Literature DB >> 18515811

A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study.

Mushtaqur Rahman1, Rebecca K Simmons, Anne-Helen Harding, Nicholas J Wareham, Simon J Griffin.   

Abstract

BACKGROUND: Randomized trials have demonstrated that Type 2 diabetes is preventable among high-risk individuals. To date, such individuals have been identified through population screening using the oral glucose tolerance test.
OBJECTIVE: To assess whether a risk score comprising only routinely collected non-biochemical parameters was effective in identifying those at risk of developing Type 2 diabetes.
METHODS: Population-based prospective cohort (European Prospective Investigation of Cancer-Norfolk). Participants aged 40-79 recruited from UK general practices attended a health check between 1993 and 1998 (n = 25 639) and were followed for a mean of 5 years for diabetes incidence. The Cambridge Diabetes Risk Score was computed for 24 495 individuals with baseline data on age, sex, prescription of steroids and anti-hypertensive medication, family history of diabetes, body mass index and smoking status. We examined the incidence of diabetes across quintiles of the risk score and plotted a receiver operating characteristic (ROC) curve to assess discrimination.
RESULTS: There were 323 new cases of diabetes, a cumulative incidence of 2.76/1000 person-years. Those in the top quintile of risk were 22 times more likely to develop diabetes than those in the bottom quintile (odds ratio 22.3; 95% CI: 11.0-45.4). In all, 54% of all clinically incident cases occurred in individuals in the top quintile of risk (risk score > 0.37). The area under the ROC was 74.5%.
CONCLUSION: The risk score is a simple, effective tool for the identification of those at risk of developing Type 2 diabetes. Such methods may be more feasible than mass population screening with biochemical tests in defining target populations for prevention programmes.

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Year:  2008        PMID: 18515811     DOI: 10.1093/fampra/cmn024

Source DB:  PubMed          Journal:  Fam Pract        ISSN: 0263-2136            Impact factor:   2.267


  51 in total

1.  Different type 2 diabetes risk assessments predict dissimilar numbers at 'high risk': a retrospective analysis of diabetes risk-assessment tools.

Authors:  Benjamin J Gray; Richard M Bracken; Daniel Turner; Kerry Morgan; Michael Thomas; Sally P Williams; Meurig Williams; Sam Rice; Jeffrey W Stephens
Journal:  Br J Gen Pract       Date:  2015-11-05       Impact factor: 5.386

Review 2.  The potential of novel biomarkers to improve risk prediction of type 2 diabetes.

Authors:  Christian Herder; Bernd Kowall; Adam G Tabak; Wolfgang Rathmann
Journal:  Diabetologia       Date:  2014-01       Impact factor: 10.122

3.  An algorithm to predict risk of type 2 diabetes in Turkish adults: contribution of C-reactive protein.

Authors:  A Onat; G Can; H Yüksel; E Ayhan; Y Dogan; G Hergenç
Journal:  J Endocrinol Invest       Date:  2010-10-27       Impact factor: 4.256

4.  Depression and risk of type 2 diabetes: the potential role of metabolic factors.

Authors:  N Schmitz; S S Deschênes; R J Burns; K J Smith; A Lesage; I Strychar; R Rabasa-Lhoret; C Freitas; E Graham; P Awadalla; J L Wang
Journal:  Mol Psychiatry       Date:  2016-02-23       Impact factor: 15.992

5.  Risk scores for predicting type 2 diabetes: using the optimal tool.

Authors:  M Alssema; D Vistisen; M W Heymans; G Nijpels; C Glümer; P Z Zimmet; J E Shaw; M Eliasson; C D A Stehouwer; A G Tabák; S Colagiuri; K Borch-Johnsen; J M Dekker
Journal:  Diabetologia       Date:  2011-06-10       Impact factor: 10.122

6.  Risk scores for predicting type 2 diabetes: comparing axes and spades.

Authors:  N J Wareham; S J Griffin
Journal:  Diabetologia       Date:  2011-03-05       Impact factor: 10.122

7.  San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study.

Authors:  Mohammadreza Bozorgmanesh; Farzad Hadaegh; Azadeh Zabetian; Fereidoun Azizi
Journal:  Int J Public Health       Date:  2010-03-09       Impact factor: 3.380

8.  A prediction model for type 2 diabetes risk among Chinese people.

Authors:  K Chien; T Cai; H Hsu; T Su; W Chang; M Chen; Y Lee; F B Hu
Journal:  Diabetologia       Date:  2008-12-05       Impact factor: 10.122

9.  Impact of an informed choice invitation on uptake of screening for diabetes in primary care (DICISION): trial protocol.

Authors:  Eleanor Mann; A Toby Prevost; Simon Griffin; Ian Kellar; Stephen Sutton; Michael Parker; Simon Sanderson; Ann Louise Kinmonth; Theresa M Marteau
Journal:  BMC Public Health       Date:  2009-02-20       Impact factor: 3.295

10.  Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study.

Authors:  Philippa J Talmud; Aroon D Hingorani; Jackie A Cooper; Michael G Marmot; Eric J Brunner; Meena Kumari; Mika Kivimäki; Steve E Humphries
Journal:  BMJ       Date:  2010-01-14
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