Gut microbiota and metabolomic alterations in newborns of mothers with gestational diabetes mellitus

Article information

Clin Exp Pediatr. 2026;69(1):26-35

Publication date (electronic) : 2025 October 22

doi : https://doi.org/10.3345/cep.2025.01074

Wan-Hsin Su, MD ¹

, Yi-Wei Wang, MD ¹, Chien-Chang Chen, MD^,¹^,²

, Ming-Wei Lai, MD, PhD ¹^,², Hsun-Chin Chao, MD ¹^,², Ming-Chou Chiang, MD ²^,³

, Ren-Huei Fu, MD ²^,³

, Pai-Jui Yeh, MD ¹

¹Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Children’s Medical Center, Chang Gung Memorial Hospital, Taoyuan City, Taiwan

²Chang Gung University College of Medicine, Taoyuan City, Taiwan

³Division of Neonatology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital, Taoyuan Taiwan

Corresponding author: Chien-Chang Chen, MD. Chang Gung Children’s Medical Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 5 Fu-Hsing Street, Guishan District, Taoyuan City, 33305, Taiwan Email: cgj2841@yahoo.com

Received 2025 June 27; Revised 2025 July 28; Accepted 2025 August 6.

Abstract

Background

Gestational diabetes mellitus (GDM) is a common complication of pregnancy associated with various perinatal risks in mothers and heightened risks of long-term obesity and metabolic syndrome in their children. Understanding the effect of GDM on infant health is crucial. Infant gut colonization has generated significant interest owing to its profound impact on health and potential role in later disease development.

Purpose

Here we conducted a thorough analysis of the microbiota and metabolome of neonatal meconium to understand how GDM in mothers affects microbial colonization in the early lives of their offspring.

Methods

This study included 49 healthy-term neonates born to mothers with GDM (n=29) and normoglycemic mothers (n=20) between March 2022 and February 2023 at Chang Gung Memorial Hospital (Linkou branch). Fecal samples were collected in sterilized containers before the infants reached 5 days of age. To analyze the meconium microbiota, 16S rRNA gene sequencing was performed, and proton nuclear magnetic resonance was used to examine the metabolome.

Results

Neonates born to mothers with diet-controlled GDM exhibited a notable decrease in α-diversity and a shift in β-diversity compared to those born to normoglycemic mothers. A functional analysis revealed increased peroxisome proliferator-activated receptor and adipocytokine signaling pathway activation in the GDM group. Metabolomic analysis revealed significant changes in the fumarate and succinate levels, indicating metabolic shifts associated with maternal GDM.

Conclusion

These findings highlight the potential effects of pregnancy-related complications on the establishment of gut bacteria in neonates. Further comprehensive studies are required to understand the long-term implications of these microbial changes on infant health.

Keywords: Gestational diabetes mellitus; Neonatal gut microbiota; Meconium metabolomics; Microbial diversity; Metabolic shifts

Key message

Question: Does maternal gestational diabetes mellitus (GDM) affect newborn gut microbiota and metabolomic profiles?

Finding: Neonates born to mothers with diet-controlled GDM exhibited reduced gut microbiota α-diversity, altered β-diversity, and metabolic shifts, including changes in fumarate and succinate levels, with peroxisome proliferator-activated receptor and adipocytokine signaling pathway activation.

Meaning: Maternal GDM affects early microbial colonization and metabolism in newborns and may have longterm health implications.

Graphical abstract. Graphical abstract illustrating the gut microbiota diversity and metabolomic changes in newborns of mothers with gestational diabetes mellitus. The graphical abstract was created in BioRender. Su, W. (2025) https://BioRender.com/moaaux1.

Introduction

Gestational diabetes mellitus (GDM) is a significant metabolic disorder that occurs during pregnancy, affecting approximately 10%–15% of pregnancies worldwide [1-3]. It is associated with multiple adverse perinatal outcomes, including an increased risk of preterm birth, macrosomia, and neonatal hypoglycemia [4-7]. Beyond immediate perinatal complications, evidence suggests that maternal GDM contributes to long-term metabolic consequences in offspring, including an increased probability of obesity, insulin resistance, and metabolic syndrome [8]. Despite these established associations, the underlying mechanisms by which GDM influences neonatal metabolic programming remain unclear [9].

One possible cause of these effects is the neonatal gut microbiota, which plays a crucial role in maintaining metabolic homeostasis, developing the immune system, and influencing long-term disease susceptibility [10-13]. The initial colonization of the neonatal gut represents a critical window in early life, shaping lifelong health trajectories. Recent research has highlighted the significance of maternal factors, including GDM, delivery mode, perinatal antibiotics, and breastfeeding, in influencing microbial colonization patterns [14,15]. However, existing studies have produced inconsistent findings, and comprehensive investigations that integrate microbial and metabolic alterations in neonates are still limited [12,16].

This study examines the impact of maternal GDM on the composition of the neonatal gut microbiota and metabolic profiles. We conducted an integrated multi-omics analysis of neonatal meconium samples to characterize microbial dysbiosis and metabolome in infants born to mothers with GDM, aiming to identify biomarkers that signal metabolic dysfunction.

Methods

1. Study design and participants

This prospective study included healthy-term neonates (gestational age ≥37 weeks and ≤41⁺⁶ weeks) born at Linkou Chang Gung Memorial Hospital and admitted to the baby room between March 2022 and February 2023, along with their mothers. All mothers provided written informed consent for themselves and their neonates. All mothers underwent a routine 75-g oral glucose tolerance test (OGTT) at 24–28 weeks during pregnancy. Maternal GDM was diagnosed through OGTT if one or more of the following glucose criteria were met: fasting plasma glucose ≥92 mg/dL, 1-hour posttest glycemia ≥180 mg/dL, or 2-hour posttest glycemia ≥153 mg/Dl [17-19]. The mothers and their newborns were divided into 2 categories based on their OGTT results: GDM and non-GDM. The exclusion criteria were newborns transferred to the neonatal intensive care unit (NICU) or observation nursing unit (OBN), any condition that required a laboratory survey in the baby room, any unexpected medication use in the baby room, any abnormalities other than G6PD deficiency detected during newborn screening, a failed hearing test, mothers with GDM undergoing pharmacotherapy, preexisting diabetes, any other pregnancy-related complications, chronic diseases, and antibiotic usage within 3 months.

The mother’s age, body mass index (BMI), underlying disease, delivery mode, past medical history, laboratory data, and examination results were collected from medical charts. The neonate’s gender, gestational age, birth body height and weight, birth history, laboratory data, and examination results were also collected.

2. Meconium collection

Fecal samples were collected in a sterilized container by well-trained nurses in the baby room within the first 3–5 days of birth to ensure proper collection and avoid contamination. The samples were transported to the laboratory within 12 hours, and their microbiota were profiled using 16S rRNA gene sequencing. The metabolome was examined via proton nuclear magnetic resonance (1HNMR).

3. DNA extraction and polymerase chain reaction amplification

A sample pellet of approximately 0.2 g of wet sludge was subjected to DNA extraction using a QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany), following the manufacturer’s instructions. The extracted genomic DNA was analyzed via 1.5% agarose gel electrophoresis and then stored at -20℃ until further use. The amplicons from triplicate anaerobic sludge samples were pooled in equal amounts and subjected to emulsion polymerase chain reaction before sequencing.

4. 16S rRNA gene sequencing, sequence analysis, and phylogenetic classification

The V3–V4 regions of the 16S rRNA gene were amplified using specific primers, purified, and sequenced on the MiSeq Pyrosequencer (Illumina, Inc., USA) following the manufacturer’s instructions. After pyrosequencing, all raw reads were processed using the QIIME standard pipeline [20]. Using the clustered sequences of the 16S rRNA, alpha diversity statistics, including the Chao1 richness estimator (CRE) and the Shannon diversity index (SDI), were calculated for each sample using MOTHUR [21].

5. Taxonomic and statistical analyses

Taxonomic analyses were performed by classifying each sequence using the RDP Naïve Bayesian rRNA Classifier Version 2.5 program and the database of the Michigan State University Center for Microbial Ecology Ribosomal Database Project (RDP) (http://rdp.cme.msu.edu/), with a 50% bootstrap value.

6. Proton nuclear magnetic resonance

The metabolic profile of the meconium was analyzed via 1H NMR using a Bruker Advance III HD console with a 14.1-T magnet operating at 1 H 600 MHz. It was equipped with a 5-mm inverse triple resonance CryoProbe (1H/13C/15N) featuring cold preamplifiers for both ¹H and ¹³C, along with a z-axis gradient and automated tuning and matching capabilities. In addition, the system included a Bruker SampleJet operating in 5-mm shuttle mode, complete with a cooling rack to maintain the sample temperature at 279 K.

The sample preparation involved adding 630 mL of fecal water supernatant to an Eppendorf tube, along with 70 mL of D2O containing 1-mM TSP and 3-mM NaN3. Then, the samples were centrifuged at 12,000 g for 5 minutes at 277 K. Next, 600 μL of supernatant was transferred to a 4-inch SampleJet NMR tube. During data analysis, spectral regions downfield of 9.50 ppm and upfield of 0.5 ppm were excluded. The data were acquired and processed using Topspin 3.2, and all experiments were conducted under automation using the IconNMR program.

7. Statistical analysis

Statistical comparisons were performed using 2-tailed t tests or 1-way analysis of variance with GraphPad Prism 5 software (La Jolla, USA) and IBM SPSS Statistics ver. 22.0 (IBM Co., USA). Differences in patient characteristics were evaluated using Fisher exact test for categorical variables and the t test for continuous variables.

For microbiota data, α-diversity indices were compared using the SDI, amplicon sequence variants, the CRE, and the abundance-based coverage estimator (ACE). Partial least squares discriminant analysis (PLS-DA) was employed to calculate the difference in β-diversity between the GDM and control groups, with delivery mode as a covariate. The abundance of genera in each group was analyzed using Metastats. Correlation networks for significantly differentiated genera were established utilizing Spearman’s correlation (rho <-0.3 or rho >0.3; FDR-corrected P<0.05). Statistical tests were performed using STAMP version 2.1.3 with a corrected P value to compare the relative proportions of predicted metagenomic functions. For metabolomics, OPLS-DA (orthogonal PLS-DA), performed using SIMCA-P 13.0 (Umetrics, Sweden), was used to reveal differences in metabolomic profiles. Pathway analyses of metabolomics data were conducted using MetaboAnalyst (http://www.metaboanalyst.ca/) to predict enriched pathways of differential metabolites.

8. Ethics approval

The study protocol complied with the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital (IRB No.202200115B0).

Results

1. Patient selection and classification

In total, 53 infants were initially enrolled in the study, including 22 in the non-GDM group and 31 in the GDM group (Fig. 1). Subsequently, 4 infants (2 from each group) were excluded due to admission to the NICU or OBN, resulting in a final sample of 20 infants in the non-GDM group and 29 in the GDM group.

Fig. 1.

Patient grouping flow chart. GDM, gestational diabetes mellitus; NICU, neonatal intensive care unit; OBN, observation nursing unit.

2. Demographic and clinical characteristics

Table 1 presents the demographic and clinical features of the study participants. The average age of mothers was significantly (P<0.05 unless stated otherwise) higher in the GDM group (35.0±4.4 years) than in the non-GDM group (32.2±4.1 years). The antepartum BMI did not significantly differ between the groups, with values of 27.8±4.1 in the GDM group and 26.2±3.0 in the non-GDM group.

Table 1.

Descriptive data of enrolled children

Regarding neonates, the average gestational age was significantly lower in the GDM group. Mean birth weight and length were also greater in that group but the differences were not statistically significant. The mode of delivery was similar between the groups, with cesarean section rates of 44.8% in the GDM group and 40% in the non-GDM group. In addition, the newborn sex ratio did not significantly differ between the groups.

3. Microbial correlation with metabolomic findings

Fig. 2 illustrates the relative abundances of gut microbiota in individual samples at various taxonomic levels, highlighting distinct microbial profiles between groups at the family and genus levels. At the phylum level, there was a notable decrease in Firmicutes in the GDM group, a bacterial phylum known for its role in butyrate production [22-24]. This suggests a potential impact on the metabolic function of the gut microbia, particularly with respect to the production of short-chain fatty acids (SCFAs). At the genus and species levels, a significant decrease in Eubacterium and Eubacterium hallii was observed in the GDM group. These bacteria are associated with the tricarboxylic acid (TCA) cycle, suggesting potential disruptions of microbial contributions to host energy metabolism [25-27]. These microbial changes may provide an explanatory framework for the observed metabolomic differences between GDM and non-GDM individuals, warranting further investigation into their functional implications.

Fig. 2.

(A) Relative abundance of the gut microbiota in individual samples at the family and genus levels. The bar graphs illustrate the mean relative composition of the bacteria in the patient’s stool samples over time representative of the gut microbiota. Each color represents the corresponding taxon group in the legend. (B) Relative abundance of Firmicutes, Eubacterium, and Eubacterium halii in each group.

4. Microbiota diversity of the meconium

To understand the impact of maternal GDM on the diversity of the gut microbiota of neonates, we conducted a comparative analysis of α-diversity and β-diversity between the GDM and non-GDM groups. The α-diversity indices are shown in Fig. 3. The SDI was notably lower in the GDM group (P=0.015), indicating a decrease in diversity. Although the number of operational taxonomic units (OTUs), CRE, and ACE also showed a trend toward lower diversity in the GDM group, these changes were not statistically significant. For β-diversity, we performed PLSDA, which revealed distinct clustering patterns between the 2 groups. This suggests a strong association between GDM and changes in the composition of the microbiota.

Fig. 3.

(A) Alpha diversity based on the Shannon diversity index, observed operational taxonomic units, Chao1 Index, and the abundance-based coverage estimator. ^*P<0.05. (B) Beta-diversity as determined using a partial least squares discriminant analysis. GDM, gestational diabetes mellitus; PLS, partial least squares.

5. Functional pathway analysis

We further performed a functional pathway analysis, which revealed distinct differences in pathway abundance between the groups. A heatmap was used to illustrate the relative abundance of metabolic and signaling pathways across various physiological systems (Fig. 4). Notably, the peroxisome proliferator-activated receptor (PPAR) signaling pathway and adipocytokine signaling pathway, both involved in metabolic regulation, exhibited significantly higher relative abundance in GDM individuals.

Fig. 4.

Predictive functional profiling using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software. GDM, gestational diabetes mellitus; PPAR, peroxisome proliferator-activated receptor.

6. Metabolomic analysis of the meconium

Principal component analysis and variable importance in projection (VIP) scores were used to identify metabolites that could significantly differentiate between the GDM and non-GDM groups (Fig. 5). Key metabolites with VIP scores >2 included organic acids (fumarate, malic acid, succinate), essential amino acids (tryptophan, lysine, tyrosine), ethanolamine, amino penton, cresol, and nicotinate. A volcano plot identified fumarate and succinate as the most significantly altered metabolites, exhibiting a fold change (FC) >1.5.

Fig. 5.

Principal component analysis (A) and partial least squares discriminant analysis (B) score plots for all metabolite features. (C) Volcano plot showing significantly altered metabolites with fold changes (FCs) ≥1.5 between the GDM and non-GDM groups. (D) Comparison of the significantly altered metabolites, fumarate and succinate, between the groups (^*P<0.1; ^**P<0.05). GDM, gestational diabetes mellitus; PC1, first principal component; PC2, second principal component; Conc., concentration.

Further statistical analysis revealed that the levels of fumarate and succinate were significantly different between the 2 groups. These metabolites play crucial roles in the TCA cycle, influencing the metabolism of carbohydrates, lipids, and amino acids. In addition, they are essential for mitochondrial function and energy production. Succinate is not only a metabolic byproduct but also an intermediate in butyrate production, thereby linking it to butyrate-producing bacterial communities [28,29].

7. Short-chain fatty acids

Next, SCFAs were analyzed to compare their concentrations between the groups. As illustrated in Fig. 6, the amounts of acetate, propionate, butyrate, and valerate were lower in the GDM group. Although a downward trend in SCFA metabolism was observed in the GDM group, the differences between the 2 groups did not reach statistical significance.

Fig. 6.

Comparison of acetate (A), propionate (B), butyrate (C), and valerate (D) levels between groups. GDM, gestational diabetes mellitus.

Discussion

This study investigated the impact of GDM on the microbiota and metabolomic profiles of the meconium of neonates. Our findings demonstrate significant differences in maternal and neonatal characteristics, microbial diversity, and metabolomic composition between infants born to mothers with and without GDM.

Maternal age was significantly higher in the GDM group, consistent with previous reports linking advanced maternal age with an increased risk of GDM [30,31]. In addition, infants in the GDM group had a significantly lower gestational age, suggesting a potential influence of GDM on pregnancy duration. However, no significant differences were observed in birth weight, birth length, mode of delivery, or newborn gender distribution, highlighting the complex interplay between maternal metabolic status and neonatal outcomes.

A key finding is the significant reduction in microbial diversity, as indicated by the lower SDI in the GDM group. Reduced microbial diversity in early life is associated with an increased risk of metabolic and immune-related disorders [32,33]. Although other α-diversity indices, such as OTU counts, CRE, and ACE, showed a trend toward reduced diversity in the GDM group, these differences did not reach statistical significance. Furthermore, β-diversity analysis revealed distinct clustering patterns among the groups, indicating a substantial shift in microbial composition associated with maternal GDM.

At the taxonomic level, we observed a notable reduction in the abundance of Firmicutes, a phylum crucial for the production of SCFAs, in the GDM group, including butyrate [22-24,34]. Moreover, the decreased levels of Eubacterium, including Eubacterium hallii, in the GDM group may explain the observed differences, as these taxa are involved in the TCA cycle and butyrate synthesis [22,23,25-27]. These results suggest that maternal metabolic dysregulation may interfere with the initial colonization and functionality of the neonatal microbiome, potentially impacting metabolic programming and immune development [31].

Similar results were noted in the functional pathway analysis, where the PPAR and adipocytokine signaling pathways showed significantly higher relative abundance in the GDM group. These findings align with previous research, indicating a potential role for lipid metabolism dysregulation in the pathophysiology of GDM [27,35]. Furthermore, they also highlight the potential involvement of metabolic and endocrine dysfunctions in GDM, particularly through PPAR and adipocytokine signaling, warranting further investigation into their roles in disease progression and therapeutic targeting.

In metabolomic analyses, we identified fumarate and succinate—key intermediates of the TCA cycle—as significantly altered in neonates born to mothers with GDM. These metabolites are central to energy metabolism [36-38] and have emerged as signaling molecules implicated in metabolic disorders [39], inflammatory and autoimmune diseases [39-43], and cancer [40-42,44]. Their altered levels may reflect early metabolic programming driven by maternal hyperglycemia, potentially predisposing offspring to metabolic syndrome, immune dysregulation, and chronic disease later in life. Notably, succinate’s role in butyrate production links microbial composition with metabolic function, and the observed reduction in butyrate-producing bacteria in the GDM group may contribute to these disturbances [16,28,29]. These findings underscore a critical connection between maternal metabolism, neonatal metabolic shifts, and long-term health, suggesting further mechanistic studies on metabolite-related pathways.

In addition, the identification of key metabolites with high VIP scores, including organic acids, essential amino acids, and ethanolamine, reinforces the influence of maternal GDM on neonatal metabolic pathways. Given the crucial role of fumarate and succinate in mitochondrial function, their dysregulation may have long-term metabolic consequences, underscoring the need for further research into their potential as biomarkers for early assessment of metabolic risk [45].

