case study of gestational diabetes

• Research article
• Open access
• Published: 07 February 2020

Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review

• Louise Craig 1 ,
• Rebecca Sims 1 ,
• Paul Glasziou 1 &
• Rae Thomas   ORCID: orcid.org/0000-0002-2165-5917 1  

BMC Pregnancy and Childbirth volume  20 , Article number:  76 ( 2020 ) Cite this article

29k Accesses

70 Citations

46 Altmetric

Metrics details

Gestational diabetes mellitus (GDM) - a transitory form of diabetes induced by pregnancy - has potentially important short and long-term health consequences for both the mother and her baby. There is no globally agreed definition of GDM, but definition changes have increased the incidence in some countries in recent years, with some research suggesting minimal clinical improvement in outcomes. The aim of this qualitative systematic review was to identify the psychosocial experiences a diagnosis of GDM has on women during pregnancy and the postpartum period.

We searched CINAHL, EMBASE, MEDLINE and PsycINFO databases for studies that provided qualitative data on the psychosocial experiences of a diagnosis of GDM on women across any stage of pregnancy and/or the postpartum period. We appraised the methodological quality of the included studies using the Critical Appraisal Skills Programme Checklist for Qualitative Studies and used thematic analysis to synthesis the data.

Of 840 studies identified, 41 studies of diverse populations met the selection criteria. The synthesis revealed eight key themes: initial psychological impact; communicating the diagnosis; knowledge of GDM; risk perception; management of GDM; burden of GDM; social support; and gaining control. The identified benefits of a GDM diagnosis were largely behavioural and included an opportunity to make healthy eating changes. The identified harms were emotional, financial and cultural. Women commented about the added responsibility (eating regimens, appointments), financial constraints (expensive food, medical bills) and conflicts with their cultural practices (alternative eating, lack of information about traditional food). Some women reported living in fear of risking the health of their baby and conducted extreme behaviours such as purging and starving themselves.

A diagnosis of GDM has wide reaching consequences that are common to a diverse group of women. Threshold cut-offs for blood glucose levels have been determined using the risk of physiological harms to mother and baby. It may also be advantageous to consider the harms and benefits from a psychosocial and a physiological perspective. This may avoid unnecessary burden to an already vulnerable population.

Peer Review reports

Gestational diabetes mellitus (GDM) is diagnosed by elevated blood glucose in pregnancy though the definition has changed repeatedly since its first description in the 1960’s [ 1 , 2 ]. The most frequently reported perinatal consequence of GDM is macrosomia (usually defined as a neonate weighing over 4 kg) which can increase the risk of caesarean section and shoulder dystocia. For the mother, there are also potential longer-term consequences including an increased risk of type 2 diabetes post-pregnancy and/or in later life [ 3 ]. The investigators of a large international Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study aimed to identify a cut-point in the continuum to decide the blood glucose level (BGL) thresholds that should be used to define GDM [ 4 ]. However, a definitive cut-point was not identified and using the HAPO data the International Association of the Diabetes and Pregnancy Study Groups (IADSPG) consensus panel recommended a BGL threshold associated with the risk of adverse infant outcomes (such as risk of macrosomia, excess infant adiposity and neonatal hyperinsulinemia) [ 5 ]. This change was controversial, and there is currently a lack of an agreed standard for diagnosing high blood glucose in pregnancy.

Pregnancy can be a vulnerable period when a woman is adapting and responding to changes in body perceptions, such as loss of strength or fitness, which can result in reduced self-esteem and depression [ 6 ]. Many women report depression and anxiety during pregnancy which often includes worry for the baby’s wellbeing [ 7 , 8 ]. A diagnosis of a health condition such as GDM could have a detrimental effect on a pregnant woman’s quality of life due to fears that the illness may affect her and/or her baby [ 9 ]. This has potential to convert pregnancy, a natural process, into one associated with risks, ill-health and increased surveillance [ 10 ]. Understanding a women’s response to the GDM diagnosis and its psychological impact has emerged as an important issue [ 11 ]. Some studies report women describing the initial response as one of ‘shock’ [ 12 , 13 ], ‘sadness’ and ‘guilt’ [ 13 ]. A women’s acceptance of risk and fear of complications is likely to influence the acceptability of various interventions. Therefore, it is imperative to amalgamate the findings of these studies to synthesise the array of potential psychosocial consequences of a diagnosis of GDM.

In many countries the prevalence of GDM is rising [ 14 , 15 , 16 ]. Some of this is due to the increasing age at which women are becoming pregnant, an increase in obesity amongst women, more testing during pregnancy, and better recording during pregnancy. However, much of the rise has occurred since 2013 when some countries adopted the new IADPSG criteria and testing regimen for gestational diabetes. This resulted in the anomalous position that two women in two countries with exactly the same glucose levels may or may not be diagnosed with GDM depending on the country’s definition. Caution had been previously raised that the new IADPSG definition would increase prevalence of women diagnosed with GDM by two-to-three-fold [ 17 ].

Despite a significant increase in prevalence of GDM after the introduction of the new IADPSG criteria [ 15 , 16 ], some pre-post studies suggest negligible clinical improvement in the adverse outcomes measured [ 17 , 18 ]. Findings from a qualitative study of 19 women of different cultural backgrounds investigating women’s experiences of a GDM diagnosis reported that the diagnostic criteria itself was viewed as ‘confusing’ by some women and treatment for their ‘borderline’ condition unnecessary [ 19 ].

Although multiple studies have considered the impact of a diagnosis of GDM, a systematic review to synthesise the evidence around the emotional impact of a diagnosis at different stages, i.e. time of diagnosis, after diagnosis, at the delivery of the baby, and post-delivery, is lacking. The findings could inform healthcare clinicians of women’s attitudes and the consequences of a diagnosis and illuminate potential opportunities to provide support and advise. Therefore, in this systematic review, we aim to synthesise the evidence of the psychosocial experiences a diagnosis of GDM has on women during pregnancy and the postpartum period.

We followed the Enhancing Transparency in Reporting the Synthesis of Qualitative Research Guidelines (ENTREQ; Additional file  1 : Table S1) [ 20 ]. We included primary studies published in peer-review journals that:

included pregnant women with a current diagnosis or women with a history of GDM;

provided qualitative data on the psychosocial experiences of a diagnosis of GDM on women across any stage of pregnancy and/or the postpartum period; and

where participants have provided an account of their experience or perspective of living with GDM

No restrictions were placed on country, written language, or year of publication.

Studies were excluded, if:

the primary aim was to identify barriers and/or facilitators to service as these focused on the management of GDM rather than the GDM diagnosis; or

participants were women diagnosed with diabetes before pregnancy

Abstracts, letters, editorials and commentaries were also excluded.

Search methods for identification of studies

The search strategy (MEDLINE version provided in the Additional file  1 ) was developed using a combination of Medical Subject Headings terms centred around three key areas: i) gestational diabetes mellitus ii) diagnostic testing for gestational diabetes mellitus and iii) patient experiences. The Systematic Review Accelerator software was used to translate the search strategy for each of the different databases and to remove duplicated articles [ 21 ]. We searched CINAHL, EMBASE, MEDLINE and PsycINFO databases from inception to April 2018. Forward and backward citation searching of included studies was conducted.

Selection process

A single reviewer (LC) screened the titles and abstracts of retrieved references using Endnote Version X7.7.1. Potentially eligible full-texts were independently reviewed by LC and RS with conflicts resolved via discussion. Two full-text studies published in Portuguese were first translated using Google Translate and then validated by a researcher with both spoken and written Portuguese language skills located within our research network.

Data extraction

All data labelled as results or findings including themes, categories, theories were extracted and imported into NVivo Version 12 by LC. Study characteristics were extracted by LC which included study location, reported research aims, study design, methodology and the analytical approach. Information about the diagnostic criteria used to determine GDM in women was also extracted.

Data synthesis and analysis

To synthesise the findings, we used a thematic synthesis described by Thomas and Harden [ 22 ]. Thematic synthesis has the potential for conclusions to be drawn based on common aspects across otherwise heterogeneous studies and produce findings that directly inform health practitioners [ 22 , 23 ]. Coding was inductive, with codes derived from the data. First, extracted text relevant to patient experiences and perspectives was coded line by line. A subset of studies ( n  = 5) were coded independently by LC and RS to develop a coding framework. Disagreements were resolved by discussion. LC and RS coded a further subset ( n  = 4) and established an inter-rater reliability of Kappa = 0.87. Following this, LC applied the coding framework to the remaining studies. New codes were iteratively developed as new concepts arose.

Second, relationships between the codes were identified by LC to form the basis of descriptive themes across the studies. Similar codes were grouped to generate themes and less frequently used codes were classified into sub-themes. In the final stage, analytical themes were developed to ‘go beyond’ the primary studies to amalgamate and interpret the findings. The relevant quotes to support each theme were tabulated.

Quality assessment

As recommended by the Cochrane Qualitative Research Methods Group, we assessed the quality of the included studies using the Critical Appraisal Skills Programme Qualitative Checklist (CASP). This tool uses a systematic approach to appraise three key areas: study validity, an evaluation of methodological quality, and an assessment of external validity [ 24 ]. Critical appraisal was conducted by one reviewer (LC) for all studies, with second reviewer appraisal (RS) for a sub-set of included papers. The findings from the two reviewers were compared and any contrasting items were discussed and re-reviewed.

The search identified 840 studies. After deduplication and screening of titles and abstracts 88 full-text articles were assessed (Fig.  1 ). Seven further articles were identified through citation searching. Data were extracted from 41 studies meeting eligibility criteria and were included in the review [ 11 , 12 , 13 , 19 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ].

Prima flow diagram

Study characteristics

The studies reflected a variety of sampling methods and data collection methods. For example, interviews were conducted in 34 studies [ 10 , 12 , 13 , 25 , 27 , 28 , 30 , 31 , 32 , 34 , 35 , 36 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 60 , 61 ], focus group methods were used in three [ 19 , 32 , 37 ], and interviews and focus groups were used in two studies [ 29 , 51 ]. Two studies used a mixed method approach [ 26 , 59 ]. The sample sizes ranged from 6 to 57 women. Eighteen studies were conducted in Europe, 10 in Australia, 9 in North America, and 2 studies each in Asia and South America. Table  1 details the characteristics of the included studies.

Quality appraisal

Most studies were assessed as high quality (Additional file  1 : Table S2). Study aims were stated in all but one study [ 47 ]. As the purpose of all included studies was to explore or gain knowledge, opinions or attitudes about GDM, the qualitative methods employed in all the studies were appropriate. Different study designs were used and in some cases the lack of reporting details made judgments of the appropriateness of study methods difficult. Data were collected in a way that addressed the research issue, however, a few authors did not discuss or report details such as saturation of data [ 31 , 47 , 56 , 59 ]. The relationship between researcher and participants was considered in only two studies [ 51 , 61 ]. Appropriateness of data analysis was assessed as “unclear” when there was a lack of details about how themes were derived.

Thematic analyses

Eight themes were generated from the data synthesis: 1. initial psychological impact; 2. communicating the diagnosis; 3. knowledge of GDM; 4. risk perception; 5. management of GDM; 6. burden of GDM; 7. social support; and 8. gaining control. The relevant quotes to support each theme are presented in Table  2 .

Initial psychological impact

When initially diagnosed with GDM, most women reported reactions such as self-blame, failure, fear, sadness, concern and confusion. Women often focused on the uncertainty of diagnostic prognosis and some considered it to be a life-altering experience. Some women felt lost and unsure what to do next. Often women felt an overwhelming sense of vulnerability and guilt. In some cases, the diagnosis was received positively and was viewed as an opportunity for lifestyle improvements. For example, some women viewed the diagnosis as a ‘ wake up’ call and were grateful for the chance to intervene and potentially prevent adverse outcomes for their babies and themselves. Some women viewed gaining less weight than expected during their pregnancy as a benefit of having a GDM diagnosis.

Communicating the diagnosis

Communication with healthcare professionals (HCPs) and their families was a common theme throughout the findings of the included studies. Generally, the level and quality of communication with HCPs was mixed – with some women reporting positive and informative encounters, while others described brief encounters with overly technical language and unsupportive consultations. The main issues were limited time available to spend with the HCP, lack of continuity of care and lack of understanding about the role of the HCP at follow-up. In some instances, women felt that GDM was not a topic that HCPs were keen to discuss - ‘the nurses, they never talked to me about my gestational diabetes’. [ 23 ] The level and quality of information provided was often conflicting, confusing or insufficient. Areas of contention were appropriate foods and the dietary changes that should be made.

Some women formed a dependency on HCPs to know what to do and on the electronic reminders for follow-up appointments and monitoring. Often women reported having no choice in treatment resulting in them feeling threatened and frustrated. Often women were automatically booked in for a caesarean section without consultation or lived in fear of this occurring. One woman referred to GDM as being over medicalised. Receiving limited information also prompted women to independently seek information about the impact and management of GDM from other sources such as the internet. However, some women found the internet limited for specific information or confusing.

Knowledge of GDM

Women had varying levels of understanding of GDM which impacted on their initial reaction to the diagnosis. Those who were able to explain the cause of GDM were able to process and accept the diagnosis more readily than those who had little understanding of GDM, or were confused as to how GDM occurred. Lack of knowledge also extended to and impacted on relatives. Some women stated that they would have preferred to be more prepared to receive the diagnosis by having early knowledge about the testing for diabetes. Women reported being on a steep learning curve, especially the onerous approach of dietary trial and error whereby women learnt what foods would increase their blood glucose level (BGL) and what food to avoid. Women also reported challenges in adopting new habits to manage their GDM, including understanding food labels and nutritional values of food. Often this required a trial and error approach. There was also a lack of understanding about the impact of GDM on their baby with some women believing it would be transmitted to their baby via breastmilk.

Risk perception

Women’s perception of risk were reported before the diagnosis of GDM, after they were diagnosed in pregnancy, and after the delivery. Some women attempted to understand their level of risk in context of family history. Some were very surprised by the diagnosis, especially if they were asymptomatic; and some women found it difficult to come to terms with the diagnosis. There was uncertainty about the severity of the condition. Some women considered the condition to be mild, downplaying the disease and believing that too much ‘ fuss’ was being made about GDM and other women doubted the diagnosis and its seriousness. Women often discussed: the adverse effects that GDM would have on her baby; frustration that the focus was on risks to the baby and less so them; their worry about the consequences for the future; and questioned the impact of insulin on the baby. Women worried that their diet was too restrictive for their growing baby and would not provide the nutrients that the baby required. Some women held the view that GDM was a temporary condition and would disappear once the baby was born, and many women reverted to old eating habits after the baby’s birth. Often women referred to the birth as a ‘ moment of truth ’ or as an endpoint to their GDM. This was also reflected in the level of care that the women received after the birth of their baby.

Managing GDM

Dietary management-related stress was commonly reported amongst interviewed women and was experienced by both insulin and non-insulin users. Stress and frustrations often occurred as a consequence of an unexpected abnormal blood glucose reading following strict adherence to dietary advice. Maintaining stable BGL was an ongoing struggle and in some cases the burden proved too much, with a few women ceasing employment. Insulin users described the process as a ‘ roller coaster ’ as well as the emotional and physical discomfort of injecting, while non-insulin users often became obsessed with a well-controlled diet, with some women viewing this as a way to avoid the use of insulin. Conversely, some women felt relieved when they were transitioned onto insulin, as it reduced the need for dietary restriction.

Making lifestyle changes was considered stringent and restrictive by the majority of women, and for some required ‘ major restructuring’ to their diet and daily routines to incorporate exercise. Some women reported extreme behaviours, including falsifying blood glucose readings, self-starvation and hiding their condition, including from family members. Often the impact of non-adherence to lifestyle changes resulted in guilt and belief that the baby would know they have cheated. Other pregnancy related ailments and the need to care for other children interfered with the ability to make the required changes. Women who had a specific culture-related diet discussed the impact and difficulty of applying or tailoring the dietary recommendations to their diet.

The key motivator to making required lifestyle changes, despite the associated hardships, was to minimise the risks to their unborn baby. Women prioritised the health of the baby over their own health and were willing to do anything to ensure that the health of their baby was not compromised. Over time, management of the GDM became a part of their normal routine for many women. However, some women expressed a desire to have a ‘ normal’ pregnancy similar to their friends, discussing that a diagnosis of GDM made them feel as though their pregnancy was atypical, leading to defining their pregnancy as ‘ abnormal ’ or as an ‘ illness ’. For one woman, it made her feel like an ‘ illegal’ person.

Burden of GDM

Women reported that a diagnosis of GDM came with extra responsibility, which added pressure whilst trying to juggle life commitments such as work, childcare, and daily living responsibilities. Monitoring and treating GDM placed burden on women’s daily routines and most woman agreed that taking BGL measurements was time consuming and disruptive. There was a constant need to prudently plan meals and co-ordinate the attendance at additional hospital appointments, all of which were considered time intensive, especially with travel and wait times. Women expressed that GDM consumed a lot of their thinking time e.g., ‘ I think about diabetes everyday’ and felt that they had to acknowledge GDM all the time and became ‘ super-conscious’ . In some instances, women reported a GDM diagnosis took away some of the ‘ joy of pregnancy ’ . One woman described her pregnancy as a ‘ misfortune’ . Women mentioned the financial burden of buying healthier food – ‘it would take lots of money just because it is so expensive to eat healthy’. [ 25 ] Women also considered the physical burden of GDM such as fatigue and the side effects of treatment such as insulin. There was a longer-term impact on family planning, where in some cases women decided not to have another child because they were fearful of enduring a similar restrictive and stressful pregnancy due to GDM.

Social support

Social support, including family and HCP support, was an important aspect for women during their experience of a GDM diagnosis. Changes in lifestyle often had an overflow effect, with other family members adopting healthier lifestyles. Women not in their country of birth, and without family, often reported feeling isolated and lonely. Disappointment and isolation were also expressed by some women when they perceived a lack of healthcare system support. This often occurred postnatally when the expectations of postpartum care were high, however, in reality, support was absent. In some cases, women were stigmatised by their families and in a few cases received undesirable feedback that they were not doing enough to protect their unborn child.

Gaining control

Control was a frequently used word when women described living with and managing a GDM diagnosis. Initially women reported a lack of control especially over their emotions, however, over time women transitioned from feeling like a victim of diabetes, to being active agents in controlling their GDM. The terms ‘ balance’ and ‘ adjustment’ were used to describe how some women tried to offset the strict compliance and active self-management with reducing their risk to their unborn baby and their own future risk of developing diabetes after pregnancy. Some women reported feeling empowered as their pregnancies progressed, especially when they gained more knowledge about GDM and what action they could take to accept and make sense of the diagnosis. Taking control included realising the changes that were required to their lifestyle, self-initiated care, and self-education. Often investigating alternative options, such as natural remedies outside those recommended by HCPs, provided women with some autonomy in managing their condition and some believed that it was a safer option to medication.

Summary of main findings

This synthesis of the qualitative evidence of women’s experiences of being diagnosed with GDM highlighted the psychosocial consequences a diagnosis of GDM can have on women. The purported benefits of a GDM diagnosis identified from our review, were largely behavioural and included an opportunity to improve health, prevent excessive weight gain, control weight during pregnancy, and prompts to make healthy eating changes. However, the purported harms included the added responsibility (eating regimens, appointments), financial constraints (expensive food, medical bills), and conflicts with their cultural practices (alternative eating, lack of information about traditional food). The psychosocial consequences were wide reaching and often resulted in significant social isolation with women only sharing their diagnosis with partners. Furthermore, there were a few reports of over-medicalisation due to a GDM diagnosis, with the perception that HCPs were often authoritarian, focusing on physiological aspects, with little attempt to involve women in decision making. This is noteworthy considering a non-GDM pregnancy has already come under scrutiny as being over-medicalised with increasing levels of unnecessary intervention [ 62 ].

Women from studies included in our review frequently reported inconsistent information provision. Limited GDM information provision has been identified in another systematic review regarding healthcare seeking for GDM during the postpartum period [ 63 ]. In contrast, findings from another study which aimed to evaluate satisfaction with obtaining a diagnosis of GDM concluded that the majority of women were satisfied with their experience of being diagnosed [ 64 ]. Further, women in the latter study associated poor GDM control with perinatal complications and an increased risk of type 2 diabetes following pregnancy [ 64 ].

Another key finding from this review was low awareness of the potential risks of GDM, particularly in the long-term. Low health literacy levels could be one factor to explain knowledge deficits and understanding of GDM, especially given the sociodemographic diverse population included in this review. One study found that low literacy among disadvantaged women had a significant impact on their understanding of GDM information [ 65 ]. Other research found that women who live in an English-speaking country but primarily speak a non-English language, have lower rates of dietary awareness compared with their English speaking counterparts, and this may affect compliance to dietary interventions [ 66 ]. Therefore, it is important that new educational interventions are developed to target those with lower health literacy as well as cultural factors when diagnosing and managing multi-ethnic populations with GDM [ 66 ].

Interestingly, women with a borderline diagnosis of GDM did not seem as concerned as other women and in some cases were dismissive of the diagnosis and the potential consequences. Similarly, in a study which specifically included women with a borderline diagnosis of GDM, the majority of women reported that they were not worried by the diagnosis [ 67 ]. For some women, the potential transitory nature of GDM was emphasised and some reported that it didn’t seem like a real illness. The diagnostic criteria for GDM has previously been compared with the established criteria used to classify a condition as a disease. This comparison revealed disparity which Goer, in 1996, used to suggest that GDM did not pose a serious health risk, was neither easily nor accurately diagnosed, was not treated effectively and that treatment outweighed the risks of the condition [ 68 ]. Therefore, the levels of heightened psychological distress as reported by the women in our review, may actually be unnecessary and others have gone as far as saying that GDM is an example of ‘obstetric iatrogenesis’ [ 69 ].

The findings of this review did underline a few unmet service needs with recurring themes around the lack of individualised care and its continuity, lack of choice regarding important aspects of care such as birthing options, and the scarcity of comprehensive follow-up. There was a sense of abandonment amongst women after delivery in that they had experienced intensive intervention and then nothing. This could be viewed as a missed opportunity to capitalise on the motivation to make changes during pregnancy. Researchers have previously highlighted that adherence to postpartum screening and continued lifestyle modifications to prevent future diabetes seems to dissipate after birth, possibly because the driver to protect their unborn child is no longer there [ 70 ].

The studies included in our review had participants of varying cultures sampled from countries with different GDM definitions. However, there appeared no difference in the qualitative outcomes between studies/countries. In our review, the experiences of women diagnosed with GDM suggest psychosocial harms appear to outweigh the qualitative benefits. Quantitative studies [ 14 , 15 ] that report prevalence increases in GDM after the IADSPG [ 71 ] definition changed, also report minimal improvements to maternal and infant physical outcomes.

This synthesis of women’s experiences of a GDM diagnosis could be used to inform the content of communication materials both before and after a GDM diagnosis. For example, an awareness of GDM testing and basic information including cultural adaptations to dietary guidelines and addressing misconceptions around breastfeeding. There is also an opportunity for HCPs to use teachable moments with women who have been identified at risk of developing type 2 diabetes post-pregnancy and offer supportive, effective advice about lifestyle changes. This is particularly relevant considering a previous review highlighted a significant time is spent in sedentary behaviour during pregnancy [ 72 ]. A study which examined HCPs views of healthcare provision to women with GDM showed that HCPs themselves perceived that there was a shortfall in GDM education [ 73 ]. There are also signals for service improvement and potential for service redesign, such as increasing community-delivered care for women diagnosed with GDM. This would assist in alleviating the burden on women to attend hospital appointments and potentially offer flexible appointment times. Follow-up appointments post-pregnancy could be made with consideration of other appointments such as maternal and child health milestones and breastfeeding weaning classes, and could also focused on healthy eating for both mother and baby.

Strengths and limitations

This systematic review included studies with women of different demographic characteristics and multicultural samples. The themes identified were represented in the majority of studies which increased the internal validity. The relatively high participation rate in the included studies, and that most studies were conducted during pregnancy or shortly after delivery, contributes to the external validity of our study. Although some participants were interviewed antenatally and some postnatally, this distribution over different gestational stages assists the generalisability of the study findings.

The comparison of coding between authors, discussion of the results and reaching consensus was a robust approach to improve the credibility of the results. Overall, the quality of most studies was good, however, a third of the studies used convenience methods to recruit participants which could contribute to sampling bias and limit the external validity of our findings. Only two studies adequately described the facilitator’s prior experience and the relationship between the participants and the facilitator/researcher. Unfortunately, this review did not capture the perception of HCPs which might be used to explain some of the behaviours and attitudes of the women, particularly in relation to communication of the diagnosis and information provision. Finally, although the data were collected from diverse populations, the majority of the countries in which research were conducted in were high-income countries, which could be considered to have more established and evidence-based healthcare systems than low-income countries.

Further research

A previous study has suggested the need for more research on the benefits and harms of alternative treatment choices for women with GDM [ 33 ]. The findings from this review suggest a need for more investigation around the psychosocial benefits and harms of a diagnosis of GDM. Given some women viewed treatment of ‘borderline GDM’ as unimportant, a new model of care based on stratification or individual level of risk for pregnancy and birth complications could be further explored. This may reduce the need for all women to be labelled as having GDM and negate unnecessary anxiety and burden for those at the lower ‘borderline’ threshold. This would then potentially offer tailored treatment options, improve shared-decision making, and improve women’s knowledge about how a diagnosis of GDM might affect them.

Consequences of a GDM diagnosis are multidimensional and highly contextual. Despite the psychosocial challenges frequently experienced, many women (driven by the innate response to safeguard their unborn baby) were able to gradually adapt to the required lifestyle changes and monitoring regimens. Perhaps a question is whether some of them should have to. There is opportunity to improve lifestyle and to assist the prevention of diabetes after pregnancy, however, this needs to be managed alongside the potential harms of a GDM diagnosis such as the negative psychological impact and social isolation. In the context of rising prevalence [ 14 , 15 , 16 , 17 ], potential minimal clinical [ 14 , 15 , 16 ] improvements, and the wide range of psychosocial experiences identified in this study, the findings of this review highlight the need for HCPs to consider the implications that a GDM diagnosis may have on women. It is essential that women diagnosed with GDM receive consistent evidence-based information and ongoing psychological and social support.

Availability of data and materials

The datasets generated during the current systematic review are available from the lead author upon request.

