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Published on: November 16, 2011
Logistic model of glucose-regulated C-peptide secretion: hysteresis pathway disruption in impaired fasting glycemia
Daniel M Keenan1, Rita Basu, Yan Liu
1Department of Statistics, University of Virginia, Charlottesville, VA, USA.
Abstract:
The present analysis tests the hypothesis that quantifiable disruption of the glucose-stimulated insulin-secretion dose-response pathway mediates impaired fasting glycemia (IFG) and type 2 diabetes mellitus (DM). To this end, adults with normal and impaired fasting glycemia (NFG, n = 30), IFG (n = 32), and DM (n = 14) were given a mixed meal containing 75 g glucose. C-peptide and glucose were measured over 4 h, 13 times in NFG and IFG and 16 times in DM (age range 50-57 yr, body mass index 28-32 kg/m(2)). Wavelet-based deconvolution analysis was used to estimate time-varying C-peptide secretion rates. Logistic dose-response functions were constructed analytically of the sensitivity, potency, and efficacy (in the pharmacological sense of slope, one-half maximal stimulation, and maximal effect) of glucose's stimulation of prehepatic insulin (C-peptide) secretion. A hysteresis changepoint time, demarcating unequal glucose potencies for onset and recovery pathways, was estimated simultaneously. According to this methodology, NFG subjects exhibited distinct onset and recovery potencies of glucose in stimulating C-peptide secretion (6.5 and 8.5 mM), thereby defining in vivo hysteresis (potency shift -2.0 mM). IFG patients manifested reduced glucose onset potency (8.6 mM), and diminished C-peptide hysteretic shift (-0.80 mM). DM patients had markedly decreased glucose potency (18.8 mM), reversal of C-peptide's hysteretic shift (+4.5 mM), and 30% lower C-peptide sensitivity to glucose stimulation. From these data, we conclude that a dynamic dose-response model of glucose-dependent control of C-peptide secretion can identify disruption of in vivo hysteresis in patients with IFG and DM. Pathway-defined analytic models of this kind may aid in the search for prediabetes biomarkers.
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