Severe Diabetic Complications Research Articles

#2265 The challenge of renal biopsy in diabetic patients: evaluation of a single center large cohort from North-West Italy

Abstract Background and Aims Diabetic kidney disease is one of the most severe long-term complications of diabetes. Currently, the international guidelines suggest diagnosis of diabetic nephropathy to be achieved on a clinical ground only. However, in the heterogeneous cluster of different conditions putatively co-existing with diabetes, renal biopsy is a unique tool for an accurate diagnosis and a guide for patient management. The aim of the study was to retrospectively analyze monocentric case history of kidney biopsies performed in diabetic patients over a period of 10 years. Patient's data were collected both at the time of histological examination and during follow-up. Method Data of renal biopsies (RB) performed in patients with diabetes mellitus from 1st January 2014 to 31th July 2023, were collected. Patient identification was performed by reviewing clinical histories and histopathological charts. Data on the indication for RB were collected from the medical report of the hospitalisation when the procedure was performed or from the visit when the indication was given. Atypical features were categorized according to an adaptation of the 2007 KDOQI guidelines for diabetes and chronic kidney disease. The significance of baseline differences was determined by the chi-squared test, Fisher's exact test or the unpaired t-test, as appropriate. Significance of univariate analysis were confirmed through the use of multivariate analysis. Correlation analysis, linear regression and Cox regression were also performed. A two-sided P-value <0.05 was statistically significant. All statistical analyses were performed using SPSS version 26.0 (IBM, Armonk, NY, USA). Results Among 1990 patients (pts) who underwent kidney biopsy during the study period, 297 had type 1 or type 2 DM (14.9%) and were therefore included in the study, regardless of diagnostic suspicion and histological findings. Histological examination revealed diabetic nephropathy (DN) in 112 cases (group 1), DN associated with another kidney disease in 56 cases (group 2) and nondiabetic renal diseases alone in the remaining 129 cases (62%) (group 3). In group 2 the kidney disease most frequently associated with DN was focal and segmental glomerulosclerosis, FSGS (24/56 pts) followed by IgA nephropathy, IgAN (14/56 pts). In this population the histological examination led to specific therapeutic indications in 22 patients. In group 3 the most frequently observed kidney disease was FSGS (37/112 pts) followed by ANCA-vasculitis (14/112 pts), membranous nephropathy (14/112 pts) and IgAN (10/112). In this population, histological examination led to a specific therapeutic indication in 67 pts. To determine predictors for histopathological diagnosis of DKD, we ran a logistic regression model. The covariates used in this model were the duration of diabetes, presence of retinopathy, hypertension, serum creatinine values, eGFR values and 24-hours proteinuria. None of them was found to be statistically significant. It should also be noted that 47 out of 112 pts (42%) with diabetic nephropathy had no retinopathy. In contrast, 9 pts (7%) with histological evidence of NDRD had diabetic retinopathy. 215 out of 297 pts had a follow-up of at least 6 months. During a mean follow up of 41.6±31.8 months, 53 pts (24 in group 1, 15 in group 2 and 14 in group 3) reached end stage kidney disease (16.5 months). In our population the presence of DN associated with another biopsy-proven nephropathy was especially prevalent. Having double nephropathy conferred a relative risk-RR for dialysis of 1.8089 (95% CI 1.0094 to 3.2415, P = .0464). Conclusion To our knowledge this is the largest cohorts of diabetic patients receiving renal biopsy even reported in a Western country. Our data proved kidney biopsy not to be an academic exercise in diabetic pts. Clinical and laboratory features do not predict histologic findings. Renal biopsy allowed the identification of unsuspecting nondiabetic kidney injuries and double nephropathies, which could be susceptible to different outcomes, and paved the pathway for treatment of potentially manageable histological changes.

Tribbles pseudokinase 3 promoted renal fibrosis by regulating the expression of DNA damage-inducible transcript 3 in diabetic nephropathy.

