Biological Aging Affects the Rate of Cognitive Decline in Middle-aged and Elderly Populations: A Cohort Study Based on CHARLS
Abstract
To investigate the relationship between biological aging and the rate of cognitive decline in middle-aged and elderly populations.
Methods
Longitudinal tracking data of cognitive function were obtained from the China Health and Retirement Longitudinal Study (CHARLS). We employed the Klemera and Doubal method (KDM) to estimate biological age (BA), and calculate the biological aging index (BAI) and biological aging type (BAT). A multivariate linear regression model was employed to analyze the relationships between baseline BAI, BAT, and cognitive function scores. Based on the baseline analysis, a mixed-effects model was used to examine the longitudinal associations between baseline BAI, BAT, and cognitive function during follow-up.
Results
A total of 5897 participants were included in the study. BAI was found to be negatively associated with baseline cognitive function scores, with the partial regression coefficient (β) (95% CI) being －0.185 (－0.231, －0.139) (P < 0.001). Compared with the lagged aging group, the premature aging group had lower cognitive function scores (β [95% CI]: －0.741 [－0.966, －0.516]). For age and sex, for each additional year of baseline BAI, cognitive function scores declined by an average of 0.012 (95% CI: －0.019, －0.005) points per year after adjusting for age and sex, and declined by 0.011 (95% CI: －0.018, －0.004) points per year after adjusting for other covariates. Compared with participants with lagged aging, those with premature aging experienced, on average, an additional decline of 0.042 (95% CI: －0.075, 0.009) points per year in cognitive function scores after adjusting for age and sex alone, and by 0.039 (95% CI: －0.072, －0.007) points per year after adjusting for other covariates.
Conclusion
Biological aging affects the rate of cognitive decline in middle-aged and elderly populations. A higher BAI is associated with a faster decline in cognitive function. Compared with those with lagged aging, individuals with premature aging exhibit a more rapid rate of cognitive decline.
Keywords: Aging, Biological age, Cognitive function
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NICHOLS E, STEINMETZ J D, VOLLSET S E, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health, 2022, 7(2): e105-e125. doi: 10.1016/S2468-2667(21)00249-8.
LAI W L, XIA Y L, FU Y T, et al. Diagnostic value of phosphorylated tau217 and other plasma biomarkers for cognitive dysfunction in the populations of Deyang City, Sichuan Province, China. J Sichuan Univ (Med Sci), 2024, 55(6): 1520-1526. doi: 10.12182/20241160206.
Chinese Society of Dementia and Cognitive Impairment. Chinese Society of Dementia and Cognitive Impairment. Chinese expert consensus on the diagnosis and treatment of mild cognitive impairment due to Alzheimer's disease 2024. Chin J Neurol Psychiatry, 2024, 57(7): 715-737. doi: 10. 3760/cma.j.cn113694-20240320-00172.
JIA L, DU Y, CHU L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health, 2020, 5(12): e661-e671. doi: 10.1016/s2468-2667(20)30185-7.
LIVINGSTON G, HUNTLEY J, SOMMERLAD A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet, 2020, 396(10248): 413-446. doi: 10.1016/s0140-6736 (20)30367-6.
LIVINGSTON G, SOMMERLAD A, ORGETA V, et al. Dementia prevention, intervention, and care. Lancet, 2017, 390(10113): 2673-2734. doi: 10.1016/s0140-6736(17)31363-6.
CHEN L, FAN J N, SUN D J Y, et al. Progress in research of measurements of biological age. Chin J Epidemiol, 2021, 42(9): 1683-1688. doi: 10.3760/cma.j.cn112338-20201210-01396.
ELLIOTT M L, BELSKY D W, KNODT A R, et al. Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Mol Psychiatry, 2021, 26(8): 3829-3838. doi: 10. 1038/s41380-019-0626-7.
FORRESTER S N, MCMANUS D D, SACZYNSKI J S, et al. A cross-sectional analysis of racial differences in accelerated aging and cognitive function among patients with atrial fibrillation: the SAGE-AF study: forrester, accelerated aging and cognitive function. EClinicalMedicine, 2021, 39: 101060. doi: 10.1016/j.eclinm.2021.101060.
WU J W, YAQUB A, MA Y, et al. Biological age in healthy elderly predicts aging-related diseases including dementia. Sci Rep, 2021, 11(1): 15929. doi: 10.1038/s41598-021-95425-5.
ZHAO Y, HU Y, SMITH J P, et al. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol, 2014, 43(1): 61-68. doi: 10.1093/ije/dys203.
KLEMERA P, DOUBAL S. A new approach to the concept and computation of biological age. Mech Ageing Dev, 2006, 127(3): 240-248. doi: 10.1016/j.mad.2005.10.004.
LEVINE M E. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci, 2013, 68(6): 667-674. doi: 10.1093/gerona/gls233.
CAO L M, CHEN S T, WANG T. Progress in research of quantification of biological age. Chin J Epidemiol, 2023, 44(3): 516-520. doi: 10.3760/cma.j. cn112338-20220814-00709.
HÄGG S, JYLHÄVÄ J. Sex differences in biological aging with a focus on human studies. Elife, 2021, 10: e63425. doi: 10.7554/eLife.63425.
KUO P L, SCHRACK J A, LEVINE M E, et al. Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging. Nat Aging, 2022, 2(7): 635-643. doi: 10.1038/s43587-022-00243-7.
KARLSSON I K, ERICSSON M, WANG Y, et al. Epigenome-wide association study of level and change in cognitive abilities from midlife through late life. Clin Epigenetics, 2021, 13(1): 85. doi: 10.1186/s13148-021-01075-9.
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