Flare Report #5

This is part of the ongoing tracking of the flare or loss of remission from myalgic encephalomyelitis through microbiome studies. You may view my prior reports below:

I have added a lot to the site and analysis over the last month — so this will be a very long post.

First Symptom Prediction – almost 100% accurate

The artificial intelligence agent that does prediction continues to astound me for it’s general accuracy. The results of the latest microbiome uploaded is below:

#1 is reasonably correct, I live with two of them (excluding myself) — and #3 comes in because both have histamine and mast cell issues… Does this mean no more snogging with the wife?. #2 and #4 (confirmed by conventional lab tests) are definites, as are 5,7,8,9 (been known for 30 years), 11,12,13,14,15. The only possible miss is #6. So 14/15 for correctness (for AI that is awesome).

Some symptoms missed that it got right on earlier samples:

For Data Science Nerds out there, there are 161 1-tuple symptoms (covering 77 symptoms) . That is, some symptoms are associated with different shifts in different bacteria — but still the same symptoms.

Why is this important?

If we can predict symptoms accurately from bacteria, this implies (does not prove) that the bacteria are causing the symptoms. This implies if we shift the amount of the bacteria we may reduce symptoms. Getting a high success rate on predicted symptoms (in spike of some nasty issues with having good samples) means that not only is this viable — but that the method being used is reliable and not vaporwear or speculation.

I have gotten rid of symptoms by following the suggestions

If you read the prior reports, I have a lot less symptoms now. I have been following the suggestions determined by the same artificial intelligence process. It appears to be working.

Back to MY Symptoms

The physical fatigue is massive after any activity. I would not attempt to walk to the local bus stop — or even down and back on our long driveway. If I am in town, I figure that I am good for about three city blocks. Symptoms when I push things include massive sweats, hiccups, clumsiness etc.

On the bright side, there were no neurological symptoms like brain fog predicted — and as you can see by recent enhancements and these posts. There is no sign of cognitive issues — purely physical issue. For someone in information processing — that means working remotely is viable. Doing a commute into the city for work is not. “If it ain’t in my Zipcode, it is too far!”

Key Bacteria Shifts

I have covered Lactobacillus and Bifidobacterium in my post on Seed Probiotics failing to persist. I will switch to selective charts for this section:

I have zero of Akkermansia muciniphila (35.27% of Samples) just other species
High levels are associated with super athletes and it converts lactic acid into fuel for the body. … I have almost zero
Lactobacillus collapsed and making some comeback
Bifidobacterium went from median levels to zero and still has not returned
Betaproteobacteria attempted to rally and then collapsed.
Sutterellaceae (family) followed a similar pattern
Actinobacteria class (class) also collapsed

Faecalibacterium boosts the immune system. It’s collapse result in increased risk of multiple conditions.
Candidatus Soleaferrea went sky high while on Seed Probiotics.

Some Ubiome Charts

Anti-inflammatory Microbes

Butyrate producing microbes are starting to recover, quite a distance to go
Polyamine producing microbes are back to prior range
Propionate producing microbes are moving upwards (traditionally I have been high for this one). So some distance to go still
Serotonin has made a good recovery, but GABA is way below my past levels

Incidental Charts

Diversity did not change — there was a shift in the population.

Firmicutes/Bacteroidetes Ratio

This is a ratio that is often cited in research articles — I view it as a hang over from pre-16s data when measuring bacteria was much harder. I did a post on this ages ago and have largely ignored it due to a statistical gut feeling that it was not very useful. I decided to look at these numbers manually and perhaps automate a chart or report in coming days.

Bottom 95% of samples — remaining values were huge

From this chart, I computed the percentile for each measure and got the chart below for my samples.

Dramatic Changes with onset, reversed and perhaps, overcorrected


When I added the probiotic mixture feature to the site, I saw that Seed was a bad choice. I took a sample (the one above) and sent the ubiome sample off. The next day I stopped taking it because if was NOT on my recommended probiotics list for my specific microbiome.

The ones on the list FOR ME are shown below. I find that I must remind people that this is specific suggestion for MY MICROBIOME results — your list may be very different.

