A reader wrote:

Hi Ken – I’m attempting to use MBP. I recall you linked (a long time ago) to a section that showed a list of probiotics useful in colitis, with each probiotic having the studies. How would I arrive to that page?

Where do I get the probiotics?

I prefer single species — and where I get mine?

• Single species with (almost) no fillers. There are precisely three sources that I use:
  • Custom Probiotics :they list all of their strains — many are researched on the above list. No other ingredients just the bacteria.
  • Maple Life Science™: No strains yet, but shipments usually have manufactured date within 4 weeks of arrival (i.e. FRESH). Contains FOS
  • Bulk Probiotics: US based Newbie — but has some species not available at the other two sites. No other ingredients just the bacteria. Specifically, Lactobacillus Jensenii that has great potential for Crohn’s disease.
• NOTE: none of these sell though retail outlets. This keeps their costs down and their product fresh.

There is a multiple step/layer approach that I would use. The sequence is below:

#1 Researched Probiotics Available Retail

Go to https://microbiomeprescription.com/library/ProbioticSearch and type in the name of the condition.

You will see the list of probiotics with the ones with the most studies listed first.

Below it are the links to the studies. You should read at least some of them – some may report “no effect”. The list is generated by a data gathering algorithm on the species and the condition and the condition may be mentioned in passing and not directly referenced or tested.

#2 Use Perplexity AI

Go to https://www.perplexity.ai and just ask “What is the most effective probiotic for…”. I use this AI because it provided links to data sources so I can verify the response

A typical response is below

#3 Probiotic Via Published Microbiome Shifts

In this case we are using the MP engine to generate suggestions. It is inferior in reliability to the above methods but is a reasonable fall back. Go to Medical Conditions with Microbiome Shifts from US National Library of Medicine ( https://microbiomeprescription.com/Library/PubMed ) and search for the condition

You can click the PDF download (which report TOP items only) or click Candidates (all items)

Surprise, surprise! #1 and #2 agree with Perplexity!!! vsl#3 is not listed because we do not have strain information for this mixture of probiotics. Remember – these are computed SOLELY from the microbiome shifts without reference to any studies on ulcerative colitis!

#4 Probiotic via Citizen Science

Goto Symptoms associated with conditions reported for Samples https://microbiomeprescription.com/Library/Symptoms Enter the name — in this case it was not found (not enough samples that people have annotated with ulcerative colitis). In that case, you may want to go over to symptoms you have; for example Diarrhea. This data is conditioned on the lab that the contributor was using and layered by taxa level.

I will be adding a suggestions icon to this page shortly, and you can then review those suggestions. Clicking on the bowl, produces suggestions that can be filtered to probiotics.

#1 and #2 above are #1 and #3 with the most studied one show in above!

Clicking on the symptom name takes you to the symptom page listing all of the bacteria

This will then use the expert system to generate suggestions which can be filtered to probiotics

This is ranked not by the number of studies published but by the ones with the greatest estimated impact by the expert system. We see Mutaflor, L. rhamnosus – and many familiar ones in the researched strain list.

Bottom Line

We have 4 ways of selecting probiotics. For the specific question asked, all 4 converge to the same items. In terms of clinical use, we see that the suggestions based solely on bacteria shifts going through the expert system appear to be cross-validated. IMHO, this explicit expert system works better than the typical MD would.

Backstory

I’ve found your work through PR forum and then your website, it’s really interesting reading all that information and feeling so related to the issues.

For the last year I’ve been house bounded and sometimes bed bounded, all coming from ignoring one year of daily sandy-yellowish diarrhea along with a series of chronic sinus infections that one after another left me in a worst health state to finally being isolated fearing further infections.

I’ve been reading and investigating, trying methylation protocols and probiotics. The probiotic I took was fairly simple (bifidobacterium bifidum and lactobacillus acidophilus) but gave me pretty bad reaction which got me thinking about histamine release, one Google search showed me that those strains are not histamine releases but what it does (acidophilus) is increase D-lactic production and what other bacteria could do this? Streptococous, the one which probably caused my chronic sinusitis.

This is how I ended up thinking that I really need to know if I have those kind of bacterial overgrowth in my guts and reading your work thougth that you could have some advice to start learning how to do it.

Analysis

Given that sinus is significant, this study may be worth a read: Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes, 2024.

I looked at “Leaked” from Oral Cavity and while the count places him at 49%ile, the number of bacteria was much longer than usual — a lot of different bacteria at low levels which suggests a new statistics to add in the next revision.

Going Forward

Since symptoms are entered, I am just using the Just Give Me Suggestions with Symptoms button.

The suggestions are below, trimmed a little

• sucralose
• lauric acid(fatty acid in coconut oil,in palm kernel oil,)
• enterococcus faecium (probiotic)
• garlic (allium sativum)
• momordia charantia(bitter melon, karela, balsam pear, or bitter gourd)
• Tudca – Tauroursodeoxycholic acid , a supplement
• lactobacillus casei (probiotics)
• whole-grain barley
• fruit/legume fibre
• bifidobacterium infantis,(probiotics)
• dates
• grape polyphenols
• naringenin(grapefruit) (Flavonoid)
• Cacao
• rosa rugosa (Rosavin – an adaptogen)
• Hesperidin (polyphenol)
• Nicotine, Nicotine Patch
• galla chinensis (herb)
• Grapefruit seed extract
• lactobacillus gasseri (probiotics)
• thyme (thymol, thyme oil)
• foeniculum vulgare,fennel
• Turmeric
• N-Acetyl Cysteine (NAC),
• Onion

I will create two extracts: one for probiotics and one for teas (i.e. herbs).

Looking at probiotics, we have a good number that would allow easy rotation of probiotics. Two are usually difficult to obtain are removed: lactobacillus kefiri and lactobacillus sakei . Most are available at my usual two preferred sources: Custom Probiotics and Indian Bulk Exporter (Maple Life Sources). See this list for sources not available there. By rotation, I mean 20-50 BCFU daily of one probiotic for 2 weeks and then change to another probiotic.

• enterococcus faecium
• lactobacillus casei [CB,MLS]
• bifidobacterium infantis, [CB,MLS]
• lactobacillus gasseri [CB,MLS]
• lactobacillus jensenii [MS]
• lactobacillus salivarius [CB,MLS]
• pediococcus acidilactic
• bifidobacterium breve [CB,MLS]
• bifidobacterium pseudocatenulatum,
• bifidobacterium longum [CB,MLS]
• bacillus coagulans

While taking herbs as capsules or oils is very viable, herbal teas has the advantage of effecting the oral cavity (and thus sinus) and extends through the entire system.

• thyme
• fennel
• anise
• chamomile
• eugenol (from clove oil, nutmeg, cinnamon, basil,bay leaf.)
• peppermint
• sage
• tulsi
• neem
• oregano
• ginger

Items to avoid or reduce

• Slippery Elm
• wheat bran, rice bran
• xylan (prebiotic)
• inulin (prebiotic)
• arabinogalactan (prebiotic)
• stevia, saccharin
• resistant starch, resistant maltodextrin, slow digestible carbohydrates
• Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
• red wine
• pectin
• l-citrulline (supplement)
• berberine (supplement)
• Pulses
• apples, nuts, banana
• galacto-oligosaccharides (prebiotic)
• low-fat diets, high beef diet, high red meat, animal-based diet, hypocaloric hyperproteic diet
• iron
• Pork
• kefir
• bacillus licheniformis,(probiotics)
• fasting
• galactose (milk sugar)
• propionate
• Guaiacol (polyphenol)

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.

After a good night sleep after doing the video below, I realized the next step in this journey. With hundreds of bacteria in the microbiome the question arises which ones to focus on if the condition is not a known single bacteria condition. I.e. Crohn’s, Asthma vs Tuberculosis, Ulcers.

