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.

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	196
p < 0.001	172
p < 0.0001	154
p < 0.00001	140

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 Bifidobacterium below, we see that the average is above and the median below.

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).

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	28.802	25.926	24.224	27.059
Phocaeicola	genus	12.357	10.786	9.314	11.306
Phocaeicola vulgatus	species	7.043	5.751	3.394	4.929
Bacteroides uniformis	species	2.909	2.723	1.553	1.958
Bacteroides thetaiotaomicron	species	1.234	1.065	0.458	0.734
Coprococcus	genus	1.23	1.44	0.737	0.552
Roseburia faecis	species	0.969	1.217	0.577	0.455
Bilophila	genus	0.417	0.347	0.207	0.319
Bifidobacterium	genus	0.534	0.953	0.131	0.035
Bacteroides stercoris	species	2.066	1.543	0.033	0.123
Blautia coccoides	species	0.776	0.917	0.592	0.504
Bilophila wadsworthia	species	0.395	0.339	0.197	0.281
Mediterraneibacter	genus	0.885	0.708	0.278	0.326
Butyricimonas	genus	0.217	0.186	0.108	0.154
Hathewaya	genus	0.314	0.277	0.155	0.201
Hathewaya histolytica	species	0.314	0.277	0.155	0.201
Bacteroides rodentium	species	0.435	0.39	0.186	0.231
Bifidobacterium longum	species	0.237	0.326	0.051	0.012
Lachnobacterium	genus	0.197	0.327	0.075	0.041
Bacteroides stercorirosoris	species	0.234	0.191	0.135	0.164

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. Look at Bacteroides uniformis below, we see that the average is above and the median below

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Bifidobacterium breve	species	0.64	7.8	26.6	41.4
Anaerococcus hydrogenalis	species	1.66	7.2	18.2	11

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
Niabella aurantiaca	species	0.002	0.34	44.3	544	183
Psychroflexus	genus	0.002	0.31	43.7	357	111
Psychroflexus gondwanensis	species	0.002	0.31	43.7	357	111
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.33	41.6	398	131
Psychrobacter glacialis	species	0.002	0.37	38.6	660	246
Niabella	genus	0.002	0.38	36	584	222
Chromatium	genus	0.002	0.38	34.4	517	198
Chromatium weissei	species	0.002	0.38	34.2	516	198
Lentibacillus	genus	0.002	0.38	34	510	196
Lentibacillus salinarum	species	0.002	0.38	33.7	494	190
Viridibacillus neidei	species	0.002	0.38	33.3	469	180
Thermoanaerobacterium	genus	0.002	0.41	30	483	196
Thiomicrospira	genus	0.002	0.39	29.3	335	130
Sporosarcina pasteurii	species	0.002	0.41	29.2	439	178
Thiorhodococcus	genus	0.002	0.42	29.1	578	243
Thermoanaerobacterium islandicum	species	0.002	0.41	29	476	196
Syntrophomonas sapovorans	species	0.002	0.42	29	534	223
Sporosarcina	genus	0.002	0.42	27.5	443	185
Thermodesulfovibrio thiophilus	species	0.002	0.44	25.5	536	237
Oenococcus	genus	0.002	0.45	24.8	604	273

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 longum	species	0.012	2.27	260.2	870	1971
Bifidobacterium	genus	0.035	2.04	241.7	1266	2586
Methylobacillus glycogenes	species	0.003	0.4	232.5	1250	497
Methylobacillus	genus	0.003	0.41	217.2	1249	516
Corynebacterium	genus	0.0085	0.42	201.9	1163	486
Bilophila	genus	0.3185	0.55	162.8	2247	1236
Erysipelothrix muris	species	0.015	0.55	161	2153	1179
Psychrobacter glacialis	species	0.002	0.37	156.5	660	246
Niabella aurantiaca	species	0.002	0.34	153.5	544	183
Methylonatrum	genus	0.004	0.53	145.2	1620	866
Methylonatrum kenyense	species	0.004	0.53	145.2	1620	866
Catonella morbi	species	0.01	0.56	144	1968	1099
Catonella	genus	0.01	0.56	141.4	1966	1104
Erysipelothrix	genus	0.0155	0.57	139.9	2151	1232
Niabella	genus	0.002	0.38	137	584	222
Megasphaera elsdenii	species	0.0045	0.41	132.7	640	260
Bacteroides thetaiotaomicron	species	0.734	0.6	130.1	2389	1422
Veillonella parvula	species	0.003	1.9	128.6	666	1266
Alkalithermobacter paradoxus	species	0.004	0.55	125.8	1537	853
Odoribacter denticanis	species	0.005	0.57	124.1	1642	928

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	0.61	11.2	1344	814
Dethiosulfovibrio	genus	0.004	0.66	7.6	1500	994
Tetragenococcus doogicus	species	0.003	0.67	7.2	1360	910

