Continuous glucose monitoring system in diabetes in pregnancy: a narrative review

Abstract

Diabetes in pregnancy increases maternal and fetal risks as well as the burden of chronic complications and comorbidities associated with this condition. In addition to HbA1c and blood glucose monitoring (BGM), continuous glucose monitoring systems (CGM) provide a complementary tool that enables comprehensive glycemic assessments and improves glycemic control. This review highlights the clinical value of CGM in the management of diabetes in pregnancy, encompassing type 1 diabetes, type 2 diabetes, gestational diabetes mellitus (GDM), but also early GDM. CGM derived metrics, such as time in range (TIR) and mean glucose levels, are associated with adverse pregnancy outcomes, emphasizing the importance of optimizing glycemic control. Overall, CGM is a valuable tool for detecting glucose fluctuations in pregnancies complicated by all forms of diabetes.

Introduction

Diabetes in pregnancy is common in clinical practice. Hyperglycemia in pregnancy, either from preexisting diabetes, defined as any type of diabetes diagnosed before the pregnancy, or even hyperglycemia first detected in pregnancy, affects about 16% of pregnancies globally [1]. Although the pooled prevalences of pre-existing type 1 and type 2 diabetes were 0.3% (95%CI 0.2–0.4) and 0.2% (95%CI 0.0-0.9) respectively, the prevalence of pre-existing diabetes doubled from 0.5% (95%CI 0.1-1.0) to 1.0% (95%CI 0.6–1.5) from 1990 to 2020 [2].

Pre-existing diabetes confers significantly greater maternal and fetal risk, primarily related to the degree of hyperglycemia as well as chronic complications and comorbidities associated with diabetes [3].

The global rise of type 2 diabetes among younger people has contributed to an increased prevalence of this type of diabetes during pregnancy [2, 4]. Optimizing glycemic outcomes is equally important for individuals with type 2 diabetes as it is for those with type 1 diabetes [5]. Maternal T2D has been associated with a higher risk of stillbirth and perinatal mortality compared with maternal T1D [6]. Among women with gestational diabetes, 30–70% have early gestational diabetes (i.e., hyperglycemia occurring before 20 weeks of gestation) [7]. These women tend to experience worse pregnancy outcomes compared to those with late gestational diabetes (i.e., hyperglycemia detected between 24 and 28 weeks of gestation) [8].

Beyond HbA1c and Blood glucose monitoring (BGM), new methods have been introduced, namely continuous glucose monitoring systems (CGM), which include real-time continuous glucose monitoring (rtCGM), and intermittently scanned continuous glucose monitoring (isCGM) systems [9, 10]. Continuous glucose monitoring (CGM) provides a comprehensive assessment of glycemic status by accurately measuring glucose levels. This reduces the need for frequent fingerstick monitoring while improving glycemic control, lowering the risk of both clinically evident and undetected hypoglycemia and hyperglycemia, including during the overnight period. However, these benefits must be weighed against considerations such as cost, accuracy, and appropriate interpretation of CGM-derived metrics [3, 11].

The objective of this review was to perform a comprehensive critical view about the clinical value of CGM in pregnant women with diabetes, with a particular focus on the CGM metrics that correlate with maternal and fetal outcomes.

CGM accuracy

Continuous glucose monitoring (CGM) measures interstitial glucose, which correlates well with plasma glucose [9]. There are currently two types of CGM systems available: real-time (rtCGM) and intermittently scanned (isCGM) [12]. Current rtCGM systems measure and continuously stream glucose data in real time. The isCGM systems also measure glucose continuously but require the user to actively scan the sensor to access glucose information with and without the use of alarms [10, 13].

The accuracy, reliability, and safety of CGM systems must be demonstrated through well-designed studies [12, 14]. The mean absolute relative difference (MARD) parameter is often used to describe the measurement performance for CGM [15]. However, other parameters beyond MARD are important for assessing safety and clinical relevance, such as performance across the dynamic glycemic range from hypoglycemia to hyperglycemia [16], error grid analyses, and robust accuracy studies [15, 17].

