nursing case study on gestational diabetes mellitus

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Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review

Gwendolyn patience mensah.

1 School of Nursing and Midwifery, College of Health Sciences, University of Ghana, Ghana Legon

Wilma ten Ham‐Baloyi

2 Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth South Africa

Dalena (R.M.) van Rooyen

Sihaam jardien‐baboo.

3 Department of Nursing Science, Nelson Mandela University, Port Elizabeth South Africa

Aims and objectives

An integrative literature review searched for, selected, appraised, extracted and synthesized data from existing available guidelines on the nursing management of gestational diabetes mellitus as no such analysis has been found.

Early screening, diagnosis and management of gestational diabetes mellitus are important to prevent or reduce complications during and postpregnancy for both mother and child. A variety of guidelines exists, which assist nurses and midwives in the screening, diagnosis and management of gestational diabetes mellitus.

An integrative literature review.

The review was conducted in June 2018 following an extensive search of available guidelines according to an adaptation of the stages reported by Whittemore and Knafl (2005, Journal of Advanced Nursing , 52, 546). Thus, a five‐step process was used, namely formulation of the review question, literature search, critical appraisal of guidelines identified, data extraction and data analysis. All relevant guidelines were subsequently appraised for rigour and quality by two independent reviewers using the AGREE II tool. Content analysis was used analysing the extracted data.

Following extraction and analysis of data, two major themes were identified from eighteen ( N  = 18) guidelines. These were the need for early screening and diagnosis of gestational diabetes mellitus and for nursing management of gestational diabetes mellitus (during pregnancy, intra‐ and postpartum management). Various guidelines on the nursing management of gestational diabetes mellitus were found; however, guidelines were not always comprehensive, sometimes differed in their recommended practices and did not consider a variety of contextual barriers to the implementation of the recommendations.

Critically, scrutiny of the guidelines is required, both in terms of the best evidence used in their development and in terms of the feasibility of implementation for its context.

Relevance to clinical practice

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of gestational diabetes mellitus that provide guidance for nurse–midwives on maternal and postpartum follow‐up care for women at risk or diagnosed with gestational diabetes mellitus.

1. INTRODUCTION

The prevalence of gestational diabetes mellitus (GDM) varies per country but is estimated to be approximately 15% among pregnant women globally (Zhu & Zhang, 2016 ). However, the global prevalence is expected to increase due to increasing numbers of overweight and obese women of reproductive age (Guariguata, Linnenkamp, Beagley, Whithing, & Cho, 2014 ; Kampmann et al., 2015 ). During 2003–2014, the prevalence of pregnant women with overweight and obesity increased in high middle‐income countries mainly due to increased caloric supply and urbanization and in upper middle‐ and lower middle‐income countries as a result of the decreased employment of women in agricultural activities (Chen, Xu, & Yan, 2018 ). GDM is defined as any degree of glucose intolerance with onset or first recognition during pregnancy (American Diabetes Association [ADA], 2010 ). GDM characterizes the most common metabolic complication of pregnancy and is related to maternal complications such as hypertension, pre‐eclampsia, caesarean section, infection and polyhydramnios. It is also related to foetal morbidity in terms of macrosomia, birth trauma, hypoglycaemia, hypocalcaemia, hypomagnesemia, hyperbilirubinemia, respiratory distress syndrome and polycythemia (Mitanchez, Yzydorczyk, & Simeoni, 2015 ; Rafiq, Hussain, Jan, & Najar, 2015 ).

Additionally, women diagnosed with GDM are considerably more at risk for impaired glucose tolerance and are up to six times more likely to develop type 2 diabetes 5–10 years postpregnancy compared with women with normal glucose levels in pregnancy (Work Loss Data Institute, 2016 ). Furthermore, children from women with GDM have a higher likelihood of developing obesity and of having impaired glucose tolerance as well as diabetes, either in childhood or in early adulthood (World Health Organization [WHO], 2016 ).

Some risk factors that are identified for developing GDM include age (the risk for GDM increases with age), being overweight or obese, extreme weight gain during pregnancy and a family history of diabetes. Additional risk factors related to an increased frequency of GDM include GDM during an earlier pregnancy, a history of stillbirth or giving birth to an infant with congenital abnormalities and detection of glucose in the urine as well as ethnic background (Anna, van der Ploeg, Cheung, Huxley, & Bauman, 2008 ; Evensen, 2012 ; Kampmann et al., 2015 ; Khan, Ali, & Khan, 2013 ).

Early screening and diagnosis of GDM is therefore important to prevent or reduce complications during and postpregnancy for both mother and child. Most countries use selective screening, based on the known risk factors. Although selective screening could miss GDM cases, it could also assist nursing management by focussing health resources on women with the highest risk of complications, specifically in contexts where resources are scarce. Likewise, screening early in pregnancy for pre‐existent diabetes by determining fasting glucose is justified, especially in the context of increased existence of diabetes mellitus type 2 in young women, which often remains undiagnosed (Kampmann et al., 2015 ).

Once women are diagnosed with GDM, management includes lifestyle modifications in terms of a diet high in dietary fibre (specifically fruit and cereal) and with a low glycaemic index, as well as routine monitoring of blood glucose levels during and postpregnancy. Additionally, if needed, the GDM is treated by means of insulin, metformin and glyburide to ensure the long‐term health of the pregnant woman and her baby (ADA, 2015 ; Poomalar, 2015 ).

A guideline, developed from rigorous evidence, would assist nurses and midwives in the screening, diagnosis and management of GDM. As they are often the first point of care for women, this is particularly important in contexts where medical care is scarce. Although some guidelines on the management of GDM exist, they are often designed for medical practitioners. No study was found that summarized best practice guidelines regarding the nursing management of GDM. This study therefore searched for, selected, appraised, extracted and synthesized data from existing available guidelines to guide the development of a best practice guideline for the nursing management of GDM.

An integrative literature review was conducted following a five‐step process adopted from Whittemore and Knafl ( 2005 ). The processes proceeded as follows: Step 1: Formulation of the review question; Step 2: Literature searching; Step 3: Critical appraisal of evidence; Step 4: Data extraction; and Step 5: Data analysis. The integrative literature review was conducted by the first author, under supervision of the second and third authors, both of whom are experienced in conducting integrative literature reviews. The study was part of a larger study that aimed to develop a best practice guideline for the nursing management of GDM during the ante‐, intra‐ and postnatal periods.

2.1. Formulation of the review question

The review question (Step 1) was formulated according to the PICO format. The elements of the question were as follows: P – Population = Women; I – Issue = nursing management of GDM (including screening, diagnosis and management); C – Context = nursing and health institutions; O – Outcome = to inform best practices on the nursing management of GDM. The review question was therefore formulated as follows: What existing evidence is available to inform best practices on the nursing management of women diagnosed with GDM?

2.2. Literature searching process

The literature searching process (Step 2) was conducted with the assistance of an experienced librarian in selecting the databases and keywords. Inclusion and exclusion criteria were established to guide the search and selection process.

2.2.1. Sources of literature

Databases were thoroughly searched using the following search engines: BioMed Central, EBSCOhost (CINAHL, ERIC, Health Source: Nursing/Academic Edition, MasterFILE Premier, MEDLINE), JSTOR, PUBMED CENTRAL, SAGE, ScienceDirect, Google Scholar, Scopus and Wiley Online Library. A manual search for guidelines was performed, using Google Scholar and Google, accessing organizations specialized in developing best practice guidelines. These included Canadian Practice Guidelines, National Guidelines Clearinghouse (NGC), National Institute for Health and Clinical Excellence (NICE), Guidelines International Network, Scottish Intercollegiate Guidelines Network (SIGN), New Zealand Guidelines Group, National Health and Medical Research Council, Registered Nurses’ Association of Ontario, American College of Obstetricians and Gynaecologists, American Diabetes Association and Health Service Executives. Grey literature, such as unpublished theses and dissertations, responding to the management of GDM were also considered.

2.2.2. Key words

With the assistance of an experienced librarian, the combination of key words “guideline*” and “evidence‐based practice” and “gestational diabetes mellitus” AND “nurs* manage* OR nurs* intervention*” and “pregnan*, antenatal, intra‐natal OR postnatal*” was used. The combination of keywords used was adapted per database, if necessary, to obtain all relevant guidelines.

2.2.3. Inclusion and exclusion criteria

Guidelines were included that focussed on the nursing management of GDM where any of the following aspects are addressed: early screening for GDM and its management, self‐monitoring of blood glucose levels, lifestyle modifications and/or insulin administration. Studies published in English were used as this is the language the authors are proficient in. Guidelines published between 2004–2018 were included, and the most updated version of guidelines was included. Guidelines focussing on the management of type 1 or type 2 diabetes mellitus only were excluded as were guidelines that did not consider the practices of nurses or midwives in GDM management.

2.2.4. Search and selection process

The search for appropriate guidelines was conducted in June 2018. All guidelines that fitted the criteria for the study were retrieved and selected for inclusion. Guidelines that did not meet the required criteria were excluded. The inclusion and exclusion criteria were applied by both the first author and the fourth author (who served as an independent reviewer). Consensus regarding the inclusion and exclusion of relevant articles was reached between the authors. The search and selection process of the included guidelines is illustrated in Figure ​ Figure1’s 1 ’s PRISMA flow chart (Moher, Liberati, Tetzlaff, Altman, & PRISMA Group, 2009 ).

PRISMA flow of studies through the review (adapted from Moher et al., 2009 )

Figure ​ Figure1 1 shows that 28 guidelines were found in the literature search and retained for full‐text review. Seven guidelines were excluded, based on the study criteria, and two duplicates were excluded. Nineteen guidelines fulfilled the review criteria and were included for critical appraisal.

2.3. Critical appraisal

The AGREE II instrument was used to critically appraise the guidelines (Step 3). AGREE II consists of 23 appraisal items organized within six domains, followed by two global rating items for an overall assessment. Each domain captures a specific aspect of guideline quality. All AGREE II items were rated on a 7‐point scale (1 – “Strongly disagree”, when no relevant information was given, to 7 – “Strongly agree”, when the quality of reporting was exceptional and the criterion was fully met) (Brouwers et al., 2010 ). The rating for each item was done depending on the completeness and quality of reporting.

The overall score allocated to each guideline appraised was expressed as a percentage of the maximum possible score of 161. Guidelines with a score of 60 per cent were included as they were considered to have more rigour than guidelines with a lower score. Similarly, they were considered to contribute more weight to the discussion and recommendations derived from the review. Consensus was reached between the two reviewers (the first and fourth author), as a result of which one of the nineteen guidelines was excluded owing to poor rigour. A total of 18 guidelines were included for data extraction (Figure ​ (Figure1 1 ).

2.4. Data extraction process

After critical appraisal, data were extracted from eighteen guidelines (Step 4). This process was completed by the first and fourth authors, working independently. Data extraction focused on material relating to early screening and diagnosis of GDM and the nursing management of GDM.

2.5. Data analysis process

Thematic data analysis was used to systematically synthesize the extracted data of each guideline and develop themes (Step 5) (Burls, 2009 ). Consensus was achieved between the authors on the themes.

2.6. Ethical statement

The study obtained ethics from the University's Faculty Postgraduate Studies Committee (ethics number: H14‐HEA‐NUR‐32). The authors adhered to principles of honesty and transparency in reporting the data. Consent was not obtained, since this study had no participants.

Data extracted from the eighteen guidelines resulted in two main themes. They are, in outline, as follows: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management) (Table ​ (Table1). 1 ). Table ​ Table1 1 shows that most guidelines mentioned the nursing management of GDM during pregnancy ( N  = 17), followed by early screening and diagnosis of GDM ( N  = 16) and postpartum nursing management of GDM ( N  = 14). Intrapartum nursing management of GDM was least mentioned by the guidelines ( N  = 7). Table ​ Table2 2 provides a summary of the main recommendations per guideline, which are further discussed below.

Themes per guideline

Main recommendations per guideline

3.1. Early screening and diagnosis of GDM

Guidelines encourage early screening of the pregnant woman for possible identification and diagnosis of GDM, which can only be achieved if pregnant women are screened during antenatal visits. Scottish Intercollegiate Guidelines Network [SIGN] ( 2017 ) mentions a programme that must be designed for all pregnant women for early detection and treatment of GDM. Once women are screened and the results of the blood glucose tests fall within levels that can be diagnosed as GDM, the woman is considered as having GDM.

The timing of screening differs in the various guidelines. Most guidelines agree that early screening must be done at 24–28 weeks of gestation (American Association of Clinical Endocrinologists & American College of Endocrinology [AACE/ACE], 2010 ; Blumer et al., 2013 ; Diabetes Australia/Royal Australian College of General Practitioners [RACGP], 2016 ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; International Federation of Gynaecology & Obstetrics [FIGO], 2015 ; United States Preventative Services Taskforce [USPSTF], 2014 ) (see Table ​ Table2). 2 ). However, some guidelines recommend this to be done as early as possible or in the first trimester (Diabetes Australia/RACGP, 2016 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Society for Endocrinology, Metabolism, & Diabetes of South Africa [SEMDSA], 2017 ; FIGO, 2015 ). This often includes women that are at risk for developing GDM and, if negative, screening is repeated at 24–28 weeks of gestation (Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; FIGO, 2015 ). The International Diabetes Federation ( 2009 ) specifically recommends determination of the women's risk of developing GDM at the first antenatal visit.

The method of screening recommended also differs. Most guidelines recommend the 2‐hr 75 g oral glucose tolerance test (OGTT) to aid with the diagnosis of GDM, while some guidelines opt for other tests, including the 50 g glucose challenge (Diabetes Australia/RACGP, 2016 ; USPSTF, 2014 ), the 2‐step screening test (Permanente, 2018 ) and the HbA1c (Queensland, 2015 ; FIGO, 2015 ). However, the AACE/ACE ( 2010 ) advises against the use of the HbA1c as a screening method to diagnose GDM, while NICE ( 2015 ) does not encourage the use of other screening tests (including fasting plasma glucose, random blood glucose, HbA1c, glucose challenge tests or urinalysis for glucose) to determine the risk of a woman developing GDM. Although the 2‐hr 75 g OGTT is recommended in most guidelines, its blood glucose values to diagnose GDM differ slightly. While some (Blumer et al., 2013 ; Queensland, 2015 ; SIGN, 2017 ; SEMDSA, 2017 ; WHO, 2013 ) recommend a fasting plasma glucose of 5.1–6.9 mM, 1‐hr value of >10.0 mM and 2‐hr value 8.5–11.0, according to NICE ( 2015 ), fasting values are <5.6 mM and 2 hr 7.8mM.

Specific aspects needing consideration during early screening and diagnosis are identified by various guidelines. For example, Blumer et al. ( 2013 ) recommend that the 75g OGTT be done after at least eight (8) hours night fast but not more than fourteen (14) hours. They further recommend that the usual intake of carbohydrates by the pregnant woman should not be reduced on the days preceding the OGTT test and the pregnant woman must be seated throughout the procedure. The International Diabetes Federation ( 2009 ) recommends that women that are at high risk for developing GDM should be offered healthy lifestyle advice during their first visit when screening is done. FIGO ( 2015 ) is the only guideline that considers low‐ and high‐resource contexts in their recommendations. FIGO ( 2015 ) recommends the use of a plasma‐calibrated hand‐held glucometer with properly stored test strips to measure plasma glucose in primary care settings, particularly in low‐resource countries (where a close‐by laboratory or facilities for proper storage and transport of blood samples to a distant laboratory may not exist). Using a plasma‐calibrated hand‐held glucometer may be more convenient and reliable than test results from a laboratory done on inadequately handled and transported blood samples.

3.2. Nursing management of GDM

Nursing management of GDM is a theme that is consistently featured in the guidelines that were included in the review. GDM management includes glycaemic control and monitoring and lifestyle modifications (diet and physical activity/exercise). Recommendations included those that should be used during pregnancy and intra‐ and postpartum.

3.2.1. During pregnancy

Glycaemic control and monitoring during pregnancy must be done, for example, once a week and thereafter every 2–3 weeks until delivery (International Diabetes Federation, 2009 ), to keep blood glucose levels within acceptable ranges for pregnancy (AACE/ACE, 2010 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Permanente, 2018 ; SIGN, 2017 ; USPSTF, 2014 ; WHO, 2013 ). This is especially so where the woman is commenced on insulin therapy (AACE/ACE, 2010 ). According to Blumer et al., ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), Diabetes Australia/RACGP ( 2016 ) and ADA ( 2018 ), acceptable ranges are fasting blood sugar <5.3 mM, 1 hr pre‐prandial <7.8 mM and 2 hr postprandial <6.7 mM. Women with GDM must be encouraged to do self‐monitoring of blood glucose (ADA, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; FIGO, 2015 ). FIGO ( 2015 ) recommends that self‐monitoring should be done at least daily (low‐resource settings) and up to 3–4 times a day (high‐resource settings).

As lifestyle moderations are the first line of treatment (ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; Permanente, 2018 ; FIGO, 2015 ; USPSTF, 2014 ), pharmacological treatment should only be provided if lifestyle moderations are inadequate to keep blood glucose targets within acceptable levels after 1–2 weeks (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ; Permanente, 2018 ). The preferred pharmacological treatment is insulin (AACE/ACE, 2010 ; ADA, 2018 ; International Diabetes Federation, 2009 ; Permanente, 2018 ; SEMDSA, 2017 ; FIGO, 2015 ), while metformin and glyburide can be used as effective alternatives (AACE/ACE, 2010 ; SIGN, 2017 ; FIGO, 2015 ) if not contraindicated or unacceptable for the woman (NICE, 2015 ). However, metformin should be prescribed/continued under specialist supervision (SEMDSA, 2017 ) but is not approved in Australia (Diabetes Australia/RACGP, 2016 ).