Abbreviations

Blood glucose level

Critical Appraisal Skills Programme Checklist (Qualitative)

Enhancing Transparency in Reporting the Synthesis of Qualitative Research

• Gestational diabetes mellitus

Hyperglycemia and Adverse Pregnancy Outcomes

Health care professional

International Association of the Diabetes and Pregnancy Study Groups

O'Sullivan JB, Mahan CM. Criteria for the oral glucose tolerance test in pregnancy. Diabetes. 1964;13:278–85.

CAS   PubMed   Google Scholar  

Mishra S, Rao CR, Shetty A. Trends in the diagnosis of gestational diabetes mellitus. Scientifica (Cairo). 2016;2016:5489015.

Google Scholar  

Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes. Syst Rev. 2002;25(10):1862–8. https://doi.org/10.2337/diacare.25.10.1862 .

Article   Google Scholar  

HAPO Study Cooperative Research Group. Hyperglycaemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002.

McIntyre HD, Gibbons KS, Lowe J, Oats JJN. Development of a risk engine relating maternal glycemia and body mass index to pregnancy outcomes. Diabetes Res Clin Pract. 2018;139:331–8.

Article   PubMed   Google Scholar  

Kamysheva E, Skouteris H, Wertheim EH, et al. Examination of a multi-factorial model of body-related experiences during pregnancy: The relationships among physical symptoms, sleep quality, depression, self-esteem, and negative body attitudes. Body Image. 2008;5(2):152–63 https://doi.org/10.1016/j.bodyim.2007.12.005 .

Carolan-Olah M, Barry M. Antenatal stress: an Irish case study. Midwifery. 2014;30(3):310–6. https://doi.org/10.1016/j.midw.2013.03.014 [published Online First: 2013/05/21].

Ferreira CR, Orsini MC, Vieira CR, et al. Prevalence of anxiety symptoms and depression in the third gestational trimester. Arch Gynecol Obstet. 2015;291(5):999–1003. https://doi.org/10.1007/s00404-014-3508-x [published Online First: 2014/10/15].

Dalfra MG, Nicolucci A, Bisson T, et al. Quality of life in pregnancy and post-partum: a study in diabetic patients. Qual Life Res. 2012;21(2):291–8. https://doi.org/10.1007/s11136-011-9940-5 .

Article   CAS   PubMed   Google Scholar  

Evans MK, O’Brien B. Gestational diabetes: the meaning of an at-risk pregnancy. Qual Health Res. 2005;15(1):66–81.

Devsam BU, Bogossian FE, Peacock AS. An interpretive review of women's experiences of gestational diabetes mellitus: proposing a framework to enhance midwifery assessment. Women Birth. 2013;26(2):E69–76.

Carolan-Olah M, Duarte-Gardea M, Lechuga J, et al. The experience of gestational diabetes mellitus (GDM) among Hispanic women in a U.S. border region. Sex Reprod Healthc. 2017;12:16–23. https://doi.org/10.1016/j.srhc.2016.11.003 .

Persson M, Winkvist A, Mogren I. ‘From stun to gradual balance’—women’s experiences of living with gestational diabetes mellitus. Scand J Caring Sci. 2010;24(3):454–62. https://doi.org/10.1111/j.1471-6712.2009.00735.x .

Cade TJ, Polyakov A, Brennecke SP. Implications of the introduction of new criteria for the diagnosis of gestational diabetes: a health outcome and cost of care analysis. BMJ Open. 2019;9(1):e023293–e93. https://doi.org/10.1136/bmjopen-2018-023293 .

Article   PubMed   PubMed Central   Google Scholar  

Sexton H, Heal C, Banks J, et al. Impact of new diagnostic criteria for gestational diabetes. J Obstet Gynaecol Res. 2018;44(3):425–31. https://doi.org/10.1111/jog.13544 [published Online First: 2018/01/13].

Erjavec K, Poljičanin T, Matijević R. Impact of the implementation of new WHO diagnostic criteria for gestational diabetes mellitus on prevalence and perinatal outcomes: a population-based study. J Pregnancy. 2016;2016:2670912. https://doi.org/10.1155/2016/2670912 .

Feldman RK, Tieu RS, Yasumura L. Gestational diabetes screening the international association of the diabetes and pregnancy study groups compared with carpenter-coustan screening. Obstet Gynecol. 2016;127(1):10–7. https://doi.org/10.1097/aog.0000000000001132 .

Pocobelli G, Yu O, Fuller S, et al. One-step approach to identifying gestational diabetes mellitus: association with perinatal outcomes. Obstet Gynecol. 2018;132(4):859–67. https://doi.org/10.1097/aog.0000000000002780 [published Online First: 2018/08/22].

Draffin CR, Alderdice FA, McCance DR, et al. Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: a qualitative study. Midwifery. 2016;40:141–7. https://doi.org/10.1016/j.midw.2016.06.019 .

Tong A, Flemming K, McInnes E, et al. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 2012;12(1):181. https://doi.org/10.1186/1471-2288-12-181 .

Rathbone J, Carter M, Hoffmann T, et al. Better duplicate detection for systematic reviewers: evaluation of systematic review assistant-deduplication module. Syst Rev. 2015;4:6. https://doi.org/10.1186/2046-4053-4-6 [published Online First: 2015/01/16].

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):45. https://doi.org/10.1186/1471-2288-8-45 .

Lucas PJ, Baird J, Arai L, et al. Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews. BMC Med Res Methodol. 2007;7(1):4. https://doi.org/10.1186/1471-2288-7-4 .

Group CCQM. Chapter 4: critical appraisal of qualitative research. In: Noyes J, Booth A, Hannes K, Harden A, Harris J, Lewin S, Lockwood C, editors. Supplementary guidance for inclusion of qualitative research in cochrane systematic reviews of interventions. Version 1; 2011.

Abraham K, Wilk N. Living with gestational diabetes in a rural community. MCN Am J Matern Child Nurs. 2014;39(4):239–45.

Araujo MF, Pessoa SM, Damasceno MM, et al. Gestational diabetes from the perspective of hospitalized pregnant women. Rev Bras Enferm. 2013;66(2):222–7.

Bandyopadhyay M, Small R, Davey MA. Attendance for postpartum glucose tolerance testing following gestational diabetes among south Asian women in Australia: a qualitative study. Int J Gynecol Obstet. 2015;131:E149.

Bandyopadhyay M, Small R, Davey MA, et al. Lived experience of gestational diabetes mellitus among immigrant South Asian women in Australia. Aust N Z J Obstet Gynaecol. 2011;51(4):360–4. https://doi.org/10.1111/j.1479-828X.2011.01322.x .

Carolan M. Women’s experiences of gestational diabetes self-management: a qualitative study. Midwifery. 2013;29(6):637–45. https://doi.org/10.1016/j.midw.2012.05.013 .

Doran F. Gestational diabetes mellitus: perspectives on lifestyle changes during pregnancy and post-partum, physical activity and the prevention of future type 2 diabetes. Aust J Prim Health. 2008;14(3):85–92.

Doran F, Davis K. Gestational diabetes mellitus in Tonga: insights from healthcare professionals and women who experienced gestational diabetes mellitus. N Z Med J. 2010;123(1326):59–67.

PubMed   Google Scholar  

Eades CE, France EF, Evans JMM. Postnatal experiences, knowledge and perceptions of women with gestational diabetes. Diabet Med. 2018;35(4):519–29.

Figueroa Gray M, Hsu C, Kiel L, et al. “It's a very big burden on me”: Women’s experiences using insulin for gestational diabetes. Matern Child Health J. 2017;21(8):1678–85.

Ge L, Albin B, Hadziabdic E, et al. Beliefs about health and illness and health-related behavior among urban women with gestational diabetes mellitus in the south east of China. J Transcult Nurs. 2016;27(6):593–602.

Ge L, Wikby K, Rask M. ‘Is gestational diabetes a severe illness?’ Exploring beliefs and self-care behaviour among women with gestational diabetes living in a rural area of the south east of China. Aust J Rural Health. 2016;24(6):378–84. https://doi.org/10.1111/ajr.12292 .

Han S, Middleton PF, Bubner TK, et al. Women’s views on their diagnosis and management for borderline gestational diabetes mellitus. J Diabetes Res. 2015;2015:209215. https://doi.org/10.1155/2015/209215 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hirst JE, Tran TS, Do MAT, et al. Women with gestational diabetes in Vietnam: a qualitative study to determine attitudes and health behaviours. BMC Pregnancy Childbirth. 2012;12:81.

Hjelm K, Bard K, Apelqvist J. Gestational diabetes: prospective interview-study of the developing beliefs about health, illness and health care in migrant women. J Clin Nurs. 2012;21(21–22):3244–56.

Hjelm K, Bard K, Apelqvist J. A qualitative study of developing beliefs about health, illness and healthcare in migrant African women with gestational diabetes living in Sweden. BMC Womens Health. 2018;18(1):34.

Hjelm K, Bard K, Berntorp K, et al. Beliefs about health and illness postpartum in women born in Sweden and the Middle East. Midwifery. 2009;25(5):564–75.

Hjelm K, Bard K, Nyberg P, et al. Swedish and middle-eastern-born women’s beliefs about gestational diabetes. Midwifery. 2005;21(1):44–60.

Hjelm K, Berntorp K, Apelqvist J. Beliefs about health and illness in Swedish and African-born women with gestational diabetes living in Sweden. J Clin Nurs. 2012;21(9–10):1374–86. https://doi.org/10.1111/j.1365-2702.2011.03834.x .

Hjelm K, Berntorp K, Frid A, et al. Beliefs about health and illness in women managed for gestational diabetes in two organisations. Midwifery. 2008;24(2):168–82.

Hui AL, Sevenhuysen G, Harvey D, et al. Stress and anxiety in women with gestational diabetes during dietary management. Diabetes Educ. 2014;40(5):668–77.

Kaptein S, Evans M, McTavish S, et al. The subjective impact of a diagnosis of gestational diabetes among ethnically diverse pregnant women: a qualitative study. Can. 2015;39(2):117–22. https://doi.org/10.1016/j.jcjd.2014.09.005 .

Kilgour C, Bogossian FE, Callaway L, et al. Postnatal gestational diabetes mellitus follow-up: Australian women’s experiences. Women Birth. 2015;28(4):285–92. https://doi.org/10.1016/j.wombi.2015.06.004 .

Lawson EJ, Rajaram S. A transformed pregnacy – the psychological consequences of gestational diabetes. Sociol Health Illn. 1994;16(4):536–62.

Lie MLS, Hayes L, Lewis-Barned NJ, et al. Preventing type 2 diabetes after gestational diabetes: women’s experiences and implications for diabetes prevention interventions. Diabet Med. 2013;30(8):986–93.

Neufeld HT. Food perceptions and concerns of aboriginal women coping with gestational diabetes in Winnipeg, Manitoba. J Nutr Educ Behav. 2011;43(6):482–91. https://doi.org/10.1016/j.jneb.2011.05.017 .

Nielsen JH, Olesen CR, Kristiansen TM, et al. Reasons for women’s non-participation in follow-up screening after gestational diabetes. Women Birth. 2015;28(4):e157–63. https://doi.org/10.1016/j.wombi.2015.04.006 .

Parsons J, Sparrow K, Ismail K, et al. Experiences of gestational diabetes and gestational diabetes care: a focus group and interview study. BMC Pregnancy Childbirth. 2018;18:25.

Pennington AVR, O’Reilly SL, Young D, et al. Improving follow-up care for women with a history of gestational diabetes: perspectives of GPs and patients. Aust J Prim Health. 2017;23(1):66–74.

Rafii F, Vasegh Rahimparvar SF, Keramat A, et al. Procrastination as a key factor in postpartum screening for diabetes: A qualitative study of Iranian women with recent gestational diabetes. Iran Red Crescent Med J. 2017;19(5). https://doi.org/10.5812/ircmj.44833 .

Razee H, van der Ploeg HP, Blignault I, et al. Beliefs, barriers, social support, and environmental influences related to diabetes risk behaviours among women with a history of gestational diabetes. Health Promot J Austr. 2010;21(2):130–7.

Salomon IMM, Soares SM. Understanding the impact of gestational diabetes diagnosis. Revista Mineira de Enfermagem. 2004;8(3):349–57.

Svensson L, Nielsen KK, Maindal HT. What is the postpartum experience of Danish women following gestational diabetes? A qualitative exploration. Scand J Caring Sci. 2018;32(2):756–64.

Tang JW, Foster KE, Pumarino J, et al. Perspectives on prevention of type 2 diabetes after gestational diabetes: a qualitative study of Hispanic, African-American and White women. Matern Child Health J. 2015;19(7):1526–34. https://doi.org/10.1007/s10995-014-1657-y .

Tierney M, O'Dea A, Danyliv A, et al. Factors influencing lifestyle behaviours during and after a gestational diabetes mellitus pregnancy. Health Psychol Behav Med. 2015;3(1):204–16. https://doi.org/10.1080/21642850.2015.1073111 .

Trutnovsky G, Panzitt T, Magnet E, et al. Gestational diabetes: women's concerns, mood state, quality of life and treatment satisfaction. J Matern Fetal Neonatal Med. 2012;25(11):2464–6. https://doi.org/10.3109/14767058.2012.683900 .

Wah YYE, McGill M, Wong J, et al. Self-management of gestational diabetes among Chinese migrants: a qualitative study. Women Birth. 2019;32(1):e17–e23.

Whitty-Rogers J, Caine V, Cameron B. Aboriginal women’s experiences with gestational diabetes mellitus: a participatory study with mi'kmaq women in Canada. ANS Adv Nurs Sci. 2016;39(2):181–98. https://doi.org/10.1097/ANS.0000000000000115 .

Johanson R, Newburn M, Macfarlane A. Has the medicalisation of childbirth gone too far? BMJ. 2002;324(7342):892–5.

Van Ryswyk E, Middleton P, Shute E, et al. Women's views and knowledge regarding healthcare seeking for gestational diabetes in the postpartum period: a systematic review of qualitative/survey studies. Diabetes Res Clin Pract. 2015;110(2):109–22.

Goldstein RF, Gibson-Helm ME, Boyle JA, et al. Satisfaction with diagnosis process for gestational diabetes mellitus and risk perception among Australian women. Int J Gynaecol Obstet. 2015;129(1):46–9.

Carolan M. Diabetes nurse educators’ experiences of providing care for women, with gestational diabetes mellitus, from disadvantaged backgrounds. J Clin Nurs. 2014;23(9–10):1374–84. https://doi.org/10.1111/jocn.12421 .

Yuen L, Wong VW. Gestational diabetes mellitus: challenges for different ethnic groups. World J Diabetes. 2015;6(8):1024–32. https://doi.org/10.4239/wjd.v6.i8.1024 [published Online First: 2015/07/25].

Carolan M, Steele C, Margetts H. Knowledge of gestational diabetes among a multi-ethnic cohort in Australia. Midwifery. 2010;26(6):579–88. https://doi.org/10.1016/j.midw.2009.01.006 .

Goer H. Gestational diabetes: the emperor has no clothes. The Birth Gazette Summertown: Second Foundation; 1996. p. 32–5.

Han S, Bubner T, Middleton PF, et al. A qualitative study of women's views on diagnosis and management for borderline gestational diabetes. J Paediatr Child Health. 2013;49:128. https://doi.org/10.1111/jpc.12133 .

Nielsen KK, Kapur A, Damm P, et al. From screening to postpartum follow-up - the determinants and barriers for gestational diabetes mellitus (GDM) services, a systematic review. BMC Pregnancy Childbirth. 2014;14:41. https://doi.org/10.1186/1471-2393-14-41 .

International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International Association of Diabetes and Pregnancy Study Groups Recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82.

Article   PubMed Central   Google Scholar  

Fazzi C, Saunders DH, Linton K, et al. Sedentary behaviours during pregnancy: a systematic review. Int J Behav Nutr Phys Act. 2017;14:32. https://doi.org/10.1186/s12966-017-0485-z .

Van Ryswyk E, Middleton P, Hague W, et al. Clinician views and knowledge regarding healthcare provision in the postpartum period for women with recent gestational diabetes: A systematic review of qualitative/survey studies. Diabetes Res Clin Pract. 2014;106(3):401–11 https://doi.org/10.1016/j.diabres.2014.09.001 .

Download references

Acknowledgements

Not applicable.

LC is supported by a National Health and Medical Research Council Partnership Centre for Health System Sustainability grant (#9100002). RS and RT are supported by a National Health and Medical Research Council Program grant (#1106452) and PG is supported by a NHMRC Research Fellowship (#1080042). The funders had no role in design, data collection, analysis, interpretation or writing of the manuscript.

Author information

Authors and affiliations.

Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Australia

Louise Craig, Rebecca Sims, Paul Glasziou & Rae Thomas

You can also search for this author in PubMed   Google Scholar

Contributions

PG, LC and RT conceived the project design, LC conducted the search, extracted, analysed and interpreted the data assisted by RS. RT assisted in data interpretation. All authors contributed to the drafting of the manuscript and approve the final version.

Corresponding author

Correspondence to Rae Thomas .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1:.

Table S1. Enhancing Transparency in Reporting the Synthesis of Qualitative Research Guidelines Checklist. Table S2. Assessment of quality of included studies using the CASP tool.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Craig, L., Sims, R., Glasziou, P. et al. Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review. BMC Pregnancy Childbirth 20 , 76 (2020). https://doi.org/10.1186/s12884-020-2745-1

Download citation

Received : 24 September 2019

Accepted : 15 January 2020

Published : 07 February 2020

DOI : https://doi.org/10.1186/s12884-020-2745-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Systematic review
• Qualitative
• Diagnostic impacts

BMC Pregnancy and Childbirth

ISSN: 1471-2393

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Diabetes & Primary Care
• Vol:25 | No:02

Interactive case study: Gestational diabetes

• 10 May 2023

Share this article + Add to reading list – Remove from reading list ↓ Download pdf

Diabetes & Primary Care ’s series of interactive case studies is aimed at all healthcare professionals in primary and community care who would like to broaden their understanding of diabetes.

These two cases provide an overview of gestational diabetes (GDM). The scenarios cover the screening, identification and management of GDM, as well as the steps that should be taken to screen for, and ideally prevent, development of type 2 diabetes in the long term post-pregnancy.

The format uses typical clinical scenarios as tools for learning. Information is provided in short sections, with most ending in a question to answer before moving on to the next section.

Working through the case studies will improve our knowledge and problem-solving skills in diabetes care by encouraging us to make evidence-based decisions in the context of individual cases.

Readers are invited to respond to the questions by typing in their answers. In this way, we are actively involved in the learning process, which is hopefully a much more effective way to learn.

By actively engaging with these case histories, I hope you will feel more confident and empowered to manage such presentations effectively in the future.

Holly is a 31-year-old lady who is now 26 weeks into her first pregnancy. She sees you with a 3-day history of dysuria and frequency of micturition. There is no history of abdominal pain or fever.

A urine dipstick reveals a positive test for nitrites and the presence of white cells. It also shows glycosuria ++.

What is your assessment of Holly’s situation?

Nadia is a 34-year-old lady of Indian ethnic origin who is now 24 weeks into her second pregnancy, her last pregnancy being 7 years ago. Nadia’s BMI is 32.4 kg/m 2 and her father has type 2 diabetes. GDM was not, however, diagnosed during her first pregnancy and her first baby was born at term weighing 3.8 kg.

How would you assess Nadia’s risk of acquiring gestational diabetes?

By working through this interactive case study, we will consider the following issues and more:

• The risk factors for developing gestational diabetes.
• Investigations and how to interpret them.
• Effects of gestational diabetes on outcomes for the mother and offspring.
• Which treatments for diabetes are considered safe and effective in gestational diabetes.
• What arrangements should be set in place for future screening of diabetes post-pregnancy.

Click here to access the case study .

Diabetes Distilled: Diabetes-related foot ulcers – detailed advice for primary care

Conference over coffee: diabetes and obesity within multiple long-term condidions, lada – assessing diabetes in a non-overweight younger person, challenges and opportunities in reducing risk of diabetes-related cardiovascular disease: making every contact count, diabetes distilled: pneumonia hospitalisation associated with long- and short-term risk of cardiovascular mortality, editorial: a tribute to dr michael mosley, pcds news: obesity survey results.

Review and guidelines highlight opportunities for primary care to really make a difference.

25 Jul 2024

The interactions between diabetes, obesity and long-term conditions, including cardiovascular disease, chronic kidney disease and cancer.

23 Jul 2024

The characteristics and clinical implications of LADA, its differential diagnosis and its possible management strategies.

18 Jul 2024

Exploring the unique opportunities general practice nurses have to promote primary and secondary prevention of CVD.

17 Jul 2024

Sign up to all DiabetesontheNet journals

• CPD Learning
• Journal of Diabetes Nursing
• Diabetes Care for Children & Young People
• The Diabetic Foot Journal
• Diabetes Digest

Useful information

• Terms and conditions
• Privacy policy
• Editorial policies and ethics

By clicking ‘Subscribe’, you are agreeing that DiabetesontheNet.com are able to email you periodic newsletters. You may unsubscribe from these at any time. Your info is safe with us and we will never sell or trade your details. For information please review our Privacy Policy .

Are you a healthcare professional?  This website is for healthcare professionals only. To continue, please confirm that you are a healthcare professional below.

We use cookies  responsibly to ensure that we give you the best experience on our website. If you continue without changing your browser settings, we’ll assume that you are happy to receive all cookies on this website.  Read about how we use cookies .

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Gestational diabetes...

Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis

• Related content
• Peer review
• Wenrui Ye , doctoral student 1 2 ,
• Cong Luo , doctoral student 3 ,
• Jing Huang , assistant professor 4 5 ,
• Chenglong Li , doctoral student 1 ,
• Zhixiong Liu , professor 1 2 ,
• 1 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 2 Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
• 3 Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 4 National Clinical Research Centre for Mental Disorders, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• 5 Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• Correspondence to: F Liu liufangkun{at}csu.edu.cn
• Accepted 18 April 2022

Objective To investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.

Design Systematic review and meta-analysis.

Data sources Web of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.

Review methods Cohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.

Results 156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.

Conclusions When adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

Review registration PROSPERO CRD42021265837.

• Download figure
• Open in new tab
• Download powerpoint

Introduction

Gestational diabetes mellitus is a common chronic disease in pregnancy that impairs the health of several million women worldwide. 1 2 Formally recognised by O’Sullivan and Mahan in 1964, 3 gestational diabetes mellitus is defined as hyperglycaemia first detected during pregnancy. 4 With the incidence of obesity worldwide reaching epidemic levels, the number of pregnant women diagnosed as having gestational diabetes mellitus is growing, and these women have an increased risk of a range of complications of pregnancy. 5 Quantification of the risk or odds of possible adverse outcomes of pregnancy is needed for prevention, risk assessment, and patient education.

In 2008, the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study recruited a large multinational cohort and clarified the risks of adverse outcomes associated with hyperglycaemia. The findings of the study showed that maternal hyperglycaemia independently increased the risk of preterm delivery, caesarean delivery, infants born large for gestational age, admission to a neonatal intensive care unit, neonatal hypoglycaemia, and hyperbilirubinaemia. 6 The obstetric risks associated with diabetes, such as pregnancy induced hypertension, macrosomia, congenital malformations, and neonatal hypoglycaemia, have been reported in several large scale studies. 7 8 9 10 11 12 The HAPO study did not adjust for some confounders, however, such as maternal body mass index, and did not report on stillbirths and neonatal respiratory distress syndrome, raising uncertainty about these outcomes. Other important pregnancy outcomes, such as preterm delivery, neonatal death, and low Apgar score in gestational diabetes mellitus, were poorly reported. No comprehensive study has assessed the relation between gestational diabetes mellitus and various maternal and fetal adverse outcomes after adjustment for confounders. Also, some cohort studies were restricted to specific clinical centres and regions, limiting their generalisation to more diverse populations.

By collating the available evidence, we conducted a systematic review and meta-analysis to quantify the short term outcomes in pregnancies complicated by gestational diabetes mellitus. We evaluated adjusted associations between gestational diabetes mellitus and various adverse outcomes of pregnancy.

This meta-analysis was conducted according to the recommendations of Cochrane Systematic Reviews, and our findings are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (table S16). The study was prospectively registered in the international database of prospectively registered systematic reviews (PROSPERO CRD42021265837).

Search strategy and selection criteria

We searched the electronic databases PubMed, Web of Science, Medline, and the Cochrane Database of Systematic Reviews with the keywords: “pregnan*,” “gestatio*” or “matern*” together with “diabete*,” “hyperglycaemia,” “insulin,” “glucose,” or “glucose tolerance test*” to represent the exposed populations, and combined them with terms related to outcomes, such as “pregnan* outcome*,” “obstetric* complicat*,” “pregnan* disorder*,” “obstetric* outcome*,” “haemorrhage,” “induc*,” “instrumental,” “caesarean section,” “dystocia,” “hypertensi*,” “eclampsia,” “premature rupture of membrane,” “PROM,” “preter*,” “macrosomia,” and “malformation,” as well as some abbreviated diagnostic criteria, such as “IADPSG,” “DIPSI,” and “ADIPS” (table S1). The search strategy was appropriately translated for the other databases. We included observational cohort studies and control arms of trials, conducted after 1990, that strictly defined non-gestational diabetes mellitus (control) and gestational diabetes mellitus (exposed) populations and had definite diagnostic criteria for gestational diabetes mellitus (table S2) and various adverse outcomes of pregnancy.

Exclusion criteria were: studies published in languages other than English; studies with no diagnostic criteria for gestational diabetes mellitus (eg, self-reported gestational diabetes mellitus, gestational diabetes mellitus identified by codes from the International Classification of Diseases or questionnaires); studies published after 1990 that recorded pregnancy outcomes before 1990; studies of specific populations (eg, only pregnant women aged 30-34 years, 13 only twin pregnancies 14 15 16 ); studies with a sample size <300, because we postulated that these studies might not be adequate to detect outcomes within each group; and studies published in the form of an abstract, letter, or case report.

We also manually retrieved reference lists of relevant reviews or meta-analyses. Three reviewers (WY, CL, and JH) independently searched and assessed the literature for inclusion in our meta-analysis. The reviewers screened the titles and abstracts to exclude ineligible studies. The full texts of relevant records were then retrieved and assessed. Any discrepancies were resolved after discussion with another author (FL).

