Protective and Risk Factors in Daily Life Associated with Cognitive Decline of Older Adults

Fang TONG ¹ Hao Yang ² Haidong Yu ^3* Le-wen Sui ^1* Jing-Yuan Yao ^1* Chen-Lei Shi ^1* Qiao-Yuan Yao ^1* Mei-Fang Shi ^3* Cheng-Lang Qian ^2* Gang Li ^2* Chao Zhao ^2* Hui-Jing Wang ^1*

• ¹ Shanghai University of Medicine and Health Sciences, Shanghai, China
• ² Huashan Hospital, Fudan University, Shanghai, Shanghai Municipality, China
• ³ Youyi Road Community Health Service Centre for Baoshan District, Shanghai, China

Background: Cognitive decline is a chronic condition which is characterized by a loss of the ability to remember, learn, and pay attention to complex tasks. Many older people are now suffering from cognitive decline, which decreases life quality and leads to disability. This study aimed to identify the risk and protective factors for cognitive decline of the older people from daily life and establish a predictive model using logistic regression. Methods: We investigated 3,790 older people with health examination and questionnaires which included information associated with physical condition, lifestyle factors, and cognitive status. Single-factor comparison, principal component analysis with a Manova-Wilk test, multiple linear regression, and logistic regression were performed to filter the risk and protective factors regarding cognitive decline of older individuals. Then a predictive model using logistic regression was established based on the most significant protective and risk factors. Results: We found a significant separation along the coordinate axis between people with normal and declined cognition by principal component analysis, as confirmed by the Manover-Wilk test. Single-factor comparison, multiple linear regression and logistic regression implied that gender, age, hypertension level, height, dietary habit, physical-exercise duration, physical-exercise history, and smoking history could be closely linked with cognitive decline. We also observed significant differences in height, physical exercise duration, physical-exercise years, and smoking years between the male and female of the participants. ROCs of the predictive model by logistic regression were plotted, with AUC values of 0.683 and 0.682 respectively for the training and testing sets. Although an effective predictive model is thought to have AUC over 0.7, we still believe that the present model is acceptable because the value is close to the threshold. Conclusion: The protective factors of cognitive decline for older people were male gender, height, keeping moderate exercising, and nicotine stimulation, and the risk factors included age, female gender, vegetarianism and hypertension. Except for the genetic factor, differences in lifestyle, such as smoking and exercise habits, may contribute to the observed differences in cognitive function between genders. The significant results could be utilized in the practice for the early intervention of cognitive decline in aged people.