Distinct Early-Life Gut Microbiota Patterns Across SGA, AGA, and LGA Infants

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Small-for-gestational-age (SGA), appropriate-for-gestational-age (AGA), and large-for-gestational-age (LGA) infants may exhibit distinct microbial maturation patterns that could influence later metabolic and developmental outcomes. Methods: We prospectively enrolled 42 late preterm and term infants and classified them into SGA (n=12), AGA (n=20), and LGA (n=10). Serial fecal samples were collected at four postnatal time windows (0–7, 8–14, 15–28, and 29–80 days). 16S rRNA gene sequencing using Oxford Nanopore MinION characterized microbial composition, diversity, and community networks. Bioinformatic analyses included alpha- and beta-diversity metrics, co-occurrence network analysis, and functional pathway inference using PICRUSt2 mapped to MetaCyc and KEGG databases. Clinical variables including feeding pattern and antibiotic exposure were assessed. Results: Relative abundances did not differ significantly at the phylum or genus levels. However, Streptococcus salivarius and Streptococcus spp. abundance significantly increased in late LGA infants. Alpha diversity was significantly higher in the late SGA infants than the early LGA infants. Beta diversity analysis revealed significant microbial separation, with the late SGA infants forming a distinct microbial community from early AGA, early SGA, and late LGA infants. Co-occurrence network analysis revealed a stable gut microbiota in LGA infants. Conclusion: These findings highlight birth weight–dependent divergence in early gut microbiome development, suggesting that initial growth status shapes microbial maturation patterns and may influence subsequent health trajectories. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What is Known Abnormal fetal growth is associated with increased neonatal morbidity and long-term metabolic risk. Early-life gut microbiota plays an important role in immune and metabolic development. What is New This study provides the first longitudinal comparison of gut microbiome development in SGA, AGA, and LGA infants born ≥35 weeks. SGA infants show delayed microbial stabilization and distinct microbial interaction patterns linked to early growth. Introduction Abnormal fetal growth refers to deviations from normal growth rates, potentially affecting neonatal health and postnatal outcomes. Such growth abnormalities significantly increase perinatal mortality and morbidity risk. Newborns are classified by birth weight (BW) as small-for-gestational-age (SGA; BW 90th percentile). SGA infants exhibit an elevated risk of neonatal complications, including neonatal asphyxia, hypoglycemia, bronchopulmonary dysplasia, and neurodevelopmental delay [1-3]. Meanwhile, LGA infants are prone to obstetric challenges, including traumatic delivery, hypoglycemia, and increased neonatal mortality risk [4, 5]. The gut microbiome composition and metabolite profiles of infants with low BW or SGA infants differ significantly from those of infants with normal BW [6]. These variations are not solely attributable to BW and are closely linked to developmental programming influenced by the gut microbiome. The maternal nutritional environment during pregnancy plays a pivotal role in shaping the infant gut microbiome, significantly influencing long-term health outcomes [7]. As the early gut microbiome composition is crucial for immune development and metabolic function, growth disruptions observed in SGA infants may increase long-term health risks. Investigating these associations can provide valuable insights for developing novel therapeutic strategies to improve health outcomes in SGA infants. Previous neonatal microbiome studies have primarily focused on low birth weight or preterm infants [8-11], In contrast, gut microbiota in late-preterm or full-term SGA infants has been less studied, and LGA infants remain a largely understudied population. We hypothesized that infants born as SGA, AGA, and LGA would exhibit distinct postnatal gut microbiome developmental trajectories. Therefore, we examined the early-life gut microbiome of infants born at ≥35 weeks’ gestation to evaluate how birthweight-for-gestational-age status influences microbial maturation and subsequent health outcomes. Materials and Methods Study population and fecal sampling We prospectively enrolled 42 infants admitted to the newborn nursery unit or NICU of Hanyang University Hospital in Seoul, Republic of Korea, between September 2021 and January 2024. Written informed consent was obtained from the parents of all infants. This study was approved by the Institutional Review Board of Hanyang University Medical Center (2023-04-047) and registered at ClinicalTrials.gov (NCT06812091). All procedures were performed in accordance with the standard guidelines for human research ethics and regulations. Fecal samples were collected from the infants enrolled in the study 0–7, 8–14, 15–28, and 29–80 days after birth (Figure 1). Cases in which informed consent could not be obtained, infants born before 34 weeks of gestation, and those with birth weight percentiles outside the eligibility criteria were excluded. Consequently, 42 late preterm and term infants were included and classified into three groups: SGA, AGA, and LGA. SGA and LGA were defined as BW below the 10th percentile and above the 90th percentile, respectively, for gestational age according to the 2013 Fenton Preterm Growth Chart for preterm infants [12] and the World Health Organization child growth standards [13] for term infants. Clinical data for these infants were collected from medical records and analyzed to compare the factors likely to influence gut microbiome composition during the perinatal period. Antibiotic use was defined as at least one antibiotic administration before fecal sample collection. Fecal DNA extraction for ONT MinION sequencing Fecal samples were collected from 42 enrolled infants and newborns admitted to the NICU of the Hanyang University Hospital (Seoul, South Korea) using the Fecal Swab Sampling Kit (Copan, Italy) storaged at ˗80 °C. Fecal microbiome DNA was extracted using a ZymoBIOMICS DNA Miniprep Kit (ZymoBIOMICS, USA) following the manufacturer’s instruction. ONT MinION library preparation and sequencing The genomic DNA was subjected to 16S rRNA sequencing using Oxford Nanopore Technologies ONT, according to the manufacturer’s protocol. The variable regions (V1–V9) of the 16S rRNA gene were amplified using a 16S Barcoding Kit (SQK-16S024) in a MiniAmp™ Plus Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). Briefly, 10 ng DNA was amplified using a PCR polymerase reagent mixture of LongAmp Hot Start Taq 2X Master Mix (New England Biolabs, USA) and 10 µL each barcoded primer (18). Agencourt AMPure XP magnetic beads (Beckman Coulter, USA) were used to purify the PCR amplicons. An Invitrogen Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to quantify the purified PCR amplicon concentrations. Finally, the purified amplicons were pooled at a total concentration of 100 fmol in a final volume of 10 µL. A MinION Flow Cell (R9.4.1, Oxford Nanopore Technologies, Oxford, UK) was used to sequence the 16S rRNA amplicons. Raw FASTQ data were collected for up to 72 h in super-accurate mode with barcode and adaptor trimming using a GPU-based ONT guppy basecaller (v5.0.16, Oxford Nanopore Technologies, Oxford, UK). Bioinformatic and statistical analysis ONT 16S rRNA sequencing data were acquired in FASTQ format using MinKNOW software (v21.06.13, Oxford Nanopore Technologies), and the QIIME2 (v2022.2) pipeline was used to analyze the data and assess microbial taxonomy [14]. The clustered operational taxonomic units were taxonomically classified using VSERCH global alignment with an 85% identity threshold and 0.8 query alignment coverage against the SILVA 16S rRNA reference database [15]. The generated taxonomic profile encompassed levels from phylum to species, and the relative abundances of taxa were determined for comparison. Alpha-diversity metrics and rarefaction curves were generated using a rooted phylogenetic tree. Beta diversity was analyzed through Bray–Curtis dissimilarity and weighted/unweighted UniFrac distances, utilizing principal coordinate analysis [16] To investigate the bacterial–bacterial interactions within each experimental group, we performed co-occurrence analysis and calculated the modularity and clustering coefficient to determine their distribution status. To further characterize how one bacterial taxon may influence another within the gut environment, we constructed a co-occurrence network based on taxa that appeared in at least half of all analyzed samples. Relative abundance data were used to compute pairwise associations, and these relationships were quantified through Spearman’s rank correlation using the Hmisc package (v5.2-2) implemented in R Studio (version 1.1456). Only correlations showing a strong positive or negative association (rs > 0.5 or rs < −0.5) and remaining significant after FDR correction (p < 0.05) were retained for network