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 longum	species	0.012	2.27	260.2	870	1971
Bifidobacterium	genus	0.035	2.04	241.7	1266	2586
Methylobacillus glycogenes	species	0.003	0.4	232.5	1250	497
Methylobacillus	genus	0.003	0.41	217.2	1249	516
Corynebacterium	genus	0.0085	0.42	201.9	1163	486
Bilophila	genus	0.3185	0.55	162.8	2247	1236
Erysipelothrix muris	species	0.015	0.55	161	2153	1179
Psychrobacter glacialis	species	0.002	0.37	156.5	660	246
Niabella aurantiaca	species	0.002	0.34	153.5	544	183
Methylonatrum	genus	0.004	0.53	145.2	1620	866
Methylonatrum kenyense	species	0.004	0.53	145.2	1620	866
Catonella morbi	species	0.01	0.56	144	1968	1099
Catonella	genus	0.01	0.56	141.4	1966	1104
Erysipelothrix	genus	0.0155	0.57	139.9	2151	1232
Niabella	genus	0.002	0.38	137	584	222
Megasphaera elsdenii	species	0.0045	0.41	132.7	640	260
Bacteroides thetaiotaomicron	species	0.734	0.6	130.1	2389	1422
Veillonella parvula	species	0.003	1.9	128.6	666	1266
Alkalithermobacter paradoxus	species	0.004	0.55	125.8	1537	853
Odoribacter denticanis	species	0.005	0.57	124.1	1642	928

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

Some literature suggesting that the model’s suggestions are reasonable:

• Bifidobacterium breve Bif11 supplementation improves depression-related neurobehavioural and neuroinflammatory changes in the mouse. Neuropharmacology (Neuropharmacology ) Vol: 229 Issue: Pages: 109480 Pub: 2023 May 15 ePub: 2023 Mar 1 Authors Sushma G,Vaidya B,Sharma S,Devabattula G,Bishnoi M,Kondepudi KK,Sharma SS
• Heat-sterilized Bifidobacterium breve prevents depression-like behavior and interleukin-1ß expression in mice exposed to chronic social defeat stress. Brain, behavior, and immunity (Brain Behav Immun ) Vol: Issue: Pages: Pub: 2021 May 29 ePub: 2021 May 29 Authors Kosuge A,Kunisawa K,Arai S,Sugawara Y,Shinohara K,Iida T,Wulaer B,Kawai T,Fujigaki H,Yamamoto Y,Saito K,Nabeshima T,Mouri A
• Bifidobacterium breve BB05 alleviates depressive symptoms in mice via the AKT/mTOR pathway.
  Frontiers in nutrition (Front Nutr ) Vol: 12 Issue: Pages: 1529566 Pub: 2025 ePub: 2025 Jan 30 Authors Pan Y,Huang Q,Liang Y,Xie Y,Tan F,Long X
• Lipid and Energy Metabolism of the Gut Microbiota Is Associated with the Response to Probiotic Bifidobacterium breve Strain for Anxiety and Depressive Symptoms in Schizophrenia.
  Journal of personalized medicine (J Pers Med ) Vol: 11 Issue: 10 Pages: Pub: 2021 Sep 30 ePub: 2021 Sep 30 Authors Yamamura R,Okubo R,Katsumata N,Odamaki T,Hashimoto N,Kusumi I,Xiao J,Matsuoka YJ
• Towards a psychobiotic therapy for depression: Bifidobacterium breve CCFM1025 reverses chronic stress-induced depressive symptoms and gut microbial abnormalities in mice. Neurobiology of stress (Neurobiol Stress ) Vol: 12 Issue: Pages: 100216 Pub: 2020 May ePub: 2020 Mar 20 Authors Tian P,O’Riordan KJ,Lee YK,Wang G,Zhao J,Zhang H,Cryan JF,Chen W
  

