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	177
p < 0.001	160
p < 0.0001	138
p < 0.00001	124

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 is 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	10.366	12.277	11.415	9.08
Faecalibacterium	genus	10.881	12.843	12.131	9.826
Lachnospira	genus	2.401	2.738	1.9	1.418
Coprococcus	genus	1.071	1.443	0.737	0.428
Phocaeicola dorei	species	2.699	2.916	0.418	0.128
Parabacteroides	genus	2.385	2.634	1.724	1.989
Clostridium	genus	2.005	1.854	1.359	1.6
Roseburia faecis	species	0.951	1.215	0.576	0.457
Bacteroides caccae	species	1.59	0.852	0.286	0.402
Mediterraneibacter	genus	0.805	0.713	0.277	0.381
Bacteroides thetaiotaomicron	species	1.104	1.071	0.463	0.561
Lachnospira pectinoschiza	species	0.547	0.667	0.336	0.249
Bifidobacterium	genus	0.761	0.94	0.127	0.042
Bacteroides cellulosilyticus	species	1.396	0.836	0.076	0.151
Blautia wexlerae	species	0.869	0.569	0.314	0.386
Bilophila	genus	0.343	0.35	0.21	0.278
Anaerotruncus	genus	0.284	0.184	0.136	0.203
Akkermansia muciniphila	species	2.398	1.325	0.05	0.117
Akkermansia	genus	2.398	1.325	0.051	0.117
Anaerotruncus colihominis	species	0.259	0.173	0.133	0.198

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 %
Actinobacillus porcinus	species	0.61	6.9	24.5	40.2
Slackia faecicanis	species	1.53	7.8	44.8	29.2
Mogibacterium vescum	species	1.79	11.5	32.2	18

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.

Psychrobacter glacialis	species	0.002	0.37	30.5	664	247
Niabella	genus	0.002	0.39	27.4	583	226
Thermoanaerobacterium	genus	0.002	0.4	24.4	485	195
Chromatium	genus	0.002	0.41	24.2	508	206
Chromatium weissei	species	0.002	0.41	24.1	507	206
Thermoanaerobacterium islandicum	species	0.002	0.41	23.6	478	195
Syntrophomonas sapovorans	species	0.002	0.42	22.5	536	226
Sporosarcina pasteurii	species	0.002	0.42	21.9	440	184
Thermodesulfovibrio thiophilus	species	0.002	0.43	21	543	236
Sporosarcina	genus	0.002	0.43	20.6	444	191
Oenococcus	genus	0.002	0.45	20.1	609	272
Thermodesulfovibrio	genus	0.002	0.45	19.5	629	285
Helicobacter suncus	species	0.002	0.47	18.3	768	361
Desulfofundulus	genus	0.002	0.46	18.2	496	227
Herbaspirillum magnetovibrio	species	0.002	0.51	13.6	447	226
Streptococcus infantis	species	0.003	0.54	12	804	437
Sphingomonas	genus	0.002	0.53	11.9	457	242
Desulfotomaculum defluvii	species	0.003	0.56	11.3	1022	570
Alkalibacterium	genus	0.003	0.57	10.5	906	514
Hydrogenophilus	genus	0.003	0.58	10.2	1149	662

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
Methylonatrum	genus	0.005	0.35	374.2	1861	655
Methylonatrum kenyense	species	0.005	0.35	374.2	1861	655
Anaerotruncus colihominis	species	0.198	0.41	365.1	2712	1113
Anaerotruncus	genus	0.203	0.42	340.3	2688	1141
Odoribacter denticanis	species	0.006	0.41	291	1856	760
Luteolibacter	genus	0.017	0.38	245.6	1225	468
Luteolibacter algae	species	0.017	0.39	240.4	1214	468
Finegoldia	genus	0.0115	0.41	212.1	1210	501
Anaerococcus	genus	0.012	0.4	206.3	1099	444
Eggerthella sinensis	species	0.006	0.44	197.2	1289	568
Finegoldia magna	species	0.008	0.4	195.4	1014	408
Coprococcus	genus	0.4285	1.87	191.4	1379	2577
Desulfovibrio fairfieldensis	species	0.0395	0.4	175.1	868	347
Mogibacterium	genus	0.023	0.54	170.4	2154	1159
Bifidobacterium	genus	0.04245	1.8	169.2	1390	2505
Rubritalea	genus	0.004	0.43	168.7	969	415
Bifidobacterium longum	species	0.016	1.9	167.6	986	1876
Lysobacter	genus	0.004	0.36	164.9	657	236
Porphyromonas	genus	0.013	0.54	164.7	2156	1174
Psychrobacter glacialis	species	0.002	0.37	158	664	247

