I was messaged below:

Hello, could you tell me which antimicrobials are okay to use without killing the good bacteria? I have hydrogen SIBO, methane SIBO, and hydrogen sulfide SIBO. I don’t want to make things worse because I no longer have bifidobacteria, lactobacilli, and Oxalobacter in particular. And I don’t want to take something broad-spectrum.

I was especially wondering about clove and thyme. I also have fungal issues and yeast in my body, possibly related to mold. Could you explain how to tell whether an antimicrobial is harmful to the good bacteria? Thank you

What is defined as good or bad?

The issue is not that simple as “good” or “bad”. Too much of a “good” bacteria is associated with a variety of conditions. Let us look at the research for two commonly believed “good” bacteria:

• Lactobacillus is reported HIGH (from 119 studies) with
  • Allergies
  • Alzheimer’s disease
  • Amyotrophic lateral sclerosis (ALS) Motor Neuron
  • Asthma
  • Atherosclerosis
  • Atrial fibrillation
  • Autism
  • Carcinoma
  • Celiac Disease
  • Chronic Obstructive Pulmonary Disease (COPD)
  • Cognitive Function
  • Colorectal Cancer
  • COVID-19
  • Crohn’s Disease
  • Depression
  • Endometriosis
  • Fibromyalgia
  • Graves’ disease
  • Heart Failure
  • High Histamine/low DAO
  • hypertension (High Blood Pressure
  • Hypoxia
  • Inflammatory Bowel Disease
  • Insomnia
  • Irritable Bowel Syndrome
  • ischemic stroke
  • Juvenile idiopathic arthritis
  • Liver Cirrhosis
  • Long COVID
  • Metabolic Syndrome
  • Mood Disorders
  • multiple chemical sensitivity [MCS]
  • Multiple Sclerosis
  • Multiple system atrophy (MSA)
  • Neuropathy (all types)
  • Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic
  • Obesity
  • Osteoporosis
  • Parkinson’s Disease
  • Polycystic ovary syndrome
  • primary biliary cholangitis
  • Primary sclerosing cholangitis
  • Psoriasis
  • rheumatoid arthritis (RA),Spondyloarthritis (SpA)
  • Schizophrenia
  • Small Intestinal Bacterial Overgrowth (SIBO)
  • Stress / posttraumatic stress disorder
  • Systemic Lupus Erythematosus
  • Type 1 Diabetes
  • Type 2 Diabetes
  • Ulcerative colitis
  • Unhealthy Ageing
• Bifidobacterium is reported HIGH (from 120 studies) with
  • ADHD
  • Allergic Rhinitis (Hay Fever)
  • Allergies
  • Alzheimer’s disease
  • Amyotrophic lateral sclerosis (ALS) Motor Neuron
  • Ankylosing spondylitis
  • Anorexia Nervosa
  • Asthma
  • Atherosclerosis
  • Atrial fibrillation
  • Autism
  • Autoimmune Disease
  • Brain Trauma
  • Breast Cancer
  • Carcinoma
  • Cerebral Palsy
  • Cognitive Function
  • Colorectal Cancer
  • COVID-19
  • Crohn’s Disease
  • Depression
  • Endometriosis
  • Epilepsy
  • Fibromyalgia
  • Functional constipation / chronic idiopathic constipation
  • Graves’ disease
  • Hashimoto’s thyroiditis
  • Hyperlipidemia (High Blood Fats)
  • hypertension (High Blood Pressure
  • Hypoxia
  • Inflammatory Bowel Disease
  • Irritable Bowel Syndrome
  • ischemic stroke
  • Juvenile idiopathic arthritis
  • Liver Cirrhosis
  • Long COVID
  • Metabolic Syndrome
  • Mood Disorders
  • Multiple Sclerosis
  • NonCeliac Gluten Sensitivity
  • Obesity
  • Parkinson’s Disease
  • Polycystic ovary syndrome
  • Premenstrual dysphoric disorder
  • Psoriasis
  • rheumatoid arthritis (RA),Spondyloarthritis (SpA)
  • Rosacea
  • Schizophrenia
  • Sleep Apnea
  • Stress / posttraumatic stress disorder
  • Type 2 Diabetes
  • Ulcerative colitis
  • Vitiligo

There are a very small number of bacteria deemed absolutely bad.

• Bacillus anthracis.
• Francisella tularensis.
• Clostridium botulinum 

From Dangerous Microbes, 2018

The Human Need for Simplicity versus Biological Reality

I am a high functioning autistic spectrum individual. Others in the spectrum include those with photographic memory and complete memory recall. I lack those, but where I excel is my tolerance for complexity and uncertainty.

Across my 50-year career in software development, I’ve noticed that code I find straightforward often overwhelms other developers. One once remarked, “Any JavaScript file over 200 lines is black magic to me,” while reviewing what I considered a simple application. That experience reflects something broader: people naturally seek simplicity, even when reality is irreducibly complex.

In the same way, many approach microbiome science by labeling bacteria as “good” or “bad.” This reduction helps those who feel saturated by excessive detail—but the truth is far more nuanced.

The Evolution of Microbiome Prescription

For more than a decade, my goal with the Microbiome Prescription project has been simple in principle:

1. Accept scientific evidence—a microbiome test.
2. Compute suggestions aimed at correcting dysbiosis.
3. Provide direct links to supporting literature.

(See an example for Depression.)

The biggest challenge lies in determining which bacteria should shift, and in what direction. My early approach relied on lab-provided ranges: if a value was above range, reduce it; if below, increase it. But this method failed. Lab ranges are based on naïve averages and assume normal distributions. After teaching Ph.D.-level statistics, I knew better—bacterial populations follow heavily skewed distributions, not bell curves.

The next phase was to use symptom-annotated samples to mathematically model bacterial associations. When a new sample arrived, the system forecasted likely symptoms. Users checked which symptoms applied, improving both the model and predictive power.

Subsequent tests validated these forecasts: 53 predictions improved, while 19 worsened. It became clear that “gut health” cannot be captured by any single number. The ecosystem is too complex.

“No Protocol Can Serve Two Symptoms”

This phrase is an adaptation of Matthew 6:24: “No man can serve two masters.” When multiple symptoms are modeled independently, the results often conflict—what helps one symptom can worsen another. The earlier data illustrates this problem: 53 improvements, 19 regressions.

Rather than fighting symptoms individually, I began shifting focus toward the overall trajectory of health.

From Symptom Fighting to Health Trekking

A turning point came during an experiment using odds ratios derived from annotated microbiome samples—this time ranking bacteria by percentiles instead of percentages. Different labs report percentages inconsistently; percentiles normalize those variations (as discussed in this review).

Using 1,000 healthy individuals’ shotgun results from PrecisionBiome.EU, I noticed a striking pattern: “Asymptomatic: No Health Issues” consistently ranked as the top prediction.

That insight simplified everything. Instead of juggling countless symptom-specific models (10, 20, or even 200 symptoms), we can statistically track a single target—how far a sample deviates from “asymptomatic.” See definition here.

Now we’re just juggling one ball.

Reality vs. Model

The refined model depends on detailed microbiome tests—at least 16s sequencing, shotgun preferred—and percentile rankings for each bacterium. Unfortunately, most labs don’t provide percentile data. From Biomesight and Ombre, I can derive percentiles accurately from their percentage data. Some others attempt to estimate percentiles by assuming a bell curve—again, incorrect.