Health education should be provided on GDM and glycaemic control, especially on recognizing the signs of hypoglycaemia and treatment of those signs. Women should be made aware of the implications of GDM for the woman and the foetus and of steps to achieve management of GDM. Family members should be taught how to use the glucometer, as well as the management principles and importance of long‐term follow‐up (Diabetes Coalition of California, 2012 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ).

In terms of diet, it is recommended that pregnant women with GDM receive nutrition counselling (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; SIGN, 2017 ; USPSTF, 2014 ), preferably from a dietician familiar with GDM (ADA, 2018 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must make it a point to involve all the necessary healthcare professionals (Queensland, 2015 ) and preferably those with expertise in GDM (International Diabetes Federation, 2009 ; SEMDSA, 2017 ). A healthy diet should be high in vegetables and protein (Permanente, 2018 ) and low in GI (International Diabetes Federation, 2009 ; NICE, 2015 ). The recommended diet should consist of a minimum intake of 1,600–1,800 kcal/day and carbohydrate intake limited to 35%–45% of total calories (Blumer et al., 2013 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Weight gain in the pregnant woman with GDM must also be checked according to her BMI (Ministry of Health Malaysia, 2017 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must encourage the pregnant woman with GDM to stick to the diet or nutrition planned with the dietician and also to monitor her blood glucose levels as scheduled.

In terms of exercise, moderate exercise is recommended, such as a 30 min’ (at least 10‐min periods) (Queensland, 2015 ) walk after meals (Blumer et al., 2013 ; NICE, 2015 ) or 1 hr a day (Permanente, 2018 ). Education should also be given about armchair exercises (American College of Obstetrics & Gynaecology [ACOG], 2018a ).

To provide the best nursing management for GDM, a customized plan of care, especially for women at high risk, should be developed (NICE, 2015 ) that is individualized and culturally sensitive (International Diabetes Federation, 2009 ). This care plan could also include checks of blood pressure and dipstick urine protein every 1–2 weeks (resourced settings) or monthly (low‐resource settings; FIGO, 2015 ; International Diabetes Federation, 2009 ; Queensland, 2015 ) as well as an ultrasound between 30–32 weeks of gestation to estimate foetal weight (Queensland, 2015 ) or every four weeks from 28–36 weeks of gestation (Ministry of Health Malaysia, 2017 ).

3.2.2. Intrapartum

Although guidelines differ regarding the delivery time and mode, most agree with an elective induction of 38–40 weeks to reduce the risk for stillbirths (Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ). A caesarean section around 40 weeks plus 6 days is recommended, but this should be done before that time for those with comorbidities or maternal or foetal complications (NICE, 2015 ; Queensland, 2015 ). The primary objective of the intrapartum nursing management of GDM is to maintain maternal euglycemia to prevent neonatal hypoglycaemia, which is caused by the hyperinsulinemia in the baby due to hyperglycaemia in the mother. Close monitoring of women with GDM during labour and delivery should therefore be done (ACOG, 2018a ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Queensland, 2015 ; SEMDSA, 2017 ) at least once an hour (ACOG, 2018a ) or, according to NICE ( 2015 ), every thirty (30) minutes till delivery. Maternal blood glucose levels must be maintained between 4.0 mM–7.0 mM (Diabetes Canada, 2018 ; Diabetes Coalition of California, 2012 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ). To achieve these blood glucose levels, the woman should be given enough glucose during labour to help her to cope with the high level of energy demands for labour and for delivery so as to prevent the woman from having hypoglycaemia (Diabetes Canada, 2018 ; NICE, 2015 ; SEMDSA, 2017 ). NICE ( 2015 ) recommends that, if the capillary plasma glucose is above 7 mM, intravenous dextrose and insulin infusion must be given during labour and delivery, although the guideline does not specify how much.

3.2.3. Postpartum

Postpartum nursing management of GDM constitutes a critical challenge when treating women with GDM. Various guidelines selected for synthesis focus on postpartum management. It is recommended blood glucose‐lowering medication should be lowered immediately after delivery (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Although guidelines recommend postpartum blood glucose screening for early detection of diabetes mellitus, impaired glucose tolerance or impaired fasting glucose (ACOG, 2018a ), they differ on when this should be done. Most guidelines recommend 6 weeks when the woman comes for postnatal follow‐up (Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ) or between 6–12/13 weeks (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Queensland, 2015 ). Blumer et al. ( 2013 ) is the only guideline that recommends, besides the 6‐ to 12‐week screening, that blood glucose monitoring should also be done 24–72 hr after delivery. This is to rule out high blood glucose levels just after delivery.

Most guidelines prefer a follow‐up of screening varying between 1 year (NICE, 2015 ; Permanente, 2018 ; SEMDSA, 2017 ) and 3 years (ACOG, 2018a ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ). According to ADA ( 2018 ), risk factors should be considered when deciding the timeframe for follow‐up screening. According to Diabetes Canada ( 2018 ), emails and phone calls can be used to remind women for their follow‐up screening. The method of screening recommended also differs, although a 2‐hr 75 g OCTT seems to be the most frequently used, as recommended by nine ( N  = 9) guidelines. ACOG ( 2018a ) recommend that women with impaired glucose tolerance or with impaired fasting glucose must be referred as early as practicable for prevention therapy.

In addition, women with a history of GDM must be counselled on preventative lifestyle modifications to reduce the risk of type 2 diabetes (ACOG, 2018a ; ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; SIGN, 2017 ; FIGO, 2015 ) specifically regarding their diet, weight control and exercise requirements (SIGN, 2017 ). Referral to a dietician can be done (Diabetes Canada, 2018 ). According to NICE ( 2015 ) women should be educated specifically with regard to the signs and symptoms of hyperglycaemia. Education on the risk of developing GDM in subsequent pregnancies should be included as well as the benefits of optimizing postpartum and inter‐pregnancy weight (Queensland, 2015 ).

Various guidelines (American College of Obstetrics and Gynaecology [ACOG], 2018b ; International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ) recommend that women with GDM should be encouraged to breastfeed their newborns immediately after delivery, thereby helping to prevent hypoglycaemia in the newborn. It is recommended that continuous breastfeeding should be done for at least 3–4 months postpartum (Diabetes Canada, 2018 ; SIGN, 2017 ) or longer (Ministry of Health Malaysia, 2017 ) as this helps to reduce childhood obesity, glucose intolerance and diabetes later in life. However, caution should be advised regarding maternal hypoglycaemia if breastfeeding (SEMDSA, 2017 ) and skilled lactation support is therefore recommended (Queensland, 2015 ; FIGO, 2015 ). Finally, extra attention is also required to detect early signs of genitourinary, uterine and surgical site infections (in the case of an episiotomy and caesarean delivery; FIGO, 2015 ).

4. DISCUSSION

4.1. comprehensiveness of the guidelines.

Several guidelines from a variety of healthcare organizations, associations or health departments were found that include aspects relevant to the nursing management of GDM. Not all guidelines focus on all aspects (namely glycaemic control, monitoring and treatment and lifestyle moderations, including diet and physical activity/exercise) and phases of the nursing management of GDM (during pregnancy, intrapartum as well as postpartum) as only 8 ( N  = 8) of the guidelines reviewed include all phases of the management of GDM (ACOG, 2018a ; Diabetes Canada, 2018 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; SEMDSA, 2017 ; FIGO, 2015 ). There were guidelines which cover some of the phases or the nursing management of GDM in general. For example, the SIGN ( 2017 ) guideline does not focus on the nursing management of GDM during labour and delivery but does provide general recommendations on what should be done during pregnancy and postdelivery. NGC ( 2013 ) also does not discuss intrapartum nursing management of GDM but gives recommendations on the testing and diagnosis of pregnant women.

Guidelines also differed in the level of descriptiveness employed. Guidelines that were generally more descriptive with their recommendations included those from Blumer et al. ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), NICE ( 2015 ), SEMDSA ( 2017 ) and Diabetes Canada ( 2018 ). Additionally, variances in best practices regarding screening and diagnosis as well as the nursing management of GDM were observed. It is thus recommended that existing guidelines should be scrutinized in respect of their level of descriptiveness, together with the latest best evidence and of the quality of the evidence used to develop the recommendations in the guidelines.

4.2. Quality of evidence

Not all guidelines reviewed included the level or grades of evidence used for each recommendation and various levels or grades were used. This is required to select a recommendation for implementation that fits the context best and will yield the best outcomes for both mother and child. For example, some of the guidelines included did not use a grading system for evidence or references when citing the recommendations (Diabetes Coalition of California, 2012 ; NGC, 2013 ; NICE, 2015 ; Permanente, 2018 ), while others did not use a grading system for the evidence included, but did use a variety of evidence when citing the recommendations (International Diabetes Federation, 2009 ; Queensland, 2015 ; SEMDSA, 2017 ; USPSTF, 2014 ). Other guidelines included grading systems for the evidence of which an A–D grading system was the most commonly used which was adapted from the American Diabetes Association ( 2018 ). Grade A refers to clear evidence from well‐conducted, generalizable RCTs, grade B includes supportive evidence from well‐conducted cohort studies, while grade C and grade D refers to supportive evidence from poorly controlled or uncontrolled studies as well as expert consensus or clinical experience, respectively. Some guidelines included a variety of evidence supporting the recommendations (grade A–D) (AACE/ACE, 2010 ; Diabetes Australia/RACG, 2016 ; WHO, 2013 ), with two guidelines mainly using grade A and B evidence (ADA, 2018 ; Blumer et al., 2013 ), another two guidelines mainly using grade B and C evidence (ACOG, 2018a ; SIGN, 2017 ) and a fifth guideline mainly using grade C and D evidence to support the recommendations (Diabetes Canada, 2018 ). FIGO ( 2015 ) used the 2019 grading system, including mostly moderate quality evidence (+++) and very low‐quality evidence (+), while the guideline by the Ministry of Health Malesia ( 2017 ) used a grading system from the United States/Canadian Preventive Services Task Force ( 2001 ) where level I (at least one properly conducted RCT) and level III (expert opinions) were mostly used to support the recommendations. Therefore, in this review it was impossible to make a valid statement for each recommendation that was based on evidence grades/levels. A systematic review is therefore recommended which extends beyond the AGREEII tool that was undertaken in this study to summarize the overall strength of evidence of each recommendation, such as the screening, diagnosis and nursing management of GDM during pregnancy, intrapartum and postpartum care and the overall quality of each particular guideline. Additionally, only two guidelines considered the input from the woman in the management of GDM (International Diabetes Federation, 2009 ; NICE, 2015 ). Any recommendation or care plan developed should be discussed with the woman diagnosed with GDM and her permission should be obtained to implement the recommended care practices.

4.3. Resources/Barriers

Only one guideline considered the context in terms of low/high resources (FIGO, 2015 ). The reality is that most low‐resource countries are unable to implement some of the recommendations, such as, for example, universal 75‐g OGTT or self‐monitoring every day (FIGO, 2015 ). The possible barriers to the implementation of the recommendations caused by a lack of resources were not addressed in most of the guidelines. For example, several barriers to maternal health related to GDM have been identified. These include the lack of trained healthcare professionals; high staff turnover; lack of standard protocols and diagnostic tools, consumables and equipment; inadequate levels of financing of health services and treatment; and lack of or poor referral systems, feedback mechanisms and follow‐up systems.

Further barriers relate to distance to health facility; perceptions of female body size and weight gain/loss related to pregnancy; practices related to a pregnant women's diet; societal negligence of women's health; lack of decision‐making power among women regarding their own health; the role of women in society and expectations that the pregnant woman move to her maternal home for delivery; and lack of adherence to recommended postpartum screening and low continued lifestyle modifications ( 2017 , & Stray‐Pederson, 2 2017 ; Nielsen, Courten, & Kapur, 2012 ; Nielsen, Kapur, Damm, Courten, & Bygbjerg, 2014 ). Additionally, a recent delivery experience, baby's health issues, personal and family adjustment to the new baby, a negative experience of medical care and services and concerns about postpartum and future health (as in, for example, fear of being informed that they have diabetes) were specifically related to the barriers to postpartum follow‐up care (Bennett et al., 2011 ).

The barriers cited should be considered when implementing the recommendations offered by the guidelines. Further, an integration of health services should be offered as well as communication between the different healthcare professionals is required. Integration of health services can be done when postpartum follow‐up of a mother can be combined with the child's vaccination and routine paediatric care.

4.4. Recommendations

Kaiser and Razurel ( 2013 ) examined the determinants of health behaviours during the postpartum period in GDM patients. They found that the women's physical activity and diet do not often meet the recommended health‐promoting actions. Risk perception, health beliefs, social support and self‐efficacy were the main factors that were identified as having an impact on the adoption of health behaviours. GDM clients are encouraged to engage in lifestyle modifications or healthy behaviours during the postpartum period. It is important, therefore, to identify the factors that may influence these clients to continue with healthy behaviours (Kaiser & Razurel, 2013 ).

Education of the woman diagnosed with GDM on the screening, and management (including preventative lifestyles) is imperative and will assist in addressing some of the above‐mentioned barriers. Education, as mentioned by most guidelines, should preferably be given by nurses and/or midwives to all pregnant women that are at risk or diagnosed with GDM. Furthermore, the healthcare professionals will need to be trained on pregnancy‐specific lifestyle modifications, treatment and screening for complications (International Diabetes Federation, 2009 ). Finally, it is particularly important for low‐resource settings that availability of trained healthcare professions, self‐monitoring equipment and insulin supply, and laboratory resources for clinical monitoring of glucose control and assessment of renal damage (International Diabetes Federation, 2009 ) should be prioritized in national budgets for health care.

No contextualized guideline on the nursing management of GDM is available for contexts where women with GDM deal with specific challenges such as factors related to the health system, or socioeconomic and cultural conditions that may impose barriers to the implementation of the best practice. It is therefore recommended that, prior to the implementation, a context analysis should be conducted to identify specific barriers to its implementation. This was confirmed by FIGO ( 2015 ) who mentioned that local decisions will be required to decide whether a selective or universal approach will be used for each individual patient. Additionally, further research of the barriers is required to develop contextualized guidelines considering the challenges some women and some health systems may have in accessing or providing adequate maternal health care. The developed contextualized guidelines could then be piloted. Piloting will be done to determine how the guidelines could have a positive effect on the nursing management of GDM while considering the input from the pregnant women as well as possible barriers or resource constraints towards its implementation.

4.5. Limitations

Some limitations of the study were observed. A comprehensive search of a variety of databases available to the authors was used with the assistance of an experienced librarian. However, limited databases were available, and some organizations/ developers of guidelines were not subscribed to so some guidelines may have been missed. Although the reviewer possessed wide experience in appraising the guidelines, more independent reviewers could have reduced possible bias in the selection process of the guidelines.

5. CONCLUSION

Data extracted from the eighteen guidelines resulted in two main themes: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management). Although a variety of guidelines on the management of GDM were found, guidelines were not always comprehensive, sometimes differed in recommended practices and did not consider barriers to the implementation of the recommendations.

6. RELEVANCE TO CLINICAL PRACTICE

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of GDM. The findings can be used by nurse–midwives when conducting maternal and postpartum follow‐up care for women at risk or diagnosed with GDM. However, critically scrutinizing the guidelines in terms of the best evidence used in their development and feasibility of the implementation of the recommendations for its context is required. Additionally, education of women with GDM could assist in addressing any barriers such as certain harmful health beliefs, a lack of social support and self‐efficacy to provide the best maternal health care. Further research is recommended to determine the strength of evidence of each recommendation and the development and implementation of a contextual guideline on the management of GDM that considers possible barriers and resource constraints towards its implementation.

CONFLICT OF INTEREST

The authors have no conflicts of interest to disclose.

ACKNOWLEDGEMENTS

The authors would like to thank Vicki Igglesden for editing the manuscript.

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Gestational Diabetes Mellitus Case Study

Gestational diabetes mellitus (GDM), also known as type III diabetes mellitus, is one of the most common types of diabetes mellitus and considered the most common complication of pregnancy. This health problem is like pregnancy-induced hypertension (PIH) that develops during pregnancy and disappears after the delivery of the fetus, or as the maternal body returns to its pre-pregnant state. Gestational diabetes mellitus may or may not with co-existing maternal diabetes. It heightens the level of diabetes (if with previous diabetes) by a notch in response to the rise in fetal carbohydrate demand. 40% of pregnant mothers who develop GDM will eventually develop non-insulin-dependent diabetes mellitus (NIDDM or type II DM) within 5 years.

FACTS ABOUT INSULIN

Knowing the facts about insulin facilitates the understanding of gestational diabetes mellitus. Or any form of diabetes for that matter. This creates/develops ideas on how and why such health problems occur.

• The insulin is a normal body hormone that is produced by the beta cells of the Islets of Langerhans in the pancreas.
• The release of insulin is regulated by negative feedback in response to high glucose levels. The high glucose level may come from excessive glucagon action or high carbohydrate intake.
• The insulin secretion of the pancreas and its action on the liver makes it maintain a normal value of 80-120 mg/dL.
• Carbohydrates— utilization of glucose by the cells
• Proteins— conversion of amino acids to replace muscle tissues
• Fats— conversion of excess glucose to fatty acids and store them to adipose tissues
• Endothelial and nerve cells are the only cells/tissues that can use glucose even without insulin.
• Low insulin level causes the rise in plasma glucose concentration and glycosuria.
• Diabetes mellitus develops as the body secretes a low amount or as body cells reject its utilization.

