This person has been using microbiome prescription to reduce the symptoms with success and with objective measurements of improved microbiome. His MD is willing to prescribe antibiotics and the top three items (from hundreds possible) are all used by ME/CFS specialist — indicating that the model is in agreement with clinical experience of ME/CFS specialist (a.k.a. Cross-Validation).

This is a follow up to these prior posts:

• Dec 30, 2021. Rosacea, Circulation and mild CFS.
• Apr 27, 2022 Follow up Microbiome Analysis from a prior post

Why Follow Up Posts are important

The first item is simple, does the model and suggestion appear to work. Everything is theoretically computed. The second item is that encourages people to try suggestions

Foreword – 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.

Comparisons between Samples

First, I do not know the best way to compare samples — what I usually do is put all of the numbers side by side. Special attention needs to be paid to Lab Read Quality. A poorer read quality results in less bacteria being identified.

Lab Quality is a measure of the total number of bacteria counted. The processing of a sample may detect just 30,000 bacteria or 300,000 bacteria. This impacts the number of bacteria detected and also the accuracy of the measures.

Criteria	8/31/2021	12/3/2021	3/25/2022	8/11/2022
Lab Read Quality	7.8	3.6	6.2	5.5
Bacteria Reported By Lab	461	379	479	383
Bacteria Over 99%ile	7	5	3	3
Bacteria Over 95%ile	20	24	11	13
Bacteria Over 90%ile	32	40	21	23
Bacteria Under 10%ile	283	123	237	189
Bacteria Under 5%ile	222	66	143	107
Bacteria Under 1%ile	161	9	44	23
Rarely Seen 1%	3	2	14	7
Rarely Seen 5%	9	7	33	14
Pathogens	37	30	44	31
Outside Range from JasonH	4	4	7	7
Outside Range from Medivere	15	15	15	15
Outside Range from Metagenomics	6	6	8	8
Outside Range from MyBioma	7	7	7	7
Outside Range from Nirvana/CosmosId	18	18	23	23
Outside Range from XenoGene	5	5	7	7
Outside Lab Range (+/- 1.96SD)	14	9	6	8
Outside Box-Plot-Whiskers	41	58	38	33
Outside Kaltoft-Moldrup	211	100	123	111
Condition Est. Over 99%ile	0	0	0	0
Condition Est. Over 95%ile	4	3	1	1
Condition Est. Over 90%ile	9	6	5	7
Enzymes Over 99%ile	17	19	30	10
Enzymes Over 95%ile	105	82	219	68
Enzymes Over 90%ile	139	126	296	183
Enzymes Under 10%ile	783	369	514	645
Enzymes Under 5%ile	542	186	264	423
Enzymes Under 1%ile	271	37	49	86
Compounds Over 99%ile	33	28	62	47
Compounds Over 95%ile	140	127	231	254
Compounds Over 90%ile	346	307	298	338
Compounds Under 10%ile	310	227	297	308
Compounds Under 5%ile	211	111	224	173
Compounds Under 1%ile	132	47	67	65

The next table is also very dependent of Lab Read Quality. The apparent improvement on 12/3/2021 is likely artificial because the counts are low due to low read quality.

	8/31/2021	12/3/2021	3/25/2022	8/11/2022
Percentile	Genus	Genus	Genus	Genus
0 – 9	73	24	51	51
10-19	15	18	32	24
20 – 29	12	13	18	12
30 – 39	4	10	9	14
40 – 49	6	8	9	3
50 – 59	4	8	7	2
60 – 69	4	4	9	3
70 – 79	7	10	7	10
80 – 89	7	4	8	5
90 – 99	14	18	8	8

	8/31/2021	12/3/2021	3/25/2022	8/11/2022
Percentile	Species	Species	Species	Species
0 – 9	87	29	57	58
10-19	24	21	29	24
20 – 29	14	15	21	16
30 – 39	10	16	14	14
40 – 49	2	6	14	3
50 – 59	12	9	17	10
60 – 69	9	10	10	7
70 – 79	8	9	14	7
80 – 89	7	15	5	4
90 – 99	11	13	10	9

So how to interpret this wall of numbers? People can cherry-pick the numbers to say improvement or no improvement. The difference of lab read quality is a big factor because they impact the count for most of the items above. The Outside Box-Plot-Whiskers numbers show continued improvement. In short, the changes shown were less than I was hoping to see.

There is one more method of comparison — using special studies. In this case we see the average matches. Doing a little math, the expected drop of percentage due to lab quality size between 8/31/2021 and 8/11/2022 is a 10% drop. Those that exceeded 20% are color with 😊 below. Nothing became 10% worse. Note that the 😊 also agrees with comparing to 3/25/2022 (the prior sample). Other items remained unchanged. Items reported by this person are 😧 – Strong issue, 😟 – a bit of an issue

Study	8/31/2021	12/3/2021	3/25/2022	8/11/2022	Average
Inflammatory bowel disease	49	32	47	47	43.8
Small intestinal bacterial overgrowth (SIBO) 😟	51	29	48	34😊	40.5
Allergic Rhinitis (Hay Fever) 😧	41	27	45	41	38.5
Autism	45	34	44	27😊	37.5
COVID19 (Long Hauler)	40	24	43	29😊	34.0
Irritable bowel syndrome 😟	40	22	44	30😊	34.0
Alcohol intolerance or Medication sensitivities	43	23	39	29😊	33.5
Histamine or Mast Cell issues 😧	44	17	43	24😊	32.0
Post-exertional malaise 😧	36	23	40	29😊	32.0
ME/CFS without IBS 😟	39	22	36	30😊	31.8
Poor gut motility	42	21	40	24😊	31.8
Brain Fog 😧	37	23	33	33	31.5
Depression	32	27	32	34	31.3
Allergies And Food Sensitivity 😧	38	23	36	25😊	30.5
Cold Extremities 😧	42	23	33	22😊	30.0
Intolerance of Extremes of Heat and Cold 😟	38	16	39	27😊	30.0
ME/CFS with IBS 😟	36	21	36	27	30.0
Bloating 😧	37	22	34	26😊	29.8
General Fatigue 😧	34	20	31	34	29.8
Unrefreshed sleep	31	22	36	28	29.3
Constipation	37	21	29	25😊	28.0
High Anxiety 😟	33	17	33	29	28.0
Easily irritated 😟	32	16	34	23😊	26.3
Tinnitus (ringing in ear) 😟	24	15	29	22	22.5
Chronic Fatigue Syndrome (CFS/ME) 😧	25	21	20	23	22.3
Average	37.8	22.4	37.0	28.9	31.5
Lab Quality	7.8	3.6	6.2	5.5

What is my conclusion? Most of the measures above deteriorates into noise with the exception of data from Special Studies, where we seen improvement in many measures, but not all. In one real statistical sense this makes sense: many are based on common sense and the ones showing clear improvement on statistical significance.

Going Forward

For most of my prior posts used the logical reasoning and clinical studies (which used different labs and software than the samples that I was looking at). With the special studies, we have upped our game (potentially) – the bacteria deemed significant were determined by the same lab and software of our sample, plus the study sizes was much larger than published clinical studies — hence better detection.

To build the consensus I will use the special studies, I filtered to reported issues and high percentage of matches, namely:

• Allergic Rhinitis (Hay Fever)
• Histamine or Mast Cell issues
• Post-exertional malaise
• Brain Fog
• Allergies And Food Sensitivity
• Cold Extremities
• General Fatigue

Remember that most of the special studies found that infrequent bacteria with a low value was what was statistically significant. This is turning the usual logic on it’s head. As I state, this is all experimental but based on studies and statistics.

The top suggestions are below

• navy bean
• Pulses ( dry peas, beans, lentils, chickpeas, etc)
• arabinoxylan oligosaccharides (prebiotic)
• non-starch polysaccharides
• red wine
• wheat bran
• proton-pump inhibitors (prescription)
• l-citrulline
• xylan (prebiotic)
• pea (fiber, protein)

Antibiotics

As expected, most antibiotics and prescription drugs are to be avoided. A few with positive impact includes:

• macrolide ((antibiotic)s) – used by Dr. Cecile Jadin in treating ME/CFS
• sulfonamide (antibiotic)s – suggested for treated ME/CFS [2019]

In terms of generic suggestions, rifaximin (antibiotic)s is by far the top antibiotics, cited here on Health Rising: Rifaxamin – citing use by Dr. Teitelbaum, Dr. Peterson, De De Meirleir and Dr. Myhill (all ME/CFS specialists).

In short, all of the top suggested antibiotics are applicable. My personal approach would be do all three of them in a pulse manner a la Jadin, 10 days on, 20 days off and then move to the next one.

Both above and generic suggestions have proton-pump inhibitors (prescription) being the top choice for other prescription drugs.

Probiotics

The top probiotics list have the usual dilemma: both e.coli probiotics and lactobacillus probiotics. It’s a dilemma because they tend to be hostile to each other. My typical rotation resolution would be 2 weeks of each and then move to the next:

• saccharomyces boulardii (probiotics)
• e.coli probiotics (take it with d-ribose, on the to take list and a food for e.coli)
• bifidobacterium
• lactobacillus
• Equilibrium and/or Prescript Assist

I should note that some are strong to be avoided (watch out for mixtures!!!)

KEGG Suggestions

The KEGG suggestions top items were the bacteria found in Equilibrium and Prescription Assist, except for the top choice, Escherichia coli. A probiotic suggested by Dr. Myhill, a ME/CFS specialist in the UK. The next common conventional items are

Bottom Line

The suggestions above were done solely from special studies. The key question is are they reasonable? I would say yes based on the antibiotics suggestions — all of them have been reported to help ME/CFS patients. We also have agreement between KEGG probiotics and these suggestions.

There is a potential conceptual symmetry between the two approaches (working off extremes and using special studies that are often dealing with rare low bacteria). Bacteria influences each other in very complex ways.

The full list of suggestions is available above.

I am hoping this will be a model for other Long COVID people to start the recovery process. This person used Biomesight. Those results allow the data from special studies to be used on his microbiome sample.

A word of warning, tests like GI-MAPS will not report on most of the bacteria found to be low in the Special Studies — you need much more detail reports!

Suggested Parallel Reading: CFS Patient after COVID using the Special Studies Results

Foreword – 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.

Backstory

COVID in February 2021. 37M at the time, athletic/fit. Crossfit x 3 a week, playing football weekly Only mild gastritis prior to COVID. No other health issues. Moderate severity Covid, lots of symptoms.

And then Long COVID and CFS/ME type of symptoms mostly fatigue, PEM and GI problems (pain, food intolerance, bloating..etc) I’d say it’s a moderate/mild case of CFS/ME. But after 18 months still not back to previous levels, can’t walk too long otherwise i crash. I’d say i am around 75%.

