Dr Ganapathi Bantwal, Professor & Head of Department-Endocrinology, St. John’s Medical College & Hospital, Bangalore highlights that for decades, HbA1c has been the gold standard for assessing diabetes control, providing an average of blood glucose levels over several months. However, with the development of Continuous Glucose Monitoring (CGM) systems, we now have access to an array of new metrics, including Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR), mean glucose, and Glycemic Variability (GV)
The landscape of diabetes management is evolving rapidly, driven by advances in technology and data-driven insights. For decades, HbA1c has been the gold standard for assessing diabetes control, providing an average of blood glucose levels over several months. However, with the development of Continuous Glucose Monitoring (CGM) systems, we now have access to an array of new metrics, including Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR), mean glucose, and Glycemic Variability (GV)[1]. These metrics offer real-time insights into blood glucose control, unlike HbA1c, and provide information on both hyperglycemia and hypoglycemia.
Recent research has identified GV as a potential marker and independent predictor of diabetic complications, highlighting its importance in diabetes management. However, the widespread adoption of CGM systems faces limitations, including affordability especially in non-reimbursed markets such as India, personal choice, limited accessibility, and technological barriers. Consequently, Self-Monitoring of Blood Glucose (SMBG) remains a widely used method for diabetes self-management. SMBG provides immediate spot glucose measurements, serving as the foundation for insulin dosing, managing hypoglycemia, physical activity, stress, and illness. Its value in informed decision-making for short- and long-term glycemic control is undeniable.
However, there is limited evidence to date that SMBG data can be pooled and expressed as TIR, TBR, TAR, mean glucose, and GV, similar to CGM systems. To address this gap, cloud-based Diabetes Management Systems (DMS) have emerged as a promising solution. Recent studies have shown that enhancing SMBG data analysis through DMS can significantly improve metabolic control in patients with type 1 diabetes (T1D). These improvements are driven by automated analysis and more robust oversight, allowing healthcare providers to contextualise SMBG data and make fact-based decisions.
Correlation between HbA1c and PIR
One key aspect of SMBG data analysis is the concept of Points in Range (PIR). PIR quantifies the number of data points falling within the target range of 70 to 180 mg/dL (3.9 to 10 mmol/L)[2], aligning with CGM guidelines. Research has revealed a significant correlation between HbA1c and PIR calculated through DMS. This correlation not only supports the utility of PIR as a valid marker of glycemic control but also underscores the additional insights offered by DMS in managing patients with both type 1 (T1D) and type 2 diabetes (T2D) who perform SMBG[3].
Leveraging cloud-based DMS to provide Integrated Personalised Diabetes Management (iPDM)
SMBG data can be uploaded and managed efficiently using cloud-based solutions. These integrated systems includes a Bluetooth-enabled glucometer, a smartphone app for self-management, and a web portal for healthcare providers (HCPs) to provide iPDM based on real-time analyses. The DMS analyses blood glucose data obtained with the glucometer, which is wirelessly transferred to the web portal via the mobile app. Importantly, this system functions even when the app is unavailable. Furthermore, during in-office visits, BG data stored in the meter can be downloaded using the Smart devices, offering a convenient and efficient means of data transfer.
Conclusion
SMBG, when coupled with cloud-based DMS and the concept of PIR, presents a valuable opportunity to provide integrated personalised diabetes management. It enables healthcare providers to leverage new metrics for a deeper understanding of glycemic control. The correlation between SMBG and PIR emphasises the utility of this approach, and the ability to integrate SMBG data at no additional cost opens avenues for improving decision-making processes. This helps improve access to diabetes care and make it available to a wider set of patients.
References:
[1] https://pubmed.ncbi.nlm.nih.gov/32669240/
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814148/
[3] https://pubmed.ncbi.nlm.nih.gov/32669240/
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