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?.

​Quick Best Probiotics

For details, see bottom

• Bifidobacterium breve
• Bifidobacterium longum
• Bifidobacterium adolescentis

Lacticaseibacillus (one of the lactobacillus probiotics) is very excessive and Lactobacillus probiotics should generally be avoided. Check your yogurt labels!

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	134
p < 0.001	125
p < 0.0001	119
p < 0.00001	105

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 Average	Reference Average	Symptom Median	Reference Median
Phocaeicola vulgatus	species	7.372	5.774	3.427	5.031
Faecalibacterium	genus	12.482	12.784	12.073	10.514
Phocaeicola	genus	10.908	10.854	9.369	10.392
Blautia	genus	8.97	8.448	7.176	6.431
Lachnospira	genus	1.863	2.746	1.899	1.168
Roseburia	genus	3.574	2.822	1.778	2.222
Phocaeicola dorei	species	1.717	2.935	0.43	0.038
Parabacteroides	genus	3.252	2.611	1.724	2.116
Bacteroides uniformis	species	2.938	2.727	1.571	1.917
Oscillospira	genus	2.656	2.349	1.952	2.285
Parabacteroides distasonis	species	1.943	1.228	0.604	0.911
Clostridium	genus	1.959	1.857	1.364	1.665
Sutterella	genus	1.834	1.64	1.244	1.49
Sutterella wadsworthensis	species	0.734	0.657	0.05	0.262
Coprococcus	genus	1.112	1.438	0.73	0.53
Lachnospira pectinoschiza	species	0.369	0.67	0.34	0.162
Novispirillum	genus	1.036	0.864	0.095	0.259
Insolitispirillum	genus	1.035	0.865	0.095	0.259
Insolitispirillum peregrinum	species	1.035	0.865	0.095	0.259
Bacteroides thetaiotaomicron	species	1.09	1.072	0.466	0.628

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. Excessive Lacticaseibacillus (one of the lactobacillus probiotics) is very excessive.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Collinsella tanakaei	species	2.43	19.9	37.4	15.4
Anaerofustis stercorihominis	species	2.06	12.7	36.3	17.6
Anaerofustis	genus	1.98	11.4	36.3	18.3
Lacticaseibacillus	genus	1.83	9.2	38.5	21

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
Moraxella caviae	species	0.002	0.21	22.6	81	17
Moraxella	genus	0.002	0.25	19.1	83	21
Rickettsiella	genus	0.002	0.26	17.4	76	20
Treponema porcinum	species	0.002	0.32	14.3	84	27
Clostridium hveragerdense	species	0.002	0.43	9.4	102	44
Streptococcus infantis	species	0.003	0.55	7.8	808	442
Desulfotomaculum defluvii	species	0.003	0.56	7.4	1033	576
Alkalibacterium	genus	0.003	0.57	6.8	914	521
Hydrogenophilus	genus	0.003	0.58	6.7	1166	671

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
Phocaeicola dorei	species	0.038	2.32	335.2	1171	2721
Corynebacterium	genus	0.012	0.32	324.4	1279	413
Odoribacter denticanis	species	0.006	0.41	294.3	1881	771
Lachnospira pectinoschiza	species	0.162	2.18	293.4	1260	2744
Sporotomaculum	genus	0.004	0.38	268.1	1329	500
Oribacterium	genus	0.035	2.14	264.9	1145	2451
Slackia	genus	0.0465	0.47	256.2	2336	1094
Oribacterium sinus	species	0.035	2.11	255.9	1151	2432
Luteolibacter	genus	0.017	0.39	243.3	1238	479
Luteolibacter algae	species	0.017	0.39	238.1	1227	479
Collinsella intestinalis	species	0.009	0.41	234.5	1330	542
Collinsella	genus	0.108	0.48	233.8	2126	1011
Blautia obeum	species	0.10995	1.98	223.4	1271	2520
Lachnobacterium	genus	0.03	1.92	202	1284	2463
Johnsonella ignava	species	0.0429	0.53	200.3	2571	1356
Johnsonella	genus	0.0429	0.53	199.5	2571	1358
Eggerthella sinensis	species	0.006	0.44	196.6	1296	574
Adlercreutzia equolifaciens	species	0.013	0.49	191	1674	814
Pontibacter	genus	0.004	0.42	190.9	1085	456
Pontibacter niistensis	species	0.004	0.42	189.6	1082	456

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.

