This post started out seeking to confirm or debunk the claim located here.

The method was very simple because we have a continuous stream of samples from before COVID, before the COVID vaccination and after the majority of people uploading samples would have been vaccinated. If this massive change is happening then the pre-COVID bifidobacterium count (by lab) would be much higher than the post-COVID vaccination bifidobacterium counts.

My results: there was no statistical significance between the averages

• Pre 2020-01-01: Average Count 20380 on 118 samples, Std Dev 98300
• Post 2022-06-01: Average Count 26111 on 406 samples, Std Dev 72700

That is a 28% increase when a decrease was expected from the above talk.

I am open data, so you can pull the data and check the calculations:

Volatility of Numbers

I was also curious to see if there was any apparent month by month pattern, so I pulled the statistics for biidobacterium, shown below. It is illuminating to a statistician like me, perhaps confusing or concerning to people with poor understanding of statistics (who would expect the numbers from month to month to be similar).

		Thryve			BiomeSight
Year	Month	Average	Std Dev	Obs	Average	Std Dev	Obs
2020	7	32438	131646	24	27929	35826	14
2020	8	25456	43405	21	7683	8948	9
2020	9	13410	19329	17	18501	22566	14
2020	10	84056	148144	18	4370	7390	20
2020	11	18598	34049	9	4926	8197	13
2020	12	10078	17108	16	1841	2718	29
2021	1	68152	172405	20	12436	20675	32
2021	2	101600	163980	30	17289	55509	45
2021	3	57957	103248	17	14482	33774	33
2021	4	21979	42967	30	7700	24436	46
2021	5	24693	51744	56	14257	33608	38
2021	6	28166	84491	39	21465	85762	51
2021	7	47023	105209	39	22620	67229	51
2021	8	60283	82398	43	18427	79784	37
2021	9	62438	92929	28	12002	19635	41
2021	10	13121	29924	24	5922	8565	38
2021	11	11515	27095	57	9996	24966	58
2021	12	28582	80191	17	11498	29919	63
2022	1	15114	28760	38	7076	15149	50
2022	2	24816	59069	32	11707	27202	52
2022	3	10486	23995	33	20243	51539	47
2022	4	10207	21580	57	7916	18288	69
2022	5	33471	82497	80	10304	23719	81
2022	6	23861	60126	53	8053	21994	235
2022	7	26797	109435	40	10709	20439	62
2022	8	67707	132108	60	8085	17190	85
2022	9	13926	17622	28	12635	21332	92
2022	10	9090	14049	45	9627	21171	89
2022	11	10296	19034	39	7293	12150	61
2022	12	3186	6194	21	10887	21390	42
2023	1	10224	18215	41	13302	21612	89
2023	2	10604	22883	52	9103	19781	72
2023	3	65953	78173	30	13566	34256	57

My conclusion is that you need to have two things to get good results:

• All of the samples should be processed by the same lab at the same time. Different batches of reagents may cause different results.
• You need good sample sizes, at least 100+
• You need to be very very careful not to cherry pick data (example below)

An example from Thryve/Ombre data above, with a sample size of 30, the average was 101600. Later a sample size of 21 reported just 3186. Conclusion: going back to school caused family bifidobacterium to tank!

On sample size of 100 issue:

This is an update of my 2016 post: Low Iron – A Gut Bacteria Connection. One key addition is that Vitamin A supplementation may have significant positive impact.

The microbiota shifts the iron sensing of intestinal cells [2016]. “The amount of iron in the diet directly influences the composition of the microbiota. Inversely, the effects of the microbiota on iron homeostasis have been little studied….Commensal organisms (Bacteroides thetaiotaomicron VPI-5482 and Faecalibacterium prausnitzii A2-165) and a probiotic strain (Streptococcus thermophilus LMD-9) led to up to 12-fold induction of ferritin in colon.”

Probiotics to Take

• Probiotic strain Lactobacillus plantarum 299v increases iron absorption from an iron-supplemented fruit drink: a double-isotope cross-over single-blind study in women of reproductive age[ 2015 ].[2017] [2019] [2020] [2021]
• Effects of Lactobacillus casei on Iron Metabolism and Intestinal Microflora in Rats Exposed to Alcohol and Iron.[2022]
• “No significant differences were found between the two yogurts (one with live  cultures and the other without) in terms of their effects on serum iron, AST and ALT levels.” [2013] – so only certain  bacteria are involved. Most lactobacillus do not have an effect [2007]
• Oral administration of Bifidobacterium longum CECT 7347 ameliorates gliadin-induced alterations in liver iron mobilisation [2013].
• “we show that a panel of probiotics are not able to respond to increased iron availability, and identify an isolate of Streptococcus thermophilus NCIMB 41856 that can increase growth rate in response to increased iron availability.” [2011]
• Vitamin A modulates the expression of genes involved in iron bioavailability [2012] [2019]
• Heat-Killed Lactococcus lactis subsp. cremoris H61 Altered the Iron Status of Young Women: A Randomized, Double-Blinded, Placebo-Controlled, Parallel-Group Comparative Study [2022]

Likely No or Negative Effect

• “Bifidobacterium bifidum.. decreased amounts of calcium and iron released from bread.” [2012]
• Probiotics Lactobacillus reuteri DSM 17938 and Lactobacillus casei CRL 431 modestly increase growth, but not iron and zinc status, among Indonesian children aged 1-6 years [2013].
• “we show that a panel of probiotics are not able to respond to increased iron availability, and identify an isolate of Streptococcus thermophilus NCIMB 41856 that can increase growth rate in response to increased iron availability.” [2011]
• Probiotic bacteria reduce salmonella typhimurium intestinal colonization by competing for iron [2013]. “Thus, iron availability impacts S. Typhimurium growth, and E. coli Nissle reduces S. Typhimurium intestinal colonization by competing for this limiting nutrient.” – So E.Coli probiotics and iron deficiency may not play well together.
  • ” Intestinal bacteria compete for the essential nutrient iron, leading to replacement of pathogenic Salmonella by the probiotic Escherichia coli Nissle, which is better equipped with iron acquisition systems, and resolution of infectious colitis.” [2013]

This is a person as in this prior set of posts, A Microbiome Trek Continues thru the land of ME/CFS.

There are two key differences that needs to be understood

• Difference in numbers reported (of bacteria and percentiles)
• Will the suggestions change?

Comparison of Results: Thorne to Biomesight

The samples were only a few weeks apart, so similar data. The ratio of bacteria reporting is 6x more for Thorne than Biomesight, so the expectation would be similar shifts for most of the others.

Key Difference: Bacteria Percentiles come from Thorne, the other percentiles are computed against a composite of other samples (until we get sufficient samples). The Conditions, Enzymes and Compound estimates are likely unreliable (we compare against all tests and not other samples from the same procession) and we will ignore in this analysis.

Many of the criteria are identical between tests: Outside Range for JasonH, Medivere, Metagenomics , MyBioma, Nirvana/CosmosId and XenoGene. So for people using those criteria — there is no difference between the tests.

The Bacteria over 90% and Bacteria under 10% are a simple statistic to understand. 10% should be under 10% and 10% above the 90%ile to have a balance microbiome.

With Thorne we have 3226 bacteria and true randomness then you would expect around 322 in each group. We find 239 over 90%, close, but a whopping 1577 under 10% — that 48% of all bacteria, not 10%!!! In other words, we have a massive number of different bacteria at low levels. It is not a problem of a few bacteria being too high (which is a common belief about gut dysfunction), but many only have token amounts.

For Biomesight, we have 503, and thus would expect 50 and 50. For over 90%ile, we have just 25, and for under 10%, 108 bacteria. The high %ile is just 50% of expected and 200% of expected for low with Biomesight; Thorne is just 75% of expected for high, but a massive 489% of expected for low.

Criteria	Thorne Sample	Biomesight
Bacteria Reported By Lab	3226	503
Bacteria Over 99%ile	193	10
Bacteria Over 95%ile	212	18
Bacteria Over 90%ile	239	25
Bacteria Under 10%ile	1577	108
Bacteria Under 5%ile	1411	44
Bacteria Under 1%ile	1106	3
Different Labs – Items Skipped
Pathogens	162	34
Outside Range from JasonH	6	6
Outside Range from Medivere	16	16
Outside Range from Metagenomics	7	7
Outside Range from MyBioma	5	5
Outside Range from Nirvana/CosmosId	17	17
Outside Range from XenoGene	35	35
Outside Lab Range (+/- 1.96SD)	189	8
Outside Box-Plot-Whiskers	685	27
Outside Kaltoft-Møldrup	1753	91
Condition Est. Over 99%ile	6	6
Condition Est. Over 95%ile	15	7
Condition Est. Over 90%ile	24	10
Enzymes Over 99%ile	93	10
Enzymes Over 95%ile	673	118
Enzymes Over 90%ile	1131	647
Enzymes Under 10%ile	312	150
Enzymes Under 5%ile	262	75
Enzymes Under 1%ile	183	12
Compounds Over 99%ile	230	100
Compounds Over 95%ile	498	463
Compounds Over 90%ile	684	606
Compounds Under 10%ile	1350	599
Compounds Under 5%ile	1336	580
Compounds Under 1%ile	1324	569

Comparison of Results: Thorne to Ombre

The Bacteria over 90% and Bacteria under 10% are a simple statistic to understand. If you have 3226 bacteria and true randomness then you would expect around 322 in each group.

• For Ombre we would expect 59 over 90%ile and under 10%ile. close. We have 22 or 37% of expected for low %ile and 117 or 200% of expected for low percentile.

Many of the criteria are identical between tests: Outside Range for JasonH, Medivere, Metagenomics , MyBioma, Nirvana/CosmosId and XenoGene. So for people using those criteria — there is no difference between the tests.

Criteria	Thorne Sample	Ombre
Bacteria Reported By Lab	3226	588
Bacteria Over 99%ile	193	2
Bacteria Over 95%ile	212	10
Bacteria Over 90%ile	239	22
Bacteria Under 10%ile	1577	117
Bacteria Under 5%ile	1411	67
Bacteria Under 1%ile	1106	6
Different Labs – Items Skipped
Pathogens	162	34
Outside Range from JasonH	7	7
Outside Range from Medivere	14	14
Outside Range from Metagenomics	5	5
Outside Range from MyBioma	8	8
Outside Range from Nirvana/CosmosId	18	18
Outside Range from XenoGene	46	46
Outside Lab Range (+/- 1.96SD)	189	5
Outside Box-Plot-Whiskers	685	34
Outside Kaltoft-Møldrup	1753	129
Condition Est. Over 99%ile	6	0
Condition Est. Over 95%ile	15	0
Condition Est. Over 90%ile	24	0
Enzymes Over 99%ile	93	0
Enzymes Over 95%ile	673	9
Enzymes Over 90%ile	1131	101
Enzymes Under 10%ile	312	165
Enzymes Under 5%ile	262	65
Enzymes Under 1%ile	183	2
Compounds Over 99%ile	230	38
Compounds Over 95%ile	498	236
Compounds Over 90%ile	684	332
Compounds Under 10%ile	1350	248
Compounds Under 5%ile	1336	159
Compounds Under 1%ile	1324	33

My personal opinion is that Thorne is better because the more bacteria reported, the greater the statistical significance of over and under representation. On the positive side, all three samples agree on the shifts of bacteria patterns

Analysis

The distribution continues to match a common pattern with ME/CFS microbiomes, an over abundance of low percentile bacteria. This is also seen with the prior Biomesight sample. This shift is made much stronger with Thorne because more genus and species are reported. It also emphasis the shifts seen above.

Percentile	Genus	Species
0 – 9	417	628
10 – 19	85	82
20 – 29	60	85
30 – 39	42	69
40 – 49	34	32
50 – 59	59	339
60 – 69	26	60
70 – 79	16	28
80 – 89	15	31
90 – 99	21	142

Percentile	Genus	Species
0 – 9	24	33
10 – 19	32	37
20 – 29	15	23
30 – 39	9	11
40 – 49	9	16
50 – 59	8	14
60 – 69	8	9
70 – 79	10	17
80 – 89	7	15
90 – 99	6	10

Treatment Dilemma

The usual algorithm is to increase bacteria with low percentiles and decrease those with high percentiles. When you have a huge numbers of low percentile then the question arises: Do you really want to increase these, or do you want to eliminate them entirely to get them off the radar? It is a valid question, but to do that, we have to make increase/eliminate suggestions on 417 genus. That is not practical (given the sparseness of data and limited knowledge of so many genus). My working hypothesis is that keeping to the usual algorithm is the best way to go. Let the bacteria make the determination of winners and losers.

Going Forward

I am going to build two consensus reports. One for Thorne and one for the latest Biomesight, then use the “Uber Consensus” report on the Multiple Samples tab. The purpose is to see whether there are really significant differences in suggestions between the two sample reports.

We are going to do 4 basic suggestions for each:

•  Standard Lab Ranges (+/- 2 Std Dev) Thorne: 53 bacteria,BiomeSight: 8 bacteria
•  Box Plot Whisker Thorne: 86 bacteria,BiomeSight: 21 bacteria
•  Kaltoft-Moltrup Normal Ranges Thorne: 215 bacteria,BiomeSight: 70 bacteria
•  Percentile in top or bottom 10% Thorne: 1508 bacteria,BiomeSight: 130 bacteria

The results had 581 suggestions. I did a Pivot tables of Take Counts against Avoid Counts to visualize the similarities between each set of suggestions going into the uber suggestions. I read the pivot table below as indicating that the suggestions were equivalent with 73 items being to Take with no Avoid, and a further 72 items with just 1 avoid. We have lots of choices in agreement

Counts	Takes
Avoid	0	1	2	3	4	5	6	7	8
0		4	5	16	23	1	1	1	22
1	3	2	3	7	11	6		72
2	3	3	11	10	19	5	29
3	20	6	9	11	5	47
4	15	18	9	4	29	1		1
5	12	10	1	44		1
6	3	2	25			1
7		37
8	13

What are some of the top suggestions?

• bacillus subtilis (probiotics)
• whole-grain barley
• high fiber diet
• Cacao
• noni
• dates
• aspartame (sweetner) — which is always a tricky one to suggest
• Taxifolin
• Sriracha sauce
• wasabi
• Cilantro

As with the prior reports, Escherichia coli probiotics is at the top of the KEGG computed probiotics, typical for ME/CFS. From the consensus, we have:

• bacillus subtilis (probiotics)
• bifidobacterium infantis,(probiotics)
• bifidobacterium pseudocatenulatum,(probiotics)
• clostridium butyricum (probiotics),Miya,Miyarisan
• lactobacillus reuteri (probiotics) – which produces a powerful natural antibiotic, Reuterin
• bifidobacterium pseudocatenulatum li09,bifidobacterium catenulatum li10 (probiotics)

The absence of most Lactobacillus is not surprising because they are hostile to Escherichia Coli. My pivot conference report from 1998 had this bacteria being low in ME/CFS patients. With Thorne, we can get actual numbers (16s numbers for Escherichia Coli are questionable). This person Thorne Results is at the 27%ile for Escherichia and 29%ile for Escherichia Coli, which is consistent with that conference report and the KEGG computation for probiotics.

Out of interest, I looked for the %ile on the Thorne results of the consensus suggested probiotics:

• bacillus subtilis (probiotics) – 66%il1
• bifidobacterium infantis,(probiotics) – 0%ile
• bifidobacterium pseudocatenulatum,(probiotics) – 16 %ile
• clostridium butyricum (probiotics),Miya,Miyarisan – 92%ile
• lactobacillus reuteri (probiotics) – 32%ile

Having the actual percentiles for the strains used in probiotics allows us to tune the suggestions. In this case, we should skip any probiotics with bacillus subtilis or clostridium butyricum. There is no point in taking them. On the other side, you have confirmation that the suggested probiotics are likely to have an impact. I give the Thorne results a big 👍 because you can actually determine the probiotics that you likely not benefit from. The 16s results only report a few probiotic species (with questionable accuracy).

The Extras from the Thorne Results

This person did not see the next data on Thorne’s Web Site — but it was in the data CSV file to upload. The virus count with a few having percentiles. The ones without percentiles are rare ones without data.

And Fungi too!

For both of these sets of data, values over 90% should be researched. Fungi are of special concern because they can both be treated often and may also indicate a mold issue around the person.

User Feedback

Thanks Ken! .. and yikes . Mold is my nemesis. I’ve been trying to figure if I’ve had mold / moldy house for years. I did an ermi for the entire house and got a 2 which is really low. Recently I did an air test all around the house and it was pretty low minus a car which I knew was an issue and I’ve been trying to avoid. I’m trying to decipher if it’s a past issue and I can’t detox or current 

Attached is my air test.

Question: yellow highlights on Thorne. Those were 90%+ , there were some non highlighted 90% + results. Should I only research the ones you highlighted?

I should have a new mold urine test result coming too which I’ll send your way once I get it!

Answers: As a general and very rough rule, count the number of items reported (Virus: 35, Fungi:50). Take this number and multiple by a percentile – say 99%ile, and round up. For Virus and Fungi it is 1. This is the number of false positives that would be expected with 99% or higher.

