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%

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.

More work to do…

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.

• The effect of real-ambient PM2.5 exposure on the lung and gut microbiomes and the regulation of Nrf2 [2023] “Our study demonstrated the detrimental impacts of PM2.5 on the lung and gut microbiome by inhaled exposure to air pollution”
• Data on lung and intestinal microbiome after air pollution exposure in ageing rats. [2023]
• PM_2.5 exposure associated with microbiota gut-brain axis: Multi-omics mechanistic implications from the BAPE study [2022] “We also found links between PM_2.5 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 PM_2.“
• Airborne PM_2.5-Induced Hepatic Insulin Resistance by Nrf2/JNK-Mediated Signaling Pathway. [2017]
• The toxicity of ambient fine particulate matter (PM2.5) to vascular endothelial cells. [2021]
• Effect of PM concentration on anemia blood indicators reduced by air purifiers. [2023]
• Exposure to atmospheric pollutants is associated with alterations of gut microbiota in spontaneously hypertensive rats. [2019]
• Exposure to concentrated ambient PM_2.5 alters the composition of gut microbiota in a murine model. [2018]
• Intratracheally instillated diesel PM_2.5 significantly altered the structure and composition of indigenous murine gut microbiota [2021]
• Longitudinal survey of microbiome associated with particulate matter in a megacity [2020] ” Sample stratification based on Air Quality Index (AQI) demonstrated that many microbial species, including those associated with human, dog, and mouse feces, exhibit AQI-dependent incidence dynamics.”
• Short-term exposure to urban PM_2.5 particles induces histopathological and inflammatory changes in the rat small intestine [2022] ” Results showed that short-term exposure to PM_2.5 induces mucosal lesions and reduces IL1β levels in the small intestine but not colon. ‘
• . Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age. Gut Microbes. 2022

For one condition that has been studied, pollution has a life-long impact, even if the exposure was for a relatively short period of time.

• Air Pollution and Autism Spectrum Disorders: Causal or Confounded? ” the overall evidence for a causal association between air pollution and ASD is increasingly compelling.”
• Traffic-Related Air Pollution, Particulate Matter, and Autism [2013] “Exposure to traffic-related air pollution, nitrogen dioxide, PM_2.5, 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.
  

• 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.

From AirNow

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.

A reader asked:

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.

Metabolic independence drives gut microbial colonization and resilience in health and disease [2023]

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.

This post is a parallel to my earlier post Multiple Chemical Sensitivity (MCS) – A Cause Found?. We will not assume anything. Just let the data speak.

We have 181 samples annotated with SIBO. Ombre Labs: 57, uBiome: 57 and Biomesight: 57.

Literature Review

I found one study that does a very good job

Association between Gut Dysbiosis and the Occurrence of SIBO, LIBO, SIFO and IMO [2023] states “Bacteria characteristic of small intestinal bacterial overgrowth include Streptococcus, Staphylococcus, Bacteroides, and Lactobacillus. “

Microbiome Associations

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
• 📚622 different bacteria produces Methane.

CompoundName	percentile	Obs
Methylaminoacrylate C20255 – has NH2 and Hydrogen	61.5	129
Aminoacrylate C20253 – has NH2 and Hydrogen	61.5	129
D-Erythritol 1-phosphate C21294	60.7	134
N-Acyl-L-homoserine C18061 – a signaling molecule	60.3	126
2-O-[6-O-Octanoyl-alpha-D-glucopyranosyl-(1->6)-alpha-D-glucopyranosyl]-D-glycerate	39.8	44
(2R,3R)-Dihydroflavonol	38.6	67
6-Phospho-2-dehydro-D-gluconate	36.9	41
p-Hydroxybenzyl alcohol	34.8	56
4-Hydroxybenzaldehyde	34.8	56

Bottom Line

I have made the bacteria found above available on the site under Research tab

The page resulting looks like this:

With results like shown below. Note that on the Add with have gluten-free and on the Remove: wheat, barley.

