"Subclassification of obesity for precision prediction of cardiometabol" by Daniel E. Coral, Femke Smit et al.
 

Subclassification of obesity for precision prediction of cardiometabolic diseases

Daniel E. Coral, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden. daniel.coral@med.lu.se.
Femke Smit, Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands. f.smit@maastrichtuniversity.nl.
Ali Farzaneh, Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Alexander Gieswinkel, Preventive Cardiology and Preventive Medicine, Center for Cardiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
Juan Fernandez Tajes, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden.
Thomas Sparsø, Department of Pharmacometrics, Novo Nordisk A/S, Søborg, Denmark.
Carl Delfin, Department of Pharmacometrics, Novo Nordisk A/S, Søborg, Denmark.
Pierre Bauvain, Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1190-EGID, Lille, France.
Kan Wang, Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Marinella Temprosa, Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Rockville, MD, USA.
Diederik De Cock, Biostatistics and Medical Informatics Research Group, Department of Public Health, Vrije Universiteit Brussel, Brussels, Belgium.
Jordi Blanch, Nutrition, Eumetabolism and Health Group, Institut d'Investigació Biomèdica de Girona (IDIBGI-CERCA), Girona, Spain.
José Manuel Fernández-Real, Nutrition, Eumetabolism and Health Group, Institut d'Investigació Biomèdica de Girona (IDIBGI-CERCA), Girona, Spain.
Rafael Ramos, Nutrition, Eumetabolism and Health Group, Institut d'Investigació Biomèdica de Girona (IDIBGI-CERCA), Girona, Spain.
M Kamran Ikram, Departments of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Maria F. Gomez, Diabetic Complications Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Malmö, Sweden.
Maryam Kavousi, Departments of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Marina Panova-Noeva, Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany.
Philipp S. Wild, Preventive Cardiology and Preventive Medicine, Center for Cardiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
Carla van der Kallen, School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
Michiel Adriaens, Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
Marleen van Greevenbroek, School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
Ilja Arts, Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
Carel Le Roux, Diabetes Complications Research Centre, Conway Institute, University College Dublin, Dublin, Ireland.
Fariba Ahmadizar, Data Science and Biostatistics Department, Julius Global Health, University Medical Center Utrecht, Utrecht, The Netherlands.
Timothy M. Frayling, Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK.
Giuseppe N. Giordano, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden.
Ewan R. Pearson, Population Health and Genomics, University of Dundee, Dundee, UK.
Paul W. Franks, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden. paul.franks@med.lu.se.

Document Type

Journal Article

Publication Date

10-24-2024

Journal

Nature medicine

DOI

10.1038/s41591-024-03299-7

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; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10). 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.

Department

Biostatistics and Bioinformatics

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