Genetic Analysis of INSR Gene Variants and Their Association with Metabolic and Physiological Markers of Insulin Resistance in the Iraqi Population

Hadeel S. Hadi

doi:10.51699/cajmns.v7i3.3306

Authors

Hadeel S. Hadi Biology Department, College of Science, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

DOI:

https://doi.org/10.51699/cajmns.v7i3.3306

Keywords:

lipid metabolism, glucose homeostasis, insulin resistance, INSR gene

Abstract

Background: The insulin receptor (INSR) gene encodes a conserved signaling polypeptide which trigger a broad spectrum of functions loke glucose metabolic regulation, growth control, and neuronal function. Numerous genetic variants associated with insulin resistance have been identified, but physiological associations between specific mutations in the Iraqi population are not well-described. Objective: To describe the polymorphisms for INSR gene among insulin resistant individuals and relate them to fasting blood glucose, insulin, lipid fraction, and HOMA-IR. Methods: A total of 150 participants were divided into cases of IR and healthy matched controls (75 each). The INSR gene was sequenced by whole-exon using next-generation sequencing of the Illumina NovaSeq 6000 platform. Genetic analysis consisted of rare variant burden test (collapsing method), logistic regression per allele under dominant, additive and recessive genetic models and haplotype using Haploview. We assessed population stratification using principal component analysis (PCA). A post hoc power analysis indicated >95% power at α = 0.05 with n = 75 per group. Results: The study detected 23 INSR variants (8 novel). It has also revealed 64% of 7 825 insulin-resistant individuals had a pathogenic variant compared to 12% of the controls (OR =1.2,95% CI 5.8–30.1, p<0.001); this high prevalence likely reflects ascertainment enrichment due to strict inclusion criteria and may not reflect population-based frequencies in unselected cohorts. A rare variant burden test has been used to show for cases an excess of rare pathogenic variants (OR = 1·8, p<0·001). A clinical-genetic predictive model showed discrimination with an AUC of 0.91 (95% CI 0.85–0.97) in the selected cohort. Bioinformatic prediction of in silico function highlighted that 58.3% of pathogenic variants blocked autophosphorylation within the tyrosine kinase domain, elucidating underlying mechanisms for impaired PI3K-AKT signaling. Conclusion: INSR genetic variants are highly associated with insulin resistance and metabolic abnormalities. The observed high variant prevalence within our Middle Eastern cohort, alongside a previously established and validated predictive model (AUC = 0.91), provides support for the clinical utility of INSR genetic screening as an early risk stratification tool in a precision medicine framework.

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