Risk prediction models for the development of diabetes in Mauritian Indians
ABSTRACT To develop risk prediction models of future diabetes in Mauritian Indians.
Three thousand and ninety-four Mauritian Indians (1141 men, aged 20-65 years) without diabetes in 1987 or 1992 were followed up to 1992 or 1998. Subjects underwent repeated oral glucose tolerance tests and diabetes was diagnosed according to 2006 World Health Organization/International Diabetes Federation criteria. Cox regression models for interval censored data were performed using data from 1544 randomly selected participants. Predicted probabilities for diabetes were calculated and validated in the remaining 1550 subjects.
Over 11 years of follow-up, there were 511 cases of diabetes. Among variables tested, family history of diabetes, obesity (body mass index, waist circumference) and glucose were significant predictors of diabetes. Predicted probabilities derived from a simple model fitted with sex, family history of diabetes and obesity ranged from 0.05 to 0.64 in men and 0.03 to 0.49 in women. To predict the onset of diabetes, area under the receiver operating characteristic (ROC) curve (AROC) of predicted probabilities was 0.62 (95% confidence interval, 0.56-0.68) in men and 0.64 (0.59-0.69) in women. At a cut-off point of 0.12, the sensitivity and specificity were 0.72 (0.71-0.74) and 0.47 (0.45-0.49) in men and 0.77 (0.75-0.78) and 0.50 (0.48-0.52) in women, respectively. Addition of fasting plasma glucose (FPG) to the model improved the prediction slightly [AROC curve 0.70 (0.65-0.76) in men, 0.71 (0.67-0.76) in women].
A diabetes prediction model based on obesity and family history yielded moderate discrimination in Mauritian Indians, which was slightly inferior to the model with the FPG but may be useful in low-income countries to promote identification of people at high risk of diabetes.
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ABSTRACT: Despite the well-established link between body mass index (BMI) and diabetes mellitus (DM), it remains unclear whether this association is more pronounced at certain levels of education. This study assessed the modifying effect of educational attainment on the associations between BMI and DM-as well as the joint associations of BMI and education with DM-in low-income countries (LICs) and middle-income countries (MICs). The authors used cross-sectional data from 160 381 participants among 49 LICs and MICs in the World Health Survey. Overweight and obesity levels were defined using WHO's classification. Educational attainment was classified in four categories: 'no formal education', 'some/completed primary school', 'secondary/high school completed' and 'college and beyond'. We used random-intercept multilevel logistic regressions to investigate the modifying influence of educational attainment on the associations of different BMI levels-as well as their joint associations-with DM. We found positive associations between excessive BMI and DM at each education level in both LICs and MICs. We found that the joint associations of BMI and education with DM were larger than the product of their separate single associations among females in LICs. With joint increases in BMI and education, males and females in LICs had similar increased odds of DM, but males had higher such odds than females in MICs. BMI and education are associated with the DM, but the associations seem to differ in complex ways between LICs and MICs and by gender.Journal of epidemiology and community health 03/2014; 68(8). DOI:10.1136/jech-2013-203200 · 3.29 Impact Factor
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ABSTRACT: OBJECTIVE To develop a New Chinese Diabetes Risk Score for screening undiagnosed type 2 diabetes in China.RESEARCH DESIGN AND METHODS Data from the China National Diabetes and Metabolic Disorders Study conducted from June 2007 to May 2008 comprising 16,525 men and 25,284 women aged 20-74 years were analyzed. Undiagnosed type 2 diabetes was detected based on fasting plasma glucose ≥7.0 mmol/L or 2-h plasma glucose ≥11.1 mmol/L in people without a prior history of diabetes. β-Coefficients derived from a multiple logistic regression model predicting the presence of undiagnosed type 2 diabetes were used to calculate the New Chinese Diabetes Risk Score. The performance of the New Chinese Diabetes Risk Score was externally validated in two studies in Qingdao: one is prospective with follow-up from 2006 to 2009 (validation 1) and another cross-sectional conducted in 2009 (validation 2).RESULTSThe New Chinese Diabetes Risk Score includes age, sex, waist circumference, BMI, systolic blood pressure, and family history of diabetes. The score ranges from 0 to 51. The area under the receiver operating curve of the score for undiagnosed type 2 diabetes was 0.748 (0.739-0.756) in the exploratory population, 0.725 (0.683-0.767) in validation 1, and 0.702 (0.680-0.724) in validation 2. At the optimal cutoff value of 25, the sensitivity and specificity of the score for predicting undiagnosed type 2 diabetes were 92.3 and 35.5%, respectively, in validation 1 and 86.8 and 38.8% in validation 2.CONCLUSIONS The New Chinese Diabetes Risk Score based on nonlaboratory data appears to be a reliable screening tool to detect undiagnosed type 2 diabetes in Chinese population.Diabetes care 10/2013; 36(12). DOI:10.2337/dc13-0593 · 8.57 Impact Factor
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ABSTRACT: To evaluate the performance and cost-effectiveness of two screening methods to identify undiagnosed diabetes at primary care settings among a Chinese population. Two screening methods using a fasting capillary glucose (FCG) test or a Chinese diabetes risk score (DRS) at primary care settings followed by diagnostic tests were compared. The performance of FCG and DRS was evaluated by using receiver operating characteristic (ROC) curve analysis. The main economic outcome measures were the total cost of screening per 1000 persons, proportion of undiagnosed diabetes detected, and cost per undiagnosed diabetes identified from the societal perspective. Among all participants, 14.6% (1349/9232) had undiagnosed diabetes defined by fasting plasma glucose ≥7.0mmol/l and/or 2h plasma glucose ≥11.1mmol/l and/or hemoglobin A1c ≥6.5%. At the optimal cutoff point of 6.1mmol/l for FCG and 14 for DRS, the sensitivity was 65.1% and 65.8%, and specificity was 72.4% and 55.2%, respectively. The area under the ROC curve was 75.3% for FCG and 63.7% for DRS (P<0.001). Based on the input costs, the total cost of screening 1000 persons was ¥64,000 ($9143) for FCG and ¥81,000 ($11,571) for DRS. The average cost per case identified was ¥674 ($96) for FCG at cutoff point of 6.1mmol/l and ¥844 ($121) for DRS at score of 14. The incremental cost per case identified was ¥17,000 ($2429) for DRS compared to FCG. The dominance relations between strategies remained with the changed in sensitivity analysis. As a first-line screening tool for undiagnosed diabetes, the FCG test performed better than the DRS in primary care settings in China. The non-invasive and layperson-oriented DRS was feasible and detected more cases but more expensive. No strategy has strong dominance that was both more effective and less costly. The favorable strategy will depend on if the purpose of the screening program is to identify more cases or to have lower cost per case.09/2013; DOI:10.1016/j.pcd.2013.08.003