Another ME/CFS Microbiome Followup

This is a follow up to a prior post:

This person did his tests using and then transfer the data to This allows us to use special studies to select bacteria. I am also, as part of my own learning (as well as the readers), going to do some comparison between the OmbreLabs and BiomeSight reports on the same data (i.e. FASTQ files).

I had another sample analyzed at Ombre, and there were already changes in my flora, even in a short amount of time. And they correlate with me feeling a bit better. So thank you. I’m still trying to crunch the data and make sense of the new results, and other than your great Dr. AI, I am using this new feature by Ombre which I find very clear (old sample first, new sample after)

Why Follow Up Posts are important

The first item is simple, does the model and suggestion appear to work. Everything is theoretically computed, not based on clinical practice or clinical studies. The second item is that these posts encourages people to try suggestions, or to do “self-serve” with the site.

Foreword – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that appears to have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

Comparisons between Samples

See this other review of a series of ME/CFS microbiomes from another person that I recently did, ME/CFS Follow Up Microbiome Samples. If you are new to this series, you may wish to review A new specialized selection of suggestions based on statistical significance for symptoms.

First, I do not know the best way to compare samples — what I usually do is put all of the numbers side by side. Special attention needs to be paid to Lab Read Quality. A poorer read quality results in less bacteria being identified.

Lab Quality is a measure of the total number of bacteria counted. The processing of a sample may detect just 30,000 bacteria or 300,000 bacteria. This impacts the number of bacteria detected and also the accuracy of the measures.

Also, Special Studies Percentage Matches is helpful to interpret these numbers better.

CriteriaBS 6/6BS 7/19OL 6/6O 7/19
Lab Read Quality2.
Bacteria Reported By Lab280497365628
Bacteria Over 99%ile2756
Bacteria Over 95%ile24312724
Bacteria Over 90%ile49584951
Bacteria Under 10%ile17621860
Bacteria Under 5%ile5301028
Bacteria Under 1%ile01317
Rarely Seen 1%0409
Rarely Seen 5%418840
Outside Range from JasonH4472
Outside Range from Medivere17171616
Outside Range from Metagenomics7777
Outside Range from MyBioma991414
Outside Range from Nirvana/CosmosId22222323
Outside Range from XenoGene661111
Outside Lab Range (+/- 1.96SD)6131014
Outside Box-Plot-Whiskers70846461
Outside Kaltoft-Moldrup70113112182
Condition Est. Over 99%ile1100
Condition Est. Over 95%ile2400
Condition Est. Over 90%ile5622
Enzymes Over 99%ile3101315
Enzymes Over 95%ile46326982
Enzymes Over 90%ile9051155411
Enzymes Under 10%ile10221955138
Enzymes Under 5%ile451322267
Enzymes Under 1%ile64752
Compounds Over 99%ile97104126
Compounds Over 95%ile5676385397
Compounds Over 90%ile292313533548
Compounds Under 10%ile72125183248
Compounds Under 5%ile3964109127
Compounds Under 1%ile5211617
Note: I just cut and pasted from “Multiple Samples” tab to Excel to make the above table.

What are the key things seen above (most of the numbers are similar):

  • Sample Quality are the same (expected from using the same FASTQ file)
  • Ombre reports more bacteria
  • Outside Range from Jason Hawrelak show a major improvement with Ombre Labs and no change with BiomeSight
    • As a historic notes, Jason’s numbers were developed using uBiome labs (adding more fuzziness to everything).
    • I view this major improvement per OmbreLab, to indicate the person’s improvement.
  • For Enzymes we see more high production rates and less low production rate with Ombre
    • Remember that enzymes are estimated based on the bacteria reported and is an estimate only.
    • The percentiles for both Ombre and BiomeSight are based on other samples from the same lab (they are NOT intermixed – I removed that earlier this year)
  • For Compounds, we see the same thing!

