I have just added a new page, Symptom Associations, you can progressively enter symptoms. After each one is added, the page recalculates and show the odds, given that set of symptoms, of potential additional symptoms.

If you have a sample from Biomesight, Ombre or Thorne, you can get a better estimates of the odds of developing this symptom specific to your microbiome!

And then by clicking Suggestions, get suggestions to reduce the risk of developing it.

Probiotics

Substances to Take

Substances to Avoid

Summary

The suggestions are computed explicitly for your microbiome and the bacteria identified for a symptoms. These may be counter-indicated for other symptom treatment. These may often contradict “universal solutions for bloating” from internet hearsay.

Below is a video walkthrough

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?. See also:

• ​Odds Ratio Snapshot: ME/CFS without IBS
• Odds Ratio Snapshots: Official Diagnosis: Chronic Fatigue Syndrome (CFS/ME)

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	198
p < 0.001	172
p < 0.0001	156
p < 0.00001	141

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 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	27.459	25.974	24.269	26.821
Lachnospira	genus	3.093	2.706	1.886	2.4
Bacteroides uniformis	species	3.057	2.713	1.545	2.007
Phocaeicola dorei	species	3.667	2.865	0.395	0.77
Sutterella	genus	1.72	1.64	1.24	1.465
Coprococcus	genus	1.24	1.442	0.737	0.566
Phascolarctobacterium	genus	0.674	0.578	0.396	0.538
Bacteroides cellulosilyticus	species	1.194	0.835	0.075	0.176
Bilophila	genus	0.46	0.344	0.208	0.288
Bifidobacterium	genus	0.539	0.956	0.129	0.052
Bilophila wadsworthia	species	0.45	0.336	0.198	0.273
Bacteroides fragilis	species	0.93	0.841	0.05	0.113
Anaerofilum	genus	0.323	0.266	0.105	0.166
Sutterella wadsworthensis	species	0.608	0.662	0.061	0.009
Blautia obeum	species	0.66	0.567	0.235	0.189
Hathewaya	genus	0.334	0.276	0.155	0.2
Hathewaya histolytica	species	0.334	0.276	0.155	0.2
Mediterraneibacter	genus	0.753	0.714	0.278	0.321
Bacteroides rodentium	species	0.415	0.39	0.186	0.223
Lachnobacterium	genus	0.313	0.321	0.074	0.042

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 %
Mogibacterium vescum	species	1.55	9.2	27.8	17.9
Sphingomonas	genus	1.61	10	26.1	16.3
Prevotella bivia	species	1.51	8.4	29.1	19.3
Neisseria mucosa	species	1.84	13.4	20	10.9
Sphingobium	genus	1.63	8	18.3	11.2

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
Alcanivorax	genus	0.002	0.27	59.8	378	103
Isoalcanivorax	genus	0.002	0.27	59.8	367	99
Isoalcanivorax indicus	species	0.002	0.27	59.8	367	99
Niabella aurantiaca	species	0.002	0.33	52.4	533	174
Psychroflexus	genus	0.002	0.3	50.4	356	108
Psychroflexus gondwanensis	species	0.002	0.3	50.4	356	108
Salidesulfovibrio	genus	0.002	0.32	47.5	387	125
Salidesulfovibrio brasiliensis	species	0.002	0.32	47.5	387	125
Psychrobacter glacialis	species	0.002	0.37	45.6	658	241
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.34	43.7	395	135
Niabella	genus	0.002	0.37	42.5	572	213
Thauera	genus	0.002	0.36	38.9	378	137
Viridibacillus neidei	species	0.002	0.38	38.6	467	177
Thiorhodococcus pfennigii	species	0.002	0.37	38.4	405	150
Chromatium	genus	0.002	0.39	36.6	500	197
Lentibacillus	genus	0.002	0.39	36.5	497	196
Thermoanaerobacterium	genus	0.002	0.39	36.5	486	191
Chromatium weissei	species	0.002	0.39	36.4	499	197
Pontibacillus halophilus	species	0.002	0.38	36	411	158
Lentibacillus salinarum	species	0.002	0.4	36	481	190

