GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273166/primaryOK200Methylation profilingHomo sapiensMethylation profiling by genome tiling arrayhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273166GEOGSEfalseGut microbiome signatures associate with DNA methylation–based biological agingRecent advances in machine learning have applied novel tools to aging research, yet the relationship between the gut microbiome and epigenetic aging remains underexplored. This proof-of-concept study investigates whether gut microbial composition is associated with biological aging pace independent of chronological age. Using paired 16S rRNA gene sequencing and DNA methylation data from 123 monocyte-enriched samples in a cohort enriched for Native Hawaiian and Pacific Islander participants, we developed "EpiBiome" models to predict epigenetic age acceleration residuals and DunedinPACE, a DNA methylation biomarker that estimates the instantaneous pace of biological aging. Models predicting residuals of traditional clocks (Horvath, Levine, GrimAge2) showed no predictive signal at either taxonomic rank. By contrast, the EpiBiome-Accel model for DunedinPACE reached statistical significance at both the species level (R² = 0.152, Spearman ρ = 0.408, p = 0.012; permutation p < 0.001) and the genus level (R² = 0.099, permutation p = 0.036). Adding chronological age as a feature did not improve performance (ΔR² = −0.046 at species level), indicating age-independence. SHAP analysis of the species-level ElasticNet model identified Bifidobacterium adolescentis as the dominant contributor and the strongest predictor of decelerated aging, with Succinivibrio dextrinosolvens showing the strongest association with accelerated aging. These findings nominate specific gut taxa as hypothesis-generating candidates for mechanistic follow-up, rather than as individual-level diagnostic markers.Recent advances in machine learning have applied novel tools to aging research, yet the relationship between the gut microbiome and epigenetic aging remains underexplored. This proof-of-concept study investigates whether gut microbial features are associated with biological aging rates independent of chronological time. Using paired 16S rRNA gene sequencing and DNA methylation data from 123 monocyte-enriched samples, we developed "EpiBiome" models to predict epigenetic age acceleration residuals and DunedinPACE, a DNA methylation biomarker that estimates the instantaneous pace of biological aging. While models predicting residuals of traditional clocks (Horvath, Levine, GrimAge2) showed minimal signal, the EpiBiome-Accel model for DunedinPACE showed a modest association (R2=0.125), identifying a microbial signature correlated with the pace of aging. SHAP analysis indicated that Bifidobacterium adolescentis was the top feature associated with decelerated aging, while Faecalibacterium prausnitzii and Gemmiger formicilis were top predictors of accelerated aging in this context. These findings suggest that specific gut taxa are associated with biological aging independent of chronological time. This study offers preliminary evidence linking the gut microbiome to the pace of aging, presenting a potential non-invasive framework for future investigation into the gut-epigenetic aging axis.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 sapiens