Vaccine, COVID, Long COVID

For other analysis of Microbiomes. see Analysis Posts on Long COVID and ME/CFS.


My mom got the AstraZeneca Vaccine last year, after which she didn’t really have any major problems, so later she got her 2nd shot with BionTech/Pfizer. Shortly after she caught Covid. While the course of the disease was very mild, she experienced severe hair loss in the following days, which reverted 6 months later. Also, she started feeling tired fast and could not work anymore (nurse). That was about a year and a half ago.

She developed hypertension after she received the vaccination for COVID

As of now, she still has the same issue with CFS, though it’s gotten better on most days. Some days she gets a crash and doesn’t feel too good. What’s helping her is going outside twice to three times a day for extended walks, and she says when she goes into the pine forest nearby she feels refreshed afterwards.

Her CFS isn’t as severe as my brothers, though it still restricts her from working.\

The Lab used was BiomeSight which ships world wide. An equivalent alternative in the US is Ombre.


I am going to do a pro-forma review, i.e. a process that other can follow easily.

My Profile/Health Analysis

  • Potential Medical Conditions Detected
    • hypertension (High Blood Pressure 78%ile (12 of 35) prevalence 47%, so likely (and confirmed)
    • Acne 48%ile (4 of 20) , prevalence 47% — so very unlikely.
  • Bacteria deemed Unhealthy
  • Dr. Jason Hawrelak Recommendations – 89%ile

Since we have a condition, Long COVID or ME/CFS, we look at:

Looking at balance there was no strong shift to the lower or upper.

This leads to using several filtered sets of suggestions:

Proactive Factors

Going to Medical Conditions with Microbiome Shifts from US National Library of Medicine and sorting by status can be used to look at risks of slipping into additional issues. In this case IBS and SIBO are shown — both are commonly associated with ME/CFS. Coronary artery disease has been associated with COVID (“The risk of heart failure increased by 72%” [2022]). These could be included in building consensus suggestions.


Building Consensus

We use the 2 above and the following

Kegg Computed Probiotics

Escherichia coli is the top one, which agrees with the Alison Hunter Memorial Foundation presentations in 1998. E.Coli does not get reported in 16s reports and hence tends to be ignored in studies :-(.

Other ones included (in amount of contribution to deficient enzymes):

Consensus Report

There was a good long list of items that were suggested by all 5 suggestions sets. A few of note are below:

Avoids included the following:

See attached for details.

Bottom Line

This patient history and their microbiome are in agreement. The antibiotics suggestions (off label usage) matches what has been used by some ME/CFS specialists. Light exercise (within tolerance and without causing Post-exertional malaise (PEM)) has been reported to improve ME/CFS and is often suggested by AI. A reader forwarded me this study on walking in the forest: A comparative study of the physiological and psychological effects of forest bathing (Shinrin-yoku) on working age people with and without depressive tendencies [2019] and Effect of forest bathing trips on human immune function [2010] which hints that location, location, location is important. It even comes in a COVID presentation factor, Green spaces, especially forest, linked to lower SARS-CoV-2 infection rates: A one-year nationwide study [2022]. As a FYI, I do “Shinrin-yoku” whenever the weather permits with my three corgis.

Working on posts

I explicitly checked against the new list of Bacteria Triggering Coagulation and Micro clots, and they were none at over 75%ile; so coagulation is unlikely to part of the situation. I view coagulation as a potential feedback loop to keep CFS/Long COVID going. The coagulation drops oxygen levels which encourages the growth of bacteria that produces coagulation – a nasty feed back loop.

Postscript – 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 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.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Microbiome and Age

Some people may ask “Does anyone know the list of gut microbes does ameliorate aging?” looking for a magazine of magical bullets to reverse aging. Unfortunately, there are far more bacteria then will fit into a magazine.

From Literature we can see the main genus involved. Remember every genus has many species. Every species have many strains.

From The gut microbiome as a modulator of healthy ageing [2022]
  • “The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up.” Gut microbiome pattern reflects healthy ageing and predicts survival in humans[2021]

This data has been added to Medical Conditions with Microbiome Shifts from US National Library of Medicine. With the following generic suggestions, which is also available to be tuned to your specific uploaded microbiome.

While we have over 4000 samples, most of the samples are from people dealing with health issues. The average number of matches for each age group (when given) is shown below. If your own values is significantly above the number under Matches, you should have some concerns. We do see the number increases around 70.

