Metabolic differences in the maternal gut microbiome precede the birth of large-for-gestational-age infants: A nested case-control study

Metabolic differences in the maternal gut microbiome precede the birth of large-for-gestational-age infants: A nested case-control study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Metabolic differences in the maternal gut microbiome precede the birth of large-for-gestational-age infants: A nested case-control study View ORCID Profile Anusha T. Antony , View ORCID Profile Fatemeh Mohseni , View ORCID Profile Gaël Toubon , View ORCID Profile Unnur Guðnadóttir , View ORCID Profile Lars Engstrand , View ORCID Profile Emma Fransson , View ORCID Profile Ina Schuppe-Koistinen , View ORCID Profile Nele Brusselaers , View ORCID Profile Luisa W. Hugerth doi: https://doi.org/10.1101/2025.10.08.25337564 Anusha T. Antony 1 Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anusha T. Antony Fatemeh Mohseni 1 Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fatemeh Mohseni Gaël Toubon 2 Food and Nutrition Science, Life Sciences, Chalmers University of Technology Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gaël Toubon Unnur Guðnadóttir 3 Department of Women’s and Children’s Health, Karolinska Institutet , Solna, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Unnur Guðnadóttir Lars Engstrand 4 Centre for Translational Microbiome Research, Department of Microbiology , Tumor and Cell Biology (MTC), Karolinska Institutet , Solna, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lars Engstrand Emma Fransson 4 Centre for Translational Microbiome Research, Department of Microbiology , Tumor and Cell Biology (MTC), Karolinska Institutet , Solna, Sweden 5 Department of Women’s and Children’s health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma Fransson Ina Schuppe-Koistinen 4 Centre for Translational Microbiome Research, Department of Microbiology , Tumor and Cell Biology (MTC), Karolinska Institutet , Solna, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ina Schuppe-Koistinen Nele Brusselaers 3 Department of Women’s and Children’s Health, Karolinska Institutet , Solna, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nele Brusselaers Luisa W. Hugerth 1 Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luisa W. Hugerth For correspondence: luisa.hugerth{at}scilifelab.se Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Neonatal size influences newborn health and is affected by prenatal factors, such as the maternal gut microbiome, which impacts maternal metabolism and fetal growth. Methods A nested case-control study (1:2 matched) was conducted within the Swedish Maternal Microbiome (SweMaMi, 2017–2021) cohort to investigate the associations between maternal gut microbiome profiles during pregnancy and the risk of delivering large-for-gestational-age (LGA; birth weight >90th percentile) infants compared to appropriate-for-gestational-age (AGA; birth weight 10-90th percentile) infants, with a focus on dietary influence. Shotgun metagenomics enabled taxonomic and functional profiling of fecal samples collected during early (TP1: 10–20 weeks; n = 245:490) and late pregnancy (TP2: 28–32 weeks; n = 157:314). Additionally, we analyzed within-group changes in microbiome diversity from TP1 to TP2 to compare longitudinal patterns between the groups. Results At TP1, microbial species richness was higher in mothers of LGA infants (p = 0.01, 95% CI: 3.26 to 24.9), but this difference was not observed at TP2 (p = 0.30, 95% CI: –7.04 to 22.7). Beta diversity (overall microbial structure) did not differ significantly between groups at either time point. Within-sample diversity, as measured by the Shannon index, showed a significantly greater decline in the LGA group compared to the AGA group (p = 0.03, 95% CI: –0.13 to –0.007), suggesting a loss of microbial diversity specific to the LGA group over time. Evenness also declined in both groups, but the change was significantly more pronounced in the LGA group (p = 0.047, 95% CI: –0.02 to –0.0001. At TP1, the LGA group was functionally enriched in proteolytic pathways, while the AGA, group was associated with short-chain fatty acid (SCFA) production, a connection that remained at TP2. In contrast, the LGA group displayed a shift toward a pathway involved in glucose production. Limitations Reliance on self-reported dietary data, lack of physical activity and pre-pregnancy diet information, and a highly educated study population may limit generalizability. Conclusion Early pregnancy gut microbiome diversity and function, along with maternal factors, may contribute to the risk of fetal overgrowth. While microbiome diversity converges between cases and controls at a later time-point, this is not enough to mitigate risk. 1. Introduction Birth weight is an important parameter for assessing a neonate’s health ( 1 ). Reference growth charts are a crucial tool for assessing the overall well-being of infants and children ( 2 ). Among several available growth charts ( 3 ), the WHO Child Growth Standards are widely regarded as robust