Association between lean body mass and osteoarthritis: a cross-sectional study from the NHANES 2007–2018

被引:0
作者
Hongrui Lu [3 ]
Zifan Zhuang [2 ]
Gengjian Wang [1 ]
Mengyun Zhang [1 ]
Chang Yang [1 ]
Dongyan Wang [1 ]
机构
[1] The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, No. 411, Gogol Street, Nangang District, Harbin
[2] Guang’anmen Hospital, China Academy of Traditional Chinese Medicine, Beijing
[3] Heilongjiang University of Traditional Chinese Medicine, Harbin
关键词
Cross-sectional study; Large-scale; Lean body mass; Osteoarthritis; Stratified analysis;
D O I
10.1038/s41598-025-98795-2
中图分类号
学科分类号
摘要
The prevention of osteoarthritis through controlling body measurements has received increasing attention in recent years, but the relationship between lean body mass (LBM) and osteoarthritis remains unclear. Hence, we explored this association through the data from the National Health and Nutrition Examination Survey (2007–2018). The present study enrolled 31,172 participants. To explore the correlation between LBM and osteoarthritis, we utilized logistic regression equations to explore associations between covariates, exposures, and outcomes. We used multivariate regression models to further control confounding factors. Restricted cubic splines were employed to investigate non-linear relationships. And the inflection point was identified by recursive algorithm. Furthermore, stratified analyses of gender and age were conducted. Osteoarthritis was negatively correlated with LBM [0.74 (0.67, 0.80)] in the model adjusting for all confounders. Based on the restricted cubic spline curve, an inflection point of 52.26 kg was found to confirm the non-linear relationship. LBM was negatively correlated with osteoarthritis at 0.56 (0.48, 0.64) before the inflection point, and the association was not statistically significant afterward. This large-scale study revealed that LBM was non-linearly correlated with osteoarthritis in the general American population. Differences in age and gender were further identified. © The Author(s) 2025.
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