Performance of Stepwise Screening Methods in Identifying Individuals at High Risk of Type 2 Diabetes in an Iranian Population

Document Type : Original Article

Authors

1 Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Barwon Health, Geelong, VIC, Australia

3 School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia

4 Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia

5 Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

6 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

7 Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

8 WHO Collaborating Centre on Implementation Research for Prevention & Control of NCDs, University of Melbourne, Melbourne, VIC, Australia

Abstract

Background
Recent evidence recommended stepwise screening methods for identifying individuals at high risk of type 2 diabetes to be recruited in the lifestyle intervention programs for the prevention of the disease. This study aims to assess the performance of different stepwise screening methods that combine non-invasive measurements with lab-based measurements for identifying those with 5-years incident type 2 diabetes.
 
Methods 
3037 participants aged ≥30 years without diabetes at baseline in the Tehran Lipid and Glucose Study (TLGS) were followed. Thirty-two stepwise screening methods were developed by combining a non-invasive measurement (an anthropometric measurement (waist-to-height ratio, WtHR) or a score based on a non-invasive risk score [Australian Type 2 Diabetes Risk Assessment Tool, AUSDRISK]) with a lab-based measurement (different cut-offs of fasting plasma glucose [FPG] or predicted risk based on three lab-based prediction models [Saint Antonio, SA; Framingham Offspring Study, FOS; and the Atherosclerosis Risk in Communities, ARIC]). The validation, calibration, and usefulness of lab-based prediction models were assessed before developing the stepwise screening methods. Cut-offs were derived either based on previous studies or decision-curve analyses.
 
Results 
203 participants developed diabetes in 5 years. Lab-based risk prediction models had good discrimination power (area under the curves [AUCs]: 0.80-0.83), achieved acceptable calibration and net benefits after recalibration for population’s characteristics and were useful in a wide range of risk thresholds (5%-21%). Different stepwise methods had sensitivity ranged 20%-68%, specificity 70%-98%, and positive predictive value (PPV) 14%-46%; they identified 3%-33% of the screened population eligible for preventive interventions.
 
Conclusion 
Stepwise methods have acceptable performance in identifying those at high risk of incident type 2 diabetes.

