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

I just got an email asking for which bacteria are involved with hypersensitivity to noise. This post is just presenting the tables derived from the methodology described in this technical post: Odds Ratios and the Microbiome. 

Here are some odds ratios using BiomeSight data. Odds Low means when the reading is below the Median and Odds High above the Median (of those with this symptom). We use the symptom median to get balanced (same approximate size) categories. Using an average results in poorer results.

A 1.2 in Odds Low, means that having less then typical/median increases your odds, i.e. you want to increase the amount.

At first look for probiotics, we see:

• Bifidobacterium adolescentis
• Bifidobacterium longum
• Lactococcus

I also note that Odds Low really dominant, i.e. too little of a lot of different bacteria. This hints at Prescript-Assist®/SBO Probiotic with 22 different unusual probiotics as being a possible candidate as well as General Biotics/Equilibrium.

Tax_Name	tax_Rank	Odds Low	Odd High
Collinsella tanakaei	species	0.74	1.67
Segatella paludivivens	species	1.56	0.73
Viridiplantae	kingdom	1.51	0.65
Peptostreptococcus stomatis	species	1.47	0.69
Bacteroides salyersiae	species	1.42	0.74
Neisseriales	order	1.42	0.63
Bifidobacterium	genus	1.42	0.77
Bifidobacterium adolescentis	species	1.41	0.76
Bifidobacteriales	order	1.41	0.77
Bifidobacteriaceae	family	1.41	0.77
genistoids sensu lato	clade	1.40	0.72
rosids	clade	1.40	0.72
Rothia	genus	1.40	0.72
core genistoids	clade	1.40	0.72
Crotalarieae	tribe	1.40	0.72
Fabaceae	family	1.40	0.72
Papilionoideae	subfamily	1.40	0.72
50 kb inversion clade	clade	1.40	0.72
Fabales	order	1.40	0.72
fabids	clade	1.40	0.72
Desulfosporosinus	genus	1.39	0.76
Gunneridae	clade	1.39	0.73
Streptophytina	subphylum	1.39	0.73
Tracheophyta	clade	1.39	0.73
Embryophyta	clade	1.39	0.73
eudicotyledons	clade	1.39	0.73
Spermatophyta	clade	1.39	0.73
Magnoliopsida	class	1.39	0.73
Mesangiospermae	clade	1.39	0.73
Euphyllophyta	clade	1.39	0.73
Streptophyta	phylum	1.39	0.73
Pentapetalae	clade	1.39	0.73
Bifidobacterium choerinum	species	1.38	0.77
Neisseria	genus	1.38	0.67
Bifidobacterium adolescentis JCM 15918	strain	1.37	0.79
Lysobacter	genus	0.79	1.37
Neisseriaceae	family	1.37	0.65
Rothia	genus	1.36	0.75
Actinomycetota	phylum	1.36	0.79
Bifidobacterium gallicum	species	1.35	0.75
Catenibacterium mitsuokai	species	1.35	0.72
Planococcus	genus	1.34	0.57
Bifidobacterium indicum	species	1.34	0.75
Planococcus columbae	species	1.34	0.58
Enterobacter	genus	1.34	0.80
Morganellaceae	family	1.33	0.64
Clostridium chartatabidum	species	1.33	0.78
Sutterella stercoricanis	species	1.32	0.78
Aeromonadales	order	1.32	0.75
Mesoplasma entomophilum	species	1.31	0.78
Rothia mucilaginosa	species	1.30	0.78
Entomoplasmataceae	family	1.30	0.79
Entomoplasmatales	order	1.30	0.79
Eukaryota	superkingdom	1.30	0.71
Mesoplasma	genus	1.30	0.79
Succinivibrio	genus	1.30	0.76
Bifidobacterium longum	species	1.29	0.81
Succinivibrionaceae	family	1.29	0.81
Ruminococcus callidus	species	1.29	0.78
Streptococcus cristatus	species	1.28	0.58
Tepidibacter	genus	1.28	0.82
Catenibacterium	genus	1.28	0.76
Atopobium fossor	species	1.28	0.62
Rivulariaceae	family	1.27	0.82
Dyadobacter	genus	1.27	0.63
Actinomycetes	class	1.27	0.82
Oribacterium	genus	1.27	0.82
Clostridium cadaveris	species	1.26	0.83
Micrococcaceae	family	1.26	0.72
Micromonosporaceae	family	1.26	0.65
Micromonosporales	order	1.26	0.65
Streptococcus sanguinis	species	1.25	0.73
Citrobacter	genus	1.25	0.83
Oribacterium sinus	species	1.25	0.83
Acinetobacter	genus	1.25	0.75
Salisaetaceae	family	1.25	0.59
Salisaeta	genus	1.25	0.59
Salisaeta longa	species	1.24	0.59
Thermosediminibacterales	order	1.23	0.83
Candidatus Tammella caduceiae	species	1.23	0.76
Lachnobacterium	genus	1.23	0.84
Alishewanella	genus	1.23	0.62
Heliorestis	genus	1.23	0.84
Actinocatenispora	genus	1.22	0.65
Azospirillum	genus	1.22	0.80
Candidatus Tammella	genus	1.22	0.77
Bifidobacterium catenulatum PV20-2	strain	1.22	0.84
Lactococcus	genus	1.22	0.83
Bifidobacterium subtile	species	1.22	0.84
Negativicoccus	genus	1.22	0.82
Succinivibrio dextrinosolvens	species	1.21	0.85
Opisthokonta	clade	1.21	0.71
Eumetazoa	clade	1.21	0.71
Metazoa	kingdom	1.21	0.71
Caloramator indicus	species	1.21	0.85
Desulfurisporaceae	family	1.21	0.78
Desulfurispora	genus	1.21	0.78
Desulfurispirillum alkaliphilum	species	1.21	0.81
Streptococcus milleri	species	1.21	0.79
Coprococcus eutactus	species	1.21	0.84
Desulfurispora thermophila	species	1.21	0.78
Herbaspirillum magnetovibrio	species	1.20	0.59
Phocaeicola massiliensis	species	1.20	0.85
Prevotella dentasini	species	1.20	0.79
Collinsella intestinalis	species	1.20	0.81
Pseudomonas	genus	1.20	0.85
Coraliomargarita akajimensis	species	1.20	0.67
Coraliomargaritaceae	family	1.20	0.67
Coraliomargarita	genus	1.20	0.67
Pseudomonadaceae	family	1.20	0.85
Bilateria	clade	1.20	0.72

