This is a follow up the earlier blog post: Son and Daughter with Autism Analysis from a year ago. There has been a lot of changes of the site and revisions of algorithms.

Comparing Siblings

We know from studies that members in the same family often share about 27% of the same strains. Unfortunately with 16s tests (Biomesight, Ombre), we do not get strain information just species information.

Using the new refactor citizen science symptoms (see New Special Studies on Symptoms ), we are presently surprised! We have many forecast symptoms being the same which supports the observation cited above of share taxa, likely at the strain level.

It does hint that less time with each other and a lot more time with other (ideally normal) children may have benefits to the microbiome. Some of the changes may be connected to gender:

• About twice as many women as men experience depression [Mayo]
• Increased inflammation is seen in the periphery in both depression and fatigue. [2019] which agrees with the daughter having a lower Anti inflammatory Bacteria Score

Measure	Daughter	Son
Anti inflammatory Bacteria Score	25.6%ile	89%ile
Buytrate Bacteria Score	95.9%ile	78%ile
Histamine Producers	21.8%ile	15.3%
Autism From PubMed	53/97 (1%ile) Prior: 54/97 (1%ile)	73/97 (11%ile) Prior: 53/97 (1%ile)
Forecast Symptoms	Official Diagnosis: Depression 28 % match on 7 taxa DePaul University Fatigue Questionnaire : Blurred Vision 25 % match on 8 taxa	Neurological-Sleep: Inability for deep (delta) sleep 23 % match on 13 taxa Age: 10-20 17 % match on 23 taxa DePaul University Fatigue Questionnaire : Forgetting what you are trying to say 16 % match on 31 taxa

Next looking at Percentages of Percentiles, we see significant differences. Unfortunately, we do not have gender and age reference tables, so interpretation is fuzzy.

Potential Medical Conditions Detected had nothing significant for either child. Both are at 95.6%ile on Dr. Jason Hawrelak Recommendations (they were 98.9 and 99.7%iles before) .

• Akkermansia: Low for both
• Bifidobacterium: Low for both
• Blautia: High for son, Low for daughter
• Lactobacillus Very High for son only
• Faecalibacterium prausnitzii: High for both

Detail Comparison

The thing that stands out is that the Son has a lot more Enzymes out of range (with the resulting substrates(consumers) and products also being out of range).

Criteria	Daughter	Son
Lab Read Quality	6.7	10.9
PubMed Bacteria Matches for Autism	1%ile (53/97)	11%ile (73/97)
Outside Range from JasonH	5	5
Outside Range from Medivere	18	18
Outside Range from Metagenomics	9	9
Outside Range from MyBioma	9	9
Outside Range from Nirvana/CosmosId	23	23
Outside Range from XenoGene	51	51
Outside Lab Range (+/- 1.96SD)	11	22
Outside Box-Plot-Whiskers	98	76
Outside Kaltoft-Moldrup	132	248
Bacteria Reported By Lab	842	757
Bacteria Over 99%ile	13	11
Bacteria Over 95%ile	28	45
Bacteria Over 90%ile	66	61
Bacteria Under 10%ile	66	285
Bacteria Under 5%ile	22	214
Shannon Diversity Index	3.064	2.807
Simpson Diversity Index	0.07	0.088
Chao1 Index	29791	24924
Rarely Seen 5%	61	98
Pathogens	36	35
Kegg Compounds Low	973	1001
Kegg Compounds High	43	162
Kegg Enzymes Low	89	265
Kegg Enzymes High	98	381
Kegg Products Low	55	152
Kegg Products High	52	209
Kegg Substrates Low	46	148
Kegg Substrates High	58	229

Looking at KEGG Derived Probiotic suggestions, the list is full of the soil based bacteria found in Prescript-Assist®/SBO Probiotic or Energybalance / ColoBiotica 28 Colon Support or General Biotics/Equilibrium. There was no probiotic above my usual threshold from the consensus, so the above seems to be worth a try.

KEGG Suggested supplements has nothing significant for the daughter, but for the son we have the following being very significant:

• Serine
• Threonine
• Glutamine
• Cysteine
• Arginine

A complex amino-acid supplement may be worth an experiment.

As an experiment (and trying to avoid two different kid diet), I did an uber-consensus from each child’s with tons of prescription medication but only one thing above my usual 50% of highest value.

• Vitamin B1,thiamine hydrochloride
• Vitamin B6,pyridoxine hydrochloride
• vitamin B7, biotin
• gluten-free diet
• sorghum
• melatonin supplement
• GABA

Son Compared to Prior Sample

We can see the spike in low percentile bacteria. This raises the question, has he had COVID (or a COVID vaccine) prior to the sample being done. These spikes show themselves also via Kaltoft-Moldrup and Box-Plot-Whiskers which are both sensitive to this pattern.

Criteria	Current Sample	Old Sample
Lab Read Quality	10.9	4.4
Outside Range from JasonH	4	4
Outside Range from Medivere	17	17
Outside Range from Metagenomics	10	10
Outside Range from MyBioma	13	13
Outside Range from Nirvana/CosmosId	27	27
Outside Range from XenoGene	49	49
Outside Lab Range (+/- 1.96SD)	22	22
Outside Box-Plot-Whiskers	76	100
Outside Kaltoft-Moldrup	248	89
Bacteria Reported By Lab	757	708
Bacteria Over 90%ile	61	82
Bacteria Under 10%ile	285	26
Shannon Diversity Index	2.807	2.451
Simpson Diversity Index	0.088	0.15
Chao1 Index	24924	19183
Lab: Thryve
Pathogens	35	30
Condition Est. Over 90%ile	2	0
Kegg Compounds Low	1001	1048
Kegg Compounds High	162	132
Kegg Enzymes Low	265	115
Kegg Enzymes High	381	296
Kegg Products Low	152	74
Kegg Products High	209	191
Kegg Substrates Low	148	69
Kegg Substrates High	229	212
Anti inflammatory Bacteria Score	89.2%ile	83.2%ile
Buytrate Bacteria Score	77.9%ile	90.2%ile
Histamine Producers	15.3%ile	38.2%ile

From this weekend update of special studies, we can get a count of bacteria shifts strongly associated to symptoms.

• Old Sample: 32 taxa
• Latest Sample: 60 taxa

Daughter Compared to Prior Sample

First the numbers which are usually similar to the prior sample.

Criteria	Current Sample	Old Sample
Lab Read Quality	6.7	3.1
Outside Range from JasonH	6	6
Outside Range from Medivere	19	19
Outside Range from Metagenomics	7	7
Outside Range from MyBioma	12	12
Outside Range from Nirvana/CosmosId	26	26
Outside Range from XenoGene	47	47
Outside Lab Range (+/- 1.96SD)	11	61
Outside Box-Plot-Whiskers	98	203
Outside Kaltoft-Moldrup	132	134
Bacteria Reported By Lab	842	852
Bacteria Over 90%ile	66	202
Bacteria Under 10%ile	66	10
Shannon Diversity Index	3.064	3.411
Simpson Diversity Index	0.07	0.028
Chao1 Index	29791	35210
Lab: Thryve
Pathogens	36	35
Condition Est. Over 90%ile	0	0
Kegg Compounds Low	973	1027
Kegg Compounds High	43	80
Kegg Enzymes Low	89	44
Kegg Enzymes High	98	171
Kegg Products Low	55	29
Kegg Products High	52	86
Kegg Substrates Low	46	26
Kegg Substrates High	58	111
Anti inflammatory Bacteria Score	25.5%ile	28%ile
Buytrate Bacteria Score	95.9%ile	74.5%ile
Histamine Producers	21.7%ile	28.7%ile

From this weekend update of special studies, we can get a count of bacteria shifts strongly associated to symptoms.

• Old Sample 53
• Latest Sample: 39

Out of curiosity, I compared the symptom associated outliers. We found 3 are matches (of these 39) and one not matches for the taxa reported for each. That is close to the expected percentage of the same strains for people in the same house.

Bacteria Name	Daughter	Son
Negativicutes	Too High	Too High
Acidobacteriia	Too Low	Too Low
Bacteroides eggerthii	Too High	Too Low
Propionibacteriales	Too Low	Too Low

Going Forward

Autism has challenges because of its complex nature. This is compounded by a low number of samples to work from for Citizen Science analysis. The shifts reported from PubMed have a high pattern match with people who do not have autism.

I am going to try building a consensus for each by doing two itemsL

• “Just give Me Suggestions”
• Doing PubMed Autism on [Changing Microbiome]

• [All Bacteria identified by special studies]

The rationale is that the last one identify the bacteria that appears to be symptom causing in many people. We have a very poor match from what we do have a match for. This is not surprising because autism is a very wide spectrum.

We then see six sets of suggestion

Son

When I look at the details we have over 150 items with 6 recommended take (i.e. everyone agrees)

The probiotics that have no known adverse risk for any bacteria is below. The high value is 510.

• 245 Bacillus clausii (Probiotics) – Spor Sun, Enterogemina
• 96 lactobacillus sakei (probiotics) – Nasobiotex, Lanto Sinus

Daughter

When I look at the details we have just 15 items with 6 recommended take (i.e. everyone agrees)

The probiotics that have no known adverse risk for any bacteria are low in computed benefit, so I would ignore them.

• Their values are low: 16/31 out of a high value of 301

Bottom Line

The failure to find significant matching patterns is a bit of a frustration to me. What we did find had very good agree for the son with 150+ items having each of the size suggestion set agreeing for the take. For the daughter, it was not as strong: 15 for 6 sets being in agreement, and 50 with 5 sets being in agreement.