Over the past two decades, CGM systems have undergone substantial improvements in accuracy and overall performance [18]. Integrated CGM (iCGM) Food and Drug Administration (FDA)-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing) [19]. These systems are both safe and effective in reducing HbA1c and hypoglycemia, while increasing the amount of time spend within target glycemic range [18]. To date, the following CGM systems are classified as iCGM devices: Abbott FreeStyle Libre 2, Abbott FreeStyle Libre 2 Plus, Abbott FreeStyle Libre 3, Abbott FreeStyle Libre 3 Plus, Dexcom G6, Dexcom G7 and Senseonics Eversense E3 [19].

For many years, the Yellow Springs Instruments (YSI) has been the reference standard for validating the accuracy of blood glucose meters (BGMs) and CGM systems. The overall MARD for Dexcom G6 compared with YSI was 10.3%, with even higher accuracy when the sensor was worn on the posterior upper arm (8.7%). For the Dexcom G7, the MARD was 9.5%. The FreeStyle Libre has also demonstrated safety for use in pregnancy, with a MARD of 11.8%. However, this value was based on comparisons with capillary blood glucose (fingerstick) measurements rather than matched CGM-YSI pairs [20–22]. These results support the use of CGM in pregnant women with diabetes in clinical settings.

Several studies have assessed the accuracy, user acceptability, and safety of CGM specifically in pregnant populations. This is particularly important when considering CGM-specific product‐evaluation criteria to ensure that a device has been adequately assessed for safety, performance and benefits within the population of pregnant women with diabetes [16]. Importantly, The European Society’s Position Statement on the accuracy of the systems has been published, including the proposed parameters for clinical investigation and device validation in this population [23].

1. Preexisting type 1 diabetes in pregnancy

The presence of preexisting T1D in pregnancy increases the risk of adverse maternal and neonatal outcomes, including preeclampsia, cesarean delivery, preterm birth, macrosomia, and congenital anomalies [24]. A recent systematic review [24] concluded that preexisting diabetes confers a more than threefold increased odds of both stillbirth and perinatal mortality [25].

The Continuous Glucose Monitoring in Pregnant Women With Type 1 Diabetes Trial (CONCEPTT) was a randomized controlled trial that evaluated the addition of real-time CGM (rtCGM) to standard care, including optimization of pre- and postprandial glucose targets versus standard care. The trial enrolled 325 women (215 pregnant, 110 planning pregnancy) with T1D. Compared to the control group, pregnant CGM users spent more time within target glucose range (68% vs. 61%; p = 0.0034) and had less time above the target (27% vs. 32%; p = 0.0279). No differences were observed in severe hypoglycaemia episodes (18 CGM and 21 control) or time spent hypoglycemic (3% vs. 4%; p = 0.10). In addition, CGM use was associated with reductions in large-for-gestational-age births, length of infant hospital stays, and severe neonatal hypoglycemia [26].

In another observational cohort study of 186 pregnant women with T1D in Sweden (92 using rtCGM and 94 using isCGM devices), despite no significant differences in glucose patterns were observed between the two devices, overall, lower mean glucose, lower standard deviation, and higher percentage of time in range (TIR) were associated with lower risks of large-for-gestational-age births and other adverse neonatal outcomes [27]. In a small prospective, observational study, 28 pregnant individuals with T1D were monitored during the early pregnancy, with both intermittently scanning CGM (isCGM) without alert functionality and blinded real-time CGM for 7 days. While mean sensor glucose was similar between groups, the isCGM exhibited a higher percentage of time below range (TBR) [28].

