Differences in the gut microbiome of obese and non-obese pregnant women: a matched cohort study in Sweden

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The causative role of the microbiome in obesity is known from both mouse and human studies. In parallel, the gut microbiome has been implicated in the most common complications of pregnancy, including preterm birth, gestational diabetes mellitus, and gestational hypertension. Despite obesity being a well-established risk factor for these complications, the composition of the gut microbiome of obese pregnant individuals has not yet been studied. Results The differences in the gut microbiome of lean and obese persons persist during gestation. Obese individuals have a less diverse and less rich microbiome throughout all trimesters of pregnancy. In the first trimester, 53 species differ significantly between lean and obese individuals, and 60 in the early third trimester, after adjusting for confounders. Additionally, obese individuals harbored a consistently lower potential to produce propionate in their gut microbiomes, even after adjustments. Conclusions Gut microbes adapt to pregnancy similarly, but some crucial differences between lean and obese the groups persist, even in late pregnancy. This may be a potential underexplored mechanism explaining the increased rates of pregnancy complications among obese patients. Diet, probiotics, or medication interventions to correct the gut microbiome of pregnant obese individuals could potentially improve their pregnancy outcomes. Gut microbiome Obesity Pregnancy Dysbiosis Preeclampsia Diabetes Hypertension Diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Obesity is a significant public health problem in almost all middle- and high-income countries. According to WHO, one out of five individuals will be obese by 2025 [ 1 ]. The global rise in obesity can be attributed to an increasingly obesogenic environment (e.g., reduced energy expenditure, higher prices of fresh produce, food additives, and lower availability of healthy food [ 2 ]), but animal and human evidence also supports a role for early-life microbiome disturbances, such as antibiotic usage, in promoting obesity [ 3 ]. Generally, obesity has been associated with gut microbiome imbalance (dysbiosis), lower microbiota diversity, and reduced microbial gene richness [ 4 ]. Recent studies propose that a high-fat diet induces dysbiosis and subsequent systemic inflammation [ 5 , 6 ], which may partially explain obesity-associated dysbiosis. This inflammatory cascade is implicated in conditions like infertility, reduced conception rates, and early pregnancy loss, which are also associated with vaginal dysbiosis [ 7 – 9 ]. This suggests a close interaction between diet, microbiome, gut health, and reproductive health. Studying the maternal microbiome has implications not only for pregnancy but for the lifelong health of the neonate. A systematic review reported alterations in the maternal microbiome throughout pregnancy, resulting in elevated levels of bacterial byproducts in the mother's bloodstream [ 10 ], including short-chain fatty acids (SCFA), amino acids, vitamin K and B-complex vitamins [ 11 ]. These, in turn, can enter the fetal circulation, leading to potential repercussions for both physical and mental health outcomes and influencing the colonization of the infant's microbiome [ 10 ]. Additionally, animals colonized with the gut microbiota of obese individuals tend to gain weight even with unchanged food intake and activity levels [ 4 ]. The same could be true for newborn humans; establishing a neonate's initial microbial community can either impose health advantages or pose a risk for future diseases [ 6 ]. Pregnant women with a higher Body Mass Index (BMI) are an increasing group in antenatal care worldwide and in Sweden [ 12 ]. They need to be studied more thoroughly to get better care to minimize the risk of complications during their pregnancies. Here, we leverage the most extensive longitudinal pregnancy microbiome study to date, the SweMaMi cohort [ 13 ], to examine the variation in the gut microbiome in obese pregnant women and see if it differs from that of normal-weight pregnant women in terms of species richness, diversity, specifically altered species, and critical microbial gene functions. Materials and Methods Cohort The Swedish Maternal Microbiome (SweMaMi) study is a prospective cohort study examining the microbiome during pregnancy, with adverse pregnancy outcomes, including preterm birth and pregnancy loss, as its primary outcomes [13]. Its study protocol has been approved by the Regional Ethics Board, Stockholm, Sweden (2017/1118-31). All participants were at least 18 years old at enrollment and provided informed consent when completing the first online questionnaire. Participants filled in extensive questionnaires on health and pregnancy before gestational week 20 and were consequently sent microbiome collection kits for home sampling. The same procedure was repeated around gestational week 30 and postpartum [13] (but in this work we only analyzed samples collected during pregnancy). The questionnaires in Swedish and English are available from Zenodo: https://doi.org/10.5281/zenodo.15369670 Here, we conducted a nested case-control study comparing the gut microbes of 274 obese pregnant women (pre-pregnancy BMI > 30) individually matched to 746 lean controls (BMI < 25) in both the second and third trimesters. Cases were selected if they had a self-reported BMI greater than 30 before pregnancy, and fecal microbiome data were available at least for the first time point. Cases were individually matched 1-to-3 with lean controls (BMI 18.5-25) based on the following matching criteria: parity (nulliparous or parous), gestational week of first fecal samples (±1 week), and age (±5 years). DNA extraction, sequencing and annotation DNA was extracted, sequenced and annotated as previously reported [13]. Briefly, samples were sequenced on MGI instruments using paired-end 150 bp reads and a median library size of 61 million reads per sample (range: 21.3-116.5 million). Samples were trimmed with fastp [14], human DNA reads were removed by annotating with kraken2 [15] on a reference containing only the human Ghc38 genome, and reads were annotated to the species level using the Metaphlan4 pipeline [16] (table S1), as well as functionally annotated with the Humann2 pipeline. To derive the estimated abundance of gut metabolic modules (GMMs) [17], the gene families annotated from Humann2 [18] were regrouped to KEGG pathways using the built-in script humann_regroup_table.py in Humann2. The abundance table of the regrouped KEGG pathways was then used as input for Omixer-RPM [19] to estimate the abundance table of GMMs (Table S2). Categorical and ordinal variables on baseline maternal health Validated scales were used where possible, including the Pregnancy Unique Quantification of Emesis (PUQE) [20] for measuring nausea and vomiting, the Perceived Stress Scale (PSS-4) [21] for assessing stress, and the Edinburgh Postnatal Depression Scale (EPDS) [22] for evaluating mental distress. Additionally, some variables used are standard to all SweMaMi analyses, but not validated. These include: Bowel transit time. Based on the question “How do your stools typically look like?” and the Bristol stool scale [23]. Only Bristol 3-4: regular transit. Including Bristol 1-4 only: slow transit. Including Bristol 3-7 only: fast transit. Including both 1-2 and 5-7: varied transit. Socioeconomic score: one point each was given to having a bachelor’s degree or higher, working full-time, and marriage or cohabitation Diet score: five-point scale, with one point each given for fruits, vegetables or whole grain bread daily and two possible additional point for sweetened beverages or other sweets once a week or more seldom Biostatistics All analyses were conducted in the R programming environment, using libraries “vegan” [24], “ggpubr”, “ggplot2” [25], “dplyr” [26], “tidyverse”[27] , “phyloseq” [28], “microbiome” [29], and “ANCOMBC” [30]. Alpha-diversity was measured as observed species richness, as well as Shannon’s entropy and Pielou’s evenness. The distance between samples was calculated using either Aitchison’s or Jaccard’s distances. Univariable PERMANOVA were computed using the libraries “vegan” and “ape” [24, 31], and significant variables were selected for further analysis in multivariable models using a stepwise forward selection approach. The differential abundance of bacterial species and gut metabolic modules was calculated using ANCOMBC v.2.2. These analyses were performed in both univariable and multivariable models which included primiparity (yes/no), age (in years), and diet score (0-4). Additionally, the second model included colonic transit time as -1 (slow transit), 0 (regular transit) and +1 (fast transit). Subjects with “various” transit times were excluded from this sub-analysis. Species and modules with a BH-adjusted p-value less than 0.05 were considered significantly different; however, only those with an absolute log2-fold change greater than 0.5 are plotted and discussed. Results Cohort description Participants were recruited from all Swedish regions (fig. 1) . The median BMI in our control group (n = 746) was 22 kg/m² (IQR: 19-24 kg/m²), whereas in our cases (n = 274), it was 33 kg/m² (IQR: 30-60 kg/m²) (table 1) . The mean age was 32 years in both groups. Some differences were observed in alcohol consumption (more common in the control group) and higher use of probiotics, fibers, and vitamins in the lean group, whereas a higher proportion of polycystic ovary syndrome (PCOS), use of antidepressants, and animal contact was observed in the obese group (table 1) . In relation to outcomes of pregnancy, a higher proportion of gestational diabetes mellitus (GDM) and preeclampsia was observed in