Data extraction

Three independent researchers (WY, CL, and JH) extracted data from the included studies with a predesigned form. If the data were not presented, we contacted the corresponding authors to request access to the data. We extracted data from the most recent study or the one with the largest sample size when a cohort was reported twice or more. Sociodemographic and clinical data were extracted based on: year of publication, location of the study (country and continent), design of the study (prospective or retrospective cohort), screening method and diagnostic criteria for gestational diabetes mellitus, adjustment for conventional prognostic factors (defined as maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension), and the proportion of patients with gestational diabetes mellitus who were receiving insulin. For studies that adopted various diagnostic criteria for gestational diabetes mellitus, we extracted the most recent or most widely accepted one for subsequent analysis. For studies adopting multivariate logistic regression for adjustment of confounders, we extracted adjusted odds ratios and synthesised them in subsequent analyses. For unadjusted studies, we calculated risk ratios and 95% confidence intervals based on the extracted data.

Studies of women with gestational diabetes mellitus that evaluated the risk or odds of maternal or neonatal complications were included. We assessed the maternal outcomes pre-eclampsia, induction of labour, instrumental delivery, caesarean section, shoulder dystocia, premature rupture of membrane, and postpartum haemorrhage. Fetal or neonatal outcomes assessed were stillbirth, neonatal death, congenital malformation, preterm birth, macrosomia, low birth weight, large for gestational age, small for gestational age, neonatal hypoglycaemia, neonatal jaundice, respiratory distress syndrome, low Apgar score, and admission to the neonatal intensive care unit. Table S3 provides detailed definitions of these adverse outcomes of pregnancy.

Risk-of-bias assessment

A modified Newcastle-Ottawa scale was used to assess the methodological quality of the selection, comparability, and outcome of the included studies (table S4). Three independent reviewers (WY, CL, and JH) performed the quality assessment and scored the studies for adherence to the prespecified criteria. A study that scored one for selection or outcome, or zero for any of the three domains, was considered to have a high risk of bias. Studies that scored two or three for selection, one for comparability, and two for outcome were regarded as having a medium risk of bias. Studies that scored four for selection, two for comparability, and three for outcome were considered to have a low risk of bias. A lower risk of bias denotes higher quality.

Data synthesis and analysis

Pregnant women were divided into two groups (gestational diabetes mellitus and non-gestational diabetes mellitus) based on the diagnostic criteria in each study. Studies were considered adjusted if they adjusted for at least one of seven confounding factors (maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension). For each adjusted study, we transformed the odds ratio estimate and its corresponding standard error to natural logarithms to stabilise the variance and normalise their distributions. Summary odds ratio estimates and their 95% confidence intervals were estimated by a random effects model with the inverse variance method. We reported the results as odds ratio with 95% confidence intervals to reflect the uncertainty of point estimates. Unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy were quantified and summarised (table S6 and table S14). Thereafter, heterogeneity across the studies was evaluated with the τ 2 statistics and Cochran’s Q test. 17 18 Cochran’s Q test assessed interactions between subgroups. 18

We performed preplanned subgroup analyses for factors that could potentially affect gestational diabetes mellitus or adverse outcomes of pregnancy: country status (developing or developed country according to the International Monetary Fund ( www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm ), risk of bias (low, medium, or high), screening method (universal one step, universal glucose challenge test, or selective screening based on risk factors), diagnostic criteria for gestational diabetes mellitus (World Health Organization 1999, Carpenter-Coustan criteria, International Association of Diabetes and Pregnancy Study Groups (IADPSG), or other), and control for body mass index. We assessed small study effects with funnel plots by plotting the natural logarithm of the odds ratios against the inverse of the standard errors, and asymmetry was assessed with Egger’s test. 19 A meta-regression model was used to investigate the associations between study effect size and proportion of patients who received insulin in the gestational diabetes mellitus population. Next, we performed sensitivity analyses by omitting each study individually and recalculating the pooled effect size estimates for the remaining studies to assess the effect of individual studies on the pooled results. All analyses were performed with R language (version 4.1.2, www.r-project.org ) and meta package (version 5.1-0). We adopted the treatment arm continuity correction to deal with a zero cell count 20 and the Hartung-Knapp adjustment for random effects meta models. 21 22

Patient and public involvement

The experience in residency training in the department of obstetrics and the concerns about the association between gestational diabetes mellitus and health outcomes inspired the author team to perform this study. We also asked advice from the obstetrician and patients with gestational diabetes mellitus about which outcomes could be included. The covid-19 restrictions meant that we sought opinions from only a limited number of patients in outpatient settings.

Characteristics of included studies

Of the 44 993 studies identified, 156 studies, 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 involving 7 506 061 pregnancies, were eligible for the analysis of adverse outcomes in pregnancy ( fig 1 ). Of the 156 primary studies, 133 (85.3%) reported maternal outcomes and 151 (96.8%) reported neonatal outcomes. Most studies were conducted in Asia (39.5%), Europe (25.5%), and North America (15.4%). Eighty four (53.8%) studies were performed in developed countries. Based on the Newcastle-Ottawa scale, 50 (32.1%) of the 156 included studies showed a low or medium risk of bias and 106 (67.9%) had a high risk of bias. Patients in 35 (22.4%) of the 156 studies never used insulin during the course of the disease and 63 studies (40.4%) reported treatment with insulin in different proportions of patients. The remaining 58 studies did not report information about the use of insulin. Table 1 summarises the characteristics of the study population, including continent or region, country, screening methods, and diagnostic criteria for the included studies. Table S5 lists the key excluded studies.

Search and selection of studies for inclusion

Characteristics of study population

• View inline

Associations between gestational diabetes mellitus and adverse outcomes of pregnancy

Based on the use of insulin in each study, we classified the studies into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. We reported odds ratios with 95% confidence intervals after controlling for at least minimal confounding factors. In studies with no insulin use, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and an infant born large for gestational age (1.57, 1.25 to 1.97) ( fig 2 and fig S1). In studies with insulin use, adjusted for confounders, the odds of an infant born large for gestational age (odds ratio 1.61, 95% confidence interval 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31) were higher in women with than in those without gestational diabetes mellitus ( fig 3) . In studies that did not report the use of insulin, women with gestational diabetes mellitus had increased odds ratio for pre-eclampsia (1.46, 1.21 to 1.78), induction of labour (1.88, 1.16 to 3.04), caesarean section (1.38, 1.20 to 1.58), premature rupture of membrane (1.13, 1.06 to 1.20), congenital malformation (1.18, 1.10 to 1.26), preterm delivery (1.51, 1.19 to 1.93), macrosomia (1.48, 1.13 to 1.95), neonatal hypoglycaemia (11.71, 7.49 to 18.30), and admission to the neonatal intensive care unit (2.28, 1.26 to 4.13) (figs S3 and S4). We found no clear evidence for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and infant born small for gestational age between women with and without gestational diabetes mellitus in all three subgroups ( fig 2, fig 3, and figs S1-S4). Table S6 shows the unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy.

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies in patients who never used insulin during the course of the disease (no insulin use). NA=not applicable

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies where different proportions of patients were treated with insulin (insulin use). NA=not applicable

Subgroup, meta-regression, and sensitivity analyses

Subgroup analyses, based on risk of bias, did not show significant heterogeneity between the subgroups of women with and without gestational diabetes mellitus for most adverse outcomes of pregnancy ( table 2 and table 3 ), except for admission to the neonatal intensive care unit in studies where insulin use was not reported (table S7). Significant differences between subgroups were reported for country status and macrosomia in studies with (P<0.001) and without (P=0.001) insulin use ( table 2 and table 3 ), and for macrosomia (P=0.02) and infants born large for gestational age (P<0.001) based on adjustment for body mass index in studies with insulin use (table S8). Screening methods contributed significantly to the heterogeneity between studies for caesarean section (P<0.001) and admission to the neonatal intensive care unit (P<0.001) in studies where insulin use was not reported (table S7). In most outcomes, the estimated odds were lower in studies that used universal one step screening than those that adopted the universal glucose challenge test or selective screening methods ( table 2 and table 3 ). Diagnostic criteria were not related to heterogeneity between the studies for all of the study subgroups (no insulin use, insulin use, insulin use not reported). The subgroup analysis was performed only for outcomes including ≥6 studies.

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with no insulin use

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with insulin use

We applied meta-regression models to evaluate the modification power of the proportion of patients with insulin use when sufficient data were available. Significant associations were found between effect size estimate and proportion of patients who had received insulin for the adverse outcomes caesarean section (estimate=0.0068, P=0.04) and preterm delivery (estimate=−0.0069, P=0.04) (table S9).

In sensitivity analyses, most pooled estimates were not significantly different when a study was omitted, suggesting that no one study had a large effect on the pooled estimate. The pooled estimate effect became significant (P=0.005) for low birth weight when the study of Lu et al 99 was omitted, however (fig S5). We found evidence of a small study effect only for caesarean section (Egger’s P=0.01, table S10). Figure S6 shows the funnel plots of the included studies for various adverse outcomes (≥10 studies).

Principal findings

We have provided quantitative estimates for the associations between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for confounding factors, through a systematic search and comprehensive meta-analysis. Compared with patients with normoglycaemia during pregnancy, patients with gestational diabetes mellitus had increased odds of caesarean section, preterm delivery, low one minute Apgar score, macrosomia, and an infant born large for gestational age in studies where insulin was not used. In studies with insulin use, patients with gestational diabetes mellitus had an increased odds of an infant born large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit. Our study was a comprehensive analysis, quantifying the adjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy. The study provides updated critical information on gestational diabetes mellitus and adverse outcomes of pregnancy and would facilitate counselling of women with gestational diabetes mellitus before delivery.

To examine the heterogeneity conferred by different severities of gestational diabetes mellitus, we categorised the studies by use of insulin. Insulin is considered the standard treatment for the management of gestational diabetes mellitus when adequate glucose levels are not achieved with nutrition and exercise. 179 Our meta-regression showed that the proportion of patients who had received insulin was significantly associated with the effect size estimate of adverse outcomes, including caesarean section (P=0.04) and preterm delivery (P=0.04). This finding might be the result of a positive linear association between glucose concentrations and adverse outcomes of pregnancy, as previously reported. 180 However, the proportion of patients who were receiving insulin indicates the percentage of patients with poor glycaemic control in the population and cannot reflect glycaemic control at the individual level.

Screening methods for gestational diabetes mellitus have changed over time, from the earliest selective screening (based on risk factors) to universal screening by the glucose challenge test or the oral glucose tolerance test, recommended by the US Preventive Services Task Force (2014) 181 and the American Diabetes Association (2020). 182 The diagnostic accuracy of these screening methods varied, contributing to heterogeneity in the analysis.

Several studies have tried to pool the effects of gestational diabetes mellitus on pregnancy outcomes, but most focused on one outcome, such as congenital malformations, 183 184 macrosomia, 185 186 or respiratory distress syndrome. 187 Our findings of increased odds of macrosomia in gestational diabetes mellitus in studies where insulin was not used, and respiratory distress syndrome in studies with insulin use, were similar to the results of previous meta-analyses. 188 189 The increased odds of neonatal respiratory distress syndrome, along with low Apgar scores, might be attributed to disruption of the integrity and composition of fetal pulmonary surfactant because gestational diabetes mellitus can delay the secretion of phosphatidylglycerol, an essential lipid component of surfactants. 190

Although we detected no significant association between gestational diabetes mellitus and mortality events, the observed increase in the odds of neonatal death (odds ratio 1.59 in studies that did not report the use of insulin) should be emphasised to obstetricians and pregnant women because its incidence was low (eg, 3.75% 87 ). The increased odds of neonatal death could result from several lethal complications, such as respiratory distress syndrome, neonatal hypoglycaemia (3.94-11.71-fold greater odds), and jaundice. These respiratory and metabolic disorders might increase the likelihood of admission to the neonatal intensive care unit.

For the maternal adverse outcomes, women with gestational diabetes mellitus had increased odds of pre-eclampsia, induction of labour, and caesarean section, consistent with findings in previous studies. 126 Our study identified a 1.24-1.46-fold greater odds of pre-eclampsia between patients with and without gestational diabetes mellitus, which was similar to previous results. 191

Strengths and limitations of the study

Our study included more studies than previous meta-analyses and covered a range of maternal and fetal outcomes, allowing more comprehensive comparisons among these outcomes based on the use of insulin and different subgroup analyses. The odds of adverse fetal outcomes, including respiratory distress syndrome (P=0.002), neonatal jaundice (P=0.05), and admission to the neonatal intensive care unit (P=0.005), were significantly increased in studies with insulin use, implicating their close relation with glycaemic control. The findings of this meta-analysis support the need for an improved understanding of the pathophysiology of gestational diabetes mellitus to inform the prediction of risk and for precautions to be taken to reduce adverse outcomes of pregnancy.

The study had some limitations. Firstly, adjustment for at least one confounder had limited power to deal with potential confounding effects. The set of adjustment factors was different across studies, however, and defining a broader set of multiple adjustment variables was difficult. This major concern should be looked at in future well designed prospective cohort studies, where important prognostic factors are controlled. Secondly, overt diabetes was not clearly defined until the IADPSG diagnostic criteria were proposed in 2010. Therefore, overt diabetes or pre-existing diabetes might have been included in the gestational diabetes mellitus groups if studies were conducted before 2010 or adopted earlier diagnostic criteria. Hence we cannot rule out that some adverse effects in newborns were related to prolonged maternal hyperglycaemia. Thirdly, we divided and analysed the subgroups based on insulin use because insulin is considered the standard treatment for the management of gestational diabetes mellitus and can reflect the level of glycaemic control. Accurately determining the degree of diabetic control in patients with gestational diabetes mellitus was difficult, however. Finally, a few pregnancy outcomes were not accurately defined in studies included in our analysis. Stillbirth, for example, was defined as death after the 20th or 28th week of pregnancy, based on different criteria, but some studies did not clearly state the definition of stillbirth used in their methods. Therefore, we considered stillbirth as an outcome based on the clinical diagnosis in the studies, which might have caused potential bias in the analysis.

Conclusions

We performed a meta-analysis of the association between gestational diabetes mellitus and adverse outcomes of pregnancy in more than seven million women. Gestational diabetes mellitus was significantly associated with a range of pregnancy complications when adjusted for confounders. Our findings contribute to a more comprehensive understanding of adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

What is already known on this topic

The incidence of gestational diabetes mellitus is gradually increasing and is associated with a range of complications for the mother and fetus or neonate

Pregnancy outcomes in gestational diabetes mellitus, such as neonatal death and low Apgar score, have not been considered in large cohort studies

Comprehensive systematic reviews and meta-analyses assessing the association between gestational diabetes mellitus and adverse pregnancy outcomes are lacking

What this study adds

This systematic review and meta-analysis showed that in studies where insulin was not used, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean delivery, preterm delivery, low one minute Apgar score, macrosomia, and an infant large for gestational age in the pregnancy outcomes

In studies with insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of an infant large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit

Future primary studies should routinely consider adjusting for a more complete set of prognostic factors

Ethics statements

Ethical approval.

Not required.

Data availability statement

Table S11 provides details of adjustment for core confounders. Supplementary data files contain all of the raw tabulated data for the systematic review (table S12). Tables S13-15 provide the raw data and R language codes used for the meta-analysis.

Contributors: WY and FL developed the initial idea for the study, designed the scope, planned the methodological approach, wrote the computer code and performed the meta-analysis. WY and CL coordinated the systematic review process, wrote the systematic review protocol, completed the PROSPERO registration, and extracted the data for further analysis. ZL coordinated the systematic review update. WY, JH, and FL defined the search strings, executed the search, exported the results, and removed duplicate records. WY, CL, ZL, and FL screened the abstracts and texts for the systematic review, extracted relevant data from the systematic review articles, and performed quality assessment. WY, ZL, and FL wrote the first draft of the manuscript and all authors contributed to critically revising the manuscript. ZL and FL are the study guarantors. ZL and FL are senior and corresponding authors who contributed equally to this study. All authors had full access to all the data in the study, and the corresponding authors had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The research was funded by the National Natural Science Foundation of China (grants 82001223 and 81901401), and the Natural Science Foundation for Young Scientist of Hunan Province, China (grant 2019JJ50952). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Natural Science Foundation of China and the Natural Science Foundation for Young Scientist of Hunan Province, China for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The dissemination plan targets a wide audience, including members of the public, patients, patient and public communities, health professionals, and experts in the specialty through various channels: written communication, events and conferences, networks, and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Saravanan P ,
• Diabetes in Pregnancy Working Group ,
• Maternal Medicine Clinical Study Group ,
• Royal College of Obstetricians and Gynaecologists, UK
• O’Sullivan JB ,
• Hartling L ,
• Dryden DM ,
• Guthrie A ,
• Vandermeer B ,
• McIntyre HD ,
• Catalano P ,
• Mathiesen ER ,
• Metzger BE ,
• HAPO Study Cooperative Research Group
• Persson M ,
• Balsells M ,
• García-Patterson A ,
• Simmonds M ,
• Murphy HR ,
• Roland JM ,
• East Anglia Study Group for Improving Pregnancy Outcomes in Women with Diabetes (EASIPOD)
• Magnuson A ,
• Simmons D ,
• Sumeksri P ,
• Wongyai S ,
• González González NL ,
• Bellart J ,
• Guillén MA ,
• Herranz L ,
• Barquiel B ,
• Hillman N ,
• Burgos MA ,
• Pallardo LF
• Higgins JP ,
• Thompson SG ,
• Davey Smith G ,
• Schneider M ,
• Sweeting MJ ,
• Sutton AJ ,
• Hartung J ,
• IntHout J ,
• Ioannidis JP ,
• Alberico S ,
• Montico M ,
• Barresi V ,
• Multicentre Study Group on Mode of Delivery in Friuli Venezia Giulia
• Alfadhli EM ,
• Anderberg E ,
• Ardawi MS ,
• Nasrat HA ,
• Al-Sagaaf HM ,
• Kenealy T ,
• Barakat MN ,
• Youssef RM ,
• Al-Lawati JA
• Ibrahim I ,
• Eltaher F ,
• Benhalima K ,
• Hanssens M ,
• Devlieger R ,
• Verhaeghe J ,
• Berggren EK ,
• Boggess KA ,
• Stuebe AM ,
• Jonsson Funk M
• Korucuoglu U ,
• Aksakal N ,
• Himmetoglu O
• Bodmer-Roy S ,
• Cousineau J ,
• Broekman BFP ,
• GUSTO study group
• Catalano PM ,
• Cruickshank JK ,
• Chanprapaph P ,
• Cheung NW ,
• Lopez-Rodo V ,
• Rodriguez-Vaca D ,
• Benchimol M ,
• Carbillon L ,
• Benbara A ,
• Pharisien I ,
• Scifres CM ,
• Gorban de Lapertosa S ,
• Salzberg S ,
• DPSG-SAD Group
• Zijlmans AB ,
• Rademaker D ,
• Djelmis J ,
• Mulliqi Kotori V ,
• Pavlić Renar I ,
• Ivanisevic M ,
• Oreskovic S
• Domanski G ,
• Ittermann T ,
• Donovan LE ,
• Edwards AL ,
• Torrejón MJ ,
• Ekeroma AJ ,
• Chandran GS ,
• McCowan L ,
• Eagleton C ,
• Erjavec K ,
• Poljičanin T ,
• Matijević R
• Ethridge JK Jr . ,
• Feleke BE ,
• Feleke TE ,
• Forsbach G ,
• Cantú-Diaz C ,
• Vázquez-Lara J ,
• Villanueva-Cuellar MA ,
• Alvarez y García C ,
• Rodríguez-Ramírez E
• Gonçalves E ,
• Gortazar L ,
• Flores-Le Roux JA ,
• Benaiges D ,
• Gruendhammer M ,
• Brezinka C ,
• Lechleitner M
• Hedderson MM ,
• Ferrara A ,
• Hillier TA ,
• Pedula KL ,
• Morris JM ,
• Hossein-Nezhad A ,
• Maghbooli Z ,
• Vassigh AR ,
• Massaro N ,
• Streckeisen S ,
• Ikenoue S ,
• Miyakoshi K ,
• Raghav SK ,
• Jensen DM ,
• Sørensen B ,
• Rich-Edwards JW ,
• Kreisman S ,
• Tildesley H
• Kachhwaha CP ,
• Kautzky-Willer A ,
• Bancher-Todesca D ,
• Weitgasser R ,
• Keikkala E ,
• Mustaniemi S ,
• Koivunen S ,
• Keshavarz M ,
• Babaee GR ,
• Moghadam HK ,
• Kgosidialwa O ,
• Carmody L ,
• Gunning P ,
• Kieffer EC ,
• Carman WJ ,
• Sanborn CZ ,
• Viljakainen M ,
• Männistö T ,
• Kachhawa G ,
• Laafira A ,
• Griffin CJ ,
• Johnson JA ,
• Lapolla A ,
• Dalfrà MG ,
• Ragazzi E ,
• De Cata AP ,
• Norwitz E ,
• Leybovitz-Haleluya N ,
• Wainstock T ,
• Hinkle SN ,
• Grantz KL ,
• Lopez-de-Andres A ,
• Carrasco-Garrido P ,
• Gil-de-Miguel A ,
• Hernandez-Barrera V ,
• Jiménez-García R
• Luengmettakul J ,
• Sunsaneevithayakul P ,
• Talungchit P
• Macaulay S ,
• Munthali RJ ,
• Dunger DB ,
• Makwana M ,
• Bhimwal RK ,
• El Mallah KO ,
• Kulaylat NA ,
• Melamed N ,
• Vandenberghe H ,
• Jensen RC ,
• Kibusi SM ,
• Munyogwa MJ ,
• Patient C ,
• Miailhe G ,
• Legardeur H ,
• Mandelbrot L
• Minsart AF ,
• N’guyen TS ,
• Ratsimandresy R ,
• Ali Hadji R
• Matsumoto T ,
• Morikawa M ,
• Sugiyama T ,
• Knights SJ ,
• Olayemi OO ,
• Mwanri AW ,
• Ramaiya K ,
• Abalkhail B ,
• Nguyen TH ,
• Nguyen CL ,
• Minh Pham N ,
• Nicolosi BF ,
• Vernini JM ,
• Kragelund Nielsen K ,
• Andersen GS ,
• Nybo Andersen AM
• Ogonowski J ,
• Miazgowski T ,
• Czeszyńska MB ,
• Kuczyńska M ,
• Miazgowski T
• Morrish DW ,
• O’Sullivan EP ,
• O’Reilly M ,
• Dennedy MC ,
• Gaffney G ,
• Atlantic DIP collaborators
• Ovesen PG ,
• Rasmussen S ,
• Kesmodel US
• Ozumba BC ,
• Premuzic V ,
• Zovak Pavic A ,
• Bevanda M ,
• Mihaljevic S ,
• Ramachandran A ,
• Snehalatha C ,
• Clementina M ,
• Sasikala R ,
• Redman LM ,
• LIFE-Moms Research Group
• Spanish Group for the Study of the Impact of Carpenter and Coustan GDM thresholds
• Bowker SL ,
• Montoro MN ,
• Lawrence JM
• Kamalanathan S ,
• Saldana TM ,
• Siega-Riz AM ,
• Savitz DA ,
• Thorp JM Jr .
• Savona-Ventura C ,
• Schwartz ML ,
• Lubarsky SL
• Segregur J ,
• Buković D ,
• Milinović D ,
• Cavkaytar S ,
• Shahbazian H ,
• Nouhjah S ,
• Shahbazian N ,
• McElduff A ,
• Sheffield JS ,
• Butler-Koster EL ,
• McIntire DD ,
• Saigusa Y ,
• Nakanishi S ,
• Templeton A ,
• Sirimarco MP ,
• Guerra HM ,
• Lisboa EG ,
• Sletner L ,
• Yajnik CS ,
• Soliman A ,
• Al Rifai H ,
• Soonthornpun S ,
• Soonthornpun K ,
• Aksonteing J ,
• Thamprasit A
• Srichumchit S ,
• Sugiyama MS ,
• Roseveare C ,
• Basilius K ,
• Madraisau S
• Hansen BB ,
• Mølsted-Pedersen L
• Caswell A ,
• Holliday E ,
• Petrović O ,
• Crnčević Orlić Ž ,
• Vambergue A ,
• Nuttens MC ,
• Goeusse P ,
• Biausque S ,
• van Hoorn J ,
• von Katterfeld B ,
• McNamara B ,
• Langridge AT
• Wahabi HA ,
• Esmaeil SA ,
• Alzeidan RA
• Esmaeil S ,
• Mamdouh H ,
• Wahlberg J ,
• Nyström L ,
• Persson B ,
• Arnqvist HJ
• Nankervis A ,
• Weijers RN ,
• Bekedam DJ ,
• Smulders YM
• Bleicher K ,
• Saunders LD ,
• Demianczuk NN
• Homer CSE ,
• Sullivan EA
• American Diabetes Association
• U.S. Preventive Services Task Force
• Tabrizi R ,
• Lankarani KB ,
• Leung-Pineda V ,
• Gronowski AM
• Bryson CL ,
• Ioannou GN ,
• Rulyak SJ ,
• Critchlow C

• Previous Article
• Next Article

Presentation

Clinical pearls, case study: a 36-year-old woman with type 2 diabetes and pregnancy.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Get Permissions

Diane M. Karl; Case Study: A 36-Year-Old Woman With Type 2 Diabetes and Pregnancy. Clin Diabetes 1 January 2001; 19 (1): 24–25. https://doi.org/10.2337/diaclin.19.1.24

Download citation file:

• Ris (Zotero)
• Reference Manager

C.M. is a 36-year-old Spanish-speaking Mexican-American woman with a 3-year history of type 2 diabetes. She was seen in her primary physician’s office because of a missed menstrual period; a pregnancy test was positive.

Her past obstetrical history included five vaginal deliveries and six miscarriages. All of her previous pregnancies occurred before the diagnosis of diabetes. Her previous medical care was in Mexico. She was never told of any glucose problem during her pregnancies, and she does not know the birth weights of her children. At the time of referral, she was 8 weeks pregnant and taking glyburide 10 mg twice daily. She was checking her blood glucose once daily in the morning with typical readings between 180 and 220 mg/dl on a plasma-referenced meter. Family history was positive for diabetes in her mother.

