Category: Home

Diabetic nephropathy genetic factors

Diabetic nephropathy genetic factors

Diwbetic addition, Nootropic for Seniors most meta-analyses, a small number of afctors were included, so the results should Supports effective nutrient breakdown interpreted geneticc caution. Finest Orange Extract KR, Bakris GL, Bilous RW, Chiang JL, de Boer IH, Goldstein-Fuchs J, et al. Open menu Brazil. Three of the 11 initial SNPs had their association confirmed, two were borderline and the remaining did not show a significant association with the development of DN proteinuria or CKD 25 Table 4. Accepted : 26 August

Diabetic nephropathy genetic factors -

However, the association between rs and DKD was not affected by adjustment for body mass index BMI , suggesting that the locus affects DKD through another mechanism than an increase in BMI Indeed, the FTO locus has been highlighted as a pleiotropic one, associated with multiple biomarkers and traits such as sweet vs.

salty taste preference through modifying the regulatory properties of enhancers targeting the IRX3 and IRX5 gene expression in various tissues 71 , The SUrrogate markers for Micro- and Macrovascular hard endpoints for Innovative diabetes Tools SUMMIT Consortium GWAS meta-analysis of DKD in T2D included 5, individuals of European ancestry and with T2D at the discovery stage.

The variant is in LD with the lead eQTL association signal for GABRR1 expression in multiple tissues Extended to individuals with T1D and other ethnicities, the joint meta-analysis involved up to 40, subjects with diabetes.

However, meta-analysis with individuals with T1D 18 revealed no loci for dichotomous DKD phenotypes. Nevertheless, variants in the UMOD and PRKAG2 loci, previously associated with eGFR and CKD in the general population 73 , 74 , were associated with eGFR also in individuals with diabetes Table 2 Meta-analysis of the DNCRI [T1D 49 ] and SUMMIT consortia [both T1D 18 and T2D 19 ], excluding the overlap between the consortia, and harmonized for the 10 phenotype definitions of DKD for available cohorts, included nearly 27, individuals with diabetes Expression of multiple lead genes correlated with renal phenotypes, e.

In addition to the disease-specific cohorts, large population-based biobanks allow analyses of an increasing number of samples and phenotypes.

A GWAS on DKD in the UK Biobank included 13, unrelated individuals with diabetes and of European origin.

Of note, the heritability estimate for DKD, defined based on ICD codes E GWAS on DKD and eGFR identified variants in the UMOD and PRKAG2 loci Meta-analysis with the SUMMIT T2D study further identified a novel variant, rs, associated with the combined DKD definition.

The variant is associated with alternative gene splicing of the NID1 gene 50 , encoding for nidogen-1, a sulfated glycoprotein involved in the development of GBM, where it binds to laminin and type IV collagen Another study in the UK Biobank, although focused on heritability estimates for diabetic micro- and macrovascular complications, additionally found a variant rs near PLD1 associated with DKD self-reported or medical records ; as well as variants in WSCD2 and SETDB2 associated with ESKD and in LOC associated with microalbuminuria In addition to the dichotomous case-control definitions of DKD, GWASs have also explored albuminuria and eGFR as continuous traits in individuals with diabetes Figure 1.

Only few studies have identified variants with genome-wide significance for albuminuria Table 3 or eGFR Table 2 , and most of these loci were identified in diabetes-specific sub-analyses of larger general population studies.

Figure 1 GWAS on DKD, albuminuria, and eGFR in diabetes. Point size indicates the number of samples. The GLRA3 gene encodes the α3 subunit of glycine receptors. In pancreatic α-cells, glycine receptors stimulate glucagon release in response to glycine, thus counterbalancing the effects of insulin Interestingly, the association with albuminuria was only evident among individuals with a h urine collection.

Because exercise can acutely increase albuminuria due to excess hemodynamic pressure 84 , the authors hypothesized that the variant might affect renal sensitivity to hemodynamic pressure Of note, in the eQTLGen database, the rs variant is associated with the expression of the HPGD gene, encoding for the hydroxyprostaglandin dehydrogenase that catalyzes the prostaglandin catabolic pathway; prostaglandins are locally acting vasodilators and regulate renal hemodynamics in the kidneys Another GWAS on albuminuria included 54, individuals from the general population, confirming the previously identified CUBN locus 86 for albuminuria.

RAB38 expression was found higher in the tubules of individuals with DKD compared to healthy controls, and Rab38 knockout resulted in higher urinary albumin concentrations in diabetic rat models A larger study including , individuals, of which 51, individuals with diabetes, identified eight loci associated with albuminuria in diabetes; all had larger effect among individuals with diabetes, and four KAZN , MIRHG - BCL11A , FOXP2 , and CDH2 were only found in the secondary analysis limited to diabetes Finally, a GWAS including , individuals with diabetes from the CKD Genetics CKDGen consortium and large biobank studies identified 29 genome-wide significant loci for eGFR, including 27 novel loci for eGFR in diabetes; among these, variants near SH3BP4 and LOXL4 were not associated with eGFR in the 1,, individuals without diabetes In the general population, nearly genetic loci have been identified for eGFR in meta-analyses, including over 1.

