The Use of Continuous Glucose Monitoring in Comparison to Self-Monitoring of Blood Glucose in Gestational Diabetes: A Systematic Review

Abstract

Background:

Continuous glucose monitoring (CGM) has emerged as an important tool for managing gestational diabetes mellitus (GDM), offering real-time glucose data and the potential for improved glycemic control. Unlike traditional self-monitoring of blood glucose (SMBG), which provides intermittent readings, CGM captures continuous glucose fluctuations, including postprandial and nocturnal changes, which are critical in GDM management.

Objective:

This systematic review aimed to assess the effectiveness of CGM compared with SMBG in managing glycemic control in women with GDM, focusing on key glycemic metrics such as time in range (TIR) and glycemic variability (GV), and exploring their associations with maternal and neonatal outcomes.

Methods:

A comprehensive search of PubMed and Google Scholar was conducted, adhering to PRISMA guidelines. Studies included randomized controlled trials, observational studies, and prospective cohort studies comparing CGM and SMBG, with 35 studies ultimately reviewed.

Results:

Compared with SMBG, CGM demonstrated significant improvements in maintaining TIR and reducing GV, which correlated with favorable maternal and neonatal outcomes, including lower rates of large-for-gestational-age (LGA) infants, preterm birth, and NICU (neonatal intensive care unit) admissions. Furthermore, CGM detected more hyperglycemic and hypoglycemic events, particularly nocturnal fluctuations. However, the studies also highlighted the need for standardized metrics to optimize CGM use in GDM management.

Conclusion:

Continuous glucose monitoring offers substantial advantages over SMBG for managing GDM by providing continuous, real-time glucose data, enabling timely treatment adjustments. These findings support the integration of CGM into clinical practice to improve maternal and neonatal outcomes in GDM. Further research is needed to establish standardized CGM metrics specific to GDM management.

Introduction

The role of continuous glucose monitoring (CGM) in managing all individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D), particularly those on insulin or multiple oral hypoglycemic agents is widely accepted. ¹ The role of CGM in other diabetes conditions like gestational diabetes mellitus (GDM) is less clear although it is increasingly being used for managing GDM due to its ability to provide real-time data and improve overall glucose control during pregnancy. Traditional self-monitoring of blood glucose (SMBG) using handheld glucose meters ² offers only intermittent readings of glucose levels, which can miss critical fluctuations, particularly postprandial and nocturnal spikes. Studies have shown that CGM enhances maternal glycemic control, especially in maintaining time in range (TIR) and reducing glycemic variability (GV), both of which are essential for minimizing risks to both mother and fetus. ³ Improved glycemic control is linked to better neonatal outcomes, and maintaining TIR and reducing GV can mitigate the risks of adverse perinatal outcomes associated with hyperglycemia. ⁴ Thus, CGM is a valuable addition to SMBG for comprehensive glucose monitoring during pregnancy. ⁵

A key advantage of CGM in GDM is its ability to detect glucose fluctuations that SMBG may miss, enabling timely actions to reduce both hyperglycemia and hypoglycemia. Recent advancements, such as time in tight range (TITR), have been explored to assess glycemic control, although specific TITR targets for GDM have not yet been formally established. ⁶ This change in clinical standards reflects a greater focus on maintaining individualized glycemic targets throughout pregnancy. Continuous glucose monitoring’s real-time glucose monitoring helps with clinical decisions and allows for timely insulin adjustments, thus reducing complications like large-for-gestational-age (LGA) infants.

