically healthy obese”. We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify “at risk” individuals for targeted intervention and monitoring.

Date: 2023
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DOI: 10.1038/s41467-023-41963-7

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