See also:

• The taxonomy nightmare before Christmas….
• The taxonomy nightmare before Christmas… Episode III

Several readers have emailed this article (or news story on it) Current microbiome analyses may falsely detect species that are not actually present. or The virtual microbiome: A computational framework to evaluate microbiome analyses. This is not a surprise, it was reported earlier

Common approaches to analyzing DNA from a community of microbes, called a microbiome, can yield erroneous results, in large part due to the incomplete databases used to identify microbial DNA sequences.

The process is equivalent to naming a person’s last name from a random DNA sample of a person.

 To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information and optimizing the use of this information.

The analogy of “The process is equivalent to naming a person’s last name from a random DNA sample of a person” is a good description of the issue. If you get more people DNA is the database, the odds of correctly identifying the person’s birth last name increases…. naming the bacteria species or strain has the same issue.

For the purposes of Microbiome Prescription, it is not a significant factor because the Artificial Intelligence is based on odds and probability (just like finding the name). For a human, you may identify that it is likely a Norwegian or Dane and thus the last name likely ends with a “sen” with 4.6% odds of being a Jensen (see more here). It is significant if your ideology requires absolute answers.

The script of the first Episode from 2019 is repeated below.

The taxonomy nightmare before Christmas…

This post is intended to educate people more on the technical aspects of the microbiome. I am not talking about taking 4 samples from one stool and sending it to 4 different testing company. I am talking about one sample sent to one testing company which then provided their analysis and a FASTQ file. The raw data.

What is a FASTQ file (besides being megabytes big)? It is the DNA (technically the RNA) of the bacteria in the stool. It looks like this (using the 4 letters that DNA has):

CCGGACTACACGGGTTTCTAATCCTGTTTGATACCCACTCTTTCGAGCATCAGTGTCAGTTGCAGTCCAGTGAGCAGCCTTCGCAATCGGAGTTCATCGTTATATCTAAGCATTTCACCGCTACACAACGAATTCCGCACACCTCTA

The file that I am using as text would be around 16 megabytes. This data comes from a lab machine. The company then processes it through their software to match up sequences to bacteria.

In this post, I am using the FASTQ from uBiome and getting reports on the bacteria from:

• ubiome
• thryve inside
• biomesight
• sequentia biotech.

Naively, one would expect almost identical results. What I got is shown in detail below. At a high level we had the following taxa counts reported

• ubiome – 253
• thryve inside – 632
• biomesight – 558
• sequentia biotech 366

I did a more technical post on my other blog. From some providers, a taxonomy may be 40% on another 2% or even none… ugly!

The headaches!

Number One Issue: You cannot, repeat cannot, compare a taxonomy report from one lab with another. EVER!

• I have 8 uBiome reports and 2 Thryve reports. I can compare the uBiome to each other and the Thryve to each other. I can never mix their direct taxonomy reports !

Number Two Issue: If I wish to compare different lab reports, I MUST obtain the FastQ files from each lab and process them thru the same provider. The FastQ files are the raw data! For me, I prefer to push them through multiple providers which means that the 10 reports suddenly become 40 or 50 different reports in my site.

• This means a lot more work for the typical user. It also means that guidance, like that from Jason Hawrelak Criteria for Healthy Gut, would need to be revised to be provider specific!

For more details with examples, see The problem with “official” ranges from labs

My Headaches

I have revised my site to show data by specific provider (while keeping the across all provider data still available). A lot of pages to revise and test.

Share this:

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

Backstory

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.

Analysis

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
  • Prevotella copri – 86%ile,, 20% of the microbiome
• Dr. Jason Hawrelak Recommendations – 89%ile
  • Low Bifidobacterium
  • Low Akkermansia
  • Low Lactobacillus
  • Low Roseburia
  • High Bacteroidia

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

• PubMed data first ( After logging in, Go to https://microbiomeprescription.com/Library/PubMed pick the sample OR see bottom of Changing Microbiome tab)
  • Long COVID – 3%ile
  • ME/CFS without IBS – 82%ile
  • ME/CFS with IBS 53%ile
  • Chronic Fatigue Syndrome 43%ile
• Citizen Science
  • ME/CFS without IBS ( 544 candidate bacteria) with highest z-score of 11.1
  • ME/CFS with IBS ( 440 candidate bacteria) with highest z-score of 9.5
  • COVID19 (Long Hauler) ( 201 candidate bacteria) with highest z-score of 10.5

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

This leads to using several filtered sets of suggestions:

• PubMed: ME/CFS without IBS
• Citizen Science: ME/CFS without IBS ( 544 candidate bacteria) with highest z-score of 11.1

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.

