Edited by: Margarita de Veciana, Eastern Virginia Medical School, United States
Reviewed by: Joseph Aloi, Wake Forest Baptist Medical Center, United States; Nirav Dhanesha, The University of Iowa, United States
This article was submitted to Clinical Diabetes, a section of the journal Frontiers in Endocrinology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Hyperglycemia during pregnancy without a history of diabetes is known as gestational diabetes mellitus (GDM), with worldwide prevalence of 5–10% before 2010 (
Once a pregnant mother is diagnosed with GDM, she will be treated with either diet, medication (i.e., insulin), or both. In addition, she will be required to monitor her own glucose level closely using self-monitoring of blood glucose (SMBG) that involves finger pricking up to seven times daily. However, SMBG provides an incomplete picture of the daily glucose profile due to long intervals between finger pricking, and inaccurate self-reported measures which heavily rely on patients' compliance (
In the past decade, continuous glucose monitoring (CGM)—a new device that uses a subcutaneous sensor to measure interstitial fluid glucose levels—has been developed and advanced greatly in terms of clinical application (
In this review, we aimed to summarize and evaluate the use of CGM technology in pregnancies complicated by GDM, especially pertaining to feasibility, acceptability and efficacy (i.e., improvement in clinical outcomes and treatment effect). We hypothesized that there is strong evidence on the potential values of CGM in detecting more complete gestational glucose profiling, more episodes of dysglycemia, and in improving pregnancy outcomes and glycemic control among pregnant women with GDM.
We conducted a broad search (conducted by QY and verified by L-JL) on PubMed, Scopus, and Web of Science using possible combination of terms from two themes featuring CGM and GDM, as shown in
Literature search flowchart.
Characteristics of included studies.
1 | Yogev et al. ( |
72-h MiniMed, Medtronic Twice: 1x baseline, 1x post-treatment (2–4 weeks later) | Prospective observational study | CGM vs. SBGM use | Differences in glucose levels and insulin regimen | 1. Under-controlled study 2. Small number of patients, 3. No clinical difference in perinatal outcome between CGM and SBGM due to lack of power | |
2 | Chen et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM use | Daily glycemic profile | 1. Non-standardized data analyses between two study sites | |
3 | Ben-Haroush et al. ( |
72 h MiniMed Medtronic Once | Prospective observational study | CGM vs. SMBG use | Peak postprandial glucose levels | 1. Small sample size 2. No data on association between postprandial glucose levels and pregnancy outcomes | |
4 | Buhling et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM use | Glucose profile | 1. Small number of patients 2. No assessment of the clinical significance of hyperglycemic intervals | |
5 | Yogev et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM | Hypoglycemic episodes | 1. Did not study perinatal or maternal outcomes 2. No significant hypoglycemic events were identified in any patients | |
6 | Buhling et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM use and diet | Postprandial glucose time to peak Postprandial glucose values | 1. Unable to detect clinical outcome differences due to lack of power from small number of study subjects | |
7 | Cypryk et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM | Glycemic control | 1. Small number of study subjects 2. No fetal/obstetric outcomes measured | |
8 | McLachlan et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SBGM use | Clinical usefulness Patient assessment of usefulness Accuracy of CGMS | 1. No blinded third party for assessing CGMS results 2. No statistical analysis of data 3. Not all target women agreed to participate which could lead to overestimation of usefulness of CGMS | |
9 | Kestila et al. ( |
Average 47.4-hr MiniMed, Medtronic Once | Prospective observational study | CGM vs. SBGM use | Determination of medical intervention | 1. Study was not powered to detect differences in obstetrical outcome such as macrosomia. 2. Some mothers were not treated with antihyperglycemic medication even though indicated based on CGMS values | |
