Exploring relevant factors of cognitive impairment in the elderly Chinese population using Lasso regression and Bayesian networks

Qiao Chen; Tianyi Zhou; Cong Zhang; Xiaoni Zhong

doi:10.1016/j.heliyon.2024.e27069

Exploring relevant factors of cognitive impairment in the elderly Chinese population using Lasso regression and Bayesian networks

Heliyon. 2024 Feb 24;10(5):e27069. doi: 10.1016/j.heliyon.2024.e27069. eCollection 2024 Mar 15.

Authors

Qiao Chen^{1

2}, Tianyi Zhou¹, Cong Zhang¹, Xiaoni Zhong¹

Affiliations

¹ College of Public Health, Chongqing Medical University, Chongqing, 400016, China.
² Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China.

Abstract

Older adults are highly susceptible to developing cognitive impairment(CI). Various factors contribute to the prevalence of CI, but the potential relationships among these factors remain unclear. This study aims to explore the relevant factors associated with CI in Chinese older adults and analyze the potential relationships between CI and these factors.We analyzed the data on 6886 older adults aged≥60 from the China Health and Retirement Longitudinal Study (CHARLS) 2018. Lasso regression was initially used to screening variables. Bayesian Networks(BNs) were used to identify the correlates of CI and potential associations between factors. After screening with Lasso regression, 11 variables were finally included in the BNs. The BNs, by establishing a complex network relationship, revealed that age, education, and indoor air pollution were the direct correlates affecting the occurrence of CI in older adults. It also indicated that marital status indirectly influenced CI through age, and residence indirectly linked to CI through two pathways: indoor air pollution and education.Our findings underscore the effectiveness of BNs in unveiling the intricate network linkages among CI and its associated factors, holding promising applications. It can serve as a reference for public health departments to address the prevention of CI in the elderly.

Keywords: Bayesian networks; CHALRS; Cognitive impairment; Lasso; Older adults.