Composition of the intestinal microbiota during mid- to late pregnancy in women with gestational diabetes mellitus

Composition of the intestinal microbiota during mid- to late pregnancy in women with gestational diabetes mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Composition of the intestinal microbiota during mid- to late pregnancy in women with gestational diabetes mellitus Yaxin Wang, Nana Liu, Yin Sun, Liangkun Ma, Xuanjin Yang, Suhan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3595611/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Determine the composition and characteristics of intestinal microbiota in GDM patients during mid- and late pregnancy, and identify possible differences in bacterial composition based on comparisons with a normal healthy population. Methods The participants were recruited at 24 to 28 weeks of gestation, and stool samples were collected twice in the second and third trimesters, and were examined based on next-generation sequencing. Test results and pregnancy outcomes were recorded, and baseline conditions and intestinal microecological composition were compared between the two groups. Results Compared with the healthy control group, the composition ratio of Bacteroides and Megamonas was significantly increased, whereas that of Bifidobacterium , Dialister , and Escherichia coli was significantly reduced. At the species level, the combination of Eubacterium hallii , Butyrate-producing Bacterium GM2.1, and Clostridium disporicum enabled an effective discrimination between the two groups (AUC = 93.64%, P < 0.05, 95% CI: 89.83–97.45%). Conclusion Compared with the healthy control group, we detected significant differences in the composition ratio of gut microbiota during late pregnancy in the gestational diabetes group, and also observed a reduction in bacterial diversity and an increase in microbial disorder. Biological sciences/Microbiology Health sciences/Endocrinology Health sciences/Pathogenesis Pregnancy GDM Intestinal microbiota Mid- to late pregnancy Figures Figure 1 Figure 2 Figure 3 Figure 4 Brief Synopsis We found several specific bacteria in the GDM population that could potentially be used for disease identification in the future. （The trial was registered at ClinicalTrials.gov, NCT: 03916354 . URL: https://clinicaltrials.gov/ct2/results?cond=&term=03916354&cntry=&state=&city=&dist= ） Introduction Gestational diabetes mellitus (GDM) is among the most common complications encountered during pregnancy, defined as the initial diagnosis of hyperglycemia during pregnancy [ 1 ]. GDM is closely associated with maternal and neonatal adverse outcomes [ 2 ] and obesity [ 3 ]. For example, this condition substantially raises the risk of preeclampsia, delivery by cesarean section, and infant morbidities such as diabetic fetopathy [ 4 , 5 ]. It has previously been established that advanced maternal age, obesity [ 6 , 7 ], and insulin resistance [ 8 ] contribute to an increased risk of GDM. In addition, as a typical metabolic disease, GDM is also believed to be associated with disturbances of the gut microbiota. In recent years, studies on the intestinal microbiota have revealed the important role of intestinal microecology. The gut microbiota has been established to constitute the second largest gene pool in humans and has been demonstrated to be closely associated with the occurrence and development of a range of diseases [ 16 – 18 ]. Moreover, it has been found that the composition of this microbiome changes during pregnancy [ 19 ]. It is speculated that the intestinal microbiota and their metabolic activities (intestinal dysbiosis) may play essential roles in body weight control, energy homeostasis, fermentation, and the absorption of non-digestible carbohydrate, and well as the development of insulin resistance. Accordingly, it is probable that the composition of the gut microbiota is also linked to the pathogenesis of several metabolic disorders, including obesity, diabetes mellitus, and GDM [ 9 , 10 ], which would be consistent with the fact that microbiome composition in other parts of the body is also linked to overall health [ 11 – 14 ]. In addition, probiotic interventions have been demonstrated to improve the insulin resistance of GDM subjects [ 15 ]. Collectively, the findings these studies indicate that the occurrence of GDM is closely associated with changes in the gut microbiota. The mechanisms associated with gut microecology and the occurrence of disease have yet to be sufficiently elucidated, and thus it would be highly desirable to established whether GDM is linked to a characteristic composition of the gut microbiota. To this end, we used 16S ribosomal RNA (rRNA) gene sequencing metagenomics to compare the composition of the gut microbiome in GDM subjects with that in healthy gestational controls during mid- and late pregnancy. On the basis of the results obtained, we discuss the species composition characteristics of intestinal microorganisms in the disease state, and in an attempt to develop markers for the identification of this state, we performed random forest analysis. Materials and Methods Subjects In this study, we recruited 100 pregnant women who underwent oral glucose tolerance testing at 24 to 28 weeks gestation in the Peking Union Medical College Hospital from 7/5/2019 to 31/12/2021, among whom there were 50 pregnant women with GDM and 50 healthy pregnant women. GDM was diagnosed in accordance with the guidelines of the International Association of the Diabetes and Pregnancy Study Groups, in which subjects were administered 75 g oral OGTT. Pregnant women who exhibited blood glucose levels higher than at least one of the assessed cut-off values (fasting-5.1 mmol/L, 1 h-10.0 mmol/L, or 2 h-8.5 mmol/L) were diagnosed as having GDM. In addition, we collected information relating to baseline characteristics, as well as pregnancy and neonatal outcomes. Finally, 49 pregnant women with GDM and 42 healthy pregnant women were followed up to delivery (Fig. 1 ). The criteria for inclusion in the GDM group were meeting the GDM diagnostic criteria, a natural pregnancy, and monocyesis. The exclusion criteria were as follows: hypertensive disorders during pregnancy, type 2 diabetes mellitus complicated with pregnancy, severe dyslipidemia, etc.; serious complications during pregnancy; use of antibiotics 1 month prior to pregnancy and during pregnancy; severe chronic diseases prior to pregnancy; and refusal to sign an informed consent form. This study was registered as Clinical Trail NCT03916354 and reviewed by the Ethics Committee of Peking Union Medical College Hospital (HS-1875). Collection of stool samples Given that the intestinal microbiome would remain relatively constant for a certain period of time, pregnant women were requested to collect feces at home at two different time points during pregnancy. Subjects were required to collect at least 250 mg of feces into a sterile test tube (PSP® Spin Stool DNA Plus Kit) containing preservative solution in the second trimester (24–28 weeks) and third trimester (36 weeks prior to delivery). The samples were transferred to the laboratory wherein they were stored at -80°C for subsequent DNA extraction. Microbial genome sequencing Each sample was identified according to a barcode sequence and PCR amplification primer sequence. Having removed the barcode and primer sequences, the FLASH program [ 20 ] was used to assemble reads to obtain raw tags. The raw tags thus obtained were filtered according to the standard of QIIME [ 21 ]. Closed-reference clustering of quality-controlled sequences into operational taxonomic units (OTUs) was performed at the 97% identity threshold against the Greengenes 13_5 97% OTU reference sequences using VSEARCH [ 22 ] (via q2-vsearch). OTU annotation analysis was performed using the Mothur [ 23 ] and SSUrRNA databases of SILVA132 [ 24 ] (threshold 0.8 ~ 1). Shannon and Simpson index values were calculated using QIIME (version 1.9.1), and the R program version 3.5.1 was used to analyze the differences between groups regarding alpha diversity indices. UniFrac distances were determined using QIIME, and principal coordinates analysis (PCoA) was performed using the WGCNA, stats, and ggplot2 packages in R. The functions of annotated OTUs were predicted using PICRUSt [ 25 ]. Taxonomic and functional differences were analyzed using STAMP [ 26 ], and correlations between genus abundance and clinical parameters were calculated based on Spearman correlation analysis using the WGCNA corAndPvalue function. Statistical analysis Statistical analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA). The Mann–Whitney U-test was performed to compare the alpha diversity/richness and differences in the relative abundance of the main (higher than 1% per group and per patient) bacterial phyla and genera between the groups. P-values < 0.01 were considered statistically significant to control multiple comparisons in the relative abundance. A standard P-value < 0.05 was considered as significant for all other analyses. PCoA, principal component (PCA), and Metastat Complex heat map analyses were performed using R (Version 2.15.3), beta diversity analysis was performed using Qiime (Version 1.9.1), and linear discriminant analysis was performed using Lefse software (Version 1.0). Results Clinical Index Among the initial 50 patients in each of the GDM and healthy controls groups, 49 and 42, respectively, were followed up until delivery. The mean ages of GDM and control group subjects were 33 (32–36.5) and 32 (29–34) years old, respectively, and the corresponding mean gestational times were 39 (38–39) and 39(38–40) weeks. Baseline data of the two groups (Table 1 ) were statistically different with respect to age (P = 0.018) and gestational age at delivery (P = 0.006), respectively. There were no statistically significant differences in terms of gravidity, parity, pre-pregnancy BMI, height, weight, or neonatal weight. Table 1 A comparison of baseline data between GDM and normal controls Characteristic