RESUMEN
The causal association of educational attainment (EA) with intervertebral disc degeneration (IVDD) or low back pain (LBP), and the mediating effect of metabolic syndrome (MetS) in this association, is not studied to date. In this study, using summary statistics of genome-wide association studies primarily conducted in the individuals of European ancestry, Mendelian randomization (MR) analyses were performed to investigate: (1) the total and direct effects of EA on IVDD and LBP, (2) bidirectional associations of EA with MetS or the components of MetS, (3) causal effects of MetS or its components on IVDD and LBP, and (4) mediating effects of MetS or its components on the causal associations of EA with IVDD and LBP. Univariable MR analysis demonstrated that genetically proxied EA was inversely associated with IVDD (ORIVW: 0.90; 95 % CI: 0.87-0.92) and LBP (ORIVW: 0.86; 95 % CI: 0.84-0.89). Consistent results were obtained after adjusting for potential confounders (cognition, economic level, smoking traits, and metabolic factors). Mediation analysis proved that the effect of EA on IVDD mediated by MetS, waist circumference, and high-density lipoprotein cholesterol was 11.38 %, 9.22 %, and 2.17 %, respectively. Besides, MetS mediated 8.42 % and waist circumference mediated 5.81 % of the EA effects on LBP, respectively. Our findings provided support for MetS mediating the causal protective effects of EA on IVDD and LBP, which provided causal evidence to the etiology and intervention targets of IVDD and LBP.
RESUMEN
Numerous observational studies have shown that obesity (OB) is a significant risk factor in the occurrence and progression of osteoarthritis (OA), but the underlying molecular mechanism between them remains unclear. The study aimed to identify the key genes and pathogeneses for OA with OB. We obtained two OA and two OB datasets from the gene expression omnibus (GEO) database. First, the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify key genes for diagnosing OA with OB, and then the nomogram and receiver operating characteristic (ROC) curve were conducted to assess the diagnostic value of key genes. Second, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the pathogenesis of OA with OB. Third, CIBERSORT was created to investigate immunocyte dysregulation in OA and OB. In this study, two genes (SOD2, ZNF24) were finally identified as key genes for OA with OB. These two key genes had high diagnostic values via nomogram and ROC curve calculation. Additionally, functional analysis emphasized that oxidative stress and inflammation response were shared pathogenesis of OB and AD. Finally, in OA and OB, immune infiltration analysis showed that SOD2 closely correlated to M2 macrophages, regulatory T cells, and CD8 T cells, and ZNF24 correlated to regulatory T cells. Overall, our findings might be new biomarkers or potential therapeutic targets for OA and OB comorbidity.