You appear to have an issue. Your mold test fortunately identifies the genus and where it is located. The HVAC and washing machine hints that it is brought into the house, likely on clothes. The attic appears free of the ones that you are high in. I noticed that Malassezia is not reported in the mold test.

Bottom Line

IMHO, getting a Thorne sample is a definite should do at least once. Why? some of your issues may be fungi or virus related. The difference for suggestions of using Thorne, BiomeSight or Ombre is slight. The differences are reasonable given the sparseness of the data that we have for suggestions.

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.

My uber focus for the last few years has been on the microbiome. The reasons are simple: relatively rich amount of data to work from, detail tests can be done without a Physician’s Order, and treatment can often be done without a prescription.

In no way am I saying that the microbiome is the complete picture. It is simply the easiest to doddle in.

The analogy of a dice is good to get the entire picture. Actually two dice … because often you feel like crap as a result of a roll of the die in the craps game of life.

Some Sides of The Die

The following are the sides that come quickly into mind, they are likely more

1. SNP/DNA issues. Many conditions have associations with specific DNA mutations.
2. Infections (Past or Present)
3. Environment
4. Minerals
5. Vitamins
6. Organic Acid and Other Metabolites
7. Microbiome
8. Epigenetics

Chances are that a condition will develop when two (or more) die are rolled with bad values

Worked Example

I am using Chronic Fatigue Syndrome (CFS) / Myalgic Encephalomyelitis (ME) because I am most familar with the existing literature. The same can be done for many other conditions – for example Autism.

SNP/DNA for ME/CFS

A few examples of findings

• Genetic association study in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) identifies several potential risk loci [2022]
• Autoimmunity-Related Risk Variants in PTPN22 and CTLA4 Are Associated With ME/CFS With Infectious Onset [2020]
• Glucocorticoid receptor DNA methylation and childhood trauma in chronic fatigue syndrome patients [2018]
• Coagulation Issues can be DNA based (i.e. Sticky Blood or Micro-Clots). For example, I have the Prothrombin G20210A mutation which was a direct contributor to my own ME/CFS
• IgG subclass deficiencies in chronic fatigue syndrome. [1988]

Infections (Current or Past)

Side Note: Many cancers are associated with specific virial infections.

• The persistent viral infections in the development and severity of myalgic encephalomyelitis/chronic fatigue syndrome [2023]
• Viral studies of chronic fatigue syndrome.[1994]
• Studies on enterovirus in patients with chronic fatigue syndrome. [1994]
• Incidence, risk and prognosis of acute and chronic fatigue syndromes and psychiatric disorders after glandular fever. [1998]
• Post-Lyme borreliosis syndrome: a meta-analysis of reported symptoms. [2005]
• Share 
• [Systemic Mycoplasma blood infection in fibromyalgia and chronic fatigue syndrome]. [2004]

Environment

• Stress: May be work, childhood etc. It was a trigger for my own ME/CFS
  • Stress-Induced Transcriptomic Changes in Females with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Reveal Disrupted Immune Signatures [2023]
  • Intimate Partner Violence and the Risk of Developing Fibromyalgia and Chronic Fatigue Syndrome [2021]
  • Glucocorticoid receptor DNA methylation and childhood trauma in chronic fatigue syndrome patients [2018]
  • Effects of early childhood trauma on hypothalamic-pituitary-adrenal (HPA) axis function in patients with Chronic Fatigue Syndrome [2014]
• Fungi and Mold (at home or work)
  • Fungal spores: hazardous to health? [1999]
  • Is there any relation between moldy building exposure and chronic fatigue syndrome? [2006]
• Chemical Exposures
  • [Chronic fatigue syndrome and multiple chemical hypersensitivity after insecticide exposure]. [2005]
  • Chronic fatigue and organophosphate pesticides in sheep farming: a retrospective study amongst people reporting to a UK pharmacovigilance scheme [2003]
  • A preliminary investigation of chlorinated hydrocarbons and chronic fatigue syndrome. [1995]
  • Exposure to Gulf War Illness-related agents leads to the development of chronic pain and fatigue. [2021]

Minerals

This can be a function of environment, diet, water quality.

• Vitamin and mineral status in chronic fatigue syndrome and fibromyalgia syndrome: A systematic review and meta-analysis [2017] 
• Does Coenzyme Q10 Plus Selenium Supplementation Ameliorate Clinical Outcomes by Modulating Oxidative Stress and Inflammation in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome? [2022]
• Multivitamin mineral supplementation in patients with chronic fatigue syndrome. [2014]
• Improved chronic fatigue symptoms after removal of mercury in patient with increased mercury concentration in hair toxic mineral assay: a case. [2012]
• Magnesium and chronic fatigue syndrome. [1991]

Vitamins

• Analysis of dietary intake and selected nutrient concentrations in patients with chronic fatigue syndrome.[1998]
• Efficacy of Vitamin D Supplementation in the Improvement of Clinical Status in Patients Diagnosed with Fibromyalgia Syndrome: A Systematic Review. [2022]
• Neurochemical abnormalities in chronic fatigue syndrome: a pilot magnetic resonance spectroscopy study at 7 Tesla [2022]
• Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy.

Organic Acid and Other Metabolites

Within this, stomach acid and blood pH is included.

• Evidence of widespread metabolite abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: assessment with whole-brain magnetic resonance spectroscopy. [2020]
• Hematologic and urinary excretion anomalies in patients with chronic fatigue syndrome. [2007]
• Urinary and plasma organic acids and amino acids in chronic fatigue syndrome. [2005]
• Lowered oxygen saturation and increased body temperature in acute COVID-19 largely predict chronic fatigue syndrome and affective symptoms due to Long COVID: A precision nomothetic approach. [2023]

Microbiome

A quick copy and paste. For many other conditions, see this page.

📓 Potential role of microbiome in Chronic Fatigue Syndrome/Myalgic Encephalomyelits (CFS/ME). Scientific reports (Sci Rep ) Vol: 11 Issue 1 Pages: 7043 Pub: 2021 Mar 29 Epub: 2021 Mar 29 Authors Lupo GFD , Rocchetti G , Lucini L , Lorusso L , Manara E , Bertelli M , Puglisi E , Capelli E , Summary Html Article Publication

📓 Gut Microbiota Interventions With <i>Clostridium butyricum</i> and Norfloxacin Modulate Immune Response in Experimental Autoimmune Encephalomyelitis Mice. Frontiers in immunology (Front Immunol ) Vol: 10 Issue Pages: 1662 Pub: 2019 Epub: 2019 Jul 23 Authors Chen H , Ma X , Liu Y , Ma L , Chen Z , Lin X , Si L , Ma X , Chen X , Summary Html Article Publication

📓 Correction to: Open-label pilot for treatment targeting gut dysbiosis in myalgic encephalomyelitis/chronic fatigue syndrome: neuropsychological symptoms and sex comparisons. Journal of translational medicine (J Transl Med ) Vol: 16 Issue 1 Pages: 39 Pub: 2018 Feb 23 Epub: 2018 Feb 23 Authors Wallis A , Ball M , Butt H , Lewis DP , McKechnie S , Paull P , Jaa-Kwee A , Bruck D , Summary Html Article Publication

📓 Potential role of dengue virus, chikungunya virus and Zika virus in neurological diseases. Memorias do Instituto Oswaldo Cruz (Mem Inst Oswaldo Cruz ) Vol: 113 Issue 11 Pages: e170538 Pub: 2018 Oct 29 Epub: 2018 Oct 29 Authors Vieira MADCES , Costa CHN , Linhares ADC , Borba AS , Henriques DF , Silva EVPD , Tavares FN , Batista FMA , Guimarães HCL , Martins LC , Monteiro TAF , Cruz ACR , Azevedo RDSDS , Vasconcelos PFDC , Summary Html Article Publication

📓 Human Gut-Derived Commensal Bacteria Suppress CNS Inflammatory and Demyelinating Disease. Cell reports (Cell Rep ) Vol: 20 Issue 6 Pages: 1269-1277 Pub: 2017 Aug 8 Epub: Authors Mangalam A , Shahi SK , Luckey D , Karau M , Marietta E , Luo N , Choung RS , Ju J , Sompallae R , Gibson-Corley K , Patel R , Rodriguez M , David C , Taneja V , Murray J , Summary Html Article Publication

📓 Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome (Microbiome ) Vol: 5 Issue 1 Pages: 44 Pub: 2017 Apr 26 Epub: 2017 Apr 26 Authors Nagy-Szakal D , Williams BL , Mishra N , Che X , Lee B , Bateman L , Klimas NG , Komaroff AL , Levine S , Montoya JG , Peterson DL , Ramanan D , Jain K , Eddy ML , Hornig M , Lipkin WI , Summary Html Article Publication

📓 A Pair of Identical Twins Discordant for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Differ in Physiological Parameters and Gut Microbiome Composition. The American journal of case reports (Am J Case Rep ) Vol: 17 Issue Pages: 720-729 Pub: 2016 Oct 10 Epub: 2016 Oct 10 Authors Giloteaux L , Hanson MR , Keller BA , Summary Html Article

📓 Support for the Microgenderome: Associations in a Human Clinical Population. Scientific reports (Sci Rep ) Vol: 6 Issue Pages: 19171 Pub: 2016 Jan 13 Epub: 2016 Jan 13 Authors Wallis A , Butt H , Ball M , Lewis DP , Bruck D , Summary Html Article Publication

📓 Chronic fatigue syndrome patients have alterations in their oral microbiome composition and function. PloS one (PLoS One ) Vol: 13 Issue 9 Pages: e0203503 Pub: 2018 Epub: 2018 Sep 11 Authors Wang T , Yu L , Xu C , Pan K , Mo M , Duan M , Zhang Y , Xiong H , Summary Publication Publication

📓 Gut-associated lymphoid tissue, gut microbes and susceptibility to experimental autoimmune encephalomyelitis. Beneficial microbes (Benef Microbes ) Vol: 7 Issue 3 Pages: 363-73 Pub: 2016 Jun Epub: 2016 Feb 3 Authors Stanisavljevic S , Lukic J , Momcilovic M , Miljkovic M , Jevtic B , Kojic M , Golic N , Mostarica Stojkovic M , Miljkovic D , Summary Publication Publication

📓 Increased d-lactic Acid intestinal bacteria in patients with chronic fatigue syndrome. In vivo (Athens, Greece) (In Vivo ) Vol: 23 Issue 4 Pages: 621-8 Pub: 2009 Jul-Aug Epub: Authors Sheedy JR , Wettenhall RE , Scanlon D , Gooley PR , Lewis DP , McGregor N , Stapleton DI , Butt HL , DE Meirleir KL , Summary

Epigenetics

This is where an event, like stress, causes the behavior of DNA to change. Your DNA is the same, just a “switch” is turned on or off.

• Dynamic Epigenetic Changes during a Relapse and Recovery Cycle in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome [2022]
• Altered endothelial dysfunction-related miRs in plasma from ME/CFS patients.[2021]
• Changes in DNA methylation profiles of myalgic encephalomyelitis/chronic fatigue syndrome patients reflect systemic dysfunctions. [2020]
• Lower ambulatory blood pressure in chronic fatigue syndrome. [2009]

Going Forward with Treatment

My attitude is evidence based action with testable models. If you walk into a physician’s office, it is unlikely that they will be aware with the many sides of the dice. Usually, they want simple “follow the recipe book” cases where what to do is clear.

For myself, I had the luxury of unbelievable, unlimited, medical coverage for a few years. I found some of the DNA issues, and to quote a physician “You are extremely lucky with that mutation, it is very treatable” — I became a piracetam addict when needed. Most people do not have that luxury.

Looking at 8 items above, I ask the same question:

• Is it objective measurable?
  • Can you get the test (willing MD, cost)
• Is it treatable?
  • Do we have actual clinical studies showing treatment is effective?
    • Is the treatment just symptom relief or remission?
  • What are the risk of side-effects?

If getting information from a test is not clearly actionable, then it does not help with treatment and not worth the expense. Testing for testing sake is a luxury for the rich.

My Criteria in evaluating new proposed models. “

Many people will advocate that just one of these 8 sides of the die needs to be done for a cure. IMHO, if the model does not address most of these factors, it is likely to work for only a few.

For me, the Microbiome model appears the best to use.

• Microbiome tests are cheap and do not require a MD to be involved — Objective
• We have hundreds of studies showing substances alters the microbiome
• Risk of side-effects with non-prescription items is low

And it is connected to the other factors above well.

• Many of the organic acid and metabolites are produced by the microbiome. Thus correcting the microbiome is likely to resolve this I compute many of these using Kyoto Encyclopedia of Genes and Genomes data.
• Vitamins and Mineral absorption is deeply influences by the microbiome too!

If you have DNA information, for example on your methylation, this impacts your microbiome and the reverse. Being tested for DNA SNPs that does not have effective treatment is a waste of money. The individual’s microbiome is greatly influenced by their DNA. They co-exist and co-operate. In some cases, the microbiome bacteria can produce anticoagulants and fibrinolytics which can counter some coagulation issues.

WARNING ON PEOPLE PROPOSING MODELS

Over the last 30 years, I have constantly seen people proposing this model or that model. Usually the model is focusing on a single aspect of one the die sides above. For ME/CFS, it was the search for an occult virus that was the root cause of this condition. This often comes out of a need to reduce to the simple in whatever specialty that the researcher or physician is trained in. The wages of over-specialization in modern medicine. Be wary of any model that does not offer a concrete explanation for all of the laboratory results in the literature. Often models will cherry-pick studies and ignore the majority of other studies, or do vague hand waving.

The cause is almost never just one of the above factors, but typically many.

Recently I have been getting several emails from people with status updates. I thought that I should share a few of them. I have not gotten any negative feedback (Are all of my readers Canadians who are too polite to complain?)

Also good news. I’ve managed to correct much of my microbiome. I’ve reversed the NIH gnavus ans prausnitzii signature that is very common in ME. My prausnitzii is now 21%! It was something like 0.2 two years ago.

And my lactobacillus and bifido are in the very bottom range of healthy for the first time in two years. Also three lactobacillus strains from vivomixx appeared on my 16s for the first time. Proving that even artificial probiotics can populate the gut albeit temporarily. 

I intend to continue and get another test in three months. While my physical symptoms have improved a lot my light sensitivity and brainfog are still not great to be honest and I have no idea why. Leaky gut can I think be ruled out becuass gnavus is so low and I barely react to eggs anymore (I’ve had issues with eggs since I was a kid). But I have some work to do still.

I really love the website and get a ton of use out of it.  I hope you stick with it and keep updating it and making it better.

Mold is what got me initially too.  Retrospectively.  When we moved to our new house is when my decline actually started.  And then when we redid our master bath, there was a bunch of mold, and I got real sick real fast.

In essence, I had all the triggers.  Every single one of these:

1. Viral or bacterial exposure (listed in order of severity) – COVID and RSV
2. Trauma – to intestines
3. Food poisoning – Bacterial and fungal
4. Prolonged Stress – Luxonis.com startup, I’m the founder
5. Environmental Toxins – Mold in our MASTER BEDROOM

Oh forgot to mention I took lactobacillus Rhamnosus based my my research before I noticed your big red note to not take it and other lactobacillus because they block the impact of heparin.  I think that’s what really got me!

Haven’t pooped for 3 days since that mistake!  Before that pooped every day for 14. 

As an update, I’m nearly 5 weeks in and am beginning to feel better.  My energy levels are perhaps the best they’ve been in the last 5 years.  I’ve still got a very long way to go but the results thus far are promising!

I’m taking 5-7 foods/ supplements, 2-3X a day.  And every 2 weeks I’m rotating all of it to prevent antibiotic resistance.  In another month I plan to retest myself and make the necessary adjustments to my protocol.  

The other reason I’m writing is that a friend with similar fatigue issues and a histamine intolerance has just gotten tested and is joining my journey to recovery. 