LabAge RangeMatches
OmbreLabAge: 30-405.1
OmbreLabAge: 40-504.8
OmbreLabAge: 50-604.7
OmbreLabAge: 60-705.2
OmbreLabAge: 70-806.7
BiomeSightAge: 30-404.8
BiomeSightAge: 40-504.5
BiomeSightAge: 50-604.6
BiomeSightAge: 60-704.2
BiomeSightAge: 70-803.9
BiomeSightAge: 80-907

I am 70, and decided to look at the last few years of samples. I noticed a blimp with a relapse of ME/CFS which slowly declined with remission.

Sample DateMatchesComment
October 20, 20195
December 6, 20197ME/CFS Relapse
December 13, 20197
February 23, 20205
October 29, 20206
July 27, 20216
September 9, 20214
January 24, 20224
May 23, 20224
September 18, 20225
December 1, 20225
Using OmbreLab tests

Using BiomeSight processing (which allows my earlier ubiome data to be added). We see the unhealthy spike with ME/CFS

Sample DateMatches
November 6, 20173
March 16, 20185Work Stress
March 19, 20197ME/CFS Flare
April 9, 20197
February 23, 20205
November 17, 20204
September 9, 20216
January 24, 20224

REMEMBER: Quality of processing of samples can vary greatly. The above should be taken with 0.1 grams of NaCl.

Example of Getting Suggestions

I used Microbiome Prescription site to identify these 4/5 and get suggestions. First, note that different labs detect things differently (See The taxonomy nightmare before Christmas…). The bacteria selections done below are based on the percentile ranking (> 75%ile or < 25%ile) of other lab results from the same lab.

Top Suggestions

What we see is that 5+4 = 8 bacteria of concern — only Enterobacteriaceae was shared between labs.

I then went over to Multiple Samples Tab and looked at the multiple sample Consensus

With the results shown below

The last two are interesting, with the consequence being a shift from chicken to using beef (and with likely smaller portions).

Bottom Line

As shown above, I would recommend getting your FASTQ files processed by both OmbreLab and BiomeSight … a continuing part of The taxonomy nightmare before Christmas… Then do both through this system and getting a Consensus report across samples.

Bacteria Triggering Coagulation and Micro clots

The question of which bacteria may induce coagulation issues and micro clots with Myalgic encephalomyelitis/chronic fatigue syndrome and Long COVID has been an interest for many years (pre-COVID). This week I started digging (again) and this time we got sufficient information to do a sharing post.

Blood coagulation often accompanies bacterial infections and sepsis and is generally accepted as a consequence of immune responses. Though many bacterial species can directly activate individual coagulation factors, they have not been shown to directly initiate the coagulation cascade that precedes clot formation. Here we demonstrated, using microfluidics and surface patterning, that the spatial localization of bacteria substantially affects coagulation of human and mouse blood and plasma. Bacillus cereus and Bacillus anthracis, the anthrax-causing pathogen, directly initiated coagulation of blood in minutes when bacterial cells were clustered.

Spatial localization of bacteria controls coagulation of human blood by ‘quorum acting‘ [2008]

In Gut Microbiota and Coronary Plaque Characteristics [2022] we actually get some names:

  • Paraprevotella had a positive correlation with fibrinogen
  • Succinatimonas had positive correlations with fibrinogen and homocysteine
  • Bacillus had positive correlations with fibrinogen and high-sensitivity C-reactive protein
  • ParaprevotellaSuccinatimonas, and Bacillus were also associated with greater plaque volume

Helicobacter pylori, Chlamydia pneumoniae, Mycoplasma pneumoniae, Haemophilus influenzae, Streptococcus pneumoniae, Staphylococcus aureus, Streptococcus pyogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae, Bartonella henselae and Escherichia coli, causing infections may increase the risk of thrombotic complications through platelet activation or may lead to an inflammatory reaction involving the fibrinolytic system. Acinetobacter, Burkholderia pseudomallei [2020]

“The found slight increases in FVIII:C and CRP levels might support the hypothesis that a vancomycin-induced gram-negative shift in the gut microbiome could induce increased systemic inflammation and thereby a procoagulant state.” [2021]

Porphyromonas gingivalis initiates coagulation and secretes polyphosphates – A mechanism for sustaining chronic inflammation? [2022]

“significantly abundant genera were observed in the coronary thrombus in the patients: Escherichia, 1.25%; Parabacteroides, 0.25%; Christensenella, 0.0%; and Bacteroides, 7.48%. ” [2020]

Bottom Line

I have added this data to the Medical Conditions with Microbiome Shifts from US National Library of Medicine page.

Cross Validation

This means do prediction agree with reasonable expectation.

