The typical pipeline processing finds the closest match of 16s or shotgun to a reference library. Conceptually this is fine, but when the closest match is not a bacteria found in humans (or very rarely), then the match may have zero value. It is speculative information for information sake.

Some examples from the taxon reported in retail microbiome reports:

• Sharpea azabuensis:  isolated from the faeces of thoroughbred horses in 2008
• Olsenella timonensis:isolated in 2015
• Phoenicibacter congonensis: isolated in 2019 from a Pygmy
• Gillisia: From marine environment
• Macrococcus: Found in food animals
• Clostridium chauvoei:  causative agent of blackleg, a wide spread serious infection of cattle and sheep with high mortality

At the very least, the matching should be done to those reported in humans.

This creates a challenge for the clinician — there is no literature on these bacteria.

But to the capable statistician…

They can often be very useful for determining odds ratios for a specific condition or general good health. A suitably large dataset is needed (thousand of samples). This leaves the clinician between the rock (no literature or studies) and a hard place (“magical” statistical odds ratios).