Xia Hao, Meng Chen, Chen Rong, Mao Chao, Li Chuan, Xu Yongqing
Department of Orthopaedic, 920Th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, 212 Daguan Road, Xishan District, Kunming, Yunnan, China.
Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, China.
Sci Rep. 2025 Aug 11;15(1):29334. doi: 10.1038/s41598-025-07711-1.
Although previous studies have assessed the effect of sleep traits on osteoarthritis (OA) risk, the association between the complex interplay of multiple sleep patterns and OA risk remains uncertain. We included participants who were free of OA at baseline based the UK Biobank. We evaluated the associations of five sleep behaviors with the risk of OA using Cox proportional hazard regression models. To explore the metabolic profile of sleep patterns, we regressed the sleep score on 167 standardized metabolites using ten iterations of LASSO model with ten-fold cross-validation. Restricted cubic splines (RCS) with four knots were used in the fully adjusted model to explore the potential non-linear association of sleep score and metabolic profile with OA risk. We discovered that individuals with poor sleep patterns experienced a notably higher incidence of OA (HR, 1.23, 95% CI, 1.18 to 1.28, P = 2.69 × 10). Furthermore, the risk of hand OA specifically was 1.29 times higher among those with poor sleep patterns compared to those with healthy sleep patterns (HR, 1.29, 95% CI, 1.12 to 1.49, P = 4.23 × 10). Individuals belonging to the highest quintile of metabolic score exhibited a 1.14-fold elevated risk of OA compared to those in the lowest quintile (HR, 1.14; 95% CI, 1.08 to 1.20; P = 3.09 × 10). Our findings have important public health implications as we provide an objective and more comprehensive evaluation of sleep patterns, and novel insights into the mechanisms linking sleep patterns and OA through metabolic profile.
尽管先前的研究已经评估了睡眠特征对骨关节炎（OA）风险的影响，但多种睡眠模式的复杂相互作用与OA风险之间的关联仍不确定。我们纳入了英国生物银行中基线时无OA的参与者。我们使用Cox比例风险回归模型评估了五种睡眠行为与OA风险的关联。为了探究睡眠模式的代谢特征，我们使用具有十折交叉验证的LASSO模型进行十次迭代，将睡眠评分与167种标准化代谢物进行回归分析。在完全调整模型中使用具有四个节点的受限立方样条（RCS）来探究睡眠评分和代谢特征与OA风险之间潜在的非线性关联。我们发现，睡眠模式较差的个体患OA的发生率显著更高（风险比[HR]，1.23；95%置信区间[CI]，1.18至1.28；P = 2.69×10）。此外，与睡眠模式健康的个体相比，睡眠模式较差的个体患手部OA的风险高出1.29倍（HR，1.29；95%CI，1.12至1.49；P = 4.23×10）。代谢评分处于最高五分位数的个体患OA的风险比最低五分位数的个体高1.14倍（HR，1.14；95%CI，1.08至1.20；P = 3.09×10）。我们的研究结果具有重要的公共卫生意义，因为我们提供了对睡眠模式的客观且更全面的评估，并通过代谢特征对睡眠模式与OA之间的联系机制有了新的见解。
