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Highlights
• CGM data from the 1st and 2nd trimesters was collected in pregnant women.
• Early CGM metrics provide valuable insights into glucose trends in pregnancy.
• Early CGM mean glucose predicts pregnancy outcomes better than OGTT.
• 1st trimester CGM mean glucose predicts large fetal size in top birth weight group.
INTRODUCTION
The oral glucose tolerance test (OGTT) is conventionally used for diagnosing maternal glucose metabolism issues disorders and predicting adverse neonatal outcomes, including increased higher birth weight [1-3], even in women without preexisting or gestational diabetes mellitus (GDM) [3]. However, the OGTT’s validity as the gold standard test has been increasingly questioned in recent years due to the lack of universally standardized procedural methods and diagnostic thresholds [4]. Another key limitation of the routine OGTT is its timing in mid-pregnancy [5], which may fail to detect earlier metabolic dysfunctions.
Studies have shown that 30% to 70% of women with GDM experience hyperglycemia at gestational ages (GAs) as early as <20 weeks of gestation [6,7]. Early fetal hyperinsulinemia preceding universal GDM screening has been associated with macrosomia [8], and early pregnancy hyperglycemia has been associated with an increased risk of neonates being large for gestational age (LGA) [9]. Additionally, maternal hyperglycemia exposure by 20 weeks of gestation has been associated with increased fetal adiposity [10], and growth velocity [11]. All of these studies, however, examined associations with higher infant birth weight exclusively in women with hyperglycemia or diagnosed GDM [8-11]. However, evidence suggests that any degree of glucose intolerance during pregnancy including elevated maternal blood glucose levels, and increased glycaemic variability may negatively impact pregnancy outcomes even in women without established diabetes or GDM [12,13]. Furthermore, the consequences of maternal glucose concentrations on fetal growth do not occur at definite thresholds but rather across a continuum [4]. This underscores the need for a reliable screening method in early pregnancy to identify women at risk of delivering infants with high birth weight even in the absence of overt diabetes.
A systematic review and meta-analysis have demonstrated using the continuous glucose monitoring (CGM) to track glycaemic variation and glycaemic control in pregnancy was associated with lower neonatal birth weight [6]. It provides average daily glucose values as well as a complete 24-hour glucose profile which offers a more robust understanding of glucose fluctuations throughout the day, including the percentage of time spent within the target glucose range (% time in range [TIR]) as well as periods spent above (% time above range [TAR]) and below the target (% time below range [TBR]) glucose range [7]. These values have been found to be useful for predicting adverse neonatal outcomes early in pregnancy [14]. The introduction of CGM into routine clinical practice to detect glycaemic parameters has greatly advanced care for mothers with diabetes mellitus in pregnancy [6], but little is known about their use in women with no pre-existing diabetes. To date, only one study has provided evidence linking early glycemic monitoring between 11 and 15 weeks of pregnancy to an increased risk of neonates being classified as LGA at birth, occurring prior to the diagnosis of GDM [15]. However, this study was limited to CGM data collected during a single trimester of pregnancy, and focused exclusively on a population of overweight and obese women at risk for metabolic dysregulation [15].
In this study, we aimed to evaluate the efficacy of CGM during pregnancy by examining the relationship between CGM-derived metrics in both the first and second trimesters with infant birth weight in a population of pregnant women with no pre-existing diabetes. Additionally, the study aims to compare these findings with the correlation between infant birth weight and glucose values obtained from the conventional mid-pregnancy three-point OGTT (fasting, 0-hour, and 2-hour). We hypothesize that the CGM-derived metrics from the first and second trimesters are superior to the conventional mid-pregnancy OGTT glucose values (fasting, 0-hour, and 2-hour) in predicting high infant birth weight.
METHODS
The study was approved by the Sing Health Centralised Institutional Review Board (reference number 2018/2128). All participants gave written informed consent in accordance with the Declaration of Helsinki (Clinical trial identification number: NCT05123248). We used data from the Integrating the Use of Calibration-Free Continuous Monitoring for Pregnancy Glucose Profiling (I-PROFILE) study which recruited 239 pregnant Singapore citizens or permanent residents in their first trimester of pregnancy between December 2018 and December 2022. Out of the 219 participants randomized in the study, 113 were included into this study and 106 were excluded. The current study sample of 113 participants was characterized by a higher proportion of Chinese individuals, a greater percentage of highly educated women, fewer cases of GDM diagnosed at 24 to 28 weeks, a higher proportion of participants randomized to the unblinded CGM group, and a lower average pre-pregnancy body mass index (ppBMI; P<0.05) (data not shown).
