Caution: Special Studies Suggestions

Special Studies are a conceptual thought experiment. The logic is simple, identify the bacteria that are have major statistical significance from the reference. Then use these bacteria with the weight that each has being the z-score to generate suggestions.

The first reviews that I did using them had good results and agreement with my preferred trio to build consensus, namely:

This last week I have gotten several emails from people who got counter-indicated suggestions. I have verified that for their samples, it produces contrary suggestions.

Digging into the mathematics and fuzzy logic being used, I see several possible failure points that I want to slowly investigate. The top failure points are:

  • Using the z-score for the weight to give for each desired shifts. A different formula may resolve it. Two candidate formula are:
    • z-score * incidence of bacteria being seen
    • z-score * function(bacteria count) — with many possible functions
    • The z-score cut off is too low, I am using 5.0 at present, it may need to be raised to a higher value.
  • The criteria for picking a bacteria to include may not be specific enough, so a lot of bacteria that are fine are included. This can result in excessive noise in the suggestions
  • The data available for suggestions at the species levels that we are working are insufficient (and in some cases, may not exist). A lot of the species flagged are rarely seen in studies showing changes.

Bottom Line

Use the suggestions generated with great caution. If they compliment the suggestions from the three preferred consensus methods listed above — good. If they contradict, keep to the original consensus method — I have been getting consistent report that they work. The special studies suggestions are getting inconsistent results.

A special study with a z-score below 6.6 is very suspect and should be ignored.

Brain Fog – Recap and what is known

A reader contacted me over a new post on Biomesight – How to reduce brain fog. He was concerned over the content (knowing that I have often researched and posted on brain fog), so I am doing this post to provide some clarity on brain fog. (Bad pun: Remove some fog from brain fog)

What is Brain Fog?

  • The term brain fog is a vague term that has been defined in the literature as a combination of the following more accurate (and measurable by tests) conditions. A better term is executive dysfunction [2015] or Cognitive Fatigue [2014]. The literature goes back to at least 1989. I know from personal experience, I have taken them from professional psychologist, and other in the family has too.
  • Some people will perform badly on all tests, other will perform poorly on certain tests only.
Tests used to evaluate objectively brain fog, Executive Dysfunction [2015]

Related forms include

IMHO, if you do not have the majority of the above, the term may be misapplied.

For different diseases, what constitues brain fog can vary, for example:

The clinical picture typically affects visuo-spatial immediate memory (g = − 0.55, p = 0.007), reading speed (g = − 0.82, p = 0.0001) and graphics gesture (g = − 0.59, p = 0.0001). Analysis also revealed difficulties in several processes inherent in episodic verbal memory (storage, retrieval, recognition) and visual memory (recovery) and a low efficiency in attentional abilities.

Systematic review and meta-analysis of cognitive impairment in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [2022]

Common Conditions Having it

The list of conditions having issues somewhere in the executive function space is large, just a few are listed below.

Many of the above have distinct microbiome signatures and thus the hope of getting a universal microbiome signature for brain fog is an ideological belief. This appears to be confirmed in the analysis from Special Studies … Brain fog strongest z-score is just 5.2. This is lowest significance level of 26 items evaluated, the next lowest is General Fatigue. IMHO, there may be no true significance, the z-scores numbers were not adjusted for False discovery rate and incidence of reporting.

Most people will agree that there is no magic cause or microbiome signature for general fatigue — it could be an issue with iron levels, excessive lactic acid (impairment in clearing it), blood circulation issues, respiratory issues etc.

Brain fog could be described as mental fatigue and thus the same wide variety of issues can be involved. For ME/CFS, the dominant causes for brain fog, according to the literature are mentioned in some of my prior posts:

For Long COVID, we should also consider damage to the lungs impacting oxygen levels.

Concerns about Biomesight – How to reduce brain fog

My first concern is simple, the belief of there being a common microbiome pattern is very questionable. There are likely patterns, for example, a microbiome pattern that results in higher d-lactic acid production; a pattern that results in lower d-lactic acid production; a pattern that inhibits one of the many steps in the coagulation cascade; a pattern that overloads one of the many steps in the coagulation cascade; a pattern that causes vascular constriction; a pattern that cause inflammation; a pattern that inhibits the absorption of iron…and on and on. There is not a single pattern that applies to all.

My second concern is a failure to cross validate/document. D-lactic acid is well associated with brain fog in the literature, ” Dlactic acidosis is characterized by brain fogginess (BF) and elevated D-lactate“[2018], Recent research indicates that dlactic acid may inhibit the supply of energy from astrocytes to neurons involved with memory formation.  [2010]. It is not mentioned once in the post (as of this time).

When a statement like this is made “Unsurprisingly, high ethanol producers in the gut based on research findings (separate from what we are seeing from the Biomesight dataset) is associated with brain fog.” I would expect links to these research papers to be included for the reader to follow up. Ethanol is drinking alcohol, booze – which has me very curious about the links and especially if they are seen with many of the conditions cited above.

I am very curious because I use KEGG data to see if any compound production/consumption was statistically significant in Special Study: Neurocognitive: Brain Fog, and ethanol was not found to be statistically significant. Searching for ethanol on Pub Med, we find “Lactic acidosis and acute ethanol intoxication [1994]” and “SEVERE LACTIC ACIDOSIS SECONDARY TO ACUTE ALCOHOL INTOXICATION” [2021] but that was from explicit ethanol (alcohol) consumption.

