title: A novel risk classification paradigm for patients with impaired glucose tolerance and high cardiovascular risk.
abstract: We used baseline data from the NAVIGATOR trial to (1) identify risk factors for diabetes progression in those with impaired glucose tolerance and high cardiovascular risk, (2) create models predicting 5-year incident diabetes, and (3) provide risk classification tools to guide clinical interventions. Multivariate Cox proportional hazards models estimated 5-year incident diabetes risk and simplified models examined the relative importance of measures of glycemia in assessing diabetes risk. The C-statistic was used to compare models; reclassification analyses compare the models' ability to identify risk groups defined by potential therapies (routine or intensive lifestyle advice or pharmacologic therapy). Diabetes developed in 3,254 (35%) participants over 5 years median follow-up. The full prediction model included fasting and 2-hour glucose and hemoglobin A1c (HbA1c) values but demonstrated only moderate discrimination for diabetes (C = 0.70). Simplified models with only fasting glucose (C = 0.67) or oral glucose tolerance test values (C = 0.68) had higher C statistics than models with HbA1c alone (C = 0.63). The models were unlikely to inappropriately reclassify participants to risk groups that might receive pharmacologic therapy. Our results confirm that in a population with dysglycemia and high cardiovascular risk, traditional risk factors are appropriate predictors and glucose values are better predictors than HbA1c, but discrimination is moderate at best, illustrating the challenges of predicting diabetes in a high-risk population. In conclusion, our novel risk classification paradigm based on potential treatment could be used to guide clinical practice based on cost and availability of screening tests.
authors.affiliation: Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom. angelyn.bethel@dtu.ox.ac.uk
authors.forename: M Angelyn
authors.initials: MA
authors.lastname: Bethel
authors.forename: Antonio R
authors.initials: AR
authors.lastname: Chacra
authors.forename: Prakash
authors.initials: P
authors.lastname: Deedwania
authors.forename: Gregory R
authors.initials: GR
authors.lastname: Fulcher
authors.forename: Rury R
authors.initials: RR
authors.lastname: Holman
authors.forename: Trond
authors.initials: T
authors.lastname: Jenssen
authors.forename: Steven E
authors.initials: SE
authors.lastname: Kahn
authors.forename: Naomi S
authors.initials: NS
authors.lastname: Levitt
authors.forename: John J V
authors.initials: JJ
authors.lastname: McMurray
authors.forename: Robert M
authors.initials: RM
authors.lastname: Califf
authors.forename: Sotirios A
authors.initials: SA
authors.lastname: Raptis
authors.forename: Laine
authors.initials: L
authors.lastname: Thomas
authors.forename: Jie-Lena
authors.initials: JL
authors.lastname: Sun
authors.forename: Steven M
authors.initials: SM
authors.lastname: Haffner
mesh_terms: D002318:Cardiovascular Diseases / Q000453:epidemiology*; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D018149:Glucose Intolerance / Q000453:epidemiology*; D005951:Glucose Tolerance Test; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D018570:Risk Assessment; D013997:Time Factors