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
Oscillatoria corallinae	species	0.003	0.3	254.6	861	262
Oscillatoria	genus	0.003	0.3	254.6	861	262
Methylobacillus glycogenes	species	0.003	0.4	232.4	1244	493
Tetragenococcus	genus	0.004	0.44	231.8	1606	702
Methylobacillus	genus	0.003	0.41	217.1	1243	512
Parapedobacter	genus	0.004	0.4	198.1	1032	414
Parapedobacter koreensis	species	0.004	0.4	197.6	1031	414
Anaerofilum	genus	0.166	0.53	187.1	2498	1335
Erysipelothrix	genus	0.016	0.54	172.3	2176	1165
Erysipelothrix muris	species	0.0155	0.54	165	2135	1156
Filifactor villosus	species	0.006	0.34	161.5	588	200
Lysobacter	genus	0.004	0.36	160.8	642	231
Psychrobacter glacialis	species	0.002	0.37	160	658	241
Niabella aurantiaca	species	0.002	0.33	156.5	533	174
Methylonatrum	genus	0.004	0.53	146.3	1607	854
Methylonatrum kenyense	species	0.004	0.53	146.3	1607	854
Niabella	genus	0.002	0.37	138.7	572	213
Schaalia odontolytica	species	0.003	0.45	128.5	783	353
Holdemania	genus	0.026	0.59	128.5	2155	1265
Bacteroides heparinolyticus	species	0.003	0.49	122.1	954	472

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	0.6	13.3	1329	803
Dethiosulfovibrio	genus	0.004	0.66	9.2	1498	988
Tetragenococcus doogicus	species	0.003	0.66	9	1347	891
Hydrocarboniphaga daqingensis	species	0.004	0.7	6.8	1591	1112

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
Oscillatoria	genus	0.003	0.3	254.6	861	262
Oscillatoria corallinae	species	0.003	0.3	254.6	861	262
Methylobacillus glycogenes	species	0.003	0.4	232.4	1244	493
Tetragenococcus	genus	0.004	0.44	231.8	1606	702
Methylobacillus	genus	0.003	0.41	217.1	1243	512
Parapedobacter	genus	0.004	0.4	198.1	1032	414
Parapedobacter koreensis	species	0.004	0.4	197.6	1031	414
Anaerofilum	genus	0.166	0.53	187.1	2498	1335
Erysipelothrix	genus	0.016	0.54	172.3	2176	1165
Erysipelothrix muris	species	0.0155	0.54	165	2135	1156
Filifactor villosus	species	0.006	0.34	161.5	588	200
Lysobacter	genus	0.004	0.36	160.8	642	231
Psychrobacter glacialis	species	0.002	0.37	160	658	241
Niabella aurantiaca	species	0.002	0.33	156.5	533	174
Methylonatrum	genus	0.004	0.53	146.3	1607	854
Methylonatrum kenyense	species	0.004	0.53	146.3	1607	854
Niabella	genus	0.002	0.37	138.7	572	213
Schaalia odontolytica	species	0.003	0.45	128.5	783	353
Holdemania	genus	0.026	0.59	128.5	2155	1265
Bacteroides heparinolyticus	species	0.003	0.49	122.1	954	472