Suggestions

• Antibiotics List for MDs produced a list of many antibiotics often prescribed for ME/CFS by some specialist ✅, including:
  • macrolide ((antibiotic)s) ✅
  • azithromycin,(antibiotic)s ✅
  • doxycycline (antibiotic)s ✅
  • penicillin-moxalactam (antibiotic)s
  • I strongly favor Dr. Cecile Jadin approach which would be do each month a course (7-10 days) of one of the above and then move on to the next one in the following month. (See Manly Conference February 1999)

Building Consensus

We use the 2 above and the following

• Standard Lab Ranges (+/- 2 Std Dev)
• Box Plot Whisker
• Kaltoft-Moltrup Normal Ranges

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):

• Brevibacillus brevis
• Bacillus licheniformis
• Bacillus subtilis
• Bacillus subtilis subsp. natto
• Akkermansia muciniphila
• Clostridium butyricum
• Lactiplantibacillus plantarum
• Enterococcus faecalis

Consensus Report

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

• raffinose(sugar beet)
• Apple
• Slippery Elm
• fructo-oligosaccharides (prebiotic)
• Lard
• bifidobacterium (probiotics)
• palm kernel meal
• almonds/ almond skins
• zinc
• jerusalem artichoke (prebiotic)
• resveratrol (grape seed/polyphenols/red wine)

Avoids included the following:

• oregano (origanum vulgare, oil) |
• lactobacillus salivarius (probiotics)
• neem , tulsi
• high carbohydrate diet
• Baking Soda (Sodium Bicarbonate)
• chicken

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.

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.

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.

• “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.

Lab	Age Range	Matches
OmbreLab	Age: 30-40	5.1
OmbreLab	Age: 40-50	4.8
OmbreLab	Age: 50-60	4.7
OmbreLab	Age: 60-70	5.2
OmbreLab	Age: 70-80	6.7
BiomeSight	Age: 30-40	4.8
BiomeSight	Age: 40-50	4.5
BiomeSight	Age: 50-60	4.6
BiomeSight	Age: 60-70	4.2
BiomeSight	Age: 70-80	3.9
BiomeSight	Age: 80-90	7

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 Date	Matches	Comment
October 20, 2019	5
December 6, 2019	7	ME/CFS Relapse
December 13, 2019	7
February 23, 2020	5
October 29, 2020	6
July 27, 2021	6
September 9, 2021	4
January 24, 2022	4
May 23, 2022	4
September 18, 2022	5
December 1, 2022	5

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

Sample Date	Matches
November 6, 2017	3
March 16, 2018	5	Work Stress
March 19, 2019	7	ME/CFS Flare
April 9, 2019	7
February 23, 2020	5
November 17, 2020	4
September 9, 2021	6
January 24, 2022	4

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.

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.

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
• Paraprevotella, Succinatimonas, 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

• Curcumin, hemostasis, thrombosis, and coagulation [2018] “support the positive activity of curcumin in hemostasis, anticoagulation, and fibrinolysis.”
• Consumption of a garlic clove a day could be beneficial in preventing thrombosis [1995]
• Evaluation of thiamine as adjunctive therapy in COVID-19 critically ill patients: a two-center propensity score matched study [2021] “it was associated with a lower incidence of thrombosis.”
• Antiatherothrombotic effects of nicotinic acid [2023] –
• Vitamin B12 And Folic Acid Reduce The Risk Of Blood Clots In The Brain [2005]

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 Reported	Lab	Reported	Not Reported
COVID	BiomeSight	2.44	2.23
Fully Recovered from COVID (No Long Covid)	BiomeSight	2.28
ME/CFS	es-xenogene	6	3.25
	SequentiaBiotech	2.5	1.3
	OmbreLabs	2.08	1.94
	American Gut	5.74	3.10
	BiomeSight	2.29	2.27
	uBiome	1.54	1.51

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

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

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!

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 (Lachnospiraceae, Corynebacterium, 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

“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]

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

• Improvements in sleep indices during exam stress due to consumption of a Bifidobacterium longum. [2020]
• Probiotics, prebiotics and postbiotics for better sleep quality: a narrative review [2022]
• The gut microbiome as a target for adjuvant therapy in insomnia disorder. [2022]
• Effects of Lactobacillus plantarum PS128 on Depressive Symptoms and Sleep Quality in Self-Reported Insomniacs: A Randomized, Double-Blind, Placebo-Controlled Pilot Trial. [2021]
• Effects of Probiotic NVP-1704 on Mental Health and Sleep in Healthy Adults: An 8-Week Randomized, Double-Blind, Placebo-Controlled Trial. [2021]
• Effects of Supplementation with Lactobacillus Probiotics on Insomnia Treatment. [2021]
• Para-psychobiotic Lactobacillus gasseri CP2305 ameliorates stress-related symptoms and sleep quality. [2017]

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.

