医学
全国健康与营养检查调查
骨质疏松症
逻辑回归
接收机工作特性
四分位数
人口
线性回归
内科学
列线图
统计
环境卫生
置信区间
数学
作者
Zhiwen Liu,H. Jian,Zijing Peng,Sicheng Xiong,Zhihai Zhang
标识
DOI:10.3389/fnut.2025.1508127
摘要
Objective This study aimed to explore the association between the Dietary Inflammatory Index (DII) and the prevalence of osteoporosis in the U.S. population, using data from the National Health and Nutrition Examination Survey (NHANES) 2003–2010. Methods Data from 7,290 participants in the NHANES 2003–2010 survey were analyzed. The relationship between the DII and osteoporosis was evaluated using weighted multivariate logistic regression, and potential non-linear associations were explored through restricted cubic spline (RCS) regression. Subgroup analyses were conducted with stratified models, and the findings were depicted in a forest plot. To pinpoint key dietary factors associated with osteoporosis, we applied least absolute shrinkage and selection operator (LASSO) regression. These factors were integrated into a nomogram for risk prediction, with the model’s discriminative ability assessed via the receiver operating characteristic (ROC) curve. Results Osteoporosis patients had higher DII scores than those without the condition (1.61 vs. 1.18, p < 0.001). After adjusting for covariates, participants in the highest DII quartile had an 88% greater risk of osteoporosis (OR: 1.88, 95% CI: 1.41–2.52, P for trend <0.001). Restricted cubic spline analysis confirmed a linear relationship between DII and osteoporosis risk. Subgroup analyses showed similar patterns across different groups, as illustrated by the forest plot. LASSO regression identified key dietary factors, which were used to build a nomogram with an AUC of 83.6%, indicating strong predictive accuracy. Conclusion A higher DII is strongly linked to increased osteoporosis risk, underscoring the importance of reducing dietary inflammation to help prevent osteoporosis.
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