Biological Age Prediction Based On Gut Microbiome And Its Application In Aging Assessment

In recent years,the growing number of elderly people and the increased rising risk of age-related chronic diseases have put an enormous medical and economic strain on society.According to studies,the composition of the gut microbiome changes with age and that changes in microbial structure also affect the aging process.However,the pattern of interaction between age and gut microbiome,as well as the mechanism of flora’s impact on body aging are still unclear.In this study,we first explored the age-related variations in gut microbiome species,metabolic pathways as well as the interactions to clarify the changes of gut microbiome with age.Secondly,an age prediction model based on multi-dimensional data of gut microbiome was constructed in order to achieve the characterization of the biological age of gut microbiome and the identification of age-related microbial markers.Finally,the correlation between the biological age of gut microbiome and the degree of disease and aging was verified using public and independent aging population cohorts,and aging assessment based on the biological age of gut microbiome was achieved.To provide a theoretical basis for analyzing the role of the gut microbiome in the aging process of the host,developing targeted dietary intervention strategies based on gut microbiome to intervene in aging,thus achieving precise nutritional health.The main findings are as follows:(1)Analysis of the changing pattern of gut microbiome with age based on a large dataset of gut microbiome.First,age was an important factor influencing the diversity of the gut microbiome,elderly people demonstrate reduced species and metabolic pathway diversity with increasing age compared to younger populations.Secondly,Akkermansia muciniphila,Ruminococcus gnavus,Prevotella copri,Faecalibacterium prausnitzii,Bifidobacterium adolescentis and Bifidobacterium longum were the differential species significantly associated with age.Ketogluconate metabolism,heme b biosynthesis from glycine,sulfur oxidation,isoprene biosynthesis,methylerythritol phosphate pathway and methanogenesis from acetate were the differential metabolic pathways significantly associated with age.Finally,the species associated co-expression network module exhibited enrichment of inflammation-associated microorganisms with age,as well as migration of species of non-gut community context sources.The metabolic pathway co-expression network model showed an age-related decrease in pathways related to branching amino acids and short-chain fatty acid synthesis,and an increase in pathways related to short-chain fatty acid utilization and partial cofactor synthesis by the gut microbiome.(2)An age prediction model based on multi-dimensional data of gut microbiome was constructed to realize the characterization of the biological age of gut microbiome.The ensemble age prediction model was first constructed using a stacking strategy,which was able to achieve an effective prediction of host chronological age(R~2 of 0.544)and helps to improve the utilization of multi-omics data.Secondly,Finegoldia magna,Pseudomonas aeruginosa and the leucine and isoleucine degradation pathways were age-related microbial markers.Positively age-related species indicated a higher propensity for gas production,while negatively related species had a higher propensity for indole and acetate production.Finally,gut microbiome metagenomic and metabolic data had the best synergistic age prediction effect,which suggests that gut microbiome and its associated metabolites may be the effector influencing the host aging process.(3)Aging assessment by the biological age of gut microbiome was implemented based on public and independent validation cohorts.First,analysis of host multimorbidity showed that the biological age of gut microbiome was more effective in characterizing host disease than chronological age,with most diseases leading to a significant increase in residuals for biological age of gut microbiome.Further analysis based on a public frailty population cohort indicated that the biological age of gut microbiome and its prediction residuals also increased with aggravation of frailty in elderly people,and were able to identify differences in frailty in the population of the same age.Finally,a validation cohort based on recruited elderly population achieved the identification of people with different aging degrees based on the biological age of gut microbiome,proving the possibility of aging assessment based on the biological age of gut microbiome.