Construction and Validation of an Osteoporosis Risk Prediction Model for Elderly Population based on National Health and Nutrition Examination Survey Data.

内容摘要

Osteoporosis is a systemic bone disease characterized by low bone mass and destruction of bone microstructure, which is prone to fractures. It is more common in the elderly. Against the context of global population aging, osteoporotic fractures have become a major public health burden. To construct and validate a nomogram model for predicting osteoporosis risk in elderly individuals aged &#x2265;60 years based on National Health and Nutrition Examination Survey (NHANES) data. This cross-sectional study included 3,422 participants aged &#x2265;60 years from the NHANES database (2017-March 2020). Osteoporosis was defined as a dual-energy X-ray absorptiometry (DXA) T-score &#x2264; -2.5. Demographic characteristics, biochemical indicators, anthropometric measurements, and inflammation-related indices were collected. Univariate and multivariate logistic regression analyses were used to identify independent predictors of osteoporosis. The dataset was randomly divided into a training set (70%) and a validation set (30%). A nomogram model was then developed and validated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). The results showed that female gender (5.92[3.21,10.91], P < 0.001), age (1.06[1.03,1.09], P < 0.001), height (0.94[0.91,0.96], P < 0.001), BMI(0.97[0.95,1], P = 0.038), drinking history (1.78[1.02,3.12], P = 0.043), history of kidney disease (1.96[1.06,3.62], P = 0.033), previous hypercholesterolemia (1.47[1.03,2.09], P = 0.036), and difficulty falling asleep (1.6 [1.12,2.28], P = 0.010) were independent predictors of osteoporosis in the elderly. The nomogram demonstrated its good discrimination ability with AUC values of 0.806 in the training set and 0.793 in the validation set. Calibration curves showed good agreement between predicted and observed outcomes, and DCA confirmed clinical applicability. This study developed a clinically applicable prediction model to identify elderly individuals at high risk of osteoporosis, aiding early intervention and reducing adverse outcomes.