Fight Aging!

The gut microbiome changes with age in ways the provoke chronic inflammation and tissue dysfunction. Animal studies demonstrate that restoring a youthful gut microbiome to old individuals extends life and improves health. Thus we would expect favorable changes to the gut microbiome to be reflected in any good alternative measure of aging, such as aging clocks. With that in mind, in today's open access paper the authors report on the development of algorithms based on gut microbiome composition that are predictive of epigenetic clock results. This allows identification of specific microbial species that may be harmful or helpful in the matter of the pace of aging.

The eventual destination for this field of research is to produce probiotic or other forms of therapy that can permanently adjust the composition of the gut microbiome in a controlled way. Reduce the numbers of bad species, increase the numbers of good species, and do this for at least hundreds of different species. At present a number of approaches can rejuvenate the gut microbiome with a single treatment, but in an uncontrolled way. For example, fecal microbiota transplantation from a young donor. Animal studies show that fecal microbiota transplantation produces sizable benefits, but for human medicine, given the present regulatory environment, widespread use of such a gut microbiome altering therapy is only likely given complete control over both the contents of the therapy and the outcomes of the therapy.

Gut microbiome signatures associate with DNA methylation-based biological aging

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 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 including 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) and the genus level (R2 = 0.099,). 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 reveal specific gut taxa as hypothesis-generating candidates for mechanistic follow-up, rather than as individual-level diagnostic markers.