Human genetics of diabetic nephropathy

Abstract

Diabetic vascular complications (DVCs) affecting several important organ systems of human body such as cardiovascular system contribute a major public health problem. Genetic factors contribute to the risk of diabetic nephropathy (DN). Genetics variants, structural variants (copy number variation) and epigenetic changes play important roles in the development of DN. Apart from nucleus genome, mitochondrial DNA (mtDNA) plays critical roles in regulation of development of DN. Epigenetic studies have indicated epigenetic changes in chromatin affecting gene transcription in response to environmental stimuli, which provided a large body of evidence of regulating development of diabetes mellitus. This review focused on the current knowledge of the genetic and epigenetic basis of DN. Ultimately, identification of genes or genetic loci, structural variants and epigenetic changes contributed to risk or protection of DN will benefit uncovering the complex mechanism underlying DN, with crucial implications for the development of personalized medicine to diabetes mellitus and its complications.

• Diabetic nephropathy
• epigenetic
• genetics
• interactions of gene by environment
• mitochondrial DNA

Introduction

The vascular complications of diabetes involve several important organ systems, such as the eyes, the kidneys and the cardiovascular system, and are often classified as either microvascular complications, such as diabetic nephropathy (DN) and diabetic retinopathy (DR), or macrovascular complications, including diabetic cardiovascular complications (DCCs).1^,2^,3 Owing to the high prevalence of diabetes, DN is a serious public health problem and is a global burden leading to renal replacement therapy interventions worldwide.1^,3 A large body of evidence indicates that major risk factors, such as long-term diabetes, poor control of blood glucose and elevated blood pressure are responsible for the onset and progression of DN.3^,4 In addition, there is now evidence for the role of genetic factors in DN.5 The DN can be considered a classic example of a human complex disease attributed to genetic factors, environmental factors and interactions between them.

Clinical factors that contribute to the onset and progression of DN, including elevated blood pressure, long duration of diabetes and poor control of blood glucose, have not been consistently identified in different studies. Therefore, patients cannot be stratified with respect to their risk of developing DN based only upon clinical or procedural risk factors. There is some evidence that genetic factors can explain part of the excessive risk of DN independently of conventional clinical variables. A large body of evidence for the role of genetic factors in DN has been generated over several decades. Genetics studies have clearly demonstrated that susceptibility to DN attributed to a familial basis.6 Approximately 30% of the variance of a quantitative trait for DN (urinary albumin excretion rate) can be explained by familial factors.7

Currently, multiple genetic approaches have been used to identify which genetic loci or genes are risk factors for developing this complex disease. Recently, two classical genetic approaches, genetic linkage analysis and genetic association analysis, have been used to identify genetic susceptibility variants or genes.8 Genetic linkage analyses have been performed under the “rare variant” hypothesis to identify genetic loci using extended families or sibling pairs. In contrast, genetic association analyses under the “common variant” hypothesis have identified genetic susceptibility variants via a dense marker map.8 Genetic alterations in mitochondrial DNA (mtDNA) also play a role in the development of diabetic complications. Genetic analysis of mtDNA, including mtDNA genome association analysis and copy number analysis have provided insights into the underlying mechanisms of DN.9 A large body of evidence from a study on epigenetic changes in chromatin induced by environmental stimuli implicated alterations in gene transcription in the development of DN.10 Such genetic approaches may also be used for identifying diabetic patients at high risk of complications so that patients may benefit from intensive prevention programs.

This review focuses on the current knowledge of the genetic and epigenetic basis of DN and summarizes data from previous genetic studies regarding susceptibility genetic variants and epigenetic changes that influence DN.

