This is a follow up to Ability to Predict Symptoms with 99.9% probability using Bacteria Incidence alone. We compute a threshold for each bacteria; then compute the odds ratio using this threshold and add it to the computations. Because of the larger number of samples, Biomesight results dominated and only those are shown.

Most have a cumulative value around 100 [i.e. log(OR)=100] and a few have very small values suggesting that the root association is not the microbiome for those conditions.

Biomesight

Symptom Name	Tax_rank	Cumulative	Cnt
Autonomic Manifestations: irritable bowel syndrome	species	103.4	279
Autonomic Manifestations: light-headedness	species	106.0	251
Autonomic Manifestations: nausea	species	104.5	230
Autonomic Manifestations: Neurally mediated hypotension (NMH)	species	1.5	1
Autonomic Manifestations: Orthostatic intolerance	species	105.2	253
Autonomic Manifestations: Postural orthostatic tachycardia syndrome (POTS)	species	115.7	264
General: Fatigue	species	94.8	319
General: Headaches	species	100.8	269
General: Myalgia (pain)	species	100.1	237
Neurological: Confusion	species	83.7	179
Neurological: Difficulty processing information (Understanding)	species	105.1	265
Neurological: Disorientation	species	82.2	165
Neurological: emotional overload	species	95.9	252
Neurological: fasciculations	species	74.3	162
Neurological: Impairment of concentration	species	121.3	276
Neurological: Short-term memory issues	species	110.4	280
Neurological: Spatial instability and disorientation	species	2.9	2
Neurological: Word-finding problems	species	103.6	286
Neurological-Audio: hypersensitivity to noise	species	113.4	282
Neurological-Sleep: Chaotic diurnal sleep rhythms (Erratic Sleep)	species	80.2	173
Neurological-Vision: inability to focus eye/vision	species	93.4	204
Neurological-Vision: photophobia (Light Sensitivity)	species	115.4	257
Post-exertional malaise: Inappropriate loss of physical and mental stamina,	species	114.8	278
Sleep: Unrefreshed sleep	species	98.3	306

I am off from my usual day job until the new year. In the new year I know that I will be very busy because my firm just landed a contract for a major software product that I am the principal for. I decided to give myself a challenge to explore on these down days:

How accurately can you prediction symptoms from the presence or absence of bacteria reported ALONE with different 16s test.

For those familiar with various forms of Artificial Intelligence, that approach is often used. It reduces the problem to a collection of true/false. For most microbiologists, it is a road not even thought about, lest travelled.

To make the challenge harder, I required the data to have a P value of 0.001. The analysis demanded bacteria-symptom associations with a stringent P-value of 0.001 (Chi² > 10.83), exceeding typical microbiome study thresholds. 

I have four contributed and annotated datasets:

• 16s
  • uBiome: 791 samples
  • Ombre (formerly Thryve): 1,319 samples
  • Biomesight: 4,436 samples
• Shotgun
  • Thorne : 253 samples

Using these datasets, I explored the strength of relationships based on Odds Ratio. A subsequent post includes using odds ratio based on a threshold of bacteria which will get much higher values. A high cumulative value indicates a very strong microbiome basis of the symptom and thus remediation.

For details, see the methodology in : New Standards for Microbiome Analysis? Also, taking amount of each bacteria into consider is shown in Ability to Predict Symptoms with 99.9% probability using Bacteria Incidence and Amount

Relationships were quantified using Odds Ratios (OR) at consistent taxonomy levels to avoid dependence, with cumulative log(OR) indicating symptom-microbiome strength (higher values suggest robust basis for remediation). ​

The Tax Rank indicates what is likely the most effective level to use for investigation (i.e. highest discrimination ability).

Thorne

Symptom Name	Tax_rank	Cumulative	Cnt
General: Fatigue	species	25.26	37

Ombre / Thryve

SymptomName	Tax_rank	Cumulative	Cnt
Autonomic Manifestations: Orthostatic intolerance	genus	21.39	23
General: Fatigue	species	7.72	32
General: Headaches	genus	27.26	37
General: Myalgia (pain)	species	8.49	31
Neurological: Confusion	species	1.31	2
Neurological: Difficulty processing information (Understanding)	species	9.05	19
Neurological: Disorientation	species	1.40	3
Neurological: emotional overload	species	4.74	11
Neurological: Impairment of concentration	genus	22.71	32
Neurological: Word-finding problems	genus	15.24	15
Neurological-Audio: hypersensitivity to noise	genus	29.58	43
Neurological-Sleep: Chaotic diurnal sleep rhythms (Erratic Sleep)	genus	35.01	41
Neurological-Vision: inability to focus eye/vision	genus	42.34	53
Neurological-Vision: photophobia (Light Sensitivity)	genus	47.87	64
Post-exertional malaise: Inappropriate loss of physical and mental stamina,	species	20.20	45
Sleep: Unrefreshed sleep	species	21.19	49

uBiome

While no longer in existence, sharing numbers may be interesting.

Symptom Name	Tax_rank	Cumulative	Cnt
General: Fatigue	species	5.69	15
General: Headaches	species	3.12	7
General: Myalgia (pain)	species	1.74	6
Neurological: Confusion	species	2.82	3
Neurological: Difficulty processing information (Understanding)	species	1.57	6
Neurological: emotional overload	species	6.08	17
Neurological: fasciculations	strain	1.25	3
Neurological: Impairment of concentration	species	14.29	18
Neurological: Short-term memory issues	species	0.94	7
Neurological: Spatial instability and disorientation	species	1.82	1
Neurological: Word-finding problems	species	6.38	11
Neurological-Audio: hypersensitivity to noise	species	7.71	11
Neurological-Sleep: Chaotic diurnal sleep rhythms (Erratic Sleep)	species	7.72	6
Neurological-Vision: inability to focus eye/vision	species	11.63	14
Neurological-Vision: photophobia (Light Sensitivity)	species	10.57	13
Sleep: Unrefreshed sleep	species	5.47	11

