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> Disorders > Disease or Syndrome > Diabetic Nephropathy

Diabetic Nephropathy

Diabetic Nephropathy is a serious complication of diabetes mellitus that involves kidney damage.
It is characterized by persistent albuminuria, progressive decline in glomerular filtration rate, and elevated blood pressure.
Early detection and management of diabetic nephropathy is crucial to prevent or delay the progression to end-stage renal disease.
The etiology involves a complex interplay of genetic, metabolic, and hemodynamic factors.
Effective treatment strategies focus on glycemic control, blood pressure management, and the use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers.
Continued research is needed to better understand the pathologenesis and identify novel therapeutic targets for this disabling condition.

Most cited protocols related to «Diabetic Nephropathy»

1
CKD-EPI Study of GFR Estimation
The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) conducted the study under a cooperative agreement with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). CKD-EPI collaborators provided data from clinical research studies and clinical populations.3 (link) GFR measurements were based on urinary or plasma clearance of exogenous filtration markers. Data from studies of urinary clearance of iothalamate were used for development and internal validation, and data from studies of other filtration markers were used for external validation. We included 13 studies with 5352 participants, who were randomly divided into separate data sets for development (3522) and internal validation (1830) (see Table S1a in the Supplementary Appendix, available with the full text of this article at NEJM .org). We included 5 other studies with 1119 participants for external validation (Table S1b in the Supplementary Appendix). We excluded studies involving transplant recipients because our preliminary analyses showed large variations among these studies in the relationship between serum cystatin C levels and measured GFR. The institutional review boards of all participating institutions approved the study.
The NIDDK was substantially involved in the design of the study and in the collection, analysis, and interpretation of the data; the NIDDK was not required to approve the final manuscript before submission for publication. The first author had full access to all the data in the study, vouches for the integrity of the data and the accuracy of the data analysis for the CKD-EPI database, and wrote the first draft of the manuscript. For a list of collaborators who provided data, see the Supplementary Appendix.
Inker L.A., Schmid C.H., Tighiouart H., Eckfeldt J.H., Feldman H.I., Greene T., Kusek J.W., Manzi J., Van Lente F., Zhang Y.L., Coresh J, & Levey A.S. (2012). Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. The New England journal of medicine, 367(1), 20-29.
Publication 2012
Diabetic Nephropathy Digestive System Ethics Committees, Research Filtration Iothalamate Plasma Population Group Post-gamma-Globulin Serum Transplant Recipients Urine
2
NIDDK-Funded Metabolic Study
The study was funded by a cooperative agreement with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), which allows the NIDDK substantial involvement in the design of the study and in the collection, analysis, and interpretation of the data. The NIDDK was not required to approve publication of the finished manuscript.
Levey A.S., Stevens L.A., Schmid C.H., Zhang Y.(., Castro AF I.I.I., Feldman H.I., Kusek J.W., Eggers P., Van Lente F., Greene T, & Coresh J. (2009). A New Equation to Estimate Glomerular Filtration Rate. Annals of internal medicine, 150(9), 604-612.
Publication 2009
Diabetic Nephropathy Digestive System
3
TARA Study: African-American Body Characteristics
Body characteristics for participants in the TARA study are shown in Table 1. Subjects were 223 African Americans (Table 1, 43.5% male), age 35 years (34.8 ± 7.7), range 20–50 years, BMI 30.0 ± 7.7, range 18.5–54.7 kg/m2. The BMI range of TARA participants was similar to that of the BetaGene subjects. Data from the TARA subjects have been previously reported (18 (link),19 (link)). Subjects were born in the United States and each subject reported that both parents were African-American. Oral glucose tolerance tests revealed previously unknown impaired glucose tolerance in 25% and diabetes in 2%. Twenty percent were hypertensive. Women were premenopausal; 41% of the females were obese. Recruitment was accomplished through flyers, newsletters, and websites. The institutional review board of National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) approved the study, and all subjects gave informed consent.
Hip circumference in all TARA participants was measured by a single observer (N.G.S.) over nonrestrictive underwear or light-weight shorts, at the level of the maximum extension of the buttocks posteriorly in a horizontal plane. The mean of the three determinations was recorded. Similar to the BetaGene study, whole-body composition measurements were performed with a Hologic QDR4500A dual-energy X-ray absorptiometer (Hologic) in the array mode using software version 5.71A.