This does lead me to a model for alcohol intolerance developing with ME/CFS, which I posted here [Alcohol Intolerance in ME/CFS – A Model].

How does special studies compared to Biomesight post

The table below shows no agreement between my special studies and their findings. We used different statistical process, but finding not a single agreement should be a red flag on relying on the data. While I have a smaller sample (approximately 1/3), the data processing to get the microbiome data was identical.

BacteriaBiomeSight
Post
Special Studies
Z-Score
Escherichia coli (species)5.3
Lactiplantibacillus pentosus (species) 5.1
Shuttleworthia (genus) 5.1
Escherichia (genus) 4.5
Veillonella (genus) 4.4
Veillonella dispar (species) 4.4
Staphylococcus pseudolugdunensis (species) 4.2
Clostridium cellulovorans (species) 4.1
Class DeltaproteobacteriaX
Species Bacteroides uniformisX
Species Bacteroides cellulosilyticusX
Species Phascolarctobacterium faeciumX
Genus BacteroidesX
Species Anaerotruncus colihominisX
Species Faecalibacterium prausnitziiX
Genus PrevotellaX

Bottom Line

I do not believe that we can aggregate all microbiome samples reporting brain fog into a single set and find a universal pattern to address a priori. The numbers from Special Study: Neurocognitive: Brain Fog were the weakest of all special studies and, based on some other recent work in progressed, results may be adversely affected by sampling bias, sample quality, and false detection rate.

A mother with Premenstrual Dysphoric Disorder

Back Story For Mother

“Lots of high % potential health stuff. In real time, mother’s very hormone sensitive . Gets like a Premenstrual dysphoric disorder (PMDD) state , feels depressed. Struggles with anxiety and depression in general . Notice she’s been getting allergies. During the birth of 2nd child two years ago; mother had to get vancomycin because she was strep B positive. When the family get sick, the mother gets the sickest of the 4 of us unfortunately. Mother caught COVID in Sep 2022. Mother’s cycles in 2021 started to be heavy where before that never happened. As a kid, mother was on Accutane a lot. When mothers results through biomesight I also got a message that said unusually small file size or something along those lines.”

Foreword – and Reminder

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

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

Mother Overview

  • Jason Hawrelak Recommendations – 99.7%ile (better than most), High Roseburia, Blautia. The others are low

Checking on the two studies that I could find Premenstrual dysphoric disorder

If you can locate any other studies, please send to me.

Tax NameShiftPercentile
Bacteroidia (NCBI:200643 )Low4%ile
Anaerotaenia (NCBI:1843206 )Highnot reported by lab
Bifidobacterium (NCBI:1678 )High80%ile
Blautia (NCBI:572511 )High98%ile
Butyricicoccus (NCBI:580596 )Lownot reported by lab
Collinsella (NCBI:102106 )High0%ile
Extibacter (NCBI:1918452 )Lownot reported by lab
Megasphaera (NCBI:906 )Low92%ile
Parabacteroides (NCBI:375288 )Low67%ile
Bacteroidetes (NCBI:976 )Low4%ile

This suggests that we have a general match with the literature and should include in our consensus, a hand picked set of suggestions.

There is an abundance of bacteria which are there, but at low levels.

We have an impressive list of bacteria deemed unhealthy. Note that counts are often small (agreeing with the abundance of bacteria at low levels seen above).

NameRankPercentileCountCommentMore Info
Actinomycesgenus87230PathogenCitation
Anaerotruncus colihominisspecies782380Not Healthy PredictorCitation
Bacteroides fragilisspecies745659PathogenCitation
Blautia productaspecies751440Not Healthy PredictorCitation
Clostridiumgenus7221469PathogenCitation
Collinsellagenus010High COVID RiskCitation
Corynebacteriumgenus891520PathogenCitation
Doreagenus9723040Increased COVID riskCitation
Eggerthella lentaspecies993469Not Healthy PredictorCitation
Finegoldia magnaspecies91830Infectious bacteriaCitation
Granulicatella adiacensspecies8480Not Healthy PredictorCitation
Leptospiragenus92160PathogenCitation
Peptoniphilus hareispecies6750Infectious bacteriaCitation
Peptoniphilus lacrimalisspecies78170Infectious bacteriaCitation
Peptostreptococcusgenus83170PathogenCitation
Peptostreptococcus stomatisspecies88150Not Healthy PredictorCitation
Prevotella timonensisspecies951890Infectious bacteriaCitation
Ruminococcus gnavusspecies9962200Not Healthy PredictorCitation
Staphylococcus haemolyticusspecies7240PathogenCitation
Streptococcus vestibularisspecies65370Not Healthy PredictorCitation
Veillonella atypicaspecies94859Not Healthy PredictorCitation

Potential Medical Conditions Detected had at 99%ile, Schizophrenia, which suggests some overlap with PMDD.

Going Forward

I added a new choice to the site that gets canned suggestions for Bacteria Deemed Unhealthy above because of the large number of bacteria flagged in this sample.

The new option

The suggestions for this person with this new option.

33 bacteria was selected.