Recently I had major success is identifying bacteria associated to hundreds of symptoms. This was done by using the percentile ranking of each microbiome sample over the population and then examining the counts of the top and bottom 15%ile (simplified version). These should be equal if there is no relationship of the bacteria to the symptom. The difference allows a Chi2 value and probability to be computed for significance. Comparing the averages perform very poorly for finding equivalent statistical significance.

The result was a list of bacteria where the high in these end 15%ile was associated with symptoms / diagnosis. This allows us to filter out the noise from the significance in a sample. The question arises, should all of these be used or is there a critical limit? The current count is 1,960 taxa-shifts with one or more

To address this issue, I decided to plot the counts.

Applying a log function found a region became a straight line. Two apparent inflection points are suggested. Applying different function kept showing the same inflection points.

Bacteria with high and low associated to Symptoms

There are two species in this list. Both are reported in published clinical studies often.

Bacteria	Rank
Faecalibacterium prausnitzii	species
Dorea formicigenerans	species
Weissella	genus
Faecalibacterium	genus
Acidaminococcaceae	family
Acidaminococcales	order

The Short List of Bacteria

We see many bacteria not usually associated plus two familiar ones. Increases means more are in the 85-100%ile, decrease means more are in the 0-15%ile

Bacteria	Rank	Direction	Count of Associations
Thermodesulfobacteriota	phylum	increases	460
Rhodothermota	phylum	increases	247
Rhodothermia	class	increases	247
Rhodothermales	order	increases	247
Actinomycetota	phylum	decrease	146
Holdemania	genus	increases	135
Faecalibacterium	genus	decrease	133
Coriobacteriaceae	family	decrease	125
Coriobacteriales	order	decrease	120
[Clostridium] thermoalcaliphilum	species	increases	116
Coriobacteriia	class	decrease	115
Lactobacillaceae	family	decrease	114
Bifidobacterium	genus	decrease	113
Sphingobacteriaceae	family	increases	113

Longer List of Bacteria

Bacteria	Rank	Shift	Count
Oscillospiraceae	family	decrease	108
Anaerotruncus colihominis	species	increases	107
Actinomycetes	class	decrease	106
Bacteroides	genus	increases	106
Hathewaya	genus	increases	105
Sharpea azabuensis	species	increases	105
Bacillota	phylum	decrease	105
Sharpea	genus	increases	105
Anaerotruncus	genus	increases	104
Sphingobacteriia	class	increases	104
Sphingobacteriales	order	increases	104
Lactobacillales	order	decrease	101
Ruminococcaceae	family	decrease	101
Erysipelothrix muris	species	increases	100
Erysipelothrix	genus	increases	96
Veillonellaceae	family	increases	94
Bacteroides caccae	species	increases	92
Clostridia	class	decrease	90
Bacteroides fluxus	species	increases	89
Eubacteriales	order	decrease	89
Ethanoligenens	genus	increases	89
Finegoldia	genus	increases	88
Hathewaya histolytica	species	increases	87
Hymenobacter xinjiangensis	species	increases	87
Roseburia faecis	species	decrease	85
Blautia obeum	species	decrease	84
[Clostridium] leptum	species	increases	83
Hymenobacter	genus	increases	82
Negativicutes	class	increases	80
Terrabacteria group	clade	decrease	79
Bifidobacteriaceae	family	decrease	79
Bacilli	class	decrease	78
Bifidobacterium adolescentis	species	decrease	78
Marvinbryantia	genus	increases	77
Phocaeicola vulgatus	species	increases	77
FCB group	clade	increases	76
Odoribacter denticanis	species	increases	75
Mediterraneibacter	genus	increases	74
Faecalibacterium prausnitzii	species	decrease	73
Eggerthella	genus	increases	72
Bifidobacteriales	order	decrease	72
Lachnoclostridium	genus	increases	70
Acidaminococcales	order	increases	70
Odoribacter	genus	increases	70
Acidaminococcaceae	family	increases	70
Blautia schinkii	species	increases	69
Dorea formicigenerans	species	increases	69
Collinsella	genus	decrease	69
Bacteroides stercoris	species	decrease	69
Lactobacillus	genus	decrease	68
Pseudomonadota	phylum	increases	67
Odoribacteraceae	family	increases	67
Peptoniphilaceae	family	increases	67
Fusobacteriaceae	family	decrease	66
Dorea formicigenerans	species	decrease	66
Amedibacillus dolichus	species	increases	65
Amedibacillus	genus	increases	65
Candidatus Glomeribacter	genus	increases	64
Eggerthella lenta	species	increases	63
Finegoldia magna	species	increases	63
Oscillatoria corallinae	species	increases	62
Ruminiclostridium cellobioparum subsp. termitidis	subspecies	decrease	62
Oscillatoria	genus	increases	62
Tissierellales	order	increases	62
Ruminiclostridium cellobioparum	species	decrease	62
Hymenobacteraceae	family	increases	62
Pseudoflavonifractor	genus	increases	61
Mycobacteriales	order	increases	61
Tissierellia	class	increases	60
Lachnoanaerobaculum	genus	decrease	60
Coprococcus	genus	decrease	59
Blautia	genus	increases	59
Collinsella aerofaciens	species	decrease	59
Verrucomicrobiota	phylum	increases	59
Veillonellales	order	increases	58
[Ruminococcus] gnavus	species	increases	58
Selenomonas infelix	species	increases	58
Eubacterium ventriosum	species	decrease	58
Lactonifactor	genus	increases	57
Bacteroides cellulosilyticus	species	increases	57
Enterococcaceae	family	decrease	57
Fusobacterium	genus	decrease	57
Alistipes putredinis	species	increases	57
Sutterella	genus	decrease	56
Anaerotignum lactatifermentans	species	increases	56
Anaerotignum	genus	increases	56
Clostridium akagii	species	increases	56
Burkholderiaceae	family	increases	55
Akkermansiaceae	family	increases	55
Turicibacter	genus	decrease	55
Butyricimonas	genus	increases	55
Segatella copri	species	decrease	55
Mycoplasmoidales	order	increases	55
Turicibacteraceae	family	decrease	55
Turicibacter sanguinis	species	decrease	55
Akkermansia muciniphila	species	increases	54
Sporomusaceae	family	increases	54
Limnobacter	genus	increases	54
Limnobacter litoralis	species	increases	54
Lactobacillus rogosae	species	decrease	54
PVC group	clade	increases	54
Thiocapsa	genus	increases	53
Sphingobacterium bambusae	species	increases	53
Enterobacteriaceae	family	increases	53
Pseudoflavonifractor capillosus	species	increases	53
Blautia hansenii	species	increases	53
Akkermansia	genus	increases	53
Marvinbryantia formatexigens	species	increases	52
Enterococcus	genus	decrease	52
Corynebacteriaceae	family	increases	52
Lachnospira	genus	increases	52
Lachnospira eligens	species	increases	52
Corynebacterium	genus	increases	52
Mycoplasmataceae	family	increases	52
delta/epsilon subdivisions	clade	decrease	52
Bacteroides faecis	species	increases	52
Bacteroides gallinarum	species	increases	52
Veillonella	genus	increases	51
Weissella	genus	increases	51
Dialister invisus	species	increases	51
Blautia hydrogenotrophica	species	increases	51
Lactonifactor longoviformis	species	increases	50
Dorea longicatena	species	decrease	50

Best Test/Lab?

The above is based on percentile and not the typical percentage. The results from Clinical Microbiomics A/S (a.k.a. CosmosId) provides percentile ranking again a regionalized healthy population. In the US, their tests are available retail from Thorne. They will soon be available retail from Precision Biome in the EU.

