Continuous glucose monitoring takes center stage
John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.
In February, we discussed the benefits of using machine learning (ML) to improve screening for diabetes and for managing the disorder. In some situations, ML can more accurately detect the presence of prediabetes, for instance. Similarly, there’s research to show that the right algorithms can improve the treatment of Type 1 diabetes. But there’s also mounting evidence to suggest ML can benefit Type 2 patients.
One of the problems clinicians and patients have in managing Type 2 diabetes is the fact that medical care is episodic. Patients may see their physician or nurse practitioner once every few months, which means most of the time, they are on their own. And while it is true that glucose meters let patients monitor their blood glucose (BG) levels on a daily basis, those readings have their limitations. Many patients hesitate to take enough readings because finger pricks hurt. It can also be difficult to determine the need for changes in one’s diet, medication, and physical activity based on a few daily BG readings. And while glycated hemoglobin A1c provides a picture of one’s long-term metabolic control, it doesn’t provide immediate feedback on how to respond to day-to-day changes in food intake, stress levels, exercise and the like.
Mounting evidence suggests that continuous glucose monitoring (CGM) is a game changer that will profoundly improve a Type 2 diabetic’s ability to control their condition — at least for those patients willing to commit the time and energy needed. Advances in hardware technology and artificial intelligence have ushered in a new approach to metabolic control that takes advantage of CGM. As the name implies, CGM provides patients with continuous, immediate readings of their BG levels, about once every 5 minutes. This is accomplished with the help of a subdermal sensor and a wireless connection to a glucose meter. Some systems will also send this data to a tablet or smartphone, which can then be used to make more informed decisions on how to adjust one’s carbohydrate intake, for instance, or to alert a patient about a particular food or beverage that’s spiking their BG levels. The system will also alert users about unexpected hypoglycemic episodes before they become dangerous.
The Endocrine Society and the American Diabetes Association consider CGM standard of care for insulin-dependent Type 1 diabetes,1 but until recently, CGM platforms were considered too expensive and not efficacious enough to justify their use in type 2 diabetes. That thinking is changing, however. Tejaswi Kompala, MD, and Aaron Neinstein, MD, with the Division of Endocrinology, University of California, San Francisco, point out: “High costs and uncertainty over efficacy and necessity have kept CGM from widespread use in people with T2D. However, the newest CGM models, the Abbott Freestyle Libre and Dexcom G6, have begun to overcome many of these technical barriers to use of CGM systems. The sensors are inserted painlessly, are small enough to fit easily under clothing, can remain in place for 10 to 14 days.” 2
Devices like the Freestyle Libre really are an important advance in patient self-care because they eliminate the need to fingerprints. * Combining this technology with machine learning and other types of AI is helping clinicians and patients interpret a massive influx of data into actionable insights. Several vendors, including Livongo, Canary Health, and Omada Health,* have entered this space, providing patients with easy-to-navigate digital tools that take advantage of the new technology. Livongo, for instance, combines clinical decision support with patient support that includes customized glucose meters and nurse coaches who send personalized messages to patients in need of advice. The Livongo program uses 4 technologies it refers to as AI+AI, which represents Aggregate, Interpret, Apply, and Iterate. It aggregates data from a variety of sources, including its custom-built devices, as well as a patient’s age, gender, zip code, medical claims, and pharmacy claims. The aggregated data is interpreted to create a unique data set it calls Health Signals, which are derived from its applications, devices, coaches, and other sources. It uses these signals to build relevant healthcare messages and outputs for its apps. The Apply in AI+AI refers to the applications linked to its glucose meter, BP cuff, and digital scale, as well as the “human applications,” namely, its coaching system and care coordination team. Finally, the system folds the signals it has generated back into the AI engine to make the system smarter.3
The recent entry of UnitedHealthcare, the largest healthcare insurer in the US., into this area should send a strong signal to clinicians and Type 2 patients alike: This approach has merit! Major 3rd party payors don’t invest their dollars without doing their due diligence to determine that their offerings will likely be cost-effective. Brian Thompson, CEO of the company’s Medicare and Retirement unit, summed up the potential for ML-enhanced diabetes care: “Continuous glucose monitoring can be a game changer for people enrolled in our Medicare Advantage plans, as the data can be translated into personalized information that can be acted upon in real time.”
*Products and services mentioned are not endorsements. Mayo Clinic has no financial relationship with any of these vendors.
1. Peters AL, Ahmann AJ, Battelino T et al. Diabetes Technology—Continuous Subcutaneous Insulin Infusion Therapy and Continuous Glucose Monitoring in Adults: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab 101: 3922–3937, 2016.
2. Kompala T. Neinstein A. A New Era: Increasing Continuous Glucose Monitoring Use in Type 2 Diabetes. Evidence-Based Diabetes Management, March 2019, Volume 25, Issue 4.
3. Cerrato, P. Halamka, J. Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. CRC Press/HIMSS, 2020, p. 38-39.