ANATOMY AND PHYSIOLOGY

A normal body uses insulin as a channel for glucose to enter the cells for utilization. This process is also applicable to the fetus (during pregnancy) for growth and development. As the fetus grows, the maternal body executes an automatic response by doubling the level of glucose level through lowering insulin secretion and with the aid of some gestational hormones that antagonize the effects of insulin, a process known as a protective mechanism. Along with this, this mechanism causes the rise of placental lactogen, estrogen, and progesterone to cause the following effects: 1. antagonizes the effects of insulin, 2. prolong the elevation of stress hormones (cortisol, epinephrine, and glucagon), and 3. degradation of insulin by the placenta. The total effect of these mechanisms raises the maternal glucose level for fetal usage. Hyperglycemia normally occurs with a protective mechanism that predisposes a pregnant mother in the triggering of her pre-diabetic state or heightens an existing diabetes mellitus.

The effects of pregnancy on diabetes mellitus are summarized as:

• The first trimester— glucose level is relatively stable or may decrease
• The second trimester— there is a rapid increase in glucose level
• The third trimester— there is a rapid decrease in glucose level and return to its pre-pregnant state.

CAUSES AND INCIDENCE

The primary cause is almost the same as the other types of diabetes . The inability of the body to produce or synthesize a sufficient amount of insulin in response to glucose level (as in type I DM), or the body’s rejection of insulin (as in type II DM) shows a significant relationship on the development of any form of diabetes. The existence of either of these problems, plus, the interaction of the protective mechanisms in pregnancy doubles the occurrence of GDM.

The incidence of gestational diabetes mellitus is almost 3% in all pregnancies and 2% in all women with diabetes before pregnancy.

GDM causes a high incidence of fetal morbidity and unwanted complications such as polyhydramnios and macrosomia in fetus.

RISK FACTORS

For some clear and unclear pathological reasons, the following are considered the risk factors in the occurrence/development of GDM:

• Family history of DM
• Age of 45 or older (when got pregnant)
• Previous delivery of a baby weighing 9 lbs or more
• History of any autoimmune disease
• Belonging to/with ethnic background from African Americans, Latino, and Native Americans
• History of previous GDM
• With any level of hypertension
• With elevated high-density lipoprotein

SIGNS AND SYMPTOMS

The clinical manifestations of gestational diabetes mellitus coincide with the signs and symptoms of the other types of diabetes mellitus. These are popularly known as the “3 P’s” or polydipsia (excessive thirst), polyphagia (excessive hunger), and polyuria (frequent urination). Aside from these manifestations, there are also other signs and symptoms that are general manifestations and pregnancy-specific manifestations.

PATHOPHYSIOLOGY

COMPLICATIONS

The chronic effects or the uncontrolled glucose level during pregnancy would lead to the development of the following complications:

• Urinary tract infection (UTI)
• Infertility
• Preterm labor and delivery
• Pregnancy-induced hypertension (PIH)- pre-eclampsia and eclampsia
• Congenital anomalies
• Spontaneous abortion

Also, a woman who developed or experienced gestational diabetes mellitus is expected to have type II diabetes mellitus within 5 years for the rest of her life.

The prognosis or the chance of the mother and/or fetus for survival depends on the maternal ability to tolerate and adjust to high glucose levels, medical management, and obedience to the treatment regimen. This means that the more cooperative and responsive the mother to the treatment regimen is, the better chances of both maternal and fetal well being are.

The performance of the following diagnostic tests aims to determine the level of diabetes present in the pregnant mother and determine its extent of damage or impending effects. This serves as the basis for the plan of care for the mother and the fetus.

• Blood glucose monitoring— this can either be done through fasting blood sugar (FBS) or randomly. This reveals the glucose level and indicates the plan of care needed.
• Glucose tolerance test (GTT)— to evaluate the response of insulin to loading glucose.
• Glycated hemoglobin (Glycohemoglobin)— measures glycemic control by evaluating the attachment of glucose to freely permeable erythrocytes during their whole life cycle.
• C-peptide Assay (connecting peptide assay)— useful when the presence of insulin antibodies interferes with direct insulin assay.
• Fructosamine assay— is much more useful than glycosylated hemoglobin tests in cases of hemoglobin variants.
• Urine glucose and ketone monitoring— may be performed in cases where blood glucose monitoring is not available, but, is not as accurate as of the former.
• Amniocentesis
• Non-stress test

NURSING DIAGNOSES

• Altered nutrition, more or less than body requirements related to weight gain.
• High-risk pregnancy: high risk for infection, ketosis, fetal demise, cephalopelvic disproportion, polyhydramnios, congenital anomalies, preterm labor.
• Knowledge deficit related to disease and insulin use and interaction.

The overall goal of management for gestational diabetes mellitus is the control of the maternal glucose level and keep it on a normal or near-normal level to prevent the development of complications that might compromise both the mother and the fetus. The most significant of these managements is the use of insulin. This is the most potent, yet, requires accuracy and monitoring of its unwanted effect (hypoglycemia) that brings immediate danger to both the mother and the fetus. Proper timing, dosage, and knowledge on counteractions of its over-reaction are vital concepts to be incorporated in health education.

Along with this, health promotion and disease prevention activities like diet, exercise, and fetal monitoring are of great importance.

NURSING MANAGEMENTS

History taking on:

• First presentation of the manifestations of diabetes (3 P’s)
• First diagnosis of DM
• Family members with DM

Review of systems:

• Weight gain, increasing fatigue/weakness/tiredness
• Skin lesions, infections, hydration, signs of poor wound healing
• Changes in vision—floaters, halos, blurred vision, dry/burning eyes, cataract, glaucoma
• Gingivitis, periodontal disease
• Orthostatic hypotension, cold extremities, weak pedal pulses
• Diarrhea, constipation, early satiety, bloating, flatulence, hunger and thirst
• Frequent urination, nocturia, vaginal discharge
• Numbness and tingling of the extremities, decrease pain and temperature sensation

Intervention

1. Nutrition

• Assess the timing and content of meals
• Instruct on importance of a well-balanced diet
• Explain the importance of exercise
• Plan for a weight reduction course

2. Insulin use

• Encourage verbalization of feelings
• Demonstrate and explain insulin therapy
• Allow the client to do self-administration
• Review mastery of the whole process

3.   Injury from hypoglycemia

• Monitor maternal blood glucose level
• Instruct on insulin-activity-diet interaction
• Teach on the signs and symptoms of hypoglycemia
• Teach/present list of things/foods that need to be available at all times (in cases of hypoglycaemic attacks)
• Have an identification band indicating the health condition (DM) for fainting instances

4.  Activity tolerance

• Plan for regular exercise
• Increase carbohydrate intake before exercise
• Instruct to avoid exercise if blood glucose level exceeds 250 mg/dL and urine ketones are present
• Advise to use abdomen for insulin injection if arms and legs are used for exercise

5.  Skin integrity

• Avoid alcohol use, instead, lotion
• Teach on proper foot care
• Advice to stop smoking and alcohol use

6. Fetal well-being

• Continuous monitoring of fetal activities and fetal heart tone
• Monitor fetal activities during maternal activities
• Monitor early signs of labor
• Advice to report of any discharge coming from the vagina
• Monitor daily weight and advice to report on rapid weight gain

7. Educative

• Teach on lifestyle modifications
• Advice to see  psychologists with other family members for therapy on the possibilities of fetal abnormalities
• Advice to call emergency response team in cases of emergency
• Advise to religiously follow health instructions  
• Bodyweight is within the normal range for the age of gestation.
• Demonstrates proper technique in self-administration of insulin
• No episodes of hypoglycemia as claimed by the client
• No skin problems/lesions
• Verbalizes readiness on the possible fetal defects.
•   Stable fetal heart rate

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Clinical pearls, case study: complicated gestational diabetes results in emergency delivery.

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Ginny Lewis; Case Study: Complicated Gestational Diabetes Results in Emergency Delivery. Clin Diabetes 1 January 2001; 19 (1): 25–26. https://doi.org/10.2337/diaclin.19.1.25

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A.R. is a 33-year-old caucasian woman initially diagnosed with diabetes during a recent pregnancy. The routine glucose challenge test performed between 28 and 29 weeks gestation was elevated at 662 mg/dl. A random glucose completed 1–2 days later was also elevated at 500 mg/dl. A follow-up HbA 1c was elevated at 11.6%. Additional symptoms included a 23-lb weight loss over the past 3–4 weeks with ongoing “flu-like” symptoms, including fatigue, nausea, polyuria, and polydypsia.

A.R. had contacted her obstetrician’s office when her symptoms first appeared and was told to contact her primary care provider for the “flu” symptoms. She had called a nurse triage line several times over the previous 2–3 weeks with ongoing symptoms and was told to rest and take fluids.

She presented to her primary care provider 3 days after the HbA 1c was drawn for ongoing evaluation of hyperglycemia. At that time, she was symptomatic for diabetic ketoacidosis. She was hospitalized and started on an insulin drip.

A.R.’s hospitalization was further complicated with gram-negative sepsis, adult respiratory distress syndrome, and Crohn’s disease with a new rectovaginal fistula. She was intubated as her respiratory status continued to decline and was transferred to a tertiary medical center for ongoing management. She required an emergency Caesarian section at 30 1/7 weeks gestation due to increased fetal distress.

A.R. had no family history of diabetes with the exception of one sister who had been diagnosed with gestational diabetes. Her medical history was significant for Crohn’s disease diagnosed in 1998 with no reoccurrence until this hospitalization. Her pre-pregnancy weight was 114–120 lb. She had gained 25 lb during her pregnancy and lost 23 lb just before diagnosis.

A.R.’s blood glucose levels improved postpartum, and the insulin drip was gradually discontinued. She was discharged on no medications.

At her 2-week postpartum visit, home blood glucose monitoring indicated that values were ranging from 72 to 328 mg/dl, with the majority of values in the 200–300 mg/dl range. A repeat HbA 1c was 8.7%. She was restarted on insulin.

1.  What is the differential diagnosis of gestational diabetes versus type 1 diabetes?

2.  At what point during pregnancy should insulin therapy be instituted for blood glucose control?

3.  How can communication systems be changed to provide for integration of information between multiple providers?

Gestational diabetes is defined as “any degree of carbohydrate intolerance with onset first recognized during pregnancy. This definition applies whether insulin ... is used for treatment and whether or not the condition persists after pregnancy.” 1 Risk assessment is done early in the pregnancy, with average-risk women being tested at 24–28 weeks’ gestation and low-risk women requiring no additional testing. 1 , 2 A.R. met the criteria for average risk based on age and a first-degree family member with a history of gestational diabetes.

Screening criteria for diagnosing diabetes include 1 ) symptoms of diabetes plus casual plasma glucose >200 mg/dl (11.1 mmol/l), or   2 ) fasting plasma glucose >126 mg/dl (7.0 mmol/l), or   3 ) 2-h plasma glucose >200 mg/dl (11.1 mmol/l) during an oral glucose tolerance test (OGTT). 3 For women who do not meet the first two criteria, a glucose challenge test (GCT) measuring a 1-h plasma glucose following a 50-g oral glucose load is acceptable. For those women who fail the initial screen, practitioners can then proceed with the OGTT. 1  

In A.R.’s case, she most likely would have met the first criterion if a casual blood glucose had been measured. She had classic symptoms with weight loss, fatigue, polyuria, and polydypsia. Her 1-h plasma glucose following the glucose challenge was >600 mg/dl, which suggests that her casual glucose would also have been quite high.

Medical nutrition therapy (MNT) is certainly a major part of diabetes management. However, with this degree of hyperglycemia, MNT would not be adequate as monotherapy. Treatment for gestational diabetes includes the use of insulin if fasting blood glucose levels are >95 mg/dl (5.3 mmol/l) or 2-h postprandial values are >120 mg/dl (6.7 mmol/l). 1  

Several days passed from the time of A.R.’s initial elevated blood glucose value and the initiation of insulin therapy. While HbA 1c values cannot be used for diagnostic purposes, in this case they further confirmed the significant degree of hyperglycemia.

Plasma blood glucose values initially improved in the immediate postpartum period. A.R. was sent home without medications but instructed to continue home glucose monitoring.

At her 2-week postpartum visit, whole blood glucose values were again indicating progressive hyperglycemia, and insulin was restarted. A.R.’s postpartum weight was 104 lb—well below her usual pre-pregnancy weight of 114–120 lb. Based on her ethnic background, weight loss, abrupt presentation with classic diabetes symptoms, and limited family history, she was reclassified as having type 1 diabetes.

In immune-mediated, or type 1, diabetes, b-cell destruction can be variable, with a slower destruction sometimes seen in adults. 3 Presentation of type 1 diabetes can also vary with modest fasting hyperglycemia that can quickly change to severe hyperglycemia and/or ketoacidosis in the presence of infection or other stress. 3 A.R. may have had mild hyperglycemia pre-pregnancy that increased in severity as the pregnancy progressed.

The final issue is communication among multiple health care providers. A.R. was part of a system that uses primary care providers, specialists, and triage nurses. She accessed all of these providers as instructed. However, the information did not seem to be clearly communicated among these different types of providers. A.R. called triage nurses several times with her concerns of increased fatigue, nausea, and weight loss. The specialist performed her glucose challenge with follow-up through the primary care office. It seems that if all of these providers had the full information about this case, the diagnosis could have been made more easily, and insulin could have been initiated more quickly.

1.  Hyperglycemia diagnosed during pregnancy is considered to be gestational diabetes until it is reclassified in the postpartum period. Immune-mediated diabetes can cause mild hyperglycemia that is intensified with the increased counterregulatory hormone response during pregnancy.

2.  Insulin therapy needs to be instituted quickly for cases in which MNT alone is inadequate.

3.  The GCT is an appropriate screening test for an average-risk woman with no symptoms of diabetes. In the face of classic symptoms of diabetes, a casual plasma glucose test can eliminate the need for the glucose challenge.

4.  As part of the health care industry, we need to continue to work on information systems to track patient data and share data among multiple providers. Patients can become lost in an ever-expanding system that relies on “protocols” and does not always allow for individual differences or for cases with unusual presentation.

Ginny Lewis, ARNP, FNP, CDE, is a nurse practitioner at the Diabetes Care Center of the University of Washington School of Medicine in Seattle.

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

Interactive case study: Gestational diabetes

• 10 May 2023

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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 .

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Gestational Diabetes Nursing Diagnosis and Nursing Care Plans

Last updated on May 16th, 2022 at 12:47 pm

Gestational Diabetes Nursing Care Plans Diagnosis and Interventions

Gestational Diabetes NCLEX Review and Nursing Care Plans

       Gestational Diabetes is a pregnancy-related type of diabetes. It causes elevated blood sugar level which can be detrimental to both the mother and baby’s health during pregnancy.

Like any other complications of pregnancy, gestational diabetes is seemingly alarming but risks may be reduced by controlling the blood sugar level of the mother.

This can be achieved by modifying diet and appropriate exercise.

Medications are likely needed if these interventions are not enough. It is essential to keep blood sugar at normal level to ensure healthy pregnancy and safe delivery.

Gestational diabetes usually disappears after giving birth. However, women who have had gestational diabetes are at risk for recurrence in next pregnancies and even developing Type 2 diabetes in the near future.

A regular blood sugar level check is necessary to note any changes.

Signs and Symptoms of Gestational Diabetes

• Polydipsia – increased thirst
• Polyuria – increased urinary frequency
• mouth dryness
• fatigue or tiredness

The mother can be asymptomatic and the condition can only be diagnosed when she goes to her prenatal visits.

Causes of Gestational Diabetes

The exact cause of gestations diabetes is still unknown.

However, the risk factors that contribute to its development include: being overweight or obese , previous gestational diabetes or prediabetes, a lack of physical activity, diabetes in an immediate family member,  polycystic ovary syndrome (PCOS), and previously delivering a baby weighing more than 9 pounds (4.1 kilograms).

In addition to these, women who are Black, Hispanic, American Indian and Asian American have a higher risk of developing gestational diabetes.

Complications of Gestational Diabetes

Failure to manage gestational diabetes may cause elevation in blood sugar levels which can greatly affect the mother and her baby.

It may also increase the likelihood of delivering thru Cesarean section .

The fetus may be at risk for having the following conditions:

• Fetal macrosomia. This term used for excessive birth weight, typically weighs 9 pounds or more which makes them at risk for birth injuries. It also increases the need for surgical delivery
• Early preterm birth. High blood sugar level may precipitate early labor and delivery prior to the expected delivery date
• Serious breathing disorders such as newborn respiratory distress syndrome (NRDS) which are common in preterm newborns
• Hypoglycemia . Low blood sugar after birth and risk for having type 2 diabetes and obesity later in life
• Stillbirth or fetal death before or shortly after delivery

The mother may be at risk for having the following conditions:

• Hypertension . Elevated blood pressure can lead to a serious complication such as preeclampsia that may put the mother and the baby’s life at risk.
• Delivery via C-Section. Macrosomia can cause the baby to become wedged in the birth canal causing difficulty in vaginal delivery.
• Diabetes. It can be either developed on the next pregnancy or as the mother gets older.

