Nondiabetic Kidney Disease Research Articles

Sodium-Glucose Co-Transporter 2 Inhibitors: Mechanism of Action and Efficacy in Non-Diabetic Kidney Disease from Bench to Bed-Side.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are currently available for the management of type 2 diabetes mellitus. SGLT2i acts by inhibiting renal SGLT2, thereby increasing glucosuria and lowering serum glucose. Recent trials are emerging supporting a role for SGLT2i irrespective of the diabetic status pointing towards that SGLT2i have other mechanisms of actions beyond blood sugar control. In this review, we will shed light on the role of this group of medications that act as SGLT2i in non-diabetics focusing on pre-clinical and clinical data highlighting the mechanism of renoprotection and effects of SGLT2i in the non-diabetic kidneys.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes

BackgroundsAccumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD). ObjectivesWe aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD. MethodsTotal 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance. ResultsAll enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10−6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (β = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (β = −3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10−6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI. ConclusionsΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10−6 mm2/s and <66 × 10−6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.

A 10-year retrospective cohort of diabetic patients in a large medical institution: Utilizing multiple machine learning models for diabetic kidney disease prediction.

As the prevalence of diabetes steadily increases, the burden of diabetic kidney disease (DKD) is also intensifying. In response, we have utilized a 10-year diabetes cohort from our medical center to train machine learning-based models for predicting DKD and interpreting relevant factors. Employing a large dataset from 73,101 hospitalized type 2 diabetes patients at The First Affiliated Hospital of Zhengzhou University, we analyzed demographic and medication data. Machine learning models, including XGBoost, CatBoost, LightGBM, Random Forest, AdaBoost, GBDT (gradient boosting decision tree), and SGD (stochastic gradient descent), were trained on these data, focusing on interpretability by SHAP. SHAP explains the output of the models by assigning an importance value to each feature for a particular prediction, enabling a clear understanding of how individual features influence the prediction outcomes. The XGBoost model achieved an area under the curve (AUC) of 0.95 and an area under the precision-recall curve (AUPR) of 0.76, while CatBoost recorded an AUC of 0.97 and an AUPR of 0.84. These results underscore the effectiveness of these models in predicting DKD in patients with type 2 diabetes. This study provides a comprehensive approach for predicting DKD in patients with type 2 diabetes, employing machine learning techniques. The findings are crucial for the early detection and intervention of DKD, offering a roadmap for future research and healthcare strategies in diabetes management. Additionally, the presence of non-diabetic kidney diseases and diabetes with complications was identified as significant factors in the development of DKD.

Association of FRMD3 and ACE Gene Polymorphisms with Diabetic Nephropathy and Non-Diabetic Kidney Disease

Association of FRMD3 and ACE Gene Polymorphisms with Diabetic Nephropathy and Non-Diabetic Kidney Disease

Rapidly Progressive Renal Failure in Type 2 Diabetic versus Non-diabetic Patients: Changing Trends, Natural History, and Outcome in an Indian Tertiary Care Center.

Rapidly progressive renal failure (RPRF) is not typical of diabetic nephropathy and suggests non-diabetic kidney disease (NDKD). We conducted an analysis of the data of RPRF patients (28 diabetic and 88 non-diabetic patients) with doubled creatinine over 2 weeks to 3 months and/or presented with >4 mg serum creatinine without prior renal disease to ascertain the types of lesions and compare the patients' histopathology. The primary outcome was dependence on dialysis at 1 year. Anti-neutrophilic cytoplasmic antibody-associated pauci-immune glomerulonephritis was the most common cause of RPRF in both groups. No particular lesion was more frequent in either group. Dependence on dialysis at 1 year was similar in both groups and was associated with dependence on dialysis at presentation but not diabetes. Crescentic glomerulonephritis was the most common in non-diabetic patients (57.9 vs. 25%, P = 0.002), and acute tubular necrosis (ATN) was seen in diabetic patients (21.4 vs. 11.4%, P = 0.179). Both factors were associated with adverse renal outcomes. Diffuse global glomerulosclerosis at presentation suggested a poor outcome in both groups. Diabetic nephropathy was seen in 14.29%, and its presence did not affect the outcome. The etiology of RPRF in diabetic patients has changed and is similar to that in non-diabetic patients, with no specific lesions predominating. Diabetic nephropathy does not alter the outcome for those with RPRF. Diffuse global glomerulosclerosis, being on dialysis at presentation, and ATN in a diabetic patient indicate a poor outcome and need close follow-up. Diabetic retinopathy should not prevent us from investigating for NDKD.

