Diabetic Kidney Disease In Type Research Articles

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

WCN24-1295 Prevalence and factors predicting non diabetic kidney disease in type 2 diabetic patients

WCN24-1295 Prevalence and factors predicting non diabetic kidney disease in type 2 diabetic patients

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.

To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively. The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD. To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation. Prospective. One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49). 1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values. The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve. Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05. The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972). The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD. 2 TECHNICAL EFFICACY: Stage 2.

Serum Aldosterone Level in Different Stages of Diabetic Kidney Disease in Type 2 Diabetes Mellitus

Serum Aldosterone Level in Different Stages of Diabetic Kidney Disease in Type 2 Diabetes Mellitus

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves. 44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR. Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.

Association of elevated extracellular HSP72 in albuminuria with systemic inflammation and disease progression in type 2 diabetic kidney disease

BackgroundSub-clinical inflammation in hyperglycemia is tied to the pathogenesis of diabetic kidney disease (DKD). Though well known for its immunostimulatory function, the significance of extracellular heat shock protein 72 (eHSP72) in DKD is not well studied. We aimed to determine the association of extracellular HSP72 with systemic inflammation and the progression of DKD, and explore its possible clinical significance in DKD. Methods160 type 2 diabetic individuals were enrolled in the study. Their anthropometric data, routine biochemical parameters, urinary renal function parameters, and blood count parameters were estimated. Plasma from patients’ blood samples were used to estimate HSP72 and interleukin 1β (IL-1β) using sandwich immunoassays. ResultsPlasma eHSP72 is elevated in DKD. Pairwise comparisons showed the drastic elevation of eHSP72 in the presence of albuminuria. A significant positive relationship was observed between plasma levels of eHSP72 and IL-1β. eHSP72 levels did not statistically differ between micro and macro-albuminuric DKD. However, it was inversely associated with estimated glomerular filtration rate, the index of disease severity, independent of age, gender, diabetes duration and absolute monocyte count. At a cutoff of 0.52 ng/ml, with sensitivity of 64.1 % and specificity of 69.2 %, plasma eHSP72 differentiated the presence of DKD in type 2 diabetics with statistical significance. ConclusionThe positive relationship of eHSP72 and IL-1β with worsening DKD likely indicates their participation in immunostimulatory pathways of renal fibrosis. eHSP72 may be closely linked to albuminuria-induced tubular injury and likely contributes to fibrotic changes in the progression of DKD. From our study, we infer the possible clinical significance of eHSP72 as a marker of sub-clinical renal damage in DKD, and the implication of IL-1β-associated mechanisms in DKD progression.

Omega-3 fatty acids supplementation improves early-stage diabetic nephropathy and subclinical atherosclerosis in pediatric patients with type 1 diabetes: A randomized controlled trial

Numerous studies have evaluated the beneficial effects of omega-3 fatty acids on inflammatory, autoimmune and renal diseases. However, data about the effects of omega-3 fatty acids on diabetic kidney disease in type 1 diabetes mellitus (T1DM) are lacking. This randomized-controlled trial assessed the effect of oral omega-3 supplementation on glycemic control, lipid profile, albuminuria level, kidney injury molecule-1 (KIM-1) and carotid intima media thickness (CIMT) in pediatric patients with T1DM and diabetic nephropathy. Seventy T1DM patients and diabetic nephropathy were enrolled with a mean age 15.2±1.96 years and median disease duration 7 years. Patients were randomly assigned into two groups; intervention group which received oral omega-3 fatty acids capsules (1g daily). The other group received a matching placebo and served as a control group. Both groups were followed-up for 6 months with assessment of fasting blood glucose (FBG), HbA1c, fasting lipids, urinary albumin creatinine ratio (UACR), KIM-1 and CIMT. After 6 months, omega-3 fatty acids adjuvant therapy for the intervention group resulted in a significant decrease in FBG, HbA1c, triglycerides, total cholesterol, LDL-cholesterol, UACR, KIM-1 and CIMT, whereas, HDL-cholesterol was significantly higher post-therapy compared with baseline levels and compared with the control group (p<0.05). Baseline KIM-1 levels were positively correlated to HbA1c, UACR and CIMT. Supplementation with omega-3 fatty acids was safe and well-tolerated. Omega-3 fatty acids as an adjuvant therapy in pediatric T1DM patients with diabetic nephropathy improved glycemic control, dyslipidemia and delayed disease progression and subclinical atherosclerosis among those patients. This trial was registered under ClinicalTrials.gov Identifier no. NCT05980026.

