Exploring the mortality and cardiovascular outcomes with SGLT-2 inhibitors in patients with T2DM at dialysis commencement: a health global federated network analysis.

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT-2is) have demonstrated associations with lowering cardiovascular outcomes in patients with type 2 diabetes mellitus (T2DM). However, the impact of SGLT-2is on individuals at dialysis commencement remains unclear. The aim of this real-world study is to study the association between SGLT-2is and outcomes in patients with T2DM at dialysis commencement. METHODS: This is a retrospective cohort study of electronic health records (EHRs) of patients with T2DM from TriNetX Research Network database between January 1, 2012, and January 1, 2024. New-users using intention to treatment design was employed and propensity score matching was utilized to select the cohort. Clinical outcomes included major adverse cardiac events (MACE) and all-cause mortality. Safety outcomes using ICD-10 codes, ketoacidosis, urinary tract infection (UTI) or genital infection, dehydration, bone fracture, below-knee amputation, hypoglycemia, and achieving dialysis-free status at 90 days and 90-day readmission. RESULTS: Of 49,762 patients with T2DM who initiated dialysis for evaluation, a mere 1.57% of patients utilized SGLT-2is within 3 months after dialysis. 771 SGLT-2i users (age 63.3 ± 12.3 years, male 65.1%) were matched with 771 non-users (age 63.1 ± 12.9 years, male 65.8%). After a median follow-up of 2.0 (IQR 0.3-3.9) years, SGLT-2i users were associated with a lower risk of MACE (adjusted Hazard Ratio [aHR] = 0.52, p value < 0.001), all-cause mortality (aHR = 0.49, p < 0.001). SGLT-2i users were more likely to become dialysis-free 90 days after the index date (aHR = 0.49, p < 0.001). No significant differences were observed in the incidence of ketoacidosis, UTI or genital infection, hypoglycemia, dehydration, bone fractures, below-knee amputations, or 90-day readmissions. CONCLUSIONS: Our findings indicated a lower incidence of all-cause mortality and MACE after long-term follow-up, along with a higher likelihood of achieving dialysis-free status at 90 days in SGLT-2i users. Importantly, they underscored the potential cardiovascular protection and safety of SGLT-2is use in T2DM patients at the onset of dialysis.

Simultaneous detection of multiple urinary biomarkers in patients with early-stage diabetic kidney disease using Luminex liquid suspension chip technology.

Background: Several urinary biomarkers have good diagnostic value for diabetic kidney disease (DKD); however, the predictive value is limited with the use of single biomarkers. We investigated the clinical value of Luminex liquid suspension chip detection of several urinary biomarkers simultaneously. Methods: The study included 737 patients: 585 with diabetes mellitus (DM) and 152 with DKD. Propensity score matching (PSM) of demographic and medical characteristics identified a subset of 78 patients (DM = 39, DKD = 39). Two Luminex liquid suspension chips were used to detect 11 urinary biomarkers according to their molecular weight and concentration. The biomarkers, including cystatin C (CysC), nephrin, epidermal growth factor (EGF), kidney injury molecule-1 (KIM-1), retinol-binding protein4 (RBP4), α1-microglobulin (α1-MG), ß2-microglobulin (ß2-MG), vitamin D binding protein (VDBP), tissue inhibitor of metalloproteinases-1 (TIMP-1), tumor necrosis factor receptor-1 (TNFR-1), and tumor necrosis factor receptor-2 (TNFR-2) were compared in the DM and DKD groups. The diagnostic values of single biomarkers and various biomarker combinations for early diagnosis of DKD were assessed using receiver operating characteristic (ROC) curve analysis. Results: Urinary levels of VDBP, RBP4, and KIM-1 were markedly higher in the DKD group than in the DM group (p < 0.05), whereas the TIMP-1, TNFR-1, TNFR-2, α1-MG, ß2-MG, CysC, nephrin, and EGF levels were not significantly different between the groups. RBP4, KIM-1, TNFR-2, and VDBP reached p < 0.01 in univariate analysis and were entered into the final analysis. VDBP had the highest AUC (0.780, p < 0.01), followed by RBP4 (0.711, p < 0.01), KIM-1 (0.640, p = 0.044), and TNFR-2 (0.615, p = 0.081). However, a combination of these four urinary biomarkers had the highest AUC (0.812), with a sensitivity of 0.742 and a specificity of 0.760. Conclusions: The urinary levels of VDBP, RBP4, KIM-1, and TNFR-2 can be detected simultaneously using Luminex liquid suspension chip technology. The combination of these biomarkers, which reflect different mechanisms of kidney damage, had the highest diagnostic value for DKD. However, this finding should be explored further to understand the synergistic effects of these biomarkers.