In working with Microbiome Prescription, I experimented with various prediction approaches before settling on a workaround that, in many cases, could successfully predict the top 10 symptoms for new microbiome samples, with individuals confirming about 80% of them as accurate reflections of their own symptoms. Though this solution was adequate for practical needs, it was admittedly less than ideal in theory. Recently, I recognized that a more robust and principled prediction algorithm is achievable. The aim of this post is to walk through that process, making it accessible for anyone interested in trying this more rigorous approach.

Accurate prediction identifies the key bacteria that should be altered with statistical justification.

An odds ratio (OR) is a measure of association that describes the odds of a disease, symptom, or event occurring in one group compared to another, often used in medical and epidemiological studies to estimate the strength of risk factors or the effectiveness of interventions.​

Understanding Odds Ratios

• The odds ratio is calculated by dividing the odds of the event in the exposed group by the odds in the non-exposed group.​
• OR > 1 indicates higher odds of disease with the exposure or risk factor; OR < 1 indicates reduced odds; OR = 1 means no difference in odds between groups.​
• Odds ratios are especially used in case-control studies, but also in cohort and cross-sectional studies, and they can approximate risk ratios when the disease or symptom is rare.​

Using Multiple Odds Ratios in Disease Analysis

When you have several odds ratios related to a disease, there are several key uses:

• Compare the magnitude of different risk factors: By looking at the odds ratios for various exposures (e.g., smoking, age group, genetic markers), you can identify which exposures are most strongly associated with the disease.​​
• Synthesize evidence: Meta-analysis allows combining odds ratios from multiple studies to produce a summary effect estimate, which helps determine overall strength of association and consistency across populations.​

Example Table of Interpreting Odds Ratios

Exposure/Risk Factor	Odds Ratio	Interpretation
Smoking	3.5 ​	Exposure increases odds
Physical Activity	0.7 ​	Exposure decreases odds
High BMI	1.2 ​	Exposure slightly increases odds
Family History	4.0 ​	Strong increased odds

These odds ratios can guide targeted interventions, identify priority risk factors, and inform clinical decision-making or public health policy.​

Each odds ratio’s confidence interval should be considered to determine statistical significance: if it includes 1, the specific association may not be statistically meaningful.​

Summary

The charts below used naïve odds ratio computation (ignoring Probability) of people with declared symptom and those without this symptoms. If you use Zero(0) as a threshold, we correctly predicted 74% of the time for those with symptoms and those without symptoms.