The correlation between serum uric acid and diabetic kidney disease in type 1 diabetes patients in Anhui, China

Background/AimTo assess the correlation between serum uric acid (UA) level and diabetic kidney disease (DKD) in Type 1 diabetes (T1DM) patients in Anhui, China.MethodsA total of 231 patients diagnosed with T1DM in our hospital were enrolled between January 2014 and December 2016. Urinary albumin-creatinine ratio (ACR) in patients with hyperuricemia was compared with those without hyperuricemia. The relationship between serum UA level and urinary ACR was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DKD.ResultsThe average serum UA levels were 257.7 [215.0, 338.0]μmol/L. The median levels of urinary ACR were significantly higher in patients with hyperuricemia than those without hyperuricemia. In multiple stepwise regression analysis, Serum UA levels were positively correlated with the urinary ACR. The logistic multivariate regression analysis showed that hyperuricemia (OR: 5.24, 95% CI: 1.40—19.65, P = 0.014) had an independent positive correlation with DKD in T1DM patients, and the odds of Serum UA to DKD were both elevated as the serum UA levels rose no matter whether adjustment for traditional confounders. The area under the receiver operating characteristic curve was 0.62 (95% CI: 0.55–0.70) in assessing the discrimination of the serum UA level for DKD in T1DM patients.ConclusionsIn Chinese patients with T1DM, the serum UA level is positively correlated with urinary ACR and DKD. The correlation between Serum UA and DKD gradually increases with serum UA levels. Serum UA level is not a good predictor for DKD in T1DM patients. Serum UA may directly contribute to initiating DKD, while it has little direct but an indirect effect on an already established DKD in T1DM patients.

Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria-uACR < 30 mg/g; microalbuminuria-uACR 30-300 mg/g; macroalbuminuria-uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum-arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine-p-cresyl sulfate.

Urinary metabolites associate with the presence of diabetic kidney disease in type 2 diabetes and mediate the effect of inflammation on kidney complication

AimsDiabetic kidney disease (DKD) is the one of the leading causes of end-stage kidney disease. Unraveling novel biomarker signatures capable to identify patients with DKD is favorable for tackle the burden. Here, we investigated the possible association between urinary metabolites and the presence of DKD in type 2 diabetes (T2D), and further, whether the associated metabolites improve discrimination of DKD and mediate the effect of inflammation on kidney involvement was evaluated.MethodsTwo independent cohorts comprising 192 individuals (92 DKD) were analyzed. Urinary metabolites were analyzed by targeted metabolome profiling and inflammatory cytokine IL-18 were measured by ELISA. Differentially expressed metabolites were selected and mediation analysis was carried out.ResultsSeven potential metabolite biomarkers (i.e., S-Adenosyl-L-homocysteine, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid) were identified using the discovery and validation design. In the pooled analysis, propionic acid, oxoadipic acid, leucine, isovaleric acid, isobutyric acid, and indole-3-carboxylic acid were markedly and independently associated with DKD. The composite index of 7 potential metabolite biomarkers (CMI) mediated 32.99% of the significant association between the inflammatory IL-18 and DKD. Adding the metabolite biomarkers improved the discrimination of DKD.ConclusionsIn T2D, several associated urinary metabolites were identified to improve the prediction of DKD. Whether interventions aimed at reducing CMI also reduce the risk of DKD especially in patients with high IL-18 needs further investigations.

