Back

Oral-gut microbiome profiles in environmentally matched dizygotic triplets discordant for autism spectrum disorder: an exploratory study

Duarte, N. T.; Faria, C. B.; Fonseca, J. V. d. S.; de Oliveira, F. M.; Sabino, E. C.; Braz da Silva, P. H.; Martins, F.; Gallottini, M.

2026-08-18 dentistry and oral medicine
10.64898/2026.08.14.26360454 medRxiv
Show abstract

Background/Objectives. Autism spectrum disorder (ASD) has been associated with microbiome alterations, but the relative contribution of environmental and individual factors remains unclear. This study explored oral and gut microbiome profiles in environmentally matched dizygotic triplets discordant for ASD. Materials and Methods. Triplets in the 5-9 year age range, including one child with ASD and two neurotypical siblings, underwent standardized oral examination. Oral tongue-dorsum and rectal swab samples were analyzed by 16S rRNA sequencing. Taxonomic composition and beta diversity were evaluated descriptively. Results. Dominant bacterial phyla were broadly similar across siblings, but oral microbial profiles showed greater interindividual variation. The participant with ASD had the highest dental biofilm accumulation, predominance of Streptococcus, and reduced representation of several secondary genera. One neurotypical sibling with mild gingival inflammation showed greater representation of Fusobacterium, Prevotella, and Leptotrichia. Beta diversity demonstrated clearer interindividual separation among oral than gut samples. Conclusions. Individual-specific factors may influence microbiome patterns even under highly similar environmental and dietary conditions. These findings support further investigation of the oral microbiome as a complementary component of ASD microbiome research.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.