Diabetes risk score in Oman: A tool to identify prevalent type 2 diabetes among Arabs of the Middle East

Al-Lawati, Jawad; Tuomilehto, Jaakko

doi:10.1016/j.diabres.2007.01.013

Cited by 122 publications

(128 citation statements)

References 25 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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“…Established risk scores and tools were calculated within the Mesy-Bepo cohort according to the information provided in the respective publications (7,17,18).…”

Section: Discussionmentioning

confidence: 99%

Decision trees as a simple-to-use and reliable tool to identify individuals with impaired glucose metabolism or type 2 diabetes mellitus

Hische

Luis-Dominguez²,

Pfeiffer³

et al. 2010

European Journal of Endocrinology

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Objective: The prevalence of unknown impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or type 2 diabetes mellitus (T2DM) is high. Numerous studies demonstrated that IFG, IGT, or T2DM are associated with increased cardiovascular risk, therefore an improved identification strategy would be desirable. The objective of this study was to create a simple and reliable tool to identify individuals with impaired glucose metabolism (IGM). Design and methods: A cohort of 1737 individuals (1055 controls, 682 with previously unknown IGM) was screened by 75 g oral glucose tolerance test (OGTT). Supervised machine learning was used to automatically generate decision trees to identify individuals with IGM. To evaluate the accuracy of identification, a tenfold cross-validation was performed. Resulting trees were subsequently re-evaluated in a second, independent cohort of 1998 individuals (1253 controls, 745 unknown IGM). Results: A clinical decision tree included age and systolic blood pressure (sensitivity 89.3%, specificity 37.4%, and positive predictive value (PPV) 48.0%), while a tree based on clinical and laboratory data included fasting glucose and systolic blood pressure (sensitivity 89.7%, specificity 54.6%, and PPV 56.2%). The inclusion of additional parameters did not improve test quality. The external validation approach confirmed the presented decision trees. Conclusion: We proposed a simple tool to identify individuals with existing IGM. From a practical perspective, fasting blood glucose and blood pressure measurements should be regularly measured in all individuals presenting in outpatient clinics. An OGTT appears to be useful only if the subjects are older than 48 years or show abnormalities in fasting glucose or blood pressure.

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“…Type 2 diabetes is the most common type of diabetes and globally about 90% of all cases of diabetes are type 2 diabetes [8]. There have been several models, namely, 'The global diabetes model' [9,10], 'The diabetes risk score' [11], the 'Archimedes diabetes model' [12,13], the Diabetes risk score in Oman [14], the 'Genetic Risk Score' [15]. All these models predict risk of future complications associated with type 2 diabetes in people with already diagnosed type 2 diabetes.…”

Section: Type 2 Diabetes Personalized Risk Evaluation Systemmentioning

confidence: 99%

Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

Verma¹,

Fiasché

Cuzzola³

et al. 2011

Engineering Applications of Neural Networks

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Abstract.A novel ontology based type 2 diabetes risk analysis system framework is described, which allows the creation of global knowledge representation (ontology) and personalized modeling for a decision support system. A computerized model focusing on organizing knowledge related to three chronic diseases and genes has been developed in an ontological representation that is able to identify interrelationships for the ontology-based personalized risk evaluation for chronic diseases. The personalized modeling is a process of model creation for a single person, based on their personal data and the information available in the ontology. A transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk for chronic disease. This approach aims to provide support for further discovery through the integration of the ontological representation to build an expert system in order to pinpoint genes of interest and relevant diet components.

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Diabetes risk score in Oman: A tool to identify prevalent type 2 diabetes among Arabs of the Middle East

Cited by 122 publications

References 25 publications

A prediction model for type 2 diabetes risk among Chinese people

A prediction model for type 2 diabetes risk among Chinese people

Decision trees as a simple-to-use and reliable tool to identify individuals with impaired glucose metabolism or type 2 diabetes mellitus

Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach

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