Probiotic Species	Impact	Good Count	Bad Count
Faecalibacterium prausnitzii	256.62	8	0
Blautia hansenii	181.61	22	1
Bifidobacterium breve	89.17	15	0
Bifidobacterium longum	79.15	15	2
Blautia wexlerae	59.88	7	0
Bifidobacterium adolescentis	58.57	14	2
Segatella copri	41.79	3	0
Bifidobacterium bifidum	17.98	11	2
Bifidobacterium catenulatum	15.57	8	0
Escherichia coli	14.66	11	0
Enterococcus faecalis	14.27	42	46
Akkermansia muciniphila	11.57	12	9
Lactobacillus helveticus	9.86	43	53
Bifidobacterium animalis	8.17	7	0
Enterococcus faecium	5.05	14	34
Streptococcus thermophilus	2.3	4	0
Enterococcus durans	2.14	25	23
Veillonella atypica	1.34	12	0
Clostridium butyricum	0.67	13	26
Bacillus subtilis	0.51	28	33
Lacticaseibacillus paracasei	0.23	13	5
Lactococcus lactis	0.15	2	5
Limosilactobacillus fermentum	0.12	10	17
Lactiplantibacillus pentosus	0.1	6	4
Heyndrickxia coagulans	0.04	8	14
Ligilactobacillus salivarius	-0.05	2	10
Lactiplantibacillus plantarum	-0.08	3	6
Lacticaseibacillus casei	-0.17	1	10
Lacticaseibacillus rhamnosus	-0.18	2	5
Limosilactobacillus vaginalis	-0.22	25	51
Leuconostoc mesenteroides	-0.26	4	13
Bifidobacterium pseudocatenulatum	-0.35	7	24
Lactobacillus crispatus	-0.63	2	31
Limosilactobacillus reuteri	-0.78	17	22
Lactobacillus acidophilus	-0.83	5	33
Odoribacter laneus	-1.78	0	3
Lactobacillus jensenii	-3.23	18	55
Pediococcus acidilactici	-7.22	26	33
Lactobacillus johnsonii	-13.52	36	39
Parabacteroides goldsteinii	-21.27	0	13
Parabacteroides distasonis	-27.34	0	11
Bacteroides uniformis	-285.55	0	11
Bacteroides thetaiotaomicron	-294.72	0	10

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	149
p < 0.001	121
p < 0.0001	96
p < 0.00001	79

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 Bacteroides uniformis below, we see that the average is above and the median below.

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).

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	28.504	25.674	23.872	27.629
Bacteroides uniformis	species	3.078	2.678	1.51	1.977
Coprococcus	genus	1.258	1.458	0.75	0.539
Bifidobacterium	genus	0.677	0.974	0.135	0.064
Bacteroides cellulosilyticus	species	1.06	0.822	0.073	0.126
Bilophila	genus	0.397	0.343	0.206	0.252
Bilophila wadsworthia	species	0.385	0.335	0.196	0.239
Alkaliphilus	genus	0.251	0.299	0.07	0.041
Alkaliphilus crotonatoxidans	species	0.245	0.291	0.065	0.037
Collinsella	genus	0.146	0.188	0.058	0.036
Bifidobacterium longum	species	0.221	0.338	0.052	0.03
Bacteroides stercorirosoris	species	0.216	0.19	0.134	0.156
Collinsella aerofaciens	species	0.138	0.175	0.056	0.036
Caloramator mitchellensis	species	0.798	0.873	0.054	0.035
Anaerotruncus	genus	0.195	0.186	0.136	0.155
Bacteroides salyersiae	species	0.262	0.367	0.022	0.004
Bacteroides faecis	species	0.145	0.118	0.055	0.071
Anaerotruncus colihominis	species	0.184	0.174	0.133	0.147
Oxalobacter	genus	0.036	0.03	0.018	0.027
Luteibacter anthropi	species	0.051	0.08	0.016	0.009

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. Look at Bacteroides uniformis below, we see that the average is above and the median below

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Bifidobacterium scardovii	species	0.71	6.6	12.3	17.2

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
Niabella aurantiaca	species	0.002	0.34	79.6	484	165
Psychrobacter glacialis	species	0.002	0.37	78.5	594	218
Niabella	genus	0.002	0.39	66	516	201
Viridibacillus neidei	species	0.002	0.38	61.6	426	163
Actinopolyspora	genus	0.002	0.4	61.5	489	195
Chromatium	genus	0.002	0.4	61.4	473	187
Chromatium weissei	species	0.002	0.4	61.1	472	187
Lentibacillus	genus	0.002	0.4	58.9	459	184
Lentibacillus salinarum	species	0.002	0.41	56.7	444	180
Thermoanaerobacterium	genus	0.002	0.42	53.1	438	183
Thiorhodococcus	genus	0.002	0.44	53.1	524	229
Thermoanaerobacterium islandicum	species	0.002	0.42	51.3	432	183
Syntrophomonas sapovorans	species	0.002	0.44	50.8	482	211
Thermodesulfovibrio thiophilus	species	0.002	0.44	49.5	481	213
Thermodesulfovibrio	genus	0.002	0.47	46.8	552	258
Desulfofundulus	genus	0.002	0.45	46.7	449	201
Helicobacter suncus	species	0.002	0.49	46.3	669	325
Oenococcus	genus	0.002	0.47	45.1	529	249
Vagococcus penaei	species	0.003	0.49	36.1	427	211
Viridibacillus	genus	0.002	0.5	34.8	437	220