High Level Overview

Looking at Health Indication, we find no significant medical conditions flagged (consistent with prior life style). There is one bacteria of potential concern: Prevotella copri, accounting for a whooping 56% of the microbiome! It is interesting that this was also seen in another recent review, see CFS Patient after COVID using the Special Studies Results. In terms of Dr. Jason Hawrelak Recommendations – he’s at the 99.7%ile — extremely healthy!

Using Special Studies

Interpreting the updated table shown below can get a little complicated (i.e. not naively simple) see Special Studies Percentage Matches for details

We are going to use the 7 items below – items matching his reported issues. In an independent study that I did, I found that the pattern dims over time as the microbiome evolves. His person is 20 months post-COVID.

• COVID19 (Long Hauler)
• Small intestinal bacterial overgrowth (SIBO)
• ME/CFS with IBS
• Inflammatory bowel disease
• Post-exertional malaise
• General Fatigue
• Bloating

The Prevotella copri concerns me because it’s the mastodon in the room (bigger than an elephant, and a bit hairier!). This specific bacteria is NOT typical for long COVID, but I suspect many will find one or another tyrant to dominate in excess in the face of massive minority representation– hence check for high bacteria counts with high percentile. It was also high in the study cited above, CFS Patient after COVID using the Special Studies Results. I went thru the My Biome View to tag the ones that have a high percentile with with a large count. The purpose is to inhibit these, so they will not inhibit everything else.

The results were almost the opposite of the consensus below for B-Vitamins. It presents a dilemma, a choice that needs to be made. At the moment, I favor the working from the special studies approach (pending feedback from people who tried it). Conceptually, it is a more probable approach — incidentally, it is not the approach usually done (and those approaches, historically, have had very little success to date).

The Consensus

I did not want to toss in any more sets of suggestions. From the start we saw the dominate item in his microbiome was undergrowth of a multitude of bacteria and the domination of one — we have gotten what helped the weak and inhibits the strong.

I found the avoids to be an interesting combination, no red meat and no chicken (matching Reduced choline on the to take) .

We see that something like a B-Complex should be avoided. I discuss this issue more in the other blog post that I cited above.

Computed Probiotics from KEGG Enzymes

This produced a few items that are reasonably easy to get as single species probiotics. Remember, these are calculated by a totally different mechanism – using the genes of the bacteria in your microbiome and the genes in these bacteria. The top items were:

• Escherichia coli (which is near the top of our to take list)
• Bacillus subtilis (on our to take list) and other similar bacillus species
• Akkermansia muciniphila (which shows up once, with a positive value (74.5) on our consensus)
• Clostridium butyricum (a minor avoid on the consensus with 3 for and 4 against)
• Lactobacillus plantarum (a minor avoid on the consensus with 3 for and 4 against)

My basic take-away are using just 3 probiotics in weekly or fortnight rotations (one at a time)

• Escherichia coli (Mutaflor or Symbioflor-2)
• Bacillus subtilis many sources
• Akkermansia muciniphila (available only in the US unfortunately)

If one of the above cannot be obtained, I would suggest using Clostridium butyricum or Lactobacillus plantarum as the third element in the rotation.

Supplements suggested by KEGG

Although this is using an old algorithm that I have not updated, the list is below.

• alpha-galactosidase (Enzyme) – Percentile: 11
• Amylase (Enzyme) – Percentile: 8 – On Consensus: Take
• beta-alanine – Percentile: 2 – On Consensus: Take
• Glycine – Percentile: 4 – – On Consensus: Minor avoid
• iron – Percentile: 7 – On Consensus: Take
• L-Cysteine – Percentile: 3 – On Consensus: Major avoid
• L-glutamine – Percentile: 14 – On Consensus: Major avoid
• L-Histidine – Percentile: 12 – On Consensus: Take
• L-methionine – Percentile: 10 – On Consensus: Major avoid
• L-Serine – Percentile: 11 – On Consensus: Take
• L-Threonine – Percentile: 16
• magnesium – Percentile: 4 – On Consensus: Take
• NADH – Percentile: 4
• Selenocysteine – Percentile: 4
• zinc – Percentile: 16 – On Consensus: Take

Remember we are dealing with fuzzy data, my usual rule is do positive stuff where there is universal agreement, avoid stuff that are negative or where there are contradictions (I do like playing dice with my health).

Bottom Line

Because of the special studies and this person using the appropriate lab, this was actually a simple analysis to do. The traditional analysis showed “nothing wrong”, a familiar restrain from medical professionals to Long COVID patients. Our special studies and distribution by percentile showed things are wrong. Having 56% of the bacteria being Prevotella copri is saying something is very wrong.

I often try to use analogy of human populations to explain what I see. In this case, we have dozens of small tribes battling each other allowing a dominating force to seize most of the space. There are many historic examples, often under the name of “Divide and Conquer”.

In this example, the high number of low representation bacteria we saw in the overview matched the high number of low number of bacteria we observed in our special studies.

After two months of trying the suggestions, I hope this reader will do a new sample to see how well things shift from these suggestions.

Questions

Q: “Excuse me if I’m missing something but is there any reason why we are focusing on only Commensals,  Prevotella, why not on Probiotics at all?  I understand it’s way above the range, and it’d like to keep it low ideally, but what about the rest of Microbiome?”

A1: First “the rest of the microbiome” issue – the obvious response is a simple “If it is not broken, don’t fix it”. The above analysis used over 100 different bacteria. Our focus is on the bacteria where there is significant statistically evidence that they are connected to Long COVID. The numbers above are general health. As cited above, with Dr. Jason Hawrelak General Health Recommendations you are better than 99.7% of people. There is a huge variation in recommended ranges coming from labs and specialists — who are you going to rely upon? I am a statistician and I follow the numbers (and the z-scores), in other words, not working off opinion based largely on treating people who do not have Long COVID. I am NOT focusing on commensals, I am focusing on what was shown to be statistically significant.

A2: “why not on Probiotics at all” — Excuse me, I name four key probiotics: Escherichia coli (Mutaflor or Symbioflor-2 are retail products), Bacillus subtilis et al (microbiome labs/ megasporebiotic looks like a good commercial choice), Akkermansia muciniphila (Pendulum) and Clostridium butyricum (miyarisan). You will find alternative brands for some using the probiotic page.

If you mean bifidobacterium and lactobacillus probiotics — they are not indicated in general, in fact, they often appear in the to-avoid list. Example, Long COVID often has brain fog, see this study: Brain fogginess, gas and bloating: a link between SIBO, probiotics and metabolic acidosis [2018] which calls out those two as contributors.

Comment: To answer your question, I’ve had lots of symptoms in the beginning, but for now only mild fatigue and PEM plus gut issues, so I’d say definite ME/CFS with IBS, some Rhinitis, Alcohol intolerance and Long hauling.

I have recently changed the display below to show the percentage of matched instead of just the number of bacteria matches (the number will appear if you hover over the link as a tool tip). The numbers may be prone to misinterpretation, hence this technical page.

The candidate bacteria comes from special studies — it is important to note that often these bacteria are rarely seen, so having a 100% match is effectively impossible. We also have the dilemma of a single sample versus a collection of samples.

The rule that I am using is simple, a match must:

• Have the bacteria (if it is missing, it is not deemed a match)
• The bacteria count must be either:
  • below the study mean – 3 standard deviations of the mean if the study found it to be a low mean value against the reference population
  • above the study mean + 3 standard deviations of the mean if the study found it to be a high mean value against the reference population

Naively, assuming a normal distribution, the odds of a single match is around 1%, so with 200 items to check, we would expect 1% for a random person.

You should NOT view these as predictive, for example both ME/CFS with IBS and ME/CFS without IBS are on the list with the same value!!! Instead, your existing condition(s) should be used to select only the ones that apply to you. You could arbitrarily do all of the high ones — I do have a concern about that approach, you are creating noise that may make suggestions less effective.

One last item is the quality of the read (i.e. how many bacteria was actually detected in the sample). Since we are dealing with rare bacteria, bacteria (that are actually there) may not be detected and thus you have a lower percentage match. So do not view the percentage as absolute. but relative to others in the sample.

This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of fatigue. Each person use their own subject definition of fatigue, thus we do not expect strong statistical associations (and do not get it!)

Study Populations:

I include values for Special Studies: General ME/CFS below

Symptom	Reference	Study
General Fatigue	1095	134

• Bacteria Detected with z-score > 2.6: found 158 items, highest value was 5.3 (ME/CFS was 6.6)
• Enzymes Detected with z-score > 2.6: found 410 items, highest value was 6.0 (ME/CFS was 4.5)
• Compound Detected with z-score > 2.6: found 67 items, highest values was -4/4 (ME/CFS was 3.1)

So we have a weaker bacteria signature but stronger enzymes and compound signature than ME/CFS. Many people marking one will mark the other… so get your sodium chloride crystals out!

Interesting Significant Bacteria

All bacteria found significant had too low levels. The list of those with a z-score over 5 is small. Low Prevotella copri and Escherichia coli which appears on special studies on many co-morbid symptoms. The good news, is that there is work ongoing to produce a prevotella copri probiotic and several Escherichia coli probiotics are available.

We do see a few overgrowth These are seen only in some subsets.