First, there is a body of literature indicating that the microbiome plays an important part in many conditions. The last one in this list of citations agrees with my hypothesis for the last 7 years: “Multi-kingdom gut microbiota analyses define CONDITION-X severity and in some case the syndrome.“

Citation
Reversion of Gut Microbiota during the Recovery Phase in Patients with Asymptomatic or Mild COVID-19: Longitudinal Study. Microorganisms (Microorganisms ) Vol: 9 Issue 6 Pages: Pub: 2021 Jun 7 Epub: 2021 Jun 7 Authors Kim HN , Joo EJ , Lee CW , Ahn KS , Kim HL , Park DI , Park SK , Summary Html Article Publication
The gut microbiome of COVID-19 recovered patients returns to uninfected status in a minority-dominated United States cohort. Gut microbes (Gut Microbes ) Vol: 13 Issue 1 Pages: 1-15 Pub: 2021 Jan-Dec Epub: Authors Newsome RC , Gauthier J , Hernandez MC , Abraham GE , Robinson TO , Williams HB , Sloan M , Owings A , Laird H , Christian T , Pride Y , Wilson KJ , Hasan M , Parker A , Senitko M , Glover SC , Gharaibeh RZ , Jobin C , Summary Html Article Publication
Gut Microbiota May Not Be Fully Restored in Recovered COVID-19 Patients After 3-Month Recovery. Frontiers in nutrition (Front Nutr ) Vol: 8 Issue Pages: 638825 Pub: 2021 Epub: 2021 May 13 Authors Tian Y , Sun KY , Meng TQ , Ye Z , Guo SM , Li ZM , Xiong CL , Yin Y , Li HG , Zhou LQ , Summary Html Article Publication
Gut Microbiota Interplay With COVID-19 Reveals Links to Host Lipid Metabolism Among Middle Eastern Populations. Frontiers in microbiology (Front Microbiol ) Vol: 12 Issue Pages: 761067 Pub: 2021 Epub: 2021 Nov 5 Authors Al Bataineh MT , Henschel A , Mousa M , Daou M , Waasia F , Kannout H , Khalili M , Kayasseh MA , Alkhajeh A , Uddin M , Alkaabi N , Tay GK , Feng SF , Yousef AF , Alsafar HS , Summary Publication
Gut microbiota dynamics in a prospective cohort of patients with post-acute COVID-19 syndrome. Gut (Gut ) Vol: Issue Pages: Pub: 2022 Jan 26 Epub: 2022 Jan 26 Authors Liu Q , Mak JWY , Su Q , Yeoh YK , Lui GC , Ng SSS , Zhang F , Li AYL , Lu W , Hui DS , Chan PK , Chan FKL , Ng SC , Summary Publication
Alterations in microbiota of patients with COVID-19: potential mechanisms and therapeutic interventions. Signal transduction and targeted therapy (Signal Transduct Target Ther ) Vol: 7 Issue 1 Pages: 143 Pub: 2022 Apr 29 Epub: 2022 Apr 29 Authors Wang B , Zhang L , Wang Y , Dai T , Qin Z , Zhou F , Zhang L , Summary Publication
Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization. Gastroenterology (Gastroenterology ) Vol: 159 Issue 3 Pages: 944-955.e8 Pub: 2020 Sep Epub: 2020 May 20 Authors Zuo T , Zhang F , Lui GCY , Yeoh YK , Li AYL , Zhan H , Wan Y , Chung ACK , Cheung CP , Chen N , Lai CKC , Chen Z , Tso EYK , Fung KSC , Chan V , Ling L , Joynt G , Hui DSC , Chan FKL , Chan PKS , Ng SC , Summary Publication
Alterations in Fecal Fungal Microbiome of Patients With COVID-19 During Time of Hospitalization until Discharge. Gastroenterology (Gastroenterology ) Vol: 159 Issue 4 Pages: 1302-1310.e5 Pub: 2020 Oct Epub: 2020 Jun 26 Authors Zuo T , Zhan H , Zhang F , Liu Q , Tso EYK , Lui GCY , Chen N , Li A , Lu W , Chan FKL , Chan PKS , Ng SC , Summary Publication
Challenges in the Management of SARS-CoV2 Infection: The Role of Oral Bacteriotherapy as Complementary Therapeutic Strategy to Avoid the Progression of COVID-19. Frontiers in medicine (Front Med (Lausanne) ) Vol: 7 Issue Pages: 389 Pub: 2020 Epub: 2020 Jul 7 Authors d`Ettorre G , Ceccarelli G , Marazzato M , Campagna G , Pinacchio C , Alessandri F , Ruberto F , Rossi G , Celani L , Scagnolari C , Mastropietro C , Trinchieri V , Recchia GE , Mauro V , Antonelli G , Pugliese F , Mastroianni CM , Summary Publication
It Ain`t Over `Til It`s Over: SARS CoV-2 and Post-infectious Gastrointestinal Dysmotility. Digestive diseases and sciences (Dig Dis Sci ) Vol: Issue Pages: Pub: 2022 Mar 30 Epub: 2022 Mar 30 Authors Coles MJ , Masood M , Crowley MM , Hudgi A , Okereke C , Klein J , Summary Publication
Integrated analysis of gut microbiome and host immune responses in COVID-19. Frontiers of medicine (Front Med ) Vol: 16 Issue 2 Pages: 263-275 Pub: 2022 Apr Epub: 2022 Mar 8 Authors Xu X , Zhang W , Guo M , Xiao C , Fu Z , Yu S , Jiang L , Wang S , Ling Y , Liu F , Tan Y , Chen S , Summary Publication
Respiratory dysfunction three months after severe COVID-19 is associated with gut microbiota alterations. Journal of internal medicine (J Intern Med ) Vol: 291 Issue 6 Pages: 801-812 Pub: 2022 Jun Epub: 2022 Mar 17 Authors Vestad B , Ueland T , Lerum TV , Dahl TB , Holm K , Barratt-Due A , Kåsine T , Dyrhol-Riise AM , Stiksrud B , Tonby K , Hoel H , Olsen IC , Henriksen KN , Tveita A , Manotheepan R , Haugli M , Eiken R , Berg Å , Halvorsen B , Lekva T , Ranheim T , Michelsen AE , Kildal AB , Johannessen A , Thoresen L , Skudal H , Kittang BR , Olsen RB , Ystrøm CM , Skei NV , Hannula R , Aballi S , Kvåle R , Skjønsberg OH , Aukrust P , Hov JR , Trøseid M , NOR-Solidarity study group. , Summary Publication
Dissecting the role of the human microbiome in COVID-19 via metagenome-assembled genomes. Nature communications (Nat Commun ) Vol: 13 Issue 1 Pages: 5235 Pub: 2022 Sep 6 Epub: 2022 Sep 6 Authors Ke S , Weiss ST , Liu YY , Summary Publication
Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome. Nature communications (Nat Commun ) Vol: 13 Issue 1 Pages: 6806 Pub: 2022 Nov 10 Epub: 2022 Nov 10 Authors Liu Q , Su Q , Zhang F , Tun HM , Mak JWY , Lui GC , Ng SSS , Ching JYL , Li A , Lu W , Liu C , Cheung CP , Hui DSC , Chan PKS , Chan FKL , Ng SC , Summary Publication

Looking at what is statistically significant in samples uploaded to the citizen science site, we see that only BiomeSight has sufficient data. It identifies 36 taxa/bacteria at present. With more samples coming in, this may increase. Before looking at the bacteria, I like to look at the enzymes — why? different bacteria produce the same enzymes so it is an elegant way of clustering bacteria by what they produce.

KEGG Enzyme data

In this case, I am blown away on the number of statistically significant shifts! If you want more information about these enzymes, see BRENDA or/and Kyoto Encyclopedia of Genes and Genomes.

There are 197 with P < 0.001. Give there is 8000+ enzymes, that suggests around 8 may be false positives.