This is a continuation of my last post looking at Multiple Chemical Sensitivity (MCS) – A Cause Found? This resulted in a set of bacteria identified that appear to be associated.
For other analysis of people with ME/CFS see this page

Backstory

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.

Rank	Bacteria	Importance
genus	Roseburia	3.4
species	Roseburia faecis	2.5

Looking at the different Health Filters 🕵️

• Potential Medical Conditions Detected – nothing of concern
• Bacteria deemed Unhealthy : Only Prevotella copri stands out, this is associated with mold, fungi
  • Source may be internal, in food, or external.
  • Of special concern because of this study: Moist and Mold Exposure is Associated With High Prevalence of Neurological Symptoms and MCS in a Finnish Hospital Workers Cohort [2020]
• Dr. Jason Hawrelak Recommendations: 98.8%ile – so no major concerns

Going over to [Special Studies] / [ From Special Studies] we see the typical matches — which also matches his symptoms:

As a result of the MCS Research post, I went over to the [Research Features] tab and picked the new experimental item shown below.

The likely bacteria are pre-selected.

Remember that we are not dealing with a single bacteria causing the problem, rather different combinations of bacteria forming a “cartel”

Building Suggestions

To speed (and simplify) the process, we start with the ‘Just give me Suggestions’ on the profile tab. This build a consensus using:

•  Standard Lab Ranges (+/- 2 Std Dev)
•  Box Plot Whisker
•  Kaltoft-Moltrup Normal Ranges
• 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:

• cannabinoids
• high animal protein diet
• Followed by many antibiotics interspersed with the following
• thiamine hydrochloride (vitamin B1)
• melatonin supplement
• Vitamin B-12
• Hesperidin (polyphenol)
• linseed(flaxseed)
• Caffeine
• Arbutin (polyphenol)

Filter and looking for “5”

• Probiotics: nothing
• Amino Acid, Diet Style, Foods, Herb or Spice – listed above
• Amino Acids, above plus
  • diosmin,(polyphenol)
  • luteolin (flavonoid)
  • Guaiacol (polyphenol)
• Prebiotics
  • chitooligosaccharides (prebiotic)
• Sugars
  • chitosan,(sugar)
• Vitamins
  • pyridoxine hydrochloride (vitamin B6)
  • retinoic acid,(Vitamin A derivative)
  • vitamin b7 biotin (supplement) (vitamin B7)
  • vitamin b3 (niacin)

The smallness of this list indicates the problem of trying to serve many masters / addressing different goals.

Flipping to Avoids (Sorting by Avoid Count), we find nothing that is all “5” with the 4’s including: Iron, Vitamin E, Vitamin A, Vitamin D.

Returning to MCS specific probiotics – we see some (bacillus subtilis, clostridium butyricum aka Miyarisan, and bifidobacterium longum bb536 ). None of these items appear in the [Only] Positive Impact commercial probiotics – indicating that while overall positive, there are some negative impacts).

Bottom Line

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).

Q: Suggestions for labs for fungi etc?

Xenogene – Enevia Health which tests for these

Or

Thorne Labs:   https://www.thorne.com/products/dp/gut-health-test

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"

Time to Refresh What we know

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.

Multiple Chemical Sensitivity [2022]

• “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]. 

• More than half (59%) of the mast cell activation syndrome group met criteria for chemical intolerance. [2021]
• Moist and Mold Exposure is Associated With High Prevalence of Neurological Symptoms and MCS in a Finnish Hospital Workers Cohort [2020]

Microbiome and MCS

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

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).