Her height was 62 inches, and her weight was 198 lb. Other than mild acanthosis nigricans and obesity, her physical examination was normal. She had no retinopathy and no evidence of neuropathy. Her glycosylated hemoglobin (HbA 1c ) level was 10.5% (normal <6.0%), and an office capillary blood glucose 4 h after lunch was 201 mg/dl.

She was started on insulin immediately and her glyburide was discontinued. She began monitoring her glucose before and after each meal, making daily adjustments in insulin. She received nutrition education with an appropriate calorie intake plus an emphasis on frequent smaller meals and limited carbohydrate intake. Within 1 week, her plasma glucose values were in the target range for pregnancy, but in the following week she had a spontaneous miscarriage. After her miscarriage, she discontinued insulin on her own and resumed taking glyburide 10 mg twice daily.

1.  Is there a relationship between C.M.’s diabetes and her adverse obstetrical history?

2.  What should have been done before her recent pregnancy to increase the odds of a favorable outcome?

3.  What considerations affect the choice of therapy for her diabetes now?

In the past, most diabetic women who conceived had type 1 diabetes. Today, however, we see an increasing number of women who have preconception type 2 diabetes. One reason is the tendency for many women to delay pregnancy until a later age. Another important factor, however, is the increasing number of children and young adults, especially in minority groups, who are developing type 2 diabetes. 1  

The presence of diabetes in a woman of childbearing years is a special challenge. Blood glucose control during the first 2 months of pregnancy is critical to normal organ development. Commonly, however, women do not seek medical attention until after this period of early fetal development. Many women do not yet realize they are pregnant during this important period, especially if the pregnancy is not planned, which is the situation in well over half of all pregnancies. For this reason, preconception counseling must be an important aspect of management in all diabetic women of childbearing years, regardless of whether there is an expressed desire to conceive. 2 , 3  

Even though C.M.’s diabetes was diagnosed 3 years ago, the fact that she is already poorly controlled on maximal sulfonylurea treatment suggests a longer duration of diabetes. This supports the possibility that her poor obstetrical history may have been related to undiagnosed (and, therefore, uncontrolled) diabetes. Certainly during her most recent pregnancy, C.M. was poorly controlled during the critical period of organ development, possibly leading to an anomaly incompatible with fetal viability.

Comprehensive preconception counseling is now indicated for C.M. Oral diabetic medications have not been adequately studied for safety during pregnancy. Therefore, a woman who is taking oral medication and who wishes to conceive should be switched to insulin, and control should be established before she becomes pregnant. If C.M. plans another pregnancy or if she is not actively using birth control, she needs to resume insulin treatment.

Even patients whose diabetes is well controlled with diet and exercise are almost certain to require insulin during the later stages of gestation, when insulin resistance increases markedly. Preparing patients for this likelihood and teaching insulin administration as part of preconception counseling is advisable. Before pregnancy occurs is the ideal time to address any patient fears and misconceptions about insulin treatment.

For a woman of childbearing age who does not wish to become pregnant, choice of therapy can be important. Insulin resistance, almost universally present in type 2 diabetes, may be associated with decreased fertility. This is most clearly evident in polycystic ovary syndrome. 4 Oral diabetic medications that reduce insulin resistance, such as metformin and thiazolidinediones, 5 may also restore fertility. Thus, a previously infertile patient with type 2 diabetes may become unexpectedly pregnant after starting an insulin-sensitizing medication unless she is counseled regarding the need for birth control.

1.  Preconception counseling is important for all women with diabetes, type 1 or type 2, who are in their childbearing years, since many pregnancies are not planned and poor glucose control early in pregnancy is associated with a higher incidence of major congenital defects.

2.  Especially in minority populations, increasing numbers of women with type 2 diabetes who are treated with oral medications may be in their childbearing years. There are not adequate safety data to recommend the use of oral diabetic medications during pregnancy.

3.  Oral diabetic medications that reduce insulin resistance may increase fertility in women previously unable to conceive.

Diane M. Karl, MD, is medical director of diabetes services at Adventist Health and an assistant professor of clinical medicine at Oregon Health Sciences University in Portland, Ore.

Email alerts

• Online ISSN 1945-4953
• Print ISSN 0891-8929
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Relationship between melatonin receptor 1B (rs10830963 and rs1387153) with gestational diabetes mellitus: a case-control study and meta-analysis

Affiliations.

• 1 Department of Obstetries and Gynecology, The First Clinical Medical College of Three Gorges University, Yichang Central People's Hospital, Yichang, China.
• 2 Department of Obstetries and Gynecology, The First Clinical Medical College of Three Gorges University, Yichang Central People's Hospital, Yichang, China. [email protected].
• PMID: 26563312
• DOI: 10.1007/s00404-015-3948-y

Purpose: Variants rs10830963 (C/G) and rs1387153 (C/T) in MTNR1B have been shown with an increased risk of developing type 2 diabetes and gestational diabetes mellitus. However, the results are still controversial, and evidence was not satisfied. Hence, a case-control study and a further meta-analysis will be performed in this study.

Methods: We recruited 674 GDM patients and 690 controls from Jan 2010 and Jan 2014. The SNPs were genotyped by ABI TaqMan SNP Genotyping Assays. MTNR1B rs10830963 and rs1387153 single nucleotide polymorphisms (SNPs) were performed for association analysis. Then a systematic search of all relevant studies was conducted. A meta-analysis was performed to prove the relationship between melatonin receptor 1B (rs10830963 and rs1387153) with GDM.

Results: The case-control study presented that G allele of the rs10830963 and T allele of rs1387153 were significantly associated with increased risk of GDM. The further meta-analysis included other five studies showed that the frequency of MTNR1B rs10830963 G allele and rs1387153 T allele are higher in GDM patients.

Conclusion: The case-control study proved that the risk allele (G allele) of rs10830963 and (T allele) of rs1387153 lead to a higher risk for GDM. The further meta-analysis provides additional evidence supporting the above results. Due to the limited data currently available in different race population, further studies with large sample sizes are required.

Keywords: Gene; Gestational diabetes mellitus; Melatonin receptor 1B; Meta-analysis; Polymorphism.

PubMed Disclaimer

Similar articles

• Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction. Popova PV, Klyushina AA, Vasilyeva LB, Tkachuk AS, Vasukova EA, Anopova AD, Pustozerov EA, Gorelova IV, Kravchuk EN, Li O, Pervunina TM, Kostareva AA, Grineva EN. Popova PV, et al. Front Endocrinol (Lausanne). 2021 Apr 19;12:628582. doi: 10.3389/fendo.2021.628582. eCollection 2021. Front Endocrinol (Lausanne). 2021. PMID: 33953693 Free PMC article.
• MTNR1B genetic polymorphisms as risk factors for gestational diabetes mellitus: a case-control study in a single tertiary care center. Alharbi KK, Al-Sulaiman AM, Shedaid KMB, Al-Shangiti AM, Marie M, Al-Sheikh YA, Ali Khan I. Alharbi KK, et al. Ann Saudi Med. 2019 Sep-Oct;39(5):309-318. doi: 10.5144/0256-4947.2019.309. Epub 2019 Oct 3. Ann Saudi Med. 2019. PMID: 31580701 Free PMC article.
• The roles of ADIPOQ rs266729 and MTNR1B rs10830963 polymorphisms in patients with gestational diabetes mellitus: A meta-analysis. Bai Y, Tang L, Li L, Li L. Bai Y, et al. Gene. 2020 Mar 10;730:144302. doi: 10.1016/j.gene.2019.144302. Epub 2019 Dec 26. Gene. 2020. PMID: 31884106 Review.
• Melatonin receptor 1 B polymorphisms associated with the risk of gestational diabetes mellitus. Kim JY, Cheong HS, Park BL, Baik SH, Park S, Lee SW, Kim MH, Chung JH, Choi JS, Kim MY, Yang JH, Cho DH, Shin HD, Kim SH. Kim JY, et al. BMC Med Genet. 2011 Jun 10;12:82. doi: 10.1186/1471-2350-12-82. BMC Med Genet. 2011. PMID: 21658282 Free PMC article.
• Relationship between melatonin receptor 1B and insulin receptor substrate 1 polymorphisms with gestational diabetes mellitus: a systematic review and meta-analysis. Zhang Y, Sun CM, Hu XQ, Zhao Y. Zhang Y, et al. Sci Rep. 2014 Aug 22;4:6113. doi: 10.1038/srep06113. Sci Rep. 2014. PMID: 25146448 Free PMC article. Review.
• Membrane Melatonin Receptors Activated Cell Signaling in Physiology and Disease. Nikolaev G, Robeva R, Konakchieva R. Nikolaev G, et al. Int J Mol Sci. 2021 Dec 31;23(1):471. doi: 10.3390/ijms23010471. Int J Mol Sci. 2021. PMID: 35008896 Free PMC article. Review.
• Genetic Studies of Gestational Diabetes and Glucose Metabolism in Pregnancy. Powe CE, Kwak SH. Powe CE, et al. Curr Diab Rep. 2020 Nov 9;20(12):69. doi: 10.1007/s11892-020-01355-3. Curr Diab Rep. 2020. PMID: 33165656 Free PMC article. Review.
• Chronological Age Interacts with the Circadian Melatonin Receptor 1B Gene Variation, Determining Fasting Glucose Concentrations in Mediterranean Populations. Additional Analyses on Type-2 Diabetes Risk. Sorlí JV, Barragán R, Coltell O, Portolés O, Pascual EC, Ortega-Azorín C, González JI, Estruch R, Saiz C, Pérez-Fidalgo A, Ordovas JM, Corella D. Sorlí JV, et al. Nutrients. 2020 Oct 29;12(11):3323. doi: 10.3390/nu12113323. Nutrients. 2020. PMID: 33138317 Free PMC article.
• A functional polymorphism rs10830963 in melatonin receptor 1B associated with the risk of gestational diabetes mellitus. Huang B, Wang YK, Qin LY, Wei Q, Liu N, Jiang M, Yu HP, Yu XY. Huang B, et al. Biosci Rep. 2019 Dec 20;39(12):BSR20190744. doi: 10.1042/BSR20190744. Biosci Rep. 2019. PMID: 31808503 Free PMC article.

Publication types

• Search in MeSH

Related information

• Gene (GeneRIF)
• Nucleotide (RefSeq)
• Protein (RefSeq)

LinkOut - more resources

Full text sources.

• Genetic Alliance
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Immunometabolic profiling related with gestational diabetes mellitus: a nested case-control study of CD4 + T cell phenotypes and glycemic traits

• Original Article
• Published: 15 August 2024

Cite this article

• Xiaohui Wei 1 ,
• Zhuo Sun 1 ,
• Na Wang 2 ,
• Zequn Deng 1 ,
• Wenyun Li 1 ,
• Tao Ying 1 ,
• Yuwei Liu   ORCID: orcid.org/0000-0002-4541-3824 1 &
• Gengsheng He 1  

14 Accesses

Explore all metrics

To investigate immunometabolic associations of CD4 + T cell phenotypes with gestational diabetes mellitus (GDM).

A nested case-control study was conducted comprising 53 pairs of GDM patients and matched controls within a prospective cohort. Metabolomic signatures related to both CD4 + T cell phenotypes and glycemic traits among pregnant women were investigated by weighted gene co-expression network analysis (WGCNA). Multivariable-adjusted generalized linear models were used to explore the associations of CD4 + T cell phenotypes and selected metabolites with GDM. Mediation analysis was conducted to evaluate the mediating effect of selected metabolites on the relationship between CD4 + T cell phenotypes and glycemic traits.

Higher levels of Treg cells (OR per SD increment (95%CI): 0.57 (0.34, 0.95), p  = 0.031) and increased expression of Foxp3 (OR per SD increment (95%CI): 0.59 (0.35, 0.97), p  = 0.039) and GATA3 (OR per SD increment (95%CI): 0.42 (0.25, 0.72), p  = 0.002) were correlated with a decreased risk of GDM. Plasma pyruvaldehyde, S-adenosylhomocysteine (SAH), bergapten, and 9-fluorenone mediated the association between Tregs and fasting plasma glucose (FPG), with mediation proportions of 46.9%, 39.6%, 52.4%, and 56.9%, respectively.

Conclusions

Treg cells and Foxp3 expressions were inversely associated with GDM risk, with potential metabolic mechanisms involving metabolites such as pyruvaldehyde and SAH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Metabolomic and genetic architecture of gestational diabetes subtypes

Metabolomic Biomarkers, Metabolite Patterns, and Gestational Diabetes Mellitus

Data availability

Restrictions apply to the availability of all data analyzed during this study to preserve patient confidentiality or because they were used under license. The corresponding author will, on request, detail the restrictions and any conditions under which access to some data may be provided.

Sweeting A, Wong J, Murphy HR, Ross GP (2022) A clinical update on gestational diabetes Mellitus. Endocr Rev 43(5):763–793. https://doi.org/10.1210/endrev/bnac003

Article   PubMed   PubMed Central   Google Scholar  

International Diabetes Federation (2021) IDF Diabetes Atlas, 10th edn. International Diabetes Federation Brussels, Belgium

Google Scholar  

Ye W, Luo C, Huang J, Li C, Liu Z, Liu F (2022) Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ 377:e67946. https://doi.org/10.1136/bmj-2021-067946

Article   Google Scholar  

Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL (2020) Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ 369:m1361. https://doi.org/10.1136/bmj.m1361

Clausen TD, Mathiesen ER, Hansen T, Pedersen O, Jensen DM, Lauenborg J, Damm P (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia. Diabetes Care 31(2):340–346. https://doi.org/10.2337/dc07-1596

Article   PubMed   Google Scholar  

Zhu H, Zhao Z, Xu J, Chen Y, Cai J, Shi C, Zhou L, Zhu Q, Ji L (2023) Comprehensive landscape of the T and B-cell repertoires of newly diagnosed gestational diabetes mellitus. Genomics 115(5):110681. https://doi.org/10.1016/j.ygeno.2023.110681

Article   CAS   PubMed   Google Scholar  

McElwain CJ, McCarthy FP, McCarthy CM (2021) Gestational diabetes Mellitus and maternal Immune Dysregulation: what we know so far. Int J Mol Sci 22(8). https://doi.org/10.3390/ijms22084261

Dong C (2021) Cytokine regulation and function in T cells. Annu Rev Immunol 39:51–76. https://doi.org/10.1146/annurev-immunol-061020-053702

Schober L, Radnai D, Spratte J, Kisielewicz A, Schmitt E, Mahnke K, Fluhr H, Uhlmann L, Sohn C, Steinborn A (2014) The role of regulatory T cell (Treg) subsets in gestational diabetes mellitus. Clin Exp Immunol 177(1):76–85. https://doi.org/10.1111/cei.12300

Article   CAS   PubMed   PubMed Central   Google Scholar  

Yang Y, Liu L, Liu B, Li Q, Wang Z, Fan S, Wang H, Wang L (2018) Functional Defects of Regulatory T Cell through Interleukin 10 mediated mechanism in the induction of gestational diabetes Mellitus. DNA Cell Biol 37(3):278–285. https://doi.org/10.1089/dna.2017.4005

Sheu A, Chan Y, Ferguson A, Bakhtyari MB, Hawke W, White C, Chan YF, Bertolino PJ, Woon HG, Palendira U, Sierro F, Lau SM (2018) A proinflammatory CD4(+) T cell phenotype in gestational diabetes mellitus. Diabetologia 61(7):1633–1643. https://doi.org/10.1007/s00125-018-4615-1

Wang N, Deng Z, Wen L, Ding Y, He G (2021) Relationships between maternal dietary patterns and blood lipid levels during pregnancy: a prospective cohort study in Shanghai, China. Int J Environ Res Public Health 18(7). https://doi.org/10.3390/ijerph18073701

Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7):412–419. https://doi.org/10.1007/BF00280883

Smith JP, Shen Y, Strauss J, Zhe Y, Zhao Y (2012) The Effects of Childhood Health on Adult Health and SES in China. Econ Dev Cult Change 61(1):127–156. https://doi.org/10.1086/666952

National Institute of Nutrition and Food Safety (2018) China Food Composition. Peking University Medical Press Beijing

National Institute of Nutrition and Food Safety (2019) China Food Composition. Peking University Medical Press Beijing

Mahmoud F, Al-Ozairi E (2013) Inflammatory cytokines and the risk of cardiovascular complications in type 2 diabetes. Dis Markers 35(4):235–241. https://doi.org/10.1155/2013/931915

Yuan N, Zhang HF, Wei Q, Wang P, Guo WY (2018) Expression of CD4 + CD25 + Foxp3 + Regulatory T cells, Interleukin 10 and transforming growth factor beta in newly diagnosed type 2 Diabetic patients. Exp Clin Endocrinol Diabetes 126(2):96–101. https://doi.org/10.1055/s-0043-113454

Winer S, Chan Y, Paltser G, Truong D, Tsui H, Bahrami J, Dorfman R, Wang Y, Zielenski J, Mastronardi F, Maezawa Y, Drucker DJ, Engleman E, Winer D, Dosch HM (2009) Normalization of obesity-associated insulin resistance through immunotherapy. Nat Med 15(8):921–929. https://doi.org/10.1038/nm.2001

Eller K, Kirsch A, Wolf AM, Sopper S, Tagwerker A, Stanzl U, Wolf D, Patsch W, Rosenkranz AR, Eller P (2011) Potential role of regulatory T cells in reversing obesity-linked insulin resistance and diabetic nephropathy. Diabetes 60(11):2954–2962. https://doi.org/10.2337/db11-0358

Fabrizi M, Marchetti V, Mavilio M, Marino A, Casagrande V, Cavalera M, Moreno-Navarrete JM, Mezza T, Sorice GP, Fiorentino L, Menghini R, Lauro R, Monteleone G, Giaccari A, Fernandez RJ, Federici M (2014) IL-21 is a major negative regulator of IRF4-dependent lipolysis affecting Tregs in adipose tissue and systemic insulin sensitivity. Diabetes 63(6):2086–2096. https://doi.org/10.2337/db13-0939

Al-Jaber H, Al-Mansoori L, Elrayess MA (2021) GATA-3 as a potential therapeutic target for insulin resistance and type 2 diabetes Mellitus. Curr Diabetes Rev 17(2):169–179. https://doi.org/10.2174/1573399816666200705210417

El-Arabey AA, Abdalla M (2022) GATA3 as an immunomodulator in obesity-related metabolic dysfunction associated with fatty liver disease, insulin resistance, and type 2 diabetes. Chem Biol Interact 366:110141. https://doi.org/10.1016/j.cbi.2022.110141

Al-Jaber H, Mohamed NA, Govindharajan VK, Taha S, John J, Halim S, Alser M, Al-Muraikhy S, Anwardeen NR, Agouni A, Elhissi A, Al-Naemi HA, Al-Mansoori L, Elrayess MA (2022) In Vitro and in vivo validation of GATA-3 suppression for induction of adipogenesis and improving insulin sensitivity. Int J Mol Sci 23(19). https://doi.org/10.3390/ijms231911142

Chakraborty S, Karmakar K, Chakravortty D (2014) Cells producing their own nemesis: understanding methylglyoxal metabolism. IUBMB Life 66(10):667–678. https://doi.org/10.1002/iub.1324

Shuck SC, Wuenschell GE, Termini JS (2018) Product studies and mechanistic analysis of the reaction of Methylglyoxal with Deoxyguanosine. Chem Res Toxicol 31(2):105–115. https://doi.org/10.1021/acs.chemrestox.7b00274

Lai S, Lopez GE, Zoukari T, Ki P, Shuck SC (2022) Methylglyoxal and its adducts: induction, Repair, and Association with Disease. Chem Res Toxicol 35(10):1720–1746. https://doi.org/10.1021/acs.chemrestox.2c00160

Ahmed N, Babaei-Jadidi R, Howell SK, Beisswenger PJ, Thornalley PJ (2005) Degradation products of proteins damaged by glycation, oxidation and nitration in clinical type 1 diabetes. Diabetologia 48(8):1590–1603. https://doi.org/10.1007/s00125-005-1810-7

Han Y, Randell E, Vasdev S, Gill V, Gadag V, Newhook LA, Grant M, Hagerty D (2007) Plasma methylglyoxal and glyoxal are elevated and related to early membrane alteration in young, complication-free patients with type 1 diabetes. Mol Cell Biochem 305(1–2):123–131. https://doi.org/10.1007/s11010-007-9535-1

Kong X, Ma MZ, Huang K, Qin L, Zhang HM, Yang Z, Li XY, Su Q (2014) Increased plasma levels of the methylglyoxal in patients with newly diagnosed type 2 diabetes 2. J Diabetes 6(6):535–540. https://doi.org/10.1111/1753-0407.12160

Riboulet-Chavey A, Pierron A, Durand I, Murdaca J, Giudicelli J, Van Obberghen E (2006) Methylglyoxal impairs the insulin signaling pathways independently of the formation of intracellular reactive oxygen species. Diabetes 55(5):1289–1299. https://doi.org/10.2337/db05-0857

Ng Y, Ramm G, Lopez JA, James DE (2008) Rapid activation of Akt2 is sufficient to stimulate GLUT4 translocation in 3T3-L1 adipocytes. Cell Metab 7(4):348–356. https://doi.org/10.1016/j.cmet.2008.02.008

Zhang W, Thompson BJ, Hietakangas V, Cohen SM (2011) MAPK/ERK signaling regulates insulin sensitivity to control glucose metabolism in Drosophila. PLoS Genet 7(12):e1002429. https://doi.org/10.1371/journal.pgen.1002429

Stipanuk MH (2004) Sulfur amino acid metabolism: pathways for production and removal of homocysteine and cysteine. Annu Rev Nutr 24:539–577. https://doi.org/10.1146/annurev.nutr.24.012003.132418

Ganguly P, Alam SF (2015) Role of homocysteine in the development of cardiovascular disease. Nutr J 14:6. https://doi.org/10.1186/1475-2891-14-6

Huang T, Ren J, Huang J, Li D (2013) Association of homocysteine with type 2 diabetes: a meta-analysis implementing mendelian randomization approach. BMC Genomics 14:867. https://doi.org/10.1186/1471-2164-14-867

Zheng Y, Deng HY, Qiao ZY, Gong FX (2021) Homocysteine level and gestational diabetes mellitus: a systematic review and meta-analysis. Gynecol Endocrinol 37(11):987–994. https://doi.org/10.1080/09513590.2021.1967314

Becker A, Henry RM, Kostense PJ, Jakobs C, Teerlink T, Zweegman S, Dekker JM, Nijpels G, Heine RJ, Bouter LM, Smulders YM, Stehouwer CD (2003) Plasma homocysteine and S-adenosylmethionine in erythrocytes as determinants of carotid intima-media thickness: different effects in diabetic and non-diabetic individuals. Hoorn Study Atherosclerosis 169(2):323–330. https://doi.org/10.1016/s0021-9150(03)00199-0

Liang Y, Xie L, Liu K, Cao Y, Dai X, Wang X, Lu J, Zhang X, Li X (2021) Bergapten: a review of its pharmacology, pharmacokinetics, and toxicity. Phytother Res 35(11):6131–6147. https://doi.org/10.1002/ptr.7221

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 81773413 and 81861138007), the Shanghai Pujiang Program (No. 21PJD005), and the Shanghai 3-Year Public Health Action Plan (Grant No. GWV-10.1-XK11). We thank all mothers for participating in this study and all clinic and research staff for running it.

This work was supported by the National Natural Science Foundation of China (Grant Nos. 81773413 and 81861138007), the Shanghai Pujiang Program (No. 21PJD005), and the Shanghai 3-Year Public Health Action Plan (Grant No. GWV-10.1-XK11).

Author information

Authors and affiliations.

School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, No.130, Dong’an Road, Xuhui District, Shanghai, 200032, China

Xiaohui Wei, Zhuo Sun, Zequn Deng, Wenyun Li, Tao Ying, Min Wu, Yuwei Liu & Gengsheng He

Nursing Department, Obstetrics and Gynaecology Hospital of Fudan University, Shanghai, 200011, China

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: Gengsheng He, Yuwei Liu; Methodology: Yuwei Liu, Xiaohui Wei; Data curation and investigation: Na Wang, Zhuo Sun, Zequn Deng; Validation: Wenyun Li, Tao Ying, Min Wu; Formal analysis, visualization and writing - original draft preparation: Xiaohui Wei; Writing - review and editing: Gengsheng He, Yuwei Liu, Xiaohui Wei; Funding acquisition: Gengsheng He, Yuwei Liu; Project administration: Gengsheng He, Na Wang; Supervision: Gengsheng He. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yuwei Liu or Gengsheng He .

Ethics declarations

Ethics approval.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Research Committee of the Obstetrics and Gynecology Hospital of Fudan University (Ethics Approval number 2017-74).

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Managed by Annunziata Lapolla.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Wei, X., Sun, Z., Wang, N. et al. Immunometabolic profiling related with gestational diabetes mellitus: a nested case-control study of CD4 + T cell phenotypes and glycemic traits. Acta Diabetol (2024). https://doi.org/10.1007/s00592-024-02338-6

Download citation

Received : 07 April 2024

Accepted : 06 July 2024

Published : 15 August 2024

DOI : https://doi.org/10.1007/s00592-024-02338-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• CD4 + T cells
• Gestational diabetes mellitus
• Nested case-control study
• Find a journal
• Publish with us
• Track your research

• Allergy & Immunology
• Anesthesiology
• Critical Care
• Dermatology
• Diabetes & Endocrinology
• Emergency Medicine
• Family Medicine
• Gastroenterology
• General Surgery
• Hematology - Oncology
• Hospital Medicine
• Infectious Diseases
• Internal Medicine
• Multispecialty
• Ob/Gyn & Women's Health
• Ophthalmology
• Orthopedics
• Pathology & Lab Medicine
• Plastic Surgery
• Public Health
• Pulmonary Medicine
• Rheumatology
• Transplantation
• Today on Medscape
• Business of Medicine
• Medical Lifestyle
• Science & Technology
• Medical Students
• Pharmacists

What Every Provider Needs to Know About Type 1 Diabetes

Miriam E. Tucker

August 16, 2024

In July 2024, a 33-year-old woman with type 1 diabetes was boating on a hot day when her insulin delivery device slipped off. By the time she was able to exit the river, she was clearly ill, and an ambulance was called. The hospital was at capacity. Lying in the hallway, she was treated with fluids but not insulin, despite her boyfriend repeatedly telling the staff she had diabetes. She was released while still vomiting. The next morning, her boyfriend found her dead.

