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Subclassification of obesity for precision prediction of cardiometabolic diseases

Coral, Daniel E. LU orcid ; Smit, Femke ; Farzaneh, Ali ; Gieswinkel, Alexander ; Tajes, Juan Fernandez LU ; Sparsø, Thomas ; Delfin, Carl LU orcid ; Bauvin, Pierre ; Wang, Kan and Temprosa, Marinella , et al. (2024) In Nature Medicine
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... (More)
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. (Less)
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organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Nature Medicine
publisher
Nature Publishing Group
external identifiers
  • pmid:39448862
  • scopus:85207719659
ISSN
1546-170X
DOI
10.1038/s41591-024-03299-7
language
English
LU publication?
yes
id
a77846b7-9195-4269-ae09-855cee84a45b
date added to LUP
2024-12-04 10:57:35
date last changed
2025-04-04 13:54:23
@article{a77846b7-9195-4269-ae09-855cee84a45b,
  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 × 10−10; 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 &lt; 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.}},
  author       = {{Coral, Daniel E. and Smit, Femke and Farzaneh, Ali and Gieswinkel, Alexander and Tajes, Juan Fernandez and Sparsø, Thomas and Delfin, Carl and Bauvin, Pierre and Wang, Kan and Temprosa, Marinella and De Cock, Diederik and Blanch, Jordi and Fernández-Real, José Manuel and Ramos, Rafael and Ikram, M. Kamran and Gomez, Maria F. and Kavousi, Maryam and Panova-Noeva, Marina and Wild, Philipp S. and van der Kallen, Carla and Adriaens, Michiel and van Greevenbroek, Marleen and Arts, Ilja and Le Roux, Carel and Ahmadizar, Fariba and Frayling, Timothy M. and Giordano, Giuseppe N. and Pearson, Ewan R. and Franks, Paul W.}},
  issn         = {{1546-170X}},
  language     = {{eng}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Medicine}},
  title        = {{Subclassification of obesity for precision prediction of cardiometabolic diseases}},
  url          = {{http://dx.doi.org/10.1038/s41591-024-03299-7}},
  doi          = {{10.1038/s41591-024-03299-7}},
  year         = {{2024}},
}