Diabetic Nephropathy

Diabetic nephropathy (DN) is a leading cause of end-stage kidney disease worldwide and is associated with a high risk of cardiovascular events and mortality. Despite current standard therapies including renin-angiotensin system inhibitors (ACEI/ARB), glycemic control, and blood pressure management, disease progression remains inadequately controlled in many patients. Naoxintong Capsule is a traditional Chinese medicine formulation composed of 16 herbal ingredients (Astragalus membranaceus, Prunus persica seed, Carthamus tinctorius, Paeonia lactiflora, Angelica sinensis, Ligusticum chuanxiong, Boswellia carterii, Commiphora myrrha, Salvia miltiorrhiza, Achyranthes bidentata, Cinnamomum cassia, Morus alba, Spatholobus suberectus, Buthus martensii, Pheretima aspergillum, and Hirudo nipponica). It has been widely used in China for cardiovascular and cerebrovascular diseases, with demonstrated effects on endothelial protection, anti-inflammation, anti-thrombosis, and plaque stabilization. This multicenter, randomized, double-blind, placebo-controlled trial aims to evaluate the efficacy and safety of Naoxintong Capsule added to standard conventional therapy in slowing the progression of diabetic nephropathy and providing cardiovascular protection. A total of 410 patients with Type 2 diabetic nephropathy will be randomized (1:1) to receive either Naoxintong Capsules (4 capsules, 3 times daily) or matching placebo, in addition to standard therapy, for 12 months. The primary outcome is the annual rate of decline in estimated glomerular filtration rate (eGFR). Secondary outcomes include changes in proteinuria, CKD stage progression, time to end-stage kidney disease, major adverse cardiovascular events, TCM syndrome scores, and quality of life measures.

Chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM) is a major cause of morbidity, commonly associated with persistent albuminuria and progressive renal decline. Reducing albuminuria is a key therapeutic goal to slow disease progression. Sodium-glucose cotransporter-2 inhibitors like dapagliflozin and non-steroidal mineralocorticoid receptor antagonists such as finerenone have independently shown significant renoprotective effects. Their combined use may provide additive benefits. This open-label randomized controlled trial will be conducted in the Department of Nephrology, Chittagong Medical College Hospital, Bangladesh, including 88 patients with CKD and T2DM. Participants will be randomized into two groups: one receiving dapagliflozin 10 mg plus finerenone 10 mg daily, and the other receiving dapagliflozin 10 mg alone for eight weeks. The primary outcome will be the change in urinary albumin-to-creatinine ratio (UACR). Secondary outcomes include serum creatinine, estimated glomerular filtration rate (eGFR), and serum potassium. Safety and adverse events will also be monitored. Data will be analyzed using SPSS version 27. This study aims to assess whether combination therapy is more effective than dapagliflozin alone in reducing albuminuria and may help guide treatment strategies in diabetic CKD.

The study aims to examine the relationship between the non-high-density lipoprotein cholesterol-to-high-density lipoprotein cholesterol ratio (NHHR) and CKD in patients with DM.

Diabetic kidney disease (DKD) is one of the most common microvascular complications of type 2 diabetes mellitus (T2DM), affecting up to 40% of diabetic patients and accounting for the leading cause of end-stage renal disease worldwide \[1\]. The progression of DKD involves multiple mechanisms, including oxidative stress, endothelial dysfunction, and most importantly, chronic inflammation \[2\]. Systemic inflammation plays a central role in renal injury by promoting glomerular and tubulointerstitial damage. the neutrophil-to-lymphocyte ratio (NLR) and systemic inflammatory index (SII) has emerged as a readily accessible markers of subclinical inflammation. Elevated NLR and SII levels have been significantly associated with increased urinary albumin excretion and decreased estimated glomerular filtration rate (eGFR) in T2DM patients \[3\]. It was demonstrated that patients in the highest NLR tertile had a higher prevalence of DKD, independent of confounders \[4\]. High-sensitivity C-reactive protein (hsCRP), is widely used to evaluate systemic inflammation. Recent studies have shown a strong association between elevated hsCRP levels and DKD development \[5\].Some studies provided genetic evidence supporting a causal relationship between higher hsCRP and diabetic nephropathy \[6\]. Monocyte chemoattractant protein-1 (MCP-1) is a chemokine involved in monocyte recruitment to inflamed renal tissues. Elevated serum and urinary MCP-1 levels have been found to predict microalbuminuria and eGFR decline in T2DM patients \[7,8\]. Identifying these markers may help in early diagnosis, risk stratification, and monitoring progression of DKD.

