Association between multimorbidity and memory-related diseases among middle-aged and older adults: Evidence from the China Health and Retirement Longitudinal Study

Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

Introduction

Memory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs.

Background

MDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs.

Methods

Data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson's correlation analysis demonstrated a robust negative association (r = -0.188, p < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant (B = -0.002, β = -0.142, t = -14.393, 95% CI = -0.002, -0.001, p = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI's role in enhancing predictive abilities, with the DT model demonstrating superior performance.

Conclusion

This study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.

The effect of body mass index on self-rated health in middle-aged and older adults: evidence from the China health and retirement longitudinal study

BACKGROUND AND AIMS

Health promotion for middle-aged and older people has received a lot of attention recently in the context of healthy aging. Furthermore, it is unclear how body mass index (BMI) presently affects self-rated health (SRH), a reliable and representative indicator of health.

METHODS

This study used longitudinal follow-up data from the China Health and Retirement Longitudinal Study (CHARLS). Systematic collection of information on the socio-demographic, lifestyle, and health status of the subjects. Binary logistic regression was used to investigate the relationship between BMI and SRH, and gender-specific variations were examined. Subgroup analysis was used to examine interactions, and the results of the research stability were demonstrated.

RESULTS

After adjusting for age, gender, education level, marital status, place of residence, number of chronic diseases, alcohol consumption, smoking, depressive symptoms, and SRH at baseline, it was found that obesity grade 1 and obesity grade 2 were good contributors to SRH compared to normal weight individuals, and this association was different in males and females. According to the results of the subgroup analyses, those under 65 years old, with junior high school or less education, with a spouse, residing in a city, having one chronic disease, and not smoking or drinking, respectively, all had stable positive associations between obesity and SRH.

CONCLUSIONS

Our findings suggest that obesity may be associated with good SRH. Teams of healthcare professionals should revisit the potential impact of obesity among middle-aged and older adults and focus on developing prevention strategies for morbid obesity.