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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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• Open access
• Published: 02 January 2024

Supporting self-management in women with pre-existing diabetes in pregnancy: a mixed-methods sequential comparative case study

• Katelyn Sushko 1 ,
• Patricia Strachan 1 ,
• Michelle Butt 1 ,
• Kara Nerenberg 2 &
• Diana Sherifali 1  

BMC Nursing volume  23 , Article number:  1 ( 2024 ) Cite this article

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Metrics details

Introduction

Maternal glycemia is associated with pregnancy outcomes. Thus, supporting the self-management experiences and preferences of pregnant women with type 1 and type 2 diabetes is crucial to optimize glucose control and perinatal outcomes.

Research design and methods

This paper describes the mixed methods integration of a sequential comparative case study. The objectives are threefold, as we integrated the quantitative and qualitative data within the overall mixed methods design: (1) to determine the predictors of glycemic control during pregnancy; (2) to understand the experience and diabetes self-management support needs during pregnancy among women with pre-existing diabetes; (3) to assess how self-management and support experiences helpe to explain glycemic control among women with pre-existing diabetes in pregnancy. The purpose of the mixing was to integrate the quantitative and qualitative data to develop rich descriptive cases of how diabetes self-management and support experiences and preferences in women with type 1 and type 2 diabetes during pregnancy help explain glucose control. A narrative approach was used to weave together the statistics and themes and the quantitative results were integrated visually alongside the qualitative themes to display the data integration.

The quantitative results found that women achieved “at target” glucose control (mean A1C of the cohort by the third visit: 6.36% [95% Confidence Interval 6.11%, 6.60%]). The qualitative findings revealed that feelings of fear resulted in an isolating and mentally exhausting pregnancy. The quantitative data also indicated that women reported high levels of self-efficacy that increased throughout pregnancy. Qualitative data revealed that women who had worked hard to optimize glycemia during pregnancy were confident in their self-management. However, they lacked support from their healthcare team, particularly around self-management of diabetes during labour and delivery.

Conclusions

The achievement of optimal glycemia during pregnancy was motivated by fear of pregnancy complications and came at a cost to women’s mental health. Mental health support, allowing women autonomy, and the provision of peer support may improve the experience of diabetes self-management during pregnancy. Future work should focus on developing, evaluating and implementing interventions that support these preferences.

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What is already known on this topic

• Pregnant women living with type 1 and type 2 diabetes have an increased risk of perinatal complications, including fetal and infant death.

• As maternal glycemia is associated with pregnancy outcomes, supporting women in diabetes self-management may optimize glycemia and reduce perinatal complications.

What this study adds

• Women who achieved optimal glycemia during pregnancy reported high levels of self-efficacy in diabetes self-management.

• Diabetes self-management negatively impacted women’s mental health and made for an isolating pregnancy experience.

• Mental health support, peer support and autonomy in diabetes self-management is preferred by patients to improve their pregnancy experiences.

How this study might affect research, practice or policy

• Peer support and mental health interventions were unavailable for study participants.

• Policies supporting maternal self-management of diabetes during labour and delivery were also lacking.

• Appropriate peer and mental health interventions, as well as policies to support autonomy of self-management during labour and delivery, are required.

• Future research should focus on developing interventions related to these desired supports and implementing them into the standard of care for this population.

With the rising prevalence of overweight and obesity and an older average maternal age during childbirth, type 2 diabetes in pregnancy has been steadily increasing [ 1 , 2 , 3 ]. The incidence of type 1 diabetes has also been rising, with an etiology that remains largely unknown [ 4 ]. These factors have contributed to the increased prevalence of pre-existing type 1 and type 2 diabetes in pregnancy, affecting 0.5–2.4% of pregnancies worldwide [ 5 , 6 , 7 , 8 , 9 ].

Pregnancies impacted by pre-existing diabetes are at an increased risk for many complications, from congenital anomalies to fetal and infant death [ 10 ]. Maternal glycemia, measured by glycosylated hemoglibin A1C (A1C), is closely linked to perinatal morbidity and mortality; each 0.1% increase of periconception A1C above 4.9% confers a 2% and 3% relative increase in fetal and infant death, respectively [ 11 ]. As a result, women experience a heavy burden of diabetes self-management during pregnancy, typically occurring outside of the health care system. During pregnancy, when additional stressors compound the stresses of everyday life, there may be an increased occurrence of mental health disorders. The prevalence of mental health disorders among adults with diabetes is already higher when compared to those without diabetes. Thus, women with diabetes in pregnancy may be even more likely to be affected by mental health disorders during pregnancy [ 12 ].

Supporting women in diabetes self-management is important to reduce mental stress, optimize glycemia during pregnancy and subsequently improve perinatal outcomes. How to best support women with pre-existing diabetes during pregnancy in self-management to attain optimal glycemia is not well understood. Currently, women with diabetes in pregnancy are supported in multiple ways [ 1 ]. Interventions include preconception care and counselling, care by a multidisciplinary team and education regarding the importance of self-monitoring of blood glucose, recommendations for weight gain and insulin administration, among others [ 1 ]. Throughout the duration of pregnancy, women attend appointments with the healthcare team to reinforce these concepts, including from endocrinologists and obstetricians to nurses and dietitians [ 1 ]. An exploration of this topic is therefore of importance to a variety of professionals of the multidisciplinary team.Women with pre-existing diabetes in pregnancy are unique in their self-management experiences and preferences compared to women with gestational diabetes. First, the management of pregnant women with pre-existing type 1 and type 2 diabetes is more complex than women with gestational diabetes due to their higher risk of experiencing serious perinatal complications and the need for insulin therapy [ 10 ]. Furthermore, in type 1 and type 2 diabetes, attention during pregnancy is focused on titrating insulin dosing using pens, continuous infusion sets (e.g., pumps) and continuous glucose monitors, while avoiding hypoglycemia. This is in contrast to the general focus on nutrition and exercise-related interventions for many women with gestational diabetes [ 1 ]. Glycemic targets during pregnancy among women with pre-existing diabetes are also much more stringent than those for non-pregnant adults with diabetes [ 1 ]. As such, the experiences and supports that women with pre-existing diabetes during pregnancy need likely differ from those with gestational diabetes and non-pregnant adults with diabetes. The objectives are threefold, as we integrated the quantitative and qualitative data within the overall mixed methods design: (1) to determine the predictors of glycemic control during pregnancy; (2) to understand the experience and diabetes self-management support needs during pregnancy among women with pre-existing diabetes; (3) to assess how self-management and support experiences helpe to explain glycemic control among women with pre-existing diabetes in pregnancy.

This paper represents the mixed methods integration of a four-phased mixed methods sequential comparative case study [ 13 , 14 ]. This is a complex mixed methods design that involves the integration of diverse types of data (quantitative and qualitative) to develop enhanced analyses and case descriptions of the topic of interest [ 13 , 14 ]. This design provides detailed and contextualized data that is beneficial when there is a need to portray and understand complex variation regarding the subject under study [ 13 ]. Both the quantitative and qualitative phases received ethics approval from the Hamilton Integrated Research Ethics Board (REB #14–222 and #13,847).

Quantiative phase

The quantitative phase consisted of an analysis of quantitative data collected as part of the ‘Assessing the Determinants of Pregestational Diabetes in Pregnancy: A Prospective Cohort Study.’ This study took place at the Maternal-Fetal Medicine clinic at McMaster University Medical Center in Ontario, Canada between April 2014 to November 2019. Consecutive convenience sampling was employed to recruit eligible participants who met the following criteria: (1) a diagnosis of type 1 or type 2 diabetes; (2) attending the Maternal-Fetal Medicine clinic at McMaster University Medical Centre clinic for obstetrical care; and (3) age 18 years or older. A total of 111 participants were recruited (type 1 diabetes, n = 55; type 2 diabetes, n = 56). Data were collected three times during pregnancy, between 0 and 16 weeks (time point 1 (T1)); 17–28 weeks (time point 2 (T2)); and 29–40 weeks (time point 3 (T3)). Participants completed a demographic questionnaire, surveys to measure self-efficacy for Diabetes scale), self-care behaviors (Summary of Diabetes Self-Care Activities and Measures) and satisfaction with medical care (Patient Assessment of Care for Chronic Conditions scale). Glycemic control was assessed via self-report of A1C and confirmation with medical charts. Descriptive statistics were completed to understand the distribution of participant demographic and clinical characteristics, and participant levels of self-efficacy, self-care and care satisfaction. Independent Samples t-Tests, Chi-squared tests and Fisher’s exact tests explored differences in baseline variable distribution, stratified by diabetes type. Linear mixed-effects modelling was used to explore trends in glycaemic control and examine self-efficacy, self-care and care satisfaction as predictors of A1C. Linear mixed-effects modeling was employed given non-independence in the data across timepoints – data was collected from the same participants at each timepoint. To control for potential confounding factors on the relationship between self-efficacy, self-care, care satisfaction and glycaemic control, we adjusted for participant age, diabetes duration, ethnicity, education level, household income and insurance coverage [ 15 ]. SPSS (IBM Corp. Released 2021. IBM SPSS Statistics for Macintosh, Version 28) was used to perform all statistical analyses.

Qualitative phase

The qualitative phase was conducted between March and July 2022 and employed a qualitative description design. We used the principles of purposeful sampling to recruitment women aged 18 years or older, with type 1 and type 2 diabetes, who were currently or who were previously pregnant. A total of 12 women were recruited (type 1 diabetes, n = 6; type 2 diabetes, n = 6). The sample of women interviewed in the qualitative study attended the Maternal-Fetal Medicine clinic at McMaster University Medical Centre. However, they were not the same women included in the quantitative phase due to the different times of study conduct (April 2014 to November 2019 for the quantitative phase and March to July 2022 for the qualitative phase). Women participated in individual semi-structured interviews to describe their experience of managing diabetes and determine their needs regarding diabetes self-management education and support during pregnancy. Individual interviews were the primary means of data collection. The interviews were conducted face to face via videoconferencing (Zoom) with an approximate duration of 30–60 min. All interviews were audiorecorded. Baseline demographic and clinical characteristics were collected before the interview and supplementary field notes were written immediately after. The recorded audio was transcribed verbatim and imported into NVivo (NVivo. QSR International; 2020) for analysis. Conventional content analyses, as described by Hsieh and Shannon was employed [ 16 ].

Mixed methods phase

In the context of this study, the use of a mixed-methods sequential comparative case study was ideal as we aimed to develop detailed and particularized information about the self-management experiences and preferences of women with pre-existing diabetes during pregnancy. Furthermore, we expected that the self-management experiences and preferences during pregnancy might vary based on diabetes type. Thus, the use of the mixed methods sequential comparative case study enabled us to understand the potential variation between these two populations. Our goal was to portray realistic and practical information about the evidence on this topic to guide subsequent research in designing, evaluating, and implementing self-management education and support interventions for this population.

We have previously published the study protocol [ 14 ], and the quantitative [ 15 ] and qualitative phases [ 16 ]. These provide details regarding our methodology. Briefly, the sequence of the mixed methods study was as follows: (1) Phase I: Prospective cohort; (2) Phase II: Planning the qualitative data collection; (3) Phase III: Qualitative descriptive; (4) Phase IV: Integration of quantitative and qualitative findings and case construction (Appendix A ).

Mixed methods integration

The purpose of the mixed methods procedures was to integrate the quantitative and qualitative data. The goal was to develop a rich analysis and description of the diabetes self-management and support experiences and preferences during pregnancy of women with pre-existing diabetes and how these factors may help explain glycemia. Through the integration of the quantitative and qualitative data, cases were developed and refined based on these experiences and preferences. We used Creswell and Plano Clark’s recommendations for mixed methods research integration procedures to guide the mixing process [ 13 ]. Integration first occurred following the completion of the quantitative study when we analyzed the results to plan the interview guide. It also informed the participant selection approach for the qualitative study. The second integration, reported in the current paper, illustrates the sequential mixing of the quantitative and qualitative results and the development of cases to represent the main findings.

We incorporated Stake’s approach to instrumental and collective case studies [ 17 , 18 ], which is utilized when the goal of the study is to facilitate an understanding of a phenomenon of interest [ 17 ], particularly for social sciences and human services research [ 19 ]. Stake’s approach allows for researcher flexibility and values the emergence of cases as the study progresses, aligning with the sequential ordering of our mixed methods approach [ 19 ]. We endeavoured to understand how factors related to self-management support (e.g., diabetes management behaviours and self-efficacy) and the pregnancy experience, help to explain glycemic control among women with pre-existing diabetes. In the instrumental and collective derivatives of Stake’s approach to case study research, cases are developed through categorical aggregation, based on repeated patterns and categories that emerge following researcher immersion in the data [ 18 ]. Also in keeping with Stake’s approach, we utilized methodological, data source and investigator triangulation to promote the validity of the case developemnt, as Stake’s approach values intituion and impression over rigid, preplanned case definitions and binding [ 18 ]. Finally, cases were defined through: (1) holistic (considers the connectivity of the phenomoenon and its context); (2) empirical (observations/data); and (3) interpretive (intuition or researchers) approaches, recognizing that each case is a complex and integrated entity, which is not limited or bound to working parts [ 17 , 18 ]. Following our quantitative study, we developed an interview guide for the qualitative study to ensure that similar questions were asked across participants. Data source triangulation was achieved when participants provided similar answers across questions [ 18 ]. Investigator triangulation was attained when the first author conferred with the senior author throughout case construction [ 18 ]. To promote study transparency, quality and rigor, we followed the Good Reporting of a Mixed Methods Study tool (Appendix B ) [ 20 ].

Our mixed-methods sequential comparative case study findings are presented in two approaches.

First, results are presented using a narrative approach, weaving together the quantitative statistics and the qualitative themes. Second, we created a joint display to present the quantitative results and qualitative findings alongside cases that were derived from the mixed methods integration.

Quantitative results

In phase I (cohort), we explored the trends in glycemia and assessed self-efficacy [ 21 ], self-care [ 22 ] and care satisfaction [ 23 ] during pregnancy in 111 women (55, type 1 diabetes; 56, type 2 diabetes), across three time points during the perinatal period (time point one, zero to 16 weeks; time point two, 17 to 28 weeks; time point three, 29 to 40 weeks). Overall, the cohort’s average A1C was “at target” ( ≤  6.5%) by time point two and remained “at target” at time point three. Measurements of self-efficacy and care satisfaction were relatively high among the cohort (Table  1 ). A one unit increase in self-efficacy (e.g., total score of 8 to a total score of 9) was associated with a mean reduction in A1C of 0.22% (95% CI -0.42, -0.02, p  = 0.03). In using the self-care [ 21 ] tool, every one unit increase in the exercise sub-score (e.g., total score of four to a total score of five) was associated with a mean reduction in A1C of 0.11% (95% CI, -0.22, -0.01, p = 0.04). These associations were present after adjustment for confounders [ 15 ].

