Recurrent Gestational Diabetes Mellitus: A Narrative Review and Single-Center Experience

Clinical Risk Factors and First Gestational 75 g OGTT May Predict Recurrent and New-Onset Gestational Diabetes in Multiparous Women

Background: Women who experience gestational diabetes mellitus (GDM) during their first pregnancy are at a high risk of developing GDM again in subsequent pregnancies. Even mothers with no previous history of GDM may develop the condition in a new pregnancy. Methods: In this retrospective cross-sectional observational study, 759 multiparous women tested for GDM in two successive pregnancies using the 75 g OGTT (IADPSG criteria) were enrolled. The OGTT was performed at 24-28 weeks' gestation or earlier if there was a history of GDM. Participants were categorized into four groups: women with normal glucose tolerance (NGT) in both pregnancies (n = 493), women with a first occurrence of GDM in their second pregnancy (n = 74), women with non-recurrent GDM in their second pregnancy (n = 92), and women with recurrent GDM in their second pregnancy (n = 100). Results: Intergroup comparisons revealed clinical predictors of GDM in the first pregnancy (family history of type 2 diabetes, PCOS, advanced maternal age, pregravid obesity) and in the second pregnancy (interpregnancy BMI gain), as well as predictors of recurrent GDM (pregravid obesity, PCOS). A positive correlation was observed between the OGTT glucose levels of consecutive pregnancies. Adjusted logistic regression indicated that a higher 1-h post-load glucose level (≥130 mg/dL) during the first pregnancy significantly increased the likelihood of new-onset GDM in the second pregnancy (OR: 2.496), whereas a higher 2-h post-load glucose level (≥153 mg/dL) at the first diagnostic OGTT increased the likelihood of recurrent GDM (OR: 2.214). Conclusions: Clinical risk factors and post-load glucose levels during the first gestational 75 g OGTT can help predict new-onset or recurrent GDM in multiparous women.

Adverse pregnancy outcomes with respect to treatment modalities in women with gestational diabetes mellitus at a rural tertiary care teaching hospital

Objectives

To estimate the prevalence of gestational diabetes mellitus (GDM) and compare adverse pregnancy outcomes with respect to treatment modalities in a peri-urban teaching hospital in Telangana.

Methods

A prospective study was conducted on GDM cases delivered from January 2019 to March 2020. GDM was diagnosed using a two-step procedure of screening using IADPSG criteria. Women diagnosed with GDM were divided into four groups - diet group, metformin group, metformin plus insulin group and insulin group based on the treatment modalities. Adverse pregnancy outcomes of the women managed with different treatment modalities were recorded.

Results

Good glycaemic control (FBS, P = 0.04, 2 hrs PLBS, P = 0.01) was achieved in diet and metformin groups. Incidence of Gestational hypertension (P = 0.01) and preeclampsia (P = 0.01) were found to be higher in the insulin group when compared to the metformin and insulin group, metformin group and diet group. No difference was noted with respect to polyhydramnios, preterm birth, premature rupture of membranes, induction labour and caesarean delivery rates between the treatment groups. Apgar score at 5 min of <7 (P = 0.02), neonatal intensive care unit admissions for >24 hrs (P = 0.03) and neonatal hypoglycaemia (P = 0.01) were found to be higher in insulin-required groups. Rates of shoulder dystocia, stillbirth, early neonatal death within 1 week and respiratory distress did not vary significantly between the treatment groups.

Conclusion

Universal screening of women for GDM and multidisciplinary management of women once diagnosed tend to lessen maternal and fetal complications. Metformin can be an effective, cheaper and non-invasive alternative to insulin in the management of GDM.

Impact of previous gestational diabetes management on perinatal outcomes in subsequent pregnancies affected by gestational diabetes mellitus

OBJECTIVE

To determine the impact of prior gestational diabetes mellitus (GDM) on perinatal outcomes in a subsequent GDM pregnancy.