This story was shared by a friend of the woman in a Facebook group for people with type 1 diabetes and later confirmed by the boyfriend in a separate heartbreaking post. While it may be an extreme case, encounters with a lack of knowledge about type 1 diabetes in healthcare settings are quite common, sometimes resulting in serious adverse consequences.

In my 50+ years of living with the condition, I've lost track of the number of times I've had to speak up for myself, correct errors, raise issues that haven't been considered, and educate nonspecialist healthcare professionals about even some of the basics.

Type 1 diabetes is an autoimmune condition in which the insulin-producing cells in the pancreas are destroyed, necessitating lifelong insulin treatment. Type 2, in contrast, arises from a combination of insulin resistance and decreased insulin production. Type 1 accounts for just 5% of all people with diabetes, but at a prevalence of about 1 in 200, it's not rare. And that's not even counting the adults who have been misdiagnosed as having type 2 but who actually have type 1.

As a general rule, people with type 1 diabetes are more insulin sensitive than those with type 2 and more prone to both hyper- and hypoglycemia. Blood sugar levels tend to be more labile and less predictable, even under normal circumstances. Recent advances in hybrid closed-loop technology have been extremely helpful in reducing the swings, but the systems aren't foolproof yet. They still require user input (ie, guesswork), so there's still room for error.

Managing type 1 diabetes is challenging even for endocrinologists. But here are some very important basics that every healthcare provider should know:

We Need Insulin 24/7

Never, ever withhold insulin from a person with type 1 diabetes, for any reason. Even when not eating — or when vomiting — we still need basal (background) insulin, either via long-acting analog or a pump infusion. The dose may need to be lowered to avoid hypoglycemia, but if insulin is stopped, diabetic ketoacidosis will result. And if that continues, death will follow.

This should be basic knowledge, but I've read and heard far too many stories of insulin being withheld from people with type 1 in various settings, including emergency departments, psychiatric facilities, and jails. On Facebook, people with type 1 diabetes often report being told not to take their insulin the morning before a procedure, while more than one has described "sneaking" their own insulin while hospitalized because they weren't receiving any or not receiving enough.

On the flip side, although insulin needs are very individual, the amount needed for someone with type 1 is typically considerably less than for a person with type 2. Too much can result in severe hypoglycemia. There are lots of stories from people with type 1 diabetes who had to battle with hospital staff who tried to give them much higher doses than they knew they needed.

The American Diabetes Association recommends that people with type 1 diabetes who are hospitalized be allowed to wear their devices and self-manage to the degree possible. And please, listen to us when we tell you what we know about our own condition.

Fasting Is Fraught

I cringe every time I'm told to fast for a test or procedure. Fasting poses a risk for hypoglycemia in people with type 1 diabetes, even when using state-of-the-art technology. Fasting should not be required unless absolutely necessary, especially for routine lab tests.

Saleh Aldasouqi, MD, chief of endocrinology at Michigan State University, East Lansing, Michigan, has published several papers on a phenomenon he calls "Fasting-Evoked En Route Hypoglycemia in Diabetes," in which patients who fast overnight and skip breakfast experience hypoglycemia on the way to the lab.

"Patients continue taking their diabetes medication but don't eat anything, resulting in low blood sugar levels that cause them to have a hypoglycemic event while driving to or from the lab, putting themselves and others at risk," Aldasouqi explained, adding that fasting often isn't necessary for routine lipid panels .

If fasting is necessary, as for a surgical procedure that involves anesthesia, the need for insulin adjustment — NOT withholding — should be discussed with the patient to determine whether they can do it themselves or whether their diabetes provider should be consulted.

But again, this is tricky even for endocrinologists. True story: When I had my second carpal tunnel surgery in July 2019, my hand surgeon wisely scheduled me for his first procedure in the morning to minimize the length of time I'd have to fast. (He has type 1 diabetes himself, which helped.) My endocrinologist had advised me, per guidelines, to cut back my basal insulin infusion on my pump by 20% before going to bed.

But at bedtime, my continuous glucose monitor (CGM) showed that I was in the 170 mg/dL's and rising, not entirely surprising since I'd cut back on my predinner insulin dose knowing I wouldn't be able to eat if I dropped low later. I didn't cut back the basal.

When I woke up, my glucose level was over 300 mg/dL. This time, stress was the likely cause. (That's happened before.) Despite giving myself several small insulin boluses that morning without eating, my blood sugar was still about 345 mg/dL when I arrived at the hospital. The nurse told me that if it had been over 375 mg/dL, they would have had to cancel the surgery, but it wasn't, so they went ahead. I have no idea how they came up with that cutoff.

Anyway, thankfully, everything went fine; I brought my blood sugar back in target range afterward and healed normally. Point being, type 1 diabetes management is a crazy balancing act, and guidelines only go so far.

We Don't React Well to Steroids

If it's absolutely necessary to give steroids to a person with type 1 diabetes for any reason, plans must be made in advance for the inevitable glucose spike. If the person doesn't know how to adjust their insulin for it, please have them consult their diabetes provider. In my experience with locally injected corticosteroids, the spike is always higher and longer than I expected. Thankfully, I haven't had to deal with systemic steroids, but my guess is they're probably worse.

Procedures Can Be Pesky

People who wear insulin pump and/or CGMs must remove them for MRI and certain other imaging procedures. In some cases — as with CGMs and the Omnipod insulin delivery device that can't be put back on after removal — this necessitates advance planning to bring along replacement equipment for immediately after the procedure.

Diabetes devices can stay in place for other imaging studies, such as X-rays, most CT scans, ECGs, and ultrasounds. For heaven's sake, don't ask us to remove our devices if it isn't totally necessary.

In general, surprises that affect blood sugar are a bad idea. I recently underwent a gastric emptying study. I knew the test would involve eating radioactive eggs, but I didn't find out there's also a jelly sandwich with two slices of white bread until the technician handed it to me and told me to eat it. I had to quickly give myself insulin, and of course my blood sugar spiked later. Had I been forewarned, I could have at least "pre-bolused" 15-20 minutes in advance to give the insulin more time to start working.

Another anecdote: Prior to a dental appointment that involved numbing my gums for an in-depth cleaning, my long-time dental hygienist told me "be sure to eat before you come." I do appreciate her thinking of my diabetes. However, while that advice would have made sense long ago when treatment involved two daily insulin injections without dose adjustments, now it's more complicated.

Today, when we eat foods containing carbohydrates, we typically take short-acting insulin, which can lead to hypoglycemia if the dose given exceeds the amount needed for the carbs, regardless of how much is eaten. Better to not eat at all (assuming the basal insulin dose is correct) or just eat protein. And for the provider, best to just tell the patient about the eating limitations and make sure they know how to handle.

Duh, We Already Have Diabetes

I've heard of at least four instances in which pregnant women with type 1 diabetes have been ordered to undergo an oral glucose tolerance test to screen for gestational diabetes. In two cases, it was a "can you believe it?!" post on Facebook, with the women rightly refusing to take the test.

But in May 2024, a pregnant woman reported she actually drank the liquid, her blood sugar skyrocketed, she was vomiting, and she was in the midst of trying to bring her glucose level down with insulin on her own at home. She hadn't objected to taking the test because "my ob. gyn. knows I have diabetes," so she figured it was appropriate.

I don't work in a healthcare setting, but here's my guess: The ob. gyn. Hadn't actually ordered the test but had neglected to UN-order a routine order for a pregnant patient who already had diabetes and obviously should NOT be forced to drink a high-sugar liquid for no reason. If this is happening in pregnancies with type 1 diabetes, it most certainly could be as well for those with preexisting type 2 diabetes. Clearly, something should be done to prevent this unnecessary and potentially harmful scenario.

In summary, I think I speak for everyone living with type 1 diabetes in saying that we would like to have confidence that healthcare providers in all settings can provide care for whatever brought us to them without adding to the daily burden we already carry. Let's work together.

Reviewed by Saleh Aldasouqi, MD, chief of endocrinology at Michigan State University.

Miriam E. Tucker is a freelance journalist based in the Washington, DC, area. She is a regular contributor to Medscape Medical News, with other work appearing in the Washington Post, NPR's Shots blog, and Diatribe. She is on X @MiriamETucker.

Send comments and news tips to [email protected] .

TOP PICKS FOR YOU

• Perspective
• Drugs & Diseases
• Global Coverage
• Additional Resources
• Biosimilars May Finally Stop the Rocketing Cost of Insulin
• Aging and Type 1 Diabetes: 'Complete Picture' 40 Years On
• With Type 1 Diabetes Delay Possible, Focus Now on Screening
• Diseases & Conditions Pediatric Type 1 Diabetes Mellitus
• Diseases & Conditions Type 1 Diabetes Mellitus
• Drugs dulaglutide
• Diseases & Conditions Ketosis-Prone Type 2 Diabetes
• Type 1 Diabetes Mellitus
• Pediatric Type 1 Diabetes Mellitus
• Type 2 Diabetes Mellitus
• Pediatric Type 2 Diabetes Mellitus
• Fast Five Quiz: How Much Do You Know About Diabetic Neuropathy?
• Fast Five Quiz: Atrial Fibrillation and Diabetes
• What the Eyes Tell You: 15 Abnormalities of the Lens
• Diabetes Mellitus Type 1 News & Perspectives
• Are Immune Therapies for Type 1 Diabetes Worthwhile?
• 'No Excuses': Training for the Olympics with Type 1 Diabetes

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 17 August 2024

Risk factors for necrotizing enterocolitis in small-for-gestational-age infants: a matched case–control study

• Xiang-Ping Ding 1 ,
• Xiang-Wen Hu 1 ,
• Shi Chen 2 ,
• Zheng-Li Wang 1 , 2 ,
• Lu-Quan Li 1 , 2 &
• Wen-Yan Tang 1 , 3  

Scientific Reports volume  14 , Article number:  19098 ( 2024 ) Cite this article

Metrics details

• Gastroenterology
• Gastrointestinal diseases
• Medical research
• Risk factors

Few studies have focused on the risk factors for necrotizing enterocolitis (NEC) in small for gestational age (SGA) infants. The aim of this study was to identify the risk factors for NEC in SGA newborns. This study included consecutive SGA neonates admitted to a tertiary hospital in Jiangxi Province, China from Jan 2008 to Dec 2022. Patients with NEC (Bell’s stage ≥ II) were assigned to the NEC group. Gestational age- and birth weight-matched non-NEC infants born during the same period at the same hospital were assigned to the control group. The risk factors associated with NEC were analyzed with univariate and logistic regression models. During the study period, 2,912 SGA infants were enrolled, 150 (5.15%) of whom developed NEC. In total, 143 patients and 143 controls were included in the NEC and control groups, respectively. Logistic regression analysis revealed that sepsis ( OR 2.399, 95% CI 1.271–4.527, P  = 0.007) and anemia ( OR 2.214, 95% CI 1.166–4.204, P  = 0.015) might increase the incidence of NEC in SGA infants and that prophylactic administration of probiotics ( OR 0.492, 95% CI 0.303–0.799, P  = 0.004) was a protective factor against NEC. Therefore, sepsis, anemia and a lack of probiotic use are independent risk factors for NEC in SGA infants.

Similar content being viewed by others

Maternal, neonatal, and nutritional risk factors for medical and surgical necrotizing enterocolitis

Spontaneous intestinal perforation in premature infants: a national study

Initial surgery for spontaneous intestinal perforation in extremely low birth weight infants is not associated with mortality or in-hospital morbidities

Introduction.

Necrotizing enterocolitis (NEC) is a common and devastating gastrointestinal emergency of preterm birth that occurs in 7–12% of very low birth weight infants 1 , 2 . The mortality rate associated with NEC ranges from 20 to 40%, and survivors are at increased risk for poor long-term growth and neurodevelopmental impairment despite improvements in medical technology and neonatal care over the past several years 3 , 4 . The exact etiology of NEC remains unclear, but multiple factors, such as formula feeding, prematurity, low birth weight, intestinal ischemia and abnormal microbial colonization, are considered risk factors 5 , 6 , 7 . Classification as small for gestational age (SGA) is assigned if a newborn has a birth weight < 10th percentile for their gestational age 8 , suggesting possible intrauterine growth retardation and growth insufficiency. The risk of developing NEC in SGA neonates is more than double that in appropriate for gestational age (AGA) neonates 9 . However, the risk factors associated with the development of NEC in SGA infants remain unclear. To our knowledge, few studies have focused on the risk factors for NEC in SGA infants. The aim of this study was to identify the potential risk factors for NEC in SGA infants.

Clinical features

During the study period, 2,912 SGA infants were admitted to the Department of Neonatology, Jiangxi Hospital Affiliated to the Children's Hospital of Chongqing Medical University (CHCMU). Among these infants, 150 (5.15%) developed NEC (Bell’s stage ≥ II), 143 of whom were eligible for enrollment; the other 7 patients were excluded because they were discharged from the hospital during the first 24 h of hospitalization (n = 4) and had incomplete information (n = 3). Consequently, 143 matched SGA infants without NEC were included in the control group.

Table 1 shows the comparison of demographic characteristics between the two groups. No differences in neonatal baseline factors or maternal factors were found between the two groups ( P  > 0.05). Compared with non-NEC infants, infants with NEC required a longer duration of hospitalization ( P  = 0.046) and had higher overall mortality ( P  = 0.000). After adjustment, the mortality rate in the NEC group (Table 1 ).

was still significantly greater than that in the control group ( P  = 0.000).

Table 2 shows the risk factors associated with NEC identified with the univariate analysis. A higher incidence of anemia and sepsis and a lower incidence of prophylactic probiotic administration were found in neonates with NEC ( P  < 0.05). In infants with NEC, sepsis occurred at a mean of 2 (range 1–5) days before the onset of NEC; the time of sepsis onset after birth and the timing of NEC onset are presented in Fig.  1 . No differences in the incidence of patent ductus arteriosus, respiratory failure, apnea, respiratory distress syndrome or polycythemia were found between the two groups ( P  > 0.05).

The time of sepsis onset after birth and the timing of NEC onset.

The most important risk factors for NEC in SGA infants in the less than P3 and P3–P10 subgroups are shown in Table 3 . Specifically, sepsis was an important risk factor for NEC in SGA infants in the less than P3-P10 subgroup ( P  = 0.012), and anemia was an important risk factor for NEC in the P3 subgroup ( P  = 0.034). Additionally, prophylactic probiotics appeared to reduce the incidence of NEC in the P3 group ( P  = 0.005).

Table 4 shows the independent risk factors identified by the multivariate logistic regression model. Neonatal anemia ( P  = 0.015) and sepsis ( P  = 0.007) were considered independent risk factors for NEC in SGA infants, and SGA infants prophylactically administered probiotics were less likely to have NEC ( P  = 0.004).

To further clarify whether the presence of these risk factors affects the prognosis of NEC in infants, we compared subgroups of NEC survivors and nonsurvivors. Table 5 shows that the presence of sepsis significantly increased the mortality of NEC infants ( P  = 0.016), whereas the prophylactic administration of probiotics might reduce their mortality ( P  = 0.000). Here, anemia did not increase the mortality of NEC patients ( P  = 0.732).

SGA infants are considered a high-risk population for NEC 9 , 10 . In this population, the incidence of NEC in SGA infants was 5.15% (150/2912). Other studies have reported that the incidence rate of NEC in SGA infants is between 3.2% and 6.02% 9 , 11 . Notably, a multicenter survey of the Chinese population revealed that the incidence rate of NEC in SGA infants may be as high as 20.41% 12 .

However, the exact risk factors for NEC in SGA infants remain unclear. We found that neonatal anemia and sepsis were risk factors for NEC, and prophylactic administration of probiotics might decrease the incidence of NEC in SGA infants. This study may provide scientific evidence for prevention and treatment strategies for NEC.

We found that SGA infants with sepsis were more vulnerable to NEC. The mechanism by which sepsis causes NEC is thought to be multifactorial. Bacteria from hematogenous and gut-derived infections can directly destroy intestinal epithelial cells, and bacterial products such as endotoxins can cause necrosis of the intestinal tract 13 , 14 . Various inflammatory mediators, such as platelet-activating factor, tumor necrosis factor-α, interleukin (IL)-1, IL-6 and IL-10, contribute to the onset and progression of NEC 15 , 16 . We also found that anemia was associated with the development of NEC in SGA infants. Anemia can impair splanchnic perfusion, including that in the intestine, resulting in increased anaerobic metabolism and the production of byproducts such as lactic acid 17 , 18 . Additionally, anemia can impair the normal maturation of vascular autoregulation in the intestine 19 . These effects can trigger a cascade of events leading to ischemic-hypoxemic mucosal gut injury, predisposing neonates to NEC 17 , 18 .

Finally, we found that prophylactic probiotics were associated with a lower incidence of NEC in SGA infants. Several clinical trials have demonstrated that probiotic administration after birth decreases the incidence of NEC in neonates 20 , 21 . Inappropriate bacterial colonization in the gastrointestinal tract plays an key role in the development of NEC. Probiotics may promote the colonization of beneficial microbiota, inhibit the growth of pathogens, improve the function of the gut mucosal barrier, and prevent the incidence of NEC 5 , 22 . Therefore, a lack of probiotic use may be associated with a higher incidence of NEC 23 , 24 .

In this study, the overall mortality rate of the NEC group was significantly greater than that of the control group, and after adjusting for mortality due to NEC, the mortality rate in the NEC group was still significantly greater than that of the control group. Sepsis, anemia, respiratory failure, and other factors might significantly increase the mortality rate of patients with NEC 7 , 25 , 26 ; therefore, the higher mortality rate in the NEC group observed here might be closely related to the presence of multiple comorbidities in the SGA infants themselves.

There are several limitations in this study, including errors and bias inherent to the retrospective nature of the study. Moreover, this was only a single-center study and might not represent the characteristics of the entire Chinese SGA population. Therefore, prospective multicenter studies are needed to clarify the high-risk factors for NEC in the SGA population.

In conclusion, sepsis, anemia and a lack of probiotic use were independent risk factors for NEC in SGA infants in the present study. Thus, more attention should be given to SGA neonates with anemia and sepsis in future medical practices. Additionally, prophylactic probiotic use may reduce the incidence of NEC in SGA neonates.

Study population

This study was designed as a 1:1 matched case–control study. Consecutive SGA neonates who were admitted to the Department of Neonatology, Jiangxi Hospital Affiliated to Children’s Hospital of Chongqing Medical University (CHCMU) from Jan 2008 to Dec 2022, were included. This retrospective study was approved by the Ethics Committee of Jiangxi Hospital Affiliated to CHCMU (Approval No. 2016–19), and use of the database containing the evaluated data was permitted by the Ethics Committees of Jiangxi Hospital Affiliated to CHCMU. The requirement for informed consent was waived by the Ethics Committee of Jiangxi Hospital Affiliated to CHCMU. All study protocols were carried out in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. SGA was defined as a birth weight < 10th percentile for each newborn’s gestational age according to the growth chart for Chinese neonates 8 , 27 . SGA neonates with diagnosed NEC (Bell’s stage ≥ II) were included in the NEC group 28 . The SGA neonates without NEC admitted during the same period to the same hospital (the admission time of the control group infants did not differ from that of the NEC group by more than 3 months) were screened as possible controls; those matched for gestational age (difference of < 3 days) and birth weight (difference of < 100 g) were selected. When there were multiple candidate infants , one infant was randomly selected for inclusion in the control group by a computer. Neonates whose medical information was incomplete or who were discharged from the hospital during the first 24 h of hospitalization were excluded from the study.

Data collection

The demographic characteristics, including maternal factors such as maternal age, antibiotic exposure during pregnancy, antenatal glucocorticoid exposure, gestational hypertension, intrauterine cholestasis during pregnancy, anemia during pregnancy, gestational diabetes mellitus, premature rupture of the membrane (> 18 h), fetal distress and meconium-stained amniotic fluid, were recorded. The neonatal factors included gender, gestational age, birth weight, mode of delivery, feeding type, and Apgar score at 1 and 5 min. Risk factors prior to the occurrence of NEC, such as neonatal anemia, sepsis, patent ductus arteriosus, respiratory failure, apnea, respiratory distress syndrome and polycythemia, were also recorded. Laboratory examinations and clinical outcomes were collected retrospectively from the hospital’s neonatal database. Neonatal anemia was defined as a hemoglobin or hematocrit concentration greater than 2 standard deviations below the mean for postnatal age 29 . Sepsis that developed prior to the onset of NEC was diagnosed on the basis of clinical manifestations and the growth of bacteria on blood culture and ancillary tests such as leukopenia (WBC < 5 × 10 9 /L) or leukocytosis (WBC > 25 × 10 9 /L for ≤ 3 days or WBC > 20 × 10 9 /L for > 3 days), a platelet count < 100 × 10 9 /L, an immature-to-total neutrophil ratio (I:T ratio) ≥ 0.16, and a C-reactive protein > 8 mg/L 30 , 31 , 32 . NEC was defined according to the modified Bell’s criteria as Bell Stage II or greater 28 , 33 . All infants were treated with suitable and necessary interventions according to their conditions, with the possible interventions including cessation of enteral feeding, nasogastric suction and parenteral nutrition, antibiotic therapy and surgical intervention. The data were collected, reviewed, deidentified, and anonymously analyzed by the authors, and the Ethics Committee of Jiangxi Hospital Affiliated to CHCMU waived the requirement for informed consent because of the anonymized nature of the data and the scientific purpose of the study.

Statistical analysis

All analyses were conducted using SPSS 24.0 (SPSS Inc., Chicago, IL, USA). The Kolmogorov‒Smirnov test was used to assess the normality of continuous variables. Normally distributed variables were analyzed using Student’s t test, and skewed variables were analyzed with the Mann‒Whitney U test. The chi-square test and Fisher’s exact test were used to compare categorical variables between the two groups. All potential risk factors related to NEC incidence were included in the multivariate regression model to identify independent risk factors for NEC. P  < 0.05 was considered statistically significant.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Neu, J. Necrotizing enterocolitis: The mystery goes on. Neonatology 106 , 289–295. https://doi.org/10.1159/000365130 (2014).

Article   PubMed   Google Scholar  

Thänert, R., Keen, E. C., Dantas, G., Warner, B. B. & Tarr, P. I. Necrotizing enterocolitis and the microbiome: Current status and future directions. J. Infect. Dis. 223 , S257-s263. https://doi.org/10.1093/infdis/jiaa604 (2021).

Duess, J. W. et al. Necrotizing enterocolitis, gut microbes, and sepsis. Gut Microbes 15 , 2221470. https://doi.org/10.1080/19490976.2023.2221470 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Vaidya, R. et al. Long-term outcome of necrotizing enterocolitis and spontaneous intestinal perforation. Pediatrics 150 , e2022056445. https://doi.org/10.1542/peds.2022-056445 (2022).

Neu, J. & Walker, W. A. Necrotizing enterocolitis. N. Engl. J. Med. 364 , 255–264. https://doi.org/10.1056/NEJMra1005408 (2011).

Niño, D. F., Sodhi, C. P. & Hackam, D. J. Necrotizing enterocolitis: New insights into pathogenesis and mechanisms. Nat. Rev. Gastroenterol. Hepatol. 13 , 590–600. https://doi.org/10.1038/nrgastro.2016.119 (2016).

Samuels, N., van de Graaf, R. A., de Jonge, R. C. J., Reiss, I. K. M. & Vermeulen, M. J. Risk factors for necrotizing enterocolitis in neonates: A systematic review of prognostic studies. BMC Pediatr. 17 , 105. https://doi.org/10.1186/s12887-017-0847-3 (2017).

Chen, Y., Wu, L., Zou, L., Li, G. & Zhang, W. Update on the birth weight standard and its diagnostic value in small for gestational age (SGA) infants in China. J. Matern. Fetal Neonatal Med. 30 , 801–807. https://doi.org/10.1080/14767058.2016.1186636 (2017).

Ree, I. M. et al. Necrotizing enterocolitis in small-for-gestational-age neonates: A matched case-control study. Neonatology 105 , 74–78. https://doi.org/10.1159/000356033 (2014).

Su, Y. et al. Risk factors for necrotizing enterocolitis in neonates: A meta-analysis. Front. Pediatr. 10 , 1079894. https://doi.org/10.3389/fped.2022.1079894 (2022).

Liu, C. et al. Association of small-for-gestational-age status with mortality and morbidity in very preterm Chinese infants. J. Matern. Fetal Neonatal Med. 36 , 2258257. https://doi.org/10.1080/14767058.2023.2258257 (2023).

Huang, X. R. et al. Real-world evidence regarding the growth of very premature infants with small for gestational age after birth: A multicenter survey in China. BMC Pediatr. 23 , 437. https://doi.org/10.1186/s12887-023-04245-1 (2023).

Hackam, D. & Caplan, M. Necrotizing enterocolitis: Pathophysiology from a historical context. Semin Pediatr Surg. 27 , 11–18. https://doi.org/10.1053/j.sempedsurg.2017.11.003 (2018).

Kumar, R. et al. Inflammatory biomarkers and physiomarkers of late-onset sepsis and necrotizing enterocolitis in premature infants. medRxiv https://doi.org/10.1101/2023.06.29.23292047 (2023).

Luo, J., Yu, S., Xu, J., Sun, X. & Wang, R. Deciphering the role of TNF-α-induced protein 8-like 2 in the pathogenesis of necrotizing enterocolitis in neonatal rats. Exp. Ther. Med. 26 , 443. https://doi.org/10.3892/etm.2023.12142 (2023).

Yan, X. L. et al. Succinate aggravates intestinal injury in mice with necrotizing enterocolitis. Front. Cell Infect. Microbiol. 12 , 1064462. https://doi.org/10.3389/fcimb.2022.1064462 (2022).

Maheshwari, A., Patel, R. M. & Christensen, R. D. Anemia, red blood cell transfusions, and necrotizing enterocolitis. Semin. Pediatr. Surg. 27 , 47–51. https://doi.org/10.1053/j.sempedsurg.2017.11.009 (2018).

Singh, R. et al. Association of necrotizing enterocolitis with anemia and packed red blood cell transfusions in preterm infants. J. Perinatol. 31 , 176–182. https://doi.org/10.1038/jp.2010.145 (2011).