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	92.35	3	4
Bifidobacterium breve	56.65	15	1
Bifidobacterium longum	50.98	17	1
Bifidobacterium adolescentis	37.32	13	1
Segatella copri	15.2	8	0
Bifidobacterium bifidum	11.81	15	3
Bifidobacterium catenulatum	10.46	13	0
Lactobacillus helveticus	10.43	57	69
Pediococcus acidilactici	8.91	36	43
Bifidobacterium animalis	5.43	8	1
Enterococcus faecalis	2.2	39	50
Escherichia coli	1.33	3	1
Bifidobacterium pseudocatenulatum	1.28	25	33
Enterococcus faecium	1.2	21	32
Clostridium butyricum	1.04	21	22
Streptococcus thermophilus	0.9	3	2
Limosilactobacillus reuteri	0.7	25	36
Limosilactobacillus fermentum	0.29	10	9
Parabacteroides distasonis	0.27	2	1
Bacillus subtilis	0.23	38	33
Lactiplantibacillus pentosus	0.19	10	2
Blautia wexlerae	0.14	2	1
Lacticaseibacillus paracasei	0.09	14	8
Leuconostoc mesenteroides	0.08	10	14
Ligilactobacillus salivarius	0.08	6	6
Heyndrickxia coagulans	-0.08	13	24
Lacticaseibacillus casei	-0.08	3	8
Enterococcus durans	-0.11	21	21
Lacticaseibacillus rhamnosus	-0.16	3	11
Lactobacillus acidophilus	-0.44	11	25
Lactobacillus crispatus	-0.48	5	28
Odoribacter laneus	-1.18	0	5
Limosilactobacillus vaginalis	-1.25	27	57
Lactobacillus jensenii	-1.41	20	53
Lactobacillus johnsonii	-2.2	39	39
Parabacteroides goldsteinii	-2.71	4	8
Akkermansia muciniphila	-3.27	7	22
Blautia hansenii	-59.22	1	10
Bacteroides uniformis	-67.68	1	9
Bacteroides thetaiotaomicron	-79.78	1	9

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?.

ME/CFS with IBS is coming!​

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	174
p < 0.001	155
p < 0.0001	137
p < 0.00001	128

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 Faecalibacterium prausnitzii 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
Faecalibacterium prausnitzii	species	12.725	12.194	11.315	12.88
Faecalibacterium	genus	13.315	12.757	11.945	13.394
Roseburia	genus	2.622	2.846	1.811	1.203
Parabacteroides	genus	3.056	2.609	1.716	2.291
Bacteroides uniformis	species	3.043	2.72	1.558	2.035
Phocaeicola dorei	species	2.81	2.912	0.402	0.806
Oscillospira	genus	2.618	2.346	1.95	2.273
Novispirillum	genus	0.964	0.863	0.091	0.295
Insolitispirillum	genus	0.964	0.864	0.091	0.295
Insolitispirillum peregrinum	species	0.964	0.864	0.091	0.295
Parabacteroides goldsteinii	species	0.945	0.556	0.131	0.319
Clostridium	genus	1.962	1.855	1.361	1.533
Parabacteroides merdae	species	0.857	0.741	0.297	0.469
Bacteroides cellulosilyticus	species	1.222	0.841	0.075	0.244
Caloramator	genus	1.265	0.927	0.102	0.22
Bacteroides ovatus	species	1.258	1.523	0.6	0.482
Ruminococcus bromii	species	0.838	0.789	0.164	0.269
Pedobacter	genus	1.274	0.989	0.552	0.651
Bacteroides xylanisolvens	species	0.45	0.559	0.338	0.255
Bifidobacterium	genus	0.434	0.953	0.126	0.056

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 Dehalobacterium below, we see that is occurs much more often.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Dehalobacterium	genus	1.47	7.7	56	38.1
Ammonifex thiophilus	species	1.48	7.5	50.7	34.3
Ammonifex	genus	1.48	7.4	50.7	34.3
Pontibacter niistensis	species	1.45	6.8	51.3	35.4
Pontibacter	genus	1.45	6.7	51.3	35.5
Nodularia baltica	species	2.03	14.5	24.7	12.1
Nodularia	genus	2.03	14.5	24.7	12.1
Nodularia	genus	2.03	14.5	24.7	12.1
Desulfonatronovibrio	genus	1.92	12.8	26	13.5
Microcoleus	genus	1.88	12.3	26.7	14.2
Microcoleus antarcticus	species	1.88	12.3	26.7	14.2
Paraburkholderia	genus	1.54	6.7	32.7	21.2
Roseospira	genus	1.62	7.8	30	18.5
Paraburkholderia phenoliruptrix	species	1.62	7.6	29.3	18.1
Rhodovibrio	genus	1.57	6.7	29.3	18.7
Rhodovibrio sodomensis	species	1.57	6.7	29.3	18.7
Clostridium acetireducens	species	1.72	8.1	23.3	13.5

