Screening for Diabetes

The U.K. National Screening Committee provides criteria against which screening programs can be evaluated (http://www.nsc.nhs.uk/pdfs/ criteria.pdf). Using these criteria, screening for diabetes in the general population was deemed not to be warranted in 2001 (1) because 1) the benefits of early diagnosis and treatment had not been proved, 2) screening for diabetes to reduce cardiovascular disease had not been shown to be effective, 3) disadvantages of screening were not quantified, and 4) the clinical management of those with diabetes should be optimized before instituting a screening program. A more recent 2007 report from the U.K. on screening for type 2 diabetes concluded that the case for screening was somewhat stronger given the possible “options for reduction of cardiovascular disease” (mainly with statins) and “because of the rising prevalence of obesity and hence diabetes” (2). Further, since the 2001 evaluation, some of the possible disadvantages of screening have been quantified and found not to be of great harm (3–5). In this issue of Diabetes Care, the Diabetes Risk Calculator that aims to detect both undiagnosed diabetes as well as prediabetes is proposed (6). A number of other screening tools have already been developed in various populations and are reviewed in the U.K. report (2). In particular, for the U.S., Herman et al. (7) developed a simple questionnaire based on National Health and Nutrition Examination Survey (NHANES) II data using a classification tree approach. The questionnaire included age, weight for height, exercise, diabetes in the family, and delivery of a large baby. It is currently proposed by the American Diabetes Association as a “Diabetes Risk Test” (available at http:// www.diabetes.org/risk-test.jsp). The Diabetes Risk Calculator (6) was developed and validated using American data from NHANES III (1988–94) and studied 7,000 men and women aged 20 years. Diabetes and pre-diabetes were defined by fasting plasma glucose, and in about half of the participants aged 40–75 years, 2-h glucose concentrations following an oral glucose tolerance test were also used. Thus, the glucose phenotype identified by the tool is not homogeneous. A total of 18 potential explanatory variables were reviewed, all of which would be known to an individual. Two methods were compared for the development of a calculator: logistic regression and classification and regression tree (CART) analysis. Details are presented in a technical report prepared for GlaxoSmithKline available in the online appendix of the article in this issue (6). For logistic regression, two methods of variable selection were used to detect “diabetes pre-diabetes”: the best model with k variables and forward stepwise selection. Pragmatically, the same equation was also used for predicting undiagnosed diabetes alone, even though there appear to be differences in the variables that would be chosen to predict the two entities (sex is included for one but not the other). Threshold values were determined to achieve sensitivities of 80%, a better criterion than the usual optimum threshold, which corresponds to maximizing (sensitivity specificity). The corresponding positive predictive values are not given. Ethnicity was not included for technical reasons because SAS is not able to cope with a four-class variable in best model logistic regressions, but the stepwise technique could still have been used. This important variable has been included in the CART classification and found to be a discriminator. The final logistic regression model included eight variables: age, sex, weight, height, waistto-hip ratio, BMI, high blood pressure, and familial diabetes. Although all of these variables were statistically significant, it is probable that some of the highly correlated anthropometric variables could be deleted without loss to the capacity of the model to predict undiagnosed d iabe te s . Indeed , in the formulation of the CART model, neither BMI nor waist-to-hip ratio was included as a possible variable to enter into the model. The final CART model required 10 variables: age, sex, weight, height, waist, high blood pressure, familial diabetes, exercise, ethnicity, and gestational diabetes mellitus. The areas under the receiver operating characteristic curves for the two techniques were similar, as seen in Fig. 7 of the technical report. The logistic model is quickly dismissed because CART is said to be of “equivalent accuracy but greater ease of use.” The two methods have not been compared on equal grounds because different variables were used in developing the two techniques. Further, the two methods were essentially compared by the areas under the receiver operating characteristic curves, which were very similar. Other characteristics for the CART method are difficult to compare with those of the logistic regression because the thresholds have been set to have a sensitivity of 80%. As for the ease of use, the results from both techniques need to be written as an additive score to provide a simple prescreening score. As a clinical tool, I am not sure whether a busy physician would take the time to go through a chart to calculate the probability of a patient having diabetes or pre-diabetes. It needs to be put into a more useable format, as has been done for the Diabetes Risk Test (7). A diabetes risk calculator could be developed in an electronic format or as a Web-based facility to prescreen for undiagnosed diabetes or pre-diabetes. It might be a useful tool for patients who would like to estimate their risk of diabetes. One of the unmet criteria for screening given above is hard evidence that screening for diabetes reduces cardiovascular risk. One trial, the Anglo-DanishDutch Study of Intensive Treatment in People with Screen Detected Diabetes in Primary Care, or ADDITION, Study, a randomized clinical trial that aims to study whether systematic screening and subsequent cardiovascular risk reduction have benefits on morbidity and mortality (8), is underway. Over 3,000 primary care patients were recruited in the U.K., Denmark, and the Netherlands, and we now await the results from this 5-year trial. Diabetic individuals with an isolated 2-h hyperglycemia following an oral charge of glucose are not detected by a fasting hyperglycemia and deserve special attention. They tend to be older and slimmer, and more are women (9). Although (obviously) the prevalence of diabetes is increased when these individuals are included, the current article does not provide information on this group, and we are told that “the lack of oral glucose tolerance data for some participants did not materially affect the stability of the results.” It would be very useful to have a E d i t o r i a l s