Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease: an exploratory study

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

Integrated analysis of blood DNA methylation, genetic variants, circulating proteins, microRNAs, and kidney failure in type 1 diabetes

Variation in DNA methylation (DNAmet) in white blood cells and other cells/tissues has been implicated in the etiology of progressive diabetic kidney disease (DKD). However, the specific mechanisms linking DNAmet variation in blood cells with risk of kidney failure (KF) and utility of measuring blood cell DNAmet in personalized medicine are not clear. We measured blood cell DNAmet in 277 individuals with type 1 diabetes and DKD using Illumina EPIC arrays; 51% of the cohort developed KF during 7 to 20 years of follow-up. Our epigenome-wide analysis identified DNAmet at 17 CpGs (5'-cytosine-phosphate-guanine-3' loci) associated with risk of KF independent of major clinical risk factors. DNAmet at these KF-associated CpGs remained stable over a median period of 4.7 years. Furthermore, DNAmet variations at seven KF-associated CpGs were strongly associated with multiple genetic variants at seven genomic regions, suggesting a strong genetic influence on DNAmet. The effects of DNAmet variations at the KF-associated CpGs on risk of KF were partially mediated by multiple KF-associated circulating proteins and KF-associated circulating miRNAs. A prediction model for risk of KF was developed by adding blood cell DNAmet at eight selected KF-associated CpGs to the clinical model. This updated model significantly improved prediction performance (c-statistic = 0.93) versus the clinical model (c-statistic = 0.85) at P = 6.62 × 10-14. In conclusion, our multiomics study provides insights into mechanisms through which variation of DNAmet may affect KF development and shows that blood cell DNAmet at certain CpGs can improve risk prediction for KF in T1D.

Role of epigenetically regulated inflammation in renal diseases

In recent decades, renal disease research has witnessed remarkable advances. Experimental evidence in this field has highlighted the role of inflammation in kidney disease. Epigenetic dynamics and immunometabolic reprogramming underlie the alterations in cellular responses to intrinsic and extrinsic stimuli; these factors determine cell identity and cell fate decisions and represent current research hotspots. This review focuses on recent findings and emerging concepts in epigenetics and inflammatory regulation and their effect on renal diseases. This review aims to summarize the role and mechanisms of different epigenetic modifications in renal inflammation and injury and provide new avenues for future research on inflammation-related renal disease and drug development.

Identification of Genes Associated with Decreasing Abundance of Monocytes in Long-Term Peritoneal Dialysis Patients

Purpose

Chronic kidney disease (CKD) will become an end-stage renal disease (ESRD) at stage 5. Peritoneal dialysis (PD) is required for renal replacement therapy. This study aims to identify monocytes-related genes in peritoneal cells from long-term PD (LPD) patients and short-term PD (SPD) patients.

Methods

Bulk RNA-seq data (GSE125498 dataset) and ScRNA-seq data (GSE130888) were downloaded to identify differentially expressed genes, monocytes-related genes, and monocytes marker genes in LPD patients. Immune infiltration was analyzed in the GSE125498 dataset. Core genes associated with monocytes changes were screened out, followed by functional analysis and expression validation using RT-PCR.

Results

Monocytes are the most abundant immune cell in PD. The number of monocytes was remarkably decreased in LPD compared with SPD. A total of 16 up-regulated core genes negatively correlated with the abundance of monocytes were obtained in LPD. The expression of 16 core genes was lower in monocyte clusters than that in other cell clusters. In addition, LCK, CD3G, CD3E, CD3D, and LAT were involved in the signaling pathways of Th1 and Th2 cell differentiation, T cell receptor signaling pathway, and Th17 cell differentiation. CD2 was involved in hematopoietic cell lineage signaling pathway.

Conclusion

Identification of monocytes related-genes and related signaling pathways could be helpful in understanding the molecular mechanism of monocytes changes during PD.

Genetic and epigenetic background of diabetic kidney disease

Diabetic kidney disease (DKD) is a severe diabetic complication that affects up to half of the individuals with diabetes. Elevated blood glucose levels are a key underlying cause of DKD, but DKD is a complex multifactorial disease, which takes years to develop. Family studies have shown that inherited factors also contribute to the risk of the disease. During the last decade, genome-wide association studies (GWASs) have emerged as a powerful tool to identify genetic risk factors for DKD. In recent years, the GWASs have acquired larger number of participants, leading to increased statistical power to detect more genetic risk factors. In addition, whole-exome and whole-genome sequencing studies are emerging, aiming to identify rare genetic risk factors for DKD, as well as epigenome-wide association studies, investigating DNA methylation in relation to DKD. This article aims to review the identified genetic and epigenetic risk factors for DKD.

