In the same email I got an second challenge: “Father In Law – Diabetes, Heart conditions and High Blood Pressure” with samples of her.
Foreword – and Reminder
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.
Analysis
In this case we have an even higher lab quality than the wife, but a lot less bacteria reported. This means that the microbiome is likely a lot less fragmented than the wife.
Criteria
Current Sample
Lab Read Quality
11.5
Bacteria Reported By Lab
628
Bacteria Over 99%ile
4
Bacteria Over 95%ile
27
Bacteria Over 90%ile
48
Bacteria Under 10%ile
330
Bacteria Under 5%ile
295
Bacteria Under 1%ile
237
Rarely Seen 1%
12
Rarely Seen 5%
41
Pathogens
50
Outside Range from JasonH
7
Outside Range from Medivere
21
Outside Range from Metagenomics
9
Outside Range from MyBioma
4
Outside Range from Nirvana/CosmosId
25
Outside Range from XenoGene
6
Outside Lab Range (+/- 1.96SD)
14
Outside Box-Plot-Whiskers
54
Outside Kaltoft-Møldrup
246
Condition Est. Over 99%ile
1
Condition Est. Over 95%ile
1
Condition Est. Over 90%ile
8
Dr. Jason Hawrelak Recommendations has him at the 75%ile — so off, but not really bad. Following the same pattern of analysis as the wife (since we have no matching special studies):
One item really jumps out — Burdock Root (Gobo in Japan)- which is available as a supplement if not available as a fresh vegetable. It is high in Inulin (but inulin is much lower, just 81 — so other components may be playing a significant role)
Bottom Line
The diet style is a major contrast with the wife — this creates the frustration of needing almost a double food preparation. To address this issue, I imported both consensus list into Excel, used a VlookUp function to display the values besides each modifier and then identified items that are positive for both and then order by the total of each.
This allows one menu to be used for both of them. Perhaps a little less effective, but likely a lot less frustrating (and thus better compliance). I attached it as an example.
This person did his tests using OmbreLabs.com and then transfer the data to biomesight.com. This allows us to use special studies to select bacteria. I am also, as part of my own learning (as well as the readers), going to do some comparison between the OmbreLabs and BiomeSight reports on the same data (i.e. FASTQ files).
I had another sample analyzed at Ombre, and there were already changes in my flora, even in a short amount of time. And they correlate with me feeling a bit better. So thank you. I’m still trying to crunch the data and make sense of the new results, and other than your great Dr. AI, I am using this new feature by Ombre which I find very clear (old sample first, new sample after)
Why Follow Up Posts are important
The first item is simple, does the model and suggestion appear to work. Everything is theoretically computed, not based on clinical practice or clinical studies. The second item is that these posts encourages people to try suggestions, or to do “self-serve” with the site.
Foreword – and Reminder
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that appears to 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.
First, I do not know the best way to compare samples — what I usually do is put all of the numbers side by side. Special attention needs to be paid to Lab Read Quality. A poorer read quality results in less bacteria being identified.
Lab Quality is a measure of the total number of bacteria counted. The processing of a sample may detect just 30,000 bacteria or 300,000 bacteria. This impacts the number of bacteria detected and also the accuracy of the measures.
Note: I just cut and pasted from “Multiple Samples” tab to Excel to make the above table.
What are the key things seen above (most of the numbers are similar):
Sample Quality are the same (expected from using the same FASTQ file)
Ombre reports more bacteria
Outside Range from Jason Hawrelak show a major improvement with Ombre Labs and no change with BiomeSight
As a historic notes, Jason’s numbers were developed using uBiome labs (adding more fuzziness to everything).
I view this major improvement per OmbreLab, to indicate the person’s improvement.
For Enzymes we see more high production rates and less low production rate with Ombre
Remember that enzymes are estimated based on the bacteria reported and is an estimate only.
The percentiles for both Ombre and BiomeSight are based on other samples from the same lab (they are NOT intermixed – I removed that earlier this year)
For Compounds, we see the same thing!
KEGG Computed Enzymes
I was curious what the top items were. Most of the bacteria are the bacteria only available in Equilibrium and PrescriptAssist, excluding those and looking at the top few — we see similar suggestions (and note E.Coli is not always #1 for ME/CFS people on all tests, just a frequent pattern what dates back to 1998 in some conference papers from Australia).
