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World Journal of Diabetes logoLink to World Journal of Diabetes
. 2021 Oct 15;12(10):1765–1777. doi: 10.4239/wjd.v12.i10.1765

Diabetic kidney disease: Are the reported associations with single-nucleotide polymorphisms disease-specific?

Marek Saracyn 1,2, Bartłomiej Kisiel 3, Maria Franaszczyk 4, Dorota Brodowska-Kania 5,6, Wawrzyniec Żmudzki 7, Robert Małecki 8, Longin Niemczyk 9, Przemysław Dyrla 10, Grzegorz Kamiński 11, Rafał Płoski 12, Stanisław Niemczyk 13
PMCID: PMC8554375  PMID: 34754377

Abstract

BACKGROUND

The genetic backgrounds of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) have not been fully elucidated.

AIM

To examine the individual and cumulative effects of single-nucleotide polymorphisms (SNPs) previously associated with DKD on the risk for ESKD of diabetic etiology and to determine if any associations observed were specific for DKD.

METHODS

Fourteen SNPs were genotyped in hemodialyzed 136 patients with diabetic ESKD (DKD group) and 121 patients with non-diabetic ESKD (NDKD group). Patients were also re-classified on the basis of the primary cause of chronic kidney disease (CKD). The distribution of alleles was compared between diabetic and non-diabetic groups as well as between different sub-phenotypes. The weighted multilocus genetic risk score (GRS) was calculated to estimate the cumulative risk conferred by all SNPs. The GRS distribution was then compared between the DKD and NDKD groups as well as in the groups according to the primary cause of CKD.

RESULTS

One SNP (rs841853; SLC2A1) showed a nominal association with DKD (P = 0.048; P > 0.05 after Bonferroni correction). The GRS was higher in the DKD group (0.615 ± 0.260) than in the NDKD group (0.590 ± 0.253), but the difference was not significant (P = 0.46). The analysis of associations between GRS and individual factors did not show any significant correlation. However, the GRS was significantly higher in patients with glomerular disease than in those with tubulointerstitial disease (P = 0.014) and in those with a combined group (tubulointerstitial, vascular, and cystic and congenital disease) (P = 0.018).

CONCLUSION

Our results suggest that selected SNPs that were previously associated with DKD may not be specific for DKD and may confer risk for CKD of different etiology, particularly those affecting renal glomeruli.

Keywords: Diabetic kidney disease, Chronic kidney disease, End-stage kidney disease, Single-nucleotide polymorphism, Diabetes mellitus

Core Tip: The genetic background of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) has not been fully elucidated. This study on a large population of dialyzed patients shows that single-nucleotide polymorphisms (SNPs) previously described in diabetes mellitus type 2 patients with DKD are not associated with the risk for ESKD of a diabetic background. Instead, the analyzed SNPs seem to correlate with glomerular kidney disease. These findings suggest that chronic kidney disease of different etiologies but the same dominant location of the pathological processes may share a common genetic background.

INTRODUCTION

A diabetes mellitus (DM) epidemic is underway; in 2019, approximately 463 million people worldwide were estimated to have DM, and half of those cases were considered undiagnosed. By 2045, the prevalence is expected to increase to 700 million cases[1-3]. In Poland, around 3.5 million people (9.1% of the total population) suffer from DM[4]. Roughly 30% of patients with type 1 DM (DM1) and 40% with type 2 DM (DM2) develop a serious complication of the small renal vessels: diabetic kidney disease (DKD)[5,6]. The occurrence of DKD considerably worsens the long-term prognosis — the risk of premature death in end-stage kidney disease (ESKD) patients requiring renal replacement therapy (RRT) is 18-fold greater than in the general population[7]. Understandably, DKD is the strongest single predictor of death in a diabetic patient[8]. It is estimated that the number of DKD-associated deaths in DM patients increased by 94% from 1990 to 2012[9]. The recognized risk factors for DKD are hyperglycemia, acute kidney injury, hypertension, and obesity; however, age, gender, race, and family history are also influential factors[10].

Both clinical and epidemiological studies on DKD show a familial association of the disease[11-13], suggesting that heredity plays an important role in its development. Investigators aiming to elucidate the genetic basis of DKD have used two different approaches: candidate gene studies (CGS) and genome-wide association studies (GWAS). During the last two decades, CGS have tested more than 150 loci for their potential relationship with the renal complications of DM[14-16] and have reported an association between several single-nucleotide polymorphisms (SNPs) and albuminuria, glomerular filtration rate (GFR), DKD, and ESKD in both DM1 and DM2 patients. The majority of the proteins encoded by these genes are components of nephron (glomerulus, epithelium, and ion channels), but some are related to the extracellular matrix, immune response, phagocytosis, and cell migration[17]. During the last decade, GWAS have replaced CGS as the study method of choice. The results of GWAS have confirmed some of the CGS findings and have also revealed new associations[18,19]. These initial findings must be corroborated in replication studies in different populations.

With the increasing number of identified genetic risk factors for multifactorial diseases, tools that assess the cumulative impact of these factors on disease risk have been developed. One such tool is the genetic risk score (GRS), which our group has successfully applied in earlier studies[20,21]. GRS is particularly useful in situations wherein the individual effect carried by a single genetic variant is small. Previous studies have shown the capability of GRS to measure the genetic risk of various multifactorial diseases, including myocardial infarction, atrial fibrillation, stroke, rheumatoid arthritis, and psoriasis[22]. Thus, several studies have used this approach to evaluate the risk of renal complications in DM[23-25].

Previous analyses have searched for associations between genetic markers and the risk of diabetic renal complications but may have overlooked a critical issue: whether these genetic markers are specific for DKD or are, rather, associated with the risk for chronic kidney disease (CKD), regardless of its explicit underlying cause.

The aim of our study was to examine the individual and cumulative effects of SNPs previously described in DM2 patients with DKD on the risk for ESKD of diabetic etiology in a population of hemodialyzed (HD) patients and to determine if any associations observed were specific for DKD.

MATERIALS AND METHODS

This study was approved by the Military Institute of Medicine Ethics Committee, Warsaw, Poland. Informed consent was obtained from each patient. All procedures were performed in accordance with the Helsinki Declaration of 1975, revised in 1983.

Patients and controls

From an initial group of 1246 ESKD/HD patients, 136 consecutive patients with DM2 and DKD as the primary cause of ESKD were included in this study; they formed the DKD group. The control group was composed of 121 age-matched ESKD/HD patients with non-DKD (NDKD); this was the NDKD group. The inclusion criteria were as follows: (1) ESKD treated by hemodialysis; and (2) Age ≥ 18 years. The exclusion criterion was the presence of malignancy. The patients were recruited from the Military Institute of Medicine in Warsaw, Poland, and the Mazovian Centers of Dialysis in the following cities in Poland: Radom, Ciechanów, Grodzisk Mazowiecki, Maków Mazowiecki, Sokołów Podlaski, Skierniewice, Warszawa Międzylesie, Wołomin, and Otwock. Patients were classified as DKD or NDKD on the basis of histopathological examinations and diagnoses made by two independent nephrologists in each of the Mazovian Dialysis Centers. Nineteen DKD patients and 11 NDKD patients were excluded from the study because of poor DNA quality or incomplete genotyping data; the final DKD and NDKD groups consisted of 117 and 110 patients, respectively. Nineteen patients in the NDKD group were classified by nephrologists as having kidney disease of complex pathogenesis (CP group). A structured questionnaire was used to collect data regarding age, gender, smoking habits, body mass index (BMI), history of kidney disease in the pre-ESKD/HD period, presence and volume of diuresis, and coexistence of hypertension and current treatment. Then, the patients were re-classified on the basis of the 2012 Kidney Disease Improving Global Outcomes (KDIGO) guidelines and CKD classification system based on the primary cause of their kidney disease. These classifications included (1) glomerular disease (GD); (2) tubulointerstitial disease (TID); (3) vascular disease (VD); and (4) cystic and congenital disease (CCD). Patients with CP were considered separately[26].

