Gut microbiome signatures associate with DNA methylation-based biological aging

Area of Science:

• Microbiology
• Genetics
• Computational Biology

Background:

• The gut microbiome's role in aging is increasingly recognized, but its connection to epigenetic aging pace is not well understood.
• Machine learning offers novel approaches to analyze complex biological data in aging research.

Purpose of the Study:

• To investigate the association between gut microbial composition and biological aging pace, independent of chronological age.
• To develop predictive models for epigenetic age acceleration using gut microbiome data.

Main Methods:

• Utilized 16S rRNA gene sequencing and DNA methylation data from 123 monocyte-enriched samples.
• Developed "EpiBiome" models to predict epigenetic age acceleration residuals and DunedinPACE.
• Employed SHAP analysis to identify key microbial taxa associated with aging pace.

Main Results:

• EpiBiome-Accel models significantly predicted DunedinPACE at both species (R²=0.152) and genus (R²=0.099) levels.
• Model performance was not improved by adding chronological age, indicating age-independence.
• Bifidobacterium adolescentis was identified as a key predictor of decelerated aging, while Succinivibrio dextrinosolvens was associated with accelerated aging.

Conclusions:

• Specific gut microbial taxa are associated with the pace of biological aging.
• Findings suggest potential microbial targets for future mechanistic studies on aging.
• Gut microbiome composition may serve as a biomarker for biological aging pace, distinct from chronological age.