2026-04-03T17:51:56Zhttps://recercat.cat/oai/request

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

00925njm 22002777a 4500 dc Coral Candelo, Daniel Esteban author Smit, Femke F. author Farzaneh, Ali author Gieswinkel, Alexander author Fernández-Tajes, Juan author Sparsø, Thomas author Blanch, Jordi author Fernández-Real, Jose Manuel author Ramos , Rafel author 2025-06-05T08:32:14Z 2025-06-05T08:32:14Z 2024 2025-02 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. http://hdl.handle.net/11351/13213 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 Subclassification of obesity for precision prediction of cardiometabolic diseases