A nomogram model for predicting malnutrition among older hospitalized patients with type 2 diabetes: a cross—sectional study in China

BACKGROUND: Malnutrition remains a pervasive issue among older adults, a prevalence that is markedly higher among those diagnosed with diabetes. The primary objective of this study was to develop and validate a risk prediction model that can accurately identify instances of malnutrition among elderly hospitalized patients with type 2 diabetes mellitus (T2DM) within a Chinese demographic. METHODS: This cross-sectional study was conducted between August 2021 and August 2022, we enrolled T2DM patients aged 65 years and above from endocrinology wards. The creation of a nomogram for predicting malnutrition was based on risk factors identified through univariate and multivariate logistic regression analyses. The predictive accuracy of the model was evaluated by the receiver operating characteristic curve (ROC),the area under the ROC (AUC), the concordance index (C-index), and calibration curves. RESULTS: The study included a total of 248 older T2DM patients, with a recorded malnutrition prevalence of 26.21%. The identified critical risk factors for malnutrition in this cohort were body mass index, albumin, impairment in activities of daily living, dietary habits, and glycosylated hemoglobin. The AUC of the nomogram model reached 0.914 (95% CI: 0.877—0.951), with an optimal cutoff value of 0.392. The model demonstrated a sensitivity of 80.0% and a specificity of 88.5%. Bootstrap-based internal verification results revealed a C-index of 0.891, while the calibration curves indicated a strong correlation between the actual and predicted malnutrition risks. CONCLUSIONS: This study underscores the critical need for early detection of malnutrition in older T2DM patients. The constructed nomogram represents a practical and reliable tool for the rapid identification of malnutrition among this vulnerable population.