From https://chat.openai.com/chat

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).

Yin-Yang

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.

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]

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.

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:

• “Serratiopeptidase is a proteolytic enzyme that has been used for reducing inflammation, it has antiedemic, analgesic, fibrinolytic and caesinolytic properties.” [2016]
• Bromelain influences blood coagulation by increasing the serum fibrinolytic ability and by inhibiting the synthesis of fibrin, a protein involved in blood clotting [2012]
• Many strains of Bacillus are fibrinolytic
  • A strain, Bacillus subtilis LD-8547 produced DFE with high fibrinolytic activity has been isolated in our lab previously. [2012]
  • Fibrinolytic characteristics of Bacillus subtilis G8 isolated from natto [2021]
  • Bacillus coagulans with fibrinolytic activity [2021]

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

Reservations

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.

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 Name	Condition Name	Same Direction Percentage	Different Direction Percentage
Alzheimer’s disease	Chronic Kidney Disease	86.7	13.3
ADHD	Chronic Kidney Disease	85.7	14.3
Chronic Fatigue Syndrome	ME/CFS with IBS	85.7	14.3
Chronic Urticaria (Hives)	Obesity	85.7	14.3
Chronic Fatigue Syndrome	ME/CFS without IBS	85.2	14.8
Chronic Urticaria (Hives)	obsessive-compulsive disorder	84.6	15.4
Chronic Urticaria (Hives)	Ulcerative colitis	83.3	16.7
Colorectal Cancer	Small Intestinal Bacterial Overgrowth  (SIBO)	83.3	16.7
Histamine Issues,Mast Cell Issue, DAO Insufficiency	obsessive-compulsive disorder	83.3	16.7
Brain Trauma	hypertension (High Blood Pressure	83.3	16.7
Allergic Rhinitis (Hay Fever)	Chronic Fatigue Syndrome	83.3	16.7
Ankylosing spondylitis	Rosacea	83.3	16.7
Parkinson’s Disease	Anorexia Nervosa	83.3	16.7
rheumatoid arthritis (RA),Spondyloarthritis (SpA)	Small Intestinal Bacterial Overgrowth  (SIBO)	83.3	16.7
Brain Trauma	Multiple Sclerosis	81.8	18.2
Hyperlipidemia (High Blood Fats)	Multiple Sclerosis	81.8	18.2
hypertension (High Blood Pressure	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	81.8	18.2
Graves’ disease	Bipolar Disorder	81.8	18.2
Chronic Urticaria (Hives)	Inflammatory Bowel Disease	81.3	18.8
Stress / posttraumatic stress disorder	Systemic Lupus Erythematosus	80.8	19.2
ME/CFS without IBS	Long COVID	80	20
Chronic Kidney Disease	Bipolar Disorder	80	20
Alzheimer’s disease	Multiple Sclerosis	80	20
ADHD	obsessive-compulsive disorder	78.6	21.4
gallstone disease (gsd)	Ulcerative colitis	78.6	21.4
Osteoarthritis	Long COVID	78.6	21.4
Systemic Lupus Erythematosus	Anorexia Nervosa	77.8	22.2
ADHD	Multiple Sclerosis	77.3	22.7
Chronic Urticaria (Hives)	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	76.9	23.1
Parkinson’s Disease	Small Intestinal Bacterial Overgrowth  (SIBO)	76.9	23.1
Chronic Urticaria (Hives)	Long COVID	76.5	23.5
Gastroesophageal reflux disease (Gerd) including Barrett’s esophagus	Long COVID	76.5	23.5
Chronic Kidney Disease	Systemic Lupus Erythematosus	76.2	23.8
hypertension (High Blood Pressure	obsessive-compulsive disorder	76	24
Inflammatory Bowel Disease	Small Intestinal Bacterial Overgrowth  (SIBO)	75	25
Histamine Issues,Mast Cell Issue, DAO Insufficiency	Schizophrenia	75	25
