A person who suffered from Multiple Chemical Sensitivity(MCS) for many years before it progressed into Mast Cell Syndrome(MCAS) forward an article, “Chemical Intolerance and Mast Cell Activation: A Suspicious Synchronicity“, 2023. At the same time, my understanding of the complex nature of the microbiome also made a leap forward. For those interested, see these three very technical posts:

• Using Mean and Standard Deviation for Bacteria is Inappropriate.
• Significant Bacteria and Their Thresholds – Part 1
• Significant Bacteria and Their Thresholds – Part 2

I decided to look at Mast Cell Activation Syndrome again in the hope of gaining insight into treatment possibilities.

The samples being using are donated by readers from various labs with symptoms being self-declared. Symptoms may not agree with clinical definitions. All of the data is freely available for those that are highly skilled with statistics at Citizen Science Distribution.

First, MCS::MCAS

	With MCAS	With MCS	WITH MCAS and MCS	With Any Symptoms
Count	305	219	62	3025
Percentage	10%	7.2%	2%

• If MCAS and MCS are independent, we would expect 10% x 7.2% or 0.72% overall. We have 3 times more than expected.
• The chi-square statistic is 19.3693. The p-value is .000011. VERY SIGNIFICANT CONNECTION.

This disagrees on face value with the reported “Our outcomes confirm the previously published study where the majority of MCAS patients also have CI. ” For this to be true, With MCAS and MCS would be > 150. Differences in methodology may be the cause for this disagreement, but regardless, we see that a person with MCAS is around three times more likely to have had MCS. I read this as suggesting that MCS is a precursor for a class of MCAS. Having MCS prior is not required to developing MCAS; but having MCS means the odds of getting MCAS are much increased.

Looking at Bacterium

I am going to use samples processed through Biomesight only because it is the largest sample set.

For MCS

The table below is filtered to those with P < 0.001 at the genus level with the highest first (P < 5.19132E-05).

Name	Direction
Actinocatenispora	Low
Hathewaya	High
Thauera	Low
Devosia	Low
Thiocapsa	Low
Deferribacter	Low
Viridibacillus	Low
Candidatus Tammella	Low
Coraliomargarita	Low
Geothrix	Low
Desulfosporosinus	Low
Glutamicibacter	Low
Denitratisoma	Low
Catenibacterium	Low
Desulforamulus	Low
Geobacter	Low
Neisseria	Low
Nonomuraea	Low
Agromyces	Low
Anaerotruncus	High
Oenococcus	Low
Saccharopolyspora	Low
Lentibacillus	Low

MCAS

The table below is filtered to those with P < 0.001 at the genus level with the highest first (P < 6.25726E-07).

Name	Direction
Emticicia	Low
Pseudoramibacter	Low
Parascardovia	Low
Rickettsia	Low
Calothrix	Low
Nonomuraea	Low
Marinospirillum	Low
Azospirillum	Low
Neisseria	Low
Viridibacillus	Low
Helicobacter	Low
Peptacetobacter	Low
Nitrosococcus	Low
Avibacterium	Low
Schaalia	Low
Propionigenium	Low
Flammeovirga	Low
Oligella	Low
Erysipelothrix	High
Geobacter	Low
Catenibacterium	Low
Pontibacter	Low
Isoalcanivorax	Low
Faecalitalea	Low
Jonesia	Low
Thalassospira	Low
Amedibacillus	High
Arthrobacter	Low
Hathewaya	High

MCAS and MCS

The table below is filtered to those with P < 0.001 with the highest first (P < 1.85255E-05). The sample size is much smaller, so fewer items were significant, hence all ranks are shown.

Name	Rank	Direction
Chloroflexota	phylum	Low
Anaerolineae	class	Low
Eggerthella sinensis	species	Low
Desulfofundulus	genus	Low

Probiotic Remedies?

Because there are simply no published studies on most of the above bacterium, I went over to the R2 site to compute candidate probiotics. Note: Some of these probiotics are still in development or available only in some countries.