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
Psychrobacter glacialis	species	0.002	0.37	144.3	594	218
Niabella aurantiaca	species	0.002	0.34	134.5	484	165
Niabella	genus	0.002	0.39	116.9	516	201
Bacteroides heparinolyticus	species	0.003	0.49	111.1	858	423
Actinopolyspora	genus	0.002	0.4	107.5	489	195
Chromatium	genus	0.002	0.4	106	473	187
Chromatium weissei	species	0.002	0.4	105.4	472	187
Viridibacillus neidei	species	0.002	0.38	102	426	163
Lentibacillus	genus	0.002	0.4	100.9	459	184
Thiorhodococcus	genus	0.002	0.44	96.8	524	229
Lentibacillus salinarum	species	0.002	0.41	96.2	444	180
Helicobacter suncus	species	0.002	0.49	94.8	669	325
Thermoanaerobacterium	genus	0.002	0.42	90.2	438	183
Syntrophomonas sapovorans	species	0.002	0.44	89.9	482	211
Thermodesulfovibrio	genus	0.002	0.47	88.2	552	258
Thermodesulfovibrio thiophilus	species	0.002	0.44	87.8	481	213
Thermoanaerobacterium islandicum	species	0.002	0.42	87	432	183
Oenococcus	genus	0.002	0.47	83.9	529	249
Hydrogenophilus	genus	0.003	0.58	81.2	1019	589
Desulfofundulus	genus	0.002	0.45	81	449	201

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	0.62	24.8	1203	751
Dethiosulfovibrio	genus	0.004	0.68	17.5	1353	917
Tetragenococcus doogicus	species	0.003	0.69	15.8	1214	836
Hydrocarboniphaga daqingensis	species	0.004	0.71	13.8	1453	1032
Mycoplasmopsis	genus	0.005	0.72	13.3	1600	1147
Pediococcus	genus	0.004	0.76	8.7	1168	886
Propionispora	genus	0.005	0.77	7.9	1374	1060
Propionispora hippei	species	0.005	0.77	7.9	1374	1060
Tetragenococcus	genus	0.003	0.78	6.7	1186	931

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
Psychrobacter glacialis	species	0.002	0.37	144.3	594	218
Niabella aurantiaca	species	0.002	0.34	134.5	484	165
Niabella	genus	0.002	0.39	116.9	516	201
Bacteroides heparinolyticus	species	0.003	0.49	111.1	858	423
Actinopolyspora	genus	0.002	0.4	107.5	489	195
Chromatium	genus	0.002	0.4	106	473	187
Chromatium weissei	species	0.002	0.4	105.4	472	187
Viridibacillus neidei	species	0.002	0.38	102	426	163
Lentibacillus	genus	0.002	0.4	100.9	459	184
Thiorhodococcus	genus	0.002	0.44	96.8	524	229
Lentibacillus salinarum	species	0.002	0.41	96.2	444	180
Helicobacter suncus	species	0.002	0.49	94.8	669	325
Thermoanaerobacterium	genus	0.002	0.42	90.2	438	183
Syntrophomonas sapovorans	species	0.002	0.44	89.9	482	211
Thermodesulfovibrio	genus	0.002	0.47	88.2	552	258
Thermodesulfovibrio thiophilus	species	0.002	0.44	87.8	481	213
Thermoanaerobacterium islandicum	species	0.002	0.42	87	432	183
Oenococcus	genus	0.002	0.47	83.9	529	249
Hydrogenophilus	genus	0.003	0.58	81.2	1019	589
Desulfofundulus	genus	0.002	0.45	81	449	201

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
Bifidobacterium breve	53.54	16	0
Blautia hansenii	52.64	12	0
Bifidobacterium longum	48.23	19	0
Segatella copri	45.23	5	0
Bifidobacterium adolescentis	35.47	15	0
Lactobacillus helveticus	20.58	75	11
Akkermansia muciniphila	18.74	16	1
Faecalibacterium prausnitzii	15.59	3	0
Bifidobacterium bifidum	10.9	16	0
Bifidobacterium catenulatum	10.1	15	0
Enterococcus faecalis	9.15	55	10
Escherichia coli	6.29	8	0
Bifidobacterium animalis	4.92	7	0
Lactobacillus johnsonii	3.79	51	5
Enterococcus faecium	3.77	28	3
Enterococcus durans	3.18	39	1
Streptococcus thermophilus	1.64	3	0
Blautia wexlerae	1.56	1	0
Limosilactobacillus vaginalis	1.06	34	6
Lactobacillus jensenii	0.85	41	10
Limosilactobacillus reuteri	0.52	23	6
Clostridium butyricum	0.49	19	3
Bifidobacterium pseudocatenulatum	0.37	23	0
Bacillus amyloliquefaciens group	0.31	44	11
Lacticaseibacillus paracasei	0.31	10	1
Lactobacillus acidophilus	0.27	24	1
Veillonella atypica	0.27	6	0
Heyndrickxia coagulans	0.26	20	2
Lactobacillus crispatus	0.24	17	2
Ligilactobacillus salivarius	0.16	9	0
Lacticaseibacillus casei	0.14	7	2
Lactiplantibacillus pentosus	0.14	5	0
Bacillus subtilis group	0.13	22	4
Limosilactobacillus fermentum	0.12	13	5
Lactococcus lactis	0.12	5	0
Leuconostoc mesenteroides	0.11	14	1
Lactiplantibacillus plantarum	0.1	4	1
Lacticaseibacillus rhamnosus	0.09	4	0
Pediococcus	0.07	4	0
Bacillus	0.07	2	0
Bacillus subtilis	-0.32	31	12
Pediococcus acidilactici	-2.33	28	12
Parabacteroides goldsteinii	-13.74	0	8
Parabacteroides distasonis	-27.7	0	8
Bacteroides uniformis	-246.88	0	13
Bacteroides thetaiotaomicron	-265.51	0	14

