Diabetes Care Volume 38, October 2015
e172
RESPONSE TO COMMENT ON HEMPE ET AL.
The Hemoglobin Glycation Index Identifies Subpopulations With Harms or Benefits From Intensive Treatment in the ACCORD Trial. Diabetes Care 2015;38:1067–1074
James M. Hempe,1 Shuqian Liu,2 Leann Myers,3 Robert J. McCarter,4 John B. Buse,5 and Vivian Fonseca2
e-LETTERS – COMMENTS AND RESPONSES
Diabetes Care 2015;38:e172–e173 | DOI: 10.2337/dci15-0001 Drs. Riddle and Gerstein (1) suggest renaming the hemoglobin glycation index (HGI) to reflect the possibility that interindividual variation in HGI in the ACCORD trial could be an artifact of how blood glucose was measured. HGI is the difference between an individual’s observed HbA1c and a predicted HbA1c derived by inserting a time-matched blood glucose measurement into a regression equation describing the linear population relationship between blood glucose and HbA1c. In our analysis of ACCORD (2), we used baseline HbA1c and fasting plasma glucose (FPG) to show that high HGI at baseline (i.e., HbA1c higher than predicted by FPG) was associated with worse outcomes. Riddle and Gerstein correctly note that FPG provides no information about person-to-person differences in glucose control during the day. One might thus conclude that high HGI calculated using FPG was associated with adverse outcomes in ACCORD because HbA1c reflects person-to-person differences in blood glucose dynamics that are missed by FPG. But this suggestion ignores prior studies showing that adverse outcomes are associated with high HGI when more comprehensive assessments of blood glucose dynamics are used to calculate HGI. For example, HGI based on mean
blood glucose derived from seven-point profile measurements was positively associated with microvascular disease in the Diabetes Control and Complications Trial (DCCT) (3). The glycation gap (4), calculated the same way as HGI except that glycated serum protein replaces blood glucose in the regression equation, is positively associated with both HGI and adverse diabetes outcomes. Persistent person-to-person differences in HGI and the glycation gap have been observed in enough studies (5) that the question is not whether the phenomenon exists, but why. Riddle and Gerstein’s concern highlights the need to determine if persistent person-to-person differences in HbA1c measured by HGI are of analytical or biological origin. Glycohemoglobin standardization programs make HbA1c an unlikely source of analytical bias, except perhaps as bias due to differences in erythrocyte turnover rates. Otherwise, HGI could only be an analytical artifact if the method used to estimate blood glucose concentration produced results that were persistently lower or higher than true blood glucose in some patients but not others. Although conceptually possible with FPG or patient meter data, this explanation seems unlikely in studies where blood glucose
was estimated based on glycated serum protein or mean glucose from profile sets or continuous glucose monitoring. Ultimately, either HbA1c reflects hemoglobin exposure to glucose the same way in everyone or it does not. If it does, then HGI is an analytical artifact. If not, then it behooves us to delve more deeply into the biochemistry of nonenzymatic hemoglobin glycation in search of the underlying mechanisms. Given the historical precedent, we see no reason to change the name of the hemoglobin glycation index. We agree with Riddle and Gerstein, however, that HGI should be considered a “clinically helpful trigger for reassessment of both glycemic targets and treatment tactics for individual patients” regardless of the source of population variation in HbA1c measured by HGI. Funding. This work was supported by the
National Heart, Lung, and Blood Institute (R01-HL-110395). Duality of Interest. No potential conflicts of interest relevant to this article were reported.
References 1. Riddle MC, Gerstein HC. Comment on Hempe et al. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial. Diabetes Care 2015;38:1067–1074 (Letter).
1
Department of Pediatrics, Louisiana State University Health Sciences Center and Children’s Hospital Research Institute for Children, New Orleans, LA Department of Medicine, Tulane University Health Sciences Center, New Orleans, LA 3 Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 4 Research Division of Biostatistics and Study Methodology, Children’s National Medical Center, Washington, DC 5 Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 2
Corresponding author: James M. Hempe,
[email protected]. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
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Diabetes Care 2015;38:e170–e171. DOI: 10.2337/ dc15-1073 2. Hempe JM, Liu S, Myers L, McCarter RJ, Buse JB, Fonseca V. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial. Diabetes Care 2015;38:1067–1074
Hempe and Associates
3. McCarter RJ, Hempe JM, Gomez R, Chalew SA. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes. Diabetes Care 2004;27:1259–1264 4. Rodr´ıguez-Segade S, Rodr´ıguez J, Garc´ıa Lopez JM, Casanueva FF, Cami~ na F. Estimation of the glycation gap in diabetic patients with
stable glycemic control. Diabetes Care 2012; 35:2447–2450 5. Soros AA, Chalew SA, McCarter RJ, Shepard R, Hempe JM. Hemoglobin glycation index: a robust measure of hemoglobin A1c bias in pediatric type 1 diabetes patients. Pediatr Diabetes 2010;11:455–461
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