Limitations in activities of daily living increase the risk of stroke in older Chinese adults: a population-based longitudinal study

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.

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

10.1101/2021.04.16.21255644 ◽

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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Trends in disability of instrumental activities of daily living among older Chinese adults, 1997-2006: population based study

BMJ Open ◽

10.1136/bmjopen-2017-016996 ◽

2017 ◽

Vol 7 (8) ◽

pp. e016996 ◽

Cited By ~ 12

Author(s):

Yajun Liang ◽

Anna-Karin Welmer ◽

Jette Möller ◽

Chengxuan Qiu

Keyword(s):

Daily Living ◽

Total Sample ◽

Population Based ◽

Chinese Adults ◽

Nutrition Survey ◽

Population Based Study ◽

Iadl Disability ◽

Older Chinese ◽

Older Chinese Adults ◽

Over Time

BackgroundData on trends for disability in instrumental activity of daily living (IADL) are sparse in older Chinese adults.ObjectivesTo assess trends in prevalence and incidence of IADL disability among older Chinese adults and to explore contributing factors.DesignPopulation based study.Setting15 provinces and municipalities in China.SubjectsParticipants (age ≥60) were from four waves of the China Health and Nutrition Survey, conducted in 1997 (n=1533), 2000 (n=1581), 2004 (n=2028) and 2006 (n=2256), and from two cohorts constructed within the national survey: cohort 1997–2004 (n=712) and cohort 2000–2006 (n=823).MeasurementsIADL disability was defined as inability to perform one or more of the following: shopping, cooking, using transportation, financing and telephoning. Data were analysed with logistic regression and generalised estimating equation models.ResultsThe prevalence of IADL disability significantly decreased from 1997 to 2006 in the total sample and in all of the subgroups by age, sex, living region and IADL items (all ptrend<0.05). The incidence of IADL disability remained stable from cohort 1997-2004 to cohort 2000-2006 in the total sample and in all of the subgroups (all p>0.10). The recovery rate from IADL disability significantly increased over time in those aged 60–69 years (p=0.03). Living in a rural area or access to local clinics for healthcare was less disabling over time (ptrend<0.02).ConclusionsThe prevalence of IADL disability decreased among older Chinese adults during 1997–2006, whereas the incidence remained stable. The declining prevalence of IADL disability might be partly due to the decreased duration of IADL disability, and to improvements in living conditions and healthcare facilities over time.

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