title: Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes.
abstract: BACKGROUND
  Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect.
  
  
  METHODS
  Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8).
  
  
  RESULTS
  The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552*θ1 + 0.007645*θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well.
  
  
  CONCLUSIONS
  The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.
authors.affiliation: Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
authors.forename: Boris P
authors.initials: BP
authors.lastname: Kovatchev
authors.affiliation: Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA mb6nt@virginia.edu.
authors.forename: Marc D
authors.initials: MD
authors.lastname: Breton
mesh_terms: D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis*; D006801:Humans; D008297:Male; D008875:Middle Aged
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: HbA1c estimation; average glycemia; dynamical tracking; self-monitored blood glucose