Using the five methods described in Technical Note: The Four Winds of Microbiome Analysis, I ran these method on all of the data on the citizen science site of Microbiome Prescription testing for all symptoms that have been self-reported from users of Ombre Labs and Biomesight retail microbiome tests. The data from each lab was done is insolation (you cannot mix data from different processions flows, see The taxonomy nightmare before Christmas… for how the results from the same FASTQ files are reported by 4 different processing flows).

My criteria for deeming a genus significant was:

• At least one method reported P < 0.01
• At least two methods reported P < 0.05

The 2 @ P < 0.05 is a bit of shooting from the hip; I expect some correlation between methods but not sufficient to have that adjusted P value to be outside of the range 0.0025 and 0.01. The statistics on significant genus found is below. The 2 @ P < 0.05 produce only a small contributions,

Significant Count	P < .01	P < .05
2312	0	2
241	0	3
5	0	4
55576	1	1
17714	1	2
10835	1	3
6280	1	4
711	1	5
28660	2	2
4361	2	3
12876	2	4
3646	2	5
13523	3	3
6636	3	4
4700	3	5
49501	4	4
9004	4	5
29060	5	5

326 Symptoms Had significant Statistical Associations

An example of symptoms with a number of associations is shown below. You may examine them here Citizen Science Symptoms To Genus Special Studies.

There are some great contrasts between these two labs

With the next set being very interesting. I know that there is a significant subset of 0-20 years old autistic children whose parent have done their microbiome and uploaded. This appears to be reflected in the data. It does call into questions the 10-20 yo associations because of the likely over-representation of autism in this range group.

All of this data is freely available at:

Drilling Down to Genus Involved

If on the above page you click on the count, you will be taken to a sortable table showing the genus. In the example below, we look at the most significant (i.e. P < 0.01 for all five methods).

Looking at Lachnobacterium, we see the expected pattern

• The odds of seeing this genus for people with mast cell issues in slightly elevated (1.037)
• The percentage seen is 3.2x what the average for others are
• The percentile is about 1.19x higher

Looking at Lactococcus we see a more confusing picture

• The genus is seen less often (0.928)
• The amount seen with this genus is found is actually much higher (2.057)
• The percentile ranking is slightly lower (0.943)

Both Percentage and Percentile numbers are the maximum using paired and unpaired statistics which may partially account for an apparent contradiction.

Bottom Line

The purpose of this post was to illustrate the data produced from using the five different ways of finding statistically significant association of genus to symptoms. For many readers, this data may be difficult to accept because it disagree with the common sense view of the microbiome that they are working with.

For example, if a genus is seen more often, then you would expect the average amount to be higher. This is often false looking at real data. Understanding the microbiome means discarding simple mechanical models and understanding a complex world of interactions with cascading consequences.

Note: Why we have so many association… Sample Size!

For each of these analysis we have over 1000 annotated samples. As sample size increases, the ability to detect significance goes up significantly.

Pending Work

Tuning parameters:

The numbers will be changing as I tune thresholds. At present:

• I raised the number of times that a genus need to be reported in annotated samples to 36 (i.e. around 0.3% prevelance)
• Number of cases with symptoms reported to 30

This reduced the volume of results (we are not saying less significance — we are filtering by rarer occurrence).

The next step is applying this to an individual microbiome result for a person with one or more symptoms. This means determining which bacteria are the greatest probable contributors and the weight to be given to each for determining a course of microbiome modification.

This is one of a continuing set of posts on Microbiome Analysis: Technical Notes on Microbiome Analysis.

Many studies use just one method of analysis: Means of the Counts for bacteria very often seen. The reason is likely conditioning from their education and not knowing how to handle a variety of statistical complexities.

For my analysis I tend to use the following four methods:

• Means of Counts for those reporting this bacteria [Reported]
• Means of Counts with zero for those not reporting [All]
• Prevalence (see Technical Note: Prevalence, Average and Not Reported) [Prevalence]
• Means of Percentiles for those reporting this bacteria [Percentile]

Mini-lessons on the methods

For those folks who may be rusty on technical aspects

Means of Counts for those reporting this bacteria [Reported]

With this method we compute the average and variance measures for each group based on the percentage of each bacteria in the sample, for each sample in the two test groups (with and without brain fog). Not reported values are ignored.

From these numbers we then compute the t-test statistic (see Hypothesis Test for a Difference in Two Population Means ). From this t-test statistic, we lookup or compute the probability of them being the same. If there is less than 1% chance of the two sets being the same, then we say P < 0.01; 5% chance is P < 0.05; 0.1% change is P < 0.001.

Means of Counts with zero for those not reporting [All]

With this method we compute the average and variance measures for each group based on the percentage of each bacteria in the sample, for each sample in the two test groups (with and without brain fog). Not reported values are deemed to be zero.

From these numbers we then compute the t-test statistic (see Hypothesis Test for a Difference in Two Population Means ). From this t-test statistic, we lookup or compute the probability of them being the same. If there is less than 1% chance of the two sets being the same, then we say P < 0.01; 5% chance is P < 0.05; 0.1% change is P < 0.001.

Prevalence (see Technical Note: Prevalence, Average and Not Reported) [Prevalence]

With this method we determine the percentage of time that a bacteria is seen in each group. A simple example would be the incidence of finding salmonella bacteria in people with food poisoning may be 80% and in people without it, 10%.

The method is well known and described here: Comparing Two Independent Population Proportions.

In this case, we obtain a z-score instead of a t-test statistics. From the z-score, we lookup or compute the probability of them being the same. If there is less than 1% chance of the two sets being the same, then we say P < 0.01; 5% chance is P < 0.05; 0.1% change is P < 0.001.

Means of Percentiles for those reporting this bacteria [Percentile]

With this method we compute the average and variance measures for each group based on the percentile of the bacteria in the sample across some reference set. In this case, not reported values are ignored.

