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