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Article

A Pilot Study on the Fecal Microbiota in Mexican Women with Gestational Diabetes Mellitus and Their Newborns

by
Dennise Lizárraga
1,2,
Alejandra García-Gasca
2,*,
Teresa García-Gasca
3,
Gertrud Lund
4,
Abraham Guerrero
1,
Efrén Peraza-Manjarrez
5 and
Bruno Gómez-Gil
1,*
1
Laboratory of Microbial Genomics, Centro de Investigación en Alimentación y Desarrollo, Avenida Sábalo Cerritos s/n, Mazatlán 82112, Mexico
2
Laboratory of Molecular Biology and Tissue Culture, Centro de Investigación en Alimentación y Desarrollo, Avenida Sábalo Cerritos s/n, Mazatlán 82112, Mexico
3
Laboratory of Molecular and Cellular Biology, Facultad de Ciencias Naturales, Universidad Autónoma de Querétaro, Avenida de las Ciencias s/n, Juriquilla, Querétaro 76230, Mexico
4
Department of Genetic Engineering, Centro de Investigación y de Estudios Avanzados del IPN, Libramiento Norte Carretera Irapuato León Kilómetro 9.6, Carr Panamericana, Irapuato 36821, Mexico
5
Gynecology and Obstetrics Department, Instituto Vidalia, Hospital Sharp Mazatlán, Avenida Rafael Buelna y Dr. Jesús Kumate s/n, Mazatlán 82126, Mexico
*
Authors to whom correspondence should be addressed.
Diabetology 2024, 5(5), 464-475; https://doi.org/10.3390/diabetology5050034
Submission received: 23 July 2024 / Revised: 18 September 2024 / Accepted: 20 September 2024 / Published: 25 September 2024
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Abstract

:
Introduction: The gut microbiota plays important roles in physiological and immune processes. Some metabolic disorders and complications during pregnancy, such as gestational diabetes mellitus (GDM), have been associated with alterations in the gut microbiota. The aim of this study was to characterize alterations in fecal microbiota (as a non-invasive proxy of the gut microbiota) by 16S rRNA (V3-V4) taxonomic fingerprinting in Mexican women with GDM and their newborns. Methods: A total of 17 pregnant women (GDM = 8 and control = 9) were recruited between April 2021 and August 2022, and fecal samples were collected in the third trimester of gestation and during cesarean section. A total of 16 newborns (GDM = 8 and control = 8) participated in the study; meconium samples were taken immediately after birth. Results: The maternal microbiota (both at the third trimester and cesarean section) exhibited higher alpha diversity and a different bacterial community structure compared to that of their newborns. The maternal fecal microbiota of the third trimester from the GDM group showed a significant increase in the abundance of Firmicutes, Lachnospiraceae, Ruminococcaceae, Blautia, Rosebura, and Faecalibacterium, and a significant decrease in Peptostreptococcaceae, Anaerococcus, and Finegoldia, compared to the control group; these taxa correlated with blood glucose levels (except for Ruminococcaceae) but not with body mass index (BMI). No significant differences were observed between GDM and control groups in the relative abundance of maternal fecal microbiota collected in the cesarean section. The meconium microbiota of newborns from mothers with GDM showed a significant increase in Faecalibacterium compared with newborns from normoglycemic mothers and correlated with the mothers’ BMI and fasting glucose levels. Conclusions: The results indicate that GDM is associated with alterations in the fecal microbiota of women with GDM in the third trimester, in particular, with taxa known to be associated with metabolic disorders and other types of diabetes, and modifications in the meconium microbiota of their newborns.

