Continuous Glucose Monitoring in NHP Models
Discover continuous glucose monitoring as an enhanced technique for preclinical blood glucose measurement in NHP models of diabetes.
Challenges in Glucose Monitoring in NHP Studies
NHPs are the most clinically-relevant preclinical model for T2D research, closely mimicking human disease, including disease progression, obesity, and complications. There are challenges to be faced when using NHPs in diabetes studies, however, particularly with routinely used tests such as blood glucose measurements.
Blood glucose tests in NHPs are carried out using the same methods as humans – with finger sticks and glucometer readings. These can be performed multiple times per day to assess blood glucose changes. However, this periodic blood sampling is far from ideal as:
- It generates a limited number of data points – snapshots of glucose levels throughout the day.
- Discrete measurements might not identify important trends and fluctuations that vary with time, diet, and exercise.
- The measurements are invasive and induce stress.
The stress from blood sampling procedures lowers the accuracy of blood glucose measurements, which can confound results if glucose levels are a key study endpoint.
New technologies have been developed for continuous glucose monitoring in the clinic and translating these back to preclinical studies can start to overcome these challenges.
Continuous Glucose Monitoring (CGM) in Preclinical NHP Studies
CGM has had a huge impact in improving the quality of life of diabetic patients, and has also been validated for preclinical studies, with a multitude of benefits.
CGM automatically and continuously tracks blood glucose levels at regular intervals. A glucose sensor is implanted in the patient/model, connected to a transmitter, and sends readings wirelessly to a receiver for remote signal collection.
Within NHP preclinical studies, CGM provides continuous, remote, and robust blood glucose monitoring. This allows continuous blood glucose data collection that can be viewed in real time. Full patterns of circadian rhythms and response to a variety of variables can be monitored with ease.
CGM also reduces the frequency of blood sampling and required sedation/anesthesia, and the stress of repeatedly moving study animals. Some preclinical CGM devices can also simultaneously monitor body temperature and physical activity, and CGM can also be used accurately during other tests (e.g. clamp studies and tolerance tests).
Validating CGM to Assess NHP Blood Glucose Trends
CGM has been validated to confirm the accuracy of readings vs traditional glucometer tests, and to see how glucose fluctuations alter under different study conditions. NHPs including both normoglycemic and diabetic animals were assessed using the Data Sciences International HD-XG CGM device.
Daily Blood Glucose Fluctuations
Blood glucose fluctuations were monitored during daily NHP activities, to record patterns and changes observed by each testing method. Interestingly, normoglycemic and diabetic NHPs showed different circadian blood glucose changes by CGM.
Normoglycemic NHPs had lower blood glucose during most of the day compared to the night, while the diabetic NHPs showed the inverse pattern This could be due to insulin resistance and relative insulin deficiency in diabetic NHPs, comparable to the hormonal changes termed the “dawn phenomenon” observed in diabetic patients.
Significantly, these observations would not have been captured from conventional, limited glucometer data points, showing a major benefit of CGM.
Glucose Tolerance Test Comparisons
Blood glucose levels were also compared to glucometer readings for a range of glucose tolerance tests, with the method correlating well across IVGTT and OGTT for normoglycemic and diabetic NHPs.
Over the whole study, CGM blood glucose readings were highly correlated with glucometer results, validating the implantable HD-XG for preclinical CGM use.
CGM data provide a novel approach for dynamic analyses of blood glucose and compound efficacy studies. Validation of CGM in NHPs has shown correlation to currently used techniques, and has the added benefit of enhancing study data through reducing disruption and stress to the models.