In a real-world retrospective study, the clinical effectiveness through electronic medical records was investigated in 160 pregnant women with T1D, comparing CGM and BGM. The CGM group had a higher proportion of participants meeting HbA1c goals throughout pregnancy and postpartum (P < 0.01 in each time period) with lower rates of macrosomia (12.8% CGM vs. 29.4% BGM, P = 0.01) [29]. However, a separate retrospective single-center study conducted in Qatar evaluated electronic medical records of 265 pregnant women with T1D. No significant associations were found between CGM use and improved maternal (mean changes in HbA1c) or fetal and neonatal outcomes [30].

The National Pregnancy in Diabetes Audit 2021 and 2022 in England reported that CGM users have improved pregnancy outcomes in women with T1D, including lower rates of preterm births (39.5% vs. 43.9%), large-for-gestational-age birthweight (45.6% vs. 53.5%) and neonatal care unit admission (44.8% vs. 48.5%) [31].

CGM use during T1D pregnancy has shown that even modest changes in TIR and in other common CGM metrics are associated with meaningful differences in the risk of adverse neonatal and also in selected maternal outcomes [32]. Importantly, the cost-effectiveness of CGM compared to BGM must also be considered [33]. Several analyses suggest that routine rtCGM use in pregnant women with T1D results in significant cost savings, largely due to reductions in neonatal intensive care unit (NICU) admissions, which was shown in England (£3743) [34] and United States ($2,903 with CGM use) [35]. In addition, a reduction in the risk of pre-term birth was seen in Australia ($3275 per prevented pre-term birth) [36]. As a result, the Brazilian Diabetes Society and the American Diabetes Association (ADA) Standards of Care recommend CGM to pregnant individuals with T1D to help them to achieve glycemic goals: TIR (63 to 140 mg/dl): >70%; time above range [TAR] (> 140 mg/dl): <25%; TBR level 1 (< 63 mg/dl): <4%; TBR level 2 (< 54 mg/dl): <1%; glycemic variability: ≤36% [3, 9].

2. Preexisting type 2 diabetes in pregnancy

Despite the urgent need and potential benefits of diabetes technology use for pregnant women with type 2 diabetes (T2D), studies remain scarce [37, 38]. CGM could offer an alternative to traditional glucose monitoring with capillary blood glucose, improving compliance and engagement with diabetes care.

In a randomized controlled trial (RCT) study of 124 pregnant women with T2D using isCGM, the intervention group showed significantly lower glycated albumin levels after two weeks (14.6 ± 2.2 vs. 16.8 ± 2.7; p < 0.001), greater TIR (69 ± 10% vs. 62 ± 11%; p < 0.001), and reduced TAR (25 ± 7% vs. 31 ± 8%; p < 0.001) compared to the control group. Notably, low glucose episodes during the nighttime period (00:00–05:59 AM) were detected in 31.2% and 26.6% of cases (depending on time interval) in the isCGM group, compared to only 8.3% in the BGM group, suggesting that isCGM better than traditional BGM in identifying nocturnal hypoglycemia. Furthermore, the frequency of positive urine ketone tests was significantly lower in the isCGM group (42 ± 5% vs. 54 ± 5%; p < 0.001) [39].

In contrast, other studies that assessed mixed cohorts with both T1D and T2D did not demonstrate significant differences in glycemic control or pregnancy outcomes with CGM [40, 41]. It is worth noting that two RCTs that reported T2D outcomes as subgroup analyses were not powered for such analyses, as only 20% and 27% of participants, respectively, had T2D [42].

Observational studies provide additional data. A retrospective cohort study evaluating 65 pregnant women with T2D and GDM found that both isCGM and rtCGM improved glycemic control. However, only patients who achieved TIR > 70% with CGM (57% of the study population) experienced a lower likelihood of adverse neonatal and maternal outcomes [43].