BiomeSight

Symptom Name	Tax_rank	Cumulative	Cnt
Autonomic Manifestations: irritable bowel syndrome	species	3.87	10
Autonomic Manifestations: light-headedness	species	8.07	15
Autonomic Manifestations: nausea	species	2.55	11
Autonomic Manifestations: Neurally mediated hypotension (NMH)	species	1.46	1
Autonomic Manifestations: Postural orthostatic tachycardia syndrome (POTS)	species	1.87	11
General: Fatigue	species	7.65	20
General: Headaches	species	3.94	15
General: Myalgia (pain)	species	1.63	10
Neurological: Confusion	species	4.93	6
Neurological: Difficulty processing information (Understanding)	species	0.63	9
Neurological: emotional overload	species	0.81	10
Neurological: fasciculations	genus	3.61	9
Neurological: Impairment of concentration	species	2.10	5
Neurological: Short-term memory issues	species	2.41	5
Neurological: Spatial instability and disorientation	species	2.91	4
Neurological: Word-finding problems	species	2.68	21
Neurological-Audio: hypersensitivity to noise	genus	2.53	6
Neurological-Vision: inability to focus eye/vision	species	2.10	4
Neurological-Vision: photophobia (Light Sensitivity)	species	9.46	28
Post-exertional malaise: Inappropriate loss of physical and mental stamina,	species	2.44	12
Sleep: Unrefreshed sleep	species	1.75	17

Summary

Nota Bene: the above is the cumulative of the log values. It is assumed that for each bacteria, the highest odd ratio is used /hit. A value of 0.81 means exp(0.81) = 2.25 is the highest odds ratio possible if the sample hits every child highest odds ratio. A value of 8.07 becomes odds ratio of 3188.

All of the above are 16s tests which typically are viewed accurate to species at best. The difference of test processing is strongly exhibited in the table below. For background on the challenge on a lack of standardization in microbiome testing, see my post from 6 years ago: The taxonomy nightmare before Christmas…

Symptom Name	Ombre	uBiome	BiomeSight
General: Fatigue	7.72	5.69	7.65
General: Headaches	27.26	3.12	3.94
General: Myalgia (pain)	8.49	1.74	1.63
Neurological: Confusion	1.31	2.82	4.93
Neurological: Difficulty processing information (Understanding)	9.05	1.57	0.63
Neurological: emotional overload	4.74	6.08	0.81
Neurological: Impairment of concentration	22.71	14.29	2.10
Neurological: Word-finding problems	15.24	6.38	2.68
Neurological-Audio: hypersensitivity to noise	29.58	7.71	2.53
Neurological-Vision: inability to focus eye/vision	42.34	11.63	2.10
Neurological-Vision: photophobia (Light Sensitivity)	47.87	10.57	9.46
Sleep: Unrefreshed sleep	21.19	5.47	1.75

Looking at the counts:

Symptom Name	Ombre	uBiome	BiomeSight
General: Fatigue	32	15	20
General: Headaches	37	7	15
General: Myalgia (pain)	31	6	10
Neurological: Confusion	2	3	6
Neurological: Difficulty processing information (Understanding)	19	6	9
Neurological: emotional overload	11	17	10
Neurological: Impairment of concentration	32	18	5
Neurological: Word-finding problems	15	11	21
Neurological-Audio: hypersensitivity to noise	43	11	6
Neurological-Vision: inability to focus eye/vision	53	14	4
Neurological-Vision: photophobia (Light Sensitivity)	64	13	28
Sleep: Unrefreshed sleep	49	11	17

I view these stark differences due to the fragments of RNA that each test looks at to make the identification of bacteria. It is those RNA fragments that is important.

All of the data used above is available for download.

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

A reader asked about gluten sensitivity profile in an email. Here are the results. The short form for probiotics:

• Bifidobacterium breve
• Bifidobacterium longum
• Bifidobacterium adolescentis
• AVOID LACTOBACILLUS

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	162
p < 0.001	146
p < 0.0001	131
p < 0.00001	116

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?

tax_name	Rank	Symptom Average	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	27.548	26	24.269	26.905
Faecalibacterium prausnitzii	species	12.695	12.196	11.329	12.474
Roseburia	genus	2.324	2.857	1.809	1.382
Lachnospira	genus	3.173	2.711	1.885	2.302
Oscillospira	genus	2.668	2.345	1.947	2.323
Bacteroides uniformis	species	2.839	2.728	1.565	1.903
Parabacteroides	genus	3.138	2.607	1.719	2.022
Clostridium	genus	2.087	1.851	1.363	1.531
Pedobacter	genus	1.315	0.988	0.552	0.706
Coprococcus	genus	1.13	1.442	0.73	0.604
Bacteroides thetaiotaomicron	species	0.943	1.077	0.464	0.59
Bifidobacterium	genus	0.352	0.955	0.129	0.028
Hathewaya histolytica	species	0.442	0.273	0.154	0.251
Hathewaya	genus	0.442	0.273	0.154	0.251
Ruminococcus bromii	species	1.039	0.783	0.167	0.262
Bacteroides cellulosilyticus	species	1.266	0.839	0.076	0.155
Bilophila	genus	0.415	0.348	0.209	0.285
Bilophila wadsworthia	species	0.393	0.34	0.199	0.262
Dorea	genus	0.329	0.488	0.295	0.242
Bacteroides rodentium	species	0.361	0.393	0.186	0.235

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

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. In this case two specific probiotic species are seen rarely and thus, supplementation could be inferred.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Bifidobacterium breve	species	0.57	8.8	23.6	41.3
Bifidobacterium catenulatum	species	0.6	6.7	21.6	35.9

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
Thiorhodococcus mannitoliphagus	species	0.002	0.2	37.9	132	27
Cystobacter	genus	0.002	0.21	37.4	131	27
Psychrobacter glacialis	species	0.002	0.36	33.8	675	243
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.36	30.3	393	140
Niabella	genus	0.002	0.38	29	585	224
Viridibacillus neidei	species	0.002	0.39	27	470	182
Thiorhodococcus	genus	0.002	0.43	22.9	579	247
Thermodesulfovibrio thiophilus	species	0.002	0.44	21.5	541	236
Oenococcus	genus	0.002	0.45	20.7	614	275
Thermodesulfovibrio	genus	0.002	0.45	20.1	626	284
Helicobacter suncus	species	0.002	0.46	19.6	765	355
Viridibacillus	genus	0.002	0.5	14.8	488	244
Desulfotomaculum defluvii	species	0.003	0.56	11.6	1017	569
Alkalibacterium	genus	0.003	0.57	10.6	899	514
Sporotomaculum syntrophicum	species	0.003	0.58	10.4	1127	652
Pelagicoccus	genus	0.002	0.58	10.1	842	487
Treponema	genus	0.003	0.58	9.7	593	342
Olivibacter soli	species	0.002	0.57	9.5	457	262
Hydrogenophilus	genus	0.003	0.59	9.5	1133	671
Mycoplasma iguanae	species	0.002	0.58	9.1	458	266

