Subclassification of obesity for precision prediction of cardiometabolic diseases

Authors

• 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.

APA Citation

Coral, Daniel E.; Smit, Femke; Farzaneh, Ali; Gieswinkel, Alexander; Tajes, Juan Fernandez; Sparsø, Thomas; Delfin, Carl; Bauvain, Pierre; Wang, Kan; Temprosa, Marinella; De Cock, Diederik; Blanch, Jordi; Fernández-Real, José Manuel; Ramos, Rafael; Ikram, M Kamran; Gomez, Maria F.; Kavousi, Maryam; Panova-Noeva, Marina; Wild, Philipp S.; van der Kallen, Carla; Adriaens, Michiel; van Greevenbroek, Marleen; Arts, Ilja; Le Roux, Carel; Ahmadizar, Fariba; Frayling, Timothy M.; Giordano, Giuseppe N.; Pearson, Ewan R.; and Franks, Paul W., "Subclassification of obesity for precision prediction of cardiometabolic diseases" (2024). GW Authored Works. Paper 5779.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/5779

Department

Biostatistics and Bioinformatics