the obese group. Table 1: Main descriptive characteristics of the control and case groups. Significant differences (p<0.05) are highlighted in bold. Timepoint Variable Lean n(%) Obese n(%) p-value Background Alcohol 3 months before pregnancy 662 (88.7) 211 (83.0) 0.025 Animal contact 337 (45.2) 158 (62.2) <0.001 Antidepressant use 34 (4.6) 27 (10.7) <0.001 Autoimmune disease 2 (0.2) 1 (0.4) 1.0 Eating Disorder 82 (11.1) 26 (10.5) 0.86 Ever smoking 48 (6.4) 20 (7.8) 0.52 First pregnancy 293 (39.3) 99 (38.9) 0.99 Hypothyroidism 74 (9.9) 32 (12.6 ) 0.28 Natural conception 654 (87.7) 226 (88.9) 0.65 Previous misscariage (1 to 2) 92 (12.3 ) 85 (33.4) <0.001 Previous misscariages (3+) 28 (3.8) 17 (6.7) <0.001 SES score 0 1 (0.1) 1 (0.39) <0.001 SES score 1 59 (7.8) 47 (18.5) SES score 2 225 (30.2) 77 (30.3) SES score 3 461 (61.7) 129 (50.8) Time-point 1 Antibiotic usage 49 (6.6) 24 (9.4) 0.16 daily fiber consumption 650 (87.1) 200 (78.4) 0.0017 GI medication during pregnancy 3 (0.4) 3 (1.2) 0.35 Bristol rate:Fast 201 (26.9) 82 (32.4) <0.001 Bristol rate:Normal 170 (22.8) 39 (15.4) Bristol rate:Slow 202 (27.11) 54 (21.3) Bristol rate: Various 172 (23.1) 78 (30.8) Time-point 2 Antibiotics during pregnancy 78 (11.9) 34 (14.5) 0.36 daily fiber consumption 571 (76.5) 176 (69.2) 0.026 GI medication during pregnancy 3 (0.4) 6 (2.4) 0.013 Bristol rate:Fast 146 ( 22.3) 85 (36.1) <0.001 Bristol rate:Normal 152 ( 23.2) 31 (13.2) Bristol rate:Slow 206 (31.34) 45 (19.1) Bristol rate: Various 143 (21.9) 69 (29.4 ) Any time in pregnancy Probiotic consumption 128 (17.2) 27 (10.7) 0.016 Alcohol during pregnancy 62 (8.3) 9 (3.5) 0.015 Vegetarian diet 109 (14.6) 26 (10.3 ) 0.1 Vegan diet 12 (1.6) 4 (1.6) 1.0 Pregnancy outcomes Gestational diabetes mellitus 17 (2.2) 46 (18.1) <0.001 Preeclampsia 31 (4.1) 23 (9.1) 0.004 Preterm delivery 61 (8.1%) 20 (7.8%) 0.9 Taxonomy and within-sample diversity At the phylum level, a significant increase in Actinobacteria was observed from time point 1 to time point 2 for obese women cases, with a p-value of 0.019. In contrast, analysis in controls between the two time points showed a significant decrease in Euryarchaeota (p-value = 0.024) and Verrucomicrobia (p-value < 0.001), and an increase in Candidatus Melainabacteria (p-value = 0.019) (Fig. S1). Several differences were observed at the species level (fig. 2). Lean subjects had higher observed species richness at both points (mean: 300.7 vs 277.3; 308.5 vs. 282.5; both p < 0.0001) as well as higher diversity (Shannon’s entropy mean: 4.05 vs 3.92; 4.07 vs 3.89; both p < 0.0003) and evenness (Pielou’s evenness mean: 0.71 vs 0.701; 0.714 vs 0.692; both p < 0.05). While both cases and controls become more species-rich in the second time-point compared to the first (fig S2), this increase reaches statistical significance only among lean individuals (p = 0.00247). Beta-diversity (between groups) The distance between samples was calculated both in terms of presence/absence (Jaccard’s distance) and quantitatively (Aitchison distance). For both distance metrics and time points, lean and obese samples were significantly different (time point 1: Aitchison’s R² = 0.003, Jaccard’s R² = 0.004; time point 2: Aitchison’s R² = 0.005, Jaccard’s R² = 0.006; all p-values = 0.001 on 999 permutations). However, several other characteristics, including demographics (age, socioeconomic score), diet (fiber intake, plant-based diets), and mental health (stress scores, depression scores, use of neuropsychiatric medication) were also significantly correlated with the gut microbiome at both time points (Fig. 3; fig. S3). Beta-diversity (between time-points) The beta diversity between samples at time point one and those at time point two was compared separately within the obese group and the lean group. In both the case and control groups (Figure S4), a significantly lower total pairwise beta diversity was observed at time point 2 compared with time point 1, indicating that samples are more similar to each other in the third trimester. The magnitude of this decrease was higher in the case group, suggesting that obese individuals exhibit a more significant similarity in their gut microbial composition than non-obese individuals at both time points, particularly at time point 2. Differentially abundant species Based on the beta-diversity analysis and previous knowledge, we decided to analyze differentially abundant species with both a univariable model and two multivariable models. Both multivariable models account for primiparity, age and diet quality, with the second model additionally considering the Bristol stool form scale. The multivariable Model 1 revealed that 15 species were significantly enriched in obese individuals, while 17 species were significantly enriched in the control group. In late pregnancy, these numbers increased to 20 and 25 (fig S5). The multivariable model 2, which accounts for Bristol stool score and excludes subjects with irregular transit (alternating fast and slow), found 26 enriched species in the obese group and 27 in the control group in the early pregnancy while, in late pregnancy these numbers increased to 27 and 33 respectively (fig 4a and 4b; suppl. table S5 and S8). The univariable model identified 14 species as differentially enriched in obese women, while 17 were found to be enriched in control women during their early pregnancy (Fig. S4a). In late pregnancy, these numbers increased to 22 and 27 (Fig. S4b). However, there was little overlap between the species that were differentially abundant in early and late pregnancy (Tables S3-S8). Differentially abundant gut-metabolic modules From the sample’s total functional gene family complement, we mapped and analyzed a subset of gene families to KEGG pathways, which are known to affect cardiometabolic health. The univariable model identified two significantly abundant GMMs at time point 1 and one significantly abundant GMM at time point 2 (Fig. 5a, 5b). In multivariable model 1, 15 GMMs were found to be significantly abundant at time point 1, and one GMM was significantly abundant at time point 2 (Fig. 5c, 5d). For multivariable model 2, a total of four GMMs were found to be significantly abundant at time point 1, and no GMM was significantly different at time point 2 (Fig. 5E, 5F). Like the result of species-level differential analysis, the number of differentially abundant GMMs decreased at the late stage of pregnancy. Propionate production was the only consistent marker significantly more abundant in the lean individuals in five out of six models (tables S9 - S14). Discussion Our results from this national pregnancy cohort showed that the richness of the microbiome is, on average, increasing during pregnancy (only significantly in the lean population) and that beta-diversity is decreasing in both lean and obese women, in contrast to Koren et al. [ 32 ] who found dramatic changes in gut microbiota from the first to the third trimester of pregnancy. In the same study, an overall increase in Proteobacteria and Actinobacteria was observed, accompanied by a reduction in richness, a phenomenon that was only noted in the obese group. As in other studies [ 33 , 34 ], we observed that the overall composition of the gut microbiome is primarily associated with large bowel transit time, diet, and BMI. Beta diversity between the groups was also shown to be significantly different by both quantitative (Aitchison) and qualitative (Jaccard; presence/absence) metrics. Bristol stool form scale, BMI, antibiotics, diet and mental health were correlated to gut microbiome in both time points, as is shown from previous studies [ 33 , 34 ] Furthermore, studies have reported an increase in the abundance of Actinobacteria and Proteobacteria [ 35 ] in maternal obesity, whereas Actinobacteria changed in this work. Previous studies have shown a shift in gut microbiome composition during pregnancy toward more energy-producing communities, which are believed to be necessary for fetal growth [ 32 ]. These include Firmicutes, Proteobacteria, and Actinobacteria, where the latter has significant effects on the maintenance of the gut barrier and the induction of Treg cells [ 36 ]. In our models, mostly Firmicutes species were differentially abundant between lean and obese women at each time point, which is expected in populations with a Western-style diet [ 37 ]. Lean individuals also tended to have a higher diversity in time point 2; however, this difference wasn’t statistically significant. At the species level, we observed that lean subjects had higher species richness, Shannon diversity index and Pielou’s evenness index at both time points than the obese subjects. Although a more species-rich microbiome was observed in both groups at the second time point, this was only statistically for the lean group (Pielou’s evenness and Fig. S2 ). This could depend on statistical power, as the lean subjects were matched 3-to-1 with the obese. Additionally, we found significant differences in species composition between our groups, even after adjusting for age, primiparity, diet score, and Bristol stool form scale. Interestingly, vaginal species were detected in fecal samples in both groups: Lactobacillus iners in time point one in the lean group and Gardnerella vaginalis in time point two for the obese group. [ 38 ]. Lactobacilli in the gut decrease inflammation and keep the intestinal barrier function [ 39 ]. Van Baarlen et al. reviewed how different types of Lactobacilli improve the immune status of an organism, referring to various mechanisms. For example, Lactobacillus plantarum produces Lipoteichoic acid (LTA) and specific components of peptidoglycan (PGN), which can modulate the inhibitor of NF-κB kinase (IKK)/nuclear factor (NF)-κB. In epithelial cells, NF-κB nuclear activity leads to the expression of pro-inflammatory cytokines and chemokines, which is tightly regulated