Maternal pre-pregnancy BMI influences breast milk composition, infant gut microbiome development, and early-life growth of term infants

Maternal pre-pregnancy BMI influences breast milk composition, infant gut microbiome development, and early-life growth of term infants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Maternal pre-pregnancy BMI influences breast milk composition, infant gut microbiome development, and early-life growth of term infants Rasmus Jakobsen, Julie Astono, Frederik Beck, Trine Jakobsen, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6075035/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Breast milk is the optimal nutrition for infants, yet individual variations in its composition and effects on infant growth remain unclear. This study examined human milk (HM) metabolome and microbiome dynamics in relation to infant growth and gut microbiome (GM) maturation in 164 exclusively breastfeeding Danish mother-infant dyads over the first three months. Results showed distinct temporal shifts in in HM metabolome and microbiome as well as infant GM composition. Maternal pre-pregnancy BMI correlated with HM metabolite profiles, infant growth, and GM diversity and composition. However, HM and GM maturity scores were not correlated, suggesting independent development. Notably, HM oligosaccharide clusters were linked to neonatal gut bacteria, including multiple Bifidobacterium spp. These findings indicate that maternal BMI may influence infant gut microbiome development and growth through changes in HM composition. Biological sciences/Microbiology/Microbial communities/Microbiome Health sciences/Health care/Nutrition Human milk microbiome human milk metabolome infant gut microbiome maternal overweight Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Human milk (HM) contains all essential nutrients for the growing infant, and there is substantial evidence that breastfeeding protects against both acute and chronic illnesses 1 , 2 . These beneficial effects may partially be mediated through the gut microbiome (GM), as whether the infant is breastfed or formula fed is the strongest determinant of GM composition during infancy, with effects lasting well into childhood 3 – 5 . The nutrient composition of HM varies between individuals and is influenced by multiple maternal factors, including geographical location and ethnicity, gestational age at delivery, weight, diet, secretor status, and mastitis 6 . The composition of HM changes significantly across the first six months of lactation 7 , 8 . During the first years of life, the GM also undergoes rapid compositional changes, and achieving a mature microbiome within 1–2 years is strongly correlated with later beneficial health outcomes 9 – 11 . Human milk oligosaccharides (HMOs) are a group of complex sugars highly abundant in HM, reaching concentrations of 10–15 g/L, often exceeding the total amount of protein 12 , 13 . Since HMOs are indigestible, their primary function appears to be as substrates for gut microbes 14 . Several studies report associations between individual HMOs with infant growth outcomes, but findings are inconsistent and sometimes contradictory 15 . In addition to nutrients, HM contains a distinct microbiome with bacterial loads of 10 3 up to 10 5 CFU/ml 16 . However, the extent of HM-to-gut colonisation and its direct significance for the gut microbiota is still debated 17 – 20 . Maternal obesity is a significant risk factor for excessive foetal growth 21 , infant overweight and childhood obesity 22 , 23 . High maternal pre-pregnancy BMI (ppBMI) has been associated with altered HM composition 24 , and with higher amounts of milk glucose monosaccharides 25 . However, the overall evidence linking HM composition with later obesity is highly inconsistent 26 . Whether maternal ppBMI also affects the HM microbiome and whether microbial transfer from HM to the gut plays a role in intergenerational obesity is still inconclusive 27 . This study recruited 164 Danish mother-infant dyads from the MaInHealth birth cohort to study the interrelationships between HM composition and infant gut microbiome development during the first three months of life. The study focused particularly on the impact of maternal BMI and other maternal factors on these systems and how they affect early-life infant growth. Materials and methods Participants and Sample Collection Infants and their mothers who provided samples for this study were recruited as part of the MaInHealth (Maternal Infant Health) cohort established in Aarhus, Denmark, to investigate the natural HM variation and its possible effects on offspring metabolism and gut microbiota 28 . Pregnant women were recruited from Aarhus University Hospital, Aarhus, Denmark, from 2019 to 2021. Informed consent was obtained from both parents per the Declaration of Helsinki II. Ethical approval for this study was granted by The Central Jutland Regional Committee on Health Research Ethics (journal number 1-10-72-296-18v6). The study is registered at ClinicalTrials.gov, with the identifier NCT05111990. Women included in the study were healthy, non-smokers, expecting to give birth vaginally, and planning to breastfeed for the first four to six months. Infants included were healthy, with birth weights of 2500-5000g and were born full-term, i.e. gestational week 37 +0 or later. See the study protocol for a detailed project description, recruitment, and exclusion criteria 28 . Briefly, HM was collected by the mother in a 40 mL sterile container within the first week after giving birth and at one, two, and three months post-partum. Around 20 ml of foremilk was collocated at each sampling, avoiding sampling the first few drops. Faecal samples of approximately 2 g were collected at one, two and three months post-partum from the first faeces passed after HM sample collection. Samples were stored in the participants’ own freezer at -20 °C for up to two weeks. Subsequently, the samples were collected and transported on dry ice to the Department of Food Science, Aarhus University, where they were stored at -80 °C until further analysis. HM samples were thawed, thoroughly mixed, split into 1 ml aliquots, and returned to storage at -80 °C. Metabolomics and microbiome characterisations each used one 1 ml HM aliquot. Faecal samples were thawed, and approximately 250 mg of faecal sample was transferred to a 1.5 ml Eppendorf tube and mixed at a 1:5 ratio (w/v) with PBS buffer (0.13 M NaCl, 0.0100 M Na2HPO4, 0.0027 M KCl, 0.0018 M KH2PO4, pH ~ 7.4) by vortexing and centrifuged at 10,000 × g for 10 min at, 4℃. The pellet was frozen at -80℃ before subsequent DNA extraction, and the supernatant was transferred to a separate tube for metabolomics analysis. 1 H Nuclear Magnetic Resonance Spectroscopy Metabolomics Analysis of Milk HM samples for 1 H nuclear magnetic resonance (NMR)-based metabolomics were processed following a standard protocol for milk-based metabolomics as described previously 29 . In brief, samples were thawed in a water bath and kept on ice while Amicon Ultra 0.5-ml 10-kDa spin filters (Millipore, Billerica, MA, USA) were washed three times. After washing, samples were centrifuged at 4,000 × g at 4°C for 10 minutes to skim off the fat layer, transferred to spin filters and centrifuged at 10,000 g at 4°C for 60 minutes. Subsequently, 400 µl of the filtered milk was placed into individual 5-mm NMR tubes, and 200 µl of D 2 O containing 0.05% 3-(trimethylsilyl) propionic acid (TSP, Sigma-Aldrich, Saint-Louis, MO, USA) was added to each tube. 1 H NMR spectra acquisition was performed using a Bruker NEO 600 spectrometer equipped with a 5-mm 1H BBI probe, operating at a temperature of 300 K and a 1H frequency of 600.03 MHz. All spectra were referenced to the TSP signal at 0 ppm, with a line-broadening function of 0.3 Hz applied before Fourier transformation. Phase and baseline corrections were performed manually and automatically using Topspin 4.09 (Bruker Biospin, Rheinstetten, Germany). Identification of Metabolites and Statistical Analysis To ensure correct metabolite identification and quantification and to identify metabolites in HM, the acquired spectra were analysed using Chenomx NMR suite 10.1 (Chenomx Inc., Edmonton, AB, Canada) with the Chenomx standard metabolite library and an in-house HMO library. Metabolites were normalised using total metabolite count, using a weighted normalisation to 215 mM lactose. Since lactose constitutes >80% of the total concentration of the HM metabolites detected by NMR-based metabolomics, using total metabolite count would be the same as adjusting for lactose which significantly varies between stages of lactation 30 . A weighted normalisation with lactose weight 3 and total metabolites weight 1 was therefore used: a lactose factor normalising to the average concentration in HM of 215 mM (L-factor) and a total metabolite (excluding lactose) across all samples (T-factor) were combined as the factor (L-factor×3+T-factor)/4. Metabolite concentrations were centred but not scaled prior to analysis. Sequencing of bacterial communities in mother’s milk and infant faecal samples DNA extraction Samples were randomised before DNA extraction and amplicon sequencing. Samples were thawed at 4 C° before DNA extraction. Milk samples were centrifuged at 12000 × g for 20 minutes at 4 C°, the fat layer mechanically removed with a sterile cotton swab, and the supernatant carefully removed. DNA was then extracted from the resulting pellet using the Bead-Beat Micro AX Gravity Kit (A&A Biotechnology, Gdynia, Poland) per the manufacturer's instructions. Sterile MilliQ water was used as negative control during the entire extraction pipeline as well as during subsequent PCR and sequencing steps. The DNA purity and concentration were determined by NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, USA) and using the Qubit™ 1x dsDNA high sensitivity kit on a Varioskan Flash (Thermo Fisher Scientific, USA), respectively. Library Preparation and Sequencing A 16S rRNA gene amplicon library was constructed by amplifying the 16S rRNA gene using primers targeting the 16S V1-V9 regions, as described previously 31 . After each PCR reaction, PCR amplicons were cleaned using SpeedBeads TM magnetic carboxylate (obtained from Sigma Aldrich). The size of barcoded PCR products (approximately 1500 bp) was checked by 1.5% agarose gel electrophoresis. Sequencing libraries consisting of pooled barcoded PCR products from up to 196 samples were prepared following the ligation sequencing protocol SQK-LSK110 (Oxford Nanopore Technologies, Oxford, UK). They were loaded on an R9.1.4 flow cell and sequenced on a GridIONX5 for 72 hours (Oxford Nanopore Technologies, Oxford, UK). Pre-processing and filtering Raw read data was processed using the long amplicon consensus analysis (LACA) pipeline for de-novo clustering and taxonomic classification of long-read amplicons 32 . This pipeline employs multiple de-novo clustering approaches to control the sequencing error rate of Oxford Nanopore sequencing data and assign taxonomy to the resulting consensus sequences using the SILVA 33 v138.1 database. Bioinformatics analysis Initially, the dataset was purged for zOTUs, which were detected in less than 5% of the samples and with mean relative abundances below 0.05%, with the resulting dataset still maintaining 98 % of the total reads. R version 4.2.1 34 was used for subsequent data analysis and presentation. The data and complete code used are available, as described in the data availability section. The main packages used were Phyloseq 35 , vegan 36 , ampvis2 37 , microeco 38 , ggpubr 39 and ggplot2 40 . Alpha-diversity analysis was based on raw read counts, rarefied to a median depth of 44,574 reads. Cumulative sum scaling (CSS) was applied before calculating beta-diversity metrics to add weight to less abundant zOTUs. Metabolic maturation Sparse partial least squares (sPLS) for microbiome data and partial least squares (PLS) regression models were built with sample age in days as the response variable and ASV relative abundances (CSS