Early identification of sarcopenia in patients with diabetes mellitus combined with osteoporosis: development and validation of a gender-specific nomogram - Yu, Mingzhong; Su, Yunyun; Wang, Ping; Pan, Min (Frontiers in Endocrinology, 2025)
ObjectiveThe aim of this study was to develop a predictive model to screen for sarcopenia in patients with type 2 diabetes mellitus (T2DM) combined with osteoporosis, with a view to identifying and intervening early in those at high risk of falls and fractures, thereby reducing the risk of disability and death in the elderly.MethodsClinical data collection, physical performance evaluations, and dual-energy X-ray absorptiometry were performed on 847 patients with T2DM combined with osteoporosis. Risk factors for sarcopenia were identified using the least absolute shrinkage and selection operator method. Furthermore, a sex-specific nomogram was constructed based on these indicators to predict the occurrence of sarcopenia, and the predictive efficacy and clinical value of the model were evaluated by receiver operating characteristic curve and decision curve analysis.ResultsThe prevalence of sarcopenia in patients with T2DM combined with osteoporosis was 33.88%, with men having a significantly higher prevalence than women. Among male patients, body mass index, 25-hydroxyvitamin D, and calcium levels were associated with a decreased risk of sarcopenia, whereas age and weight-adjusted waist index were associated with an increased risk. In female patients, body mass index and creatine kinase were associated with a decreased risk of sarcopenia, while age, weight-adjusted waist index, and low-density lipoprotein cholesterol were associated with an increased risk. The area under the receiver operating characteristic curve of the nomogram was 91.2% in males and 84.5% in females, showing high predictive accuracy.ConclusionsIn this study, gender-specific nomograms were successfully established, which provided an effective tool for early screening of sarcopenia in patients with T2DM combined with osteoporosis. These models help healthcare professionals identify individuals at high risk of falls and fractures, facilitating timely preventive measures and reducing the burden on the social healthcare system.