Bacteria	Reference Mean	Study	Z-Score
Lactiplantibacillus pentosus (species)	114	22	5.3
Prevotella copri (species)	68098	21864	5.2
Gammaproteobacteria (class)	14382	5944	5.2
Veillonella (genus)	4022	2324	5.1
Escherichia coli (species)	829	196	5

Interesting Enzymes

Most enzymes found significant had too low levels. A few were higher (12 of 410), which are listed in a second table below

Enzyme	Reference Mean	Study Mean	Z-Score
[cysteine desulfurase]-S-sulfanyl-L-cysteine:[molybdopterin-synthase sulfur-carrier protein]-Gly-Gly sulfurtransferase (2.8.1.11)	5407	2420	6
3-(cis-5,6-dihydroxycyclohexa-1,3-dien-1-yl)propanoate:NAD+ oxidoreductase (1.3.1.87)	597	144	5.5
3-phenylpropanoate,NADH:oxygen oxidoreductase (2,3-hydroxylating) (1.14.12.19)	611	148	5.5
deoxyribocyclobutadipyrimidine pyrimidine-lyase (4.1.99.3)	4311	1668	5.5
tRNA-uridine65 uracil mutase (5.4.99.26)	4201	1733	5.4
S-adenosyl-L-methionine:23S rRNA (guanine2069-N7)-methyltransferase (2.1.1.264)	5850	3067	5.3
propanoyl-CoA:oxaloacetate C-propanoyltransferase (thioester-hydrolysing, 1-carboxyethyl-forming) (2.3.3.5)	1575	501	5.2
N4-acetylcytidine amidohydrolase (3.5.1.135)	3083	1279	5.2
ATP:D-tagatose 6-phosphotransferase (2.7.1.101)	415	112	5.1
(2S,3R)-3-hydroxybutane-1,2,3-tricarboxylate pyruvate-lyase (succinate-forming) (4.1.3.30)	1524	484	5.1
acyl-CoA:sn-glycerol-3-phosphate 1-O-acyltransferase (2.3.1.15)	3385	1438	5.1
S-adenosyl-L-methionine:tRNA (cytidine32/uridine32-2′-O)-methyltransferase (2.1.1.200)	3437	1390	5.1
2-(glutathione-S-yl)-hydroquinone:glutathione oxidoreductase (1.8.5.7)	3263	1099	5.1
galactitol-1-phosphate:NAD+ oxidoreductase (1.1.1.251)	1042	292	5
acetyl-CoA:[elongator tRNAMet]-cytidine34 N4-acetyltransferase (ATP-hydrolysing) (2.3.1.193)	3430	1387	5
S-adenosyl-L-methionine:23S rRNA (uracil747-C5)-methyltransferase (2.1.1.189)	3379	1385	5
ATP phosphohydrolase (ABC-type, thiamine-importing) (7.6.2.15)	3637	1454	5
[50S ribosomal protein L16]-L-Arg81,2-oxoglutarate:oxygen oxidoreductase (3R-hydroxylating) (1.14.11.47)	3448	1440	5
7,8-dihydroneopterin 3′-triphosphate diphosphohydrolase (3.6.1.67)	3719	1634	5
ATP:N-acetyl-D-glucosamine 6-phosphotransferase (2.7.1.59)	3383	1369	5

Enzyme	Reference Mean	Study Mean	Z-Score
CTP:N,N’-diacetyllegionaminate cytidylyltransferase (2.7.7.82)	59568	76504	-2.9
1-phosphatidyl-1D-myo-inositol:a very-long-chain (2’R)-2′-hydroxy-phytoceramide phosphoinositoltransferase (2.7.1.227)	39144	50749	-2.9
1,5-anhydro-D-mannitol:NADP+ oxidoreductase (1.1.1.292)	65010	81029	-2.9
alginate oligosaccharide 4-deoxy-alpha-L-erythro-hex-4-enopyranuronate-(1->4)-hexananopyranuronate lyase (4.2.2.26)	79725	99196	-2.8
phosphatidylglycerophosphate phosphohydrolase (3.1.3.27)	87820	105463	-2.7
chondroitin-sulfate-ABC endolyase (4.2.2.20)	71773	88107	-2.7
chondroitin-sulfate-ABC exolyase (4.2.2.21)	71773	88107	-2.7
arabinogalactan 4-beta-D-galactanohydrolase (3.2.1.89)	107701	128121	-2.7
cephalosporin-C acetylhydrolase (3.1.1.41)	86153	104494	-2.7
UDP-N-acetyl-alpha-D-glucosamine 4-epimerase (5.1.3.7)	83349	100761	-2.6
N-sulfo-D-glucosamine sulfohydrolase (3.10.1.1)	59634	73685	-2.6
6-alpha-D-glucan 6-glucanohydrolase (3.2.1.11)	64035	78529	-2.6
GDP-alpha-D-mannose:2-O-alpha-D-mannosyl-1-phosphatidyl-1D-myo-inositol 6-alpha-D-mannosyltransferase (configuration-retaining) (2.4.1.346)	43744	55284	-2.6

Interesting Compound

Unlike most of the special studies we have many compounds that are significant. I have listed the high and low in separate tables below. Spot checking most of these found no useful information. For Maltodextrin which becomes  glucose would fit with fatigue — i.e. low sugar being produced.

Of the low items, the following appear to be available as supplements and potentially could help with fatigue

• Maltodextrin
• Inosine
• Thymine (Vitamin B1)
• Cytosine (as N-acetylcysteine)

Names	Z-Score
beta-D-Ribofuranose (C16639)	4
1,2-Diacyl-3-alpha-D-glucosyl-sn-glycerol (C06364)	3.8
Coproporphyrin III (C05770)	3.3
Cys-Gly (C01419)	3.2
Deoxyinosine (C05512)	3.2
UDP-N-acetyl-alpha-D-glucosamine 3′-phosphate (C20245)	3.2
Inosine (C00294)	3.1
Carboxylate (C00060)	3.1
Thymine (C00178)	2.9
3-(4-Hydroxyphenyl)pyruvate (C01179)	2.9
Acetoacetyl-CoA (C00332)	2.8
beta-D-Galactosyl-(1->3)-N-acetyl-D-galactosamine (C07278)	2.8
Prokaryotic ubiquitin-like protein (C21177)	2.8
dTDP (C00363)	2.7
tRNA with a 3′ CCA end (C19085)	2.7
Cytosine (C00380)	2.7
alpha-D-Aldose 1-phosphate (C00991)	2.7
5′-O-Phosphonoadenylyl-(3′->5′)-adenosine (C22092)	2.7
Hexadecanoic acid (C00249)	2.6
Maltodextrin (C01935)	2.6
Hydroquinone (C15603)	2.6

Names	Z-Score
beta-D-Ribopyranose (C08353)	-4
Chitobiose (C01674)	-3.6
Deoxyadenosine (C00559)	-3.5
Glutaredoxin (C07292)	-3
tRNA(Leu) (C01645)	-3
Lacto-N-biose (C06372)	-3
alpha-D-Glucosamine 1-phosphate (C06156)	-3
Pantetheine 4′-phosphate (C01134)	-2.9
UDP-N-acetylmuramate (C01050)	-2.9
L-Formylkynurenine (C02700)	-2.9
tRNA uridine (C00868)	-2.9
(S)-3-Hydroxybutanoyl-CoA (C01144)	-2.9
Amino acid (C00045)	-2.8
1,2-Diacyl-sn-glycerol (C00641)	-2.8
L-Glutamate 5-semialdehyde (C01165)	-2.8
Taurine (C00245)	-2.8
tRNA with a 3′ cytidine (C19078)	-2.8
tRNA precursor (C02211)	-2.8
Sucrose (C00089)	-2.8
UTP (C00075)	-2.8
beta-D-Glucose 1-phosphate (C00663)	-2.8
UDP-N-acetylmuramoyl-L-alanyl-gamma-D-glutamyl-L-lysine (C05892)	-2.7
tRNA(Lys) (C01646)	-2.7
L-Threonine (C00188)	-2.7
2-Succinylbenzoate (C02730)	-2.7
Oxygen (C00007)	-2.7
UDP-sugar (C05227)	-2.7
Phenyl acetate (C15583)	-2.7
Isopentenyl diphosphate (C00129)	-2.7
Cyclomaltodextrin (C00973)	-2.7

Similarly, items that are too high are likely to be avoided, including the following

• Sucrose
• Taurine
• Threonine

All of these suggestions are theoretical based in the model. Some literature to consider that appears to confirm the above (a.k.a. cross-validation)

• A caffeine-maltodextrin mouth rinse counters mental fatigue [2018]
• Effect of inosine supplementation on aerobic and anaerobic cycling performance [1996]
• Thiamine and fatigue in inflammatory bowel diseases: an open-label pilot study [2013]

Bottom Line

II was not expecting much from this special study. I was pleased to see some suggestions being generated that can be implemented (or soon will be)

• Probiotics:
  • Escherichia coli
  • Prevotella copri
• Supplements
  • Maltodextrin
  • Inosine
  • Thymine (Vitamin B1)
  • Cytosine (as N-acetylcysteine)

In looking at the suggestions below, remember we are using two very different models. Above we use KEGG data to identify what the bacteria are producing (the items going to the farmer’s market). Below, we use what has been reported to influence the population of the bacteria that we are too low in (i.e. “Fertilizer”)

This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does.

• “Motility” is a term used to describe the contraction of the muscles that mix and propel contents in the gastrointestinal (GI) tract. [Src] thus it has similarity to constipation (See Special Studies: Constipation)

• “An excess of intracolonic saturated long-chain fatty acids (SLCFAs) was associated with enhanced bowel motility in NMS rats. Heptadecanoic acid (C17:0) and stearic acid (C18:0), as the most abundant odd- and even-numbered carbon SLCFAs in the colon lumen, can promote rat colonic muscle contraction and increase stool frequency” [2018]

Study Populations:

Symptom	Reference	Study
Poor gut motility	1171	55

• Bacteria Detected with z-score > 2.6: found 170 items, highest z-score value was 8.8
• Enzymes Detected with z-score > 2.6: found 336 items, highest z-score value was 6.4
• Compound Detected with z-score > 2.6: found No items

Interesting Significant Bacteria

All bacteria that was found significant are too low. This is a common pattern for most of the special studies and really challenge the internet myth of the cause being too many bad bacteria. One bacteria really stands out — and there is ongoing work on making this one bacteria, Prevotella copri , available as a probiotics!

Bacteria	Reference Mean	Study	Z-Score
Prevotella copri (species)	64568	5498	8.8
Sutterella stercoricanis (species)	3098	410	7.5
Prevotella paludivivens (species)	144	26	7.1
Prevotella (genus)	72220	20587	6.2
Alkalibacterium (genus)	102	21	6
Prevotellaceae (family)	79801	32549	5.2
Leptospiraceae (family)	67	24	5.2
Leptospira (genus)	67	24	5.2
Leptospirales (order)	67	24	5.2
Leptospira licerasiae (species)	67	24	5.2
Ruminiclostridium (genus)	1000	348	5.1
Phocaeicola sartorii (species)	807	375	5.1

• “For example, abundances of Lactobacillus, Prevotella and Alistipes spp. are significantly decreased in patients with constipation ” [2018]

Interesting Enzymes

Most of the enzymes are too low, however a few are too high which is not the usual pattern seen in other of these special studies.

