Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study


  • Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs) is a multicentre study that is designed to reflect the demography of the pregnant population in the USA.

  • GO MOMs participants wear blinded continuous glucose monitoring devices at four timepoints over the course of pregnancy and complete oral glucose tolerance tests at both 10–14 weeks’ and 24–28 weeks’ gestation, generating a uniquely valuable data resource for characterising the longitudinal glycaemic profile of pregnancy.

  • The study design and sample size for GO MOMs will support development and validation of predictive criteria for gestational diabetes in pregnant individuals at 24–28 weeks’ gestation and large-for-gestational-age birth weight in newborns using early pregnancy data.

  • Multiple laboratory measures and chart abstraction data will complement glycaemic measurements.

  • As in any observational, longitudinal study, confounders and missing data may be limitations of the GO MOMs study.


Gestational diabetes mellitus (GDM) affects 7.8% of pregnant individuals in the USA1–4 and is associated with an increased risk of adverse pregnancy outcomes.5–8 Further, it is a harbinger of long-term metabolic disease in affected parents and children exposed in utero.5–8 Randomised trials have demonstrated that treatment of GDM in the third trimester results in a reduction in the frequency of large-for-gestational-age birth weight (LGA) and other adverse perinatal outcomes.9–14 Therefore, in the USA, all pregnant individuals accessing prenatal care are typically screened for GDM between 24 and 28 weeks’ gestation.15 16

Due to the increasing prevalence of both obesity and pre-diabetes, along with the recognition that individuals who meet traditional GDM criteria early in pregnancy have a greater risk of adverse outcomes than those diagnosed later, there is growing interest in first trimester identification of hyperglycaemia.17 Although treatment for GDM in the third trimester does not seem to result in improvements in long-term sequelae for exposed neonates, whether treatment of hyperglycaemia in early pregnancy would be associated with decreased metabolic risk in children exposed in utero is not well understood.18–20 Available evidence suggests that the mechanism by which in utero exposure to hyperglycaemia might lead to obesity and metabolic disease later in life is through fetal hyperinsulinaemia and the resultant accrual of excess adipose tissue.21 Studies of amniotic fluid insulin levels demonstrate that fetal hyperinsulinaemia occurs as early as 15 weeks’ gestation, preceding the gestational age at which GDM is usually diagnosed22–24 and suggesting that intervention at earlier gestational ages than the current standard might prevent long-term sequelae. However, available trials of screening and treatment at less than 20 weeks’ gestation based on conventional GDM diagnostic criteria have not consistently demonstrated a beneficial effect on birth weight or other adverse perinatal outcomes.25–28

Although the International Association of Diabetes and Pregnancy Study Groups (IADPSG),29 the American Diabetes Association (ADA)16 and the American College of Obstetricians and Gynecologists (ACOG)15 recommend early pregnancy diabetes screening for individuals at increased risk, a 2021 United States Preventive Services Task Force guideline concluded that evidence was insufficient to assess the balance of benefits and drawbacks of screening for hyperglycaemia before 24 weeks’ gestation.30 In addition, the optimal method and criteria for diagnosing hyperglycaemia in early pregnancy have not been established.31–39 As a result, various testing modalities including haemoglobin A1c (A1c), fasting glucose and oral glucose tolerance tests (OGTTs) are used with variable criteria applied.

In 2017, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) convened a workshop to identify research gaps in GDM and highlighted three areas related to early diagnosis of GDM as priorities for further investigation: (1) diagnostic criteria and definitions, (2) alternative markers for diagnosis and (3) effects of early diagnosis and treatment on outcomes.20 The NIDDK-supported Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) was designed to address these gaps. The goal of GO MOMs is to use early pregnancy glycaemia to predict late pregnancy GDM (diagnosed with traditional criteria) and LGA. A secondary objective is to provide a comprehensive, longitudinal description of changes in glucose throughout pregnancy leveraging continuous glucose monitoring (CGM). A substudy, the GO MOMs Nutrition Study, is collecting dietary information on a subset of participants and will link dietary components to maternal glycaemia and offspring outcomes. It is hoped that these data will elucidate improved approaches for GDM screening in early pregnancy and inform future clinical trials of early GDM treatment.

