Integrative multi-omics Mendelian randomization reveals key lipid metabolism genes as therapeutic targets for...

Dysregulated lipid metabolism contributes to diabetic nephropathy (DN), but the genetic links remain unclear. This study identified lipid metabolism genes associated with DN risk using summary-data-based Mendelian randomization (SMR). SMR screened 757 lipid metabolism genes by integrating large-scale blood methylation quantitative trait locus (mQTL; n = 1,980), expression QTL (eQTL; n = 31,684), and protein QTL (pQTL; n = 54,219) datasets with FinnGen genome-wide association study (5,042 cases/79,344 controls), followed by validation in GCST005881 (5,908 cases/4,967 controls). Functional enrichment, protein-protein interaction, and mQTL-eQTL integration were performed. Nephroseq data were used to examine tissue-level expression-renal function correlations. External bulk and single-cell transcriptomic datasets were analyzed to assess stage- and cell-type-specific expression. CERS2, MED27, and PLA2G1B were prioritized as DN-associated risk genes. Genetically predicted higher expression of CERS2 (OR = 1.246, p_SMR = 0.027) and MED27 (OR = 1.551, p_SMR = 0.032) increased DN risk. Methylation at cg26058502 (CERS2: OR = 0.604, FDR = 1.61 × 10-23) and cg13628444 (MED27: OR = 0.662, FDR = 3.82 × 10^-10) demonstrated negative regulatory effects on gene expression. PLA2G1B was linked to increased risk at methylation (cg16396488: OR = 1.369, p_SMR = 7.82 × 10^-4) and protein (OR = 3.057, p_SMR = 0.005) levels. However, in the Nephroseq database, higher expression of all three genes correlated with better kidney function. External bulk transcriptomics showed transient upregulation of CERS2 and MED27 in early DN but not advanced stages. PLA2G1B exhibited no differential expression or association with renal function. Single-cell analysis demonstrated broad detection of CERS2 and MED27 across renal cell types with subset enrichment, whereas PLA2G1B expression was sparse, with no evident disease-associated shifts. These findings indicate discordance between genetically inferred risk effects and observed expression patterns, suggesting complex regulatory mechanisms.