Patient satisfaction and acceptability of isCGM have also been explored. In one study, approximately two-thirds of pregnant women with T2D expressed satisfaction with the convenience and reliability of isCGM. Moreover, 86% expressed willingness to use the device in future pregnancies and 90% would recommend it to others [44]. Another study involving 50 pregnant women with T2D that completed the satisfaction questionnaire revealed that most participants found isCGM useful, worthwhile and easy to use; 47 (94%) would recommend isCGM to others. Self-reported frequency of glucose testing four times per day increased from 36 to 68% (P = 0.001), compared with prior finger-stick measurements [45]. In a small prospective pilot study, the use of CGM provided clinicians with additional information for T2D (ten of 55 pregnant women), including detection of postprandial glucose excursions and nocturnal hypoglycemia that may not have been identified through standard monitoring [46].

Although CGM has been well established for improving glycemic control and pregnancy outcomes in individuals with T1D, its potential in T2D remains underexplored. Improvements in glycemic outcomes may also be partially mediated by behavioral changes or shifts in clinical decision-making prompted by CGM data. As a result, given the severity of pregnancy complications in T2D, further investigation into the use of CGM as an intervention is necessary to evaluate the efficacy, safety, and cost-effectiveness of CGM as a standard intervention in this population.

3. Gestational diabetes

The most common form of hyperglycemia during pregnancy is gestational diabetes (GDM) [1, 47]. CGM may offer substantial benefits in women with GDM, as demonstrated by several clinical trials.

The FLAMINGO study was an open-label, randomized controlled trial that recruited 100 women with GDM between 24 and 28 weeks of gestation. Participants were randomly allocated to either isCGM Freestyle Libre (n = 50) or BGM (n = 50). While no significant differences were observed regarding mean glucose between the groups (p = 0.437), the use of isCGM was associated with a significantly lower fasting (p = 0.027) and postprandial glucose levels (p = 0.034) during the first 4 weeks following GDM diagnosis, compared to the control group. Moreover, the incidence of fetal macrosomia was significantly higher in the control group (OR 5.63; 95% CI 1.16–27.22) [48]. In another clinical trial involving women with GDM and HbA1c levels below 6%, those using rtCGM had similar TIR and HbA1c levels before delivery compared to the BGM group. However, a significantly higher proportion of patients in the rtCGM group achieved gestational weight gain within the recommendations (59.7% versus 40.3%, p = 0.046). CGM might improve patients’ awareness of self-management, leading to better weight control [49].

A meta-analysis of six randomized clinical trials involving 482 patients with GDM found that the use of CGM was associated with lower HbA1c levels at the end of pregnancy (mean difference: -0.22; 95%CI -0.42 to -0.03) compared to BGM. Additionally, women using CGM also experienced less gestational weight gain (mean difference: -1.17, 95%CI -2.15 to -0.19), and their concepts had lower birth weight (mean difference: -116.26, 95%CI -224.70 to -7.81). No differences were observed in the other outcomes evaluated [50].

Taken together, these findings suggest that CGM may improve both maternal and fetal outcomes in women with GDM. Therefore, its use could be considered in selected patients, particularly those who may benefit from enhanced glucose monitoring and improved self-management.

Early diagnosis of GDM and use of CGM

Historically, GDM has been considered a condition induced by pregnancy, typically emerging in the late second trimester [51]. More recently, it has become clear that pregnancies early affected by hyperglycemia, although not meeting the criteria for overt diabetes, are at a greater risk of complications and need for insulin treatment [52]. Early gestational diabetes (eGDM) is defined as GDM detected before 20 weeks of gestation [53]. The Treatment of Booking Gestational Diabetes Mellitus (TOBOGM) trial demonstrated that, among women with risk factors for GDM, early treatment of the condition significantly reduces perinatal complications — primarily by decreasing incidence of neonatal respiratory distress — and shortens the length of stay in neonatal care, compared to the traditional approach of initiating treatment between 24 and 28 weeks of gestation. Of note, when managing women with mild GDM, it is important to consider the non-significant increase in the rate of small-for-gestational-age (SGA) neonates observed even in the subgroup of women with lower glycemic range on the oral glucose tolerance test (OGTT) [52].