10 | Seshiah et al. ( |
72 h MiniMed Medtronic Once | Prospective observational study | CGM use | Postprandial time to peak | 1. Only 3 SMBG measurements per day 2. Small sample size | |
11 | Dalfra et al. ( |
48-h GlucoDay S (GDS) Menarini Diagnostics Twice: 1 in 2nd trimester 1 in 3rd trimester | Prospective observational study | CGM vs. SBGM use | Relationship between glycemic profiles and fetal growth | 1. Small number of patients 2. BMI in women with diabetes was significantly higher before pregnancy compared to controls | |
12 | Mazze et al. ( |
72-h Guardian, Medtronic Once during 3rd trimester | Prospective, observational study | CGM vs. SBGM use in glyburide vs. insulin vs. diet | Diurnal glucose patterns of women | 1. Only 3 days of testing for each sensor 2. Women were not randomly selected | |
13 | Colatrella et al. ( |
72-h MiniMed, Medtronic Once | Prospective observational study | Suckling effect, CGM vs. SBGM use | Glucose profiles | 1. Significant difference in BMI between groups 2. It cannot be ruled out that differences in glycemic profiles between groups could be due to diet | |
14 | Dalfra et al. ( |
48 h GlucoDay Once each trimester | Prospective observational study | CGM use | Glycemic variabilityHbA1c | 1. Small sample size 2. Short monitoring period 3. Multiple recruitment centers with subsequent pooling of study population | |
15 | Su et al. ( |
72-h MiniMed, Medtronic Once | Prospective observational study | CGM vs. SBGM use | Glycemic variability and its association with B cell function | 1. Study was not powered to detect associations between glycemic variability and pregnancy outcomes | |
16 | Hernandez et al. ( |
72 h MiniMed Medtronic Twice | Randomized crossover study | Higher vs. lower carbohydrate diets | Postprandial glucose levels and insulin levels | 1. Small study sample 2. Short duration 3. Highly controlled diet exposure | |
17 | Yu et al. ( |
72-h MiniMed, Medtronic In SMBG group, twice (1st and 5th week of study) In SMBG and CGM group, every 2-4 weeks from study start to delivery | Prospective observational study | CGM vs. SBGM use, insulin vs. diet | Maternal complications: PE, miscarriage, IUFD, cesarean delivery. Neonatal outcomes: GA, preterm birth, BW, BW percentile, neonatal complications | 1. Sample size inadequate for substantial positive cases of neonatal complications 2. In routine care group CGM data was analyzed in relation to pregnancy outcomes even though they were obtained only in 1st and 5th week of study | |
18 | Kusunoki et al. ( |
48-h MiniMed, Medtronic Once during the first 3 weeks of GDM diagnosis | Prospective observational study | CGM vs. SMBG use | Postprandial hyperglycemia HbA1c | 1. No controls of healthy pregnant women | |
19 | Sung et al. ( |
6–7 days Seven and Seven Plus, Dexcom Avg use 4.8 days | Prospective observational study | CGM vs. SMBG use, food diary effect | Primary outcome: BW percentile Secondary outcomes: unplanned operative delivery and macrosomia | 1. The GDM diagnostic criteria was changed midway through study 2. Data was collected only during 24–28 weeks gestation 3. Sample size was underpowered to detect differences in secondary outcomes | |
20 | Wang et al. ( |
72 h MiniMed Medtronic Once | Prospective observational study | CGM use | Glycemic variability | 1. Sample size was underpowered to detect differences in subgroups 2. Factors such as physical activating and emotional stress could not be controlled and could affect glycemic variability | |
21 | Alfadhli et al. ( |
3–7 days Minimed Medtronic Once | Prospective open label randomized controlled study | CGM vs. SMBG use | Maternal glycemic control Pregnancy outcomes Glucose variability | 1. Single use of CGM 2. Small sample size | |
22 | Carreiro et al. ( |
72-h Minimed, Medtronic Once | Prospective observational study | CGM vs. SMBG use | Glucose profiles Effects of dietary counseling on glucose profiles | 1. Pooled glucose profiles is one summary point rather than all measurements. 2. No analysis on perinatal outcomes 3. No evaluation of the same patients before and after dietary counseling with CGM | |
23 | Wei et al. ( |