Diagnosis of Gestational Diabetes

• Screening tests – usually done during the second trimester which is between 24- and 28-weeks of pregnancy and during the prenatal visit for those who are at high risk.
• Initial glucose challenge test- a blood sugar below 140 mg/dL (7.8 mmol/L) can be considered normal
• Follow-up glucose tolerance testing

Treatment of Gestational Diabetes

The following may help in prevention and treatment of gestational diabetes:

• Blood sugar monitoring. Gestational diabetes can be treated through lifestyle modification. Blood sugar monitoring (one in the morning and after meals) also helps in managing blood sugar levels. An individual’s lifestyle plays an important role in maintaining their blood sugar at a normal level. The mother’s food choices and daily activities can improve or negatively affect her blood sugar. It’s important to set a pregnancy weight gain goal with the dietitian.
• Proper Nutrition. It’s important to get the daily nutrition by consuming foods that are high in nutrients such as fruits, vegetables, whole grains and lean protein. Foods that are high in fat and highly refined sugars should be avoided. A meal plan based on one’s preference, food habits and blood sugar can be of great help.
• Regular Exercise. Exercise not only relieves pregnancy discomfort but also helps a lot in lowering blood sugar. Everyday activities such as walking, doing household chores and gardening are also beneficial.
• Insulin administration. If the lifestyle modifications are inadequate then insulin injections may be incorporated in the management. Close monitoring of the baby’s condition thru ultrasounds and other diagnostics will be done throughout the pregnancy.

Nursing Care Plans for Gestational Diabetes

Diabetes is a medical condition that involves excessive glucose (sugar) levels in the blood due to the little or no production of the hormone insulin, or the presence of insulin resistance.

Despite not having a cure, diabetes can be controlled by effective medical and nursing management, as well as the patient’s strict adherence to prescribed medication, lifestyle changes, and blood sugar monitoring.

The following nursing care plans can be used to assess, plan, manage, and monitor the symptoms and effects of diabetes to a patient.

Gestational Diabetes Nursing Care Plan 1

Nursing Diagnosis: Deficient Knowledge related to new diagnosis of gestational diabetes as evidenced by patient’s verbalization of “I want to know more about my new diagnosis and care”

Desired Outcome: At the end of the health teaching session, the patient will be able to demonstrate sufficient knowledge of gestational diabetes and its management.

Gestational Diabetes Nursing Care Plan 2

Nursing Diagnosis: Fatigue related to decreased metabolic energy production as evidenced by overwhelming lack of energy, verbalization of tiredness, generalized weakness, blood sugar level of  210 mg/dL, and shortness of breath upon exertion

Desired Outcome : The patient will demonstration active participation in necessary and desired activities and demonstrate increase in activity levels.

Gestational Diabetes Nursing Care Plan 3

Risk for Imbalanced Nutrition: Less than Body Requirements

Nursing Diagnosis: Risk for Imbalanced Nutrition: Less than Body Requirements related to lack of ability to make use of nutrients appropriately secondary to gestational diabetes.

Desired Outcomes:

• The patient will express an understanding of the treatment management process and the necessity of regular self-assessment.
• The patient will attain the required fasting blood sugar levels, between 60 to 100 mg/dl, and no higher than 140 mg/dl after meals.
• The patient will increase body weight by at least 24 to 30 pounds prenatally or according to the recommended pre-pregnancy weight.
• The patient will not develop diabetic ketoacidosis and signs and symptoms such as weakness, fruity-scented breath, excessive thirst, frequent urination, confusion , and complications.

Gestational Diabetes Nursing Care Plan 4

Risk for Maternal Injury

Nursing Diagnosis: Risk for Maternal Injury related to changes in diabetic control secondary to gestational diabetes.

• The patient will maintain a normal blood sugar level.
• The patient will be free of signs and symptoms of maternal injury related to gestational diabetes.

Gestational Diabetes Nursing Care Plan 5

Risk for Fetal Injury

Nursing Diagnosis: Risk for Fetal Injury related to elevated maternal serum blood glucose levels secondary to gestational diabetes.

• The fetus will remain safe and pregnancy is maintained until it reaches maturity.
• The fetus will display a reactive normal stress test, a negative result in OCT and CST.

Nursing References

Ackley, B. J., Ladwig, G. B., Makic, M. B., Martinez-Kratz, M. R., & Zanotti, M. (2020).  Nursing diagnoses handbook: An evidence-based guide to planning care . St. Louis, MO: Elsevier.  Buy on Amazon

Gulanick, M., & Myers, J. L. (2022).  Nursing care plans: Diagnoses, interventions, & outcomes . St. Louis, MO: Elsevier. Buy on Amazon

Ignatavicius, D. D., Workman, M. L., Rebar, C. R., & Heimgartner, N. M. (2018).  Medical-surgical nursing: Concepts for interprofessional collaborative care . St. Louis, MO: Elsevier.  Buy on Amazon

Silvestri, L. A. (2020).  Saunders comprehensive review for the NCLEX-RN examination . St. Louis, MO: Elsevier.  Buy on Amazon

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This information is not intended to be nursing education and should not be used as a substitute for professional diagnosis and treatment.

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• Open access
• Published: 10 April 2024

Maternal vitamin D status and risk of gestational diabetes mellitus in twin pregnancies: a longitudinal twin pregnancies birth cohort study

• Da-yan Li 1 , 2 , 3   na1 ,
• Lan Wang 3 , 4   na1 ,
• Li Li 3 , 4   na1 ,
• Shuwei Zhou 3 , 4 ,
• Jiangyun Tan 3 , 4 ,
• Chunyan Tang 3 , 4 ,
• Qianqian Liao 3 , 4 ,
• Ting Liu 3 , 4 ,
• Li Wen 3 , 4 &
• Hong-bo Qi 3 , 4  

Nutrition Journal volume  23 , Article number:  41 ( 2024 ) Cite this article

Metrics details

Gestational diabetes mellitus (GDM) is a common complication of pregnancy, with significant short-term and long-term implications for both mothers and their offspring. Previous studies have indicated the potential benefits of vitamin D in reducing the risk of GDM, yet little is known about this association in twin pregnancies. This study aimed to investigate maternal vitamin D status in the second trimester and examine its association with the risk of GDM in twin pregnancies.

We conducted a prospective cohort study based on data from the Chongqing Longitudinal Twin Study (LoTiS). Peripheral blood serum was collected from the mothers in the second trimester to measure 25(OH)D concentrations. GDM was diagnosed at 23–26 weeks of gestation using a 75-g 2-h oral glucose tolerance test. We used multivariable logistic regression analyses to examine the correlations between vitamin D status and the risk of GDM.

Of the total participants, 93 (29.9%) women were diagnosed with GDM. The mean serum 25(OH)D concentration in the second trimester was 31.1 ± 11.2 ng/mL, and the rate of vitamin D insufficiency and deficiency were 23.5% and 18.7%, respectively. Compared to women with a 25(OH)D concentration < 30 ng/mL, those with a 25(OH)D concentration ≥ 30 ng/mL had a significantly lower risk of GDM (RR 0.61; 95% CI: 0.43, 0.86), especially those who were overweight before pregnancy (RR 0.32; 95% CI: 0.16, 0.64). The restricted cubic splines model showed an inverted J-shaped relationship between vitamin D concentrations and GDM risk.

Conclusions

The risk of GDM was significantly reduced in twin pregnant women with vitamin D concentrations ≥ 30 ng/mL in the second trimester.

Trial registration

ChiCTR-OOC-16,008,203. Retrospectively registered on 1 April 2016.

Peer Review reports

Gestational diabetes mellitus (GDM) is defined as diabetes diagnosed in the second or third trimester of pregnancy in women who did not have clearly overt diabetes prior to gestation according to the American Diabetes Association (ADA) [ 1 ]. GDM is one of the most common pregnancy complications and exhibits varying prevalence rates ranging from 7.1% to 27.6% worldwide according to country, ethnicity and diagnostic thresholds [ 2 ]. The prevalence of GDM among the Chinese population ranges from 17.5% to 18.9% [ 3 ], while in Europe and North America, the prevalence is lower, at 7.1-7.8% [ 2 ]. GDM has been found to have short- and long-term adverse effects on both mothers and their offspring, including an increased risk of hypertensive diseases of pregnancy, cesarean deliveries and macrosomia at birth during the perinatal period, as well as a higher risk of type 2 diabetes in mothers and metabolic complications in offspring later in life [ 4 ].

Given the potential negative effects of GDM, it is crucial to identify the risk factors associated with its development. Accumulative studies have reported an association between vitamin D status and GDM prevalence [ 5 , 6 ], with vitamin D deficiency being linked to an increased risk of developing GDM [ 7 , 8 , 9 ]. Nevertheless, it is noteworthy that all the aforementioned studies have focused primarily on singleton pregnancies, and there is a lack of comprehensive exploration of vitamin D concentrations and status in twin pregnancies and their association with the development of GDM.

With the development of assisted reproductive technology and delayed childbearing, the rate of twin births has exceeded 3% [ 10 ]. When the same diagnostic criteria are used to diagnose GDM, twin pregnancies are found to have a higher prevalence of GDM than singleton pregnancies in the same geographical region [ 11 , 12 , 13 ]. This may be attributed to older age, larger placental areas and greater gestational weight gain in twin pregnant women [ 14 ]. However, studies on the impact of GDM on perinatal outcomes in twin pregnancies have reported conflicting results. Studies conducted in North America revealed that GDM is associated with an increased risk of cesarean section, preterm delivery and large-for-gestational age (LGA) neonates in twin pregnancies [ 15 , 16 , 17 , 18 ]. However, Lin et al. reported that the perinatal outcomes of women with twin pregnancies with GDM are comparable to those without GDM in a Chinese population [ 19 ]. In our previous prospective investigation, we found that twin pregnancies with GDM are related to an elevated risk of gestational hypertension, childhood overweight at 6 months [ 20 ] and preterm delivery [ 21 ]. This suggests that GDM may affect the health of both twin pregnant women and their offspring. Therefore, it is worth exploring whether there is a correlation between vitamin D status and the occurrence of GDM in twin pregnancies.

The aim of the present study was to investigate the vitamin D concentrations and status in the second trimester and to examine their associations with the development of GDM in twin pregnancies. To achieve this goal, we utilized a longitudinal birth cohort of twin pregnancies from Southwest China.

Study design and participants

This study was conducted as part of the Chongqing Longitudinal Twin Study (LoTiS), which is an ongoing prospective study conducted at the First Affiliated Hospital of Chongqing Medical University and Chongqing Health Center for Women and Children in China [ 22 ]. Chongqing is located in southwestern China at a latitude of 29.35° N and has a humid subtropical monsoon climate with insufficient sunshine (1000–1400 h per year). The LoTiS study recruited twin pregnant women aged 20–40 years who began receiving prenatal care at 11–16 weeks of gestation in the study centers. The twin birth cohort was launched in January 2016; by February 2019, a total of 439 women were recruited at the first visit, and 333 women had completed all the required visits during the pregnancy period. The study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (No. 201530). All the methods and procedures carried out in this study were in accordance with the principles of the Declaration of Helsinki as revised in 2008. Written informed consent was obtained from each participant at recruitment.

In the current study, women were eligible for inclusion if they had peripheral blood samples collected in the second trimester, underwent a 75-g oral glucose-tolerance test (OGTT) between 23 and 26 weeks of gestation, and had complete pregnancy records. Women with any of the following conditions were excluded from the study: preexisting chronic metabolic diseases, such as hypertension or type 2 diabetes; fetal complicated with severe malformation and complications, such as twin-to-twin transfusion syndrome and intrauterine death of one or both fetuses.

Vitamin D measurement

Peripheral blood samples were collected from mothers in the second trimester (23–26 weeks of gestation) by using a coagulation-promoting blood collection tube. Serum samples were centrifuged for 10 min at 4℃ and 3000 rpm, and transferred to -80 ℃ freezers within 3 h for long-term storage. Serum 25(OH)D 3 and 25(OH)D 2 concentrations were measured by high-performance liquid chromatography- electrospray tandem mass spectrometry (HPLC-MS/MS, Waters, USA), which is the gold standard measurement method. The intra-assay and inter-assay coefficients of variation were < 15%, indicating good repeatability.

The concentration of 25(OH)D was calculated by summing the concentrations of 25(OH)D 3 and 25(OH)D 2 . The women were categorized into three 25(OH)D status groups according to the Endocrine Society guidelines: 25(OH)D concentrations below 20 ng/mL were classified as deficient, concentrations ranging from 20 to 30 ng/mL were considered insufficient, and concentrations above 30 ng/mL were considered sufficient [ 23 ].

Diagnosis of GDM

GDM was diagnosed after the 75 g 2-h OGTT if ≥ 1 of the following plasma glucose values was met or exceeded according to the International Association of Diabetes and Pregnancy Study Group (IADPSG): a fasting plasma glucose (FPG) level ≥ 5.1 mmol/L, a 1-h plasma glucose (PG-1 h) level ≥ 10.0 mmol/L, or a 2-h plasma glucose (PG-2 h) level ≥ 8.5 mmol/L [ 24 ].

Data collection

We collected data on maternal age (< 35 y, ≥ 35 y), height, prepregnancy weight, weight at 12 weeks, weight at 24 weeks, education level (≤ 12 y, > 12 y), employment status (employed, unemployed), smoking status before pregnancy, chorionicity (monochorionic, dichorionic), mode of conception (naturally conceived, conceived by assisted reproductive technology), parity (0, ≥ 1), family history of diabetes, gestational age and season of blood sample collection (summer/autumn, winter/spring). Prepregnancy BMI (kg/m 2 ) was calculated as the ratio of weight (kg) to squared height (m 2 ) (< 24 and ≥ 24 kg/m 2 ), which was derived from self-reported prepregnancy weight and measured height at the first visit.

Statistical analysis

Continuous variables are expressed as the means and standard deviations and were analyzed using Student’s t test or one-way analysis of variance. Categorical variables are expressed as count and percentage and were analyzed using the chi-squared test or Fisher’s exact test. Multivariate logistic regression models were utilized to estimate the relative ratio (RR) and 95% confidence interval (CI) for GDM risk related to vitamin D status. Adjusted confounders included maternal age, prepregnancy BMI, education level, employment status, parity, mode of conception and family history of diabetes. Additionally, we employed a restricted cubic spline (RCS) regression model to further examine the nonlinear association between vitamin D concentrations and GDM risk.

All the statistical analyses were conducted in Stata 15.0 (StataCorp, College Station, TX, USA).

Characteristics of the participants according to GDM status

After exclusions of twin pregnancies due to the death of one or both twins, complicated with severe fetal malformation and complications, preexisting hypertension/type 2 diabetes, lost to follow-up and missing peripheral blood samples, a total of 311 twin pregnant women were included in the current study (Fig.  1 ). Among them, 93 (29.9%) were diagnosed with GDM (Fig.  1 ).

Flowchart showing selection of participants included in this analysis from LoTiS study

Table  1 presents the participant characteristics according to GDM status. Overall, compared to women uncomplicated with GDM, women complicated with GDM tended to be older (30.0 ± 3.9 vs. 29.0 ± 3.9, p  = 0.031) and were more likely to have a BMI higher than 24.0 kg/m 2 before pregnancy (26.9% vs. 17.0%, p  = 0.045). There were no significant differences between the GDM and non-GDM groups in terms of education level, employment status, primipara, mode of conception, chorionicity, smoking status before pregnancy, family history of diabetes and season of sampling. Importantly, women complicated with GDM had significantly lower concentrations of 25(OH)D and a lower proportion of vitamin D sufficiency than women uncomplicated with GDM (27.8 ± 9.9 vs. 32.5 ± 11.4, p  < 0.001; 44.1% vs. 63.8%, p  = 0.002). The distribution of serum 25(OH)D concentrations between the two groups is presented in Fig.  2 .

Comparison of vitamin D concentrations between women complicated with GDM and without GDM. (***) represents p  < 0.001

Comparisons of vitamin D concentrations in the second trimester according to the maternal characteristics

As shown in Table  2 , the average concentration of 25(OH)D in the second trimester was 31.1 ± 11.2 ng/mL, with vitamin D sufficiency present in 57.9% of mothers, vitamin D insufficiency in 23.5% and vitamin D deficiency in 18.7%. A significant difference in the mean 25(OH)D concentration was observed among twin pregnant women with different modes of conception. Women who conceived with the aid of assisted reproductive technology had a lower mean 25(OH)D concentration (29.3 ± 10.9 vs. 32.2 ± 11.3, p  = 0.030). There were no significant differences in the mean 25(OH)D concentration between the other maternal characteristics and the season of sampling.

Association between vitamin D status and the risk of GDM

The multivariate regression analyses performed to determine the association between vitamin D status in the second trimester and the risk of GDM are summarized in Table  3 . Compared to women with vitamin D sufficiency, women with vitamin D insufficiency had a higher risk of developing GDM (RR 1.98; 95% CI: 1.37, 2.87; p  < 0.001). After adjusting for maternal age, prepregnancy BMI, education level, employment status, parity, mode of conception and family history of diabetes, the association between vitamin D insufficiency and GDM risk remained significant (RR 1.85; 95% CI: 1.28, 2.67; p  = 0.001). Women with vitamin D deficiency did not have an increased risk of GDM according to either the unadjusted or the adjusted model.

In the subgroup analysis, a significant increase in GDM risk was observed in both the vitamin D insufficiency group (RR 3.55; 95% CI: 1.75, 7.20; p  = 0.001) and vitamin D deficiency group (RR 2.38; 95% CI: 1.03, 5.53; p  = 0.043) among overweight women compared to the vitamin D sufficiency group after adjustments were made for confounding factors (Table  3 ). Age did not modify the association between vitamin D insufficiency and GDM risk, as increased GDM risks were observed in both the vitamin D insufficiency group among both twin pregnant women aged ≥ 35 years (RR 2.88; 95% CI: 1.25, 6.61; p  = 0.013) and those aged < 35 years (RR 1.67; 95% CI: 1.09, 2.56; p  = 0.018) after adjusting for confounding factors (Table  3 ).

Furthermore, we examined the association of vitamin D sufficiency with the incidence of GDM (Fig.  3 ). Women with vitamin D concentrations ≥ 30 ng/mL had a reduced risk of developing GDM compared to those with vitamin D concentrations < 30 ng/mL (RR 0.61; 95% CI: 0.43, 0.86; p  = 0.005) after adjusting for potential confounding factors. The effect modification by prepregnancy BMI remained significant, as overweight women with sufficient vitamin D had a reduced risk of GDM (RR 0.32; 95% CI: 0.16, 0.64; p  = 0.001).