for term infants, while the Fenton growth chart is uniquely advantageous for preterm infants. The Fenton chart provides a specific reference for monitoring growth before term age and is designed to transition seamlessly to WHO standards as the infant approaches term ( 4 ). Newborns are classified into three weight categories based on gestational age and sex according: large-for-gestational-age (LGA) if their birth weight is above the 90th percentile for gestational age; appropriate-for-gestational-age (AGA), including those between the 10th and 90th percentiles, and small-for-gestational-age (SGA) for those below the 10th percentile. A birth weight higher than 4500g, irrespective of gestational age and sex, is known as macrosomia, a condition associated with increased risk for maternal and neonatal complications ( 5 ). The prevalence of large babies varies worldwide due to maternal health, nutrition, and genetics ( 4 , 6 ). Over the past 30 years, the UK, Norway, Sweden, Denmark, France, the USA, and Canada have seen rising birth weights. Globally, macrosomia and LGA occur in 3% to 15% of pregnancies ( 7 ), attributed to risk factors such as higher maternal age, increased pre-pregnancy Body Mass Index (BMI), gestational diabetes, excessive gestational weight gain, maternal depression during early pregnancy, and prenatal stress ( 6 , 8 – 11 ). Maternal complications associated with a large fetus include a higher risk of Caesarian section, postpartum hemorrhage, and perineal lacerations, which increase with the infant’s birth weight ( 8 , 12 , 13 ). LGA neonates face risks such as birth asphyxia, trauma, and hypoglycemia ( 12 , 14 ). In the long term, these infants may be more prone to obesity, diabetes, and cardiovascular diseases ( 12 , 13 ), making LGA a global health concern ( 13 ). During pregnancy, the body undergoes significant hormonal, metabolic, and immunological changes to support fetal development ( 15 , 16 ). Additionally, the maternal gut microbiome also undergoes significant transformations, shifting from a more stable and comparable composition in the first trimester to more distinct changes by the third trimester ( 16 ). These transformations are typically marked by a decrease in alpha diversity and an expansion in beta diversity ( 16 ). These changes may be linked to the maternal metabolic profile. Microbial metabolites produced by the maternal gut microbiota play a crucial role in influencing the host’s metabolism during pregnancy( 17 ). These metabolic interactions are also relevant for fetal development and the potential for pregnancy complications ( 17 ). Recent studies suggest that maternal gut microbiome dysbiosis is linked to various pregnancy-related complications. For instance, a case-control study ( 15 ) found that women who gave birth to neonates with fetal growth restriction (FGR) had gut microbiota profiles distinct from those of healthy controls, implying a potential influence on fetal development ( 15 ). Another study ( 18 ) reported that changes in the gut microbiome can affect glucose metabolism, contributing to the onset of gestational diabetes mellitus (GDM) ( 18 ). Additionally, a study ( 19 ) profiling women in their first trimester found that those who later developed GDM exhibited altered gut microbiota, elevated pro-inflammatory cytokines, and reduced short-chain fatty acid levels months before diagnosis ( 19 ). While recent human studies have linked gut microbiota to preterm birth, more research is needed to clarify this relationship ( 20 ). A previous nested case-control study ( 6 ) conducted in pregnant women explored the maternal gut microbiome in early pregnancy in relation to macrosomia, suggesting that microbial composition and function may influence birth weight ( 6 ). Aditionally, an observational prospective cohort study ( 9 ) conducted during late pregnancy found that gut microbiota composition may play a key role in excessive fetal growth. The study hypothesized that a relatively high abundance of the Firmicutes phylum combined with a relatively low abundance of the Prevotella genus could be linked to LGA by influencing metabolic pathways ( 9 ). Nevertheless, the mechanistic link between specific microbial shifts and LGA outcomes remains incompletely understood, highlighting the need for more research. Among the many factors influencing human gut microbiota, diet is one of the most significant and readily manipulated ( 21 , 22 ). Dietary intake during pregnancy significantly influences the maternal gut microbiome, which plays a crucial role in maternal health and fetal development ( 6 , 23 ). A mouse study ( 24 ) suggested that the gut microbiome may contribute to GDM, with dietary interventions potentially improving glucose tolerance by altering microbial communities ( 24 ). Additionally, an observational study ( 25 ) of 105 pregnant women revealed that diet-related factors and the gut microbiome significantly influenced postprandial glucose responses in those with diet-treated GDM ( 25 ). While the relationship between maternal diet, gut microbiome, and fetal