This is a follow up the earlier blog post: Son and Daughter with Autism Analysis from a year ago. There has been a lot of changes of the site and revisions of algorithms.
Comparing Siblings
We know from studies that members in the same family often share about 27% of the same strains. Unfortunately with 16s tests (Biomesight, Ombre), we do not get strain information just species information.
Using the new refactor citizen science symptoms (see New Special Studies on Symptoms ), we are presently surprised! We have many forecast symptoms being the same which supports the observation cited above of share taxa, likely at the strain level.
It does hint that less time with each other and a lot more time with other (ideally normal) children may have benefits to the microbiome. Some of the changes may be connected to gender:
About twice as many women as men experience depression [Mayo]
Increased inflammation is seen in the periphery in both depression and fatigue. [2019] which agrees with the daughter having a lower Anti inflammatory Bacteria Score
Measure
Daughter
Son
Anti inflammatory Bacteria Score
25.6%ile
89%ile
Buytrate Bacteria Score
95.9%ile
78%ile
Histamine Producers
21.8%ile
15.3%
Autism From PubMed
53/97 (1%ile) Prior: 54/97 (1%ile)
73/97 (11%ile) Prior: 53/97 (1%ile)
Forecast Symptoms
Official Diagnosis: Depression 28 % match on 7 taxa
DePaul University Fatigue Questionnaire : Blurred Vision 25 % match on 8 taxa
Neurological-Sleep: Inability for deep (delta) sleep 23 % match on 13 taxa
Age: 10-20 17 % match on 23 taxa
DePaul University Fatigue Questionnaire : Forgetting what you are trying to say 16 % match on 31 taxa
Next looking at Percentages of Percentiles, we see significant differences. Unfortunately, we do not have gender and age reference tables, so interpretation is fuzzy.
Potential Medical Conditions Detected had nothing significant for either child. Both are at 95.6%ile on Dr. Jason Hawrelak Recommendations (they were 98.9 and 99.7%iles before) .
The thing that stands out is that the Son has a lot more Enzymes out of range (with the resulting substrates(consumers) and products also being out of range).
KEGG Suggested supplements has nothing significant for the daughter, but for the son we have the following being very significant:
Serine
Threonine
Glutamine
Cysteine
Arginine
A complex amino-acid supplement may be worth an experiment.
As an experiment (and trying to avoid two different kid diet), I did an uber-consensus from each child’s with tons of prescription medication but only one thing above my usual 50% of highest value.
We can see the spike in low percentile bacteria. This raises the question, has he had COVID (or a COVID vaccine) prior to the sample being done. These spikes show themselves also via Kaltoft-Moldrup and Box-Plot-Whiskers which are both sensitive to this pattern.
Criteria
Current Sample
Old Sample
Lab Read Quality
10.9
4.4
Outside Range from JasonH
4
4
Outside Range from Medivere
17
17
Outside Range from Metagenomics
10
10
Outside Range from MyBioma
13
13
Outside Range from Nirvana/CosmosId
27
27
Outside Range from XenoGene
49
49
Outside Lab Range (+/- 1.96SD)
22
22
Outside Box-Plot-Whiskers
76
100
Outside Kaltoft-Moldrup
248
89
Bacteria Reported By Lab
757
708
Bacteria Over 90%ile
61
82
Bacteria Under 10%ile
285
26
Shannon Diversity Index
2.807
2.451
Simpson Diversity Index
0.088
0.15
Chao1 Index
24924
19183
Lab: Thryve
Pathogens
35
30
Condition Est. Over 90%ile
2
0
Kegg Compounds Low
1001
1048
Kegg Compounds High
162
132
Kegg Enzymes Low
265
115
Kegg Enzymes High
381
296
Kegg Products Low
152
74
Kegg Products High
209
191
Kegg Substrates Low
148
69
Kegg Substrates High
229
212
Anti inflammatory Bacteria Score
89.2%ile
83.2%ile
Buytrate Bacteria Score
77.9%ile
90.2%ile
Histamine Producers
15.3%ile
38.2%ile
Histamine dropping is usually a good sign
From this weekend update of special studies, we can get a count of bacteria shifts strongly associated to symptoms.
Old Sample: 32 taxa
Latest Sample: 60 taxa
Daughter Compared to Prior Sample
First the numbers which are usually similar to the prior sample.
Criteria
Current Sample
Old Sample
Lab Read Quality
6.7
3.1
Outside Range from JasonH
6
6
Outside Range from Medivere
19
19
Outside Range from Metagenomics
7
7
Outside Range from MyBioma
12
12
Outside Range from Nirvana/CosmosId
26
26
Outside Range from XenoGene
47
47
Outside Lab Range (+/- 1.96SD)
11
61
Outside Box-Plot-Whiskers
98
203
Outside Kaltoft-Moldrup
132
134
Bacteria Reported By Lab
842
852
Bacteria Over 90%ile
66
202
Bacteria Under 10%ile
66
10
Shannon Diversity Index
3.064
3.411
Simpson Diversity Index
0.07
0.028
Chao1 Index
29791
35210
Lab: Thryve
Pathogens
36
35
Condition Est. Over 90%ile
0
0
Kegg Compounds Low
973
1027
Kegg Compounds High
43
80
Kegg Enzymes Low
89
44
Kegg Enzymes High
98
171
Kegg Products Low
55
29
Kegg Products High
52
86
Kegg Substrates Low
46
26
Kegg Substrates High
58
111
Anti inflammatory Bacteria Score
25.5%ile
28%ile
Buytrate Bacteria Score
95.9%ile
74.5%ile
Histamine Producers
21.7%ile
28.7%ile
From this weekend update of special studies, we can get a count of bacteria shifts strongly associated to symptoms.
Old Sample 53
Latest Sample: 39
Out of curiosity, I compared the symptom associated outliers. We found 3 are matches (of these 39) and one not matches for the taxa reported for each. That is close to the expected percentage of the same strains for people in the same house.
Autism has challenges because of its complex nature. This is compounded by a low number of samples to work from for Citizen Science analysis. The shifts reported from PubMed have a high pattern match with people who do not have autism.
I am going to try building a consensus for each by doing two itemsL
“Just give Me Suggestions”
Doing PubMed Autism on [Changing Microbiome]
[All Bacteria identified by special studies]
The rationale is that the last one identify the bacteria that appears to be symptom causing in many people. We have a very poor match from what we do have a match for. This is not surprising because autism is a very wide spectrum.
We then see six sets of suggestion
Son
When I look at the details we have over 150 items with 6 recommended take (i.e. everyone agrees)
The probiotics that have no known adverse risk for any bacteria is below. The high value is 510.
When I look at the details we have just 15 items with 6 recommended take (i.e. everyone agrees)
The probiotics that have no known adverse risk for any bacteria are low in computed benefit, so I would ignore them.
Their values are low: 16/31 out of a high value of 301
Bottom Line
The failure to find significant matching patterns is a bit of a frustration to me. What we did find had very good agree for the son with 150+ items having each of the size suggestion set agreeing for the take. For the daughter, it was not as strong: 15 for 6 sets being in agreement, and 50 with 5 sets being in agreement.
Questions
I assume higher anti-inflammatory score is better – Daughter was 25% and Son was 89%
Deep Sleep with Son – 23% match that he has deep sleep issues is pretty strong?
Does not jump out, but indicates that microbiome is playing a role.
Son – lot more missed enzymes – what is that do you believe and probiotics help with that?
I avoid the word “believe”. A rational assumption is that disruption of enzymes compare to others impacts how the cells (including brain cells) react.
Spike in low percentage bacteria – likely long Covid for Son means he has less good bacteria now?
I avoid the words “good” or “bad” bacteria. Any bacteria far enough from typical values become bad; disruption to the microbiome and the body. Theses spikes are typically seen (pattern matching) with two conditions: Long COVID and ME/CFS. A common symptom of these two issues are cognitive issues – for example: memory, ability to learn, etc.
Histamine – Higher percentage is worse correct? Daughter was 21% and son was 15%
The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.
The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.
I would say some small subjective improvements since last time, but no major changes. Reminder: I have a friendly MD in terms of antibiotics.
Metronidazole was on top in the last samples, I did it back then.