Despite these advancements, there is no standardized guidance on the best use of CGM in GDM, especially regarding which metrics predict better maternal and fetal outcomes. The current body of literature also varies widely in study designs, CGM protocols, and outcome measures, complicating comparisons and limiting clear recommendations for clinical practice. In addition, the American Diabetes Association (ADA) notes that there is insufficient data to support the widespread use of CGM in all individuals with GDM, indicating a critical need for targeted research to determine optimal CGM targets specifically for these populations.^5,7

This systematic review aimed to assess the available data on (1) the effectiveness of CGM for GDM diagnosis and prediction, and (2) the effect of CGM compared with SMBG on glycemic control and pregnancy outcomes in women with GDM. Specifically, we focused on the impact of CGM on key glycemic metrics, such as TIR and GV, and explored the association between CGM-derived TIR and maternal and neonatal outcomes. By analyzing these factors, this review seeks to clarify the clinical benefits of CGM in GDM management and its potential to improve both maternal and fetal health outcomes.

Methods

Study Design and Selection

This systematic review followed the PRISMA guidelines to assess the impact of CGM on glycemic control and pregnancy outcomes in women with GDM. Studies were included if they compared CGM with SMBG and focused on key glycemic metrics, such as TIR and GV, and associated maternal and neonatal outcomes.

Eligibility Criteria

Inclusion criteria for aim 1 were the following:

• Studies involving pregnant women screened for GDM.

• Studies that used CGM metrics to predict GDM.

• Randomized controlled trials (RCTs), observational studies.

Inclusion criteria for aim 2 were the following:

• Studies involving pregnant women diagnosed with GDM.

• Studies using CGM to assess glycemic control, regardless of the comparator (eg, between treatment arms, blinded vs unblinded CGM, or CGM vs SMBG).

• Randomized controlled trials (RCTs), observational studies.

Exclusion criteria were:

• Studies involving pregnant women with type 1 or type 2 diabetes.

• Review articles, case studies, or systematic reviews.

• Non-English publications.

• Studies lacking full text.

Information Sources and Search Strategy

A comprehensive search was conducted using PubMed and Google Scholar, along with citation searching, from inception until January 2025. The search strategy involved using terms related to CGM, GDM, and SMBG. Full details of the search strategy are available in the supplementary files.

Study Selection and Screening

The initial search yielded 301 references. After removing duplicates (53 references), a total of 248 studies were screened for eligibility. Studies that did not meet the inclusion criteria were excluded (164 studies), whereas 61 studies were assessed for eligibility. Manual search resulted in an additional two studies that were included. Finally, 35 studies were included in the review (Figure 1).

Figure 1.

PRISMA flow chart.

Data Extraction and Synthesis

Data were extracted from the studies on key variables, including study design, sample size, participant demographics, CGM metrics (TIR, GV, hyperglycemia/hypoglycemia detection), and maternal and neonatal outcomes (eg, birth weight, LGA, preterm birth, NICU [neonatal intensive care unit] admission). A narrative synthesis was performed to summarize the findings. Meta-analysis was not planned due to heterogeneity in study designs, differences in CGM devices used, and differential outcome reporting.

Results

Study Characteristics

The 35 studies included (Table 1) involved a total of 5627 participants. ^{8 –42} These studies explored the impact of CGM on glycemic metrics, variability, and maternal-fetal outcomes in pregnancies affected by GDM or the association of CGM-derived glycemic metrics with the results of oral glucose tolerance test (OGTT) in pregnant women. Sample sizes varied widely, from smaller pilot studies with less than 20 participants to larger cohorts exceeding 300, providing a comprehensive assessment of CGM’s role in GDM management.

Table 1.

Studies of CGM in Gestational Diabetes (In Alphabetical Order of Authors).