SNP selection

SNPs previously associated with DKD in DM2 patients were selected for this study[27-38]. The inclusion criteria for SNP selection were as follows: (1) Association with DKD in the presence of DM2 confirmed in a GWAS, meta-analysis, or large-scale case-control study; (2) Odds ratio (OR) ≥ 1.2 (or ≤ 0.83); and (3) Minor allele frequency ≥ 0.1 in a Caucasian population (based on data from the HapMap CEU). The exclusion criteria were as follows: (1) Association limited to non-Caucasian populations; or (2) Lack of studies on Caucasian populations. On the basis of these criteria, 14 SNPs were selected for genotyping. However, five of the genotyped SNPs were excluded from the analysis because of a low genotyping success rate (< 80%) or Hardy-Weinberg equilibrium (HWE) deviation: rs9521445 (MYO16/IRS2), rs1801133 (MTHFR), rs2241766 (ADIPOQ), rs5186 (AGTR1), and rs4880 (SOD2). The complete list of the analyzed SNPs is shown in Supplementary Table 1.

Genotyping

The salting out method was used to isolate DNA form whole blood samples[39]. For SNP genotyping, a custom array was designed (Taqman® OpenArray® Genotyping Plate, Custom Format 16 QuantStudio™ 12 K Flex, Life Technologies, Carlsbad, CA, United States), and genotyping was performed following the manufacturer’s protocols on a QuantStudio™ 12 K Flex Real-Time PCR System (Applied Biosystems, Foster City, CA, United States).

Statistical analysis

The statistical methods used in this study were reviewed by Kisiel B of the Clinical Research Support Center, Military Institute of Medicine, Warsaw, Poland. The differences in allele distribution between cases and controls as well as HWE were evaluated using the PLINK v1.07 statistical software package[40]. The deviation from HWE was considered significant at P < 0.05. The rest of statistical analyses was undertaken with the use of Statistica 12 package (StatSoft Inc). A Bonferroni correction (P value × number of tested SNPs) was used to adjust for multiple comparisons. To evaluate the cumulative risk of multiple loci a GRS (computed by summing the products of the number of risk alleles and the natural logarithm of the OR for each SNP) was calculated. For the calculation of GRS we used ORs from previous studies (Supplementary Table 1). The GRSs of the DKD and NDKD groups were compared using Student’s t-test, whereas the GRSs of GD, VD, TID, CCD, and CP groups were compared using ANOVA. Pearson's test was used to assess the correlations between different parameters.

RESULTS

The clinical and demographic data of the subjects are presented in Table 1. The DKD and NDKD groups differed significantly with respect to BMI (P = 0.01) and treatment with beta-blockers (P = 0.015).

Table 1.

Study and control group characteristics

 DKD (n = 117)
NDKD (n = 110)
P value
Age (yr) 68.94 (7.90) 69.87 (10.86) 0.46
Male sex (%) 57 (48.72) 66 (60.00) 0.088
Time-to-dialysis (yr) 5.25 (5.16) 6.23 (6.09) 0.20
Rapid progression of CKD (TTD ≤ 3 mo) (%) 6 (5.13) 6 (5.45) 0.91
Fast progression of CKD (TTD ≤ 1 yr) (%) 29 (24.79) 21 (19.09) 0.30
Slow progression of CKD (TTD > 5 yr) (%) 37 (31.62) 43 (39.09) 0.24
BMI (kg/m2) 30.21 (16.35) 26.01 (5.15) 0.01
Smoking ever (%) 52 (44.44) 58 (53.21)1 0.19
Preserved diuresis (%) 97 (82.91) 85 (77.27) 0.29
24 h diuresis > 500 mL (%) 49 (41.88) 46(41.82) 0.99
Hypertension (%) 114 (97.44) 102 (92.73) 0.099
ACE-I (%) 50 (43.48)2 39 (39.39)4 0.54
ARB (%) 17 (14.78)2 12 (11.76)5 0.51
Beta-blockers (%) 89 (76.72)3 91 (89.22)5 0.015
Statins (%) 76 (66.09)2 57 (58.16)6 0.23
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1

Data available for 109 patients.

2

Data available for 115 patients.

3

Data available for 116 patients.

4

Data available for 99 patients.

5

Data available for 102 patients.

6

Data available for 98 patients.

Significant differences are highlighted in bold. ACE-I: Angiotensin-converting-enzyme inhibitors; ARB: Angiotensin II receptor blockers; BMI: Body mass index; CKD: Chronic kidney disease; DKD: Diabetic kidney disease; NDN: Non-diabetic kidney disease; TTD: Time-to-dialysis (time between the diagnosis of chronic kidney disease and start of hemodialysis).

SNP associations with DKD are detailed in Table 2. Only one SNP (rs841853) showed a nominal association with DKD (P = 0.048; P > 0.05 after Bonferroni correction).

Table 2.

Single-nucleotide polymorphisms’ associations with diabetic kidney disease

SNP
DKD
NDKD
OR
P value
E
N
E
N
rs1617640 123 111 119 101 0.94 (0.65-1.36) 0.78
rs841853 84 150 60 160 1.49 (1.00-2.23) 0.048
rs1800783 67 167 69 151 0.88 (0.59-1.31) 0.53
rs1531343 23 211 17 203 1.30 (0.68-2.51) 0.43
rs1800470 91 143 93 127 0.87 (0.60-1.26) 0.46
rs759853 94 140 74 146 1.32 (0.90-1.94) 0.15
rs1801282 40 194 35 185 1.06 (0.66-1.79) 0.73
rs13293564 104 130 91 129 1.13 (0.78-1.65) 0.51
rs2268388 29 205 32 188 0.83 (0.48-1.43) 0.50
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DKD: Diabetic kidney disease; E: Effect allele; NDKD: Non-diabetic kidney disease; N: Non-effect allele; OR: Odds ratio; SNP: Single nucleotide polymorphism.

To evaluate the cumulative risk of multiple loci, we calculated GRS; it was slightly higher in the DKD group (0.615 ± 0.260) than in the NDKD group (0.590 ± 0.253), but the difference was not significant (P = 0.46). The GRS difference remained not significant (P = 0.34) after the exclusion of 19 CP patients from the NDKD group: 0.615 ± 0.260 for the DKD group and 0.582 ± 0.244 for the NKD group. The analysis of associations between GRS and the individual factors of gender, diuresis, and rate of CKD progression in the DKD and NDKD groups showed a significant correlation of GRS with diuresis in the NDKD group (0.643 ± 0.22 for diuresis > 500 mL vs 0.535 ± 0.253 for diuresis < 500 mL, P = 0.035, Table 3).

Table 3.