generation. A layout algorithm was then applied to generate an interactive visualization. In this network, each node represents bacterial taxa, and edges visualization in which nodes represent bacterial taxa and edges indicate statistically significant interactions, with edge thickness corresponding to the strength of association. In parallel with the co-occurrence network analysis, 16S rRNA taxonomic profiles were analyzed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) package to assess the functional potential of the identified microbiota [17]. The relative abundances of functional pathways were determined using the MetaCyc metabolic pathway database [18]. Univariate analyses were performed to compare the infant characteristics. The Shapiro–Wilk normality test was conducted to evaluate continuous variable normality. All values were analyzed using one-way analysis of variance; however, the dominant species within the same sample were compared using a t -test. Multiple comparisons were performed using Tukey’s honest significant difference test, and the results were considered statistically significant at p < 0.05. Statistical analyses were performed using the SAS software (version 9.4; SAS Institute, USA) and GraphPad Prism version 10.0.4 (GraphPad Software Inc., USA) Results Demographic characteristics In total, 42 infants were categorized into three groups based on their BWs: SGA (n = 12), AGA (n = 20), and LGA (n = 10). The clinical characteristics of the infants are summarized in Table 1. The mean gestational age (GA) did not differ significantly between the groups (SGA, 37.9 ± 1.8 weeks; AGA, 38.1 ± 1.5 weeks; LGA, 37.2 ± 1.6 weeks; p = 0.318). The proportion of cesarean section delivery rates (SGA, 58.3%; AGA, 70.0%; LGA, 70.0%; p = 0.831) also did not differ significantly. All infants were initially breastfed, with formula supplementation provided as needed, and most received mixed feeding; no significant differences in feeding type were observed between the groups. Antibiotic use was observed in 25 infants and differed significantly between the groups ( p = 0.02). All infants received empirical ampicillin and gentamicin in the early neonatal period, typically for ≤5 days; although antibiotic use was highest in the LGA group (90%), only two infants required a change in regimen. Antibiotic exposure is well known to influence the gut microbiome. However, in this study population, the commonly used antibiotics— ampicillin and gentamicin —are unlikely to have had a substantial impact on microbial diversity [19]. In particular, recent studies have shown that short-term (1–7 days) antibiotic exposure during the early neonatal period does not exert an immediate or lasting effect on microbiome diversity [20], suggesting that antibiotic use in our cohort was unlikely to have significantly affected the overall results Diversity analysis of gut microbiome between SGA/AGA/LGA infants To compare the gut microbial community of SGA, AGA, and LGA infants, we analyzed the microbial α- and β-diversities. The Chao1 and ACE indices significantly increased in SGA_15–80 infants compared to LGA_0–14 infants. The Simpson index in SGA_15–80 infants was significantly higher than in SGA_0–14 and LGA_0–14 infants, and the Shannon indices in the SGA_15–80 and AGA_15–80 infants were significantly higher than those in the SGA_0–14 and LGA_0–14 infants (Figure 2A). These results indicated that richness increased with age, although not in LGA infants. Beta diversity showed that the bacterial community of SGA_15–80 infants was significantly distinct from AGA_0–14, SGA_0–14, and LGA_15–80 infants, and indicating a unique gut microbial community in SGA infants over time (Figure 2B). Comparison of gut microbiome composition among SGA, AGA, and LGA infants At the phylum level, Firmicutes dominated all groups (>60%), followed by Proteobacteria, showing a non-significant increasing trend from SGA to LGA (Figure 3A). At the genus level, Enterococcus predominated within the first 14 days across all groups and declined thereafter (Figure 3B), whereas Streptococcus was relatively enriched in LGA_15–80 infants and Klebsiella in SGA_15–80 infants. At the species level, Enterococcus faecium was the most abundant taxon in all groups, while the second most abundant species differed by growth category ( Klebsiella pneumoniae in SGA, Staphylococcus epidermidis in AGA, and Streptococcus salivarius in LGA; Figure 3C). Streptococcus spp. were significantly more abundant in LGA infants during the early postnatal period, and this enrichment persisted after day 14 for S. salivarius. Temporal analyses revealed marked shifts in microbial composition after postnatal day 14 (Supplementary figure 1). Several taxa increased in SGA_15–80 infants (K. pneumoniae, Streptococcus spp ., and Lactobacillus gasseri ), whereas Lactobacillus rhamnosus increased in AGA_15–80 infants and S. salivarius in LGA_15–80 infants. E. faecium increased early (0–14 days) but declined thereafter in all groups, with the largest decrease observed in SGA infants. In contrast, Clostridioides difficile remained overall more abundant in SGA infants despite postnatal fluctuations (Supplementary figure 2). Co-occurrence analysis in SGA/AGA/LGA infants The SGA infants exhibited significantly increased modularity without changes in clustering coefficient. However, AGA and LGA infants showed age-dependent increments in clustering coefficient without affecting modularity. In SGA_0–14 infants, Firmicutes were strongly restricted by Proteobacteria, Klebsiella , Enterobacter , K. aerogenes , E. cloacae , and E. faecium , which also showed synergistic relationships (Figure 4A). However, during days 15–80, these deleterious relationships decreased, and Firmicutes restricted Proteobacteria, Klebsiella , and K. pneumoniae , resulting in a more stable bacterial community in SGA_15–80 infants (Figure 4B). In AGA_0-14 infants, Firmicutes were dominated by Proteobacteria and pathogenic species, such as K. pneumoniae , and E. cloacae , but this pattern was entirely reversed in AGA_15–80 infants. In LGA_0–14 infants, Firmicutes restricted Proteobacteria, but over time, Proteobacteria limited Firmicutes, forming a stable gut microbial community distinct dominated by Streptococcus spp . Collectively, each group developed a unique gut microbiome differently, indicating that initial BW shapes gut microbiome changes over time. Association between microbiome and postnatal growth Streptococcus spp. showed positive correlations with all neonatal growth parameters (BW, HC, HT, and BMI; p < 0.01). Firmicutes and Actinobacteria were positively associated with BW and HC or HT (p < 0.05), whereas Proteobacteria was negatively correlated with HC, suggesting a potential adverse effect on early head growth (Supplementary figure 3). Association between gut metabolites and weight gain In SGA infants, PICRUSt-based functional prediction revealed that those with catch-up growth (≥10% weight gain at 3 months) showed enrichment of microbial pathways related to amino acid biosynthesis and energy metabolism compared with those without catch-up growth (Figure 5A). In contrast, among AGA infants, catch-up growth was associated with increased abundance of pathways involved in nucleotide and cell wall biosynthesis, indicating enhanced microbial replication and metabolic capacity (Figure 5B). Discussion In this study, we characterized the gut microbiota composition in SGA, AGA, and LGA infants and analyzed their microbial diversity and community networks over time. To the best of our knowledge, this is the first and most comprehensive analysis of the early life gut microbiome of SGA, AGA, and LGA infants. Richness indices in SGA infants increase significantly with age While microbial richness progressively increased in SGA and AGA infants, it remained comparatively low in LGA infants throughout the study period. This sustained low diversity in the LGA group may be partially attributable to the high rate of early antibiotic exposure (90%) observed in our cohort, which is known to reduce microbial richness. However, this lower diversity was not indicative of instability; rather, it coincided with the establishment of a distinct community dominated by Streptococcus . This suggests that the LGA gut environment, potentially under antibiotic selection pressure, favors a specific, stable colonization pattern different from the dynamic expansion observed in SGA and AGA infants. Beta diversity analysis further highlighted that SGA infants followed a unique trajectory compared to other infants, reinforcing that gut microbiota development in SGA infants follows a unique trajectory. The observed differences in microbial stability may have long-term implications in metabolic and immune programming. Notably, gut microbiota composition during early life plays a crucial role in shaping metabolic outcomes, with variations in microbial richness linked to future health risks, including obesity and metabolic syndrome [21-23]. Early colonization and pathogen control by Firmicutes, Enterococcus, and Streptococcus Across all groups, Firmicutes