	Study (Author, year & ref no.)	Sample size and population	Study design	CGM metrics assessed	Key findings	Trimester(s) when CGM administered	Frequency of CGM use
1.	Afandi et al 8	25 GDM patients (Ramadan fasting)	Observational	TIR, Hypoglycemia, Hyperglycemia	CGM captured more glycemic events, including fasting hypoglycemia, compared to SMBG	Second or third trimester	3+ days
2.	Baghel et al 9	50 GDM patients	Observational	MAGE, TAR, TIR	Higher MAGE (74.58 ± 16.83 mg/dL) in LGA group vs non-LGA group (49.86 ± 12.83 mg/dL, P = .002). Increased TAR (15.71%) and reduced TIR (50.85%) in LGA group.	32-34 weeks	2 weeks
3.	Bastobbe et al 10	50 GDM patients	Observational	FGM vs SMBG	No significant differences in NICU admissions, preterm delivery, or C-sections, but improved patient satisfaction with FGM	20-25 weeks (GA at diagnosis)	not mentioned
4.	Chen et al 11	300 GDM patients	Cohort study	Hypoglycemia detection	CGM detected hypoglycemia for 132 minutes daily in insulin-treated patients vs 94 minutes for diet-controlled patients	24-32 weeks	72 hours
5.	Crowther et al 12	958 women with mild GDM	RCT	Glycemic control	Intensive glycemic control reduced serious perinatal outcomes.	24-34 weeks	Regular monitoring until delivery
6.	Cypryk et al 13	150 GDM patients (diet & insulin controlled)	Observational	GV, TIR, SMBG	Wider GV range in CGM (40-244 mg/dL) compared to SMBG, asymptomatic glucose fluctuations detected by CGM	24-28 weeks	72 hours
7.	Dalfra et al 14	30 GDM women	Observational	Maternal-fetal outcomes	CGM use linked to reduced neonatal complications	Each trimester	2 days each trimester
8.	Di Filippo et al 15	100 GDM patients	Randomized	TIR, treatment modifications	60% of participants had treatment modifications based on CGM, enabling individualized management	12-35 weeks	7 days
9.	DiFillippo et al 16	81 participants (NGT & GDM)	Observational	TIR	TIR of 75%, 81% preference for CGM over OGTT, CGM triangulation revealed 9 OGTT misclassifications	24-28 weeks	7 days
10.	Durnwald et al 17	120 GDM women	Observational	Glycemic control	All glycemic metrics, including mean glucose and the percent time spent exceeding both 120 and 140 mg/dL (6.7 and 7.8 mmol/L), were found to be significantly higher as early as 13-14 weeks of gestation among individuals in the GDM group compared with the No GDM group.	Recruited <17 weeks	10 days
11.	Fishel Bartal et al 18	300 pregnant women (GDM screening group)	Observational study	Time in Range (TIR), Time Above Range (TAR)	Participants with TAR >140 mg/dL for ≥10% of CGM readings had significantly increased adverse neonatal outcomes including higher LGA (33.3% vs 1.3%) and neonatal hypoglycemia requiring IV glucose (40% vs 9.2%).	<=30 weeks gestation	10 days CGM use
12.	Gáborová et al 19	96 pregnant women	Observational	AUC, glucose peaks	Significant differences in glycemic patterns between GDM and healthy controls.	2nd and 3rd trimester	6 days total
13.	Gupta et al 20	120 GDM patients	Cross-sectional	Postprandial glucose, TAR	CGM detected higher postprandial glucose and TAR in women diagnosed as GDM by IADPSG but normoglycemic by alternate criteria	<20 weeks	4 days
14.	Kestilä et al 21	100 GDM patients	Randomized	Postprandial glucose, Hypoglycemia detection	CGM identified more GDM patients needing antihyperglycemic treatment than SMBG	Not specified	3 days
15.	Lai et al 22	120 GDM women	RCT	TIR	No difference in GV, HbA1c levels or perinatal adverse events between the use of CGM and SMBG. CGM group showed better gestational weight control and a lower birth weight of newborns	24-28 weeks	Every 4 weeks for 3 times during the study
16.	Lane et al 23	82 pregnant women	RCT	TIR, TAR	Comparable glycemic outcomes between real-time and blinded CGM.	24-28 weeks	7 days
