Gastroesophageal reflux disease and hyperlipidaemia
Description
We hypothesized that gastroesophageal reflux disease (GERD) and hyperlipidaemia (HLP) share common pathogenic mechanisms, and that GERD may have a causal effect on HLP risk. A combined approach using bioinformatics and Mendelian randomization (MR) was employed to identify shared genes, common pathways, and to evaluate the causal relationship between these two conditions.What the Data Shows:332 differentially expressed genes (DEGs) identified from the GSE41687 dataset (GERD), including 152 upregulated and 180 downregulated genes. 1,086 DEGs identified from the GSE6054 dataset (HLP), including 595 upregulated and 491 downregulated genes.173 Gene Ontology (GO) terms enriched from the 11 shared DEGs between GERD and HLP, covering Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories.KEGG pathway enrichment results for the 11 shared DEGs, highlighting pathways potentially common to both diseases.Complete list of 75 single nucleotide polymorphisms (SNPs) used as instrumental variables in the MR analysis, with their effect estimates, standard errors, P‑values, and F‑statistics.Full MR analysis results, including estimates from five different MR methods (IVW, MR Egger, Weighted median, Simple mode, Weighted mode), with corresponding P‑values and odds ratios.Notable Findings:11 shared DEGs were identified between GERD and HLP, including 5 upregulated genes and 6 downregulated genes.KEGG enrichment analysis revealed that Renin secretion, Cholesterol metabolism, and Glycerolipid metabolism may represent common underlying mechanisms between the two diseases.MR analysis demonstrated a significant causal link between GERD and HLP (IVW P = 3.82×10⁻⁹ in the primary analysis; P = 0.0025 in the replication analysis). All sensitivity tests (Cochran's Q, MR‑Egger intercept, leave‑one‑out) confirmed the robustness of the results.How the Data Was Gathered:Bioinformatics analysis: Gene expression data for GERD (GSE41687) and HLP (GSE6054) were obtained from the GEO database. Differential expression analysis was performed using the GEO2R online tool with thresholds of P < 0.05 and |log₂FC| > 1. Shared DEGs were identified by intersecting the upregulated and downregulated genes from both datasets. GO and KEGG enrichment analyses were conducted using the clusterProfiler R package (P < 0.05).Mendelian randomization analysis: GWAS summary statistics for GERD (exposure) were obtained from the IEU OpenGWAS database. HLP outcome data were sourced from the MRC-IEU Consortium and the FinnGen Consortium. SNPs with genome‑wide significance (P < 5×10⁻⁸) and independent of each other (r² < 0.001, clumping distance > 10,000 kb) were selected as instrumental variables. The TwoSampleMR R package was used for all MR analyses, including the IVW, MR Egger, Weighted median, Simple mode, and Weighted mode methods. Sensitivity analyses included Cochran's Q test, MR‑Egger intercept test, funnel plots, and leave‑one‑out analysis.