MCS

I enclosed the full list because you want to make sure NOT to take any with a Net being negative. Also, the safest are those with BAD being Zero (0)

Tax_Name	Tax_Rank	Good	Bad	Net
Christensenella minuta	species	194	29	165
Aspergillus oryzae	species	138	0	138
Faecalibacterium prausnitzii	species	185	78	107
Anaerobutyricum hallii	species	162	58	104
Enterococcus faecium	species	124	37	87
Blautia hansenii	species	122	37	85
Lactiplantibacillus plantarum	species	64	0	64
Roseburia intestinalis	species	118	60	58
Bifidobacterium catenulatum	species	53	0	53
Priestia megaterium	species	47	0	47
Bacillus pumilus	species	43	0	43
Bacteroides thetaiotaomicron	species	37	0	37
Latilactobacillus sakei	species	37	0	37
Bifidobacterium breve	species	32	0	32
Levilactobacillus brevis	species	31	0	31
Parabacteroides distasonis	species	31	0	31
Parabacteroides goldsteinii	species	54	28	26
Pediococcus pentosaceus	species	25	0	25
Limosilactobacillus reuteri	species	23	0	23
Shouchella clausii	species	23	0	23
Lactiplantibacillus argentoratensis	species	23	0	23
Bifidobacterium longum	species	20	0	20
Bifidobacterium adolescentis	species	39	21	18
Blautia wexlerae	species	74	57	17
Lactococcus cremoris	species	36	21	15
Enterococcus faecalis	species	14	0	14
Bifidobacterium pseudocatenulatum	species	13	0	13
Limosilactobacillus vaginalis	species	12	0	12
Lactobacillus kefiranofaciens	species	12	0	12
Lactococcus lactis	species	11	0	11
Clostridium beijerinckii	species	11	0	11
Streptococcus thermophilus	species	10	0	10
Leuconostoc mesenteroides	species	10	0	10
Segatella copri	species	37	29	8
Phocaeicola coprocola	species	27	21	6
Bacillus subtilis	species	26	21	5
Lactobacillus crispatus	species	11	11	0
Lactiplantibacillus pentosus	species	0	11	-11
Bacteroides uniformis	species	20	32	-12
Limosilactobacillus mucosae	species	0	14	-14
Lacticaseibacillus casei	species	0	17	-17
Bacillus cereus	species	33	54	-21
Bacillus licheniformis	species	0	22	-22
Ligilactobacillus salivarius	species	11	41	-30
Lactobacillus jensenii	species	0	36	-36
Akkermansia muciniphila	species	12	50	-38

MCAS

Tax_Name	Tax_Rank	Good	Bad	Net
Christensenella minuta	species	83	0	83
Aspergillus oryzae	species	68	0	68
Enterococcus faecium	species	58	0	58
Faecalibacterium prausnitzii	species	53	0	53
Roseburia intestinalis	species	53	0	53
Anaerobutyricum hallii	species	51	0	51
Blautia wexlerae	species	44	0	44
Bacillus pumilus	species	28	0	28
Priestia megaterium	species	27	0	27
Levilactobacillus brevis	species	25	0	25
Latilactobacillus sakei	species	25	0	25
Lactiplantibacillus argentoratensis	species	23	0	23
Blautia hansenii	species	22	0	22
Limosilactobacillus fermentum	species	21	0	21
Shouchella clausii	species	20	0	20
Limosilactobacillus reuteri	species	18	0	18
Lactiplantibacillus plantarum	species	17	0	17
Bacillus subtilis	species	16	0	16
Bifidobacterium animalis	species	15	0	15
Bifidobacterium animalis subsp. lactis	subspecies	15	0	15
Lactobacillus acidophilus	species	14	0	14
Clostridium butyricum	species	13	0	13
Bifidobacterium adolescentis	species	12	0	12
Ligilactobacillus salivarius	species	11	0	11
Hafnia alvei	species	11	0	11
Bacteroides uniformis	species	0	15	-15
Lacticaseibacillus rhamnosus	species	0	16	-16
Bacteroides fragilis	species	0	23	-23

Bottom Line

The most confidence is to work on probiotics only with the following being strongly recommended.