Other main risk factors for CKD include hypertension, obesity, and high age, all commonly seen among individuals with T2D in particular. In individuals with T1D, the majority of DKD is due to diabetic nephropathy. On the contrary, the renal lesions in kidney biopsies of DKD in T2D are heterogeneous, and a substantial proportion of the biopsies do not show the typical characteristics of diabetic nephropathy However, kidney biopsies are rarely taken, and DKD is defined as any CKD in an individual with diabetes Therefore, the question arises, how much of the genetic background of DKD is shared with the CKD and eGFR in the general population?

The DKD loci identified in individuals with T1D in the DNCRI consortium did not replicate in the general population GWAS for eGFR 49 ; conversely, the loci associated with eGFR in the general population 92 were not associated with DKD in T1D apart from the UMOD locus On the contrary, some of the first findings for DKD in T2D included the UMOD and PRKAG2 loci known from the general population 19 , as well as the APOL1 variant responsible for the majority of kidney failures in AAs The CKDGen GWAS on eGFR including , individuals, of which 16, with diabetes, found that the effect size of the eGFR loci identified in the full population were highly correlated between individuals with and without diabetes correlation coefficient of 0.

A more recent study on eGFR from the CKDGen consortium, including nearly 1. They identified seven eGFR loci with significant difference in individuals with and without diabetes, as well as four loci with suggestive difference; in all but one, the effect was more pronounced or exclusively seen among individuals with diabetes Similarly, in a GWAS for eGFR decline studied as a longitudinal trait in the general population, the effect sizes of the nine identified variants were on average two-fold higher in individuals with diabetes Finally, the effect of the rs variant in the CUBN locus—the major locus for albuminuria—was larger among individuals with diabetes compared to those without diabetes In addition, a rare CUBN variant rs had three times stronger effect in individuals with T2D compared with those without Furthermore, rs was associated with higher eGFR but only in the non-diabetes population, suggesting pleiotropic effects on both kidney function measures In the DNCRI-SUMMIT GWAS meta-analysis for DKD, the similarity of DKD with kidney traits in the general population of note, including individuals with diabetes was assessed on a genome-wide scale instead of single-variant level, using the LD score regression approach.

The albuminuria-based DKD definition, including microalbuminuria, was genetically correlated with microalbuminuria in the general population, both in the pooled analysis, and separately for individuals with T1D or T2D; of note, the correlation was over two-fold stronger in individuals with T2D.

In addition, the eGFR-based CKD definition was also correlated with eGFR and CKD in individuals with T2D, but not in T1D despite more than three times more individuals with T1D The analysis suggests that DKD in T2D has a larger proportion of shared genetic background with the general population, e.

The LD score regression with cardiometabolic and other traits further suggested that a proportion of the genetic background of DKD is shared with genetic risk factors, e.

However, the confidence intervals remain large, and further studies are needed to estimate the proportion of risk attributable to each risk factor. Some interesting discrepancies also exist between DKD and the general population: For example, the missense variant rs in COL4A3 is one of the strongest findings for DKD in T1D, but the effect is modified by glycemia, and the variant does not seem to affect kidney traits in the general population.

Rare mutations in both COL4A3 and COL4A4 cause Alport syndrome, a monogenic disease of basement membranes that frequently leads to ESKD, as well as thin basement membrane nephropathy and focal segmental glomerulosclerosis Some studies have suggested a correlation between the genetic risk factors predisposing to insulin resistance or T2D and DKD 18 , 19 , Of note, these studies found no correlation between genetic risk factors predisposing to T1D and DKD.

However, among the lead variants for DKD, albuminuria, or eGFR in diabetes, only the albuminuria-associated FTO locus [rs 47 ] has been associated with T2D. While common variants have a large effect on complex traits at the population level 43 , the low frequency and rare variants can have a high impact on the individual level In particular, protein-altering variants PAVs , i.

To identify chromosomal regions harboring rare variants for DKD, a linkage study based on GWAS data of 6, FinnDiane study participants included small pedigrees such as sib-ships, parent-offspring pairs, and more distant relations, with, altogether, individuals, all with T1D.

Many of these regions harbor genes in which mutations cause rare syndromes with kidney complications, such as ARHGAP24 associated with focal segmental glomerulosclerosis 99 and FRAS1 associated with the familial Fraser syndrome Overlap with loci causing rare kidney syndromes supports the role of rare variants in the development of DKD.