A walk thru using Microbiome Prescription

People with mast cell issues typical treatment approach is:

• Reduce foods high in histamine
• Avoid probiotics that produces histamine
• Use DAO supplements.

There is a fourth leg that should be added to this three legged stool. Probiotics (and other substances) that impacts DAO levels. Some could argue that they impact DAO because they produce histamines. That may be true in some rare cases, but many of the items listed below are not known to produce histamine.

More information on PubMed: Histamine Intolerance-The More We Know the Less We Know. A Review [2021].

The production and activity of DAO can be regulated by various factors, including hormones, inflammatory mediators, and the presence of substrates like histamin. Difference probiotics produces different enzymes which may inhibit or assist with the production of DAO.

The probiotics, impact and study used are listed below:

• Increases DAO
  • Saccharomyces boulardii [1999]
    • Supported by: [Clinical observation of saccharomyces boulardii combined with cetirizine hydrochloride in children allergic rhinitis] [2017]
  • Clostridium butyricum, Bifidobacterium infantis [2019]
  • Mixture of Clostridium butyricum, Bacillus licheniformis, Bacillus subtilis [2023]
    • Comment: I suspect the increase above was due to CB only
  • Clostridium butyricum [2020]
  • Clostridium butyricum [2019]
    • Supported by: Allergic rhinitis management:environment control and patient education [2017]
    • Oral administration of Clostridium butyricum CGMCC0313-1 inhibits β-lactoglobulin-induced intestinal anaphylaxis in a mouse model of food allergy [2017]
  • Lactobacillus johnsonii  [2021]
    • Supported by A trial of adding Lactobacillus johnsonii EM1 to levocetirizine for treatment of perennial allergic rhinitis in children aged 7-12 years [2012]
• Decreases DAO
  • Akkermansia muciniphila [2020]
  • Bacillus amyloliquefaciens [2022]
  • Bacillus licheniformis [2013]
  • Bacillus subtilis [2023]
  • Bacillus coagulans [2021]
  • Bacillus coagulans, Lactobacillus plantarum [2022]
  • Clostridium butyricum [2018]
  • Clostridium butyricum [2016]
  • Lactobacillus plantarum [2019]
  • Lactobacillus plantarum [2017]
  • Bifidobacterium Mixture [2017]
  • Bifidobacterium bifidum Combined with Bifidobacterium longum subsp. infantis [2023]
  • Lactobacillus paracasei, L. rhamnosus, L. acidophilus, and Bifidobacterium lactis  [2018]
  • Bifidobacterium animalis subsp. lactis [2022]
  • Bifidobacterium, Lactobacillus and Streptococcus [2014]
  • Lactococcus petauri, Bacillus siamensis [2022]
  • Lactobacillus paracasei  [2022]
  • Pediococcus pentosaceus [2022]
  • Streptococcus thermophiles [2020]
  • Dietary live yeast and mannan-oligosaccharide [2016]
  • Sodium butyrate and probiotics [2022]
  • Sodium Butyrate [2022]

Reviewing the studies is recommended. In some cases DAO levels were raised due to infection, i.e. Escherichia coli K88; and the probiotic may be inhibiting the increase due to the infection though reducing the infection and not actually impacting DAO levels.

Other Common Supplements and DAO or Histamine

Niacin

• Excess nicotinamide increases plasma serotonin and histamine levels[2013] Note: this is non-flushing version of niacin (chemically different)
  • [Nicotinamide and histamine shock]. 1947
  • Mechanism of histamine-release inhibition by nicotinamide in in vitro antigen-antibody reaction [1961]
  • “nicotinamide partially inhibits the in vitro release of histamine ” [1963]
  • “Release of PGD2 [due to niacin] was not accompanied by a release of histamine which was assessed by quantification of plasma levels of the histamine metabolite,” [1989]
  • CONCLUSION: Niacin by itself does not cause histamine release (except with excessive use of one form of niacin)
• “Moreover, it[Niacin] led to a significant rise in DAO levels on day 30 (p < 0.05). Niacin supplementation significantly reduced the LPS levels on day 30 (p < 0.05).” [1992]

What is needed are control studies measuring DAO levels in humans with different probiotics. The above illustrates that probiotics are likely to make good or bad differences.

Back Story

• 47yo Male
• 1976-2012 – Sustained 7 concussions playing contact sports, minor short term symptoms and fully recovered.  Fit, active, and employed as adult. Satisfying family/social life. Higher intensity physical training 5-6 days per week outside or in gym. No drugs/alcohol since 2003, no medical issues or medications. 
• 2012 – Post Concussion Syndrome – Fell and hit head skiing, diagnosed with concussion, symptoms did not resolve. No detectable damage on MRI. 

Symptoms

• Fatigue, brain fog, intense and unpredictable nerve pain in head 
• Cognitive and visual processing issues
• Sensitivity to light, loud noise, busy environments
• GI – constipation
• Multiple food, chemical, and environmental sensitivities
• Overall functionality reduced, still able to socialize, drive, complete tasks, problem solve, etc… sporadically and in short increments. Physical fitness mostly unaffected, continued to exercise, incorporated yoga/gyrotonic.
• Divorced
• Received disability benefits in 2019
• chronic sinus issues that started after concussion and worsened with vaccine. 
• diagnosed w nasal mold colonization, nasal biofilms, and chronic staph  aureus.

2021 – mRNA Vaccine Injury (2 shots Pfizer)

Symptoms:

• Total exercise intolerance, sympathetic activation, anxiety and confusion
• Exacerbation of pre-existing symptoms – severe fatigue, brain fog, food/chemical/medication/supplement sensitivities, constipation
• Neuropathy – pain, tingling, numbness in legs and feet. 
• Minimal activity, short walks, occasional driving short distances, most time spent resting

Tests

• Microclotting confirmed by visual sample analysis
• Elevated Leukotiene 4 + typical symptoms suggests MCAS
• Reactivated EBV – elevated latent IFN-gamma, lytic IFN-gamma, lytic IL-2 (Infectolabs)
• Autoantibodies (Celltrends)
• Spike protein present in non-classical monocytes 5 months post-vax
• OATS – apergillus, clostridia, metabolic dysfunction

Research

We appear to have two happenstances cascading:

• Concussion
• COVID Shots – which build immunity by causing a reaction similar to COVID. This reaction includes some microbiome shifts.

Concussion

There is some literature here:

• Alterations to the gut microbiome after sport-related concussion and subconcussive impacts in a collegiate football players cohort [2022]
  •  Decrease in Eubacterium rectale [Agathobacter rectalis], and Anaerostipes hadrus
• Association of Blast Exposure in Military Breaching with Intestinal Permeability Blood Biomarkers Associated with Leaky Gut [2024]
  • the plasma level of Zonulin was significantly increased after post blast exposure, indicating that blast may contribute to the impairment of the gut barrier in the paracellular pathway “
• Visceral hypersensitivity induced by mild traumatic brain injury via the corticotropin-releasing hormone receptor: An animal model [2023]
  • “Mild blast-induced traumatic brain injury (bTBI) induces various gut symptoms resembling human irritable bowel syndrome (IBS) “
• Timing matters: Sex differences in inflammatory and behavioral outcomes following repetitive blast mild traumatic brain injury [2023]
  • Repetitive blast exposure resulted in both similar (e.g., increased IL-6), and disparate (e.g., IL-10 increase only in females) patterns of acute serum and brain cytokine as well as gut microbiome changes in female and male mice. Chart is below

• Age matters: Microbiome depletion prior to repeat mild traumatic brain injury differentially alters microbial composition and function in adolescent and adult rats [2022]
  • increased Clostridium innocuum and Erysipelatoclostridium 
  • reductions in Bacteroides and Clostridium Sensu Stricto

Reductions in Bacteroides have been associated with irritable bowel syndrome (IBS) development and identified after stroke [87]. Bacteroides are imperative for the maintenance of intestinal barrier integrity, with supplementation being associated with increased tight junction proteins [88]. Reductions in Clostridium sensu stricto have been associated with reduced butyrate production and Alzheimer’s disease [89,90]. 