None were found

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
Phocaeicola dorei	species	0.038	2.32	335.2	1171	2721
Corynebacterium	genus	0.012	0.32	324.4	1279	413
Odoribacter denticanis	species	0.006	0.41	294.3	1881	771
Lachnospira pectinoschiza	species	0.162	2.18	293.4	1260	2744
Sporotomaculum	genus	0.004	0.38	268.1	1329	500
Oribacterium	genus	0.035	2.14	264.9	1145	2451
Slackia	genus	0.0465	0.47	256.2	2336	1094
Oribacterium sinus	species	0.035	2.11	255.9	1151	2432
Luteolibacter	genus	0.017	0.39	243.3	1238	479
Luteolibacter algae	species	0.017	0.39	238.1	1227	479
Collinsella intestinalis	species	0.009	0.41	234.5	1330	542
Collinsella	genus	0.108	0.48	233.8	2126	1011
Blautia obeum	species	0.10995	1.98	223.4	1271	2520
Lachnobacterium	genus	0.03	1.92	202	1284	2463
Johnsonella ignava	species	0.0429	0.53	200.3	2571	1356
Johnsonella	genus	0.0429	0.53	199.5	2571	1358
Eggerthella sinensis	species	0.006	0.44	196.6	1296	574
Adlercreutzia equolifaciens	species	0.013	0.49	191	1674	814
Pontibacter	genus	0.004	0.42	190.9	1085	456
Pontibacter niistensis	species	0.004	0.42	189.6	1082	456

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	174.48	6	5
Bifidobacterium breve	35.77	18	0
Bifidobacterium longum	31.47	17	0
Bifidobacterium adolescentis	23.42	16	0
Bifidobacterium bifidum	7.27	11	0
Blautia wexlerae	7.06	3	3
Bifidobacterium catenulatum	6.17	8	0
Bifidobacterium animalis	3.25	7	0
Bacillus subtilis	2.42	22	12
Escherichia coli	2.37	3	2
Clostridium butyricum	0.85	9	13
Veillonella atypica	0.38	12	5
Segatella copri	0.33	1	1
Heyndrickxia coagulans	-0.07	6	8
Bifidobacterium pseudocatenulatum	-0.09	8	5
Leuconostoc mesenteroides	-0.17	3	6
Limosilactobacillus reuteri	-0.36	9	17
Lactiplantibacillus plantarum	-0.36	0	3
Lacticaseibacillus rhamnosus	-0.37	1	5
Lactiplantibacillus pentosus	-0.44	0	3
Ligilactobacillus salivarius	-0.48	4	7
Lacticaseibacillus casei	-0.52	1	6
Akkermansia muciniphila	-0.54	4	10
Lactobacillus acidophilus	-0.59	8	9
Limosilactobacillus fermentum	-0.61	11	11
Odoribacter laneus	-0.69	0	3
Lactobacillus crispatus	-0.76	3	13
Lacticaseibacillus paracasei	-1.13	3	7
Lactococcus lactis	-1.4	1	3
Enterococcus durans	-2.91	15	11
Lactobacillus jensenii	-3.05	25	28
Lactobacillus helveticus	-3.9	62	34
Limosilactobacillus vaginalis	-4.08	19	44
Enterococcus faecium	-4.7	9	17
Enterococcus faecalis	-11.45	45	39
Bacteroides thetaiotaomicron	-12.36	3	6
Parabacteroides goldsteinii	-15.08	4	14
Streptococcus thermophilus	-15.77	0	9
Bacteroides uniformis	-18.42	4	4
Lactobacillus johnsonii	-23.91	33	36
Pediococcus acidilactici	-37.58	18	34
Parabacteroides distasonis	-74.46	1	9
Blautia hansenii	-77.3	12	9