The Association of Neonatal Gut Microbiota Community State Types with Birth Weight

Abstract

Background: while most gut microbiota research has focused on term infants, the health outcomes of preterm infants are equally important. Very-low-birth-weight (VLBW) or extremely-low-birth-weight (ELBW) preterm infants have a unique gut microbiota structure, and probiotics have been reported to somewhat accelerate the maturation of the gut microbiota and reduce intestinal inflammation in very-low preterm infants, thereby improving their long-term outcomes. The aim of this study was to investigate the structure of gut microbiota in ELBW neonates to facilitate the early identification of different types of low-birth-weight (LBW) preterm infants.

Methods: a total of 98 fecal samples from 39 low-birth-weight preterm infants were included in this study. Three groups were categorized according to different birth weights: ELBW (n = 39), VLBW (n = 39), and LBW (n = 20). The gut microbiota structure of neonates was obtained by 16S rRNA gene sequencing, and microbiome analysis was conducted. The community state type (CST) of the microbiota was predicted, and correlation analysis was conducted with clinical indicators. Differences in the gut microbiota composition among ELBW, VLBW, and LBW were compared. The value of gut microbiota composition in the diagnosis of extremely low birth weight was assessed via a random forest-machine learning approach.

Results: we briefly analyzed the structure of the gut microbiota of preterm infants with low birth weight and found that the ELBW, VLBW, and LBW groups exhibited gut microbiota with heterogeneous compositions. Low-birth-weight preterm infants showed five CSTs dominated by Enterococcus, Staphylococcus, Klebsiella, Streptococcus, Pseudescherichia, and Acinetobacter. The birth weight and clinical indicators related to prematurity were associated with the CST. We found the composition of the gut microbiota was specific to the different types of low-birth-weight premature infants, namely, ELBW, VLBW, and LBW. The ELBW group exhibited significantly more of the potentially harmful intestinal bacteria Acinetobacter relative to the VLBW and LBW groups, as well as a significantly lower abundance of the intestinal probiotic Bifidobacterium. Based on the gut microbiota's composition and its correlation with low weight, we constructed random forest model classifiers to distinguish ELBW and VLBW/LBW infants. The area under the curve of the classifiers constructed with Enterococcus, Klebsiella, and Acinetobacter was found to reach 0.836 by machine learning evaluation, suggesting that gut microbiota composition may be a potential biomarker for ELBW preterm infants.

Conclusions: the gut bacteria of preterm infants showed a CST with Enterococcus, Klebsiella, and Acinetobacter as the dominant genera. ELBW preterm infants exhibit an increase in the abundance of potentially harmful bacteria in the gut and a decrease in beneficial bacteria. These potentially harmful bacteria may be potential biomarkers for ELBW preterm infants.

Keywords: 16S rRNA gene sequencing; community state type; gut microbiota; infant; low birth weight; machine learning; neonate.

Figure 1

Analysis of gut microbiota diversity in low-birth-weight preterm infants. (A) Visual analysis of clinical data for three groups of preterm infants; (B) saturation curve analysis based on species richness; (C) comparison of similarities and differences in ELBW, LBW, and VLBW gut microbiota composition data by ANOSIM; (D) Venn plot illustrating shared and unique OTUs among the three groups; (E,F) composition of gut microbiota at the genus level among the three groups; (G) CPCoA explained 2.65% of the total variation in gut microbiota composition among the groups, and there were significant differences between the groups (p < 0.05); (H) NMDS analysis used to rank the gut microbiota of the ELBW, VLBW, and LBW groups. The Bray–Curtis index was calculated for the three groups to generate the NMDS to visualize the similarities among the gut microbiota, and the results showed that there was a significant difference in the distribution of the gut microbiota among the three groups (p < 0.05).

Figure 2

The distribution of bacteria at the genus level in the gut microbiota. (A–C) Volcano plots revealing differentially abundant gut microbiota among the VLBW, ELBW, and LBW groups. Red represents significantly high-abundance bacteria, while green represents significantly low-abundance bacteria. (D–F) Manhattan plots revealing the distribution of gut microbiota among the three groups.

Figure 3

The relationships among the five clusters of samples identified through clustering analysis and the diversity of the gut microbiota. (A) MENs method based on 16S rRNA high-throughput sequencing and visualization tools to analyze the interrelationships among gut microorganisms between groups. (B) The gap statistic method was used to analyze the optimal number of clusters based on the Bray–Curtis distance of the incoming samples; the results show that 5 was the optimal k value. (C) Ordination analysis of eigenvalue obtained from MDS. (D) NMDS visualization based on the first four eigenvectors obtained by MDS. (E) Demonstration of 5 CSTs samples based on the NMDS method. (F) Heatmap showcasing the variations in the abundance of driver intestinal bacteria across the five CST sample.

Figure 4

The correlation analysis between the five CST samples and clinical phenotypes. (A) Significant differences between the five CST samples and clinical phenotypes (*** p < 0.001, ** p < 0.01, * p < 0.05); a, b, and c are defined as using the significant difference letter marking method to arrange all the means from largest to smallest. Any difference with the same marking letter is not significant, and any difference with a different marking letter is significant. (B) Significant linear correlation between the five CST samples and clinical phenotypes.

Figure 5

Intestinal bacteria may be a potential biomarkers for ELBW premature infants. (A) The abundances of intestinal bacteria Acinetobacter_ASV_46, Acinetobacter_ASV_49, Acinetobacter_ASV_51, and Acinetobacter_ASV_54 were significantly elevated in the ELBW infant group. The abundances of intestinal bacteria Bifidobacterium_ASV_107 and Klebsiella_ASV_2 were significantly reduced in the ELBW group of infants. ** p < 0.01, * p < 0.05. (B,C) Intestinal bacteria can be used as potential biomarkers for ELBW preterm infants. (B) Based on the random forest model, the potential use of intestinal bacteria in the classification of ELBW preterm infants was evaluated. The results showed that the AUC value of the top-three intestinal bacteria in the classification of ELBW preterm infants was 0.836. (C) Rank of intestinal bacterial markers.