The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes

Article (PDF Available)inDiabetic Medicine 29(8):1067-73 · December 2011with23 Reads
DOI: 10.1111/j.1464-5491.2011.03545.x · Source: PubMed
Abstract
Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower during falling glucose than during rising glucose. Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn concurrently. Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95%CI 6.1-11.6) vs. 1.5 min (95%CI -2.6 to 5.5, P<0.005). Using the time shift optimization method, results were similar, with a lag that was higher for rising than for falling segments [8.3 (95%CI 5.8-10.7) vs. 1.5 min (95% CI -2.2 to 5.2), P<0.001]. Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments. In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling than when rising.

Figures

Article: Care Delivery
The effect of rising vs. falling glucose level on
amperometric glucose sensor lag and accuracy in Type 1
diabetes
W. K. Ward
1,2
, J. M. Engle
1
, D. Branigan
1
, J. El Youssef
2
, R. G. Massoud
1
and J. R. Castle
2
1
Legacy Health System, Legacy Research Institute and
2
Division of Endocrinology, Diabetes and Clinical Nutrition, Oregon Health and Science University, Portland,
OR, USA
Accepted 6 December 2011
Abstract
Background Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between
blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower
during falling glucose than during rising glucose.
Methods Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference
blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we
excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from
duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two
ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the
optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn
concurrently.
Results Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95% CI
6.1–11.6) vs. 1.5 min (95% CI –2.6 to 5.5, P < 0.005). Using the time shift optimization method, results were similar, with a
lag that was higher for rising than for falling segments [8.3 (95% CI 5.8–10.7) vs. 1.5 min (95% CI –2.2 to 5.2), P < 0.001].
Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments.
Conclusions In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay
measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling
than when rising.
Diabet. Med. 29, 1067–1073 (2012)
Keywords biotechnology, continuous blood glucose monitoring, Type 1 diabetes
Introduction
Amperometric continuous glucose monitors that are designed
for use in the subcutaneous space are intended to measure
glucose in interstitial fluid. During changing glucose levels,
there is typically a lag of the sensor signal behind blood glucose,
as recently reviewed [1]. The physiologic component of this lag
results from the time required to transport glucose from the
plasma to the interstitial fluid. The instrumental (analytic)
component [2] results from the time required for analytes to
diffuse through sensor elements before signal transduction. The
data processing component results from noise filtering [1].
Ideally, the use of continuous glucose monitors in persons
with diabetes provides early warning for extremes of
glycaemia. However, lag impairs accuracy during changing
glucose at times when accuracy is most needed. Failure to
detect falling glucose can lead to confusion, stupor and coma.
Some workers found that the magnitude of sensor lag was
less during falling glucose than during rising glucose. This was
first observed in humans [3] and shortly thereafter in animals
[4]. It was suggested that, during high insulin effect, the decline
in plasma glucose results from glucose transport from the
interstitial fluid into cells. Thus, it made sense that the decline
in interstitial fluid glucose in this state precedes the decline in
Correspondence to: W. K. Ward, Legacy Health System, Legacy Research
Institute, 1225 NE 2nd Avenue, Portland, OR 97232, USA.
E-mail: wardk@ohsu.edu
DIABETICMedicine
DOI: 10.1111/j.1464-5491.2011.03545.x
ª 2011 The Authors.
Diabetic Medicine ª 2011 Diabetes UK
1067
plasma. In contrast, during low insulin effect and low periph-
eral glucose uptake, glucose is transported from the liver or gut
into plasma, then later diffuses into the interstitial fluid [4,5].
Findings similar to those of Sternberg et al. and Thome-Duret
et al. were found with a transdermal sensor [6].
In contrast with these findings, other groups reported that
sensor glucose lagged behind plasma glucose, regardless of
whetherglucosewasrisingorfalling and regardless of insulin
effect [2,7,8].
Table 1 is a summary of the literature on the
topic of sensor delay. The methods used in these studies varied
widely and it is likely that there are many sources of error that
can confound lag measurement. For example, in a recent study,
we found that calibration error can perturb measurements of
sensor lag [9].