GDM(n = 49) Health women(n = 42) P value Age 33 (32 ~ 36.5) 32 (29 ~ 34) 0.018* Gravidity 2 (1 ~ 2.5) 2 (1 ~ 2) 0.646 Parity 1 (1 ~ 2) 1 (1 ~ 2) 0.438 Pre-pregnancy BMI 22.46 (19.78 ~ 24.28) 21.05 (19.65 ~ 22.68) 0.112 Height (cm) 163.00 (160.00 ~ 167.00) 163 (162 ~ 166.5) 0.592 Weight (kg) 58 (53.25 ~ 64.5) 56.5 (52.5 ~ 61.25) 0.217 Gestational age 39 (38 ~ 39) 39 (38 ~ 40) 0.006* Baby weight 3230 (3025 ~ 3530) 3395 (3025 ~ 3632.5) 0.471 GDM: Gestational diabetes mellitus, BMI: Body mass index *Statistically significant at P ＜0.05 Alpha and beta diversity A Venn chart displaying the numbers of intestinal flora detected in the two assessed groups (Fig. 2 .a) revealed 4342 and 4778 distinct OTUs in the GDM and healthy control groups, respectively, among which, 3196 types were found in the intestinal flora of both groups. These numbers thus indicate a reduction in microbial diversity in the GDM group compared with that in the control group. Further analysis of the microbiota beta diversity of the two groups (Fig. 2 .b) indicated that whereas the distribution of microbiota in the GDM population tended to be aggregated, that of microbiota in control group individuals was relatively dispersed. The horizontal and vertical coordinates of a Bray–Curtis PCoA plot explained 12.78% and 6.89% of the variation between the two groups, respectively. Compared with the control group, there was a significant reduction in the diversity of gut microorganisms in the GDM group (P < 0.001). Shannon, Simpson, Chao1, and ACE indices were used to assess the α diversity of the intestinal flora (Fig. 2 .c.d.e.f), among which, we recorded significant increases in values of the Shannon (P = 1.08E-11) and Simpson (P = 1.09E-12) indices, and significant reductions in Chao1 (P = 0.02264) and ACE ( P = 0.004667 ) index values in the GDM group. These findings thus indicate that disease status may be associated with large variations in the gut microbiota of different pregnant women, with a general reduction in microbial abundance. However, the interactions between different bacterial species may be more complex. Relative abundance of gut microbes In order to better distinguish the composition of intestinal microbes in different populations, we only focused on bacteria with a relative abundance greater than 1% (Attached Table 1 ). The composition of intestinal microbiota was found to differ significantly different between the GDM and control populations, and here we describe composition characteristics of the gut microbiota in GDM subjects at the phylum, genus, and species levels. At the phylum level The proportion of Firmicutes in the GDM group (58.98%) was significantly higher than that in the healthy control group (48.67%) (P < 0.01), whereas in contrast the proportion of Actinobacteria in GDM group (4.90%) was significantly lower than that in control group (14.66%) (P < 0.01), as was the proportion of Proteobacteria (3.38% vs. 5.37%, P = 0.013). Furthermore, we established that the ratio of Firmicutes to Bacteroidetes in the GDM group (0.5898/0.3169 = 1.86) was higher than that in the control group (0.4867/0.3040 = 1.60). At the genus level. Among the different bacterial genera, we detected a significant increase in the proportion of Lactobacillus in the GDM group (1.01%) compared with that in the control group (0.31%) (P = 0.022). Similar significant increases were detected for the proportions of Bacteroides (21.43% vs. 16.54%, P = 0.004) and Megamonas (2.06% vs. 0.29%, P = 0.006). In contrast, compared with those in control group, we detected significant reductions in the proportions of Bifidobacterium in (3.26% vs. 8.42%, Figure. 3c, P < 0.001) and Dialister (0.88% vs. 2.25%, Figure. 3d, P < 0.001) in the GDM group. At the genus level, Bacteroides accounted for the largest proportion of intestinal flora in both groups, but there were significant differences in the proportions of the different bacteria (Figure. 3a). At the species level In terms of individual species, the proportion of Bacteroides stercoris in the GDM group (1.62%) was found to be significantly higher than that in the control group (1.09%) (P = 0.001), as was the proportion of Bacteroides coprocola (1.50% vs. 0.67%, Fig. 3 f, P < 0.001). Contrastingly, compared with the control group, significant reductions were observed for the proportions of Bifidobacterium pseudocatenulatum (2.46% vs. 6.56%, Fig. 3 e, P = 0.001) and Escherichia coli (0.63% vs. 1.58%, P = 0.004) in the GDM group. There were significant differences in the proportion of intestinal flora between the two groups at the species level (Fig. 3 b). Discussion The diverse roles of the microbiome with respect to health-related physiological processes and the development of different diseases have yet to be sufficiently elucidated. Some scholars have proposed that the physiological adaptation of the microbial pattern, present in pregnancy, is altered in women with metabolic diseases, such as GDM, as a consequence increased inflammation, IR, and weight gain in this population [ 27 , 28 ]. However pregnant women with GDM typically maintain a stable gut microbiome for a period of time, as GDM status may interfere with the flexibility of the maternal gut microbiome, thus limiting the ability of GDM patients to respond to dietary interventions [ 29 ]. In the present study, we found that compared with healthy controls, the gut microbiota in GDM group subjects was characterized by reductions in species OTUs and beta diversity. Moreover, the distribution of species OTUs in GDM group subjects was observed to be more aggregated than that in control group individuals, with this difference in distribution found to be significantly different. Such aggregated distribution is presumed to be a characteristic manifestation of the disease state, although the specific discriminative criteria need to be further defined. In addition, we also detected significant changes in the α diversity of the GDM group microbiota compared with that in the healthy controls. Specifically, we recorded increases in Shannon and Simpson index values, and reduction in those of the Chao1 and ACE indices. These findings accordingly indicate that disease status can lead to large variations and a reduced abundance in gut microbiota among pregnant women, which can modify the interactions between different bacterial species, thereby complicating the diagnosis and treatment of GDM. We also detected significant differences with respect to the species composition ratios in GDM group and healthy control group subjects. At the phylum level, the proportion of Firmicutes in the GDM group was significantly higher than that detected in the control group, whereas in contrast, we detected significant reductions in the proportion of Actinobacteria and Proteobacteria in the GDM group. Similar differential patterns were detected at the genus level, with the proportions of Lactobacillus , Bacteroides , and Megamonas being significantly higher in the GDM group than in the control group, and the proportions of Bifidobacterium and Dialister being significantly lower Furthermore, at the species level, the proportions of Bacteroides stercoris and Bacteroides coprocola were found be significantly higher in the GDM group than in the controls. whereas the proportions of Bifidobacterium pseudocatenulatum and Escherichia coli were significantly lower (Attached Table 1 ). However, there tends to be a lack of consensus regarding changes in gut microecology during pregnancy. Whereas the findings of some studies have indicated that the gut microbiome undergoes distinct changes during pregnancy, other studies have found little evidence in this respect [ 27 , 30 ]. For example, in a study of gut microbiota in non-pregnant women, Fugmann et al. [ 31 ] found that the proportion of Firmicutes in women with a history of GDM was smaller than that in women without GDM. Wang et al. [ 32 ] similarly detected a lower Firmicutes composition in the oral microbiome of GDM women, although did not identify similar difference in the gut microbiome. In contrast, Jost et al. [ 33 ], who followed up seven healthy pregnant women from the third trimester to the postpartum period, found that the maternal microbiota was dominated by Firmicutes . In addition, some scholars believe that an imbalance in the ratio between Firmicutes and Bacteroidetes may represent a manifestation of biological disorder [ 34 – 36 ], which is consistent with the findings of the present study, in which we found that the Firmicutes / Bacteroidetes ratio in GDM group subjects (0.5898/0.3169 = 1.86) was higher than that in the controls (0.4867/0.3040 = 1.60). However, further clinical trials are needed to determine appropriate threshold values for difference in specific proportions. By displaying our data in the form of box plot, we have more intuitively demonstrated the common bacteria characterized by significant differences in composition ratios between the GDM and healthy control group subjects, such as Bifidobacterium (Fig. 3 c) and Dialister ( Fig. 3 d) at the genus level and Bifidobacterium pseudocatenulatum (Fig. 3 e) and Bacteroides coprocola (Fig. 3 f) at the species level. Given that the proportions of gut microorganisms can vary between groups, we considered this as a means whereby disease states could be distinguished. In this regard, we used linear discriminant analysis (LEfSe, LDA > 2.0) to identify 14 differential OTUs that could be applied in discriminating between the two groups (Fig. 4 a), with those OTUs enriched in the GDM group mainly belonging to Lachnospiraceae and Bacteroidetes , whereas depleted OTUs were mainly from the Bifidobacterium and Actinobacteria . At present, machine learning, such as random forest analysis, is increasingly being applied in the field of medical diagnosis [ 37 , 38 ], and using this approach, researchers have established that specific combinations of different gut microbes can be used to effectively distinguish GDM individuals from healthy controls. For example, in a study published in 2017, which