normalised) or metabolite levels as the explanatory variables from all samples, including cross-validation to avoid overfitting. Confidence intervals of the metabolite coefficients in the PLS model were based on the internal PLS cross-validation models. The metabolic/bacterial maturation age is the predicted age solely based on the bacterial or metabolite profiles, and the metabolic/bacterial z-scores are defined as the difference between the actual age and the predicted age for each sample. Correlation between the predicted metabolic or bacterial maturation age to clinical and anthropometric factors was performed using the Pearson correlation. Results Participant and sample characteristics This study includes samples from 164 healthy mother-infant dyads consisting of HM samples collected at day 3-90 (4 time points) and infant faecal samples at 30-90 days (3 time points), along with extensive anthropometrics and clinical data ( Figure 1A ). The average pre-pregnancy BMI was 27±5.4 and the mean gestational age was 283±2.5 days with average birth weights of 3704±45 g ( Table 1 ). Among the infants, 59% had older siblings, and 52% were female. HM metabolites were characterised using 1 H nuclear magnetic resonance (NMR) and HM and infant faecal bacterial communities by V1-V9 16S rRNA gene Nanopore long-read amplicon sequencing. After filtering and quality control, 570 HM metabolite samples, 495 HM microbiome samples, and 348 infant faecal microbiome samples were included for analysis. Table 1. Characteristics of 164 Mother-infant dyads enrolled in the MaInHealth cohort . Variable n Mean Min Pctl. 25 Pctl. 75 Max BMI (kg/m 2 ) 164 27 ± 5.2 19 23 29 46 BMI group Normal weight 71 43% Overweight 54 33% Obese 39 24% Maternal age (years) 164 31 ± 4 20 28 34 43 Gestational diabetes 163 No 155 95% Yes 8 5% Siblings 164 No 75 46% Yes 89 54% Delivery mode 164 C-section 14 9% Vaginal 150 91% Infant sex 162 Female 84 52% Male 78 48% Gestational age (Days) 160 283 ± 7.9 258 278 289 296 Birth weight (g) 159 3704 ± 45 2.7 3.4 4 5 Secretor status 164 Non-secretor 39 24% Secretor 125 76% Lewis status 164 Lewis negative 7 4% Lewis positive 157 96% Continuous data is presented as means ± standard deviation. Categorical data are presented as numbers included in each category. BMI: body mass index, C-section: Caesarean section, n: number. Human milk metabolome changes over lactation and is strongly influenced by secretor status 1 H NMR metabolome analysis of the HM samples identified 70 metabolites, including 20 amino acids and derivatives, 18 oligosaccharides, 13 energy-metabolism related, 9 fatty acids and derivatives, 5 simple sugars, 3 food and 1 microbially derived metabolites ( Table S2 ). Lactose was by a large margin the most abundant detected metabolite at 215±7 mmol/ml, followed by citrate at 3.9±1.3 mmol/ml, various HMO’s and simple sugars. HM samples showed a significant shift in the overall metabolite profile over time. Day 3 profiles were the most distinct ( Figure 1B ), and individual metabolite concentrations changed significantly over time ( Figure 1D, Table S2 ). Mothers were classified as secretors or non-secretors dependent on the presence of the predominant resonance in 2’-Fucosyllactose, and α1-2 fucosyl linkage and Lewis status as determined by the presence of an α1-4 fucosyl linkage in Lacto-N-difucohexaose (LNDFH) I and II 8 . 74% of the mothers were classified as secretors and 96% as Lewis-positive ( Table 1 ). Principle Coordinate Analysis of HM metabolome profiles showed complete separation of HM samples by secretor status ( Figure 2C ). Examining the loadings plot, the majority of variance was explained by 2’- and 3-Fucosyllactose (2’-FL and 3-FL) for component 1 and unclassified HMO 5.10 for component 2, which likely corresponds to secretor-status and time effects, respectively ( Figure S2 ). We did not perform a detailed analysis of the impact of Lewis status due to the low number of Lewis-negative mothers (n=7). Comparing metabolite profiles, HM from non-secretors had undetectable levels of the fucosylated oligosaccharides 2’-FL and LNDFH I but relatively high levels of lacto-N-tetraose (LNT) and several unclassified HMOs ( Figure 2E ). Human and infant faecal microbiomes mature over time The HM microbiomes comprised Streptococcus , Staphylococcus , Lacticaseibacillus , Escherichia-Shigella , and Bacteroides members ( Figure 2AB ). In contrast, the microbiomes of infant faecal samples were mainly composed of Bifidobacterium, Bacteroides, Escherichia-Shigella, Klebsiella and Streptococcus members. Bacterial composition showed significant between-sample (β) diversity shifts for both sample types ( Figure 2CD ). In contrast, within-sample (α) diversities did not significantly differ over time for neither HM nor infant faecal samples ( Figure S1AB ). In the HM samples, the relative abundance of Staphylococcus and Gamella significantly decreased from day 3 to day 90, while Escherichia-Shigella, lactobacilli and Acinetobacter increased ( Figure 1E ). For faecal samples, the relative abundance of Streptococcus significantly decreased while Actinomyces , Enterococcus and Lacticaseibacillus increased from day 30 to 90 ( Figure 1F ). HM and infant faecal bacterial communities both showed modest changes in average sample composition over time. However, individual amplicon sequence variant (ASV) persistence across time points was high in faecal samples but significantly lower in HM samples. Comparing ASV-level persistence between the most abundant HM genera, we found that ASVs identified as Gamella and Staphylococcus members were significantly more persistent across samples, while Lactococcus was the least persistent ( Figure S1C ). In comparison, all high-abundance ASVs in infant faecal samples showed high persistence, with no significant differences between genera ( Figure S1D ). The lower persistence of HM ASVs is likely due to the detection threshold rather than actual presence/absence, as the HM microbiome had markedly higher variance, even at the genus level ( Figure S1EF ). As the MAINHEALTH cohort was designed to study the influence of maternal ppBMI on HM composition and associations to infant metabolism and gut colonisation, we next examined these associations. HM metabolic profiles from overweight or obese (OWOB) mothers showed moderate yet significant differences in overall metabolite profiles and significant differences in levels of multiple HMOs and simple sugars compared to normal-weight mothers ( Figure 3AB ). HM bacterial profiles for OWOB mothers had significantly lower within-sample diversity and significant differences in community composition compared to normal-weight samples ( Figure 3CD ). OWOB samples were characterised by increased Gamella and decreased Escherichia-Shigella, Bifidobacterium , Acinetobacter and Pseudomonas relative abundance compared to HM from mothers with ppBMI < 25 ( Figure 3E ). Conversely, faecal samples from infants from OWOB mothers had significantly higher within-sample diversity while also differing significantly in community composition ( Figure 3FG ). Though not highly abundant, Klebsiella, Ruminococcus and Erysipelatoclostridium were found to be significantly higher in abundance in faecal samples from offspring of OWOB mothers compared to those whose mothers had ppBMI < 25 ( Figure 3H). Milk metabolome and milk and faecal microbiomes show distinct correlations with clinical factors and anthropometrics We next examined how maternal and infant clinical and anthropometric risk factors correlated with HM metabolome and bacterial communities. We correlated each factor with bacterial or metabolite community composition to get an overview of which factors had the strongest associations ( Figure 4 ). Expectedly, the HM metabolite profile correlated strongly to secretor status, followed by weight-for-age z-score (ΔWAZ), change in height-for-age z-score (ΔHAZ), Lewis status, change in weight-for-length/height z-score (ΔWHZ), maternal antibiotics, and maternal postpartum BMI change (ΔBMI 3 months) ( Figure 4A ). HM bacterial composition correlated mainly with, in decreasing order by R 2 values: ΔHAZ, ΔWHZ, and ΔWAZ, followed by maternal antibiotics, breast infection, infant antibiotics, and breastfeeding issues ( Figure 4C ). Infant faecal microbiome composition correlated mainly with, in decreasing order by R 2 values: number of siblings, infant antibiotics, ΔWAZ, ΔWAZ, C-section, breast infection, and breastfeeding issues ( Figure 4E ). While effect size comparisons provide an overview of the overall correlation of factors with bacterial or metabolite composition, they do not show which bacteria or metabolites influence this correlation nor their directionality. To examine associations of specific bacteria or metabolites with clinical features and anthropometric factors, we used redundancy analysis (RDA). HM metabolite RDA ( Figure 4B) showed two unclassified HMOs, one of them sialylated, positively associated with maternal ppBMI and negatively with days after birth and glutamine levels. Post-birth change in maternal BMI was positively associated with LNT. On the orthogonal axis, 2’-FL, LNDFH I and Lactodifucotetraose (LDFT) are, as expected, positively correlated with secretor status, along with birth weight and secretor status. Interestingly, the non-secretor associated 3-FL and LNDFH II were associated with ΔHAZ, ΔWHZ, and ΔWAZ. For the HM bacterial composition ( Figure 4D ) , Clostridium positively correlates with days after birth, and shows negative associations to Gemella, Staphylococcus, and infant antibiotics. Interestingly, ΔHAZ and ΔWAZ correlated with faecal-associated Bacteroides, Lactobacilli , Bifidobacterium, and Faecalibacterium , all of which were negatively associated with ppBMI, breast infection, and breastfeeding issues. Analysis of infant faecal bacteria ( Figure 4F ) showed that Bifidobacterium abundance positively correlated with the number of siblings, secretor status, and antibiotic treatments while negatively associated with Velilonella , Klebsiella , and Clostridium . On the orthogonal axis, Streptococcus aligned with C-section and positive ΔWAZ and ΔHAZ scores, while these were negatively correlated with Collinsella, Bacteroides, Shigella, breastfeeding issues, and days after birth. Microbial and metabolic maturation Several attempts have been made to model the maturation of infant intestinal microbial communities over time, and one approach is to score the “maturity” of each sample by comparing it to the average change in sample profiles over time. This is generally referred to as the microbiome maturity score and, correspondingly, the microbiome-for-age score when adjusted for sample age 41–43 . Similarly, urine metabolites have also been used to determine the metabolic age of children 44,45 . Here, we predicted each sample's microbial/metabolic age using two-component sPLS/PLS models. We then compared the predicted maturities of each set of corresponding samples within each dyad. HM metabolic maturity correlated with maturity scores of both HM and faecal bacterial communities ( Figure 5AB ). In contrast, HM and faecal samples' bacterial maturity did not significantly correlate ( Figure 5C ). However, when samples were stratified by days of life, no significant correlations were detected between neither bacterial nor metabolic maturity scores ( Figure 5DEF ). This suggests that the HM metabolome matures parallel to HM and infant faecal bacterial communities. However, high maturity in one score at a given time point does not correspond to high maturity in other scores. We then correlated maturity scores to clinical and anthropometric features, using both the average maturity within each dyad and maturity-for-age z-scores, i.e. the difference between the sample maturity score and the average score for the time point. Infant faecal maturity correlated with maternal antibiotics, while faecal maturity-for-age scores correlated negatively with ΔWAZ score in the first six months ( Figure 5G ). HM bacterial maturity and maturity-for-age both correlated with