Using percentiles is not common in life and physical science, it is used occasionally in economics. The use of percentiles transform the percentages in the first two methods into a uniform distribution. There are other methods — see Transforming Non-Normal Distribution to Normal Distribution.

From the percentile we then compute the t-test statistic (see Hypothesis Test for a Difference in Two Population Means ). From this t-test statistic, we lookup or compute the probability of them being the same. If there is less than 1% chance of the two sets being the same, then we say P < 0.01; 5% chance is P < 0.05; 0.1% change is P < 0.001.

Means of Percentiles for those reporting this bacteria [Percentile] — NOT DONE

With this method we compute the average and variance measures for each group based on the percentile of the bacteria in the sample across some reference set. In this case, reported values are used. In terms of a reference set, if the prevalence is 50% and the reference set only uses reported values, we simply adjust the numbers as follows:

Percentile(Include Null) =Percentile (Not Null)+ (100- Prevalence Percentage)

Using percentiles is not common in life and physical science, it is used occasionally in economics. The use of percentiles transform the percentages in the first two methods into a uniform distribution.

From the percentile we then compute the t-test statistic (see Hypothesis Test for a Difference in Two Population Means ). From this t-test statistic, we lookup or compute the probability of them being the same. If there is less than 1% change of the two sets being the same, then we say P < 0.01; 5% chance is P < 0.05; 0.1% change is P < 0.001.

Note on Including or Excluding Null Values

If you exclude null values, you will often be indirectly including prevalence into the statistics measurement. Including null values, you are indirectly excluding prevalence. IMHO, to get the most amount of information from the data, do both.

Analysis Pattern

I have a great preference for percentiles because it transforms the VERY non-normal distribution of bacteria into a uniform distribution. One complicating factor with the common 16s tests is the number of reads required to deem a bacteria is there with a reliable measure. Many strains are single reads. If you require more reads, then the number of taxonomy items report drops quickly as shown in the table below.

To give a concrete example with real data, I am using the samples donated to my citizen science site that were processed through the UK based 16s provider, Biomesight.com. I am going to take a subset of those who entered self-reporting symptoms and divide them into two groups:

• Neurocognitive: Brain Fog issues reported (N:328)
• Neurocognitive: Brain Fog issues not reported (N:700)

The high number of Brain Fog is likely a byproduct of Long Covid in the population with those people willing to beat the bushes to find answers.

I am going to look only at genus level for illustration. A prior analysis found that species significance was more pronounced. I attached the data summary below. You can also download the data from Microbiome Prescription Citizen Science Data Repository and do your own data grinding. (Examine the data summary to determine the direction of shifts.)

Probability	All	Reported	Prevalence	Percentile	Concurrent
< .01	68	20	5	42	0
<.05	158	61	23	102	1
< .10	241	102	36	147	0

Concurrent agreement between Significant Bacteria is quite dramatic as just one bacteria stands out: Oribacterium. If we exclude Prevalence, we find more.

• < .01
  • Desulfosporosinus
  • Blautia
  • Sporolactobacillus
  • Gallionella
  • Paenisporosarcina
  • Planococcus
  • Limnobacter
• < .05
  • Phascolarctobacterium
  • Helicobacter
  • Alkalihalobacillus
  • Natronincola
  • Alkaliphilus
  • Oribacterium
  • Cerasicoccus
  • Salisaeta
  • Anaerobranca
  • Acetomicrobium
  • Erysipelothrix
  • Thioalkalivibrio
  • Chryseobacterium
  • Anaerotruncus
  • Desulfurispora
  • Lysinibacillus
  • Halanaerobium
  • Allochromatium
  • Oleomonas
  • Marinospirillum
  • Moorella
  • Agrococcus
• < .10
  • Alishewanella
  • Halomonas
  • Ruminobacter
  • Mobiluncus
  • Leptothrix
  • Dehalogenimonas
  • Gemella
  • Dorea
  • Rikenella
  • Brenneria
  • Adlercreutzia
  • Anaeroplasma
  • Candidatus
  • Endobugula
  • Actinotignum
  • Methylocella
  • Ligilactobacillus
  • Bergeyella
  • Dickeya

Let us look at < .10 above, we have 641 genus, so .1 * 641 = 64 false positive would be expected. If you went down that path, you ignored that < .1 occurred over THREE measures. Assuming independence of each measure (for simplicity), then we have (0.1)³ or < 0.001; in other words, 0.001 * 641 = 0.6 false positive. Effectively, every genus listed above is statistically significant at < 0.001 for Neurocognitive: Brain Fog.

Some Visual Representations

I tend to use visuals to better understand processes. In this case, contrary to my expectations, we have quite dramatic differences in appearance. The first one looks like a normal distribution and the others definitely not normal distributions.

Bottom Line

The purpose of this post was to illustrate that no single method of determining significance is ideal. The use of prevalence is ideal for infrequently seen bacteria but is unlikely to produce results for commonly seen bacteria. It is important to understand the differences and ,in practice, do all five as a pro forma practice for microbiome data analysis.

The next question is simple, how do you treat people with brain fog? My own approach is to obtain a detailed microbiome sample (in this case, biomesight’s) and then identify which genus are sufficiently matching this pattern. From that matching, then use the fuzzy logic expert systems at Microbiome Prescription.com to suggest supplements, probiotics, diets and prescription drugs. There may be alternatice approaches to detemine a treatment approach.

I am by training a mathematician, a statistician and operations researcher. For years, I have seen different indices proposed and “Frankly, my dear, I don’t give a damn“. I deem them to be the equivalent of Passing with the Wind. Apologies for the pun.

The problem is that they are using the art of clinical or research experience and NOT the science of mathematics.