The first purpose of the present study was to compare sensor
lag during falling vs. rising glucose. Because we were interested
in studying lag during substantial rates of change, we also
asked whether meal carbohydrate of high glycaemic index
would increase the glucose rate of change and thus create more
opportunities to measure sensor lag. We were also interested in
the magnitude of the difference in glucose levels resulting from
meals of differing glycaemic index values.
Subjects and methods
Meal study
Seven subjects with Type 1 diabetes mellitus on subcutaneous
insulin pump therapy were recruited from clinics in Portland,
Oregon, USA. Patients who were pregnant or had cardiovas-
cular, cerebrovascular, kidney or liver disease were excluded.
Other exclusion criteria included oral or parenteral cortico-
steroid use, visual or physical impairments that impede the
use of a continuous glucose monitoring device, insulin
allergy, hypoglycaemia unawareness, insulin resistance defined
as requiring more than 200 units of insulin per day or
gastroparesis.
A total of 14 experiments, each with two meals, were com-
pleted in three men and four women aged 45.0 4.5 years,
whose duration of diabetes was 28.3 3.8 years. HbA
1c
was
58 2mmol mol (7.3 0.2%) and BMI 26.1 1.2 kg m
2
.
For each experiment, two meals were served 195 min apart and
both were accompanied by continuous glucose monitoring. On
one study day, two high glycaemic index meals were given and,
on the other study day, two low glycaemic index meals were
given. The glycaemic index is the area under the 2-h blood
glucose response curve for a given food, compared with glucose
[10]. The glycaemic index was 75 for high glycaemic index
meals and 30 for low glycaemic index meals. The order (high
glycaemic index vs. low glycaemic index) was randomized.
The Dexcom Seven Plus subcutaneous sensor (Dexcom,
Inc., San Diego, CA, USA) was inserted the day prior to the
study to allow time for signal stabilization. Subjects took no
bolus insulin after 05.00 h on the day of the study. Aspart
insulin (Novo-Nordisk Inc., Princeton, NJ, USA) was delivered
subcutaneously by a portable insulin pump. Each subject’s
standard basal insulin infusion rates were used throughout the
day and were identical for both study days. Arterialized venous
blood glucose samples were obtained every 15 min using a
HemoCue Glucose 201 Analyzer (Hemocue AB, Engelholm,
Table 1 A summary of published reports that have compared the magnitude of sensor lag measured during rising glucose (or low insulin effect) to lag
measured during falling glucose (or high insulin effect)
Authors, year Reference
Type of
sensor Subjects
Lower lag
found during
high insulin effect?
Sternberg et al., 1996 [3] Microdialysis Type 1 diabetes—humans Yes, but only in
50% of subjects
Thome-Duret et al., 1996 [4] Wilson-Reach sensor
(GOX, non-mediated)
Type 1 diabetes—rats Yes
Wilhelm et al., 2006 [21] MiniMed (GOX,
non-mediated)
Type 1 diabetes—humans *
Schmidke et al., 1998 [7] Heller sensor (GOX,
osmium-mediated)
Non-diabetic rats No
Aussedat et al., 2000 [5] Wilson-Reach Non-diabetic rats Yes
Rebrin et al., 1999 [8] MiniMed Non-diabetic dogs No
Kulcu et al., 2003 [6] Cygnus, GOX,
transdermal
Type 1 and Type 2
diabetes—humans
Yes
Boyne et al., 2003 [2] MiniMed Type 1 diabetes—humans No
Kamath et al., 2009 [12] Dexcom Seven+
(GOX, non-mediated)
Type 1 diabetes—humans Apparently yes,
although not
directly stated
*Compared with the rising glucose phase, during rapid decline after oral glucose and insulin administration, there was less of a lag of the
sensor (vs. capillary blood as a reference), but there was also consistent calibration error (the sensor substantially underestimated blood
glucose at high glucose levels), so this finding is difficult to interpret.
GOX, glucose oxidase.
ª 2011 The Authors.