Differentiating Non-Diabetic Kidney Diseases from Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients with Nephrotic Syndrome

Differentiating Non-Diabetic Kidney Diseases from Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients with Nephrotic Syndrome

Study of prevalence, clinical profile, and predictors of rapid progression in diabetic kidney disease.

A significant proportion of diabetic kidney disease (DKD) experience a rapid decline in eGFR, leading to end-stage kidney disease (ESKD) within months. This single-centered retrospective cohort study aimed to assess the prevalence, clinical profile, and predictors for rapid progression in type 2 diabetes mellitus (T2DM) patients with DKD. Three hundred fifty-nine T2DM patients with DKD between January 2018 and 2022 were included and those with superimposed non-diabetic kidney disease, chronic kidney disease 5, and < 6months follow-up were excluded. They were classified as rapid and non-rapid progressors based on the annual eGFR decline of > 5ml/min/1.73 m2/year. The primary outcome analyzed was the progression to ESKD. The secondary outcomes were the onset of microvascular and macrovascular complications and predictors for rapid progression as well as ESKD. In a median follow-up of 3.5years, 61.3% were rapid progressors (mean eGFR decline of 15.4ml/1.73m2/year) and 38.7% were non-rapid progressors (mean eGFR decline 1.8ml/1.73m2/year. Among rapid progressors, 61.4% reached ESKD. Severe proteinuria, the presence of retinopathy, and acute kidney injury (AKI) episodes were strong predictors of rapid progression. Cardiovascular disease and diabetic retinopathy (microvascular complications) were significantly higher among rapid progressors and had a mortality rate of 7.2%. The majority of type 2 DKD patients were rapid progressors and two-thirds of them developed ESKD. The prevalence of hypertension, cardiovascular disease, diabetic retinopathy, AKI episodes, and mortality was higher in rapid progressors. Severe proteinuria and diabetic retinopathy were found to be strong predictors for rapid eGFR decline and its progression to ESKD.

Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study.

The continuous glucose monitoring (CGM) system has become a popular evaluation tool for glucose fluctuation, providing a detailed description of glucose change patterns. We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), despite similarities in change patterns, because of different etiologies. Unlike Fourier transform, continuous wavelet transform (CWT) is able to simultaneously analyze the time and fre-quency domains of oscillating data. To investigate whether CWT can detect glucose fluctuations in T1DM. The 60-d and 296-d glucose fluctuation data of patients with T1DM (n = 5) and T2DM (n = 25) were evaluated respectively. Glucose data obtained every 15 min for 356 d were analyzed. Data were assessed by CWT with Morlet form (n = 7) as the mother wavelet. This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change. The frequency and enclosed area (0.02625 scalogram value) of 18 emerged signals were compared. The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables. The high frequency at midnight (median: 75 Hz, cycle time: 19 min) and middle frequency at noon (median: 45.5 Hz, cycle time: 32 min) were higher in T1DM vs T2DM (median: 73 and 44 Hz; P = 0.006 and 0.005, respectively). The area of the > 100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs T2DM. In a day, the lower frequency zone (15-35 Hz) was more frequent and the area was larger in T2DM than in T1DM. The three-dimensional scatter diagrams, which consist of the time of day, frequency, and area of each signal after CWT, revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min. Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM (odds ratio: 1.33, 95% confidence interval: 1.08-1.62; P = 0.006). CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.

Analysis of influencing factors and interaction of body weight and disease outcome in patients with prediabetes.