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
Bifidobacterium	genus	0.028	2.37	347.9	1154	2736
Tetragenococcus	genus	0.004	0.44	234.4	1638	719
Bifidobacterium adolescentis	species	0.004	2.03	215.7	1074	2176
Hathewaya histolytica	species	0.2505	0.52	202.8	2568	1345
Hathewaya	genus	0.2505	0.52	202.2	2567	1346
Psychrobacter glacialis	species	0.002	0.36	168.1	675	243
Anaerotruncus	genus	0.1785	0.57	155.7	2439	1383
Caloramator uzoniensis	species	0.006	0.51	153.9	1408	712
Bifidobacterium choerinum	species	0.0055	1.87	151.9	917	1718
Mogibacterium	genus	0.022	0.57	145.4	2115	1195
Methylonatrum	genus	0.004	0.54	145	1627	872
Methylonatrum kenyense	species	0.004	0.54	145	1627	872
Anaerotruncus colihominis	species	0.1705	0.58	143	2415	1403
Hymenobacter xinjiangensis	species	0.007	0.53	137.4	1486	795
Niabella	genus	0.002	0.38	135.9	585	224
Streptococcus australis	species	0.0095	0.57	127.5	1773	1010
Leptolyngbya laminosa	species	0.0045	0.44	125.9	698	304
Leptolyngbya	genus	0.0045	0.44	125.8	701	306
Bifidobacterium longum	species	0.0195	1.73	124	1047	1814
Vagococcus	genus	0.003	0.48	119.9	841	403

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	8.9	1354	818

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
Bifidobacterium	genus	0.028	2.37	347.9	1154	2736
Tetragenococcus	genus	0.004	0.44	234.4	1638	719
Bifidobacterium adolescentis	species	0.004	2.03	215.7	1074	2176
Hathewaya histolytica	species	0.2505	0.52	202.8	2568	1345
Hathewaya	genus	0.2505	0.52	202.2	2567	1346
Psychrobacter glacialis	species	0.002	0.36	168.1	675	243
Anaerotruncus	genus	0.1785	0.57	155.7	2439	1383
Caloramator uzoniensis	species	0.006	0.51	153.9	1408	712
Bifidobacterium choerinum	species	0.0055	1.87	151.9	917	1718
Mogibacterium	genus	0.022	0.57	145.4	2115	1195
Methylonatrum	genus	0.004	0.54	145	1627	872
Methylonatrum kenyense	species	0.004	0.54	145	1627	872
Anaerotruncus colihominis	species	0.1705	0.58	143	2415	1403
Hymenobacter xinjiangensis	species	0.007	0.53	137.4	1486	795
Niabella	genus	0.002	0.38	135.9	585	224
Streptococcus australis	species	0.0095	0.57	127.5	1773	1010
Leptolyngbya laminosa	species	0.0045	0.44	125.9	698	304
Leptolyngbya	genus	0.0045	0.44	125.8	701	306
Bifidobacterium longum	species	0.0195	1.73	124	1047	1814
Vagococcus	genus	0.003	0.48	119.9	841	403

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	103.8	16	0
Bifidobacterium longum	93.37	17	2
Bifidobacterium adolescentis	68.66	14	2
Enterococcus faecalis	42.16	59	24
Lactobacillus johnsonii	30.97	46	20
Segatella copri	24.16	6	0
Bifidobacterium bifidum	21.18	12	3
Bifidobacterium catenulatum	18.93	10	0
Akkermansia muciniphila	15.46	8	19
Lactobacillus helveticus	10.82	46	44
Bifidobacterium animalis	9.67	7	0
Pediococcus acidilactici	6.74	31	20
Enterococcus faecium	6.47	21	26
Blautia wexlerae	2.6	2	1
Streptococcus thermophilus	2.28	7	1
Escherichia coli	2.23	4	2
Clostridium butyricum	2.07	21	3
Enterococcus durans	0.62	27	16
Lactococcus lactis	0.47	8	2
Bacillus subtilis	0.24	14	27
Bifidobacterium pseudocatenulatum	0.17	18	12
Limosilactobacillus fermentum	0.11	8	8
Veillonella atypica	0.07	14	3
Heyndrickxia coagulans	0.01	9	12
Limosilactobacillus reuteri	-0.01	12	13
Lacticaseibacillus paracasei	-0.03	4	10
Lactobacillus crispatus	-0.04	13	13
Ligilactobacillus salivarius	-0.04	1	6
Leuconostoc mesenteroides	-0.09	8	8
Lacticaseibacillus casei	-0.13	2	9
Lactiplantibacillus plantarum	-0.13	0	6
Lactiplantibacillus pentosus	-0.14	0	6
Lacticaseibacillus rhamnosus	-0.17	1	8
Lactobacillus acidophilus	-0.21	16	22
Limosilactobacillus vaginalis	-0.31	32	29
Odoribacter laneus	-0.45	0	3
Lactobacillus jensenii	-1.03	30	26
Blautia hansenii	-3.61	0	14
Parabacteroides goldsteinii	-38.45	0	16
Parabacteroides distasonis	-48.84	0	11
Bacteroides uniformis	-147.38	2	10
Bacteroides thetaiotaomicron	-157.07	2	8
Faecalibacterium prausnitzii	-278.2	2	7

Prior posts for this person are linked below

• The ME/CFS Quest for Health [May 2025]
• Ongoing ME/CFS Journey [Jan 2025]
• ME/CFS Continues Improvement + Lab Read Quality Issues [Feb 2024]
• Update on ME/CFS Person [Sep 2023]
• Follow up Microbiome Analysis from a prior post[Apr 2022]
• Rosacea, Circulation and mild CFS [Dec 2021]

I am going to review using the traditional analysis. My initial impression is that suggested retesting and plotting the next course correction was missed. As with sailing a boat, this can sail a person to an unintended spot. The last section is trying some work in progress on his sample(s). This is experimental work which I have high hope on yielding much finer identification of the critical bacteria that should be addressed.

Analysis

My usual starting point for multiple tests is compare forecasts of symptoms: New sample is 2025-11, old sample is 2025-03. Things have gotten worse.

