Simple Risk Score to Identify Population at Risk of Impaired Glucose Tolerance in the Thai Population

Research Article

Austin J Endocrinol Diabetes. 2014;1(4): 1019.

Simple Risk Score to Identify Population at Risk of Impaired Glucose Tolerance in the Thai Population

Bumrerraj S1, Kaczorowski J2, Kessomboon P1, Thinkhamrop B3 and Rattarasarn C4*

1Department of Community Medicine, Khon Kaen University, Thailand

2Department of Family Medicine and Emergency Medicine, Université de Montréal, Canada

3Department of Biostatistics and Demography, Khon Kaen University, Thailand

4Department of Medicine, Mahidol University, Thailand

*Corresponding author: Rattarasarn C, Department of Medicine, Ramathibodi Hospital, Mahidol University, Thailand,

Received: July 02, 2014; Accepted: July 28, 2014; Published: July 31, 2014


People with impaired glucose tolerance (IGT) have increased risk of diabetes mellitus and cardiovascular diseases. To identify this high risk group, it needs an oral glucose tolerance test (OGTT) which is time-consuming and difficult to perform in primary care setting. The risk score could be helpful for selection of only those high risk people for OGTT. The objective of this study was to develop the risk score for prediction of IGT in the Thai population. Data from 857 civil service workers of whom 106 (12.4%) had IGT, were collected. A risk score model was developed using logistic regression analysis and the area-under-ROC curve (AUC) was used to compare each model. Age, history of diabetes in 1st-degree relatives, history of hypertension, BMI, waist, waist-height ratio were significantly associated with IGT. The model with waist-height ratio had slightly higher AUC (0.728; 95%CI 0.680-0.777) than, but was not different from, the model with BMI (0.714; 95% CI 0.665-0.763) or waist (0.713; 95% CI 0.663-0.762).The simple risk score model was age + (5 x history of diabetes in 1st-degree relatives) + (10 x waist-height ratio) + (history of hypertension). Risk scores of at least 98 can be used as a cutoff point to predict IGT with the sensitivity and the specificity of 71.3 and 64.4% respectively. This risk score might be appropriate for risk stratifying in people at risk of IGT in the community.

Keywords: Community; Impaired glucose tolerance; Pre-diabetes; Risk score; Waist to height ratio


AUC: Area under the ROC Curve; BMI: Body Mass Index; IFG: Impaired Fasting Glucose; IGT: Impaired Glucose Tolerance; OGTT: Oral Glucose Tolerance Test; ROC: Receiver-operating Characteristics; WHt: Waist to Height Ratio


The early phase of abnormal control of glucose regulation, before progressing to diabetes, is designated as the pre-diabetes stages (impaired fasting glucose: IFG and impaired glucose tolerance: IGT). The prevalence of IGT in Thailand is unknown. However, the prevalence of IGT in the South-East Asia region as reported by the International Diabetes Federation is more than twice that of diabetes (estimated to be 13.2%) [1]. Identification of people at risk of diabetes or pre-diabetes stage coupled with appropriate diagnostic tests, and lifestyle or pharmacological interventions could be one method to reduce or delay the onset of new diabetes cases.

Using the oral glucose tolerance test (OGTT), a gold standard for IGT diagnosis, is often not practical and is time-consuming to perform in primary care settings. The development of a valid risk score could be helpful for selection of only those people at high risk for OGTT. Currently, there are very few risk scores in the literatures that are developed specifically for IGT. Most of the risk scores are developed for predicting diabetes among people with pre-diabetes [2]. Those that aim to screen for IGT require several blood chemistry measurements, which might not be practical for screening at population level [3,4]. Although it is true that several diabetes and IGT risk scores are presently available [5-8], the diagnostic properties of these risk scores differ when applied to different populations [9]. Therefore, there is a need to develop the IGT risk score aimed specifically for the Thai population. This study aimed to develop a simple risk score specifically for prediction of IGT in the Thai population [10].


