DNA methylation profiling identifies epigenetic differences between early versus late stages of diabetic chronic kidney disease.

BACKGROUND: We investigated a cross-sectional epigenome-wide association study of patients with early and late diabetes-associated chronic kidney disease (CKD) to identify possible epigenetic differences between the two groups as well as changes in methylation across all stages of diabetic CKD. We also evaluated the potential of using a panel of identified 5'-C-phosphate-G-3' (CpG) sites from this cohort to predict the progression of diabetic CKD. METHODS: This cross-sectional study recruited 119 adults. DNA was extracted from blood using the Qiagen QIAampDNA Mini Spin Kit. Genome-wide methylation analysis was performed using Illumina Infinium MethylationEPIC BeadChips (HM850K). Intensity data files were processed and analysed using the minfi and MissMethyl packages for R. We examined the degree of methylation of CpG sites in early versus late diabetic CKD patients for CpG sites with an unadjusted P-value <0.01 and an absolute change in methylation of 5% (n = 239 CpG sites). RESULTS: Hierarchical clustering of the 239 CpG sites largely separated the two groups. A heat map for all 239 CpG sites demonstrated distinct methylation patterns in the early versus late groups, with CpG sites showing evidence of progressive change. Based on our differentially methylated region (DMR) analysis of the 239 CpG sites, we highlighted two DMRs, namely the cysteine-rich secretory protein 2 (CRISP2) and piwi-like RNA-mediated gene silencing 1 (PIWIL1) genes. The best predictability for the two groups involved a receiver operating characteristics curve of eight CpG sites alone and achieved an area under the curve of 0.976. CONCLUSIONS: We have identified distinct DNA methylation patterns between early and late diabetic CKD patients as well as demonstrated novel findings of potential progressive methylation changes across all stages (1-5) of diabetic CKD at specific CpG sites. We have also identified associated genes CRISP2 and PIWIL1, which may have the potential to act as stage-specific diabetes-associated CKD markers, and showed that the use of a panel of eight identified CpG sites alone helps to increase the predictability for the two groups.

Beyond Glucose Monitoring: Multianalyte Sensor Use in Diabetes.

The incidence, prevalence, mortality, and health expenditure associated with diabetes continue to grow, despite efforts. Use of multianalyte sensors which detect glucose as well as key analytes such as ketones, lactate, insulin, uric acid, and electrolytes, may provide additional information to guide earlier identification and management of diabetes and its complications. We undertook a narrative review using a systematic approach in May 2023, with a bridge search undertaken in April 2024. Four biomedical databases were searched: MEDLINE (Ovid), Embase, Emcare, and Cochrane Library. Searches for grey literature were conducted in ClinicalTrials.gov, Google Scholar, and websites of relevant organisations. Included studies incorporated articles on multianalyte sensors in diabetes and single-analyte sensors proposing integration into multianalyte diabetes management, with no limits placed on publication date and study design. Data were screened and extracted using CovidenceTM software. Overall, 11 articles were included of which eight involved multianalyte sensors (involving glucose and other analytes), and four single-analyte sensors (measuring non-glucose substances for proposed future integration into multianalyte systems). Analytes examined were ketones (n=3); lactate (n=4); uric acid (n=3), insulin (n=1) and potassium (n=1). Results demonstrated that in vitro and in vivo measurements of multi and single-analyte sensors accurately and reliably corresponded with human capillary and serum samples. While literature on this topic is sparse, our review demonstrated that measurement of glucose and other analytes can be feasibly undertaken using multi and single-analyte sensors. More studies in humans are needed to establish clinical utility in diabetes self-management and assist with technological improvements.

Blood Plasma Metabolites in Diabetes-Associated Chronic Kidney Disease: A Focus on Lipid Profiles and Cardiovascular Risk.

