OBSTETRICS
Fasting Capillary Glucose as a Screening Test
for Ruling Out Gestational Diabetes Mellitus
Valerie Anderson,1 Chang Ye, MSc,1 Mathew Sermer, MD,2 Philip W. Connelly, PhD,3,4
Anthony J. G. Hanley, PhD,1,3,5 Bernard Zinman, CM MD,1,3,6 Ravi Retnakaran, MD1,3,6
1
Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto ON
2
Division of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto ON
3
Division of Endocrinology, University of Toronto, Toronto ON
4
Keenan Research Centre in the Li Ka Shing Knowledge Institute of St . Michael’s Hospital, Toronto ON
5
Department of Nutritional Sciences, University of Toronto, Toronto ON
6
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto ON
Abstract
Résumé
Objective: A common approach to screening for gestational diabetes
mellitus (GDM) is the testing of all pregnant women with a one-hour,
50 g glucose challenge test (GCT), followed by a diagnostic oral
glucose tolerance test (OGTT) when the GCT is positive
(≥ 7 .8 mmol/L) . As only a small subset of those with a positive GCT
will have GDM, many more women undergo the OGTT than may be
necessary . In this context, we hypothesized that measurement of
fasting capillary glucose (FCG) could provide a strategy for reducing
the number of unnecessary OGTTs . Thus, we sought to identify a
threshold level of FCG below which GDM could be ruled out following
a positive GCT, without need for the OGTT .
Objectif : Une approche courante pour le dépistage du diabète
sucré gestationnel (DSG) consiste à soumettre toutes les
femmes enceintes à une épreuve de charge en glucose (ECG,
soit la glycémie une heure après l’ingestion de 50 g de glucose),
suivie de la tenue d’une épreuve d’hyperglycémie provoquée par
voie orale (EHPVO) diagnostique lorsque l’ECG s’avère positive
(≥ 7,8 mmol/l) . Puisque seul un faible sous-ensemble des
femmes ayant obtenu des résultats positifs à l’ECG présenteront
un DSG, un grand nombre de femmes sont donc inutilement
soumises à une EHPVO . Dans ce contexte, nous avons émis
l’hypothèse selon laquelle la mesure de la glycémie capillaire
à jeun (GCJ) pourrait fournir une stratégie qui permettrait de
réduire le nombre d’EHPVO menées inutilement . Ainsi, nous
avons cherché à identifier un seuil de GCJ en deçà duquel la
présence possible d’un DSG pourrait être écartée à la suite
de l’obtention de résultats positifs à l’ECG, sans devoir avoir
recours à l’EHPVO .
Methods: Following a positive GCT, 888 women underwent
measurement of FCG prior to their OGTT . We evaluated the test
characteristics of FCG for identifying the 209 women diagnosed with
GDM on the OGTT .
Results: Fasting capillary glucose was positively associated with each
glucose measurement on the OGTT (all P < 0 .001) and inversely
related to insulin sensitivity and pancreatic beta-cell function (both
P < 0 .001) . As FCG increased, the prevalence of GDM progressively
rose (P < 0 .001) . However, the area under the curve of the receiveroperating characteristic curve for FCG in predicting GDM was
modest (0 .67) . Although using an FCG threshold of 4 .8 mmol/L could
reduce the number of OGTTs by 28 .4%, this approach would miss
18 .2% of cases of GDM .
Conclusion: Fasting capillary glucose is associated with glycemia,
insulin sensitivity, and pancreatic beta-cell function . However, a
single FCG measurement is insufficient for reliably ruling out GDM
after an abnormal GCT .
J Obstet Gynaecol Can 2013;35(6):515–522
Key Words: Fasting capillary glucose, gestational diabetes,
screening, glucometer
Competing Interests: None declared .
Received on November 29, 2012
Accepted on February 15, 2013
Méthodes : Après avoir obtenu des résultats positifs à l’ECG,
888 femmes ont été soumises à la mesure de la GCJ avant la
tenue d’une EHPVO . Nous avons évalué la valeur prévisionnelle
de la GCJ pour ce qui est de l’identification des 209 femmes ayant
obtenu un diagnostic de DSG à la suite de l’EHPVO .