Over this last weekend I revisited with more rigor my “percentages of percentiles” approach and found that I could create a An Eubiosis Index for the Microbiome without using Art, just mathematics. Eubiosis means the opposite of dysbiosis.

When you go to my profile, you will see your index and a revised chart below.

Below the chart, you can click to see the numbers. In case, we see that high percentile dominates.

That’s all folks

If you want technical details see An Eubiosis Index for the Microbiome. The computation is simple and other sites are free to use it with appropriate acknowledgement..

Eubiosis is a measure of Representative-ness in the Gut

If you look at a place of employment, ideally you would see each part of society represented in the employees. For example:

• 50% males and 50% females
• 62% white
• 19% Hispanic or Latino
• 12.4% Black or Africian American
• 2% with Autism
• 3% Native American
• .01 with Down Syndrome
• etc

A place that matches (or close to matches) could be said to have 100% Eubiosis – that is the percentage expected in reflected in the employees. This does not reflect this firms profitability or employee turnover rates or any of a dozen of other measures. It is an adjunct measure that is statistically based. Most estimates of gut health are subjective, often based on beliefs or for a specific type of condition. This measure can be low for someone in good health with no symptoms, but the odds are low.

To illustrate this, the following is from one individual over time. Genus and Overall are from special studies.

The Eubiosis and Hawrelak both indicates worsening in the latest sample (my usual first question is: Any virus caught between samples? Second question is any vaccinations in the 6 weeks prior to the sample). Overall Citizen Science being high for symptoms indicates a strong pattern match to symptoms — which implies a worsening microbiome.

Below we have a bad Eubiosis, 3.9 — why is it bad because the amounts in each range is very far from the expected (statistically). The blue and red bars should be close to each other.

This note in a continuation of an earlier note:

Technical Notes: Percentages of Percentiles for Health Measure?

I am a statistician and operations research person by training and experience. I tend to take novel approaches to many issues based on mathematics. These are recorded in this series of notes

!Technical Notes on Microbiome Analysis

I have some 4,600 different unique microbiome samples uploaded to my citizen science site. Most of these are from people with gut issues. Some are from health hackers (i.e. no issues).

A simple Chi² experiment using percentages of percentiles is done. I bucketized the genus data into 10% percentile ranges resulting in 10 buckets, compute the chi² and thus we have 9 degree of freedom.

Genus has adequate counts per sample. Species reporting is often very sparse for some tests (depending on the number of reads that the lab set as a threshold, etc.). Genus gives the highest count for a specific taxonomy rank in this dataset.

I then proceeded to plot the values to see what the data looks like. Note the significance levels for 9 degree of freedom below

• 14.7 is 0.1
• 16.9 is 0.05
• 19.02 is 0.025
• 21.7 is 0.01
• 27.9 is 0.001

Over all of the values, we do see some extreme values

But let us look a less extreme values

We now need to do a little math assuming there was no significance, i.e. the numbers were happening random.

• 0.1 (aka 10%) means that 90% of the samples would be expected to have a chi_² value of 14.7 or less. We have 1130/4600 or 24% of the samples

We could start working to lower values, but using 14.7 means that 1/4 of the samples at this value may not have dysbiosis. Taking 0.1 and this ratio, we can estimate that we have around a 97% chance of correctly identifying dysbiosis

General conclusion is that a gut without dysbiosis would have a chi_² value of 15 or below for genus.

The Challenge of Getting a “Health Index” for the Microbiome

Looking at a variety of microbiome testing sites I see a lot of “flying by the seats of their pants” being tossed out. IMHO, these sites are soiling their pants — somewhat appropriate for this business :-).

I believe we can create a statistically valid index that works solely off the numbers and not some idealized concept of what a healthy gut should be. We use the above analysis to create this index.

People like have a percentage number for a healthy gut, then the following is suggested (which is actually a percentile ranking):

• Under 15: 100% good
• Over 15: 100 – (Percentile over those over 15)

For lack of a better name (and keeping with naming practices for indices), I will call this the Lassesen Eubiosis Index with 100% being no apparent sign of dysbiosis and good eubiosis.

From the set of samples used above, I extracted a reference table (which may vary according to the test used and the population used). Since I know that the majority of samples have dysbiosis issues, this is likely a reasonable guideline.

Eubiosis Index	Chi2
90	18
80	21.3
70	24.5
60	29.1
50	35.6
40	45.2
30	65.3
20	106
10	168
5	248

The joy of this approach is that it simple, statistically valid and is taxonomy agnostic. No judgement calls are being made on good or bad bacteria.

Example of 100% Eubiosis

We see a dip at the 50-59%ile range but this minor disturbance does not register as a likely dysbiosis.

A reader wrote

Another thing I’ve noticed that helps, perhaps 10% of what Amoxicillin helps, is Lauricidin (Monolaurin). I was able to get some sleep last night because I decided to try it.

Monolaurin was not on the top of his recommendations list. It has mixed impact. So the question arises, can we use this response to better identify the bacteria connected with this change of symptoms.

Monolaurin does not come in high, actually low.

Clicking under PubMed we see the bacteria impacted (for better or worse). We have a list (118 in this case). I just added Happy and Sad emoji to clarify if it is a good 😁 or bad 😢 shift

This can still be a long list.

Going over to Citizen Science Special Studies and filtering to sleep issues we find some there – which hints that these may be the key bacteria.

Bottom Line

This is a slow process — for this person, we got clarification quickly.

