# Super donor assessment tool for oral microbiome transplantation

## TL;DR

This study introduces a new tool to identify ideal donors for oral microbiome transplants to prevent dental caries.

## Contribution

The novel SDAT tool uses MCDM and AHP to systematically rank donors for oral microbiome transplantation.

## Key findings

- Model 1 donors showed the lowest microbial diversity and health-associated genera.
- Model 3 had higher diversity and disease-associated taxa like Treponema and Capnocytophaga.
- Network analysis revealed distinct microbial community structures across donor models.

## Abstract

Oral microbiome transplantation (OMT) involves transferring microbiota from donor to recipient. However, selecting suitable donors remains challenging due to a lack of standardised guidelines. This study developed a novel super donor assessment tool (SDAT) combining a multi-criteria decision-making (MCDM) process and an analytical hierarchical process (AHP) to identify OMT “super donors” for dental caries prevention.

This cross-sectional study used four sequential screening phases with data from 93 healthy participants, capturing socio-demographics, lifestyle, dietary and oral health behaviours. The SDAT employed MCDM, AHP, combining criteria with normalised and weighted ranks to establish the top 10 donors for three models: “Optimal donor” (Model 1), “Ideal donor” (Model 2), and “Sub-optimal donor” (Model 3). Donor plaque samples underwent 16S ribosomal RNA amplicon sequencing for microbial profiling, examining alpha and betadiversity, differential abundance, and network analysis.

Alpha diversity analysis showed significant differences among groups (Kruskal-Wallis p < 0.001), with Model 1 showing the lowest diversity and Model 3 the highest. Beta diversity analysis using Permutational Multivariate Analysis of Variance revealed significant differences in microbial community composition (R² = 0.19, p = 0.001). Differential abundance analysis (False Discovery Rate < 0.05, controlling for age and sex) identified health-associated genera (Neisseria, Lautropia, Streptococcus, Veillonella) in Model 1, whereas Model 3 showed higher levels of disease-associated taxa (Treponema, Capnocytophaga). Network analysis revealed that Model 1 was organised around Actinomyces and Prevotella, Model 2 around Rothia and Haemophilus, and Model 3 was dominated by pathogenic taxa.

SDAT provides a systematic, transparent framework for super-donor selection, ensuring precision and reproducibility in donor rankings. The scoring system standardises the donor selection process, the effectiveness of donor screening, and reduces the risk of adverse events for OMT.

The online version contains supplementary material available at 10.1186/s12866-025-04630-z.