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.

The Microbiome as a Key to Health

Continue reading “The Journey Begins with your microbiome”

Gender based Microbiome Shifts for ME/CFS and other conditions

A question was ask – are there significant gender differences with ME/CFS. A partial answer is possible from our citizen science data (Available here). The number of bacteria identify as statistical significant drops because we are reducing sample sizes. The table below shows the shifts that are seen in common with P < 0.01.

For Symptom of ME/CFS

SourceTax_nametax_rankMaleFemaleMale_Chi2FeMale_Chi2
thryveThermodesulfobacteriaphylumincreasesincreases234.0375138.4544
biomesightVerrucomicrobiaceaefamilyincreasesincreases8.3333337.262051
biomesightRhodothermaeotaphylumincreasesincreases179.2217.3071
biomesightAkkermansiaceaefamilyincreasesincreases8.7183789.965634
biomesightErysipelothrix murisspeciesincreasesincreases9.53388910.08333
biomesightAkkermansiagenusincreasesincreases8.7183789.965634
biomesightRhodothermalesorderincreasesincreases179.2217.3071
biomesightAkkermansia muciniphilaspeciesincreasesincreases8.7183789.965634
biomesightErysipelothrixgenusincreasesincreases9.6632899.663289
biomesightRhodothermiaclassincreasesincreases179.2217.3071
biomesightThermodesulfobacteriaphylumincreasesincreases281.1738299.9112

ME/CFS With IBS

We find differences here.

SourceTax_nametax_rankTaxonMaleFemaleMale_Chi2FeMale_Chi2
biomesightSutterellagenus40544decreaseincreases8.33333311.25018
biomesightRhodothermalesorder1853224increasesincreases139.9274114.5716
biomesightDoreagenus189330increasesdecrease18.7516.17875
biomesightRhodothermiaclass1853222increasesincreases139.9274114.5716
biomesightThermodesulfobacteriaphylum200940increasesincreases280.3333187.9779
biomesightSutterellaceaefamily995019decreaseincreases8.33333311.25018
biomesightAlcaligenaceaefamily506decreaseincreases8.3333339.120714
biomesightRhodothermaeotaphylum1853220increasesincreases139.9274114.5716

ME/CFS Without IBS

We found no differences yet (given the sample size)

SourceTax_nametax_rankTaxonMaleFemaleMale_Chi2FeMale_Chi2
biomesightBacteroides fluxusspecies626930increasesincreases7.3551617.910588
biomesightThermodesulfobacteriaphylum200940increasesincreases124.4571170.4624

Irritable Bowel Syndrome

Following up from above and noting that there is a gender bias in incidence, we find some differences

thryveThermodesulfobacteriaphylum200940increasesincreases252.823295.10095
biomesightRhodothermalesorder1853224increasesincreases125.1467110.6182
biomesightRhodothermiaclass1853222increasesincreases125.1467110.6182
biomesightThermodesulfobacteriaphylum200940increasesincreases314.4971174.6182
biomesightRhodothermaeotaphylum1853220increasesincreases125.1467110.6182
biomesightSharpea azabuensisspecies322505increasesincreases16.185266.80625
biomesightSharpeagenus519427increasesincreases16.185266.80625
thryveMycoplasmagenus2093increasesdecrease12.8152420.3229
thryveMycoplasmataceaefamily2092increasesdecrease14.8858120.3229
thryvePhocaeicola vulgatusspecies821increasesdecrease7.89349217.06273
thryveMycoplasmatalesorder2085increasesdecrease14.8858126.01485

Depression

Another condition with a gender association

SourceTax_nametax_rankTaxonMaleFemaleMale_Chi2FeMale_Chi2
thryveThermodesulfobacteriaphylum200940increasesincreases227.7557148.4336
thryveParabacteroides distasonisspecies823decreaseincreases9.11835613.46941
thryveEubacterium oxidoreducensspecies1732decreaseincreases12.995076.76
biomesightRhodothermalesorder1853224increasesincreases121.200291.125
biomesightRhodothermiaclass1853222increasesincreases121.200291.125
biomesightThermodesulfobacteriaphylum200940increasesincreases223.4402189.2431
biomesightRhodothermaeotaphylum1853220increasesincreases121.200291.125
thryveLactobacillus rogosaespecies706562decreasedecrease23.8836812.12781

Symptom: Problems remembering things

This is one of the characteristics of ME/CFS, Long Covid, etc

SourceTax_nametax_rankTaxonMaleFemaleMale_Chi2FeMale_Chi2
thryveThermodesulfobacteriaphylum200940increasesincreases316.4446120.0944
biomesightRhodothermalesorder1853224increasesincreases171.7445133.3333
biomesightRhodothermiaclass1853222increasesincreases171.7445133.3333
biomesightThermodesulfobacteriaphylum200940increasesincreases369.0078289.0992
biomesightOdoribacteraceaefamily1853231increasesincreases12.793117.962632
biomesightRhodothermaeotaphylum1853220increasesincreases171.7445133.3333
biomesightAcetivibriogenus35829decreaseincreases9.18086517.49208
biomesightOdoribactergenus283168increasesincreases9.33494912
biomesightAcetivibrio alkalicellulosispecies320502decreaseincreases9.18086519.95636
biomesightHathewaya histolyticaspecies1498decreaseincreases9.1808657.262051
biomesightHathewayagenus1769729decreaseincreases9.1808657.262051
biomesight[Clostridium] thermoalcaliphilumspecies29349increasesincreases7.356.880909
thryveIntestinimonasgenus1392389decreaseincreases168.552727
thryveIntestinimonas butyriciproducensspecies1297617decreaseincreases16.486469.992258
ubiomeBacteroides sp. EBA5-17species447029increasesdecrease9.0555777.314286

Symptom: Worsening of symptoms with stress.

