Quantifying Biobehavioral Determinants of Risk for Severe
Hypoglycemia in Type 1 Diabetes*
Boris
Kovatcheva
Daniel
Coxa
Raina
Robevab
Linda
Gonder-Fredericka
Leon
Farhic
William
Clarked
aDepartment of Psychiatric Medicine
cDepartment of Medicine
dDepartment of Pediatrics
University of Virginia Health System
Charlottesville, VA 22908
bDepartment of Mathematical Sciences
Sweet Briar College
Sweet Briar, VA
*This research was supported by grants
RO1 DK-51562 and RO1 DK-28288 from the National Institutes of Health and by
LifeScan Inc, Milpitas, CA.
ABSTRACT
This study investigates,
through a biobehavioral risk model, the perception, awareness, and
self-treatment behavior accompanying hypoglycemia in type 1 diabetes, and their
relationship with self-monitoring blood glucose (SMBG) profiles and episodes of
severe hypoglycemia (SH).
Ninety-six adults with
type 1 diabetes, aged 35 ± 8 years, with a duration of diabetes of 16 ± 10
years, HbA1c
8.6 ±
1.8%, 43 of whom had a recent history of SH, used a hand-held computer over a
month to complete 70 behavior-assessment trials followed by SMBG. For the next
6 months all subjects recorded occurrence of SH. Symptom awareness and
self-treatment behavior indices accounted for 53% of the risk for hypoglycemia
observed in SMBG profiles. Future SH episodes were equally dependent on two
factors: symptom awareness and self-treatment/SMBG profile. Combined with
subjects' history of SH, these factors explained 52% of future SH.
We conclude that a field
assessment of awareness and self-treatment behaviors yields credible estimates
of frequency of hypoglycemia and long-term risk for SH, thus providing a basis
for behavioral intervention.
Key Words: [severe] hypoglycemia, awareness, self-regulation,
self-monitoring of blood glucose
INTRODUCTION
Extensive studies, including the 10-year Diabetes Control and
Complications Trial (DCCT)1 and its European counterpart2 have demonstrated that the most effective long‑term
control of type 1 diabetes mellitus (T1DM) is the strict maintenance of blood
glucose (BG) levels within the normal range. The DCCT proved that chronic high
BG levels cause many complications in multiple body systems over time, but, at
the same time, intensive insulin therapy resulted in an increased risk of
hypoglycemia.3 Without corrective action, hypoglycemia can
rapidly progress to severe hypoglycemia (SH), a condition identified as low BG
resulting in stupor, seizure, or unconsciousness that precludes self‑
treatment.3 If the patient does not receive treatment
during a SH episode, death can occur. It is estimated that approximately 4% of
deaths in all T1DM patients are attributed to SH.4 Recently, other negative consequences of SH
have been documented: a magnetic resonance imaging (MRI) study reported cases
in which occurrences of SH were associated with anatomical changes in the brain5; other studies report permanent cognitive
dysfunction associated with SH.6-8 Consequently, hypoglycemia has been identified
as the major barrier to improved glycemic control,9,10 and the progression of complications is most
rapid for patients in whom glycemic control is worst. In short, patients with
T1DM face the life‑long clinical optimization problem of maintaining
strict glycemic control without increasing their risk for hypoglycemia. Thus,
it is imperative that the biologic and behavioral factors contributing to the
occurrence of hypoglycemia be well understood.
In
addition to intensive insulin therapy, research has identified many other
factors contributing to increased likelihood of hypoglycemia. For example,
patients who do not hormonally counterregulate during insulin-induced
hypoglycemia were reported to be 250 times more likely to have future SH than
those who do hormonally counterregulate,10 and patients who report loss of hypoglycemic
symptoms are reported to be three times more likely to have SH in the immediate
future.11 All of these risk factors (ie, deficits in
counterregulation, hypoglycemic unawareness, and intensive insulin therapy)
appear to be associated with hypoglycemia-associated autonomic failure.12 Specifically, episodes of hypoglycemia (<3.9
mmol/L) can cause hormonal counterregulation to be delayed or weakened in
response to a low BG event occurring in the next 24 to 48 hours. In this case,
only extremely low BG would trigger counterregulation and, when it occurs,
epinephrine secretion may be inadequate to keep BG from falling further.