Bergman R.N., Stefanovski D., Buchanan T.A., Sumner A.E., Reynolds J.C., Sebring N.G., Xiang A.H, & Watanabe R.M. (2011). A Better Index of Body Adiposity. Obesity (Silver Spring, Md.), 19(5), 1083-1089.
Publication 2011
African American Buttocks Childbirth Diabetes Mellitus Diabetic Nephropathy Digestive System Digestive System Disorders Ethics Committees, Research Females Human Body Intolerances, Glucose Kidney Kidney Diseases Light Males Measure, Body Obesity Oral Glucose Tolerance Test Parent Radiography Woman
4
Cardiometabolic Traits Genetic Association
To explore the relationship between BMI and an array of cardiometabolic traits and diseases, association results for the 97 BMI index SNPs were requested from 13 GWAS meta-analysis consortia: DIAGRAM (type 2 diabetes)56 (link), CARDIoGRAM-C4D (CAD)57 (link), ICBP (systolic and diastolic blood pressure (SBP, DBP))58 (link), GIANT (waist-to-hip ratio, hip circumference, and waist circumference, each unadjusted and adjusted for BMI)13 (link),59 , GLGC (HDL, low density lipoprotein cholesterol, triglycerides, and total cholesterol)60 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)61 (link)–63 (link), ADIPOGen (BMI-adjusted adiponectin)64 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate, and overall CKD)65 (link),66 (link), ReproGen (age at menarche, age at menopause)67 (link),68 (link), GENIE (diabetic nephropathy)69 (link),70 (link). Proxies (r2 > 0.8 in CEU) were used when an index SNP was unavailable.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
ADIPOQ protein, human Albumins Cholesterol Cholesterol, beta-Lipoprotein Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Genie Genome-Wide Association Study Gigantism Glomerular Filtration Rate Glucose Insulin Menarche Menopause Pressure, Diastolic Systole Triglycerides Urine Waist-Hip Ratio Waist Circumference
5
Cohort Study of Korean CKD Patients
Each participating center will enroll approximately 250 consecutive individuals over a 5-year period from 2011 until 2015, totaling 2,450 adult patients with CKD who provide written informed consent. The participating individuals will be monitored for approximately 10 years until death or until ESRD occurs.
The KNOW-CKD will enroll ethnic Korean patients with CKD who range in age between 20 years and 75 years. The CKD stages from 1 to 5 (predialysis), based on the eGFR, is calculated using the four-variable Modification of Diet in Renal Disease (MDRD) equation as follows:
eGFR (ml/min per 1.73 m2) = 175 × [serum Cr (mg/dl)] -1.154 × [age]-0.203 × [0.742 if female] × [1.212 if black], using serum creatinine concentrations measured at a central laboratory and an assay traceable to the international reference material [12 (link)].
Excluded subjects are those who 1) are unable or unwilling to give written consent, 2) have previously received chronic dialysis or organ transplantation, 3) have heart failure (NYHA class 3 or 4) or liver cirrhosis (Child-Pugh class 2 or 3), 4) have a past or current history of malignancy, 5) are currently pregnant, or 6) have a single kidney due to trauma or kidney donation.
We defined and allocated the specific causes of the CKD into four subgroups: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), and polycystic kidney disease (PKD). The definition of the subgroup is defined by the pathologic diagnosis, in the event that the biopsy result is available. Otherwise, the subgroup classification depends on the clinical diagnosis. GN is defined by the presence of glomerular hematuria or albuminuria with or without an underlying systemic disease causing glomerulonephritis. The diagnosis of DN is based on albuminuria in a subject with type 2 diabetes mellitus and the presence of diabetic retinopathy. HTN is defined by the patient’s hypertension history and the absence of a systemic illness associated with renal damage. Unified ultrasound criteria [13 (link)] will be used to diagnose PKD. The other causative diseases will be categorized as ‘unclassified’.
Oh K.H., Park S.K., Park H.C., Chin H.J., Chae D.W., Choi K.H., Han S.H., Yoo T.H., Lee K., Kim Y.S., Chung W., Hwang Y.H., Kim S.W., Kim Y.H., Kang S.W., Park B.J., Lee J, & Ahn C. (2014). KNOW-CKD (KoreaN cohort study for Outcome in patients With Chronic Kidney Disease): design and methods. BMC Nephrology, 15, 80.
Publication 2014
Adult Biological Assay Biopsy Child Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Diabetic Retinopathy Diagnosis Dialysis Diet EGFR protein, human Glomerulonephritis Heart Failure Hematuria High Blood Pressures Hypertensive Nephropathy Kidney Kidney Diseases Kidney Failure, Chronic Kidney Glomerulus Koreans Liver Cirrhosis Malignant Neoplasms Organ Transplantation Patients Polycystic Kidney Diseases Renal Agenesis, Unilateral Serum Ultrasonics Woman Wounds and Injuries

