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Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK)
© The Author(s). 2016
Received: 6 February 2016
Accepted: 15 July 2016
Published: 25 July 2016
Screening for undiagnosed diabetes is not widely undertaken due to the high costs and invasiveness of blood sampling. Simple non-invasive tools to identify high risk individuals can facilitate screening. The main objectives of this study are to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores.
Data were available from a representative sample of 4276 adults without diagnosed diabetes. In a jack-knife approach two thirds of the sample was used for the development of the risk score and the remainder for the validation. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity and osmotic symptoms were significantly associated with undiagnosed diabetes (age most to osmotic symptoms least). Individual scores were generated for these factors using the beta coefficient values obtained in multiple logistic regression. A cut-off value of sum = 31 was determined by ROC curve analysis.
The area under the ROC curve of the risk score for prevalent diabetes was 0.78 (CI 0.73–0.82). In the sample 36.3 % were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3 % respectively. For Sri Lankans the AUC for the CRS and IDRS were 0.72 and 0.66 repectively.
This simple non-invasive screening tool can identify 80 % of undiagnosed diabetes by selecting 40 % of Sri Lankan adults for confirmatory blood investigations.
Diabetes mellitus is increasing worldwide . Type 2 diabetes accounts for over 90 % of prevalent cases in high risk populations such as South Asians and Pima Indians [2, 3]. Among those with type 2 diabetes 30 to 76 % remain undiagnosed according to different estimates [2, 4–7]. In a national level epidemiological study conducted in 2006 in Sri Lanka, the diabetes prevalence was reported as 10.3 % with 36 % remaining undiagnosed . Early diagnosis is important as in the United Kingdom Prospective Diabetes Study (UKPDS), 37 % of those newly diagnosed with type 2 diabetes had retinopathy . A cross-sectional study in Sri Lanka reported 15 % retinopathy and 25 % neuropathy in newly diagnosed diabetic subjects . These data suggest that type 2 diabetes may remain undiagnosed for many years leading to adverse diabetes related outcomes.
Early diagnosis and optimisation of therapy may improve outcomes in patients with diabetes, as tight control of blood glucose and blood pressure has been shown to reduce the incidence of microvascular complications in type 2 diabetic subjects [10, 11] and control of lipids reduces macrovascular disease and mortality in people with diabetes . This reduction of risk of microvascular complications, myocardial infarcton and death from any cause persisted in 10 years of post-trial followup . Since the diagnosis of diabetes requires invasive blood sampling and laboratory investigations, there is an ongoing debate about the benefit and the potential harm of screening for diabetes [14–17]. A recent study from UK has reported limited psychological impact on patients from screening for diabetes . We hypothesise that if sensitive, specific and low cost tools (that are non-invasive) can be developed, large scale community level diabetes screening can be undertaken with a potential for the improvement of outcomes in otherwise undiagnosed people.
Questionnaire based risk screening tools have been developed for the detection of diabetes in other populations [16, 19–21]. The aim of this study was to develop and validate a screening tool for undiagnosed diabetes in Sri Lanka based on demographic information. The performance of the risk tools developed for White Europeans in the UK by Griifin et al.  and Asian Indians by Ramachandran et al.  were compared with our risk score.
Data from 4276 subjects without previously diagnosed diabetes from the Sri Lanka Diabetes and Cardiovascular Study (SLDCS) were used for risk score development and validation. SLDCS was a nationally representative study conducted in seven out of the nine provinces in Sri Lanka using a probability cluster-sampling technique. The study was approved by the Ethical Review Committee of the Faculty of Medicine – University of Colombo. Informed written consent was obtained from all participants. The sampling and data collection of this study has been reported previously . Two thirds of this study population were randomly selected for the development of the risk score. The remaining third of the subjects were used for validation.
We used methods adopted for the development of the Finnish and Cambridge Risk scores [16, 21, 22]. We performed univariate regression analysis to select variables that were associated with undiagnosed diabetes. Variables that could be easily measured in the community with minimal expertise and resources were included. We did not include area of residence, occupation and income. Multiple logistic regression analysis was carried out with newly diagnosed diabetes as the dependent variable and those variables identified from the univariate analyses at significance level <0.05.