The trend of prediabetes progressing to type 2 diabetes mellitus (T2DM) is prominent, and effective intervention can lead to a return to prediabetes. Exploring the factors influencing the outcome of prediabetes is helpful to guide clinical intervention. The weight change in patients with prediabetes has not attracted much attention. To explore the interaction between body weight and the factors affecting the progression of prediabetes to T2DM. We performed a retrospective analysis of 236 patients with prediabetes and 50 with normal glucose tolerance (NGT), and collected clinical data and follow-up results of all patients. Based on natural blood glucose outcomes, we classified 66 patients with progression to T2DM into the disease progression (DP) group, and 170 patients without progression to T2DM into the disease outcome (DO) group. We analyzed the factors that influenced prediabetes outcome and the influence of body weight on prediabetes blood glucose outcome by unconditional logistic regression. A general linear model (univariate) was used to analyze the inter-action between body weight and independent influencing factors. There were 98 cases of impaired fasting glucose (IFG), 90 cases of impaired glucose tolerance (IGT), and 48 cases of coexistent IFG and IGT. The body weight, waist circumference, body mass index, fasting blood glucose, and 2 h plasma glucose of patients with IFG, IGT, and coexistent IFG and IGT were higher than those in patients with NGT (P < 0.05). Logistic regression analysis showed that body weight, glycosylated hemoglobin, uric acid, fasting insulin, and homeostatic model assessment for insulin resistance were independent factors affecting progression of prediabetes to T2DM (P < 0.05). Receiver operating characteristic curve analysis showed that the area under the curve predicted by the above indicators combined was 0.905 [95% confidence interval (CI): 0.863-0.948], which was greater than that predicted by each indicator alone. Logistic regression analysis with baseline body weight as an independent variable showed that compared with body weight 1, the odds ratio (95%CI) of body weight 3 was 1.399 (1.142-2.126) (P = 0.033). There was a multiplicative interaction between body weight and uric acid (β = 1.953, P = 0.005). High body weight in patients with prediabetes is an independent risk factor for progression to T2DM, and the risk of progression is increased when coexisting with high uric acid level.

Accessibility and utilization of healthcare services among diabetic patients: Is diabetes a poor man's ailment?

Diabetes is a non-communicable ailment that has adverse effects on the individual's overall well-being and productivity in society. The main objective of this study was to examine the empirical literature concerning the association between diabetes and poverty and the accessibility and utilization of medical care services among diabetic patients. The diabetes literature was explored using a literature review approach. This review revealed that diabetes is an ailment that affects all individuals irrespective of socioeconomic status; however, its pre-valence is high in low-income countries. Hence, despite the higher prevalence of diabetes in developing countries compared with developed countries, diabetes is not a poor man's ailment because it affects individuals of all incomes. While the number of diabetic patients that access and utilize diabetes medical care services has increased over the years, some personal and institutional factors still limit patients' access to the use of diabetes care. Also, there is a lacuna in the diabetes literature concerning the extent of utilization of available healthcare services by diabetic patients.

Effect of individualized nutrition interventions on clinical outcomes of pregnant women with gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) can lead to excessive pregnancy weight gain (PWG), abnormal glucolipid metabolism, and delayed lactation. Therefore, it is necessary to provide appropriate and effective interventions for pregnant women with GDM. To clarify the effects of individualized nutrition interventions on PWG, glucolipid metabolism, and lactation in pregnant women with GDM. The study population consisted of 410 pregnant women with GDM who received treatment at the Northern Jiangsu People's Hospital of Jiangsu Province and Yangzhou Maternal and Child Health Hospital between December 2020 and December 2022, including 200 who received routine in-terventions [control (Con) group] and 210 who received individualized nutrition interventions [research (Res) group]. Data on PWG, glucolipid metabolism [total cholesterol, (TC); triglycerides (TGs); fasting blood glucose (FPG); glycosylated hemoglobin (HbA1c)], lactation time, perinatal complications (cesarean section, premature rupture of membranes, postpartum hemorrhage, and pregnancy-induced hypertension), and neonatal adverse events (premature infants, fetal macrosomia, hypo-glycemia, and respiratory distress syndrome) were collected for comparative analysis. The data revealed markedly lower PWG in the Res group vs the Con group, as well as markedly reduced TG, TC, FPG and HbA1c levels after the intervention that were lower than those in the Con group. In addition, obviously earlier lactation and statistically lower incidences of perinatal complications and neonatal adverse events were observed in the Res group. Individualized nutrition interventions can reduce PWG in pregnant women with GDM, improve their glucolipid metabolism, and promote early lactation, which deserves clinical promotion.