Associations between genetic risk score and different parameters in diabetic kidney disease and non-diabetic kidney disease patients

Parameter
DKD + NDKD, n = 227
DKD, n = 117
NDKD1, n = 91
GRS, mean ± SD
P value
GRS, mean ± SD
P value
GRS, mean ± SD
P value
Rapid progression (TTD ≤ 3 mo vs > 3 mo) 0.602 ± 0.233 vs 0.603 ± 0.258 0.99 0.518 ± 0.273 vs 0.620 ± 0.260 0.34 0.687 ± 0.167 vs 0.574 ± 0.248 0.27
Fast progression (TTD ≤ 1 yr vs > 1 yr) 0.592 ± 0.246 vs 0.606 ± 0.260 0.75 0.600 ± 0.229 vs 0.620 ± 0.270 0.71 0.522 ± 0.253 vs 0.593 ± 0.242 0.30
Slow progression (TTD > 5 yr vs ≤ 5 yr) 0.596 ± 0.256 vs 0.607 ± 0.258 0.77 0.629 ± 0.286 vs 0.608 ± 0.249 0.69 0.583 ± 0.230 vs 0.580 ± 0.257 0.96
Diuresis (preserved diuresis vs no diuresis) 0.614 ± 0.260 vs 0.557 ± 0.239 0.18 0.622 ± 0.265 vs 0.578 ± 0.235 0.49 0.605 ± 0.244 vs 0.503 ± 0.235 0.09
24h diuresis > 500 mL (> 500 mL vs ≤ 500 mL) 0.630 ± 0.244 vs 0.583 ± 0.265 0.17 0.621 ± 0.258 vs 0.611 ± 0.263 0.83 0.643 ± 0.220 vs 0.535 ± 0.253 0.035
Male sex (males vs females) 0.639 ± 0.265 vs 0.591 ± 0.255 0.31 0.617 ± 0.263 vs 0.586 ± 0.249 0.37 0.589 ± 0.251 vs 0.568 ± 0.235 0.70
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1

Cases with complex pathogenesis excluded from analysis.

DKD: Diabetic kidney disease; NDKD: Non-diabetic kidney disease; GRS: Genetic risk score; TTD: Time-to-dialysis (time between the diagnosis of chronic kidney disease and start of hemodialysis). Significant differences are highlighted in bold.

We analyzed the distribution of GRS in the GD, TID, VD, CCD, and CP groups using ANOVA; the differences between groups were not significant (Table 4). However, post hoc analysis showed a significantly higher GRS in the GD group (0.628 ± 0.256) than in the TID group (0.461 ± 0.218) (P = 0.014) as well as a higher GRS in the GD group (0.628 ± 0.256) than in the combined TID+VD+CCD group (0.536 ± 0.235) (P = 0.018). The analysis of associations between GRS and the individual factors of gender, diuresis, and rate of CKD progression did not show any significant correlation in the GD group (Supplementary Table 2).

Table 4.

The distribution of genetic risk score in particular end-stage kidney disease subgroups

Group
GRS
P value
Post-hoc analysis
Classification: (1) GD, (2) TID, (3) VD, (4) CCD, (5) CP
0.09 GD vs TID P = 0.014
GD vs VD P = 0.12
GD (n = 146) 0.628 ± 0.256 GD vs CCD P = 0.61
TID (n = 16) 0.461 ± 0.218 GD vs CP P = 0.97
VD (n = 33) 0.551 ± 0.269 TID vs VD P = 0.24
CCD (n = 13) 0.590 ± 0.138 TID vs CCD P = 0.18
CP (n = 19) 0.629 ± 0.298 TID vs CP P = 0.051
VD vs CCD P = 0.64
VD vs CP P = 0.29
CCD vs CP P = 0.66
Classification: (1) GD, (2) TID+VD+CCD, (3) CP
GD (n = 146) 0.628 ± 0.256 0.055 GD vs TID+VD+CCD P = 0.018
TID+VD+CCD (n = 62) 0.536 ± 0.235 GD vs CP P = 0.97
CP (n = 19) 0.629 ± 0.298 TID+VD+CCD vs CP P = 0.16
Classification: (1) GD, (2) TID+VD+CCD+CP
GD 0.628 ± 0.256 0.051 N/A
TID+VD+CCD+CP 0.558 ± 0.253
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Genetic risk score is presented as mean ± SD. GRS: Genetic risk score; GD: Glomerular disease; TID: Tubulointerstitial disease; VD: Vascular diseases; CCD: Cystic and congenital disease; CP: Complex pathogenesis; N/A: Not applicable.

DISCUSSION

To the best of our knowledge, this study may be the first to assess the genetic risk of DKD and other nephropathies leading to ESKD and RRT in a relatively large group of HD patients. Unexpectedly, none of the selected SNPs previously described in DM2 patients and associated with DKD development significantly correlated with DKD in our group of ESKD patients requiring hemodialysis, except one (rs841853) that showed a nominal association with DKD (P = 0.048; P > 0.05 after Bonferroni correction). Moreover, the GRS composed of all genotyped SNPs, carrying the cumulative risk of all tested loci, did not differ significantly between the DKD and NDKD groups and did not associate with any of the analyzed clinical parameters, including the rate of renal failure progression. This observation may have three explanations: (1) The analyzed SNPs are not associated with DKD in a Polish population; (2) The analyzed SNPs are associated with earlier stages of DKD; or (3) The analyzed SNPs are not specific for DKD but are associated with different kidney diseases and their progression.

It should be emphasized that many earlier studies on the genetic background of DKD have used DM patients without DKD as controls. This approach has identified risk factors for DKD development but has been unable to assess their specificities. That is, the genetic factors that have been pinpointed to be associated with DKD are not necessarily specific for DKD. To address this issue, we re-classified our patients on the basis of the KDIGO guidelines and found that GRS was significantly higher in the GD group than in the TID group and the combined TID+VD+CCD group. This observation suggests that these SNPs that were previously described as associated with DKD are in fact associated with the risk of CKD and ESKD of different etiologies, particularly those primarily affecting renal glomeruli.

In recent years, research has focused on the genetic basis of DKD. Nearly 160 genes have been implicated in DKD development as found in CDS and GWAS, and there appears to be a multigenic etiology of disease as gauged by GRS[14-19]. In a study involving 1100 DM2 patients, Wang et al[23] demonstrated that a GRS composed of six SNPs in genes involved in lipid metabolism (PON1, PON2, CETP), hemostasis (ITGA2), and inflammation (LTA1, LTA3) was associated with a significantly higher risk of DKD; risks were lower when SNPs were individually assessed. Todd et al[24] examined a GRS composed of 32 validated BMI loci to determine the relationship between BMI and macroalbuminuria, ESKD, and DKD and found that a 1 kg/m2 higher BMI increased the risk of macroalbuminuria by 28%, of ESKD by 43%, and of DKD by 33%. Barbieux et al[25] explored the association between 18 CKD-related SNPs and CKD G5 in 1,300 DM2 patients and 300 DM1 patients; however, neither a single SNP nor the 18-SNP GRS was linked to the deterioration of renal function, need for RRT, or death. Our study cannot be directly compared with the ones referenced above because it followed a completely different, exclusively DKD-related set of SNPs and a dissimilar study design in which non-diabetic patients were used as controls for diabetic ESKD patients.

The main finding of our study is that SNPs reported to be associated with DKD alone may, in fact, be correlated with CKD of different etiology. In this context, two papers are worth mentioning. A large-scale GWAS on 130000 subjects of European ancestry by Pattaro et al[41] showed that 53 known and novel SNP loci were highly correlated with GFR in individuals with or without diabetes; the effects on both patient groups were of similar magnitude. A study of even larger scale (including over 280000 subjects from the Million Veteran Program, over 765000 subjects from the CKD Gen Consortium, and more than 90000 diabetic patients) published by Hellwege et al[42] made similar observations: 32 SNP loci (17 known and 15 new) were significantly and with similar effect size associated with GFR in both diabetic and non-diabetic groups. Of course, reduced GFR is not synonymous with CKD (or DKD) and ESKD; however, the findings by Pattaro et al[41] and Hellwege et al[42] raise the possibility that CKDs of different etiologies may, to some extent, share a common genetic basis.