Odds ratios quantify the likelihood of disease or symptoms given exposures and allow comparison and synthesis of risk across different factors or populations. When handling multiple odds ratios, use them to identify, adjust for, and summarize the impact of risk factors on disease occurrence.​

Applying to the Microbiome

We encounter some challenges here. Consider this constructed example:

• Bacteria Foo has OR of 1.5 when the microbiome exceeds 5%
• Bacteria Bar has OR of 2 when the microbiome exceeds 3%
• Bacteria Foo and Bar are associated.

If a sample has both, the OR is not 1.5 x 2 or 3.0. Instead, we need to know much they influence each other, i.e. the R². We can estimate this from Microbiome Taxa R² Site. Suppose that R² is 0.5, significant inference.

The Odds ratio is thus reduced to 2.66 from 3.0.

Odds Ratios and Continuous Values

Odds ratios are commonly used for binary data, such as smoker versus non-smoker or high school graduation status. Continuous data can also be categorized; for example, instead of treating smoking as simply yes/no, you might use metrics like the number of cigarettes smoked per day or packs per week. Similarly, the microbiome data can be categorized, though caution is needed to avoid over-interpreting sparse data. A rough guideline from many studies suggests a minimum of 30 cases and 30 controls are needed to calculate an odds ratio with basic reliability. For data on the lower end, it can be helpful to binarize using the median rather than the mean. This is important because bacterial abundances tend to be highly skewed—using the mean often results in about 70% of samples falling below it and 30% above, whereas the median splits the data evenly with 50% below and 50% above.

Example: Brain Fog

Here are some odds ratios using BiomeSight data. Odds Low means when the reading is below the Median and Odds High above the Median (of those with this symptom). We use the symptom median to get balanced (same approximate size) categories.

A few quick take away:

• Probiotics such as Bifidobacterium, Ligilactobacillus, Lactococcus lactis, Lactiplantibacillus
  • Bifidobacterium catenulatum subsp. kashiwanohense (OR 1.37) is the preferred one!
  • Ligilactobacillus: Ligilactobacillus salivarius is the only one available retail
  • Lactiplantibacillus: Lactiplantibacillus plantarum is the only one available retail
  • Veillonella atypica is offered as FITBIOMICS V•Nella Lactic Acid Metabolizing Probiotic …
    • Note: Brain fog is often ascribed to too much Lactic Acid.

