Biomesight microbiome samples. The methodology for data acquisition is outlined in New Standards for Microbiome Analysis?. Self-described: Official Diagnosis: Mast Cell Dysfunction​

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	131
p < 0.001	118
p < 0.0001	106
p < 0.00001	94

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 Avarage	Reference Average	Symptom Median	Reference Median
Phocaeicola dorei	species	4.464	2.872	0.399	0.92
Roseburia	genus	3.065	2.833	1.786	2.058
Sutterella	genus	1.711	1.643	1.259	1.022
Parabacteroides merdae	species	0.558	0.75	0.306	0.09
Clostridium	genus	1.977	1.856	1.363	1.566
Bacteroides thetaiotaomicron	species	1.754	1.057	0.464	0.659
Coprococcus	genus	1.271	1.435	0.73	0.597
Mediterraneibacter	genus	1.174	0.706	0.279	0.386
Bacteroides caccae	species	1.451	0.864	0.29	0.19
Bacteroides cellulosilyticus	species	1.26	0.845	0.076	0.158
Lachnospira pectinoschiza	species	0.67	0.663	0.334	0.257
Blautia obeum	species	0.593	0.572	0.233	0.303
Bilophila	genus	0.363	0.35	0.211	0.272
Hathewaya histolytica	species	0.426	0.275	0.156	0.205
Hathewaya	genus	0.427	0.276	0.156	0.205
Sutterella wadsworthensis	species	0.845	0.655	0.058	0.011
Veillonella criceti	species	0.279	0.237	0.124	0.168
Bacteroides rodentium	species	0.338	0.393	0.187	0.231
Akkermansia	genus	1.582	1.353	0.053	0.011
Akkermansia muciniphila	species	1.582	1.354	0.053	0.011

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.

Nothing found that was significant

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
Sulfobacillus acidophilus	species	0.002	0.39	10.8	84	33
Sulfobacillus	genus	0.002	0.39	10.8	84	33
Caldanaerobacter hydrothermalis	species	0.002	0.43	9.8	96	41
Caldanaerobacter	genus	0.002	0.43	9.8	96	41
Desulfotomaculum defluvii	species	0.003	0.56	8.1	1032	578
Pelagicoccus	genus	0.002	0.57	7.4	859	490
Alkalibacterium	genus	0.003	0.57	7.4	907	518
Hydrogenophilus	genus	0.003	0.58	7.3	1162	670
Sporotomaculum syntrophicum	species	0.003	0.59	6.8	1138	668

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
Nostoc	genus	0.003	0.34	295.7	1214	408
Bacillus	genus	0.006	0.43	277.1	1954	837
Erysipelothrix	genus	0.018	0.47	250.1	2346	1111
Psychrobacter	genus	0.003	0.39	239.8	1254	492
Sharpea	genus	0.025	0.4	233	1278	514
Methylobacillus glycogenes	species	0.003	0.4	232.7	1286	519
Sharpea azabuensis	species	0.025	0.41	226.8	1264	514
Methylobacillus	genus	0.003	0.42	219.3	1287	537
Erysipelothrix muris	species	0.017	0.5	218.7	2274	1130
Candidatus Tammella caduceiae	species	0.003	0.41	205.7	1155	478
Paenibacillus	genus	0.003	0.39	205.3	1016	398
Candidatus Tammella	genus	0.003	0.42	200.1	1170	494
[Ruminococcus] torques	species	0.04	0.51	189.3	1921	971
Holdemania	genus	0.028	0.53	187	2315	1225
Streptococcus oralis	species	0.003	0.47	185.8	1453	686
Amedibacillus dolichus	species	0.024	0.5	184.4	1759	879
Amedibacillus	genus	0.024	0.5	184	1758	879
Haemophilus parainfluenzae	species	0.01	1.91	173.7	1023	1952
Haemophilus	genus	0.01	1.89	170.3	1035	1959
Luteolibacter	genus	0.015	0.46	169.7	1177	541

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.