I decide to compare 2025-11 to 2024-12 and see the latest sample is still worse, but not as bad. In other words, the gains made over the summer has been lost. This person is in a northern climate so seasonal variation could (theoretically) be significant.

As often, we have a high hit rate of projected symptoms against actual symptoms.

Current Back Story

The first line is reflected above.

I have not been feeling so well lately (since the last year).

I would say that my symptoms has become worse.

Earlier it has always felt as I have done some progress but the last 12 months it has been the opposite. 

Earlier I got rid of my muscle and joint pain but it has come back and I have much bigger issues with my red nose and my body feels very stressed.

Also feel very bloated.

A summary of my biggest issues:

• Get the red nose (some form of rosacea). 
• Feel fatigued (both physically and mentally). 
• Feeling stressed. 
• Brain fog.
• Bloated.
• Lots of gas – I fart and burps a lot. 
• Issues with allergies
• Muscle and joint pain

For the last 4 years I’ve been eating large amounts of rye and oats.

Around 150-200 gram of rye bread every day.

Around 70 gram of oats every day.

Been eating low fat, low protein and high carb (specially from rye, oats, apple juice and potatoes) because this diet seem to reduce my symptoms.

As soon as I start to eat high meat and high fat my symptoms get worse.

Traditional Analysis

First, I am doing the “traditional” analysis before exploring some work in progress to improve suggestions further. The process is simple, pick Beginner-Symptoms, mark symptoms and get suggestions. This is the process that seems to produce the best results. Other choices are intended to satisfy people with different assumptions. The site purpose is allow people to use the data according to their beliefs about the best way.

Despite having 42 symptoms entered, this boiled down to just 20 bacteria. Many related symptoms are connected to the same bacteria.

Investigate: “As soon as I start to eat high meat and high fat my symptoms”

Looking at the consensus report we see:

• high red meat: -155
• Pork : -22
• Chicken : -14

Which agrees with his reported response. On the other side, generic “fat” is a significant plus– so the type of fat seems to be critical.

Investigate Current Eating Habits: eating large amounts of rye and oats.

The suggestions are intended to be course corrections for the microbiome. Keeping on a course for too long may end up running aground on mudflats (instead of the original reefs that the course correction was intended for).

• Avena sativa {Oats} is a -16
• Secale cereale {Rye} +276
• Hordeum vulgare {Barley} -225
• Avena sativa x Hordeum vulgare {barley,oat} -240
• ß-glucan {Beta-Glucan} -110
• wheat, amaranth,quinoa, taro,buckwheat were all negatives

The question is why rye is ok and other grains are not? It may be due to some composition aspect or a side-effect of having sparse data. It looks like some change of diet is suggested.

The positive food items appear to be:

• fruit: grapes, especially lingonberries, cranberries (and likely cloudberries!), citric fruits, bananas
• fish: fish oil,
• legumes: beets, nuts, Asparagus, Rhubarb but NOT Pulses, Beans
• Quinoa – which unlike cereals above is gluten free

Vitamins

The list is pretty short.

• To take: Vitamin B6,7 and 12 but no other B vitamins (i.e. no mixtures)
• Iron and Vitamin D are fine

Probiotics

There are two approaches here.

• Using PubMed Studies only (sparse data, negative impacts almost never published)
• Using Inference from Microbiome Taxa R² Site.
• A third way is in the next section with is like the R2 site, but using a 16s Biomesight reference set instead of a shotgun.

PubMed Studies

The top ones, in descending order are (with strike thru on ones that are hard to obtain):

• Latilactobacillus sakei {Lactobacillus sakei}
• Lacticaseibacillus paracasei {L.paracasei}
• Enterococcus faecium sf 68 {bioflorin}
• Escherichia coli Nissle 1917 {Mutaflor} os Symbioflor-2
• Lentilactobacillus buchneri {Lactobacillus buchneri}
• Lactococcus lactis {Streptococcus lactis}
• bifidobacterium bifidum {B. bifidum}
• aspergillus oryzae {koji}
• Bacillus amyloliquefaciens group {B. Amyloliquefaciens}

Microbiome Taxa R2 Site

We end up with just 2 “safe” (positive impact only)

• Bacillus pumilus
• Escherichia coli

This leaves only one with 100% consensus: E.Coli (Mutaflor, Symbioflor-2). Looking at PubMed with a net positive with R2, we also have

• Bifidobacterium bifidum

My Own Experience

While fighting ME/CFS, I retested about 6 weeks after getting the results of the last test. I noticed that suggestions swing back and worth a lot – but I kept following them. Often there can be a battle between “common sense beliefs” and what the algorithms find. Avoiding something during one cycle and then taking it the next cycle seemed “irrational”. I borrowed from physical processes the concept of “microbiome oscillations” and stopped worrying about the swings.

My personal advice is simple, get results and then do suggestions for 6-8 week and do another test. With test processing delays, it means about 10-12 weeks on each set of suggestions

Going Forward — and a new Algorithm

Recently I have been working on an Odds Ratio investigation. The reason is simple, the Odds ratio gives an objective measure of the importance of each bacteria for the symptoms. The new approach uses Odds Ratio to determine the odds of a bacteria causing a symptom. The odds tells me the importance. If you are interested in more technical data, see:

• New Standards for Microbiome Analysis?
• Naive Testing of Odds Ratio for Predicting Symptoms

I will be trying it out on his data. The databases involved are about 160GB with processing often taking 20 minutes for each processing state, so they are on my “garage” high performance server (nerd talk: 64GB of memory, fast M.2 NVMe  2TB drive for disk) and strictly for research/exploration at the moment.

Key differences

• we are going to estimate symptoms a different way than traditional (using odds)
• we are likely to have 100+ bacteria to shift

Predicted Symptoms Rank Order

Using the Odds Ratio approach we get the following predictions that agrees with his reported symptoms/characteristics.