Diabetic nephropathy (DN) is a severe complication of prolonged diabetes, impacting millions worldwide with an increasing incidence. This study investigates the role of tribbles pseudokinase 3 (TRIB3), a protein implicated in the progression of DN, focusing on its mechanisms underlying glomerular damage. Through analysis of the Gene Expression Omnibus (GEO) database, we identified TRIB3 among differentially expressed genes in streptozotocin (STZ)-treated C57BL/6J mice. Both in vitro and in vivo experiments were conducted to examine the effects of TRIB3 inhibition on high glucose (HG)-induced damage in podocytes and DN mouse models. The results demonstrated that TRIB3 inhibition reduced inflammatory responses and extracellular matrix (ECM) production in MPC5 cells, mediated by the downregulation of DNA damage-inducible transcript 3 (DDIT3) - a critical regulator of proinflammatory cytokine secretion and ECM synthesis. Inhibiting TRIB3 decreased inflammatory factors and ECM deposition in diabetic mice in vivo, confirming its pivotal role in DN pathogenesis. These findings indicate that TRIB3 and its interaction with DDIT3 contribute significantly to DN by promoting inflammatory cascades and ECM accumulation, presenting potential therapeutic targets for managing the disease.

Omega-3-Index bei Patient*innen mit schweren diabetischen Augenkomplikationen

Summary Background The aim of the study was to assess the omega‑3 index and the proportions of other relevant fatty acids (FAs) in patients undergoing vitrectomy due to severe diabetic ocular complications. According to evidence, an optimal omega‑3 index range is 8–11% and a high level of saturated FAs correlate with advanced diabetic retinopathy (DR). Methods Patients with diabetes mellitus (DM) undergoing vitrectomy due to diabetic macular edema or vitreous hemorrhage secondary to proliferative DRP were recruited for this study. Prior to surgery, the omega‑3 index, defined as the proportion of omega‑3 FAs in the membranes of erythrocytes, and the proportions of omega‑6, omega‑9, saturated and trans FAs were assessed using “high-sensitivity” gas chromatography with flame ionization detection. Results In total, 12 patients with DM type 1 (n = 2) and type 2 (n = 10) were enrolled. Their age was 69 ± 12 years and their HbA1c averaged 7.6 ± 1.2%. The omega‑3 index was 4.5 ± 1.2%. There was a moderate negative correlation between HbA1c and the omega‑3 index (r = −0.51, p = 0.09). The proportions of other FAs were within the normal ranges. However, the proportions of omega‑6 and saturated FAs were in the upper range (33.3 ± 2.2% and 41.1 ± 1.3%, respectively). Conclusion Patients with severe diabetic ocular complications who require vitreoretinal surgery are generally under-supplied with omega‑3 FAs and over-supplied with omega‑6 and saturated FAs. Patients with diabetes might consequently benefit from early omega‑3 FA supplementation and a reduction of saturated FAs in order to prevent or at least slow down the development of DRP and ultimately preserve vision.

Painful forms of diabetic neuropathy

Diabetic neuropathy is a frequent and severe degenerative complication of diabetes. The diagnosis is easily performed in painful symptomatic patients. Sensitivity disorders responsible for numbness, tingling, and loss of feeling are part and parcel of diabetic foot syndrome and require investigation in view of preventing trophic ulcers. To date, there exists no specific treatment for diabetic neuropathy possibly preventable by careful control of metabolic disorder. Effective management of diabetic patients would make it possible to limit the dramatic consequences of diabetic neuropathy while at the same time acting on other complications.

Tectorigenin protects against cardiac fibrosis in diabetic mice heart via activating the adiponectin receptor 1-mediated AMPK pathway