Questions

1. I assume higher anti-inflammatory score is better – Daughter was 25% and Son was 89%
   • Correct (conventional wisdom): Inflammation and Mitochondrial Dysfunction in Autism Spectrum Disorder[2020] “Treatments of ASD children and adults with probiotics, sodium butyrate and butyrate-inducing diets”
2. Deep Sleep with Son – 23% match that he has deep sleep issues is pretty strong?
   • Does not jump out, but indicates that microbiome is playing a role.
3. Son – lot more missed enzymes – what is that do you believe and probiotics help with that?
   • I avoid the word “believe”. A rational assumption is that disruption of enzymes compare to others impacts how the cells (including brain cells) react.
4. Spike in low percentage bacteria – likely long Covid for Son means he has less good bacteria now?
   • I avoid the words “good” or “bad” bacteria. Any bacteria far enough from typical values become bad; disruption to the microbiome and the body. Theses spikes are typically seen (pattern matching) with two conditions: Long COVID and ME/CFS. A common symptom of these two issues are cognitive issues – for example: memory, ability to learn, etc.
5. Histamine – Higher percentage is worse correct?  Daughter was 21% and son was 15% 
   • Lower levels are better, especially since histamines appears to be a factor with autism. See Altered expression of histamine signaling genes in autism spectrum disorder [2017]
6. Higher Butryate percentage is better?  95% daughter / 78% son
   • Correct, Inflammation and Mitochondrial Dysfunction in Autism Spectrum Disorder[2020] “Treatments of ASD children and adults with probiotics, sodium butyrate and butyrate-inducing diets”

The Journey Begins with your microbiome

Thanks for joining me!

This is a companion site to the analysis site at: 

https://microbiomeprescription.com/

The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.

The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.

Open data and Open source are our mottos!

Continue reading “The Journey Begins with your microbiome” →Posted on April 27, 2019 by lassesenEdit

Biomesight #4 Sample: IBS and COVID

We have a varied history with some storms blowing us off courses. Here’s a list of the tests and prior blog posts:

• 2018: Irritable Bowel Syndrome (IBS) diagnosis
• 2021-11-18 – IBS + BioNTech COVID Vaccine -> ME/CFS? [Jan 6,2022]
• 2022-05-20 – Follow up Analysis for ME/CFS (After COVID) [Jun 16,2022]
• 2023-06-22 – Repeat IBS + Vaccine + COVID [Jul 22,2022]
• 2023-09-04

His comments are short:

• I would say some small subjective improvements since last time, but no major changes. Reminder: I have a friendly MD in terms of antibiotics.
• Metronidazole was on top in the last samples, I did it back then.
  • Comment: Metronidazole is no longer at the top but dropped down to 16% of the highest value. It appears to have done its magic in reducing the bacteria pointing to it as a tool..

Base Analysis

When people have multiple samples, I like to do side-by-side comparisons, especially when someone has been doing some of the suggestions suggested. The suggestions are computed and may not always work. Expert Systems and AI are not perfect; they typically do better than a person with only a few years of experience that has training in the discipline (better consistency, remember more facts, etc). How are we doing objectively?

Scores

We see two positive shifts in the latest sample: Increase of Anti inflammatory Bacteria Score and decrease of Histamine Producers.

Percentages of Percentiles

We see a lot of bouncing around between samples. The middle two images matches the typical pattern seen with ME/CFS and Long COVID. Those shifts have faded over the last 3 months with a different pattern appearing indicating a different dialect of gut dysfunction.

Multi-Vector Comparison

The main numbers are below. The take away, less bacteria that are in the high percentile range (at 95%ile, 10 -> 28 -> 23 -> 8). The numbers bounce around with the middle two being similar and the other two also similar. There are no really clear shift in these measures.

Criteria	11/18/2021	5/20/2022	6/22/2023	9/4/2023
Lab Read Quality	8.1	5.5	4.7	7.2
Outside Range from JasonH	6	6	9	9
Outside Range from Medivere	16	16	15	15
Outside Range from Metagenomics	8	8	7	7
Outside Range from MyBioma	5	5	6	6
Outside Range from Nirvana/CosmosId	20	20	23	23
Outside Range from XenoGene	29	29	35	35
Outside Lab Range (+/- 1.96SD)	7	6	17	3
Outside Box-Plot-Whiskers	36	69	54	38
Outside Kaltoft-Moldrup	93	48	47	88
Bacteria Reported By Lab	652	508	542	558
Bacteria Over 99%ile	7	4	6	2
Bacteria Over 95%ile	10	28	23	8
Bacteria Over 90%ile	29	42	36	22
Bacteria Under 10%ile	208	41	50	175
Bacteria Under 5%ile	180	19	8	157
Shannon Diversity Index	1.853	1.826	1.272	1.556
Simpson Diversity Index	0.056	0.038	0.087	0.09
Rarely Seen 1%	2	2	7	1
Rarely Seen 5%	14	5	21	8
Pathogens	41	24	29	36

From Special Studies

The top match was the same on all of the samples, with an increase when there was actually COVID.

Criteria	11/18/2021	5/20/2022	6/22/2023	9/4/2023
COVID19 (Long Hauler)	28%ile	33%ile	41%ile	28%ile
Next one:	15%ile	26%ile	20%ile	13%ile

The “next one” dropping implies some reduction of dysbiosis

Health Analysis

Using Dr. Jason Hawrelak Recommendations, there are many items on the edge of being in range with some items of interest (I strike out those that are unlikely to be of great concern):

• Faecalibacterium prausnitzii at 27% of the microbiome or 96%ile
• Akkermansia — 0.009 % of the microbiome or 35%ile
• Bifidobacterium 0.016 % of the microbiome or 16%ile
• Bacteroides – 27% of microbiome, or 64%ile

Additionally, two indicate increased risk of Candida (new feature just added)

• Phocaeicola dorei at 10% of the microbiome or 91%ile
• Faecalibacterium prausnitzii at 27% of the microbiome or 96%ile

I would suggest a test for candida to be safe. The data suggests a risk. If confirmed, candida would contribute significantly to gut dysbiosis [The interplay between gut bacteria and the yeast Candida albicans[2021]). I did a “back-flip” check of the top prescription items, and all of them reduces Candida (studies cited below).

Addendum – Predicted Symptoms

This was just added to the site today as a further refactor based on New Special Studies on Symptoms data. These are from [My Profile Tab]

Criteria	11/18/2021	5/20/2022	6/22/2023	9/4/2023
Forecast Major Symptoms	Neurological: Cognitive/Sensory Overload 40 % match on 25 taxa DePaul University Fatigue Questionnaire : Racing heart 38 % match on 13 taxa DePaul University Fatigue Questionnaire : Difficulty falling asleep 37 % match on 27 taxa DePaul University Fatigue Questionnaire : Difficulty finding the right word 35 % match on 20 taxa	Autonomic Manifestations: urinary frequency dysfunction 66 % match on 6 taxa Immune Manifestations: Bloating 37 % match on 45 taxa Neurological-Audio: hypersensitivity to noise 35 % match on 28 taxa	None	Neurological-Sleep: Chaotic diurnal sleep rhythms (Erratic Sleep) 50 % match on 18 taxa Neurological: Spatial instability and disorientation 37 % match on 16 taxa

This can be helpful for judging possible severity (and potential improvement of some symptoms), for example: Neurological: Cognitive/Sensory Overload. See [Special Studies] tab.

• 2021 – 40% matches
• 2022- 24% matches
• 6/22/23 – 16% matches
• 9/4/2023 – 4% matches

Going Forward

COVID has had quite an impact on this microbiome. I am going to just go with the “Just Give Me Suggestions” option with the addition of what matched his diagnosis:

• Irritable Bowel Syndrome  (68 %ile) 7 of 68

To explain a bit more. First I click the button below

And then click I could click the consensus report to see what the top items are:

Which are shown below.

In this case, I want to add Irritable Bowel Syndrome suggestions (on the Changing Microbiome Tab)

Instead of the usual 4 packages of suggestions, we have 5

When we look at the consensus report we see the same items there, but the values have increased.

The intent is put a little bias on the numbers towards specific conditions of greatest concern.

PDF Suggestions

I tend to favor the PDF suggestions because it simplifies things for many readers. Also the PDF gives a good list of citations (never complete) used to make the citations to persuade MDs to see that the suggestions are based on studies — a lot of studies.

The PDF suggestions are below (using the consensus view is another option for those more technically orientated). I clip from the PDF to keep the blog simpler for the typical reader.

This is a little longer list than usual, so I went to the consensus report to get priority data. Top value was 618, so 309 is the 50% threshold.

• Top Items:
  • cinnamon (oil. spice): 571
  • green tea: 435
  • Hesperidin (polyphenol): 428
  • omega-3 fatty acids: 395
  • rosmarinus officinalis,rosemary: 395
  • N-Acetyl Cysteine (NAC): 354
• Vitamins:
  • Vitamin B1,thiamine hydrochloride: 390
  • vitamin B7, biotin: 322
  • Vitamin C (ascorbic acid): 305
  • Vitamin B6,pyridoxine hydrochloride: 305
• Probiotics.
  • lactobacillus salivarius (probiotics): 402
  • lactobacillus rhamnosus (probiotics): 339
    • lactobacillus rhamnosus gg (probiotics): -352, thus there is a risk and I would avoid.
  • lactobacillus kefiri (NOT KEFIR): 292 –Note: For sale in Italy

These appear to be of low influence with the exception of l.bulgaricus:

• mutaflor escherichia coli nissle 1917 (probiotics): 1.41 (only in 1 of the 5 suggestions sets)
• lactobacillus bulgaricus (probiotics): 221 (on 4 of the 5 suggestions sets)
• lactobacillus helveticus: 99 (on 2 of 5 suggestions sets)

Minor note: quercetin with resveratrol is an avoid, quercetin is a take. resveratrol by itself is a negative (-113). At times, you need to look at the technical details/consensus to clarify things; the data we are using is incomplete and sparse…. If clearly contradictory suggestions appear, then don’t do them (thing an abundance of caution).