Full details of the I-PROFILE study were previously published [16]. This prospective, observational study was conducted in KK Women’s and Children’s Hospital (KKH) which is a major public hospital in Singapore. Inclusion criteria included women of Chinese, Malay, or Indian descent, aged 21 and above with singleton pregnancies. Individuals with serious skin conditions (e.g., eczema) that might interfere with compliance to the study, or those with pre-existing chronic diseases (e.g., kidney disease, type 1 diabetes mellitus [T1DM], or type 2 diabetes mellitus [T2DM]) were excluded from participation.
Continuous glucose monitoring
At the recruitment visit in the first trimester of pregnancy (9 to 13 weeks gestation), participants had a CGM sensor (FreeStyle Libre Pro, Abbott Diabetes Care, Alameda, CA, USA) inserted into the back of either the right or left upper arm on day 0, which was worn for up to 14 days. Glucose levels were recorded every 15 minutes. Participants were fitted with a new sensor at the second trimester clinic visit (18 to 23 weeks of gestation). Data from the CGM were extracted for all participants with a minimum wear-time of 7 out of 14 days (50% of data captured) and the following variables were calculated from CGM readings for each participant by an automated software EasyGV version 9.0.R2 (https://innovation.ox.ac.uk/licence-details/glycaemic-variability-calculator-easygv): mean amplitude glycemic excursion (MAGE), standard deviation (SD), mean glucose, and % of time spent in glucose target ranges. MAGE quantifies major swings of glycemia and excludes minor ones and is considered the gold standard for assessing intra-day glycaemic variability [14]. The % of time in target ranges were defined as: %TIR (61 to 140 mg/dL), %TAR (>140 mg/dL), and %TBR (61 mg/dL) [16,17].
Ascertainment of infant birth weight
Birth weight obtained from hospital records were reported as birth weight tertile categories with higher tertile groups corresponding to increasing birth weight ranges.
Covariates
GA was determined by a dating ultrasound scan performed after 8 weeks gestation. Participants were followed up at the recruitment visit in the first trimester of pregnancy (9 to 13 weeks) and at 18 to 23 weeks gestation in the second trimester of pregnancy. Questionnaires were administered for information on demographics, socio-economic status, lifestyle, obstetric and medical history. Pre-pregnancy weight was self-reported while height in early pregnancy was measured in the antenatal care clinic at KKH using the Avamech B1000-M. A ppBMI (kg/m2) was calculated as pre-pregnancy weight (kg) divided by height2 (m2). For assessing GDM, any glucose value in the 75 g OGTT above the threshold; fasting plasma glucose (FPG) ≥92 mg/dL, 1-hour post‐load plasma glucose ≥180 mg/dL, or 2-hour postload plasma glucose ≥153 mg/dL was considered a positive result [5].
Statistical analysis
Only complete case and outcome analyses (participants without missing CGM-derived metrics and OGTT glucose values, confounders and outcomes, n=113) were used for analysis (Fig. 1). Univariate analyses were conducted to describe and compare the demographic factors, anthropometric measurements, medical history, CGM wear and OGTT readings between birth weight tertile groups using the one-way analysis of variance (ANOVA) or the Kruskal–Wallis test for continuous variables, and the chi-square and Fisher’s exact tests were used for categorical variables. Descriptive statistics for numerical variables were presented as mean±SD, or median (interquartile range [IQR]) and number (%) for categorical variables.
All CGM-derived metrics and OGTT glucose values were standardized before performing the multinomial logistic regression. Multinomial logistic regression was performed to estimate the odds ratios (95% confidence intervals [CIs]) of infants being categorized in the higher birth weight tertile groups (2nd or 3rd tertiles) compared to the lowest tertile (1st tertile) group, per-1 SD change in each of the CGM-derived metrics (mean±SD, MAGE, %TIR, %TAR, and %TBR) and OGTT glucose values at 3 timepoints (fasting, 1-hour, and 2-hour glucose). The model was adjusted for parity, ethnicity, maternal education level, age, ppBMI, GA at delivery, GDM diagnosis, and CGM sensor type (blinded or unblinded) [16]. Missing values for the covariate maternal ppBMI (n=4 missing data) were imputed by chained equations on 20 imputed datasets which included all exposures, outcomes, and covariates as predictors, and the results of the 20 datasets and pooled analyses were presented.
