Comprehensive Factors for Predicting the Complications of Diabetes
Mellitus: A Systematic Review

Madurapperumage   Anuradha   Erandathi; William Yu Chung      Wang; Michael      Mayo; Ching-Chi      Lee
doi:10.2174/0115733998271863231116062601
Abstract

Background: This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years. The most common complications of diabetes, diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases are considered in the study.
Method: The features used to predict the complications are identified and categorised by scrutinising the standards of electronic health records.
Result: Overall, 102 research articles have been reviewed, resulting in 59 frequent features being identified. Nineteen attributes are recognised as a standard in all four considered complications, which are age, gender, ethnicity, weight, height, BMI, smoking history, HbA1c, SBP, eGFR, DBP, HDL, LDL, total cholesterol, triglyceride, use of insulin, duration of diabetes, family history of CVD, and diabetes. The existence of a well-accepted and updated feature set for health analytics models to predict the complications of diabetes mellitus is a vital and contemporary requirement. A widely accepted feature set is beneficial for benchmarking the risk factors of complications of diabetes.
Conclusion: This study is a thorough literature review to provide a clear state of the art for academicians, clinicians, and other stakeholders regarding the risk factors and their importance.
[1]
Rawshani A, Rawshani A, Sattar N, et al. Relative prognostic importance and optimal levels of risk factors for mortality and cardiovascular outcomes in type 1 diabetes mellitus. Circulation  2019; 139(16): 1900-12.
 [http://dx.doi.org/10.1161/CIRCULATIONAHA.118.037454] [PMID: 30798638]

[2]
Vadiveloo T, Jeffcoate W, Donnan PT, et al. Amputation-free survival in 17,353 people at high risk for foot ulceration in diabetes: A national observational study. Diabetologia  2018; 61(12): 2590-7.
 [http://dx.doi.org/10.1007/s00125-018-4723-y] [PMID: 30171278]

[3]
Braffett BH, Gubitosi-Klug RA, Albers JW, et al. Risk factors for diabetic peripheral neuropathy and cardiovascular autonomic neuropathy in the diabetes control and complications trial/epidemiology of diabetes interventions and complications (DCCT/EDIC) study. Diabetes  2020; 69(5): 1000-10.
 [http://dx.doi.org/10.2337/db19-1046] [PMID: 32051148]

[4]
Writing Group for the DCCT/EDIC Research Group Coprogression of cardiovascular risk factors in type 1 diabetes during 30 years of follow-up in the DCCT/EDIC study. Diabetes Care  2016; 39(9): 1621-30.
 [http://dx.doi.org/10.2337/dc16-0502] [PMID: 27436274]

[5]
Hainsworth DP, Bebu I, Aiello LP, et al. Risk factors for retinopathy in type 1 diabetes: The DCCT/EDIC study. Diabetes Care  2019; 42(5): 875-82.
 [http://dx.doi.org/10.2337/dc18-2308] [PMID: 30833368]

[6]
Roglic J. WHO Global Report on Diabetes: A Summary. Int J Noncommun Dis  2016; 1(1): 3-8.

[7]
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ  2017; 357(357): j2099.
 [http://dx.doi.org/10.1136/bmj.j2099] [PMID: 28536104]

[8]
Ganeshkumar P, Gopalakrishnan S. Systematic reviews and meta-analysis: Understanding the best evidence in primary healthcare. J Family Med Prim Care  2013; 2(1): 9-14.
 [http://dx.doi.org/10.4103/2249-4863.109934] [PMID: 24479036]

[9]
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med  2009; 6(7): e1000097.
 [http://dx.doi.org/10.1371/journal.pmed.1000097] [PMID: 19621072]

[10]
Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform  2008; 77(5): 291-304.
 [http://dx.doi.org/10.1016/j.ijmedinf.2007.09.001] [PMID: 17951106]

[11]
Gliklich RE, Leavy MB, Dreyer NA. Tools and technologies for registry interoperability, registries for evaluating patient outcomes: A user’s guide.In: Addendum 2, Ed. 3rd. US: Rockville (MD): Agency for Healthcare Research and Quality. 2019.