Interestingly, one suggestive linkage peak was observed in the NID1 locus, recently associated with DKD in T2D The gene and protein expression were attenuated in human diabetic proximal tubules and in mouse kidney injury models The GWAS genotyping chips cover only a portion of the PAVs, and genotype imputation quality largely depends on the variant minor allele count in the reference sample and can be limited for rare variants , A whole-exome sequencing WES on DKD, including individuals with T1D, did not find any variants or genes reaching robust exome-wide significance 18 but found suggestive evidence of association, e.

However, the discriminatory analyses suggest that the ERAP2 and NPEPPS may be primarily associated with diabetes per se , subsequently leading to DKD While WES mainly covers the protein-coding sequence, a whole-genome sequencing WGS study of 76 Finnish sibling pairs with T1D but discordant for DKD found significant enrichment of variants in DKD in gene promoter and enhancer regions, as well as for specific transcription factor binding sites , but larger studies are required to pinpoint the most relevant regulatory regions.

A recent multi-ethnic WGS in 23, individuals identified three novel rare intronic variants for eGFR in the general population , and larger WGS for DKD are needed to identify the rare variants contributing to DKD.

Studies focusing on epigenetic modifications have emerged in an increasing number during the last years. Epigenetic modifications can be described as chemical modifications of the DNA or RNA that can induce changes in gene expression without changing the underlying sequence.

Thus, epigenetic changes may vary between tissues, cell types, and developmental stages and can even be affected by environmental factors. Furthermore, in disease states, the methylation patterns can change either as a cause or a consequence of the disease Although epigenetic changes are dynamic, there is evidence that epigenetic modifications, such as DNA methylation, persist in blood years after acute illness or metabolic changes in the body , Consequently, epigenetic factors have been suggested as an underlying mechanism for metabolic memory , Metabolic memory in diabetes refers to the sustained harmful effect of hyperglycaemia on diabetic complications, initially observed in the DCCT-EDIC study, even after improved glycaemic control , In line with this observation, subsequent work in DCCT-EDIC has identified several epigenetic changes associated with metabolic memory , DNA methylation is the most frequently studied epigenetic modification and occurs at cytosine bases of cytosine—phosphate—guanine dinucleotide sites CpGs in the DNA sequence.

In addition to DNA methylation, additional epigenetic modifications exist, such as histone modifications acetylation and methylation , and their role in DKD has also been explored. For example, dysregulation of histone H3 lysine 27 trimethylation H3K27me3 in TGF-β1—induced gene expression has been associated with DKD Histone modifications associated with DKD are reviewed, e.

Although whole-genome bisulfite sequencing for the analysis of the methylome has been done for DKD, sample sizes have been small However, this number of CpGs only accounts for a small amount of all the CpGs in the genome, totalling up to ~30 million Contrary to the GWAS, which initially yielded few significant loci with increasing number of findings with larger studies, in EWAS, the use of varying thresholds, combined with unaddressed inflated test statistics especially in the early EWAS , has led to a quite varying number of identified methylation loci in the studies performed so far.

Most EWASs performed on DKD have examined DNA methylation in blood. Still, other tissues have been used, such as kidney samples micro-dissected into kidney tubules and even saliva The epigenetic changes observed in the kidney tissue likely reflect the local changes more accurately.

Indeed, EWAS on fibrosis in kidney tissue samples identified 65 differentially methylated CpGs that were enriched on kidney regulatory regions Another promising target tissue for studying kidney disease would be the urine, which can be collected non-invasively and easily from larger datasets.

Urine, however, contains few nucleated cells and extracting a sufficient amount of DNA from urine has turned out to be a challenge An intronic SNP rs in the same UNC13B gene was identified for DKD in T1D in a prior genetic association study including genetic variants in candidate genes More recent methylation arrays, with higher coverage have enabled identification of additional CpGs.

Using the K array, Smyth et al. identified 53 CpGs within 23 genes with differential methylation in participants with CKD, of which approximately half had T1D. Of the 23 genes, six were in genes that are biological candidates for kidney disease: CUX1 , ELMO1 , FKBP5 , INHBA-AS1 , PTPRN2 , and PRKAG2 Of these, genetic variants within the PRKAG2 , encoding a protein kinase involved in cellular energy metabolism, have also been associated with eGFR in GWAS on kidney disease, both in individuals with and without diabetes 19 , 73 , The most recent and largest study, including 1, individuals with T1D, identified 32 sites with altered methylation in DKD 77 , of which 23 were specific to the EPIC array.

Methylation levels at seven CpGs were epigenome-wide significantly and differentially methylated after accounting for differences in multiple clinical risk factors HbA 1c , HDL cholesterol, triglycerides, BMI, smoking, and duration of diabetes , in addition to age, sex, and six cell-type proportions.

