Human Salivary Microbial Community Data for BMI Inference in Forensic Research

Saliva samples from a pilot study were analyzed using 16S rRNA sequencing to characterize microbial community composition. The dataset includes results from linear discriminant analysis and Kruskal–Wallis tests identifying 22 microbial taxa as potential BMI-associated markers. Author Jingyan Ji published the data on figshare under a CC-BY-4.0 license, with a last update timestamp of 2026-04-21.

Use Cases

• Train random forest models for BMI category inference based on microbial marker abundance.
• Identify microbial taxa with differential abundance across BMI groups using statistical tests mentioned in the description.
• Validate the separation between normal-weight and overweight groups via principal coordinates analysis.
• Explore the forensic application of saliva-associated microbial indicators for phenotypic profiling.

Strengths

• Identifies 22 specific microbial taxa, including genera like Neisseria and Veillonella, as potential BMI indicators.
• Includes results from a constructed random forest model for BMI category inference.
• Data is openly shared under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small in scale at 896.7 KB, indicating a limited pilot study.

Provenance

Source

figshare, author Jingyan Ji.

Collection Method

Saliva sample collection and 16S rRNA sequencing.

Freshness

Last updated 2026-04-21 05:24:34; freshness should be verified.