Leonard Schlessinger

The prevention or delay of type 2 diabetes: American Diabetes Association and National Institute of Diabetes, Digestive and Kidney Diseases

D iabetes is one of the most costly and burdensome chronic diseases of our time and is a condition that is increasing in epidemic proportions in the U.S. and throughout the world (1). The complications resulting from the disease are a significant cause of morbidity and mortality and are associated with the damage or failure of various organs such as the eyes, kidneys, and nerves. Individuals with type 2 diabetes are also at a significantly higher risk for coronary heart disease, peripheral vascular disease, and stroke, and they have a greater likelihood of having hypertension, dyslipidemia, and obesity (2–6). There is also growing evidence that at glucose levels above normal but below the diabetes threshold diagnostic now referred to as pre-diabetes, there is a substantially increased risk of cardiovascular disease (CVD) and death (5,7–10). In these individuals, CVD risk factors are also more prevalent (5–7,9,11–14), which further increases the risk but is not sufficient to totally explain it. In contrast to the clear benefit of glucose lowering to prevent or retard the progression of microvascular complications associated with diabetes (15– 18,21), it is less clear whether the high rate of CVD in people with impaired glucose homeostasis, i.e., those with impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or diabetes, is caused by elevated blood glucose levels or will respond to treatments that lower blood glucose. Epidemiological studies have shown a clear relationship (19,20), whereas intervention trials in people with diabetes suggest, but have not demonstrated, a clear benefit of glycemic control (15,16,21,22). Additionally, there are no studies that have investigated a benefit of glucose lowering on macrovascular disease in subjects with only pre-diabetes (IFG or IGT) but not diabetes. Although the treatment of diabetes has become increasingly sophisticated, with over a dozen pharmacological agents available to lower blood glucose, a multitude of ancillary supplies and equipment available, and a clear recognition by health care professionals and patients that diabetes is a serious disease, the normalization of blood glucose for any appreciable period of time is seldom achieved (23). In addition, in well-controlled socalled “intensively” treated patients, serious complications still occur (15–18,21), and the economic and personal burden of diabetes remains. Furthermore, microvascular disease is already present in many individuals with undiagnosed or newly diagnosed type 2 diabetes (11,24– 28). Given these facts, it is not surprising that studies have been initiated in the last decade to determine the feasibility and benefit of various strategies to prevent or delay the onset of type 2 diabetes. Two early reports (29,30) suggested that changes in lifestyle can prevent diabetes, but weaknesses in study design limited their general relevance. Recently, however, four well-designed randomized controlled trials have been reported (31–35). In the Finnish study (31), 522 middleaged (mean age 55 years) obese (mean BMI 31 kg/m) subjects with IGT were randomized to receive either brief diet and exercise counseling (control group) or intensive individualized instruction on weight reduction, food intake, and guidance on increasing physical activity (intervention group). After an average follow-up of 3.2 years, there was a 58% relative reduction in the incidence of diabetes in the intervention group compared with the control subjects. A strong correlation was also seen between the ability to stop the progression to diabetes and the degree to which subjects were able to achieve one or more of the following: lose weight (goal of 5.0% weight reduction), reduce fat intake (goal of 30% of calories), reduce saturated fat intake (goal of 10% of calories), increase fiber intake (goal of 15 g/1,000 kcal), and exercise (goal of 150 min/week). No untoward effects of the lifestyle interventions were observed. In the Diabetes Prevention Program (DPP) (32–34), the 3,234 enrolled subjects were slightly younger (mean age 51 years) and more obese (mean BMI 34 kg/m) but had nearly identical glucose intolerance compared with subjects in the Finnish study. About 45% of the participants were from minority groups (e.g, AfricanAmerican, Hispanic), and 20% were 60 years of age. Subjects were randomized to one of three intervention groups, which included the intensive nutrition and exercise counseling (“lifestyle”) group or either of two masked medication treatment groups: the biguanide metformin group or the placebo group. The latter interventions were combined with standard diet and exercise recommendations. After an average follow-up of 2.8 years (range 1.8–4.6 years), a 58% relative reduction in the progression to diabetes was observed in the lifestyle group (absolute incidence 4.8%), and a 31% relative reduction in the progression of diabetes was observed in the metformin group (absolute incidence 7.8%) compared with control subjects (absolute incidence 11.0%). ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Diabetes Risk Calculator: A simple tool for detecting undiagnosed diabetes and pre-diabetes

OBJECTIVE—The objective of this study was to develop a simple tool for the U.S. population to calculate the probability that an individual has either undiagnosed diabetes or pre-diabetes. RESEARCH DESIGN AND METHODS—We used data from the Third National Health and Nutrition Examination Survey (NHANES) and two methods (logistic regression and classification tree analysis) to build two models. We selected the classification tree model on the basis of its equivalent accuracy but greater ease of use. RESULTS—The resulting tool, called the Diabetes Risk Calculator, includes questions on age, waist circumference, gestational diabetes, height, race/ethnicity, hypertension, family history, and exercise. Each terminal node specifies an individuals probability of pre-diabetes or of undiagnosed diabetes. Terminal nodes can also be used categorically to designate an individual as having a high risk for 1) undiagnosed diabetes or pre-diabetes, 2) pre-diabetes, or 3) neither undiagnosed diabetes or pre-diabetes. With these classifications, the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic area for detecting undiagnosed diabetes are 88%, 75%, 14%, 99.3%, and 0.85, respectively. For pre-diabetes or undiagnosed diabetes, the results are 75%, 65%, 49%, 85%, and 0.75, respectively. We validated the tool using v-fold cross-validation and performed an independent validation against NHANES 1999–2004 data. CONCLUSIONS—The Diabetes Risk Calculator is the only currently available noninvasive screening tool designed and validated to detect both pre-diabetes and undiagnosed diabetes in the U.S. population.

Methods for Building and Validating Equations for Physiology-Based Mathematical Models Glucose Metabolism and Type 2 Diabetes in the Archimedes Model

Objectives. Describe steps for deriving and validating equations for physiology processes for use in mathematical models. Illustrate the steps using glucose metabolism and Type 2 diabetes in the Archimedes model. Methods and Results. The steps are as follows: identify relevant variables, describe their relationships, identify data sources that relate the variables, correct for biases in data sources, use curve fitting algorithms to estimate equations, validate the accuracy of curve fitting against empirical data, perform partially and fully independent external validations, examine any discrepancies to determine causes and make corrections, and periodically update and revalidate equations as necessary. Specific methods depend on the available data. Specific data sources and methods are illustrated for equations that represent the cause of Type 2 diabetes and its effect on fasting plasma glucose in the Archimedes model. Methods for validating the equations are illustrated. Applications enabled by including physiological equations in healthcare models are discussed. Conclusions. The methods can be used to derive equations that represent the relationships between physiological variables and the causes of diseases and that validate well against empirical data.