In the GLAM prospective observational study in a cohort of low-risk pregnancies in women without pregestational diabetes (n = 768), those who went on to develop GDM as diagnosed by OGTT presented with higher mean glucose, greater SD and a higher percent time spent > 120 mg/dL (median 23% vs. 12%, p < 0.001), and higher percent time > 140 mg/dL (7.4% vs. 2.7%) throughout the gestational period prior to OGTT diagnosis. Use of CGM showed that these glycemic trends were measurable as early as 13–14 weeks gestation, compared to pregnant women who did not develop GDM. These glycemic patterns and trends remained consistent throughout pregnancy from 13-weeks onwards for women ultimately diagnosed with GDM using OGTT, compared to those who did not develop GDM [54].

In a study exploring the potential value of continuous glucose monitoring (CGM) in early pregnancy for predicting GDM, 103 Asian pregnant women with overweight or obesity used blinded isCGM (FreeStyle Libre Pro) devices in early pregnancy and underwent universal GDM screening at 24–28 weeks of gestation. Models based on early pregnancy risk factors and CGM-derived parameters were compared for their predictive values for GDM and pregnancy outcomes. The study identified 18 cases of GDM, with CGM-derived novel parameters showing superior performance (e.g., area under the curve: 0.953 vs. 0.722) in predicting incident GDM compared to traditional risk models. These novel parameters also significantly differentiated risk for primary cesarean and large-for-gestational age (LGA) babies [55]. Although the findings suggest that CGM has potential clinical utility in the first trimester for predicting GDM and adverse pregnancy outcomes, especially in individuals with overweight or obesity, there is a need for further studies to evaluate maternal and neonatal outcomes.

CGM-derived metrics and adverse pregnancy outcomes

CGM enables the assessment of glucose levels over 24-h period across multiple days. As such, it offers a more comprehensive understanding of maternal glycemic patterns, allowing for the identification of time-specific excursions, such as asymptomatic nocturnal hypoglycemia and postprandial hyperglycemia, events that could be missed with conventional BGM or HbA1c measurement [56]. Several studies have explored the impact of CGM-derived metrics on adverse pregnancy outcomes.

In the CONCEPTT trial [57], which included women with T1D, the proportion of participants achieving > 70% TIR was 7.7%,10.2%, and 35.5% during the first, second and third trimesters, respectively. Achieving TIR and especially TAR targets at 24 and 34 weeks was associated with reduced rates of preterm birth and LGA [57]. Two additional studies reported that each 5% increase in TIR (approximately 1.2 h/day) was associated with a 20–28% reduction in the odds of neonatal morbidity among women with either T1D or T2D [58–60].

The GlucoMOMS randomized controlled trial (RCT) was designed to track longitudinal changes in glucose levels throughout pregnancy [61]. A post-hoc analysis of this study found that in individuals with T2D or insulin-treated GDM, a higher glucose area under the curve (AUC) above the target range was associated with higher risk of LGA. In T1D, the mean glucose was the major determinant of LGA with no evidence suggesting that other CGM metrics contributed to adverse pregnancy outcomes [62].

In a systematic review of RCT and quasi-experimental studies, compared to self BGM, CGM was associated with a significant reduction in HbA1c and possibly large for gestational age across diabetes in pregnancy, with greatest benefits in T1D, followed by GDM. Mean sensor glucose and pregnancy TIR were important CGM metrics for reducing large for gestational age. In fact, increased pregnancy woman with T1D TIR and for GDM and T1D decreased mean sensor glucose were associated with decreased risk for large for gestational age [63].