Effects of insulin aspart and metformin on gestational diabetes mellitus and inflammatory markers

Diabetes mellitus is one of the most common causes of chronic kidney disease. Kidney involvement in patients with diabetes has a wide spectrum of clinical presentations ranging from asymptomatic to overt proteinuria and kidney failure. The development of kidney disease in diabetes is associated with structural changes in multiple kidney compartments, such as the vascular system and glomeruli. Glomerular alterations include thickening of the glomerular basement membrane, loss of podocytes, and segmental mesangiolysis, which may lead to microaneurysms and the development of pathognomonic Kimmelstiel-Wilson nodules. Beyond lesions directly related to diabetes, awareness of the possible coexistence of nondiabetic kidney disease in patients with diabetes is increasing. These nondiabetic lesions include focal segmental glomerulosclerosis, IgA nephropathy, and other primary or secondary renal disorders. Differential diagnosis of these conditions is crucial in guiding clinical management and therapeutic approaches. However, the relationship between diabetes and the kidney is bidirectional; thus, new-onset diabetes may also occur as a complication of the treatment in patients with renal diseases. Here, we review the complex and multifaceted correlation between diabetes and kidney diseases and discuss clinical presentation and course, differential diagnosis, and therapeutic opportunities offered by novel drugs.

Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes.

Diabetes mellitus is one of the most common causes of chronic kidney disease. Kidney involvement in patients with diabetes has a wide spectrum of clinical presentations ranging from asymptomatic to overt proteinuria and kidney failure. The development of kidney disease in diabetes is associated with structural changes in multiple kidney compartments, such as the vascular system and glomeruli. Glomerular alterations include thickening of the glomerular basement membrane, loss of podocytes, and segmental mesangiolysis, which may lead to microaneurysms and the development of pathognomonic Kimmelstiel-Wilson nodules. Beyond lesions directly related to diabetes, awareness of the possible coexistence of nondiabetic kidney disease in patients with diabetes is increasing. These nondiabetic lesions include focal segmental glomerulosclerosis, IgA nephropathy, and other primary or secondary renal disorders. Differential diagnosis of these conditions is crucial in guiding clinical management and therapeutic approaches. However, the relationship between diabetes and the kidney is bidirectional; thus, new-onset diabetes may also occur as a complication of the treatment in patients with renal diseases. Here, we review the complex and multifaceted correlation between diabetes and kidney diseases and discuss clinical presentation and course, differential diagnosis, and therapeutic oppor-tunities offered by novel drugs.

Gut microbiome supplementation as therapy for metabolic syndrome.

The gut microbiome is defined as an ecological community of commensal symbiotic and pathogenic microorganisms that exist in our body. Gut microbiome dysbiosis is a condition of dysregulated and disrupted intestinal bacterial homeostasis, and recent evidence has shown that dysbiosis is related to chronic inflammation, insulin resistance, cardiovascular diseases (CVD), type 2 diabetes mellitus (T2DM), and obesity. It is well known that obesity, T2DM and CVD are caused or worsened by multiple factors like genetic predisposition, environmental factors, unhealthy high calorie diets, and sedentary lifestyle. However, recent evidence from human and mouse models suggest that the gut microbiome is also an active player in the modulation of metabolic syndrome, a set of risk factors including obesity, hyperglycemia, and dyslipidemia that increase the risk for CVD, T2DM, and other diseases. Current research aims to identify treatments to increase the number of beneficial microbiota in the gut microbiome in order to modulate metabolic syndrome by reducing chronic inflammation and insulin resistance. There is increasing interest in supplements, classified as prebiotics, probiotics, synbiotics, or postbiotics, and their effect on the gut microbiome and metabolic syndrome. In this review article, we have summarized current research on these supplements that are available to improve the abundance of beneficial gut microbiota and to reduce the harmful ones in patients with metabolic syndrome.