Keywords

References

  1. Li R, Qu S, Zhang P, et al. Economic evaluation of combined diet and physical activity promotion programs to prevent type 2 diabetes among persons at increased risk: a systematic review for the community preventive services task force. Ann Intern Med. 2015;163(6):452-460. doi:10.7326/m15-0469
  2. Sun Y, You W, Almeida F, Estabrooks P, Davy B. The effectiveness and cost of lifestyle interventions including nutrition education for diabetes prevention: a systematic review and meta-analysis. J Acad Nutr Diet. 2017;117(3):404-421.e36. doi:10.1016/j.jand.2016.11.016
  3. Zhang X, Devlin HM, Smith B, et al. Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: a systematic review and meta-analysis. PLoS One. 2017;12(5):e0176436. doi:10.1371/journal.pone.0176436
  4. Haw JS, Galaviz KI, Straus AN, et al. Long-term sustainability of diabetes prevention approaches: a systematic review and meta-analysis of randomized clinical trials. JAMA Intern Med. 2017;177(12):1808-1817. doi:10.1001/jamainternmed.2017.6040
  5. Balk EM, Earley A, Raman G, Avendano EA, Pittas AG, Remington PL. Combined diet and physical activity promotion programs to prevent type 2 diabetes among persons at increased risk: a systematic review for the Community Preventive Services Task Force. Ann Intern Med. 2015;163(6):437-451. doi:10.7326/m15-0452
  6. Saaristo T, Moilanen L, Korpi-Hyövälti E, et al. Lifestyle intervention for prevention of type 2 diabetes in primary health care: one-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D). Diabetes Care. 2010;33(10):2146-2151. doi:10.2337/dc10-0410
  7. Saaristo T, Peltonen M, Keinänen-Kiukaanniemi S, et al. National type 2 diabetes prevention programme in Finland: FIN-D2D. Int J Circumpolar Health. 2007;66(2):101-112. doi:10.3402/ijch.v66i2.18239
  8. Albright AL, Gregg EW. Preventing type 2 diabetes in communities across the U.S.: the National Diabetes Prevention Program. Am J Prev Med. 2013;44(4 Suppl 4):S346-351. doi:10.1016/j.amepre.2012.12.009
  9. Lee CMY, Versace VL, Malo JA, Shaw JE, Dunbar JA, Colagiuri S. Screening for diabetes prevention with diabetes risk scores-a balancing act. Diabetes Res Clin Pract. 2018;135:120-127. doi:10.1016/j.diabres.2017.11.009
  10. Oldenburg B, Absetz P. Lost in translation: overcoming the barriers to global implementation and exchange of behavioral medicine evidence. Transl Behav Med. 2011;1(2):252-255. doi:10.1007/s13142-011-0051-1
  11. Oldenburg B, Absetz P, Dunbar JA, Reddy P, O'Neil A. The spread and uptake of diabetes prevention programs around the world: a case study from Finland and Australia. Transl Behav Med. 2011;1(2):270-282. doi:10.1007/s13142-011-0046-y
  12. Khunti K, Gillies CL, Taub NA, et al. A comparison of cost per case detected of screening strategies for type 2 diabetes and impaired glucose regulation: modelling study. Diabetes Res Clin Pract. 2012;97(3):505-513. doi:10.1016/j.diabres.2012.03.009
  13. Hippisley-Cox J, Coupland C. Development and validation of risk prediction algorithm (QThrombosis) to estimate future risk of venous thromboembolism: prospective cohort study. BMJ. 2011;343:d4656. doi:10.1136/bmj.d4656
  14. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33 Suppl 1(Suppl 1):S62-69. doi:10.2337/dc10-S062
  15. Chatterton H, Younger T, Fischer A, Khunti K. Risk identification and interventions to prevent type 2 diabetes in adults at high risk: summary of NICE guidance. BMJ. 2012;345:e4624. doi:10.1136/bmj.e4624
  16. Zafari N, Lotfaliany M, Mansournia MA, Khalili D, Azizi F, Hadaegh F. Optimal cut-points of different anthropometric indices and their joint effect in prediction of type 2 diabetes: results of a cohort study. BMC Public Health. 2018;18(1):691. doi:10.1186/s12889-018-5611-6
  17. Lotfaliany M, Oldenburg B, Azizi F, Khalili D. Validation, Calibration, and clinical usefulness of findrisc, ausdrisk, and ADA risk scores for detecting 5-year incident type 2 diabetes in Tehran Lipid and Glucose Study (TLGS). In: 17th Biennial European Conference. Leiden, The Netherlands: Society for Medical Decision Making; 2018:PS2-5.
  18. Lotfaliany M, Hadaegh F, Asgari S, et al. Non-invasive risk prediction models in identifying undiagnosed type 2 diabetes or predicting future incident cases in the Iranian population. Arch Iran Med. 2019;22(3):116-124.
  19. 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):575-581. doi:10.7326/0003-4819-136-8-200204160-00006
  20. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D'Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007;167(10):1068-1074. doi:10.1001/archinte.167.10.1068
  21. Bozorgmanesh M, Hadaegh F, Ghaffari S, Harati H, Azizi F. A simple risk score effectively predicted type 2 diabetes in Iranian adult population: population-based cohort study. Eur J Public Health. 2011;21(5):554-559. doi:10.1093/eurpub/ckq074
  22. Bozorgmanesh M, Hadaegh F, Zabetian A, Azizi F. San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study. Int J Public Health. 2010;55(4):315-323. doi:10.1007/s00038-010-0130-y
  23. 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):2013-2018. doi:10.2337/diacare.28.8.2013
  24. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163. doi:10.1136/bmj.d7163
  25. Azizi F, Ghanbarian A, Momenan AA, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials. 2009;10:5. doi:10.1186/1745-6215-10-5
  26. Glycemic targets: standards of medical care in diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S55-S64. doi:10.2337/dc18-S006
  27. World Health Organization (WHO). Definition and Diagnosis of Diabetes Mellitus and Intermediate HyperglycemiaWHO; 2006.
  28. Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2009.
  29. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180-187. doi:10.1001/jama.286.2.180
  30. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13:1. doi:10.1186/s12916-014-0241-z
  31. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-574. doi:10.1177/0272989x06295361
  32. Baker SG, Van Calster B, Steyerberg EW. Evaluating a new marker for risk prediction using the test tradeoff: an update. Int J Biostat. 2012;8(1). doi:10.1515/1557-4679.1395
  33. McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY. Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. Diabetes Care. 2003;26(3):758-763. doi:10.2337/diacare.26.3.758
  34. Mann DM, Bertoni AG, Shimbo D, et al. Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US cohort: the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol. 2010;171(9):980-988. doi:10.1093/aje/kwq030
  35. 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. doi:10.1136/bmj.e5900
  36. Masconi KL, Matsha TE, Erasmus RT, Kengne AP. Recalibration in validation studies of diabetes risk prediction models: a systematic review. Int J Stat Med Res. 2015;4(4):347-69. doi:10.6000/1929-6029.2015.04.04.5
  37. Chen L, Magliano DJ, Balkau B, et al. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192(4):197-202. doi:10.5694/j.1326-5377.2010.tb03507.x
  38. Wareham NJ, Griffin SJ. Risk scores for predicting type 2 diabetes: comparing axes and spades. Diabetologia. 2011;54(5):994-995. doi:10.1007/s00125-011-2101-0
  39. Mansournia MA, Altman DG. Population attributable fraction. BMJ. 2018;360:k757. doi:10.1136/bmj.k757
  40. Bartley AC. Evaluating Goodness-of-Fit for a Logistic Regression Model Using the Hosmer-Lemeshow Test on Samples from a Large Data Set [dissertation]. Columbus, Ohio: Ohio State University; 2014.
  41. Cowie CC, Rust KF, Byrd-Holt DD, et al. Prevalence of diabetes and high risk for diabetes using A1C criteria in the U.S. population in 1988-2006. Diabetes Care. 2010;33(3):562-568. doi:10.2337/dc09-1524
International Journal of Health Policy and Management
Volume 11, Issue 8
August 2022
Pages 1391-1400

Files

History

  • Receive Date: 20 May 2020
  • Revise Date: 01 February 2021
  • Accept Date: 10 March 2021
  • First Publish Date: 05 May 2021

Share

How to cite

Statistics