METHODS

This retrospective cohort study included 544 multiparous patients with two consecutive pregnancies between 2012-2019, where the second (index) pregnancy was affected by GDM. The primary exposure was prior GDM diagnosis, categorized into medical and dietary management. The primary outcome was a composite including need for pharmacotherapy, large-for-gestational age, or neonatal hypoglycemia. Adjusted odds ratios (aOR) were calculated using multivariable logistic regression controlling for maternal age, pre-pregnancy body mass index, and gestational age at GDM diagnosis in the index pregnancy.

RESULTS

Of the 544 patients, 164 (30.1%) had prior GDM. Prior GDM significantly increased the likelihood of composite outcome compared to no prior GDM (74.4% vs. 57.4%; P < 0.001). After adjusting for confounders, prior GDM remained significantly associated with the composite outcome (aOR 2.03, 95% confidence interval [CI] 1.31-3.15). Stratifying by prior GDM treatment modality, a significant association was found for prior pharmacotherapy-controlled GDM (aOR 3.29, 95% CI 1.64-6.59), but not for prior diet-controlled GDM (aOR = 1.54, 95% CI 0.92-2.60).

CONCLUSION

A history of pharmacotherapy-controlled GDM in a previous pregnancy increases odds of adverse perinatal outcomes in a subsequent GDM pregnancy.

First-Trimester Triglyceride-Glucose Index and Triglyceride/High-Density Lipoprotein Cholesterol are Predictors of Gestational Diabetes Mellitus Among the Four Surrogate Biomarkers of Insulin Resistance

Purpose

This study seeks to assess the potential of early pregnancy Triglyceride Glucose Index (TyG), triglyceride to High-Density Lipoprotein Cholesterol ratio (TG/HDL-c), Low-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol ratio (LDL-C/HDL-C), and Total Cholesterol to High-Density Lipoprotein Cholesterol ratio (TC/HDL-C) in predicting Gestational Diabetes Mellitus (GDM).

Patients and Methods

A total of 1073 adults singleton pregnant women were enrolled from June 2017 to September 2019. Complete anthropometric data and lipid profiles were measured in the first trimester (before 12 weeks gestation) and a 75g oral glucose tolerance test (OGTT) at 24-28 weeks was performed. Based on OGTT results, participants were categorised into Normal Glucose Tolerance (NGT) group (n=872) and GDM group (n=201). General data, laboratory test results, and surrogate insulin resistance indicators such as TyG index, TG/HDL-C, LDL-C/HDL-C, and TC/HDL-C were documented and compared. To compare differences between the two groups, t-test was used, Spearman correlation analysis and linear regression analysis were performed to establish associations between these indicators and insulin resistance in GDM. Receiver Operating Characteristic (ROC) curves were generated to compare the thresholds of these indicators for predicting GDM during pregnancy and to quantify overall diagnostic accuracy.

Results

Individuals with GDM had higher TyG, TG/HDL-C, and LDL-C/HDL-C levels (P < 0.001), but with no significant difference observed in TC/HDL-C. All four ratios were positively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), yet only TyG emerged as an independent risk factor for HOMA-IR. The Area under the Curve (AUC) of TyG index (0.692) was comparable to that of HOMA-IR (0.703). The cut-off points for TyG index, TG/HDL-C, and HOMA-IR in predicting GDM were 7.088, 0.831, and 1.8, respectively. HOMA-IR exhibited the highest sensitivity (79.1%), while TyG index (64.3%) and TG/HDL-C ratio (64.3%) demonstrated better specificity compared to HOMA-IR (56.3%). LDL-C/HDL-C and TC/HDL-C offered no discernible predictive advantage.

Conclusion

Early pregnancy TyG index and TG/HDL-C can aid in identifying pregnant women at risk for GDM, potentially facilitating early and effective intervention to improve prognosis. TyG index exhibited superior predictive capability compared to TG/HDL-C.