Résultats : La glycémie capillaire à jeun a été positivement associée
à chacune des mesures de la glycémie obtenues au moyen
de l’EHPVO (toutes P < 0,001) et s’est avérée inversement
proportionnelle à l’insulinosensibilité et à la fonction des cellules
β pancréatiques (toutes deux P < 0,001) . La GCJ s’est avérée
directement proportionnelle à la prévalence du DSG (P < 0,001) .
Toutefois, la surface sous la courbe de la fonction d’efficacité du
récepteur pour ce qui est de la valeur prévisionnelle de la GCJ en
matière de DSG était modeste (0,67) . Bien que l’utilisation d’un
seuil de GCJ de 4,8 mmol/l puisse réduire le nombre de EHPVO
de 28,4 %, 18,2 % des cas de DSG passeraient alors inaperçus .
Conclusion : La glycémie capillaire à jeun est associée à la
glycémie, à l’insulinosensibilité et à la fonction des cellules β
pancréatiques . Toutefois, une seule mesure de la GCJ ne s’avère
pas suffisante pour écarter de façon fiable la présence possible du
DSG à la suite de l’obtention de résultats anormaux à l’ECG .
JUNE JOGC JUIN 2013 l 515
OBSTETRICS
INTRODUCTION
S
creening for gestational diabetes mellitus has become a
standard element of obstetrical practice because of the
adverse maternal and neonatal outcomes associated with
exposure to maternal hyperglycemia.1,2 These outcomes
include increased rates of macrosomia, birth trauma,
shoulder dystocia, and Caesarean section.1–4 Although
protocols for GDM screening vary by jurisdiction, a
common approach is the universal testing of all pregnant
women in the late second trimester with a one-hour 50 g
glucose challenge test, followed by referral for a diagnostic
oral glucose tolerance test in those in whom the GCT is
positive. In the general population, however, the GCT has
been shown to have a false-positive rate as high as 83%.5
Thus, many more pregnant women undergo the diagnostic
OGTT than may be necessary. As the test is costly, timeconsuming, and unpleasant for many women, strategies
directed towards refining the screening process to reduce
the number of unnecessary OGTTs could hold significant
clinical implications.
Measurement of fasting capillary glucose, using a pointof-care hand-held glucometer, might be one such strategy.
The advantages of capillary glucose measurement by
glucometer include the immediacy of results, the ease of
use, and the minimal invasiveness compared with venous
blood draw. Of note, there has been limited previous
evaluation of FCG as a screening test for GDM and only
in specific ethnic populations at very high6 or very low7 risk
of GDM. In this context, we hypothesized that for GDM
screening in the general obstetric population a possible
strategy would be to use FCG to reduce the number of
unnecessary OGTTs following initial GCT screening.
Thus, our objective in this study was to identify in women
ABBREVIATIONS
AUC
area under the curve
FCG
fasting capillary glucose
GCT
glucose challenge test
GDM
gestational diabetes mellitus
IADPSG
International Association of Diabetes and Pregnancy
Study Groups
HOMA-IR
Homeostasis Model Assessment of Insulin Resistance
1/HOMA-IR
inverse of the HOMA-IR
IGI/HOMA-IR insulinogenic index divided by HOMA-IR
ISSI-2
Insulin Secretion-Sensitivity Index-2
NDDG
National Diabetes Data Group
NGT
normal glucose tolerance
OGTT
oral glucose tolerance test
ROC
receiver-operating-characteristic
516 l JUNE JOGC JUIN 2013
with a positive GCT a threshold level of FCG below which
GDM could be ruled out without need for the OGTT.
METHODS
This analysis was conducted as part of an ongoing
observational study in which a cohort of women
recruited at the time of antepartum screening for GDM is
undergoing metabolic characterization in pregnancy. The
study protocol has been described previously.8,9 In brief,
the standard obstetrical practice at our institution is for all
pregnant women to undergo screening for GDM at 24 to
28 weeks’ gestation. This screening consists of a one-hour
50 g GCT, followed by a diagnostic OGTT if the result of
the GCT is abnormal (defined as a plasma glucose level
≥ 7.8 mmol/L at one hour after ingestion of the glucose
load). In this study, women are recruited either before or
after the GCT, and all participants undergo a three-hour
100 g OGTT regardless of the GCT result. All participants
provide written informed consent. The current analysis
was restricted to women with a singleton pregnancy and a
GCT ≥ 7.8 mmol/L (n = 888).