[12]
Clarivate. Web of Science Platform. Clarivate  Available from: https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/ (Accessed July 01 2023)

[13]
Andersen ST, Witte DR, Dalsgaard EM, et al. Risk factors for incident diabetic polyneuropathy in a cohort with screen-detected type 2 diabetes followed for 13 years: ADDITION-Denmark. Diabetes Care  2018; 41(5): 1068-75.
 [http://dx.doi.org/10.2337/dc17-2062] [PMID: 29487078]

[14]
Gnatiuc L, Herrington WG, Halsey J, et al. Sex-specific relevance of diabetes to occlusive vascular and other mortality: A collaborative meta-analysis of individual data from 980 793 adults from 68 prospective studies. Lancet Diabetes Endocrinol  2018; 6(7): 538-46.
 [http://dx.doi.org/10.1016/S2213-8587(18)30079-2] [PMID: 29752194]

[15]
Andersen ST, Witte DR, Andersen H, et al. Risk-factor trajectories preceding diabetic polyneuropathy: ADDITION-Denmark. Diabetes Care  2018; 41(9): 1955-62.
 [http://dx.doi.org/10.2337/dc18-0392] [PMID: 29987164]

[16]
Anderson SG, Shoo H, Saluja S, et al. Social deprivation modifies the association between incident foot ulceration and mortality in type 1 and type 2 diabetes: A longitudinal study of a primary-care cohort. Diabetologia  2018; 61(4): 959-67.
 [http://dx.doi.org/10.1007/s00125-017-4522-x] [PMID: 29264632]

[17]
Herder C, Kannenberg JM, Huth C, et al. Proinflammatory cytokines predict the incidence and progression of distal sensorimotor polyneuropathy: KORA F4/FF4 Study. Diabetes Care  2017; 40(4): 569-76.
 [http://dx.doi.org/10.2337/dc16-2259] [PMID: 28174259]

[18]
Matsushita K, Kwak L, Yang C, et al. High-sensitivity cardiac troponin and natriuretic peptide with risk of lower-extremity peripheral artery disease: The Atherosclerosis Risk in Communities (ARIC) Study. Eur Heart J  2018; 39(25): 2412-9.
 [http://dx.doi.org/10.1093/eurheartj/ehy106] [PMID: 29579246]

[19]
Gurney JK, Stanley J, Rumball-Smith J, York S, Sarfati D. Postoperative death after lower-limb amputation in a national prevalent cohort of patients with diabetes. Diabetes Care  2018; 41(6): 1204-11.
 [http://dx.doi.org/10.2337/dc17-2557] [PMID: 29622543]

[20]
Gurney JK, Stanley J, York S, Rosenbaum D, Sarfati D. Risk of lower limb amputation in a national prevalent cohort of patients with diabetes. Diabetologia  2018; 61(3): 626-35.
 [http://dx.doi.org/10.1007/s00125-017-4488-8] [PMID: 29101423]

[21]
Boyko EJ, Seelig AD, Ahroni JH. Limb- and person-level risk factors for lower-limb amputation in the prospective seattle diabetic foot study. Diabetes Care  2018; 41(4): 891-8.
 [http://dx.doi.org/10.2337/dc17-2210] [PMID: 29439130]

[22]
Fesseha BK, Abularrage CJ, Hines KF, et al. Association of hemoglobin A1c and wound healing in diabetic foot ulcers. Diabetes Care  2018; 41(7): 1478-85.
 [http://dx.doi.org/10.2337/dc17-1683] [PMID: 29661917]

[23]
Potier L, Roussel R, Velho G, et al. Lower limb events in individuals with type 2 diabetes: evidence for an increased risk associated with diuretic use. Diabetologia  2019; 62(6): 939-47.
 [http://dx.doi.org/10.1007/s00125-019-4835-z] [PMID: 30809716]

[24]
Seferovic JP, Pfeffer MA, Claggett B, et al. Three-question set from Michigan Neuropathy Screening Instrument adds independent prognostic information on cardiovascular outcomes: Analysis of ALTITUDE trial. Diabetologia  2018; 61(3): 581-8.
 [http://dx.doi.org/10.1007/s00125-017-4485-y] [PMID: 29098323]

[25]
Smith-Strøm H, Igland J, Østbye T, et al. The effect of telemedicine follow-up care on diabetes-related foot ulcers: A cluster-randomized controlled noninferiority trial. Diabetes Care  2018; 41(1): 96-103.
 [http://dx.doi.org/10.2337/dc17-1025] [PMID: 29187423]