constituted the dominant phylum, in line with previous studies linking Firmicutes to energy metabolism, fetal growth, and increased birth weight [23, 24]. as well as to gut barrier stabilization and immunomodulatory effects that support intestinal homeostasis [25]. In our cohort, this dominance was closely related to the balance with Proteobacteria. In the early postnatal period, particularly in LGA_0–14 infants, Firmicutes were already able to suppress Proteobacteria and genera such as Escherichia-Shigella and E. coli , which are recognized as pathogenic microorganisms associated with disrupted intestinal ecology and feeding intolerance in vulnerable neonates [24, 26]. A similar Firmicutes-driven, pathogen-limiting configuration emerged later in SGA_15–80 and AGA_15–80 infants, suggesting that LGA infants may acquire a Firmicutes-dominated, pathogen-suppressive gut environment largely through prenatal influences, whereas SGA and AGA infants gradually converge toward this pattern during postnatal growth. At the genus and species levels, early colonization was characterized by high abundances of Enterococcus faecium across all groups, consistent with the role of Enterococcus as a common early-life colonizer that is well adapted to the neonatal gut environment [27-29]. As Enterococcus declined over time, overall microbial diversity increased, indicating a natural shift from a simple early community toward a more complex, Firmicutes-enriched ecosystem. In LGA infants, this transition was accompanied by a persistent enrichment of Streptococcus spp., particularly S. salivarius , from the early to the late period. Certain Streptococcus taxa have been associated with weight gain and catch-up growth in infants [30, 31]. and the short chain fatty acid production, which are linked to healthier growth trajectories [23, 32] Taken together, these findings suggest that key Firmicutes taxa—including Enterococcus in the earliest phase and Streptococcus in later stages—contribute to the establishment of a stable, pathogen-suppressive gut environment, with the timing and configuration of this process differing according to birthweight-for-gestational-age status. LGA infants have a stable bacteria–bacteria interaction Co-occurrence network analysis revealed distinct bacterial interactions in each group. In SGA infants, Firmicutes were initially restricted by Proteobacteria and Klebsiella , indicating an unstable gut environment with a high potential for dysbiosis. However, these negative interactions diminished in SGA_15–80 infants, suggesting a gradual microbial community stabilization. In contrast, AGA infants demonstrated a shift, wherein Firmicutes transitioned from being restricted to regulating other taxa, leading to a stable gut microbiota as the infants matured. These findings are consistent with those of previous studies, suggesting that microbial network stability in early life is critical for long-term health outcomes [30, 31, 33]. The delayed stabilization observed in SGA infants may increase susceptibility to inflammatory conditions, metabolic dysregulation, and impaired nutrient absorption [34, 35]. Metabolic pathways associated with weight gain Our analysis suggests that early postnatal weight gain is closely linked to functional maturation using the KEGG pathway [27, 36] of the gut microbiome, consistent with previous studies [21, 37]. In SGA infants, enhanced weight gain was associated with increased microbial pathways involved in amino acid biosynthesis and energy metabolism, indicating an anabolic and fermentative microbial environment that may support catch-up growth. In AGA infants, greater weight gain was linked to increased abundance of nucleotide and cell wall biosynthesis pathways, suggesting higher microbial proliferation and structural activity. These shifts imply that the microbial functional capacity, particularly biosynthetic and energy-yielding potential, may contribute to or reflect growth trajectories during early infancy. Clinical implications and considerations Our results suggest that early microbial interventions, such as probiotics targeting Lactobacillus spp., may be beneficial in modulating the gut microbiota composition and mitigating potential risks in SGA infants. Nevertheless, further longitudinal studies are needed to determine whether these microbial differences persist into later childhood and how they correlate with metabolic and neurodevelopmental outcomes. Limitations This study has some limitations. First, we could not elucidate the direct mechanisms underlying the differences in the gut microbiome composition among the infants. However, this was partially mitigated by analyzing gut microbiome co-occurrence networks and inferred metabolic pathways. Second, identifying apparent statistical differences was challenging due to the lack of participants and samples. Finally, as antibiotics can significantly alter gut microbial composition and function, this factor may have influenced the observed differences in microbial pathways. Abbreviations AGA = Appropriate-for-Gestational-Age; BMI = Body Mass Index; BW = Birth Weight; CUG = Catch-Up Growth; DNA = Deoxyribonucleic Acid; FDR = False Discovery Rate; GA = Gestational Age; GPU = Graphics Processing Unit; HC = Head Circumference; HT = Height; IRB = Institutional Review Board; KEGG = Kyoto Encyclopedia of Genes and Genomes; LGA = Large-for-Gestational-Age; NICU = Neonatal Intensive Care Unit; NCUG = Non–Catch-Up Growth; ONT = Oxford Nanopore Technologies; OTU = Operational Taxonomic Unit; PCR = Polymerase Chain Reaction; PICRUSt2 = Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2; QIIME2 = Quantitative Insights Into Microbial Ecology 2; rRNA = Ribosomal Ribonucleic Acid; SAS = Statistical Analysis System; SGA = Small-for-Gestational-Age; spp. = Species plural notation; TCA = Tricarboxylic Acid Cycle. Declarations Conflict of Interest Disclosures (includes financial disclosures): The authors have no conflicts of interest to disclose. Clinical trial number : not applicable. Acknowledgments We authors gratefully acknowledge our colleagues and clinical reseach coordinator (Su yeon Kim) at Hanyang University MEB (Medicine-Engineering-Bio) Center. Funding: This work was funded by: The Korea Institute of Energy Technology Evaluation and Planning (KETEP) of the Republic of Korea (RS-2023-00255939) Hanyang University MEB (Global Center for Developmental Disorders, HY-202400000002957) The research fund of Hanyang University (HY-202400000001280) The National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2023-00219983). Author contributions: JK Hwang, SM Lim, SH Kim conceptualized and designed the study. J-K Hoh, B-H Jeon, and H-K Park supervised the study. 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Identification of microflora related to growth performance in pigs based on 16S rRNA sequence analyses. AMB Express. 2020;10(1):192. Table Table 1. Baseline characteristics of participants SGA group (12) AGA group (2 0 ) LGA group (1 0 ) p -value GA (weeks, mean ± SD) 37.9 ± 1.8 38.1 ± 1.5 37.2 ± 1.6 0.318 BW (gram, mean ± SD) 2338 ± 411 3145 ± 323 3921 ± 241 < 0.00 1 Male, n (%) 6 (50) 13 (6 5 ) 8 (80) 0.347 Cesarean section, n (%) 7 (58.3) 14 ( 70 ) 7 (70) 0.831 Exclusive whole milk, n (%)* 2 (16.7) 8 ( 40 ) 2 ( 2 0) 0.173 Ponderal index 2.26 ± 0.40 2.62 ± 0.29 2.79 ± 0.23 <0.001 Infants admitted to the NICU, n (%) 12 (100) 16 (80) † 9 (90) ǂ 0.1 Hospital stay (days, mean ± SD ) 22 ± 8 8 ± 6 12 ± 6 <0.001 Use of antibiotics, n (%) § 8 (66.7) 8 ( 40 ) 9 (90) 0.0 2 Antibiotics use (days, median, IQR) 2.5 (2-6) 5 (2-6.5) 3.5 (2-5) 0.44 *All other infants received mixed feeding (whole milk and breast milk) †Transient tachypnea of the newborn (n=4) , respiratory distress syndrome (n=5) , ventricular septal defect (VSD, n=1) , patent ductus arteriosus (PDA, n=1) , neonatal jaundice (n=3) , bradycardia (n=1) , and cleft palate (n=1) ǂTransient tachypnea of the newborn (n= 3) , respiratory distress syndrome (n= 4) , sepsis (n=2), patent ductus arteriosus (n=1) § All but one infant received antibiotic treatment within the first three days of life. The initial empirical antibiotics were ampicillin plus gentamicin (SGA, n = 7; AGA, n = 7; LGA, n = 9) or cefazolin (SGA, n = 1; AGA, n = 1). In three infants, the antibiotic regimen was modified during treatment: two who initially received ampicillin plus gentamicin were switched to piperacillin/tazobactam plus amikacin (n = 1) and vancomycin plus amikacin (n = 1), respectively, while one patient was changed to cefazolin . Additional Declarations No competing interests reported. Supplementary Files supple1.tif supple3.tif supple2.tif Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in European Journal of Pediatrics → Version 1 posted Editorial decision: Revision requested 07 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 07 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 26 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8699714","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588208887,"identity":"31903b7f-0266-4e65-aada-bc089691db77","order_by":0,"name":"Jae