development has been the subject of increasing research efforts, specific studies directly linking these factors to the risk of LGA are currently limited. Addressing this gap is critical given the known health implications of LGA for both mother and child. In this nested case-control study, we compared the gut microbiome profiles of mothers who delivered large-for-gestational-age (LGA) neonates with those who delivered appropriate-for-gestational-age (AGA) neonates. We used shotgun metagenomics to investigate the taxonomic and functional profiles of the microbiome during early (TP1) and late pregnancy (TP2). Additionally, we analysed within-group changes in microbiome diversity from TP1 to TP2 (Δalpha-diversity) to compare longitudinal patterns between the groups. Finally, we assessed whether maternal dietary factors could explain the differences in their microbiome. 2. Materials and Methods 2.1 Data Collection The study utilized samples from the Swedish Maternal Microbiome (SweMaMi) cohort, which recruited 5,423 pregnant women before 20 weeks of gestation between November 2017 and February 2021 in Sweden ( 26 ). The research was approved by the Regional Ethics Committee in Stockholm, Sweden (2017/1118-31) and conducted in accordance with international ethical guidelines. Data collection occurred at two time points (TP): gestational weeks 10–20 (TP1 or early pregnancy) and 28–32 (TP2 or late pregnancy. Participants completed online questionnaires and submitted fecal samples using at-home kits. Neonatal birth weight, gestational age at birth, and sex data were obtained from the Medical Birth Registry ( 26 ) and neonates were classified as large-for-gestational-age (LGA; birth weight > 90th percentile) or appropriate-for-gestational-age (AGA; birth weight between the 10th and 90th percentiles) using the Fenton growth chart ( 4 ). SGA infants were excluded from this study. Participants included in this project were pregnant women who provided fecal samples and gave birth to a live infant. The primary outcome was infant gestational size at birth, grouped into LGA (case) and AGA (control) categories. The exposure was the maternal gut microbiome, assessed through fecal samples collected at two gestational time points, TP1 and TP2. Optimal 1:2 case-control matching was conducted using propensity score matching, implemented via the matchit function from the MatchIt package ( 27 ), with the matching method set to ’nearest’. Matching was based on maternal age, pre-pregnancy BMI, and gestational week at sampling. Exclusion criteria were incomplete or missing questionnaire data, failed sample collection, subsequent pregnancies within the cohort, and sequencing errors. To assess within-group changes in gut microbial diversity (Δalpha diversity), only participants with samples available from both time points were included. Potential confounders were identified a priori and included maternal dietary factors ( 23 ) (self-reported frequency), maternal age, pre-pregnancy BMI, gestational weight gain, parity, smoking status, alcohol consumption (during pregnancy and three months before pregnancy), Perceived stress score ( 28 ), Edinburgh postnatal depression scale ( 29 ), Swedish born, antibiotics taken up to three months before pregnancy, gestational diabetes and socioeconomic status, as these may influence both the maternal microbiome and infant gestational size ( 6 , 8 – 11 ). Questionnaire data collected at TP1 and TP2 also included validated measures of demographic and lifestyle information. All variables and measurement tools are detailed in Supplementary Table S1. 2.2 Extraction, Sequencing and Data Annotation Samples were collected by the participants at home, with a spoon attached to the tube’s lid and posted to Karolinska Institutet in DNA/RNA-shield buffer (Zymo Research, California, USA). Samples were kept at -80°C after arrival. For DNA extraction, samples were mechanically lysed in FastPrep-24 beads before chemical extraction with the ZymoBIOMICS 96 MagBead DNA protocol. Library preparation was performed with a brief PCR amplification step in a MGISP-960. 850-880 bp fragments were selected by electrophoresis with 4200 TapeStation. Fragments were then normalized to the same concentration and circularised. Sequencing was performed using MGI sequencers (MGI Tech Co., G400 or T7 models) on paired 150 base pair reads. Data quality control and annotation used the StaG-mwc (v0.5.1) workflow ( 30 ) based on Snakemake (v4.8.1) ( 31 ). This workflow started with FastP (v0.23.2) ( 32 ) for trimming adapter sequences and low-quality bases. Host reads were then removed through mapping to the human reference genome GRCh38 with Kraken2 (v2.1.2) ( 33 ). Finally, the sequences were taxonomically annotated with Metaphlan4.0 ( 34 ). HUMAnN2 (HMP Unified Metabolic Analysis Network) was used for the functional analysis of metagenomic data ( 35 ). It works in conjunction with MetaPhlAn to provide functional profiling of metagenomes, gene family and pathway abundance quantification, and metabolic potential analysis of microbial communities. This