Twin Pregnancy Complicated by Gestational Diabetes Mellitus: Maternal and Neonatal Outcomes

Context

The risk of gestational diabetes mellitus (GDM) in twin pregnancies is more than double that of singleton pregnancies. Although twin pregnancies present unique challenges for fetal growth and prenatal management, the approach to GDM diagnosis and treatment is the same regardless of plurality. Data on pregnancy outcomes for individuals with GDM and a twin pregnancy are limited and conflicting.

Objective

To describe the maternal characteristics associated with GDM in twin pregnancies and to assess the associated pregnancy outcomes compared to twin pregnancies unaffected by GDM.

Methods

A retrospective cohort study was conducted at Mayo Clinic, Rochester, Minnesota, USA, and included predominantly Causasian women aged 18 to 45 years who received prenatal care for a twin pregnancy from 2017-2022. Maternal characteristics and a broad spectrum of pregnancy outcomes were evaluated. Universal GDM screening involved a 50 g oral glucose challenge test +/- a 100 g oral glucose tolerance test.

Results

GDM was diagnosed in 23% pregnancies (n = 104/452). Compared to those without, women with GDM had known risk factors including a higher prepregnancy body mass index (31.1vs 26.3 kg/m2; P < .01) and a prior history of GDM (21.7 vs 5.9%; P < .01). There were no differences in maternal pregnancy complications or neonatal outcomes between groups. Attendance at postpartum glucose testing among women with GDM was poor at 27.9% (29/104).

Conclusion

These data suggest that women with twin pregnancies share a similar GDM risk profile to those with singleton pregnancies and provide reassuring evidence that current management for GDM twin pregnancies produces similar outcomes to twin pregnancies without GDM.

Beta-cell compensation and gestational diabetes

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance in pregnant women without a previous diagnosis of diabetes. While the etiology of GDM remains elusive, the close association of GDM with increased maternal adiposity and advanced gestational age implicates insulin resistance as a culpable factor for the pathogenesis of GDM. Pregnancy is accompanied by the physiological induction of insulin resistance in the mother secondary to maternal weight gain. This effect serves to spare blood glucose for the fetus. To overcome insulin resistance, maternal β-cells are conditioned to release more insulin into the blood. Such an adaptive response, termed β-cell compensation, is essential for maintaining normal maternal metabolism. β-cell compensation culminates in the expansion of β-cell mass and augmentation of β-cell function, accounting for increased insulin synthesis and secretion. As a result, a vast majority of mothers are protected from developing GDM during pregnancy. In at-risk pregnant women, β-cells fail to compensate for maternal insulin resistance, contributing to insulin insufficiency and GDM. However, gestational β-cell compensation ensues in early pregnancy, prior to the establishment of insulin resistance in late pregnancy. How β-cells compensate for pregnancy and what causes β-cell failure in GDM are subjects of investigation. In this mini-review, we will provide clinical and preclinical evidence that β-cell compensation is pivotal for overriding maternal insulin resistance to protect against GDM. We will highlight key molecules whose functions are critical for integrating gestational hormones to β-cell compensation for pregnancy. We will provide mechanistic insights into β-cell decompensation in the etiology of GDM.

Interconception care in Australian general practice: a qualitative study

BACKGROUND

GPs provide care for women across the lifespan. This care currently includes preconception and postpartum phases of a woman's life. Interconception care (ICC) addresses women's health issues between pregnancies that then have impact on maternal and infant outcomes, such as lifestyle and biomedical risks, interpregnancy intervals, and contraception provision. However, ICC in general practice is not well established.

AIM

To explore GP perspectives about ICC.

DESIGN AND SETTING

Qualitative interviews were undertaken with GPs between May and July 2018.

METHOD

Eighteen GPs were purposively recruited from South-Eastern Australia. Audiorecorded semi- structured interviews were transcribed verbatim and analysed thematically using the Framework Method.

RESULTS

Most participants were unfamiliar with the concept of ICC. Delivery was mainly opportunistic, depending on the woman's presenting need. Rather than a distinct and required intervention, participants conceptualised components of ICC as forming part of routine practice. GPs described many challenges including lack of clarity about recommended ICC content and timing, lack of engagement and perceived value from mothers, and time constraints during consultations. Facilitators included care continuity and the availability of patient education material.

