A reader with Multiple Chemistry Sensitivity(MCS) read my Light Sensitivity Exploration post and asked me to look at her sample because her MCS has been getting worse and she is hoping to slow and ideally reverse it. She does not want to become an anchorite with complete isolation from people. On her symptom list it is:
Comorbid: Multiple Chemical Sensitivity
Mast Cell Activation Syndrome (Next Post)
Looking at Comorbidity from our contributed data
14% of people with Photo Sensitivity have MCS, but 89% have MCAS/Histamine issues
17% of people with MCAS/Histamine issues have MCS, but 51% have Photo Sensitivity
Photo Sensitivity
MCS
MCAS
Photo Sensitivity
427
60
383
MCS
238
135
MCAS
753
I am going to skip the explorations that I did in the earlier post. As with prior post, Odds Ratio has better fine level identification.
Classic
Odds Ratio
Bacteria Considered
85
103
Bacteria In Common
17
15
Species
6
37
Genus
16
35
Family
24
13
Order
17
10
Class
10
4
Since Light Sensitivity and MCS tends to go hand in hand, I did a comparison of the net Log(odd ratio) between people. A person without these issues is expected to have a Log(Odds Ratio) < 0. This Post’s anchorite has moderate light sensitivity in reality.
Person
Light Sensitivity
MCS
Last Post Person
11.8
17.3
Anchorite
5.7
16.4
This feature is now available on the web site for samples from Biomesight, Ombre, Thorne and uBiome. How to get to it and use it is shown below.
Probiotics Suggestions for MCS
The full list is below (remember only probiotic bacteria reported by Biomesight are included). The list for the Last Post person for MCS was very similar.
Tax_name
Impact
Bifidobacterium longum
2.22
Bifidobacterium adolescentis
1.92
Enterococcus faecalis
1.89
Bifidobacterium breve
1.83
Clostridium butyricum
1.78
Faecalibacterium prausnitzii
1.73
Streptococcus thermophilus
1.54
Bifidobacterium bifidum
0.89
Bifidobacterium catenulatum
0.8
Ruminobacter amylophilus
0.55
Bifidobacterium animalis
0.51
Bifidobacterium pseudocatenulatum
0.51
Enterococcus durans
0.47
Lactobacillus johnsonii
0.4
Leuconostoc mesenteroides
0.33
Roseburia faecis
0.32
Veillonella atypica
0.23
Lacticaseibacillus paracasei
0.2
Phocaeicola coprophilus
0.18
Bacillus velezensis
0.08
Lactobacillus acidophilus
-0.05
Bacteroides thetaiotaomicron
-0.23
Bacteroides uniformis
-0.3
Limosilactobacillus reuteri
-0.3
Bacillus amyloliquefaciens group
-0.3
Lentilactobacillus parakefiri
-0.31
Phocaeicola dorei
-0.32
Bacillus subtilis group
-0.32
Limosilactobacillus vaginalis
-0.32
Enterococcus faecium
-0.33
Bacillus subtilis
-0.7
Lactobacillus helveticus
-0.85
Lactobacillus jensenii
-0.85
Pediococcus acidilactici
-1.05
I should note that Pediococcus acidilactici is a high take for Light Sensitivity and a take for MCS for the light sensitive person. It is a to be avoided for the Anchorite in both cases. This goes back to the old saying “No probiotics can serve two people with the same symptoms” (Matt 6:24, Microbiome Translation).
Take Suggestions
These match the general pattern seen for Long COVID and ME/CFS
Modifier
Net
Take
Avoid
(2->1)-beta-D-fructofuranan {Inulin}
133
137
4
dietary fiber
82
106
25
oligosaccharides {oligosaccharides}
78
90
12
Slow digestible carbohydrates. {Low Glycemic}
77
106
29
Fiber, total dietary
69
91
21
fruit
60
80
21
Lactobacillus plantarum {L. plantarum}
50
67
18
fruit/legume fibre
48
67
19
fructo-oligosaccharides
48
51
3
synthetic disaccharide derivative of lactose {Lactulose}
46
48
2
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
38
46
8
Cichorium intybus {Chicory}
36
39
3
wheat
35
40
6
Hordeum vulgare {Barley}
34
44
11
whole-grain diet
33
46
13
ß-glucan {Beta-Glucan}
33
38
5
High-fibre diet {Whole food diet}
32
48
16
Bovine Milk Products {Dairy}
32
46
13
resistant starch
32
40
8
Avoid Suggestions
We have a few herbs or spices showing up as an avoid. When we look at MCAS, we see a very atypical avoid list.
Nitrogen Oxide x Particulate Matter {Urban air pollutant}
-6
2
8
High-protein diet {Atkins low-carbohydrate diet}
-6
4
10
vegetarians
-6
4
10
low fodmap diet
-6
8
15
Azadirachta indica {Neem}
-4
0
4
Silver nanoparticles {Colloidal silver}
-4
0
4
Summary
The new offering is easy to use, just follow the video above. Remember, most symptoms are caused by combinations of bacteria that alters the metabolites (chemicals) that the body gets. There are many distinct combinations that can produce a symptom. Above is NOT a general guidance, it shows the results for a specific person using their microbiome. The suggestions for your microbiome may be different. Testing is not optional if you want to make progress.
My daughter’s light sensitivity is now so bad, she’s screaming in pain at daylight and won’t let her flatmate put up the blinds! Of course it’s related to her autism. Now we’ve uploaded her new sample, is there anything implicated in her current dysbiosis that might lessen this? She is tormented by this..
I believe we just have enough data to get some traction. I will first use the new Odds Ratio because it give an objective measurement of the importance of each bacteria. Second, I will use the older methodology to simply get a second opinion of which bacteria (unfortunately, this does not indicate importance of each bacteria).
There are three symptom choices related. The difference in count is a reflection of when the symptom was added (the earliest one had the highest count).
DePaul University Fatigue Questionnaire : Abnormal sensitivity to light 259 samples
Other:Light sensitivity (photophobia) 5
The sample above was done using biomesight and we have 148 different bacteria using Odds that are statistically significant for increasing or reducing the odds.
The Odds of her having light sensitivity is quite high: log(Odds)=11.8,
These notes document ongoing work on this issue. The goal is both to address her request and to deepen our understanding of how the MP classic method compares to the newer Odds Ratio approach. The MP classic method has produced good results so far, and Odds Ratios may further improve them. For details on how Odds Ratios are calculated, see this related post: Odds Ratio for the Microbiome 101.
In subsequent posts I will look at two symptoms that are very often seen with light sensisitivy:
Multiple Chemical Sensitivity
Mast Cell Activation Syndrome
Comparison of “MP Classic” and Odds Ratio Algorithms
Across all symptoms, using Biomesight data, we see consistent patterns in which bacterial levels are involved. The Odds Ratio analysis focuses on more specific bacterial taxa and is therefore more targeted. For example, instead of simply indicating low Lactobacillus, the Odds Ratio can highlight a particular species such as Lactobacillus reuteri. This higher resolution enables more precise selection of probiotics.
