Recently I have gotten some messages concerned about Eubiosis scores dropping. Eubiosis is a measure of evenness of the bacteria representation. It cannot be used to identify which bacteria needs to be changed. It is a representation of the Chi_² value of the genus converted to %ile with values over 80%ile deemed 100%.

What is statistically ideal is:

Below is an example of a low score of 1.2%

Dr. Jason Hawrelak score is 28%ile, MHI-A Ratio is 47%

It is saying that you have very few bacteria genus that have high representations and a ton of bacteria with high representation. This patterns suggest that the gut has become destabilized (which if you have dysbiosis is a good thing) but has not stabilized.

Same Person – Prior Pattern

The person has improved compared to this pattern. The “peak: was at 10-19 and above it has shifted to 10-29 range. Dr. Jason Hawrelak score is 46%ile MHI-A Ratio 67%

Same Person — further back

This is a better looking pattern. But remember this is not a primary measure for a gut score, but an adjunct dimension to be considered. For example, Dr. Jason Hawrelak score is a low 5%ile. MHI-A Ratio 66%ile

Bottom Line

There is no single number that represents an accurate gut health score. DO NOT DEEM EUBIOSIS to be such. It should be considered with items like: Dr. Jason Hawrelak score at least, and MHI-A Ratio ( see Development and Validation of a Novel Microbiome-Based Biomarker of Post-antibiotic Dysbiosis and Subsequent Restoration). Microbiome Prescription gives a multitude of different numbers estimating gut health based on literature, or in this case, statistics.

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

Source	Tax_name	tax_rank	Male	Female	Male_Chi2	FeMale_Chi2
thryve	Thermodesulfobacteria	phylum	increases	increases	234.0375	138.4544
biomesight	Verrucomicrobiaceae	family	increases	increases	8.333333	7.262051
biomesight	Rhodothermaeota	phylum	increases	increases	179.2	217.3071
biomesight	Akkermansiaceae	family	increases	increases	8.718378	9.965634
biomesight	Erysipelothrix muris	species	increases	increases	9.533889	10.08333
biomesight	Akkermansia	genus	increases	increases	8.718378	9.965634
biomesight	Rhodothermales	order	increases	increases	179.2	217.3071
biomesight	Akkermansia muciniphila	species	increases	increases	8.718378	9.965634
biomesight	Erysipelothrix	genus	increases	increases	9.663289	9.663289
biomesight	Rhodothermia	class	increases	increases	179.2	217.3071
biomesight	Thermodesulfobacteria	phylum	increases	increases	281.1738	299.9112

ME/CFS With IBS

We find differences here.

Source	Tax_name	tax_rank	Taxon	Male	Female	Male_Chi2	FeMale_Chi2
biomesight	Sutterella	genus	40544	decrease	increases	8.333333	11.25018
biomesight	Rhodothermales	order	1853224	increases	increases	139.9274	114.5716
biomesight	Dorea	genus	189330	increases	decrease	18.75	16.17875
biomesight	Rhodothermia	class	1853222	increases	increases	139.9274	114.5716
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	280.3333	187.9779
biomesight	Sutterellaceae	family	995019	decrease	increases	8.333333	11.25018
biomesight	Alcaligenaceae	family	506	decrease	increases	8.333333	9.120714
biomesight	Rhodothermaeota	phylum	1853220	increases	increases	139.9274	114.5716

ME/CFS Without IBS

We found no differences yet (given the sample size)

Source	Tax_name	tax_rank	Taxon	Male	Female	Male_Chi2	FeMale_Chi2
biomesight	Bacteroides fluxus	species	626930	increases	increases	7.355161	7.910588
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	124.4571	170.4624

Irritable Bowel Syndrome

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

thryve	Thermodesulfobacteria	phylum	200940	increases	increases	252.8232	95.10095
biomesight	Rhodothermales	order	1853224	increases	increases	125.1467	110.6182
biomesight	Rhodothermia	class	1853222	increases	increases	125.1467	110.6182
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	314.4971	174.6182
biomesight	Rhodothermaeota	phylum	1853220	increases	increases	125.1467	110.6182
biomesight	Sharpea azabuensis	species	322505	increases	increases	16.18526	6.80625
biomesight	Sharpea	genus	519427	increases	increases	16.18526	6.80625
thryve	Mycoplasma	genus	2093	increases	decrease	12.81524	20.3229
thryve	Mycoplasmataceae	family	2092	increases	decrease	14.88581	20.3229
thryve	Phocaeicola vulgatus	species	821	increases	decrease	7.893492	17.06273
thryve	Mycoplasmatales	order	2085	increases	decrease	14.88581	26.01485