Methods and analysis

GO MOMs is being conducted at seven clinical centres (table 1), the Biostatistics Research Center (BRC) at Northwestern University Data Analysis and Coordinating Center (Chicago, IL) and the Central Laboratory at the Advanced Research and Diagnostic Laboratory at University of Minnesota (Minneapolis, MN). The protocol and study design were developed by the GO MOMs steering committee, which consists of investigators from each clinical site, the BRC, the Central Laboratory and the NIDDK.

Table 1

Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study centres

Eligibility, recruitment process and consent

Inclusion and exclusion criteria are described in table 2. Prior to initial study procedures at 10–14 weeks’ gestation, participants must have an ultrasound confirming dating and a viable, singleton gestation. During the first study visit, participants undergo a 75-gram, 2-hour OGTT and haemoglobin A1c measurement. Both the OGTT and the haemoglobin A1c are performed because the OGTT is the most sensitive method for detection of diabetes and the haemoglobin A1c is currently the most commonly used method to diagnose diabetes. If haemoglobin A1c >6.5%, fasting glucose >126 mg/dL or 2-hour glucose >200 mg/dL, the participant is excluded, having met criteria for overt diabetes.16 Participants who do not meet these criteria are eligible to continue in the study, and results below these thresholds remain masked to participants, their providers and research staff.

Table 2

Glycemic Observation and Metabolic Outcomes in Mothers and Offspring inclusion and exclusion criteria

Recruitment strategies leverage each site’s unique clinical and electronic medical record (EMR) infrastructure. Once identified, potential participants are approached to gauge interest and provide details on study participation. Written informed consent is obtained prior to study procedures.

Study cohort

The study opened to enrolment in April 2021. Recruitment is projected to continue until January 2025. We expect the GO MOMs cohort to represent the demography of the population of US pregnant individuals, including race and ethnicity, body weight distribution and the adult reproductive age spectrum.40


Table 3 summarises the primary, secondary and exploratory outcomes.

Table 3

GO MOMs primary, secondary and exploratory outcomes

Primary outcome in pregnant individuals: GDM

GDM is ascertained between 24 and 28 weeks’ gestation according to Carpenter-Coustan criteria applied to a 3-hour, 100-gram OGTT.15 16 We chose the Carpenter-Coustan criteria because they are the most commonly used criteria for GDM diagnosis in the USA. Given that the 1-hour non-fasting glucose challenge test (GCT) has imperfect sensitivity,41 GO MOMs participants forego the 1-hour GCT and all complete the 3-hour diagnostic OGTT for GDM ascertainment.

Primary outcome in newborns LGA

LGA, defined as birth weight greater than the 90th percentile for gestational age and sex according to a 2017 USA based reference,42 is the primary newborn outcome. Fetal overgrowth is the most common clinically relevant consequence of maternal hyperglycaemia, increases morbidity in the perinatal period, and confers long-term metabolic risk in childhood.43 The LGA coprimary outcome will facilitate development of new early pregnancy hyperglycaemia screening criteria for prediction of fetal overgrowth.


GO MOMs will develop a prediction model, incorporating glycaemic and clinical measures, which can be used in early pregnancy to identify individuals who will have GDM and/or deliver LGA infants (figure 1).

Figure 1
Figure 1

GO MOMs Primary Analyses Statistical analyses for the primary objectives will evaluate predictive models for the gestational diabetes and large for gestational age birth weight co-primary outcomes. Training and validation data sets will be identified a priori with all observations from a subset of sites used for training, and observations from the remaining sites use for validation.

Glycaemic predictors

Models will incorporate summary measures of CGM or OGTT data obtained at visit 1 (V1). ‘Excess time above range’ will be the primary summary measure of CGM data to best reflect hyperglycaemia. The primary predictor will be nocturnal percentage of time above range, calculated from midnight to 06:00.44 Overnight hyperglycaemia has previously been linked to LGA in GDM.45 A range of cut-off values for both the glucose threshold defining ‘above range’ and the percentage of time spent above a given threshold to define ‘excess’ will be evaluated. Other CGM metrics including 24-hour time above range and mean glucose will also be evaluated. Additionally, timed OGTT glucose measurements obtained at V1 will be evaluated as predictors of outcomes, starting with the IADPSG criteria for GDM46 (fasting ≥92 mg/dL, 1 hour ≥180 mg/dL, 2 hours ≥153 mg/dL) and exploring various cut-offs for each timepoint to develop a dichotomous predictor using the V1 OGTT values. Secondary and exploratory analyses will incorporate continuous versions of CGM and OGTT data, CGM predictors from other gestational time points, and additional glycaemic and metabolic laboratory measures including maternal triglycerides, free fatty acids, haemoglobin A1c, C-peptide, insulin and calculated insulin physiology indices, complete blood count, and alternative glycated markers (glycated CD59, glycated albumin, 1,5-anhydroglucitol).