Back Story

• I took many antibiotics for ear infections as a kid, mainly amoxicillin.
• At 12 started extreme period cramps.
• At 18 discovered an ovarian cyst the size of a lemon, then two months later was the size of a grapefruit.
• Had a c section like surgery to remove it along with 1/3 of the left ovary.
• After a month of painkillers and whatever else they gave me, I was in severe pain, exhaustion and bowel function stopped.
• Started rounds of Drs and tests- after a year was diagnosed with FM CFS IBS and within 3 yrs MCS.
• Was homebound and severely allergic to chemicals for 6 years until I saw a chiropractor in Vegas who did energy work, spinal manipulation, high red meat diet and 6 litres of water/ day.
• Improvement lasted 4 months and I was able to work but then crashed.
• I chased it for 10 years, saw many chiros that did this work but never felt as well as the first time.
• I switched diet again in 2013 to eating more animal protein and veggies and spent 6 months in Costa Rica and was 50 % better.
• I came back to Montreal and moved into a new place where everything was offgassing and crashed within a week.
• I took four pills of cipro in 2015 and developed right side pain and sciatic. Since 2018 constant sometimes severe pain in the right leg and hip.
• I was diagnosed with Sjogrens in 2017 and Hypothyroidism in 2019 and put on cytomel.
• I have two tiny nodules on my thyroid. I had a tiny cyst in my uterus that burst in 2018.
• I was diagnosed with Vulvodynia in 2021. It flares in response to certain foods, stress and I think, histamines.
• They tested me thoroughly for lyme and treated for bartonella with 1 1/2 months of doxy in may 2022.
• My gynecologist told me last week that I may have endometriosis. I’ve been in perimenopause according to the thyroid doc. I have felt it for a few years now.

My main symptoms are:

Muscle pain, PEM, exhaustion, brain fog, memory issues, constipation ( it is better as long as I stay on top of it), bloating and gas (mostly with fodmaps), gut pain, bad mood, very stressed and angry, very emotional, sleeping issues, crying every day, hopelessness….

Analysis

A long history of microbiome altering events. First Percentages of Percentiles below, which is more extreme than most samples that I have reviewed.

The top predicted symptoms appear to be spot on. Despite all of the other shifts, this appears to persist. A good number of symptoms were correctly predicted.

Going over to health analysis, we actually have a moderate list for General Health Predictors.

Other items:

• Anti inflammatory Bacteria Score 26.3 %ile
• Histamine Producers 79.5 %ile — common with ME/CFS
• Oxalate degrading 0 %ile — suggesting risk of hyperoxaluria or kidney stones.
• SIBO is reported, which does not appear reflected in the fecal sample.
  • Hydrogen 38.7 %ile
  • Hydrogen sulfide (H2S) 43.3 %ile
  • Methane 4.3 %ile
• Dr. Jason Hawrelak Recommendations results in 56.4%ile, so in the middle of what is seen in the samples.
  

PubMed Medical Conditions

None were listed as being significant, so I looked at some of the conditions reported. Remember, having multiple conditions can mask the signature patterns.

• rosacea: 0 matches
• endometriosis: 2 of 45 (33%ile)
• Hypothyroidism 
• Sjogrens 1 of 35 (23%ile)
• FM: 1 of 35 (31%ile_
• CFS: 0 of 64 (0%Ile)
  • ME/CFS with IBS 0 of 18 (0 %ile)
• IBS: 1 of 68 (8%ile)

So we will include none of these in building suggestions. When there are multiple conditions, patterns are often altered (unfortunately).

Going Forward

The usual “just give me suggestions” (which does 4 different ways of selecting bacteria) plus Special Studies on symptoms

The PDF sections are shown below to give an overview.

Going over to the detail report to address some specific questions. The high was 708 (antibiotics)

• Probiotics: all of them were weak suggestions(< 184) with bifidobacterium (probiotics), lactobacillus casei (probiotics) and clostridium butyricum (probiotics),Miya,Miyarisan being the top ones
• Prebiotics: similar, with mastic gum (prebiotic) being the best. avoid: inulin (prebiotic), resistant starch
• Herbs and Spices: garlic (allium sativum) being the best, avoid: berberine, oregano (origanum vulgare, oil), xylan (prebiotic), oligosaccharides (prebiotic)
• Diet Style: avoids included: ketogenic diet, animal-based diet, low protein diet, mediterranean diet
• Sugars: sucralose, avoid: lactulose, galactose (milk sugar), xylitol, saccharin
• Vitamins: avoids: zinc,vitamin a
• Antibiotics (which have been used to treat CFS)
  • chloroquine diphosphate,(prescription)[CFS]
  • dapsone (antibiotic)[CFS]
  • neomycin (antibiotic)s[CFS]
  • lymecycline (antibiotic)[CFS]
  • Weaker for: metronidazole (antibiotic)s[CFS]
  • But avoid: amoxicillin (antibiotic)s[CFS], minocycline (antibiotic)s[CFS]

Bottom Line

A complex history with a hodgepodge microbiome. Antibiotics occupy the top section of the suggestions — but those are always tricky. “Dr. Knows Best” will often attempt to persuade the patient to take a different one (in complete ignorance of the microbiome impacts).
I would suggest 3 months of the above suggestions and then another microbiome sample to see what has changed.

Questions

Q: My only question is which new feature shows rifaximin as higher up on suggestions?

A: Just enter the name in the consensus (which is now the default screen for “Just Give Me Suggestions”)

Note the stacks of books on the right, it will show the studies that this was based on, in this case 207 citations. There are some 685 bacteria flagged as being atypical, a number of bacteria far higher than usual and indicates severe disruption.

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 can compute items to take, those computations do not provide information on rotations etc.

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.

In general, I avoid comparing opinions/suggestions from different microbiome resources. Like my earlier The taxonomy nightmare before Christmas… post; some resources may be sufficient/adequate for some people; for others it is not. My criteria for both tends to be simple:

• More data, and more complete data, tends to better results
  • For the Microbiome it means that Shotgun Analysis where the data is uploadable, complete (often 5000+ items) and has percentile ranking is my preference
  • For the Analysis it means how many substances are considered (MP: 2065), are all interactions considered (MP: 2.5 million), how many different ways of doing analysis are offered (MP: lots).