Jiang, Z., Ye, G., Zhang, S. & Zhang, L. Association of anemia and platelet activation with necrotizing enterocolitis with or without sepsis among low birth weight neonates: A case-control study. Front. Pediatr. 11 , 1172042. https://doi.org/10.3389/fped.2023.1172042 (2023).

Healy, D. B. et al. Neonatal outcomes following introduction of routine probiotic supplementation to very preterm infants. Acta Paediatr. 112 , 2093–2101. https://doi.org/10.1111/apa.16923 (2023).

Zhou, K. Z. et al. Probiotics to prevent necrotizing enterocolitis in very low birth weight infants: A network meta-analysis. Front. Pediatr. 11 , 1095368. https://doi.org/10.3389/fped.2023.1095368 (2023).

Lin, H. C. et al. Oral probiotics prevent necrotizing enterocolitis in very low birth weight preterm infants: A multicenter, randomized, controlled trial. Pediatrics 122 , 693–700. https://doi.org/10.1542/peds.2007-3007 (2008).

Barbian, M. E. & Patel, R. M. Probiotics for prevention of necrotizing enterocolitis: Where do we stand?. Semin. Perinatol. 47 , 151689. https://doi.org/10.1016/j.semperi.2022.151689 (2023).

Wang, H. et al. Probiotics to prevent necrotizing enterocolitis and reduce mortality in neonates: A meta-analysis. Medicine 102 , e32932. https://doi.org/10.1097/md.0000000000032932 (2023).

Bracho-Blanchet, E. et al. Prognostic factors related to mortality in newborns with necrotising enterocolitis. Cir. Cir. 83 , 286–291. https://doi.org/10.1016/j.circir.2015.02.002 (2015).

Zhang, Y. et al. Predictive scores for mortality in full-term infants with necrotizing enterocolitis: Experience of a tertiary hospital in Southwest China. World. J. Pediatr. 12 , 202–208. https://doi.org/10.1007/s12519-015-0063-x (2016).

Zhu, L. et al. Chinese neonatal birth weight curve for different gestational age. Zhonghua Er Ke Za Zhi. 53 , 97–103 (2015).

PubMed   Google Scholar  

Bell, M. J. et al. Neonatal necrotizing enterocolitis. Therapeutic decisions based upon clinical staging. Ann. Surg. 187 , 1–7. https://doi.org/10.1097/00000658-197801000-00001 (1978).

Aher, S., Malwatkar, K. & Kadam, S. Neonatal anemia. Semin. Fetal Neonatal Med. 13 , 239–247. https://doi.org/10.1016/j.siny.2008.02.009 (2008).

Subspecialty Group of Neonatology Pediatric Society Chinese Medical Association, Editorial Board Chinese Journal of Pediatrics. Protocol for diagnosis and treatment of neonatal septicemia. Zhonghua Er Ke Za Zhi. 41 , 897–899 (2003).

Google Scholar  

Camacho-Gonzalez, A., Spearman, P. W. & Stoll, B. J. Neonatal infectious diseases: Evaluation of neonatal sepsis. Pediatr. Clin. North Am. 60 , 367–389. https://doi.org/10.1016/j.pcl.2012.12.003 (2013).

Shah, B. A. & Padbury, J. F. Neonatal sepsis: An old problem with new insights. Virulence 5 , 170–178. https://doi.org/10.4161/viru.26906 (2014).

Walsh, M. C. & Kliegman, R. M. Necrotizing enterocolitis: Treatment based on staging criteria. Pediatr. Clin. North Am. 33 , 179–201. https://doi.org/10.1016/s0031-3955(16)34975-6 (1986).

Download references

Author information

Authors and affiliations.

Department of Neonatology, Jiangxi Hospital Affiliated to Children’s Hospital of Chongqing Medical University, Nanchang, 330103, People’s Republic of China

Xiang-Ping Ding, Xiang-Wen Hu, Zheng-Li Wang, Lu-Quan Li & Wen-Yan Tang

Key Laboratory of Pediatrics in Chongqing, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Neonatal Diagnosis and Treatment Center, Children’s Hospital of Chongqing Medical University, Chongqing, 400014, People’s Republic of China

Shi Chen, Lu Guo, Zheng-Li Wang, Yu He & Lu-Quan Li

Jiangxi Maternal and Child Health Hospital, Maternal and Child Health Hospital of Nanchang Medical College, Nan Chan, 330038, Jiangxi, China

Wen-Yan Tang

You can also search for this author in PubMed   Google Scholar

Contributions

All the authors made substantial contributions to the study. Xiang-Ping Ding collected the clinical data drafted the manuscript. Xiang-Wen Hu, Shi Chen helped to collect the clinical information. Lu Guo and Zheng-Li Wang analyzed the data, Lu-Quan Li contributed to the critical revision. Wen-Yan Tang supervised the project and contributed to the conception and design of the study. Xiang-Ping Ding, Xiang-Wen Hu, Shi Chen, Lu Guo, Zheng-Li Wang, Lu-Quan Li and Wen-Yan Tang provided the final approval of the manuscript.

Corresponding author

Correspondence to Wen-Yan Tang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ding, XP., Hu, XW., Chen, S. et al. Risk factors for necrotizing enterocolitis in small-for-gestational-age infants: a matched case–control study. Sci Rep 14 , 19098 (2024). https://doi.org/10.1038/s41598-024-70351-4

Download citation

Received : 21 November 2023

Accepted : 14 August 2024

Published : 17 August 2024

DOI : https://doi.org/10.1038/s41598-024-70351-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Search Menu
• Sign in through your institution
• Advance articles
• Editor's Choice
• Supplement Archive
• Cover Archive
• IDSA Guidelines
• IDSA Journals
• The Journal of Infectious Diseases
• Open Forum Infectious Diseases
• Photo Quizzes
• State-of-the-Art Reviews
• Voices of ID
• Author Guidelines
• Open Access
• Why Publish
• IDSA Journals Calls for Papers
• Advertising and Corporate Services
• Advertising
• Journals Career Network
• Reprints and ePrints
• Sponsored Supplements
• Branded Books
• About Clinical Infectious Diseases
• About the Infectious Diseases Society of America
• About the HIV Medicine Association
• IDSA COI Policy
• Editorial Board
• Self-Archiving Policy
• For Reviewers
• For Press Offices
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Conclusions, supplementary data.

• < Previous

Favorable Antiviral Effect of Metformin on SARS-CoV-2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of COVID-19

D. R. B. and J. D. H. contributed equally to this work.

Potential conflicts of interest. J. B. B. reports contracted fees and travel support for contracted activities for consulting work paid to the University of North Carolina by Novo Nordisk; grant support by NIH, PCORI, Bayer, Boehringer-Ingelheim, Carmot, Corcept, Dexcom, Eli Lilly, Insulet, MannKind, Novo Nordisk, and vTv Therapeutics; personal compensation for consultation from Alkahest, Altimmune, Anji, Aqua Medical Inc, AstraZeneca, Boehringer-Ingelheim, CeQur, Corcept Therapeutics, Eli Lilly, embecta, GentiBio, Glyscend, Insulet, Mellitus Health, Metsera, Moderna, Novo Nordisk, Pendulum Therapeutics, Praetego, Stability Health, Tandem, Terns Inc, and Vertex.; personal compensation for expert testimony from Medtronic MiniMed; participation on advisory boards for Altimmune, AstraZeneca, and Insulet; a leadership role for the Association of Clinical and Translational Science; and stock/options in Glyscend, Mellitus Health, Pendulum Therapeutics, Praetego, and Stability Health. M. A. P. receives consulting fees from Opticyte and Cytovale. A. B. K. has served as an external consultant for Roche Diagnostics; received speaker honoraria from Siemens Healthcare Diagnostics, the American Kidney Fund, the National Kidney Foundation, the American Society of Nephrology, and Yale University Department of Laboratory Medicine; research support unrelated to this work from Siemens Healthcare Diagnostics, Kyowa Kirin Pharmaceutical Development, the Juvenile Diabetes Research Foundation, and the NIH; support for travel from College of American Pathologists Point-Of-Care Testing Committee; participation on an advisory board for the Minnesota Newborn Screening Advisory Committee; grants from NIH and JDRF for multiple unrelated clinical research projects and Kyowa Kirin Pharmaceutical Development and Siemens Healthcare Diagnostics for unrelated clinical research studies; and leadership roles for the American Board of Clinical Chemistry, Association for Diagnostics and Laboratory Medicine (ADLM) Evidence-Based Laboratory Medicine Subcommittee, and ADLM Academy Test Utilization Committee. M. R. R. reports consulting fees from 20/20 Gene Systems for coronavirus disease 2019 testing. D. B. R. reports grants from the NIH NCATS ACTIV-6 Steering Committee Chair. K. C. reports stock or stock options for United Health Group. C. T. B. reports consulting fees from NCATS/DCRI and the ACTIV-6 Executive Committee and support for travel from Academic Medical Education. All other authors report no potential conflicts.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

• Article contents
• Figures & tables

Carolyn T Bramante, Kenneth B Beckman, Tanvi Mehta, Amy B Karger, David J Odde, Christopher J Tignanelli, John B Buse, Darrell M Johnson, Ray H B Watson, Jerry J Daniel, David M Liebovitz, Jacinda M Nicklas, Ken Cohen, Michael A Puskarich, Hrishikesh K Belani, Lianne K Siegel, Nichole R Klatt, Blake Anderson, Katrina M Hartman, Via Rao, Aubrey A Hagen, Barkha Patel, Sarah L Fenno, Nandini Avula, Neha V Reddy, Spencer M Erickson, Regina D Fricton, Samuel Lee, Gwendolyn Griffiths, Matthew F Pullen, Jennifer L Thompson, Nancy E Sherwood, Thomas A Murray, Michael R Rose, David R Boulware, Jared D Huling, COVID-OUT Study Team , Favorable Antiviral Effect of Metformin on SARS-CoV-2 Viral Load in a Randomized, Placebo-Controlled Clinical Trial of COVID-19, Clinical Infectious Diseases , Volume 79, Issue 2, 15 August 2024, Pages 354–363, https://doi.org/10.1093/cid/ciae159

• Permissions Icon Permissions

Metformin has antiviral activity against RNA viruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mechanism appears to be suppression of protein translation via targeting the host mechanistic target of rapamycin pathway. In the COVID-OUT randomized trial for outpatient coronavirus disease 2019 (COVID-19), metformin reduced the odds of hospitalizations/death through 28 days by 58%, of emergency department visits/hospitalizations/death through 14 days by 42%, and of long COVID through 10 months by 42%.

COVID-OUT was a 2 × 3 randomized, placebo-controlled, double-blind trial that assessed metformin, fluvoxamine, and ivermectin; 999 participants self-collected anterior nasal swabs on day 1 (n = 945), day 5 (n = 871), and day 10 (n = 775). Viral load was quantified using reverse-transcription quantitative polymerase chain reaction.

The mean SARS-CoV-2 viral load was reduced 3.6-fold with metformin relative to placebo (−0.56 log 10 copies/mL; 95% confidence interval [CI], −1.05 to −.06; P = .027). Those who received metformin were less likely to have a detectable viral load than placebo at day 5 or day 10 (odds ratio [OR], 0.72; 95% CI, .55 to .94). Viral rebound, defined as a higher viral load at day 10 than day 5, was less frequent with metformin (3.28%) than placebo (5.95%; OR, 0.68; 95% CI, .36 to 1.29). The metformin effect was consistent across subgroups and increased over time. Neither ivermectin nor fluvoxamine showed effect over placebo.

In this randomized, placebo-controlled trial of outpatient treatment of SARS-CoV-2, metformin significantly reduced SARS-CoV-2 viral load, which may explain the clinical benefits in this trial. Metformin is pleiotropic with other actions that are relevant to COVID-19 pathophysiology.

NCT04510194.

(See the Invited Commentary by Siedner and Sax on pages 292–4.)

COVID-OUT was a multisite, phase 3, quadruple-blinded, placebo-controlled, randomized clinical trial to test whether outpatient treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevented severe coronavirus disease 2019 (COVID-19) [ 1 ].

The selection of metformin was motivated by in silico modeling, in vitro data, and human lung tissue data that showed that metformin decreased SARS-CoV-2 viral growth and improved cell viability [ 2–4 ]. The in silico modeling identified protein translation as a key process in SARS-CoV-2 replication, similar to protein mapping of SARS-CoV-2 [ 3 ]. Metformin inhibits the mechanistic target of rapamycin (mTOR) [ 5 ], which controls protein translation [ 6 , 7 ]. Metformin has shown in vitro antiviral actions against the Zika virus and against hepatitis C via mTOR inhibition [ 8–11 ].

Severe COVID-19 was defined using a binary, 4-part composite outcome (1 reading <94% SpO 2 on a home oximeter/emergency department visit/hospitalization/death) through 14 days and was not significant. After removing the 1 oxygen reading <94% component per the prespecified statistical analysis plan (SAP), metformin reduced the odds of emergency department visits/hospitalizations/death by day 14 by 42%, of hospitalization/death by day 28 by 58%, and of long COVID diagnoses by day 300 by 42% [ 1 , 12 ].

Here, we present the viral load quantification from samples obtained during the COVID-OUT trial. The trial used a 2 × 3 factorial design of parallel treatments to efficiently assess 3 medications: immediate-release metformin, ivermectin, and fluvoxamine at doses not previously studied in COVID-19 trials.

Study Design, Sample, and Oversight

COVID-OUT was an investigator-initiated, multisite, phase 3, quadruple-blinded, placebo-controlled randomized clinical trial ( Supplementary Tables 1 and 2 ) [ 1 ] that enrolled from 30 December 2020 to 28 January 2022. COVID-OUT was decentralized to prevent SARS-CoV-2 spread. The participants, care providers, investigators, and outcomes assessors remained blinded to treatment allocation.

Institutional review boards (IRBs) at each site and the Advarra Central IRB approved the protocol. An independent data and safety monitoring board (DSMB) monitored safety and efficacy. All analyses and covariates were prespecified in the SAP, which was submitted to the DSMB before enrollment ended and submitted in February 2022 with the primary outcome manuscript and then published [ 1 ]. An independent monitor oversaw study conduct per the Declaration of Helsinki, Good Clinical Practice Guidelines, and local requirements.

COVID-OUT excluded low-risk individuals, limiting enrollment to standard-risk adults aged 30 to 85 years with a body mass index (BMI) in the overweight or obesity categories, documented + SARS-CoV-2 within 3 days, and no prior confirmed SARS-CoV-2 infection. Pregnant women were randomized to metformin or placebo and not to ivermectin or fluvoxamine. Exclusion criteria included hospitalized, symptom onset >7 days prior, and unstable heart, liver, or kidney failure [ 1 ].

Metformin dosing was as follows: 500 mg on day 1, 500 mg twice daily on days 2–5, and 500 mg in the morning and 1000 mg in the evening on days 6–14. Fluvoxamine dosing was as follows: 50 mg on day 1 and 50 mg twice daily on days 2–14. Ivermectin dosing was as follows: a median of 430  µg/kg/day (range, 390 to 470  µg/kg/day) for 3 days.

Clinical and Virologic End Points

The primary end point was severe COVID-19 by day 14, defined using a binary, 4-part composite end point: 1 reading <94% SpO 2 on home oximeter/emergency department visit/hospitalization/death due to COVID-19. Secondary end points included hospitalization or death by day 28 and long COVID over the 10-month follow-up. The virologic secondary end point was overall viral load in follow-up, adjusted for baseline viral load as prespecified in the SAP.

Self-collection of anterior nares samples was an optional component of the randomized trial. Supply chain shortages caused administrative censoring of 78 participants who did not receive materials for collecting day 1, day 5, or day 10 samples; 3 did not receive materials for day 5 or day 10 samples ( Supplementary Figure 1, Supplementary Tables 3–6 ).

Laboratory Procedures

Participants received written instructions with pictures on self-collecting from the anterior mid-turbinate, which has excellent concordance with professionally collected nasal swabs [ 13 ]. Viral load was measured via reverse-transcription quantitative polymerase chain reaction using N1 and N2 targets in the SARS-CoV-2 nucleocapsid protein, with relative cycle threshold values converted to absolute copy number via calibration to droplet digital polymerase chain reaction. Detailed methods can be found in Supplementary Table 7 .

While participant self-collection may vary between participants, self-collection of samples is done by the same individual at baseline and follow-up. Thus, participant self-collection may have less variability between baseline and follow-up than when study or clinical staff obtain samples.

Statistical Analyses

We evaluated randomized study drug assignment on the impact of log 10 -transformed viral load on day 5 and day 10 with a linear Tobit regression model where the effect of study drugs was allowed to differ on day 5 and day 10. This was decided a priori as a rigorous analytic approach to account for left censoring due to the viral load limit of quantification. Repeated measures were accounted for using clustered standard errors within participants. Analyses of viral loads estimated the adjusted mean reduction averaged over time and the adjusted mean reduction at day 5 and day 10. We evaluated impact over time on the probability of viral load being undetectable using generalized estimating equations with a logistic link; estimates are reported as adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

The COVID-OUT trial was a 2 × 3 factorial design of parallel distinct treatments ( Supplementary Table 2 ). All analyses were adjusted for baseline viral load, vaccination status, time since last vaccination for those vaccinated before enrollment, receipt of other study medications within factorial trial, laboratory that processed the nasal swabs, and exact time and date of specimen collection. Additional details and the results of the analyses with dropping of adjustment variables are presented in Supplementary Tables 8 and 9 .

To handle missing values, we used multiple imputation with chained equations to multiply impute missing viral load outcomes and vaccination status. Missing covariate information was jointly imputed along with missing outcomes using random forests for the univariate imputation models. Along with outcome and vaccination status information, imputation models were informed by sex, BMI, symptom duration, race/ethnicity, baseline comorbidities, clinical outcomes, and enrollment time categorized by the dominant pandemic variant. Complete case analysis without imputation of missing data is presented in Supplementary Figures 2–4 . Heterogeneity of effect was assessed across a priori subgroups of baseline characteristics. Starting metformin in <4 days of symptom onset is a subgroup that aligns with antiviral trials and reflects real-world use, as metformin is widely available.

Among 1323 randomized participants in the COVID-OUT trial, 999 (76%) chose to participate in the optional substudy and provided at least 1 nasal swab sample ( Table 1 , Supplementary Figure 1 ). The demographics of the participants who submitted swabs were similar to those who did not submit nasal swabs ( Supplementary Tables 3–5 ). Day 1 samples were provided by 945 participants, 871 provided day 5 samples, and 775 provided day 10 samples ( Supplementary Table 6 ). The overall viral load was a median of 4.88 log 10 copies/mL (interquartile range [IQR], 2.99 to 6.18) on day 1, 1.90 (IQR, 0 to 3.93) on day 5, and 0 (IQR, 0 to 1.90 with 0 representing the limit of quantification) on day 10.

Baseline Characteristics of Participants Who Submitted Any Nasal Swab

Values are percent (n) or median (interquartile range) unless specified. Cardiovascular disease defined as hypertension, hyperlipidemia, coronary artery disease, past myocardial infarction, congestive heart failure, pacemaker, arrhythmias, or pulmonary hypertension.

Abbreviation: BMI, body mass index.

a Unknown n = 22.

The overall mean SARS-CoV-2 viral load reduction with metformin was −0.56 log 10 copies/mL (95% CI, −1.05 to −0.06) greater than placebo across all follow-up ( P = .027). The antiviral effect of metformin compared with placebo was −0.47 log 10 copies/mL (95% CI, −0.93 to −0.014) on day 5 and −0.64 log 10 copies/mL (95% CI, −1.42 to 0.13) on day 10 ( Figure 1 ). Neither ivermectin nor fluvoxamine had virologic effect ( Figure 2 , Supplementary Figure 2 , Supplementary Tables 8–10 ).

Effect of metformin versus placebo on viral load over time, detectable viral load, and rebound viral load. A , Adjusted mean change in log10 copies per milliliter (viral load) from baseline (day 1) to day 5 and day 10 for metformin (lower line) and placebo (upper line). Mean change estimates are based on the adjusted, multiply imputed Tobit analysis (the primary analytic approach) that corresponds to the overall metformin analysis presented in Figure 2 . B , Adjusted percent of viral load samples that were detectable at day 1, day 5, and day 10. The percent viral load detected estimates were based on the adjusted, multiply imputed logistic generalized estimating equations (GEE) analysis corresponding to the overall metformin analysis depicted in Figure 3 . Odds ratios correspond to adjusted effects on the odds ratio scale. C , Bar chart depicting the percent of participants whose day 10 viral load was greater than the day 5 viral load and the odds ratio for having viral load rebound using the multiply imputed logistic GEE. Abbreviation: CI, confidence interval.

Overall results for metformin, ivermectin, and fluvoxamine on viral load; heterogeneity of treatment effect of metformin versus placebo. This is a forest plot that depicts the effect of active medication compared with control on log10 copies per milliliter (viral load), overall, and at day 5 and day 10. Viral Effect* denotes the adjusted mean change in viral load in log10 copies per milliliter with 95% confidence intervals for the adjusted mean change. Analyses were conducted using the primary analytic approach, a multiply imputed Tobit model. The vertical dashed line indicates the value for a null effect. The top 3 rows show ivermectin, the next 3 rows show fluvoxamine, and the following 3 rows show metformin. Below these, the effect of metformin compared with placebo is shown by a priori subgroups of baseline characteristics. Abbreviation: CI, confidence interval.

When the adjustment covariates were dropped one at a time—baseline viral load, vaccination status, time since last vaccination, other study medications within the factorial trial, and the laboratory processing the nasal swabs—in addition to dropping all adjustment covariates, the results were similar. The range in the estimated average effect was −0.51 log 10 copies/mL (95% CI, −1.04 to 0.01; P = .056) to −0.66 log 10 copies/mL (95% CI, −1.215 to −0.097; P = .021) with the latter arising from the unadjusted model ( Supplementary Table 9 ).

Those in the metformin group were less likely to have a detectable viral load than those in the placebo group (OR, 0.72; 95% CI, .55 to .94; Figure 1) . This effect was higher at day 10 (OR, 0.65; 95% CI, .43 to .98) when 1500 mg/d of metformin was being prescribed than at day 5 (OR, 0.79; 95% CI, .60 to 1.05) when 1000 mg/d was prescribed. Viral rebound was defined as having a higher viral load at day 10 than day 5. In the placebo group, 5.95% (22 of 370) of participants had viral rebound compared with 3.28% (12 of 366) in the metformin group (adjusted OR, .68; 95% CI, .36 to 1.29) for metformin compared with placebo ( Figure 1) .

Metformin's effect on continuous viral load and conversion to undetectable viral load was consistent across a priori identified subgroups of baseline characteristics ( Figures 2 and 3 ). Subgroups should be interpreted with caution because of low power, risk of making multiple comparisons without correction, and sparse data bias. One subgroup warrants additional detail for interpretation. The antiviral effect on geometric log 10 scale was greater among those with baseline viral loads <100 000 copies/mL (mean −1.17 log 10 copies/mL reduction) than among those with >100 000 copies/mL (mean −0.49 log 10 copies/mL reduction); although the reduction in absolute copies per milliliter would be greater among those with higher viral loads ( Figures 2 and 3 ). Mean, median viral load levels are presented in Supplementary Table 11 ; sensitivity analyses are presented in Supplementary Figures 5–7 and Supplementary Table 12 .

Overall results for metformin, ivermectin, and fluvoxamine on detectability of viral load; heterogeneity of treatment effect of metformin versus placebo. This is a forest plot that depicts the effect of active medication compared with control on the proportion of participants with a detectable viral load, overall and at days 5 and 10. Estimate* denotes the adjusted mean risk difference in the percent of samples with detected viral load with 95% confidence intervals for the adjusted risk difference. The vertical dashed line indicates the value for a null effect. The estimated risk differences are derived from the adjusted, multiply imputed logistic generalized estimating equations (GEE) analytic approach. The top 3 rows show ivermectin, the next 3 rows show fluvoxamine, and the following 3 rows show metformin. Below these, the effect of metformin compared with placebo is shown by a priori subgroups of baseline characteristics. Abbreviation: CI, confidence interval.

In the virologic end point of the COVID-OUT phase 3, randomized trial, metformin significantly reduced SARS-CoV-2 viral load over 10 days [ 1 ]. The mean reduction was −0.56 log 10 copies/mL greater than placebo. The antiviral response is consistent with the statistically significant and clinically relevant effects of metformin in preventing clinical outcomes: severe COVID-19 (emergency department visit, hospitalization, or death) through day 14, hospitalization or death by day 28, and the diagnosis of long COVID [ 1 , 12 ]. The magnitude of effect on clinical outcomes was larger when metformin was started earlier in the course of infection at <4 days from symptom onset, with metformin reducing the odds of severe COVID-19 by 55% (OR, 0.45; 95% CI, .22 to .93) and of long COVID by 65% (hazard ratio = 0.35; 95% CI, .15 to .95; Figure 4) . An improved effect size for clinical outcomes when therapies are started earlier in the course of infection is consistent with an antiviral action [ 14 ].

Overview of results from the COVID-OUT trial. This is a forest plot that combines the severe, acute coronavirus disease 2019 outcome as well as the long-term follow-up outcome from the COVID-OUT trial [ 1 , 12 ]. Two a priori subgroups from the COVID-OUT trial are also presented: pregnant individuals and those who started the study drug within 4 days of symptom onset, to match the primary analytic sample of other antivirals. Abbreviations: COVID-19, coronavirus disease 2019; ITT, intention to treat; mITT, modified intention to treat; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

The objective of the COVID-OUT trial was to determine whether metformin prevented severe COVID-19. Severe COVID-19 was defined with a binary, 4-part composite outcome (<94% SpO 2 on a home oximeter/emergency department visit/hospitalization/death) at a time when the implications of “silent hypoxia” were unknown and fears of overwhelmed emergency services caused concern that deaths would occur at home before patients reached the emergency department. As a scientific community, we now understand that 1 reading below 94% is not severe COVID-19. An accurate definition of severe COVID-19 (emergency department visit/hospitalization/death) was ascertained within the same data-generation process. In such situations, recommendations are sometimes made based on the totality of evidence from a single randomized trial [ 15–17 ].

