Towards a Biomarker of Aging Based on the Gut Microbiome

A low-cost, low-effort way to accurately assess biological age, meaning the burden of molecular damage and the countless harmful cellular reactions to that damage, would greatly speed development of rejuvenation therapies. Ideally researchers would be able to apply a therapy and then within a month obtain a measure of how greatly it affects aging. At present the only reliable way to fully assess means of slowing or reversing aging is to run life span studies, which are slow and expensive in mice, and simply not feasible in humans.

Thus a fair amount of effort is presently devoted to the development of biomarkers and combinations of biomarkers that might one day serve this purpose. In this preprint paper, researchers outline their work on the use of the gut microbiome as a basis for a biomarker of aging. It is known that characteristic changes occur in the microbiome with age, many of them detrimental and associated with the development of age-related disease, but there is a high degree of variability between individuals and study populations. Thus these results will certainly need a much broader replication as a part of any further development.

Although infant microbiome succession is well studied and can be used to assess the risks of various health conditions, its transition to adult microbiome is less understood. More so, composition variability attributed to geographic location, medical history, diet, and other factors make it hard to analyze adult microbiomes as effectively as those of infants. Age-related studies of human microbiome have failed to produce a straightforward theory of gut flora aging.

Some studies indicate decreasing biodiversity in the elderly gut. However, that is not the case for all data sets, and elderly healthy people may have microbiomes as diverse as the younger population. Other findings include changes in specific taxa abundance in aging microbiota. Such bacterial genera as Bacteroides, Bifidobacterium, Blautia, Lactobacilli, Ruminococcus have been shown to decrease in the elderly, while Clostridium, Escherichia, Streptococci, Enterobacteria increase. However, these patterns are not strictly established as results vary greatly across different studies. This may be attributed to different methodologies as well as unbalanced data sets that may contain people of different lifestyles.

Despite these complications, the consensus is that the elderly gut has lower counts of short chain fatty acid (SCFA) producers such as Roseburia and Faecalibacterium and an increased number of aerotolerant and pathogenic bacteria. Such shifts can lead to dysbiosis, which in turn contributes to the onset of multiple age-related diseases.

The standard way of separating the gut microbiome into three chronological states - child, adult, and elderly microbiomes - lack a clear set of rules. Among them, adult microbiome remains the greatest mystery. It has no established succession stages, as in newborns, and does not normally reflect gradient detrimental processes typical for an old organism. This poses a question whether normal adult microbiome progresses at all or it is in a state of stasis. Considering the aging process is gradual and involves accumulation of damage and other deleterious changes (as also indicated by a number of biomarkers such as DNA methylation clocks), it is logical to suppose that gut microbiome succession is also gradual. However, attempts to use microbiome-derived features to predict chronological age have been inconclusive.

Here, we developed a method of predicting the biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals. Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations. This approach has allowed us to define two lists of 95 intestinal biomarkers of human aging. We further show that this list can be reduced to 39 taxa that convey the most information on their host's aging. Overall, we show that (a) microbiological profiles can be used to predict human age; and (b) microbial features selected by models are age-related.