The Relationship between Hemoglobin A1c and the Glucose Management Indicator and Glucose Metrics in Children and Adolescents with Type 1 Diabetes Mellitus Using Automated Insulin Delivery Systems
PDF

Original Article

The Relationship between Hemoglobin A1c and the Glucose Management Indicator and Glucose Metrics in Children and Adolescents with Type 1 Diabetes Mellitus Using Automated Insulin Delivery Systems

J Clin Res Pediatr Endocrinol 2026;18(2):314-322 DOI: 10.4274/jcrpe.galenos.2025.2025-10-11
, , , , , ,
PDF
Permissions

Abstract

Objective

Hemoglobin A1c (HbA1c) remains the standard biomarker for long-term glycemic control in type 1 diabetes mellitus (T1D), butis incapable of capturing short-term glucose variability and acute excursions. This limitation is especially relevant in children with T1D who use continuous glucose monitoring systems (CGMS) and automated insulin delivery (AID) systems. The objective of this study was to evaluate the temporal relationship between HbA1c and the glucose management indicator (GMI), and any further associations with CGMS-derived glycemic parameters over a 12 weeks in youth with T1D using AID.

Methods

This retrospective, cross-sectional, observational study, included children and adolescents with T1D using the Medtronic MiniMed 780G™ system. CGMS data covering the 12 weeks prior to HbA1c measurement were analyzed in two-week intervals. Correlations between HbA1c, GMI, and CGMS metrics were assessed.

Results

The study cohort numbered 81; 46 (57%) were female. Median age at T1D diagnosis was 8.1 (interquartile range: 4.3-10.8) years. HbA1c correlated positively with all GMI values, with the strongest correlation observed for the last six-week GMI (r=0.728, p<0.001). The mean difference between HbA1c and last 12-week GMI was 0.57% (95% confidence interval: -1.13 to  2.27). GMI demonstrated stronger correlations than HbA1c with time in range (TIR), time above range (TAR) and time below range (TBR). Notably, in individuals with similar TIR (~70%), HbA1c values varied widely (6.6-9.6%/48-81 mmol/mol), while GMI remained stable (6.8-7.1%).

Conclusion

The strongest correlation between HbA1c and the most recent 6-week GMI suggests that HbA1c reflects relatively recent glycemic trends.  GMI also showed closer alignment with CGMS-derived indices such as TIR, TAR and TBR, suggesting better sensitivity in capturing day-to-day glycemic variability. We suggest GMI offers a more sensitive and clinically actionable estimate of glycemic control, supporting its integration into routine care for children with T1D using AID.

Keywords: T1D, AID, CGMS, GMI, sampling period

Graphical Abstract

Read the full text

5 sections · 3 figures · 3 tables · PDF

Create your free account to read the full text.

  • Full text with figures and tables
  • PDF and supplementary files
  • Access on every Galenos journal
Get free access

Already have an account?

Sign in
or
Continue with Google

One Galenos account works on every Galenos journal site.

References

1

Güven Baysal Ş, Çiftci N, Dündar İ, Büyükavcı MA, Yağın FH, Çamtosun E, Gümüş Doğan D, Akıncı A. Chronic disease management of children followed with type 1 diabetes mellitus. J Clin Res Pediatr Endocrinol. 2023;15:172-181. Epub 2023 Jan 26

2

Hatun Ş, Gökçe T, Can E, Eviz E, Karakuş KE, Smart C, Hanas R, Yeşiltepe Mutlu G. Current management of type 1 diabetes in children: guideline-based expert opinions and recommendations. J Clin Res Pediatr Endocrinol. 2024;16:245-255. Epub 2024 Mar 15

3

Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, Brown AS, Heinemann L, Aleppo G, Ryan DB, Riddlesworth TD, Cefalu WT. Glucose management indicator (GMI): a new term for estimating a1c from continuous glucose monitoring. Diabetes Care. 2018;41:2275-2280.