Only BiomeSight was used in the Special Studies (because of higher sample population). The person’s rating for each of the symptoms (2 – worst, 0 -none) is also added.
Why did the number increased so much? Look below at Lab Sample quality! We cannot pick a percentage match as being critical — because that percentage depends very much on lab quality!
BS 6/6
BS 7/19
Person
Symptom
2.1
5.4
Lab Quality
13
25
2
Allergies And Food Sensitivity
13
20
2
Bloating
11
24
2
Brain Fog
9
34
2
Depression
13
23
2
Easily irritated
8
22
2
General Fatigue
11
23
2
High Anxiety
12
20
2
Histamine or Mast Cell issues
13
23
1.5
Chronic Fatigue Syndrome (CFS/ME)
11
20
1.5
irritable bowel syndrome
13
23
1.5
ME/CFS with IBS
12
30
1
Alcohol intolerance or Medication sensitivities
10
23
1
Intolerance of Extremes of Heat and Cold
9
17
1
Post-exertional malaise
21
30
1
Small intestinal bacterial overgrowth (SIBO)
12
28
1
Unrefreshed sleep
16
28
0
Allergic Rhinitis (Hay Fever)
23
28
0
Autism
12
22
0
Cold Extremities
15
20
0
Constipation
21
29
0
COVID19 (Long Hauler)
26
45
0
Inflammatory bowel disease
8
23
0
ME/CFS without IBS
11
21
0
Poor gut motility
8
20
0
Tinnitus (ringing in ear)
Intrepretation
As cited in the introduction, the person reported feeling better. We also see a major improvement against Jason Hawrelak Criteria for a healthy gut (using Ombre numbers). With both labs we see an increased of rarely seen bacteria — which is open to many interpretations; statistically both increases looks like a move towards a typical gut. 5% of 628 bacteria is 31, we see 40.
These suggestions agrees with the top KEGG suggestions (despite being calculated in a totally different way — one set used Genomics and one set used Clinical Trials)
Going over to vitamins, the strongest take is Ferric citrate. We have almost all of the B-vitamins being strong avoid — this is contrary to the conventional treatment wisdom which says vitamin B helps ME/CFS. I discuss this in a prior post and speculate that the reason that Vitamin B is low in blood test ME/CFS is that part of the microbiome dysfunction are bacteria that are greedy for vitamin B, hence it does not get to the body. Conceptually this speculation is testable with a lab reactor using the microbiome from a ME/CFS person.
Starving out bacteria that consumes B-Vitamins may be one path
“This is too complicated” is what I can hear some people saying. This analysis digs into the nature of the data which is really not needed for most people. It is likely of interest to those treating microbiome dysfunctions as it illustrates many of the challenges in interpreting.
For most people, the process stays the same:
Upload the data
Try several different ways of generating suggestions
Look at the consensus
Why is consensus important? Simple, we have very incomplete data and also have limited accuracy with the microbiome tests. Going the consensus approach is similar to using a Monte Carlo Simulation, an appropriate approach to deal with complex processes with many parameters that are fuzzy.
This person has been using microbiome prescription to reduce the symptoms with success and with objective measurements of improved microbiome. His MD is willing to prescribe antibiotics and the top three items (from hundreds possible) are all used by ME/CFS specialist — indicating that the model is in agreement with clinical experience of ME/CFS specialist (a.k.a. Cross-Validation).
The first item is simple, does the model and suggestion appear to work. Everything is theoretically computed. The second item is that encourages people to try suggestions
Foreword – and Reminder
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.
Comparisons between Samples
First, I do not know the best way to compare samples — what I usually do is put all of the numbers side by side. Special attention needs to be paid to Lab Read Quality. A poorer read quality results in less bacteria being identified.
Lab Quality is a measure of the total number of bacteria counted. The processing of a sample may detect just 30,000 bacteria or 300,000 bacteria. This impacts the number of bacteria detected and also the accuracy of the measures.
