Association between diabetic retinopathy and interleukin-related gene polymorphisms: a machine learning aided meta-analysis.

BACKGROUND

Diabetic retinopathy (DR) is a severe complication of diabetes and a common cause of visual loss in adults. We aimed to assess the correlation between IL gene-related SNPs and the incidence of DR and attempted to predict DR with combined mutation site detection.

METHODS

A systematic search of databases was performed up to August 2019. Five genetic models were used to analyze associations. Machine learning methods were implemented to improve SNP-related disease prediction.

RESULTS

Sixteen trials assessing a total of 7221 patients were included in our meta-analysis. IL6/rs1800795, rs1800796, and IL10/rs1800896 were analyzed. For the IL-6 gene, there was no significant association between rs1800795 and the incidence of DR (allelic model: OR, 1.091; 95% CI, 0.892-1.334; = .396). There was no significant correlation between rs1800796 (allelic model: OR, 1.135; 95% CI, 0.678-1.901; = .63), rs1800896 (allelic model: OR, 1.047; 95% CI, 0.788-1.392; = .752) and the incidence of DR. Unfortunately, the machine learning results also showed that the combined detection of two SNPs could not accurately predict DR occurrence.

CONCLUSION

rs1800795 and rs1800796 in the IL-6 gene and rs1800896 in IL-10 gene are not related to the incidence of DR. Mutations in multiple SNPs for each DR patient still need to be specifically assessed to increase prediction accuracy.