Recommendations for Individuals

Before ordering a microbiome test, confirm that it allows downloadable data with:

• Percentile and percentage values.
• Bacteria identified by NCBI Taxon numbers.

Recommended providers: Ombre or Biomesight (for better percentile reliability).

After testing, upload your results to Microbiome Prescription and simply click to start analysis.

Older analytical methods remain available and effective for many users, though progress may plateau for some. See Another ME/CFS Microbiome Update for details.

This is part of this continuing saga with this person. Prior posts and the labs shown below. Repairing the microbiome is not a single test, take a pill, and you are done. It may be like a long journey by sail through the fjords of Norway: a lot of course corrections!

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

Person’s Summary

I would say that there is no improvement since the last test. So this is still applicable:

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 18 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 4-5 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.

In this analysis, I am going to look at:

• Changes since the last sample
• Review a new approach that is being incorporated
• Looking at suggestions and the difference between the new approach and the traditional approach

At the end, I suggest following the new approach with the motivation that the traditional approach has appeared to have stalled. The microbiome adapts to antibiotics and diet changes; rotation to alternatives often seems to be needed to keep destabilizing the microbiome dysbiosis.

Changes Between Samples

Going to Old UI/Multiple Samples we compare symptom matching values. We see that just 1 of 42 showed improvement.

Looking at the new Odds Ratio data, we see the number of bacteria identified as critical in different samples below / Odds Estimate. I am not clear on the meaning and significance…

• Odds Estimate: The higher the number, the more likely that the person is healthy
• Number of Bacteria: Not reliable because different bacteria contribute differently to health.

• 2026-03-06: 59 / 1632
• 2025-11-17: 36 / 1588
• 2025-03-30: 20 / 1671
• 2024-12-03: 21 / 1561
• 2024-09-02: 36 / 1611
• 2024-01-22: 58 / 1586
• 2023-09-12: 38 / 678
• 2023-02-22: 52 / 1707
• 2022-08-11: 30 / 886
• 2022-03-25: 24 / 1037
• 2021-12-03: 15 / 1287
• 2021-08-31: 49 / 757

My general reading is that from 2021-2024 there was improvement and now the person is in a new stable healthier state but with still dysbiosis. I am hoping that the switch to an alternative view of solving his health may result in further improvement. In other words, rotation of approaches.

Another View on the Same Issue

In my recent post, Turning Fixing the Microbiome Upside Down!, I introduced a different way to think about repairing the microbiome. A human–society analogy might make it clearer.

Imagine your city is struggling with homelessness, vagrancy, and petty crime. The usual response—especially in the U.S.—is to send in the police. Round up those panhandling on the streets! In microbiome terms, that’s like identifying “bad bacteria” and launching an attack.

But there’s another approach: offer housing, mental health care, and job training. You don’t punish people—you help them heal and reintegrate.

Traditionally, Microbiome Prescription has focused on detecting problematic bacteria and trying to suppress or adjust them. The challenge is that most people have many interconnected symptoms. Research often shows that substance X improves one symptom but worsens another. You end up chasing symptoms—fixing one only to see another emerge or intensify.

A more holistic alternative, which has only recently become possible, is to guide the person’s microbiome toward a naturally healthy state instead. See this post: Mathematically Derived Healthy Microbiome.

Recently I asked the head of a microbiome testing company, what statistical evidence do you have for what is a healthy or desired microbiome profile. How do you obtain the importance of each bacteria? He knew that using means and standard deviation were invalid because of the high skew with the data. His response was requesting his staff to remedy this situation, looking at odds-ratio as a starting point.

---

Evaluation

I am a modeler, not a medical professional. Modelers try putting together mathematics using available data and use that to generate predictions. Once the predictions are made, they are evaluated against any available facts.

Above we have some observations from the person, the model does not know this information — so we can evaluate predictions against this data.

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.

Comparing Suggestions

	Health	Symptoms	Novice
Rye	-0.11	-183.5	-352.3
Oats	0.15	-179.2	-742.6
Apple	2.34	-385.1	-351.5
Potato	1.32	-1102.7	-1418.5
High Meat	2.2	155.9	763
Raw Meat	-2.78	105.5	21.2
High Fat Diet	-.79	154.4	-246.1
high-saturated fat diet	-.35	96	-291.1
low-fat diet	0.86	195.9	378.4
Range of Values	-6.82 to 5.43	-2075 to 1359	-2296 to 1461
Best to Take	Catechol {Catecholamines}	restricted-fiber diet {low fiber diet}	restricted-fiber diet {low fiber diet}

Each of the above depends heavily on the bacteria selected and the threshold used. It is interesting to see that the new “Make Healthy” is a clear winner against his observations.

What is particularly interesting with the “Make Healthy” is that values were computed for 1,632 substances. Looking at the list os suggestions, we do not have a mass of antibiotics seen on the other lists. We are not focused on reducing bad bacteria, rather on improving the good bacteria, and letting those address the bad bacteria. The top items are below.

• Bromelain
• N-acetylneuraminic acid {Sialic acid} : a bioactive sugar involved in cell signaling, glycoproteins, glycolipids, and is abundant in the brain; it is linked to cognitive function, memory, and immune function in general mechanistic descriptions
• 1,2-dithiolane-3-pentanoic acid {a-Lipoic acid (ALA)}
• pediococcus acidilactici {P acidilactici}
• lactobacillus paragasseri {L. Paragasseri}
• Rubus fruticosus {Blackberry}
• Eutrema japonicum {wasabi}
• Cucurbita pepo {Pumpkin}
• Alkalihalobacillus clausii {Bacillus clausii }
• Faecalibacterium prausnitzii {Faecalibacterium prausnitzii}
• Capra hircus milk {Goat milk}
• 1,4-linked ß-D-xylopyranosyl {arabinoxylan}
• Gamma-Aminobutyric Acid {GABA}
• Ulmus rubra {slippery elm}
• Musa acuminata {Banana}

My impression is that this is a much friendlier set of suggestions. In fact, the bottom of the list (to avoid) are pages of antibiotics and prescription drugs.

Probiotics Exploration

There are two ways of getting probiotics:

• Using published studies on their impact. In general, each study describes one or two bacteria impacted. This results in low data
• Using the R2 Associations: This is a modelling of their impact with hundreds of bacteria impact estimated.

The new Healthy Algorithm includes R2 recommendations

We got the following suggestions

We will explore how different algorithms evaluate these.

Bacteria	Healthy With R2	Healthy With Studies	Symptoms With Studies	Novice with Studies
Streptococcus thermophilus	1988	-1 to 4	-132 to -139	-132 to -139
Enterococcus faecalis	1291	.6	-579	-579
Bifidobacterium infantis	1124	-2	76	76
Bifidobacterium breve	377	1	213	213
Bifidobacterium longum	365	1	-1423	-1423
Bacillus thuringiensis	96	n/a	n/a	n/a
Pichia kudriavzevii	38	n/a	n/a	n/a
Acidipropionibacterium acidipropionici	22	n/a	n/a	n/a
Aspergillus oryzae	15	.1 – 3	-192 to 8	-192 to 8
Lactobacillus acidophilus	4	0	-574	-574

Keeping to the “When in disagreement, leave it out” a.k.a. Minimal Risk a.k.a. “Do not harm”, we have

• Top choice is Bifidobacterium breve
• Reasonable choice is Bifidobacterium infantis

Alternatively, Streptococcus thermophilus high value, cheap, and easy availability — it is a good candidate to try a 2-4 week experiment.

