A nomogram to predict sarcopenia in older adults: a multi-center study in rural China.

BACKGROUND

Sarcopenia is closely associated with activity limitations, falls and fractures, all serving as major causes of physiological deterioration and even mortality in older adults. The purpose of this study was to develop and validate a novel nomogram for predicting the risk of sarcopenia in Chinese older adults from rural areas.

METHODS

Overall, 611 older adults were enrolled from the Thyroid Diseases in Older Population (TOPS) study and randomly assigned to the training and validation datasets in a ratio of 8:2. Based on the risk factors for sarcopenia identified by stepwise multivariate logistic regression, a nomogram to predict sarcopenia in older adults was created. Its discrimination ability, calibration accuracy and clinical utility were assessed via the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Additionally, a web-based nomogram was developed.

RESULTS

A nomogram to predict sarcopenia in older adults was created by incorporating the following variables: age (years), sex, body mass index (BMI, kg/m2), free triiodothyronine (FT3), total cholesterol (TC), triglyceride (TG), and TG/HDL-C. The area under the curve (AUC) of the nomogram was 0.813 (95% CI 0.775–0.846) in the training dataset and 0.819 (95% CI 0.739–0.883) in the validation dataset. Both calibration curves and DCA verified high calibration accuracy and clinical utility of the nomogram.

CONCLUSIONS

A nomogram is created based on routine laboratory values of blood testing, showing an outstanding ability to predict sarcopenia in older adults from rural areas in China accurately, specifically and cost-effectively.