Qualitative results

In phase III (qualitative description), we described the experience of managing diabetes during pregnancy and identified the self-management education and support preferences among 12 women (6, type 1 diabetes; 6, type 2 diabetes). We identified eight qualitative themes within two overarching categories: (1) themes describing patient experiences of managing diabetes in pregnancy; and (2) themes identifying preferences for diabetes self-management education and support during pregnancy. In general, women described the experience of managing diabetes during pregnancy as terrifying, isolating, mentally exhausting and they had feelings about being out of control. Preferences were expressed for individualized healthcare, mental health support and support from peers and the healthcare team [ 16 ].

When conceiving this study, we examined the literature and hypothesized that glycemic control would be sub-optimal among this cohort. Thus, we planned to quantify glycemic control through the cohort study, use findings from the qualitative study to explain the reasons behind sub-optimal control and develop suggestions to optimize glycemia based on participant experiences and preferences. However, after analyzing and integrating the quantitative and qualitative data, we found that a different story emerged. In contrast to the hypothesized findings, the study cohort demonstrated high self-efficacy and achieved on target A1C. Thus, in addition to using the qualitative study to explore the reasons behind glycemic control, we explored the pregnancy experience through participant-derived suggestions and preferences for support. As the findings did not mirror the hypotheses, and in accordance with Stake’s case study approach [ 19 ], we modified how we constructed the cases from the plan outlined in the protocol [ 14 ]. Rather than developing cases based on variation in glycemia and covariates (e.g., self-efficacy, care satisfaction) across diabetes type, we used Stake’s approach that facilitated the highlighting of repeated patterns in the data to aid in the construction of cases for support in pregnancy that were participant-derived. Our mixed-methods apporach produced confirmatory and expansionary insights and as a result, three cases regarding participant-suggested self-management support preferences emerged: (1) Mental Health Support; (2) Support for Autonomy in Self-Management; and (3) Peer Support. Figure  1 depicts the constructed cases and supporting quantitative and qualitative data in a joint display. Below we have used a narrative approach that weaves together the quantitative statistics and the qualitative themes to further display the data integration.

Joint-Display Table with Box Plots Depicting Quantitative Results (Good Glycemic Control, High Self-Efficacy, Low Satisfaction with Care) Side-by-Side with Qualitative Text of Participant-Derived Cases for Support during Pregnancy. Note: The box plots visually show the distribution of glycemic control (mean 6.36 [95% CI 6.11–6.60]), self-efficacy (range of the scale: 0 to 10; cohort mean 7.96 ± 1.20) and care satisfaction (range of the scale score: 0 to 5; cohort mean 3.39 ± 0.81) by the third follow-up

Women in the study cohort demonstrated optimal glycemia across both types of diabetes, meeting the recommended national A1C guidelines of ≤ 6.5% [ 14 ]. By the third follow-up, the overall mean A1C remained “at target” at 6.36% (95% CI 6.11%, 6.60%). Confidence in self-management, represented by self-efficacy, was also relatively high and improved throughout pregnancy. By the third follow-up, the overall mean score on the self-efficacy scale was 7.96 (SD 1.20), out of a total possible score of 10 [ 15 ].

Upon reflecting on the qualitative findings in light of the quantitative results, we hypothesized that lower glycemic control may have manifested from women feeling out of control and fearing diabetes-induced pregnancy complications (“You feel guilty when your blood sugar is high… you’re like, what important body part is being formed right now? And am I ruining it?” ). Unfortunately, the price of such tight control was an isolating and mentally exhausting pregnancy experience. (“It’s like I can’t, I feel like I can’t be a normal person because I’m constantly having to check my blood sugar, constantly having to remember to take my insulin or if I’m going out like I have to make sure ‘OK do you have your insulin? Do you have your meter just in case your sensor goes wrong?’… I just I wish I was a normal person, but I’m not.”). Such experiences contributed to the first constructed case, the desire for Mental Health Support (“ The piece that wasn’t a part of the high-risk clinic was the mental health piece and… I don’t think that people look at it so seriously. And I don’t think anybody talked to me about like the fact that like I was feeling stressed out… So, I think that was something that was missing”). Possibly due to feeling out of control and scared, women put a lot of effort and research into their diabetes management, resulting in feelings of self-confidence (“… My A1C was 5.3… you get to feel annoyingly confident because you’re like ‘Look at me, I’m doing better than a real pancreas’” ).

After women had worked hard to have tight control during pregnancy and were confident in their self-management, they wanted to be able to manage insulin administration during labour and delivery ( “Very early on I was asking like ‘I want to control my diabetes at the end” ; “I had, you know built up the courage ‘cause you’re like, ‘I’m gonna tell this doctor how I want things done’ and so I was like ‘I want to be in charge. I don’t want to take off my pump, like under no circumstances.’” ). However, they found that they lacked support from the healthcare team to have autonomy in self-management during labour (“ They were like ‘That’s not what we do here. The protocol here is you will be put on an insulin IV’… Just basically flat out told me ‘No that’s not what we do here.’ …If I pushed back, it was always like ‘We have to do this or your baby’s at risk of dying…” ; “So like at the end of the day I did end up having to go on the insulin IV… They did a terrible job. My blood sugar level was perfect when I went in… of course, my blood sugar went up. And it did not go down for the rest of labour and delivery…” ). These reported experiences were the basis for the development of the second case, the desire for Support for Autonomy in Self-Management.

Perceiving a lack of support from the healthcare team, women found that they needed to turn to their diabetes community for advice regarding diabetes management and thus the third constructed case was the desire for Peer Support ( “I have gastroparesis too… I found that my Endo and OB, that wasn’t even something that they’d ever really considered.” ; “I remember just asking like ‘All I want from you [endocrinologist] is like a suggestion on where my insulin like starting point should be… And he … wouldn’t help me at all… … I reached out to my community, to kind of get a read on what I thought was pretty standard [pump settings for labour and delivery]…” ). In addition to looking to the diabetes community for advice on diabetes management during pregnancy, women expressed a desire for regular companionship with fellow mothers with pre-existing diabetes who would understand their unique day-to-day struggles ( “My best friend is great. She’s wonderful …I can go to her and I can be like, ‘Oh my, my blood sugars are all over the place and everything’ but all she’s gonna say is ‘Oh, you know, like you got this, you can do it’… but I need a diabetic best friend… so I know that like there’s somebody else in my corner, that’s actually going through this and understands everything…” ; “It would have been nice to have that in-person connection to someone else like me… to connect to somebody else who would have been, like you know, someone who’s not just on Facebook. That would have been nice…”).

The quantitative results indicate that participants were confident in diabetes management. As a result, they achieved optimal glycemia during pregnancy. This finding contrasted with expectations developed from the existing literature showing that women with type 1 and type 2 diabetes have difficulty reaching glycemic targets during pregnancy [ 24 , 25 , 26 ]. The qualitative findings, in contrast, show a different perspective: women appeared to be in significant mental distress and wanted support during pregnancy from peers and professionals. The finding of the need for peer support among those with diabetes in pregnancy has also been found in existing literature [ 27 , 28 ]. Thus, integrating both findings resulted in an apparent discordance: women may be confident in diabetes self-management, yet, they still desired support during a challenging pregnancy. There was also an expressed desire for autonomy in diabetes management during labour and delivery. The quantitative results show that women maintained optimal glycemia during pregnancy by closely measuring carbohydrate intake and carefully administering insulin, changing the timing and dosing as they progressed through the pregnancy trimesters. However, once they began to labour, they were forced to surrender diabetes management to the healthcare team. The stripping of autonomy at this critical time was another contributor to the significant distress that women revealed. The findings of this integration have important implications for future research and policies that affect clinical practice focused on this population.

Implications for clinical practice

Women in our study demonstrated high self-efficacy and achieved optimal glycemia. Their motivations were clear; the main priority was to avoid potential diabetes-induced complications for their infant. The fear of consequences for their infant and the strain of stringent self-management resulted in poor mental health. This is an example of the previously noted discordance: women appeared confident in diabetes management and achieved optimal glycemic control but they still had a strong desire for mental health support. Unfortunately, participants expressed that the healthcare team did not address their mental health concerns. Diabetes is often concomitant with mental health disorders among non-pregnant adults. Depression, for example, is two to three times more common among those with diabetes than those without diabetes [ 29 ]. Anxiety disorders are also more common among adults with diabetes, with a 20% higher prevalence when compared to the background population [ 30 ]. Pregnancy is a time when women are particularly susceptible to mental illness [ 31 ]. Research on mental health disorders and pre-existing diabetes in pregnancy is limited. However, a recent meta-analysis that studied women with gestational and pre-existing diabetes in combination found that they are at a significantly higher risk of developing depression during pregnancy than women without diabetes [ 31 ]. Few studies have focused on mental health among women with diabetes [ 31 ]. Thus, there is an opportunity for future research to examine more closely the prevalence of mental health disorders in pregnancy among women with pre-existing diabetes and to develop interventions to optimize mental health during this vulnerable time.

Implications for research

Although the women were confident in their diabetes management and met their target glucose levels during pregnancy, they voiced a strong desire for peer support. The previously noted discordance remained: women were high functioning from a diabetes self-management perspective but still wanted connection and support. Some women explained that they engaged with peers in national and international online support groups for expectant mothers with pre-existing diabetes. Others described interacting with expectant mothers in their friend group who did not have diabetes. Neither of these social connections provided them with sufficient support. Thus, they wished for in-person peer support facilitated by the healthcare team. Research on peer support during pregnancy without diabetes is limited but shows that peer support may be beneficial for improving mental health outcomes. A narrative review of six studies on peer support and the development of postpartum depression showed some evidence for lower Edinburgh Postpartum Depression Scale scores following peer support interventions and reports of positive experiences and maternal satisfaction [ 32 ]. Other research indicated the beneficial effects of peer support on mood and anxiety may occur by decreasing feelings of isolation and stress [ 33 ]. To our knowledge, research focused on in-person peer support during pregnancy for pre-existing diabetes is limited to one study that assessed the need for peer support among those with gestational and type 2 diabetes, finding that almost half of the participants were interested in such an intervention [ 34 ]. Independent of diabetes, the literature indicates that up to 21% of women experience mood disorders during pregnancy and early parenthood [ 32 ]. Mental health disorders are even more common during a pregnancy complicated by pre-existing diabetes [ 30 ]. As such, the evidence indicates that peer support may contribute to reduced isolation and stress during pregnancy and early parenthood for women without diabetes [ 32 ], future research should focus on developing such interventions for those with pre-existing diabetes who are even more vulnerable to such feelings.

Implications for policy

The quantitative results showed that the women in our study were confident in diabetes self-management, meeting recommended glycemic targets throughout pregnancy. However, once they began to labour, they were forced to allow the healthcare team to take over diabetes management. Women reported this was to the detriment of their physical and mental health: their glucose levels were allowed to run dangerously high, causing them significant stress and frustration. Thus, there was a strong desire for autonomy and diabetes self-management during labour. Currently, the policies at the regional centre where we conducted our studies do not support diabetes self-management during labour. Thus, the women in our study reported that they had to turn off their insulin pump, for example, and receive insulin via an intravenous controlled by the healthcare team. Management by the healthcare team was problematic because women revealed that when their glucose levels became too high, the team would not consider their requests to increase the insulin dose. This resulted in glucose levels near diabetic ketoacidosis, delayed hospital discharge and caused women to have to administer their insulin from home without the knowledge of the healthcare team. In other clinical settings, diabetes self-management using insulin pumps, for example, during short surgeries, is commonplace [ 35 ]. Established protocols exist in parts of the United States, Canada, Australia and Europe [ 36 , 37 , 38 , 39 ] and current research indicates that insulin pump use during labour is safe and may result in improved glycemic control [ 40 , 41 , 42 , 43 , 44 , 45 ]. Thus, it is imperative for policymakers and healthcare team members to use knowledge translation strategies that bridge the gap between evidence and clinical practice. Knowledge translation strategies that are effective for policymakers include providing information packages, one-on-one meetings and tailored summaries [ 46 ]. Effective strategies for healthcare team members include education workshops, webinars and in-services [ 46 ]. The implementation of these strategies to facilitate change in clinical practice is critical to allow for autonomy and diabetes self-management during labour.

Strengths and limitations

Our mixed methods study has several strengths. First, through the quantitative phase, we demonstrated the prevalence and correlates of self-management support and glycemic control during pregnancy in women with type 1 and type 2 diabetes, offering insights related to women with type 2 diabetes: a population of women with limited research. We also revealed the diabetes self-management support experiences and preferences during pregnancy in this population in the qualitative phase. Finally, our mixed methods integration generated cases that provided deeper understandings regarding participant-derived support preferences related to diabetes self-management among women with type 1 and type 2 diabetes in pregnancy using methodological, data source and investigator triangulation to promote the validity of the case study.

However, our study also had limitations. First, the quantitative phase had several limitations, including the reliance on self-reported data surveys. Further details can be found in the published paper [ 15 ]. The qualitative phase also had limitations, including a relatively small sample size. This resulted in limited transferability of the study findings. Finally, the mixed methods integration had limitations. One of these was the need to find a method for case construction that better fit with the repeated and emerging findings in the data. In the original protocol, we described our plan to construct cases based on variation in glycemia and covariates (e.g., self-efficacy, care satisfaction) across diabetes type, using the Diverse Case Method [ 47 ] for case Sect [ 14 ]. This was based on the idea that glycemic control might be sub-optimal among our population and there could be variation in glycemic control and self-efficacy across the different diabetes types. As the study results revealed that this was generally not the case, we had to modify how we developed the cases. However, the guiding literature on mixed methods case study designs suggests a sequential and flexible approach to case development and emphasizes the importance of allowing the emergence of cases as the research progresses [ 13 ]. Thus, we used Stake’s approach to case construction that allowed for the development of cases based on emerging, repeated patterns in the data.

