A Risk Score for Predicting Incident Diabetes in the Thai Population

Abstract

OBJECTIVE -The objective of this study was to develop and evaluate a risk score to predict people at high risk of diabetes in Thailand.RESEARCH DESIGN AND METHODS -A Thai cohort of 2,677 individuals, aged 35-55 years, without diabetes at baseline, was resurveyed after 12 years. Logistic regression models were used to identify baseline risk factors that predicted the incidence of diabetes; a simple model that included only those risk factors as significant (P Ͻ 0.05) when adjusted for each other was developed. The coefficients from this model were transformed into components of a diabetes score. This score was tested in a Thai validation cohort of a different 2,420 individuals.RESULTS -A total of 361 individuals developed type 2 diabetes in the exploratory cohort during the follow-up period. The significant predictive variables in the simple model were age, BMI, waist circumference, hypertension, and history of diabetes in parents or siblings A cutoff score of 6 of 17 produced the optimal sum of sensitivity (77%) and specificity (60%). The area under the receiver-operating characteristic curve (AUC) was 0.74. Adding impaired fasting glucose or impaired glucose tolerance status to the model slightly increased the AUC to 0.78; adding low HDL cholesterol and/or high triglycerides barely improved the model. The validation cohort demonstrated similar results.CONCLUSIONS -A simple diabetes risk score, based on a set of variables not requiring laboratory tests, can be used for early intervention to delay or prevent the disease in Thailand. Adding impaired fasting glucose or impaired glucose tolerance or triglyceride and HDL cholesterol status to this model only modestly improves the predictive ability. Diabetes Care 29:1872-1877, 2006A remarkable worldwide increase in the number of people with type 2 diabetes has been predicted (1). Prevalence rates in the developing world, particularly in the Asia-Pacific region, are already high and expected to rise more quickly than elsewhere. In Thailand, the prevalence of type 2 diabetes among the population aged Յ35 years was 9.6% in 2001, an increase of 20% over a period of 5 years (2). Cardiovascular disease (CVD) is one of the leading causes of death in Thailand (3), and individuals with diabetes have a two to fourfold greater risk of developing CVD than those without (4). The burden of diabetes and its complications, which include other diseases besides CVD (5), imposes a massive load on the Thai health care system. Lifestyle modification has been proven to effectively prevent and delay the development of diabetes (6 -8).Therefore, early recognition of and intervention for the condition will be beneficial, particularly as cardiovascular complications set in early after the onset of diabetes (9). Delay and lack of detection of the disease are mostly due to patients being asymptomatic during the early stage of the disease; hence an accurate screening tool to identify those at high risk of developing diabetes will be of great value. Knowledge of the risk of diabetes could enhance...

Determinants of New-Onset Diabetes Among 19,257 Hypertensive Patients Randomized in the Anglo-Scandinavian Cardiac Outcomes Trial–Blood Pressure Lowering Arm and the Relative Influence of Antihypertensive Medication

“…These studies have shown that although the prevalence continues to increase with age, the incidence of diabetes plateaus in elderly individuals. Our study is consistent with several other observational studies in finding alcohol intake to be protective (25,26) and increased triglycerides to be a putative risk factor for NOD (27,28). Somewhat counterintuitively, raised total cholesterol appeared to be protective in these analyses, although this finding too has been reported in other trials (8 -10).…”

Glucose challenge test screening for prediabetes and undiagnosed diabetes

“…Comparisons with other studies Previous studies of screening have focused on risk factor questionnaires, glucose-related measures and risk factor combinations of demographics, anthropometrics and laboratory measurements. Questionnaires are inexpensive and some have been reported to provide AROC for diabetes of~0.80 [21], comparable to that of RPG in the present study, with most providing AROC 0.70-0.75 [22]. In our population, the Diabetes Risk Calculator [23] provided AROC 0.70 for diabetes and 0.67 for prediabetes 110 .…”

Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

“…Combining information on FPG or impaired fasting glucose with a simple diabetes risk score has been reported to increase predictive ability [7,8,[12][13][14][15][16][17]. A study reported that screening models using the combination of HbA 1c , BMI and FPG accurately identified individuals at risk of future clinically diagnosed diabetes [22], although the factors that remained significant were different from those found in the present study.…”

“…Various rules have been developed to predict the incidence of diabetes in different ethnic groups [2][3][4][5]. Routine clinical markers available without laboratory testing have been shown to be predictive of the development of diabetes [6][7][8][9][10][11][12][13][14][15][16][17][18] and adding biochemical measures, in particular fasting plasma glucose (FPG), improves predictive accuracy [7,8,[12][13][14][15][16][17]. On the other hand, adding complex data such as the results of oral glucose tolerance tests and measurements of insulin levels and inflammatory markers into a simple clinical model only minimally improves risk prediction while increasing cost and inconvenience [10,19].…”