Subclassification of obesity for precision prediction of cardiometabolic diseases

Daniel E. Coral; Femke Smit; Ali Farzaneh; Alexander Gieswinkel; Juan Fernandez-Tajes; Thomas Sparso; Carl Delfin; Pierre Bauvain; Kan Wang; Marinella Temprosa; Diederik De Cock; Jordi Blanch; José-Manuel Fernández-Real; Rafael Ramos; M. Kamran Ikram; Maria F Gomez; Maryam Kavousi; Marina Panova-Noeva; Philipp S. Wild; Carla van der Kallen; Michiel Adriaens; Marleen M. J. van Greevenbroek; Ilja C. W. Arts; Carel W. Le Roux; Fariba Ahmadizar; Timothy M. Frayling; Giuseppe Nicola Giordano; Ewan Pearson; Paul W. Franks

doi:10.1038/s41591-024-03299-7

Subclassification of obesity for precision prediction of cardiometabolic diseases

Daniel E. Coral (Lead / Corresponding author)
, Femke Smit (Lead / Corresponding author)
, Ali Farzaneh
, Alexander Gieswinkel
, Juan Fernandez-Tajes
, Thomas Sparso
, Carl Delfin
, Pierre Bauvain
, Kan Wang
, Marinella Temprosa
, Diederik De Cock
, Jordi Blanch
, José-Manuel Fernández-Real
, Rafael Ramos
, M. Kamran Ikram
, Maria F Gomez
, Maryam Kavousi
, Marina Panova-Noeva
, Philipp S. Wild
, Carla van der Kallen

Michiel Adriaens, Marleen M. J. van Greevenbroek, Ilja C. W. Arts, Carel W. Le Roux, Fariba Ahmadizar, Timothy M. Frayling, Giuseppe Nicola Giordano, Ewan Pearson, Paul W. Franks (Lead / Corresponding author)

Diabetes Endocrinology and Reproductive Biology

Research output: Contribution to journal › Article › peer-review

38 Citations (Scopus)

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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 < 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.

Original language	English
Journal	Nature Medicine
DOIs	https://doi.org/10.1038/s41591-024-03299-7
Publication status	Published - 24 Oct 2024

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Cite this

Coral, D. E., Smit, F., Farzaneh, A., Gieswinkel, A., Fernandez-Tajes, J., Sparso, T., Delfin, C., Bauvain, P., Wang, K., Temprosa, M., De Cock, D., Blanch, J., Fernández-Real, J.-M., Ramos, R., Ikram, M. K., Gomez, M. F., Kavousi, M., Panova-Noeva, M., Wild, P. S., ... Franks, P. W. (2024). Subclassification of obesity for precision prediction of cardiometabolic diseases. Nature Medicine. https://doi.org/10.1038/s41591-024-03299-7

@article{05e552f0224f431c82c1f8279f6e8974,

title = "Subclassification of obesity for precision prediction of cardiometabolic diseases",

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 \textasciitilde{}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.",

author = "Coral, \{Daniel E.\} and Femke Smit and Ali Farzaneh and Alexander Gieswinkel and Juan Fernandez-Tajes and Thomas Sparso and Carl Delfin and Pierre Bauvain and Kan Wang and Marinella Temprosa and \{De Cock\}, Diederik and Jordi Blanch and Jos{\'e}-Manuel Fern{\'a}ndez-Real and Rafael Ramos and Ikram, \{M. Kamran\} and Gomez, \{Maria F\} and Maryam Kavousi and Marina Panova-Noeva and Wild, \{Philipp S.\} and \{van der Kallen\}, Carla and Michiel Adriaens and \{van Greevenbroek\}, \{Marleen M. J.\} and Arts, \{Ilja C. W.\} and \{Le Roux\}, \{Carel W.\} and Fariba Ahmadizar and Frayling, \{Timothy M.\} and Giordano, \{Giuseppe Nicola\} and Ewan Pearson and Franks, \{Paul W.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = oct,

day = "24",

doi = "10.1038/s41591-024-03299-7",

language = "English",

journal = "Nature Medicine",

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Coral, DE, Smit, F, Farzaneh, A, Gieswinkel, A, Fernandez-Tajes, J, Sparso, T, Delfin, C, Bauvain, P, Wang, K, Temprosa, M, De Cock, D, Blanch, J, Fernández-Real, J-M, Ramos, R, Ikram, MK, Gomez, MF, Kavousi, M, Panova-Noeva, M, Wild, PS, van der Kallen, C, Adriaens, M, van Greevenbroek, MMJ, Arts, ICW, Le Roux, CW, Ahmadizar, F, Frayling, TM, Giordano, GN, Pearson, E & Franks, PW 2024, 'Subclassification of obesity for precision prediction of cardiometabolic diseases', Nature Medicine. https://doi.org/10.1038/s41591-024-03299-7

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 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85207719659

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 -

Subclassification of obesity for precision prediction of cardiometabolic diseases

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