Diabetic cardiomyopathy (DCM) is a common severe complication of diabetes that occurs independently of hypertension, coronary artery disease, and valvular cardiomyopathy, eventually leading to heart failure. Previous studies have reported that Tectorigenin (TEC) possesses extensive anti-inflammatory and anti-oxidative stress properties. In this present study, the impact of TEC on diabetic cardiomyopathy was examined. The model of DCM in mice was established with the combination of a high-fat diet and STZ treatment. Remarkably, TEC treatment significantly attenuated cardiac fibrosis and improved cardiac dysfunction. Concurrently, TEC was also found to mitigate hyperglycemia and hyperlipidemia in the DCM mouse. At the molecular level, TEC is involved in the activation of AMPK, both in vitro and in vivo, by enhancing its phosphorylation. This is achieved through the regulation of endothelial-mesenchymal transition via the AMPK/TGFβ/Smad3 pathway. Furthermore, it was demonstrated that the level of ubiquitination of the adiponectin receptor 1 (AdipoR1) protein is associated with TEC-mediated improvement of cardiac dysfunction in DCM mice. Notably the substantial reduction of myocardial fibrosis. In conclusion, TEC improves cardiac fibrosis in DCM mice by modulating the AdipoR1/AMPK signaling pathway. These findings suggest that TEC could be an effective therapeutic agent for the treatment of diabetic cardiomyopathy.

Variants APOA5 p.G185C and p.S19W associated with early-onset severe hypertriglyceridemia-induced pancreatitis and diabetes complications in a Vietnamese girl

An imbalance of glucose and severely high triglyceride are the key characteristics driving hypertriglyceridemia and hyperlipidemic pancreatitis. Hereditary factors considerably promote the increase of triglyceride levels and secondary complications. This study explored the clinical characteristics and genetic causes of a young girl with recurrent severe hypertriglyceridemia-developed pancreatitis at 9-year-olds and diabetes mellitus appearance at 14-year-olds. At 20 years old, she was admitted to the hospital in critical condition and had pancreatitis pain with triglyceride level at 26.22 mmol/L, HbA1c 15%, and glucose at 21.3 mmol/L. Whole exome sequencing analysis showed that she had two heterozygous variants of the APOA5 gene, (p.G185C and p.S19W), one homozygous variant of the APOE gene (p.C156R), which were previously reported to cause critical hypertriglyceridemia condition of patients. The results and analysis propose these variations were the underlying causes of a heightened of patient's plasma triglycerides and lead metabolic complication onset at a young age. These findings shed light on the molecular mechanism of her elevated plasma triglyceride and the early onset of diabetes following hyperlipidemic pancreatitis.

SIRT1/P53 in retinal pigment epithelial cells in diabetic retinopathy: a gene co-expression analysis and He-Ying-Qing-Re formula treatment.

Diabetic retinopathy (DR) is a severe diabetic complication that leads to severe visual impairment or blindness. He-Ying-Qing-Re formula (HF), a traditional Chinese medicinal concoction, has been identified as an efficient therapy for DR with retinal vascular dysfunction for decades and has been experimentally reported to ameliorate retinal conditions in diabetic mice. This study endeavors to explore the therapeutic potential of HF with key ingredients in DR and its underlying novel mechanisms. Co-expression gene modules and hub genes were calculated by weighted gene co-expression network analysis (WGCNA) based on transcriptome sequencing data from high-glucose-treated adult retinal pigment epithelial cell line-19 (ARPE-19). The chromatographic fingerprint of HF was established by ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-Q-TOF-MS). The molecular affinity of the herbal compound was measured by molecular docking. Reactive oxygen species (ROS) was measured by a DCFDA/H2DCFDA assay. Apoptosis was detected using the TUNEL Assay Kit, while ELISA, Western blot, and real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used for detecting the cytokine, protein, and mRNA expressions, respectively. Key compounds in HF were identified as luteolin, paeoniflorin, and nobiletin. For WGCNA, ME-salmon ("protein deacetylation") was negatively correlated with ME-purple ("oxidative impairment") in high-glucose-treated ARPE-19. Luteolin has a high affinity for SIRT1 and P53, as indicated by molecular docking. Luteolin has a hypoglycemic effect on type I diabetic mice. Moreover, HF and luteolin suppress oxidative stress production (ROS and MDA), inflammatory factor expression (IL-6, TNF-α, IL1-β, and MCP-1), and apoptosis, as shown in the in vivo and in vitro experiments. Concurrently, treatment with HF and luteolin led to an upregulation of SIRT1 and a corresponding downregulation of P53. Using HF and its active compound luteolin as therapeutic agents offers a promising approach to diabetic retinopathy treatment. It primarily suppressed protein acetylation and oxidative stress via the SIRT1/P53 pathway in retinal pigment epithelial cells.

