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	202
p < 0.001	173
p < 0.0001	145
p < 0.00001	129

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	27.633	25.884	24.121	26.729
Bacteroides uniformis	species	2.985	2.705	1.527	2.059
Novispirillum	genus	0.784	0.876	0.085	0.174
Insolitispirillum	genus	0.784	0.877	0.086	0.174
Insolitispirillum peregrinum	species	0.784	0.877	0.086	0.174
Bacteroides cellulosilyticus	species	1.024	0.836	0.073	0.151
Bilophila	genus	0.376	0.348	0.206	0.265
Bifidobacterium	genus	0.607	0.969	0.131	0.072
Bilophila wadsworthia	species	0.365	0.339	0.196	0.255
Blautia obeum	species	0.654	0.563	0.228	0.281
Hathewaya histolytica	species	0.318	0.275	0.154	0.188
Hathewaya	genus	0.318	0.275	0.154	0.188
Lachnobacterium	genus	0.327	0.32	0.075	0.049
Anaerotruncus	genus	0.213	0.184	0.136	0.159
Bifidobacterium longum	species	0.248	0.33	0.05	0.029
Oribacterium	genus	0.123	0.131	0.074	0.053
Anaerotruncus colihominis	species	0.2	0.173	0.133	0.153
Oribacterium sinus	species	0.116	0.127	0.072	0.053
Odoribacter	genus	0.194	0.197	0.123	0.139
Megamonas	genus	0.42	0.439	0.003	0.018

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 %
Prevotella bivia	species	1.42	9.5	26.9	19

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	77.8	360	99
Isoalcanivorax	genus	0.002	0.27	77.3	349	95
Isoalcanivorax indicus	species	0.002	0.27	77.3	349	95
Niabella aurantiaca	species	0.002	0.34	69.1	505	172
Psychroflexus	genus	0.002	0.3	66.9	345	105
Psychroflexus gondwanensis	species	0.002	0.3	66.9	345	105
Salidesulfovibrio	genus	0.002	0.32	65.7	376	120
Salidesulfovibrio brasiliensis	species	0.002	0.32	65.7	376	120
Deferribacter autotrophicus	species	0.002	0.32	65.4	366	116
Deferribacter	genus	0.002	0.32	64.5	368	118
Psychrobacter glacialis	species	0.002	0.38	62.5	623	237
Pelagicoccus croceus	species	0.002	0.32	62.3	357	116
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.34	60.6	372	125
Bacillus ferrariarum	species	0.002	0.34	58.4	350	118
Niabella	genus	0.002	0.39	56.6	542	211
Actinopolyspora	genus	0.002	0.39	55.8	505	195
Chromatium	genus	0.002	0.39	54.7	480	185
Chromatium weissei	species	0.002	0.39	54.4	479	185
Viridibacillus neidei	species	0.002	0.38	53.7	445	170
Segetibacter aerophilus	species	0.002	0.36	53.7	353	126

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
Methylobacillus glycogenes	species	0.003	0.4	217.9	1190	477
Methylobacillus	genus	0.003	0.42	203.2	1190	496
Psychrobacter glacialis	species	0.002	0.38	143.2	623	237
Niabella aurantiaca	species	0.002	0.34	140.5	505	172
Alcanivorax	genus	0.002	0.27	133.4	360	99
Isoalcanivorax	genus	0.002	0.27	131	349	95
Isoalcanivorax indicus	species	0.002	0.27	131	349	95
Niabella	genus	0.002	0.39	122.8	542	211
Salidesulfovibrio	genus	0.002	0.32	117.8	376	120
Salidesulfovibrio brasiliensis	species	0.002	0.32	117.8	376	120
Actinopolyspora	genus	0.002	0.39	117.1	505	195
Deferribacter autotrophicus	species	0.002	0.32	115.9	366	116
Psychroflexus	genus	0.002	0.3	115.2	345	105
Psychroflexus gondwanensis	species	0.002	0.3	115.2	345	105
Deferribacter	genus	0.002	0.32	114.9	368	118
Chromatium	genus	0.002	0.39	112.4	480	185
Chromatium weissei	species	0.002	0.39	111.9	479	185
Helicobacter suncus	species	0.002	0.46	111.6	717	333
Pelagicoccus croceus	species	0.002	0.32	110	357	116
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.34	109.4	372	125