These seven included two intergenic CpGs on chromosome 19 and four CpGs located within genes PTBP3 , NME7 , SLC1A5 , and SLC27A3 and one CpG within a long non-coding RNA LINC Figure 2 Chromosomal ideogram including CpGs methylation associated with kidney disease DKD and ESKD , fibrosis, eGFR, or albuminuria in diabetes.

Hypermethylated CpGs in kidney disease vs. controls are denoted by a dark blue colour and hypomethylated by a light blue colour. CpGs appearing among top loci in multiple studies on kidney disease in diabetes denoted by a red color.

Methylation levels at only a few CpGs have been associated with DKD in multiple studies Figure 2. Consequently, one-third of the differentially methylated CpGs identified for DKD or eGFR in studies using the EPIC array 77 , , were novel and not available on previous arrays Supplementary Table 1.

However, methylation loci that have been repeatedly associated with DKD, include CpG within genes C5orf66 , FKBP5 77 , , and PIP5K1C 77 , In addition, higher methylation at the intergenic CpG cg, located on chromosome 19 within a zinc finger gene cluster, has been repeatedly associated not only with DKD and eGFR in diabetes 77 , , but also with CKD and eGFR in the general population 78 , , , as well as eGFR in other more specific cohorts, such as men with human immunodeficiency virus HIV Although most of the DNA methylation association studies performed on DKD have covered the whole genome, targeted approaches have been undertaken as well.

Swan et al. evaluated DNA methylation levels associated with DKD for CpGs located within genes influencing mitochondrial function in individuals with long term T1D A few CpGs identified as differentially methylated in DKD to date Figure 2 also appear in EWAS on traits that are considered risk factors for DKD.

TXNIP encodes for the thioredoxin-interacting protein, which by binding to thioredoxin induces oxidative stress and apoptosis. Intriguingly, methylation levels at cg are also under genetic influence by SNPs located within the SLC2A1 gene encoding for the glucose transporter 1 GLUT1 A recent EWAS on DKD performed a systematic trait enrichment analysis and found significant overlap with EWAS findings for traits and diseases such as aging, smoking, systolic and diastolic blood pressure, eGFR, and HbA 1c org , accessed 31 January, found an overlap with DKD risk factors including dyslipidemia CpGs within SLC1A5 , TXNIP , and CPT1A , HbA 1c TXNIP , blood pressure CpGs within SLC1A5 , TXNIP , CPT1A , and PTBP3 and obesity CpGs within SLC1A5 , TXNIP , CPT1A , and FKBP5 ; Supplementary Table 2.

For example, in the CPT1A gene, methylation at cg was associated with DKD in T1D 77 and has been robustly associated with the triglycerides in the general population. CPT1A encodes a key enzyme in the fatty acid metabolism, namely, the hepatic isoform of carnitine palmitoyl transferase 1 , controlling the fatty acid flux in the liver.

In addition, the genetic variants in the gene were also associated with triglycerides and HDL cholesterol in a recent GWAS Several studies have provided evidence suggesting that changes in DNA methylation patterns could be used to predict DKD or its progression.

Using 91 kidney tissue samples, Gluck et al. found that information from differentially methylated CpGs in the kidneys helped them to predict kidney disease progression However, the utility of kidney tissue—specific DNA methylation patterns as potential biomarkers remain limited, as individuals with DKD do not routinely undergo kidney biopsy.

As an alternative, a study with methylation data from individuals constructed methylation risk scores for phenotypes based on electronic health records and suggested that blood methylation was particularly good in identifying individuals with pre-existing kidney failure and related traits An EWAS in American Indians with diabetes identified methylation levels at 77 CpG sites associated with eGFR decline over a 6-year period Methylation at two CpGs cg and cg in FSTL5 improved prediction of eGFR decline even when baseline eGFR and Albumin-to-creatinine ratio ACR were included in the model In addition, in T1D, methylation levels at baseline can be used to predict progression of DKD.

In total, 20 of the 32 differentially methylated CpGs in DKD in T1D predicted future progression to kidney failure in individuals with DKD, 13 even after accounting for eight clinical risk factors Furthermore, methylation at the two intergenic CpGs located within the zinc finger gene cluster on chromosome 19 predicted kidney failure, independent of baseline eGFR.

Because of the dynamic nature of epigenetic changes, the methylation changes observed at CpGs in DKD can be either a cause or a consequence of the disease. To separate the causal methylation changes from the consequential, EWASs have also attempted Mendelian randomization, which uses genetic information to infer causality 77 , , Although these analyses have been partly hampered by the lack of genetic variants influencing CpG methylation, some causal associations have been observed.

For example, Mendelian randomization suggested that higher methylation levels at cg located within the REV1 gene reduces the risk of DKD in T1D On the other hand, no evidence for causality was found for cg TXNIP or cg between ZNFP and ZNFZNF20 , suggesting that methylation changes observed at these sites are consequential to kidney disease or its other manifestations, e.