Consequently, autonomic symptoms may also be delayed or dampened, making it
much more likely that hypoglycemia will be undetected, thus increasing the risk
of SH. In general, studies that investigated the occurrence of
hypoglycemia-related symptoms found that such warning signs occur and are
recognized by patients in less than 50% of all hypoglycemic episodes and are
associated with low BG levels (3.9 mmol/L and below).13-16 This means that about half of all hypoglycemic
episodes are asymptomatic (ie, unrecognized). In many cases, even if such
hypoglycemic episodes are recognized, the patient's BG level may be too low to
allow for self-treatment due to neuroglycopenia's interference with
cognitive/motor functioning. In addition to symptom awareness/unawareness, the
accuracy and the promptness of self-treatment of low BG contributes to the risk
of progression of hypoglycemia into SH and would be generally related to the
magnitude of BG fluctuations in the low BG range.
In order to
describe formally the behavioral precursors to (severe) hypoglycemia, we have
developed and validated a biopsychobehavioral model postulating that SH is a
consequence of a complex interplay between physiologic, psychologic, and
behavioral factors.17-21 The model is based on the concept of stochastic
transitions22 that describe sequentially the links between
symptom awareness, self-treatment judgments, and behaviors. These assessments
are included in the biopsychobehavioral model that contains seven steps, each
having a theoretical binary outcome of yes or no. These sequential steps are linked by paths reflecting the increase
or decrease of patients' idiosyncratic risk of SH through the steps of the
model. We continue this research by presenting a detailed analysis of the
biobehavioral sequence: history of SH, symptom awareness, self-treatment
behaviors, self-monitoring BG profiles, and long-term risk for future SH. We
will demonstrate that long-term risk of SH is linked to autonomic failure,
while neuroglycopenia and self-treatment behavior predict short-term BG
excursions into hypoglycemia. In order to do so, we will introduce appropriate
tools that quantify perception and awareness of autonomic and neuroglycopenic
symptoms, self-treatment behavior, and self-monitoring BG profiles using data
collected in patients' natural environment.
MATERIALS AND METHODS
Subjects
Ninety-six individuals, 58 women and 38
men, who had T1DM for at least 2 years and were taking insulin since the time
of diagnosis were recruited through advertisements in newsletters and diabetic
clinics and through direct referrals. All subjects were routinely using
self-monitoring devices to measure their BG. Their average age at the time of
recruitment was 35 years (standard deviation [SD] = 8), the average duration of
diabetes was 16 years (SD = 10) and the average daily insulin dose was 0.58
U/kg (SD = 0.19). Since the goal of this research was to study risk factors for
SH, subjects who had problems with recurrent SH were preferentially recruited.
History of SH was recorded as the number of SH episodes in the previous year.
The preferential recruitment resulted in 43 participants who reported having at
least two SH episodes in the previous year (SH group) and 53 who reported none
(No SH group). The SH group included 45% of all subjects, which is greater than
the estimated 4% to 22% of T1DM patients who have problems with SH.3 Consequently, the incidence of SH in this study
was high compared with reports from population-based studies.
Procedure
Subjects were invited to an orientation
meeting in groups of 4 to 10. They were informed about the study, their
questions were answered, written informed consent was obtained, and blood
samples were collected for determination of HbA1c. Each subject then
participated in four consecutive data collections: (1) screening questionnaire,
(2) hand-held computer field behavioral assessment, (3) parallel
self-monitoring of blood glucose (SMBG) using LifeScan OneTouch Profile meters,
and (4) subsequent 6 months of diaries of moderate/SH.
The
screening questionnaire included
demographic data and history of subjects' diabetes, including detailed
description of the SH episodes in the previous year (history of SH).