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	21	1269	777
Dethiosulfovibrio	genus	0.004	0.67	14.6	1417	946
Tetragenococcus doogicus	species	0.003	0.68	12.6	1279	876
Hydrocarboniphaga daqingensis	species	0.004	0.72	9.8	1499	1078
Mycoplasmopsis	genus	0.005	0.72	9.7	1661	1199
Pediococcus	genus	0.004	0.75	7.2	1217	913
Propionispora hippei	species	0.005	0.76	6.8	1449	1101
Propionispora	genus	0.005	0.76	6.7	1448	1102

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.

Methylobacillus glycogenes	species	0.003	0.4	217.9	1190	477
Methylobacillus	genus	0.003	0.42	203.2	1190	496
Psychrobacter glacialis	species	0.002	0.38	143.2	623	237
Niabella aurantiaca	species	0.002	0.34	140.5	505	172
Alcanivorax	genus	0.002	0.27	133.4	360	99
Isoalcanivorax	genus	0.002	0.27	131	349	95
Isoalcanivorax indicus	species	0.002	0.27	131	349	95
Niabella	genus	0.002	0.39	122.8	542	211
Salidesulfovibrio	genus	0.002	0.32	117.8	376	120
Salidesulfovibrio brasiliensis	species	0.002	0.32	117.8	376	120
Actinopolyspora	genus	0.002	0.39	117.1	505	195
Deferribacter autotrophicus	species	0.002	0.32	115.9	366	116
Psychroflexus	genus	0.002	0.3	115.2	345	105
Psychroflexus gondwanensis	species	0.002	0.3	115.2	345	105
Deferribacter	genus	0.002	0.32	114.9	368	118
Chromatium	genus	0.002	0.39	112.4	480	185
Chromatium weissei	species	0.002	0.39	111.9	479	185
Helicobacter suncus	species	0.002	0.46	111.6	717	333
Pelagicoccus croceus	species	0.002	0.32	110	357	116
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.34	109.4	372	125

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	38.64	13	0
Bifidobacterium longum	34.39	12	0
Bifidobacterium adolescentis	25.49	11	0
Segatella copri	21.76	5	0
Akkermansia muciniphila	16.19	13	7
Lactobacillus helveticus	10.85	70	36
Bifidobacterium bifidum	7.8	16	2
Bifidobacterium catenulatum	6.98	14	0
Escherichia coli	3.79	8	0
Lactobacillus johnsonii	3.74	52	16
Bifidobacterium animalis	3.53	8	0
Pediococcus acidilactici	3.33	31	26
Enterococcus faecalis	2.72	47	26
Enterococcus durans	2.31	50	14
Enterococcus faecium	1.83	28	22
Streptococcus thermophilus	1.28	2	1
Clostridium butyricum	0.9	24	11
Limosilactobacillus reuteri	0.82	27	12
Lactococcus lactis	0.28	6	3
Leuconostoc mesenteroides	0.26	18	12
Lacticaseibacillus paracasei	0.25	20	8
Ligilactobacillus salivarius	0.25	16	5
Limosilactobacillus fermentum	0.23	19	14
Bacillus subtilis	0.15	37	29
Bifidobacterium pseudocatenulatum	0.14	29	9
Lactiplantibacillus plantarum	0.12	3	1
Limosilactobacillus vaginalis	0.11	43	36
Lactiplantibacillus pentosus	0.1	7	2
Veillonella atypica	0.09	3	1
Lacticaseibacillus casei	0.05	11	10
Lactobacillus crispatus	-0.02	14	15
Lactobacillus acidophilus	-0.08	18	13
Lacticaseibacillus rhamnosus	-0.1	2	8
Heyndrickxia coagulans	-0.16	14	21
Odoribacter laneus	-0.4	0	2
Parabacteroides goldsteinii	-0.43	2	4
Lactobacillus jensenii	-1.69	36	30
Faecalibacterium prausnitzii	-3.39	1	4
Blautia hansenii	-12.12	2	2
Blautia wexlerae	-12.46	0	1
Bacteroides uniformis	-136.48	0	9
Bacteroides thetaiotaomicron	-148.97	0	9