48–72-h Gold MiniMed, Medtronic Once | Prospective, observational, open-label randomized controlled trial | CGM vs. SMBG use, diet vs. insulin | Maternal: GWG, cesarean section Neonatal: BW Apgar score at 5 min HbA1c levels Glucose variability | 1. Small sample size with no significant differences in outcomes 2. Education management was not blinded (Hawthorne effect) | |
24 | Naik et al. ( |
72-h MiniMed Medtronic Once | Prospective observational study | CGM vs. SMBG, Medical nutrition intervention vs. insulin | Masked hypoglycemia (interstitial glucose levels <2.7 mmol/L [48.6 mg/dL] for >30 min without symptoms detected by CGM) | 1. More women in CGM group underwent cesarean section but most of them were elective 2. Small sample size | |
25 | Panyakat et al. ( |
72 h iPro2 Medtronic Once | Prospective observational study | CGM use | Glycemic variability Pregnancy outcomes | 1. Low incidence of perinatal outcomes 2. Study conducted in third trimester only | |
26 | Paramasivam et al. ( |
7 days iPro2 Medtronic Three times | Prospective randomized open label controlled trial | CGM vs. SMBG use | HbA1c | 1. Small sample size 2. Unblinded participants and practitioners | |
27 | Pustozerov et al. ( |
7 days iPro2 Medtronic | Prospective observational trial | Mobile app, CGM use vs. SMBG | Postprandial peak glucose levels Postprandial time to peak FBG | 1. Self-reported food intake 2. Small sample size 3. Accuracy of prediction models has not been proven | |
28 | Scott et al. ( |
Up to 14 days FreeStyle Libre FGM system (Abbott Diabetes Care) Once | Prospective observational trial | CGM vs. SMBG | Accuracy User acceptability Safety evaluation | 1. Sample size not powered to detect accuracy between subgroups 2. Short term study | |
29 | Voormolen et al. ( |
5–7 day iPRO2 retrospective CGM, Medtronic Once in every 6 weeks | Open label, multicenter, randomized controlled trial | CGMS vs. SMBG use | Primary outcome: macrosomia, Secondary outcomes: BW, neonatal and maternal morbidity HbA1cGlucose variability | 1. Enrollment took place over more than 4 years 2. High number of patients refused continued use of CGM 3. Cannot compare CGM related outcomes b/c not blinded |
We summarize the major findings categorized below:
Two studies which focused on user acceptability found that CGM use was generally well-tolerated by patients. McLachlan et al. demonstrated that CGM was able to provide an additional 62% of information that is missing in patients' self-recorded glucose diaries (
Other outcomes of articles.
1 | McLachlan et al. ( |
1. Positive user feedback |
2 | Colatrella et al. ( |
Suckling did not affect blood glucose profiles significantly. |
3 | Wang et al. ( |
Previous GDM vs. w/out previous GDM: |
4 | Scott et al. ( |
CGM vs. SMBG: |
Two RCTs and 1 prospective cohort showed no differences in any maternal and fetal outcomes between CGM use and SMBG use among GDM pregnancies, while 3 other studies, including 1 RCT and 2 prospective cohorts, reported otherwise.
Major findings of articles with focus on pregnancy outcomes.
1 | Kestila et al. ( |
CGM vs. SMBG: |
CGM vs. SMBG: |
2 | Yu et al. ( |
CGM vs. SMBG: |
CGM vs. SMBG: |
3 | Sung et al. ( |
Hyperglycemia and BW |
|
4 | Alfadhli et al. ( |
CGM vs. SMBG: |
CGM vs. SMBG: BW: Nil |
5 | Wei et al. ( |
CGM vs. SMBG: |
CGM vs. SMBG: BW: Nil |
6 | Voormolen et al. ( |
CGM vs. SMBG: |
CGM vs. SMBG: |
Wei et al. randomly assigned 120 pregnant women with GDM to either CGM (
In addition, one prospective observational study also found no significant differences in maternal outcomes between CGM and SMBG groups. Kestila et al. enrolled 73 women with GDM and assigned 36 to CGM and 37 to SMBG (
On the contrary, 2 studies found significant differences. Voormolen et al. randomized 109 women with GDM to CGM vs. standard treatment (SMBG 4–8 times daily and HbA1c levels every 4 weeks) (
The same studies also compared fetal outcomes between CGM and SMBG groups and all three RCTs found no significant differences (
Two prospective observational studies that compared CGM vs. SMBG to detect dysglycemia concluded that CGM detected more hypoglycemia and hyperglycemia incidents. Chen et al. conducted a study in Israel (
Major findings of articles focused on dysglycemia and glycemic profiling.