Looking at the suggestions, they appear to be full of items connected to ME/CFS or to blood thinning

The artificial intelligence producing these suggestions knows nothing about coagulation, it made these suggestions to solely reduce the bacteria identified above. Bacteria which may cause coagulation.

We would expect more matches for high bacteria levels (defined as > 75%ile) of the bacteria identified above with people with Long COVID and people with ME/CFS. This appears to be shown in the data. The reason that exogene has a very high number is that it reports on all of the candidate bacteria — which is not the case for 16s tests. Second, we see post-COVID people with full recovery having less matches then the combination of samples which includes those that provided no information (and which would likely contain some Long COVID and ME/CFS people)

Condition ReportedLabReportedNot Reported
Fully Recovered from COVID (No Long Covid)BiomeSight2.28
American Gut5.743.10
Filtered to sufficient samples. Numbers above are based on the number of matches found

The list of bacteria above is known to be incomplete but the above results does suggest at least a partial identification of the bacteria responsible for coagulation and micro clots.

This post from 2015, may be of interest to people with coagulation / micro-clots issues: Coagulation: Thick Blood Supplements for CFS and Long COVID

Supplements to avoid before bed…

This is based on bacteria identified in Sleep and the Microbiome – Some Notes. Bacteria level shifts through the day and you do not want to feed the bacteria that are associated with sleep issues. This is theoretical lists that ignores the magnitude of shifts.

To Avoid Before Bed

  • arabinoxylan oligosaccharides (prebiotic)
  • bacillus subtilis (probiotics)
  • berberine
  • bifidobacterium longum (probiotics)
  • bile (acid/salts)
  • Burdock Root
  • Fisetin
  • ginger
  • glycine
  • inulin (prebiotic)
  • iron
  • lactobacillus casei (probiotics)
  • lactobacillus reuteri (probiotics)
  • lactobacillus rhamnosus gg (probiotics)
  • omega-3 fatty acids
  • saccharomyces boulardii (probiotics)
  • salt (sodium chloride)
  • Slippery Elm
  • sodium butyrate
  • vitamin d
  • walnuts
  • wheat
From Social Media
From Social Media

Fine to take

These items will have a reducing impact on at least one of the bacteria. Items in bold has the highest impact.

  • Arbutin (polyphenol)
  • bacillus amyloliquefaciens (probiotic)
  • bacillus coagulans (probiotics)
  • Baking Soda (Sodium Bicarbonate)
  • bentonite
  • Caffeine
  • camelina seed
  • cannabinoids
  • chitooligosaccharides (prebiotic)
  • diosmin,(polyphenol)
  • extra virgin olive oil
  • galacto-oligosaccharides (prebiotic)
  • Hesperidin (polyphenol)
  • l-glutamine
  • linseed(flaxseed)
  • luteolin (flavonoid)
  • melatonin supplement
  • N-Acetyl Cysteine (NAC),
  • pyridoxine hydrochloride (vitamin B6)
  • quercetin
  • resveratrol (grape seed/polyphenols/red wine)
  • sodium stearoyl lactylate
  • thiamine hydrochloride (vitamin B1)
  • Vitamin B-12
  • vitamin b3 (niacin)
  • vitamin b7 biotin (supplement) (vitamin B7)
  • Vitamin C (ascorbic acid)
  • xylan (prebiotic)

So we have melatonin supplement, camelina seed and a glass of red wine to take with some B-vitamins at bed time!

Sleep and the Microbiome – Some Notes

A special edition blog for the sleepless… Many studies are looking at the microbiome with co-morbid conditions — making conclusions difficult.

  • “Growing evidence suggests bi-directional links between gut microbiota and sleep quality as shared contributors to health.” [2023]
  • “Contrary to expectations, timed feeding rendered animals more sensitive to stress” [2023] — so eating by the clock and not the light impacts stress negatively.
  • “In older adults, shorter sleep duration is associated with an increase in pro-inflammatory bacteria whereas increasing sleep quality is positively associated with an increase of beneficial Verrucomicrobia and Lentisphaerae phyla.” [2022]
  • Lachnoclostridium (genus) correlates positively with sleep efficiency, Blautia (genus) correlates negatively [2022]
  • “several taxa (LachnospiraceaeCorynebacterium, and Blautia) were negatively correlated with sleep measures” [2017]
  • Blautia and Eubacterium hallii were microbe markers in the sleep-disordered population” [2022]
  • “Relative abundances of Streptococcus salivarius and Veillonella were independent predictors of sleep disturbances in MHE patients” [2022]
  • “class Mollicutes in subjects with poor sleep quality were lower than in the healthy individuals. [2022]
  • “The relative abundance of Sutterella was significantly lower (0.38% vs. 1.25%) and that of Pseudomonas was significantly higher (0.14% vs. 0.08%) in short sleepers than in normal sleepers” [2021]