Therapeutic Effect of Silver Nanoparticles in the Management of Diabetic Ulcers: A Systematic Review and Meta-Analysis on RCTs.

The skin, as the body's largest organ, plays vital roles in sensory functions, temperature regulation, and protection against pathogens and injuries. Skin wounds, which disrupt its integrity, can result from various factors, including diseases such as diabetes. Diabetic foot ulcers are a severe complication of diabetes, often leading to amputations. This systematic review explores the therapeutic potential of silver nanoparticles in the management of diabetic ulcers. Seven studies published between 2016 and 2023 were included in this review. Also, 4 studies were included in the meta-analysis. These studies investigated the application of silver nanoparticles, primarily in dressing forms, for diabetic ulcer treatment. A systematic search strategy was employed, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The results show that silver nanoparticles do not have a significant difference in improving DFU healing rates. SilvrSTAT Gel, a dressing containing silver nanoparticles, outperformed traditional dressings, leading to a substantial percentage of ulcers healing within weeks. Comparative studies also indicated that silver nanoparticles were at least as effective as alternative treatments, such as nano-chitosan dressings, and showed potential for combination therapy with growth factors. This review underscores the promise of silver nanoparticles, a nanotechnology-based approach, in accelerating the healing of diabetic ulcers while providing antimicrobial benefits. Despite some limitations, including variations in treatment regimens and a lack of long-term outcome data, these findings show there is no clinical evidence for using Nanosilver for the healing process of DFU. Silver nanoparticles currently do not have sufficient clinical evidence for healing the DFU; however, in some studies, they had noticeable effects on the rate of wound healing.

Transfer learning approach for diabetic retinopathy detection using residual network

The condition known as diabetic retinopathy is a severe and common complication of diabetes. It affects the retina, which is a light-sensitive organ inside the eye, and it can lead to blindness or loss of vision. It is therefore important to improve the diagnosis and classification of this disorder. In deep learning, transfer learning is a process that aims to improve the performance of a given task by taking advantage of the knowledge that has been acquired from another. The main idea of this approach is to speed up the learning process by applying the obtained knowledge to a new task. In this paper, with the help of migration learning, a pre-trained deep learning model known as InceptionV3 was used to classify fundus images the Diabetic Retinopathy 2015 Data Colored Resized database in five categories according to the severity of the lesions. It was able to achieve a 92.314% accuracy on a test set.

A novel approach for intelligent diagnosis and grading of diabetic retinopathy

Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model. To solve the problem caused by imbalanced data distribution, our model adopts a more balanced strategy by allocating an equal number of channels to feature maps representing various DR categories. Furthermore, we introduce a CAM-EfficientNetV2-M encoder dedicated to encoding input retinal fundus images for feature vector generation. The number of parameters of our encoder is 52.88 M, which is less than RegNet_y_16gf (80.57 M) and EfficientNetB7 (63.79 M), but the corresponding kappa value is higher. Additionally, in order to take advantage of the binocular relationship, we input fundus retinal images from both eyes of the patient into the network for features fusion during the training phase. We achieved a kappa value of 86.62% on the EyePACS dataset and 86.16% on the Messidor-2 dataset. Experimental results on these representative datasets for diabetic retinopathy (DR) demonstrate the exceptional performance of our DRGCNN model, establishing it as a highly competitive intelligent classification model in the field of DR. The code is available for use at https://github.com/Fat-Hai/DRGCNN.

Integrated 16S rRNA sequencing and metabolomic analysis reveals the potential protective mechanism of Germacrone on diabetic nephropathy in mice.

