Metabolomic disorders caused by an imbalance in the gut microbiota are associated with central precocious puberty

Abstract

Background: Central precocious puberty (CPP) is characterized by the premature activation of the hypothalamic-pituitary-gonadal axis, resulting in early onset of sexual development. The incidence of CPP has been rising in recent years, with approximately 90% of cases lacking a clearly identifiable etiology. While an association between precocious puberty and gut microbiota has been observed, the precise causal pathways and underlying mechanisms remain poorly understood. The study aims to investigate the potential mechanisms through which gut microbiota imbalances may contribute to CPP.

Methods: In this study, clinical information and fecal samples were collected from 50 CPP patients and 50 healthy control subjects. The fecal samples were analyzed by 16S rDNA sequencing and UPLC-MS/MS metabolic analysis. Spearman correlation analysis was used to identify the relationships between gut microbiota and metabolites.

Results: The gut microbiota composition in CPP patients was significantly different from that in healthy controls, characterized by an increased abundance of Faecalibacterium and a decreased abundance of Anaerotruncus. Additionally, significant differences were observed in metabolite composition between the CPP and control groups. A total of 51 differentially expressed metabolites were identified, with 32 showing significant upregulation and 19 showing significant downregulation in the CPP group. Furthermore, Spearman correlation analysis indicated that gut microbiota dysbiosis may contribute to altered metabolic patterns in CPP, given its involvement in the regulation of several metabolic pathways, including phenylalanine and tyrosine biosynthesis and metabolism, the citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, and tryptophan metabolism.

Conclusions: The study revealed the gut microbial and metabolite characteristics of CPP patients by integrating microbiome and metabolomics analyses. Moreover, several key metabolic pathways involved in the onset and progression of CPP were identified, which were regulated by gut microbiota. These findings broaden the current understanding of the complex interactions between gut microbial metabolites and CPP, and provide new insights into the pathogenesis and clinical management of CPP.

Keywords: 16S rDNA; GnRH; central precocious puberty; gut microbiota; metabolomics.

Figure 1

Discrepancy in the structure and diversity of the gut microbiota in the CPP and control groups. (A) The sequence distribution across length intervals. Read length was kept within the 250-500 bp range, with those shorter than 250 bp excluded. The X-axis represents sequence length, and the Y-axis shows the count of reads. (B) Venn diagram of OTUs. Each color corresponds to a specific group. The overlapping region represents OTUs shared by both groups, while the non-overlapping areas represent unique OTUs in each group. (C) Histogram of microbial abundances in the CPP group and control group at the genus level. (D) Comparison of the α-diversity index (Shannon index) between the CPP and control group. The X-axis represents the groups, and the Y-axis represents the Shannon index. The box plot presents 5 statistics: minimum, first quartile, median, third quartile, and maximum. An asterisk “*” means 0.01<p<0.05. (E) Adonis analysis (unweighted UniFrac analysis). The X-axis represents the first principal coordinate, and the Y-axis represents the second principal coordinate. Percentage refers to the contribution of the corresponding principal coordinate to the difference of samples; the points represent individual samples; the horizontal and vertical box plots represent value distributions of the two groups on corresponding principal coordinates.

Figure 2

Analysis of different microbiota between the CPP and control groups. (A) LEfSe analysis. The LDA score was used to detect differential abundance between the two groups at the phylum, class, order, family, and genus levels. Bacterial taxa with LDA scores above the threshold (minimum of 2) were considered biomarkers with significant differences. Red represents the control group, and blue represents the CPP group. (B) The Wilcoxon test was used to analyze different microbiota constituents at the genus level. The X-axis shows genera name, and the Y-axis shows the log2 value of relative abundance.

Figure 3

Identification of differential metabolites between the CPP and control groups. (A) PLS-DA score plots of the metabolic profiles from the CPP and control groups. Blue represents the CPP group, and red represents the control group. (B) Volcano map of differential metabolites. Metabolites with a P value < 0.05 and an absolute value of log2FC > 0 were considered significantly different. The red dots (right side) represent metabolites that were increased in the CPP group, and blue dots (left side) indicate those that were decreased. (C) The representative differential metabolites with the highest rank (smaller P value and larger FC value) between the CPP group and the control group were 3,4-dihydroxyhydrocinnamic acid and HPHPA ***means P < 0.001. (D) Z score heatmap of differential metabolites. The X-axis represents individual samples, and the Y-axis represents the metabolites. The red and blue bands at the top represent the control group and the CPP group, respectively. The relative values represented by colors are displayed at the bottom of the figure, with red indicating higher levels of the metabolite and green indicating lower levels.

Figure 4

Analysis of metabolic pathways. The larger the circle in the figure, the greater the influence of the metabolic pathway on the grouping. A redder color represents a smaller P value, indicating that these pathways warrant greater attention.

Figure 5

Spearman correlation analysis between differential microbiota at the genus level and differential metabolites. The X-axis represents the microbiota, and the Y-axis represents the metabolites. The color of the grid represents the correlation coefficient of the corresponding metabolite-microbiota. As shown in the figure, warm colors represent positive correlations, while cold colors represent negative correlations. Darker colors indicate stronger correlations. Statistical significance is marked as follows: *P < 0.05, **P < 0.01, ***P < 0.001.