On the morning of their OGTT, participants came to the
hospital laboratory after an overnight fast. They completed
interviewer-administered questionnaires that provided data
pertaining to demographic information (age, ethnicity),
medical history (medications, comorbidities), reproductive
and obstetrical history (parity, previous GDM), the current
pregnancy (complications, illnesses, medications), and
family history. Anthropometric measurements of height
(to nearest 0.5 cm) and weight (to nearest 0.1 kg) were
obtained using a medical scale. Fasting capillary glucose
was measured by Lifescan SureStepFlexx glucose meter
(LifeScan Canada Ltd, Burnaby BC).
For the three-hour 100 g OGTT, venous blood samples
were drawn for measurement of plasma glucose and
insulin at baseline (fasting) and at 30, 60, 120, and 180
minutes after ingestion of the 100 g glucose load. Specific
insulin was measured using the Roche Modular system
and the electrochemiluminescence immunoassay kit
(Roche, Montreal, QC). The plasma glucose and insulin
measurements on the OGTT enabled assessment of glucose
tolerance status and the pathophysiologic determinants of
diabetes: insulin sensitivity and pancreatic beta-cell function.
As previously described,8 glucose tolerance status on the
OGTT was classified as follows: GDM, defined by two or
more glucose values above National Diabetes Data Group
criteria10; gestational impaired glucose tolerance, defined
as only one glucose value exceeding NDDG criteria; or
normal glucose tolerance. Insulin sensitivity was assessed
Fasting Capillary Glucose as a Screening Test for Ruling Out Gestational Diabetes Mellitus
by the Matsuda index, a validated measure of insulin action
that can be obtained from the OGTT.11 The inverse of the
Homeostasis Model Assessment of Insulin Resistance was
determined as a secondary measure of insulin sensitivity.12
Pancreatic beta-cell function was assessed by the Insulin
Secretion-Sensitivity Index-2, a validated measure that can
be obtained from the OGTT.13,14 The insulinogenic index
divided by HOMA-IR was determined as a secondary
measure of beta-cell function.15
All analyses were conducted with SAS 9.2 software (SAS
Institute, Cary, NC). The study population was stratified
into four groups based on the FCG. For each group,
we identified the median and interquartile range of
continuous variables and the proportions of categorical
variables. Continuous variables were compared across
the groups by Kruskal-Wallis test. Categorical variables
were compared by chi-square test or Fisher exact test.
Spearman correlation analysis was performed to evaluate
the associations of FCG with measures of metabolic
function. The test characteristics of FCG for prediction
of GDM were determined by calculating the sensitivity,
specificity, positive predictive value, negative predictive
value, positive likelihood ratio, and negative likelihood
ratio associated with different thresholds of FCG. In
addition, we determined the proportion of OGTTs that
could be averted by not performing the test in women
whose FCG was below each of these thresholds. Receiveroperating-characteristic analysis was performed to assess
the discriminative capacity of FCG for predicting GDM.
The Mount Sinai Hospital Research Ethics Board approved
the study protocol.
RESULTS
Relationship Between Fasting Capillary
Glucose and Metabolic Function
The study population consisted of 888 pregnant women
with an abnormal GCT, of whom 209 had GDM on the
OGTT. The characteristics of the study population were
stratified into the following four groups: FCG ≤ 4.5 mmol/L
(n = 132), FCG between 4.6 and 5.0 mmol/L inclusive
(n = 309), FCG between 5.1 and 5.5 mmol/L inclusive
(n = 237), and FCG ≥ 5.6 mmol/L (n = 210) (Table 1).
The GDM clinical risk factors of age, pre-pregnancy BMI,
and prevalence of family history of diabetes progressively
increased from the lowest to the highest FCG group
(P = 0.008, P < 0.001, and P = 0.032, respectively).
Gestational age at the time of the OGTT was slightly higher
in women with FCG between 4.6 and 5.0 mmol/L inclusive
(P = 0.049). Otherwise, there were no significant differences
between the groups with respect to ethnicity, parity, previous
GDM, and weight gain in pregnancy to the time of the
OGTT.