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	36.5	538	182
Psychrobacter glacialis	species	0.002	0.37	31.7	667	249
Niabella	genus	0.002	0.38	29.1	578	222
Viridibacillus neidei	species	0.002	0.38	28.5	474	180
Thermodesulfovibrio thiophilus	species	0.002	0.45	20.6	538	240
Oenococcus	genus	0.002	0.45	20.4	611	276
Thermodesulfovibrio	genus	0.002	0.46	19.3	625	289
Helicobacter suncus	species	0.002	0.47	19.2	771	362
Viridibacillus	genus	0.002	0.5	15.3	491	244
Desulfotomaculum defluvii	species	0.003	0.55	12.2	1021	565
Streptococcus infantis	species	0.003	0.55	12	804	443
Hydrogenophilus	genus	0.003	0.58	10.6	1149	664
Alkalibacterium	genus	0.003	0.58	10.3	894	517
Pelagicoccus	genus	0.002	0.58	10.2	846	489
Olivibacter soli	species	0.002	0.56	10.1	462	261
Salisaeta longa	species	0.002	0.57	10	509	290
Treponema	genus	0.003	0.57	10	592	340
Salisaeta	genus	0.002	0.57	9.8	508	291
Sporotomaculum syntrophicum	species	0.003	0.59	9.8	1111	655
Clostridium taeniosporum	species	0.003	0.61	8.9	1353	820

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
Candidatus Amoebophilus asiaticus	species	0.016	0.44	307	2547	1120
Candidatus Amoebophilus	genus	0.016	0.44	307	2547	1120
Oscillatoria corallinae	species	0.003	0.32	247.4	874	276
Oscillatoria	genus	0.003	0.32	247.4	874	276
Parabacteroides goldsteinii	species	0.319	0.49	237.7	2558	1260
Rhodothermus	genus	0.034	0.49	230.3	2454	1213
Rhodothermus clarus	species	0.034	0.49	230.2	2452	1212
Paenibacillus	genus	0.003	0.39	200.8	1001	394
Granulicatella	genus	0.0025	2.25	192	570	1280
Listeria	genus	0.003	0.29	190.5	563	162
Listeria innocua	species	0.003	0.29	189.2	561	162
Acidaminobacter hydrogenoformans	species	0.003	0.35	185.2	721	252
Acidaminobacter	genus	0.003	0.35	184.7	722	253
Candidatus Glomeribacter	genus	0.004	0.45	182.1	1264	575
Psychrobacter glacialis	species	0.002	0.37	157.7	667	249
Niabella aurantiaca	species	0.002	0.34	151.1	538	182
Methylonatrum	genus	0.004	0.54	145.2	1632	875
Methylonatrum kenyense	species	0.004	0.54	145.2	1632	875
Hymenobacter xinjiangensis	species	0.007	0.53	139.5	1482	788
Niabella	genus	0.002	0.38	133.8	578	222

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	8.9	1353	820

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
Candidatus Amoebophilus	genus	0.016	0.44	307	2547	1120
Candidatus Amoebophilus asiaticus	species	0.016	0.44	307	2547	1120
Oscillatoria	genus	0.003	0.32	247.4	874	276
Oscillatoria corallinae	species	0.003	0.32	247.4	874	276
Parabacteroides goldsteinii	species	0.319	0.49	237.7	2558	1260
Rhodothermus	genus	0.034	0.49	230.3	2454	1213
Rhodothermus clarus	species	0.034	0.49	230.2	2452	1212
Paenibacillus	genus	0.003	0.39	200.8	1001	394
Granulicatella	genus	0.0025	2.25	192	570	1280
Listeria	genus	0.003	0.29	190.5	563	162
Listeria innocua	species	0.003	0.29	189.2	561	162
Acidaminobacter hydrogenoformans	species	0.003	0.35	185.2	721	252
Acidaminobacter	genus	0.003	0.35	184.7	722	253
Candidatus Glomeribacter	genus	0.004	0.45	182.1	1264	575
Psychrobacter glacialis	species	0.002	0.37	157.7	667	249
Niabella aurantiaca	species	0.002	0.34	151.1	538	182
Methylonatrum	genus	0.004	0.54	145.2	1632	875
Methylonatrum kenyense	species	0.004	0.54	145.2	1632	875
Hymenobacter xinjiangensis	species	0.007	0.53	139.5	1482	788
Niabella	genus	0.002	0.38	133.8	578	222