Clinical predictors

Predictive models that incorporate clinical factors and CGM or OGTT data will be compared with models including only clinical factors, specifically maternal age, body mass index (BMI) and family history of diabetes. These were selected based on clinical factors that have been commonly used in previous reports of clinical predictive models for GDM.47–53 While previously published predictors for GDM and LGA have sometimes included race and ethnicity, GO MOMs will develop a model that does not incorporate race and ethnicity variables. Race and ethnicity are social constructs which, when included in prediction models, could perpetuate healthcare disparities.54 55 Additional clinical factors may also be evaluated for their contributions to predictive accuracy.

Study procedures


Table 4 summarises the study procedures following enrolment. To capture glycaemic changes across pregnancy, we conduct visits and place CGM devices at four timepoints: 10–14 (V1), 16–20 (visit 2: V2), 24–28 (visit 3: V3) and 32–36 (visit 4: V4) weeks’ gestation. We administer a 2-hour 75-gram OGTT at V1 and a 3-hour 100-gram OGTT at V3. Within 72 hours of delivery, neonatal anthropometric measures are obtained. A postpartum follow-up survey occurs remotely between 4 and 13 weeks after delivery. Medical record abstraction occurs 30–90 days after delivery and captures relevant diagnoses and hospital readmissions. The timing of the gestational windows for the V1 and V3 in-person study visits was chosen to facilitate data collection for the primary analyses. The gestational windows for V2 and V4 were chosen to capture an additional early pregnancy time point (V2) and to facilitate assessment of the impact of GDM treatment (V4) prior to term.

Table 4

GO MOMs visits and procedures

Continuous glucose monitoring

CGM provides a nuanced, detailed determination of dynamic glycaemic patterns by measuring interstitial glucose every 5 min, providing up to 288 glucose readings per day. We chose the Dexcom G6 Pro CGM (Dexcom, San Diego, CA) because of its demonstrated accuracy in pregnancy,56 ability to blind glucose values and lack of need for fingerstick calibration. Based on a participant’s preference, the CGM is placed on the abdomen, upper arm, buttock or lower back/hip. CGM devices are used in ‘blinded’ mode so that participants, providers and the GO MOMs research team do not observe participants’ CGM data. For 10 days after each visit, the participant wears the device and subsequently returns it to the clinical centre. CGM data are downloaded for quality assessment and summary by the BRC. If there are less than 5 days of data, participants are asked to repeat the CGM for a full 10-day wear if this can be accomplished during the study visit window.

Oral glucose tolerance tests

Both the 2-hour 75 g (V1) and 3-hour 100-gram (V3) OGTTs are conducted after an overnight fast (≥8 hours duration). After fasting samples are drawn (table 4), participants consume the oral glucose load within 10 min. Timing of hourly sample collection is based on when OGTT beverage consumption begins. To minimise in-vitro glycolysis,57 blood samples from the OGTT are immediately placed on ice, centrifuged within 15 min of collection and promptly frozen at −80°C. Samples are shipped to the study’s central laboratory where they are assayed within 80 hours of collection. A backup plasma sample from each draw time is stored at each local site at −80°C to allow for re-evaluation of glucose levels in case of primary sample loss or error. Glucose is measured in EDTA plasma by a hexokinase method on the Roche Cobas c502 chemistry analyzer (Roche Diagnostics, Indianapolis, IN); the inter-assay CV is 2.4% at a mean concentration of 98.5 mg/dL and 3.1% at a mean concentration of 229.8 mg/dL. Additional periodic monitoring of the central laboratory glucose assay is assessed via measurement of value-assigned standard reference material from the National Institute of Standards and Technology and accuracy-based proficiency testing programmes that compare results to those obtained by a reference method procedure. Average glucose values for the study population are reviewed periodically by the study’s laboratory committee to evaluate for sample drift over time. Masked OGTT results are reviewed by the same committee to evaluate for potential within-OGTT sample swaps. Both participants and study personnel are masked to the results from the V1 OGTT and A1c, unless the results are consistent with overt type 2 diabetes by ADA criteria.16 Results of the 3-hour OGTT similarly remain masked unless the glucose levels are consistent with GDM by Carpenter-Coustan criteria.16 If overt diabetes (V1) or GDM criteria are met (V3), the BRC shares results with the clinical centre, who notifies the participant’s obstetric provider. Additional safety criteria for unblinding the 3-hour OGTT results include a fasting glucose of ≥126 mg/dL or a 2-hour or 3-hour glucose value of ≥250 mg/dL. If GDM is diagnosed at V3, it is treated according to standard practice by local obstetric providers.

