Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming

Prenatal exposure to ambient air pollution and risk of fetal overgrowth: Systematic review of cohort studies

OBJECTIVES

Fetal overgrowth has detrimental effects on both the mother and the fetus. The global issue of ambient air pollution has been found to contribute to fetal overgrowth through various pathways. This study aimed to identify the association between prenatal exposure to ambient air pollution and the risk of fetal overgrowth.

METHODS

We identified articles between January 2013 and February 2024 by searching the Web of Sciences(WoS), PubMed, Proquest, Scopus, and Google Scholar databases. Quality assessment was performed using the Newcastle Ottawa scale. This review was provided based on the PRISMA guideline and registered with PROSPERO, "CRD42023488936".

RESULTS

The search generated 1719 studies, of which 22 cohort studies were included involving 3,480,041 participants. Results on the effects of air pollutants on fetal overgrowth are inconsistent because they vary in population and geographic region. But in general, the results indicate that prenatal exposure to air pollutants, specifically PM2.5, NO2, and SO2, is linked to a higher likelihood of fetal overgrowth(macrosomia and large for gestational age). Nevertheless, the relationship between CO and O3 pollution and fetal overgrowth remains uncertain. Furthermore, PM10 has a limited effect on fetal overgrowth. It is essential to consider the time that reproductive-age women are exposed to air pollution. Exposure to air pollutants before conception and throughout pregnancy has a substantial impact on the fetus's vulnerability to overgrowth.

CONCLUSIONS

Fetal overgrowth has implications for the health of both mother and fetus. fetal overgrowth can cause cardiovascular diseases, obesity, type 2 diabetes, and other diseases in adulthood, so it is considered an important issue for the health of the future generation. Contrary to popular belief that air pollution leads to intrauterine growth restriction and low birth weight, this study highlights that one of the adverse consequences of air pollution is macrosomia or LGA during pregnancy. Therefore governments must focus on implementing initiatives that aim to reduce pregnant women's exposure to ambient air pollution to ensure the health of future generations.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Association of Placental Tissue Metabolite Levels with Gestational Diabetes Mellitus: a Metabolomics Study

The purpose of the study is to investigate the metabolic characteristics of placental tissue in patients diagnosed with gestational diabetes mellitus (GDM). Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) was employed to qualitatively and quantitatively analyze the metabolites in placental tissues obtained from 25 healthy pregnant women and 25 pregnant women diagnosed with GDM. Multilevel statistical methods are applied to process intricate metabolomics data. Meanwhile, we applied machine learning techniques to identify biomarkers that could potentially predict the risk of long-term complications in patients with GDM as well as their offspring. We identified 1902 annotated metabolites, out of which 212 metabolites exhibited significant differences in GDM placentas. In addition, the study identifies a set of risk biomarkers that effectively predict the likelihood of long-term complications in both pregnant women with GDM and their offspring. The accuracy of this panel was measured by the area under the receiver operating characteristic curve (ROC), which was found to be 0.992 and 0.960 in the training and validation sets, respectively. This study enhances our understanding of GDM pathogenesis through metabolomics. Furthermore, the panel of risk markers identified could prove to be a valuable tool in predicting potential long-term complications for both GDM patients and their offspring.

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Maternal and Child Health, Non-Communicable Diseases and Metabolites

Mothers influence the health and disease trajectories of their children, particularly during the critical developmental windows of fetal and neonatal life reflecting the gestational-fetal and lactational-neonatal phases. As children grow and develop, they are exposed to various stimuli and insults, such as metabolites, that shape their physiology and metabolism to impact their health. Non-communicable diseases, such as diabetes, cardiovascular disease, cancer and mental illness, have high global prevalence and are increasing in incidence. Non-communicable diseases often overlap with maternal and child health. The maternal milieu shapes progeny outcomes, and some diseases, such as gestational diabetes and preeclampsia, have gestational origins. Metabolite aberrations occur from diets and physiological changes. Differential metabolite profiles can predict the onset of non-communicable diseases and therefore inform prevention and/or better treatment. In mothers and children, understanding the metabolite influence on health and disease can provide insights for maintaining maternal physiology and sustaining optimal progeny health over the life course. The role and interplay of metabolites on physiological systems and signaling pathways in shaping health and disease present opportunities for biomarker discovery and identifying novel therapeutic agents, particularly in the context of maternal and child health, and non-communicable diseases.

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BACKGROUND

Metabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.

METHODS

Within a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.

RESULTS

A total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference β: 0.07; 95% CI: 0.02, 0.11) and 38:6 (β: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.

CONCLUSIONS

Our data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.

Metabolomics in gestational diabetes mellitus: A review

Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.

Nontargeted metabolomics reveals the potential mechanism underlying the association between birthweight and metabolic disturbances

AIMS

The aim of this study was to characterize the metabolites associated with small- and large-gestational-age newborns in maternal and cord blood, and to investigate potential mechanisms underlying the association between birthweight and metabolic disturbances.

RESEARCH DESIGN AND METHODS

We recorded detailed anthropometric data of mother-offspring dyads. Untargeted metabolomic assays were performed on 67 pairs of cord blood and maternal fasting plasma samples including 16 pairs of small-for-gestational (SGA, < 10th percentile) dyads, 28 pairs of appropriate-for-gestational (AGA, approximate 50 percentile) dyads, and 23 pairs of large-for-gestational (LGA, > 90th percentile) dyads. The association of metabolites with newborn birthweight was conducted to screen for metabolites with U-shaped and line-shaped distributions. The association of metabolites with maternal and fetal phenotypes was also performed.

RESULTS

We found 2 types of metabolites that changed in different patterns according to newborn birthweight. One type of metabolite exhibited a "U-shaped" trend of abundance fluctuation in the SGA-AGA-LGA groups. The results demonstrated that cuminaldehyde level was lower in the SGA and LGA groups, and its abundance in cord blood was negatively correlated with maternal BMI (r = -0.352 p = 0.009) and weight gain (r = -0.267 p = 0.043). 2-Methoxy-estradiol-17b 3-glucuronide, which showed enrichment in the SGA and LGA groups, was positively correlated with homocysteine (r = 0.44, p < 0.001) and free fatty acid (r = 0.42, p < 0.001) in maternal blood. Serotonin and 13(S)-HODE were the second type of metabolites, denoted as "line-shaped", which both showed increasing trends in the SGA-AGA-LGA groups in both maternal and cord blood and were both significantly positively correlated with maternal BMI before pregnancy. Moreover, cuminaldehyde, serotonin, 13(S)-HODE and some lipid metabolites showed a strong correlation between maternal and cord blood.

CONCLUSIONS

These investigations demonstrate broad-scale metabolomic differences associated with newborn birthweight in both pregnant women and their newborns. The U-shaped metabolites associated with both the SGA and LGA groups might explain the U-shaped association between birthweight and metabolic dysregulation. The line-shaped metabolites might participate in intrauterine growth regulation. These observations might help to provide new insights into the insulin resistance and the risk of metabolic disturbance of SGA and LGA babies in adulthood and might identify potential new markers for adverse newborn outcomes in pregnant women.

Plasma Exosomal miRNAs Associated With Metabolism as Early Predictor of Gestational Diabetes Mellitus

To date, the miRNA expression profile of plasma exosomes in women whose pregnancy is complicated by gestational diabetes mellitus (GDM) has not been fully clarified. In this study, differentially expressed miRNAs in plasma exosomes were identified by high-throughput small-RNA sequencing in 12 pregnant women with GDM and 12 with normal glucose tolerance (NGT) and validated in 102 pregnant women with GDM and 101 with NGT. A total of 22 exosomal miRNAs were found, five of which were verified by real-time qPCR. Exosomal miR-423-5p was upregulated, whereas miR-122-5p, miR-148a-3p, miR-192-5p, and miR-99a-5p were downregulated in women whose pregnancy was complicated by GDM. IGF1R and GYS1 as target genes of miR-423-5p, and G6PC3 and FDFT1 as target genes of miR-122-5p were associated with insulin and AMPK signaling pathways and may participate in the regulation of metabolism in GDM. The five exosomal miRNAs had an area under the curve of 0.82 (95%CI, 0.73, ∼0.91) in early prediction of GDM. Our study demonstrates that dysregulated exosomal miRNAs in plasma from pregnant women with GDM might influence the insulin and AMPK signaling pathways and could contribute to the early prediction of GDM.

Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology

BACKGROUND

The major challenge for obstetrics is the prediction and prevention of the great obstetrical syndromes. We propose that defining obstetrical diseases by the combination of clinical presentation and disease mechanisms as inferred by placental pathology will aid in the discovery of biomarkers and add specificity to those already known.

OBJECTIVE

To describe the longitudinal profile of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and the PlGF/sFlt-1 ratio throughout gestation, and to determine whether the association between abnormal biomarker profiles and obstetrical syndromes is strengthened by information derived from placental examination, eg, the presence or absence of placental lesions of maternal vascular malperfusion.

STUDY DESIGN

This retrospective case cohort study was based on a parent cohort of 4006 pregnant women enrolled prospectively. The case cohort of 1499 pregnant women included 1000 randomly selected patients from the parent cohort and all additional patients with obstetrical syndromes from the parent cohort. Pregnant women were classified into six groups: 1) term delivery without pregnancy complications (n=540; control); 2) preterm labor and delivery (n=203); 3) preterm premature rupture of the membranes (n=112); 4) preeclampsia (n=230); 5) small-for-gestational-age neonate (n=334); and 6) other pregnancy complications (n=182). Maternal plasma concentrations of PlGF and sFlt-1 were determined by enzyme-linked immunosorbent assays in 7560 longitudinal samples. Placental pathologists, masked to clinical outcomes, diagnosed the presence or absence of placental lesions of maternal vascular malperfusion. Comparisons between mean biomarker concentrations in cases and controls were performed by utilizing longitudinal generalized additive models. Comparisons were made between controls and each obstetrical syndrome with and without subclassifying cases according to the presence or absence of placental lesions of maternal vascular malperfusion.

RESULTS

1) When obstetrical syndromes are classified based on the presence or absence of placental lesions of maternal vascular malperfusion, significant differences in the mean plasma concentrations of PlGF, sFlt-1, and the PlGF/sFlt-1 ratio between cases and controls emerge earlier in gestation; 2) the strength of association between an abnormal PlGF/sFlt-1 ratio and the occurrence of obstetrical syndromes increases when placental lesions of maternal vascular malperfusion are present (adjusted odds ratio [aOR], 13.6 vs 6.7 for preeclampsia; aOR, 8.1 vs 4.4 for small-for-gestational-age neonates; aOR, 5.5 vs 2.1 for preterm premature rupture of the membranes; and aOR, 3.3 vs 2.1 for preterm labor (all P<0.05); and 3) the PlGF/sFlt-1 ratio at 28 to 32 weeks of gestation is abnormal in patients who subsequently delivered due to preterm labor with intact membranes and in those with preterm premature rupture of the membranes if both groups have placental lesions of maternal vascular malperfusion. Such association is not significant in patients with these obstetrical syndromes who do not have placental lesions.

CONCLUSION

Classification of obstetrical syndromes according to the presence or absence of placental lesions of maternal vascular malperfusion allows biomarkers to be informative earlier in gestation and enhances the strength of association between biomarkers and clinical outcomes. We propose that a new taxonomy of obstetrical disorders informed by placental pathology will facilitate the discovery and implementation of biomarkers as well as the prediction and prevention of such disorders.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

Scoping Review of the Relationship Between Gestational Diabetes Mellitus and the Neonatal and Infant Gut Microbiome

OBJECTIVE

To conduct a scoping review to examine the relationship between a diagnosis of gestational diabetes mellitus (GDM) and the neonatal and infant gut microbiome from 0 to 1 year of age.

DATA SOURCES

We searched PubMed, Scopus, Embase, and CINAHL for articles with key terms "microbiome" and "gestational diabetes mellitus."

STUDY SELECTION

We included articles published in English in peer-reviewed journals between 2012 and 2021 that were reports of original research studies in which researchers used next-generation sequencing for analysis of the fecal microbiome and collected meconium or transitional stool from neonates and infants.

DATA EXTRACTION

We identified nine studies with a combined sample size of 1,279 neonates and infants. We extracted data, including title, authors, sample size, study design, methods, findings, significance, and limitations. We extracted and charted confounding variables such as treatment of GDM, body mass index, gestational age at birth, antibiotic use, mode of birth, and feeding method.

DATA SYNTHESIS

Gestational diabetes mellitus may alter the neonatal and infant gut microbiome because neonates and infants of women with GDM had altered composition and diversity compared to neonates and infants of women without GDM.

CONCLUSION

Mechanisms by which the neonatal and infant microbiome changes in response to GDM are poorly understood and need to be evaluated in future research. Further study of how GDM plays a role in the initial seeding of the microbiome, how the maternal microbiome may affect fetal metabolic programming, and how the neonatal microbiome leads to the future development of obesity and glucose intolerance is critical. Future studies should include larger sample sizes, appropriate collection of potential confounding variables, assessment of maternal interventions for GDM, and longitudinal designs to further understand potential associations with long-term detrimental outcomes such as obesity and impaired glucose tolerance.

Comparison of Diagnostic Values of Maternal Arginine Concentration for Different Pregnancy Complications: A Systematic Review and Meta-Analysis

Abnormal arginine metabolism contributes to the development of intrauterine growth restriction (IUGR), preeclampsia (PE), and gestational diabetes mellitus (GDM), which increase the health burden of mothers and induce adverse birth outcomes. However, associations between maternal arginine concentration and different pregnancy complications have not been systematically compared. The PubMed, ScienceDirect, and Web of Science databases were searched for peer-reviewed publications to evaluate the diagnostic value of plasma arginine concentration in complicated pregnancies. Standardized mean difference (SMD) of the arginine concentration was pooled by a random effects model. The results show that increased maternal arginine concentrations were observed in IUGR (SMD: 0.48; 95% CI: 0.20, 0.76; I² = 47.0%) and GDM (SMD: 0.46; 95% CI: 0.11, 0.81; I² = 82.3%) cases but not in PE patients (SMD: 0.21; 95% CI: −0.04, 0.47; I² = 80.3%) compared with the normal cohorts. Subgroup analyses indicated that the non-fasting circulating arginine concentration in third trimester was increased significantly in GDM and severe IUGR pregnancies, but the change mode was dependent on ethnicity. Additionally, only severe PE persons were accompanied by higher plasma arginine concentrations. These findings suggest that maternal arginine concentration is an important reference for assessing the development of pregnancy complications.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

Fetal programming: could intrauterin life affect health status in adulthood?

Intrauterine life is one of the most important periods of life. As the development of the fetus continues, the mechanisms that affect adult health also begin to mature. With the hypothesis denoted "fetal programming," it is thought that the presence of endocrinological disorders, toxins, infectious agents, the nutritional status of a mother, and nutrients related to placental functionality all have an effect on future life. Therefore, the fetus must adapt to the environment for survival. These adaptations may be involved the redistribution of metabolic, hormonal, or cardiac outputs in an effort to protect the brain, which is one of the important organs, as well as the slowing of growth to meet nutritional requirements. Unlike lifestyle changes or treatments received in adult life, the early developmental period tends to have a lasting effect on the structure and functionality of the body. In this review, fetal programming and the effects of fetal programming are discussed.

Metabolic changes in female rats exposed to intrauterine Hyperglycemia and post-weaning consumption of high-fat diet

