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Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality

Author

  • Hugo Pomares-Millan
  • Alaitz Poveda
  • Naeimeh Atabaki Pasdar
  • Jonas Björk
  • Mattias Ohlsson
  • Nick Giordano
  • Paul Franks

Other contributions

  • Ingegerd Johansson

Summary, in English

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person's degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual's cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as 'sensitive' to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup.

Department/s

  • EXODIAB: Excellence of Diabetes Research in Sweden
  • Genetic and Molecular Epidemiology
  • EPI@LUND
  • Surgery and public health
  • eSSENCE: The e-Science Collaboration
  • EpiHealth: Epidemiology for Health
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

Publishing year

2022

Language

English

Pages

1-13

Publication/Series

Nutrients

Volume

14

Issue

15

Document type

Journal article

Publisher

MDPI AG

Topic

  • Cardiac and Cardiovascular Systems

Status

Published

Research group

  • Genetic and Molecular Epidemiology
  • EPI@LUND
  • Surgery and public health
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

ISBN/ISSN/Other

  • ISSN: 2072-6643