Taxonomy Rank
MP Classic
Odds Ratio
Species
1727
13541
Genus
5130
10040
Family
8463
6158
Order
5860
3269
Class
3663
1437
Overview of all Samples
The list of bacteria that DOUBLES or more the odds when present in larger amounts
Bacteria
Rank
Odds Ratio
Salidesulfovibrio
genus
5.9
Salidesulfovibrio brasiliensis
species
5.9
Ethanoligenens
genus
4.9
Peptoniphilus lacrimalis
species
4.3
Slackia faecicanis
species
4.2
Collinsella tanakaei
species
3.8
Finegoldia magna
species
3.5
Viviparoidea
superfamily
3.5
Architaenioglossa
order
3.5
Rivularia
genus
3.5
Viviparidae
family
3.5
Rivularia atra
species
3.5
Rivularia
genus
3.5
Finegoldia
genus
3.4
Lysobacter
genus
3.4
Desulfovibrio fairfieldensis
species
3.3
Aerococcaceae
family
3.3
Anaerococcus
genus
3.2
Streptococcus anginosus
species
3.1
Luteolibacter
genus
3
Luteolibacter algae
species
3
Anaerotruncus colihominis
species
3
Odoribacter denticanis
species
3
Filifactor
genus
2.8
Lactobacillus gallinarum
species
2.8
Peptoniphilus asaccharolyticus
species
2.8
Selenomonas infelix
species
2.7
Corynebacterium striatum
species
2.7
Adlercreutzia equolifaciens
species
2.6
Streptococcus anginosus group
species group
2.6
Glutamicibacter soli
species
2.6
Anaerotruncus
genus
2.5
Rubritaleaceae
family
2.5
Rubritalea
genus
2.5
Gardnerella
genus
2.4
Oscillatoriales
order
2.3
Amedibacillus dolichus
species
2.3
Amedibacillus
genus
2.3
Glutamicibacter
genus
2.2
Anaerococcus prevotii
species
2.2
Azospirillum palatum
species
2.2
Eggerthella sinensis
species
2.2
Sphingomonas abaci
species
2.2
Alcanivorax
genus
2.1
Alcanivoracaceae
family
2.1
Haploplasma
genus
2.1
Haploplasma cavigenitalium
species
2.1
Isoalcanivorax
genus
2.1
Isoalcanivorax indicus
species
2.1
Oscillatoriaceae
family
2.1
Selenomonadales
order
2.1
Nisaea nitritireducens
species
2.1
Anaerococcus tetradius
species
2.1
Selenomonadaceae
family
2.1
Lactobacillus acidophilus
species
2.1
Anaerococcus lactolyticus
species
2.1
On the other end, the bacteria that reduces the odds when present in higher amounts are:
Propionibacteriales
order
0.1
Dyadobacter
genus
0.3
Herbaspirillum magnetovibrio
species
0.3
Calditrichia
class
0.4
Calditrichales
order
0.4
Calditrichaceae
family
0.4
Caldithrix
genus
0.4
Calditrichota
phylum
0.4
Desulfitobacteriaceae
family
0.4
Bifidobacterium adolescentis
species
0.4
Bifidobacterium longum
species
0.4
In terms of probiotics, we see some quick observations: good and bad.
Two Lactobacillus probiotics significantly increases the odds — i.e. AVOID, especially yogurts!
Two Bifidobacterium species (and the genus as a whole) significantly decreases the odds — TAKE A LARGER DOSAGE.
Looking at this specific sample
We found no lactobacillus at all, and Bifidobacterium adolescentis is too low. Bifidobacterium longum was found but the amount was significant for reducing the risk.
Getting best probiotics via modelling
This is done using the Correlation Coefficient between bacteria from the R2 site (using the lab specific numbers). We focused solely on the bacteria that increased the odds significantly, and then compute the probiotics (based on only the species what Biomesight reports) that will shift them in the right direction.
Tax_name
Impact
Pediococcus acidilactici
4.28
Bacillus amyloliquefaciens group
3.89
Limosilactobacillus vaginalis
2.95
Bifidobacterium
2.5
Enterococcus faecalis
1.73
Bifidobacterium pseudocatenulatum
1.6
Leuconostoc mesenteroides
1.6
Heyndrickxia coagulans (bacillus coagulans)
1.53
Bifidobacterium longum
1.49
Clostridium butyricum
1.46
Lacticaseibacillus paracasei
1.35
Lactococcus lactis
1.33
Bifidobacterium breve
1.28
Lactobacillus helveticus
1.27
Enterococcus faecium
1.24
Bacillus subtilis group
1.16
Lactiplantibacillus plantarum
1.08
Bifidobacterium bifidum
0.96
Bifidobacterium adolescentis
0.84
Taking these same bacteria using the odds ratios and our usual suggestions engine, we get the following as the top suggestions.
Modifier
Net
Take
Avoid
Slow digestible carbohydrates. {Low Glycemic}
37
52
16
dietary fiber
29
45
16
Fiber, total dietary
24
38
14
fruit
22
34
12
fruit/legume fibre
20
32
12
(2->1)-beta-D-fructofuranan {Inulin}
20
23
3
High-fibre diet {Whole food diet}
19
32
13
oligosaccharides {oligosaccharides}
19
26
6
whole-grain diet
18
25
7
Lactobacillus plantarum {L. plantarum}
17
29
12
bifidobacterium
15
16
1
wheat
12
14
2
The Avoids. I noticed that Bofutsushosan is an avoid. This is a promoter of Akkermansia — which was on our avoid probiotics list. There appears to be reasonable consistency although we are using two different sources and mechanism to get these suggestions.
Modifier
Net
Take
Avoid
high-fat diets
-8
3
11
Ganoderma sichuanense {Reishi Mushroom}
-5
1
6
Pulvis ledebouriellae compositae {Bofutsushosan}
-4
0
5
2-aminoacetic acid {glycine}
-4
0
4
Bacteriophages LH01,T4D,LL12,LL5 {PreforPro}
-4
0
4
laminaria hyperborea {Cuvie}
-4
0
4
low protein diet
-4
1
6
D-glucose {Glucose}
-4
1
6
Ferrum {Iron Supplements}
-4
1
5
Ulmus rubra {slippery elm}
-4
2
6
Honey {Honey }
-4
2
6
Going Old School Suggestions
This is done the usual way but we temporarily clear all of the symptomsand then just marked this single symptom. We are wanting to focus solely on this one horrible symptom.
Clicking on this one symptom, we then get 10 bacteria associated
And also suggestions. I note some agreements between the methods:
Disagreement: Bifidobacterium Longum – this gets interesting because the Odds Ratio indicate that the amount of Bifidobacterium Longum present was sufficient to reduce the odds to below 1.0
Summary
I generally favor a consensus of recommendations as the safest course. In this case, my impression is that using Odds Ratios leads to better identification of the bacteria involved (10 versus 24 for this sample), with the added benefit of indicating the relative importance of each bacterium. With Odds Ratios, the thresholds for being too high or too low are symptom-specific, rather than some magical universal cutoff that applies to all conditions.
Believing that there is one magic reference range for any bacteria is simply naive and ignoring the data.
I need to do some more refining of the code as well as enhancement to handle multiple symptoms concurrently; in time, this will be added to the sight.
Using Odds Ratio is now available on the site. The video below shows how to access it.
Technical Notes
Doing a low level comparison between the “classic forecast method” and the “Odds Ratio method I generated the table below. The Odds Ratio identified bacteria at a much more at a finer level (species) and most people would interpret that as being more targeted and likely better outcomes.
Measure
Classic
Odds Ratio
Bacteria Considered
115
148
Bacteria in common
20
20
Species
8
57
Genus
22
51
Family
33
21
Order
23
10
Class
14
3
This also implies that only Genus and Species should be considered with Odds Ratio. Statistically this is preferred to reduce the amount of double counting.
Revisiting Suggestions using only Genus and Species with Odds Ratio
The R2 Probiotics are similar. Most probiotics are more challenging to obtain — see this page for known sources. The avoids are:
Lactobacillus johnsonii
Akkermansia muciniphila
Bacillus subtilis
Note: Pediococcus acidilactici and L.Plantarum (positive) mixtures is likely the easiest to obtain.
By Kenneth Lassesen, B.Sc.(Statistics), M.Sc.(Operations Research)
Odds Ratio and Chi2 are two sides of the same coin. The worth of this coin is far more than the fourréesseen with studies using averages.