Importantly, the four groups displayed clear metabolic
differences with respect to insulin sensitivity, pancreatic
beta-cell function, and glucose tolerance. Notably, both
insulin sensitivity (Matsuda index and 1/HOMA-IR,
both P < 0.001) and beta-cell function (ISSI-2 and
IGI/HOMA-IR, both P < 0.001) progressively decreased
across the groups as FCG increased. Furthermore, each of
the plasma glucose values on the OGTT (fasting, 1 hour, 2
hour, 3 hour) showed a continuous increase across the four
groups (all P < 0.001). Most importantly, the FCG groups
differed markedly with respect to glucose tolerance status
on the OGTT (P < 0.001). Indeed, as FCG rose across the
groups, the prevalence of GDM increased (from 12.9% to
16.2% to 22.4% to 42.4%), while the prevalence of NGT
decreased (from 69.7% to 67.3% to 56.1% to 36.7%).
On Spearman correlation analysis, FCG was positively
associated with fasting venous glucose (r = 0.78, P < 0.001)
and with each post-challenge glucose value on the OGTT
(1 hour glucose: r = 0.39, P < 0.001; 2 hour glucose r = 0.24,
P < 0. 001; 3 hour glucose: r = 0.14, P < 0.001). Moreover,
FCG was inversely related to both insulin sensitivity
(Matsuda index: r = −0.44, P < 0.001; 1/HOMA-IR:
r = −0.51, P < 0.001) and beta-cell function (ISSI-2: r = −0.56,
P < 0.001; IGI/HOMA-IR: r = −0.55, P < 0.001).
Fasting Capillary Glucose and Screening for GDM
Having demonstrated that FCG is associated with
metabolic function, we next sought to characterize its
potential as a screening test for ruling out GDM. The
distribution of FCG in the women who had GDM on
the OGTT (n = 209) and those who did not have GDM
(n = 679) is shown in Figure 1. Of note, there was
considerable overlap in the distribution of FCG between
these two patient populations, suggesting that there was no
clear threshold level of FCG that can be used to rule out
GDM. Indeed, even at a threshold as low as 4.5 mmol/L,
there were still 13 women with FCG at or below this
level who had GDM on the OGTT. To formally evaluate
the potential capacity of FCG for ruling out GDM, we
determined its test characteristics in this regard. Different
thresholds of FCG for predicting GDM and their
associated sensitivity, specificity, positive predictive value,
negative predictive value, positive likelihood ratio, negative
likelihood ratio, and the percentage of OGTTs that
could be averted if they were not performed in women
whose FCG was below this level are shown in Table 2.
For example, an FCG threshold of 4.8 mmol/L could
eliminate the need for an OGTT in 28.4% of women
JUNE JOGC JUIN 2013 l 517
518 l JUNE JOGC JUIN 2013
9 .8
8 .4
GCT, mmol/L
0 .9
1/HOMA-IR
14 .7
IGI/HOMA-IR
(4 .9–7 .5)
7 .8
6 .1
2-hour glucose, mmo/L
12 .9
GDM
16 .2
16 .5
67 .3
6 .7
8 .0
9 .2
4 .4
11 .5
767 .9
0 .7
4 .8
8 .4
10
22 .7
4 .2
9 .4
33 .7
57
IQR: interquartile range; DM: diabetes mellitus; GIGT: gestational impaired glucose tolerance .