Because he has an antibiotic friendly MD, the following are the TOP antibiotics with notes:

• gentamicin (antibiotic)s: 618, an Aminoglycoside
  • “Since gentamicin has minimal gastrointestinal absorption,…it has applications in several clinical scenarios, such as bacterial septicemia, meningitis, urinary tract infections, gastrointestinal tract (including peritonitis), and soft tissue infection,” NIH StatPearls
  • Antifungal Activity of Gentamicin B1 against Systemic Plant Mycoses [2020], i.e. candida
• trimethoprim (antibiotic)s: 552
  • “The FDA-Approved indications include acute infective exacerbation of chronic bronchitis, otitis media in pediatrics only, travelers diarrhea for treatment and prophylaxis, urinary tract infections, shigellosis, pneumocystis jirovecii, pneumonia/pneumocystis carinii pneumonia (PJP/PCP), and toxoplasmosis, both as prophylaxis and treatment. ” NIH StatPearlls
• atorvastatin (prescription): 459
  • medications decrease cholesterol production in the liver.  NIH StatPearls
  • Antifungal effect of Atorvastatin against Candida species in comparison to Fluconazole and Nystatin [2019]
• ofloxacin (antibiotic)s : 386, sparfloxacin (antibiotic): 381 – these are Quinolones (i.e. fluoroquinolone) many people have issues with using these, see this post for discussion: Update #4 on ME/CFS Person

CFS Antibiotics are also above the threshold. Since the prior sample had a strong Long COVID or ME/CFS Profile, I would be inclined to include one of those below in the antibiotic rotation. The microbiome cannot make a diagnosis of most things, with most ME/CFS microbiomes there is a particular pattern which you had in your last sample but which has disappeared from your current sample which looks more like your first sample. I read this as recovering from ME/CFS….  in likely a fragile state since relapse is very common with ME/CFS.

• azithromycin,(antibiotic)s[CFS]: 349
• amoxicillin (antibiotic)s[CFS]: 327
• Of those below, I would favor: minocycline (antibiotic)s[CFS]: 293

My own experience is that it is better to overcure ME/CFS and when there are signs of recovery…. no backflips of joy or running marathons; keep doing slow walks that becomes a bit further each week for 6-12 months. Your microbiome is fragile and can quickly slip back.

I prefer to use the strategy of going for prescription items that are both suggested from the microbiome and been shown to help with one or more of the diagnosis conditions. This usually encounter low resistance from physicians — they are clueless for the microbiome, but very accepting of published studies. An antibiotic that is used as a prophylaxis usually encounter little resistance.

KEGG Suggestions

This is done by using information from the bacteria found with some fudge factors. I am in discussion with some Ph.D. candidates to build this concept directly from the FASTQ files and will hopefully have this as an added feature next year.

The KEGG probiotics is the usual pattern for ME/CFS and Long COVID with the top one being the usual, with the top reasonably available ones for other families shown below. I usually like to compare the values with those from consensus to minimum risk (i.e. two thumbs up, we do; mixed, we skipped)

• 170.6 Escherichia coli:
  • 47 mutaflor escherichia coli nissle 1917 (probiotics)
• 50 Bifidobacterium bifidum (and the rest lower)
  • -240 bifidobacterium bifidum (probiotics) 
  • Other bifido are positive on both, especially bifidobacterium longum bb536 
• 56 Lactobacillus plantarum subsp. plantarum
  • -132 lactobacillus plantarum (probiotics)
  • Other Lacto are positive, especially lactobacillus salivarius (probiotics)
• 49 Akkermansia muciniphila – not in consensus
• 37 Lactococcus lactis – not in consensus

KEGG Supplements

From the list, we will look only at those with a z-score (statistical significance) over 2. After each we put the consensus value (if it is listed)

• Phenylalanine
• Tryptophan -50
• Cysteine: + 459, N-Acetyl Cysteine (NAC),
• Aspartic acid
• Biotin (Vitamin H): + 392 Vitamin B7, biotin
• Serine
• Tyrosine

Only two items are with high confidence.

How to Proceed Suggestions

The suggestions should be thought as influencers. The human population is often a good analogy or parable for the microbiome population. Each influencer shifts the population in the desired direction. Based on Cecile Jadin’s work and several studies, I am a firm believer in short duration (1-2 weeks) of each influencers. Just as with human influencers, people stop listening if the same person just keeps droning on and on. If a different person starts speaking, you get persuaded more. If a mob start to shout, yet a different human behavior will occur. In terms of the microbiome, “stop listening” means mutations that are resistant to the item will start to increase. Items line vitamins and minerals can be taken continuously; items that are likely to have bacteria resistance developed should be taken for a week and then another item replace it.

The items to rotate:

• Antibiotics listed above
• Probiotics: lactobacillus salivarius and lactobacillus bulgaricus
• Herbs and spices: cinnamon, ginger, black cumin, thyme, rosemary, quercetin (suggests just before each antibiotic with a few days of overlap because it has potential synergistic activity with antibiotics [2020], [2016],[2018] )

Remember our goal is to destabilize a stable microbiome dysfunction.

Questions and Answers

While there has not been significant changes in many of the vectors between this sample and the prior sample from a few months earlier, there has been two significant objective changes:

• Significant improvement of Anti inflammatory Bacteria Score (higher) and Histamine Producers (Lower).
• The lost of the ME/CFS – Long COVID spike in the 0-9%ile

Q: Do you/should I use the colored list now instead of the consensus list?

• Either are fine, the color list (from PDF) is what I tend to use in post because it is easier for new readers to understand (and automatically sent on new uploads). The consensus page is more complex but allows people to apply their own logic and priorities.

Q: “Quercetin (suggests just before each antibiotic with a few days of overlap because it has potential synergistic activity with antibiotics”

• Yes, there are several studies similar to this one. Potential synergistic activity of quercetin with antibiotics against multidrug-resistant clinical strains of Pseudomonas aeruginosa,[2020]

Q: I just did Mutaflor for 8 days and felt really tired all the time (but in the end I also got a flu/cold, so maybe that was the reason and not mutaflor). Nevertheless, if it was a herx reaction, I wonder if I should have taken it for longer until the reaction disappeared? (I stopped it 4 days ago.) Not sure if this question even makes sense.

• My personal choice would be to keep taking it for at least a week (perhaps 2). Remember that the traditional pattern for a herx is feeling bad for X hours and then things get better. The duration of the feeling bad usually decrease from day to day. Catching a cold makes interpretation challenging.

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 can compute items to take, those computations do not provide information on rotations etc.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

In reviewing many papers with the microbiome I noticed that often the researchers restrict their examinations to the taxa that is reported in all samples. I suspect this is due to a lack of sufficient statistical training and/or not understanding the natures of the microbiome.

Recently I came across these papers that uses an approach that I often have used, working off relative frequency of detection a.k.a. prevalence.

• A predictive index for health status using species-level gut microbiome profiling [2020]
• Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles [2023]
• GMWI-webtool: a user-friendly browser application for assessing health through metagenomic gut microbiome profiling [2023]

This post is going to use samples available at Microbiome Prescription Citizen Science site. We are going to restrict to one lab source and divide the data into two groups based on their self-declare symptoms and diagnosis.

• Patients with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) declared [Obs: 271]
• Patients without Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) declared and other status declared (for example: “Asymptomatic” [Obs:569]

Naive First Pass

We are going to take the average count for each group ignoring no values reported. We are going to restrict it to taxa where we have at least 30 non-zero values [1,564 taxa]. We found some 77 taxa with a t-score over 2.81 (p < 0.005)

taxa name	taxa rank	Shift	T_score
Prevotella copri	species	low in ME/CFS	-5.27
Prevotella	genus	low in ME/CFS	-4.52
Sporolactobacillaceae	family	low in ME/CFS	-4.2
Sporolactobacillus putidus	species	low in ME/CFS	-4.19
Sporolactobacillus	genus	low in ME/CFS	-4.19
Prevotellaceae	family	low in ME/CFS	-4.1
Firmicutes	phylum	high in ME/CFS	3.94
Blautia	genus	high in ME/CFS	3.91
Cetobacterium ceti	species	high in ME/CFS	3.89
Cetobacterium	genus	high in ME/CFS	3.84

Deeming Not Reported to be Zero

In this case we have 78 taxa with a t-score over 2.81 with slight changes of t-scores.

taxa name	taxa rank	Shift	T_score
Prevotella copri	species	low in ME/CFS	-5.31
Sporolactobacillaceae	family	low in ME/CFS	-4.63
Sporolactobacillus putidus	species	low in ME/CFS	-4.62
Sporolactobacillus	genus	low in ME/CFS	-4.62
Prevotella	genus	low in ME/CFS	-4.5
Prevotella oulorum	species	low in ME/CFS	-4.35
Prevotellaceae	family	low in ME/CFS	-4.08
Bifidobacterium gallicum	species	low in ME/CFS	-3.97
Firmicutes	phylum	high in ME/CFS	3.94
Blautia	genus	high in ME/CFS	3.91

Prevalence

We followed the same process as above and limited things to a Chi-2 probability of < 0.005 (as used above) We ended up with 65 taxa.

Comparing these two lists, we found only 6 taxa in common

• Bifidobacterium angulatum
• Propionigenium modestum
• Pseudomonas viridiflava
• Cetobacterium ceti
• Cetobacterium
• Propionigenium

The next result is that we have 78+65 – 6 = 137 statistically significant bacteria with p < 0.005.

Bottom Line

There are at least two different statistical ways of determining significance. IMHO, the prevalence approach is likely to be a superior tool for diagnosis purposes because it is possible to compute the probability of a match to the above patterns despite some bacteria not being reported.

The full list of bacteria is listed here.

A reader raised a valid question which actually triggers other related questions.

You seem to like the “percentage of percentiles” measurement, but I’m not convinced it’s being analyzed appropriately. As I understand it, you first convert to percentiles, getting numbers in [0, 100]. I think this is fine. Then you histogram these percentiles. Because each lab will perform the same measurements every time, I think this is also fine. However, the result is compositional data in the sense of Aitchison, and it should be analyzed in a manner consistent with that. For compositional data, a chi^2 test is inappropriate because it relies on the number of species (or genera) measured.

My suggestion is to apply a centered logratio transform to each person’s percentages and fit a normal distribution to the transformed data. To determine whether someone’s microbiome deviates significantly, calculate a multivariate normal tail probability. Beware that the covariance matrix will be rank deficient (you’re in a ten-dimensional space, but there are only nine parameters because percentages sum to 100). You may want a robust fit because it’s reasonable to expect that the microbiome of someone ill might be an outlier.