Kyoon Hwang","email":"","orcid":"","institution":"Hanyang University Guri Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Kyoon","lastName":"Hwang","suffix":""},{"id":588208891,"identity":"3384a724-495f-4ccc-b246-d8457ea16748","order_by":1,"name":"Sung Min Lim","email":"","orcid":"","institution":"Hanyang University Seoul 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11:25:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8699714/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8699714/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00431-026-06903-9","type":"published","date":"2026-04-20T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102593626,"identity":"25d586f4-63f2-4530-ae8c-8d542f728108","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":691653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfant recruitment flow chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/3a3dd667df9b90d2014fcc1a.png"},{"id":102593628,"identity":"d6128f99-df98-49d6-9c91-495c03c14742","added_by":"auto","created_at":"2026-02-13 11:51:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":817767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of gut microbial richness, diversities, and PCoA of microbial communities in the SGA, AGA, and LGA infants before and after 14 days of life\u003c/strong\u003e. (a) Richness and alpha diversity: Chao1 estimator, ACE, Shannon index, Simpson, (b) Beta diversity: Bray–Curtis dissimilarity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/bc380b19d5e7ab656b42b561.png"},{"id":102593627,"identity":"39bf3b2e-86de-4f8d-8dee-f0ac9ee9c8a4","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1695479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut microbiome composition in SGA, AGA, and LGA infants (left panel) and changes in composition before and after 14 days of age (right panel).\u003c/strong\u003e Relative abundances at the phylum level (a), genus level (b), and species level (c). \u003csup\u003e*\u003c/sup\u003e0.05 \u0026lt; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1 (tendency), \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05 (significant).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/595cd64b319b5bf04b23fb51.png"},{"id":102593621,"identity":"576b4ad7-cffe-4eca-9b80-0668e884ba3f","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4678406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-occurrence network with modularity values and clustering coefficient.\u003c/strong\u003e Interrelationships among bacterial taxa in SGA (red circle), AGA (green circle), and LGA (blue circle) infants during the first 0–14 (a), 15–80 (b). The link width represents a strong correlation, and the circle size corresponds to the proximity of a bacterium to all other bacterial communities in the network. The circle size in the co-occurrence analysis result indicates the influence of each taxon in the bacterial community, with red and blue lines indicating detrimental and beneficial relationships, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/70cfc8ef40d65b2f7635e911.png"},{"id":102593624,"identity":"4f8ff572-831e-4419-b796-5ccce2fa285a","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1139911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between gut microbial metabolic pathways (inferred from KEGG) and postnatal weight gain using PICRUSt.\u003c/strong\u003e Functional profiles were inferred from 16S rRNA sequencing data and mapped to KEGG pathways, (a) SGA group, (b) AGA group. NCUG and CUG represent infants with \u0026lt;10% and ≥10% weight gain over the first 3 months, respectively. The bar plot shows the relative abundance (%) of the selected KEGG pathways related to amino acid biosynthesis, energy metabolism, and fermentation processes. Error bars represent standard deviations across the subjects in each group.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/b0ebbcfe8dacb1fff0d003de.png"},{"id":107927785,"identity":"57f0c32a-99b2-430e-a81c-dd621982c6ab","added_by":"auto","created_at":"2026-04-27 16:04:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7692788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/d1a7d997-8d8d-40fa-9793-ab0e804a28f0.pdf"},{"id":102593622,"identity":"109bc222-6de7-4c3d-9c4c-d285381fa05f","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4683074,"visible":true,"origin":"","legend":"","description":"","filename":"supple1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/4fb7893e834a4cd811a45b91.tif"},{"id":102593625,"identity":"f4e17130-8f9d-474a-b18d-1a1aea2a0a56","added_by":"auto","created_at":"2026-02-13 11:51:05","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5863780,"visible":true,"origin":"","legend":"","description":"","filename":"supple3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/35b718707651c4f112f8832a.tif"},{"id":102593629,"identity":"27834c02-c950-4864-8083-9e19bce011d4","added_by":"auto","created_at":"2026-02-13 11:51:06","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5861688,"visible":true,"origin":"","legend":"","description":"","filename":"supple2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8699714/v1/a364f1488e459bc44f67ae85.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct Early-Life Gut Microbiota Patterns Across SGA, AGA, and LGA Infants","fulltext":[{"header":"What is Known","content":"\u003cul\u003e\n \u003cli\u003eAbnormal fetal growth is associated with increased neonatal morbidity and long-term metabolic risk.\u003c/li\u003e\n \u003cli\u003eEarly-life gut microbiota plays an important role in immune and metabolic development.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is New\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThis study provides the first longitudinal comparison of gut microbiome development in SGA, AGA, and LGA infants born \u0026ge;35 weeks.\u003c/li\u003e\n \u003cli\u003eSGA infants show delayed microbial stabilization and distinct microbial interaction patterns linked to early growth.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eAbnormal fetal growth refers to deviations from normal growth rates, potentially affecting neonatal health and postnatal outcomes. Such growth abnormalities significantly increase perinatal mortality and morbidity risk. Newborns are classified by birth weight (BW) as small-for-gestational-age (SGA; BW \u0026lt; 10th percentile), appropriate-for-gestational-age (AGA; 10th percentile \u0026le; BW \u0026le; 90th percentile), or large-for-gestational-age (LGA; BW \u0026gt; 90th percentile). SGA infants exhibit an elevated risk of neonatal complications, including neonatal asphyxia, hypoglycemia, bronchopulmonary dysplasia, and neurodevelopmental delay [1-3]. Meanwhile, LGA infants are prone to obstetric challenges, including traumatic delivery, hypoglycemia, and increased neonatal mortality risk [4, 5].\u003c/p\u003e\n\u003cp\u003eThe gut microbiome composition and metabolite profiles of infants with low BW or SGA infants differ significantly from those of infants with normal BW [6]. These variations are not solely attributable to BW and are closely linked to developmental programming influenced by the gut microbiome. The maternal nutritional environment during pregnancy plays a pivotal role in shaping the infant gut microbiome, significantly influencing long-term health outcomes [7]. As the early gut microbiome composition is crucial for immune development and metabolic function, growth disruptions observed in SGA infants may increase long-term health risks. Investigating these associations can provide valuable insights for developing novel therapeutic strategies to improve health outcomes in SGA infants.\u003c/p\u003e\n\u003cp\u003ePrevious neonatal microbiome studies have primarily focused on low birth weight or preterm infants [8-11], In contrast, gut microbiota in late-preterm or full-term SGA infants has been less studied, and LGA infants remain a largely understudied population.