Purpose: New-onset diabetes after transplantation (NODAT) is a major complication after kidney transplantation. The risk factors for NODAT include the use of calcineurin inhibitors as part of the immunosuppressive regimen, among which tacrolimus has the most pronounced diabetogenic effect. Both NODAT and type 2 diabetes mellitus (T2DM) share several risk factors. Recent studies have identified a number of common genetic variants associated with increased risk of T2DM. Here we report the results of our study on the potential effect of single nucleotide polymorphisms (SNPs) previously associated with T2DM on the risk of NODAT in kidney transplant patients medicated with tacrolimus.
Methods: Seven SNPs in six genes known to increase the risk of T2DM in Caucasians were genotyped by means of TaqMan assays in 235 kidney transplant patients medicated with tacrolimus: rs4402960 and rs1470579 in IGF2BP2; rs1111875 in HHEX; rs10811661 upstream of CDKN2A/B; rs13266634 in SLC30A8; rs1801282 in PPARG; rs5215 in KCNJ11. The TCF7L2 rs7903146 SNP was also included in the multivariate analysis.
Results: None of the analyzed SNPs was significantly associated with the risk of NODAT. However, the IGF2BP2 rs4402960 T allele was present significantly more frequently among patients diagnosed with NODAT more than 2 weeks after transplantation (p = 0.048). Mean (± standard deviation) number of the analyzed alleles tended to be lower in patients without NODAT (6.19 ± 1.71) than in NODAT patients (6.58 ± 1.1.95; p = 0.09) and significantly lower compared to late-onset NODAT patients (7.03 ± 1.88; p = 0.018). Multivariate analysis confirmed the significance of 'diabetogenic' allele number in late-onset NODAT development [odds ratio (OR) 1.37, 95 % confidence interval (CI) 1.05-1.78; p = 0.017]. Additionally, individuals carrying >7 of the analyzed 'diabetogenic' alleles were at a significantly higher risk of NODAT (OR 2.17, 95 % CI 1.18-3.99; p = 0.015).
Conclusions: Complex analysis of genotypes increasing the risk of diabetes may lead to the identification of NODAT susceptibility predictors.