Year: 2022
Type: article
Study design: Observational Study
Abstract: The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising > 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. 'Physical activity' and 'General Health' were the exposure categories containing the highest number of 'tentative signals' in analyses assessing the average association of lifestyle variables, while 'Tobacco use' was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions.
Source: Scientific Reports
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,190
Awards: G1197151823, G1513236869, G1558193350, G2004031785, G2412805558, G2563633029, G263730981, G2786770482, G3032835362, G3918735012, G424085733, G5117813090, G583733011, Stratification of Obesity Phenotypes to Optimize Future Therapy, G6373074959, G67379338, LUDC-IRC för individanpassad medicin i diabetes, G804694337, G8616623527, Data science tools to identify robust exposure-phenotype associations for precision medicine
OpenAlex ID: W4220738872