The antiviral effect in this phase 3, randomized trial is also consistent with emerging data from other trials. In a phase 2, randomized trial with 20 participants, the metformin group had better clinical outcomes, achieved an undetectable viral load 2.3 days faster than placebo ( P = .03), and had a larger proportion of patients with an undetectable viral load at 3.3 days in the metformin group ( P = .04) [ 18 ]. A recent in vitro study showed that metformin decreased infectious SARS-CoV-2 titers and viral RNA in 2 cell lines, Caco2 and Calu3, at a clinically appropriate concentration [ 19 ].

Conversely, an abandoned randomized trial testing extended-release metformin 1500 mg/d without a dose titration did not report improved SARS-CoV-2 viral clearance at day 7 [ 20 ]. Several differences between the Together Trial and the COVID-OUT trial are important for understanding the data. First, the Together Trial allowed individuals already taking metformin to enroll and be randomized to placebo or more metformin [ 20 , 21 ]. To compare starting metformin versus placebo, the authors excluded those already taking metformin at baseline and reported that emergency department visit or hospitalization occurred in 9.2% (17 of 185) randomized to metformin compared with 14.8% (27 of 183) randomized to placebo (relative risk, 0.63; 95% confidence interval, .35 to 1.10, Probability of superiority = 0.949) [ 22 ]. Thus, the Together Trial results for starting metformin versus placebo are similar. Second, 1500 mg/day without escalating the dose over 6 days would cause side effects, especially if the study participant was already taking metformin [ 23 ]. Third, extended-release and immediate-release metformin have different pharmacokinetic properties. Immediate-release metformin has higher systemic exposure than extended-release metformin, which may improve antiviral actions, but this is not known [ 24 , 25 ]. Given the similar clinical outcomes between immediate and extended-release, a direct comparison of the 2 may be important for understanding pharmacokinetics against SARS-CoV-2.

In comparison with other SARS-CoV-2 antivirals, when considering all enrolled participants, at day 5, the antiviral effect over placebo was 0.47 log 10 copies/mL for metformin, 0.30 log 10 copies/mL for molnupiravir, and 0.80 log 10 copies/mL for nirmatrelvir/ritonavir [ 26 , 27 ]. At day 10, the viral load reduction over blinded placebo was 0.64 log 10 copies/mL for metformin, 0.35 log 10 copies/mL for nirmatrelvir, and 0.19 log 10 copies/mL for molnupiravir [ 26 , 27 ]. We note that the 3 trials enrolled different populations and at different times and locations during the pandemic. In the COVID-OUT metformin trial, half were vaccinated [ 1 , 12 ].

The magnitude of metformin's antiviral effect was larger at day 10 than at day 5 overall and across subgroups, which correlates with the dose titration from 1000 mg on days 2–5 to 1500 mg on days 6–14. The dose titration to 1500 mg over 6 days used in the COVID-OUT trial was faster than typical use. When used chronically, that is, for diabetes, prediabetes, or weight loss, metformin is slowly titrated to 2000 mg daily over 4–8 weeks. While metformin's effect on diabetes control is not consistently dose-dependent, metformin's gastrointestinal side effects are known to be dose-dependent [ 25 ]. Thus, despite what appears to be dose-dependent antiviral effects, a faster dose titration should likely only be considered in individuals with no gastrointestinal side effects from metformin.

When assessing for heterogeneity of effect, metformin was consistent across subgroups. Metformin's antiviral effect in vaccinated versus unvaccinated of −0.48 versus −0.86 log 10 copies/mL at day 10 mirrors nirmatrelvir, for which the effect in seropositive participants was smaller than in the overall trial population, −0.13 versus −0.35 log 10 copies/mL at day 10 [ 26 ]. Effective primed memory B- and T-cell anamnestic immunity prompting effective response by day 5 in vaccinated persons may account for this trend in both trials. Subgroups should be interpreted with caution because of low power and multiple comparisons [ 28 ].

Both nirmatrelvir and molnupiravir are pathogen-directed antiviral agents. Therapeutics may have an important role in targeting host factors rather than viral factors, as targeting the host may be less likely to induce drug-resistant viral variants through mutation–selection [ 11 , 29 ]. We did not study the mechanism for the antiviral activity or an antiinflammatory action in this trial. Previous work has shown that metformin's inhibition of mTOR complex 1 may depend on AMP-activated protein kinase (AMPK) at low doses but not high doses [ 5 ]. An AMPK-independent inhibition of mTOR may be more efficient. Additionally, metformin demonstrates a dose-dependent ability to inhibit interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha in the presence of lipopolysaccharide, inflammatory products that correlate with COVID-19 severity [ 30 , 31 ].

In addition to antiviral activity, metformin appears to have relevant antiinflammatory actions. In mice without diabetes, metformin inhibited mitochondrial ATP and DNA synthesis to evade NLRP3 inflammasome activation [ 32 ]. In macrophages of mice without diabetes infected with SARS-CoV-2, metformin inhibited inflammasome activation, IL-1 production, and IL-6 secretion and also increased the IL-10 antiinflammatory response to lipopolysaccharide, thereby attenuating lipopolysaccharide-induced lung injury [ 32 ]. In a recent assay of human lung epithelial cell lines, metformin inhibited the cleavage of caspase-1 by NSP6, inhibiting the maturation and release of IL-1, a key factor that mediates inflammatory responses [ 7 ]. The idea of pleiotropic effects is being embraced in novel therapeutics being developed for both antiviral and anti-inflammatory actions [ 33 ].

Strengths of our study include the large sample size and detailed participant information collected, including the exact time and date of specimen collection. One limitation was the sampling time frame of only day 1, day 5, and day 10 due to limited resources. By day 10 post-randomization, 77% of participants in the placebo group and 86% in the metformin group had an undetectable viral load. As viral load is lower in vaccinated persons [ 34 ], this degree of undetectable viral loads differs from findings from earlier clinical trials conducted in unvaccinated participants without known prior infection [ 26 , 27 ]. Sampling earlier and more frequently, that is, day 1, day 3, day 6, and day 9 in future trials, may better characterize differences in viral shedding earlier in the infection and over time, dependent on the duration of therapy and timing of enrollment.

Future work could assess whether synergy exists between metformin and direct SARS-CoV-2 antivirals, as previous work showed that metformin improved sustained virologic clearance of hepatitis C virus and improved outcomes in other respiratory infections [ 35–37 ]. The biophysical modeling that motivated this trial predicts additive/cooperative effects in combination with transcription inhibitors. Combination therapy might decrease selective pressure, and metformin has few medication interactions, so treatment with metformin could continue beyond 5 days while home medications are restarted. Additionally, continuing metformin could reduce symptom rebound, given its effects on T-cell immunity [ 38 , 39 ]. Further data are needed to understand whether decreased viral load and faster viral clearance decrease onward transmission of SARS-CoV-2.

Metformin is safe in children and pregnant individuals with and without preexisting diabetes [ 40–42 ]. Individuals with or without diabetes do not need to check blood sugar when taking metformin. Historical concerns about lactic acidosis were driven by other biguanides; metformin does not increase risk of lactic acidosis [ 43 ]. Metformin improves outcomes in patients with heart, liver, and kidney failure, as well as during hospitalizations and perioperatively [ 44–48 ].

In a large randomized, controlled trial conducted in nonhospitalized, standard-risk adults, metformin reduced the incidence of severe COVID-19 by day 14, of hospitalizations by day 28, and of long COVID diagnosis by day 300. In this virologic analysis, we found a corresponding significant reduction in viral load with metformin compared with placebo and a lower likelihood of viral load rebound. While 22% of participants in the trial were enrolled during the Omicron era, metformin has not been assessed in individuals with a history of prior infection and thus should be trialed in the current state of the pandemic. Metformin is currently being trialed in low-risk adults [ 49 ].

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Disclaimer. The funders had no influence on the design or conduct of the trial and were not involved in data collection or analysis, writing of the manuscript, or decision to submit for publication. The authors assume responsibility for trial fidelity and the accuracy and completeness of the data and analyses.

Financial support . The fluvoxamine placebo tablets were donated by the Apotex Pharmacy. The ivermectin placebo and active tablets were donated by the Edenbridge Pharmacy. The trial was funded by the Parsemus Foundation, Rainwater Charitable Foundation, Fast Grants, and the UnitedHealth Group Foundation. C. T. B. was supported by grants (KL2TR002492 and UL1TR002494) from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) and by a grant (K23 DK124654) from the National Institute of Diabetes and Digestive and Kidney Diseases of the NIH. J. B. B. was supported by a grant (UL1TR002489) from NCATS. J. M. N. was supported by a grant (K23HL133604) from the National Heart, Lung, and Blood Institute (NHLBI) of the NIH. D. J. O. was supported by the Institute for Engineering in Medicine, University of Minnesota Office of Academic and Clinical Affairs COVID-19 Rapid Response Grant, the Earl E. Bakken Professorship for Engineering in Medicine, and by grants (U54 CA210190 and P01 CA254849) from the National Cancer Institute of the NIH. D. M. L. receives funding from NIH RECOVER (OT2HL161847). L. K. S. was supported by NIH grants (18X107CF6 and 18X107CF5) through a contract with Leidos Biomedical and by grants from the HLBI of the NIH (T32HL129956) and the NIH (R01LM012982 and R21LM012744). M. A. P. receives grants from the Bill and Melinda Gates Foundation (INV-017069), Minnesota Partnership for Biotechnology and Medical Genomics (00086722) and NHLBI (OT2HL156812).

Bramante CT , Huling JD , Tignanelli CJ , et al.  Randomized trial of metformin, ivermectin, and fluvoxamine for Covid-19 . New Engl J Med 2022 ; 387 : 599 – 610 .

Google Scholar

Castle BT , Dock C , Hemmat M , et al.  Biophysical modeling of the SARS-CoV-2 viral cycle reveals ideal antiviral targets. bioRxiv 111237 [Preprint]. June 16, 2020. Available from: https://doi.org/10.1101/2020.05.22.111237 .

Gordon DE , Jang GM , Bouhaddou M , et al.  A SARS-CoV-2 protein interaction map reveals targets for drug repurposing . Nature 2020 ; 583 : 459 – 68 .

Schaller MA , Sharma Y , Dupee Z , et al.  Ex vivo SARS-CoV-2 infection of human lung reveals heterogeneous host defense and therapeutic responses . JCI Insight 2021 ; 6 : e148003 .

Howell JJ , Hellberg K , Turner M , et al.  Metformin inhibits hepatic mTORC1 signaling via dose-dependent mechanisms involving AMPK and the TSC complex . Cell Metab 2017 ; 25 : 463 – 71 .

Bailey CJ . Metformin: historical overview . Diabetologia 2017 ; 60 : 1566 – 76 .

Garcia EY . Flumamine, a new synthetic analgesic and antiflu drug . Philippine Med Assoc 1950 ; 26 : 287 – 93 .

Singh S , Singh PK , Suhail H , et al.  AMP-activated protein kinase restricts Zika virus replication in endothelial cells by potentiating innate antiviral responses and inhibiting glycolysis . J Immunol 2020 ; 204 : 1810 – 24 .

Cheng F , Ramos da Silva S , Huang IC , Jung JU , Gao SJ . Suppression of Zika virus infection and replication in endothelial cells and astrocytes by PKA inhibitor PKI 14-22 . J Virol 2018 ; 92 : e02019 – 17 .

Del Campo JA , García-Valdecasas M , Gil-Gómez A , et al.  Simvastatin and metformin inhibit cell growth in hepatitis C virus infected cells via mTOR increasing PTEN and autophagy . PLoS One 2018 ; 13 : e0191805 .

Maiese K . The mechanistic target of rapamycin (mTOR): novel considerations as an antiviral treatment . Curr Neurovasc Res 2020 ; 17 : 332 – 7 .

Bramante CT , Buse JB , Liebovitz DM , et al.  Outpatient treatment of COVID-19 and incidence of post-COVID-19 condition over 10 months (COVID-OUT): a multicentre, randomised, quadruple-blind, parallel-group, phase 3 trial . Lancet Infect Dis 2023 ; 23 : 1119 – 29 .

Mannan N , Raihan R , Parvin US , et al.  Detection of SARS-CoV-2 RNA by reverse transcription-polymerase chain reaction (RT-PCR) on self-collected nasal swab compared with professionally collected nasopharyngeal swab . Cureus 2022 ; 14 : e25618 .

Gil Martínez V , Avedillo Salas A , Santander Ballestín S . Antiviral therapeutic approaches for SARS-CoV-2 infection: a systematic review . Pharmaceuticals (Basel) 2021 ; 14 : 736 .

Pocock SJ , Rossello X , Owen R , Collier TJ , Stone GW , Rockhold FW . Primary and secondary outcome reporting in randomized trials: JACC state-of-the-art review . J Am Coll Cardiol 2021 ; 78 : 827 – 39 .

Pocock SJ , Stone GW . The primary outcome fails—what next? New Engl J Med 2016 ; 375 : 861 – 70 .

Dahlöf B , Devereux RB , Kjeldsen SE , et al.  Cardiovascular morbidity and mortality in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol . Lancet 2002 ; 359 : 995 – 1003 .

Ventura-Lopez C , Cervantes-Luevano K , Aguirre-Sanchez JS , et al.  Treatment with metformin glycinate reduces SARS-CoV-2 viral load: an in vitro model and randomized, double-blind, phase IIb clinical trial . Biomed Pharmacother 2022 ; 152 : 113223 .

Parthasarathy H , Tandel D , Siddiqui AH , Harshan KH . Metformin suppresses SARS-CoV-2 in cell culture . Virus Res 2022 ; 323 : 199010 .

Reis G , Dos Santos Moreira Silva EA , Medeiros Silva DC , et al.  Effect of early treatment with metformin on risk of emergency care and hospitalization among patients with COVID-19: the TOGETHER randomized platform clinical trial . Lancet Reg Health Am 2022 ; 6 : 100142 .

Expression of concern – Effect of early treatment with metformin on risk of emergency care and hospitalization among patients with COVID-19: the TOGETHER randomized platform clinical trial. Lancet Reg health Am 2024 ; 31 : 100703 .

Mills EJ . Email communications to D. Boulware and C. Bramante on 1/5/24, 1/12/24 .

Henry RR , Frias JP , Walsh B , et al.  Improved glycemic control with minimal systemic metformin exposure: effects of metformin delayed-release (metformin DR) targeting the lower bowel over 16 weeks in a randomized trial in subjects with type 2 diabetes . PLoS One 2018 ; 13 : e0203946 .

DeFronzo RA , Buse JB , Kim T , et al.  Once-daily delayed-release metformin lowers plasma glucose and enhances fasting and postprandial GLP-1 and PYY: results from two randomised trials . Diabetologia 2016 ; 59 : 1645 – 54 .

Buse JB , DeFronzo RA , Rosenstock J , et al.  The primary glucose-lowering effect of metformin resides in the gut, not the circulation: results from short-term pharmacokinetic and 12-week dose-ranging studies . Diabetes Care 2016 ; 39 : 198 – 205 .

Hammond J , Leister-Tebbe H , Gardner A , et al.  Oral nirmatrelvir for high-risk, nonhospitalized adults with Covid-19 . New Engl J Med 2022 ; 386 : 1397 – 408 .

Jayk Bernal A , Gomes da Silva MM , Musungaie DB , et al.  Molnupiravir for oral treatment of Covid-19 in nonhospitalized patients . New Engl J Med 2022 ; 386 : 509 – 20 .

Greenland S , Mansournia MA , Altman DG . Sparse data bias: a problem hiding in plain sight . BMJ 2016 ; 352 : i1981 .

Pérez-Pérez M-J , Saiz J-C , Priego E-M , Martín-Acebes MA . Antivirals against (re)emerging flaviviruses: should we target the virus or the host? ACS Med Chem Lett 2022 ; 13 : 5 – 10 .

Rodrigues TS , de Sa KSG , Ishimoto AY , et al.  Inflammasomes are activated in response to SARS-CoV-2 infection and are associated with COVID-19 severity in patients . J Exp Med 2021 ; 218 : e20201707 .

Hyun B , Shin S , Lee A , et al.  Metformin down-regulates TNF-α secretion via suppression of scavenger receptors in macrophages . Immune Netw 2013 ; 13 : 123 – 32 .

Xian H , Liu Y , Rundberg Nilsson A , et al.  Metformin inhibition of mitochondrial ATP and DNA synthesis abrogates NLRP3 inflammasome activation and pulmonary inflammation . Immunity 2021 ; 54 : 1463 – 77.e11 .

Barnette KG , Gordon Michael S , Rodriguez D , et al.  Oral sabizabulin for high-risk, hospitalized adults with Covid-19: interim analysis . NEJM Evidence 2022 ; 1 : EVIDoa2200145 .

Bramante CT , Proper JL , Boulware DR , et al.  Vaccination against SARS-CoV-2 is associated with a lower viral load and likelihood of systemic symptoms . Open Forum Infect Dis 2022 ; 9 : ofac066 .

Goto M , Perencevich EN . Metformin and infections: what is the next step in this decades-long story? Clin Infect Dis 2023 ; 76 : 1245 – 6 .

Mohammed T , Bowe M , Plant A , Perez M , Alvarez CA , Mortensen EM . Metformin use is associated with lower mortality in veterans with diabetes hospitalized with pneumonia . Clin Infect Dis 2023 ; 76 : 1237 – 44 .

Yu J-W , Sun L-J , Zhao Y-H , Kang P , Yan B-Z . The effect of metformin on the efficacy of antiviral therapy in patients with genotype 1 chronic hepatitis C and insulin resistance . Int J Infect Dis 2012 ; 16 : e436 – 41 .

Wabitsch S , McCallen JD , Kamenyeva O , et al.  Metformin treatment rescues CD8+ T-cell response to immune checkpoint inhibitor therapy in mice with NAFLD . J Hepatol 2022 ; 77 : 748 – 60 .

Xu L , Wang X , Chen Y , et al.  Metformin modulates T cell function and alleviates liver injury through bioenergetic regulation in viral hepatitis . Front Immunol 2021 ; 12 : 638575 .

Boggess KA , Valint A , Refuerzo JS , et al.  Metformin plus insulin for preexisting diabetes or gestational diabetes in early pregnancy: the MOMPOD randomized clinical trial . JAMA 2023 ; 330 : 2182 – 90 .

Dunne F , Newman C , Alvarez-Iglesias A , et al.  Early metformin in gestational diabetes: a randomized clinical trial . JAMA 2023 ; 330 : 1547 – 56 .

Mauras N , DelGiorno C , Hossain J , et al.  Metformin use in children with obesity and normal glucose tolerance — effects on cardiovascular markers and intrahepatic fat . J Pediatr Endocrinol Metab 2012 ; 25 : 33 – 40 .

Smith FC , Stocker SL , Danta M , et al.  The safety and pharmacokinetics of metformin in patients with chronic liver disease . Aliment Pharmacol Ther 2020 ; 51 : 565 – 75 .

Zhang X , Harmsen WS , Mettler TA , et al.  Continuation of metformin use after a diagnosis of cirrhosis significantly improves survival of patients with diabetes . Hepatology 2014 ; 60 : 2008 – 16 .

Eurich DT , Weir DL , Majumdar SR , et al.  Comparative safety and effectiveness of metformin in patients with diabetes mellitus and heart failure: systematic review of observational studies involving 34,000 patients . Circ Heart Fail 2013 ; 6 : 395 – 402 .

Clegg LE , Jing Y , Penland RC , et al.  Cardiovascular and renal safety of metformin in patients with diabetes and moderate or severe chronic kidney disease: observations from the EXSCEL and SAVOR-TIMI 53 cardiovascular outcomes trials . Diabetes Obes Metab 2021 ; 23 : 1101 – 10 .

Chang LL , Umpierrez GE , Inzucchi SE . Management of hyperglycemia in hospitalized, non-critically ill adults . New Engl J Med 2022 ; 387 : 1040 – 2 .

Reitz KM , Marroquin OC , Zenati MS , et al.  Association between preoperative metformin exposure and postoperative outcomes in adults with type 2 diabetes . JAMA Surgery 2020 ; 155 : e200416 .

Narayanasamy S , Curtis LH , Hernandez AF , et al.  Lessons from COVID-19 for pandemic preparedness: proceedings from a multistakeholder think tank . Clin Infect Dis 2023 ; 77 : 1635 – 43 .

Author notes

• coronavirus
• antiviral agents
• outpatients
• viral load result
• severe acute respiratory syndrome
• post-acute covid-19 syndrome

Supplementary data

Email alerts

• Repurposing Revisited: Exploring the Role of Metformin for Treatment of COVID-19

More on this topic

Related articles in pubmed, citing articles via, looking for your next opportunity.

• Recommend to your Library

Affiliations

• Online ISSN 1537-6591
• Print ISSN 1058-4838
• Copyright © 2024 Infectious Diseases Society of America
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier Sponsored Documents

Gestational diabetes mellitus: Case definition & guidelines for data collection, analysis, and presentation of immunization safety data

Alisa kachikis.

a University of Washington, Seattle, WA, USA

Linda O. Eckert

Christie walker, eugene oteng-ntim.

b London School of Hygiene and Tropical Medicine, UK

c King’s College London, UK

Rama Guggilla

d George Institute for Global Health, India

Manish Gupta

e Barts Health NHS Trust, London, UK

Manasi Patwardhan

f Wayne State University, USA

Ronald Mataya

g Loma Linda University, USA

h University of Malawi College of Medicine, Malawi

Tamala Mallett Moore

i Sanofi Pasteur, USA

Ana Maria Alguacil-Ramos

j Dirección General de Salud Pública, Conselleria de Sanidad Universal y Salud Pública, Spain

k Fundación para el Fomento de la Investigación Sanitaria y Biomédica (FISABIO), Spain

Cheryl Keech

Michael gravett.

m Global Alliance to Prevent Prematurity and Stillbirth, An Initiative of Seattle Children’s Hospital, USA

Helen Murphy

n University of East Anglia/Cambridge University Hospitals NHS Foundation Trust, UK

Sonali Kochhar

o Global Healthcare Consulting, Delhi, India

q Erasmus University Medical Center, Rotterdam, The Netherlands

Nancy Chescheir

p University of North Carolina, Chapel Hill, USA

Associated Data

1. preamble, 1.1. need for developing case definitions and guidelines for data collection, analysis, and presentation for gestational diabetes mellitus as an adverse event following immunization.

Gestational diabetes mellitus (GDM) is a common condition in pregnancy that can result in significant morbidity and mortality to both mother and fetus. According to the International Diabetes Federation (IDF), about 16.8% of live-births are born to women with hyperglycemia in pregnancy [1] . Approximately 16% of these women will have pre-existing diabetes mellitus, diagnosed prior to pregnancy or during the first trimester of pregnancy. The remainder will have GDM. The incidence of GDM follows the incidence of insulin-resistance and type 2 diabetes mellitus (T2DM) in a given country’s population [2] . The prevalence of GDM can range anywhere from 1% to 15% depending on screening methods used, risk factors and ethnicity [3] . The Global Burden of Disease Project and IDF estimate that the rates of T2DM, including those of reproductive-age women, will continue to rise annually especially in low- and middle-income countries (LMICs) due to increasing risk factors such as obesity and sedentary lifestyle [4] .

The pathophysiology for GDM centers around the inability of a pregnant woman to develop an adequate insulin response to a glucose load to maintain her blood sugar in a normal range. This is due to decreasing insulin sensitivity as the pregnancy progresses. Risk factors for GDM include family history of diabetes, GDM in prior pregnancy, ethnicity and obesity. However it has been found that screening based on these factors will miss approximately 50% of women with GDM [5] . GDM places mothers at increased risk for gestational hypertension, pre-eclampsia and cesarean section during pregnancy [6] . In addition, women with a history of GDM are at higher risk for developing T2DM in the future [7] . Fetal complications of pregnancies with GDM include increased risk of macrosomia, operative delivery, shoulder dystocia, birth trauma and neonatal hypoglycemia and hyperbilirubinemia. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study demonstrated a continuous association between maternal glucose levels and increased birth weight, cesarean section deliveries and neonatal hyperinsulinemia [8] . In addition, in utero exposure to maternal hyperglycemia may predispose to obesity and insulin resistance later in life [9] , [10] . Given the risk for significant maternal and fetal morbidity and mortality in pregnancies complicated by GDM, strict glycemic control during pregnancy is recommended [11] , [12] it is also important to be cognizant of medications that may cause transient hyperglycemia or that exacerbate hyperglycemia in mothers with GDM, such as beta-adrenergic agents and corticosteroids that are often administered to women with threatened preterm labor [13] .

The association between maternal immunization and GDM, whether the development or exacerbation of the disease, or even the mitigation of disease, has not been well studied and is unknown. Multiple large prospective and retrospective vaccination studies have included GDM as a potential adverse outcome, often relying on ICD-9 or ICD-10 codes for diagnosis. While these studies did not find an increased incidence of GDM after maternal immunization, they had multiple confounders [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] . One of these confounders is the specific timing of vaccinations in pregnancy in relation to the diagnosis of GDM. Vaccinations are predominantly administered in the first and third trimesters, and GDM testing and diagnosis occurs in the second and third trimesters. This inevitably leads to the diagnosis of GDM after vaccine administration has occurred or concurrently. This timing makes determining the actual effect of vaccines on glucose tolerance difficult. Other medications, such as corticosteroids and beta-mimetics, are known to alter glucose tolerance, although only transiently [13] . Ideally, studies that examine the effect of vaccines on glucose tolerance would include a time period close to the vaccination administration, perhaps 0–14 days. Since this definition is not currently in use the studies included in this review have not limited the time between vaccination and diagnosis of GDM. Another confounder to the prior studies is that the true incidence of GDM is not known and ranges widely based on the population examined and the diagnostic criteria used [23] . If the baseline incidence in the study population is not known then determining the change in incidence after vaccination is not feasible. For this review, we have focused on the development of GDM as a possible adverse event following vaccination. We have excluded treatment of GDM, as well as maternal, fetal and neonatal complications attributed to GDM as these poor outcomes were likely due to GDM and other co-morbidities, not directly to the vaccine.

There is wide variation for the diagnostic criteria for GDM globally depending on country, consensus statements and resources available. Recommendations for GDM screening in pregnancy, usually between 24 and 28 weeks gestational age, are increasingly becoming universal, however, are often based on risk factors in resource-limited settings. While the gold standard for diagnosis of GDM is an oral glucose tolerance test, the blood glucose cut-offs often vary between and within countries, and sampling methodology can range from laboratory results based on venous serum samples to plasma samples using calibrated handheld glucometers [24] . In resource-limited settings, alternative methods of diagnosis have been proposed including fasting glucose levels, glucosuria or diagnosis based on other risk factors.