by NF-κB inhibitors [ 40 ]. In the obese group, all models identified a significantly higher abundance of Gardnerella vaginalis at time-point two. G. vaginali s is a bacterium that can form thick biofilms in the vaginal epithelium and is tightly associated with bacterial vaginosis [ 41 ] and premature preterm rupture of membranes [ 42 ]. In the gut, the presence of G. vaginalis is associated with a 16% increase in the risk of irritable bowel syndrome (IBS) [ 43 ]. Another hypothesis is that the use of antibiotics in treating bacterial vaginosis may contribute to intestinal dysbiosis. More studies need to understand how this dysbiosis may affect pregnancy and whether it could be used as a marker or as a potential therapeutic target. Prevotella corporis , typically found in the oral cavity, was also found to be more abundant in the obese group at point 2 in all models. These observations suggest that other microbiomes, such as the oral and vaginal, may also be worth exploring in relation to obesity and reproductive outcomes. Furthermore, we found that Isoptericola variabilis and Roseburia inulinovasum were most over-represented in the obese group. Isoptericola spp. are more commonly found in soils or the guts of insects; therefore, the significance of this finding is unclear. Interestingly, Roseburia spp. are butyrate producers typically associated with gut eubiosis [ 44 ]. The species that characterized the lean group were mostly still not isolated and named (GGB, genus-level genomic bins). When analyzing gut metabolic modules, only propionate production was consistently found to be significantly more abundant in the lean subjects. Roy et al . demonstrated that propionate and butyrate significantly reduced the inflammation-induced expression of pro-inflammatory mediators in the human placenta and adipose tissue of pregnant women [ 45 ]. They also demonstrated that propionate and butyrate significantly improved insulin sensitivity, suggesting a potential role for short-chain fatty acids in preventing and treating Gestational Diabetes Mellitus (GDM). The risk of GDM increases by approximately 4% for every 1 kg/m² increase in BMI during pregnancy [ 46 ]. Another study showed a significantly reduced levels of intestinal bacterial metabolites such as propionate in pregnant women experiencing preeclampsia. [ 47 ], whose risk doubles for every 5–7 kg/m 2 increase in BMI [ 48 ]. Our project has several important strengths, but also some limitations that affect data collection and analysis. It is a large, well-characterized cohort of pregnant women nationwide, with comprehensive data collected on various pregnancy outcomes, allowing for robust statistical analyses. Microbiome sampling from the gut at two time points, combined with longitudinal data collection across Sweden, makes the population more representative of the general population. The use of advanced, cutting-edge sequencing technology for metagenomic analyses of bacterial DNA and its functions will help us understand the significance and potential role of microbiomes in various outcomes. Furthermore, Sweden’s health registries, which are highly complete and up to date, allow us to cross-reference and minimize biases stemming from incorrectly self-reported data. Finally, using both Swedish and English questionnaires, as well as participant information, enabled a broader recruitment of participants. However, home-based sampling limits sampling and excludes additional samples that could have been useful, such as blood samples. The inclusion of patients occurred during pregnancy; therefore, pre-pregnancy microbiome samples were not collected. It may be essential to have pre-pregnancy data to facilitate screening before pregnancy, allowing for comparison of the eventual microbiome changes that occur during pregnancy. Another limitation is that the questionnaires included limited information on diet, sleep and physical activity. As in most studies, there is an over-representation of participants from higher socioeconomic backgrounds and those living in large cities, which may limit the generalizability of the findings to a more diverse population. In particular, while participation in the study was mostly from women living in larger cities, the highest obesity rates in Sweden are found in smaller towns and villages [ 49 ]. While our data conclusively demonstrates differences in the gut microbiome of lean and obese pregnant women, future work will need to assess whether these differences can be causally linked to various pregnancy outcomes. If so, interventions and monitoring strategies may need to be adapted for obese women, whose baseline microbiome is already distinct from the lean population. Conclusion The gut microbiomes of lean and obese women adapt to pregnancy similarly, but some crucial differences between the groups persist, even in late pregnancy (week 30). More research is needed to understand the potential gut-vaginal cross-talk during pregnancy and its significance during the gestational period. Gut microbiome composition may be one factor modulating the differential risk of various pregnancy complications between lean and obese individuals. If microbiome-based screening can be utilized, or if microbiome-altering approaches become more widely available to obese patients during pregnancy, it could profoundly alter the risk profile for a large portion of pregnancies. Declarations Ethics approval and consent to participate The SweMaMi study protocol has been approved by the Regional Board of Ethics, Stockholm, Sweden (2017/1118-31). All participants were at least 18 years old at enrollment and provided informed consent when completing the first online questionnaire. The study complied with the Declaration of Helsinki and the General Data Protection Regulation (GDPR) regulations. Consent for publication Not applicable Clinical trial number Not applicable Availability of data and materials The metagenomic sequences, along with selected metadata variables, have been submitted to ENA (project number PRJEB81814). Competing interests The Centre for Translational Microbiome Research (CTMR) and associated authors (NB, EF, ISK., LE, LWH) have been partly funded by Ferring Pharmaceuticals Funding Ferring Pharmaceuticals has funded this work with an unrestricted research grant to LE. The study was also supported by grants from the Swedish state, as per the agreement between the Swedish government and the county councils, known as the ALF agreement (Region Stockholm). LWH and BT were supported by the SciLifeLab and Wallenberg Data-Driven Life Science Program (KAW 2020.0239). Authors' contributions Conception - EWI, EF, EP, ISK, LE, NB Design - LWH Data acquisition and analysis - BT, EP, KC, UG Data interpretation – EF, EP, EWI, ISK, LWH, NB Initial draft - EP, BT, KC, LWH Text revision - EF, EWI, ISK, LE, LWH, NB, UG All authors have read and approved the final version. Acknowledgments We would like to express our gratitude to all volunteers in the SweMaMi cohort and all previous members of the Centre for Translational Microbiome Research. References World Obesity Day Report. World Obesity Federation; 2020. NCD Risk Factor Collaboration (NCD-RisC). Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight. Elife. 2021;10:e60060. Vallianou N, Dalamaga M, Stratigou T, Karampela I, Tsigalou C. Do antibiotics cause obesity through long-term alterations in the gut microbiome? A review of current evidence. Curr Obes Rep. 2021;10:244–62. 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Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science. 2016;352:565–9. Beckers KF, Flanagan JP, Sones JL. Microbiome and pregnancy: focus on microbial dysbiosis coupled with maternal obesity. Int J Obes (Lond). 2024;48:439–48. Binda C, Lopetuso LR, Rizzatti G, Gibiino G, Cennamo V, Gasbarrini A. Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Dig Liver Dis. 2018;50:421–8. Severino A, Tohumcu E, Tamai L, Dargenio P, Porcari S, Rondinella D, et al. The microbiome-driven impact of western diet in the development of noncommunicable chronic disorders. Best Pract Res Clin Gastroenterol. 2024;:101923. Romero R, Hassan SS, Gajer P, Tarca AL, Fadrosh DW, Bieda J, et al. The vaginal microbiota of pregnant women who subsequently have spontaneous preterm labor and delivery and those with a normal delivery at term. Microbiome. 2014;2:18. Li C, Peng K, Xiao S, Long Y, Yu Q. The role of Lactobacillus in inflammatory bowel disease: from actualities to prospects. Cell Death Discov. 2023;9:361. van Baarlen P, Wells JM, Kleerebezem M. Regulation of intestinal homeostasis and immunity with probiotic lactobacilli. Trends Immunol. 2013;34:208–15. Muzny CA, Taylor CM, Swords WE, Tamhane A, Chattopadhyay D, Cerca N, et al. An updated conceptual model on the pathogenesis of bacterial vaginosis. J Infect Dis. 2019;220:1399–405. Kacerovsky M, Pliskova L, Bolehovska R, Lesko D, Gerychova R, Janku P, et al. Cervical Gardnerella vaginalis in women with preterm prelabor rupture of membranes. PLoS One. 2021;16:e0245937. Agnello M, Carroll LN, Imam N, Pino R, Palmer C, Varas I, et al. Gut microbiome composition and risk factors in a large cross-sectional IBS cohort. BMJ Open Gastroenterol. 2020;7:e000345. Nie K, Ma K, Luo W, Shen Z, Yang Z, Xiao M, et al. Roseburia intestinalis: A beneficial gut organism from the discoveries in genus and species. Front Cell Infect Microbiol. 2021;11:757718. Roy R, Nguyen-Ngo C, Lappas M. Short-chain fatty acids as novel therapeutics for gestational diabetes. J Mol Endocrinol. 2020;65:21–34. Najafi F, Hasani J, Izadi N, Hashemi-Nazari S-S, Namvar Z, Mohammadi S, et al. The effect of prepregnancy body mass index on the risk of gestational diabetes mellitus: A systematic review and dose-response meta-analysis. Obes Rev. 2019;20:472–86. Jin J, Gao L, Zou X, Zhang Y, Zheng Z, Zhang X, et al. Gut dysbiosis promotes preeclampsia by regulating macrophages and trophoblasts. Circ Res. 2022;131:492–506. Sohlberg S, Stephansson O, Cnattingius S, Wikström A-K. Maternal body mass index, height, and risks of preeclampsia. Am J Hypertens. 