• Aspergillus oryzae
• Enterococcus faecium
• Bacillus pumilus
• Bacillus subtilis
• Lactiplantibacillus plantarum a.k.a. Lactobacillus plantarum
• Bifidobacterium catenulatum
• Bifidobacterium breve
• Levilactobacillus brevis a.k.a. Lactobacillus brevis
• Latilactobacillus sakei a.k.a. Lactobacillus sakei
• Limosilactobacillus reuteri a.k.a. Lactobacillus reuteri
• Shouchella clausii a.k.a. Bacillus Clausii
• Lactobacillus acidophilus
• Ligilactobacillus salivarius a.k.a. Lactobacillus salivarius

The top one for both is Aspergillus oryzae. This is likely unfamiliar to most people. It is also known as Shirayuri Koji. It is available on Amazon, not as a probiotic but cooking additive!! It is in Koji Rice. It is also solid as strong wakamoto w

With Tariffs ordering from Japan can get expensive, https://www.yami.com/ ships from the US, so no tariffs costs!

CAUTION: This is based on modelled data and not verified by clinical studies. IMHO, it is likely a superior set of suggestions than other more “conventional” approaches.

In an earlier post (Significant Bacteria and Their Thresholds – Part 1), I raised that issue and a EU colleague, Valentina Goretzki, suggested that I take data from 1 thousand shotgun samples from healthy individuals to illustrate the problem.

Microbiome data distributions frequently display extreme skewness—often greater than 20. In such cases, computing mean and standard deviation is simply incorrect.  My friend “Perplexity” writes Mean and standard deviation become inappropriate measures for computing significance if the distribution’s skewness is substantial—specifically, when the absolute skewness exceeds ±2.

The result was about two thousand bacterium that occurs at least 60 times in these samples could be plotted as shown below.

It is clear that non-parametric methods are needed to compute “healthy ranges”. For those with just basic statistics, this may become a significant challenge.

Conventional medical science tend to think of one bacteria to one condition. These are known as Single-Bacterium Diseases.

Single-Bacterium Diseases

• Tuberculosis — caused by Mycobacterium tuberculosis​
• Diphtheria — caused by Corynebacterium diphtheriae​
• Cholera — caused by Vibrio cholerae​
• Leprosy (Hansen’s disease) — caused by Mycobacterium leprae​
• Whooping cough (Pertussis) — caused by Bordetella pertussis​
• Tetanus — caused by Clostridium tetani​
• Typhoid fever — caused by Salmonella typhi​
• Syphilis — caused by Treponema pallidum​
• Gonorrhea — caused by Neisseria gonorrhoeae​
• Lyme disease — caused by Borrelia burgdorferi​
• Gastric ulcer — caused by Helicobacter pylori​
• Strep throat — caused by Streptococcus pyogenes​
• Urinary tract infection — most commonly caused by Escherichia coli​
• Pneumonia — can be caused by Streptococcus pneumoniae​
• Meningitis — can be caused by Neisseria meningitidis (meningococcus), or Streptococcus pneumoniae​
• Bacterial vaginosis — often caused by Gardnerella vaginalis​

There are other conditions that could be cause by any one of several bacterium, but not bacterium cooperating with each other.

When we enter the world of microbiome dysbiosis, this simplicity disappears.

Case Study of number of bacterium associated with many symptoms

We return to our collection of 4,290 unique samples with 327 symptoms having statistical significant bacterium discussed in Significant Bacteria and Their Thresholds – Part 1. Restricting our data to associations with P < 0.01, the graph below shows the number of bacteria associated with each symptom.

My view is that symptoms arise from the metabolites produced by a specific combination of bacteria. Examining the data from KEGG: Kyoto Encyclopedia of Genes and Genomes, we see that some metabolites can be produced by hundreds of different bacterium. Some bacteria associated with a symptom may actually be due to secondary effects—reflecting shifts caused by other species—so distinguishing causal bacteria from merely correlated ones remains difficult.

A practical working hypothesis for reducing or eliminating a symptom is therefore to normalize the bacteria associated with it. A rational approach is to start with those that have the strongest association.

