Predictive modeling for incident and prevalent diabetes risk evaluation

References

• Beagley J, Guariguata L, Weil C, et al. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract 2014;103(2):150-60
   PubMed Web of Science ®Google Scholar
• Clarke PM, Glasziou P, Patel A, et al. Event rates, hospital utilization, and costs associated with major complications of diabetes: a multicountry comparative analysis. PLoS Med 2010;7(2):e1000236
   PubMed Web of Science ®Google Scholar
• Harris MI, Eastman RC. Early detection of undiagnosed diabetes mellitus: a US perspective. Diabetes Metab Res Rev 2000;16(4):230-6
   PubMed Web of Science ®Google Scholar
• Campbell IW. Type 2 diabetes mellitus: ‘the silent killer’. Pract Diabetes Int 2001;18(6):187-91
   Google Scholar
• World Health Organization. Screening for type 2 diabetes: report of a world health organization and international diabetes federation meeting. 2003
   Google Scholar
• Pan X-R, Li G-W, Hu Y-H, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care 1997;20(4):537-44
   PubMed Web of Science ®Google Scholar
• Knowler WC, Barrett-Connor E, Fowler SE. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346(6):393
   PubMed Web of Science ®Google Scholar
• Tuomilehto J, Lindström J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344(18):1343-50
   PubMed Web of Science ®Google Scholar
• Knowler WC, Hamman RF, Edelstein SL. Prevention of type 2 diabetes with troglitazone in the Diabetes Prevention Program. Diabetes 2005;54(4):1150
   PubMed Web of Science ®Google Scholar
• Unwin N, Shaw J, Zimmet P, et al. Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention. Diabet Med 2002;19(9):708-23
   PubMed Web of Science ®Google Scholar
• Edelstein SL, Knowler WC, Bain RP, et al. Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies. Diabetes 1997;46(4):701-10
   PubMed Web of Science ®Google Scholar
• Ramachandran A, Snehalatha C, Naik RAS, et al. Significance of impaired glucose tolerance in an Asian Indian population: a follow-up study. Diabetes Res Clin Pract 1986;2(3):173-8
   PubMedGoogle Scholar
• Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008;359(21):2208-19
   PubMed Web of Science ®Google Scholar
• Talmud PJ, Hingorani AD, Cooper JA, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 2010;340:b4838
   PubMed Web of Science ®Google Scholar
• Park KS. The search for genetic risk factors of type 2 diabetes mellitus. Diabetes Metab J 2011;35(1):12-22
   PubMedGoogle Scholar
• Herron CA. Screening in diabetes mellitus: report of the Atlanta workshop. Diabetes Care 1979;2(4):357-62
   PubMedGoogle Scholar
• National Diabetes Data Group. Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. National diabetes data group. Diabetes 1979;28(12):1039-57
   PubMed Web of Science ®Google Scholar
• American Diabetes Association. Screening for diabetes. Diabetes Care 1989;12(8):588-90
   PubMedGoogle Scholar
• American Diabetes Association. American diabetes alert. Diabetes Forecast 1993;46(3):54-5
   Google Scholar
• Stern MP, Morales PA, Valdez RA, et al. Predicting diabetes: moving beyond impaired glucose tolerance. Diabetes 1993;42(5):706-14
   PubMedGoogle Scholar
• Herman WH, Smith PJ, Thompson TJ, et al. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes Care 1995;18(3):382-7
   PubMed Web of Science ®Google Scholar
• Herman WH, Smith PJ, Thompson TJ, et al. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes Care 1995;18(3):382-7
   PubMed Web of Science ®Google Scholar
• Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011;343:d7163
   PubMed Web of Science ®Google Scholar
• Brown N, Critchley J, Bogowicz P, et al. Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review. Diabetes Res Clin Pract 2012;98(3):369-85
   PubMedGoogle Scholar
• Collins GS, Mallett S, Omar O, et al. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011;9(1):103
