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.

Modifier	Net	Take	Avoid
Ferrum {Iron Supplements}	-26	4	30
high-fat diets	-15	9	24
Ethyl alcohol {Grain alcohol}	-8	3	10
high red meat	-7	0	8
5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a]quinolizinium {Berberine}	-7	11	18
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.

This morning I got this email:

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).

• Neurological-Vision: photophobia (Light Sensitivity) 431 samples
• 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 symptoms and 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:

• Avoids: Honey, Ganoderma sichuanense {Reishi Mushroom},laminaria hyperborea {Cuvie}, etc
• Takes: whole-grain diet, oligosaccharides
• 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.

Tax_name	Impact	Possible Source
Pediococcus acidilactici	4.28	Imagilin / NutriLots
Bacillus amyloliquefaciens group	3.1	only in big mixtures 🙁
Limosilactobacillus vaginalis	1.79	n/a
Bifidobacterium pseudocatenulatum	1.6	only in big mixtures 🙁
Leuconostoc mesenteroides	1.6	Bulk Probiotics / Leuconostoc Mesenteroides Probiotic Powder
Clostridium butyricum	1.46	Many sources
Lacticaseibacillus paracasei	1.35	danactive drink and many others
Lactococcus lactis	1.33	Bulk Probiotics / Lactococcus Lactis Probiotic Powder
Bifidobacterium	1.04

The To Take List

Modifier	Net	Take	Avoid
Slow digestible carbohydrates. {Low Glycemic}	34	47	12
dietary fiber	29	40	11
Fiber, total dietary	23	35	11
fruit	20	30	10
oligosaccharides {oligosaccharides}	20	24	4
High-fibre diet {Whole food diet}	19	29	10
fruit/legume fibre	19	28	9
whole-grain diet	18	24	5
(2->1)-beta-D-fructofuranan {Inulin}	17	18	1
bifidobacterium	12	12	0
Lactobacillus plantarum {L. plantarum}	11	22	11
wheat	11	12	1
3,3′,4′,5,7-pentahydroxyflavone {Quercetin}	10	11	1
Bovine Milk Products {Dairy}	9	13	4
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)	9	10	1
polyphenols	8	12	4

The To Avoid List

high-fat diets	-6	1	7
Honey {Honey }	-5	1	6
Pulvis ledebouriellae compositae {Bofutsushosan}	-4	0	5
2-aminoacetic acid {glycine}	-4	0	4
laminaria hyperborea {Cuvie}	-4	0	4
Vaccinium myrtillus {Bilberry}	-4	0	4
D-glucose {Glucose}	-4	1	6
Sodium Chloride {Salt}	-4	1	5
Ferrum {Iron Supplements}	-4	1	5
Ulmus rubra {slippery elm}	-4	2	6
2-Amino-5-(carbamoylamino)pentanoic acid {Citrulline}	-3	0	3
Lactotransferrin {Lactoferrin}	-3	0	3
Sus domesticus {Pork}	-3	0	3
Ganoderma sichuanense {Reishi Mushroom}	-3	1	4
low protein diet	-3	1	3
Theobroma cacao {Cacao}	-3	2	5

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 fourrées seen 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: $\frac{300}{90}=3.333$90300=3.333.
• Compute odds for control group: $\frac{600}{700}\approx 0.857$700600≈0.857.
• Odds ratio: $OR=\frac{3.333}{0.857}\approx 3.89$ 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	100
Thryve	Condition: Type 1 Diabetes	100
Thryve	Neuroendocrine Manifestations: abnormal appetite	100
BiomeSight	Autonomic Manifestations: delayed postural hypotension	100
Thryve	Physical: Long term antibiotics(over 6 months)	100
Thryve	Comorbid: Electromagnetic Sensitivity (EMF)	100
BiomeSight	Physical: Bad Air Quality	100
BiomeSight	Neuroendocrine Manifestations: marked diurnal fluctuation	100
Thryve	Physical: Amalgam fillings	100
BiomeSight	Comorbid: Reactive Hypoglicemia	100
Thryve	Comorbid: Sugars cause sleep or cognitive issues	100
BiomeSight	Official Diagnosis: Dermatitis (all types)	100
Thryve	Physical: Steps Per Day 2000-4000	100
Thryve	Neuroendocrine Manifestations: Painful menstrual periods	100
Thryve	General: Anhedonia (inability to feel pleasure)	100
BiomeSight	Virus: Parvovirus positive (B19)	100
BiomeSight	Blood Type: FUT2 secretor	100
Thryve	Official Diagnosis: High Blood Pressure (Hypertension)	100
Thryve	DePaul University Fatigue Questionnaire : Poor hand to eye coodination	100
Thryve	Infection: Coxsackie	100
Thryve	Neuroendocrine Manifestations: marked diurnal fluctuation	100

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 $P<0.05$.” 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.