---

The Naïve Approach

A well-educated medical professional typically follows this reasoning:

1. Identify which bacteria are outside the normal range and linked to the patient’s symptoms.
2. Determine whether each bacterium is elevated or reduced.
3. Review substances known to influence these bacteria.
4. Recommend lifestyle or dietary changes based on those substances.

In practice, certain substances may be counter-indicated for other bacteria that are also out of range. This is often overlooked, as many professionals adopt a “find the first substances that address the bacterium shift” approach. This sometimes makes the patient worse.

Microbiome Prescription uses a manually curated database containing over 7.4 million relationships between substances and specific bacteria. Because of this depth, these potential conflicts are often identified and the risk of adverse effects is reduced.

A professional in this situation would reasonably expect to see a chart such as the one below for each of the bacterium associated with a symptom. The chart, table or other items giving a desired range of values.

With dozens of bacteria out of range, there is no clear objective ability to rank these bacteria by importance. There are a variety of speculative punts that could be tried:

• Rank them by the volume of bacteria
• Rank them by the deviation from the reference range, i.e.(value – mean)/standard deviation
• Rank them by any of many possible algorithms that could be tossed at this issue.

Turning the issue upside down

Let us take the concrete example promised in the earlier post: Long COVID. We have 538 samples with Long COVID in our population of 4,290 contributed samples. This is about 12% of the samples.

Filtering the associations to those bacterium with P < 0.0001; our highest priority or weight. We obtain the table below. While there are many bacteria, some are tightly related according to lineage:

Mycoplasmatota; Mollicutes; Anaeroplasmatales; Anaeroplasmataceae; Anaeroplasma

Taxon	tax_name	tax_rank
1737405	Tissierellales	order
626933	Odoribacter laneus	species
186332	Anaeroplasmatales	order
186333	Anaeroplasmataceae	family
85630	16SrX (Apple proliferation group)	species group
102260	16SrXV (Hibiscus witches’-broom group)	species group
47565	Candidatus Phytoplasma prunorum	species
2086	Anaeroplasma	genus

Filtering the associations to those bacterium with P < 0.001; we get a second table shown below. One of the bacteria is Lactobacillus jensenii, which is available as a probiotic (I have some in my fridge) — but we do not know if we want to increase or decrease this bacteria.

Taxon	tax_name	tax_rank
2330	Halanaerobium	genus
1381	Atopobium minutum	species
972	Halanaerobiaceae	family
724	Haemophilus	genus
194	Campylobacter	genus
28128	Prevotella corporis	species
72294	Campylobacteraceae	family
33037	Anaerococcus vaginalis	species
38288	Corynebacterium genitalium	species
42857	Moorella group	norank
45254	Dysgonomonas capnocytophagoides	species
45404	Beijerinckiaceae	family
47420	Hydrogenophaga	genus
102261	Candidatus Phytoplasma brasiliense	species
109790	Lactobacillus jensenii	species
89061	Weissella thailandensis	species
215579	Schlegelella	genus
382673	Syntrophomonas cellicola	species
386414	Hoylesella timonensis	species
1963360	Parachlamydiales	order
1853231	Odoribacteraceae	family

We could continue onwards and look at the 40 bacterium associated with P < 0..01 and 48 bacterium with P < .05. While potentially important, because of the lesser degree of association, we will ignore them here. My preference is always to favor highest probability and thus would only look at those in the above two tables.

Question: Is Lactobacillus Jensenii too high or too low

Some medical practitioners would hear the word “Lactobacillus” and immediately say “Take it” because they have a (questionable) belief that Lactobacillus will help everything! Is this the case here?

The Data for Lactobacillus Jensenii

The table below shows the data. The percentages have been transformed to percentiles for better presentation with a count of the occurrences at each. One of the first items some people will note is that this bacterium is not reported often; but there is enough data to get a P < 0.001 using Chi² . I disagree with the approach seen in some papers, to only examine very commonly reported bacterium.