To address this issue, we reviewed recent publications regarding the possible relationship of our SNPs with CKD/ESKD of etiology other than DKD[43-50]. Most reports, whether on small or large populations, testing single or many variants, did not include the exact SNPs selected for our study, although they may have involved the same genes. For example, Hellwege et al[42] found an association between GFR and NOS3 (nitric oxide synthase type 3) (rs3918226), but it was a different variation, which showed only a moderate linkage disequilibrium with our NOS3 SNP (rs1800783) (R2 = 0.17)[42]. Similarly, our SNPs were not any of those used in a GWAS by Wuttke et al[43], the largest meta-analysis to date. Essential differences exist between our study and the other studies cited, and simple comparisons are not applicable. First, the aforementioned studies characterized associations between SNPs and GFR in the general population or in specific subpopulations such as diabetic patients, whereas our study aimed to delineate relationships between the selected SNPs and ESKD. Undoubtedly, changes in GFR are not synonymous with ESKD. In fact, a GWAS by Lin et al[44] indicated that the majority of loci do not overlap between early stages of CKD and ESKD, suggesting that distinct genetic factors may dominate at different stages of CKD. Second, a GWAS detects only the strongest associations, as corrections for a number of analyses are routinely used, possibly neglecting more nuanced but still meaningful effects on outcome measures. Third, studies on the general population, which include patients with CKDs of different etiologies, may simply not pick up disease-specific associations due to a water-downed subject pool.

The association of the SNPs selected for our study with CKDs of different etiologies, and not only DKD, is supported by the fact that their corresponding loci carry information affecting the structure and function of nephrons or interstitial renal tissue, the disorders of which are not restricted to diabetic etiology. In fact, the most common causes of CKD, such as DKD, hypertensive nephropathy, atherosclerotic nephropathy, renal mass reduction, obstructive nephropathy, and glomerular diseases, share many common pathophysiological pathways[45]. The factor initiating kidney injury is clearly different, but the progression of renal damage may involve mutual mechanisms, such as the renin-angiotensin-aldosterone system; endothelial, podocyte, mesangial, and tubular cell activation; platelet activation; common cytokine activation pathways (transforming growth factor-β1); the spread of inflammation; and repair of damaged tissues (fibrosis). A shared pathophysiology is even more probable in diseases affecting the same region of the kidney — in this case, the renal glomeruli.

This study has several strengths. First, the study group included only patients with ESKD, which addresses the suggestion made by earlier research that different CKD stages may be driven by distinctive genetic factors. Second, non-diabetic ESKD patients comprised our control group; this allowed us to answer the question whether the SNPs previously associated with DKD are specific for that type of CKD; indeed, we demonstrated that the SNPs in question were not specific for ESKD of diabetic etiology.

We must also acknowledge the major limitation of this study: its size. The study group was relatively small for a genetic analysis. However, owing to our strict inclusion criteria, the initial population of approximately 1200 patients with ESKD requiring RRT from dialysis centers in Mazovia, Poland, which has a general population of about 5.5 million, was winnowed down to those patients with documented DKD as the primary cause of ESKD and an appropriate, non-DKD control group with ESKD of etiology other than diabetes. Due to relatively small study group our results need to be confirmed in studies on larger populations.

CONCLUSION

Our study may be the first to demonstrate that selected SNPs that were previously associated with DKD may not be specific for DKD and may confer genetic risk for CKDs of different etiologies, particularly those affecting renal glomeruli. Our results need to be confirmed in studies on larger populations.

ARTICLE HIGHLIGHTS

Research background

A diabetes mellitus (DM) epidemic is underway; 40% patients with type 2 DM (DM2) develop a serious complication, diabetic kidney disease (DKD), and the occurrence of DKD considerably worsens the long-term prognosis. However, the genetic backgrounds of DKD and end-stage kidney disease (ESKD) have not been fully elucidated.

Research motivation

Previous studies have searched for associations between genetic markers and the risk of diabetic renal complications but may have overlooked a critical issue: whether these genetic markers are specific for DKD or are, rather, associated with the risk for chronic kidney disease and ESKD itself, regardless of its explicit underlying cause.

Research objectives

The aim of our study was to examine the individual and cumulative effects (as a genetic risk score, GRS) of SNPs previously described in DM2 patients with DKD on the risk of diabetic ESKD in a population of hemodialyzed patients and to determine if any associations observed were specific for DKD.

Research methods

Fourteen SNPs were genotyped in 136 patients with diabetic ESKD (DKD group) and 121 patients with non-diabetic ESKD (NDKD group). Patients were also classified on the basis of the KDIGO guidelines and CKD classification system based on the primary cause of CKD, such as glomerular disease (GD), tubulointerstitial disease (TID), vascular disease (VD), and cystic and congenital disease (CCD). Patients with complex pathogenesis (CP) were considered separately. The distribution of alleles was compared between diabetic and non-diabetic groups as well as between different sub-phenotypes. The weighted multilocus GRS was calculated to estimate the cumulative risk conferred by all SNPs. The distribution of GRS was then compared between the DKD and NDKD groups as well as in the groups according to KDIGO guidelines.

Research results

One SNP (rs841853; SLC2A1) showed a nominal association with DKD (P = 0.048; P > 0.05 after Bonferroni correction). The GRS was higher in the DKD group (0.615 ± 0.260) than in the NDKD group (0.590 ± 0.253), but the difference was not significant (P = 0.46). The analysis of associations between GRS and the individual factors of gender, diuresis, and rate of CKD progression in either study group did not show any significant correlation. However, the GRS was significantly higher in the GD group than in the TID group (P = 0.014) and the combined TID+VD+CCD group (P = 0.018).

Research conclusions

Our study may be the first to demonstrate that selected SNPs that were previously associated with DKD may not be specific for DKD and may confer genetic risk for CKDs of different etiologies, particularly those affecting renal glomeruli.

Research perspectives

Further studies are needed to confirm a similar correlation of other SNPs previously associated with DKD with the development and etiology of ESKD.

ACKNOWLEDGEMENTS

We thank all medical staff members and technicians of dialysis centers who agreed to participate in this study.

Footnotes

Institutional review board statement: This study was approved by the Military Institute of Medicine Ethics Committee, Warsaw, Poland (approval No. 5/WIM/2010).

Informed consent statement: All involved patients gave their informed consent.

Conflict-of-interest statement: The authors declare that they have no competing financial interests. No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Manuscript source: Invited manuscript

Corresponding Author's Membership in Professional Societies: Polish Society of Nephrology; Polish Society of Endocrinology; Polish Society of Medical Biology; European Renal Association-European Dialysis and Transplant Association.

Peer-review started: March 16, 2021

First decision: May 3, 2021

Article in press: September 7, 2021

Specialty type: Endocrinology and metabolism

Country/Territory of origin: Poland

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P-Reviewer: Rostoker G S-Editor: Gao CC L-Editor: A P-Editor: Guo X

Contributor Information

Marek Saracyn, Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland; Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland. msaracyn@interia.pl.

Bartłomiej Kisiel, Clinical Research Support Center, Military Institute of Medicine, Warsaw 04-141, Poland.

Maria Franaszczyk, Department of Medical Biology, Molecular Biology Laboratory, Institute of Cardiology, Warsaw 04-628, Poland.

Dorota Brodowska-Kania, Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland; Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland.

Wawrzyniec Żmudzki, Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland.

Robert Małecki, Department of Nephrology, Międzyleski Specialist Hospital in Warsaw, Warsaw 04-749, Poland.

Longin Niemczyk, Department of Nephrology, Dialysis and Internal Diseases, Warsaw Medical University, Warsaw 02-097, Poland.

Przemysław Dyrla, Department of Gastroenterology, Military Institute of Medicine, Warsaw 04-141, Poland.

Grzegorz Kamiński, Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland.

Rafał Płoski, Department of Medical Genetics, Medical University of Warsaw, Warsaw 02-106, Poland.

