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

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