In conclusion, overall, women with pre-existing diabetes in this cohort study are able to achieve tight glycemic control during pregnancy. They are motivated and display high self-efficacy in diabetes self-management. However, the achievement of optimal glycemia appeared to be driven by fear, which took a toll on their mental health and pregnancy experience. Thus, women desired mental health support, support for autonomy in self-management and peer support. We plan to use the findings from this study to provide the basis for the development, evaluation and implementation of interventions related to these participant-described support preferences.

Availability of data and materials

Study participants were advised that their raw data would remain confidential and not be shared publicly, particularly due to the sensitive nature of the interview questions. Upon reasonable request, the data are available from the corresponding author.

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KS is supported through graduate scholarships. DS acknowledges the Heather M. Arthur Population and Health Research Institute/Hamilton Health Sciences Chair in Inter-Professional Health Research. KN acknowledges Heart and Stroke and the Canadian Institute of Health Research for the Women’s Heart and Brain Midcareer Research Chair.

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Additional file 1: appendix a. figure s1..

Study Flow Diagram. Appendix B. Table S1. Application of the Good Reporting of a Mixed Methods Study (GRAMMS) Checklist 16 .

Additional file 2: Table S1.

Participant Baseline Characteristics, Stratified by Type of Diabetes

Additional file 3: Table S2.

Predictors of A1C, Stratified by Type of Diabetes.

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● The patient has type 2 diabetes, obesity related, with nonproliferative retinopathy, distal sensory neuropathy, and microalbuminuria. She has poor glycemic management. She needs intensive insulin therapy to decrease the risks of spontaneous abortion, fetal macrosomia, and the neonatal complications of diabetic pregnancy, including hypoglycemia, hypocalcemia, hyperbilirubinemia, and polycythemia. (See "Interactive diabetes case 11: A 34-year-old pregnant patient with type 2 diabetes – A1" .)

● The patient has type 2 diabetes, obesity related, with nonproliferative retinopathy, distal sensory neuropathy, and microalbuminuria. She has poor glycemic management. She needs intensive insulin therapy to decrease the risk of congenital malformations and also the risks of spontaneous abortion, fetal macrosomia, and the neonatal complications of diabetic pregnancy, including hypoglycemia, hypocalcemia, hyperbilirubinemia, and polycythemia. (See "Interactive diabetes case 11: A 34-year-old pregnant patient with type 2 diabetes – A2" .)

● The patient has type 2 diabetes, obesity related, with nonproliferative retinopathy, distal sensory neuropathy, and microalbuminuria. She has poor glycemic management. She needs urgent referral for a therapeutic abortion to prevent the maternal and fetal complications of diabetic pregnancy. (See "Interactive diabetes case 11: A 34-year-old pregnant patient with type 2 diabetes – A3" .)

Insulin Use During Gestational and Pre-existing Diabetes in Pregnancy: A Systematic Review of Study Design

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• Kristin Castorino 1 ,
• Beatrice Osumili 2 ,
• Theophilus Lakiang 3 ,
• Kushal Kumar Banerjee 3 ,
• Andrea Goldyn 4 &
• Carolina Piras de Oliveira 4  

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Introduction

Insulin is the first-line pharmacologic therapy for women with diabetes in pregnancy. However, conducting well-designed randomized clinical trials (RCTs) and achieving recommended glycemic targets remains a challenge for this unique population. This systematic literature review (SLR) aimed to understand the evidence for insulin use in pregnancy and the outcome metrics most often used to characterize its effect on glycemic, maternal and fetal outcomes in gestational diabetes mellitus (GDM) and in pregnant women with diabetes.

An SLR was conducted using electronic databases in Medline, EMBASE via Ovid platform, evidence-based medicine reviews (2010–2020) and conference proceedings (2018–2019). Studies were included if they assessed the effect of insulin treatment on glycemic, maternal or fetal outcomes in women with diabetes in pregnancy. Studies on any type of diabetes other than gestational or pre-existing diabetes as well as non-human studies were excluded.

In women diagnosed with GDM or pre-existing diabetes, most studies compared treatment of insulin with metformin ( n  = 35) followed by diet along with lifestyle intervention ( n  = 24) and glibenclamide ( n  = 12). Most studies reporting on glycemic outcomes compared insulin with metformin ( n  = 22) and glibenclamide ( n  = 4). Fasting blood glucose was the most reported clinical outcome of interest. Among the studies reporting maternal outcomes, method of delivery and delivery complications were most commonly reported. Large for gestational age, stillbirth and perinatal mortality were the most common fetal outcomes reported.

This SLR included a total of 108 clinical trials and observational studies with diverse populations and treatment arms. Outcomes varied across the studies, and a lack of consistent outcome measures to manage diabetes in pregnant women was observed. This elucidates a need for global consensus on study design and standardized clinical, maternal and fetal outcomes metrics.

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Diabetes is the most prevalent antenatal complication of pregnancy and can be subdivided into two types: pregestational and gestational diabetes mellitus (GDM) [ 1 ]. The prevalence of diabetes in pregnancy has been increasing in the USA [ 2 ]. About 1–2% of pregnant women have pre-existing diabetes, and approximately 1–14% of all pregnancies are affected by GDM [ 3 , 4 ]. Women diagnosed with diabetes during pregnancy are at an increased risk to develop other maternal complications such as gestational hypertension, preeclampsia and hypoglycemia, which subsequently can lead to the development of type 2 diabetes (T2D) later in life [ 3 ]. They are also at a higher risk to undergo cesarean section or have premature delivery. In addition, diabetes in pregnancy is associated with a risk of developing fetal complications such as macrosomia and neonates with large for gestational age (LGA), small for gestational age, premature birth, neonatal respiratory distress, asphyxia, neonatal hypoglycemia and congenital anomalies [ 5 , 6 ].

The recommendations from current standard of care of diabetes management in pregnant women are beyond regular blood glucose level monitoring, lifestyle behavioral changes, medical nutrition therapy (MNT), physical exercise and pharmacotherapy (metformin, glyburide or insulin) [ 7 ]. Insulin is considered the most efficacious pharmacotherapy for all types of diabetes in pregnancy, including GDM and pregestational diabetes [ 8 ]. The 2023 update of the the American Diabetes Association (ADA) guidelines, The American College of Obstetricians and Gynecologists-2018 (ACOG-2018) and International Diabetes Federation (IDF) guidelines recommend use of insulin as a first-line pharmacological therapy for management of pre-existing diabetes and GDM over other oral anti-diabetic agents [ 9 , 10 , 11 , 12 ].

Recent advances in insulin therapy are focused on improving the pharmacokinetics and pharmacodynamics of insulin. These goals enable prolonged profile of action, flexible dosing regimen and reduce the risk of hypoglycemia [ 13 ]. However, well-powered randomized clinical trials (RCTs) in pregnant women with diabetes are often conducted well after non-pregnant populations, if it is done at all, which leads to delayed implementation of evidence-based practices for insulin use in pregnancy. In addition, designing studies to demonstrate the achievement of stringent glycemic targets as recommended by the guidelines remains challenging for this unique population [ 14 ]. A variety of insulins have been commercially available globally, many of which have limited data on their use in pregnancy. Real-world barriers such as access to insulin or newer insulins, access to glucose monitoring and delayed prenatal care can further make adhering to guidelines difficult, if not impossible. Considering the different insulin options available in the global market and understanding the use and effects of types of insulin and/or insulin regimens on glycemic, maternal and fetal outcomes may support clinical practice. This may as well aid in improving study designs for treatment of diabetes in pregnancy. Therefore, to assess and evaluate the current body of evidence including RCTs and real-world observational data, we performed a systematic literature review (SLR) to better understand and summarize the evidence for insulin use in pregnancy to harmonize future study design in this special population.

Study Design

Search strategy.

A comprehensive search was conducted to identify relevant studies using electronic databases in Medline, EMBASE via Ovid platform and evidence-based medicine reviews from 1 January 2010 to 25 August 2020. In addition, manual (hand) searches were performed for relevant conference abstracts that were published from 2018 to 2019.

Inclusion and Exclusion Criteria

The eligibility for assessing the relevance of each article for data extraction was based on the population, intervention, comparison, outcomes and study design (PICOS) criteria (Supplementary Table 1). Inclusion criteria for the selection of articles consisted of studies that were RCTs, non-RCTs and observational studies (Supplementary Table 1). Studies were included with perinatal women diagnosed with either gestational, pre-existing diabetes (type 1 diabetes [T1D] or type 2 diabetes [T2D]) or mixed population (pregnant women with GDM, T1D or T2D). Specific glycemic (fasting blood glucose [FBG], post prandial glucose [PPG] and time in range), maternal (prevalence of hypoglycemia, cesarean section, preterm labor, hypertension, induced labor and preterm delivery) and fetal (fetal mortality, fetal morbidity and LGA) outcomes were included in this review (Supplementary Table 1). Studies on any type of diabetes other than gestational diabetes or pre-existing T1D or T2D as well as non-human studies were excluded.

Study Selection and Data Extraction

The DistillerSR tool, a cloud-based literature review software, was used to screen, compile and manage all the identified studies. Two independent reviewers screened the identified studies based on their titles and abstracts against the eligibility criteria. Subsequently, full-text articles were retrieved for full-text screening against eligibility criteria. A third, independent reviewer resolved any uncertainties/conflicts between the two reviewers. The reasons for exclusion are reported in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram (Fig.  1 ). DistillerSR was used to extract data from the included studies. Details of study characteristics, patient characteristics, interventions and outcomes of interest were extracted in the data extraction form. Studies with multiple publications were identified and linked to the primary study; all relevant data were extracted under the primary study. Identification and screening of the available literature was performed in accordance with PRISMA statement [ 15 ], the Centre for Reviews and Dissemination [ 16 ] and the Cochrane Collaboration [ 17 ].

PRISMA flow diagram presenting number of studies included and excluded at each stage of screening

Quality Assessment

The quality of the included RCTs was assessed using the quality assessment checklist, in accordance with the recommendations by the Centre for Reviews and Dissemination’s Guidance for Undertaking Reviews in Health Care (NICE, 2019) [ 18 ]. The quality of observational studies was assessed using the Newcastle-Ottawa Scale, 2019 [ 19 ]. Three factors were considered to score the quality of included observational studies: selection, comparability and outcomes assessment.

Ethical Approval

This article is based on previously conducted studies and does not contain any studies with human participants or animals.

Study Selection

A total of 2628 citations were retrieved after initial search through electronic databases and conference proceedings (Fig.  1 ). After removing duplicates, 2614 articles were assessed for title-abstract screening. Subsequently, 835 articles were assessed for full-text screening. Overall, 724 records were excluded, and 111 publications, representing 108 unique studies were included in the SLR (Fig.  1 ).

Study and Patient Characteristics

Of the total 108 included studies, 30 were clinical trials, 74 were observational studies, and 1 was a quasi-experimental study. In three studies the study designs were not clear. The RCTs and observational studies included in this review covered perinatal women across different continents, like America, Europe, Asia, Oceania, Africa and/or multinational.

Details on patient characteristics including maternal age, gestational weight, gestational age at diagnosis and treatment initiation and relevant obstetrical history are given in Tables  1 and 2 . Study characteristics are summarized in Supplementary Table 2 and 3, and treatment interventions along with types of insulin utilized by the women diagnosed with GDM or pre-existing diabetes are summarized in Supplementary Tables 4 and 5.

Glycemic Outcomes in People with GDM and Pre-existing Diabetes

Of the 108 included studies, 21 clinical trials and 20 observational studies reported the clinical outcomes of interest (FBG, PPG, glycemic range and glycemic variability) in women with GDM (Table  3 ). Six clinical trials and 12 observational studies reported the clinical outcomes of interest in women with pre-existing diabetes and mixed population (Table  4 ).

Evidence from Clinical Trials

In women diagnosed with GDM, majority of the trials compared an insulin regimen [basal only, basal/bolus, or bolus only] to metformin ( n  = 13) (Table  3 ). In addition, few trials compared insulin to glibenclamide/glyburide ( n  = 3), (Table  3 ). The difference in the glycemic outcomes in women treated with insulin versus other therapies varied across the trials and provided very low-quality of evidence for the outcomes. The study design varied widely across the trials.

FBG was the most reported clinical outcome ( n  = 22). Some RCTs ( n  = 3) reported a significantly better ( p  ≤ 0.01) FBG in the metformin-treated group compared to those with insulin [ 20 , 21 , 22 ]. Two RCTs by Zawiejska et al. and Khan et al. compared glycemic control in women diagnosed with GDM in response to insulin and metformin and reported significantly better FBG in the insulin-treated groups compared to other therapies ( p  ≤ 0.01) [ 23 , 24 ]. Arshad et al. compared insulin with diet therapy and exercise and reported a significantly better FBG in the diet-treated group compared to those treated with insulin [ 25 ].

In an RCT by Somani et al. with no differences in glycemic outcomes between the metformin and insulin groups at baseline, higher PPG levels were reported in group treated with insulin compared to those treated with metformin ( p  = 0.005) [ 26 ]. In an RCT by Ji et al. with mixed population, a significant improvement in PPG and time in range (TIR) was observed with insulin detemir compared to insulin neutral protamine Hagedorn (NPH) ( p  < 0.001) [ 27 ].

Evidence from Observational Studies

In women with GDM, most observational studies that reported clinical outcomes of interest compared insulin to diet/MNT ( n  = 6), metformin ( n  = 5), combination of metformin and/or diet and/or lifestyle interventions ( n  = 4). Additionally, other studies reported a comparison between different types of insulin ( n  = 3), insulin versus no insulin ( n  = 1) and insulin versus glyburide ( n  = 1) (Table  3 ). Five studies showed significant improvement in FBG and PPG among those managed with other therapies compared to the insulin-treated group [ 28 , 29 , 30 , 31 , 32 ] ( p  < 0.05). These observational studies provide an insight into the real-world use of insulin within this specific population, highlighting that potential barriers of insulin use may be limiting its full benefits in optimizing glycemic control.

Maternal Outcomes in People with GDM and Pre-existing Diabetes

Of the 108 included studies, 18 clinical trials and 44 observational studies reported the maternal outcomes of interest (prevalence of hypoglycemia, cesarean section, preterm labor, hypertension, induced labor and preterm delivery) in women with GDM (Table  5 ). Maternal outcomes in women with diabetes prior to pregnancy and mixed population were reported in 6 trials and 18 observational studies (Table  6 ).

Most trials included in this study had small numbers of participants and no prolonged follow-up after the treatment. Some of the included trials had unclear risk of bias due to lack of blinding, unclear methods of randomization and selective reporting of outcomes. The primary outcomes of interest were different across the included studies.

Most included trials reported no difference in the proportion of cesarean sections among women treated with metformin versus insulin [ 21 , 24 , 26 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. However, two RCTs by Galal et al. and Hassan et al. reported a significantly higher rate ( p  ≤ 0.05) of cesarean sections in the insulin-treated group [ 20 , 40 ], while an RCT by Ijas et al. reported a lower rate of cesarean section in the insulin-treated group versus metformin ( p  = 0.047) [ 41 ]. In three RCTs by Khan et al., Mirzamoradi et al. and Huhtala et al., numerically higher rates of preterm delivery, preeclampsia and induced labor were observed in the insulin-treated group relative to comparator group using oral anti-diabetic agents [ 24 , 42 , 43 ]. Other RCTs by Galal et al., Niromanesh et al. and Hassan et al. reported a numerically higher incidence of preterm delivery and induced labor in the group treated with metformin versus insulin [ 20 , 39 , 40 ]. In women with pre-existing diabetes, an open-label, randomized study by Ainuddin et al. reported a significantly high rate of incidence of pregnancy-induced hypertension in the insulin-treated group compared to only metformin group and metformin and insulin-treated group [ 44 ], while an RCT by Ji et al. demonstrated a numerically higher incidence of gestational hypertension in the insulin NPH-treated group compared to the insulin detemir-treated-group [ 27 ].

Among women diagnosed with GDM, across different interventions, retrospective analyses revealed that cases of cesarean section and preterm delivery were higher in women managed with insulin than in those managed with other interventions such as diet/MNT, metformin and metformin + insulin [ 28 , 32 , 45 , 46 ]. Compared with other interventions, insulin did not show a significant difference in the rate of gestational hypertension and induced labor in women treated with insulin and those managed with lifestyle modification [ 47 , 48 ] or metformin [ 49 , 50 ]. In the mixed population, a prospective cohort study by Negrato et al. compared insulin glargine with NPH and reported a significantly higher rate of preeclampsia in the NPH-treated group compared to the glargine-treated group ( p  < 0.0001) in women diagnosed with diabetes prior to pregnancy [ 51 ].

Maternal Hypoglycemia in Clinical Trials and Observational Studies

The overall rate of hypoglycemia in women with GDM and a mixed population was significantly higher in the insulin-treated group compared to metformin and metformin with additional insulin therapy [ 27 , 52 , 53 ]. Contrarily, a significantly lower incidence of hypoglycemia was reported with insulin ( p  < 0.001) compared to glyburide [ 54 , 55 ].

Fetal Outcomes in Women with GDM and Pre-existing Diabetes or Mixed Population

Of the 108 included studies, 7 RCTs and 24 observational studies reported fetal outcomes of interest in women with GDM, and 2 trials and 14 observational studies reported fetal outcomes of interest in women diagnosed with diabetes prior to pregnancy and a mixed population (Tables  7 , 8 ). Most of the included studies scored low to moderate on the Newcastle-Ottawa Scale and quality assessment checklist; they had limited power, relatively small sample size, long individual study period and a high drop-out rate.

A number of studies on women with GDM and pre-existing diabetes reported a numerically higher proportion of LGA in women treated with insulin compared to women treated with metformin [ 34 , 36 , 41 , 56 ], with a significant difference ( p  = 0.001) reported by Eid et al. [ 34 ]. In an RCT, Hod et al. compared insulin detemir with NPH in pregnant women diagnosed with diabetes and reported a significantly higher rate of LGA in the group treated with insulin NPH compared to the group treated with insulin detemir [ 57 ]. Other RCTs by Ainuddin et al. and Mukopadhyay et al. reported a lower proportion of LGA in women treated with basal/bolus insulin compared to metformin + insulin and glibenclamide, respectively [ 44 , 58 ]. In another RCT, Somani et al. compared stillbirth in women treated with insulin (regular or NPH or both) versus metformin. This trial reported one case of stillbirth in the insulin-treated group compared to no stillbirth in the metformin group ( p  = 0.32) [ 26 ].

In women diagnosed with GDM, three retrospective cohort studies by Koren et al., Castillo et al. and Hedderson et al. compared insulin versus glyburide and reported no substantial differences in the proportion of LGA in between the treatment groups [ 55 , 59 , 60 ]. However, other retrospective analyses by Simeonova-Krstevska et al., Benhalima et al. and Bogdanet et al. reported a significantly higher proportion of LGA in the insulin-treated group compared to diet/MNT and metformin ( p  < 0.0001– p  < 0.05) [ 28 , 32 , 46 ]. In women diagnosed with diabetes prior to pregnancy, one retrospective database review by Neff et al. reported a significantly higher rate of delivery of LGA in mothers treated with CSII-aspart and NPH compared to those treated using MDI-aspart and NPH ( p  = 0.03) [ 61 ]. Most of the studies did not report stillbirth, with only five studies reporting this outcome. Perinatal mortality among women with pre-existing diabetes was reported in retrospective studies by Bartal et al., Abell et al. and Billionnet et al., and no differences across the treatment arms were observed [ 48 , 62 , 63 ]. However, in a prospective cohort study by Negrato et al., a significantly higher rate of perinatal mortality ( p  = 0.028) in pregnant women diagnosed with diabetes prior to pregnancy was reported among NPH-treated women compared to those treated with glargine [ 51 ].

We conducted an SLR that assessed the paradigm of reported insulin use in pregnant women with diabetes, as well as the outcomes, including recommended clinical parameters related to glycemic control as part of their treatment goals and maternal and fetal outcomes. The wide variety in outcomes of interest when comparing insulin use with other anti-diabetic agents across the included studies makes it extremely difficult and potentially misleading to summarize findings and make management recommendations, illustrating the need for standardization of study design with consistent glycemic and maternal/fetal efficacy outcomes to evaluate the use of glucose-lowering medications in pregnancy.

Glycemic outcomes of interest were reported in 27 clinical trials and 32 observational studies. Notably, while 1-h and 2-h PPGs are the recommended treatment goals in patients with GDM, many of the studies captured in this review focused on HbA 1c as a primary outcome measure. Furthermore, compared to the non-pregnant population, there are very few well-powered RCTs evaluating insulin use in pregnancy. Ji et al. published a well-designed RCT in 2020 showing that in pregnant women diagnosed with diabetes prior to pregnancy, a significant improvement in PPG and TIR was observed among those treated with detemir compared with insulin NPH as basal insulin. Both groups received the short-acting human insulin three times a day before the meals [ 27 ]. These results increase the options for women requiring basal insulin therapy for diabetes management in pregnancy [ 27 ]. Use of continuous glucose monitoring (CGM) was also observed to be effective in improving glycemic range metrics in women treated with insulin. However, at the time of this SLR there was limited evidence to draw a conclusive statement on the impact of CGM role in improving glycemic outcomes for diabetes in pregnancy. Overall, there was no clear consensus between the study outcomes and use of various intervention types and regimens. The quality of the included studies was assessed, and they were found to be low on evidence with high risk of bias. Therefore, we could not conclude which intervention type or regimen was best for pregnant women with diabetes.

Maternal outcomes such as hypoglycemia, preeclampsia, cesarean delivery, preterm delivery and induced labor were reported in 18 RCTs, which may be due to the difficulty in collecting these outcome measures. They were reported more frequently in studies designed to compare an insulin regimen to another regimen such as in women treated with insulin versus those treated with metformin, diet/MNT and other anti-diabetic agents [ 20 , 28 , 32 , 40 , 45 , 46 , 64 ]. Most of the studies included in this review used insulin therapy as the last option of treatment, after the failure of nutritional therapy or in association with other drug interventions such as metformin and/or sulfonylureas. This suggests that these patients could have had more severe insulin resistance and/or deficiency than the other patients, and this would likely confound glycemic, maternal and fetal outcomes. Heterogeneity was observed across maternal outcomes among the studies, including rates of cesarean delivery, gestational age at delivery and induction of labor. The plausible reason for heterogeneity could be due to various ethnic groups, study designs, treatment requirements and selection criteria.

The most common fetal complication reported across the included studies for any type of diabetes during pregnancy was LGA, confirming that these patients were mostly in hyperglycemic state, a common cause of LGA. Other common neonatal outcomes observed, commonly associated with LGA and the mother’s hyperglycemia, included the rate of complications such as preterm birth and neonatal hypoglycemia. Across studies covered in this review, insulin was associated with fewer cases of LGA only compared with glibenclamide, as observed in a study by Mukopadhyay et al. that compared insulin and glibenclamide for treatment of GDM [ 58 ]. In accordance with another meta-analysis, women treated with glibenclamide reported the highest incidence of LGA, preeclampsia, neonatal hypoglycemia and preterm birth; metformin (plus insulin when required) had the lowest risk of macrosomia, pregnancy hypertension, LGA, preterm birth and low birth weight [ 65 ]. Overall, there was no clear evidence of the risk of delivery of LGA in those born of mothers with diabetes treated with insulin versus other oral anti-diabetic agents. Based on the current results, it is difficult to make a conclusive affirmation of the most effective form of treatment to reduce incidence of neonatal complications in pregnant women with diabetes.

Across the included studies, treatments with metformin and diet/MNT were associated with better clinical, maternal and fetal outcomes than those treated with insulin therapy. However, the studies did not provide enough evidence on whether insulin can help achieve improved outcomes compared with other therapies. Overall, the quality of the evidence of RCTs ranged from low to moderate, whereas for observational studies the quality ranged from low to good. A variety of methods was used to diagnose GDM in the included studies. Furthermore, it is difficult to draw conclusions about the optimal approach to treatment of diabetes in pregnancy because of inconsistencies in the criteria for management of glucose targets, patient adherence to treatment, clinical outcome measures across studies and lack of long-term safety data.

The current SLR included clinical trials and observational studies with diverse populations and treatment arms. Some studies lacked appropriate sample size, and many studies utilized a variety of methods for diagnosis of GDM. Data on pregnant women diagnosed with diabetes prior to pregnancy were very limited. Furthermore, high-quality studies are needed to identify the optimal treatment regimens for women with diabetes in pregnancy who are treated with insulin.

There were clear limitations to the current SLR. With limited evidence and meta-analyses, the included studies did not provide sufficient evidence to identify clear differences between the various insulin types and regimens. Most of the included studies did not adjust for other potential confounding factors such as maternal age, educational status, income, ethnicity and other factors that might influence the results; therefore, findings should be interpreted with caution. This SLR included clinical trials and observational studies with varied populations and treatment arms. For some studies, sample size was small, and many studies did not report statistical tests for significance. In the included studies, there was no consensus on the types of outcome measures reported in pregnant women with diabetes. Most of the studies reported that there was no evidence of clear-cut benefit of one intervention type or regimen over the other. Hence, no firm conclusions or management recommendations could be made about different insulin types and regimens in pregnant women with diabetes. Future trials are required that are multi-centered, randomized, well-powered and of improved methodological quality with standardization of glycemic and maternal/fetal efficacy outcome measures. Furthermore, more research is warranted with larger groups of pregnant women, with transparent reporting of how the trials were conducted, and that reports clinical, maternal and fetal outcomes.

In summary, the findings of this review were comparable to the existing reviews evaluating treatment of diabetes in pregnancy. There is a tremendous paucity of well-designed RCTs and no consensus for the study design and definition of diabetes in pregnancy in the existing literature. We identified a variety of definitions being used that did not always overlap. We observed that the lack of standard diagnosis also results in a diversity of outcomes that are used in clinical practice to evaluate optimal medical management in pregnant women with diabetes. It would be helpful for the practitioners and patient populations if the outcomes were consistently defined and reported globally. According to the ADA Management of Diabetes in Pregnancy guidelines, the standard treatment goals for pregnant women with diabetes are aimed at maintaining target blood glucose levels (fasting glucose 70–95 mg/dl [3.9–5.3 mmol/l], 1-h postprandial glucose 110–140 mg/dl [6.1–7.8 mmol/l] and/or 2-h postprandial glucose 100–120 mg/dl [5.6–6.7 mmol/l]) to prevent maternal and fetal complications, achieved through stringent glucose monitoring and insulin therapy [ 11 ]. However, the universal adoption of these recommendations in the real-world is limited, as we identified in the observational studies analyzed, and some misalignment still exists in randomized clinical trials as well. This makes identifying any real-world association of the effectiveness of insulin in maternal and fetal outcomes difficult. With the increased access to CGM, the collection of glycemic values will increase, and more glycemic outcome data will be generated. However, this will require a more standardized approach, especially without a clear consensus on clinically relevant CGM metrics for GDM and T2D.

Conducting well-designed RCTs to evaluate the efficacy of various insulins or insulin regimens in this unique population remains an area that requires specialized attention. There is a need to be better aligned on clinical endpoints to study pregnant populations to delineate what treatment or therapies unequivocally demonstrate improvement in maternal and neonatal outcomes, especially with introduction of innovative insulin formulations and improved technologies that evaluate glucose management.

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Beatrice Osumili, Theophilus Lakiang, Carolina Piras De Oliveira, and Kristin Castorino contributed to the conception and design of the study. Beatrice Osumili, Theophilus Lakiang, Carolina Piras De Oliveira, and Kushal Kumar Banerjee were involved in data collection. All authors Goldyn contributed to the interpretation of study results, provided critical revisions, and have read and approved the final version of the manuscript.

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Beatrice Osumili, Kushal Kumar Banerjee, Andrea Goldyn, and Carolina Piras De Oliveira are full-time employees and shareholders of Eli Lilly and Company. Theophilus Lakiang was an employee of Eli Lilly and Company at the time this research was conducted and is currently an employee of GE Healthcare. Kristin Castorino receives research support provided to her institution from Dexcom, Abbott, Medtronic, Novonordisk, Ely Lilly, and Insulet and consulting fees from Dexcom. Theophilus Lakiang: Author affiliation has changed since the time this research was conducted. Assigned affiliation is the institution of employment at the time this research was conducted.

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Castorino, K., Osumili, B., Lakiang, T. et al. Insulin Use During Gestational and Pre-existing Diabetes in Pregnancy: A Systematic Review of Study Design. Diabetes Ther (2024). https://doi.org/10.1007/s13300-024-01541-6

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Interactive case study: Gestational diabetes

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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.

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• http://orcid.org/0000-0002-8530-6900 Katelyn Sushko 1 ,
• Diana Sherifali 1 ,
• Kara Nerenberg 2 ,
• Patricia H Strachan 3 ,
• Michelle Butt 1
• 1 School of Nursing , McMaster University Faculty of Health Sciences , Hamilton , Ontario , Canada
• 2 Medicine, Obstetrics & Gynaecology and Community Health Sciences , University of Calgary Cumming School of Medicine , Calgary , Alberta , Canada
• 3 Nursing , McMaster University Faculty of Health Sciences , Hamilton , Ontario , Canada
• Correspondence to Ms Katelyn Sushko; sushkokj{at}mcmaster.ca

Introduction For women with pre-existing type 1 and type 2 diabetes, glycaemic targets are narrow during the preconception and prenatal periods to optimise pregnancy outcomes. Women aim to achieve glycaemic targets during pregnancy through the daily tasks of diabetes self-management. Diabetes self-management during pregnancy involves frequent self-monitoring of blood glucose and titration of insulin based on glucose measures and carbohydrate intake. Our objective is to explore how self-management and support experiences help explain glycaemic control among women with pre-existing diabetes in pregnancy.

Methods and analysis We will conduct a four-phased mixed-methods sequential comparative case study. Phase I will analyse the data from a prospective cohort study to determine the predictors of glycaemic control during pregnancy related to diabetes self-management among women with pre-existing diabetes. In phase II, we will use the results of the cohort analysis to develop data collection tools for phase III. Phase III will be a qualitative description study to understand women’s diabetes education and support needs during pregnancy. In phase IV, we will integrate the results of phases I and III to generate unique cases representing the ways in which self-management and support experiences explain glycaemic control in pregnancy.

Ethics and dissemination The phase I cohort study received approval from our local ethics review board, the Hamilton Integrated Ethics Review Board. We will seek ethics approval for the phase III qualitative study prior to its commencement. Participants will provide informed consent before study enrolment. We plan to publish our results in peer-reviewed journals and present our findings to stakeholders at relevant conferences/symposia.

• Diabetes in pregnancy
• QUALITATIVE RESEARCH
• DIABETES & ENDOCRINOLOGY

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https://doi.org/10.1136/bmjopen-2022-062777

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STRENGTHS AND LIMITATIONS OF THIS STUDY

Mixed-methods sequential comparative case study designs facilitate the development of detailed and nuanced information.

However, the single-centre design of the cohort study will limit the generalisability of our findings.

The use of qualitative methods may further limit study generalisability.

Introduction

Pre-existing diabetes in pregnancy.

There has been a rise over the past 20 years in the prevalence of pre-existing diabetes (type 1 or type 2 diabetes) in pregnancy. Currently, pre-existing diabetes affects approximately 1% or 4 000 000 pregnancies in the USA annually 1 2 Worldwide, other countries are also experiencing a similar phenomenon, contributing to what has been called the ‘diabetes pandemic.’ 3–8

The increased occurrence of pre-existing diabetes in pregnancy presents a clear threat to maternal–child health. Compared with women without diabetes, infants of women with pre-existing diabetes have an increased risk of experiencing congenital anomalies and stillbirths, and infant death—relative risk (RR) 1.86 (95% CI 1.49 to 2.33) and RR 2.33 (95% CI 1.59 to 3.43), respectively. 3 Infants born to mothers with diabetes also experience increased postbirth complications, including macrosomia, respiratory distress and hypoglycaemia. For example, up to 60% of infants born to mothers with type 1 or type 2 diabetes may be macrocosmic 9 ; respiratory distress syndrome is approximately twofold higher (OR 2.66 (95% CI 2.06 to 3.44)) among infants of mothers with pre-existing diabetes compared with infants of non-diabetic mothers 10 ; and the occurrence of neonatal hypoglycaemia is approximately 27% among infants born to mothers with diabetes, compared with 3% in the background population. 11

Role of diabetes self-management education and support

Research suggests that glycaemic management is associated with perinatal complications. 12 Thus, glycaemic targets are narrow during the preconception and prenatal periods to optimise pregnancy outcomes. 13 Women with pre-existing diabetes in pregnancy achieve glycaemic targets through the daily tasks of diabetes self-management, which includes frequent self-monitoring of blood glucose and accurate titration of insulin doses to blood glucose measures and carbohydrate intake. 13 However, the evidence suggests that expectant mothers often struggle to meet recommended glycaemic targets. A large cohort study in the UK that followed women from conception to delivery found that only 14.3% of those with type 1 diabetes and 37.0% of those with type 2 diabetes met recommended glycaemic targets during early pregnancy (less than 13 weeks gestation). 14 Therefore, recent attention has focused on promoting diabetes self-management education and diabetes self-management support during pregnancy.

Diabetes self-management education focuses on individual goal setting, problem-solving and patient empowerment strategies. The intent is to ensure that patients have knowledge regarding their condition to sufficiently collaborate in decision-making with their healthcare providers and receive tailored care. 15 16 Clinical practice guidelines suggest that self-management support should augment education. Self-management support may include activities that reinforce and enhance education and behaviours. Support strategies include text messages, email reminders, automatic phone reminders, peer support and mobile health interventions, among others. 16 Specifically, such strategies aim to improve patient self-efficacy, confidence and one’s ability to optimally self-manage diabetes. Among non-pregnant adults with diabetes, systematic reviews and meta-analyses indicate that self-management education and support interventions improve clinically important outcomes, 16 including improved glycaemic control and reduced diabetes complications, such as foot amputations. 17 Thus, diabetes self-management education and support may improve glycaemic control and other clinically important outcomes among women with diabetes in pregnancy. However, the existing research on diabetes self-management education and support in pregnancy is limited and primarily focused on gestational diabetes mellitus. 18

Our objective is to explore how self-management and support experiences help explain glycaemic control among women with pre-existing diabetes in pregnancy.

Methods and analysis

Study design overview.

We will conduct a four-phased mixed-methods sequential comparative case study. This mixed-methods design will begin with the analysis of collected quantitative data. A phase of qualitative data collection and analysis will follow the quantitative phase. The study will conclude by integrating the quantitative and qualitative findings to generate unique cases. The mixed-methods sequential comparative case design is ideal because we aim to develop detailed information about diabetes self-management among women with pre-existing diabetes during pregnancy. Furthermore, diabetes self-management during pregnancy varies based on diabetes type (type 1 or type 2). Thus, the mixed-methods sequential comparative case design will portray this variation in self-management in the form of constructed cases that can be compared and contrasted. Ultimately, it is our goal that the information from the generated cases will guide subsequent research in designing, evaluating and implementing self-management education and support interventions for women with pre-existing diabetes in pregnancy.

The research questions are threefold, as we will integrate the quantitative and qualitative data within the overall mixed-methods design.

Quantitative research question.

What are the predictors of glycaemic control during pregnancy among women with pre-existing diabetes?

Qualitative research question

What is the experience of managing diabetes during pregnancy?

What are the diabetes self-management education and support needs during pregnancy among women with pre-existing diabetes?

Mixed-methods research question

How do the self-management and support experiences of women with pre-existing diabetes in pregnancy help explain their glycaemic control?

Figure 1 provides a diagram depicting the study flow. Phase I will involve the analysis of data from a prospective cohort study to determine the predictors of glycaemic control during pregnancy related to diabetes self-management (eg, the level of self-efficacy) among women with pre-existing diabetes. Phase II will use the results of the cohort data analysis to inform the interview guide for phase III. Phase III will be a qualitative descriptive study to understand the diabetes education and support needs during pregnancy among women with pre-existing diabetes. Phase IV will integrate the results of phases I and III to generate unique cases representing the various ways in which self-management and support experiences explain glycaemic control in pregnancy.

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Provides a diagram depicting the study flow.

Study phases I, II, III and IV

Study phase i: prospective cohort, study design and setting.

Phase I will involve the analysis of quantitative data collected as part of the ‘Assessing the Determinants of Pregestational Diabetes in Pregnancy: A Prospective Cohort Study.’ This study took place at the Maternal-Fetal Medicine clinic at McMaster University Medical Center in Ontario, Canada. Ethics approval was granted by the Hamilton Integrated Research Ethics Board (REB #14-222).

Participants and recruitment

Consecutive convenience sampling was employed to recruit eligible participants who met the following criteria: (1) a diagnosis of type 1 or type 2 diabetes; (2) attending the Maternal-Fetal Medicine clinic at McMaster University Medical Centre clinic for obstetrical care and (3) age 18 years or older.

Sample size calculation

The minimum required sample size was calculated by selecting the following options in G*Power: (1) test family, F tests; (2) statistical test, linear multiple regression: Fixed model, R2 deviation from zero and (3) type of power analysis, a priori: compute required sample size—given α, power and effect size. 19 20 The required sample size varied based on if Cohen’s effect size was small, medium or large. 21 In a meta-analysis exploring the effect of nurse-led diabetes self-management education on A1C, Tshiananga et al 22 found that nurse-led diabetes self-management education had a medium effect on A1C. Therefore, using an alpha of 0.05, 80% power, a medium effect size of 0.15 21 and accounting for three predictors (self-efficacy, self-care and care satisfaction), the minimum required sample size is 77. A total of 111 women were recruited as part of the ‘Assessing the Determinants of Pregestational Diabetes in Pregnancy: A Prospective Cohort Study.’

Data collection

Data collection occurred from April 2014 to November 2019. Data were collected three times during pregnancy, between 0 to 16 weeks (time point 1 (T1)); 17–28 weeks (time point 2 (T2)); and 29–40 weeks (time point 3 (T3)). Participants completed a demographic questionnaire, which inquired about characteristics such as age, ethnicity, marital status, household income, education level, living arrangements and employment status.

Participants also completed a survey to assess the following clinical characteristics:

Current type of diabetes (type 1 or type 2).

Gestational diabetes in a previous pregnancy.

Duration of diabetes.

Method of diabetes treatment (insulin pump or multiple daily injections).

Daily frequency of self-monitoring of blood glucose.

Status of insurance coverage for diabetes supplies.

Gestational age.

Multiple or single gestation.

Use of assistive reproductive technology.

Glycaemic control was assessed through participant self-report of A1C at each time point. The self-reported values were confirmed with the medical chart.

Self-efficacy was measured using an eight-item Likert scale called the Self-Efficacy for Diabetes scale. 23 Participants rated their confidence in activities, such as knowing what to do when their blood glucose is higher or lower than the target. Responses ranged from 1 to 10 (not at all confident to totally confident). The total score is the mean of the eight-item responses, with a maximum score of 10. A higher score indicates higher self-efficacy in diabetes management. 23

Self-care was assessed using the Summary of Diabetes Self-Care Activities Measure. 24 Eleven questions over four categories asked participants to indicate how many days in the last week they performed a variety of self-care behaviours. The scale sections included diet, exercise, blood glucose testing and foot care. The scores from each subsection are averaged create a total score, with a maximum score of 7. A greater frequency of performed activities indicates better self-management and adherence to treatment. 24

Care satisfaction was assessed using the Patient Assessment of Care for Chronic Conditions scale. 25 This scale measured the degree to which a patient perceived that their medical care was congruent with the Chronic Care Model. 25 The Chronic Care Model involves optimising the following components—healthcare organisation, delivery system design, clinical information systems, decision-support, self-management support and community resources. 25 This tool had 20 items that asked participants to quantify the care they received from their healthcare team over the past 6 months. Participants indicated how often they were given choices about treatment or asked to talk about their treatment goals. Responses ranged from none of the time (1 point) to always (five points). There were five subscales: Patient Activation, Delivery System Design/Decision Support, Goal Setting, Problem-Solving/Contextual Counselling and Follow-up/Coordination. The overall score is an average of the combined subscale scores, with a maximum score of five. Higher scores indicate that the patient is receiving care congruent with the chronic care model. 25

Data analysis

We will conduct descriptive statistics to understand the distribution of participant demographic and clinical characteristics, and participant levels of self-efficacy, self-care and care satisfaction. We plan to explore differences in variable distribution, stratified by diabetes type. We will use linear mixed-effects modelling to explore trends in glycaemic control and examine self-efficacy, self-care and care satisfaction as predictors of A1C. To control for potential confounding factors on the relationship between self-efficacy, self-care, care satisfaction and glycaemic control, we will adjust for participant age, diabetes duration, ethnicity, education level, household income and insurance coverage. The decision to control for these factors was based on the knowledge that they may be independently associated with both the proposed independent variables (self-efficacy, self-care and care satisfaction) and dependent variables (A1C), making them potential confounders of any association between the independent and dependent variables. For example, evidence indicates that diabetes duration is associated with self-efficacy 26 and glycaemic control among non-pregnant adults with type 2 diabetes. 27

Study phase II: planning the qualitative data collection

In phase II, we will use the results of the quantitative data analysis, which aims to determine the predictors of glycaemic control during pregnancy for women with pre-existing diabetes, to develop the interview guide for phase III. Using the quantitative results to inform the qualitative interviews will allow us to focus on areas of the quantitative results that require further exploration.

Study phase III: qualitative description

We will use a qualitative description design for Phase III. Fundamental qualitative description allows researchers to gather rich narrations from participants regarding the phenomenon of interest. 28 The Consolidated Criteria for Reporting Qualitative Studies will be used to guide the reporting of the phase III. 29

Albert Bandura’s Theory of Self-Efficacy will be used as a framework to guide this study phase. The Theory of Self-Efficacy proposes that individuals can exercise control over their behaviour. 30 Two integral concepts of the Theory of Self-Efficacy include efficacy expectations and outcome expectations. Bandura describes efficacy expectations as a person’s judgement in their ability to complete a certain task. On the other hand, outcomes expectations represent what a person thinks will occur as a result of successfully completing a task. 31 Bandura further outlines that personal efficacy is derived from four sources: performance accomplishments, vicarious experiences, verbal persuasion and physiological state. 31 Performance accomplishments represent a person’s past experiences, both positive and negative, of performing the targeted behaviour. This is the source that has the most influence on the development of personal efficacy. High or low personal efficacy is also developed vicariously through viewing someone else performing the desired behaviour. Verbal persuasion—encouragement from another person—as well as the physiological state—a person’s bodily sensation in response to a stressful situation—also influence a person’s confidence in their capabilities. These sources of information come together to shape a person’s perceived ability to accomplish a task. 31 Supporting patients to engage in healthy behaviours to the best of one’s ability presents a challenge for healthcare providers, even for diseases that can be self-managed, such as diabetes. 32 Therefore, the notion of self-efficacy within an understanding of the impact of the social determinants of health is key, for it strongly influences the initiation and maintenance of behaviour change, a component essential in chronic disease management. 33 Arguably, understanding one’s self-efficacy is paramount for women with pre-existing in pregnancy, as there is a limited window of time within which self-management education and support can be provided to optimise diabetes-related and pregnancy-related outcomes.

We will use the principles of purposeful sampling to recruitment women aged 18 years or older, with type 1 and type 2 diabetes, who are currently or who were previously pregnant. These women will participate in individual semi-structured interviews to describe their experience of managing diabetes and determine their needs regarding diabetes self-management education and support during pregnancy. Additional sampling strategies such as snowball sampling and theoretical sampling, in which initial data analysis guides future recruitment to explore emerging themes, will also be used. 34 The guidelines regarding sample sizes in fundamental qualitative description studies focus on recruiting an adequate number of participants to generate descriptions of the phenomenon of interest. 35 Sample sizes are usually small to facilitate in-depth exploration of participant descriptions. 35 For qualitative description studies that employ individual interviews, sample sizes are typically in the range of eight to 20 participants. 35 The sample size for this study will be between 10 and 20 participants. Data saturation will guide the completion of recruitment and data collection. 35

Interviews provide first-hand knowledge regarding participant experiences. 36 As such, individual interviews will be the primary means of data collection. A literature review and the phase I study results will inform the development of the semi-structured interview guide. The interviews will be conducted face to face via videoconferencing (Zoom, WebEx, Skype or Microsoft Teams) or by telephone and will have an approximate duration of 30–60 min. All interviews will be audiorecorded. The primary researcher (KS) will conduct all interviews to maintain consistency. Several pilot interviews will be completed with the first few recruited participants to evaluate the appropriateness of the interview guide. The interview questions may be modified based on the pilot interviews. 37 Questions may also be added or removed as the number of interviews progresses, depending on emerging themes and content. We will collect baseline demographic and clinical characteristics before the interview and write supplementary field notes immediately after. We will also verbally summarise the interview with the participants and ask them to confirm or expand on the summary as a way of member checking. 38

Following the completion of the interviews, the recorded audio will be transcribed verbatim and imported into NVivo (NVivo. QSR International ; 2020) for analysis. The goal of data analysis in qualitative description is to elicit the participant’s viewpoint regarding the phenomenon of interest and remain close to the surface of the data. 35 Therefore, we will employ conventional content analyses, as described by Hsieh and Shannon. 39 This method of analysis is appropriate for studies with the aim of description because it allows codes and categories to be derived directly from the data rather than from preconceived ideas informed by existing literature or theories. 39 Content analysis in this study will begin with repeated reading of interview transcripts to facilitate immersion in the data. We will then identify codes through a line-by-line review and highlight relevant concepts. Simultaneous note-taking and reflection on initial impressions will allow code labelling derived from the interview text. We will then group related codes into 10–15 categories and develop definitions for each. 39

Study phase IV: integration of quantitative and qualitative findings and case construction

The purpose of the mixed-methods procedures will be to integrate the quantitative and qualitative data to develop a deep description and analysis of diabetes self-management in women with type 1 and type 2 diabetes during pregnancy. The recommendations by Creswell and Clark for integration procedures will guide our mixing process. 40

The mixed-methods integration will occur following the completion of the qualitative study when the results of the cohort data analysis and the qualitative interview findings are combined to construct cases. We will integrate the quantitative and qualitative data following their separate analyses through data displays and the development of meta-inferences. 40 The Diverse Case Method 41 will be used to construct cases that describe how diabetes self-management and support experiences explain glycaemic control in pregnancy. For categorical variables, such as diabetes type, we will construct cases for each category. For example, we will select participant groups with type 1 diabetes and good and poor glycaemic control and participant groups with type 2 diabetes and good and poor glycaemic control to assemble cases. For continuous variables, such as self-efficacy score, we will create cases using high compared with low values of the variable. For example, we will choose participant groups with high compared with low levels of self-efficacy and examine differences in their glycaemic control. Supporting data will then be selected from the qualitative interview results to contextualise and complete case construction.

Displaying the data will be done in several ways to link the quantitative and qualitative phases. We will represent the points of integration in two ways. First, we will develop a statistics-by-theme joint-display table to present the cases constructed from the quantitative and qualitative data. 40 The joint display will depict the quantitative results alongside the qualitative themes to portray the results of the mixed-methods integration. 40 Second, we will mix the data in our write-up of the study results by using an approach that weaves together quantitative statistics with narrative themes.

This study will use a mixed-methods design to provide a comprehensive understanding of how self-management and support experiences influence glycaemic control for women with diabetes in pregnancy. Specifically, a better understanding will be gained of the following: the prevalence and correlates of self-management support and glycaemic control in women with pre-existing diabetes in pregnancy; and the self-management experience of women with pre-existing diabetes in pregnancy.

This study will also lay the groundwork for future research that could include collecting further quantitative data to confirm the results—locally, regionally, provincially and nationally. The study results also have the potential to inform medical care for high-risk patients with pre-existing diabetes during the critical finite, intensive period of pregnancy. However, this study also has limitations. Specifically, the single-centre design of the cohort study and the use of qualitative methods will limit the generalisability of our findings. In addition, our study is subject to biases inherent in self-report data, such as recall bias. In an attempt to address recall bias, we have made the recall period short (6 months or less), are studying participants with a chronic disease and made the duration of the study relatively short (over the 9 months of pregnancy), all factors known to be related to impact recall bias. 42

Patient and public involvement

Patients and the public were not involved in the protocol design. We plan to make study results available to participants on request. We also plan to use the results of this study to provide the basis for the development, evaluation and implementation of a patient-centred intervention based on the constructed cases to inform models of self-management education and support including the use of technology, peer support and health coaching interventions, among others.

Ethics statements

Patient consent for publication.

Not applicable.

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Contributors The study concept and design and plans for data collection and analysis were conceptualised by KS and DS with support from KN, PHS and MB. The manuscript was drafted by KS and DS, KN, PHS and MB contributed to its critical revision. All authors have reviewed and approved the final manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Read the full text or download the PDF:

Diabetes crisis in pregnancy: a case report

Affiliation.

• 1 University of Maryland Medical Center, Baltimore, MD 21201, USA. [email protected]
• PMID: 19474583
• DOI: 10.1097/JPN.0b013e3181a2c044

Optimal maternal, fetal, and neonatal outcomes are the goal of care for pregnant women with preexisting diabetes. Women with a long history of poorly managed diabetes begin pregnancy with a deficit that poses additional challenges for the patient and the healthcare team. The following case study presents a woman who had a history of type 1 diabetes that was poorly controlled and experienced an incidence of severe hypoglycemia with serious sequelae.

Publication types

• Case Reports
• Cesarean Section, Repeat
• Critical Care
• Diabetes Mellitus, Type 1 / complications
• Diabetes Mellitus, Type 1 / psychology
• Diabetes Mellitus, Type 1 / therapy*
• Emergency Medical Services
• Fetal Monitoring
• Hypoglycemia / etiology
• Hypoglycemia / therapy*
• Intensive Care, Neonatal
• Needs Assessment
• Nursing Assessment
• Patient Care Planning
• Patient Compliance / psychology
• Patient Education as Topic
• Postnatal Care
• Pregnancy in Diabetics / psychology
• Pregnancy in Diabetics / therapy*
• Prenatal Care
• Seizures / etiology

• Open access
• Published: 11 April 2024

Glucose control during pregnancy in patients with type 1 diabetes correlates with fetal hemodynamics: a prospective longitudinal study

• Patrik Simjak 1 , 2 ,
• Katerina Anderlova 1 , 3 ,
• Dagmar Smetanová 2 ,
• Michal Kršek 3 ,
• Miloš Mráz 4 &
• Martin Haluzík 4  

BMC Pregnancy and Childbirth volume  24 , Article number:  264 ( 2024 ) Cite this article

Metrics details

Maternal diabetes adversely affects fetal cardiovascular system development. Previous studies have reported that the fetuses of mothers with diabetes exhibit both structural and functional changes; nevertheless, prior studies have not examined the association between glucose control and fetal cardiac morphology and performance. Thus, the objective was to determine the association between fetal cardiac morphology and function and maternal glucose control in type 1 diabetes and to compare the differences in measured cardiac parameters between the fetuses of mothers with diabetes and healthy controls.

In this prospective, longitudinal case-control study — including 62 pregnant women with type 1 diabetes mellitus and 30 healthy pregnant women — fetal cardiac assessment using B-mode, M-mode, and spectral pulsed-wave Doppler was performed in the second and third trimesters. In women with T1DM, glycated hemoglobin and data obtained from glucose sensors — including the percentage of time in, below, and above the range (TIR, TBR, and TAR, respectively), and coefficient of variation (CV) — were analyzed across three time periods: the last menstrual period to 13 (V1), 14–22 (V2), and 23–32 weeks (V3) of gestation. Fetal cardiac indices were compared between groups, and the correlation between glucose control and fetal cardiac indices was assessed.

At 28–32 weeks, the fetuses of women with T1DM exhibited increased left ventricular end-diastolic length, relative interventricular septum thickness, right ventricular cardiac output, and pulmonary valve peak systolic velocity compared with healthy controls. At 18–22 weeks, pulmonary and aortic valve diameters, left and right ventricular stroke volumes, and left cardiac output inversely correlated with the CV and glycated hemoglobin levels at V1 and V2. Furthermore, at 28–32 weeks, pulmonary and aortic valve diameters, left ventricular stroke volume, cardiac output, and right/left atrioventricular valve ratio inversely correlated with the TBR at V1, V2, and V3. Moreover, diastolic functional parameters correlated with the TAR and glycated hemoglobin levels, particularly after the first trimester.

In women with T1DM, maternal hyperglycemia during pregnancy correlates with fetal diastolic function, whereas glucose variability and hypoglycemia inversely correlate with fetal left ventricular systolic function in the second and third trimesters.

Key message

This prospective longitudinal study demonstrates that in women with T1DM, maternal hyperglycemia during pregnancy correlates with fetal diastolic function. In contrast, glucose variability and hypoglycemia inversely correlate with fetal left ventricular systolic function in the second and third trimesters.

Peer Review reports

Type 1 diabetes mellitus (T1DM) is a chronic metabolic disease caused by absolute insulin deficiency due to the autoimmune destruction of pancreatic B-cells. Usually diagnosed in childhood or early adulthood, T1DM inevitably affects reproduction. The reported prevalence of T1DM in pregnancy is 4.1–4.7 per 1000 pregnancies [ 1 , 2 ]. Maternal diabetes adversely affects fetal development in various ways, such as by significantly affecting the fetal cardiovascular system. Previous studies have reported that the fetuses of mothers with diabetes have more globular hearts, increased right and left sphericity indices, and subclinical systolic dysfunction in the second half of pregnancy [ 3 , 4 ]; fetal hyperinsulinemia owing to enhanced maternal-fetal glucose transport is thought to be the underlying cause. Nevertheless, prior studies have not examined the association between glucose control, and fetal cardiovascular morphology and performance. Additionally, T1DM and gestational diabetes (GDM) were often combined, regardless of the differences in etiopathogenesis and clinical presentation.

Glucose sensors have been used in the care of pregnant women with T1DM for several years. This creates an opportunity for the noninvasive, instant monitoring of glycemia at any time during the day, thereby enabling early treatment decisions. These wearables perform real-time continuous glucose monitoring (CGM) to alert the user when preset glucose targets are exceeded or flash glucose monitoring (FGM) that indicates glycemia when a reader is applied to the sensor. While recent studies have confirmed the safety and accuracy of both methods during pregnancy [ 5 , 6 ], CGM is associated with improved neonatal outcomes, presumably due to better diabetes compensation [ 7 ].

The adoption of glucose sensors allowed for the development of core metrics for understanding glycemic status — including the time in, below, and above the range (TIR, TBR, and TAR, respectively) — and the coefficient of variation (CV), which describe the effectiveness of treatment in more detail than the traditionally used glycated hemoglobin (A1c). Notably, the high sensitivity of the sensors also enables the detection of glycemic fluctuations in pregnant women with a negative oral glucose tolerance test [ 8 ]. Thus, these sensors provide an entirely new opportunity to study the relationship between diabetes compensation and the development of pregnancy-related complications.

The primary objective of this prospective study was to determine the association between fetal cardiac morphology and function and maternal glucose control in T1DM. Secondarily, we aimed to compare the differences in measured cardiac parameters between the fetuses of mothers with T1DM and healthy controls.

Subjects and methods

Study population.

This prospective, longitudinal case-control study included 64 consecutively recruited pregnant women with T1DM, and 32 matched healthy pregnant women. Pregnant women attending the combined first-trimester screening between April 2018 and December 2022 were recruited. The general exclusion criteria were multiple pregnancies, and fetal structural or chromosomal abnormalities diagnosed during the pregnancy. Women assigned to the control group had a standard 75 g oral glucose tolerance test performed between 24 and 28 weeks of gestation. Two women met the American Diabetes Association (ADA) criteria for diagnosis of GDM and were excluded from the analysis. All participants provided written informed consent for all study procedures, and the study protocol was approved by the Human Ethics Review Board.

Information on the maternal body mass index (BMI), age, race, method of conception (natural or assisted by in-vitro fertilization), cigarette smoking during pregnancy, and parity were recorded at the first visit. In women with T1DM, the disease duration (in completed years) was calculated, and diabetes-related morbidity, sensor type, and treatment modalities were recorded.

In all women with T1DM, glucose monitoring was initiated either before pregnancy or during the first trimester; a FGM system (FreeStyle® Libre™; Abbott, Inc.) or glucose sensors (G6®; DexCom, Inc., San Diego, CA, USA; or Guardian™ 4; Medtronic, Inc., Minneapolis, MN, USA) for real-time CGM of the interstitial fluid were used. Women with diabetes were followed-up every 4 weeks; if necessary, the diabetologist adjusted the treatment to ensure optimal disease control. The data obtained from sensors — including the TIR (time spent in target [3.5–7.8 mmol/l]), TBR (time spent below target [< 3.5 mmol/l]), TAR (time spent above target [> 7.8 mmol/l]) percentages — A1c, and CV were analyzed across three time periods: the last menstrual period to 13 weeks (V1); 14–22 weeks (V2); and 23–32 weeks (V3) of gestation. Insulin was administered via an insulin pump or multiple daily injections.

Standard perinatal outcomes were recorded after delivery, including gestational age, mode of delivery, birthweight, the incidence of preeclampsia, neonatal hypoglycemia, congenital malformations, NICU admission and umbilical artery pH.

Echocardiography and ultrasound assessment

In all women, fetal B-mode, M-mode, and spectral pulsed-wave (PW) Doppler examinations were performed in the second and early third trimesters (18–22 weeks and 28–32 weeks of gestation, respectively) as a part of the routine prenatal ultrasound examinations. One investigator (P.S.) performed all fetal ultrasound examinations using a VolusonTM E10 BT 18 ultrasound system (GE Healthcare, Chicago, Il, USA). All measurements were performed using a convex-array obstetric transducer (C2-9). M-mode was used to assess ventricular free and septal wall thicknesses, and chamber dimensions. An apical, basal, or lateral four-chamber view in B-mode was used to obtain measurements regarding the cardiac axis, cardiothoracic index, ventricular length, and ventricular and semilunar valve dimensions, as appropriate. The ventricular sphericity index was calculated as the ventricular end-diastolic diameter/end-diastolic length. The relative wall thicknesses of the ventricles and interventricular septum (IVS) were estimated as (2 × free wall or septal wall thickness)/ventricular end-diastolic diameter. PW Doppler with an angle correction of < 45° was used to obtain Doppler signals from the inflow and outflow tracts for the evaluation of diastolic and systolic function. The left ventricular (LV) myocardial performance index (MPI) was obtained from a single cardiac cycle by placing the sample volume at the junction of the anterior mitral valve leaflet and left outflow tract to simultaneously display ventricular filling and emptying. For the right ventricular (RV) MPI, inflow and outflow pulsed Doppler signals were obtained separately, and MPI was only calculated if the difference between the fetal heart rate in the inflow and outflow tracts was < 5 beats per minute. Fetal biometry was performed in all women, and the pulsatility indices of the uterine and umbilical arteries were assessed.

Intra- and interobserver reproducibility

The same investigator (P.S.) repeated the measurements on 20 randomly selected fetal echocardiograms obtained in the second and third trimesters in the same cardiac cycle. A second observer (D.S.) repeated the measurements using the same echocardiogram. Intraclass correlation coefficients (ICCs) were calculated to assess intra- and interobserver variability.

Statistical analysis

Sample size and power calculations were performed based on the assumption that in the second trimester, the mean septal thickness increases by 10% in the fetuses of women with poorly controlled diabetes compared with controls [ 9 ]. Assuming a standard deviation of 10%, with an alpha of 0.05 and a power of 85%, the required sample size should be 39 with an enrollment ratio of 2:1. The enrollment ratio in favor of women with T1DM was chosen with the primary outcome of the study in mind and accounted for the lower propensity of healthy pregnant women to comply with the study protocol.

The Shapiro-Wilk test was performed to assess the data distribution for normality. Normally distributed continuous variables were presented as mean ± SD, and nonnormally distributed variables as median (interquartile range). Nominal variables were presented as numbers (percentages). Maternal and fetal parameters were compared between groups using the independent-samples Student’s t -test or Mann-Whitney U-test for continuous variables, and Chi-squared test for categorical variables. The Friedman test was used to detect differences in diabetes compensation across the three time periods. Pearson’s correlation test was used to assess the relationship between diabetes compensation and fetal cardiac indices.

Pregnancy characteristics and diabetes control

In total, 94 pregnant women consented to participate in the study, including 64 with T1DM and 32 healthy controls; two women from the T1DM group were excluded from the analysis due to fetal anomalies (common arterial trunk and caudal regression syndrome); two women in the healthy control group met the American Diabetes Association (ADA) criteria for diagnosis of GDM and were excluded. No significant differences in the baseline population characteristics were observed between the groups, excluding a higher BMI in women with T1DM. As expected, birthweight was higher, and cesarean section, preeclampsia, and neonatal hypoglycemia were more frequent in the T1DM group.

The demographic characteristics and perinatal outcomes of the enrolled women are summarized in Table  1 . Of the 62 women with T1DM, 52 started using glucose sensors before conception, whereas the remaining started in the first trimester. CGM and FGM were performed in 44 and 18 women, respectively. Insulin was administered via insulin pump to 33 (53.2%) women, whereas 29 (46.8%) received multiple daily injections. Characteristics of women with type 1 diabetes mellitus are summarized in Table  2 . In women with T1DM, the mean A1c levels gradually decreased from preconception to V2, and then increased at V3. The same pattern was observed in the control group, but A1c was significantly lower in all visits. As expected, in women T1DM, A1c correlated with the TAR and CV during pregnancy. By contrast, A1c inversely correlated with the TIR. Furthermore, an inverse correlation was observed between the TBR at V1 (Table S1 ). Compared with the first trimester, women spent more TIR and less TAR at subsequent intervals. The glucose control of women in both study groups is summarized in Table  3 . The ADA criterion for good glucose control, a TIR > 70%, was achieved in 41% of women with diabetes at all follow-up periods.

Cardiac geometry and function

The groups were comparable regarding the estimated fetal weight and pulsatility indices in the umbilical and uterine arteries. No differences in fetal cardiac geometry and function were observed between the groups at 18–22 weeks. At 28–32 weeks, the fetuses of women with T1DM exhibited increased LV end-diastolic length, relative IVS thickness, RV cardiac output (RV-CO), and pulmonary valve peak systolic velocity compared with the fetuses of healthy controls. A summary of the cardiac parameters in women with T1DM and controls in the second and third trimesters is presented in Table  4 . The ICCs indicated moderate to excellent (ICC: 0.60–0.99) intra- and interobserver reliabilities (Table S2 ).

An inverse correlation between the CV and echocardiographic parameters was observed between 18 and 22 weeks. Specifically, pulmonary valve diameter and RV stroke volume (RV-SV) decreased with increasing CV prior to 14 weeks ( r  = − 0.51, P  = 0.001; r  = − 0.35, P  = 0.033; respectively). Additionally, the CVs at V1 and V2 inversely correlated with the LV-SV ( r  = − 0.33, P  = 0.043; r  = − 0.41, P  = 0.008; respectively), and aortic valve (AV) diameter and LV-CO at V2 ( r  = − 0.31, P  = 0.046; r  = − 0.39, P  = 0.011; respectively). An inverse correlation was also observed between A1c and AV diameter at V1 ( r  = − 0.32, P  = 0.018), and LV-SV and LV-CO at V1 and V2 ( r  = − 0.37, P  = 0.006; r  = − 0.27, P  = 0.042; r  = − 0.31, P  = 0.024; r − 0.27, P  = 0.041; respectively). Thus, increased glycemic variability and A1c in the first half of pregnancy impaired mid-gestation ventricular systolic function, and decreased semilunar valve diameters contributing to decreased SVs.

At 28–32 weeks, the AV diameter inversely correlated with the TBR at V1, V2, and V3 ( r  = − 0.38, P  = 0.006; r  = − 0.35, P  = 0.008; r  = − 0.37, P  = 0.005; respectively), but positively correlated with A1c at V3 ( r  = 0.30; P  = 0.025). The TBR also inversely correlated with LV systolic function, represented by LV-SV ( r  = − 0.33, P  = 0.029; r  = − 0.32, P  = 0.017; r  = − 0.29, P  = 0.024; for V1, V2, V3 respectively) and LV-CO ( r  = − 0.35, P  = 0.023; r  = − 0.34, P  = 0.011; r  = − 0.29, P  = 0.029; for V1, V2, V3 respectively; Fig.  1 ). Moreover, LV-SV correlated with A1c at V3 ( r  = 0.27; P  = 0.047).

Scatterplots demonstrating the negative correlation between left ventricular cardiac output (LV-CO) and time below range (TBR) in the first 14 weeks (V1; r = -0.35, P  = 0.023), at 14–22 weeks (V2; r = -0.34, P  = 0.011), and at 23–28 weeks (V3; r = -0.29, P  = 0.029) of gestation

By contrast, diastolic function markers correlated with A1c and TAR. At 28–32 weeks, the TAR correlated with RV A ( r  = 0.29, P  = 0.009; r  = 0.32, P  = 0.017; r  = 0.29, P  = 0.047; for V1, V2, V3 respectively), LV E ( r  = 0.30, P  = 0.034; r  = 0.36, P  = 0.008; for V2, V3 respectively), and LV A ( r  = 0.29, P  = 0.031 for V3). Similarly, A1c correlated with RV E ( r  = 0.33, P  = 0.014; r  = 0.32, P  = 0.020; for V2, V3 respectively), RV A ( r  = 0.30, P  = 0.007; r  = 0.36, P  = 0.006; for V2, V3 respectively), and LV E ( r  = 0.28; P  = 0.036; for V3). The correlations between glucose control and fetal cardiac geometry and function are summarized in Table  5 .

This study presents a detailed analysis of fetal cardiovascular hemodynamics during the second and early third trimesters of patients with T1DM in relation to glucose control, and a comparison with healthy controls. The results demonstrate that in the study population, where 41% of women achieved the recommended glucose control target (> 70% of the TIR) throughout pregnancy, significant changes in cardiac geometry and function were only observed in the early third trimester. Additionally, this study demonstrates for the first time that glucose variability and maternal hypoglycemia affect LV performance in the second and third trimesters. By contrast, hyperglycemia during pregnancy affects the fetal diastolic function. Thus, optimal control of T1DM enhances fetal hemodynamics. However, the extent to which improvements in fetal hemodynamics can translate into improved clinical outcomes remains to be elucidated.

Although the pathophysiology of impaired fetal cardiac development in maternal diabetes is complex and incompletely understood, increased transplacental glucose transport and subsequent fetal hyperinsulinemia are thought to be the main causes [ 10 ]. Fetal hyperinsulinemia can alter placental mRNA expression, leading to the dysregulation of insulin/insulin-like growth factor (IGF) systems [ 11 ]. IGF-1 is a potent stimulator of cell growth, and experimental studies have demonstrated that it promotes prenatal cardiomyocyte growth [ 12 ]. Additionally, a positive correlation between cord blood IGF-1 bioavailability and IVS thickness was observed in the newborns of mothers with diabetes [ 13 ]. Other major factors that influence fetal heart morphology and function include increased oxidative stress, subclinical low-grade inflammation, maternal obesity, triglyceridemia, and placental dysfunction [ 13 , 14 , 15 ].

Similar to previously published studies, we demonstrated distinct changes in fetal cardiac morphology in the fetuses of mothers with T1DM during the third trimester, compared with controls. These included increased heart area, greater LV-EDL, and a thicker IVS [ 16 ]. Other studies have reported a more globular heart shape with increased ventricular sphericity indices [ 4 , 17 ]; however, the cohorts in these studies mainly comprised women with gestational diabetes and fetal echocardiography was performed later in the third trimester, potentially explaining the noted discrepancies. A recent meta-analysis confirmed IVS thickening in the fetuses of women with T1DM; however, increased septal thickness has also been observed in the second trimester [ 16 ].

Previous research on pregnant women with diabetes has also revealed impaired fetal cardiac function, which can be demonstrated using various ultrasound examination techniques at different stages of pregnancy. The very first detectable manifestation of fetal heart function is the heart rate. One study found that pregnant women with pregestational diabetes (type 1 and 2) had a higher fetal heart rate in the first trimester compared to healthy women, regardless of their BMI [ 18 ]. However, our study found that the fetal heart rate was similar in both groups during the later trimesters.

Another parameter, fetal MPI, has been assessed by two recent meta-analyses concerning diabetes in pregnancy [ 16 , 19 ]. In the study by Depla et al., fetal MPI in women with pregestational diabetes and controls were comparable, but Sirico et al. observed higher MPI in fetuses of diabetic mothers in the third trimester. Although both were published in a similar time period, each had different study inclusion criteria, which affected the results. Nevertheless, the MPI may be confounded by coexisting complications that are common in women with diabetes, such as maternal obesity, fetal macrosomia, or placental function. We did not observe differences in left or right ventricular MPI between the groups, possibly due to the comparable estimated fetal weight and uteroplacental Doppler indices between the groups or the limited number of study participants. Nevertheless, fetal MPI was unrelated to glucose control in our cohort.

Furthermore, we failed to demonstrate impaired diastolic function in the second and early third trimesters using spectral PW Doppler, which is consistent with the results of another published study [ 20 ]. However, a lower diastolic strain rate was observed in this study, suggesting that speckle-tracking echocardiography is a more sensitive method for assessing fetal heart dysfunction [ 20 ]. These subtle subclinical changes likely precede diastolic dysfunction, as demonstrated using conventional ultrasound parameters during the third trimester [ 21 ]. A similar impairment of diastolic function was also observed in the fetuses of mothers with gestational diabetes [ 22 ]; thus, hyperglycemia-induced cardiac remodeling and the consequent fetal adaptation may be responsible for this phenomenon. Indeed, we observed a positive correlation between the percentage of time spent in hyperglycemia, A1c, and diastolic function, mainly regarding the RV A wave. In line with our findings, a lower right E/A ratio was observed in the fetuses of women with poorly controlled pregestational diabetes [ 20 , 23 ].

Regarding fetal systolic function in pregnancies complicated by T1DM, the evidence is inconclusive. A higher RV-SV was observed; however, the difference in CO was not significant after correcting for estimated fetal weight. A similar finding was observed at the end of the third trimester in a mixed cohort including the fetuses of mothers with GDM and T1DM [ 4 ]. In another study, RV systolic impairment was demonstrated using speckle-tracking echocardiography [ 24 ]. Although a difference in the LV-CO was not observed in the second and early third trimesters in the fetuses of women with T1DM, another study demonstrated a significant decrease in LV-CO at term [ 4 ]. In these fetuses, LV-CO was restored to values comparable to those in the healthy population early after birth, suggesting a suppressive effect of diabetes on heart function. A novel finding of our study was that LV-CO inversely correlated with glucose variability and A1c in the second trimester, and the percentage of time spent in hypoglycemia in the third trimester, independent of fetal weight.