Epigenome-wide association study of diabetic chronic kidney disease progression in the Korean population: the KNOW-CKD study

Since the etiology of diabetic chronic kidney disease (CKD) is multifactorial, studies on DNA methylation for kidney function deterioration have rarely been performed despite the need for an epigenetic approach. Therefore, this study aimed to identify epigenetic markers associated with CKD progression based on the decline in the estimated glomerular filtration rate in diabetic CKD in Korea. An epigenome-wide association study was performed using whole blood samples from 180 CKD recruited from the KNOW-CKD cohort. Pyrosequencing was also performed on 133 CKD participants as an external replication analysis. Functional analyses, including the analysis of disease-gene networks, reactome pathways, and protein-protein interaction networks, were conducted to identify the biological mechanisms of CpG sites. A phenome-wide association study was performed to determine the associations between CpG sites and other phenotypes. Two epigenetic markers, cg10297223 on AGTR1 and cg02990553 on KRT28 indicated a potential association with diabetic CKD progression. Based on the functional analyses, other phenotypes (blood pressure and cardiac arrhythmia for AGTR1) and biological pathways (keratinization and cornified envelope for KRT28) related to CKD were also identified. This study suggests a potential association between the cg10297223 and cg02990553 and the progression of diabetic CKD in Koreans. Nevertheless, further validation is needed through additional studies.

DNA methylation markers for kidney function and progression of diabetic kidney disease

Epigenetic markers are potential biomarkers for diabetes and related complications. Using a prospective cohort from the Hong Kong Diabetes Register, we perform two independent epigenome-wide association studies to identify methylation markers associated with baseline estimated glomerular filtration rate (eGFR) and subsequent decline in kidney function (eGFR slope), respectively, in 1,271 type 2 diabetes subjects. Here we show 40 (30 previously unidentified) and eight (all previously unidentified) CpG sites individually reach epigenome-wide significance for baseline eGFR and eGFR slope, respectively. We also develop a multisite analysis method, which selects 64 and 37 CpG sites for baseline eGFR and eGFR slope, respectively. These models are validated in an independent cohort of Native Americans with type 2 diabetes. Our identified CpG sites are near genes enriched for functional roles in kidney diseases, and some show association with renal damage. This study highlights the potential of methylation markers in risk stratification of kidney disease among type 2 diabetes individuals.

Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case-control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case-control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10^-6). Telomere length was also significantly reduced (p = 6.6 × 10^-5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10^-8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

Differential methylation in CD44 and SEC23A is associated with time preference in older individuals

Time preference is a measure used to ascertain the level of which individuals prefer smaller, immediate rewards over larger, delayed rewards. We explored how an individual's time preference associates with their epigenetic profile. Time preferences were ascertained by asking participants of the Northern Ireland COhort for the Longitudinal study of Ageing to make a series of choices between two hypothetical income scenarios. From these, eight 'time preference' categories were derived, ranging from "patient" to "impatient" on an ordinal scale. The Infinium High Density Methylation Assay, MethylationEPIC (Illumina) was used to evaluate the status of 862,927 CpGs. Time preference and DNA methylation data were obtained for 1648 individuals. Four analyses were conducted, assessing the methylation patterns at single site resolution between patient and impatient individuals using two adjustment models. In this discovery cohort analysis, two CpG sites were identified with significantly different levels of methylation (p < 9 × 10^-8) between the individuals allocated to the patient group and the remaining population following adjustment for covariates; cg08845621 within CD44 and cg18127619 within SEC23A. Neither of these genes have previously been linked to time preference. Epigenetic modifications have not previously been linked to time preference using a population cohort but they may represent important biomarkers of accumulated, complex determinants of this trait. Further analysis is warranted of both the top-ranked results and of DNA methylation as an important link between measurable biomarkers and health behaviours.

Shared peripheral blood biomarkers for Alzheimer’s disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis

Background

Alzheimer's disease (AD), type 2 diabetes mellitus (T2DM), and Major Depressive Disorder (MDD) have a higher incidence rate in modern society. Although increasing evidence supports close associations between the three, the mechanisms underlying their interrelationships remain elucidated.

Objective

The primary purpose is to explore the shared pathogenesis and the potential peripheral blood biomarkers for AD, MDD, and T2DM.

