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?.

In my weekly review, I noticed this new study: “Analysis of gut microbiota in Restless Legs Syndrome: searching for a metagenomic signature” Dec 2025 That identifies “a statistically significant decrease in the abundance of Lachnoclostridium and Flavonifractor genera in RLS compared to CTRL“. Just two bacteria. I was surprised to see so few reported. Below you will see my results (all all of the source data is available for download for those that wish to verify the numbers). I was able to get 137 significant shifts​​

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	137
p < 0.001	126
p < 0.0001	119
p < 0.00001	103

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	26.797	26.017	24.337	28.038
Phocaeicola	genus	10.593	10.852	9.365	10.564
Bacteroides uniformis	species	2.92	2.724	1.555	2.385
Ruminococcus	genus	6.675	5.576	4.377	4.823
Clostridium	genus	2.042	1.855	1.36	1.803
Oscillospira	genus	2.898	2.345	1.952	2.313
Parabacteroides	genus	2.741	2.622	1.723	2.016
Bacteroides cellulosilyticus	species	1.359	0.843	0.075	0.312
Parabacteroides merdae	species	0.823	0.745	0.298	0.531
Pedobacter	genus	1.05	0.999	0.553	0.742
Coprococcus	genus	1.193	1.438	0.735	0.558
Sutterella	genus	1.369	1.651	1.251	1.095
Roseburia faecis	species	1.282	1.205	0.573	0.709
Novispirillum	genus	0.845	0.866	0.091	0.225
Insolitispirillum	genus	0.845	0.867	0.093	0.222
Insolitispirillum peregrinum	species	0.845	0.867	0.093	0.222
Ruminococcus bromii	species	1.015	0.784	0.169	0.292
Blautia coccoides	species	0.726	0.915	0.593	0.47
Caloramator	genus	1.013	0.937	0.103	0.211
Blautia hansenii	species	1.19	1.035	0.717	0.824

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.

Nothing significant was found

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
Streptococcus ursoris	species	0.002	0.26	21.6	98	25
Actinopolymorpha	genus	0.002	0.35	17.4	150	52
Actinopolymorpha rutila	species	0.002	0.34	17.3	138	47
Helicobacter suncus	species	0.002	0.47	13.1	771	363
Thermodesulfovibrio	genus	0.002	0.47	13.1	627	293
Desulfotomaculum defluvii	species	0.003	0.55	8.5	1037	569
Bacteroides helcogenes	species	0.002	0.44	8.5	80	35
Bifidobacterium pullorum	species	0.002	0.49	8.3	139	68
Hydrogenophilus	genus	0.003	0.58	7.2	1162	670
Pelagicoccus	genus	0.002	0.58	6.9	850	493
Sporotomaculum syntrophicum	species	0.003	0.58	6.8	1134	663

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
Caldicellulosiruptor	genus	0.027	0.46	279.5	2572	1189
Bifidobacterium longum	species	0.012	2.23	257.1	891	1990
Hymenobacter xinjiangensis	species	0.008	0.43	239.6	1608	696
Hymenobacter	genus	0.008	0.47	223.6	1832	854
Thermicanus	genus	0.189	0.5	211.1	2254	1135
Bifidobacterium gallicum	species	0.0035	2.21	204.6	654	1448
Anaerotruncus colihominis	species	0.178	0.53	201	2532	1331
Bacteroides cellulosilyticus	species	0.312	0.53	191.2	2463	1311
Candidatus Glomeribacter	genus	0.004	0.46	179.2	1282	592
Anaerotruncus	genus	0.18	0.55	170.6	2488	1379
Segatella	genus	0.016	1.8	167.3	1347	2426
Staphylococcus	genus	0.004	0.43	167.3	943	402
Erysipelothrix	genus	0.0165	0.55	167.2	2231	1220
Porphyromonas	genus	0.013	0.55	165.7	2172	1185
Erysipelothrix muris	species	0.015	0.55	158.9	2186	1211
Clostridium	genus	1.803	0.57	158.3	2551	1454
Emticicia oligotrophica	species	0.007	0.54	156.7	1944	1057
Caloramator uzoniensis	species	0.0065	0.51	155.5	1416	716
Emticicia	genus	0.007	0.55	154	1942	1062
Bifidobacterium choerinum	species	0.005	1.86	151.4	927	1728

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.

