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	182
p < 0.001	164
p < 0.0001	146
p < 0.00001	130

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 the bacterua below, we see that for some the average is above and the median below. Should one increase or decrease this bacteria?

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). IMHO using average value instead of median will often result in a worse situation for the patient

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	31.102	25.834	24.217	30.409
Faecalibacterium	genus	10.531	12.875	12.155	9.178
Faecalibacterium prausnitzii	species	10.193	12.301	11.47	8.958
Phocaeicola vulgatus	species	6.283	5.788	3.427	4.247
Bacteroides uniformis	species	3.229	2.71	1.559	2.11
Ruminococcus	genus	5.96	5.586	4.394	3.874
Coprococcus	genus	1.313	1.436	0.738	0.483
Clostridium	genus	2.067	1.85	1.359	1.612
Phocaeicola dorei	species	3.715	2.873	0.412	0.196
Bacteroides thetaiotaomicron	species	1.612	1.049	0.463	0.593
Bacteroides cellulosilyticus	species	1.092	0.844	0.075	0.179
Lachnospira pectinoschiza	species	0.549	0.668	0.337	0.245
Ruminococcus bromii	species	0.784	0.791	0.174	0.083
Bifidobacterium	genus	0.574	0.95	0.128	0.045
Bilophila wadsworthia	species	0.387	0.34	0.199	0.273
Bilophila	genus	0.395	0.348	0.209	0.281
Lachnobacterium	genus	0.222	0.325	0.076	0.028
Sutterella wadsworthensis	species	0.711	0.657	0.059	0.011
Dorea	genus	0.494	0.482	0.292	0.336
Hathewaya	genus	0.381	0.275	0.155	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.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Halanaerobium	genus	1.58	7.7	26.4	16.7

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
Isoalcanivorax	genus	0.002	0.26	53.9	371	98
Isoalcanivorax indicus	species	0.002	0.26	53.9	371	98
Alcanivorax	genus	0.002	0.27	53.8	382	102
Niabella aurantiaca	species	0.002	0.33	43.2	545	182
Pelagicoccus croceus	species	0.002	0.32	40.7	378	122
Psychrobacter glacialis	species	0.002	0.38	35.8	660	250
Niabella	genus	0.002	0.38	35	585	221
Viridibacillus neidei	species	0.002	0.38	32.9	472	179
Chromatium	genus	0.002	0.39	32.1	515	200
Chromatium weissei	species	0.002	0.39	32	514	200
Sporosarcina pasteurii	species	0.002	0.4	28.6	444	179
Thiorhodococcus	genus	0.002	0.42	27.4	578	245
Syntrophomonas sapovorans	species	0.002	0.42	27	536	227
Sporosarcina	genus	0.002	0.42	26.9	448	186
Lysinibacillus	genus	0.002	0.42	25.9	401	167
Thermodesulfovibrio thiophilus	species	0.002	0.45	23.5	540	243
Oenococcus	genus	0.002	0.46	22.6	601	277
Thermodesulfovibrio	genus	0.002	0.47	22.1	625	292
Helicobacter suncus	species	0.002	0.48	21.7	761	363
Viridibacillus	genus	0.002	0.5	17.6	486	242

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
Lachnobacterium	genus	0.028	2.07	243	1197	2474
Bifidobacterium longum	species	0.0135	2.14	228.2	900	1929
Paenibacillus	genus	0.003	0.38	208.1	999	384
Erysipelothrix	genus	0.017	0.51	205.7	2244	1135
Anaerobranca zavarzinii	species	0.005	1.98	195.5	1046	2066
Anaerobranca	genus	0.005	1.98	195.5	1046	2066
Erysipelothrix muris	species	0.016	0.52	191.4	2194	1135
Slackia	genus	0.009	1.9	182.1	1161	2202
Legionella shakespearei	species	0.003	0.37	158.9	659	243
Bacteroides	genus	30.409	0.57	158.2	2511	1422
Faecalibacterium	genus	9.178	1.76	157.2	1423	2508
Niabella aurantiaca	species	0.002	0.33	155.3	545	182
Eubacterium callanderi	species	0.007	0.54	155.2	1882	1016
Holdemania	genus	0.027	0.56	153.4	2222	1244
Psychrobacter glacialis	species	0.002	0.38	152.7	660	250
Bifidobacterium	genus	0.045	1.73	146.5	1413	2447
Methylonatrum	genus	0.004	0.54	142.5	1617	870
Methylonatrum kenyense	species	0.004	0.54	142.5	1617	870
Amedibacillus dolichus	species	0.022	0.54	141.7	1678	912
Amedibacillus	genus	0.022	0.54	141.4	1677	912

