title: Model-Based Quantification of Glucagon-Like Peptide-1-Induced Potentiation of Insulin Secretion in Response to a Mixed Meal Challenge.
abstract: BACKGROUND
  Glucagon-like peptide-1 (GLP-1) is a powerful insulin secretagogue that is secreted in response to meal ingestion. The ability to quantify the effect of GLP-1 on insulin secretion could provide insights into the pathogenesis and treatment of diabetes. We used a modification of a model of GLP-1 action on insulin secretion using data from a hyperglycemic clamp with concomitant GLP-1 infusion. We tested this model using data from a mixed meal test (MMT), thereby measuring GLP-1-induced potentiation of insulin secretion in response to a meal.
  
  
  METHODS
  The GLP-1 model is based on the oral C-peptide minimal model and assumes that over-basal insulin secretion depends linearly on GLP-1 concentration through the parameter Π, representing the β-cell sensitivity to GLP-1. The model was tested on 62 subjects across the spectrum of glucose tolerance (age, 53 ± 1 years; body mass index, 29.7 ± 0.6 kg/m(2)) studied with an MMT and provided a precise estimate of both β-cell responsivity and Π indices. By combining Π with a measure of L-cell responsivity to glucose, one obtains a potentiation index (PI) (i.e., a measure of the L-cell's function in relation to prevailing β-cell sensitivity to GLP-1).
  
  
  RESULTS
  Model-based measurement of GLP-1-induced insulin secretion demonstrates that the PI is significantly reduced in people with impaired glucose tolerance, compared with those with normal glucose tolerance.
  
  
  CONCLUSIONS
  We describe a model that can quantitate the GLP-1-based contribution to insulin secretion in response to meal ingestion. This methodology will allow a better understanding of β-cell function at various stages of glucose tolerance.
authors.affiliation: 1 Department of Information Engineering, University of Padua , Padua, Italy .
authors.forename: Chiara
authors.initials: C
authors.lastname: Dalla Man
authors.affiliation: 1 Department of Information Engineering, University of Padua , Padua, Italy .
authors.forename: Francesco
authors.initials: F
authors.lastname: Micheletto
authors.affiliation: 2 Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic College of Medicine , Rochester, Minnesota.
authors.forename: Matheni
authors.initials: M
authors.lastname: Sathananthan
authors.affiliation: 2 Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Mayo Clinic College of Medicine , Rochester, Minnesota.
authors.forename: Adrian
authors.initials: A
authors.lastname: Vella
authors.affiliation: 1 Department of Information Engineering, University of Padua , Padua, Italy .
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D001786:Blood Glucose / Q000378:metabolism; D002096:C-Peptide; D004435:Eating / Q000502:physiology; D052216:Glucagon-Like Peptide 1 / Q000378:metabolism*; D015309:Glucose Clamp Technique; D018149:Glucose Intolerance / Q000097:blood; D064368:Healthy Volunteers; D006801:Humans; D007328:Insulin / Q000097:blood / Q000378:metabolism*; D000078790:Insulin Secretion; D050417:Insulin-Secreting Cells / Q000378:metabolism; D016014:Linear Models; D062407:Meals / Q000502:physiology*; D008875:Middle Aged
chemical_list: D001786:Blood Glucose; D002096:C-Peptide; D007328:Insulin; D052216:Glucagon-Like Peptide 1