Evidence map›Paper›PMID 40510800›Full record

ArticleFrontiers in cellular and infection microbiology2025

Characterization of the salivary microbiome in healthy individuals under fatigue status.

Xianhui Peng, Na Han, Yanan Gong, Lihua He, Yanli Xu, Di Xiao, Tingting Zhang, Yujun Qiang, Xiuwen Li, Wen Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xianhui PengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Na HanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanan GongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Lihua HeNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanli XuHebei University of Engineering, Affiliated Hospital, College of Medicine, Handan, Hebei, China.
Di XiaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Tingting ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yujun QiangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiuwen LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Wen Zhang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jianzhong Zhang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & Aims: Limited understanding exists regarding the characteristics and biological significance of the salivary microbiome in healthy individuals experiencing physiological fatigue. This study aimed to delineate the structural and functional alterations in the salivary microbiome of healthy individuals undergoing physiological fatigue compared to energetic controls, and to explore its potential as a biomarker for fatigue status. Methods: A cohort of 7 healthy individuals experiencing acute physiological fatigue (induced by prolonged study and confirmed via electroencephalography; Fatigue group, FTG) and 63 energetic healthy controls (Energetic group, ENG) were enrolled. Saliva samples were collected, from which microbial DNA was extracted. The V3-V4 hypervariable region of the 16S rRNA gene was subsequently sequenced using high-throughput technology. Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively. Results: The FTG group exhibited significantly diminished alpha diversity (Simpson index, p=0.01071) relative to the ENG group. Beta diversity analysis demonstrated significant dissimilarities in microbial community structure between the groups (p<0.05). Taxonomic profiling revealed a significant enrichment in the relative abundance of potential periodontopathogenic genera, including Conclusion: This study provides novel evidence that physiological fatigue induces significant structural and functional alterations in the salivary microbiome of healthy individuals. These perturbations include diminished microbial diversity, disrupted community architecture, enrichment of potential opportunistic pathogens, and marked shifts in key metabolic pathways, particularly those governing neuroactivity and energy metabolism. These findings suggest that the salivary microbiome may be implicated in the physiological regulation of fatigue, potentially via an "oral-microbiome-brain axis," and underscore its potential as a source of non-invasive biomarkers for assessing fatigue status. Further mechanistic investigations are warranted to elucidate these interactions.

Indexed as

BacteriaFatigueMicrobiotaSalivaAdultBiomarkersComputational BiologyDNA, BacterialFemaleHealthy VolunteersHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedRNA, Ribosomal, 16SSequence Analysis, DNABiomarkersDNA, BacterialRNA, Ribosomal, 16S16S rRNA sequencingbiomarkersfatiguehealthy individualssalivary microbiome

Identifiers

PMID40510800
PMCPMC12159056

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.