[26]
Spreen MI, Gremmels H, Teraa M, et al. Diabetes is associated with decreased limb survival in patients with critical limb ischemia: pooled data from two randomized controlled trials. Diabetes Care  2016; 39(11): 2058-64.
 [http://dx.doi.org/10.2337/dc16-0850] [PMID: 27612499]

[27]
Hallström S, Svensson AM, Pivodic A, et al. Risk factors and incidence over time for lower extremity amputations in people with type 1 diabetes: An observational cohort study of 46,088 patients from the Swedish National Diabetes Registry. Diabetologia  2021; 64(12): 2751-61.
 [http://dx.doi.org/10.1007/s00125-021-05550-z] [PMID: 34494137]

[28]
Štotl I, Blagus R, Urbančič-Rovan V. Individualised screening of diabetic foot: Creation of a prediction model based on penalised regression and assessment of theoretical efficacy. Diabetologia  2022; 65(2): 291-300.
 [http://dx.doi.org/10.1007/s00125-021-05604-2] [PMID: 34741637]

[29]
Levitt Katz LE, White NH. El ghormli L, et al Risk factors for diabetic peripheral neuropathy in adolescents and young adults with type 2 diabetes: Results from the today study. Diabetes Care  2022; 45(5): 1065-72.
 [http://dx.doi.org/10.2337/dc21-1074] [PMID: 34716210]

[30]
Siddiqui MK, Kennedy G, Carr F, et al. Lp-PLA2 activity is associated with increased risk of diabetic retinopathy: A longitudinal disease progression study. Diabetologia  2018; 61(6): 1344-53.
 [http://dx.doi.org/10.1007/s00125-018-4601-7] [PMID: 29623345]

[31]
Deal JA, Sharrett AR, Rawlings AM, et al. Retinal signs and 20-year cognitive decline in the atherosclerosis risk in communities study. Neurology  2018; 90(13): e1158-66.
 [http://dx.doi.org/10.1212/WNL.0000000000005205] [PMID: 29490915]

[32]
Wang T, Hong JL, Gower EW, et al. Incretin-based therapies and diabetic retinopathy: Real-world evidence in older U.S. adults. Diabetes Care  2018; 41(9): 1998-2009.
 [http://dx.doi.org/10.2337/dc17-2285] [PMID: 30012674]

[33]
Drinkwater JJ, Davis TME, Turner AW, Bruce DG, Davis WA. Incidence and determinants of intraocular lens implantation in type 2 diabetes: The fremantle diabetes study phase II. Diabetes Care  2019; 42(2): 288-96.
 [http://dx.doi.org/10.2337/dc18-1556] [PMID: 30523034]

[34]
Cheung CY, Sabanayagam C, Law AK, et al. Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy. Diabetologia  2017; 60(9): 1770-81.
 [http://dx.doi.org/10.1007/s00125-017-4333-0] [PMID: 28623387]

[35]
Sasongko MB, Widyaputri F, Sulistyoningrum DC, et al. Estimated resting metabolic rate and body composition measures are strongly associated with diabetic retinopathy in indonesian adults with type 2 diabetes. Diabetes Care  2018; 41(11): 2377-84.
 [http://dx.doi.org/10.2337/dc18-1074] [PMID: 30213883]

[36]
Douros A, Filion KB, Yin H, et al. Glucagon-like peptide 1 receptor agonists and the risk of incident diabetic retinopathy. Diabetes Care  2018; 41(11): 2330-8.
 [http://dx.doi.org/10.2337/dc17-2280] [PMID: 30150234]

[37]
Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care  2018; 41(12): 2509-16.
 [http://dx.doi.org/10.2337/dc18-0147] [PMID: 30275284]

[38]
Forster RB, Garcia ES, Sluiman AJ, et al. Retinal venular tortuosity and fractal dimension predict incident retinopathy in adults with type 2 diabetes: the Edinburgh Type 2 Diabetes Study. Diabetologia  2021; 64(5): 1103-12.
 [http://dx.doi.org/10.1007/s00125-021-05388-5] [PMID: 33515071]

[39]
Sandoval-Garcia E, McLachlan S, Price AH, et al. Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia  2021; 64(10): 2215-27.
 [http://dx.doi.org/10.1007/s00125-021-05499-z] [PMID: 34160658]

[40]
Wykoff CC, Khurana RN, Nguyen QD, et al. Risk of blindness among patients with diabetes and newly diagnosed diabetic retinopathy. Diabetes Care  2021; 44(3): 748-56.
 [http://dx.doi.org/10.2337/dc20-0413] [PMID: 33472864]