ppBMI, while HM metabolome maturity negatively correlated with breast infection and breastfeeding issues. Finally, HM maturity-for-age positively correlated with the number of siblings and maternal antibiotics use but negatively with breastfeeding issues and BMI. Bacteria-metabolite correlations in human milk and infant gut Considering that HM was the sole source of exogenous nutrients for the microbial communities in the HM and gut, we next aimed to determine specific metabolite-bacteria correlations. Using regularised canonical correlation analysis (RCCA), associations between HM metabolite concentrations and bacterial abundances within each mother-infant dyad were determined. The strongest correlations for HM bacteria constituted three main correlation clusters, each with a distinct profile of HMOs ( Figure 5A ). The first consisted of Staphylococcus and Streptococcus ASV’s positively correlated to three unclassified HMOs; the second consisted of lactobacilli, clostridia and bifidobacteria, positively correlated to 3-Fucolsyllactose and LNDFH II; while the third cluster contained a more diverse set of skin-associated bacteria, negatively correlated to three unclassified HMOs. Infant faecal bacteria correlated with multiple HM metabolites, mainly oligosaccharides but also simple sugars and amino acids ( Figure 5B ). More specifically, two unclassified HMOs positively correlated to a cluster of Bifidobacterium , Bacteroides , Faecalibacterium and Streptococcus ASVs while negatively correlating with a cluster consisting of Streptococcus , Staphylococcus , Enterococcus and Bacteroides . A third cluster comprised multiple B. dentium ASVs and Trueperella , Blautia and Escherichia-Shigella spp. positively correlated with myo-inositol and negatively with betaine. Since bifidobacteria comprise more than half of the infant gut bacterial relative abundance and play a central role in early-life gut health 46,47 , we also performed a separate RCCA correlating the infant gut bifidobacteria fraction with HM metabolites. In line with the analysis using all infant gut bacteria, we detected sets of bifidobacteria correlating with distinct HM metabolome profiles ( Figure 5C ). Most bifidobacteria associations comprised two clusters with opposite correlations to two unclassified HMOs. Interestingly, both clusters comprised mixed infant-associated Bifidobacterium species, with B. longum found in both. Again, B. dentium clustered separately. Discussion Here, we studied the interrelationships between HM composition, maternal factors, and infant gut microbiome development among 164 Danish mother-infant dyads during the first three months after birth. We observed distinct temporal changes in the HM metabolome, microbiome and infant gut microbiome composition over time, finding distinct associations with maternal factors and infant anthropometrics. We also found significant correlations between maternal ppBMI and HM metabolite profile as well as offspring early-life gut microbial diversity and composition. While HM and infant faecal bacterial communities were found to mature at independent rates, we detected clusters of bacteria correlating to HM HMOs for both communities. The stable, low-diversity infant GM we observe matches findings of previous studies of exclusively breastfed infants 48 , 49 , and this simple, Bifidobacterium -dominated GM has also been found to be optimal in early life development 50 , 51 . Notably, the GM of infants with normal weight mothers were substantially less diverse relative to those with OWOB mothers. This could imply that transgenerational obesity might be caused by excessive infant GM diversity, i.e. loss of bifidobacteria dominance, rather than transfer of specific detrimental bacteria. Interestingly, we observed an association between ppBMI and higher infant GM Klebsiella relative abundance, a bacterium previously found to correlate with early-life antibiotic use and later childhood obesity 52 . HM from OWOB mothers had increased relative abundance of multiple bacterial genera, including Escherichia Shigella, Lactobacillus, Bifidobacterium, Gamella and Acinetobacter . Whether these indicate a specific high-BMI HM microbiome signature or overweight-associated alterations to the overall maternal microbiome requires further study. Previous smaller-scale studies similarly found that maternal obesity negatively correlated with the relative abundance of infant GM Bifidobacterium, while associations for other genera were more contradictory 27 . In the present study, the majority of HM metabolite associations with maternal BMI were for LNDFH I and unclassified HMOs. Interestingly, we did not find significant correlations between 2´- and 3-FL and maternal ppBMI. While these have been associated with ppBMI and infant adiposity across multiple studies, the reported directions conflicted 53 , 54 . Other studies have linked ppBMI to altered HMO profiles, though findings are inconsistent and often time-point-dependent 55 . HM myo-inositol levels also significantly differed between ppBMI groups but have not previously been linked to maternal BMI or adiposity, despite its importance in infant neural development 56 . However, myo-inositol levels in infant urine was previously found to decrease rapidly from one to three months of life, indicating increased catabolism in this period 45 . While ppBMI was associated with HM microbiome and metabolites, the association with faecal microbial composition was less clear. This suggests that the direct colonisation of the infant GM by HM bacteria is a minor vector for transfer of the maternal overweight phenotype. Maternal postpartum weight loss had a moderate correlation with milk composition, but ranked low in associations for both HM microbiome and GM. Since the rate of postpartum weight loss can be considered a proxy for maternal energy intake, energy expenditure, and diet, this could indicate a modest effect of these on microbial maturation. We observed that both HM bacterial and metabolite compositions showed strong correlations with three-month infant anthropometrics. Overall, HMO profiles have been consistently linked to infant growth but with inconclusive associations for individual HMOs 57 . The correlation to HM microbiome was less expected, as only few significant associations between infant growth and neither HM nor infant gut microbiomes have previously been described. The presence of siblings strongly correlated with infant GM composition but had low associations with HM metabolites and bacteria. Several large-scale studies have similarly identified the presence of older siblings as a main determinant of early-life GM development. Breastfeeding complications were among the factors strongest correlated with both HM and infant faecal microbiomes, as well as for HM metabolome maturity. A previous large-scale study (n = 393) found that the breastfeeding method (i.e. pumping versus breast) was strongly correlated with HM microbiota 58 . Since breastfeeding covers various complications affecting feeding frequency and volume, our findings support the importance of considering these when instigating the HM microbiome. Remarkably, no correlations were found between HM metabolome maturation scores and either HM bacterial or infant GM scores from the same dyad at a given time. This suggests independent maturation of HM and infant gut bacterial communities, with minimal impact of HM microbes on the Bifidobacterium -dominated GM. The only correlation between maturity scores and growth metrics was a 6-month change in WAZ score, which correlated negatively with infant faecal maturity-for-age scores. However, what constitutes the optimal growth rate is ambiguous, as both excessive early-life weight gain and stunted growth have been associated adverse childhood growth and development in multiple studies 59 , 60 . Maternal ppBMI significantly correlated with both average HM bacterial maturity and maturity-for-age scores. However, the correlation with HM metabolite maturity was only moderate. This suggests that maternal BMI affects the HM microbiome in a manner not mediated through the HM metabolome – or at least not among the compounds we were able to measure. Two previous studies of single mother-infant dyads have examined longitudinal correlations of HM components with infant gut metabolome and microbiome. One study focused on the transition to solid food, with solid food introduction being clearly reflected in the faecal metabolome, while the faecal microbiome had a more chaotic response 61 . A second study found that the HM HMO profile was stable throughout the lactation period but detected significant changes in GM composition 62 . In concordance with our study, both indicate independent maturation of HM and GM trajectories. Within HM samples Lactobacillus and Clostridium spp. relative abundance correlated with 3-FL and LNDFH II, in line with previous findings that lactobacilli utilise 3-FL 63 . The distinct clustering of Staphylococcus spp. and other skin-associated bacteria which correlated with specific unclassified HMOs, suggests targeted growth promotion of particular strains by specific HMOs 64 , 65 . In infant faecal samples Bifidobacterium spp. were mainly positively associated with two unclassified HMOs. However, a subcluster of bifidobacteria were negatively correlated with the same HMOs and positively correlated with 3-SL. This indicates sialidase activity, which has been shown for both B. infantis and B. bifidum 66 , 67 . The lack of clear clustering by species, especially B. longum , could reflect strain-level differences in HMO metabolism since bifidobacteria have been found to differ significantly at strain level in nutrient preferences 68 – 70 . The negative correlations between Streptococcus , Staphylococcus , and Bacteroides with specific HMOs likely indicate outgrowth by more efficient utilisers rather than direct inhibition. In conclusion, we found that while HM metabolome, HM microbiome and infant GM correlate strongly with infant growth trajectories after birth they have distinct associations with maternal ppBMI and other clinical and anthropometric factors. Combined with the independent maturation trajectories of HM and infant GM this highlights the complex mechanisms linking maternal factors to infant growth outcomes. Further investigation into these connections may pave the way for interventions to optimize infant development and prevent obesity via personalised nutrition, prebiotics, and probiotics. Strengths and weaknesses The lack of maternal dietary information was a significant limitation, as both caloric intake and diet composition could have influenced milk production. Another major limitation is that we did not measure the total volume of milk consumed. Since we used only the skimmed milk fraction for microbiome analysis, we could have undercounted certain bacteria preferentially associated with the fat fraction 71 , 72 . It should also be noted that there was a large degree of collinearity between many of the variables in exploratory analysis, such as those used in the present setting. Due to general population trends, the effects of maternal age, ppBMI, and the presence of siblings can be challenging to disentangle. Since our cohort mainly consisted of healthy women with tertiary education, selection bias cannot be excluded, affecting the representativity of the participating population. Lastly, most of our participants were of a Western ethnic background, which may also limit the generalizability of the results. Declarations Contributions Conceptualization and funding: D.S.N., U.K.S., and NU; Recruitment and sample collection, J.A, K.O.P, R.A.L C.B.S, J.F. and U.K.S.; Laboratory analysis and data generation: J.A, K.O.P, R.A.L, E.V.J., C.B.S, F.B.B., T.K.J. and R.R.J.; Statistics and bioinformatics analysis: R.R.J.; writing – original draft: R.R.J.; writing – review and editing: D.S.N, J.A., J.F., K.O.P, U.K.S., (M.A.R). All authors have approved the manuscript. Disclosures Funding was obtained from Arla Food for Health. The authors declare no competing interests. Acknowledgements Data was generated by accessing research infrastructure at Copenhagen University and Aarhus University, including equipment financed by FOODHAY (Food and Health Open Innovation Laboratory, Danish Roadmap for Research Infrastructure). Data availability Raw sequencing data is available at the European Nucleotide Archive (ENA) at (https://www.ebi.ac.uk/ena/browser/view/PRJEB82744). All R code used for the statistical analysis is available online at (https://github.com/RasmusRiemer/MAINHEALTH). Clinical data used in this study cannot be made freely available to protect the privacy of the participants, in accordance with the Danish Data Protection Act and European Regulation 2016/679 of the European Parliament and of the Council (GDPR). However, data can be made available upon request after agreement reached with University of Copenhagen and other participating institutions via the corresponding authors Rasmus Riemer Jakobsen ( [email protected] ) and Dennis Sandris Nielsen ( [email protected] ), who aim at responding to any request within 2 weeks. Once an agreement has been reached between the parties, the data will be available for any research covered by the ethical permission under which the original study was carried out. Access will be granted as long as reasonably needed to carry out the envisioned analysis. References Beyerlein, A. & Von Kries, R. Breastfeeding and body composition in children: will there ever be conclusive empirical evidence for a protective effect against overweight? Am J Clin Nutr 94 , S1772–S1775 (2011). Ladomenou, F., Moschandreas, J., Kafatos, A., Tselentis, Y. & Galanakis, E. Protective effect of exclusive breastfeeding against infections during infancy: a prospective study. 