Most recents protocols related to «Diabetic Nephropathy»

1
Accessing Design-Associated Print Files
Design-associated print files and software for described components can be found in the supplement for use by academic researchers under a material transfer agreement (MTA) from the Technology Advancement Office (TAO) of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), an institute of the National Institutes of Health (NIH). Interested parties should contact [email protected].
Jaime M.D., Salem G.H., Martinez D.J., Karott S., Flores A., Palmer C.D., Mahadevaraju S., Krynitsky J., Garmendia-Cedillos M., Anderson S., Harbison S., Pohida T.J., Ludington W.B, & Oliver B. (2023). Whole Animal Feeding FLat (WAFFL): a complete and comprehensive validation of a novel, high-throughput fly experimentation system. G3: Genes|Genomes|Genetics, 13(3), jkad012.
Publication 2023
Diabetic Nephropathy Dietary Supplements Digestive System
2
Defining Diabetic Complications: A Comprehensive Approach
Macrovascular complications are composed of cardiovascular disease, cerebrovascular disease, and peripheral arterial occlusive disease (PAOD); whereas microvascular complications comprise neuropathy, nephropathy, and retinopathy [1 (link), 2 (link)]. In this study, patients with any one of the listed conditions including coronary heart disease, myocardial ischemia and/or infarction, angina, congestive heart failure, arrhythmia, and a history of percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass graft surgery (CABG) were referred to have cardiovascular disease. The cerebrovascular disease is defined as a group of diseases including transient ischemic attack (TIA), ischemic stroke, and hemorrhagic stroke. PAOD is defined as a composite of following status, such as having symptom of intermittent claudication, abnormal foot assessment with reduced or absent pulse over dorsalis pedis artery and/or posterior tibial artery, and a history of percutaneous transluminal angioplasty (PTA), peripheral artery bypass surgery, or amputation. Moreover, diabetic polyneuropathy comprises patients who had neurologic symptoms or aberrant neurologic physical examinations such as decrease/loss of vibratory or pinprick sensation tested by hemi-quantified tuning fork and single-stranded nylon, respectively, on either foot. Patients with diabetic retinopathy are defined as those who had one of the following conditions including macular degeneration, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), blindness, or receiving laser therapy of retina in the past. Estimated glomerular filtration rate (eGFR), expressed in ml/min/1.73 m2, was calculated using the equation from Modification of Diet in Renal Disease (MDRD) [22 (link)]. Finally, diabetic kidney disease (DKD) in this study was defined as eGFR < 60 ml/min/1.73 m2 or albuminuria defined as a spot urine albumin to creatinine ratio (UACR) ≥ 30 mg/g.
Ho L.J., Sheu W.H., Lo S.H., Yeh Y.P., Hwu C.M., Huang C.N., Hsieh C.H, & Kuo F.C. (2023). Unhealthy lifestyle associated with increased risk of macro- and micro-vascular comorbidities in patients with long-duration type 2 diabetes: results from the Taiwan Diabetes Registry. Diabetology & Metabolic Syndrome, 15, 38.
Publication 2023
Age-Related Macular Degeneration Albumins Amputation Angina Pectoris Arterial Occlusive Diseases Arteries Blindness Cardiac Arrhythmia Cardiovascular Diseases Cerebrovascular Disorders Congestive Heart Failure Coronary Arteriosclerosis Coronary Artery Bypass Surgery Creatinine Diabetic Nephropathy Diabetic Polyneuropathies Diabetic Retinopathy Dietary Modification Foot Glomerular Filtration Rate Heart Disease, Coronary Hemorrhagic Stroke Infarction Intermittent Claudication Kidney Diseases Laser Therapy Neurologic Examination Neurologic Symptoms Nylons Operative Surgical Procedures Patients Percutaneous Transluminal Angioplasty Percutaneous Transluminal Coronary Angioplasty Peripheral Vascular Diseases Pulse Rate Retina Retinal Diseases Stroke, Ischemic Tibial Arteries, Posterior Transient Ischemic Attack Urine Vibration
3
Demographic and Complication Factors in CKD
Demographic characteristics and complications were recorded, including age, sex, body mass index (BMI), education level, dialysis duration, systolic blood pressure, diastolic blood pressure, pulse pressure, smoking, diabetes mellitus, hypertension, primary kidney disease, and history of CVD. Education level was recorded as the highest school level for which a diploma was obtained: primary school or below, middle school, high school and beyond. Primary kidney disease includes chronic glomerulonephritis, diabetic nephropathy, and others (such as IgA nephropathy, nephrotic syndrome, lupus nephritis, Sjogren's syndrome, and ANCA-associated vasculitis). CVD information was obtained from medical history reviews, and CVD was recorded if any of the following conditions were present: angina pectoris, grade III or IV congestive heart failure (as classified based on the guidelines of the New York Heart Association), transient ischemic attack, myocardial infarction, and cerebrovascular accident.