17.	Law et al 24	150 GDM patients	Cohort study	Nocturnal hyperglycemia, TIR	CGM identified nocturnal hyperglycemia in 47% of participants, compared to 18% in SMBG, with implications for LGA infants	30-32 weeks	7 days
18.	Liang et al 25	200 GDM patients	Cohort study	TIR, Preterm birth, NICU admissions	Lower preterm birth rates (12%) and NICU admissions (18%) for GDM patients with TIR >75%	24-28 weeks	14 days
19.	Liang et al 26	109 GDM women	Prospective cohort	LGA, NICU outcomes	Higher TAR associated with increased neonatal risks.	24-28 weeks	14 days
20.	Majewska et al 27	92 pregnant women	Observational	Fetal macrosomia	Significantly lower incidence of fetal macrosomia in FGM vs SMBG group	2nd and 3rd trimester	7 days
21.	Marquez-Pardo et al 28	100 GDM patients	Cohort	Postprandial glucose	Postprandial glucose linked with macrosomia and LGA	26-32 weeks	6 days
22.	Mazze et al 29	50 GDM patients	Randomized	GV	Higher glucose variability in GDM pregnancies (IQR 35 mg/dL) compared to normal pregnancies (IQR 23 mg/dL).	2nd or 3rd trimesters	3 days in third trimester
23.	O’Malley et al 30	100 GDM patients	Observational	TIR	No significant difference in TIR between CGM and SMBG groups, but CGM users reported greater convenience	24 weeks	2 weeks at 32 and 36 weeks
24.	Pandey et al 31	50 GDM patients	Randomized	TIR	No significant difference in TIR between metformin (69.9%) and insulin (70.1%) groups	Throughout pregnancy	14 days
25.	Paramasivam et al 32	50 GDM women	RCT	HbA1c	At delivery, more mothers with GDM who used CGM achieved an HbA1c < 40 mmol/mol (5.8%) compared with mothers receiving standard care (92% vs 68%, P < .05).	28, 32, 36 weeks	6 days per trimester
26.	Quah et al 33	50 pregnant women	Randomized	TIR, TAR	Unblinded CGM users showed higher TIR values in the first and third trimesters, but differences not statistically significant	1st to 3rd trimester	14 days each trimester
27.	Rademaker et al 34	200 GDM patients	Cohort study	AUC, MBG, TAR, GV	Increased AUC and second-trimester mean glucose levels linked to higher LGA risk	12, 18, 24, 30, 36 weeks	5 days
28.	Scifres et al 35	40 obese pregnant women	Observational	GV, glucose patterns	Higher glycemic variability significantly associated with increased risk of pregnancy-related complications, including preeclampsia and large-for-gestational-age infants.	12-36 weeks	5 days per interval
29.	Scott et al 36	22 GDM women	Pilot study	TIR, patient adherence	High CGM adherence and user satisfaction.	24-32 weeks	6-14 days
30.	Shen et al 37	60 GDM patients	Cohort study	GV, HSSV, Mean glucose levels	Each 1-SD increase in nighttime mean glucose and hours spent in severe variability mode (HSSV) associated with increased birth weight percentile (6.0 and 6.3 percentage points respectively).	28 weeks	5-14 days
31.	Tartaglione et al 38	50 pregnant women	Observational	Hyper/hypoglycemia	Effective detection of undiagnosed glycemic abnormalities.	24-28 weeks	7 days
32.	Voormolen et al 39	154 pregnant women	Observational	CGM feasibility	Feasible CGM use, high acceptability by patients.	Before 30 weeks	5-7 days every 6 weeks
33.	Wei et al 40	300 pregnant women	Prospective cohort	GV metrics	No significant difference in major obstetric or neonatal outcomes between CGM and SMBG groups; however, CGM significantly reduced excessive gestational weight gain (33.3% vs 56.4%, P = .039). Early CGM initiation associated with lower weight gain.	~24 weeks	48-72 hours
34.	Yu et al 41	50 GDM patients	Randomized	SDBG, MAGE, MODD	Significant reductions in GV metrics among CGM users after 4 weeks	24-28 weeks	72 hours
35.	Zaharieva et al 42	45 GDM women	Randomized	Hyperglycemia detection	CGM detected more hyperglycemic episodes than SMBG	24-28 weeks	7 days