1 | Chen et al. ( |
↑ Hypoglycemia |
Hyperglycemia and Hba1c: Nil |
2 | Ben-Haroush et al. |
T1D vs. GDM: |
|
3 | Buhling et al. ( |
GDM vs. NDP: |
|
4 | Yogev et al. ( |
GDM vs. NDP: |
|
5 | Buhling et al. ( |
GDM vs. NDP: |
|
6 | Cypryk et al. ( |
GDM vs. NDP: |
|
7 | McLachlan et al. |
||
8 | Seshiah et al. ( |
GDM vs. NDP: |
|
9 | Dalfra et al. ( |
GDM w/insulin vs. GDM |
|
10 | Mazze et al. ( |
GDM vs. NDP: |
|
11 | Colatrella et al. ( |
GDM vs. NDP: |
|
12 | Dalfra et al. ( |
GDM vs. NDP: |
|
13 | Su et al. ( |
GDM vs. NDP: |
|
14 | Kusunoki et al. ( |
Postprandial glucose levels and |
|
15 | Wang et al. ( |
pGDM vs. w/out pGDM: |
|
16 | Alfadhli et al. ( |
CGM vs. SMBG |
|
17 | Carreiro et al. ( |
GDM vs. NDP: |
|
18 | Naik et al. ( |
GDM vs. NDP: |
|
19 | Panyakat et al. ( |
CGM: |
|
20 | Pustozerov et al. |
GDM vs. NDP: |
As expected, all three studies focusing on glucose profiling found that women with GDM had higher average glucose levels and that CGM was better than SMBG in detecting subtle glucose changes. Yogev et al. enrolled 117 patients (82 GDM vs. 35 NDP) and identified more asymptomatic hypoglycemic episodes (i.e., no dizziness or sweating due to hypoglycemia) by using CGM in the GDM group compared to the controls (25 vs. 0,
The majority of research (4 out of 5, 80%) showed that patients with GDM had higher postprandial glucose levels. Naik et al. enrolled 30 women (20 GDM vs. 10 NDP) and found 1 h postprandial glucose levels were higher in women with GDM than NDP [7.21 mmol/L (130 mg/dL) vs. 5.98 mmol/L (108 mg/dL)
In terms of postprandial time to peak, there was no consensus among researchers. Buhling et al. conducted two studies focused on postprandial time to peak. Initially, they found that patients with GDM had longer time to peak compared to patients with NDP (54 vs. 47 min,
For postprandial time to resume, only one study by Ben-Haroush et al. enrolled 45 women with GDM and found that women with diet-treated GDM took a longer time to return to pre-prandial glucose levels than those with insulin-treated GDM (134 vs. 111 min,
Glycemic variability was consistently found to be increased in patients with GDM compared to NDP. Mazze et al. enrolled 76 patients (51 NDP, 25 GDM) and found significantly higher glucose variability in the GDM group compared to NDP [Interquartile range (IQR): 1.94 mmol/L (35 mg/dL) vs. 1.3 mmol/L (23 mg/dL),
Interestingly, there was one study which compared mothers of NDP with and without history of GDM. Wang et al. recruited 96 women (48 NDP with prior GDM and 48 without) and found that those with prior GDM had higher MBG [6.5 mmol/L (117 mg/dL) vs. 5.9 mmol/L (106 mg/dL),
However, one study did report contrasting results relating to glycemic variability from the findings above. Cypryk et al. enrolled 19 pregnant women (12 GDM, 7 NDP) and found no significant differences between groups for mean 24 h glycaemia, mean glucose level during the night, and duration of glycaemia below 3.3 mmol/L [60 mg/dL] or above 6.7 mmol/L [120 mg/dL], regardless of whether CGM or SMBG was used to measure the parameters (
All four studies focused on using CGM to make medication adjustments in patients with GDM found that CGM led to more treatment changes. Yogev et al. recruited 2 GDM patients and CGM was worn twice (once at baseline and once at 4 weeks after treatment) (
Major findings of articles with focus on clinical and intervention utility.