Statistical significance was determined at P<0.05 when reporting demographic data and univariate analysis. Statistical analyses were performed using STATA software version 18.0 (StataCorp LP, College Station, TX, USA).
RESULTS
Among the 239 recruited study participants, 66 did not complete the study. Of the remaining 173 participants who completed the study a further 60 were excluded due to insufficient CGM data collected or missing OGTT data. Overall, 113 participants were included in the final analysis which examined the difference between CGM-derived metrics in the first trimester and second trimester, and mid-pregnancy OGTT glucose values (fasting, 1-hour, and 2-hour) on infant birth weight (Fig. 1). Amongst the 173 participants who complete the study, baseline characteristics of excluded participants did not differ from those who were included (Supplementary Table 1).
Characteristics of study participants by birth weight tertiles
There were no significant differences in maternal characteristics across birth weight tertiles, except for GA at delivery and infant birth weight. Women in the highest infant birth weight tertile (3rd) had the greatest GA and infant birth weight at delivery (P<0.001) (Table 1).
CGM-derived metrics in the first trimester by birth weight tertiles
In the first trimester, higher mean glucose was associated with the highest tertile (3rd) birth weight compared to the lowest (1st) (mean glucose levels were 80±8.8 mg/dL vs. 76±9.0 mg/dL, P<0.05 with P=0.03 across tertiles). Higher %TIR was associated with the highest (3rd tertile) birth weight compared to the lowest (1st) (median %TIR: 86.2 [IQR, 70.5 to 93.6] vs. 78.4 [IQR, 58.0 to 87.1], P<0.05, with a trend of P=0.08 across tertiles). Lower %TBR was significantly associated with the highest (3rd tertile) birth weight tertile compared to the lowest (1st) (median %TBR: 12.6 [IQR, 4.7 to 29.4] vs. 21.5 [IQR, 11.3 to 42.2], P<0.05), and also compared to the 2nd tertile (12.6 [IQR, 4.7 to 29.4] vs. 19.6 [IQR, 6.4 to 39.7], P<0.05), with a trend of P=0.06 across tertiles (Table 2).
CGM-derived metrics in the second trimester by birth weight tertiles
In the second trimester, higher %TIR was associated with the highest (3rd) birth weight tertile compared to the lowest (1st) (median glucose levels: 89.0% [IQR, 77.6% to 94.2%] vs. 81.7% [IQR, 68.7% to 91.5%], P<0.05) and the second (2nd) (89.0% [IQR, 77.6% to 94.2%] vs. 82.9% [IQR, 61.4% to 92.2%], P< 0.05) with a trend of P=0.07 across tertiles. Lower %TAR was associated with higher birth weight tertiles in the 2nd tertile compared to the lowest (1st) tertile (0.2% [IQR, 0.0% to 0.9%] vs. 0.6% [IQR, 0.2% to 1.6%], P<0.05). Lower %TBR was significantly associated with the highest birth weight tertile (3rd) compared to the 2nd tertile (median glucose levels: 7.9% [IQR, 4.5% to 17.3%] vs. 15.5% [IQR, 6.5% to 33.7%], P<0.05) (Table 2).
OGTT fasting, 1-hour, and 2-hour glucose values between 24 and 38 weeks by birth weight tertile
There were no significant differences in OGTT parameters either across or between birth weight tertiles (P>0.05) (Table 2).
Association of CGM-derived metrics and OGTT fasting, 1-hour, and 2-hour glucose values with infant birth weight tertiles
After adjusting for confounders, there were no significant associations (P>0.05), apart from mean glucose. Specifically, a 1-SD increase in mean glucose during the first trimester was associated with a higher likelihood of being categorized in the highest birth weight tertile (adjusted odds ratio [AOR], 3.11; 95% CI, 1.18 to 8.21; P=0.022) (Table 3).
DISCUSSION
Our study is the first to show that, in women without pre-existing diabetes, first trimester mean glucose levels measured by CGM independently predict infant birth weight tertiles. The observation that a 1-SD increase in first trimester mean glucose associates with higher odds of highest birth weight tertile (AOR, 3.11; 95% CI, 1.18 to 8.21) suggests a critical early window for intervention, with important implications for clinical practice. No significant associations were found with other CGM-derived metrics or mid-pregnancy glucose values from the OGTT.
