A risk scores for predicting prevalence of diabetes in the LAO population

  • Souphaphone Louangdouangsithidet
  • Wiroj Jiamjarasrangsi
  • Suwimol SapwarobolEmail author
Original Article


To develop risk scores for predicting the prevalence of diabetes in the Lao population. This was a cross-sectional study of both men and women (age 30 to 70 years) living in rural villages of the Vientiane municipality in the Lao PDR. Multiple logistic regressions with backward stepwise selection were used; the diabetes risk score was derived from the β-coefficient. Performance of the score was determined by the area under the receiver operating characteristic curve (AUC), the sensitivity, the specificity, and the positive predictive value for the specified cut-off value. The prevalence of undiagnosed diabetes was 7%. The factors included in the predictive in model were 17 (40 to 70 years of age) + 14 (high waist circumference) + 11 (hypertension) + 7 (family history of diabetes). A cut-off point of risk scores of 29.5 out of 49 produced the optimal sum, leading to a sensitivity of 0.75, a specificity of 0.55, a positive predictive value of 17.8%, and an AUC of 0.70. Data suggested that the combination of age, waist circumference, hypertension, and family history of diabetes could be utilized to identify Lao individuals at high risk of undiagnosed diabetes. The generalizability for other Lao population needs further investigation.


Risk assessment model Diabetes prevalence Risk score Undiagnosed diabetes Lao diabetes prevalence 



This study was funded by the 90th Anniversary of Chulalongkorn University Ratchadaphiseksomphot Endowment Fund and Research grant of Faculty of Allied Health Sciences, Chulalongkorn University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the National Institute of Public Health National Ethics Committee for Health Research (NECHR), the Lao People’s Democratic Republic, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. The clinical trial number is NCT03311802 (


