Risk prediction models for the development of diabetes in Mauritian Indians

Gao, Weiguo; Qiao, Qing; Pitkäniemi, Janne; Magliano, Dianna J.; Shaw, Jonathan E.; Söderberg, Stefan; Zimmet, Paul; Chitson, Pierrot; Knowlessur, S; Alberti, George; Tuomilehto, J

doi:10.1111/j.1464-5491.2009.02810.x

Cited by 28 publications

(40 citation statements)

References 19 publications

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“…Combining information on FPG or impaired fasting glucose with a simple diabetes risk score has been reported to increase predictive ability [7,8,[12][13][14][15][16][17]. A study reported that screening models using the combination of HbA 1c , BMI and FPG accurately identified individuals at risk of future clinically diagnosed diabetes [22], although the factors that remained significant were different from those found in the present study.…”

Section: Discussionmentioning

confidence: 46%

“…Various rules have been developed to predict the incidence of diabetes in different ethnic groups [2][3][4][5]. Routine clinical markers available without laboratory testing have been shown to be predictive of the development of diabetes [6][7][8][9][10][11][12][13][14][15][16][17][18] and adding biochemical measures, in particular fasting plasma glucose (FPG), improves predictive accuracy [7,8,[12][13][14][15][16][17]. On the other hand, adding complex data such as the results of oral glucose tolerance tests and measurements of insulin levels and inflammatory markers into a simple clinical model only minimally improves risk prediction while increasing cost and inconvenience [10,19].…”

Section: Introductionmentioning

confidence: 99%

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Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

et al. 2012

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Aims/hypothesis The aims of this study were to assess the clinical significance of introducing HbA 1c into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals. Methods The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA 1c ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA 1c or both to NLA. Results The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA 1c scores was non-significant (0.836 vs 0.837; p00.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA 1c had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA 1c resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p<0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively.

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Section: Discussionmentioning

confidence: 46%

Section: Introductionmentioning

confidence: 99%

Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

et al. 2012

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“…This difference seemed attributable to the different BMI-adjusted education–DM associations found in LICs versus MICs as well. Different from LICs, higher education level has been associated with decreased odds of DM among Mauritian Indians23 and people in Lithuania 24. Even accounting for BMI, such negative education–DM association was found in studies done in Brazil25 and China 26.…”

Section: Discussionmentioning

confidence: 92%

Global educational disparities in the associations between body mass index and diabetes mellitus in 49 low-income and middle-income countries

Wang

Stronks

Arah³

2014

J Epidemiol Community Health

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BackgroundDespite 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).MethodsThe 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.ResultsWe 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.ConclusionsBMI and education are associated with the DM, but the associations seem to differ in complex ways between LICs and MICs and by gender.

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“…Two studies reported examining the association of individual risk predictors with patient outcome after adjusting for age and sex [27] and age and cohort [30]. Nine studies (23%) included all risk predictors in the multivariable analysis [25,26,33,36,39,49,51,53,61].…”

Section: Resultsmentioning

confidence: 99%

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

Collins

Mallett

Omar

et al. 2011

BMC Med

416

419

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BackgroundThe 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.MethodsWe 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.ResultsThirty-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%).ConclusionsWe 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.

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Risk prediction models for the development of diabetes in Mauritian Indians

Abstract: 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.

Cited by 28 publications

References 19 publications

Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

Global educational disparities in the associations between body mass index and diabetes mellitus in 49 low-income and middle-income countries

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

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