Continuous Glucose Monitors Useful for Predicting Pre-Diabetes

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When employed as a prescreening tool, care must be taken to avoid overdiagnosis of healthy individuals without diabetes.

Continuous glucose monitors (CGMs) may be able to detect pre-diabetes, according to results of a recent study. Most individuals with prediabetes are unaware of their condition, and an estimated 75 million Americans with prediabetes are currently undiagnosed, Jaycee M. Kaufman, medical R&D scientist with the Department of Science, Ontario Tech University, Oshawa, Canada wrote in Mayo Clinical Proceedings: Digital Health.1

As a result, Kaufman and other researchers looked to CGMs to help detect pre-diabetes. CGMs provide information-rich time-series data that can be analyzed for not only glucose levels but also the dynamic properties of glycemic variability, Kaufman pointed out.

“An opportunity exists with CGMs to create a T2D [type 2 diabetes] prescreening method to assess the risk of dysglycemia without requiring a blood draw or visit to a laboratory. Furthermore, CGMs provide an opportunity to assess glucose dynamics in an individual’s everyday life, which may provide an additional metric of health that cannot be determined under controlled conditions,” Kaufman wrote.

She said that a mathematical model of glucose homeostasis was recently developed for CGM data. The model accurately reproduces both positive (hyperglycemic) and negative (hypoglycemic) excursions when combined with a computational procedure that can easily be run on a mobile device. “Moreover, pilot studies have suggested that tuned model parameters have the potential to be used as biomarkers for diabetic status.”

The research team used a novel analysis method that distinguishes individuals with impaired glucose homeostasis (IGH) from individuals with effective glucose homeostasis (EGH). The functional assessment of glucose homeostasis (FLAG) method is used to compare the distribution of homeostasis model parameters from CGM data with representative parameter distributions from populations without diabetes, with prediabetes, and with T2D. Then, an individual is classified as having IGH if the observed distribution is closest to representative populations with prediabetes or T2D.

“The primary endpoint is to develop a classification system to identify individuals with dysfunctional glucose homeostasis and distinguish individuals with T2D and prediabetes from individuals without a diabetes diagnosis,” Kaufman wrote.

When employing a prescreening tool, care must be taken to avoid overdiagnosis of healthy individuals while still maintaining specificity for individuals with prediabetes and T2D, she added. Overdiagnosis of prediabetes and T2D is a real concern for the medical community—even with an increase in prevalence, according to Kaufman. “A prediabetes diagnosis may lead to issues with insurance, employment, or self-image and increase the burdens and costs of health care.”

“A simple solution is to exclusively use CGMs and their corresponding analysis methods as a checkpoint or precursor to accepted blood tests (oral glucose tolerance test and HbA1c measurement) and not for diagnostics. This approach would also decrease the frequency of unnecessary blood tests and give physicians a better understanding of the patient’s glucose control in normal, day-to-day situations,” Kaufman wrote.

Rather than ordering blood work on complaints of hyperglycemia-related symptoms, for example, a physician would prescribe a CGM to be worn for 2 weeks. “The necessity for blood tests could be re-evaluated after the 2-week CGM period,” she wrote.

To test this method, the research team recruited 384 participants from two studies between October 2020 and June 2022. They were equipped with a CGM that collected interstitial glucose data automatically for 2 weeks.

A physician determined whether the participants were diabetic, prediabetic, or healthy according to the American Diabetes Association guidelines. A mathematical model of glucose homeostasis was fitted to the glucose data and the participants were classified into the following two groups on the basis of their glucose homeostasis parameters: effective and impaired.

The homeostasis classification resulted in a specificity, sensitivity of individuals with prediabetes, and sensitivity of individuals with T2D of 0.78, 0.86, and 1.00, respectively, for women and 0.71, 0.86, and 1.00, respectively, for men. This sensitivity was similar to that of HbA1c measurement (sensitivity of 0.89 for women and 0.90 for men for prediabetes and a sensitivity of 1.00 for T2D) and superior to that of the oral glucose tolerance test (sensitivity of 0.18 for women and 0.24 for men for prediabetes and a sensitivity of 0.75 for women and 0.86 for men for T2D).

“Overall, the individuals classified as impaired had increased glucose variability metrics than the individuals classified as effective,” Kaufman wrote. “This classification had a sensitivity similar to HbA1c measurement and superior to [oral glucose tolerance tests].”

References
1. Kaufman J, van Veen L, Fossat Y. Screening for impaired glucose homeostasis: A novel metric of glyemic control. Mayo Clin Proc Digital Health. 2023;1(2):189-200. doi:10.1016/j.mcpdig.2023.02.008
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