In a June 2009 study, McKenna et al. hypothesized that a risk prediction tool relying primarily on measurements from glucose and HbA1C lack specificity, and incorporating markers from multiple biological pathways that underlie disease pathogenesis would combine the advantages of robust risk prediction with the clinical convenience of a routine blood test. Their objective was to validate the DRS, a simple fasting blood test assessing multiple biomarkers that predict 5-year risk of type 2 diabetes, on an independent population. The test was performed on the Botnia cohort, a Finnish family-based study designed to identify genetic factors associated with development of type 2 diabetes. The participants had 5-year outcomes available for the study. The authors concluded that the DRS stratified the Botnia cohort according to 5-year risk of type 2 diabetes accurately; the DRS reclassified at-risk patients into categories of low, moderate and high risk; DRS effectively redistributed patients among presumed high-risk groups; and DRS may be useful in developing effective diabetes prevention programs for patients identified as highly vulnerable to diabetes. (3)
In July 2009, Kolberg et al. conducted a study to develop a model for assessing the 5-year risk of developing diabetes from a panel of 64 circulating candidate biomarkers. Subjects were selected from the Inter99 cohort, a longitudinal population-based study of about 6,600 Danes in a nested case-control design with the primary outcome of 5-year conversion to type 2 diabetes. Nondiabetic subjects, aged 39 years, with BMI ≥25 kg/m2 at baseline were selected. Baseline fasting serum samples from 160 individuals who developed type 2 diabetes and from 472 who did not were tested. An ultrasensitive immunoassay was used to measure of 58 candidate biomarkers in multiple diabetes-associated pathways, along with six routine clinical variables. Statistical learning methods and permutation testing were used to select the most informative biomarkers. Risk model performance was estimated using a validated bootstrap bias-correction procedure. A model using six biomarkers (adiponectin, C-reactive protein, ferritin, interleukin-2 receptor A, glucose, and insulin) was developed for assessing an individual’s 5-year risk of developing type 2 diabetes. This model has a bootstrap-estimated area under the curve of 0.76, which is greater than that for A1C, fasting plasma glucose, fasting serum insulin, BMI, sex-adjusted waist circumference, a model using fasting glucose and insulin, and a noninvasive clinical model. The authors concluded that a model incorporating six circulating biomarkers provides an objective and quantitative estimate of the 5-year risk of developing type 2 diabetes, performs better than single risk indicators and a noninvasive clinical model, and provides better stratification than fasting plasma glucose alone. (4)
Also in July 2009, Urdea et al. conducted a validation of a multimarker model for assessing risk of type 2 diabetes from the 5-year prospective study of 6,784 Danish people (Inter99). They described the training and validation of the PreDx™ Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. (5)
In a paper presented at the 6th World Congress on Prevention of Diabetes and its Complications, April 8–11, 2010, in Dresden, Germany, Kolberg et al. discussed performance of a multi-marker diabetes risk score on the insulin resistance atherosclerosis study (IRAS), a multi-ethnic U.S. cohort. Their study concluded that 1) the performance of the DRS does not differ across the different ethnic subpopulations in the IRAS study population, an important consideration when the test is being applied in routine clinical practice; 2) DRS is better than other risk assessment tools, including fasting plasma glucose, fasting insulin, BMI, and HOMA-IR; and 3) DRS provides additional value over the current clinical practice of using fasting glucose levels to identify individuals at elevated risk for incident diabetes. The DRS test can easily be implemented in clinical practice and identifies individuals who are genuinely at high risk for developing type 2 diabetes and for whom diabetes prevention strategies should be emphasized. (6)
The above 4 studies were sponsored by, or otherwise connected to Tethys Bioscience. The studies were case series, and no randomized controlled trials have been done that show improved outcomes by knowing this risk score for patients who exhibit standard risk factors for developing type 2 diabetes. Therefore, at this time PreDx DRS is considered experimental, investigational and unproven.
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