Stanisław Niemczyk, Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland.

Data sharing statement

Technical appendix, statistical code, and dataset available from the corresponding author at msaracyn@interia.pl.

References

  • 1.Alicic RZ, Rooney MT, Tuttle KR. Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clin J Am Soc Nephrol. 2017;12:2032–2045. doi: 10.2215/CJN.11491116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Piccoli GB, Grassi G, Cabiddu G, Nazha M, Roggero S, Capizzi I, De Pascale A, Priola AM, Di Vico C, Maxia S, Loi V, Asunis AM, Pani A, Veltri A. Diabetic Kidney Disease: A Syndrome Rather Than a Single Disease. Rev Diabet Stud. 2015;12:87–109. doi: 10.1900/RDS.2015.12.87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843. doi: 10.1016/j.diabres.2019.107843. [DOI] [PubMed] [Google Scholar]
  • 4.Niklas A, Marcinkowska J, Kozela M, Pająk A, Zdrojewski T, Drygas W, Piwońska A, Kwaśniewska M, Kozakiewicz K, Tykarski A. Blood pressure and cholesterol control in patients with hypertension and hypercholesterolemia: the results from the Polish multicenter national health survey WOBASZ II. Pol Arch Intern Med. 2019;129:864–873. doi: 10.20452/pamw.15013. [DOI] [PubMed] [Google Scholar]
  • 5.United States Renal Data System. Annual Data Report: Epidemiology of Kidney Disease in the United States, Bethesda, MD, National Institute of Diabetes and Digestive and Kidney Diseases, 2015. [cited 14 March 2021]. In: United States Renal Data System [Internet]. Available from: https://www.usrds.org/annual-data-report/
  • 6.Reutens AT. Epidemiology of diabetic kidney disease. Med Clin North Am. 2013;97:1–18. doi: 10.1016/j.mcna.2012.10.001. [DOI] [PubMed] [Google Scholar]
  • 7.Groop PH, Thomas MC, Moran JL, Wadèn J, Thorn LM, Mäkinen VP, Rosengård-Bärlund M, Saraheimo M, Hietala K, Heikkilä O, Forsblom C FinnDiane Study Group. The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes. 2009;58:1651–1658. doi: 10.2337/db08-1543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Dousdampanis P, Trigka K, Mouzaki A. Tregs and kidney: From diabetic nephropathy to renal transplantation. World J Transplant. 2016;6:556–563. doi: 10.5500/wjt.v6.i3.556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, Saran R, Wang AY, Yang CW. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382:260–272. doi: 10.1016/S0140-6736(13)60687-X. [DOI] [PubMed] [Google Scholar]
  • 10.Taal MW. Risk factors and chronic kidney disease. In: Skorecki K. Brenner and Rector’s The Kidney, 10th ed. Amsterdam, Elsevier, 2015: 669-692. [Google Scholar]
  • 11.Freedman BI, Bostrom M, Daeihagh P, Bowden DW. Genetic factors in diabetic nephropathy. Clin J Am Soc Nephrol. 2007;2:1306–1316. doi: 10.2215/CJN.02560607. [DOI] [PubMed] [Google Scholar]
  • 12.Sandholm N, Groop PH. Genetic basis of diabetic kidney disease and other diabetic complications. Curr Opin Genet Dev. 2018;50:17–24. doi: 10.1016/j.gde.2018.01.002. [DOI] [PubMed] [Google Scholar]
  • 13.Guo J, Rackham OJL, Sandholm N, He B, Österholm AM, Valo E, Harjutsalo V, Forsblom C, Toppila I, Parkkonen M, Li Q, Zhu W, Harmston N, Chothani S, Öhman MK, Eng E, Sun Y, Petretto E, Groop PH, Tryggvason K. Whole-Genome Sequencing of Finnish Type 1 Diabetic Siblings Discordant for Kidney Disease Reveals DNA Variants associated with Diabetic Nephropathy. J Am Soc Nephrol. 2020;31:309–323. doi: 10.1681/ASN.2019030289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hanson RL, Craig DW, Millis MP, Yeatts KA, Kobes S, Pearson JV, Lee AM, Knowler WC, Nelson RG, Wolford JK. Identification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide polymorphism association study. Diabetes. 2007;56:975–983. doi: 10.2337/db06-1072. [DOI] [PubMed] [Google Scholar]
  • 15.Maeda S, Imamura M, Kurashige M, Araki S, Suzuki D, Babazono T, Uzu T, Umezono T, Toyoda M, Kawai K, Imanishi M, Hanaoka K, Maegawa H, Uchigata Y, Hosoya T. Replication study for the association of 3 SNP loci identified in a genome-wide association study for diabetic nephropathy in European type 1 diabetes with diabetic nephropathy in Japanese patients with type 2 diabetes. Clin Exp Nephrol. 2013;17:866–871. doi: 10.1007/s10157-013-0797-5. [DOI] [PubMed] [Google Scholar]
  • 16.Sandholm N, Salem RM, McKnight AJ, Brennan EP, Forsblom C, Isakova T, McKay GJ, Williams WW, Sadlier DM, Mäkinen VP, Swan EJ, Palmer C, Boright AP, Ahlqvist E, Deshmukh HA, Keller BJ, Huang H, Ahola AJ, Fagerholm E, Gordin D, Harjutsalo V, He B, Heikkilä O, Hietala K, Kytö J, Lahermo P, Lehto M, Lithovius R, Osterholm AM, Parkkonen M, Pitkäniemi J, Rosengård-Bärlund M, Saraheimo M, Sarti C, Söderlund J, Soro-Paavonen A, Syreeni A, Thorn LM, Tikkanen H, Tolonen N, Tryggvason K, Tuomilehto J, Wadén J, Gill GV, Prior S, Guiducci C, Mirel DB, Taylor A, Hosseini SM DCCT/EDIC Research Group, Parving HH, Rossing P, Tarnow L, Ladenvall C, Alhenc-Gelas F, Lefebvre P, Rigalleau V, Roussel R, Tregouet DA, Maestroni A, Maestroni S, Falhammar H, Gu T, Möllsten A, Cimponeriu D, Ioana M, Mota M, Mota E, Serafinceanu C, Stavarachi M, Hanson RL, Nelson RG, Kretzler M, Colhoun HM, Panduru NM, Gu HF, Brismar K, Zerbini G, Hadjadj S, Marre M, Groop L, Lajer M, Bull SB, Waggott D, Paterson AD, Savage DA, Bain SC, Martin F, Hirschhorn JN, Godson C, Florez JC, Groop PH, Maxwell AP. New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet. 2012;8:e1002921. doi: 10.1371/journal.pgen.1002921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Gu HF. Genetic and Epigenetic Studies in Diabetic Kidney Disease. Front Genet. 2019;10:507. doi: 10.3389/fgene.2019.00507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Nicolas A, Fatima S, Lamri A, Bellili-Muñoz N, Halimi JM, Saulnier PJ, Hadjadj S, Velho G, Marre M, Roussel R, Fumeron F. ABCG8 polymorphisms and renal disease in type 2 diabetic patients. Metabolism. 2015;64:713–719. doi: 10.1016/j.metabol.2015.03.005. [DOI] [PubMed] [Google Scholar]