Nothing found that was significant

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
Nostoc	genus	0.003	0.34	295.7	1214	408
Bacillus	genus	0.006	0.43	277.1	1954	837
Erysipelothrix	genus	0.018	0.47	250.1	2346	1111
Psychrobacter	genus	0.003	0.39	239.8	1254	492
Sharpea	genus	0.025	0.4	233	1278	514
Methylobacillus glycogenes	species	0.003	0.4	232.7	1286	519
Sharpea azabuensis	species	0.025	0.41	226.8	1264	514
Methylobacillus	genus	0.003	0.42	219.3	1287	537
Erysipelothrix muris	species	0.017	0.5	218.7	2274	1130
Candidatus Tammella caduceiae	species	0.003	0.41	205.7	1155	478
Paenibacillus	genus	0.003	0.39	205.3	1016	398
Candidatus Tammella	genus	0.003	0.42	200.1	1170	494
[Ruminococcus] torques	species	0.04	0.51	189.3	1921	971
Holdemania	genus	0.028	0.53	187	2315	1225
Streptococcus oralis	species	0.003	0.47	185.8	1453	686
Amedibacillus dolichus	species	0.024	0.5	184.4	1759	879
Amedibacillus	genus	0.024	0.5	184	1758	879
Haemophilus parainfluenzae	species	0.01	1.91	173.7	1023	1952
Haemophilus	genus	0.01	1.89	170.3	1035	1959
Luteolibacter	genus	0.015	0.46	169.7	1177	541

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

Probiotic Species	Impact	Good Count	Bad Count
Akkermansia muciniphila	36.08	18	6
Segatella copri	32.72	5	2
Bifidobacterium breve	21.31	8	5
Bifidobacterium longum	19.06	8	6
Bifidobacterium adolescentis	14.38	8	7
Lactobacillus helveticus	9.11	48	27
Streptococcus thermophilus	7.97	8	2
Lactobacillus johnsonii	7.28	22	26
Bifidobacterium bifidum	4.35	7	2
Bifidobacterium catenulatum	4.24	8	0
Parabacteroides goldsteinii	4.11	5	9
Bifidobacterium animalis	1.97	7	0
Lactococcus lactis	1.1	6	2
Veillonella atypica	1.02	11	3
Clostridium butyricum	0.97	7	9
Limosilactobacillus vaginalis	0.86	20	29
Odoribacter laneus	0.76	2	0
Enterococcus durans	0.7	11	20
Limosilactobacillus fermentum	0.11	2	15
Leuconostoc mesenteroides	-0.07	3	7
Lacticaseibacillus paracasei	-0.07	2	9
Lacticaseibacillus rhamnosus	-0.09	0	2
Bifidobacterium pseudocatenulatum	-0.1	5	6
Heyndrickxia coagulans	-0.1	3	9
Ligilactobacillus salivarius	-0.14	1	6
Lactobacillus crispatus	-0.15	3	7
Lactiplantibacillus plantarum	-0.19	0	4
Lactiplantibacillus pentosus	-0.21	0	4
Lacticaseibacillus casei	-0.21	0	7
Lactobacillus acidophilus	-0.22	8	12
Bacillus subtilis	-0.26	8	27
Limosilactobacillus reuteri	-0.42	5	16
Lactobacillus jensenii	-1.41	15	29
Pediococcus acidilactici	-1.86	17	32
Enterococcus faecium	-2.58	7	22
Enterococcus faecalis	-9.41	28	50
Parabacteroides distasonis	-9.5	9	4
Blautia wexlerae	-13.3	1	3
Escherichia coli	-24.86	1	12
Blautia hansenii	-26.65	7	6
Faecalibacterium prausnitzii	-116.83	3	3
Bacteroides uniformis	-167.73	1	11
Bacteroides thetaiotaomicron	-178.11	1	11

Comments on this Condition

Two of the above sections reported nothing significant found. This implies that the microbiome plays a secondary role. The bacteria shifts are more likely consequences of the condition than triggers of the condition. Regardless, there is a potential that the above probiotics may modify the severity of the condition.

It is unclear if the shifts are due to anti-histamine and other drugs usage.