Potential of CGM for Diagnosis and Prediction of GDM

Studies have shown that CGM can play a significant role in GDM diagnosis by providing CGM data, which may help identify potential misclassifications and improve diagnostic accuracy. A pilot study by Di Fillippo et al ¹⁶ aiming to assess the Freestyle Libre Pro 2 acceptability as a diagnostic test for GDM found no significant difference between the group with negative OGTT (NGT) and GDM group in terms of CGM parameters. CGM triangulation analysis suggested OGTT screening resulted in five false positives and four false negatives. ¹⁶

Durnwald et al demonstrated that individuals who were later diagnosed with GDM by OGTT at 24 to 34 weeks’ gestation, had significantly higher mean glucose levels and spent more time above 120 and 140 mg/dL as early as 13 to 14 weeks of gestation compared with those who did not develop GDM, suggesting that CGM can discern patterns associated with the condition, well before the standard OGTT screening window. Consequently, the early identification of such glycemic profiles through CGM in the first trimester, may present an opportunity for the timely identification of individuals at elevated GDM risk, potentially facilitating earlier interventions aimed at reducing hyperglycemia-related risks. ¹⁷

Lim et al demonstrated that CGM-derived metrics, such as the coefficient of variation (CV), mean amplitude of glycemic excursions (MAGE), and glucose management indicator (GMI%) were more effective in predicting GDM-related complications compared to traditional risk factors like body mass index and maternal age in predicting GDM-related complications. The study reported an area under the curve (AUC) of 0.953 for CGM’s ability to predict GDM and related complications, suggesting that CGM can be a powerful tool in early identification and management of high-risk pregnancies. ⁴³

Afandi et al ⁸ examined the use of CGM and SMBG in GDM patients and found that CGM captured a broader spectrum of glycemic data, identifying hypoglycemia and hyperglycemia during fasting hours, which SMBG missed.

Glycemic Variability

Cypryk et al reported a wider GV range in CGM (40-244 mg/dL) compared with SMBG (50-180 mg/dL) in diet-controlled GDM pregnancies. Insulin-treated patients experienced longer periods of hyperglycemia, averaging 394 min/d above 120 mg/dL. In addition, CGM revealed asymptomatic glucose fluctuations missed by SMBG, reinforcing CGM’s utility in managing GV. ¹³

The Effect of CGM Compared to SMBG on Glycemic Control

Pandey et al ³¹ reported no significant difference in TIR between metformin (69.9%) and insulin (70.1%) groups, indicating comparable glycemic control in both treatment arms. Quah et al ³³ investigated the impact of CGM feedback in early pregnancy, revealing that unblinded CGM users showed trends toward higher TIR values, (83.2% and 90.2% in the first and third trimesters) compared with blinded CGM users (75.2% in the first trimester and 82.8% in the third trimester), although these differences were not statistically significant (P > .05).

O’Malley et al ³⁰ reported that TIR remained stable (82.7% to 87.8%) throughout monitoring and did not differ between CGM and SMBG groups, with CGM users reporting greater convenience despite minimal differences in glycemic outcomes. Yu et al ⁴¹ demonstrated significant reductions in GV metrics, including standard deviation of blood glucose (SDBG), MAGE, and mean of daily differences (MODD), among CGM users after four weeks.

Hyperglycemia and Hypoglycemia Detection

Kestilä et al ²¹ found that CGM identified 31% of GDM patients needing antihyperglycemic treatment compared with 8% detected by SMBG (P =.0149). CGM captured earlier postprandial glucose peaks (~51 minutes after meals) that SMBG missed, reinforcing CGM’s role in detecting hyperglycemia more effectively. Compared with treatment decisions based on SMBG, CGM data led to therapeutic adjustments in 76% of Israeli women and all American participants, indicating that CGM revealed previously undetected glycemic patterns that prompted changes in insulin or dietary regimens. ¹¹ Conversely, studies by Law et al ²⁴ highlighted CGM’s efficacy in detecting nocturnal hyperglycemia, with CGM identifying overnight hyperglycemia in 47% of participants compared with only 18% in SMBG, and an average glucose level of 6.2 mmol/L in mothers who delivered LGA infants compared with 5.5 mmol/L in others (P < .05).