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Paquette, A. F. et al. The human milk component myo-inositol promotes neuronal connectivity. Proc Natl Acad Sci U S A 120 , (2023). Ma, J., Palmer, D. J., Geddes, D., Lai, C. T. & Stinson, L. Human Milk Microbiome and Microbiome-Related Products: Potential Modulators of Infant Growth. Nutrients 14 , (2022). Moossavi, S. et al. Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. Cell Host Microbe 25 , 324-335.e4 (2019). Cheung, Y. B., Yip, P. S. F. & Karlberg, J. P. E. Fetal growth, early postnatal growth and motor development in Pakistani infants. Int J Epidemiol 30 , 66–72 (2001). Ong, K. & Loos, R. Rapid infancy weight gain and subsequent obesity: Systematic reviews and hopeful suggestions. Acta Paediatr 95 , 904–908 (2006). Conta, G. et al. Longitudinal Multi-Omics Study of a Mother-Infant Dyad from Breastfeeding to Weaning: An Individualized Approach to Understand the Interactions Among Diet, Fecal Metabolome and Microbiota Composition. Front Mol Biosci 8 , 1 (2021). Komatsu, Y. et al. Dynamic Associations of Milk Components With the Infant Gut Microbiome and Fecal Metabolites in a Mother–Infant Model by Microbiome, NMR Metabolomic, and Time-Series Clustering Analyses. Front Nutr 8 , 813690 (2022). Salli, K. et al. Selective Utilization of the Human Milk Oligosaccharides 2′-Fucosyllactose, 3-Fucosyllactose, and Difucosyllactose by Various Probiotic and Pathogenic Bacteria. J Agric Food Chem 69 , 170–182 (2021). Hunt, K. M. et al. Human milk oligosaccharides promote the growth of staphylococci. Appl Environ Microbiol 78 , 4763–4770 (2012). Lin, A. E. et al. Human milk oligosaccharides inhibit growth of group B Streptococcus. Journal of Biological Chemistry 292 , 11243–11249 (2017). Lawson, M. A. E. et al. Breast milk-derived human milk oligosaccharides promote Bifidobacterium interactions within a single ecosystem. The ISME Journal 2019 14:2 14 , 635–648 (2019). Nishiyama, K. et al. Two extracellular sialidases from Bifidobacterium bifidum promote the degradation of sialyl-oligosaccharides and support the growth of Bifidobacterium breve. Anaerobe 52 , 22–28 (2018). Devika, N. T. & Raman, K. Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models. Sci Rep 9 , (2019). LoCascio, R. G., Desai, P., Sela, D. A., Weimer, B. & Mills, D. A. Broad conservation of milk utilization genes in Bifidobacterium longum subsp. infantis as revealed by comparative genomic hybridization. Appl Environ Microbiol 76 , 7373–7381 (2010). Sela, D. A. et al. The genome sequence of Bifidobacterium longum subsp. infantis reveals adaptations for milk utilization within the infant microbiome. Proc Natl Acad Sci U S A 105 , 18964 (2008). Lima, S. F., De Souza Bicalho, M. L. & Bicalho, R. C. Evaluation of milk sample fractions for characterization of milk microbiota from healthy and clinical mastitis cows. PLoS One 13 , (2018). Sun, L., Dicksved, J., Priyashantha, H., Lundh & Johansson, M. Distribution of bacteria between different milk fractions, investigated using culture-dependent methods and molecular-based and fluorescent microscopy approaches. J Appl Microbiol 127 , 1028–1037 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files JakobsenetalHumanmilkinfantmicrobiomeinteractionssuplementrydatancoms.docx Supplementary figures and tables NCOMMS2514177RS.pdf Reporting summary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Principal component analysis (PCA) plot of HM metabolites, by (B) time point or (C) secretor status; significance by PERMANOVA, pairwise value represents the least significant FDR corrected pairwise comparison between groups. Metabolite concentrations by (D) time point or (E) secretor status, showing the 15 most abundant metabolites; analysed by Wilcox Rank-sum test and FDR corrected. Abbreviations: GA; Gestational age, HMO; human milk oligosaccharide, LNDFH; Lacto-N-difucohexaose. LNT; lacto-N-tetraose.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/e2d03ab3c2c50bc7d95b02eb.png"},{"id":78346686,"identity":"dc97064a-38f8-47fd-9d78-e4292d710ee4","added_by":"auto","created_at":"2025-03-12 09:42:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":427122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial composition of human milk and infant faecal samples change over time.\u003c/strong\u003e\u003cem\u003e Relative abundance of (B) human milk and (C) infant faecal bacteria summarised at genus level, ordered bottom-to-top by mean relative abundance. Legend orders match plot ordering. (DE) PCoA plots of Bray-Curtis dissimilarities by time point; p-values by dbPERMANOVA comparisons between time points. Pairwise values represent the least significant pairwise comparison between groups. (FG) Relative abundance of the 15 most abundant genera by time point; Wilcoxon rank-sum test using FDR correction.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/2ee0d37146c04f7759f2de02.png"},{"id":78346695,"identity":"d528fbb4-4538-4ae6-9ddd-c0312b1709c8","added_by":"auto","created_at":"2025-03-12 09:42:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaternal pre-pregnancy BMI effects human milk and infant faecal compositions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman milk metabolites: (A) PCA plot using Euclidian distance by BMI group and (B) concentrations of the top 15 most abundant compounds compared between BMI groups. Human milk and infant\u003c/em\u003e \u003cem\u003ebacterial: (C,F) Shannon diversity by BMI group, and (D,G) PCoA plot of Bray-Curtis dissimilarities by BMI group and (E,H) genus level differential abundance plots of the 15 most abundant genera. P-values FDR corrected after Wilcoxon rank-sum test or PERMANOVA for between-sample comparisons. Abbreviations: NW; normal weight, OB; obese, OW; overweight, LNDFH; Lacto-N-difucohexaose, LNT; lacto-N-tetraose.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/728ccaec7b1fae9869aefd5c.png"},{"id":78348209,"identity":"7f5aaee4-1eac-4d8c-bea5-4f04037d35cb","added_by":"auto","created_at":"2025-03-12 09:58:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1873326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolite and microbiome compositions are strongly associated to growth, antibiotics use and presence of siblings. \u003c/strong\u003e\u003cem\u003eEffect size R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e of (A) human milk metabolites, (C) human milk bacterial and (E) infant faecal bacterial and association with clinical and anthropometric factors determined by db-PERMANOVA adjusted for time point, using Euclidian distance for metabolites and Bray-Curtis dissimilarities for bacteria; FRD-adjusted p-values. Redundancy analysis (RDA) ordination plot showing the correlation of features with bacterial community structure or metabolite composition showing the contribution of individual (B) human milk metabolites, (D) human milk bacteria and (F) Infant faecal bacteria; the top 10 most abundant taxa and the top 10 highest effect size features are shown. Abbreviations: ΔHAZ; change in height-for-age z-score, ΔWAZ; weight-for-age z-score, ΔWHZ; change in weight-for-length/height z-score, ΔBMI 3 months; change in maternal BMI three months after birth, BMI; maternal pre-pregnancy BMI, LDFT; \u003c/em\u003elactodifucotetraose,\u003cem\u003e LNT; lacto-N-tetraose, LNDFH; Lacto-N-difucohexaose.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/439ac22fab6a23af0bdef8c6.png"},{"id":78347012,"identity":"4ffd677c-00a5-4220-9e3f-22b06246c320","added_by":"auto","created_at":"2025-03-12 09:50:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial and metabolic maturation rates are independent, with distinct associations to clinical features. \u003c/strong\u003e\u003cem\u003eMaturity score correlations across all time-points, and stratified by time points for (A,D) human milk metabolome vs human milk bacterial maturity, (B,E) human milk metabolome vs infant faecal bacterial maturity and (C,F) human milk vs infant faecal bacterial maturity; significance by Pearson correlation. (G) Average maturity and maturity-for-age score correlations to clinical factors and anthropometrics; significant Pearson correlations (p\u0026gt;0.05 after FDR correction) are shown. Maturity scores were calculated using a PLS/sPLS models, using actual sample age as response variable and metabolite levels/bacterial relative abundances as explanatory variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/b7f88cf17414ed756720e6e2.png"},{"id":78348214,"identity":"480960ce-078c-4bfc-ab9f-0f931214b553","added_by":"auto","created_at":"2025-03-12 09:58:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":418795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacteria-metabolite correlations in human milk and the infant gut. \u003c/strong\u003e\u003cem\u003eRegularised Canonical Correlation Analysis (RCCA) for (A) human milk bacteria versus human milk metabolites, (B) infant faecal bacteria versus human milk metabolites and (C) infant faecal bifidobacteria vs human milk metabolites using CSS normalised ASV-abundances and centred metabolite concentrations and cross-validation tuning of each model, displaying correlations \u0026gt; 0.2 for Figures A and C and \u0026gt;0.225 for B., arranged by Euclidian hierarchical clustering. Abbreviations: 3SL; 3′‐Sialyllactose, HMO; human milk oligosaccharide, LNDFH; Lacto-N-difucohexaose.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/85ab5904df883817f1a4f159.png"},{"id":80302220,"identity":"b2d77b40-0bdc-4746-83ba-409f96010c48","added_by":"auto","created_at":"2025-04-10 09:29:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5219280,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/7d59e867-c6ca-435e-bac1-a66244c1d278.pdf"},{"id":78346688,"identity":"a8c076c9-9649-41c9-ad83-2a944525dc61","added_by":"auto","created_at":"2025-03-12 09:42:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":985849,"visible":true,"origin":"","legend":"Supplementary figures and tables","description":"","filename":"JakobsenetalHumanmilkinfantmicrobiomeinteractionssuplementrydatancoms.docx","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/61e4c66223494257e16cb536.docx"},{"id":78348536,"identity":"41b41b8e-321a-4c6f-a223-20a965fbcea9","added_by":"auto","created_at":"2025-03-12 10:06:44","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5908137,"visible":true,"origin":"","legend":"Reporting summary","description":"","filename":"NCOMMS2514177RS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6075035/v1/11ec82257b397fd7312f7edf.