The β coefficient values obtained in the logistic regression analysis (without stepwise elimination) were used to derive a risk score. The sum of β coefficients in multiple regression analysis can be used to derive cumulative regression coefficients . Data were analysed using SPSS version 14 (SPSS Inc, Chicago IL, USA). Similar methods have been adopted in the development of the Finnish and Cambridge Risk scores [16, 21, 22]. The score was based on the beta coefficient multiplied by a factor of ten to allow integer scores. The total score was calculated as the sum of the individual weighted scores.
Diabetes was diagnosed using the 1989 WHO criteria using fasting and 2-h OGTT plasma glucose . Physical activity was recorded (all activities including occupational, daily living and leisure time) using the short format of International Physical Activity Questionnaire (IPAQ) and categorised as low, moderate and active according to the IPAQ categorisation . Hypertension was diagnosed when systolic blood pressure was ≥140 mmMg/ diastolic blood pressure ≥90 mmMg or if the subjects were on antihypertensive medications . Balanitis was defined as persistent itching or soreness of the glans penis and vulvitis as persistent itching, soreness and discharge from the vulva. Frequent thirst, polyuria and nocturia were considered as osmotic symptoms.
The performance of the risk score in detecting prevalent undiagnosed diabetes was evaluated using receiver-operating characteristic (ROC) curve analysis. The risk score value with combined highest sensitivity and specificity was considered the optimal cut-off value. The risk score was validated in the remaining one third of the population. The performance of the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS), two Chinese risk scores and a Thai risk score [16, 22, 27–29] when applied to the Sri Lankan population were compared with the newly derived risk score (SLDSRISK).
Characteristics of the sample according to gender
2-h PG (mmol/l)
Waist circumference (cm)
Male sex (%)
Physical inactivity (%)
Family history of diabetes (%)
Gestational diabetes (%)
Osmotic symptoms (%)
Balanitis or vulvitis (%)
Characteristics of the sample according to development and validation cohort
2-h PG (mmol/l)
Waist circumference (cm)
Male sex (%)
Physical inactivity (%)
Family history of diabetes (%)
Gestational diabetes (%)
Osmotic symptoms (%)
Balanitis or vulvitis (%)
Multiple logistic regression analysis and individual scores for variables included in the risk score
Variables (p value for diabetes)
Odds ratio (95 % CI)
Contribution to Risk Score (10 × β)
Age (years) (p = 0.000)
BMI (kg/m2) (p = 0.000)
Waist circumference (cm) (p = 0.000)
F < 70 / M <75
F (70–79)/M (75–84)
F ≥ 80/M ≥ 85
Hypertension (p = 0.000)
Family history of diabetes (p = 0.000)
Physical activity level (p = 0.008)
Gestational diabetes (p = 0.270)
Balanitis or vulvitis (p = 0.034)
Osmotic symptoms (p = 0.024)
When we applied SLDRISK to the remaining one third of the sample (cohort 1450 people, 68 with newly diagnosed Type 2 diabetes, 38.9 % had a score above 31 (risk-score positive). The risk score has a sensitivity of 77.9 % and a specificity of 63.0 %. The score has a positive predictive value (PPV) of 9.4 % and a negative predictive value (NPV) of 98.3 % for predicting prevalent diabetes in the remaining one third of the subjects (Fig. 1a). The ROC curve analysis in the validation sample showed an AUC of 0.74.
Comparison of SLDRISK with Cambridge, Indian, Chineses and Thai Diabetes Risk Scores
The performance of SLDRISK, CRS and the IDRS in the Sri Lankan adult population
Positive predictive value (%)
Negative predictive value (%)
The performance of SLDRISK compared to two Chinese risk scores and a Thai risk score in the Sri Lankan adult population
In this study we have developed and validated a tool for community level screening of undiagnosed diabetes in a high risk South Asian population. The SLDRISK can be used by local health care workers in a village setting or in health centres wihtout needing hospital or laboratory support. It can be used with minimal expertise. The AUC of 0.78 for the SLDRISK indicated satisfactory predictability of prevalent undiagnosed diabetes in Sri Lankan adults. The score would detect 78 % of those with undiagnosed diabetes in the community by identifying 39 % of risk score positive adults in the general population. This non-invasive tool has the potential to be used for targeting individuals for definitive diabetes diagnostic screening at a population level.
Many systematic reviews have been done over the years on diabetic risk scores. Collins et al. reviewed 39 similar studies done before 2011 . Age, Family history of diabetes, hypertension, BMI and waist circumference were the most frequenly used risk predictors in those studies. All these risk predictors are included in our study as well.