S-methyl-5′-thioadenosine:phosphate S-methyl-5-thio-alpha-D-ribosyl-transferase (2.4.2.28)	3740	1373	6.4
n/a (3.4.14.13)	1270	248	5.9
D-alanine:2-oxoglutarate aminotransferase (2.6.1.21)	2687	959	5.9
1-aminocyclopropane-1-carboxylate aminohydrolase (isomerizing) (3.5.99.7)	1428	337	5.9
succinyl-CoA:3-oxo-acid CoA-transferase (2.8.3.5)	1307	291	5.8
N-acyl-D-amino acid amidohydrolase (3.5.1.81)	1777	396	5.8
4-hydroxyphenylpyruvate:oxygen oxidoreductase (hydroxylating, decarboxylating) (1.13.11.27)	1446	225	5.6
carotenoid beta-end group lyase (ring-opening) (5.5.1.19)	1450	134	5.6
ATP:L-threonine O3-phosphotransferase (2.7.1.177)	2320	486	5.6
n/a (3.4.15.6)	1585	391	5.6
hydrogen-sulfide:flavocytochrome c oxidoreductase (1.8.2.3)	1164	108	5.5
15-cis-phytoene:acceptor oxidoreductase (lycopene-forming) (1.3.99.31)	2233	902	5.5
all-trans-zeta-carotene:acceptor oxidoreductase (1.3.99.26)	2233	902	5.5
15-cis-phytoene:acceptor oxidoreductase (zeta-carotene-forming) (1.3.99.29)	2233	902	5.5
15-cis-phytoene:acceptor oxidoreductase (neurosporene-forming) (1.3.99.28)	2233	902	5.5
[SoxY protein]-S-sulfosulfanyl-L-cysteine sulfohydrolase (3.1.6.20)	1183	106	5.5
CTP:5,7-diacetamido-3,5,7,9-tetradeoxy-L-glycero-alpha-L-manno-nonulosonic acid cytidylyltransferase (2.7.7.81)	1139	119	5.5
medium-chain acyl-CoA:electron-transfer flavoprotein 2,3-oxidoreductase (1.3.8.7)	1300	325	5.4
2-carboxy-2,5-dihydro-5-oxofuran-2-acetate lyase (ring-opening) (5.5.1.2)	1332	198	5.3
sulfite:oxygen oxidoreductase (1.8.3.1)	1199	107	5.3
alkane,reduced-rubredoxin:oxygen 1-oxidoreductase (1.14.15.3)	1087	107	5.3
4-hydroxybenzoate,NADPH:oxygen oxidoreductase (3-hydroxylating) (1.14.13.2)	1319	203	5.2
protocatechuate:oxygen 3,4-oxidoreductase (ring-opening) (1.13.11.3)	1319	199	5.2
dihydro-NAD(P):oxygen oxidoreductase (H2O2-forming) (1.6.3.5)	1290	145	5.2
glutaryl-CoA:electron-transfer flavoprotein 2,3-oxidoreductase (decarboxylating) (1.3.8.6)	1257	258	5.2
3-methylcrotonoyl-CoA:carbon-dioxide ligase (ADP-forming) (6.4.1.4)	1114	205	5.1
N-acyl-L-homoserine-lactone amidohydrolase (3.5.1.97)	1044	126	5.1
4a-hydroxytetrahydrobiopterin hydro-lyase (6,7-dihydrobiopterin-forming) (4.2.1.96)	1314	223	5.1

Too High Enzymes

Enzyme	Reference Mean	Study Mean	Z-Score
L-leucyl-tRNALeu:[protein] N-terminal L-lysine/L-arginine leucyltransferase (2.3.2.6)	130285	170931	-2.9
polyphosphate phosphohydrolase (3.6.1.11)	171217	211918	-2.8
guanosine-5′-triphosphate-3′-diphosphate 5′-phosphohydrolase (3.6.1.40)	171217	211918	-2.8
1-deoxy-D-xylulose 5-phosphate:thiol sulfurtransferase (2.8.1.10)	198195	242062	-2.7
(R)-2-carboxy-2,5-dihydro-5-oxofuran-2-acetate carboxy-lyase (4,5-dihydro-5-oxofuran-2-acetate-forming) (4.1.1.44)	199133	242528	-2.7
ATP phosphohydrolase (P-type, Ca2+-transporting) (7.2.2.10)	234154	280063	-2.7
acetyl-CoA:maltose O-acetyltransferase (2.3.1.79)	206724	252634	-2.7
3-methylbut-3-en-1-yl-diphosphate:ferredoxin oxidoreductase (1.17.7.4)	239452	285669	-2.7
L-aspartate:tRNAAsp ligase (AMP-forming) (6.1.1.12)	244534	289637	-2.6
5,10-methylenetetrahydrofolate:dUMP C-methyltransferase (2.1.1.45)	245078	290417	-2.6
D-arabinose aldose-ketose-isomerase (5.3.1.3)	188214	231505	-2.6
L-fucose aldose-ketose-isomerase (5.3.1.25)	188214	231505	-2.6
5,6,7,8-tetrahydrofolate:NADP+ oxidoreductase (1.5.1.3)	247230	291626	-2.6

Bottom Line

While poor gut motility is often assumed to be due too many of some bacteria, the evidence suggestions that not enough is the more likely cause. There appears to be no simple model or answer.

• “The gut bacterium Prevotella copri (P. copri) has been shown to lower blood glucose levels in mice as well as in healthy humans, and is a promising candidate for a next generation probiotic aiming at prevention or treatment of obesity and type 2 diabetes” [2021]
• Cultivation of the gut bacterium Prevotella copri DSM 18205^T using glucose and xylose as carbon sources
• Prevotella copri as Next Generation Probiotics [firm that provides different strains for research purposes]

Prevotella copri will hopefully be available as a probiotic in a few year. There are two natural sources for P.Copri : Beer and Sauerkraut [2020], which may be an experiment for those that are prone to poor gut motility..

• “This species is more prevalent in non-Western populations likely due to its association with high fibre low fat diets” [2022]
• “Across all ethnicities, only coffee consumption was associated with an increased Prevotella relative abundance ” [2022]
• “Ancient stool samples suggest Westernization leads to P. copri underrepresentation” [2019]

The real bottom line is changing diet significantly. Consider some Indian style of food as part of supper every day, some examples ready to heat are here.

It should be noted that the B-Vitamins below are likely in the avoid list because they are usually provided thru meat in traditional diet.

This is a devastating mental infection of many people suffering severe ongoing health issues. The mythology is simple “Fix the root cause, and you will get better!” For acute, send yourself to the hospital, diseases this may be true, but there is another class of conditions where it is false.

Example, you developed rickets and developed skeletal deformities such as:

• Bowed legs or knock knees
• Thickened wrists and ankles
• Breastbone projection.

We know the root cause, not sufficient vitamin D. Will taking vitamin-D correct the skeletal deformities? No. Treatment will slow progression. You have lung cancer because you are a heavy smoker, will stop smoking cure lung cancer? You have Long COVID, ah the cure is to always wear a N95 mask?

Yes There was a Cause likely, but…

For items dealing with the microbiome, the cause starts a microbiome cascade that keeps going onwards. Think of a land slide, things are changed. There are side-effects like impact on fish or even getting into towns. So people start trying to cure the landslide by clearing the river or building a new road or…. and those attempt at curing, could cause more problems.

The best example that is well documented is the Bergen’s Giardia Infection. The root cause was Giardia infection. They eliminated the giardia — but the IBS, ME/CFS issues remained. They very well documented the root cause and dealt with it. No magical recovery.

Going Forward

I view many conditions as being supported (in a few cases totally caused) by the microbiome. Finding the root cause is very very unlikely to impact treatment and the way back to health. Focus on what is contributing to your current state and not ancient history!

I just banned someone called Ross Walter

Why? he has twice attacked me ad hominem (i.e. an attack on the person). I have made no secret that I am a high functioning ASD person (functioning in terms of mathematics), and that I did not learn to speak or form sentences until I was 9 y.o. I know that items like grammar are a great weakness. To attack a person with a recognized disability, for a disability is neither polite nor acceptable.
I apologize if my grammar is not perfect — my blog is not intended to be a literary masterpiece, but to convey data!

Alternative Title: Climate Change and your guts!

This is a common symptom for both ME/CFS and Long COVID. This is reported often in samples uploaded. We examine if the data reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does.

My default view is that this symptom is likely due to vesicular constriction/inflammation or “sticky blood” (which contains many possible coagulation issues). This results in slow blood flow causing issues with transferring heat to and from the body.

• temperature intolerance is generally placed under dysautonomia

Study Populations:

Symptom	Reference	Study
Intolerance of extremes of heat and cold	1159	54

• Bacteria Detected with z-score > 2.6: found 247 items, highest value was 8.8
• Enzymes Detected with z-score > 2.6: found 501 items, highest value was 6.7
• Compound Detected with z-score > 2.6: found No items

The number of bacteria and enzymes that are significant hints that it is a complex scenario, possibly with different subsets.

Interesting Significant Bacteria

All bacteria found significant had too low levels. The dominant order is Anaeroplasmatales and three significant genus are Holdemanella, Eubacterium, and Escherichia. Every one of the 247 bacteria found significant was LOW.

Bacteria	Reference Mean	Study	Z-Score
Holdemanella biformis (species)	3370	350	8.8
Holdemanella (genus)	3379	350	8.8
Eubacterium (genus)	2507	586	7.8
Lysobacter deserti (species)	29	11	6.5
Anaerolineae (class)	87	35	5.7
Opitutae (class)	163	46	5.5
Puniceicoccaceae (family)	158	44	5.5
Legionellales (order)	82	41	5.4
Leptospiraceae (family)	67	22	5.4
Leptospira (genus)	67	22	5.4
Leptospirales (order)	67	22	5.4
Leptospira licerasiae (species)	67	22	5.4
Bifidobacterium cuniculi (species)	79	26	5.3
Lactiplantibacillus pentosus (species)	116	22	5.3
Puniceicoccales (order)	110	37	5.3
Legionellaceae (family)	81	41	5.2
Legionella (genus)	81	41	5.2
Anaeroplasmataceae (family)	8102	20	5.2
Anaeroplasma (genus)	8102	20	5.2
Anaeroplasmatales (order)	8102	20	5.2
Chloroflexi (phylum)	128	55	5.1
Cerasicoccus arenae (species)	537	80	5.1
Caldilineaceae (family)	90	35	5.1
Caldilinea (genus)	90	35	5.1
Caldilineales (order)	90	35	5.1
Caldilinea tarbellica (species)	90	35	5.1
Caldilineae (class)	90	35	5.1
Escherichia (genus)	6016	1367	5.1
Cerasicoccus (genus)	312	60	5

Interesting Enzymes

Atypical distribution for enzymes in these studies, the number of highs and lows were of the same magnitude. 268 enzymes were low and 233 enzymes were high (see 2nd table) but none above our listing threshold of 5.0.