Associations between vitamin D levels and the risk of GDM, and stratified by pre-pregnancy body mass index levels (< 24.0 vs. ≥ 24.0) and age (< 35 vs. ≥ 354.0). Adjusted for maternal age, prepregnancy BMI, education level, occupation, parity, mode of conception and family history of diabetes. ( ● ) represents vitamin D concentrations < 30 ng/mL; (■) representsvitamin D concentrations ≥ 30 ng/mL

Associations between vitamin D concentrations and the risk of GDM

The RCS model showed an inverted J-shaped association between vitamin D concentrations and the risk of GDM. This association was observed after adjusting for maternal age, prepregnancy BMI, education level, employment status, parity, mode of conception and family history of diabetes (Fig.  4 ). Break-point analysis showed that the knot of the steep downward trend was 30 ng/mL. There was no significant association between vitamin D concentration and GDM when the 25(OH)D concentration was < 30 ng/mL, while the risk of GDM decreased when the 25(OH)D concentration was ≥ 30 ng/mL.

Nonlinear association between vitamin D levels in the second trimester and GDM risk by restricted cubic spline curve, maternal age, prepregnancy BMI, education level, occupation, parity, mode of conception and family history of diabetes were adjusted. A vitamin D concentration of 20 ng/mL was selected as the reference level. The area between dashed lines represents 95% CIs. Knots were located at the 5th, 35th, 65th and 95th percentiles

In the current cohort study, the results demonstrated that the average concentration of 25(OH)D in the second trimester among twin pregnant women was 31.1 ± 11.2 ng/mL, and 57.9%, 23.5% and 18.7% of women had sufficient, insufficient and deficient vitamin D levels, respectively. A nonlinear association between vitamin D concentrations and the incidence of GDM was observed. A vitamin D concentration above 30 ng/mL in the second trimester was found to be a protective factor against the development of GDM. This protective effect was more pronounced in twin pregnant women who were overweight prior to pregnancy.

Vitamin D deficiency is a prevalent public health issue, particularly among pregnant women. In our study, the average concentration of vitamin D in twin pregnant women in the second trimester was 31.1 ng/mL, which was higher than that observed in singleton pregnant women in China during the same trimester [ 25 , 26 , 27 ]. This difference may be attributed to the fact that twin pregnancies are widely recognized as high-risk pregnancies in clinical practice, leading to better compliance among twin pregnant women with prenatal health management recommendations, such as more frequent ultrasound examinations and nutritional supplementation. However, we observed that twin pregnant women who conceived with the assistance of assisted reproductive technology had lower vitamin D concentrations compare to women who conceived naturally. This may be due to the health issues commonly associated with women undergoing assisted reproductive technology, such as infertility or hormonal imbalances, traumatic procedures like embryo transfer, and the use of additional medications. These factors may impact the absorption and metabolism of vitamin D. Individual variations in vitamin D supplementation may also contribute to the observed differences.

Extensive research has been conducted on the association between vitamin D levels and the occurrence of GDM in singleton pregnancies. There are conflicting reports exist regarding the association between vitamin D levels during early pregnancy and the development of GDM. Some studies have figured out that vitamin D deficiency during early pregnancy is associated with an increased risk of GDM [ 7 , 8 , 28 , 29 , 30 ], while other studies have not supported this association [ 31 , 32 , 33 ]. A systematic review and meta‑analysis consisting of 37,838 pregnant women concluded that lower levels of vitamin D in early pregnancy were associated with a higher risk of developing GDM. However, in some of the included studies, vitamin D concentrations were measured in the second trimester, which limits the applicability of the findings [ 5 ]. In terms of the correlation between second trimester vitamin D levels and the occurrence of GDM, eighteen studies utilized a prospective cohort or nested case-control study design to measure vitamin D levels at 24–28 weeks of gestation. Among these studies, eleven studies reported a positive association between vitamin D deficiency and GDM risk [ 5 , 6 ]. These varying results may be attributed to several factors, such as the study design, sample size, methods used to determine vitamin D levels, region and latitude. For instance, most nested case-control investigations concluded a positive association between vitamin D deficiency and higher risk of GDM. Four studies conducted on Chinese women have consistently reported an association between vitamin D levels in the second trimester and GDM risk [ 25 , 26 , 27 , 34 ]. Hence, we assessed the vitamin D concentration in twin pregnant women in the second trimester and investigated its association with GDM.

In the current study, we discovered that vitamin D insufficiency in the second trimester was associated with an elevated risk of GDM. This association was more pronounced among overweight women, which aligns with the findings of a previous study that reported a stronger association between vitamin D and GDM risk among overweight/obese women [ 26 ]. However, we did not observe a connection between vitamin D deficiency in the second trimester and a higher risk of GDM. Therefore, we speculated that there might be a nonlinear relationship between vitamin D concentrations and GDM risk. Previous studies have shown that GDM risk was significantly reduced among pregnant women with vitamin D concentrations ≥ 20 ng/mL [ 25 , 28 ], or decreased among those with vitamin D concentrations > 35 ng/mL [ 35 ], or decreased among those with vitamin D concentrations 25–40 ng/mL [ 32 ] in singleton pregnancies, suggesting the existence of a threshold concentration for vitamin D that determines the significance of its association with GDM risk. In our study, nonlinear association analysis revealed an inverted J-shaped relationship between vitamin D concentrations and the risk of GDM. A vitamin D level of 30 ng/mL was identified as the threshold that significantly reduced the risk of GDM in twin pregnant women. The variations in the identified thresholds of vitamin D, which affect GDM risk, across different studies may be ascribed to inconsistent population characteristics, diagnostic criteria for GDM and timing of vitamin D measurement.

Several biological mechanisms have been proposed to elucidate the role of vitamin D in regulating glucose metabolism. First, vitamin D may enhance the peripheral/hepatic uptake of glucose, which can help decrease glucose levels [ 36 ]. Second, vitamin D deficiency may impair pancreatic β-cell functions, thereby compromising the secretion of insulin [ 37 ]. Third, vitamin D plays a role in immune system regulation. It has been suggested that dysregulation of the immune system during pregnancy may contribute to the development of GDM, and vitamin D may help modulate immune responses and promote a balanced immune system, potentially reducing the risk of GDM. Finally, vitamin D deficiency can exacerbate inflammation and oxidative stress in the pancreas and other organs, leading to insulin resistance [ 37 ]. Compared to singleton pregnant women with GDM, twin pregnant women with GDM are more likely to have abnormal postprandial blood glucose levels, which is more likely attribute to insulin resistance than impaired pancreatic islet β cell function [ 21 , 38 , 39 , 40 ]. Thus, higher vitamin D concentrations are significant for alleviating insulin resistance and reducing the risk of GDM in twin pregnant women. This also explains why the threshold of vitamin D, which can affect the incidence of GDM, is higher in twin pregnancies than in singleton pregnancies in the Chinese population under the same diagnostic criteria for GDM [ 25 ].

The strength of our study lies in the specific study population. To our knowledge, this was the first study to investigate the association between vitamin D status and the risk of GDM in a population of women with twin pregnancies. However, there are several limitations that should be considered in this study. One limitation was the single-center study design of this study, which limits the generalizability of the findings. Another limitation was the lack of accurate data on vitamin D supplementation during the second trimester. The questionnaire used to assess vitamin D supplementation frequency had only two options: “daily” and “sometimes or less frequently”. Further detailed investigations are needed to understand the associations among vitamin D supplementation, vitamin D absorption, and the causal mechanism underlying the relationship between vitamin D supplementation and GDM. Finally, the lack of certain biological indicators related to GDM, such as glycosylated hemoglobin and insulin, limits our ability to fully explain the effect of vitamin D on GDM.

In twin pregnant women with vitamin D concentrations < 30 ng/mL in the second trimester, the risk of GDM was significantly reduced in those with vitamin D levels ≥ 30 ng/mL in the second trimester. There was a nonlinear association between vitamin D concentrations and the incidence of GDM, with 30 ng/mL considered as the cutoff for the vitamin D concentration that could significantly reduce the risk of GDM in twin pregnancies. Further multicenter research is needed to provide more evidence elucidating the relationship between vitamin D and GDM in twin pregnancies.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon request.

Abbreviations

America Diabetes Association

assistant reproductive technology

body mass index

confidence interval

gestational diabetes mellitus

International Association of Diabetes and Pregnancy Study Groups

large-for-gestational age

Longitudinal Twin Study

oral glucose tolerance test

restricted cubic spline

relative ratio

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Acknowledgements

The authors would like to thank all the families, health professionals and researchers who contributed to this cohort study.

This work was supported by the National Key Research and Development Program of China (2023YFC2705900) and Chongqing Science and Technology Foundation (CSTB2023NSCQ-MSX0384).

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Da-yan Li and Lan Wang contributed equally to this work.

Authors and Affiliations

Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

Department of Obstetrics and Gynecology, Banan Hospital of Chongqing Medical University, Chongqing, 401320, China

Department of Obstetrics and Gynecology, Women and Children’s Hospital of Chongqing Medical University, Longshan Road 120, Yubei District, Chongqing, 401147, China

Da-yan Li, Lan Wang, Li Li, Shuwei Zhou, Jiangyun Tan, Chunyan Tang, Qianqian Liao, Ting Liu, Li Wen & Hong-bo Qi

Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Chongqing, 401147, China

Lan Wang, Li Li, Shuwei Zhou, Jiangyun Tan, Chunyan Tang, Qianqian Liao, Ting Liu, Li Wen & Hong-bo Qi

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Contributions

D.L., L.Wang., L.Wen. and H.Q. designed the research protocol; D.L., L.Wang., L.Wen., L.L., S.Z., J.T. and T.L. conducted the study; L.Wen., C.T. and Q.L. analysed the data; D.L. and L.Wang. drafted the manuscript; L.Wen. and H.Q. critically revised the manuscript; L.Wen. and H.Q. were responsible for the final contents. All authors reviewed and approved the final manuscript.

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Correspondence to Li Wen or Hong-bo Qi .

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Li, Dy., Wang, L., Li, L. et al. Maternal vitamin D status and risk of gestational diabetes mellitus in twin pregnancies: a longitudinal twin pregnancies birth cohort study. Nutr J 23 , 41 (2024). https://doi.org/10.1186/s12937-024-00944-2

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Diabetes Mellitus

The major sources of the glucose that circulates in the blood are through the absorption of ingested food in the gastrointestinal tract and formation of glucose by the liver from food substances.

• Diabetes mellitus is a group of metabolic diseases that occurs with increased levels of glucose in the blood.
• Diabetes mellitus most often results in defects in insulin secretion, insulin action, or even both.

Classification

The classification system of diabetes mellitus is unique because research findings suggest many differences among individuals within each category, and patients can even move from one category to another, except for patients with type 1 diabetes.

• Diabetes has major classifications that include type 1 diabetes , type 2 diabetes , gestational diabete s, and diabetes mellitus associated with other conditions.
• The two types of diabetes mellitus are differentiated based on their causative factors, clinical course, and management.

Pathophysiology

Diabetes Mellitus has different courses of pathophysiology because of it has several types

• Insulin is secreted by beta cells in the pancreas and it is an anabolic hormone.
• When we consume food, insulin moves glucose from blood to muscle , liver, and fat cells as insulin level increases.
• The functions of insulin include the transport and metabolism of glucose for energy, stimulation of storage of glucose in the liver and muscle, serves as the signal of the liver to stop releasing glucose, enhancement of the storage of dietary fat in adipose tissue, and acceleration of the transport of amino acid into cells.
• Insulin and glucagon maintain a constant level of glucose in the blood by stimulating the release of glucose from the liver.

Type 1 Diabetes Mellitus

• Type 1 diabetes mellitus is characterized by destruction of the pancreatic beta cel ls.
• A common underlying factor in the development of type 1 diabetes is a genetic susceptibility .
• Destruction of beta cells leads to a decrease in insulin production, unchecked glucose production by the liver and fasting hyperglycemia.
• Glucose taken from food cannot be stored in the liver anymore but remains in the blood stream.
• The kidneys will not reabsorb the glucose once it has exceeded the renal threshold, so it will appear in the urine and be called glycosuria .
• Excessive loss of fluids is accompanied by excessive excretion of glucose in the urine leading to osmotic diuresis.
• There is fat breakdown which results in ketone production , the by-product of fat breakdown.

Type 2 Diabetes Mellitus

• Type 2 diabetes mellitus has major problems of insulin resistance and impaired insulin secretion .
• Insulin could not bind with the special receptors so insulin becomes less effective at stimulating glucose uptake and at regulating the glucose release.
• There must be increased amounts of insulin to maintain glucose level at a normal or slightly elevated level.
• However, there is enough insulin to prevent the breakdown of fats and production of ketones.
• Uncontrolled type 2 diabetes could lead to hyperglycemic, hyperosmolar nonketotic syndrome .
• The usual symptoms that the patient may feel are polyuria, polydipsia, polyphagia, fatigue, irritability, poorly healing skin wounds, vaginal infections, or blurred vision.

Gestational Diabetes Mellitus

• With gestational diabetes mellitus ( GDM ) , the pregnant woman experiences any degree of glucose intolerance with the onset of pregnancy.
• The secretion of placental hormones causes insulin resistance , leading to hyperglycemia.
• After delivery, blood glucose levels in women with GDM usually return to normal or later on develop type 2 diabetes.

Epidemiology

Diabetes mellitus is now one of the most common disease all over the world. Here are some quick facts and numbers on diabetes mellitus.

• More than 23 million people in the United States have diabetes, yet almost one-third are undiagnosed.
• By 2030, the number of cases is expected to increase more than 30 million.
• Diabetes is especially prevalent in the elderly ; 50% of people older than 65 years old have some degree of glucose intolerance.
• People who are 65 years and older account for 40% of people with diabetes.
• African-Americans and members of other racial and ethnic groups are more likely to develop diabetes.
• In the United States, diabetes is the leading cause of non-traumatic amputations, blindness in working-age adults, and end-stage renal disease .
• Diabetes is the third leading cause of death from disease.
• Costs related to diabetes are estimated to be almost $174 billion annually.

The exact cause of diabetes mellitus is actually unknown, yet there are factors that contribute to the development of the disease.

• Genetics. Genetics may have played a role in the destruction of the beta cells in type 1 DM.
• Environmental factors. Exposure to some environmental factors like viruses can cause the destruction of the beta cells.
• Weight. Excessive weight or obesity is one of the factors that contribute to type 2 DM because it causes insulin resistance.
• Inactivity. Lack of exercise and a sedentary lifestyle can also cause insulin resistance and impaired insulin secretion.
• Weight. If you are overweight before pregnancy and added extra weight, it makes it hard for the body to use insulin.
• Genetics. If you have a parent or a sibling who has type 2 DM, you are most likely predisposed to GDM.

Clinical Manifestations

Clinical manifestations depend on the level of the patient’s hyperglycemia.

• Polyuria or increased urination.  Polyuria occurs because the kidneys remove excess sugar from the blood, resulting in a higher urine production.
• Polydipsia or increased thirst. Polydipsia is present because the body loses more water as polyuria happens, triggering an increase in the patient’s thirst.
• Polyphagia or increased appetite. Although the patient may consume a lot of food but glucose could not enter the cells because of insulin resistance or lack of insulin production.
• Fatigue and weakness . The body does not receive enough energy from the food that the patient is ingesting.
• Sudden vision changes. The body pulls away fluid from the eye in an attempt to compensate the loss of fluid in the blood, resulting in trouble in focusing the vision.

• Tingling or numbness in hands or feet. Tingling and numbness occur due to a decrease in glucose in the cells.
• Dry skin. Because of polyuria, the skin becomes dehydrated.
• Skin lesions or wounds that are slow to heal. Instead of entering the cells, glucose crowds inside blood vessels, hindering the passage of white blood cells which are needed for wound healing .
• Recurrent infections.  Due to the high concentration of glucose, bacteria thrives easily.

Appropriate management of lifestyle can effectively prevent the development of diabetes mellitus.

• Standard lifestyle recommendations, metformin, and placebo are given to people who  are at high risk for type 2 diabetes.
• The 16-lesson curriculum of the intensive  program of lifestyle modifications focused on weight reduction of greater than 7% of initial body weight and physical activity of moderate intensity.
• It also included behavior modification strategies that can help patients achieve their weight reduction goals and participate in exercise.

Complications

If diabetes mellitus is left untreated, several complications may arise from the disease

• Hypoglycemia. Hypoglycemia occurs when the blood glucose falls to less than 50 to 60 mg/dL because of too much insulin or oral hypoglycemic agents, too little food, or excessive physical activity.
• Diabetic Ketoacidosis . DKA is caused by an absence or markedly inadequate amounts of insulin and has three major features of hyperglycemia, dehydration and electrolyte loss, and acidosis.
• Hyperglycemic Hyperosmolar Nonketotic Syndrome. HHNS is a serious condition in which hyperosmolarity and hyperglycemia predominate with alteration in the sense of awareness.

Assessment and Diagnostic Findings

Hypoglycemia may occur suddenly in a patient considered hyperglycemic because their blood glucose levels may fall rapidly to 120 mg/dL or even less.