Sex-difference of multifactorial intervention on cardiovascular and mortality risk in DKD: post-hoc analysis of a randomised clinical trial.

OBJECTIVE: Women with type 2 diabetes experience higher cardiovascular and mortality risk than men possibly because of a sub-optimal cardio-protective treatment. We evaluated whether an intensive multifactorial therapy (MT) produces similar protective effect on development of adverse outcomes in women and men. RESEARCH DESIGN AND METHODS: Nephropathy in Diabetes type 2 study is an open-label cluster randomized trial comparing the effect of Usual Care (UC) or MT of main cardiovascular risk factors (blood pressure < 130/80 mmHg, HbA1c < 7%, LDL < 100 mg/dL, and total cholesterol < 175 mg/dL) on cardiovascular and mortality risk in patients with type 2 diabetes. In this post-hoc analysis, we stratified patients by sex to compare the occurrence of MACEs (primary endpoint) and all-cause death (secondary endpoint) between women (104 MT and 105 UC) and men (103 MT and 83 UC). RESULTS: Achievement of therapeutic goals was similar by sex, with 44% and 47% of women and men in MT achieving at least 3 targets vs. 16% and 20% of women and men in UC. During a median follow-up of 13.0 years, we recorded 262 MACE (48.5% in women) and 189 deaths (53.6% in women). Compared to the UC group, the risk of MACE in the MT group was reduced by 52% in women and by 44% in men (P = 0.11). Conversely, the reduction in mortality risk by MT was greater in women (44% versus 12%, P = 0.019). CONCLUSIONS: MT similarly reduces the risk of MACEs in either sex. This therapeutic approach is associated with a survival advantage in women as compared with men and it may represent an important rationale to motivate physicians in overcoming their therapeutic inertia often encountered in female patients as well as to encourage patients of both sexes at improving their adherence to multidrug therapy.

Can immature granulocytes and neutrophil-lymphocyte ratio be biomarkers to evaluate diabetic nephropathy?: A cross-sectional study.

AIMS: We aimed to examine the role of circulating immature granulocytes (IGs) in assessing Diabetic Nephropathy (DN) mainly and also associations of other leukocyte parameters with DN. METHODS: In this retrospective cross-sectional study, a total of 164 Diabetes Mellitus patients were grouped as normoalbuminuric and microalbuminuric according to urinary albumin excretion in the course of admission. Neutrophil-lymphocyte ratio (NLR), IG count (IG#) and IG percentage (IG%) levels were compared between the groups. The value of IG# and IG% levels in detecting microalbuminuria was analyzed with the Receiver operating characteristic (ROC) curve. RESULTS: NLR was remarkably higher in the microalbuminuric group (p = 0.036). Correlation results in the microalbuminuric group were as follows: A feeble positive correlation between neutrophil count (NEU#) and serum creatinine and albumin-to- creatinine ratio (ACR) (p = 0.036, r = 0.261; p = 0.005, r = 0.347, respectively), a feeble positive correlation between lymphocyte count (LYM#) and estimated glomerular filtration rate (p = 0.021, r = 0.285). Correlation results in the normooalbuminuric group were as follows: A feeble positive correlation between NEU# and ACR (p = 0.043, r = 0.204), a feeble negative correlation between LYM# and serum creatinine (p = 0.042, r = -0.205), a poor positive correlation between IG# and ACR and HBA1C% (p = 0.048, r = 0.199; p = 0.004, r = 0.290, respectively), a positive poor correlation between IG% and HBA1C% (p = 0.019, r = 0.235). Area under the ROC curve values for IG# and IG% were not statistically noteworthy in detecting microalbuminuria (p = 0.430; p = 0.510, respectively). CONCLUSIONS: IG# and IG% values are insufficient to predict immediate microalbuminuria, but could be considered a weak biomarker for renal damage in normoalbuminuric (<30 mg/g) diabetic patients. Further researches are needed for the use of leukocyte parameters in evaluating DN.