1068 Diabetic Medicine ª 2011 Diabetes UK
DIABETICMedicine Lag in continuous glucose monitors during glucose change W. K. Ward et al.
Sweden). After blood glucose remained stable at
3.8–9.4 mmol l (70–170 mg dl) for 30 min, the first meal was
given.
Pre-meal insulin dosing was based on each subject’s usual
insulin:carbohydrate ratio and was the same for all four meals.
Sensors were calibrated using the Hemocue blood glucose
analyser just prior to the first meal.
Closed-loop study
In order to further investigate sensor lag and to obtain addi-
tional comparisons between Dexcom Seven Plus continuous
glucose monitor data and corresponding blood glucose, we also
evaluated data from a previously published closed-loop study
[11]. A total of 24 sensor data sets were analysed (12 closed-
loop experiments in which each subject wore two sensors),
with a mean study duration of 25 h. A data set is defined as all
data for one sensor obtained over the course of one experiment.
The two sensors were widely separated, on either side of the
abdomen. Sensors were placed before the experiment started,
as above, and sensors were calibrated every 6 h. Blood glucose
was obtained every 10 min.
Data analysis
In order to measure the lag of sensor glucose behind blood
glucose, there must be a change in glucose. Therefore, a data
segment is defined as the data collected over a time period
during which the magnitude of the change in sensor signal is
sufficient to calculate sensor delay. All data sets in both
studies were analysed in order to capture all data segments in
which blood glucose was changing at a rate of +0.028 mmol l
(+0.5 mg dl) or )0.028 mmol l (–0.5 mg dl) per min over a
time period of at least 25 min. For a data segment to be
included in the analysis, it was necessary to have a consistent
monotonic rise or fall of glucose with a coefficient of deter-
mination (r
2
)of 0.82 for the changing blood glucose and
sensor glucose values. In addition, to avoid confounding
effects on sensor lag, we did not include sensor data segments
with substantial calibration error, which can occur by drift or
incorrect designation of background current. In particular, the
data from the two plateaus (i.e. before and after the change
segment) were analysed for sensor accuracy. Specifically, we
included only those data segments whose mean absolute
relative difference values obtained at the lower and upper
plateaus were £ 15%. The data from the change segment
itself was not subjected to this accuracy test, because such a
test would inappropriately exclude data sets with a large lag.
The rationale for excluding inaccurate sensor records is
explained further by the three panels in the Supporting
Information (Fig. S1), which show how calibration error
impairs sensor accuracy. As shown in panels (b) and (c), data
segments with substantial sensor calibration error (shown here
as error in the upper plateau segments) make computation of
lag duration difficult.
For the meal study, there were a total of 18 rising glucose
sensor data segments and 19 falling glucose segments that met
the criteria for rate of change, duration and r
2
. Of these seg-
ments, there were 13 rising and 12 falling segments that also
met the plateau accuracy criteria. For the closed-loop study,
there were 51 rising sensor data segments and 56 falling glucose
segments that met the criteria for rate of change, duration and
r
2
. Of these segments, there were 34 rising and 36 falling seg-
ments that also met the plateau accuracy criteria.
The calculated lag results of both sensors in the same subject
over the same period of time are not independent observations.
For this reason, when both sensors at any given time point met
all the acceptance criteria, the two lag calculations were com-
bined (averaged) and reported as a single observation. After
combining the lag results from the duplicate sensors and taking
closed-loop data and the meal study data together, there were a
total of 36 rising and 36 falling segments.
Table 2 shows the
interrelationships among numbers of subjects, experiments,
data sets and data segments.
Sensor time lag was determined for each sensor using two
methods. The first method was the linear regression delay
method. For each rising glucose and for each falling glucose
sensor data set, excluding the plateau segments, least-squares
linear regression lines were calculated for the blood glucose
values (vs. time) and for the sensor values (vs. time). The lag
time was defined as the time difference between the two
regression lines at the vertical (glucose level) midpoint between
the low plateau and the high plateau. An example of mea-
surement of delay in this fashion is shown in
Fig.1.
