Subclassification of obesity for precision prediction of cardiometabolic diseases

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10<sup>-10</sup>; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10<sup>-14</sup>). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4-15 additional correct interventions and 37-135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.

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Publication Details

Journal
Nature Medicine
Published
2024-10-24
DOI
https://doi.org/10.1038/s41591-024-03299-7
Citations
44
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
15.74

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article

Subclassification of obesity for precision prediction of cardiometabolic diseases

Diederik De Cock, Femke Smit, Carel Wynand le Roux, Juan Fernández Tajes et al.
44 citations
Nature Medicine
Diabetes, Cardiovascular Risks, and Lipoproteins
15.74
article

Subclassification of obesity for precision prediction of cardiometabolic diseases

Diederik De Cock, Femke Smit, Carel Wynand le Roux, Juan Fernández Tajes, Marinella Temprosa, Jordi Blanch, Philipp S. Wild, Marleen M. J. van Greevenbroek, Paul W. Franks, Timothy M. Frayling, Giuseppe N. Giordano, Ilja C.W. Arts, Fariba Ahmadizar, Pierre Bauvin, Daniel Coral, Maria F. Gomez, Rafel Ramos, Carl Delfin, Thomas Sparsø, Maryam Kavousi, Carla Kallen, Michiel Adriaens, M. Kamran Ikram, Marina Panova‐Noeva, Ali Farzaneh, Ewan R. Pearson, José Manuel Fernández‐Real, Alexander Gieswinkel, Kan Wang
article en
44 citations

Abstract

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10<sup>-10</sup>; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10<sup>-14</sup>). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4-15 additional correct interventions and 37-135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.

Nature MedicineVol. 31(2)
University College Dublin (IE), Milken Institute (US), Boehringer Ingelheim (Germany) (DE), Vrije Universiteit Brussel (BE), Harvard University (US), Inserm (FR), University of Dundee (GB), Lund University (SE), Novo Nordisk (Denmark) (DK), George Washington University (US), Johannes Gutenberg University Mainz (DE), Université de Lille (FR), University of Exeter (GB), Universitat de Girona (ES), Maastricht University Medical Centre (NL), Institut Pasteur de Lille (FR), Instituto de Salud Carlos III (ES), Erasmus MC (NL), University Medical Center Utrecht (NL), Centre Hospitalier Universitaire de Lille (FR), Maastricht University (NL), University Medical Center of the Johannes Gutenberg University Mainz (DE), Institut d'Investigació Biomèdica de Girona (ES), Centro de Investigación Biomédica en Red (ES), German Centre for Cardiovascular Research (DE), Genomics (United Kingdom) (GB), Hospital Universitari de Girona Doctor Josep Trueta (ES), Institute of Molecular Biology (DE)
Sanofi, European Federation of Pharmaceutical Industries and Associations, Centres de Recerca de Catalunya, Health Foundation Limburg, European Commission, Stiftelsen för Strategisk Forskning, Erasmus Universiteit Rotterdam, Generalitat de Catalunya, Erasmus Medisch Centrum, Lunds Universitet, Institució Catalana de Recerca i Estudis Avançats, Hjärt-Lungfonden, Novo Nordisk, Stiftung Rheinland-Pfalz für Innovation, Vetenskapsrådet, Stichting De Weijerhorst, Innovative Medicines Initiative, Instituto de Salud Carlos III, European Regional Development Fund, CARIM School for Cardiovascular Diseases, Universiteit Maastricht, Care and Public Health Research Institute, Universiteit Maastricht, NUTRIM School of Nutrition and Translational Research in Metabolism, Cilag
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Openalex Percentile: Top 1%
Diabetes, Cardiovascular Risks, and Lipoproteins
15.74
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