Age matters: Microbiome depletion prior to repeat mild traumatic brain injury differentially alters microbial composition and function in adolescent and adult rats [2022]

Treatment

• Involvement of Microbiome Gut-Brain Axis in Neuroprotective Effect of Quercetin in Mouse Model of Repeated Mild Traumatic Brain Injury [2023]
• “probiotic supplementation (Lactobacillus acidophilus or Clostridium butyricum) improved neurologic function.” [2022]
• ” Findings from this trial support the feasibility, acceptability, and safety of supplementation with an anti-inflammatory/immunoregulatory probiotic, Lactobacillus reuteri DSM 17938, among Veterans with PPC and PTSD symptoms.” [2020]

COVID Shots

Explicit studies are rare. What we do have is a variety of studies between high and low responder which enumerates the difference of bacteria. We do not clearly know which goes in any specific direction. We do know the bacteria that are likely to change.

• Association between Gut Microbiota Composition and Long-Term Vaccine Immunogenicity following Three Doses of CoronaVac [2024]
  • increase of Eubacterium rectale, Collinsella aerofaciens, Streptococcus salivarius
• Gut microbiota composition is associated with SARS-CoV-2 vaccine immunogenicity and adverse events [2022]
  • Increases Bifidobacterium adolescentis, Prevotella copri, Megamonas  
• Stability of gut microbiome after COVID-19 vaccination in healthy and immuno-compromised individuals [2024]
  • Increase Akkermansia muciniphila in some cases, but in general no change

The high response group were primarily characterized by a predominance of Enterococcus faecium, Prevotella bivia, Actinomyces massiliensis, Veillonella dispar, Veillonella_sp_T11011_6, Eubacterium_sp_CAG_38, Ruminococcus torques, Actinomyces odontolyticus, while Alistipes putredinis, Allisonella histaminiformans, Bacteroides clarus, Clostridium lavalense, Clostridium asparagiforme, Bacteroides eggerthii, Coprobacter fastidiosus, Sutterella parvirubra, and Blautia coccoides are more abundant in the low response group 

Plasma and urine proteomics and gut microbiota analysis reveal potential factors affecting COVID-19 vaccination response [2024]

Analysis

We have two results to work from: Biomesight report and an OATS (Organic Acid) report.

Looking at Forecasted symptoms we have a high rate of pattern matches. This is hopeful because it implies we now have strongly suspected bacteria.

We also see a significant shift of bacteria that are atypically over represented.

The reader noticed the dominance of some unusual bacteria (species and the genus they belong to). These amount to be 28.4% of the microbiome. These two bacteria usually average 3.5% of the microbiome.

For Caloramator, the probiotics most documented to reduce are: lactobacillus plantarum and lactobacillus casei; fructo-oligosaccharides (prebiotic) (BUT inulin increases).

As a starting point, we will do [Just give me Suggestions include Symptoms]. Since we have some condition specific suggestions above, let us see where they rank.

• quercetin,resveratrol: 297; quercetin: 123
• clostridium butyricum (probiotics),Miya,Miyarisan: 187
• lactobacillus acidophilus (probiotics) : 67
• lactobacillus reuteri (probiotics): 96

This type of cross validation is nice to see — everything known to help TBI/concussion is in the recommended to take list. It builds confidence in the suggestions being generated.

The next thing is to see which of the bacteria shifts cited in the above literature. We go over the the Bacteria tree. We filter out those not usually reported by Biomesight and ended with just one (which illustrates the benefit of shot-gun tests).

• reductions in Bacteroides: was at 32%ile

Other incidental measures of note:

• Anti Inflammatory Ratio: 20% (so inflammation is likely)

Other Issues

Last, do we see Staphylococcus aureus in the sample? No.

Elevated Leukotriene E4 (LTE4)  is an accepted indicator of Mast Cell Activation Syndromes, see Diagnosis, Classification and Management of Mast Cell Activation Syndromes (MCAS) in the Era of Personalized Medicine[2020].  I will look at LTE4 only and not the much bigger MCAS. LTE4 depends on  5-lipoxygenase (EC:1.13.11.34) [ Leukotrienes and inflammation [1990]]. This gives us some possible paths to explore:

• Natural anti-inflammatory products and leukotriene inhibitors as complementary therapy for bronchial asthma [2010]
  •  Boswellia serrata, Curcuma longa and Glycyrrhiza (AKA Boswellia, Tumeric, Licorice)
  • These are also cited in Efficacy of Herbal Medicines on Lung Function in Asthma: a systematic review and meta-analysis of randomized controlled trials[2023] with additional atypical herbs.
• Effect of plasma α‐tocopherol on leukotriene E4 excretion in genetic vitamin E deficiency – Kohlschütter – 1997
  • LTE4 excretion proved inversely correlated to plasma α-tocopherol levels.
  • Modulation of the endogenous leukotriene production by fish oil and vitamin E

Unfortunately, there are no reported bacteria associated with 5-lipoxygenase (EC:1.13.11.34) . What we find is:

• Targeting Mammalian 5-Lipoxygenase by Dietary Phenolics as an Anti-Inflammatory Mechanism: A Systematic Review (mdpi.com)
  • “Some (poly)phenolics such as caffeic acid [found in Barley, Coffee], hydroxytyrosol [from Olive Oil}, resveratrol, curcumin, nordihydroguaiaretic acid (NDGA) [TOXIC ISSUES], and quercetin have been reported to reduce the formation of 5-LOX eicosanoids in vitro”
  • From Suggestions:
    • caffeic acid [found in Barley:266, Coffee:87],
    • hydroxytyrosol [from Olive Oil: 207 / 173, olea europaea,olive leaf: 98],
    • resveratrol:55,
    • curcumin: 291,
    • quercetin: 135

Again, we have cross-validations happening, i.e. the expert system suggestions and clinical studies are in agreement.

OATS Tests

Next we see what the OATS results produces. Only one was out of range where we have data

• (S)-Malate was to high

I should point out that something is very funky with the OATS report. Every value below is within their declared range but their graphics show it is out of range.

The result was 326 probiotics — specifically, probiotics that are known to consume Malate. We have no significant additions.

Action Plan

First item of note, above we have 100% cross validation on what the fuzzy logic expert system says should help with clinical studies of what helps given his particulars. This hints that other suggestions are far more likely to help than hurt.

The easy set of suggestions are quercetin, clostridium butyricum (probiotics),lactobacillus acidophilus, lactobacillus reuteri (probiotics) because they are double recommended: both clinical studies and the microbiome. Since with this history, getting any prescription drugs is unlikely, the consensus suggestion leads me to consider the following (filtered for availability etc):

• Probiotics (no more than 2 at a time, and rotate to different ones every 2 weeks)
  • lactobacillus casei
  • bifidobacterium animalis lactis
  • lactobacillus paracasei
  • lactobacillus rhamnosus gg
  • mutaflor escherichia coli nissle 1917 (if available)
  • bifidobacterium longum
  • lactobacillus brevis
  • clostridium butyricum
  • lactobacillus plantarum
  • If possible, get single species OR mixtures containing ONLY the above.
• Supplements:
  • Hesperidin (polyphenol)
  • luteolin (flavonoid)
  • quercetin
  • Vitamin B-12
  • Vitamin B1,thiamine hydrochloride
  • selenium
  • chicory (prebiotic)
  • mastic gum (prebiotic)
  • Bromelain
  • N-Acetyl Cysteine (NAC),
• Diet and Food
  • high-fat diets / high-saturated fat diet
  • whey
  • garlic (allium sativum)
  • thyme (thymol, thyme oil)
  • rosmarinus officinalis,rosemary
  • sorghum
  • grapes
  • Cacao
  • whole-grain barley
  • Take Far infrared Sauna if available

Ideally, the reader will use all of the details and avoid all of the negative values ones if possible

The high-fat diets / high-saturated fat diet suggestion takes a little figuring. I deduce that being high in fat from:

• Coconut oil 1 2 3 4
• Palm oil and palm kernel oil

IMHO, This is the general problem with diet data — they contain vast baskets of substances: some good and some bad; with a wide variety of definitions. I prefer working substance by substance.