The simplest case is how often is a specific bacteria reported with the control versus study groups. This is easy computed and can be placed in a table such as the one below
Control (without Symptom)
Study (or with Symptom)
Bacteria Seen
300
90
Bacteria Not Seen
600
700
Just looking at the table, it is obvious that this bacteria is less likely to be seen in a study group. We can just drop these numbers in a page like this one, and get the results.
Converting to odds ratio is simple:
Compute odds for study group: 90300=3.333.
Compute odds for control group: 700600≈0.857.
Odds ratio: that seeing this bacteria put you likely not in the study group
Or 1/3.89 = 0.257 if seeing this bacteria, places you in the study group
Second Tier: The amount
This is identical to the above, except there is a little mathematics needed to compute the best range of bacteria for odds ratio.
At 0.04%
Control (without Symptom)
Study (or with Symptom)
Above or Equal
100
60
Below
200
30
Again a simple computation with great statistical significant.
And again the Odds Ratio is calculated the same as above.
100/60 = 1.66
200/30 = 6.66
OR = 1.66 / 6.66 = 0.25 (or 4.00 for the reverse.
We have a tri-state odds ratio
Bacteria not seen: 0.257 of having symptom (i.e. bacteria is rarely seen with symptom)
Bacteria see but above or equals to 0.04%: 3.89 * 4 =15.56
Bacteria see but below 0.04%: 3.89 * .25 = 0.9725, almost no effect.
In this example, we used above or below 0.04%; we could have also used in the range (0.03 to 0.07) or not in the range.
Key points
Use only bacteria with P < 0.001 or better
Check Present or not Present
There is a finite enumeration of possible ranges when a bacteria present.
With today’s powerful computers, this is not a challenge
Check all bacteria that satisfies the minimum size constraint for the function used for the 2×2 table
For some symptoms we have:
over 450 bacteria with significant odds ratios for some conditions.
Highest Odds ratio over 92 for some bacteria
Performance
This data is based on self-declared symptoms from users. Often the symptoms entered are incomplete (some users had over 100 symptoms entered). While not rigorous, this appears to work for getting sample annotations entered in a citizen science context and for demonstration of the concept. There was enough consistency of data to get results.
The best news: The following had the Odds Ratio > 1.0, over a dozen in the sampling and agreement with entered symptoms.
Source
SymptomName
Accurate %
BiomeSight
Official Diagnosis: Mood Disorders
100
Thryve
DePaul University Fatigue Questionnaire : Frequently get words or numbers in the wrong order
100
Thryve
Autism: More Repetitive Movements
100
Thryve
Autonomic Manifestations: cardiac arrhythmias
100
Thryve
Condition: Acne
100
Thryve
DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness
100
Thryve
DePaul University Fatigue Questionnaire : Feeling like you have a temperature
100
Thryve
Official Diagnosis: Diabetes Type 1
100
Thryve
Neurological: Spatial instability and disorientation
Looking at the biggest sets. we see very good performance for some symptoms and poor performance for items like gender. Unrefreshing Sleep is interesting:
Unrefreshed sleep: 88.6% accurate
Unrefreshing Sleep, that is waking up feeling tired: 36.7% accurate
Is the cause, the fineness of definition (and lack of clarity by users entering) or some other issues?
Source
Symptom
% Correct
Size
BiomeSight
General: Fatigue
98.70317
694
BiomeSight
Neurocognitive: Brain Fog
98.18182
660
BiomeSight
Sleep: Unrefreshed sleep
88.57616
604
BiomeSight
Neurocognitive: Difficulty paying attention for a long period of time
75.54113
462
BiomeSight
Immune Manifestations: Bloating
90.13761
436
BiomeSight
DePaul University Fatigue Questionnaire : Fatigue
85.96491
399
BiomeSight
Gender: Male
59.79644
393
BiomeSight
Comorbid: Histamine or Mast Cell issues
88.0102
392
BiomeSight
Official Diagnosis: COVID19 (Long Hauler)
97.87798
377
BiomeSight
DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired
36.66667
360
BiomeSight
Neurocognitive: Can only focus on one thing at a time
63.76404
356
BiomeSight
Neuroendocrine Manifestations: worsening of symptoms with stress.
70.26239
343
BiomeSight
Neurological-Audio: Tinnitus (ringing in ear)
60.71429
336
BiomeSight
Neurocognitive: Problems remembering things
47.00599
334
BiomeSight
Age: 30-40
97.14286
315
BiomeSight
DePaul University Fatigue Questionnaire : Post-exertional malaise, feeling worse after doing activities that require either physical or mental exertion
92.33227
313
BiomeSight
Neurocognitive: Absent-mindedness or forgetfulness
62.7907
301
BiomeSight
Sleep: Daytime drowsiness
69.33333
300
BiomeSight
Post-exertional malaise: General
85.95318
299
BiomeSight
Immune Manifestations: Constipation
83.22148
298
Lab Performance
Identification by Age exhibits the reality of all labs are not equal. If Odds Ratios from the microbiome was not statistically significant for estimating age, we would see 14% for accuracy. We far exceed that.
Lab
Symptom
Accuracy
Size
BiomeSight
Age: 0-10
86.2
29
Ombre
Age: 0-10
76.3
59
BiomeSight
Age: 10-20
80
25
Ombre
Age: 10-20
94.7
19
BiomeSight
Age: 20-30
58.5
135
Ombre
Age: 20-30
64.7
34
BiomeSight
Age: 30-40
97.1
315
Ombre
Age: 30-40
66.3
104
BiomeSight
Age: 40-50
22.2
203
Ombre
Age: 40-50
71.4
63
BiomeSight
Age: 50-60
29.7
111
Ombre
Age: 50-60
61.7
47
BiomeSight
Age: 60-70
52.5
59
Ombre
Age: 60-70
18.1
83
BiomeSight
Age: 70-80
90
20
This difference of labs is seen with other symptoms — some of which has associations reported in the literature.
Source
SymptomName
Ratio
Size
BiomeSight
General: Depression
67.7
195
Ombre
General: Depression
13.9
108
BiomeSight
General: Fatigue
98.7
694
Ombre
General: Fatigue
20.8
149
BiomeSight
General: Headaches
71.6
197
Ombre
General: Headaches
15.5
103
Summary
The use of odds ratios provides statistically significant evidence for identifying probable symptoms. While not definitive—acknowledging that few diagnostic tests achieve complete certainty—the results demonstrate that both the selected testing method and its interpretation (for example, in relation to bacterial associations) materially influence diagnostic accuracy.
In clinical contexts, reliance on odds ratios offers greater methodological rigor than studies reporting merely “higher or lower levels of certain bacteria with .” A notable clinical strength of this approach lies in its capacity to generate a structured list of potential symptoms for further inquiry, including those that patients may not have initially disclosed.
Nota Bene: It should be noted that the observed error rate is likely attributable, at least in part, to underreporting of symptoms. Patients often disclose only the symptoms they perceive as most severe, thereby introducing reporting bias into the dataset.
The table below shows the accuracy from 4 different labs. It is not a surprise that Shotgun data is more accurate than 16s tests.
I just pushed a new feature that is shown in the video below
The differences of the two methods are:
Old method looks at the level of bacteria that you have only. This is ideal for making suggestions because we want to alter what is there.
New, Odds Ratio method, looks at which bacteria were found (or missing) and their levels. Addressing missing bacteria is not trivial — unless it is a known probiotics species (99% are not)
Odds Ratio is likely more accurate because it considers what is there, the amount and what is missing. It is not an ideal choice for computing suggestions.
Remember Odds Ratios do not say you will have a symptom, it merely indicates increased odds of having the symptom.
Example below of two samples taken 5 years apart. Earlier sample, patient was 68, later sample 73. In general reflects well the symptoms reported.
For many months, using R2 Associations to select probiotics used a generic database from PrecisionBiome. Over the year end holidays, I computed the R2 Associations based on data from specific labs. This is far more accurate and have just been added for the following labs:
Biomesight (best because biggest dataset)
Ombre
uBiome
Thorne (smallest dataset and not as much data).
At the bottom of the suggestion page you will see a new section like below:
The range of numbers can vary greatly.
Which is best? We have multiple ways of computing probiotics. The ideal would be to do only ones that each way advocates. When there are Good and Bad counts, having a positive good and zero for bad is ideal.
The reality is that this rarely happens. I tend to favor Good count much bigger than Bad with a high positive benefit. Our knowledge is sparse and often studies results fail to duplicate. I tend to favor this method because it is an Fuji apple to Fuji apple comparison instead of the Crab Apple to Watermelon comparison that published studies tend to be.
A reader forwarded a Bulgaria supplier site to me. I was delighted to see their offerings!
Probiotics availability is a complex area with national laws restricting access. A good example is the US: if you are not producing a grandfathered species then there is a massive amount of testing to get approved for sale. A good example is Mutaflor, E.Coli Nissle 1917, which cannot be sold in the US despite a literal century of safe use in Europe.
This is further complicated because a probiotic claiming to be a specific species may be tested and depending on the test used be found to not be there, a different species or as claimed. There is no standardization of microbiome testing See this post for the background.
In most of Western Countries, there is a huge profit margin for probiotics, 10x or 20x the cost of production is not unusual. Often manufacturers will often prevent the import of foreign probiotics citing safety or lack of “in country safety tests”. Some people may find that they cannot import those below.
Bottom line: I take claims of species in a probiotic on face value. See bottom on selecting probiotics given a microbiome sample; most of these have very few clinical studies in English. I will be adding these to the probiotic search page over the next week.
The predicted / model impact of each probiotic above can be estimated from this page.
Over the next week, I will attempt to add modelled impact on each of these combinations on a microbiome sample using the link below on the suggestions page.
Today, I got this message on Facebook from some one who got relief for Anhedonia from advice on Microbiome Prescription.
Anhedonia is the diminished ability or loss of interest in experiencing pleasure from activities once enjoyed, feeling emotionally flat, numb, or empty, and it’s a core symptom of depression, schizophrenia, substance use disorders, PTSD, and Parkinson’s
Hi Ken, I’ve taken Enterococcus Faecium Probiotic Powder (Dopamine Support) from Bulkprobiotics for 2 weeks and it has made me laugh at things (which I’ve been unable to do since having anhedonia) but when I stopped the laughter also stopped. How can I make this probiotic stay more permanently in my system? What do I feed it?
To answer that question, we need to look at literally four paths of suggestions. This pattern applies to mono-bacteria requests. The paths are:
Use the association “R2” site based on data from PrecisionBiome.eu to see if any probiotics are known to increase /feed/ it.
Use the associations based on data from Biomesight.com to see if any probiotics are known to increase /feed/ it. This sits on my local server only at present. It’s a fat database.
Doing a search, we link to this page. And then need to sort by “Probiotic” and then “R2” to see which probiotics have the greatest impact. We double click the “R2” to have the highest values first.
Not all of these probiotics are currently available retail in every country. The most likely to be available are listed on this page. Search by name for providers:
Lactococcus cremoris: Not available in isolation. Swedish Filmjölk (SE) / Filmjölk would be my first choice.
Search for bacteria here, takes us to this page. Here we are not dealing solely with probiotics but everything. Clicking on effect to see what has the most evidence/studies, we see most studies report on items that decrease, i.e. your avoids.
Putting “increases” in the search box, we then see this list of items (I leave the rest to you)
Lastly, we click on probiotics to see that list. NOTE: PubMed does not evaluate relative impact (R2 does). We are measuring only the confidence (via number of studies) that it will do some impact.
This a little more obtuse, we are looking for supplements that may be consumed by this bacteria. To get there, you need to login and then change display level to Advance.
Modify your diet to remove the foods with significant evidence of inhibiting Enterococcus Faecium
Bonus
I took your last sample through the condition Odds Ratio algorithm that I am working on with the following results. I was surprised/delighted that every positive prediction agreed with your reported symptoms. In time, this will evolve into better suggestions because of better identification of the key bacteria involved.
SymptomName
Strength
HasSymptom
Immune Manifestations: Bloating
26.7
1
Sleep: Unrefreshed sleep
25.1
1
Comorbid: High Anxiety
23
1
Neurocognitive: Problems remembering things
21.8
1
Neurocognitive: Difficulty paying attention for a long period of time
This document presents the results of statistical analysis on symptoms from viable, self-annotated Biomesight microbiome samples. The methodology for data acquisition is outlined in New Standards for Microbiome Analysis?.
Quick Best Probiotics
For details, see bottom
Bifidobacterium breve
Bifidobacterium longum
Bifidobacterium adolescentis
Lacticaseibacillus (one of the lactobacillus probiotics) is very excessive and Lactobacillus probiotics should generally be avoided. Check your yogurt labels!
Tables have been refined to display only genus- and species-level taxa, the 20 most prominent entries per group, and associations achieving statistical significance (P < 0.01).
The following sections provide the processed data, accompanied by guidance on interpretation and application. Counts of significant bacterial taxa are included, reflecting the application of non-standard but rigorously validated statistical approaches to extensive sample and reference populations, where statistical power derives from dataset scale.
Significance
Genus
p < 0.01
134
p < 0.001
125
p < 0.0001
119
p < 0.00001
105
Averages and Medians
I prefer medians over averages. Medians are the values where half of the people have less and half has more. If the data was a bell-curve, then the values will almost be the same… with bacteria that happens rarely. Look at the bacterua below, we see that for some the average is above and the median below. Should one increase or decrease this bacteria?
If symptom median is higher than reference median, it means there is more of this bacteria. If lower, then less. This ignores how often the bacteria is seen (we average only over reports). IMHO using average value instead of median will often result in a worse situation for the patient
tax_name
Rank
Symptom Average
Reference Average
Symptom Median
Reference Median
Phocaeicola vulgatus
species
7.372
5.774
3.427
5.031
Faecalibacterium
genus
12.482
12.784
12.073
10.514
Phocaeicola
genus
10.908
10.854
9.369
10.392
Blautia
genus
8.97
8.448
7.176
6.431
Lachnospira
genus
1.863
2.746
1.899
1.168
Roseburia
genus
3.574
2.822
1.778
2.222
Phocaeicola dorei
species
1.717
2.935
0.43
0.038
Parabacteroides
genus
3.252
2.611
1.724
2.116
Bacteroides uniformis
species
2.938
2.727
1.571
1.917
Oscillospira
genus
2.656
2.349
1.952
2.285
Parabacteroides distasonis
species
1.943
1.228
0.604
0.911
Clostridium
genus
1.959
1.857
1.364
1.665
Sutterella
genus
1.834
1.64
1.244
1.49
Sutterella wadsworthensis
species
0.734
0.657
0.05
0.262
Coprococcus
genus
1.112
1.438
0.73
0.53
Lachnospira pectinoschiza
species
0.369
0.67
0.34
0.162
Novispirillum
genus
1.036
0.864
0.095
0.259
Insolitispirillum
genus
1.035
0.865
0.095
0.259
Insolitispirillum peregrinum
species
1.035
0.865
0.095
0.259
Bacteroides thetaiotaomicron
species
1.09
1.072
0.466
0.628
Bacteria Incidence – How often is it reported
The common sense belief is that if a bacteria is reported more often, then the amount should be higher. This is often not true. The microbiome is a complex thing. Excessive Lacticaseibacillus (one of the lactobacillus probiotics) is very excessive.