\u003c/p\u003e\n\u003cp\u003eWe hypothesized that infants born as SGA, AGA, and LGA would exhibit distinct postnatal gut microbiome developmental trajectories. Therefore, we examined the early-life gut microbiome of infants born at \u0026ge;35 weeks\u0026rsquo; gestation to evaluate how birthweight-for-gestational-age status influences microbial maturation and subsequent health outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population and fecal sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe prospectively enrolled 42 infants admitted to the newborn nursery unit or NICU of Hanyang University Hospital in Seoul, Republic of Korea, between September 2021 and January 2024. Written informed consent was obtained from the parents of all infants. This study was approved by the Institutional Review Board of Hanyang University Medical Center (2023-04-047) and registered at ClinicalTrials.gov (NCT06812091). All procedures were performed in accordance with the standard guidelines for human research ethics and regulations. Fecal samples were collected from the infants enrolled in the study 0\u0026ndash;7, 8\u0026ndash;14, 15\u0026ndash;28, and 29\u0026ndash;80 days after birth (Figure 1). Cases in which informed consent could not be obtained, infants born before 34 weeks of gestation, and those with birth weight percentiles outside the eligibility criteria were excluded. Consequently, 42 late preterm and term infants were included and classified into three groups: SGA, AGA, and LGA. SGA and LGA were defined as BW below the 10th percentile and above the 90th percentile, respectively, for gestational age according to the 2013 Fenton Preterm Growth Chart for preterm infants [12] and the World Health Organization child growth standards [13] for term infants. Clinical data for these infants were collected from medical records and analyzed to compare the factors likely to influence gut microbiome composition during the perinatal period. Antibiotic use was defined as at least one antibiotic administration before fecal sample collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFecal DNA extraction for ONT MinION sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFecal samples were collected from 42 enrolled infants and newborns admitted to the NICU of the Hanyang University Hospital (Seoul, South Korea) using the Fecal Swab Sampling Kit (Copan, Italy) storaged at ˗80 \u0026deg;C. Fecal microbiome DNA was extracted using a ZymoBIOMICS DNA Miniprep Kit (ZymoBIOMICS, USA) following the manufacturer\u0026rsquo;s instruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eONT MinION library preparation and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genomic DNA was subjected to 16S rRNA sequencing using Oxford Nanopore Technologies ONT, according to the manufacturer\u0026rsquo;s protocol. The variable regions (V1\u0026ndash;V9) of the 16S rRNA gene were amplified using a 16S Barcoding Kit (SQK-16S024) in a MiniAmp\u0026trade; Plus Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). Briefly, 10 ng DNA was amplified using a PCR polymerase reagent mixture of LongAmp Hot Start Taq 2X Master Mix (New England Biolabs, USA) and 10 \u0026micro;L each barcoded primer (18). Agencourt AMPure XP magnetic beads (Beckman Coulter, USA) were used to purify the PCR amplicons. An Invitrogen Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to quantify the purified PCR amplicon concentrations. Finally, the purified amplicons were pooled at a total concentration of 100 fmol in a final volume of 10 \u0026micro;L. A MinION Flow Cell (R9.4.1, Oxford Nanopore Technologies, Oxford, UK) was used to sequence the 16S rRNA amplicons. Raw FASTQ data were collected for up to 72 h in super-accurate mode with barcode and adaptor trimming using a GPU-based ONT guppy basecaller (v5.0.16, Oxford Nanopore Technologies, Oxford, UK).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatic and statistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eONT 16S rRNA sequencing data were acquired in FASTQ format using MinKNOW software (v21.06.13, Oxford Nanopore Technologies), and the QIIME2 (v2022.2) pipeline was used to analyze the data and assess microbial taxonomy [14]. The clustered operational taxonomic units were taxonomically classified using VSERCH global alignment with an 85% identity threshold and 0.8 query alignment coverage against the SILVA 16S rRNA reference database [15].\u003c/p\u003e\n\u003cp\u003eThe generated taxonomic profile encompassed levels from phylum to species, and the relative abundances of taxa were determined for comparison. Alpha-diversity metrics and rarefaction curves were generated using a rooted phylogenetic tree. Beta diversity was analyzed through Bray\u0026ndash;Curtis dissimilarity and weighted/unweighted UniFrac distances, utilizing principal coordinate analysis\u0026nbsp;[16] To investigate the bacterial\u0026ndash;bacterial interactions within each experimental group, we performed co-occurrence analysis and calculated the modularity \u0026nbsp;and clustering coefficient to determine their distribution status. To further characterize how one bacterial taxon may influence another within the gut environment, we constructed a co-occurrence network based on taxa that appeared in at least half of all analyzed samples. Relative abundance data were used to compute pairwise associations, and these relationships were quantified through Spearman\u0026rsquo;s rank correlation using the Hmisc package (v5.2-2) implemented in R Studio (version 1.1456). Only correlations showing a strong positive or negative association (rs\u0026thinsp;\u0026gt;\u0026thinsp;0.5 or rs\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;0.5) and remaining significant after FDR correction (p \u0026lt; 0.05) were retained for network generation. A layout algorithm was then applied to generate an interactive visualization. In this network, each node represents bacterial taxa, and edges visualization in which nodes represent bacterial taxa and edges indicate statistically significant interactions, with edge thickness corresponding to the strength of association. In parallel with the co-occurrence network analysis, 16S rRNA taxonomic profiles were analyzed using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) package to assess the functional potential of the identified microbiota [17]. The relative abundances of functional pathways were determined using the MetaCyc metabolic pathway database [18].\u003c/p\u003e\n\u003cp\u003eUnivariate analyses were performed to compare the infant characteristics. The Shapiro\u0026ndash;Wilk normality test was conducted to evaluate continuous variable normality. All values were analyzed using one-way analysis of variance; however, the dominant species within the same sample were compared using a \u003cem\u003et\u003c/em\u003e-test. Multiple comparisons were performed using Tukey\u0026rsquo;s honest significant difference test, and the results were considered statistically significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05. Statistical analyses were performed using the SAS software (version 9.4; SAS Institute, USA) and GraphPad Prism version 10.0.4 (GraphPad Software Inc., USA)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 42 infants were categorized into three groups based on their BWs: SGA (n = 12), AGA (n = 20), and LGA (n = 10). The clinical characteristics of the infants are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eThe mean gestational age (GA) did not differ significantly between the groups (SGA, 37.9 \u0026plusmn; 1.8 weeks; AGA, 38.1 \u0026plusmn; 1.5 weeks; LGA, 37.2 \u0026plusmn; 1.6 weeks; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.318). The proportion of cesarean section delivery rates (SGA, 58.3%; AGA,\u0026nbsp;70.0%; LGA, 70.0%; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.831) also did not differ significantly. All infants were initially breastfed, with formula supplementation provided as needed, and most received mixed feeding; no significant differences in feeding type were observed between the groups.\u003c/p\u003e\n\u003cp\u003eAntibiotic use was observed in 25 infants and differed significantly between the groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.02). All infants received empirical ampicillin and gentamicin in the early neonatal period, typically for \u0026le;5 days; although antibiotic use was highest in the LGA group (90%), only two infants required a change in regimen. Antibiotic exposure is well known to influence the gut microbiome. However, in this study population, the commonly used antibiotics\u0026mdash;\u003cstrong\u003eampicillin\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003egentamicin\u003c/strong\u003e\u0026mdash;are unlikely to have had a substantial impact on microbial diversity [19]. In particular, recent studies have shown that \u003cstrong\u003eshort-term (1\u0026ndash;7 days) antibiotic exposure during the early neonatal period\u003c/strong\u003e does not exert an\u003cstrong\u003e\u0026nbsp;immediate or lasting effect\u003c/strong\u003e on microbiome diversity [20], suggesting that antibiotic use in our cohort was unlikely to have significantly affected the overall results\u003c/p\u003e\n\u003cp\u003eDiversity analysis of gut microbiome between SGA/AGA/LGA infants\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo compare the gut microbial community of SGA, AGA, and LGA infants, we analyzed the microbial \u0026alpha;- and \u0026beta;-diversities. The Chao1 and ACE indices significantly increased in SGA_15\u0026ndash;80 infants compared to LGA_0\u0026ndash;14 infants. The Simpson index in SGA_15\u0026ndash;80 infants was significantly higher than in SGA_0\u0026ndash;14 and LGA_0\u0026ndash;14 infants, and\u0026nbsp;the Shannon indices in the SGA_15\u0026ndash;80 and AGA_15\u0026ndash;80 infants were significantly higher than those in the SGA_0\u0026ndash;14 and LGA_0\u0026ndash;14 infants (Figure 2A). These results indicated that richness increased with age, although\u0026nbsp;not in LGA infants. Beta diversity\u0026nbsp;showed\u0026nbsp;that the bacterial community of SGA_15\u0026ndash;80 infants was significantly distinct from AGA_0\u0026ndash;14, SGA_0\u0026ndash;14, and LGA_15\u0026ndash;80 infants, and\u0026nbsp;indicating\u0026nbsp;a unique gut microbial community\u0026nbsp;in SGA infants\u0026nbsp;over time (Figure 2B).