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.
Highlights
Aging is a complex, inevitable, and multifactorial process, characterized by functional deterioration, physiological damage, and multiple age-related diseases [1]
In the prediction model developed by machine learning methods, the model can automatically identify those interactive relationship from the data and if it is unnecessary to specify interactions
We examined the associations of the best Machine learning (ML)-Biological age (BA) and Klemera and Doubal method (KDM)-BA with physical disability and mortality during the follow-up period
Summary
Inevitable, and multifactorial process, characterized by functional deterioration, physiological damage, and multiple age-related diseases [1]. Traditional methods have been demonstrated to perform well in predicting adverse aging outcomes [7–10], they may encounter obstacles when dealing with complex, multidimensional data. Among such multidimensional data, there are complex interactions among the features such as the interaction between vitamin D and albumin on mortality [12], and most of the current models were developed based on regression methods. The researcher needs to manually input the predefined interactions Missing those complex interactions in the regression model may result in an inaccurate prediction of outcomes. More studies are required to validate the application values of ML in other populations and with more features
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