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

Archimedes, San Francisco, California, USA.
Diabetes care (Impact Factor: 8.42). 06/2008; 31(5):1040-5. DOI: 10.2337/dc07-1150
Source: PubMed


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.
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.
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 individual's 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.
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.

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    • "). This validated algorithm uses BMI, age, race, immediate family history of diabetes, and personal history of hypertension or gestational diabetes to categorize respondents as having low, medium, or high risk for type 2 diabetes (Heikes et al. 2008). "
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    ABSTRACT: Genetic testing for chronic disease susceptibility may motivate young adults for preventive behavior change. This nationally representative survey gave 521 young adults hypothetical scenarios of receiving genetic susceptibility results for heart disease, type 2 diabetes, and stroke and asked their (1) interest in such testing, (2) anticipated likelihood of improving diet and physical activity with high- and low-risk test results, and (3) readiness to make behavior change. Responses were analyzed by presence of established disease-risk factors. Respondents with high phenotypic diabetes risk reported increased likelihood of improving their diet and physical activity in response to high-risk results compared with those with low diabetes risk (odds ratio (OR), 1.82 (1.03, 3.21) for diet and OR, 2.64 (1.24, 5.64) for physical activity). In contrast, poor baseline diet (OR, 0.51 (0.27, 0.99)) and poor physical activity (OR, 0.53 (0.29, 0.99)) were associated with decreased likelihood of improving diet. Knowledge of genetic susceptibility may motivate young adults with higher personal diabetes risk for improvement in diet and exercise, but poor baseline behaviors are associated with decreased intention to make these changes. To be effective, genetic risk testing in young adults may need to be coupled with other strategies to enable behavior change.
    Journal of community genetics 02/2013; 4(2). DOI:10.1007/s12687-013-0140-6
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    • "The AUC was 0.65 in men and 0.66 in women for the FINDRISC score predicting impaired fasting glucose, impaired glucose tolerance, or undiagnosed diabetes, and 0.72 and 0.75 for detecting metabolic syndrome [18]. The AUC of the Diabetes Risk Calculator was 0.70 for detecting impaired fasting glucose, impaired glucose tolerance, or undiagnosed diabetes [19]. These modest AUC values indicate that many people who will develop T2D are not identified as being at increased risk by these risk scores, and that many that will not develop the disease are labeled as increased risk. "
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    ABSTRACT: Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions.
    Current Diabetes Reports 09/2011; 11(6):511-8. DOI:10.1007/s11892-011-0235-6 · 3.08 Impact Factor
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    • "The number of participants included in developing risk prediction models was clearly reported in 35 (90%) studies. In the four studies where this was not clearly reported, the number of events was not reported [26,34,49,56]. The median number of participants included in model development was 2,562 (interquartile range (IQR) 1,426 to 4,965). "
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    ABSTRACT: The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults. We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance. Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%). We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.
    BMC Medicine 09/2011; 9(1):103. DOI:10.1186/1741-7015-9-103 · 7.25 Impact Factor
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