Association of ELMO1 Genetic Polymorphism (rs741301) with the Progression of Diabetic Kidney Disease in Balinese Patients with Type 2 Diabetes Mellitus

Background: Diabetic kidney disease (DKD) is one of diabetes mellitus microvascular complications. Engulfment and cell motility 1 (ELMO1) protein interacts with dedicator of cytokinesis 180 (DOCK180) and cyclooxygenase (COX)-2, which affects gene expression in extracellular matrix (ECM) and causes glomerular damage in several mechanisms, such as ECM accumulation and renal tubules thickening. Single nucleotide polymorphism (SNP) rs741301 is one of the ELMO1 genetic polymorphisms involved in DKD. The aim of this study was to evaluate the association between ELMO1 rs741301 polymorphism and DKD in type 2 diabetes mellitus (T2DM) among Balinese.Materials and methods: This study was an observational analytical study with case-control method. Subjects were divided into control and case groups comprising 40 subjects each. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of DNA from T2DM patients were performed to detect the polymorphism in ELMO1 rs741301. Genotype and allele distribution obtained from this study was analyzed by chi-square (χ2) test and Hardy-Weinberg equilibrium law (p&lt;0.05; CI: 95%).Results: There was no significant difference between genotype or allele distribution of ELMO1 rs741301 with DKD incidence. Genotype AA against GG had odds ratio (OR) of 0.793 (p=0.814), AG against GG had OR of 0.602 (p=0.674), and A allele against G allele had OR of 0.761 (p=0.509).Conclusion: There is no significant relationship between ELMO1 rs741301 polymorphism and DKD in T2DM patients among Balinese.Keywords: ELMO1 gene, diabetic kidney disease, polymorphism

Association between hemoglobin glycation index and diabetic kidney disease in type 2 diabetes mellitus in China: A cross- sectional inpatient study.

To investigate the association between Hemoglobin Glycation Index (HGI) and Diabetic Kidney Disease (DKD) in Chinese type 2 diabetic individuals and to construct a risk score based on HGI to predict a person's risk of DKD. We retrospectively analyzed 1622 patients with type 2 diabetes mellitus (T2DM). HGI was obtained by calculating the fasting plasma glucose (FPG) level into the formula, and they were grouped into low HGI group (L-HGI), medium HGI group (H-HGI) and high HGI group (H-HGI) according to tri-sectional quantile of HGI. The occurrence of DKD was analyzed in patients with different levels of HGI. Multivariate logistics regression analysis was used to analyze the risk factors of DKD in patients with T2DM. A total of 1622 patients with T2DM were enrolled in the study. Among them, 390 cases were DKD. The prevalence of DKD among the three groups was 16.6%, 24.2% and 31.3%. The difference was statistically significant (P = 0.000). There were significant differences in age (P=0.033), T2DM duration (P=0.005), systolic blood pressure (SBP) (P=0.003), glycosylated hemoglobin (HbA1c) (P=0.000), FPG (P=0.032), 2-hour postprandial plasma glucose (2h-PPG) (P=0.000), fasting C-peptide FCP (P=0.000), 2-hour postprandial C-peptide (2h-CP) (P=0.000), total cholesterol (TC) (P=0.003), low density lipoprotein cholesterol (LDL-C) (P=0.000), serum creatinine (sCr) (P=0.001), estimated glomerular filtration rate (eGFR) (P=0.000) among the three groups. Mantel-Haenszel chi-square test showed that there was a linear relationship between HGI and DKD (x2=177.469, p < 0.001). Pearson correlation analysis showed that with the increase of HGI level the prevalence of DKD was increasing (R= 0.445, P=0.000). It was indicated by univariate logistic regression analysis that individuals in H-HGI was more likely to develop DKD (OR: 2.283, 95% CI: 1.708~ 3.052) when compared with L-HGI. Adjusted to multiple factors, this trend still remained significant (OR: 2.660, 95% CI: 1.935~ 3.657). The combined DKD risk score based on HGI resulted in an area under the receiver operator characteristic curve (AUROC) of 0.702. High HGI is associated with an increased risk of DKD. DKD risk score may be used as one of the risk predictors of DKD in type 2 diabetic population.

Association of weight-adjusted waist index and diabetic kidney disease in type 2 diabetes mellitus.