69 .7
17 .4
NGT
GIGT
Glucose tolerance status, %
3-hour glucose, mmol/L
(6 .4–8 .9)
8 .4
1-hour glucose, mmol/L
(7 .4–9 .6)
4 .1
(4 .0–4 .3)
(11 .2–21 .5)
(768 .8–1099)
(0 .6–1 .5)
(3 .7–9 .6)
(8 .1–9 .0)
(7 .0–13 .6)
Fasting glucose, mmol/L
OGTT
915 .3
ISSI-2
Beta-cell function
6 .2
Matsuda Index
Insulin sensitivity
22 .2
Pre-pregnancy BMI (kg/m )
Weight gain in pregnancy, kg
10 .6
>1
2 .3
34 .9
1
Previous GDM, %
54 .6
0
Parity, %
56 .6
15 .2
13 .6
Family history of DM, %
51 .5
13 .6
Other
14 .9
72 .7
69 .9
29
34
(5 .6–7 .6)
(7 .1–9 .0)
(8 .2–10 .2)
(4 .2–4 .5)
(8 .6–16 .1)
(655 .5–915 .7)
(0 .5–0 .9)
(3 .3–6 .3)
(8 .0–9 .1)
(7 .7–13 .0)
(21 .0–25 .7)
(28–31)
(31–37)
IQR
4 .6 ≤ FCG ≤ 5 .0 mmol/L
n = 309
Asian
(20 .2–25 .4)
(28–30)
(30–35)
IQR
White
2
29
Mean weeks’ gestation
Ethnicity, %
33
Mean age, years
n = 132
FCG ≤ 4 .5 mmol/L
Table 1. Characteristics of study population stratified by fasting capillary glucose level
42 .4
22 .4
36 .7
7 .1
8 .8
10 .5
5 .1
5 .2
524 .2
0 .3
2 .8
8 .8
10
26
8 .1
13 .8
36 .7
49 .5
66 .2
20 .5
14 .3
65 .2
29
34
21 .0
(5 .4–7 .7)
(7 .2–9 .4)
(8 .5–10 .7)
(4 .5–4 .8)
(5 .8–12 .9)
(555 .5–816 .9)
(0 .3–0 .7)
(2 .7–5 .2)
(8 .1–9 .1)
(6 .8–13 .6)
(21 .5–28 .8)
(28–30)
(31–37)
(6 .1–8 .1)
(7 .8–9 .9)
(9 .3–11 .7)
(4 .9–5 .4)
(3 .2–7 .5)
(424 .9–619 .3)
(0 .2–0 .5)
(2 .0–4 .2)
(8 .2–9 .4)
(6 .5–13 .0)
(22 .9–30 .5)
(28–30)
(31–38)
IQR
FCG > 5 .5 mmol/L
n = 210
21 .5
56 .1
6 .7
8 .3
9 .6
4 .6
8 .8
668 .6
0 .5
3 .8
8 .5
9 .5
23 .7
7 .2
14 .8
33 .8
51 .5
55 .7
17 .7
13 .1
69 .2
29
34
IQR
5 .1 ≤ FCG ≤ 5 .5 mmol/L
n = 237
P
<0 .001
<0 .001
<0 .001
<0 .001
<0 .001
<0 .001
<0 .001
<0 .001
<0 .001
0 .002
0 .501
<0 .001
0 .059
0 .428
0 .032
0 .662
0 .049
0 .008
OBSTETRICS
Fasting Capillary Glucose as a Screening Test for Ruling Out Gestational Diabetes Mellitus
Figure 1. Histogram showing the distribution of fasting capillary glucose in women who had GDM (n = 209) and
women who did not have GDM (n = 679)
with an abnormal GCT but would miss 18.2% of cases of
GDM. Similarly, a threshold of 4.6 mmol/L could reduce
the need for OGTTs in 14.9% of this patient population
but at a cost of still missing 8.1% of GDM cases.
Lastly, the ROC curve for FCG as a screening test for
GDM is shown in Figure 2. This curve shows no clear
threshold for optimizing test characteristics, consistent
with the modest AUC ROC of 0.67.
DISCUSSION
In this study, we have demonstrated that FCG in pregnancy
is associated with metabolic function as measured on the
OGTT. Specifically, FCG was positively associated with
each glucose measurement on the OGTT and inversely
related to insulin sensitivity and pancreatic beta-cell
function. Moreover, the prevalence of GDM progressively
increased as FCG increased within the study population.
However, despite these findings, its test characteristics for
predicting GDM were such that a single FCG measurement
after an abnormal GCT was insufficient for reliably ruling
out GDM in practice.