Personally, I would be tempted to try the following pattern (starting at a low dosage and increasing):

• 3 weeks of Streptococcus thermophilus (up to 10 BCFU)
• 2 weeks of Bifidobacterium breve (up to 20 BCFU)
• 2 weeks of Bifidobacterium infantis (up to 20 BCFU)

Bottom Line

My personal choice would be to go with the “Healthy Algorithm” for the following reasons:

• The traditional approach has appeared to stall, time for a change
• It is heavily based on very statistical significance over the entire scope of bacteria involved (i.e. dense data) but it has not been validated by clinical studies.
• I have always been unhappy about clinical studies because the data is:
  • Messy (typically in the context of one or another medical condition)
  • Small sample sizes
  • Low resolution to bacteria

I am also curious to see how well the “Healthy Algorithm” performs.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

A reader asked why different models to generate suggestions for their microbiome disagreed for a probiotic.

Two primary data sources exist regarding the effects of Bifidobacterium longum subsp. infantis: clinical studies and computational modeling data. I pulled some statistics from my databases.

---

Two primary data sources exist regarding the effects of Bifidobacterium longum subsp. infantis: clinical studies and computational modeling data.

1. Clinical studies (PubMed):

• A total of 34 clinical studies were identified, many of which involved participants with existing medical conditions.
• Across these studies, an impact was reported on only 24 bacterial taxa.
  • Among these, six taxa showed consistent results (replication)
• The remaining 18 taxa were each reported as impacted in only a single study, indicating a lack of replication.
• Statistical significance in these studies was typically defined as (P < 0.05).
• Moreover, each study employed distinct microbiome testing methodologies, contributing to substantial variability—a phenomenon sometimes referred to as the “taxonomy nightmare.”

2. Computational model (R2 Model):

• Analysis using the R2 Model (link) identified statistically strong associations between B. longum subsp. infantis and 73 bacterial taxa among healthy individuals.
• The statistical significance threshold in this dataset was generally (P < 0.00001),
• All samples were processed using a standardized microbiome testing pipeline, eliminating cross-platform variability.

Interpretation:
Medical professionals predominantly rely on clinical trial data when evaluating probiotic efficacy, often without critically assessing methodological consistency or statistical robustness. Consequently, computational models such as R2—despite their reproducibility and rigor—are often perceived as opaque or “black box” approaches.

Example: Bacteroides 

The following probiotics report different results (i.e. one study report increases, a different study report decreases)

• bacillus subtilis {B.Subtilis }
• Bifidobacterium animalis {B. animalis}
• bifidobacterium longum {B.Longum }
• Lacticaseibacillus casei {L. casei}
• Lacticaseibacillus paracasei {L.paracasei}
• Lacticaseibacillus rhamnosus {l. rhamnosus}
• lactobacillus acidophilus {L. acidophilus}
• Lactobacillus plantarum {L. plantarum}
• Limosilactobacillus fermentum {L. fermentum}
• Limosilactobacillus reuteri {L. Reuteri}

Example: Clostridium

The following probiotics report different results (i.e. one study report increases, a different study report decreases)

• bacillus subtilis {B.Subtilis }
• Bifidobacterium animalis {B. animalis}
• bifidobacterium longum {B.Longum }
• Heyndrickxia coagulans {B. coagulans}
• Lactobacillus plantarum {L. plantarum}
• Limosilactobacillus reuteri {L. Reuteri}

Example: Lachnospiraceae

The following probiotics report different results (i.e. one study report increases, a different study report decreases)

• bacillus subtilis {B.Subtilis }
• bifidobacterium longum {B.Longum }
• Lacticaseibacillus paracasei {L.paracasei}
• lactobacillus acidophilus {L. acidophilus}

My academic training is in Operations Research with a large dosage of lateral thinking (Edward de Bono).

A classic story is below:

One famous story of operations research success during the war involved an analysis of Allied bombers returning from bombing missions over Europe. The military analyzed the location of shrapnel damage and bullet holes in returning bombers, to identify where to place additional armor on aircraft. Operations researchers were brought in at the last minute to do a “confirmatory” analysis, but they recommended that additional armor be placed on bombers everywhere except the places with damage or bullet holes! The operations researchers realized that analyzing damage to returning bombers involved a sampling error. It was the bombers that did not return that needed extra protection—and they needed it in the most vulnerable places (the places not damaged on the returning bombers).

In the past, most medical work has focused on bacterial shifts statistically linked to individual symptoms. Think of the “bullet holes” problem: the literature usually looks at one symptom at a time, while real patients often present with dozens.

Recently, I started using odds ratios instead. Most people know odds ratios from smoking and cancer risk, but they can be applied broadly—for example, the odds of working at Microsoft if you graduated from a particular university.

Using about 5,500 samples and roughly 350 symptoms, I built odds tables and then tested those odds ratios against a reference set of healthy individuals. To my surprise, sample after sample showed the highest odds for being asymptomatic, far more often than I expected.

On reflection, this implies we now have a well-defined, statistically grounded model of a healthy (asymptomatic) microbiome. That was the “lightbulb” moment.

Instead of hunting for individual “holes” and trying to patch them, we should look at all the shifts away from this asymptomatic model. Once those shifts and their contributions (odds ratios) are identified, we can use published research to determine what is most likely to normalize the microbiome. A long list of symptoms (bullet holes) stops being the target; the real target becomes making the microbiome asymptomatic.

Some past links:

• Significance Ordered list of Bacteria Associated with Autism using Odds Ratio
• Mathematically Derived Healthy Microbiome
• Generalized Odds Ratio to predict Symptoms from microbiome reports
• Open Access Odds Tables

A walk through on applying the above methodology pattern.

After seeing the post Mathematically Derived Healthy Microbiome, a colleague asked me to show the Odds Ratio for Autism – ordered with most significance on top. Autism has many causes with significant literature indicating that some behaviors are associated to some bacteria.

From the samples upload (Ombre, etc) or transferred (Biomesight) to my Citizen Science site (Microbiome Prescription), I computed the following odds ratios in the same manner (percentile based). All of the data that I am using is available for download for other people to explore. The current matrix of Odds ratio is available at Key Bacteria for Symptoms using Percentiles (Lab Agnostic) – R2.

Contribute Your Data

If you have a child or person with ASD, please contribute your microbiome sample and annotated it with symptoms. The Odds tables are planned to be recomputed every 6 months as data increases.