Our findings suggest that maternal GDM is associated with distinct changes in the meconium microbiota and metabolomic profiles of neonates, which may have implications for metabolic programming in early life. With growing evidence linking early microbial dysbiosis to metabolic disorders later in life, it is crucial to understand the mechanisms underlying these changes. Although the sample size is limited, the strict inclusion criteria and thorough multiomics analysis strengthen the reliability of the findings. Future studies with larger cohorts and participation from multiple centers are needed to confirm and build upon these observations. Additionally, a major limitation of the current study is the lack of paired maternal microbiome data, which restricts the ability to directly evaluate vertical microbial transmission. While future research should ideally include synchronized sampling of both maternal and neonatal microbiota to better understand how maternal microbiota affects early-life microbial colonization, substantial existing evidence emphasizes the critical role of the maternal microbiome in shaping the fetal gut microbiota during pregnancy [46]. We review the published literature, some previous studies have demonstrated that maternal gut microbial composition may significantly impact neonatal microbial acquisition and metabolic programming [32,33,47]. If the possible mechanism of action is explored, the maternal gut microbiota may modulate both systemic and local immune responses by translocating microbial-derived products, such as lipopolysaccharides, SCFAs, and extracellular vesicles, across the intestinal barrier, thereby influencing immune cell activity and maternal-fetal immune tolerance [48,49]. Consistent with this, a recent maternal-neonate pairs study found notable shifts in microbiota and plasma metabolites in both GDM mothers and their neonates compared to healthy controls. Several bacterial genera and metabolites showed similar patterns in maternal and neonatal samples, suggesting that GDM-related intrauterine conditions may drive parallel microbial and metabolic disruptions in both mother and infant [50]. Furthermore, future research could utilize long-term studies to investigate whether these microbial and metabolic changes persist beyond the neonatal period and contribute to metabolic dysregulation in childhood and adulthood.

In conclusion, maternal GDM influences the meconium microbiota diversity and metabolomic profiles of neonates, with notable reductions in microbial diversity and changes in key metabolites involved in the TCA cycle. These findings underscore the importance of early-life microbial and metabolic programming, suggesting that fumarate and succinate may be potential biomarkers for metabolic disturbances associated with GDM. Further research is needed to elucidate the long-term implications of these changes and to explore potential interventions to optimize neonatal health outcomes in the context of maternal GDM.

Notes

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Funding

This study was supported by Chang Gung Memorial Hospital research project grant CMRPG3L1751-3L1752 and National Science Council research project grant 108-2314-B-182A-089-.

Acknowledgments

The authors thank all of the children and their parents or caregivers for participating in this study. The authors also extend their gratitude to the medical staff for their clinical care.

Author Contribution

Conceptualization: CC; Data curation: WS, YW, CC, MC, RF; Formal analysis: WS, YW, CC; Funding acquisition: CC; Methodology: WS, YW, CC; Project administration: WS, CC; Visualization: WS, CC; Writing - original draft: WS, CC; Writing - review & editing: WS, CC, ML, HC, PY

References

1. Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF Diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group's Criteria. Diabetes Res Clin Pract 2022;183:109050.

2. Tantengco OA, Aquino IM, Asis JL, Tan JJ, Uy MN, Pacheco EP. Research trends in gestational diabetes mellitus in Southeast Asia: a bibliometric analysis (1975-2020). Diabetes Metab Syndr 2021;15:102202.

3. Amarra MS, Chong MF, Titapant V, Somprasit C, Rogacion J, Irwinda R, et al. ILSI Southeast Asia symposium: prevalence, risk factors, and actions to address gestational diabetes in selected Southeast Asian countries. Eur J Clin Nutr 2021;75:1303–8.

4. Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The pathophysiology of gestational diabetes mellitus. Int J Mol Sci 2018;19:3342.

5. Damm P, Houshmand-Oeregaard A, Kelstrup L, Lauenborg J, Mathiesen ER, Clausen TD. Gestational diabetes mellitus and long-term consequences for mother and offspring: a view from Denmark. Diabetologia 2016;59:1396–9.

6. Buchanan TA, Xiang AH, Page KA. Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol 2012;8:639–49.

7. Jaskolka D, Retnakaran R, Zinman B, Kramer CK. Sex of the baby and risk of gestational diabetes mellitus in the mother: a systematic review and meta-analysis. Diabetologia 2015;58:2469–75.

8. Ferrocino I, Ponzo V, Gambino R, Zarovska A, Leone F, Monzeglio C, et al. Changes in the gut microbiota composition during pregnancy in patients with gestational diabetes mellitus (GDM). Sci Rep 2018;8:12216.

9. Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, et al. Dynamics and Stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 2015;17:690–703.

10. Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Bäckhed HK, et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 2012;150:470–80.

11. Ostrea EM, Morales V, Ngoumgna E, Prescilla R, Tan E, Hernandez E, et al. Prevalence of fetal exposure to environmental toxins as determined by meconium analysis. Neurotoxicology 2002;23:329–39.

12. Chu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat Med 2017;23:314–26.

13. Tremaroli V, Bäckhed F. Functional interactions between the gut microbiota and host metabolism. Nature 2012;489:242–9.