Multiple Sclerosis	Small Intestinal Bacterial Overgrowth  (SIBO)	75	25
Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	Stress / posttraumatic stress disorder	75	25
Chronic Kidney Disease	Graves’ disease	75	25
gallstone disease (gsd)	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	75	25
Cerebral Palsy	Parkinson’s Disease	75	25
Rosacea	Type 1 Diabetes	75	25
Chronic Urticaria (Hives)	Crohn’s Disease	73.7	26.3
Chronic Urticaria (Hives)	Psoriasis	72.7	27.3
Depression	ME/CFS with IBS	72.7	27.3
neuropsychiatric disorders (PANDAS, PANS)	Parkinson’s Disease	72.7	27.3
Irritable Bowel Syndrome	Osteoarthritis	72.7	27.3
Histamine Issues,Mast Cell Issue, DAO Insufficiency	Inflammatory Bowel Disease	72.7	27.3
Chronic Fatigue Syndrome	Functional constipation / chronic idiopathic constipation	72.7	27.3
Asthma	Celiac Disease	72.7	27.3
rheumatoid arthritis (RA),Spondyloarthritis (SpA)	Hidradenitis Suppurativa	72.7	27.3
ADHD	Alzheimer’s disease	72.2	27.8
Allergies	Psoriasis	72.2	27.8
Brain Trauma	Parkinson’s Disease	72.2	27.8
Depression	Fibromyalgia	72.2	27.8
Atherosclerosis	Histamine Issues,Mast Cell Issue, DAO Insufficiency	72	28
Inflammatory Bowel Disease	Ulcerative colitis	71.8	28.2
Chronic Kidney Disease	Stress / posttraumatic stress disorder	71.4	28.6
Atherosclerosis	ME/CFS with IBS	71.4	28.6
ADHD	Functional constipation / chronic idiopathic constipation	71.4	28.6
Ankylosing spondylitis	Gastroesophageal reflux disease (Gerd) including Barrett’s esophagus	71.4	28.6
rheumatoid arthritis (RA),Spondyloarthritis (SpA)	Rosacea	71.4	28.6
Chronic Kidney Disease	Long COVID	70.6	29.4
gallstone disease (gsd)	rheumatoid arthritis (RA),Spondyloarthritis (SpA)	70.6	29.4
Multiple Sclerosis	Anorexia Nervosa	70.6	29.4
Inflammatory Bowel Disease	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	70.4	29.6
hypertension (High Blood Pressure	Inflammatory Bowel Disease	70	30
Cerebral Palsy	Long COVID	70	30
Chronic Kidney Disease	COVID-19	69.6	30.4
Cerebral Palsy	obsessive-compulsive disorder	69.2	30.8
Cerebral Palsy	Psoriasis	69.2	30.8
Atherosclerosis	Osteoarthritis	69.2	30.8
Bipolar Disorder	obsessive-compulsive disorder	69.2	30.8
ADHD	Anorexia Nervosa	69.2	30.8
Alzheimer’s disease	Anorexia Nervosa	69.2	30.8
Hyperlipidemia (High Blood Fats)	rheumatoid arthritis (RA),Spondyloarthritis (SpA)	69.2	30.8
hypertension (High Blood Pressure	Osteoporosis	69.2	30.8
Inflammatory Bowel Disease	Anorexia Nervosa	69.2	30.8
Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	Anorexia Nervosa	69.2	30.8
Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	Ulcerative colitis	69.2	30.8
Fibromyalgia	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	69.2	30.8
Chronic Kidney Disease	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	69.2	30.8
Chronic Kidney Disease	Osteoporosis	69.2	30.8
Chronic Kidney Disease	Inflammatory Bowel Disease	68.8	31.3
Chronic Kidney Disease	Multiple Sclerosis	68.8	31.3
gallstone disease (gsd)	Irritable Bowel Syndrome	68.8	31.3
ME/CFS with IBS	COVID-19	68.8	31.3
Irritable Bowel Syndrome	Small Intestinal Bacterial Overgrowth  (SIBO)	68.8	31.3
hypertension (High Blood Pressure	Bipolar Disorder	68.8	31.3
Allergic Rhinitis (Hay Fever)	Systemic Lupus Erythematosus	68.8	31.3
Atherosclerosis	Small Intestinal Bacterial Overgrowth  (SIBO)	68.8	31.3
Chronic Kidney Disease	Colorectal Cancer	68.8	31.3
Chronic Kidney Disease	Depression	68.4	31.6
gallstone disease (gsd)	COVID-19	68.4	31.6
Atherosclerosis	Liver Cirrhosis	68.3	31.7