We are currently using the annotations below, the table below used “Official Diagnosis: Autism” :

• Autism: More Unusual Responses to an Unexpected Event
• Autism: More Abnormal Body Posturing or Facial Expressions
• Autism: More Abnormal Tone of Voice
• Autism: More Avoidance of Eye Contact or Poor Eye Contact
• Autism: More Behavioral Disturbances
• Autism: More deficits in Language Comprehension
• Autism: More Decrease in Speaking
• Autism: More Inappropriate Social Interaction
• Autism: More Intense Focus on One Topic
• Autism: More Lack of Empathy
• Autism: More Flat or Monotonous Speech
• Autism: More Repeating Words or Phrases
• Autism: More Repetitive Movements
• Autism: More Self-Abusive Behaviors
• Autism: More Sleep Disturbances
• Autism: Less Unusual Responses to an Unexpected Event
• Autism: Less Abnormal Body Posturing or Facial Expressions
• Autism: Less Abnormal Tone of Voice
• Autism: Less Avoidance of Eye Contact or Poor Eye Contact
• Autism: Less Behavioral Disturbances
• Autism: Less deficits in Language Comprehension
• Autism: Less Decrease in Speaking
• Autism: Less Inappropriate Social Interaction
• Autism: Less Intense Focus on One Topic
• Autism: Less Lack of Empathy
• Autism: Less Flat or Monotonous Speech
• Autism: Less Repeating Words or Phrases
• Autism: Less Repetitive Movements
• Autism: Less Self-Abusive Behaviors
• Autism: Less Sleep Disturbances
• Autism: High Functioning
• Autism: Spectrum (not full)

Usage: This is based on multiple labs using percentiles to compensate for their processing differences. Your lab may not report on some of the bacteria — if not reported, ignore.