Practicalities and importance of assessing urine albumin excretion in type 2 diabetes: A cutting-edge update.

Type 2 diabetes (T2D) is associated with increased risk for chronic kidney disease (CKD). It is estimated that 40 % of people with diabetes have CKD, which consequently leads to increase in morbidity and mortality from cardiovascular diseases (CVDs). Diabetic kidney disease (DKD) is leading cause of CKD and end-stage renal disease (ESRD) globally. On the other hand, DKD is independent risk factor for CVDs, stroke and overall mortality. According to the guidelines, using spot urine sample and assessing urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) are both mandatory methods for screening of CKD in T2D at diagnosis and at least annually thereafter. Diagnosis of CKD is confirmed by persistent albuminuria followed by a progressive decline in eGFR in two urine samples at an interval of 3 to 6 months. However, many patients with T2D remain underdiagnosed and undertreated, so there is an urgent need to improve the screening by detection of albuminuria at all levels of health care. This review discusses the importance of albuminuria as a marker of CKD and cardiorenal risk and provides insights into the practical aspects of methods for determination of albuminuria in routine clinical care of patients with T2D.

CYSTATIN C-A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes.

Chronic kidney disease (CKD) is a microvascular complication that frequently affects numerous patients diagnosed with diabetes. For the diagnosis of CKD, the guidelines recommend the identification of the urinary albumin/creatinine ratio and the determination of serum creatinine, based on which the estimated rate of glomerular filtration (eGFR) is calculated. Serum creatinine is routinely measured in clinical practice and reported as creatinine-based estimated glomerular filtration rate (eGFRcr). It has enormous importance in numerous clinical decisions, including the detection and management of CKD, the interpretation of symptoms potentially related to this pathology and the determination of drug dosage. The equations based on cystatin C involve smaller differences between race groups compared to GFR estimates based solely on creatinine. The cystatin C-based estimated glomerular filtration rate (eGFRcys) or its combination with creatinine (eGFRcr-cys) are suggested as confirmatory tests in cases where creatinine is known to be less precise or where a more valid GFR estimate is necessary for medical decisions. Serum creatinine is influenced by numerous factors: age, gender, race, muscle mass, high-protein diet, including protein supplements, and the use of medications that decrease tubular creatinine excretion (H2 blockers, trimethoprim, fenofibrate, ritonavir, and other HIV drugs). The low levels of creatinine stemming from a vegetarian diet, limb amputation, and conditions associated with sarcopenia such as cirrhosis, malnutrition, and malignancies may lead to inaccurately lower eGFRcr values. Therefore, determining the GFR based on serum creatinine is not very precise. This review aims to identify a new perspective in monitoring renal function, considering the disadvantages of determining the GFR based exclusively on serum creatinine.

Panel miRNAs are potential diagnostic markers for chronic kidney diseases: a systematic review and meta-analysis.

BACKGROUND: Accurate detection of kidney damage is key to preventing renal failure, and identifying biomarkers is essential for this purpose. We aimed to assess the accuracy of miRNAs as diagnostic tools for chronic kidney disease (CKD). METHODS: We thoroughly searched five databases (MEDLINE, Web of Science, Embase, Scopus, and CENTRAL) and performed a meta-analysis using R software. We assessed the overall diagnostic potential using the pooled area under the curve (pAUC), sensitivity (SEN), and specificity (SPE) values and the risk of bias by using the QUADAS-2 tool. The study protocol was registered on PROSPERO (CRD42021282785). RESULTS: We analyzed data from 8351 CKD patients, 2989 healthy individuals, and 4331 people with chronic diseases. Among the single miRNAs, the pooled SEN was 0.82, and the SPE was 0.81 for diabetic nephropathy (DN) vs. diabetes mellitus (DM). The SEN and SPE were 0.91 and 0.89 for DN and healthy controls, respectively. miR-192 was the most frequently reported miRNA in DN patients, with a pAUC of 0.91 and SEN and SPE of 0.89 and 0.89, respectively, compared to those in healthy controls. The panel of miRNAs outperformed the single miRNAs (pAUC of 0.86 vs. 0.79, p < 0.05). The SEN and SPE of the panel miRNAs were 0.89 and 0.73, respectively, for DN vs. DM. In the lupus nephritis (LN) vs. systemic lupus erythematosus (SLE) cohorts, the SEN and SPE were 0.84 and 0.81, respectively. Urinary miRNAs tended to be more effective than blood miRNAs (p = 0.06). CONCLUSION: MiRNAs show promise as effective diagnostic markers for CKD. The detection of miRNAs in urine and the use of a panel of miRNAs allows more accurate diagnosis.