EnzymeName	ECKEY	Src	WithMean	WithoutMean	TScore	DF
7alpha-hydroxysteroid:NAD+ 7-oxidoreductase	1.1.1.159	BiomeSight	14681	2479	4.01	657
acetyl-CoA:glycerone phosphate C-acetyltransferase	2.3.1.245	BiomeSight	49551	27444	3.56	701
acetyl-CoA:heparan-alpha-D-glucosaminide N-acetyltransferase	2.3.1.78	BiomeSight	26909	9394	4.02	671
alginate beta-D-mannuronate—uronate lyase	4.2.2.3	BiomeSight	26933	9472	4.02	681
CTP:phosphoglutamine cytidylyltransferase	2.7.7.103	BiomeSight	27471	13156	3.97	702
L-alanine:glyoxylate aminotransferase	2.6.1.44	uBiome	16083	2029	3.40	309
NAD+:glycine ADP-D-ribosyltransferase (sulfide-adding)	2.4.2.59	BiomeSight	48398	26181	3.60	699
phosphoenolpyruvate:glycerone phosphotransferase	2.7.1.121	BiomeSight	68289	41808	3.60	702
sn-glycerol-1-phosphate:NAD(P)+ 2-oxidoreductase	1.1.1.261	BiomeSight	29070	11440	3.55	700

Dropping by Lab and using Percentiles Only

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

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

EnzymeName	percentile	obs
[2.7.1.85] ATP:cellobiose 6-phosphotransferase	34.6	64
[4.1.1.31] phosphate:oxaloacetate carboxy-lyase (adding phosphate, phosphoenolpyruvate-forming)	35.2	122
[3.2.1.199] 6-sulfo-alpha-D-quinovosyl diacylglycerol 6-sulfo-D-quinovohydrolase	36.1	61
[1.17.2.1] nicotinate:cytochrome 6-oxidoreductase (hydroxylating)	36.7	63
[3.5.1.18] N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase	37.9	122
[2.7.1.211] protein-Npi-phospho-L-histidine:sucrose Npi-phosphotransferase	38	122
[1.2.1.99] 4-(gamma-L-glutamylamino)butanal:NAD(P)+ oxidoreductase	38.1	63
[1.17.98.4] formate:[oxidized hydrogenase] oxidoreductase	38.3	121
[3.4.23.51]	38.4	117
[2.7.1.195] protein-Npi-phospho-L-histidine:2-O-alpha-mannopyranosyl-D-glycerate Npi-phosphotransferase	38.4	94
[1.1.1.65] pyridoxine:NADP+ 4-oxidoreductase	38.4	98
[2.1.1.172] S-adenosyl-L-methionine:16S rRNA (guanine1207-N2)-methyltransferase	38.5	120
[2.1.1.264] S-adenosyl-L-methionine:23S rRNA (guanine2069-N7)-methyltransferase	38.6	121
[3.5.1.32] N-benzoylamino-acid amidohydrolase	38.7	63
[2.7.7.12] UDP-alpha-D-glucose:alpha-D-galactose-1-phosphate uridylyltransferase	38.7	122
[2.4.1.342] ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining)	38.9	121
[4.1.2.22] D-fructose-6-phosphate D-erythrose-4-phosphate-lyase (adding phosphate; acetyl-phosphate-forming)	39	122
[4.1.2.9] D-xylulose-5-phosphate D-glyceraldehyde-3-phosphate-lyase (adding phosphate; acetyl-phosphate-forming)	39	122
[3.2.1.122] alpha-maltose-6′-phosphate 6-phosphoglucohydrolase	39	122
[1.1.1.9] xylitol:NAD+ 2-oxidoreductase (D-xylulose-forming)	39.1	83
[6.3.1.12] D-aspartate:[beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n ligase (ADP-forming)	39.2	122
[4.1.2.40] D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming)	39.2	122
[3.7.1.12] cobalt-precorrin 5A acylhydrolase	39.2	122
[1.3.1.28] (2S,3S)-2,3-dihydro-2,3-dihydroxybenzoate:NAD+ oxidoreductase	39.2	73
[5.3.1.26] D-galactose-6-phosphate aldose-ketose-isomerase	39.3	115
[2.7.1.19] ATP:D-ribulose-5-phosphate 1-phosphotransferase	39.4	62
[2.7.1.208] protein-Npi-phospho-L-histidine:maltose Npi-phosphotransferase	39.4	122
[2.7.1.207] protein-Npi-phospho-L-histidine:lactose Npi-phosphotransferase	39.4	118
[3.2.1.185] beta-L-arabinofuranoside non-reducing end beta-L-arabinofuranosidase	39.4	72
[1.1.1.130] 3-dehydro-L-gulonate:NAD(P)+ 2-oxidoreductase	39.5	72
[3.2.1.85] 6-phospho-beta-D-galactoside 6-phosphogalactohydrolase	39.5	117
[5.1.1.10] amino-acid racemase	39.7	108
[3.2.1.10] oligosaccharide 6-alpha-glucohydrolase	39.7	122
[1.1.1.203] uronate:NAD+ 1-oxidoreductase	39.7	61
[3.5.2.9] 5-oxo-L-proline amidohydrolase (ATP-hydrolysing)	39.7	120
[2.7.1.191] protein-Npi-phospho-L-histidine:D-mannose Npi-phosphotransferase	39.8	122
[4.2.1.30] glycerol hydro-lyase (3-hydroxypropanal-forming)	39.8	64
[3.4.17.19] Carboxypeptidase Taq	39.9	122
[2.7.1.189] ATP:(S)-4,5-dihydroxypentane-2,3-dione 5-phosphotransferase	39.9	72
[3.2.1.46] D-galactosyl-N-acylsphingosine galactohydrolase	63.3	65

