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

Genome-wide characterization of 54 urinary metabolites reveals molecular impact of kidney function

Dissecting the genetic mechanisms underlying urinary metabolite concentrations can provide molecular insights into kidney function and open possibilities for causal assessment of urinary metabolites with risk factors and disease outcomes. Proton nuclear magnetic resonance metabolomics provides a high-throughput means for urinary metabolite profiling, as widely applied for blood biomarker studies. Here we report a genome-wide association study meta-analysed for 3 European cohorts comprising 8,011 individuals, covering both people with type 1 diabetes and general population settings. We identify 54 associations (p < 9.3 × 10-10) for 19 of 54 studied metabolite concentrations. Out of these, 33 were not reported previously for relevant urinary or blood metabolite traits. Subsequent two-sample Mendelian randomization analysis suggests that estimated glomerular filtration rate causally affects 13 urinary metabolite concentrations whereas urinary ethanolamine, an initial precursor for phosphatidylcholine and phosphatidylethanolamine, was associated with higher eGFR lending support for a potential protective role. Our study provides a catalogue of genetic associations for 53 metabolites, enabling further investigation on how urinary metabolites are linked to human health.

Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine

Chronic kidney disease (CKD) is a major worldwide health concern because of its progressive nature and complex biology. Traditional diagnostic and therapeutic approaches usually fail to account for disease heterogeneity, resulting in low efficacy. Precision medicine offers a novel approach to studying kidney disease by combining omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. By identifying discrete disease subtypes, molecular biomarkers, and therapeutic targets, these technologies pave the way for personalized treatment approaches. Multi-omics integration has enhanced our understanding of CKD by revealing intricate molecular linkages and pathways that contribute to treatment resistance and disease progression. While pharmacogenomics offers insights into expected responses to personalized treatments, single-cell and spatial transcriptomics can be utilized to investigate biological heterogeneity. Despite significant development, challenges persist, including data integration concerns, high costs, and ethical quandaries. Standardized data protocols, collaborative data-sharing frameworks, and advanced computational tools such as machine learning and causal inference models are required to address these challenges. With the advancement of omics technology, nephrology may benefit from improved diagnostic accuracy, risk assessment, and personalized care. By overcoming these barriers, precision medicine has the potential to develop novel techniques for improving patient outcomes in kidney disease treatment.

Blood methylation biomarkers are associated with diabetic kidney disease progression in type 1 diabetes

Background

DNA methylation differences are associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we aimed to study DNA methylation in a prospective setting in the Finnish Diabetic Nephropathy Study type 1 diabetes (T1D) cohort.

Methods

We analysed baseline blood sample-derived DNA methylation (Illumina's EPIC array) of 403 individuals with normal albumin excretion rate (early progression group) and 373 individuals with severe albuminuria (late progression group) and followed-up their DKD progression defined as decrease in eGFR to <60 mL/min/1.73m2 (early DKD progression group; median follow-up 13.1 years) or end-stage kidney disease (ESKD) (late DKD progression group; median follow-up 8.4 years). We conducted two epigenome-wide association studies (EWASs) on DKD progression and sought methylation quantitative trait loci (meQTLs) for the lead CpGs to estimate genetic contribution.

Results

Altogether, 14 methylation sites were associated with DKD progression (P<9.4×10-8). Methylation at cg01730944 near CDKN1C and at other CpGs associated with early DKD progression were not correlated with baseline eGFR, whereas late progression CpGs were strongly associated. Importantly, 13 of 14 CpGs could be linked to a gene showing differential expression in DKD or chronic kidney disease. Higher methylation at the lead CpG cg17944885, a frequent finding in eGFR EWASs, was associated with ESKD risk (HR [95% CI] = 2.15 [1.79, 2.58]). Additionally, we replicated meQTLs for cg17944885 and identified ten novel meQTL variants for other CpGs. Furthermore, survival models including the significant CpG sites showed increased predictive performance on top of clinical risk factors.