Tax_Name	tax_Rank	Odds Low	Odd High
Cerasicoccus arenae	species	1.59	0.71
Polyangia	subclass	1.47	0.72
Lelliottia	genus	1.42	0.75
Lelliottia amnigena	species	1.42	0.75
Microcoleaceae	family	0.82	1.41
Myxococcia	class	1.38	0.71
Myxococcales	order	1.38	0.71
Myxococcota	phylum	1.38	0.71
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	1.37	0.74
Denitratisoma	genus	0.87	1.37
Microcoleus antarcticus	species	0.81	1.36
Microcoleus	genus	0.81	1.36
Desulfosporosinus	genus	1.34	0.80
Trabulsiella	genus	1.33	0.80
Rivulariaceae	family	1.32	0.79
Segatella paludivivens	species	1.32	0.79
Prosthecobacter	genus	1.32	0.73
Ligilactobacillus	genus	1.31	0.77
Enterobacter cloacae complex	species group	1.30	0.80
Peptostreptococcus stomatis	species	1.30	0.80
Alcanivorax	genus	0.93	1.30
Alcanivoracaceae	family	0.93	1.30
Tepidanaerobacter syntrophicus	species	1.30	0.79
Tepidanaerobacter	genus	1.30	0.79
Tepidanaerobacteraceae	family	1.30	0.79
Hoylesella loescheii	species	1.29	0.81
Thermosediminibacterales	order	1.28	0.81
Enterobacter hormaechei	species	1.28	0.82
Slackia isoflavoniconvertens	species	0.84	1.27
Bifidobacterium choerinum	species	1.27	0.82
Desulfovibrio simplex	species	1.27	0.80
Chromatium	genus	0.90	1.27
Lactococcus fujiensis	species	1.27	0.67
Chromatium weissei	species	0.90	1.27
Klebsiella	genus	1.27	0.82
Klebsiella/Raoultella group	no rank	1.27	0.82
Veillonella atypica	species	1.26	0.82
Isoalcanivorax	genus	0.94	1.26
Isoalcanivorax indicus	species	0.94	1.26
Schaalia turicensis	species	1.25	0.72
Lactococcus lactis	species	1.25	0.83
Bifidobacteriaceae	family	1.24	0.84
Bifidobacteriales	order	1.24	0.84
Chloroflexota	phylum	1.24	0.79
Salidesulfovibrio brasiliensis	species	0.92	1.24
Salidesulfovibrio	genus	0.92	1.24
Enterobacter	genus	1.24	0.81
Bifidobacterium	genus	1.24	0.84
Actinomycetota	phylum	1.24	0.84
Acholeplasma hippikon	species	0.85	1.23
Mycoplasmataceae	family	1.23	0.82
Mycoplasmatales	order	1.23	0.82
Bifidobacterium angulatum	species	1.23	0.82
Clostridium nitrophenolicum	species	0.85	1.23
Bacteroides uniformis	species	0.85	1.22
Lactococcus	genus	1.22	0.83
Lactiplantibacillus	genus	1.22	0.84
Mycoplasma	genus	1.22	0.82
Filifactor villosus	species	0.88	1.22
Anaerolineae	class	1.21	0.85
Veillonella denticariosi	species	0.89	1.21
Actinomycetes	class	1.21	0.85
Acidimicrobium	genus	1.21	0.79
Cerasicoccaceae	family	1.21	0.79
Cerasicoccus	genus	1.21	0.79
Mycoplasmoidales	order	1.21	0.81
Parabacteroides gordonii	species	1.21	0.84
Thioalkalivibrio jannaschii	species	1.21	0.63
Candidatus Blochmanniella camponoti	species	1.21	0.79
Thioalkalivibrio	genus	1.21	0.63
Acidimicrobiaceae	family	1.21	0.77
Bifidobacterium adolescentis	species	1.21	0.85
Bifidobacterium longum	species	1.21	0.85

That’s it for the moment

Also, see the links below for by-request tables

• Odds Ratio Snapshots: Histamine or Mast Cell issues
• Odds Ratio Snapshots: Official Diagnosis: Chronic Fatigue Syndrome (CFS/ME)
• Odds Ratio Snapshot: ME/CFS without IBS
• Odds Ratio Snapshot: ME/CFS with IBS
• Odds Ratio Snapshots: Unrefreshed sleep
• Odds Ratio Snapshots: Neurocognitive: Brain Fog
• Odds Ratio Snapshots: Histamine or Mast Cell issues
• Odds Ratio Snapshots: Unrefreshed sleep
• Odds Ratio Snapshots: Neurocognitive: Brain Fog
• Odds Ratios for Neurological-Audio: hypersensitivity to noise

The next step is seeing how these odds ratio perform against samples and against the old algorithm. Stay tune.

Special note: This is not based on using averages of healthy populations, but more on the skewness of the distribution of those with the symptom. It is a different way of thinking about the issue.

caveat emptor

The table above applies only and exclusively with Biomesight data. For an explanation of why, see The taxonomy nightmare before Christmas… If you use a different lab, you will need to get that lab to crunch their numbers in the same manner as detailed above

This morning I was trouble shooting an upload issue on Ombre CSV data — the reason was “they changed the format again!“. While triaging the issues I saw a lot of counts of “1” in the sample that I was working with. A count of 1 means that only one unit of bacteria was detected. Most microbiologists would deem that to be unreliable, the bacteria may not actually be present, i.e. a “Ghost Bacteria Identification”.

As a result, I look at the 16s tests that has been uploaded to compute the percentages of ghosts in samples.

16s Test from	Average	Lowest Rate	Highest Rate	Bacteria Reported
Biomesight	22.1%	0%	35.3%	611
Ombre	28.8%	0%	41.1%	694
Medivere	20.5%	19%	22.3%	756
BiomeSightRdp	11%	1.9%	20.0%	476
CerbaLab	13.9%	0%	24%	Over 600
SequentiaBiotech	1.4%	0%	5%	313
CosmosId	0.01%	0%	0.28%	463

The numbers above suggests that reporting on ghosts results in more bacteria reports — which is a good marketing strategy. It is a questionable service to the consumers.