Genetic linkage analysis

Genetic linkage analysis detects the chromosomal location of disease genes, and is based on the observation that genes that are physically close together on a chromosome remain linked during meiosis.11^,12 For this approach to be successful, it is very important to define a specific phenotype associated with each gene. Two classical approaches, parametric tests and non-parametric tests, are commonly used in genetic linkage analyses.13–16 It is also critical to choose proper genetic markers for genetic linkage analysis. Microsatellite markers are most widely used owing to microsatellite loci being linked to highly polymorphic regions with greater than the combined paternity index (CPI).17 For candidate gene analysis, candidate genes of known sequence and location are identified that may be involved in disease pathogenesis, and these are often selected based on their physiological functions. In contrast, genome-wide screens are a more powerful approach that can be used to screen the whole human genome for gene linkage or association with a disease without making any assumptions regarding disease pathogenesis.18^,19 This type of approach has been used successfully to identify the susceptibility genetic loci for DN. Genetic linkage analysis often consists of the following steps: identifying linked loci, confirming linked loci, fine mapping of confirmed loci and then testing genes in the linked region in functional studies.20

Candidate genes linkage studies for DN have examined functional polymorphisms that affect the activity of candidate pathways, including the nitric oxide, renin–angiotensin and bradykinin systems. Other pathways involved with glucose metabolism and homeostasis, lipid production and insulin resistance have been explored owing to common mechanisms that may involve them in several disease processes. Despite this, there has been no consistent and reproducible identification of genetic loci or candidate genes for DN risk or protection, although this may be attributed to several factors such as small sample size or extensive genetic and phenotypic heterogeneity. A partial list of detected chromosome regions for DN is provided in Table 1.21–27 Imperator et al. performed the first genome linkage scan for DN in Pima Indians.21 They observed the strongest evidence of linkage to chromosome 7q and further evidence of linkage to chromosomes 3, 9 and 20. One of these regions contains the angiotensin II type 1 receptor gene (AGTR1), for which linkage evidence has been found in another study of families with type 1 diabetes mellitus (T1DM) using a similar approach.28 A further genome-wide linkage study of a large Turkish kindred sample containing multiple individuals with type 2 diabetes mellitus (T2DM) revealed a strong linkage of DN to chromosome 18.22 Evidence for nephropathy loci on chromosomes 3, 7p and 18q has also been detected in a genome-wide scan for DN in African-American families.24 Several of these linkage locations are consistent between studies. In summary, several chromosome regions throughout genome have been found to link to DN. Interestingly, chromosome 3 has been found to link the complications.

Table 1. Genetic linkage analysis for diabetic nephropathy.

Chromosome region	Population studied	Reference
2q14.1	Caucasian and African-Americans	26
3q	Pima Indians	21^,27
7p	African-American-Americans	24^,27
7q	Caucasian and African-Americans	25
7q21.1, 7q21.3	Caucasian and African-Americans	26
10p15.3	Caucasian and African-Americans	26
14q23.1	Caucasian and African-Americans	26
15q26.3	Caucasian and African-Americans	26
16q13	Japanese	23
18q, 18q22.3-23	Turkish	22^,24
20p	Caucasian and African-Americans	27
22q	Caucasian and African-Americans	25

Genetic association analysis

Compared to the limited resolution of genetic linkage studies, genetic association studies are more sensitive and may detect minor susceptibility genes contributing less than 5% of the total genetic contribution to a disease.8^,11 The approach used for this type of analysis is based on comparing the frequency of the allele studied in unrelated patients with matched controls. If the allele appears significantly more frequently in patients than in controls, then it is considered to be associated with the disease.29 Single nucleotide polymorphisms (SNPs) are the most important genetic markers for genetic association analysis, due to the abundance of SNPs covering the entire human genome at a high density.30–32 Candidate gene association analyses use candidate genes of a known sequence and location that are considered involved in the disease pathology. However, approaches based on prior hypothesis have a limited power to detect novel genetic variants. Instead, a non-prior hypothesis is a more powerful approach for identifying gene(s) association with a disease by screening the whole human genome. Genome-wide association studies (GWAS) became a reality following the publication of the HapMap of the human genome.33 Recently, several genes associated with type 2 diabetes have been reproducibly identified using GWAS.34 Genetic association-based gene mapping consists of the following steps: genome-wide association using tag SNPs, confirming SNP association, gene identification and then functional studies.