Tax_Name	Tax_Rank	Increased Odds of Autism
Mediterraneibacter gnavus	species	Above 18.2609 %ile
Phocaeicola plebeius	species	Above 11.9622 %ile
Dorea longicatena	species	Below 12.1069 %ile
Segatella	genus	Above 18.4336 %ile
Halomonadaceae	family	Above 7.377 %ile
Bacteroides nordii	species	Above 17.6211 %ile
Acidaminobacter	genus	Above 6.015 %ile
Segatella copri	species	Above 17.6471 %ile
Erysipelatoclostridium	genus	Below 15.7082 %ile
Ruminococcus albus	species	Above 20.1852 %ile
Ruminococcus	genus	Above 18.808 %ile
Oscillibacter	genus	Below 13.1897 %ile
Spirosomataceae	family	Above 7.5362 %ile
Acidaminobacter hydrogenoformans	species	Above 5.5138 %ile
Rikenellaceae	family	Below 13.1509 %ile
Microcoleaceae	family	Above 8.9855 %ile
Campylobacterales	order	Above 6.4151 %ile
Limosilactobacillus fermentum	species	Above 2.7523 %ile
Caldilineae	class	Above 15.1111 %ile
Caldilineales	order	Above 15.1111 %ile
Paraprevotella	genus	Above 16 %ile
Caldilineaceae	family	Above 15.1786 %ile
Desulfotomaculaceae	family	Above 10.6448 %ile
Thermoclostridium	genus	Above 0.3236 %ile
Slackia	genus	Above 0.4184 %ile
Liquorilactobacillus	genus	Above 19.6721 %ile
Epsilonproteobacteria	class	Above 9.7804 %ile
Hespellia	genus	Below 11.8474 %ile
Cytophagaceae	family	Above 13.3333 %ile
Mollicutes	class	Above 16.3636 %ile
Phascolarctobacterium succinatutens	species	Above 1.1628 %ile
Ruminiclostridium	genus	Above 15.7895 %ile
Mediterraneibacter	genus	Above 18.8889 %ile
Alistipes finegoldii	species	Below 13.5572 %ile
Mycoplasmatota	phylum	Above 15.7576 %ile
Campylobacterota	phylum	Above 10.9434 %ile
Acetivibrio	genus	Above 17.7227 %ile
Rhodocyclaceae	family	Above 5.8824 %ile
Bacteroides graminisolvens	species	Above 15.5637 %ile
Blautia luti	species	Below 19.1045 %ile
Moraxellales	order	Above 10 %ile
Bacteroides intestinalis	species	Above 14.4105 %ile
Ruminococcus bromii	species	Above 21.6477 %ile
Subdoligranulum variabile	species	Below 19.68 %ile
Erysipelotrichaceae	family	Below 7.1149 %ile
Sporobacter	genus	Below 10.4876 %ile
Moraxellaceae	family	Above 8.5389 %ile
Phocaeicola coprocola	species	Above 17.8068 %ile
Streptomyces	genus	Above 4.7619 %ile
Oribacterium	genus	Above 21.5398 %ile
Ruminococcus callidus	species	Above 19.5072 %ile
Bacteroides xylanisolvens	species	Above 16.1884 %ile
Phocaeicola salanitronis	species	Above 12.2807 %ile
Anaeroplasmataceae	family	Above 22.2222 %ile
Anaeroplasmatales	order	Above 21.875 %ile
Flavonifractor plautii	species	Below 18.4022 %ile
Phascolarctobacterium faecium	species	Above 0.5482 %ile
Oscillatoriales	order	Above 17.4962 %ile
Chromatiales	order	Above 9.2994 %ile
Mogibacterium timidum	species	Above 16.4659 %ile
Butyrivibrio proteoclasticus	species	Below 64.8889 %ile
Fusicatenibacter	genus	Below 23.7641 %ile
Oxalobacter	genus	Above 4.6875 %ile
Flavonifractor	genus	Below 14.7527 %ile
Microbacterium	genus	Above 9.8655 %ile
Catenibacterium	genus	Above 1.7241 %ile
Rhodocyclales	order	Above 7.1429 %ile
Alkalithermobacter thermoalcaliphilus	species	Above 15.8345 %ile
Erwiniaceae	family	Above 10.0437 %ile
Marvinbryantia	genus	Below 12.4525 %ile
Blautia faecis	species	Below 22.905 %ile
Leuconostocaceae	family	Above 20.868 %ile
Papillibacter	genus	Below 14.9367 %ile
Bacillales incertae sedis	no rank	Above 14.2752 %ile
Dehalococcoidia	class	Above 5.9406 %ile
Bifidobacterium pullorum	species	Above 17.7914 %ile
Thomasclavelia	genus	Below 14.4953 %ile
Pseudomonas	genus	Above 15.6522 %ile
Opitutia	class	Above 4.4444 %ile
Lactonifactor	genus	Below 14.891 %ile
Bacteroides stercoris	species	Above 16.6144 %ile
Dialister invisus	species	Above 1.2915 %ile
Catonella	genus	Above 21.3216 %ile
Megasphaera	genus	Above 3.8685 %ile
Myxococcia	class	Above 1.4011 %ile
Myxococcales	order	Above 1.4011 %ile
Myxococcota	phylum	Above 1.4011 %ile
Pseudomonadaceae	family	Above 15.6522 %ile
Hyphomonadaceae	family	Above 38.3387 %ile
Alistipes	genus	Below 13.8175 %ile
Limosilactobacillus	genus	Above 19.2529 %ile
Anaerosporobacter	genus	Below 21.1679 %ile
Kiloniellaceae	family	Above 2.7586 %ile
Nostocales	order	Above 2.008 %ile
[Ruminococcus] lactaris	species	Below 71.3383 %ile
Lactobacillus iners	species	Above 13.8614 %ile
Peptostreptococcus anaerobius	species	Above 1.4493 %ile
Luteibacter anthropi	species	Below 72.4754 %ile
Archaea	superkingdom	Above 2.8571 %ile
Intestinimonas	genus	Below 15.1786 %ile
Odoribacter laneus	species	Below 64.1221 %ile
Campylobacteraceae	family	Above 13.0536 %ile
Actinomycetota	phylum	Above 9.7956 %ile
Sphingomonadales	order	Above 17.3913 %ile
Finegoldia	genus	Above 14.8472 %ile
Fusobacterium nucleatum	species	Above 0.4831 %ile
Methanobrevibacter	genus	Above 2.8037 %ile
Sphingobacterium	genus	Above 0.7639 %ile
Rhodobacterales	order	Above 25.9259 %ile
Thalassospiraceae	family	Above 3.352 %ile
Desulforamulus	genus	Above 0 %ile
Intestinibacter	genus	Below 30.7692 %ile
unclassified Clostridiales	family	Below 21.5559 %ile
Sphingobacteriaceae	family	Above 9.8826 %ile
Euryarchaeota	phylum	Above 2.6432 %ile
Shuttleworthella	genus	Above 21.4575 %ile
Sphingobacteriia	class	Above 9.9902 %ile
Sphingobacteriales	order	Above 9.9902 %ile
Hoylesella	genus	Above 18.541 %ile
Thiotrichaceae	family	Below 75.077 %ile
Gemella	genus	Above 9.4512 %ile
Syntrophomonas	genus	Above 17.9842 %ile
Bifidobacterium pseudocatenulatum	species	Above 13.4831 %ile
Oscillospira	genus	Below 6.1783 %ile
Blautia obeum	species	Above 19.6197 %ile
Porphyromonas asaccharolytica	species	Above 0.7843 %ile
Eubacteriaceae	family	Above 17.632 %ile
Lachnobacterium	genus	Above 15.0525 %ile
Oscillatoriophycideae	subclass	Above 6.9444 %ile
Parasutterella	genus	Below 19.2101 %ile
Methanobacteria	class	Above 2.8112 %ile
Campylobacter	genus	Above 13.3588 %ile
Thiotrichales	order	Above 1.0707 %ile
Bifidobacterium animalis	species	Below 34.058 %ile
Nocardioidaceae	family	Above 25 %ile
Coprococcus	genus	Above 18.905 %ile
Cyanophyceae	class	Above 12.3288 %ile
Dorea formicigenerans	species	Below 7.2871 %ile
Streptosporangiales	order	Above 7.2072 %ile
Anaerofilum	genus	Above 10.9091 %ile
Alistipes putredinis	species	Below 19.8198 %ile
Prevotella disiens	species	Above 4.7782 %ile
Methanobacteriaceae	family	Above 2.8689 %ile
Methanobacteriales	order	Above 2.8689 %ile
Catonella morbi	species	Above 19.6413 %ile
Fusobacteriaceae	family	Above 10.978 %ile
Butyricimonas synergistica	species	Below 44.385 %ile
Acetivibrio straminisolvens	species	Below 69.1722 %ile
Desulfitobacteriaceae	family	Above 11.0647 %ile