Comment: Metronidazole is no longer at the top but dropped down to 16% of the highest value. It appears to have done its magic in reducing the bacteria pointing to it as a tool..
Base Analysis
When people have multiple samples, I like to do side-by-side comparisons, especially when someone has been doing some of the suggestions suggested. The suggestions are computed and may not always work. Expert Systems and AI are not perfect; they typically do better than a person with only a few years of experience that has training in the discipline (better consistency, remember more facts, etc). How are we doing objectively?
Scores
We see two positive shifts in the latest sample: Increase of Anti inflammatory Bacteria Score and decrease of Histamine Producers.
Percentages of Percentiles
We see a lot of bouncing around between samples. The middle two images matches the typical pattern seen with ME/CFS and Long COVID. Those shifts have faded over the last 3 months with a different pattern appearing indicating a different dialect of gut dysfunction.
Multi-Vector Comparison
The main numbers are below. The take away, less bacteria that are in the high percentile range (at 95%ile, 10 -> 28 -> 23 -> 8). The numbers bounce around with the middle two being similar and the other two also similar. There are no really clear shift in these measures.
Criteria
11/18/2021
5/20/2022
6/22/2023
9/4/2023
Lab Read Quality
8.1
5.5
4.7
7.2
Outside Range from JasonH
6
6
9
9
Outside Range from Medivere
16
16
15
15
Outside Range from Metagenomics
8
8
7
7
Outside Range from MyBioma
5
5
6
6
Outside Range from Nirvana/CosmosId
20
20
23
23
Outside Range from XenoGene
29
29
35
35
Outside Lab Range (+/- 1.96SD)
7
6
17
3
Outside Box-Plot-Whiskers
36
69
54
38
Outside Kaltoft-Moldrup
93
48
47
88
Bacteria Reported By Lab
652
508
542
558
Bacteria Over 99%ile
7
4
6
2
Bacteria Over 95%ile
10
28
23
8
Bacteria Over 90%ile
29
42
36
22
Bacteria Under 10%ile
208
41
50
175
Bacteria Under 5%ile
180
19
8
157
Shannon Diversity Index
1.853
1.826
1.272
1.556
Simpson Diversity Index
0.056
0.038
0.087
0.09
Rarely Seen 1%
2
2
7
1
Rarely Seen 5%
14
5
21
8
Pathogens
41
24
29
36
From Special Studies
The top match was the same on all of the samples, with an increase when there was actually COVID.
Criteria
11/18/2021
5/20/2022
6/22/2023
9/4/2023
COVID19 (Long Hauler)
28%ile
33%ile
41%ile
28%ile
Next one:
15%ile
26%ile
20%ile
13%ile
The “next one” dropping implies some reduction of dysbiosis
Health Analysis
Using Dr. Jason Hawrelak Recommendations, there are many items on the edge of being in range with some items of interest (I strike out those that are unlikely to be of great concern):
Faecalibacterium prausnitzii at 27% of the microbiome or 96%ile
Faecalibacterium prausnitzii at 27% of the microbiome or 96%ile
I would suggest a test for candida to be safe. The data suggests a risk. If confirmed, candida would contribute significantly to gut dysbiosis [The interplay between gut bacteria and the yeast Candida albicans[2021]). I did a “back-flip” check of the top prescription items, and all of them reduces Candida (studies cited below).
Addendum – Predicted Symptoms
This was just added to the site today as a further refactor based on New Special Studies on Symptoms data. These are from [My Profile Tab]
Criteria
11/18/2021
5/20/2022
6/22/2023
9/4/2023
Forecast Major Symptoms
Neurological: Cognitive/Sensory Overload 40 % match on 25 taxa
DePaul University Fatigue Questionnaire : Racing heart 38 % match on 13 taxa
DePaul University Fatigue Questionnaire : Difficulty falling asleep 37 % match on 27 taxa
DePaul University Fatigue Questionnaire : Difficulty finding the right word 35 % match on 20 taxa
Autonomic Manifestations: urinary frequency dysfunction 66 % match on 6 taxa Immune Manifestations: Bloating 37 % match on 45 taxa
Neurological-Audio: hypersensitivity to noise 35 % match on 28 taxa
None
Neurological-Sleep: Chaotic diurnal sleep rhythms (Erratic Sleep) 50 % match on 18 taxa
Neurological: Spatial instability and disorientation 37 % match on 16 taxa
This can be helpful for judging possible severity (and potential improvement of some symptoms), for example: Neurological: Cognitive/Sensory Overload. See [Special Studies] tab.
2021 – 40% matches
2022- 24% matches
6/22/23 – 16% matches
9/4/2023 – 4% matches
Going Forward
COVID has had quite an impact on this microbiome. I am going to just go with the “Just Give Me Suggestions” option with the addition of what matched his diagnosis:
To explain a bit more. First I click the button below
And then click I could click the consensus report to see what the top items are:
Which are shown below.
In this case, I want to add Irritable Bowel Syndrome suggestions (on the Changing Microbiome Tab)
Instead of the usual 4 packages of suggestions, we have 5
When we look at the consensus report we see the same items there, but the values have increased.
The intent is put a little bias on the numbers towards specific conditions of greatest concern.
PDF Suggestions
I tend to favor the PDF suggestions because it simplifies things for many readers. Also the PDF gives a good list of citations (never complete) used to make the citations to persuade MDs to see that the suggestions are based on studies — a lot of studies.
The PDF suggestions are below (using the consensus view is another option for those more technically orientated). I clip from the PDF to keep the blog simpler for the typical reader.
This is a little longer list than usual, so I went to the consensus report to get priority data. Top value was 618, so 309 is the 50% threshold.
Minor note: quercetin with resveratrol is an avoid, quercetin is a take. resveratrol by itself is a negative (-113). At times, you need to look at the technical details/consensus to clarify things; the data we are using is incomplete and sparse…. If clearly contradictory suggestions appear, then don’t do them (thing an abundance of caution).
Because he has an antibiotic friendly MD, the following are the TOP antibiotics with notes:
“Since gentamicin has minimal gastrointestinal absorption,…it has applications in several clinical scenarios, such as bacterial septicemia, meningitis, urinary tract infections, gastrointestinal tract (including peritonitis), and soft tissue infection,” NIH StatPearls
“The FDA-Approved indications include acute infective exacerbation of chronic bronchitis, otitis media in pediatrics only, travelers diarrhea for treatment and prophylaxis, urinary tract infections, shigellosis, pneumocystis jirovecii, pneumonia/pneumocystis carinii pneumonia (PJP/PCP), and toxoplasmosis, both as prophylaxis and treatment. ” NIH StatPearlls
CFS Antibiotics are also above the threshold. Since the prior sample had a strong Long COVID or ME/CFS Profile, I would be inclined to include one of those below in the antibiotic rotation. The microbiome cannot make a diagnosis of most things, with most ME/CFS microbiomes there is a particular pattern which you had in your last sample but which has disappeared from your current sample which looks more like your first sample. I read this as recovering from ME/CFS…. in likely a fragile state since relapse is very common with ME/CFS.
My own experience is that it is better to overcure ME/CFS and when there are signs of recovery…. no backflips of joy or running marathons; keep doing slow walks that becomes a bit further each week for 6-12 months. Your microbiome is fragile and can quickly slip back.
I prefer to use the strategy of going for prescription items that are both suggested from the microbiome and been shown to help with one or more of the diagnosis conditions. This usually encounter low resistance from physicians — they are clueless for the microbiome, but very accepting of published studies. An antibiotic that is used as a prophylaxis usually encounter little resistance.
KEGG Suggestions
This is done by using information from the bacteria found with some fudge factors. I am in discussion with some Ph.D. candidates to build this concept directly from the FASTQ files and will hopefully have this as an added feature next year.
The KEGG probiotics is the usual pattern for ME/CFS and Long COVID with the top one being the usual, with the top reasonably available ones for other families shown below. I usually like to compare the values with those from consensus to minimum risk (i.e. two thumbs up, we do; mixed, we skipped)
The suggestions should be thought as influencers. The human population is often a good analogy or parable for the microbiome population. Each influencer shifts the population in the desired direction. Based on Cecile Jadin’s work and several studies, I am a firm believer in short duration (1-2 weeks) of each influencers. Just as with human influencers, people stop listening if the same person just keeps droning on and on. If a different person starts speaking, you get persuaded more. If a mob start to shout, yet a different human behavior will occur. In terms of the microbiome, “stop listening” means mutations that are resistant to the item will start to increase. Items line vitamins and minerals can be taken continuously; items that are likely to have bacteria resistance developed should be taken for a week and then another item replace it.