sought to improve the predictive power of the model, researchers attempted to provide taxonomic and functional information for unknown or unanalyzed species to enhance discrimination, and accordingly succeeded in increasing the area under the receiver operating characteristic (ROC) curve (AUC) from 0.80 (95%CI = 0.73 0.86) to 0.91 (95%CI = 0.87 0.96) [ 39 ]. However, the best combination of the model still required the inclusion 20 gut microbes. In the present study, we sought to combine different numbers of bacteria with inter-group differences at the species level and generate respective ROC curves (Fig. 4 b). Contrary to our expectations, we found that a peak AUC value could be obtained using combination of considerably fewer bacteria ( Eubacterium hallii , Butyrate-producing bacterium GM2.1, and Clostridium disporicum ) (Fig. 4 c). This combined marker panel could distinguish GDM individuals from healthy controls, with an AUC of 93.64% (95%CI: 89.83–97.45%, P < 0.001, Fig. 4 d). This finding will facilitate the identification of pregnant women with GDM, contribute to disease management and treatment, and even provide a detection basis and therapeutic assistance for assessment and improvement of the intestinal status of the postpartum GDM population and newborns. Moreover, we anticipate that it will provide a new model for the diagnosis and treatment of gestational diabetes mellitus, which warrants further evaluation. The composition ratio of Eubacterium hallii , Clostridium disporicum , and Butyrate-producing bacterium GM2.1 in the GDM group was 0.9%, 0.1%, and 0.03%, respectively. Although the composition of these bacteria is relatively small, they were nevertheless enriched in the GDM group. As a reference, these bacteria have been linked to possible disease mechanisms identified in previous studies. For example, the lactic acid or butyrate produced by bacteria can regulate intestinal permeability and induce intestinal inflammation, thereby leading to the occurrence of diabetes [ 40 , 41 ]. Clostridium disporicum and other bacteria are characterized as producers of the cell wall component lipopolysaccharide (LPS), and by increasing intestinal permeability, dysregulation of the intestinal flora has been shown promote increases in levels of LPS entering the systemic circulation, thereby leading to inflammation and metabolic dysfunction [ 42 ]. In addition, the findings of studies on intestinal microbial function have indicated that LPS biosynthesis and export are involved in the regulation of blood glucose levels [ 39 ]. This will accordingly be a focus of future research to further investigate the mechanisms whereby intestinal microbes influence the occurrence and development of disease, thereby enabling the development of effective interventions. Among the limitations of this study was the fact that stool samples were not collected from the assessed populations during the early stages of pregnancy, and consequently, although AUC values could be used to explain the difference between different populations, we were unable to identify candidate predictor bacteria during early pregnancy. In addition, all the subjects assessed in this study were recruited from a single institute, and thus prior to commencing further clinical studies, additional large-scale multicenter studies should be conducted to verify the findings reported herein. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution All authors participated in the interpretation of study results, and in the drafting, critical revision, and approval of the final version of the manuscript. Yaxin Wang and Yin Sun wrote the main manuscript. Yaxin Wang and Nana Liu analyzed the data and prepared figures 1-4. Yin Sun and Liangkun Ma conducted the overall design of the study. Xuanjin Yang and Suhan Zhang collected the data and assisted the research. Funding This study was funded by The National Key Research and Development Program of China（Grant number：2022YFC2703304); Medical and Health Technology Innovation Project of Chinese Academy of Medical Sciences（Grant number. 2020-I2M-2-009、2021-I2M-1-023) ; Recommendations for weight gain in women with gestational diabetes mellitus (Grant number. 20191901); Clinical and Translational Medicine Research Fund of the Central Public welfare Research Institutes of the Chinese Academy of Medical Sciences (Grant number. 2019XK320007) Ethical Statement and consent to participate This study was registered as Clinical Trail NCT03916354 and reviewed by the Ethics Committee of Peking Union Medical College Hospital (HS-1875). All subjects signed informed consent and all experimental protocols were approved by a named institutional and licensing committee. All the steps/ methods were performed in accordance with the relevant guidelines and regulations. Consent to publish All subjects signed informed consent for the relevant study and for the publication of the final data. Acknowledgement We would like to thank Beijing Norhe Zhiyuan Technology Co., LTD for its contribution to the detection of intestinal flora samples and the interpretation of results. Availability of Data and Material The data that support the findings of this study are available from the corresponding author upon reasonable request. References World Health Organization, Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy (World Health Organization, Geneva, 2013) Metzger BE, Coustan DR, Trimble ER. Hyperglycemia and adverse pregnancy outcomes. Clin Chem. 2019;65:937–938. O. Verier-Mine, Outcomes in women with a history of gestational diabetes. Screening and prevention of type 2 diabetes. Lit. Rev. Diabetes Metab. 36, 595–616 (2010) Ostlund I, Haglund B, Hanson U. Gestational diabetes and preeclampsia. Eur J Obstet Gynecol Reprod Biol. 2004;113:12–16. Billionnet C, Mitanchez D, Weill A, Nizard J, Alla F, Hartemann A, Jacqueminet S. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia. 2017;60:636–644. Lao TT, Ho LF, Chan BC, Leung WC. Maternal age and prevalence of gestational diabetes mellitus. Diabetes Care. 2006;29:948–949. Moore Simas TA, Waring ME, Callaghan K, Leung K, Ward Harvey M, Buabbud A, Chasan-Taber L. Weight gain in early pregnancy and risk of gestational diabetes mellitus among Latinas. Diabetes Metab. 2019;45:26–31. Kampmann U, Madsen LR, Skajaa GO, Iversen DS, Moeller N, Ovesen P. Gestational diabetes: a clinical update. World J Diabetes. 2015;6:1065–1072. D. Zhang, Y. Huang, D. Ye, Intestinal dysbiosis: an emerging cause of pregnancy complications? Med. Hypotheses 84, 233–236 (2015) P.D. Cani, L. Geurts, S. Matamoros, H. Plovier, T. Duparc, Glucose metabolism: Focus on gut microbiota, the endocannabinoid system and beyond. Diabetes Metab. 40, 246–257 (2014) W.G. Wade, The oral microbiome in health and disease. Pharmacol. Res. 69, 137–143 (2013) N. Makiura, M. Ojima, Y. Kou, N. Furuta, N. Okahashi, S. Shizukuishi, A. Amano, Relationship of Porphyromonas gingivalis with glycemic level in patients with type 2 diabetes following periodontal treatment. Oral. Microbiol. Immunol. 23, 348–351 (2008) K. Aagaard, K. Riehle, J. Ma, N. Segata, T.A. Mistretta, C. Coarfa, S. Raza, S. Rosenbaum, I. Van den Veyver, A. Milosavljevic, D. Gevers, C. Huttenhower, J. Petrosino, J. Versalovic, Metagenomic approach to characterization of the vaginal microbiome signature in pregnancy. PLoS ONE 7, e36466 (2012) R. Romero, S.S. Hassan, P. Gajer, A.L. Tarca, D.W. Fadrosh, L.Nikita, M. Galuppi, R.F. Lamont, P. Chaemsaithong, J. Miranda, T. Chaiworapongsa, J. Ravel, The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome 2, 4 (2014) Dolatkhah N, Hajifaraji M, Abbasalizadeh F, Aghamohammadzadeh N, Mehrabi Y, Abbasi MM. Is there a value for probiotic supplements in gestational diabetes mellitus? A randomized clinical trial. J Health Popul Nutr. 2015;33:25. Clemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on human health: an integrative view. Cell. 2012;148:1258–1270. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–1031. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S,Zhang W,Guan Y,Shen D, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Backhed HK, Gonzalez A., Werner J.J, Angenent L.T, Knight R. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell. 2012;150:470–480. Magoč T, Salzberg SL: FLASH: fast length adjustment of short reads to improve genome assemblies . Bioinformatics (Oxford, England) 2011, 27(21):2957-2963. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI et al: QIIME allows analysis of high-throughput community sequencing data. Nature methods 2010, 7(5):335-336. Rognes T, Flouri T, Nichols B, Quince C, Mahé F: VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016, 4:e2584. Edgar RC: UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods 2013, 10(10):996-998. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, PepliesJ, Glöckner FO: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research 2013, 41(Database issue):D590-596. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI: PICRUSt2 for prediction of metagenome functions. Nature biotechnology 2020, 38(6):685-688. Parks DH, Tyson GW, Hugenholtz P, Beiko RG: STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics (Oxford, England) 2014, 30(21):3123-3124. C.R. Taddei, R.V. Cortez, R. Mattar, M.R. Torloni, S. Daher, Microbiome in normal and pathological pregnancies: a literature overview. Am. J. Reprod. Immunol. 