Graphical Abstract

1. Introduction

Microbial communities form complex relationships with each other; in the human body, most of them reside in the gastrointestinal tract, the so-called “gut microbiota”, which usually rely on the fecal microbiota and do not represent the general composition of the intestinal microbiota [1]. Human gut microbiota have been well documented due to its importance in health and well-being, as it improves digestion and the metabolism of nutrients in the intestine, helps modulate the immune system, and acts as a defense barrier against harmful bacteria [2,3]. Alterations in the gut microbiota have been associated with diabetes mellitus, obesity, allergies, and autoimmune diseases, among others [4,5,6,7,8]; some pregnancy outcomes, such as preeclampsia and gestational diabetes mellitus (GDM), have also been associated with alterations in gut microbiota composition [9,10].
GDM is a type of diabetes first detected between 24 and 28 weeks of gestation due to increased blood glucose levels, which are normally restored by the end of pregnancy [11]; nevertheless, both the mother and the newborn become susceptible to developing chronic degenerative diseases throughout their lives, such as type-2 diabetes mellitus and obesity [12,13]. Insulin resistance (IR) is one of the metabolic disorders present in GDM and is associated with health implications in the mother and her offspring [14]; furthermore, the gut microbiota play an important role in carbohydrate metabolism and, therefore, contributes to IR [15]. Similarly, alterations in gut microbiota composition (such as differences in alpha and beta diversity, as well as variations in the relative abundance of some taxa) have been reported in women with GDM in the second [16,17], and in the third trimester of pregnancy [18,19]. Some taxa have been related to metabolic disorders during GDM; for example, Mokkala et al. [20] found a positive association between blood glucose levels and IR with the family Ruminococcaceae in fecal samples at 12.5 weeks of gestation in women with GDM; similarly, Liu et al. [10] observed a positive correlation between Streptococcus, Vellonella, Haemophilus, and Actinomyces with cholesterol levels, and Prevotella with lysophosphatidyl glycerol levels in women with GDM.
The seeding of the intestinal microbiota begins early in life, and alterations in bacterial composition have been associated with the development of pathological conditions throughout life [21]. It has been reported that the gut microbiota of one-week-old infants born to mothers with GDM showed an increased abundance of Actinobacteria and Bacteroidetes. In contrast, infants born to normoglycemic mothers had an increase in Staphylococcus, Ralstonia, Lactobacillus, and some members of Enterobacteriaceae [22]. Soderborg et al. [23] reported that two-week-old infants born to mothers with GDM presented a decrease in Lactobacillus, Flavonifractor, Erysipelotrichaceae, and Gammaproteobacteria but an increase in Phascolarctobacterium, compared with infants born to normoglycemic mothers.
In Mexico, the overall reported prevalence of GDM in 2012 was 30.1% [24], while in 2018, it was 23.7% in public healthcare institutions [25], a higher figure than the global prevalence of 14% [13], indicating a population at high risk of developing metabolic diseases such as type-2 diabetes mellitus, cardiovascular disease, and obesity; hence, this study aims to analyze alterations in fecal microbiota caused by GDM in Mexican women and their newborns.

2. Materials and Methods

2.1. Patient Recruitment and Follow-Up

Patient recruitment was carried out from April 2021 to August 2022 in Mazatlán, Mexico, by gynecologist members of the Board of Gynecology and Obstetrics of Mazatlán, Mexico. Inclusion criteria were single pregnancy, live fetus, cesarean delivery, and age ≥ 28 years. Exclusion criteria were multiple pregnancies, stillbirth, in vitro fertilization treatment, other types of diabetes (excluding GDM), and antibiotic treatment. All protocols were approved by the Institutional Ethics Committee for Research (CEI) of the Research Center for Food and Development (CIAD) (CEI-CIAD, approval number CE/002/2020).
Out of 20 participants recruited, only 17 finished the trial (GDM = 8 and control = 9). All participants signed an informed consent form and filled out a questionnaire with their medical history and additional information. Meconium was collected from 16 neonates (GDM = 8 and control = 8). GDM was diagnosed according to the World Health Organization criteria [26]. Briefly, between the gestational weeks 24 and 28, each participant underwent a 75 g oral glucose tolerance test (OGTT). GDM was diagnosed when blood glucose coincided with at least one of the following values: 92–125 mg/dL (fasting), ≥180 mg/dL (one hour post load), and/or 153–199 mg/dL (two hours post load). All patients diagnosed with GDM received proper treatment to control glucose levels and avoid complications. Such treatments consisted of dietary adjustments, exercise, and (in some cases) metformin.
Weight and height data were collected to calculate BMI at their first prenatal care appointment (first trimester). The OGTT was performed between weeks 24 and 28, and each participant provided a fecal sample using a sterile swab between weeks 28 and 39 (third trimester). Approximately one week before the planned cesarean section (C-section), patients were tested for current or previous SARS-CoV-2 infection (using RT-qPCR and IgG/IgM antibodies).

