Luo Di, Xie Linguo, Zhang Jingdong, Liu Chunyu
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, 23 Pingjiang Road, Hexi District, Tianjin, 300000, People's Republic of China.
Biol Direct. 2025 Mar 31;20(1):42. doi: 10.1186/s13062-025-00627-w.
Osteoporosis and kidney stones share several common pathophysiological risk factors, and their association is well-established. However, previous studies have primarily focused on environmental mediators, such as diet, and the precise mechanism linking these two conditions remains unclear.
The relationship between osteoporosis and kidney stones was analyzed using weighted multivariate logistic regression, employing data from five cycles of the National Health and Nutrition Examination Survey (NHANES) from 2007-2010, 2013-2014, and 2017-2020. Gene expression data from the Gene Expression Omnibus (GEO) microarray database were integrated with machine learning techniques to identify key genes involved in both osteoporosis and kidney stones. Common targets were then identified through the Comparative Toxicogenomics Database (CTD) and GeneCards. GMFA enrichment analysis was performed to identify shared biological pathways. Additionally, drug prediction and molecular docking were employed to further investigate the pharmacological relevance of these targets.
Analysis of the NHANES database confirmed a strong association between osteoporosis and kidney stones. Weighted multivariate logistic regression showed that osteoporosis (OR: 1.41; 95% CI 1.11-1.79; P < 0.001) and bone loss (OR: 1.24; 95% CI 1.08-1.43; P < 0.001) were significantly correlated with an increased risk of kidney stones. Three hub genes-WNT1, AKT1, and TNF-were identified through various analytical methods. GMFA revealed that the mTOR signaling pathway is a key shared pathway. Molecular docking studies further confirmed the pharmacological relevance of these targets, demonstrating strong binding affinity between drugs and the proteins involved, consistent with previous findings.
Bone loss is associated with an increased risk of kidney stones. Targeting the mTOR signaling pathway may offer a potential therapeutic approach for treating both osteoporosis and kidney stones.
骨质疏松症和肾结石有若干共同的病理生理风险因素，它们之间的关联已得到充分证实。然而，以往研究主要集中在环境介导因素，如饮食，而连接这两种病症的精确机制仍不清楚。
利用2007 - 2010年、2013 - 2014年和2017 - 2020年五个周期的美国国家健康与营养检查调查（NHANES）数据，通过加权多变量逻辑回归分析骨质疏松症与肾结石之间的关系。来自基因表达综合数据库（GEO）微阵列数据库的基因表达数据与机器学习技术相结合，以识别参与骨质疏松症和肾结石的关键基因。然后通过比较毒理基因组学数据库（CTD）和基因卡片（GeneCards）确定共同靶点。进行基因集功能富集分析（GMFA）以识别共享的生物学途径。此外，采用药物预测和分子对接进一步研究这些靶点的药理学相关性。
对NHANES数据库的分析证实骨质疏松症与肾结石之间存在强关联。加权多变量逻辑回归显示，骨质疏松症（比值比：1.41；95%置信区间1.11 - 1.79；P < 0.001）和骨质流失（比值比：1.24；95%置信区间1.08 - 1.43；P < 0.001）与肾结石风险增加显著相关。通过各种分析方法确定了三个枢纽基因——WNT1、AKT1和TNF。GMFA显示，雷帕霉素靶蛋白（mTOR）信号通路是一个关键的共享途径。分子对接研究进一步证实了这些靶点的药理学相关性，表明药物与相关蛋白质之间具有很强的结合亲和力，与先前的研究结果一致。
骨质流失与肾结石风险增加相关。针对mTOR信号通路可能为治疗骨质疏松症和肾结石提供一种潜在的治疗方法。