Additional biospecimen collection

Maternal blood and urine are obtained at each visit for additional laboratory measurements and future use (table 5).

Table 5

Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs) laboratory measures and biospecimens


Participant height is measured at V1 using a Seca Stadiometer 217 (portable) or Detecto Adult Stadiometer (non-portable). Weight is measured using a calibrated Seca Scale 869 at V1 through V4. These measurements are obtained two times; if the first two measurements differ by ≥0.5 cm for height or ≥0.5 kg for weight, a third measurement is taken. Measurements of newborns are obtained within 72 hours of delivery and include length using an Ellard length board, weight using a calibrated Seca 334 scale, flank skinfold using a calibrated Harpenden calliper, and abdominal circumference using the Gulick II tape measure. Newborn measurements are obtained two times; if the first two measurements differ by ≥0.5 cm for length, ≥10 g for weight, ≥0.5 mm for flank skinfold or ≥0.5 cm for abdominal circumference, a third measurement is taken.

Biospecimens are shipped to the GO MOMs Central Laboratory. Glucose, insulin, C-peptide, haemoglobin A1c and complete blood counts are assayed within 80 hours of collection.


At V1, staff interview participants about obstetrical history, family history of diabetes, alcohol and tobacco use, medical conditions, medication use, food insecurity and other social and demographic information. Information about medical conditions, medication use, alcohol and tobacco use are updated at V2, V3 and V4. To gather data on sleep, which may be associated with glycaemia during pregnancy,58 participants complete the Pittsburgh Sleep Quality Index survey at V1 and V3. At V4, participants complete questionnaires on their perceptions of CGM and OGTT; those diagnosed with GDM also answer questions about nutritional management. The remote postpartum survey includes questions about breast feeding and maternal and newborn hospital admissions. Questionnaires are available in both English and Spanish.

Chart abstraction and adjudication

Data abstracted from each site’s EMR are used to identify the newborn primary outcome and the predefined secondary and exploratory outcomes. Adjudication committees review outcomes requiring decision making beyond what is noted in the EMR and GDM cases diagnosed outside the study.

Nutrition substudy

A subset of participants who enrolled in GO MOMs between February 2023 and February 2024 are participating in the GO MOMs Nutrition Study. Substudy participants complete six 24-hour dietary recalls using the Automated Self-Administered 24-Hour Dietary Assessment Tool.59 Two recalls occur during the V1 and V3 study visits and four unannounced recalls occur after each of the four study visits on a random day during the CGM wear period.

Risk to participants

The study is considered minimal risk to participants; risks are described to participants during the informed consent process. These include risks associated with blood drawing (eg, local pain, irritation, bruising, anxiety, syncope), oral glucose tolerance testing (eg, headache, vomiting, symptoms of hypoglycaemia), collection of health information (eg, loss of confidentiality) and CGM placement/wear (eg, local pain, skin irritation, bleeding). At each visit, participants are asked about their experience with CGM wear and whether any problems occurred, such as skin irritation. If skin irritation from CGM occurs, the study team works with the participant to determine if future CGM placements should occur, with the option of using a barrier film spray to protect skin. Adverse events are reviewed by site investigators, recorded in the study database and reported to the IRB and Observational Study Monitoring Board (OSMB) when appropriate.

