OBJECTIVEDiscordance between HbA1c and fructosamine estimations in the assessment of glycemia is often encountered. A number of mechanisms might explain such discordance, but whether it is consistent is uncertain. This study aims to coanalyze paired glycosylated hemoglobin (HbA1c)-fructosamine estimations by using fructosamine to determine a predicted HbA1c, to calculate a glycation gap (G-gap) and to determine whether the G-gap is consistent over time.RESEARCH DESIGN AND METHODSWe included 2,263 individuals with diabetes who had at least two paired HbA1c-fructosamine estimations that were separated by 10 ± 8 months. Of these, 1,217 individuals had a third pair. The G-gap was calculated as G-gap = HbA1c minus the standardized fructosamine-derived HbA1c equivalent (FHbA1c). The hypothesis that the G-gap would remain consistent in individuals over time was tested.RESULTSThe G-gaps were similar in the first, second, and third paired samples (0.0 ± 1.2, 0.0 ± 1.3, and 0.0 ± 1.3, respectively). Despite significant changes in the HbA1c and fructosamine, the G-gap did not differ in absolute or relative terms and showed no significant within-subject variability. The direction of the G-gap remained consistent.CONCLUSIONSThe G-gap appears consistent over time; thus, by inference any key underlying mechanisms are likely to be consistent. G-gap calculation may be a method of exploring and evaluating any such underlying mechanisms.

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OBJECTIVEThe “glycation gap” (G-gap), an essentially unproven concept, is an empiric measure of disagreement between HbA1c and fructosamine, the two indirect estimates of glycemic control. Its association with demographic features and key clinical outcomes in individuals with diabetes is uncertain.RESEARCH DESIGN AND METHODSThe G-gap was calculated as the difference between measured HbA1c and a fructosamine-derived standardized predicted HbA1c in 3,182 individuals with diabetes. The G-gap’s associations with demographics and clinical outcomes (retinopathy, nephropathy, macrovascular disease, and mortality) were determined.RESULTSDemographics varied significantly with G-gap for age, sex, ethnic status, smoking status, type and duration of diabetes, insulin use, and obesity. A positive G-gap was associated with retinopathy (odds ratio 1.24 [95% CI 1.01–1.52], P = 0.039), nephropathy (1.55 [1.23–1.95], P < 0.001), and, in a subset, macrovascular disease (1.91 [1.18–3.09], P = 0.008). In Cox regression analysis, the G-gap had a “U”-shaped quadratic relationship with mortality, with both negative G-gap (1.96 [1.50–2.55], P < 0.001) and positive G-gap (2.02 [1.57–2.60], P < 0.001) being associated with a significantly higher mortality.CONCLUSIONSWe confirm published associations of G-gap with retinopathy and nephropathy. We newly demonstrate a relationship with macrovascular and mortality outcomes and potential links to distinct subpopulations of diabetes.