[41]
Coca SG, Nadkarni GN, Huang Y, et al. Plasma biomarkers and kidney function decline in early and established diabetic kidney disease. J Am Soc Nephrol  2017; 28(9): 2786-93.
 [http://dx.doi.org/10.1681/ASN.2016101101] [PMID: 28476763]

[42]
Hadjadj S, Cariou B, Fumeron F, et al. Death, end-stage renal disease and renal function decline in patients with diabetic nephropathy in French cohorts of type 1 and type 2 diabetes. Diabetologia  2016; 59(1): 208-16.
 [http://dx.doi.org/10.1007/s00125-015-3785-3] [PMID: 26486355]

[43]
Barr ELM, Barzi F, Hughes JT, et al. High baseline levels of tumor necrosis factor receptor 1 are associated with progression of kidney disease in indigenous australians with diabetes: The eGFR follow-up study. Diabetes Care  2018; 41(4): 739-47.
 [http://dx.doi.org/10.2337/dc17-1919] [PMID: 29367427]

[44]
Heinzel A, Kammer M, Mayer G, et al. Validation of plasma biomarker candidates for the prediction of egfr decline in patients with type 2 diabetes. Diabetes Care  2018; 41(9): 1947-54.
 [http://dx.doi.org/10.2337/dc18-0532] [PMID: 29980527]

[45]
Peters KE, Davis WA, Ito J, et al. Identification of novel circulating biomarkers predicting rapid decline in renal function in type 2 diabetes: the fremantle diabetes study phase II. Diabetes Care  2017; 40(11): 1548-55.
 [http://dx.doi.org/10.2337/dc17-0911] [PMID: 28851702]

[46]
Mottl AK, Gasim A, Schober FP, et al. Segmental sclerosis and extracapillary hypercellularity predict diabetic ESRD. J Am Soc Nephrol  2018; 29(2): 694-703.
 [http://dx.doi.org/10.1681/ASN.2017020192] [PMID: 29180393]

[47]
Vistisen D, Andersen GS, Hulman A, Persson F, Rossing P, Jørgensen ME. Progressive decline in estimated glomerular filtration rate in patients with diabetes after moderate loss in kidney function—even without albuminuria. Diabetes Care  2019; 42(10): 1886-94.
 [http://dx.doi.org/10.2337/dc19-0349] [PMID: 31221677]

[48]
Baker NL, Hunt KJ, Stevens DR, et al. Association between inflammatory markers and progression to kidney dysfunction: Examining different assessment windows in patients with type 1 diabetes. Diabetes Care  2018; 41(1): 128-35.
 [http://dx.doi.org/10.2337/dc17-0867] [PMID: 29118060]

[49]
Pilemann-Lyberg S, Hansen TW, Tofte N, et al. Uric acid is an independent risk factor for decline in kidney function, cardiovascular events, and mortality in patients with type 1 diabetes. Diabetes Care  2019; 42(6): 1088-94.
 [http://dx.doi.org/10.2337/dc18-2173] [PMID: 30885950]

[50]
Rhee CM, Kovesdy CP, Ravel VA, et al. Association of glycemic status during progression of chronic kidney disease with early dialysis mortality in patients with diabetes. Diabetes Care  2017; 40(8): 1050-7.
 [http://dx.doi.org/10.2337/dc17-0110] [PMID: 28592525]

[51]
Ku E, McCulloch CE, Mauer M, Gitelman SE, Grimes BA, Hsu C. Association between blood pressure and adverse renal events in type 1 diabetes. Diabetes Care  2016; 39(12): 2218-24.
 [http://dx.doi.org/10.2337/dc16-0857] [PMID: 27872156]

[52]
Perkins BA, Bebu I, de Boer IH, et al. Risk factors for kidney disease in type 1 diabetes. Diabetes Care  2019; 42(5): 883-90.
 [http://dx.doi.org/10.2337/dc18-2062] [PMID: 30833370]

[53]
Lee CH, Cheung CYY, Woo YC, et al. Prospective associations of circulating adipocyte fatty acid-binding protein levels with risks of renal outcomes and mortality in type 2 diabetes. Diabetologia  2019; 62(1): 169-77.
 [http://dx.doi.org/10.1007/s00125-018-4742-8] [PMID: 30267180]