​

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	135
p < 0.001	100
p < 0.0001	83
p < 0.00001	69

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 Bacteroides uniformis below, we see that the average is above and the median below.

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).

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	27.303	25.835	24.008	26.554
Bacteroides uniformis	species	3.026	2.68	1.498	2.016
Phocaeicola dorei	species	3.35	2.83	0.379	0.672
Coprococcus	genus	1.354	1.444	0.739	0.612
Bifidobacterium	genus	0.698	0.975	0.136	0.064
Bacteroides cellulosilyticus	species	0.883	0.849	0.07	0.138
Bilophila	genus	0.41	0.34	0.206	0.25
Bacteroides rodentium	species	0.416	0.387	0.179	0.221
Bilophila wadsworthia	species	0.4	0.331	0.197	0.235
Bifidobacterium longum	species	0.244	0.336	0.052	0.03
Anaerotruncus	genus	0.197	0.185	0.136	0.156
Anaerotruncus colihominis	species	0.187	0.173	0.132	0.15
Collinsella	genus	0.146	0.189	0.057	0.042
Collinsella aerofaciens	species	0.139	0.176	0.054	0.042
Anaerobranca zavarzinii	species	0.14	0.159	0.015	0.009
Anaerobranca	genus	0.14	0.159	0.015	0.009
Bifidobacterium adolescentis	species	0.281	0.305	0.013	0.007
Oxalobacter	genus	0.033	0.03	0.018	0.023
Bifidobacterium choerinum	species	0.037	0.052	0.012	0.007
Acholeplasma hippikon	species	0.051	0.042	0.006	0.01

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. Look at Bacteroides uniformis below, we see that the average is above and the median below

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Aggregatibacter	genus	0.75	7.4	18.5	24.7
Prevotella bivia	species	1.29	6.6	24.4	19
Bifidobacterium scardovii	species	0.71	7.1	12.4	17.3

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
Psychrobacter glacialis	species	0.002	0.37	81	584	215
Chromatium	genus	0.002	0.38	69.5	466	175
Chromatium weissei	species	0.002	0.38	69.2	465	175
Niabella	genus	0.002	0.4	65.2	500	199
Actinopolyspora	genus	0.002	0.4	64.4	482	191
Thiorhodococcus	genus	0.002	0.43	57.4	516	221
Syntrophomonas sapovorans	species	0.002	0.43	55.9	477	203
Thermodesulfovibrio thiophilus	species	0.002	0.44	52.9	480	210
Thermodesulfovibrio	genus	0.002	0.46	50.9	554	255
Oenococcus	genus	0.002	0.46	50.7	528	241
Helicobacter suncus	species	0.002	0.49	46.3	656	324
Desulfofundulus	genus	0.002	0.47	41.9	434	206
Caldithrix	genus	0.002	0.52	36.7	541	282
Desulfotomaculum defluvii	species	0.003	0.56	36.2	906	508
Viridibacillus	genus	0.002	0.52	33.7	430	222
Streptococcus infantis	species	0.003	0.56	33.2	707	396
Sporotomaculum syntrophicum	species	0.003	0.58	32.4	996	582
Alkalibacterium	genus	0.003	0.58	31.6	792	456
Hydrogenophilus	genus	0.003	0.59	31.6	994	585
Pelagicoccus	genus	0.002	0.58	30.7	750	433