Conclusions

Our EWAS on early DKD progression identified a podocyte-specific CDKN1C locus. EWAS on late progression proposed novel CpGs for ESKD risk and confirmed previously known sites for kidney function. Since DNA methylation signals could improve disease course prediction, a combination of blood-derived methylation sites could serve as a potential prognostic biomarker.

Collagen formation, function and role in kidney disease

Highly abundant in mammals, collagens define the organization of tissues and participate in cell signalling. Most of the 28 vertebrate collagens, with the exception of collagens VI, VII, XXVI and XXVIII, can be categorized into five subgroups: fibrillar collagens, network-forming collagens, fibril-associated collagens with interrupted triple helices, membrane-associated collagens with interrupted triple helices and multiple triple-helix domains with interruptions. Collagen peptides are synthesized from the ribosome and enter the rough endoplasmic reticulum, where they undergo numerous post-translational modifications. The collagen chains form triple helices that can be secreted to form a diverse array of supramolecular structures in the extracellular matrix. Collagens are ubiquitously expressed and have been linked to a broad spectrum of disorders, including genetic disorders with kidney phenotypes. They also have an important role in kidney fibrosis and mass spectrometry-based proteomic studies have improved understanding of the composition of fibrosis in kidney disease. A wide range of therapeutics are in development for collagen and kidney disorders, including genetic approaches, chaperone therapies, protein degradation strategies and anti-fibrotic therapies. Improved understanding of collagens and their role in disease is needed to facilitate the development of more specific treatments for collagen and kidney disorders.

Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations

Key Points

The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits. However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations. Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.

Background

CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.

Methods

This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.

Results

The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.

Conclusions

In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease

AIM

Diabetes is increasing in prevalence worldwide, with a 20% rise in prevalence predicted between 2021 and 2030, bringing an increased burden of complications, such as diabetic kidney disease (DKD). DKD is a leading cause of end-stage kidney disease, with significant impacts on patients, families and healthcare providers. DKD often goes undetected until later stages, due to asymptomatic disease, non-standard presentation or progression, and sub-optimal screening tools and/or provision. Deeper insights are needed to improve DKD diagnosis, facilitating the identification of higher-risk patients. Improved tools to stratify patients based on disease prognosis would facilitate the optimisation of resources and the individualisation of care. This review aimed to identify how multiomic approaches provide an opportunity to understand the complex underlying biology of DKD.

METHODS

This review explores how multiomic analyses of DKD are improving our understanding of DKD pathology, and aiding in the identification of novel biomarkers to detect disease earlier or predict trajectories.

RESULTS

Effective multiomic data integration allows novel interactions to be uncovered and empathises the need for harmonised studies and the incorporation of additional data types, such as co-morbidity, environmental and demographic data to understand DKD complexity. This will facilitate a better understanding of kidney health inequalities, such as social-, ethnicity- and sex-related differences in DKD risk, onset and progression.

CONCLUSION

Multiomics provides opportunities to uncover how lifetime exposures become molecularly embodied to impact kidney health. Such insights would advance DKD diagnosis and treatment, inform preventative strategies and reduce the global impact of this disease.

Identification of GTF2I Polymorphisms as Potential Biomarkers for CKD in the Han Chinese Population : Multicentric Collaborative Cross-Sectional Cohort Study

Key Points

Genetic factors are key players in CKD, with two linked single-nucleotide polymorphisms in the GTF2I gene, associated with CKD susceptibility in the Taiwanese population. Individuals with specific GTF2I genotypes (CT/TT for rs117026326 and CT/CC for rs73366469) show higher CKD prevalence and earlier onset. Men with the specific genotypes of rs117026326 and rs73366469 face a heightened CKD risk compared with women, particularly at lower eGFR.