Another common symptom of ME/CFS

SourceTax_nametax_rankTaxonMaleFemaleMale_Chi2FeMale_Chi2
thryveThermodesulfobacteriaphylum200940increasesincreases282.4023185.22
biomesightThermoanaerobacterales Family III. Incertae Sedisfamily543371decreaseincreases22.004548.491649
biomesightSharpeagenus519427increasesincreases17.5562512.38345
biomesightHathewayagenus1769729decreaseincreases16.9861211.70814
biomesightRhodothermalesorder1853224increasesincreases142.9353188.8704
biomesightHathewaya histolyticaspecies1498decreaseincreases16.9861211.70814
biomesightSharpea azabuensisspecies322505increasesincreases17.5562512.97965
biomesightRhodothermiaclass1853222increasesincreases142.9353188.8704
biomesightThermodesulfobacteriaphylum200940increasesincreases352.2616362.7038
biomesightAcetivibrio alkalicellulosispecies320502decreaseincreases12.658188.491649
biomesightRhodothermaeotaphylum1853220increasesincreases142.9353188.8704
biomesightAcetivibriogenus35829decreaseincreases12.658188.491649

Other Symptoms with Significant Gender Differences in patterns

  • Immune Manifestations: Abdominal Pain
  • Sleep: Unrefreshed sleep
  • Comorbid: High Anxiety
  • General: Fatigue
  • Neurological-Audio: hypersensitivity to noise
  • DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired
  • DePaul University Fatigue Questionnaire : Fatigue
  • Neurocognitive: Brain Fog
  • Neurocognitive: Problems remembering things
  • DePaul University Fatigue Questionnaire : Anxiety/tension
  • General: Myalgia (pain)
  • Immune Manifestations: Constipation
  • Post-exertional malaise: Rapid muscular fatigability,
  • Neuroendocrine Manifestations: Poor gut motility
  • Comorbid: Restless Leg
  • Comorbid: Small intestinal bacterial overgrowth (SIBO)
  • DePaul University Fatigue Questionnaire : Difficulty finding the right word
  • DePaul University Fatigue Questionnaire : Mood swings
  • DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness
  • Sleep: Problems falling asleep
  • Sleep: Problems staying asleep

New Clues Into Mystery of Itch – Exploration

A reader pointed me at S. aureus drives itch and scratch-induced skin damage through a V8 protease-PAR1 axis [2021]. There is a prescription drug, PAR-1 INHIBITORS, that appears to help (with some risks).

It is not all strains of Staphylococcus aureus, but about 10% of the strains.

Normally, I look at modifying the gut microbiome — but many items are likely to help. So the question becomes, what are possible for use as skin ointments?

From the list of inhibitors, likely candidates are:

  • Zinc or silver ointments
  • acetic acid (vinegar) – likely diluted, possibly with a sprayer
  • The following available as oils, mixed with creams:
    • oregano oil (2nd high studies)
    • thyme oil (MOST STUDIES)
    • lauric oil / coconut oil
    • clove oil
    • cinnamon oil
    • peppermint oil
    • coriander oil
  • Other items that may be semi liquid:
  • Following in solution
    • aspartame (sweetener)
    • saccharin
    • stevia
    • sucralose

The following should NOT be applied to the skin:

  • Olive oil

User Feedback

A person with this issue looked over the list and found that the items in the above list that she has tried, reduced the itch.

The obvious cheapest solution to try is simple: a shower with soap (ideally antibacterial soap). Followed by using a spray bottle with vinegar that is allowed to dry on the skin.

Other items that inhibits: [2012]

  • paroxetine
  • hydroxyzine
  • atomoxetine
  • bencyclane fumarate

Jason Hawrelak Criteria for Healthy Gut – Revisited

This is an update Jason Hawrelak Criteria for Healthy Gut. His criteria is based on percentages and used by medical practitioners around the world. I have three significant collections of samples and decided to find out how these percentages translate to percentile for each lab.

They are similar and not similar. For example 50% of people will have low Akkermansia using uBiome while Biomesight increases it to 77%. Alistipes — are never out of range for Biomesight while 90% of people using uBiome would be too high.