Herbs and spices: cinnamon, ginger, black cumin, thyme, rosemary, quercetin (suggests just before each antibiotic with a few days of overlap because it has potential synergistic activity with antibiotics [2020], [2016],[2018] )
Remember our goal is to destabilize a stable microbiome dysfunction.
Questions and Answers
While there has not been significant changes in many of the vectors between this sample and the prior sample from a few months earlier, there has been two significant objective changes:
Significant improvement of Anti inflammatory Bacteria Score (higher) and Histamine Producers (Lower).
The lost of the ME/CFS – Long COVID spike in the 0-9%ile
Q: Do you/should I use the colored list now instead of the consensus list?
Either are fine, the color list (from PDF) is what I tend to use in post because it is easier for new readers to understand (and automatically sent on new uploads). The consensus page is more complex but allows people to apply their own logic and priorities.
Q: “Quercetin (suggests just before each antibiotic with a few days of overlap because it has potential synergistic activity with antibiotics”
Q: I just did Mutaflor for 8 days and felt really tired all the time (but in the end I also got a flu/cold, so maybe that was the reason and not mutaflor). Nevertheless, if it was a herx reaction, I wonder if I should have taken it for longer until the reaction disappeared? (I stopped it 4 days ago.) Not sure if this question even makes sense.
My personal choice would be to keep taking it for at least a week (perhaps 2). Remember that the traditional pattern for a herx is feeling bad for X hours and then things get better. The duration of the feeling bad usually decrease from day to day. Catching a cold makes interpretation challenging.
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 reviewing many papers with the microbiome I noticed that often the researchers restrict their examinations to the taxa that is reported in all samples. I suspect this is due to a lack of sufficient statistical training and/or not understanding the natures of the microbiome.
Recently I came across these papers that uses an approach that I often have used, working off relative frequency of detection a.k.a. prevalence.
This post is going to use samples available at Microbiome Prescription Citizen Science site. We are going to restrict to one lab source and divide the data into two groups based on their self-declare symptoms and diagnosis.
Patients with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) declared [Obs: 271]
Patients without Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) declared and other status declared (for example: “Asymptomatic” [Obs:569]
Naive First Pass
We are going to take the average count for each group ignoring no values reported. We are going to restrict it to taxa where we have at least 30 non-zero values [1,564 taxa]. We found some 77 taxa with a t-score over 2.81 (p < 0.005)
taxa name
taxa rank
Shift
T_score
Prevotella copri
species
low in ME/CFS
-5.27
Prevotella
genus
low in ME/CFS
-4.52
Sporolactobacillaceae
family
low in ME/CFS
-4.2
Sporolactobacillus putidus
species
low in ME/CFS
-4.19
Sporolactobacillus
genus
low in ME/CFS
-4.19
Prevotellaceae
family
low in ME/CFS
-4.1
Firmicutes
phylum
high in ME/CFS
3.94
Blautia
genus
high in ME/CFS
3.91
Cetobacterium ceti
species
high in ME/CFS
3.89
Cetobacterium
genus
high in ME/CFS
3.84
Deeming Not Reported to be Zero
In this case we have 78 taxa with a t-score over 2.81 with slight changes of t-scores.
taxa name
taxa rank
Shift
T_score
Prevotella copri
species
low in ME/CFS
-5.31
Sporolactobacillaceae
family
low in ME/CFS
-4.63
Sporolactobacillus putidus
species
low in ME/CFS
-4.62
Sporolactobacillus
genus
low in ME/CFS
-4.62
Prevotella
genus
low in ME/CFS
-4.5
Prevotella oulorum
species
low in ME/CFS
-4.35
Prevotellaceae
family
low in ME/CFS
-4.08
Bifidobacterium gallicum
species
low in ME/CFS
-3.97
Firmicutes
phylum
high in ME/CFS
3.94
Blautia
genus
high in ME/CFS
3.91
Prevalence
We followed the same process as above and limited things to a Chi-2 probability of < 0.005 (as used above) We ended up with 65 taxa.
tax_Name
Tax_Rank
Prevalence in MECFS %
Prevalence Control %
Difference
Chi2
FoldChange
Deferribacteres
phylum
33.6
20
13.5
14
1.7
Erysipelothrix inopinata
species
21
10.7
10.3
14
2
Deferribacterales
order
33.6
20
13.5
14
1.7
Deferribacteraceae
family
33.6
20
13.5
14
1.7
Deferribacteres
class
33.6
20
13.5
14
1.7
Mogibacterium vescum
species
27.7
15.8
11.9
13
1.8
Haploplasma cavigenitalium
species
8.5
2.8
5.7
13
3
Haploplasma
genus
8.5
2.8
5.7
13
3
Gluconobacter
genus
15.1
6.9
8.3
13
2.2
Prosthecobacter fluviatilis
species
7.7
2.5
5.3
12
3.1
Comparing these two lists, we found only 6 taxa in common
Bifidobacterium angulatum
Propionigenium modestum
Pseudomonas viridiflava
Cetobacterium ceti
Cetobacterium
Propionigenium
The next result is that we have 78+65 – 6 = 137 statistically significant bacteria with p < 0.005.
Bottom Line
There are at least two different statistical ways of determining significance. IMHO, the prevalence approach is likely to be a superior tool for diagnosis purposes because it is possible to compute the probability of a match to the above patterns despite some bacteria not being reported.
A reader raised a valid question which actually triggers other related questions.
You seem to like the “percentage of percentiles” measurement, but I’m not convinced it’s being analyzed appropriately. As I understand it, you first convert to percentiles, getting numbers in [0, 100]. I think this is fine. Then you histogram these percentiles. Because each lab will perform the same measurements every time, I think this is also fine. However, the result is compositional data in the sense of Aitchison, and it should be analyzed in a manner consistent with that. For compositional data, a chi^2 test is inappropriate because it relies on the number of species (or genera) measured.
My suggestion is to apply a centered logratio transform to each person’s percentages and fit a normal distribution to the transformed data. To determine whether someone’s microbiome deviates significantly, calculate a multivariate normal tail probability. Beware that the covariance matrix will be rank deficient (you’re in a ten-dimensional space, but there are only nine parameters because percentages sum to 100). You may want a robust fit because it’s reasonable to expect that the microbiome of someone ill might be an outlier.
For more information about compositional data, see Aitchison, J., “The Statistical Analysis of Compositional Data,” Journal of the Royal Statistical Society. Series B (Methodological) Vol. 44, No. 2 (1982), pp. 139-177; Aitchison, J., “The Statistical Analysis of Compositional Data,” Chapman & Hall, London, 1986; and Aitchison, J. “A Concise Guide to Compositional Data Analysis,” unpublished manuscript, 2005, available online (just Google). For other approaches to compositional data analysis, see Greenacre, Michael; Grunsky, Eric; Bacon-Shone, John; Erb, Ionas; Quinn, Thomas, “Aitchison’s Compositional Data Analysis 40 Years On: A Reappraisal,” arXiv:2201.05197, 13 Jan 2022, to appear in Statistical Science.
What is the statistical basis for other Diversity Indices?
How to calculate these numbers is well determined — they seem to be brilliant ideas tossed out there that seems to fit the data for some study. For some background, see this page. The problem is a lack of rigor, especially statistical rigor.
Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible.
The problem is an absence of a native statistical model. For example, it does not fit the usual ones.
Normal distribution – “The distribution that shall rule them all” because that is what is always assumed and what is usually taught outside of mathematics department (who knows better)
The key question is simple, what is the distribution underlying diversity Indices? We read ” In the literature of biodiversity, according to Ricotta (2005), there are a “jungle” of biological measures of diversity.”[2017]. Zheng’s A new diversity estimator[2017] in Journal of Statistical Distributions and Applications where he states “There are many other open problems built on this connection between birthday problem and diversity measures. ” The problem is this, the birthday problems deals with 366 discrete well defined boxes that are well defined. Dealing with the microbiome, we lack these boxes. Consider a measure of a microbiome sample in 2000, there are a large number of different bacteria species in Lactobacillus. Today, we have these species no longer placed in 1 genus, but 25 genus [2020] including:
Acetilactobacillus,
Agrilactobacillus,
Amylolactobacillus,
Apilactobacillus,
Bombilactobacillus,
Companilactobacillus,
Dellaglioa,
Fructilactobacillus,
Furfurilactobacillus,
Holzapfelia,
Lacticaseibacillus,
Lactiplantibacillus,
Lapidilactobacillus,
Latilactobacillus,
Lentilactobacillus,
Levilactobacillus,
Ligilactobacillus,
Limosilactobacillus,
Liquorilactobacillus,
Loigolactobacilus,
Paucilactobacillus,
Schleiferilactobacillus,
Secundilactobacillus.
With the same strains/species, our diversity indices will be very different because our boxes are arbitrary and “soft” unlike the days of the year or the roll of a dice.
Back to percentage of percentiles
While I show genus and species in the table for ease of understanding of the typical reader, I originally did it solely with the lowest identifiable levels (the “atoms” or the microbiome) – species. At the species level, it is not compositional. There is no composition! Looking at the data that was actually received, I noticed many genus had no species listed. In some cases, the genus had species, but none of the known ones were detected. In other cases, the test did not report any species in over 3000 test results.
On this basis I decided to use try using both species and genus. I soon discovered that they almost always exhibit a similar pattern and chi^2. At this point, I opted for benefiting my readers and not as much rigor as some would like. We could do the lowest taxonomical level reporting across the hierarchy as one solution.
This approach ends up with us side-stepping the classification issues cited above. We are dealing with distinctive, non-overlapping events (a bacteria being identified) and then convert them to percentile giving use a continuous uniform distribution for each of these independent events. IMHO, at this point we have a good model to chi^2 test. We are not dealing with measuring a population, just a sample.
In answer to “a chi^2 test is inappropriate because it relies on the number of species (or genera) measured.” is missing the point. If I get two bags of coins from the bank and then flip them to determine if they are biased — whether the bags contains 1000 or 100,000 coins is significant only on the ability to determine the margin of error. The number of species/genus is only significant in that sense. If there is a strong bias with a small number, then having more will not change the bias.
For samples coming from uBiome, Ombre/Thryve and Biomesight there are two important numbers reported. [Count] and [Count_Norm].
[Count] is the number coming from the lab equipment, the bacteria detected.
[Count_Norm] is the above number scaled to be out of one million (1,000,000)
The question arises, if you have low lab quality and the number of bad bacteria also dropped: Is this an actual improvement or a false improvement due to low lab quality?
To partially answer that question, I pulled biomesight samples (biggest collection) and plotted the data. Shown below:
Vertical axis is number of bacteria reported
Horizontal axis is lab quality measure
As is shown, there is a relationship.
Using this data and sample values of 4.3 and 8.4, we compute 546 and 643 for expected bacteria (just use the formula on the chart). This means that with 4.3 we expect only 85% (546/643 * 100) of the count seen with 8.4.
So we see the changes below are likely true improvements: (Left is 4.3, right is 8.4)
For bacteria reported by lab, the numbers suggests the left column has less odd bacteria and the gut microbiome may becoming more uniform.
Today I was asked if the numbers shown on various sights like the one below, ” if this result is correlated with the severity of the patient?”. The answer is No. The usual reason that such numbers appear on sites is to satisfy customers asking for such numbers. Simple, easy to understand number.
Businesses want to make customers happy — so they literally cook-up a number to show on their reports. There is no research supporting any of the magical numbers that I have seen. Some one put together some numeric formula to generate the numbers.
For Biomesight, the logic is shown on the page with the right display
If you sum up the values for each dial, you get the total. When you view the percentage, it is not so obvious: 100%, 69%, 88%, 85% looks like a complex formula is being used.
Analogy: Give me a Health Measure for a Person
The microbiome is very much like a person. How would you create a single value for a person?
Probiotics –> Income
Commensals –> Savings
Pathobionts –> Debts
Diversity –> Health?
Married and marriage status
Chronic Conditions
Health Status
Height
Weight
Gender
etc
Would a person with no debt, good savings, a low income and married with 5 kids be a higher or lower measure than someone with moderate debt, high income, small savings and no relationships?
Are there any studies?
What about the ratios used in the literature?
Bacteroides/Bifidobacterium Ratio
Bacteroides/Clostridium Ratio
Blautia/Bacteroides Ratio
Firmicutes/Bacteroidetes Ratio
Prevotella/Bacteroides Ratio
Well, the sample that I am looking at have 43%ile, 98%ile, 7.2%ile, 58%ile and 19%ile. The numbers are all over the place!
Their latest study states: ” accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased)” So 1 in five times, they will misclassify a healthy person as having a disease.
An example of the algorithm being used is below.
This is just trying to determine if the person is healthy or not — nothing about severity.
My approach is simple, I simply flagged the bacteria deemed to be unhealthy. Just list them.
Latest microbiome results are in. Seems like my microbiome is stubborn and stuck these last few tests. Wondering if I should just use this test suggestions exclusively or combine with my prior Thorne test
We have two sample – one via BiomeSight and one via Thorne. This post is going to do two things:
Look at Suggestions – by combining both sets of suggestions using the Uber Consensus
Look at the differences between the reports.
We also review “which is better”. My focus is clinical application to individuals — not research papers; answer at bottom.
Uber Consensus
The process has become very simple — “Just give me Suggestions!” on both samples and then going to uber consensus as illustrated by the video below.
The result was excellent agreement on suggestion between each set of results. The CSV files are attached below.
Items less than 100 should be ignored (accuracy of measurement limits). There are a few dramatic differences.
Bacteria Name
Thorne Count
BiomeSight Count
Firmicutes
396799
529540
Actinobacteria
60610
2100
Bacteroidetes
461289
448230
Proteobacteria
6095
18150
Chlorobi
36
429
Acidobacteria
35
100
Cyanobacteria
83
20
Spirochaetes
85
30
Verrucomicrobia
59
10
Chloroflexi
77
50
Tenericutes
54
30
Deinococcus-Thermus
48
30
Fibrobacteres
4
10
Synergistetes
17
20
By Count
Looking at Percentiles next
Bacteria Name
Thorne %ile
BiomeSight %ile
Chlorobi
25
84
Actinobacteria
85
33
Acidobacteria
34
81
Spirochaetes
81
36
Cyanobacteria
31
1
Deinococcus-Thermus
55
29
Firmicutes
14
37
Chloroflexi
67
50
Verrucomicrobia
14
1
Tenericutes
13
2
Proteobacteria
10
18
Synergistetes
6
4
Bacteroidetes
55
56
Fibrobacteres
1
0
By Percentile ranking
We have Bacteroidetes in agreement with both — but for the rest…
At the genus level
Bacteria Name
Thorne Count
BiomeSight Count
Bacteroides
180054
397640
Blautia
16470
107220
Roseburia
16793
73640
Faecalibacterium
109196
152890
Corynebacterium
43413
820
Ruminococcus
9177
44170
Phocaeicola
223209
199669
Parabacteroides
11855
31940
Phascolarctobacterium
6101
23980
Dorea
36
13000
Sutterella
16
11339
Oscillospira
0
8250
Coprococcus
6120
12589
Eggerthella
6491
760
Pseudobutyrivibrio
149
5790
Lachnospira
11593
6230
Prevotella
954
4260
Anaerostipes
9303
6310
Clostridium
2039
4960
Pedobacter
46
2410
Odoribacter
4077
2060
Bifidobacterium
2783
1019
Escherichia
75
1610
Porphyromonas
1372
150
Mediterraneibacter
14831
13629
Bilophila
6
1110
Veillonella
75
1160
Desulfovibrio
1900
1250
Streptococcus
1477
840
Acetivibrio
33
470
Chlorobaculum
6
429
Finegoldia
1339
920
Gemella
17
400
Enterococcus
585
220
Paenibacillus
376
20
Mogibacterium
39
370
Acetobacterium
15
340
Serratia
47
350
Eubacterium
517
240
Megasphaera
35
290
Selenomonas
52
290
Bacillus
248
10
Caldicellulosiruptor
11
240
Campylobacter
235
10
Slackia
16
240
Sphingobacterium
48
270
Caloramator
10
190
Staphylococcus
181
10
Hathewaya
8
170
Peptoniphilus
656
800
Peptostreptococcus
6
150
Microbacterium
125
10
Adlercreutzia
525
620
Rhodothermus
6
90
Erysipelothrix
12
90
Acidaminococcus
12
90
Hymenobacter
80
10
Negativicoccus
115
50
Collinsella
74
10
Rhodococcus
67
10
Dialister
25
80
Anaerococcus
336
390
Pseudoclostridium
8
60
Moorella
9
60
Vibrio
60
10
Caldilinea
1
50
Brochothrix
2
50
Mycobacterium
67
20
Neisseria
57
10
Pectinatus
7
50
Thermoclostridium
16
50
Alkaliphilus
9
40
Shewanella
31
60
Lactobacillus
57
30
Leptospira
4
30
Deinococcus
35
10
Tetragenococcus
5
30
Ethanoligenens
34
10
Weissella
10
30
Gulosibacter
1
20
Pseudoclavibacter
2
20
Kocuria
28
10
Meiothermus
2
20
Stenotrophomonas
28
10
Symbiobacterium
3
20
Devosia
4
20
Dysgonomonas
34
20
Azoarcus
21
10
Leuconostoc
9
20
Glaciecola
1
10
Turicibacter
21
30
Pelotomaculum
1
10
Parascardovia
2
10
Lentibacillus
2
10
Actinopolyspora
2
10
Kitasatospora
2
10
MLOs
3
10
Ochrobactrum
3
10
Rickettsia
3
10
Luteibacter
3
10
Fibrobacter
4
10
Pediococcus
14
20
Halanaerobium
6
10
Dyadobacter
14
10
Mycoplasma
17
20
Thauera
9
10
Lysobacter
11
10
By Counts
Looking at the percentile rankings — the absolute numbers may vary greatly, but what about relative percentiles?
