What Is Glucotyping?

Patients who have normoglycemia still at risk for glucose spikes and diabetes; detection of glucose dysregulation patterns for individuals can now be categorized and assessed using a new method called glucotyping.

Diabetes is diagnosed in many people worldwide, with about 30 million of the United States population being affected. Even more people have prediabetes and are not being treated to prevent the progression of type 2 diabetes. With continuous glucose monitoring, it was found that individuals who have not been diagnosed with diabetes  experience frequent elevations in their blood glucose levels. These readings have been found to be as high as glucose levels in the diabetes range. The detection of glucose dysregulation patterns for individuals can now be categorized and assessed using a new method called glucotyping. The glucotyping method advances the already existing continuous glucose monitoring (CGM) systems.

At the Clinical Translational Research Unit (CTRU) of Stanford University, a study was done to characterize the glycemic patterns (glucotyping) of 57 healthy individuals who were not diagnosed with diabetes prior to the study. Subjects were recruited from the San Francisco Bay area via newspaper advertisements and informational community lectures. Participants were required to be free of any major organ disease, diagnosis of diabetes, uncontrolled hypertension, malignancy, chronic inflammatory conditions, or use of any diabetes medications.

Patients were evaluated with CGM in their natural environments at home and with standardized meals to observe the patterns of glucose dysregulations. The standardized meals were to be consumed at breakfast, as this was the time that participants had a stable baseline glucose measurement. The meals consisted of either cereal and milk, (low fiber and high sugar), bread with peanut butter (high fat and high protein), or a protein bar (moderate fat and moderate protein). These breakfast meals were to be eaten for 2 consecutive days. The values measured from the CGM were then compared to diabetes diagnostic measurements, including insulin resistance and insulin secretion. After visual inspection of the monitors, there were three different patterns of glucose variability. They were classified as either low, moderate, or severe variability. The amount of time spent in each pattern was determined for all patients as well. It was found that most patients spent time in the low variability glucose pattern while there were others who spent time in the moderate to severe glucose pattern. The patterns were then classified into “glucotypes” as follows: low variability (glucotype L), moderate variability (glucotype M), and severe variability (glucotype S). Each pattern was not only associated with the amount of variability but also the mean concentration of glucose.

Glucose tolerance was assessed for each participant with 75 grams of glucose after an overnight fast. Glucose and insulin were measured at baseline, 30 minutes, and 120 minutes after administration of glucose. HbA1c, triglyceride, and HDL cholesterol levels were measured from the baseline sample. Steady-state plasma insulin (SSPI) and steady-state plasma glucose (SSPG) concentrations were measured after an infusion of octreotide, insulin, and glucose was administered to each participant. This infusion was administered after the overnight fast. Higher SSPG values indicated higher insulin resistance. The insulin secretion rate was estimated during this same sampling of blood following the oral glucose test. The Insulin SECretion (SIEC) software was used to measure insulin secretion rates via C-peptide measurements.

There was a positive clinical correlation between the glucotypes and metabolic measurements such as HbA1c, fasting blood glucose, OGTT, BMI, and age. The higher the variability, the higher these values were and the lower the variability, the lower these values were (p<0.05). Comparing the three standardized meals, the cereal and milk was associated with more severe responses, whereas the high protein, high fat, and high fiber meals were associated with low to moderate responses.

In conclusion, it was found that patients who were never diagnosed with prediabetes or diabetes can have spikes in their blood glucose that would be in the diabetes range. It has also been noted that while some individuals respond differently to different meals, there are still some foods that will result in blood glucose levels in the diabetes range in most adults. With this finding, it is safe to say that diets can be personally modified for each patient to manage glucose spikes. Also, as continuous glucose monitoring systems become more efficient and more cost-friendly for patients, the progression of diabetes and cardiovascular disease can be prevented and better managed for those patients at risk.

Practice Pearls:

• Patients with normoglycemia are still at risk for developing prediabetes and eventually diabetes.
• Continuous glucose monitoring and categorization of glucose patterns can help determine association of glucose dysregulation and diet.
• Low variable glucose readings are associated with lower metabolic measurements such as HbA1c, fasting blood glucose, BMI, SSPG, and OGTT.
• Higher variable glucose readings were associated with higher metabolic measures.
• Certain diets consisting of high sugar content can elevate glucose to diabetes levels in the majority of adults with normoglycemia.
• Advancing CGM and diet modification can help prevent prediabetes and ultimately diabetes and cardiovascular disease.

Reference for glucotyping:

Hall, Heather, et al. “Glucotypes Reveal New Patterns of Glucose Dysregulation.” PLOS Biology, vol. 16, no. 7, 2018, doi:10.1371/journal.pbio.2005143.

Amanda Cortes LECOM School of Pharmacy PharmD Candidate Class of 2019

 

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