Retest

I would suggest a retest with Biomesight in 3 months (if the reader consents, I would be glad to do a follow up post). Remember these are not generic suggestions for anyone with a concussion but based on the individual’s microbiome with cross referencing to the literature to develop a clean consensus of what may help.

Questions and Answers

Q: What does with “Symptoms” mean?

• Using “With Symptoms” uses the bacteria associations found from donated samples (i.e. see  Citizen Science Symptoms To Bacteria Studies (microbiomeprescription.com))
• Ideally, the user will do the time consuming process of checking the suggestions against the literature (which I did above for illustration).
• There is NO direct linkage using studies of symptoms to supplements by the engine. That is technically possible, but would require major funding to hire qualified people to enter the data.
  • Experience have found that 85-95% of suggestions that has studies for a conditions are in agreement.

Q: Do you use studies on my conditions to pick bacteria?

• If there are sufficient studies, then yes. In your case there is not. Clinical studies on conditions often have contradictory results for a vast number of reasons: the lab and software used; the diet of the people in the studies; often low significance (often P < 0.05 is cited, with our lab specific analysis we typically use P < 0.001 as a criteria).

Q: The high Bilophila on my Biomesight got my attention. Is that something that MP.com does not identify specifically to address but rather it just corrects as part of the overall microbiome rebalancing?

• MP does a holistic analysis — so things that may reduce Bilophila but also shifts others bacteria in the wrong direction may be eliminated.
  • “The typical MP matrix to solve is around 60 taxa (up to 430 in some cases) by 2092 possible modifiers – thus an array of some 12,000 to 800,000 elements to consider. The Monte Carlo method typically uses 5 runs resulting in 60,000 to 4,000,000 elements evaluated.” [blog]

Q: The prebiotic that shows up as #1 rec for me is Prefor Pro, but you have chicory. Is that due to availability?

• The retailed probiotic selection is based on the species in the probiotics ignoring additives and relative amounts (often not declared). If there are issues with these additives, then just move down the list.

Q: Do you think using the condition specific bacteria shifts is a better approach than the bacteria identified by the AI as “likely to be causing my symptoms”?

• Definitely, my observations from feedback is that targeting those bacteria do moderate or eliminate symptoms.

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.

This is part of a series of technical notes. If this interests you, you may wish to view others in the series.

A colleague wrote me:

I am currently researching companies who are using machine learning and LLM approaches for Microbiome analysis I wanted to know your opinion why this approach fails/ is less precise than your [MicrobiomePrescription MP] Fuzzy Logic Expert System with Monte Carlo method.

I have been doing a wide variety of Artificial Intelligence development professionally since 1988 for firms including Microsoft, Amazon, Verizon and Starbucks. I also have a reasonable science background including High School General Science teacher and College Chemistry and Physics instructor.

In the early days of Microbiome Prescription I tried a panacea of technics such as Random Forest, linear and non linear regression, supervised learning, etc. The results were less than acceptable. A friend, Richard Sprague, also ex-Microsoft, who worked as Citizen Scientist in Residence for uBiome for a while set up a series of meeting with the teams of the Allen Institute for AI [AI2]. The consensus working with those experts was that my direction of using the expert system model was far superior than what they could come up with.

How does Machine Learning and LLM work?

The core mechanism is pattern recognition of key words and phrases. This allows a numeric representation of the literature to be created, i.e. [subject #,verb #, object #]. When someone asks about subject, a set of equivalent subject # is obtained, a set of equivalent verb # is obtained and we just lookup the data. The number of records may be used to determine priority or most probable outcome.

This leads to the problem with many implementations, phrase recognition. To illustrate consider two articles, one mentions: Limosilactobacillus reuteri and the other mentions for Lactobacillus reuteri. A microbiologist knows that these are the same, but the typical ML or LLM does not. Do we end up with two collections of information? Since both Limosilactobacillus and Lactobacillus are also taxological units, do we get this species information incorrectly applied to the genus?

This is further complicated going to studies dealing with strains. Compare these two species: Escherichia coli O157:H7 and Escherichia coli Nissle 1917. One causes food poisoning, and the other is a probiotic shown to improves Crohn’s disease. Strains often have multiple identifiers and retail names. For example, Enterococcus faecium SF68 is also known as Enterococcus faecium NCIMB 10415 and sold retail as Bioflorin for humans and multiple brands in veterinary practice.

So the number 1 issue is correct identification. We also have some name collisions: Bacteroidetes is the name of both a class [Taxon 200643] and a phylum [Taxon 976] (which is now called Bacteroidota). A knowledgeable reader can reason out what is meant.

Taxonomy Hierarchy Inference

Information on the microbiome is sparse. A microbiome practitioner asked about what would reduce Lactobacillus balticus may discover that there is no literature on it. This practitioner would then infer that whatever reduces Lactobacillus would reduce it. This is an inference which MP does (remember it is an expert system mimicking the behavior of a human expert). MP takes it one step further by recognizing that is now classified as Limosilactobacillus balticus. Instead of items impacting Lactobacillus, it will use items impacting Limosilactobacillus. The suggestions are more probable to be correct.

What to address question

The various AI systems may well scrap ranges from studies to apply to a microbiome sample. Results are not consistent from lab to lab [the back story is this 2019 post: The taxonomy nightmare before Christmas…] . This means that reference ranges are more inconsistent as a consequence. Below are some range examples from commercial tests

The reference range dilemma is why MP generates suggestions based on reference ranges determine by multiple methods: Average +/- Std Dev, Box Plot Whiskers, ranges from specific sources (including the processing lab in some cases), and patent pending algorithms. Each reference range is determined from a large collection of sample processed by the same lab. The suggestions using each of these reference ranges are then aggregated into a “consensus” report (i.e. Monte Carlo method).

“We are using AI to get Suggestions” – marketing hype!

A few years ago, microbiome testing companies would attempt to get creditability by claiming their suggestions were created by registered dietitian. Today, “AI” is the replacement. If you ask about what AI methodology is being used, the size and scope of the data behind, etc. 99% of the time you will be given a “It’s proprietary! We cannot disclose it“.

They may be truthful that is coming from AI, for example, someone asked https://www.perplexity.ai/, “Which foods reduces Fusobacterium” (Example answer – Fusobacterium nucleatum is what is cited). They copy the answer into their database to show their customers. Thus it is true that the answer is coming from AI; but it is a one-dimensional blinkered answer that will often leaves their customer worse. This AI is ignorant that Fusobacterium prausnitzii [The bacteria formerly known as Faecalibacterium prausnitzii] belongs to it. This bacteria has lots of studies that are ignored by the AI! The AI appears ignorant that two studies report both quercetin, mastic gum (prebiotic) reduces it. It likely has the data but has misclassified it.

My observations of reviewing many sites is that suggestions are scoped to a single bacteria and ignores side-effects on other bacteria that would also be impacted. MP uses holistic algorithms [which is what would be expected when someone has done Ph.D. courses on Integer and Non-Integer Programming Optimization]. The typical MP matrix to solve is around 60 taxa (up to 430 in some cases) by 2092 possible modifiers – thus an array of some 12,000 to 800,000 elements to consider. The Monte Carlo method typically uses 5 runs resulting in 60,000 to 4,000,000 elements evaluated.