allows for identifying and quantifying metabolic modules, particularly Gut Metabolic Modules (GMM) ( 36 ), in the sample data. 2.3 Statistical Analysis The normality of variables was assessed by histogram. Variables are expressed as mean ± standard deviation (SD) or numbers and percentages. The comparison of continuous variables between two groups was performed by 2-tailed independent sample t -test. Categorical variables were compared using the χ 2 test. All statistical analyses and plotting were done using R Statistical Software (versions 4.2.3 and 4.4.2) ( 37 ). Data was cleaned using packages from the tidyverse v2.0.0 ( 38 ) and plotted with the ggplot2 v3.5.1 ( 39 ), ggrain v0.0.4 ( 40 ). p-value < 0.05 was considered statistically significant and corrected for multiple testing with the Benjamini-Hochberg method ( 41 ). Alpha diversity was evaluated through three key metrics: Shannon’s Diversity Index, Pielou’s Evenness, and Observed Species Richness, all calculated with the vegan package (v2.6-4/2.6.8) ( 42 ). Differences in alpha-diversity between the LGA and AGA groups were determined using a t -test. To assess longitudinal changes in gut microbiome diversity, within-subject differences in alpha diversity between TP1 and TP2 (Δalpha diversity) were computed. These values were compared between groups using an independent two-sample t- test and visualized using raincloud plots. Beta-diversity was assessed using Jaccard, Aitchison, and Bray–Curtis distance metrics, which were computed using the ’vegdist’ function from Vegan. These distance metrics were analyzed with a permutational multivariable analysis of variance (PERMANOVA) and visualized using Principal Coordinates Analysis (PCoA) plots generated with the ggplot2 package. Aitchison distance was calculated as a robust Aitchison, which CLR-transforms values above zero. Univariable analysis was conducted for each variable, and a multivariable model was constructed using variables that were significant in the univariable analysis. Associations between dietary variables and gut microbiome alpha diversity were assessed using linear models. PERMANOVA was used as a univariate test for each variable to assess the effect of dietary factors on beta diversity. To analyze the functional annotation data from gut microbiome samples of LGA and AGA groups, a multi-step approach was followed. Modules with a prevalence below 15% were filtered out to reduce noise and to focus on robust biological signals. The data were converted to binary format for modules with a prevalence between 15% and 85%, and either Fisher’s Exact Test or Chi-square test was applied, depending on data variance and test requirements. For high-prevalence modules (>85%), logistic regression was applied, modelling group status (Control = 0, Case = 1) as the outcome and module abundance as the predictor. 3. Results 3.1 Baseline Demographics and Descriptive Statistics A total of 5,423 pregnant participants from the SweMaMi cohort were initially identified and classified into two groups: LGA cases (n = 305) and AGA controls (n = 2,444). After applying exclusion criteria and performing a 1:2 case-control matching, 735 participants remained at TP1, comprising 245 LGA cases and 490 AGA controls. At TP2, the matched sample included 245 LGA cases and 314 AGA controls. Figure 1 illustrates the participant selection and matching process. Download figure Open in new tab Figure 1. Sample selection and exclusion. N represents the number of subjects left/removed in each step. LGA: Large for Gestational Age. AGA: Appropriate for Gestational Age The demographic and clinical characteristics of participants are presented in Table 1a for TP1 and Table 1b for TP2. The LGA group exhibited a higher prevalence of primiparity and excessive gestational weight gain during pregnancy (p < 0.05). Other variables, including maternal self-reported food frequency intakes (daily fiber, sweet drinks), smoking status, and socioeconomic status, did not differ significantly between the LGA and AGA groups (p > 0.05), as detailed in Table 1a , Table 1b , and Supplementary Table S2a, S2b. View this table: View inline View popup Download powerpoint Table 1a. Characteristics of Study Participants for TP1 View this table: View inline View popup Download powerpoint Table 1b. Characteristics of Study Participants for TP2 3.2 The relationship between Gut Microbiome and LGA: Alpha and Beta diversity Observed species richness was higher in the LGA case at TP1 (p = 0.01, Table S3, Figure 2B ), but no significant differences were observed at TP2 (p = 0.30, Table S3 Figure 3B ), and other alpha diversity indices did not show significant differences at either time point (p > 0.05, Figure 2 , Figure 3 , Table S3). Download figure Open in new tab Figure 2. Comparison of alpha diversity between the LGA and AGA groups at TP1. Violin plot representing: A Diversity (Shannon’s diversity). B Richness (Observed species), C Evenness (Pielou’s). P < 0.05 indicates statistical significance, ns = not significant. Download figure Open in new tab Figure 3. Comparison