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	62.66	15	0
Bifidobacterium longum	56.01	15	0
Enterococcus faecalis	54.6	58	27
Bifidobacterium adolescentis	41.58	13	0
Lactobacillus johnsonii	34.13	51	29
Bifidobacterium bifidum	12.62	11	0
Bifidobacterium catenulatum	11.34	9	1
Enterococcus faecium	10.41	17	14
Segatella copri	5.95	1	0
Bifidobacterium animalis	5.68	7	0
Streptococcus thermophilus	3.3	6	0
Veillonella atypica	1.16	16	0
Pediococcus acidilactici	1.12	45	14
Clostridium butyricum	0.98	20	3
Enterococcus durans	0.95	36	6
Lactococcus lactis	0.38	6	3
Lactobacillus jensenii	0.16	21	26
Ligilactobacillus salivarius	0.07	7	3
Lacticaseibacillus paracasei	0.07	7	17
Lacticaseibacillus casei	-0.1	1	7
Lactiplantibacillus pentosus	-0.11	5	16
Limosilactobacillus fermentum	-0.16	15	19
Lactiplantibacillus plantarum	-0.17	0	7
Lactobacillus crispatus	-0.18	2	12
Leuconostoc mesenteroides	-0.33	8	6
Lacticaseibacillus rhamnosus	-0.34	1	23
Bacillus subtilis	-0.39	25	42
Lactobacillus acidophilus	-0.54	14	14
Heyndrickxia coagulans	-0.74	13	34
Limosilactobacillus reuteri	-0.82	30	29
Odoribacter laneus	-0.82	0	3
Limosilactobacillus vaginalis	-1.11	28	40
Bifidobacterium pseudocatenulatum	-1.17	11	16
Lactobacillus helveticus	-2.19	34	79
Escherichia coli	-2.29	4	6
Bacteroides uniformis	-4.13	3	4
Bacteroides thetaiotaomicron	-4.76	3	4
Blautia wexlerae	-37.77	1	6
Akkermansia muciniphila	-50.11	0	30
Parabacteroides goldsteinii	-67.85	0	18
Parabacteroides distasonis	-111.53	0	6
Blautia hansenii	-130.51	0	27
Faecalibacterium prausnitzii	-378.85	2	9

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	239
p < 0.001	206
p < 0.0001	184
p < 0.00001	164

Below is a walkthru that may help some people understand the statistics.

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 Phocaeicola 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
Phocaeicola	genus	9.816	10.961	9.483	8.286
Blautia	genus	9.805	8.323	7.101	7.832
Bacteroides uniformis	species	3.057	2.699	1.512	2.029
Oscillospira	genus	2.751	2.316	1.925	2.255
Parabacteroides	genus	2.604	2.627	1.714	2.007
Bacteroides cellulosilyticus	species	1.145	0.824	0.07	0.218
Clostridium	genus	2.093	1.835	1.359	1.501
Pedobacter	genus	1.134	0.986	0.545	0.647
Ruminococcus bromii	species	0.824	0.788	0.16	0.261
Bacteroides caccae	species	1.079	0.855	0.282	0.371
Akkermansia muciniphila	species	1.789	1.315	0.047	0.132
Akkermansia	genus	1.789	1.314	0.047	0.132
Blautia hansenii	species	1.093	1.033	0.713	0.786
Bifidobacterium	genus	0.627	0.965	0.133	0.061
Bilophila	genus	0.374	0.348	0.206	0.275
Bacteroides rodentium	species	0.484	0.382	0.183	0.234
Sutterella wadsworthensis	species	0.647	0.66	0.06	0.012
Bilophila wadsworthia	species	0.355	0.34	0.198	0.241
Acetivibrio	genus	0.357	0.259	0.099	0.141
Acetivibrio alkalicellulosi	species	0.343	0.25	0.094	0.134

