On Microbiome Prescription we have Medical Conditions with Microbiome Shifts from US National Library of Medicine with some 91 entries. It would be good for everyone if we increase the number of entries.

YOU can help make it happen!

• Find a condition of concern to you or someone you know.
• Go to https://pubmed.ncbi.nlm.nih.gov/
• Type in the name with ” 16s microbiome “, for example, with “pregnancy”
  

Click Search. you will hopefully get a few dozen (or hundreds) of studies.

Next is the long haul part. Go thru them (Example URL).

• For many, the title alone tells you to skip it!! For example Interaction between Microbes and Host in Sow Vaginas in Early Pregnancy.
• Remember, we are not dealing with vaginal microbiome on the site, so you can skip all of those too!
• Others may be interesting: Effects of galactooligosaccharides on maternal gut microbiota, glucose metabolism, lipid metabolism and inflammation in pregnancy: A randomized controlled pilot study.
• Look at the summary or the full study if available
• Look for bacteria names. We need to know the name and how they shift. i.e.

And compared with the placebo group, the GOS group had a higher abundance of Paraprevotella and Dorea, but lower abundance of LachnospiraceaeUCG_001.

• Then comes the thinking part. Trying to describe the results. In this case, we see that the focus is “gestational diabetes mellitus (GDM)”
• If not a good fit for your focus, add it to a new list to come back to later.

In the above case, a new search (gestational diabetes mellitus 16s microbiome) returned 70+ studies. This is a good candidate for a new collection to add.

Sending the Information to Me

I would suggest putting the links (i.e. like https://pubmed.ncbi.nlm.nih.gov/36471554/ ) in a text file or excel and sending to me.

The information sent (when added to database) is available to everyone! This is citizen science! As I remarked to someone earlier today “I do not have a business model, I have a pro bono model“.

The encoded data can be used to evaluate yours and others microbiome against the patterns reported.

That is it!!! You spend the time so others do not have to and can act on their challenges with better information!

Email the lists to Research@microbiomeprescription.com

A reader wrote about feeling worse

Have you ever heard from anyone who felt significantly worse on antibiotics?  Since 2020 when I had ebv and iv antibiotics for an infection I can’t tolerate antibiotics like I used to.  Low dose doxy 50mg makes me feel very unwell within a few hours, clarithromycin is better tolerated but if I take that I get severe abdominal pain after meals and reduced physical function which only pro and prebiotics fix.  If I take a broad spectrum like metronidazol that completely fucks me up (pardon my french!) and I have to get into bed as I cannot stay awake.

There can be multiple causes, the most likely is a Jarisch–Herxheimer reaction a.k.a. “die-off”. (see this article). I have written about these for many years with a few prior posts.

• Changes of Microflora producing Herxheimer Reactions
• Controlling a Herxheimer Reaction
• Reminder: Probiotic can cause major herx!
• Probiotic Herxheimer Reactions

There are other possible causes, for example, something is being feed and more chemicals are being dumped into your system. That is best determines with lab tests.

So to see explore possibilities (I deal with probabilities, not certainty), For your sample go to My Profile. You will see the new link there under Special Reports. Other common possible causes are probiotics that produce d-lactic acid (often causing brain-fog) or histamines.

Enter what causes you to feel worse on the left size, click compute and see which bacteria are likely being lowered.

A friend got this diagnosis from a naturopath examining blood cells using a microscope. Visual inspection of blood lacks the degree of objectivity that I would prefer (AI imaging of the microscope slides is what I would like to see things evolve to). I went to Microbiome Prescription’s Medical Conditions with Microbiome Shifts from US National Library of Medicine and then look at the candidate suggestions for Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic .

For information about the Artificial Intelligence approach, see AI Generated Diet Suggestion for Medical Conditions. Note: This is not machine learning but old school fuzzy expert systems.

Out of curiosity, I did a few cross validations for the highest to take and highest to avoid. Everything was in agreement. The few suggested items that I checked were shown to have the desired impact on NAFLD.

The purpose of cross validation is to see how well the Artificial Intelligence Logic (and assumptions) are performing.

I decided to do a deeper cross validation because I found that the treatment literature was relatively abundant. REMEMBER the AI only knows the bacteria shifts and nothing about the diagnosis or treatments.