Candidate gene association studies have identified several important genes associated with DN, including ACE, FABP2, ENPP1 and GLUT1. An association between the I/D polymorphism in ACE and the development of DN have been reported in Brazilian patients with T2DM.35 Interestingly, patients with a D allele (DD/ID) were at a greater risk for overt DN than incipient DN, suggesting that the I/D polymorphism in ACE is linked to different kinds of DN.36 However, inconsistent results regarding the association of the ACE I/D polymorphism with DN in T1DM and T2DM patients were found in several studies, possibly due to ethnic differences between the populations studied.37 FABP2 is also a gene candidate for predisposition to DN, as it has been linked to microalbuminuria in patients with T2DM.38 Interestingly, the FABP2 rs1799883 polymorphism is associated with altered protein conformation, and an association of the T allele with different stages of DN has been reproducibly observed in independent samples of white American subjects with T2DM.35 Gene candidates for insulin resistance (IR) can also be considered as DN candidates since insulin resistance is a common characteristic of patients with T1DM and T2DM.39^,40 Following the discovery that an ENPP1 polymorphism is associated with IR, a candidate gene association study was conducted in patients with T1DM to investigate a possible association between advanced DN and the ENPP1 rs1805101 polymorphism.41^,42 This study indicated a greater risk for early onset of end-stage renal disease (ESRD) in patients with this allele, and the association was confirmed using the transmission disequilibrium test.42 GLUT1 polymorphisms have also been considered as candidate risk factors for DN due to their being associated with early kidney alterations, and because GLUT1 functions as a glucose transporter in the kidneys. In a genetic study, GLUT1 polymorphisms associated with DN were also examined. In addition to identifying genetic variants, genetic studies have also been performed to assess environmental factors. Considering smoking as an environmental factor, one study showed that the GLUT1 rs4673 polymorphism was more frequently found in smokers with persistent proteinuria than in normoalbuminuric patients. The finding that CT and TT genotypes were independently associated with a greater risk of overt DN among smokers has since been confirmed using multiple logistic regression analysis.43 Our experience in candidate genes analysis has also allowed us to identify some genes that can be related to the development and severity of DN.

A whole genome association scan was performed in the Genetics of Kidneys in Diabetes (GoKinD) study using a large sample of diabetic subjects with or without DN and characterized by long duration of diabetes, and replication of the strongest results from this study was sought in the DCCT/EDIC cohort.44 Eleven SNPs located in four distinct chromosomal regions were found to associate most strongly with DN. The strongest genetic association was with a SNP located on chromosome 9p near FRMD3, and other three linked SNPs were located near CHN2 and CPVL on chromosome 7p, CARS on 11p, and in an intergenic region at 13q (Table 2). Association of DN with the 9q and 11p loci was reproduced in the EDIC/DCCT cohort with nominal significance, and a recent meta-analysis of four studies from Japan confirmed that the SNP at 13q is associated with nephropathy in T2DM subjects.45 The findings have provided potential insights into novel pathways underlying the etiology of DN. Candidate genes such as FRMD3, CARS and CPVL play crucial roles in the development of DN.46–48 Anyway, the findings from GWAS could benefit further research to expand our knowledge of the pathogenesis of DN.

Table 2. Genetic association analysis for diabetic nephropathy.