of alpha diversity between the LGA and AGA groups at TP2. Violin plot representing: A Diversity (Shannon’s diversity). B Richness (Observed species), C Evenness (Pielou’s), P < 0.05 indicates statistical significance, ns = not significant. Within-subject changes in alpha diversity between TP1 and TP2 (Δalpha diversity, LGA = 157, AGA = 314), showed a substantial decline in Shannon diversity in the LGA group (p = 0.025, Table S4, Figure 4(A) ), which was not observed in the AGA group. Evenness declined over time in both groups, but the change was larger in the LGA group (p = 0.047, Table S4, Figure 4 (B)). For observed species shift, the LGA group exhibited a decrease between time-points, while the AGA increased. (Table S4; Figure 4(C) ). However, these changes are not statistically significant (p = 0.14). Download figure Open in new tab Figure 4. Δalpha diversity between the LGA and AGA groups: Raincloud plot representing Shannon’s diversity. B Richness (Observed species), C Evenness (Pielou’s), P < 0.05 indicates statistical significance. Beta diversity was visualized through PCoA plots for TP1 ( Figure 5 ; LGA = 245, AGA = 490) and TP2 ( Figure 6 ; LGA = 157, AGA = 314). The Adonis test (PERMANOVA) revealed no differences in gut microbiome composition between the LGA and AGA groups at either time point. At TP1, primiparity was significantly associated with microbiome composition, as measured by Jaccard similarity, Aitchison distance, and Bray-Curtis distance ( Table 2 ). Additionally, being born in Sweden was significantly associated with microbiome composition, as determined by Jaccard similarity Aitchison distance, and Bray-Curtis distance ( Table 2 ). At TP2, age significantly impacted microbiome composition, as indicated by Jaccard similarity ( Table 2 ). Download figure Open in new tab Figure 5. Beta Diversity at TP1: LGA vs. AGA. Plots illustrating group differences using Jaccard similarity (A), Aitchison (B), and Bray-Curtis (C) distances. P < 0.05 (PERMANOVA), R² explained by group. Download figure Open in new tab Figure 6. Beta Diversity at TP2: LGA vs. AGA. Plots illustrating group differences using Jaccard similarity (A), Aitchison (B), and Bray-Curtis (C) distances. p < 0.05 (PERMANOVA), R² explained by group View this table: View inline View popup Download powerpoint Table 2. Association between confounders and microbiome composition 3.4 Effects of Dietary Factors on Gut Microbiome Diversity and Composition The findings indicated a positive association between daily intake of dietary fiber and vegetables and increased gut microbiome diversity at both time points (TP1 and TP2) (p < 0.05, Table 3 ). Specifically, fruit consumption was linked to higher species richness (p = 0.0020, Table 3 ) and Shannon diversity index (p = 0.049) at TP2. Although the intake of sugary and sugar-free drinks had minimal negative effects on alpha diversity (Shannon and Evenness) at both TP1 and TP2, sugary drinks at TP2 were associated with lower richness (p = 0.017, Table 3 ). Furthermore, unhealthy diets were strongly associated with lower microbiome diversity at both time points ( Table 3 ). An omnivorous diet was associated with higher richness at both TP1 and TP2 ( Table 3 ), while fish intake showed a positive association with observed species at both time points ( Table 3 ). Other dietary variables did not demonstrate a significant association with microbiome diversity (p > 0.05). Additionally, no dietary variables showed different effects on alpha diversity between the LGA and AGA groups at both time points (p > 0.05, Supplementary Table S5). Dietary factors did not show any significant association with beta diversity at TP1 ( Table 4 ). At TP2, the consumption of sugar-free drinks significantly impacted microbiome composition, as indicated by Jaccard similarity and Aitchison distance ( Table 4 ). View this table: View inline View popup Table 3. Association between dietary intake and microbiome diversity View this table: View inline View popup Table 4. Effects of dietary factors on microbiome composition 3.5 Metabolic Module Profiling: Case-Control Comparison At TP1, an analysis of modules with intermediate prevalence (15% - 85%) identified two input modules that were more prevalent in LGA than AGA: MF0032: Glutamate Degradation III (LGA: 69.38%, AGA: 60.40%; p = 0.0214) and MF0040: Proline Degradation (LGA: 55.5%, AGA: 44.89%; p = 0.00849) ( Figure 7 ). Download figure Open in new tab Figure 7: Prevalence of GMM in LGA and AGA groups at TP1. Modules with a prevalence between 15% and 85% were included. * indicate modules with statistically significant differences between the LGA and AGA groups (* = p < 0.05, **= p 85%), univariable logistic regression analysis revealed significant associations with LGA status. Two amino-acid degradation modules were positively associated with LGA: MF0034: alanine degradation II (OR = 1.07, 95% CI: 1.01– 1.13, p = 0.01). and MF0055: arginine degradation V (OR = 1.23, 95% CI: 1.01–1.13, p = 0.01). In contrast, several modules were negatively associated with LGA, including input