Taxa NameTaxa RankPercentageuBiome PercentileOmbre PercentileBiomesight Percentile
Akkermansiagenus1 – 548 – 8071 – 9177 – 93
Alistipesgenus0 – 0.30 – 100 – 330 – 100
Bacteroidesgenus0 – 200 – 320 – 480 – 45
Bacteroidiaclass0 – 350 – 240 – 400 – 45
Bifidobacteriumgenus2.5 – 578 – 9178 – 8790 – 95
Bilophila wadsworthiaspecies0 – 0.150 – 320 – 430 – 44
Blautiagenus5 – 1015 – 6032 – 7224 – 69
Desulfovibriogenus0 – 0.150 – 460 – 420 – 72
Escherichia colispecies0 – 0.10 – 1000 – 750 – 88
Eubacteriumgenus0 – 150 – 1000 – 990 – 100
Faecalibacterium prausnitziispecies10 – 1580 – 9550 – 6946 – 69
Fusobacteriumgenus0 – 0.010 – 400 – 660 – 72
Lactobacillusgenus0.01 – 123 – 939 – 7546 – 99
Methanobrevibactergenus0 – 0.010 – 70 – 330 – 33
Oxalobactergenus0.01 – 10 – 10038 – 10035 – 100
Prevotellagenus0 – 250 – 1000 – 890 – 88
Pseudomonadotaphylum0 – 40 – 520 – 760 – 54
Roseburiagenus5 – 1051 – 8685 – 9681 – 95
Ruminococcusgenus0 – 150 – 1000 – 9810- 95

This post is intended to illustrate that percentages cannot be determined by one lab and applied to another. Percentile appears to be more robust.

Evolution of Addressing Microbiome/Gut issues

There are generations of approaches. Often limited to the knowledge available at the time

Generation #1: Eat Fermented Foods as a Cure All

This dates back millennium in the east and the west. It helps some, and thus is validated as working (for some at least). For example, Garum in ancient Greece

Generation #2: Yogurt and Probiotics

In western culture, The Russian biologist and Nobel laureate Ilya Mechnikov, from the Institut Pasteur in Paris, was influenced by Grigorov’s work and hypothesized that regular consumption of yogurt was responsible for the unusually long lifespans of Bulgarian peasants.[25] Believing Lactobacillus to be essential for good health, Mechnikov worked to popularize yogurt as a foodstuff throughout Europe. [Wikipedia]

Generation #3: Bacteria Shifts

This arose out of the new technologies to identify bacteria in better detail. This was in the 1950’s and later [Experience with antibiotics. II. Shifts in bacterial flora in man].

There are several generation of technology involved here.

“A significant difference in gut microbial composition between SARS-CoV-2 positive and negative samples was observed, with Klebsiella and Agathobacter being enriched in the positive cohort.”

The gut microbiome of COVID-19 recovered patients returns to uninfected status in a minority-dominated United States cohort [2021]

These studies indicates an increase or decrease in the average for populations. There is no thresholds where the odds change nor relative magnitude. This is further complicated by non-replication by other researchers — the reason is often because on non-standardization of microbiome analysis

Generation #4: Lab Specific Shifts with critical levels and contributions

Using large dataset and techniques such as those described in Symptoms with Ability to Predict from Microbiome Results. We have the ability to set threshold and determine the relative importance. The table below is for Long COVID based on one lab’s pipeline. We can easy see the pattern — often, it is a relatively rare bacteria(low prevalence) that is seen in significant levels in Long COVID patients

This allows identification of the genus (or other ranks) that may be ascribe to the condition if over the 84%ile. It also allows the relative importance of each to be evaluated since there may be multiple targeted bacteria. Chi2 value is a reasonable proxy for importance.

Moving up the taxonomical rank, we see at the ORDER level that one order is really significant.

Bottom Line

IMHO, this last method allows superior identification of bacteria involved with conditions and symptoms using two simple cutoff points: <= 16%ile and >=84%ile. Other cutoff points are possible,
We can then look at a patient’s microbiome (assuming suitable lab-pipeline) and identify with statistical accuracy which bacteria are involved. Not only can we identify the bacteria — we can determine the relative importance of each bacteria.

Strong Genus association to many conditions

This week I refactor the genus association algorithm resulting in clearer results. I also change it so the common person can understand what is being reported.

The core is that once we convert percentage to percentiles, we end up with a “flat” or uniform distribution. For any genus, we have the same number in 0-10%ile, 50-60%ile and 90-100%. If there is no association, we should see the same number in the 0-16%ile and 84-100%ile. If there are not, we can compute the statistical significance (I picked p < 0.01 or one chance in 100 of not being a true association).

Below, we will cover 2 pages and a FYI:

Extreme Associations

Processing without considering genus (i.e. all tax ranks) The following association occurs with extremely high statistical associations to many conditions.

This does not mean that it is a cause, but may indicate these bacteria prosper with the disruption associated with the condition. An example is below

Note that these are almost always present, it is when the percentile ranking exceeds 84%ile that we have a strong indicator which is illustrated below with two distributions. Note that the amount is small.

Unfortunately, restricting to genus level resulted in nothing.

Overview by symptom

This lists all of the symptoms found significant in various lab processing pipeline. The number depends on the number of samples contributed and the number of samples annotated with symptoms. This page is recomputed and updated on the 2nd of each month; more data means more associations.