Maternal and Fetal Outcomes

Several studies have shown that CGM use in GDM management leads to improved maternal and neonatal outcomes through more precise glucose monitoring and individualized treatment adjustments. Afandi et al ⁸ found that CGM enabled more accurate dietary and insulin adjustments, reducing hyperglycemia episodes to 5.65% compared with 14.2% in SMBG. Di Filippo et al ¹⁶ reported that 60% of participants had treatment modifications based on CGM data, enabling individualized management. Liang et al documented lower preterm birth rates (12% vs 25%) and NICU admissions (18% vs 30%) for GDM patients achieving TIR >75%. Additional studies reported similar improvements, with reduced insulin adjustment needs and fewer preeclampsia incidences among CGM users. ²⁵

Further supporting these associations, Fishel Bartal et al ¹⁸ observed that while pregnant women undergoing GDM screening and simultaneously using CGMS had a mean TIR of 78%, participants with time above the target range (TAR > 140 mg/dL) for ≥10% of readings had significantly higher adverse neonatal outcomes including increased LGA incidence (33.3% vs 1.3%, P = .002) and hypoglycemia requiring IV glucose (40% vs 9.2%, P = .007). Liang et al ²⁵ reported that TAR values above 5% were significantly associated with increased risks of LGA (odds ratio [OR] = 2.18; 95% confidence interval [CI] = 1.35-3.52) and preterm birth risks (OR = 2.17; 95% CI = 1.11-4.24), suggesting that maintaining daily mean blood glucose (MBG) below 4.8 mmol/L is crucial for mitigating neonatal complications, conversely, participants with Time Below Range (TBR) ≥ 4 % experienced a 39 % lower odds of LGA (OR = 0.61; 95% CI = 0.42-0.88). Rademaker et al ³⁴ further linked increased area under the curve (AUC) and second-trimester mean glucose levels to higher LGA risk, reinforcing that GV metrics provide critical insights into fetal outcomes.

Majewska et al ²⁷ observed a significantly lower incidence of fetal macrosomia in the flash glucose monitoring (FGM) group compared to SMBG (4% vs 20%, P < .05), although there were no significant differences in birth weight percentile or neonatal hypoglycemia rates between the groups. Bastobbe et al ¹⁰ reported comparable perinatal outcomes between FGM and SMBG groups, with no significant differences in NICU admissions, preterm delivery, or C-section rates, though patient satisfaction improved markedly with FGM use. The relationship between GV and adverse outcomes was further demonstrated by Baghel et al ⁹ who reported significantly elevated MAGE values in the LGA group (74.58 ± 16.83 mg/dL) compared with the non-LGA (49.86 ± 12.83 mg/dL, P = .002), along with higher TAR (15.71%) and lower TIR (50.85%).

While most studies supporting improved outcomes have evaluated CGM-based interventions, Crowther et al conducted a cluster-randomized trial assessing the impact of tighter versus less tight glycemic targets, independent of monitoring modality. Although this study did not report a reduction in LGA incidence with tighter control, it demonstrated significant reductions in perinatal death, birth trauma, and shoulder dystocia, emphasizing the complex interplay between glucose control intensity and specific neonatal outcomes. ¹²

Other metrics captured through CGM have also shown prognostic value. Shen et al ³⁷ identified nighttime mean glucose levels and time spent in severe variability mode (HSSV) as significant predictors of increased birth weight percentile, suggesting the importance of nighttime glycemic control in managing fetal overgrowth. Márquez-Pardo et al ²⁸ highlighted that elevated postprandial glucose levels were strongly associated with macrosomia and LGA risk, reinforcing the importance of post-meal glucose control as a contributor to TIR improvements.

