Abstract
Objective
Research design and methods
Results
Conclusions
Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Diabetes Research and Clinical PracticeReferences
American Diabetes Association. Standards of Medical Care in Diabetes—2022. Diabetes Care 1 January 2022; 45 (Supplement_1). Univer https://doi.org/10.2337/dc22-Sint.
Ruospo M, Saglimbene VM, Palmer SC, De Cosmo S, Pacilli A, Lamacchia O, Cignarelli M, Fioretto P, Vecchio M, Craig JC, Strippoli GF. Glucose targets for preventing diabetic kidney disease and its progression. Cochrane Database Syst Rev. 2017 Jun 8;6(6):CD010137. doi: 10.1002/14651858.CD010137.pub2. PMID: 28594069; PMCID: PMC6481869.
- Diabetic kidney disease in the elderly: prevalence and clinical correlates.BMC Geriatr. 2018; 18
- Renal Insufficiency And Cardiovascular Events (RIACE) Study Group. Clinical significance of nonalbuminuric renal impairment in type 2 diabetes.J Hypertens. 2011 Sep; 29 (PMID: 21738053): 1802-1809https://doi.org/10.1097/HJH.0b013e3283495cd6
De Cosmo S, Rossi MC, Pellegrini F, et al. Kidney dysfunction and related cardiovascular risk factors among patients with type 2 diabetes. Nephrol Dial Transplant 2014; 29:657–662.
- CAPTURE: a multinational, cross-sectional study of cardiovascular disease prevalence in adults with type 2 diabetes across 13 countries.Cardiovasc Diabetol. 2021; 20
- CAPTURE: A cross-sectional study on the prevalence of cardiovascular disease in adults with type 2 diabetes in Italy.Nutr Metab Cardiovasc Dis. 2022 Jan 29; S0939–4753: 00043-00046https://doi.org/10.1016/j.numecd.2022.01.026
Hemmingsen B, Lund SS, Gluud C, Vaag A, Almdal T, Hemmingsen C, Wetterslev J. Targeting intensive glycaemic control versus targeting 15;(6):CD008143. doi: 10.1002/14651858.CD008143.pub2. Update in: Cochrane Database Syst Rev. 2013;11:CD008143. PMID: 21678374.
Strippoli GF, Bonifati C, Craig M, Navaneethan SD, Craig JC. Angiotensin converting enzyme inhibitors and angiotensin II receptor antagonists for preventing the progression of diabetic kidney disease. Cochrane Database Syst Rev. 2006 Oct 18;2006(4):CD006257. doi: 10.1002/14651858.CD006257. PMID: 17054288; PMCID: PMC6956646.
- SGLT-2 inhibitors and nephroprotection: current evidence and future perspectives.J Hum Hypertens. 2021; 35: 12-25https://doi.org/10.1038/s41371-020-00393-4
- Nephroprotective effects of GLP-1 receptor agonists: where do we stand?.J Nephrol. 2020; 33: 965-975https://doi.org/10.1007/s40620-020-00738-9
- GLP-1 Receptor Agonists and Kidney Protection.Medicina (Kaunas). 2019 May 31; 55: 233https://doi.org/10.3390/medicina55060233. PMID: 31159279; PMCID: PMC6630923
- Chronic kidney disease and end-stage renal disease-a review produced to contribute to the report ‘the status of health in the European union: towards a healthier Europe’.NDT Plus. 2010; 3: 213-224
- Effect of Intensive Versus Standard Blood Glucose Control in Patients With Type 2 Diabetes Mellitus in Different Regions of the World: Systematic Review and Meta-analysis of Randomized Controlled Trials.J Am Heart Assoc. 2015; 4: e001577
- The “Early Treatment” Approach Reducing Cardiovascular Risk in Patients with Type 2 Diabetes: A Consensus From an Expert Panel Using the Delphi Technique.Diabetes Ther. 2021 May; 12: 1445-1461
- Assessing the performance of prediction models: a framework for traditional and novel measures.Epidemiology. 2010; 21: 128-138