\u003c/p\u003e\n\u003cp\u003eComparison of gut microbiome composition among SGA, AGA, and LGA infants\u003c/p\u003e\n\u003cp\u003eAt the phylum level, Firmicutes dominated all groups (\u0026gt;60%), followed by Proteobacteria, showing a non-significant increasing trend from SGA to LGA (Figure 3A). At the genus level, Enterococcus predominated within the first 14 days across all groups and declined thereafter (Figure 3B), whereas Streptococcus was relatively enriched in LGA_15\u0026ndash;80 infants and Klebsiella in SGA_15\u0026ndash;80 infants.\u003c/p\u003e\n\u003cp\u003eAt the species level, \u003cem\u003eEnterococcus faecium\u003c/em\u003e was the most abundant taxon in all groups, while the second most abundant species differed by growth category (\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e in SGA, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e in AGA, and \u003cem\u003eStreptococcus salivarius\u003c/em\u003e in LGA; Figure 3C). Streptococcus spp. were significantly more abundant in LGA infants during the early postnatal period, and this enrichment persisted after day 14 for \u003cem\u003eS. salivarius.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTemporal analyses revealed marked shifts in microbial composition after postnatal day 14 (Supplementary figure 1). Several taxa increased in SGA_15\u0026ndash;80 infants \u003cem\u003e(K. pneumoniae, Streptococcus spp\u003c/em\u003e., and \u003cem\u003eLactobacillus gasseri\u003c/em\u003e), whereas \u003cem\u003eLactobacillus rhamnosus\u003c/em\u003e increased in AGA_15\u0026ndash;80 infants and \u003cem\u003eS. salivarius\u003c/em\u003e in LGA_15\u0026ndash;80 infants. \u003cem\u003eE. faecium\u003c/em\u003e increased early (0\u0026ndash;14 days) but declined thereafter in all groups, with the largest decrease observed in SGA infants. In contrast, \u003cem\u003eClostridioides difficile\u003c/em\u003e remained overall more abundant in SGA infants despite postnatal fluctuations (Supplementary figure\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eCo-occurrence analysis in SGA/AGA/LGA infants\u003c/p\u003e\n\u003cp\u003eThe SGA infants exhibited significantly increased modularity without\u0026nbsp;changes\u0026nbsp;in clustering coefficient. However, AGA and LGA infants\u0026nbsp;showed age-dependent\u0026nbsp;increments in clustering coefficient\u0026nbsp;without affecting modularity. In SGA_0\u0026ndash;14 infants, Firmicutes were strongly restricted by Proteobacteria, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eK.\u003c/em\u003e\u003cem\u003e\u0026nbsp;aerogenes\u003c/em\u003e, \u003cem\u003eE. cloacae\u003c/em\u003e, and \u003cem\u003eE. faecium\u003c/em\u003e,\u0026nbsp;which also showed\u0026nbsp;synergistic relationships\u0026nbsp;(Figure\u0026nbsp;4A). However, during days 15\u0026ndash;80, these deleterious relationships decreased, and Firmicutes restricted Proteobacteria, \u003cem\u003eKlebsiella\u003c/em\u003e, and \u003cem\u003eK.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003epneumoniae\u003c/em\u003e, resulting in a more stable bacterial community in SGA_15\u0026ndash;80 infants (Figure\u0026nbsp;4B).\u003c/p\u003e\n\u003cp\u003eIn AGA_0-14 infants,\u0026nbsp;Firmicutes were dominated by Proteobacteria and pathogenic species, such as\u0026nbsp;\u003cem\u003eK.\u003c/em\u003e\u003cem\u003e\u0026nbsp;pneumoniae\u003c/em\u003e, and \u003cem\u003eE. cloacae\u003c/em\u003e, but\u0026nbsp;this pattern was entirely reversed in AGA_15\u0026ndash;80 infants. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn LGA_0\u0026ndash;14 infants, Firmicutes restricted Proteobacteria, but over time, Proteobacteria limited Firmicutes,\u0026nbsp;forming\u0026nbsp;a stable gut microbial community distinct\u0026nbsp;dominated by\u0026nbsp;\u003cem\u003eStreptococcus spp\u003c/em\u003e. Collectively, each group\u0026nbsp;developed a unique gut\u0026nbsp;microbiome differently,\u0026nbsp;indicating\u0026nbsp;that initial BW\u0026nbsp;shapes\u0026nbsp;gut microbiome changes over time.\u003c/p\u003e\n\u003cp\u003eAssociation between microbiome and postnatal growth\u003c/p\u003e\n\u003cp\u003eStreptococcus spp. showed positive correlations with all neonatal growth parameters (BW, HC, HT, and BMI; p \u0026lt; 0.01). Firmicutes and Actinobacteria were positively associated with BW and HC or HT (p \u0026lt; 0.05), whereas Proteobacteria was negatively correlated with HC, suggesting a potential adverse effect on early head growth (Supplementary figure\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eAssociation between gut metabolites and weight gain\u003c/p\u003e\n\u003cp\u003eIn SGA infants, PICRUSt-based functional prediction revealed that those with catch-up growth (\u0026ge;10% weight gain at 3 months) showed enrichment of microbial pathways related to amino acid biosynthesis and energy metabolism compared with those without catch-up growth (Figure 5A).\u003c/p\u003e\n\u003cp\u003eIn contrast, among AGA infants, catch-up growth was associated with increased abundance of pathways involved in nucleotide and cell wall biosynthesis, indicating enhanced microbial replication and metabolic capacity (Figure 5B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we characterized the gut microbiota composition in SGA, AGA, and LGA infants and analyzed their microbial diversity and community networks over time. To the best of our knowledge, this is the first and most comprehensive analysis of the early life gut microbiome of SGA, AGA, and LGA infants.\u003c/p\u003e\n\u003cp\u003eRichness indices in SGA infants increase significantly with age\u003c/p\u003e\n\u003cp\u003eWhile microbial richness progressively increased in SGA and AGA infants, it remained comparatively low in LGA infants throughout the study period. This sustained low diversity in the LGA group may be partially attributable to the high rate of early antibiotic exposure (90%) observed in our cohort, which is known to reduce microbial richness. However, this lower diversity was not indicative of instability; rather, it coincided with the establishment of a distinct community dominated by \u003cem\u003eStreptococcus\u003c/em\u003e. This suggests that the LGA gut environment, potentially under antibiotic selection pressure, favors a specific, stable colonization pattern different from the dynamic expansion observed in SGA and AGA infants. Beta diversity analysis further highlighted that SGA infants followed a unique trajectory\u0026nbsp;compared to other infants, reinforcing that gut microbiota development in SGA infants follows a unique trajectory. The observed differences in microbial stability may have long-term implications in metabolic and immune programming. Notably, gut microbiota composition during early life plays a crucial role in shaping metabolic outcomes, with variations in microbial richness linked to future health risks, including obesity and metabolic syndrome [21-23].\u003c/p\u003e\n\u003cp\u003eEarly colonization and pathogen control by Firmicutes, Enterococcus, and Streptococcus\u003c/p\u003e\n\u003cp\u003eAcross all groups, Firmicutes constituted the dominant phylum, in line with previous studies linking Firmicutes to energy metabolism, fetal growth, and increased birth weight [23, 24]. as well as to gut barrier stabilization and immunomodulatory effects that support intestinal homeostasis [25]. In our cohort, this dominance was closely related to the balance with Proteobacteria. In the early postnatal period, particularly in LGA_0\u0026ndash;14 infants, Firmicutes were already able to suppress Proteobacteria and genera such as \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e, which are recognized as pathogenic microorganisms associated with disrupted intestinal ecology and feeding intolerance in vulnerable neonates [24, 26]. A similar Firmicutes-driven, pathogen-limiting configuration emerged later in SGA_15\u0026ndash;80 and AGA_15\u0026ndash;80 infants, suggesting that LGA infants may acquire a Firmicutes-dominated, pathogen-suppressive gut environment largely through prenatal influences, whereas SGA and AGA infants gradually converge toward this pattern during postnatal growth.\u003c/p\u003e\n\u003cp\u003eAt the genus and species levels, early colonization was characterized by high abundances of \u003cem\u003eEnterococcus faecium\u003c/em\u003e across all groups, consistent with the role of \u003cem\u003eEnterococcus\u003c/em\u003e as a common early-life colonizer that is well adapted to the neonatal gut environment [27-29]. As \u003cem\u003eEnterococcus\u003c/em\u003e declined over time, overall microbial diversity increased, indicating a natural shift from a simple early community toward a more complex, Firmicutes-enriched ecosystem. In LGA infants, this transition was accompanied by a persistent enrichment of \u003cem\u003eStreptococcus\u003c/em\u003e spp., particularly \u003cem\u003eS. salivarius\u003c/em\u003e, from the early to the late period. Certain \u003cem\u003eStreptococcus\u003c/em\u003e taxa have been associated with weight gain and catch-up growth in infants [30, 31]. and the\u0026nbsp;short chain fatty acid\u0026nbsp;production, which are linked to healthier growth trajectories [23, 32] Taken together, these findings suggest that key Firmicutes taxa\u0026mdash;including \u003cem\u003eEnterococcus\u003c/em\u003e in the earliest phase and \u003cem\u003eStreptococcus\u003c/em\u003e in later stages\u0026mdash;contribute to the establishment of a stable, pathogen-suppressive gut environment, with the timing and configuration of this process differing according to birthweight-for-gestational-age status.