Methods

We downloaded the microarray data of AD, MDD, and T2DM from the Gene Expression Omnibus database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis to identify differentially expressed genes. We took the intersection of differentially expressed genes to obtain co-DEGs. Then, we performed GO and KEGG enrichment analysis on the common genes in the AD, MDD, and T2DM-related modules. Next, we utilized the STRING database to find the hub genes in the protein-protein interaction network. ROC curves were constructed for co-DEGs to obtain the most diagnostic valuable genes and to make drug predictions against the target genes. Finally, we conducted a present condition survey to verify the correlation between T2DM, MDD and AD.

Results

Our findings indicated 127 diff co-DEGs, 19 upregulated co-DEGs, and 25 down-regulated co-DEGs. Functional enrichment analysis showed co-DEGs were mainly enriched in signaling pathways such as metabolic diseases and some neurodegeneration. Protein-protein interaction network construction identified hub genes in AD, MDD and T2DM shared genes. We identified seven hub genes of co-DEGs, namely, SMC4, CDC27, HNF1A, RHOD, CUX1, PDLIM5, and TTR. The current survey results suggest a correlation between T2DM, MDD and dementia. Moreover, logistic regression analysis showed that T2DM and depression increased the risk of dementia.

Conclusion

Our work identified common pathogenesis of AD, T2DM, and MDD. These shared pathways might provide novel ideas for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosing and treating.

Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease

The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.

Reduced methylation correlates with diabetic nephropathy risk in type 1 diabetes

Diabetic nephropathy (DN) is a polygenic disorder with few risk variants showing robust replication in large-scale genome-wide association studies. To understand the role of DNA methylation, it is important to have the prevailing genomic view to distinguish key sequence elements that influence gene expression. This is particularly challenging for DN because genome-wide methylation patterns are poorly defined. While methylation is known to alter gene expression, the importance of this causal relationship is obscured by array-based technologies since coverage outside promoter regions is low. To overcome these challenges, we performed methylation sequencing using leukocytes derived from participants of the Finnish Diabetic Nephropathy (FinnDiane) type 1 diabetes (T1D) study (n = 39) that was subsequently replicated in a larger validation cohort (n = 296). Gene body-related regions made up more than 60% of the methylation differences and emphasized the importance of methylation sequencing. We observed differentially methylated genes associated with DN in 3 independent T1D registries originating from Denmark (n = 445), Hong Kong (n = 107), and Thailand (n = 130). Reduced DNA methylation at CTCF and Pol2B sites was tightly connected with DN pathways that include insulin signaling, lipid metabolism, and fibrosis. To define the pathophysiological significance of these population findings, methylation indices were assessed in human renal cells such as podocytes and proximal convoluted tubule cells. The expression of core genes was associated with reduced methylation, elevated CTCF and Pol2B binding, and the activation of insulin-signaling phosphoproteins in hyperglycemic cells. These experimental observations also closely parallel methylation-mediated regulation in human macrophages and vascular endothelial cells.

Epigenetics in Obesity and Diabetes Mellitus: New Insights

A long-term complication of obesity is the development of type 2 diabetes (T2D). Patients with T2D have been described as having epigenetic modifications. Epigenetics is the post-transcriptional modification of DNA or associated factors containing genetic information. These environmentally-influenced modifications, maintained during cell division, cause stable changes in gene expression. Epigenetic modifications of T2D are DNA methylation, acetylation, ubiquitylation, SUMOylation, and phosphorylation at the lysine residue at the amino terminus of histones, affecting DNA, histones, and non-coding RNA. DNA methylation has been shown in pancreatic islets, adipose tissue, skeletal muscle, and the liver. Furthermore, epigenetic changes have been observed in chronic complications of T2D, such as diabetic nephropathy, diabetic retinopathy, and diabetic neuropathy. Recently, a new drug has been developed which acts on bromodomains and extraterminal (BET) domain proteins, which operate like epigenetic readers and communicate with chromatin to make DNA accessible for transcription by inhibiting them. This drug (apabetalone) is being studied to prevent major adverse cardiovascular events in people with T2D, low HDL cholesterol, chronic kidney failure, and recent coronary events. This review aims to describe the relationship between obesity, long-term complications such as T2D, and epigenetic modifications and their possible treatments.

Longitudinal Epigenome-Wide Analysis of Kidney Transplant Recipients Pretransplant and Posttransplant

Introduction

Kidney transplantation remains the gold standard of treatment for end-stage renal disease (ESRD), with improved patient outcomes compared with dialysis. Epigenome-Wide Association Analysis (EWAS) of DNA methylation may identify markers that contribute to an individual's risk of adverse transplant outcomes, yet only a limited number of EWAS have been conducted in kidney transplant recipients. This EWAS aimed to interrogate the methylation profile of a kidney transplant recipient cohort with minimal posttransplant complications, exploring differences in samples pretransplant and posttransplant.