Diabetic nephropathy (DN) is a severe complication of diabetes and the leading cause of end-stage renal disease and death. Germacrone (Ger) possesses anti-inflammatory, antioxidant and anti-DN properties. However, it is unclear whether the improvement in kidney damage caused by Ger in DN mice is related to abnormal compositions and metabolites of the gut microbiota. This study generates a mouse model of DN to explore the potent therapeutic ability and mechanism of Ger in renal function by 16S rRNA sequencing and untargeted fecal metabolomics. Although there is no significant change in microbiota diversity, the structure of the gut microbiota in the DN group is quite different. Serratia_marcescens and Lactobacillus_iners are elevated in the model group but significantly decreased after Ger intervention ( P<0.05). Under the treatment of Ger, no significant differences in the diversity and richness of the gut microbiota are observed. An imbalance in the intestinal flora leads to the dysregulation of metabolites, and non-targeted metabolomics data indicate high expression of stearic acid in the DN group, and oleic acid could serve as a potential marker of the therapeutic role of Ger in the DN model. Overall, Ger improves kidney injury in diabetic mice, in part potentially by reducing the abundance of Serratia_marcescens and Lactobacillus_iners, as well as regulating the associated increase in metabolites such as oleic acid, lithocholic acid and the decrease in stearic acid. Our research expands the understanding of the relationship between the gut microbiota and metabolites in Ger-treated DN. This contributes to the usage of natural products as a therapeutic approach for the treatment of DN via microbiota regulation.

Dual branch deep learning network for detection and stage grading of diabetic retinopathy

Diabetic retinopathy is a severe complication of diabetes that can lead to permanent blindness if not treated promptly. Early and accurate diagnosis of the disease is essential for successful treatment. This paper introduces a deep learning method for the detection and stage grading of diabetic retinopathy, using a single fundus retinal image. Our model utilizes transfer learning, employing two state-of-the-art pre-trained models as feature extractors and fine-tuning them on a new dataset. The proposed model is trained on a large multi-center dataset, including the APTOS 2019 dataset, obtained from publicly available sources. It achieves remarkable performance in diabetic retinopathy detection and stage classification on the APTOS 2019, outperforming the established literature. For binary classification, the proposed approach achieves an accuracy of 98.50%, a sensitivity of 99.46%, and a specificity of 97.51%. In stage grading, it achieves a quadratic weighted kappa of 93.00%, an accuracy of 89.60%, a sensitivity of 89.60%, and a specificity of 97.72%. The proposed approach serves as a reliable screening and stage grading tool for diabetic retinopathy, offering significant potential to enhance clinical decision-making and patient care.

Exploration of AI-powered DenseNet121 for effective diabetic retinopathy detection.

Diabetic Retinopathy (DR) is a severe complication of diabetes that damages the retina and affects approximately 80% of patients with diabetes for 10years or more. This condition primarily impacts young and productive individuals, resulting in significant long-term medical complications for patients and society. The early stages of diabetic retinopathy often advance without noticeable symptoms, resulting in delayed identification and intervention. Therefore, develop approaches employing transfer learning methodologies to enhance early detection capabilities, facilitating timely diagnosis and intervention to mitigate the progression of diabetic retinopathy. This study introduces a transfer learning approach for detecting four stages of DR: No DR, Mild, Moderate, and Severe. The methods AlexNet, VGG16, ResNet50, Inception v3, and DenseNet121 are utilized and trained using the Kaggle DR dataset. To assess the efficiency of the suggested improved network, the Kaggle dataset is employed to analyze four performance metrics: Sensitivity, Precision, Accuracy, and F1 score. DenseNet121 demonstrated superior accuracy among the two models, outperforming other models, making it a suitable option for automatic DR sign detection. The integration of the DenseNet121 model shows great promise in transforming the timely identification and treatment of DR, resulting in enhanced patient results in the long run and alleviating the burden on society.

Advances in Research Related to MicroRNA for Diabetic Retinopathy.

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and is one of the primary causes of blindness in the working-age population in Europe and the United States. At present, no cure is available for DR, but early detection and timely intervention can prevent the rapid progression of the disease. Several treatments for DR are known, primarily ophthalmic treatment based on glycemia, blood pressure, and lipid control, which includes laser photocoagulation, glucocorticoids, vitrectomy, and antivascular endothelial growth factor (anti-VEGF) medications. Despite the clinical efficacy of the aforementioned therapies, none of them can entirely shorten the clinical course of DR or reverse retinopathy. MicroRNAs (miRNAs) are vital regulators of gene expression and participate in cell growth, differentiation, development, and apoptosis. MicroRNAs have been shown to play a significant role in DR, particularly in the molecular mechanisms of inflammation, oxidative stress, and neurodegeneration. The aim of this review is to systematically summarize the signaling pathways and molecular mechanisms of miRNAs involved in the occurrence and development of DR, mainly from the pathogenesis of oxidative stress, inflammation, and neovascularization. Meanwhile, this article also discusses the research progress and application of miRNA-specific therapies for DR.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

BackgroundDiabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.MethodsWe downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.ResultsIn both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).ConclusionThis study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.