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I also added AOR / Probiotic-3: the best of the Mixed list with a net value of 3.0

Where as Seed was negative

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What happened when I changed probiotics?

I was definitely willing to change because symptom improvement had stopped. The main symptom was having a 15-20 minute window of even mild physical activity before severe fatigue sets in.

Switching to the above probiotics, a lot of changes within 48 hours:

  • Sore throat (same as when I started with Barley porridge) – my body is fighting something
  • Niacin flushes whenever I take some (suggesting that vascular constriction is happening from toxins released by dying bacteria)
  • Night sweats stopped. Running hot at night but without sweats.
  • Nose started running, sneezing
  • Groggy feeling starting about 1 hr after taking them and lasting for 2-3 hours. Reminds me of an antibiotic herx.

You will have to wait until the next installment for the microbiome changes.

Recently added Bed Time Oral Probiotics based on this post about ME/CFS oral bacteria being different than controls. Two of those in the list and on hand, were pressed capsules so I could just let them dissolve solely in the mouth:

With both, I slept hard and had night sweats when I took them (but none if I did not). This has been reported also by another reader.

What is next?

There were a few significant changes on the suggestions from this sample, so I will do those for 7-10 days and then take another sample. Each sample results in a change of course, especially when additional data has been added to the artificial intelligence database.

I am not happy with the very limited physical activity. My three dogs are even more unhappy — they have not had any of their customary weekend walks in the Cascades for months.

Maurice, Winston, Angie

Seed Probiotics – No Persistence

I just got my 2nd microbiome done at the end of two months of the new Seed Probiotic. Conclusion: No persistence despite being human strains.

See my prior posts on Seed:

The conclusion is clear using the recent enhancements to the http://microbiomeprescription.azurewebsites.net site, “My Taxon Time Line” available to those who have uploaded two or more (you need two samples to get a time line!). Below are before and at the end of each of the two months with Seed.

By Lactobacillus Species

Bottom Line

If this probiotic shows up on your recommended list — then take it. It’s effect should be to shift the balance — but do not expect persistence. If you wish to increase Lactobacillus, you should be taking bacillus probiotics, see this earlier post by someone with high lactobacillus and bifidobacterium.

Misc Notes on the Microbiome

Proteins can cause a microbiome cascade that leads to disease

a neurologist at Johns Hopkins School of Medicine, and his team created an animal model of the disease by injecting particular proteins into the stomachs of mice. About a month later, the animals showed symptoms of Parkinson€  ’²s disease. The model not only demonstrates how the disease protein can travel up from the gut to the brain, but also presents nonmotor symptoms rarely seen in other animal models.

Source – It was not bacteria added, just proteins — which alter the microbiome and cascaded from there.

Commensal bacteria from the mouth, skin, and gut produce an ortholog of the human protein Ro60. Some of these bacteria and bacterial Ro60 activated T cells from the blood of a patient with lupus. Ro60-specific antibodies from lupus patients also bound to bacterial Ro60, suggesting commensals could have a hand in activating antibody-producing B cells involved in the autoimmune disease.

source – similar to above, bacteria produce proteins activate nasty things

Is this why ME/CFS are so tired?

Veillonella acts like a “metabolic sink” for lactate, the scientists suspect, converting into fuel the by-product of hard-working muscles runners blame for their aching legs in the latter part of long-distance races. The bacteria turn lactate into propionate, a short-chain fatty acid that’s a source of energy and can also be an anti-inflammatory, a Harvard-affiliated team of scientists reported Monday in Nature Medicine


Using our contributed data: LOW Veillonella is associated at a statistically significant level with:

  • General: Fatigue
  • Neurological-Audio: hypersensitivity to noise
  • Autonomic Manifestations: Postural orthostatic tachycardia syndrome (POTS)
  • Neuroendocrine Manifestations: sweating episodes
  • Neuroendocrine Manifestations: intolerance of extremes of heat and cold
  • Immune Manifestations: tender lymph nodes
  • Immune Manifestations: general malaise
  • Sleep: Waking up early in the morning (e.g. 3 AM)
  • Autonomic: Dizziness or fainting
  • Post-exertional malaise: Post-exertional malaise
  • Post-exertional malaise: Next-day soreness after everyday activities
  • Official Diagnosis: Chronic Fatigue Syndrome

This suggests that the Veillonella probiotics being considered for fitness jocks may also cause a major improvement with ME/CFS. Until that happens, we have to depend on things that feed it and avoid items that diminish it. What is know is listed here.