There has been limited previous study of FCG as a screening
test for GDM.6,7 In a low-risk population of 3616 Swedish
women, of whom only 55 had GDM, Fadl et al.7 reported
that FCG was a useful screening test for GDM. However,
the authors noted that the GCT offered better screening
test characteristics than did FCG.7 Agarwal et al.6 studied
a high-risk population of 1465 women in the United Arab
Emirates, of whom 196 had GDM. They reported that
FCG had an AUC ROC of 0.83 for the prediction of
GDM and could be used as a screening test to reduce the
number of OGTTs required for diagnosis of GDM.6 It
should be recognized, however, that the generalizability of
JUNE JOGC JUIN 2013 l 519
OBSTETRICS
Table 2. Test characteristics of different thresholds of FCG for prediction of GDM
FCG,
mmol/L
Sensitivity,
%
Specificity,
%
PPV,
%
NPV,
%
LR+
LR−
OGTTs
averted, %
≥ 4 .1
99 .0
2 .1
23 .7
87 .5
1 .01
0 .46
1 .8
≥ 4 .2
99 .0
4 .0
24 .1
93 .1
1 .03
0 .24
3 .3
≥ 4 .3
97 .6
6 .5
24 .3
89 .8
1 .04
0 .37
5 .5
≥ 4 .4
94 .3
9 .9
24 .4
84 .8
1 .05
0 .58
8 .9
≥ 4 .5
93 .8
13 .5
25 .0
87 .6
1 .08
0 .46
11 .8
≥ 4 .6
91 .9
16 .9
25 .4
87 .1
1 .11
0 .48
14 .9
≥ 4 .7
86 .6
24 .2
26 .0
85 .4
1 .14
0 .55
21 .6
≥ 4 .8
81 .8
31 .5
26 .9
84 .9
1 .19
0 .58
28 .4
≥ 4 .9
79 .0
39 .2
28 .5
85 .8
1 .30
0 .54
34 .9
≥ 5 .0
70 .3
50 .1
30 .2
84 .6
1 .41
0 .59
45 .3
≥ 5 .5
45 .5
79 .8
40 .9
82 .6
2 .25
0 .68
73 .9
≥ 6 .0
21 .1
95 .3
57 .9
79 .7
4 .47
0 .83
91 .4
≥ 6 .5
8 .6
99 .1
75 .0
77 .9
9 .75
0 .92
97 .3
PPV: positive predictive value; NPV: negative predictive value; LR+: positive likelihood ratio; LR-: negative likelihood ratio;
Percentage of OGTTs averted = (true negative + false negative) / total number of subjects .
Figure 2. Receiver operating characteristic curve for fasting capillary glucose as a screening test for
GDM (with FCG thresholds of 4.6 to 5.0 mmol/L inclusive labelled)
520 l JUNE JOGC JUIN 2013
Fasting Capillary Glucose as a Screening Test for Ruling Out Gestational Diabetes Mellitus
the findings of these two previous studies is limited by the
fact that their respective patient populations represented
extremes in the risk and prevalence of GDM.
In this context, our study extends this literature in three key
ways. First, unlike the previous studies, we have evaluated
FCG in relation to clinical risk factors and pathophysiologic
determinants of GDM. Specifically, we found that FCG is
associated with clinical risk factors for GDM such as prepregnancy BMI, family history of diabetes mellitus, and
maternal age. Furthermore, in showing its associations with
insulin resistance and beta-cell dysfunction, we demonstrate
that FCG is a marker of the pathophysiology that leads
to GDM. Second, we have studied the use of FCG in a
multi-ethnic Canadian population. This population may be
more representative of a typical intermediate-risk obstetric
population than the high- and low-risk ethnic groups studied
previously.6,7 For example, in a similar vein, previous studies
have suggested that the relative benefits of fasting plasma
glucose measurement (i.e., from venous blood sample) for
identifying GDM may differ between women from the
United Arab Emirates and those from Canada.16–18 Lastly,
we tested the measurement of FCG after initial screening
with the GCT. This approach enabled evaluation of FCG in
a specific clinical setting in which it has not been previously
studied, but one which may facilitate a reduction in the
number of OGTTs needed for GDM screening.