80, e12993 (2018) O. Koren, J.K. Goodrich, T.C. Cullender, A. Spor, K. Laitinen, H.K. Bäckhed, A. Gonzalez, J.J. Werner, L.T. Angenent, R. Knight,F. Bäckhed, E. Isolauri, S. Salminen, R.E. Ley, Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470–480 (2012) Metagenomics analysis of gut microbiota in response to diet intervention and gestational diabetes in overweight and obese women: a randomised, double- blind, placebo-controlled clinical trial D.B. DiGiulio, B.J. Callahan, P.J. McMurdie, E.K. Costello, D.J.Lyell, A. Robaczewska, C.L. Sun, D.S.A. Goltsman, R.J. Wong,G. Shaw, D.K. Stevenson, S.P. Holmes, D.A. Relman, Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112,11060–11065 (2015) M. Fugmann, M. Breier, M. Rottenkolber, F. Banning, U. Ferrari,V. Sacco, H. Grallert, K.G. Parhofer, J. Seissler, T. Clavel, A. Lechner, The stool microbiota of insulin resistant women with recent gestational diabetes, a high risk group for type 2 diabetes. Sci. Rep. 5, 13212 (2015) J. Wang, J. Zheng, W. Shi, N. Du, X. Xu, Y. Zhang, P. Ji, F. Zhang, Z. Jia, Y. Wang, Z. Zheng, H. Zhang, F. Zhao, Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut 67, 1614–1625 (2018) T. Jost, C. Lacroix, C. Braegger, C. Chassard, Stability of the maternal gut microbiota during late pregnancy and early lactation. Curr. Microbiol. 68, 419–427 (2014) S.M. Nelson, P. Matthews, L. Poston, Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum. Reprod. Update 16, 255–275 (2010) R.E. Ley, F. Backhed, P. Turnbaugh, C.A. Lozupone, R.D.Knight, J.I. Gordon, Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005) Microbiome and its relation to gestational diabetes Ren Z, Li A, Jiang J, Zhou L, Yu Z, Lu H, Xie H, Chen X, Shao L, Zhang R, et al. Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma. Gut. 2019;68:1014–1023. Potter JM, Hickman PE, Oakman C, Woods C, Nolan CJ. Strict preanalytical oral glucose tolerance test blood sample handling is essential for diagnosing gestational diabetes mellitus. Diabetes Care. 2020;43:1438–1441. Connections between the human gut microbiome and gestational diabetes mellitus Peng L, Li Z-R, Green RS et al. Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. J Nutr 2009;139(9):1619–25. Vaarala O, Atkinson MA, Neu J. The “perfect storm” for type 1 diabetes: the complex interplay between intestinal Brun P, Castagliuolo I, Leo VD et al. Increased intestinal permeability in obese mice: new evidence in the pathogenesis of nonalcoholic steatohepatitis. Am J Physiol Gastrointest Liver Physiol 2007;292(2):G518–25. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3595611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":249223677,"identity":"23470a13-7fb6-4345-8f8f-f6e712fae725","order_by":0,"name":"Yaxin Wang","email":"","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaxin","middleName":"","lastName":"Wang","suffix":""},{"id":249223678,"identity":"22c63d51-74fa-4d8b-881e-18d348da51c2","order_by":1,"name":"Nana Liu","email":"","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nana","middleName":"","lastName":"Liu","suffix":""},{"id":249223679,"identity":"19adc297-ed8a-44b5-82ef-cd7ea286269e","order_by":2,"name":"Yin Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYLCCBAYbEMVGkpY0UrUwMBwmQYv8jNxjEg93nM8zuHb42QOGmjuEtRjcyEuTSDxzu9jgdpq5AcOxZ0Rokcgxk0hsu5244XYOmwRjw2FiHAbWco4ELQw3wFoOkKDF4MwbY4vEtuRiydtpZhIJx4hxWHuO4c2fbXZ5fLeTn0l8qCHGYQwMLBIMoNhkgJOEAfMHEhSPglEwCkbBSAQAoeU6y2bKFSAAAAAASUVORK5CYII=","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Sun","suffix":""},{"id":249223680,"identity":"559fdd3d-7c9e-4bb0-b17c-de623b65cceb","order_by":3,"name":"Liangkun Ma","email":"","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liangkun","middleName":"","lastName":"Ma","suffix":""},{"id":249223681,"identity":"193db8f5-7571-458b-bfa6-52003ea6b28f","order_by":4,"name":"Xuanjin Yang","email":"","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuanjin","middleName":"","lastName":"Yang","suffix":""},{"id":249223682,"identity":"a99f6184-215a-4797-992d-311569cd2d51","order_by":5,"name":"Suhan Zhang","email":"","orcid":"","institution":"National Clinical Research Center for Obstetric \u0026 Gynecologic Diseases, PUMC Hospital, CAMS and PUMC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suhan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-11-11 12:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3595611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3595611/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46570696,"identity":"49b2e7ea-a943-4627-94a0-33f6ad514a0a","added_by":"auto","created_at":"2023-11-16 15:55:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106789,"visible":true,"origin":"","legend":"\u003cp\u003eA flow chart of subject follow-up. * Severe complications and obstetric complications during pregnancy include eclampsia and pregnancy-induced heart disease. The use of antibiotic drugs may affect the intestinal microbiota, and thus individuals using antibiotics should be excluded. The main reasons for loss of follow-up were late pregnancy without a timely birth check-up or failure to provide qualified stool samples.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3595611/v1/12a244422885ee88d20abfc7.png"},{"id":46570695,"identity":"9da6bc1b-5311-4c4e-ad79-e57970d4111b","added_by":"auto","created_at":"2023-11-16 15:55:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59266,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobiota α and β diversity. (a). \u003cem\u003eVenn\u003c/em\u003e diagram: Red represents the number of gut microbes unique to the gestational diabetes mellitus (GDM) group, green represents the number of gut microbes unique to the healthy control (HC) group, and blue indicates the number of gut microbes common to the subjects in both groups (b). \u003cem\u003ePCoA\u003c/em\u003e plot: The red range represents the distribution of intestinal microorganisms in the GDM group and the green range represents the distribution of intestinal microorganisms in the HC group. The horizontal coordinates represent the difference between the two groups on the horizontal axis (PC1 variation = 12.78%), and the vertical coordinates represent the difference between the two groups on the vertical axis (PC2 variation = 6.89%). (c). \u003cem\u003eSimpson\u003c/em\u003e index graph: Red represents the GDM group, green represents the HC group, and the vertical axis shows the index values. (d). \u003cem\u003eShannon\u003c/em\u003e index graph: Red represents the GDM group, green represents the HC group, and the vertical axis shows the index values. (e). \u003cem\u003eChao1\u003c/em\u003e index graph: Red represents the GDM group, green represents the HC group, and the vertical axis shows the index values. (f). \u003cem\u003eACE\u003c/em\u003eindex graph: Red represents the GDM group, green represents the HC group, and the vertical axis shows the index values.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3595611/v1/1eaab538a6ad8f94ae2e81fa.jpeg"},{"id":46570697,"identity":"14fdb253-f0db-4767-93f4-e96a6992e473","added_by":"auto","created_at":"2023-11-16 15:55:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268915,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of the composition of bacterial. (a). A bar chart showing species composition at the genus level. The horizontal axis is the comparison between the gestational diabetes mellitus (GDM) and healthy control (HC) groups, and the vertical axis is the relative abundance of species. The legend on the right-hand side indicates the colors used to represent the different bacterial genera. (b). Species composition at the species level. The horizontal axis is the comparison between the GDM and HC groups and the vertical axis is the relative abundance of species. The legend on the right-hand side indicates the colors used to represent the different bacterial species. (c). Box plot showing the differences between the groups for \u003cem\u003eBifidobacterium \u003c/em\u003eat the genus level. Red represents the GDM group, green represents the HC group, and the vertical axis shows the relative abundance values. (d). Box plot showing the differences between the groups for \u003cem\u003eDialister\u003c/em\u003e at the genus level. Red represents the GDM group, green represents the HC group, and the vertical axis shows the relative abundance values. (e). Box plot showing differences between the groups for \u003cem\u003eBifidobacterium pseudocatenulatum\u003c/em\u003e at the species level. Red represents the GDM group, green represents the HC group, and the vertical axis shows the relative abundance values. (f). Box plot showing differences between the groups for \u003cem\u003eBacteroides coprocola\u003c/em\u003e at the species level. Red represents the GDM group, green represents the HC group, and the vertical axis shows the relative abundance values.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3595611/v1/37fb8e6c76c0e319ca6c47f2.png"},{"id":46570698,"identity":"a5427ab6-633f-411d-92a5-77b418444358","added_by":"auto","created_at":"2023-11-16 15:55:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191744,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant (Lefse) and receiver operating characteristic (ROC) curve analyses. (a) Lefse analysis: A total of 14 differential bacteria between the gestational diabetes mellitus (GDM) and healthy control (HC) groups were identified. Red indicates dominant bacteria in the GDM group, and green represents bacteria dominant in the HC group. (b) ROC curves of bacteria with inter-group differences at the species level. Different colors represent curves with a different number of bacterial combinations, diagonal lines represent the reference line, the horizontal axis represents specificity, the vertical axis represents sensitivity, and the right-hand scale marks the area under the curve (AUC) values of curves with different colors; (c). AUC prediction performance curves of different numbers of bacteria. The horizontal axis represents the number of bacteria, and the vertical axis represents the AUC value, showing a gradual downward trend; (d). The combined ROC curve of \u003cem\u003eEubacterium hallii\u003c/em\u003e, Butyrate-producing bacterium GM2.1, and \u003cem\u003eClostridium\u003c/em\u003e \u003cem\u003edisporicum\u003c/em\u003e, with the diagonal line as the reference line, AUC = 93.64%.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3595611/v1/9b82a1b56c1ef7e1de9ea848.png"},{"id":48711230,"identity":"53beac85-d0c2-4205-b7da-55bb1ad5b319","added_by":"auto","created_at":"2023-12-23 07:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3595611/v1/c3259fd4-6421-4cd3-80b9-bb46a973b249.