Enzyme	Reference Mean	Study Mean	Z-Score
(S)-2-hydroxyglutarate:quinone oxidoreductase (1.1.5.13)	2214	398	6.7
(2S,3R)-3-hydroxybutane-1,2,3-tricarboxylate pyruvate-lyase (succinate-forming) (4.1.3.30)	1444	360	6.3
propanoyl-CoA:oxaloacetate C-propanoyltransferase (thioester-hydrolysing, 1-carboxyethyl-forming) (2.3.3.5)	1491	387	6.3
propanoate:CoA ligase (AMP-forming) (6.2.1.17)	1894	483	6.1
D-galactaro-1,4-lactone lyase (ring-opening) (5.5.1.27)	1698	393	5.8
thiosulfate:ferricytochrome-c oxidoreductase (1.8.2.2)	1381	272	5.4
gamma-butyrobetainyl-CoA:electron-transfer flavoprotein 2,3-oxidoreductase (1.3.8.13)	1400	441	5.4
glutarate, 2-oxoglutarate:oxygen oxidoreductase ((S)-2-hydroxyglutarate-forming) (1.14.11.64)	1299	297	5.4
(E)-4-(trimethylammonio)but-2-enoyl-CoA:L-carnitine CoA-transferase (2.8.3.21)	1386	440	5.4
L-carnitinyl-CoA hydro-lyase [(E)-4-(trimethylammonio)but-2-enoyl-CoA-forming] (4.2.1.149)	1444	321	5.4
2-dehydrotetronate isomerase (5.3.1.35)	1406	482	5.3
(2S,3S)-2-hydroxybutane-1,2,3-tricarboxylate hydro-lyase [(Z)-but-2-ene-1,2,3-tricarboxylate-forming] (4.2.1.79)	1297	439	5.2
3-phenylpropanoate,NADH:oxygen oxidoreductase (2,3-hydroxylating) (1.14.12.19)	581	142	5.2
1-aminocyclopropane-1-carboxylate aminohydrolase (isomerizing) (3.5.99.7)	1431	416	5.2
n/a (3.4.23.49)	1307	326	5.2
3-(cis-5,6-dihydroxycyclohexa-1,3-dien-1-yl)propanoate:NAD+ oxidoreductase (1.3.1.87)	568	140	5.2
ATP:2′-deoxyadenosine 5′-phosphotransferase (2.7.1.76)	1974	344	5.2
carotenoid beta-end group lyase (ring-opening) (5.5.1.19)	1452	201	5.1
RNA-3′-phosphate:RNA ligase (cyclizing, AMP-forming) (6.5.1.4)	1220	316	5.1
NTP:deoxycytidine 5′-phosphotransferase (2.7.1.74)	1885	287	5.1
L-threonate:NAD+ 2-oxidoreductase (1.1.1.411)	1410	492	5.1
succinyl-CoA:3-oxo-acid CoA-transferase (2.8.3.5)	1307	363	5.1
acyl-CoA,ferrocytochrome b5:oxygen oxidoreductase (6,7 cis-dehydrogenating) (1.14.19.3)	1063	277	5
L-carnitine:CoA ligase (AMP-forming) (6.2.1.48)	1373	517	5
hydrogen-sulfide:flavocytochrome c oxidoreductase (1.8.2.3)	1165	161	5
L-methionine methanethiol-lyase (deaminating; 2-oxobutanoate-forming) (4.4.1.11)	1272	288	5
L-carnitine,NAD(P)H:oxygen oxidoreductase (trimethylamine-forming) (1.14.13.239)	1186	320	5
4-hydroxy-2-oxoglutarate glyoxylate-lyase (pyruvate-forming) (4.1.3.16)	29	9	5

Bottom Line

Temperature intolerance is not an independently study topic. Existing studies are usually in the context of some other condition. This suggests that this is the first study on it’s microbiome.

•  “Diabetes is often associated with orthostatic hypotension and temperature intolerance.” [2005]

The suggestions below has some surprises on the to avoid list: Vitamins B-3, B-12, Curcumin,  N-Acetyl Cysteine (NAC) sitting high in the list with antibiotics. The absence of probiotics in the to take suggestions is note worthy for those that view probiotics as ‘cure all’. Not listed, but conceptually worth considering, are the E.Coli probiotics (Symbioflor-2 or Mutaflor).

Recently I have see pea appear often and I recall that peas were served with most meals as a child. Peas has largely disappear from the western menu. I am starting to wonder if having pea soup 3 times a week would help a lot of people.

This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of constipation.

Study Populations:

We have 2 symptom annotations that could be included

• Comorbid: Constipation and diarrhea (not explosions) – 29 only
• Comorbid: Constipation and Explosions (not diarrhea) – 11 only
• Immune Manifestations: Constipation – 9.7 max z-score (70)
  • All of the above Constipation – 9.9 max z-score (83)

Taking all together we get 83 samples with an max z-score of 9.9

Symptom	Reference	Study
Constipation	1123	83

• Bacteria Detected with z-score > 2.6: found 123 items, highest value was 9.9
• Enzymes Detected with z-score > 2.6: found 511 items, highest value was 5.2
• Compound Detected with z-score > 2.6: found No items

Clearly some bacteria have strong associations, but the number of enzymes that are significant suggests that they may be more dimensions to the issue.

Previous studies have shown that the gut microbiota of constipated patients differs from healthy controls; however, many discrepancies exist in the findings, and no clear link has been confirmed between chronic constipation and changes in the gut microbiota.

Gut Microbiota Composition Changes in Constipated Women of Reproductive Age [2021]

The reason that I do not have Constipation on my US National Library of Medicine Studies list is that the studies are usually in the context of another microbiome altering condition. For example: Effects of Fermented Milk Containing Lacticaseibacillus paracasei Strain Shirota on Constipation in Patients with Depression [2021]

Interesting Significant Bacteria

Unusually bacteria was found significant with both high (13) and low (110). This far exceed the count expected as the false detection rate, so we should include and cite them.

Bacteria	Reference Mean	Study	Z-Score
Prevotella copri (species)	67087	7150	9.9
Prevotella (genus)	74786	17755	8
Prevotellaceae (family)	82267	29219	6.8
Veillonella (genus)	4003	2102	5
Lactiplantibacillus pentosus (species)	117	26	5

UNLIKE most of the other studies, we had a significant number of too many bacteria (far more than expected with a False Detection Rate).

Bacteria	Reference Mean	Study	Z-Score
Tannerellaceae (family)	24592	34890	-3.6
Parabacteroides (genus)	24587	34665	-3.6
Porphyromonadaceae (family)	26584	36605	-3.4
Bacteroides uniformis (species)	25682	42170	-3.2
Bacteroidaceae (family)	281711	337344	-3.1
Desulfosporosinus auripigmenti (species)	18	28	-3
Hathewaya histolytica (species)	2607	4093	-2.8
Bacteroides (genus)	232904	276348	-2.8
Oscillospira (genus)	22836	28653	-2.8
Anaerotruncus colihominis (species)	1739	2436	-2.8
Anaerotruncus (genus)	1834	2520	-2.7
Hathewaya (genus)	2618	4058	-2.7
Parabacteroides merdae (species)	7249	11459	-2.6

There is some agreement with studies on these findings, but as cited above — results are not consistent in studies.

• Fecal bacterial microbiota in constipated patients before and after eight weeks of daily Bifidobacterium infantis 35624 administration [2022] , “Oscillospira, was the taxon most strongly correlated”
• “high abundances of Prevotella,… may be the main factors in curing constipation.[2020]
• ” studies have found that Bacteroides were more abundant in colonic mucosa of patients with chronic constipation” [2017]

Interesting Enzymes

As with bacteria we had both too few and too many.

Enzyme	Reference Mean	Study Mean	Z-Score
UDP-N-acetyl-alpha-D-mannosamine:NAD+ 6-oxidoreductase (1.1.1.336)	121077	167870	-4.1
GDP-alpha-D-mannose:2-O-alpha-D-mannosyl-1-phosphatidyl-1D-myo-inositol 6-alpha-D-mannosyltransferase (configuration-retaining) (2.4.1.346)	43257	68972	-4.1
UDP-2-acetamido-3-amino-2,3-dideoxy-alpha-D-glucuronate:2-oxoglutarate aminotransferase (2.6.1.98)	44942	68064	-4.1
acetyl-CoA:UDP-2-acetamido-3-amino-2,3-dideoxy-alpha-D-glucuronate N-acetyltransferase (2.3.1.201)	67863	98332	-4
chondroitin AC lyase (4.2.2.5)	62785	90081	-3.8
alpha-N-acetyl-D-glucosaminide N-acetylglucosaminohydrolase (3.2.1.50)	170810	221135	-3.7
glycine:H-protein-lipoyllysine oxidoreductase (decarboxylating, acceptor-amino-methylating) (1.4.4.2)	188544	239012	-3.7
L-leucyl-tRNALeu:[protein] N-terminal L-lysine/L-arginine leucyltransferase (2.3.2.6)	129313	167839	-3.7
5-phospho-alpha-D-ribose 1,2-cyclic phosphate 2-phosphohydrolase (alpha-D-ribose 1,5-bisphosphate-forming) (3.1.4.55)	185457	234894	-3.7
(2E,6E)-farnesyl-diphosphate:isopentenyl-diphosphate farnesyltranstransferase (adding 5 isopentenyl units) (2.5.1.90)	195115	245998	-3.6

Enzyme	Reference Mean	Study Mean	Z-Score
propanoyl-CoA:oxaloacetate C-propanoyltransferase (thioester-hydrolysing, 1-carboxyethyl-forming) (2.3.3.5)	1509	500	5.2
(2S,3R)-3-hydroxybutane-1,2,3-tricarboxylate pyruvate-lyase (succinate-forming) (4.1.3.30)	1458	500	4.9
N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase (3.5.1.18)	101418	72762	4.7
ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining) (2.4.1.342)	77187	49189	4.6
3-(cis-5,6-dihydroxycyclohexa-1,3-dien-1-yl)propanoate:NAD+ oxidoreductase (1.3.1.87)	580	183	4.5
3-phenylpropanoate,NADH:oxygen oxidoreductase (2,3-hydroxylating) (1.14.12.19)	593	189	4.4
D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming) (4.1.2.40)	89078	62686	4.4
UDP-N-acetyl-alpha-D-glucosamine:lipopolysaccharide N-acetyl-D-glucosaminyltransferase (2.4.1.56)	1077	376	4.3
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)	87698	61735	4.3
penicillin amidohydrolase (3.5.1.11)	83364	57721	4.3

Bottom Line

While constipation is often assumed to be due too many of some bacteria, the evidence suggestions that not enough is the more likely cause. There appears to be no simple model or answer.

Prevotella copri will hopefully be available as a probiotic in a few year. There are two natural sources for P.Copri : Beer and Sauerkraut [2020], which may be an experiment for those that are prone to constipation..

Looking at the suggestions — Constipation caused by Antibiotics!

This is not a “new discovery” — rather it appears to confirm that the mathematic model being used is reasonable and thus Dr. Artificial Intelligence suggestions are reasonable!