[54]
Colombo M, McGurnaghan SJ, Bell S, et al. Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus. Diabetologia  2020; 63(3): 636-47.
 [http://dx.doi.org/10.1007/s00125-019-05052-z] [PMID: 31807796]

[55]
Penno G, Russo E, Garofolo M, et al. Evidence for two distinct phenotypes of chronic kidney disease in individuals with type 1 diabetes mellitus. Diabetologia  2017; 60(6): 1102-13.
 [http://dx.doi.org/10.1007/s00125-017-4251-1] [PMID: 28357502]

[56]
Mayer G, Heerspink HJL, Aschauer C, et al. Systems biology–derived biomarkers to predict progression of renal function decline in type 2 diabetes. Diabetes Care  2017; 40(3): 391-7.
 [http://dx.doi.org/10.2337/dc16-2202] [PMID: 28077457]

[57]
Vaisar T, Durbin-Johnson B, Whitlock K, et al. Urine complement proteins and the risk of kidney disease progression and mortality in type 2 diabetes. Diabetes Care  2018; 41(11): 2361-9.
 [http://dx.doi.org/10.2337/dc18-0699] [PMID: 30150236]

[58]
Bjornstad P, Laffel L, Lynch J, et al. Elevated serum uric acid is associated with greater risk for hypertension and diabetic kidney diseases in obese adolescents with type 2 diabetes: An observational analysis from the treatment options for type 2 diabetes in adolescents and youth (TODAY) study. Diabetes Care  2019; 42(6): 1120-8.
 [http://dx.doi.org/10.2337/dc18-2147] [PMID: 30967435]

[59]
Skupien J, Warram JH, Smiles AM, Stanton RC, Krolewski AS. patterns of estimated glomerular filtration rate decline leading to end-stage renal disease in type 1 diabetes. Diabetes Care  2016; 39(12): 2262-9.
 [http://dx.doi.org/10.2337/dc16-0950] [PMID: 27647852]

[60]
Bidadkosh A, Lambooy SPH, Heerspink HJ, et al. Predictive properties of biomarkers GDF-15, NTproBNP, and hs-TnT for morbidity and mortality in patients with type 2 diabetes with nephropathy. Diabetes Care  2017; 40(6): 784-92.
 [http://dx.doi.org/10.2337/dc16-2175] [PMID: 28341782]

[61]
Lin CY, Hsieh MC, Kor CT, Hsieh YP. Association and risk factors of chronic kidney disease and incident diabetes: A nationwide population-based cohort study. Diabetologia  2019; 62(3): 438-47.
 [http://dx.doi.org/10.1007/s00125-018-4788-7] [PMID: 30607465]

[62]
Vistisen D, Andersen GS, Hulman A, et al. A validated prediction model for end-stage kidney disease in type 1 diabetes. Diabetes Care  2021; 44(4): 901-7.
 [http://dx.doi.org/10.2337/dc20-2586] [PMID: 33509931]

[63]
Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia  2021; 64(7): 1504-15.
 [http://dx.doi.org/10.1007/s00125-021-05444-0] [PMID: 33797560]

[64]
Bjornstad P. El ghormli L, Hughan KS, et al Effects of metabolic factors, race-ethnicity, and sex on the development of nephropathy in adolescents and young adults with type 2 diabetes: Results from the today study. Diabetes Care  2022; 45(5): 1056-64.
 [http://dx.doi.org/10.2337/dc21-1085] [PMID: 34531309]

[65]
Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group Risk factors for cardiovascular disease in type 1 diabetes. Diabetes  2016; 65(5): 1370-9.
 [http://dx.doi.org/10.2337/db15-1517] [PMID: 26895792]

[66]
Bragg F, Li L, Yang L, et al. Risks and population burden of cardiovascular diseases associated with diabetes in China: A prospective study of 0.5 million adults. PLoS Med  2016; 13(7): e1002026.
 [http://dx.doi.org/10.1371/journal.pmed.1002026] [PMID: 27379518]

[67]
Wong ND, Zhao Y, Patel R, et al. Cardiovascular risk factor targets and cardiovascular disease event risk in diabetes: A pooling project of the atherosclerosis risk in communities study, multi-ethnic study of atherosclerosis, and jackson heart study. Diabetes Care  2016; 39(5): 668-76.
 [http://dx.doi.org/10.2337/dc15-2439] [PMID: 27208374]