- Baseline quality-of-care data from a quality-improvement program implemented by a network of diabetes outpatient clinics.Diabetes Care. 2008; 31: 2166-2168
- Four-year impact of a continuous quality improvement effort implemented by a network of diabetes outpatient clinics: the AMD-Annals initiative.Diabet Med. 2010; 27: 1041-1048
- A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604
- A combined comorbidity score predicted mortality in elderly patients better than existing scores.J ClinEpidemiol. 2011; 64 (PMID: 21208778): 749-759https://doi.org/10.1016/j.jclinepi.2010.10.004
- Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.Stat Med. 2004; 23 (PMID:15211606): 2109-2123
- Prediction models need appropriate internal, internal-external, and external validation.J Clin Epidemiol. 2016; 69: 245-247https://doi.org/10.1016/j.jclinepi.2015.04.005
- Diabetic Nephropathy: New Risk Factors and Improvements in Diagnosis.Rev Diabet Stud. 2015; 12: 110-118https://doi.org/10.1900/RDS.2015.12.110
- Gender Differences in Diabetic Kidney Disease: Focus on Hormonal, Genetic and Clinical Factors.Int J Mol Sci. 2021 May 28; 22: 5808https://doi.org/10.3390/ijms22115808. PMID: 34071671; PMCID: PMC8198374
Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med. 2012;9(11):e1001344. doi: 10.1371/journal.pmed.1001344. Epub 2012 Nov 20. PMID: 23185136; PMCID: PMC3502517.
- Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.BMC Med Res Methodol. 2012; 12: 82
- Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD).Int J Environ Res Public Health. 2021 Nov 30; 18: 12649
Blech I, Katzenellenbogen M, Katzenellenbogen A, Wainstein J, Rubinstein A,Harman-Boehm I, Cohen J, Pollin TI, Glaser B. Predicting diabetic nephropathy using a multifactorial genetic model. PLoS One. 2011 Apr 14;6(4):e18743. doi:10.1371/journal.pone.0018743. PMID: 21533139; PMCID: PMC3077408.
- Prediction of ESRD and Death Among People With CKD: The Chronic Renal Impairment in Birmingham (CRIB) Prospective Cohort Study.Am J Kidney Dis. 2010; 56: 1082-1094
- A risk score for chronic kidney disease in the general population.Am J Med. 2012; 125: 270-277
- Development of risk prediction equations for incident chronic kidney disease.JAMA. 2019; 322: 2104
- Chronic Kidney Disease Prognosis Consortium. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis.Lancet. 2012 Nov 10; 380: 1662-1673
- ADVANCE Collaborative Group. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes.J Am Soc Nephrol. 2009 Aug; 20: 1813-1821
- Risk factors of gender for renal progression in patients with early chronic kidney disease.Medicine (Baltimore). 2016 Jul; 95: e4203
- Diabetes mellitus with normal renal function is associated with anaemia.Diabetes Metab Res Rev. 2014; 30: 291-296
- Anaemia in diabetic patients with chronic kidney disease—prevalence and predictors.Diabetologia. 2006; 49: 1183-1189
- Impact of diabetes on haemoglobin levels in renal disease.Diabetologia. 2006; 50: 26-31
- Higher prevalence of anemia with diabetes mellitus in moderate kidney insufficiency: The Kidney Early Evaluation Program.Kidney Int. 2005; 67: 1483-1488
Kuo IC, Lin HY, Niu SW, Lee JJ, Chiu YW, Hung CC, Hwang SJ, Chen HC. Anemia modifies the prognostic value of glycated hemoglobin in patients with diabetic chronic kidney disease. PLoS One. 2018 Jun 22;13(6):e0199378. doi: 10.1371/journal.pone.0199378.