Background

CKD poses a global health challenge, but its molecular mechanisms are poorly understood. Genetic factors play a critical role, and phenome-wide association studies and genome-wide association studies shed light on CKD's genetic architecture, shared variants, and biological pathways.

Methods

Using data from the multicenter collaborative precision medicine cohort, we conducted a retrospective prospectively maintained cross-sectional study. Participants with comprehensive information and genotyping data were selected, and genome-wide association study and phenome-wide association study analyses were performed using the curated Taiwan Biobank version 2 array to identify CKD-associated genetic variants and explore their phenotypic associations.

Results

Among 58,091 volunteers, 8420 participants were enrolled. Individuals with CKD exhibited higher prevalence of metabolic, cardiovascular, autoimmune, and nephritic disorders. Genetic analysis unveiled two closely linked single-nucleotide polymorphisms, rs117026326 and rs73366469, both associated with GTF2I and CKD (r 2 = 0.64). Further examination revealed significant associations between these single-nucleotide polymorphisms and various kidney-related diseases. The CKD group showed a higher proportion of individuals with specific genotypes (CT/TT for rs117026326 and CT/CC for rs73366469), suggesting potential associations with CKD susceptibility (P < 0.001). Furthermore, individuals with these genotypes developed CKD at an earlier age. Multiple logistic regression confirmed the independent association of these genetic variants with CKD. Subgroup analysis based on eGFR demonstrated an increased risk of CKD among carriers of the rs117026326 CT/TT genotypes (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07 to 1.24; P < 0.001; OR, 1.32, 95% CI, 1.04 to 1.66; P = 0.02, respectively) and carriers of the rs73366469 CT/CC genotypes (OR, 1.13; 95% CI, 1.05 to 1.21; P < 0.001; OR, 1.31; 95% CI, 1.08 to 1.58; P = 0.0049, respectively). In addition, men had a higher CKD risk than women at lower eGFR levels (OR, 1.35; 95% CI, 1.13 to 1.61; P < 0.001).

Conclusions

Our study reveals important links between genetic variants GTF2I and susceptibility to CKD, advancing our understanding of CKD development in the Taiwanese population and suggesting potential for personalized prevention and management strategies. More research is needed to validate and explore these variants in diverse populations.

DeSUMOylation of RBMX regulates exosomal sorting of cargo to promote renal tubulointerstitial fibrosis in diabetic kidney disease

INTRODUCTION

Diabetic kidney disease (DKD) has become the primary cause of chronic renal failure in China, and renal tubulointerstitial fibrosis plays a central role in DKD progression. Urinary exosomes, which reflect kidney changes, are largely influenced by RNA-binding proteins (RBPs) in their miRNA content.

OBJECTIVES

Our research aimed to determine the effect of the RNA-binding protein RBMX on exosomal miRNA in DKD.

METHODS

We introduced a higher level of Rbmx into diabetic mice using an adenoassociated virus and isolated exosomes from their kidney tissue through advanced centrifugation techniques and specialized kits. We then conducted a series of tests, including qRT-PCR, Western blot, MitoSOX, ATP luminescence, coimmunoprecipitation, SUMOylation assays, RNA immunoprecipitation, and confocal microscopy.

RESULTS

RBMX is found in higher levels in DKD and contributes to worsening kidney fibrosis, mitochondrial damage, and miRNA mismanagement in exosomes. It specifically binds with miR-26a, miR-23c, and miR-874 within the exosomes. This dysfunction may be linked to changes in RBMX SUMOylation. These miRNAs seem to protect against mitochondrial damage in kidney cells by targeting CERS6.

CONCLUSION

DeSUMOylation of RBMX plays a crucial role in determining the makeup of miRNAs in kidney cell exosomes, impacting the protective miRNAs which regulate mitochondrial damage through their interaction with CERS6 mRNA, ultimately affecting mitochondrial health in DKD.