Previous studies examining CGM-derived mean glucose values in pregnant women with different types of diabetes (preexisting T1DM or T2DM [18], T1DM only [18-20], and GDM only [21]) have reported higher CGM-derived mean glucose values to be associated with the increased odds of LGA neonates. In contrast, a study conducted in a population specific to overweight/obese women without pre-existing T1DM or T2DM did not find significant associations with CGM mean glucose measured in the first trimester of pregnancy [15]. Interestingly, the only study investigating a patient population similar to ours—non-diabetic women—found that higher maternal early pregnancy non-fasting glucose levels, measured from blood samples, were associated with increased fetal growth rates from late pregnancy onward, culminating in larger birth size [22]. Although, the limitations in these studies were that Liang et al. [21] and Feig et al. [19] measured CGM-derived metrics only in the late second or early third trimester, while Rademaker et al. [20] studied insulin-treated diabetes, and Geurtsen et al. [22] measured glucose levels in blood samples. In contrast, our study demonstrates these associations as early as the first trimester in women without pre-existing T1DM or T2DM. Our findings are most in keeping with those of Scott et al. [18], who demonstrated that lower average glucose levels in the first trimester were associated with normal birth weight, albeit in a cohort of pregnant women with T1DM. Thus, this study introduces novel insights by emphasizing the importance of optimizing maternal glycemia early in pregnancy, even in women without pre-existing diabetes.
The mechanism underlying why glucose levels in the first trimester play a more critical role in establishing the foundation for fetal growth, compared to mid-trimester glucose tolerance measured by OGTT, can be attributed to the negative impact of impaired glucose control on placental development. Specifically, disrupted glucose regulation in early pregnancy can impair early placentation, leading to placental insufficiency and subsequent effects on fetal growth, by stimulating increased fetal adiposity and growth [23]. While the mid-trimester OGTT provides a snapshot of glucose tolerance, first trimester mean glucose values from CGM offer a more comprehensive measure of glucose exposure during this critical developmental window [22]. Furthermore, CGM-derived mean glucose in early pregnancy may provide a more sensitive indicator of subtle glucose variations and chronic hyperglycemia [7,14] than the OGTT, which focuses on post-load glucose levels and can miss day-to-day fluctuations.
Compared to conventional measures like glycosylated hemoglobin (HbA1c) [24] and glycated albumin [25], mean glucose values derived from CGM are aggregating data from frequent, real-time glucose measurements obtained at regular intervals (every 15 minutes). This method provides a more precise representation of overall glucose trends and variability by consolidating a large number of data points over a given time period, allowing for the detection of short-term fluctuations, glycemic excursions, and time spent within or outside target glucose ranges [7]. In contrast, HbA1c [24] and glycated albumin [25] provide retrospective averages of glycemic control, but do not offer real-time insights into glucose levels or fluctuations. Furthermore, HbA1c can underestimate maternal glycemia during pregnancy [24].
While prior studies have consistently reported the association of higher mean glucose with increased risk for higher neonatal birth weight or LGA, the association between other CGM-derived metrics varies. Despite a small sample size, we observed associations between CGM metrics (%TIR and %TBR) and increased infant birth weight. Our univariate analysis showed that higher %TIR and lower %TBR in the first and second trimesters were consistently associated with the highest infant birth weight tertiles.
However, these associations were no longer significant after adjusting for confounders in the multivariate model. In contrast our observations, specifically with %TIR, a study involving 386 pregnant women with T1DM only from two international multicenter trials [18], along with 186 women with T1DM only from a separate single-center study [26], reported that only a lower %TIR was associated with risk of LGA. Additionally, in a study of 117 pregnant women with both T1DM and T2DM, lower %TIR and higher %TAR was associated with the risk of LGA [27]. Our findings however do align with a few previous studies in pregnant women with GDM which demonstrated that both lower %TBR [21,26], while higher %TIR was associated with the risk of LGA [21]. Additionally, another study involving 1,302 pregnant women with GDM, all three CGM glycemic control metrics—%TIR, %TAR, and %TBR—were found to be associated with LGA [21]. This study reported that %TIR and %TAR was positively associated, while %TBR was inversely associated with the risk of LGA [21]. Overall, comparisons to previous studies are challenging due to differences in study populations, often involving women with GDM or pre-existing T1DM or T2DM.