• Serum glucose:  Increased 200–1000 mg/dL or more.
• Serum acetone  ( ketones ):  Strongly positive.
• Fatty acids:  Lipids, triglycerides, and cholesterol level elevated.
• Serum osmolality:  Elevated but usually less than 330 mOsm/L.
• Glucagon:  Elevated level is associated with conditions that produce (1) actual hypoglycemia, (2) relative lack of glucose (e.g., trauma , infection ), or (3) lack of insulin. Therefore, glucagon may be elevated with severe DKA despite hyperglycemia.
• Glycosylated hemoglobin  ( HbA 1C ):  Evaluates glucose control during past 8–12 wk with the previous 2 wk most heavily weighted. Useful in differentiating inadequate control versus incident-related DKA (e.g., current upper respiratory infection [URI]). A result greater than 8% represents an average blood glucose of 200 mg/dL and signals a need for changes in treatment.
• Serum insulin:  May be decreased/absent (type 1) or normal to high (type 2), indicating insulin insufficiency/improper utilization (endogenous/exogenous). Insulin resistance may develop secondary to formation of antibodies.
• Electrolytes :
• Sodium:  May be normal, elevated, or decreased.
• Potassium :  Normal or falsely elevated (cellular shifts), then markedly decreased.
• Phosphorus:  Frequently decreased.
• Arterial blood gases  ( ABGs ):  Usually reflects low pH and decreased HCO 3  (metabolic acidosis) with compensatory respiratory alkalosis.
• CBC:  Hct may be elevated ( dehydration ); leukocytosis suggest hemoconcentration, response to stress or infection.
• BUN:  May be normal or elevated ( dehydration /decreased renal perfusion).
• Serum amylase:  May be elevated, indicating acute pancreatitis as cause of DKA.
• Thyroid function tests:  Increased thyroid activity can increase blood glucose and insulin needs.
• Urine:  Positive for glucose and ketones; specific gravity and osmolality may be elevated.
• Cultures and sensitivities:  Possible UTI, respiratory or wound infections.

Medical Management

Here are some medical interventions that are performed to manage diabetes mellitus.

• Normalize insulin activity . This is the main goal of diabetes treatment — normalization of blood glucose levels to reduce the development of vascular and neuropathic complications.
• Intensive treatment. Intensive treatment is three to four insulin injections per day or continuous subcutaneous insulin infusion, insulin pump therapy plus frequent blood glucose monitoring and weekly contacts with diabetes educators.
• Exercise caution with intensive treatment. Intensive therapy must be done with caution and must be accompanied by thorough education of the patient and family and by responsible behavior of patient.
• Diabetes management has five components and involves constant assessment and modification of the treatment plan by healthcare professionals and daily adjustments in therapy by the patient.

Nutritional Management

• The foundations. Nutrition, meal planning , and weight control are the foundations of diabetes management.
• Consult a professional. A registered dietitian who understands diabetes management has the major responsibility for designing and teaching this aspect of the therapeutic plan.
• Healthcare team should have the knowledge. Nurses and other health care members of the team must be knowledgeable about nutritional therapy and supportive of patients who need to implement nutritional and lifestyle changes.
• Weight loss. This is the key treatment for obese patients with type 2 diabetes.
• How much weight to lose? A weight loss of as small as 5% to 10% of the total body weight may significantly improve blood glucose levels.
• Other options for diabetes management. Diet education, behavioral therapy, group support, and ongoing nutritional counselling should be encouraged.

Meal Planning

• Criteria in meal planning . The meal plan must consider the patient’s food preferences, lifestyle, usual eating times, and ethnic and cultural background.
• Managing hypoglycemia through meals. To help prevent hypoglycemic reactions and maintain overall blood glucose control, there should be consistency in the approximate time intervals between meals with the addition of snacks as needed.
• Assessment is still necessary.  The patient’s diet history should be thoroughly reviewed to identify his or her eating habits and lifestyle.
• Educate the patient. Health education should include the importance of consistent eating habits, the relationship of food and insulin, and the provision of an individualized meal plan.
• The nurse ‘s role. The nurse plays an important role in communicating pertinent information to the dietitian and reinforcing the patients for better understanding .

Other Dietary Concerns

• Alcohol consumption. Patients with diabetes do not need to give up alcoholic beverages entirely, but they must be aware of the potential adverse of alcohol specific to diabetes.
• If a patient with diabetes consumes alcohol on an empty stomach , there is an increased likelihood of hypoglycemia.
• Reducing hypoglycemia. The patient must be cautioned to consume food along with alcohol, however, carbohydrate consumed with alcohol may raise blood glucose.
• How much alcohol intake? Moderate intake is considered to be one alcoholic beverage per day for women and two alcoholic beverages per day for men.
• Artificial sweeteners. Use of artificial sweeteners is acceptable, and there are two types of sweeteners: nutritive and nonnutritive.
• Types of sweeteners. Nutritive sweeteners include all of which provides calories in amounts similar to sucrose while nonnutritive have minimal or no calories.
• Exercise. Exercise lowers blood glucose levels by increasing the uptake of glucose by body muscles and by improving insulin utilization.
• A person with diabetes should exercise at the same time and for the same amount each day or regularly.
• A slow, gradual increase in the exercise period is encouraged.

Using a Continuous Glucose Monitoring System

• A continuous glucose monitoring system is inserted subcutaneously in the abdomen and connected to the device worn on a belt.
• This can be used to determine whether treatment is adequate over a  24-hour period.
• Blood glucose readings are analyzed after 72 hours when the data has been downloaded from the device.

Testing for Glycated Hemoglobin

• Glycated hemoglobin or glycosylated hemoglobin, HgbA1C, or A1C reflects the average blood glucose levels over a period of approximately 2 to 3 months.
• The longer the amount of glucose in the blood remains above normal, the more glucose binds to hemoglobin and the higher the glycated hemoglobin becomes.
• Normal values typically range from 4% to 6% and indicate consistently near-normal blood glucose concentrations.

Pharmacologic Therapy

• Exogenous insulin. In type 1 diabetes, exogenous insulin must be administered for life because the body loses the ability to produce insulin.
• Insulin in type 2 diabetes. In type 2 diabetes, insulin may be necessary on a long-term basis to control glucose levels if meal planning and oral agents are ineffective.
• Self-Monitoring Blood Glucose (SMBG). This is the cornerstone of insulin therapy because accurate monitoring is essential.
• Human insulin. Human insulin preparations have a shorter duration of action because the presence of animal proteins triggers an immune response that results in the binding of animal insulin.
• Rapid-acting insulin. Rapid-acting insulins produce a more rapid effect that is of shorter duration than regular insulin.
• Short-acting insulin. Short-acting insulins or regular insulin should be administered 20-30 minutes before a meal , either alone or in combination with a longer-acting insulin.
• Intermediate-acting insulin. Intermediate-acting insulins or NPH or Lente insulin appear white and cloudy and should be administered with food around the time of the onset and peak of these insulins.
• The rapid-acting and short-acting insulins are expected to cover the increase in blood glucose levels after meals; immediately after the injection.
• Intermediate-acting insulins are expected to cover subsequent meals, and long-acting insulins provide a relatively constant level of insulin and act as a basal insulin.
• Approaches to insulin therapy. There are two general approaches to insulin therapy: conventional and intensive.
• Conventional regimen. Conventional regimen is a simplified regimen wherein the patient should not vary meal patterns and activity levels.
• Intensive regimen. Intensive regimen uses a more complex insulin regimen to achieve as much control over blood glucose levels as is safe and practical.
• A more complex insulin regimen allows the patient more flexibility to change the insulin doses from day to day in accordance with changes in eating and activity patterns.
• Methods of insulin delivery. Methods of insulin delivery include traditional subcutaneous injections, insulin pens, jet injectors, and insulin pumps.
• Insulin pens use small prefilled insulin cartridges that are loaded into a pen-like holder.
• Insulin is delivered by dialing in a dose or pushing a button for every 1- or 2-unit increment administered.
• Jet injectors deliver insulin through the skin under pressure in an extremely fine stream.
• Insulin pumps involve continuous subcutaneous insulin infusion with the use of small, externally worn devices that closely mimic the function of the pancreas.
• Oral antidiabetic agents may be effective for patients who have type 2 diabetes that cannot be treated by MNT and exercise alone.
• Oral antidiabetic agents . Oral antidiabetic agents include sulfonylureas , biguanides, alpha-glucosidase inhibitors, thiazolidinediones, and dipeptidyl-peptidase-4.
• Half of all the patients who used oral antidiabetic agents eventually require insulin, and this is called secondary failure .
• Primary failure occurs when the blood glucose level remains high 1 month after initial medication use.

Nursing Management

Nurses should provide accurate and up-to-date information about the patient’s condition so that the healthcare team can come up with appropriate interventions and management.

For the complete and comprehensive nursing care plan and management of patients with diabetes, please visit 20 Diabetes Mellitus Nursing Care Plans

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• Research article
• Open access
• Published: 10 April 2024

Potential risk of gestational diabetes mellitus in females undergoing in vitro fertilization: a pilot study

• Yehia Moustafa Ghanem 1 ,
• Yasser El Kassar 2 ,
• May Mohamed Magdy 1 ,
• Mohamed Amara 3 &
• Noha Gaber Amin   ORCID: orcid.org/0000-0002-8225-5052 1  

Clinical Diabetes and Endocrinology volume  10 , Article number:  7 ( 2024 ) Cite this article

Metrics details

Most of the cases of hyperglycemia during pregnancy are attributed to gestational diabetes mellitus (GDM) (75–90%). Women diagnosed with GDM are at an increased risk for complications during pregnancy and delivery. This observational prospective study aimed to investigate the potential risk of GDM among Egyptian females following in vitro fertilization (IVF) pregnancies compared to spontaneous pregnancies (SC).

This prospective cohort study included normoglycemic females without any history of dysglycemia before this conception. Subjects were divided according to the type of conception into two age and BMI-matched groups: (IVF group): 55 pregnant females conceived by IVF, and (SC group) spontaneous pregnancy: 55 pregnant females conceived spontaneously. A one-step oral glucose tolerance test (OGTT) was performed at gestational weeks 20 and 28 for all study subjects.

The incidence of GDM was statistically significantly higher in the IVF group compared to the spontaneous pregnancy (SC) group (20 and 5.5%, respectively), p  = 0.022 at week 28. On comparing the incidence of GDM on early screening at week 20 in both groups, the incidence of GDM in the IVF group was significantly higher (16.4%) compared to (3.6%) in the spontaneous pregnancy (SC) group, p  = 0.026.

Conclusions

IVF may have an increased potential risk for GDM. Moreover, the diagnosis of GDM may occur early (week 20), highlighting the need for precise and early screening for GDM in IVF pregnancies.

Introduction

The number of pregnancies resulting from in vitro fertilization (IVF) is increasing due to the rise in the prevalence of infertility globally. IVF is one of the most effective forms of assisted reproductive technology (ART). IVF-achieved pregnancies are associated with a higher risk for both obstetric and perinatal complications compared to spontaneous pregnancies [ 1 , 2 ]. Although several studies have reported an increased prevalence of GDM among IVF pregnancies, further investigations are needed to determine the exact time for GDM screening and the management of GDM in IVF pregnancies [ 3 , 4 , 5 ].

Gestational diabetes mellitus (GDM) is defined as a condition where a woman without a previous diagnosis of diabetes experiences high blood glucose levels during pregnancy. GDM is one of the most frequent maternal complications during pregnancy, affecting 10-25% of pregnancies worldwide [ 6 ]. GDM is the most common type of diabetes during pregnancy, representing 75–90% of cases of diabetes during pregnancy [ 7 ].

Factors that increase the risk of GDM include polycystic ovary syndrome, history of GDM in a previous pregnancy, family history of a first-degree relative with type 2 DM, advanced maternal age (over 35 years of age), being overweight, obesity, pre-existing hypertension, smoking during pregnancy, [ 8 ] and assisted reproductive technology (ART) treatment such as in vitro fertilization (IVF) [ 9 ].

Pregnancy complicated with GDM is associated with adverse acute and long-term consequences for both the mother and the infant [ 10 ]. GDM increases the risk of pre-eclampsia, depression, and the necessity of a Caesarean section [ 11 ]. Newborns born to mothers with poorly controlled gestational diabetes are at increased risk of macrosomia, hypoglycemia, and jaundice. If untreated, it can also result in stillbirth. In addition, in the long term, children are at a higher risk of being overweight and at a higher risk of developing type 2 diabetes. Furthermore, a long-term follow-up study reported that a high percentage of women with GDM will develop type 2 diabetes [ 10 , 11 ].

Further multicenter studies are required to confirm the relation between IVF and the prevalence of GDM, whether this is attributed to pre-existing medical conditions or the IVF technique itself. This study aimed to determine the potential risk of GDM among Egyptian females following in vitro fertilization (IVF) pregnancies compared to spontaneous pregnancies. This study also aimed to evaluate the time of diagnosis of GDM in IVF pregnancies compared to spontaneous pregnancies.

Subjects and methods

Study design.

This prospective cohort study design recruited 110 primigravid pregnant females (aged 18-39 at the time of conception). At the outpatient clinic, participants planning for pregnancy and having medical records at the registry of El Shatby Hospital (the central maternity hospital in Alexandria) were invited to participate in the study from January 2021 to September 2021.

Medical records of the first pre-pregnancy visit and the first ante-natal care visit at week [ 4 , 5 , 6 ] of gestation confirmed normoglycemia according to the ADA criteria for the diagnosis of diabetes [ 12 ]. Subjects were screened for gestational diabetes at week 20 and week 28 of gestation using the 75 g 2-hour Oral Glucose Tolerance Test (OGTT) as recommended by the International Association of Diabetes in Pregnancy Study Groups (IADPSG) recommendations [ 13 ]. GDM was diagnosed based on a single reading above three thresholds: Fasting 92 mg/dL, 1-h 180 mg/dL, and 2-h 153 mg/dL.

Study subjects were divided into two groups:

IVF group: 55 pregnant females who IVF conceived. Our institute’s IVF treatment protocol agreed with the standard international guidelines. The IVF technique applied was gonadotrophin-releasing hormone (GnRH) antagonist in In-vitro-fertilization/Intracytoplasmic sperm injection (IVF/ICSI) cycles [ 14 ]. The different infertility underlying etiologies for IVF included maternal structural and mechanical factors (48%), unexplained factors (20%), and male factors (32%).

Spontaneous pregnancy (SC) group: 55 pregnant females who conceived spontaneously.

The ethics committee of Alexandria University approved the study design. The participating study population signed an informed consent before any study-related procedure occurred. The study followed the criteria set by the Declaration of Helsinki. Confidentiality and personal privacy were respected at all levels of the study. Patients felt free to withdraw from the study at any time without any consequences.

Exclusion criteria

Subjects with a history of pre-pregnancy existing diabetes, a history of any previous glucose intolerance or pre-diabetes, or a history of GDM in previous pregnancies were excluded from the study. We also excluded subjects with acanthosis nigricans and cases of PCOS according to Rotterdam criteria of diagnosis [ 15 ]. Subjects of age more than 39 years at the time of conception, smokers, twin pregnancies, thyroid dysfunction, or any known other chronic condition. Patients taking medications that may affect glucose homeostasis, including corticosteroid medications, anti-inflammatory drugs, antidepressants, bronchodilators, nicotine, thyroid hormones, and growth hormones, were excluded from the study.

The following was performed for all the study subjects:

Baseline visit: At the first antenatal visit at 4-6 weeks of gestation.

History Taking and review of the medical records:

The medical records of the recruited pregnant females were reviewed using a computerised sheet including all studied data for each subject to exclude undiagnosed pre-existing diabetes and check the HbA1c at the confirmation of pregnancy. A thorough history of any chronic diseases, drug history, family history of diabetes, hypertension, and other medical history was taken.

Medical records were checked to report pre-pregnancy data, including.

Body weight, height, and body mass index (BMI) (kg/meter 2 )

Vital signs: Blood pressure measurement and pulse examination.

Laboratory assessment:

Participants were referred to the same central laboratory after 8-10 hours of overnight fasting. Blood samples were collected to assess the fasting plasma glucose level (FPG), plasma glucose level after 2 hours (2-hour PP) of 75 g anhydrous glucose load, and HbA1c to exclude pre-pregnancy existing diabetes at the first ant-natal visit.

Evaluation At 20 weeks of gestation for GDM using one-step OGTT.

Evaluation At 28 weeks of gestation for GDM using one-step OGTT [ 16 ].

Sample collection and preparation

Venous blood samples were obtained for all lab tests.

OGTT was assessed by 8-10 hour fasting of a patient all night; the sample was collected in the morning in a grey top (Na fluoride/K oxalate) tube and centrifuged at 1100-2000 g for a minimum of 10 minutes, 75 g of glucose given immediately and after 1 hour and two-hour sample were collected again.

HbA1c was collected in a vacutainer tube containing Na2-EDTA and centrifuged (3000 rpm) for serum preparation.

Statistical analysis

All statistical analyses were performed using the SPSS software (version 20.0; IBM Corporation, Armonk, NY, USA). Continuous data were presented as mean ± SD, and categorical data as numbers and percentages. We used the student’s t -test to compare the normally distributed quantitative variables between the two main groups. The chi-square test was used to compare the categorical variables between the main groups, given that Fisher Exact and Monte-Carlo Exact tests were used instead in case of violating the chi-square test assumptions. The crude odds ratio with its 95% confidence interval was estimated for testing the risk of developing GDM among the IVF and the spontaneous pregnancy groups. A P value < 0.05 was considered significant.

Table  1 shows the study population’s baseline characteristics.

The baseline comparison between the two studied groups showed no significant difference regarding age, BMI, systolic blood pressure, and diastolic blood pressure. However, the presence of a positive family history of diabetes was statistically significantly higher among the SC group compared to the IVF pregnancy group. It was also noted that only 9.1 and 7.3% had normal BMI in the IVF group and the SC Group, respectively, while most of both groups were either overweight or obese.