Background: We investigated a cross-sectional metabolomic analysis of plasma and urine of patients with early and late stage diabetes associated chronic kidney disease (CKD), inclusive of stages 1-5 CKD, to identify potential metabolomic profiles between the two groups. Methods: This cross-sectional study recruited 119 adults. Metabolomic biomarkers were quantified in 119 non-fasted plasma and 57 urine samples using a high-throughput proton Nuclear Magnetic Resonance platform. Analyses were conducted using R with the ggforestplot package. Linear regression models were minimally adjusted for age, sex, and body mass index and p-values were adjusted for multiple comparisons using the Benjamini-Hockberg method with a false discovery rate of 0.05. Results: Apolipoprotein A1 concentration (ApoA1) was reduced (adj. p = 0.04) and apolipoprotein B/apolipoprotein A1 ratio (ApoB/ApoA1) was increased (adj. p = 0.04) in late CKD compared with early CKD. Low-density lipoprotein triglyceride (LDL-TG) had an increased concentration (adj. p = 0.01), while concentrations of high-density lipoprotein cholesterol (HDL-C) were reduced (adj. p = 0.04) in late CKD compared to early stages of disease. Conclusion: Our results highlight the presence of abnormal lipid metabolism namely significant reduction in the protective ApoA1 and significant increase in atherogenic ApoB/ApoA1 ratio. The study also demonstrates significantly elevated levels of triglyceride-rich lipoproteins such as LDL-TG. We illustrate the significant reduction in protective HDL-C in individuals with diabetic CKD. It explores a detailed plasma lipid profile that significantly differentiates between the late and early CKD groups as well as each CKD stage. The study of complex metabolite profiles may provide additional data required to enable more specific cardiovascular risk stratification.

Cardiorenal Interactions: A Review.

A complex interaction occurs between cardiac and renal function. They are intricately tied together, and a range of disorders in both the heart and kidneys can alter the function of the other. The pathophysiology is complex, and these conditions are termed cardiorenal syndromes. They can be acute and/or chronic in nature, they result in and from hemodynamic consequences, systemic congestion, and metabolic abnormalities, and they lead to dysfunction of both the heart and kidneys. The aim of this article is to provide a review for cardiologists and intensivists who are treating patients for whom cardiac and renal interactions may complicate their picture. We review acute kidney injuries, management of the complications of renal dysfunction, renal replacement therapy, and cardiorenal syndromes.

Il existe une interaction complexe entre la fonction cardiaque et la fonction rénale. Elles sont étroitement liées, et un éventail de troubles cardiaques et rénaux peuvent altérer la fonction de l'autre. Ces maladies dont la physiopathologie est complexe sont appelées syndromes cardiorénaux. Elles peuvent être aiguës et/ou chroniques de nature, elles entraînent des conséquences hémodynamiques, une congestion systémique et des anomalies métaboliques, ou résultent de celles-ci, et elles mènent à la dysfonction du cœur ou des reins. L'objectif du présent article est d'offrir une revue aux cardiologues et aux intensivistes qui traitent des patients dont les interactions cardiaques et rénales peuvent compliquer leur tableau. Nous passons en revue les atteintes rénales aiguës, la prise en charge des complications de la dysfonction rénale, le traitement de substitution rénale et les syndromes cardiorénaux.

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

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day "gold standard" biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas-namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

Identification of Potential Biomarkers of Chronic Kidney Disease in Individuals with Diabetes: Protocol for a Cross-sectional Observational Study.

BACKGROUND: The importance of identifying people with diabetes and progressive kidney dysfunction relates to the excess morbidity and mortality of this group. Rates of cardiovascular disease are much higher in people with both diabetes and kidney dysfunction than in those with only one of these conditions. By the time these people are identified in current clinical practice, proteinuria and renal dysfunction are already established, limiting the effectiveness of therapeutic interventions. The identification of an epigenetic or blood metabolite signature or gut microbiome profile may identify those with diabetes at risk of progressive chronic kidney disease, in turn providing targeted intervention to improve patient outcomes. OBJECTIVE: This study aims to identify potential biomarkers in people with diabetes and chronic kidney disease (CKD) associated with progressive renal injury and to distinguish between stages of chronic kidney disease. Three sources of biomarkers will be explored, including DNA methylation profiles in blood lymphocytes, the metabolomic profile of blood-derived plasma and urine, and the gut microbiome. METHODS: The cross-sectional study recruited 121 people with diabetes and varying stages (stages 1-5) of chronic kidney disease. Single-point data collection included blood, urine, and fecal samples in addition to clinical data such as anthropometric measurements and biochemical parameters. Additional information obtained from medical records included patient demographics, medical comorbidities, and medications. RESULTS: Data collection commenced in January 2018 and was completed in June 2018. At the time of submission, 121 patients had been recruited, and 119 samples remained after quality control. There were 83 participants in the early diabetes-associated CKD group with a mean estimated glomerular filtration rate (eGFR) of 61.2 mL/min/1.73 m2 (early CKD group consisting of stage 1, 2, and 3a CKD), and 36 participants in the late diabetic CKD group with a mean eGFR of 23.9 mL/min/1.73 m2 (late CKD group, consisting of stage 3b, 4, and 5), P<.001. We have successfully obtained DNA for methylation and microbiome analyses using the biospecimens collected via this protocol and are currently analyzing these results together with the metabolome of this cohort of individuals with diabetic CKD. CONCLUSIONS: Recent advances have improved our understanding of the epigenome, metabolomics, and the influence of the gut microbiome on the incidence of diseases such as cancers, particularly those related to environmental exposures. However, there is a paucity of literature surrounding these influencers in renal disease. This study will provide insight into the fundamental understanding of the pathophysiology of CKD in individuals with diabetes, especially in novel areas such as epigenetics, metabolomics, and the kidney-gut axis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16277.