Criteria
8/31/2021
12/3/2021
3/25/2022
8/11/2022
Lab Read Quality
7.8
3.6
6.2
5.5
Bacteria Reported By Lab
461
379
479
383
Bacteria Over 99%ile
7
5
3
3
Bacteria Over 95%ile
20
24
11
13
Bacteria Over 90%ile
32
40
21
23
Bacteria Under 10%ile
283
123
237
189
Bacteria Under 5%ile
222
66
143
107
Bacteria Under 1%ile
161
9
44
23
Rarely Seen 1%
3
2
14
7
Rarely Seen 5%
9
7
33
14
Pathogens
37
30
44
31
Outside Range from JasonH
4
4
7
7
Outside Range from Medivere
15
15
15
15
Outside Range from Metagenomics
6
6
8
8
Outside Range from MyBioma
7
7
7
7
Outside Range from Nirvana/CosmosId
18
18
23
23
Outside Range from XenoGene
5
5
7
7
Outside Lab Range (+/- 1.96SD)
14
9
6
8
Outside Box-Plot-Whiskers
41
58
38
33
Outside Kaltoft-Møldrup
211
100
123
111
Condition Est. Over 99%ile
0
0
0
0
Condition Est. Over 95%ile
4
3
1
1
Condition Est. Over 90%ile
9
6
5
7
Enzymes Over 99%ile
17
19
30
10
Enzymes Over 95%ile
105
82
219
68
Enzymes Over 90%ile
139
126
296
183
Enzymes Under 10%ile
783
369
514
645
Enzymes Under 5%ile
542
186
264
423
Enzymes Under 1%ile
271
37
49
86
Compounds Over 99%ile
33
28
62
47
Compounds Over 95%ile
140
127
231
254
Compounds Over 90%ile
346
307
298
338
Compounds Under 10%ile
310
227
297
308
Compounds Under 5%ile
211
111
224
173
Compounds Under 1%ile
132
47
67
65
The next table is also very dependent of Lab Read Quality. The apparent improvement on 12/3/2021 is likely artificial because the counts are low due to low read quality.
8/31/2021
12/3/2021
3/25/2022
8/11/2022
Percentile
Genus
Genus
Genus
Genus
0 – 9
73
24
51
51
10-19
15
18
32
24
20 – 29
12
13
18
12
30 – 39
4
10
9
14
40 – 49
6
8
9
3
50 – 59
4
8
7
2
60 – 69
4
4
9
3
70 – 79
7
10
7
10
80 – 89
7
4
8
5
90 – 99
14
18
8
8
8/31/2021
12/3/2021
3/25/2022
8/11/2022
Percentile
Species
Species
Species
Species
0 – 9
87
29
57
58
10-19
24
21
29
24
20 – 29
14
15
21
16
30 – 39
10
16
14
14
40 – 49
2
6
14
3
50 – 59
12
9
17
10
60 – 69
9
10
10
7
70 – 79
8
9
14
7
80 – 89
7
15
5
4
90 – 99
11
13
10
9
So how to interpret this wall of numbers? People can cherry-pick the numbers to say improvement or no improvement. The difference of lab read quality is a big factor because they impact the count for most of the items above. The Outside Box-Plot-Whiskers numbers show continued improvement. In short, the changes shown were less than I was hoping to see.
There is one more method of comparison — using special studies. In this case we see the average matches. Doing a little math, the expected drop of percentage due to lab quality size between 8/31/2021 and 8/11/2022 is a 10% drop. Those that exceeded 20% are color with 😊 below. Nothing became 10% worse. Note that the 😊 also agrees with comparing to 3/25/2022 (the prior sample). Other items remained unchanged. Items reported by this person are 😧 – Strong issue, 😟 – a bit of an issue
What is my conclusion? Most of the measures above deteriorates into noise with the exception of data from Special Studies, where we seen improvement in many measures, but not all. In one real statistical sense this makes sense: many are based on common sense and the ones showing clear improvement on statistical significance.
Going Forward
For most of my prior posts used the logical reasoning and clinical studies (which used different labs and software than the samples that I was looking at). With the special studies, we have upped our game (potentially) – the bacteria deemed significant were determined by the same lab and software of our sample, plus the study sizes was much larger than published clinical studies — hence better detection.
To build the consensus I will use the special studies, I filtered to reported issues and high percentage of matches, namely:
Remember that most of the special studies found that infrequent bacteria with a low value was what was statistically significant. This is turning the usual logic on it’s head. As I state, this is all experimental but based on studies and statistics.
In terms of generic suggestions, rifaximin (antibiotic)s is by far the top antibiotics, cited here on Health Rising: Rifaxamin – citing use by Dr. Teitelbaum, Dr. Peterson, De De Meirleir and Dr. Myhill (all ME/CFS specialists).
In short, all of the top suggested antibiotics are applicable. My personal approach would be do all three of them in a pulse manner a la Jadin, 10 days on, 20 days off and then move to the next one.
The top probiotics list have the usual dilemma: both e.coli probiotics and lactobacillus probiotics. It’s a dilemma because they tend to be hostile to each other. My typical rotation resolution would be 2 weeks of each and then move to the next:
The KEGG suggestions top items were the bacteria found in Equilibrium and Prescription Assist, except for the top choice, Escherichia coli. A probiotic suggested by Dr. Myhill, a ME/CFS specialist in the UK. The next common conventional items are
Bottom Line
The suggestions above were done solely from special studies. The key question is are they reasonable? I would say yes based on the antibiotics suggestions — all of them have been reported to help ME/CFS patients. We also have agreement between KEGG probiotics and these suggestions.
There is a potential conceptual symmetry between the two approaches (working off extremes and using special studies that are often dealing with rare low bacteria). Bacteria influences each other in very complex ways.
I am hoping this will be a model for other Long COVID people to start the recovery process. This person used Biomesight. Those results allow the data from special studies to be used on his microbiome sample.
A word of warning, tests like GI-MAPS will not report on most of the bacteria found to be low in the Special Studies — you need much more detail reports!
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.
Backstory
COVID in February 2021. 37M at the time, athletic/fit. Crossfit x 3 a week, playing football weekly Only mild gastritis prior to COVID. No other health issues. Moderate severity Covid, lots of symptoms.
And then Long COVID and CFS/ME type of symptoms mostly fatigue, PEM and GI problems (pain, food intolerance, bloating..etc) I’d say it’s a moderate/mild case of CFS/ME. But after 18 months still not back to previous levels, can’t walk too long otherwise i crash. I’d say i am around 75%.
High Level Overview
Looking at Health Indication, we find no significant medical conditions flagged (consistent with prior life style). There is one bacteria of potential concern: Prevotella copri, accounting for a whooping 56% of the microbiome! It is interesting that this was also seen in another recent review, see CFS Patient after COVID using the Special Studies Results. In terms of Dr. Jason Hawrelak Recommendations – he’s at the 99.7%ile — extremely healthy!
imbalance with a lot of different low count bacteria
Using Special Studies
Interpreting the updated table shown below can get a little complicated (i.e. not naively simple) see Special Studies Percentage Matches for details
We are going to use the 7 items below – items matching his reported issues. In an independent study that I did, I found that the pattern dims over time as the microbiome evolves. His person is 20 months post-COVID.
COVID19 (Long Hauler)
Small intestinal bacterial overgrowth (SIBO)
ME/CFS with IBS
Inflammatory bowel disease
Post-exertional malaise
General Fatigue
Bloating
The Prevotella copri concerns me because it’s the mastodon in the room (bigger than an elephant, and a bit hairier!). This specific bacteria is NOT typical for long COVID, but I suspect many will find one or another tyrant to dominate in excess in the face of massive minority representation– hence check for high bacteria counts with high percentile. It was also high in the study cited above, CFS Patient after COVID using the Special Studies Results. I went thru the My Biome View to tag the ones that have a high percentile with with a large count. The purpose is to inhibit these, so they will not inhibit everything else.
The results were almost the opposite of the consensus below for B-Vitamins. It presents a dilemma, a choice that needs to be made. At the moment, I favor the working from the special studies approach (pending feedback from people who tried it). Conceptually, it is a more probable approach — incidentally, it is not the approach usually done (and those approaches, historically, have had very little success to date).
The Consensus
I did not want to toss in any more sets of suggestions. From the start we saw the dominate item in his microbiome was undergrowth of a multitude of bacteria and the domination of one — we have gotten what helped the weak and inhibits the strong.
I found the avoids to be an interesting combination, no red meat and no chicken (matching Reduced choline on the to take) .
We see that something like a B-Complex should be avoided. I discuss this issue more in the other blog post that I cited above.