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Maternal pre-pregnancy BMI influences breast milk composition, infant gut microbiome development, and early-life growth of term infants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman milk (HM) contains all essential nutrients for the growing infant, and there is substantial evidence that breastfeeding protects against both acute and chronic illnesses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These beneficial effects may partially be mediated through the gut microbiome (GM), as whether the infant is breastfed or formula fed is the strongest determinant of GM composition during infancy, with effects lasting well into childhood\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The nutrient composition of HM varies between individuals and is influenced by multiple maternal factors, including geographical location and ethnicity, gestational age at delivery, weight, diet, secretor status, and mastitis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The composition of HM changes significantly across the first six months of lactation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. During the first years of life, the GM also undergoes rapid compositional changes, and achieving a mature microbiome within 1\u0026ndash;2 years is strongly correlated with later beneficial health outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHuman milk oligosaccharides (HMOs) are a group of complex sugars highly abundant in HM, reaching concentrations of 10\u0026ndash;15 g/L, often exceeding the total amount of protein\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Since HMOs are indigestible, their primary function appears to be as substrates for gut microbes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Several studies report associations between individual HMOs with infant growth outcomes, but findings are inconsistent and sometimes contradictory\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In addition to nutrients, HM contains a distinct microbiome with bacterial loads of 10\u003csup\u003e3\u003c/sup\u003e up to 10\u003csup\u003e5\u003c/sup\u003e CFU/ml\u003csup\u003e16\u003c/sup\u003e. However, the extent of HM-to-gut colonisation and its direct significance for the gut microbiota is still debated\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMaternal obesity is a significant risk factor for excessive foetal growth\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, infant overweight and childhood obesity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. High maternal pre-pregnancy BMI (ppBMI) has been associated with altered HM composition\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and with higher amounts of milk glucose monosaccharides\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, the overall evidence linking HM composition with later obesity is highly inconsistent\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Whether maternal ppBMI also affects the HM microbiome and whether microbial transfer from HM to the gut plays a role in intergenerational obesity is still inconclusive\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study recruited 164 Danish mother-infant dyads from the MaInHealth birth cohort to study the interrelationships between HM composition and infant gut microbiome development during the first three months of life. The study focused particularly on the impact of maternal BMI and other maternal factors on these systems and how they affect early-life infant growth.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003eParticipants and Sample Collection\u003c/h2\u003e\n\u003cp\u003eInfants and their mothers who provided samples for this study were recruited as part of the MaInHealth (Maternal Infant Health) cohort established in Aarhus, Denmark, to investigate the natural HM variation and its possible effects on offspring metabolism and gut microbiota\u003csup\u003e28\u003c/sup\u003e. Pregnant women were recruited from Aarhus University Hospital, Aarhus, Denmark, from 2019 to 2021.\u0026nbsp;Informed consent was obtained from both parents\u0026nbsp;per the Declaration of Helsinki II.\u0026nbsp;Ethical approval for this study was granted by The Central Jutland Regional Committee on Health Research Ethics (journal number 1-10-72-296-18v6). The study is registered at ClinicalTrials.gov, with the identifier NCT05111990. Women included in the study were healthy, non-smokers, expecting to give birth vaginally, and planning to breastfeed for the first four to six months. Infants included were healthy, with birth weights of 2500-5000g and were born full-term, i.e. gestational week 37\u003csup\u003e+0\u003c/sup\u003e or later. \u0026nbsp; See the study protocol for a detailed project description, recruitment, and exclusion criteria\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBriefly, HM was collected by the mother in a 40 mL sterile container within the first week after giving birth and at one, two, and three months post-partum. Around 20 ml of foremilk was collocated at each sampling, avoiding sampling the first few drops. Faecal samples of approximately 2 g were collected at one, two and three months post-partum from the first faeces passed after HM sample collection. Samples were stored in the participants\u0026rsquo; own freezer at -20 \u0026deg;C for up to two weeks. Subsequently, the samples were collected and transported on dry ice to the Department of Food Science, Aarhus University, where they were stored at -80 \u0026deg;C until further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHM samples were thawed, thoroughly mixed, split into 1 ml aliquots, and returned to storage at -80 \u0026deg;C. Metabolomics and microbiome characterisations each used one 1 ml HM aliquot. Faecal samples were thawed, and approximately 250 mg of faecal sample was transferred to a 1.5 ml Eppendorf tube and mixed at a 1:5 ratio (w/v) with PBS buffer (0.13 M NaCl, 0.0100 M Na2HPO4,\u0026nbsp;0.0027 M KCl, 0.0018 M KH2PO4, pH ~ 7.4) by vortexing and centrifuged at 10,000\u0026nbsp;\u0026times;\u0026nbsp;g for 10 min at, 4℃. The pellet was frozen at -80℃\u0026nbsp;before subsequent DNA extraction, and the supernatant was transferred to a separate tube for metabolomics analysis.\u003c/p\u003e\n\u003ch2\u003e\u003csup\u003e1\u003c/sup\u003eH Nuclear Magnetic Resonance Spectroscopy Metabolomics Analysis of Milk\u003c/h2\u003e\n\u003cp\u003eHM samples for \u003csup\u003e1\u003c/sup\u003eH nuclear magnetic resonance (NMR)-based metabolomics were processed following a standard protocol for milk-based metabolomics as described previously\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn brief, samples were thawed in a water bath and kept on ice while Amicon Ultra 0.5-ml 10-kDa spin filters (Millipore, Billerica, MA, USA) were washed three times. After washing, samples were centrifuged at 4,000 \u0026times; g at 4\u0026deg;C for 10 minutes to skim off the fat layer, transferred to spin filters and centrifuged at 10,000 g at 4\u0026deg;C for 60 minutes. Subsequently, 400 \u0026micro;l of the filtered milk was placed into individual 5-mm NMR tubes, and 200 \u0026micro;l of D\u003csub\u003e2\u003c/sub\u003eO containing 0.05% 3-(trimethylsilyl) propionic acid (TSP, Sigma-Aldrich, Saint-Louis, MO, USA) was added to each tube.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH NMR spectra acquisition was performed using a Bruker NEO 600 spectrometer equipped with a 5-mm 1H BBI probe, operating at a temperature of 300 K and a 1H frequency of 600.03 MHz. All spectra were referenced to the TSP signal at 0 ppm, with a line-broadening function of 0.3 Hz applied before Fourier transformation. Phase and baseline corrections were performed manually and automatically using Topspin 4.09 (Bruker Biospin, Rheinstetten, Germany).\u003c/p\u003e\n\u003ch2\u003eIdentification of Metabolites and Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eTo ensure correct metabolite identification and quantification and to identify metabolites in HM, the acquired spectra were analysed using Chenomx NMR suite 10.1 (Chenomx Inc., Edmonton, AB, Canada) with the Chenomx standard metabolite library and an in-house HMO library. Metabolites were normalised using total metabolite count, using a weighted normalisation to 215 mM lactose. Since lactose constitutes \u0026gt;80% of the total concentration of the HM metabolites detected by NMR-based metabolomics, using total metabolite count would be the same as adjusting for lactose which significantly varies between stages of lactation\u003csup\u003e30\u003c/sup\u003e. A weighted normalisation with lactose weight 3 and total metabolites weight 1 was therefore used: a lactose factor normalising to the average concentration in HM of 215 mM (L-factor) and a total metabolite (excluding lactose) across all samples (T-factor) were combined as the factor (L-factor\u0026times;3+T-factor)/4. Metabolite concentrations were centred but not scaled prior to analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSequencing of bacterial communities in mother\u0026rsquo;s milk and infant faecal samples\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eDNA extraction\u003c/h2\u003e\n\u003cp\u003eSamples were randomised before DNA extraction and amplicon sequencing. Samples\u0026nbsp;were thawed at 4 C\u0026deg; before DNA extraction.\u0026nbsp;Milk samples were centrifuged at 12000 \u0026times; g for 20 minutes at 4 C\u0026deg;, the fat layer mechanically removed with a sterile cotton swab,\u0026nbsp;and the supernatant carefully removed. DNA was then extracted from the resulting pellet using the Bead-Beat Micro AX Gravity Kit (A\u0026amp;A Biotechnology, Gdynia, Poland) per the manufacturer\u0026apos;s instructions. Sterile MilliQ water was used as negative control during the entire extraction pipeline as well as during subsequent PCR and sequencing steps. The DNA purity and concentration were determined by NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, USA) and using the Qubit\u0026trade; 1x dsDNA high sensitivity kit on a Varioskan Flash (Thermo Fisher Scientific, USA), respectively.\u003c/p\u003e\n\u003ch2\u003eLibrary Preparation and Sequencing\u003c/h2\u003e\n\u003cp\u003eA 16S rRNA gene amplicon library was constructed by amplifying the 16S rRNA gene using\u0026nbsp;primers targeting the 16S V1-V9 regions, as described previously\u003csup\u003e31\u003c/sup\u003e. After each PCR reaction, PCR amplicons were cleaned using\u0026nbsp;SpeedBeads\u003csup\u003eTM\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003emagnetic carboxylate\u0026nbsp;(obtained from Sigma Aldrich). The size of barcoded PCR products (approximately 1500 bp) was checked by 1.5% agarose gel electrophoresis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSequencing libraries consisting of pooled barcoded PCR products from up to 196 samples were prepared following the ligation sequencing protocol SQK-LSK110 (Oxford Nanopore Technologies, Oxford, UK). They were loaded on an R9.1.4 flow cell and sequenced on a GridIONX5 for 72 hours (Oxford Nanopore Technologies, Oxford, UK).\u003c/p\u003e\n\u003ch2\u003ePre-processing and filtering\u003c/h2\u003e\n\u003cp\u003eRaw read data was processed using the long amplicon consensus analysis (LACA) pipeline for \u003cem\u003ede-novo\u0026nbsp;\u003c/em\u003eclustering and taxonomic classification of long-read amplicons\u003csup\u003e32\u003c/sup\u003e. This pipeline employs multiple \u003cem\u003ede-novo\u003c/em\u003e clustering approaches to control the sequencing error rate of Oxford Nanopore sequencing data and assign taxonomy to the resulting consensus sequences using the SILVA\u003csup\u003e33\u003c/sup\u003e v138.1 database.