For more information about compositional data, see Aitchison, J., “The Statistical Analysis of Compositional Data,” Journal of the Royal Statistical Society. Series B (Methodological) Vol. 44, No. 2 (1982), pp. 139-177; Aitchison, J., “The Statistical Analysis of Compositional Data,” Chapman & Hall, London, 1986; and Aitchison, J. “A Concise Guide to Compositional Data Analysis,” unpublished manuscript, 2005, available online (just Google). For other approaches to compositional data analysis, see Greenacre, Michael; Grunsky, Eric; Bacon-Shone, John; Erb, Ionas; Quinn, Thomas, “Aitchison’s Compositional Data Analysis 40 Years On: A Reappraisal,” arXiv:2201.05197, 13 Jan 2022, to appear in Statistical Science.

What is the statistical basis for other Diversity Indices?

How to calculate these numbers is well determined — they seem to be brilliant ideas tossed out there that seems to fit the data for some study. For some background, see this page. The problem is a lack of rigor, especially statistical rigor.

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. 

Conceptual and statistical problems associated with the use of diversity indices in ecology [2009]

The problem is an absence of a native statistical model. For example, it does not fit the usual ones.

• Normal distribution – “The distribution that shall rule them all” because that is what is always assumed and what is usually taught outside of mathematics department (who knows better)
• Continuous uniform distribution
• Poisson distribution
• Erlang distribution – which I used professionally for many years’
• Cauchy distribution
• For even more choices, see this list

The key question is simple, what is the distribution underlying diversity Indices? We read ” In the literature of biodiversity, according to Ricotta (2005), there are a “jungle” of biological measures of diversity.”[2017]. Zheng’s A new diversity estimator[2017] in Journal of Statistical Distributions and Applications where he states “There are many other open problems built on this connection between birthday problem and diversity measures. ” The problem is this, the birthday problems deals with 366 discrete well defined boxes that are well defined. Dealing with the microbiome, we lack these boxes. Consider a measure of a microbiome sample in 2000, there are a large number of different bacteria species in Lactobacillus. Today, we have these species no longer placed in 1 genus, but 25 genus [2020] including:

• Acetilactobacillus,
• Agrilactobacillus,
• Amylolactobacillus,
• Apilactobacillus,
• Bombilactobacillus,
• Companilactobacillus,
• Dellaglioa,
• Fructilactobacillus,
• Furfurilactobacillus,
• Holzapfelia,
• Lacticaseibacillus,
• Lactiplantibacillus,
• Lapidilactobacillus,
• Latilactobacillus,
• Lentilactobacillus,
• Levilactobacillus,
• Ligilactobacillus,
• Limosilactobacillus,
• Liquorilactobacillus,
• Loigolactobacilus,
• Paucilactobacillus,
• Schleiferilactobacillus,  
• Secundilactobacillus.

With the same strains/species, our diversity indices will be very different because our boxes are arbitrary and “soft” unlike the days of the year or the roll of a dice.

Back to percentage of percentiles

While I show genus and species in the table for ease of understanding of the typical reader, I originally did it solely with the lowest identifiable levels (the “atoms” or the microbiome) – species. At the species level, it is not compositional. There is no composition! Looking at the data that was actually received, I noticed many genus had no species listed. In some cases, the genus had species, but none of the known ones were detected. In other cases, the test did not report any species in over 3000 test results.

On this basis I decided to use try using both species and genus. I soon discovered that they almost always exhibit a similar pattern and chi^2. At this point, I opted for benefiting my readers and not as much rigor as some would like. We could do the lowest taxonomical level reporting across the hierarchy as one solution.

This approach ends up with us side-stepping the classification issues cited above. We are dealing with distinctive, non-overlapping events (a bacteria being identified) and then convert them to percentile giving use a continuous uniform distribution for each of these independent events. IMHO, at this point we have a good model to chi^2 test. We are not dealing with measuring a population, just a sample.

In answer to “a chi^2 test is inappropriate because it relies on the number of species (or genera) measured.” is missing the point. If I get two bags of coins from the bank and then flip them to determine if they are biased — whether the bags contains 1000 or 100,000 coins is significant only on the ability to determine the margin of error. The number of species/genus is only significant in that sense. If there is a strong bias with a small number, then having more will not change the bias.

For samples coming from uBiome, Ombre/Thryve and Biomesight there are two important numbers reported. [Count] and [Count_Norm].

• [Count] is the number coming from the lab equipment, the bacteria detected.
• [Count_Norm] is the above number scaled to be out of one million (1,000,000)

The question arises, if you have low lab quality and the number of bad bacteria also dropped: Is this an actual improvement or a false improvement due to low lab quality?

To partially answer that question, I pulled biomesight samples (biggest collection) and plotted the data. Shown below:

• Vertical axis is number of bacteria reported
• Horizontal axis is lab quality measure

As is shown, there is a relationship.

Using this data and sample values of 4.3 and 8.4, we compute 546 and 643 for expected bacteria (just use the formula on the chart). This means that with 4.3 we expect only 85% (546/643 * 100) of the count seen with 8.4.

So we see the changes below are likely true improvements: (Left is 4.3, right is 8.4)

For bacteria reported by lab, the numbers suggests the left column has less odd bacteria and the gut microbiome may becoming more uniform.

For anyone interested in doing their own charting and analysis, the raw data is https://citizenscience.microbiomeprescription.com/

Today I was asked if the numbers shown on various sights like the one below, ” if this result is correlated with the severity of the patient?”. The answer is No. The usual reason that such numbers appear on sites is to satisfy customers asking for such numbers. Simple, easy to understand number.

Businesses want to make customers happy — so they literally cook-up a number to show on their reports. There is no research supporting any of the magical numbers that I have seen. Some one put together some numeric formula to generate the numbers.

For Biomesight, the logic is shown on the page with the right display

If you sum up the values for each dial, you get the total. When you view the percentage, it is not so obvious: 100%, 69%, 88%, 85% looks like a complex formula is being used.

Analogy: Give me a Health Measure for a Person

The microbiome is very much like a person. How would you create a single value for a person?

• Probiotics –> Income
• Commensals –> Savings
• Pathobionts –> Debts
• Diversity –> Health?
  • Married and marriage status
  • Chronic Conditions
  • Health Status
  • Height
  • Weight
  • Gender
  • etc

Would a person with no debt, good savings, a low income and married with 5 kids be a higher or lower measure than someone with moderate debt, high income, small savings and no relationships?

Are there any studies?

What about the ratios used in the literature?

• Bacteroides/Bifidobacterium Ratio
• Bacteroides/Clostridium Ratio
• Blautia/Bacteroides Ratio
• Firmicutes/Bacteroidetes Ratio 
• Prevotella/Bacteroides Ratio

Well, the sample that I am looking at have 43%ile, 98%ile, 7.2%ile, 58%ile and 19%ile. The numbers are all over the place!

We also have two studies:

• A predictive index for health status using species-level gut microbiome profiling [2020]
• Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles [2023] using 8000+ samples

Their latest study states: ” accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased)” So 1 in five times, they will misclassify a healthy person as having a disease.

An example of the algorithm being used is below.

This is just trying to determine if the person is healthy or not — nothing about severity.

My approach is simple, I simply flagged the bacteria deemed to be unhealthy. Just list them.

How to use it:

Back Story

Latest microbiome results are in. Seems like my microbiome is stubborn and stuck these last few tests. Wondering if I should just use this test suggestions exclusively or combine with my prior Thorne test 

We have two sample – one via BiomeSight and one via Thorne. This post is going to do two things:

• Look at Suggestions – by combining both sets of suggestions using the Uber Consensus
• Look at the differences between the reports.

We also review “which is better”. My focus is clinical application to individuals — not research papers; answer at bottom.

Uber Consensus

The process has become very simple — “Just give me Suggestions!” on both samples and then going to uber consensus as illustrated by the video below.

The result was excellent agreement on suggestion between each set of results. The CSV files are attached below.

Differences between Reports

I compared two things between the reports:

• Percentage of the bacteria in the microbiome
• Percentile of the bacteria in the microbiome

At the Phylum Level

Items less than 100 should be ignored (accuracy of measurement limits). There are a few dramatic differences.

Bacteria Name	Thorne Count	BiomeSight Count
Firmicutes	396799	529540
Actinobacteria	60610	2100
Bacteroidetes	461289	448230
Proteobacteria	6095	18150
Chlorobi	36	429
Acidobacteria	35	100
Cyanobacteria	83	20
Spirochaetes	85	30
Verrucomicrobia	59	10
Chloroflexi	77	50
Tenericutes	54	30
Deinococcus-Thermus	48	30
Fibrobacteres	4	10
Synergistetes	17	20

Looking at Percentiles next

Bacteria Name	Thorne %ile	BiomeSight %ile
Chlorobi	25	84
Actinobacteria	85	33
Acidobacteria	34	81
Spirochaetes	81	36
Cyanobacteria	31	1
Deinococcus-Thermus	55	29
Firmicutes	14	37
Chloroflexi	67	50
Verrucomicrobia	14	1
Tenericutes	13	2
Proteobacteria	10	18
Synergistetes	6	4
Bacteroidetes	55	56
Fibrobacteres	1	0