[68]
Steinarsson AO, Rawshani A, Gudbjörnsdottir S, Franzén S, Svensson AM, Sattar N. Short-term progression of cardiometabolic risk factors in relation to age at type 2 diabetes diagnosis: A longitudinal observational study of 100,606 individuals from the Swedish National Diabetes Register. Diabetologia  2018; 61(3): 599-606.
 [http://dx.doi.org/10.1007/s00125-017-4532-8] [PMID: 29318343]

[69]
Wright AK, Kontopantelis E, Emsley R, et al. Cardiovascular risk and risk factor management in type 2 diabetes mellitus. Circulation  2019; 139(24): 2742-53.
 [http://dx.doi.org/10.1161/CIRCULATIONAHA.118.039100] [PMID: 30986362]

[70]
Armstrong AC, Ambale-Venkatesh B, Turkbey E, et al. Association of cardiovascular risk factors and myocardial fibrosis with early cardiac dysfunction in type 1 diabetes: The diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care  2017; 40(3): 405-11.
 [http://dx.doi.org/10.2337/dc16-1889] [PMID: 27986796]

[71]
Andersen ST, Witte DR, Fleischer J, et al. Risk factors for the presence and progression of cardiovascular autonomic neuropathy in type 2 diabetes: ADDITION-Denmark. Diabetes Care  2018; 41(12): 2586-94.
 [http://dx.doi.org/10.2337/dc18-1411] [PMID: 30305347]

[72]
Braffett BH, Dagogo-Jack S, Bebu I, et al. Association of insulin dose, cardiometabolic risk factors, and cardiovascular disease in type 1 diabetes during 30 years of follow-up in the DCCT/EDIC Study. Diabetes Care  2019; 42(4): 657-64.
 [http://dx.doi.org/10.2337/dc18-1574] [PMID: 30728218]

[73]
Standl E, Stevens SR, Lokhnygina Y, et al. Confirming the bidirectional nature of the association between severe hypoglycemic and cardiovascular events in type 2 diabetes: Insights from EXSCEL. Diabetes Care  2020; 43(3): 643-52.
 [http://dx.doi.org/10.2337/dc19-1079] [PMID: 31882409]

[74]
Elder DHJ, Singh JSS, Levin D, et al. Mean HbA 1C and mortality in diabetic individuals with heart failure: A population cohort study. Eur J Heart Fail  2016; 18(1): 94-102.
 [http://dx.doi.org/10.1002/ejhf.455] [PMID: 26663216]

[75]
Lee KY, Hwang BH, Kim TH, et al. Computed tomography angiography images of coronary artery stenosis provide a better prediction of risk than traditional risk factors in asymptomatic individuals with type 2 diabetes: A long-term study of clinical outcomes. Diabetes Care  2017; 40(9): 1241-8.
 [http://dx.doi.org/10.2337/dc16-1844] [PMID: 28663384]

[76]
Jin JL, Cao YX, Zhang HW, et al. Lipoprotein(a) and cardiovascular outcomes in patients with coronary artery disease and prediabetes or diabetes. Diabetes Care  2019; 42(7): 1312-8.
 [http://dx.doi.org/10.2337/dc19-0274] [PMID: 31076417]

[77]
Pop-Busui R, Braffett BH, Zinman B, et al. Cardiovascular autonomic neuropathy and cardiovascular outcomes in the diabetes control and complications trial/epidemiology of diabetes interventions and complications (DCCT/EDIC) study. Diabetes Care  2017; 40(1): 94-100.
 [http://dx.doi.org/10.2337/dc16-1397] [PMID: 27803120]

[78]
Wan EYF, Fung CSC, Yu EYT, Fong DYT, Chen JY, Lam CLK. Association of visit-to-visit variability of systolic blood pressure with cardiovascular disease and mortality in primary care chinese patients with type 2 diabetes: A retrospective population-based cohort study. Diabetes Care  2017; 40(2): 270-9.
 [http://dx.doi.org/10.2337/dc16-1617] [PMID: 27899498]

[79]
Strelitz J, Ahern AL, Long GH, et al. Moderate weight change following diabetes diagnosis and 10 year incidence of cardiovascular disease and mortality. Diabetologia  2019; 62(8): 1391-402.
 [http://dx.doi.org/10.1007/s00125-019-4886-1] [PMID: 31062041]