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 %
Ethanoligenens	genus	1.35	12.4	62.9	46.6
Porphyromonas asaccharolytica	species	1.6	18.7	31.7	19.8
Mogibacterium vescum	species	1.68	21.3	29.2	17.4
Dehalobacterium	genus	1.31	8.7	49.5	37.7
Acholeplasma hippikon	species	1.49	14	33.5	22.5
Aggregatibacter	genus	0.56	15.3	14	24.8
Anaerococcus	genus	1.29	7.4	46.4	35.9
Peptoniphilus asaccharolyticus	species	1.3	7.4	44.7	34.4
Slackia faecicanis	species	1.35	8.7	38.8	28.8
Sporosarcina pasteurii	species	1.67	17.5	23.6	14.1
Finegoldia magna	species	1.29	6.7	42.4	32.9
Sporosarcina	genus	1.64	16.2	23.6	14.4
Shewanella upenei	species	1.34	7.4	33	24.7
Meiothermus	genus	1.34	7.2	31.2	23.3
Meiothermus granaticius	species	1.34	7.1	30.7	22.9
Actinobacillus pleuropneumoniae	species	0.58	10.7	10.9	18.7
Varibaculum	genus	1.53	10.8	20.6	13.4
Halanaerobium	genus	1.42	8.2	23.4	16.4
Anaerococcus vaginalis	species	1.37	7.2	25.6	18.7
Erysipelothrix inopinata	species	1.47	8.6	19.8	13.4

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
Isoalcanivorax	genus	0.002	0.25	82.4	350	88
Isoalcanivorax indicus	species	0.002	0.25	82.4	350	88
Alcanivorax	genus	0.002	0.26	81.9	359	92
Salidesulfovibrio	genus	0.002	0.3	70	371	110
Salidesulfovibrio brasiliensis	species	0.002	0.3	70	371	110
Niabella aurantiaca	species	0.002	0.33	69.1	507	169
Psychroflexus	genus	0.002	0.3	66.1	348	105
Psychroflexus gondwanensis	species	0.002	0.3	66.1	348	105
Deferribacter autotrophicus	species	0.002	0.31	64.6	360	112
Deferribacter	genus	0.002	0.31	63.7	362	114
Pelagicoccus croceus	species	0.002	0.32	61.9	368	119
Psychrobacter glacialis	species	0.002	0.38	60.9	622	235
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.34	58.8	372	125
Bacillus ferrariarum	species	0.002	0.33	58.7	354	117
Segetibacter aerophilus	species	0.002	0.33	58.5	360	120
Niabella	genus	0.002	0.39	55.5	540	208
Segetibacter	genus	0.002	0.35	54.9	362	126
Actinopolyspora	genus	0.002	0.38	54.7	509	195
Lentibacillus	genus	0.002	0.38	53.8	484	185
Lentibacillus salinarum	species	0.002	0.38	52.9	468	179