The rationale for evaluating the use of FCG in women who
have had an abnormal GCT relates to the pathophysiology
of GDM. GDM develops in women who have a chronic
defect in pancreatic beta-cell function such that they are
unable to secrete sufficient insulin to maintain euglycemia
in the setting of the severe insulin resistance of late
pregnancy.3 Evaluation of beta-cell function is generally
more reliable under stimulatory test conditions (i.e., such
as a glucose challenge), as opposed to static test conditions
such as a fasting measurement.13,15 Therefore, we reasoned
that FCG likely could not take the place of the GCT for
initial GDM screening. Indeed, this hypothesis is supported
by the mixed results of previous studies comparing fasting
plasma glucose with the GCT for GDM screening.19–21
Accordingly, we reasoned that it may be more appropriate
to use the FCG in conjunction with the GCT, rather than
supplanting the GCT. Specifically, it may be possible to use
FCG measurement as a means for reducing the number
of unnecessary OGTTs required after an abnormal GCT.
Using this strategy, however, yielded an AUC ROC of
only 0.67 for predicting GDM. Furthermore, although
the single FCG measurement could reduce the number
of OGTTs, it does so at the cost of missing a substantial
number of women with GDM. These data thus suggest
that a single FCG measurement is insufficient to rule out
GDM. Nevertheless, it should also be recognized that its
associations with glycemia, insulin resistance, and betacell dysfunction indicate that FCG still holds potential for
playing a role in GDM screening. For example, it may be
that multiple FCG measurements are required to reliably
rule out GDM after an abnormal GCT. In other words,
if the initial FCG is below a given threshold, then repeat
sampling at specified intervals (such as 3 days later) may
be warranted so that the OGTT is only triggered when
the FCG is consistently above a certain threshold. Such an
approach might improve the capacity of this test to reliably
rule out GDM. Thus, further study (ideally by randomized
controlled trial) is indicated to evaluate strategies for using
serial FCG measurements to reduce the number of OGTTs
required after an abnormal GCT.A potential limitation of
this study is that GDM was diagnosed using NDDG criteria
for the three-hour 100 g OGTT, since protocols for GDM
screening vary by jurisdiction. Recently, the International
Association of Diabetes and Pregnancy Study Groups
has proposed the use of lower glycemic thresholds for
diagnosing GDM by two-hour 75 g OGTT.1 It should be
recognized, however, that since the NDDG thresholds
are more stringent than the recent IADPSG criteria (and
hence identify more severe dysglycemia), the observed
insufficiency of the single FCG measurement to rule out
GDM is likely to apply to the IADPSG criteria as well. The
IADPSG has also recommended that all pregnant women
undergo an OGTT at 24 to 28 weeks’ gestation without
prior screening by GCT. Our findings relating FCG to
glycemia, insulin resistance, and beta-cell dysfunction are
notable in light of previous criticism that the IADPSG
criteria will lead to an increase in the number of OGTTs
performed.22,23 Specifically, our findings suggest that
strategies for using serial FCG measurements to reduce the
number of OGTTs required with the IADSPG approach
may warrant consideration.
CONCLUSION
Fasting capillary glucose showed significant associations with
metabolic parameters on the OGTT, including glycemia,
insulin resistance, and pancreatic beta-cell dysfunction. Even
though the findings of this study indicate that a single FCG
measurement could not be used to reliably rule out GDM,
its associations with the metabolic parameters described
suggest that FCG holds potential for use in GDM screening.
Indeed, strategies may exist whereby FCG could be used to
reduce the number of unnecessary OGTTs associated with
GDM screening. Further study of serial FCG measurement
during pregnancy is warranted to refine the screening
process for GDM.
JUNE JOGC JUIN 2013 l 521
OBSTETRICS
ACKNOWLEDGEMENTS
The authors wish to thank Mount Sinai Hospital
Department of Pathology and Laboratory Medicine
and Patient Care Services. This study was supported by
operating grants from the Canadian Institutes of Health
Research (CIHR) (MOP-84206) and the Canadian Diabetes
Association (CDA) (OG-3–08–2543-RR). Anthony J. G.
Hanley holds a Tier-II Canada Research Chair in Diabetes
Epidemiology. Bernard Zinman holds the Sam and
Judy Pencer Family Chair in Diabetes Research at Mount
Sinai Hospital and University of Toronto. Ravi Retnakaran
holds an Ontario Ministry of Research and Innovation
Early Researcher Award.
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