CONCLUSION

Findings indicate that ICC is not a familiar concept for GPs, who feel that they have limited capacity to deliver such care. Further research to evaluate patient perspectives and potential models of care is required before ICC improvements can be developed, trialled, and evaluated. These models could include the colocation of multidisciplinary services and services in combination with well-child visits.

Motivation for Behavior Change among Women with Recent Gestational Diabetes and Their Partners-A Qualitative Investigation among Participants in the Face-It Intervention

Promoting diet and physical activity is important for women with recent gestational diabetes mellitus (GDM) and their partners to reduce the risk of future type 2 diabetes (T2D). The study aimed to understand how motivation for changing diet and physical activity behaviors among women with recent GDM and their partners was experienced after participation in the Danish Face-it intervention. Fourteen couples' interviews were conducted. Data analysis followed a reflexive thematic analysis. Guided by self-determination theory and interdependence theory, we identified four themes affecting couples' motivation for health behavior change: (1) The need to feel understood after delivery; (2) adjusting health expectations; (3) individual and mutual preferences for health behaviors; and (4) the health threat of future T2D as a cue to action. We found that couples in general perceived the Face-it intervention as useful and motivating. Using couple interviews increased our understanding of how the women and partners influenced each other's perspectives after a GDM-affected pregnancy and thus how targeting couples as opposed to women alone may motivate health behavior change.

Gestational diabetes mellitus and COVID-19: The epidemic during the pandemic

During the global coronavirus disease 2019 (COVID-19) pandemic, people worldwide have experienced an unprecedented rise in psychological distress and anxiety. In addition to this challenging situation, the prevalence of diabetes mellitus (DM), a hidden epidemic, has been steadily increasing in recent years. Lower-middle-income countries have faced significant barriers in providing accessible prenatal care and promoting a healthy diet for pregnant women, and the pandemic has made these challenges even more difficult to overcome. Pregnant women are at a higher risk of developing complications such as hyper-tension, preeclampsia, and gestational diabetes, all of which can have adverse implications for both maternal and fetal health. The occurrence of gestational diabetes has been on the rise, and it is possible that the pandemic has worsened its prevalence. Although data is limited, studies conducted in Italy and Canada suggest that the pandemic has had an impact on gestational diabetes rates, especially among women in their first trimester of pregnancy. The significant disruptions to daily routines caused by the pandemic, such as limited exercise options, indicate a possible link between COVID-19 and an increased likelihood of experiencing higher levels of weight gain during pregnancy. Notably, individuals in the United States with singleton pregnancies are at a significantly higher risk of excessive gestational weight gain, making this association particularly important to consider. Although comprehensive data is currently lacking, it is important for clinical researchers to explore the possibility of establishing correlations between the stress experienced during the pandemic, its consequences such as gestational gain weight, and the increasing incidence of gestational DM. This knowledge would contribute to better preventive measures and support for pregnant individuals during challenging times.

Diabetes during Pregnancy: A Transgenerational Challenge

For many years, gestational diabetes mellitus (GDM) has been defined as “a glucose intolerance of variable magnitude that begins or is first diagnosed in pregnancy” and that, in most cases, resolves after delivery [...]

Prediction of recurrent gestational diabetes mellitus: a retrospective cohort study

BACKGROUND

Women after gestational diabetes mellitus (GDM) are at increased risk for development of GDM recurrence. It was the aim of our study to evaluate factors for prediction of risk of recurrence.

METHODS

In this retrospective cohort study we included 159 women with GDM and a subsequent pregnancy. Putative risk factors for GDM recurrence were analyzed by logistic regression models. Results were compared to a cohort of age-matched women without GDM as controls (n = 318).