Statistical considerations

Statistical analyses for the primary objectives will evaluate predictive models based on logistic regression for GDM and LGA primary outcomes (figure 1). Training and validation data sets will be identified a priori with all observations from a subset of sites used for training, and observations from the remaining sites used for validation. First, predictive models using clinical variables at V1 will be developed for GDM and LGA. Model parameters will be estimated using 10-fold cross-validation in the training data set, maintaining equal GDM and LGA outcome frequencies across rounds of cross-validation.

After finalising predictive models using clinical variables, models that incorporate summary measures of V1 CGM or OGTT data will be developed. Predictive model parameters using CGM or OGTT data summaries will be estimated using 10-fold cross-validation in the training data set, maintaining equal GDM and LGA outcome frequencies across rounds of cross-validation. Improvements in predictive accuracy for models that add CGM or OGTT data to clinical factors will be examined. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using assumed values of 5.6% GDM (the most recent published prevalence estimate at the time of study design3) and 10% LGA population prevalence will be used for primary reporting. PPV and NPV will also be estimated using the outcome prevalence in GO MOMs. Values of PPV and NPV will be evaluated across the full range of potential cut-offs in logistic regression models, and cut-offs meeting desired NPV and maximising PPV under cross-validation will be selected to define optimal predictive models based on clinical factors alone, CGM and clinical factors, and OGTT and clinical factors. Formal statistical hypothesis testing to compare the optimal predictive model with CGM and clinical factors vs clinical factors alone, and the optimal predictive model with OGTT and clinical factors versus clinical factors alone, will be conducted using methods that account for the paired nature of PPV values under different tests in the same population.60 Predictive accuracy metrics for models optimised in the training data will be independently estimated along with 95% CIs in the validation data set.

Predictive modelling for GDM and LGA will be expanded in secondary analyses to discern whether models including clinical variables and CGM-based or OGTT-based criteria in the V1 time frame demonstrate comparable predictive performance across subgroups according to self-identified race and ethnicity and socioeconomic variables. This will be examined by evaluating statistical interaction terms between the subgroup and the CGM-based and OGTT-based and other clinical variables in logistic regression models, and by examining PPV, NPV, sensitivity and specificity point estimates and 95% CIs within subgroups. In addition to V1 CGM and OGTT metrics and clinical risk factors, the added predictive contribution of continuous summaries of CGM and OGTT data and additional laboratory measures for primary outcomes will be explored.

While outcome prediction is a priority for GO MOMs, statistical analyses will also be performed to explore associations of clinical, CGM and laboratory data obtained at V1–V4 with secondary maternal and newborn outcomes. Statistical methods will include regression modelling and generalised longitudinal linear mixed models. Exploratory dynamic risk prediction models using CGM data over the course of pregnancy will also be examined. Mediation analyses using structural equation models will be conducted to estimate the effects of GDM management, if diagnosed, on the association between maternal glycaemia and newborn outcomes.

Sample size and power

Target enrolment for GO MOMs is 2150 participants. Sample size calculations were based on the primary aims to assess the predictive capacity of CGM and OGTT summary measures at V1 for the primary outcomes. Evaluation of GDM and LGA outcomes will be viewed as separate analyses and 5% two-sided type I error will be used for evaluation of each outcome. Since two formal hypothesis tests will be conducted for each outcome (ie, the evaluation of each outcome with predictive models comparing clinical factors alone to models also including V1 CGM or OGTT data), results will be considered statistically significant at two-sided p<0.025 according to Bonferroni correction to maintain overall 5% type I error.

