A simple screening tool to identify women with previously undiagnosed prediabetes and diabetes mellitus in the community

Indu Waidyatilaka, Pulani Lanerolle, Sunethra Atukorala, Rajitha Wickremasinghe, Noel Somasundaram, Angela de Silva


Background: In the current context of rising prevalence of non-communicable diseases (NCD), simple low-cost screening tools are essential for identifying individuals who have glucose dysregulation at its early stages. Therefore, we developed and validated a screening tool for dysglycemia (defined as HbA1c≥5.7%) with the potential to identify undiagnosed prediabetes and as well as diabetes mellitus.

Methods: A sample of 2800 women representative of Colombo Municipal Council area was screened using fasting blood glucose for dysglycemia. All (n=272) newly diagnosed dysglycemics and a further 345 normoglycemics were recruited following confirmation of glycemic status by HbA1c, to enable ROC analysis. A pretested questionnaire and the International physical activity questionnaire (IPAQ) validated for Sri Lanka were used to generate variables for the risk score.

Results: A risk score for dysglycemia with a sensitivity of 87% and specificity of 87% and AUC of 0.941 was developed with two common symptoms of dysglycaemia, history of recent increase in frequency of passing urine and recent reduction in vision, one common food related practice, inability to resist sugary food and one indicator of sedentary behavior, TV viewing time and a single anthropometric measurement, waist circumference.

Conclusions: A tool to identify prediabetes is currently unavailable and this new tool fills this gap. Further, the tool is designed to include women with previously undiagnosed diabetes mellitus. Inclusion of lifestyle parameters having a known association with dysglycemia increased the strength of the tool. Early identification will ensure targeting of interventions at the point of maximum effect.


Screening tool, Prediabetes, Diabetes, Dysglycemia, Asia, Sri Lanka

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Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, 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.

Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. New England J Med. 2001;344(18):1343-50.

Hopper I, Billah B, Skiba M, Krum H. Prevention of diabetes and reduction in major cardiovascular events in studies of subjects with prediabetes: meta-analysis of randomised controlled clinical trials. Europ J Cardiovascular Prevent Rehabilitat. 2011;18(6):813-23.

World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia 2006. Available at http://www.who. int/diabetes/publications/Definition%20and%20diagnosis%20of%20diabetes_new.pdf. Accessed 26 April 2019.

Brannick B, Wynn A, Dagogo-Jack S. Prediabetes as a toxic environment for the initiation of microvascular and macrovascular complications. Exp Biol Med. 2016;241(12):1323-31.

Sörensen BM, Houben AJ, Berendschot TT, Schouten JS, Kroon AA, van der Kallen CJ, et al. Prediabetes and type 2 diabetes are associated with generalized microvascular dysfunction clinical perspective. Circulation. 2016;134(18):1339-52.

Abdul-Ghani M, DeFronzo RA, Jayyousi A. Prediabetes and risk of diabetes and associated complications: impaired fasting glucose versus impaired glucose tolerance: does it matter?. Curr Opinion Clin Nutr Metabol Care. 2016;19(5):394-9.

Garber AJ, Abrahamson MJ, Barzilay JI, Blonde L, Bloomgarden ZT, Bush MA, et al. Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm–2016 executive summary. Endocr Pract. 2016;22(1):84-113.

Ranil J, Ranasinghe P, Byrne NM, Soares MJ, Katulanda P, Hills AP. Prevalence and trends of the diabetes epidemic in South Asia: a systematic review and meta-analysis. BMC Public Health. 2012;12 (1):380.

Jaana L, Louheranta A, Mannelin M, Rastas M, Salminen V, Eriksson J, et al. The Finnish diabetes prevention study (DPS). Diabetes Care. 2003;26(12):3230-6.

Tcheugui E, Justin B, Mayige M, Ogbera AO, Sobngwi E, Kengne AP. Screening for hyperglycemia in the developing world: Rationale, challenges and opportunities. Diabetes Res Clin Practice. 2012;98(2):199-208.

Katulanda P, Constantine GR, Mahesh JG, Sheriff R, Seneviratne RDA, Wijeratne S, et al. Prevalence and projections of diabetes and pre‐diabetes in adults in Sri Lanka-Sri Lanka diabetes, cardiovascular study (SLDCS). Diabet Med. 2008; 25(9):1062-9.

Douglas N, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163.

D'Agostino RB, Ralph B, Grundy S, Sullivan LM, Wilson P. and CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;282(2):180-7.

Simmons RK, Harding AH, Wareham NJ, Griffin SJ. 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.

Katulanda P, Hill NR, Stratton I, Sheriff R, De Silva SDN, Matthews DR. Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK). BMC Endocrine Disorders. 2016;16(1):42.

Waidyatilaka I, Lanerolle P, Wickremasinghe R, Atukorala S, Somasundaram N, de Silva A. Sedentary Behaviour and Physical Activity in South Asian Women: Time to Review Current Recommendations? PLoS One. 2013;8(3):e58328.

Waidyatilaka I, de Silva A, de Lanerolle-Dias M, Wickremasinghe R, Atukorala S, Somasundaram N, et al. Lifestyle patterns and dysglycaemic risk in urban Sri Lankan women. Br J Nutr. 2014;112(6):952-7.

American Diabetic Association. Diagnosis and classification of diabetes mellitus. Position statement. Diabet Care. 2012;35(1):64-71.

Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2013;35:1381-95.

Arambepola C. Abdominal obesity and its association with selected risk factors of coronary heart disease in an adult population in the district of Colombo. Thesis (MD in Community Medicine), Postgraduate Institute of Medicine, University of Colombo; 2004.

Stewart A, Marfell-Jones M, Olds T, de Ridder H. International standards for anthropometric assessment. Lower Hutt, New Zealand: International Society for Advancement of Kineanthropometry; 2011.

Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabet Care. 2003;26(3):725-31.

Devin M, Bertoni AG, Shimbo D, Carnethon MR, Chen H, Jenny NS, 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): 980-8.

Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian diabetes risk score for screening for undiagnosed diabetic subjects. J Assoc Physicians India. 2005;53(9):759-63.

Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, et al. A prediction model for type 2 diabetes risk among Chinese people. Diabetologia. 2009;52(3):443.

Navin BR, van Valkengoed IG, Mairuhu G, Holleman F, Hoekstra JB, Michels BP, 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.