| Title: | Unraveling the shared genetic architecture of osteoarthritis and metabolic traits through multi-omics insights |
| Journal: | Osteoarthritis and Cartilage |
| Published: | 28 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41167325/ |
| DOI: | https://doi.org/10.1016/j.joca.2025.10.010 |
Publication 19123
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Abstract
OBJECTIVE: Osteoarthritis (OA) often coexists with metabolic traits (MTs), causing significant disability. Our study aims to uncover the shared genetic mechanisms between OA and MTs, revealing novel OA-MT related genes, proteins and pathways.</p>
DESIGN: We first explored the clinical associations between OA and MTs based on UK Biobank data. Using GWAS statistics for 9 OA subtypes and 51 MTs, we identified both global and regional genetic correlations. Multi-trait GWAS helped revealed credible genes and relevant pathways through various methods. Protein-level analyses were also conducted to identify key proteins. We developed polygenic scores (PGS), machine learning models and drug repurposing strategies were explored to translate these findings into clinical applications.</p>
RESULTS: We identified 152 trait pairs with significant associations and 709 local regions linked to OA-MT. Key SNVs like rs13135092 (SLC39A8) and rs34811474 (ANAPC4) were associated with multiple OA-MT pairs. Lipid and glucose metabolism emerged as central pathways, with tissue-specific enrichment analyses revealing key gene clusters in hepatocytes, arteries, and brain regions. Protein-level analyses identified 205 protein subgroups. PGS integrating MTs outperformed model based solely on OA, improving AUC by 17.5%. Causal gene-based models showed strong diagnostic accuracy (average AUC = 0.875 in external cohorts). Drug prediction highlighted fenofibrate as a promising treatment among 71 candidates.</p>
CONCLUSIONS: This study provides new insights into the genetic links between OA and MTs. We identified genes, proteins, and pathways related to comorbidities, revealing shared mechanisms, highlighting the potential of integrating metabolic factors to improve OA prediction, diagnosis, and treatment.</p>
10 Keywords
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Machine Learning
- Male
- Multifactorial Inheritance
- Multiomics
- Osteoarthritis
- Polymorphism, Single Nucleotide
11 Authors
- Binzhi Liao
- Yumeng Mu
- Mengliang Luo
- Yinjiang Liu
- Qingtao Yang
- Haibo Yang
- Jihua Ye
- Yuanyuan Wu
- Feifei Cheng
- Xianding Sun
- Mao Nie
1 Application
| Application ID | Title |
|---|---|
| 91850 | Using phenome-wide mendelian randomization and machine leaning to identify novel drug targets for aging and aging-related disease |
Enabling scientific discoveries that improve human health