Association Between Trajectory Patterns of Body Mass Index Change Up to 10 Months and Early Gut Microbiota in Preterm Infants

The impact of gut microbiota on morbidities in preterm infants

The gut microbiota undergoes substantial development from birth, and its development in the initial years of life has a potentially lifelong effect on the health of the individual. However, various factors can disrupt the development of the gut microbiota, leading to a condition known as dysbiosis, particularly in preterm infants. Current studies involving adults have suggested that the gut microbiota not only influences the gut but also has multidimensional effects on remote organs; these pathways are often referred to as the gut-organ axis. Imbalance of the gut microbiota may lead to the development of multiple diseases. Recent studies have revealed that gut dysbiosis in preterm infants may cause several acute morbidities-such as necrotizing enterocolitis, late-onset sepsis, bronchopulmonary dysplasia, and retinopathy of prematurity-and it may also influence long-term outcomes including neurodevelopment and somatic growth. This review mainly presents the existing evidence regarding the relationships between the gut microbiota and these morbidities in preterm infants and explores the role of the gut-organ axis in these morbidities. This paper thus offers insights into the future perspectives on microbiota interventions for promoting the health of preterm infants.

Gut microbiota in preterm infants with late-onset sepsis and pneumonia: a pilot case-control study

BACKGROUND

Late-onset sepsis (LOS) and pneumonia are common infectious diseases, with high morbidity and mortality in neonates. This study aimed to investigate the differences in the gut microbiota among preterm infants with LOS, or pneumonia, and full-term infants. Furthermore, this study aimed to determine whether there is a correlation between intestinal pathogenic colonization and LOS.

METHODS

In a single-center case‒control study, 16 S rRNA gene sequencing technology was used to compare gut microbiota characteristics and differences among the LOS group, pneumonia group, and control group.

RESULTS

Our study revealed that the gut microbiota in the control group was more diverse than that in the LOS group and pneumonia group (P < 0.05). No significant differences in diversity were detected between the LOS and pneumonia groups (P > 0.05). Compared with the control group, the abundances of Akkermansia, Escherichia/Shigella, and Enterococcus increased, while the abundances of Bacteroides and Stenotrophomonas decreased in the LOS and pneumonia groups. The pathogenic bacteria in infants with LOS were consistent with the distribution of the main bacteria in the intestinal microbiota. An increase in Escherichia/Shigella abundance may predict a high risk of LOS occurrence, with an area under the curve (AUC) of 0.773.

CONCLUSION

Changes in the gut microbiota composition were associated with an increased risk of LOS and pneumonia. The dominant bacteria in the gut microbiota of the LOS group were found to be associated with the causative pathogen of LOS. Moreover, preterm infants exhibiting an elevated abundance of Escherichia/Shigella may be considered potential candidates for predicting the onset of LOS.

Association between gut microbiota at 3.5 years of age and body mass index at 5 years: results from two French nationwide birth cohorts

BACKGROUND/OBJECTIVES

The relationship between gut microbiota and changes in body mass index (BMI) or pediatric overweight in early life remains unclear, and information regarding the preterm population is scarce. This study aimed to investigate how the gut microbiota at 3.5 years of age is associated with (1) later BMI at 5 years, and (2) BMI z-score variations between 2 and 5 years in children from two French nationwide birth cohorts.

SUBJECTS/METHODS

Bacterial 16S rRNA gene sequencing was performed to profile the gut microbiota at 3.5 years of age in preterm children (n = 143, EPIPAGE 2 cohort) and late preterm/full-term children (n = 369, ELFE cohort). The predicted abundances of metabolic functions were computed using PICRUSt2. Anthropometric measurements were collected at 2 and 5 years of age during medical examinations or retrieved from children's health records. Statistical analyses included multivariable linear and logistic regressions, random forest variable selection, and MiRKAT.