Pentoxifylline ameliorates subclinical atherosclerosis progression in patients with type 2 diabetes and chronic kidney disease: a randomized pilot trial.

BACKGROUND: Diabetic kidney disease (DKD) is associated with a higher risk of cardiovascular disease (CVD). Pentoxifylline (PTF), a nonselective phosphodiesterase inhibitor with anti-inflammatory, antiproliferative, and antifibrotic actions, has demonstrated renal benefits in both clinical trials and meta-analyses. The present work aimed to study the effects of PTF on the progression of subclinical atherosclerosis (SA) in a population of patients with diabetes and moderate to severe chronic kidney disease (CKD). METHODS: In this open-label, randomized controlled, prospective single-center pilot study the evolution of carotid intima-media thickness (CIMT) and ankle-brachial index (ABI) were determined in 102 patients with type 2 diabetes mellitus and CKD assigned to PTF, aspirin or control groups during 18 months. We also determined the variations in the levels of inflammatory markers and Klotho (KL), a protein involved in maintaining cardiovascular health, and their relationship with the progression of SA. RESULTS: Patients treated with PTF presented a better evolution of CIMT, increased KL mRNA levels in peripheral blood cells (PBCs) and reduced the inflammatory state. The progression of CIMT values was inversely related to variations in KL both in serum and mRNA expression levels in PBCs. Multiple regression analysis demonstrated that PTF treatment and variations in mRNA KL expression in PBCs, together with changes in HDL, were significant determinants for the progression of CIMT (adjusted R2 = 0.24, P < 0.001) independently of traditional risk factors. Moreover, both variables constituted protective factors against a worst progression of CIMT [OR: 0.103 (P = 0.001) and 0.001 (P = 0.005), respectively]. CONCLUSIONS: PTF reduced SA progression assessed by CIMT variation, a beneficial effect related to KL gene expression in PBCs. TRIAL REGISTRATION: The study protocol code is PTF-AA-TR-2009 and the trial was registered on the European Union Drug Regulating Authorities Clinical Trials (EudraCT #2009-016595-77). The validation date was 2010-03-09.

Association of serum syndecan-1 concentrations with albuminuria in type 2 diabetes.

INTRODUCTION: Syndecan (SDC)-1 is a transmembrane heparan sulfate proteoglycan and is a major component of endothelial glycocalyx (EG). This study aimed to investigate the association of serum SDC-1 concentration as a marker of EG degradation with albuminuria in type 2 diabetes. METHODS: We included 370 patients with type 2 diabetes and 219 individuals with no diabetes. The individuals with estimate glomerular filtration rate <30 mL/min/1.73 m2 were excluded. RESULTS: Serum SDC-1 concentration was higher in type 2 diabetes than in no diabetes. The presence of diabetes was independently associated with log [SDC-1] in multivariate analysis. In type 2 diabetes, serum SDC-1 concentration was correlated with log [urinary albumin-to-creatinine ratio (ACR)]. Moreover, log [SDC-1] was an independent determinant of log [ACR] after adjustment for known risk factors of albuminuria. CONCLUSIONS: Serum SDC-1 concentration was higher in patients with type 2 diabetes compared to individuals with no diabetes and an independent determinant of ACR. This study implicates the role of the EG degradation in albuminuria in type 2 diabetes.

Plasma type IV collagen levels as a potential biomarker for early detection of nephropathy in diabetes mellitus patients.