For what reduces Blautia click here (melatonin supplement and camelina seed). Click here for Lachnoclostridium list (also includes melatonin supplement and camelina seed). Since populations change during the day (See Changing your Microbiome Results by when you take your sample!) you want to avoid substances that FEED these bacteria likely 4+ hours before bed. For list of items, see Supplements to avoid before bed…

Shift them to the morning times

Gut microbiome diversity is associated with sleep physiology in humans [2019]

“men with poor sleep (PSQI >5) tended to have lower alpha-diversity compared to men with normal sleep (Faith’s PD, beta= -0.15; 95% CI:-0.30-0.01, p=0.06). Sleep regularity was significantly associated with  robust Aitchison distances (RPCA) and (phylogenetic-RPCA) PRPCA, even after adjusting for site, batch, age, ethnicity, body mass index, diabetes, antidepressant and sleep medication use, and health behaviors”

  • the top 5 positively associated with sleep regularity were Faecalibacterium prausnitzii G, OEMS01 sp0900199405, Oscillibacter valericigenes, Faecalibacterium prausnitzii A, and Faecalibacterium prausnitzii C.
  • [Poorer sleep] associated with Ruthenibacterium lactatiformans, Bacteroides uniformis, Alistipes putredinis, and Escherichia dysenteriae
Association of subjective and objective measures of sleep with gut microbiota composition and diversity in older men: The Osteoporotic Fractures in Men (MrOS) study [2023]
Gut microbiota alterations in response to sleep length among African-origin adults [2021]

Many Probiotics have some effect

My personal experience is that for most probiotics, taking just before bedtime helps with sleep. I say most — because a few of them will actually cause issues with falling a sleep.
If you have single strains probiotics, you may wish to experiment with the impact of individual strains. Take one strain consistently at bed time, with a significant dosage, for a few days to see the impact (if any). One’s that cause wakefulness, may be ones you should take in the morning.

Ken Lassesen

Bacteria are very very rarely bad or good

A reader messaged me this

hello I do not want to bother, I have a question in the laboratories of my country, in the microbiota tests they put veillonella as virulent, but in a recent publication of microbiome prescription I saw that it could be a solution, why do the laboratories attribute virulence to it?

My Answer

That is equivalent to saying “Italians are criminals”. Why would someone say that? “Some Italians belong to the Mafia”

Veillonella is a genus of gram-negative, anaerobic bacteria that are commonly found in the human oral cavity, gastrointestinal tract, and respiratory tract. While some strains of Veillonella can cause infections, particularly in individuals with compromised immune systems, the majority of strains are considered to be non-virulent or opportunistic pathogens. Some studies have suggested that Veillonella may play a role in certain disease states, such as periodontal disease, but more research is needed to fully understand the potential pathogenic mechanisms of this genus.


For a lab to creditably state that, the lab would need to identify the specific strain. Veillonella is a genus, composed of many species, each species is composed of many strains. In terms of our Italian allergy, Italians come from many regions of Italy (species), within each regions are many families (strains). There may be some of these families that tend to being Mafia, others may tend to be priests (and eventually Popes).


My attitude is that Yin and yang is a better way of viewing bacteria. Bacteria are out of balance. Too many poor people results in high crime rates (out of desperation), Too many rich people results in low class mobility (the only people that get ahead are their friends, “old school ties”). The “right balance” for a well functioning society varies by country — for example, Iceland versus Haiti. Similarly, your DNA and diet influences what the right balance should be.

This family’s favorite and most effective probiotic is Mutaflor, an Escherichia coli probiotic. All E.Coli is not bad, trying to eliminate all E.Coli is likely a very dumb choice.

Changing your Microbiome Results by when you take your sample!

While working on a different blog post, I came across this study with a nice collection of charts to illustrate the importance of taking samples at the same time of day! It also makes implication that microbiome testing firms should be asking for the time of day that samples was taken (and provide gender, age and time of sample reference ranges — if they want to be creditable)

Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock [2015]

Showing Gender Differences (M, F)
Differences with Sleep Issues (BMal1 Circadian Clock Protein)

See The Circadian Clock Protein BMAL1 Acts as a Metabolic Sensor In Macrophages to Control the Production of Pro IL-1β [2021] for more information on BMAL1

Bottom Line

I have often compared the microbiome to a city. If you do a opinion survey, when in the city you take the survey and the time of day has a huge impact. Taking it at 7am in the morning, you will be bias for office workers. At 2pm, likely female shoppers. At 4am likely make shift workers.