We evaluated the influence of the hyperglycemic intrauterine environment and post-weaning consumption of a high-fat diet on the glycemia, insulin, lipid and immunological profile of rat offspring in adulthood. Female rats received citrate buffer (Control - C) or Streptozotocin (a beta cell-cytotoxic drug to induce diabetes - D) on post-natal day 5. In adulthood, these rats were mated to obtain female offspring, who were fed a standard diet (SD) or high-fat diet (HFD) from weaning to adulthood (n = 10 rats/group). OC/SD and OC/HFD represent female offspring of control mothers and received SD or HFD, respectively; OD/SD and OD/HFD represent female offspring of diabetic mothers and received SD or HFD, respectively. At adulthood, the Oral Glucose Tolerance Test (OGTT) was performed and, next, the rats were anesthetized and euthanized. Pancreas was collected and analyzed, and adipose tissue was weighted. Blood samples were collected to determine biochemical and immunological profiles. The food intake was lower in HFD-fed rats and visceral fat weight was increased in the OD/HFD group. OC/HFD, OD/SD, and OD/HFD groups presented glucose intolerance and lower insulin secretion during OGTT. An impaired pancreatic beta-cell function was shown in the adult offspring of diabetic rats, regardless of diet. Interleukin (IL)-6 and IL-10 concentrations were lower in the OD/HFD group and associated to a low-grade inflammatory condition. The fetal programming was responsible for impaired beta cell function in experimental animals. The association of maternal diabetes and post-weaning high-fat diet is responsible for greater glucose intolerance, impaired insulin secretion and immunological change.

Mild Gestational Diabetes and Adverse Pregnancy Outcome: A Systemic Review and Meta-Analysis

Background and Objectives: Mild gestational diabetes (GDM) refers to the gestational hyperglycemia, which does not fulfill the diagnostic criteria for GDM. The results of studies on adverse pregnancy outcomes among women with mild GDM are controversial. Therefore, the aim of this systematic review and meta-analysis was to investigate the impact of mild GDM on the risk of adverse maternal and neonatal outcomes. Methods: A thorough literature search was performed to retrieve articles that investigated adverse maternal and neonatal outcomes in women with mild GDM in comparison with non-GDM counterparts. All populations were classified to three groups based on their diagnostic criteria for mild GDM. Heterogeneous and non-heterogeneous results were analyzed using the fixed/random effects models. Publication bias was assessed using the Harbord test. DerSimonian and Laird, and inverse variance methods were used to calculate the pooled relative risk of events. Subgroup analysis was performed based on mild GDM diagnostic criteria. Quality and risk of bias assessment were performed using standard questionnaires. Results: Seventeen studies involving 11,623 pregnant women with mild GDM and 53,057 non-GDM counterparts contributed to the meta-analysis. For adverse maternal outcomes, the results of meta-analysis showed that the women with mild GDM had a significantly higher risk of cesarean section (pooled RR: 1.3, 95% CI 1.2-1.5), pregnancy-induced hypertension (pooled RR: 1.4, 95% CI 1.1-1.7), preeclampsia (pooled RR: 1.3, 95% CI 1.1-1.5) and shoulder dystocia (pooled RR: 2.7, 95% CI 1.5-5.1) in comparison with the non-GDM population. For adverse neonatal outcomes, the pooled relative risk of macrosomia (pooled RR = 0.4, 95% CI: 1.1-1.7), large for gestational age (pooled RR = 1.7, 95% CI: 1.3-2.3), hypoglycemia (pooled RR = 1.6, 95% CI: 1.1-2.3), hyperbilirubinemia (pooled RR = 1.1, 95% CI: 1-1.3), 5 min Apgar <7 (pooled RR = 1.6, 95% CI: 1.1-2.4), admission to the neonatal intensive care unit (pooled RR = 1.5, 95% CI: 1.1-2.1), respiratory distress syndrome (pooled RR = 3.2, 95% CI: 1.8-5.5), and preterm birth (pooled RR = 1.4, 95% CI: 1.1-1.7) was significantly increased in the mild GDM women as compared with the non-GDM population. However, the adverse events of small for gestational age and neonatal death were not significantly different between the groups. Analysis of composite maternal and neonatal outcomes revealed that the risk of those adverse outcomes in the women with mild GDM in all classifications were significantly higher than the non-GDM population. Also, the meta-regression showed that the magnitude of those increased risks in both composite maternal and neonatal outcomes was similar. Conclusion: The risks of sever adverse neonatal outcomes including small for gestational age and neonatal mortality are not increased with mild GDM. However, the increased risks of most adverse maternal and neonatal outcomes are observed. The risks have similar magnitudes for all mild GDM diagnostic classifications.