Abstract
Abstract Background As the individual ages, the immune system decreases in activity while chronic systemic inflammation increases. The microbiome is also affected by age, decreasing in beneficial microbes while increasing in pathogenic, inflammation inducing microbes with corresponding changes in their metabolic profile. While aging is known to affect both, links between the two have been hard to uncover. Methods Four young (age 3-6 years) and 12 old (age>18 years) Rhesus macaques were recruited for the study. PBMCs and plasma were collected to investigate immune cell subsets by flow cytometry and plasma cytokines by bead based multiplex cytokine analysis respectively. Stool samples were collected by ileal loop for microbiome analysis by shotgun metagenomics and serum, gut microbial lysate and microbe-free fecal extract was used to determine metabolomics by mass-spectrometry. Results Our aging NHP model recaptured many of the features of the age-associated immune alterations, with increased inflammation and alterations in immune cells subsets with lower number of CD4 T cells and a trend of age associated alterations in maturation subsets in older animals with lower naïve and higher central memory CD4 T cells. Older animals showed a significantly different microbiome from young animals with lower abundance of Firmicutes and higher Archaeal and Proteobacterial species. Correlation analysis showed a link between microbes in older animals with systemic inflammation. Analysis of metabolites in the serum and feces showed significant differences between specific metabolites between young and older animals that can influence age-associated morbidities. Conclusion These data support the age associated alterations in microbiome profile and its association with persistent systemic inflammation and metabolome changes. Further mechanistic studies are needed to understand the relationship between inflammation and microbiome. Nevertheless, this NHP model recapitulates human age associated changes in immune, inflammatory and microbiome profiles and can be useful for designing future studies targeting microbiome modulations in aging.