Note Taxa identification is fuzzy and should never be assumed to be “correct”. The same FASTQ file processed thru ubiome, Ombre, Biomesight and Sequentia biotech; resulted in different genus being reported with different amounts. Clearly, the associations is processing pipeline dependent.

Genus identification

Looking at Immune Manifestations: Constipation we can compare results across different tests

We see the 3 are in consensus for Butyricimonas being increased and one is silent. We see 2 are in consensus for Lachnobacterium being increased, and two are silent (at the moment, waiting for more data). Two are in consensus for Desulfosporosinus being decreased with two silent.

The lab processing pipeline is very significant for detection rate (for Butyricimonas , one detects it 57% or the time and another lab 77% of the time) and the amount reported.

Technical Note: Microbiome Analysis is Fuzzier than a Peach with Mold!!

This is a post in this series !Technical Notes on Microbiome Analysis

The Question

A reader forwarded this study to me:

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences [2018]

While this paper is dealing with fungi the tables can be eye opening for some people. A suitable quote from the paper “When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%), all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA.

My expertise is in statistics, operational research and artificial intelligence, with good expertise in reading medical studies; so I asked a colleague who has a Ph.D. in Molecular Genetics. His casual comments were:

There are several studies with ASVs out there. Especially the recent ones. Clustering pipeline is what matters here. But I agree that full length gives better taxonomic assignment.
Problem is full length is twice as expensive. So my point is when using V4, you will achieve incredibly better taxonomic assignments with ASV vs OTU. However, full length or V3-V5 gives a better resolution.

He also shared this graphic from Zymo Research. The V4 often cost around $50 and the full length can be 3-4x more.

What is ASV?

  • ASV stands for amplicon sequence variants.
  • OTU stands for operational taxonomic units

ChatGPT gives a good common man explanation:


Both methods aim to characterize and quantify the diversity of microorganisms in a given sample, but they differ in their underlying algorithms and conceptual frameworks.

  1. Amplicon Sequence Variants (ASVs):
    • ASVs are derived from high-throughput sequencing data by clustering sequences that differ by as little as a single nucleotide. This means that ASVs are defined at a very fine level of sequence resolution.
    • The goal of ASVs is to represent individual unique sequences within a dataset, thereby capturing the most detailed information about the microbial community present in a sample.
    • ASVs are typically generated using algorithms like DADA2 (Divisive Amplicon Denoising Algorithm 2), which infer exact sequence variants and correct sequencing errors.
    • ASVs are considered more accurate in capturing true biological diversity but may be more sensitive to sequencing errors.
  2. Operational Taxonomic Units (OTUs):
    • OTUs are clusters of similar sequences that are defined based on a chosen sequence similarity threshold (commonly 97% similarity for bacterial 16S rRNA gene sequences).
    • The 97% similarity threshold is often used to group sequences into OTUs to approximate the species level, although this can vary depending on the marker gene and research goals.
    • OTUs are generated using methods such as UCLUST, UPARSE, or others that involve sequence clustering. The resulting OTUs represent a consensus sequence for each cluster.
    • OTUs are considered more tolerant to sequencing errors, but they may group together closely related species or strains into the same cluster.

In summary, the main difference lies in the level of sequence resolution. ASVs aim for the highest possible resolution by identifying unique sequences, while OTUs represent clusters of similar sequences based on a chosen threshold. The choice between ASVs and OTUs depends on the specific research goals, the desired level of taxonomic resolution, and considerations related to sequencing error handling and computational resources.


To translate into human terms: ASV identifies criminals by fingerprints or DNA, while OTU identifies by the image from a security camera.

A Dilemma for Direct-To-Retail Tests

My colleague words makes the points clearly: Problem is full length is twice as expensive. Consumers are not knowledgeable about the differences but are very cost aware. The cheapest and least reliable way is often the norm. A direct to retail test costing less than $400 is likely to use the more inaccurate processes.

A Dilemma for Data from Studies

We encounter the same issue often for studies, budget! Searching the US National Library of Medicine for ASV, I get 2,955 results

Searching the US National Library of Medicine for OTU, I get 9,180 results. We also see that ASV is replacing OTU starting around 2021.

This means that many studies published before 2021 may have correctly identified the bacteria impacted as little as 50% of the time. So, does Barley increases or decreases Bifidobacterium?

In addition to possible confounders with selection of control and subjects in the study, we must now consider the possibility of misidentification of the bacteria. For myself and microbiome prescription’s expert system, this is not a major issue because we are using a fuzzy logic expert system. Suggestions are based on most probable given the data available.

Many medical practitioners (MDs and naturopaths) are not trained in this area and resort to a naïve deterministic approach.

Additional Suggested Literature

A comparison of bioinformatics pipelines for compositional analysis of the human gut microbiome [2023]

The differences of the same sample, Bacterial genera profile. Top 10 most abundant bacterial genera per pipeline resulted in a total of 16 unique genera.

