Characterization of gut microbiota in preterm infants at discharge: a single-center observational study

Introduction

The early-life period is critical for gut microbiota establishment, characterized by high variability and marked susceptibility to disruptions with potential short- and long-term health consequences (1,2). Preterm infants, with their immature gastrointestinal and immune systems, face elevated risks of aberrant microbial colonization that may profoundly impact lifelong health trajectories (3,4). Growing evidence links gut microbiota dysbiosis to diverse conditions, including gastrointestinal diseases, metabolic disorders (e.g., infections, obesity and diabetes), immune-mediated pathologies (e.g., asthma), and neurodevelopmental impairments (5-7).

Current research is focused on comparing the diversity and composition of the gut microbiota in preterm infants with those in term infants (8); evaluating factors that influence the microbiota, such as gestational age (GA), delivery mode, feeding practices, antibiotic use, and probiotic supplementation (9-12); and correlating the gut microbiota with outcomes in preterm infants (4,13-15). However, critical knowledge gaps persist regarding discharge-stage microbiota characteristics and their determinants in preterm infants. A cross-sectional study at this transitional juncture could delineate clinically actionable microbial signatures and identify modifiable factors for optimizing post-discharge care and hospitalization management.

This study therefore aimed to characterize GA-stratified gut microbiota profiles at discharge in preterm infants and elucidate key clinical determinants, addressing an unmet need in neonatal microbiota research. We present this article in accordance with the STROBE reporting checklist (available at https://pm.amegroups.com/article/view/10.21037/pm-24-86/rc).

Methods

Study cohort

This study enrolled 103 preterm infants (GA, 28–36⁺⁶ weeks) admitted to the neonatal department of The Second Affiliated Hospital of Guangxi Medical University between December 2021 and December 2023. Fecal samples were collected 0–2 days before discharge for 16S rRNA sequencing. Concurrently, a control group of 32 healthy full-term infants, hospitalized for 3–7 days post-birth due to prenatal high-risk factors was enrolled. Participants were stratified into four groups: very preterm (VP: 28≤ GA ≤31⁺⁶ weeks, n=20), moderately preterm (MP: 32≤ GA ≤34⁺⁶ weeks, n=34), late preterm (LP: 35≤ GA ≤36⁺⁶ weeks, n=49), and term infants (n=32). Additional subgroup analyses were conducted for antibiotic exposure and extrauterine growth restriction (EUGR). A total of 43 preterm infants were excluded due to congenital anomalies, gastrointestinal disorders, necrotizing enterocolitis (NEC), inherited metabolic diseases, or abandonment. In the full-term group, we excluded a total of 17 infants who met either of the following exclusion criteria: (I) post-term birth (GA ≥42 weeks), or (II) apparent initial health status followed by the development of clinically significant conditions (including infection, gastrointestinal symptoms, seizures, or respiratory distress) requiring medical intervention during the hospitalization. Discharge criteria included: weight ≥1.8 kg, established oral feeding competence, consistent weight gain, stable thermoregulation, and 24-hour cardiopulmonary stability without incubator support. EUGR was defined as discharge weight below the 10th percentile for postmenstrual age (PMA).

During antibiotic treatment, probiotic supplementation (Bifidobacterium longum, Lactobacillus acidophilus, and Enterococcus faecalis; 0.15×10⁷ CFU each) was administered twice daily with ≥4-hour intervals from antibiotics, continuing for at least 5–7 days post-cessation or to discharge per institutional protocol. Clinical data collection encompassed: gender, birth weight, GA, delivery mode, 5-minute Apgar score, PMA, weight at discharge (WAD), formula feeding (FF), postnatal age, day of probiotic, day of antibiotic and major complications. The study design is detailed in Figure 1.

Figure 1 Design flowchart. EUGR, extrauterine growth restriction; GA, gestational age; LP, late preterm infant; MP, moderately preterm infant; PMA, postmenstrual age; T, term infant; VP, very preterm infant.