  • 19.Sandholm N, Haukka JK, Toppila I, Valo E, Harjutsalo V, Forsblom C, Groop PH. Confirmation of GLRA3 as a susceptibility locus for albuminuria in Finnish patients with type 1 diabetes. Sci Rep. 2018;8:12408. doi: 10.1038/s41598-018-29211-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Saracyn M, Kisiel B, Bachta A, Franaszczyk M, Brodowska-Kania D, Żmudzki W, Szymański K, Sokalski A, Klatko W, Stopiński M, Grochowski J, Papliński M, Goździk Z, Niemczyk L, Bober B, Kołodziej M, Tłustochowicz W, Kamiński G, Płoski R, Niemczyk S. Value of multilocus genetic risk score for atrial fibrillation in end-stage kidney disease patients in a Polish population. Sci Rep. 2018;8:9284. doi: 10.1038/s41598-018-27382-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kisiel B, Kisiel K, Szymański K, Mackiewicz W, Biało-Wójcicka E, Uczniak S, Fogtman A, Iwanicka-Nowicka R, Koblowska M, Kossowska H, Placha G, Sykulski M, Bachta A, Tłustochowicz W, Płoski R, Kaszuba A. The association between 38 previously reported polymorphisms and psoriasis in a Polish population: High predicative accuracy of a genetic risk score combining 16 Loci. PLoS One. 2017;12:e0179348. doi: 10.1371/journal.pone.0179348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Kisiel B, Kruszewski R, Juszkiewicz A, Raczkiewicz A, Bachta A, Kłos K, Duda K, Maliborski A, Szymański K, Płoski R, Saracyn M, Niemczyk S, Kisiel K, Tłustochowicz M, Tłustochowicz W. Common atherosclerosis genetic risk factors and subclinical atherosclerosis in rheumatoid arthritis: the relevance of disease duration. Rheumatol Int. 2019;39:327–336. doi: 10.1007/s00296-018-4186-y. [DOI] [PubMed] [Google Scholar]
  • 23.Wang Y, Luk AO, Ma RC, So WY, Tam CH, Ng MC, Yang X, Lam V, Tong PC, Chan JC. Predictive role of multilocus genetic polymorphisms in cardiovascular disease and inflammation-related genes on chronic kidney disease in Type 2 diabetes--an 8-year prospective cohort analysis of 1163 patients. Nephrol Dial Transplant. 2012;27:190–196. doi: 10.1093/ndt/gfr343. [DOI] [PubMed] [Google Scholar]
  • 24.Todd JN, Dahlström EH, Salem RM, Sandholm N, Forsblom C FinnDiane Study Group, McKnight AJ, Maxwell AP, Brennan E, Sadlier D, Godson C, Groop PH, Hirschhorn JN, Florez JC. Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease. Diabetes. 2015;64:4238–4246. doi: 10.2337/db15-0254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Barbieux P, György B, Gand E, Saulnier PJ, Ducrocq G, Halimi JM, Feigerlova E, Hulin-Delmotte C, Llaty P, Montaigne D, Rigalleau V, Roussel R, Sosner P, Zaoui P, Ragot S, Marre M, Tregouët DA, Hadjadj S SURDIAGENE Study Group; French JDRF Diabetic Nephropathy Collaborative Research Initiative (Search for genes determining time to onset of ESRD in T1D patients with proteinuria) No prognostic role of a GWAS-derived genetic risk score in renal outcomes for patients from French cohorts with type 1 and type 2 diabetes. Diabetes Metab. 2019;45:494–497. doi: 10.1016/j.diabet.2018.01.016. [DOI] [PubMed] [Google Scholar]
  • 26.Kidney International Supplements. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int . 2013;3:1–150. doi: 10.1038/ki.2013.243. [DOI] [PubMed] [Google Scholar]
  • 27.Nazir N, Siddiqui K, Al-Qasim S, Al-Naqeb D. Meta-analysis of diabetic nephropathy associated genetic variants in inflammation and angiogenesis involved in different biochemical pathways. BMC Med Genet. 2014;15:103. doi: 10.1186/s12881-014-0103-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Pezzolesi MG, Poznik GD, Skupien J, Smiles AM, Mychaleckyj JC, Rich SS, Warram JH, Krolewski AS. An intergenic region on chromosome 13q33.3 is associated with the susceptibility to kidney disease in type 1 and 2 diabetes. Kidney Int. 2011;80:105–111. doi: 10.1038/ki.2011.64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.McKnight AJ, Patterson CC, Sandholm N, Kilner J, Buckham TA, Parkkonen M, Forsblom C, Sadlier DM, Groop PH, Maxwell AP Warren 3/UK GoKinD Study Group. Genetic polymorphisms in nitric oxide synthase 3 gene and implications for kidney disease: a meta-analysis. Am J Nephrol. 2010;32:476–481. doi: 10.1159/000321340. [DOI] [PubMed] [Google Scholar]
  • 30.Alkayyali S, Lajer M, Deshmukh H, Ahlqvist E, Colhoun H, Isomaa B, Rossing P, Groop L, Lyssenko V. Common variant in the HMGA2 gene increases susceptibility to nephropathy in patients with type 2 diabetes. Diabetologia. 2013;56:323–329. doi: 10.1007/s00125-012-2760-5. [DOI] [PubMed] [Google Scholar]
  • 31.Jia H, Yu L, Gao B, Ji Q. Association between the T869C polymorphism of transforming growth factor-beta 1 and diabetic nephropathy: a meta-analysis. Endocrine. 2011;40:372–378. doi: 10.1007/s12020-011-9503-0. [DOI] [PubMed] [Google Scholar]
  • 32.Mooyaart AL, Valk EJ, van Es LA, Bruijn JA, de Heer E, Freedman BI, Dekkers OM, Baelde HJ. Genetic associations in diabetic nephropathy: a meta-analysis. Diabetologia. 2011;54:544–553. doi: 10.1007/s00125-010-1996-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Li T, Shi Y, Yin J, Qin Q, Wei S, Nie S, Liu L. The association between lipid metabolism gene polymorphisms and nephropathy in type 2 diabetes: a meta-analysis. Int Urol Nephrol. 2015;47:117–130. doi: 10.1007/s11255-014-0843-6. [DOI] [PubMed] [Google Scholar]
  • 34.Zhou TB, Drummen GP, Jiang ZP, Li HY. Methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and diabetic nephropathy susceptibility in patients with type 2 diabetes mellitus. Ren Fail. 2015;37:1247–1259. doi: 10.3109/0886022X.2015.1064743. [DOI] [PubMed] [Google Scholar]
  • 35.Cui W, Du B, Zhou W, Jia Y, Sun G, Sun J, Zhang D, Yuan H, Xu F, Lu X, Luo P, Miao L. Relationship between five GLUT1 gene single nucleotide polymorphisms and diabetic nephropathy: a systematic review and meta-analysis. Mol Biol Rep. 2012;39:8551–8558. doi: 10.1007/s11033-012-1711-z. [DOI] [PubMed] [Google Scholar]
  • 36.Cai Y, Zeng T, Chen L. Association of adiponectin polymorphisms with the risk of diabetic nephropathy in type 2 diabetes: a meta-analysis. J Diabetes. 2015;7:31–40. doi: 10.1111/1753-0407.12166. [DOI] [PubMed] [Google Scholar]