Computed Probiotics from KEGG Enzymes
This produced a few items that are reasonably easy to get as single species probiotics. Remember, these are calculated by a totally different mechanism – using the genes of the bacteria in your microbiome and the genes in these bacteria. The top items were:
Although this is using an old algorithm that I have not updated, the list is below.
alpha-galactosidase (Enzyme) – Percentile: 11
Amylase (Enzyme) – Percentile: 8 – On Consensus: Take
beta-alanine – Percentile: 2 – On Consensus: Take
Glycine – Percentile: 4 – – On Consensus: Minor avoid
iron – Percentile: 7 – On Consensus: Take
L-Cysteine – Percentile: 3 – On Consensus: Major avoid
L-glutamine – Percentile: 14 – On Consensus: Major avoid
L-Histidine – Percentile: 12 – On Consensus: Take
L-methionine – Percentile: 10 – On Consensus: Major avoid
L-Serine – Percentile: 11 – On Consensus: Take
L-Threonine – Percentile: 16
magnesium – Percentile: 4 – On Consensus: Take
NADH – Percentile: 4
Selenocysteine – Percentile: 4
zinc – Percentile: 16 – On Consensus: Take
Remember we are dealing with fuzzy data, my usual rule is do positive stuff where there is universal agreement, avoid stuff that are negative or where there are contradictions (I do like playing dice with my health).
Bottom Line
Because of the special studies and this person using the appropriate lab, this was actually a simple analysis to do. The traditional analysis showed “nothing wrong”, a familiar restrain from medical professionals to Long COVID patients. Our special studies and distribution by percentile showed things are wrong. Having 56% of the bacteria being Prevotella copri is saying something is very wrong.
I often try to use analogy of human populations to explain what I see. In this case, we have dozens of small tribes battling each other allowing a dominating force to seize most of the space. There are many historic examples, often under the name of “Divide and Conquer”.
In this example, the high number of low representation bacteria we saw in the overview matched the high number of low number of bacteria we observed in our special studies.
After two months of trying the suggestions, I hope this reader will do a new sample to see how well things shift from these suggestions.
Questions
Q: “Excuse me if I’m missing something but is there any reason why we are focusing on only Commensals, Prevotella, why not on Probiotics at all? I understand it’s way above the range, and it’d like to keep it low ideally, but what about the rest of Microbiome?”
A1: First “the rest of the microbiome” issue – the obvious response is a simple “If it is not broken, don’t fix it”. The above analysis used over 100 different bacteria. Our focus is on the bacteria where there is significant statistically evidence that they are connected to Long COVID. The numbers above are general health. As cited above, with Dr. Jason Hawrelak General Health Recommendations you are better than 99.7% of people. There is a huge variation in recommended ranges coming from labs and specialists — who are you going to rely upon? I am a statistician and I follow the numbers (and the z-scores), in other words, not working off opinion based largely on treating people who do not have Long COVID. I am NOT focusing on commensals, I am focusing on what was shown to be statistically significant.
Comment:To answer your question, I’ve had lots of symptoms in the beginning, but for now only mild fatigue and PEM plus gut issues, so I’d say definite ME/CFS with IBS, some Rhinitis, Alcohol intolerance and Long hauling.
I have recently changed the display below to show the percentage of matched instead of just the number of bacteria matches (the number will appear if you hover over the link as a tool tip). The numbers may be prone to misinterpretation, hence this technical page.
The candidate bacteria comes from special studies — it is important to note that often these bacteria are rarely seen, so having a 100% match is effectively impossible. We also have the dilemma of a single sample versus a collection of samples.
The rule that I am using is simple, a match must:
Have the bacteria (if it is missing, it is not deemed a match)
The bacteria count must be either:
below the study mean – 3 standard deviations of the mean if the study found it to be a low mean value against the reference population
above the study mean + 3 standard deviations of the mean if the study found it to be a high mean value against the reference population
Naively, assuming a normal distribution, the odds of a single match is around 1%, so with 200 items to check, we would expect 1% for a random person.
You should NOT view these as predictive, for example both ME/CFS with IBS and ME/CFS without IBS are on the list with the same value!!! Instead, your existing condition(s) should be used to select only the ones that apply to you. You could arbitrarily do all of the high ones — I do have a concern about that approach, you are creating noise that may make suggestions less effective.
One last item is the quality of the read (i.e. how many bacteria was actually detected in the sample). Since we are dealing with rare bacteria, bacteria (that are actually there) may not be detected and thus you have a lower percentage match. So do not view the percentage as absolute. but relative to others in the sample.
This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of fatigue. Each person use their own subject definition of fatigue, thus we do not expect strong statistical associations (and do not get it!)
Bacteria Detected with z-score > 2.6: found 158 items, highest value was 5.3 (ME/CFS was 6.6)
Enzymes Detected with z-score > 2.6: found 410 items, highest value was 6.0 (ME/CFS was 4.5)
Compound Detected with z-score > 2.6: found 67 items, highest values was -4/4 (ME/CFS was 3.1)
So we have a weaker bacteria signature but stronger enzymes and compound signature than ME/CFS. Many people marking one will mark the other… so get your sodium chloride crystals out!