The second method was the time-shift optimization method,
as reported by Kamath et al. [12] and similar to the method of
Breton and Kovatchev [13] and Kovatchev et al. [14]. In this
method, least-squares regression lines were initially calculated
as above, with no time shift. Then, the sensor data were time
shifted in 5-min increments and decrements in order to obtain
the optimal time shift which maximized the coefficient of
determination calculated for the combined blood glucose and
sensor glucose readings. For each data set, a total of 15 coef-
ficients of determination were calculated for time shifts from
35 to 35 min.
For determination of the effect of glycaemic index on
glycaemia, we compared blood glucose area under the curve
between the two groups. Area under the curve was calculated
over 3 h after meals as described [15].
Sensor accuracy was determined by calculating the absolute
relative (per cent) difference and the relative difference between
sensor glucose and blood glucose. The absolute relative dif-
ference is the percentage difference between the sensor glucose
value and the arterialized venous reference blood glucose
value, calculated as follows: [(reference glucose–sensor glu-
cose) reference glucose] · 100. The relative difference (per
cent bias) is a signed value and calculated as follows: (reference
glucose–sensor glucose) · 100. Positive and negative relative
difference values indicate overestimates and underestimates,
respectively.
ª 2011 The Authors.
Diabetic Medicine ª 2011 Diabetes UK
1069
DIABETICMedicineOriginal article
Statistics
Data for the outcome variables (duration of lag as measured
both by regression delay and by time shift optimization; and
measures of sensor accuracy and error) were analysed for dis-
tribution and symmetry in the rising and falling data sets. In
these data sets, the shape of the distribution was not perfectly
consistent, in part because the size of the data sets was not large.
The magnitude of the Pearson skewness coefficients was gen-
erally between 0 and 0.5 (indicating a substantial degree of
symmetry) although one lag set coefficient was 0.8. As, in some
cases, there was more than one evaluable segment per subject in
any given day, we used generalized estimating equations for
comparison of sensor lag and sensor accuracy (Stata, ver-
sion 10; StataCorp, College Station, TX, USA). The generalized
estimating equation method of analysis is designed primarily for
Gaussian data sets, although it can handle some non-Gaussian
behaviour. To be consistent with generalized estimating equa-
tions, we report central tendency as mean and 95% confidence
intervals. For other comparisons, including glucose slope during
glycaemic index studies, t-tests were used, as indicated.
In addition, to further address the issue of independence of
data (potential correlation within data sets obtained in indi-
vidual subjects), when there were two sensors for any given
change segment, the data from the two were combined (aver-
aged).
Results
High glycaemic index vs. low glycaemic index conditions
In high glycaemic index meals (vs. low glycaemic index meals),
we found only a small, non-significant difference in the number
of evaluable glucose change segments and no difference in
absolute relative difference or relative difference. For these
reasons, for all subsequent data analysis in this paper, the high
and low glycaemic index conditions were analysed together.
Sensor lag: rising vs. falling glucose
Using the linear regression delay method, the lag time of the
sensor glucose behind blood glucose was substantially greater
for rising segments than for falling segments [8.9 min (6.1–
11.6) vs. 1.5 min (–2.6 to 5.5), P < 0.005]. Using the time shift
optimization method, results were similar, with a lag that was
Table 2 Numerical interrelationships among number of subjects, experiments, data sets and glucose data segments
Meal
study Comments
Closed-loop
study
Comments
Subjects 7 One sensor per subject 14 Two sensors per subject
Experiments 14 Two experiments per subject
(one high glycaemic
index, one low glycaemic index)
23 Five subjects had one experiment,
nine subjects had two experiments
Data sets 14 One sensor data set
per experiment
46 Two sensor data sets per experiment
Rising glucose
data segments
13 Of the 14 data sets, there
were 11 that had one
evaluable rising segment,
one that had two
evaluable rising segments
and two that
had no evaluable rising segment
34 Of the 46 data sets, there were nine that
had one evaluable rising segment, five
that had two evaluable rising segments,
five that had three evaluable rising segments
and 27 that had no evaluable rising segment
Rising segments after
combination of
duplicate sensors
13 (No change, single sensor
per experiments)
23 In 11 sensor pairs, both sensors met all inclusion
criteria and thus were combined (averaged),
leaving 23
Falling glucose
data segments
12 Of the 14 data sets, there
were six that had
one evaluable falling segment,
three that had
two evaluable falling segments
and five that
had no evaluable falling segment
36 Of the 46 data sets, there were seven that had one
evaluable falling segment, four that had two
evaluable falling segments, three that had
three evaluable falling segments, three that had
four evaluable falling segments
and 29 that had no evaluable falling segment
Falling segments after
combination of
duplicate sensors
12 (No change, single sensor
per experiments)
24 In 12 sensor pairs, both sensors met all inclusion
criteria and thus were combined (averaged),
leaving 24
Combined data from both studies
Total rising data segments 36 13 (meal) + 23 (closed loop) = 36
Total falling data segments 36 12 (meal) + 24 (closed loop) = 36
ª 2011 The Authors.