Other Studies Related to CGM Use in GDM

Several additional studies further support CGM’s role in capturing glycemic abnormalities and offering broader insights beyond standard diagnostic frameworks. Lane et al ²³ compared real-time CGM with blinded CGM and found comparable TIR values between the two groups (88.8% vs 91.6%, P = .1), suggesting that real-time feedback may not confer additional glycemic benefit in all cases. Gupta et al explored the diagnostic implications of CGM by evaluating women classified as GDM by the IADPSG (International Association of Diabetes and Pregnancy Study Groups) criteria but considered normoglycemic by alternate guidelines such as UK NICE (National Institute for Health and Care Excellence), Canadian CDA (Canadian Diabetes Association), and Indian DIPSI (Diabetes in Pregnancy Study Group, India) guidelines. These women exhibited significantly higher postprandial glucose levels and greater TAR, indicating that CGM may identify hyperglycemia overlooked by traditional screening protocols. ²⁰ Mazze et al ²⁹ reported increased GV in GDM pregnancies compared to normal pregnancies, with an interquartile range of 35 versus 23 mg/dL, underscoring CGM’s sensitivity in capturing fluctuations that may be clinically meaningful.

Gupta et al further demonstrated CGM’s ability to detect early glycemic abnormalities by comparing women with elevated HbA1c levels (≥5.5%) in early pregnancy to those with HbA1c <5.5%. The elevated HbA1c group showed significantly higher postprandial glucose levels and greater glucose excursions, reinforcing the role of CGM in early identification of at-risk individuals that SMBG might not detect. ⁴⁴ In one of the earlier studies evaluating CGM in pregnancy, Chen (2003) documented CGM detected hypoglycemia for an average of 132 minutes daily in insulin-treated patients versus 94 minutes for diet-controlled patients. In addition, nocturnal hypoglycemia lasting over 30 minutes, mostly asymptomatic, was detected in 14 patients (all insulin-treated), reinforcing the limitations of SMBG. ¹¹

Collectively, these studies highlight the broader utility of CGM not only for improving glycemic control but also for enhancing diagnostic accuracy, identifying unrecognized risks, and informing clinical decisions in a manner that traditional glucose monitoring methods may not consistently achieve.

Discussion

Key Message / Main Findings

This review highlights the advantages of CGM over SMBG in managing GDM. Across studies, CGM was administered at varying times during pregnancy, primarily between the second and third trimesters. The frequency of CGM use ranged from a few days to several weeks. According to the CGM guidelines, glucose levels between 63 and 140 mg/dL is recommended for women with GDM. Although the exact time to be spent in this range has not been established (Battalinos reference). CGM’s ability to capture continuous glycemic data enables timely adjustments in treatment, minimizing the risks associated with hyperglycemia and hypoglycemia, particularly during critical periods like postprandial and nocturnal phases. Results from several studies also suggest that CGM may improve GDM diagnostics, underscoring CGM’s broader applicability and potential as a valuable tool in GDM detection and management. These results collectively support the integration of CGM into clinical practice to optimize outcomes for both mothers and infants in GDM pregnancies.

