Novel Risk Factors and the Prediction of Type 2 Diabetes in the Atherosclerosis Risk in Communities (ARIC) Study

Department of Pediatrics, Division of Academic General Pediatrics, University of Minnesota, Minneapolis, Minnesota.
Diabetes care (Impact Factor: 8.42). 08/2012; 36(1). DOI: 10.2337/dc12-0609
Source: PubMed


The objective of this study was to determine potential added value of novel risk factors in predicting the development of type 2 diabetes beyond that provided by standard clinical risk factors.RESEARCH DESIGN AND METHODS
The Atherosclerosis Risk in Communities (ARIC) Study is a population-based prospective cohort study in four U.S. communities. Novel risk factors were either measured in the full cohort or in a case-control sample nested within the cohort. We started with a basic prediction model, previously validated in ARIC, and evaluated 35 novel risk factors by adding them independently to the basic model. The area under the curve (AUC), net reclassification index (NRI), and integrated discrimination index (IDI) were calculated to determine if each of the novel risk factors improved risk prediction.RESULTSThere were 1,457 incident cases of diabetes with a mean of >7.6 years of follow-up among 12,277 participants at risk. None of the novel risk factors significantly improved the AUC. Forced expiratory volume in 1 s was the only novel risk factor that resulted in a significant NRI (0.54%; 95% CI: 0.33-0.86%). Adiponectin, leptin, γ-glutamyl transferase, ferritin, intercellular adhesion molecule 1, complement C3, white blood cell count, albumin, activated partial thromboplastin time, factor VIII, magnesium, hip circumference, heart rate, and a genetic risk score each significantly improved the IDI, but net changes were small.CONCLUSIONS
Evaluation of a large panel of novel risk factors for type 2 diabetes indicated only small improvements in risk prediction, which are unlikely to meaningfully alter clinical risk reclassification or discrimination strategies.

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Available from: Mark Pereira, Aug 06, 2014
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    • "To provide improved predictive power over conventional risk factors, genetic testing must be sensitive and specific in discriminating subjects who will develop the disease on follow up from those who will not73. In this line, it has been recognized that genetic variants so far identified do not substantially improve the discriminative accuracy of disease prediction based on conventional risk factors74. Even genetic models incorporating thousands of additional putative common variants are likely to offer limited improvement73. "
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