4

Wright LA, Hirsch IB. Metrics beyond hemoglobin A1C in diabetes management: time in range, hypoglycemia, and other parameters. Diabetes Technol Ther. 2017;19(Suppl 2):16-26.

5

Afridi Z, Rauf SA, Ashraf SMN, Haque MA. Transformative advances in continuous glucose monitoring and the impact of FDA over-the-counter approval on diabetes care. Health Sci Rep. 2025;8:e70747.

6

De Block C, Vertommen J, Manuel-y-Keenoy B, Van Gaal L. Minimally-invasive and non-invasive continuous glucose monitoring systems: indications, advantages, limitations and clinical aspects. Curr Diabetes Rev. 2008;4:159-168.

7

Funtanilla VD, Candidate P, Caliendo T, Hilas O. Continuous glucose monitoring: a review of available systems. P T. 2019;44:550-553.

8

Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Wilson DM, Xing D, Beck RW, Block J, Bode B, Fox LA, Hirsch I, Kollman C, Laffel L, Ruedy KJ, Steffes M, Tamborlane WV. Hemoglobin A1c and mean glucose in patients with type 1 diabetes: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized trial. Diabetes Care. 2011;34:540-544. Epub 2011 Jan 25

9

Riddlesworth TD, Beck RW, Gal RL, Connor CG, Bergenstal RM, Lee S, Willi SM. Optimal sampling duration for continuous glucose monitoring to determine long-term glycemic control. Diabetes Technol Ther. 2018;20:314-316. Epub 2018 Mar 22

10

Bundak R, Bas F, Furman A, Günöz H, Darendeliler F, Saka N, Poyrazoğlu S, Neyzi O. Sitting height and sitting height/height ratio references for Turkish children. Eur J Pediatr. 2014;173:861-869. Epub 2014 Jan 9

11

de Bock M, Agwu JC, Deabreu M, Dovc K, Maahs DM, Marcovecchio ML, Mahmud FH, Nóvoa-Medina Y, Priyambada L, Smart CE, DiMeglio LA. International Society for Pediatric and Adolescent Diabetes Clinical Practice Consensus Guidelines 2024: glycemic targets. Horm Res Paediatr. 2024;97;546-554. Epub 2024 Dec 19

12

Gomez-Peralta F, Choudhary P, Cosson E, Irace C, Rami-Merhar B, Seibold A. Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin. Diabetes Obes Metab. 2022;24:599-608. Epub 2022 Feb 8

13

Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and glucose management indicator discordance: a real-world analysis. Diabetes Technol Ther. 2021;23:253-258. Epub 2020 Dec 1

14

Piona C, Marigliano M, Mozzillo E, Rosanio F, Zanfardino A, Iafusco D, Maltoni G, Zucchini S, Piccinno E, Delvecchio M, Maffeis C. Relationships between HbA1c and continuous glucose monitoring metrics of glycaemic control and glucose variability in a large cohort of children and adolescents with type 1 diabetes. Diabetes Res Clin Pract. 2021;177:108933. Epub 2021 Jun 30

15

Lenters-Westra E, Fokkert M, Kilpatrick ES, Schleicher E, Pilla S, English E, van Dijk P. Managing discordance between HbA 1c and glucose management indicator. Diabet Med. 2025;42:e70023. Epub 2025 Mar 23

16

Cohen RM, Franco RS, Khera PK, Smith EP, Lindsell CJ, Ciraolo PJ, Palascak MB, Joiner CH. Red cell life span heterogeneity in hematologically normal people is sufficient to alter HbA1c. Blood. 2008;112:4284-4291. Epub 2008 Aug 11

17

Lledó-García R, Kalicki RM, Uehlinger DE, Karlsson MO. Modeling of red blood cell life-spans in hematologically normal populations. J Pharmacokinet Pharmacodyn. 2012;39:453-462. Epub 2012 Jul 31

18

Osterman-Golkar SM, Vesper HW. Assessment of the relationship between glucose and A1c using kinetic modeling. J Diabetes Complications. 2006;20:285-294.