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.

Back Story

I heard from a group of long covid patients that you’re offering specific recommendations after they provide you results from a microbiome sample analysis. Would you happen to still be offering those?

My partner and I have had covid 8 times, and unfortunately have over 20 cardiovascular, neurological and pulmonological long covid symptoms. I had my sample analyzed through BiomeSight(attached below).

Happy to follow any process that you may have for this.

The process is simple

• Upload (Ombre or Thorne) your data to Microbiome Prescription upload page, OR
  • Transfer your data from BiomeSight. 📹 PC, 📹 Phone
• Send me an email (Ken /at\ Lassesen.com with:
  • Your Back Story
  • Permission to use the above information anonymously for a blog post
    • This is required to avoid issues with Health Insurance Portability and Accountability Act of 1996 (HIPAA) or equivalent in different area of the world.
• Posts are done usually on a first come, first serve basis — usually within 2 weeks.
  • I do not do private consults. I am not licensed to provide medical advice. I am a statistician and Artificial Intelligence engineer.
• If you choose to share the post with your Medical Professional and they are interested in discussing the post with me on a zoom or equivalent call, I will make the time. My temperament is to teach and share knowledge.
• There is no cost. If the suggestions work, feel free to buy me a coffee as a thank you.

My preference is for people to do “self serve”, examples of other analysis: Analysis Posts on Long COVID and ME/CFS

Analysis

I am doing a more detail analysis first. While similar to other ME/CFS samples (i.e. over representation of bacteria in the 0-9%ile range), we do not have the under representation in the 90-99%ile range.

Percentile	Genus	Species
0 – 9	64	94
10 – 19	6	11
20 – 29	10	15
30 – 39	15	18
40 – 49	15	17
50 – 59	19	20
60 – 69	19	31
70 – 79	19	31
80 – 89	26	39
90 – 99	46	40

The Likely Key Bacteria Causing Above show two genus, both with relatively low importance (Usually there is at least one over 3.5).

Rank	Bacteria	Importance	Percentage of Microbiome
genus	Ruminococcus	2.8	15% (100 %ile)
genus	Oscillospira	2.5	10% (95%ile)

Going over to Bacteria deemed Unhealthy, we see a likely why for getting COVID eight times! Two bacteria have been associated with getting COVID from studies. This suggests that the chemicals produced (or consumed) by these bacteria creates a friendly environment for the virus

Name	Rank	Percentile	Count	Comment	More Info
Anaerotruncus colihominis	species	73	2000	Not Healthy Predictor	Citation
Collinsella	genus	94	8620	High COVID Risk	Citation
Dorea	genus	85	8350	Increased COVID risk	Citation
Prevotella copri	species	76	2160	Over 70%ile Indicator of mycotoxin present	Citation

Looking at Dr. Jason Hawrelak Recommendations, we are at 95.6%ile with almost the not ideal being too low (which is likely caused by Ruminococcus and Oscillospira taking excessive space in the microbiome).