14. Asbury MR, Butcher J, Copeland JK, Unger S, Bando N, Comelli EM, et al. Mothers of preterm infants have individualized breast milk microbiota that changes temporally based on maternal characteristics. Cell Host Microbe 2020;28:669–82.e4.

15. Moossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, et al. Composition and variation of the human milk microbiota are influenced by maternal and early-life factors. Cell Host Microbe 2019;25:324–35.e4.

16. Chen T, Qin Y, Chen M, Zhang Y, Wang X, Dong T, et al. Gestational diabetes mellitus is associated with the neonatal gut microbiota and metabolome. BMC Med 2021;19:120.

17. International Association of Diabetes and Pregnancy Study Groups Consensus Panel, Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 2010;33:676–82.

18. Lapolla A, Dalfrà MG, Ragazzi E, De Cata AP, Fedele D. New International Association of the Diabetes and Pregnancy Study Groups (IADPSG) recommendations for diagnosing gestational diabetes compared with former criteria: a retrospective study on pregnancy outcome. Diabet Med 2011;28:1074–7.

19. Liao S, Mei J, Song W, Liu Y, Tan YD, Chi S, et al. The impact of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) fasting glucose diagnostic criterion on the prevalence and outcomes of gestational diabetes mellitus in Han Chinese women. Diabet Med 2014;31:341–51.

20. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335–6.

21. Schloss PD, Gevers D, Westcott SL. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 2011;6e27310.

22. Duncan SH, Barcenilla A, Stewart CS, Pryde SE, Flint HJ. Acetate utilization and butyryl coenzyme A (CoA):acetate- CoA transferase in butyrate-producing bacteria from the human large intestine. Appl Environ Microbiol 2002;68:5186–90.

23. Louis P, Duncan SH, McCrae SI, Millar J, Jackson MS, Flint HJ. Restricted distribution of the butyrate kinase pathway among butyrate-producing bacteria from the human colon. J Bacteriol 2004;186:2099–106.

24. Scott KP, Martin JC, Campbell G, Mayer CD, Flint HJ. Whole-genome transcription profiling reveals genes upregulated by growth on fucose in the human gut bacterium "Roseburia inulinivorans". J Bacteriol 2006;188:4340–9.

25. Wei J, Zhang Y, Dalbeth N, Terkeltaub R, Yang T, Wang Y, et al. Association between gut microbiota and elevated serum urate in two independent cohorts. Arthritis Rheumatol 2022;74:682–91.

26. Roager HM, Licht TR. Microbial tryptophan catabolites in health and disease. Nat Commun 2018;9:3294.

27. Zhou S, Liu L, Ye B, Xu Y, You Y, Zhu S, et al. Gut microbial metabolism is linked to variations in circulating non-high density lipoprotein cholesterol. EBioMedicine 2024;104:105150.

28. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 2016;165:1332–45.

29. Hetzel M, Brock M, Selmer T, Pierik AJ, Golding BT, Buckel W. Acryloyl-CoA reductase from Clostridium propionicum. An enzyme complex of propionyl-CoA dehydrogenase and electron-transferring flavoprotein. Eur J Biochem 2003;270:902–10.

30. Schummers L, Hutcheon JA, Hacker MR, VanderWeele TJ, Williams PL, McElrath TF, et al. Absolute risks of obstetric outcomes by maternal age at first birth: a population-based cohort. Epidemiology 2018;29:379–87.

31. Li Y, Ren X, He L, Li J, Zhang S, Chen W. Maternal age and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of over 120 million participants. Diabetes Res Clin Pract 2020;162:108044.

32. Hu J, Nomura Y, Bashir A, Fernandez-Hernandez H, Itzkowitz S, Pei Z, et al. Diversified microbiota of meconium is affected by maternal diabetes status. PLoS One 2013;8e78257.

33. Su M, Nie Y, Shao R, Duan S, Jiang Y, Wang M, et al. Diversified gut microbiota in newborns of mothers with gestational diabetes mellitus. PLoS One 2018;13e0205695.

34. Wu N, Zhou J, Mo H, Mu Q, Su H, Li M, et al. The gut microbial signature of gestational diabetes mellitus and the association with diet intervention. Front Cell Infect Microbiol 2022;11:800865.