Examples of LLM gone bad

Most experts know that Mutaflor is Escherichia coli Nissle 1917 and is clearly names as such in publicly accessible papers on the National Library of Medicine. So this response is one of AI’s famous hallucinations; hallucinations are not possible from expert systems.

Bottom Line

Microbiome testing firms may correctly claim[in a legal sense] they are AI based. If they refuse to fully disclose the methodology being used (ideally on their site), then the safest assumption is that they got a summer intern to ask one of the LLM’s the questions and just copied the answers into the database. Without full disclosure, they simply cannot be trusted.

If you are considering using AI because some “hot shot evangelist or venture capitalist” is pushing for it; then — look at the above issues and insists on documentation on how each of these issues will be addressed. Until there is clear, understandable documentation on these issues, “The suitability of AI has not been shown for the proposed AI implementation” and stop wasting time and money!

MP uses a very old model of AI that requires manual data curation being feed to the expert system. This a hallmark of expert systems.

I wish to give a special thanks to Juan Pablo C., Assistant professor in Universidad Mayor, Chile and former Senior Bioinformatician for uBiome for bringing to my attention the article below. He was then kind enough to point me at appropriate data sources to allow me to implement this new measure.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes

The detection of oral bacteria in faecal samples has been associated with inflammation and intestinal diseases. The increased relative abundance of oral bacteria in faeces has two competing explanations: either oral bacteria invade the gut ecosystem and expand (the ‘expansion’ hypothesis), or oral bacteria transit through the gut and their relative increase marks the depletion of other gut bacteria (the ‘marker’ hypothesis). … By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and could potentially identify cases where therapies are needed to rebuild the resident microbiome rather than protect against invading oral bacteria.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes [May 2024]

I have implemented a new measure on Microbiome Prescription to estimate the amount of leakage. It is located in the Health Analysis section (a large and often slow loading page)

The report gives a percentile ranking based on the total for genus that are Oral Bacteria and then lists the specific genus and species present

As with other study specific pages, you can hand select bacteria of interest or click on the bacteria to get more information about the bacteria. Of special note is Prevotella being listed as there is evidence suggesting that it increases due to fungi in the environment – thus the gut microbiome depletion is likely why it can take up residency.

I will be looking to modify the suggestions algorithms to exclude oral bacteria and thus have the suggestions focused on gut microbiota depletion as a treatment option.

For other analysis for ME/CFS see this list.

Concerns

“For the Ombre/Thryve sample data, dated 12/31/24, I found the following:

• Rickettsiales – order – 31%ile High
• Rickettsiaceae – family – 34%ile High
• Rickettsieae – tribe – 34%ile High
• Parasutterella excrementihominis – species – 69%ile High

The reason these bacteria stand out to me are because my CFS journey,
like many, began with a Lyme diagnosis 20 years ago.
And as testing and research improved, Lyme became synonymous with
“co-infections” like Rickettsia and Ehrlichia and a whole host of
others in the “Lyme Soup”.”

Parasutterella excrementihominis has been present in all samples, varying from 61-89%ile. For Rickettsiaceae, we see in much older samples

• 2022-04-11 at 6%ile
• 2022-11-01 at 18%ile

All bacteria wax and wane over time. Personally, I would not be concerned unless it is constantly above 75%ile for pathogenic. It is is over, then take your concerns to your MD for appropriate testing to confirm..

For this person we have a lot of symptom forecasts[new algorithm] matching reported symptoms

Analysis

Ombre Lab Processing

We are looking at the Ombre Lab Report data below. These are a lot of swings over the last 4 sample. Looking at the prior (likely the worse)

• Eubiosis was the worse
• Bacteria Under 10%ile was the highest
• Outside Kaltoft-Moldrup was the highest
• Chao1 Index was the highest

A lot of other measures had little or no change (i.e. JasonH, Microba Co-Biome,,Nirvana/CosmosId etc) suggesting low sensitivity to detect shifts. Some hits at a continuous shift (Simpson Diversity Index.

The wide variety of lab read quality makes reliable comparisons difficult. The Lab Read Quality bounces around, and with that, other values may echo these shifts (i.e. up to 20% shifts for some measures). A low read quality means less bacteria are reported, for example, when it was low, the Outside Kaltoft-Moldrup has low, when it was high, the value became high.

Another way to view it is this: If 10% are out of range and 660 are reported then we have 66. If we have 940 in another report then we would expect 94. This could be misread as a 94/66 or a 43% increase in out of range bacteria. Technically, it is more complicated but that should explain the problem.

Criteria	12/31/2023	10/2/2023	7/28/2023	4/18/2023
Lab Read Quality	5.6	12.6	8	4.9
Eubiosis	53.4	8.2	38.8	83.1
Rickettsiaceae	34%ile	0	0	0
Outside Range from GanzImmun Diagostics	17	17	14	14
Outside Range from JasonH	5	5	5	5
Outside Range from Lab Teletest	32	32	25	25
Outside Range from Medivere	17	17	17	17
Outside Range from Metagenomics	8	8	7	7
Outside Range from Microba Co-Biome	5	5	6	6
Outside Range from MyBioma	16	16	9	9
Outside Range from Nirvana/CosmosId	24	24	26	26
Outside Range from Thorne (20/80%ile)	310	310	210	210
Outside Range from XenoGene	51	51	41	41
Outside Lab Range (+/- 1.96SD)	36	20	7	19
Outside Box-Plot-Whiskers	114	97	71	69
Outside Kaltoft-Moldrup	164	239	141	79
Bacteria Reported By Lab	765	941	762	663
Bacteria Over 90%ile	101	82	42	58
Bacteria Under 10%ile	60	302	102	28
Shannon Diversity Index	2.953	3.099	2.987	2.62
Simpson Diversity Index	0.043	0.053	0.079	0.098
Chao1 Index	23041	38183	26135	15707
Pathogens	28	25	23	15
Condition Est. Over 90%ile	1	1	1	1
Top 10 forecast symptoms matching	9	9	10	n/a

Rickettsiaceae was seen in earlier samples:

• 2022-04-11 at 6%ile
• 2022-11-01 at 18%ile

Biomesight Reporting

One sample could not be successfully processed thru Biomesight. Biomesight indicated that there are ongoing technical issues with their processing of Thryve FastQ files.

Criteria	12/12/2023	7/28/2023	4/18/2023
Lab Read Quality	5.6	8	4.9
Eubiosis	42.8	80.8	74.3
Outside Range from GanzImmun Diagostics	13	14	14
Outside Range from JasonH	3	5	5
Outside Range from Lab Teletest	16	23	23
Outside Range from Medivere	12	13	13
Outside Range from Metagenomics	5	8	8
Outside Range from Microba Co-Biome	1	2	2
Outside Range from MyBioma	8	4	4
Outside Range from Nirvana/CosmosId	11	23	23
Outside Range from Thorne (20/80%ile)	144	250	250
Outside Range from XenoGene	20	23	23
Outside Lab Range (+/- 1.96SD)	38	22	25
Outside Box-Plot-Whiskers	149	97	81
Outside Kaltoft-Moldrup	99	123	60
Bacteria Reported By Lab	664	696	618
Bacteria Over 90%ile	122	93	61
Bacteria Under 10%ile	38	46	9
Shannon Diversity Index	1.565	1.242	1.507
Simpson Diversity Index	0.037	0.057	0.078
Chao1 Index	17542	14092	10705
Shannon Diversity Percentile	41.2	10.8	32.6
Simpson Diversity Percentile	32.4	55.9	72
Chao1 Percentile	88.4	75.6	53.8
Lab: BiomeSight
Pathogens	25	23	19
Condition Est. Over 90%ile	1	1	1
Top 10 forecast symptoms matching	7	n/a	9

Bottom line, the variability of 16s Lab Quality leaves too much fogginess for interpretations 🙁

Revised Symptom Forecasting

This algorithm is similar to the Eubiosis algorithm. We compute the expected number of matches to bacteria shifts associated with the symptoms. The expected theoretical threshold by randomness is 16%. A higher number indicate increased odds, a lower number decreased odds. This is based on the existing annotated samples uploaded. It is not definitive and often there can be multiple subsets of bacteria associated with a symptom. The match is on too much or too few of a collection of bacteria

The checkmarks are the entered symptoms, the list are the predictions from most likely to lesser.