Clinical Utility of CGM in GDM Management

Continuous glucose monitoring is promising for improving glycemic control in GDM, especially by enhancing TIR and reducing instances of hyperglycemia. TIR improvements with CGM have been associated with better pregnancy outcomes, such as fewer instances of LGA infants. Consistent with our review, CGM studies in other diabetes situations highlight similar advantages, where continuous tracking has been shown to reduce hospitalizations by providing detailed, real-time data for managing glucose variability. ⁴⁵ Continuous glucose monitoring has shown the ability to detect postprandial and nocturnal glucose abnormalities that may be missed by standard diagnostic methods, enabling more targeted dietary and insulin interventions to address specific glycemic patterns during critical periods of the day. ³⁸ Studies such as those by Afandi et al and Wei et al, highlight the importance of this feature, showing that CGM helps identify post-meal glucose increases, which are a significant contributor to GDM risks. The continuous monitoring provided by CGM enables more timely interventions and adjustments to insulin and diet, potentially preventing prolonged episodes of hyperglycemia.^8,46 This ability to track glucose fluctuations is linked to better outcomes. In addition, the ability of CGM to detect both hyperglycemia and hypoglycemia events more effectively than SMBG further supports its role in enhancing glucose control and minimizing the risks associated with glucose excursions. These findings collectively emphasize the critical role of CGM in improving maternal and fetal health by maintaining glucose levels within target ranges and reducing variability. Lim et al ⁴³ further validated the clinical utility of CGM in diagnosing GDM by showing that CGM-derived parameters, such as %CV, MAGE, and GMI%, were superior to traditional risk factors in predicting GDM and related complications. This highlights the importance of early CGM use in identifying pregnancies at risk of complications like LGA, emphasizing the potential of CGM as a proactive approach in managing GDM before complications arise.

Continuous glucose monitoring provides a continuous stream of data, offering a high-frequency glucose profile that allows for the detection of GV and fluctuations that SMBG’s limited sampling cannot capture. In GDM, where rapid changes in glucose levels are common, this comprehensive tracking is crucial for timely treatment adjustments. Continuous glucose monitoring helps manage GV effectively, reducing risks such as overnight hypoglycemia and postprandial spikes, both of which can contribute to complications in pregnancy. ²³ Studies have shown that CGM’s frequent data collection enables better glucose management during pregnancy by helping maintain more stable glucose levels. ⁵

Continuous glucose monitoring’s ability to reduce GV has been linked to better maternal health outcomes. Improvements in GV metrics, such as reductions in standard deviation of blood glucose (SDBG), mean amplitude of glycemic excursion (MAGE), and MODD, have been observed with CGM, reinforcing its advantage over SMBG in detecting and managing glucose fluctuations. Controlling GV is particularly crucial during the second trimester, as higher AUC and elevated mean glucose levels during this period have been associated with increased LGA risk.^34,41 Advancements in CGM technology, such as ambulatory glucose profiling (AGP) and trend indicators, further enhance its clinical utility and empowers both patients and healthcare professionals to make informed, real-time decisions. This continuous monitoring approach, combined with smart alerts for hypo- and hyperglycemia, offers a more dynamic and proactive method for glucose management.^4,30,47

Limitations of the Current Evidence and Future Directions

While our review shows that CGM is helpful for managing GDM, the current literature has some limitations. Many studies were small, observational, or pilot studies, which can introduce bias and make it difficult to apply the findings to a wider population. Observational studies provide useful real-world insights but lack the robustness of randomized controlled trials (RCTs). A key issue is the lack of consistent definitions and measurements for CGM metrics in GDM. For example, studies often differ in how they define TIR and GV making it hard to compare results or set clinical guidelines. Future research should focus on creating standardized CGM thresholds for GDM to improve care and guide clinical decisions more effectively. Only a few studies were designed to assess the impact of CGM use on pregnancy outcomes.

The cost of the CGM will play a big role in the use of CGM in GDM if it is to be extended to women with GDM in low- and middle-income countries (LMICs) like India which have a large burden of GDM.^48,49 Reimbursement of the cost of CGM or providing it free of cost to women with GDM could help to improve the uptake of CGM globally and possibly improve pregnancy outcomes as well.

Conclusion

In summary, this review shows that CGM is a promising approach to improve the management of GDM by increasing TIR and reducing GV. Continuous glucose monitoring provides real-time glucose data, allowing patients and health care professionals to make timely adjustments and maintain more stable glucose levels. This can lower the risk of complications related to high or low blood sugar. Compared with traditional SMBG methods, CGM offers a more comfortable for patients, more precise and proactive way to manage blood sugar levels during pregnancy, supporting better outcomes for both mother and baby. Further studies are needed to confirm the effect of CGM use on pregnancy outcomes and to establish the most appropriate glycemic targets and TIR in GDM.