Reading Stool and Protein Panels Alongside Your Genome

A patient handed me two reports this week. A stool microbiome analysis with a dysbiosis index, and a plasma panel claiming her kidney age is nine years older than her birth certificate. Her standard labs are essentially clean, except a GGT that's been creeping for four years. She wants to know which report to believe.

Neither one on its own. The useful question is whether any finding in those reports agrees with something a completely different method also found — and for a different reason.

Okay, walk me through the stool report first. What is it actually measuring?

Most commercial stool tests use 16S rRNA gene sequencing — they copy and read a single marker gene every bacterium carries, then match those reads to a reference database. That tells you who is present, roughly to the genus level. It does not tell you what they're doing.

The report has species-level calls all the way through.

Then it's claiming more than the method supports. Short-amplicon 16S reliably stops at genus. And because the output is relative abundance — proportions summing to one — one organism expanding can mathematically shrink every other, so a flagged depletion may just be somebody else's bloom.

What about the recommendation for three specific probiotic strains and a botanical antimicrobial?

That's a commercial layer on top of a research-grade measurement. There are no guideline bodies endorsing strain-specific probiotic prescriptions from 16S composition data. That recommendation is marketed, not evidenced.

Now the protein panel. Thousands of proteins, a kidney-age estimate. How is that generated?

High-plex proteomics — platforms like SomaScan or Olink measure thousands of proteins simultaneously from a plasma sample. The organ-age estimates come from machine-learning models trained on cohort data. The models are real; the clinical validation of acting on a single organ-age score is not there yet.

So I should ignore the kidney-age number entirely?

Not ignore — contextualize. If her urinary albumin-creatinine ratio, eGFR trend, and uric acid are all normal, that proteomic signal is an unvalidated outlier. If two or three of those are also drifting, now you have convergence across independent layers and that's worth pursuing.

Which brings me back to her creeping GGT and the fasting insulin you mentioned. Those are standard labs.

Right, and those are validated clinical signals. A rising GGT over four years even within the reference range correlates with hepatic fat accumulation and insulin resistance in prospective cohort data — not trial-level evidence for intervention, but robust observational association. Fasting insulin trending upward is mechanistically consistent. That's two standard measurements pointing the same direction.

And if the stool report also shows reduced Faecalibacterium prausnitzii — a bacterium associated with butyrate production — does that add to the picture?

It's consistent with it, but composition data is the weakest layer. Gene presence from shotgun metagenomics — which sequences all the DNA, not just the marker gene — would be stronger, and actual stool butyrate measurement stronger still. Consistency is suggestive; convergence from independent methods is what you act on.

So what's the one thing you'd want me to take away?

Convergence across layers that couldn't share the same error is the only signal strong enough to change management. Your standard labs found it here. The panels added colour, not weight.