Clinical Research Directory

Family Investigation of Nephropathy and Diabetes (F.I.N.D.)

The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in eleven centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center based at the Phoenix office of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK-Phoenix) will conduct family-based linkage studies among American Indian populations in the southwestern United States. Participants (index cases) with diabetes and kidney disease will initially be recruited, and their parents and siblings will also be invited to participate. Genetic material from these participants will be used to genotype markers throughout the genome. Linkage analysis will be conducted to identify particular chromosomal regions containing genes that influence susceptibility to diabetic kidney disease. Linkage analyses will also be used to identify genes influencing traits related to diabetic kidney disease, such as serum creatinine, urinary protein excretion, plasma glucose levels, blood pressure and blood lipid levels. Regions that show evidence for linkage will then be examined in more detail, with both genetic linkage and association studies, to attempt to identify the specific genes that influence diabetic kidney disease, or related traits. The identification of genes that influence susceptibility to diabetic kidney disease will lead to a better understanding of how kidney disease develops. In the long run, this may lead to improved treatment and prevention of diabetic kidney disease.

The Non-high-density Lipoprotein Cholesterol (Non-HDL-C) to High-density Lipoprotein Cholesterol (HDL-C) Ratio (NHHR) and Its Association With Occurrence of Diabetic Nephropathy in Patients With Diabetes Mellitus

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.

Type 2 Diabetes and Associated Factors in Mexico

The goal of this observational cross-sectional multicenter study is to identify clinical, lifestyle, metabolic, inflammatory, and genetic factors associated with glycemic control and complications of Type 2 Diabetes (T2D) in adult patients in Mexico. The main questions it aims to answer are: Which molecular and clinical factors are associated with poor glycemic control (HbA1c \> 7%)? Which factors are linked to the presence of diabetic nephropathy (GFR \< 60 mL/min/1.73 m²)? Participants: Provide informed consent and clinical history. Undergo a clinical and physical evaluation (including six-minute walk test). Complete lifestyle, dietary, and therapeutic adherence questionnaires. Provide blood samples for biochemical, inflammatory, and transcriptomic (RNA-Seq) analysis. Researchers integrate clinical, biochemical, and transcriptomic data using statistical modeling to identify a characteristic molecular fingerprint of poor metabolic control and diabetes-related complications.

Evaluating The Roles of Novel Inflammatory Markers Compared to MCP-1 in Type 2 Diabetic Nephropathy

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.

Study of Genome-associated Mechanisms of Diabetic Nephropathy and Evaluation of Nephroprotective Therapy in Patients With Type 2 Diabetes Mellitus in the Kazakh Population

The incidence of diabetes remains a serious global health issue, demanding close attention and the development of comprehensive strategies for prevention, early diagnosis, and effective treatment . According to the International Diabetes Federation (IDF), in 2021, the number of people with diabetes reached 537 million, and it is projected to rise to 783 million by 2045. In the Asia-Pacific region, the number of people with diabetes has reached approximately 206 million . One of the most common complications of diabetes is diabetic nephropathy. In developed countries, such as the United States and European nations, diabetic nephropathy is a leading cause of chronic kidney disease, placing a significant burden on the healthcare system. In light of this, the development of more effective methods for early detection of diabetic nephropathy remains a relevant objective. Current research focuses extensively on identifying kidney biomarkers that can signal early kidney damage in the disease's initial stages. Moreover, in recent years, genome-wide association studies (GWAS) have shown that diabetic nephropathy has a genetic component and that susceptibility may vary among different ethnic groups. This underscores the need to develop personalized treatments that consider patients' genetic characteristics. In emerging economies, such as Kazakhstan, the issue of diabetic nephropathy is also becoming increasingly relevant. The rise in diabetes cases, especially in urban populations, has led to a growing number of chronic kidney disease cases and associated disabilities. This results in significant economic costs for the healthcare system, as the treatment of end-stage renal disease patients requires considerable resources. In Kazakhstan, a particularly important aspect is the lack of studies focused on the genetic and clinical characteristics of diabetic nephropathy in the local population. The project is particularly valuable due to its emphasis on the Kazakh population, which is underrepresented in global studies. This focus allows for the identification of risk factors and specific characteristics of diabetic nephropathy unique to this ethnic group. Such an approach will substantially enhance the country's scientific and technical capacity and boost Kazakhstan's competitiveness on the international scientific stage. Given the above, research into genome-associated mechanisms of diabetic nephropathy and evaluation of the effectiveness of nephroprotective therapy in patients with type 2 diabetes within the Kazakh population represent scientific novelty and practical significance. Such studies will help improve the effectiveness of therapeutic and rehabilitation measures, contribute to identifying genetic variations specific to the Kazakh population, and improve patients' quality of life. This will also have a positive impact on healthcare economics and enhance patients' quality of life, while contributing to the global genetic data regarding the Kazakh population.

The Intelligent Prevention And Control System And Strategy For The Whole Disease Cycle Of Diabetic Nephropathy

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.

Assessement of the Knoweldge ,Attitudes,Practices of Diabetic Kidney Disease Among Diabetic Patients

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

Repository of Novel Analytes Leading to Autoimmune, Inflammatory and Diabetic Nephropathies (RENAL AID)

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.

Raman Spectroscopy Diagnosis of Kidney Diseases

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.

A Cohort Study on the Efficacy of Bariatric Surgery for Rapidly Progressing Diabetic Nephropathy

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.

Comparative Effectiveness Of Low Extra-Corporeal Shock Wave Versus Acetylcholine Iontophoresis On Type2 Diabetic Neuropathy

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