Sample collection and 16S rRNA sequencing

Fecal samples were collected from each group of infants 0–2 days before discharge, placed in sterile fecal collection containers, and immediately stored at −80 ℃ for subsequent 16S rRNA sequencing. Total deoxyribonucleic acid (DNA) was extracted from the fecal samples using the cetyltrimethylammonium bromide (CTAB) method. The V3–V4 variable region of the 16S rRNA gene was amplified by PCR using the upstream primer 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and the downstream primer 806R (5'-GGACTACHVGGGTWTCTAAT-3') carrying barcode sequences (16). The PCR products were purified (AxyPrep DNA Gel Extraction Kit) and quantified (Quantus™ Fluorometer) prior to library construction. Paired-end sequencing (2×300 bp) was performed on the Illumina NovaSeq 6000 platform (Majorbio Bio-Pharm Technology Co., Ltd.) with ≥50,000 reads per sample. The operational taxonomic unit (OTU) clustering was performed at 97% similarity threshold.

Bioinformatics and statistical analysis

The Shannon and Simpson indices of α-diversity were calculated using the Mothur (17) software (https://mothur.org/wiki/calculators/). The similarity of microbial community structures between samples was examined using Principal Coordinate Analysis (PCoA) based on the Bray-Curtis distance algorithm, combined with the Kruskal-Wallis and Wilcoxon rank-sum tests, to analyze whether the differences in microbial community structures between sample groups were significant. The Mantel test, a nonparametric test for assessing the correlation between two matrices, was employed for correlation analysis between clinical factors and the relative abundance of gut microbiota. Species with Spearman correlation coefficients and P values <0.05 were selected for correlation analysis (18).

Statistical analysis

SPSS26.0 was used for the parallel double entry of demographic and clinical data. For continuous variables, non-normally distributed data are presented as median (interquartile range), while baseline characteristics of the mothers and infants are presented as the mean ± standard deviation for normally distributed continuous variables and frequencies for categorical variables. Statistical significance was defined as two-tailed P<0.05.

Ethical considerations

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics board of Second Affiliated Hospital of Guangxi Medical University [No. 2021-KY(0410)] and informed consent was taken from all individual participants’ parents or legal guardians.

Results

Comparison of clinical information among groups

Significant intergroup variations emerged in baseline parameters, including GA, birth weight, delivery mode, feeding type, discharge weight, complications and PMA (Table 1). Both VP and MP groups showed comparable outcomes at discharge, with similar PMA (36.2±1.14 vs. 36.2±1.13 weeks; P=0.89) and discharge weight (2,269.5±256.5 vs. 2,315.3±332.1 g; P=0.60).

Gut microbiota diversity comparison at discharge

The fecal microbiota composition at discharge was dominated by Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes (Figure 2A) at phylum level. Among these, Firmicutes showed the only significant intergroup variation (P=0.02, Figure 2B).

Figure 2 Comparison on phylum level. (A) Barplot on phylum level. (B) Comparison of barplot on phylum level. LP, late preterm infant; MP, moderately preterm infant; T, term infant; VP, very preterm infant.

At the genus level (Figure 3A), Enterococcus and Clostridium were the sole taxa with significant abundance differences (P<0.001 and P=0.02, respectively; Figure 3B). PCoA revealed significant separation among groups (R²=0.057, P=0.005, Figure 4). PCoA coordinates demonstrated significant associations with clinical parameters: negative correlation with PMA (R²=0.095, P<0.001, Figure 5A) and positive correlation with feeding proportion (FP) (R²=0.136, P<0.001, Figure 5B).

Figure 3 Comparison on genus level. (A) Barplot on genus level. (B) Comparison of barplot on genus level. LP, late preterm infant; MP, moderately preterm infant; T, term infant; VP, very preterm infant.

Figure 4 PCoA of beta diversity on genus level. LP, late preterm infant; MP, moderately preterm infant; PCoA, Principal Coordinate Analysis; T, term infant; VP, very preterm infant.

