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	226
p < 0.001	199
p < 0.0001	176
p < 0.00001	157

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	28.633	25.842	24.114	27.576
Phocaeicola vulgatus	species	6.593	5.744	3.347	4.644
Phocaeicola	genus	11.777	10.78	9.291	10.397
Bacteroides uniformis	species	3.031	2.707	1.52	2.104
Parabacteroides	genus	2.552	2.631	1.714	2.058
Bacteroides thetaiotaomicron	species	1.27	1.056	0.455	0.678
Clostridium	genus	2.004	1.847	1.352	1.569
Oscillospira	genus	2.45	2.348	1.944	2.144
Bacteroides cellulosilyticus	species	1.032	0.84	0.07	0.237
Parabacteroides merdae	species	0.854	0.737	0.287	0.44
Coprococcus	genus	1.071	1.461	0.739	0.59
Pedobacter	genus	1.232	0.98	0.548	0.695
Novispirillum	genus	0.816	0.872	0.087	0.229
Insolitispirillum	genus	0.816	0.873	0.089	0.229
Insolitispirillum peregrinum	species	0.816	0.873	0.089	0.229
Bacteroides caccae	species	1.135	0.854	0.281	0.38
Bifidobacterium	genus	0.513	0.969	0.134	0.048
Bilophila	genus	0.395	0.347	0.206	0.276
Parabacteroides goldsteinii	species	0.565	0.571	0.13	0.194
Bilophila wadsworthia	species	0.376	0.339	0.197	0.256

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 catenulatum	species	0.69	8.3	25.2	36.2
Thiomonas thermosulfata	species	1.41	8	29.4	20.9
Aggregatibacter	genus	0.65	7.8	16	24.4
Desulfurispirillum	genus	1.45	8.4	25.5	17.5
Desulfurispirillum alkaliphilum	species	1.45	8.1	25.2	17.4
Actinobacillus pleuropneumoniae	species	0.6	8.4	11	18.5
Bifidobacterium cuniculi	species	0.62	7.8	12	19.3
Desulfonatronovibrio	genus	1.48	7.5	19.9	13.5
Erysipelothrix inopinata	species	1.47	7.4	19.9	13.5
Paraprevotella xylaniphila	species	1.6	9.2	16.6	10.4
Candidatus Phytoplasma phoenicium	species	1.59	8.7	16	10.1
Trichodesmium	genus	1.56	6.6	12.9	8.3

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
Alcanivorax	genus	0.002	0.26	75.2	375	98
Isoalcanivorax	genus	0.002	0.26	74.5	365	95
Isoalcanivorax indicus	species	0.002	0.26	74.5	365	95
Nostoc flagelliforme	species	0.002	0.27	67.3	316	84
Salidesulfovibrio	genus	0.002	0.3	63.2	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	63.2	381	115
Niabella aurantiaca	species	0.002	0.34	62.1	524	176
Mycoplasmopsis lipophila	species	0.002	0.27	61.2	277	75
Psychroflexus	genus	0.002	0.3	60.4	348	105
Psychroflexus gondwanensis	species	0.002	0.3	60.4	348	105
Deferribacter autotrophicus	species	0.002	0.31	59.6	374	117
Pelagicoccus croceus	species	0.002	0.32	59.2	380	120
Deferribacter	genus	0.002	0.32	59	377	119
Psychrobacter glacialis	species	0.002	0.37	55.3	636	238
Thermodesulfatator atlanticus	species	0.002	0.3	53.6	276	83
Thermodesulfatator	genus	0.002	0.3	53.6	276	83
Segetibacter aerophilus	species	0.002	0.34	53.1	364	122
Bacillus ferrariarum	species	0.002	0.34	52.1	356	120
Niabella	genus	0.002	0.38	51.8	560	212
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.35	51.6	379	131

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	150	636	238
Niabella aurantiaca	species	0.002	0.34	148.2	524	176
Desulfotomaculum	genus	0.004	0.51	147.5	1394	711
Alcanivorax	genus	0.002	0.26	145.8	375	98
Isoalcanivorax	genus	0.002	0.26	142.8	365	95
Isoalcanivorax indicus	species	0.002	0.26	142.8	365	95
Niabella	genus	0.002	0.38	132.4	560	212
Salidesulfovibrio	genus	0.002	0.3	127.5	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	127.5	381	115
Bacteroides cellulosilyticus	species	0.237	0.59	127.2	2243	1325
Actinopolyspora	genus	0.002	0.38	125.5	524	198
Bifidobacterium	genus	0.048	1.68	125.3	1392	2332
Nostoc flagelliforme	species	0.002	0.27	122.8	316	84
Geobacillus	genus	0.003	0.43	122.4	672	291
Bacteroides heparinolyticus	species	0.003	0.49	122.1	928	454
Erysipelothrix muris	species	0.014	0.59	121.6	2029	1195
Pelagicoccus croceus	species	0.002	0.32	120.7	380	120
Deferribacter autotrophicus	species	0.002	0.31	120.3	374	117
Deferribacter	genus	0.002	0.32	119.9	377	119
Psychroflexus	genus	0.002	0.3	117.5	348	105