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
Methylobacillus glycogenes	species	0.003	0.4	215.7	1186	478
Methylobacillus	genus	0.003	0.42	201.4	1185	496
Streptococcus oralis	species	0.003	0.47	177.9	1355	632
Erysipelothrix muris	species	0.015	0.53	167.1	2060	1096
Desulfotomaculum	genus	0.004	0.49	156.3	1370	678
Erysipelothrix	genus	0.015	0.55	148	2061	1143
Niabella aurantiaca	species	0.002	0.33	145.2	507	169
Psychrobacter glacialis	species	0.002	0.38	144.7	622	235
Alcanivorax	genus	0.002	0.26	142.5	359	92
Isoalcanivorax	genus	0.002	0.25	141.6	350	88
Isoalcanivorax indicus	species	0.002	0.25	141.6	350	88
Caloramator fervidus	species	0.045	0.58	131	2131	1235
Salidesulfovibrio	genus	0.002	0.3	126.8	371	110
Salidesulfovibrio brasiliensis	species	0.002	0.3	126.8	371	110
Porphyromonas	genus	0.012	0.58	125.1	1974	1145
Niabella	genus	0.002	0.39	124.6	540	208
Actinopolyspora	genus	0.002	0.38	119.5	509	195
Psychroflexus	genus	0.002	0.3	117.4	348	105
Psychroflexus gondwanensis	species	0.002	0.3	117.4	348	105
Deferribacter autotrophicus	species	0.002	0.31	116.8	360	112

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	0.6	21.7	1272	762
Dethiosulfovibrio	genus	0.004	0.65	15.5	1446	943
Tetragenococcus doogicus	species	0.003	0.66	14.9	1290	845
Hydrocarboniphaga daqingensis	species	0.004	0.7	11.3	1531	1065
Mycoplasmopsis	genus	0.005	0.72	9.6	1681	1206
Pediococcus	genus	0.004	0.72	8.6	1222	885
Tetragenococcus	genus	0.003	0.74	7.6	1268	938
Carboxydocella ferrireducens	species	0.004	0.75	6.8	1215	912

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.

Methylobacillus glycogenes	species	0.003	0.4	215.7	1186	478
Methylobacillus	genus	0.003	0.42	201.4	1185	496
Streptococcus oralis	species	0.003	0.47	177.9	1355	632
Erysipelothrix muris	species	0.015	0.53	167.1	2060	1096
Desulfotomaculum	genus	0.004	0.49	156.3	1370	678
Erysipelothrix	genus	0.015	0.55	148	2061	1143
Niabella aurantiaca	species	0.002	0.33	145.2	507	169
Psychrobacter glacialis	species	0.002	0.38	144.7	622	235
Alcanivorax	genus	0.002	0.26	142.5	359	92
Isoalcanivorax	genus	0.002	0.25	141.6	350	88
Isoalcanivorax indicus	species	0.002	0.25	141.6	350	88
Caloramator fervidus	species	0.045	0.58	131	2131	1235
Salidesulfovibrio	genus	0.002	0.3	126.8	371	110
Salidesulfovibrio brasiliensis	species	0.002	0.3	126.8	371	110
Porphyromonas	genus	0.012	0.58	125.1	1974	1145
Niabella	genus	0.002	0.39	124.6	540	208
Actinopolyspora	genus	0.002	0.38	119.5	509	195
Psychroflexus	genus	0.002	0.3	117.4	348	105
Psychroflexus gondwanensis	species	0.002	0.3	117.4	348	105
Deferribacter autotrophicus	species	0.002	0.31	116.8	360	112