%-ile Range	HasTotal	HasNotTotal
Not Present	344	3852
0.00	1	3
4.21	0	15
20.00	2	22
45.26	2	13
61.05	1	3
65.26	0	3
68.42	0	3
71.58	0	5
76.84	0	2
78.95	1	1
81.05	0	1
82.11	1	0
83.16	1	0
84.21	0	1
85.26	0	2
87.37	0	1
88.42	0	1
89.47	1	0
90.53	0	1
91.58	1	0
92.63	1	0
93.68	0	1
94.74	0	1
95.79	0	1
96.84	0	1
97.89	0	1
98.95	0	1
100.00	0	1

Doing a little more aggregation we get the table below. If a person has no L. Jensenii they have a 8% chance of having Long COVID, If there is any present, the odds increases 13% chance, a higher amount pushes it up to 17% (double the odds).

Conclusion: L. Jensenii probiotics are a definite to be avoided probiotic for Long COVID

%ile Range	HasTotal	HasNotTotal	Ratio
Not Present	344	3852	0.08
0.00	1	3	0.25
4.21	0	15	0
20.00	2	22	0.08
Over 20	9	44	0.17
Over All	12	84	0.13

Danger Will Robinson: Do not over simplify

Looking at another bacteria with P < 0.0001, we see charted below. The bacteria is commonly reported.

What is evident is that the association is range sensitive, and thus reference ranges:

• Below 17%ile is out of range
• Between 28%ile and 34%ile is out of range
• Over 60% is out of range

Many microbiologists would say that this does not make sense. At this point I should remind people of quorum sensing with bacteria.

Quorum sensing is a communication process that allows bacteria to sense and respond to their population density using chemical signaling molecules called autoinducers. Each bacterium produces and releases autoinducers into its environment. As the population grows, the concentration of these molecules increases. When a threshold level is reached, autoinducers bind to specific receptors, triggering changes in gene expression that coordinate group behaviors such as biofilm formation, virulence factor secretion, sporulation, and bioluminescence.

At this point, many minds may be going into ‘statistical culture shock‘. For me, it makes complete sense and is often seen across nature. They are sometimes termed “islands of stability” in some sciences. In our case “islands of symptoms” would be a more accurate name.

We may end up committed blasphemy against conventional linear mechanic thinking!

Examples:

Alloys With Maximum Strength at Specific Composition

• Iron-Carbon Steel: In carbon steel, maximum strength is usually achieved at a carbon content near 0.8% (known as “eutectoid steel”), where the steel forms a very fine pearlite structure on cooling. Both lower and higher carbon percentages reduce ductility or create brittleness, decreasing usable strength for many applications.​
• Aluminum-Copper Alloy (Al-Cu): The precipitation strengthening of aluminum alloys, such as in the Al-Cu system, reaches maximum effectiveness at about 4–5% copper by weight. Below or above this optimal range, strengthening diminishes because either not enough or too much second phase is created.​
• Nickel-Iron Alloys: For instance, “Permalloy” (a nickel-iron alloy) typically reaches its desired magnetic properties with about 80% nickel and 20% iron. Changes in this ratio result in reduced magnetic strength, which can be considered a parallel with mechanical strength in many alloys.​

Wildlife Systems with Optimal Mixtures

• Animal Diversity in Food Webs: Studies show that increasing the number of animal species generally increases total animal biomass and plant consumption rates, up to a point. Beyond this, higher diversity leads to increased intraguild predation (animals eating each other), which can reduce overall community efficiency and stability.​
• Keystone Species: Some ecosystems depend on a particular balance among keystone species and others. Removing or adding too many can severely weaken the system, in analogy to alloys with optimal composition.​
• Biodiversity-Function Relationship: The stability and strength of ecosystems typically follow a nonlinear relationship with species richness: there is a “sweet spot” where ecosystem functions (like carbon sequestration or primary production) are maximized.

Nuclear Islands of Stability

• The best-known island is around atomic numbers (Z) 114 to 126 and neutron number (N) 184, where theoretical calculations suggest nuclei could have half-lives of minutes, days, or potentially even years, instead of the microseconds typical for super heavy elements nearby.

Other Physical Sciences

• The term may be used metaphorically to describe stable orbital configurations or regions where system dynamics are less chaotic.

Returning to Long COVID

With Tissierellales we have a complex behavior. With Lactobacillus Jensenii we have a simple “if present, reduce it” finding. I returned to the lists above and attempted to identify those with a simple finding with a P < 0.05 for the odds ratios. This is shown in the table below.

