Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics
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 remain...
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2025-03-01
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| Online Access: | https://doi.org/10.1186/s13062-025-00627-w |
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| author | Di Luo Linguo Xie Jingdong Zhang Chunyu Liu |
| author_facet | Di Luo Linguo Xie Jingdong Zhang Chunyu Liu |
| author_sort | Di Luo |
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| description | 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. |
| format | Article |
| id | doaj-art-e8343a69733e451784f823c0bb7a19e8 |
| institution | OA Journals |
| issn | 1745-6150 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Biology Direct |
| spelling | doaj-art-e8343a69733e451784f823c0bb7a19e82025-08-20T01:53:07ZengBMCBiology Direct1745-61502025-03-0120112010.1186/s13062-025-00627-wExploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformaticsDi Luo0Linguo Xie1Jingdong Zhang2Chunyu Liu3Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical UniversityDepartment of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical UniversityDepartment of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical UniversityDepartment of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical UniversityAbstract 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.https://doi.org/10.1186/s13062-025-00627-wKidney stonesOsteoporosisBioinformatics analysisMTOR signaling pathwayDrug target |
| spellingShingle | Di Luo Linguo Xie Jingdong Zhang Chunyu Liu Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics Biology Direct Kidney stones Osteoporosis Bioinformatics analysis MTOR signaling pathway Drug target |
| title | Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics |
| title_full | Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics |
| title_fullStr | Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics |
| title_full_unstemmed | Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics |
| title_short | Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics |
| title_sort | exploring the association between osteoporosis and kidney stones a clinical to mechanistic translational study based on big data and bioinformatics |
| topic | Kidney stones Osteoporosis Bioinformatics analysis MTOR signaling pathway Drug target |
| url | https://doi.org/10.1186/s13062-025-00627-w |
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