tax_name
Rank
Incidence Odds Ratio
Chi2
Symptoms %
Reference %
Collinsella tanakaei
species
2.43
19.9
37.4
15.4
Anaerofustis stercorihominis
species
2.06
12.7
36.3
17.6
Anaerofustis
genus
1.98
11.4
36.3
18.3
Lacticaseibacillus
genus
1.83
9.2
38.5
21
More or Less often based on Symptom Median All Incidence
This is a little more complex to understand. If we compute the mid point for people with the symptom, then if the bacteria was not involved then half of the reference should be above this value and half below this value. If not, it means that the symptom tends to over or under growth.
tax_name
Rank
Symptom Median
Odds Ratio
Chi2
Below
Above
Moraxella caviae
species
0.002
0.21
22.6
81
17
Moraxella
genus
0.002
0.25
19.1
83
21
Rickettsiella
genus
0.002
0.26
17.4
76
20
Treponema porcinum
species
0.002
0.32
14.3
84
27
Clostridium hveragerdense
species
0.002
0.43
9.4
102
44
Streptococcus infantis
species
0.003
0.55
7.8
808
442
Desulfotomaculum defluvii
species
0.003
0.56
7.4
1033
576
Alkalibacterium
genus
0.003
0.57
6.8
914
521
Hydrogenophilus
genus
0.003
0.58
6.7
1166
671
More or Less often based on Reference Median All Incidence
This is like the above, but with a different line in the sand. Instead of the median of those with the condition, we use the median of the reference set.
tax_name
Rank
Reference Median
Odds Ratio
Chi2
Below
Above
Phocaeicola dorei
species
0.038
2.32
335.2
1171
2721
Corynebacterium
genus
0.012
0.32
324.4
1279
413
Odoribacter denticanis
species
0.006
0.41
294.3
1881
771
Lachnospira pectinoschiza
species
0.162
2.18
293.4
1260
2744
Sporotomaculum
genus
0.004
0.38
268.1
1329
500
Oribacterium
genus
0.035
2.14
264.9
1145
2451
Slackia
genus
0.0465
0.47
256.2
2336
1094
Oribacterium sinus
species
0.035
2.11
255.9
1151
2432
Luteolibacter
genus
0.017
0.39
243.3
1238
479
Luteolibacter algae
species
0.017
0.39
238.1
1227
479
Collinsella intestinalis
species
0.009
0.41
234.5
1330
542
Collinsella
genus
0.108
0.48
233.8
2126
1011
Blautia obeum
species
0.10995
1.98
223.4
1271
2520
Lachnobacterium
genus
0.03
1.92
202
1284
2463
Johnsonella ignava
species
0.0429
0.53
200.3
2571
1356
Johnsonella
genus
0.0429
0.53
199.5
2571
1358
Eggerthella sinensis
species
0.006
0.44
196.6
1296
574
Adlercreutzia equolifaciens
species
0.013
0.49
191
1674
814
Pontibacter
genus
0.004
0.42
190.9
1085
456
Pontibacter niistensis
species
0.004
0.42
189.6
1082
456
More or Less often based on Symptom Median High Incidence
Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.
None were found
More or Less often based on Reference Median High Incidence
Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.
tax_name
Rank
Reference Median Freq
Odds Ratio
Chi2
Below
Above
Phocaeicola dorei
species
0.038
2.32
335.2
1171
2721
Corynebacterium
genus
0.012
0.32
324.4
1279
413
Odoribacter denticanis
species
0.006
0.41
294.3
1881
771
Lachnospira pectinoschiza
species
0.162
2.18
293.4
1260
2744
Sporotomaculum
genus
0.004
0.38
268.1
1329
500
Oribacterium
genus
0.035
2.14
264.9
1145
2451
Slackia
genus
0.0465
0.47
256.2
2336
1094
Oribacterium sinus
species
0.035
2.11
255.9
1151
2432
Luteolibacter
genus
0.017
0.39
243.3
1238
479
Luteolibacter algae
species
0.017
0.39
238.1
1227
479
Collinsella intestinalis
species
0.009
0.41
234.5
1330
542
Collinsella
genus
0.108
0.48
233.8
2126
1011
Blautia obeum
species
0.10995
1.98
223.4
1271
2520
Lachnobacterium
genus
0.03
1.92
202
1284
2463
Johnsonella ignava
species
0.0429
0.53
200.3
2571
1356
Johnsonella
genus
0.0429
0.53
199.5
2571
1358
Eggerthella sinensis
species
0.006
0.44
196.6
1296
574
Adlercreutzia equolifaciens
species
0.013
0.49
191
1674
814
Pontibacter
genus
0.004
0.42
190.9
1085
456
Pontibacter niistensis
species
0.004
0.42
189.6
1082
456
Summary
A large number of bacterial taxa exhibit shifts with P < 0.01 in association with this condition. The subsequent challenge is determining how to modulate these taxa, since the volume of candidates exceeds what most individuals can practically consider. Moreover, for many of the taxa identified, there is no published evidence in the U.S. National Library of Medicine describing how to alter their abundance.
A deep optimization model, such as the one implemented on the Microbiome Taxa R2 site, can be used to inform probiotic selection. This model provides coverage for each identified taxon and infers which probiotics are most likely to shift their levels. Its output may then be integrated with more conventional recommendations derived from literature indexed in the U.S. National Library of Medicine where such evidence exists, with the two recommendation sets reconciled by giving priority to probiotic-based suggestions.
Development of a dedicated database based on Biomesight samples is in progress. The current model uses data contributed by PrecisionBiome, and datasets generated with differing laboratory processing pipelines cannot be safely combined, as discussed in The taxonomy nightmare before Christmas…. Once the Biomesight-specific database is complete, an option for generating (offline-only) personalized suggestions will be added to the Microbiome Prescription website.
Probiotics Suggestions
The following are based on a simplified algorithm using R2 data for Biomesight. These are tentative numbers subject to future refinements. Bacteria listed are only for probiotics detected with Biomesight tests. Probiotics include some that are available only in some countries and some that are pending approval for retail sale.
Good Count: Number of bacteria expected to shift in desired direction
Bad Count: Number of bacteria expected to shift in wrong direction
Impact: Estimator of impact based on Chi-2, Slope and R2 vectors
A reader asked about gluten sensitivity profile in an email. Here are the results. The short form for probiotics:
Bifidobacterium breve
Bifidobacterium longum
Bifidobacterium adolescentis
AVOID LACTOBACILLUS
This document presents the results of statistical analysis on symptoms from viable, self-annotated Biomesight microbiome samples. The methodology for data acquisition is outlined in New Standards for Microbiome Analysis?.
Tables have been refined to display only genus- and species-level taxa, the 20 most prominent entries per group, and associations achieving statistical significance (P < 0.01).
The following sections provide the processed data, accompanied by guidance on interpretation and application. Counts of significant bacterial taxa are included, reflecting the application of non-standard but rigorously validated statistical approaches to extensive sample and reference populations, where statistical power derives from dataset scale.