Sphingomonadaceae	family	Above 14.7826 %ile
Coprococcus eutactus	species	Above 21.8324 %ile
Proteus	genus	Above 1.626 %ile
Ralstonia	genus	Above 19.214 %ile
Slackia heliotrinireducens	species	Above 9.0498 %ile
Dialister	genus	Above 0.8889 %ile
Erysipelotrichia	class	Below 6.7775 %ile
Erysipelotrichales	order	Below 6.7775 %ile
Holdemania filiformis	species	Below 22.4044 %ile
Haemophilus parainfluenzae	species	Above 3.6927 %ile
Actinomycetaceae	family	Above 0.4348 %ile
Turicibacter sanguinis	species	Above 0.6925 %ile
Fusicatenibacter saccharivorans	species	Below 23.9819 %ile
Bacteroides clarus	species	Above 12.8788 %ile
Bacillaceae	family	Above 13.4783 %ile
Methanomada group	clade	Above 2.8112 %ile
Cytophagia	class	Above 15.7842 %ile
Cytophagales	order	Above 15.7842 %ile
Phocaeicola	genus	Below 8.5865 %ile
Leuconostoc	genus	Above 16.9725 %ile
Parvimonas	genus	Above 23.6842 %ile
Selenomonadaceae	family	Above 0.4348 %ile
Amedibacillus dolichus	species	Above 3.3113 %ile
Caulobacteraceae	family	Below 50.2994 %ile
Amedibacillus	genus	Above 3.3113 %ile
Desulfohalotomaculum	genus	Above 43.3912 %ile
Gemellaceae	family	Above 9.4512 %ile
Hyphomonadales	order	Above 47.2843 %ile
Azoarcus	genus	Above 15.3846 %ile
Entomoplasmatales	order	Above 13.0693 %ile
Azospirillum	genus	Above 9.7778 %ile
Haemophilus	genus	Above 3.9924 %ile
Cellvibrionales	order	Above 7.5221 %ile
Halochromatium	genus	Above 2.9703 %ile
Bacteroides fragilis	species	Above 2.8061 %ile
Methanobrevibacter smithii	species	Above 3.3816 %ile
Catenibacterium mitsuokai	species	Above 17.7419 %ile
Slackia isoflavoniconvertens	species	Above 22.1662 %ile
Mycoplasma	genus	Above 14.6054 %ile
Thermoclostridium caenicola	species	Below 77.4232 %ile
Azospirillaceae	family	Above 2.7027 %ile
Enterococcaceae	family	Above 1.4286 %ile
Desulfitobacterium	genus	Below 24.6575 %ile
Parasutterella excrementihominis	species	Below 20.5943 %ile
Eubacterium	genus	Above 17.9028 %ile
Moorellales	order	Below 82.1855 %ile
Balneola	genus	Above 10.1695 %ile
Alkaliphilus	genus	Above 3.7152 %ile
Acetivibrio alkalicellulosi	species	Above 16.1781 %ile
Fusobacteriales	order	Above 13.2867 %ile
Blautia wexlerae	species	Below 9.2321 %ile
Pseudoramibacter	genus	Below 64.5833 %ile
Weissella	genus	Above 13.3333 %ile
Acidaminococcus	genus	Above 10.6489 %ile
Fusobacteriia	class	Above 13.2867 %ile
Tepidibacter	genus	Below 65.4206 %ile
Psychrobacter glacialis	species	Above 0 %ile
Veillonella dispar	species	Above 12.6816 %ile
Bacteroides cellulosilyticus	species	Above 16.8103 %ile
Negativicoccus	genus	Above 8.7805 %ile
Actinobacillus porcinus	species	Below 74.971 %ile
Chitinophagaceae	family	Above 15.565 %ile
Chitinophagia	class	Above 15.032 %ile
Chitinophagales	order	Above 15.032 %ile
Cyanobacteriota	phylum	Above 13.5965 %ile
Planifilum fimeticola	species	Below 82.4012 %ile
Clostridium perfringens	species	Above 7.6923 %ile
Bacteroides salyersiae	species	Above 11.583 %ile
Parabacteroides johnsonii	species	Above 2.8571 %ile
Syntrophomonadaceae	family	Above 5.7348 %ile
Mycoplasmataceae	family	Above 15.814 %ile
Alistipes shahii	species	Below 8.6694 %ile
Cystobacterineae	suborder	Above 5.6338 %ile
Succinivibrio	genus	Below 54.6053 %ile
Moryella	genus	Below 21.0046 %ile
Parvimonas micra	species	Above 25 %ile
Bacteroides rodentium	species	Above 17.799 %ile
Succinivibrio dextrinosolvens	species	Below 52.6786 %ile
Brochothrix thermosphacta	species	Below 77.5635 %ile
Bifidobacteriaceae	family	Above 7.0069 %ile
Bifidobacteriales	order	Above 6.9565 %ile
Brochothrix	genus	Above 5.2632 %ile
Fusobacterium	genus	Above 9.7345 %ile
Tissierellia	class	Below 3.0928 %ile
Novispirillum	genus	Below 81.6863 %ile
Mannheimia	genus	Below 66.2442 %ile
Blautia glucerasea	species	Above 14.2222 %ile
Fusobacteriota	phylum	Above 13.1944 %ile
Desulfofarcimen	genus	Above 25.0965 %ile
Tindallia magadiensis	species	Below 89.2784 %ile
Desulfurispora thermophila	species	Above 11.4504 %ile
Acidaminococcus fermentans	species	Below 56.3139 %ile
Pectobacteriaceae	family	Above 30.531 %ile
Mycoplasmatales	order	Above 14.9425 %ile
Paraprevotella clara	species	Above 3.7736 %ile
Desulfurisporaceae	family	Above 11.2805 %ile
Desulfurispora	genus	Above 11.2805 %ile
Lelliottia	genus	Above 0 %ile
Oxalobacter formigenes	species	Above 4.5016 %ile
Terrisporobacter	genus	Below 24.9482 %ile
Caloramator indicus	species	Below 49.9016 %ile
Ignavibacteriota	phylum	Above 61.7766 %ile
Odoribacter splanchnicus	species	Below 14.0381 %ile
Symbiobacterium	genus	Below 77.561 %ile
Anaerotruncus colihominis	species	Above 16.0959 %ile
Microbacteriaceae	family	Above 23.1441 %ile
Nevskiaceae	family	Below 81.761 %ile
Peptococcus niger	species	Above 6.6667 %ile
Bifidobacterium ruminantium	species	Above 17.8862 %ile
Streptococcus anginosus	species	Above 10.4683 %ile
Dysgonomonas	genus	Above 10.4478 %ile
Parabacteroides goldsteinii	species	Above 8.9385 %ile
Coprobacter	genus	Below 13.4328 %ile
Acholeplasma hippikon	species	Above 6.1644 %ile
Cyanobacteriota/Melainabacteria group	clade	Above 13.5135 %ile
Tissierellia incertae sedis	no rank	Above 12.1242 %ile
Hallella	genus	Above 11.1888 %ile
Rhodospirillaceae	family	Above 1.6726 %ile
Halomonas	genus	Above 29.5673 %ile
Lysobacterales	order	Above 6.9565 %ile
Bacteroides gallinarum	species	Above 14.6222 %ile
Anaerococcus	genus	Above 0.2985 %ile
Desulfovibrio piger	species	Above 7.1429 %ile
Clostridium putrefaciens	species	Above 37.2294 %ile
Facklamia	genus	Above 65.4525 %ile
Brenneria	genus	Above 31.5927 %ile
Blautia hydrogenotrophica	species	Above 20.7045 %ile
Alkaliphilus peptidifermentans	species	Above 50 %ile
Segatella albensis	species	Above 20.9738 %ile
Kribbella	genus	Above 25 %ile
Dysgonomonadaceae	family	Above 10.8497 %ile
Actinomyces	genus	Above 0.8811 %ile
Sphingomonas	genus	Above 20.9821 %ile
Rothia mucilaginosa	species	Above 6.8493 %ile
Selenomonas	genus	Below 75.154 %ile
Acidimicrobiia	class	Above 24.8434 %ile
Rothia	genus	Above 13.8614 %ile
Caldicellulosiruptor	genus	Below 71.5325 %ile
Coprococcus catus	species	Above 20.9663 %ile
Acidimicrobiales	order	Above 24.8434 %ile
Pseudomonadales	order	Above 10 %ile
Holophagaceae	family	Above 11.46 %ile
Geothrix	genus	Above 11.7002 %ile
Butyricimonas virosa	species	Above 6.2366 %ile
Mycoplasmoidales	order	Above 1.8242 %ile
Holophagales	order	Above 11.8196 %ile
Clostridium frigoris	species	Above 3.2216 %ile
Thermodesulfobacteriota	phylum	Above 0.0907 %ile
Halanaerobiales	order	Above 22.0503 %ile
