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Journal of Diabetes Research
Volume 2016 (2016), Article ID 8790235, 9 pages
Research Article

Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting

1CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
2Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
3Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
4Área de Investigación y Desarrollo, Asociación Benéfica PRISMA, Lima, Peru
5Centro Nacional de Alimentación y Nutrición, Instituto Nacional de Salud, Lima, Peru
6Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
7Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
8Universidad Peruana Cayetano Heredia, Lima, Peru

Received 16 June 2016; Accepted 27 July 2016

Academic Editor: Ulrike Rothe

Copyright © 2016 Antonio Bernabe-Ortiz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey () and the CRONICAS Cohort Study (). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62–0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61–0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.