Plasma lipids, alcohol intake frequency and risk of Osteoarthritis: a Mendelian randomization study

BACKGROUD

Plasma lipids and alcohol intake frequency have been reported to be associated with the risk of osteoarthritis (OA). However, it remains inconclusive whether plasma lipids and alcohol intake frequency play a role in the development of OA.

METHODS

The study employed a comprehensive genome-wide association database to identify independent genetic loci strongly linked to plasma lipids and alcohol intake frequency, which were used as instrumental variables. The causal association between plasma lipids, alcohol intake frequency, and the risk of OA was then analyzed using two-sample Mendelian randomization methods such as inverse variance weighted (IVW), MR-Egger regression, and weighted median estimator (WME), with odds ratios (ORs) as the evaluation criteria.

RESULTS

A total of 392 SNPs were included as instrumental variables in this study, including 32 for total cholesterol (TC), 39 for triglycerides (TG), 170 for high-density lipoproteins (HDL), 60 for low-density lipoproteins (LDL), and 91 for alcohol intake frequency. Using the above two-sample Mendelian Randomization method to derive the causal association between exposure and outcome, with the IVW method as the primary analysis method and other MR analysis methods complementing IVW. The results of this study showed that four exposure factors were causally associated with the risk of OA. TC obtained a statistically significant result for IVW (OR = 1.207, 95% CI: 1.018-1.431, P = 0.031); TG obtained a statistically significant result for Simple mode (OR = 1.855, 95% CI: 1.107-3.109, P = 0.024); LDL obtained three statistically significant results for IVW, WME and Weighted mode (IVW: OR = 1.363, 95% CI: 1.043-1.781, P = 0.023; WME: OR = 1.583, 95% CI: 1.088-2.303, P = 0.016; Weighted mode: OR = 1.521, 95% CI: 1.062-2.178, P = 0.026). Three statistically significant results were obtained for alcohol intake frequency with IVW, WME and Weighted mode (IVW: OR = 1.326, 95% CI: 1.047-1.678, P = 0.019; WME: OR = 1.477, 95% CI: 1.059-2.061, P = 0.022; Weighted mode: OR = 1.641, 95% CI: 1.060-2.541, P = 0.029). TC, TG, LDL, and alcohol intake frequency were all considered as risk factors for OA. The Cochran Q test for the IVW and MR-Egger methods indicated intergenic heterogeneity in the SNPs contained in TG, HDL, LDL, and alcohol intake frequency, and the test for pleiotropy indicated a weak likelihood of pleiotropy in all causal analyses.

CONCLUSIONS

The results of two-sample Mendelian randomization analysis showed that TC, TG, LDL, and alcohol intake frequency were risk factors for OA, and the risk of OA increased with their rise.

Identification of potential autophagy-related genes in steroid-induced osteonecrosis of the femoral head via bioinformatics analysis and experimental verification

PURPOSE

Steroid-induced osteonecrosis of the femoral head (SONFH) is a refractory orthopaedic hip joint disease that occurs in young- and middle-aged people. Previous experimental studies have shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remains unclear. We aimed to identify and validate the key potential autophagy-related genes involved in SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis.

METHODS

The GSE123568 mRNA expression profile dataset, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples, was downloaded from the Gene Expression Omnibus (GEO) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes involved in SONFH were screened by intersecting the GSE123568 dataset with the set of autophagy genes. The differentially expressed autophagy-related genes involved in SONFH were identified with R software. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially expressed autophagy-related genes involved in SONFH were conducted by using R software. Then, the correlations between the expression levels of the differentially expressed autophagy-related genes involved in SONFH were confirmed with R software. Moreover, the protein-protein interaction (PPI) network was analysed by using the Search Tool for the Retrieval of Interacting Genes (STRING), significant gene cluster modules were identified with the MCODE Cytoscape plugin, and hub genes among the differentially expressed autophagy-related genes involved in SONFH were screened by using the CytoHubba Cytoscape plugin. Finally, the expression levels of the hub genes of the differentially expressed autophagy-related genes involved in SONFH were validated in hip articular cartilage specimens from necrotic femur heads (NFHs) by using the GSE74089 dataset and further verification by qRT-PCR.

RESULTS

A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood samples of SONFH patients and non-SONFH patients based on the defined criteria, including 25 upregulated genes and 9 downregulated genes. The GO and KEGG pathway enrichment analyses revealed that these 34 differentially expressed autophagy-related genes involved in SONFH were particularly enriched in death domain receptors, the FOXO signalling pathway and apoptosis. Correlation analysis revealed significant correlations among the 34 differentially expressed autophagy-related genes involved in SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. Ten hub genes were identified by using the MCC algorithms of CytoHubba. The GSE74089 dataset showed that TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 was significantly downregulated in the hip cartilage specimens, which was consistent with the GSE123568 dataset. TNFSF10, PTEN and BCL2L1 were detected with consistent expression by qRT-PCR.

CONCLUSIONS

Thirty-four potential autophagy-related genes involved in SONFH were identified via bioinformatics analysis. TNFSF10, PTEN and BCL2L1 might serve as potential drug targets and biomarkers because they regulate autophagy. These results expand the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.