Building the Consensus

I am building from:

  • Hand Picked (see above focused on PMDD) (4 picked)
  • Unhealthy Suppression – just added (33 picked)
  • Outside Range from Nirvana/CosmosId (5 picked)
  • Outside Lab Range (+/- 1.96SD) (6 picked)
  • Outside Box-Plot-Whiskers (60 picked)
  • Outside Kaltoft-Moldrup (95 picked)

The top items to suggested to take are shown below (meat and B-vitamins are concentric):

The top items to suggested to avoid are shown below

Probiotics

We have a challenge here, take lactobacillus rhamnosus gg (probiotics) by itself is a -409, but in combination with propionibacterium freudenreichii it is 190-263. This suggests that bacteria as a supplement ( Nutricology/Securil ) or lots of real Emmental cheese (which uses it).

Similarly lactobacillus salivarius (probiotics) is a negative -275 but Lactobacillus salivarius UCC118 is a positive 285. I would avoid it, instead of playing probiotic roulette.

The next one on the list as a reasonable candidate is lactobacillus fermentum (probiotics), then mutaflor escherichia coli nissle 1917 (probiotics).

Going over to KEGG suggestions, the top choices (not in Prescript Assist which is a negative) are (in order)

Which points to microbiome labs/ megasporebiotic OR organic 3 / primal soil (see probiotic page for other choices)

My suggestion for rotation would be:

Curiosity Question from User

“for curiosity’s sake, is there any way i can find the small number of actionable bacteria affected by all these foods?”

taxonTax NameTax RankModifier that Impacts
186802Eubacterialesorder1421
816Bacteroidesgenus1398
815Bacteroidaceaefamily1396
543Enterobacteriaceaefamily1384
28116Bacteroides ovatusspecies1382
29523Bacteroides sp.species1381
626931Bacteroides oleiciplenusspecies1381
357276Phocaeicola doreispecies1381
817Bacteroides fragilisspecies1381
820Bacteroides uniformisspecies1381
387090Phocaeicola coprophilusspecies1380
674529Bacteroides faecisspecies1380
1365140Bacteroides sp. J1511species1380
246791Bacteroides sp. 35BE35species1380
371599Bacteroides sp. XB12Bspecies1380
1244208Bacteroides sp. 2011_Ileo_VSA_D11species1380
151276Bacteroides coprosuisspecies1380
93975Bacteroides sp. AR29species1380
338188Bacteroides finegoldiispecies1380
One answer to the question
ModifierModifier typeBacteria Impacted
berberineHerb or Spice2101
glycyrrhizic acid (licorice)Herb or Spice2067
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)Prebiotics and similar2054
triphalaHerb or Spice2008
gentamicin (antibiotic)sAntibiotics, Antivirals etc1961
lactobacillus plantarum (probiotics)Probiotics1856
azithromycin,(antibiotic)sAntibiotics, Antivirals etc1829
trimethoprim (antibiotic)sAntibiotics, Antivirals etc1810
lactobacillus reuteri (probiotics)Probiotics1795
ironVitamins, Minerals and similar1738
Slippery ElmHerb or Spice1718
neomycin (antibiotic)sAntibiotics, Antivirals etc1717
zincVitamins, Minerals and similar1686
foeniculum vulgare (Fennel)Herb or Spice1686
folic acid,(supplement Vitamin B9)Vitamins, Minerals and similar1666
amoxicillin (antibiotic)sAntibiotics, Antivirals etc1661
resistant starchPrebiotics and similar1650
N-Acetyl Cysteine (NAC),flavonoids, polyphenols etc1624
streptomycin (antibiotic)sAntibiotics, Antivirals etc1620
cinnamon (oil. spice)Herb or Spice1586
The Alternative answer

Best Talks from ISB Virtual Microbiome Series 2022

I attended this series of talks and for the readers of this blog I link to the videos in terms of my preferences below. The first ones are very technical, Day Three is much easier watching.
Unfortunately, they did not post the videos, talk by talks..

  • Dr. Katherine Amato on Day 3 (hour 3) is likely the best starting one.
  • Dr. Poyet Day 3 (hour 1) on Industrialization Microbiome
  • Dr. Iraola Day 3 (hour 0) – on the Microbiomes of South America etc
  • Day 3 hour 2 — very interesting on the impact on the microbiome of anal intercourse…. there is significant impact.

Fatigue — candidate cause: Hypoxia

This is a summary of items that may be the cause. This assumes that a lack of oxygen getting to tissue (or the brain) is the cause. Note that if the brain is tired(oxygen starved), that there may be false signals saying the body is exhausted because the brain does not have the energy to manage the body. The sources are typically from Chronic Fatigue Syndrome. Some of these may be related to the microbiome (lack of studies). It is suspected that many people are borderline (have no apparent issues) and then some event pushes them over the edge into fatigue.

This is intended as a checklist to review with your medical professional. Items should be objectively excluded by actual tests and not subjectively excluded (often a response if the professional does not know how to test). See Hypoxia[2022] and Hypoxia (medical)

Coagulation Issues

The typical scenario seems to be an inherited or acquired (epigenetic — sometimes from a virus infection) coagulation defect. Some step of the coagulation cascade gets “constipated” with the result being what is often called “sticky blood”. It only takes one step.

If the blood gets thick, it gets slow and hence less oxygen gets to the body and the brain.

Most “blood thinners” only impacts one of these steps (with heparin being the main exception). Taking excessive supplements that impacts the same step may result in long bleeding time or easy bruising.

Note: We are talking about sub-clinical (i.e. not having a stroke or blood clot) levels. The lab levels may not be abnormal, just low (or high).