Whatever you are using may be sufficient. If it is not, then read on.

This person asked for my help on Facebook explicitly and to properly answer, I need to do some comparison of analysis, interpretations and suggestions.

ME/CFS for 9 years. LC from vax injury 2.5 years.

I’ve been following the biomesight recommendations for 18 months and my gut has improved massively. I’ve just completed my third biomesight test and results are in. I have been experimenting with nicotine patches for 6 months now and my fatigue and pem symptoms have improved massively.

However, my most recent results are back and they have never been worse! Do you think Nicotine has a really negative impact on our guts? I can’t explain why everything is soo much worse.

Ken Lassesen / Troy Roach this could be one that you guys could help on.

For info: my gut doesn’t actually feel worse, but the results are terrible .

From a facebook User

Analysis

The reader is relying on BiomeSight evaluation. IMHO there is no single magical number or formula but many features that needs to be examined. Below is a table of the three test results meta-information. Remember that I am use the same measurement of bacteria data as Biomesight.

My general impressions is improvement is continuing despite Biomesight indicating not. Why?

• Shannon, Simpson and Chao1 Diversity Percentile all moved towards 50%ile from extremes, a good sign.
  • Biomesight Diversity score started at 100% (ideal) and went downwards; completely opposite read to mine.
• Outside Kaltoft-Moldrup are the ranges that I have the most confidence in, and they continued to drop

Criteria	12/7/2023	2/24/2203	6/7/2022
Lab Read Quality	4.4	7.3	10.3
Lab Quality Adjustment Percentage	79.7	89.7	100
Outside Range from JasonH	6	7	7
Outside Range from Medivere	13	17	17
Outside Range from Metagenomics	7	9	9
Outside Range from MyBioma	4	4	4
Outside Range from Nirvana/CosmosId	23	17	17
Outside Range from XenoGene	41	40	40
Outside Lab Range (+/- 1.96SD)	23	16	24
Outside Box-Plot-Whiskers	79	61	120
Outside Kaltoft-Moldrup	53	115	120
Bacteria Reported By Lab	677	709	866
Bacteria Over 90%ile	51	32	86
Bacteria Under 10%ile	56	263	244
Shannon Diversity Index	1.723	1.959	1.914
Simpson Diversity Index	0.068	0.046	0.026
Chao1 Index	13468	14912	20849
Shannon Diversity Percentile	64.4	91.8	87.9
Simpson Diversity Percentile	64.6	43.4	18.2
Chao1 Percentile	72.5	80.4	93.8
Lab: BiomeSight
Pathogens	37	31	37
Condition Est. Over 90%ile	0	1	4

• Biomesight (BS) and Microbiome Prescription (MP) appear to be using different list of pathobionts
  • 7 Dec 2023: MP reported 37, BS cites 49%
  • 24 Feb 2023: MP reported 31, BS cites 72%
  • 7 Jun 2022: MP reported 37, BS cites 36%

The Percentage of Percentiles

The charts are below — we see in the older samples that the 0-9%ile spike that is typical of ME/CFS has disappeared in the latest sample. My preferred single measure of gut health, Chi² has moved from 60 to 49 to 45. Significant improvement.

Conclusion: Biomesight simple evaluation of overall health may be misleading because it is too simple an algorithm.

Health Analysis

• General Health Predictors (based on various studies) No significant changes
  • 2022-06-07: 10 bacteria
  • 2023-02-24: 9 bacteria
  • 2023-12-07: 10 bacteria
• Hydrogen Peroxide (See Antibacterial Effects of Hydrogen Peroxide and Methods for Its Detection and Quantitation ). Increasing which is a good sign
  • 2022-06-07: 36%ile
  • 2023-02-24: 24%ile
  • 2023-12-07: 44.9%

Nicotine Patches Question

Nicotine is one of the modifiers consider by Microbiome Prescription Expert systems.

• 2022-06-07: Nicotine patch was a low positive
• 2023-02-24: Nicotine patch was a positive, 5x higher than above
• 2023-12-07: Nicotine patch was a positive, less than above but 3x the first value.

Suggestions Comparisons

Biomesight just gives suggestions without any attempt to prioritize them. Looking at the suggestions from the latest sample(reader sent the PDF); we list them below. The highest Priority from Microbiome Prescription was 927 and lowest was -906.

Below are Biomesight suggestions followed by how Microbiome Prescription ranks them.

• Prebiotics
  • Arabinogalactan: Massive Avoid: -906 (based on 331 interactions)
  • Galactooligosaccharides: Avoid -233
  • Guar gum: Avoid -106
  • Gum arabic: Avoid -107
  • Lactose (not in lactose intolerant)
  • Lactulose: Minor take: 81
  • Milk oligosaccharides: Major avoid -233
  • Pectin: Major Avoid: -570
  • Raffinose: Minor avoid: -65
  • Resistant starch: Minor avoid: -26
  • Resveratrol: Avoid -230
  • Stachyose: Avoid – 422
  • Xylooligosaccharides: Avoid: -390
  • Chitooligosaccharides: Minor avoid: -22
  • Yeast beta-glucan: Minor take: 38
  • Psyllium: Minor take: 65
  • Colostrum: Minor Avoid -95
  • Quercetin: Minor take: 39
• Herbs and Spices
  • Triphala: Minor take: 43
  • Cinnamon: Minor take: 53
  • Ginger: Take: 108
  • Oregano: Take 138
  • Turmeric: Take: 218
  • Thyme: Take: 434
  • Curcumin: Take 180
  • Garlic: Take 200
  • Lauric acid: Take 200
  • Niacin: Major take 732
  • Cranberries: Avoid -22 / -685 (for flour)
  • Olive leaf: Take 179
  • Slippery elm: Major avoid: -803
  • Codonopsis pilosula: Avoid -113
  • Shen Ling Bai Zhu San: Minor take 26
  • high fiber diet: Avoid -99
• Probiotics
  • Bifidobacterium longum: Avoid -120
  • Lactobacillus rhamnosus: Avoid -471
  • Bacillus coagulans: Avoid -289

So we have a few agreements but a lot of disagreements. It may be just “the change of microbiome environment shock” with either sets of suggestions is causing improvement.