• Reduce Beijerinckiaceae, family, With odds being almost 2x for those with Long COVID, i.e. 0.059 vs 0.026
• Increase Parachlamydiales, order, With odds being more than 10x for not having Long COVID, i.e.
  0.067 vs 0.1

Other have the odds being close to each other. For example, Tissierellales (0.978 vs 0.973). In the next post, we will examine more of the complex behavior ones.

Correction	Tax_name	Tax_rank	Symptom Odds	No Symptom Odds
	Anaeroplasma	genus	0.146	0.135
	Candidatus Phytoplasma prunorum	species	0.722	0.738
	16SrX (Apple proliferation group)	species group	0.722	0.735
	Anaeroplasmatales	order	0.146	0.135
	Anaeroplasmataceae	family	0.146	0.135
	Odoribacter laneus	species	0.494	0.506
	Tissierellales	order	0.978	0.973
	16SrXV (Hibiscus witches’-broom group)	species group	0.02	0.013
	Candidatus Phytoplasma brasiliense	species	0.02	0.014
	Lactobacillus jensenii	species	0.034	0.021
	Odoribacteraceae	family	0.961	0.941
Increase	Parachlamydiales	order	0.067	0.1
	Schlegelella	genus	0.062	0.042
	Syntrophomonas cellicola	species	0.039	0.019
Reduce	Hoylesella timonensis	species	0.348	0.295
	Weissella thailandensis	species	0.028	0.016
	Campylobacteraceae	family	0.419	0.386
	Campylobacter	genus	0.388	0.354
	Haemophilus	genus	0.775	0.745
	Halanaerobiaceae	family	0.222	0.184
	Atopobium minutum	species	0.042	0.027
	Halanaerobium	genus	0.222	0.184
	Prevotella corporis	species	0.497	0.457
	Anaerococcus vaginalis	species	0.213	0.215
	Corynebacterium genitalium	species	0.031	0.019
Reduce	Moorella group	norank	0.534	0.447
	Dysgonomonas capnocytophagoides	species	0.042	0.053
Reduce	Beijerinckiaceae	family	0.059	0.026
Reduce	Hydrogenophaga	genus	0.034	0.014

Summary

This post explains my approach for ranking which bacteria should be targeted for change, primarily using the P value to guide priority. I compared two types of bacteria: one that is rare and one that is common. Rare bacteria are usually omitted from standard analyses because their scarcity makes conventional measures—such as calculating the mean and standard deviation—poor indicators of significance. Overlooking these bacteria is a methodological error, especially when the data skew exceeds 2, making such traditional metrics inappropriate.

It’s important to keep in mind that bacteria engage in quorum sensing, which influences the metabolites they produce and likely affects the symptoms observed. For some bacteria, the difference in their relative abundance between people with and without a particular symptom can be substantial and may be all that is needed for significance.

When I see lab present a patient data with images like below, I roll my eyes! “It looks likes a bell curve, it smells like a bell curve,….”

Some reports gives event less information with no ranges and a “normal” — which is computed how? Some choices are below.

• Arithmetic Mean:
  The most common type, calculated by summing all the values and dividing by the number of values.
  Arithmetic Mean=1n∑i=1nxiArithmetic Mean=n1∑i=1nxi
• Geometric Mean:
  The nth root of the product of all values, commonly used for ratios or percent changes.
  Geometric Mean=∏i=1nxinGeometric Mean=n∏i=1nxi
• Harmonic Mean:
  The reciprocal of the arithmetic mean of the reciprocals of the data, useful for rates or ratios (e.g., average speed).
  Harmonic Mean=n∑i=1n1xiHarmonic Mean=∑i=1nxi1n
• Quadratic Mean (Root Mean Square, RMS):
  The square root of the average of the squares of the numbers, often used in engineering and physics.
  RMS=1n∑i=1nxi2RMS=n1∑i=1nxi2
• Weighted Mean:
  The mean where each value has its own (possibly different) weight, calculated as:
  Weighted Mean=∑i=1nwixi∑i=1nwiWeighted Mean=∑i=1nwi∑i=1nwixi
• Truncated (or Trimmed) Mean:
  The mean calculated after removing a specified percentage of the largest and smallest values to reduce the effect of outliers.
• Median (sometimes referred to as a kind of mean):
  The middle value when the data are sorted. While technically not a “mean,” it is often referenced in the context of central tendency.
• Mode:
  The most frequently occurring value in the set. Also not a “mean,” but sometimes grouped with measures of central tendency.