Microbiome Analysis via OTU and ASV-Based Pipelines—A Comparative Interpretation of Ecological Data in WWTP Systems [2022]

  • Additional recent work has shown that individual pipelines themselves may be biased toward certain phyla [15,21]
  • The Illumina sequencing output reported an average quality of Q30 ≥ 81.9%.

Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold [2021]

  • Based on mock communities, ASV-based approaches had a higher sensitivity in detecting the bacterial strains present, sometimes at the expense of specificity [1720]
  • OTUs detected much higher amounts of Verrucomicrobiae in the seston and sediment samples than were detected by the ASV approach. These differences are surprising given that both OTU and ASV approaches classified sequences to the same database.

Bottom Line

In dealing with microbiomes in a clinical setting, we have multiple fuzziness:

  • The actual bacteria being reported (and the amount) is not reliable (in the common sense of that word), it is probable.
  • When trying to modify the microbiome, the impact on the reported bacteria is not reliable (in the common sense of that word), it is probable.

This means using a single study has significant risk. With a diverse collections of studies and facts, then a fuzzy logic expert system results in significantly reduced risk and a higher probability of successful manipulation. It also illustrates why the Large Language Model (i.e. ChatGPT style) is very inappropriate. and likely machine learning also.

As of this writing, Microbiome Prescription has 10,390 Citations from US National Library of Medicine resulting in 2,415,340 facts in it’s expert system.

Anticholinergic items should avoid taking if over 70 or with brain fog

A reader contacted me because she was concerned about cognitive issues after she started Dramamine. It is a microbiome modifier.

IMHO Brain Fog should be a condition that should result in de-prescribing or stopping any of the following.

From healthline, we have the following list

Over the counter anticholinergics

  • diphenhydramine (Benadryl, Tylenol PM, Advil PM, Unisom SleepGels, DimedrolDaedalon, and Nytol.)
  • brompheniramine (DimetappmDimetapp, Bromfenex, Dimetane, and Lodrane)
  • dimenhydrinate (Dramamine or Gravol)
    Dimenhydrinate is marketed under many brand names: in the U.S., Mexico, Turkey and Thailand as Dramamine; in Serbia as Dimigal; in Ukraine as Driminate; in Canada, Costa Rica, and India as Gravol; in Iceland as Gravamin; in Russia and Croatia as Dramina; in South Africa and Germany as Vomex; in Australia and Austria as Vertirosan; in Brazil as Dramin; in Colombia as Mareol; in Ecuador as Anautin; in Hungary as Daedalon; in Indonesia as Antimo; in Italy as Xamamina or Valontan; in Peru as Gravicoll; in Poland and Slovakia as Aviomarin;[18] in Portugal as Viabom, Vomidrine, and Enjomin; in Spain as Biodramina; in Israel as Travamin; and in Pakistan as Gravinate.[19]
  • doxylamine (Unisom SleepTabs, Diclectin, Diclegis)

Prescription anticholinergics

  • atropine (Atropen)
  • belladonna alkaloids
  • benztropine mesylate (Cogentin)
  • clidinium
  • cyclopentolate (Cyclogyl)
  • darifenacin (Enablex)
  • dicylomine
  • fesoterodine (Toviaz)
  • flavoxate (Urispas)
  • glycopyrrolate
  • homatropine hydrobromide
  • hyoscyamine (Levsinex)
  • ipratropium (Atrovent)
  • orphenadrine
  • oxybutynin (Ditropan XL)
  • propantheline (Pro-banthine)
  • scopolamine
  • methscopolamine
  • solifenacin (VESIcare)
  • tiotropium (Spiriva)
  • tolterodine (Detrol)
  • trihexphenidyl
  • trospium

For more, from Drugs.com: Anticholinergic Drugs to Avoid in the Elderly

Some Literature

The amount of literature is HUGE, I will just cite back to September 2023….

Which Bacteria may be causing the Cognitive Declines?

Many of the drugs above are in the Microbiome Prescription database. Many of them impacts the same bacteria [ DECREASING] — implying that the cognitive loss may be connected with microbiome alteration.

The obvious way to improve recovery appears to be the following probiotics:

In Common Across DrugsBacteria
19Enterobacteriaceae
18Escherichia
18Escherichia coli
18Kluyvera
18Streptococcaceae
18Streptococcus
18Streptococcus parasanguinis
17Akkermansia
17Akkermansia muciniphila
17Bacteroidaceae
17Bacteroides
17Bacteroides caccae
17Bacteroides fragilis
17Bacteroides ovatus
17Bacteroides thetaiotaomicron
17Bacteroides uniformis
17Bacteroides xylanisolvens
17Bifidobacteriaceae
17Bifidobacterium
17Bifidobacterium adolescentis
17Bifidobacterium longum
17Bilophila
17Bilophila wadsworthia
17Blautia
17Clostridiaceae
17Clostridioides
17Clostridioides difficile
17Clostridium
17Collinsella
17Collinsella aerofaciens
17Coprococcus
17Coriobacteriaceae
17Desulfovibrionaceae
17Dorea
17Dorea formicigenerans
17Eggerthella
17Eggerthella lenta
17Eubacteriaceae
17Eubacterium
17Fusobacteriaceae
17Fusobacterium
17Fusobacterium nucleatum
17Lachnospiraceae
17Lacticaseibacillus paracasei
17Lactobacillus
17Lactobacillus casei group
17Odoribacter
17Odoribacter splanchnicus
17Parabacteroides
17Parabacteroides distasonis
17Parabacteroides merdae
17Peptoclostridium
17Peptostreptococcaceae
17Phocaeicola vulgatus
17Porphyromonadaceae
17Prevotella
17Prevotellaceae
17Roseburia
17Roseburia hominis
17Roseburia intestinalis
17Ruminococcaceae
17Ruminococcus
17Streptococcus salivarius
17Veillonella
17Veillonella parvula
17Veillonellaceae
17Verrucomicrobiaceae

