{"title":"Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics.","authors":"Di Luo, Linguo Xie, Jingdong Zhang, Chunyu Liu","doi":"10.1186/s13062-025-00627-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9164,"journal":{"name":"Biology Direct","volume":"20 1","pages":"42"},"PeriodicalIF":5.7000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956445/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology Direct","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13062-025-00627-w","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
背景:骨质疏松症和肾结石有几个共同的病理生理危险因素,它们之间的联系是明确的。然而,之前的研究主要集中在环境介质上,如饮食,而将这两种情况联系起来的确切机制尚不清楚。方法:利用2007-2010年、2013-2014年和2017-2020年全国健康与营养检查调查(NHANES) 5个周期的数据,采用加权多元logistic回归分析骨质疏松症与肾结石的关系。来自基因表达综合(GEO)微阵列数据库的基因表达数据与机器学习技术相结合,以确定与骨质疏松症和肾结石相关的关键基因。然后通过比较毒物基因组学数据库(CTD)和GeneCards确定共同靶点。进行GMFA富集分析以确定共享的生物学途径。此外,通过药物预测和分子对接进一步研究这些靶点的药理相关性。结果:对NHANES数据库的分析证实了骨质疏松症和肾结石之间的强烈关联。加权多因素logistic回归显示骨质疏松症(OR: 1.41;95% ci 1.11-1.79;结论:骨质流失与肾结石风险增加有关。靶向mTOR信号通路可能为治疗骨质疏松症和肾结石提供潜在的治疗方法。
期刊介绍:
Biology Direct serves the life science research community as an open access, peer-reviewed online journal, providing authors and readers with an alternative to the traditional model of peer review. Biology Direct considers original research articles, hypotheses, comments, discovery notes and reviews in subject areas currently identified as those most conducive to the open review approach, primarily those with a significant non-experimental component.