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oai:recercat.cat:11351/132132025-10-24T07:46:59Zcom_2072_451667com_2072_378040col_2072_451668

Subclassification of obesity for precision prediction of cardiometabolic diseases Coral Candelo, Daniel Esteban Smit, Femke F. Farzaneh, Ali Gieswinkel, Alexander Fernández-Tajes, Juan Sparsø, Thomas Blanch, Jordi Fernández-Real, Jose Manuel Ramos , Rafel Institut Català de la Salut [Coral DE] Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden. [Smit F] Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands. [Farzaneh A] Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands. [Gieswinkel A] Preventive Cardiology and Preventive Medicine, Center for Cardiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany. [Fernandez Tajes J] Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Helsingborg, Sweden. [Sparsø T] Department of Pharmacometrics, Novo Nordisk A/S, Søborg, Denmark. [Blanch J, Fernandez-Real JM, Ramos R] Nutrition, Eumetabolism and Health Group, Institut d'Investigació Biomèdica de Girona (IDIBGI-CERCA), Girona, Spain. Departament de Ciències Mèdiques, Universitat de Girona, Girona, Spain. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain. Unitat de Diabetis, Endocrinologia i Nutrició, Hospital Universitari de Girona Doctor Josep Trueta, Institut Català de la Salut (ICS), Girona, Spain Hospital Universitari de Girona Dr Josep Trueta Diabetis no-insulinodependent Malalties cardiovasculars Obesitat DISEASES::Cardiovascular Diseases DISEASES::Nutritional and Metabolic Diseases::Metabolic Diseases::Glucose Metabolism Disorders::Diabetes Mellitus::Diabetes Mellitus, Type 2 DISEASES::Nutritional and Metabolic Diseases::Nutrition Disorders::Overnutrition::Obesity Other subheadings::Other subheadings::Other subheadings::/epidemiology DISEASES::Pathological Conditions, Signs and Symptoms::Signs and Symptoms::Body Weight::Overweight::Obesity Other subheadings::Other subheadings::Other subheadings::/complications ENFERMEDADES::enfermedades cardiovasculares ENFERMEDADES::enfermedades nutricionales y metabólicas::enfermedades metabólicas::trastornos del metabolismo de la glucosa::diabetes mellitus::diabetes mellitus tipo II ENFERMEDADES::enfermedades nutricionales y metabólicas::trastornos nutricionales::hipernutrición::obesidad Otros calificadores::Otros calificadores::Otros calificadores::/epidemiología ENFERMEDADES::afecciones patológicas, signos y síntomas::signos y síntomas::peso corporal::sobrepeso::obesidad Otros calificadores::Otros calificadores::Otros calificadores::/complicaciones Malalties cardiovasculars; Diabetis tipus 2; Obesitat Enfermedades cardiovasculares; Diabetes Mellitus Tipo 2; Obesidad Cardiovascular diseases; Diabetes Mellitus Type 2; Obesity 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 × 10-10; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10-14). 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. Open access funding provided by Lund University. 2025-06-05T08:32:14Z 2025-06-05T08:32:14Z 2024 2025-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Coral DE, Smit F, Farzaneh A, Gieswinkel A, Fernandez Tajes J, Sparsø T, et al. Subclassification of obesity for precision prediction of cardiometabolic diseases. Nat Med. 2025 Feb;31(2):534-543. 1546-170X http://hdl.handle.net/11351/13213 10.1038/s41591-024-03299-7 39448862 http://hdl.handle.net/11351/13213 eng Nature Medicine;31(2) https://doi.org/10.1038/s41591-024-03299-7 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf application/pdf application/vnd.openxmlformats-officedocument.spreadsheetml.sheet application/pdf Nature Publishing Company Scientia