Low Iron Or Impaired Heme

Hemoglobin is the iron-containing oxygen-transportmetalloprotein in the red blood cells of all vertebrates  Hemoglobin in the blood carries oxygen from the respiratory organs (lungs or gills) to the rest of the body (i.e. the tissues). There it releases the oxygen to permit aerobic respiration to provide energy to power the functions of the organism in the process called metabolism. [wikipedia]. Hemogloblin can be inhibited by some chemicals (Carbon Monoxide [2022] and the pH of the blood (Bohr effect [2021]) are the most common. pH is influenced by the microbiome.

Iron insufficiency and hypovitaminosis D in adolescents with chronic fatigue and orthostatic intolerance. Restless leg syndrome (RLS) is  negatively correlated with ferritin / iron [2012] and RLS is also co-morbid with CFS (i.e. the less iron, the more likely you will have RLS).

For example, Staphylococcus aureus is known to adversely impact iron [2022]

Malformed Red Blood Cells

Back in 1997, Dr. Les Simpson in New Zealand wrote “Myalgic Encephalomyelitis (ME): A Haemorheological Disorder Manifested as Impaired Capillary Blood Flow” observed this in CFS patients. “So it has been proposed – first at the Cambridge Symposium on ME in 1990 and in another article in 1998, that ME is a dysfunctional state resulting from reduced rates of capillary blood flow due to the presence of shape-changed, poorly-deformable red cells” [source]

It should be noted that SPECT scans show the expected effects of shape-
changed, poorly deformable red cells in reducing cerebral blood flow in
regions which by chance have smaller than usual capillaries.

TheBMJ, Chronic fatigue syndrome or myalgic encephalomyelitis [2007]

Inflammatory hypoxia

Hypoxia and inflammation are frequently co-incidental microenvironmental features of sites of concentrated physiological or pathological immune activity.

Hypoxia activates hypoxia-inducible factor, which is a major regulator of multiple aspects of immune cell function. Consequently, hypoxia plays a key role in the regulation of immunity and inflammation.

The impact of hypoxia on immunity and inflammation is site-specific and cell type-specific.

Pharmacological hydroxylase inhibition, which activates hypoxia-sensitive pathways, is profoundly protective in multiple models of inflammation.

Regulation of immunity and inflammation by hypoxia in immunological niches [2017]

Low Blood Volume

David Bell, M.D. did a study in 1995 (twenty years ago) finding about low blood volume. Cort Johnson has an 2015 update by Dr. Bell that is worth reading. “In contrast, ME patients have a volume that can be as low as 50% of normal.”

Small Heart Size

“A considerable number of CFS patients have a small heart. Small heart syndrome may contribute to the development of CFS as a constitutional factor predisposing to fatigue, and may be included in the genesis of CFS.” [2008]

” Echocardiographic examination revealed that CFS patients with “small heart” had an actually small LV chamber and poor cardiac performance. Cardiac functional changes evaluated by repeated examinations appeared to be directly associated with the severity of their symptoms. Small heart syndrome with impaired cardiac function may contribute to the development of CFS through low cardiac output as a constitutional factor.” [2009]

Enzymes and thus Microbiome

“We’ve discovered that the muscles regulate oxygen consumption in a very precise way using the oxygen-sensitive enzyme FIH,”

How muscles regulate their oxygen consumption [2018]

” cytochrome P450 (CYP), monoamine oxygenase (MAO), and cyclooxygenase (COX). CYP enzymes are central players.. are important for therapeutic intervention and treatment of neurological and inflammatory diseases”

Analysis of activity and inhibition of oxygen-dependent enzymes by optical respirometry on the LightCycler system [2010]

Obstructive Sleep Apnea

Note that blood platelets activation is part of the coagulation cascade above.

Snoring, collapse of upper airways and intermittent hypoxia are main causes of smoldering systemic inflammation in patients suffering from obstructive sleep apnea. The systematic inflammation is considered one of the key mechanisms leading to significant cardiovascular complications. Blood platelets, formerly not even recognized as cells, are currently gaining attention as crucial players in the immune continuum. Platelet surface is endowed with receptors characteristic for cells classically belonging to the immune system, which enables them to recognize pathogens, immune complexes, and interact in a homo- and heterotypic aggregates.

Obstructive Sleep Apnea: From Intermittent Hypoxia to Cardiovascular Complications via Blood Platelets [2018]

Simplified Suggestions for Microbiome Adjustments

For the last few months I have been working with someone that runs a Long COVID support group. This has resulted in more modifiers being added. One of the outcomes has just been added to the site. She requested that a simplified set of suggestions be added to the site to make her life easier. This consists of items she picked from her experience dealing with the group.

These simplified suggestions has been added everywhere (tell me if I missed a page). If you use GI Map or similar reports, it is available after you have transferred and return to adjust suggestions. You should see these options on most suggestion pages now. To get this new condensed report, just click the checkbox.

Common Elements

GI Map and similar

The report is a CDV file (loads into Excel or other spreadsheet programs)

After loading, into Excel, you will need to adjust column widths

First load
After re-formatting as a table

If an item is a Take, then a clinical dosage is given (if known). Clinical Dosages are those reported from studies listed on the U.S. National Library of Medicine Clinical Trials site.

Example from a 16s Sample

Other sections are shown below.

CAUTIONS

At the bottom of the page are some essential reminders.

Some of the clinical dosages above may be problematic with some medical conditions. Dosages should be reviewed by a medical professional before starting. The suggestions are based on a mathematical model. dosages are from clinical studies.