Anyone who regularly reads peer-reviewed medical studies on the microbiome will notice findings reported as bacteria being “too high” or “too low,” with phrases like “trending” when statistical significance isn’t reached. Frankly, my reaction to 95% of these papers is an eye-roll, as the statistical methods used are often inappropriate for the data at hand. With multiple degrees in statistics, professional memberships, and experience, I’m acutely aware of both best practices and common pitfalls.

Microbiome data distributions frequently display extreme skewness—often greater than 20. In such cases, computing mean and standard deviation is simply incorrect.  My friend “Perplexity” writes Mean and standard deviation become inappropriate measures for computing significance if the distribution’s skewness is substantial—specifically, when the absolute skewness exceeds ±2.

Despite this, using these metrics remains standard in high school statistics and unfortunately persists in many life science studies. This “comfort zone” approach does nothing but cloud true findings in microbiome science.

My alternative methodology uses a much larger, highly annotated dataset—over 4,290 unique samples generously donated to Microbiome Prescription., most transferred from Biomesight.com. Importantly, these samples are uniformly processed and richly annotated with symptoms rather than diagnoses, yielding superior analytical clarity.

My Natural Questions to ask

Natural for a statistician that is.

• For people with a symptom or diagnosis
  • What are the significant bacteria associating (and likely causing) the symptom
  • What is the threshold levels for these bacteria
    • I use levels and not level because I have observed the same symptom may occur with a bacteria outside of a specific range. That is, too high or too low. I have also encountered this reported in a few studies, often hidden under a term like “altered microbiome”.
    • There is a dangerous assumption in the literature that significant bacteria must be either too high or too low. I unfortunately know Kierkegaard’s “Either/Or” well.
    • There are no universal threshold for all symptoms, each has its own
• For people without a symptom with a statistical model but with dysbiosis
  • How do you determine which bacteria are significant?
  • What is the threshold levels for these bacteria

Over the last decade, these are important questions because they lead directly to treatment suggestions.

They are also significant in evaluating progress. At present I have a forecasting algorithm that has a high prediction rate for symptoms from a microbiome. The forecasting algorithm also is useful for evaluating progress, an example for a recent sample the donor asked me to review.

Prediction

The checks indicates that the donor agrees that they have this symptom.

Monitoring

The person above followed the suggestions and the subsequent test results are shown below.

What are the most common bacteria associated with symptoms?

This is a generic question that is useful for health practitioners to know. For example, Kristina Mitts, of Mind Mood Microbiome and who I frequently correspond with, or Dr. Jason Hawrelak.  

Using more appropriate statistical methods on our sample of 4,292 distinct different samples; we found significant bacteria identified over 327 symptoms resulting in the following statistical significances.

Significance: P <	Count
0.05	13,855
0.01	12,411
0.001	7,614
0.0001	5,532

So what are the top one for each of these significance?

Overall Significance

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	165
35833	Bilophila wadsworthia	species	142
35832	Bilophila	genus	139
818	Bacteroides thetaiotaomicron	species	137
1426	Parageobacillus thermoglucosidasius	species	133
118884	Gammaproteobacteria incertae sedis	no rank	125
871324	Bacteroides stercorirosoris	species	124
120580	Symbiobacterium toebii	species	122
53244	Desulfonatronovibrio	genus	122
543349	Symbiobacteriaceae	family	122
2733	Symbiobacterium	genus	122
1498	Hathewaya histolytica	species	122
454155	Paraprevotella xylaniphila	species	120