Hand-held computer (HHC) assessment: Subjects were instructed to use the
Psion P-250 (Psion Corp, England) HHC whenever they believed that their BG was
low or high, based on internal or external cues, and before their routine SMBG.
For each subject, seventy trials were stored over a 3- to 4-week period. At
each trial, the HHC collected data on two sets of potential sources of information
concerning hypoglycemia status: current symptoms and preceding self-treatment
behaviors.
Symptoms: Subjects rated on a scale from 0 to 6
four common autonomic symptoms (sweating, pounding heart, trembling,
jittery/tense/stressed feelings) and four common neuroglycopenic symptoms
(difficulty concentrating/slow thinking, light-headedness/dizziness, visual
disturbance, uncoordinated movements).
Self-treatment behaviors: At each trial, the HHC asked the
subjects to recall their previous BG and to rate the subsequent amount of food
eaten, insulin taken, and exercise on a three-point scale: more, less, or
usual. At the end of each trial, the HHC prompted the subjects to measure BG
and enter the reading. Since it requires at least 45 seconds for someone to
lance a finger, collect a blood sample, and generate a BG reading, a trial was
considered invalid and excluded from the data analysis if less than 45 seconds
elapsed between the prompt "measure BG" and the actual BG entry. Six percent of
all trials were excluded for this reason, leaving 4111 records for analysis. We
have previously demonstrated that the HHC assessment yields very reliable and
useful findings.23-25
SMBG
profile: Parallel to the
HHC assessment, subjects were instructed to measure their BG four times a day
using LifeScan OneTouch Profile meters (lifescan Inc, Milpitas, CA). This
yielded 135 ± 53 SMBG readings per subject over a month. These data were electronically
downloaded and the low BG index of each subject was computed. The low BG index
is a statistic based on our previously published transformation of the BG
measurements scale (26) and has been repeatedly validated and proven to be
the best predictor of SH from SMBG data (27, 28, 29, 30).
Monthly
diaries of SH: For the
following 6 months, the subjects recorded in diaries any occurrence of SH
together with the date and time of the episode. The diaries were mailed in
monthly and documented a total of 215 SH episodes (2.4 ± 5.3 per subject).
RESULTS
Biobehavioral model of hypoglycemia (Figure
1): The risk for hypoglycemia in T1DM is a continuous process that is
contingent on patients' perception and awareness of hypoglycemic symptoms,
quality of judgment of their condition, and accuracy and swiftness of execution
of corrective actions. The short-term outcome of this control is reflected by
SMBG profiles, while the long-term outcome is reflected by the frequency of
events such as SH. To formally describe this process, we introduce a
biobehavioral model of hypoglycemia (Figure 1) that consists of sequential steps
and appropriate indices developed to quantify each step:
Step 0 is history of SH, assessed by screening questionnaires.
Step 1 quantifies symptom awareness by introducing Autonomic and Neuroglycopenic Symptom
Awareness Indices. These indices are summary scores, based on the ratings
of autonomic/ neuroglycopenic symptoms during hypoglycemia recorded in 70 HHC
trials. Each index is computed as a logistic function of autonomic or
neuroglycopenic symptom ratings, optimized with respect to subjects' history of
SH. In other words, each index is given by the formula:
100/(1+exp[-Z{sx}])
Where Z(sx) =a1Sx1+a2Sx2+a3Sx3+a4Sx4+a5
is a linear combination of four autonomic or four neuroglycopenic symptoms,
respectively, with coefficients optimized to differentiate SH versus No SH
subjects (ie, on the basis of Step 0). Symptom Awareness Indices range between
0 and 100.
