Genetic polymorphisms in DNA repair gene XRCC1 and the risk of diabetic polyneuropathy

TL;DR

This study finds that genetic variations in the XRCC1 gene are linked to the risk and severity of diabetic polyneuropathy in type 2 diabetes patients.

Contribution

The study introduces machine learning to analyze genetic and clinical factors in diabetic polyneuropathy progression.

Findings

XRCC1 Arg399Gln and Arg194Trp SNPs are significantly associated with DPN risk under codominant and dominant models.

The A-T haplotype is identified as a prominent risk factor for DPN.

Machine learning highlights XRCC1 SNPs and disease duration as key factors in advanced DPN stages.

Abstract

This study aimed to investigate the association between XRCC1 Arg399Gln and Arg194Trp single nucleotide polymorphisms (SNPs) and the risk and severity of polyneuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM). The genotyping of SNPs was achieved in 732 contributors, including diabetic subjects with and without polyneuropathy and controls, using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). In addition, by using advanced statistical techniques, including machine learning methodologies, to analyze the data.The results indicated a significant link between both SNPs and DPN risk under both codominant and dominant models, respectively, with the A and T alleles as risk variants. Haplotype analysis further established the A-T haplotype as a prominent risk factor. The disease severity was associated with the 399 A/A and combined (G/A + A/A) genotypes, as well as the 194 C/T and combined (C/T + T/T) genotypes. In advanced DPN stages, random Forest (RF) highlighted both XRCC1 SNPs, and disease duration as the top three contributing factors. SHAP analysis corroborated the 194 C/T genotype of and the 399 A/A genotype were strongly linked to severe disease manifestations, particularly when coexisting with prolonged illness duration, advanced age, elevated HDL, and reduced LDL levels. Our findings substantiate the association of XRCC1 Arg399Gln and Arg194Trp SNPs with both susceptibility to and progression of DPN in T2DM patients. The integration of machine learning methodologies augments clinical decision-making by refining diagnostic precision and facilitating personalized treatment strategies.

The online version contains supplementary material available at 10.1038/s41598-026-35213-1.

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