2.2. Sample Collection

Duplicate stool and meconium samples were collected from pregnant mothers and their newborns, respectively, using a sterile swab and stored in absolute ethanol at −80 °C until further use. Two fecal samples were collected from the mother, the first during the third trimester (self-collected between 28 and 39 weeks of gestation), the second on the day of C-section by the obstetrician after cesarean delivery, and the meconium was collected from the newborn while checking for anal patency during the initial health check.

2.3. Sequencing and Bioinformatics

All DNA samples were extracted and sequenced in Novogene (Sacramento, CA, USA); the V3–V4 region of the 16S gene (2 × 250 bp) was sequenced using the Illumina PE250 platform. The amplification primers used were 341F-CCTACGGGNGGCWGCAG and 805R-GACTACHVGGGTATCTAATCC.
Raw sequences were trimmed and merged with our pipeline pair-end_cleaner https://github.com/GenomicaMicrob/pair-end_cleaner (accessed on 20 February 2024). Chimeras were removed with the chimera_detector https://github.com/GenomicaMicrob/chimera_detector (accessed on 20 February 2024). Once the sequences were clean, they were clustered into operational taxonomic units (OTUs), and taxonomic assignment was carried out with mg_classifier https://github.com/GenomicaMicrob/mg_classifier (accessed on 20 February 2024) using the SILVA database v138; both scripts are based on vsearch [27]. The data were imported, analyzed and graphed in RStudio (R v4.2.2.) using Phyloseq https://github.com/joey711/phyloseq (accessed on 20 February 2024) [28]. A rarefaction curve was created (Supplementary Figure S1), which was used to conduct subsequent analyses. Relative abundances were obtained at the phylum, family, and genus levels; alpha diversity was estimated with Chao1 and Shannon indexes. Also, the Vegan package v2.6.4 (https://github.com/vegandevs/vegan, accessed on 20 February 2024) was used to determine beta diversity, with a PCoA principal coordinate analysis based on the Bray–Curtis distance using RStudio (R v 4.2.2.).

2.4. Statistics

Statistical tests for mothers’ and newborns’ characteristics, the relative abundance of bacterial taxa, and alpha diversity were performed with the SigmaPlot software (v 12.0). Continuous variables were compared with the t-test (for normally distributed data) and the Mann–Whitney U test (for data with non-normal distribution). Categorical variables were compared with the chi-square and Fisher’s exact tests. Spearman’s correlation was used to identify taxa associated with BMI and maternal glucose levels from the OGTT. Significant differences in beta diversity were determined using PERMANOVA (permutational multivariate analysis of variance) with the Vegan package and the Adonis function v0.4.1 with 999 permutations in RStudio (R v 4.2.2.). A 95% confidence level was employed, and a p value lower than 0.05 was considered statistically significant.

3. Results

3.1. Characteristics of the Patients

Table 1 shows the characteristics of the patients participating in the study. Weight and BMI at the beginning of the first trimester were significantly higher in women with GDM compared to normoglycemic women (p = 0.040 and p = 0.016, respectively). As expected, fasting, one-, and two-hour glucose levels were significantly higher in the GDM group compared to the control group (p < 0.001, p ≤ 0.001 and p = 0.001, respectively). Conversely, maternal age, height, family history of diabetes, gestational week of C-section, and SARS-CoV-2 infection did not differ significantly across experimental groups. Likewise, neonates of either experimental group showed no significant differences in sex, weight, height, abdominal circumference, and incidence of macrosomia.

3.2. Fecal and Meconium Bacterial Diversity

Datasets are available as Supplementary Files S1 (metadata), S2 (OTUs), and S3 (Taxa). We first compared the alpha bacterial diversity of maternal fecal samples collected during the third trimester to those collected on the day of the C-section, in addition to neonate meconium microbiota. The microbiota of the maternal fecal samples were significantly higher compared to neonate meconium samples (p < 0.001). However, alpha diversity did not differ significantly across the GDM and control groups (Figure 1).
Next, we estimated bacterial beta diversity using the principal coordinate analysis (PCoA) based on the Bray–Curtis distance. Bacterial communities differed between maternal feces and offspring meconium, which was statistically confirmed with PERMANOVA (p = 0.001) (Figure 2A). Similarly, bacterial communities differed significantly across maternal fecal samples collected during the third trimester and on the C-section day (p = 0.001), but not between GDM and control groups (Figure 2B,C, respectively). However, beta diversity in the meconium of newborns showed a significant difference (p = 0.027) between the GDM and the control groups (Figure 2D).
The above indicates that the maternal fecal microbiota present a higher alpha diversity compared to that of the neonate meconium (Figure 1), but neither were affected by GDM. Furthermore, the structure of maternal fecal bacterial communities (represented by the beta diversity) differed from that of the newborn meconium, while only the latter differed between GDM and control groups (Figure 2).

3.3. Relative Abundance of Maternal Fecal Microbiota

The most abundant taxa and differences between groups and rarefaction curves are presented in Supplementary File S3 and Supplementary Figure S1. The five most abundant phyla in maternal fecal microbiota were Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Cyanobacteria. Taxa that showed significant differences in the relative abundance (%) between GDM and control groups were restricted to fecal samples collected during the third trimester (p < 0.05) (Table 2).
In particular, Firmicutes’ relative abundance was increased in the GDM group compared to the control group (p = 0.032) in the third trimester. Likewise, at the family level, the relative abundances of Lachnospiraceae, Ruminococcaceae, Veillonellaceae, and Lachnospiraceae were significantly increased in the GDM group, while the opposite was observed of Peptostreptococcaceae. At the genus level, the GDM group showed a significant increase in Blautia, Roseburia, and Faecalibacterium and a significant decrease in Anaerococcus and Finegoldia.
Next, we compared the microbiota of fecal samples at the third trimester versus cesarean section within the control and GDM groups. Within the control group, only Veillonellaceae showed a significant increase in relative abundance in fecal samples taken during the cesarean section compared to third-trimester samples (Table 3). Conversely, within the GDM group, all taxa (except Firmicutes, Veillonellaceae, and Peptostreptococcaceae) showed significant differences in bacterial relative abundance of fecal samples taken during the third trimester and the cesarean section (Table 3).
Overall, the results indicate that the fecal microbiota from Mexican women with GDM during the third trimester present a different bacterial taxonomic profile than that of healthy women. This profile consists of an increase in Firmicutes, Lachnospiraceae, Ruminococcaceae, Blautia, Faecalibacterium, and Roseburia, and a decrease in Peptostreptococcaceae, Anaerococcus, and Finegoldia (Figure 3). The results also suggest that these changes are transient, as significant differences in the relative abundance of all taxa were no longer observed during the cesarean section (Table 2).

3.4. Relative Abundance of Newborn Meconium Microbiota

The most abundant taxa in the meconium and the differences between the GDM and control groups are shown in Table S1. The most abundant phyla (top five) in meconium were Proteobacteria, Firmicutes, Cyanobacteria, Actinobacteria, and Bacteroidetes. The bacterial relative abundance showed no significant differences between the GDM and control groups at the phylum and family levels. However, at the genus level, Faecalibacterium was significantly increased in the newborns of women with GDM compared to the newborns of healthy women (p = 0.005) (Figure 3).

3.5. Correlation Analysis of Bacterial Relative Abundance with Maternal BMI and Blood Glucose Levels

Spearman’s correlation analysis was performed to uncover potential associations between the relative abundance of taxa, maternal BMI, and/or blood glucose levels (Figure 3 and Supplementary Table S1).
The relative abundance of specific taxa correlated significantly with blood glucose levels, while none correlated with BMI. For instance, Firmicutes positively correlated with fasting and 2 h glucose levels (0.673, p = 0.007 and 0.600, p = 0.022, respectively). At the family level, Lachnospiraceae correlated positively with fasting, 1 h, and 2 h glucose levels (0.807, p < 0.001, 0.855, p < 0.001 and 0.692, p = 0.005, respectively) while Peptostreptococcaceae showed a negative correlation with fasting glucose levels (−0.548, p = 0.041). At the genus level, Blautia, Faecalibacterium, and Roseburia showed positive correlations with fasting glucose (0.759, p = 0.001, 0.803, p < 0.001 and 0.759, p = 0.001, respectively), 1 h (0.912, p < 0.001), 0.705, p = 0.004 and 0.719, p = 0.003, respectively) and 2 h glucose levels (0.692, p = 0.005, 0.705, p = 0.004 and 0.613, p = 0.018, respectively), whereas Anaerococcus and Finegoldia showed negative correlation with fasting glucose levels (−0.641, p = 0.013 and −0.597, p = 0.023, respectively). Finally, meconium positively correlated with Faecalibacterium and the mother’s BMI (0.537, p = 0.030) and fasting glucose level (0.599, p = 0.013). Altogether, these results suggest that changes in the maternal fecal microbiota could be associated with blood glucose levels, and these alterations (together with the BMI) could be associated with changes in the meconium microbiota in newborns of women with GDM.

4. Discussion

In this study, maternal fecal microbiota showed greater alpha diversity than that of newborn meconium, as well as differences in bacterial communities, as shown by the beta diversity. Previous studies have reported similar results in meconium samples obtained a few days (3 to 5) or within the first hours after birth [22,29]. He and co-workers [29] also explored the maternal origin of meconium microbiota, suggesting that it may originate from different sources (vaginal, fecal, amniotic fluid, and saliva), finding higher similarities with amniotic fluid. Maternal fecal samples during the third trimester and delivery showed significant differences in beta diversity, suggesting that bacterial communities change during late pregnancy, which could be due to hormonal changes in estradiol and progesterone, as these hormones have been reported to affect microbial composition [30,31].
No differences were found in alpha and beta diversity in maternal fecal samples between the control and GDM groups. However, significant differences in beta diversity were observed in the meconium of infants born to mothers with GDM. Other studies have shown differences in fecal microbiota composition in six-month-old infants born to mothers with GDM [32], suggesting that GDM could affect microbial composition several months after birth.
The altered taxa uncovered in the fecal microbiota in women with GDM during the third trimester, such as Firmicutes, Lachnospiraceae, Ruminococcaceae, Blautia, Roseburia, Faecalibacterium, Peptostreptococcaceae, Anaerococcus, and Finegoldia, have previously been reported to be associated with other types of diabetes and/or metabolic disorders. In 2016, Moreno-Indias and colleagues [33] reported that IR in obese individuals was associated with an increase in Firmicutes. Another study reported that Lachnospiraceae alters glucose metabolism in people with type-1 and type-2 diabetes mellitus (T2DM) [34]. Furthermore, Rumicococcaceae was detected in fecal samples from pregnant women at week 12.5, who later developed GDM, and relative abundance correlated with glucose levels and IR [20]. Other studies have reported an increase in Blautia and Roseburia in patients with T2DM and GDM, respectively [35,36]. Our results show an increase in the fecal and meconium samples of Faecalibacterium from women with GDM and their offspring, respectively. While this is partly consistent with a finding of some Faecalibacterium OTUs in women with GDM [18], it is at odds with the negative association previously reported between Faecalibacterium and T2DM [35]. Other studies have reported that a decrease in Peptostreptococcaceae, Anaerococcus, and Finegoldia is negatively associated with T2DM [37,38]. These reports suggest that GDM alters the gut (fecal) microbiota composition associated with IR and glucose metabolism (also present in T1 and T2DM). The taxa reported in the present study (except Ruminococcaceae) showed correlations with blood glucose levels determined by the OGTT performed between the second and third trimester of pregnancy.
Interestingly, fecal microbiota showed no significant differences between GDM and control groups at the time of delivery. Once GDM was diagnosed, patients received treatment to control glucose levels and avoid complications. These treatments included dietary adjustments and exercise; five patients (62.5%) used metformin. Metformin has been reported to alter the gut microbiota in treatment-naïve people for T2DM, resulting in improved glucose tolerance [39]. Therefore, after treating GDM, glucose levels would be expected to be within normal values, which could help restore the fecal microbiota at the time of delivery.
The increase in Faecalibacterium in the meconium of infants born to mothers with GDM is intriguing. Faecalibacterium has been reported to be beneficial for health, as decreased levels are known to have been associated with inflammatory bowel disease, colorectal cancer, dermatitis, and depression, among others [40]. Faecalibacterium produces butyrate as the end product of glucose fermentation [41], a short-chain fatty acid important for intestinal health and energy homeostasis [42]. Thus, an increase in Faecalibacterium in the meconium of babies born to women with GDM could have a protective role. However, increased levels of Faecalibacterium in fecal samples of 1- to 2-year-old infants have been reported to be associated with problems in cognitive development (e.g., motor skills, visual reception, and language, measured using the Mullen Early Learning Scale). Furthermore, increased levels of Faecalibacterium are also used as a diagnostic criterion of the autism spectrum disorder (ASD) [43] and/or associated with ASD diagnosed in 4 to 6-year-old infants [44]. Offspring of mothers with GDM are at high risk of developing cognitive problems such as ASD, with a prevalence mean reported of 16.3%, as well as motor and memory deficits that can persist until adulthood [45,46,47]. These reports suggest that GDM may alter microbiota seeding in the newborn, and these changes could be related to cognitive development. However, more studies are needed to understand the association between microbiota seeding and neurological development in children of mothers with GDM.

5. Conclusions

This pilot study indicates that GDM in Mexican women is associated with alterations in the maternal fecal microbiota during the third trimester of gestation; these alterations are associated with blood glucose levels. Furthermore, we found that GDM alters the microbiota seeding in newborns, which may have implications for postnatal health. Further studies are needed to confirm the reported findings using a larger sample, including the analysis of fecal microbiota at an earlier stage of pregnancy, to know whether some of the taxa altered in GDM serve as early markers even before hyperglycemia is detected in the mother. Moreover, the status of children born to mothers with GDM with an increased abundance of Faecalibacterium should be followed, to evaluate a possible association between Faecalibacterium and the general health status of the infants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diabetology5050034/s1, Supplementary File S1: Metadata; Supplementary File S2: OTUs; Supplementary File S3: Taxa; Supplementary Figure S1: Rarefaction curves; Supplementary Table S1: Relative abundance and Spearman’s correlation.

Author Contributions

Conceptualization, A.G.-G., B.G.-G. and D.L.; methodology, B.G.-G., E.P.-M. and D.L.; formal analysis, B.G.-G., A.G.-G., A.G. and D.L.; investigation, B.G.-G., A.G.-G. and D.L.; resources, A.G.-G.; writing—original draft preparation, A.G.-G., B.G.-G. and D.L.; writing—review and editing, B.G.-G., A.G.-G., G.L., T.G.-G., A.G. and E.P.-M.; project administration and funding acquisition, A.G.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the UC MEXUS-CONACYT Collaborative Grant CN-19-39.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee for Research at CIAD (CEI-CIAD, approval number CE/002/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Acknowledgments