Data were collected from a consecutive sample of volunteer civil service workers in Khon Kaen province, Thailand from June to December 2009. Pregnant women and people with known diabetes were excluded. The volunteers were advised to fast for at least 8 h on the night before the day of the OGTT. The venous blood samples were in tubes with the standard preservations and kept in ice prior to glucose analysis. The time delay between blood collection and laboratory assay was about 1-3 h. Anthropometric measurements that included weight, height, and waist circumference was collected during the 2 h waiting time. Weights were taken with a digital weight scale using kg unit. Height measurements were taken with a metal scale using cm unit. Waist circumference was measured in cm at midline between lower costal margin and iliac crest. Waist to height ratio (WHt) was calculated as waist circumference (cm) divided by height (cm). Blood pressure were taken twice (at least one minute apart) after 15 min rest. Both systolic and diastolic pressures were recorded as an average between the two measurements as integer numbers. Gender was recorded as either male or female. Age was recorded as an integer number. History of hypertension was obtained by asking “Has your blood pressure ever been higher than 140/90 mmHg?” Family history of diabetes within 1st-degree relatives was obtained by asking “Have your parents or siblings ever been diagnosed with diabetes?” Smoking history was obtained by asking “Have you ever been a regular smoker?” Drinking history was obtained by asking “Do you currently drink more than 1 alcoholic drink per day or not?” The answers to these questions were recorded as dichotomous variables. Average carbohydrate intake per day was estimated using the Thai nutritional division portion size [6]. Number of min per week of moderately active activities or exercise was recorded and the level of activity was classified using the metabolic equivalent time classification [7]. Coffee drinking was recorded as the number of cups per day. People who reported drinking more than one cup per day were considered to be coffee drinkers. For the analysis of risk factors, the outcome was classified as negative or positive for IGT. By this definition, the negative for IGT included all the normal OGTT glucose results and IFG.

Statistical methods

IGT risk score was calculated by the stepwise logistic regression approach using the results from OGTT as a gold standard. Data were analyzed with STATA 10. After all risk factors were individually analyzed for crude odds ratio and to make sure that no important factors were missed, those with odds ratio more than one and p-value of less than 0.20 were selected for the multivariate model. Factors associated with obesity (waist circumference, BMI, and WHt) were taken out and reintroduced into the model one at a time. The receiver-operating characteristics (ROC) curve was constructed for each model. The area under the ROC curve (AUC) was computed and was used to compare each model. The model with AUC near one is considered to have high performance. The AUC near 0.5 is considered to have poor performance.

Ethical consideration

The study was approved by Khon Kaen University Ethics Committee for Human Research. Each participant was assigned a unique study identification not corresponding to their names or hospital numbers. The result of the OGTT was reported to each participant in a private confidential letter.


Of 1,160 people who were verbally asked and signed written consented forms to participate in the study, 897 had a complete data set and underwent OGTT. After exclusion of people with diabetes, there were 857 subjects available for analysis. The characteristics and OGTT results of subjects were shown in Table 1. Fifty-six percent was female, mean age and BMI were 39.2 years (SD 12.05) and 23.9 kg/ m2 (SD 3.77) respectively. Of 857 subjects, only 106 (12.4%) had IGT (Table 1). The crude odds ratios for each factor were shown in Table 2. Factors with p-value < 0.20 were age, waist circumference, weight, height, BMI, WHt, history of diabetes in 1st degree relatives, currently on treatment of hypertension or known to have high blood pressure. Weight and height were drop because they were already included as part of other anthropometric factors. The model’s estimation and the corresponding AUCs were shown in Table 3. When the factors associated with obesity (BMI, waist circumference, and WHt) were introduced into the model one at a time, AUC of the model that included WHt was slightly higher (0.7285; 95% CI 0.680-0.777) than that of model that included BMI (0.7137; 95% CI 0.665-0.763) or waist circumference (0.7129; 95% CI 0.663-0.762). However, none were significantly different. In the final model, the beta coefficients were rounded up to the nearest integer which had negligible effect on the AUC (0.728; 95% CI 0.679-0.777). The final equation was as follows: