case study of gestational diabetes mellitus

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

Interactive case study: Gestational diabetes

• 10 May 2023

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

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

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

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

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

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

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

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

What is your assessment of Holly’s situation?

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

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

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

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

Click here to access the case study .

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Case Presentation

Clinical pearls, article information, case report: managing pregnancy with type 1 diabetes using a do-it-yourself artificial pancreas system.

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Apoorva Ravindranath Waikar , Tanima Arora , Meagan Haynes , William V. Tamborlane , Laura M. Nally; Case Report: Managing Pregnancy With Type 1 Diabetes Using a Do-It-Yourself Artificial Pancreas System. Clin Diabetes 1 October 2021; 39 (4): 441–444. https://doi.org/10.2337/cd20-0128

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The stakes are high when pregnancies are complicated by type 1 diabetes. In addition to the maternal risks, there are numerous fetal complications that can result from diabetes, including spontaneous abortion, fetal anomalies, neonatal birth trauma, macrosomia, neonatal hypoglycemia, hyperbilirubinemia, and fetal demise ( 1 ). Consequently, the American Diabetes Association (ADA) recommends stringent glycemic targets in pregnant women with type 1 diabetes to prevent adverse maternal and fetal outcomes.

Recommendations include self-monitoring of both fasting and postprandial blood glucose and a target A1C of <6% without significant hypoglycemia. Preprandial glucose testing is also recommended in women with pregestational diabetes using insulin pumps or basal-bolus therapy so that premeal rapid-acting insulin doses can be adjusted ( 2 ). The following individual glucose targets are recommended for type 1 diabetes: fasting glucose <90 mg/dL, 1-hour postprandial glucose <130–140 mg/dL, and 2-hour postprandial glucose <120 mg/dL ( 2 ).

These strict glycemic targets are difficult to achieve, especially given the insulin resistance commonly observed during the second and third trimesters of pregnancy. Indeed, women with gestational and pregestational diabetes have average A1C levels ranging from 7.25 to 8.15%, values that are considerably higher than ADA targets during pregnancy ( 3 ). Despite the availability of continuous glucose monitoring (CGM) systems since 1999, none have been approved by the U.S. Food and Drug Administration (FDA) for use in pregnancy. Moreover, no insulin pumps or hybrid-closed loop (HCL) automated insulin delivery systems have been approved for use during pregnancy. The lack of FDA-approved diabetes management technologies poses a significant limitation to pregnant women with type 1 diabetes.

Individuals in the type 1 diabetes community have developed open-source software and hardware to create a do-it-yourself artificial pancreas system (DIY APS) that allows for automated insulin dose adjustments. These systems include algorithms that, when coupled with an insulin pump and CGM system, mimic pancreatic β-cell function ( 4 ). A DIY APS requires a processor capable of receiving insulin pump and CGM data and an algorithm to control the rate of insulin delivery using a compatible insulin pump ( 5 ). With these systems, many patients with type 1 diabetes have reported improvements in their quality of life, more time in the target glucose range, better sleep, less frequent and less severe hypoglycemia, lower A1C, greater confidence in diabetes management, more energy, and fewer mood swings ( 3 ).

The use of an innovative and patient-developedDIY APS during pregnancy has not been reported previously. Herein, we present a unique case of type 1 diabetes management during pregnancy in which a DIY APS where the “Loop” algorithm on an iPhone interface was used ( 5 ). The patient provided written informed consent for the publication of this case report.

This case involves a first pregnancy of a 30-year-old woman with type 1 diabetes since the age of 6 years. Before conception, she used a DIY APS with a Medtronic pump and Dexcom CGM system that allowed for her to manage diabetes using the Loop application that she built and installed on her iPhone. Sensor glucose data, insulin doses, and carbohydrate intake were visible through the Loop application and Apple Health app. She had optimized her glycemic control before conception, with an A1C of 6.0%. During the month before conception, her overall mean sensor glucose was 134 mg/dL with a coefficient of variation (CV) of 41%, indicating healthy glycemic control. During this time, her CGM metrics indicated 77% of time in range (TIR; 70–180 mg/dL), 5% of time <70 mg/dL, and 17% of time >180 mg/dL.

During pregnancy, she noted an increase in her insulin requirements. Table 1 lists the insulin-to-carbohydrate ratios and insulin sensitivity factors she used during each trimester of pregnancy. Her A1C levels remained ≤5.7% throughout the pregnancy. She maintained optimal glycemic control, with 87% TIR in the third trimester. Details of her CGM metrics are listed in Table 2 . The patient was managed by the Maternal Fetal Medicine Clinic at Yale New Haven Hospital by a team of physicians, nurses, and diabetes educators knowledgeable in type 1 diabetes management.

A1C Levels and Insulin Doses at the End of Each Trimester of Pregnancy

Sensor Glucose Metrics During Each Trimester of Pregnancy Using Recommended Pregnancy-Specific Target Ranges ( 10 )

Sensor glucose metrics for each trimester of pregnancy are presented. Excellent sensor usage (>90%) occurred throughout the pregnancy. Of note, while pregnancy-specific targets are presented in the table, the overall time spent in the target range of 70–180 mg/dL through the entire pregnancy was 87%.

The patient presented for induction of labor at 39 weeks’ gestation and delivered a healthy female infant via cesarean section due to arrest of descent. The newborn weighed 2,910 g (16th percentile for age based on Centers for Disease Control and Prevention [CDC] growth charts) and measured 49.53 cm in length (54th percentile for age based on CDC growth charts), and thus her size was appropriate for gestational age.

The infant required temporary monitoring in the Neonatal Intensive Care Unit for hypoglycemia, which resolved within 24 hours of birth with a combination of breast milk and formula feeding. The infant was otherwise healthy and both mother and baby were discharged on the third day after delivery.

What are the challenges of type 1 diabetes management during pregnancy?

How can we incorporate the use of diabetes technology during pregnancy?

What are the advantages of using a DIY APS in pregnancy?

Although the increased risk of adverse pregnancy outcomes in women with type 1 diabetes is well established, FDA approval of the use of diabetes technologies in this population is lacking. The CONCEPTT (Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial) showed that use of real-time CGM during pregnancy in women with type 1 diabetes was associated with improved neonatal outcomes compared with using multiple fingerstick blood glucose measurements ( 6 ). However, in the United States, only 36% of the 214 pregnant or recently pregnant women who participated in the T1D Exchange clinic registry between 2010 and 2013 reported using CGM ( 7 ). Despite the potential benefits of CGM, the FDA has not approved the use of these devices in pregnancy. Further, newly developed HCL systems that have improved time in healthy glucose ranges in nonpregnant individuals with type 1 diabetes have not been approved for use during pregnancy. These currently available HCL systems do not allow for individuals to set pregnancy-specific targets, thus limiting options for insulin-dependent women who become pregnant. Further, glucose targets in the commercially available HCL systems are not customizable and are typically too high for pregnancy.

Although DIY APS technology has not been formally studied in pregnant women with diabetes, it provides the necessary flexibility in establishing individualized target ranges and automatic adjustments to basal rates when blood glucose levels are anticipated to rise above or fall below prespecified targets, thus helping users improve their TIR with less thought, less effort, and the potential for improved glycemic control and better sleep ( 8 , 9 ). The degree of individualization possible with this type of technology is greater than will be achievable with commercial systems in the near future. The benefits of DIY APS systems should be balanced with risks, including the lack of a formal body to address technical issues that may arise, making DIY APS users largely responsible for finding solutions through the online community, as well as potential issues with lack of support or knowledge from their diabetes provider.

Consensus recommendations state that pregnant women with type 1 diabetes should have >70% TIR (63–140 mg/dL), <25% of time above range (>140 mg/dL), and 5% of time below range (<4% of time <63 mg/dL and <1% of time <54 mg/dL) ( 10 ). Our patient was able to meet these targets during the third trimester and reported improved sleep and less diabetes-related anxiety throughout her pregnancy. Large-for-gestational-age infants are more commonly observed in mothers who have sustained hyperglycemia during pregnancy. Therefore, it is noteworthy that the opposite was true in this patient’s infant, whose weight was at the 16th percentile and length was at the 54th percentile. Normal weight in this baby suggests that maternal blood glucose levels were not substantially increased.

Using a DIY APS requires a high level of motivation and a strong understanding of diabetes technology. Those who choose to use this type of technology tend to be very actively engaged in and adherent to their diabetes management regimen. Hence, a DIY APS may not be appropriate for the general type 1 diabetes population. Nevertheless, our patient’s experience illustrates that these systems can be effectively used to meet and maintain safe glucose levels throughout pregnancy.

Pregnant women have the strictest glycemic targets of any group with diabetes, yet no CGM or insulin pump technology is FDA-approved for this population.

Some women with type 1 diabetes who become pregnant have chosen to use a DIY APS to achieve these targets because of the ability such systems afford to set individualized target glucose ranges and insulin dose settings with more ease and flexibility than commercially available insulin pump and HCL systems.

Enthusiasm for DIY APS technology continues to grow in the diabetes online community, and further studies are needed to support its use and facilitate more widespread acceptance.

Duality of Interest

W.V.T. is a consultant for Medtronic. L.M.N. has received funding from the National Institutes of Health and product support from Dexcom for investigator-initiated research. No other potential conflicts of interest relevant to this article were reported.

This work was supported by National Institutes of Health grant K12 DK094714 (W.V.T. principal investigator).

Author Contributions

A.R.W., T.A., and L.M.N. wrote the manuscript. M.H. cared for the patient during pregnancy and provided data for the case report. L.M.N. conceived the idea for the case report. W.V.T. and M.H. reviewed and edited the manuscript before submission. L.M.N. is the guarantor of this work and, as such, had full access to all the data and takes full responsibility for the integrity of data and the accuracy of data analysis.

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Obstetrics and Gynaecology

At a glance, fourth edition errol r. norwitz, john o. schorge.

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Case Studies

Case 9: gestational diabetes.

A 28-year-old G 4 P 2 presents to your office for a routine prenatal visit at 24 weeks’ gestation. Her physical examination is unremarkable and fetal wellbeing is reassuring. You recommend testing for gestational diabetes mellitus (GDM).

1. What is GDM?

Show Answer

Correct answer: GDM refers to any form of glucose intolerance with the onset of pregnancy or first recognized during pregnancy, and complicates approximately 5% of all pregnancies. It likely includes some women who have undiagnosed pregestational diabetes.

2. Should everyone be screened for GDM? If so, at what gestational age should they be screened?

Correct answer: Patients with GDM are typically asymptomatic. There is a small cohort of pregnant women in whom routine screening for GDM is not cost-effective. These are women under age 25 who have normal body mass index (BMI 2 ), no first-degree relatives with diabetes, no risk factors (such as a history of GDM, insulin resistance/PCOS [polycystic ovarian syndrome], a prior macrosomic infant, a prior unexplained late fetal demise, and women with persistent glycosuria), and who are not members of ethnic or racial groups with a high prevalence of diabetes (such as Hispanic, Native American, Asian, or African–American). As such patients are rare, most experts and organizations recommend screening for GDM in all pregnant women. The ideal time to screen for GDM is 24–28 weeks of gestation. For women at high risk of developing GDM (listed above), early screening for GDM is recommended at the first prenatal visit. If the early screen is negative, it should be repeated at 24–28 weeks.

3. Her 1-hour GLT is 182 mg/dL. Does she have GDM?

Correct answer: The most common screening test for GDM is the glucose load test (GLT) – also known as the glucose challenge test (GCT) – which is a non-fasting 50-g oral glucose challenge followed by a venous plasma glucose measurement at 1 hour. Most authorities consider the GLT to be positive if the 1-hour glucose measurement is >140 mg/dL. Use of a lower cut-off (such as >130 mg/dL) will increase the detection rate of women with GDM, but will result in a substantial increase in the false-positive rate.

There is no GLT cut-off that should be regarded as diagnostic of GDM . A definitive diagnosis of GDM requires a 3-hour glucose tolerance test (GTT). In pregnancy, the GTT involves 3 days of carbohydrate loading followed by a 100-g oral glucose challenge after an overnight fast. Venous plasma glucose is measured fasting and at 1 hour, 2 hours, and 3 hours. Although there is agreement that two or more abnormal values are required to confirm the diagnosis, there is little consensus about the glucose values that define the upper range of normal in pregnancy (see below). Most institutions use the National Diabetes Data Group (NDDG) or Carpenter and Coustan cut-offs. Measurement of glycated hemoglobin (HbA1c) levels is not useful in making the diagnosis of GDM, although it may be useful in the diagnosis of pregestational diabetes.

Plasma glucose values (mg/dL) (mmol/L) *

Sacks et al.

Carpenter and Coustan

* Values in parentheses are mmol/L.

4. All four values of her 3-hour GTT are elevated and her fasting glucose level is 127 mg/dL. How would you manage her GDM? How long would you allow her to try dietary restriction before adding a hypoglycemic agent?

Correct answer: GDM poses little risk to the mother. Such women are not at risk of diabetic ketoacidosis (DKA), which is primarily a disease of absolute insulin deficiency. However, GDM has been associated with an increase in infant birth trauma and perinatal morbidity and mortality. The risk to the fetus/infant is directly related to its size. Fetal macrosomia is defined as an estimated fetal weight (not birthweight) of ≥4,500 g. It is a single cut-off that is unrelated to gestational age, the sex of the baby, or the presence or absence of diabetes, or to the actual birthweight.

The goal of antepartum treatment of GDM is to prevent fetal macrosomia and its resultant complications by maintaining maternal blood glucose at desirable levels throughout gestation, defined as a fasting glucose level 95 mg/dL, treatment can be started immediately because “you can’t diet more than fasting.”

Insulin (which has to be given several times a day by injection) remains the “gold standard” for the medical management of GDM. The use of oral hypoglycemic agents has traditionally been avoided in pregnancy because of concerns over fetal teratogenesis and prolonged neonatal hypoglycemia. However, recent studies suggest that second-generation hypoglycemic agents (glyburide, glipizide) do not cross the placenta, are safe in pregnancy, and can achieve adequate glycemic control in 85% of pregnancies complicated by GDM.

5. The estimated fetal weight at 38 weeks’ gestation is 4,600 g (10 lb 2 oz). She has had six prior uncomplicated vaginal deliveries. How would you counsel her about delivery?

Correct answer: As noted above, the complications of GDM are related primarily to fetal macrosomia, including an increased risk of cesarean section delivery, operative vaginal delivery, and birth injury to both the mother (vaginal, perineal, and rectal trauma) and fetus (including orthopedic and neurologic injury). Shoulder dystocia with resultant brachial plexus injury (Erb’s palsy) is a serious consequence of fetal macrosomia, and further increased in the setting of GDM because the macrosomia of diabetes is associated with increased diameters in the upper thorax of the fetus.

The use of elective cesarean section delivery to reduce the risk of maternal and fetal birth injury in the setting of fetal macrosomia remains controversial. According to current ACOG guidelines, an elective cesarean section delivery at or after 39 weeks’ gestation should be recommended for all non-diabetic women who have a fetus with an estimated fetal weight (EFW) ≥5,000 g (or ≥4,500 g in a diabetic individual) to minimize the risk of birth trauma. Furthermore, it is recommended that a discussion be held about the safest route of delivery with non-diabetic women who have a fetus with an EFW ≥4,500 g (or ≥4,000 g in a diabetic individual) and that this discussion be documented in the medical record.

6. After extensive counseling, the couple decline elective cesarean section delivery. She is now 38 weeks’ gestation. How should she be managed at this point in time?

Correct answer: If the patient declines elective cesarean section delivery, spontaneous labor should be awaited. Induction of labor for so-called “impending macrosomia” does not decrease the risk of cesarean section delivery or intrapartum complications, and is therefore not routinely recommended. If she is still undelivered at 41 weeks’ gestation, she should be counseled again about induction of labor and/or elective cesarean section.

During labor, maternal glucose levels should be maintained at 100–120 mg/dL to minimize the risk of intrapartum fetal hypoxic–ischemic injury. Continuous fetal monitoring is recommended throughout labor and the progress of labor should be carefully charted. Internal monitors such as an intrauterine pressure catheter (IUPC) and/or fetal scalp electrode can be used, if indicated. Neonatal blood glucose levels should be measured within 1 hour of birth and early feeding encouraged.

Delivery of the fetus and placenta effectively removes the source of the anti-insulin (counter-regulatory) hormones that cause GDM. As such, no further management is required in the immediate postpartum period. A 2-hour non-pregnant GTT should be performed at 6–8 weeks postpartum in all women with GDM to exclude pre-gestational diabetes.

See Chapter 45.

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The potential causal effect of the pre-pregnancy dietary phytochemical index on gestational diabetes mellitus: a prospective cohort study

• Neda Heidarzadeh-Esfahani 1 ,
• Javad Heshmati 2 ,
• Reihaneh Pirjani 3 ,
• Ashraf Moini 3 , 4 ,
• Mehrnoosh shafaatdoost 5 ,
• Mahnaz Esmaeili 6 ,
• Azar Mardi-Mamaghani 7 ,
• Seyyed Mostafa Nachvak 1 &
• Mahdi Sepidarkish 8  

BMC Pregnancy and Childbirth volume  24 , Article number:  447 ( 2024 ) Cite this article

Metrics details

Phytochemicals are non-nutritive bioactive compounds with beneficial effects on the metabolism of glucose. This study aimed to clarify the possible causal effect of the pre-pregnancy dietary phytochemical index (DPI) on gestational diabetes mellitus (GDM).

In this prospective cohort study 1,856 pregnant women aged 18–45 years who were in their first trimester, were recruited and followed up until delivery. The dietary intakes of participants were examined using an interviewer-administered validated 168-item semi-quantitative food frequency questionnaire (FFQ). Inverse probability weighting (IPW) of propensity scores (PS), estimated from the generalized boosted model (GBM) were used to obtain a adjusted risk ratio (aRR) for potential confounders.

During the follow-up period, 369 (19.88%) women were diagnosed with GDM. DPI scores ranged from 6.09 to 89.45. There was no association between DPI scores and GDM (aRR: 1.01, 95% confidence interval [CI]: 0.92, 1.08; p trend = 0.922). When comparing DPI quartile 4 (most pro-phytochemical content) to quartile 1 (few phytochemical contents), there was no significant difference between them (aRR: 0.97; 95% CI: 0.75, 1.25; p  = 0.852). Also, there was no significant difference between DPI quartile 3 and quartile 1 (aRR: 1.04; 95% CI: 0.81, 1.34; p  = 0.741) as well as DPI quartile 2 and quartile 1 (aRR: 0.92; 95% CI: 0.71, 1.21; p  = 0.593).

Conclusions

Although this data did not support the association between pre-pregnancy DPI scores and GDM, further cohort studies to ascertain the causal association between them are warranted.

Peer Review reports

Women with GDM experience glucose intolerance in the second or third trimester of pregnancy without any clear manifest diabetes before pregnancy [ 1 ]. GDM prevalence was reported to range from 9.3 to 25.5% (average 17.8%) and the prevalence has been increasing worldwide [ 2 , 3 ]. GDM is associated with an increased risk for many short-term and long-term consequences for both mother and offspring, including obesity, impaired glucose metabolism, and cardiovascular disease [ 4 , 5 , 6 , 7 , 8 ]. Thus, it is important to come up invaluable approach for GDM prevention and management.

There is now substantial evidence that maternal dietary patterns before and during pregnancy prevent or delay the development of GDM [ 9 , 10 , 11 , 12 ] however, the focus has been on identifying crucial risk factors during pregnancy. Recently, an increasing interest has emerged in the worthwhile effects of plant-based dietary patterns and phytochemical plant-derive bioactive compounds for the management of GDM [ 13 , 14 ].

Phytochemicals are biologically non-nutritive bioactive compounds divided into several classes, including: Alkaloids, Glycosides, organosulfur compounds (thiosulfinate and isothiocyanates) phenolic compounds (flavonoids, phenolic acids, hydroxycinnamic acids, lignans, polyphenols, and stilbenoids), tannins, Terpenes, saponins, Anthraquinones, essential oils, and steroids [ 15 , 16 ].

The DPI, which was proposed and developed for the first time by McCarty, is determined according to the percent of daily energy intake derived from phytochemical-rich foods such as fruits, vegetables, legumes, whole grains, nuts, seeds, soy products, juices (fruit and vegetable), and other plant foods [ 17 ].

DPI have been inversely associated with risk of cardiovascular disease [ 18 , 19 ], insulin resistance [ 20 ], metabolic syndrome [ 21 ], and cancer [ 21 ]. Plausible mechanisms underlying causes of beneficial traits of phytochemicals on non-communicable diseases are antioxidant and anti-inflammatory effects, enhanced glycemic control, regulated body weight, improved insulin sensitivity, and gut microbiota [ 22 , 23 , 24 ].

Limited observational studies have investigated associations between DPI and the improvement of glucose tolerance and insulin sensitivity [ 20 , 25 ]. According to our review, there is no study presenting the association between DPI and GDM. Denoting the association between DPI and glycemic indices among affected women may provide the obvious starting point for GDM prevention and treatment. Hence, in the present study, we aimed to determine the possible causal effect of the pre-pregnancy DPI on GDM.

Study design and participants

We conducted a prospective cohort study - Mothers and their children’s health (MATCH) study at the Arash Women’s Hospital in Tehran, Iran between February 2020 and January 2023. The details of this study and further information on methods have been described previously [ 26 ]. The MATCH protocol was approved by the institutional review boards of the Tehran University of Medical Sciences (Protocol number: IR.TUMS.MEDICINE.REC.1398.576).

Briefly, the pregnant women aged from 18 to 45 years who were at less than 12 weeks of gestation, and attending antenatal care in Arash Women’s Hospital in Tehran were included between February 2020 and August 2021. Furthermore, women who reported a previous diagnosis of metabolic or chronic diseases, following a special diet, using certain food supplements (except for pregnancy supplements such as iron or folate), suffering from physical, mental, cognitive disability, and having an unusual total energy intake (< 800 or > 4200 kcal/day), were excluded from the current study. Total daily energy intake by summing up the calories from all food items were reported in 168-item semi-quantitative food frequency questionnaire (FFQ).

Data collection

Ten trained observers completed a structured questionnaire through face-to-face interviews to obtain sociodemographic, history of underlying disease, and lifestyle variables, including smoking, alcohol, dietary pattern, physical activity, and sleep quality pre-pregnancy and early pregnancy. The quantity and quality of physical activity was assessed by the International Physical Activity Questionnaire (IPAQ) using Metabolic Equivalent of Tasks (METs) [ 26 ]. Also, the anthropometric indices, including weight, height, waist, and hip circumferences were measured by our trained staff accurately.

Dietary intakes assessment

In the first visit, dietary intake was evaluated by using an interviewer-administrated 168-item FFQ that contains questions about the type/brand, cooking methods, frequency, and the amount of all foods and drinks they consumed during the one-year leading up to the pregnancy. The validity and reliability of FFQ were confirmed in the Tehran Lipid and Glucose Study (TLGS) in Iran [ 27 ]. For the FFQ data, portion sizes will be converted to grams per week per food item by two experienced nutritionists.

Exposure assessment

The DPI was determined based on the method developed by McCarty; [PI = (daily energy derived from phytochemical-rich foods (kcal)/total daily energy intake (kcal)) × 100] [ 17 ]. Fruits and vegetables (except potatoes), legumes, whole grains, nuts, soy products, olives, and olive oil were categorized into phytochemical-rich foods. Natural fruit and vegetable juices such as tomato sauces were included in the fruit and vegetable groups due to their high phytochemical content.

Outcome assessment

Screening and diagnosis of GDM were carried out according to the results of the one-step method which includes a fasting glucose test followed by a 75-gram, 2-hour diagnostic oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. Using the one-step method, women were considered to have screened positive for GDM if they had a serum glucose value fasting ≥ 92 mg/dl, 1-hour ≥ 180 mg/dl, and 2-hour ≥ 153 mg/dl [ 28 ].

Statistical analysis

We presented continuous baseline characteristics as mean (± SD) or median (interquartile range, IQR) and compared using one-way analysis of variance and independent t-test. Also, we expressed categorical variables as numbers (percentages) and compared using Chi-square test. We used multiple imputations based on chained equations, which fill in missing values in multiple variables iteratively using a sequence of univariate imputation models with a fully conditional specification of prediction equations. We used the generalized boosted model (GBM) for the estimation of participants’ propensity scores for DPI, so that covariate imbalance between the exposed (quartiles 1–3 (Q1–Q3) for DPI) and non-exposed groups was minimized. We employed ‘TWANG’ package to estimate propensity scores using an automated, nonparametric machine learning method, and generalized boosted models based on 10,000 regression trees. We selected the minimal sufficient variables using directed acyclic graphs (DAGs), based on the web tool dagitty.net (Fig.  1 ) [ 29 ]. We evaluated the association between DPI and the incidence of GDM by calculating adjusted risk ratios (aRRs) and corresponding 95% confidence intervals based on the weighted modified Poisson regression with the inverse probability weight (IPTW). In addition, we stratified the analysis based on age to determine whether the risk of GDM affected by it. The data processing and statistical analysis were performed using the Stata statistical package version 17 (Stata Corp LP, College Station, TX, USA) and R statistical software (Version 4.2.1; The R Foundation for Statistical Computing, Vienna, Austria).

Directed acyclic graphs, A: Unadjusted, B: Adjusted. BMI, Body Mass Index; GDM, Gestational Diabetes Mellitus; FH, Family History

The flow diagram in Fig.  2 depicts the number of pregnant women examined at each time point, as well as those lost to follow-up and the reasons for dropout. A total of 3,285 women were enrolled from 1 February 2020 and January 2023. Based on initial screening, 2,103 women were eligible for inclusion in the study, of whom 1,856 had complete and 247 (11.74%) had incomplete follow-up data. We excluded 1,182 participants for the following reasons: (І) plan to deliver elsewhere ( n  = 486, 41.11%); (Π) gestational age > 12 Weeks ( n  = 328, 27.74%); (III) multiple pregnancies ( n  = 125, 10.57%); (IV) metabolic or chronic diseases ( n  = 114; 9.64%); (V) following a special diet ( n  = 32; 2.70%); and (VI) declined ( n  = 97, 8.20%) (Fig.  2 ).

Flow Diagram of Study Participants. † Gestational Diabetes Mellitus; flow diagram showing participant recruitment from enrolment to corresponding numbers of women who were and were not diagnosed with gestational diabetes. Reasons and number of exclusions are stated accordingly

The demographic and clinical characteristics of women with and without GDM as well as across quartiles of DPI are presented in Tables  1 and 2 , respectively. At the study baseline, the mean age and BMI of the included women were 32.9 ± 6.1 years and 25.9 ± 8.3 kg/m2, respectively. 7.5% ( n  = 149) of study participants were employed and 45.5% ( n  = 845) had an academic education. During the follow-up period, 369 (19.88%) women were diagnosed with GDM. Women with GDM were older (34.8 ± 5.7 vs. 32.4 ± 5.9; p  < 0.001), heavier (67.9 ± 13.1 vs. 65.2 ± 12.4; p  < 0.001), more likely to be current smokers (90/369, 24.4% vs. 278/1,487, 18.7%; p  = 0.140), pre-existing diabetes (83/369, 22.5% vs. 106/1,487, 7.1%; p  < 0.001), and had a higher frequency of family history of diabetes than controls (174/369, 47.1% vs. 572/1,487, 38.5%; p  = 0.002).

The DPI score of the women’s diet ranged from 6.1 to 89.4 with a median (IQR) of 40.3 (19.8). Also, the DPI score of women across quartile categories in the first, second and third quartiles was 30.9, 40.3, and 50.8, respectively. Pregnant women in the highest quartile had a higher frequency of pre-existing diabetes, GDM, and a family history of diabetes. Also, they had a higher pre-existing BMI and dietary caloric intake (kcal/day). The overall mean DIP in the women with and without GDM were 41.5 ± 13.6 and 41.1 ± 13.8, respectively ( p  = 0.614).

We outlined the crude and multivariate-adjusted risk ratios (aRRs) for the association between DPI and GDM in Table  3 . We found no significant association between DPI and GDM in the crude model (crude RR: 1.03, 95% CI: 0.95, 1.12, p  = 0.413). This association remained non-significant after adjustment for potential confounders, including body mass index (kg/m2), occupation, age, hypertension, education, and gastrointestinal diseases (aRR: 1.01, 95% CI: 0.92, 1.08, p  = 0.922). The crude RR of GDM in a quartile with the highest DPI scores (Q4), compared to that with the lowest scores (Q1), was 1.06 (95% CI: 0.82, 1.38, p  = 0.612). After additional adjustment for potential confounders, including body mass index (kg/m2), occupation, age, hypertension, education, and gastrointestinal diseases, associations were attenuated but remained non-significant (aRR: 0.97, 95% CI: 0.75, 1.25, p  = 0.852).

In stratified analyses, we studied whether the effects of DFI on GDM could be modified by age. In these analyses, the main analyses showed similar results by age categories. We found no significant association between DPI and GDM in all age categories (Tables  4 , 5 and 6 ).

The purpose of the current study was to evaluate the causal effect of calorie intake from phytochemical-rich foods on GDM, using propensity score to minimize potential confounding factors. In this prospective study, after accounting for non-dietary covariates such as body mass index (kg/m2), occupation, age, hypertension, education, and gastrointestinal diseases, the association between the higher load of calorie intake from phytochemical-rich foods and occurrence of GDM was found to be nonsignificant after a 9-months follow-up in pregnant women.

To our knowledge, this work was the first study observed the association between DPI and GDM. However, some studies have examined the association between DPI and glucose homeostasis disruption which showed controversial results [ 20 , 25 , 30 , 31 , 32 , 33 ]. For instance, the finding of mentioned prospective study are aligned with some observational studies on DPI and hyperglycemia. In a cross-sectional investigation on 2,326 adults aged between 20 and 70 years which aimed to investigate the association between DPI and metabolic syndrome, no significant association was yielded between DPI and the prevalence of high serum FBS in crud and full adjustments model [ 33 ]. Firouzabadi et al. conducted a cross-sectional study reported no significant association between odds of hyperglycemia in men and women across quartiles of DPI in both crude model and after adjusting for age, energy intake, marital status, educational status, occupation physical activity, and smoking status [ 31 ].

In stark contrast, however, in the Tehran Lipid and Glucose cohort study across 1,141 participants with an average of three years of follow-up, showed considerably a reduction risk of insulin insensitivity (OR = 0.11, 95% CI: 0.05, 0.24), insulin resistance (OR = 0.48, 95% CI: 0.25, 0.93) and hyperinsulinmia (OR = 0.14, 95% CI: 0.07, 0.25) in higher quartiles of DPI after adjustment for non-dietary factors [ 20 ]. The potential protective impact of phytochemicals is attributed to their antioxidant properties, enhancement of beta cell function, promotion of insulin response, and reduction of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like polypeptide-1 (GLP-1) levels. These mechanisms are considered key in the pathophysiological effects of phytochemicals [ 20 ]. In agreement with this finding, Delshad Aghdam et al. found that the risk of hyperglycemia significantly decreased by 88% (OR = 0.12, 95% CI: 0.02, 0.82) after adjusted for age, sex, total energy intake (kcal/day), physical activity (MET/min/week), BMI (kg/m2), diabetes duration (year), total insulin dose (unit/day), education and dietary supplement intake in participants with T1DM in the highest tertile of DPI [ 30 ]. Moreover, the case-control study which denoted a high level of DPI score is related to a lower risk of prediabetes (OR = 0.09, 95% CI: 0.03, 0.25). Also this study showed individuals in the higher quartiles of DPI had significantly lower FBG and OGTT (p-trend < 0.001) [ 25 ]. In contrast, we did not observe any statistically significant association between DPI and OGTT in women with GDM.

In addition, in a case-control study with 210 diabetic women, a significant negative association of DPI with FBS ( p  = 0.04) was observed in the case group with diabetic nephropathy [ 32 ].

The fact that studies are inconsistent might be due to differences in sample size, methodology, different dietary intake assessment, and eligibility criteria (most of them excluded pregnant women).

It is worth noting that in this study we used DPI which is practically useful to induce synergetic clinical functions of phytochemicals isolated from various types of foods and it can bring in its wake modulating physiologically [ 17 ]. By contrast, the majority of findings from prior studies can be drawn from certain phytochemicals and their effects on GDM.

The results of the present study are in line with the findings of a longitudinal cohort study conducted on pregnant with twins in China which indicated that no significant association was shown between the risk of GDM and vegetables and fruit-based pattern [ 34 ].

Our findings is in accordance with a recent meta-analysis of 12 epidemiological studies revealed that there was not any significant interplay between consuming polyphenol-rich fruits, seeds, and whole grains with GDM, nonetheless, the highest adherence to the Mediterranean diet (MedDiet) associated with lower risk of GDM [ 14 ]. Meanwhile, the inverse association of MedDiet with GDM has been appraised in another systematic review and meta-analysis of observational studies [ 35 ]. MedDiet is associated with better control of lipid and glycemic profiles [ 36 , 37 , 38 ], and eventually lower incident risk in type 2 diabetes [ 39 ]. As a matter of fact, the protection effect of MedDiet was mainly manifested via its content of poly and mono-unsaturated fatty acids by modulating inflammatory processes [ 40 ]. Moreover, the above-mentioned studies were mainly conducted in Western and American countries.

On the other hand, two prospective studies have presented the association between whole grain and the risk of GDM [ 41 , 42 ] but with inconsistent findings. In a prospective cohort study in China, a whole grain-sea food pattern was associated with an increased occurrence of GDM (OR = 1.73, 95% CI: 1.10, 2.74) because of environmental contaminants [ 42 ]. In stark contrast, however, in the PREWICE II cohort study, Tryggvadottir EA et al., reported a higher median concentration of total alkylresorcinols of plasma as a whole-grain consumption biomarker in pregnancies women plasma without GDM rather than women diagnosed with GDM (209 nmol/L vs. 163 nmol/L, respectively; p  < 0.001) [ 41 ]. The possible mechanism might be that whole-grain diet contained fiber and phytochemical components increased gut health, and improved glycemic response [ 23 , 43 ].

Meanwhile, one study that has prospectively examined the correlation of fruit intake during pregnancy with GDM incidence, suggested that fresh fruit intake is inversely associated with the risk of GDM [ 44 ]. However, fruits were not been categorized in detail, which might result in misinformation about fruit type.

We are unaware of published prospective studies which assessed the DPI in relation to GDM. Pregnancy outcomes in relation to GDM adversely have imposed an immense burden on the global health system [ 45 ]. Hence, prevention and management of GDM should be getting as a high priority straight. This study is the largest to date to provide data that has investigated the correlation between DPI and GDM risk.

In this study, several strengths and limitations were present. There are no cohort studies have used propensity scores to evaluate the association between DPI and GDM which can preclude bias related to potential confounding variables. The propensity scoring implementation can control confounding by balancing covariates between exposed and non-exposed groups [ 46 ].

Moreover, strong recall bias may present through dietary assessment tool which assessed with FFQ. However, the use of a validated FFQ to collect dietary intake information, and standardized clinical assessments, as well as the prospective large sample size setting in this study can rule out mentioned bias. In this work, Iranian population with the same ethnicity diversity were recruited in order to limiting generalizability.

Inheritance limitation of DPI such as failing to add up non-caloric phytochemical-rich foods like green and black tea and spices should be considered. Furthermore, we failed to conduct dietary questionnaires during the early or pre-pregnancy which can interpret the relationship between DPI and GDM more clearly. Previous data reported that dietary patterns are not, however, varied during pregnancy [ 47 ].

In summary, according to our finding, this prospective cohort study among pregnant women suggest that the DPI has no impact on GDM. More research is needed to determine the exact association between DPI and GDM.

Data availability

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

Abbreviations

Confidence interval

Directed acyclic graphs

• Dietary phytochemical index

Food frequency questionnaires

• Gestational diabetes mellitus

Generalized boosted model

Inverse probability weighting

Inverse probability weight

Interquartile range

Propensity scores

Standard deviation

Tehran Lipid and Glucose Study

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Acknowledgements

The authors are grateful to all the women who consented to participate. We are also grateful to research teams at our participating clinical centers such as Arash Women’s Hospital in Tehran.

This research was supported by the Tehran and Kermanshah University of Medical Sciences.

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Nutritional Sciences Department, School of Nutrition Sciences and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran

Neda Heidarzadeh-Esfahani & Seyyed Mostafa Nachvak

University of Ottawa Heart Institute, University of Ottawa, Ottawa, Canada

Javad Heshmati

Department of Obstetrics and Gynecology, Arash Women’s Hospital, Tehran University of Medical Sciences, Tehran, Iran

Reihaneh Pirjani & Ashraf Moini

Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran

Ashraf Moini

Department of Clinical Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran, Iran

Mehrnoosh shafaatdoost

Department of Genetics, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran

Mahnaz Esmaeili

Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran

Azar Mardi-Mamaghani

Population, Family and Spiritual Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran

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M.S., M.N., contributed to the study conception and design. Material preparation, data collection, and data analysis were performed by N.H., M.S, J.H., AM, M.Sh., A.M-M, M.E. and R.P. The manuscript was written by N.H, M.S., and M.N. All authors read and approved the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Seyyed Mostafa Nachvak or Mahdi Sepidarkish .

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Heidarzadeh-Esfahani, N., Heshmati, J., Pirjani, R. et al. The potential causal effect of the pre-pregnancy dietary phytochemical index on gestational diabetes mellitus: a prospective cohort study. BMC Pregnancy Childbirth 24 , 447 (2024). https://doi.org/10.1186/s12884-024-06643-4

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Gestational Metabolic Risk: A Narrative Review of Pregnancy-Related Complications and of the Effectiveness of Dietary, Exercise and Lifestyle Interventions during Pregnancy on Reducing Gestational Weight Gain and Preventing Gestational Diabetes Mellitus

Affiliations.

• 1 Department of Medicine, University of Ioannina, University Campus, 45110 Ioannina, Greece.
• 2 3rd Department of Cardiology, "Sotiria" Chest Diseases Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
• 3 Department of Research for General Medicine and Primary Health Care, Faculty of Medicine, University of Ioannina, University Campus, 45110 Ioannina, Greece.
• 4 Department of Critical Care, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, Mezourlo, 41335 Larissa, Greece.
• 5 Department of Neurosurgery, University Hospitals Sussex NHS Foundation Trust, Brighton BN2 5BE, UK.
• 6 Department of Gastroenterology, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, Mezourlo, 41335 Larissa, Greece.
• 7 Department of Internal Medicine-Endocrinology, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, Mezourlo, 41335 Larissa, Greece.
• PMID: 38929991
• PMCID: PMC11204633
• DOI: 10.3390/jcm13123462

Objective: This study is a Narrative Review that aims at investigating the implications of obesity, excessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM). Additionally, this Review seeks to explore the effectiveness of nutrition, and/or exercise interventions during pregnancy on reducing GWG and preventing GDM. Materials and Methods: The search in literature included studies that identified obesity, GWG, GDM and associated risks during pregnancy. Also, SR and MA focusing on interventions including diet, or physical activity (PA), or combined (i.e., lifestyle interventions) and their impact on metabolic risk during pregnancy, were identified through searches in PubMed, Cochrane Database of Systematic Reviews (CDSRs), and Scopus. Results: The study findings suggest that lifestyle interventions during pregnancy may be effective in reducing excessive GWG. Regarding the prevention of GDM, results from studies evaluating lifestyle interventions vary. However, significant and less controversial results were reported from studies assessing the efficacy of exercise interventions, particularly in high-risk pregnant women. Conclusions: Lifestyle interventions during pregnancy may reduce excessive GWG. Exercise during pregnancy may prevent GDM, especially in high-risk pregnant women. Future research is warranted to tailor lifestyle interventions for optimal effectiveness during pregnancy.

Keywords: GDM; GWG; PA; diet; exercise; nutrition; obesity.

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• Int J Reprod Biomed
• v.19(9); 2021 Sep

Gestational diabetes mellitus: Major risk factors and pregnancy-related outcomes: A cohort study

Azam kouhkan.

1 Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.

2 Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.

Laily Najafi

3 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran.

Mojtaba Malek

4 Research Center for Prevention of Cardiovascular Disease, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran.

Hamid Reza Baradaran

Roya hosseini.

5 Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.

Alireza Khajavi

6 Student Research Committee, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Mohammad Ebrahim Khamseh

Gestational diabetes mellitus (GDM) is a major pregnancy endocrine problem that has several confirmed risk factors and is associated with adverse pregnancy-related outcomes (PRO).

To evaluate the relationship between GDM diagnosis and the associated risk factors of PRO (maternal, intrapartum, perinatal, and neonatal) in accordance with International Association of Diabetes and Pregnancy Study Groups criteria.

Materials and Methods

This prospective cohort study was performed with 531 singleton parturient (265 GDM and 266 non-GDM). They were selected consecutively from referral hospitals, and the maternal, intrapartum, perinatal, and neonatal outcomes were assessed.

The major risk factors influencing the GDM diagnosis were maternal age, obesity, family history of diabetes, previous history of GDM, and previous history of macrosomia. In the comparison of PRO between the groups, significant associations were detected for emergency cesarean delivery, preeclampsia, polyhydramnios, premature rupture of membrane, preterm delivery, and neonatal hyperbilirubinemia in the GDM group. In the multivariate logistic regression analysis, a previous history of stillbirth was significantly associated with maternal and perinatal outcomes. The odds ratios (CI 95%) of the PRO in the women with a GDM diagnosis were: maternal = 2.43 (1.51-3.90), intrapartum = 2.05 (1.35-3.11), perinatal = 2.00 (1.29-3.10), and neonatal = 1.68 (1.08-2.62). The PRO was significantly correlated with GDM diagnosis, but not with the risk factors.

The adverse pregnancy outcomes were significantly correlated with GDM diagnosis, and the outcomes were not directly affected by the risk factors. Given the related adverse outcomes for mothers and offspring, early screening and management of GDM is necessary especially in Asians and in low-/middle-income countries.

1. Introduction

Chronic diseases such as diabetes have become one of the major public health problems in recent years. One of the main forms of diabetes is gestational diabetes mellitus (GDM), which is recognized as glucose intolerance, and is diagnosed initially during pregnancy. It could affect between 1.3% and 18.6% of pregnancies in Iran (1), depending on the studied population and the diagnostic criteria used.

The pregnancies complicated by GDM are associated with feto-maternal sequelae. The adverse pregnancy-related outcomes (PRO) are spontaneous abortions, macrosomia, intrauterine growth restriction (IUGR), premature rupture of membranes (PROM), neonatal hypoglycemia, respiratory distress, and the need for neonatal intensive care unit (NICU) admission (2). Maternal poor glycemic control is associated with a high prevalence of adverse perinatal outcomes (3).

The most common risk factors for GDM diagnosis are higher age and body mass index (BMI), previous history of GDM, first-degree relatives with diabetes, and adverse obstetric outcomes (4). Parturients diagnosed with GDM are at an increased risk of obesity, metabolic syndrome, and type-II diabetes mellitus for themselves in the future and their offspring in later life (5). Due to the high incidence of metabolic syndrome and genetic predisposition among Asians, they are more likely to have GDM. Therefore, with an increase in GDM globally, identifying major risk factors and adverse feto-maternal outcomes and providing appropriate care to women developing GDM could substantially impact the health of large numbers of parturients and offspring.

The screening criteria, ideal timing for screening, risk factors, and feto-maternal complications of GDM remain under debate. Considering the importance of early detection and appropriate GDM diagnosis, the International Association of Diabetes and Pregnancy Study Groups (IADPSG) (6) has identified new diagnostic criteria, which distinguish and help manage GDM and the adverse outcomes to prevent further complications.

The current study was undertaken to evaluate the relationship between GDM diagnosis and the associated risk factors of PRO in accordance with the IADPSG criteria.

2. Materials and Methods

This prospective cohort study was carried out between April 2015 and July 2017. The present study was performed on 531 single women, who were selected consecutively from referral hospitals (Kamali, Akbarabadi and Arash); comprising of GDM and non-GDM pregnancies. The sample size was calculated using the G power software (version 3.1), using power = 90%, α = 5%, β = 0.1, d = 0.05, and the prevalence of GDM = 3.41% and 4.9% (1, 7). Considering the probable drop rate of 5%, the sample size calculated each group was 265 parturient. This sample size was confirmed by the prevalence of feto-maternal outcomes (5.1% preterm in Iran (8), 5% large-for-gestational age [GA] (9), 7.9% meconium-stained amniotic fluid (10) and 8% preeclampsia (11)). The study population was comprised of 265 GDM and 266 non-GDM parturient.

The exclusion criteria were parturients who were transferred out at any GA, smoking or substance abuse, abortion, multifetal pregnancy, gross fetal anomalies, overt diabetes, chronic hypertension, systemic disorders, and use of systemic medications.

A complete history was taken and a physical evaluation was performed for all parturients by a trained physician. Maternal baseline demographic characteristics, clinical and obstetrical parameters, laboratory data and anthropometric variables were obtained from existing antenatal records and face-to-face interviews which were conducted in the first and following prenatal visits by trained observers. For height and weight measurement, a calibrated digital scale (Seca gmbh & co. kg., Germany) was used. The BMI measured in the first trimester, was the best predictor of prepregnancy BMI; it is considered by dividing the weight (kilograms) by the square of the height (meters) (12). Standard measurement of blood pressure (BP) was recorded for all the parturients; defined as a seated position, relax after at least 5 min, refrain from talking or moving and cease smoking and drinking tea or coffee, or eating food for at least half an hr.

GDM is diagnosed at 24-28 wk of gestation using a “one-step strategy” (75-gr 2-hr oral glucose tolerance test) which is well-defined according to the American Diabetes Association/IADPSG criteria (6, 13, 14). The test should be done within 8-14 hr of overnight fasting. Based on the aforementioned guidelines criteria, a diagnosis of GDM can be made when one of the following values is met or exceeded in the one-step strategy: 0-hr (fasting) ≥ 92 mg/dL; 1-hr ≥ 180 mg/dL; or 2-hr ≥ 153 mg/dL.

The non-GDM group was defined as those with a normal oral glucose tolerance test. The enzymatic calorimeter method was used for blood glucose measurement by means of a standard kit (EliTech kit, France). The major risk factors which influenced the GDM diagnosis were defined as follows: maternal age > 35 yr, obesity (BMI ≥ 30 kg/m 2 ), family history of diabetes, previous history of GDM, and previous history of macrosomia (neonate weight ≥ 4000 gr) (14).

The following maternal outcomes were included: preeclampsia (as BP ≥ 140/90 mmHg and a positive proteinuria [at least 1+ dipstick, 30 mg/dl] shown by random urine sample or ≥ 300 mg/24 hr, or a urine protein-to-creatinine ratio of ≥ 0.3 after 20 wk of pregnancy (15)), oligohydramnios ( < 5 th percentile of amniotic fluid volume expected for GA), and polyhydramnios (amniotic fluid index > 24 cm expected for GA in the amniotic sac).

The following intrapartum outcomes were included: emergency cesarean section (CS), PROM (rupture of the membrane more than 1 hr before the onset of labor), and preterm delivery (delivery before 37 wk of gestation).

Perinatal outcomes were defined as fetal death, macrosomia (birth weight at delivery > 4,000 gr), IUGR (growth < the third percentile for GA), first- and fifth-min Apgar score > 7, and congenital malformation.

Neonatal outcomes were defined as NICU admission, neonatal hypoglycemia (16), neonatal hyperbilirubinemia (17), and neonatal respiratory distress (18). Neonatal variables were examined by a pediatrician in all cases after delivery.

Ethical considerations

The ethics committee of Iran University of Medical Sciences, Tehran, Iran approved the study protocol (Code: IR.IUMS.REC.1393.24991) and a written informed consent was signed by all parturients and their spouses included in the study. All procedures performed were in accordance with the ethical standards of institute of endocrinology and metabolism, Iran University of Medical Sciences, Tehran, Iran and with the 1964 Helsinki Declaration and its later amendments.

Statistical analysis

The discrete and continuous variables are reported using the number (percent) and mean (standard deviation [SD]), respectively. To compare the continuous variables between GDM and non-GDM groups, t test and Mann-Whitney test were used, depending on the variables' distribution is in accordance with the normal distribution or not, respectively. In addition, the Chi-square test was the tool for comparing the categorical variables between GDM and non-GDM groups. Finally, the responses which owned significant levels between two groups were fitted in the ordinal logistic regression model. Besides, the variables of clinical important but non-significant in group-comparison step were also put in the model.

The analyses were performed using the statistical software Statistical Package for the Social Sciences (SPSS) (version 16, SPSS Inc., Chicago, IL, USA) Stata (ver. 12). The significance level was chosen to be 0.05.

A total of 531 parturients including 265 GDM and 266 non-GDM parturients were included in this study.

The average gravidity in non-GDM and GDM groups was 1.86 ± 0.93 and 2.20 ± 1.00, respectively (p = 0.0001). The parity average in the non-GDM and GDM groups was 0.66 ± 0.78 and 0.90 ± 0.81, respectively (p = 0.0005). The demographic and reproductive characteristics of the parturients are summarized in Table I.

The relationship between GDM and the major risk factors which influenced the GDM diagnosis (maternal age > 35 yr, obesity [BMI ≥ 30 kg/m 2 ], family history of diabetes, previous history of GDM, and previous history of macrosomia [neonate weight ≥ 4000 gr]), were compared between the two groups, using the Chi-square test. The analysis showed significant association between GDM and maternal age > 35 yr (p = 0.0001), obesity [BMI ≥ 30 kg/m 2 ] (p = 0.03), family history of diabetes (p = 0.0001), previous history of GDM (p = 0.0001), and previous history of macrosomia (p = 0.01). Finally, at multivariate logistic regression analysis was used to evaluate the effect of these five risk factors (adjusted for maternal characteristics) on the odds of GDM development (dependent variable), and the same results as above was obtained, with few alterations (findings are not shown).

Table II presents the comparison of the PRO (maternal, intrapartum, perinatal, and neonatal) between the two groups and the association of the aforementioned outcomes with the GDM group.

This study, measured the effect of major risk factors on the PRO (maternal, intrapartum, perinatal, and neonatal) in the GDM group; the results are presented in table III.

Table IV shows the results from the multivariate logistic regression analysis, which was performed on the risk factors for maternal, intrapartum, perinatal, and neonatal outcomes in the study population. The results demonstrated that a previous history of stillbirth was significantly associated with maternal and perinatal outcomes (Table V). The maternal outcomes included preeclampsia, oligohydramnios and polyhydramnios; the intrapartum outcomes were emergency CS, PROM and preterm delivery; the perinatal outcomes were fetal death, macrosomia, IUGR, Apgar 1, 5 and congenital malformation; and the neonatal outcomes were NICU admission, neonatal hypoglycemia, neonatal hyperbilirubinemia, and neonatal respiratory distress.

In an additional analysis, the four aforementioned outcomes were significantly correlated with GDM diagnosis; as the ORs (95% CI, p-value) demonstrate: maternal = 2.43 (1.51-3.90, 0.0001), intrapartum = 2.05 (1.35-3.11, 0.001), perinatal = 2.00 (1.29-3.10, 0.002), and neonatal = 1.68 (1.08-2.62, 0.02).

Demographic and reproductive characteristics of the parturients

The comparison of pregnancy-related outcomes in GDM and non-GDM groups

The effect of major risk factors on PRO (maternal, intrapartum, perinatal, and neonatal) in the GDM group

The multivariate logistic regression analysis for the risk factors on maternal, intrapartum, perinatal, and neonatal outcomes in the study population

4. Discussion

The aim of the present study was to evaluate the relationship between the GDM diagnosis and the associated risk factors of PRO in accordance with the IADPSG criteria.

The major risk factors that were associated with the GDM diagnosis were maternal age, obesity, family history of diabetes, previous history of GDM, and previous history of macrosomia. In the comparison of PRO between groups, the significant associations were detected for emergency CS delivery, preeclampsia, polyhydramnios, premature rupture of membrane, preterm delivery, and neonatal hyperbilirubinemia in the GDM group. A previous history of stillbirth was significantly associated with maternal and perinatal outcomes. All PRO were significantly correlated with GDM diagnosis, but not with the risk factors.

Early identification of GDM is crucial because it affects clinical decision-making. Optimal GDM management, including lifestyle alterations, medical nutrition therapy, insulin therapy, and antepartum fetal observation, may decrease the perinatal morbidity and mortality associated with GDM (13). However, screening and accurately detecting GDM in asymptomatic pregnant women is controversial. Screening is done using a one-step strategy at least once at or about 24 wk of gestation unless there are suggestions for it to be done earlier (2). Healthcare providers should recognize and screen high-risk groups of pregnant women to detect and manage GDM earlier. Our data showed that major risk factors (such as age, obesity, family history of diabetes, previous history of GDM, and previous history of macrosomia) significantly increased the risk of GDM. In most countries, early screening is performed based on parameters such as belonging to a specific ethnic group related to a high GDM prevalence, advanced maternal age, prepregnancy obesity, history of diabetes in first-degree relatives, previous history of GDM, previous large-for-GA babies, and glucosuria. Early screening using only traditional risk factors may increase the likehood of missing GDM cases (19). Therefore, we suggest that both traditional risk factors and new biomarkers should be researched in large populations and with varied ethnic groups.

In our regional literature review, we detected a varied range of GDM prevalence values from 1.3% to 18.6% in Iranian pregnant women (1). Ethnicity seems to play a principal role as well: the trend of GDM in the Asian population is increasing; also increased insulin resistance is observed at much lower BMI levels in Asian compared with Europeans (20). Although South Asians are a high-risk population, screening, risk factors and complications of GDM are still debated (21). The recognized risk factors (such as age, BMI, family history of diabetes, previous history of macrosomia/congenital malformation, and previous history of GDM) were mainly attained from studies of European populations (22). Furthermore, there was a Malaysian and Iranian study which examined this relationship (2, 23) and found similar results. Keshavarz and colleagues showed that older age, a family history of diabetes, obesity, previous macrosomia, and glycosuria can be risk factors for GDM (23). In another study, the risk of GDM was greater in parturients aged > 35 yr with a fourfold increase that was related to pancreatic β-cell function and insulin-sensitivity falling with age (24). In addition, a 2.45-fold increase of risk has been attributed to obesity (2), which is related to the elevation of insulin resistance that occurs as a result of obesity (19). In the present study, GDM risk was higher by 3.08-fold and 1.7-fold in women > 35 yr and with a BMI ≥ 30 kg/m 2 , respectively. The placenta and adipose tissue that produce a large number of diabetogenic adipokines. Tumor necrosis factor-alpha, which is a as diabetogenic adipokine, plays an important role in insulin-resistance pathways and may induce adverse feto-maternal outcomes (25). In two studies, a family history of DM was presented in 77.7% and 16.3% of GDM cases (26, 27). Our findings showed that a family history of diabetes increased the risk of GDM by 2.90-fold. Keshavarz and colleagues suggested that screening based on risk factors alone could miss 16% of GDM cases (23). A recent systematic review evaluated the cost-effectiveness of identification and/or treatment of GDM. This study found that neither early screening nor treating GDM seems to be convincingly cost-effective in high-income countries, but they suggested that early screening and proper detection of GDM might be worthwhile in low-/middle- incomes countries due to different health systems and other health priorities (28).

The present study revealed that adverse PRO such as emergency CS, preeclampsia, polyhydramnios, PROM, preterm delivery, and neonatal hyperbilirubinemia were significantly associated with GDM diagnosis (OR = 1.88, 3.64, 2.44, 4.53, 2.52, and 2.00, respectively) compared to non-GDM women. All the PROs were significantly correlated with GDM diagnosis. Other studies have shown that the risk of fetal macrosomia (29) may be increased by a diagnosis of GDM, and a greater potential risk may exist for shoulder dystocia in macrosomic infants (30). Additionally, CS delivery, spontaneous miscarriage, preterm delivery, a low Apgar score, the need for NICU admission, hypoglycemia, congenital malformations and respiratory distress syndrome occured more commonly among the infants of GDM parturients (2). Another Iranian study reported a higher risk of pregnancy complications and adverse feto-maternal outcomes with GDM (23). In GDM parturients, a higher risk of having a non-spontaneous vaginal delivery such as operative deliveries was observed in GDM paturients compared with non-GDM women (OR = 1.9) (2). GDM is not only associated with short-term adverse feto-maternal outcomes, such as macrosomia, increased CS rates, hypertensive disorders, and fetal hyperinsulinemia (31), but also significantly increases the risk for long-term adverse events for both mothers and their offspring (19).

Nilofer and co-workers and Opara and colleagues found that hyperbilirubinemia is a common adverse event in GDM, which complicated 20% and 57.4% of the infants they studied, respectively; this resulted from excessive red blood cell breakdown in association with polycythemia because of immature bilirubin conjugation by the liver of the neonate (26, 32). In contrast to our findings, Logakodie and colleagues revealed no significant difference in the fetal outcomes of GDM parturients, which may be due to the tight glycemic control of GDM status (2). In the present study, the major risk factors were not associated with PRO. According to our multivariate logistic regression analysis, after group adjustment, a previous history of stillbirth was an independent risk factor for maternal and perinatal outcomes. Unlike in our study, Jolly and colleagues who observed that maternal obesity was associated with adverse pregnancy outcomes; however, they found similar results to our study in relation to the association of GDM with adverse pregnancy outcomes (24).

5. Limitations

In the present study, we could not evaluate the socioeconomic status or dietary patterns of the parturients.

6. Conclusion

In conclusion, we found that the PRO were significantly correlated with GDM diagnosis, but not with the risk factors. Given the increased rate of GDM worldwide, healthcare providers should attend to risk factors for early diagnosis of GDM. In view of the long- and short-term adverse related outcomes for mothers and offspring, early screening and appropriate management of GDM are necessary, especially in Asians and in low-/middle-income countries. Detection of predictive factors of GDM should be researched in these populations.

Conflict of Interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Acknowledgements

This study was registered, funded, and supported by Iran University of Medical Sciences (IUMS), Tehran, Iran at 2015 by Grant No. 93-03-122-24991.

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