Symptom Name	Strength
Age: 30-40	11.2
Sleep: Unrefreshed sleep	10.9
Comorbid: Small intestinal bacterial overgrowth (SIBO)	9.1
Immune Manifestations: Inflammation (General)	9
General: Fatigue	9
DePaul University Fatigue Questionnaire : Tingling feeling	8.8
Neuroendocrine: Cold limbs (e.g. arms, legs hands)	8.5
Neuroendocrine Manifestations: cold extremities	8.3
Neurocognitive: Brain Fog	8.1
Post-exertional malaise: Worsening of symptoms after mild mental activity	8
DePaul University Fatigue Questionnaire : Fatigue	7.9
Gender: Male	7.9
DePaul University Fatigue Questionnaire : Muscle Pain (i.e., sensations of pain or aching in your muscles. This does not include weakness or pain in other areas such as joints)	7.8
Immune Manifestations: Bloating	7.5
DePaul University Fatigue Questionnaire : Allergies	7.4
DePaul University Fatigue Questionnaire : Muscle weakness	6.9
DePaul University Fatigue Questionnaire : Post-exertional malaise, feeling worse after doing activities that require either physical or mental exertion	6.7
DePaul University Fatigue Questionnaire : Rash	6.5
Post-exertional malaise: Mentally tired after the slightest effort	6.3
Comorbid: Histamine or Mast Cell issues	6.3
Post-exertional malaise: Next-day soreness after everyday activities	6
Post-exertional malaise: Muscle fatigue after mild physical activity	6
Official Diagnosis: Mast Cell Dysfunction	5.9
Neuroendocrine Manifestations: worsening of symptoms with stress.	5.9
Post-exertional malaise: Worsening of symptoms after mild physical activity	5.8
DePaul University Fatigue Questionnaire : Does physical activity make you feel worse	5.7
DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired	5.7
Immune: Flu-like symptoms	5.7
DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness	5.5
Neurological-Audio: hypersensitivity to noise	5.2
Immune Manifestations: Inflammation of skin, eyes or joints	5.1

Looking at the existing estimates, we see far greater separation in weight/estimates. I favor separation because that implies much better focus on bacteria.

1.  DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness ✅ – [83.2%]
2.  DePaul University Fatigue Questionnaire : Muscle weakness ✅ – [83.1%]
3.  Neuroendocrine Manifestations: cold extremities ✅ – [83.1%]
4.  Neurocognitive: Brain Fog ✅ – [82.7%]
5.  DePaul University Fatigue Questionnaire : Muscle Pain (i.e., sensations of pain or aching in your muscles. This does not include weakness or pain in other areas such as joints) ✅ – [82.7%]
6.  Post-exertional malaise: Worsening of symptoms after mild physical activity ✅ – [82.4%]
7.  Post-exertional malaise: Next-day soreness after everyday activities ✅ – [82.3%]
8.  General: Fatigue ✅ – [82.3%]
9.  Sleep: Unrefreshed sleep ✅ – [82.2%]
10.  Comorbid: Small intestinal bacterial overgrowth (SIBO) ✅ – [82.1%]
11.  Immune Manifestations: Bloating ✅ – [81.9%]
12.  DePaul University Fatigue Questionnaire : Fatigue ✅ – [81.7%]
13.  Official Diagnosis: Chronic Fatigue Syndrome (CFS/ME) ✅ – [81.5%]
14.  Neurological-Audio: hypersensitivity to noise ✅ – [81.5%]
15.  DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired ✅ – [81.4%]
16.  DePaul University Fatigue Questionnaire : Difficulty staying asleep ✅ – [81.3%]
17.  Official Diagnosis: Autoimmune Disease ✅ – [81.2%]
18.  Immune Manifestations: Inflammation (General) ✅ – [81.2%]
19.  DePaul University Fatigue Questionnaire : Easily irritated – [81.2%]
20.  Neuroendocrine Manifestations: worsening of symptoms with stress. ✅ – [81.2%]

Key Bacteria identified

The new approach identifies these bacteria to target, with their relative importance (Weight). I just did another post on a ME/CFSer, Microbiome Interpretation – Questions From A User. Megamonas also was her top one.

tax name	tax rank	Weight	Target
Megamonas	genus	108.9	Too High
Klebsiella oxytoca	species	95.8	Too High
Ruminococcus bromii	species	-17.9	Too Low
Eubacteriales	order	-16.1	Too Low
Clostridia	class	-15.1	Too Low
Megamonas funiformis	species	15	Too High
Bacillota	phylum	-14.2	Too Low
Ruminococcaceae	family	-14.2	Too Low
Segatella	genus	13.6	Too High
Oscillospiraceae	family	-13.4	Too Low
Ruminococcus	genus	-12.4	Too Low
Terrabacteria group	clade	-12.4	Too Low
Segatella copri	species	11.9	Too High
Bacteroidia	class	11.4	Too High
Bacteroidales	order	11.4	Too High
Bacteroidota/Chlorobiota group	clade	10.3	Too High
Bacteroidota	phylum	10.1	Too High
FCB group	clade	9.8	Too High
Prevotella	genus	9.7	Too High
Lachnospiraceae	family	-9.3	Too Low
Prevotellaceae	family	9	Too High
Phocaeicola vulgatus	species	8.4	Too High
Akkermansiaceae	family	6.7	Too High
Bacteroides uniformis	species	6	Too High
Pseudomonadota	phylum	5.9	Too High
Gammaproteobacteria	class	5.2	Too High
Yersinia	genus	4.8	Too High
Verrucomicrobiota	phylum	4.3	Too High
Akkermansia	genus	4.1	Too High
Verrucomicrobiales	order	4	Too High

The traditional approach identifies the list below. There is relatively little overlap. My ‘gut’ reading is that those above are likely a better candidate set than those below.

Bacteria	Rank	Shift
Thiotrichales	order	High
Sharpea	genus	High
Selenomonas	genus	High
Ruminococcus	genus	High
Negativicutes	class	Low
Johnsonella	genus	High
Holdemania	genus	High
Erysipelothrix	genus	High
Dorea	genus	High
Desulfovibrionia	class	Low
delta/epsilon subdivisions	clade	Low
Cyanophyceae	class	Low
Cyanobacteriota/Melainabacteria group	clade	Low
Coprococcus	genus	High
Chlorobiota	phylum	High
Chlorobiia	class	High
Chlorobiaceae	family	High
Chlorobaculum	genus	High
Actinomycetota	phylum	Low
Actinomycetes	class	Low

Suggestions

Since two bacteria dominates, I ran the suggestion algorithm only on those two bacteria. The results are below and very similar to the results from the traditional approach. “All algorithms lead to the same suggestions”. Doing the full list cited above, produced very similar suggestions.