2012;25:120–5. Nationella riktlinjer för vård vid obesitas. Socialstyrelsen; 2020. Additional Declarations No competing interests reported. Supplementary Files supplementarytables.xlsx 250331Supplementaryfigures.docx Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 10 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 20 May, 2025 Editor invited by journal 19 May, 2025 Submission checks completed at journal 16 May, 2025 First submitted to journal 16 May, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-6518171","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467146540,"identity":"bf551583-1a35-492e-9ddc-af4ca4ce7f7e","order_by":0,"name":"Evangelos Patavoukas","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Evangelos","middleName":"","lastName":"Patavoukas","suffix":""},{"id":467146541,"identity":"e13ef35e-f3d7-41f4-bbfb-12206c4d28e9","order_by":1,"name":"Bangzhuo Tong","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Bangzhuo","middleName":"","lastName":"Tong","suffix":""},{"id":467146542,"identity":"fd02f26e-35af-4236-9542-0bdebd5f2b04","order_by":2,"name":"Kyriakos Charalampous","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Kyriakos","middleName":"","lastName":"Charalampous","suffix":""},{"id":467146543,"identity":"9de06444-c3b5-4fe3-819d-c669456d78d8","order_by":3,"name":"Unnur Guðnadóttir","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Unnur","middleName":"","lastName":"Guðnadóttir","suffix":""},{"id":467146544,"identity":"a7ae30b2-b6dd-4cc3-a60e-0c9f514da0b6","order_by":4,"name":"Nele Brusselaers","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Nele","middleName":"","lastName":"Brusselaers","suffix":""},{"id":467146545,"identity":"296834d1-49bc-462b-82fa-fb41339c9e63","order_by":5,"name":"Ina Schuppe-Koistinen","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Ina","middleName":"","lastName":"Schuppe-Koistinen","suffix":""},{"id":467146547,"identity":"75bdcdd6-496f-42d2-ab65-dc71d1349dc9","order_by":6,"name":"Lars Engstrand","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Lars","middleName":"","lastName":"Engstrand","suffix":""},{"id":467146550,"identity":"1ffee254-3af6-4eb1-8a8b-cbbc79008161","order_by":7,"name":"Emma Fransson","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Fransson","suffix":""},{"id":467146551,"identity":"b763b4f4-ef7f-4b5e-98a8-aee53b268ce9","order_by":8,"name":"Eva Wiberg Itzel","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"Wiberg","lastName":"Itzel","suffix":""},{"id":467146554,"identity":"7cb70d4f-017e-41d5-b14e-a7da5229cfa9","order_by":9,"name":"Luisa Hugerth","email":"data:image/png;base64,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","orcid":"","institution":"Uppsala University","correspondingAuthor":true,"prefix":"","firstName":"Luisa","middleName":"","lastName":"Hugerth","suffix":""}],"badges":[],"createdAt":"2025-04-24 07:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518171/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-04473-8","type":"published","date":"2025-11-15T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84276158,"identity":"843c3046-dd6d-4a97-9419-7804a11fe350","added_by":"auto","created_at":"2025-06-10 05:38:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":288670,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of lean and obese participants. Colors are given on a log scale to facilitate the visualization of less densely populated regions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/d144fad0c25289dffd61f882.png"},{"id":84276207,"identity":"023cb8b7-d135-4e60-944a-836a6754afd4","added_by":"auto","created_at":"2025-06-10 05:39:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe gut microbiome of obese pregnant participants is less even, rich and diverse at both time points compared with the gut microbiome of control participants. \u003c/strong\u003eComparisons were made between cases and controls within each time point. A red line marks the median of each distribution\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/09ec970d2dffc6ff6479594c.png"},{"id":84276159,"identity":"4c409898-33ae-426d-8a12-805f32f1aba4","added_by":"auto","created_at":"2025-06-10 05:39:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBristol stool form scale, BMI, antibiotics and probiotics are major contributors to differences between samples at both time points\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePermanova R² (length of the bar) and p-value (color scale) based on Aitchison distance at early and late pregnancy\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/3a1a87f8b28634e7484d49e1.png"},{"id":84276198,"identity":"9fc7a005-bbba-47e7-989f-c0eebbe39c77","added_by":"auto","created_at":"2025-06-10 05:39:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":835470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMostly Firmicutes species exhibit differential abundance between lean and obese pregnant women in the multivariable model adjusted for primiparity, age, diet score, and colonic transit time.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe bubble plot displays the differentially abundant species identified using ANCOM-BC2, with adjustments for primiparity, age, diet score, and colonic transit time. The x-axis represents the log2-fold change of the species in the groups, while the y-axis displays the names of the differentially abundant species. Species in the blue shadow of each bubble plot are enriched in obese women, while species in the red shadow are enriched in control women. The color of each dot indicates its phylum. a) Time point 1, b) Time point 2.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/5d34c2b76be49a9bcdb6dd74.png"},{"id":84276160,"identity":"9f49a24c-5426-429e-bedc-63d6bf00e1ec","added_by":"auto","created_at":"2025-06-10 05:39:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eVolcano plots of the differential abundance calculated in an unadjusted analysis using ANCOM-BC2.\u003c/strong\u003e\u003c/em\u003e Volcano plot of the unadjusted model at a) time point 1 and b) time point 2. Volcano plot of adjusted model 1 at c) time point 1 and d) time point 2. Volcano plot of adjusted model 2 (e) at time point 1 and (f) at time point 2. Gut metabolic modules with log2 fold change values less than 0 are more abundant in lean controls, and those with log2 fold change values greater than 0 are more abundant in obese individuals. Significantly abundant GMMs were highlighted in black and labeled. The vertical red dashed line represents the threshold of the q value at 0.05, while the horizontal red dashed lines represent the thresholds of log2 fold change at -0.5 and 0.5, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/f1f3a5e2844ca22f6a0b2c37.png"},{"id":96105912,"identity":"da88525e-0bb4-4d28-8a5c-c4a15574297e","added_by":"auto","created_at":"2025-11-17 16:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2562267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/0aa19f57-633f-487a-a24b-a499147e6015.pdf"},{"id":84278635,"identity":"a46a9736-3c49-4191-950a-ce0ef21b1404","added_by":"auto","created_at":"2025-06-10 06:09:19","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8202896,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/048e8764ccf592e171033e33.xlsx"},{"id":84276213,"identity":"714aff6c-424d-4d4b-b635-cbf283149555","added_by":"auto","created_at":"2025-06-10 05:39:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3580062,"visible":true,"origin":"","legend":"","description":"","filename":"250331Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6518171/v1/8a221da0bd5f98a88f5e78ce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences in the gut microbiome of obese and non-obese pregnant women: a matched cohort study in Sweden","fulltext":[{"header":"Background","content":"\u003cp\u003eObesity is a significant public health problem in almost all middle- and high-income countries. According to WHO, one out of five individuals will be obese by 2025 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global rise in obesity can be attributed to an increasingly obesogenic environment (e.g., reduced energy expenditure, higher prices of fresh produce, food additives, and lower availability of healthy food [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]), but animal and human evidence also supports a role for early-life microbiome disturbances, such as antibiotic usage, in promoting obesity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Generally, obesity has been associated with gut microbiome imbalance (dysbiosis), lower microbiota diversity, and reduced microbial gene richness [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent studies propose that a high-fat diet induces dysbiosis and subsequent systemic inflammation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which may partially explain obesity-associated dysbiosis. This inflammatory cascade is implicated in conditions like infertility, reduced conception rates, and early pregnancy loss, which are also associated with vaginal dysbiosis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This suggests a close interaction between diet, microbiome, gut health, and reproductive health.