- Natural history and risk factors for diabetic kidney disease in patients with T2D: lessons from the AMD-annals.J Nephrol. 2019; 32: 517-525
Grams ME, Sang Y, Ballew SH, Carrero JJ, Djurdjev O, Heerspink HJL, Ho K, Ito S, Marks A, Naimark D, Nash DM, Navaneethan SD, Sarnak M, Stengel B, Visseren FLJ, Wang AY, Köttgen A, Levey AS, Woodward M, Eckardt KU, Hemmelgarn B, Coresh J. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 2018 Jun;93(6):1442-1451. doi: 10.1016/j.kint.2018.01.009. Epub 2018 Mar 29. Erratum in: Kidney Int. 2018 Nov;94(5):1025-1026.
- MASTERPLAN Study Group. Validation of the kidney failure risk equation in European CKD patients.Nephrol Dial Transplant. 2013; 28: 1773-1779
- Risk prediction models for patients with chronic kidney disease: a systematic review.Ann Intern Med. 2013; 158: 596-603https://doi.org/10.7326/0003-4819-158-8- 201304160-00004
- Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD.Clin J Am Soc Nephrol. 2020 Oct 7; 15: 1424-1432
- Development and validation of a general population renal risk score.Clin J Am Soc Nephrol. 2011 Jul; 6: 1731-1738
- A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods.J Clin Epidemiol. 2013; 66: 268-277https://doi.org/10.1016/j.jclinepi.2012.06.020
van Rijn MHC, van de Luijtgaarden M, van Zuilen AD, et al. Prognostic models for chronic kidney disease: a systematic review and external validation. Nephrol Dial Transplant 2020;gfaa155. doi:10.1093/ ndt/gfaa155.
- An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford.UK BMC Med. 2016; 14https://doi.org/10.1186/s12916-016-0650-2
- ADVANCE Collaborative Group. Prediction of kidney-related outcomes in patients with type 2 diabetes.Am J Kidney Dis. 2012 Nov; 60 (Epub 2012 Jun 12. PMID: 22694950): 770-778https://doi.org/10.1053/j.ajkd.2012.04.025
- Risk Prediction for Early CKD in Type 2 Diabetes.Clin J Am Soc Nephrol. 2015 Aug 7; 10: 1371-1379
- Development and validation of Risk Equations for Complications of type 2 Diabetes (RECODe) using individual participant data from randomised trials.Lancet Diabetes Endocrinol. 2017; 5: 788-798https://doi.org/10.1016/S2213-8587(17)30221-8
- Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK).Am J Kidney Dis. 2015 Mar; 65: 394-402
- Derivation and validation of a renal risk score for people with type 2 diabetes.Diabetes Care. 2013; 36: 3113-3120
- Japan Diabetes Complications Study Group; Japanese Elderly Diabetes Intervention Trial Group. Predicting macro- and microvascular complications in type 2 diabetes: the Japan Diabetes Complications Study/the Japanese Elderly Diabetes Intervention Trial risk engine.Diabetes Care. 2013 May; 36: 1193-1199
- Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes.Sci Rep. 2021; 11
Allen A, Iqbal Z, Green-Saxena A, Hurtado M, Hoffman J, Mao Q, Das R. Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Res Care. 2022 Jan;10(1):e002560.
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Syst Rev. 2021 Nov 1; 10: 288https://doi.org/10.1186/s13643-021-01841-z. PMID: 34724973; PMCID: PMC8561867
- External validation of prognostic models for chronic kidney disease among type 2 diabetes.J Nephrol. 2022; 35: 1637-1653
- Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study.BMJ. 2021 Sep; 28n2134https://doi.org/10.1136/bmj.n2134
- Risk factors for the development of albuminuria and renal impairment in type 2 diabetes–the Swedish National Diabetes Register (NDR).Nephrol Dial Transplant. 2011; 26: 1236-1243
- Development and validation of a predictive model for chronic kidney disease progression in type 2 diabetes mellitus based on a 13-year study in Singapore.Diabetes Res Clin Pract. 2017; 123: 49-54