Depression

Another condition with a gender association

Source	Tax_name	tax_rank	Taxon	Male	Female	Male_Chi2	FeMale_Chi2
thryve	Thermodesulfobacteria	phylum	200940	increases	increases	227.7557	148.4336
thryve	Parabacteroides distasonis	species	823	decrease	increases	9.118356	13.46941
thryve	Eubacterium oxidoreducens	species	1732	decrease	increases	12.99507	6.76
biomesight	Rhodothermales	order	1853224	increases	increases	121.2002	91.125
biomesight	Rhodothermia	class	1853222	increases	increases	121.2002	91.125
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	223.4402	189.2431
biomesight	Rhodothermaeota	phylum	1853220	increases	increases	121.2002	91.125
thryve	Lactobacillus rogosae	species	706562	decrease	decrease	23.88368	12.12781

Symptom: Problems remembering things

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

Source	Tax_name	tax_rank	Taxon	Male	Female	Male_Chi2	FeMale_Chi2
thryve	Thermodesulfobacteria	phylum	200940	increases	increases	316.4446	120.0944
biomesight	Rhodothermales	order	1853224	increases	increases	171.7445	133.3333
biomesight	Rhodothermia	class	1853222	increases	increases	171.7445	133.3333
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	369.0078	289.0992
biomesight	Odoribacteraceae	family	1853231	increases	increases	12.79311	7.962632
biomesight	Rhodothermaeota	phylum	1853220	increases	increases	171.7445	133.3333
biomesight	Acetivibrio	genus	35829	decrease	increases	9.180865	17.49208
biomesight	Odoribacter	genus	283168	increases	increases	9.334949	12
biomesight	Acetivibrio alkalicellulosi	species	320502	decrease	increases	9.180865	19.95636
biomesight	Hathewaya histolytica	species	1498	decrease	increases	9.180865	7.262051
biomesight	Hathewaya	genus	1769729	decrease	increases	9.180865	7.262051
biomesight	[Clostridium] thermoalcaliphilum	species	29349	increases	increases	7.35	6.880909
thryve	Intestinimonas	genus	1392389	decrease	increases	16	8.552727
thryve	Intestinimonas butyriciproducens	species	1297617	decrease	increases	16.48646	9.992258
ubiome	Bacteroides sp. EBA5-17	species	447029	increases	decrease	9.055577	7.314286

Symptom: Worsening of symptoms with stress.

Another common symptom of ME/CFS

Source	Tax_name	tax_rank	Taxon	Male	Female	Male_Chi2	FeMale_Chi2
thryve	Thermodesulfobacteria	phylum	200940	increases	increases	282.4023	185.22
biomesight	Thermoanaerobacterales Family III. Incertae Sedis	family	543371	decrease	increases	22.00454	8.491649
biomesight	Sharpea	genus	519427	increases	increases	17.55625	12.38345
biomesight	Hathewaya	genus	1769729	decrease	increases	16.98612	11.70814
biomesight	Rhodothermales	order	1853224	increases	increases	142.9353	188.8704
biomesight	Hathewaya histolytica	species	1498	decrease	increases	16.98612	11.70814
biomesight	Sharpea azabuensis	species	322505	increases	increases	17.55625	12.97965
biomesight	Rhodothermia	class	1853222	increases	increases	142.9353	188.8704
biomesight	Thermodesulfobacteria	phylum	200940	increases	increases	352.2616	362.7038
biomesight	Acetivibrio alkalicellulosi	species	320502	decrease	increases	12.65818	8.491649
biomesight	Rhodothermaeota	phylum	1853220	increases	increases	142.9353	188.8704
biomesight	Acetivibrio	genus	35829	decrease	increases	12.65818	8.491649

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

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:
  • Vitamin D
  • glycerol monolaurate (Monolaurin)
  • Chlorine Dioxide Solution (CDS)
• 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.

