Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging

Yipeng Song; Yang S Liu; Fernanda Talarico; Yanbo Zhang; Jake Hayward; Mengzhe Wang; Eleni Stroulia; Roger A Dixon; Russell Greiner; Xinmin Li; Andrew Greenshaw; Jie Sui; Bo Cao

doi:10.1159/000534015

Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging

Yipeng Song, Yang S Liu, Fernanda Talarico, Yanbo Zhang, Jake Hayward, Mengzhe Wang, Eleni Stroulia, Roger A Dixon, Russell Greiner, Xinmin Li, Andrew Greenshaw, Jie Sui, Bo Cao^* (Corresponding Author)

^*Corresponding author for this work

Psychology

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

1 Citation (Scopus)

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
Pages (from-to)	1394-1403
Number of pages	10
Journal	Gerontology
Volume	69
Issue number	12
Early online date	19 Sept 2023
DOIs	https://doi.org/10.1159/000534015
Publication status	Published - 1 Dec 2023

Bibliographical note

Acknowledgement
This 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 Statement
Data 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Song, Y., Liu, Y. S., Talarico, F., Zhang, Y., Hayward, J., Wang, M., Stroulia, E., Dixon, R. A., Greiner, R., Li, X., Greenshaw, A., Sui, J., & Cao, B. (2023). Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging. Gerontology, 69(12), 1394-1403. https://doi.org/10.1159/000534015

Song, Y, Liu, YS, Talarico, F, Zhang, Y, Hayward, J, Wang, M, Stroulia, E, Dixon, RA, Greiner, R, Li, X, Greenshaw, A, Sui, J & Cao, B 2023, 'Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging', Gerontology, vol. 69, no. 12, pp. 1394-1403. https://doi.org/10.1159/000534015

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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.",

keywords = "Aging, CLSA, Biological age, Healthy longevity, Machine learning",

author = "Yipeng Song and Liu, {Yang S} and Fernanda Talarico and Yanbo Zhang and Jake Hayward and Mengzhe Wang and Eleni Stroulia and Dixon, {Roger A} and Russell Greiner and Xinmin Li and Andrew Greenshaw and Jie Sui and Bo Cao",

note = "Acknowledgement This 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{\textquoteright}s and Related Disorders (SynAD) program, and University of Alberta Hospital Foundation. ",

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TY - JOUR

T1 - Associations between differential aging and lifestyle, environment, current, and future health conditions

T2 - Findings from Canadian Longitudinal Study on Aging

AU - Song, Yipeng

AU - Liu, Yang S

AU - Talarico, Fernanda

AU - Zhang, Yanbo

AU - Hayward, Jake

AU - Wang, Mengzhe

AU - Stroulia, Eleni

AU - Dixon, Roger A

AU - Greiner, Russell

AU - Li, Xinmin

AU - Greenshaw, Andrew

AU - Sui, Jie

AU - Cao, Bo

N1 - Acknowledgement This 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.

PY - 2023/12/1

Y1 - 2023/12/1

N2 - 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.

AB - 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.

KW - Aging

KW - CLSA

KW - Biological age

KW - Healthy longevity

KW - Machine learning

U2 - 10.1159/000534015

DO - 10.1159/000534015

M3 - Article

C2 - 37725932

SN - 0304-324X

VL - 69

SP - 1394

EP - 1403

JO - Gerontology

JF - Gerontology

IS - 12

ER -

Associations between differential aging and lifestyle, environment, current, and future health conditions: Findings from Canadian Longitudinal Study on Aging

Abstract

Bibliographical note

Data Availability Statement

Keywords

UN SDGs

Access to Document

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Cite this