Cardio-reno-vascular protection in type 2 diabetes mellitus: new insights into pharmacotherapeutic management

INTRODUCTION

From 2008 and following the withdrawal of rosiglitazone, obligatory cardiovascular outcomes trials are performed for glucose lowering drugs introduced to the market to ensure their cardiovascular (CV) safety. Paradoxically, these studies have demonstrated CV safety but also shown additional cardio-reno-vascular protection of some therapeutic agents. Additionally, nonsteroidal mineralocorticoid receptor antagonists (ns-MRA) have emerged as novel drugs for cardio - and renoprotection in type 2 diabetes (T2D) and chronic kidney disease (CKD). In addition to atherosclerotic CV disease, heart failure (HF) and CKD are important clinical problems in T2D leading to poor quality of life and premature death as such cardio-reno-vascular protection is an important clinical issue.

AREAS COVERED

We provide new insights into pharmacotherapeutic cardio-reno-vascular protection in T2D based on the new glucose lowering drugs and ns-MRA. PUB MED/CINAHL/Web of Science/Scopus were searched (May 2024).

EXPERT OPINION

The conventional glucose lowering approach alone which was implemented for decades is now replaced by the use of disease modifying drugs which lower the rates of CV events, HF decompensation, hospitalization due to HF, slow progression of CKD and all-cause mortality. Indeed, the choice of medications in T2D should be focused on underlying co-morbidities with cardio-reno-vascular protection rather than a gluco-centric approach.

Multi-omics characterization of type 2 diabetes associated genetic variation

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.

Diabetes and Renal Complications: An Overview on Pathophysiology, Biomarkers and Therapeutic Interventions

Diabetic kidney disease (DKD) is a major microvascular complication of both type 1 and type 2 diabetes. DKD is characterised by injury to both glomerular and tubular compartments, leading to kidney dysfunction over time. It is one of the most common causes of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Persistent high blood glucose levels can damage the small blood vessels in the kidneys, impairing their ability to filter waste and fluids from the blood effectively. Other factors like high blood pressure (hypertension), genetics, and lifestyle habits can also contribute to the development and progression of DKD. The key features of renal complications of diabetes include morphological and functional alterations to renal glomeruli and tubules leading to mesangial expansion, glomerulosclerosis, homogenous thickening of the glomerular basement membrane (GBM), albuminuria, tubulointerstitial fibrosis and progressive decline in renal function. In advanced stages, DKD may require treatments such as dialysis or kidney transplant to sustain life. Therefore, early detection and proactive management of diabetes and its complications are crucial in preventing DKD and preserving kidney function.

Novel genetic markers for chronic kidney disease in a geographically isolated population of Indigenous Australians: Individual and multiple phenotype genome-wide association study

BACKGROUND

Chronic kidney disease (CKD) is highly prevalent among Indigenous Australians, especially those in remote regions. The Tiwi population has been isolated from mainland Australia for millennia and exhibits unique genetic characteristics that distinguish them from other Indigenous and non-Indigenous populations. Notably, the rate of end-stage renal disease is up to 20 times greater in this population compared to non-Indigenous populations. Despite the identification of numerous genetic loci associated with kidney disease through GWAS, the Indigenous population such as Tiwi remains severely underrepresented and the increased prevalence of CKD in this population may be due to unique disease-causing alleles/genes.

METHODS

We used albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) to estimate the prevalence of kidney disease in the Tiwi population (N = 492) in comparison to the UK Biobank (UKBB) (N = 134,724) database. We then performed an exploratory factor analysis to identify correlations among 10 CKD-related phenotypes and identify new multi-phenotype factors. We subsequently conducted a genome-wide association study (GWAS) on all single and multiple phenotype factors using mixed linear regression models, adjusted for age, sex, population stratification, and genetic relatedness between individuals.