Diabetic nephropathy represents a microvascular complication related to type 2 diabetes mellitus (T2DM) that ultimately causes end-stage renal disease. Our study aimed to evaluate the association of plasma type IV collagen with albuminuria status and to assess the clinical significance of plasma type IV collagen as a potential biomarker in the early stage of diabetic nephropathy. The study comprised 75 participants diagnosed with T2DM allocated equally (n=25) into three groups: (A) normal albuminuria levels, (B) microalbuminuria, and (C) macroalbuminuria, depending on their urine albumin-to-creatinine ratio. A comparative analysis was conducted between these groups and a control group (D, n=15). The enzyme-linked immunosorbent assay (ELISA) method was employed for measuring plasma type IV collagen levels. The results revealed that plasma type IV collagen levels were significantly higher in T2DM groups than in the control group. Moreover, diabetic patients without albuminuria had significantly higher plasma type IV collagen levels than the control group (p < 0.001). Furthermore, albuminuria levels among diabetic patient groups were significantly increased as albuminuria categories increased (p < 0.001). A significant positive correlation existed between plasma type IV collagen and glycated hemoglobin (HbA1c) levels in the macroalbuminuric diabetic group. Our study employed the receiver operating characteristic (ROC) curve analysis to determine plasma type IV collagen diagnostic utility in macroalbuminuria prediction. The ROC curve analysis revealed that type IV collagen can significantly determine macroalbuminuric patients at a cutoff value of 2.25 with sensitivity, specificity, positive predictive value, and negative predictive value of 68%, 100%, 100%, and 75.8%, respectively (p < 0.001). In conclusion, plasma type IV collagen levels might serve as a valuable predictor of albuminuria onset in patients with T2DM.

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.

Objective: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. Methods: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance. Conclusion: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. Registration: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

Association of diabetes with cardiovascular calcification and all-cause mortality in end-stage renal disease in the early stages of hemodialysis: a retrospective cohort study.

BACKGROUND: The main goal of this study was to examine how diabetes, cardiovascular calcification characteristics and other risk factors affect mortality in end-stage renal disease (ESRD) patients in the early stages of hemodialysis. METHODS: A total of 285 ESRD patients in the early stages of hemodialysis were enrolled in this research, including 101 patients with diabetes. Survival time was monitored, and general data, biochemical results, cardiac ultrasound calcification of valvular tissue, and thoracic CT calcification of the coronary artery and thoracic aorta were recorded. Subgroup analysis and logistic regression were applied to investigate the association between diabetes and calcification. Cox regression analysis and survival between calcification, diabetes, and all-cause mortality. Additionally, the nomogram model was used to estimate the probability of survival for these individuals, and its performance was evaluated using risk stratification, receiver operating characteristic, decision, and calibration curves. RESULTS: Cardiovascular calcification was found in 81.2% of diabetic patients (82/101) and 33.7% of nondiabetic patients (62/184). Diabetic patients had lower phosphorus, calcium, calcium-phosphorus product, plasma PTH levels and lower albumin levels (p < 0.001). People with diabetes were more likely to have calcification than people without diabetes (OR 5.66, 95% CI 1.96-16.36; p < 0.001). The overall mortality rate was 14.7% (42/285). The risk of death was notably greater in patients with both diabetes and calcification (29.27%, 24/82). Diabetes and calcification, along with other factors, collectively predict the risk of death in these patients. The nomogram model demonstrated excellent discriminatory power (area under the curve (AUC) = 0.975 at 5 years), outstanding calibration at low to high-risk levels and provided the greatest net benefit across a wide range of clinical decision thresholds. CONCLUSIONS: In patients with ESRD during the early period of haemodialysis, diabetes significantly increases the risk of cardiovascular calcification, particularly multisite calcification, which is correlated with a higher mortality rate. The risk scores and nomograms developed in this study can assist clinicians in predicting the risk of death and providing individualised treatment plans to lower mortality rates in the early stages of hemodialysis.

Glucocorticoid does not improve the renal prognosis of patients with diabetic nephropathy combined with acute tubulointerstitial nephritis: a retrospective analysis.