Kim et al. used Mendelian randomization in the opposite direction, i. During the last 5 years, both GWAS and EWAS have identified an expanding number of genetic loci for DKD. Nearly 80 genetic loci have reached genome-wide statistical significance for DKD, albuminuria, or eGFR in diabetes to date.

Much of this increase is not only due to larger meta-analyses of existing diabetes cohorts but also due to CKD studies in the general population including a substantial number of individuals with diabetes, as well as general population biobank studies.

Even larger meta-analyses combining multiple biobank studies are likely to result in more genetic loci contributing to DKD. One of the major challenges of such studies will be how to best ascertain cases with DKD, either based on ICD codes that do not capture DKD well, self-reported DKD, or single measurements of albuminuria or eGFR, both of which vary over time.

General population biobanks may also be affected by selection bias including healthier than average individuals , leading to a limited number of individuals with severe DKD or ESKD or with long-lasting diabetes: As DKD takes decades to develop 6 , ideal study controls would only include individuals with diabetes without DKD despite a long diabetes duration.

The number of identified genetic loci now also allows comparison of the findings and the genetic overlap between general population CKD and DKD in T1D and T2D.

The general population loci for eGFR seem to affect eGFR also in individuals with diabetes, especially those with T2D For some variants, the effect size is markedly higher in the individuals with diabetes than in those without e.

On the other hand, genetic risk factors for DKD in T1D seem to differ from the general population These support the notions from the clinical and epidemiological studies suggesting that individuals with T2D can have either DKD, non-DKD, or both, whereby individuals with T1D mainly develop diabetic nephropathy with a different pathophysiology from the general CKD 11 , Therefore, future genetic studies on DKD will need to balance between maximizing the number of samples any diabetes, or even the general population with focus on diabetes but with a more heterogeneous phenotype, and a cleaner DKD phenotype in T1D with diabetic nephropathy as a more likely underlying cause, but with a more limited number of samples.

GWASs on DKD have been performed in various populations beyond the European ancestry 46 — 48 , and some of the identified variants are population-specific, e.

For many complex diseases, such as T2D, extension to further populations, as well as larger multi-ancestry GWAS meta-analyses have yielded novel genetic susceptibility loci by increasing the total sample size and capturing additional variants with ancestry-correlated heterogeneity in the allelic effect sizes , Multi-ancestry GWASs also provide improved fine-mapping resolution of the detected association signals, i.

Therefore, such multi-ancestry studies are likely to reveal novel loci with improved fine-mapping for DKD as well. On the contrary, homogenous study populations may be particularly important in sequencing studies aiming to identify rare genetic risk factors for DKD.

Although there are known differences in the methylation pattern of a number of CpGs between different ethnicities , there is a lack of ethnic diversity in EWAS, which are based mainly on individuals of European ancestry , A recent multi-ancestry EWAS on kidney function revealed several population-specific methylation patterns for eGFR in the general population with little overlap between African and European populations.

These discrepancies, however, could be due to both genetic and environmental differences between the different ethnic groups. The expansion of EWAS datasets in DKD to include multi-ancestry populations is still lacking. The GWASs have also enabled creation of polygenic risk scores PRSs that may be used for risk stratification and identification of affected traits and phenotypes.

In general population, PRS on eGFR was associated with incident CKD and kidney failure in the Atherosclerosis Risk in Communities study with 8. In diabetes, smaller studies have shown that genetic risk scores for DKD improved the prediction of DKD in Han Chinese with T2D In the ADjuVANt Chemotherapy in the Elderly ADVANCE trial with individuals with T2D, a multi-phenotype PRS, based on variants from the general population GWAS, predicted micro- and macrovascular complications and suggested that the PRS can identify high-risk individuals, who would benefit from intensified diabetes treatment ; similarly, a general population PRS for coronary artery disease CAD was associated with CAD also among individuals with T1D However, no large-scale PRS for DKD have yet been published, and larger GWASs on DKD are needed to create diabetes-specific PRS for DKD and to assess their utility compared to general population PRS.

To date, several CpG sites with altered methylation levels in DKD have been identified across the genome. Understanding the underlying mechanism behind these changes would be critical, i. In addition, methylation levels are also influenced by the genetics.

Insights to the complex network behind the findings might therefore require integrating DNA methylation results with results from multiple other sources such as GWAS as well as transcriptomic and proteomic data.

Some efforts in that direction have already been made. Indeed, a recent study demonstrated that DNA methylation explains a larger fraction of kidney disease heritability than gene expression by integrating GWAS data with methylomic and transcriptomic data obtained from kidney tissue samples DNA methylation markers have proven useful for the prediction of DKD progression.