   PubMed Web of Science ®Google Scholar
• Buijsse B, Simmons RK, Griffin SJ, et al. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011;33(1):46-62
   PubMedGoogle Scholar
• Thoopputra T, Newby D, Schneider J, et al. Survey of diabetes risk assessment tools: concepts, structure and performance. Diabetes Metab Res Rev 2012;28(6):485-98
   PubMedGoogle Scholar
• Altman DG, Vergouwe Y, Royston P, et al. Prognosis and prognostic research: validating a prognostic model. BMJ 2009;338:b605
   PubMed Web of Science ®Google Scholar
• Glümer C, Vistisen D, Borch-Johnsen K, et al. Risk scores for type 2 diabetes can be applied in some populations but not all. Diabetes Care 2006;29(2):410-14
   PubMedGoogle Scholar
• de Sousa AGP, Pereira AC, Marquezine GF, et al. Derivation and external validation of a simple prediction model for the diagnosis of type 2 diabetes mellitus in the Brazilian urban population. Eur J Epidemiol 2009;24(2):101-9
   PubMedGoogle Scholar
• Hanif M, Valsamakis G, Dixon A, et al. Detection of impaired glucose tolerance and undiagnosed type 2 diabetes in UK South Asians: an effective screening strategy. Diabetes Obes Metab 2008;10(9):755-62
   PubMedGoogle Scholar
• Bindraban NR, van Valkengoed IG, Mairuhu G, et al. Prevalence of diabetes mellitus and the performance of a risk score among Hindustani Surinamese, African Surinamese and ethnic Dutch: a cross-sectional population-based study. BMC Public Health 2008;8(1):271
   PubMedGoogle Scholar
• Spijkerman AM, Yuyun MF, Griffin SJ, et al. The performance of a risk score as a screening test for undiagnosed hyperglycemia in ethnic minority groups data from the 1999 health survey for england. Diabetes Care 2004;27(1):116-22
   PubMedGoogle Scholar
• Investigators A. The atherosclerosis risk in communit (aric) stui) y: design and objectwes. Am J Epidemiol 1989;129(4):687-702
   PubMedGoogle Scholar
• Williams D, Wareham N, Brown D, et al. Undiagnosed glucose intolerance in the community: the Isle of Ely Diabetes Project. Diabet Med 1995;12(1):30-5
   PubMedGoogle Scholar
• Vartiainen E, Laatikainen T, Peltonen M, et al. Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol 2010;39(2):504-18
   PubMed Web of Science ®Google Scholar
• Lin J-W, Chang Y-C, Li H-Y, et al. Cross-sectional validation of diabetes risk scores for predicting diabetes, metabolic syndrome, and chronic kidney disease in Taiwanese. Diabetes Care 2009;32(12):2294-6
   PubMedGoogle Scholar
• Alssema M, Vistisen D, Heymans M, et al. The evaluation of screening and early detection strategies for type 2 diabetes and impaired glucose tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes. Diabetologia 2011;54(5):1004-12
   PubMedGoogle Scholar
• Lewis JD, Schinnar R, Bilker WB, et al. Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research. Pharmacoepidemiol Drug Saf 2007;16(4):393-401
   PubMed Web of Science ®Google Scholar
• Neter J, Kutner MH, Nachtsheim CJ, et al. Applied linear statistical models. Volume 4. Irwin; Chicago: 1996
   Google Scholar
• Bang H, Edwards AM, Bomback AS, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med 2009;151(11):775-83
   PubMedGoogle Scholar
• Al Khalaf M, Eid M, Najjar H, et al. Screening for diabetes in Kuwait and evaluation of risk scores. EMHJ 2010;16(7):725-31
   Google Scholar
• Al-Lawati J and Tuomilehto J. Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East. Diabetes Res Clin Pract 2007;77(3):438-44
   PubMedGoogle Scholar
• Baan CA, Ruige JB, Stolk RP, et al. Performance of a predictive model to identify undiagnosed diabetes in a health care setting. Diabetes Care 1999;22(2):213-19
   PubMed Web of Science ®Google Scholar
• Glümer C, Carstensen B, Sandbæk A, et al. A Danish Diabetes Risk Score for Targeted Screening The Inter99 study. Diabetes Care 2004;27(3):727-33
   PubMedGoogle Scholar
