title: The potential for a two-stage diabetes risk algorithm combining non-laboratory-based scores with subsequent routine non-fasting blood tests: results from prospective studies in older men and women.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Primary Care and Population Health, UCL Medical School, Glasgow, UK. goya@pcps.ucl.ac.uk
authors.forename: S G
authors.initials: SG
authors.lastname: Wannamethee
authors.forename: O
authors.initials: O
authors.lastname: Papacosta
authors.forename: P H
authors.initials: PH
authors.lastname: Whincup
authors.forename: M C
authors.initials: MC
authors.lastname: Thomas
authors.forename: C
authors.initials: C
authors.lastname: Carson
authors.forename: D A
authors.initials: DA
authors.lastname: Lawlor
authors.forename: S
authors.initials: S
authors.lastname: Ebrahim
authors.forename: N
authors.initials: N
authors.lastname: Sattar
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000453:epidemiology; D005260:Female; D006403:Hematologic Tests; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology
chemical_list: D015415:Biomarkers; D002097:C-Reactive Protein