Step 2 quantifies self-treatment behaviors pertinent to low BG. We
introduce a Self-treatment [Behavior]
Index based on patients' HHC reports of changes in insulin, food, and
exercise following a specific BG level. Essentially the index is a conditional
estimate of patients' self-treatment reaction (change in insulin, food,
exercise) to a previous BG level, computed according to a previously published
table.18 For example, if a patient's BG is low and
subsequently s/he increases her/his insulin dose, or eats less, or exercises
more, a "penalty" is imposed. This "penalty" is 1, 2, or 3, depending on the
range of the previous BG: 6.2 to 3.9 mmol/L, 3.9 to 6 mmol/L, or below 3
mmol/L, respectively. If the patient takes an appropriate action, the "penalty"
is zero.
The self-treatment index
is the average "penalty score" across 70 HHC trials, calibrated between 0 and 100,
with higher scores indicating inappropriate treatment responses.
Step 3 includes the
low BG index, computed from SMBG data, using a previously published formula.28,30 This Index could theoretically range between 0
and 100.
Step 4 is directly
accessible by prospective diaries recording the occurrence of moderate/SH
together with the date and time of each episode.
The analysis of awareness
and self-treatment indices, Low BG Index, history of SH and prospective records
of SH yielded the following results:
Retrospectively, the two
subject groups, No SH and SH, were differentiated by (1) Autonomic Symptom
Awareness Index, t = 3.5, P <
.001; (2) Neuroglycopenic Symptom Awareness Index, t = 2.3, P < .05; (3) Self-Treatment
Behavior Index, t = -3.6, P <
.001, and the Low BG Index, t = -4.2, P
< .001. Table 1 presents the means, standard deviations, t and P values for these comparisons.
Prospectively, the
Autonomic Symptom Awareness Index correlated with future SH episodes, r =
-0.53, P < .001, while the
Self-Treatment Behavior Index correlated with subjects' Low BG Index, r = 0.73,
P < .001. None of the Symptom
Awareness Indices correlated with the Self-Treatment Behavior Index.
A linear regression predicting subjects' low BG index from awareness
and self-treatment indices had r2 = 53% and was statistically
significant (F = 51, P <
.0001). The partial correlations of the Neuroglycopenic Symptom Awareness
Index and Self-Treatment Behavior Index were -0.23 and 0.72, respectively;
both variables entered significantly into the model.
A linear regression model using Autonomic and Neuroglycopenic
Symptom Awareness and Low BG Indices, in combination with history
of SH, to predict future SH episodes in the following 6 months had
r2=52% and was significant (F = 24, P
< .0001). Table 2 presents the partial correlations of the variables
in the model together with their significance.
In the set of four
variables (Autonomic and Neuroglycopenic Symptom Awareness, Self-Treatment
Behavior and Low BG Indices), factor analysis identified two orthogonal
factors: Factor 1, associated with Self-Treatment and Low BG Indices (factor
loads of 0.93 and 0.91, respectively); and Factor 2, associated with the
Autonomic and Neuroglycopenic Symptom Awareness Indices (factor loads of 0.84
and 0.85, respectively). Both factors differentiated SH from No SH
subjects, P levels < .01). Most importantly, future SH was almost equally
dependent on these two factors (loads of 0.55 and -0.49, respectively). Thus,
we can conclude that the risk of SH can be (statistically) presented as a
linear combination of the independent Factor 1 and Factor 2 (Figure 2).
DISCUSSION
We have previously
developed and reported a biopsychobehavioral model of SH that presents
in detail the possible paths through which a low BG episode could
progress into SH.21
We now generalize this model with a formal description
of behavioral patterns leading to hypoglycemia and test the model
using HHC technology for implementation in the field of our behavioral
assessments. The general idea of the formal biobehavioral model of
hypoglycemia is that the daily control of T1DM is a continuous process
dependent on patients' perception and awareness of hypoglycemic symptoms,
quality of judgment of their condition, and accuracy and swiftness
of execution of corrective actions. This process has a memory (the
history of SH episodes) and receives continuous feedback from SMBG.
In the long-term, the quality of hypoglycemic control is reflected
by frequency of SH. In the short term, patients can rely on SMBG profiles,
such as the Low BG Index. In order to fully understand this process,
we need two sets of estimates: one for each of its steps and another
for the relationships between its sequential steps. Step 1
and Step 2 of the model (Figure 1) are complex sets of psychobehavioral
characteristics that have their own internal structure, which we previously
presented.