We have Bacteroidetes in agreement with both — but for the rest…

At the genus level

Bacteria Name	Thorne Count	BiomeSight Count
Bacteroides	180054	397640
Blautia	16470	107220
Roseburia	16793	73640
Faecalibacterium	109196	152890
Corynebacterium	43413	820
Ruminococcus	9177	44170
Phocaeicola	223209	199669
Parabacteroides	11855	31940
Phascolarctobacterium	6101	23980
Dorea	36	13000
Sutterella	16	11339
Oscillospira	0	8250
Coprococcus	6120	12589
Eggerthella	6491	760
Pseudobutyrivibrio	149	5790
Lachnospira	11593	6230
Prevotella	954	4260
Anaerostipes	9303	6310
Clostridium	2039	4960
Pedobacter	46	2410
Odoribacter	4077	2060
Bifidobacterium	2783	1019
Escherichia	75	1610
Porphyromonas	1372	150
Mediterraneibacter	14831	13629
Bilophila	6	1110
Veillonella	75	1160
Desulfovibrio	1900	1250
Streptococcus	1477	840
Acetivibrio	33	470
Chlorobaculum	6	429
Finegoldia	1339	920
Gemella	17	400
Enterococcus	585	220
Paenibacillus	376	20
Mogibacterium	39	370
Acetobacterium	15	340
Serratia	47	350
Eubacterium	517	240
Megasphaera	35	290
Selenomonas	52	290
Bacillus	248	10
Caldicellulosiruptor	11	240
Campylobacter	235	10
Slackia	16	240
Sphingobacterium	48	270
Caloramator	10	190
Staphylococcus	181	10
Hathewaya	8	170
Peptoniphilus	656	800
Peptostreptococcus	6	150
Microbacterium	125	10
Adlercreutzia	525	620
Rhodothermus	6	90
Erysipelothrix	12	90
Acidaminococcus	12	90
Hymenobacter	80	10
Negativicoccus	115	50
Collinsella	74	10
Rhodococcus	67	10
Dialister	25	80
Anaerococcus	336	390
Pseudoclostridium	8	60
Moorella	9	60
Vibrio	60	10
Caldilinea	1	50
Brochothrix	2	50
Mycobacterium	67	20
Neisseria	57	10
Pectinatus	7	50
Thermoclostridium	16	50
Alkaliphilus	9	40
Shewanella	31	60
Lactobacillus	57	30
Leptospira	4	30
Deinococcus	35	10
Tetragenococcus	5	30
Ethanoligenens	34	10
Weissella	10	30
Gulosibacter	1	20
Pseudoclavibacter	2	20
Kocuria	28	10
Meiothermus	2	20
Stenotrophomonas	28	10
Symbiobacterium	3	20
Devosia	4	20
Dysgonomonas	34	20
Azoarcus	21	10
Leuconostoc	9	20
Glaciecola	1	10
Turicibacter	21	30
Pelotomaculum	1	10
Parascardovia	2	10
Lentibacillus	2	10
Actinopolyspora	2	10
Kitasatospora	2	10
MLOs	3	10
Ochrobactrum	3	10
Rickettsia	3	10
Luteibacter	3	10
Fibrobacter	4	10
Pediococcus	14	20
Halanaerobium	6	10
Dyadobacter	14	10
Mycoplasma	17	20
Thauera	9	10
Lysobacter	11	10

Looking at the percentile rankings — the absolute numbers may vary greatly, but what about relative percentiles?

Bacteria Name	Thorne %ile	Biomesight %ile
Ochrobactrum	2	2
Actinopolyspora	1	1
Halanaerobium	1	1
MLOs	1	1
Glaciecola	1	1
Lentibacillus	1	1
Pelotomaculum	1	1
Parascardovia	1	1
Luteibacter	1	1
Phocaeicola	89	89
Rickettsia	1	0
Pediococcus	10	9
Fibrobacter	2	0
Mycoplasma	5	3
Alkaliphilus	1	3
Finegoldia	85	88
Kitasatospora	3	0
Thauera	5	1
Streptococcus	55	50
Turicibacter	12	17
Peptoniphilus	64	58
Hathewaya	1	8
Clostridium	18	11
Desulfovibrio	61	69
Eubacterium	38	46
Symbiobacterium	1	9
Enterococcus	88	79
Sphingobacterium	13	23
Pseudoclavibacter	1	11
Anaerococcus	72	83
Eggerthella	98	86
Gulosibacter	0	12
Lactobacillus	23	11
Bifidobacterium	55	43
Leuconostoc	2	14
Shewanella	35	47
Prevotella	50	63
Corynebacterium	99	86
Collinsella	13	0
Oscillospira	0	16
Faecalibacterium	49	65
Meiothermus	1	17
Caloramator	1	19
Coprococcus	39	57
Lysobacter	18	0
Odoribacter	81	63
Adlercreutzia	63	81
Pedobacter	13	31
Dyadobacter	20	1
Dysgonomonas	24	4
Mediterraneibacter	69	90
Devosia	1	22
Acetivibrio	5	27
Thermoclostridium	9	32
Ethanoligenens	25	1
Dialister	11	35
Veillonella	16	41
Pectinatus	1	27
Porphyromonas	88	62
Moorella	1	28
Negativicoccus	66	39
Lachnospira	51	21
Rhodothermus	1	32
Tetragenococcus	1	32
Acetobacterium	3	34
Anaerostipes	65	96
Bilophila	1	33
Ruminococcus	14	47
Weissella	2	35
Parabacteroides	42	75
Acidaminococcus	4	39
Pseudoclostridium	1	37
Leptospira	1	42
Serratia	34	75
Slackia	4	45
Phascolarctobacterium	56	97
Erysipelothrix	4	46
Sutterella	1	46
Bacteroides	39	87
Roseburia	43	91
Escherichia	28	77
Selenomonas	21	73
Deinococcus	54	1
Megasphaera	18	72
Brochothrix	1	56
Kocuria	58	2
Mogibacterium	17	74
Stenotrophomonas	63	3
Azoarcus	61	0
Caldilinea	0	61
Caldicellulosiruptor	2	64
Mycobacterium	87	24
Hymenobacter	68	1
Blautia	5	73
Paenibacillus	87	19
Neisseria	69	0
Pseudobutyrivibrio	25	95
Campylobacter	75	1
Gemella	4	82
Peptostreptococcus	1	81
Chlorobaculum	1	84
Staphylococcus	85	0
Vibrio	91	2
Bacillus	92	1
Rhodococcus	91	0
Dorea	1	93
Microbacterium	94	1

Bottom Line

I have never had much belief in the absolute accuracy of the bacteria named or the count. Why? Simple, I understand the statistical process being used and its weakness. I will leave arguments over “which is better” and “which is accurate” to others.

My focus and concern is to improve the microbiome. With sparse data and the great complexity involved, I am actually very pleased that the suggestions are in agreement. The suggestions are computed using fuzzy logic expert systems. The noise in the data and the statistical processes involved seem to be smoothed out by this Artificial Intelligence engine approach.

Score: Labs: -2, Microbiome Prescription 2

Which is better?. My focus is clinical application to individuals, both give similar suggestions using the Fuzzy Logic Expert System. There is no difference in that sense.

I have been doing periodic review of this person’s sample. He just got his latest results and it was a shocker (of the wrong type!). This is worth a review.

Comparing Samples Overtime

At the typical analysis level there has been no change in these broad criteria since the last sample:

• Outside Range from JasonH
• Outside Range from Medivere
• Outside Range from Metagenomics
• Outside Range from MyBioma
• Outside Range from Nirvana/CosmosId
• Outside Range from XenoGene

Why compare over 90%ile to under 10%ile? The reason is probability – we are converting the data to a uniform distribution for all of the bacteria. This allows for reliable statistical significance to be determined for all of the bacteria. If things are “normal” then the ratio should be 1.0 The further from 1, the more abnormal. This is independent of any assumptions on bacteria distributions.

Looking at over 90% and under 10%, our expected count are 64 for both (10% of 639)

• We have 20 over 90%ile, so we have under representation of dominant
• We have 273 under 10%ile, the typical over representation of low levels of many bacteria seenwith most ME/CFS people
• The ratios is higher at 13.7 compare to prior ratios (8.2, 11,3,3,6.5)

Where we see differences

• Outside Kaltoft-Moldrup count returned to the size of the very first sample.
• Compounds over 90%ile to under 10%ile (which should be 1.0 theoretically) had been close to 1.0 on the prior 3 samples, jumped up to 8.8:1. This was not as bad as the first sample with a 12.9:1 ration.
• Enzymes over 90%ile to under 10%ile, continue to be bias towards low with a 2.7:1 ratio (prior 3.5, 1.7, 2.9, 3.2)
• Conditions: jumped from none over 90%ile to 13!

My general impression is that ground has been lost. This is the first time that subsequent results appear to be worse. WHAT HAPPENED!???!!???!!!

Criteria	2/22/2022	8/11/2022	3/25/2022	12/3/2021	8/31/2021
Lab Read Quality	9.7	5.5	6.2	3.6	7.8
Bacteria Reported By Lab	639	461	593	445	551
Bacteria Over 99%ile	4	3	3	5	15
Bacteria Over 95%ile	11	13	11	24	23
Bacteria Over 90%ile	20	23	21	40	35
Bacteria Under 10%ile	273	189	237	123	227
Bacteria Under 5%ile	219	107	143	66	192
Bacteria Under 1%ile	175	23	44	9	167
Lab: BiomeSight
Rarely Seen 1%	6	7	14	2	3
Rarely Seen 5%	22	14	33	7	9
Pathogens	37	32	46	31	38
Outside Range from JasonH	7	7	4	4	6
Outside Range from Medivere	15	15	15	15	19
Outside Range from Metagenomics	8	8	6	6	7
Outside Range from MyBioma	7	7	7	7	8
Outside Range from Nirvana/CosmosId	23	23	18	18	21
Outside Range from XenoGene	32	32	36	36	39
Outside Lab Range (+/- 1.96SD)	7	8	6	9	14
Outside Box-Plot-Whiskers	38	33	38	58	41
Outside Kaltoft-Moldrup	210	111	123	100	211
Condition Est. Over 99%ile	5	0	0	0	7
Condition Est. Over 95%ile	9	0	0	0	15
Condition Est. Over 90%ile	13	0	0	0	29
Enzymes Over 99%ile	35	10	30	19	72
Enzymes Over 95%ile	100	68	219	82	162
Enzymes Over 90%ile	191	183	296	126	192
Enzymes Under 10%ile	520	645	514	369	616
Enzymes Under 5%ile	375	423	264	186	450
Enzymes Under 1%ile	219	86	49	37	272
Compounds Over 99%ile	23	47	62	28	44
Compounds Over 95%ile	72	254	231	127	86
Compounds Over 90%ile	126	338	298	307	98
Compounds Under 10%ile	1104	308	297	227	1265
Compounds Under 5%ile	1068	173	224	111	1241
Compounds Under 1%ile	1045	65	67	47	1206

What Happened?