GI Map vs Biomesight for Long Covid after ME/CFS

One of the purposes of these blog posts is to learn. My thinking and thoughts are there with the ability of people to correct, to comment and to learn. This post looks at GI-MAP and Biomesight report for the same person, taken at the same time. The key objectives are:

Foreword – and Reminder

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

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

Simple Back Story

” I got COVID at the end of Sept 2020 and have been much worse ever since.

We have 2 samples from Biomesight April,2022 and Sept 2022 and will see if the microbiome shifts reflect getting worse

GI-MAP vs BiomeSight

We are comparing different lab results, so reading: The taxonomy nightmare before Christmas… is strongly recommended. While results may disagree, they may be both technically accurate given the testing methodologies.

BacteriaGI MAPBiomeSight
Percentile
Enterobacter spp. ( Enterobacter)High78%ile
Bacteroidetes ( Bacteroidetes)High81%ile
Firmicutes ( Firmicutes)High8%ile
Bacillus spp. (Bacillus)High47%ile (seen in 66%)
Enterococcus faecalisHighNot reported (Seen in 1%)
Enterococcus faeciumHighNot reported (seen in 2.2%)
Methanobacteriaceae (family)HighNot reported (seen in 27%)

As is seen clearly, there are differences — some very striking (Firmicutes). I will leave it to the test providers to offer explanations on differences of techniques, etc. See Below One of the key issues is how labs handle not detected for determining ranges: some will exclude it from the calculation (thus range IF detected) and others will give it a zero value and include it.

I was curious about Bacillus distribution, which is shown below

Explanation of differences from GI-MAP

COMMENT FROM DIAGNOSTIC SOLUTIONS LAB

Thank you for contacting us and providing an opportunity for us to comment.  Metagenomic sequencing has been popular for untargeted characterization of microbiome composition, whereas qPCR is used primarily for accurate quantitation of selected targets (microbes and genes of interest) especially those with particular clinical implications, such as common pathogens and opportunists. Metagenomic sequencing and qPCR are not considered competing methodologies for the same purpose, so it’s not an apples-to-apples comparison. 

A major difference between the two methods is the type of quantitation that they provide. Metagenomic sequencing results are expressed as relative abundance (usually as a percentage), whereas qPCR results are expressed as absolute abundance (usually microbial cells per gram of stool). With relative abundance, it’s not possible to directly determine how much of a given taxon (species, genus, etc) is actually present, since its relative abundance is dependent upon the relative abundance of all of the other detected taxa. A couple of example references are included below. 

The differences between relative and absolute abundance are likely key factors contributing to any differences in results between GI-MAP and metagenomic tests. From a clinical standpoint, there are demonstrated advantages of using methods, such as qPCR, that provide absolute quantitation, since absolute quantitation has been shown in research studies to be more effective in identifying true correlations with quantitative clinical markers. Examples of clinical markers that are included on GI-MAP include calprotectin, secretory IgA, pancreatic elastase and occult blood.

A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities

https://pubmed.ncbi.nlm.nih.gov/32444602/

“Thus, an inherent limitation of methods that use relative abundance is that they cannot determine whether an individual taxon is more abundant or less abundant (the direction of the change) or by how much (the magnitude of the change) between two experimental conditions or samples.”

Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

https://pubmed.ncbi.nlm.nih.gov/34117246/

“Our results demonstrated that quantitative approaches performed better than their relative and compositional counterparts when it comes to identifying taxon–metadata associations or studying taxon–taxon interactions. The observed performance gap among the methods profiled widened with increasing unevenness of taxa distributions, as exemplified by our analyses of the blooming scenario.”

Explanation from BiomeSight

As many of your readers know, it is not possible to compare Biomesight and GI MAP directly due to the differences in what and how it is measured. The strength of a 16s test is in the ability to see the overall ecosystem and this technology is widely used today in clinical and academic research, often alongside shotgun or other WGS techniques. It allows for coverage of a range of bacteria that’s not included in more focused PCR tests.

Additionally, Biomesight already has around 400 samples from the long covid community allowing for better profiling of the condition. We have shared around half of these samples’ results with MicrobiomePrescription enabling a strong comparison base.

Here’s some of our blog articles covering our findings from the study:

https://biomesight.com/blog/long-covid-study-update-1

https://biomesight.com/blog/the-role-of-lps-in-long-covid

Suggestions from GIMAP

Without applying PUBMED filtering

All of the above bacteria were used.

With PubMed Filtering

Only the following bacteria was selected. This filtering is not available on the site because the quality of select drops too low (as seen here).

Bacteria NameAnalysisTaxonomy Hierarchy Level
  BacteroidetesToo Highphylum
  FirmicutesToo Highphylum

Suggestions from BiomeSight

Without Filtering – using Kaltoft-Moldrup for bacteria selection

We have 70 bacteria selected

Using Special Studies

We have 93 bacteria selected.

Getting Worst with Long COVID

Taking ME/CFS as a typical pattern, there tend to be three main paths:

  • Slow or Spontaneous Remission
  • Steady state with waxing and waning
  • Slow progressive deterioration, sometimes ending in complete system failue

There are two others, suicide from hopelessness and Remission cause by specific treatment (which was my personal case).

The reader had a early-COVID sample which allows comparisons. It is especially nice that the Lab Read Quality are similar, which makes interpretation more robust.

CriteriaCurrent SampleOld Sample
Lab Read Quality10.310
Bacteria Reported By Lab639507
Bacteria Over 99%ile63
Bacteria Over 95%ile2613
Bacteria Over 90%ile4026
Bacteria Under 10%ile198284
Bacteria Under 5%ile173229
Bacteria Under 1%ile155161
Lab: BiomeSight
Rarely Seen 1%37
Rarely Seen 5%1625
Pathogens3436
Outside Range from JasonH1010
Outside Range from Medivere1919
Outside Range from Metagenomics99
Outside Range from MyBioma77
Outside Range from Nirvana/CosmosId1919
Outside Range from XenoGene66
Outside Lab Range (+/- 1.96SD)148
Outside Box-Plot-Whiskers6742
Outside Kaltoft-Moldrup181186
Condition Est. Over 99%ile00
Condition Est. Over 95%ile00
Condition Est. Over 90%ile30
Enzymes Over 99%ile80
Enzymes Over 95%ile2814
Enzymes Over 90%ile5230
Enzymes Under 10%ile150240
Enzymes Under 5%ile120161
Enzymes Under 1%ile9088
Compounds Over 99%ile78
Compounds Over 95%ile37101
Compounds Over 90%ile146360
Compounds Under 10%ile94443
Compounds Under 5%ile60162
Compounds Under 1%ile35101

A summary of the above would be:

  • More bacteria are in play over time, post COVID
    • More bacteria at extreme high values
    • Less bacteria at extreme low values
  • No difference seen for canned selection of bacteria ( JasonH thru XenoGene)
  • More extreme values seen from Outside Lab Range (+/- 1.96SD), and Outside Box-Plot-Whiskers
  • Enzyme production became more extreme over time, after COVID
  • Compound production extreme values dropped a lot

This agrees with Special Studies on a large population (n > 150) of long COVID, especially for Compounds. There were no statistically significant compounds detected. There were statistically significant bacteria and enzymes detected.

Looking at the From Special Studies, we see that the degree of match for all of the top items have increased significantly (with the lab quality being similar), which is in agreement with “getting worse”

Older SampleLatest Sample
Inflammatory bowel disease52% match63% match
Small intestinal bacterial overgrowth (SIBO)45% 54%
Depression43%50%
ME/CFS without IBS42%50%
COVID19 (Long Hauler)41%46%
Unrefreshed sleep 41%51%

Which set of Suggestions is likely best?

Using BiomeSight data with Long COVID with Special Studies is by far the best. It detects 93 different bacteria at a detail level (low hierarchy – species, z-scores for each above 2.6) versus GI-MP with PubMed Long COVID studies which detects just 2 bacteria at the highest hierarchy level (phylum). While GI-MAP may be technically superior, there are two important factors:

  • The number of bacteria reported is significantly less in GI-MAP. Ideally, they will, in time, provide a deep report with NCBI numbers available for download or automatic transfer.
    • Volume of data and volume of different bacteria has an exponential impact on the ability to detect what has statistical significance.
  • We do not have a curated set of samples with Long COVID with GI-MAP. In other words, it does not point to the specific bacteria we should focus on.

Bottom Line

I am going to skip building a consensus here. The Special Studies filter for Long COVID feels right for a starting point. Doing the suggestions for 3 months and then doing a retest would be my way forward (after reviewing with your medical professional)

What we have not touched is probiotics.


From Special Studies: COVID19 (Long Hauler)

From KEGG data, the top species were Azospirillum lipoferum, Streptomyces venezuelae, Azospirillum brasilense — all sitting in 110-120 range. These are from probiotics like Equilibrium and Prescript Assist (various versions) –  Prescript-Assist®/SBO Probiotic sits at the top of mixed impact.

Scanning down the list for familiar names, we see

Remember: The above are specific species, you may wish to view the Probiotic Mixtures page to see which species are in each mixture. My take for simplicity would be: Prescript Assist, Symbioflo-2 (E.Coli probiotic),  miyarisan (Clostridium butyricum) and one of those listed above containing lactobacillus fermentum (Limosilactobacillus fermentum).

Reminder: One set of probiotic suggestions is obtained from the (very few) studies using the probiotics species and seeing what is affected. The other set of suggestions is obtained from the genomics of the bacteria that you have and the genomics of probiotic species. Two very different approaches which lead to the same probiotics.

Questions

This person is a new person to microbiome intrepretation (with a dose of brain fog)

Q: Are the items on the green add or increase lists the things I should be adding to my diet to help lower the levels of the bad bacteria I have.?

A: Correct, Some items have a ruler beside it, 📏, This is a link to dosages used in clinical studies (i.e. likely both safe dosages, and likely sufficient dosage to cause change. They are slowly being added for more and more modifiers.

I have shown suggestions from BOTH of your samples below, you will note that they are very similar.

Your latest sample
Your earlier sample

For example for walnuts we see that 40-80 gram/day (1.5 to 4 ounce) or 1/3 – 2/3 cups/day.

A: The Red ones are ones to reduce or eliminate from your diet when possible. These are substance that encourages the bad shifts. Often there can be surprises when the items to avoid are those often suggested. Best example is Vitamin B12 — I have a hypothesis (which some people agree with from their experience) that there are greedy bacteria in the gut that thrives on B-12 (thus accounting for low B-12 levels in the blood). It is suggested that B-Vitamins be taken by injection or transdermal (via skin patches).

Q: What should be the priorities in terms of diet and supplements to help reduce the levels of bad bacteria? 