Significance
Genus
p < 0.01
162
p < 0.001
146
p < 0.0001
131
p < 0.00001
116
Averages and Medians
I prefer medians over averages. Medians are the values where half of the people have less and half has more. If the data was a bell-curve, then the values will almost be the same… with bacteria that happens rarely. Look at the bacterua below, we see that for some the average is above and the median below. Should one increase or decrease this bacteria?
tax_name
Rank
Symptom Average
Reference Average
Symptom Median
Reference Median
Bacteroides
genus
27.548
26
24.269
26.905
Faecalibacterium prausnitzii
species
12.695
12.196
11.329
12.474
Roseburia
genus
2.324
2.857
1.809
1.382
Lachnospira
genus
3.173
2.711
1.885
2.302
Oscillospira
genus
2.668
2.345
1.947
2.323
Bacteroides uniformis
species
2.839
2.728
1.565
1.903
Parabacteroides
genus
3.138
2.607
1.719
2.022
Clostridium
genus
2.087
1.851
1.363
1.531
Pedobacter
genus
1.315
0.988
0.552
0.706
Coprococcus
genus
1.13
1.442
0.73
0.604
Bacteroides thetaiotaomicron
species
0.943
1.077
0.464
0.59
Bifidobacterium
genus
0.352
0.955
0.129
0.028
Hathewaya histolytica
species
0.442
0.273
0.154
0.251
Hathewaya
genus
0.442
0.273
0.154
0.251
Ruminococcus bromii
species
1.039
0.783
0.167
0.262
Bacteroides cellulosilyticus
species
1.266
0.839
0.076
0.155
Bilophila
genus
0.415
0.348
0.209
0.285
Bilophila wadsworthia
species
0.393
0.34
0.199
0.262
Dorea
genus
0.329
0.488
0.295
0.242
Bacteroides rodentium
species
0.361
0.393
0.186
0.235
If symptom median is higher than reference median, it means there is more of this bacteria. If lower, then less. This ignores how often the bacteria is seen (we average only over reports). IMHO using average value instead of median will often result in a worse situation for the patient
Bacteria Incidence – How often is it reported
The common sense belief is that if a bacteria is reported more often, then the amount should be higher. This is often not true. The microbiome is a complex thing. In this case two specific probiotic species are seen rarely and thus, supplementation could be inferred.
tax_name
Rank
Incidence Odds Ratio
Chi2
Symptoms %
Reference %
Bifidobacterium breve
species
0.57
8.8
23.6
41.3
Bifidobacterium catenulatum
species
0.6
6.7
21.6
35.9
More or Less often based on Symptom Median All Incidence
This is a little more complex to understand. If we compute the mid point for people with the symptom, then if the bacteria was not involved then half of the reference should be above this value and half below this value. If not, it means that the symptom tends to over or under growth.
tax_name
Rank
Symptom Median
Odds Ratio
Chi2
Below
Above
Thiorhodococcus mannitoliphagus
species
0.002
0.2
37.9
132
27
Cystobacter
genus
0.002
0.21
37.4
131
27
Psychrobacter glacialis
species
0.002
0.36
33.8
675
243
Rickettsia marmionii Stenos et al. 2005
species
0.002
0.36
30.3
393
140
Niabella
genus
0.002
0.38
29
585
224
Viridibacillus neidei
species
0.002
0.39
27
470
182
Thiorhodococcus
genus
0.002
0.43
22.9
579
247
Thermodesulfovibrio thiophilus
species
0.002
0.44
21.5
541
236
Oenococcus
genus
0.002
0.45
20.7
614
275
Thermodesulfovibrio
genus
0.002
0.45
20.1
626
284
Helicobacter suncus
species
0.002
0.46
19.6
765
355
Viridibacillus
genus
0.002
0.5
14.8
488
244
Desulfotomaculum defluvii
species
0.003
0.56
11.6
1017
569
Alkalibacterium
genus
0.003
0.57
10.6
899
514
Sporotomaculum syntrophicum
species
0.003
0.58
10.4
1127
652
Pelagicoccus
genus
0.002
0.58
10.1
842
487
Treponema
genus
0.003
0.58
9.7
593
342
Olivibacter soli
species
0.002
0.57
9.5
457
262
Hydrogenophilus
genus
0.003
0.59
9.5
1133
671
Mycoplasma iguanae
species
0.002
0.58
9.1
458
266
More or Less often based on Reference Median All Incidence
This is like the above, but with a different line in the sand. Instead of the median of those with the condition, we use the median of the reference set.
tax_name
Rank
Reference Median
Odds Ratio
Chi2
Below
Above
Bifidobacterium
genus
0.028
2.37
347.9
1154
2736
Tetragenococcus
genus
0.004
0.44
234.4
1638
719
Bifidobacterium adolescentis
species
0.004
2.03
215.7
1074
2176
Hathewaya histolytica
species
0.2505
0.52
202.8
2568
1345
Hathewaya
genus
0.2505
0.52
202.2
2567
1346
Psychrobacter glacialis
species
0.002
0.36
168.1
675
243
Anaerotruncus
genus
0.1785
0.57
155.7
2439
1383
Caloramator uzoniensis
species
0.006
0.51
153.9
1408
712
Bifidobacterium choerinum
species
0.0055
1.87
151.9
917
1718
Mogibacterium
genus
0.022
0.57
145.4
2115
1195
Methylonatrum
genus
0.004
0.54
145
1627
872
Methylonatrum kenyense
species
0.004
0.54
145
1627
872
Anaerotruncus colihominis
species
0.1705
0.58
143
2415
1403
Hymenobacter xinjiangensis
species
0.007
0.53
137.4
1486
795
Niabella
genus
0.002
0.38
135.9
585
224
Streptococcus australis
species
0.0095
0.57
127.5
1773
1010
Leptolyngbya laminosa
species
0.0045
0.44
125.9
698
304
Leptolyngbya
genus
0.0045
0.44
125.8
701
306
Bifidobacterium longum
species
0.0195
1.73
124
1047
1814
Vagococcus
genus
0.003
0.48
119.9
841
403
More or Less often based on Symptom Median High Incidence
Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.
tax_name
Rank
Symptom Median Freq
Odds Ratio
Chi2
Below
Above
Clostridium taeniosporum
species
0.003
0.6
8.9
1354
818
More or Less often based on Reference Median High Incidence
Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.
tax_name
Rank
Reference Median Freq
Odds Ratio
Chi2
Below
Above
Bifidobacterium
genus
0.028
2.37
347.9
1154
2736
Tetragenococcus
genus
0.004
0.44
234.4
1638
719
Bifidobacterium adolescentis
species
0.004
2.03
215.7
1074
2176
Hathewaya histolytica
species
0.2505
0.52
202.8
2568
1345
Hathewaya
genus
0.2505
0.52
202.2
2567
1346
Psychrobacter glacialis
species
0.002
0.36
168.1
675
243
Anaerotruncus
genus
0.1785
0.57
155.7
2439
1383
Caloramator uzoniensis
species
0.006
0.51
153.9
1408
712
Bifidobacterium choerinum
species
0.0055
1.87
151.9
917
1718
Mogibacterium
genus
0.022
0.57
145.4
2115
1195
Methylonatrum
genus
0.004
0.54
145
1627
872
Methylonatrum kenyense
species
0.004
0.54
145
1627
872
Anaerotruncus colihominis
species
0.1705
0.58
143
2415
1403
Hymenobacter xinjiangensis
species
0.007
0.53
137.4
1486
795
Niabella
genus
0.002
0.38
135.9
585
224
Streptococcus australis
species
0.0095
0.57
127.5
1773
1010
Leptolyngbya laminosa
species
0.0045
0.44
125.9
698
304
Leptolyngbya
genus
0.0045
0.44
125.8
701
306
Bifidobacterium longum
species
0.0195
1.73
124
1047
1814
Vagococcus
genus
0.003
0.48
119.9
841
403
Summary
A large number of bacterial taxa exhibit shifts with P < 0.01 in association with this condition. The subsequent challenge is determining how to modulate these taxa, since the volume of candidates exceeds what most individuals can practically consider. Moreover, for many of the taxa identified, there is no published evidence in the U.S. National Library of Medicine describing how to alter their abundance.