  1. 1.
    International Diabetes Federation. IDF diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013.Google Scholar
  2. 2.
    Eddy DM, Schlessinger L, Kahn R. Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. Ann Intern Med. Aug 16 2005;143(4):251–64.CrossRefGoogle Scholar
  3. 3.
    Collins GS, Mallett S, Omar O, Yu L-M. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9(1):103.CrossRefGoogle Scholar
  4. 4.
    Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008;371(9626):1783–9.CrossRefGoogle Scholar
  5. 5.
    Gillies CL, Abrams KR, Lambert PC, et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. Vol 3342007.Google Scholar
  6. 6.
    Pongchaiyakul C, Kotruchin P, Wanothayaroj E, Nguyen TV. An innovative prognostic model for predicting diabetes risk in the Thai population. Diabetes Res Clin Pract. Nov 2011;94(2):193–8.CrossRefGoogle Scholar
  7. 7.
    Peduzzi P, Concato J, Kemper E, Holford T, Feinstein A. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9.CrossRefGoogle Scholar
  8. 8.
    Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India. 2009;57:163–70.Google Scholar
  9. 9.
    Zhao X, Zhu X, Zhang H, Zhao W, Li J, Shu Y, et al. Prevalence of diabetes and predictions of its risks using anthropometric measures in southwest rural areas of China. BMC Public Health. 2012;12:821.CrossRefGoogle Scholar
  10. 10.
    Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52.CrossRefGoogle Scholar
  11. 11.
    Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013;34(28):2159–219.CrossRefGoogle Scholar
  12. 12.
    American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care. 2013;36(Suppl 1):S11–66.CrossRefGoogle Scholar
  13. 13.
    Ramachandran A, Snehalatha C, Vijay V, Wareham N, Colagiuri S. Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes Res Clin Pract. 2005;70(1):63–70.CrossRefGoogle Scholar
  14. 14.
    Chaturvedi V, Reddy KS, Prabhakaran D, et al. Development of a clinical risk score in predicting undiagnosed diabetes in urban Asian Indian adults: a population-based study. CVD Prev Control. 2008;3(3):141–51.CrossRefGoogle Scholar
  15. 15.
    King H, Keuky L, Seng S, Khun T, Roglic G, Pinget M. Diabetes and associated disorders in Cambodia: two epidemiological surveys. Lancet. 2005;366(9497):1633–9.CrossRefGoogle Scholar
  16. 16.
    Ta MT, Nguyen KT, Nguyen ND, Campbell LV, Nguyen TV. Identification of undiagnosed type 2 diabetes by systolic blood pressure and waist-to-hip ratio. Diabetologia. 2010;53(10):2139–46.CrossRefGoogle Scholar
  17. 17.
    Ramachandran A, Snehalatha C, Dharmaraj D, Viswanathan M. Prevalence of glucose intolerance in Asian Indians: urban-rural difference and significance of upper body adiposity. Diabetes Care. 1992;15(10):1348–55.CrossRefGoogle Scholar
  18. 18.
    Conen D, Ridker PM, Mora S, Buring JE, Glynn RJ. Blood pressure and risk of developing type 2 diabetes mellitus: the Women’s health study. Eur Heart J. Dec 2007;28(23):2937–43.CrossRefGoogle Scholar
  19. 19.
    WHO EC. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–63.CrossRefGoogle Scholar
  20. 20.
    Glumer C, Vistisen D, Borch-Johnsen K, Colagiuri S. Risk scores for type 2 diabetes can be applied in some populations but not all. Diabetes Care. 2006;29(2):410–4.CrossRefGoogle Scholar
  21. 21.
    Ruige JB, JNDd N, Kostense PJ, Bouter LM, Heine RJ. Performance of an NIDDM screening questionnaire based on symptoms and risk factors. Diabetes Care. 1997;20(4):491–6.CrossRefGoogle Scholar
  22. 22.
    Baan CA, Ruige JB, Stolk RP, Witteman JC, Dekker JM, Heine RJ, et al. Performance of a predictive model to identify undiagnosed diabetes in a health care setting. Diabetes Care. Feb 1999;22(2):213–9.CrossRefGoogle Scholar
  23. 23.
    Kekalainen P, Sarlund H, Pyorala K, Laakso M. Hyperinsulinemia cluster predicts the development of type 2 diabetes independently of family history of diabetes. Diabetes Care. Jan 1999;22(1):86–92.CrossRefGoogle Scholar
  24. 24.
    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. Apr 16 2002;136(8):575–81.CrossRefGoogle Scholar
  25. 25.
    T TJ V, Eriksson J. Epidemiology of type 2 diabetes in Europids. In: Alberti K, Zimmet P, DeFronzo R, Keen H, editors. In International textbook of diabetes mellitus. 2nd ed. New York: John Wiley and Sons; 1997. p. 125–42.Google Scholar
  26. 26.
    Harris MI, Flegal KM, Cowie CC, Eberhardt MS, Goldstein DE, Little RR, et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. The third National Health and nutrition examination survey, 1988-1994. Diabetes Care. Apr 1998;21(4):518–24.CrossRefGoogle Scholar
  27. 27.
    Borch-Johnsen K, Andrew N, Beverley B, Svend L, Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria The DECODE study group. European Diabetes Epidemiology Group Diabetes Epidemiology: collaborative analysis of diagnostic criteria in Europe. Lancet. 1999;354(9179):617–21.CrossRefGoogle Scholar
  28. 28.
    Mbanya V, Hussain A, Kengne AP. Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: a systematic literature search. Prim Care Diabetes. 2015;9(5):317–29.CrossRefGoogle Scholar
  29. 29.
    Rathmann W, Martin S, Haastert B, Icks A, Holle R, Löwel H, et al. Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA survey 2000. Arch Intern Med. Feb 28 2005;165(4):436–41.CrossRefGoogle Scholar

Copyright information

© Research Society for Study of Diabetes in India 2018

Authors and Affiliations

  1. 1.Food and Nutrition Program, Department of Nutrition and Dietetics, Faculty of Allied Health SciencesChulalongkorn UniversityBangkokThailand
  2. 2.Department of Preventive and Social Science, Faculty of MedicineChulalongkorn UniversityBangkokThailand
  3. 3.Department of Nutrition and Dietetics, Faculty of Allied Health SciencesChulalongkorn UniversityBangkokThailand

Personalised recommendations