Metabolically unhealthy obesity (MUO) is characterized by adipose tissue dysfunction and insulin resistance (IR), though the causal genetic drivers remain elusive. We applied multi-omics Mendelian randomization (MR) to identify suggestive genetically associated glycolysis-related candidates and molecular signals linked to IR in MUO. We integrated transcriptomic data from MUO/MHO subcutaneous adipose tissue (GSE156906) to screen the MUO-related glycolysis genes. By integrating IR GWAS data with DNA methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs), and protein QTLs (pQTLs) information, we performed Summary-data-based MR (SMR) analysis to investigate the genetic association between MUO-related glycolysis genes and the IR. Colocalization analysis was performed to determine if identified signals share a causal genetic variant. Additionally, tissue-specific validations were performed using GTEx v8 eQTL data. In MUO adipose tissue, 205 glycolysis genes were downregulated and 89 upregulated ( p < 0.05). Founded on the MUO-related glycolysis genes, our analysis identified 64 mQTLs, 11 eQTLs and 7 pQTL associated with IR risk in the discovery dataset. Colocalization analysis provides suggestive evidence in support of these associations. Based on the mQTL-eQTL analysis, HKDC1 (cg05532178, cg11762346) showed convergent SMR and colocalization evidence suggestive of an association with IR in the discovery cohort. Tissue-specific SMR analyses showed directionally consistent negative associations between HKDC1 expression and IR across metabolically relevant tissues. Furthermore, based on the eQTL-pQTL analysis, we identified that ITGAM showed directionally consistent associations at both transcriptomic and proteomic levels with IR risk, though these associations reached only nominal significance and colocalization support was modest. These findings provide exploratory multi-omics evidence for candidate genes potentially linking glycolytic dysregulation to insulin resistance in obesity. Further validation in independent cohorts and experimental systems is warranted.
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Zhong et al. (2026) studied this question.
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