1070 Diabetic Medicine ª 2011 Diabetes UK
DIABETICMedicine Lag in continuous glucose monitors during glucose change W. K. Ward et al.
higher for rising than for falling segments [8.3 min (5.8–10.7)
vs.1.5min(2.2to5.2),P < 0.001].
An example of sensor lag during rising and falling glucose
levels is shown in Fig. 1. Panel (a) shows the raw sensor and
raw blood glucose data and panel (b) shows the method of lag
measurement in the rising segment using the linear regression
delay method. The least-squares regression lines are fit to the
ascending blood glucose and sensor data, not including the
plateau periods. The lag value (difference between the two
regression lines) is taken at the vertical midpoint.
We also observed that, for the glucose change segments, the
slope of rising glucose tended to be steeper than that of falling
glucose. The magnitude of the glucose rate of change was
0.075 0.026 mmol l (1.36 0.46 mg dl) (rising, mean
sd) vs. 0.05 0.017 mmol l (0.92 0.30 mg dl) per min
(falling, P < 0.001, unpaired t-test). Because of this finding, we
wondered whether high rates of change contributed to the
greater lag in rising vs. falling segments. However, there was no
correlation between the absolute value of the rate of change
and the lag for all segments (r
2
= 0.027 for rising segments and
r
2
= 0.020 for falling segments, neither significant).
Comparison of sensor error during rising vs. falling glucose
Sensor error data are shown in Table 3. As the direction of
error was important, the primary metric of sensor error was the
(signed) relative difference, i.e. per cent bias. We also calculated
the absolute relative difference (per cent difference), a more
global, unsigned metric of accuracy. For all data pairs of each
rising and falling segment that met all inclusion criteria, a mean
and median relative difference and absolute relative difference
were calculated. The relative difference values were greater
during rising glucose because sensor delays during rising
glucose predictably led to underestimates (positive values).
Although delays that occur during falling glucose will lead to
sensor overestimates (negative values), the small delays that
occurred during glucose decline minimized this effect. As
shown in Table 3, the general metric of accuracy, the absolute
relative difference, was lower (indicating greater accuracy)
during falling glucose than during rising glucose.
Effect of glycaemic index on postprandial glucose rise
The high glycaemic index breakfast, compared with the low
glycaemic index breakfast, produced a much greater rise in
blood glucose as measured as the area under the curve
[583 113 vs. 61 135 min · (mmol l), 10 497 2028 vs.
1096 2428 min · (mg dl), P = 0.01]. The slope of the
blood glucose from 20 to 80 m after the high glycaemic index
6
8
10
12
14
16
1380
1410
1440
1470
1500
1530
1560
1590
1620
1650
Glucose (mmol/l)
Elapsed study time (min)
Subject E97
6
8
10
12
14
16
1350
1380
1410
1440
1470
1500
1530
1560
1590
Glucose (mmol/l)
Elapsed study time (min)
Subject E97:
delay at 50% half rise = 11.7 min
(a)
(b)
FIGURE 1 (a) Typical raw data from a period during which glucose rose
then fell. Note that the sensor glucose values (blue) lagged behind blood
glucose (red) as glucose was rising, but not as glucose was falling. (b)
Quantitative measurement of the lag obtained during rising glucose from
the data shown in (a). The time difference between the regression lines for
changing blood glucose (dotted red line) and changing sensor glucose
(dotted blue line) obtained halfway between the lower plateau and the
upper plateau (vertical midpoint) is indicated as the dark horizontal bar,
the length of which is calculated to be 11.7 min.