Our study did not find any significant associations with other CGM metrics representing glycaemic variability such as MAGE and SD. In the literature, the relationship between glycemic variability and neonatal outcomes remains unclear. While several studies have examined blood glucose fluctuations in pregnant women and their relationship to adverse neonatal outcomes, such as LGA infants or high birth weight, the findings have not been consistent. Moreover, most of these studies have been limited to women with GDM [21,28,29]. Panyakat et al. [28] found no statistically significant associations between CGM-derived glycemic variability metrics, including SD and MAGE with birth weight percentiles. Yu et al. [29] reported that MAGE during the first week of pregnancy was an independent risk factor for adverse neonatal outcomes, including LGA. Furthermore, by the fifth week, a strong correlation between MAGE, birth weight, and birth weight percentiles was identified [29]. Liang et al. [21] highlighted the significance of CGM-derived glycemic variability metrics, particularly MAGE, in relation to neonatal birth weight. The studies mentioned above show inconsistent views on the impact of CGM-derived glycemic variability markers on the occurrence of adverse neonatal outcomes in women with GDM.
In our study, the OGTT glucose values (fasting, 1-hour, and 2-hour), showed no association with infant birth weight, even in univariate analysis. A study on Hyperglycemia and Pregnancy Outcomes (HAPO) demonstrated a strong correlation between elevated FPG levels, as well as 1-hour and 2-hour OGTT results during weeks 24–32 with LGA infants [11,30]. The null associations between OGTT glucose values (fasting, 1-hour, and 2-hour) and birth weight in our study may be attributed to the overall healthy birth weight distribution (2,500 to 4,000 g) in 92% of study sample. The average infant birth weight was 3,073.5 g, with only one infant exceeding 4,000 g at 37.4 weeks of gestation. The limited occurrence of high birth weights, coupled with the small sample size limited ability to categorize LGA infants and appropriately power the study and may explain the lack of association typically observed with OGTT glucose levels.
Overall, our findings do suggest a need to reconceptualize glycemic monitoring in pregnancy. The traditional reliance on mid-pregnancy OGTT may result in missed opportunities for early intervention. The emergence of CGM technology offers a potential solution, enabling earlier identification of at-risk pregnancies and timely implementation of preventive measures. This perspective is supported by robust evidence. A meta-analysis of over 11,000 pregnancies demonstrated that interventions for hyperglycemia were most effective when initiated before 15 weeks’ gestation [3]. Furthermore, studies have shown that first trimester OGTT measurements can predict adverse neonatal outcomes earlier than traditional mid-pregnancy screening [31]. These findings underscore the potential value of early monitoring and intervention. CGM technology offers distinct advantages over conventional OGTT screening. Whereas OGTT provides isolated glucose measurements at specific timepoints, CGM captures comprehensive glycemic patterns over extended periods (7 to 14 days), reflecting daily fluctuations in real-world conditions [7]. This continuous monitoring provides a more nuanced understanding of glucose metabolism during pregnancy, potentially enabling more precise risk assessment and personalized intervention strategies.
Our study has several notable strengths. The prospective design focused exclusively on healthy pregnant women without pre-existing diabetes, addressing an important gap in the literature. High compliance with CGM wear in early pregnancy enabled comprehensive analysis of glycemic patterns and their relationship to infant birth weight. This approach is particularly relevant given meta-analytic evidence demonstrating that even in non-diabetic pregnancies, graded associations exist between glucose concentrations and adverse perinatal outcomes [3].
However, important limitations must be acknowledged. The relatively small sample size limits the generalizability of our findings and prevents us from definitively concluding that the OGTT does not contribute to the prediction of birth weight outcomes. Additionally, the birth weight distribution in our cohort, while predominantly normal, prevented analysis of CGM metrics specifically in cases of LGA infants. While our results suggest promising applications for CGM in early pregnancy monitoring, larger studies are needed to validate these findings and establish the utility of universal CGM during pregnancy.
In conclusion, our findings show that early pregnancy CGM may offer predictive insights into fetal growth that surpass traditional mid-pregnancy screening. By capturing glycemic patterns during critical periods of fetal development, CGM enables earlier identification and intervention in at-risk pregnancies, potentially transforming the prevention of adverse neonatal outcomes, even in healthy pregnancies. Further studies with larger and more diverse populations are needed to validate and confirm these findings.
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