Residential Address Amplifies Health Disparities and Risk of Infection in Individuals With Diabetic Foot Ulcers.

To determine the association between social determinants of health (SDOH) and a diagnosis of diabetic foot ulcer (DFU) infection. Targeted interrogation of electronic health record data using novel search engines to analyze individuals with a DFU infection during a 5-year period (2013-2017) was performed. We extracted geolocated neighborhood data and SDOH characteristics from the National Neighborhood Data Archive and used univariate and multiple logistic regression to evaluate associations with outcomes in the population with diabetes. Among 4.3 million people overall and 144,564 individuals with diabetes seen between 2013 and 2017, 8,351 developed DFU, of which cases 2,252 were complicated by a DFU infection. Sex interactions occurred, as men who experienced a DFU infection more frequently identified as having nonmarried status than their female counterparts. For the population with DFU infection, there were higher rates for other SDOH, including higher neighborhood disadvantaged index score, poverty, nonmarriage, and less access to physician/allied health professionals (all P < 0.01). In multiple logistic regression, those individuals who developed DFU infection came from neighborhoods with greater Hispanic and/or foreign-born concentrations (odds ratio 1.11, P = 0.015). We found significant differences in neighborhood characteristics driving a higher risk for DFU infection in comparisons with the grouping of individuals with diabetes overall, including increased risk for individuals with Hispanic and/or foreign-born immigration status. These data strongly support the need to incorporate SDOH, particularly ethnic and immigration status, into triage algorithms for DFU risk stratification to prevent severe diabetic foot complications and move beyond biologic-only determinants of health.

Renal-protective effects of Chinese medicinal herbs and compounds for diabetic kidney disease in animal models: protocol for systematic review and meta-analysis

BackgroundDiabetic kidney disease (DKD) is a common and severe complication of diabetes that can lead to end-stage renal disease with no cure. The first-line drugs recommended by clinical guidelines fail to achieve satisfactory effects for people with DKD. A Chinese herbal medicine Tangshen Qushi Formula (TQF) shows preliminary efficacy and safety in preserving renal function for people with DKD, but the effects on comprehensive renal outcomes remain unclear. We will conduct a systematic review and meta-analysis to evaluate the effects of TQF herbs and their compounds identified from ultra-high performance liquid chromatography-MS/MS in diabetic animal models with renal outcomes.MethodsThis protocol complies with the guideline Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. We will include studies investigating the effects of TQF herbs and compounds on diabetic rats or mice with renal outcomes. Six electronic databases will be searched from their inception to February 2023. Quality assessment will be conducted using SYRCLE’s risk of bias tool. Standardized or weighted mean differences will be estimated for renal outcomes (creatinine, urea, proteinuria, histological changes, oxidative stress, inflammation, and kidney fibrosis). Data will be pooled using random-effects models. Heterogeneity across studies will be expressed as I2. Sensitivity analyses will explore treatment effects in adjusted models and within subgroups. Funnel plots and Egger’s test will be used to explore publication bias.DiscussionThe results of this review will provide valuable insights into the potential effects of TQF in managing DKD. The limitation is that the included studies will be animal studies from specific databases, and the interpretation of the findings must be cautious.Systematic review registrationPROSPERO CRD42023432895. Registered on 19 July 2023 (https://www.crd.york.ac.uk/PROSPERO/#recordDetails).

