Multimorbidity, depressive symptoms and disability in activities of daily living amongst middle-aged and older Chinese: Evidence from the China Health and Retirement Longitudinal Study

2021 ◽  
Vol 295 ◽  
pp. 703-710 ◽  
Author(s):  
Shunzhuang Peng ◽  
Sijiu Wang ◽  
Xing Lin Feng
Keyword(s):  
Depressive Symptoms ◽  
Longitudinal Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Middle Aged ◽  
Older Chinese

scholarly journals A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xingqi Cao ◽  
Guanglai Yang ◽  
Xurui Jin ◽  
Liu He ◽  
Xueqin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Longitudinal Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Gradient Boosting ◽  
Biological Aging ◽  
Middle Aged ◽  
Chinese Adults ◽  
Older Chinese ◽  
Older Chinese Adults

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population.Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure—Klemera and Doubal method-BA (KDM-BA) we previously developed—with physical disability and mortality, respectively.Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA.Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

scholarly journals A machine learning based aging measure among middle-aged and older Chinese adults: the China Health and Retirement Longitudinal Study

2021 ◽  
Author(s):  
Xingqi Cao ◽  
Guanglai Yang ◽  
Xurui Jin ◽  
Liu He ◽  
Xueqin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Longitudinal Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Gradient Boosting ◽  
Biological Aging ◽  
Middle Aged ◽  
Chinese Adults ◽  
Older Chinese ◽  
Older Chinese Adults

Background: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Methods: We used data from 9,771 middle-aged and older (≥45 years) Chinese adults in the China Health and Retirement Longitudinal Study. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure - Klemera and Doubal method -biological age (KDM-BA) we previously developed - with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed best, resulting in a ML-BA with R-squared value of 0.270 and MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1% to 7% (per one-year increment in ML-BA, all P <0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and help facilitate the understanding of the aging process.

The Association of Hearing Impairment and Its Severity with Physical and Mental Health among Chinese Middle-aged and Older Adults

2020 ◽  
Author(s):  
Xin Ye ◽  
Dawei Zhu ◽  
Siyuan Chen ◽  
Ping He
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Depressive Symptoms ◽  
Health Outcomes ◽  
Activities Of Daily Living ◽  
Hearing Impairment ◽  
Daily Living ◽  
Hearing Impaired ◽  
Physical And Mental Health ◽  
Middle Aged

Abstract Background: Hearing loss is a common chronic condition which can be closely related with people’s health. However, current studies on this topic are quite limited in developing countries, and few with standardized audiometric measurement and multiple health outcomes. Therefore, we aimed to explore the association between hearing impairment and its severity with physical and mental health among Chinese middle-aged and older adults. Methods: We obtained data from two sources: (1) China Health and Retirement Longitudinal Study (CHARLS) 2011, 2013, and 2015, in which hearing impairment was measured by asking whether participants aged 45 years old had hearing problems; and (2) Hearing Survey 2019, the baseline survey of a randomized controlled trial conducted in Shandong Province of China, including 376 middle-aged and older participants. The severity of hearing impairment was identified by pure tone average of hearing thresholds at 0.5, 1, 2, and 4 kHz. Results: In CHARLS, 1248 (8.36%) participants suffered from hearing impairment at baseline, and hearing-impaired individuals were more likely to have chronic diseases, impaired activities of daily living (ADLs), impaired instrumental activities of daily living (IADLs) and depressive symptoms. For the 376 hearing-impaired participants in Hearing Survey 2019, 30.32%, 38.30% and 31.38% of them had moderate, severe and profound hearing impairment, respectively. As the severity of hearing impairment increased, individuals were likely to have impaired ADLs, impaired IADLs and depressive symptoms. Conclusions: Hearing impairment and its severity were closely related to multiple physical and mental health outcomes among Chinese middle-aged and older adults. Actions should be taken to prevent and treat hearing impairment, so as to improve people’s health and well-being.

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