Per above, nothing was 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
Caldicellulosiruptor	genus	0.027	0.46	279.5	2572	1189
Bifidobacterium longum	species	0.012	2.23	257.1	891	1990
Hymenobacter xinjiangensis	species	0.008	0.43	239.6	1608	696
Hymenobacter	genus	0.008	0.47	223.6	1832	854
Thermicanus	genus	0.189	0.5	211.1	2254	1135
Bifidobacterium gallicum	species	0.0035	2.21	204.6	654	1448
Anaerotruncus colihominis	species	0.178	0.53	201	2532	1331
Bacteroides cellulosilyticus	species	0.312	0.53	191.2	2463	1311
Candidatus Glomeribacter	genus	0.004	0.46	179.2	1282	592
Anaerotruncus	genus	0.18	0.55	170.6	2488	1379
Segatella	genus	0.016	1.8	167.3	1347	2426
Staphylococcus	genus	0.004	0.43	167.3	943	402
Erysipelothrix	genus	0.0165	0.55	167.2	2231	1220
Porphyromonas	genus	0.013	0.55	165.7	2172	1185
Erysipelothrix muris	species	0.015	0.55	158.9	2186	1211
Clostridium	genus	1.803	0.57	158.3	2551	1454
Emticicia oligotrophica	species	0.007	0.54	156.7	1944	1057
Caloramator uzoniensis	species	0.0065	0.51	155.5	1416	716
Emticicia	genus	0.007	0.55	154	1942	1062
Bifidobacterium choerinum	species	0.005	1.86	151.4	927	1728

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
Segatella copri	52.28	8	0
Bifidobacterium breve	43.16	17	0
Enterococcus faecalis	37.79	37	33
Bifidobacterium longum	37.42	18	0
Bifidobacterium adolescentis	28.09	17	0
Lactobacillus johnsonii	20.64	28	28
Lactobacillus helveticus	14.2	24	41
Blautia wexlerae	13.66	3	1
Bifidobacterium bifidum	8.5	11	0
Bifidobacterium catenulatum	7.59	9	0
Enterococcus faecium	5.85	12	18
Akkermansia muciniphila	4.13	8	21
Bifidobacterium animalis	3.76	7	0
Bacillus subtilis	2.13	20	21
Clostridium butyricum	1.21	10	11
Bifidobacterium pseudocatenulatum	0.2	8	7
Ligilactobacillus salivarius	0.05	2	5
Limosilactobacillus fermentum	0.04	9	7
Limosilactobacillus reuteri	0.02	11	17
Enterococcus durans	-0.01	18	14
Heyndrickxia coagulans	-0.01	5	6
Lactococcus lactis	-0.03	5	2
Veillonella atypica	-0.04	2	11
Leuconostoc mesenteroides	-0.06	5	7
Lactiplantibacillus pentosus	-0.06	1	3
Lactiplantibacillus plantarum	-0.07	1	4
Lacticaseibacillus rhamnosus	-0.07	0	5
Lacticaseibacillus casei	-0.08	1	6
Lactobacillus crispatus	-0.1	3	10
Lactobacillus acidophilus	-0.3	5	11
Limosilactobacillus vaginalis	-0.47	16	34
Streptococcus thermophilus	-0.92	1	4
Lactobacillus jensenii	-1.13	18	24
Odoribacter laneus	-2.2	0	3
Pediococcus acidilactici	-5.98	23	28
Escherichia coli	-28.64	0	12
Parabacteroides goldsteinii	-37.48	0	19
Blautia hansenii	-39.47	4	19
Parabacteroides distasonis	-42.82	0	9
Faecalibacterium prausnitzii	-239.3	3	5
Bacteroides uniformis	-252.61	2	7
Bacteroides thetaiotaomicron	-284.92	2	10