Comparison of a Clinical Model, the Oral Glucose Tolerance Test, and Fasting Glucose for Prediction of Type 2 Diabetes Risk in Japanese Americans

McNeely, Marguerite J.; Boyko, Edward J.; Leonetti, Donna L.; Kahn, Steven E.; Fujimoto, Wilfred Y.

doi:10.2337/diacare.26.3.758

Cited by 93 publications

(76 citation statements)

References 20 publications

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“…Stern and colleagues also constructed the San Antonia model to predict diabetes risk in Mexican Americans and non-Hispanic whites during a 7.5-year follow-up period [13] and this model was validated among Japanese Americans during a 10 year follow-up period [14]. The San Antonia model included biomarkers such as fasting glucose, blood pressure and HDL-cholesterol, in addition to age, sex, obesity and family history of diabetes.…”

Section: Discussionmentioning

confidence: 99%

“…The standard-error estimates and the confidence intervals were obtained based on 1,000 bootstrap samples. Finally, we compared the performance of the proposed prediction model with that of various prediction models derived from other populations, including Cambridge [8][9][10], Prospective Cardiovascular Münster (PROCAM) [30], San Antonia [13,14] and Framingham [5]. AUC was used to compare the discriminatory capabilities of these models and our simple points model.…”

Section: Measurement Of Biochemical Markersmentioning

confidence: 99%

“…Routinely available and easily collected clinical and lifestyle-related information has been found to be effective for identifying diabetes cases [6][7][8][9][10][11][12]. In addition, a prediction model has been developed in Mexican Americans [13] and further tested in Japanese Americans [14]. This San Antonia model included a variety of diabetes risk factors, including age, sex, ethnicity, fasting glucose, systolic blood pressure, HDL-cholesterol, body mass index and family history, to generate a prediction points system and appeared to have a predictive power similar to that of the diagnostic criteria for metabolic syndrome [13,15].…”

Section: Introductionmentioning

confidence: 99%

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A prediction model for type 2 diabetes risk among Chinese people

et al. 2008

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Aims/hypothesis A range of prediction rules for the onset of type 2 diabetes have been proposed. However, most studies have been conducted in white groups and it is not clear whether these models apply to Asian populations. The purpose of this study was to construct a simple points model for predicting incident diabetes among Chinese people.Methods We estimated the 10 year risk of diabetes in a cohort study of middle-aged and elderly participants who were free from diabetes at baseline. Cox regression coefficients were used to construct the simple points model and the discriminatory ability of the resulting prediction rule was determined using AUC and net reclassification improvement and integrated discrimination improvement statistics. Fivefold random splitting was used to test the internal validity and obtain bootstrap estimates of the AUC. Results Of the 2,960 participants without diabetes at the baseline examination, 548 developed type 2 diabetes during a median 10 year follow-up period. Age (four points), elevated fasting glucose (11 points), body mass index (eight points), triacylglycerol (five points), white blood cell count (four points) and a higher HDL-cholesterol (negative four points) were found to strongly predict diabetes incidence in a multivariate model. The estimated AUC for the model was 0.702 (95% CI 0.676-0.727). This model performed better than existing prediction models developed in other populations, including the Prospective Cardiovascular Münster, Cambridge, San Antonia and Framingham models for diabetes risk. Conclusions/interpretation We have constructed a model for predicting the 10 year incidence of diabetes in Chinese people that could be useful for identifying individuals at high risk of diabetes in the Chinese population.

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Section: Discussionmentioning

confidence: 99%

Section: Measurement Of Biochemical Markersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A prediction model for type 2 diabetes risk among Chinese people

et al. 2008

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“…Several prediction models for incident type 2 diabetes based on clinical risk factors have been suggested (6,16,17) because of the ease of administration, convenience, acceptability to patients, and lower cost in comparison with OGTT. Data from The San Antonio Heart Study demonstrated that a simple clinical prediction model resulted in better identification of subjects at risk than OGTT during a 7-to 8-year follow-up period (16).…”

Section: Discussionmentioning

confidence: 99%

“…age, gender, fasting plasma glucose (FPG), serum lipids, BP, family history of diabetes, and anthropometric measurements (5). Clinical prediction models consisting of risk factors seem to be equivalent to OGTT in the prediction of future type 2 diabetes in younger subjects at short follow-up, but not in older subjects or during longer follow-up (6). To our knowledge, however, only very few studies have explored these relationships at follow-up beyond 10 years.…”