RESULTS

The Firmicutes to Bacteroidetes (F/B) ratio at 3.5 years was positively associated with the BMI z-score at 5 years. Several genera were positively ([Eubacterium] hallii group, Fusicatenibacter, and [Eubacterium] ventriosum group) or negatively (Eggerthella, Colidextribacter, and Ruminococcaceae CAG-352) associated with the BMI z-scores at 5 years. Some genera were also associated with variations in the BMI z-scores between 2 and 5 years of age. Predicted metabolic functions, including steroid hormone biosynthesis, biotin metabolism, glycosaminoglycan degradation, and amino sugar and nucleotide sugar metabolism, were associated with lower BMI z-scores at 5 years. The unsaturated fatty acids biosynthesis pathway was associated with higher BMI z-scores.

CONCLUSIONS

These findings indicate that the gut microbiota at 3.5 years is associated with later BMI during childhood, independent of preterm or term birth, suggesting that changes in the gut microbiota that may predispose to adult obesity begin in early childhood.

The Microbiome in the Obesity-Breast Cancer Axis: Diagnostic and Therapeutic Potential

A growing body of evidence has demonstrated a relationship between the microbiome, adiposity, and cancer development. The microbiome is emerging as an important factor in metabolic disease and cancer pathogenesis. This review aimed to highlight the role of the microbiome in obesity and its association with cancer, with a particular focus on breast cancer. This review discusses how microbiota dysbiosis may contribute to obesity and obesity-related diseases, which are linked to breast cancer. It also explores the potential of the gut microbiome to influence systemic immunity, leading to carcinogenesis via the modulation of immune function. This review underscores the potential use of the microbiome profile as a diagnostic tool and treatment target, with strategies including probiotics, fecal microbiota transplantation, and dietary interventions. However, this emphasizes the need for more research to fully understand the complex relationship between the microbiome, metabolic disorders, and breast cancer. Future studies should focus on elucidating the mechanisms underlying the impact of the microbiome on breast cancer and exploring the potential of the microbiota profile as a biomarker and treatment target.

The relationship between gut microbiota and neonatal pathologic jaundice: A pilot case-control study

Background and objective

Neonatal jaundice is a common clinical disease in neonates. Pathologic jaundice is more harmful to neonates. There are a few studies on the biomarkers of pathologic jaundice and the correlation between gut microbiota and clinical indices. Therefore, we aimed to reveal the characteristics of gut microbiota in pathologic jaundice, provide potential biomarkers for the diagnosis of pathologic jaundice, and find the correlation between gut microbiota and clinical indices.

Methods

Fourteen neonates with physiologic jaundice were recruited into a control group (Group A). Additionally, 14 neonates with pathologic jaundice were recruited into a case group (Group B). The microbial communities were analyzed using 16S rDNA sequencing. LEfSe and the differences in the relative abundance of gut microbiota were used to identify different bacteria among the two groups. The ROC curve was used to assess effective biomarkers for pathologic jaundice. Spearman’s rank-sum correlation coefficient was used to evaluate the correlation between gut microbiota and clinical indices.

Results

There were no differences in the total richness or diversity of gut microbiota between the two groups. At the phylum and genus levels, compared with the control group, Bacteroidetes (p = 0.002) and Braydrhizobium (p = 0.01) were significantly higher, while Actinobacteria (p = 0.003) and Bidfldobacterium (p = 0.016) were significantly lower in the case group. Bacteroidetes were valuable in differentiating pathologic jaundice from physiologic jaundice by the ROC curve, and the area under the ROC curve (AUC) value was 0.839 [95%CI (0.648–0.995)]. In the case group, Bacteroidetes were negatively associated with total bilirubin (TBIL) (p &lt; 0.05). In the control group, Bacteroidetes were positively associated with TBIL (p &lt; 0.05).

Conclusion

Bacteroidetes could be used as biomarkers to identify pathologic jaundice and Bacteroidetes are positively associated with bilirubin levels.