RESULTS

The overall risk of GDM recurrence was 72.3% (115/159). Risk factors of recurrence were a body mass index (BMI) ≥ 30 kg/m² before the index pregnancy (odds ratio (OR) 2.8 [95% CI 1.3-6.2], p = 0,008), a BMI ≥ 25 kg/m² before the subsequent pregnancy (OR 2.7 [95% CI 1.3-5.8]. p = 0.008), a positive family history (OR 4.3 [95% CI 1.2-15.4], p = 0.016) and insulin treatment during the index pregnancy (OR 2.3 [95% CI 1.1-4.6], p = 0.023). Delivery by caesarean section (index pregnancy) was of borderline significance (OR 2.2 [95% CI 0.9-5.2], p = 0.069). Interpregnancy weight gain, excessive weight gain during the index pregnancy and fetal outcome where not predictive for GDM recurrence. Neonates after GDM revealed a higher frequency of transfer to intensive care unit compared to healthy controls (OR 2.3 [95% CI 1.1-4.6], p = 0.0225). The best combined risk model for prediction of GDM recurrence including positive family history and a BMI ≥ 25 kg/m² before the subsequent pregnancy revealed moderate test characteristics (positive likelihood ratio 7.8 [95% CI 1.1-54.7] and negative likelihood ratio 0.7 [95% CI 0.6-0.9]) with a positive predictive value of 96.6% in our cohort.

CONCLUSIONS

A positive family history of diabetes mellitus in combination with overweight or obesity were strongly associated with recurrence of a GDM in the subsequent pregnancy. Normalization of the pregravid BMI should be an effective approach for reducing the risk of GDM recurrence.

Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.

A Mobile Phone App for the Prevention of Type 2 Diabetes in Malaysian Women With Gestational Diabetes Mellitus: Protocol for a Feasibility Randomized Controlled Trial

BACKGROUND

Over 50% of women with a history of gestational diabetes mellitus (GDM) will develop type 2 diabetes (T2D) in later life. Asian women experience a disproportionate risk of both GDM and T2D compared to women from other ethnic backgrounds. Lifestyle interventions and behavior change can delay or even prevent the onset of T2D. We have developed a digitalized diabetes prevention intervention for the prevention of T2D in Malaysian women with GDM.

OBJECTIVE

The protocol describes a randomized controlled trial (RCT) to test the feasibility of undertaking a definitive trial of a diabetes prevention intervention, including a smartphone app and group support. Secondary aims are to summarize anthropometric, biomedical, psychological, and lifestyle outcomes overall and by allocation group, and to undertake a process evaluation.

METHODS

This is a two-arm parallel feasibility RCT. A total of 60 Malaysian women with GDM will be randomized in the antenatal period to receive the intervention or standard care until 12 months post partum. The intervention is a diabetes prevention intervention delivered via a smartphone app developed based on the Information-Motivation-Behavioral Skills model of behavior change and group support using motivational interviewing. The intervention provides women with tailored information and support to encourage weight loss through adapted dietary intake and physical activity. Women in the control arm will receive standard care. The Malaysian Ministry of Health's Medical Research and Ethics Committee has approved the trial (NMRR-21-1667-60212).

RESULTS

Recruitment and enrollment began in February 2022. Future outcomes will be published in peer-reviewed health-related research journals and presented at national, regional, or state professional meetings and conferences. This publication is based on protocol version 2, January 19, 2022.

CONCLUSIONS

To our knowledge, this will be the first study in Malaysia that aims to determine the feasibility of a digital intervention in T2D prevention among women with GDM. Findings from this feasibility study will inform the design of a full-scale RCT in the future.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05204706; https://clinicaltrials.gov/ct2/show/NCT05204706.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37288.

Prediction of gestational diabetes based on explainable deep learning and fog computing

Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However, early prediction is preferable because it may decrease the risk. The continuous monitoring of the mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this research is to provide a comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers, which are: (i) Internet of things (IoT) Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IoT sensors to aggregate vital signs from pregnancies using invasive and non-invasive sensors. The vital signs are then transmitted to fog nodes to be processed and finally stored in the cloud layer. The main contribution in this research is located in the fog layer producing the GDM module to implement two influential tasks which are as follows: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free up the cache space for new incoming data items. The cache replacement is very important in the case of the healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the occurrence of GDM in the second trimester of the pregnancy. To evaluate our model, we extracted data from 16,354 pregnant women from the medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data, and laboratory tests were aggregated. The results of the prediction model are superior to the state-of-the-art (ACC = 0.957, AUC = 0.942). Regarding explainability, we used Shapley additive explanation (SHAP) framework to provide local and global explanations for the developed models. Overall, the proposed framework is medically intuitive and allows the early prediction of GDM with a cost-effective solution.