modules (MF0036: isoleucine degradation and MF0041: valine degradation I), one central module (MF0073: pyruvate: ferredoxin oxidoreductase), and output modules (MF0087: acetyl-CoA to crotonyl-CoA, MF0089: butyrate production II, MF0102: sulfate reduction (dissimilatory)) ( Figure 8A ; Supplementary Table S6). Download figure Open in new tab Figure 8. Forest Plot: Odds Ratios (OR) and 95% Confidence Intervals (CI) for High-Prevalence (>85%) Modules Associated with LGA vs. AGA at TP1(A), TP2 (B). Results from univaraible logistic regression. p-values (p < 0.05) were used to determine statistical significance. At TP2, no modules of intermediate prevalence were significantly different (Supplementary Figure S1). However, for high-prevalence modules, there was one positive and three negative associations with LGA (Table S7; Figure 8B ). The input module MF0005: starch degradation was positively associated with LGA, while the input module MF0036: isoleucine degradation was negatively associated. Additionally, the output modules MF0073: pyruvate: ferredoxin oxidoreductase and MF0087: acetyl-CoA to crotonyl-CoA exhibited negative associations with LGA group. 4. Discussion This nested case-control study investigated gut microbiota composition, its longitudinal changes, and functional profiles in pregnancies leading to large-for-gestational-age (LGA) and appropriate-for-gestational-age (AGA) infants, also considering the influence of maternal diet. Our key findings reveal significant differences in gut microbiota observed species richness between LGA and AGA groups in early pregnancy, which subsequently decreased as gestation advanced. We also found that unhealthy maternal diets were strongly associated with lower microbiome diversity at both early and late pregnancy time points, with a consistent impact across both groups. Furthermore, longitudinal analysis of within-group changes indicated differences in Shannon diversity and Evenness between the groups, and functional profiling demonstrated distinct abundances of certain gut microbial metabolites (GMM) at both time points. The LGA group at TP1 showed higher species richness (p < 0.05) than the AGA group, but no differences were observed in Shannon diversity or evenness at either time point. As pregnancy progressed, microbial species richness in the LGA group decreased, leading to no significant difference between groups at TP2. No differences in beta diversity were detected between the LGA and AGA groups. These results indicate that although alpha diversity (specifically species richness) differed at TP1, the overall microbial community structure was similar between groups. Consistent with our TP2 results, an observational cohort study of women with LGA neonates in the third trimester ( 9 ) found no statistically significant differences in overall gut microbial diversity or community composition, further supporting the idea that late pregnancy might not reveal key microbial shifts associated with LGA ( 6 ). While no studies have directly compared the maternal microbiome of mothers with LGA and AGA neonates, research on macrosomia provides useful insights. A case-control study ( 6 ) found that the overall gut microbial diversity of mothers of macrosomic babies did not significantly differ from controls. Most existing studies have focused on late pregnancy, and data on maternal gut microbiota in early pregnancy in relation to birth weight, particularly LGA, remain scarce. Although not directly comparable, a the Women First Trial ( 43 ) assessed the maternal gut microbiome and metabolome at 12 and 34 weeks of gestation, finding that mothers of low birth weight (LBW) infants had higher microbial evenness at 12 weeks compared to those with normal birth weight (NBW) infants. This suggests early pregnancy microbial features may influence birth weight outcomes. Research on the impact of pregnancy itself on the gut microbiome is conflicting. While some studies have reported changes in either alpha or beta diversity, others have found no significant alterations . We observed notable shifts in alpha-diversity (Shannon and Evenness) from TP1 to TP2 between the AGA and LGA groups. Initially, the two groups had significant differences in gut microbial diversity, but the LGA group converged towards the diversity observed in the AGA, in line with previous SweMaMi studies . The Shannon index, reflecting both species richness and evenness, suggests these changes are tied to the abundance of certain less common taxa. Identifying these taxa could offer insights into their effects on maternal health and neonatal outcomes in LGA pregnancies. Our analysis further examined the functional profiles of the gut microbiome at two time points, focusing on known gut-metabolic modules. All modules associated with the LGA group fall within the input module category of the GMM framework, which represents the initial steps of substrate conversion and either feeds into the central modules or drives cross-feeding interactions ( 36 ). This suggests a potential microbial functional bias toward early-stage