Post Script:

A comment on this post wrote “I take Huperzine A and always wonder if that helps me out a little. (I can’t take any of those strong central anticholinergics anyway though!)“.

Huperzine A, the active ingredient derived from the traditional Chinese herb, is an efficacious, selective, and reversible acetylcholinesterase inhibitor (AChEI) 

So, does it impact cognitive issues in these groups? There is no clear evidence (mixed results in most reviews)

What is the best diet in your opinion?

This is a repost of a post from 6 years ago.

To this I should add, the goal is to disrupt dysbiosis.  Keeping the same items allows the dysbiosis to adapt. So, Keto for no more than 6 weeks, gluten free (if you tolerate gluten) for no more than 6 weeks, etc. Sticking religiously to a “cure-all diet” rarely ends well.

Once the dysbiosis is resolved, then the approach below should be considered.


I have been asked this often. My answer is extremely logical but not what you will get from most health experts (and unfortunately, may not be easy to determine for some).

The Diet….

Very simple — the type of diet that your ancestors ate 300+ years ago! Diet changes gene expression, i.e. microbiome AND dna adapts.

Last year, researchers discovered that these kinds of environmental genetic changes can be passed down for a whopping 14 generations in an animal – the largest span ever observed in a creature, in this case being a dynasty of C. elegans nematodes (roundworms)…. Usually, environmental changes to genetic expression only last a few generations.
…  studies have shown that both the children and grandchildren of women who survived the Dutch famine of 1944-45 were found to have increased glucose intolerance in adulthood.Scientists Have Observed Epigenetic Memories Being Passed Down For 14 Generations

From a post that I did three years ago:

Some nuggets that I found in a Christmas Present…

My wife gave me “Danish Cookbooks” by Carol Gold. This is NOT a cook book, but rather an academic study of cookbooks published in Denmark.  I’m 100% Danish and very interested in history.

I have always been inclined towards going for ancestral diet patterns, and did Paleo for a while. My problem with Paleo is that it is more idealogical based than actual (scientific) archeologically based. It is also trying to jump the diet back thousands of years which effectively ignores how our bacteria evolved to meet our changes of diet.

A diet based on typical diet of your ancestors 400 – 1400 years ago is likely a better choice. You avoid the newly introduced foods, for example, potatoes. You also avoid process foods and modern additives. On the plus side, your gut bacteria is likely closer to the optimized bacteria your ancestors evolved from eating the same food for a thousand years.

In this book, I found two gems from the historical records:

  • We have decreased the use of spice considerably — in 1600, the common spices were:
    • cumin, anise, coriander, dill, fennel, lavender, sage, rosemary, mint, bay leaves, cloves, pepper, saffron, thyme, marjoram, nutmeg, cardamon, ginger, cinnamon, hyssop, wormwood, lemon balm, angelica-root.
    • “The issue here is … the use of seasonings in general slackens” p.47
    • Many of these spices (like wormwood and ginger) have strong antibacterial characteristics which would have kept some gut bacteria families in control well.
  • “Their most common food was meat” p. 122
  • White (wheat) bread was very uncommon, expensive, and typically seen only in upper class homes on special occasions(not as part of the regular menus). It appears that most of the carbohydrates came from Rye Bread.

I am sure that some readers who favor a diet that is vegan or vegetarian on ideological grounds would object to these suggestions.  My response is simple, if your ancestors were vegetarians for centuries or millenniums (as some friends who were born in India can validly claim), then that is the right diet without any doubts.

Evidence shows that gut bacteria is inherited through generations — hence it is good to know what your ancestors ate because your gut bacteria have likely adapted to that diet. Given my heritage (which likely applies to people from the UK, Poland, northern France and Germany etc), this boils down to:

  • Rye Bread without any wheat flour
  • Meat and Fish (especially since the family seemed to always been within 5 miles of the coast back to 1500..)
  • Vegetables:

No potatoes — they really did not enter my ancestor dies until the early 1800’s – after one of my great-grandfathers was born. Little or no sugar (“Worldwide through the end of the medieval period, sugar was very expensive[1] and was considered a “fine spice“,[2] but from about the year 1500, technological improvements and New World sources began turning it into a much cheaper bulk commodity.” – Wikipedia)

This Wikipedia article may be a helpful start for many.