Bifidobacterium angulatum	species	Above 62.8713 %ile
Aeromonadales	order	Above 9.6916 %ile
Desulfofarcimen acetoxidans	species	Above 15.7895 %ile
Kribbellaceae	family	Above 25 %ile
Bifidobacterium	genus	Above 5.9083 %ile
Varibaculum	genus	Above 5.814 %ile
Peptoniphilus lacrimalis	species	Above 7.362 %ile
Geothrix fermentans	species	Above 15.8965 %ile
Ignavibacteria	class	Above 64.4684 %ile
Holophagae	class	Above 11.5899 %ile
Filifactor	genus	Above 10.1299 %ile
Jonesiaceae	family	Above 13.4831 %ile
Oxalobacteraceae	family	Above 0.7782 %ile
Jonesia	genus	Above 16.8539 %ile
Caloramator	genus	Above 12.8554 %ile
Coriobacteriales	order	Above 9.8845 %ile
Actinomycetes	class	Above 9.2105 %ile
Streptococcus	genus	Above 8.3879 %ile
Peptostreptococcaceae incertae sedis	no rank	Above 0.6667 %ile
Eubacteriales incertae sedis	no rank	Above 0 %ile
Ignavibacteriales	order	Above 67.0256 %ile
Anaerococcus vaginalis	species	Above 16.3934 %ile
Luteibacter	genus	Below 71.123 %ile
Campylobacter ureolyticus	species	Above 3.0769 %ile
Chromatiaceae	family	Above 0 %ile
Synergistaceae	family	Above 5.8824 %ile
Sporomusaceae	family	Above 6.7227 %ile
Comamonadaceae	family	Above 0.4348 %ile
Paenibacillus	genus	Above 13.1004 %ile
Caldicellulosiruptoraceae	family	Below 71.5325 %ile
Caldicellulosiruptorales	order	Below 71.5325 %ile
Alteromonadales	order	Above 6.1135 %ile
Oribacterium sinus	species	Above 19.4444 %ile
Ethanoligenens harbinense	species	Above 50.4541 %ile
Thermoactinomycetaceae	family	Above 9.5097 %ile
Anaerofustis stercorihominis	species	Above 38.6449 %ile
Eubacteriales Family XII. Incertae Sedis	family	Above 2.0528 %ile
Fibrobacterota	phylum	Above 1.0753 %ile
Phocaeicola coprophilus	species	Above 29.3286 %ile
Clostridium intestinale	species	Above 73.8625 %ile
Thermoanaerobacterales	order	Above 2.7397 %ile
Hymenobacteraceae	family	Below 70.5882 %ile
Bacteroides stercorirosoris	species	Below 75.1353 %ile
Piscirickettsiaceae	family	Above 12.4339 %ile
Anaerofustis	genus	Above 36.3409 %ile
Schaalia	genus	Above 0.3974 %ile
Niabella	genus	Above 19.977 %ile
Sporotomaculum hydroxybenzoicum	species	Above 21.3542 %ile
Coriobacteriaceae	family	Above 9.8845 %ile
Enterorhabdus	genus	Below 12.9278 %ile
Bacteroides reticulotermitis	species	Above 89.5652 %ile
Gordonibacter	genus	Below 14.9123 %ile
Anaerobranca	genus	Above 0.8 %ile
Turicibacter	genus	Above 6.7941 %ile
Hyphomicrobiales	order	Above 8.2609 %ile
Romboutsia ilealis	species	Above 34.0909 %ile
Desulfobacteraceae	family	Above 25.9312 %ile
Furfurilactobacillus	genus	Above 8.6957 %ile
Acetitomaculum	genus	Above 25 %ile
Mobiluncus	genus	Above 23.7569 %ile
Micrococcaceae	family	Above 14.2857 %ile
Bacillus	genus	Above 7.2581 %ile
Oceanospirillales	order	Above 11.4537 %ile
Desulfobacterales	order	Above 26.9333 %ile
Caulobacterales	order	Below 48.9641 %ile
Ruminococcus flavefaciens	species	Above 46.0116 %ile
Ethanoligenens	genus	Above 25.8193 %ile
Acetivibrio clariflavus	species	Above 13.9151 %ile
Bifidobacterium indicum	species	Above 10.7143 %ile
Streptococcaceae	family	Above 8.2902 %ile
Candidatus Soleaferrea	genus	Below 45.3465 %ile
Bacteroides helcogenes	species	Above 34.5133 %ile
Streptococcus sanguinis	species	Above 13.4211 %ile
Alistipes indistinctus	species	Above 53.0568 %ile
Peptostreptococcus	genus	Above 12.3636 %ile
Furfurilactobacillus siliginis	species	Above 20 %ile
Phocaeicola vulgatus	species	Below 7.2567 %ile
Propionibacteriales	order	Above 31.7391 %ile
Eisenbergiella tayi	species	Below 19.2009 %ile
Metamycoplasmataceae	family	Above 1.8622 %ile
Adlercreutzia equolifaciens	species	Above 3.2319 %ile
Turicibacteraceae	family	Above 6.7941 %ile
Coprobacillus	genus	Above 9.8039 %ile
Atopobium	genus	Above 18.5567 %ile
Thiohalobacter	genus	Above 29.4118 %ile
Blautia hansenii	species	Above 11.9804 %ile
Streptococcus anginosus group	species group	Above 10.7595 %ile
Lysobacteraceae	family	Above 3.913 %ile
Shigella	genus	Above 46.5517 %ile
Akkermansia muciniphila	species	Above 5.5556 %ile
Bacillales Family X. Incertae Sedis	family	Above 10.1538 %ile
Roseburia intestinalis	species	Above 9.0909 %ile
Cyclobacteriaceae	family	Above 29.0323 %ile
Nevskiales	order	Below 76.0638 %ile
Ehrlichia ovina	species	Below 62.1113 %ile
Terrabacteria group	clade	Above 10.1363 %ile
Pseudomonadota	phylum	Above 9.9365 %ile
Chloroflexota	phylum	Above 13.0491 %ile
Peptostreptococcaceae	family	Above 8.982 %ile
[Ruminococcus] torques	species	Above 21.6405 %ile
Nitrospirales	order	Above 25 %ile
Rhodospirillales	order	Above 2.8415 %ile
Subdoligranulum	genus	Below 20 %ile
Thiothrix	genus	Below 75.0602 %ile
Planifilum	genus	Below 80.1641 %ile
Nitrospiraceae	family	Above 18.9655 %ile
Acidimicrobiaceae	family	Above 22.5806 %ile
Porphyromonas	genus	Above 13.3333 %ile
Nitrospirota	phylum	Above 25 %ile
Bacteroides caccae	species	Above 4.7619 %ile
Johnsonella	genus	Above 13.5845 %ile
Thiohalobacter thiocyanaticus	species	Above 29.4118 %ile
Streptococcus parasanguinis	species	Above 9.0129 %ile
Sutterella stercoricanis	species	Above 10.1669 %ile
Lentisphaerota	phylum	Above 28.3688 %ile
Selenomonadales	order	Above 4.8902 %ile
Nitrospiria	class	Above 25 %ile
Robinsoniella	genus	Above 13.8554 %ile
Paenibacillaceae	family	Above 7.8603 %ile
Betaproteobacteria	class	Above 9.0039 %ile
Thermoanaerobacterium	genus	Above 33.8028 %ile
Veillonella	genus	Above 5.5736 %ile
Phocaeicola sartorii	species	Above 15.1724 %ile
Prevotella veroralis	species	Above 25 %ile
Halobacteroidaceae	family	Above 41.573 %ile
Rhodovibrionaceae	family	Above 17.7616 %ile
Nocardiopsaceae	family	Above 37.8917 %ile
Tessaracoccus	genus	Above 22.1675 %ile
Thiobacillus thiophilus	species	Above 21.7317 %ile
Negativicoccus succinicivorans	species	Above 8.6842 %ile
Hungateiclostridium	genus	Above 37.8815 %ile
unclassified Burkholderiales	family	Above 20.3488 %ile
Proteinivoraceae	family	Above 4.1509 %ile
Paucilactobacillus	genus	Above 24.2915 %ile
Synergistia	class	Above 4.2553 %ile
Synergistales	order	Above 4.2553 %ile
Leuconostoc miyukkimchii	species	Above 46.3768 %ile
Bifidobacterium catenulatum	species	Above 70.1105 %ile
Synergistota	phylum	Above 4.2553 %ile
Flavobacterium	genus	Above 25 %ile
Lentisphaeria	class	Above 24.1135 %ile
Finegoldia magna	species	Above 7.4468 %ile
Gallionellaceae	family	Above 30.8458 %ile
[Clostridium] scindens	species	Above 68.559 %ile
Victivallales	order	Above 25.8567 %ile
Thiobacillus	genus	Above 47.0588 %ile
Morganellaceae	family	Above 16.3717 %ile
