Ep 229 – Continuous glucose monitoring

Continuous glucose monitoring (CGM) has moved from being a specialist technology used mainly in type 1 diabetes to becoming one of the most important developments in modern glucose management. What began as a way of collecting more frequent glucose measurements has evolved into a platform capable of displaying trends, predicting future glucose changes and, when integrated with insulin pumps, automatically influencing treatment. More recently, CGM has expanded into type 2 diabetes, consumer health, early diagnostic research and telemedicine, while artificial intelligence is opening the possibility of increasingly individualised prediction and decision support. At the same time, researchers are working on longer-lasting and potentially non-invasive sensors. In this episode, Dr Roger Henderson revisits how CGM developed, looks at where the technology is heading, what these advances could mean for clinical practice, and the challenges GPs need to consider as glucose monitoring becomes increasingly predictive and automated.

Key take-home points

• CGM has transformed glucose monitoring from intermittent snapshots into continuous physiological data. This allows clinicians and patients to see glucose trends and variability that fingerprick testing can miss.
• The technology has evolved rapidly since the first commercial CGM was approved in 1999. Modern sensors are smaller, more accurate, require less calibration and can be worn for substantially longer periods.
• The major shift now is from measurement towards prediction. Machine-learning models can analyse glucose trajectories and potentially identify impending hypoglycaemia or hyperglycaemia before they occur.
• Artificial intelligence may make CGM increasingly personalised. Emerging approaches, including foundation models and time-series forecasting, could incorporate an individual’s glucose patterns, lifestyle and metabolic responses.
• Prediction will need to be accompanied by appropriate clinical validation. An algorithmic forecast is not equivalent to a clinical diagnosis or certainty, and clinicians will need to understand performance and limitations.
• CGM is increasingly moving beyond insulin-treated type 1 diabetes. Over-the-counter systems are opening the technology to people with non-insulin-treated diabetes and, in some cases, people without diabetes.
• NICE recommends that all adults and children with type 1 diabetes are offered a choice of real-time continuous glucose monitoring (rtCGM) or intermittently scanned continuous glucose monitoring (isCGM, or flash monitoring) based on individual needs and preferences.
• Adults with type 2 diabetes on multiple daily injections should also be offered isCGM if they experience recurrent hypoglycaemia, have hypo unawareness, cannot self-monitor their blood glucose by capillary monitoring, or if they have been advised to test at least eight times a day.
• Consumer CGM creates opportunities but also raises concerns about interpretation. Normal physiological glucose fluctuations may be mistaken for disease, particularly when the clinical significance of individual excursions is uncertain.
• Automated insulin delivery represents one of the clearest examples of CGM becoming an active treatment technology. CGM data can drive algorithms that adjust insulin delivery and improve glycaemic control while reducing some of the cognitive burden of diabetes management.
• Longer sensor life is another important direction of development. Implantable systems and research into substantially longer-lasting sensors could further reduce the practical burden of continuous monitoring.
• Non-invasive glucose monitoring remains an attractive but technically difficult goal. Optical, radiofrequency and other approaches are being investigated, but reliable non-invasive systems have yet to replace established invasive or minimally invasive CGM technology.
• The future value of CGM will depend less on generating more data and more on converting data into useful decisions.

Key references

1. Cohen N. Aust Fam Physician. 2015;44(5):284-7.
2. Lewis DM, et al. BMJ. 2023;380:e072420. doi: 10.1136/bmj-2022-072420.
3. Mulvihill C, et al. BMJ. 2022;379:o2418. doi: 10.1136/bmj.o2418.
4. Carr ALJ, et al. Diabetologia. 2026;69(6):1413-1431. doi: 10.1007/s00125-026-06707-4.