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

Qixiang Yuan's meta-analysis aggregates 22 public 16S rRNA saliva cohorts from PubMed (2016–2024), comprising 7,750 samples. The study identifies core microbiota and uses machine learning to build multi-disease prediction models. The dataset and findings were published on figshare in April 2026.

Use Cases

• Training multi-class disease classifiers based on identified core microbial genera.
• Analyzing gender differences in oral microbiota composition.
• Benchmarking healthy baseline salivary microbiome profiles.
• Investigating associations between microbial structure and physiological or pathological states like colorectal cancer.

Strengths

• Aggregates data from 22 cohorts and 7,750 samples, providing a substantial meta-analysis scale.
• Reports robust model performance with AUC values ranging from 0.898 to 1.0.
• Identifies nine specific core microbiota genera, such as g:Streptococcus.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data tables is unknown, which may limit suitability assessment.
• The primary file is a 100.0 KB PDF, suggesting the dataset's scope is limited to summary results rather than raw sequence data.

Provenance

Source

figshare, author Qixiang Yuan

Collection Method

Meta-analysis of public 16S rRNA sequence data from PubMed.

Time Range

Cohorts from 2016 to 2024

Freshness

Last updated 2026-04-15 05:46:07; freshness should be verified.

Geography

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