BACKGROUND AND OBJECTIVES: In clinical practice, some patients are diagnosed with diabetic nephropathy (DN) combined with acute tubulointerstitial nephritis (ATIN) through renal biopsy. There is relatively little research on the treatment and prognosis of such patients, and no consensus exists on the use of glucocorticoid for treatment. Therefore, our study explores the progression of DN combined with ATIN and the renal outcomes after treatment with glucocorticoid. METHODS: This study retrospectively analyzed patients diagnosed with DN combined with ATIN through renal biopsy at our center from January 1, 2015, to December 31, 2021. We collected general patient information, laboratory indicators, renal pathology indicators, and the glucocorticoid usage after kidney biopsy. Follow-up data were collected from medical records. Statistical analysis methods included t-tests, non-parametric tests, and chi-square tests. Univariate and multivariate Cox regression analyses were used to evaluate the risk factors for renal endpoint events in patients. Statistical significance was defined as p-values < 0.05. RESULTS: In this study, a total of 67 patients were included. The subjects were divided into two groups based on whether they received glucocorticoid treatment: 33 patients in the steroid group and 34 in the non-steroid group. In the steroid group, 19 patients reached the renal endpoint event, which was significantly higher than in the non-steroid group (57.58% vs. 29.41%, p = 0.038). Univariate Cox regression analysis showed that serum creatinine (HR = 1.008, p < 0.001), albumin (HR = 0.919, p < 0.001), 24-h urinary protein (HR = 1.093, p = 0.002), hemoglobin (HR = 0.964, p = 0.001), triglycerides (HR = 1.12, p = 0.04), and the use of glucocorticoid (HR = 2.507, p = 0.019) were influencing factors for renal endpoint events in patients with DN combined with ATIN. Multivariate Cox regression analysis showed that albumin (HR = 0.863, p = 0.003) was an independent risk factor for renal endpoint events in patients with DN combined with ATIN. CONCLUSIONS: The use of glucocorticoid in treatment does not improve renal prognosis in patients with DN combined with ATIN. Lower levels of albumin are associated with a worse renal prognosis.

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

Mortality and cardiovascular events in diabetes mellitus patients at dialysis initiation treated with glucagon-like peptide-1 receptor agonists.

BACKGROUND: Glucagon-like Peptide-1 Receptor Agonists (GLP-1RAs) have demonstrated efficacy in improving mortality and cardiovascular (CV) outcomes. However, the impact of GLP-1RAs therapy on cardiorenal outcomes of diabetic patients at the commencement of dialysis remains unexplored. PURPOSE: This study aimed to investigate the long-term benefits of GLP-1RAs in type 2 diabetic patients at dialysis commencement. METHODS: A cohort of type 2 diabetic patients initializing dialysis was identified from the TriNetX global database. Patients treated with GLP-1RAs and those treated with long-acting insulin (LAI) were matched by propensity score. We focused on all-cause mortality, four-point major adverse cardiovascular events (4p-MACE), and major adverse kidney events (MAKE). RESULTS: Among 82,041 type 2 diabetic patients initializing dialysis, 2.1% (n = 1685) patients were GLP-1RAs users (mean ages 59.3 years; 55.4% male). 1682 patients were included in the propensity-matched group, treated either with GLP-1RAs or LAI. The main causes of acute dialysis in this study were ischemic heart disease (17.2%), followed by heart failure (13.6%) and sepsis (6.5%). Following a median follow-up of 1.4 years, GLP-1RAs uses at dialysis commencement was associated with a reduced risk of mortality (hazard ratio [HR] = 0.63, p < 0.001), 4p-MACE (HR = 0.65, p < 0.001), and MAKE (HR = 0.75, p < 0.001). This association was particularly notable in long-acting GLP-1RAs users, with higher BMI, lower HbA1c, and those with eGFR > 15 ml/min/1.73m2. GLP-1RAs' new use at dialysis commencement was significantly associated with a lower risk of MACE (p = 0.047) and MAKE (p = 0.004). Additionally, GLP-1RAs use among those who could discontinue from acute dialysis or long-term RAs users was associated with a lower risk of mortality, 4p-MACE, and MAKE. CONCLUSION: Given to the limitations of this observational study, use of GLP-1RAs at the onset of dialysis was associated with a decreased risk of MACE, MAKE, and all-cause mortality. These findings show the lack of harm associated with the use of GLP-1RAs in diabetic patients at the initiation of acute dialysis.

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

BACKGROUND: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial. OBJECTIVE: To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning. METHODOLOGY: A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability. RESULTS: Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97. CONCLUSION: Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.

Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.

BACKGROUND: Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored. METHODS: This study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls. RESULTS: The study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN. CONCLUSION: This research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment.

Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review).

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end­stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in­depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Protein glycation products associate with progression of kidney disease and incident cardiovascular events in individuals with type 1 diabetes.

