Gut microbiome combined with metabolomics reveals biomarkers and pathways in central precocious puberty
Xiaoyan Huang1, Jixiong Chen2, Haozhe Zou3
1Department of Genetics, Metabolism and Endocrinology, Hainan Women and Children's Medical Center, Haikou, Hainan, China.
Insights
This study reveals gut microbiome and metabolite changes in central precocious puberty (CPP). These findings help identify potential diagnostic markers and therapeutic targets for CPP in children.
Area of Science:
- Endocrinology
- Microbiome Research
- Metabolomics
Background:
- Central precocious puberty (CPP) is a common endocrine disorder in children.
- Gut microbes influence hormone secretion, but their role in CPP is unclear.
Purpose of the Study:
- To investigate gut microbial and metabolic alterations in CPP.
- To identify potential diagnostic and therapeutic markers for CPP.
Main Methods:
- 16S rRNA sequencing and untargeted metabolomics on 91 CPP patients and 59 controls.
- Bioinformatic and statistical analyses of microbiome and metabolite data.
- Machine learning classifier development for CPP discrimination.
Main Results:
- Identified distinct gut microbial and metabolite profiles in CPP patients.
- Developed a classifier with high accuracy (AUC 0.832-1.00) for CPP detection.
- Linked nitric oxide synthesis to CPP progression and identified Streptococcus as a potential CPP marker.
Conclusions:
- Multi-omics approach successfully discriminated CPP patients.
- Identified potential molecular markers for CPP diagnosis and treatment.
Background:
Central precocious puberty (CPP) is a common disease in prepubertal children and results mainly from disorders in the endocrine system. Emerging evidence has highlighted the involvement of gut microbes in hormone secretion, but their roles and downstream metabolic pathways in CPP remain unknown.
Methods:
To explore the gut microbes and metabolism alterations in CPP, we performed the 16S rRNA sequencing and untargeted metabolomics profiling for 91 CPP patients and 59 healthy controls. Bioinformatics and statistical analyses, including the comparisons of alpha and beta diversity, abundances of microbes, were undertaken on the 16S rRNA gene sequences and metabolism profiling. Classifiers were constructed based on the microorganisms and metabolites. Functional and pathway enrichment analyses were performed for identification of the altered microorganisms and metabolites in CPP.
Results:
We integrated a multi-omics approach to investigate the alterations and functional characteristics of gut microbes and metabolites in CPP patients. The fecal microbiome profiles and fecal and blood metabolite profiles for 91 CPP patients and 59 healthy controls were generated and compared. We identified the altered microorganisms and metabolites during the development of CPP and constructed a machine learning-based classifier for distinguishing CPP. The Area Under Curves (AUCs) of the classifies were ranged from 0.832 to 1.00. In addition, functional analysis of the gut microbiota revealed that the nitric oxide synthesis was closely associated with the progression of CPP. Finally, we investigated the metabolic potential of gut microbes and discovered the genus Streptococcus could be a candidate molecular marker for CPP treatment.
Conclusions:
Overall, we utilized multi-omics data from microorganisms and metabolites to build a classifier for discriminating CPP patients from the common populations and recognized potential therapeutic molecular markers for CPP through comprehensive analyses.
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