This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of allergy.

Sub-Series Study Populations:

We have 5 symptom annotations that this sub-series are examining, I tried different combination to see which resulted in a higher z-score to identify probable siblings (in a statistical sense)

• New food sensitivities – 12.1 z-score
• Medication sensitivities -8.8 z-score
  • Combined, dropped to 6.4
• Alcohol intolerance – 8.6 z-score
  • with Medication sensitivities 9.9 z-score and less bacteria
• Allergic Rhinitis (Hay Fever) – 8.6 z-score
  • When combined with any of the above, a major drop of z-scores
• Allergies – 9.2 z-score
  • With new food sensitivities – 9.9 z-score
  • When combined with any of the other, a major drop of z-scores

We have broken this down into 3 sub-groups of microbiome shifts:

• Allergies and Food Sensitivity
• Alcohol intolerance + Medication sensitivities
• Allergic Rhinitis (Hay Fever) (this study)

Symptom	Reference	Study
Allergic Rhinitis (Hay Fever)	1161	42

• Bacteria Detected with z-score > 2.6: found 222 items, highest value was 8.6
• Enzymes Detected with z-score > 2.6: found 250 items, highest value was 6.8
• Compound Detected with z-score > 2.6: found No items

Interesting Significant Bacteria

All bacteria found significant (except 1) had too low levels. This is a common pattern found with these studies, it is not “bad bacteria bogie man bacteria” but an absence of “upstanding citizens bacteria”.

The key bacteria are very different from the other two studies in this sub-series. We do see one retail level probiotic in the list: Bifidobacterium animalis (species)

Bacteria	Reference Mean	Study	Z-Score
Desulfovibrio (genus)	2770	625	8.6
Bacteroides stercoris (species)	14185	2169	7.8
Desulfovibrio simplex (species)	213	27	6.9
Phocaeicola sartorii (species)	807	276	6.4
Opitutae (class)	164	40	6.1
Actinobacillus pleuropneumoniae (species)	56	16	6
Planifilum (genus)	80	34	5.9
Planifilum fimeticola (species)	80	34	5.9
Puniceicoccaceae (family)	159	40	5.9
Cerasicoccus (genus)	313	36	5.8
Actinobacillus porcinus (species)	182	62	5.4
Veillonella (genus)	3935	1927	5.3
Desulfurispora thermophila (species)	68	35	5.3
Desulfurispora (genus)	68	35	5.3
Tepidanaerobacteraceae (family)	55	24	5.3
Thermoactinomycetaceae (family)	81	37	5.3
Rhodanobacteraceae (family)	838	249	5.3
Eggerthella sinensis (species)	179	45	5.2
Lactiplantibacillus pentosus (species)	120	25	5.2
Tepidanaerobacter (genus)	54	24	5.1
Tepidanaerobacter syntrophicus (species)	54	24	5.1
Absiella (genus)	728	74	5.1
delta/epsilon subdivisions (subphylum)	6122	3174	5.1
Veillonellales (order)	17250	11649	5
Bifidobacterium animalis (species)	1160	124	5

Looking at published studies we see many close matches, with most of the species cited in those studies being too low.

• Phocaeicola sartorii (species) <=> Phocaeicola massiliensis (NCBI:204516 )
• Bifidobacterium animalis (species) <=> Bifidobacterium (NCBI:1678 )
• Clostridium chartatabidum (species), Clostridia (class) <=> Clostridium butyricum (NCBI:1492 )
• Dialister invisus (species) <=> Dialister succinatiphilus (NCBI:487173 )

Note: This study and published studies suffer from the issues described in The taxonomy nightmare before Christmas…

Interesting Enzymes

All 250 enzymes found significant had too low levels.

Enzyme	Reference Mean	Study Mean	Z-Score
chorismate hydro-lyase (3-[(1-carboxyvinyl)oxy]benzoate-forming) (4.2.1.151)	1654	360	6.8
dolichyl-diphosphooligosaccharide:protein-L-asparagine N-beta-D-oligopolysaccharidotransferase (2.4.99.18)	1611	318	6.8
S-adenosyl-L-methionine:3-[(1-carboxyvinyl)-oxy]benzoate adenosyltransferase (HCO3–hydrolysing, 6-amino-6-deoxyfutalosine-forming) (2.5.1.120)	1624	363	6.8
dehypoxanthine futalosine:S-adenosyl-L-methionine oxidoreductase (cyclizing) (1.21.98.1)	1615	363	6.7
2-amino-3,7-dideoxy-D-threo-hept-6-ulosonate:NAD+ oxidoreductase (deaminating) (1.4.1.24)	1548	345	6.6
ATP phosphohydrolase (ABC-type, tungstate-importing) (7.3.2.6)	1378	302	6.6
hydrogen:ferricytochrome-c3 oxidoreductase (1.12.2.1)	1388	320	6.6
[TtuB sulfur-carrier protein]-Gly-NH-CH2-C(O)SH:tRNA (5-methyluridine54-2-O)-sulfurtransferase (2.8.1.15)	1359	307	6.5
isethionate sulfite-lyase (4.4.1.38)	1524	318	6.5
ATP phosphohydrolase (ABC-type, capsular-polysaccharide-exporting) (7.6.2.12)	949	239	6.5
6-amino-6-deoxyfutalosine deaminase (3.5.4.40)	1667	459	6.3
hydrogen-sulfide:[DsrC sulfur-carrier protein],acceptor oxidoreductase (1.8.99.5)	1304	323	6.2
futalosine ribohydrolase (3.2.2.26)	1241	311	6.1
siroheme carboxy-lyase (4.1.1.111)	1340	531	6
propanoyl-CoA:oxaloacetate C-propanoyltransferase (thioester-hydrolysing, 1-carboxyethyl-forming) (2.3.3.5)	1479	366	5.9
S-methyl-5′-thioadenosine:phosphate S-methyl-5-thio-alpha-D-ribosyl-transferase (2.4.2.28)	3646	1532	5.8
(2S,3R)-3-hydroxybutane-1,2,3-tricarboxylate pyruvate-lyase (succinate-forming) (4.1.3.30)	1430	379	5.6
2-dehydrotetronate isomerase (5.3.1.35)	1399	447	5.4
L-threonate:NAD+ 2-oxidoreductase (1.1.1.411)	1405	446	5.3
5-aminopentanoate:2-oxoglutarate aminotransferase (2.6.1.48)	2363	797	5.2
(S)-3-amino-2-methylpropanoate:2-oxoglutarate aminotransferase (2.6.1.22)	2356	796	5.2
(2S,3S)-2-hydroxybutane-1,2,3-tricarboxylate hydro-lyase [(Z)-but-2-ene-1,2,3-tricarboxylate-forming] (4.2.1.79)	1290	383	5.1
3-deoxy-alpha-D-manno-octulopyranosonate:oxygen 8-oxidoreductase (1.1.3.48)	699	196	5
GTP:molybdenum cofactor guanylyltransferase (2.7.7.77)	17411	9662	5
1-phosphatidyl-1D-myo-inositol-4,5-bisphosphate 4-phosphohydrolase (3.1.3.78)	1912	326	5

Bottom Line

We appear to have general agreement with published studies. The purpose of this series is to identify the shifts using a lab/analysis that is available to anyone world wide with the ability to identify the bacteria causing the issue with reliability and higher statistical significance than most studies.

This is a common symptom for many people with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of allergy.

Sub-Series Study Populations:

We have 5 symptom annotations that this sub-series are examining, I tried different combination to see which resulted in a higher z-score to identify probable siblings (in a statistical sense)

• New food sensitivities – 12.1 z-score
• Medication sensitivities -8.8 z-score
  • Combined, dropped to 6.4
• Alcohol intolerance – 8.6 z-score
  • with Medication sensitivities 8.6 z-score and less bacteria
• Allergic Rhinitis (Hay Fever) – 8.6 z-score
  • When combined with any of the above, a major drop of z-scores
• Allergies – 9.2 z-score
  • With new food sensitivities – 9.9 z-score
  • When combined with any of the other, a major drop of z-scores

We have broken this down into 3 sub-groups of microbiome shifts:

• Allergies and Food Sensitivity
• Alcohol intolerance + Medication sensitivities (this study).
• Allergic Rhinitis (Hay Fever)

Symptom	Reference	Study
Alcohol intolerance + Medication sensitivities	1144	59

• Bacteria Detected with z-score > 2.6: found 130 items, highest value was 8.6
• Enzymes Detected with z-score > 2.6: found 507 items, highest value was 7.9
• Compound Detected with z-score > 2.6: found No items

The bacteria that was most significant was NOT Prevotella copri (species) which we saw with Special Studies: Allergies And Food Sensitivity.

Interesting Significant Bacteria

All bacteria found significant (except 1) had too low levels. This is a common pattern found with these studies, it is not “bad bacteria bogie man bacteria” but an absence of “upstanding citizens bacteria”. The signature is very interesting because it is specific Bifidobacterium species — despite this, the Bifidobacterium genus was not found to be statistically significant. This drops us into the odd space of general Bifidobacterium probiotics likely not be effective, but specific strains may be(unfortunately those strains are not available as probiotics). There is one retail probiotics that contains Enterbacter, General Biotics/Equilibrium which give an experiment to try.

Bacteria	Reference Mean	Study	Z-Score
Bifidobacterium gallicum (species)	3698	136	8.6
Bifidobacterium catenulatum subsp. kashiwanohense (subspecies)	316	55	6.9
Bifidobacterium kashiwanohense PV20-2 (strain)	315	55	6.8
Enterobacter (genus)	1361	127	5.6
Bifidobacterium angulatum (species)	183	20	5.2
Gammaproteobacteria (class)	14010	5636	5
Aeromonadaceae (family)	157	25	5

Interesting Enzymes

All 507 enzymes found significant had too low levels. This places it into the same class of massive enzyme disruptions as seen in Special Studies: Depression . As with that one, apologies for the massive list — I am keeping to my practice for these studies of listing everything with 5.0 z-score or higher.

