Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

AIM: To develop strategies based on simple clinical assessment and blood markers to identify older individuals at high risk for Type 2 diabetes. METHODS: A prospective study of non-diabetic men (n = 3523) and women (n = 3404) aged 60-79 years followed for 7 years, during which there were 297 incident cases of Type 2 diabetes. Logistic regression was used to develop scores to predict incident cases, starting with clinical predictors and adding blood markers that predicted the incidence of diabetes. Receiving operating characteristic analyses were used to assess improvement in prediction. RESULTS: The area under the curve for a simple clinical assessment score, which included age, sex, family history of diabetes, smoking status, BMI, waist circumference, hypertension and recall of doctor diagnosis of coronary heart disease was 0.765 (0.740, 0.791); sensitivity and specificity in the top quintile of the score were 50.3 and 81.4%, respectively. Addition of simple fasting blood markers HDL cholesterol, triglyceride and glucose improved prediction [area under the curve = 0.817 (0.793, 0.840), P < 0.0001; sensitivity 63.8%; specificity 82.0%]. An alternative model adding blood markers not dependent on fasting yielded similar results. Further addition of C-reactive protein made no improvement. Blood measurements made small differences to reclassification of risk in those in the lowest three quintiles of the non-laboratory score. CONCLUSION: In large population settings, simple clinical assessments could be used in the first instance to identify older adults who would benefit from further testing with routine (non-fasting) blood markers to identify those at most likely to be at elevated diabetes risk.

Original publication




Journal article


Diabet Med

Publication Date





23 - 30


Aged, Algorithms, Area Under Curve, Biomarkers, C-Reactive Protein, Diabetes Mellitus, Type 2, Female, Hematologic Tests, Humans, Logistic Models, Male, Middle Aged, Prospective Studies, Risk Assessment, Risk Factors, United Kingdom