• The effect of antibacterial soap with 1.5% triclocarban on Staphylococcus aureus in patients with atopic dermatitis [2000] – reduces, not eliminated
• Bactericidal effects of triclosan in soap both in vitro and in vivo [2015] – the soap needs to be kept on the skin for a while to be effective. i.e. not just wash with soap and immediately rinse.
• Effects of an Antibacterial Soap on the Ecology of Aerobic Bacterial Flora of Human Skin [1975]
• Microbicidal effects of plain soap vs triclocarban-based antibacterial soap [2016]

Other items that inhibits: [2012]

• paroxetine
• hydroxyzine
• atomoxetine
• bencyclane fumarate
  

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 Name	Taxa Rank	Percentage	uBiome Percentile	Ombre Percentile	Biomesight Percentile
Akkermansia	genus	1 – 5	48 – 80	71 – 91	77 – 93
Alistipes	genus	0 – 0.3	0 – 10	0 – 33	0 – 100
Bacteroides	genus	0 – 20	0 – 32	0 – 48	0 – 45
Bacteroidia	class	0 – 35	0 – 24	0 – 40	0 – 45
Bifidobacterium	genus	2.5 – 5	78 – 91	78 – 87	90 – 95
Bilophila wadsworthia	species	0 – 0.15	0 – 32	0 – 43	0 – 44
Blautia	genus	5 – 10	15 – 60	32 – 72	24 – 69
Desulfovibrio	genus	0 – 0.15	0 – 46	0 – 42	0 – 72
Escherichia coli	species	0 – 0.1	0 – 100	0 – 75	0 – 88
Eubacterium	genus	0 – 15	0 – 100	0 – 99	0 – 100
Faecalibacterium prausnitzii	species	10 – 15	80 – 95	50 – 69	46 – 69
Fusobacterium	genus	0 – 0.01	0 – 40	0 – 66	0 – 72
Lactobacillus	genus	0.01 – 1	23 – 93	9 – 75	46 – 99
Methanobrevibacter	genus	0 – 0.01	0 – 7	0 – 33	0 – 33
Oxalobacter	genus	0.01 – 1	0 – 100	38 – 100	35 – 100
Prevotella	genus	0 – 25	0 – 100	0 – 89	0 – 88
Pseudomonadota	phylum	0 – 4	0 – 52	0 – 76	0 – 54
Roseburia	genus	5 – 10	51 – 86	85 – 96	81 – 95
Ruminococcus	genus	0 – 15	0 – 100	0 – 98	10- 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.

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.

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 Association
• Overview by symptoms Citizen Science Symptoms To Genus Special Studies (microbiomeprescription.com)
• Genus identification

Extreme Associations

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

• Thermodesulfobacteriota
• Rhodothermales
• Rhodothermia
• Rhodothermota

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.

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 [17–20]
• 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.

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, Dimedrol, Daedalon, and Nytol.)
• brompheniramine (Dimetappm, Dimetapp, 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….

• Association of systemic anticholinergic medication use and accelerated decrease in lung function in older adults [2024]
• Oxybutynin-Associated Cognitive Impairment: Evidence and Implications for Overactive Bladder Treatment [2024] “a first-generation anticholinergic, accounts for more than half of all OAB medication prescriptions” “associated with adverse cognitive outcomes”
• Increased anticholinergic medication use in middle-aged and older autistic adults and its associations with self-reported memory difficulties and cognitive decline [2023]
  “Among older adults, anticholinergic medications are associated with cognitive impairment and risk for cognitive disorders, including Alzheimer’s disease.”
• Clinical pharmacist-led assessment and management of anticholinergic burden and fall risk in geriatric patients [2023] “Risk of falls increased 2.50 times in patients with high anticholinergic burden”
• Anticholinergic deprescribing interventions for reducing risk of cognitive decline or dementia in older adults with and without prior cognitive impairment [2023] “There is insufficient evidence to reach any conclusions on the effects of anticholinergic burden reduction interventions on cognitive outcomes in older adults with or without prior cognitive impairment. ” — that is, stopping may not reverse it effects.
• Drugs with a negative impact on cognitive functions (Part 2):… [2023]
• Association of anticholinergic drug exposure with the risk of dementia among older adults in Japan: The LIFE Study [2023] “Several types of anticholinergic drugs used by older adults in Japan are associated with an increased risk of dementia. “
• Low-Income Older Adults’ Vulnerability to Anticholinergic Medication-Associated Frailty [2023]
• Association between recently raised anticholinergic burden and risk of acute cardiovascular events: nationwide case-case-time-control study [2023] “An association was found between recently raised anticholinergic burden and increased risk of acute cardiovascular events. Furthermore, a greater increase in anticholinergic burden was associated with a higher risk of acute cardiovascular events.”
  

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:

• Escherichia coli (Mutaflor, Symbioflor 2)
• Akkermansia muciniphila (Pendulum)
• Bifidobacterium adolescentis
• Bifidobacterium longum
• Lactobacillus casei group

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

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)

• Effect of Huperzine A on Cognitive Function and Perception of Effort during Exercise: A Randomized Double-Blind Crossover Trial [2021] “Huperzine A does not enhance cognitive function during exercise despite it being marketed as a cognitive enhancer”

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:
  • Cauliflower
  • Artichoke
  • Rutabaga
  • Turnip
  • Beets “From the Middle Ages, beetroot was used as a treatment for a variety of conditions, especially illnesses relating to digestion and the blood.”
  • For more, see http://www.oldcook.com/en/medieval-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.