Modifier	Net
fruit/legume fibre	263
fruit	241
Fiber, total dietary	217
Chitosan	217
Slow digestible carbohydrates. {Low Glycemic}	210
oolong teas	205
polyphenols	203
resveratrol-pterostilbene x Quercetin  {quercetin x resveratrol}	202
(2->1)-beta-D-fructofuranan {Inulin}	201
3,3′,4′,5,7-pentahydroxyflavone {Quercetin}	200
High-fibre diet {Whole food diet}	199
Citrus limon  {Lemon}	194
5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a]quinolizinium {Berberine}	193
Lonicera periclymenum {Epazote}	184
Grape Polyphenols {Grape Flavonoids}	177
tea	177
dietary fiber	169
red wine	168
grapes	163
Linum usitatissimum {Flaxseed}	160
Lactobacillus plantarum {L. plantarum}	158
Camellia Linnaeus {camellia}	158
Coffee	150
Hydrastis canadensis {Goldenseal}	148
Ethanoic acid {Vinegar}	148
Ulmus rubra {slippery elm}	145
Ligilactobacillus salivarius {L. salivarius}	144
Ginkgo biloba {Ginkgo}	139
Cola aspartame {Diet Cola}	138
pseudo-cereals  {amaranth,quinoa, taro,buckwheat}	137
Agaricus bisporus {White button mushrooms}	134
Theobroma cacao {Cacao}	131
Caffeine	127
Lacticaseibacillus rhamnosus {l. rhamnosus}	124
fucoidan {Brown Algae Extract}	122
Litchi chinensis {lychee fruit}	121
coptis chinensis {Chinese goldthread }	121

Probiotics using R2

First I used only the top two bacteria to see what is suggested with a very targeted set.

• Bacillus subtilis 75
• Lactobacillus jensenii 60

Below are pushing the full set of identified bacteria through BiomeSight R2 matrix, then filtered to positive impact with no risk. Escherichia coli (cited above) continues to be a take. Bacillus subtilis would be my fall back suggestion for a probiotic. It is marginally negative on the consensus report and not cited on other R2 suggestion list. For others candidates

• Lactobacillus jensenii was one for and no comment
• Lactococcus lactis is a one for and one against
• Lactobacillus helveticus is one strong against and no comment
• Lacticaseibacillus casei is one strong against and no comment
• Akkermansia muciniphila is two strong avoid

I tend to do a variation of the traditional “Do no harm”, minimize the risk of adverse shifts.

Probiotic	Net Impact	Good Count	Bad Count
Bacillus subtilis	81.9	5	0
Lactobacillus jensenii	68.5	2	0
Lactococcus lactis	26.8	2	0
Lactobacillus helveticus	21.4	4	0
Lacticaseibacillus casei	21.4	2	0
Segatella copri	21.2	3	0
Lactiplantibacillus pentosus	21	5	0
Akkermansia muciniphila	18.7	4	0
Bacillus amyloliquefaciens group	18.3	1	0
Enterococcus faecium	16.3	1	0
Limosilactobacillus fermentum	13.6	1	0
Heyndrickxia coagulans	13.4	3	0
Enterococcus durans	12.4	2	0
Pediococcus acidilactici	11	1	0
Enterococcus faecalis	10.6	2	0
Leuconostoc mesenteroides	10.5	1	0
Escherichia coli	4.3	3	0

Bottom Line

The purpose of this post was to evaluate suggestions for a regular reader. The secondary goal was to see how well a new approach that I am developing is working. This new approach produces different targeted bacteria with very similar suggestions generated, the most significance difference is far more targeted probiotics for the symptoms based on the same lab data.

The one interesting aspect is that the key bacteria (just 2) were clearly identified. These two bacteria alone produced suggestions similar to the bigger bacteria selection. I do like this narrow bacteria selection of key bacteria and will likely do a few more samples to further explore things.

Follow Up

I decided to look at all of his samples with the new algorithm to look for patterns. Megamonas stands out as the one that most frequently appears and disappears. Klebsiella oxytoca and Morganellaceae are the next candidates.

Upload Date	Top Bacteria
2021-09-24	Megamonas genus 108.9 Too High Lachnospiraceae family -56.2 Too Low Eubacteriales order -47.3 Too Low Bacillota phylum -46.6 Too Low Clostridia class -46.4 Too Low Terrabacteria group clade -44.8 Too Low
2021-09-24	Bacillota phylum -46.5 Too Low Eubacteriales order -45.1 Too Low Terrabacteria group clade -45 Too Low Clostridia class -44.1 Too Low Lachnospiraceae family -43.6 Too Low
2022-04-19	Klebsiella oxytoca species 95.8 Too High Morganellaceae family 91 Too High
2022-09-04	Oscillospiraceae family 17.7 Too High Ruminococcaceae family 17.4 Too High Bacteroidaceae family -16 Too Low Bacteroides genus -15.9 Too Low
2023-03-15	Megamonas genus 108.9 Too High Lachnospiraceae family -26 Too Low
2023-09-26	Megamonas genus 108.9 Too High
2024-02-13	Bacteroidaceae family -27.3 Too Low Bacteroides genus -27.3 Too Low Phocaeicola dorei species -21.4 Too Low
2024-09-25	Segatella genus 13.6 Too High Segatella copri species 11.9 Too High
2025-04-22	Morganellaceae family 91.6 Too High
2025-12-08	Megamonas genus 108.9 Too High Klebsiella oxytoca species 95.8 Too High

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

A reader wrote the following to me. This post and associate video is an attempt to answer.

When I compare my first two analyses from Biomesight under “Multiple Samples,” the distribution of bacteria improves toward normal—assuming that the reduction in “Lab Read Quality” from 39.3 to 23.5 does not mean that the latest sample is a false positive. I have read your article “Lab Quality Versus Bacteria Reported,” but I am too brain fogged to really understand the content. 

In fact, “Symptom Pattern Matching” states that there was only one improvement and 150 deteriorations. Subjectively, I feel differently, not really better, but not much worse either. That’s why I don’t know how to interpret these results. (Better: nausea, migraine, constipation—Worse: brain fog, fatigue, sleep, muscle strength. So, speculation that: MCAS is improving, but ME-CFS continues to progressively worsen).

The suggestions under “To Avoid” have changed under “Food.” It is interesting to note that, before I was aware of these suggestions, they were already reflected in my appetite or aversion to certain foods; so it is reassuring that my body perception actually corresponds to the measured facts and the statistically supported conclusions. 

Unfortunately, my subjective perception of a change, but not an improvement, would then also correspond with the “outside ranges” and “symptom pattern matching.” (Objectively, I measure heart rate variability, pulse, and temperature with a simple GARMIN device and thermometer.)