This person sent the following notes

• I’ve got COVID in October, I feel as I have fully recovered.
• I have a little bit more energy than before.
• My body feels extremely stressed, I have started to get a pressure over the neck / thyroid when I get totally stressed out.
• My sleep is much better. I have been following Andrew Hubermans protocol for sleep, which had a great impact on me. 
• Would be great if I could get some recommendations for food, supplements, antibiotics etc.

Ah, the person feels like he has recovered but his microbiome is still recovering. We have a clean explanation for the regression! Our goal is now to try to stop Long COVID from setting in.

The Conditions matches include: hypercholesterolemia (High Cholesterol), Hyperlipidemia (High Blood Fats), Hypertension (High Blood Pressure, Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic and Atherosclerosis. None of those are concerning –they were not matches last time and thus should be viewed as transient red herrings. Looking at PUBMED Long COVID explicitly, we see good news: Long COVID   (29 %ile) 37 of 212. However when we go over to Special Studies, the very top one is

•  41 % match COVID19 (Long Hauler), the next match was 21% –this really sticks out!
  • All Prior samples also had Long Hauler at the top too.

Going Forward

To build our consensus, we will do the usual and toss in our top Special Studies one.

•  41 % match COVID19 (Long Hauler) – 77 bacteria
•  Standard Lab Ranges (+/- 2 Std Dev) – 7 bacteria
•  Box Plot Whisker – 25 bacteria
•  Kaltoft-Moltrup Normal Ranges – 104 bacteria

Remember that we have a massive over representation of low %ile bacteria — hence some selection methods produce must larger numbers.

The suggestions downloads are below.

The top items are two similar probiotic mixtures from studies:

• lactobacillus rhamnosus gg,lactobacillus,rhamnosus,propionibacterium freudenreichii,bif (probiotics)
• lactobacillus rhamnosus gg,lactobacillus,rhamnosus,propionibacterium freudenreichii,bifidobacterium breve (probiotics)
• lactobacillus gasseri (probiotics)
• Turmeric

Looking at probiotics and the components above — bifidobacterium breve (probiotic) and propionibacterium freudenreichii appear to be the best of the components. The last one is used to make Emmental and Jarlsberg cheese or is available as a probiotic: Nutricology/Securil.

• Escherichia coli is the top KEGG suggestions (and symbioflor 2 e.coli probiotics, mutaflor escherichia coli nissle 1917 (probiotics) and colinfant e.coli probiotics) are on the consensus to-take list
• Bacillus subtilis /natto is also on the KEGG suggestions and also on the consensus to-take list
• Bifidobacterium breve is similar on both lists
• lactobacillus casei (probiotics) is also on both lists
• lactobacillus rhamnosus (probiotics) has contradictor results.

Given the general hostility between lactobacillus and E.Coli, plus the risk or lactic acid issue with lactobacillus, I would suggest avoiding lactobacillus casei initially, perhaps try it in a later cycle. A similar contradiction happened with different forms of cranberry as shown below. We want absolutely clean positive choices.

I reran the suggestions with everything — in case any prescription items may be of special interest.

The top antibiotics included:

• rifaximin (antibiotic)s “Xifaxin has been used with success to rebalance gut flora and relieve gut symptoms in chronic fatigue syndrome, IBS, inflammatory bowel disorder and others.” [Src]
• amoxicillin (antibiotic)s (cause a prior ME/CFS review to go into remission for 5 months see Sudden Illness Triggered ME/CFS)
• metronidazole (antibiotic)s improved 75% of me/cfs patients, see Metronidazole – Dosages Review [2017]
• ofloxacin (antibiotic)s – a quinolone antibiotics, this class. NOT RECOMMENDED — while used with some success [ME Association], this class of antibiotics is known to cause some persistent/permanent side effects

As always, I prefer the Cecile Jadin approach of taking a single course, take a break and then take a different antibiotics.

Looking at Foods

The top food suggested was Protein powder, whey based, protein >70%, unfortified (ignore the unfortified – go for the standard used by ME/CFS Physicians: Undenatured Whey). This is a recommended with good results by many ME/CFS physicians like Teitelbaum and Dr. Paul Cheney. The second choice was duck liver which is not easy to obtain in many places (with other liver below). #3 items was Rye, whole grain flour, and near the top of the list: Confectionery, peanut, chocolate-coated which I chuckled over: See my 2012 post, Honestly! Chocolate!!! (Yes 11 years ago!) and 2013 post, Peanut Butter – a complex food?

I personally have always love Liver pâté! There was something that always felt so good after eating. Liverwurst is essentially the same food.

So for a Scandinavian, this is almost going to a church social!! Liver pâté on Dark 100% Rye bread with Jarlsberg cheese also on Dark 100% Rye bread!

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 can compute items to take, those computations do not provide information on rotations etc.

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.

Foreword

This post started out with a title of “Post-Acute COVID-19 Syndrome vs Myalgic Encephalomyelitis – Similarities and Differences“. It scope was pretty obvious — compare microbiome shifts from these two sibling conditions. Expectations was a bland informational review.

The result was calling into question the typical assumption that we could isolate symptoms and conditions to specific bacteria. I follow the statistics and discovered that you can get a magnitude better statistical significance by moving beyond bacteria. In coming weeks, I hope to code up suggestions AI based on this enlightenment.

My starting point

In my last post, Long COVID – an update, I did a comparison between the citizen science data and the literature published on the US Library of Medicine. In terms of symptoms, these two appear the same — but at the microbiome (and enzyme levels) how similar are they?

• Post-Acute COVID-19 Syndrome (PCAS), also known as Long COVID
• Myalgic Encephalomyelitis (ME), also known as Chronic Fatigue Syndrome (CFS)

One frustrating aspect of many studies on the US Library of Medicine for many conditions is simple: results are not replicated in subsequent studies for the same condition. Historically I have viewed this as a result of different equipment and different reference libraries. In many cases the bacteria deemed significant are often different and when they did report the same bacteria, they report opposite shifts!

This post explores some of these issues, and came to an interesting conclusion.

Study Caveats

The studies on the US Library of Medicine compare people with the condition to healthy controls. With the citizen science data that is almost impossible to do. If a person has gotten a microbiome test, they likely have some condition(s) and thus are not healthy controls!!

This is not all bad. It means that when we find things that are statistically significant they are differentiators against other people with microbiome issues. That is, how are people with ME different than people with FM and IBS. Conceptually, we are more likely to identify the key features for these conditions and not key features for auto-immune conditions or a gut disturbance in general. It is a nuisance difference, but may be a very important nuisance.

Comparison that we will review are from:

• US Pubmed — bacteria reported by both with direction
  • List for ME
  • List for PACS
• KEGG Enzymes shifts from Citizen Science (using only Biomesight data)
• Bacteria shifts from Citizen Science (using only Biomesight data)

For citizen science we may have many uploaded samples annotated both with PCAS and ME. To resolve this conflict, ME will contain only samples with ME and without PCAS. Both ME and PCAS have many, many comorbid symptoms which may also come into play. Many of the pure ME samples are before COVID swept the world, hence relatively clean. PCAS are more recent samples.

For PACS citizen science data, we have only significance difference identified from Biomesight data, hence we will compare those only.

Scope	ME	PACS	Same
US National Library of Medicine	68	233	25
Enzymes – Citizen Science with p < 0.001	228	199	31
Bacteria – Citizen Science	109	36	0

I must admit that finding no bacteria in common with the same lab and the same reference library was a little bit of a surprise. One explanation is that microbiome dysfunctions evolve over time. People with PACS have had it less then 3 years, likely an average of just 1 year. People with ME has had it often for 30+ years. Comparing the two may be similar to comparing a one bottle of grape juice to a bottle of vintage wine.

Details for Common Bacteria from US National Library of Medicine

In the table below: H indicates High, L indicates Low.

Note that Bacteroides are reported high and low in different studies, suggesting there are subsets of each condition

tax_rank	tax_Name	Direction
class	Bacteroidia	H
family	Bacteroidaceae	H
family	Clostridiaceae	H
family	Lachnospiraceae	L
genus	Anaerostipes	L
genus	Bacteroides	H
genus	Bacteroides	L
genus	Bifidobacterium	L
genus	Coprobacillus	H
genus	Coprococcus	L
genus	Dorea	L
genus	Eggerthella	H
genus	Enterococcus	H
genus	Faecalibacterium	L
genus	Lactobacillus	L
genus	Streptococcus	H
genus	Turicibacter	H
order	Eubacteriales	L
phylum	Bacteroidetes	H
phylum	Firmicutes	L
phylum	Fusobacteria	H
species	Anaerobutyricum hallii	L
species	Enterocloster bolteae	H
species	Faecalibacterium prausnitzii	L
species	Ruminococcus gnavus	H

Details for Shared Enzymes with p < 0.001

In recent posts for conditions comorbid with ME, PACS, I found that enzyme analysis had greater statistical significance than bacteria. All of these posts reported higher enzyme levels were significant with these conditions.

The result for items shared that had p < 0.001 was almost overwhelming!

