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Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality
Department Of Clinical Sciences Malmö, Malmö, Sweden.
Department Of Clinical Sciences Malmö, Malmö, Sweden.ORCID iD: 0000-0001-6349-3955
Department Of Clinical Sciences Malmö, Malmö, Sweden.
Umeå University, Umea, Sweden.
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2022 (English)In: Nutrients, E-ISSN 2072-6643, Vol. 14, no 15, article id 3171Article in journal (Refereed) Published
Abstract [en]

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. © 2022 by the authors.

Place, publisher, year, edition, pages
Basel: MDPI, 2022. Vol. 14, no 15, article id 3171
Keywords [en]
cardiometabolic risk factors, lifestyle, prediction interval, quantile random forests, risk assessment, sensitivity
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:hh:diva-49085DOI: 10.3390/nu14153171ISI: 000839709400001Scopus ID: 2-s2.0-85136515127OAI: oai:DiVA.org:hh-49085DiVA, id: diva2:1724975
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Note

Funding text 1: P.W.F. has received research grants from numerous diabetes drug companies and fees as consultant from Novo Nordisk, Lilly, and Zoe Ltd., London, UK; He is currently the Scientific Director in Medical Science at the Novo Nordisk Foundation. Other authors declare no conflict of interests.

Funding text 2: This project received funding from the Swedish Research Council, Strategic Research Area Exodiab, (Dnr 2009-1039), the Swedish Foundation for Strategic Research (IRC15-0067), the Swedish Research Council, Linnaeus grant (Dnr 349-2006-237), and the European Research Council (CoG-2015_681742_NASCENT). J.B received funding from the Swedish Research Council (Artificially Intelligent use of Registers (AIR Lund), Dnr 2019-00198). N.A.-P. received funding from the Swedish Research Council (Avtals-ID: 2021-06714_3) and the Henning och Johan Throne-Holsts Foundation.

Available from: 2023-01-09 Created: 2023-01-09 Last updated: 2023-08-28Bibliographically approved

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Ohlsson, Mattias

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