As seen in another post over multiple samples, we have a good accuracy in predicting symptoms from the microbiome.

Going Forward

Again, using Just give me suggestions include Symptoms is how we are going to proceed. And then add in the two Special Studies. This results in 7 packages of suggestions for each lab. Since we have two different interpretations (Biomesight and Thryve) of the FastQ files. We then do an Uber Consensus merging 14 packages of suggestions!

And the merge (on [Multiple Samples] tab)

Thresholds: High is 383 thus 190 or higher, Low is -462 this -231 or lower

First we are going to look on what ALL 14 sets of suggestions agreed upon

• linseed(flaxseed)
• high red meat
• high animal protein diet
• lactobacillus rhamnosus (probiotics)
• low-fat high-complex carbohydrate diet

So low-fat animal protein. There were no avoids that everyone agreed upon.

Usual High Level Review

• Amino Acids and flavonoids
  • fumarate
  • propionate
  • l-taurine
  • l-glutamine
  • glutamate
  • melatonin supplement
  • luteolin (flavonoid)
  • Arbutin (polyphenol)
  • diosmin,(polyphenol)
  • Hesperidin (polyphenol)
• Diet and Food
  • vegetable/fruit juice-based diets
  • high animal protein diet
  • gluten-free diet
  • linseed(flaxseed)
  • high red meat
  • Kale
• Probiotics
  • bifidobacterium longum bb536 (probiotics)
  • lactobacillus rhamnosus (probiotics)
• Vitamins
  • Vitamin B1,thiamine hydrochloride
  • Vitamin B9,folic acid
  • Vitamin B6,pyridoxine hydrochloride
  • vitamin B7, biotin
  • Vitamin C (ascorbic acid)
  • Vitamin B-12
  • vitamin d

Remember to check suggested dosages here. A common issue is taking token amounts which are unable to effect changes.

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 result 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 anyone. Always review with your knowledgeable medical professional.

For other analysis for ME/CFS see this list.

Prior Posts:

• A review of a ME/CFS Microbiome Jan 2021
• Evidence of ME/CFS improving using Microbiome Data May 2022
• Comparing 4 ME/CFS Samples with New Tool Jul 2022
• A Microbiome Trek Continues thru the land of ME/CFS. Feb 2023
• Biomesight vs Thorne Tests – Differences Mar 2023
• Post Exertional Malaise (PEM) with diminished ME/CFS Nov 2023

Analysis

This person had been doing duel interpretation of their FastQ files (Thyrve and BiomeSight). The results are mixed. A common problem comparing results is not there, the Lab Quality is similar for most of the samples,

Criteria	2/2/2024	3/23/2023	10/29/2022	8/22/2022	7/21/2022	4/30/2022
Lab Read Quality	4.9	8.7	5.1	5	4.9	4.8
Eubiosis	100% 🙂	36%	50%	66%	87%	74%
Outside Range from GanzImmun Diagostics	13	13	18	18	16	16
Outside Range from JasonH	6	6	6	6	7	7
Outside Range from Lab Teletest	26	26	27	27	29	29
Outside Range from Medivere	17	17	17	17	17	17
Outside Range from Metagenomics	9	9	8	8	9	9
Outside Range from Microba Co-Biome	9	9	9	9	11	11
Outside Range from MyBioma	8	8	8	8	9	9
Outside Range from Nirvana/CosmosId	19 🙂	19	22	22	24	24
Outside Range from Thorne (20/80%ile)	217 🙂	217	300	300	216	216
Outside Range from XenoGene	48	48	53	53	45	45
Outside Lab Range (+/- 1.96SD)	26	4	6	16	8	4
Outside Box-Plot-Whiskers	104	34	36	120	73	44
Outside Kaltoft-Moldrup	126	161	85	125	102	70
Bacteria Reported By Lab	694	632	604	843	752	575
Bacteria Over 90%ile	65 🙁	28	30	87	47	37
Bacteria Under 10%ile	76	140	28	61	51	25
Shannon Diversity Index	3.107	3.028	2.987	3.227	3.126	3.121
Simpson Diversity Index	0.044	0.085	0.076	0.045	0.088	0.075
Chao1 Index	16886	13972	13231	27357	20690	9772
Pathogens	29	30	25	34	35	28
Forecasted	4	3	n/a	n/a	4	4

We have a few matches with the latest symptom forecast. Remember this is pattern matching over symptoms that likely have dozen of subsets.

Going Forward

We are going to do the [Just give me suggestions include Symptoms] . This gives us 5 packages of suggestions.

Looking only at what is suggested by all 5 sets of suggestions

• whey
• aloe vera
• Tributyrin
• Shen Ling Bai Zhu San

• Avoids
  • non-starch polysaccharides
  • proton-pump inhibitors (prescription)
  • lactobacillus plantarum (probiotics)
  • blueberry
  • Limosilactobacillus fermentum (probiotic)
  • alcoholic beverages

Highest value was 280, thus a threshold of 140, lowest value was -220, thus a threshold of -110. What was VERY DIFFERENT then most samples, there were not a swarm of prescription items hogging the top. Also the range of priorities was much less than most samples examined. This implies less severe dysbiosis (or less that we can do, i.e. suggestions).

Takes

• Amino Acids and Supplements
  • epicatechin
• Foods and Herbs
  • aloe vera
  • Shen Ling Bai Zhu San
  • eggs
• Probiotics and Prebiotics – nothing
• Antibiotics: The top two are all common with ME/CFS
  • clarithromycin (antibiotic)s[CFS]
  • metronidazole (antibiotic)s[CFS]

Avoids

• alcoholic beverages
• blueberry
• pea (fiber, protein)
• dairy
• Slippery Elm
• Rhubarb Peony Decoction
• Limosilactobacillus fermentum (probiotic)
• non-starch polysaccharides
• proton-pump inhibitors (prescription)
• Slippery Elm
• Bile Acid Sequestrant
• glycerol monolaurate (Monolaurin)

Antibiotics as FYI

The top 3 antibiotics that had all 5 agreement on were also often used for ME/CFS:

• clarithromycin (antibiotic)s[CFS]
• metronidazole (antibiotic)s[CFS]
• azithromycin,(antibiotic)s[CFS]

Shot in the Dark for PEM

PEM is an ongoing issue for this person. With the new symptoms algorithms, I decided to run both with only PEM selected. The top results are

• Take
  • inulin (prebiotic)
  • Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
  • arabinogalactan (prebiotic)
  • pectin
  • clostridium butyricum (probiotics),Miya,Miyarisan
  • barley,oat
  • l-proline
  • soy
• Avoid
  • Hesperidin (polyphenol)
  • gluten-free diet
  • Curcumin
  • Vitamin C (ascorbic acid)
  • thyme (thymol, thyme oil)
  • Vitamin B1,thiamine hydrochloride (and most B-vitamins)
  • low fodmap diet
  • Arbutin (polyphenol)
  • diosmin,(polyphenol)

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.

