Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Genome-wide Analysis Reflects Novel 5-Hydroxymethylcytosines Implicated in Diabetic Nephropathy and the Biomarker Potential

Aim

Diabetic nephropathy (DN) has become the most common cause of end-stage renal disease (ESRD) in most countries. Elucidating novel epigenetic contributors to DN can not only enhance our understanding of this complex disorder, but also lay the foundation for developing more effective monitoring tools and preventive interventions in the future, thus contributing to our ultimate goal of improving patient care.

Methods

The 5hmC-Seal, a highly selective, chemical labeling technique, was used to profile genome-wide 5-hydroxymethylcytosines (5hmC), a stable cytosine modification type marking gene activation, in circulating cell-free DNA (cfDNA) samples from a cohort of patients recruited at Zhongnan Hospital, including T2D patients with nephropathy (DN, n = 12), T2D patients with non-DN vascular complications (non-DN, n = 29), and T2D patients without any complication (controls, n = 14). Differentially analysis was performed to find DN-associated 5hmC features, followed by the exploration of biomarker potential of 5hmC in cfDNA for DN using a machine learning approach.

Results

Genome-wide analyses of 5hmC in cfDNA detected 427 and 336 differential 5hmC modifications associated with DN, compared with non-DN individuals and controls, and suggested relevant pathways such as NOD-like receptor signaling pathway and tyrosine metabolism. Our exploration using a machine learning approach revealed an exploratory model comprised of ten 5hmC genes showing the possibility to distinguish DN from non-DN individuals or controls.

Conclusion

Genome-wide analysis suggests the possibility of exploiting novel 5hmC in patient-derived cfDNA as a non-invasive tool for monitoring DN in high risk T2D patients in the future.

High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study

INTRODUCTION

Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression.

METHODS

Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites' ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites.

RESULTS

Six eGFR slope models selected 9-30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease.

CONCLUSIONS

Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.

Epigenomic signatures in age-related macular degeneration: Focus on their role as disease modifiers and therapeutic targets

Epigenetics is characterized by molecular modifications able to shape gene expression profiles in response to inner and external stimuli. Therefore, epigenetic elements are able to provide intriguing and useful information for the comprehension and management of different human conditions, including aging process, and diseases. On this subject, Age-related Macular Degeneration (AMD) represents one of the most frequent age-related disorders, dramatically affecting the quality of life of older adults worldwide. The etiopathogenesis is characterized by an interplay among multiple genetic and non-genetic factors, which have been extensively studied. Nevertheless, a deeper dissection of molecular machinery associated with risk, onset, progression and effectiveness of therapies is still missing. In this regard, epigenetic signals may be further explored to disentangle disease etiopathogenesis, the possible therapeutic avenues and the differential response to AMD treatment. This review will discuss the epigenomic signatures mostly investigated in AMD, which could be applied to improve the knowledge of disease mechanisms and to set-up novel or modified treatment options.