As CO is dependent on the diameter of the corresponding semilunar valve and fetal heart rate an inverse correlation between the TBR and AV diameter contributed to this finding. Notably, up to 40% of women experience severe hypoglycemia during pregnancy [ 25 ]. Our finding that hypoglycemia impairs heart function is also supported by an earlier study that reported decreased fetal heart rate variability during maternal hypoglycemia episodes [ 26 ]. Nevertheless, maintaining a sufficient fetal CO is crucial to ensure adequate perfusion of the placenta, especially when fetal oxygen requirements peak in the late third trimester. Furthermore, women with T1DM exhibit increased placental angiogenesis, leading to a larger distribution volume and thus, a decreased afterload [ 27 ]. Strict compensation for diabetes with frequent episodes of hypoglycemia can lead to chronic hypoxia that may not manifest as overt fetal growth restriction, as the fetuses of pregnant women with diabetes are often predisposed to being macrosomic. We hypothesized that in most susceptible fetuses with diabetic fetopathy, this may even result in sudden intrauterine fetal demise in women with seemingly good compensation. Thus, with respect to strict glycemic control in pregnant women with T1DM, caution is necessary to prevent potentially harmful episodes of maternal hypoglycemia, especially in the third trimester.

This is the first prospective longitudinal study to evaluate the association between fetal cardiac geometry and function and glucose control in a cohort of women with T1DM. It highlights the importance of optimal glucose control in women with T1DM during pregnancy, as maternal hyperglycemia during pregnancy correlates with fetal diastolic function, whereas glucose variability and hypoglycemia inversely correlate with fetal left ventricular systolic function in the second and third trimesters.

The main strength of this study was the prospective monitoring of glucose control using glucose sensors, and ultrasound evaluation of fetal cardiac function by a multidisciplinary team of specialists in diabetology and fetal medicine. Another strength was the consecutive recruitment of women with diabetes to minimize selection bias. During the study period, the estimated weight and routine Doppler indices of fetuses of women with diabetes did not differ from those of healthy controls; thus, we believe that these variables had negligible confounding effects on the presented results.

The main limitation of the present study was that the sample size was too small to allow for adjustment for potential confounders. The reason for the inclusion of fewer healthy controls was the lower willingness of women to comply with the study protocol, which required all ultrasound examinations and delivery at the investigating centre. Recruitment of healthy controls was also undermined by anti-epidemic measures during the SARS-CoV 19 pandemic.

The ultrasound examinations were performed by a single examiner who was not blinded to the diagnosis of diabetes, which implies the possibility of bias. Therefore, the measurements were repeated by the second blinded observer resulting in moderate to excellent interobserver agreements.

Data availability

The raw data can be obtained on request from the corresponding author.

Abbreviations

Glycated hemoglobin

Atrial contraction peak velocity

Aortic valve

Cardiac output

Coefficient of variation

Early diastolic peak velocity

End-diastolic diameter

End-diastolic length

Estimated fetal weight

Interventricular septum

Left atrioventricular valve

Left ventricle

Myocardial performance index

Pulsatility index

Pulmonary valve

Right atrioventricular valve

Right ventricle

Stroke volume

Type 1 diabetes

Time above range

Time below range

Time in range

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Funding for this work was provided by the Czech Health Research Council (NU20-01-00067) and the National Institute for Research of Metabolic and Cardiovascular Diseases (Programme EXCELES, ID Project No. LX22NPO5104), funded by the European Union – Next Generation EU.

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M.H. and K.A. designed the study; K.A., P.S., D.S., M.M. collected the data; M.M. and M.K. analyzed the data and interpreted the results; P.S. drafted the manuscript. All authors reviewed the results and approved the manuscript.

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Simjak, P., Anderlova, K., Smetanová, D. et al. Glucose control during pregnancy in patients with type 1 diabetes correlates with fetal hemodynamics: a prospective longitudinal study. BMC Pregnancy Childbirth 24 , 264 (2024). https://doi.org/10.1186/s12884-024-06462-7

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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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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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DOI : https://doi.org/10.1186/s12937-024-00944-2

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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]

Disclaimer: 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.

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

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NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

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• Clin Nutr Res
• v.5(4); 2016 Oct

In-depth Medical Nutrition Therapy for a Woman with Diabetes: From Pregnancy to Delivery

Miyoung jang.

Department of Food Service and Nutrition Care, Seoul National University Hospital, Seoul 03080, Korea.

MeeRa Kweon

Diabetes in pregnancy is associated with higher rates of miscarriage, pre-eclampsia, preterm labor, and fetal malformation. To prevent these obstetric and perinatal complications, women with diabetes have to control levels of blood sugar, both prior to and during pregnancy. Thus, individualized medical nutrition therapy for each stage of pregnancy is essential. We provided in-depth medical nutrition therapy to a 38-year-old pregnant woman with diabetes at all stages of pregnancy up to delivery. She underwent radiation therapy after surgery for breast cancer and was diagnosed with diabetes. At the time of diagnosis, her glycated hemoglobin level was 8.3% and she was planning her pregnancy. She started taking an oral hypoglycemic agent and received education regarding the management of diabetes and preconception care. She became pregnant while maintaining a glycated hemoglobin level of less than 6%. We provided education program for diabetes management during the pregnancy, together with insulin therapy. She experienced weight loss and ketones were detected; furthermore, she was taking in less than the recommended amount of foods for the regulation of blood sugar levels. By giving emotional support, we continued the counseling and achieved not only glycemic control but also instilled an appreciation of the importance of appropriate weight gain and coping with difficulties. Through careful diabetes management, the woman had a successful outcome for her pregnancy, other than entering preterm labor at 34 weeks. This study implicated that the important things in medical nutrition therapy for pregnant women with diabetes are frequent follow-up care and emotional approach through the pregnancy process.

INTRODUCTION

Pregnancy is associated with changes in insulin sensitivity, which may in turn lead to changes in plasma glucose levels. For women with diabetes, or those who are developing diabetes during their pregnancy, these changes can contribute to outcomes with risk [ 1 ]. Diabetes in pregnancy is associated with higher rates of miscarriage, pre-eclampsia, preterm labor, and fetal malformation [ 2 ].

The risks can be minimized by optimal glycemic control, both prior to and throughout the pregnancy [ 3 , 4 ]. It is achieved throughout comprehensive preconception care including other factors such as genetic risks, health status, reproductive history, exposure to environmental toxins, and immunization, in addition to lifestyle risk factors [ 5 ]. It can also be addressed through a multidisciplinary approach to community-based management of diabetes before and during pregnancy [ 6 ].

Women with pre-existing diabetes who are planning pregnancy or who have become pregnant should receive counseling on preconception care that highlights the importance of glycemic control targeting a level as close to normal as is safely possible (ideally A1c < 6.5%) to reduce the risk of congenital anomalies [ 7 , 8 ]. Fasting, pre-prandial and postprandial self-monitoring of blood glucose (SMBG), are recommended in both of gestational diabetes mellitus and pregestational diabetes in pregnancy to achieve glycemic control [ 7 ].

The purpose of this case report is to share our experience of delivering medical nutrition therapy to pregnant woman with diabetes. This case report was approved, and the requirement for informed consent waived, by the Institutional Review Board of the Seoul National University College of Medicine (H-1609-009-789).

First visit

In August 2014, a 38-year-old woman visited our nutrition care center. She had undergone radiation therapy after surgery for breast cancer and was diagnosed with diabetes. She wanted to become pregnant and decided to receive hormone therapy later. Her A1c value was 8.3% and the fasting glucose sugar level was 175 mg/dL. She started taking an oral hypoglycemic agent, metformin (500 mg per day), and was referred for diabetes education.

She was 153 cm in height and 60 kg in weight (body mass index [BMI] = 25.6 kg/m 2 ). She had an irregular diet pattern including having all meals at once. She preferred noodles and rice and drank beer frequently. She had tried several regimens for weight-loss, but any weight loss did not sustained for a long time. Her usual energy intake was approximately 1,774 kcal/day. Her carbohydrate intake was irregular due to a lack of knowledge about diet therapy for diabetes, as evidenced by an A1c value of 8.3%, and the patient’s report of irregular diet pattern and preference for noodles. We provided a nutrition intervention by formulating a meal plan with the recommended number of calories, and providing information on cooking methods and appropriate snacks. We also recommended to limit alcohol intake with regular exercise and educated the patient regarding pre-conception care of diabetes.

Second visit

In August 2015, the patient attended the clinic center for the second time. She had maintained an A1c value with less than 6%. She tried to become pregnant and was successful. She requested diabetes education at a gestational age of 6 weeks and her medication was discontinued. She was 59 kg in weight, giving a BMI of 25.2 kg/m 2 . The patient’s A1c value was 5.4%. She has reduced not only the intake of energy and carbohydrate but also the consumption frequency of noodles and alcohol after receiving nutrition counseling a year ago. Through these efforts and drug therapy, her blood glucose level has been kept near normal. But, she had reduced her carbohydrate intake excessively; she often did not have breakfast and sometimes ate only meat and vegetables without carbohydrate containing foods. Her usual energy intake was 1,474 kcal/day; carbohydrates accounted for less than 45% of her total energy intake. We determined that her carbohydrate intake was inadequate. We provided a daily menu comprising three meals and three snacks to control blood sugar without producing ketones. We explained that morning urine ketone testing would be helpful to determine whether she was consuming adequate calories and carbohydrate to ensure optimal growth and development of her baby. Also, we explained the need for frequent SMBG and the impact of the relationship between food intake and physical activity on blood glucose level.

Third visit

After 2 weeks (i.e., at 8 weeks of gestation), she visited the clinic center again. Her 1-hour postprandial glucose level was elevated to 140–200 mg/dL. Urine ketone test results were 1 positive on three mornings over a 2-week period. She had reduced her carbohydrate intake to regulate her blood sugar and performed 1 hour of exercise after every meal. She consumed snacks at night irregularly. She started insulin therapy; i.e., 4 units of Humalog (insulin lispro) before each meal. We educated her on the importance of appropriate carbohydrate intake and regular night snacks for prevention of ketones on insulin therapy during pregnancy. We recommended reducing exercise to 30 minutes after every meal.

Fourth visit

After 2 weeks, the patient visited us again (i.e., at 10 weeks of gestation). She had bad morning sickness that reduced her appetite markedly. In addition, she was attempting to reduce her carbohydrate intake so as not to increase her insulin dose. Her body weight had decreased by 1.5 kg during the past 2 weeks and by more than 2.5 kg compared to the pre-pregnancy value. Ketonuria was still evident, and her urine was darker. Her energy intake was approximately 930 kcal per day, 70% of the required level. After being noticed that ketones were still detected, she expressed worries regarding their impact on the fetus. We counseled the patient on appropriate energy intake and provided the recipe with cooking direction to increase caloric density. We educated food exchange models, especially carbohydrate containing foods. Also, we provided emotional support to alleviate her sense of uneasiness.

Fifth visit

After a further 2 weeks, the patient revisited the clinic center (i.e., at 12 weeks of gestation). Her carbohydrate intake had increased as she was now consuming bread, pasta, rice cakes, and cereal instead of rice. The frequency of ketone detection had also decreased.

Her body weight was 58.4 kg. When her morning fasting blood glucose level was high, the insulin lispro dose was increased by 2 units. After 12 weeks of gestation, her nutrition requirements had increased. We repeated the education component of the therapy, adjusted her meal plan and encouraged to increase her energy intake.

Sixth visit

The patient visited the clinic center at 32 weeks of gestation. Her morning fasting blood glucose level was maintained at 95–105 mg/dL. Therefore, 4 units of humulin N were added to her medication, to be taken at night. The patient’s body weight was 66.9 kg, which represented a 7.9 kg increase over the course of her pregnancy. Based on Institute of Medicine (IOM) guidelines, her weight gain during pregnancy was appropriate [ 9 ]. Her energy intake was adequate, and urine ketone tests were negative. We consulted the diet tips that meet the demand for the third trimester of pregnancy and night snacks to prevent hypoglycemia or urine ketone.

Seventh visit

The patient gave birth to a male infant weighing 2.5 kg, after entering preterm labor at 34 weeks. Glycemic control was good at the time of birth (glycated hemoglobin = 5.2%), and overall weight gain during pregnancy was in the normal range, at 8.3%. Furthermore, no ketones were detected in her urine. After the birth, the patient’s medication was changed into oral hypoglycemic agents and her blood sugar level was maintained within the normal range. She planned to breastfeed her baby and requested information on dietary management strategies to achieve a healthy weight and control her diabetes. We educated the patient on the energy and protein intake requirements for breastfeeding and encouraged SMBG.

A summary of the parameters recorded during the seven visits is provided in Table 1 .

GA, gestational age; SMBG, self-monitoring of blood glucose; FBS, fasting blood sugar; PP1, postprandial 1 hour.

This case study focuses on medical nutrition therapy provided to a woman with diabetes from pregnancy to delivery. The patient achieved close to normal glucose levels through effective preconception counseling and had a successful pregnancy. Furthermore, glycemic control during pregnancy was good, with daily insulin therapy and glucose monitoring.

In addition to monitoring blood sugar, pregnant women with diabetes also have to monitor for ketones in the urine [ 10 ]. Our patient had difficulty in reducing the production of ketones during her pregnancy. Ketones are produced when the body catabolizes stored or ingested fat for energy, which occurs when an insufficient quantity of carbohydrate is consumed. The patient decreased her carbohydrate intake to reduce the frequency of insulin injections and the insulin dose. Moreover, her carbohydrate intake was insufficient due to morning sickness. The patient became cognizant of the importance of adequate carbohydrate after receiving counseling from a dietitian, and so the problem was overcome.

A meal plan based on each individual’s needs is one of the most important elements of treatment for patients with diabetes. The meal plan should ensure that the adequate proportions of carbohydrates, protein, fat, vitamins, and minerals, which are needed for diabetes care and healthy pregnancy, are delivered. Dietitians with knowledge of the effects of various foods ondiabetes patients should assist with developing meal plans based on the individual needs of the patient [ 10 ]. We formed an intimate relationship with our patient during seven nutrition consultations and thus were able to provide an appropriate meal plan and level of emotional support.

Conflict of Interest: The authors declare that they have no competing interests.

Author Contributions: All the authors read, commented on, and contributed to the submitted manuscript. The research proposal was registered by Dal Lae Ju and nutrition counseling was performed by Miyoung Jang and MeeRa Kweon. The manuscript was written by Miyoung Jang and finalized by Dal Lae Ju and Misun Park.