Abstract
BACKGROUND: Sarcopenia raises the risk of falls, fractures, and death, in addition to having a direct impact on the form and function of the skeletal muscles. Thus, our research aimed to create and evaluate a nomogram indicating the probability that middle-aged and older adults may acquire sarcopenia. METHODS: In this study, data from the National Health and Nutrition Examination Survey covering 2011 to 2018 were split randomly into training and validation datasets. We then used univariate and multivariate logistic regression analysis to create a nomogram prediction model. The model's accuracy was measured using calibration curves, and its clinical applicability and discriminatory power were evaluated using receiver operating characteristic curves and decision curve analysis. RESULTS: This study encompassed 39,175 participants, with 5,342 participants in the training dataset and 2,290 participants in the validation dataset. Age, race, education, poverty income ratio, energy, protein, carbohydrate, albumin, blood uric acid, serum iron, diabetes, depression, arthritis, and leisure-time physical activity were independent risk variables for sarcopenia by multivariate logistic regression analysis. A nomogram risk prediction model was then built, and in the training and validation datasets, the model's operating characteristic curves values were 0.720 (95% CI 0.701-0.739) and 0.705 (95% CI 0.685-0.725), respectively. The calibration curves showed high agreement between the model's predictions and the actual results. Decision curve analysis showed that the nomogram model was beneficial across a range of probability thresholds. CONCLUSION: The nomogram demonstrated high clinical predictability, enabling clinicians to utilize this tool to identify the incidence of sarcopenia in middle-aged and elderly individuals.