Diabetic nephropathy is one of the most severe microvascular complications of diabetes and a major cause of premature death and disability. It has become a leading cause of end-stage renal failure both in China and worldwide, consuming substantial medical resources. The establishment of a comprehensive regulatory system for the development and progression of diabetic nephropathy is a critical need for effective prevention and control. However, there is a lack of representative cohorts covering the entire disease cycle of diabetic nephropathy both domestically and internationally, creating technical bottlenecks in comprehensively describing its developmental patterns. This project aims to expand and integrate existing large-sample natural population cohorts and prospective follow-up cohorts covering the entire disease cycle of diabetes and diabetic nephropathy. It will construct a panoramic life database to identify risk factors, clinical phenotypes, and multimodal biomarkers at different disease stages. By integrating multi-organ interactions (e.g., kidney, eye), the project will establish novel imaging and functional assessment technologies for microvascular complications. Utilizing artificial intelligence to process multimodal medical data, it will build and validate risk prediction models and evaluation systems for the entire disease cycle of diabetic nephropathy. The project will develop effective intelligent prevention and treatment strategies for diabetic nephropathy, promote the adoption of new technologies, and establish a medical quality control system to provide services for medical institutions. Ultimately, it aims to improve and sustain medical quality, reducing the incidence of end-stage renal disease.

This research aims to evaluate the levels of knowledge , attitudes and practices, of diabetic kidney disease among diabetic patients.

A central goal of this data repository is to collect data from a large population of subjects with a variety of renal disease states. Cohorts will include subjects with diabetes, inflammatory/autoimmune and transplant related renal conditions. Additionally, the repository will have the capacity to store biospecimens and electronic data in control subjects without established renal disease. This initiative will provide an opportunity to compare data from various disease states and controls with the objective of determining clinical and biological factors that predict disease progression, response to therapy and identify discriminating noninvasive clinical and biological features that predict renal biopsy findings.

This research plan, from January 2021 to December 2024, aims to collect serum and morning urine from patients diagnosed with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, and focal segmental glomerulosclerosis the Nephrology Department of Qianfoshan Hospital in Shandong Province, through renal biopsy. These samples will be scanned using a Raman spect to obtain Raman spectral data. The scattering peaks in the Raman spectra will be analyzed using Origin software for Gaussian curve fitting. The position of the peaks will used to query relevant literature to identify the corresponding chemical bonds and confirm the presence of compounds. The intensity and area of the chemical substance peaks in the Raman will be calculated and used to plot calibration curves, thereby establishing a quantitative analysis equation. This equation will be used to accurately calculate the concentration of each analyte in serum and urine samples. Based on the average concentration data for each patient group, multivariate analysis methods, such as principal component analysis (PCA) and Mahalanis distance discriminant model, will be used to classify and predict the disease types. The preliminary data for this study comes from the Nephrology Department ofianfoshan Hospital, where different types of glomerular diseases have been pathologically classified using tools such as light microscopy, electron microscopy, and immunoforescence microscopy. By combining Raman spectroscopy technology and statistical analysis, this study aims to establish a non-invasive and efficient diagnostic tool to assist in the of kidney diseases and predict treatment outcomes.

Establish a cohort of diabetic kidney disease(DKD) patients with intensive weight loss treatment to evaluate the impact of intensive weight loss treatment on the renal prognosis of DKD and construct a prediction model for the improvement of renal outcomes after weight loss.

In our thesis, we will compare the effectiveness of low-intensity shock wave versus acetylcholine iontophoresis as a possible new therapy for microcirculation changes in type 2 diabetic neuropathy