Figure 5 Regression analysis of PC1 and clinical factors. (A) Linear regression of PCoA with PMA. (B) Linear regression of PCoA with FP. FP, feeding proportion; LP, late preterm infant; MP, moderately preterm infant; PCoA, Principal Coordinate Analysis; PMA, postmenstrual age; T, term infant; VP, very preterm infant.

Microbiota-clinical parameter correlations

Given the multifactorial clinical interactions, we performed Mantel tests with network heatmap visualization to evaluate overall microbiota-clinical relationships, supplemented by Spearman’s correlation heatmap for taxon-specific associations (Figure 6).

Figure 6 Spearman correlation heatmap of the relationship between gut microbiota and clinical factors. *, P<0.05; **, P<0.01; ***, P<0.001. BW, birth weight; CS, cesarean section; DOA, day of antibiotic; DOP, day of probiotic; FF, formula feeding; GA, gestational age; M, male; PA, postnatal age; PMA, postmenstrual age; WAD, weight at discharge.

Key genus-level correlations included (display only the correlation analysis results with P<0.01):

• Enterococcus: Negative associations with GA, birth weight, PMA, and WAD (all P<0.01); positive correlation with FF (P<0.001).
• Escherichia-Shigella: No significant correlations.
• Klebsiella: Positive correlations with PMA (P<0.001) and cesarean section (CS) (P=0.003).
• Enterobacteriaceae: Positive associations with PMA, WAD (both P<0.001), and CS (P=0.002).
• Bifidobacterium: Negative correlation with PMA (P=0.008).
• Staphylococcus: No significant correlations.
• Streptococcus: Positive associations with PMA and GA (P=0.005).
• Enterobacter: Positive associations with PMA and CS (both P=0.008).
• Bacteroides: Negative correlation with CS (P=0.007).
• Clostridium: Positive associations with PMA (P=0.003); negative correlation with GA and birth weight (P=0.006 and P=0.008).

The top ten species showed no significant associations with sex or antibiotic exposure duration.

Mantel test network analysis (Figure 7) revealed distinct microbiota-clinical associations across groups: the VP group demonstrated a positive microbiota-PMA correlation (P=0.008), the LP group showed associations with birth date and CS (P=0.004), while the T group exhibited links to antibiotic duration and PMA (P=0.02).

Figure 7 Mantel test network heatmap of the relationship in each group between gut microbiota and clinical factors. *, P<0.05; **, P<0.01; ***, P<0.001. AD; BW, birth weight; CS, cesarean section; DOP, day of probiotic; FF, formula feeding; GA, gestational age; LP, late preterm; M, male; MP, moderately preterm; PA, postnatal age; PMA, postmenstrual age; VP, very preterm; WAD, weight at discharge.

Subanalysis: extrauterine growth restriction of VP and MP

To control for the effects of maturation, we compared VP and MP groups with matched PMA at discharge (36.2±1.14 vs. 36.2±1.13 weeks; P=0.89, Table 1). The VP group demonstrated significantly enriched Klebsiella (P=0.03) and Streptococcus (P=0.02) abundances compared to MP infants (Figure 8).

Figure 8 Analysis of differences in genus-level bacterial community composition between groups VP and MP. *, P<0.05. MP, moderately preterm infant; VP, very preterm infant.

Antibiotic exposure subanalysis

Based on physiologically stable late-preterm infants (GA 34–36 weeks), we compared antibiotic-exposed (A, n=11) vs. non-exposed (NA, n=26) subcohorts. This population’s baseline homogeneity in feeding patterns, clinical complications, and developmental-stage microbiota minimized confounding. The NA group exhibited significantly higher abundances of Bifidobacterium (P=0.009) and Clostridium (P=0.03) at discharge (Figure 9).

Figure 9 Analysis of differences in genus-level bacterial community composition between the A group versus the non-A group. *, P<0.05; **, P<0.01. A, antibiotic-exposed; non-A, non-antibiotic-exposed.