I don’t know enough about statistics and am not fully mentally present, so I would be grateful if you could provide a few assessments of the measured changes, if you have the time. 

https://youtu.be/JXP9UWQVU_A

Sample Comparison

With two or more samples, the ability to compare samples using symptom forecasts is intended to give a good indicator of change. In most cases, there is significant improvement. In this case, things became worse.

There can be many causes:

• Catching a virus, food poisoning, etc between samples
• Ignoring the “Avoids” – items that feed the bad bacteria
• Trying to follow two sets of advice that have not been reconcilled
  • A health consultant and microbiome prescription suggestions.

“Feeling the same” with these minor shifts is not unexpected. Remembering how you were tend to be unreliable; bad memories fail fast.

Getting Suggestions is Easy, Picking Bacteria is not

Microbiome Prescription is constantly update on what influence bacteria from new studies every week. At present if has a 7,432,372 Modifier-to-Bacteria relationships in its database. Given a set of bacteria, their shifts and the relative importance of each bacteria, the suggestions are a relatively simple computation.

Picking Bacteria

If you go to the typical alternative health practitioners, or just ask on line, you may not need to get a microbiome tests. From your symptoms, they will speculate on the issue and give suggestions that they will swear works. In reality, they may appear to work because the patients that they worked for, will come back to see them again. For those where it does not work, they will move on to the next “expert”.

Testing labs will often provide a reference range for some bacteria and thus identify if you have too much or too low. There are many technical issues using these. Personally, I avoid using them — but to make people happy, several are provided on the site (Old UI).

The Simple UI gives a few canned choices for selecting the bacteria and then does the easy part, computing suggestions. The numbers of bacteria vary greatly.

A new algorithm in development, identified 2 very critical bacteria (HIGH WEIGHT) and 20 minor bacteria. The goal of the new algorithm is to better pinpointing the key bacteria and their impact.to look

• Both Samples had the same top item
  • Megamonas, genus, weight 109, too high on both tests
  • Morganellaceae, famiy, weight 92, too high on latest test only,

The addition of Morganellaceae in the latest sample may account for the worse report.

Going forward, I would look at what decreases these two bacteria. I ran your latest data through the suggestion algorithm and attach the full set of suggestions in excel.

The main take items from the new algorithm are below

• Slow digestible carbohydrates. {Low Glycemic}
• dietary fiber
• Fiber, total dietary
• fruit
• fruit/legume fibre
• High-fibre diet {Whole food diet}
• Lactobacillus plantarum {L. plantarum}
• (2->1)-beta-D-fructofuranan {Inulin}
• oligosaccharides {oligosaccharides}
• 5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a]quinolizinium {Berberine}
• bacillus,lactobacillus,streptococcus,saccharomyces probiotic
• 3,3′,4′,5,7-pentahydroxyflavone {Quercetin}
• fucoidan {Brown Algae Extract}
• Lacticaseibacillus rhamnosus {l. rhamnosus}
• yogurt
• grapes
• polyphenols
• Grape Polyphenols {Grape Flavonoids}
• bacillus
• bifidobacterium longum {B.Longum }
• Linum usitatissimum {Flaxseed}
• resveratrol-pterostilbene x Quercetin {quercetin x resveratrol}
• ß-glucan {Beta-Glucan}
• Saccharomyces cerevisiae var boulardii {S. boulardii}
• Outer Layers of Triticum aestivum {Wheat Bran}
• tea

Compared to the existing using symptoms. They are similar and both have top items of:

• Lactobacillus plantarum
• fruit
• dietary fiber
  

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

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	226
p < 0.001	199
p < 0.0001	176
p < 0.00001	157

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 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	28.633	25.842	24.114	27.576
Phocaeicola vulgatus	species	6.593	5.744	3.347	4.644
Phocaeicola	genus	11.777	10.78	9.291	10.397
Bacteroides uniformis	species	3.031	2.707	1.52	2.104
Parabacteroides	genus	2.552	2.631	1.714	2.058
Bacteroides thetaiotaomicron	species	1.27	1.056	0.455	0.678
Clostridium	genus	2.004	1.847	1.352	1.569
Oscillospira	genus	2.45	2.348	1.944	2.144
Bacteroides cellulosilyticus	species	1.032	0.84	0.07	0.237
Parabacteroides merdae	species	0.854	0.737	0.287	0.44
Coprococcus	genus	1.071	1.461	0.739	0.59
Pedobacter	genus	1.232	0.98	0.548	0.695
Novispirillum	genus	0.816	0.872	0.087	0.229
Insolitispirillum	genus	0.816	0.873	0.089	0.229
Insolitispirillum peregrinum	species	0.816	0.873	0.089	0.229
Bacteroides caccae	species	1.135	0.854	0.281	0.38
Bifidobacterium	genus	0.513	0.969	0.134	0.048
Bilophila	genus	0.395	0.347	0.206	0.276
Parabacteroides goldsteinii	species	0.565	0.571	0.13	0.194
Bilophila wadsworthia	species	0.376	0.339	0.197	0.256

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 %
Bifidobacterium catenulatum	species	0.69	8.3	25.2	36.2
Thiomonas thermosulfata	species	1.41	8	29.4	20.9
Aggregatibacter	genus	0.65	7.8	16	24.4
Desulfurispirillum	genus	1.45	8.4	25.5	17.5
Desulfurispirillum alkaliphilum	species	1.45	8.1	25.2	17.4
Actinobacillus pleuropneumoniae	species	0.6	8.4	11	18.5
Bifidobacterium cuniculi	species	0.62	7.8	12	19.3
Desulfonatronovibrio	genus	1.48	7.5	19.9	13.5
Erysipelothrix inopinata	species	1.47	7.4	19.9	13.5
Paraprevotella xylaniphila	species	1.6	9.2	16.6	10.4
Candidatus Phytoplasma phoenicium	species	1.59	8.7	16	10.1
Trichodesmium	genus	1.56	6.6	12.9	8.3