Parameters of glycemic profile

A 75 g OGTT to screen for GDM was done twice, at weeks 20 and 28 of pregnancy. At week 20 of pregnancy, 16.4% in the IVF group had GDM compared to 3.6% in the SC group, which was statistically significant with p -value = 0.026. At week 28 of pregnancy, 20% in the IVF group had GDM compared to 5.5% in the SC group, which was statistically significant with p -value  =  0.022, as shown in Table  2 .

Comparing the incidence of GDM among the IVF & and the spontaneous pregnancy groups

IVF pregnancies had a five-fold increased risk for GDM compared to the SC pregnancies at week 20 of pregnancy, and four-fold increased risks at 28 weeks of gestation, as shown in Table 2 .

The present work aimed to assess the potential risk of GDM among Egyptian females conceived by IVF procedures (IVF) compared to spontaneous (SC) pregnancies. This study also aimed to investigate the time for screening of GDM in IVF pregnancies.

In our study, the in vitro fertilization (IVF) and SC pregnancy groups were age- and BMI-matched. The mean pre-pregnancy BMI was 29.74 ± 5.33 kg/m 2 in the IVF group and 32.02 ± 6.17 kg/m 2 in the SC pregnancies group, with a non-significant difference between both groups ( p  = 0.068). Overweight and obesity are major risk factors for GDM. The prevalence of GDM globally is increasing parallel to the rising surge of obesity in the reproductive age among females [ 17 ]. Kouhkan et al. [ 18 ] demonstrated that overweight and obese women had roughly three- and five-fold increases in the odds of developing GDM, respectively. Provost et al. [ 19 ] reported a higher rate of being overweight (22.9%) and obese (17.8%) among women undergoing ART. Torloni et al. [ 14 ], in a meta-analysis of 70 studies, reported that the risk of developing GDM in overweight and obese women in spontaneous pregnancies was nearly 2- and 4-fold higher in comparison to women with a normal BMI. Moreover, they demonstrated that in women with BMI > 25 kg/m 2 , adding 1 kg/m 2 BMI increased the risk of developing GDM by approximately 0.92%.

Regarding the potential risk of GDM following ART, Xiong et al. reported a linear positive association between pre-pregnancy body weight and the risk of GDM in a population-based cohort study. These findings support the recommendations for pre-pregnancy weight intervention, especially before starting ART procedures [ 20 ].

Our results demonstrated a statistically significant higher incidence of GDM in the IVF group compared to the spontaneous pregnancy group at 20 and 28 weeks of pregnancy, p  = 0.026 and 0.022, respectively. Also, an increased risk of developing GDM in the IVF group than in the spontaneous pregnancy group at 20 weeks (OR 5.185, 95% CI: 1.06 – 25.23), at 28 weeks (OR 4.333, 95% CI: 1.14 – 16.52). This was in harmony with the results of the Pandey et al. meta-analysis [ 21 ], which reported that the relative risk (95% CI) of having gestational diabetes was 1.48 (1.33–1.66) in IVF conceptions when compared with spontaneous conceptions with an absolute increased risk (95% CI) of 1% (1–1%). Cai et al. [ 22 ] showed that IVF pregnancies were related to a higher rate of GDM alongside raised fasting and 2-hour OGTT blood glucose levels in the late second trimester, particularly in overweight and obese mothers. Results of the Mohammadi et al. metanalysis [ 23 ] demonstrated a significant increase in GDM among women who conceived by ART in comparison to those who conceived spontaneously (pooled relative risk = 1.51, 95% confidence interval = 1.18–1.93). In agreement with our observations, Thomakos et al. reported that 37.6% of the IVF pregnancy group was diagnosed with GDM before the 24th week of gestation [ 24 ].

Also, a meta-analysis study by Bosdou et al. [ 4 ], which included 63,760 females who got pregnant after ART (GDM was present in 4776) and 1,870,734 females who got pregnant spontaneously (GDM in 158,526), revealed a higher risk of GDM after ART versus Spontaneous conceptions (RR 1.95, 95% CI 1.56–2.44). Our observations emphasise the importance of recognising IVF as an important risk factor for GDM, and raising awareness among clinicians would help better prevent GDM in the future. Moreover, current guidelines do not have specific recommendations regarding screening and management for GDM in IVF pregnancies; our data may be used to further evaluate the benefit of early screening for GDM (before 24-28 weeks of gestation) in IVF pregnancies [ 13 ].

Many females undergoing ART conceptions have significant risk factors for GDM, such as advanced maternal age, obesity, multiple pregnancies and polycystic ovary syndrome (PCOS), suggesting a potential relationship between GDM and ART [ 25 ]. Another explanation may be attributed to the high dose of gestational hormones administered with IVF techniques, which may precipitate metabolic derangements, insulin resistance, and glucose intolerance. However, further studies are required to establish these effects [ 21 , 26 ].

Our study has some limitations, mainly because this was performed in one centre; thus, the results may not apply to other populations. A significant limitation of our study was the lack of funding to investigate a larger sample size, as the post-hoc power analysis based on the given results of a total sample size of 110 (55 per group) is estimated to be 70% given that GDM was statistically significantly higher in IVF group compared to spontaneous pregnancy group (20 and 5.5% respectively). Thus, we recommend further multicentric research, recruiting a larger sample size. This study aimed to test the potential increased incidence of GDM among IVF-induced pregnancies. Thus, further research is recommended to investigate various risk factors implicated.

GDM is a common health problem in our community. The risk of GDM is increased by five-fold among IVF-induced pregnancies compared to spontaneous pregnancies. Our observations shed light on the recognition of IVF as a risk factor for GDM and that early screening for GDM in IVF pregnancies may help the early diagnosis of GDM.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

• Gestational diabetes
• In vitro fertilization

Assisted reproductive technology

Polycystic ovarian syndrome

Oral glucose tolerance test

type 2 diabetes

Glycosylated hemoglobin

Body mass index

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Department of Internal Medicine; Unit of Diabetes Lipidology & Metabolism, Faculty of Medicine, Alexandria University, 17 Champollion Street Azarita, Alexandria, Egypt

Yehia Moustafa Ghanem, May Mohamed Magdy & Noha Gaber Amin

Department of Obstetrics and Gynecology, Faculty of Medicine, Alexandria University, Alexandria, Egypt

Yasser El Kassar

Department of Internal Medicine, Faculty of Medicine, Fayoum University, Fayoum, Egypt

Mohamed Amara

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All authors contributed to the study concept and design. Material preparation, data collection and analysis were performed by [ MM ], [ NGA ] and [ YEK ]. The first draft of the manuscript was written by [ YMG ], [ NGA ], and [ MA ], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Noha Gaber Amin .

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The ethics committee of Alexandria University approved the study design. The study followed the criteria set by the Declaration of Helsinki. Confidentiality and personal privacy were respected at all levels of the study.

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Ghanem, Y.M., El Kassar, Y., Magdy, M.M. et al. Potential risk of gestational diabetes mellitus in females undergoing in vitro fertilization: a pilot study. Clin Diabetes Endocrinol 10 , 7 (2024). https://doi.org/10.1186/s40842-024-00164-x

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DOI : https://doi.org/10.1186/s40842-024-00164-x

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Global research team finds no clear link between maternal diabetes during pregnancy and ADHD in children

by The University of Hong Kong

An international research team led by Professor Ian Wong Chi-kei, Head of the Department of Pharmacology and Pharmacy at LKS Faculty of Medicine of the University of Hong Kong (HKUMed) has just provided valuable evidence through a 20-year longitudinal study to address the longstanding debate concerning the potential impact of maternal diabetes on attention-deficit/hyperactivity disorder (ADHD) in children.

This study, analyzing real-world data from more than 3.6 million mother-baby pairs in China's Hong Kong, Taiwan, New Zealand, Finland, Iceland, Norway and Sweden, showed that maternal diabetes during pregnancy is unlikely to be a direct cause of ADHD. The findings were published on 8 April in Nature Medicine .

Maternal diabetes and ADHD risk

Globally, approximately 16% of women have high blood sugar levels during pregnancy, and the prevalence of diabetes during pregnancy has been on the rise owing to factors like obesity and older maternal age. This can negatively affect the baby's brain and nervous system development.

ADHD is one of the most common neurodevelopmental disorders in children , which can have severe negative consequences. Individuals with ADHD are prone to poor outcomes such as emotional problems , self-harm , substance misuse, educational underachievement, exclusion from school, difficulties in employment and relationships, and even criminality.

The impact of maternal diabetes on the risk of ADHD in children has been a subject of debate because of inconsistent findings in previous studies. As a result, concerns regarding pregnancies in women with diabetes and the potential connection to the risk of ADHD in children have persisted.

Recognizing the importance of identifying risk factors for ADHD, especially for women of childbearing age, the cross-regional study utilized population-based data from China's Hong Kong, Taiwan, New Zealand, Finland, Iceland, Norway and Sweden to comprehensively assess the association between maternal diabetes and the risk of ADHD in offspring.

Findings challenge previous studies

This extensive study, which included a remarkable sample size of more than 3.6 million mother-child pairs from 2001 to 2014, with follow-up until 2020, yielded crucial observations regarding the association between maternal diabetes during pregnancy and the risk of ADHD.

The research team first found that children born to mothers with any type of diabetes, whether before or during pregnancy, had a slightly higher risk of ADHD compared to unexposed children, with a hazard ratio of 1.16. The study further identified elevated risks of ADHD for both gestational diabetes (diabetes during pregnancy) and pregestational diabetes (diabetes before pregnancy).

The hazard ratio for gestational diabetes was 1.10, indicating a modestly increased risk, whereas the hazard ratio for pregestational diabetes was 1.39, suggesting a more substantial association.

However, an intriguing finding emerged when the research team compared the risk of ADHD between siblings with discordant exposure to gestational diabetes and found no significant difference.

This unexpected result indicates that the previously identified risk of ADHD when children were exposed to gestational diabetes during pregnancy is likely due to shared genetic and familial factors, rather than gestational diabetes per se. These findings challenge previous studies that suggested maternal diabetes during or before pregnancy could heighten the risk of ADHD in children.

Research significance

According to Professor Ian Wong Chi-kei, Lo Shiu Kwan Kan Po Ling Professor in Pharmacy, and Head of the Department of Pharmacology and Pharmacy, HKUMed, the process of coordinating with scholars from around the world analyzing cross-regional cases spanning more than 20 years was no mean feat. This collaborative effort aimed to establish a comprehensive understanding of the matter at hand.

"In contrast to previous studies, which hypothesized that maternal diabetes during pregnancy could significantly increase the risk of ADHD, our study found only a modest association between maternal diabetes and ADHD in children after considering the intricate interplay of various influential factors. Notably, sibling comparisons showed this association is likely influenced by shared genetic and familial factors, particularly in the case of gestational diabetes," explained Professor Wong.

He highlighted the need for deliberate consideration and future research. "This implies that women who are planning pregnancy should look at their holistic risk profile rather than focusing solely on gestational diabetes ," he said. "Moving forward, it is crucial for future research to investigate the specific roles of genetic factors and proper blood sugar control during different stages of embryonic brain development in humans."

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ORIGINAL RESEARCH article

Outdoor artificial light at night exposure and gestational diabetes mellitus: a case–control study.

• 1 National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Pediatrics, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, China
• 2 Precision and Smart Imaging Laboratory, Beijing Friendship Hospital, Capital Medical University, Beijing, China
• 3 Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

Objective: This study aims to explore the association between outdoor artificial light at night (ALAN) exposure and gestational diabetes mellitus (GDM).

Methods: This study is a retrospective case–control study. According with quantiles, ALAN has been classified into three categories (Q1-Q3). GDM was diagnosed through oral glucose tolerance tests. Conditional logistic regression models were used to evaluate the association between ALAN exposure and GDM risk. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association. Restricted cubic spline analysis (RCS) was utilized to investigate the no liner association between ALAN and GDM.

Results: A total of 5,720 participants were included, comprising 1,430 individuals with GDM and 4,290 matched controls. Pregnant women exposed to higher levels of ALAN during the first trimester exhibited an elevated risk of GDM compared to those with lower exposure levels (Q2 OR = 1.39, 95% CI 1.20–1.63, p  < 0.001); (Q3 OR = 1.70, 95% CI 1.44–2.00, p  < 0.001). Similarly, elevated ALAN exposure during the second trimester also conferred an increased risk of GDM (second trimester: Q2 OR = 1.70, 95% CI 1.45–1.98, p  < 0.001; Q3 OR = 2.08, 95% CI 1.77–2.44, p  < 0.001). RCS showed a nonlinear association between ALAN exposure and GDM risk in second trimester pregnancy, with a threshold value of 4.235.

Conclusion: Outdoor ALAN exposure during pregnancy is associated with an increased risk of GDM.

1 Introduction

Exposure to artificial light at night (ALAN) has emerged as a progressively ubiquitous environmental hazard within contemporary society ( 1 ). Over the past several decades, urbanization and shifts in modern lifestyle have led to a continuous escalation of ALAN in our daily lives ( 2 ). While ALAN offers convenience and safety, it also brings forth an array of potential health concerns ( 3 ).

It is worth noting that recent research has employed satellite remote sensing data to validate the correlations between ALAN and a range of human health issues, including obesity ( 4 ), metabolic syndrome ( 5 ), sleep disorder ( 6 , 7 ), and cancer ( 8 ). Furthermore, emerging evidence suggests an association between ALAN and the risk of type 2 diabetes (Minjee ( 9 – 11 )). However, the relationship between outdoor ALAN exposure and gestational diabetes mellitus (GDM) remains poorly understood.

The mechanisms through which ALAN impacts human health remain unclear; however, research indicates that ALAN can disrupt circadian rhythms in humans and other organisms, thereby influencing various physiological processes and behavioral patterns ( 12 , 13 ). Exposure to ALAN may even lead to suppressed secretion of melatonin, a hormone that plays a crucial role in regulating sleep and other physiological functions ( 14 ). Furthermore, ALAN may impact the functioning of other endocrine systems, such as the secretion of adrenal corticosteroids and insulin regulation ( 15 ).

GDM is a condition characterized by abnormal blood glucose levels during pregnancy ( 16 ). Reports indicate that the prevalence of GDM varies across different countries and regions, with a notably higher incidence of 14.8% reported in China, making it a noteworthy public health concern in the country ( 17 ). This increased prevalence can primarily be attributed to behavioral and environmental risk factors ( 18 ). For mothers, having GDM can lead to heightened risks of pregnancy complications such as hypertension ( 19 ) and preterm birth ( 20 ), along with an elevated risk of developing type 2 diabetes later in life ( 21 ). Additionally, GDM can have enduring consequences for the newborn, including neonatal cardiovascular health ( 22 ) and respiratory distress syndrome ( 23 ). Consequently, the identification of potential risk factors for gestational diabetes is of paramount importance in mitigating the risks posed to both mothers and their offspring.

Pregnant women constitute a unique population group, as they are more susceptible to the influence of environmental factors during pregnancy due to hormonal effects ( 24 ). Current research suggests that exposure to ALAN may have adverse effects on fetal size and the metabolism of offspring ( 25 , 26 ). Hence, this study postulates that ALAN among pregnant women may is the risk of GDM through alterations in circadian rhythms and metabolism. The primary objective of this study is to investigate the association between outdoor ALAN exposure and gestational diabetes, aiming to address existing knowledge gaps and offer pertinent public health recommendations.

2 Materials and methods

2.1 study population.

This retrospective case–control study was conducted at the China-Japan Friendship Hospital. The geographic distribution of the study participants is illustrated in Figure 1 . Participants were selected based on specific inclusion criteria, which included: (1) residence in Beijing; (2) delivery at the China-Japan Friendship Hospital; (3) maternal age ≥ 18 years; (4) singleton pregnancies; (5) live-born infants. Exclusion criteria encompassed: (1) missing residential address ( n  = 1,122); (2) presence of complications during pregnancy, such as gestational hypertension, placental abruption, etc. ( n  = 320); (3) missing information on age, delivery date, last menstrual period (LMP) date, and other related data ( n  = 670). A 1:3 propensity score matching was performed based on nation and offspring sex to select the control group. The final study comprised 5,720 participants, and the workflow is depicted in Figure 2 .

Figure 1 . Geographical distribution of participants in Beijing. ALAN: artificial light at night; Red dots represent GDMs, and green dots represent controls. GDM, gestational diabetes mellitus.

Figure 2 . Flowchart of the study. LMP, Last Menstrual Period; GDM, Gestational diabetes mellitus; NDVI, normalized difference vegetation index; PM 2.5 , ambient fine particulate matter; PM 10 , ambient inhalable particulate matter.

The retrospective case–control study design precluded the acquisition of informed consent from the participants. Nevertheless, this approach aligns with the ethical review approved by the Ethics Committee of the China-Japan Friendship Hospital (Ethics Review Number: 2023-KY-137), which acknowledges the impracticality of obtaining informed consent in retrospective research studies.

2.2 Assessment of outdoor ALAN

In this study, ALAN measurements were obtained using the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), which offers superior spatial resolution, enhanced temporal resolution, an extended spectral range, and advanced calibration and correction when compared to the Operational Linescan System of Defense Meteorological Satellite Program (OLS-DMSP) ( 27 ). Commencing in April 2012, NPP-VIIRS captures data within the wavelength range of 500 nm to 900 nm, with a spatial resolution of 500 m × 500 m at the Equator ( 28 ). Monthly NPP-VIIRS nighttime light data for the period from 2013 to 2020 were obtained from the Earth Observation Group. 1 The unit of measurement is nanowatts per square centimeter per steradian (nW/cm 2 /sr), which quantifies the radiative intensity per unit area, accounting for solid angles in all directions.

