title: Comparing incident diabetes as defined by fasting plasma glucose or by HbA(1c). The AusDiab, Inter99 and DESIR studies.
abstract: OBJECTIVE
  We examined the ability of fasting plasma glucose and HbA(1c) to predict 5-year incident diabetes for an Australian cohort and a Danish cohort and 6-year incident diabetes for a French cohort, as defined by the corresponding criteria.
  
  
  METHODS
  We studied 6025 men and women from AusDiab (Australian), 4703 from Inter99 (Danish) and 3784 from DESIR (French), not treated for diabetes and with fasting plasma glucose < 7.0 mmol/l and HbA(1c) < 48 mmol/mol (6.5%) at inclusion. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l and/or treatment for diabetes or as HbA(1c) ≥ 48 mmol/mol (6.5%) and/or treatment for diabetes.
  
  
  RESULTS
  For AusDiab, incident fasting plasma glucose-defined diabetes was more frequent than HbA(1c) -defined diabetes (P(McNemar)<0.0001), the reverse applied to Inter99 (P(McNemar) < 0.007) and for DESIR there was no difference (P(McNema)=0.17). Less than one third of the incident cases were detected by both criteria. Logistic regression models showed that baseline fasting plasma glucose and baseline HbA(1c) predicted incident diabetes defined by the corresponding criteria. The standardized odds ratios (95% confidence interval) for HbA(1c) were a little higher than for fasting plasma glucose, but not significantly so. They were respectively, 5.0 (4.1-6.1) and 4.1 (3.5-4.9) for AusDiab, 5.0 (3.6-6.8) and 4.8 (3.6-6.3) for Inter99, 4.8 (3.6-6.5) and 4.6 (3.6-5.9) for DESIR.
  
  
  CONCLUSIONS
  Fasting plasma glucose and HbA(1c) are good predictors of incident diabetes defined by the corresponding criteria. Despite Diabetes Control and Complications Trial-alignment of the three HbA(1c) assays, there was a large difference in the HbA(1c) distributions between these studies, conducted some 10 years ago. Thus, it is difficult to compare absolute values of diabetes prevalence and incidence based on HbA(1c) measurements from that time.
authors.affiliation: Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Epidemiology of Diabetes, Obesity and Chronic Kidney Diseases over the Lifecourse, Villejuif Université Paris Sud, UMRS 1018, Villejuif, France. soraya.soulimane@inserm.fr
authors.forename: S
authors.initials: S
authors.lastname: Soulimane
authors.forename: D
authors.initials: D
authors.lastname: Simon
authors.forename: J E
authors.initials: JE
authors.lastname: Shaw
authors.forename: P Z
authors.initials: PZ
authors.lastname: Zimmet
authors.forename: S
authors.initials: S
authors.lastname: Vol
authors.forename: D
authors.initials: D
authors.lastname: Vistisen
authors.forename: D J
authors.initials: DJ
authors.lastname: Magliano
authors.forename: K
authors.initials: K
authors.lastname: Borch-Johnsen
authors.forename: B
authors.initials: B
authors.lastname: Balkau
mesh_terms: D000328:Adult; D000368:Aged; D001315:Australia / Q000453:epidemiology; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism*; D015331:Cohort Studies; D003718:Denmark / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000453:epidemiology; D005215:Fasting / Q000378:metabolism*; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D011237:Predictive Value of Tests; D012307:Risk Factors; D055815:Young Adult
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human