In a large observational study involving 937 pregnant women without diabetes, real-time CGM demonstrated that glucose levels remained relatively stable throughout gestation. The overall mean ± SD glucose during gestation was 98 ± 7 mg/dL. The median percentage time within 63–120 mg/dL was 86% (IQR: 82–89%), while the median percentage of time within 63–140 mg/dL was 95% (IQR: 93–96%). The median percentage time < 63 mg/dL was 1.8%, the median percentage time > 120 mg/dL was 11%, and the median percentage time > 140 mg/dL was 2.5%. The mean post-prandial peak glucose was 126 ± 22 mg/dL, and mean post-prandial glycemic excursion was 36 ± 22 mg/dL. Higher mean glucose levels were low to moderately associated with pregnant individuals with higher body mass index (BMI) (103 ± 6 mg/dL for BMI ≥ 30.0 kg/m2 vs. 96 ± 7 mg/dL for BMI 18.5–<25 kg/m2, r = 0.35) [64].

In a prospective observational study involving 162 pregnant women with GDM, who underwent 7-day CGM at 30–32 weeks’ gestation, mean glucose was significantly higher in women who delivered LGA infants (111.6 mg/dL vs. 104.4 mg/dL, p = 0.025). Functional data analysis revealed that this was largely driven by elevated nocturnal glucose values (108.0 ± 18.0 vs. 99.0 ± 14.4 mg/dL, p = 0.005) [65]. These findings highlight the potential of CGM in identifying nocturnal hyperglycemia, which may contribute to fetal overgrowth.

Liang et al. explored the relationship between CGM-derived metrics and pregnancy outcomes in 1302 pregnant women with GDM. Higher risks of adverse pregnancy outcomes were associated with increased TAR, nighttime mean blood glucose (MBG), daytime MBG, and daily MBG. Additionally, a higher TAR was linked to an increased risk of NICU admission. The authors further identified potential thresholds values for TAR (2.5%) and daily MBG (86.4 mg/dL) to distinguish women with and without any adverse pregnancy outcome [66].

Therefore, CGM provides a more comprehensive assessment of fetal exposure to maternal glucose over a 24-h period. In pregnancies complicated by preexisting diabetes, individuals with suboptimal or poorly controlled glucose levels had an increased risk of adverse pregnancy outcomes. Sustained maternal hyperglycemia, particularly overnight, appears to be associated with a higher risk of adverse pregnancy outcomes than transient glucose spikes or glycemic variability [67]. Higher CGM mean glucose in the second and third trimesters is associated with LGA in T1D and GDM [27, 32, 65, 68], making it one of the most relevant glycemic predictors of this complication [27].

Despite these insights, no studies to date have directly assessed the benefit of specific alarm thresholds for hypoglycemia and hyperglycemia during pregnancy. Alarm limits may be set near the recommended BGM targets for pregnancy, and in selected cases, more stringent limits can be applied. However, a careful orientation is crucial to avoid severe iatrogenic hypoglycemia and alarm fatigue. On the other hand, strict hyperglycemia alarms during this period can help to guide behavior changes, such as food choices and physical activity. Alarm thresholds should be reassessed and individualized throughout pregnancy as needed [69].

The 2025 ADA standards in Diabetes Care recently added a recommendation that CGM metrics may be used in combination with blood glucose monitoring to achieve optimal pre- and postprandial glycemic goals in pregnancy [3], prior the 2024 standards that CGM metrics did not recommend it as a substitute for BGM metrics [70]. The international consensus on Time in Range (TIR) in 2019 endorses pregnancy glucose goal ranges and goals for TIR for pregnant women with T1D using CGM as reported on the ambulatory glucose profile. Due to the lack of evidence on CGM targets for women with GDM or T2D in pregnancy, percentages of TIR, TBR, and TAR have not been included in that report [71]. Battarbee and colleagues developed a stepwise approach to evaluating CGM data for pregnant with diabetes (Fig. 1) [72]. Yet not all CGM devices can currently be adjusted to these pregnancy-specific targets [32]. Therefore, although useful, more evidence is warranted to provide definitive recommendations, and in line with guidelines, we also consider that CGM metrics should be used in combination with blood glucose monitoring to improve glycemic control in pregnancy.