Table 3 Sensor accuracy and error
Glucose sensor error accuracy metrics
Relative
difference*
Absolute
relative
difference
Rising Mean –8.1 11.0
95% CI –5.8 to –10.3 9.3–12.6
Median –6.6 9.7
n (data pairs) 36 36
Falling Mean 2.3 8.5
95% CI 0.02–4.6 7.0–9.9
Median 1.2 7.4
n (data pairs) 36 36
P rising vs. falling < 0.001 < 0.001
*Relative (per cent) difference is defined as [(sensor glucose–
reference glucose) reference glucose] · 100. The magnitude of
relative difference was greater (and more negative) during rising
vs. falling glucose, indicating that the sensor error was sub-
stantially greater when glucose was rising, at a time when lag
was greater.
Absolute relative difference refers to absolute value of the
relative difference and was lower when glucose was falling,
indicating greater accuracy in that condition.
ª 2011 The Authors.
Diabetic Medicine ª 2011 Diabetes UK
1071
DIABETICMedicineOriginal article
breakfast was similarly steeper than after the low glycaemic
index breakfast [0.07 0.01 vs 0.01 + 0.02 (mmol l) min,
1.3 0.2 vs. 0.2 0.3 (mg dl) min, P = 0.002, paired t-test].
There was not a significant difference in postprandial glucose
area under the curve or post-meal glucose slopes between the
high and low glycaemic index treatments for the lunch meal
[240 213 vs. 143 45 min · mmol l, 4314 3837 vs.
2579 817 min · (mg dl), P = NS]. These results showed
that the glycaemic index effect was greater with the first meal
than with the second.
Discussion
We compared the temporal relationship of sensor glucose val-
ues to simultaneously obtained, frequently sampled blood
glucose values in persons with Type 1 diabetes when glucose
was rising vs. falling. We allowed glucose level to vary rather
than use glucose clamps for this study, as we wished to study
situations encountered during typical living conditions. The
clinical relevance of this study pertains to the accuracy of such
devices in situations during which glucose is changing. If there
were substantial and fixed sensor lag under all conditions, there
would be a systematic error that could be clinically problematic
as glucose declines. Because hypoglycaemia can have dangerous
consequences, early warning alarms from the device are very
important.
In general, the degree of lag in this study was relatively short,
as was shown with the same continuous sensor studied by
Kamath et al. [12]. When we compared different directions of
glucose change, we found that the duration of lag during falling
glucose was significantly shorter than during rising glucose. In
many cases, the lag during falling glucose was less than zero,
suggesting that sensor glucose changed before blood glucose
changed. The difference in lag (rising vs. falling) was highly
significant when measured either with a linear regression delay
method or a time-shift optimization method, the latter being
similar to a method used by others [12,14]. The lag results and
statistical results using these two methods yielded very similar
results.
In order to ensure sufficient rates of change and to minimize
calibration error (to minimize confounding of lag measure-
ment), we excluded those glucose change segments of shallow
slope, those of short duration, those in which the rise or fall
was inconsistent, and those with substantial sensor inaccuracy.
The fact that we selected segments with low noise and high
sensor accuracy may contribute to the differences of our
findings compared with those of other groups [2,7,8]. In
addition, these groups used sensors made by different manu-
facturers and, in some cases, studied animals instead of
humans [7,8]. Our finding of a shorter lag during falling
glucose, a time of high insulin effect, is in general agreement
with those of several other workers [3–6]. A high insulin effect
causes glucose to be pulled into cells from the interstitial fluid,
causing a reduction of interstitial fluid glucose first, followed
by diffusion of glucose down the concentration gradient from
plasma to interstitial fluid, leading to decline of plasma
glucose [5].