metabolic pathways in this group. Many gut bacteria are auxotrophic, relying on cross-feeding interactions to obtain essential nutrients such as amino acids from other community members( 44 , 45 ). Several LGA-associated modules reflect pathways central to microbial cross-feeding. In contrast, the metabolic modules enriched in the AGA group predominantly reflect the input, central, and output module categories. At TP1, mothers of LGA infants had of greater proteolytic degradation potential in their guts compared to the AGA group. Proteolytic degradation is linked to microbial cross-feeding dynamics( 36 , 45 ), but is also associated with low-grade inflammation ( 36 ). Some microbial metabolites can promote inflammation by influencing immune cell activation and signaling pathways ( 46 ). Inflammation during pregnancy can influence placental function, including nutrient transporter expression and activity. Changes in placental nutrient transfer efficiency may promote increased nutrient delivery to the fetus, contributing to fetal overgrowth( 47 ). This agrees well with our finding of greater species richness in TP1, as modeling studies have found that increased frequencies of almost all amino acid auxotrophies are associated with greater microbiome diversity ( 44 ). In contrast with the LGA group, the AGA group associated with modules involved in SCFA synthesis, a product of microbial fermentation. Notably, this includes output modules responsible for butyrate production, a key SCFA known for its multiple physiological roles. Butyrate contributes to maintaining gut epithelial integrity, modulating immune responses, and exerting anti-inflammatory effects ( 48 ). Its anti-inflammatory properties have been demonstrated in an in vitro model of preterm birth ( 49 ), highlighting its potential relevance during pregnancy. Furthermore, an in vivo mouse study ( 50 ) investigating the role of SCFAs during pregnancy found that mid-gestation SCFA supplementation increased placental weight, total placental volume, fetal vascular volume, and surface area factors that support optimal placental function and fetal development ( 50 ). Importantly, this mid-gestation window corresponds to TP1 (gestational weeks 10–20) in our cohort, aligning the findings with our study timeline. SCFAs are enriched early in pregnancy and linked to biological events like embryonic and placental development. Supplementing with probiotics or butyrate modulates placental GPR43 signaling, protecting against morphological and oxidative injury ( 51 ), suggesting a potential role for SCFAs in supporting fetal development. Additionally, the metabolite H₂S, the product of dissimilatory sulfate reduction, is a potent signaling molecule with concentration-dependent effects: at low to moderate levels, it is anti-inflammatory and aids tissue repair in the GI tract, but excessive amounts can be toxic ( 52 ). Dietary protein intake has been positively associated with increased H₂S production in the gut ( 53 ). Although this module may be influenced by protein consumption, our questionnaire did not directly assess participants’ dietary protein intake. All in all, the functional analysis found a pro-inflammatory profile in LGA, compared to AGA. This is specially interesting since cases and controls were matched for age and BMI, which are otherwise positively correlated with gut inflammation ( 54 , 55 ). Inflammation driven by specific gut microbial activities early in pregnancy may in turn alter placental nutrient transport, thereby predisposing to the delivery of an LGA infant. These findings emphasize the need to consider early gestational microbial dynamics as contributors to fetal overgrowth risk. Further studies are needed to dissect these functional-microbial interactions and their impact on fetal development. Because the gut microbiome is strongly shaped by diet, we further assessed the association of various dietary components, including fiber, fruits, vegetables, and sugar intake, with both alpha and beta diversity of the gut microbiota. Higher fiber intake was strongly associated with microbial diversity at both time-points, in agreement with the known beneficial effects of fiber. Dietary fiber intake influences gut microbiota composition and SCFA production ( 56 ). SCFAs such as acetate, propionate, and butyrate, produced from fiber fermentation, may improve insulin sensitivity and metabolic health ( 9 ). Conversely, higher intake of sugar-sweetened beverages was associated with lower species richness at TP2. These beverages have been linked to lower microbial diversity and can negatively impact metabolic health and oxidative stress during pregnancy, potentially leading to gestational diabetes ( 57 , 58 ). As an alternative to sugar, artificial sweeteners are being studied for their potential to alter gut microbiota, potentially causing glucose intolerance and metabolic issues ( 59 ). Our findings suggest a link between sugar-free drinks and changes in microbial structure in late pregnancy. Additionally, fish intake was positively associated with gut microbiome