TY - JOUR
T1 - Subclassification of obesity for precision prediction of cardiometabolic diseases
AU - Coral, Daniel E.
AU - Smit, Femke
AU - Farzaneh, Ali
AU - Gieswinkel, Alexander
AU - Fernandez-Tajes, Juan
AU - Sparso, Thomas
AU - Delfin, Carl
AU - Bauvain, Pierre
AU - Wang, Kan
AU - Temprosa, Marinella
AU - De Cock, Diederik
AU - Blanch, Jordi
AU - Fernández-Real, José-Manuel
AU - Ramos, Rafael
AU - Ikram, M. Kamran
AU - Gomez, Maria F
AU - Kavousi, Maryam
AU - Panova-Noeva, Marina
AU - Wild, Philipp S.
AU - van der Kallen, Carla
AU - Adriaens, Michiel
AU - van Greevenbroek, Marleen M. J.
AU - Arts, Ilja C. W.
AU - Le Roux, Carel W.
AU - Ahmadizar, Fariba
AU - Frayling, Timothy M.
AU - Giordano, Giuseppe Nicola
AU - Pearson, Ewan
AU - Franks, Paul W.
PY - 2024/10/24
Y1 - 2024/10/24
N2 - Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing sub-populations 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 ~ 173K). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, 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 (HR in women: 1.05, 95%CI:1.03, 1.06, p = 4.19x10-10; HR in men: 1.05, 95%CI:1.04, 1.06, p= 9.33x10-14). 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 to 15 additional correct interventions, and 37 to 135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.
AB - Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing sub-populations 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 ~ 173K). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, 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 (HR in women: 1.05, 95%CI:1.03, 1.06, p = 4.19x10-10; HR in men: 1.05, 95%CI:1.04, 1.06, p= 9.33x10-14). 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 to 15 additional correct interventions, and 37 to 135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.
UR - http://www.scopus.com/inward/record.url?scp=85207719659&partnerID=8YFLogxK
U2 - 10.1038/s41591-024-03299-7
DO - 10.1038/s41591-024-03299-7
M3 - Article
C2 - 39448862
SN - 1078-8956
JO - Nature Medicine
JF - Nature Medicine
ER -