Enzyme Name	ECKEY	With Long COVID	Without Long COVID	T-Score	DF	Probability
(S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming)	4.1.3.4	130357	52076	13.57	666	P < 0.001
4-amino-5-aminomethyl-2-methylpyrimidine aminohydrolase	3.5.99.2	151693	69126	13.48	666	P < 0.001
dihydrourocanate:acceptor oxidoreductase	1.3.99.33	135133	55921	13.44	666	P < 0.001
nucleoside-triphosphate diphosphohydrolase	3.6.1.9	145528	63523	13.38	666	P < 0.001
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP or NAD+)	6.5.1.6	129475	51779	13.34	666	P < 0.001
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP, ADP or GTP)	6.5.1.7	129475	51779	13.34	666	P < 0.001
acetyl-CoA:kanamycin-B N6′-acetyltransferase	2.3.1.82	128673	51662	13.32	666	P < 0.001
acetyl-CoA:2-deoxystreptamine-antibiotic N3-acetyltransferase	2.3.1.81	130200	53104	13.28	666	P < 0.001
(1->4)-alpha-D-galacturonan reducing-end-disaccharide-lyase	4.2.2.9	128038	50989	13.28	666	P < 0.001
alpha-maltose-6′-phosphate 6-phosphoglucohydrolase	3.2.1.122	130496	53951	13.28	666	P < 0.001
D-serine ammonia-lyase (pyruvate-forming)	4.3.1.18	128576	51987	13.23	666	P < 0.001
ATP phosphohydrolase (ABC-type, iron(III) enterobactin-importing)	7.2.2.17	130373	54180	13.15	666	P < 0.001
protein-Npi-phospho-L-histidine:D-mannose Npi-phosphotransferase	2.7.1.191	140701	62807	13.14	666	P < 0.001
protein-Npi-phospho-L-histidine:D-mannitol Npi-phosphotransferase	2.7.1.197	129284	53723	13.06	666	P < 0.001
ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining)	2.4.1.342	134654	58028	13.04	665	P < 0.001
aryl-ester hydrolase	3.1.1.2	151171	72377	13.02	666	P < 0.001
ATP phosphohydrolase (ABC-type, Fe3+-transporting)	7.2.2.7	142152	66111	12.86	666	P < 0.001
palmitoyl-CoA hydrolase	3.1.2.2	149499	71728	12.83	666	P < 0.001
ATP:[protein]-L-tyrosine O-phosphotransferase (non-specific)	2.7.10.2	131925	57360	12.75	666	P < 0.001
D-psicose 3-epimerase	5.1.3.30	143416	68284	12.74	666	P < 0.001
D-tagatose 3-epimerase	5.1.3.31	143416	68284	12.74	666	P < 0.001
penicillin amidohydrolase	3.5.1.11	139138	66165	12.62	666	P < 0.001
D-aspartate:[beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n ligase (ADP-forming)	6.3.1.12	145356	70261	12.57	666	P < 0.001
2′-(5-triphosphoribosyl)-3′-dephospho-CoA:apo-[citrate (pro-3S)-lyase] 2′-(5-phosphoribosyl)-3′-dephospho-CoA-transferase	2.7.7.61	149196	74697	12.46	666	P < 0.001
ATP:3′-dephospho-CoA 5-triphospho-alpha-D-ribosyltransferase	2.4.2.52	149957	75781	12.39	666	P < 0.001
2,4,6/3,5-pentahydroxycyclohexanone 2-isomerase	5.3.99.11	146254	73538	12.37	666	P < 0.001
acetyl-CoA:citrate CoA-transferase	2.8.3.10	150367	76318	12.36	666	P < 0.001
L-aspartate:tRNAAsx ligase (AMP-forming)	6.1.1.23	132939	61720	12.22	666	P < 0.001
4-phospho-D-erythronate:NAD+ 3-oxidoreductase	1.1.1.409	134073	62914	12.19	666	P < 0.001
4-phospho-D-threonate:NAD+ 3-oxidoreductase	1.1.1.408	134073	62914	12.19	666	P < 0.001
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP)	6.5.1.1	143883	68128	12.15	666	P < 0.001
ATP:D-erythronate 4-phosphotransferase	2.7.1.220	133631	62696	12.13	666	P < 0.001
ATP:D-threonate 4-phosphotransferase	2.7.1.219	133631	62696	12.13	666	P < 0.001
ATP:D-glycero-alpha-D-manno-heptose 7-phosphate 1-phosphotransferase	2.7.1.168	156157	78491	11.99	666	P < 0.001
2-lysophosphatidylcholine acylhydrolase	3.1.1.5	160874	86814	11.71	666	P < 0.001
UDP-alpha-D-glucose:1,2-diacyl-sn-glycerol 3-alpha-D-glucosyltransferase	2.4.1.337	159960	87248	11.57	666	P < 0.001
N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase	3.5.1.18	151482	82849	11.49	666	P < 0.001
acetate:holo-[citrate-(pro-3S)-lyase] ligase (AMP-forming)	6.2.1.22	163245	90493	11.44	666	P < 0.001
D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming)	4.1.2.40	140814	72879	11.44	666	P < 0.001
L-iditol:NAD+ 2-oxidoreductase	1.1.1.14	181524	109886	11.34	666	P < 0.001
alkylated-DNA glycohydrolase (releasing methyladenine and methylguanine)	3.2.2.21	172169	89028	11.33	666	P < 0.001
oligosaccharide 6-alpha-glucohydrolase	3.2.1.10	161628	93712	11.12	666	P < 0.001
(3S)-citryl-CoA oxaloacetate-lyase (acetyl-CoA-forming)	4.1.3.34	185375	105009	10.73	666	P < 0.001
S-adenosyl-L-methionine:tRNA (adenine22-N1)-methyltransferase	2.1.1.217	157938	92817	10.69	666	P < 0.001
S-adenosyl-L-methionine:16S rRNA (cytidine1409-2′-O)-methyltransferase	2.1.1.227	183689	114027	10.56	666	P < 0.001
S-adenosyl-L-methionine:23S rRNA (cytidine1920-2′-O)-methyltransferase	2.1.1.226	183689	114027	10.56	666	P < 0.001
sn-glycerol 3-phosphate:quinone oxidoreductase	1.1.5.3	177064	110486	10.54	666	P < 0.001
L-glutamate:tRNAGlx ligase (AMP-forming)	6.1.1.24	165448	97402	10.38	666	P < 0.001
D-glycero-D-manno-heptose 7-phosphate aldose-ketose-isomerase	5.3.1.28	213475	135248	10.25	666	P < 0.001
beta-D-glucose 1,6-phosphomutase	5.4.2.6	253383	169933	8.98	666	P < 0.001
type II site-specific deoxyribonuclease	3.1.21.4	245864	165266	8.77	666	P < 0.001
uroporphyrinogen-III carboxy-lyase (coproporphyrinogen-III-forming)	4.1.1.37	266562	184417	8.59	666	P < 0.001
ATP phosphohydrolase (ABC-type, Ni2+-importing)	7.2.2.11	141054	86367	8.56	666	P < 0.001
acetyl-CoA:oxalate CoA-transferase	2.8.3.19	133160	80149	8.41	666	P < 0.001
5-phospho-alpha-D-ribose 1,2-cyclic phosphate 1,2-diphosphophosphohydrolase	3.1.4.57	134813	82183	8.33	666	P < 0.001
Carboxypeptidase Taq	3.4.17.19	136153	84074	8.31	666	P < 0.001
(2E)-2-enoyl-CoA:NADP+ 4-oxidoreductase	1.3.1.34	134146	81935	8.31	666	P < 0.001
2′-deoxyribonucleoside 5′-monophosphate phosphohydrolase	3.1.3.89	136444	84251	8.19	666	P < 0.001
cellobiose 2-epimerase	5.1.3.11	224933	147685	8.17	666	P < 0.001
type III site-specific deoxyribonuclease	3.1.21.5	200155	141657	8.10	666	P < 0.001
D-galactonate hydro-lyase (2-dehydro-3-deoxy-D-galactonate-forming)	4.2.1.6	138933	88181	8.02	666	P < 0.001
protein-Npi-phospho-L-histidine:maltose Npi-phosphotransferase	2.7.1.208	138609	88325	7.98	666	P < 0.001
L-aspartate:NAD(P)+ oxidoreductase (deaminating)	1.4.1.21	245150	169471	7.90	666	P < 0.001
protein-Npi-phospho-L-histidine:sucrose Npi-phosphotransferase	2.7.1.211	144741	96084	7.85	666	P < 0.001
protein-Npi-phospho-L-histidine:N-acetyl-D-glucosamine Npi-phosphotransferase	2.7.1.193	152088	103970	7.64	666	P < 0.001
N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol 4-epimerase	5.1.3.26	259434	182387	7.64	666	P < 0.001
UDP-sugar sugarphosphohydrolase	3.6.1.45	156303	108944	7.63	666	P < 0.001
rubredoxin:superoxide oxidoreductase	1.15.1.2	147572	101699	7.40	666	P < 0.001
UDP-2-acetamido-2,6-dideoxy-L-talose:NADP+ oxidoreductase	1.1.1.367	244790	176296	7.33	666	P < 0.001
(4-O-methyl)-D-glucuronate—lignin ester hydrolase	3.1.1.117	265763	190685	7.31	666	P < 0.001
alkylated-DNA glycohydrolase (releasing methyladenine and methylguanine)	3.2.2.20	306951	231407	7.30	666	P < 0.001
S-adenosyl-L-methionine:16S rRNA (cytosine967-C5)-methyltransferase	2.1.1.176	192348	143978	7.28	666	P < 0.001
ATP:molybdopterin-synthase adenylyltransferase	2.7.7.80	297993	218788	7.25	666	P < 0.001
UDP-N-acetyl-alpha-D-glucosamine hydro-lyase (inverting; UDP-2-acetamido-2,6-dideoxy-beta-L-arabino-hex-4-ulose-forming)	4.2.1.115	287717	211984	7.24	666	P < 0.001
UDP-2-acetamido-2,6-dideoxy-beta-L-talose 2-epimerase	5.1.3.28	245857	178308	7.23	666	P < 0.001
ATP phosphohydrolase (ABC-type, molybdate-importing)	7.3.2.5	157815	111902	7.18	666	P < 0.001
sucrose:(1->4)-alpha-D-glucan 4-alpha-D-glucosyltransferase	2.4.1.4	148095	102284	7.16	666	P < 0.001
UDP-N-acetyl-2-amino-2-deoxy-alpha-D-glucuronate:NAD+ 3-oxidoreductase	1.1.1.335	172209	124841	7.08	666	P < 0.001
an acyl-[acyl-carrier protein]:phosphate acyltransferase	2.3.1.274	185445	139090	7.05	666	P < 0.001
FMNH2:NAD(P)+ oxidoreductase	1.5.1.39	156478	107179	7.02	666	P < 0.001
cellobiose:phosphate alpha-D-glucosyltransferase	2.4.1.20	165795	117785	6.98	666	P < 0.001
4-beta-D-xylan xylohydrolase	3.2.1.37	308412	233667	6.87	666	P < 0.001
crossover junction endodeoxyribonuclease	3.1.21.10	341116	260719	6.82	666	P < 0.001
nucleoside-2′,3′-cyclic-phosphate 3′-nucleotidohydrolase	3.1.4.16	321464	242830	6.82	666	P < 0.001
UDP-glucuronate 4-epimerase	5.1.3.6	280650	207501	6.80	666	P < 0.001
S-adenosyl-L-methionine:precorrin-2 C20-methyltransferase	2.1.1.130	293271	227548	6.79	666	P < 0.001
S-adenosyl-L-methionine:cobalt-factor-II C20-methyltransferase	2.1.1.151	293124	227408	6.78	666	P < 0.001
dipeptidase E	3.4.13.21	204290	149503	6.73	666	P < 0.001
GTP:alpha-D-mannose-1-phosphate guanylyltransferase	2.7.7.13	331462	252998	6.69	666	P < 0.001
ATP:1,2-diacyl-sn-glycerol 3-phosphotransferase	2.7.1.107	333291	259697	6.61	666	P < 0.001
L-selenocysteine selenide-lyase (L-alanine-forming)	4.4.1.16	337172	260622	6.56	666	P < 0.001
1,4-beta-D-mannooligosaccharide:phosphate alpha-D-mannosyltransferase	2.4.1.319	300427	227352	6.54	666	P < 0.001
4-O-beta-D-mannopyranosyl-N-acetyl-D-glucosamine:phosphate alpha-D-mannosyltransferase	2.4.1.320	300427	227352	6.54	666	P < 0.001
sortase B	3.4.22.71	161358	119363	6.52	666	P < 0.001
cobalt-precorrin 5A acylhydrolase	3.7.1.12	160731	118900	6.48	666	P < 0.001
6-carboxy-5,6,7,8-tetrahydropterin ammonia-lyase	4.3.99.3	329010	253328	6.47	666	P < 0.001
precorrin-8 11,12-methylmutase	5.4.99.60	161359	119806	6.46	666	P < 0.001
precorrin-8X 11,12-methylmutase	5.4.99.61	161359	119806	6.46	666	P < 0.001
UDP-alpha-D-glucose:NAD+ 6-oxidoreductase	1.1.1.22	341877	267968	6.45	666	P < 0.001
4-O-beta-D-mannopyranosyl-D-glucopyranose:phosphate alpha-D-mannosyltransferase	2.4.1.281	294563	221546	6.44	666	P < 0.001
O-acetyl-ADP-D-ribose carboxylesterase	3.1.1.106	232755	178380	6.39	666	P < 0.001
2′-deoxyribonucleoside-5′-diphosphate:thioredoxin-disulfide 2′-oxidoreductase	1.17.4.1	355018	281224	6.39	666	P < 0.001
3′-ribonucleotide phosphohydrolase	3.1.3.6	303202	229568	6.38	666	P < 0.001
acetyl-CoA:alkane-alpha,omega-diamine N-acetyltransferase	2.3.1.57	341806	269466	6.32	666	P < 0.001
5,10-methylenetetrahydrofolate:glycine hydroxymethyltransferase	2.1.2.1	356606	283241	6.30	666	P < 0.001
carbonic acid hydro-lyase (carbon-dioxide-forming)	4.2.1.1	321295	248957	6.29	666	P < 0.001
L-isoleucine:tRNAIle ligase (AMP-forming)	6.1.1.5	364094	292209	6.24	666	P < 0.001
DNA-6-O-methylguanine/DNA-4-O-methylthymine:[protein]-L-cysteine S-methyltransferase	2.1.1.63	365615	293975	6.16	666	P < 0.001
NADH:hydroperoxide oxidoreductase	1.11.1.26	323275	252045	6.11	666	P < 0.001
L-glutamate:tRNAGlu ligase (AMP-forming)	6.1.1.17	321496	262066	6.08	666	P < 0.001
bleomycin hydrolase	3.4.22.40	314461	243175	6.08	666	P < 0.001
L-cysteine-S-conjugate thiol-lyase (deaminating; 2-aminoprop-2-enoate-forming)	4.4.1.13	363963	293237	6.08	666	P < 0.001
UDP-alpha-D-glucose:alpha-D-galactose-1-phosphate uridylyltransferase	2.7.7.12	170072	129650	6.05	666	P < 0.001
peptidoglycan amidohydrolase	3.5.1.28	363233	293964	5.99	666	P < 0.001
adenine-DNA deoxyribohydrolase (adenine-releasing)	3.2.2.31	355124	285300	5.95	666	P < 0.001
D-phosphoglycerate 2,3-phosphomutase (2,3-diphosphoglycerate-dependent)	5.4.2.11	319582	250208	5.95	666	P < 0.001
tRNA N6-(3-methylbut-2-en-1-yl)-adenine37:sulfur-(sulfur carrier),S-adenosyl-L-methionine C2-(methylsulfanyl)transferase	2.8.4.3	370115	301224	5.93	666	P < 0.001
10-formyltetrahydrofolate:5′-phosphoribosylglycinamide N-formyltransferase	2.1.2.2	363374	295358	5.89	666	P < 0.001
peptidase Do	3.4.21.107	276660	224637	5.87	666	P < 0.001
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (NAD+)	6.5.1.2	372463	304412	5.85	666	P < 0.001
methionyl aminopeptidase	3.4.11.18	372462	304426	5.85	666	P < 0.001
prenyl-diphosphate:adenine37 in tRNA prenyltransferase	2.5.1.75	371863	303924	5.84	666	P < 0.001
ATP:pyridoxal 5′-phosphotransferase	2.7.1.35	329032	261786	5.80	666	P < 0.001
L-rhamnose aldose-ketose-isomerase	5.3.1.14	307393	241401	5.80	666	P < 0.001
peptide-methionine:thioredoxin-disulfide S-oxidoreductase [methionine (R)-S-oxide-forming]	1.8.4.12	257798	203935	5.76	666	P < 0.001
deoxyribonuclease IV	3.1.21.2	353617	287404	5.73	666	P < 0.001
alpha-L-rhamnoside rhamnohydrolase	3.2.1.40	318053	252744	5.68	666	P < 0.001
ATP phosphohydrolase (ABC-type, Zn2+-importing)	7.2.2.20	331254	264520	5.66	666	P < 0.001
L-rhamnulose-1-phosphate (S)-lactaldehyde-lyase (glycerone-phosphate-forming)	4.1.2.19	307632	243590	5.63	666	P < 0.001
6-deoxy-6-sulfo-D-fructose:D-glyceraldehyde-3-phosphate glyceronetransferase	2.2.1.14	173	33	5.60	41	P < 0.001
ATP:L-rhamnulose 1-phosphotransferase	2.7.1.5	319257	255641	5.50	666	P < 0.001
ATP:glycerol 3-phosphotransferase	2.7.1.30	271460	223970	5.50	666	P < 0.001
(R)-S-lactoylglutathione methylglyoxal-lyase (isomerizing; glutathione-forming)	4.4.1.5	341379	280079	5.36	666	P < 0.001
exodeoxyribonuclease V	3.1.11.5	224710	181406	5.36	666	P < 0.001
NAD(H) phosphohydrolase	3.6.1.22	335898	275329	5.26	666	P < 0.001
L-alanyl-D-glutamate epimerase	5.1.1.20	211984	168065	5.24	666	P < 0.001
CTP:2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase	2.7.7.60	342240	289619	5.12	666	P < 0.001
beta-D-fructofuranoside fructohydrolase	3.2.1.26	204815	169106	4.91	666	P < 0.001
beta-lactam hydrolase	3.5.2.6	328892	276729	4.76	666	P < 0.001
GTP 7,8-8,9-dihydrolase (diphosphate-forming)	3.5.4.25	363191	310173	4.54	666	P < 0.001
D-ribulose 5-phosphate formate-lyase (L-3,4-dihydroxybutan-2-one 4-phosphate-forming)	4.1.99.12	363190	310173	4.54	666	P < 0.001
5-amino-6-(D-ribitylamino)uracil butanedionetransferase	2.5.1.78	363021	310046	4.54	666	P < 0.001
5-amino-6-(5-phospho-D-ribitylamino)uracil:NADP+ 1′-oxidoreductase	1.1.1.193	356070	302730	4.53	666	P < 0.001
2,5-diamino-6-hydroxy-4-(5-phospho-D-ribosylamino)pyrimidine 2-aminohydrolase	3.5.4.26	356070	302730	4.53	666	P < 0.001
D-mannose-6-phosphate aldose-ketose-isomerase	5.3.1.8	349959	298038	4.51	666	P < 0.001
5′-ribonucleotide phosphohydrolase	3.1.3.5	363417	312818	4.40	666	P < 0.001
peptidoglycan-N-acetylglucosamine amidohydrolase	3.5.1.104	334065	289136	4.35	666	P < 0.001
fragilysin	3.4.24.74	7726	2137	4.30	487	P < 0.001
UTP:alpha-D-glucose-1-phosphate uridylyltransferase	2.7.7.9	180042	146829	4.27	666	P < 0.001
adenosylcobinamide-GDP:alpha-ribazole ribazoletransferase	2.7.8.26	336101	289980	4.27	666	P < 0.001
Fe(II):oxygen oxidoreductase ([FeO(OH)]core-producing)	1.16.3.2	343886	295642	4.23	666	P < 0.001
D-stereospecific aminopeptidase	3.4.11.19	146732	114608	4.23	666	P < 0.001
D-glycerate:NAD+ oxidoreductase	1.1.1.29	304790	258395	4.21	666	P < 0.001
ATP:dTMP phosphotransferase	2.7.4.9	182727	149804	4.20	666	P < 0.001
ATP:UDP-N-acetyl-alpha-D-glucosamine 3′-phosphotransferase	2.7.1.176	7304	3121	4.15	664	P < 0.001
L-histidinol:NAD+ oxidoreductase	1.1.1.23	360776	312129	4.13	666	P < 0.001
queuosine34 in tRNA:acceptor oxidoreductase	1.17.99.6	365803	318709	4.13	666	P < 0.001
L-lysine:tRNALys ligase (AMP-forming)	6.1.1.6	372837	325856	4.10	666	P < 0.001
(R)-lactate hydro-lyase (adding N-acetyl-D-glucosamine 6-phosphate; N-acetylmuramate 6-phosphate-forming)	4.2.1.126	315409	268045	4.06	666	P < 0.001
CoA-[4′-phosphopantetheine]:apo-[acyl-carrier protein] 4′-pantetheinephosphotransferase	2.7.8.7	170465	139721	4.06	666	P < 0.001
D-altronate hydro-lyase (2-dehydro-3-deoxy-D-gluconate-forming)	4.2.1.7	307568	262854	4.05	666	P < 0.001
(6R)-6beta-hydroxy-1,4,5,6-tetrahydronicotinamide-adenine dinucleotide 6-epimerase	5.1.99.6	373721	327886	3.98	666	P < 0.001
UTP:L-glutamine amido-ligase (ADP-forming)	6.3.4.2	369633	324526	3.96	666	P < 0.001
beta-D-4-deoxy-Delta4-GlcAp-(1->3)-beta-D-GalNAc6S hydrolase	3.2.1.180	300245	258391	3.94	666	P < 0.001
D-erythro-1-(imidazol-4-yl)glycerol-3-phosphate hydro-lyase [3-(imidazol-4-yl)-2-oxopropyl-phosphate-forming]	4.2.1.19	361792	316233	3.94	666	P < 0.001
1-(5-phospho-beta-D-ribosyl)-ATP:diphosphate phospho-alpha-D-ribosyl-transferase	2.4.2.17	361822	316271	3.94	666	P < 0.001
2-phospho-4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol CMP-lyase (cyclizing; 2-C-methyl-D-erythritol 2,4-cyclodiphosphate-forming)	4.6.1.12	375199	329906	3.93	666	P < 0.001
1-(5-phospho-beta-D-ribosyl)-5-[(5-phospho-beta-D-ribosylamino)methylideneamino]imidazole-4-carboxamide aldose-ketose-isomerase	5.3.1.16	361727	316261	3.93	666	P < 0.001
[poly-N-acetyl-D-glucosaminyl-(1->4)-(N-acetyl-D-muramoylpentapeptide)]-diphosphoundecaprenol:[N-acetyl-D-glucosaminyl-(1->4)-N-acetyl-D-muramoylpentapeptide]-diphosphoundecaprenol disaccharidetransferase	2.4.1.129	367331	322381	3.93	666	P < 0.001
5-[(5-phospho-1-deoxy-D-ribulos-1-ylamino)methylideneamino]-1-(5-phospho-beta-D-ribosyl)imidazole-4-carboxamide D-erythro-1-(imidazol-4-yl)glycerol 3-phosphate-lyase (L-glutamine-hydrolysing; 5-amino-1-(5-phospho-beta-D-ribosyl)imidazole-4-carboxamide-forming)	4.3.2.10	363190	317925	3.91	666	P < 0.001
dTDP-alpha-D-glucose 4,6-hydro-lyase (dTDP-4-dehydro-6-deoxy-alpha-D-glucose-forming)	4.2.1.46	363385	318389	3.90	666	P < 0.001
ATP:(d)CMP phosphotransferase	2.7.4.25	370117	325801	3.88	666	P < 0.001
L-histidinol-phosphate:2-oxoglutarate aminotransferase	2.6.1.9	372117	327934	3.85	666	P < 0.001
5,10-methylenetetrahydrofolate,FADH2:dUMP C-methyltransferase	2.1.1.148	143685	115112	3.81	666	P < 0.001
orotidine-5′-phosphate carboxy-lyase (UMP-forming)	4.1.1.23	376841	333051	3.81	666	P < 0.001
N2-formyl-N1-(5-phospho-D-ribosyl)glycinamide:L-glutamine amido-ligase (ADP-forming)	6.3.5.3	376690	332992	3.80	666	P < 0.001
alpha-D-galactoside galactohydrolase	3.2.1.22	351869	309262	3.80	666	P < 0.001
oligonucleotidase	3.1.13.3	361085	318401	3.79	666	P < 0.001
ATP:pseudouridine 5′-phosphotransferase	2.7.1.83	143192	114831	3.78	666	P < 0.001
citrate(isocitrate) hydro-lyase (cis-aconitate-forming)	4.2.1.3	370344	327004	3.78	666	P < 0.001
adenosine-3′(2′),5′-bisphosphate 3′(2′)-phosphohydrolase	3.1.3.7	363418	321345	3.74	666	P < 0.001
adenylyl-molybdopterin:molybdate molybdate transferase (AMP-forming)	2.10.1.1	155862	128309	3.71	666	P < 0.001
sn-glycerol-3-phosphate:NAD(P)+ 2-oxidoreductase	1.1.1.94	370438	328604	3.70	666	P < 0.001
isocitrate:NADP+ oxidoreductase (decarboxylating)	1.1.1.42	361230	319382	3.70	666	P < 0.001
short-chain acyl-CoA:electron-transfer flavoprotein 2,3-oxidoreductase	1.3.8.1	140972	113231	3.70	666	P < 0.001
acetyl-CoA:oxaloacetate C-acetyltransferase [thioester-hydrolysing, (pro-S)-carboxymethyl-forming]	2.3.3.1	362515	321195	3.65	666	P < 0.001
UDP-2,4-diacetamido-2,4,6-trideoxy-beta-L-altropyranose hydrolase	3.6.1.57	5601	2056	3.64	644	P < 0.001
D-mannonate hydro-lyase (2-dehydro-3-deoxy-D-gluconate-forming)	4.2.1.8	329439	288135	3.63	666	P < 0.001
ribonuclease M5	3.1.26.8	146813	119961	3.60	666	P < 0.001
(S)-4-hydroxymandelate:oxygen 1-oxidoreductase	1.1.3.46	148308	122183	3.58	666	P < 0.001
S-adenosyl-L-methionine:tRNA (carboxymethyluridine34-5-O)-methyltransferase	2.1.1.229	86	34	3.50	87	P < 0.001
5,10-methylenetetrahydrofolate:tRNA (uracil54-C5)-methyltransferase	2.1.1.74	146047	120006	3.48	666	P < 0.001
aldehyde:ferredoxin oxidoreductase	1.2.7.5	140099	114474	3.47	666	P < 0.001
cobalt-precorrin-6B:NAD+ oxidoreductase	1.3.1.106	150101	125120	3.41	666	P < 0.001
precorrin-6B:NADP+ oxidoreductase	1.3.1.54	150101	125120	3.41	666	P < 0.001
(3S)-3-hydroxyacyl-CoA hydro-lyase	4.2.1.17	146259	121343	3.40	666	P < 0.001
coproporphyrinogen-III:S-adenosyl-L-methionine oxidoreductase (decarboxylating)	1.3.98.3	183567	156750	3.31	666	P < 0.001