There is hence no uniformly accepted definition of Gestational Diabetes Mellitus. This is a missed opportunity, as data comparability across trials or surveillance systems would facilitate data interpretation and promote the scientific understanding of GDM in general, and for our purposes, any possible relationship of maternal vaccination with the development of GDM.

1.2. Methods for the development of the case definition and guidelines for data collection, analysis, and presentation for gestational diabetes mellitus as an adverse events following immunization

Following the process described on the Brighton Collaboration Website http://www.brightoncollaboration.org/internet/en/index/process.html , the Brighton Collaboration Gestational Diabetes Working Group was formed in 2016 and included members from clinical, academic, public health, and industry backgrounds. The composition of the working and reference group as well as results of the web-based survey completed by the reference group with subsequent discussions in the working group can be viewed at: http://www.brightoncollaboration.org/internet/en/index/working_groups.html .

To guide the decision-making for the case definition and guidelines, a literature search was performed using MEDLINE and Embase databases. Due to the extensive and diverse topic of gestational diabetes mellitus, the search was limited to systematic reviews conducted in the prior five years. The search term for Pubmed is shown below and was modified for Embase search terminology:

• ((“pregnancy induced diabetes” AND diagnosis) Filters:  published in the last 5 years; Humans; English)
• (gestational diabetes/diagnosis OR (Diabetes mellitus AND diagnosis AND pregnancy) OR “diabetes in pregnancy” OR Glucose intolerance of pregnancy OR hyperglycemia in pregnancy OR hyperglucosuria) Filters:  Systematic Reviews; published in the last 5 years; Humans; English)

A separate search was done to identify any studies or reports associating gestational diabetes mellitus with immunizations and vaccinations, using MEDLINE, Embase, the Cochrane Database of Systematic Reviews, Clinical Key medical reference books, and the Centers for Disease Control and Prevention (CDC) and National Institutes for Health (NIH) websites. The following search string was used:

• (( maternal  NEXT/1 ( vaccin ∗  OR  immuniz ∗  OR  immunis ∗ )) OR ((( 'vaccine' /exp/mj OR  'immunization' /exp/mj OR  vaccin ∗ :ti OR  immuniz ∗ :ti OR  immunis ∗ :ti OR  revaccin ∗ :ti OR  postvaccin ∗ :ti OR  reimmuni ∗ :ti OR  postimmuni ∗ ) AND ( 'pregnancy' /exp/mj OR  'child bearing' :ti OR  'childbearing' :ti OR  'gestation' :ti OR  'gravidity' :ti OR (( labor  OR  labor ) NEXT/1  presentation ):ti OR pregnan ∗ :ti OR  'pregnant woman' /exp/mj OR  'expectant mother' /exp/mj OR ( expectant  NEXT/1  mother ∗ ) AND ( 'pregnancy diabetes mellitus' /exp AND diabetes  NEAR/2 ( gestational  OR  pregnancy ))

Two committee members reviewed the literature search results for duplications and appropriateness to the topic, retaining 111 of 209 included documents. Of the search for gestational diabetes related to vaccinations, 11 of 14 were retained after review. Differences in literature review were adjudicated by a third committee member.

In addition, we identified latest edition Obstetrics and Gynecology text books in common usage in North America, Europe and Africa and reviewed four of these for definitions of GDM. Fifteen national endocrine and obstetrics and gynecology guidelines were reviewed. A flow diagram of identified sources is shown in Fig. 1 .

Flow diagram describing pathway for source identification.

Each member of the Gestational Diabetes work group was assigned approximately eight of the 141 articles to review for identification of a working definition of gestational diabetes and the preferred method of diagnosing it or for describing any association of gestational diabetes as a complication of vaccination.

Findings of the literature search included varying definitions of gestational diabetes mellitus in the literature as well as different diagnostic criteria described in the systematic reviews and research studies. In the majority of cases, oral glucose tolerance tests were used to diagnose gestational diabetes with venous blood draws. The specific glucose tolerance test as well as the glucose level cut-offs vary between reviews and consensus guidelines. An inventory spreadsheet of definitions and diagnostic criteria of the 141 pieces of literature as well as a summary page comparing the most common guidelines for the definition of gestational diabetes was made available to working group members. The full reference list of documents, consensus guidelines and textbooks are available upon request. Please contact the corresponding author for further information.

1.3. Rationale for selected decisions about the case definition of gestational diabetes mellitus as an adverse event following immunization

1.3.1. the term gestational diabetes mellitus.

• – Different terminology

Alternate terminology for GDM includes “pregnancy-induced hyperglycemia.” Diabetes in pregnancy is frequently used to describe pregestational diabetes or is used as an umbrella term for pregestational diabetes AND GDM.

1.3.2. Related term(s) of gestational diabetes mellitus

Pregestational diabetes mellitus (DM): Pregestational DM is the diagnosis of diabetes mellitus prior to pregnancy. Two subtypes are frequently described:

• – Type 1 DM: Type 1 DM typically has an onset early in life usually due to an autoimmune process. It necessitates insulin therapy. It is commonly considered to result from insufficient production of insulin in the pancreas.
• – Type 2 DM: Type 2 DM is the more common form of pregestational DM. It is characterized by insulin resistance or relative insulin deficiency and can be treated by dietary and lifestyle modifications, oral agents and/or insulin therapy.

1.3.3. Formulating a case definition that reflects diagnostic certainty: weighing specificity versus sensitivity

It needs to be emphasized that the grading of definition levels is entirely about diagnostic certainty, not clinical severity of an event. Thus, a clinically very severe event may appropriately be classified as Level Two or Three rather than Level One if it could reasonably be of non-GDM etiology. Detailed information about the severity of the event should additionally always be recorded, as specified by the data collection guidelines.

The number of symptoms and/or signs that will be documented for each case may vary considerably. The case definition has been formulated such that the Level 1 definition is highly specific for the condition. As maximum specificity normally implies a loss of sensitivity, two additional diagnostic levels have been included in the definition, offering a stepwise increase of sensitivity from Level One down to Level Three, while retaining an acceptable level of specificity at all levels. In this way it is hoped that all possible cases of GDM can be captured.

1.3.4. Rationale for individual criteria or decision made related to the case definition

1.3.4.1. laboratory findings.

Laboratory findings are crucial to the diagnosis of GDM. Please see laboratory criteria listed below (Section 2 ).

1.3.5. Timing post immunization

Timing criteria for considering GDM as a possible adverse event from vaccination are important. We considered only vaccinations given during pregnancy, not prior to pregnancy. GDM is usually diagnosed in the second or third trimester of pregnancy, related to increasing pregnancy-related insulin resistance. Some medications used in pregnancy, such as corticosteroids and beta-mimetics, which can result in transient hyperglycemia which can last from hours to several days. Given the paucity of identified information about GDM associated with vaccination, we are unable to provide an evidence-based estimate of time interval for the possible development of GDM following maternal immunization. Future studies on time intervals between maternal immunizations and GDM are needed (please see Section 3.2 ).

Timed criteria should be used, since development of hyperglycemia in pregnancy can occur at any time in the second and third trimesters of pregnancy and vaccinations are usually administered in the 1st and 3rd trimesters of pregnancy.

We postulate that a definition designed to be a suitable tool for testing causal relationships requires ascertainment of the outcome (e.g. GDM) independent from the exposure (e.g. immunizations). Therefore, to avoid selection bias, a restrictive time interval from immunization to onset of GDM should not be an integral part of such a definition. Instead, where feasible, details of this interval should be assessed and reported as described in the data collection guidelines.

Further, GDM usually occurs outside the controlled setting of a clinical trial or hospital. In some settings it may be impossible to obtain a clear timeline of the event, particularly in less developed or rural settings. In order to avoid selecting against such cases, the Brighton Collaboration case definition avoids setting arbitrary time frames.

1.3.6. Differentiation from other (similar/associated) disorders

• – Pregestational DM: It can be difficult to distinguish pregestational DM from GDM, especially if there is late entry to prenatal care.
• – Elements to differentiate pregestational DM from GDM include timing and trimester of diagnosis and severity of hyperglycemia as well as postnatal testing results. Further details are available in Section 2 . Please see below.

1.4. Guidelines for data collection, analysis and presentation

As mentioned in the overview paper, the case definition is accompanied by guidelines that are structured according to the steps of conducting a clinical trial, i.e. data collection, analysis and presentation. Neither case definition nor guidelines are intended to guide or establish criteria for management of ill infants, children, or adults. Both were developed to improve data comparability.

1.5. Periodic review

Similar to all Brighton Collaboration case definitions and guidelines, review of the definition with its guidelines is planned on a regular basis (i.e. every three to five years) or more often if needed.

2. Case definition of gestational diabetes mellitus 3

2.1. for all levels of diagnostic certainty.

Gestational diabetes mellitus (GDM) is a clinical syndrome characterized by

The absence of pre-gestational diabetes diagnosis defined by

• • Previous diagnosis of diabetes while not pregnant
• • First trimester hemoglobin A1c level of ⩾ 6.5% (47.5 mmol/mol)
• • First trimester fasting blood glucose 126 mg/dL/⩾7 mmol/L

Identification of sustained hyperglycemia during pregnancy not due to other known causes (i.e. corticosteroids, beta-mimetics, etc.)

2.2. Level 1 of diagnostic certainty

Absence of pregestational diabetes mellitus diagnosis in the first trimester as defined above with level 1–2 certainty for gestational age using GAIA definition for gestational age (please see Appendix A)

Diagnosis of gestational diabetes based on a positive internationally recognized oral glucose tolerance test (see below “major criteria”) using venous blood sample/samples.

2.3. Level 2 of diagnostic certainty

Diagnosis of gestational diabetes based on positive internationally recognized oral glucose tolerance test (see below “major criteria”) using capillary blood sample/samples.

2.4. Level 3 of diagnostic certainty

Absence of pregestational diabetes mellitus diagnosis in the first trimester as defined above with at least level 3 certainty for gestational age using GAIA definition for gestational age (please see Appendix A)

Diagnosis of gestational diabetes based on positive internationally recognized oral glucose tolerance test (see below “major criteria”) using venous blood or capillary blood sample/samples

Diagnosis of gestational diabetes based on fasting plasma glucose of 5.1–6.9 mmol/l (92–125 mg/dL) using venous or capillary blood samples.

Glucometers should be calibrated according to local standards/research protocols.

All participants in maternal immunization trials should have at minimum a fasting venous blood or capillary glucose sample prior to vaccination.

2.5. Insufficient evidence for diagnosis of gestational diabetes mellitus

Blood glucose cannot be measured

Elevated postprandial blood glucose level without confirmatory fasting venous blood or capillary glucose level

Use of Hemoglobin A1c alone for the diagnosis of GDM without a diagnostic oral glucose tolerance test (OGTT) or elevated fasting plasma glucose level

Clinical and laboratory findings such as glucosuria, fundal height greater than dates, obesity, prior history of GDM or family history for the diagnosis of gestational diabetes mellitus without a diagnostic test.

2.6. Major and minor criteria used in the case definition of gestational diabetes mellitus

OGTT (Oral glucose tolerance test); IADPSG (International Association of Diabetes and Pregnancy Study Groups); WHO (World Health Organization); NICE (The National Institute for Health and Care Excellence, UK); NDDG (National Diabetes Data Group) (see Table 1 ).

Diagnostic oral glucose tolerance tests based on organization or country guidelines.

OGTT (Oral glucose tolerance test); IADPSG (International Association of Diabetes and Pregnancy Study Groups); WHO (World Health Organization); NICE (The National Institute for Health and Care Excellence, UK); NDDG (National Diabetes Data Group).

Ideally, a postpartum or interpregnancy glucose tolerance test would be performed to confirm that the diagnosis of diabetes mellitus is confined to pregnancy and to exclude diabetes mellitus outside of pregnancy. Postpartum or interpregnancy GTTs, however, are infrequently performed and therefore the absence of this test would not be exclusionary.

3. Guidelines for data collection, analysis and presentation of gestational diabetes mellitus

It was the consensus of the Brighton Collaboration Gestational Diabetes Mellitus Working Group to recommend the following guidelines to enable meaningful and standardized collection, analysis, and presentation of information about gestational diabetes. However, implementation of all guidelines might not be possible in all settings. The availability of information may vary depending upon resources, geographical region, and whether the source of information is a prospective clinical trial, a post-marketing surveillance or epidemiological study, or an individual report of gestational diabetes. Also, as explained in more detail in the overview paper in this volume, these guidelines have been developed by this working group for guidance only, and are not to be considered a mandatory requirement for data collection, analysis, or presentation.

3.1. Data collection

These guidelines represent a desirable standard for the collection of data on availability following immunization to allow for comparability of data, and are recommended as an addition to data collected for the specific study question and setting. The guidelines are not intended to guide the primary reporting of GDM to a surveillance system or study monitor. Investigators developing a data collection tool based on these data collection guidelines also need to refer to the criteria in the case definition, which are not repeated in these guidelines.

Guidelines 1–44 below have been developed to address data elements for the collection of adverse event information as specified in general drug safety guidelines by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use [28] , and the form for reporting of drug adverse events by the Council for International Organizations of Medical Sciences [29] . These data elements include an identifiable reporter and patient, one or more prior immunizations, and a detailed description of the adverse event, in this case, of GDM following immunization. The additional guidelines have been developed as guidance for the collection of additional information to allow for a more comprehensive understanding of GDM following immunization.

3.1.1. Source of information/reporter

For all cases and/or all study participants, as appropriate, the following information should be recorded:

• (1) Date of report.
• (2) Name and contact information of person reporting (Footnote 2 ) and/or diagnosing the GDM as specified by country-specific data protection law.
• (3) Name and contact information of the investigator responsible for the subject, as applicable.
• (4) Relation to the patient (e.g., immunizer [clinician, nurse], family member [indicate relationship], other).

3.1.2. Vaccinee/control

3.1.2.1. demographics.

• (5) Case/study participant identifiers (e.g. first name initial followed by last name initial with medical record number/booking number/subject number) or alpha-numeric code (or in accordance with country-specific data protection laws).
• (6) Date of birth, age, and sex.
• (7) For infants: Gestational age and birth weight and length, and whether multiple gestation. Infant’s name and identifier (medical record number/booking number/subject number or alpha-numeric code) should also be recorded.

3.1.2.2. Clinical and immunization history

• (8) For the purposes of this definition, any hyperglycemia or diabetes diagnosis prior to current pregnancy or between pregnancies.
• (9) Past medical history, including hospitalizations, underlying diseases/disorders, pre-immunization signs and symptoms including identification of indicators for, or the absence of, a history of allergy to vaccines, vaccine components or medications; food allergy; allergic rhinitis; eczema; asthma. Risk factors for GDM including family history of diabetes, GDM in prior pregnancies, eating habits and physical activity, weight or body mass index (BMI) at first prenatal visit.
• (10) Any medication history (other than treatment for the event described) prior to, during, and after immunization including prescription and non-prescription medication as well as medication or treatment with long half-life or long term effect. (e.g. immunoglobulins, blood transfusion and immunosuppressants).
• (11) Immunization history (i.e. previous immunizations and any adverse event following immunization (AEFI)), in particular occurrence of GDM after a previous immunization.

3.1.3. Details of the immunization

• (12) Date and time of immunization(s).
• (13) Description of vaccine(s) (name of vaccine, manufacturer, lot number, dose (e.g. 0.25 mL, 0.5 mL, etc.) and number of dose if part of a series of immunizations against the same disease).
• (14) If applicable, description of diluent (manufacturer, lot number, amount (e.g. 0.25 mL, 0.5 mL, etc.)
• (15) The anatomical sites (including left or right side) of all immunizations (e.g. vaccine A in proximal left lateral thigh, vaccine B in left deltoid).
• (16) Route and method of administration (e.g. intramuscular, intradermal, subcutaneous, and needle-free (including type and size), other injection devices).
• (17) Needle length and gauge.

3.1.4. The adverse event

• (18) For all cases at any level of diagnostic certainty and for reported events with insufficient evidence, the criteria fulfilled to meet the case definition should be recorded.

Specifically document:

• (19) Clinical description of signs and symptoms of GDM, and if there was medical confirmation of the event (i.e. patient seen by qualified health professional).
• (20) Date/time of onset, 4 first observation 5 and diagnosis, 6 end of episode 7 and final outcome. 8
• (21) Concurrent signs, symptoms, and diseases.
• • Values and units of routinely measured parameters (e.g. blood glucose levels including fasting and postprandial measurements, temperature, blood pressure) – in particular those indicating the severity of the event;
• • Method of measurement (e.g. serum sampling or fingerstick for capillary glucose measurements, type of thermometer, oral or other route, duration of measurement, etc.);
• • Results of laboratory examinations, surgical and/or pathological findings and diagnoses if present.
• (23) Treatment given for GDM especially specify whether diet-controlled or whether hyperglycemic agents were used and dosing.
• (24) Outcome (Footnote 7 ) at last observation.
• (25) Objective clinical evidence supporting classification of the event as “serious”. 9
• (26) Exposures other than the immunization 24 h before and after immunization (e.g. food, environmental, pharmaceutical) considered potentially relevant to the reported event.

3.1.5. Miscellaneous/general

• • Biologic characteristics of the vaccine e.g. live attenuated versus inactivated component vaccines.
• • Biologic characteristics of the vaccine-targeted disease.
• • Biologic characteristics of GDM including patterns identified in previous trials (e.g. early-phase trials).
• • Biologic characteristics of the vaccinee (e.g. nutrition, underlying disease like immunosuppressing illness).
• (28) The duration of follow-up reported during the surveillance period should be predefined likewise. It should aim to continue to resolution of the event.
• (29) Methods of data collection should be consistent within and between study groups, if applicable.
• (30) Follow-up of cases should attempt to verify and complete the information collected as outlined in data collection guidelines 1–25.
• (31) Investigators of patients with GDM should provide guidance to reporters to optimize the quality and completeness of information provided.
• (32) Reports of GDM should be collected throughout the study period regardless of the time elapsed between immunization and the adverse event. If this is not feasible due to the study design, the study periods during which safety data are being collected should be clearly defined.

3.2. Data analysis

The following guidelines represent a desirable standard for analysis of data on GDM to allow for comparability of data, and are recommended as an addition to data analyzed for the specific study question and setting.

• (33) Reported events should be classified in one of the following five categories including the three levels of diagnostic certainty. Events that meet the case definition should be classified according to the levels of diagnostic certainty as specified in the case definition. Events that do not meet the case definition should be classified in the additional categories for analysis.

Event classification in 5 categories 10

Event meets case definition

• (1) Level 1: Criteria as specified in the GDM case definition
• (2) Level 2: Criteria as specified in the GDM case definition
• (3) Level 3: Criteria as specified in the GDM case definition

Event does not meet case definition

Additional categories for analysis

• (4) Reported GDM with insufficient evidence to meet the case definition 11
• (5) Not a case of GDM 12
• (34) The interval between immunization and reported GDM could be defined as the date/time of immunization to the date/time of onset (Footnote 3 ) of the first abnormal glucose measurement. If few cases are reported, the concrete time course could be analyzed for each; for a large number of cases, data can be analyzed in the following increments:

Subjects with gestational diabetes mellitus by interval to presentation

• (35) The duration of a possible GDM could be analyzed as the interval between the date/time of onset (Footnote 2 ) of the first symptoms and/or signs consistent with the definition and the end of episode (Footnote 6 ) and/or final outcome (Footnote 7 ). Whatever start and ending are used, they should be used consistently within and across study groups.
• (36) If more than one measurement of a particular criterion is taken and recorded, the value corresponding to the greatest magnitude of the adverse experience could be used as the basis for analysis. Analysis may also include other characteristics like qualitative patterns of criteria defining the event.
• (37) The distribution of data (as numerator and denominator data) could be analyzed in predefined increments (e.g. measured values, times), where applicable. Increments specified above should be used. When only a small number of cases is presented, the respective values or time course can be presented individually.
• (38) Data on GDM obtained from subjects receiving a vaccine should be compared with those obtained from an appropriately selected and documented control group(s) to assess background rates of hypersensitivity in non-exposed populations, and should be analyzed by study arm and dose where possible, e.g. in prospective clinical trials.

Ultimately, careful analysis of data is necessary given overlap between usual timing of vaccination in pregnancy and routine GDM screening between 24 and 28 weeks gestational age in order to avoid misleading conclusions regarding GDM and associations with vaccinations purely based on timing of vaccine administration and GDM diagnosis.

3.3. Data presentation

These guidelines represent a desirable standard for the presentation and publication of data on GDM following immunization to allow for comparability of data, and are recommended as an addition to data presented for the specific study question and setting. Additionally, it is recommended to refer to existing general guidelines for the presentation and publication of randomized controlled trials, systematic reviews, and meta-analyses of observational studies in epidemiology (e.g. statements of Consolidated Standards of Reporting Trials (CONSORT), of Improving the quality of reports of meta-analyses of randomized controlled trials (QUORUM), and of meta-analysis Of Observational Studies in Epidemiology (MOOSE), respectively) [30] , [31] , [32] .

• (39) All reported events of GDM should be presented according to the categories listed in guideline 32.
• (40) Data on possible GDM events should be presented in accordance with data collection guidelines 1–25 and data analysis guidelines 32–37.
• (41) Terms to describe GDM such as “well-controlled”, “poorly controlled”, “low-grade”, “mild”, “moderate”, “high”, “severe” or “significant” are highly subjective, prone to wide interpretation, and should be avoided, unless clearly defined.
• (42) Data should be presented with numerator and denominator (n/N) (and not only in percentages), if available.

Although immunization safety surveillance systems denominator data are usually not readily available, attempts should be made to identify approximate denominators. The source of the denominator data should be reported and calculations of estimates be described (e.g. manufacturer data like total doses distributed, reporting through Ministry of Health, coverage/population based data, etc.).

• (43) The incidence of cases in the study population should be presented and clearly identified as such in the text.
• (44) If the distribution of data is skewed, median and range are usually the more appropriate statistical descriptors than a mean. However, the mean and standard deviation should also be provided.
• • The study design.
• • The method, frequency and duration of monitoring for GDM.
• • The trial profile, indicating participant flow during a study including drop-outs and withdrawals to indicate the size and nature of the respective groups under investigation.
• • The type of surveillance (e.g. passive or active surveillance).
• • The characteristics of the surveillance system (e.g. population served, mode of report solicitation).
• • The search strategy in surveillance databases.
• • Comparison group(s), if used for analysis.
• • The instrument of data collection (e.g. standardized questionnaire, diary card, report form).
• • Whether the day of immunization was considered “day one” or “day zero” in the analysis.
• • Whether the date of onset (Footnote 3 ) and/or the date of first observation (Footnote 4 ) and/or the date of diagnosis (Footnote 5 ) was used for analysis.
• • Use of this case definition for GDM, in the abstract or methods section of a publication. 13

The findings, opinions and assertions contained in this consensus document are those of the individual scientific professional members of the working group. They do not necessarily represent the official positions of each participant’s organization (e.g., government, university, or corporation). Specifically, the findings and conclusions in this paper are those of the authors and do not necessarily represent the views of their respective institutions.

Acknowledgements

The authors are grateful for the support and helpful comments provided by the Brighton Collaboration and the reference group (see https://brightoncollaboration.org/public/what-we-do/setting-standards/case-definitions/groups.html for reviewers), as well as other experts consulted as part of the process. These experts include Dr. Alastair McKelvey, Subspecialist in Maternal-Fetal Medicine and Associate Professor in Obstetrics and Gynaecology at the Norfolk and Norwich University Hospitals NHS Foundation Trust, UK, as well as Lara Handler, Medicine Librarian, UNC Health Sciences Library, Monica Weiss-Nolen, Information Scientist at Sanofi Pasteur, USA, and Sonja Banga, at Sanofi Pasteur, Canada. The authors are also grateful to Jan Bonhoeffer, Jorgen Bauwens of the Brighton Collaboration Secretariat and Sonali Kochhar of Global Healthcare Consulting for final revisions of the final document. Finally, we would like to acknowledge the Global Alignment of Immunization Safety Assessment in Pregnancy (GAIA) project, funded by the Bill and Melinda Gates Foundation.

3 If the reporting center is different from the vaccinating center, appropriate and timely communication of the adverse event should occur.

4 The date and/or time of onset is defined as the time post immunization, when the GDM is first detect via diagnostic criteria described above. GDM is usually screen detected and therefore asymptomatic. This may only be possible to determine in retrospect.

5 The date and/or time of first observation of the first sign or symptom indicative for GDM can be used if date/time of onset is not known.

6 The date of diagnosis of an episode is the day post immunization when the event met the case definition at any level.

7 The end of an episode is defined as the time the event no longer meets the case definition at the lowest level of the definition.

8 E.g. recovery to pre-immunization health status, spontaneous resolution, therapeutic intervention, persistence of the event, sequelae, death.

9 An AEFI is defined as serious by international standards if it meets one or more of the following criteria: (1) it results in death, (2) is life-threatening, (3) it requires inpatient hospitalization or results in prolongation of existing hospitalization, (4) results in persistent or significant disability/incapacity, (5) is a congenital anomaly/birth defect, (6) is a medically important event or reaction.

10 To determine the appropriate category, the user should first establish, whether a reported event meets the criteria for the lowest applicable level of diagnostic certainty, e.g. Level three. If the lowest applicable level of diagnostic certainty of the definition is met, and there is evidence that the criteria of the next higher level of diagnostic certainty are met, the event should be classified in the next category. This approach should be continued until the highest level of diagnostic certainty for a given event could be determined. Major criteria can be used to satisfy the requirement of minor criteria. If the lowest level of the case definition is not met, it should be ruled out that any of the higher levels of diagnostic certainty are met and the event should be classified in additional categories four or five.

11 If the evidence available for an event is insufficient because information is missing, such an event should be categorized as “Reported GDM with insufficient evidence to meet the case definition”.

12 An event does not meet the case definition if investigation reveals a negative finding of a necessary criterion (necessary condition) for diagnosis. Such an event should be rejected and classified as “Not a case of GDM”.

13 Use of this document should preferably be referenced by referring to the respective link on the Brighton Collaboration website ( http://www.brightoncollaboration.org ).

Appendix A Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.vaccine.2017.01.043 .

Appendix A. Supplementary material