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BackgroundThe associations of sarcopenia with adverse health status have highlighted the importance of sarcopenia research and intervention. This study was designed to analyze the characteristics of aging-related differences in appendicular skeletal muscle mass (ASM), handgrip strength (HS), gait speed (GS) and their associated factors in older Chinese, in order to generate guidance for sarcopenia intervention in this population.MethodsPopulation-based cross-sectional study. The criteria proposed by Asian Working Group for Sarcopenia were used to define low ASM, HS, and GS. The time required for five repeated chair stands (RCS) was also measured to evaluate physical performance. The differences of continuous variables were compared using one-way ANOVA tests and the Pearson correlation was used to analyze the relationship of each measurement adjusted by gender and age. Stepwise logistic regression was used to determine associated factors of low HS and low physical performance.ResultsThe data were analyzed in a total of 218 younger adults (aged 20–59, 76 males, 142 females) and 461 older adults (≥60 year, 207 males and 254 females). There were significant differences among age groups for HS, GS, and RCS while females were found to have significantly lower HS and GS values. ASM was significantly correlated with HS but not with other measures. Correlations among HS and GS, RCS were influenced by age differences. In the older group, unstructured daily routine (OR = 2.77) was associated with the risk of low GS, while physical exercise (OR = 0.27), and engaging in hobbies (OR = 0.11) were associated with faster GS. Co-morbidity (OR = 1.99) was associated with the risk of reduced performance of RCS, while engaging in hobbies was associated with faster RCS performance (OR = 0.35).ConclusionsMuscle strength and physical performance varied with aging in older Chinese. Measures of GS, HS, and RCS provide a readily available and effective method for assessing the risk of functional mobility decline. Maintaining a healthy life style and physical activity throughout life is beneficial for older people to improve their physical performance, especially in the early stages of aging.

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Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital. After data cleaning, 4,378 cases and 50 attributes are stored and collected for the data set. Through selecting the most feasible method, the cost parameter of CSHM is adapted to deal with imbalance of the dataset. In the experiment, 3940 samples are used for training and the rest 438 samples for testing. Although the accuracy of positive samples is barely acceptable (62.16%), the results suggest that the vast majority (98.4%) of those predicted positive instances are real positives. To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future.

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