The CRS risk score which showed an AUC of 0.80 for the White Europeans was less predictive for the Sri Lankan population (AUC 0.66). The positive predictive value of CRS for Sri Lankans at an optimal cut-off value of 0.052 was 6.2 % compared to 9.4 % in the SLDRISK. The IDRS performed comparable to the SLDRISK in Sri Lankans (AUC 0.72 vs. 0.78, PPV 8.8 vs. 9.4). This highlights the importance of using ethnic specific tools in epidemiology and disease screening. In the SLDRISK we have used common diabetes symptoms that were not used in the CRS and the IDRS to increase the sensitivity of the risk score.
Our projections of diabetes in Sri Lanka indicate a prevalence of 13.9 % in 2030 . Other countries in South Asia like India, Pakistan and Bangladesh are also having high prevalence of diabetes [31–37]. This has necessitated effective primary and secondary preventive measures to prevent the rising prevalence as well as diabetes related adverse health outcomes in this ethnic group. Therefore, low cost and non-invasive epidemiological tools such as SLDRISK are of particular value in South Asian countries with limited healthcare resources and high disease burden.
In conclusion, we have developed and validated a simple, low-cost and a non-invasive tool for stepwise community level screening of undiagnosed diabetes in a high risk South Asian population in Sri Lanka. Our risk score is comparable to the one developed for Asian Indians by Ramachandran et al. and can be used in Sri Lanka and perhaps among other South Asians for community level screening programmes for diabetes.
CRS, Cambridge Risk Score; IDRS, Indian Diabetes Risk Score; BMI, body mass index; ROC, receiver operating characteristic; AUC, area under the curve; SLDCS, Sri Lanka Diabetes and Cardiovascular Study; IPAQ, International Physical Activity Questionnaire; SLDRISK, Sri Lanka Diabetes Risk Score
The additional support provided from the Oxford Centre for Diabetes Endocrinology and Metabolism, UK and the NIHR Biomedical Research Centre Programme is gratefully acknowledged. PK is a Commonwealth Postgraduate Scholar. We thank the Diabetes Association of Sri Lanka and the World Health Organization Office in Colombo for the support for lipid assays. The authors thank all individuals and institutions who helped and worked for the SLDCS (http://www.ocdem.ox.ac.uk/_asset/file/annual-report-2010.pdf).
The National Science Foundation of Sri Lanka was the main source of funding for the SLDCS. Funding body did not interfere with design of the study, data collection, data interpretation or writning the manuscript.
Availability of data and materials
The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.
PK developed the research methodology and coordinated data collection and reviewed final manscript. NRH contributed to develop research methodology and to write the manuscript. IS analysed data and reviewed manuscript. RS was involved in developing research methodology and coordinated data analysis and reviewed manuscript. SDNDS contributed to results and discussion parts of the paper, contributed to data analysis and coordinated submission process. DRM coordinated OCDEM in research partnership reviewed the manuscript and developed research concept. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The study was approved by the Ethical Review Committee of the Faculty of Medicine – University of Colombo. Informed written consent was obtained from all participants.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Wild S, et al. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53.View ArticlePubMedGoogle Scholar
- Katulanda P, 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(3):1062–9.View ArticlePubMedGoogle Scholar
- Sicree R, Shaw J, Zimmet P. Diabetes and impaired glucose tolerance. In: Gan D, editor. Diabetes atlas. Belgium: International Diabetes Federation; 2006. p. 22–37.Google Scholar
- Aekplakorn W, et al. Prevalence and management of diabetes and associated risk factors by regions of Thailand: Third National Health Examination Survey 2004. Diabetes Care. 2007;30(8):2007–12.View ArticlePubMedGoogle Scholar
- Cowie CC, et al. Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population: National Health And Nutrition Examination Survey 1999–2002. Diabetes Care. 2006;29(6):1263–8.View ArticlePubMedGoogle Scholar
- Gu D, et al. Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Diabetologia. 2003;46(9):1190–8.View ArticlePubMedGoogle Scholar
- Ramachandran A, et al. Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India. Diabetologia. 2004;47(5):860–5.View ArticlePubMedGoogle Scholar