Using US National Library of Medicine studies:

• for Long COVID   (64 %ile) 56 of 212 matches
• but for COVID-19 only (15 %ile) 22 of 107

Using Special Studies, we see (in decreasing order) that the signature is closest to Long COVID.

•  35 % match COVID19 (Long Hauler)
•  22 % match Bloating
•  21 % match Easily irritated
•  20 % match Cold Extremities

On your matching of signature to symptoms, it does seem to be spot on with the long covid, bloating, easily irritated and cold extremity symptoms.

Feedback from reader on 1st draft

Research Features for Special Studies v.2 had no matches – adding to my observations of those results being low usability.

I am going to do the ‘Just give me Suggestions!’ button (which does the three typical selections of bacteria, plus Likely Key Bacteria Causing Above.

The quick take away with strong evidence include:

• Melatonin, NAC, Quercetin, Arbutin (polyphenol), Guaiacol (polyphenol), Hesperidin, Luteolin, Polyphenols, Resveratrol
• doxycycline, azithromycin – if you have a willing MD
• Gluten Free Diet in general BUT Wheat germ, crude , high-protein diet (low-fat diets – eggs especially)
• Whey (non denatured), Flaxseed, broccoli, Far infrared Sauna
• Black Cumin, Cinnamon, Fennel, Ginger, Glycyrrhizin/Pure Licorice, Grape seed extract (mostly Resveratrol cited above), Milk Thistle seed, Olive leaf extract, Rosemary, Wormwood
• Racetams, Vinpocetine (Both are known to help cognitive issues)
• AVOID: inulin, jerusalem artichoke, Resistant starch
• Probiotics: Mutaflor e.coli probiotics (Germany), symbioflor 2 e.coli probiotics (Germany),  enterococcus faecium, lactobacillus casei [typically Yakult]. Avoid probiotic mixtures
• Vitamins: B1,B2,B6,B7,B12, C
• Minerals: Magnesium, Selenium, Zinc AVOID: iron supplements

The downloads of simplified and consensus is below.

Questions from Reader

I usually send early and second drafts to readers to improve posts. Some of the questions sent are:

• I was incorrectly under the assumption that antibiotics and antivirals will always kill beneficial and detrimental bacteria, until I read your Antibiotics will kill everything — Not article. I have taken azithromycin injections on average 3x a year and 5 courses of 2 month long doxycycline cycles over the past 6 years.  My question is, can we infer from the data as to how long one should take antibiotics like doxycycline or azithromycin? My presumption is that regular microbiome samples should be taken 1/mo, ideally 1/week, until bacteria populations affected by these drugs are in optimal ranges. What are your thoughts?
  • Most antibiotics come from nature and usually from a bacteria that tries to reduce competitors. Usually they are discovered because it is effective against a troublesome bacteria (and ignore side-effects on anything else).
  • When to stop is a complex question. There are a variety of ways that a bacteria is “gone” but hibernating. My attitude is to take just one course if there is a specific infection. The goal is to knock the bacteria back sufficiently that your natural immune system can take over the fight. For changing a dysfunction microbiome where there could dozens of different bacteria in the cartel, I favor Cecile Jadin approach of alternating types of antibiotics with 7-10 days on and 20 days off between each course. It seems to be more effective against antibiotic resistance that often develops when the same antibiotic is regularly used. I have seen studies on sewerage treatment confirming this.
  • There is no “optimal range” IMHO, there are “typical ranges” with the problem that different folks will use different methods to determine those. I favor the Kaltoft-Moldrup ranges. See  The problem with “official” ranges from labs.
•  Do you have an article or any other info on the Cold Extremities symptoms caused by particular microbiome signatures? This issue has plagued me all my life and I’d really like to fix it if you have any suggestions on places to look.
  • I do not recall doing anything specific because there can be multiple paths to this symptoms. Some obvious ones:
    • Some forms of coagulation or vascular constriction issues that results in slow blood flow. Your saturated Oxygen Levels may be fine, just heat loss from taking too long to reach the extremities. This can also occur with inflammation.
      • This was the case for me. A genetic defect caused thick blood — my particular defect was easy to compensate for.
    • Hypothyroidism is another possibility
•  I am currently taking OMNi-BiOTIC(sold under AllergoSan USA) Stress Release pre and probiotics everyday, alongside a prebiotic called Omni Logic Plus. I saw that your report mentioned “Avoid Probiotic Mixtures”, so I will be reevaluating this strategy.
  – One scoop of the Omni Logic Plus prebiotic contains 1g of Fructo-Oligosaccharides and 1g of Galacto-Oligosaccharides, and causes me intense, painful symptoms of bloating for 24 hours. 
  -The Simple Suggestions sheet recommends 15gm FOS and 10gm GOS daily. Do you think I should be experiencing bloating alongside these prebiotics, and how should I work up to 15gm/10gm? What exactly is happening in the microbiome when this ramping of prebiotics occurs?
  • Remember these are suggestions only, many suggestions!! Since it causes painful bloating, I would not do.
  • The dosages are UPPER LIMITS that are deemed safe (has been used in clinical studies). The purpose is to show what may be therapeutic levels are. See this page. Often people dosages are closer to homeopathic than therapeutic. 🙁 . Then we hear on forums “I tried it and it did nothing”
  • You may wish to get the Oligosaccharides directly from food and not as supplements. This is always my preference.
•  Some of these suggestions counteract each other, such as Mutaflor e.coli probiotics (Germany), and doxycyline. Do you recommend prioritizing one or the other first?
  • See Suggestions Contradictions — Limits of Certainty for why contradictions occur. My usual preference is natural substances over prescription, second item is availability. If you can get a MD to prescribe an antibiotic while you wait for Mutaflor or Symbioflor to arrive — then the sequence resolves itself.
  • One thing to remember is this: We want to destabilize the bad microbiome. Doing everything at once will likely gain some ground initially but you then get stuck in trench warfare as the other side learns how to respond. I usually suggest breaking suggestions into 4-8 lists of 1-2 weeks duration and just rotate what you take. I refer to this as the resistance approach. It worked for the Americans against England in 1776, French Resistance against Nazi Germany, and Vietnamese (Viet Kong) against Americans in Vietnam, and Afghanistan against the British, Russia and the US. No great victory just constant small ones.
•  I noticed that you recommend high dose B1 Thiamine at 1.8gm/day and a high protein/low fat diet overall. I understand that there’s a variety of other dietary suggestions here, but would you recommend that a purely carnivore diet be tried? Have you noticed the carnivore diet having any permanent beneficial impact on the microbiome?
  • Short answer, B-vitamins are often nicknamed “Beef-Vitamins” because that is where most can be sourced from. On the flip side, getting the vitamins out of the beef, depends on stomach acid and what bacteria are there.
  • You can use the food site, to see how much B12 and B6 are in various foods. 100 grams of beef liver (3-4 oz) gives 0.13 mg of B-12. It is unlikely you will get therapeutic dosages from eating beef.

My usual advice is to do things for 4-8 weeks and then retest. You are sailing your microbiome to safer waters though an archipelago. The winds and the charts will often require many course corrections.

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.

Back Story

I have been suffering from fatigue for about 8 months now and would like your advice. I have seen multiple doctors now and they have not been able to figure anything out despite running multiple labs. My fatigue started around August of 2022 when I was put on antibiotics. I had an infected wound from a martial art that got infected. After that, I got c diff from a friend I had been taking care of, so in late August I was put on another course of antibiotics. Then in September I got c diff again and was put on another course of antibiotics. My fatigue became very severe at this point. I could barely make it out of bed, etc. I still have fatigue now. I can walk around but I get tired pretty easily. I still cannot go to the gym or exercise. Just the thought alone of pushing my body seems tiring. Please assist.