Polymorphism	Candidate gene	Reference
rs1805101	ENPP1/PC-1	17^,39
rs35448603	CATALASE	41
rs4673	CYBA	43
rs1800625	RAGE	44
rs1800624	RAGE	44
rs1799883	FABP2	45
rs1801282	PPARγ2	46
rs39059	CPVL/CHN2	21
rs39075	CPVL/CHN2	21
rs1888747	FRMD3	21
rs10868025	FRMD3	21
rs739401	CARS	21
rs451041	CARS	21
rs1411766	IRS2/MYO16	21
rs39075	CPVL/CHN2	21
rs1888746	FRMD3	21
rs13289150	FRMD3	21
rs451041	CARS	21

In summary, genes of ACE, FABP2, ENPP1 and GLUT1 were found to associate with DN in candidate gene analysis, and whole genomic association study indicated that FRMD3, CHN2 and CPVL, CARS were associated with this disease.

mtDNA association analysis

Mitochondrial DNA (mtDNA) is a non-genomic DNA located within mitochondria, which are the structures within eukaryotic cells that convert the chemical energy from food into ATP. The mitochondrial genome is highly compact, consisting of double-stranded circular mtDNA greater than 16 kb in length. In humans, each cell contains between several hundred and more than a thousand mitochondria, and each mitochondrion contains 2–10 copies of mtDNA. The number of mitochondria and mtDNA copies can vary dramatically in response to energy demand and under different physiological conditions, and are tightly controlled by mitochondrial biogenesis. The consequence of mtDNA mutation may be a change in the protein-coding sequence, which may affect organism metabolism. Alterations in mitochondrial biogenesis may be the underlying pathological factors for several human complex diseases such as diabetes mellitus or DN.49–51 Further, there is compelling evidence for a genetic predisposition to diabetes complications.52–58

Single mtDNA mutations and mitochondrial haplogroups are associated with T2DM, and many studies have evaluated mtDNA variation in T2DM patients. For instance, a mtDNA genome-wide association analysis investigated a potential role for mtDNA in diabetic complications59 and reported that mitochondrial haplogroup distributions observed in T2DM patients were characterized by the development of DN (Table 3). The study found that the incidence of retinopathy was significantly increased in subjects harboring H haplotype mtDNAs. There is also evidence that haplogroup H3 patients have an increased probability of developing different complication. For example, the study showed that mitochondrial haplogroups H3, U3 and V were risk factors for DN. This compelling analysis of grouped complications provides some initial clues concerning the role of mitochondrial haplogroups in modulating the course of the diabetes mellitus.

Table 3. mtDNA association analysis for diabetic nephropathy.

Haplogroup	Phenotype	p Value	Reference
U3	Nephropathy	0.0154	59
V	Renal failure	0.0103	59
H3	Renal failure	0.0143	59

Copy number variant analysis

Recent discoveries have revealed that large segments of DNA can vary in copy number between individuals. A copy number variation (CNV) is a segment of DNA in which copy number differences have been found in two or more genomes; the segment may range from one kilobase to several megabases in size.60 CNVs can encompass genes, leading to dosage imbalances, and this may play an important role both in human disease and in drug response. It was first realized that DNA CNV is a widespread and common phenomenon among humans after the completion of the human genome project.61^,62 CNVs can lead to variations in dosage sensitive genes, which may contribute to a substantial amount of human phenotypic variability and disease susceptibility.63^,64 In GWAS, the raw intensity data generated from SNP genotyping can be mined for copy number information.65^,66 No published genetic study to date has performed copy number variation analysis to identify associations between CNVs and DN. However, an analysis has been conducted to detect CNVs associated with T1DM. The study performed a genome-wide CNV analysis on a cohort of 20 unrelated adults with T1DM and a control cohort of 20 subjects, and identified 39 CNVs as enriched or depleted in T1DM versus control using the Affymetrix SNP Array, which suggested that these variants may be involved in the development of T1DM.67