Sample size was determined based on evaluation of PPV for GDM and LGA at a set NPV for each. Published literature suggests that PPV for GDM using clinical factors that are identifiable early in pregnancy is approximately 0.20.47–53 Less information is available for LGA, but some reports indicate that a PPV of 0.15 is possible using maternal clinical risk factors that are identifiable early in pregnancy.61 Clinical factors in these predictive models uniformly included age and BMI and several also included family history of diabetes. GO MOMs was designed to detect a clinically meaningful increase in PPV from 0.20 to 0.40 for GDM and from 0.15 to 0.30 for LGA. Calculations were performed assuming 5.6% population prevalence for GDM based on the most recent published estimate at the time of study design3 and 10% population prevalence for LGA. NPV was held constant for GDM at 0.97 and for LGA at 0.95. Sample size calculations were based on simulation studies using hypothesis testing methodology that accounts for the paired nature of PPV values under different tests in the same population.60 Dependence was induced between observations of PPV under the null and alternative hypotheses using normally distributed random effects with mean 0 and between-subject variance ranging 0.1–1 (intraclass correlation coefficient 0–0.5). To detect the proposed improvements in PPV for GDM at 90% power, 860 GO MOMs participants with observed data for the primary GDM outcome are required. A sample size of 860 with complete data provides approximately 99% power to detect an increase in PPV from 0.15 to 0.30 at a constant NPV of 0.95 for the LGA primary outcome.

Existing literature does not support hypotheses about the exact CGM-based or OGTT-based criteria at V1 to be evaluated as predictors of GDM and LGA. Thus, a range of statistical summaries for CGM data and cut-off values for OGTT data will be explored. Given this, independent training and validation GO MOMs data sets are paramount. As noted above, 860 GO MOMs dyads will be required for hypothesis testing for improvements in PPV. Two times this number of GO MOMs participants will be enrolled, and training and validation data sets of equal sizes will be identified prior to predictive model development. Training and validation data sets will be designated after completion of data collection, but prior to formal statistical analyses. The full data from each site will be placed either into the training or validation data sets, maintaining comparable demographic and clinical characteristics and outcome frequencies across the two data sets. A sample size of 860 in the validation data set will provide 95% CIs with half-width of 0.10 surrounding the PPV estimate. Precision at this level is thought to be necessary to motivate potential changes in clinical practice for early screening.

In summary, 1720 participants (860 each in training and validation data sets) are required to accomplish the GO MOMs primary objectives. Assuming 80% of participants complete the study with observed outcomes, a total enrolment of 2150 participants is required.

Analyses of associations for secondary outcomes will collectively use observations from all 2150 participants. For analyses investigating associations of predictors with dichotomous secondary outcomes, assuming R2 of the primary predictor with other model covariates of up to 0.4, this sample size affords 90% power at nominal two-sided p<0.05 to detect ORs in the range of 1.25–1.60 for a continuous predictor higher by one SD for outcome frequencies ranging 0.05–0.30. ORs in the range of 1.59–4.31 are detectable for dichotomous predictors with frequencies ranging 0.05–0.30 for both the outcome and predictor. For continuous secondary outcomes, again assuming R2 of the primary predictor with other model covariates of up to 0.4, adjusted mean differences ranging 0.22–0.46 SD are detectable at 90% power at nominal two-sided p<0.05 for dichotomous predictors with frequencies ranging 0.05–0.30. Partial correlation of 0.10 is detectable for a continuous predictor with a continuous outcome. All calculations assume 80% of participants complete the study and have observed outcomes. Analyses of exploratory outcomes are not formally powered.

Patient and public involvement

During study development, participants who had previously enrolled in a similar pregnancy study at the Massachusetts General Hospital site62 were invited to provide feedback and input into study design, recruitment plan and research protocol via focus groups. At the Yale site, investigators convened a stakeholder meeting with community members and leaders from organisations in the local black and Latino communities for feedback on the study plans. The participants in the focus groups and stakeholder meeting strongly supported the scientific rationale and the importance of the study to relevant communities. Input from the focus groups and stakeholder meetings informed the timing of study visits to minimise participant burden, the participant remuneration plan, the strategy for participant engagement and retention, the sharing of individual-level data and the methods for dissemination of study results, among other study details. GO MOMs participants who have consented to future contact will be sent a letter about the results of the study after study completion and publication of results will be made available to the relevant wider patient communities.

Ethics and dissemination

The GO MOMs OSMB, an independent review group appointed by NIDDK, reviewed the study protocol and granted approval in October 2020. Vanderbilt University’s Institutional Review Board (Nashville, TN) granted protocol approval in January 2021 under IRB #202214. The BRC oversees certification of study personnel training to ensure standardisation of study conduct and data collection across sites.