\u003c/p\u003e\n\u003cp\u003eLGA infants have a stable bacteria\u0026ndash;bacteria interaction\u003c/p\u003e\n\u003cp\u003eCo-occurrence network analysis revealed distinct bacterial interactions in each group. In SGA infants, Firmicutes were initially restricted by Proteobacteria and \u003cem\u003eKlebsiella\u003c/em\u003e, indicating an unstable gut environment with a high potential for dysbiosis. However, these negative interactions diminished in SGA_15\u0026ndash;80 infants, suggesting a gradual microbial community stabilization. In contrast, AGA infants demonstrated a shift, wherein Firmicutes transitioned from being restricted to regulating other taxa, leading to a stable gut microbiota as the infants matured.\u0026nbsp;These findings are consistent with those of previous studies, suggesting that microbial network stability in early life is critical for long-term health outcomes [30, 31, 33]. The delayed stabilization observed in SGA infants may increase susceptibility to inflammatory conditions, metabolic dysregulation, and impaired nutrient absorption [34, 35].\u003c/p\u003e\n\u003cp\u003eMetabolic pathways associated with weight gain\u003c/p\u003e\n\u003cp\u003eOur analysis suggests that early postnatal weight gain is closely linked to functional maturation using the KEGG pathway [27, 36] of the gut microbiome, consistent with previous studies [21, 37]. In SGA infants, enhanced weight gain was associated with increased microbial pathways involved in amino acid biosynthesis and energy metabolism, indicating an anabolic and fermentative microbial environment that may support catch-up growth. In AGA infants, greater weight gain was linked to increased abundance of nucleotide and cell wall biosynthesis pathways, suggesting higher microbial proliferation and structural activity. These shifts imply that the microbial functional capacity, particularly biosynthetic and energy-yielding potential, may contribute to or reflect growth trajectories during early infancy.\u003c/p\u003e\n\u003cp\u003eClinical implications and considerations\u003c/p\u003e\n\u003cp\u003eOur results suggest that early microbial interventions, such as probiotics targeting \u003cem\u003eLactobacillus\u003c/em\u003e spp., may be beneficial in modulating the gut microbiota composition and mitigating potential risks in SGA infants. Nevertheless, further longitudinal studies are needed to determine whether these microbial differences persist into later childhood and how they correlate with metabolic and neurodevelopmental outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. First, we could not elucidate the direct mechanisms underlying the differences in the gut microbiome composition among the infants. However, this was partially mitigated by analyzing gut microbiome co-occurrence networks and inferred metabolic pathways. Second, identifying apparent statistical differences was challenging due to the lack of participants and samples. Finally, as antibiotics can significantly alter gut microbial composition and function, this factor may have influenced the observed differences in microbial pathways.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAGA = Appropriate-for-Gestational-Age; BMI = Body Mass Index; BW = Birth Weight; CUG = Catch-Up Growth; DNA = Deoxyribonucleic Acid; FDR = False Discovery Rate; GA = Gestational Age; GPU = Graphics Processing Unit; HC = Head Circumference; HT = Height; IRB = Institutional Review Board; KEGG = Kyoto Encyclopedia of Genes and Genomes; LGA = Large-for-Gestational-Age; NICU = Neonatal Intensive Care Unit; NCUG = Non\u0026ndash;Catch-Up Growth; ONT = Oxford Nanopore Technologies; OTU =\u0026nbsp;Operational Taxonomic Unit; PCR = Polymerase Chain Reaction; PICRUSt2 = Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2; QIIME2 = Quantitative Insights Into Microbial Ecology 2; rRNA = Ribosomal Ribonucleic Acid; SAS = Statistical Analysis System; SGA = Small-for-Gestational-Age; spp. = Species plural notation; TCA = Tricarboxylic Acid Cycle.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures (includes financial disclosures):\u003c/strong\u003e The authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe authors gratefully acknowledge our colleagues and clinical reseach coordinator (Su yeon Kim) at Hanyang University MEB (Medicine-Engineering-Bio) Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was funded by:\u003c/p\u003e\n\u003cp\u003eThe Korea Institute of Energy Technology Evaluation and Planning (KETEP) of the Republic of Korea (RS-2023-00255939)\u003c/p\u003e\n\u003cp\u003eHanyang University MEB (Global Center for Developmental Disorders, HY-202400000002957)\u003c/p\u003e\n\u003cp\u003eThe research fund of Hanyang University (HY-202400000001280)\u003c/p\u003e\n\u003cp\u003eThe National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2023-00219983).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJK Hwang, SM Lim, SH Kim conceptualized and designed the study. J-K Hoh, B-H Jeon, and H-K Park supervised the study. JK Hwang, SM Lim, M-J Kwak, and H-K Park were involved in the data collection and initial statistical analysis. G Musfata and RS Tanpure interpreted the data. JK Hwang, SM Lim, and SH Kim wrote and edited the manuscript. M-J Kwak, J-K Hoh and B-H Jeon interpreted the results and drafted the manuscript. All authors have reviewed and approved the revised manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Authors declare that they have no competing interests. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u003c/strong\u003e All the raw data files of the metagenomic sequencing used in this study are available in the National Center for Biotechnology Information (NCBI) Short Read Archive under Bioproject accession number PRJNA1268722 (https://www.ncbi.nlm.nih.gov/sra/?term= PRJNA1268722).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFlenady V, Koopmans L, Middleton P, Froen JF, Smith GC, Gibbons K, et al. Major risk factors for stillbirth in high-income countries: a systematic review and meta-analysis. Lancet. 2011;377(9774):1331-40.\u003c/li\u003e\n\u003cli\u003eNobile S, Marchionni P, Carnielli VP. 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Gut microbiota and BMI throughout childhood: the role of firmicutes, bacteroidetes, and short-chain fatty acid producers. Sci Rep. 2022;12(1):3140.\u003c/li\u003e\n\u003cli\u003eSun Y, Zhang S, Nie Q, He H, Tan H, Geng F, et al. Gut firmicutes: Relationship with dietary fiber and role in host homeostasis. Crit Rev Food Sci Nutr. 2023;63(33):12073-88.\u003c/li\u003e\n\u003cli\u003eSokol H, Pigneur B, Watterlot L, Lakhdari O, Berm\u0026uacute;dez-Humar\u0026aacute;n LG, Gratadoux JJ, et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci U S A. 2008;105(43):16731-6.\u003c/li\u003e\n\u003cli\u003eIndiani CMdSP, Rizzardi KF, Castelo PM, Ferraz LFC, Darrieux M, Parisotto TM. Childhood obesity and firmicutes/bacteroidetes ratio in the gut microbiota: a systematic review. Childhood obesity. 2018;14(8):501-9.\u003c/li\u003e\n\u003cli\u003eLiu L, Ao D, Cai X, Huang P, Cai N, Lin S, Wu B. Early gut microbiota in very low and extremely low birth weight preterm infants with feeding intolerance: a prospective case-control study. J Microbiol. 2022;60(10):1021-31.\u003c/li\u003e\n\u003cli\u003eGonzalez S, Selma-Royo M, Arboleya S, Martinez-Costa C, Solis G, Suarez M, et al. Levels of Predominant Intestinal Microorganisms in 1 Month-Old Full-Term Babies and Weight Gain during the First Year of Life. Nutrients. 2021;13(7).\u003c/li\u003e\n\u003cli\u003eRiboulet E, Verneuil N, La Carbona S, Sauvageot N, Auffray Y, Hartke A, Giard JC. Relationships between oxidative stress response and virulence in Enterococcus faecalis. J Mol Microbiol Biotechnol. 2007;13(1-3):140-6.\u003c/li\u003e\n\u003cli\u003eYounge NE, Newgard CB, Cotten CM, Goldberg RN, Muehlbauer MJ, Bain JR, et al. Disrupted Maturation of the Microbiota and Metabolome among Extremely Preterm Infants with Postnatal Growth Failure. Sci Rep. 2019;9(1):8167.\u003c/li\u003e\n\u003cli\u003eTadros JS, Llerena A, Sarkar A, Johnson R, Miller EM, Gray HL, Ho TTB. Postnatal growth and gut microbiota development influenced early childhood growth in preterm infants. Front Pediatr. 2022;10:850629.\u003c/li\u003e\n\u003cli\u003eLi S, Ma X, Mei H, Chang X, He P, Sun L, et al. Association between gut microbiota and short-chain fatty acids in children with obesity. Sci Rep. 2025;15(1):483.