Microbiome Outliers

I have just gotten back results from Thryve. I have processed them thru BiomeSight.com giving me two interpretations of my sample. To better understand why I prefer this, read The taxonomy nightmare before Christmas…. In this post, I will look at the Thryve interpretation.

Before starting an analysis, it is good to note what has happened between samples and any specific issues you are hoping to address. My last sample was in February 2020. At that point, I was likely close to full recovery from ME/CFS but still be careful. Subsequently to that sample:

  • Two hospitalization for sepsis with lots of antibiotics
  • Last sepsis residue included high blood pressure (hypertension).
  • Instead of using prescriptions to address high blood pressure (I am taking no prescription drugs of any type – making me an exception for someone in the late 60’s), I have been taking appropriate supplements for it (see Hypertension – What we know, where the items and studies are detailed) . Almost all of the items are anti-inflammatories also.
  • Some weight loss (15 kilos, 30 lbs) – possibly a side-effect of the antibiotics.
  • I have noticed that I am sleeping longer and deeper. Smart watch reports sleep quality as “Excellent”

Questions being asked:

  • Should I drop some of the supplements for hypertension? BP is often 100/70 today, so there is wiggle room.
  • Any new supplements that I should consider?

My impression is that I am in good health for my age. Still have weight to take off.


I have added two massive sets of data from KEGG: Kyoto Encyclopedia of Genes and Genomes since February. For this post, I have had to add a few more pages to do this analysis. There are almost 4000 facets of your microbiome that are available. This is mentally overwhelming to most people. The outliers attempts to filter these down to a manageable number.

Bacteria, like people, allow substitutions for work function. We need to be careful not to get caught up in specific bacteria — “all of your beer brew masters must be born within 10 miles of Munich”. This Maori from New Zealand may be the equivalent or better job.

We need to focus on what they produce and not what they are.

I will start with pages that I call Outliers – that is, values that may warrant deeper examination.

These are all located on the first dropdown menu as shown below.

Outliers are values that are rarely seen

Values that shows up depends very much on what is reported by the software looking at the sample. There are two sets of results that I want to look at:

  • Those reported by almost everybody (i.e. 1300 samples or more). That is 97% shown above
    • For these I am concerned about very high and very low values (bottom and top 3%)
  • Those reported by few people (3% of 1300, or 40 samples or less). If the values are high or low is not significant because their presence is the oddity.

All of the outlier pages allow you is increase or decrease the ranges to be examined.

Items listed will explain why they were included in the list.

My goal is to identify these items for later follow up (Medical Professional or PubMed research)

End Products

Most end products are reported for most samples.

KEGG Modules

KEGG Enzymes

Actual Results – Using Thryve’s Data

Compare to February, I expected things to improve. I was actually surprise at the degree of normalization seen across thousands of measures (literally!) as shown below. February results are shown first, followed by October.

All items have normalized
All common values have normalized
All common values have normalized

Sick Me Results

If I hop back to a time that Chronic Fatigue Syndrome was active, we see a massive number of Kegg Module and Enzymes that were low (botton 3%ile)

Bottom Line

These new pages appear to work for identifying the processes that are off. Some of them can lead to immediate suggestions for supplements. For example, I noticed D-ribose enzymes on the list above, looking at more detail of this sample and filtering… I see that everything connected to d-ribose is low. Logical conclusion, supplement with D-Ribose. Surprise, surprise — the literature agrees!

Results: D-ribose, which was well-tolerated, resulted in a significant improvement in all five visual analog scale (VAS) categories: energy; sleep; mental clarity; pain intensity; and well-being, as well as an improvement in patients’ global assessment. Approximately 66% of patients experienced significant improvement while on D-ribose, with an average increase in energy on the VAS of 45% and an average improvement in overall well-being of 30% (p < 0.0001).

The use of D-ribose in chronic fatigue syndrome and fibromyalgia: a pilot study (2006)

Unfortunately, there is a lot of items that could be listed. Each one may take significant time to research.

This is enough material for a single post. More coming.