Looking at the weaker statistical significance (where we expect 80 to be false positive) we have just 87 items. In other words, the ones below should likely be excluded from any analysis

Enzyme Name	ECKEY	With Long COVID	Without Long COVID	T-Score	DF	Probability
protein-Npi-phospho-L-histidine:D-glucose Npi-phosphotransferase	2.7.1.199	150395	126046	3.30	666	P < 0.01
ATP:4-methyl-5-(2-hydroxyethyl)thiazole 2-phosphotransferase	2.7.1.50	189360	164062	3.30	666	P < 0.01
D-glucose-6-phosphate:NAD+ oxidoreductase	1.1.1.361	368	88	3.26	359	P < 0.01
ATP:alpha-D-Man-(1->2)-alpha-D-Man-(1->2)-[alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->2)-alpha-D-Man-(1->2)]n-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-GlcNAc-diphospho-ditrans,octacis-undecaprenol 3-phosphotransferase	2.7.1.181	386	91	3.26	346	P < 0.01
S-adenosyl-L-methionine:3-O-phospho-alpha-D-Man-(1->2)-alpha-D-Man-(1->2)-[alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->2)-alpha-D-Man-(1->2)]n-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-GlcNAc-diphospho-ditrans,octacis-undecaprenol 3-phospho-methyltransferase	2.1.1.294	386	91	3.26	346	P < 0.01
S-adenosyl-L-methionine:23S rRNA (guanine745-N1)-methyltransferase	2.1.1.187	147722	123211	3.25	666	P < 0.01
kanosamine-6-phosphate phosphohydrolase	3.1.3.92	364	88	3.25	363	P < 0.01
kanosamine 6-phosphate:2-oxoglutarate aminotransferase	2.6.1.104	368	92	3.24	365	P < 0.01
ATP:L-glutamine N5-phosphotransferase	2.7.3.13	241	33	3.23	54	P < 0.01
GDP-beta-L-fucose:beta-D-Gal-(1->3)-alpha-D-GalNAc-(1->3)-alpha-D-GalNAc-diphospho-ditrans,octacis-undecaprenol alpha-1,2-fucosyltransferase	2.4.1.308	390	89	3.22	326	P < 0.01
UDP-alpha-D-galactose:N-acetyl-alpha-D-galactosaminyl-R beta-1,3-galactosyltransferase (configuration-inverting)	2.4.1.122	390	89	3.22	326	P < 0.01
UDP-alpha-D-glucose:N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol beta-1,3-glucosyltransferase	2.4.1.305	390	89	3.22	326	P < 0.01
UDP-N-acetyl-alpha-D-galactosamine:N-acetyl-alpha-D-galactosaminyl-diphospho-ditrans,octacis-undecaprenol alpha-1,3-N-acetyl-D-galactosyltransferase	2.4.1.306	390	89	3.22	326	P < 0.01
fluoroacetyl-CoA hydrolase	3.1.2.29	242821	211022	3.20	666	P < 0.01
GDP-alpha-D-mannose:alpha-D-Man-(1->2)-alpha-D-Man-(1->2)-[alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->2)-alpha-D-Man-(1->2)]n-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-Man-(1->3)-alpha-D-GlcNAc-diphospho-ditrans,octacis-undecaprenol 2,3-alpha-mannosyltransferase (configuration-retaining)	2.4.1.371	387	97	3.20	347	P < 0.01
GDP-alpha-D-mannose:alpha-D-mannosyl-(1->3)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol 3-alpha-mannosyltransferase (configuration-retaining)	2.4.1.349	387	97	3.20	347	P < 0.01
carbamoyl-phosphate:L-ornithine carbamoyltransferase	2.1.3.3	193666	169029	3.19	666	P < 0.01
ATP:L-homoserine O-phosphotransferase	2.7.1.39	169424	144404	3.19	666	P < 0.01
GDP-alpha-D-mannose:N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol 3-alpha-mannosyltransferase (configuration-retaining)	2.4.1.348	386	98	3.18	346	P < 0.01
16S rRNA-uridine516 uracil mutase	5.4.99.19	191228	167210	3.17	666	P < 0.01
2,3-dihydroxybenzoate:oxygen 3,4-oxidoreductase (ring-opening)	1.13.11.14	56	24	3.17	72	P < 0.01
ATP:molybdopterin adenylyltransferase	2.7.7.75	150983	127838	3.16	666	P < 0.01
GDP-beta-L-colitose:beta-D-galactopyranosyl-(1->3)-N-acetyl-D-glucosamine L-colitosyltransferase (configuration-inverting)	2.4.1.341	391	96	3.16	326	P < 0.01
pyrimidine-nucleoside:phosphate (2′-deoxy)-alpha-D-ribosyltransferase	2.4.2.2	160966	137755	3.13	666	P < 0.01
ATP phosphohydrolase (ABC-type, D-galactose-importing)	7.5.2.11	150190	126661	3.13	666	P < 0.01
protein-Npi-phospho-L-histidine:D-fructose Npi-phosphotransferase	2.7.1.202	150170	127151	3.12	666	P < 0.01
diphosphate phosphohydrolase	3.6.1.1	204902	180774	3.10	666	P < 0.01
2-oxo-acid carboxy-lyase (aldehyde-forming)	4.1.1.1	356	95	3.09	358	P < 0.01
NAD+:diphthamide-[translation elongation factor 2] N-(ADP-D-ribosyl)transferase	2.4.2.36	391	103	3.08	326	P < 0.01
GDP-4-dehydro-alpha-D-rhamnose 3-hydro-lyase	4.2.1.168	391	103	3.08	326	P < 0.01
acetyl-CoA:L-glutamate N-acetyltransferase	2.3.1.1	198282	174552	3.07	666	P < 0.01
(8S)-3′,8-cyclo-7,8-dihydroguanosine 5′-triphosphate lyase (cyclic pyranopterin phosphate-forming)	4.6.1.17	164816	142092	3.06	666	P < 0.01
GTP 3′,8-cyclase [(8S)-3′,8-cyclo-7,8-dihydroguanosine 5′-triphosphate-forming]	4.1.99.22	207283	180149	3.05	666	P < 0.01
ADP-L-glycero-D-manno-heptose 6-epimerase	5.1.3.20	33154	22635	3.03	666	P < 0.01
malonyl-CoA:malonyl-CoA malonyltransferase (decarboxylating, phloroglucinol-forming)	2.3.1.253	56	9	3.03	47	P < 0.01
N2-acetyl-L-ornithine:L-glutamate N-acetyltransferase	2.3.1.35	195917	172469	3.03	666	P < 0.01
dTDP-3-amino-3,4,6-trideoxy-alpha-D-glucose:2-oxoglutarate aminotransferase	2.6.1.106	48	25	3.01	61	P < 0.01
L-threonine ammonia-lyase (2-oxobutanoate-forming)	4.3.1.19	287308	259880	3.01	666	P < 0.01
acetyl-CoA:dTDP-4-amino-4,6-dideoxy-alpha-D-glucose N-acetyltransferase	2.3.1.209	391	109	3.01	326	P < 0.01
acetyl-CoA:GDP-4-amino-4,6-dideoxy-alpha-D-mannose N-acetyltransferase	2.3.1.227	391	109	3.01	326	P < 0.01
acetyl-CoA:polysialic-acid O-acetyltransferase	2.3.1.136	391	109	3.01	326	P < 0.01
UDP-alpha-D-galactose:N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol 3-beta-galactosyltransferase (configuration-inverting)	2.4.1.303	391	109	3.01	326	P < 0.01
phosphoenolpyruvate:protein-L-histidine Npi-phosphotransferase	2.7.3.9	198327	175154	2.99	666	P < 0.01
beta-D-galactopyranosyl-(1->3)-N-acetyl-D-hexosamine:phosphate galactosyltransferase	2.4.1.211	157215	134837	2.98	666	P < 0.01
acyl phosphoate:sn-glycerol 3-phosphate acyltransferase	2.3.1.275	188180	165885	2.97	666	P < 0.01
(S)-3-hydroxybutanoyl-CoA:NADP+ oxidoreductase	1.1.1.157	152442	130608	2.95	666	P < 0.01
prephenate:NAD+ oxidoreductase (decarboxylating)	1.3.1.12	209257	186558	2.95	666	P < 0.01
GDP-beta-L-fucose:beta-D-galactosyl-(1->3)-N-acetyl-beta-D-glucosaminyl-R 4I-alpha-L-fucosyltransferase (configuration-inverting)	2.4.1.65	22456	12648	2.95	648	P < 0.01
GDP-beta-L-fucose:beta-D-galactosyl-(1->4)-N-acetyl-D-glucosaminyl-R 3-alpha-L-fucosyltransferase (configuration-inverting)	2.4.1.152	22456	12648	2.95	648	P < 0.01
S-adenosyl-L-methionine:tRNA (cytidine34/5-carboxymethylaminomethyluridine34-2′-O)-methyltransferase	2.1.1.207	203298	180300	2.95	666	P < 0.01
Ste24 endopeptidase	3.4.24.84	8054	5270	2.94	642	P < 0.01
dTDP-4-amino-4,6-dideoxy-alpha-D-glucose:2-oxoglutarate aminotransferase	2.6.1.33	392	116	2.94	327	P < 0.01
formate:NADP+ oxidoreductase	1.17.1.10	9300	6092	2.93	618	P < 0.01
[biotin carboxyl-carrier protein]-N6-carboxybiotinyl-L-lysine:acetyl-CoA:carboxytransferase	2.1.3.15	344844	310616	2.92	666	P < 0.01
2-hydroxy-2H-chromene-2-carboxylate—(3E)-4-(2-hydroxyphenyl)-2-oxobut-3-enoate isomerase	5.99.1.4	48	17	2.92	88	P < 0.01
acetyl-CoA:acetyl-CoA C-acetyltransferase	2.3.1.9	152724	131247	2.92	666	P < 0.01
1H-pyrrole-2-carbonyl-[peptidyl-carrier protein]:FADH2 oxidoreductase (chlorinating)	1.14.19.56	57	8	2.92	43	P < 0.01
phosphoenolpyruvate:D-erythrose-4-phosphate C-(1-carboxyvinyl)transferase (phosphate-hydrolysing, 2-carboxy-2-oxoethyl-forming)	2.5.1.54	201875	179097	2.91	666	P < 0.01
SpoIVB peptidase	3.4.21.116	158665	137029	2.90	666	P < 0.01
ATP:adenosine 5′-phosphotransferase	2.7.1.20	8550	5745	2.90	614	P < 0.01
ATP:selenide, water phosphotransferase	2.7.9.3	160218	139060	2.89	666	P < 0.01
CTP:molybdenum cofactor cytidylyltransferase	2.7.7.76	158341	136662	2.89	666	P < 0.01
ATP phosphohydrolase (ABC-type, polar-amino-acid-importing)	7.4.2.1	199197	176858	2.86	666	P < 0.01
6-phospho-beta-D-glucosyl-(1->4)-D-glucose glucohydrolase	3.2.1.86	151359	130522	2.85	666	P < 0.01
D-mannonate:NAD+ 5-oxidoreductase	1.1.1.57	156942	135744	2.83	666	P < 0.01
acetyl-CoA:N-terminal L-alanyl-[S5 protein of 30S ribosome] N-acetyltransferase	2.3.1.267	334249	305071	2.82	666	P < 0.01
23S rRNA-uridine955/2504/2580 uracil mutase	5.4.99.24	173982	152979	2.79	666	P < 0.01
aspartyl aminopeptidase	3.4.11.21	174811	154321	2.75	666	P < 0.01
adenine aminohydrolase	3.5.4.2	165689	145730	2.73	666	P < 0.01
D-threo-aldose:NAD+ 1-oxidoreductase	1.1.1.122	318	146	2.72	529	P < 0.01
N-acyl-D-glucosamine-6-phosphate 2-epimerase	5.1.3.9	169311	149193	2.71	666	P < 0.01
(S)-2-haloacid halidohydrolase	3.8.1.2	155173	135018	2.71	666	P < 0.01
peptide-L-methionine:thioredoxin-disulfide S-oxidoreductase [L-methionine (S)-S-oxide-forming]	1.8.4.11	312546	283931	2.71	666	P < 0.01
S-adenosyl-L-methionine:precorrin-3B C17-methyltransferase	2.1.1.131	161683	141568	2.70	666	P < 0.01
S-adenosyl-L-methionine:cobalt-factor III C17-methyltransferase (ring contracting)	2.1.1.272	161548	141442	2.69	666	P < 0.01
23S rRNA-uridine2605 uracil mutase	5.4.99.22	356849	324293	2.69	666	P < 0.01
4-hydroxybenzoyl-CoA hydrolase	3.1.2.23	8515	5695	2.67	538	P < 0.01
phosphate-monoester phosphohydrolase (alkaline optimum)	3.1.3.1	350542	317857	2.67	666	P < 0.01
aceneuramate pyruvate-lyase (N-acetyl-D-mannosamine-forming)	4.1.3.3	308399	281017	2.65	666	P < 0.01
ADP-D-ribose ribophosphohydrolase	3.6.1.13	323656	296452	2.65	666	P < 0.01
Repressor LexA	3.4.21.88	194850	174370	2.64	666	P < 0.01
pectin pectylhydrolase	3.1.1.11	273240	244908	2.64	666	P < 0.01
S-methyl-5′-thioinosine:phosphate S-methyl-5-thio-alpha-D-ribosyl-transferase	2.4.2.44	8312	5582	2.62	547	P < 0.01
hydrogen:ferredoxin oxidoreductase	1.12.7.2	8245	5720	2.61	609	P < 0.01
phosphonoacetate phosphonohydrolase	3.11.1.2	9209	6261	2.60	489	P < 0.01
sortase A	3.4.22.70	170941	151298	2.59	666	P < 0.01

I will leave it to the professional microbiologists to make sense of the 190 enzymes above. I should point out that the levels with Long COVID are always higher than the control. I saw the same pattern with Salicylate sensitive – Data And Research, but not as extreme (as with much fewer significant enzymes).

Bacteria Found Significant

The strongest associations are shown below. It is interesting to note that the t-scores are lower for bacteria than for enzymes. The clustering by enzymes was beneficial. We expect less than 2 false positives in this list. Note that the degrees of freedom (Df) is reflected of frequency found. df=38 means that 40 out of 668 samples reported this bacteria.

Tax rank	Taxa Name	With Long COVID	Without Long COVID	T-Score	Df	Probability
species	Faecalibacterium prausnitzii	138151	109673	3.78	666	P < 0.001
genus	Planococcus	41	25	3.89	118	P < 0.001
genus	Anaeroplasma	19328	3766	3.49	74	P < 0.001
class	Mollicutes	5774	2610	3.65	642	P < 0.001
genus	Coriobacterium	173	32	5.41	38	P < 0.001
species	Coriobacterium glomerans	173	32	5.41	38	P < 0.001
genus	Kushneria	54	28	3.62	148	P < 0.001
phylum	Tenericutes	5774	2610	3.65	642	P < 0.001
order	Anaeroplasmatales	19328	3766	3.49	74	P < 0.001
family	Anaeroplasmataceae	19328	3766	3.49	74	P < 0.001
species	Planococcus columbae	41	24	4.85	108	P < 0.001

The less significant ones are shown below:

Tax rank	Taxa name	With Long COVID	Without Long COVID	T-Score	Df	Probability
species	Lactobacillus letivazi	28	11	3.93	12	P < 0.01
species	Clostridium fallax	84	22	3.53	32	P < 0.01
species	Corynebacterium xerosis	755	90	3.34	48	P < 0.01
species group	Pseudomonas syringae group	60	25	3.17	46	P < 0.01
species	Corynebacterium kutscheri	186	34	3.12	33	P < 0.01
genus	Comamonas	289	51	3.11	61	P < 0.01
species	Peptoniphilus indolicus	150	30	3.11	67	P < 0.01
species	Prevotella oulorum	87	52	3.06	72	P < 0.01
order	Sphingobacteriales	40382	31662	2.99	667	P < 0.01
class	Sphingobacteriia	40382	31662	2.99	667	P < 0.01
family	Azonexaceae	60	20	2.98	60	P < 0.01
genus	Dechloromonas	60	20	2.98	60	P < 0.01
family	Rhodocyclaceae	71	50	2.97	413	P < 0.01
genus	Marinospirillum	42	28	2.87	156	P < 0.01
species	Euzebya tangerina	64	46	2.79	370	P < 0.01
class	Nitriliruptoria	64	46	2.79	370	P < 0.01
order	Euzebyales	64	46	2.79	370	P < 0.01
family	Euzebyaceae	64	46	2.79	370	P < 0.01
genus	Euzebya	64	46	2.79	370	P < 0.01
species	Clostridium caliptrosporum	26	17	2.72	37	P < 0.01
genus	Pedobacter	12917	9686	2.72	656	P < 0.01
species	Sphingobacterium bambusae	480	315	2.70	579	P < 0.01
species	Prevotella corporis	1883	645	2.69	315	P < 0.01
species	Dechloromonas hortensis	34	21	2.68	56	P < 0.01
family	Sphingobacteriaceae	35765	28616	2.59	666	P < 0.01

At this point, our first set of bacteria to look at are ones with a high df (and thus most commonly seen). This is a short list when we drop parent and child with the same numbers:

Tax rank	Taxa name	With Long COVID	Without Long COVID	TScore	Df	Probability
order	Sphingobacteriales	40382	31662	2.99	667	P < 0.01
species	Faecalibacterium prausnitzii	138151	109673	3.78	666	P < 0.001
family	Sphingobacteriaceae	35765	28616	2.59	666	P < 0.01
genus	Pedobacter	12917	9686	2.72	656	P < 0.01
phylum	Tenericutes	5774	2610	3.65	642	P < 0.001

Comparing to published studies above

We have no agreement on identification of significant bacteria. The only bacteria in common was Faecalibacterium prausnitzii. This bacteria was cited in just one Pubmed studies on Long COVID. Many studies reported on it during the active COVID infection. This study reported it was lower. We found that it was higher. That study sample size was considerable smaller than us : 106 with Long COVID and 68 Control; they did not mention the significance level but used the term often seen when there was poor significance: “were characterized by”. This study cites “Faecalibacterium prausnitzii showed the largest inverse correlations with PACS at 6 months.” We are dealing with samples with different periods since onset.