Bacteria Name
Thorne %ile
Biomesight %ile
Ochrobactrum
2
2
Actinopolyspora
1
1
Halanaerobium
1
1
MLOs
1
1
Glaciecola
1
1
Lentibacillus
1
1
Pelotomaculum
1
1
Parascardovia
1
1
Luteibacter
1
1
Phocaeicola
89
89
Rickettsia
1
0
Pediococcus
10
9
Fibrobacter
2
0
Mycoplasma
5
3
Alkaliphilus
1
3
Finegoldia
85
88
Kitasatospora
3
0
Thauera
5
1
Streptococcus
55
50
Turicibacter
12
17
Peptoniphilus
64
58
Hathewaya
1
8
Clostridium
18
11
Desulfovibrio
61
69
Eubacterium
38
46
Symbiobacterium
1
9
Enterococcus
88
79
Sphingobacterium
13
23
Pseudoclavibacter
1
11
Anaerococcus
72
83
Eggerthella
98
86
Gulosibacter
0
12
Lactobacillus
23
11
Bifidobacterium
55
43
Leuconostoc
2
14
Shewanella
35
47
Prevotella
50
63
Corynebacterium
99
86
Collinsella
13
0
Oscillospira
0
16
Faecalibacterium
49
65
Meiothermus
1
17
Caloramator
1
19
Coprococcus
39
57
Lysobacter
18
0
Odoribacter
81
63
Adlercreutzia
63
81
Pedobacter
13
31
Dyadobacter
20
1
Dysgonomonas
24
4
Mediterraneibacter
69
90
Devosia
1
22
Acetivibrio
5
27
Thermoclostridium
9
32
Ethanoligenens
25
1
Dialister
11
35
Veillonella
16
41
Pectinatus
1
27
Porphyromonas
88
62
Moorella
1
28
Negativicoccus
66
39
Lachnospira
51
21
Rhodothermus
1
32
Tetragenococcus
1
32
Acetobacterium
3
34
Anaerostipes
65
96
Bilophila
1
33
Ruminococcus
14
47
Weissella
2
35
Parabacteroides
42
75
Acidaminococcus
4
39
Pseudoclostridium
1
37
Leptospira
1
42
Serratia
34
75
Slackia
4
45
Phascolarctobacterium
56
97
Erysipelothrix
4
46
Sutterella
1
46
Bacteroides
39
87
Roseburia
43
91
Escherichia
28
77
Selenomonas
21
73
Deinococcus
54
1
Megasphaera
18
72
Brochothrix
1
56
Kocuria
58
2
Mogibacterium
17
74
Stenotrophomonas
63
3
Azoarcus
61
0
Caldilinea
0
61
Caldicellulosiruptor
2
64
Mycobacterium
87
24
Hymenobacter
68
1
Blautia
5
73
Paenibacillus
87
19
Neisseria
69
0
Pseudobutyrivibrio
25
95
Campylobacter
75
1
Gemella
4
82
Peptostreptococcus
1
81
Chlorobaculum
1
84
Staphylococcus
85
0
Vibrio
91
2
Bacillus
92
1
Rhodococcus
91
0
Dorea
1
93
Microbacterium
94
1
By Percentile
Bottom Line
I have never had much belief in the absolute accuracy of the bacteria named or the count. Why? Simple, I understand the statistical process being used and its weakness. I will leave arguments over “which is better” and “which is accurate” to others.
My focus and concern is to improve the microbiome. With sparse data and the great complexity involved, I am actually very pleased that the suggestions are in agreement. The suggestions are computed using fuzzy logic expert systems. The noise in the data and the statistical processes involved seem to be smoothed out by this Artificial Intelligence engine approach.
Score: Labs: -2, Microbiome Prescription 2
Which is better?. My focus is clinical application to individuals, both give similar suggestions using the Fuzzy Logic Expert System. There is no difference in that sense.
I have been doing periodic review of this person’s sample. He just got his latest results and it was a shocker (of the wrong type!). This is worth a review.
Comparing Samples Overtime
At the typical analysis level there has been no change in these broad criteria since the last sample:
Outside Range from JasonH
Outside Range from Medivere
Outside Range from Metagenomics
Outside Range from MyBioma
Outside Range from Nirvana/CosmosId
Outside Range from XenoGene
Why compare over 90%ile to under 10%ile? The reason is probability – we are converting the data to a uniform distribution for all of the bacteria. This allows for reliable statistical significance to be determined for all of the bacteria. If things are “normal” then the ratio should be 1.0 The further from 1, the more abnormal. This is independent of any assumptions on bacteria distributions.
Looking at over 90% and under 10%, our expected count are 64 for both (10% of 639)
We have 20 over 90%ile, so we have under representation of dominant
We have 273 under 10%ile, the typical over representation of low levels of many bacteria seenwith most ME/CFS people
The ratios is higher at 13.7 compare to prior ratios (8.2, 11,3,3,6.5)
Where we see differences
Outside Kaltoft-Moldrup count returned to the size of the very first sample.
Compounds over 90%ile to under 10%ile (which should be 1.0 theoretically) had been close to 1.0 on the prior 3 samples, jumped up to 8.8:1. This was not as bad as the first sample with a 12.9:1 ration.
Enzymes over 90%ile to under 10%ile, continue to be bias towards low with a 2.7:1 ratio (prior 3.5, 1.7, 2.9, 3.2)
Conditions: jumped from none over 90%ile to 13!
My general impression is that ground has been lost. This is the first time that subsequent results appear to be worse. WHAT HAPPENED!???!!???!!!
