Updated risk factors should be used to predict development of diabetes.
OBJECTIVE
Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information.


METHODS
Among non-diabetic participants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a "landmark" model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction.


RESULTS
A total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72-0.74]) than for the baseline model (0.67 [95% CI 0.66-0.68]). The landmark model improved classification to modest (<20%), moderate (20%-40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10-0.16) and an integrated discrimination index of 0.01 (95% CI 0.003-0.013).


CONCLUSIONS
Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.
Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK. Electronic address: angelyn.bethel@dtu.ox.ac.uk.
Mary Angelyn
MA
Bethel
Wilmington VA Medical Center, Wilmington, DE, USA. Electronic address: kristen.hyland@va.gov.
Kristen A
KA
Hyland
Federal University of São Paulo, São Paulo, Brazil. Electronic address: chacra@unifesp.br.
Antonio R
AR
Chacra
University of California-San Francisco Program at Fresno and the Veterans Affairs Central California Health Care System, Fresno, CA, USA. Electronic address: deed@fresno.ucsf.edu.
Prakash
P
Deedwania
Royal North Shore Hospital, University of Sydney, Sydney, New South Wales, Australia. Electronic address: gfulcher@med.usyd.edu.au.
Gregory R
GR
Fulcher
Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK. Electronic address: rury.holman@dtu.ox.ac.uk.
Rury R
RR
Holman
Oslo University Hospital Rikshospitalet, Oslo Institute of Clinical Medicine, and the University of Tromsø, Tromsø, Norway. Electronic address: trond.jenssen@rikshospitalet.no.
Trond
T
Jenssen
Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa. Electronic address: naomi.levitt@uct.ac.za.
Naomi S
NS
Levitt
British Heart Foundation, University of Glasgow, Glasgow, UK. Electronic address: john.mcmurray@glasgow.ac.uk.
John J V
JJV
McMurray
National and Kapodistrian University of Athens, Athens, Greece. Electronic address: boutati@otenet.gr.
Eleni
E
Boutati
Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA. Electronic address: laine.thomas@dm.duke.edu.
Laine
L
Thomas
Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA. Electronic address: jielena.sun@dm.duke.edu.
Jie-Lena
JL
Sun
San Antonio, TX, USA. Electronic address: traffic15@satx.rr.com.
Steven M
SM
Haffner
D001786:Blood Glucose / Q000032:analysis; D002318:Cardiovascular Diseases / Q000150:complications / Q000453:epidemiology / Q000517:prevention & control; D003131:Combined Modality Therapy; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control*; D003925:Diabetic Angiopathies / Q000453:epidemiology / Q000517:prevention & control; D058065:Diabetic Cardiomyopathies / Q000453:epidemiology / Q000517:prevention & control; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D014943:Global Health / Q000639:trends; D005951:Glucose Tolerance Test; D019456:Health Transition; D000070497:Healthy Lifestyle; D006801:Humans; D015994:Incidence; D008297:Male; D008954:Models, Biological; D017410:Practice Guidelines as Topic; D011236:Prediabetic State / Q000097:blood / Q000150:complications / Q000503:physiopathology / Q000628:therapy*; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors
D001786:Blood Glucose
Diabetes risk prediction; Fasting plasma glucose; Impaired glucose tolerance; Post-prandial glucose; Type 2 diabetes