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
Psychrobacter glacialis	species	0.002	0.37	141.3	584	215
Bacteroides heparinolyticus	species	0.003	0.49	114	849	412
Chromatium	genus	0.002	0.38	113.2	466	175
Chromatium weissei	species	0.002	0.38	112.6	465	175
Odoribacter denticanis	species	0.005	0.56	110.5	1455	822
Niabella	genus	0.002	0.4	109.6	500	199
Actinopolyspora	genus	0.002	0.4	107	482	191
Thiorhodococcus	genus	0.002	0.43	99	516	221
Syntrophomonas sapovorans	species	0.002	0.43	93.7	477	203
Thermodesulfovibrio	genus	0.002	0.46	91.2	554	255
Helicobacter suncus	species	0.002	0.49	89.5	656	324
Thermodesulfovibrio thiophilus	species	0.002	0.44	89.5	480	210
Oenococcus	genus	0.002	0.46	89.2	528	241
Desulfotomaculum defluvii	species	0.003	0.56	81.7	906	508
Sporotomaculum syntrophicum	species	0.003	0.58	76.8	996	582
Hydrogenophilus	genus	0.003	0.59	74.9	994	585
Desulfosporosinus	genus	0.0025	1.67	71.5	600	1004
Clostridium taeniosporum	species	0.003	0.62	70.1	1184	734
Desulfofundulus	genus	0.002	0.47	69.5	434	206
Alkalibacterium	genus	0.003	0.58	68.1	792	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.

tax_name	Rank	Symptom Median Freq	Odds Ratio	Chi2	Below	Above
Clostridium taeniosporum	species	0.003	0.62	27.2	1184	734
Mycoplasmopsis edwardii	species	0.005	0.67	21.1	1580	1053
Dethiosulfovibrio	genus	0.004	0.67	20	1335	893
Tetragenococcus doogicus	species	0.003	0.67	18.8	1206	813
Hydrocarboniphaga daqingensis	species	0.004	0.72	13.8	1422	1022
Mycoplasmopsis	genus	0.005	0.74	12	1550	1142
Pediococcus	genus	0.004	0.75	10	1141	855
Tetragenococcus	genus	0.003	0.76	9.2	1183	899
Propionispora hippei	species	0.005	0.77	8.5	1348	1039
Propionispora	genus	0.005	0.77	8.5	1348	1039
Phocaeicola coprocola	species	0.004	0.78	7.2	1068	836
Porphyromonas canis	species	0.005	0.8	6.6	1382	1100

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
Psychrobacter glacialis	species	0.002	0.37	141.3	584	215
Bacteroides heparinolyticus	species	0.003	0.49	114	849	412
Chromatium	genus	0.002	0.38	113.2	466	175
Chromatium weissei	species	0.002	0.38	112.6	465	175
Odoribacter denticanis	species	0.005	0.56	110.5	1455	822
Niabella	genus	0.002	0.4	109.6	500	199
Actinopolyspora	genus	0.002	0.4	107	482	191
Thiorhodococcus	genus	0.002	0.43	99	516	221
Syntrophomonas sapovorans	species	0.002	0.43	93.7	477	203
Thermodesulfovibrio	genus	0.002	0.46	91.2	554	255
Helicobacter suncus	species	0.002	0.49	89.5	656	324
Thermodesulfovibrio thiophilus	species	0.002	0.44	89.5	480	210
Oenococcus	genus	0.002	0.46	89.2	528	241
Desulfotomaculum defluvii	species	0.003	0.56	81.7	906	508
Sporotomaculum syntrophicum	species	0.003	0.58	76.8	996	582
Hydrogenophilus	genus	0.003	0.59	74.9	994	585
Desulfosporosinus	genus	0.0025	1.67	71.5	600	1004
Clostridium taeniosporum	species	0.003	0.62	70.1	1184	734
Desulfofundulus	genus	0.002	0.47	69.5	434	206
Alkalibacterium	genus	0.003	0.58	68.1	792	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

Probiotic Species	Impact	Good Count	Bad Count
Segatella copri	11.24	1	0
Lactobacillus helveticus	10.17	2	2
Bifidobacterium breve	6.17	1	0
Bifidobacterium longum	5.98	1	0
Bifidobacterium adolescentis	4.67	1	0
Bifidobacterium bifidum	1.67	1	0
Bifidobacterium catenulatum	1.04	1	0
Blautia wexlerae	0.71	1	0
Bifidobacterium animalis	0.7	1	0
Enterococcus faecalis	0.17	2	3
Limosilactobacillus reuteri	0.14	3	0
Enterococcus durans	0.11	5	1
Lactobacillus johnsonii	0.11	3	0
Lactococcus lactis	0.09	1	0
Limosilactobacillus vaginalis	0.03	3	1
Bifidobacterium pseudocatenulatum	0.02	2	1
Escherichia coli	0.02	1	0
Lacticaseibacillus rhamnosus	0.02	1	0
Enterococcus faecium	0.01	1	1
Akkermansia muciniphila	-0.01	0	1
Ligilactobacillus salivarius	-0.01	0	1
Bacillus subtilis	-0.02	4	3
Lactobacillus jensenii	-0.02	2	2
Lactobacillus crispatus	-0.03	1	3
Pediococcus acidilactici	-1.09	2	3
Bacteroides uniformis	-32.26	0	1
Bacteroides thetaiotaomicron	-33.35	0	1

Updated: Dec 3, 2025 correcting some computations errors.