Criteria
2/22/2022
8/11/2022
3/25/2022
12/3/2021
8/31/2021
Lab Read Quality
9.7
5.5
6.2
3.6
7.8
Bacteria Reported By Lab
639
461
593
445
551
Bacteria Over 99%ile
4
3
3
5
15
Bacteria Over 95%ile
11
13
11
24
23
Bacteria Over 90%ile
20
23
21
40
35
Bacteria Under 10%ile
273
189
237
123
227
Bacteria Under 5%ile
219
107
143
66
192
Bacteria Under 1%ile
175
23
44
9
167
Lab: BiomeSight
Rarely Seen 1%
6
7
14
2
3
Rarely Seen 5%
22
14
33
7
9
Pathogens
37
32
46
31
38
Outside Range from JasonH
7
7
4
4
6
Outside Range from Medivere
15
15
15
15
19
Outside Range from Metagenomics
8
8
6
6
7
Outside Range from MyBioma
7
7
7
7
8
Outside Range from Nirvana/CosmosId
23
23
18
18
21
Outside Range from XenoGene
32
32
36
36
39
Outside Lab Range (+/- 1.96SD)
7
8
6
9
14
Outside Box-Plot-Whiskers
38
33
38
58
41
Outside Kaltoft-Moldrup
210
111
123
100
211
Condition Est. Over 99%ile
5
0
0
0
7
Condition Est. Over 95%ile
9
0
0
0
15
Condition Est. Over 90%ile
13
0
0
0
29
Enzymes Over 99%ile
35
10
30
19
72
Enzymes Over 95%ile
100
68
219
82
162
Enzymes Over 90%ile
191
183
296
126
192
Enzymes Under 10%ile
520
645
514
369
616
Enzymes Under 5%ile
375
423
264
186
450
Enzymes Under 1%ile
219
86
49
37
272
Compounds Over 99%ile
23
47
62
28
44
Compounds Over 95%ile
72
254
231
127
86
Compounds Over 90%ile
126
338
298
307
98
Compounds Under 10%ile
1104
308
297
227
1265
Compounds Under 5%ile
1068
173
224
111
1241
Compounds Under 1%ile
1045
65
67
47
1206
What Happened?
This person sent the following notes
I’ve got COVID in October, I feel as I have fully recovered.
I have a little bit more energy than before.
My body feels extremely stressed, I have started to get a pressure over the neck / thyroid when I get totally stressed out.
My sleep is much better. I have been following Andrew Hubermans protocol for sleep, which had a great impact on me.
Would be great if I could get some recommendations for food, supplements, antibiotics etc.
Ah, the person feels like he has recovered but his microbiome is still recovering. We have a clean explanation for the regression! Our goal is now to try to stop Long COVID from setting in.
The Conditions matches include: hypercholesterolemia (High Cholesterol), Hyperlipidemia (High Blood Fats), Hypertension (High Blood Pressure, Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic and Atherosclerosis. None of those are concerning –they were not matches last time and thus should be viewed as transient red herrings. Looking at PUBMED Long COVID explicitly, we see good news: Long COVID (29 %ile) 37 of 212. However when we go over to Special Studies, the very top one is
41 % matchCOVID19 (Long Hauler), the next match was 21% –this really sticks out!
All Prior samples also had Long Hauler at the top too.
Going Forward
To build our consensus, we will do the usual and toss in our top Special Studies one.
Given the general hostility between lactobacillus and E.Coli, plus the risk or lactic acid issue with lactobacillus, I would suggest avoiding lactobacillus casei initially, perhaps try it in a later cycle. A similar contradiction happened with different forms of cranberry as shown below. We want absolutely clean positive choices.
If different results from similar versions of something — then AVOID.
I reran the suggestions with everything — in case any prescription items may be of special interest.
The top antibiotics included:
rifaximin (antibiotic)s “Xifaxin has been used with success to rebalance gut flora and relieve gut symptoms in chronic fatigue syndrome, IBS, inflammatory bowel disorder and others.” [Src]
ofloxacin (antibiotic)s – a quinolone antibiotics, this class. NOT RECOMMENDED — while used with some success [ME Association], this class of antibiotics is known to cause some persistent/permanent side effects
As always, I prefer the Cecile Jadin approach of taking a single course, take a break and then take a different antibiotics.
I personally have always love Liver pâté! There was something that always felt so good after eating. Liverwurst is essentially the same food.
So for a Scandinavian, this is almost going to a church social!! Liver pâté on Dark 100% Rye bread with Jarlsberg cheese also on Dark 100% Rye bread!
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 post started out with a title of “Post-Acute COVID-19 Syndrome vs Myalgic Encephalomyelitis – Similarities and Differences“. It scope was pretty obvious — compare microbiome shifts from these two sibling conditions. Expectations was a bland informational review.
The result was calling into question the typical assumption that we could isolate symptoms and conditions to specific bacteria. I follow the statistics and discovered that you can get a magnitude better statistical significance by moving beyond bacteria. In coming weeks, I hope to code up suggestions AI based on this enlightenment.
My starting point
In my last post, Long COVID – an update, I did a comparison between the citizen science data and the literature published on the US Library of Medicine. In terms of symptoms, these two appear the same — but at the microbiome (and enzyme levels) how similar are they?
Post-Acute COVID-19 Syndrome (PCAS), also known as Long COVID
Myalgic Encephalomyelitis (ME), also known as Chronic Fatigue Syndrome (CFS)
One frustrating aspect of many studies on the US Library of Medicine for many conditions is simple: results are not replicated in subsequent studies for the same condition. Historically I have viewed this as a result of different equipment and different reference libraries. In many cases the bacteria deemed significant are often different and when they did report the same bacteria, they report opposite shifts!
This post explores some of these issues, and came to an interesting conclusion.
Study Caveats
The studies on the US Library of Medicine compare people with the condition to healthy controls. With the citizen science data that is almost impossible to do. If a person has gotten a microbiome test, they likely have some condition(s) and thus are not healthy controls!!
This is not all bad. It means that when we find things that are statistically significant they are differentiators against other people with microbiome issues. That is, how are people with ME different than people with FM and IBS. Conceptually, we are more likely to identify the key features for these conditions and not key features for auto-immune conditions or a gut disturbance in general. It is a nuisance difference, but may be a very important nuisance.
Comparison that we will review are from:
US Pubmed — bacteria reported by both with direction
KEGG Enzymes shifts from Citizen Science (using only Biomesight data)
Bacteria shifts from Citizen Science (using only Biomesight data)
Citizen Science Samples
Studies on US National Library of Medicine
For citizen science we may have many uploaded samples annotated both with PCAS and ME. To resolve this conflict, ME will contain only samples with ME and without PCAS. Both ME and PCAS have many, many comorbid symptoms which may also come into play. Many of the pure ME samples are before COVID swept the world, hence relatively clean. PCAS are more recent samples.
For PACS citizen science data, we have only significance difference identified from Biomesight data, hence we will compare those only.
Scope
ME
PACS
Same
US National Library of Medicine
68
233
25
Enzymes – Citizen Science with p < 0.001
228
199
31
Bacteria – Citizen Science
109
36
0
Entities reported as significant or found significant
I must admit that finding no bacteria in common with the same lab and the same reference library was a little bit of a surprise. One explanation is that microbiome dysfunctions evolve over time. People with PACS have had it less then 3 years, likely an average of just 1 year. People with ME has had it often for 30+ years. Comparing the two may be similar to comparing a one bottle of grape juice to a bottle of vintage wine.
Details for Common Bacteria from US National Library of Medicine
In the table below: H indicates High, L indicates Low.
Note that Bacteroides are reported high and low in different studies, suggesting there are subsets of each condition
tax_rank
tax_Name
Direction
class
Bacteroidia
H
family
Bacteroidaceae
H
family
Clostridiaceae
H
family
Lachnospiraceae
L
genus
Anaerostipes
L
genus
Bacteroides
H
genus
Bacteroides
L
genus
Bifidobacterium
L
genus
Coprobacillus
H
genus
Coprococcus
L
genus
Dorea
L
genus
Eggerthella
H
genus
Enterococcus
H
genus
Faecalibacterium
L
genus
Lactobacillus
L
genus
Streptococcus
H
genus
Turicibacter
H
order
Eubacteriales
L
phylum
Bacteroidetes
H
phylum
Firmicutes
L
phylum
Fusobacteria
H
species
Anaerobutyricum hallii
L
species
Enterocloster bolteae
H
species
Faecalibacterium prausnitzii
L
species
Ruminococcus gnavus
H
From https://microbiomeprescription.com/Library/PubMed
Details for Shared Enzymes with p < 0.001
In recent posts for conditions comorbid with ME, PACS, I found that enzyme analysis had greater statistical significance than bacteria. All of these posts reported higher enzyme levels were significant with these conditions.
The result for items shared that had p < 0.001 was almost overwhelming!
One of them caught my eye, heparin-sulfate lyase, because micro-clots and “thick blood” are associated with these conditions with good results reported from the use of heparin for some patients.