• Wilson PW, Meigs JB, Sullivan L, et al. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham offspring study. Arch Intern Med 2007;167(10):1068-74
   PubMed Web of Science ®Google Scholar
• Griffin S, Little P, Hales C, et al. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev 2000;16(3):164-71
   PubMed Web of Science ®Google Scholar
• Liu M, Pan C, and Jin M. A Chinese diabetes risk score for screening of undiagnosed diabetes and abnormal glucose tolerance. Diabetes Technol Ther 2011;13(5):501-7
   PubMedGoogle Scholar
• Schmidt MI, Duncan BB, Bang H, et al. Identifying individuals at high risk for diabetes the atherosclerosis risk in communities study. Diabetes Care 2005;28(8):2013-18
   PubMed Web of Science ®Google Scholar
• Gao W, Dong Y, Pang Z, et al. A simple Chinese risk score for undiagnosed diabetes. Diabet Med 2010;27(3):274-81
   PubMedGoogle Scholar
• Lindström J, Tuomilehto J. The Diabetes Risk Score A practical tool to predict type 2 diabetes risk. Diabetes Care 2003;26(3):725-31
   PubMed Web of Science ®Google Scholar
• Stern M, Williams K, Eddy D, et al. Validation of prediction of diabetes by the Archimedes model and comparison with other predicting models. Diabetes Care 2008;31(8):1670-1
   PubMedGoogle Scholar
• Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 2012;345:e5900
   PubMedGoogle Scholar
• Echouffo-Tcheugui JB, Dieffenbach SD, Kengne AP. Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review. Diabetes Res Clin Pract 2013;101(3):255-69
   PubMedGoogle Scholar
• Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker. Heart 2012;98(9):683-90
   PubMed Web of Science ®Google Scholar
• Association AD. Diagnosis and classification of diabetes mellitus. Diabetes care 2008;31(Suppl 1):S55-60
   PubMedGoogle Scholar
• Simmons R, Harding AH, Wareham N, et al. Do simple questions about diet and physical activity help to identify those at risk of Type 2 diabetes? Diabet Med 2007;24(8):830-5
   PubMedGoogle Scholar
• Steyerberg EW. Clinical prediction models. Springer, New York; 2009
   Google Scholar
• Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches data from the epidemiological study on the insulin resistance syndrome (DESIR). Diabetes Care 2008;31(10):2056-61
   PubMed Web of Science ®Google Scholar
• Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 2002;136(8):575-81
   PubMed Web of Science ®Google Scholar
• Moons KG, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606
   PubMedGoogle Scholar
• Stern MP, Williams K, González-Villalpando C, et al. Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care 2004;27(11):2676-81
   PubMedGoogle Scholar
• Cameron A, Magliano D, Zimmet P, et al. The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study. J Intern Med 2008;264(2):177-86
   PubMedGoogle Scholar
• Abdul-Ghani MA, Lyssenko V, Tuomi T, et al. Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes results from the botnia study. Diabetes Care 2009;32(2):281-6
   PubMed Web of Science ®Google Scholar
• Chien K, Cai T, Hsu H, et al. A prediction model for type 2 diabetes risk among Chinese people. Diabetologia 2009;52(3):443-50
   PubMedGoogle Scholar
• Alssema M, Feskens E, Bakker S, et al. Finse vragenlijst redelijk goede voorspeller van het optreden van diabetes in Nederland. Ned Tijdschr Geneeskd 2008;152(44):2418-24
   PubMedGoogle Scholar
• Mainous AG, Diaz VA, Everett CJ. Assessing risk for development of diabetes in young adults. Ann Fam Med 2007;5(5):425-9
   PubMedGoogle Scholar
• Sun F, Tao Q, Zhan S. An accurate risk score for estimation 5-year risk of type 2 diabetes based on a health screening population in Taiwan. Diabetes Res Clin Pract 2009;85(2):228-34
   PubMedGoogle Scholar
• Rahman M, Simmons RK, Harding A-H, et al. A simple risk score identifies individuals at high risk of developing type 2 diabetes: a prospective cohort study. Fam Pract 2008;25(3):191-6
   PubMed Web of Science ®Google Scholar