Interesting Successful Clinical Trial for Long COVID and ?ME/CFS ?

A key take-away is the importance of bacteria that triggers coagulation and inflammation.

A reader forwarded me this link,

A Randomized Controlled Trial of the Efficacy of Systemic Enzymes and Probiotics in the Resolution of Post-COVID Fatigue

The study concludes:

This study demonstrates that a 14 days supplementation of ImmunoSEB + ProbioSEB CSC3 resolves post-COVID-19 fatigue. The proposed supplement regimen significantly reduces the burden of both, physical and mental fatigue and is effective as an early intervention in the recovery of COVID-19 patients, many of whom continue to experience severe fatigue including muscle weakness and “brain fog” several months after initial infection. 

The substances used are very familiar to readers of my CFS Remission blog. They are:

  • Bacillus coagulans LBSC (DSM 17654)
  • Bacillus subtilis PLSSC (ATCC SD 7280)
  • Bacillus clausii 088AE (MCC 0538)
  • Serratiopeptidase,
  • Bromelain,
  • Amylase,
  • Lysozyme,
  • Peptidase,
  • Catalase,
  • Papain,
  • Glucoamylase
  • Lactoferrin

Some quick notes with citations for new readers:

This points back to the research and demonstration done by Dave Berg at Hemex Labs . “Berg and Joseph Brewer studied coagulation in CFS patients and concluded that approximately 85% of chronic fatigue syndrome patients had hypercoagulation, “[src].

My personal experience with the Hemex approach is good and put me into remission in 2000. Objective measurements showed coagulation in some parts of the coagulation cascade with piracetam and heparin being my favorite cocktail (both taken sublingual).

This may be of interest to some: Bacteria Triggering Coagulation and Micro clots


The study ended at two weeks with no ongoing tracking of patients. My observations of ME/CFS people over several decades has been that short term remission is common with a slow regression back to fatigue. An excellent examples are ME/CFS in Australia doing Fecal Matter Transplants with remission within 48 hours and relapse in 4-8 weeks. Just as some bacteria (bacillus cited above) reduces coagulation, other bacteria triggers coagulation. If those triggering bacteria are not adequately suppressed then the fatigue and brain fog will return over time. Think of a leaking dike, you bring in the pumps and remove the water behind the dike, 3 weeks later the water is back — you need to fix the leak in the dike also.

A reader pointed out that the product is available on Amazon for $40. So a cheap experiment to try! If you do try it– please add your experience as a comment on this post.

Condition Progression Using the Microbiome

Today I ran some queries to see how many progressions between the conditions that I have could be inferred from the current data. The following were used:

  • The taxon must be exact matches (no parent of a taxa or taxa children)
  • We count the number of times that the taxa shifts are the same direction, or are different
  • Each condition must share at least 10 bacteria.
  • All of the data (with sources) can be found here for people to dig further into relationships.

The results are below for those with 65% the same or more. Some are very expected, some are not

