ArticleFrontiers in nutrition2024
Development and validation of a predicative model for identifying sarcopenia in Chinese adults using nutrition indicators (AHLC).
Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Sarcopenia, a condition characterized by low muscle mass, plays a critical role in the health of older adults. Early identification of individuals at risk is essential to prevent sarcopenia-related complications. This study aimed to develop a predictive model using readily available clinical nutrition indicators to facilitate early detection. Methods: A total of 1,002 participants were categorized into two groups: 819 with normal skeletal muscle mass (SMM) and 183 with low muscle mass (sarcopenia). A predictive model was developed for sarcopenia risk via multivariate logistic regression, and its performance was assessed using four analyses: receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), a nomogram chart, and external validation. These methods were used to evaluate the model's discriminative ability and clinical applicability. Results: In the low-SMM group, more females (55.73% vs. 40.42%) and older individuals (median 61 vs. 55 years) were observed. These patients had lower albumin (41.00 vs. 42.50 g/L) and lymphocyte levels (1.60 vs. 2.02 × 10 Conclusion: Our study employs serum nutrition indicators to aid clinicians in promoting healthier aging. The AHLC model stands out for weight-independent evaluations. This novel approach could assess sarcopenia risk in the Chinese population, thereby enhancing aging and quality of life.
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