BACKGROUND: Despite improved glycemic treatment, the impact of glycation on pathological consequences may persist and contribute to adverse clinical outcomes in diabetes. In the present study we investigated the association between serum protein glycation products and progression of kidney disease as well as incident major adverse cardiovascular events (MACE) in type 1 diabetes. METHODS: Fructosamine, advanced glycation end products (AGEs), and methylglyoxal-modified hydro-imidazolone (MG-H1) were measured from baseline serum samples in the FinnDiane study (n = 575). Kidney disease progression was defined as steep eGFR decline (> 3 mL/min/1.73 m2/year) or progression of albuminuria (from lower to higher stage of albuminuria). MACE was defined as acute myocardial infarction, coronary revascularization, cerebrovascular event (stroke), and cardiovascular death. RESULTS: Fructosamine was independently associated with steep eGFR decline (OR 2.15 [95% CI 1.16-4.01], p = 0.016) in the fully adjusted model (age, sex, baseline eGFR). AGEs were associated with steep eGFR decline (OR 1.58 per 1 unit of SD [95% CI 1.07-2.32], p = 0.02), progression to end-stage kidney disease (ESKD) (HR 2.09 per 1 unit of SD [95% CI 1.43-3.05], p < 0.001), and pooled progression (to any stage of albuminuria) (HR 2.72 per 1 unit of SD [95% CI 2.04-3.62], p < 0.001). AGEs (HR 1.57 per 1 unit of SD [95% CI 1.23-2.00], p < 0.001) and MG-H1 (HR 4.99 [95% CI 0.98-25.55], p = 0.054) were associated with incident MACE. MG-H1 was also associated with pooled progression (HR 4.19 [95% CI 1.11-15.89], p = 0.035). Most AGEs and MG-H1 associations were no more significant after adjusting for baseline eGFR. CONCLUSIONS: Overall, these findings suggest that protein glycation products are an important risk factor for target organ damage in type 1 diabetes. The data provide further support to investigate a potential causal role of serum protein glycation in the progression of diabetes complications.

Growth Differentiation Factor 15 and Matrix Metalloproteinase 3 in Plasma as Biomarkers for Neuropathy and Nephropathy in Type 1 Diabetes.

Diabetic neuropathy and nephropathy are common complications of type 1 diabetes (T1D). The symptoms are often elusive in the early stages, and available diagnostic methods can be improved using biomarkers. Matrix metalloproteinase 3 (MMP-3) has been identified in the kidneys and is thought to be involved in diabetic nephropathy. Growth differentiation factor 15 (GDF-15) has been suggested to have positive effects in diabetes, but is otherwise associated with adverse effects such as cardiovascular risk, declined kidney function, and neurodegeneration. This study aims to investigate plasma MMP-3 and GDF-15 as systemic biomarkers for diabetic neuropathy and nephropathy in T1D. The study involves patients with childhood-onset T1D (n = 48, age 38 ± 4 years) and a healthy control group (n = 30, age 38 ± 5 years). Neurophysiology tests, evaluations of albuminuria, and measurements of routine biochemical markers were conducted. The neuropathy impairment assessment (NIA) scoring system, where factors such as loss of sensation and weakened reflexes are evaluated, was used to screen for symptoms of neuropathy. MMP-3 and GDF-15 concentrations were determined in heparinized plasma using ELISA kits. In total, 9 patients (19%) had albuminuria, and 25 (52%) had diabetic neuropathy. No significant differences were found in MMP-3 concentrations between the groups. GDF-15 levels were higher in T1D, with median and interquartile range (IQR) of 358 (242) pg/mL in T1D and 295 (59) in controls (p < 0.001). In the merged patient group, a positive correlation was found between MMP-3 and plasma creatinine, a negative correlation was found between MMP-3 and estimated glomerular filtration rate (eGFR; rho = -0.358, p = 0.012), and there was a positive correlation between GDF-15 and NIA (rho = 0.723, p < 0.001) and high-sensitive C-reactive protein (rho = 0.395, p = 0.005). MMP-3 was increased in macroalbuminuria and correlated positively with NIA only in the nine T1D patients with albuminuria (rho = 0.836, p = 0.005). The present study indicates that high MMP-3 is associated with low eGFR, high plasma creatinine, and macroalbuminuria, and that GDF-15 can be a biomarker for diabetic neuropathy in T1D. MMP-3 may be useful as biomarker for neuropathy in T1D with albuminuria.