Gene–environment interaction analysis

Current genetic association analyses are designed to detect strong and direct associations of a SNP, or clusters of SNPs, with disease.68^,69 However, in the context of complex diseases, scanning for strong associations may miss important genetic variants specific to subpopulations, defined by their exposure to particular environmental factors. Interactions of functional gene polymorphisms with environmental factors play a substantial role in disease risk.70 Thus, in a genome-wide association study, gene–environment interactions are worth further investigation.71 First, gene–environment interactions can reveal fundamental biological mechanisms and the effects of individual components on a complex mixture, and can be important for risk prediction and for evaluating the benefit of changes in modifiable environmental factors.72–74 Gene–environment studies have been performed for exposure-related diseases such as asthma, lung cancer and T2DM.75^,76 For example, sunlight exposure has a much stronger influence on skin cancer risk in fair-skinned humans than in individuals with an inherited tendency to darker skin.77 Evidence for a gene–environment interaction has been found in a genetic study for DN.43 The study showed that the T allele of the rs4673 polymorphism was more frequently seen in smokers with ESRD or persistent proteinuria than in normoalbuminuric patients (p value = 0.045) when patients were stratified for smoking or non-smoking. Multiple logistic regression analysis confirmed that two different genotypes were independently associated with a greater risk of overt DN among smokers. It is therefore necessary to perform further studies to investigate possible gene–environment interactions following the previous GWAS for diabetes and DN.

Histone modification

Epigenetics is the study of changes in gene expression caused by mechanisms other than those that change the underlying DNA sequence, including DNA methylation, histone modification and microRNAs, and helps to explain how cells with identical DNA can differentiate into different cell types with different phenotypes. Epigenetic modifications can be passed from one cell generation to the next and between generations of humans. Epigenetic effects may also be regulated by environment factors, making them potentially important pathogenic mechanisms in complex human diseases such as T2DM or DN. It is important that the epigenome is precisely regulated and maintained, and this relies on mechanisms that write and erase specific chemical modifications to the chromatin template. For example, methylation of CpG dinucleotides is inversely correlated with gene expression in the human genome. miRNAs are also involved in epigenetic regulation. To complement the classical control of gene expression mediated by regulatory factors, the action of chromatin modifying enzymes provides an important mechanism for transcriptional control, and the action of miRNAs can fine-tune specific cellular responses.

Recently, epigenetic studies have specified that histone modifications play a role in diabetes and its complications. Histone acetyltransferases (HATs) and Histone deacetylases (HDACs) have been shown to play important roles in regulating several target genes related to diabetes.10^,78 A study found changes in histone acetylation within the promoters of inflammatory genes in monocytes from patients with both T1DM and T2DM relative to controls.79 Interestingly, and with potential relevance to diabetes, oxidized lipids can induce histone acetylation at inflammatory gene promoters in a CREB/p300 (HAT)-dependent manner that drives increased gene expression.80 Furthermore, p300 plays an important role in PARP and NF-γB signaling pathways in diabetic retina, kidney and heart, resulting in increased extracellular matrix (ECM) components linked to DN.81–86 Epigenetic studies have demonstrated that HDACs play an important role in TGF-β1-mediated ECM production and kidney fibrosis in DN.87–89 These studies show that HDACs play a role in the pathogenesis of renal fibrosis and in models of chronic renal injury induced by TGF-β1 modulation of key protective genes. Several studies support the idea that the histone methyltransferase (HMT) SET7/9 plays a role in regulating NF-γB expression and in inflammatory gene expression via promoter H3K4 methylation in response to inflammatory stimuli prevalent in the diabetic milieu.90–92 Moreover, increased H3K4 me and recruitment of SET7/9 to the insulin promoter region are considered to play important roles in regulating the development of diabetic complications.85^,86