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Composition of the intestinal microbiota during mid- to late pregnancy in women with gestational diabetes mellitus","fulltext":[{"header":"Brief Synopsis","content":"\u003cp\u003eWe found several specific bacteria in the GDM population that could potentially be used for disease identification in the future.\u003c/p\u003e\n\u003cp\u003e（The trial was registered at ClinicalTrials.gov, NCT: 03916354 . URL:\u003c/p\u003e\n\u003cp\u003ehttps://clinicaltrials.gov/ct2/results?cond=\u0026amp;term=03916354\u0026amp;cntry=\u0026amp;state=\u0026amp;city=\u0026amp;dist= ）\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) is among the most common complications encountered during pregnancy, defined as the initial diagnosis of hyperglycemia during pregnancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. GDM is closely associated with maternal and neonatal adverse outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and obesity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For example, this condition substantially raises the risk of preeclampsia, delivery by cesarean section, and infant morbidities such as diabetic fetopathy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It has previously been established that advanced maternal age, obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and insulin resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] contribute to an increased risk of GDM. In addition, as a typical metabolic disease, GDM is also believed to be associated with disturbances of the gut microbiota. In recent years, studies on the intestinal microbiota have revealed the important role of intestinal microecology. The gut microbiota has been established to constitute the second largest gene pool in humans and has been demonstrated to be closely associated with the occurrence and development of a range of diseases [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, it has been found that the composition of this microbiome changes during pregnancy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is speculated that the intestinal microbiota and their metabolic activities (intestinal dysbiosis) may play essential roles in body weight control, energy homeostasis, fermentation, and the absorption of non-digestible carbohydrate, and well as the development of insulin resistance. Accordingly, it is probable that the composition of the gut microbiota is also linked to the pathogenesis of several metabolic disorders, including obesity, diabetes mellitus, and GDM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which would be consistent with the fact that microbiome composition in other parts of the body is also linked to overall health [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, probiotic interventions have been demonstrated to improve the insulin resistance of GDM subjects [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Collectively, the findings these studies indicate that the occurrence of GDM is closely associated with changes in the gut microbiota.\u003c/p\u003e \u003cp\u003eThe mechanisms associated with gut microecology and the occurrence of disease have yet to be sufficiently elucidated, and thus it would be highly desirable to established whether GDM is linked to a characteristic composition of the gut microbiota. To this end, we used 16S ribosomal RNA (rRNA) gene sequencing metagenomics to compare the composition of the gut microbiome in GDM subjects with that in healthy gestational controls during mid- and late pregnancy. On the basis of the results obtained, we discuss the species composition characteristics of intestinal microorganisms in the disease state, and in an attempt to develop markers for the identification of this state, we performed random forest analysis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eSubjects\u003c/p\u003e \u003cp\u003eIn this study, we recruited 100 pregnant women who underwent oral glucose tolerance testing at 24 to 28 weeks gestation in the Peking Union Medical College Hospital from 7/5/2019 to 31/12/2021, among whom there were 50 pregnant women with GDM and 50 healthy pregnant women. GDM was diagnosed in accordance with the guidelines of the International Association of the Diabetes and Pregnancy Study Groups, in which subjects were administered 75 g oral OGTT. Pregnant women who exhibited blood glucose levels higher than at least one of the assessed cut-off values (fasting-5.1 mmol/L, 1 h-10.0 mmol/L, or 2 h-8.5 mmol/L) were diagnosed as having GDM. In addition, we collected information relating to baseline characteristics, as well as pregnancy and neonatal outcomes. Finally, 49 pregnant women with GDM and 42 healthy pregnant women were followed up to delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The criteria for inclusion in the GDM group were meeting the GDM diagnostic criteria, a natural pregnancy, and monocyesis. The exclusion criteria were as follows: hypertensive disorders during pregnancy, type 2 diabetes mellitus complicated with pregnancy, severe dyslipidemia, etc.; serious complications during pregnancy; use of antibiotics 1 month prior to pregnancy and during pregnancy; severe chronic diseases prior to pregnancy; and refusal to sign an informed consent form. This study was registered as Clinical Trail NCT03916354 and reviewed by the Ethics Committee of Peking Union Medical College Hospital (HS-1875).\u003c/p\u003e \u003cp\u003eCollection of stool samples\u003c/p\u003e \u003cp\u003eGiven that the intestinal microbiome would remain relatively constant for a certain period of time, pregnant women were requested to collect feces at home at two different time points during pregnancy. Subjects were required to collect at least 250 mg of feces into a sterile test tube (PSP\u0026reg; Spin Stool DNA Plus Kit) containing preservative solution in the second trimester (24\u0026ndash;28 weeks) and third trimester (36 weeks prior to delivery). The samples were transferred to the laboratory wherein they were stored at -80\u0026deg;C for subsequent DNA extraction.\u003c/p\u003e \u003cp\u003eMicrobial genome sequencing\u003c/p\u003e \u003cp\u003eEach sample was identified according to a barcode sequence and PCR amplification primer sequence. Having removed the barcode and primer sequences, the FLASH program [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used to assemble reads to obtain raw tags. The raw tags thus obtained were filtered according to the standard of QIIME [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Closed-reference clustering of quality-controlled sequences into operational taxonomic units (OTUs) was performed at the 97% identity threshold against the Greengenes 13_5 97% OTU reference sequences using VSEARCH [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (via q2-vsearch). OTU annotation analysis was performed using the Mothur [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and SSUrRNA databases of SILVA132 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (threshold 0.8\u0026thinsp;~\u0026thinsp;1). Shannon and Simpson index values were calculated using QIIME (version 1.9.1), and the R program version 3.5.1 was used to analyze the differences between groups regarding alpha diversity indices. UniFrac distances were determined using QIIME, and principal coordinates analysis (PCoA) was performed using the WGCNA, stats, and ggplot2 packages in R. The functions of annotated OTUs were predicted using PICRUSt [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Taxonomic and functional differences were analyzed using STAMP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and correlations between genus abundance and clinical parameters were calculated based on Spearman correlation analysis using the WGCNA corAndPvalue function.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA). The Mann\u0026ndash;Whitney U-test was performed to compare the alpha diversity/richness and differences in the relative abundance of the main (higher than 1% per group and per patient) bacterial phyla and genera between the groups. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered statistically significant to control multiple comparisons in the relative abundance. A standard P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant for all other analyses. PCoA, principal component (PCA), and Metastat Complex heat map analyses were performed using R (Version 2.15.3), beta diversity analysis was performed using Qiime (Version 1.9.1), and linear discriminant analysis was performed using Lefse software (Version 1.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eClinical Index\u003c/p\u003e \u003cp\u003eAmong the initial 50 patients in each of the GDM and healthy controls groups, 49 and 42, respectively, were followed up until delivery. The mean ages of GDM and control group subjects were 33 (32\u0026ndash;36.5) and 32 (29\u0026ndash;34) years old, respectively, and the corresponding mean gestational times were 39 (38\u0026ndash;39) and 39(38\u0026ndash;40) weeks. Baseline data of the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were statistically different with respect to age (P\u0026thinsp;=\u0026thinsp;0.018) and gestational age at delivery (P\u0026thinsp;=\u0026thinsp;0.006), respectively. There were no statistically significant differences in terms of gravidity, parity, pre-pregnancy BMI, height, weight, or neonatal weight.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison of baseline data between GDM and normal controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDM(n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth women(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (32\u0026thinsp;~\u0026thinsp;36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (29\u0026thinsp;~\u0026thinsp;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026thinsp;~\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026thinsp;~\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.46 (19.78\u0026thinsp;~\u0026thinsp;24.