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	0.26	75.2	375	98
Isoalcanivorax	genus	0.002	0.26	74.5	365	95
Isoalcanivorax indicus	species	0.002	0.26	74.5	365	95
Nostoc flagelliforme	species	0.002	0.27	67.3	316	84
Salidesulfovibrio	genus	0.002	0.3	63.2	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	63.2	381	115
Niabella aurantiaca	species	0.002	0.34	62.1	524	176
Mycoplasmopsis lipophila	species	0.002	0.27	61.2	277	75
Psychroflexus	genus	0.002	0.3	60.4	348	105
Psychroflexus gondwanensis	species	0.002	0.3	60.4	348	105
Deferribacter autotrophicus	species	0.002	0.31	59.6	374	117
Pelagicoccus croceus	species	0.002	0.32	59.2	380	120
Deferribacter	genus	0.002	0.32	59	377	119
Psychrobacter glacialis	species	0.002	0.37	55.3	636	238
Thermodesulfatator atlanticus	species	0.002	0.3	53.6	276	83
Thermodesulfatator	genus	0.002	0.3	53.6	276	83
Segetibacter aerophilus	species	0.002	0.34	53.1	364	122
Bacillus ferrariarum	species	0.002	0.34	52.1	356	120
Niabella	genus	0.002	0.38	51.8	560	212
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.35	51.6	379	131

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
Psychrobacter glacialis	species	0.002	0.37	150	636	238
Niabella aurantiaca	species	0.002	0.34	148.2	524	176
Desulfotomaculum	genus	0.004	0.51	147.5	1394	711
Alcanivorax	genus	0.002	0.26	145.8	375	98
Isoalcanivorax	genus	0.002	0.26	142.8	365	95
Isoalcanivorax indicus	species	0.002	0.26	142.8	365	95
Niabella	genus	0.002	0.38	132.4	560	212
Salidesulfovibrio	genus	0.002	0.3	127.5	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	127.5	381	115
Bacteroides cellulosilyticus	species	0.237	0.59	127.2	2243	1325
Actinopolyspora	genus	0.002	0.38	125.5	524	198
Bifidobacterium	genus	0.048	1.68	125.3	1392	2332
Nostoc flagelliforme	species	0.002	0.27	122.8	316	84
Geobacillus	genus	0.003	0.43	122.4	672	291
Bacteroides heparinolyticus	species	0.003	0.49	122.1	928	454
Erysipelothrix muris	species	0.014	0.59	121.6	2029	1195
Pelagicoccus croceus	species	0.002	0.32	120.7	380	120
Deferribacter autotrophicus	species	0.002	0.31	120.3	374	117
Deferribacter	genus	0.002	0.32	119.9	377	119
Psychroflexus	genus	0.002	0.3	117.5	348	105

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	0.62	16	1295	805
Tetragenococcus doogicus	species	0.003	0.67	11.5	1316	881
Dethiosulfovibrio	genus	0.004	0.67	11.3	1457	981
Hydrocarboniphaga daqingensis	species	0.004	0.71	8.7	1549	1096
Mycoplasmopsis	genus	0.005	0.72	8	1709	1230

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
Psychrobacter glacialis	species	0.002	0.37	150	636	238
Niabella aurantiaca	species	0.002	0.34	148.2	524	176
Desulfotomaculum	genus	0.004	0.51	147.5	1394	711
Alcanivorax	genus	0.002	0.26	145.8	375	98
Isoalcanivorax	genus	0.002	0.26	142.8	365	95
Isoalcanivorax indicus	species	0.002	0.26	142.8	365	95
Niabella	genus	0.002	0.38	132.4	560	212
Salidesulfovibrio	genus	0.002	0.3	127.5	381	115
Salidesulfovibrio brasiliensis	species	0.002	0.3	127.5	381	115
Bacteroides cellulosilyticus	species	0.237	0.59	127.2	2243	1325
Actinopolyspora	genus	0.002	0.38	125.5	524	198
Bifidobacterium	genus	0.048	1.68	125.3	1392	2332
Nostoc flagelliforme	species	0.002	0.27	122.8	316	84
Geobacillus	genus	0.003	0.43	122.4	672	291
Bacteroides heparinolyticus	species	0.003	0.49	122.1	928	454
Erysipelothrix muris	species	0.014	0.59	121.6	2029	1195
Pelagicoccus croceus	species	0.002	0.32	120.7	380	120
Deferribacter autotrophicus	species	0.002	0.31	120.3	374	117
Deferribacter	genus	0.002	0.32	119.9	377	119
Psychroflexus	genus	0.002	0.3	117.5	348	105

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
Blautia hansenii	92.34	11	1
Bifidobacterium breve	70.82	17	3
Faecalibacterium prausnitzii	67.96	12	0
Bifidobacterium longum	64.14	19	0
Blautia wexlerae	50.13	6	0
Bifidobacterium adolescentis	46.86	15	0
Segatella copri	39.48	7	0
Enterococcus faecalis	30.68	60	31
Bifidobacterium bifidum	14.43	15	3
Lactobacillus helveticus	13.52	47	77
Bifidobacterium catenulatum	13.5	13	2
Escherichia coli	12.84	13	0
Enterococcus faecium	7.35	20	26
Bifidobacterium animalis	6.59	8	1
Lactobacillus johnsonii	1.25	40	46
Streptococcus thermophilus	1.17	3	0
Veillonella atypica	0.93	9	0
Bacillus subtilis	0.85	33	46
Clostridium butyricum	0.74	24	25
Enterococcus durans	0.45	36	23
Ligilactobacillus salivarius	0.3	12	11
Leuconostoc mesenteroides	0.23	17	12
Lactococcus lactis	0.13	5	11
Limosilactobacillus fermentum	0.08	10	12
Lacticaseibacillus paracasei	-0.08	12	28
Lacticaseibacillus casei	-0.1	0	9
Heyndrickxia coagulans	-0.22	20	35
Lactiplantibacillus plantarum	-0.26	2	9
Bifidobacterium pseudocatenulatum	-0.41	18	35
Lactiplantibacillus pentosus	-0.44	8	22
Lactobacillus crispatus	-0.46	1	32
Lactobacillus acidophilus	-0.52	11	28
Lacticaseibacillus rhamnosus	-0.6	3	28
Lactobacillus jensenii	-1.44	19	41
Odoribacter laneus	-1.54	0	4
Limosilactobacillus vaginalis	-1.79	30	52
Limosilactobacillus reuteri	-2.22	31	46
Pediococcus acidilactici	-6.8	25	23
Akkermansia muciniphila	-13.33	2	32
Parabacteroides goldsteinii	-41.6	0	18
Parabacteroides distasonis	-60.25	0	7
Bacteroides uniformis	-250.42	0	11
Bacteroides thetaiotaomicron	-261.55	1	11

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