\u003c/p\u003e \u003cp\u003eStudying the maternal microbiome has implications not only for pregnancy but for the lifelong health of the neonate. A systematic review reported alterations in the maternal microbiome throughout pregnancy, resulting in elevated levels of bacterial byproducts in the mother's bloodstream [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], including short-chain fatty acids (SCFA), amino acids, vitamin K and B-complex vitamins [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These, in turn, can enter the fetal circulation, leading to potential repercussions for both physical and mental health outcomes and influencing the colonization of the infant's microbiome [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, animals colonized with the gut microbiota of obese individuals tend to gain weight even with unchanged food intake and activity levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The same could be true for newborn humans; establishing a neonate's initial microbial community can either impose health advantages or pose a risk for future diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePregnant women with a higher Body Mass Index (BMI) are an increasing group in antenatal care worldwide and in Sweden [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They need to be studied more thoroughly to get better care to minimize the risk of complications during their pregnancies. Here, we leverage the most extensive longitudinal pregnancy microbiome study to date, the SweMaMi cohort [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], to examine the variation in the gut microbiome in obese pregnant women and see if it differs from that of normal-weight pregnant women in terms of species richness, diversity, specifically altered species, and critical microbial gene functions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eCohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Swedish Maternal Microbiome (SweMaMi) study is a prospective cohort study examining the microbiome during pregnancy, with adverse pregnancy outcomes, including preterm birth and pregnancy loss, as its primary outcomes [13]. Its study protocol has been approved by the Regional Ethics Board, Stockholm, Sweden (2017/1118-31). All participants were at least 18 years old at enrollment and provided informed consent when completing the first online questionnaire. Participants filled in extensive questionnaires on health and pregnancy before gestational week 20 and were consequently sent microbiome collection kits for home sampling. The same procedure was repeated around gestational week 30 and postpartum [13] (but in this work we only analyzed samples collected during pregnancy). The questionnaires in Swedish and English are available from Zenodo: https://doi.org/10.5281/zenodo.15369670\u003c/p\u003e\n\u003cp\u003eHere, we conducted a nested case-control study comparing the gut microbes of 274 obese pregnant women (pre-pregnancy BMI \u0026gt; 30) individually matched to 746 lean controls (BMI \u0026lt; 25) in both the second and third trimesters. Cases were selected if they had a self-reported BMI greater than 30 before pregnancy, and fecal microbiome data were available at least for the first time point. Cases were individually matched 1-to-3 with lean controls (BMI 18.5-25) based on the following matching criteria: parity (nulliparous or parous), gestational week of first fecal samples (\u0026plusmn;1 week), and age (\u0026plusmn;5 years).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDNA extraction, sequencing and annotation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDNA was extracted, sequenced and annotated as previously reported [13]. Briefly, samples were sequenced on MGI instruments using paired-end 150 bp reads and a median library size of 61 million reads per sample (range: 21.3-116.5 million). Samples were trimmed with fastp [14], human DNA reads were removed by annotating with kraken2 [15] on a reference containing only the human Ghc38 genome, and reads were annotated to the species level using the Metaphlan4 pipeline [16] (table S1), as well as functionally annotated with the Humann2 pipeline. To derive the estimated abundance of gut metabolic modules (GMMs) [17], the gene families annotated from Humann2 [18] were regrouped to KEGG pathways using the built-in script humann_regroup_table.py in Humann2. The abundance table of the regrouped KEGG pathways was then used as input for Omixer-RPM [19] to estimate the abundance table of GMMs (Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCategorical and ordinal variables on baseline maternal health\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eValidated scales were used where possible, including the Pregnancy Unique Quantification of Emesis (PUQE) [20] for measuring nausea and vomiting, the Perceived Stress Scale (PSS-4) [21] for assessing stress, and the Edinburgh Postnatal Depression Scale (EPDS) [22] for evaluating mental distress. Additionally, some variables used are standard to all SweMaMi analyses, but not validated. These include:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBowel transit time. Based on the question \u0026ldquo;How do your stools typically look like?\u0026rdquo; and the Bristol stool scale [23]. Only Bristol 3-4: regular transit. Including Bristol 1-4 only: slow transit. Including Bristol 3-7 only: fast transit. Including both 1-2 and 5-7: varied transit.\u003c/li\u003e\n \u003cli\u003eSocioeconomic score: one point each was given to having a bachelor\u0026rsquo;s degree or higher, working full-time, and marriage or cohabitation\u003c/li\u003e\n \u003cli\u003eDiet score: five-point scale, with one point each given for fruits, vegetables or whole grain bread daily and two possible additional point for sweetened beverages or other sweets once a week or more seldom\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eBiostatistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in the R programming environment, using libraries \u0026ldquo;vegan\u0026rdquo; [24], \u0026ldquo;ggpubr\u0026rdquo;, \u0026ldquo;ggplot2\u0026rdquo; [25], \u0026ldquo;dplyr\u0026rdquo; [26], \u0026ldquo;tidyverse\u0026rdquo;[27] , \u0026ldquo;phyloseq\u0026rdquo; [28], \u0026ldquo;microbiome\u0026rdquo; [29], and \u0026ldquo;ANCOMBC\u0026rdquo; [30].\u003c/p\u003e\n\u003cp\u003eAlpha-diversity was measured as observed species richness, as well as Shannon\u0026rsquo;s entropy and Pielou\u0026rsquo;s evenness. The distance between samples was calculated using either Aitchison\u0026rsquo;s or Jaccard\u0026rsquo;s distances. Univariable PERMANOVA were computed using the libraries \u0026ldquo;vegan\u0026rdquo; and \u0026ldquo;ape\u0026rdquo; [24, 31], and significant variables were selected for further analysis in multivariable models using a stepwise forward selection approach.\u003c/p\u003e\n\u003cp\u003eThe differential abundance of bacterial species and gut metabolic modules was calculated using ANCOMBC v.2.2. These analyses were performed in both univariable and multivariable models which included primiparity (yes/no), age (in years), and diet score (0-4). Additionally, the second model included colonic transit time as -1 (slow transit), 0 (regular transit) and +1 (fast transit). Subjects with \u0026ldquo;various\u0026rdquo; transit times were excluded from this sub-analysis. Species and modules with a BH-adjusted p-value less than 0.05 were considered significantly different; however, only those with an absolute log2-fold change greater than 0.5 are plotted and discussed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eCohort description\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited from all Swedish regions \u003cstrong\u003e(fig. 1)\u003c/strong\u003e. The median BMI in our control group (n = 746) was 22 kg/m\u0026sup2; (IQR: 19-24 kg/m\u0026sup2;), whereas in our cases (n = 274), it was 33 kg/m\u0026sup2; (IQR: 30-60 kg/m\u0026sup2;) \u003cstrong\u003e(table 1)\u003c/strong\u003e. The mean age was 32 years in both groups. Some differences were observed in alcohol consumption (more common in the control group) and higher use of probiotics, fibers, and vitamins in the lean group, whereas a higher proportion of polycystic ovary syndrome (PCOS), use of antidepressants, and animal contact was observed in the obese group \u0026nbsp;\u003cstrong\u003e(table 1)\u003c/strong\u003e. In relation to outcomes of pregnancy, a higher proportion of gestational diabetes mellitus (GDM) and preeclampsia was observed in the obese group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Main descriptive characteristics of the control and case groups. Significant differences (p\u0026lt;0.05) are highlighted in bold.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTimepoint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"15\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBackground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAlcohol 3 months before pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e662 (88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e211 (83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAnimal contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e337 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e158 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAntidepressant use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e34 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e27 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAutoimmune disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEating Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e82 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e26 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEver smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e48 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e20 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eFirst pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e293 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e99 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eHypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e74 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e32 (12.6 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eNatural conception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e654 