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.05 (19.65\u0026thinsp;~\u0026thinsp;22.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.00 (160.00\u0026thinsp;~\u0026thinsp;167.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (162\u0026thinsp;~\u0026thinsp;166.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (53.25\u0026thinsp;~\u0026thinsp;64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5 (52.5\u0026thinsp;~\u0026thinsp;61.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (38\u0026thinsp;~\u0026thinsp;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (38\u0026thinsp;~\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaby weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3230 (3025\u0026thinsp;~\u0026thinsp;3530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3395 (3025\u0026thinsp;~\u0026thinsp;3632.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGDM: Gestational diabetes mellitus, BMI: Body mass index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Statistically significant at \u003cem\u003eP\u003c/em\u003e＜0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlpha and beta diversity\u003c/p\u003e \u003cp\u003eA Venn chart displaying the numbers of intestinal flora detected in the two assessed groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.a) revealed 4342 and 4778 distinct OTUs in the GDM and healthy control groups, respectively, among which, 3196 types were found in the intestinal flora of both groups. These numbers thus indicate a reduction in microbial diversity in the GDM group compared with that in the control group. Further analysis of the microbiota beta diversity of the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.b) indicated that whereas the distribution of microbiota in the GDM population tended to be aggregated, that of microbiota in control group individuals was relatively dispersed. The horizontal and vertical coordinates of a Bray\u0026ndash;Curtis PCoA plot explained 12.78% and 6.89% of the variation between the two groups, respectively. Compared with the control group, there was a significant reduction in the diversity of gut microorganisms in the GDM group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Shannon, Simpson, Chao1, and ACE indices were used to assess the α diversity of the intestinal flora (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.c.d.e.f), among which, we recorded significant increases in values of the Shannon (P\u0026thinsp;=\u0026thinsp;1.08E-11) and Simpson (P\u0026thinsp;=\u0026thinsp;1.09E-12) indices, and significant reductions in Chao1 (P\u0026thinsp;=\u0026thinsp;0.02264) and ACE (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.004667\u003c/em\u003e) index values in the GDM group. These findings thus indicate that disease status may be associated with large variations in the gut microbiota of different pregnant women, with a general reduction in microbial abundance. However, the interactions between different bacterial species may be more complex.\u003c/p\u003e \u003cp\u003eRelative abundance of gut microbes\u003c/p\u003e \u003cp\u003eIn order to better distinguish the composition of intestinal microbes in different populations, we only focused on bacteria with a relative abundance greater than 1% (Attached Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The composition of intestinal microbiota was found to differ significantly different between the GDM and control populations, and here we describe composition characteristics of the gut microbiota in GDM subjects at the phylum, genus, and species levels.\u003c/p\u003e \u003cp\u003eAt the phylum level\u003c/p\u003e \u003cp\u003eThe proportion of \u003cem\u003eFirmicutes\u003c/em\u003e in the GDM group (58.98%) was significantly higher than that in the healthy control group (48.67%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas in contrast the proportion of \u003cem\u003eActinobacteria\u003c/em\u003e in GDM group (4.90%) was significantly lower than that in control group (14.66%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as was the proportion of \u003cem\u003eProteobacteria\u003c/em\u003e (3.38% vs. 5.37%, P\u0026thinsp;=\u0026thinsp;0.013). Furthermore, we established that the ratio of \u003cem\u003eFirmicutes\u003c/em\u003e to \u003cem\u003eBacteroidetes\u003c/em\u003e in the GDM group (0.5898/0.3169\u0026thinsp;=\u0026thinsp;1.86) was higher than that in the control group (0.4867/0.3040\u0026thinsp;=\u0026thinsp;1.60).\u003c/p\u003e \u003cp\u003eAt the genus level.\u003c/p\u003e \u003cp\u003eAmong the different bacterial genera, we detected a significant increase in the proportion of \u003cem\u003eLactobacillus\u003c/em\u003e in the GDM group (1.01%) compared with that in the control group (0.31%) (P\u0026thinsp;=\u0026thinsp;0.022). Similar significant increases were detected for the proportions of \u003cem\u003eBacteroides\u003c/em\u003e (21.43% vs. 16.54%, P\u0026thinsp;=\u0026thinsp;0.004) and \u003cem\u003eMegamonas\u003c/em\u003e (2.06% vs. 0.29%, P\u0026thinsp;=\u0026thinsp;0.006). In contrast, compared with those in control group, we detected significant reductions in the proportions of \u003cem\u003eBifidobacterium\u003c/em\u003e in (3.26% vs. 8.42%, Figure. 3c, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eDialister\u003c/em\u003e (0.88% vs. 2.25%, Figure. 3d, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the GDM group. At the genus level, \u003cem\u003eBacteroides\u003c/em\u003e accounted for the largest proportion of intestinal flora in both groups, but there were significant differences in the proportions of the different bacteria (Figure. 3a).\u003c/p\u003e \u003cp\u003eAt the species level\u003c/p\u003e \u003cp\u003eIn terms of individual species, the proportion of \u003cem\u003eBacteroides stercoris\u003c/em\u003e in the GDM group (1.62%) was found to be significantly higher than that in the control group (1.09%) (P\u0026thinsp;=\u0026thinsp;0.001), as was the proportion of \u003cem\u003eBacteroides coprocola\u003c/em\u003e (1.50% vs. 0.67%, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Contrastingly, compared with the control group, significant reductions were observed for the proportions of \u003cem\u003eBifidobacterium pseudocatenulatum\u003c/em\u003e (2.46% vs. 6.56%, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, P\u0026thinsp;=\u0026thinsp;0.001) and \u003cem\u003eEscherichia coli\u003c/em\u003e (0.63% vs. 1.58%, P\u0026thinsp;=\u0026thinsp;0.004) in the GDM group. There were significant differences in the proportion of intestinal flora between the two groups at the species level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe diverse roles of the microbiome with respect to health-related physiological processes and the development of different diseases have yet to be sufficiently elucidated. Some scholars have proposed that the physiological adaptation of the microbial pattern, present in pregnancy, is altered in women with metabolic diseases, such as GDM, as a consequence increased inflammation, IR, and weight gain in this population [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However pregnant women with GDM typically maintain a stable gut microbiome for a period of time, as GDM status may interfere with the flexibility of the maternal gut microbiome, thus limiting the ability of GDM patients to respond to dietary interventions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the present study, we found that compared with healthy controls, the gut microbiota in GDM group subjects was characterized by reductions in species OTUs and beta diversity. Moreover, the distribution of species OTUs in GDM group subjects was observed to be more aggregated than that in control group individuals, with this difference in distribution found to be significantly different. Such aggregated distribution is presumed to be a characteristic manifestation of the disease state, although the specific discriminative criteria need to be further defined. In addition, we also detected significant changes in the α diversity of the GDM group microbiota compared with that in the healthy controls. Specifically, we recorded increases in Shannon and Simpson index values, and reduction in those of the Chao1 and ACE indices. These findings accordingly indicate that disease status can lead to large variations and a reduced abundance in gut microbiota among pregnant women, which can modify the interactions between different bacterial species, thereby complicating the diagnosis and treatment of GDM. We also detected significant differences with respect to the species composition ratios in GDM group and healthy control group subjects. At the phylum level, the proportion of \u003cem\u003eFirmicutes\u003c/em\u003e in the GDM group was significantly higher than that detected in the control group, whereas in contrast, we detected significant reductions in the proportion of \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e in the GDM group. Similar differential patterns were detected at the genus level, with the proportions of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eMegamonas\u003c/em\u003e being significantly higher in the GDM group than in the control group, and the proportions of \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eDialister\u003c/em\u003e being significantly lower Furthermore, at the species level, the proportions of \u003cem\u003eBacteroides stercoris\u003c/em\u003e and \u003cem\u003eBacteroides coprocola\u003c/em\u003e were found be significantly higher in the GDM group than in the controls. whereas the proportions of \u003cem\u003eBifidobacterium pseudocatenulatum\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e were significantly lower (Attached Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, there tends to be a lack of consensus regarding changes in gut microecology during pregnancy. Whereas the findings of some studies have indicated that the gut microbiome undergoes distinct changes during pregnancy, other studies have found little evidence