Methods

We compared differentially methylated cytosine-phosphate-guanine sites (dmCpGs) in samples derived from peripheral blood mononuclear cells of the same kidney transplant recipients, collected both pretransplant and posttransplant (N = 154), using the Infinium MethylationEPIC microarray (Illumina, San Diego, CA). Recipients received kidneys from deceased donors and had a mean of 17 years of follow-up.

Results

Five top-ranked dmCpGs were significantly different at false discovery rate (FDR) adjusted P ≤ 9 × 10^-8; cg23597162 within JAZF1, cg25187293 within BTNL8, cg17944885, located between ZNF788P and ZNF625-ZNF20, cg14655917 located between ASB4 and PDK4 and cg09839120 located between GIMAP6 and EIF2AP3.

Conclusion

Five dmCpGs were identified at the generally accepted EWAS critical significance level of FDR adjusted P (P _FDRadj) ≤ 9 × 10^-8, including cg23597162 (within JAZF1) and cg17944885, which have prior associations with chronic kidney disease (CKD). Comparing individuals with no evidence of posttransplant complications (N = 105) demonstrated that 693,555 CpGs (89.57%) did not display any significant difference in methylation (P _FDRadj ≥ 0.05), thereby this study establishes an important reference for future epigenetic studies that seek to identify markers of posttransplant complications.

Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.

Plasma PFOA and PFOS Levels, DNA Methylation, and Blood Lipid Levels: A Pilot Study

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) is associated with blood lipids in adults, but the underlying mechanisms remain unclear. This pilot study aimed to investigate the associations between PFOA or PFOS and epigenome-wide DNA methylation and assess the mediating effect of DNA methylation on the PFOA/PFOS-blood lipid association. We measured plasma PFOA/PFOS and leukocyte DNA methylation in 98 patients enrolled from the hospital between October 2018 and August 2019. The median plasma PFOA/PFOS levels were 0.85 and 2.29 ng/mL. Plasma PFOA and PFOS levels were significantly associated with elevated total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels. There were 63/87 CpG positions and 8/11 differentially methylated regions (DMRs) associated with plasma PFOA/PFOS levels, respectively. In addition, 5 CpG positions (annotated to AFF3, CREB5, NRG2, USF2, and intergenic region) and one DMR annotated to IRF6 may mediate the association between plasma PFOA/PFOS and LDL levels (mediated proportion from 7.29 to 46.77%); two CpG positions may mediate the association between plasma PFOA/PFOS and TC levels (annotated to CREB5 and USF2, mediated proportion is around 30%). The data suggest that PFOA/PFOS exposure alters DNA methylation. More importantly, the association of PFOA/PFOS with lipid indicators was partly mediated by DNA methylation changes in lipid metabolism-related genes.

Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression

The proximal tubule is a key regulator of kidney function and glucose metabolism. Diabetic kidney disease leads to proximal tubule injury and changes in chromatin accessibility that modify the activity of transcription factors involved in glucose metabolism and inflammation. Here we use single nucleus RNA and ATAC sequencing to show that diabetic kidney disease leads to reduced accessibility of glucocorticoid receptor binding sites and an injury-associated expression signature in the proximal tubule. We hypothesize that chromatin accessibility is regulated by genetic background and closely-intertwined with metabolic memory, which pre-programs the proximal tubule to respond differently to external stimuli. Glucocorticoid excess has long been known to increase risk for type 2 diabetes, which raises the possibility that glucocorticoid receptor inhibition may mitigate the adverse metabolic effects of diabetic kidney disease.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

AIMS/HYPOTHESIS

Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.

METHODS

We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.

RESULTS

The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]).

CONCLUSIONS/INTERPRETATION

Altogether, the results point to novel genes contributing to the pathogenesis of DKD.

DATA AVAILABILITY

The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

Cardiovascular Disease in Adults with Type 1 Diabetes: Looking Beyond Glycemic Control

PURPOSE OF REVIEW

Despite improvements in treatment, people with type 1 diabetes continue to have increased cardiovascular disease (CVD) risk. Glycemic control does not fully explain this excess CVD risk, so a greater understanding of other risk factors is needed.

RECENT FINDINGS

The authors review the relationship between glycemia and CVD risk in adults with type 1 diabetes and summarize evidence regarding other factors that may explain risk beyond glycemia. Insulin resistance, weight gain, sex differences, genetics, inflammation, emerging markers of risk, including lipid subclasses and epigenetic modifications, and future directions are discussed. As glycemic control improves, an increased focus on other CVD risk factors is warranted in type 1 diabetes. Novel markers and precision medicine approaches may improve CVD prediction, but a lack of type 1 diabetes-specific guidelines for lipids, blood pressure, and physical activity are likely impediments to optimal CVD prevention in this high-risk population.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

Are Alterations in DNA Methylation Related to CKD Development?