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e226 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePrevious misscariage (1 to 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e92 (12.3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePrevious misscariages (3+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e28 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e17 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSES score 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSES score 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e59 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e47 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSES score 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e225 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e77 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSES score 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e461 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e129 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 179px;\"\u003e\n \u003cp\u003eTime-point 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAntibiotic usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e24 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003edaily fiber consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e650 (87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e200 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGI medication during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Fast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e201 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e82 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e170 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e39 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Slow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e202 (27.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e54 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate: Various\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e172 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e78 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 179px;\"\u003e\n \u003cp\u003eTime-point 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAntibiotics during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e78 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e34 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003edaily fiber consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e571 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e176 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGI medication during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e6 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Fast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e146 ( 22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e152 ( 23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e31 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate:Slow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e206 (31.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e45 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBristol rate: Various\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e143 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e69 (29.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAny time in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eProbiotic consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e128 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e27 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAlcohol during pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e62 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e9 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eVegetarian diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e109 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e26 (10.3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eVegan diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e4 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePregnancy outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e46 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePreeclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e31 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e23 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePreterm delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e61 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e20 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eTaxonomy and within-sample diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the phylum level, a significant increase in Actinobacteria was observed from time point 1 to time point 2 for obese women cases, with a p-value of 0.019. In contrast, analysis in controls between the two time points showed a significant decrease in Euryarchaeota (p-value = 0.024) and Verrucomicrobia (p-value \u0026lt; 0.001), and an increase in Candidatus Melainabacteria (p-value = 0.019) (Fig. S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral differences were observed at the species level (fig. 2). Lean subjects had higher observed species richness at both points (mean: 300.7 vs 277.3; 308.5 vs. 282.5; both p \u0026lt; 0.0001) as well as higher diversity (Shannon\u0026rsquo;s entropy mean: 4.05 vs 3.92; 4.07 vs 3.89; both p \u0026lt; 0.0003) and evenness (Pielou\u0026rsquo;s evenness mean: 0.71 vs 0.701; 0.714 vs 0.692; both p \u0026lt; 0.05). While both cases and controls become more species-rich in the second time-point compared to the first (fig S2), this increase reaches statistical significance only among lean individuals (p = 0.00247).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBeta-diversity (between groups)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe distance between samples was calculated both in terms of presence/absence (Jaccard\u0026rsquo;s distance) and quantitatively (Aitchison distance). For both distance metrics and time points, lean and obese samples were significantly different (time point 1: Aitchison\u0026rsquo;s R\u0026sup2; = 0.003, Jaccard\u0026rsquo;s R\u0026sup2; = 0.004; time point 2: Aitchison\u0026rsquo;s R\u0026sup2; = 0.005, Jaccard\u0026rsquo;s R\u0026sup2; = 0.006; all p-values = 0.001 on 999 permutations). However, several other characteristics, including demographics (age, socioeconomic score), diet (fiber intake, plant-based diets), and mental health (stress scores, depression scores, use of neuropsychiatric medication) were also significantly correlated with the gut microbiome at both time points (Fig. 3; fig. S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBeta-diversity (between time-points)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe beta diversity between samples at time point one and those at time point two was compared separately within the obese group and the lean group. In both the case and control groups (Figure S4), a significantly lower total pairwise beta diversity was observed at time point 2 compared with time point 1, indicating that samples are more similar to each other in the third trimester. The magnitude of this decrease was higher in the case group, suggesting that obese individuals exhibit a more significant similarity in their gut microbial composition than non-obese individuals at both time points, particularly at time point 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferentially abundant species\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on the beta-diversity analysis and previous knowledge, we decided to analyze differentially abundant species with both a univariable model and two multivariable models. Both multivariable models account for primiparity, age and diet quality, with the second model additionally considering the Bristol stool form scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe multivariable Model 1 revealed that 15 species were significantly enriched in obese individuals, while 17 species were significantly enriched in the control group. In late pregnancy, these numbers increased to 20 and 25 (fig S5). The multivariable model 2, which accounts for Bristol stool score and excludes subjects with irregular transit (alternating fast and slow), found 26 enriched species in the obese group and 27 in the control group in the early pregnancy while, in late pregnancy these numbers increased to 27 and 33 respectively (fig 4a and 4b; suppl. table S5 and S8). The univariable model identified 14 species as differentially enriched in obese women, while 17 were found to be enriched in control women during their early pregnancy (Fig. S4a). In late pregnancy, these numbers increased to 22 and 27 (Fig. S4b). However, there was little overlap between the species that were differentially abundant in early and late pregnancy (Tables S3-S8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferentially abundant gut-metabolic modules\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the sample\u0026rsquo;s total functional gene family complement, we mapped and analyzed a subset of gene families to KEGG pathways, which are known to affect cardiometabolic health. The univariable model identified two significantly abundant GMMs at time point 1 and one significantly abundant GMM at time point 2 (Fig. 5a, 5b). In multivariable model 1, 15 GMMs were found to be significantly abundant at time point 1, and one GMM was significantly abundant at time point 2 (Fig. 5c, 5d). For multivariable model 2, a total of four GMMs were found to be significantly abundant at time point 1, and no GMM was significantly different at time point 2 (Fig. 5E, 5F). Like the result of species-level differential analysis, the number of differentially abundant GMMs decreased at the late stage of pregnancy. Propionate production was the only consistent marker significantly more abundant in the lean individuals in five out of six models (tables S9 - S14).