Endocrines (Jul 2023)

Identification of Glucagon Secretion Patterns during an Oral Glucose Tolerance Test

  • Andrew Shahidehpour,
  • Mudassir Rashid,
  • Mohammad Reza Askari,
  • Mohammad Ahmadasas,
  • Ali Cinar

DOI
https://doi.org/10.3390/endocrines4030035
Journal volume & issue
Vol. 4, no. 3
pp. 488 – 501

Abstract

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Impaired glucagon secretion is a major component of glucose intolerance in type 2 diabetes mellitus (T2D). Glucagon secretion exhibits heterogenous patterns in individuals and across glucose tolerance diagnoses. Characterization of the range of glucagon secretion patterns can help clinicians personalize diabetes care based on glucagon characteristics in addition to glucose and insulin profiles. A total of 102 subjects with normal glucose tolerance, impaired glucose tolerance, and T2D had their glucagon profiles recorded in response to an oral glucose tolerance test. Shapelet analysis was used to identify the most descriptive patterns of early glucagon secretion, and spectral biclustering was employed to identify biclusters of associated subjects and shapelets. The dynamics of glucose, insulin, and glucagon secretion in each cluster were evaluated to identify overall patterns, and the characteristics of the subjects in each cluster were compared. Three clusters were chosen to represent the glucagon patterns. Membership in these three clusters was interpreted based on the presence or lack of extrema in the first 30 min after oral carbohydrate intake. Cluster 1 (n = 23) had a minimum at 30 min and only negative trends. Cluster 2 had a minimum at 10 min and a maximum at 20 min (n = 25). Cluster 3 (n = 40) had a maximum at 10 min and a minimum at 20 min. Subjects in cluster 1 had the lowest average fasting plasma glucose (90.17 mg/dL) and average age (41.39 years) and the highest HOMA-beta score (87.5%), while subjects in cluster 2 had the highest average fasting plasma glucose (102.56 mg/dL) and average age (53.16 years) and the lowest HOMA-beta score (55.77%). Characterization of glucagon dynamics, in addition to glucose and insulin, can aid in personalized treatment approaches and provide greater insight about the underlying dysfunction in glucose regulation.

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