Back

Diet-Driven Gut Microbiome Differences Between Cohabiting Han and Yi Ethnic Groups in Liangshan, China

Jiang, C.; tan, d.; Hu, B.; Yang, C.; Yuan, X.; He, M.; Yan, X.; Li, Y.; Li, G.

2026-01-08 microbiology
10.64898/2026.01.06.698051 bioRxiv
Show abstract

BackgroundWhile ethnic variations in gut microbiota are well-documented, their persistence in genetically distinct populations sharing the same geographic environment remains poorly understood. This study investigates the Han and Yi ethnic groups cohabiting rural Southwest China, where contrasting dietary traditions persist under identical environmental conditions. MethodsWe analyzed fecal samples from 100 healthy adults (50 Yi/50 Han) using 16S rRNA gene sequencing (V3-V4 region). Dietary intake was quantified via three 24-hour recalls validated with plasma biomarkers. Multivariate analyses (DESeq2, PERMANOVA) controlled for age, sex, and BMI covariates. ResultsYi individuals exhibited significantly higher Prevotellaceae (log2FC=1.82, padj=0.028) and Succinivibrionaceae (log2FC=2.15, padj=0.013) abundances, corresponding to their buckwheat-rich diet (58.5 {+/-} 12.3% vs Han 3.3{+/-}1.8%, p<0.001) Han microbiota showed enriched Bacteroidaceae (log2FC=1.24, padj=0.041), associated with higher animal fat intake (23.6% vs Yi 1.8%, p<0.001) Dietary factors (buckwheat/animal fat intake) explained greater microbiota variation than ethnic genetic ancestry (PERMANOVA R{superscript 2}=0.10 vs 0.07, p < 0.05) ConclusionCohabiting Han and Yi populations maintain distinct gut microbiota compositions primarily driven by dietary differences. These findings highlight diet as a stronger determinant than genetic ancestry in shaping microbial communities under shared environments. Future studies should validate these observations with metagenomic sequencing.

Matching journals

The top 4 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.