The aim of this study was to investigate the relationship between weight-adjusted-waist index (WWI) and diabetic kidney disease in individuals afflicted with type 2 diabetes. Comprehensive data were ascertained from the National Health and Nutrition Examination Survey in 2013-March 2020. Weighted univariate, multivariate logistic regression models, subgroup analyses and tests for interaction were performed. Additionally, we employed smooth curve fitting to assess linear correlations and the threshold effects were calculated by applying a binary linear regression model. Breakpoints are identified by a model with maximum likelihood ratio and a two-step recursive approach. Receiver operating characteristic curve (ROC) along with the area under the curve (AUC) value predict the capability of WWI and body mass index for diabetic kidney disease. A total of 10,661 individuals diagnosed with type 2 diabetes were included, and the overall prevalence of diabetic kidney disease was 20.74%. WWI exhibited a positive correlation with the likelihood of diabetic kidney disease in type 2 diabetes patients (OR: 1.17, 95% CI: 1.03-1.33). The results of subgroup analysis showed significant interaction for gender (P < 0.05). Among female patients, U-shaped correlations were observed with a breakpoint at 11.48. Additionally, weight-adjusted waist index (AUC = 0.664) proved to be a more effective predictor of diabetic kidney disease compared to body mass index (AUC = 0.555). In patients with type 2 diabetes, increased weight-adjusted-waist index is implicated with an increased risk of diabetic kidney disease. WWI can be used as a new anthropometric index to predict diabetic kidney disease, and its predictive ability is stronger than body mass index.

Baseline anxiety disorders are associated with progression of diabetic kidney disease in type 2 diabetes

Background Diabetic kidney disease (DKD) is a leading cause of kidney failure worldwide. Anxiety has been associated with disease progression in non-diabetes patients. We aimed to examine the prospective association between anxiety and progression of DKD in type 2 diabetes. Methods We conducted a prospective cohort study of 2040 participants with type 2 diabetes at the Diabetes Center of Shanghai General Hospital between May 2017 and June 2020. Anxiety disorders at baseline were diagnosed by a structured clinical interview based on the 10th Revision of International Classification of Disease (ICD). Progression of DKD was identified as the transition from one urinary albumin excretion rate (AER) stage to the next or the development of kidney failure during the follow-up period. Results At baseline, 403 (19.8%) had a diagnosis of anxiety disorders, of whom 107 (26.6%) also received a depression diagnosis. During a median follow-up time of 3.2 years, deterioration of the kidney status occurred in 340 (16.7%) individuals. After adjustment for potential confounders including depression or an anxiety × depression interaction term, anxiety disorders were independently related to an increased risk of progression of DKD (HR 1.539, 95% CI 1.130–2.095, p = 0.006; HR 1.536, 95% CI 1.111–2.122, p = 0.009, respectively). Conclusions Anxiety disorders at baseline, independent of possible confounders, were associated with the progression of DKD in type 2 diabetes. Whether therapeutic interventions for anxiety reduce the risk needs to be investigated.

Diabetic kidney disease in type 2 diabetes: a consensus statement from the Swiss Societies of Diabetes and Nephrology

Diabetic kidney disease is highly prevalent in patients with type 2 diabetes and is a major cause of end-stage renal disease in Switzerland. Patients with diabetic kidney disease are among the most complex patients in diabetes care. They require a multifactorial and multidisciplinary approach with the goal to slow the decline in glomerular filtration rate (GFR) and cardiovascular morbidity. With this consensus we propose an evidence-based guidance to health care providers involved in the care of type 2 diabetic patients with diabetic kidney disease.First, there is a need to increase physician awareness and improve screening for diabetic kidney disease as early intervention may improve clinical outcomes and the financial burden. Evaluation of estimated GFR (eGFR) and spot urine albumin/creatinine ratio is recommended at least annually. Once it is diagnosed, glucose control and optimisation of blood pressure control with renin-angiotensin system blockers have been recommended as mainstay management of diabetic kidney disease for more than 20 years. Recent, high quality randomised controlled trials have shown that sodium-glucose cotransporter-2 (SGLT2) inhibition slows eGFR decline and cardiovascular events beyond glucose control. Likewise, mineralocorticoid receptor antagonism with finerenone has cardiorenal protective effects in diabetic kidney disease. Glucagon-like peptide-1 (GLP1) receptor agonists improve weight loss if needed, and decrease albuminuria and cardiovascular morbidity. Lipid control is also important to decrease cardiovascular events. All these therapies are included in the treatment algorithms proposed in this consensus. With advancing kidney failure, other challenges may rise, such as hyperkalaemia, anaemia and metabolic acidosis, as well as chronic kidney disease-mineral and bone disorder. These different topics and treatment strategies are discussed in this consensus. Finally, an update on diabetes management in renal replacement therapy such as haemodialysis, peritoneal dialysis and renal transplantation is provided. With the recent developments of efficient therapies for diabetic kidney disease, it has become evident that a consensus document is necessary. We are optimistic that it will significantly contribute to a high-quality care for patients with diabetic kidney disease in Switzerland in the future.