For myself, for my offline research database, I will be excluding counts of “1”. I may also offer an option to remove them on the upload page in the future. This is not a significant issue with shotgun reports.

“Buyer beware,” or caveat emptor 

From Perplexity (Click to get sources):

In 16S microbiome sequencing, counts of “1” (single read assigned to a taxon in a sample) are generally not considered reliable for determining the true presence of that organism. Here’s why:

• Low-abundance signals (especially a single read) can easily result from sequencing errors, index hopping, cross-contamination, or misclassification in the bioinformatic pipeline.​
• Studies show that only OTUs (Operational Taxonomic Units) with higher counts (usually >10 reads, and especially >1% relative abundance) are consistently detected with high reliability and quantification accuracy.​
• Single-read taxa are much more likely to be false positives or background noise. They typically do not pass statistical filtering thresholds used in rigorous microbiome analysis.​
• Many pipelines recommend removing OTUs present in very low abundances (often <10 reads or <0.1–1% relative abundance) for reliable interpretation.​

Summary:

• Counts of “1” should be viewed as unreliable noise and not taken as meaningful evidence of that organism’s presence in your microbiome sample.
• Reliable detection begins at much higher read counts and relative abundances, with reproducibility improving rapidly as counts increase.​

Best practices:

• Filter out taxa with extremely low counts for clinical or quantitative interpretation.
• Use statistical and bioinformatic guidelines to set raw count and relative abundance thresholds for reporting results.

If you see a taxon with just one assigned read in your 16S data, consider it an artifact rather than true biological detection unless verified by other means.​

Lazy versus Old School

I have observed that many data scientists tend to push data into a model and report the results of the model. I am old school and was taught to always chart the data to look for abnormalities. Doing that revealed that microbiome data is highly skewed. I covered this in Microbiologist / Data Scientist Guide to Bacterium Statistics.

I subsequently came across an odds plot where we have an appearance similar to electron shell densities and not the nice linear model that is often assumed.

The result was a clear need to review a lot more data graphically. There are the main patterns:

• The condition line is clearly to the left of the reference line, i.e. transformed average is less
• The condition line is clearly to the right of the reference line, i.e. transformed average is more
• The condition line is on both sides of the reference line, i.e. a complex situation.
• The lines are on top of each other — no association to the symptom

Lower Transformed Average

Higher Transformed Average

Mixed Case

No Association

A Video Show

I generated a program to walk through some random bacteria and recorded them in the video below. Pause the video when you want to look at a specific chart in greater detail. My main conclusion is that often a bacteria is significant only when it is in a certain range.

400+ more over 20 minutes

Autism Only

Long COVID

A person who suffered from Multiple Chemical Sensitivity(MCS) for many years before it progressed into Mast Cell Syndrome(MCAS) forward an article, “Chemical Intolerance and Mast Cell Activation: A Suspicious Synchronicity“, 2023. At the same time, my understanding of the complex nature of the microbiome also made a leap forward. For those interested, see these three very technical posts:

• Using Mean and Standard Deviation for Bacteria is Inappropriate.
• Significant Bacteria and Their Thresholds – Part 1
• Significant Bacteria and Their Thresholds – Part 2

I decided to look at Mast Cell Activation Syndrome again in the hope of gaining insight into treatment possibilities.

The samples being using are donated by readers from various labs with symptoms being self-declared. Symptoms may not agree with clinical definitions. All of the data is freely available for those that are highly skilled with statistics at Citizen Science Distribution.

First, MCS::MCAS

	With MCAS	With MCS	WITH MCAS and MCS	With Any Symptoms
Count	305	219	62	3025
Percentage	10%	7.2%	2%

• If MCAS and MCS are independent, we would expect 10% x 7.2% or 0.72% overall. We have 3 times more than expected.
• The chi-square statistic is 19.3693. The p-value is .000011. VERY SIGNIFICANT CONNECTION.