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	219
p < 0.001	189
p < 0.0001	161
p < 0.00001	143

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 Bacteroides uniformis below, we see that the average is above and the median below.

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).

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	29.336	25.739	23.905	29.302
Phocaeicola	genus	11.847	10.761	9.194	11.373
Phocaeicola vulgatus	species	6.735	5.719	3.351	4.42
Bacteroides uniformis	species	3.25	2.682	1.524	2.07
Coprococcus	genus	1.206	1.453	0.747	0.552
Bacteroides caccae	species	1.153	0.849	0.282	0.398
Pedobacter	genus	1.174	0.983	0.548	0.659
Bilophila	genus	0.425	0.343	0.203	0.309
Bilophila wadsworthia	species	0.412	0.335	0.193	0.29
Bifidobacterium	genus	0.653	0.961	0.132	0.055
Bacteroides rodentium	species	0.403	0.39	0.179	0.23
Sutterella wadsworthensis	species	0.642	0.66	0.059	0.012
Hathewaya	genus	0.35	0.272	0.153	0.191
Hathewaya histolytica	species	0.35	0.272	0.153	0.19
Phascolarctobacterium faecium	species	0.163	0.14	0.07	0.1
Lachnobacterium	genus	0.233	0.329	0.076	0.047
Butyricimonas	genus	0.194	0.186	0.107	0.133
Anaerofilum	genus	0.266	0.269	0.105	0.13
Oribacterium	genus	0.103	0.133	0.074	0.049
Anaerotruncus	genus	0.219	0.184	0.136	0.161

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. Look at Bacteroides uniformis below, we see that the average is above and the median below.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Shewanella upenei	species	1.46	13	35.8	24.4
Methanobrevibacter	genus	0.61	10.4	13.8	22.4
Methanobrevibacter smithii	species	0.62	10.1	13.5	21.9
Slackia isoflavoniconvertens	species	0.62	9	12.7	20.4
Prosthecobacter	genus	1.68	12.9	17	10.1
Bifidobacterium cuniculi	species	0.66	7.1	12.7	19.4
Desulfomonile tiedjei	species	1.45	7.8	20.2	13.9
Desulfomonile	genus	1.44	7.6	20.2	14

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
Alcanivorax	genus	0.002	0.28	74.1	365	101
Isoalcanivorax	genus	0.002	0.28	73.2	355	98
Isoalcanivorax indicus	species	0.002	0.28	73.2	355	98
Nostoc flagelliforme	species	0.002	0.27	68.3	305	83
Pelagicoccus croceus	species	0.002	0.31	63.6	366	114
Psychroflexus	genus	0.002	0.31	63.1	348	107
Psychroflexus gondwanensis	species	0.002	0.31	63.1	348	107
Niabella aurantiaca	species	0.002	0.35	62.5	507	177
Salidesulfovibrio	genus	0.002	0.33	59.7	370	121
Salidesulfovibrio brasiliensis	species	0.002	0.33	59.7	370	121
Deferribacter autotrophicus	species	0.002	0.32	59.2	355	115
Psychrobacter glacialis	species	0.002	0.38	59.2	629	238
Deferribacter	genus	0.002	0.33	58.4	357	117
Bacillus ferrariarum	species	0.002	0.34	56	354	119
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.36	52.7	374	133
Segetibacter aerophilus	species	0.002	0.35	51.8	356	126
Thiorhodococcus pfennigii	species	0.002	0.36	51.7	392	143
Niabella	genus	0.002	0.4	51.4	543	215
Pontibacillus halophilus	species	0.002	0.37	50.7	397	147
Pontibacillus	genus	0.002	0.37	50.6	401	149

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
Bilophila	genus	0.3095	0.55	154.4	2147	1184
Psychrobacter glacialis	species	0.002	0.38	145.8	629	238
Niabella aurantiaca	species	0.002	0.35	136.6	507	177
Alcanivorax	genus	0.002	0.28	134.4	365	101
Isoalcanivorax	genus	0.002	0.28	131.4	355	98
Isoalcanivorax indicus	species	0.002	0.28	131.4	355	98
Bilophila wadsworthia	species	0.2905	0.58	127.5	2096	1222
Bacteroides heparinolyticus	species	0.003	0.49	122	921	449
Niabella	genus	0.002	0.4	119.8	543	215
Pelagicoccus croceus	species	0.002	0.31	118.5	366	114
Nostoc flagelliforme	species	0.002	0.27	116.1	305	83
Psychroflexus	genus	0.002	0.31	114.9	348	107
Psychroflexus gondwanensis	species	0.002	0.31	114.9	348	107
Salidesulfovibrio	genus	0.002	0.33	112.8	370	121
Salidesulfovibrio brasiliensis	species	0.002	0.33	112.8	370	121
Actinopolyspora	genus	0.002	0.4	111.3	501	199
Chromatium	genus	0.002	0.39	111.3	491	193
Chromatium weissei	species	0.002	0.39	110.7	490	193
Bacteroides	genus	29.302	0.62	110.5	2318	1428
Thiorhodococcus	genus	0.002	0.42	110	551	231