The absence of bacteria in common is not unexpected given differences in methodologies. This is often seen when multiple studies on the same condition are done, non-replication of results. I refer the reader to this story

Three blind microbiologists attempt to describe the microbiome of an elephant…

See Blind men and an elephant

Comparing Suggestions

We have suggestions generated from the US National Library of Medicine studies, and we also have suggests from this analysis. These suggestions are generated by the Fuzzy Logic Artificial Intelligence on the Microbiome Prescription site. How do they compare?

Running with everything that is suggested (including prescription items) by both sets of data we found:

• Agreement on 1255 items that will help
• Disagreement on 396 items (one says help, one says hurt)

This is 76% agreement on suggestions that would help. This is surprisingly good given that Faecalibacterium prausnitzii are reported in opposite directions between these two sets. The best agreement was for:

• Drug or “Non-drug” at 90.5% – with 95 items considered
• Prescription other at 93.8% – with 933 items considered

This was not surprising because the data density for these two is good. For many other things, the data density is sparse — for example, for Prebiotics, Amino acids and Probiotics. There is almost no literature on how they impact many of the bacteria cited.

How to Use this data?

The root problem is having insufficient data in many vectors. As a fuzzy logic engineer, this does not concern me — we will simply make the best use of available data in the belief that it is better than working from no data.

The system on Microbiome Prescription uses the consensus system for treatment suggestions. You can process a microbiome using PubMed studies, process it again with the above data, and then get a list where the suggestions are in agreement. At present, the microbiome sample must be processed thru Biomesight because that is how the items of significance were detected. In time, other labs could reach sufficient data to get results.

To illustrate this process, I have done a video below using a sample from someone with Long COVID.

Related Blog Posts for those who are interested:

• Special Studies on Symptoms caused by Bacteria – v.2
• Histamine Release – Literature Review And Speculation
• Salicylate sensitive – Data And Research

Data Availability: The samples and symptoms are available at: Microbiome Prescription Citizen Science Data Share

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 the post on histamines, people with Salicylate sensitivity started to contact. “Me too!”. I looked at the data available and was disappointed that only 31 people have annotated their sample with this condition.

Medical Literature

This is known as  salicylate allergy, salicylate hypersensitivity or salicylate intolerance. The traditional medical view is “It’s not clear exactly what causes salicylate allergy. “[WebMD] It was associated with Lyme Disease in 1991, and in other studies to: rheumatic diseases [1961] and systemic lupus erythematosus[1979]. It is seen in up to 7% of all patients with inflammatory bowel syndrome[2004] where as it was been estimated that 2.5%[2015] of the general population has this issue. The incidence in samples uploaded to microbiome prescription with symptom annotations appears to be 1.8%. This implies a significant microbiome contribution to this condition.

In 1980, we have The uselessness of blood salicylate levels in the diagnosis of salicylate hypersensitivity. However, the level may cause issues with blood donations being received by people with this condition, see Salicylate and acetaminophen in donated blood [1986].

Diet changes appear to have minor impact as reported in Effectiveness of Personalized Low Salicylate Diet in the Management of Salicylates Hypersensitive Patients: Interventional Study[2021] and others [2021] [2016]

In dealing with Plants we have an interesting study Overexpression of Arabidopsis NIMIN1 results in salicylate intolerance [2016] with other plant based studies [2010].

Human Trials

Only one trial could be found

After dietary supplementation with 10 g daily of fish oils rich in omega-3 PUFAs for 6-8 weeks all three experienced complete or virtually complete resolution of symptoms allowing discontinuation of systemic corticosteroid therapy. Symptoms relapsed after dose reduction.”

Control of salicylate intolerance with fish oils [2008]

KEGG: Kyoto Encyclopedia of Genes and Genomes Perspective

I am by temperament, a modeler. Look at the surface layer of information, derive some suggestions of how thing work and then, critically, test the model. Failure is information also! My first model is to assume enzymes associated with salicylates are significant.

Salicylate aka o-Hydroxybenzoic acid aka Salicylic acid is compound C00805 with the following enzymes interacting with it. Conceptually, too much of some enzymes and too few of other enzymes may be the likely cause. The list include the followin:

• 1.2.1.65 salicylaldehyde dehydrogenase
• 1.14.13.1 salicylate 1-monooxygenase;      
• 1.14.13.172 salicylate 5-hydroxylase
• 2.1.1.274  salicylate 1-O-methyltransferase
• 3.1.1.55 acetylsalicylate deacetylase  
• 4.1.1.91 salicylate decarboxylase        
• 4.2.99.21 isochorismate lyase
• 6.2.1.61 salicylate—[aryl-carrier protein] ligase    
• 6.2.1.65 salicylate—CoA ligase

Data From Microbiome Prescription Samples

See Special Studies on Symptoms caused by Bacteria – v.2 for technical detail. A related post is Histamine Release – Literature Review And Speculation.

For more information on each enzyme see BRENDA or KEGG

The most significant ones from are shown below. None are a match for the candidate list above

EC	Enzyme Name	With Sal	Without Sal	TScore	DF	Probability
1.1.1.337	(2S)-2-hydroxycarboxylate:NAD+ oxidoreductase	37077	19517	3.38	666	P < 0.001
1.3.7.1	6-oxo-1,4,5,6-tetrahydronicotinate:ferredoxin oxidoreductase	334	91	4.01	222	P < 0.001
2.1.1.245	5-methyltetrahydrosarcinapterin:corrinoid/iron-sulfur protein methyltransferase	36498	18608	3.46	666	P < 0.001
2.1.1.258	5-methyltetrahydrofolate:corrinoid/iron-sulfur protein methyltransferase	36569	18689	3.46	666	P < 0.001
2.6.1.109	8-amino-3,8-dideoxy-alpha-D-manno-octulosonate:2-oxoglutarate aminotransferase	108	35	3.90	124	P < 0.001
2.7.7.39	CTP:sn-glycerol-3-phosphate cytidylyltransferase	60861	32715	3.56	666	P < 0.001
3.4.19.11	gamma-D-glutamyl-meso-diaminopimelate peptidase	39254	21501	3.42	666	P < 0.001
3.5.2.18	6-oxo-1,4,5,6-tetrahydronicotinate amidohydrolase	334	93	3.93	215	P < 0.001
4.2.1.43	2-dehydro-3-deoxy-L-arabinonate hydro-lyase (2,5-dioxopentanoate-forming)	433	103	4.57	214	P < 0.001
5.4.99.21	23S rRNA-uridine2604 uracil mutase	63603	39414	3.33	666	P < 0.001

Looking at the next level of association, we still find none of these candidate enzymes.

EC	Enzyme Name	With Sal	Without Sal	TScore	DF	Probability
1.1.1.135	GDP-6-deoxy-alpha-D-talose:NAD(P)+ 4-oxidoreductase	279	96	2.82	263	P < 0.01
1.1.1.312	4-carboxy-2-hydroxymuconate semialdehyde hemiacetal:NADP+ 2-oxidoreductase	353	105	2.68	229	P < 0.01
1.1.1.410	D-erythronate:NAD+ 2-oxidoreductase	381	117	3.32	300	P < 0.01
1.1.99.31	(S)-mandelate:acceptor 2-oxidoreductase	265	92	2.71	210	P < 0.01
1.13.11.37	hydroxyquinol:oxygen 1,2-oxidoreductase (ring-opening)	268	88	2.66	247	P < 0.01
1.17.1.4	xanthine:NAD+ oxidoreductase	34795	17231	2.81	666	P < 0.01
1.17.9.1	4-methylphenol:oxidized azurin oxidoreductase (methyl-hydroxylating)	198	83	3.00	338	P < 0.01
1.2.1.87	propanal:NAD+ oxidoreductase (CoA-propanoylating)	25104	13840	2.72	666	P < 0.01
1.2.7.4	carbon-monoxide,water:ferredoxin oxidoreductase	51730	29588	3.18	666	P < 0.01
1.2.99.7	aldehyde:acceptor oxidoreductase (FAD-independent)	34901	17791	2.71	666	P < 0.01
1.3.1.32	3-oxoadipate:NAD(P)+ oxidoreductase	268	88	2.65	260	P < 0.01
2.3.1.101	formylmethanofuran:5,6,7,8-tetrahydromethanopterin 5-formyltransferase	289	88	3.25	207	P < 0.01
2.3.1.276	acetyl-CoA:alpha-D-galactosamine-1-phosphate N-acetyltransferase	51215	29509	3.08	666	P < 0.01
2.3.2.13	protein-glutamine:amine gamma-glutamyltransferase	49247	27040	3.22	666	P < 0.01
2.4.1.245	NDP-alpha-D-glucose:D-glucose 1-alpha-D-glucosyltransferase	111	42	3.33	213	P < 0.01
2.7.1.215	ATP:erythritol 1-phosphotransferase	12084	5590	2.73	653	P < 0.01
2.7.4.31	ATP:[5-(aminomethyl)furan-3-yl]methyl-phosphate phosphotransferase	289	88	3.25	207	P < 0.01
2.7.4.33	ADP:polyphosphate phosphotransferase	36369	19252	2.68	666	P < 0.01
2.7.7.83	UTP:N-acetyl-alpha-D-galactosamine-1-phosphate uridylyltransferase	51217	29512	3.08	666	P < 0.01
2.7.9.6	ATP:rifampicin, water 21-O-phosphotransferase	1458	427	2.99	575	P < 0.01
3.1.1.104	5-phospho-D-xylono-1,4-lactone hydrolase	12119	5680	2.70	656	P < 0.01
3.1.1.81	N-acyl-L-homoserine-lactone lactonohydrolase	12121	5591	2.73	653	P < 0.01
3.4.23.43	prepilin peptidase	104657	72966	2.58	666	P < 0.01
3.5.1.110	(Z)-3-ureidoacrylate amidohydrolase	12177	5917	2.63	661	P < 0.01
3.5.4.44	ectoine aminohydrolase	268	85	2.71	234	P < 0.01
3.5.99.5	2-aminomuconate aminohydrolase	309	106	2.72	177	P < 0.01
3.6.1.15	nucleoside-triphosphate phosphohydrolase	26554	15567	3.28	665	P < 0.01
3.6.1.7	acylphosphate phosphohydrolase	60707	38253	2.67	666	P < 0.01
4.2.1.147	5,6,7,8-tetrahydromethanopterin hydro-lyase (formaldehyde-adding, tetrahydromethanopterin-forming)	309	101	2.77	192	P < 0.01
5.3.1.15	D-lyxose aldose-ketose-isomerase	58198	37194	2.64	666	P < 0.01
5.3.1.34	D-erythrulose-4-phosphate ketose-aldose isomerase	12039	5560	2.71	642	P < 0.01
7.5.2.4	ATP phosphohydrolase (ABC-type, teichoic-acid-exporting)	25268	12222	2.91	666	P < 0.01

So, model 1 is a bust — none of the suspected enzymes showed up as being statistically significant. This echoes The uselessness of blood salicylate levels in the diagnosis of salicylate hypersensitivity[1980]. However one thing stands out…. for each of the enzymes estimates above — the amount with salicylate sensitivity is in every case more than those without it. This is unusual.It hints that the issue is over production of enzymes.

Microbiome Significances

Only Biomesight has sufficient data to do an investigation with. The most significant ones all had an interesting characteristic matching that seen with the enzymes — Too MANY.

Tax_name	Tax_rank	With Sal	Without Sal	Tscore	DF	Probability
Fusobacteriia	class	21983	3330	3.52	346	P < 0.001
Chroococcaceae	family	588	165	3.42	286	P < 0.001
Fusobacteriaceae	family	25628	3687	3.63	305	P < 0.001
Hungateiclostridiaceae	family	8534	2649	3.69	201	P < 0.001
Lachnospiraceae	family	297116	203706	4.2	667	P < 0.001
Rhodanobacteraceae	family	2453	656	3.32	478	P < 0.001
Syntrophobacteraceae	family	115	36	3.57	184	P < 0.001
Acetobacterium	genus	3977	1250	3.92	657	P < 0.001
Blautia	genus	141777	87110	4.2	667	P < 0.001
Chroococcus	genus	588	165	3.42	286	P < 0.001
Clostridium	genus	30531	18614	3.45	667	P < 0.001
Furfurilactobacillus	genus	91	38	3.48	270	P < 0.001
Hungateiclostridium	genus	8178	2229	3.94	200	P < 0.001
Luteibacter	genus	3734	764	4.07	387	P < 0.001
Fusobacteriales	order	21983	3329	3.52	346	P < 0.001
Syntrophobacterales	order	135	45	5.06	331	P < 0.001
Fusobacteria	phylum	21983	3330	3.52	346	P < 0.001
Bacteroides fluxus	species	2722	318	3.41	517	P < 0.001
Blautia glucerasea	species	4228	664	3.86	550	P < 0.001
Blautia schinkii	species	1710	265	4.14	558	P < 0.001
Caloramator uzoniensis	species	300	85	4.53	328	P < 0.001
Chroococcus minutus	species	588	165	3.42	286	P < 0.001
Clostridium akagii	species	113	43	4.37	311	P < 0.001
Furfurilactobacillus siliginis	species	91	38	3.51	271	P < 0.001
Luteibacter anthropi	species	3734	764	4.07	387	P < 0.001

Looking at the next level, we have a lot less BUT THE PATTERN OF OVER ABUNDANCE continues:

Tax_name	Tax_rank	WithMean	WithoutMean	Tscore	DF	Probability
Desulfovibrionaceae	family	8811	5037	2.61	641	P < 0.01
Xanthomonadaceae	family	2131	642	3.16	556	P < 0.01
Bilophila	genus	6608	3415	3.25	587	P < 0.01
Caldicellulosiruptor	genus	581	289	2.67	617	P < 0.01
Fusobacterium	genus	2795	683	2.76	289	P < 0.01
Holdemania	genus	572	296	3.04	589	P < 0.01
Lachnospira	genus	49628	28041	3.22	667	P < 0.01
Macrococcus	genus	2425	419	3.28	222	P < 0.01
Thiorhodococcus	genus	64	28	2.67	108	P < 0.01
Desulfovibrionales	order	8840	5039	2.63	643	P < 0.01
Bacteroides finegoldii	species	7458	2163	3.15	552	P < 0.01
Blautia obeum	species	12039	5596	2.68	642	P < 0.01
Fusobacterium gonidiaformans	species	3980	833	3.12	134	P < 0.01
Shewanella upenei	species	92	46	3	157	P < 0.01

Cross Validation of The above

We know of only one thing known to help, Omega-3 / Fish Oil. We have this information in our database and find that it decreases the following (from multiple studies):

• Lachnospira
• Lachnospiraceae
• Furfurilactobacillus siliginis
• Blautia

A single study suggests that it increases Clostridium / Clostridium akagii (Child). This is a 80% validation rate implying that the Artificial Intelligence suggestions on a person’s microbiome is likely to be effective.

Putting this data to Use

The first step is simple, take 10 g daily of fish oils rich in omega-3 for the first 6-8 weeks. Get relief. Warning: the study states that if you stop, you will revert. It is insufficient to correct the bacteria shifts.

If you do not have a BiomeSight sample on hand…

Go to Citizen Science Special Studies, Enter Salicylate in the search box. The page should give you two buttons as shown below.

The results may change as more samples are uploaded and annotated with symptoms. This research is dynamic and live!. Clicking the green button will show a list of item to take or add to your diet.

The second item is likely more important items that feed the bacteria that are too high. You want to reduce or eliminate these.

