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