A deep optimization model, such as the one implemented on the Microbiome Taxa R2 site, can be used to inform probiotic selection. This model provides coverage for each identified taxon and infers which probiotics are most likely to shift their levels. Its output may then be integrated with more conventional recommendations derived from literature indexed in the U.S. National Library of Medicine where such evidence exists, with the two recommendation sets reconciled by giving priority to probiotic-based suggestions.
Development of a dedicated database based on Biomesight samples is in progress. The current model uses data contributed by PrecisionBiome, and datasets generated with differing laboratory processing pipelines cannot be safely combined, as discussed in The taxonomy nightmare before Christmas…. Once the Biomesight-specific database is complete, an option for generating (offline-only) personalized suggestions will be added to the Microbiome Prescription website.
Probiotics Suggestions
The following are based on a simplified algorithm using R2 data for Biomesight. These are tentative numbers subject to future refinements. Bacteria listed are only for probiotics detected with Biomesight tests. Probiotics include some that are available only in some countries and some that are pending approval for retail sale.
Good Count: Number of bacteria expected to shift in desired direction
Bad Count: Number of bacteria expected to shift in wrong direction
Impact: Estimator of impact based on Chi-2, Slope and R2 vectors
I am going to review using the traditional analysis. My initial impression is that suggested retesting and plotting the next course correction was missed. As with sailing a boat, this can sail a person to an unintended spot. The last section is trying some work in progress on his sample(s). This is experimental work which I have high hope on yielding much finer identification of the critical bacteria that should be addressed.
Analysis
My usual starting point for multiple tests is compare forecasts of symptoms: New sample is 2025-11, old sample is 2025-03. Things have gotten worse.
I decide to compare 2025-11 to 2024-12 and see the latest sample is still worse, but not as bad. In other words, the gains made over the summer has been lost. This person is in a northern climate so seasonal variation could (theoretically) be significant.
As often, we have a high hit rate of projected symptoms against actual symptoms.
Current Back Story
The first line is reflected above.
I have not been feeling so well lately (since the last year).
I would say that my symptoms has become worse.
Earlier it has always felt as I have done some progress but the last 12 months it has been the opposite.
Earlier I got rid of my muscle and joint pain but it has come back and I have much bigger issues with my red nose and my body feels very stressed.
Also feel very bloated.
A summary of my biggest issues:
Get the red nose (some form of rosacea).
Feel fatigued (both physically and mentally).
Feeling stressed.
Brain fog.
Bloated.
Lots of gas – I fart and burps a lot.
Issues with allergies
Muscle and joint pain
For the last 4 years I’ve been eating large amounts of rye and oats.
Around 150-200 gram of rye bread every day.
Around 70 gram of oats every day.
Been eating low fat, low protein and high carb (specially from rye, oats, apple juice and potatoes) because this diet seem to reduce my symptoms.
As soon as I start to eat high meat and high fat my symptoms get worse.
Traditional Analysis
First, I am doing the “traditional” analysis before exploring some work in progress to improve suggestions further. The process is simple, pick Beginner-Symptoms, mark symptoms and get suggestions. This is the process that seems to produce the best results. Other choices are intended to satisfy people with different assumptions. The site purpose is allow people to use the data according to their beliefs about the best way.
Despite having 42 symptoms entered, this boiled down to just 20 bacteria. Many related symptoms are connected to the same bacteria.
Investigate: “As soon as I start to eat high meat and high fat my symptoms”
Which agrees with his reported response. On the other side, generic “fat” is a significant plus– so the type of fat seems to be critical.
Investigate Current Eating Habits: eating large amounts of rye and oats.
The suggestions are intended to be course corrections for the microbiome. Keeping on a course for too long may end up running aground on mudflats (instead of the original reefs that the course correction was intended for).
The question is why rye is ok and other grains are not? It may be due to some composition aspect or a side-effect of having sparse data. It looks like some change of diet is suggested.
While fighting ME/CFS, I retested about 6 weeks after getting the results of the last test. I noticed that suggestions swing back and worth a lot – but I kept following them. Often there can be a battle between “common sense beliefs” and what the algorithms find. Avoiding something during one cycle and then taking it the next cycle seemed “irrational”. I borrowed from physical processes the concept of “microbiome oscillations” and stopped worrying about the swings.
My personal advice is simple, get results and then do suggestions for 6-8 week and do another test. With test processing delays, it means about 10-12 weeks on each set of suggestions
Going Forward — and a new Algorithm
Recently I have been working on an Odds Ratio investigation. The reason is simple, the Odds ratio gives an objective measure of the importance of each bacteria for the symptoms. The new approach uses Odds Ratio to determine the odds of a bacteria causing a symptom. The odds tells me the importance. If you are interested in more technical data, see:
I will be trying it out on his data. The databases involved are about 160GB with processing often taking 20 minutes for each processing state, so they are on my “garage” high performance server (nerd talk: 64GB of memory, fast M.2 NVMe 2TB drive for disk) and strictly for research/exploration at the moment.
Key differences
we are going to estimate symptoms a different way than traditional (using odds)
we are likely to have 100+ bacteria to shift
Predicted Symptoms Rank Order
Using the Odds Ratio approach we get the following predictions that agrees with his reported symptoms/characteristics.
Symptom Name
Strength
Age: 30-40
11.2
Sleep: Unrefreshed sleep
10.9
Comorbid: Small intestinal bacterial overgrowth (SIBO)
9.1
Immune Manifestations: Inflammation (General)
9
General: Fatigue
9
DePaul University Fatigue Questionnaire : Tingling feeling
8.8
Neuroendocrine: Cold limbs (e.g. arms, legs hands)
8.5
Neuroendocrine Manifestations: cold extremities
8.3
Neurocognitive: Brain Fog
8.1
Post-exertional malaise: Worsening of symptoms after mild mental activity
8
DePaul University Fatigue Questionnaire : Fatigue
7.9
Gender: Male
7.9
DePaul University Fatigue Questionnaire : Muscle Pain (i.e., sensations of pain or aching in your muscles. This does not include weakness or pain in other areas such as joints)
7.8
Immune Manifestations: Bloating
7.5
DePaul University Fatigue Questionnaire : Allergies
7.4
DePaul University Fatigue Questionnaire : Muscle weakness
6.9
DePaul University Fatigue Questionnaire : Post-exertional malaise, feeling worse after doing activities that require either physical or mental exertion
6.7
DePaul University Fatigue Questionnaire : Rash
6.5
Post-exertional malaise: Mentally tired after the slightest effort
6.3
Comorbid: Histamine or Mast Cell issues
6.3
Post-exertional malaise: Next-day soreness after everyday activities
6
Post-exertional malaise: Muscle fatigue after mild physical activity
6
Official Diagnosis: Mast Cell Dysfunction
5.9
Neuroendocrine Manifestations: worsening of symptoms with stress.
5.9
Post-exertional malaise: Worsening of symptoms after mild physical activity
5.8
DePaul University Fatigue Questionnaire : Does physical activity make you feel worse
5.7
DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired
5.7
Immune: Flu-like symptoms
5.7
DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness
5.5
Neurological-Audio: hypersensitivity to noise
5.2
Immune Manifestations: Inflammation of skin, eyes or joints
5.1
Looking at the existing estimates, we see far greater separation in weight/estimates. I favor separation because that implies much better focus on bacteria.
DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness ✅ – [83.2%]
DePaul University Fatigue Questionnaire : Muscle weakness ✅ – [83.1%]
DePaul University Fatigue Questionnaire : Muscle Pain (i.e., sensations of pain or aching in your muscles. This does not include weakness or pain in other areas such as joints) ✅ – [82.7%]
Post-exertional malaise: Worsening of symptoms after mild physical activity ✅ – [82.4%]
Post-exertional malaise: Next-day soreness after everyday activities ✅ – [82.3%]
General: Fatigue ✅ – [82.3%]
Sleep: Unrefreshed sleep ✅ – [82.2%]
Comorbid: Small intestinal bacterial overgrowth (SIBO) ✅ – [82.1%]
Immune Manifestations: Bloating ✅ – [81.9%]
DePaul University Fatigue Questionnaire : Fatigue ✅ – [81.7%]
Official Diagnosis: Chronic Fatigue Syndrome (CFS/ME) ✅ – [81.5%]
Neurological-Audio: hypersensitivity to noise ✅ – [81.5%]
DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired ✅ – [81.4%]
DePaul University Fatigue Questionnaire : Easily irritated – [81.2%]
Neuroendocrine Manifestations: worsening of symptoms with stress. ✅ – [81.2%]
Key Bacteria identified
The new approach identifies these bacteria to target, with their relative importance (Weight). I just did another post on a ME/CFSer, Microbiome Interpretation – Questions From A User. Megamonas also was her top one.
tax name
tax rank
Weight
Target
Megamonas
genus
108.9
Too High
Klebsiella oxytoca
species
95.8
Too High
Ruminococcus bromii
species
-17.9
Too Low
Eubacteriales
order
-16.1
Too Low
Clostridia
class
-15.1
Too Low
Megamonas funiformis
species
15
Too High
Bacillota
phylum
-14.2
Too Low
Ruminococcaceae
family
-14.2
Too Low
Segatella
genus
13.6
Too High
Oscillospiraceae
family
-13.4
Too Low
Ruminococcus
genus
-12.4
Too Low
Terrabacteria group
clade
-12.4
Too Low
Segatella copri
species
11.9
Too High
Bacteroidia
class
11.4
Too High
Bacteroidales
order
11.4
Too High
Bacteroidota/Chlorobiota group
clade
10.3
Too High
Bacteroidota
phylum
10.1
Too High
FCB group
clade
9.8
Too High
Prevotella
genus
9.7
Too High
Lachnospiraceae
family
-9.3
Too Low
Prevotellaceae
family
9
Too High
Phocaeicola vulgatus
species
8.4
Too High
Akkermansiaceae
family
6.7
Too High
Bacteroides uniformis
species
6
Too High
Pseudomonadota
phylum
5.9
Too High
Gammaproteobacteria
class
5.2
Too High
Yersinia
genus
4.8
Too High
Verrucomicrobiota
phylum
4.3
Too High
Akkermansia
genus
4.1
Too High
Verrucomicrobiales
order
4
Too High
The traditional approach identifies the list below. There is relatively little overlap. My ‘gut’ reading is that those above are likely a better candidate set than those below.
Bacteria
Rank
Shift
Thiotrichales
order
High
Sharpea
genus
High
Selenomonas
genus
High
Ruminococcus
genus
High
Negativicutes
class
Low
Johnsonella
genus
High
Holdemania
genus
High
Erysipelothrix
genus
High
Dorea
genus
High
Desulfovibrionia
class
Low
delta/epsilon subdivisions
clade
Low
Cyanophyceae
class
Low
Cyanobacteriota/Melainabacteria group
clade
Low
Coprococcus
genus
High
Chlorobiota
phylum
High
Chlorobiia
class
High
Chlorobiaceae
family
High
Chlorobaculum
genus
High
Actinomycetota
phylum
Low
Actinomycetes
class
Low
Suggestions
Since two bacteria dominates, I ran the suggestion algorithm only on those two bacteria. The results are below and very similar to the results from the traditional approach. “All algorithms lead to the same suggestions”. Doing the full list cited above, produced very similar suggestions.
Modifier
Net
fruit/legume fibre
263
fruit
241
Fiber, total dietary
217
Chitosan
217
Slow digestible carbohydrates. {Low Glycemic}
210
oolong teas
205
polyphenols
203
resveratrol-pterostilbene x Quercetin {quercetin x resveratrol}
First I used only the top two bacteria to see what is suggested with a very targeted set.
Bacillus subtilis 75
Lactobacillus jensenii 60
Below are pushing the full set of identified bacteria through BiomeSight R2 matrix, then filtered to positive impact with no risk. Escherichia coli (cited above) continues to be a take. Bacillus subtilis would be my fall back suggestion for a probiotic. It is marginally negative on the consensus report and not cited on other R2 suggestion list. For others candidates
Lactobacillus jensenii was one for and no comment
Lactococcus lactis is a one for and one against
Lactobacillus helveticus is one strong against and no comment
Lacticaseibacillus casei is one strong against and no comment
Akkermansia muciniphila is two strong avoid
I tend to do a variation of the traditional “Do no harm”, minimize the risk of adverse shifts.
Probiotic
Net Impact
Good Count
Bad Count
Bacillus subtilis
81.9
5
0
Lactobacillus jensenii
68.5
2
0
Lactococcus lactis
26.8
2
0
Lactobacillus helveticus
21.4
4
0
Lacticaseibacillus casei
21.4
2
0
Segatella copri
21.2
3
0
Lactiplantibacillus pentosus
21
5
0
Akkermansia muciniphila
18.7
4
0
Bacillus amyloliquefaciens group
18.3
1
0
Enterococcus faecium
16.3
1
0
Limosilactobacillus fermentum
13.6
1
0
Heyndrickxia coagulans
13.4
3
0
Enterococcus durans
12.4
2
0
Pediococcus acidilactici
11
1
0
Enterococcus faecalis
10.6
2
0
Leuconostoc mesenteroides
10.5
1
0
Escherichia coli
4.3
3
0
Bottom Line
The purpose of this post was to evaluate suggestions for a regular reader. The secondary goal was to see how well a new approach that I am developing is working. This new approach produces different targeted bacteria with very similar suggestions generated, the most significance difference is far more targeted probiotics for the symptoms based on the same lab data.
The one interesting aspect is that the key bacteria (just 2) were clearly identified. These two bacteria alone produced suggestions similar to the bigger bacteria selection. I do like this narrow bacteria selection of key bacteria and will likely do a few more samples to further explore things.
Follow Up
I decided to look at all of his samples with the new algorithm to look for patterns. Megamonas stands out as the one that most frequently appears and disappears. Klebsiella oxytoca and Morganellaceae are the next candidates.
Upload Date
Top Bacteria
2021-09-24
Megamonas genus 108.9 Too High Lachnospiraceae family -56.2 Too Low Eubacteriales order -47.3 Too Low Bacillota phylum -46.6 Too Low Clostridia class -46.4 Too Low Terrabacteria group clade -44.8 Too Low
2021-09-24
Bacillota phylum -46.5 Too Low Eubacteriales order -45.1 Too Low Terrabacteria group clade -45 Too Low Clostridia class -44.1 Too Low Lachnospiraceae family -43.6 Too Low
2022-04-19
Klebsiella oxytoca species 95.8 Too High Morganellaceae family 91 Too High
2022-09-04
Oscillospiraceae family 17.7 Too High Ruminococcaceae family 17.4 Too High Bacteroidaceae family -16 Too Low Bacteroides genus -15.9 Too Low
2023-03-15
Megamonas genus 108.9 Too High Lachnospiraceae family -26 Too Low
2023-09-26
Megamonas genus 108.9 Too High
2024-02-13
Bacteroidaceae family -27.3 Too Low Bacteroides genus -27.3 Too Low Phocaeicola dorei species -21.4 Too Low
2024-09-25
Segatella genus 13.6 Too High Segatella copri species 11.9 Too High
2025-04-22
Morganellaceae family 91.6 Too High
2025-12-08
Megamonas genus 108.9 Too High Klebsiella oxytoca species 95.8 Too High
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