Partners in diabetes epidemic: A global perspective.

There is a recent increase in the worldwide prevalence of both obesity and diabetes. In this review we assessed insulin signaling, genetics, environment, lipid metabolism dysfunction and mitochondria as the major determinants in diabetes and to identify the potential mechanism of gut microbiota in diabetes diseases. We searched relevant articles, which have key information from laboratory experiments, epidemiological evidence, clinical trials, experimental models, meta-analysis and review articles, in PubMed, MEDLINE, EMBASE, Google scholars and Cochrane Controlled Trial Database. We selected 144 full-length articles that met our inclusion and exclusion criteria for complete assessment. We have briefly discussed these associations, challenges, and the need for further research to manage and treat diabetes more efficiently. Diabetes involves the complex network of physiological dysfunction that can be attributed to insulin signaling, genetics, environment, obesity, mitochondria and stress. In recent years, there are intriguing findings regarding gut microbiome as the important regulator of diabetes. Valid approaches are necessary for speeding medical advances but we should find a solution sooner given the burden of the metabolic disorder - What we need is a collaborative venture that may involve laboratories both in academia and industries for the scientific progress and its application for the diabetes control.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM. To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention. The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified. Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (P < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model (P < 0.05). The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.

Role of glycolysis in diabetic atherosclerosis.

Diabetes mellitus is a kind of typical metabolic disorder characterized by elevated blood sugar levels. Atherosclerosis (AS) is one of the most common complications of diabetes. Modern lifestyles and trends that promote overconsumption and unhealthy practices have contributed to an increase in the annual incidence of diabetic AS worldwide, which has created a heavy burden on society. Several studies have shown the significant effects of glycolysis-related changes on the occurrence and development of diabetic AS, which may serve as novel thera-peutic targets for diabetic AS in the future. Glycolysis is an important metabolic pathway that generates energy in various cells of the blood vessel wall. In particular, it plays a vital role in the physiological and pathological activities of the three important cells, Endothelial cells, macrophages and vascular smooth muscle cells. There are lots of similar mechanisms underlying diabetic and common AS, the former is more complex. In this article, we describe the role and mechanism underlying glycolysis in diabetic AS, as well as the therapeutic targets, such as trained immunity, microRNAs, gut microbiota, and associated drugs, with the aim to provide some new perspectives and potentially feasible programs for the treatment of diabetic AS in the foreseeable future.

Analysis of potential biomarkers for diabetic kidney disease based on single-cell RNA-sequencing integrated with a single-cell sequencing assay for transposase-accessible chromatin.

Diabetic kidney disease (DKD) is a renal microvascular disease caused by hyperglycemia that involves metabolic remodeling, oxidative stress, inflammation, and other factors. The mechanism is complex and not fully unraveled. We performed an integrated single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) and single-cell RNA-sequencing (scRNA-seq) analyses of kidneys from db/db and db/m mice to identify differential open chromatin regions and gene expression, particularly in genes related to proximal tubular reabsorption and secretion. We identified 9,776 differentially expressed genes (DEGs) and 884 cell type-specific transcription factors (TFs) across 15 cell types. Glucose and lipid transporters, and TFs related to the circadian rhythm in the proximal tubules had significantly higher expression in db/db mice than in db/m mice (P<0.01). Crosstalk between podocytes and tubular cells in the proximal tubules was enhanced, and renal inflammation, oxidative stress, and fibrosis pathways were activated in db/db mice. Western blotting and immunohistochemical staining results showed that Wfdc2 expression in the urine and kidneys of DKD patients was higher than that in non-diabetic kidney disease (NDKD) controls. The revealed landscape of chromatin accessibility and transcriptional profiles in db/db mice provide insights into the pathological mechanism of DKD.