HSGAGs are widely distributed on the cell surface and extracellular cell matrix of virtually every mammalian cell type and play critical role in regulating numerous functions of blood vessel wall, blood coagulation, inflammation response and cell differentiation.
This blew me away — we have over 150 people with PCAS and over 250 with ME giving us superior sample sizes. We have 145 bacteria deemed significant for one or the other. We had NONE that was in common. This gut punch gives two main options: denial and look for an excuse to disregard. or roll with the punch and roll with enzymes.
Bottom Line
The enzyme aspect is the strongest association between PACS and ME. The count is higher, but more important, we are dealing with p < 0.001 data making false positives unlikely. This leads to a model that there is no ‘magical list of bacteria involved’ but a diverse array of bacteria that may be different for each person — but united in the over production of enzymes. This shifts the microscope of research into a different light spectrum. This is very interesting and may require some new brain cells to be used.
Using this information to improve..
If Enzymes estimate trumps bacteria levels (in a statistical sense), then we need to look at the enzyme levels and deduce for each one of concern, which collection of bacteria needs reduction — even when they are in the normal range. It is the aggregation of bacteria where the issue arises, not individual bacteria or specific subsets of bacteria.
A few examples may illustrate things a little
Example: (S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming) a.k.a. EC 4.1.3.4, which was the most significant for PACS in the post: Long COVID – an update. There are some 2000+ taxon associated with it. We look at the averages for these below.
Rank
Tax_Name
With PACS
Without PACS
TScore
DF
species
Faecalibacterium prausnitzii
138151
109604
3.796775
667
species
Pseudomonas viridiflava
53
25
2.628108
32
species
Comamonas kerstersii
125
40
2.600388
54
species
Pseudomonas aeruginosa
62
31
1.824644
43
species
Emticicia oligotrophica
2303
967
1.727619
455
species
Denitratisoma oestradiolicum
42
24
1.650657
22
species
Granulicella tundricola
29
21
1.61225
48
species
Bacillus subtilis
40
19
1.374431
17
species
Niabella soli
31
24
0.963943
16
species
Ralstonia insidiosa
53
38
0.914874
36
species
Oligella ureolytica
51
32
0.89768
19
species
Glaciecola nitratireducens
27
24
0.67899
66
species
Bacillus halotolerans
32
28
0.371219
58
species
Acidaminococcus intestini
749
624
0.357674
146
species
Acinetobacter guillouiae
67
63
0.098956
18
Key Contributors to EC 4.1.3.4,
For another one, we see the pattern stronger. Below we see the difference of Faecalibacterium prausnitzii is around 30,000 units. Looking at the other contributors, we see an additional 40,000 units. These extra units doubles the shift (and thus significance) of the enzyme above that of a single bacteria. Some of these are deemed healthy usually, for example: Akkermansia muciniphila which was at the 78%ile for Without PACS and 83%ile with PCAS. Neither would be deem to be outliers.
Rank
Taxon Name
With PACS
Without PACS
t-score
DF
species
Faecalibacterium prausnitzii
138151
109604
3.796775
667
species
Sutterella wadsworthensis
9626
6772
2.380718
452
species
Aliarcobacter skirrowii
3756
21
2.223602
17
species
Akkermansia muciniphila
19096
12290
1.896922
547
species
Desulfovibrio desulfuricans
1423
469
1.769772
32
species
Emticicia oligotrophica
2303
967
1.727619
455
species
Enterococcus casseliflavus
1965
81
1.635666
38
species
Porphyromonas asaccharolytica
1350
254
1.59988
186
species
Bacteroides fragilis
8080
5595
1.523991
489
species
Bifidobacterium dentium
1454
461
1.433823
239
species
Phocaeicola dorei
35482
29075
1.396731
649
species
Corynebacterium aurimucosum
1105
407
1.275234
96
species
Bacteroides eggerthii
14379
10345
1.108857
263
species
Corynebacterium jeikeium
1897
723
0.858282
70
species
Phocaeicola coprophilus
6496
3642
0.856783
152
species
Desulfovibrio piger
2032
1534
0.848976
141
species
Megamonas funiformis
1677
1130
0.620283
90
species
Hathewaya histolytica
2890
2729
0.467066
660
species
Haemophilus parainfluenzae
1343
1250
0.282656
500
species
Mesoplasma entomophilum
1182
1069
0.230055
294
species
Phocaeicola vulgatus
51403
51213
0.034398
665
Key Contributors for EC6.1.1.6
Going Forward
The logical approach is simple to describe. For a person with the symptom, determine the enzymes which are abnormal. Determine the bacteria that are too high (even if only a little). Then use the suggestions AI Engine to determine the substances that will affect the greatest number of these bacteria to shift in the desired direction without encouraging other bacteria that could contribute to these enzymes to increase.
Now, the mathematics and complexities of this computation is a different matter but well within the power of today’s computer.
I don’t know, if you remember me, we did two reports together, and your suggestions really helped to get my microbiome back on track (which shows in the samples).
And then I got COVID in November 2021-December 2021. But I felt better with it, but unfortunately I couldn’t give up my sample while having COVID.
Anyway I wanted to ask you whether you may be interested in my case, because I had a huge, irreversible it seems, crash from 20-30 on Bell CFIDS disability scale to now under 10 and my microbiome crashed along with me . (The crash also resulted in a high number of Lorazepam intake from which I’m slowly withdrawing now. But I didn’t get a clear idea of the effect of Lorazepam on the microbiome, other then they make the slowed gut motility worse of course.)
I have a very severe and have a progressive form of ME/CFS in the way that whenever I really crash I always go down to a lower baseline and do not recover. And with most crashes I loose about 50 % of my functionality, so it just took me one year to go from very mild to very severe.
From my lowest point onwards I’ve always had to take about 4 Lorazepam to guarantee a minimum of a bearable quality of life in bed. I succeeded for 4 years not to have a major crash and did did not build up a tolerance towards Lorazepam in that time.
A lot of things seem to have reversed, what I should take before are often things that I now should avoid. What Biomesight says seems to contradict slightly from what I can gather from your site. (Yes, I know you explained why there can be contradictory results).
And I have difficulties getting the suggestions for the handpicked criteria to show. Of course I would be super glad, if you could help, but I understand if you have more interesting projects to work on. (I would of course donate for your effort, as this is the only or easiest way to say thank you),
I believe one of the differences between Microbiome Prescription(MP) and Biomesight is simply the number of studies used to make suggestions. At present, we have over 11,000 studies coded into MP, I do not know the number that Biomesight uses, but I expect less than 1%. Also, MP suggestions was written by a person that has worked professionally in Artificial Intelligence. I suspect Biomesight lacks that skill set for development. Regardless, put items not in disagreement as first priority.
Analysis — The Numbers
There is no magic number that answers questions about the microbiome. Usually, I look for abnormalities. Since the earlier post, she had 6 more microbiome samples done periodically and shown below. She is wise to regularly monitor and ideally take moderate steps (diet and supplements) to counter any concerning trends.