\u003c/p\u003e\n\u003ch2\u003eBioinformatics analysis\u003c/h2\u003e\n\u003cp\u003eInitially, the dataset was purged for zOTUs, which were detected in less than 5% of the samples and with mean relative abundances below 0.05%, with the resulting dataset still maintaining 98 % of the total reads. R version 4.2.1\u003csup\u003e34\u003c/sup\u003e was used for subsequent data analysis and presentation. The data and complete code used are available, as described in the data availability section. The main packages used were Phyloseq\u003csup\u003e35\u003c/sup\u003e, vegan\u003csup\u003e36\u003c/sup\u003e, ampvis2\u003csup\u003e37\u003c/sup\u003e,\u0026nbsp;microeco\u003csup\u003e38\u003c/sup\u003e,\u0026nbsp;ggpubr\u003csup\u003e39\u003c/sup\u003e and ggplot2\u003csup\u003e40\u003c/sup\u003e. Alpha-diversity analysis was based on raw read counts, rarefied to a median depth of 44,574 reads. Cumulative sum scaling (CSS) was applied before calculating beta-diversity metrics to add weight to less abundant zOTUs.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMetabolic maturation\u003c/h2\u003e\n\u003cp\u003eSparse partial least squares (sPLS) for microbiome data and partial least squares (PLS) regression models were built with sample age in days as the response variable and ASV relative abundances (CSS normalised) or metabolite levels as the explanatory variables from all samples, including cross-validation to avoid overfitting. Confidence intervals of the metabolite coefficients in the PLS model were based on the internal PLS cross-validation models. The metabolic/bacterial maturation age is the predicted age solely based on the bacterial or metabolite profiles, and the metabolic/bacterial z-scores are defined as the difference between the actual age and the predicted age for each sample. Correlation between the predicted metabolic or bacterial maturation age to clinical and anthropometric factors was performed using the Pearson correlation.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant and sample characteristics\u003c/h2\u003e\n\u003cp\u003eThis study includes samples from 164 healthy mother-infant dyads consisting of HM samples collected at day 3-90 (4 time points) and infant faecal samples at 30-90 days (3 time points), along with extensive anthropometrics and clinical data (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). The average pre-pregnancy BMI was 27\u0026plusmn;5.4 and the mean gestational age was 283\u0026plusmn;2.5 days with average birth weights of 3704\u0026plusmn;45 g (\u003cstrong\u003eTable 1\u003c/strong\u003e). Among the infants, 59% had older siblings, and 52% were female. HM metabolites were characterised using \u003csup\u003e1\u003c/sup\u003eH nuclear magnetic resonance (NMR) and HM and infant faecal bacterial communities by V1-V9 16S rRNA gene Nanopore long-read amplicon sequencing. After filtering and quality control, 570 HM metabolite samples, 495 HM microbiome samples, and 348 infant faecal microbiome samples were included for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of 164 Mother-infant dyads enrolled in the MaInHealth cohort\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"526\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePctl. 25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePctl. 75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBMI group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Normal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eMaternal age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e31 \u0026plusmn; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGestational diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSiblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e46%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e54%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eDelivery mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; C-section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Vaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e91%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eInfant sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e52%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e48%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGestational age (Days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e283 \u0026plusmn; 7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBirth weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3704 \u0026plusmn; 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eSecretor status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Non-secretor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Secretor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003eLewis status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lewis negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lewis positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e96%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eContinuous data is presented as means \u0026plusmn; standard deviation. Categorical data are presented as numbers included in each category. BMI: body mass index, C-section: Caesarean section, n: number.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eHuman milk metabolome changes over lactation and is strongly influenced by secretor status\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH NMR metabolome analysis of the HM samples identified 70 metabolites, including 20 amino acids and derivatives, 18 oligosaccharides, 13 energy-metabolism related, 9 fatty acids and derivatives, 5 simple sugars, 3 food and 1 microbially derived metabolites (\u003cstrong\u003eTable S2\u003c/strong\u003e). Lactose was by a large margin the most abundant detected metabolite at 215\u0026plusmn;7 mmol/ml, followed by citrate at 3.9\u0026plusmn;1.3\u0026nbsp;mmol/ml, various HMO\u0026rsquo;s and simple sugars. HM samples showed a significant shift in the overall metabolite profile over time. Day 3 profiles were the most distinct (\u003cstrong\u003eFigure 1B\u003c/strong\u003e), and individual metabolite concentrations changed significantly over time (\u003cstrong\u003eFigure 1D, Table S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMothers were classified as secretors or non-secretors dependent on the presence of the predominant resonance in 2\u0026rsquo;-Fucosyllactose, and \u0026alpha;1-2 fucosyl linkage and Lewis status as determined by the presence of an \u0026alpha;1-4 fucosyl linkage in Lacto-N-difucohexaose (LNDFH) I and II \u003csup\u003e8\u003c/sup\u003e. 74% of the mothers were classified as secretors and 96% as Lewis-positive (\u003cstrong\u003eTable 1\u003c/strong\u003e). Principle Coordinate Analysis of HM metabolome profiles showed complete separation of HM samples by secretor status (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Examining the loadings plot, the majority of variance was explained by 2\u0026rsquo;- and 3-Fucosyllactose (2\u0026rsquo;-FL and 3-FL) for component 1 and unclassified HMO 5.10 for component 2, which likely corresponds to secretor-status and time effects, respectively (\u003cstrong\u003eFigure S2\u003c/strong\u003e). We did not perform a detailed analysis of the impact of Lewis status due to the low number of Lewis-negative mothers (n=7). Comparing metabolite profiles, HM from non-secretors had undetectable levels of the fucosylated oligosaccharides 2\u0026rsquo;-FL and LNDFH I but relatively high levels of lacto-N-tetraose (LNT) and several unclassified HMOs (\u003cstrong\u003eFigure 2E\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eHuman and infant faecal microbiomes mature over time\u003c/h2\u003e\n\u003cp\u003eThe HM microbiomes comprised \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eLacticaseibacillus\u003c/em\u003e, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Bacteroides\u0026nbsp;\u003c/em\u003emembers (\u003cstrong\u003eFigure 2AB\u003c/strong\u003e). In contrast, the microbiomes of infant faecal samples were mainly composed of \u003cem\u003eBifidobacterium, Bacteroides, Escherichia-Shigella, Klebsiella\u003c/em\u003e and \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003emembers. Bacterial composition showed significant between-sample (\u0026beta;) diversity shifts for both sample types (\u003cstrong\u003eFigure 2CD\u003c/strong\u003e). In contrast, within-sample (\u0026alpha;) diversities did not significantly differ over time for neither HM nor infant faecal samples (\u003cstrong\u003eFigure S1AB\u003c/strong\u003e). \u0026nbsp;In the HM samples, the relative abundance of \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eGamella\u0026nbsp;\u003c/em\u003esignificantly decreased from day 3 to day 90, while \u003cem\u003eEscherichia-Shigella,\u0026nbsp;\u003c/em\u003elactobacilli\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAcinetobacter\u003c/em\u003e increased (\u003cstrong\u003eFigure 1E\u003c/strong\u003e). For faecal samples, the relative abundance of \u003cem\u003eStreptococcus\u003c/em\u003e significantly decreased while \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eLacticaseibacillus\u0026nbsp;\u003c/em\u003eincreased from day 30 to 90\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cstrong\u003eFigure 1F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eHM and infant faecal bacterial communities both showed modest changes in average sample composition over time. However, individual amplicon sequence variant (ASV) persistence across time points was high in faecal samples but significantly lower in HM samples. Comparing ASV-level persistence between the most abundant HM genera, we found that ASVs identified as \u003cem\u003eGamella\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e members were significantly more persistent across samples, while \u003cem\u003eLactococcus\u003c/em\u003e was the least persistent (\u003cstrong\u003eFigure S1C\u003c/strong\u003e). In comparison, all high-abundance ASVs in infant faecal samples showed high persistence, with no significant differences between genera (\u003cstrong\u003eFigure S1D\u003c/strong\u003e). The lower persistence of HM ASVs is likely due to the detection threshold rather than actual presence/absence, as the HM microbiome had markedly higher variance, even at the genus level (\u003cstrong\u003eFigure S1EF\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the MAINHEALTH cohort was designed to study the influence of maternal ppBMI on HM composition and associations to infant metabolism and gut colonisation, we next examined these associations. HM metabolic profiles from overweight or obese (OWOB) mothers showed moderate yet significant differences in overall metabolite profiles and significant differences in levels of multiple HMOs and simple sugars compared to normal-weight mothers (\u003cstrong\u003eFigure 3AB\u003c/strong\u003e). HM bacterial profiles for OWOB mothers had significantly lower within-sample diversity and significant differences in community composition compared to normal-weight samples (\u003cstrong\u003eFigure 3CD\u003c/strong\u003e). OWOB samples were characterised by increased \u003cem\u003eGamella\u0026nbsp;\u003c/em\u003eand decreased \u003cem\u003eEscherichia-Shigella, Bifidobacterium\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003erelative abundance compared to HM from mothers with ppBMI \u0026lt; 25 (\u003cstrong\u003eFigure 3E\u003c/strong\u003e). Conversely, faecal samples from infants from OWOB mothers had significantly higher within-sample diversity while also differing significantly in community composition (\u003cstrong\u003eFigure 3FG\u003c/strong\u003e). Though not highly abundant, \u003cem\u003eKlebsiella, Ruminococcus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eErysipelatoclostridium\u003c/em\u003e were\u003cem\u003e\u0026nbsp;\u003c/em\u003efound to be significantly higher in abundance in faecal samples from offspring of OWOB mothers compared to those whose mothers had ppBMI \u0026lt; 25 \u0026nbsp;(\u003cstrong\u003eFigure 3H).