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	10.2	1349	827
Dethiosulfovibrio	genus	0.004	0.67	6.7	1505	1012

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
Lachnobacterium	genus	0.028	2.07	243	1197	2474
Bifidobacterium longum	species	0.0135	2.14	228.2	900	1929
Paenibacillus	genus	0.003	0.38	208.1	999	384
Erysipelothrix	genus	0.017	0.51	205.7	2244	1135
Anaerobranca zavarzinii	species	0.005	1.98	195.5	1046	2066
Anaerobranca	genus	0.005	1.98	195.5	1046	2066
Erysipelothrix muris	species	0.016	0.52	191.4	2194	1135
Slackia	genus	0.009	1.9	182.1	1161	2202
Legionella shakespearei	species	0.003	0.37	158.9	659	243
Bacteroides	genus	30.409	0.57	158.2	2511	1422
Faecalibacterium	genus	9.178	1.76	157.2	1423	2508
Niabella aurantiaca	species	0.002	0.33	155.3	545	182
Eubacterium callanderi	species	0.007	0.54	155.2	1882	1016
Holdemania	genus	0.027	0.56	153.4	2222	1244
Psychrobacter glacialis	species	0.002	0.38	152.7	660	250
Bifidobacterium	genus	0.045	1.73	146.5	1413	2447
Methylonatrum	genus	0.004	0.54	142.5	1617	870
Methylonatrum kenyense	species	0.004	0.54	142.5	1617	870
Amedibacillus dolichus	species	0.022	0.54	141.7	1678	912
Amedibacillus	genus	0.022	0.54	141.4	1677	912

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	842.75	13	0
Blautia hansenii	196.62	20	1
Blautia wexlerae	129.19	8	0
Segatella copri	96.35	8	0
Bifidobacterium breve	64.71	16	0
Bifidobacterium longum	57.42	17	0
Bifidobacterium adolescentis	42.42	16	0
Lactobacillus helveticus	39.07	98	34
Akkermansia muciniphila	23.53	24	4
Bifidobacterium bifidum	13.29	11	2
Bifidobacterium catenulatum	11.26	8	2
Escherichia coli	8.67	7	3
Bifidobacterium animalis	6.08	7	1
Bacillus subtilis	1.26	44	38
Clostridium butyricum	0.96	19	17
Veillonella atypica	0.83	6	6
Enterococcus faecium	0.76	22	14
Enterococcus durans	0.66	26	20
Limosilactobacillus fermentum	0.51	22	3
Streptococcus thermophilus	0.32	2	3
Bifidobacterium pseudocatenulatum	0.22	20	20
Limosilactobacillus vaginalis	0.13	37	38
Lactiplantibacillus pentosus	0.13	6	1
Ligilactobacillus salivarius	0.04	7	3
Lactiplantibacillus plantarum	-0.01	3	5
Lactococcus lactis	-0.03	4	5
Lactobacillus crispatus	-0.06	1	9
Lacticaseibacillus paracasei	-0.1	5	11
Lacticaseibacillus rhamnosus	-0.17	3	6
Lactobacillus acidophilus	-0.18	10	11
Leuconostoc mesenteroides	-0.19	7	11
Lacticaseibacillus casei	-0.22	0	8
Limosilactobacillus reuteri	-0.35	17	20
Lactobacillus jensenii	-0.74	26	24
Parabacteroides goldsteinii	-6.86	4	4
Pediococcus acidilactici	-9.8	21	48
Enterococcus faecalis	-17.66	59	51
Parabacteroides distasonis	-22.69	0	4
Lactobacillus johnsonii	-58.41	48	39
Bacteroides uniformis	-547.16	0	13
Bacteroides thetaiotaomicron	-585.02	0	11