The Impact of Ethnicity on Fetal and Maternal Outcomes of Gestational Diabetes

Background and Objectives: The prevalence of gestational diabetes mellitus (GDM) significantly varies across different ethnic groups. In particular, Africans, Latinos, Asians and Pacific Islanders are the ethnic groups with the highest risk of GDM. The aim of this study was to evaluate the impact of ethnicity on pregnancy outcomes in GDM. Patients and Methods: n = 399 patients with GDM were enrolled, n = 76 patients of high-risk ethnicity (HR-GDM), and n = 323 of low-risk ethnicity (LR-GDM). Clinical and biochemical parameters were collected during pregnancy until delivery. Fetal and maternal short-term outcomes were evaluated. Results: HR-GDM had significantly higher values of glycosylated hemoglobin checked at 26−29 weeks of gestation (p < 0.001). Gestational age at delivery was significantly lower in HR-GDM (p = 0.03). The prevalence of impaired fetal growth was significantly higher in HR-GDM than LR-GDM (p = 0.009). In logistic regression analysis, the likelihood of impaired fetal growth was seven times higher in HR-GDM than in LR-GDM, after adjustment for pre-pregnancy BMI and gestational weight gain (OR = 7.1 [2.0−25.7] 95% CI, p = 0.003). Conclusions: HR-GDM had worse pregnancy outcomes compared with LR-GDM. An ethnicity-tailored clinical approach might be effective in reducing adverse outcomes in GDM.

Early-Onset of Gestational Diabetes vs. Late-Onset: Can We Revamp Pregnancy Outcomes?

BACKGROUND

We assessed risk factors, antenatal and intrapartum complications associated with early-onset Gestational diabetes mellitus (GDM) in comparison with late-onset GDM.

METHODS

This retrospective study included 161 GDM women having singleton pregnancies, without previous medical disorder and delivered at a tertiary care Hospital in Ha'il City, KSA from Dec 2020 till Jun 2021. Women diagnosed at < 24 weeks of pregnancy were grouped as early-onset GDM (n=71) and those diagnosed at ≥ 24 weeks as late-onset GDM (n=90). Both groups were matched for background variables. Chi-square and binary logistic regression analysis were applied with P-value significance at 0.05.

RESULTS

Past history of GDM, macrosomia and stillbirth were significant predictors for early-onset GDM (P value 0.000, 0.002 and 0.040 respectively). Regression analysis showed early-onset GDM significantly increases the risk for recurrent urinary tract infections (AOR 2.35), polyhydramnios (AOR 2.81), reduced fetal movements (AOR 2.13), intrauterine fetal demise (AOR 8.06), macrosomia (AOR 2.16), fetal birth trauma (2.58), low APGAR score at birth (AOR 8.06), and neonatal ICU admissions (AOR 2.65). Rate of preterm birth, hypertensive disorders, labor onset (natural vs. induced) and cesarean section and intrapartum maternal complications were same in both groups.

CONCLUSION

Early-onset GDM significantly increases certain maternal (recurrent urinary tract infections, polyhydramnios and reduced fetal movements) and fetal complications (intrauterine fetal demise, macrosomia fetal birth trauma, low APGAR score at birth and neonatal ICU admissions). Most of these adverse pregnancy outcomes can be prevented through early registration and screening, close follow up, growth ultrasounds, and provision of efficient emergency and neonatal care services.

Recent Advances in Gestational Diabetes Mellitus

The incidence of gestational diabetes mellitus (GDM) and overt diabetes in pregnancy is rising globally [...].