EUGR subanalysis

Following exclusion of 8 intrauterine growth-restricted cases, the VP and MP preterm cohort (n=46) was stratified into normal postnatal growth (N, n=28) and EUGR (n=18) subgroups. The EUGR group demonstrated significantly higher relative abundances of Enterococcus (P=0.039) and Staphylococcus (P=0.02) (Figure 10).

Figure 10 Analysis of differences in genus-level bacterial community composition between the N vs. EUGR group. *, P<0.05. EUGR, extrauterine growth restriction; ES, effect size; N, normal postnatal growth.

Discussion

Because of underdeveloped systems, preterm infants often experience prolonged hospitalization and various treatments, which may affect gut microbiota composition and colonization in the short or long term (19-23). Defining a ‘normal’ gut microbiota profile in preterm infants is challenging due to the high individual variability, sensitivity to external factors, and rapid succession of gut microbiota in early life (6). The increase in the abundance and diversity of the gut microbiota is a crucial marker of intestinal maturation (24,25). Despite attaining near-term or full-term PMA at discharge, the diversity of the gut microbiota in preterm infants seemingly exhibits a delay in our study, which is likely due to various factors.

Pattern of gut microbiota evolution in preterm infants

The colonization of gut microbiota in extremely preterm infants follows a distinct successional pattern: Firmicutes predominance establishes early, gradually shifts toward Proteobacteria colonization by PMA 28–36 weeks, and transitions to Bacteroidetes dominance by PMA 33–36 weeks (21,26). This predictable progression from aerobic to anaerobic microbial communities motivated our GA-stratified analysis. Within our study cohort (PMA 35–37 weeks at discharge), Firmicutes emerged as the sole phylum displaying statistically significant variation, driven principally by dynamic Enterococcus population shifts. Notably, preterm infants at discharge maintained genus-level profiles dominated by Enterococcus—a striking contrast to term infant microbiota compositions. Multivariate modeling revealed a consistent negative association between Enterococcus abundance and PMA, independent of postnatal age. These observations reinforce PMA (a direct biomarker of host maturity) as the principal regulator of microbial succession in preterm neonates, which aligns with established developmental paradigms (27). This intrinsic maturation trajectory exhibits particular resilience to environmental modulation (28-30), particularly in the context of the neonatal intensive care unit (NICU) environment exposure (31), especially for infants born before 32 weeks’ gestation.

Mantel test analysis identified PMA as the principal determinant of gut microbiota composition in VP infants, with Spearman’s correlation analysis further revealing Enterococcus abundance as the strongest correlate of biological maturation. In contrast, late-preterm and term infants demonstrated microbiota profiles predominantly shaped by postnatal age and cesarean delivery. Notably, our analyses uncovered a distinct microbial-clinical association: Klebsiella, Enterobacteriaceae and Enterobacter abundance exhibited significant positive correlation with CS. These findings corroborate established developmental paradigms of stage-specific microbial succession, wherein host-driven maturation mechanisms predominate before 32 weeks PMA, transitioning to environment-modulated stabilization beyond 35 weeks PMA (21). This phased progression likely arises from coordinated interactions among dynamic intestinal parameters (such as oxygen tension and pH gradients), mucus barrier development, host-microbe metabolic coordination, and immune competency maturation (21,32).

The gut microbiota of VP infants displays regulatory trajectories that are markedly distinct from those of moderate-to-LP cohorts. This notable disparity indicates the need for timely adjustments and personalization of clinical management strategies for preterm infants.

External influences on preterm gut microbiota development

The observed microbiota variations underscore the necessity to examine clinical-microbial interactions. Korpela et al. proposed that the development of gut microbiota in preterm infants typically progresses through four stages characterized by the dominance of Staphylococcus, Enterococcus, Escherichia, and Bifidobacterium (31). In breastfed infants, regardless of GA at birth, the gut microbiota gradually evolves and becomes dominated by Bifidobacterium after 30 weeks of PMA (26,33). Our findings reveal two critical deviations from this established pattern: (I) Despite universal probiotic supplementation in VP infants, Bifidobacterium colonization remained suboptimal. This is likely related to the extremely low rate of exclusive breastfeeding (0%, Table 1) within this cohort, highlighting the need for optimized nutritional strategies beyond probiotic administration. (II) The characteristic Staphylococcus decline after 35 weeks GA aligned precisely with our cohort’s discharge profile (PMA ≥35 weeks) (31).