Interesting Significant Bacteria
All bacteria found significant had too low levels. The list of those with a z-score over 5 is small. Low Prevotella copri and Escherichia coli which appears on special studies on many co-morbid symptoms. The good news, is that there is work ongoing to produce a prevotella copri probiotic and several Escherichia coli probiotics are available.
We do see a few overgrowth These are seen only in some subsets.
Bacteria
Reference Mean
Study
Z-Score
Lactiplantibacillus pentosus (species)
114
22
5.3
Prevotella copri (species)
68098
21864
5.2
Gammaproteobacteria (class)
14382
5944
5.2
Veillonella (genus)
4022
2324
5.1
Escherichia coli (species)
829
196
5
Interesting Enzymes
Most enzymes found significant had too low levels. A few were higher (12 of 410), which are listed in a second table below
Unlike most of the special studies we have many compounds that are significant. I have listed the high and low in separate tables below. Spot checking most of these found no useful information. For Maltodextrin which becomes glucose would fit with fatigue — i.e. low sugar being produced.
Of the low items, the following appear to be available as supplements and potentially could help with fatigue
In looking at the suggestions below, remember we are using two very different models. Above we use KEGG data to identify what the bacteria are producing (the items going to the farmer’s market). Below, we use what has been reported to influence the population of the bacteria that we are too low in (i.e. “Fertilizer”)
This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does.
“Motility” is a term used to describe the contraction of the muscles that mix and propel contents in the gastrointestinal (GI) tract. [Src] thus it has similarity to constipation (See Special Studies: Constipation)
“An excess of intracolonic saturated long-chain fatty acids (SLCFAs) was associated with enhanced bowel motility in NMS rats. Heptadecanoic acid (C17:0) and stearic acid (C18:0), as the most abundant odd- and even-numbered carbon SLCFAs in the colon lumen, can promote rat colonic muscle contraction and increase stool frequency” [2018]
Study Populations:
Symptom
Reference
Study
Poor gut motility
1171
55
Bacteria Detected with z-score > 2.6: found 170 items, highest z-score value was 8.8
Enzymes Detected with z-score > 2.6: found 336 items, highest z-score value was 6.4
Compound Detected with z-score > 2.6: found No items
Interesting Significant Bacteria
All bacteria that was found significant are too low. This is a common pattern for most of the special studies and really challenge the internet myth of the cause being too many bad bacteria. One bacteria really stands out — and there is ongoing work on making this one bacteria, Prevotella copri , available as a probiotics!
Bacteria
Reference Mean
Study
Z-Score
Prevotella copri (species)
64568
5498
8.8
Sutterella stercoricanis (species)
3098
410
7.5
Prevotella paludivivens (species)
144
26
7.1
Prevotella (genus)
72220
20587
6.2
Alkalibacterium (genus)
102
21
6
Prevotellaceae (family)
79801
32549
5.2
Leptospiraceae (family)
67
24
5.2
Leptospira (genus)
67
24
5.2
Leptospirales (order)
67
24
5.2
Leptospira licerasiae (species)
67
24
5.2
Ruminiclostridium (genus)
1000
348
5.1
Phocaeicola sartorii (species)
807
375
5.1
“For example, abundances of Lactobacillus, Prevotella and Alistipes spp. are significantly decreased in patients with constipation ” [2018]
Interesting Enzymes
Most of the enzymes are too low, however a few are too high which is not the usual pattern seen in other of these special studies.
While poor gut motility is often assumed to be due too many of some bacteria, the evidence suggestions that not enough is the more likely cause. There appears to be no simple model or answer.
“The gut bacterium Prevotella copri (P. copri) has been shown to lower blood glucose levels in mice as well as in healthy humans, and is a promising candidate for a next generation probiotic aiming at prevention or treatment of obesity and type 2 diabetes” [2021]
Prevotella copri will hopefully be available as a probiotic in a few year. There are two natural sources for P.Copri : Beer and Sauerkraut [2020], which may be an experiment for those that are prone to poor gut motility..