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results from this national pregnancy cohort showed that the richness of the microbiome is, on average, increasing during pregnancy (only significantly in the lean population) and that beta-diversity is decreasing in both lean and obese women, in contrast to Koren et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] who found dramatic changes in gut microbiota from the first to the third trimester of pregnancy. In the same study, an overall increase in Proteobacteria and Actinobacteria was observed, accompanied by a reduction in richness, a phenomenon that was only noted in the obese group.\u003c/p\u003e \u003cp\u003eAs in other studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], we observed that the overall composition of the gut microbiome is primarily associated with large bowel transit time, diet, and BMI. Beta diversity between the groups was also shown to be significantly different by both quantitative (Aitchison) and qualitative (Jaccard; presence/absence) metrics. Bristol stool form scale, BMI, antibiotics, diet and mental health were correlated to gut microbiome in both time points, as is shown from previous studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFurthermore, studies have reported an increase in the abundance of Actinobacteria and Proteobacteria [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in maternal obesity, whereas Actinobacteria changed in this work. Previous studies have shown a shift in gut microbiome composition during pregnancy toward more energy-producing communities, which are believed to be necessary for fetal growth [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These include Firmicutes, Proteobacteria, and Actinobacteria, where the latter has significant effects on the maintenance of the gut barrier and the induction of Treg cells [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our models, mostly Firmicutes species were differentially abundant between lean and obese women at each time point, which is expected in populations with a Western-style diet [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Lean individuals also tended to have a higher diversity in time point 2; however, this difference wasn\u0026rsquo;t statistically significant.\u003c/p\u003e \u003cp\u003eAt the species level, we observed that lean subjects had higher species richness, Shannon diversity index and Pielou\u0026rsquo;s evenness index at both time points than the obese subjects. Although a more species-rich microbiome was observed in both groups at the second time point, this was only statistically for the lean group (Pielou\u0026rsquo;s evenness and Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This could depend on statistical power, as the lean subjects were matched 3-to-1 with the obese. Additionally, we found significant differences in species composition between our groups, even after adjusting for age, primiparity, diet score, and Bristol stool form scale.\u003c/p\u003e \u003cp\u003eInterestingly, vaginal species were detected in fecal samples in both groups: \u003cem\u003eLactobacillus iners\u003c/em\u003e in time point one in the lean group and \u003cem\u003eGardnerella vaginalis\u003c/em\u003e in time point two for the obese group. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Lactobacilli in the gut decrease inflammation and keep the intestinal barrier function [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Van Baarlen \u003cem\u003eet al.\u003c/em\u003e reviewed how different types of Lactobacilli improve the immune status of an organism, referring to various mechanisms. For example, \u003cem\u003eLactobacillus plantarum\u003c/em\u003e produces Lipoteichoic acid (LTA) and specific components of peptidoglycan (PGN), which can modulate the inhibitor of NF-κB kinase (IKK)/nuclear factor (NF)-κB. In epithelial cells, NF-κB nuclear activity leads to the expression of pro-inflammatory cytokines and chemokines, which is tightly regulated by NF-κB inhibitors [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the obese group, all models identified a significantly higher abundance of \u003cem\u003eGardnerella vaginalis\u003c/em\u003e at time-point two. \u003cem\u003eG. vaginali\u003c/em\u003es is a bacterium that can form thick biofilms in the vaginal epithelium and is tightly associated with bacterial vaginosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and premature preterm rupture of membranes [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the gut, the presence of \u003cem\u003eG. vaginalis\u003c/em\u003e is associated with a 16% increase in the risk of irritable bowel syndrome (IBS) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Another hypothesis is that the use of antibiotics in treating bacterial vaginosis may contribute to intestinal dysbiosis. More studies need to understand how this dysbiosis may affect pregnancy and whether it could be used as a marker or as a potential therapeutic target. \u003cem\u003ePrevotella corporis\u003c/em\u003e, typically found in the oral cavity, was also found to be more abundant in the obese group at point 2 in all models. These observations suggest that other microbiomes, such as the oral and vaginal, may also be worth exploring in relation to obesity and reproductive outcomes.\u003c/p\u003e \u003cp\u003eFurthermore, we found that \u003cem\u003eIsoptericola variabilis\u003c/em\u003e and \u003cem\u003eRoseburia inulinovasum\u003c/em\u003e were most over-represented in the obese group. \u003cem\u003eIsoptericola spp.\u003c/em\u003e are more commonly found in soils or the guts of insects; therefore, the significance of this finding is unclear. Interestingly, \u003cem\u003eRoseburia spp.\u003c/em\u003e are butyrate producers typically associated with gut eubiosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The species that characterized the lean group were mostly still not isolated and named (GGB, genus-level genomic bins).\u003c/p\u003e \u003cp\u003eWhen analyzing gut metabolic modules, only propionate production was consistently found to be significantly more abundant in the lean subjects. Roy \u003cem\u003eet al\u003c/em\u003e. demonstrated that propionate and butyrate significantly reduced the inflammation-induced expression of pro-inflammatory mediators in the human placenta and adipose tissue of pregnant women [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. They also demonstrated that propionate and butyrate significantly improved insulin sensitivity, suggesting a potential role for short-chain fatty acids in preventing and treating Gestational Diabetes Mellitus (GDM). The risk of GDM increases by approximately 4% for every 1 kg/m\u0026sup2; increase in BMI during pregnancy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Another study showed a significantly reduced levels of intestinal bacterial metabolites such as propionate in pregnant women experiencing preeclampsia. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], whose risk doubles for every 5\u0026ndash;7 kg/m\u003csup\u003e2\u003c/sup\u003e increase in BMI [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur project has several important strengths, but also some limitations that affect data collection and analysis. It is a large, well-characterized cohort of pregnant women nationwide, with comprehensive data collected on various pregnancy outcomes, allowing for robust statistical analyses. Microbiome sampling from the gut at two time points, combined with longitudinal data collection across Sweden, makes the population more representative of the general population. The use of advanced, cutting-edge sequencing technology for metagenomic analyses of bacterial DNA and its functions will help us understand the significance and potential role of microbiomes in various outcomes. Furthermore, Sweden\u0026rsquo;s health registries, which are highly complete and up to date, allow us to cross-reference and minimize biases stemming from incorrectly self-reported data. Finally, using both Swedish and English questionnaires, as well as participant information, enabled a broader recruitment of participants.\u003c/p\u003e \u003cp\u003eHowever, home-based sampling limits sampling and excludes additional samples that could have been useful, such as blood samples. The inclusion of patients occurred during pregnancy; therefore, pre-pregnancy microbiome samples were not collected. It may be essential to have pre-pregnancy data to facilitate screening before pregnancy, allowing for comparison of the eventual microbiome changes that occur during pregnancy. Another limitation is that the questionnaires included limited information on diet, sleep and physical activity. As in most studies, there is an over-representation of participants from higher socioeconomic backgrounds and those living in large cities, which may limit the generalizability of the findings to a more diverse population. In particular, while participation in the study was mostly from women living in larger cities, the highest obesity rates in Sweden are found in smaller towns and villages [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile our data conclusively demonstrates differences in the gut microbiome of lean and obese pregnant women, future work will need to assess whether these differences can be causally linked to various pregnancy outcomes. If so, interventions and monitoring strategies may need to be adapted for obese women, whose baseline microbiome is already distinct from the lean population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe gut microbiomes of lean and obese women adapt to pregnancy similarly, but some crucial differences between the groups persist, even in late pregnancy (week 30). More research is needed to understand the potential gut-vaginal cross-talk during pregnancy and its significance during the gestational period. Gut microbiome composition may be one factor modulating the differential risk of various pregnancy complications between lean and obese individuals. If microbiome-based screening can be utilized, or if microbiome-altering approaches become more widely available to obese patients during pregnancy, it could profoundly alter the risk profile for a large portion of pregnancies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe SweMaMi study protocol has been approved by the Regional Board of Ethics, Stockholm, Sweden (2017/1118-31). All participants were at least 18 years old at enrollment and provided informed consent when completing the first online questionnaire. The study complied with the Declaration of Helsinki and the General Data Protection Regulation (GDPR) regulations.