Antibiotics can be good or bad. Each type alters the microbiome in different manners. Recently I added a new tool to help people negotiate with their MD to find the best candidate antibiotics that will address the MDs concern while looking at their microbiome impact (which is not an area they are knowledgeable). See this video for more information. What has happened, happened — let us move forward.

The antibiotic given were:

• Cephalexin (Keflex, Daxbia) for the wound – which I lack any significant information on
• Dificid (A Macrolide, fidaxomicin)  for c diff

Analysis

First, we see the type of distribution often seen with Chronic Fatigue Syndrome and Long COVID — two conditions with fatigue being a signature symptom. Many many token genus and species.

Percentile	Genus	Species
0 – 9	77	99
10 – 19	22	27
20 – 29	8	8
30 – 39	13	12
40 – 49	5	10
50 – 59	9	15
60 – 69	6	13
70 – 79	5	15
80 – 89	7	7
90 – 99	3	5

This tool also flags the bacteria that may be causing it. What was flagged is shown below — through the roof on these one bacteria (parent-child), This is known to be a very pro-inflammatory bacteria. It is 61% of the microbiome!! It is strongly associated with rheumatoid arthritis [2013] [2023] [2019]. High levels also may indicate mycotoxin (fungi) is present in the environment (house, places he goes) or person [Citation]. For more information see this excellent summary.

Rank	Name	Your value	Percentile
genus	Prevotella	618680	99
species	Prevotella copri	617760	100

The chart below shows how extreme it is.

Looking at Potential Medical Conditions Detected, we had 12 items – which is to be expected with this type of shifts, and we will ignore. It is interesting to note that Dr. Jason Hawrelak Recommendations are at the 99.7%ile — with all of the ones with issues being too low which is expected from one bacteria over-whelming the microbiome.

Antibiotics

There is a lovely study from [2013] that reports on antibiotic resistance of  Prevotella copri [Table 1]. It states “suggesting that resistance to these antibiotics amongst Prevotella … microbiota is not intrinsic, but selected by previous antibiotic treatment”. Which suggests that the multiple courses in a short period of time contributed. I have no data on the two antibiotics that he took.

This hints that taking additional antibiotics may be ineffectual.

If blood tests for fungi has not been done, it should be suggested to the treating physicians.

We have an elephant in the Igloo! Let us address this item first. Then retest after a few weeks. I asked for suggestions including antibiotics, shown below.

A good start would be Vitamin B3 (niacin). Which can cause flushing (i.e. white skin going lobster red), so a low dosage of 100 mg/day and increasing slowly. The other non-prescription items of note are:

• mastic gum (prebiotic)
• clostridium butyricum (probiotics),Miya,Miyarisan
• Possible: saccharomyces cerevisiae (probiotics) BUT saccharomyces boulardii (probiotics) is an avoid.
  • Often this is mislabeled by retail product — so I would suggest skipping it.
• glycyrrhizic acid (licorice) – I prefer Spezzatina by Amarelli  – available on Amazon and other stores
• Slippery Elm
• Triphala – an Indian herb mixture that we usually make into capsules. You could also make tea of it (if you can stomach it’s taste. It’s on Amazon.
• lactobacillus reuteri (probiotics) — not mixed with other species. Most lactobacillus and bifidobacterium are to be avoided.

Second Pass

.Should I do anything other than add b3, mastic gum, licorice, etc to my diet? Do I need to alter my diet in any way to lower the prevotella copri count? My diet right now is pretty bad. Burgers, pizza, etc. I went keto for a couple weeks a few months back to see if it made a difference and it didn’t so I figured if I was going to be miserable, I might as well enjoy what I eat.

From reader after reviewing above.

First the BAD NEWS, Keto is known to increase Prevotella copri, so the no positive impact is not unexpected. More BAD NEWS – what you are eating (high fat foods) will likely have adverse effects.