We thank Julissa Enciso-Ibarra, Alejandra Díaz-Sánchez, Rubí Hernández-Cornejo, Lisset Hernández-Cosio, and Daniel Fregoso-Rueda for assistance in the technical and logistical aspects of the project; we are also grateful to the Board of Gynecology and Obstetrics of Mazatlán, especially Virgilio Ángeles-Zatarain and Victor Arrenquín-Romero, for their help in patient recruitment and sample collection. We also thank the National Council for Science and Technology (CONACYT) and the University of California (UC MEXUS) for financial support; special thanks to Louise Laurent and Anelizze Castro-Martínez from the UC San Diego for assistance in the sequencing process.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. A box plot of the Chao1 (top) and Shannon (bottom) indexes from maternal feces and newborn meconium; red and green boxes represent the control and GDM groups, respectively.
Figure 1. A box plot of the Chao1 (top) and Shannon (bottom) indexes from maternal feces and newborn meconium; red and green boxes represent the control and GDM groups, respectively.
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Figure 2. The beta diversity of the microbiota of fecal and meconium samples estimated using principal coordinate analysis. (PCoA) based on Bray–Curtis distance. Significant p values above each graph refer to the differences across groups, circled using PEMANOVA (p < 0.05). (A) Newborn meconium (n = 16, dots enclosed with an orange line) and maternal fecal samples from the third trimester and C-section (n = 30, squares enclosed with a blue line) from the GDM and control groups (red and blue symbols, respectively); (B) maternal fecal samples from the third trimester (n = 14, blue dots enclosed with a green line) and C-section (n = 16, red squares enclosed with a red line); (C) maternal fecal samples (third trimester and C-section) with (n = 16, blue dots enclosed with a green line) or without (n = 14, red squares enclosed with a red line) GDM; (D) newborn meconium samples from mothers with (n = 8, blue dots enclosed with a green line) or without (n = 8, red squares enclosed with a red line) GDM.
Figure 2. The beta diversity of the microbiota of fecal and meconium samples estimated using principal coordinate analysis. (PCoA) based on Bray–Curtis distance. Significant p values above each graph refer to the differences across groups, circled using PEMANOVA (p < 0.05). (A) Newborn meconium (n = 16, dots enclosed with an orange line) and maternal fecal samples from the third trimester and C-section (n = 30, squares enclosed with a blue line) from the GDM and control groups (red and blue symbols, respectively); (B) maternal fecal samples from the third trimester (n = 14, blue dots enclosed with a green line) and C-section (n = 16, red squares enclosed with a red line); (C) maternal fecal samples (third trimester and C-section) with (n = 16, blue dots enclosed with a green line) or without (n = 14, red squares enclosed with a red line) GDM; (D) newborn meconium samples from mothers with (n = 8, blue dots enclosed with a green line) or without (n = 8, red squares enclosed with a red line) GDM.
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Figure 3. Fecal microbiota profile in Mexican women with GDM during the third trimester of gestation and the meconium microbiota in their newborns. Image created using BioRender.com (accessed on 10 March 2024).
Figure 3. Fecal microbiota profile in Mexican women with GDM during the third trimester of gestation and the meconium microbiota in their newborns. Image created using BioRender.com (accessed on 10 March 2024).
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Table 1. Characteristics of the participating women and their offspring. p-values with statistical significance are shown in bold.
Table 1. Characteristics of the participating women and their offspring. p-values with statistical significance are shown in bold.
GDM
(n = 8)		Control
(n = 9)		p *Data
Collection Time
Mother
Age34.37 ± 4.4332.55 ± 3.280.3481st trimester
Weight at first appointment (kg)89.50 ± 22.9369.88 ± 12.010.0401st trimester
BMI at first appointment33.82 ± 5.2926.85 ± 5.250.0161st trimester
Height (cm)1.61 ± 0.081.61 ± 0.060.9851st trimester
Family history of diabetes7 (87.50%)5 (55.55%)0.2941st trimester
Fasting glucose (mg/dL)105.91 ± 9.5681.07 ± 6.79<0.0012nd to 3rd
trimester
One-hour glucose 75 g (mg/dL)211.65 ± 49.33129.73 ± 31.88<0.0012nd to 3rd
trimester
Two-hour glucose 75 g (mg/dL)194.71 ± 62.03109.80 ± 22.080.0012nd to 3rd
trimester
Positive SARS-CoV2 IgG at third trimester **4 (50.00%)4 (57.14%)1.0003rd trimester
Gestational week at delivery **38.00 ± 0.7538.50 ± 1.190.3343rd trimester
Neonate
% Females **3 (37.50%)5 (75.00%)0.315Delivery
Weight (Kg) **3.51 ± 0.543.42 ± 0.200.645Delivery
