Gut microbiota dynamics in SAMP8 mice: insights from machine learning and longitudinal analysis.

Abstract

The gut microbiota plays a crucial role in maintaining host health, and its composition is significantly influenced by aging. The SAMP8 mouse model, known for its accelerated aging process, is widely used to study age-related changes. However, comprehensive longitudinal studies on gut microbiota dynamics in SAMP8 mice remain limited. We analyzed microbiota profiles of SAMP8 mice at 1, 3, 7, and 10 months (n = 6) using 16S rRNA sequencing. Alpha diversity (Shannon index) decreased significantly with age, while beta diversity revealed distinct clustering between young (1 and 3 months) and aged (7 and 10 months) SAMP8 mice. Firmicutes, Actinobacteria, and Deferribacteres declined significantly with age, whereas Proteobacteria and Bacteroidetes increased. At the genus level, Allobaculum and unclassified_f_Lachnospiraceae decreased significantly, whereas Ruminiclostridium_5 and Akkermansia increased significantly in older mice. Microbiota trajectory analysis identified four aging-related patterns. For biomarker discovery, the young (1 and 3 months, n = 12) and aged (7 and 10 months, n = 12) groups were compared using Random Forest analysis, which identified 11 key taxa, with Peptococcus exhibiting the highest diagnostic accuracy (area under the curve = 0.78). These findings highlight the dynamic microbiota shifts during aging and identify Peptococcus as a potential biomarker for aging, offering insights into microbiota-aging interactions and potential translational targets.

Importance: Aging is associated with profound changes in microbial composition, yet the precise trajectories and key microbial signatures of aging remain incompletely understood. This study provides a comprehensive analysis of gut microbiota dynamics in aging SAMP8 mice. By identifying significant shifts in microbial diversity, composition, and aging-related trajectories, our findings highlight the progressive restructuring of gut microbiota with age. Understanding these changes is critical for uncovering potential microbial biomarkers of aging, which could serve as diagnostic tools or therapeutic targets to promote healthy aging. Notably, we demonstrate that some key taxa, such as Peptococcus, can differentiate young and aged microbiomes with high accuracy, offering insights into the potential role of gut microbiota in aging-related health decline. These findings provide a foundation for future research aimed at microbiota-targeted interventions, such as probiotics or dietary modifications, to mitigate age-associated diseases and improve lifespan and health span.