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Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics
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Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics
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Journal Article
Exploring the association between osteoporosis and kidney stones: a clinical to mechanistic translational study based on big data and bioinformatics
2025
Overview
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.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Aged
/ Alcohol
/ Big Data
/ Biomedical and Life Sciences
/ Calculi
/ Cellular signal transduction
/ Datasets
/ Diabetes
/ Female
/ Genes
/ Humans
/ Kidney Calculi - epidemiology
/ Kidneys
/ Male
/ Methods
/ Molecular Docking Simulation
/ Osteoporosis - complications
/ Proteins
/ Software
/ Surveys
/ Urology
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