The modifications in genomic DNA methylation are involved in the regulation of normal and pathological cellular processes. The epigenetic regulation stimulates biological plasticity as an adaptive response to variations in environmental factors. The role of epigenetic changes is vital for the development of some diseases, including atherogenesis, cancers, and chronic kidney disease (CKD). The results of studies presented in this review have suggested that altered DNA methylation can modulate the expression of pro-inflammatory and pro-fibrotic genes, as well those essential for kidney development and function, thus stimulating renal disease progression. Abnormally increased homocysteine, hypoxia, and inflammation have been suggested to alter epigenetic regulation of gene expression in CKD. Studies of renal samples have demonstrated the relationship between variations in DNA methylation and fibrosis and variations in estimated glomerular filtration rate (eGFR) in human CKD. The unravelling of the genetic–epigenetic profile would enhance our understanding of processes underlying the development of CKD. The understanding of multifaceted relationship between DNA methylation, genes expression, and disease development and progression could improve the ability to identify individuals at risk of CKD and enable the choice of appropriate disease management.

Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease

Diabetes is the leading cause of kidney failure and specifically, diabetic kidney disease (DKD) occurs in up to 30% of all diabetic patients. Kidney disease attributed to diabetes is a major contributor to the global burden of the disease in terms of clinical and socio-economic impact, not only because of the risk of progression to End-Stage Kidney Disease (ESKD), but also because of the associated increase in cardiovascular (CV) risk. Despite the introduction of novel treatments that allow us to reduce the risk of future outcomes, a striking residual cardiorenal risk has been reported. This risk is explained by both the heterogeneity of DKD and the individual variability in response to nephroprotective treatments. Strategies that have been proposed to improve DKD patient care are to develop novel biomarkers that classify with greater accuracy patients with respect to their future risk (prognostic) and biomarkers that are able to predict the response to nephroprotective treatment (predictive). In this review, we summarize the principal prognostic biomarkers of type 1 and type 2 diabetes and the novel markers that help clinicians to individualize treatments and the basis of the characteristics that predict an optimal response.

Recent Advances in Epigenetics of Age-Related Kidney Diseases

Renal aging has attracted increasing attention in today’s aging society, as elderly people with advanced age are more susceptible to various kidney disorders such as acute kidney injury (AKI) and chronic kidney disease (CKD). There is no clear-cut universal mechanism for identifying age-related kidney diseases, and therefore, they pose a considerable medical and public health challenge. Epigenetics refers to the study of heritable modifications in the regulation of gene expression that do not require changes in the underlying genomic DNA sequence. A variety of epigenetic modifiers such as histone deacetylases (HDAC) inhibitors and DNA methyltransferase (DNMT) inhibitors have been proposed as potential biomarkers and therapeutic targets in numerous fields including cardiovascular diseases, immune system disease, nervous system diseases, and neoplasms. Accumulating evidence in recent years indicates that epigenetic modifications have been implicated in renal aging. However, no previous systematic review has been performed to systematically generalize the relationship between epigenetics and age-related kidney diseases. In this review, we aim to summarize the recent advances in epigenetic mechanisms of age-related kidney diseases as well as discuss the application of epigenetic modifiers as potential biomarkers and therapeutic targets in the field of age-related kidney diseases. In summary, the main types of epigenetic processes including DNA methylation, histone modifications, non-coding RNA (ncRNA) modulation have all been implicated in the progression of age-related kidney diseases, and therapeutic targeting of these processes will yield novel therapeutic strategies for the prevention and/or treatment of age-related kidney diseases.

The Role of Epigenetic Modifications in Late Complications in Type 1 Diabetes

Type 1 diabetes is a chronic autoimmune disease in which the destruction of pancreatic β cells leads to hyperglycemia. The prevention of hyperglycemia is very important to avoid or at least postpone the development of micro- and macrovascular complications, also known as late complications. These include diabetic retinopathy, chronic renal failure, diabetic neuropathy, and cardiovascular diseases. The impact of long-term hyperglycemia has been shown to persist long after the normalization of blood glucose levels, a phenomenon known as metabolic memory. It is believed that epigenetic mechanisms such as DNA methylation, histone modifications, and microRNAs, play an important role in metabolic memory. The aim of this review is to address the impact of long-term hyperglycemia on epigenetic marks in late complications of type 1 diabetes.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.