DNA methylation

DNA methylation at promoter CpG islands has been associated with gene repression that was reported in the context of tumor suppressor genes and cancer.93 In diabetic research, a recent study indicated that the insulin promoter DNA was methylated in mouse embryonic stem cells, and both the human and mouse insulin promoters were specifically demethylated in pancreatic β cells, suggesting epigenetic regulation of insulin expression.94 Another interesting recent study demonstrated that in diabetic islets, there was increased DNA methylation of the promoter of the peroxisome proliferator-activated receptor-γ (PPARγ) coactivator 1α gene (PPARGC1A).95 PPARGC1A promoter was also hypermethylated in skeletal muscles from T2D patients, but at non-CpG nucleotides. Recently, Volkmar et al. performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors.96 The study uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets. Results showed that these methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose, however, for a subgroup of the differentially methylated genes, concordant transcriptional changes were present. Interestingly, a genome-wide DNA methylation analysis for DN has been conducted in type 1 diabetes mellitus patients.97 Researchers performed DNA methylation profiling in bisulphate converted DNA from cases and controls using genomewide DNA methylation approach that enables the direct investigation of 27,578 individual cytosines at CpG loci throughout the genome, which are focused on the promoter regions of 14,495 genes. Results showed 19 CpG sites that demonstrated correlations with time to development of diabetic nephropathy. Of note, this included one CpG site located 18 bp upstream of the transcription start site of UNC13B, in which SNP rs13293564 associated with DN. This high throughput platform was able to successfully interrogate the methylation state of individual cytosines and identified 19 prospective CpG sites associated with risk of DN. In summary, these differences in DNA methylation are worthy of further follow-up in replication studies using larger cohorts of diabetic patients with and without nephropathy.

miRNA association analysis

MicroRNAs (miRNAs) are 22-nucleotide non-coding RNAs that can result in either posttranscriptional silencing or RNA degradation by binding the 3′-untranslated region of target mRNAs normally.98^,99 Importantly, miRNAs play critical roles in the tissue response to environmental stimuli without changing DNA sequence with a rapid and reversible means of gene regulation. miRNAs may also themselves be epigenetically regulated, as histone modifications and changes in chromatin structure also affect miRNA transcription and expression.100 Furthermore, miRNAs and other non-coding RNAs can also interact with transcriptional coregulators and thereby further exert epigenetic control through transcriptional regulation.101^,102

Recent findings have indicated that miRNAs play a critical role in various diseases. Tissues adversely affected by diabetes include cardiac and skeletal muscle, liver, kidney and endothelium, and miRNA expression is altered in these tissues in individuals with diabetes (Table 4). It has been established that miRNAs can regulate genes involved in biological processes such as insulin secretion, cholesterol biosynthesis, fat metabolism and adipogenesis, all of which are crucial pathways in the pathogenesis of diabetes.103–105 Specific miRNAs, including miR-192, miR-216a, miR-217 and miR-377, have been implicated in TGF-β signaling related to the pathogenesis of DN.106–109 Studies have reported that in the late stages of diabetes, abnormal expression profiles of miR-122 and miR-21 are linked to impaired liver function via disruption of the normal fatty acid metabolic system.107–112 In addition, repression of miR-133a and miR-1 was found to contribute to impaired muscle function in T2DM patients.113–117 Interestingly, a recent study uncovered a strong negative correlation between miR-126 levels and the onset of DVC.118 Further, delivery of miR-126 by apoptotic bodies protects against diet-induced atherosclerosis.119 Thus, it seems likely that miRNAs and other epigenetic factors may have important roles in the development of diabetes and metabolic disease.

Table 4. miRNA involved in diabetic nephropathy.

miRNA	Target tissue	Reference
miR-192	Kidney	122
miR-107	Pancreas, adipose	20^,38
miR-125(a/b)	Liver, vascular tissue	8^,123
miR-216a	Kidney	124^,125
miR-217	Kidney	124^,125
miR-320	Adipose, vascular endothelium	126^,127