Current studies, however, have focused on the later stages of kidney disease, when AER is severely increased or when kidney failure has occurred. EWASs at earlier stages of DKD, when AER is only moderately increased, could potentially identify additional CpGs and perhaps even more importantly, enable the prediction of early changes using DNA methylation.

Although DNA methylation scores have not yet been as extensively implemented in risk prediction as the PRSs, methylation scores show a great promise as they incorporate information from both the genes and the environment.

In a recent study, methylation scores improved the prediction of a range of clinical diagnoses and traits, including kidney disease, outperforming the predictive ability of polygenetic risk scores However, the dynamic nature of methylation as well as its tissue-specificity introduces limitations regarding causality, time span of effect, and target tissue.

By incorporating genetic information, causality can be addressed, and future studies may also be facilitated by emerging single-cell sequencing technologies that enable more targeted analyses, such as exploring the causal effects of DNA methylation at the single-cell level in the kidneys.

NS and ED revised the literature and wrote the manuscript. P-HG critically revised the manuscript for the scientific content. All authors agree to be accountable for the content of the work.

All authors contributed to the article and approved the submitted version. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. International Diabetes Federation. IDF diabetes atlas Google Scholar. Brownlee M.

Biochemistry and molecular cell biology of diabetic complications. Nature — doi: PubMed Abstract CrossRef Full Text Google Scholar. American Diabetes Association. Economic costs of diabetes in the U.

in Diabetes Care — Lithovius R, Harjutsalo V, Forsblom C, Groop PH, FinnDiane Study Group. Cumulative cost of prescription medication in outpatients with type 1 diabetes in Finland. Diabetologia — Costacou T, Orchard TJ. Cumulative kidney complication risk by 50 years of type 1 diabetes: the effects of sex, age, and calendar year at onset.

Jansson Sigfrids F, Groop P-H, Harjutsalo V. Incidence rate patterns, cumulative incidence, and time trends for moderate and severe albuminuria in individuals diagnosed with type 1 diabetes aged years: a population-based retrospective cohort study.

Lancet Diabetes Endocrinol — Thomas MC, Weekes AJ, Broadley OJ, Cooper ME, Mathew TH. The burden of chronic kidney disease in Australian patients with type 2 diabetes the NEFRON study. Med J Aust —4. Harjutsalo V, Maric C, Forsblom C, Thorn L, Waden J, Groop PH, et al.

Sex-related differences in the long-term risk of microvascular complications by age at onset of type 1 diabetes. Diabetologia —9. Schernthaner G, Schernthaner GH. Diabetic nephropathy: new approaches for improving glycemic control and reducing risk.

JNephrol — CrossRef Full Text Google Scholar. Tuomilehto J, Borch-Johnsen K, Molarius A, Forsen T, Rastenyte D, Sarti C, et al. Incidence of cardiovascular disease in type 1 insulin-dependent diabetic subjects with and without diabetic nephropathy in Finland.

Anders H-J, Huber TB, Isermann B, Schiffer M. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol — Najafian B, Mauer M. Morphologic features of declining renal function in type 1 diabetes. Semin Nephrol — Quinn M, Angelico MC, Warram JH, Krolewski AS.

Familial factors determine the development of diabetic nephropathy in patients with IDDM. Diabetologia —5. Seaquist ER, Goetz FC, Rich S, Barbosa J. Gene category Gene name Gene variant symbol Location Phenotype Ref.

Growth factors Insulin-like growth factor 1 IGF-1 12q P- Reviewer: Gao C, Yong D, Yorioka N S- Editor: Song XX L- Editor: A E- Editor: Lu YJ. Bowden DW. Genetics of diabetes complications.

Curr Diab Rep. Andersen AR , Christiansen JS, Andersen JK, Kreiner S, Deckert T. Diabetic nephropathy in Type 1 insulin-dependent diabetes: an epidemiological study.

Ewens KG , George RA, Sharma K, Ziyadeh FN, Spielman RS. Kitamura A , Hasegawa G, Obayashi H, Kamiuchi K, Ishii M, Yano M, Tanaka T, Yamaguchi M, Shigeta H, Ogata M. Diabet Med. Wang H , Zhang Z, Chu W, Hale T, Cooper JJ, Elbein SC. Molecular screening and association analyses of the interleukin 6 receptor gene variants with type 2 diabetes, diabetic nephropathy, and insulin sensitivity.

J Clin Endocrinol Metab. Kung WJ , Lin CC, Liu SH, Chaung HC. Association of interleukin polymorphisms with cytokines in type 2 diabetic nephropathy. Diabetes Technol Ther. Wang Y , Ng MC, So WY, Ma R, Ko GT, Tong PC, Chan JC. Association between tumour necrosis factor-alpha GA polymorphism and risk of nephropathy in obese Chinese type 2 diabetic patients.

