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. 2021 Dec 1:8:698851.
doi: 10.3389/fmed.2021.698851. eCollection 2021.

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

Xingqi Cao  1 , Guanglai Yang  2 , Xurui Jin  2   3 , Liu He  1 , Xueqin Li  1 , Zhoutao Zheng  1 , Zuyun Liu  1 , Chenkai Wu  2
Affiliations

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

Xingqi Cao et al. Front Med (Lausanne). .

Abstract

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.

Keywords: aging measure; biological age; disability; machine learning; mortality.

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Conflict of interest statement

XJ is employed by MindRank AI ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the analytic sample. CHARLS, the China Health and Retirement Longitudinal Study; BADL, basic activities of daily living; IADL, instrumental activities of daily living.
Figure 2
Figure 2
Correlations of chronological age with machine learning method-biological age and Klemera and Doubal method-biological age. CA, chronological age; ML-BA, Machine Learning method-biological age; KDM-BA, Klemera and Doubal method-biological age. (A) and (B) show the correlation between CA and the two measures (ML-BA and KDM-BA), respectively.
See this image and copyright information in PMC

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