• Hippisley-Cox J, Coupland C, Robson J, et al. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338:b880
   Google Scholar
• Li J, Bornstein SR, Landgraf R, et al. Validation of a simple clinical diabetes prediction model in a middle-aged, white, German population. Arch Intern Med 2007;167(22):2528-9
   PubMedGoogle Scholar
• Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 2008;359(21):2220-32
   PubMed Web of Science ®Google Scholar
• Nichols GA, Brown JB. Validating the Framingham offspring study equations for predicting incident diabetes mellitus. Am J Manag Care 2008;14(9):574-80
   PubMedGoogle Scholar
• Kahn HS, Cheng YJ, Thompson TJ, et al. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 2009;150(11):741-51
   PubMed Web of Science ®Google Scholar
• Rathmann W, Martin S, Haastert B, et al. Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med 2005;165(4):436-41
   PubMedGoogle Scholar
• Heldgaard PE, Griffin S. Routinely collected general practice data aids identification of people with hyperglycaemia and metabolic syndrome. Diabet Med 2006;23(9):996-1002
   PubMedGoogle Scholar
• Park P, Griffin S, Sargeant L, et al. The performance of a risk score in predicting undiagnosed hyperglycemia. Diabetes Care 2002;25(6):984-8
   PubMedGoogle Scholar
• Witte D, Shipley M, Marmot M, et al. Performance of existing risk scores in screening for undiagnosed diabetes: an external validation study. Diabet Med 2010;27(1):46-53
   PubMedGoogle Scholar
• Bergmann A, Li J, Wang L, et al. A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population. Horm Metab Res 2007;39(09):677-82
   PubMedGoogle Scholar
• Saaristo T, Peltonen M, Lindström J, et al. Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res 2005;2(2):67-72
   PubMedGoogle Scholar
• Franciosi M, De Berardis G, Rossi MC, et al. Use of the diabetes risk score for opportunistic screening of undiagnosed diabetes and impaired glucose tolerance the IGLOO (Impaired Glucose Tolerance and Long-Term Outcomes Observational) study. Diabetes Care 2005;28(5):1187-94
   PubMedGoogle Scholar
• Korhonen PE, Jaatinen PT, Aarnio PT, et al. Waist circumference home measurement—a device to find out patients in cardiovascular risk. Eur J Public Health 2009;19(1):95-9
   PubMedGoogle Scholar
• Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012;98(9):691-8
   PubMed Web of Science ®Google Scholar
• Absetz P, Oldenburg B, Hankonen N, et al. Type 2 Diabetes Prevention in the Real World Three-year results of the GOAL Lifestyle Implementation Trial. Diabetes Care 2009;32(8):1418-20
   PubMedGoogle Scholar
• Laatikainen T, Dunbar JA, Chapman A, et al. Prevention of type 2 diabetes by lifestyle intervention in an Australian primary health care setting: greater Green Triangle (GGT) Diabetes Prevention Project. BMC Public Health 2007;7(1):249
   PubMedGoogle Scholar
• Vermunt P, Milder I, Wielaard F, et al. An active strategy to identify individuals eligible for type 2 diabetes prevention by lifestyle intervention in Dutch primary care: the APHRODITE study. Fam Pract 2010;27(3):312-19
   PubMedGoogle Scholar
• Dhippayom T, Chaiyakunapruk N, Krass I. How diabetes risk assessment tools are implemented in practice: a systematic review. Diabetes Res Clin Pract 2014;104(3):329-42
   PubMedGoogle Scholar
• Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10(2):e1001381
   PubMed Web of Science ®Google Scholar
• Cardona-Morrell M, Rychetnik L, Morrell SL, et al. Reduction of diabetes risk in routine clinical practice: are physical activity and nutrition interventions feasible and are the outcomes from reference trials replicable? A systematic review and meta-analysis. BMC Public Health 2010;10(1):653
   PubMedGoogle Scholar
• Simmons R, Unwin N, Griffin S. International Diabetes Federation: an update of the evidence concerning the prevention of type 2 diabetes. Diabetes Res Clin Pract 2010;87(2):143-9
   PubMedGoogle Scholar