- Kohner EM, Aldington SJ, Stratton IM, Manley SE, Holman RR, Matthews DR, et al. United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non-insulin-dependent diabetes mellitus and associated risk factors. Arch Ophthalmol. 1998;116(3):297–303.View ArticlePubMedGoogle Scholar
- Weerasuriya N, et al. Long-term complications in newly diagnosed Sri Lankan patients with type 2 diabetes mellitus. QJM. 1998;91(6):439–43.View ArticlePubMedGoogle Scholar
- UKPDS. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352(9131):837–53.View ArticleGoogle Scholar
- UKPDS. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group. BMJ. 1998;317(7160):703–13.View ArticleGoogle Scholar
- Collins R, et al. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial. Lancet. 2003;361(9374):2005–16.View ArticlePubMedGoogle Scholar
- Holman RR, et al. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577–89.View ArticlePubMedGoogle Scholar
- Echouffo-Tcheugui JB, Sargeant LA, Griffin SJ. We should continue talking about screening for Type 2 diabetes. Diabet Med. 2007;24(8):924. author reply 924–5.View ArticlePubMedGoogle Scholar
- Engelgau MM, Narayan KM, Herman WH. Screening for type 2 diabetes. Diabetes Care. 2000;23(10):1563–80.View ArticlePubMedGoogle Scholar
- Griffin SJ, et al. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev. 2000;16(3):164–71.View ArticlePubMedGoogle Scholar
- Stephens JW, Williams R. Time to stop talking about screening for diabetes? Diabet Med. 2006;23(11):1163–4.View ArticlePubMedGoogle Scholar
- Eborall HC, et al. Psychological impact of screening for type 2 diabetes: controlled trial and comparative study embedded in the ADDITION (Cambridge) randomised controlled trial. BMJ. 2007;335(7618):486.View ArticlePubMedPubMed CentralGoogle Scholar
- Al-Lawati JA, 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.View ArticlePubMedGoogle Scholar
- Glumer C, et al. A Danish diabetes risk score for targeted screening: the Inter99 study. Diabetes Care. 2004;27(3):727–33.View ArticlePubMedGoogle Scholar
- Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725–31.View ArticlePubMedGoogle Scholar
- Ramachandran A, et al. Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes Res Clin Pract. 2005;70(1):63–70.View ArticlePubMedGoogle Scholar
- Kirkwood BR, Sterne JAC. Goodness of fit and regression diagnostics. In: Essential medical statistics. Malden: Blackwell Science; 2003. p. 107–17.Google Scholar
- Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.View ArticlePubMedGoogle Scholar
- Craig CL, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.View ArticlePubMedGoogle Scholar
- Chobanian AV, et al. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72.View ArticlePubMedGoogle Scholar
- Ko G, et al. A simple risk score to identify southern Chinese at high risk of diabetes. Diabet Med. 2010;27:644–9.View ArticlePubMedGoogle Scholar
- Zhou X, et al. Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nation-wide diabetes survey. Diabetes Care. 2013;36(12):3944–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Aekplakorn W, et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care. 2006;29(8):1872–7.View ArticlePubMedGoogle Scholar
- Collins GS, et al. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103.View ArticlePubMedPubMed CentralGoogle Scholar
- Hussain A, et al. Type 2 diabetes in rural and urban population: diverse prevalence and associated risk factors in Bangladesh. Diabet Med. 2005;22(7):931–6.View ArticlePubMedGoogle Scholar
- Mohan V, Farooq S, Deepa M. Prevalence of fibrocalculous pancreatic diabetes in Chennai in South India. JOP. 2008;9(4):489–92.PubMedGoogle Scholar
- Mohan V, et al. Urban rural differences in prevalence of self-reported diabetes in India–the WHO-ICMR Indian NCD risk factor surveillance. Diabetes Res Clin Pract. 2008;80(1):159–68.View ArticlePubMedGoogle Scholar
- Ramachandran A, et al. High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India. Diabetes Care. 2008;31(5):893–8.View ArticlePubMedGoogle Scholar
- Seshiah V, et al. Prevalence of gestational diabetes mellitus in South India (Tamil Nadu)–a community based study. J Assoc Physicians India. 2008;56:329–33.PubMedGoogle Scholar
- Shera AS, Jawad F, Maqsood A. Prevalence of diabetes in Pakistan. Diabetes Res Clin Pract. 2007;76(2):219–22.View ArticlePubMedGoogle Scholar
- Zargar AH, et al. Prevalence of diabetes mellitus and other abnormalities of glucose tolerance in young adults aged 20–40 years in North India (Kashmir Valley). Diabetes Res Clin Pract. 2008;82(2):276–81.View ArticlePubMedGoogle Scholar