Diabetes Care

OBJECTIVE—To describe the 1) lifestyle intervention used in the Finnish Diabetes Prevention Study, 2) short- and long-term changes in diet and exercise behavior, and 3) effect of the intervention on glucose and lipid metabolism.

RESEARCH DESIGN AND METHODS—There were 522 middle-aged, overweight subjects with impaired glucose tolerance who were randomized to either a usual care control group or an intensive lifestyle intervention group. The control group received general dietary and exercise advice at baseline and had an annual physician’s examination. The subjects in the intervention group received additional individualized dietary counseling from a nutritionist. They were also offered circuit-type resistance training sessions and advised to increase overall physical activity. The intervention was the most intensive during the first year, followed by a maintenance period. The intervention goals were to reduce body weight, reduce dietary and saturated fat, and increase physical activity and dietary fiber.

RESULTS—The intervention group showed significantly greater improvement in each intervention goal. After 1 and 3 years, weight reductions were 4.5 and 3.5 kg in the intervention group and 1.0 and 0.9 kg in the control group, respectively. Measures of glycemia and lipemia improved more in the intervention group.

CONCLUSIONS—The intensive lifestyle intervention produced long-term beneficial changes in diet, physical activity, and clinical and biochemical parameters and reduced diabetes risk. This type of intervention is a feasible option to prevent type 2 diabetes and should be implemented in the primary health care system.

OBJECTIVE—Interventions to prevent type 2 diabetes should be directed toward individuals at increased risk for the disease. To identify such individuals without laboratory tests, we developed the Diabetes Risk Score.

RESEARCH DESIGN AND METHODS—A random population sample of 35- to 64-year-old men and women with no antidiabetic drug treatment at baseline were followed for 10 years. New cases of drug-treated type 2 diabetes were ascertained from the National Drug Registry. Multivariate logistic regression model coefficients were used to assign each variable category a score. The Diabetes Risk Score was composed as the sum of these individual scores. The validity of the score was tested in an independent population survey performed in 1992 with prospective follow-up for 5 years.

RESULTS—Age, BMI, waist circumference, history of antihypertensive drug treatment and high blood glucose, physical activity, and daily consumption of fruits, berries, or vegetables were selected as categorical variables. Complete baseline risk data were found in 4,435 subjects with 182 incident cases of diabetes. The Diabetes Risk Score value varied from 0 to 20. To predict drug-treated diabetes, the score value ≥9 had sensitivity of 0.78 and 0.81, specificity of 0.77 and 0.76, and positive predictive value of 0.13 and 0.05 in the 1987 and 1992 cohorts, respectively.

CONCLUSIONS—The Diabetes Risk Score is a simple, fast, inexpensive, noninvasive, and reliable tool to identify individuals at high risk for type 2 diabetes.

OBJECTIVE: The oral glucose tolerance test (OGTT) has often been used to evaluate apparent insulin release and insulin resistance in various clinical settings. However, because insulin sensitivity and insulin release are interdependent, to what extent they can be predicted from an OGTT is unclear. RESEARCH DESIGN AND METHODS: We studied insulin sensitivity using the euglycemic-hyperinsulinemic clamp and insulin release using the hyperglycemic clamp in 104 nondiabetic volunteers who had also undergone an OGTT. Demographic parameters (BMI, waist-to-hip ratio, age) and plasma glucose and insulin values from the OGTT were subjected to multiple linear regression to predict the metabolic clearance rate (MCR) of glucose, the insulin sensitivity index (ISI), and first-phase (1st PH) and second-phase (2nd PH) insulin release as measured with the respective clamps. RESULTS: The equations predicting MCR and ISI contained BMI, insulin (120 min), and glucose (90 min) and were highly correlated with the measured MCR (r = 0.80, P < 0.00005) and ISI (r = 0.79, P < 0.00005). The equations predicting 1st PH and 2nd PH contained insulin (0 and 30 min) and glucose (30 min) and were also highly correlated with the measured 1st PH (r = 0.78, P < 0.00005) and 2nd PH (r = 0.79, P < 0.00005). The parameters predicted by our equations correlated better with the measured parameters than homeostasis model assessment for secretion and resistance, the delta30-min insulin/delta30-min glucose ratio for secretion and insulin (120 min) for insulin resistance taken from the OGTT. CONCLUSIONS: We thus conclude that predicting insulin sensitivity and insulin release with reasonable accuracy from simple demographic parameters and values obtained during an OGTT is possible. The derived equations should be used in various clinical settings in which the use of clamps or the minimal model would be impractical.

The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee (https://doi.org/10.2337/dc20-SPPC), a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.