Zhu X., Jing R., Li X., Zhang W., Tang Y, & Liu T. (2023). Left ventricular hypertrophy, carotid atherosclerosis, and cognitive impairment in peritoneal dialysis patients. BMC Cardiovascular Disorders, 23, 127.
Publication 2023
Angina Pectoris Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis Cerebrovascular Accident Congestive Heart Failure Diabetes Mellitus Diabetic Nephropathy Dialysis Glomerulonephritis Heart High Blood Pressures IGA Glomerulonephritis Index, Body Mass Kidney Diseases Lupus Nephritis Myocardial Infarction Nephrotic Syndrome Pressure, Diastolic Pulse Pressure Sjogren's Syndrome Systolic Pressure Transient Ischemic Attack
4
Cardiometabolic Factors and Diabetic Complications
Smoking was defined as a total number of ≥100 cigarettes in their lifetime (18 (link)). Low high-density lipoprotein (HDL-c) was defined as HDL-c <1.3 mmol/L. High triglyceride (HTG) was defined as TG ≥ 1.7 mmol/L. Hypertension (HTN) was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg. Diabetic retinopathy (DR) was confirmed by ophthalmoscopy. Diabetic kidney disease (DKD) was identified clinically by an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and/or urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g caused by diabetes mellitus for ≥3 months (19 (link)). Cardiovascular diseases (CVD) included angina, previous myocardial infarction, or electrocardiographic manifestations of coronary ischemia.
Liu X., Chen D., Fu H., Liu X., Zhang Q., Zhang J., Ding M., Wen J, & Chang B. (2023). Development and validation of a risk prediction model for early diabetic peripheral neuropathy based on a systematic review and meta-analysis. Frontiers in Public Health, 11, 1128069.
Publication 2023
Albumins Angina Pectoris Cardiovascular Diseases Creatinine Diabetes Mellitus Diabetic Nephropathy Diabetic Retinopathy Electrocardiography Glomerular Filtration Rate High Blood Pressures High Density Lipoproteins Low-Density Lipoproteins Myocardial Infarction Myocardial Ischemia Ophthalmoscopy Pressure, Diastolic Systolic Pressure Triglycerides Urine
5
Kidney Disease Transcriptomics Database
A single-cell sequencing database for kidney disease called the Kidney Integrative Transcriptomics (K.I.T.) database was developed by Ben Humphrey’s lab at Washington University (http://humphreyslab.com/SingleCell/) (20 (link)). To explore the distribution of hub genes in cell groups, we applied the database for analysis and visualization of the results. In one of them, we used single nucleus RNA sequencing data from diabetic nephropathy that was initially taken from the GSE131882 dataset.
Xu M., Zhou H., Hu P., Pan Y., Wang S., Liu L, & Liu X. (2023). Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning. Frontiers in Immunology, 14, 1084531.
Publication 2023
Diabetic Nephropathy Gene Expression Profiling Genes Kidney Nucleus Solitarius Renal Agenesis, Unilateral

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More about "Diabetic Nephropathy"

Diabetic kidney disease (DKD), also known as diabetic nephropathy, is a serious complication of diabetes mellitus (DM) that involves progressive kidney damage.
It is characterized by persistent albuminuria (increased protein in the urine), gradual decline in glomerular filtration rate (GFR), and elevated blood pressure.
Early detection and appropriate management of DKD are crucial to prevent or delay the progression to end-stage renal disease (ESRD).
The underlying etiology of DKD involves a complex interplay of genetic, metabolic, and hemodynamic factors.
Effective treatment strategies focus on achieving optimal glycemic control, effective blood pressure management, and the use of angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs).
Continued research, including the use of animal models like C57BL/6N mice and statistical software like SAS 9.4, JMP 13, and SPSS 15.0, is essential to better understand the pathogenesis of DKD and identify novel therapeutic targets for this disabling condition.
Diabetic kidney disease can also be referred to as diabetic nephropathy (DN), DM-associated kidney disease, or DM-related kidney disease.
Early detection and management are critical, as DKD is a leading cause of ESRD, often requiring dialysis or kidney transplantation.
By understanding the latest research and treatment approaches, healthcare providers can optimize the care and outcomes for patients with this complex and challenging complication of diabetes.