1 | Yogev et al. ( |
↑ Medical monitoring and |
|
2 | Kestila et al. ( |
↑ Medical monitoring and |
|
3 | Hernandez et al. |
↑Complex-carbohydrate |
|
4 | Wei et al. ( |
↑ Medical Monitoring and |
CGM vs. SMBG: ↓ |
5 | Panyakat et al. ( |
GWG and BW |
One trial conducted by Paramasivam et al. studied CGM vs. SMBG use in preventing HbA1c increases in 50 women with insulin-treated GDM (
Only one RCT studied GWG and found that CGM use lowered the amount of GWG compared to SMBG. Wei et al. recruited 120 women with GDM and randomized them to CGM (
Kusunoki et al. enrolled 22 patients with GDM and used CGM to find that postprandial glucose levels were positively correlated with HbA1c levels (
Sung et al. recruited 53 healthy pregnant women and followed them with gestational diabetes screening (
Hernandez et al. used CGM to monitor changes in glycaemia in a crossover study using two different diets: a conventional lower carbohydrate and higher fat diet vs. a higher-complex carbohydrate and lower fat diet (
A different study by Panyakat et al. which enrolled 55 women with GDM and investigated the associations between third trimester glucose variability parameters and pregnancy outcomes using CGM (
In this systematic review with 29 online published original articles, CGM use has better user acceptability and feedback regarding satisfaction than SMBG use in daily glucose monitoring. Furthermore, most studies showed that CGM use in pregnant women with GDM was more effective in detecting hypoglycemia, hyperglycemia and increased glycemic variability than SMBG use in both GDM subjects and non-GDM subjects. As for treatment effect, CGM use led to more frequent use of insulin, better glycemic control, and reduced GWG in patients with GDM. However, most studies showed inconclusive results regarding CGM use in order to improve maternal and fetal outcomes.
Current research shows that CGM is able to provide more comprehensive glucose data and is convenient for the patient (
In our review, there are consistent findings on the effect of CGM use on detection of dysglycemia (
One major gap in clinical knowledge—whether CGM use improves maternal and fetal outcomes—has not been filled with plausible or convincing results. Past findings are still equivocal, as described above (
There are a few other aspects of CGM which are worth exploring in future research, such as pregnant users' acceptability and effect on GWG and glycemic control. In our review, only two studies surveyed user acceptability and accuracy of CGM (
The strength of this literature review was the novelty of summarizing the clinical utility and treatment effects of CGM use on pregnancies complicated by GDM only, via a set of stringent selection criteria across three reputable medical databases. However, this review is not without limitations. First, there was a possibility of selection bias as we only included articles written in English with full text available and published in the past 2 decades. Second, when filtering articles by title, we might have excluded relevant articles that did not have the keywords (i.e., CGM, pregnancy, GDM) in their title.
In summary, there is sufficient evidence showing that CGM is effective at capturing gestational glucose profiles and improving treatment effect among pregnant women with GDM. The use of CGM provides good user acceptability, detects more dysglycemia than SMBG use, and detects higher glycemic variability in GDM pregnancies than normal pregnancies. Since CGM use is somewhat effective at improving GDM pregnancy outcomes, further research with larger sample sizes, better compliance, and longer monitoring times to detect these effects are warranted.
All datasets for this study are included in the manuscript and the supplementary files.
QY drafted the manuscript and conducted the literature search. IA edited the manuscript and partially conceived the research idea. KT edited the manuscript and provided funding for this research. L-JL verified the literature search, conceived the research idea, and edited the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Gestational diabetes
Self-monitoring of blood glucose
Continuous glucose monitoring
Randomized controlled trials
Birth weight
Gestational age
Gestational weight gain
Non-diabetic pregnancies
Internal Association of Diabetes and Pregnancy Groups
Hyperglycemia and adverse pregnancy outcome
World Health Organization
National Institute for Clinical Evidence
Type 1 Diabetes
Type 2 Diabetes
GlucoDay S
Neonatal intensive care unit
Impaired glucose tolerance
Polycystic ovarian syndrome
Body Mass Index
Interquartile range
Mean amplitude of glucose excursions
Mean of daily differences
Standard deviation of blood glucose
Mean of continuous 24 h blood glucose.