Items to Take

In priority order, the top items with AI Weight of 20 or more

Net Impact	Link	Modifier
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35678936/	soy
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36698477/	lactobacillus plantarum (probiotics)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35662935/	resistant starch
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36278802/	barley
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35101633/	vitamin d
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35782914/	Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35052765/	bifidobacterium bifidum (probiotics)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/34292103/	inulin (prebiotic)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/24475018/	lactobacillus rhamnosus gg (probiotics)
n/a		resistant maltodextrin
n/a		saccharin
n/a		fibre-rich macrobiotic ma-pi 2 diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/32718371/	lactobacillus casei (probiotics)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/33317254/	lactobacillus acidophilus (probiotics)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/23026517/	potatoes
n/a		Goji (berry,juice)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/31017556/	chicory (prebiotic)
WRONG	https://pubmed.ncbi.nlm.nih.gov/34773093/	salt (sodium chloride)
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36433820/	low protein diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35485931/	apple
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36235752/	vegetarians
RIGHT	https://pubmed.ncbi.nlm.nih.gov/30166633/	red wine
n/a		fasting
RIGHT	https://pubmed.ncbi.nlm.nih.gov/31804025/	Burdock Root
RIGHT	https://pubmed.ncbi.nlm.nih.gov/32158345/	oregano (origanum vulgare, oil) |
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35843540/	wheat
RIGHT	https://pubmed.ncbi.nlm.nih.gov/26915720/	Guaiacol (polyphenol)
WRONG	https://pubmed.ncbi.nlm.nih.gov/34773093/	high salt
n/a		merbromin
RIGHT	https://pubmed.ncbi.nlm.nih.gov/34482357/	bacillus subtilis (probiotics)

Items to Avoid

Values with AI Weight of -14 or less

RIGHT	https://pubmed.ncbi.nlm.nih.gov/36565558/	lard
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36682412/	low carbohydrate diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36223657/	l-glutamine
n/a		thiamine hydrochloride (vitamin B1)
n/a		melatonin supplement
WRONG	https://pubmed.ncbi.nlm.nih.gov/29081885/	linseed(flaxseed)
n/a		camelina seed
RIGHT	https://pubmed.ncbi.nlm.nih.gov/34534894/	dairy
WRONG	https://pubmed.ncbi.nlm.nih.gov/35269912/	Caffeine
complex		high animal protein diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/29984415/	gluten-free diet
n/a		mannooligosaccharide (prebiotic)
n/a		low fodmap diet
n/a		sodium stearoyl lactylate
Complex		high-protein diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36565558/	fat
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36670457/	smoking
RIGHT	https://pubmed.ncbi.nlm.nih.gov/35896521/	triclosan
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36771448/	high sugar diet
RIGHT	https://pubmed.ncbi.nlm.nih.gov/36445049/	Vitamin C (ascorbic acid)

Discussion

There is a strong bias to publish positive results, hence finding many n/a in the to avoid list is expected. A study show an adverse effect is unlikely to see publication. We see the following ratios:

• To Take: 22 Right to 2 Wrong, i.e. 92% correct
• To Avoid: 10 Right to 2 Wrong i.e. 83% correct

This suggests that the n/a ones above are likely to be correct.

A reader forwarded his results and ask if they could be uploaded. It was a CSV file which was a good sign. Inspecting the file I noticed two things:

• No NCBI Taxonomy numbers were included 🙁
• The report gave percentile numbers for every bacteria — a wonderful thing to see.

The reader approached Thorne support about getting the NCBI Taxonomy numbers added — with no success. After a few days of work I ended up with 99.9% success of matching their bacteria names to NCBI Taxonomy numbers. The import worked… but wait! The price is about the same as some 16s tests, but you get MORE DATA and more accurate data! See this study for the difference between 16s and Shotgun

• Thorne – Shotgun: $190.00
• Ombre – 16S: $99.99 [US Shipping Only]
• Biomesight – 16S: $208.00

This is whole-genome shotgun metagenomics which is more accurate. It provides percentiles against a much larger sample than I could hope to get. My site is focused on percentiles — so most thing flows nicely – even when there is just one sample!!!

There are items that will not work until we hit 100+ samples from Thorne (i.e. KEGG Percentile Ranking, Pub Med Condition Percentile, etc).

We substitute Percentage Match for Percentile in this section (since we are less than 100 samples)

Bottom Line

I have ordered Thorne for my next test and expect to keep using them if the pricing stays the same. These test costs are driven by technology — which keep dropping cost over time. I recall spending $1000 to get 1 Meg of RAM many decades ago, today for the same amount, I can get 320 GB of memory — that’s 320,000 x more! The same thing is happening with microbiome and DNA testing technologies.

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