The last item needs to be taken with a touch of salt and sung: “A spoonful of soil helps the microbiome recover!” We have become hyper-hygienic. See the Hygiene hypothesis. This comes from a post in 2016:

“The Amish and Hutterites are U.S. agricultural populations whose lifestyles are remarkably similar in many respects but whose farming practices, in particular, are distinct; the former follow traditional farming practices whereas the latter use industrialized farming practices….Despite the similar genetic ancestries and lifestyles of Amish and Hutterite children, the prevalence of asthma and allergic sensitization was 4 and 6 times as low in the Amish” – i.e. industrialized farming practices resulted in six times (600%) the rate of asthma and allergies. See Innate Immunity and Asthma Risk in Amish and Hutterite Farm Children(2016). This is also echoed in their farm products!!! Amish and Hutterite Environmental Farm Products Have Opposite Effects on Experimental Models of Asthma [2016]. Given a choice of buying groceries from a Hutterite farm or a Amish farm, buy the Amish (non industrialized) groceries!!!!

So I advocate not a Paleo diet, but a regional medieval-food diet (modified for modern nutritional needs). No prepared foods (talk about being extremely unnatural!), so food prepared from scratch — ideally organic with heritage seeds.

ME/CFS Continues Improvement + Lab Read Quality Issues

Prior Posts

Analysis

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

Another way to view it is this: If 10% are out of range and 400 are reported then we have 40. If we have 660 in another report then we would expect 66. This could be misread as a 66/40 or a 65% increase in out of range bacteria when the same percentage is out of range. Technically, it is more complicated but that should explain the problem.

Looking only at high read quality ( 1/22/2024, 2/22/2023, 8/31/2021) we see improvements where there are 🙂 below. This is an unfortunate aspect of 16s tests.

I have added at the bottom Forecast Symptoms compared to actual.

Criteria1/22/20249/12/20232/22/20238/11/20223/25/202212/3/20218/31/2021
Eubiosis56.41003710010068.167.4
Lab Read Quality7.93.59.75.56.23.67.8
Outside Range from GanzImmun Diagostics16161515171720
Outside Range from JasonH7777446
Outside Range from Lab Teletest20 🙂202424222225
Outside Range from Medivere16161515151519
Outside Range from Metagenomics7799778
Outside Range from Microba Co-Biome2277111
Outside Range from MyBioma5 🙂577778
Outside Range from Nirvana/CosmosId20202323181821
Outside Range from Thorne (20/80%ile)198 🙂198223223217217246
Outside Range from XenoGene24 🙂243232363639
Outside Lab Range (+/- 1.96SD)5 🙂1510119914
Outside Box-Plot-Whiskers54564236425942
Outside Kaltoft-Moldrup123 🙂70139567859140
Bacteria Reported By Lab511399666478613456572
Bacteria Over 90%ile20 🙂542624265746
Bacteria Under 10%ile108418248442999
Shannon Diversity Index1.3681.181.0381.2871.5610.8950.903
Simpson Diversity Index0.1150.0630.050.0420.0580.0220.02
Chao1 Index760350571253480531323455639209
Pathogens26 🙂253023392430
Condition Est. Over 90%ile0000000
Actual Symptoms in top 10 Forecasted581088109
Max Forecast Symptom Factor38.522.325.316.915.826.433.1

Explaining the new Symptom Forecast Algorithm

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

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

This data actually clarifies that the ideal 16+ for a factor is dependent on the Lab Read Quality and that 16 may apply to shotgun results but for 16s results, some flexibility with the 16 is warranted.

As a general FYI, hitting 80-100% correct prediction of symptoms implies that the algorithm performs well and the change of algorithm was appropriate.

It also implies that we are successfully identifying the bacteria associated with the symptoms..

The drop of matches with this sample is difficult to clearly interpret. It was not intended to be an indicator but a tool to correctly identify the bacteria of concern. Getting suggestions solely from the symptoms have been added. See the video below.

Going Forward

Again, using Just give me suggestions include Symptoms is how we are going to proceed. And then add in the two Special Studies. This results in 7 packages of suggestions.

Thresholds: High is 524 thus 260 or higher, Low is -346 this -170 or lower

For our first pass, we are going to look items that all 7 agrees upon, the list is very short

The two take list is very short. Prescription items dominates the list with metronidazole (antibiotic)s[CFS] at 524(the TOP), followed by amoxicillin (antibiotic)s[CFS], ciprofloxacin (antibiotic)s[CFS]. The top NOT-PRESCRIPTION is 232, so we will drop the threshold to 116

The avoid list is much bigger

For myself, I would try to obtain and rotate the antibiotics listed above and use Splenda where practical.

Bottom Line

This analysis has been both challenging and informative. We see that 16s Lab Read Quality can confuse analysis because it will alter many measures significantly. Care must be taken when comparing two or more samples with different Read Quality. Additionally, having the top suggestions full of prescription items means that we needed to adjust the threshold based on the top non-prescription item.

