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New Insights on Diabetes Management Through Continuous Glucose Monitoring

Researchers from the University of Virginia Center for Diabetes Technology have revealed that data gathered from continuous glucose monitors (CGMs) can effectively predict damage to the nerves, eyes, and kidneys associated with type 1 diabetes. This discovery suggests that medical professionals could potentially utilize CGM data to mitigate risks such as blindness, diabetic neuropathy, and other significant complications stemming from diabetes.

Key findings indicate that the duration patients maintain their blood sugar levels within a safe range of 70 to 180 mg/DL over a span of 14 days correlates closely with the likelihood of developing neuropathy, retinopathy, and nephropathy. This correlation holds up comparably to conventional measurements derived from hemoglobin A1c levels, a standard historically used in diabetes management.

“The pivotal 10-year Diabetes Control and Complications Trial (DCCT), involving 1,440 participants and published in 1993, established hemoglobin A1c as the benchmark for assessing complication risks in type 1 diabetes. Yet, as the prevalence of continuous glucose monitoring surges, there remains a notable gap in large-scale studies comparable in scale to the DCCT to validate CGM-based metrics as a standard for diabetes management,” explained Boris Kovatchev, PhD, who heads the UVA Center for Diabetes Technology. “The absence of substantial long-term CGM data presents various clinical and regulatory challenges, including the ongoing non-acceptance of CGM as a primary outcome measure in diabetes drug trials.”

Utilizing Historical Diabetes Data

The DCCT collected periodic hemoglobin A1c measurements from participants either monthly or quarterly, along with blood sugar profiles every three months. This invaluable data can be accessed from the archives maintained by the National Institute of Diabetes and Digestive and Kidney Diseases.

By employing advanced machine learning methodologies to analyze the DCCT datasets, researchers successfully generated simulated CGM data for all participants throughout their involvement in the trial.

The study found that a mere 14 days of data collected from these virtual CGMs could predict diabetes complications with similar efficacy as traditional hemoglobin A1c assessments. Beyond the time spent within the safe blood sugar range of 70 to 180 mg/DL, other CGM parameters, such as the duration of tight control (between 70 and 140 mg/DL) and periods where blood sugar levels exceeded 140 mg/DL, 180 mg/DL, and 250 mg/DL, also offered reliable predictions of diabetes-related complications.

As continuous glucose monitors become more common among individuals managing diabetes, these findings may significantly enhance patient care and provide researchers with valuable tools to improve diabetes management strategies moving forward.

“Conducting a study of the scale of the DCCT that incorporates continuous glucose monitoring alongside hemoglobin A1c would be extremely time-consuming and financially burdensome,” Kovatchev remarked. “Creating virtual representations of clinical trials using advanced data science techniques is the next best strategy for addressing gaps in historical data.”

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