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	8.6	1359	821

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
Methylonatrum	genus	0.005	0.35	374.2	1861	655
Methylonatrum kenyense	species	0.005	0.35	374.2	1861	655
Anaerotruncus colihominis	species	0.198	0.41	365.1	2712	1113
Anaerotruncus	genus	0.203	0.42	340.3	2688	1141
Odoribacter denticanis	species	0.006	0.41	291	1856	760
Luteolibacter	genus	0.017	0.38	245.6	1225	468
Luteolibacter algae	species	0.017	0.39	240.4	1214	468
Finegoldia	genus	0.0115	0.41	212.1	1210	501
Anaerococcus	genus	0.012	0.4	206.3	1099	444
Eggerthella sinensis	species	0.006	0.44	197.2	1289	568
Finegoldia magna	species	0.008	0.4	195.4	1014	408
Coprococcus	genus	0.4285	1.87	191.4	1379	2577
Desulfovibrio fairfieldensis	species	0.0395	0.4	175.1	868	347
Mogibacterium	genus	0.023	0.54	170.4	2154	1159
Bifidobacterium	genus	0.04245	1.8	169.2	1390	2505
Rubritalea	genus	0.004	0.43	168.7	969	415
Bifidobacterium longum	species	0.016	1.9	167.6	986	1876
Lysobacter	genus	0.004	0.36	164.9	657	236
Porphyromonas	genus	0.013	0.54	164.7	2156	1174
Psychrobacter glacialis	species	0.002	0.37	158	664	247

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	434.71	8	0
Bifidobacterium breve	64.25	17	0
Bifidobacterium longum	57.5	20	0
Bifidobacterium adolescentis	42.25	17	0
Bifidobacterium bifidum	13.35	12	2
Bifidobacterium catenulatum	11.39	11	0
Bifidobacterium animalis	6.14	7	0
Enterococcus faecium	2.64	23	25
Escherichia coli	1.67	5	8
Enterococcus faecalis	1.37	51	44
Limosilactobacillus reuteri	0.81	16	17
Streptococcus thermophilus	0.73	4	0
Bacillus subtilis	0.52	28	32
Veillonella atypica	0.5	9	2
Lactobacillus helveticus	0.34	46	44
Enterococcus durans	0.3	25	21
Lactococcus lactis	0.22	6	1
Heyndrickxia coagulans	0.12	10	11
Lacticaseibacillus paracasei	-0.02	3	6
Lactiplantibacillus pentosus	-0.02	2	2
Lactiplantibacillus plantarum	-0.06	3	5
Bifidobacterium pseudocatenulatum	-0.08	19	13
Lacticaseibacillus rhamnosus	-0.09	2	4
Lacticaseibacillus casei	-0.13	3	7
Leuconostoc mesenteroides	-0.24	4	11
Ligilactobacillus salivarius	-0.32	1	9
Clostridium butyricum	-0.4	15	20
Odoribacter laneus	-0.55	0	3
Lactobacillus crispatus	-0.65	7	24
Lactobacillus acidophilus	-1.02	9	29
Segatella copri	-1.1	4	2
Limosilactobacillus vaginalis	-1.19	25	45
Bacteroides thetaiotaomicron	-2.21	0	5
Bacteroides uniformis	-2.77	0	6
Lactobacillus jensenii	-3.6	22	51
Pediococcus acidilactici	-19.95	24	40
Blautia wexlerae	-19.99	3	2
Parabacteroides goldsteinii	-22.17	1	15
Akkermansia muciniphila	-27.03	2	26
Parabacteroides distasonis	-35.63	0	10
Blautia hansenii	-39.03	4	10
Lactobacillus johnsonii	-41.55	31	41