Single-cell transcriptomics reveals the ameliorative effect of rosmarinic acid on diabetic nephropathy-induced kidney injury by modulating oxidative stress and inflammation

Diabetic nephropathy (DN) is a severe complication of diabetes, characterized by changes in kidney structure and function. The natural product rosmarinic acid (RA) has demonstrated therapeutic effects, including anti-inflammation and anti-oxidative-stress, in renal damage or dysfunction. In this study, we characterized the heterogeneity of the cellular response in kidneys to DN-induced injury and RA treatment at single cell levels. Our results demonstrated that RA significantly alleviated renal tubular epithelial injury, particularly in the proximal tubular S1 segment and on glomerular epithelial cells known as podocytes, while attenuating the inflammatory response of macrophages, oxidative stress, and cytotoxicity of natural killer cells. These findings provide a comprehensive understanding of the mechanisms by which RA alleviates kidney damage, oxidative stress, and inflammation, offering valuable guidance for the clinical application of RA in the treatment of DN.

Natural diterpenoid EKO activates deubiqutinase ATXN3 to preserve vascular endothelial integrity and alleviate diabetic retinopathy through c-fos/focal adhesion axis

Diabetic retinopathy (DR) is one of the most prevalent severe diabetic microvascular complications caused by hyperglycemia. Deciphering the underlying mechanism of vascular injury and finding ways to alleviate hyperglycemia induced microvascular complications is of great necessity. In this study, we identified that a compound ent-9α-hydroxy-15-oxo-16-kauren-19-oic acid (EKO), the diterpenoid isolated and purified from Pteris semipinnata L., exhibited good protective roles against vascular endothelial injury associated with diabetic retinopathy in vitro and in vivo. To further uncover the underlying mechanism, we used unbiased transcriptome sequencing analysis and showed substantial impairment in the focal adhesion pathway upon high glucose and IL-1β stimulation. EKO could effectively improve endothelial focal adhesion pathway by enhancing the expression of two focal adhesion proteins Vinculin and ITGA11. We found that c-fos protein was involved in regulating the expression of Vinculin and ITGA11, a transcription factor component that was downregulated by high glucose and IL-1β stimulation and recovered by EKO. Mechanically, EKO facilitated the binding of deubiquitylation enzyme ATXN3 to c-fos protein and promoted its deubiquitylation, thereby elevating its protein level to enhance the expression of Vinculin and ITGA11. Besides, EKO effectively suppressed ROS production and restored mitochondrial function. In vivo studies, we confirmed EKO could alleviate some of the indicators of diabetic mice. In addition, protein levels of ATXN3 and focal adhesion Vinculin molecule were also verified in vivo. Collectively, our findings addressed the endothelial protective role of natural diterpenoid EKO, with emphasize of mechanism on ATXN3/c-fos/focal adhesion signaling pathway as well as oxygen stress suppression, implicating its therapeutic potential in alleviating vascular endothelium injury and diabetic retinopathy.

Prevalence, Incidence and Factors Associated with Diabetic Foot in People with Type 2 Diabetes: Systematic Review with Meta-Analysis.

Diabetic Foot is a severe chronic complication of diabetes and an important factor in the morbidity of diabetic people, resulting in high health costs and increased risk of death. To analyze the incidence, prevalence, and risk factors associated with diabetic foot in people with type 2 Diabetes Mellitus. Systematic literature review. Searches in MedLine via PubMed, LILACS, Web of Science, Scopus CINAHL, and Cochrane Library databases were performed. Inclusion of 52 studies. The R program, Metan packages, was used to calculate the meta-analysis. Given the heterogeneity of studies, the random effect was used to calculate the meta-analysis of risk factors. The meta-analysis showed that the prevalence of diabetic foot was 14% in a hospital setting and 5% in a community setting. The overall prevalence and incidence were 9% and 4%, respectively. Significant risk factors included time of DM (odds ratio [OR] =1.46, confidence interval [CI], 0.36-2.57, P = 0.009), smoking (OR = 1.46, CI, 1.16 -1.85, P< .001), glycated hemoglobin (OR = 0.96, CI, 0.50; 1.42, P< .001), peripheral arterial disease (OR = 3.38, CI, 2.07; 5.53, P < .001) and peripheral neuropathy (OR = 5.88, CI, 2.39-14.45, P<.001). Multidisciplinary monitoring, educational strategies, periodic foot examination for alterations, and early identification of risk factors are essential to prevent ulceration and reduce the disease burden.