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eClinical trial number\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe metagenomic sequences, along with selected metadata variables, have been submitted to ENA (project number PRJEB81814).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe Centre for Translational Microbiome Research (CTMR) and associated authors (NB, EF, ISK., LE, LWH) have been partly funded by Ferring Pharmaceuticals\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eFerring Pharmaceuticals has funded this work with an unrestricted research grant to LE. The study was also supported by grants from the Swedish state, as per the agreement between the Swedish government and the county councils, known as the ALF agreement (Region Stockholm). LWH and BT were supported by the SciLifeLab and Wallenberg Data-Driven Life Science Program (KAW 2020.0239).\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eConception - EWI, EF, EP, ISK, LE, NB\u003c/p\u003e\n\u003cp\u003eDesign - LWH\u003c/p\u003e\n\u003cp\u003eData acquisition and analysis - BT, EP, KC, UG\u003c/p\u003e\n\u003cp\u003eData interpretation \u0026ndash; EF, EP, EWI, ISK, LWH, NB\u003c/p\u003e\n\u003cp\u003eInitial draft - EP, BT, KC, LWH\u003c/p\u003e\n\u003cp\u003eText revision - EF, EWI, ISK, LE, LWH, NB, UG\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eWe would like to express our gratitude to all volunteers in the SweMaMi cohort and all previous members of the Centre for Translational Microbiome Research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Obesity Day Report. 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Scand J Gastroenterol. 1997;32:920\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003e Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2017.\u003c/li\u003e\n\u003cli\u003e Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2016.\u003c/li\u003e\n\u003cli\u003e Wickham H, Fran\u0026ccedil;ois R, Henry L, M\u0026uuml;ller K, Vaughan D. dplyr: A Grammar of Data Manipulation. CRAN: Contributed Packages. 2014.\u003c/li\u003e\n\u003cli\u003e Wickham H, Averick M, Bryan J, Chang W, McGowan LD, Fran\u0026ccedil;ois R, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4:1686.\u003c/li\u003e\n\u003cli\u003e McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.\u003c/li\u003e\n\u003cli\u003e Lahti L, Shetty S. microbiome R package. 2017.\u003c/li\u003e\n\u003cli\u003e Lin H, Peddada SD. 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Science. 2016;352:565\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003e Beckers KF, Flanagan JP, Sones JL. Microbiome and pregnancy: focus on microbial dysbiosis coupled with maternal obesity. Int J Obes (Lond). 2024;48:439\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003e Binda C, Lopetuso LR, Rizzatti G, Gibiino G, Cennamo V, Gasbarrini A. Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Dig Liver Dis. 2018;50:421\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003e Severino A, Tohumcu E, Tamai L, Dargenio P, Porcari S, Rondinella D, et al. The microbiome-driven impact of western diet in the development of noncommunicable chronic disorders. Best Pract Res Clin Gastroenterol. 2024;:101923.\u003c/li\u003e\n\u003cli\u003e Romero R, Hassan SS, Gajer P, Tarca AL, Fadrosh DW, Bieda J, et al. The vaginal microbiota of pregnant women who subsequently have spontaneous preterm labor and delivery and those with a normal delivery at term. 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Gut microbiome composition and risk factors in a large cross-sectional IBS cohort. BMJ Open Gastroenterol. 2020;7:e000345.\u003c/li\u003e\n\u003cli\u003e Nie K, Ma K, Luo W, Shen Z, Yang Z, Xiao M, et al. Roseburia intestinalis: A beneficial gut organism from the discoveries in genus and species. Front Cell Infect Microbiol. 2021;11:757718.\u003c/li\u003e\n\u003cli\u003e Roy R, Nguyen-Ngo C, Lappas M. Short-chain fatty acids as novel therapeutics for gestational diabetes. J Mol Endocrinol. 2020;65:21\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003e Najafi F, Hasani J, Izadi N, Hashemi-Nazari S-S, Namvar Z, Mohammadi S, et al. The effect of prepregnancy body mass index on the risk of gestational diabetes mellitus: A systematic review and dose-response meta-analysis. Obes Rev. 2019;20:472\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003e Jin J, Gao L, Zou X, Zhang Y, Zheng Z, Zhang X, et al. Gut dysbiosis promotes preeclampsia by regulating macrophages and trophoblasts. Circ Res. 2022;131:492\u0026ndash;506.\u003c/li\u003e\n\u003cli\u003e Sohlberg S, Stephansson O, Cnattingius S, Wikstr\u0026ouml;m A-K. Maternal body mass index, height, and risks of preeclampsia. Am J Hypertens. 2012;25:120\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003e Nationella riktlinjer f\u0026ouml;r v\u0026aring;rd vid obesitas. Socialstyrelsen; 2020.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, Obesity, Pregnancy, Dysbiosis, Preeclampsia, Diabetes, Hypertension, Diversity","lastPublishedDoi":"10.21203/rs.3.rs-6518171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDifferences in the gut microbiome between lean and obese individuals, even twins, have been recognized for over a decade. The causative role of the microbiome in obesity is known from both mouse and human studies. In parallel, the gut microbiome has been implicated in the most common complications of pregnancy, including preterm birth, gestational diabetes mellitus, and gestational hypertension. Despite obesity being a well-established risk factor for these complications, the composition of the gut microbiome of obese pregnant individuals has not yet been studied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe differences in the gut microbiome of lean and obese persons persist during gestation. Obese individuals have a less diverse and less rich microbiome throughout all trimesters of pregnancy. In the first trimester, 53 species differ significantly between lean and obese individuals, and 60 in the early third trimester, after adjusting for confounders. Additionally, obese individuals harbored a consistently lower potential to produce propionate in their gut microbiomes, even after adjustments.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGut microbes adapt to pregnancy similarly, but some crucial differences between lean and obese the groups persist, even in late pregnancy. This may be a potential underexplored mechanism explaining the increased rates of pregnancy complications among obese patients. Diet, probiotics, or medication interventions to correct the gut microbiome of pregnant obese individuals could potentially improve their pregnancy outcomes.\u003c/p\u003e","manuscriptTitle":"Differences in the gut microbiome of obese and non-obese pregnant women: a matched cohort study in Sweden","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 05:38:37","doi":"10.21203/rs.3.rs-6518171/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T00:20:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T20:41:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T13:31:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T08:00:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T05:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161192172914957332539990969891436021027","date":"2025-06-06T18:52:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2619724751026850982374536948505501889","date":"2025-06-05T16:31:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131354913306240209624255901741672926430","date":"2025-06-04T14:23:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182465641657757478765942550447465265170","date":"2025-06-04T10:36:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121220148358709368854437378048347009282","date":"2025-06-04T07:20:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260030635620760568259954619842256473479","date":"2025-06-04T07:01:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T06:04:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T03:29:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-19T23:12:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-16T18:31:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-05-16T18:30:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99eb6d00-42c6-4535-850b-24111101010b","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:10:31+00:00","versionOfRecord":{"articleIdentity":"rs-6518171","link":"https://doi.org/10.1186/s12866-025-04473-8","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-11-15 15:57:22","publishedOnDateReadable":"November 15th, 2025"},"versionCreatedAt":"2025-06-10 05:38:37","video":"","vorDoi":"10.1186/s12866-025-04473-8","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04473-8","workflowStages":[]},"version":"v1","identity":"rs-6518171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518171","identity":"rs-6518171","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}