diversity, supporting the Mediterranean diet’s benefits during pregnancy ( 23 ). Iron supplementation showed no significant effect on gut microbial diversity, which aligns with other studies indicating minimal impact ( 60 ). The associations observed between diet and gut microbiota in our study were not different between the LGA and AGA groups. One of the key strengths of this study is its analysis of the gut microbiome across two critical time points during pregnancy (gestational weeks 10-20 and 28-32) on a very well characterized population. Notably, our case and control groups were matched for maternal BMI, and the prevalence of gestational diabetes was very low, especially in the case group, reducing the confounding effects of obesity and gestational diabetes, which are well-known risk factors for inflammation ( 61 ) and which lead to delivering large babies as the pregnancy outcome ( 62 ). A key limitation of this study is the use of self-reported food frequency questionnaires to assess dietary intake, which can be subject to recall bias and inaccuracies in reporting. Additionally, the assessment included only a limited number of dietary items, which may not fully represent the participants’ overall dietary patterns. Furthermore, physical activity and sleeping patterns were not collected in this study. The study population exhibited a higher level of education than the average Swedish population, which may introduce selection bias. This suggests our cohort could be more health-conscious and likely to adopt healthier eating habits. Another limitation of this study is the lack of data on dietary patterns before pregnancy. Since dietary patterns can significantly impact long-term health and microbiome diversity, the absence of information on participant’s pre-pregnancy diets limits the ability to assess any potential lasting effects on the outcomes studied. Future research would benefit from including pre-pregnancy dietary assessments to provide a more comprehensive understanding of the role of nutrition in maternal health and birth outcomes. 5. Conclusion This study uniquely combines longitudinal microbiome analysis with dietary and functional assessments, providing new insights into microbiome roles in LGA risk, especially microbial diversity early in pregnancy (TP1). Longitudinally, early microbial diversity differences between the groups normalized by TP2, emphasizing the importance of early gestational monitoring. We found that functional, not overall diversity or composition, was the strongest link to fetal growth outcomes. Higher species richness in mothers of LGA infants may indicate enhanced cross-feeding and proteolytic activity, causing inflammation that could disrupt placental nutrient transport and enhance fetal growth. Conversely, SCFA synthesis pathways in the AGA group likely offer anti-inflammatory effects and support healthy development. More research is needed to identify specific bacterial species behind these differences. While diet influences overall microbial diversity, it did not differ between LGA and AGA groups, suggesting diet alone doesn’t explain these differences. Further research into additional predictors could clarify how microbiome is shaped by maternal lifestyle and influences pregnancy outcomes. Data Availability All sequencing data, and limited participant-level information, can be retrieved from the European Nucleotide Archive under project ID PRJEB81814. https://www.ebi.ac.uk/ena/browser/view/PRJEB81814 Funding Ferring Pharmaceuticals has funded the SweMaMi project with an unrestricted research grant. The study was also financed by SciLifeLab & Wallenberg Data Driven Life Science Program. References 1. ↵ Genowska A , Motkowski R , Strukcinskaite V , Abramowicz P , Konstantynowicz J . Inequalities in birth weight in relation to maternal factors: A population-based study of 3,813,757 live births . Int J Environ Res Public Health . 2022 Jan 26; 19 ( 3 ): 1384 . OpenUrl PubMed 2. ↵ Bruin C , Damhuis S , Gordijn S , Ganzevoort W . Evaluation and management of suspected fetal growth restriction . Obstet Gynecol Clin North Am . 2021 June; 48 ( 2 ): 371 – 85 . OpenUrl PubMed 3. ↵ Borghi E , de Onis M , Garza C , Van den Broeck J , Frongillo EA , Grummer-Strawn L , et al. 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Share Metabolic differences in the maternal gut microbiome precede the birth of large-for-gestational-age infants: A nested case-control study Anusha T. Antony , Fatemeh Mohseni , Gaël Toubon , Unnur Guðnadóttir , Lars Engstrand , Emma Fransson , Ina Schuppe-Koistinen , Nele Brusselaers , Luisa W. Hugerth medRxiv 2025.10.08.25337564; doi: https://doi.org/10.1101/2025.10.08.25337564 Share This Article: Copy Citation Tools Metabolic differences in the maternal gut microbiome precede the birth of large-for-gestational-age infants: A nested case-control study Anusha T. Antony , Fatemeh Mohseni , Gaël Toubon , Unnur Guðnadóttir , Lars Engstrand , Emma Fransson , Ina Schuppe-Koistinen , Nele Brusselaers , Luisa W. 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