Enzyme	Reference Mean	Study Mean	Z-Score
2-acetylphloroglucinol C-acetyltransferase (2.3.1.272)	177	28	7.9
[cysteine desulfurase]-S-sulfanyl-L-cysteine:[molybdopterin-synthase sulfur-carrier protein]-Gly-Gly sulfurtransferase (2.8.1.11)	5231	1805	7.7
S-adenosyl-L-methionine:tellurite methyltransferase (2.1.1.265)	5238	1938	7
decenoyl-[acyl-carrier protein] Delta2-trans-Delta3-cis-isomerase (5.3.3.14)	6010	2276	6.9
tRNA-uridine65 uracil mutase (5.4.99.26)	4052	1226	6.8
coproporphyrinogen:oxygen oxidoreductase (decarboxylating) (1.3.3.3)	3163	746	6.8
donor:hydrogen-peroxide oxidoreductase (1.11.1.21)	3030	672	6.7
2-(1,2-epoxy-1,2-dihydrophenyl)acetyl-CoA isomerase (5.3.3.18)	3097	1059	6.7
3-hydroxybutanoyl-CoA 3-epimerase (5.1.2.3)	2951	696	6.7
ATP:nicotinamide-nucleotide adenylyltransferase (2.7.7.1)	5076	2200	6.7
ATP:1-(beta-D-ribofuranosyl)-nicotinamide 5′-phosphotransferase (2.7.1.22)	5025	2187	6.6
isocitrate glyoxylate-lyase (succinate-forming) (4.1.3.1)	2955	730	6.6
D-amino acid:quinone oxidoreductase (deaminating) (1.4.5.1)	2485	686	6.5
L-methionine:2-oxo-acid aminotransferase (2.6.1.88)	2759	652	6.5
2-(glutathione-S-yl)-hydroquinone:glutathione oxidoreductase (1.8.5.7)	3140	748	6.5
D-glucose:ubiquinone oxidoreductase (1.1.5.2)	2200	462	6.5
methanesulfonate,FMNH2:oxygen oxidoreductase (1.14.14.34)	2239	489	6.5
alkanesulfonate,FMNH2:oxygen oxidoreductase (1.14.14.5)	2239	489	6.5
glutathione:NADP+ oxidoreductase (1.8.1.7)	5947	2494	6.5
triphosphate phosphohydrolase (3.6.1.25)	4494	2012	6.4
S-(hydroxymethyl)glutathione:NAD+ oxidoreductase (1.1.1.284)	2862	759	6.4
(S)-3-hydroxyacyl-CoA:NAD+ oxidoreductase (1.1.1.35)	3374	926	6.4
ferredoxin:NAD+ oxidoreductase (1.18.1.3)	1992	455	6.4
acyl-CoA:sn-glycerol-3-phosphate 1-O-acyltransferase (2.3.1.15)	3270	1139	6.4
S-adenosyl-L-methionine:tRNA (cytidine32/uridine32-2′-O)-methyltransferase (2.1.1.200)	3316	1093	6.3
[50S ribosomal protein L16]-L-Arg81,2-oxoglutarate:oxygen oxidoreductase (3R-hydroxylating) (1.14.11.47)	3334	1114	6.3
choline:acceptor 1-oxidoreductase (1.1.99.1)	2748	562	6.3
acetyl-CoA:glyoxylate C-acetyltransferase [(S)-malate-forming] (2.3.3.9)	3116	950	6.3
siroheme ferro-lyase (sirohydrochlorin-forming) (4.99.1.4)	2565	539	6.2
deoxyribocyclobutadipyrimidine pyrimidine-lyase (4.1.99.3)	4156	1337	6.2
acetyl-CoA:[elongator tRNAMet]-cytidine34 N4-acetyltransferase (ATP-hydrolysing) (2.3.1.193)	3309	1094	6.2
7,8-dihydroneopterin 3′-triphosphate diphosphohydrolase (3.6.1.67)	3607	1299	6.2
n/a (3.4.11.23)	3312	1083	6.2
acetyl-CoA:dTDP-4-amino-4,6-dideoxy-alpha-D-galactose N-acetyltransferase (2.3.1.210)	2766	673	6.2
ATP:N-acetyl-D-glucosamine 6-phosphotransferase (2.7.1.59)	3263	1071	6.2
L-2,4-diaminobutanoate carboxy-lyase (propane-1,3-diamine-forming) (4.1.1.86)	2127	304	6.2
chorismate pyruvate-lyase (4-hydroxybenzoate-forming) (4.1.3.40)	2596	567	6.2
protein dithiol:quinone oxidoreductase (disulfide-forming) (1.8.5.9)	3557	1276	6.2
fatty acyl-[acyl-carrier protein]:alpha-Kdo-(2->4)-alpha-Kdo-(2->6)-(acyl)-[lipid IVA] O-acyltransferase (2.3.1.243)	3322	1097	6.2
ATP:L-threonine O3-phosphotransferase (2.7.1.177)	2358	350	6.1
ditrans-octacis-undecaprenyl-diphosphate:alpha-D-Kdo-(2->4)-alpha-D-Kdo-(2->6)-lipid-A phosphotransferase (2.7.4.29)	2852	1008	6.1
D-sorbitol-6-phosphate:NAD+ 2-oxidoreductase (1.1.1.140)	2447	677	6.1
diacylglycerol-3-phosphate phosphohydrolase (3.1.3.4)	3284	1105	6.1
1,2-diacyl-sn-glycerol 3-phosphate phosphohydrolase (3.1.3.81)	3284	1105	6.1
(R)-lactate:quinone 2-oxidoreductase (1.1.5.12)	2328	510	6.1
(S)-lactate:ferricytochrome-c 2-oxidoreductase (1.1.2.3)	2368	544	6.1
methyl DNA-base, 2-oxoglutarate:oxygen oxidoreductase (formaldehyde-forming) (1.14.11.33)	2304	475	6.1
ATP:D-ribulose-5-phosphate 1-phosphotransferase (2.7.1.19)	2435	543	6.1
(2S,3S)-2,3-dihydro-2,3-dihydroxybenzoate:NAD+ oxidoreductase (1.3.1.28)	2240	476	6.1
D-mannitol-1-phosphate:NAD+ 5-oxidoreductase (1.1.1.17)	2672	727	6
2-O-(alpha-D-mannopyranosyl)-D-glycerate D-mannohydrolase (3.2.1.170)	1248	396	6
S-formylglutathione hydrolase (3.1.2.12)	2402	554	6
gamma-L-glutamyl-L-cysteinyl-glycine:spermidine amidase (3.5.1.78)	3189	1059	6
gamma-L-glutamyl-L-cysteinyl-glycine:spermidine ligase (ADP-forming) [spermidine is numbered so that atom N-1 is in the amino group of the aminopropyl part of the molecule] (6.3.1.8)	3189	1059	6
S-adenosyl-L-methionine:23S rRNA (uracil747-C5)-methyltransferase (2.1.1.189)	3257	1112	6
S-adenosyl-L-methionine:uridine in tRNA 3-[(3S)-3-amino-3-carboxypropyl]transferase (2.5.1.25)	1704	303	6
succinate-semialdehyde:NAD+ oxidoreductase (1.2.1.24)	2416	449	6
Fe(II)-siderophore:NADP+ oxidoreductase (1.16.1.9)	2344	447	6
ATP phosphohydrolase (ABC-type, taurine-importing) (7.6.2.7)	2185	521	6
geraniol:NADP+ oxidoreductase (1.1.1.183)	2470	466	6
ATP:N-acyl-D-mannosamine 6-phosphotransferase (2.7.1.60)	3204	1098	5.9
L-serine:[L-seryl-carrier protein] ligase (AMP-forming) (6.2.1.72)	2199	454	5.9
galactarate hydro-lyase (5-dehydro-4-deoxy-D-glucarate-forming) (4.2.1.42)	3015	963	5.9
propanoyl-CoA:phosphate propanoyltransferase (2.3.1.222)	1738	358	5.9
ATP:thiamine phosphotransferase (2.7.1.89)	2372	492	5.9
ATP phosphohydrolase (ABC-type, putrescine-importing) (7.6.2.16)	2302	503	5.9
ubiquinol:oxygen oxidoreductase (superoxide-forming) (1.10.3.17)	2232	475	5.9
S-adenosyl-L-methionine:23S rRNA (guanine1835-N2)-methyltransferase (2.1.1.174)	2258	485	5.9
riboflavin:NAD(P)+ oxidoreductase (1.5.1.41)	2323	491	5.9
D-erythrose 4-phosphate:NAD+ oxidoreductase (1.2.1.72)	2267	485	5.9
CDP-diacylglycerol phosphatidylhydrolase (3.6.1.26)	2239	499	5.9
D-glucarate hydro-lyase (5-dehydro-4-deoxy-D-glucarate-forming) (4.2.1.40)	2953	1125	5.9
6-phospho-D-gluconate hydro-lyase (2-dehydro-3-deoxy-6-phospho-D-gluconate-forming) (4.2.1.12)	2260	495	5.8
ATP:propanoate phosphotransferase (2.7.2.15)	2215	469	5.8
isochorismate pyruvate-hydrolase (3.3.2.1)	2164	473	5.8
(3Z/3E)-alk-3-enoyl-CoA (2E)-isomerase (5.3.3.8)	2225	487	5.8
ATP phosphohydrolase (ABC-type, L-arabinose-importing) (7.5.2.12)	2214	473	5.8
n/a (3.4.24.55)	2279	492	5.8
diacylphosphatidylethanolamine:alpha-D-Kdo-(2->4)-alpha-D-Kdo-(2->6)-lipid-A 7”-phosphoethanolaminetransferase (2.7.8.42)	2371	500	5.8
2,3-dihydroxybenzoate:L-serine ligase (6.3.2.14)	2179	611	5.8
2-O-(alpha-D-glucopyranosyl)-D-glycerate:phosphate alpha-D-glucosyltransferase (configuration-retaining) (2.4.1.352)	1217	249	5.8
pyrimidine-5′-nucleotide phosphoribo(deoxyribo)hydrolase (3.2.2.10)	2222	495	5.8
6-sulfo-alpha-D-quinovosyl diacylglycerol 6-sulfo-D-quinovohydrolase (3.2.1.199)	1113	222	5.8
(9Z)-hexadec-9-enoyl-[acyl-carrier protein]:Kdo2-lipid IVA O-palmitoleoyltransferase (2.3.1.242)	2356	502	5.8
[RNA]-adenosine-cytidine 5′-hydroxy-adenosoine ribonucleotide-3′-[RNA fragment]-lyase (cyclicizing; [RNA fragment]-3′-cytidine-2′,3′-cyclophosphate-forming and hydrolysing) (4.6.1.21)	2312	470	5.8
ATP phosphohydrolase (ABC-type, D-allose-importing) (7.5.2.8)	2146	431	5.8
6-methoxy-3-methyl-2-(all-trans-polyprenyl)-1,4-benzoquinol,acceptor:oxygen oxidoreductase (5-hydroxylating) (1.14.99.60)	2180	540	5.8
NADPH:NAD+ oxidoreductase (Si-specific) (1.6.1.1)	2204	490	5.8
