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Genetic polymorphisms in DNA repair gene XRCC1 and the risk of diabetic polyneuropathy
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  • Published: 03 February 2026

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

  • Noha A. Hashim1,
  • Hatim A. El-Baz2,
  • Zahraa I. Abo Afya3,
  • Hagar F. Gouda4 &
  • …
  • Noha M. Bakr2,5 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Genetics
  • Neuroscience

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.

Data availability

The datasets produced and/or analyzed throughout this current investigation are not available to the public but can be taken from the corresponding author upon a reasonable request.

Abbreviations

ML:

Machine Learning

RF:

Random Forest

XGboost:

Extreme Gradient boosting

OR:

Odds ratio

HWE:

Hardy-Weinberg equilibrium

AIC:

Akaike information criteria

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Acknowledgements

We acknowledged all subjects included in our study.

Funding

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Authors and Affiliations

  1. Neurology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt

    Noha A. Hashim

  2. Biochemistry Department, Biotechnology Research Institute, National Research Centre, Dokki, Giza, Egypt

    Hatim A. El-Baz & Noha M. Bakr

  3. Department of Clinical Pathology, Institute of Medical Research and Clinical studies, National Research Centre, Dokki, Giza, Egypt

    Zahraa I. Abo Afya

  4. Animal Wealth Development Department (Biostatistics Subdivision) Faculty of Veterinary Medicine, Zagazig University, Zagazig, Egypt

    Hagar F. Gouda

  5. High Throughput Molecular and Genetic Laboratory, Center of Excellence for Advanced Sciences, National Research Centre, Dokki, Giza, Egypt

    Noha M. Bakr

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Contributions

NAH, NMB and HAE prepared the idea and designed the study. NMB and HFG did the data statistical analysis. NMB and ZIA performed all the laboratory investigations and interpreted the patients’ data regarding each studied group. NAH selected the patients and the control group. All authors wrote, read, and approved the final manuscript.

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Correspondence to Noha A. Hashim.

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Hashim, N.A., El-Baz, H.A., Afya, Z.I.A. et al. Genetic polymorphisms in DNA repair gene XRCC1 and the risk of diabetic polyneuropathy. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35213-1

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  • Received: 27 April 2025

  • Accepted: 03 January 2026

  • Published: 03 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-35213-1

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Keywords

  • Diabetic polyneuropathy
  • XRCC1 Arg399GLN
  • XRCC1 Arg194Trp
  • Single nucleotide polymorphism
  • Machine learning algorithms
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