“This species is more prevalent in non-Western populations likely due to its association with high fibre low fat diets” [2022]
“Across all ethnicities, only coffee consumption was associated with an increased Prevotella relative abundance ” [2022]
“Ancient stool samples suggest Westernization leads to P. copri underrepresentation” [2019]
The real bottom line is changing diet significantly. Consider some Indian style of food as part of supper every day, some examples ready to heat are here.
This is a devastating mental infection of many people suffering severe ongoing health issues. The mythology is simple “Fix the root cause, and you will get better!” For acute, send yourself to the hospital, diseases this may be true, but there is another class of conditions where it is false.
Example, you developed rickets and developed skeletal deformities such as:
Bowed legs or knock knees
Thickened wrists and ankles
Breastbone projection.
We know the root cause, not sufficient vitamin D. Will taking vitamin-D correct the skeletal deformities? No. Treatment will slow progression. You have lung cancer because you are a heavy smoker, will stop smoking cure lung cancer? You have Long COVID, ah the cure is to always wear a N95 mask?
Yes There was a Cause likely, but…
For items dealing with the microbiome, the cause starts a microbiome cascade that keeps going onwards. Think of a land slide, things are changed. There are side-effects like impact on fish or even getting into towns. So people start trying to cure the landslide by clearing the river or building a new road or…. and those attempt at curing, could cause more problems.
The best example that is well documented is the Bergen’s Giardia Infection. The root cause was Giardia infection. They eliminated the giardia — but the IBS, ME/CFS issues remained. They very well documented the root cause and dealt with it. No magical recovery.
Going Forward
I view many conditions as being supported (in a few cases totally caused) by the microbiome. Finding the root cause is very very unlikely to impact treatment and the way back to health. Focus on what is contributing to your current state and not ancient history!
I just banned someone called Ross Walter
Why? he has twice attacked me ad hominem (i.e. an attack on the person). I have made no secret that I am a high functioning ASD person (functioning in terms of mathematics), and that I did not learn to speak or form sentences until I was 9 y.o. I know that items like grammar are a great weakness. To attack a person with a recognized disability, for a disability is neither polite nor acceptable. I apologize if my grammar is not perfect — my blog is not intended to be a literary masterpiece, but to convey data!
This is a common symptom for both ME/CFS and Long COVID. This is reported often in samples uploaded. We examine if the data reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does.
My default view is that this symptom is likely due to vesicular constriction/inflammation or “sticky blood” (which contains many possible coagulation issues). This results in slow blood flow causing issues with transferring heat to and from the body.
temperature intolerance is generally placed under dysautonomia
On Change Microbiome Tab
Study Populations:
Symptom
Reference
Study
Intolerance of extremes of heat and cold
1159
54
Bacteria Detected with z-score > 2.6: found 247 items, highest value was 8.8
Enzymes Detected with z-score > 2.6: found 501 items, highest value was 6.7
Compound Detected with z-score > 2.6: found No items
The number of bacteria and enzymes that are significant hints that it is a complex scenario, possibly with different subsets.
Interesting Significant Bacteria
All bacteria found significant had too low levels. The dominant order is Anaeroplasmatales and three significant genus are Holdemanella, Eubacterium, and Escherichia. Every one of the 247 bacteria found significant was LOW.
Bacteria
Reference Mean
Study
Z-Score
Holdemanella biformis (species)
3370
350
8.8
Holdemanella (genus)
3379
350
8.8
Eubacterium (genus)
2507
586
7.8
Lysobacter deserti (species)
29
11
6.5
Anaerolineae (class)
87
35
5.7
Opitutae (class)
163
46
5.5
Puniceicoccaceae (family)
158
44
5.5
Legionellales (order)
82
41
5.4
Leptospiraceae (family)
67
22
5.4
Leptospira (genus)
67
22
5.4
Leptospirales (order)
67
22
5.4
Leptospira licerasiae (species)
67
22
5.4
Bifidobacterium cuniculi (species)
79
26
5.3
Lactiplantibacillus pentosus (species)
116
22
5.3
Puniceicoccales (order)
110
37
5.3
Legionellaceae (family)
81
41
5.2
Legionella (genus)
81
41
5.2
Anaeroplasmataceae (family)
8102
20
5.2
Anaeroplasma (genus)
8102
20
5.2
Anaeroplasmatales (order)
8102
20
5.2
Chloroflexi (phylum)
128
55
5.1
Cerasicoccus arenae (species)
537
80
5.1
Caldilineaceae (family)
90
35
5.1
Caldilinea (genus)
90
35
5.1
Caldilineales (order)
90
35
5.1
Caldilinea tarbellica (species)
90
35
5.1
Caldilineae (class)
90
35
5.1
Escherichia (genus)
6016
1367
5.1
Cerasicoccus (genus)
312
60
5
Interesting Enzymes
Atypical distribution for enzymes in these studies, the number of highs and lows were of the same magnitude. 268 enzymes were low and 233 enzymes were high (see 2nd table) but none above our listing threshold of 5.0.
