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Dear Editor,
We are writing in response to the article “Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels” by Sun et al., published on June 16, 2023. Firstly, I would like to commend the authors for their insightful research on the incorporation of fasting and postprandial glucose levels in estimating the time in range (TIR) for patients with type 2 diabetes. Their study sheds light on an important aspect of diabetes management and offers valuable contributions to the field.
Maintaining stable blood glucose levels is essential in diabetes management. TIR refers to the amount of time an individual spends within their target glucose range (usually 3.9–10.0 mmol/L), making it a relevant parameter for monitoring glucose homeostasis [1]. While HbA1c was previously believed to be the sole criterion for evaluating long-term glucose balance in the body [2], many researchers have shown its unreliability among people with different ethnic and bodily characteristics. Consequently, measures such as glycemic variability (GV), continuous glucose monitoring (CGM), fasting plasma glucose (FPG), and postprandial plasma glucose (PPG) have gained attention in managing type 2 diabetes mellitus.
The article published by Sun et al. [3] provides a well-organized and systematic approach to comparing FPG and PPG with HbA1c in terms of glycemic control, highlighting the nonlinear relationship between TIR and glycemic parameters. The study mentions that previous research has shown severe cardiovascular symptoms even in individuals with normal HbA1c levels, yet the underlying cause of this effect remains unresolved. Although a significant correlation between HbA1c and TIR values is observed, there are still variations when considering HbA1c as a diabetes monitoring parameter. Furthermore, a recent study has demonstrated the unreliability of HbA1c in patients with anemia, hemoglobinopathies, iron deficiency, and pregnancy [4]. Therefore, the cause–effect relationship behind these results has not been fully elucidated by previous studies. The variations observed can be attributed to the diverse characteristics and ethnicities of individuals, as well as factors such as age and sex [1].
While the study by Sun et al. provides significant data through graphs showcasing the multifactorial dependence of FPG, PPG, and HbA1c on TIR, it could have included groups representing diverse populations and lifestyles. This would have helped to assess and treat people according to their specific requirements. Additionally, the paper could have discussed the mechanisms behind the unreliability of HbA1c in determining blood glucose fluctuations, as explored in previous studies. The study could have also mentioned the importance of CGM in treating various diabetic symptoms, even in individuals with normal HbA1c levels [5].
I highly appreciate that the authors provided substantial data and graphs, including univariate (R2 = 0.26, p < 0.001) and multivariate regression analysis (R2 = 0.36, p < 0.001) [3]. However, future studies and meta-analyses are required to gain further clarity on these intriguing results.
The findings of this study contribute to our understanding of glycemic control in the population studied and have implications for clinical practice. I believe that this research will stimulate further investigations and discussions within the diabetes research community.
While the study claims an absence of literature showing a correlation between HbA1c and TIR, another similar study by Aleppo [6] has demonstrated a strong inverse correlation between HbA1c and TIR. Hence, despite the prevalence of a strong correlation between TIR and FPG, PPG, and HbA1c, as shown, the univariate dependence of HbA1c and TIR also plays a major role.
Thank you for considering this Letter to the Editor for publication. We appreciate the opportunity to contribute to the scientific discourse surrounding diabetes management.
With regards,
Anshika Aggarwal and Ravneet Kaur.
References
Advani A. Positioning time in range in diabetes management. Diabetologia. 2020;63(2):242–52.
Martens T, Beck RW, Bailey R, et al. Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: a randomized clinical trial. JAMA. 2021;325(22):2262–72.
Sun R, Duan Y, Zhang Y, et al. Time in range estimation in patients with type 2 diabetes is improved by incorporating fasting and postprandial glucose levels. Diabetes Ther. 2023;14(8):1373–86.
Danne T, Nimri R, Battelino T, et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40(12):1631–40.
Lu J, Ma X, Shen Y, et al. Time in range is associated with carotid intima-media thickness in type 2 diabetes. Diabetes Technol Ther. 2020;22(2):72–8.
Aleppo G. Clinical application of time in range and other metrics. Diabetes Spectr. 2021;34(2):109–18.
Acknowledgements
Data availability
The datasets and supplementary materials used to support the findings of this research are openly available in your journal. The data can be accessed through the following repository/link: https://doi.org/10.1007/s13300-023-01432-2.
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No funding or sponsorship was received for this study or publication of this article.
Author’s Contribution
Anshika Aggarwal: Contributed in manuscript writing and literature review. Ravneet Kaur: Contributed in concept building and design of the paper.
Conflict of Interest
Anshika Aggarwal: Has nothing to disclose. Ravneet Kaur: Has nothing to disclose.
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Aggarwal, A., Kaur, R. Letter to the Editor Regarding “Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels”. Diabetes Ther 14, 1753–1755 (2023). https://doi.org/10.1007/s13300-023-01453-x
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DOI: https://doi.org/10.1007/s13300-023-01453-x