\u003c/li\u003e\n\u003cli\u003eKorpela K, Blakstad EW, Moltu SJ, Strommen K, Nakstad B, Ronnestad AE, et al. Intestinal microbiota development and gestational age in preterm neonates. Sci Rep. 2018;8(1):2453.\u003c/li\u003e\n\u003cli\u003eLevy M, Kolodziejczyk AA, Thaiss CA, Elinav E. Dysbiosis and the immune system. Nat Rev Immunol. 2017;17(4):219-32.\u003c/li\u003e\n\u003cli\u003eBiragyn A, Ferrucci L. Gut dysbiosis: a potential link between increased cancer risk in ageing and inflammaging. Lancet Oncol. 2018;19(6):e295-e304.\u003c/li\u003e\n\u003cli\u003eKhatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS computational biology. 2012;8(2):e1002375.\u003c/li\u003e\n\u003cli\u003eLi XJ, Wang M, Xue Y, Duan D, Li C, Han X, et al. Identification of microflora related to growth performance in pigs based on 16S rRNA sequence analyses. AMB Express. 2020;10(1):192.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of participants\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGA group (12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGA group (2\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLGA group (1\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGA (weeks, mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.9 \u0026plusmn; 1.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.1 \u0026plusmn; 1.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.2 \u0026plusmn; 1.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.318\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBW (gram, mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2338 \u0026plusmn; 411\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3145 \u0026plusmn; 323\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3921 \u0026plusmn; 241\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e0.00\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13 (6\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8 (80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.347\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCesarean section, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 (58.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14 (\u003c/strong\u003e\u003cstrong\u003e70\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 (70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.831\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusive whole milk, n (%)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 (16.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8 (\u003c/strong\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 (\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.173\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePonderal index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.26 \u0026plusmn; 0.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.62 \u0026plusmn; 0.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.79 \u0026plusmn; 0.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfants admitted to the NICU, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16 (80)\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9 (90)\u003csup\u003eǂ\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital stay (days,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emean \u0026plusmn; SD\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of antibiotics, n (%)\u003c/strong\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8 (66.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9 (90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotics use (days, median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 (2-6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 (2-6.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5 (2-5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*All other infants received mixed feeding (whole milk and breast milk)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026dagger;Transient tachypnea of the newborn (n=4)\u003c/strong\u003e, \u003cstrong\u003erespiratory distress syndrome (n=5)\u003c/strong\u003e, \u003cstrong\u003eventricular septal defect (VSD, n=1)\u003c/strong\u003e, \u003cstrong\u003epatent ductus arteriosus (PDA, n=1)\u003c/strong\u003e, \u003cstrong\u003eneonatal jaundice (n=3)\u003c/strong\u003e, \u003cstrong\u003ebradycardia (n=1)\u003c/strong\u003e, and \u003cstrong\u003ecleft palate (n=1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eǂTransient tachypnea of the newborn (n=\u003c/strong\u003e\u003cstrong\u003e3)\u003c/strong\u003e, \u003cstrong\u003erespiratory distress syndrome (n=\u003c/strong\u003e\u003cstrong\u003e4)\u003c/strong\u003e,\u0026nbsp;sepsis\u0026nbsp;(n=2),\u0026nbsp;\u003cstrong\u003epatent ductus arteriosus (n=1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u0026nbsp;\u003c/sup\u003eAll but one infant received antibiotic treatment within the first three days of life. The initial empirical antibiotics were \u003cstrong\u003eampicillin plus gentamicin\u003c/strong\u003e (SGA, n = 7; AGA, n = 7; LGA, n = 9) or \u003cstrong\u003ecefazolin\u003c/strong\u003e (SGA, n = 1; AGA, n = 1). In three infants, the antibiotic regimen was modified during treatment: two who initially received ampicillin plus gentamicin were switched to \u003cstrong\u003epiperacillin/tazobactam plus amikacin\u003c/strong\u003e (n = 1) and \u003cstrong\u003evancomycin plus amikacin\u003c/strong\u003e (n = 1), respectively, while one patient was changed to \u003cstrong\u003ecefazolin\u003c/strong\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8699714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8699714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBirthweight-for-gestational-age influences neonatal physiology and health, yet its role in shaping early gut microbiome development remains insufficiently defined. Small-for-gestational-age (SGA), appropriate-for-gestational-age (AGA), and large-for-gestational-age (LGA) infants may exhibit distinct microbial maturation patterns that could influence later metabolic and developmental outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe prospectively enrolled 42 late preterm and term infants and classified them into SGA (n=12), AGA (n=20), and LGA (n=10). Serial fecal samples were collected at four postnatal time windows (0–7, 8–14, 15–28, and 29–80 days). 16S rRNA gene sequencing using Oxford Nanopore MinION characterized microbial composition, diversity, and community networks. Bioinformatic analyses included alpha- and beta-diversity metrics, co-occurrence network analysis, and functional pathway inference using PICRUSt2 mapped to MetaCyc and KEGG databases. Clinical variables including feeding pattern and antibiotic exposure were assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative abundances did not differ significantly at the phylum or genus levels. However, \u003cem\u003eStreptococcus salivarius\u003c/em\u003e and \u003cem\u003eStreptococcus spp.\u003c/em\u003eabundance significantly increased in late LGA infants. Alpha diversity was significantly higher in the late SGA infants than the early LGA infants. Beta diversity analysis revealed significant microbial separation, with the late SGA infants forming a distinct microbial community from early AGA, early SGA, and late LGA infants. Co-occurrence network analysis revealed a stable gut microbiota in LGA infants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings highlight birth weight–dependent divergence in early gut microbiome development, suggesting that initial growth status shapes microbial maturation patterns and may influence subsequent health trajectories.\u003c/p\u003e","manuscriptTitle":"Distinct Early-Life Gut Microbiota Patterns Across SGA, AGA, and LGA Infants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 11:50:59","doi":"10.21203/rs.3.rs-8699714/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-07T09:11:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-28T10:46:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211088240117425710314878671232803469907","date":"2026-02-08T14:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-07T23:29:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T10:28:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T09:29:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Pediatrics","date":"2026-01-26T11:13:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0088694a-9a58-47f7-bd69-66f1f1c66e9a","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:02:06+00:00","versionOfRecord":{"articleIdentity":"rs-8699714","link":"https://doi.org/10.1007/s00431-026-06903-9","journal":{"identity":"european-journal-of-pediatrics","isVorOnly":false,"title":"European Journal of Pediatrics"},"publishedOn":"2026-04-20 15:57:39","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2026-02-13 11:50:59","video":"","vorDoi":"10.1007/s00431-026-06903-9","vorDoiUrl":"https://doi.org/10.1007/s00431-026-06903-9","workflowStages":[]},"version":"v1","identity":"rs-8699714","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8699714","identity":"rs-8699714","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}