Microbiome Prescription does a holistic approach for suggestions. It looks at the known impact on every bacteria being targeted for a modifier and makes the full details available to review (Click on the 📚). People have been double checking these citations. The decision on Arabinogalactan was based on considering 311 interactions, a few are shown below.

Another difference is that the bacteria selected is based on using 4 different algorithms to select what is of concern and then we do a Monte Carlo simulation on the suggestions.

My impression is that Biomesight considers one bacteria at a time and does not use that many studies to base a recommendation on. I do not know what extent BS consider the complexities of interactions. Biomesight would be the source of information to get better clarity on this.

So what are Microbiome Prescription Top Suggestions

I have placed a 🎯 besides those that are common suggestions

• Hesperidin (polyphenol) – 894
• Vitamin B1,thiamine hydrochloride – 861
• N-Acetyl Cysteine (NAC), 846
• Vitamin B6,pyridoxine hydrochloride 833
• Vitamin B-12 785
• vitamin B7, biotin 771
• vitamin B3,niacin 732 🎯
• acetaminophen,(prescription) Paracetamol in UK 833
• Arbutin (polyphenol) 833
• diosmin,(polyphenol) 833
• luteolin (flavonoid) 821
• Guaiacol (polyphenol) 457
• Caffeine 613 (Tea or Coffee)
• Kimchi 388 (I suspect spicy is best)
• sorghum 380
• whole-grain barley 351
• Carthamus tinctorius L,Safflower 465
• thyme (thymol, thyme oil) 434 🎯
• peppermint (spice, oil) 380
• neem 370
• sucralose 597
• mastic gum (prebiotic) 274
• lactobacillus casei (probiotics) 229 – this is the best probiotics, not a major player

This person has ME/CFS and it is extremely well documented that B-Vitamins moderates those symptoms. Microbiome Prescription shouts out that they should be taken. Biomesight only cites one B-Vitamin (with no indication of importance). Some ME/CFS studies on the top suggested B-Vitamin ( Vitamin B1, thiamine ) suggested by Microbiome Prescription are shown below.

• B-vitamins, related vitamers, and metabolites in patients with quiescent inflammatory bowel disease and chronic fatigue treated with high dose oral thiamine [2023] “Patients with chronic fatigue who reported a positive effect on fatigue after 4 weeks of high dose thiamine treatment “
• Randomised clinical trial: high-dose oral thiamine versus placebo for chronic fatigue in patients with quiescent inflammatory bowel disease [2021]
• Thiamine and fatigue in inflammatory bowel diseases: an open-label pilot study [2013]
• Response to vitamin B12 and folic acid in myalgic encephalomyelitis and fibromyalgia [2015]
• Vitamin B status in patients with chronic fatigue syndrome [1999]

Your Choice as to Path

IMHO, there is no right answer. Go with Biomesight, Go with what a medical practitioner suggests. Go with whatever you see next in an influencer YouTube.

My best answer is above, it uses a massive amount of data to compute suggestions with a complete evidence trail for people to openly challenge. I have worked professionally as an information auditor and made sure auditability was build into the expert system. I have tuned the expert system to produce good results by doing cross-validation – i.e. 80-90% of suggestions for tested conditions are known to improve that condition from independent clinical studies. In this case, the top suggestions are in agreement with what has been known to help with his specific condition: ME/CFS. MP suggestions are not random shots in the dark but heavily data driven.

It is your choice — just don’t “mix and match” suggestions from different sources.

REMEMBER: Going Biomesight and transferring data to MicrobiomePrescription gives two analysis that you can compare and potentially ask the provider for the basis of their suggestions.

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 can compute items to take, those computations do not provide information on rotations etc.

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.

This is a part of a series of Technical Notes on Microbiome Analysis

For a while I have been using a variation of this concept for 16s samples that I have reviewed. The concept is very simple to a statistician:

Percentiles is converting data into a native uniform distribution. If you sample for 1000 boxes where each box has 100 balls numbered 1-100, then you expect the distribution of the balls samples to be uniform. It they are not, then something is definitely unfair.

Concept

With the microbiome things are a little more complex because a high in a single strain may push it species into high and thus the genus into high. We could do independent levels, for example species only or genus only. The problem is that the population size starts to drop and thus the sensitivity decreases as a result.

I happen to have a small collection of shotgun samples processed through CosmosID. Their report give percentile for most of what they measure. Getting accurate percentiles requires large sample sizes.

Below I have charted the results with single percentile ranges from reports that have between 2000 and 5000 different biological units reported. I have charted using different approach (the kitchen sink and then select taxological levels).

All of these samples are from people with health issues. Note that the numbers come from rounding so 100% is just 99.5 to 100 (and not 99.5 to 100.5) so the spikes at 100 is likely twice as high.

Kitchen Sink

Filter to Species Only

Genus Level

Family Level

Bottom Line

Comparing different levels can be informative, to illustrate, we have species below with good uniformity until we hit the high levels.

Looking at the genus level for the same sample, the pattern is very different.

In this case, we drilled down into these high species and got a predominance of Corynebacterium species that fell into our 100% range (99.5-100 percentiles).