21
,22
The sequential relationship between awareness, self-treatment,
SMBG profile and future SH is stochastic (eg, a specific combination
of symptoms may or may not trigger self-treatment action; an appropriate
treatment behavior is a precursor to a safe SMBG profile only with
some probability). In addition, knowledge of SBMG readings, or profile,
as well as experience of SH results, with some probability, in feedback
about patients' awareness or behavior . This feedback may be negative
(eg, recurrent hypoglycemia may reduce symptom awareness, leading
to a "vicious cycle,"12
or positive (eg, knowledge about the SMBG profile could
lead to improvement in self-treatment behavior). Thus, the feed-forward
and feedback arrows in Figure 1 mark stochastic transitions22
and are subject to probability modeling and statistical
evaluation.
CONCLUSION
In this
article, we give quantitative representations for each step of this model and
evaluate the feed-forward relationships of the model. Further investigation and
a repeated HHC assessment at the end of the study are needed to evaluate
feedback relationships. We conclude that the Autonomic Awareness Index is
related to long-term risk for SH, while the Neuroglycopenic Index mostly
reflects patients' frequency of exposure to low BG (as estimated by the Low BG
Index). In addition, the Low BG Index depends primarily on subjects'
self-treatment behaviors. Thus, interventions to improve such behaviors would
likely reduce subjects' recurrence of hypoglycemia. The latter is even more
important in light of the finding that self-treatment behavior (and the Low BG
Index) are, to a large extent, independent of symptom awareness.
Finally, our analyses
revealed the internal structure of the biobehavioral determinants of
hypoglycemia. The occurrence of future SH episodes was largely (and
approximately equally) dependent on two clearly identified linearly independent
factors: Symptom Awareness and Self-Treatment Behavior + the resulting SMBG
profile (Figure 2). In combination with history of SH, these factors predicted
52% of the variance in future SH episodes. Interestingly, the history of SH
that is largely considered to be the single best predictor of future SH3 contributed about 16% to that prediction.
The remainder of the variance was accounted for by current characteristics of
patients' behavior. The latter gives an optimistic perspective for a behavioral
treatment intended to reduce SH in T1DM.
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Table 1: Comparison of No Severe
Hypoglycemia with Severe Hypoglycemia Subjects
|
Index
|
No
SH Mean (SD)
|
SH
mean (SD)
|
T
(2-tail P)*
|
|
Autonomic Symptom Awareness
|
98(4)
|
82(35)
|
3.3
(.001)
|
|
Neuroglycopenic Symptom Awareness
|
84(32)
|
66(44)
|
2.3
(.027)
|
|
Self-Treatment Behavior
|
58(34)
|
85(39)
|
-
3.6 (<.001)
|
|
Low Blood Glucose index
|
2.9(1.8)
|
5.2(3.3)
|
- 4.2 (<.001)
|
*All t-tests have 94° of freedom.
SD = standard deviation; SH = severe
hypoglycemia.
Table 2: Prediction of Future Severe
Hypoglycemia (r2 = 52%)
|
Variable in the Model
|
|
T (P Value)
|
|
|
0.39
|
4.0
(<.001)
|
|
Autonomic Symptom Awareness Index
|
- 0.49
|
- 5.0
(<.001)
|
|
Neuroglycopenic Symptom Awareness Index
|
0.28
|
2.4
(<.01)
|
|
Self-Treatment Behavior Index
|
0.0
|
0.4,
nonsignificant
|
|
Low Blood Glucose Index
|
0.34
|
3.4
(<.001)
|
Figure
1. Biobehavioral model
of hypoglycemia.
Figure 2. Risk for
future severe hypoglycemia is determined by two linearly independent factors
quantified by the Low Blood Glucose/Treatment Behavior Indices and
Autonomic/Neuroglycopenic Symptom Awareness Indices.