Serum anti-phospholipase A2 receptor antibody in pathological diagnosis of type 2 diabetes mellitus patients with proteinuria

Patients with diabetes mellitus complicated with proteinuria can be diabetic nephropathy (DN), diabetic complicated with non-diabetic kidney disease (NDKD), or DN with NDKD. Among these membranous nephropathy accounted for a large proportion of DN with NDRD. At present, serum anti-phospholipase A2 receptor (PLA2R) antibody is widely used in the diagnosis and evaluation of therapy in idiopathic membranous nephropathy, our study aimed to investigate the diagnostic significance of anti-PLA2R antibody in type 2 diabetes mellitus (T2DM) patients with proteinuria, providing a method for patients with contraindications of kidney biopsy. Eighty-seven T2DM patients with proteinuria who went on kidney biopsy were divided into the DN group, idiopathic membranous nephropathy (IMN) group, and others group according to their pathological results. In our study, 52.87% and 28.74% of patients were found to have IMN and diabetic nephropathy respectively. The levels of anti-PLA2R antibody, total cholesterol, triglyceride, and estimated glomerular filtration rate (eGFR) were higher in the IMN group, while the prevalence of diabetic retinopathy (DR), systolic blood pressure (SBP) and HbA1c were higher in the DN group. For T2DM patients with proteinuria, anti-PLA2R antibody (AUC = 0.904, 95%CI 0.838–0.970) has a high diagnostic value for IMN. The duration of diabetes (OR = 0.798, P = 0.030), eGFR level (OR = 1.030, P = 0.024), and positive anti-PLA2R antibody (OR = 72.727, P < 0.001) favor the diagnosis of IMN, while DR (OR = 50.234, P < 0.001), SBP (OR = 1.041, P = 0.030), and negative anti-PLA2R antibody (OR = 0.008, P = 0.001) is beneficial to the diagnosis of DN. Our study found that NDKD is not uncommon in patients with T2DM and proteinuria, and IMN was the main pathological type. Positive anti-PLA2R antibody has a strong accuracy in the diagnosis of IMN in patients with T2DM and proteinuria.

Presence of Non-Diabetic Kidney Diseases in Biopsy Specimens of Diabetic Patients' Single Center Experience.

The complications of type 2 diabetes mellitus (T2DM) are well known and one of them is diabetic chronic kidney disease (DCKD). Over time, it has become clear that patients with T2DM can have nondiabetic chronic kidney diseases (NDCKD), especially those that affect the glomeruli. Clinical indicators for identifying DCKD from NDCKD with high sensitivity and specificity have not yet been identified. Therefore, kidney biopsy remains the golden standard for DCKD diagnosis in patients with T2DM. Despite some indications for kidney biopsy, criteria for a biopsy differ between countries, regions, and doctors. The aim of the study was to analyze the biopsy findings in our T2DM population and the justification of the biopsy according to widely accepted criteria. This single center retrospective study analyzed data from 74 patients with T2DM who underwent kidney biopsy from January 2014 to January 2021. According to the biopsy data, we categorized31 patients in the DN group, patients with typical diabetic glomerulopathy, 11 patients in the mixed group, patients who had pathohistological elements for both DN and non-DN glomerulopathy, and 32 patients in the non-DN group, patients with primary glomerulopathy not linked with DM. In the non-DN and mixed groups, the most frequent glomerulopathy was mesangioproliferative glomerulonephritis, including IgA and non-IgA forms, found in 10 patients, and membranous nephropathy (MN) in 10 patients. We analyzed several parameters and only the amount of proteinuria was found to be significantly linked to biopsy findings related to DN. With the existing criteria for kidney biopsy, we managed to detect changes in the kidneys in about half of our patients with T2DM. These patients required specific treatment, different from that which we use for DCKD patients.