Antihypertensive Treatment Can Delay Cognition Decline in Middle-Aged and Older Chinese between 2011 and 2015: Results from the China Health and Retirement Longitudinal Study

2020 ◽  
Author(s):  
Chihua Li ◽  
Shumin Rui ◽  
Lambert Lumey
Keyword(s):  
Longitudinal Study ◽  
Antihypertensive Treatment ◽  
Middle Aged ◽  
Older Chinese

scholarly journals Relationship Between Sleep and Arthritis in a Middle-aged and Older Chinese Population- the China Health and Retirement Longitudinal Study

2021 ◽  
Author(s):  
Chunnan Li ◽  
Shang shaomei
Keyword(s):  
Logistic Regression ◽  
Longitudinal Study ◽  
Protective Effect ◽  
Sleep Duration ◽  
Chinese Population ◽  
P Value ◽  
Confounding Factors ◽  
Middle Aged ◽  
Older Chinese ◽  
The Relationship

Abstract Objective. To evaluate the relationship between sleep duration, sleep restless and arthritis in middle-aged and older Chinese population. Methods. A total of 4957 middle-aged and elderly people aged 45 years and above from The China Health and Retirement Longitudinal Study (CHARLS) wave 3 were included. Multivariable logistic regression was used to calculate adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) for arthritis. Results. Sleep duration was shown to have a U-shaped association with arthritis after adjusting confounding factors. Compared with <5h sleep duration per night, ORs (95 % CIs) of sleep duration 5-7,7-8 and 8-9 h per night for arthritis were 0.61 (0.52– 0.73, P value <0.001) ,0.47 (0.38-0.58, P value <0.001),0.50 (0.41,0.60, P value <0.001)and 0.50 (0.39–0.64, P value <0.001), respectively. Sleep restless was positively correlated with the prevalence of arthritis. After stratification according to sleep restless status, for those without sleep restless, 8-9 hours duration (OR=0.55, 95 % CI 0.39-0.78,P value=0.001)had the best protective effect on arthritis, while7-8 hours duration (OR=0.45, 95 % CI 0.34-0.60,P value <0.001)was best in people with sleep restless. Conclusion. In middle aged and old Chinese population, sleep duration is U-shaped associated with arthritis, and sleep restless affect the correlation.

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 &lt; 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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