Nephrol Dial Transplant. Lee SH , Lee TW, Ihm CG, Kim MJ, Woo JT, Chung JH. Genetics of diabetic nephropathy in type 2 DM: candidate gene analysis for the pathogenic role of inflammation. Nephrology Carlton. Lindholm E , Bakhtadze E, Cilio C, Agardh E, Groop L, Agardh CD. Association between LTA, TNF and AGER polymorphisms and late diabetic complications.

PLoS One. Zhao Y , Yang J, Zhang L, Li Z, Yang Y, Tang Y, Fu P. Int Urol Nephrol. Krolewski AS , Tryggvason K, Warram J, Laffel L, Housman D. Diabetic nephropathy and polymorphism in the gene coding for the alpha 1 chain of collagen IV. Kidney Int.

Chen JW , Hansen PM, Tarnow L, Hellgren A, Deckert T, Pociot F. Genetic variation of a collagen IV alpha 1-chain gene polymorphism in Danish insulin-dependent diabetes mellitus IDDM patients: lack of association to nephropathy and proliferative retinopathy. Maeda S , Haneda M, Guo B, Koya D, Hayashi K, Sugimoto T, Isshiki K, Yasuda H, Kashiwagi A, Kikkawa R.

Dinucleotide repeat polymorphism of matrix metalloproteinase-9 gene is associated with diabetic nephropathy. Hirakawa S , Lange EM, Colicigno CJ, Freedman BI, Rich SS, Bowden DW.

Evaluation of genetic variation and association in the matrix metalloproteinase 9 MMP9 gene in ESRD patients. Am J Kidney Dis. Hadjadj S , Belloum R, Bouhanick B, Gallois Y, Guilloteau G, Chatellier G, Alhenc-Gelas F, Marre M.

J Am Soc Nephrol. Boright AP , Paterson AD, Mirea L, Bull SB, Mowjoodi A, Scherer SW, Zinman B. Ng DP , Tai BC, Koh D, Tan KW, Chia KS. Lee YJ , Tsai JC. Diabetes Care. Kunz R , Bork JP, Fritsche L, Ringel J, Sharma AM. Saucă OE , Carpini SD, Zagato L, Zerbini G, Manunta P, Cojocaru D.

Romanian Journal of Diabetes Nutrition and Metabolic Diseases. Golmohamadi T , Nikzamir A, Nakhjavani M, Zahrai M, Amirzargar A, Saffari R.

Association of Angiotensin Converting Enzyme ACE Gene Polymorphism and Diabetic Nephropathy. Iranian J Publ Health. 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.

Rahimi Z , Mansouri Zaveleh O, Rahimi Z, Abbasi A. AT2R G:A polymorphism and diabetic nephropathy in type 2 diabetes mellitus patients. J Renal Inj Prev. Zintzaras E , Papathanasiou AA, Stefanidis I. Endothelial nitric oxide synthase gene polymorphisms and diabetic nephropathy: a HuGE review and meta-analysis.

Genet Med. Zeng Z , Li L, Zhang Z, Li Y, Wei Z, Huang K, He L, Shi Y. A meta-analysis of three polymorphisms in the endothelial nitric oxide synthase gene NOS3 and their effect on the risk of diabetic nephropathy. Hum Genet. Ksiazek P , Wojewoda P, Muc K, Buraczynska M. Endothelial nitric oxide synthase gene intron 4 polymorphism in type 2 diabetes mellitus.

Mol Diagn. Liu Y , Burdon KP, Langefeld CD, Beck SR, Wagenknecht LE, Rich SS, Bowden DW, Freedman BI. TC polymorphism of the endothelial nitric oxide synthase gene is associated with albuminuria in the diabetes heart study. Ezzidi I , Mtiraoui N, Mohamed MB, Mahjoub T, Kacem M, Almawi WY.

J Diabetes Complications. Buraczynska M , Ksiazek P, Zaluska W, Nowicka T, Ksiazek A. Endothelial nitric oxide synthase gene intron 4 polymorphism in patients with end-stage renal disease. Neugebauer S , Baba T, Watanabe T. Association of the nitric oxide synthase gene polymorphism with an increased risk for progression to diabetic nephropathy in type 2 diabetes.

Rahimi Z , Vaisi-Raygani A, Rahimi Z, Parsian A. Concomitant presence of endothelial nitric oxide T and angiotensin II-converting enzyme D alleles are associated with diabetic nephropathy in a Kurdish population from Western Iran. Jafari Y , Rahimi Z, Vaisi-Raygani A, Rezaei M.

Interaction of eNOS polymorphism with MTHFR variants increase the risk of diabetic nephropathy and its progression in type 2 diabetes mellitus patients. Mol Cell Biochem. Möllsten A , Lajer M, Jorsal A, Tarnow L. The endothelial nitric oxide synthase gene and risk of diabetic nephropathy and development of cardiovascular disease in type 1 diabetes.

