Our results support the hypothesis that glucose variability as quantified by several SMBG-based metrics (ADRR, GLI, SD) correlates positively with insulin sensitivity as quantified by a hyperinsulinemic euglycemic clamp procedure, irrespective of HbA1c levels. One explanation of this result is that patients with high insulin sensitivity have more difficulties controlling their diabetes because small errors in insulin dosing would result in larger errors in treatment. Thus high insulin sensitivity could cause increased glucose variability. Another possible physiological explanation is that glucose variability affects insulin sensitivity through hypoglycemia. In particular, Inouye et al.27
demonstrated that recurrent hypoglycemia in rats with diabetes increases glucose consumption. Combining the assumption that these researchers' result would hold for type 1 diabetes patients with the well-established connection between hypoglycemia and glucose variability28–30
provides a probable explanation for our findings.
On the other hand, it has been well established that episodes of antecedent hypoglycemia could cause defective epinephrine counterregulation.5–7
This relationship explains our findings of negative correlation between LBGI and maximum epinephrine concentration during hypoglycemia. In particular, because high LBGI indicates increased frequency and extent of mild hypoglycemic excursions, the mechanism of repeated hypoglycemia causing defective epinephrine counterregulation is well manifested by these data. Specifically, here we can speculate that the negative correlation of the LBGI and the ADRR with maximum epinephrine response during hypoglycemia reflects the causal effect of repeated hypoglycemia on subsequent counterregulatory deficiency. In general, correlation does not imply cause, but in this particular case assuming causality is logical. Nevertheless, the process of repeated mild hypoglycemia and counteregulatory depletion is recurrent, a “vicious cycle”31
that sometimes could be interrupted by meticulous avoidance of hypoglycemia.11,12
Thus, having a reflection of impaired counterregulatory response and increased risk for hypoglycemia that is readily available in the field from routine SMBG data could provide valuable information to patients and their physicians.
In terms of metrics, this study shows that not all measures of variability are created equal. It becomes apparent that glucose variability is a multifaceted property and that different measures capture different aspects of its manifestation. For example, traditional metrics such a MAGE and even SD tend to be more sensitive to hyperglycemic excursions and tend to ignore hypoglycemia. The reasons for that are purely numerical and have been discussed in the past:3,16
the glucose measurement scale is inherently asymmetric, the hypoglycemic range is numerically much narrower than the hyperglycemic range, and the risk for hypoglycemia increases much more rapidly with falling BG than would the risk for hyperglycemia with glucose increasing by the same increments. This asymmetry explains why certain metrics, such as the ADRR and the LBGI, which are based on a symmetrizing transformation of the BG scale,3
are more sensitive to the physiological manifestations of insulin sensitivity and impaired hypoglycemia counterregulation. Therefore, it is important to select an appropriate variability metric depending on the task in hand: if hyperglycemia and postprandial glucose excursions are of concern, MAGE would be a measure of choice; if hypoglycemia is the primary target for investigation, LBGI is arguably the best choice. ADRR was designed to optimally balance both hypo- and hyperglycemic excursions and to suppress (numerically) glucose fluctuations with the safe target range; thus, the ADRR would be preferable when overall glycemic variability and extreme glucose excursions are investigated.4
Finally, our findings support the hypothesis of existence of a less known relationship between recurrent hypoglycemia and high insulin sensitivity. In addition to our main results, we found a negative correlation between insulin sensitivity and maximum epinephrine concentration during hypoglycemia (ρ
0.04) and established that these two variables could be represented by a single underlying factor explaining 74% of their variance. However, these data do not allow further investigation of this phenomenon and cannot render a physiological explanation. One possibility would be that hepatic glucose production could be underlying both the hepatic (as opposed to the peripheral) component of insulin sensitivity and the counterregulatory response to hypoglycemia, but this cannot be established by these data. Other limitations of this study include the nonphysiologic assessment of insulin sensitivity (e.g., we used the clamp instead of a more physiologic meal test32
) and the lack of field data for the regular insulin dose of the participants, which precluded the evaluation of certain aspects of the patient treatment.
Possible directions for future research include building a detailed network model that would explain mechanistically the observed relationships and would allow for dynamical evaluation of changes in insulin sensitivity and/or counterregulatory ability from SMBG and continuous glucose monitoring data. Such a model could assist achieving a stricter glycemic control by providing real-time suggestions for more precise insulin dosing and warnings about increased risk for hypoglycemia. Of particular relevance would be the implementation of such technology into the closed-loop control systems of the near future.