Height (cm) **50.25 ± 2.1849.50 ± 2.000.486Delivery
Abdominal circumference (cm) ***31.90 ± 2.1233.10 ± 2.030.397Delivery
Macrosomia ***3 (37.50%)1 (12.50%)0.569Delivery
Continuous variables were compared using the t-test (for normally distributed data) and the Mann–Whitney U test (for data with non-normal distribution). Categorical variables were compared using the chi-square and Fisher’s exact tests. Data are shown as the mean of bacterial relative abundance in percentage ± standard deviation. * p values ≤ 0.05 were statistical different. ** n = 8 for and the control group. *** n = 7 for the control group. p-values with statistical significance are shown in bold.
Table 2. Bacterial relative abundance (%) of the most abundant taxa in maternal fecal samples collected during the third trimester and during the cesarean section.
Table 2. Bacterial relative abundance (%) of the most abundant taxa in maternal fecal samples collected during the third trimester and during the cesarean section.
Third TrimesterCesarean Section
TaxaGDMControlpGDMControlp
Firmicutes69.43 ± 10.0656.11 ± 10.36<0.0559.34 ± 10.9756.76 ± 14.060.689
Lachnospiraceae36.08 ± 9.5519.24 ± 9.99<0.0515.54 ± 10.2120.13 ± 18.950.556
Ruminococcaceae19.01 ± 8.268.93 ± 7.48<0.058.36 ± 5.366.00 ± 5.530.401
Veillonellaceae1.55 ± 1.300.37 ± 0.26<0.052.44 ± 1.811.08 ± 0.670.067
Peptostreptococcaceae0.29 ± 0.363.50 ± 2.72<0.051.03 ± 0.981.04 ± 1.070.987
Blautia7.97 ± 3.832.93 ± 2.97<0.052.98 ± 2.443.12 ± 3.800.505
Faecalibacterium7.48 ± 3.422.86 ± 2.13<0.053.32 ± 1.983.03 ± 2.681.000
Roseburia6.62 ± 4.711.69 ± 1.34<0.051.82 ± 1.843.16 ± 3.780.380
Anaerococcus0.07 ± 0.130.85 ± 0.93<0.053.13 ± 1.653.52 ± 3.070.760
Finegoldia0.03 ± 0.060.69 ± 1.07<0.053.47 ± 1.402.21 ± 1.950.162
Continuous variables were compared using the t-test (for normally distributed data) and the Mann–Whitney U test (for data with a non-normal distribution). Data are shown as the mean of bacterial relative abundance in percentage ± standard deviation. p-values with statistical significance are shown in bold.
Table 3. Bacterial relative abundance (%) in samples taken during the third trimester and cesarean section within control and GDM groups.
Table 3. Bacterial relative abundance (%) in samples taken during the third trimester and cesarean section within control and GDM groups.
ControlGDM
TaxaThird
Trimester		Cesarean
Section		pThird
Trimester		Cesarean Sectionp
Firmicutes56.11 ± 10.3656.76 ± 14.060.92569.43 ± 10.0659.34 ± 10.970.076
Lachnospiraceae19.24 ± 9.9920.13 ± 18.950.91936.08 ± 9.5515.54 ± 10.21<0.05
Ruminococcaceae8.93 ± 7.486.00 ± 5.530.41419.01 ± 8.268.36 ± 5.36<0.05
Veillonellaceae0.37 ± 0.261.08 ± 0.67<0.051.55 ± 1.302.44 ± 1.810.278
Peptostreptococcaceae3.50 ± 2.721.04 ± 1.070.1080.29 ± 0.361.03 ± 0.980.161
Blautia2.93 ± 2.973.12 ± 3.800.8527.97 ± 3.832.98 ± 2.44<0.05
Faecalibacterium2.86 ± 2.133.03 ± 2.680.8997.48 ± 3.423.32 ± 1.98<0.05
Roseburia1.69 ± 1.343.16 ± 3.780.3826.62 ± 4.711.82 ± 1.84<0.05
Anaerococcus0.85 ± 0.933.52 ± 3.070.0650.07 ± 0.133.13 ± 1.65<0.05
Finegoldia0.69 ± 1.072.21 ± 1.950.1130.03 ± 0.063.47 ± 1.40<0.05
Continuous variables were compared using the t-test (for normally distributed data) and the Mann–Whitney U test (for data with non-normal distribution). Data shown as mean of bacterial relative abundance in percentage ± standard deviation. p-values with statistical significance are shown in bold.
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Lizárraga, D.; García-Gasca, A.; García-Gasca, T.; Lund, G.; Guerrero, A.; Peraza-Manjarrez, E.; Gómez-Gil, B. A Pilot Study on the Fecal Microbiota in Mexican Women with Gestational Diabetes Mellitus and Their Newborns. Diabetology 2024, 5, 464-475. https://doi.org/10.3390/diabetology5050034

AMA Style

Lizárraga D, García-Gasca A, García-Gasca T, Lund G, Guerrero A, Peraza-Manjarrez E, Gómez-Gil B. A Pilot Study on the Fecal Microbiota in Mexican Women with Gestational Diabetes Mellitus and Their Newborns. Diabetology. 2024; 5(5):464-475. https://doi.org/10.3390/diabetology5050034

Chicago/Turabian Style

Lizárraga, Dennise, Alejandra García-Gasca, Teresa García-Gasca, Gertrud Lund, Abraham Guerrero, Efrén Peraza-Manjarrez, and Bruno Gómez-Gil. 2024. "A Pilot Study on the Fecal Microbiota in Mexican Women with Gestational Diabetes Mellitus and Their Newborns" Diabetology 5, no. 5: 464-475. https://doi.org/10.3390/diabetology5050034

APA Style

Lizárraga, D., García-Gasca, A., García-Gasca, T., Lund, G., Guerrero, A., Peraza-Manjarrez, E., & Gómez-Gil, B. (2024). A Pilot Study on the Fecal Microbiota in Mexican Women with Gestational Diabetes Mellitus and Their Newborns. Diabetology, 5(5), 464-475. https://doi.org/10.3390/diabetology5050034

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