This disagrees on face value with the reported “Our outcomes confirm the previously published study where the majority of MCAS patients also have CI. ” For this to be true, With MCAS and MCS would be > 150. Differences in methodology may be the cause for this disagreement, but regardless, we see that a person with MCAS is around three times more likely to have had MCS. I read this as suggesting that MCS is a precursor for a class of MCAS. Having MCS prior is not required to developing MCAS; but having MCS means the odds of getting MCAS are much increased.

Looking at Bacterium

I am going to use samples processed through Biomesight only because it is the largest sample set.

For MCS

The table below is filtered to those with P < 0.001 at the genus level with the highest first (P < 5.19132E-05).

Name	Direction
Actinocatenispora	Low
Hathewaya	High
Thauera	Low
Devosia	Low
Thiocapsa	Low
Deferribacter	Low
Viridibacillus	Low
Candidatus Tammella	Low
Coraliomargarita	Low
Geothrix	Low
Desulfosporosinus	Low
Glutamicibacter	Low
Denitratisoma	Low
Catenibacterium	Low
Desulforamulus	Low
Geobacter	Low
Neisseria	Low
Nonomuraea	Low
Agromyces	Low
Anaerotruncus	High
Oenococcus	Low
Saccharopolyspora	Low
Lentibacillus	Low

MCAS

The table below is filtered to those with P < 0.001 at the genus level with the highest first (P < 6.25726E-07).

Name	Direction
Emticicia	Low
Pseudoramibacter	Low
Parascardovia	Low
Rickettsia	Low
Calothrix	Low
Nonomuraea	Low
Marinospirillum	Low
Azospirillum	Low
Neisseria	Low
Viridibacillus	Low
Helicobacter	Low
Peptacetobacter	Low
Nitrosococcus	Low
Avibacterium	Low
Schaalia	Low
Propionigenium	Low
Flammeovirga	Low
Oligella	Low
Erysipelothrix	High
Geobacter	Low
Catenibacterium	Low
Pontibacter	Low
Isoalcanivorax	Low
Faecalitalea	Low
Jonesia	Low
Thalassospira	Low
Amedibacillus	High
Arthrobacter	Low
Hathewaya	High

MCAS and MCS

The table below is filtered to those with P < 0.001 with the highest first (P < 1.85255E-05). The sample size is much smaller, so fewer items were significant, hence all ranks are shown.

Name	Rank	Direction
Chloroflexota	phylum	Low
Anaerolineae	class	Low
Eggerthella sinensis	species	Low
Desulfofundulus	genus	Low

Probiotic Remedies?

Because there are simply no published studies on most of the above bacterium, I went over to the R2 site to compute candidate probiotics. Note: Some of these probiotics are still in development or available only in some countries.

MCS

I enclosed the full list because you want to make sure NOT to take any with a Net being negative. Also, the safest are those with BAD being Zero (0)

Tax_Name	Tax_Rank	Good	Bad	Net
Christensenella minuta	species	194	29	165
Aspergillus oryzae	species	138	0	138
Faecalibacterium prausnitzii	species	185	78	107
Anaerobutyricum hallii	species	162	58	104
Enterococcus faecium	species	124	37	87
Blautia hansenii	species	122	37	85
Lactiplantibacillus plantarum	species	64	0	64
Roseburia intestinalis	species	118	60	58
Bifidobacterium catenulatum	species	53	0	53
Priestia megaterium	species	47	0	47
Bacillus pumilus	species	43	0	43
Bacteroides thetaiotaomicron	species	37	0	37
Latilactobacillus sakei	species	37	0	37
Bifidobacterium breve	species	32	0	32
Levilactobacillus brevis	species	31	0	31
Parabacteroides distasonis	species	31	0	31
Parabacteroides goldsteinii	species	54	28	26
Pediococcus pentosaceus	species	25	0	25
Limosilactobacillus reuteri	species	23	0	23
Shouchella clausii	species	23	0	23
Lactiplantibacillus argentoratensis	species	23	0	23
Bifidobacterium longum	species	20	0	20
Bifidobacterium adolescentis	species	39	21	18
Blautia wexlerae	species	74	57	17
Lactococcus cremoris	species	36	21	15
Enterococcus faecalis	species	14	0	14
Bifidobacterium pseudocatenulatum	species	13	0	13
Limosilactobacillus vaginalis	species	12	0	12
Lactobacillus kefiranofaciens	species	12	0	12
Lactococcus lactis	species	11	0	11
Clostridium beijerinckii	species	11	0	11
Streptococcus thermophilus	species	10	0	10
Leuconostoc mesenteroides	species	10	0	10
Segatella copri	species	37	29	8
Phocaeicola coprocola	species	27	21	6
Bacillus subtilis	species	26	21	5
Lactobacillus crispatus	species	11	11	0
Lactiplantibacillus pentosus	species	0	11	-11
Bacteroides uniformis	species	20	32	-12
Limosilactobacillus mucosae	species	0	14	-14
Lacticaseibacillus casei	species	0	17	-17
Bacillus cereus	species	33	54	-21
Bacillus licheniformis	species	0	22	-22
Ligilactobacillus salivarius	species	11	41	-30
Lactobacillus jensenii	species	0	36	-36
Akkermansia muciniphila	species	12	50	-38