19

Beltrán del Río M, Georgiev GI, Cercone R, Tiwari M, Rilo HL. Continuous glucose monitoring analysis as predictor of islet yield and insulin requirements in autologous islet transplantation after complete pancreatectomy. J Diabetes Sci Technol. 2014;8:1097-1104. Epub 2014 Sep 4

20

Beltran Del Rio M, Tiwari M, Amodu LI, Cagliani J, Rodriguez Rilo HL. Glycated hemoglobin, plasma glucose, and erythrocyte aging. J Diabetes Sci Technol. 2016;10:1303-1307.

21

Hirsch IB, Welsh JB, Calhoun P, Puhr S, Walker TC, Price DA. Associations between HbA 1c and continuous glucose monitoring-derived glycaemic variables. Diabet Med. 2019;36:1637-1642. Epub 2019 Jul 17

22

Bosoni P, Calcaterra V, Tibollo V, Malovini A, Zuccotti G, Mameli C, Sacchi L, Bellazzi R, Larizza C. Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes. J Pediatr Endocrinol Metab. 2021;34:619-625.

23

Cichosz SL, Jensen MH, Hejlesen O. Optimal data collection period for continuous glucose monitoring to assess long-term glycemic control: revisited. J Diabetes Sci Technol. 2023;17:690-695. Epub 2022 Jan 5

24

Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, Bosi E, Buckingham BA, Cefalu WT, Close KL, Cobelli C, Dassau E, DeVries JH, Donaghue KC, Dovc K, Doyle FJ 3rd, Garg S, Grunberger G, Heller S, Heinemann L, Hirsch IB, Hovorka R, Jia W, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Levine B, Mayorov A, Mathieu C, Murphy HR, Nimri R, Nørgaard K, Parkin CG, Renard E, Rodbard D, Saboo B, Schatz D, Stoner K, Urakami T, Weinzimer SA, Phillip M. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593-1603. Epub 2019 Jun 8

25

Xing D, Kollman C, Beck RW, Tamborlane WV, Laffel L, Buckingham BA, Wilson DM, Weinzimer S, Fiallo-Scharer R, Ruedy KJ; Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring. Diabetes Technol Ther. 2011;13:351-358. Epub 2011 Feb 7

26

S Saccenti E, Hendriks MHWB, Smilde AK. Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models. Sci Rep. 2020;10:438.

27

Leelarathna L, Thabit H, Willinska ME, Bally L, Mader JK, Arnolds S, Benesch C, Pieber TR, Shah VN, Carlson AL, Bergenstal RM, Evans ML, Hovorka R; AP@home04 and APCam11 consortia. Duration of hybrid closed-loop insulin therapy to achieve representative glycemic outcomes in adults with type 1 diabetes. Diabetes Care. 2020;43:e38-e39. Epub 2020 Jan 16

28

Camerlingo N, Vettoretti M, Facchinetti A, Sparacino G, Mader JK, Choudhary P, Del Favero S. An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia. Sci Rep. 2020;10:18180.

29

Kaushal T, Tinsley L, Volkening LK, Ambler-Osborn L, Laffel L. Improvement in mean CGM glucose in young people with type 1 diabetes during 1 year of the COVID-19 pandemic. Diabetes Technol Ther. 2022;24:136-139. Epub 2021 Oct 13

30

Plachy L, Neuman V, Velichova K, Slavenko MG, Santova A, Anne Amaratunga S, Obermannova B, Kolouskova S, Pruhova S, Sumnik Z, Petruzelkova L. Telemedicine maintains good glucose control in children with type 1 diabetes but is not time saving for healthcare professionals: KITES randomized study. Diabetes Res Clin Pract. 2024;209:111602. Epub 2024 Mar 2

31

Ferber C, Mittelman SD, Moin T, Wilhalme H, Hicks R. Impact of telemedicine versus in-person pediatric outpatient type 1 diabetes visits on immediate glycemic control: retrospective chart review. JMIR Diabetes. 2024;9:e58579.