Source	Ratio	Size
BiomeSight – 16s	60.8	45069
Thorne – Shotgun	80.7	491
Ombre/Thryve – 16s	40.8	17123
uBiome – 16s	47.6	13071

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.

• Renewbiome Adlercreutzia
  • Adlercreutzia celata
  • Adlercreutzia equolifaciens
  • Mycolicibacterium aurum
• Renewbiome Akkermansia
  • Akkermansia muciniphila
• Renewbiome Alistipes
  • Alistipes finegoldii
  • Alistipes ihumii
  • Alistipes indistinctus
  • Alistipes inops
  • Alistipes shahii
  • Alistipes timonensis
  • Anaerostipes butyraticus
  • Anaerostipes caccae
  • Anaerostipes hadrus
  • Anaerostipes rhamnosivorans
• Renewbiome Anaerobutyricum
  • Anaerobutyricum hallii
  • Anaerobutyricum soehngenii
• Renewbiome Aspergillus
  • Aspergillus oryzae
• Renewbiome Aurum
  • Mycolicibacterium aurum
• Renewbiome Butyricum
  • Clostridium butyricum
• Renewbiome Cellulomonas
  • Cellulomonas cartae
• Renewbiome Christensenella
  • Christensenella minuta
• Renewbiome Ellagibacter
  • Ellagibacter isourolithinifaciens
• Renewbiome Faecalibacterium
  • Faecalibacterium prausnitzii
• Renewbiome Glutamicum
  • Corynebacterium glutamicum
• Renewbiome Gordonibacter
  • Gordonibacter faecis
  • Gordonibacter pamelaeae
  • Gordonibacter urolithinfaciens
• Renewbiome Methanobrevibacter
  • Methanobrevibacter smithii
• Renewbiome Nocardioides
  • Nocardioides simplex
• Renewbiome Oxalobacter
  • Oxalobacter formigenes
• Renewbiome Propionibacterium
  • Propionibacterium acidipropionici
  • Propionibacterium freudenreichii
• Renewbiome Bacillus
  • Bacillus amyloliquefaciens
  • Bacillus clausii
  • Bacillus coagulans
  • Bacillus halodenitrificans
  • Bacillus indicus
  • Bacillus licheniformis
  • Bacillus megaterium
  • Bacillus pantothenticus
  • Bacillus psychrosaccharolyticus
  • Bacillus pumilus
  • Bacillus subtilis
  • Bacillus velezensis
• Renewbiome Bacteroides
  • Bacteroides acidifaciens
  • Bacteroides amylophilus
  • Bacteroides coprocola
  • Bacteroides coprophilus
  • Bacteroides dorei
  • Bacteroides finegoldii
  • Bacteroides gallinarum
  • Bacteroides intestinalis
  • Bacteroides propionicifaciens
  • Bacteroides xylanisolvens
• Renewbiome Mega
  • Bifidobacterium adolescentis
  • Bifidobacterium animalis
  • Bifidobacterium animalis subsp. lactis
  • Bifidobacterium bifidum
  • Bifidobacterium breve
  • Bifidobacterium infantis
  • Bifidobacterium longum
  • Lacticaseibacillus casei
  • Lacticaseibacillus paracasei
  • Lacticaseibacillus rhamnosus
  • Lacticaseibacillus rhamnosus GG
  • Lactiplantibacillus plantarum
  • Lactobacillus acidophilus
  • Lactobacillus crispatus
  • Lactobacillus curvatus
  • Lactobacillus delbrueckii subsp. bulgaricus
  • Lactobacillus delbrueckii subsp. delbrueckii
  • Lactobacillus delbrueckii subsp. lactis
  • Lactobacillus gasseri
  • Lactobacillus helveticus
  • Lactobacillus jensenii
  • Lactobacillus johnsonii
  • Lactobacillus paragasseri
  • Lactococcus lactis subsp. cremoris
  • Lactococcus lactis subsp. lactis
  • Latilactobacillus sakei
  • Lentilactobacillus buchneri
  • Leuconostoc mesenteroides
  • Levilactobacillus brevis
  • Ligilactobacillus salivarius
  • Limosilactobacillus fermentum
  • Limosilactobacillus reuteri
  • Pediococcus acidilactici
  • Pediococcus pentosaceus
  • Saccharomyces boulardii
  • Streptococcus thermophilus
  • Weizmannia coagulans

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.