Abstract
INTRODUCTION: An aging population is a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors and chronic disease conditions.
METHODS: We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions.
RESULTS: BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking, are associated with positive BioAge gap ("older" than expected in BioAge index). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" than expected in BioAge index).
DISCUSSION/CONCLUSIONS: Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the biological aging process that is associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for guiding policy making to promote healthy longevity.
Original language | English |
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Pages (from-to) | 1394-1403 |
Number of pages | 10 |
Journal | Gerontology |
Volume | 69 |
Issue number | 12 |
Early online date | 19 Sept 2023 |
DOIs | |
Publication status | Published - 1 Dec 2023 |
Bibliographical note
AcknowledgementThis research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant
reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA dataset (Comprehensive Cohort), under Application Number 1906013. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland.
Funding Sources
This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program, Alberta Innovates, Mental Health Foundation, MITACS Accelerate program, Simon & Martina Sochatsky Fund for Mental Health, the Alberta Synergies in Alzheimer’s and Related Disorders (SynAD) program, and University of Alberta Hospital Foundation.
Data Availability Statement
Data Availability StatementData are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. To learn more about the accessibility of CLSA data sets, see https://www.clsa-elcv.ca/data-access. Further enquiries can be directed to the corresponding author.
Keywords
- Aging
- CLSA
- Biological age
- Healthy longevity
- Machine learning