Another important story

Given the immense antigenic load present in the microbiome, we hypothesized that microbiota mimotopes can be a persistent trigger in human autoimmunity via cross-reactivity. Using antiphospholipid syndrome (APS) as a model, we demonstrate cross-reactivity between non-orthologous mimotopes expressed by a common human gut commensal, Roseburia intestinalis ( R. int), and T and B cell autoepitopes in the APS autoantigen β 2-glycoprotein I (β 2GPI). 


As above, using our contributed data: HIGH Roseburia intestinalis is associated at a statistically significant level with:

  • General: Fatigue
  • Post-exertional malaise: Inappropriate loss of physical and mental stamina,
  • Sleep: Unrefreshed sleep
  • General: Myalgia (pain)
  • General: Headaches
  • Neurological: Impairment of concentration
  • Neurological: Short-term memory issues
  • Neurological: Word-finding problems
  • Neurological-Audio: hypersensitivity to noise
  • Neurological: emotional overload
  • Autonomic Manifestations: Orthostatic intolerance
  • Autonomic Manifestations: irritable bowel syndrome
  • Autonomic Manifestations: urinary frequency dysfunction
  • Post-exertional malaise: General
  • Neuroendocrine Manifestations: subnormal body temperature
  • Neuroendocrine Manifestations: cold extremities
  • Neuroendocrine Manifestations: intolerance of extremes of heat and cold
  • Neuroendocrine Manifestations: worsening of symptoms with stress.
  • Immune Manifestations: tender lymph nodes
  • Immune Manifestations: recurrent flu-like symptoms
  • Immune Manifestations: general malaise
  • Onset: Gradual
  • Neuroendocrine Manifestations: Muscle weakness
  • Neurological: Cognitive Overload
  • Neurological-Sleep: Insomnia
  • Immune Manifestations: Bloating
  • Neurological-Sleep: Night Sweats
  • Immune Manifestations: Hair loss
  • Neurological-Sleep: Inability for deep (delta) sleep
  • Neuroendocrine Manifestations: Rapid muscular fatiguability
  • Gender: Female
  • Sleep: Problems staying asleep
  • Sleep: Waking up early in the morning (e.g. 3 AM)
  • Pain: Pain or aching in muscles
  • Pain: Joint pain
  • Pain: Myofascial pain
  • Neuroendocrine: Cold limbs (e.g. arms, legs hands)
  • Neuroendocrine: Feeling hot or cold for no reason
  • Immune: Recurrent Sore throat
  • Immune: Sensitivity to smell/food/medication/chemicals
  • Neurocognitive: Problems remembering things
  • Neurocognitive: Difficulty paying attention for a long period of time
  • Neurocognitive: Can only focus on one thing at a time
  • Neurocognitive: Slowness of thought
  • Neurocognitive: Absent-mindedness or forgetfulness
  • Post-exertional malaise: Post-exertional malaise
  • Post-exertional malaise: Next-day soreness after everyday activities
  • Joint: Stiffness and swelling
  • Neurocognitive: Brain Fog

The CFS Researcher, Dave Berg back in 1999 wrote:

Our hypothesis is that a majority of individuals diagnosed as Chronic Fatigue Syndrome (CFS) &/or Fibromyalgia (FM) on clinical criteria may be defined as AntiPhospholipid Syndrome (APS) with the endothelial cell (EC) as the disease target with or without platelet activation (PA). 

Chronic Fatigue Syndrome (CFS) &/or Fibromyalgia (FM) 
as a Variation of Antiphospholipid Antibody Syndrome (APS): An Explanatory Model and Approach to Laboratory Diagnosis.

That’s it for the moment, more coming as time permits.

Comparing suggestions from uBiome and Thrive on same Sample

I will not look at the differences of what is reported. Many people have done blogs and articles complaining about differences. What I care about is the differences between analysis and suggestions — the actual application of the reports. Reader reports: “same sample, same time.”