35. Wild R, Feingold KR. Effect of pregnancy on lipid metabolism and lipoprotein levels. In: Endotext. Feingold KR, Ahmed S., Anawalt B, Blackman MR, Boyce A, Chrousos G, et al., editors. South Dartmouth (MA): MDText.com, Inc., 2000.

36. Martínez-Reyes I, Chandel NS. Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun 2020;11:102.

37. Liu L, Liu L, Wang J, Zheng Q, Jin B, Sun L. Differentiation of gestational diabetes mellitus by nuclear magnetic resonance-based metabolic plasma analysis. J Biomed Res 2021;35:351–60.

38. Atallah R, Gindlhuber J, Platzer W, Bärnthaler T, Tatzl E, Toller W, et al. SUCNR1 is expressed in human placenta and mediates angiogenesis: significance in gestational diabetes. Int J Mol Sci 2021;22:12048.

39. He W, Miao FJ, Lin DC, Schwandner RT, Wang Z, Gao J, et al. Citric acid cycle intermediates as ligands for orphan G-protein-coupled receptors. Nature 2004;429:188–93.

40. Tannahill GM, Curtis AM, Adamik J, Palsson-McDermott EM, McGettrick AF, Goel G, et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 2013;496:238–42.

41. Shibuya M, Claesson-Welsh L. Signal transduction by VEGF receptors in regulation of angiogenesis and lymphangiogenesis. Exp Cell Res 2006;312:549–60.

42. Cho H, Du X, Rizzi JP, Liberzon E, Chakraborty AA, Gao W, et al. On-target efficacy of a HIF-2α antagonist in preclinical kidney cancer models. Nature 2016;539:107–11.

43. Chouchani ET, Pell VR, Gaude E, Aksentijević D, Sundier SY, Robb EL, et al. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature 2014;515:431–5.

44. Letouzé E, Martinelli C, Loriot C, Burnichon N, Abermil N, Ottolenghi C, et al. SDH mutations establish a hypermethylator phenotype in paraganglioma. Cancer Cell 2013;23:739–52.

45. Hu T, Wu Q, Yao Q, Jiang K, Yu J, Tang Q. Short-chain fatty acid metabolism and multiple effects on cardiovascular diseases. Ageing Res Rev 2022;81:101706.

46. Lu X, Shi Z, Jiang L, Zhang S. Maternal gut microbiota in the health of mothers and offspring: from the perspective of immunology. Front Immunol 2024;15:1362784.

47. Chu DM, Antony KM, Ma J, Prince AL, Showalter L, Moller M, et al. The early infant gut microbiome varies in association with a maternal high-fat diet. Genome Med 2016;8:77.

48. Pelzer E, Gomez-Arango LF, Barrett HL, Nitert MD. Review: maternal health and the placental microbiome. Placenta 2017;54:30–7.

49. Amar J, Chabo C, Waget A, Klopp P, Vachoux C, Bermúdez-Humarán LG, et al. Intestinal mucosal adherence and translocation of commensal bacteria at the early onset of type 2 diabetes: molecular mechanisms and probiotic treatment. EMBO Mol Med 2011;3:559–72.

50. Liu J, Zhai X, Ding L, Yu M, Zhang Q, Liu J, et al. Landscapes of maternal and neonatal gut microbiome and plasma metabolome signatures and their interaction in gestational diabetes mellitus. J Nutr Biochem 2024;134:109716.

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Variable	Non-GDM group (n=20)	GDM group (n=29)	P value
Maternal
Age (yr)	32.2±4.1	35.0±4.4	0.03
Antepartum BMI (kg/m²)	26.2±3.0	27.8±4.1	0.12
Neonatal
Gestational age (wk)	39.0±0.5	38.5±0.7	0.02
Birth weight (g)	3,080.5±320.9	3,194.5±337.6	0.24
Birth length (cm)	49.4±1.9	50.1±2.2	0.21
Mode of delivery			0.74
Vaginal	12 (60)	16 (55.2)
Cesarean	8 (40)	13 (44.8)
Sex of infant			0.05
Boy	6 (30)	17 (58.7)
Girl	14 (70)	12 (41.3)