Gut Microbiome Composition Remains Stable in Individuals with Diabetes-Related Early to Late Stage Chronic Kidney Disease.

(1) Background: Individuals with diabetes and chronic kidney disease display gut dysbiosis when compared to healthy controls. However, it is unknown whether there is a change in dysbiosis across the stages of diabetic chronic kidney disease. We investigated a cross-sectional study of patients with early and late diabetes associated chronic kidney disease to identify possible microbial differences between these two groups and across each of the stages of diabetic chronic kidney disease. (2) Methods: This cross-sectional study recruited 95 adults. DNA extracted from collected stool samples were used for 16S rRNA sequencing to identify the bacterial community in the gut. (3) Results: The phylum Firmicutes was the most abundant and its mean relative abundance was similar in the early and late chronic kidney disease group, 45.99 ± 0.58% and 49.39 ± 0.55%, respectively. The mean relative abundance for family Bacteroidaceae, was also similar in the early and late group, 29.15 ± 2.02% and 29.16 ± 1.70%, respectively. The lower abundance of Prevotellaceae remained similar across both the early 3.87 ± 1.66% and late 3.36 ± 0.98% diabetic chronic kidney disease groups. (4) Conclusions: The data arising from our cohort of individuals with diabetes associated chronic kidney disease show a predominance of phyla Firmicutes and Bacteroidetes. The families Ruminococcaceae and Bacteroidaceae represent the highest abundance, while the beneficial Prevotellaceae family were reduced in abundance. The most interesting observation is that the relative abundance of these gut microbes does not change across the early and late stages of diabetic chronic kidney disease, suggesting that this is an early event in the development of diabetes associated chronic kidney disease. We hypothesise that the dysbiotic microbiome acquired during the early stages of diabetic chronic kidney disease remains relatively stable and is only one of many risk factors that influence progressive kidney dysfunction.

DNA methylation profiling of genomic DNA isolated from urine in diabetic chronic kidney disease: A pilot study.

AIM: To characterise the genomic DNA (gDNA) yield from urine and quality of derived methylation data generated from the widely used Illuminia Infinium MethylationEPIC (HM850K) platform and compare this with buffy coat samples. BACKGROUND: DNA methylation is the most widely studied epigenetic mark and variations in DNA methylation profile have been implicated in diabetes which affects approximately 415 million people worldwide. METHODS: QIAamp Viral RNA Mini Kit and QIAamp DNA micro kit were used to extract DNA from frozen and fresh urine samples as well as increasing volumes of fresh urine. Matched buffy coats to the frozen urine were also obtained and DNA was extracted from the buffy coats using the QIAamp DNA Mini Kit. Genomic DNA of greater concentration than 20µg/ml were used for methylation analysis using the HM850K array. RESULTS: Irrespective of extraction technique or the use of fresh versus frozen urine samples, limited genomic DNA was obtained using a starting sample volume of 5ml (0-0.86µg/mL). In order to optimize the yield, we increased starting volumes to 50ml fresh urine, which yielded only 0-9.66µg/mL A different kit, QIAamp DNA Micro Kit, was trialled in six fresh urine samples and ten frozen urine samples with inadequate DNA yields from 0-17.7µg/mL and 0-1.6µg/mL respectively. Sufficient genomic DNA was obtained from only 4 of the initial 41 frozen urine samples (10%) for DNA methylation profiling. In comparison, all four buffy coat samples (100%) provided sufficient genomic DNA. CONCLUSION: High quality data can be obtained provided a sufficient yield of genomic DNA is isolated. Despite optimizing various extraction methodologies, the modest amount of genomic DNA derived from urine, may limit the generalisability of this approach for the identification of DNA methylation biomarkers of chronic diabetic kidney disease.