RESULTS

Based on ACR, 20.3% of the population was at severely increased risk of CKD progression and showed elevated levels of ACR compared to the UKBB population independent of HbA1c. A GWAS of ACR revealed novel association loci in the genes MEG3 (chr14:100812018:T:A), RAB36 (rs11704318), and TIAM2 (rs9689640). Additionally, multiple phenotypes GWAS of ACR, eGFR, urine albumin, and serum creatinine identified a novel variant that mapped to the gene MEIS2 (chr15:37218869:A:G). Most of the identified variants were found to be either absent or rare in the UKBB population.

CONCLUSIONS

Our study highlights the Tiwi population's predisposition towards elevated ACR, and the collection of novel genetic variants associated with kidney function. These associations may prove valuable in the early diagnosis and treatment of renal disease in this underrepresented population. Additionally, further research is needed to comprehensively validate the functions of the identified variants/genes.

PharmGWAS: a GWAS-based knowledgebase for drug repurposing

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort

BACKGROUND

In 1990, David Barker proposed that prenatal nutrition is directly linked to adult cardiovascular disease. Since then, the relationship between adult cardiovascular risk, metabolic syndrome and birth weight has been widely documented. Here, we used the TruSeq Methyl Capture EPIC platform to compare the methylation patterns in cord blood from large for gestational age (LGA) vs adequate for gestational age (AGA) newborns from the LARGAN cohort.

RESULTS

We found 1672 differentially methylated CpGs (DMCs) with a nominal p < 0.05 and 48 differentially methylated regions (DMRs) with a corrected p < 0.05 between the LGA and AGA groups. A systems biology approach identified several biological processes significantly enriched with genes in association with DMCs with FDR < 0.05, including regulation of transcription, regulation of epinephrine secretion, norepinephrine biosynthesis, receptor transactivation, forebrain regionalization and several terms related to kidney and cardiovascular development. Gene ontology analysis of the genes in association with the 48 DMRs identified several significantly enriched biological processes related to kidney development, including mesonephric duct development and nephron tubule development. Furthermore, our dataset identified several DNA methylation markers enriched in gene networks involved in biological pathways and rare diseases of the cardiovascular system, kidneys, and metabolism.

CONCLUSIONS

Our study identified several DMCs/DMRs in association with fetal overgrowth. The use of cord blood as a material for the identification of DNA methylation biomarkers gives us the possibility to perform follow-up studies on the same patients as they grow. These studies will not only help us understand how the methylome responds to continuum postnatal growth but also link early alterations of the DNA methylome with later clinical markers of growth and metabolic fitness.

Kidney fibrosis: Emerging diagnostic and therapeutic strategies

An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.

Circulating epigenomic biomarkers correspond with kidney disease susceptibility in high-risk populations with type 2 diabetes mellitus

AIMS

To investigate epigenomic indices of diabetic kidney disease (DKD) susceptibility among high-risk populations with type 2 diabetes mellitus.

METHODS

KDIGO (Kidney Disease: Improving Global Outcomes) clinical guidelines were used to classify people living with or without DKD. Differential gene methylation of DKD was then assessed in a discovery Aboriginal Diabetes Study cohort (PROPHECY, 89 people) and an external independent study from Thailand (THEPTARIN, 128 people). Corresponding mRNA levels were also measured and linked to levels of albuminuria and eGFR.

RESULTS

Increased DKD risk was associated with reduced methylation and elevated gene expression in the PROPHECY discovery cohort of Aboriginal Australians and these findings were externally validated in the THEPTARIN diabetes registry of Thai people living with type 2 diabetes mellitus.

CONCLUSIONS

Novel epigenomic scores can improve diagnostic performance over clinical modelling using albuminuria and GFR alone and can distinguish DKD susceptibility.