Taxonomy Name	Abundance
Anaerococcus mediterraneensis	0.005611
Anaerococcus prevotii	0.006486
Bacteroides rodentium	0.001238
Corynebacteriaceae bacterium ‘ARUP UnID 227’	0.000437
Corynebacterium ammoniagenes	0.000586
Corynebacterium aurimucosum	0.1573
Corynebacterium callunae	0.00013
Corynebacterium camporealensis	0.002243
Corynebacterium casei	0.000726
Corynebacterium comes	0.000391
Corynebacterium diphtheriae	0.0755
Corynebacterium endometrii	0.001051
Corynebacterium flavescens	0.001684
Corynebacterium humireducens	0.00053
Corynebacterium imitans	0.001024
Corynebacterium jeikeium	0.01813
Corynebacterium lactis	0.000437
Corynebacterium liangguodongii	0.000558
Corynebacterium minutissimum	0.03511
Corynebacterium phocae	0.000865
Corynebacterium pseudotuberculosis	0.000233
Corynebacterium renale	0.000493
Corynebacterium resistens	0.001182
Corynebacterium riegelii	0.001321
Corynebacterium segmentosum	0.007016
Corynebacterium simulans	0.3615
Corynebacterium singulare	0.01858
Corynebacterium sp. NML 98-0116	0.001024
Corynebacterium stationis	0.000577
Corynebacterium striatum	0.04709
Corynebacterium timonense	0.001321
Corynebacterium urealyticum	0.00107
Corynebacterium uterequi	0.000642
Corynebacterium yudongzhengii	0.000689
Cutibacterium acnes	0.002298
Dehalococcoides mccartyi	0.006123
Dermabacter jinjuensis	0.01404
Dermabacter vaginalis	0.001265
Fastidiosipila sanguinis	0.003536
Finegoldia magna	0.06368
Helcococcus kunzii	0.00014
Homo sapiens	1.985
Lawsonella clevelandensis	0.003154
Mycobacterium gallinarum	0.000261
Mycobacterium sp. DL592	0.00013
Mycobacterium sp. ELW1	0.001107
Mycobacterium sp. EPa45	0.002298
Mycobacterium sp. PYR15	0.008328
Mycolicibacterium aichiense	0.000223
Negativicoccus massiliensis	0.001935
Peptoniphilus harei	0.04272
Peptoniphilus sp. ING2-D1G	0.000893
Porphyromonas asaccharolytica	0.06443
Porphyromonas bennonis	0.000521
Propionibacterium freudenreichii	0.000465
Schaalia radingae	0.001089
Streptococcus pyogenes	0.00241
Streptococcus sp. NCTC 11567	0.000149
Sutterella stercoricanis	0.000149
Tessaracoccus timonensis	0.00094
uncultured Chroococcidiopsis sp.	0.000242
uncultured Rhizobium sp.	0.000772

We could also produce single value statistical measures — for example Chi². We have an a priori expected value of 1% in each bucket.

IMHO, percentages of percentiles is likely more effective in evaluating an individual person’s gut microbiome. It seems to be able to separate the noise from what is significant, for example Corynebacterium cited above where the cause is a proliferation of species and not dominance of one species.

This has since cascaded into an Eubiosis Index.

Technical Note: An Eubiosis Index for the Microbiome

Back Story

• In 1983 or 1984 I suffered from EBV (mononucleosis)
• In 1984 or 1985 – I had appendix removed
• In 1991 I had a resurgence of fatigue like EBV reactivation, plus apparition of anxiety
• In 2004, I was bitten by a tick, I thought at the time that it was a spider. Few weeks after the bite, I had flu symptoms who last very long, like months, and some intermittent fever. When I talked about my intermittent fever to doctors, they where looking at me as if I was crazy. Later I learnt that Lyme was in the area.
• Between 2004 and 2007, lots of weird symptoms appeared. Doctors were saying it was in my head
• In 2007, I had an urinary tract infection. I took Cipro, and all my little weird symptoms that I had notice for couple of years, have worsened. I started to have mood change, internal tremors.
• Between 2007 and 2011 -I’ve met 3 neurologists, they said I maybe have multiple sclerosis, even if my MRI at this time were clear.
• In 2015, another urinary tract infection, Cipro again, symptoms once again worsened.
• In 2016, I received Multiple sclerosis, (MS), diagnosis.
  • I saw a naturopath. She run urinary test to see organic acid. And she build a protocol. I follow this protocol for 3 months, with no change.
  • I went to see a LLMD in USA for a year with some improvement.
• Between 2007 and 2011 -I’ve met 3 neurologists, they said I maybe have multiple sclerosis, even if my MRI at this time were clear. They said that I have to wait for another crisis to confirm. But they gave me
• I now have dysautomia, probably MCAS and SIBO. I also feel sick in transports. I do have intolerance to heat and cold. I have had big constipation problems for years.
  • I started to take Mutaflor[E.Coli Nissle 1917] for constipation. It’s helping.
  • I also started Akkermansia about 1 month ago.
  • B1 (1000mg/day)

We have two test results available: Biomesight and Genova test.

Analysis

The Percentage of Percentiles showed no statistically significant pattern with significance at 0.90 (we look for above .99) to be concerned.

Looking at the Health Analysis,

• Bacteroides/Clostridium Ratio is very high (97%ile_
• Anti inflammatory Bacteria Score is high (94%ile)
• Butyrate is low (1.2%ile)
• D-Lactic Acid is low [GOOD THING, high levels often are seen with brain fog and cognitive issues)
• Dopamine, Serotonin are both high (97%ile) – may account for mood issues
• Hydrogen, Hydrogen sulfide (H2S), Methane are all low with Methane being the highest (46%ile), so traditional SIBO is unlikely.

Potential Medical Conditions Detected

The following were flagged in agreement with her history:

• ME/CFS without IBS
• Fibromyalgia
• Mood Disorders
• COVID-19

And last, Intelligence at 91%ile which agrees with details from emails.