taurine, 2-oxoglutarate:oxygen oxidoreductase (sulfite-forming) (1.14.11.17)	2122	496	5.8
(deoxy)cytidine 5′-triphosphate diphosphohydrolase (3.6.1.65)	2307	500	5.7
2-(all-trans-polyprenyl)phenol,NADPH:oxygen oxidoreductase (6-hydroxylating) (1.14.13.240)	2178	535	5.7
dTDP-N-acetyl-alpha-D-fucose:N-acetyl-beta-D-mannosaminouronyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol N-acetylfucosaminyltransferase (2.4.1.325)	2308	507	5.7
ATP:[isocitrate dehydrogenase (NADP+)] phosphotransferase (2.7.11.5)	2142	516	5.7
2,3-dihydroxybenzoate:[aryl-carrier protein] ligase (AMP-forming) (6.2.1.71)	2118	604	5.7
5,6,7,8-tetrahydromonapterin:NADP+ oxidoreductase (1.5.1.50)	2159	476	5.7
glutathione gamma-glutamyl cyclotransferase (5-oxo-L-proline producing) (4.3.2.7)	2158	500	5.7
iron(III)-enterobactin hydrolase (3.1.1.108)	2204	491	5.7
N,N’-diacetylchitobiose acetylhydrolase (3.5.1.105)	2304	508	5.7
N-succinyl-L-glutamate amidohydrolase (3.5.1.96)	2172	518	5.7
protein-Npi-phospho-L-histidine:N-acetyl-D-muramate Npi-phosphotransferase (2.7.1.192)	2283	474	5.7
4-alpha-D-[(1->4)-alpha-D-glucano]trehalose glucanohydrolase (trehalose-producing) (3.2.1.141)	1335	181	5.7
acetyl-CoA:N6-hydroxy-L-lysine 6-acetyltransferase (2.3.1.102)	708	159	5.6
4-aminobutanoate:2-oxoglutarate aminotransferase (2.6.1.19)	3600	1155	5.6
ATP:(S)-4,5-dihydroxypentane-2,3-dione 5-phosphotransferase (2.7.1.189)	1415	220	5.6
inosine/xanthosine 5′-triphosphate phosphohydrolase (3.6.1.73)	2071	496	5.6
6-deoxy-6-sulfofructose-1-phosphate 2-hydroxy-3-oxopropane-1-sulfonate-lyase (glycerone-phosphate-forming) (4.1.2.57)	1346	295	5.6
protein-Npi-phospho-L-histidine:2-O-alpha-mannopyranosyl-D-glycerate Npi-phosphotransferase (2.7.1.195)	1163	234	5.6
2,3-dihydroxypropane-1-sulfonate:NAD+ 3-oxidoreductase (1.1.1.373)	1341	295	5.6
4-aminobutanal:NAD+ 1-oxidoreductase (1.2.1.19)	2237	491	5.6
N-succinyl-L-glutamate 5-semialdehyde:NAD+ oxidoreductase (1.2.1.71)	2135	535	5.6
protein-dithiol:NAD(P)+ oxidoreductase (1.8.1.8)	2151	540	5.6
succinyl-CoA:acetyl-CoA C-succinyltransferase (2.3.1.174)	1445	205	5.6
succinyl-CoA:L-arginine N2-succinyltransferase (2.3.1.109)	2135	536	5.6
3-oxo-5,6-dehydrosuberyl-CoA semialdehyde:NADP+ oxidoreductase (1.2.1.91)	1461	221	5.6
2-oxepin-2(3H)-ylideneacetyl-CoA hydrolase (3.3.2.12)	1461	221	5.6
N2-succinyl-L-arginine iminohydrolase (decarboxylating) (3.5.3.23)	2134	536	5.6
ATP phosphohydrolase (ABC-type, nitrate-importing) (7.3.2.4)	1523	142	5.6
7,8-dihydroneopterin 3′-triphosphate 2′-epimerase (5.1.99.7)	1928	468	5.5
N4-acetylcytidine amidohydrolase (3.5.1.135)	2966	1096	5.5
N2-succinyl-L-ornithine:2-oxoglutarate 5-aminotransferase (2.6.1.81)	2254	677	5.5
(1->4)-alpha-D-glucan 1-alpha-D-glucosylmutase (5.4.99.15)	1319	183	5.5
ATP:2-dehydro-3-deoxy-D-galactonate 6-phosphotransferase (2.7.1.58)	1536	299	5.5
2-dehydro-3-deoxy-6-phospho-D-galactonate D-glyceraldehyde-3-phospho-lyase (pyruvate-forming) (4.1.2.21)	1538	299	5.5
(S)-ureidoglycolate:NAD+ oxidoreductase (1.1.1.350)	1729	436	5.5
ATP:D-glyceraldehyde 3-phosphotransferase (2.7.1.28)	1512	115	5.5
ATP:glycerone phosphotransferase (2.7.1.29)	1512	115	5.5
FAD AMP-lyase (riboflavin-cyclic-4′,5′-phosphate-forming) (4.6.1.15)	1512	115	5.5
tRNA 2-(methylsulfanyl)-N6-prenyladenosine37,donor:oxygen 4-oxidoreductase (trans-hydroxylating) (1.14.99.69)	1565	158	5.5
S-methyl-5′-thioadenosine:phosphate S-methyl-5-thio-alpha-D-ribosyl-transferase (2.4.2.28)	3673	1543	5.4
primary-amine:oxygen oxidoreductase (deaminating) (1.4.3.21)	1495	217	5.4
UDP-4-amino-4-deoxy-alpha-L-arabinose:ditrans,octacis-undecaprenyl phosphate 4-amino-4-deoxy-alpha-L-arabinosyltransferase (2.4.2.53)	2107	573	5.4
(2S)-2-hydroxy-3,4-dioxopentyl phosphate aldose-ketose-isomerase (5.3.1.32)	1529	259	5.4
acyl-CoA:acetyl-CoA C-acyltransferase (2.3.1.16)	3869	1876	5.4
2,3-didehydroadipoyl-CoA:acetyl-CoA C-didehydroadipoyltransferase (double bond migration) (2.3.1.223)	1516	224	5.4
10-formyltetrahydrofolate:UDP-4-amino-4-deoxy-beta-L-arabinose N-formyltransferase (2.1.2.13)	2048	470	5.4
UDP-alpha-D-glucuronate:NAD+ oxidoreductase (decarboxylating) (1.1.1.305)	2048	470	5.4
4-amino-4-deoxy-alpha-L-arabinopyranosyl ditrans,octacis-undecaprenyl-phosphate:lipid IVA 4-amino-4-deoxy-L-arabinopyranosyltransferase (2.4.2.43)	2044	473	5.4
5-(methylsulfanyl)-D-ribulose-1-phosphate 4-hydro-lyase [5-(methylsulfanyl)-2,3-dioxopentyl-phosphate-forming] (4.2.1.109)	1552	181	5.3
UDP-4-amino-4-deoxy-beta-L-arabinose:2-oxoglutarate aminotransferase (2.6.1.87)	2070	480	5.3
phenylacetyl-CoA:oxygen oxidoreductase (1,2-epoxidizing) (1.14.13.149)	1375	278	5.3
ATP phosphohydrolase (ABC-type, D-xylose-transporting) (7.5.2.10)	1473	308	5.3
5-(methylsulfanyl)-2,3-dioxopentyl-phosphate phosphohydrolase (isomerizing) (3.1.3.77)	1689	121	5.3
N-benzoylamino-acid amidohydrolase (3.5.1.32)	1712	408	5.3
phenylacetaldehyde:NAD+ oxidoreductase (1.2.1.39)	1391	271	5.3
L-glutamate:putrescine ligase (ADP-forming) (6.3.1.11)	1902	448	5.3
iron(III)-salmochelin complex hydrolase (3.1.1.109)	727	159	5.3
2-O-(alpha-D-mannosyl)-3-phosphoglycerate phosphohydrolase (3.1.3.70)	2061	498	5.3
4-(gamma-L-glutamylamino)butanoate amidohydrolase (3.5.1.94)	1914	445	5.3
GDP-alpha-D-mannuronate:mannuronan D-mannuronatetransferase (2.4.1.33)	154	39	5.2
[mannuronan]-beta-D-mannuronate 5-epimerase (5.1.3.37)	154	39	5.2
3-(indol-3-yl)pyruvate carboxy-lyase [(2-indol-3-yl)acetaldehyde-forming] (4.1.1.74)	1802	138	5.2
quinate:quinol 3-oxidoreductase (1.1.5.8)	1523	124	5.2
betaine-aldehyde:NAD+ oxidoreductase (1.2.1.8)	2057	434	5.2
catechol:oxygen 2,3-oxidoreductase (ring-opening) (1.13.11.2)	3240	1416	5.2
1,2-dihydroxy-5-(methylsulfanyl)pent-1-en-3-one:oxygen oxidoreductase (formate- and CO-forming) (1.13.11.53)	1714	283	5.2
1,2-dihydroxy-5-(methylsulfanyl)pent-1-en-3-one:oxygen oxidoreductase (formate-forming) (1.13.11.54)	1714	283	5.2
ATP phosphohydrolase (ABC-type, thiamine-importing) (7.6.2.15)	3503	1300	5.2
citrate:N6-acetyl-N6-hydroxy-L-lysine ligase (AMP-forming) (6.3.2.38)	797	193	5.1
RX:glutathione R-transferase (2.5.1.18)	2968	1025	5.1
N2-citryl-N6-acetyl-N6-hydroxy-L-lysine:N6-acetyl-N6-hydroxy-L-lysine ligase (AMP-forming) (6.3.2.39)	795	193	5.1
putrescine:2-oxoglutarate aminotransferase (2.6.1.82)	3168	1490	5.1
UDP-alpha-D-glucose:enterobactin 5′-C-beta-D-glucosyltransferase (configuration-inverting) (2.4.1.369)	807	200	5.1
4-hydroxybutanoate:NAD+ oxidoreductase (1.1.1.61)	2322	607	5.1
D-xylonate hydro-lyase (2-dehydro-3-deoxy-D-arabinonate-forming) (4.2.1.82)	621	178	5
D-glycerate:NAD(P)+ oxidoreductase (1.1.1.60)	1888	516	5
galactitol-1-phosphate:NAD+ oxidoreductase (1.1.1.251)	997	277	5
alkylmercury mercury(II)-lyase (alkane-forming) (4.99.1.2)	577	115	5
(S)-2-hydroxyglutarate:quinone oxidoreductase (1.1.5.13)	2192	670	5
(R)-3-hydroxyacyl-CoA:NADP+ oxidoreductase (1.1.1.36)	523	154	5

Bottom Line

These two symptoms tend to be ignored by the medical establishment. There are studies on the microbiome impacting the efficiency of prescription drugs but not sensitivities or hyper-reactivity. This means that this post is likely the first study on these in existence.

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