Prior Posts

• Update on ME/CFS Person [Sep 2023]
• Follow up Microbiome Analysis from a prior post [Apr 2022]
• Rosacea, Circulation and mild CFS [Dec 2021]

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.

Criteria	1/22/2024	9/12/2023	2/22/2023	8/11/2022	3/25/2022	12/3/2021	8/31/2021
Eubiosis	56.4	100	37	100	100	68.1	67.4
Lab Read Quality	7.9	3.5	9.7	5.5	6.2	3.6	7.8
Outside Range from GanzImmun Diagostics	16	16	15	15	17	17	20
Outside Range from JasonH	7	7	7	7	4	4	6
Outside Range from Lab Teletest	20 🙂	20	24	24	22	22	25
Outside Range from Medivere	16	16	15	15	15	15	19
Outside Range from Metagenomics	7	7	9	9	7	7	8
Outside Range from Microba Co-Biome	2	2	7	7	1	1	1
Outside Range from MyBioma	5 🙂	5	7	7	7	7	8
Outside Range from Nirvana/CosmosId	20	20	23	23	18	18	21
Outside Range from Thorne (20/80%ile)	198 🙂	198	223	223	217	217	246
Outside Range from XenoGene	24 🙂	24	32	32	36	36	39
Outside Lab Range (+/- 1.96SD)	5 🙂	15	10	11	9	9	14
Outside Box-Plot-Whiskers	54	56	42	36	42	59	42
Outside Kaltoft-Moldrup	123 🙂	70	139	56	78	59	140
Bacteria Reported By Lab	511	399	666	478	613	456	572
Bacteria Over 90%ile	20 🙂	54	26	24	26	57	46
Bacteria Under 10%ile	108	41	82	48	44	29	99
Shannon Diversity Index	1.368	1.18	1.038	1.287	1.561	0.895	0.903
Simpson Diversity Index	0.115	0.063	0.05	0.042	0.058	0.022	0.02
Chao1 Index	7603	5057	12534	8053	13234	5563	9209
Pathogens	26 🙂	25	30	23	39	24	30
Condition Est. Over 90%ile	0	0	0	0	0	0	0
Actual Symptoms in top 10 Forecasted	5	8	10	8	8	10	9
Max Forecast Symptom Factor	38.5	22.3	25.3	16.9	15.8	26.4	33.1

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

• TAKE: fruit/legume fibre
• AVOID:
  • low-fat diets,
  • high sugar diet,
  • stevia,
  • kefir,
  • methionine-choline-deficient (MCD) diet,
  • saccharin,

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

• Foods:
  • Cacao
  • fruit/legume fibre
  • dates
  • lauric acid(fatty acid in coconut oil,in palm kernel oil,)
  • whole-grain barley
  • Grapefruit seed extract
  • lauric acid(fatty acid in coconut oil,in palm kernel oil,)
  • walnuts
  • Vegemite
  • Onion
  • Peanut
  • Coffee
• Herbs
  • momordia charantia(bitter melon, karela, balsam pear, or bitter gourd),
  • chamaemelum nobile,chamomile
• Probiotics (no prebiotics made it)
  • lactobacillus reuteri (probiotics)
  • bifidobacterium infantis,(probiotics)
  • lactobacillus salivarius (probiotics)
  • bifidobacterium breve (probiotic)
  • lactobacillus gasseri (probiotics)
• Sugars
  • sucralose (artificial sugar) a.k.a. Splenda,
  • raffinose(sugar beet)
  • aspartame (sweetener)

The avoid list is much bigger

• Diet: Avoid the extremes: low-fat diets, fibre-rich macrobiotic ma-pi 2 diet, high sugar diet, hypocaloric hyperproteic diet,methionine-choline-deficient (MCD) diet, high red meat, fasting
• quercetin,resveratrol, xylan (prebiotic), Human milk oligosaccharides (prebiotic, Holigos, Stachyose), resistant starch, resistant maltodextrin, arabinogalactan (prebiotic)
• cranberry bean flour, Pulses, blackcurrant
• Slippery Elm, berberine, grape seed extract
• bacillus licheniformis,(probiotics), lactobacillus plantarum (probiotics),
• stevia, saccharin, xylitol, non-starch polysaccharides

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.