Emerging evidence posits that nutritional status may rival GA as a determinant of preterm gut microbiota development (34). Comparing EUGR and normal-growth preterm infants matched for PMA at discharge; we observed persistently elevated Enterococcus and Staphylococcus levels in EUGR cases. These microbial signatures, implicated in poor weight gain among low-birth-weight infants (35,36), likely arise through two interrelated mechanisms: microbial interference with intestinal energy harvest and nutritional deficits disrupting gut environment homeostasis and enzyme-mediated maturation (37,38). Notably, while Enterococcus/Staphylococcus dominance characterizes early preterm colonization (31), their persistence in EUGR infants suggests delayed microbial succession. This developmental delay has significant clinical implications: both genera have been associated with late-onset sepsis and intestinal barrier dysfunction, underscoring the critical interplay between nutritional status, microbial ecology, and mucosal immunity (39). In the future, we will integrate metagenomic sequencing and plasma inflammatory factor profiling to delineate EUGR-specific microbial metabolic pathways and elucidate microbiota-immune crosstalk mechanisms.

To mitigate the confounding effects of physiological maturity and medical interventions, we analyzed antibiotic exposure in preterm infants at 34–36 weeks of gestation. Despite routine probiotic supplementation for bifidobacterial colonization, antibiotic-exposed infants exhibited significantly reduced relative abundances of Bifidobacterium and Clostridium—key indicators of healthy gut microbiota in preterm cohorts (40). Regrettably, our study lacked a sufficiently large non-antibiotic control group to assess antibiotic effects in VP infants. Furthermore, the lack of longitudinal data across GA constrained our ability to draw conclusions regarding the duration of these microbial alterations.

Our study provides one of the few characterizations of gut microbiota in preterm infants at discharge. However, it is limited by the exclusion of infants <28 weeks of GA and the lack of serial microbiota sampling—both critical for understanding dynamic colonization patterns. Future research should emphasize longitudinal studies to elucidate microbiota maturation trajectories. As a single-center cross-sectional study, our sample size is still relatively small and the findings may reflect regional and environmental biases; therefore, multi-center validation through large-cohort studies is essential to confirm our results

Conclusions

This study characterizes gut microbiota profiles in preterm infants at discharge across GA subgroups, providing insights for developing targeted post-discharge nutritional strategies. While microbial colonization in VP infants primarily reflects biological maturity, moderate and LP cohorts show predominant influences from environmental exposures. Notably, distinct microbial communities may demonstrate associations with unique clinical factors. This developmental disparity highlights the need for stage-specific interventions. Our findings enhance understanding of gut microbiota establishment and inform optimization of antibiotic stewardship, feeding protocols, and post-discharge care in neonatal populations.

Acknowledgments

We thank all medical staff in the Neonatology Department of The Second Affiliated Hospital of Guangxi Medical University for their contributions and support to our research project. Special thanks are extended to the team members for their work in specimen collection, management and clinical information acquisition.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://pm.amegroups.com/article/view/10.21037/pm-24-86/rc

Data Sharing Statement: Available at https://pm.amegroups.com/article/view/10.21037/pm-24-86/dss

Peer Review File: Available at https://pm.amegroups.com/article/view/10.21037/pm-24-86/prf

Funding: This study was supported by Guangxi Natural Science Foundation Project (Project No. 2017GXNSFAA198165) and Wu Jieping Medical Foundation (Project No. 320.6750.2025-9-19).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://pm.amegroups.com/article/view/10.21037/pm-24-86/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional ethics board of Second Affiliated Hospital of Guangxi Medical University [No. 2021-KY(0410)] and informed consent was taken from all individual participants’ parents or legal guardians.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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