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	0.61	19	1266	777
Dethiosulfovibrio	genus	0.004	0.68	12.3	1414	958
Tetragenococcus doogicus	species	0.003	0.69	11.3	1280	880
Hydrocarboniphaga daqingensis	species	0.004	0.7	10.4	1525	1069
Mycoplasmopsis	genus	0.005	0.71	10.2	1703	1201
Pediococcus	genus	0.004	0.75	6.6	1225	919

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.

Bilophila	genus	0.3095	0.55	154.4	2147	1184
Psychrobacter glacialis	species	0.002	0.38	145.8	629	238
Niabella aurantiaca	species	0.002	0.35	136.6	507	177
Alcanivorax	genus	0.002	0.28	134.4	365	101
Isoalcanivorax indicus	species	0.002	0.28	131.4	355	98
Isoalcanivorax	genus	0.002	0.28	131.4	355	98
Bilophila wadsworthia	species	0.2905	0.58	127.5	2096	1222
Bacteroides heparinolyticus	species	0.003	0.49	122	921	449
Niabella	genus	0.002	0.4	119.8	543	215
Pelagicoccus croceus	species	0.002	0.31	118.5	366	114
Nostoc flagelliforme	species	0.002	0.27	116.1	305	83
Psychroflexus gondwanensis	species	0.002	0.31	114.9	348	107
Psychroflexus	genus	0.002	0.31	114.9	348	107
Salidesulfovibrio brasiliensis	species	0.002	0.33	112.8	370	121
Salidesulfovibrio	genus	0.002	0.33	112.8	370	121
Actinopolyspora	genus	0.002	0.4	111.3	501	199
Chromatium	genus	0.002	0.39	111.3	491	193
Chromatium weissei	species	0.002	0.39	110.7	490	193
Bacteroides	genus	29.302	0.62	110.5	2318	1428
Thiorhodococcus	genus	0.002	0.42	110	551	231

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
Faecalibacterium prausnitzii	128.43	7	2
Segatella copri	79.14	10	0
Bifidobacterium breve	66.59	16	1
Bifidobacterium longum	59.74	16	0
Bifidobacterium adolescentis	44.18	14	0
Lactobacillus helveticus	37.56	105	31
Bifidobacterium bifidum	13.45	15	2
Akkermansia muciniphila	12.69	16	14
Bifidobacterium catenulatum	12.37	15	0
Enterococcus faecalis	10.76	70	27
Bifidobacterium animalis	6.11	7	0
Enterococcus faecium	3.69	38	18
Streptococcus thermophilus	3.24	8	0
Enterococcus durans	1.81	36	8
Clostridium butyricum	1.46	36	4
Escherichia coli	1.28	2	2
Bacillus subtilis	0.7	44	23
Limosilactobacillus vaginalis	0.69	42	19
Pediococcus acidilactici	0.68	32	42
Lactococcus lactis	0.57	11	2
Bifidobacterium pseudocatenulatum	0.53	34	11
Limosilactobacillus fermentum	0.4	14	8
Veillonella atypica	0.38	11	0
Lacticaseibacillus paracasei	0.35	17	5
Limosilactobacillus reuteri	0.34	26	11
Ligilactobacillus salivarius	0.15	7	12
Lactobacillus acidophilus	0.14	33	12
Heyndrickxia coagulans	0.09	28	13
Leuconostoc mesenteroides	0.06	17	13
Lacticaseibacillus rhamnosus	0.02	4	2
Lacticaseibacillus casei	-0.04	4	8
Lactobacillus crispatus	-0.2	13	23
Lactobacillus jensenii	-0.21	37	29
Lactiplantibacillus plantarum	-0.24	0	1
Lactiplantibacillus pentosus	-0.28	2	5
Odoribacter laneus	-0.3	0	1
Parabacteroides distasonis	-0.89	3	2
Lactobacillus johnsonii	-4.71	55	26
Parabacteroides goldsteinii	-8.88	0	11
Blautia wexlerae	-21.96	3	2
Blautia hansenii	-25.06	2	3
Bacteroides uniformis	-458.22	0	14
Bacteroides thetaiotaomicron	-489.69	0	13