Derivation and validation of diabetes risk score for urban Asian Indians

Ramachandran, Ambady; Snehalatha, C.; Vijay, V.; Wareham, Nick; Colagiuri, Stephen

doi:10.1016/j.diabres.2005.02.016

Cited by 133 publications

(136 citation statements)

References 13 publications

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“…The prediction models for the risk of diabetes can help to guide screening and interventions and to predict diabetes occurrence [3][4][5]. 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].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

A prediction model for type 2 diabetes risk among Chinese people

et al. 2008

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“…As the risk factors like increasing age, obesity, family history of diabetes, lack of physical activity, stressful environment and dietary habits for Type 2 Diabetes were well established, various associations and federations throughout the world has developed the risk scoring for the development of type 2 diabetes. Among these prediction tools, American Diabetes Association (ADA) Risk Tools, Finnish Diabetes Risk Score (FINDRISC), National Health and Nutrition Examination Survey (NHANES) risk score, and study to prevent non-insulin dependents diabetes mellitus (STOP-NIDDM) Risk Score in developed countries 4 and currently in practice in India -Indian Diabetes Risk score developed by Madras Diabetes Research Foundation and Ramachandran A et al 5,6 Almost all these questionnaires have the common risk factors with little differences according to the population race and their susceptibility and its predictive nature were all well established by many studies. This study was intended to use the IDRS risk score a screening tool for assessing the risk of developing diabetes among the semi urban community and its significance.…”

Section: Introductionmentioning

confidence: 99%

Screening for diabetes using Indian diabetes risk score

2016

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“…Most of the latter have been developed among Caucasians (10 -12). Only one diabetes risk score has been based on an Asian population (13), and this was a prevalence study carried out in a part of Asia very different from Thailand.…”

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confidence: 99%

A Risk Score for Predicting Incident Diabetes in the Thai Population

et al. 2006

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OBJECTIVE -The objective of this study was to develop and evaluate a risk score to predict people at high risk of diabetes in Thailand.RESEARCH DESIGN AND METHODS -A Thai cohort of 2,677 individuals, aged 35-55 years, without diabetes at baseline, was resurveyed after 12 years. Logistic regression models were used to identify baseline risk factors that predicted the incidence of diabetes; a simple model that included only those risk factors as significant (P Ͻ 0.05) when adjusted for each other was developed. The coefficients from this model were transformed into components of a diabetes score. This score was tested in a Thai validation cohort of a different 2,420 individuals.RESULTS -A total of 361 individuals developed type 2 diabetes in the exploratory cohort during the follow-up period. The significant predictive variables in the simple model were age, BMI, waist circumference, hypertension, and history of diabetes in parents or siblings A cutoff score of 6 of 17 produced the optimal sum of sensitivity (77%) and specificity (60%). The area under the receiver-operating characteristic curve (AUC) was 0.74. Adding impaired fasting glucose or impaired glucose tolerance status to the model slightly increased the AUC to 0.78; adding low HDL cholesterol and/or high triglycerides barely improved the model. The validation cohort demonstrated similar results.CONCLUSIONS -A simple diabetes risk score, based on a set of variables not requiring laboratory tests, can be used for early intervention to delay or prevent the disease in Thailand. Adding impaired fasting glucose or impaired glucose tolerance or triglyceride and HDL cholesterol status to this model only modestly improves the predictive ability. Diabetes Care 29:1872-1877, 2006A remarkable worldwide increase in the number of people with type 2 diabetes has been predicted (1). Prevalence rates in the developing world, particularly in the Asia-Pacific region, are already high and expected to rise more quickly than elsewhere. In Thailand, the prevalence of type 2 diabetes among the population aged Յ35 years was 9.6% in 2001, an increase of 20% over a period of 5 years (2). Cardiovascular disease (CVD) is one of the leading causes of death in Thailand (3), and individuals with diabetes have a two to fourfold greater risk of developing CVD than those without (4). The burden of diabetes and its complications, which include other diseases besides CVD (5), imposes a massive load on the Thai health care system. Lifestyle modification has been proven to effectively prevent and delay the development of diabetes (6 -8).Therefore, early recognition of and intervention for the condition will be beneficial, particularly as cardiovascular complications set in early after the onset of diabetes (9). Delay and lack of detection of the disease are mostly due to patients being asymptomatic during the early stage of the disease; hence an accurate screening tool to identify those at high risk of developing diabetes will be of great value. Knowledge of the risk of diabetes could enhance...

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Derivation and validation of diabetes risk score for urban Asian Indians

Cited by 133 publications

References 13 publications

A prediction model for type 2 diabetes risk among Chinese people

A prediction model for type 2 diabetes risk among Chinese people

Screening for diabetes using Indian diabetes risk score

A Risk Score for Predicting Incident Diabetes in the Thai Population

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