  • 37.Ding W, Wang F, Fang Q, Zhang M, Chen J, Gu Y. Association between two genetic polymorphisms of the renin-angiotensin-aldosterone system and diabetic nephropathy: a meta-analysis. Mol Biol Rep. 2012;39:1293–1303. doi: 10.1007/s11033-011-0862-7. [DOI] [PubMed] [Google Scholar]
  • 38.Tian C, Fang S, Du X, Jia C. Association of the C47T polymorphism in SOD2 with diabetes mellitus and diabetic microvascular complications: a meta-analysis. Diabetologia. 2011;54:803–811. doi: 10.1007/s00125-010-2004-5. [DOI] [PubMed] [Google Scholar]
  • 39.Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215. doi: 10.1093/nar/16.3.1215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, Garnaas M, Tin A, Sorice R, Li Y, Taliun D, Olden M, Foster M, Yang Q, Chen MH, Pers TH, Johnson AD, Ko YA, Fuchsberger C, Tayo B, Nalls M, Feitosa MF, Isaacs A, Dehghan A, d'Adamo P, Adeyemo A, Dieffenbach AK, Zonderman AB, Nolte IM, van der Most PJ, Wright AF, Shuldiner AR, Morrison AC, Hofman A, Smith AV, Dreisbach AW, Franke A, Uitterlinden AG, Metspalu A, Tonjes A, Lupo A, Robino A, Johansson Å, Demirkan A, Kollerits B, Freedman BI, Ponte B, Oostra BA, Paulweber B, Krämer BK, Mitchell BD, Buckley BM, Peralta CA, Hayward C, Helmer C, Rotimi CN, Shaffer CM, Müller C, Sala C, van Duijn CM, Saint-Pierre A, Ackermann D, Shriner D, Ruggiero D, Toniolo D, Lu Y, Cusi D, Czamara D, Ellinghaus D, Siscovick DS, Ruderfer D, Gieger C, Grallert H, Rochtchina E, Atkinson EJ, Holliday EG, Boerwinkle E, Salvi E, Bottinger EP, Murgia F, Rivadeneira F, Ernst F, Kronenberg F, Hu FB, Navis GJ, Curhan GC, Ehret GB, Homuth G, Coassin S, Thun GA, Pistis G, Gambaro G, Malerba G, Montgomery GW, Eiriksdottir G, Jacobs G, Li G, Wichmann HE, Campbell H, Schmidt H, Wallaschofski H, Völzke H, Brenner H, Kroemer HK, Kramer H, Lin H, Leach IM, Ford I, Guessous I, Rudan I, Prokopenko I, Borecki I, Heid IM, Kolcic I, Persico I, Jukema JW, Wilson JF, Felix JF, Divers J, Lambert JC, Stafford JM, Gaspoz JM, Smith JA, Faul JD, Wang JJ, Ding J, Hirschhorn JN, Attia J, Whitfield JB, Chalmers J, Viikari J, Coresh J, Denny JC, Karjalainen J, Fernandes JK, Endlich K, Butterbach K, Keene KL, Lohman K, Portas L, Launer LJ, Lyytikäinen LP, Yengo L, Franke L, Ferrucci L, Rose LM, Kedenko L, Rao M, Struchalin M, Kleber ME, Cavalieri M, Haun M, Cornelis MC, Ciullo M, Pirastu M, de Andrade M, McEvoy MA, Woodward M, Adam M, Cocca M, Nauck M, Imboden M, Waldenberger M, Pruijm M, Metzger M, Stumvoll M, Evans MK, Sale MM, Kähönen M, Boban M, Bochud M, Rheinberger M, Verweij N, Bouatia-Naji N, Martin NG, Hastie N, Probst-Hensch N, Soranzo N, Devuyst O, Raitakari O, Gottesman O, Franco OH, Polasek O, Gasparini P, Munroe PB, Ridker PM, Mitchell P, Muntner P, Meisinger C, Smit JH ICBP Consortium; AGEN Consortium; CARDIOGRAM; CHARGe-Heart Failure Group; ECHOGen Consortium, Kovacs P, Wild PS, Froguel P, Rettig R, Mägi R, Biffar R, Schmidt R, Middelberg RP, Carroll RJ, Penninx BW, Scott RJ, Katz R, Sedaghat S, Wild SH, Kardia SL, Ulivi S, Hwang SJ, Enroth S, Kloiber S, Trompet S, Stengel B, Hancock SJ, Turner ST, Rosas SE, Stracke S, Harris TB, Zeller T, Zemunik T, Lehtimäki T, Illig T, Aspelund T, Nikopensius T, Esko T, Tanaka T, Gyllensten U, Völker U, Emilsson V, Vitart V, Aalto V, Gudnason V, Chouraki V, Chen WM, Igl W, März W, Koenig W, Lieb W, Loos RJ, Liu Y, Snieder H, Pramstaller PP, Parsa A, O'Connell JR, Susztak K, Hamet P, Tremblay J, de Boer IH, Böger CA, Goessling W, Chasman DI, Köttgen A, Kao WH, Fox CS. Genetic associations at 53 Loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023. doi: 10.1038/ncomms10023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Hellwege JN, Velez Edwards DR, Giri A, Qiu C, Park J, Torstenson ES, Keaton JM, Wilson OD, Robinson-Cohen C, Chung CP, Roumie CL, Klarin D, Damrauer SM, DuVall SL, Siew E, Akwo EA, Wuttke M, Gorski M, Li M, Li Y, Gaziano JM, Wilson PWF, Tsao PS, O'Donnell CJ, Kovesdy CP, Pattaro C, Köttgen A, Susztak K, Edwards TL, Hung AM. Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. Nat Commun. 2019;10:3842. doi: 10.1038/s41467-019-11704-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, Tin A, Wang L, Chu AY, Hoppmann A, Kirsten H, Giri A, Chai JF, Sveinbjornsson G, Tayo BO, Nutile T, Fuchsberger C, Marten J, Cocca M, Ghasemi S, Xu Y, Horn K, Noce D, van der Most PJ, Sedaghat S, Yu Z, Akiyama M, Afaq S, Ahluwalia TS, Almgren P, Amin N, Ärnlöv J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boehnke M, Boerwinkle E, Boissel M, Bottinger EP, Boutin TS, Brenner H, Brumat M, Burkhardt R, Butterworth AS, Campana E, Campbell A, Campbell H, Canouil M, Carroll RJ, Catamo E, Chambers JC, Chee ML, Chen X, Cheng CY, Cheng Y, Christensen K, Cifkova R, Ciullo M, Concas MP, Cook JP, Coresh J, Corre T, Sala CF, Cusi D, Danesh J, Daw EW, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Degenhardt F, Delgado G, Demirkan A, Di Angelantonio E, Dittrich K, Divers J, Dorajoo R, Eckardt KU, Ehret G, Elliott P, Endlich K, Evans MK, Felix JF, Foo VHX, Franco OH, Franke A, Freedman BI, Freitag-Wolf S, Friedlander Y, Froguel P, Gansevoort RT, Gao H, Gasparini P, Gaziano JM, Giedraitis V, Gieger C, Girotto G, Giulianini F, Gögele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hartman CA, Hayward C, Hellwege JN, Heng CK, Hicks AA, Hofer E, Huang W, Hutri-Kähönen N, Hwang SJ, Ikram MA, Indridason OS, Ingelsson E, Ising M, Jaddoe VWV, Jakobsdottir J, Jonas JB, Joshi PK, Josyula NS, Jung B, Kähönen M, Kamatani Y, Kammerer CM, Kanai M, Kastarinen M, Kerr SM, Khor CC, Kiess W, Kleber ME, Koenig W, Kooner JS, Körner A, Kovacs P, Kraja AT, Krajcoviechova A, Kramer H, Krämer BK, Kronenberg F, Kubo M, Kühnel B, Kuokkanen M, Kuusisto J, La Bianca M, Laakso M, Lange LA, Langefeld CD, Lee JJ, Lehne B, Lehtimäki T, Lieb W Lifelines Cohort Study, Lim SC, Lind L, Lindgren CM, Liu J, Liu J, Loeffler M, Loos RJF, Lucae S, Lukas MA, Lyytikäinen LP, Mägi R, Magnusson PKE, Mahajan A, Martin NG, Martins J, März W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Melander O, Metspalu