There is still more information available on actual diet (foods that you may eat), by clicking on

We know the fish oil helps, let us verify that by clicking on [FIsh, shellfish and their products]. We see fish — specifically salmon! And Walrus liver is a to-avoid!

Returning to the red button, we see a list of off-label usage of drugs mixed into the results. The top ones:

• Risperidone is used to treat a certain mental/mood disorder called schizophrenia.
• Acarbose is used with a proper diet and exercise program to control high blood sugar in people with type 2 diabetes.
• meomycin is used to decrease the risk of infection after certain intestinal surgeries.

Step 2 Order a Suitable Test

You will likely need at least 3 tests, the data is based on Biomesight tests. Order some. If you live in the US, you could order an Ombre Test (this gets more technical because you will need to download some big files [FASTQ] from their site and then upload to Biomesight for them to process them).

You will likely be doing the above for 5-6 weeks before you get your data available on BiomeSight.

Log in to Microbiome Prescription

Go to [Research Features]

Then find on that page, the Experimental section, and click v.2 Of Special Symptom Associations

Select your latest sample (if more than one) and then the condition

The bacteria that definitely match the pattern will be automatically checked.

You should scan for any close matches and check those boxes. For example, above the without symptoms slightly.

Now click the [Add to Hand Picked LIst] button at the top

A new window will appear with the items that you have selected.

Just click [Get Suggestions] and then scope the type of suggestions you wish

Then click the Get Suggestions button on the top left

Your suggestions may be similar (the sample that I am using was for someone with this issue)

The food list may also change

WARNING: Many items suggested may trigger a Salicylate reaction. Consider doing the 6-8 weeks of Fish Oil before starting any items that could trigger a reaction.

This reaction could be caused by bacteria resisting change (similar to a Jarisch Herxheimer)

The usual pattern is to implement the changes for 4 week, do a new sample. Keep doing the changes until the results come in. So this is the likely time line for some

• Week 0: Order Kit, start general list
• Week 1: Take test #1 and send in
• Week 4: Get results, upload and get 1st personalized set of suggestions
• Week 8: Take test #2 and send in
• Week 10: Get results, upload and get 2nd personalized set of suggestions
• Week 14: Take test #3 and send in
• Week 16: Get results, upload and get 3rd personalized set of suggestions

Depending on results, repeat. Your desired result is to have NO Matches with the pattern (and hopefully have eliminated the issue).

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.

I was not happy with the first series of special studies. I left them alone and as a result of doing the Histamine Release – Literature Review And Speculation I changed the methodology and build out more infrastructure to do analysis with more statistical rigor (with thanks to Chat_GPT to speed building the new additions).

To use this feature, you need to upload a 16s sample (Ombre/Thryve, uBiome, BiomeSight) to Microbiome Prescription.

The new special studies are all done on Symptoms that could have sufficient data to detect statistical significance at p < 0.01 or p < 0.001. Many studies on the US National Library of Medicine report significance with p < 0.05. I prefer to reduce the risk of false positive results given that we have so many bacteria possible. 800 bacteria with p < 0.01 would expect 8 items by random chance. p < 0.001 would expect just 1 or less item by random chance.

I also did a more formal presentation showing the t-score, degrees of freedom and probability.

How to Find These Special Studies

Click the Research Tab and scroll to the bottom, you will see:

On this new page, select your sample and then the symptom that you are interested in. There are a lot more symptoms than in the original version

The table below show all of the taxa from the same lab that you also had reported.

If something is clearly significant, you will see P < 0.001- Too High displayed. The Degree of Freedom is the number of samples that have this bacteria. For example Thiocapsa is seen in just 151 samples, thus a rare bacteria. Look at Phascolarctobacterium, this sample has 24000, people with the condition averages 8500 and people without this symptom averages just 5000. It is clear that you have an increased risk of this condition.

You also have age range… which indicates how much you microbiome agrees with an age range (sorry, getting older is not curable!).

For a few items, you may get no rows. That means that none of the bacteria you have matched the list of ones that were found significant.

There is a lot of data there. I am planning to add hand picking bacteria to these pages eventually.

The Difference Between Version 1 and 2

One of the challenges with earlier attempts to find clear associations between bacteria and symptoms has been insufficient data. Earlier attempts made best efforts with the data that was available. Thanks to people uploading more samples, and annotating them with symptoms, we have entered a time of sufficient data for better analysis. The table below shows the difference between the penultimate version and the current version.

Aspect	Prior “V1”	Current “V2”
Sample of people with condition	Reported a specific symptom	Reported a specific symptom with Source Lab
Control Group	Everyone that did not report symptom. Including people that did not report any symptoms.	Only samples with symptoms reported, but not those with a specific symptom.
Weight for suggestions	z-score	Different between sample and mean for control group. Difference is scaled by a linear monotonic function.
Handling of multiple symptoms	“Clumsy” – bacteria get double counted often	Allow one pass to get bacteria shared across symptoms. No double counting

Nota Bene: The control group is not healthy people, just people without the specified symptoms. This is a two edge sword that many would debate.

Post Script

All of the data used is available for download at: Microbiome Prescription Citizen Science Data Share

Histamine issues can occur from consuming food high in histamines. This is the typical approach for people dealing with this issue. There is an another route that should not be overlooked — things that do not contain histamine but which triggers histamine release.

Citations

Histamine release caused by reactions to drug product and/or excipients/vehicles is a phenomenon observed in both toxicology and pharmacology studies. This type of reaction is also referred to as pseudoallergic reaction, anaphylactoid reaction or complement activation-related pseudoallergy (CARPA). 

Biomarkers in Toxicology, 2014

• “Codeine and meperidine are examples of other opioids that can induce mast cell activation with the release of histamine” [ 2020]
  • “Quaternary ammonium compounds (e.g., NMBDs) are generally weaker histamine-releasing substances than are tertiary amines such as morphine.”
• “Histamine release can occur with administration of certain opioids” [2015]
• “Histamine release is primarily caused by morphine, followed by hydromorphone,” [2009]
• “Histamine release and the severity of reactions during vancomycin administration are directly dependent on the rate of infusion.” [2007]
• “It is not clear whether histamine levels are altered following hypo– or hyperthermia seen during several clinical or experimental situations.” [2004]
• “Histamine release and non-IgE-mediated anaphylactic (anaphylactoid) reactions occur with alcuronium” [2016]

The above means that care needs to be taken if a herb, spice or supplement causes a histamine reaction. It may not be histamine in the substance, rather the substance causes mast cells to react and dump histamine. It is a significantly different situation. Other items that have been reported to cause histamine release include: Vitamin C(L-Ascorbic acid), Niacin (vitamin B3), Quercetin, Stinging nettle, Licorice root, Ginkgo biloba, Chamomile and Echinacea.

Histamine toxicity is sometimes confused with an allergic reaction to fish. Here is why:
Some kinds of fish contain naturally high levels of the chemical histidine. This chemical can be converted to histamine by bacteria [ the enzyme histidine decarboxylase EC 4.1.1.22]. In an allergic reaction, mast cells release histamine which triggers allergy symptoms. So, if a person eats fish that has a high level of histamine, the response may resemble an allergic reaction to that food.

American Academy of Allergy, Asthma & Immunology

A list of bacteria with this enzyme is here. The data comes from the KEGG: Kyoto Encyclopedia of Genes and Genomes and a collection of several thousand samples from different labs uploaded to the Microbiome Prescription web site. The top producers over all of the sample are shown below

Bacteria	Estimate Occurances	Contribution
Bacteroides fragilis [species]	1	5897.04
Eggerthella lenta [species]	1	627.29
Acinetobacter baumannii [species]	0.9	109.55
Gordonibacter pamelaeae [species]	1	99.28
Fusobacterium varium [species]	1	93.35
Clostridium perfringens [species]	1	64.31
Fusobacterium ulcerans [species]	1	57.3

Hypothesis Testing on Bacteria Conversion

The citizen science site, Microbiome Prescription, allows people to share their microbiome results from many labs and to annotate their samples with symptoms. Over 1000 samples have these annotations as shown below, so we can suggest a hypothesis and test it.

Hypothesis: People with Histamine Issues are likely to have higher counts of bacteria producing histidine decarboxylase

The results was a bit of a surprise. The Hypothesis failed dramatically! Having more bacteria producing this enzyme appears to be associated with less histamine issue!! This pattern persists across all three labs with significant data sample size.

One hypothesis that could be suggested by this data is that the histamine issue is due to the body’s base level of histamine being abnormally low and thus the body is unfamiliar with histamines and thus overreacts.

An Analogy to Consider

Imagine someone whose diet lacks ANY added sugar. After a year, he drops into a friend who makes him his favorite herbal tea. The friend, she, likes sweet tea and adds several teaspoon of sugar. This person drinks it and gets an atypical headache which confuses the friend – he drinks it regularly! The real cause is too low a base level of sugar consumption for this person to tolerate.

The above suggests that the same may be occurring with histamine reactions.

Did you know that both too much sugar and too little of sugar can cause headaches? When you consume too much sugar at once or don’t eat for an extended period of time, you can cause rapid fluctuations in your blood sugar levels which can trigger a headache. Some people are more prone to these sugar-triggered headaches.

Source

If this is a correct speculation, then the treatment approach is to encourage bacteria that produce the enzyme histidine decarboxylase EC 4.1.1.22.

Phrase 2 Checking each bacteria Taxa

While a bacteria has an enzyme, it is not a given that it is active. Our second pass is looking at the frequency of each bacteria taxa appearing in each group. If a specific taxa is significantly more or less frequent, it may be a trail worth following. Drilling down to species causes our observations to drop to the point that many data points cannot be examined for significance. We have three taxa with good sample sizes.

Remember, different labs use different software resulting in different taxa names.

Bacteria	Percent Having	Mean	SD	Subset	Lab
Bacteroides fragilis	79.2	5138	8629	Histamine	BiomeSight
	81.4	6369	16168	No Histamine
	75.6	6736	22788	Histamine	uBiome
	51.8	16628	47841	No Histamine
	94.2	7343	14279	Histamine	Ombre
	94.2	8646	27095	No Histamine
Eggerthella lenta	74	57	611	Histamine	BiomeSight
	69.8	365	346	No Histamine
	74.4	67	1323	Histamine	uBiome
	69.5	235	1502	No Histamine
	77.9	824	2407	Histamine	Ombre
	79.4	1059	4131	No Histamine
Gordonibacter pamelaeae	57.78	52	459	Histamine	uBiome
	61.2	207	413	No Histamine
	51.9	124	177	Histamine	Ombre
	46.1	187	358	No Histamine

Bacteroides fragilis for Ombre(Thryve), uBiome and BiomeSight were the most statistically significant and all have the same pattern: People reporting histamine issues had less than people that did not report histamine issues. Bacteroides fragilis is also the main source of histidine decarboxylase from the microbiome.

uBiome data was very interesting because the detection rate for Bacteroides fragilis was significantly less for samples that did not report histamine issues with the differences of means being much much more than with other labs. This hints that some part of the base pairs collection that uBiome used to determine Bacteroides fragilis in their software may be particularly important for histamine issues. To put it another way, some part of the sequence being used to determine the taxa, also appears to detect histamine issues. This leads to the possibility that specific strains of Bacteroides fragilis may result in better histamine tolerance.

uBiome and others use a reference database.  Because a 16s test only looks at a tiny portion of the microbial genome, of necessity different bioinformatics pipelines will assign slightly different microbial genus/species names for the string of base pairs they sequence.

uBiome, a company that offered microbiome testing services, used a proprietary reference database called the uBiome Microbial Insights Test (MIT) reference database for the taxonomic classification of 16S rRNA gene sequences. This database was specifically designed for the analysis of human microbiome samples, and included over 1,000 microbial taxa that were commonly found in the human gut, oral, and skin microbiomes. The database was built using a combination of publicly available 16S rRNA gene sequences and uBiome’s own sequencing data, and was regularly updated to incorporate new microbial taxa and improve the accuracy of taxonomic assignments.

From Chat_GPT

• Biomesight: SILVA SSU Ref NR 99 database,
• Ombre: GreenGenes 13_8 release
• American Gut: Greengenes 13_8 release,
• Thorne: Their 16s database is built from a combination of publicly available reference databases, including the Greengenes and SILVA databases, as well as Thorne’s own sequencing data.

Unfortunately uBiome reference database disappeared with it going into bankruptcy. Only time will tell if Thorne’s reference database will identify the key base pairs that appears to be connected to histamine issues.

What is very interesting comes from a 1999 study, “Intestinal mucosa-associated bacteria modulate rat mast cell reactivity” which reports a ConA-induced histamine release was diminished up to 71% of maximal histamine release by Bacteroides fragilis!! This supports that increasing Bacteroides fragilis may be the right way to go.

Where do we go from here for bacteria?

The above suggests that there is a bacteria taxa with a specific key base pair that is connected to histamines issues. Let us call it “Bacteroides Histamilis”(BH). This taxa has some overlap with Bacteroides fragilis (BF). They appear to occupy a similar niche in the microbiome world with an increase of BF decreasing BH (or it’s impact). We are shooting in the fog here, but it seems encouraging BF growth may reduce BH and ease histamine issues. We have know items that increases or decrease listed here: Bacteroides fragilis and a link to possible foods. The top food nutrients are iron, zinc and Riboflavin (Vitamin B2).

Going to Chat_GPT for some quick answers, we get a pleasant surprise:

Iron plays an important role in the regulation of histamine levels in the body. It is required for the activity of an enzyme called diamine oxidase (DAO), which is responsible for breaking down histamine in the gut. Low iron levels can lead to reduced DAO activity and increased histamine levels, which can contribute to histamine intolerance or sensitivity.

Zinc is also important for the activity of DAO, as well as for the regulation of histamine receptors in the body. Zinc deficiency has been linked to increased histamine levels and may contribute to histamine intolerance.

Riboflavin (vitamin B2) is required for the synthesis of DAO, and low riboflavin levels have been associated with reduced DAO activity and increased histamine levels.

Answer from Chat_CPT to question: “Is histamine sensitivity connected to iron, zinc or Riboflavin deficiency?“

So, we have an interesting cascade… the 3 key nutrients available concurrently in food that increases Bacteroides fragilis, are all associated with DAO production. Should we speculate that BH is a mutation that thrives better with low levels of Iron, Zinc and Riboflavin and the difference in base pairs is connected with this mutation? Continuing this thought experiment, would this mutation also have reduced (or no) enzyme histidine decarboxylase EC 4.1.1.22 being produced resulting in an abnormally low level of histamine on an ongoing basis and thus increased sensitivity?

A Parallel Thread in Autism?

Antihistamines have been reported to reduce some autism behaviors [2018]. For example “Altered expression of histamine signaling genes in autism spectrum disorder” [2017]. Bacteroides fragilis has been reported to be low with autism. I will leave it to others to explore this further.

One study published in 2013 found that children with autism had lower levels of Bacteroides fragilis in their gut microbiome compared to typically developing children. Another study published in 2017 found that a group of children with autism had higher levels of Bacteroides fragilis in their fecal samples compared to a control group of typically developing children.

Iron plays an important role in brain development and function, and some studies have suggested that iron deficiency during pregnancy or early childhood may increase the risk of autism.

Zinc is also important for brain development and function, and some studies have found that children with autism may have lower levels of zinc in their blood or hair compared to typically developing children.

Riboflavin (vitamin B2) is required for several important metabolic pathways in the body, and some studies have suggested that children with autism may have lower levels of riboflavin compared to typically developing children.

However, other studies have not found a significant association between these and autism.

Chat_GPT.

Bottom Line

There is a scent that specific strains of Bacteroides fragilis may be associated with histamine sensitivity. In an environment deficient of iron, zinc or riboflavin, this strain increases. This strain may not produce histidine decarboxylase EC 4.1.1.22 (epigenetics?) resulting in a much lower level of histamine in the body resulting in “sugar-shock” when a food containing histamines is consumed. We saw above a consistent pattern of having a lower count has an increased probability of histamine issues. A lower count is typically viewed as having less appropriate nutrients available – and the missing nutrients are implied by our analysis.

A common pattern seen by people with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Irritable Bowel Disease and other gut disturbances is increasing histamine issues. Mal-absorption due to gut disturbances would result in a dropping of iron, zinc and riboflavin absorption causing this “Bacteroides Histamilis” strain to dominant.

With this model, supplementation with iron, zinc and riboflavin to increase the body’s level to at least the 75%ile may result in significant improvement.

A suggestion for a study would be to measure iron, zinc and riboflavin levels of people with histamine issues against an appropriate matched control population. This may determine if deficiency of just one of this trio is sufficient, or do we need multiple deficiency.

Appendix Statistical Significance Table

The following is general data mining. Remember the lab’s software determines the taxa names using probability. Safest conclusions are when multiple labs report significance in the same direction for the same taxa. Taxa cannot be safely be applied to different labs. Labs report on different bacteria, especially at the species level, on occasion they will pick one name and a different lab will pick a different name.

REMEMBER: These may not be the cause, rather bacteria altered by the bacteria that are the cause.