Future perspectives

With the development of GWAS, many genetic polymorphisms with a possible impact on DN have been identified. However, it has been reported to inconsistent identifications of the genetic variants underlying susceptibility to DN from GWAS. Standardization of phenotypes and genotyping protocols has been considered essential for GWAS due to the methods could pooling of individual patient level data in meta-analyses to increase their power. Because DN is involved in kidney system in a diabetic background, we can consider that shared genes influence the development of both this system and of diabetes mellitus. Thus, based on data from genome-wide scans for DN, we can perform multivariant genome-wide association analysis for these systems and for diabetes mellitus. We have performed bivariant genome-wide linkage analyses for obesity and osteoporosis,120 and found several novel chromosomal regions that may influence both. We also plan to perform multivariant genome-wide association analysis for chronic kidney disease (CKD) and T2DM. Recently, pathway-based genome-wide association analyses have been conducted to identify pathways underlying complex human disease, based on data from genome-wide scans. Similar analyses should reveal pathway-based genome-wide associations for DVC.

Whole genome sequencing study with “next generation sequencing” technology is an efficient strategy to sequence the human genome in order to identify novel genes associated with rare and common disorders.121 Presently, this technique is not practical owing to the high costs and time associated with sequencing large numbers of genomes. Such technology can be considered to open a new window for genetic research of DN. Further, new genetic analysis approaches based on next generation sequencing technology combined with gene–environment interaction and pathway-based approach analyses provide a powerful tool to explore the genetic mechanisms involved in the development of DN.

Challenges in studying epigenetic must be addressed in order to better understand the contribution of epigenetic changes to the etiology of diabetes mellitus, although some of the underlying mechanisms of DN relevant to gene regulation through epigenetic changes associated with hyperglycemia have now been identified. It will be critical to apply and develop high-throughput sequencing technologies coupled to the development of more sensitive and sophisticated methodologies for studying the epigenome in order to determine the extent to which specific epigenetic events drive gene responses in patients with diabetes mellitus. This approach is leading us toward the identification of specific miRNAs as biomarkers of susceptibility and prognosis of DN. With the availability of this technology, clinicians will be able to identify at-risk individuals and prompt patients to take preventive action, as well as prescribing tailored therapeutics. It is very important to centralize and integrate this information along with clinical data to obtain the necessary insights to conquer this illness.

Conclusions

Clinical and epidemiological studies have identified a genetic component to DN, although so far no specific gene has been identified that contributes to most DN cases. In fact, data from studies aimed to identify genes or genome regions associated with DN have been quite inconsistent. The lack of more consistent results is probably due to several different factors. For example, most genetic studies have been performed in well-defined populations but different studies have looked at different ethnic groups. It should also be pointed out that a single candidate gene has been sought when several, possibly interlinked, genes are likely to be involved. Thus, joint efforts are essential to achieve robust findings in the study of genetics of DN. The successful identification of the disease at an early stage, leading to changes in lifestyle and dietary behavior, is important for prevention and control of the disease. It is expected that characterization of the genetic factors involved in the development of diabetes mellitus and its complications will lead to the understanding of the molecular pathogenesis and the development of novel therapeutic approaches. Although there are many differences between mtDNA and nuclear DNA, there is coordinated expression and interaction between the gene products of the mitochondria and nuclear genomes.

Evidence indicates that diabetic status can induce epigenetic changes. DNA methylation and related chromatin alterations are epigenetic changes induced by elevated glucose in multiple target organs and cells, which contribute to the metabolic memory of diabetic vascular complications. Changes to the histone code are reversible that is a generally accepted idea. A greater understanding of the epigenetic basis of disease could benefit further studies on personalized medicine on diabetes and its complications. It is estimated that miRNAs regulated over 30% of human genes. Emerging evidence suggests that miRNAs play an important role in insulin production, secretion and action. Diabetes or chronic hyperglycemia leads to changes in miRNA expression profiles in many tissues, such as liver, pancreas, heart and kidney. Changes in tissue miRNA levels can promote diabetes at early disease stages or later disease stages. Furthermore, recent progress in the development of how to target miRNAs in vivo may give novel clues for the treatment of diabetes and its complications in future.

Declaration of interest

This study was supported by the grant from China National Grant on Science and Technology (grant number: 30570740).