And for Bacteria deemed Unhealthy we have quite a few.

Name	Rank	Percentile	Count	Comment	More Info
[Ruminococcus] gnavus	species	98	54710	Not Healthy Predictor	Citation
Actinomyces	genus	90	250	Pathogen	Citation
Anaerotruncus colihominis	species	86	3070	Not Healthy Predictor	Citation
Bacillus	genus	92	170	Pathogen	Citation
Blautia producta	species	97	12950	Not Healthy Predictor	Citation
Collinsella	genus	0	0	High COVID Risk	Citation
Dorea	genus	99	37010	Increased COVID risk	Citation
Eggerthella lenta	species	95	1470	Not Healthy Predictor	Citation
Lactobacillus	genus	89	960	Pathogen	Citation
Legionella	genus	89	130	include notable pathogens	Citation
Ligilactobacillus salivarius	species	87	420	Not Healthy Predictor	Citation
Staphylococcus aureus	species	75	40	Skin infections, sinusitis, food poisoning	Citation
Staphylococcus haemolyticus	species	89	130	Pathogen	Citation
Streptococcus australis	species	82	210	Not Healthy Predictor	Citation
Streptococcus oralis	species	66	40	Infectious bacteria	Citation
Streptococcus sanguinis	species	87	80	Not Healthy Predictor	Citation
Streptococcus vestibularis	species	67	380	Infectious bacteria	Citation
Veillonella atypica	species	76	170	Not Healthy Predictor	Citation

I looked at her GI Effects test with the new Conditions matching (See this post) and nothing was identified by pattern matching.

Using Jason’s criteria, we see that there is a long way from health improvement.

Going Forward

With a diagnosis of Multiple sclerosis, I was curious to see the degree of pattern matching to published studies. She is at the 88%ile (i.e. her pattern the reported pattern better then 9 out of 10 people).

While it appears that Lyme played a role, the literature is very sparse on Lyme and she has no matches

Strategy

I will do the usual “Just give me suggestions’ (4 ways of picking bacteria) and then add in:

• Multiple Sclerosis
• Mood Disorders

This gives us 6 algorithms to build suggestions from. To which we add the new one to hand pick and then process. So we have 7 algorithms being used.

Review of Suggestions

My first curiosity is where does Cipro (Ciprofloxacin) set in suggestions. It is at a positive 275 our of 494. The top antibiotic is amoxicillin which is used for both ME/CFS and Lyme disease.

I was curious if there is a MS connection to either of these antibiotics and found Antibiotic Use and Risk of Multiple Sclerosis [2006] which contains a variety of gems:

• “use of penicillins(includes amoxicillin) in the 3 years before the index date decreased the risk of developing a first attack of multiple sclerosis (odds ratio = 0.5, 95% confidence interval: 0.3, 0.9 for those who used penicillins for ≥15 days compared with no use).”

For Cipro, I found no equivalent studies and some social media claiming that Cipro triggered MS in themselves.

No probiotic made it above the threshold except a particular mixture: bifidobacterium pseudocatenulatum li09,bifidobacterium catenulatum li10 (probiotics). I currently know of no retail source for this mixture (but can see a lot of studies). Neither can I locate any retail products with any form of bifidobacterium pseudocatenulatum or bifidobacterium catenulatum.

Questions and Answers

Q: In my history, you don’t seem to take into account the positive tests for borrelia and babesia, but only the diagnosis of multiple sclerosis. Am I mistaken? And is it because there are few studies on Lyme disease in relation to the microbiota? 

• Correct. I just double checked PubMed and found many articles on the microbiome of the ticks, but nothing useful for a human microbiome after being bitten. I have data on Chronic Lyme, there is not much. One example study if A Distinct Microbiome Signature in Posttreatment Lyme Disease Patients [2020]

Q: There is mention of human milk but nothing about dairy. I’m wondering if goat cheese is ok. I consume goat cheese from time to time and wondered if it’s good or bad.

• Human milk contains different sugars than goat or sheep or cow or camel or… I have data on goat and cow. Most studies have been done on using them for yogurts which alters their composition.
• Looking at the details (see YouTube video), all dairy are negative (not greatly often, but consistently negative for different dairy products), so reduce or eliminate.

Q: Does acacia fiber is consider oligofructose-enriched inulin ? I’m a bit lost. I bought acacia and wonder if it’s ok.

• Acacia fiber (a.k.a. gum) is different. There is a study comparing them, PREBIOTIC EFFECTS OF INULIN AND ACACIA GUM [2015]. Acacia fiber was not in the list for to take or to avoid, so no known harm nor benefit (apart from the usual impact on the pocket book)

Q: In the recommendations, it’s said to avoid whole grain wheat. But does it include einkorn and buckwheat ?

• No, buckwheat is not wheat, it is a seed (just like peanut is not a nut) — English can be very misleading at time!!! While it is true that Einkorn is the most primitive form of wheat on Earth, modern wheat (which is what the clinical studies used) is sufficiently different in content. “Einkorn kernels have higher protein, antioxidant (carotenoids and tocols), fructans and monounsaturated fatty acids content” [2013]. Many of those changes will cause a different effect on the microbiome.
  These are slight negative (see video), I would not be concerned about this.

Q: Alan McDonnald’s work shows that all the patients he tested with a diagnosis of multiple sclerosis were positive for at least one strain of borrelia, in addition to having their EBV reactivated. This is generally the case with Lyme. And since I’m treating Lyme, I have a lot of symptoms who alleviate.

• Unfortunately, 16s tests do badly with detecting that bacteria. Shotgun tests are 10 to 40x better at detecting this bacteria. Some level may be present in 30% if the population. See this page
  

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 can compute items to take, those computations do not provide information on rotations etc.

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.