scholarly journals Co-occurrence of cardiometabolic diseases and frailty in older Chinese adults in the Beijing Longitudinal Study of Ageing

2013 ◽  
Vol 42 (3) ◽  
pp. 346-351 ◽  
Author(s):  
Zhe Tang ◽  
Chunxiu Wang ◽  
Xiaowei Song ◽  
Jing Shi ◽  
Arnold Mitnitski ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Chinese Adults ◽  
Cardiometabolic Diseases ◽  
Older Chinese ◽  
Older Chinese Adults

scholarly journals Co-occurrence of cardiometabolic diseases and frailty in older Chinese adults in the Beijing Longitudinal Study of Ageing

2013 ◽  
Vol 42 (5) ◽  
pp. 663-663
Author(s):  
Z. Tang ◽  
C. Wang ◽  
X. Song ◽  
J. Shi ◽  
A. Mitnitski ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Chinese Adults ◽  
Cardiometabolic Diseases ◽  
Older Chinese ◽  
Older Chinese Adults

And mortality in older Chinese adults: Results from the Beijing longitudinal study of aging

2012 ◽  
Vol 16 (10) ◽  
pp. 903-907 ◽  
Author(s):  
X. Fang ◽  
J. Shi ◽  
X. Song ◽  
A. Mitnitski ◽  
Z. Tang ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Chinese Adults ◽  
Older Chinese ◽  
Older Chinese Adults

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.

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