  • Chronic Fatigue Syndrome with
    • ME/CFS with IBS
    • ME/CFS without IBS
    • ME/CFS with IBS vs ME/CFS without IBS DOES NOT SHOW UP because they only have 5 bacteria in common
  • Small Intestinal Bacterial Overgrowth  (SIBO) was not expected with
    • Colorectal Cancer
    • Rheumatoid Arthritis (RA),Spondyloarthritis (SpA)
    • Parkinson’s Disease
    • But expected with Irritable Bowel Syndrome
    • NOTE: Progression from one condition to another condition may be depend on DNA or epigenetics. If there is a high match up, it should be viewed as increased risk that may be mitigated with adjustments of the microbiome.
Condition NameCondition NameSame Direction PercentageDifferent Direction Percentage
Alzheimer’s diseaseChronic Kidney Disease86.713.3
ADHDChronic Kidney Disease85.714.3
Chronic Fatigue SyndromeME/CFS with IBS85.714.3
Chronic Urticaria (Hives)Obesity85.714.3
Chronic Fatigue SyndromeME/CFS without IBS85.214.8
Chronic Urticaria (Hives)obsessive-compulsive disorder84.615.4
Chronic Urticaria (Hives)Ulcerative colitis83.316.7
Colorectal CancerSmall Intestinal Bacterial Overgrowth  (SIBO)83.316.7
Histamine Issues,Mast Cell Issue, DAO Insufficiencyobsessive-compulsive disorder83.316.7
Brain Traumahypertension (High Blood Pressure83.316.7
Allergic Rhinitis (Hay Fever)Chronic Fatigue Syndrome83.316.7
Ankylosing spondylitisRosacea83.316.7
Parkinson’s DiseaseAnorexia Nervosa83.316.7
rheumatoid arthritis (RA),Spondyloarthritis (SpA)Small Intestinal Bacterial Overgrowth  (SIBO)83.316.7
Brain TraumaMultiple Sclerosis81.818.2
Hyperlipidemia (High Blood Fats)Multiple Sclerosis81.818.2
hypertension (High Blood PressureNonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic81.818.2
Graves’ diseaseBipolar Disorder81.818.2
Chronic Urticaria (Hives)Inflammatory Bowel Disease81.318.8
Stress / posttraumatic stress disorderSystemic Lupus Erythematosus80.819.2
ME/CFS without IBSLong COVID8020
Chronic Kidney DiseaseBipolar Disorder8020
Alzheimer’s diseaseMultiple Sclerosis8020
ADHDobsessive-compulsive disorder78.621.4
gallstone disease (gsd)Ulcerative colitis78.621.4
OsteoarthritisLong COVID78.621.4
Systemic Lupus ErythematosusAnorexia Nervosa77.822.2
ADHDMultiple Sclerosis77.322.7
Chronic Urticaria (Hives)Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic76.923.1
Parkinson’s DiseaseSmall Intestinal Bacterial Overgrowth  (SIBO)76.923.1
Chronic Urticaria (Hives)Long COVID76.523.5
Gastroesophageal reflux disease (Gerd) including Barrett’s esophagusLong COVID76.523.5
Chronic Kidney DiseaseSystemic Lupus Erythematosus76.223.8
hypertension (High Blood Pressureobsessive-compulsive disorder7624
Inflammatory Bowel DiseaseSmall Intestinal Bacterial Overgrowth  (SIBO)7525
Histamine Issues,Mast Cell Issue, DAO InsufficiencySchizophrenia7525
Multiple SclerosisSmall Intestinal Bacterial Overgrowth  (SIBO)7525
Nonalcoholic Fatty Liver Disease  (nafld) NonalcoholicStress / posttraumatic stress disorder7525
Chronic Kidney DiseaseGraves’ disease7525
gallstone disease (gsd)Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic7525
Cerebral PalsyParkinson’s Disease7525
RosaceaType 1 Diabetes7525
Chronic Urticaria (Hives)Crohn’s Disease73.726.3
Chronic Urticaria (Hives)Psoriasis72.727.3
DepressionME/CFS with IBS72.727.3
neuropsychiatric disorders (PANDAS, PANS)Parkinson’s Disease72.727.3
Irritable Bowel SyndromeOsteoarthritis72.727.3
Histamine Issues,Mast Cell Issue, DAO InsufficiencyInflammatory Bowel Disease72.727.3
Chronic Fatigue SyndromeFunctional constipation / chronic idiopathic constipation72.727.3
AsthmaCeliac Disease72.727.3
rheumatoid arthritis (RA),Spondyloarthritis (SpA)Hidradenitis Suppurativa72.727.3
ADHDAlzheimer’s disease72.227.8
Brain TraumaParkinson’s Disease72.227.8
AtherosclerosisHistamine Issues,Mast Cell Issue, DAO Insufficiency7228
Inflammatory Bowel DiseaseUlcerative colitis71.828.2
Chronic Kidney DiseaseStress / posttraumatic stress disorder71.428.6
AtherosclerosisME/CFS with IBS71.428.6
ADHDFunctional constipation / chronic idiopathic constipation71.428.6
Ankylosing spondylitisGastroesophageal reflux disease (Gerd) including Barrett’s esophagus71.428.6
rheumatoid arthritis (RA),Spondyloarthritis (SpA)Rosacea71.428.6
Chronic Kidney DiseaseLong COVID70.629.4
gallstone disease (gsd)rheumatoid arthritis (RA),Spondyloarthritis (SpA)70.629.4
Multiple SclerosisAnorexia Nervosa70.629.4
Inflammatory Bowel DiseaseNonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic70.429.6
hypertension (High Blood PressureInflammatory Bowel Disease7030
Cerebral PalsyLong COVID7030
