DISCUSSION
Our study which utilizes the CGM sensor data to analyze GV and GC
parameters, is the first to be conducted in Singaporean pregnant women
who are relatively at higher risk for GDM, compared to women of Western
ethnicity 23. In the second trimester of pregnancy, we
find that MAGE was significantly higher in the group of women who
subsequently developed GDM, compared to those who did not. Our findings
contribute to the much-needed evidence examining the associations
between GV and GC parameters early in pregnancy, and the subsequent
development of GDM in pregnant women.
Only three studies 11, 13, 16 thus far have included
MAGE from CGM data as a GV parameter when examining GDM as an outcome.
Our findings concur with the cross-sectional study by Su et al.13, which reported significantly higher levels of MAGE
in pregnant women who were diagnosed with GDM at an average of 25 weeks
of gestational age, compared to pregnant women who did not develop GDM,
and non-pregnant healthy women with normal glucose regulation.
Similarly, Dalfra et al . 16 reported an overall
trend of slightly higher MAGE levels across the first, second and third
trimester of pregnancy in women who developed GDM, compared to healthy
control, although the significance was merely borderline. However, when
MAGE levels were assessed independently in different trimesters of
pregnancy in another study by Dalfa et al., the levels of MAGE
were reported to be lower, but higher in the second and third trimester
respectively, in participants with GDM outcomes compared to the healthy
controls 11. Still, they only used CGM data after a
short wear-time of 2 days which might not be sufficient to optimally
asses glycaemic control 20, and a GDM diagnosis across
a wide timepoint with an average of 21 weeks of gestational age with an
SD of 6.3 weeks which might result in misclassifications1, possibly explaining the discrepancies in study
findings.
MAGE was the first diabetes-specific GV metric to be developed primarily
to capture mealtime-related and intra-day glucose excursions, and has
been considered a gold standard for assessing GV. It had been associated
with increased insulin resistance, and early-phase insulin secretion
deterioration 13, which is characteristic of the
pathophysiology of GDM and Type 2 Diabetes development24. Furthermore, higher MAGE levels which represents
greater glucose fluctuations had been associated with adverse maternal
and neonatal outcomes attributed by GDM, including large for gestational
age, small for gestational age, higher birth weight, neonatal
hypoglycaemia 25.
The differences in MAGE levels in the group who developed GDM compared
to the group who did not was only significantly different in the second
trimester, but not the first. This observation can be explained by the
physiological changes in the action and secretion of insulin, where
serum insulin levels progressively increase from the first to the third
trimester of pregnancy, signifying an increase in insulin resistance as
the pregnancy advances 26. This can then be correlated
to higher glucose variability exacerbated by glycaemic instability in
the later trimesters of pregnancy as seen in a study by Dalfra et
al. The authors of this study reported an overall increasing trend of
GV parameters (MAGE, SDBG, CONGA continuous overlapping net glycaemic
action, interquartile range (IQR)) from the first to the third trimester
of pregnancy in both healthy pregnant women, and in pregnant women with
GDM 16. Our findings coupled with the available
evidence from literature allude to inherent differences in MAGE levels
in pregnant women diagnosed with GDM, compared to those without GDM
which is likely to be more pronounced starting from the second trimester
of pregnancy.
In our study, MBG, SDBG, CV, J-Index, %TIR, %TAR and %TBR were not
significantly associated with GDM outcomes. However, across existing
literature these CGM-derived parameters had not been shown to be
consistently associated with GDM outcomes. Three studies reported MBG to
be significantly higher in the group of women who developed GDM13, 14, 16, one study reported significant
associations only the second trimester but not the third trimester of
pregnancy, 11 while another three reported null
associations 12, 15, 17. Amongst these studies, only
four analyzed SDBG 11, 13, 16, 17, and only two found
significant associations with GDM outcomes 13, 16, one
study reported higher SDBG in the GDM group only in the third trimester,
but lower SDBG in the second trimester compared to the controls11. So far, only one other study has analyzed CV,
J-Index and time in glucose ranges (%TIR, %TBAR and %TBR) and
reported null associations with GDM outcomes 17.
The strengths in our study lie in its’ prospective design, which enables
us to assess the temporal sequence of the GV and GC parameters measured
by the CGM in the first and second trimester of pregnancy on subsequent
GDM development. Unlike other studies which have explored similar
associations, our study has analyzed CGM data from an average of 14 days
wear-time to acquire a more accurate and meaningful interpretation of
the glucose data. Furthermore, our GDM diagnosis uses the IADPSG
criteria adopted by the World Health Organization, while other studies
in literature have used different diagnostic criteria for GDM12, 14, 16, 17, and thus different glucose thresholds
in a two- or three-time-point antenatal OGTT for diagnosis. All in all,
our findings corroborate with other studies in terms of the
applicability of CGM use in detecting GV and GC parameters during
pregnancy.
A few limitations were noted in this study. Firstly, our small sample
size limits the generalizability of its findings, and secondly, this
observational study cannot establish a causal relationship between MAGE
and the increased risk for the development of GDM.