MCAS

Tax_Name	Tax_Rank	Good	Bad	Net
Christensenella minuta	species	83	0	83
Aspergillus oryzae	species	68	0	68
Enterococcus faecium	species	58	0	58
Faecalibacterium prausnitzii	species	53	0	53
Roseburia intestinalis	species	53	0	53
Anaerobutyricum hallii	species	51	0	51
Blautia wexlerae	species	44	0	44
Bacillus pumilus	species	28	0	28
Priestia megaterium	species	27	0	27
Levilactobacillus brevis	species	25	0	25
Latilactobacillus sakei	species	25	0	25
Lactiplantibacillus argentoratensis	species	23	0	23
Blautia hansenii	species	22	0	22
Limosilactobacillus fermentum	species	21	0	21
Shouchella clausii	species	20	0	20
Limosilactobacillus reuteri	species	18	0	18
Lactiplantibacillus plantarum	species	17	0	17
Bacillus subtilis	species	16	0	16
Bifidobacterium animalis	species	15	0	15
Bifidobacterium animalis subsp. lactis	subspecies	15	0	15
Lactobacillus acidophilus	species	14	0	14
Clostridium butyricum	species	13	0	13
Bifidobacterium adolescentis	species	12	0	12
Ligilactobacillus salivarius	species	11	0	11
Hafnia alvei	species	11	0	11
Bacteroides uniformis	species	0	15	-15
Lacticaseibacillus rhamnosus	species	0	16	-16
Bacteroides fragilis	species	0	23	-23

Bottom Line

The most confidence is to work on probiotics only with the following being strongly recommended.

• Aspergillus oryzae
• Enterococcus faecium
• Bacillus pumilus
• Bacillus subtilis
• Lactiplantibacillus plantarum a.k.a. Lactobacillus plantarum
• Bifidobacterium catenulatum
• Bifidobacterium breve
• Levilactobacillus brevis a.k.a. Lactobacillus brevis
• Latilactobacillus sakei a.k.a. Lactobacillus sakei
• Limosilactobacillus reuteri a.k.a. Lactobacillus reuteri
• Shouchella clausii a.k.a. Bacillus Clausii
• Lactobacillus acidophilus
• Ligilactobacillus salivarius a.k.a. Lactobacillus salivarius

The top one for both is Aspergillus oryzae. This is likely unfamiliar to most people. It is also known as Shirayuri Koji. It is available on Amazon, not as a probiotic but cooking additive!! It is in Koji Rice. It is also solid as strong wakamoto w

With Tariffs ordering from Japan can get expensive, https://www.yami.com/ ships from the US, so no tariffs costs!

CAUTION: This is based on modelled data and not verified by clinical studies. IMHO, it is likely a superior set of suggestions than other more “conventional” approaches.

In an earlier post (Significant Bacteria and Their Thresholds – Part 1), I raised that issue and a EU colleague, Valentina Goretzki, suggested that I take data from 1 thousand shotgun samples from healthy individuals to illustrate the problem.

Microbiome data distributions frequently display extreme skewness—often greater than 20. In such cases, computing mean and standard deviation is simply incorrect.  My friend “Perplexity” writes Mean and standard deviation become inappropriate measures for computing significance if the distribution’s skewness is substantial—specifically, when the absolute skewness exceeds ±2.

The result was about two thousand bacterium that occurs at least 60 times in these samples could be plotted as shown below.

It is clear that non-parametric methods are needed to compute “healthy ranges”. For those with just basic statistics, this may become a significant challenge.