ECKey	EnzymeName
1.1.1.292	1,5-anhydro-D-mannitol:NADP+ oxidoreductase
1.12.98.4	H2:polysulfide oxidoreductase
1.7.2.2	ammonia:ferricytochrome-c oxidoreductase
1.8.7.3	CoB,CoM:ferredoxin oxidoreductase
1.8.98.4	CoB,CoM,ferredoxin:coenzyme F420 oxidoreductase
1.8.98.5	CoB,CoM,ferredoxin:H2 oxidoreductase
1.8.98.6	coenzyme B,coenzyme M,ferredoxin:formate oxidoreductase
2.3.1.201	acetyl-CoA:UDP-2-acetamido-3-amino-2,3-dideoxy-alpha-D-glucuronate N-acetyltransferase
2.7.1.227	1-phosphatidyl-1D-myo-inositol:a very-long-chain (2’R)-2′-hydroxy-phytoceramide phosphoinositoltransferase
2.7.8.12	CDP-glycerol:4-O-[(2R)-glycerophospho]-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol glycerophosphotransferase
2.7.8.36	UDP-N,N’-diacetylbacillosamine:tritrans,heptacis-undecaprenyl-phosphate N,N’-diacetylbacillosamine transferase
3.1.1.114	methyl acetate acetohydrolase
3.1.3.27	phosphatidylglycerophosphate phosphohydrolase
3.1.6.6	choline-sulfate sulfohydrolase
3.1.6.8	cerebroside-3-sulfate 3-sulfohydrolase
3.10.1.1	N-sulfo-D-glucosamine sulfohydrolase
3.2.1.11	6-alpha-D-glucan 6-glucanohydrolase
3.2.1.152	mannosylglycoprotein endo-beta-mannosidase
3.2.1.197	beta-1,2-D-mannoside mannohydrolase
3.2.1.24	alpha-D-mannoside mannohydrolase
3.4.21.26	prolyl oligopeptidase
4.1.99.1	L-tryptophan indole-lyase (deaminating; pyruvate-forming)
4.2.2.20	chondroitin-sulfate-ABC endolyase
4.2.2.21	chondroitin-sulfate-ABC exolyase
4.2.2.3	alginate beta-D-mannuronate—uronate lyase
4.2.2.8	heparin-sulfate lyase
4.3.1.7	ethanolamine ammonia-lyase (acetaldehyde-forming)
5.1.1.20	L-alanyl-D-glutamate epimerase
5.1.3.11	cellobiose 2-epimerase
5.3.1.22	hydroxypyruvate aldose-ketose-isomerase
6.1.1.13	D-alanine:poly(phosphoribitol) ligase (AMP-forming)

One of them caught my eye, heparin-sulfate lyase, because micro-clots and “thick blood” are associated with these conditions with good results reported from the use of heparin for some patients.

 HSGAGs are widely distributed on the cell surface and extracellular cell matrix of virtually every mammalian cell type and play critical role in regulating numerous functions of blood vessel wall, blood coagulation, inflammation response and cell differentiation.

Microbial heparin/heparan sulphate lyases: potential and applications [2012]

Bacteria – Citizen Science

This blew me away — we have over 150 people with PCAS and over 250 with ME giving us superior sample sizes. We have 145 bacteria deemed significant for one or the other. We had NONE that was in common. This gut punch gives two main options: denial and look for an excuse to disregard. or roll with the punch and roll with enzymes.

Bottom Line

The enzyme aspect is the strongest association between PACS and ME. The count is higher, but more important, we are dealing with p < 0.001 data making false positives unlikely. This leads to a model that there is no ‘magical list of bacteria involved’ but a diverse array of bacteria that may be different for each person — but united in the over production of enzymes. This shifts the microscope of research into a different light spectrum. This is very interesting and may require some new brain cells to be used.

Using this information to improve..

If Enzymes estimate trumps bacteria levels (in a statistical sense), then we need to look at the enzyme levels and deduce for each one of concern, which collection of bacteria needs reduction — even when they are in the normal range. It is the aggregation of bacteria where the issue arises, not individual bacteria or specific subsets of bacteria.

A few examples may illustrate things a little

Example: (S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming) a.k.a. EC 4.1.3.4, which was the most significant for PACS in the post: Long COVID – an update. There are some 2000+ taxon associated with it. We look at the averages for these below.

Rank	Tax_Name	With PACS	Without PACS	TScore	DF
species	Faecalibacterium prausnitzii	138151	109604	3.796775	667
species	Pseudomonas viridiflava	53	25	2.628108	32
species	Comamonas kerstersii	125	40	2.600388	54
species	Pseudomonas aeruginosa	62	31	1.824644	43
species	Emticicia oligotrophica	2303	967	1.727619	455
species	Denitratisoma oestradiolicum	42	24	1.650657	22
species	Granulicella tundricola	29	21	1.61225	48
species	Bacillus subtilis	40	19	1.374431	17
species	Niabella soli	31	24	0.963943	16
species	Ralstonia insidiosa	53	38	0.914874	36
species	Oligella ureolytica	51	32	0.89768	19
species	Glaciecola nitratireducens	27	24	0.67899	66
species	Bacillus halotolerans	32	28	0.371219	58
species	Acidaminococcus intestini	749	624	0.357674	146
species	Acinetobacter guillouiae	67	63	0.098956	18

For another one, we see the pattern stronger. Below we see the difference of Faecalibacterium prausnitzii is around 30,000 units. Looking at the other contributors, we see an additional 40,000 units. These extra units doubles the shift (and thus significance) of the enzyme above that of a single bacteria. Some of these are deemed healthy usually, for example: Akkermansia muciniphila which was at the 78%ile for Without PACS and 83%ile with PCAS. Neither would be deem to be outliers.

Rank	Taxon Name	With PACS	Without PACS	t-score	DF
species	Faecalibacterium prausnitzii	138151	109604	3.796775	667
species	Sutterella wadsworthensis	9626	6772	2.380718	452
species	Aliarcobacter skirrowii	3756	21	2.223602	17
species	Akkermansia muciniphila	19096	12290	1.896922	547
species	Desulfovibrio desulfuricans	1423	469	1.769772	32
species	Emticicia oligotrophica	2303	967	1.727619	455
species	Enterococcus casseliflavus	1965	81	1.635666	38
species	Porphyromonas asaccharolytica	1350	254	1.59988	186
species	Bacteroides fragilis	8080	5595	1.523991	489
species	Bifidobacterium dentium	1454	461	1.433823	239
species	Phocaeicola dorei	35482	29075	1.396731	649
species	Corynebacterium aurimucosum	1105	407	1.275234	96
species	Bacteroides eggerthii	14379	10345	1.108857	263
species	Corynebacterium jeikeium	1897	723	0.858282	70
species	Phocaeicola coprophilus	6496	3642	0.856783	152
species	Desulfovibrio piger	2032	1534	0.848976	141
species	Megamonas funiformis	1677	1130	0.620283	90
species	Hathewaya histolytica	2890	2729	0.467066	660
species	Haemophilus parainfluenzae	1343	1250	0.282656	500
species	Mesoplasma entomophilum	1182	1069	0.230055	294
species	Phocaeicola vulgatus	51403	51213	0.034398	665

Going Forward

The logical approach is simple to describe. For a person with the symptom, determine the enzymes which are abnormal. Determine the bacteria that are too high (even if only a little). Then use the suggestions AI Engine to determine the substances that will affect the greatest number of these bacteria to shift in the desired direction without encouraging other bacteria that could contribute to these enzymes to increase.

Now, the mathematics and complexities of this computation is a different matter but well within the power of today’s computer.

This request came from the person discussed in Follow up to: A German CFS Patient Experience and Analysis.

I don’t know, if you remember me, we did two reports together, and your suggestions really helped to get my microbiome back on track (which shows in the samples).

And then I got COVID in November 2021-December 2021. But I felt better with it, but unfortunately I couldn’t give up my sample while having COVID.

Anyway I wanted to ask you whether you may be interested in my case, because I had a huge, irreversible it seems, crash from 20-30 on Bell CFIDS disability scale to now under 10 and my microbiome crashed along with me . (The crash also resulted in a high number of Lorazepam intake from which I’m slowly withdrawing now. But I didn’t get a clear idea of the effect of Lorazepam on the microbiome, other then they make the slowed gut motility worse of course.)

I have a very severe and have a progressive form of ME/CFS in the way that whenever I really crash I always go down to a lower baseline and do not recover. And with most crashes I loose about 50 % of my functionality, so it just took me one year to go from very mild to very severe.

From my lowest point onwards I’ve always had to take about 4 Lorazepam to guarantee a minimum of a bearable quality of life in bed. I succeeded for 4 years not to have a major crash and did did not build up a tolerance towards Lorazepam in that time.

A lot of things seem to have reversed, what I should take before are often things that I now should avoid. What Biomesight says seems to contradict slightly from what I can gather from your site. (Yes, I know you explained why there can be contradictory results).

And I have difficulties getting the suggestions for the handpicked criteria to show. Of course I would be super glad, if you could help, but I understand if you have more interesting projects to work on. (I would of course donate for your effort, as this is the only or easiest way to say thank you),

I believe one of the differences between Microbiome Prescription(MP) and Biomesight is simply the number of studies used to make suggestions. At present, we have over 11,000 studies coded into MP, I do not know the number that Biomesight uses, but I expect less than 1%. Also, MP suggestions was written by a person that has worked professionally in Artificial Intelligence. I suspect Biomesight lacks that skill set for development. Regardless, put items not in disagreement as first priority.

Analysis — The Numbers

There is no magic number that answers questions about the microbiome. Usually, I look for abnormalities. Since the earlier post, she had 6 more microbiome samples done periodically and shown below. She is wise to regularly monitor and ideally take moderate steps (diet and supplements) to counter any concerning trends.