P < 0.05

Taxon	name	rank	Instances
2950010	Salidesulfovibrio	genus	47
221711	Salidesulfovibrio brasiliensis	species	46
658623	Chelonobacter	genus	45
69224	Erwinia psidii	species	44
213462	Syntrophobacterales	order	44
3024408	Syntrophobacteria	class	44
31977	Veillonellaceae	family	44
1843489	Veillonellales	order	43
550	Enterobacter cloacae	species	43
35832	Bilophila	genus	42
841	Roseburia	genus	41
53244	Desulfonatronovibrio	genus	41
871324	Bacteroides stercorirosoris	species	41
1260	Finegoldia magna	species	41
1498	Hathewaya histolytica	species	40

P < 0.01

Taxon	name	rank	Instances
35833	Bilophila wadsworthia	species	51
78448	Bifidobacterium pullorum	species	50
820	Bacteroides uniformis	species	47
841	Roseburia	genus	46
818	Bacteroides thetaiotaomicron	species	44
118884	Gammaproteobacteria incertae sedis	no rank	41
1769729	Hathewaya	genus	41
1426	Parageobacillus thermoglucosidasius	species	41
112902	Propionispora	genus	40
36853	Desulfitobacterium	genus	40
386414	Hoylesella timonensis	species	40
119065	unclassified Burkholderiales	family	40
1853231	Odoribacteraceae	family	40
400091	Hymenobacter xinjiangensis	species	39
209080	Propionispora hippei	species	39
871324	Bacteroides stercorirosoris	species	39
69224	Erwinia psidii	species	39
35832	Bilophila	genus	39

P < 0.001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	50
35833	Bilophila wadsworthia	species	37
35832	Bilophila	genus	32
118884	Gammaproteobacteria incertae sedis	no rank	32
658623	Chelonobacter	genus	31
246787	Bacteroides cellulosilyticus	species	31
120580	Symbiobacterium toebii	species	31
543349	Symbiobacteriaceae	family	31
253238	Ethanoligenens	genus	31
2733	Symbiobacterium	genus	31
292833	Candidatus Rhabdochlamydia	genus	30
324707	Candidatus Rhabdochlamydia crassificans	species	30
1426	Parageobacillus thermoglucosidasius	species	30
689704	Candidatus Rhabdochlamydiaceae	family	30
70190	Chroococcus	genus	29
402401	Chroococcus minutus	species	29
1890464	Chroococcaceae	family	29
283169	Odoribacter denticanis	species	28

P < 0.0001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	40
246787	Bacteroides cellulosilyticus	species	34
818	Bacteroides thetaiotaomicron	species	30
1963360	Parachlamydiales	order	30
454155	Paraprevotella xylaniphila	species	30
1426	Parageobacillus thermoglucosidasius	species	30
2733	Symbiobacterium	genus	29
543349	Symbiobacteriaceae	family	29
120580	Symbiobacterium toebii	species	29
35832	Bilophila	genus	26
191412	Chlorobiaceae	family	25
256319	Chlorobaculum	genus	25
244127	Anaerotruncus	genus	25
189723	Prevotella micans	species	25
53244	Desulfonatronovibrio	genus	25
324707	Candidatus Rhabdochlamydia crassificans	species	24
191410	Chlorobiia	class	24
35833	Bilophila wadsworthia	species	24

Summary

This is a high level overview of Significant Bacteria. The patterns above are specific for tests done by Biomesight; a lack of standardization results in using these identifications for other tests is unsafe (legal sense). Background here. IMHO, it is a moral responsibility for labs to produce similar tables.

The key findings are:

• “Common suspects” such as bifidobacterium and lactobacillus are missing!
• Large sample sizes with the same processing is critical. The processing must be the same as used in a clinical setting.
• Appropriate statistical methods must be used

Stay tune for the next part as we drill deeper into appropriate handing of data with some specific issues like Long COVID.

The input data that I used is publicly available at: https://citizenscience.microbiomeprescription.com/

Post Script

Probiotics and the above gets interesting. Take Bacteroides uniformis which is at the top of many of these tables. If we go to my bacteria association site,

We can determine the probiotics (available or pending) that will increase this bacteria (none decreases)

Again, the “cure all” lactobacillus and bifidobacterium genus is absent (apart from Ligilactobacillus ruminis which is not currently available).