[80]
Sattar N, Rawshani A, Franzén S, et al. Age at diagnosis of type 2 diabetes mellitus and associations with cardiovascular and mortality risks. Circulation  2019; 139(19): 2228-37.
 [http://dx.doi.org/10.1161/CIRCULATIONAHA.118.037885] [PMID: 30955347]

[81]
Lamb MJE, Westgate K, Brage S, et al. Prospective associations between sedentary time, physical activity, fitness and cardiometabolic risk factors in people with type 2 diabetes. Diabetologia  2016; 59(1): 110-20.
 [http://dx.doi.org/10.1007/s00125-015-3756-8] [PMID: 26518682]

[82]
Rørth R, Jhund PS, Mogensen UM, et al. Risk of incident heart failure in patients with diabetes and asymptomatic left ventricular systolic dysfunction. Diabetes Care  2018; 41(6): 1285-91.
 [http://dx.doi.org/10.2337/dc17-2583] [PMID: 29626073]

[83]
Muhammad IF, Borné Y, Östling G, et al. Arterial stiffness and incidence of diabetes: A population-based cohort study. Diabetes Care  2017; 40(12): 1739-45.
 [http://dx.doi.org/10.2337/dc17-1071] [PMID: 28971963]

[84]
Miller RG, Costacou T, Orchard TJ. Risk factor modeling for cardiovascular disease in type 1 diabetes in the pittsburgh epidemiology of diabetes complications (EDC) study: A comparison with the diabetes control and complications trial/epidemiology of diabetes interventions and complications study (DCCT/EDIC). Diabetes  2019; 68(2): 409-19.
 [http://dx.doi.org/10.2337/db18-0515] [PMID: 30409781]

[85]
Brownrigg JRW, Hughes CO, Burleigh D, et al. Microvascular disease and risk of cardiovascular events among individuals with type 2 diabetes: A population-level cohort study. Lancet Diabetes Endocrinol  2016; 4(7): 588-97.
 [http://dx.doi.org/10.1016/S2213-8587(16)30057-2] [PMID: 27216886]

[86]
Tian J, Sheng CS, Sun W, et al. Effects of high blood pressure on cardiovascular disease events among chinese adults with different glucose metabolism. Diabetes Care  2018; 41(9): 1895-900.
 [http://dx.doi.org/10.2337/dc18-0918] [PMID: 30002198]

[87]
Welsh C, Welsh P, Celis-Morales CA, et al. Glycated hemoglobin, prediabetes, and the links to cardiovascular disease. Data From UK Biobank Diabetes Care  2020; 43(2): 440-5.
 [http://dx.doi.org/10.2337/dc19-1683] [PMID: 31852727]

[88]
Read SH, van Diepen M, Colhoun HM, et al. Performance of cardiovascular disease risk scores in people diagnosed with type 2 diabetes: External validation using data from the national scottish diabetes register. Diabetes Care  2018; 41(9): 2010-8.
 [http://dx.doi.org/10.2337/dc18-0578] [PMID: 30002197]

[89]
Polovina M, Lund LH, Đikić D, et al. Type 2 diabetes increases the long‐term risk of heart failure and mortality in patients with atrial fibrillation. Eur J Heart Fail  2020; 22(1): 113-25.
 [http://dx.doi.org/10.1002/ejhf.1666] [PMID: 31822042]

[90]
Bebu I, Schade D, Braffett B, et al. Risk factors for first and subsequent cvd events in type 1 diabetes: The DCCT/EDIC study. Diabetes Care  2020; 43(4): 867-74.
 [http://dx.doi.org/10.2337/dc19-2292] [PMID: 32001614]

[91]
McGurnaghan SJ, McKeigue PM, Read SH, et al. Development and validation of a cardiovascular risk prediction model in type 1 diabetes. Diabetologia  2021; 64(9): 2001-11.
 [http://dx.doi.org/10.1007/s00125-021-05478-4] [PMID: 34106282]

[92]
Ferket BS, Hunink MGM, Masharani U, Max W, Yeboah J, Fleischmann KE. Long-term predictions of incident coronary artery calcium to 85 years of age for asymptomatic individuals with and without type 2 diabetes. Diabetes Care  2021; 44(7): 1664-71.
 [http://dx.doi.org/10.2337/dc20-1960] [PMID: 34078663]