Mol Genet Metab. Tiwari AK , Prasad P, B K T, Kumar KM, Ammini AC, Gupta A, Gupta R. Oxidative stress pathway genes and chronic renal insufficiency in Asian Indians with Type 2 diabetes. Taniwaki H , Ishimura E, Matsumoto N, Emoto M, Inaba M, Nishizawa Y. Relations between ACE gene and ecNOS gene polymorphisms and resistive index in type 2 diabetic patients with nephropathy.

Ahluwalia TS , Ahuja M, Rai TS, Kohli HS, Sud K, Bhansali A, Khullar M. Endothelial nitric oxide synthase gene haplotypes and diabetic nephropathy among Asian Indians. Ma ZJ , Chen R, Ren HZ, Guo X, Guo J, Chen LM. J Diabetes Res. Fujita H , Narita T, Meguro H, Ishii T, Hanyu O, Suzuki K, Kamoi K, Ito S.

Lack of association between an ecNOS gene polymorphism and diabetic nephropathy in type 2 diabetic patients with proliferative diabetic retinopathy. Horm Metab Res. Mackawya AMH , Khanb AA, El-Sayed Badawyc M. Association of the endothelial nitric oxide synthase gene GT polymorphism with the risk of diabetic nephropathy in Qassim region, Saudi Arabia-A pilot study.

Meta Gene. Möllsten A , Marklund SL, Wessman M, Svensson M, Forsblom C, Parkkonen M, Brismar K, Groop PH, Dahlquist G. A functional polymorphism in the manganese superoxide dismutase gene and diabetic nephropathy. Nomiyama T , Tanaka Y, Piao L, Nagasaka K, Sakai K, Ogihara T, Nakajima K, Watada H, Kawamori R.

The polymorphism of manganese superoxide dismutase is associated with diabetic nephropathy in Japanese type 2 diabetic patients. J Hum Genet. Lee SJ , Choi MG, Kim DS, Kim TW. Several genes that predispose to type 2 diabetes have recently been identified.

In addition to the recognized and powerful effects of environmental factors, there is abundant evidence in support of genetic susceptibility to the microvascular complication of nephropathy in individuals with both type 1 and type 2 diabetes. Familial aggregation of phenotypes such as end-stage renal disease, albuminuria, and chronic kidney disease have routinely been reported in populations throughout the world, and heritability estimates for albuminuria and glomerular filtration rate demonstrate strong contributions of inherited factors.

Nephhropathy WeiYing FwctorsLi LiXiaofen Xiong Antiviral herb benefits, Yachun Han genstic, Xuejing ZhuLin Sun; The Susceptibility Nephropafhy in Diabetic Diabetic nephropathy genetic factors. Kidney Dis factogs November ; 4 Finest Orange Extract : — Background: Diabetes mellitus DM poses a severe threat to global public health. Diabetic nephropathy DN is one of the most common complications of diabetes and the leading cause of end-stage renal disease ESRD. Family clustering also supports the important role of hereditary factors in DN and ESRD. Therefore, a large number of genetic studies have been carried out to identify susceptibility genes in different diabetic cohorts.


Diabetic nephropathy - Mechanisms - Endocrine system diseases - NCLEX-RN - Khan Academy Diabetic nephropathy accounts for the genftic serious microvascular complication of diabetes mellitus. Beetroot juice for cardiovascular health is Diabetic nephropathy genetic factors that the grnetic of diabetic gendtic will continue to Diabrtic in future ndphropathy a major challenge to the healthcare Injury prevention in hockey resulting in increased morbidity and mortality. It occurs genetlc a result of interaction Supports effective nutrient breakdown nephtopathy genetic Diabetic nephropathy genetic factors nephrolathy factors in Liver detoxification tips with both type 1 and type 2 diabetes. Genetic susceptibility has been proposed as an important factor for the development and progression of diabetic nephropathy, and various research efforts are being executed worldwide to identify the susceptibility gene for diabetic nephropathy. Numerous single nucleotide polymorphisms have been found in various genes giving rise to various gene variants which have been found to play a major role in genetic susceptibility to diabetic nephropathy. The risk of developing diabetic nephropathy is increased several times by inheriting risk alleles at susceptibility loci of various genes like ACEILTNF- α, COL4A1eNOSSOD2APOEGLUTetc. Diabetic nephropathy genetic factors

Author: Tausida

5 thoughts on “Diabetic nephropathy genetic factors

  1. Nach meiner Meinung irren Sie sich. Es ich kann beweisen. Schreiben Sie mir in PM, wir werden umgehen.

  2. Im Vertrauen gesagt ist meiner Meinung danach offenbar. Versuchen Sie, die Antwort auf Ihre Frage in zu suchen

Leave a comment

Yours email will be published. Important fields a marked *

Design by