Chronic Kidney DiseaseCOVID-1969.630.4
Cerebral Palsyobsessive-compulsive disorder69.230.8
Cerebral PalsyPsoriasis69.230.8
Bipolar Disorderobsessive-compulsive disorder69.230.8
ADHDAnorexia Nervosa69.230.8
Alzheimer’s diseaseAnorexia Nervosa69.230.8
Hyperlipidemia (High Blood Fats)rheumatoid arthritis (RA),Spondyloarthritis (SpA)69.230.8
hypertension (High Blood PressureOsteoporosis69.230.8
Inflammatory Bowel DiseaseAnorexia Nervosa69.230.8
Nonalcoholic Fatty Liver Disease  (nafld) NonalcoholicAnorexia Nervosa69.230.8
Nonalcoholic Fatty Liver Disease  (nafld) NonalcoholicUlcerative colitis69.230.8
FibromyalgiaNonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic69.230.8
Chronic Kidney DiseaseNonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic69.230.8
Chronic Kidney DiseaseOsteoporosis69.230.8
Chronic Kidney DiseaseInflammatory Bowel Disease68.831.3
Chronic Kidney DiseaseMultiple Sclerosis68.831.3
gallstone disease (gsd)Irritable Bowel Syndrome68.831.3
ME/CFS with IBSCOVID-1968.831.3
Irritable Bowel SyndromeSmall Intestinal Bacterial Overgrowth  (SIBO)68.831.3
hypertension (High Blood PressureBipolar Disorder68.831.3
Allergic Rhinitis (Hay Fever)Systemic Lupus Erythematosus68.831.3
AtherosclerosisSmall Intestinal Bacterial Overgrowth  (SIBO)68.831.3
Chronic Kidney DiseaseColorectal Cancer68.831.3
Chronic Kidney DiseaseDepression68.431.6
gallstone disease (gsd)COVID-1968.431.6
AtherosclerosisLiver Cirrhosis68.331.7
Ankylosing spondylitisLiver Cirrhosis68.231.8
Celiac DiseaseInflammatory Bowel Disease68.231.8
Functional constipation / chronic idiopathic constipationSchizophrenia68.231.8
Stress / posttraumatic stress disorderUlcerative colitis68.131.9
Ankylosing spondylitisBipolar Disorder67.932.1
Alzheimer’s diseaseParkinson’s Disease67.632.4
Ulcerative colitisLong COVID6733
Osteoarthritisrheumatoid arthritis (RA),Spondyloarthritis (SpA)66.733.3
Alzheimer’s diseaseInsomnia66.733.3
Amyotrophic lateral sclerosis (ALS) Motor NeuronBipolar Disorder66.733.3
Alzheimer’s diseaseBipolar Disorder66.733.3
Anorexia NervosaLong COVID66.733.3
Anorexia Nervosaobsessive-compulsive disorder66.733.3
AsthmaSystemic Lupus Erythematosus66.733.3
ADHDBipolar Disorder66.733.3
Allergic Rhinitis (Hay Fever)Stress / posttraumatic stress disorder66.733.3
ADHDChronic Fatigue Syndrome66.733.3
AcneStress / posttraumatic stress disorder66.733.3
AcneSystemic Lupus Erythematosus66.733.3
AcneLiver Cirrhosis66.733.3
Allergic Rhinitis (Hay Fever)Sjögren syndrome66.733.3
AllergiesType 1 Diabetes66.733.3
Cerebral PalsyAnkylosing spondylitis66.733.3
Chronic Fatigue SyndromeNonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic66.733.3
Cerebral PalsySystemic Lupus Erythematosus66.733.3
Cerebral PalsyInflammatory Bowel Disease66.733.3
Brain TraumaType 2 Diabetes66.733.3
Brain Traumaobsessive-compulsive disorder66.733.3
Brain TraumaIrritable Bowel Syndrome66.733.3
Fibromyalgiaobsessive-compulsive disorder66.733.3
gallstone disease (gsd)Stress / posttraumatic stress disorder66.733.3
Chronic Kidney Diseaseobsessive-compulsive disorder66.733.3
Chronic Urticaria (Hives)Systemic Lupus Erythematosus66.733.3
Crohn’s DiseaseHistamine Issues,Mast Cell Issue, DAO Insufficiency66.733.3
Histamine Issues,Mast Cell Issue, DAO InsufficiencyLiver Cirrhosis66.733.3
InsomniaLong COVID66.733.3
Inflammatory Bowel Diseaseobsessive-compulsive disorder66.733.3
Inflammatory Bowel DiseaseOsteoarthritis66.733.3
Inflammatory Bowel DiseaseME/CFS without IBS66.733.3
ME/CFS with IBSUlcerative colitis66.733.3
ME/CFS without IBSCOVID-1966.733.3
Liver Cirrhosisneuropsychiatric disorders (PANDAS, PANS)66.733.3
Liver CirrhosisME/CFS with IBS66.733.3
Multiple SclerosisMultiple system atrophy (MSA)66.733.3
Liver CirrhosisStress / posttraumatic stress disorder6634
Inflammatory Bowel Diseaserheumatoid arthritis (RA),Spondyloarthritis (SpA)65.934.1
Celiac Diseaseobsessive-compulsive disorder65.734.3
Celiac DiseaseSystemic Lupus Erythematosus65.634.4
Chronic Fatigue SyndromeSchizophrenia65.534.5
hypertension (High Blood PressureCOVID-1965.534.5
ADHDParkinson’s Disease65.434.6
Sjögren syndromeSystemic Lupus Erythematosus65.434.6
Stress / posttraumatic stress disorderobsessive-compulsive disorder65.234.8
rheumatoid arthritis (RA),Spondyloarthritis (SpA)Bipolar Disorder65.234.8
Chronic Fatigue SyndromeParkinson’s Disease65.234.8
AutismBrain Trauma65.234.8
Multiple SclerosisParkinson’s Disease65.234.8
AtherosclerosisIgA nephropathy (IgAN)6535
Chronic Kidney Diseaserheumatoid arthritis (RA),Spondyloarthritis (SpA)6535
Alzheimer’s diseaseGraves’ disease6535