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	6.33	1	0
Bacteroides thetaiotaomicron	6.22	1	0
Bifidobacterium longum	6.14	1	0
Bifidobacterium adolescentis	4.79	1	0
Bacteroides uniformis	4.21	1	0
Lactobacillus helveticus	3.61	3	10
Enterococcus faecalis	2.88	8	8
Pediococcus acidilactici	2.43	3	7
Bifidobacterium bifidum	1.71	1	0
Bifidobacterium catenulatum	1.07	1	0
Bifidobacterium animalis	0.71	1	0
Segatella copri	0.53	1	0
Bacillus subtilis	0.29	2	6
Veillonella atypica	0.09	1	0
Heyndrickxia coagulans	0.06	1	2
Limosilactobacillus fermentum	-0.01	1	2
Bacillus	-0.01	0	1
Pediococcus	-0.01	0	1
Leuconostoc mesenteroides	-0.01	0	1
Ligilactobacillus salivarius	-0.02	1	3
Lacticaseibacillus rhamnosus	-0.02	0	1
Lactiplantibacillus pentosus	-0.02	0	1
Lactiplantibacillus plantarum	-0.02	0	1
Bacillus subtilis group	-0.02	0	1
Lacticaseibacillus casei	-0.03	0	2
Enterococcus faecium	-0.04	1	5
Lacticaseibacillus paracasei	-0.05	0	3
Bifidobacterium pseudocatenulatum	-0.05	0	4
Lactobacillus crispatus	-0.06	1	4
Clostridium butyricum	-0.06	0	1
Lactobacillus acidophilus	-0.1	1	6
Enterococcus durans	-0.13	1	4
Limosilactobacillus vaginalis	-0.18	0	7
Bacillus amyloliquefaciens group	-0.19	0	8
Lactobacillus johnsonii	-0.21	2	7
Limosilactobacillus reuteri	-0.24	1	5
Odoribacter laneus	-0.34	0	1
Lactobacillus jensenii	-0.43	1	11
Parabacteroides goldsteinii	-5.4	0	3
Akkermansia muciniphila	-11.49	0	6
Parabacteroides distasonis	-14.94	0	1
Blautia wexlerae	-15.49	0	1
Blautia hansenii	-18.32	0	3

This is a brief post that draws on the analytical approach from the methodology used in Mast Cell Activation Syndrome and Multiple Chemical Sensitivity. Atypically, we are able to determine which probiotics are likely better than others. Rather than delve into the technical details—which can overwhelm those experiencing brain fog—I’m going straight to the results for this set of symptoms:

• ME/CFS – not specific
• ME/CFS – with IBS
• ME/CFS – without IBS
• IBS
• Long COVID
• IBD
• Crohn’s Disease

We are filtering to P < 0.0001 (ZScore of +/-3.72). We are also restricting to strictly safe, that is no predicted inappropriate shifts to keep the list shorted and easier to handle for the brain fogged.

The Good Count below are the number of bacterium that it has the desired effect upon. The Good value is an estimate of the amount of influence.

• The Good Count is the number of bacteria that are likely to shift in a positive direction direction.

• Good is an scaled aggregation of the R2 values for these bacteria. One bactieria may have a R2 and slope of (.9 and 1.5) another of (.2 and 5). The result is .9 * 1.5 + .2 * 5 =2.35.

ME/CFS (General)

Name	Good	Good Count
Clostridium beijerinckii	136	9
Niallia circulans	79	5

This is not unexpected because the vagueness of description results in loss of clarity.

Condition: ME/CFS with IBS

Name	Good	Good Count
Bifidobacterium adolescentis	1678	61
Bifidobacterium catenulatum	1108	40
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Condition: ME/CFS without IBS

Name	Good	Good Count
Lactococcus lactis	765	35
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Irritable Bowel Syndrome

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Long COVID

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Inflammatory Bowel Disease (IBD)

Name	Good	Good Count
Enterococcus faecium	2334	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Crohn’s Disease

Name	Good	Good Count
Christensenella minuta	2796	98
Enterococcus faecium	2334	93
Anaerobutyricum hallii	2317	93
Lactococcus cremoris	1909	77
Bifidobacterium adolescentis	1678	61
Blautia wexlerae	1645	71
Bifidobacterium catenulatum	1108	40
Lactococcus lactis	765	35
Bifidobacterium bifidum	750	31
Clostridium beijerinckii	136	9
Niallia circulans	79	5

Summary

This feature will not be added to the web site because the computations has taken hours to run and require a large amount of memory (most of the 32 GB available). In general, too low amounts dominated as the most significant pattern. It is not killing off high bacteria but encouraging low bacteria that seems to apply for these conditions.

It is interesting to note that Lactobacillus never appears. You may notice that some conditions are very similar which is not unexpected to me. There are commonality of low bacteria across conditions.

Note: ME/CFS With IBS suggestions include ME/CFS and IBS suggestions. IBS and IBD have some similarity but are different. Crohn’s disease seems more likely to be a progression of IBS and not IBD. The data model may be useful for seeing likely disease progression paths.