Criteria
Sep21
Mar22
May22
Sep22
Jan23
May23
Shannon Diversity Index
78.2
94.3
67.1
53.9
98.9
84.70
Simpson Diversity Index
30.7
40.7
44.4
17.5
42.9
48.90
Chao1 Index
53.6
66.8
81.5
36.7
65.1
61.90
Lab Read Quality
4.8
7.3
7.7
5.2
5
6.5
Bacteria Reported By Lab
612
653
717
536
636
642
Bacteria Over 99%ile
2
15
9
6
8
4
Bacteria Over 95%ile
4
20
50
33
32
12
Bacteria Over 90%ile
29
44
69
50
68
38
Bacteria Under 10%ile
44
181
181
40
53
43
Bacteria Under 5%ile
12
164
165
9
20
14
Bacteria Under 1%ile
1
140
148
1
3
0
Rarely Seen 1%
5
3
7
0
2
2
Rarely Seen 5%
16
11
21
10
19
15
Pathogens
28
29
38
31
32
34
Outside Range from JasonH
5
5
6
6
8
8
Outside Range from Medivere
12
12
19
19
19
19
Outside Range from Metagenomics
9
9
10
10
6
6
Outside Range from MyBioma
6
6
6
6
9
9
Outside Range from Nirvana/CosmosId
20
20
14
14
21
21
Outside Range from XenoGene
36
36
36
36
39
39
Outside Lab Range (+/- 1.96SD)
2
12
24
16
18
9
Outside Box-Plot-Whiskers
67
83
106
94
106
58
Outside Kaltoft-Moldrup
64
183
218
87
106
75
Condition Est. Over 99%ile
0
0
0
0
0
0
Condition Est. Over 95%ile
0
2
0
0
0
0
Condition Est. Over 90%ile
0
3
5
0
0
0
Enzymes Over 99%ile
0
0
21
0
0
0
Enzymes Over 95%ile
19
0
66
15
17
36
Enzymes Over 90%ile
68
13
119
34
27
118
Enzymes Under 10%ile
30
285
203
94
200
80
Enzymes Under 5%ile
13
225
130
41
81
27
Enzymes Under 1%ile
1
164
80
2
2
11
Compounds Over 99%ile
1
0
17
0
0
0
Compounds Over 95%ile
18
0
35
3
10
18
Compounds Over 90%ile
49
5
73
13
17
64
Compounds Under 10%ile
789
876
965
1124
1135
998
Compounds Under 5%ile
779
848
927
1092
1057
959
Compounds Under 1%ile
773
832
904
1069
1018
930
Sep21
Sep21
Mar22
Mar22
May22
May22
Sep22
Sep22
Jan23
Jan23
May23
May23
Percentile
Genus
%
Genus
%
Genus
%
Genus
%
Genus
%
Genus
%
0 – 9
7
4%
46
26%
43
23%
7
5%
11
6%
9
6%
10-19
19
11%
13
7%
9
5%
17
12%
27
15%
21
13%
20 – 29
26
15%
14
8%
15
8%
16
11%
14
8%
23
14%
30 – 39
13
8%
13
7%
16
8%
12
8%
15
9%
18
11%
40 – 49
14
8%
13
7%
14
7%
17
12%
18
10%
15
9%
50 – 59
14
8%
16
9%
14
7%
10
7%
15
9%
16
10%
60 – 69
22
13%
20
11%
18
9%
10
7%
17
10%
18
11%
70 – 79
23
13%
15
8%
22
12%
19
13%
16
9%
13
8%
80 – 89
23
13%
19
11%
18
9%
18
12%
21
12%
15
9%
90 – 99
11
6%
11
6%
22
12%
19
13%
21
12%
13
8%
Total
172
180
191
145
175
161
Sep21
Sep21
Mar22
Mar22
May22
May22
Sep22
Sep22
Jan23
Jan23
May23
May23
Percentile
%
Species
%
Species
%
Species
%
Species
%
Species
%
Species
0 – 9
5%
10
28%
60
27%
70
7%
14
10%
22
8%
16
10-19
13%
26
5%
11
5%
13
12%
23
15%
33
16%
34
20 – 29
13%
27
8%
18
6%
16
9%
18
10%
23
12%
25
30 – 39
7%
15
4%
8
6%
15
12%
23
10%
22
11%
23
40 – 49
9%
19
8%
16
9%
24
8%
15
8%
18
10%
21
50 – 59
12%
25
9%
20
12%
30
9%
18
10%
22
12%
25
60 – 69
8%
17
12%
25
8%
21
7%
14
8%
17
6%
12
70 – 79
10%
20
9%
19
7%
19
11%
22
9%
20
10%
21
80 – 89
13%
27
9%
19
7%
19
16%
30
9%
21
9%
19
90 – 99
7%
15
8%
17
12%
30
8%
15
12%
28
7%
15
201
213
257
192
226
211
We lack any data on Lorazepam and other Benzodiazepines impacts on the microbiome which complicates interpretations. I did a search on the US National Library of Medicine and found nothing useful.
More History of Patient
I discovered Pregabalin in March 22 which brought me from Bell CFIDS disability scale below 10 up to nearly bell 20-30. The ditch in the curve around May 22nd is probably me taking too much Lorazepam and Pregabalin, as I for the first time I could take care of things that needed to betaken care of.
At that time I took about 500mg metformin (which did give me energy) most of the day and stayed with my Thorne Fibre mend, Inulin (in the beginning Inulin from the Argave helped dramatically with nausea and headaches)and Acacia Fibre, sometime an Amino Acid complex, but they make me jittery. Usually completely constipated I suddenly developed a strong diarrhea along with an unbearable itching of my whole skin in August / September for which Famotidine(Pepcid) and Cromoglicic acid (Cromolyn – prescription in US) worked best. That was a time where I ate lots of cake and carbohydrates and would take Metformin (I am always hovering around the entrance point to prediabetic) afterwards. That seemed to be too much sugar, my body couldn’t deal with. After I stopped the cake , eat more vegetables again, it went away.
I have got restless legs, which are kind of turned on or off with every mayor crash. Now unfortunately they are turned on, and the only thing apart from medication that helps is when I eat complex carbohydrates lie brown rice, whole food, pasta, oat flakes etc, when I don’t I use Pramipexole.
Pregabalin been used with Fibromyalgia, a sibling condition for some, and suggested by the American Family Physician journal in 2023. Pregabalin with Lorazepam has known interactions: ” increase side effects such as dizziness, drowsiness, confusion, and difficulty concentrating.”[Src] so she is right about her loss of effectiveness.
Of the many items cited, we know what a few of them likely shifts. Others we lack data.
This missing data illustrates the challenge of trying to manipulate the microbiome — an absence of data. For antibiotics we have a reasonable amount of information, thus we can negotiate with MDs between their desired goal for the antibiotic and our goal of improving the microbiome to find a mutually acceptable compromise.
Going Forward
As part of my learning process, I evaluated each against the “Just Give Me Suggestions” consensus to see it that provide any insight. I also looked at the top items in three other classes.
This helps us evaluate possible (we do not know for certain) impact on various microbiome.
I am not a medical professional and have no clinical experience, so picking items tend to be arbitrary in most cases. I am familiar with the literature for ME/CFS and if the person has ME/CFS, I will tend to pick items that studies reporting helping.
My preference is simple.
My suggestion (given all of the fuzziness and items being taken) is to persist with the prescription items — they help both her symptoms and her microbiome! I would suggest adding the following items (see Dosages for Supplements for literature on dosage):
lactobacillus casei – at least 48 BCFU/day — this is the suggested serving size from Custom Probiotics product. Or a Yakult bottle with each meal (each bottle is 20 BCFU). Depending on availability and cost.
melatonin – 10 mg/d – in three dosages, i.e. one with each meal.
Vitamin B1, B12, C7 and C. (see above for dosages)
One additional item that I would suggest, being prediabetic is to take the Pendulum Akkermansia muciniphila probiotic. This may be a challenge to obtain in Germany (if someone is visiting the US, that may be a backdoor to get it).
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.
As a result of doing an analysis for a 19 month old toddler, I added a new option that can also be used with Transcribed tests. This post applied to the following tests:
When you logged in, you will see your saved tests, CLICK ON Review.
And then we have the details you entered below with an important column, taxon number.
Below this are conditions where your pattern matches at least 5 shifts reported in Published Studies.
There may be many items listed. This is by pattern matching and is not predictive.
If you have any of these conditions, or suspect you may have. Just click the appropriate button.
An example is below. These are tuned safest-suggestions for the matches. What do I mean by safest? It means the items are not reported in any study in the database to adversely impact any of bacteria listed. Many substances have contradictory reports on shifts — this substances are excluded.
Not Listed Condition?
This person believes they may have Autoimmune, so going to https://microbiomeprescription.com/Library/PubMed we find that it is listed.
If it is not listed, search for bacteria shifts reported and use those (please send me the studies so I may add them).
The bacteria are shown in a tree. You have to manually match between the two.
In this we have:
Escherichia ⬇️ but our sample is high,
Roseburia intestinalis ⬆️ - we are high on Roseburia, we will include it
We have only one match — this tests with limited reporting is not a good fit for this condition. Doing a test like Biomesight, Xenogene, Thorne or Ombre is likely the best choice.
We just copy the taxon number into the form at the bottom of the page, and then click suggestions.
In this case, we get a short list. Remember, doing a single bacteria means you are ignoring a lot of interactions and factors. The suggestions could feed other bacteria that are too high.
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