Criteria	Sep21	Mar22	May22	Sep22	Jan23	May23
Shannon Diversity Index	78.2	94.3	67.1	53.9	98.9	84.70
Simpson Diversity Index	30.7	40.7	44.4	17.5	42.9	48.90
Chao1 Index	53.6	66.8	81.5	36.7	65.1	61.90
Lab Read Quality	4.8	7.3	7.7	5.2	5	6.5
Bacteria Reported By Lab	612	653	717	536	636	642
Bacteria Over 99%ile	2	15	9	6	8	4
Bacteria Over 95%ile	4	20	50	33	32	12
Bacteria Over 90%ile	29	44	69	50	68	38
Bacteria Under 10%ile	44	181	181	40	53	43
Bacteria Under 5%ile	12	164	165	9	20	14
Bacteria Under 1%ile	1	140	148	1	3	0
Rarely Seen 1%	5	3	7	0	2	2
Rarely Seen 5%	16	11	21	10	19	15
Pathogens	28	29	38	31	32	34
Outside Range from JasonH	5	5	6	6	8	8
Outside Range from Medivere	12	12	19	19	19	19
Outside Range from Metagenomics	9	9	10	10	6	6
Outside Range from MyBioma	6	6	6	6	9	9
Outside Range from Nirvana/CosmosId	20	20	14	14	21	21
Outside Range from XenoGene	36	36	36	36	39	39
Outside Lab Range (+/- 1.96SD)	2	12	24	16	18	9
Outside Box-Plot-Whiskers	67	83	106	94	106	58
Outside Kaltoft-Moldrup	64	183	218	87	106	75
Condition Est. Over 99%ile	0	0	0	0	0	0
Condition Est. Over 95%ile	0	2	0	0	0	0
Condition Est. Over 90%ile	0	3	5	0	0	0
Enzymes Over 99%ile	0	0	21	0	0	0
Enzymes Over 95%ile	19	0	66	15	17	36
Enzymes Over 90%ile	68	13	119	34	27	118
Enzymes Under 10%ile	30	285	203	94	200	80
Enzymes Under 5%ile	13	225	130	41	81	27
Enzymes Under 1%ile	1	164	80	2	2	11
Compounds Over 99%ile	1	0	17	0	0	0
Compounds Over 95%ile	18	0	35	3	10	18
Compounds Over 90%ile	49	5	73	13	17	64
Compounds Under 10%ile	789	876	965	1124	1135	998
Compounds Under 5%ile	779	848	927	1092	1057	959
Compounds Under 1%ile	773	832	904	1069	1018	930

	Sep21	Sep21	Mar22	Mar22	May22	May22	Sep22	Sep22	Jan23	Jan23	May23	May23
Percentile	Genus	%	Genus	%	Genus	%	Genus	%	Genus	%	Genus	%
0 – 9	7	4%	46	26%	43	23%	7	5%	11	6%	9	6%
10-19	19	11%	13	7%	9	5%	17	12%	27	15%	21	13%
20 – 29	26	15%	14	8%	15	8%	16	11%	14	8%	23	14%
30 – 39	13	8%	13	7%	16	8%	12	8%	15	9%	18	11%
40 – 49	14	8%	13	7%	14	7%	17	12%	18	10%	15	9%
50 – 59	14	8%	16	9%	14	7%	10	7%	15	9%	16	10%
60 – 69	22	13%	20	11%	18	9%	10	7%	17	10%	18	11%
70 – 79	23	13%	15	8%	22	12%	19	13%	16	9%	13	8%
80 – 89	23	13%	19	11%	18	9%	18	12%	21	12%	15	9%
90 – 99	11	6%	11	6%	22	12%	19	13%	21	12%	13	8%
Total	172		180		191		145		175		161

	Sep21	Sep21	Mar22	Mar22	May22	May22	Sep22	Sep22	Jan23	Jan23	May23	May23
Percentile	%	Species	%	Species	%	Species	%	Species	%	Species	%	Species
0 – 9	5%	10	28%	60	27%	70	7%	14	10%	22	8%	16
10-19	13%	26	5%	11	5%	13	12%	23	15%	33	16%	34
20 – 29	13%	27	8%	18	6%	16	9%	18	10%	23	12%	25
30 – 39	7%	15	4%	8	6%	15	12%	23	10%	22	11%	23
40 – 49	9%	19	8%	16	9%	24	8%	15	8%	18	10%	21
50 – 59	12%	25	9%	20	12%	30	9%	18	10%	22	12%	25
60 – 69	8%	17	12%	25	8%	21	7%	14	8%	17	6%	12
70 – 79	10%	20	9%	19	7%	19	11%	22	9%	20	10%	21
80 – 89	13%	27	9%	19	7%	19	16%	30	9%	21	9%	19
90 – 99	7%	15	8%	17	12%	30	8%	15	12%	28	7%	15
		201		213		257		192		226		211

We lack any data on Lorazepam and other Benzodiazepines impacts on the microbiome which complicates interpretations. I did a search on the US National Library of Medicine and found nothing useful.

More History of Patient

I discovered Pregabalin in March 22 which brought me from Bell CFIDS disability scale below 10 up to nearly bell 20-30. The ditch in the curve around May 22nd is probably me taking too much Lorazepam and Pregabalin, as I for the first time I could take care of things that needed to betaken care of.

At that time I took about 500mg metformin (which did give me energy) most of the day and stayed with my Thorne Fibre mend, Inulin (in the beginning Inulin from the Argave helped dramatically with nausea and headaches)and Acacia Fibre, sometime an Amino Acid complex, but they make me jittery. Usually completely constipated I suddenly developed a strong diarrhea along with an unbearable itching of my whole skin in August / September for which Famotidine(Pepcid) and Cromoglicic acid (Cromolyn – prescription in US) worked best. That was a time where I ate lots of cake and carbohydrates and would take Metformin (I am always hovering around the entrance point to prediabetic) afterwards. That seemed to be too much sugar, my body couldn’t deal with. After I stopped the cake , eat more vegetables again, it went away. 

I have got restless legs, which are kind of turned on or off with every mayor crash. Now unfortunately they are turned on, and the only thing apart from medication that helps is when I eat complex carbohydrates lie brown rice, whole food, pasta, oat flakes etc, when I don’t I use Pramipexole.

Pregabalin been used with Fibromyalgia, a sibling condition for some, and suggested by the American Family Physician journal in 2023. Pregabalin with Lorazepam has known interactions: ” increase side effects such as dizziness, drowsiness, confusion, and difficulty concentrating.”[Src] so she is right about her loss of effectiveness.

Of the many items cited, we know what a few of them likely shifts. Others we lack data.

• cromolyn disodium salt
• famotidine
• metformin
• inulin

This missing data illustrates the challenge of trying to manipulate the microbiome — an absence of data. For antibiotics we have a reasonable amount of information, thus we can negotiate with MDs between their desired goal for the antibiotic and our goal of improving the microbiome to find a mutually acceptable compromise.

Going Forward

As part of my learning process, I evaluated each against the “Just Give Me Suggestions” consensus to see it that provide any insight. I also looked at the top items in three other classes.

Criteria	Sep21	Mar22	May22	Sep22	Jan23	May23
cromolyn disodium salt	275.5	243.9	368.9	504.3	391.1	393.3
famotidine	275.5	248.8	378.5	504.3	379.7	393.3
metformin	146.4	163.8	234.1	249.9	293	-17
inulin	-231.6	-79.1	-207.5	-203.3	-333	-157
Total	399	587	725	806	631	491
Best Probiotic	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei
Best Amino Acid	polymannuronic acid	polymannuronic acid	melatonin supplement	melatonin supplement	melatonin supplement	polymannuronic acid
Best Vitamin/Mineral	Vitamin B7	Vitamin C	Vitamin C	Vitamin B1	Vitamin B1	Vitamin B-12

This helps us evaluate possible (we do not know for certain) impact on various microbiome.

I am not a medical professional and have no clinical experience, so picking items tend to be arbitrary in most cases. I am familiar with the literature for ME/CFS and if the person has ME/CFS, I will tend to pick items that studies reporting helping.

My preference is simple.

My suggestion (given all of the fuzziness and items being taken) is to persist with the prescription items — they help both her symptoms and her microbiome! I would suggest adding the following items (see Dosages for Supplements for literature on dosage):

• lactobacillus casei – at least 48 BCFU/day — this is the suggested serving size from Custom Probiotics product. Or a Yakult bottle with each meal (each bottle is 20 BCFU). Depending on availability and cost.
• melatonin – 10 mg/d – in three dosages, i.e. one with each meal.
• Vitamin B1, B12, C7 and C. (see above for dosages)

One additional item that I would suggest, being prediabetic is to take the Pendulum Akkermansia muciniphila probiotic. This may be a challenge to obtain in Germany (if someone is visiting the US, that may be a backdoor to get it).

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 can compute items to take, those computations do not provide information on rotations etc.

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.

As a result of doing an analysis for a 19 month old toddler, I added a new option that can also be used with Transcribed tests. This post applied to the following tests:

• Bioscreen
• Biovis Microbiome Plus
• DayTwo
• Diagnostic Solution GI-Map
• GanzImmun Diagnostic A6
• GanzImmun Diagnostics AG Befundbericht
• Genova Gi Effects
• Genova Parasitology
• GI EcologiX (Invivo)
• GI360 Stool (UK)
• Gut Zoomer
• InVitaLab
• Kyber Kompakt
• Medivere Mikrobiom Plus Stuhlanalyse
• Medivere: Darm Mikrobiom Stuhltest
• Medivere: Darn Magen Diagnostik
• Medivere: Gesundsheitscheck Darm
• Metagenomics Stool (De Meirleir)
• Nordic Laboratories
• NutriPATH
• Smart Gut
• Verisana

You must save your input.

Process

Here’s an example

When you logged in, you will see your saved tests, CLICK ON Review.

And then we have the details you entered below with an important column, taxon number.

Below this are conditions where your pattern matches at least 5 shifts reported in Published Studies.

There may be many items listed. This is by pattern matching and is not predictive.

If you have any of these conditions, or suspect you may have. Just click the appropriate button.

An example is below. These are tuned safest-suggestions for the matches. What do I mean by safest? It means the items are not reported in any study in the database to adversely impact any of bacteria listed. Many substances have contradictory reports on shifts — this substances are excluded.

Not Listed Condition?

This person believes they may have Autoimmune, so going to https://microbiomeprescription.com/Library/PubMed we find that it is listed.

If it is not listed, search for bacteria shifts reported and use those (please send me the studies so I may add them).

The bacteria are shown in a tree. You have to manually match between the two.

In this we have:

• Escherichia     ⬇️  but our sample is high,
• Roseburia intestinalis   ⬆️  - we are high on Roseburia, we will include it

We have only one match — this tests with limited reporting is not a good fit for this condition. Doing a test like Biomesight, Xenogene, Thorne or Ombre is likely the best choice.

We just copy the taxon number into the form at the bottom of the page, and then click suggestions.

In this case, we get a short list. Remember, doing a single bacteria means you are ignoring a lot of interactions and factors. The suggestions could feed other bacteria that are too high.

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