Above I focused on the Elephant, let us look at the ants and mice in the igloo. This is very easy to do and I made a video of it below.

Bottom line, cut out beef that are high fat content — lean beef only. Burgers and Pizza both use cheap high fat beef. Shift to rice for your source for starch (There are rice based pizza available). No soft drinks or sweets (cut sugar way down – sugar withdrawal can be rough).

Follow up: I would suggest doing a retest after 6 weeks of trying to alter your microbiome with the above suggestions. This will give us data to do your next “diet correction”. It is usually a multi-leg journey back to health.

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 get a few emails asking about apparent contradictions from Microbiome Prescription AI Engine. With the new “Just give me suggestions!” option, they have been reduced — this post explains the root issue of these conflicts. It does not solve the issue — only time and a lot more published studies will resolve it.

Data From Studies

The data entry attempts to keep true to what is actually reported in the studies on the US National Library of Medicine. A simple example, the following are subjects of different studies:

• Grape seed extract
• Grape polyphenols
• Grape Fiber
• Resveratrol
• Grapes
• Red Wine
• Red wine polyphenols

It is human nature to try to consolidate information. There are numerous historic examples where such consolidation failed. A simple one is that all antibiotics are the same. In some nations, antibiotics are over the counter — so if you have an infection like tuberculosis, some people would walk in and buy the cheapest antibiotics expecting it to work. It is no surprise that it would have no effect. “They are almost the same” is not an approach that I subscribe to.

The second aspect of studies is that they report on different levels of the bacteria hierarchy, and rarely report on multiple levels. In some cases, the report is only on the highest levels.

When looking at your sample. You may be high at the genus level but normal in the family and order levels. So data from studies about what is impacted at the species, family, order or class level may be ignored. Some people, including medical practitioners, may consolidate this information and after reading one study that mentions a genus apply that to all related species, family, order or class — occasionally to the phylum level. The human tendency to consolidate information strikes again.

Last, the study may have been done on people with a specific condition or type of diet. Diet is often based on location in the world: India (many eat no meat), America (lots of junk food), China (high rice content). The shifts seen with some modifiers with different conditions or diets are different and sometimes in opposite directions. We may not get consistent studies. The human tendency to consolidate information by deeming everyone to be the same strikes again.

Below we have seven similar items — but very different information on what the studies report on.

Sum of Count	Column Labels
Row Labels	class	family	genus	norank	phylum	species	subspecies
grape fiber			3			1
grape polyphenols			13			3
grape seed extract	1		8			1
grapes	1	3	9			6
red wine		12	28	1		169	3
red wine polyphenols			5	1		3
resveratrol		40	72	1	6	114	6
Grand Total	2	55	138	3	6	297	9

While they are similar, there are difference between them that may be significant. One contains sugars, another contains fiber, another contains alcohol — these minor differences can alter different bacteria significantly.

This is why you may see apparent contradictions in suggestions. We a have a mixture of information about your microbiome and each of the above is a sieve of different mesh and fabric.

I choose not to consolidate information. I keep the information as reported. A medical practitioners is not able to keep all of this information in their head. They will proceed to consolidate, and consolidate and simplify – the art of medicine. The Artificial Intelligence Engine is the result of processing over 41 Gigabytes of information — that is likely far more than any medical practitioners had actually read in their career. Is the detail needed? That’s a personal judgement. I prefer to have it. Using AI, I can work with all of the information available without needing to consolidate or simplify.

When there is a contradiction – which should I choose? My standing answer, is avoid the substance. We do not know with confidence what the outcome may be. On the other hand, I often seen suggestions reinforcing each other — for example Positive: Gluten Free, Negative: Wheats.

We do not want to take gambles with our health — keep to where there is consensus.

“Just give me suggestions”

This picks common items often seen on the internet for a variety of conditions. When there are “similar” items, the one with the most data will usually be selected. It gives higher confidence. These choices are evolving as I review the data.

The intent is avoid showing contradictions, and work where we have the most data. It’s a simple best path forward.