Fig. 1.

Step-wise approach to evaluate glucose data for Diabetes Management During Pregnancy (Adapted from Battarbee et al. [72])

Discussion

Diabetes in pregnancy is associated with a substantial increase in the risk of maternal and neonatal complications, as well as higher healthcare costs [2]. Preexisting diabetes in pregnancy is complex and is associated with significant maternal and neonatal risk. Optimization of glycemic control, medication regimens, and careful attention to comorbid conditions can help mitigate these risks and ensure quality diabetes care before, during, and after pregnancy [24].

CGM has the potential to enhance diabetes management during pregnancy by providing patients and clinicians with detailed and real-time data, overcoming many of the limitations associated with traditional BGM. Substantial evidence supports the use of CGM in helping women with T1D achieve glycemic targets during pregnancy and the postpartum period. In contrast, data on CGM use in pregnant women with T2D or GDM remain limited, with larger RCTs currently underway. Additional studies are still required to determine the optimal alarm thresholds when using CGM are used during pregnancy.

Moreover, further research is needed to establish appropriate glycemic targets for CGM use in pregnant women with T2D and GDM [72]. The current decision to initiate CGM in pregnant individuals with T2D or GDM should be individualized based on treatment regimen, clinical context, requirements, comorbidities, patients preferences, and whether the CGM systems has been validated for use during pregnancy with published accuracy data [3].

In summary, overall, continuous glucose monitors are valuables tools for monitoring glucose fluctuations in pregnancies complicated by any form of diabetes. Robust studies have investigated accuracy, user acceptability, and safety evaluations in pregnant populations [21]. CGM provides real-time data and alerts, enabling early interventions that can mitigate both hypoglycemia and hyperglycemia, thereby reducing adverse maternal and neonatal outcomes associated with disglycemia [60].

Conclusion

CGM offers comprehensive glycemic assessments and can improve glycemic control. CGM may improve the management of diabetes in pregnancy, not only for individuals with T1D, but also with T2D, and even early GDM, leading to a reduction in maternal and fetal complications.

Abbreviations

ADA

American Diabetes Association

BGM

Blood glucose monitoring

BMI

Body mass index

CGM

Continuous glucose monitoring

DM

Diabetes mellitus

eGDM

Early gestational diabetes

FDA U.S.

Food and Drug Administration

GDM

Gestational diabetes mellitus

HbA1c

Hemoglobin A1C

iCGM

Integrated CGM

isCGM

Intermittently scanned continuous glucose monitoring

LGA

Large-for-gestational age

MARD

Mean absolute relative difference

MBG

Mean blood glucose

NICU

Neonatal intensive care unit

OGTT

Oral glucose tolerance test

RCT

Randomized controlled trial

rtCGM

Real time continuous glucose monitoring

T1D

Type 1 diabetes

T2D

Type 2 diabetes

TAR

Time above range

TBR

Time below range

TIR

Time in range

YSI

Yellow Springs Instruments

Author contributions

All authors made substantial contributions to the conception of the work; the acquisition, analysis or interpretation of data; and drafting the work or reviewing it critically for important intellectual content; and provided final approval of the version to be published.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. Editorial assistance and open access license were funded by Abbott Diabetes Care (Sao Paulo, Brazil).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Melanie Rodacki has received support from Abbott, NovoNordisk, Sanofi.Marcio Krakauer has received support from Abbott, Novo Nordisk, Lilly, Astra, Roche, Medtronic, Boehringer Ingelheim, EMS, Servier.Denise Reis Franco has received support from Abbott, Lilly, Novo Nordisk, Sanofi, Medtronic.Patricia Medici Dualib declares no conflicts of interest.Mauro Sancovski declares no conflicts of interest.Lenita Zajdenverg has been consultant for Novonordisk and EMS; and speaker for NovoNordisk and Chiesi.