He BB, Liu ZZ, Xu RY, Fan L, Guo R, Deng C, Xie YT, Zhou ZG, Li X. Glycated hemoglobin is not enough: The role of glycemia risk index for glycemic control assessment in type 1 diabetes. World J Diabetes 2025; 16(6): 104024 [PMID: 40548262 DOI: 10.4239/wjd.v16.i6.104024]
Corresponding Author of This Article
Xia Li, PhD, Professor, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, No. 139 Renmin Middle Road, Changsha 410011, Hunan Province, China. lixia@csu.edu.cn
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Observational Study
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He BB, Liu ZZ, Xu RY, Fan L, Guo R, Deng C, Xie YT, Zhou ZG, Li X. Glycated hemoglobin is not enough: The role of glycemia risk index for glycemic control assessment in type 1 diabetes. World J Diabetes 2025; 16(6): 104024 [PMID: 40548262 DOI: 10.4239/wjd.v16.i6.104024]
World J Diabetes. Jun 15, 2025; 16(6): 104024 Published online Jun 15, 2025. doi: 10.4239/wjd.v16.i6.104024
Glycated hemoglobin is not enough: The role of glycemia risk index for glycemic control assessment in type 1 diabetes
Bin-Bin He, Zi-Zhu Liu, Ruo-Yao Xu, Li Fan, Rui Guo, Chao Deng, Yu-Ting Xie, Zhi-Guang Zhou, Xia Li
Bin-Bin He, Zi-Zhu Liu, Ruo-Yao Xu, Li Fan, Rui Guo, Chao Deng, Yu-Ting Xie, Zhi-Guang Zhou, Xia Li, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, Changsha 410011, Hunan Province, China
Author contributions: He BB analyzed the data, and wrote the manuscript; He BB and Li X designed the study; Fan L, Liu ZZ and Xu RY collected the data; Xie YT and Guo R contributed to data curation and proofreading; Zhou ZG contributed to discussion; Li X revised the manuscript, Li X is the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project, No. 2023ZD0508201; the National Key R and D Program of China, No. 2022YFC2010100; the National Natural Science Foundation of China, No. 82070812; the Natural Science Foundation of Hunan Province, No. 2024JJ9049, No. 2023JJ30762 and No. 2021JC0003; Sinocare Diabetes Foundation, No. 2020SD08; and the National Clinical Research Center for Metabolic Diseases Clinical Diagnosis and Treatment Capacity Enhancement Program, No. 2023ZLNL003.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (No. SQ2016YFSF110035).
Informed consent statement: Written informed consent was obtained from the participants before data collection.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: The datasets of the current study are available from the corresponding author upon reasonable request.
Corresponding author: Xia Li, PhD, Professor, Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, No. 139 Renmin Middle Road, Changsha 410011, Hunan Province, China. lixia@csu.edu.cn
Received: December 16, 2024 Revised: February 25, 2025 Accepted: April 22, 2025 Published online: June 15, 2025 Processing time: 181 Days and 6.5 Hours
Abstract
BACKGROUND
Glycated hemoglobin (HbA1c), the gold standard for assessing glycemic control, has limited ability to reflect the risks of hypoglycemia and glycemic variability, raising great concerns, especially in patients with type 1 diabetes (T1D). The glycemia risk index (GRI), a composite metric derived from continuous glucose monitoring (CGM), has emerged as a potential solution by systematically integrating both hypoglycemia and hyperglycemia risks into a single interpretable score.
AIM
To evaluate whether the GRI addresses HbA1c limitations.
METHODS
We analyzed 328 patients with T1D using 681 CGM and clinical data points. Linear mixed-effects models were used to address the relationship between the GRI and HbA1c within repeated-measures data. Correlation and cluster analyses were used to assess the comprehensive GRI reflection of seven key ambulatory glucose profile parameters.
RESULTS
The GRI exhibited linear correlations with HbA1c (r = 0.53), time in range (r = -0.90), time above range (r = 0.63), time below range (TBR) (r = 0.37), and coefficient of variation (CV) (r = 0.71). It correlated strongly with TBR and CV than HbA1c. The association between HbA1c levels and GRI was influenced by TBR and CV. At a given HbA1c, each 1% increase in TBR or CV raised GRI by 1.87 [95% confidence interval (CI): 1.72-2.01] and 1.94 (95%CI: 1.80-2.10), respectively (P < 0.001). Clustering of the CGM data identified four subgroups: Moderate-risk glycemic fluctuations, high-risk hypoglycemia, optimal glycemic control, and high-risk hyperglycemia. The GRI and its components for hypoglycemia and hyperglycemia could distinguish between these subgroups.
CONCLUSION
The GRI offers a comprehensive view of glycemic control in T1D. Combining HbA1c with the GRI enables accurate assessment for managing glycemic control in patients with T1D.
Core Tip: This study highlights the glycemia risk index (GRI) as a comprehensive metric that captures the multidimensional aspects of glycemic control, effectively addressing the limitations of glycated hemoglobin (HbA1c) in type 1 diabetes (T1D). Analyzing 328 patients with T1D, GRI showed stronger correlations with time below range and coefficient of variation than HbA1c, providing a more nuanced reflection of glycemic risks. Clustering of continuous glucose monitoring data identified distinct glycemic control subgroups, demonstrating GRI’s ability to differentiate varying risks of hypoglycemia, hyperglycemia, and glycemic variability. Integrating HbA1c with the GRI enables a more accurate and holistic assessment, optimizing glycemic management in patients with T1D.