Taxa name	Taxa Rank	Lab	Histamine Mean	Control Mean	T-Score	DF	Probability
Cyanobacteria /Melainabacteria group	clade	uBiome	6444	268	4.338711	12	P < 0.001
Alphaproteobacteria	class	uBiome	26227	15210	3.283924	269	P < 0.01
Chitinophagia	class	Thryve	94	69	2.824838	330	P < 0.01
Clostridia	class	BiomeSight	619159	558435	2.713198	667	P < 0.01
Deltaproteobacteria	class	uBiome	10291	6987	2.794664	401	P < 0.01
Flavobacteriia	class	Thryve	377	93	4.138491	241	P < 0.001
Spirochaetia	class	Thryve	22731	196	2.747053	68	P < 0.01
Bifidobacteriaceae	family	uBiome	30890	14422	2.643228	474	P < 0.01
Carnobacteriaceae	family	uBiome	815	138	2.86021	182	P < 0.01
Chitinophagaceae	family	Thryve	94	69	2.818824	330	P < 0.01
Chromatiaceae	family	BiomeSight	110	74	2.962499	525	P < 0.01
Coprobacillaceae Verbarg et al. 2014	family	BiomeSight	4986	2981	2.905239	662	P < 0.01
Desulfovibrionaceae	family	uBiome	10288	6984	2.797459	401	P < 0.01
Flavobacteriaceae	family	Thryve	376	95	3.766135	215	P < 0.001
Lachnospiraceae	family	BiomeSight	244569	201657	3.461006	667	P < 0.001
Micrococcaceae	family	Thryve	2041	279	2.732836	198	P < 0.01
Odoribacteraceae	family	uBiome	13802	9604	2.953385	386	P < 0.01
Pasteurellaceae	family	uBiome	11259	3343	2.952883	325	P < 0.01
Rhodospirillaceae	family	uBiome	29014	17878	3.045209	229	P < 0.01
Rubritaleaceae	family	BiomeSight	118	51	3.018423	231	P < 0.01
Streptococcaceae	family	uBiome	12988	6609	3.294864	469	P < 0.01
Syntrophaceae	family	BiomeSight	88	34	3.602406	95	P < 0.001
Weeksellaceae	family	Thryve	826	140	2.707921	42	P < 0.01
Adlercreutzia	genus	BiomeSight	856	346	3.933686	493	P < 0.001
Anaerofustis	genus	BiomeSight	85	45	2.957077	132	P < 0.01
Anaerolinea	genus	BiomeSight	31	18	3.145373	49	P < 0.01
Anaerotruncus	genus	BiomeSight	2476	1835	2.82706	650	P < 0.01
Bacteroides	genus	uBiome	283855	247858	2.683065	471	P < 0.01
Bifidobacterium	genus	uBiome	30859	14357	2.650187	474	P < 0.01
Blautia	genus	BiomeSight	109238	86161	3.171679	667	P < 0.01
Butyrivibrio	genus	Thryve	2238	863	2.78185	414	P < 0.01
Chromatium	genus	BiomeSight	54	23	2.899706	89	P < 0.01
Chryseobacterium	genus	Thryve	112	28	5.423269	18	P < 0.001
Clostridium	genus	uBiome	9065	7003	2.64738	469	P < 0.01
Clostridium	genus	BiomeSight	24536	18253	3.27004	667	P < 0.01
Cronobacter	genus	uBiome	7047	250	3.67629	51	P < 0.001
Desulfomonile	genus	BiomeSight	94	34	3.874419	94	P < 0.001
Desulfovibrio	genus	uBiome	8575	4552	2.939696	243	P < 0.01
Granulicatella	genus	uBiome	839	137	2.901195	179	P < 0.01
Haemophilus	genus	uBiome	12156	3087	3.235353	314	P < 0.01
Henriciella	genus	Thryve	386	176	2.642494	155	P < 0.01
Hungateiclostridium	genus	BiomeSight	4457	2097	2.785968	200	P < 0.01
Limosilactobacillus	genus	uBiome	48109	2843	2.818423	44	P < 0.01
Macrococcus	genus	BiomeSight	1558	388	2.673296	222	P < 0.01
Marvinbryantia	genus	uBiome	4529	2564	2.773658	369	P < 0.01
Negativicoccus	genus	BiomeSight	1370	264	3.038088	353	P < 0.01
Odoribacter	genus	uBiome	8724	5405	3.26612	369	P < 0.01
Pelotomaculum	genus	BiomeSight	259	96	2.808263	236	P < 0.01
Rothia	genus	Thryve	2787	326	2.925505	153	P < 0.01
Rubritalea	genus	BiomeSight	118	51	3.025228	232	P < 0.01
Senegalimassilia	genus	Thryve	2823	617	3.184808	143	P < 0.01
Shuttleworthia	genus	Thryve	105	75	2.606498	318	P < 0.01
Slackia	genus	uBiome	3566	1488	4.3098	154	P < 0.001
Streptococcus	genus	uBiome	12882	6211	3.438454	465	P < 0.001
Trabulsiella	genus	BiomeSight	2381	72	2.944962	84	P < 0.01
Chryseobacterium group	norank	Thryve	112	28	5.423269	18	P < 0.001
unclassified Parabacteroides	norank	uBiome	2321	760	3.29071	91	P < 0.01
unclassified Streptococcus	norank	uBiome	9530	3840	3.188989	429	P < 0.01
Bifidobacteriales	order	uBiome	44064	18404	3.118635	363	P < 0.01
Chitinophagales	order	Thryve	94	69	2.824838	330	P < 0.01
Desulfovibrionales	order	uBiome	10291	6987	2.794631	401	P < 0.01
Eubacteriales	order	BiomeSight	614126	553568	2.710308	667	P < 0.01
Flavobacteriales	order	Thryve	377	93	4.138507	241	P < 0.001
Micrococcales	order	Thryve	1469	345	2.755253	326	P < 0.01
Pasteurellales	order	uBiome	11259	3343	2.952883	325	P < 0.01
Rhodocyclales	order	uBiome	888	327	2.799376	44	P < 0.01
Rhodospirillales	order	uBiome	27884	17148	3.002822	241	P < 0.01
Syntrophobacterales	order	BiomeSight	75	45	2.874101	331	P < 0.01
Chloroflexi	phylum	Thryve	1529	170	3.281628	262	P < 0.01
Cyanobacteria	phylum	uBiome	6444	268	4.338711	12	P < 0.001
Fibrobacteres	phylum	Thryve	180078	34	4.876895	83	P < 0.001
Firmicutes	phylum	BiomeSight	646426	582627	2.769104	667	P < 0.01
Spirochaetes	phylum	Thryve	22731	196	2.747053	68	P < 0.01
Adlercreutzia equolifaciens	species	BiomeSight	549	211	3.480457	447	P < 0.001
Alistipes putredinis	species	uBiome	14784	10219	3.567818	346	P < 0.001
Alistipes sp. NML05A004	species	uBiome	2861	1594	3.211406	270	P < 0.01
Anaerofustis stercorihominis	species	BiomeSight	85	45	2.937001	130	P < 0.01
Anaerolinea thermolimosa	species	BiomeSight	30	18	2.73704	47	P < 0.01
Anaerotruncus colihominis	species	BiomeSight	2335	1749	2.696412	650	P < 0.01
Bacteroides finegoldii	species	BiomeSight	4441	2045	2.613824	552	P < 0.01
Bacteroides heparinolyticus	species	BiomeSight	59	33	2.652157	173	P < 0.01
Bacteroides nordii	species	uBiome	5809	1153	3.833993	148	P < 0.001
Bacteroides reticulotermitis	species	Thryve	724	301	4.303933	341	P < 0.001
Bacteroides sp. 35AE37	species	uBiome	24271	10025	3.724252	219	P < 0.001
Bacteroides uniformis	species	Thryve	39266	26321	3.311011	405	P < 0.01
Bifidobacterium dentium	species	uBiome	18963	288	3.177668	33	P < 0.01
Bifidobacterium longum	species	uBiome	21851	6860	4.131341	271	P < 0.001
Bifidobacterium pseudocatenulatum	species	uBiome	34144	8671	3.752826	94	P < 0.001
Blautia glucerasea	species	BiomeSight	2184	606	2.887145	550	P < 0.01
Blautia obeum	species	BiomeSight	11262	5088	4.539525	642	P < 0.001
Chromatium weissei	species	BiomeSight	54	23	2.899706	89	P < 0.01
Corynebacterium spheniscorum	species	uBiome	12700	3296	2.737118	99	P < 0.01
Desulfohalotomaculum peckii	species	Thryve	35	16	3.191543	21	P < 0.01
Desulfomonile tiedjei	species	BiomeSight	94	34	3.874419	94	P < 0.001
Granulicatella adiacens	species	uBiome	626	100	2.637126	176	P < 0.01
Haemophilus parainfluenzae	species	uBiome	10429	3084	2.943034	310	P < 0.01
Klebsiella oxytoca	species	BiomeSight	2975	337	2.885423	113	P < 0.01
Negativicoccus succinicivorans	species	BiomeSight	1381	248	2.970997	330	P < 0.01
Pelotomaculum isophthalicicum	species	BiomeSight	259	95	2.82543	236	P < 0.01
Phocaeicola coprophilus	species	BiomeSight	18667	2437	3.75374	151	P < 0.001
Phocaeicola plebeius	species	uBiome	104385	39611	2.841081	49	P < 0.01
Porphyromonas bennonis	species	uBiome	9563	2189	2.832604	170	P < 0.01
Ruminococcus bromii	species	BiomeSight	15277	7865	2.709478	558	P < 0.01
Shuttleworthia satelles	species	Thryve	102	71	2.89508	304	P < 0.01
Slackia piriformis	species	Thryve	1676	634	2.804074	126	P < 0.01
Slackia piriformis	species	uBiome	5996	1513	4.050816	41	P < 0.001

Comments from Early Reviewers

“Interesting the connection between histamine and iron. I have some Mast Cell issues which have finally been diagnosed. I also have low ferritin [a blood protein that contains iron], although hemoglobin and even serum iron are within range…. BTW, I recall Hawrelak saying once that histamine behavior of bacteria is strain dependent, not species.”

My original motivation to get into the Microbiome was Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The Artificial Intelligence was originally tuned for that condition. Cross validation for other conditions has shown that the tuning is robust.

The intent of the site, the blog and videos is to teach people how to be self-empowered with tools and knowledge. In the case of people with ME/CFS and Long COVID, that is often an expectation too far due to cognitive issues (brain fog and other neurological disturbances). This is why many of these blog post are on people with these issues.

The Back Story

I’ve been on a downward spiral now for quite a long time (since June 2021). And ended up unable to walk and at one point, control (or feel) my right leg and both arms below the shoulder. I looked nearly identical to, and had the same state of existence as this guy, with the TPN feeding port to my heart (still in me, here) and all and being given liquids via IV because I couldn’t even drink water. Hospitalized, foley-cathetered, no bowel movements for 20+ days.

I left the hospital for 2 reasons:

1. To spend some last good days with my wife (in case my research was wrong). I ate a cheeseburger from In-And-Out Burger (and nearly died), rode in a Tesla (here), sat on the roof to enjoy sunsets (here). And generally just did a last-hoorah.
2. To enable myself to self-treat (which I started immediately upon leaving). The doctors were not allowed (by law) to prescribe what I needed (as it was off-label), and by the nature/constraints of TPN osmolality were feeding me with nearly 80% sugar through TPN (Total Parenteral Nutrition, here), which according to my research (and the impact I saw/felt my body) is HORRIBLE for this condition.

Upon leaving the hospital, I initiated self-treatment and started to recover. I can walk again. Make jokes, and write all this up. Every once a while I even dance a little. And most importantly I can eat at or above my calories each day. And I went from almost no deep sleep at all (monitored by Apple Watch 8) to 1 hour and 42 minutes as of last night.

His notes went on for 66 pages which is available here as a guest post. Well recommended reading to do with this post. Long COVID: From last days to real hope…

User Feedback After Reading Analysis

 Namely hesperidin which your suggest says to not take.  Tried that before your suggestions.  So that confirms at least to some degree your suggestions are likely right. 

I hadn’t thought of a lot of the foods I’m evaluating now.

FYI.  I was CRAVING peanuts all late 2021 and all 2022 and peanut butter (I would eat them on brown rice crackers!).   [See suggestions below]

And I was looking for E. Coli!  [See suggestions below] As it’s lacking in ME/CFS and IBS (both of which I have) and also used to produce Kineret, which is a powerful anti-inflammatory drug which is extremely beneficial for recovering from ME CFS (in my opinion) because it causes the body to stop making “thick, clotted blood”.  So E Coli makes kinert in your body!  Instead of it being cultured outside of your body and then injected.  (Which is yet another reason I was looking for it.) I already ordered symbioflor-2, but it will be here in California mid April.  [Gave link to Canadian store that will ship Mutaflor to the US]

Oh forgot to mention I took lactobacillus Rhamnosus based my my research before I noticed your big red note to not take it and other lactobacillus because they block the impact of heparin.  I think that’s what really got me! Haven’t pooped for 3 days since that mistake!  Before that pooped every day for 14. 

Analysis

We have one sample available, done via Ombre Labs.

Dr. Jason Hawrelak Recommendations – sits at 89%ile, not ideal, but not too bad.

My Profile

As seen in other reviews, there is a ton of bacteria with token representation. The numbers in each bin below should be similar counts.

Percentile	Genus	Species
0 – 9	60	78
10 – 19	36	49
20 – 29	13	20
30 – 39	13	18
40 – 49	12	12
50 – 59	12	22
60 – 69	8	14
70 – 79	6	10
80 – 89	6	21
90 – 99	9	28

Looking at Potential Medical Conditions Detected, there was only one flagged

• Unhealthy Ageing (9 of 17 bacteria matched)

Looking at Bacteria Deemed Unhealthy

The following stands out because of the association with COVID

• Alistipes – 89%ile

We also have several associated with Not Healthy Predictor

• Subdoligranulum variabile – 93%ile
• Tyzzerella nexilis – 77%ile
• Flavonifractor plautii – 100%ile
• Erysipelatoclostridium ramosum – 84%ile

And last, one that is deemed a pathogen

• Corynebacterium – 97%ile

This causes me to do an explicit hand-picked suggestion to add extra weight to these in a consensus.

Other factors

I looked at antibiotics, only rifaximin (antibiotic)s had a reasonable confidence. This antibiotic is cited often for Long COVID. See [2022] [2022] [2021]. For other drugs, again we have just one with reasonable confidence: proton-pump inhibitors (prescription)

Building Consensus Report

To our usual trinity, we add a few more

• Standard Lab Ranges (+/- 2 Std Dev) – 14 bacteria picked
• Box Plot Whisker – 37 bacteria picked
• Kaltoft-Moltrup Normal Ranges – 159 bacteria picked
• Unhealthy Ageing -13 bacteria picked
• Dr. Jason Hawrelak – 8 bacteria picked
• Hand Picked – 6 bacteria

Creating six sets of suggestions.

The top suggestions echoes a frequent suggestion for a subset of ME/CFS: Start each day with barley porridge with walnuts! Another interesting item is peanuts!!! For my own recovery it was important, see these posts from a decade ago: Peanut Butter – a complex food? [2013], Peanuts – A recommended part of diet [2015]

The top suggestions echoes a frequent suggestion for a subset of ME/CFS: Start each day with barley porridge with walnuts! Another interesting item is peanuts!!! For my own recovery it was important, see these posts from a decade ago: Peanut Butter – a complex food? [2013], Peanuts – A recommended part of diet [2015]

The fruit/legume suggestion is a bit vague — fortunately our new Diet Component helps: with the following being more explicit suggestions:

• Beans, winged (goa beans), dry, raw
• Nuts, butternuts (long walnuts), dried
• Spices, oregano, ground
• Turkey, all classes, wing, meat and skin, roasted
• sunflower seed kernels, oil roasted
• Pea, green, whole, dried
• Rosemary, dried
  

I also checked for Peanut and Peanut butter — and it was not listed (when using the nutrients alone). Remember the Food suggestions are second class — intended to be an auxiliary set of suggestions, not to be a replacement.

Probiotics

The top one from the consensus are:

• bifidobacterium infantis,(probiotics)
• bacillus subtilis (probiotics)
• bifidobacterium breve (probiotic)

The very first lactobacillus was almost 1/2 the priority of the above: lactobacillus reuteri (probiotics). I would suggest avoiding lactobacillus entirely — too high a risk of them causing brain-fog. My typical suggestions for probiotics is to take one for two weeks and then rotate to the next in the list. Remember to track any subjective or object changes (stool shape, frequency). Later you could go on to take them concurrently. Remember, may probiotics produces natural antibiotics hence you do not want to go continuously, but rotate.

Kegg Derived data were all low values, with E.Coli (Mutaflor or symbioflor-2) being the highest available single species probiotic.

Contribution	Probiotics
7	Azotobacter vinelandii
6	Azotobacter chroococcum
5	Rhodospirillum rubrum
4.9	Escherichia coli

My suggestion is to do the suggestions for 2 months and then resample and do the next course adjustment.

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.