Typically, in our study, we observed a lag of 6–12 min
during rising glucose and -6 to +6 min during falling glucose.
Using sensors made by the same manufacturer, Kamath and
colleagues found a lag of approximately +6 min when all seg-
ments were analysed together. They reported that, when glu-
cose was falling rapidly and was normal or low, the sensor read
on average 0.2–0.3 mmol l (4–6 mg dl) less than the blood
glucose [12]. This finding suggests a shorter lag during falling
glucose and agrees with our findings.
These findings suggest that, when glucose is falling, amper-
ometric sensors may be capable of providing early warning of
impending hypoglycaemia, although such accuracy cannot be
guaranteed. Early warning is beneficial for persons with
Type 1 diabetes whose glucose can fall rapidly and unexpect-
edly. Calibration error, which we were careful to minimize by
selection criteria, will tend to introduce noise in to the system
and reduce accuracy during glucose change, although we
should add that calibration error can be minimized by frequent
calibration [9]. We also acknowledge that, in our study, the
sensors were calibrated by a laboratory device (HemoCue 201;
Hemocue AB) and that subjects remained largely inactive. In
free-living subjects who use standard capillary glucose meters,
sensor accuracy will often be lower, which may impair early
detection of falling glucose.
As would be expected with a shorter lag duration, sensor
accuracy was greater (lower absolute relative difference val-
ues) during falling glucose. During rising glucose, the signed
values of relative difference (bias) were larger in magnitude
and were more negative (indicating sensor underestimates).
When glucose was falling, the relative difference was smaller
in magnitude, a finding that was predictable in view of the
shorter delays in that setting.
After the first meal of the day (but not the second), high
glycaemic index meals resulted in much higher postprandial
glucose levels vs. low glycaemic index. Several investigators
have commented on such differences between meals and have
used terms such as the ‘breakfast effect’ or ‘second meal phe-
nomenon’ to describe this effect [16–18], which might be
attributable to continued presence of the pre-breakfast insulin
bolus or a diurnal change in insulin sensitivity or other meta-
bolic factor [19,20]. In our study, the change in glucose after
meals was easily detectable using the sensor.
Limitations of this study included inclusion of data from two
different experimental protocols and the presence of some cases
of repeated measures in a single individual (although that
statistical method, generalized estimating equations, is designed
to allow comparisons in the presence of such repetitions). In
addition, some criteria for choosing glucose change segments
(e.g. the coefficient of variation) are admittedly arbitrary.
We conclude that subcutaneous amperometric glucose sen-
sors, when well-calibrated and functioning accurately, dem-
onstrate a lower lag duration when glucose is falling vs. rising,
a finding consistent with a greater insulin effect in that
ª 2011 The Authors.
1072 Diabetic Medicine ª 2011 Diabetes UK
DIABETICMedicine Lag in continuous glucose monitors during glucose change W. K. Ward et al.
condition. When glucose is falling and lag is minimal, sensor
accuracy is greater.
Competing interests
Nothing to declare.
Acknowledgements
We thank the Juvenile Diabetes Research Foundation and the
Legacy Good Samaritan Foundation for their generous finan-
cial support of this project and Legacy Clinical Research Sup-
port Services for their technical assistance. We also thank the
clinical research centre at Oregon Health and Science Univer-
sity (the Oregon Clinical and Translational Research Institute,
OCTRI) for their assistance in carrying out the closed-loop
studies. We thank Michael Lasarev for his statistical expertise,
and the journal reviewers. The NIH provided support for
OCTRI (grant UL1 RR024140) and for JRC’s salary (grant
T32 DK 007674).
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Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Figure S1. Simulations of estimated lag times during conditions
in which sensor accuracy is impaired by calibration errors.
Please note: Wiley-Blackwell are not responsible for the content
or functionality of any supporting materials supplied by the
authors. Any queries (other than for missing material) should
be directed to the corresponding author for the article.
ª 2011 The Authors.
Diabetic Medicine ª 2011 Diabetes UK
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DIABETICMedicineOriginal article
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