Section: Introductionmentioning

confidence: 99%

Follow-up duration influences the relative importance of OGTT and optimal timing of glucose measurements for predicting future type 2 diabetes

Nielsen

Pareek

Léosdottír

et al. 2016

European Journal of Endocrinology

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Objective: To examine the impact of follow-up duration on the incremental prognostic yield of a baseline oral glucose tolerance test (OGTT) for predicting type 2 diabetes and to assess the discrimination ability of blood glucose (BG) obtained at different time points during OGTT. Design: A prospective, population-based cohort study (Malmö Preventive Project) with inclusion of subjects from 1974 to 1992. Methods: A total of 5256 men without diabetes, who had BG measured at 0, 20, 40, 60, 90, and 120 min during OGTT (30 g/m 2 glucose), were followed for 30 years. Incident type 2 diabetes was recorded using registries. The performance of OGTT added to a clinical prediction model (age, body mass index (BMI), diastolic blood pressure, fasting BG, triglycerides, and family history of diabetes) was assessed using Harrell's concordance index (C-index) and integrated discrimination improvement (IDI). Results: Median age was 48 years, mean BMI 24.9 kg/m 2 , and mean fasting BG 4.7 mmol/L. Models with added post-load BG performed better than the clinical model (C-index: P = 0.08 for BG at 120 min at 5 years, otherwise P ≤ 0.045; IDI: P ≥ 0.06 for BG at 60 and 90 min at 5 years, otherwise P ≤ 0.01). With a longer follow-up duration, C-index decreased, and the C-index increase associated with OGTT was attenuated. Models including BG at 60 or 90 min performed significantly better than the model with BG at 120 min, evident beyond follow-up of 10 and 5 years, respectively. Conclusions: OGTT provided incremental prognostic yield for type 2 diabetes prediction. BG measured at 60 or 90 min provided better discrimination than BG at 120 min.

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Prediction of Incident Diabetes Mellitus in Middle-aged Adults

Wilson¹

2007

Arch Intern Med

849

775

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Background: Prediction rules for type 2 diabetes mellitus (T2DM) have been developed, but we lack consensus for the most effective approach.Methods: We estimated the 7-year risk of T2DM in middle-aged participants who had an oral glucose tolerance test at baseline. There were 160 cases of new T2DM, and regression models were used to predict new T2DM, starting with characteristics known to the subject (personal model, ie, age, sex, parental history of diabetes, and body mass index [calculated as the weight in kilograms divided by height in meters squared]), adding simple clinical measurements that included metabolic syndrome traits (simple clinical model), and, finally, assessing complex clinical models that included (1) 2-hour post-oral glucose tolerance test glucose, fasting insulin, and C-reactive protein levels; (2) the Gutt insulin sensitivity index; or (3) the homeostasis model insulin resistance and the homeostasis model insulin resistance ␤-cell sensitivity indexes. Discrimina-tion was assessed with area under the receiver operating characteristic curves (AROCs).Results: The personal model variables, except sex, were statistically significant predictors of T2DM (AROC, 0.72).In the simple clinical model, parental history of diabetes and obesity remained significant predictors, along with hypertension, low levels of high-density lipoprotein cholesterol, elevated triglyceride levels, and impaired fasting glucose findings but not a large waist circumference (AROC, 0.85). Complex clinical models showed no further improvement in model discriminations (AROC, 0.850-0.854) and were not superior to the simple clinical model. Conclusion:Parental diabetes, obesity, and metabolic syndrome traits effectively predict T2DM risk in a middleaged white population sample and were used to develop a simple T2DM prediction algorithm to estimate risk of new T2DM during a 7-year follow-up interval.

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Comparison of a Clinical Model, the Oral Glucose Tolerance Test, and Fasting Glucose for Prediction of Type 2 Diabetes Risk in Japanese Americans

Cited by 93 publications

References 20 publications

A prediction model for type 2 diabetes risk among Chinese people

A prediction model for type 2 diabetes risk among Chinese people

Follow-up duration influences the relative importance of OGTT and optimal timing of glucose measurements for predicting future type 2 diabetes

Prediction of Incident Diabetes Mellitus in Middle-aged Adults

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