The problem with “official” ranges from labs

Ranges are created by labs to be able to give answers to people asking for them. The key word is created. They may have no actually be healthy ranges for your age, gender, diet style etc. Say again! Not actually healthy ranges for you.

At the highest levels of the bacteria are phylums:  (Firmicutes and Bacteroidetes). Almost every bacteria belongs to one of these two phylums. Almost every person in the US would be unhealthy by Indian Standards — well outside of the typical ranges. And almost every person in the India would be unhealthy by US Standards — well outside of the typical ranges. If you are of Indian descent living in the U.S. and eating a mixture of Indian and Western foods… any ideas of what you healthy range should be?

The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? [2020]

The classic approach in most labs for other tests (like Vitamin D, iron, etc) is to get a collection of apparently healthy individuals from physically around where the lab is and the assume that the data will be a bell curve/normal distribution. The people are typically self-declared to be healthy – for Americans, this will usually be high in people that have a high body-mass index [BI] (i.e. overweight). We know that a high BI causes changes in the microbiome…. From that data, compute the range — see typical instructions to labs here:  Standard Lab Ranges (+/- 2 Standard Deviations). This assumption is never validated statistically on the data – lack of appropriate skills in the lab is a common cause. If you attempt to validate against almost any bacteria in the microbiome — it will fail, often extremely fail.

Research scientists knows that this is making a huge assumption and will often in their research papers use a method called Box Plot Whisker. It is definitely better but typically require more samples to establish the ranges. A lab manager will opt not to do it when he may only need to do 30 samples to get the Standard Lab Ranges, and may need 150 samples to get a good Box Plot Whisker. Why should he want to increase costs when he can go cheap and claim that he is following standard processes.

Wait! There is More!

Suppose that you get 200 “healthy samples” — we can get the ranges using Box Plot Whisker and that’s it! We now know what healthy ranges are then!!!

WRONG!!! VERY WRONG!!! The National Institute of Standards and Technology (the same people who define how long a foot is, or how many lumens a light bulb has) has made if very clear!!!!

If we have 200 samples, we will likely have 97 different ranges!!


Some of the ranges from different ways will be in significant contrast with each. To illustrate this, let us look at samples uploaded from OmbreLabs and Biomesight — they both use the same physical lab that has the same equipment — the difference is the software (“the ways”) that they use on the identically same data file!!!! We are NOT talking about two samples from the same stool; we are talking about one sample only

LabBacteria/Taxa Types
BiomeSight 4193
All consumes the same FASTQ raw data — the difference is the software they use

Looking at frequency of detection, we have some good matches at the genus level

% Detected
% Detected
Average %
Average %

And some bad ones!

% Detected
% Detected
Average %
Average %

Whose right? Both are right and both are wrong — there is no standard!!!! Right assumes a shared upon norm or consensus by people concerned.

What is my personal solution?

I am by academics and industrial experience, a statistician, operational research and Artificial Intelligence Software Engineer. The way to get the most probable solution from a difference of opinions, is to build a consensus model — take every ones suggestions and combined them!

At present I have good number of opinions that can be used, and if I get more expert opinions (and permission to use them) I will gladly add them.

I would love to see all of the labs make public the data they used to construct their ranges. Open data. I have discussed that with some of them and they deem it to be “proprietary” data. It is, in that the disclosure may reveal their mistakes and expose their ranges as questionable. Every one’s ranges are questionable (IMHO).

There is no right answer. There is no trustworthy range. A consensus answer is likely a good answer, the best that is available at the moment.