[93]
Segar MW, Patel KV, Vaduganathan M, et al. Development and validation of optimal phenomapping methods to estimate long-term atherosclerotic cardiovascular disease risk in patients with type 2 diabetes. Diabetologia  2021; 64(7): 1583-94.
 [http://dx.doi.org/10.1007/s00125-021-05426-2] [PMID: 33715025]

[94]
Bebu I, Keshavarzi S, Gao X, et al. Genetic risk factors for CVD in type 1 diabetes: The DCCT/EDIC study. Diabetes Care  2021; 44(6): 1309-16.
 [http://dx.doi.org/10.2337/dc20-2388] [PMID: 33883194]

[95]
Gubitosi-Klug R, Gao X, Pop-Busui R, et al. Associations of microvascular complications with the risk of cardiovascular disease in type 1 diabetes. Diabetes Care  2021; 44(7): 1499-505.
 [http://dx.doi.org/10.2337/dc20-3104] [PMID: 33980605]

[96]
Keshavarzi S, Braffett BH, Pop-Busui R, et al. Risk factors for longitudinal resting heart rate and its associations with cardiovascular outcomes in the DCCT/EDIC study. Diabetes Care  2021; 44(5): 1125-32.
 [http://dx.doi.org/10.2337/dc20-2387] [PMID: 33632724]

[97]
Mordi IR, Trucco E, Syed MG, et al. Prediction of major adverse cardiovascular events from retinal, clinical, and genomic data in individuals with type 2 diabetes: A population cohort study. Diabetes Care  2022; 45(3): 710-6.
 [http://dx.doi.org/10.2337/dc21-1124] [PMID: 35043139]

[98]
Dziopa K, Asselbergs FW, Gratton J, Chaturvedi N, Schmidt AF. Cardiovascular risk prediction in type 2 diabetes: A comparison of 22 risk scores in primary care settings. Diabetologia  2022; 65(4): 644-56.
 [http://dx.doi.org/10.1007/s00125-021-05640-y] [PMID: 35032176]

[99]
Barbieri S, Mehta S, Wu B, et al. Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach. Int J Epidemiol  2022; 51(3): 931-44.
 [http://dx.doi.org/10.1093/ije/dyab258] [PMID: 34910160]

[100]
Eleuteri A, Fisher AC, Broadbent DM, et al. Individualised variable-interval risk-based screening for sight-threatening diabetic retinopathy: The Liverpool Risk Calculation Engine. Diabetologia  2017; 60(11): 2174-82.
 [http://dx.doi.org/10.1007/s00125-017-4386-0] [PMID: 28840258]

[101]
Li S, Nemeth I, Donnelly L, Hapca S, Zhou K, Pearson ER. Visit-to-Visit HbA1c variability is associated with cardiovascular disease and microvascular complications in patients with newly diagnosed type 2 diabetes. Diabetes Care  2020; 43(2): 426-32.
 [http://dx.doi.org/10.2337/dc19-0823] [PMID: 31727686]

[102]
Xu Z, Arnold M, Sun L, et al. Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: Results from UK primary care electronic health records. Int J Epidemiol  2022; 51(6): 1813-23.
 [http://dx.doi.org/10.1093/ije/dyac140] [PMID: 35776101]

[103]
Pollack S, Igo RP Jr, Jensen RA, et al. Multiethnic genome-wide association study of diabetic retinopathy using liability threshold modeling of duration of diabetes and glycemic control. Diabetes  2019; 68(2): 441-56.
 [http://dx.doi.org/10.2337/db18-0567] [PMID: 30487263]

[104]
Bebu I, Braffett BH, Orchard TJ, Lorenzi GM, Lachin JM. Mediation of the effect of glycemia on the risk of CVD outcomes in type 1 diabetes: The DCCT/EDIC study. Diabetes Care  2019; 42(7): 1284-9.
 [http://dx.doi.org/10.2337/dc18-1613] [PMID: 30894365]

[105]
International Diabetes Federation.  IDF Diabetes Atlas. Brussels International Diabetes Federation 2019.
Rights & Permissions Print Cite
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/0115733998271863231116062601	Print ISSN 1573-3998
Publisher Name Bentham Science Publisher	Online ISSN 1875-6417
Current Diabetes Reviews

Comprehensive Factors for Predicting the Complications of Diabetes Mellitus: A Systematic Review

Abstract Play Pause

Related Journals

Related Books

Abstract
