ArticleBiochemical genetics2023
Exploration of the Shared Hub Genes and Biological Mechanism in Osteoporosis and Type 2 Diabetes Mellitus based on Machine Learning.
Article in Biochemical genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Cellular signaling crosstalk between osteoporosis and diabetes: Common mechanisms and therapeutic targets (Review).Biomedical reports · 2026Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.Frontiers in nutrition · 2025Article
- Identification and experimental validation of programmed cell death- and mitochondria-associated biomarkers in osteoporosis and immune microenvironment.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
A substantial amount of evidence suggests a close relationship between osteoporosis (OP) and Type 2 Diabetes Mellitus (T2DM), but the mechanisms involved remain unknown. Therefore, we conducted this study with the aim of screening for hub genes common to both diseases and conducting a preliminary exploration of common regulatory mechanisms. In the present study, we first screened genes significantly associated with OP and T2DM by the univariate logistic regression algorithm. And then, based on cross-analysis and random forest algorithm, we obtained three hub genes (ACAA2, GATAD2A, and VPS35) and validated the critical roles and predictive performance of the three genes in both diseases by differential expression analysis, receiver operating characteristic (ROC) curves, and genome wide association study (GWAS) analysis. Finally, based on gene set enrichment analysis (GSEA) and the construction of the miRNA-mRNA regulatory network, we conducted a preliminary exploration of the co-regulatory mechanisms of three hub genes in two diseases. In conclusion, this study provides promising biomarkers for predicting and treating both diseases and offers novel directions for exploring the common regulatory mechanisms of both diseases.
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Registered trials
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