2026-04-03T23:41:56Zhttps://recercat.cat/oai/requestoai:recercat.cat:11351/132132025-10-24T07:46:59Zcom_2072_451667com_2072_378040col_2072_451668
RECERCAT
author
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
2025-10-24T07:46:58Z
2025-10-24T07:46:58Z
2025-06-05T08:32:14Z2025-06-05T08:32:14Z20242025-02
http://hdl.handle.net/11351/13213
Malalties cardiovasculars; Diabetis tipus 2; ObesitatEnfermedades cardiovasculares; Diabetes Mellitus Tipo 2; ObesidadCardiovascular diseases; Diabetes Mellitus Type 2; ObesityObesity 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.
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
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
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion