If the US had a unified medical system recording information from birth until the grave, then such questions can be answered by some database queries by a skilled statistician. Unfortunately, that is not the case and access to such data is often challenging because of privacy laws.
There is an approach that may hint at answers. Look at the bacteria and direction of shifts reported for various conditions and see if there are good matches. For example, you have 10 of the shifts seen for being afraid of dogs and there are 15 shifts seen in people that are afraid of pets. You would appear to be 10/15 = 66% of the way to that other condition.
The challenge is sparse data. For some conditions we have had lots of microbiome studies and for others almost none. We can view it as shown below.
The more the bacteria are in common, the more likely you may drift from one condition to another — often added as a co-morbid condition.
Clicking will take you to a page that lists the percentage overlap and show a visualization with arrows thickness reflecting the percentage overlap. Remember that many conditions also require DNA mutations to cause the condition. This is NOT predictive, merely a factor to consider.
At the top, you can pick a different condition, set the percentage association to show (the lower the amount, the more items will appear and you may get a very busy page). Or, go over to every thing on one page.
Looking at what could cause Asthma, we see the key contributors.
When you hover over the line, you will get more information, for example Chronic Lyme. We see that we only have 3 bacteria types/taxon listed for Chronic Lyme (at this time),
Looking at Sleep Apnea, we see more bacteria are known about it.
You must balance the number of bacteria (taxons) with the percentage. High taxon counts with high percentage are likely significant. The issue keeps coming down to few studies on microbiome shifts.
Note: The numbers and charts will change over time as more data is found and entered.
The coordinator of a UK Long COVID group asked me to research and post on this topic. Endometriosis is one form of infertility that has a prevalence around 7%. When I checked the US National Library of Medicine for Endometriosis and microbiome, I found 88 studies most published in 2021 and later. It is a newly discovered association. I have added it to my PubMed Study page with bacteria listed here and a priori changes of diet to counter these shifts listed here.
I repeated searching the US National Library of Medicine for Endometriosis and microbiome for endometriosis and COVID. I found 66 studies — a volume that surprised me! A few studies that appear interesting:
Association of laparoscopically-confirmed endometriosis with long COVID–19: a prospective cohort study. [2023]. We know that long COVID has an altered microbiome and it would not be unexpected that some alterations would predispose some people to endometriosis by the microbiome alterations.
From the database I compared shifts between COVID and endometriosis, with the shift in common shown below.
Tax Name
Tax Rank
Shift
Coriobacteriaceae
family
High
Enterobacteriaceae
family
High
Atopobium
genus
High
Bacteroides
genus
High
Bifidobacterium
genus
High
Blautia
genus
High
Campylobacter
genus
High
Candida
genus
High
Corynebacterium
genus
High
Dialister
genus
Low
Escherichia
genus
High
Faecalibacterium
genus
High
Lachnospira
genus
Low
Lactobacillus
genus
Low
Odoribacter
genus
Low
Parabacteroides
genus
High
Paraprevotella
genus
Low
Prevotella
genus
High
Pseudomonas
genus
High
Ruminococcus
genus
Low
Shigella
genus
High
Streptococcus
genus
High
Eubacteriales
order
Low
Actinobacteria
phylum
High
Firmicutes
phylum
High
Proteobacteria
phylum
High
Verrucomicrobia
phylum
High
Can COVID cause Endometriosis
There is no clear evidence of that, but the table of common shifts implies that it is a reasonable hypothesis. Looking at numbers from Long COVID groups, you may see over representation (above the 7% expected). Endometriosis increased the odds (almost double) of getting COVID then seeing 14% of Long COVID population with Endometriosis could be explain by the increase risk of getting COVID.
On the brighter side, we now see that endometriosis has a significant microbiome dimension and thus treatment by microbiome manipulation becomes an option worth exploring.
I’m in my early thirties and I caught COVID in November 2022. I’ve had dizziness from the beginning which is slowly going away, shortness of breath which is going away, weakness and pains all across my body, numbness, and this general feeling of derealization. Some days it felt I couldn’t even walk up the stairs. After extensive testing it was revealed that i had EBV reactivated, toxic mold, and whatever damage was left from long COVID. The big symptom that I’m still dealing with to this day are some sort of MCAS presentation where when i eat high histamine foods, exercise, sauna, or go for too long of a walk my throat will get tight, which is pretty scary. I also get dizzier upon anaerobic exercise.
We again see the typical pattern for Long COVID and ME/CFS. Over representation of the 0-9%ile. For more information see Background on using this approach
Percentile
Genus
Species
0 – 9
77
101
10 – 19
24
20
20 – 29
16
18
30 – 39
11
18
40 – 49
12
27
50 – 59
12
19
60 – 69
15
12
70 – 79
4
15
80 – 89
13
19
90 – 99
19
24
In this case we see a number of bacteria flagged as likely causes of the above.
Looking at the standard quick health overviews, we see a massive number of bacteria of concerns. While many are of low count, they are much higher than usually seen. This is in agreement with the over representation of the 0-9%ile range.
We see similar red flags with Dr. Jason Hawrelak Recommendations
Going Forward
There are so many items of concern that most practitioners would really not know where to start. Fortunately, the Artificial Intelligence engine was built to handle such complexities. Doing the quick route, I clicked the “Just Give Me Suggestions” and then click on the more technical details which takes us over to the consensus report.
The Consensus Report is done using all possible modifiers, including antibiotics and prescription drugs (that we have information on their microbiome impact). The quick suggests auto-picks items commonly used in treating ME/CFS and Long COVID.
The top suggestions are high in antibiotics, which is atypical.
Getting antibiotic prescribed off-label is a challenge.
Reader’s Question: “Any idea how to go about getting those prescription antibiotics? “
Ideally, you can get your primary care physician to be willing to prescribe one of those above. It does not need to be the first one. If the physician suggests something not on the list, use the filter feature to see it estimated benefit. Negotiate. I attach an article on Long COVID from this week’s edition of New Scientist.
Some practitioners may be uncooperative — there are several alternative approaches.
Look for a naturopath or MD that deals with Lyme infections. It is unlikely that you have Lyme BUT the gut disruption will often result in some Lyme tests returning a false positive. Getting that positive, even a weak one, will rationalize to that practitioner the prescription of antibiotics. Some of those listed above are used by Lyme physicians.
The following are not recommended but people have reported doing these approaches:
If you live near the Mexican border, many of the antibiotics are available there without prescription.
See if you can order directly from Vet Supply shops
If you have friends travelling to Mexico (or India or many 3rd world countries), they could buy at local pharmacies and bring it home to you.
The best way is always under medical supervision!
How to explain to your MD on how these suggestions are computed?
We have been developing an Artificial Intelligence program based on the pattern of the 1972 MYCIN system developed by Stanford University in California. Unlike the popular AI systems based on machine learning or large language models (Chat GPT), we use Probabilistic Inductive Logic Programming with over a million facts manually curated from the U.S. National Library of Medicine.
LInks to more information are in above quote
Not walking the Prescription Path
Fortunately, we also have some herbs and species listed near the top. These include the following
I have no financial interest in Custom Probiotics — they are just by far the cheapest per BCFU and advocate therapeutic dosages.
I would encourage you to look at the avoids and remove the high value items. See video below.
I would not be surprised if you have a Jarisch–Herxheimer reaction from some of the above. I usually advocate the Cecile Jadin approach which is to take one or two items at a time for up to 2 weeks and then rotate to other items. Start with a low dosage and slowly work up the dosage. This will usually reduce the risk of a strong herxheimer reaction. This approach reduces the risk of antibiotic (or equivalent) resistance happening. There will always be a bacteria mutations that will tolerate specific herbs, probiotics and antibiotics. The odds of the bacteria tolerating multiple substances is very low.
I would suggest doing the rotating suggestions for 8 weeks and then retest. Always use the same lab so comparisons are valid.
Video Walkthru
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 results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.
The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.
I have an extensive collection of bacteria shifts reported from Studies on the US National Library of Medicine Studies for some 91 different conditions.
My past practice has been to deem over 75%ile to be a match if the study reported high and below 25%ile if the study reported a low. I then compute the number of hits for all samples and determine each person’s percentile. I then look at the reported incidence of the condition and see if there is a likely match.
For example, the condition is seen in 5% of the population: one person is at 45%ile – thus unlikely; another person is at 95%ile – thus borderline; a last person is at 99%ile – thus likely.
These 25%ile and 75%ile were arbitrary numbers point out of the air. I dislike arbitrary numbers. This attempts to find better numbers supported by evidence.
The root problem is that the study typically reports that the average of people with a condition is statistically different from the control group. This means that the only clear fact is whether the sample of people with this condition is higher or lower than the control group. This cannot be applied to an individual because the average is the average of a population with a wide range of values reported.
I looked at current available data and picked Depression to use for some test runs. We have 298 samples that are annotated with depression, and 226 different bacteria-shifts from studies). We divide our population into those that annotated with depression and those that annotated their sample but did not include depression.
Ombre reports 166 of the 226 bacteria from studies
BiomeSight reports 147 of the 226 bacteria from studies
uBiome reported 154 of the 226 bacteria from studies
This implies that Ombre may produce the best results, uBiome with intermediate, and Biomesight the worst.
The method is easy, finding the number of matches per sample for with depression and without depression. Then get the ratio between them. If the percentage is below 100, then we have false positives. We want the numbers to be over 100%. After going through the numbers, I came up with 109% as being a good threshold.
My first run was aggregating all lab data. We see good discrimination using 6%ile/94%ile. This drops almost in half at 10%ile/90%ile and disappears at 40%ile/60%ile. Aggregating all samples together has usually resulted in reduced statistical significance.
Percentile
With
Without
Ratio
1
1.28
1.14
112%
2
2.55
2.31
111%
3
3.70
3.39
109%
4
4.89
4.44
110%
5
6.05
5.54
109%
6
7.13
6.57
109%
7
8.12
7.63
106%
8
9.25
8.75
106%
9
10.32
9.80
105%
10
11.41
10.80
106%
15
17.11
16.48
104%
19
21.29
20.75
103%
29
32.16
31.58
102%
30
33.11
32.70
101%
The next runs are being lab specific:
Biomesight:
78 with depression,
637 without depression.
We use a criteria of a ratio of 109% or better, so 6%ile/94%ile
Percentile
With
Without
Ratio
1
1.26
1.10
115%
2
2.86
2.48
115%
3
3.99
3.57
112%
4
5.22
4.68
111%
5
6.47
5.82
111%
6
7.68
6.87
112%
7
8.51
7.96
107%
Ombre Labs:
78 sampleswith depression,
340 samples without depression
The results blew me away! I give a possible explanation below.
Percentile
With
Without
Ratio
1
0.75
0.87
86%
2
2.09
1.05
199%
3
3.42
1.70
202%
4
5.05
2.32
217%
5
6.60
3.00
220%
6
8.19
3.61
227%
7
9.59
4.27
225%
8
10.97
4.93
222%
9
12.44
5.57
223%
10
14.05
6.20
226%
11
15.54
6.85
227%
12
17.04
7.57
225%
13
18.88
8.23
229%
48
74.23
31.98
232%
And going to the defunct uBiome:
With: 103 Samples
Without: 376 Samples
It looks like using 22%ile and 78%ile yields a 109% ration or better.
Percentile
With
Without
Ratio
1
1.75
1.32
132%
2
3.09
2.06
150%
3
4.30
2.89
149%
4
5.30
3.73
142%
5
6.32
4.64
136%
6
7.17
5.59
128%
7
8.20
6.48
127%
8
9.32
7.37
126%
9
10.35
8.30
125%
10
11.19
9.20
122%
11
12.17
10.18
119%
12
13.12
11.11
118%
13
14.13
12.01
118%
14
15.14
12.91
117%
15
16.13
13.83
117%
16
16.97
14.76
115%
17
17.82
15.74
113%
18
18.76
16.63
113%
19
19.64
17.52
112%
20
20.43
18.45
111%
21
21.36
19.44
110%
22
22.16
20.35
109%
23
23.00
21.28
108%
24
24.00
22.31
108%
25
24.93
23.24
107%
26
25.92
24.18
107%
uBiome
Why does Ombre Labs blow away other Labs?
First, you need to understand the back story read The taxonomy nightmare before Christmas…[2019]. There is NO STANDARDIZATION. Different labs use different reference libraries. I would speculate that the reference libraries typically used for studies on the US National Library of Medicine is much closer to the reference libraries used by Ombre than those used by Biomesight.
It could be roughly compared to the studies using metric Bolts. Ombre nuts are metric (cm) and thus fit well. Biomesight nuts are imperial (inches), they do not fit as well, and on occasion may need to be forced. That is the likely nuts and bolts of it.
What does this mean for Predictions using PubMed Studies?
At the simplest level it means that my criteria for matching is different for each lab.
High is over 60%ile and Low is under 40%ile for Ombre
High is over 94%ile and Low is under 6%ile for Biomesight
High is over 78%ile and Low is under 22%ile for uBiome
Other labs: High is over 94%ile and Low is under 6%ile for Biomesight
We ended up with the same best to worst order for labs as predicted at the start — except the differences is a lot larger than I expected. It will likely take a week for me to modify the build processes and have numbers on Microbiome Prescription updated.
What about to Shotgun analysis (Thorne, Xenogene)? Answer is simple, we need a lot more samples from both of them.
Testing Discrimination Performance
I charted the with versus without depression for various labs below to see how strong the discrimination was. The charts are below with uBiome showing the best discrimination. At higher percentiles, ubiome is 3-4x more likely to detect depression over a not depression.
After a Facebook post, the post got this message. This post is a fuller answer
I will skip over the obvious impact of fungi and mold in your environment (home, car, work) on the microbiome. Pollutants impacting the microbiome and the immune system in well documented in recent studies.
PM2.5 exposure associated with microbiota gut-brain axis: Multi-omics mechanistic implications from the BAPE study [2022] “We also found links between PM2.5 and changes in the nervous and cardiovascular outcomes, e.g., increases of 19.77% (95% CI: -36.44, 125.69) in anxiety, 1.19% (95% CI: 0.65, 1.74) in fasting blood glucose (FBG), 2.09% (95% CI: 1.48, 2.70) in total cholesterol (TCHOL), and 0.93% (95% CI: 0.14, 1.72) in triglycerides (TG), were associated with 10 μg/m3 increase in PM2.“
Traffic-Related Air Pollution, Particulate Matter, and Autism [2013] “Exposure to traffic-related air pollution, nitrogen dioxide, PM2.5, and PM10 during pregnancy and during the first year of life was associated with autism.” – hence it impacts a child in the womb too
What can you do to limit the adverse impact?
The first step is to monitor. Know where there is an issue. We use two monitors – one outside and one inside. If I was working in the office daily, I would also have one there. Possibly a fourth one for how I commute to work (for example mold in the car). The cost was not much (buying from aliexpress.us $26 each) – and this model has an internal rechargeable battery (ideal for the commute).
The second step is to take the data and do something with it. Some examples for inside:
Use air-filters to reduce particle matter (PM) levels
Use activate-carbon filters to reduce:
Formaldehyde (HCHO)
Total Volatile Organic Compounds (TVOC).
Use dehumidifier to reduce humidity (a factor for mold)
Be aware that new items delivered could off-gas!!
We use an external shed with an ozone generator to reduce organic compound / perfumes / smells. Ozone breaks down TVOC and HCHO quickly.
For outdoor and home location,
If you work outside, you may wish to wear a half or full face mask with a N-95 or P-100 filter (we use P-100 as our standard). The half face mask gives a tight seal to the face.
In our area during the winter, many people still burn wood — you can see it on the outside meter before you can smell it.
You may wish to check the seals on doors and windows. Replace or improve them to reduce air leakage (and also save heating bills!). In some cases, you may want to consider removable masking tape (“the blue stuff”) to improve the seal.
If your furnace has an external air intake, you may wish to add filters on the intake.
Because of past wildfire smoke issue, we added in the attic a fan with good HEPA Activated Carbon filter to give the house a positive air pressure when needed. I.e. Only filter air comes in and seals that would allow pollutants to leak in, now exits our filtered air.
Left end is the filter, the fan engine and then a vent into the house.
One of the best solution is to move to a location with good air quality. There are resources on line to evaluate locations. We used them extensively before our last move.
If you have the option or desired to move — be aware of sources of pollutants at proposed places. Do an in depth investigation of the patterns for the whole year. We have a petrochemical plant to the north and one to the south – but we have a west to east wind pattern and located in a north-south valley. We also look at flood risk and marsh lands — both are high mold and fungi risks.
That’s the long and the short of it:
Studies showing that it is important
Tools to monitor
Our experience reducing the impact on our microbiome.
Hey Ken. At a talk last night by a functional med/environmental doc, she said that recent research has shown that 21% of the population has MCAS (Mast Cell Activation Syndrome). Apparently, she said, MCAS is finally being recognised by the medical profession. She went on to say that if someone with MCAS gets Covid, they almost always get Long COVID. Can you draw any conclusions between the microbiome and MCAS as you have with other conditions, and maybe relate it to your other research into Long Covid?
The joy of a citizen science site with lots of contributed data is that we can get informal insight. For more information about the probable bacteria involved see Multiple Chemical Sensitivity (MCS) – A Cause Found?
Q: Does 21% of the population has MCAS (Mast Cell Activation Syndrome)?
A: We have 336 samples annotated with MCAS or histamine issues out of 1747 annotated samples. That is 19.2%. Conclusion: We are in agreement with the research.
Q: if someone with MCAS gets COVID, they almost always get Long COVID?
A: This is a bit of a chicken and an egg question. People with CFS/ME gets MCAS. Looking at uploads prior to Long COVID appearing, we actually have 30.5% of all samples with MCS; 16.5% of samples with ME/CFS (104). The incidence of MCAS in this ME/CFS population was 58%.
We have 190 samples annotated with Long COVID but only 13 reporting MCAS. My conclusion is that all of the people she saw with MCAS were borderline ME/CFS or ME/CFS already. A likely correct statement is that someone with ME/CFS and MCAS is likely to get Long COVID. All that COVID is likely to do is to push the person further into the ME/CFS spectrum.We cannot separate Long COVID from ME/CFS.
Doing filtering of people with MCAS without ME/CFS and then Long COVID, we get a 6.4% incident rate. This suggests that MCAS without being comorbid with ME/CFS does not always get Long COVID.
Do you have a blog post where I can learn more about how percentile distribution of a 16s can give statistical insights ? You often mention the concept that a “good” sample has a even distribution…I’m trying to understand why so and what uneven distribution might mean etc
The answer is statistics!!
At each taxonomy level (species, genus, family, etc) we can reasonably assume that the count of each bacteria taxonomy are independent of others of the same rank. There may be a little correlation (if A goes up, B goes up), but in general, not significant.
When we use percentile instead of percentage, we change the information about the bacteria in a uniform distribution. Suppose you have 120 dice. You roll all of them. You expect to have around 20 with 1, 20 with 2, 20 with 3, 20 with 4, 20 with 5 and 20 with 6.
If you get 40 dice with 1 and 2 dice with a 6, you are reasonable to suspect that the dice are biased or loaded.
Instead of a 6 sided die, we use a 10 sided die — 10%ile ranges. If the number in each 10%ile range are the same, then you can assume that the die is fair OR in our case, the microbiome is balanced. If you get a great difference in each 10%ile ranges, you suspect that the die is bias OR the microbiome is unbalanced (as in an unbalance die).
Where there is over or underrepresentation gives us hints as to where there may be an issue. It does not tell us what the issue is. It simply tells us that the microbiome is unbalanced and points us at subsets of bacteria worth examining.
Wait! There is More
A reader sent me this recent paper which appears to emphasis this issue for having a high count in the 0-9%ile for inflammatory conditions.
These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments and reveal taxon-independent markers of “dysbiosis” that may explain why widespread yet typically low-abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.
The question of causal association not being found may be a matter of sample size being used and cross-interactions and cooperation between these “minor voices”. The paper data came from Fecal Matter Transplants data and is illuminating on why many FMT fail to persist.
This is a look at Small intestinal bacterial overgrowth (SIBO) from the aspect of the microbiome. Whatever is happening in the small intestine flows out of it and impacts downstream systems. Items that modifies any microbiome abnormalities will likely impact the small intestine.
We have two bacteria that are reported significant in at least 2 of the 3 labs:
Bacteroides caccae (species)
Holdemania (genus)
A total of 31 bacteria (P < 0.001) was flagged as being significant and added to our collection.
We then look at Percentile and found only Bifidobacterium adolescentis showing a significant shift.
The complete list is below. Note that almost everything is High with SIBO.
ShiftIs
tax_name
tax_rank
High
Flavobacteriia
class
High
Bacteroidia
class
High
Spongiibacteraceae
family
High
Lachnospiraceae
family
High
Bacteroidaceae
family
High
Flavobacteriaceae
family
High
Rhodocyclaceae
family
High
Anaerolinea
genus
High
Ethanoligenens
genus
High
Trabulsiella
genus
High
Bacteroides
genus
High
Hyphomicrobium
genus
High
Myroides
genus
high
Holdemania
genus
High
Pseudoflavonifractor
genus
High
Adlercreutzia
genus
High
Bacteroidales
order
High
Flavobacteriales
order
High
Oceanospirillales
order
High
Nitrosomonadales
order
High
Bifidobacterium thermophilum
species
High
Hyphomicrobium aestuarii
species
High
Anaerolinea thermolimosa
species
High
Thermodesulfovibrio thiophilus
species
High
Bacteroides sp. dnLKV9
species
High
Bacteroides sp. 35AE37
species
High
Bacteroides caccae
species
High
Adlercreutzia equolifaciens
species
High
Blautia obeum
species
High
Pseudoflavonifractor capillosus
species
Low
Bifidobacterium adolescentis
species
We have only one clear agreement with the literature: Bacteroides with a large number of species deemed significant.
Compounds Produced
SIBO is usually detected by breath tests. The question is whether the chemicals detected in the breath is associated with the compounds that bacteria produced. What we found is below. The top 4 product in this list are the likely suspect for the first stage. These likely contributed to the chemicals detected. In particular, N-Acyl-L-homoserine abundance is associated with “Over 50 Gram-negative bacteria species (including several pathogenic species) use AHLs as autoinducers and the means of their communication in Quorum sensing” In other words it is not the source, but sends signals to the source bacteria to release the chemicals detected on the breath.
Trying to “find” the specific bacteria is not practical
📚4543 different bacteria produces Hydrogen (just click to see the list)
📚3817 different bacteria produces Hydrogen Sulfide
After reading your blog for many many years I finally managed to get a microbiome sample done (Biomesight). I suffer from debilitating MCS (multiple chemical sensitivity), mold sensitivity, and associated CFS. I have been isolated at home for almost 20 years now. Pretty much unable to function in any way normally, because of these sensitivities.
I think it all started in early 2000 with food poisoning from a poorly cooked chicken, that had been left out warm for too long. After this chicken meal my body simple went haywire. My gut was so sensitive that even drinking just a glass of water produced something similar to an anaphylactic shock.
It took several months to subside, and for me to completely revamp my diet and figure out what few items I was able to eat.
My body feels toxic. Like I am being constantly poisoned. Main symptoms are severe bloating and gas, extreme irritability and mood changes, brain fog and lethargy, poor blood flow which leads to lactic acid buildup everywhere, narrowing of vision, sleepiness to the point I call it “coma sleep” (I would not wake up from these even if the fire alarm went off). Tinnitus, also have muscle twitches and probably 20 other symptoms I forget.
Fast forward to today, my MCS is so bad that I cannot even read a book because the fumes from printed ink causes a reaction. My body feels toxic. Like I am being constantly poisoned.
Through the years I have spent all my available cash on trying everything I can think of. Herbs, treatments, devices, gadgets. I have found a handful of herbs and supplements that initially gave me relief, but after a few weeks always stop working. Never found a permanent solution. Always temporary, quickly followed by developing resistance.
I’ve been reading your blog for a long time, but because of the mental and physical lethargy, it took me this long to even manage a microbiome analysis.
Do you have any experience with MCS? I am fairly certain this is a microbiome issue, but I have no clue what to do, and all my attempts have been just shooting in the dark trying to see if anything would stick.
Reader in Europe
Analysis
The typical ME/CFS pattern of over representation of 0-9%ile. This is in contrast to MCS where the cause appears to be over population of a few demanding bacteria. A “normal” microbiome will have all of the Percentile ranges being about the same number.
Percentile
Genus
Species
0 – 9
72
97
10 – 19
27
21
20 – 29
13
11
30 – 39
13
15
40 – 49
10
16
50 – 59
19
20
60 – 69
15
19
70 – 79
7
17
80 – 89
11
16
90 – 99
8
13
The likely to be important for the above shift is just a single bacteria family.
And then does a Hand Picked of items deemed important (Roseburia) cited above.
While the MCS associated bacteria above would likely be included by this, we do a fifth set of suggestions by hand picking these and getting those suggestions.
The MCS specific suggestions are below:
And the avoid:
Going over to Consensus
On that page, we sort by Take Count from highest to lowest. Items with a count of 5 addresses all concerns. The top of the list are:
Hoping for probiotics solutions failed to find any strong candidates. In terms of prescription items, these antibiotics are the top suggestions neomycin and lymecycline . It is interesting to note “Neomycin is not absorbed from the gastrointestinal tract and has been used as a preventive measure”[wikipedia]; so it’s impact is confined to the microbiome. Lymecycline is a tetracycline which also include minocycline and doxycycline — two antibiotics often used by ME/CFS specialists.
Reader Feedback
It looks like there’s not much to do, except perhaps antibiotics.
I was once prescribed ciprofloxacin for a lung infection, and all my symptoms went away for a couple days. They returned after the cipro course was completed. Later I’ve learned this antibiotic is very dangerous. So not very interested to test that particular type again.
Thiamine B1 I’ve found through experimentation that it helps a bit, as well as resveratrol (both listed among the text and images). High protein diet also helps, which was also listed.
A few herbal antibiotics also help, so the killing part does work. In fact the antibiotic route has always produced better results than the nutritional path. Well, at least the antibiotic route produces immediate results. Perhaps the nutritional path produces more sustainable results though, if I could figure it out.
The Lymecycline I would certainly want to test. Have to figure that out also.
It is nice to get feedback that the suggestions and the person’s experience doing multiple random experiments agree. The stated goal of Microbiome Prescription is to give better suggestion than random suggestions off the internet or ad hoc trials suggested by MDs.
As a FYI, I have always been an advocate for Cecile Jadin approach for antibiotics, single course, a break, then a different antibiotic (preferable a different family).
This is what is in their data file for a person (i.e. detected only) "Eukaryota","Fungi","Ascomycota","Dothideomycetes","","","","","","","","","","0.001554","0.00052608","0.0014506","77.3" "Eukaryota","Fungi","Ascomycota","Eurotiomycetes","","","","","","","","","","0.0006793","0.0017786","0.003532","2.5" "Eukaryota","Fungi","Ascomycota","Leotiomycetes","","","","","","","","","","0.0006979","0","0.0003654","90" "Eukaryota","Fungi","Ascomycota","Saccharomycetes","","","","","","","","","","0.01638","0.00071344","0.0019584","99.2" "Eukaryota","Fungi","Ascomycota","Sordariomycetes","","","","","","","","","","0.001107","0.0016878","0.0034788","18.9" "Eukaryota","Fungi","Basidiomycota","Agaricomycetes","","","","","","","","","","0.0003536","0.00054734","0.0017092","17.2" "Eukaryota","Fungi","Basidiomycota","Exobasidiomycetes","","","","","","","","","","0.0001024","0","0.00034712","52.2" "Eukaryota","Fungi","Basidiomycota","Malasseziomycetes","","","","","","","","","","0.0001582","0","0.00010162","73.3" "Eukaryota","Fungi","Basidiomycota","Tremellomycetes","","","","","","","","","","0.0002047","0.00023646","0.000608","17.9" "Eukaryota","Fungi","Basidiomycota","Ustilaginomycetes","","","","","","","","","","0.00008375","","","" "Eukaryota","Metazoa","Chordata","Mammalia","","","","","","","","","","1.985","0.0042838","0.06166","99.5"
A reader request with severe MCS motivated me to revisit Multiple Chemical Sensitivity (MCS) and literature on it. I am first going to recap my memory of what I know, a literature review, Last, analysis using data contributed to Microbiome Prescription – which did not find anything major beyond bacteria shifts that are statistically significant. We explore enzymes, products and substrates with no smoking guns found
There was a processing error — This post was revised
Long weekend tracing issue, made short. Some revisions to reduce running cost had a typo pointing one set of data (Substrate data for Samples) to the wrong lookup table to determine percentile. This has been corrected and the data audited to insure correctness.
I had mild MCS during one flare. It disappeared with remission.
Multiple Chemical Sensitivity (MCS) is not bronchial (breathing). There was a series of studies where the person was wearing a half-face mask breathing compressed air. Bringing a triggered close to the person (blind testing), trigger the response. My own wife lab results leaves a significant scent. She had a complete workup with Dave Berg, Hemex Lab and two weeks later she had a bad MCS reaction. About a week later, new blood work was sent. Everything was the same, except for a marker indicating very active coagulation. The marker had a short half-life around 2 hours. After she fully recovered, same test returned to normal. This hints at coagulation being part of the process. We did find one effective treatment for shortening the duration of a MCS episode: Olestra (as in Olestra chips). This compound sucks chemicals out of tissues [2015] [2014]
– Ken Lassesen
I attended a talk today with the AAAS (I’m a member) and Memory Cells was discuss for some autoimmune conditions. It occurs to me that these are likely a factor. These cells become uber-sensitive to specific chemical compounds and have a lasting memory. In time they may die off, but not quickly.
Caution: this post is subject to revision. The findings to date are so significant that I am posting early.
The Current Literature
First a summary from a 2022 review – it covers a lot of territory… many quotes are below
Multiple Chemical Sensitivity (MCS), a condition also known as Chemical Sensitivity (CS), Chemical Intolerance (CI), Idiopathic Environmental Illness (IEI) and Toxicant Induced Loss of Tolerance (TILT), is an acquired multifactorial syndrome characterized by a recurrent set of debilitating symptoms. The symptoms of this controversial disorder are reported to be induced by environmental chemicals at doses far below those usually harmful to most persons. They involve a large spectrum of organ systems and typically disappear when the environmental chemicals are removed. However, no clear link has emerged among self-reported MCS symptoms and widely accepted objective measures of physiological dysfunction, and no clear dose-response relationship between exposure and symptom reactions has been observed. In addition, the underlying etiology and pathogenic processes of the disorder remain unknown and disputed, although biologic and psychologic hypotheses abound. It is currently debated whether MCS should be considered a clinical entity at all.
“Not surprising, MCS is often juxtaposed to, and sometimes combined with, other diseases for which definitive diagnoses and explanations are also found wanting, including fibromyalgia (FM), Gulf War syndrome (GWS), chronic fatigue syndrome (CFS), sick building syndrome (SBS), and electromagnetic radiation exposure (ERE) [18,23,24,25,26,27,28,29,30,31,32].”
“An association was documented, however, between MCS and increased nasal airflow resistance, respiration rate, and heart rate.” [2021] Volatile chemicals can not only affect respiration, but can cause laryngeal symptoms which, in the extreme, can induce vocal cord dysfunction [82].
Changes in skin conductance were also seen in MCS patients but not in controls [193].
Compared to younger persons, those 65 years of age and older are less likely to identify themselves as chemically sensitive [88].
In accord with this concept, McKeown-Eyssen et al. [219] found, in a female cohort of 203 MCS cases and 162 controls, that the cases had higher levels of cytochrome P450 CYP2D6 (i.e., one of the most important enzymes involved in the metabolism of xenobiotics in the body) and n-acetyl transferase 2 (NAT2). More possible genes listed here.
Like the sick building syndrome [89], there is a remarkably higher prevalence in women than in men [16,18,21,90,91], with the percentage of women ranging from 60% to 88% [18,57,92,93,94].
Almost every condition in the above chart has microbiome shifts associated with it. Exposure to mold also is known to cause microbiome shifts. There appears to be no published studies exploring this possibility. At this time we have 128 people (7.3%) who have annotated their samples with Multiple Chemical Sensitivity [uBiome: 55, Ombre:39, Biomesight 25] to Microbiome Prescription.A
Executing analysis by lab, we found only one taxon reported significant in two labs. Bacteroides clarus where people with MCS had 3x the count of people not reporting MCS [BiomeSight, uBiome]. The top items of significance are shown below. All of them have much higher counts in people with MCS.
Source
probability
Tax_Name
tax_rank
with MCS
without MCS
Obs
BiomeSight
P < 0.001
Anaerotruncus
genus
3356
1834
683
BiomeSight
P < 0.001
Anaerotruncus colihominis
species
3208
1743
683
BiomeSight
P < 0.001
Bacteroides intestinalis
species
28980
2430
396
Thryve
P < 0.001
Prevotella maculosa
species
27399
5733
404
Average Counts out of a million
These have been added to the [Research] tab, as shown below.
Looking at KEGG Enzymes we have the strongest candidates below. Looking for any enzyme that is significant for 2 or more labs, we had only one: 5.3.1.15 D-lyxose aldose-ketose-isomerase (Biomesight and Thryve).