in this respect [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, in a study of gut microbiota in non-pregnant women, Fugmann et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] found that the proportion of \u003cem\u003eFirmicutes\u003c/em\u003e in women with a history of GDM was smaller than that in women without GDM. Wang et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] similarly detected a lower \u003cem\u003eFirmicutes\u003c/em\u003e composition in the oral microbiome of GDM women, although did not identify similar difference in the gut microbiome. In contrast, Jost et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who followed up seven healthy pregnant women from the third trimester to the postpartum period, found that the maternal microbiota was dominated by \u003cem\u003eFirmicutes\u003c/em\u003e. In addition, some scholars believe that an imbalance in the ratio between \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e may represent a manifestation of biological disorder [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which is consistent with the findings of the present study, in which we found that the \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u003c/em\u003e ratio in GDM group subjects (0.5898/0.3169\u0026thinsp;=\u0026thinsp;1.86) was higher than that in the controls (0.4867/0.3040\u0026thinsp;=\u0026thinsp;1.60). However, further clinical trials are needed to determine appropriate threshold values for difference in specific proportions.\u003c/p\u003e \u003cp\u003eBy displaying our data in the form of box plot, we have more intuitively demonstrated the common bacteria characterized by significant differences in composition ratios between the GDM and healthy control group subjects, such as \u003cem\u003eBifidobacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and \u003cem\u003eDialister (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) at the genus level and \u003cem\u003eBifidobacterium pseudocatenulatum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) and \u003cem\u003eBacteroides coprocola\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef) at the species level. Given that the proportions of gut microorganisms can vary between groups, we considered this as a means whereby disease states could be distinguished. In this regard, we used linear discriminant analysis (LEfSe, LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0) to identify 14 differential OTUs that could be applied in discriminating between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), with those OTUs enriched in the GDM group mainly belonging to \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e, whereas depleted OTUs were mainly from the \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt present, machine learning, such as random forest analysis, is increasingly being applied in the field of medical diagnosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and using this approach, researchers have established that specific combinations of different gut microbes can be used to effectively distinguish GDM individuals from healthy controls. For example, in a study published in 2017, which sought to improve the predictive power of the model, researchers attempted to provide taxonomic and functional information for unknown or unanalyzed species to enhance discrimination, and accordingly succeeded in increasing the area under the receiver operating characteristic (ROC) curve (AUC) from 0.80 (95%CI\u0026thinsp;=\u0026thinsp;0.73 0.86) to 0.91 (95%CI\u0026thinsp;=\u0026thinsp;0.87 0.96) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, the best combination of the model still required the inclusion 20 gut microbes. In the present study, we sought to combine different numbers of bacteria with inter-group differences at the species level and generate respective ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Contrary to our expectations, we found that a peak AUC value could be obtained using combination of considerably fewer bacteria (\u003cem\u003eEubacterium hallii\u003c/em\u003e, Butyrate-producing bacterium GM2.1, and \u003cem\u003eClostridium disporicum\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). This combined marker panel could distinguish GDM individuals from healthy controls, with an AUC of 93.64% (95%CI: 89.83\u0026ndash;97.45%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). This finding will facilitate the identification of pregnant women with GDM, contribute to disease management and treatment, and even provide a detection basis and therapeutic assistance for assessment and improvement of the intestinal status of the postpartum GDM population and newborns. Moreover, we anticipate that it will provide a new model for the diagnosis and treatment of gestational diabetes mellitus, which warrants further evaluation.\u003c/p\u003e \u003cp\u003eThe composition ratio of \u003cem\u003eEubacterium hallii\u003c/em\u003e, \u003cem\u003eClostridium disporicum\u003c/em\u003e, and Butyrate-producing bacterium GM2.1 in the GDM group was 0.9%, 0.1%, and 0.03%, respectively. Although the composition of these bacteria is relatively small, they were nevertheless enriched in the GDM group. As a reference, these bacteria have been linked to possible disease mechanisms identified in previous studies. For example, the lactic acid or butyrate produced by bacteria can regulate intestinal permeability and induce intestinal inflammation, thereby leading to the occurrence of diabetes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. \u003cem\u003eClostridium disporicum\u003c/em\u003e and other bacteria are characterized as producers of the cell wall component lipopolysaccharide (LPS), and by increasing intestinal permeability, dysregulation of the intestinal flora has been shown promote increases in levels of LPS entering the systemic circulation, thereby leading to inflammation and metabolic dysfunction [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, the findings of studies on intestinal microbial function have indicated that LPS biosynthesis and export are involved in the regulation of blood glucose levels [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This will accordingly be a focus of future research to further investigate the mechanisms whereby intestinal microbes influence the occurrence and development of disease, thereby enabling the development of effective interventions.\u003c/p\u003e \u003cp\u003eAmong the limitations of this study was the fact that stool samples were not collected from the assessed populations during the early stages of pregnancy, and consequently, although AUC values could be used to explain the difference between different populations, we were unable to identify candidate predictor bacteria during early pregnancy. In addition, all the subjects assessed in this study were recruited from a single institute, and thus prior to commencing further clinical studies, additional large-scale multicenter studies should be conducted to verify the findings reported herein.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors participated in the interpretation of study results, and in the drafting, critical revision, and approval of the final version of the manuscript. Yaxin Wang and Yin Sun wrote the main manuscript. Yaxin Wang and Nana Liu analyzed the data and prepared figures 1-4. Yin Sun and Liangkun Ma conducted the overall design of the study. Xuanjin Yang and Suhan Zhang collected the data and assisted the research.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by The National Key Research and Development Program of China（Grant number：2022YFC2703304); Medical and Health Technology Innovation Project of Chinese Academy of Medical Sciences（Grant number. 2020-I2M-2-009、2021-I2M-1-023) ; Recommendations for weight gain in women with gestational diabetes mellitus (Grant number. 20191901); Clinical and Translational Medicine Research Fund of the Central Public welfare Research Institutes of the Chinese Academy of Medical Sciences (Grant number. 2019XK320007)\u003c/p\u003e\n\u003ch2\u003eEthical Statement and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was registered as Clinical Trail NCT03916354 and reviewed by the Ethics Committee of Peking Union Medical College Hospital (HS-1875). All subjects signed informed consent and all experimental protocols were approved by a named institutional and licensing committee. All the steps/ methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003ch2\u003eConsent to publish\u003c/h2\u003e\n\u003cp\u003eAll subjects signed informed consent for the relevant study and for the publication of the final data.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Beijing Norhe Zhiyuan Technology Co., LTD for its contribution to the detection of intestinal flora samples and the interpretation of results.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Material\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization, Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy (World Health Organization, Geneva, 2013)\u003c/li\u003e\n\u003cli\u003eMetzger BE, Coustan DR, Trimble ER. Hyperglycemia and adverse pregnancy outcomes. Clin Chem. 2019;65:937\u0026ndash;938.\u003c/li\u003e\n\u003cli\u003eO. Verier-Mine, Outcomes in women with a history of gestational diabetes. Screening and prevention of type 2 diabetes. Lit. Rev. Diabetes Metab. 36, 595\u0026ndash;616 (2010)\u003c/li\u003e\n\u003cli\u003eOstlund I, Haglund B, Hanson U. Gestational diabetes and preeclampsia. Eur J Obstet Gynecol Reprod Biol. 2004;113:12\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eBillionnet C, Mitanchez D, Weill A, Nizard J, Alla F, Hartemann A, Jacqueminet S. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia. 2017;60:636\u0026ndash;644.\u003c/li\u003e\n\u003cli\u003eLao TT, Ho LF, Chan BC, Leung WC. Maternal age and prevalence of gestational diabetes mellitus. Diabetes Care. 2006;29:948\u0026ndash;949.