Correction: Adverse childhood experiences and handgrip strength among middle-aged and older adults: a cross-sectional study in China

The long-term effects of childhood circumstances on older individuals: A systematic review

Childhood experiences are known to shape individuals' development and can influence various aspects of life later on. Understanding the long-term effects is crucial for informing interventions and policies aimed at promoting healthy aging. This review aimed to explore the long-term effects of childhood experiences on older individuals. This systematic review comprised three distinct phases. Firstly, a systematic review was conducted, exploring databases such as Google Scholar, PubMed, EMBASE, PsycINFO, and the Web of Science. Out of the 2116 studies initially identified, 24 studies were selected based on the inclusion criteria. Secondly, these inclusion criteria were applied to ensure that the chosen studies specifically delved into the connection between childhood experiences and outcomes in older individuals. Finally, data extraction and synthesis techniques were employed to analyze findings, facilitating the drawing of conclusions concerning the enduring impacts of childhood experiences on the well-being of older individuals. The review's findings revealed how negative experiences in childhood continue to affect older individuals in various ways. These early-life events have far-reaching consequences, profoundly impacting their physical health, making them more susceptible to chronic diseases and weakening their immune system. Additionally, they affect mental health, leading to conditions like depression, anxiety, and substance abuse. Cognitive function is also affected, resulting in memory problems and cognitive decline. Furthermore, these experiences impact social relationships, affecting trust, emotional control, and social isolation in later life. This review highlighted the enduring influence of childhood circumstances on the health and well-being of older individuals. Policymakers and health care practitioners should consider these findings when developing strategies to support healthy aging and mitigate the long-term effects of adverse childhood experiences.

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.