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMilk metabolome and milk and faecal microbiomes show distinct correlations with clinical factors and anthropometrics\u003c/h2\u003e\n\u003cp\u003eWe next examined how maternal and infant clinical and anthropometric risk factors correlated with HM metabolome and bacterial communities. We correlated each factor with bacterial or metabolite community composition to get an overview of which factors had the strongest associations (\u003cstrong\u003eFigure 4\u003c/strong\u003e). \u0026nbsp;Expectedly, the HM metabolite profile correlated strongly to secretor status, followed by weight-for-age z-score (\u0026Delta;WAZ), change in height-for-age z-score (\u0026Delta;HAZ), Lewis status, change in weight-for-length/height z-score (\u0026Delta;WHZ), maternal antibiotics, and maternal postpartum BMI change (\u0026Delta;BMI 3 months) (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). HM bacterial composition correlated mainly with, in decreasing order by R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues:\u0026nbsp;\u0026Delta;HAZ,\u0026nbsp;\u0026Delta;WHZ, and\u0026nbsp;\u0026Delta;WAZ, followed by maternal antibiotics, breast infection, infant antibiotics, and breastfeeding issues (\u003cstrong\u003eFigure 4C\u003c/strong\u003e). Infant faecal microbiome composition correlated mainly with, in decreasing order by R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues: number of siblings, infant antibiotics,\u0026nbsp;\u0026Delta;WAZ,\u0026nbsp;\u0026Delta;WAZ, C-section, breast infection, and breastfeeding issues (\u003cstrong\u003eFigure 4E\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile effect size comparisons provide an overview of the overall correlation of factors with bacterial or metabolite composition, they do not show which bacteria or metabolites influence this correlation nor their directionality. To examine associations of specific bacteria or metabolites with clinical features and anthropometric factors, we used redundancy analysis (RDA). HM metabolite RDA (\u003cstrong\u003eFigure 4B)\u0026nbsp;\u003c/strong\u003eshowed two unclassified HMOs, one of them sialylated, positively associated with maternal ppBMI and negatively with days after birth and glutamine levels. Post-birth change in maternal BMI was positively associated with LNT. On the orthogonal axis, 2\u0026rsquo;-FL, LNDFH I and Lactodifucotetraose (LDFT) are, as expected, positively correlated with secretor status, along with birth weight and secretor status. Interestingly, the non-secretor associated 3-FL and LNDFH II were associated with\u0026nbsp;\u0026Delta;HAZ,\u0026nbsp;\u0026Delta;WHZ, and\u0026nbsp;\u0026Delta;WAZ.\u003c/p\u003e\n\u003cp\u003eFor the HM bacterial composition (\u003cstrong\u003eFigure 4D\u003c/strong\u003e)\u003cem\u003e, Clostridium\u0026nbsp;\u003c/em\u003epositively\u003cem\u003e\u0026nbsp;\u003c/em\u003ecorrelates with days after birth, and shows negative associations to \u003cem\u003eGemella, Staphylococcus,\u0026nbsp;\u003c/em\u003eand infant antibiotics. Interestingly, \u0026Delta;HAZ and \u0026Delta;WAZ correlated with faecal-associated \u003cem\u003eBacteroides,\u0026nbsp;\u003c/em\u003eLactobacilli\u003cem\u003e, Bifidobacterium,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Faecalibacterium\u003c/em\u003e, all of which were negatively associated with ppBMI, breast infection, and breastfeeding issues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of infant faecal bacteria (\u003cstrong\u003eFigure 4F\u003c/strong\u003e) showed that \u003cem\u003eBifidobacterium\u003c/em\u003e abundance positively correlated with the number of siblings, secretor status, and antibiotic treatments while negatively associated with \u003cem\u003eVelilonella\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eClostridium\u003c/em\u003e. On the orthogonal axis, \u003cem\u003eStreptococcus\u003c/em\u003e aligned with C-section and positive \u0026Delta;WAZ and \u0026Delta;HAZ scores, while these were negatively correlated with \u003cem\u003eCollinsella, Bacteroides, Shigella,\u003c/em\u003e breastfeeding issues, and days after birth.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMicrobial and metabolic maturation\u003c/h2\u003e\n\u003cp\u003eSeveral attempts have been made to model the maturation of infant intestinal microbial communities over time, and one approach is to score the \u0026ldquo;maturity\u0026rdquo; of each sample by comparing it to the average change in sample profiles over time. This is generally referred to as the microbiome maturity score and, correspondingly, the microbiome-for-age score when adjusted for sample age\u003csup\u003e41\u0026ndash;43\u003c/sup\u003e. Similarly, urine metabolites have also been used to determine the metabolic age of children\u003csup\u003e44,45\u003c/sup\u003e. Here, we predicted each sample\u0026apos;s microbial/metabolic age using two-component sPLS/PLS models. We then compared the predicted maturities of each set of corresponding samples within each dyad. HM metabolic maturity correlated with maturity scores of both HM and faecal bacterial communities (\u003cstrong\u003eFigure 5AB\u003c/strong\u003e). In contrast, HM and faecal samples\u0026apos; bacterial maturity did not significantly correlate (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). However, when samples were stratified by days of life, no significant correlations were detected between neither bacterial nor metabolic maturity scores (\u003cstrong\u003eFigure 5DEF\u003c/strong\u003e). This suggests that the HM metabolome matures parallel to HM and infant faecal bacterial communities. However, high maturity in one score at a given time point does not correspond to high maturity in other scores. We then correlated maturity scores to clinical and anthropometric features, using both the average maturity within each dyad and maturity-for-age z-scores, i.e. the difference between the sample maturity score and the average score for the time point. Infant faecal maturity correlated with maternal antibiotics, while faecal maturity-for-age scores correlated negatively with \u0026Delta;WAZ score in the first six months (\u003cstrong\u003eFigure 5G\u003c/strong\u003e). HM bacterial maturity and maturity-for-age both correlated with ppBMI, while HM metabolome maturity negatively correlated with breast infection and breastfeeding issues. Finally, HM maturity-for-age positively correlated with the number of siblings and maternal antibiotics use but negatively with breastfeeding issues and BMI.\u003c/p\u003e\n\u003ch2\u003eBacteria-metabolite correlations in human milk and infant gut\u003c/h2\u003e\n\u003cp\u003eConsidering that HM was the sole source of exogenous nutrients for the microbial communities in the HM and gut, we next aimed to determine specific metabolite-bacteria correlations. Using regularised canonical correlation analysis (RCCA), associations between HM metabolite concentrations and bacterial abundances within each mother-infant dyad were determined. The strongest correlations for HM bacteria constituted three main correlation clusters, each with a distinct profile of HMOs (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). The first consisted of \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e ASV\u0026rsquo;s positively correlated to three unclassified HMOs; the second consisted of lactobacilli, clostridia and bifidobacteria, positively correlated to 3-Fucolsyllactose and LNDFH II; while the third cluster contained a more diverse set of skin-associated bacteria, negatively correlated to three unclassified HMOs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInfant faecal bacteria correlated with multiple HM metabolites, mainly oligosaccharides but also simple sugars and amino acids (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). More specifically, two unclassified HMOs positively correlated to a cluster of \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u0026nbsp;\u003c/em\u003eand \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003eASVs while negatively correlating with a cluster consisting of \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e. A third cluster comprised multiple \u003cem\u003eB. dentium\u003c/em\u003e ASVs and \u003cem\u003eTrueperella\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e spp. positively correlated with myo-inositol and negatively with betaine.\u003c/p\u003e\n\u003cp\u003eSince bifidobacteria comprise more than half of the infant gut bacterial relative abundance and play a central role in early-life gut health\u003csup\u003e46,47\u003c/sup\u003e, we also performed a separate RCCA correlating the infant gut bifidobacteria fraction with HM metabolites. In line with the analysis using all infant gut bacteria, we detected sets of bifidobacteria correlating with distinct HM metabolome profiles (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). Most bifidobacteria associations comprised two clusters with opposite correlations to two unclassified HMOs. Interestingly, both clusters comprised mixed infant-associated \u003cem\u003eBifidobacterium\u003c/em\u003e species, \u003cem\u003ewith B. longum\u0026nbsp;\u003c/em\u003efound in both. Again, \u003cem\u003eB. dentium\u003c/em\u003e clustered separately.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we studied the interrelationships between HM composition, maternal factors, and infant gut microbiome development among 164 Danish mother-infant dyads during the first three months after birth. We observed distinct temporal changes in the HM metabolome, microbiome and infant gut microbiome composition over time, finding distinct associations with maternal factors and infant anthropometrics. We also found significant correlations between maternal ppBMI and HM metabolite profile as well as offspring early-life gut microbial diversity and composition. While HM and infant faecal bacterial communities were found to mature at independent rates, we detected clusters of bacteria correlating to HM HMOs for both communities.\u003c/p\u003e \u003cp\u003eThe stable, low-diversity infant GM we observe matches findings of previous studies of exclusively breastfed infants\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and this simple, \u003cem\u003eBifidobacterium\u003c/em\u003e-dominated GM has also been found to be optimal in early life development\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Notably, the GM of infants with normal weight mothers were substantially less diverse relative to those with OWOB mothers. This could imply that transgenerational obesity might be caused by excessive infant GM diversity, i.e. loss of bifidobacteria dominance, rather than transfer of specific detrimental bacteria. Interestingly, we observed an association between ppBMI and higher infant GM \u003cem\u003eKlebsiella\u003c/em\u003e relative abundance, a bacterium previously found to correlate with early-life antibiotic use and later childhood obesity\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. HM from OWOB mothers had increased relative abundance of multiple bacterial genera, including \u003cem\u003eEscherichia Shigella, Lactobacillus, Bifidobacterium, Gamella\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e. Whether these indicate a specific high-BMI HM microbiome signature or overweight-associated alterations to the overall maternal microbiome requires further study. Previous smaller-scale studies similarly found that maternal obesity negatively correlated with the relative abundance of infant GM Bifidobacterium, while associations for other genera were more contradictory\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, the majority of HM metabolite associations with maternal BMI were for LNDFH I and unclassified HMOs. Interestingly, we did not find significant correlations between 2´- and 3-FL and maternal ppBMI. While these have been associated with ppBMI and infant adiposity across multiple studies, the reported directions conflicted\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Other studies have linked ppBMI to altered HMO profiles, though findings are inconsistent and often time-point-dependent\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. HM myo-inositol levels also significantly differed between ppBMI groups but have not previously been linked to maternal BMI or adiposity, despite its importance in infant neural development\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. However, myo-inositol levels in infant urine was previously found to decrease rapidly from one to three months of life, indicating increased catabolism in this period\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. While ppBMI was associated with HM microbiome and metabolites, the association with faecal microbial composition was less clear. This suggests that the direct colonisation of the infant GM by HM bacteria is a minor vector for transfer of the maternal overweight phenotype. Maternal postpartum weight loss had a moderate correlation with milk composition, but ranked low in associations for both HM microbiome and GM. Since the rate of postpartum weight loss can be considered a proxy for maternal energy intake, energy expenditure, and diet, this could indicate a modest effect of these on microbial maturation.