Despite the extensive efforts of scientists, the genetic background of diabetic nephropathy (DN) has not yet been clarified. To elucidate the genetic variants that predispose to the development of DN, we conducted a systematic review and meta-analysis of all available genetic association studies (GAS) of DN. We searched in the Human Genome Epidemiology Navigator (HuGE Navigator) and PubMed for available GAS of DN. The threshold for meta-analysis was three studies per genetic variant. The association between genotype distribution and DN was examined using the generalized linear odds ratio (ORG). For variants with available allele frequencies, the examined model was the allele contrast. The pooled OR was estimated using the DerSimonian and Laird random effects model. The publication bias was assessed with Egger’s test. We performed pathway analysis of significant genes with DAVID 6.7. Genetic data of 606 variants located in 228 genes were retrieved from 360 GASs and were synthesized with meta-analytic methods. ACACB, angiotensin I-converting enzyme (ACE), ADIPOQ, AGT, AGTR1, AKR1B1, APOC1, APOE, ATP1B2, ATP2A3, CARS, CCR5, CGNL1, Carnosine dipeptidase 1 (CNDP1), CYGB-PRCD, EDN1, Engulfment and cell motility 1 (ELMO1), ENPP1, EPO, FLT4, FTO, GLO1, HMGA2, IGF2/INS/TH cluster, interleukin 1B (IL1B), IL8, IL10, KCNQ1, KNG, LOC101927627, Methylenetetrahydrofolate reductase, nitric oxide synthase 3 (NOS3), SET domain containing seven, histone lysine methyltransferase (SETD7), Sirtuin 1 (SIRT1), SLC2A1, SLC2A2, SLC12A3, SLC19A3, TCF7L2, TGFB1, TIMP1, TTC39C, UNC13B, VEGFA, WTAPP1, WWC1 as well as XYLT1 and three intergenic polymorphisms showed significant association with DN. Pathway analysis revealed the overrepresentation of six signalling pathways. The significant findings provide further evidence for genetic factors implication in DN offering new perspectives in discovery of new therapies.

Background: Despite the certain contribution of metabolic and haemodynamic factors in diabetic nephropathy (DN), many lines of evidence highlight the role of immunologic and inflammatory mechanisms. To elucidate the contribution of the immune system in the development of DN, we explored the contribution of gene variants (polymorphisms) in relevant pathophysiologic pathways. Methods: We selected six major pathways related to immune response from the Kyoto Encyclopaedia of Genes and Genomes database and thereafter we traced all available genetic association studies (GASs) involving gene variants in these pathways from PubMed and HuGE Navigator. Finally, we used meta-analytic methods for synthesizing the results of the GASs. Results: One hundred three GASs were retrieved that included 443 variants from 75 genes. Of those variants, 138 were meta-analysed and 61 produced significant results; seven variants were investigated in single GASs and showed significant association. Variants in CCL2, CCR5, IL6, IL8, EPO, IL1A, IL1B, IL100, IL1RN, GHRL, MMP9, TGFB1, VEGFA, MMP3, MMP12, IL12RB1, PRKCE, TNF and TNFRSF19 genes were associated with an increased risk of DN. Conclusions: There is evidence that variants related with immunologic response affect the course of DN. However, the present results should be interpreted with caution since the current number of available GASs is limited.

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An association study was conducted to investigate the relation between 14 variants of glucose transporter 1 gene (SLC2A1) and the risk of type 2 diabetes (T2DM) leading to nephropathy. We also performed a meta-analysis of 11 studies investigating association between diabetic nephropathy (DN) and SLC2A1 variants. The cohort included 197 cases (T2DM with nephropathy), 155 diseased controls (T2DM without nephropathy) and 246 healthy controls. The association of variants with disease progression was tested using generalized odds ratio (ORG). The risk of type 2 diabetes leading to nephropathy was estimated by the OR of additive and co-dominant models. The mode of inheritance was assessed using the degree of dominance index (h-index). We synthesized results of 11 studies examining association between 5 SLC2A1 variants and DN. ORG was used to assess the association between variants and DN using random effects models. Significant results were derived for co-dominant model of rs12407920 [OR = 2.01 (1.17–3.45)], rs841847 [OR = 1.73 (1.17–2.56)] and rs841853 [OR = 1.74 (1.18–2.55)] and for additive model of rs3729548 [OR = 0.52 (0.29–0.90)]. The mode of inheritance for rs12407920, rs841847 and rs841853 was ‘dominance of each minor allele’ and for rs3729548 ‘non-dominance’. Frequency of one haplotype (C-G-G-A-T-C-C-T-G-T-C-C-A-G) differed significantly between cases and healthy controls [p = .014]. Regarding meta-analysis, rs841853 contributed to an increased risk of DN [(ORG = 1.43 (1.09–1.88); ORG = 1.58 (1.01–2.48)] between diseased controls versus cases and healthy controls versus cases, respectively. Further studies confirm the association of rs12407920, rs841847, rs841853, as well as rs3729548 and the risk of T2DM leading to nephropathy.

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.