Ankylosing spondylitis	Liver Cirrhosis	68.2	31.8
Celiac Disease	Inflammatory Bowel Disease	68.2	31.8
Functional constipation / chronic idiopathic constipation	Schizophrenia	68.2	31.8
Stress / posttraumatic stress disorder	Ulcerative colitis	68.1	31.9
Ankylosing spondylitis	Bipolar Disorder	67.9	32.1
ADHD	Depression	67.9	32.1
Alzheimer’s disease	Parkinson’s Disease	67.6	32.4
Ulcerative colitis	Long COVID	67	33
Osteoarthritis	rheumatoid arthritis (RA),Spondyloarthritis (SpA)	66.7	33.3
Osteoporosis	COVID-19	66.7	33.3
Alzheimer’s disease	Insomnia	66.7	33.3
Amyotrophic lateral sclerosis (ALS) Motor Neuron	Bipolar Disorder	66.7	33.3
Alzheimer’s disease	Bipolar Disorder	66.7	33.3
Anorexia Nervosa	Long COVID	66.7	33.3
Anorexia Nervosa	obsessive-compulsive disorder	66.7	33.3
Asthma	Systemic Lupus Erythematosus	66.7	33.3
ADHD	Bipolar Disorder	66.7	33.3
Allergic Rhinitis (Hay Fever)	Stress / posttraumatic stress disorder	66.7	33.3
ADHD	Chronic Fatigue Syndrome	66.7	33.3
Acne	Stress / posttraumatic stress disorder	66.7	33.3
Acne	Systemic Lupus Erythematosus	66.7	33.3
Acne	Liver Cirrhosis	66.7	33.3
Allergic Rhinitis (Hay Fever)	Sjögren syndrome	66.7	33.3
Allergies	Type 1 Diabetes	66.7	33.3
Cerebral Palsy	Ankylosing spondylitis	66.7	33.3
Chronic Fatigue Syndrome	Nonalcoholic Fatty Liver Disease  (nafld) Nonalcoholic	66.7	33.3
Cerebral Palsy	Systemic Lupus Erythematosus	66.7	33.3
Cerebral Palsy	Inflammatory Bowel Disease	66.7	33.3
Brain Trauma	Type 2 Diabetes	66.7	33.3
Brain Trauma	obsessive-compulsive disorder	66.7	33.3
Brain Trauma	Irritable Bowel Syndrome	66.7	33.3
Fibromyalgia	obsessive-compulsive disorder	66.7	33.3
gallstone disease (gsd)	Stress / posttraumatic stress disorder	66.7	33.3
Depression	Gout	66.7	33.3
Chronic Kidney Disease	obsessive-compulsive disorder	66.7	33.3
Chronic Urticaria (Hives)	Systemic Lupus Erythematosus	66.7	33.3
Crohn’s Disease	Histamine Issues,Mast Cell Issue, DAO Insufficiency	66.7	33.3
COVID-19	Eczema	66.7	33.3
Histamine Issues,Mast Cell Issue, DAO Insufficiency	Liver Cirrhosis	66.7	33.3
Insomnia	Long COVID	66.7	33.3
Inflammatory Bowel Disease	obsessive-compulsive disorder	66.7	33.3
Inflammatory Bowel Disease	Osteoarthritis	66.7	33.3
Inflammatory Bowel Disease	ME/CFS without IBS	66.7	33.3
ME/CFS with IBS	Ulcerative colitis	66.7	33.3
ME/CFS without IBS	COVID-19	66.7	33.3
Liver Cirrhosis	neuropsychiatric disorders (PANDAS, PANS)	66.7	33.3
Liver Cirrhosis	ME/CFS with IBS	66.7	33.3
Multiple Sclerosis	Multiple system atrophy (MSA)	66.7	33.3
Liver Cirrhosis	Stress / posttraumatic stress disorder	66	34
Inflammatory Bowel Disease	rheumatoid arthritis (RA),Spondyloarthritis (SpA)	65.9	34.1
Celiac Disease	obsessive-compulsive disorder	65.7	34.3
Celiac Disease	Systemic Lupus Erythematosus	65.6	34.4
Chronic Fatigue Syndrome	Schizophrenia	65.5	34.5
hypertension (High Blood Pressure	COVID-19	65.5	34.5
ADHD	Parkinson’s Disease	65.4	34.6
Sjögren syndrome	Systemic Lupus Erythematosus	65.4	34.6
Stress / posttraumatic stress disorder	obsessive-compulsive disorder	65.2	34.8
rheumatoid arthritis (RA),Spondyloarthritis (SpA)	Bipolar Disorder	65.2	34.8
Chronic Fatigue Syndrome	Parkinson’s Disease	65.2	34.8
Autism	Brain Trauma	65.2	34.8
Multiple Sclerosis	Parkinson’s Disease	65.2	34.8
Atherosclerosis	IgA nephropathy (IgAN)	65	35
Asthma	COVID-19	65	35
Chronic Kidney Disease	rheumatoid arthritis (RA),Spondyloarthritis (SpA)	65	35
Alzheimer’s disease	Graves’ disease	65	35