Genetics of diabetes-associated microvascular complications

Diabetes is associated with excess morbidity and mortality due to both micro- and macrovascular complications, as well as a range of non-classical comorbidities. Diabetes-associated microvascular complications are those considered most closely related to hyperglycaemia in a causal manner. However, some individuals with hyperglycaemia (even those with severe hyperglycaemia) do not develop microvascular diseases, which, together with evidence of co-occurrence of microvascular diseases in families, suggests a role for genetics. While genome-wide association studies (GWASs) produced firm evidence of multiple genetic variants underlying differential susceptibility to type 1 and type 2 diabetes, genetic determinants of microvascular complications are mostly suggestive. Identified susceptibility variants of diabetic kidney disease (DKD) in type 2 diabetes mirror variants underlying chronic kidney disease (CKD) in individuals without diabetes. As for retinopathy and neuropathy, reported risk variants currently lack large-scale replication. The reported associations between type 2 diabetes risk variants and microvascular complications may be explained by hyperglycaemia. More extensive phenotyping, along with adjustments for unmeasured confounding, including both early (fetal) and late-life (hyperglycaemia, hypertension, etc.) environmental factors, are urgently needed to understand the genetics of microvascular complications. Finally, genetic variants associated with reduced glycolysis, mitochondrial dysfunction and DNA damage and sustained cell regeneration may protect against microvascular complications, illustrating the utility of studies in individuals who have escaped these complications.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

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.

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Single cell sequencing technologies have rapidly advanced in the last decade and are increasingly applied to gain unprecedented insights by deconstructing complex biology to its fundamental unit, the individual cell. First developed for measurement of gene expression, single cell sequencing approaches have evolved to allow simultaneous profiling of multiple additional features, including chromatin accessibility within the nucleus and protein expression at the cell surface. These multi-omic approaches can now further be applied to cells in situ, capturing the spatial context within which their biology occurs. To extract insights from these complex datasets, new computational tools have facilitated the integration of information across different data types and the use of machine learning approaches. Here, we summarize current experimental and computational methods for generation and integration of single cell multi-omic datasets. We focus on opportunities for multi-omic single cell sequencing to augment therapeutic development for kidney disease, including applications for biomarkers, disease stratification and target identification.

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

A genome-wide association study identifies a possible role for cannabinoid signalling in the pathogenesis of diabetic kidney disease

Diabetic kidney disease (DKD), also known as diabetic nephropathy, is the leading cause of renal impairment and end-stage renal disease. Patients with diabetes are at risk for DKD because of poor control of their blood glucose, as well as nonmodifiable risk factors including age, ethnicity, and genetics. This genome-wide association study (GWAS) was conducted for the first time in the Emirati population to investigate possible genetic factors associated with the development and progression of DKD. We included data on 7,921,925 single nucleotide polymorphism (SNPs) in 258 cases of type 2 diabetes mellitus (T2DM) who developed DKD and 938 control subjects with T2DM who did not develop DKD. GWAS suggestive results (P < 1 × 10-5) were further replicated using summary statistics from three cohorts with T2DM-induced DKD (Bio Bank Japan data, UK Biobank, and FinnGen Project data) and T1DM-induced DKD (UK-ROI cohort data from Belfast, UK). When conducting a multiple linear regression model for gene-set analyses, the CNR2 gene demonstrated genome-wide significance at 1.46 × 10-6. SNPs in CNR2 gene, encodes cannabinoid receptor 2 or CB2, were replicated in Japanese samples with the leading SNP rs2501391 showing a Pcombined = 9.3 × 10-7, and odds ratio = 0.67 in association with DKD associated with T2DM, but not with T1DM, without any significant association with T2DM itself. The allele frequencies of our cohort and those of the replication cohorts were in most cases markedly different. In addition, we replicated the association between rs1564939 in the GLRA3 gene and DKD in T2DM (P = 0.016, odds ratio = 0.54 per allele C). Our findings suggest evidence that cannabinoid signalling may be involved in the development of DKD through CB2, which is expressed in different kidney regions and known to be involved in insulin resistance, inflammation, and the development of kidney fibrosis.

Precision Medicine in Type 1 Diabetes

Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.