In my weekly review of new studies on PubMed of potential interest (50-150/week) I came across this one

Exploring the casual association between gut microbiome, circulating inflammatory cytokines and chronic pancreatitis: A Mendelian randomization analysis [2024]

I suspected that this may be connected to Mast Cell Activation Syndrome (MCAS), a condition that many people struggle with. I thought it would be good to do a walk-through of how I currently research things that are of interest but not explicit in the literature.

My first step is to see if it is worth while exploration. At present, I use Perplexity because it provides references for what it answers. You need to check them, one reference that I got on a different question came from [sarcasm]”the definitive medical resource: Amazon Product Information“.

The [answer] was: “Circulating inflammatory cytokines can have a significant impact on Mast Cell Activation Syndrome (MCAS)…“. If you click on [answer] you will see the full response.

Phrase I Tracking immediate leads

Returning to this new article we read:

• Sellimonas and Eubacterium ventriosum were positively associated (thus you want to reduce)
• Escherichia-Shigella, Eubacterium ruminantium and Prevotella 9(i.e. Prevotella copri) were negatively associated (thus you want to increase).

In terms of compounds we have items which we lack microbiome data for, BUT as you will see later, we have herb data for!

• CCL23 increases risk [Chemokine (C-C motif) ligand 23 (CCL23) is a small cytokine belonging to the CC chemokine family that is also known as Macrophage inflammatory protein 3 (MIP-3) Wikipedia ] 
• SIRT2, ADA and Caspase 8 decreases risk

We now have a bunch of scents to follow. Some may bear fruit.

Next step was to look up the above bacteria (and their parents) on our citizen science site.

I inspected these lists (which are at the genus level), and then look for matches. There were none 🙁

I went over to a sample from a MCAS person done with shotgun (Thorne) and a parallel Biomesight and Ombre test done at the same time. For the positively associated there were only one, reported by Ombre at 39%ile). For negatively:

• Escherichia was 50%ile (33%ile from Biomesight, 59%ile from Ombre)
• Shigella was 0%ile
• Eubacterium ruminantium was reported only by Ombre at 100%ile
• Prevotella copri was none

So the person appears to be deficient in bacteria that inhibits things. Incidentally, this person takes Mutaflor and SymbioFlor-2 (Escherichia probiotics) and have reported that it seems to moderate histamine issues.

Checking KEGG for scents:

• SIRT2 – we see NAD-dependent protein deacetylase sirtuin-2 isoform X1, this corresponds to this person feeling better from taking (flushing) niacin. Perplexity clarifies it with “Niacin (also known as vitamin B3) is a precursor for the coenzyme nicotinamide adenine dinucleotide (NAD).” and five references!
• ADA – adenosine deaminase connected to [EC:3.5.4.4] which we can seen on the site, this person BiomeSight was at 98%ile by Biomesight and 43%ile by Ombre, so situation is fine
• Caspase 8 [EC:3.4.22.61] has no associated bacteria to estimate from.

Take Away

For this person, the study and experience has reasonable agreement with this person’s positive response to Escherichia probiotics and (flushing) niacin appearing to be explained.

Phrase 2

This starts by using Perplexity to find branches of the above to explore.

• What foods and herbs reduce Sellimonas? – Nothing
• What foods or herbs reduces Eubacterium ventriosum? Nothing
• What food or herbs increases Eubacterium ruminantium? Turmeric!!! BUT THIS WAS A HALLUCINATION! The AI deemed [TRF] to be turmeric residue fiber, but in the study it was Time-restricted feeding (TRF). — this illustrates the need to check the source every time!!
• What food or herbs increases Prevotella copri? Nothing specific, suggestions about Prevotella only.

Now, if we go to Microbiome Prescription for these, example Sellimonas we have many dozens of substances with some being explicit. Clearly, Perplexity is inferior for bacteria information.

Next, we go and look at things not on microbiome prescription.

• What are foods and herbs that will increase NAD? The response does include the hallucination cited above.
  • “Niacin-rich foods: Niacin (vitamin B3) is a precursor for NAD biosynthesis in the body. Therefore, consuming foods rich in niacin can provide the raw material for NAD production.”
  • “Tryptophan-rich foods: The amino acid tryptophan can also be converted to NAD in the body, albeit at a lower efficiency than niacin.2 Foods rich in tryptophan include:Turkey, Oats, Bananas, Milk, Yogurt” The quality of references were poor (very poor), so I did a PubMed search and found:
    • “Underlying these benefits [of moderate excise] were transcriptional changes in enzymes driving the conversion of tryptophan to NAD(+), this leading to increased hepatic NAD(+) and elevated activity of the NAD(+)-” [2021]

So tryptophan rich food plus moderate exercise would be an alternative for people who cannot use (flushing) niacin.

• What foods or herbs increases adenosine deaminase ? The response was only what was to be avoided.
  • Avoiding Curcumin/turmeric is inferred, Mori Folium (Mulberry leaf), Saururi Herba (Lezpedezia bicolor):
• What foods or herbs increases Caspase 8? Nothing explicit, a lot of “this suggests”, “This implies”
• What foods or herbs decreases CCL23? Nothing

I decided to dive into PubMed for Caspase 8 with herbs and got 91 results. Some interesting ones are below. WARNING: Some of the herbs below are prohibited to be sold in the US because of risks. They are listed here for illustration only:

• “The expressions of caspase–8 and caspase-9 were significantly elevated by the Kerra extract ” [2023]
• “Furthermore, it[ leaf extract of Acorus calamus] also increased expressions of caspase-3, caspase-9, caspase-8,” [2023] FYI, available for sale. [WARNING from Web MD]
• “Tribulus terrestris (TT), a herb belonging to Zygophyllaceae family …caspase 8 genes were also upregulated” [2019] FYI, it can be purchased as a powder in bulk
• “extracted from the Chinese herb Tripterygium wilfordii Hook…was associated with activation of both caspase-3 and caspase-8” [2006]
• “isolated from Solanum incanum herb … activation of caspase-8” [2004]

Of special interest is that most of these studies were in the context of cancer with caspase-8 inhibiting the cancer. MCAS and cancer are associated: ” Our data support the view that mast cells may promote development of certain malignant tumors. These findings indicate a need for increased surveillance of certain types of cancer in MCAS patients irrespective of its individual clinical presentation.” [2017]

I did a second dive into CCL23 (and checked it alternative names too!)

• “CCL23 is produced predominantly by mast cells in systemic mastocytosis, and CCL23 plasma levels are associated with disease severity, correlating positively with established markers of disease burden, thus suggesting that CCL23 is a specific SM biomarker.” [2023]
• “Human neutrophils produce CCL23 in response to various TLR-agonists and TNFα. ” [2017]
  • This means that reducing TNFα should reduce CCL23. [perplexity gives a list with reference including: Trikatu, Allium sativum, Capsaicin, Carvacrol,Thymol. ]
  • TLR-agonists are prescription medicines… and also Fungi, Seaweed
    

Take Away

Usually I work on microbiome modifications. The reason is simple — it is pretty time consuming to maintain the database! Above, we see a secondary approach — targeting specific enzymes and compounds using PubMed. I would love to get funding to put this extra information into an equivalent (and connected) expert system.

Bottom Line

The above illustrates how AI (Large Language Models – ChatGPT and Perplexity) can assist in researching — but beware of hallucinations! and check the sources of the information.

What is interesting is that we cascaded from Circulating inflammatory cytokines to MCAS to specific compound and then to herbs. The herbs are often atypical herbs that should be researched for safety and any other known issues. Many appear to be used traditionally in eastern medicine. This approach gives new options to consider when conventional options fail. We do not know the microbiome impact of these herbs — but we do know the impact on specific compounds and enzymes of interest. I would love to build a fuzzy logic expert system in this area so that contraindications are visible and the results are sounder; unfortunately that takes skilled people and time.

As always, before starting any herbs, spices and supplements, you should review your plan with a licensed medical professional.