2.3 Outcomes and covariates

In this study, we directly acquired the diagnosis of GDM in participants from electronic health records. This diagnosis was based on the results of the 75 g oral glucose tolerance test (75 g OGTT) conducted on participants between gestational weeks 24–28. Participants were diagnosed with GDM if they met any of the following diagnostic criteria: fasting blood glucose level ≥ 5.1 mmol/L (92 mg/dL); 1-h blood glucose level ≥ 10.0 mmol/L (180 mg/dL); 2-h blood glucose level ≥ 8.5 mmol/L (153 mg/dL) ( 29 ). This study concurrently collected data on fetal sex and birth weight. Additionally, information on the following covariates was gathered: maternal race (Han, non-Han), age (years), parity (primiparous, multiparous), gravidity (1, 2, >2 times), pre-pregnancy body mass index (BMI, kg/m 2 ), and conception season (Spring, Summer, Autumn, and Winter).

2.4 Other environmental variables

Given the role of environmental factors in GDM, we incorporated environmental covariates including inhalable particulate matter (PM 10 ) and fine particulate matter (PM 2.5 ), as well as green space, into the study. The data for PM 2.5 and PM 10 were sourced from the China High-resolution Air Pollutants (CHAP) database. PM 2.5 and PM 10 data were obtained using a spatiotemporal extreme random tree model that leveraged model data to fill spatial gaps in Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction Aerosol Optical Depth satellite products. This approach integrated ground observations, atmospheric reanalysis, emissions inventories, and other large-scale data sources, generating seamless nationwide surface PM 2.5 and PM 10 data from 2000 to 2021. The ten-fold cross-validation coefficient of determination (R 2 ) for PM 2.5 data was 0.92, with a root mean square error (RMSE) of 10.76 μg/m 3 ( 30 ). For the PM 10 data, the ten-fold cross-validation yielded an R 2 of 0.9 and an RMSE of 21.12 μg/m 3 ( 31 ). The Normalized Difference Vegetation Index (NDVI) was employed as a surrogate indicator for residential greenness. NDVI is a widely utilized metric in environmental research for quantifying the density and health status of vegetation in various regions ( 32 ). This index ranges from 0 to 1, where higher NDVI values indicate denser and healthier vegetation, while lower values suggest sparse or stressed vegetation ( 33 ). In our study, NDVI was estimated based on 16-day composite images from the NASA Terra Moderate Resolution Imaging Spectroradiometer satellite. 2 After obtaining annual data for PM 2.5 , PM 10, and NDVI, we performed weighting matching for the residential locations of pregnant women and computed annual prenatal environmental pollution exposures.

2.5 Exposure time window

Participants’ residential addresses were geocoded using Baidu Maps. 3 Subsequently, we proceeded to estimate the average exposure levels during the first and second trimesters of pregnancy to investigate potential heterogeneity in the association between ALAN and GDM across different exposure windows. These exposure windows corresponded to the first and second trimesters of pregnancy, corresponding to 3 and 6 months after the last menstrual period, respectively.

2.6 Statistical analysis

Continuous variables, normally distributed, are presented as mean ± standard deviation, while categorical variables are presented as counts (percentages). Differences between groups for continuous variables were compared using t-tests or Wilcoxon tests. Differences between groups for categorical variables were compared using chi-square tests or Fisher’s exact tests.

We employed conditional logistic regression to assess the link between ALAN exposure and GDM, calculating odds ratios (ORs) with 95% confidence intervals (CIs). Initially, we established an unadjusted model, without considering any potential confounding factors. Subsequently, we adjusted for potential confounders including age, ethnicity, gravidity, parity, pre-pregnancy body mass index, and conception season. Covariate selection guided by Directed Acyclic Graph Analysis ( Supplementary Figure S1 ). Finally, while controlling for potential confounding, we further controlled for PM 2.5 , PM 10 , and NDVI. Employing Pearson correlation analysis, we identified a strong correlation between PM 2.5 and PM 10 (correlation coefficient = 0.97, p  < 0.001). To mitigate issues of multicollinearity, principal component analysis was utilized to reduce the dimensionality of PM 2.5 and PM 10 , incorporating the first principal component (PC1), which accounted for 71.65% of the variance, into the final model as a substitute for both PM 10 and PM 2.5 .

To investigate the association between exposure to ALAN and GDM, restricted cubic spline (RCS) analysis was utilized in this study. The analysis was focused on ALAN exposure in first and second trimester pregnancy, assessing its nonlinear relationship with the risk of GDM. Additionally, we conducted a stratified analysis by infant sex to examine potential effect modification and assessed the interaction between ALAN and infant sex. The inclusion of interaction terms in the model was employed to assess whether fetal sex modifies the effect of exposure on the risk of GDM.

All statistical analyses were performed using R (version 4.1.0, available at https://www.r-project.org/ ).

2.7 Sensitivity analyses

This study conducted multiple sensitivity analyses: (1) ALAN per SD increase was employed to assess the relationship with GDM ( Supplementary Tables S1, S2 ). (2) Evaluation of Han ethnicity participants was performed to assess potential influences related to ethnicity ( Supplementary Table S3 ). (3) Similar analyses were conducted within the primiparous population to assess potential differences that might arise from multiple pregnancies ( Supplementary Table S4 ). (4) Excluding participants with pre-existing diabetes prior to pregnancy ( Supplementary Table S5 ). (5) Using linear regression to investigate the effect of ALAN exposure on participants’ fasting blood glucose levels ( Supplementary Table S6 ).

3.1 Characteristics of the study population

Table 1 provides an overview of the characteristics of pregnant women and newborns in the control group ( n  = 4,290) and GDM group ( n  = 1,430). While there were no significant differences in Han Chinese ethnicity between the group, the GDM group had a slightly higher mean age (GDM: 31.85 ± 3.96 years; Controls: 30.69 ± 3.41 years, p  < 0.001). Furthermore, the GDM group showed a higher proportion of multiparous women (23.92% compared to 19.91% in the control group, p  = 0.001). Gravidity distribution also significantly differed between the groups ( p  < 0.001). The distribution of neonatal sex was similar, with 51.40% males in the control group and 51.89% males in the GDM group. Additionally, there were slight differences in neonatal length (Control: 50.67 ± 2.39 cm; GDM: 50.47 ± 2.51 cm, p  = 0.007), birth weight (Control: 3302.70 ± 479.89 g; GDM: 3270.36 ± 510.18 g, p  = 0.030), and gestation duration (Control: 276.77 ± 12.90 days; GDM: 274.92 ± 33.59 days, p  = 0.003) between the groups.

Table 1 . Characteristics of pregnant women and newborns.

3.2 Distribution of environmental factors in different trimesters

Table 2 presents the differences in outdoor ALAN levels between the GDM and Control groups. There were no statistically significant differences in PM 10 levels (Control: 102.85 ± 21.33 μg/m 3 ; Case: 103.41 ± 20.70 μg/m 3 , p  = 0.391) or PM 2.5 levels (Control: 64.87 ± 17.72 μg/m 3 ; Case: 65.90 ± 17.47 μg/m 3 , p  = 0.054) between the two groups. Similarly, the NDVI showed no significant difference (Control: 0.32 ± 0.07; Case: 0.31 ± 0.07, p  = 0.216). However, there were substantial differences in ALAN levels between the groups. In the first trimester (T1), ALAN levels were significantly higher in the GDM group (27.46 ± 16.86 nW/cm 2 /sr) compared to the Control group (24.42 ± 16.64 nW/cm 2 /sr, p  < 0.001). This trend was consistent in the second trimester (T2) (Control: 24.69 ± 16.81 nW/cm 2 /sr; Case: 27.34 ± 16.61 nW/cm 2 /sr, p  < 0.001).

Table 2 . Differences in outdoor ALAN levels between the GDM and control groups.

3.3 Association of outdoor ALAN exposure in different trimesters with GDM

In Table 3 , we present the results of conditional logistic regression models examining the association between outdoor ALAN exposure and the risk of GDM across various trimesters (T1 and T2). In the initial unadjusted model (Model 1), participants in the second (Q2) and third (Q3) quartiles of ALAN exposure exhibited significantly elevated odds of developing GDM compared to those in the first quartile (Q1) during all trimesters (all p -values <0.001). These results remained consistent after accounting for potential confounders. Specifically, for the first trimester, the ORs were as follows: Q2 OR = 1.39 (95%CI 1.20–1.63, p  < 0.001), Q3 OR = 1.70 (95%CI 1.44, 2.00, p  < 0.001). In the second trimester, the ORs were: Q2 OR = 1.70 (95%CI 1.45–1.98, p  < 0.001), Q3 OR = 2.08 (95%CI 1.77–2.44, p  < 0.001). No significant interaction between ALAN exposure and sex was observed across all models. Table 4 presents the sex-specific associations of ALAN exposure with the risk of GDM across different trimesters, along with tests for interaction. ALAN exposure exhibited consistent associations with GDM risk across trimesters, particularly among females. In our study, RCS analysis showed no significant nonlinear relationship between ALAN exposure and GDM risk in first trimester pregnancy. However, a significant nonlinear association was found in second trimester pregnancy, with a threshold value of 4.235 ( Figure 3 ).

Table 3 . Association of outdoor ALAN exposure with GDM.

Table 4 . Sex-specific associations of ALAN exposure with GDM.

Figure 3 . Restricted cubic spline analysis. (A) The association between first-trimester ALAN and GDM; (B) The relationship between second trimester ALAN and GDM; ALAN, Artificial Light at Night; GDM, Gestational Diabetes Mellitus.

4 Discussion

To investigate the association between outdoor ALAN exposure and GDM, we conducted a retrospective case–control study. Our study found a significant association between exposure to outdoor ALAN during pregnancy and an increased risk of GDM after adjusting for confounding factors. Furthermore, the association between outdoor ALAN and the risk of GDM did not differ between male and female infants. Our findings provide evidence supporting the role of outdoor ALAN in the risk of GDM among pregnant women.

In recent decades, the impact of ALAN on human health has gained global attention. Numerous studies have investigated the associations between ALAN exposure and chronic conditions such as cardiovascular diseases ( 34 ), obesity ( 35 ), and mental disorders ( 36 ). Recent research has suggested that exposure to outdoor ALAN may increase the risk of type 2 diabetes mellitus (T2DM) (Minjee ( 9 , 10 )). Furthermore, a cross-sectional study has shown a significant association between long-term exposure to higher-intensity outdoor ALAN and an increased risk of impaired glucose metabolism ( 11 ). Recent studies have elucidated the relationship between ALAN and GDM. In the United States, the risk associated with GDM has been correlated with pre-sleep exposure to light, as measured by wrist-worn activity monitors ( 37 ). Consistent with our findings, a prospective cohort study in Sichuan Province, China, utilizing satellite data to estimate outdoor ALAN exposure, offered a broader perspective on environmental exposure ( 38 ). Furthermore, a study conducted in Hefei City revealed that outdoor ALAN was associated with elevated early-pregnancy glucose homeostasis markers, yet it did not correlate with GDM risk ( 39 ). The variability in these findings may be attributed to differences in study populations and geographical locations. Our research, conducted in Beijing, a major metropolitan area, underscores the significant public health implications of addressing light pollution in densely populated urban environments. Moreover, our study surpassed traditional methods by thoroughly adjusting for critical environmental variables, including PM 2.5 , PM 10 , and NDVI, thereby reinforcing the robustness and credibility of our findings.

Exploring the critical windows of association between maternal ALAN exposure and the risk of GDM is of paramount importance for devising targeted intervention measures. The early and mid-stages of pregnancy are crucial periods for embryonic and fetal development, being particularly susceptible to external environmental influences ( 40 ). In our study, we observed that pregnant women exposed to higher levels of ALAN during the first and second trimesters exhibited an increased risk of GDM. However, considering the timing of GDM diagnosis ( 41 ), the relationship between ALAN exposure during the second trimester of pregnancy and GDM may be subject to constraints, necessitating further investigation.

The mechanisms underlying the relationship between ALAN exposure during pregnancy and the risk of GDM remain poorly understood. Several potential mechanisms may be involved. Firstly, ALAN exposure could potentially impact the risk of GDM by disrupting the circadian rhythms of pregnant women. Circadian rhythm regulation during pregnancy is critical for normal fetal and maternal physiological processes ( 42 ). ALAN may induce circadian rhythm disruption ( 43 ), leading to sleep disturbances and reduced sleep quality among pregnant women, consequently increasing the risk of GDM. Secondly, hormonal changes may play a significant role. ALAN exposure may influence hormone levels in pregnant women ( 44 ), particularly melatonin, a hormone crucial for regulating circadian rhythms during pregnancy ( 45 ). ALAN exposure might suppress melatonin secretion, potentially affecting maternal physiology and fetal development negatively. Lastly, ALAN exposure may contribute to an elevated risk of GDM by provoking alterations in inflammation and immune responses. Animal experiments have demonstrated that prolonged illumination can lead to changes in both the immune system and inflammatory processes ( 46 ). Although these mechanisms remain multifaceted and not fully elucidated, further research is needed to unravel these intricate pathways. In-depth investigations in both laboratory and epidemiological settings will contribute to a better understanding of the relationship between ALAN exposure and GDM, offering more precise directions for future intervention strategies.

This study has several limitations that warrant discussion. Firstly, in our research, we estimated outdoor ALAN exposure during pregnancy using high-resolution satellite images. However, we lacked data on indoor light exposure and whether participants used blackout curtains during the night, which could potentially lead to exposure misclassification. Future studies should consider collecting information on both indoor and outdoor light exposure. Secondly, while we adjusted for environmental confounders related to GDM, such as environmental particulate matter ( 47 ) and greenness ( 48 ) at the residential area, we did not account for other potential confounding factors, such as temperature ( 49 ), household income and education level. The absence of this information needs to be addressed and improved in future research. Thirdly, our study adopted a retrospective case–control study design, limiting the ability to establish causality between ALAN exposure and GDM. Therefore, the relationship between ALAN and GDM needs further confirmation through prospective study designs. Fourthly, the annual inclusion of study participants was not uniform ( Supplementary Table S7 ), which was due to the COVID-19 pandemic. Although the ratio of cases to controls remained consistent, this could potentially introduce a certain degree of bias. Finally, our single-center study involved participants from the Beijing area with relatively higher socioeconomic status. Caution is advised when extending the study results to regions with lower economic development. Future research should validate these findings in diverse socioeconomic contexts.

Despite these limitations, our study possesses several strengths. Firstly, we elucidated the association between ALAN exposure during pregnancy and GDM, identifying the critical exposure window for this relationship. This finding provides valuable reference for targeted intervention measures during the identified exposure window. Additionally, we conducted a series of sensitivity analyses and performed stratified analyses by newborn sex to assess the consistency and robustness of this relationship.

5 Conclusion

In summary, our study reveals that higher outdoor ALAN exposure during pregnancy is associated with an elevated risk of GDM. These findings emphasize the need for targeted interventions and further research to better understand the mechanisms underlying this relationship and mitigate the health risks associated with light pollution during pregnancy.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Ethics Committee of the China-Japan Friendship Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants' legal guardians/next of kin because this was a retrospective study and the ethics committee waived informed consent.

Author contributions

QS: Methodology, Writing – original draft, Writing – review & editing. FY: Investigation, Visualization, Writing – original draft, Writing – review & editing. JL: Investigation, Writing – original draft, Writing – review & editing. YY: Investigation, Writing – original draft, Writing – review & editing. QH: Software, Writing – original draft, Writing – review & editing YC: Data curation, Resources, Writing – original draft, Writing – review & editing. DLi: Software, Writing – original draft, Writing – review & editing. JG: Data Curation, Writing – original draft, Writing – review & editing. CW: Software, Writing – original draft, Writing – review & editing. DLv: Visualization, Writing – original draft, Writing – review & editing. LT: Investigation, Writing – original draft, Writing – review & editing. QZ: Conceptualization, Supervision, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by MOE Key Laboratory of Population Health Across Life Cycle (No: JK20225), Chinese Academy of Medical Sciences Clinical and Translational Medicine Research Project (No: 2021-I2M-C&T-B-089), Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (No: 2021-I2M-1-049), and a grant from State Key Laboratory of Resources and Environmental Information System.

Acknowledgments

We thank all the participants in this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1396198/full#supplementary-material

Abbreviations

ALAN, artificial light at night; GDM, gestational diabetes mellitus; CI, confidence interval; OR, odds ratio; OLS-DMSP, Operational Linescan System of Defense Meteorological Satellite Program; NPP-VIIRS, Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite; PM10, ambient inhalable particulate matter; PM2.5, ambient fine particulate matter; CHAP, China High Air Pollutants; NDVI, normalized difference vegetation index; RMSE, root mean square error; R2, coefficient of determination.

1. ^ https://eogdata.mines.edu/

2. ^ https://ladsweb.modaps.eosdis.nasa.gov

3. ^ https://map.baidu.com

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Keywords: gestational diabetes mellitus, outdoor artificial light, pregnancy, risk factors, air pollution

Citation: Sun Q, Ye F, Liu J, Yang Y, Hui Q, Chen Y, Liu D, Guo J, Wang C, Lv D, Tang L and Zhang Q (2024) Outdoor artificial light at night exposure and gestational diabetes mellitus: a case–control study. Front. Public Health . 12:1396198. doi: 10.3389/fpubh.2024.1396198

Received: 05 March 2024; Accepted: 02 April 2024; Published: 10 April 2024.

Reviewed by:

Copyright © 2024 Sun, Ye, Liu, Yang, Hui, Chen, Liu, Guo, Wang, Lv, Tang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qi Zhang, [email protected]

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