A, Mikaelsdottir EK, Milaneschi Y, Miliku K, Mishra PP; V. A. Million Veteran Program, Mohlke KL, Mononen N, Montgomery GW, Mook-Kanamori DO, Mychaleckyj JC, Nadkarni GN, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Noordam R, O'Connell J, O'Donoghue ML, Olafsson I, Oldehinkel AJ, Orho-Melander M, Ouwehand WH, Padmanabhan S, Palmer ND, Palsson R, Penninx BWJH, Perls T, Perola M, Pirastu M, Pirastu N, Pistis G, Podgornaia AI, Polasek O, Ponte B, Porteous DJ, Poulain T, Pramstaller PP, Preuss MH, Prins BP, Province MA, Rabelink TJ, Raffield LM, Raitakari OT, Reilly DF, Rettig R, Rheinberger M, Rice KM, Ridker PM, Rivadeneira F, Rizzi F, Roberts DJ, Robino A, Rossing P, Rudan I, Rueedi R, Ruggiero D, Ryan KA, Saba Y, Sabanayagam C, Salomaa V, Salvi E, Saum KU, Schmidt H, Schmidt R, Schöttker B, Schulz CA, Schupf N, Shaffer CM, Shi Y, Smith AV, Smith BH, Soranzo N, Spracklen CN, Strauch K, Stringham HM, Stumvoll M, Svensson PO, Szymczak S, Tai ES, Tajuddin SM, Tan NYQ, Taylor KD, Teren A, Tham YC, Thiery J, Thio CHL, Thomsen H, Thorleifsson G, Toniolo D, Tönjes A, Tremblay J, Tzoulaki I, Uitterlinden AG, Vaccargiu S, van Dam RM, van der Harst P, van Duijn CM, Velez Edward DR, Verweij N, Vogelezang S, Völker U, Vollenweider P, Waeber G, Waldenberger M, Wallentin L, Wang YX, Wang C, Waterworth DM, Bin Wei W, White H, Whitfield JB, Wild SH, Wilson JF, Wojczynski MK, Wong C, Wong TY, Xu L, Yang Q, Yasuda M, Yerges-Armstrong LM, Zhang W, Zonderman AB, Rotter JI, Bochud M, Psaty BM, Vitart V, Wilson JG, Dehghan A, Parsa A, Chasman DI, Ho K, Morris AP, Devuyst O, Akilesh S, Pendergrass SA, Sim X, Böger CA, Okada Y, Edwards TL, Snieder H, Stefansson K, Hung AM, Heid IM, Scholz M, Teumer A, Köttgen A, Pattaro C. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019;51:957–972. doi: 10.1038/s41588-019-0407-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Lin BM, Nadkarni GN, Tao R, Graff M, Fornage M, Buyske S, Matise TC, Highland HM, Wilkens LR, Carlson CS, Park SL, Setiawan VW, Ambite JL, Heiss G, Boerwinkle E, Lin DY, Morris AP, Loos RJF, Kooperberg C, North KE, Wassel CL, Franceschini N. Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study. Front Genet. 2019;10:494. doi: 10.3389/fgene.2019.00494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.López-Novoa JM, Martínez-Salgado C, Rodríguez-Peña AB, López-Hernández FJ. Common pathophysiological mechanisms of chronic kidney disease: therapeutic perspectives. Pharmacol Ther. 2010;128:61–81. doi: 10.1016/j.pharmthera.2010.05.006. [DOI] [PubMed] [Google Scholar]
  • 46.Morris AP, Le TH, Wu H, Akbarov A, van der Most PJ, Hemani G, Smith GD, Mahajan A, Gaulton KJ, Nadkarni GN, Valladares-Salgado A, Wacher-Rodarte N, Mychaleckyj JC, Dueker ND, Guo X, Hai Y, Haessler J, Kamatani Y, Stilp AM, Zhu G, Cook JP, Ärnlöv J, Blanton SH, de Borst MH, Bottinger EP, Buchanan TA, Cechova S, Charchar FJ, Chu PL, Damman J, Eales J, Gharavi AG, Giedraitis V, Heath AC, Ipp E, Kiryluk K, Kramer HJ, Kubo M, Larsson A, Lindgren CM, Lu Y, Madden PAF, Montgomery GW, Papanicolaou GJ, Raffel LJ, Sacco RL, Sanchez E, Stark H, Sundstrom J, Taylor KD, Xiang AH, Zivkovic A, Lind L, Ingelsson E, Martin NG, Whitfield JB, Cai J, Laurie CC, Okada Y, Matsuda K, Kooperberg C, Chen YI, Rundek T, Rich SS, Loos RJF, Parra EJ, Cruz M, Rotter JI, Snieder H, Tomaszewski M, Humphreys BD, Franceschini N. Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies. Nat Commun. 2019;10:29. doi: 10.1038/s41467-018-07867-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Salem RM, Todd JN, Sandholm N, Cole JB, Chen WM, Andrews D, Pezzolesi MG, McKeigue PM, Hiraki LT, Qiu C, Nair V, Di Liao C, Cao JJ, Valo E, Onengut-Gumuscu S, Smiles AM, McGurnaghan SJ, Haukka JK, Harjutsalo V, Brennan EP, van Zuydam N, Ahlqvist E, Doyle R, Ahluwalia TS, Lajer M, Hughes MF, Park J, Skupien J, Spiliopoulou A, Liu A, Menon R, Boustany-Kari CM, Kang HM, Nelson RG, Klein R, Klein BE, Lee KE, Gao X, Mauer M, Maestroni S, Caramori ML, de Boer IH, Miller RG, Guo J, Boright AP, Tregouet D, Gyorgy B, Snell-Bergeon JK, Maahs DM, Bull SB, Canty AJ, Palmer CNA, Stechemesser L, Paulweber B, Weitgasser R, Sokolovska J, Rovīte V, Pīrāgs V, Prakapiene E, Radzeviciene L, Verkauskiene R, Panduru NM, Groop LC, McCarthy MI, Gu HF, Möllsten A, Falhammar H, Brismar K, Martin F, Rossing P, Costacou T, Zerbini G, Marre M, Hadjadj S, McKnight AJ, Forsblom C, McKay G, Godson C, Maxwell AP, Kretzler M, Susztak K, Colhoun HM, Krolewski A, Paterson AD, Groop PH, Rich SS, Hirschhorn JN, Florez JC SUMMIT Consortium, DCCT/EDIC Research Group, GENIE Consortium. Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen. J Am Soc Nephrol. 2019;30:2000–2016. doi: 10.1681/ASN.2019030218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Cyrus C, Chathoth S, Vatte C, Alrubaish N, Almuhanna O, Borgio JF, Al-Mueilo S, Al Muhanna F, Al Ali AK. Novel Haplotype Indicator for End-Stage Renal Disease Progression among Saudi Patients. Int J Nephrol. 2019;2019:1095215. doi: 10.1155/2019/1095215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Thomson RJ, McMorran B, Hoy W, Jose M, Whittock L, Thornton T, Burgio G, Mathews JD, Foote S. New Genetic Loci Associated With Chronic Kidney Disease in an Indigenous Australian Population. Front Genet. 2019;10:330. doi: 10.3389/fgene.2019.00330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Gast C, Marinaki A, Arenas-Hernandez M, Campbell S, Seaby EG, Pengelly RJ, Gale DP, Connor TM, Bunyan DJ, Hodaňová K, Živná M, Kmoch S, Ennis S, Venkat-Raman G. Autosomal dominant tubulointerstitial kidney disease-UMOD is the most frequent non polycystic genetic kidney disease. BMC Nephrol. 2018;19:301. doi: 10.1186/s12882-018-1107-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Technical appendix, statistical code, and dataset available from the corresponding author at msaracyn@interia.pl.

Articles from World Journal of Diabetes are provided here courtesy of Baishideng Publishing Group Inc

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