KEGG Computed Enzymes

I was curious what the top items were. Most of the bacteria are the bacteria only available in Equilibrium and PrescriptAssist, excluding those and looking at the top few — we see similar suggestions (and note E.Coli is not always #1 for ME/CFS people on all tests, just a frequent pattern what dates back to 1998 in some conference papers from Australia).

Special Studies Numbers

Only BiomeSight was used in the Special Studies (because of higher sample population). The person’s rating for each of the symptoms (2 – worst, 0 -none) is also added.

Why did the number increased so much? Look below at Lab Sample quality! We cannot pick a percentage match as being critical — because that percentage depends very much on lab quality!

BS 6/6BS 7/19PersonSymptom
2.15.4Lab Quality
13252 Allergies And Food Sensitivity
13202 Bloating
11242 Brain Fog
9342 Depression
13232 Easily irritated
8222 General Fatigue
11232 High Anxiety
12202 Histamine or Mast Cell issues
13231.5 Chronic Fatigue Syndrome (CFS/ME)
11201.5 irritable bowel syndrome
13231.5 ME/CFS with IBS
12301 Alcohol intolerance or Medication sensitivities
10231 Intolerance of Extremes of Heat and Cold
9171 Post-exertional malaise
21301 Small intestinal bacterial overgrowth (SIBO)
12281 Unrefreshed sleep
16280 Allergic Rhinitis (Hay Fever)
23280 Autism
12220 Cold Extremities
15200 Constipation
21290 COVID19 (Long Hauler)
26450 Inflammatory bowel disease
8230 ME/CFS without IBS
11210 Poor gut motility
8200 Tinnitus (ringing in ear)


As cited in the introduction, the person reported feeling better. We also see a major improvement against Jason Hawrelak Criteria for a healthy gut (using Ombre numbers). With both labs we see an increased of rarely seen bacteria — which is open to many interpretations; statistically both increases looks like a move towards a typical gut. 5% of 628 bacteria is 31, we see 40.

Going forward

I am building a consensus report from the items marked 2 above using the Special Studies. The list is similar to other people with ME/CFS. We see 2 E.Coli probiotics (symbioflor 2 e.coli probiotics, colinfant e.coli probiotics) at the top with d-ribose (a sugar used by E.Coli). This is then followed by the earth based probiotics( General Biotics Equilibrium, Prescript Assist (Original Formula), Prescript Assist (2018 Formula)).

These suggestions agrees with the top KEGG suggestions (despite being calculated in a totally different way — one set used Genomics and one set used Clinical Trials)

The rest of the to take probiotics mainly fall into 3 groups: Saccharomyces boulardii (probiotics) bacillus (probiotics), bifidobacterium with bacillus coagulans (probiotics) being the top of this set. As is typical, lactobacillus is usually a negative.

Going over to vitamins, the strongest take is Ferric citrate. We have almost all of the B-vitamins being strong avoidthis is contrary to the conventional treatment wisdom which says vitamin B helps ME/CFS. I discuss this in a prior post and speculate that the reason that Vitamin B is low in blood test ME/CFS is that part of the microbiome dysfunction are bacteria that are greedy for vitamin B, hence it does not get to the body. Conceptually this speculation is testable with a lab reactor using the microbiome from a ME/CFS person.

Starving out bacteria that consumes B-Vitamins may be one path

Bottom Line

“This is too complicated” is what I can hear some people saying. This analysis digs into the nature of the data which is really not needed for most people. It is likely of interest to those treating microbiome dysfunctions as it illustrates many of the challenges in interpreting.

For most people, the process stays the same:

  • Upload the data
  • Try several different ways of generating suggestions
  • Look at the consensus

Why is consensus important? Simple, we have very incomplete data and also have limited accuracy with the microbiome tests. Going the consensus approach is similar to using a Monte Carlo Simulation, an appropriate approach to deal with complex processes with many parameters that are fuzzy.

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