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