In type 1 diabetes defective hormonal counterregulation is the primary obstacle to achieving tight BG control. GCR disappears during the course of the disease, but the mechanism behind the GCR impairment remains unclear. In order to understand this mechanism, we have developed a system-level approach combining in vivo
and in silico
studies to reconstruct the primary network interactions that control the glucagon release and GCR. In the course of our recent studies we have developed a mathematical model of GCR which approximates the normal GCR control axis and its impairment in diabetic rats (Farhy et al., 2008
; Farhy and McCall, 2009a
). Using this model we have proposed two key network abnormalities which might contribute to the defects in GCR: (i) absence of a switch-off trigger or in other words, lack of constant intrapancreatic repression of the α-cells which is released (switched-off) during hypoglycemia, and (ii) elevated intraislet basal (AFI) glucagon secretion. Our model-based work predicts and our animal work (Farhy et al., 2008
) supports the concept that both network abnormalities can be attacked to repair, or at least partially reverse, the GCR defects. Based on these predictions we have proposed clinical strategies which use α-cell inhibitors to repair the defective GCR and stabilize insulin deficient diabetes (Farhy and McCall, 2011
). So far however, human studies addressing these predictions are scarce. There exist some clinical data supporting the concept behind the switch-off hypothesis (Banarer et al., 2002
), but no data directly addresses this issue (ii). Therefore, the goal of this work is to start investigating the relevance of our predictions from animal data and modeling to human pathophysiology in T1DM.
The results in this work support the validity of the concept related to the second network abnormality (the GCR effects of basal hyperglucagonemia) and confirm the predictive power of our existing GCR model in the context of human physiology. Our data reflect the glucagon response to hypoglycemia in T1DM subjects under tightly controlled glucose clamp conditions (Figure ). Several metrics were used to measure the GCR and their choice was directed by the understanding that efficient defense against dangerous low blood sugar drops requires increase of glucagon levels during hypoglycemic episodes above the glucagon basal level (concentration during euglycemia). Therefore, we used measures that estimate GCR relative to the basal concentration (GLbasal
): CumG and RIG. We have found that higher BasG levels are associated with reduction of the ability of the system to respond adequately to hypoglycemia with glucagon secretion that markedly exceeds the basal levels (Table ; Figure ). In absolute terms, the maximal glucagon response to hypoglycemia (MaxG) is positively linked to the basal level, but MaxG increases slower than GLbasal
(Figure ) which supports the concept that in T1DM the GCR generally cannot significantly exceed the basal levels. Both findings appear consistent and were reproduced by our model. They are in accordance with the proposed second network abnormality and support the recently formulated hypothesis that elevation of BasG is part of the mechanism of GCR impairment in T1DM (Farhy and McCall, 2011
). We note that the very high negative correlation between RIG and GLbasal
may be partially a reflection of a limited range of the data as detected by a bootstrap procedure according to which this correlation was not significant. However, we would like to emphasize that even though this lack of detected significance does not support the concept that GLbasal
and RIG are linked, it suggests that a decrease of BasG levels as proposed elsewhere (Farhy and McCall, 2011
), may improve the effectiveness of the GCR even in the face of a limited GCR response.
Model-based analysis was used to explain and replicate the experimental observations. The simulations demonstrate that a model of glucagon secretion in which one part of the α-cells are feedback regulated and another is feedback independent (Figure ) can successfully replicate many aspects of the in vivo
behavior of the system (compare Figures and ). Except for rescaling of the model output which leaves intact its dynamic properties, no attempts were made to change the original parameters even though they have been determined to approximate the rat GCR axis. This was done in order to test the extent to which the rodent model can explain the human data without changing its basic features or adding new model components. The simulations showed that the model is consistent with the detected negative correlation between CumG and GLbasal
(Figure , left) and with the observed slower growth of MaxG with respect to GLbasal
(Figure , right). From a network control point of view this property of the system is due to a repression exerted by the high AFI (basal) glucagon secretion on the auto-feedback dependent (pulsatile) GCR as explained in our prior studies (Farhy and McCall, 2009b
). Thereby, our model is consistent with the data and provides a putative mechanism for the experimentally observed link between defective GCR and basal hyperglucagonemia. Additional simulations predict that with lowering the levels of GLbasal
(below the typical glucagon levels in T1DM) the simulated counterregulation response rapidly increases (see Table ; Figure , right), which is consistent with the previously proposed strategy to repair the defective GCR by decreasing the glucagon basal levels (Farhy and McCall, 2011
) with α-cell inhibitors.
One limitation of our model is that some of the specifics in the experimental data cannot be explained in the framework of the current construct. In particular, our model cannot account for the fact that in some study participants the glucagon response to hypoglycemia paradoxically went down
with respect to their basal levels (which accounts for some of the negative CumG values). Possible explanations for this phenomenon could be an unaccounted excessive variability or pulsatility of glucagon during euglycemia or the already mentioned putative paradoxical stimulation
of glucagon by glucose (Olsen et al., 2005
; Salehi et al., 2006
). However, none of these properties are currently part of the model, which may require further extension and refinement.