\u003c/p\u003e \u003cp\u003eWe observed that both HM bacterial and metabolite compositions showed strong correlations with three-month infant anthropometrics. Overall, HMO profiles have been consistently linked to infant growth but with inconclusive associations for individual HMOs\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The correlation to HM microbiome was less expected, as only few significant associations between infant growth and neither HM nor infant gut microbiomes have previously been described. The presence of siblings strongly correlated with infant GM composition but had low associations with HM metabolites and bacteria. Several large-scale studies have similarly identified the presence of older siblings as a main determinant of early-life GM development. Breastfeeding complications were among the factors strongest correlated with both HM and infant faecal microbiomes, as well as for HM metabolome maturity. A previous large-scale study (n = 393) found that the breastfeeding method (i.e. pumping versus breast) was strongly correlated with HM microbiota\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Since breastfeeding covers various complications affecting feeding frequency and volume, our findings support the importance of considering these when instigating the HM microbiome.\u003c/p\u003e \u003cp\u003eRemarkably, no correlations were found between HM metabolome maturation scores and either HM bacterial or infant GM scores from the same dyad at a given time. This suggests independent maturation of HM and infant gut bacterial communities, with minimal impact of HM microbes on the \u003cem\u003eBifidobacterium\u003c/em\u003e-dominated GM. The only correlation between maturity scores and growth metrics was a 6-month change in WAZ score, which correlated negatively with infant faecal maturity-for-age scores. However, what constitutes the optimal growth rate is ambiguous, as both excessive early-life weight gain and stunted growth have been associated adverse childhood growth and development in multiple studies\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Maternal ppBMI significantly correlated with both average HM bacterial maturity and maturity-for-age scores. However, the correlation with HM metabolite maturity was only moderate. This suggests that maternal BMI affects the HM microbiome in a manner not mediated through the HM metabolome – or at least not among the compounds we were able to measure. Two previous studies of single mother-infant dyads have examined longitudinal correlations of HM components with infant gut metabolome and microbiome. One study focused on the transition to solid food, with solid food introduction being clearly reflected in the faecal metabolome, while the faecal microbiome had a more chaotic response\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. A second study found that the HM HMO profile was stable throughout the lactation period but detected significant changes in GM composition\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In concordance with our study, both indicate independent maturation of HM and GM trajectories.\u003c/p\u003e \u003cp\u003eWithin HM samples \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e spp. relative abundance correlated with 3-FL and LNDFH II, in line with previous findings that lactobacilli utilise 3-FL\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The distinct clustering of \u003cem\u003eStaphylococcus\u003c/em\u003e spp. and other skin-associated bacteria which correlated with specific unclassified HMOs, suggests targeted growth promotion of particular strains by specific HMOs\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In infant faecal samples \u003cem\u003eBifidobacterium\u003c/em\u003e spp. were mainly positively associated with two unclassified HMOs. However, a subcluster of bifidobacteria were negatively correlated with the same HMOs and positively correlated with 3-SL. This indicates sialidase activity, which has been shown for both \u003cem\u003eB. infantis\u003c/em\u003e and \u003cem\u003eB. bifidum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The lack of clear clustering by species, especially \u003cem\u003eB. longum\u003c/em\u003e, could reflect strain-level differences in HMO metabolism since bifidobacteria have been found to differ significantly at strain level in nutrient preferences\u003csup\u003e\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e–\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The negative correlations between \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e with specific HMOs likely indicate outgrowth by more efficient utilisers rather than direct inhibition.\u003c/p\u003e \u003cp\u003eIn conclusion, we found that while HM metabolome, HM microbiome and infant GM correlate strongly with infant growth trajectories after birth they have distinct associations with maternal ppBMI and other clinical and anthropometric factors. Combined with the independent maturation trajectories of HM and infant GM this highlights the complex mechanisms linking maternal factors to infant growth outcomes. Further investigation into these connections may pave the way for interventions to optimize infant development and prevent obesity via personalised nutrition, prebiotics, and probiotics.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and weaknesses\u003c/h2\u003e \u003cp\u003eThe lack of maternal dietary information was a significant limitation, as both caloric intake and diet composition could have influenced milk production. Another major limitation is that we did not measure the total volume of milk consumed. Since we used only the skimmed milk fraction for microbiome analysis, we could have undercounted certain bacteria preferentially associated with the fat fraction\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. It should also be noted that there was a large degree of collinearity between many of the variables in exploratory analysis, such as those used in the present setting. Due to general population trends, the effects of maternal age, ppBMI, and the presence of siblings can be challenging to disentangle. Since our cohort mainly consisted of healthy women with tertiary education, selection bias cannot be excluded, affecting the representativity of the participating population. Lastly, most of our participants were of a Western ethnic background, which may also limit the generalizability of the results.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eConceptualization and funding: D.S.N., U.K.S., and NU; Recruitment and sample collection, J.A, K.O.P, R.A.L C.B.S, J.F. and U.K.S.; Laboratory analysis and data generation: J.A, K.O.P, R.A.L, E.V.J., C.B.S, F.B.B., T.K.J. and R.R.J.; Statistics and bioinformatics analysis: R.R.J.; writing \u0026ndash; original draft: R.R.J.; writing \u0026ndash; review and editing: D.S.N, J.A., J.F., K.O.P, U.K.S., (M.A.R). All authors have approved the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDisclosures\u003c/h2\u003e\n\u003cp\u003eFunding was obtained from Arla Food for Health. The authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eData was generated by accessing research infrastructure at Copenhagen University and Aarhus University, including equipment financed by FOODHAY (Food and Health Open Innovation Laboratory, Danish Roadmap for Research Infrastructure).\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eRaw sequencing data is available at the European Nucleotide Archive (ENA) at (https://www.ebi.ac.uk/ena/browser/view/PRJEB82744). \u0026nbsp;All R code used for the statistical analysis is available online at (https://github.com/RasmusRiemer/MAINHEALTH).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical data used in this study cannot be made freely available to protect the privacy of the participants, in accordance with the Danish Data Protection Act and European Regulation 2016/679 of the European Parliament and of the Council (GDPR). However, data can be made available upon request after agreement reached with University of Copenhagen and other participating institutions via the corresponding authors Rasmus Riemer Jakobsen ([email protected]) and Dennis Sandris Nielsen ([email protected]), who aim at responding to any request within 2 weeks. Once an agreement has been reached between the parties, the data will be available for any research covered by the ethical permission under which the original study was carried out. Access will be granted as long as reasonably needed to carry out the envisioned analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeyerlein, A. \u0026amp; Von Kries, R. 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Distribution of bacteria between different milk fractions, investigated using culture-dependent methods and molecular-based and fluorescent microscopy approaches. \u003cem\u003eJ Appl Microbiol\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 1028\u0026ndash;1037 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human milk microbiome, human milk metabolome, infant gut microbiome, maternal overweight","lastPublishedDoi":"10.21203/rs.3.rs-6075035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6075035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast milk is the optimal nutrition for infants, yet individual variations in its composition and effects on infant growth remain unclear. This study examined human milk (HM) metabolome and microbiome dynamics in relation to infant growth and gut microbiome (GM) maturation in 164 exclusively breastfeeding Danish mother-infant dyads over the first three months. Results showed distinct temporal shifts in in HM metabolome and microbiome as well as infant GM composition. Maternal pre-pregnancy BMI correlated with HM metabolite profiles, infant growth, and GM diversity and composition. However, HM and GM maturity scores were not correlated, suggesting independent development. Notably, HM oligosaccharide clusters were linked to neonatal gut bacteria, including multiple \u003cem\u003eBifidobacterium\u003c/em\u003e spp. These findings indicate that maternal BMI may influence infant gut microbiome development and growth through changes in HM composition.\u003c/p\u003e","manuscriptTitle":"Maternal pre-pregnancy BMI influences breast milk composition, infant gut microbiome development, and early-life growth of term infants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 09:42:36","doi":"10.21203/rs.3.rs-6075035/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b3c268c-2969-4aed-969a-f63b28921d46","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45564193,"name":"Biological sciences/Microbiology/Microbial communities/Microbiome"},{"id":45564194,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-04-10T09:21:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 09:42:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6075035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6075035","identity":"rs-6075035","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}