A: After reducing as many red items slowly (don’t go cold turkey!) as practical, start adding the green items with the highest confidence items according to practically (cost, ease of preparation, other issues). Again, small steps, adding one every two days. For probiotics, do one for two weeks and then change to a different one (See this post for background)

Q: Also the top three probiotics that the AI program came up with are Symbiopharm/ symbioflo, Regactiv immune and vitality and LGasseri probiotic. Is that correct? I have started taking Visbiome? 

A: Yes, Visbiome has mixed benefits, some of it’s component may shift items in the wrong way. It’s estimated confidence is 11 versus another mixed item Global Healing Center / FloraTrex with a value of 95. I would suggest taking it for two weeks (or until one of the other arrives, which ever occurs last).

Q: Also I see that Enterobacter, Bacteroides, Firmicites and Bacillus are too high. Which of these is the biggest problem and what particular bacteria in these bacteria families are over grown? 

A: Unfortunately, there is no way to detects which is the biggest problem. People will often speculate. With specials studies, we get a measure of how strongly bacteria are associated (called a z-score), For you, the most statistically significant for Long Covid that you have are

  • Catonella
  • Catonella morbi
  • much lower: Clostridia

For Chronic Fatigue Syndrome the top ones for significance that you have are:

  • Sporolactobacillus
  • Sporolactobacillus putidus
  • Prevotella copri
  • Shuttleworthia

Q: I see from watching your other videos that I fit the pattern of a CFS patients pattern with a high number of rare bacteria. Have you seen people being able to reverse this to a more healthy normal microbiome and recover? 

A: Yes, take a look at this recent review A History of Several 16s Tests and Suggestions where we see measurable objective improvement as well as the person reporting improvement of some symptoms. There are several people that are doing test-> suggestions –> retest repeat. They have been forwarding their new samples to do an update as they are done (usually 3 months between tests). The pattern has been improvement both subjectively and objectively — unfortunately, it is not an overnight recovery, rather a slow methodological journey.

Different combination of bacteria cause different symptoms so the progress is from waypoint to waypoint .

Mankind does not have an ideal Microbiome

One of the common misconception is that there is a “normal” microbiome that can be used as a reference.  Below is a chart from “Metagenomic sequencing of fecal DNA“. Diet makes a major impact on the distribution and volume of the bacteria.

  • “In a study of gut bacteria of children in Burkina Faso (in Africa), Prevotella made up 53% of the gut bacteria, but were absent in age-matched European children.”[2010]

The chart below is for healthy individuals in 12 different countries.  In some cases neighboring very similar countries (Sweden [SE] and Denmark [DK]) have very different compositions.

world

This great variation means that testing the microbiome can only be done as group of individuals living in the same area with similar eating habits…. An individual result without reference from people with the same eating habits and possibly ethnic background is very fuzzy to interpret. Yes, highlights may be common — like low E.Coli, Lactobacillus and Bifidobacteria….  but they likely apply to no more than 80-90%, others may have different shifts based on things like being gluten free and other diets.

For more information see: The gut microbiome of healthy Japanese and its microbial and functional uniqueness [2016]

Then we also find that DNA also impacts the microbiome,

Host genetic variation drives phenotype variation, and this study solidifies the notion that our microbial phenotype is also influenced by our genetic state. We have shown that the host genetic effect varies across taxa and includes members of different phyla. The host alleles underlying the heritability of gut microbes, once identified, should allow us to understand the nature of our association with these health-associated bacteria, and eventually to exploit them to promote health.

Human genetics shape the gut microbiome , 2014

People have asked me, “Did you get your microbiome done, what was it?” My honest answer was “No, such testing was not available when I last had CFS. I simply assumed that my pattern would be an appropriate match to that reported from the 1998 Australian studies”

Age changes the microbiome

” DNA of the Clostridium leptum group and pathogenic Enterobactericeae increase in the gut microbiome with age and can be detected in the same individual’s coronary plaques along with pathogenic Streptococcus spp., associating with more severe coronary atherosclerosis. ” [2019]

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The presence of the BifidobacteriumFaecalibacteriumBacteroides group, and Clostridium cluster XIVa decreased with age up to 66-80 years of age, with differences reaching statistical significance for the latter group. Interestingly, the levels of some of these microorganisms recovered in the very old age group (>80 years), with these older individuals presenting significantly higher counts of Akkermansia and Lactobacillus group than adults and the younger elderly

Age-Associated Changes in Gut Microbiota and Dietary Components Related with the Immune System in Adulthood and Old Age: A Cross-Sectional Study. [2019]

Latitude changes the Microbiome

Latitude means the distance from the equator. This may be due to sunlight-vitamin D levels.

Geographical variation of human gut microbial composition , 2014

If you exercised recently impacts the microbiome

Underlying these macro-level microbial alterations were demonstrable increases in select bacterial genera such as Veillonella (+14,229%) and Streptococcus (+438%) concomitant with reductions in Alloprevotella (-79%) and Subdolingranulum (-50%). To our knowledge, this case study shows the most rapid and pronounced shifts in human gut microbiome composition after acute exercise in the human literature. 

