The burden of sarcopenia is increasing worldwide. However, most cases of sarcopenia are undiagnosed due to the lack of simple screening tools. This study aimed to develop and validate an individualized and simple nomogram for predicting sarcopenia in older adults. A total of 180 medical examination populations aged ≥60 years were enrolled in this study. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus. The primary data were randomly divided into training and validation sets. Univariate logistic regression analysis was performed to select the risk factors of sarcopenia, which were subjected to the least absolute shrinkage and selection operator for feature selection. A nomogram was established using multivariate logistic regression analysis by incorporating the features selected in the least absolute shrinkage and selection operator regression model. The discrimination and calibration of the predictive model were verified by the concordance index, receiver operating characteristic curve, and calibration curve. In this study, 55 cases of sarcopenia were available. Risk predictors included age, albumin, blood urea nitrogen, grip strength, and calf circumference. The model had good discrimination and calibration capabilities. concordance index was 0.92 (95% confidence interval: 0.84–1.00), and the area under the receiver operating characteristic curve was 0.92 (95% confidence interval: 0.83–1.00) in the validation set. The Hosmer-Lemeshow test had a P value of .94. The predictive model in this study will be a clinically useful tool for predicting the risk of sarcopenia, and it will facilitate earlier detection and therapeutic intervention for sarcopenia.

The burden of sarcopenia is increasing. However, most cases of sarcopenia are undiagnosed due to the lack of simple screening tools. Here, we aimed to develop and validate an individualized and simple nomogram for predicting sarcopenia in older Chinese people. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus. The primary data were randomly divided into a train and validation set. Univariate logistic regression analysis was performed to select the risk factors of sarcopenia, which were subjected to the LASSO regression model for feature selection. The nomogram was built using multivariate logistic regression analysis by incorporating the features selected in the LASSO regression model. The discrimination and calibration of the predictive model were verified by the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curve. In this study, there were 55 cases of sarcopenia. Risk predictors included age, albumin (ALB), blood urea nitrogen (BUN), grip strength, and calf circumference. The model had good discrimination and calibration. C-index was 0.92 (95% confidence interval:0.84–1.00) and the area under the ROC curve (AUC) was 0.92 (95% confidence interval:0.83–1.00) in validation set. The Hosmer-Lemeshow test (HL) had a p-value of 0.94. Our predictive model will be a clinically useful tool for predicting the risk of sarcopenia. It facilitates earlier detection and therapeutic intervention for physicians and patients.

Aims: This study aims to explore the correlation between fear of dementia and insomnia among community-dwelling older adults and to examine the mediating roles of social isolation and resilience on this correlation. Methods: A total of 259 community-dwelling older adults from Mianyang, China were recruited from June 2021 to August 2021 using convenience sampling. The Chinese versions of the Fear of Dementia Scale (FODS), Lubben Social Network Scale (LSNS-6), 25-item Connor-Davidson Resilience Scale (CD-RISC-25) and Athens Insomnia Scale (AIS) were used to collect specific, study-related information from the subjects.Correlations between variables of interest were examined by Pearson analysis, and mediation analysis was conducted to explore the direct, indirect and total effects of the fear of dementia on insomnia vis-à-vis social isolation and resilience.Results: Results from 259 older adults indicated that fear of dementia and insomnia in older adults are positively correlated, that social isolation and resilience mediate the relationship between them, and that social isolation and resilience also had a statistically significant serial mediating effect. Conclusion:Fear of dementia is positively related to insomnia in older communitydwelling Chinese adults, but resilience and social support may buffer the negative impact of fear of dementia and improve sleep quality.Impact: Fear of dementia is becoming more and more common in community-dwelling older adults in China, and this emotional response to ageing and disease anxiety may be to blame for the poor sleep quality of some ageing populations. However, social support and resilience may buffer the negative impact of fear of dementia. The findings in this study indicate a need for well-trained community nurses and other health practitioners to implement targeted strategies to reduce insomnia among older adults with fear of dementia. These strategies should strengthen resilience as well as social support.