Trying a different approach aggregating all of the data and using percentile ranking. This masks most of the differences between labs and gives us large sample size, We have a very actionable item: increase Bifidobacterium by taking Bifidobacterium probiotics.
tax_name
tax_rank
percentile
Obs
Alistipes indistinctus
species
61.7
61
Anaerobutyricum hallii
species
39.8
41
Bifidobacteriales
order
37.7
106
Bifidobacterium
genus
37.1
122
Oscillospiraceae incertae sedis
norank
63.1
41
Acidobacteria
phylum
33.2
48
Butyricimonas faecihominis
species
61.2
43
Bifidobacteriaceae
family
37.4
122
Collinsella aerofaciens
species
39.1
83
Phascolarctobacterium succinatutens
species
37.3
52
Hungateiclostridiaceae
family
61.4
45
Bacteria where Percentile Average above 60%ile or below 40%ile
KEGG Enzymes
Applying this to KEGG Enzymes, we have just one of concern that is high: EC3.2.1.46 galactosylceramidase, and a big 39 that are low indicating a deficiency of enzymes is likely
Enzymes where Percentile Average above 60%ile or below 40%ile
Compound Produced
This is reflected in compound produced. With D-Galactose being the only high – matching enzyme [3.2.1.46] D-galactosyl-N-acylsphingosine galactohydrolase above,
CPID
Compound
percentile
obs
924
Ferrocytochrome
36.9
63
22336
Reduced hydrogenase
38.3
121
250
Pyridoxal
38.6
98
15767
4-(L-gamma-Glutamylamino)butanoate
38.7
63
16699
2-O-(6-Phospho-alpha-mannosyl)-D-glycerate
38.7
94
1113
D-Galactose 6-phosphate
39.2
118
196
2,3-Dihydroxybenzoate
39.3
73
4575
(4R,5S)-4,5,6-Trihydroxy-2,3-dioxohexanoate
39.5
72
615
Protein histidine
39.6
122
666
LL-2,6-Diaminoheptanedioate
39.7
122
1097
D-Tagatose 6-phosphate
39.8
115
20890
D-Glucaro-1,5-lactone
39.9
61
20889
D-Galactaro-1,5-lactone
39.9
61
124
D-Galactose
63.5
65
Compounds Consumed
As above for production and for bacteria, we have just one item with a high percentile and many with a low percentile.
CompoundName
percentile
Obs
Galactosylceramide
63.9
65
5-Oxoproline
39.9
120
Hydrogen selenide
39.9
122
3-Dehydro-L-gulonate
39.9
72
Reduced riboflavin
39.8
50
Protein N(pi)-phospho-L-histidine
39.6
122
5-Aminopentanoate
39.6
106
[SoxY protein]-S-disulfanyl-L-cysteine
39.6
43
(L-Seryl)adenylate
39.6
60
(2S,3S)-2,3-Dihydro-2,3-dihydroxybenzoate
39.6
73
6-Phospho-beta-D-galactoside
39.5
117
Sucrose
39.4
122
Hippurate
39.4
63
alpha-D-Galactose 1-phosphate
39.4
122
2-Hydroxy-3-keto-5-methylthiopentenyl-1-phosphate
39.4
52
D-Galactose 6-phosphate
39.3
115
4-Aminobutyraldehyde
39.3
47
D-Xylulose 5-phosphate
39.1
122
Benzene-1,2,4-triol
39
43
L-Arabinonate
38.6
52
Pyridoxine
38.6
98
Salicylate
38.3
44
dTDP-4-acetamido-4,6-dideoxy-alpha-D-galactose
38.3
60
Oxidized hydrogenase
38.3
121
3,4-Dihydroxyphenylacetate
38.1
47
Thymine
37.8
45
FMN-N5-peroxide
37.8
45
FMN-N5-oxide
37.8
45
Ferricytochrome
37.4
63
Hexadecenoyl-[acyl-carrier protein]
37.2
47
Protein lysine
37.1
120
2-O-(alpha-D-Glucopyranosyl)-D-glycerate
37.1
54
L-Rhamnonate
36.7
46
Geraniol
36.4
49
4-(L-gamma-Glutamylamino)butanoate
36.3
44
D-Glyceraldehyde
35.6
60
3-Hydroxybenzoate
35.3
41
(2R)-3-Sulfolactate
34.1
43
D-Glucaro-1,4-lactone
33.3
43
D-Galactaro-1,4-lactone
33.3
43
Bottom Line
The is no obvious imbalance between compounds produced and consumed. Thus the best path is to use the bacteria identified at the start. A quick select has been added to the menus.
There are a few things that could be factors, for example D-Galactaro-1,4-lactone is 33.3%ile for consumption and 39.9%ile for production. Unfortunately, the microbiome is not a closed system and whether any surplus accumulates would be too speculative.
Example of the MCS pick page for a person with MCS.
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