Temperature intolerance is not an independently study topic. Existing studies are usually in the context of some other condition. This suggests that this is the first study on it’s microbiome.
“Diabetes is often associated with orthostatic hypotension and temperatureintolerance.” [2005]
The suggestions below has some surprises on the to avoid list: Vitamins B-3, B-12, Curcumin, N-Acetyl Cysteine (NAC) sitting high in the list with antibiotics. The absence of probiotics in the to take suggestions is note worthy for those that view probiotics as ‘cure all’. Not listed, but conceptually worth considering, are the E.Coli probiotics (Symbioflor-2 or Mutaflor).
Recently I have see pea appear often and I recall that peas were served with most meals as a child. Peas has largely disappear from the western menu. I am starting to wonder if having pea soup 3 times a week would help a lot of people.
This is a common symptom for many people. This is reported often in samples, and thus being examined if it reaches our threshold for inclusion as defined in A new specialized selection of suggestions links. It does. We are not being specific about the type of constipation.
Study Populations:
We have 2 symptom annotations that could be included
Comorbid: Constipation and diarrhea (not explosions) – 29 only
Comorbid: Constipation and Explosions (not diarrhea) – 11 only
Immune Manifestations: Constipation – 9.7 max z-score (70)
All of the above Constipation – 9.9 max z-score (83)
Taking all together we get 83 samples with an max z-score of 9.9
Symptom
Reference
Study
Constipation
1123
83
Bacteria Detected with z-score > 2.6: found 123 items, highest value was 9.9
Enzymes Detected with z-score > 2.6: found 511 items, highest value was 5.2
Compound Detected with z-score > 2.6: found No items
Clearly some bacteria have strong associations, but the number of enzymes that are significant suggests that they may be more dimensions to the issue.
Previous studies have shown that the gut microbiota of constipated patients differs from healthy controls; however, many discrepancies exist in the findings, and no clear link has been confirmed between chronic constipation and changes in the gut microbiota.
Unusually bacteria was found significant with both high (13) and low (110). This far exceed the count expected as the false detection rate, so we should include and cite them.
Bacteria
Reference Mean
Study
Z-Score
Prevotella copri (species)
67087
7150
9.9
Prevotella (genus)
74786
17755
8
Prevotellaceae (family)
82267
29219
6.8
Veillonella (genus)
4003
2102
5
Lactiplantibacillus pentosus (species)
117
26
5
UNLIKE most of the other studies, we had a significant number of too many bacteria (far more than expected with a False Detection Rate).
Bacteria
Reference Mean
Study
Z-Score
Tannerellaceae (family)
24592
34890
-3.6
Parabacteroides (genus)
24587
34665
-3.6
Porphyromonadaceae (family)
26584
36605
-3.4
Bacteroides uniformis (species)
25682
42170
-3.2
Bacteroidaceae (family)
281711
337344
-3.1
Desulfosporosinus auripigmenti (species)
18
28
-3
Hathewaya histolytica (species)
2607
4093
-2.8
Bacteroides (genus)
232904
276348
-2.8
Oscillospira (genus)
22836
28653
-2.8
Anaerotruncus colihominis (species)
1739
2436
-2.8
Anaerotruncus (genus)
1834
2520
-2.7
Hathewaya (genus)
2618
4058
-2.7
Parabacteroides merdae (species)
7249
11459
-2.6
There is some agreement with studies on these findings, but as cited above — results are not consistent in studies.
While constipation is often assumed to be due too many of some bacteria, the evidence suggestions that not enough is the more likely cause. There appears to be no simple model or answer.
Prevotella copri will hopefully be available as a probiotic in a few year. There are two natural sources for P.Copri : Beer and Sauerkraut [2020], which may be an experiment for those that are prone to constipation..
Looking at the suggestions — Constipation caused by Antibiotics!
This is not a “new discovery” — rather it appears to confirm that the mathematic model being used is reasonable and thus Dr. Artificial Intelligence suggestions are reasonable!
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