Platelet Indices as Predictors of Diabetic Kidney Disease in Type 2 Diabetes Mellitus

Platelet Indices as Predictors of Diabetic Kidney Disease in Type 2 Diabetes Mellitus

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.

Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011-2018.

Diabetic kidney disease (DKD) is the most common chronic kidney disease (CKD) and has the highest prevalence of end-stage kidney disease (ESKD) globally, owing mostly to the rise in Type 2 diabetes mellitus (T2DM) correlated with obesity. Current research suggested that the immune response and inflammation may play a role in the pathophysiology of T2DM. The systemic immune-inflammation index (SII) is a novel and integrated inflammatory biomarker that has not yet been linked to DKD. We aimed to identify the potential relationship between SII and DKD. In the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018, the current cross-sectional study was conducted among adults with T2DM. SII was calculated as the platelet count × neutrophil count/lymphocyte count. DKD was diagnosed with impaired glomerular filtration rate (< 60 mL/min/1.73 m2 assessed by using the Chronic Kidney Disease Epidemiology Collaboration algorithm), albuminuria (urine albumin to creatinine ratio ≥ 30 mg/g), or both in T2DM patients. To investigate the independent association between SII and DKD, weighted univariate and multivariable logistic regression analyses and subgroup analyses were performed. The study involved 3937 patients in total, of whom 1510 (38.4%) had DKD for the diagnosis. After adjustment for covariates, multivariable logistic regression revealed that a high SII level was associated with increased likelihood of DKD (OR = 1.42, 95% CI: 1.10-1.83, P = 0.01). Subgroup analyses and interaction tests revealed that age, gender, estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), body mass index (BMI), hypertension, hyperlipidemia, anti-inflammation therapy (yes or no), metformin use (yes or no), and insulin use (yes or no) had no significant dependence on this positive relationship (all p for interaction >0.05). Our results indicate that the higher SII level is associated with DKD in T2DM patients. The SII could be a cost-effective and straightforward approach to detecting DKD. This needs to be verified in further prospective investigations.

Association of Serum Total Bilirubin and Uric Acid with Low Glomerular Filtration Rate Diabetic Kidney Disease in Type 2 Diabetic Patients.

Diabetic kidney disease (DKD) is one of the major complications of Type 2 diabetes, clinically characterized by a progressive increase in albuminuria and/or a subsequent decline in glomerular filtration rate. Identification of novel risk factors contributes to reduction in the risk of diabetic kidney disease. Bilirubin, as an antioxidant and anti-inflammatory molecule, is believed to have a protective role in kidney disease. On the other hand, uric acid is implicated in the pathogenesis of DKD due to its pro-oxidant and pro-inflammatory property in vascular tissues. A hospital based comparative cross-sectional study was conducted from October 2020 to March 2021 on 200 eligible Type 2 diabetic patients (58 with DKD and 142 without DKD) to assess the association of serum total bilirubin and serum uric acid levels with low GFR diabetic kidney disease using consecutive sampling technique. The serum total bilirubin level was significantly decreased (0.15±2.29, mean±SD) in the DKD group compared to the non-DKD group (0.19±2.26), whereas the mean±SD serum uric acid was significantly increased in the DKD group (7.13±2.21) compared to the non-DKD group (5.24±1.92). A low serum total bilirubin level was significantly associated with increased risk of DKD in multivariate analysis (AOR=2.23, 95% CI=1.55-4.13) also to high serum uric acid levels (AOR=2.09, 95% CI=1.06-4.12). Moreover, a low serum total bilirubin level was significantly associated with increased risk of DKD among patients with high serum uric acid (AOR=2.55, 95% CI=1.05-6.19). Similarly, high serum uric acid was significantly associated with increased risk of DKD among patients with low serum total bilirubin (AOR=3.49, 95% CI=1.29-9.42). Co-presence of low serum total bilirubin and high serum uric acid may be useful for stratification of DKD risk among patients with Type 2 diabetes mellitus.