Comparison tools

Reader Beware: There is no gold standard for matching rDNA to bacteria.- firms use different algorithms. Second, analysis is done using box-plot. The base data for box-plat is at present: 731 ubiome, 44 Thryve so there is a bias to ubiome’s processing.

1-15 is the Thryve sample, 7-11 is the uBiome sample

Thrye had more condition matches (113) compared to ubiome (88) – but is more bias to the low levels.

This may be explained by the number of bacteria reported, a major difference. The number for ubiome seemed very low (bad sample taken or processing issue).

  • ubiome: 335
  • thryve: 657

Let us look at what is reported by each.

So we are at the 72%ile for Thryve samples and 96%ile for uBiome samples. The median counts: 234 for ubiome, 560 for thryve.

Conclusion: Thryve on average provide more bacteria reported. I look forward to comparing SunGenomics.com tests which should have even a higher count than Thryve.


Looking at the highest level, Phylum, we see very great differences, especially in the more uncommon phylum.


This is what most interests me. The suggestions prediction algorithms are not based on any lab results but on pubmed studies. The data used may be lab-dependent.

ubiome top suggestions
thryve top suggestions

We have just one commonality: niacin.

Thryve top probiotics : Ubiome had none.

Bottom Line

This has been interesting. Which is more accurate? I do not know; clearly results are very different (as reported by many other bloggers). Is the difference because of the computer algorithm used by each lab (known to be different), natural variation in a stool sample, sample collection process, etc.

Although the literature is generally conflicting with regard to sampling methodology, it is important to consider that comparisons of data obtained using different approaches should be avoided.

The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies

All steps of the complex analysis workflow significantly influenced microbiome profiles, but the magnitude of variation caused by PCR primers for 16S rDNA amplification was clearly the largest. In order to advance microbiome research to a more standardized and routine medical diagnostic procedure, it is essential to establish uniform standard operating procedures throughout laboratories and to initiate regular proficiency testing.

Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability

Before everyone goes to Thryve — be aware of the following:

  • Box-Plot detection is based largely on ubiome samples. When I get 100 (at present 44, half-way there), I will split the reference numbers into a Thryve and a uBiome set.
  • Averages were all reversed engineered from ubiome values.
  • Symptom matching is based on submitted samples (94% from ubiome)
  • Condition Templates are based on published research which uses a wide variety of tests methodologies.

So, the reality is that the site favors uBiome data.

There is a natural desire to get absolute, precise information. We are dealing with methods that are evolving. My attitude is to take the best data currently available (factoring in costs) and use it. It is better than working from no information.

Science based mouth wash for ME/CFS

This post comes out of a 2018 article: Chronic fatigue syndrome patients have alterations in their oral microbiome composition and function. [Sep 2018]. The basic findings was:

Of these, Veillonella abundance was significantly different between CFS patients and healthy controls, with 9.81 ± 8.26% and 13.97 ± 8.91% 

In addition, many of the genera with a relative abundance greater than 1% were significantly different in CFS patients compared to healthy controls. For example, FusobacteriumPrevotellaLeptotrichia, and Campylobacter had increased abundance in CFS patients compared to healthy controls, while HaemophilusPorphyromonas, and Moraxella had decreased abundance in CFS patients compared to healthy controls.

In the past, I have written that oral/mouth bacteria was likely a reserve for ME/CFS bacteria. This study appears to confirm my earlier inference.

Constructing a Mouth Wash Conceptually

This is done by using the Genus Lab report page, The results of which summarize below (Some are in some commercial alternative mouth washes):

In terms of probiotics, the following should be dissolved in the mouth (capsules open) and not swallowed:

For dissolving in the mouth (not to swallow)

Bottom Line

The above is an a priori formulation based on the report cited above. It is interesting to note that saccharin is a to avoid (and found in some commercial mouth washes)

Self Experiment: Of the above probiotics, Shin Biofermin (JP) /S seemed the best to try — small hard tablets (Normal dosage is 18) that lends itself to dissolving in the mouth. At bedtime, I slowly dissolved two in the mouth. That night I slept the hardest that I have in months. (I have added a link to a world wide shipper on the above probiotic link)

Shin Biofermin, 120 Tablets