After the study is completed and manuscripts addressing the primary and secondary hypotheses have been developed, a limited dataset will be transmitted to the NIDDK Central Repository, under the supervision of the NIDDK, for use by other researchers. De-identified biological samples will also be stored at the NIDDK Biorepository.


Despite clinical guidelines suggesting that at-risk pregnant patients should be screened for hyperglycaemia in the first trimester, there are knowledge gaps that hinder this approach. By gathering extensive data on glycaemia and other related biomarkers starting in the first trimester, GO MOMs may be able to identify better early pregnancy criteria for predicting GDM and hyperglycaemia-associated adverse outcomes. By identifying individuals at risk for GDM and LGA in the first trimester, novel screening strategies, and ultimately, treatments can be developed to improve pregnancy health.

GO MOMs will leverage CGM technology in order to identify glycaemic patterns that may predict GDM, LGA, and other pregnancy complications better than currently employed diagnostic tools. Indeed, CGM is now being leveraged in prediction models for development of type 1 diabetes.63–66 CGM has also been used to describe glycaemic patterns in cohorts of people without diabetes, leading to improved understanding of physiological glycaemic variation.67 Similarly, GO MOMs will describe physiological glycaemic patterns across pregnancy in a large cohort.

GO MOMs will extensively characterise the metabolic profile of pregnant participants. We will use insulin and C-peptide to assess insulin resistance and determine its relationship to the glycaemic patterns identified by CGM and GDM as diagnosed by traditional OGTT. We will also examine alternative GDM biomarkers, including plasma glycated CD59, glycated albumin and 1,5-anhydroglucitol.20 Lipids, which are associated with fetal growth,68 will also be examined. Such novel biomarkers, either alone or combined with CGM, could be a more reliable and efficient way to conduct GDM screening compared with OGTT and improve problems with reproducibility, adherence, patient burden and healthcare resources associated with currently recommended screening protocols.69–72

The GO MOMs cohort will be comprised diverse individuals representing the US birthing population, allowing for generalisability and facilitating translation of findings to US clinical practice. The results will inform future clinical trials designed to prevent GDM and its sequelae. The GO MOMs cohort can also serve as a vehicle for ancillary studies. Such studies could focus on understanding the relationship between in-utero exposure to hyperglycaemia and long-term outcomes in offspring, which would have the potential to help break the intergenerational cycle of obesity and diabetes. Follow-up of the cohort could examine long-term cardiometabolic outcomes in GO MOMs parents which could lead to more effective preventive strategies for type 2 diabetes and cardiovascular disease. The stored samples from the NIDDK Biorepository will be a new resource for research on diabetes, metabolic disease and pregnancy.

Strengths of the GO MOMs study include the large sample size of pregnant participants which will facilitate a training and validation framework for any new criteria developed, the anticipated representativeness of the cohort, the detailed longitudinal glycaemic profiling using multiple assessment methods, and the use of a central laboratory and standardised processing protocol for laboratory analyses. Limitations of the study include its observational nature, which will preclude the ability to make conclusions about causality for the observed associations, limited duration of CGM monitoring in each participant, lack of physical activity data collection, and nutrition data which is limited to a subset of the study population. The decisions about CGM monitoring duration and physical activity and nutrition data collection were made in an effort to minimise participant burden and increase adherence to the study protocol.

GO MOMs has the potential to advance our understanding of the effects of hyperglycaemia throughout gestation on outcomes for birthing persons and their children. Given the association between GDM and both short-term and long-term health consequences5–8 and the large number of people affected by GDM, data generated in GO MOMs could ultimately have a large impact on population health.

Ethics statements

Patient consent for publication


Writing Group: Camille E Powe (Diabetes Unit, Endocrine Division, Department of Medicine, Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA); Kimberly K Vesco, Erin S LeBlanc (Science Programs Department, Kaiser Permanente Center for Health Research, Portland, Oregon, USA); Jennifer Sherr (Department of Pediatrics, Division of Pediatric Endocrinology, Yale University School of Medicine New Haven, Connecticut, USA); Noelia Zork, Uma M Reddy (Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Columbia University Irving Medical Center, New York, New York, USA); Maisa Feghali (Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Maternal-Fetal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA); Erika Werner (Department of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, Massachusetts, USA); Denise M Scholtens (Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA).

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