\u003c/li\u003e\n\u003cli\u003eMoore Simas TA, Waring ME, Callaghan K, Leung K, Ward Harvey M, Buabbud A, Chasan-Taber L. Weight gain in early pregnancy and risk of gestational diabetes mellitus among Latinas. Diabetes Metab. 2019;45:26\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eKampmann U, Madsen LR, Skajaa GO, Iversen DS, Moeller N, Ovesen P. Gestational diabetes: a clinical update. World J Diabetes. 2015;6:1065\u0026ndash;1072.\u003c/li\u003e\n\u003cli\u003eD. Zhang, Y. Huang, D. Ye, Intestinal dysbiosis: an emerging cause of pregnancy complications? Med. Hypotheses 84, 233\u0026ndash;236 (2015)\u003c/li\u003e\n\u003cli\u003eP.D. Cani, L. Geurts, S. Matamoros, H. Plovier, T. Duparc, Glucose metabolism: Focus on gut microbiota, the endocannabinoid system and beyond. Diabetes Metab. 40, 246\u0026ndash;257 (2014)\u003c/li\u003e\n\u003cli\u003eW.G. Wade, The oral microbiome in health and disease. Pharmacol. Res. 69, 137\u0026ndash;143 (2013)\u003c/li\u003e\n\u003cli\u003eN. Makiura, M. Ojima, Y. Kou, N. Furuta, N. Okahashi, S. Shizukuishi, A. Amano, Relationship of Porphyromonas gingivalis with glycemic level in patients with type 2 diabetes following periodontal treatment. Oral. Microbiol. Immunol. 23, 348\u0026ndash;351 (2008)\u003c/li\u003e\n\u003cli\u003eK. Aagaard, K. Riehle, J. Ma, N. Segata, T.A. Mistretta, C. Coarfa, S. Raza, S. Rosenbaum, I. Van den Veyver, A. Milosavljevic, D. Gevers, C. Huttenhower, J. Petrosino, J. Versalovic, Metagenomic approach to characterization of the vaginal microbiome signature in pregnancy. PLoS ONE 7, e36466 (2012)\u003c/li\u003e\n\u003cli\u003eR. Romero, S.S. Hassan, P. Gajer, A.L. Tarca, D.W. Fadrosh, L.Nikita, M. Galuppi, R.F. Lamont, P. Chaemsaithong, J. Miranda, T. Chaiworapongsa, J. Ravel, The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome 2, 4 (2014)\u003c/li\u003e\n\u003cli\u003eDolatkhah N, Hajifaraji M, Abbasalizadeh F, Aghamohammadzadeh N, Mehrabi Y, Abbasi MM. Is there a value for probiotic supplements in gestational diabetes mellitus? A randomized clinical trial. J Health Popul Nutr. 2015;33:25.\u003c/li\u003e\n\u003cli\u003eClemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on human health: an integrative view. Cell. 2012;148:1258\u0026ndash;1270.\u003c/li\u003e\n\u003cli\u003eTurnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027\u0026ndash;1031.\u003c/li\u003e\n\u003cli\u003eQin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S,Zhang W,Guan Y,Shen D, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eKoren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Backhed HK, Gonzalez A., Werner J.J, Angenent L.T, Knight R. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell. 2012;150:470\u0026ndash;480.\u003c/li\u003e\n\u003cli\u003eMagoč T, Salzberg SL: FLASH: fast length adjustment of short reads to improve genome assemblies . Bioinformatics (Oxford, England) 2011, 27(21):2957-2963.\u003c/li\u003e\n\u003cli\u003eCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pe\u0026ntilde;a AG, Goodrich JK, Gordon JI et al: QIIME allows analysis of high-throughput community sequencing data. Nature methods 2010, 7(5):335-336. \u003c/li\u003e\n\u003cli\u003eRognes T, Flouri T, Nichols B, Quince C, Mah\u0026eacute; F: VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016, 4:e2584. \u003c/li\u003e\n\u003cli\u003eEdgar RC: UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods 2013, 10(10):996-998. \u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, PepliesJ, Gl\u0026ouml;ckner FO: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research 2013, 41(Database issue):D590-596. \u003c/li\u003e\n\u003cli\u003eDouglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI: PICRUSt2 for prediction of metagenome functions. Nature biotechnology 2020, 38(6):685-688. \u003c/li\u003e\n\u003cli\u003eParks DH, Tyson GW, Hugenholtz P, Beiko RG: STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics (Oxford, England) 2014, 30(21):3123-3124.\u003c/li\u003e\n\u003cli\u003eC.R. Taddei, R.V. Cortez, R. Mattar, M.R. Torloni, S. Daher, Microbiome in normal and pathological pregnancies: a literature overview. Am. J. Reprod. Immunol. 80, e12993 (2018)\u003c/li\u003e\n\u003cli\u003eO. Koren, J.K. Goodrich, T.C. Cullender, A. Spor, K. Laitinen, H.K. B\u0026auml;ckhed, A. Gonzalez, J.J. Werner, L.T. Angenent, R. Knight,F. B\u0026auml;ckhed, E. Isolauri, S. Salminen, R.E. Ley, Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470\u0026ndash;480 (2012)\u003c/li\u003e\n\u003cli\u003eMetagenomics analysis of gut microbiota in response to diet intervention and gestational diabetes in overweight and obese women: a randomised, double- blind, placebo-controlled clinical trial \u003c/li\u003e\n\u003cli\u003eD.B. DiGiulio, B.J. Callahan, P.J. McMurdie, E.K. Costello, D.J.Lyell, A. Robaczewska, C.L. Sun, D.S.A. Goltsman, R.J. Wong,G. Shaw, D.K. Stevenson, S.P. Holmes, D.A. Relman, Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112,11060\u0026ndash;11065 (2015)\u003c/li\u003e\n\u003cli\u003eM. Fugmann, M. Breier, M. Rottenkolber, F. Banning, U. Ferrari,V. Sacco, H. Grallert, K.G. Parhofer, J. Seissler, T. Clavel, A. Lechner, The stool microbiota of insulin resistant women with recent gestational diabetes, a high risk group for type 2 diabetes. Sci. Rep. 5, 13212 (2015)\u003c/li\u003e\n\u003cli\u003eJ. Wang, J. Zheng, W. Shi, N. Du, X. Xu, Y. Zhang, P. Ji, F. Zhang, Z. Jia, Y. Wang, Z. Zheng, H. Zhang, F. Zhao, Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut 67, 1614\u0026ndash;1625 (2018)\u003c/li\u003e\n\u003cli\u003eT. Jost, C. Lacroix, C. Braegger, C. Chassard, Stability of the maternal gut microbiota during late pregnancy and early lactation. Curr. Microbiol. 68, 419\u0026ndash;427 (2014)\u003c/li\u003e\n\u003cli\u003eS.M. Nelson, P. Matthews, L. Poston, Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum. Reprod. Update 16, 255\u0026ndash;275 (2010)\u003c/li\u003e\n\u003cli\u003eR.E. Ley, F. Backhed, P. Turnbaugh, C.A. Lozupone, R.D.Knight, J.I. Gordon, Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070\u0026ndash;11075 (2005)\u003c/li\u003e\n\u003cli\u003eMicrobiome and its relation to gestational diabetes\u003c/li\u003e\n\u003cli\u003eRen Z, Li A, Jiang J, Zhou L, Yu Z, Lu H, Xie H, Chen X, Shao L, Zhang R, et al. Gut microbiome analysis as a tool towards targeted non-invasive biomarkers for early hepatocellular carcinoma. Gut. 2019;68:1014\u0026ndash;1023.\u003c/li\u003e\n\u003cli\u003ePotter JM, Hickman PE, Oakman C, Woods C, Nolan CJ. Strict preanalytical oral glucose tolerance test blood sample handling is essential for diagnosing gestational diabetes mellitus. Diabetes Care. 2020;43:1438\u0026ndash;1441.\u003c/li\u003e\n\u003cli\u003eConnections between the human gut microbiome and gestational diabetes mellitus\u003c/li\u003e\n\u003cli\u003ePeng L, Li Z-R, Green RS et al. Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. J Nutr 2009;139(9):1619\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eVaarala O, Atkinson MA, Neu J. The \u0026ldquo;perfect storm\u0026rdquo; for type 1 diabetes: the complex interplay between intestinal\u003c/li\u003e\n\u003cli\u003eBrun P, Castagliuolo I, Leo VD et al. Increased intestinal permeability in obese mice: new evidence in the pathogenesis of nonalcoholic steatohepatitis. Am J Physiol Gastrointest Liver Physiol 2007;292(2):G518\u0026ndash;25.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pregnancy, GDM, Intestinal microbiota, Mid- to late pregnancy","lastPublishedDoi":"10.21203/rs.3.rs-3595611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3595611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eDetermine the composition and characteristics of intestinal microbiota in GDM patients during mid- and late pregnancy, and identify possible differences in bacterial composition based on comparisons with a normal healthy population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe participants were recruited at 24 to 28 weeks of gestation, and stool samples were collected twice in the second and third trimesters, and were examined based on next-generation sequencing. Test results and pregnancy outcomes were recorded, and baseline conditions and intestinal microecological composition were compared between the two groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with the healthy control group, the composition ratio of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eMegamonas\u003c/em\u003e was significantly increased, whereas that of \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e was significantly reduced. At the species level, the combination of \u003cem\u003eEubacterium hallii\u003c/em\u003e, Butyrate-producing Bacterium GM2.1, and \u003cem\u003eClostridium disporicum\u003c/em\u003e enabled an effective discrimination between the two groups (AUC\u0026thinsp;=\u0026thinsp;93.64%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 95% CI: 89.83\u0026ndash;97.45%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCompared with the healthy control group, we detected significant differences in the composition ratio of gut microbiota during late pregnancy in the gestational diabetes group, and also observed a reduction in bacterial diversity and an increase in microbial disorder.\u003c/p\u003e","manuscriptTitle":"Composition of the intestinal microbiota during mid- to late pregnancy in women with gestational diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-16 15:55:07","doi":"10.21203/rs.3.rs-3595611/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d23bb83-3724-4492-b5bc-c43e51261c64","owner":[],"postedDate":"November 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":26308856,"name":"Biological sciences/Microbiology"},{"id":26308857,"name":"Health sciences/Endocrinology"},{"id":26308858,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2023-12-28T04:59:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-16 15:55:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3595611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3595611","identity":"rs-3595611","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}