On the positive side, we see that the revised symptom forecasts appear to perform well, actually better than I was expecting.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

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

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

A Youth’s Microbiome with Marfan

This is the son of a person with issues: Bad Diet and Antibiotics? ME/CFS like symptoms. As I have often cautioned, young people’s and children’s microbiomes are different than adults and we do not have appropriate reference ranges. We will do best efforts.

  • Tired all the time
  • Cannot gain weight (very skinny)
  • Diagnosed with Marfan syndrome (a genetic disease he has since birth)
    [editor] About 1 in 5,000 people have Marfan syndrome
  • Stomach pain
  • Disgestion issues (yellow stool and fat in stool)
  • Joint and Muscle pain
  • Dark circles around the eyes

Analysis

There is no available literature on microbiome shifts with Marfan syndrome. Marfan syndrome (MFS) results from heterozygous mutations in the fibrillin-1 gene (FBN1; OMIM #134797), located on chromosome 15 at band q21. 1 (15q21. 1), which encodes for the glycoprotein fibrillin. [MedScape]. Coagulation plays a major role in Marfan syndrome. Both procoagulation and bleeding disorders participate in the disease. Platelet function abnormalities, compatible with von Willebrand syndrome or congenital thrombocytopathies, have been identified in 29% of patients. [2014].

 FBN1 is the causative gene for Marfan syndrome, an inherited disorder of connective tissue whose major features include tall stature and arachnodactyly, ectopia lentis, and thoracic aortic aneurysm and dissection. More than one thousand individual mutations in FBN1 are associated with Marfan syndrome, making genotype-phenotype correlations difficult. [2016]

At a high level we see potential issues. [NOTE: The Eubiosis value changed for many people after a defect was detected in how percentile was computed for some bacteria – often 100% dropped, which how agrees with people symptoms ].

However, there were no statistically significant matches (this page was just updated to use the latest algorithm). This algorithm is similar to the Eubiosis algorithm. We compute the expected number of matches to bacteria shifts associated with the symptoms. The expected number by randomness is 16%. A higher number indicate increased odds, a lower number decreased odds. This is based on the existing annotated samples uploaded data. It is not definitive and often there can be multiple subsets of bacteria associated with a symptom. The match is on too much or too few of a collection of bacteria associated to symptoms.

We will still check the matching symptoms. The top one is a match for symptoms but not a match for the typical bacteria seen for this symptom. We have to hope that there is sufficient matches to be worth while.

First, looking at all of the common, popular measuring sticks –

  • General Health Predictors has a low number (just 8) with only one of possible concern: Veillonella atypica 
  • All of the usual ratios are between 20%ile and 80%ile
  • Anti inflammatory Bacteria Score is at 89%ile — very good
  • No Potential Medical Conditions Detected detected
  • Histamine issues are a potential health issue
  • Bacteria deemed Unhealthy: Bacteroides fragilis and Parabacteroides merdae are the only one, both are potentially infectious bacteria.
  • Dr. Jason Hawrelak Recommended Levels came in at 94%ile

So, everything looks like a reasonably healthy person.

Going Forward

With no clear issue associations, we are going to do two runs: One with symptoms and one without.. Using the standard ‘Just give me Suggestions’ The list below are for the items at a priority of 50% or more of the highest priority [506] [Symptoms: 440]. This limit has no rationale apart from reducing the volume of suggestions.

Suggestions

Let us look at AVOIDS, given the general healthy state, just try reducing if taking,

At this point, I attempted to do cross validation: Are there any literature of items suggested (or avoid) on improving Marfan syndrome. This is done to test the suggestions – for other conditions with lots of literature, we have usually seen 90-95% agreement. For ME/CFS, I know the literature well — for this condition, not. I found only symptom treatment (Mayo, NHS). Since this is a DNA condition and I know DNA can influence the microbiome (and the reverse), then trying to improve towards typical this youth’s microbiome is a reasonable (and low risk) approach.

Because of the platelet function abnormalities that could be present, as well as thick blood (procoagulation) — both areas that I am familiar with from ME/CFS. I checked suggestions for possible items that could be related to coagulation:

Questions and Answers

Q: Do you know why it suggest gluten Free diet? Could he have gluten intolerence/celiac maybe?

  • The why is because of that type of diet impacts on bacteria that are off balance. We cannot detect  gluten intolerance/celiac. Only the impact of that type of diet on different bacteria. Some source papers to illustrate:

Bottom Line

The thing that strikes me most about the suggestions, the items to avoid are prebiotics and probiotics; the opposite of common advice for anyone with health issue, especially digestive issues. There are no studies on PubMed for probiotics and this condition which infers to me (given the popularity of trying probiotics) that there were studies with no effect or negative effect. In the sparse literature, we did have one of the take suggestions computing matching the literature.

If this was my own child, I would likely follow the items in suggestions above. There is low risk in following them and one suggestion does cross-validate with the available literature (which has almost nothing published in this area).

I work off the axiom that a disease or conditions modifies the microbiome to be favorable to the condition. Undoing those shifts have a reasonable change of slowing, and in some cases, reversing the condition. I have had antidotal reports of this approach speeding cancer treatment.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

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

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