Salivary Microbiome Meta-Analysis for Multi-Disease Prediction, 7,750 Samples

7,750 saliva samples from 22 cohorts were analyzed to explore links between oral microbiota and disease. Qixiang Yuan's 2026 meta-analysis identified nine core microbes in healthy controls and built random forest models with high AUC scores (0.898–1.0) for disease classification. This work validates saliva microbiota as a potential source for non-invasive diagnostic markers.

Use Cases

• Training multi-disease classification models based on 16S rRNA microbial abundance data.
• Identifying core salivary microbiota signatures for establishing healthy baselines.
• Investigating gender differences in oral microbiome composition.
• Analyzing microbiota structural differences across physiological and pathological states mentioned in the description.

Strengths

• Includes 7,750 samples aggregated from 22 public cohorts.
• Multi-class random forest models demonstrated high performance (AUC: 0.898–1.0).
• Identified nine specific core microbiota genera in the negative control group.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is a 68.7 KB PDF, suggesting it contains summary results rather than the raw underlying data.
• Data may reflect temporal and source bias inherent to the PubMed search range (2016–2024).

Provenance

Source

figshare, author Qixiang Yuan

Collection Method

Meta-analysis of public 16S saliva data from PubMed (2016–2024), analyzed with QIIME2, Wekemo, and statistical modeling.

Time Range

Studies from 2016 to 2024

Freshness

Last updated 2026-04-15 05:46:12; freshness should be verified.

Geography

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