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	16	1295	805
Tetragenococcus doogicus	species	0.003	0.67	11.5	1316	881
Dethiosulfovibrio	genus	0.004	0.67	11.3	1457	981
Hydrocarboniphaga daqingensis	species	0.004	0.71	8.7	1549	1096
Mycoplasmopsis	genus	0.005	0.72	8	1709	1230

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	150	636	238
Niabella aurantiaca	species	0.002	0.34	148.2	524	176
Desulfotomaculum	genus	0.004	0.51	147.5	1394	711
Alcanivorax	genus	0.002	0.26	145.8	375	98
Isoalcanivorax	genus	0.002	0.26	142.8	365	95
Isoalcanivorax indicus	species	0.002	0.26	142.8	365	95
Niabella	genus	0.002	0.38	132.4	560	212
Salidesulfovibrio	genus	0.002	0.3	127.5	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	127.5	381	115
Bacteroides cellulosilyticus	species	0.237	0.59	127.2	2243	1325
Actinopolyspora	genus	0.002	0.38	125.5	524	198
Bifidobacterium	genus	0.048	1.68	125.3	1392	2332
Nostoc flagelliforme	species	0.002	0.27	122.8	316	84
Geobacillus	genus	0.003	0.43	122.4	672	291
Bacteroides heparinolyticus	species	0.003	0.49	122.1	928	454
Erysipelothrix muris	species	0.014	0.59	121.6	2029	1195
Pelagicoccus croceus	species	0.002	0.32	120.7	380	120
Deferribacter autotrophicus	species	0.002	0.31	120.3	374	117
Deferribacter	genus	0.002	0.32	119.9	377	119
Psychroflexus	genus	0.002	0.3	117.5	348	105

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
Blautia hansenii	92.34	11	1
Bifidobacterium breve	70.82	17	3
Faecalibacterium prausnitzii	67.96	12	0
Bifidobacterium longum	64.14	19	0
Blautia wexlerae	50.13	6	0
Bifidobacterium adolescentis	46.86	15	0
Segatella copri	39.48	7	0
Enterococcus faecalis	30.68	60	31
Bifidobacterium bifidum	14.43	15	3
Lactobacillus helveticus	13.52	47	77
Bifidobacterium catenulatum	13.5	13	2
Escherichia coli	12.84	13	0
Enterococcus faecium	7.35	20	26
Bifidobacterium animalis	6.59	8	1
Lactobacillus johnsonii	1.25	40	46
Streptococcus thermophilus	1.17	3	0
Veillonella atypica	0.93	9	0
Bacillus subtilis	0.85	33	46
Clostridium butyricum	0.74	24	25
Enterococcus durans	0.45	36	23
Ligilactobacillus salivarius	0.3	12	11
Leuconostoc mesenteroides	0.23	17	12
Lactococcus lactis	0.13	5	11
Limosilactobacillus fermentum	0.08	10	12
Lacticaseibacillus paracasei	-0.08	12	28
Lacticaseibacillus casei	-0.1	0	9
Heyndrickxia coagulans	-0.22	20	35
Lactiplantibacillus plantarum	-0.26	2	9
Bifidobacterium pseudocatenulatum	-0.41	18	35
Lactiplantibacillus pentosus	-0.44	8	22
Lactobacillus crispatus	-0.46	1	32
Lactobacillus acidophilus	-0.52	11	28
Lacticaseibacillus rhamnosus	-0.6	3	28
Lactobacillus jensenii	-1.44	19	41
Odoribacter laneus	-1.54	0	4
Limosilactobacillus vaginalis	-1.79	30	52
Limosilactobacillus reuteri	-2.22	31	46
Pediococcus acidilactici	-6.8	25	23
Akkermansia muciniphila	-13.33	2	32
Parabacteroides goldsteinii	-41.6	0	18
Parabacteroides distasonis	-60.25	0	7
Bacteroides uniformis	-250.42	0	11
Bacteroides thetaiotaomicron	-261.55	1	11