Rapid gut microbiome changes in a world-class ultramarathon runner. 2019

Some Population Studies

“We analyzed the combined microbiome data from five previous studies with samples across five continents. We clearly demonstrate that there are no consistent bacterial taxa associated with either Bacteroides– or Prevotella-dominated communities across the studies. By increasing the number and diversity of samples, we found gradients of both Bacteroides and Prevotella and a lack of the distinct clusters in the principal coordinate plots originally proposed in the “enterotypes” hypothesis. The apparent segregation of the samples seen in many ordination plots is due to the differences in the samples’ Prevotella and Bacteroides abundances and does not represent consistent microbial communities within the “enterotypes” and is not associated with other taxa across studies.” [2016]

” All Egyptian gut microbial communities belonged to the Prevotella enterotype, whereas all but one of the U.S. samples were of the Bacteroides enterotype.

  • The intestinal environment of Egyptians was characterized by higher levels of short-chain fatty acids, a higher prevalence of microbial polysaccharide degradation-encoding genes, and a higher proportion of several polysaccharide-degrading genera.
  • Egyptian gut microbiota also appeared to be under heavier bacteriophage pressure.
  • In contrast, the gut environment of U.S. children was rich in amino acids and lipid metabolism-associated compounds; contained more microbial genes encoding protein degradation, vitamin biosynthesis, and iron acquisition pathways; and was enriched in several protein- and starch-degrading genera.
  • Levels of 1-methylhistamine, a biomarker of allergic response, were elevated in U.S. guts, as were the abundances of members of Faecalibacterium and Akkermansia, two genera with recognized anti-inflammatory effects.
  • The revealed corroborating differences in fecal microbiota structure and functions and metabolite profiles between Egyptian and U.S. teenagers are consistent with the nutrient variation between Mediterranean and Western diets.” [2017]

“This suggests that similarities between the Inuit diet and the Western diet (low fiber, high fat) may lead to a convergence of community structures and diversity. However, certain species and strains of microbes have significantly different levels of abundance and diversity in the Inuit, possibly driven by differences in diet.” [2017]

Bottom Line

IMHO: There is no clear definitive benefit from doing an individual microbiome testing — there is no reference that is reliable for it on an individual basis at a fine level of details. On the other hand, having results showing abnormalities help in several ways:

  • It encourages you to make changes in eating which will usually be for the better
  • It confirms that you have significant shifts and supports the concept that the gut is causing your symptoms.

” This work supports that sex is a critical factor in colonic bacterial composition of an aged, genetically-heterogenous population. Moreover, this study establishes that the effectiveness of dietary interventions for health maintenance and disease prevention via direct or indirect manipulation of the gut microbiota is likely dependent on an individual’s sex, age, and genetic background. ” [2019]

The shortfall in available probiotics

The source of this blog comes from SCIENCE,VOLUME 377|ISSUE 6612|16 SEP 2022, , pp. 1328-1332 DOI: 10.1126/science.abm7759, Codiversification of gut microbiota with humans

 An awareness of differences in gut microbial strains between populations has already led to the notion that probiotics for treating malnutrition should be locally sourced (38).

From Bifidobacterium infantis treatment promotes weight gain in Bangladeshi infants with severe acute malnutrition. Sci. Transl. Med.14, eabk1107 (2022).

I have held to the hypothesis that a person DNA and their microbiome evolved thru their ancestors and their ancestors’ diet together. This study demonstrated that the strains diverge .

Suzuki et al. noted that the global distribution of some human gut microbial strains mirrors historical human migration patterns out of Africa … However, within a species, some strains can show remarkable population specificity. The question is whether such specificity arises from a shared evolutionary history (codiversification) between humans and their microbes.

Evolutionary history of someone from the Hebrides Islands produces a high incidence of red hair. Evolutionary history of someone from the South Africa will be black haired, and  low and high curl individuals [2017]. Similarly, their microbiome will be different and reflect the DNA inherited by their host.

This was nicely illustrated in a series of diagrams in the above article. In the middle of each circle, you will see how the different strains branched from each other:

Branching for Bidifbacterium Breve — Colors represent Regions of the world

I noticed the following in the article:

 For mother-child pairs, strain sharing is often interpreted as vertical transmission, but acquisition of strains from a shared environment cannot be excluded (8). Indeed, our data also support strain sharing between community members: Within sampling locations in Gabon and Vietnam, we observed instances of the same strains in the microbiomes of mothers and unrelated children

Mother passing bacteria on to the children — breast feeding is the likely mechanism

We hypothesized that species that codiversified with their hosts are better adapted to the host environment than those that did not….together with the observed functional attributes, such as smaller genomes and oxygen and temperature sensitivity, codiversified species likely evolved host dependency.

This means issues such as diet and environment (hot, cold, humid, dry) impacts the evolution of the bacteria in the microbiome

Bottom Line

Probiotics should be sourced locally/from same genetic heritage. This is very technically feasible — just isolate the bacteria from stools of a suitable population, sequence it to insure no undesirable genetics, culture it and produce a local version.

Personally, I chuckle because the probiotic that I have responded best to was one that was one obtained from Germans in 1917, which in a match for my ancestry (Duchy of Slesvig, and southern islands of Denmark).

If you are of East Indian Descent (perhaps living in the US), you may wish to obtain probiotics from India and not your local health store. IMHO, you should contact the distributor of any probiotic that you use to obtain the provenance of the strain being used. Demand it!

To rephrase for some:

  • If the probiotic kosher?
  • If the probiotic halal?

Unfortunately, many probiotics being sold out there are not even from human sources, little more appropriate (regional) human sources for the consumers.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillusreuteri have diverse probiotic functions[2014].

There is no Normal or Ideal Microbiome!