Bifidobacterium breve	species	Above 9.6916 %ile
Hungateiclostridiaceae	family	Above 40 %ile
Acetitomaculum ruminis	species	Above 23.5149 %ile
Phocaeicola massiliensis	species	Above 14.2857 %ile
Segatella maculosa	species	Above 1.4981 %ile
Dialister propionicifaciens	species	Above 29.4931 %ile
Clostridium chartatabidum	species	Above 32.4627 %ile
Veillonellales	order	Above 8.8109 %ile
Prevotella corporis	species	Above 9.3949 %ile
Holdemanella biformis	species	Above 10.0539 %ile
Veillonellaceae	family	Above 8.7719 %ile
Pseudobutyrivibrio	genus	Below 7.5397 %ile
Slackia piriformis	species	Above 25.266 %ile
Desulfobacteria	class	Above 26.9333 %ile
Holdemanella	genus	Above 9.6774 %ile
Bifidobacterium bombi	species	Above 35.7143 %ile
Moryella indoligenes	species	Above 64.66 %ile
Moraxella	genus	Above 22.8155 %ile
Bacteroides eggerthii	species	Above 12.9412 %ile
Niabella aurantiaca	species	Above 18.9918 %ile
Pelotomaculum	genus	Below 28.5248 %ile
Marinilabiliales	order	Above 63.5097 %ile
Enterobacterales	order	Above 1.8487 %ile
Ruminococcus champanellensis	species	Above 68.5841 %ile
Tissierellaceae	family	Above 35.6455 %ile
Balneolia	class	Above 57.1865 %ile
Intestinimonas butyriciproducens	species	Above 62.4101 %ile
Pseudoclavibacter	genus	Above 56.8182 %ile
Enterocloster	genus	Above 67.8604 %ile
Leyella	genus	Above 5.5205 %ile
Leyella stercorea	species	Above 5.5205 %ile
Blautia producta	species	Above 20.3704 %ile
canis group	species group	Below 33.3333 %ile
Hungatella	genus	Above 68.6275 %ile
Gammaproteobacteria	class	Above 8.6043 %ile
Enterobacteriaceae	family	Above 5.6522 %ile
Pedobacter	genus	Above 3.6364 %ile
Lactococcus	genus	Above 8.8803 %ile
Acidobacteriota	phylum	Above 11.1607 %ile
Thiobacillaceae	family	Above 47.0588 %ile
Sarcina	genus	Above 10.3175 %ile
Bacteroides fluxus	species	Above 8.7912 %ile
Robinsoniella peoriensis	species	Above 17.1082 %ile
Planococcaceae	family	Above 15.7895 %ile
Porphyromonas bennonis	species	Above 14.0684 %ile
Nostoc	genus	Above 20 %ile
Hoylesella buccalis	species	Above 7.5397 %ile
Balneolota	phylum	Above 50 %ile
Burkholderiales	order	Above 8.4815 %ile
Shigella dysenteriae	species	Above 46.0526 %ile
Balneolaceae	family	Above 61.2308 %ile
Clostridium kluyveri	species	Above 26.5306 %ile
Filifactor alocis	species	Above 16.3743 %ile
Anaerocolumna	genus	Above 70.852 %ile
Bacteroides uniformis	species	Below 5.7624 %ile
Verrucomicrobiota	phylum	Above 3.3663 %ile
Propionibacteriaceae	family	Above 34.657 %ile
Melioribacter roseus	species	Above 81.5172 %ile
Desulfobacterium	genus	Above 39.9329 %ile
Alloprevotella rava	species	Below 41.3699 %ile
Stella	genus	Above 24.4635 %ile
Johnsonella ignava	species	Above 13.3102 %ile
Marinilabiliaceae	family	Above 63.7987 %ile
Bifidobacterium thermacidophilum	species	Above 28.1579 %ile
Dolosigranulum pigrum	species	Above 30.9353 %ile
Anaerostipes caccae	species	Above 77.6586 %ile
Lachnospira	genus	Above 7.6068 %ile
[Eubacterium] siraeum	species	Above 71.5867 %ile
Streptococcus constellatus	species	Above 52.4476 %ile
Christensenella	genus	Above 27.3743 %ile
Senegalimassilia	genus	Above 21.4953 %ile
Bifidobacterium catenulatum PV20-2	strain	Above 89.0383 %ile
Ehrlichia	genus	Below 25 %ile
Blautia schinkii	species	Above 12.035 %ile
Pelotomaculum isophthalicicum	species	Below 28.5901 %ile
Paracholeplasma	genus	Above 79.1118 %ile
Pelosinus	genus	Above 36.1905 %ile
Coprobacillus cateniformis	species	Above 5.99 %ile
Micrococcales	order	Above 8.2609 %ile
Porphyromonas canis	species	Below 73.925 %ile
Dysgonomonas capnocytophagoides	species	Above 36.8902 %ile
Lactobacillus	genus	Above 3.4783 %ile
Dolosigranulum	genus	Above 31.8898 %ile
Spiroplasmataceae	family	Above 53.5792 %ile
Lacrimispora	genus	Above 33.0396 %ile
Dorea	genus	Below 5.3787 %ile
Propionispora	genus	Below 71.0744 %ile
Sporobacterium	genus	Above 85.8099 %ile
Balneolales	order	Above 56.8807 %ile
Bifidobacterium gallicum	species	Above 90.3963 %ile
Spiroplasma	genus	Above 51.6269 %ile
Thermicanus aegyptius	species	Above 78.268 %ile
Cellulosilyticum	genus	Above 75 %ile
Anaeroglobus geminatus	species	Above 82.1138 %ile
Prevotellaceae	family	Above 5.9783 %ile
Anaerostipes	genus	Above 10.0086 %ile
Anaeroglobus	genus	Above 81.3312 %ile
Anaeromyxobacter	genus	Above 54.0816 %ile
Collinsella	genus	Above 10.2865 %ile
Lactonifactor longoviformis	species	Above 63.6142 %ile
Lachnospira pectinoschiza	species	Above 5.41 %ile
Phocaeicola barnesiae	species	Above 65.69 %ile
Anaeromyxobacteraceae	family	Above 53.8776 %ile
Christensenellaceae	family	Above 26.6667 %ile
Lactobacillales	order	Above 9.333 %ile
Bifidobacterium cuniculi	species	Above 79.5343 %ile
Ezakiella	genus	Above 44.3787 %ile
Parascardovia	genus	Above 50 %ile
Viridibacillus	genus	Above 52.381 %ile
Desulfotomaculum	genus	Above 10.8359 %ile
Tolumonas auensis	species	Above 50.0958 %ile
Tolumonas	genus	Above 49.8102 %ile
Paraburkholderia phenoliruptrix	species	Above 50.7444 %ile
Synechococcales	order	Above 43.1111 %ile
Herbinix	genus	Above 45.8613 %ile
Bifidobacterium mongoliense	species	Above 72.963 %ile
Lawsonia intracellularis	species	Above 62.7737 %ile
Bifidobacterium dentium	species	Above 69.9454 %ile
Paraburkholderia	genus	Above 50.885 %ile
Roseburia hominis	species	Above 68.4211 %ile
Parabacteroides faecis	species	Above 70.6278 %ile
Aerococcaceae	family	Above 15.7658 %ile
Clostridium bornimense	species	Above 52.9825 %ile
Desulfocella	genus	Above 72.2063 %ile
Stomatobaculum longum	species	Above 58.5603 %ile
Bacteroidaceae	family	Below 6.1279 %ile
Bacteroides thetaiotaomicron	species	Above 8.0824 %ile
Melioribacter	genus	Above 80.9524 %ile
Melioribacteraceae	family	Above 73.4694 %ile
Lawsonia	genus	Above 63.7993 %ile
[Clostridium] innocuum	species	Above 64.8961 %ile
Terriglobia	class	Above 12.1622 %ile
Hoylesella timonensis	species	Above 15.1261 %ile
Acholeplasmataceae	family	Above 7.6923 %ile
Burkholderia	genus	Above 56.7568 %ile
Paracholeplasma vituli	species	Above 80.298 %ile
Blautia	genus	Below 6.242 %ile
Caloramator uzoniensis	species	Below 33.1201 %ile
Lachnoclostridium phytofermentans	species	Above 50 %ile
[Bacteroides] pectinophilus	species	Above 65.0641 %ile
Anaerovibrio lipolyticus	species	Below 47.1154 %ile
Bifidobacterium magnum	species	Above 64.2308 %ile
Bacteroidota/Chlorobiota group	clade	Above 10.0509 %ile
Sporosarcina	genus	Above 34.1889 %ile
Hathewaya proteolytica	species	Above 54.0616 %ile
Alicyclobacillaceae	family	Above 47.2689 %ile
Acholeplasma	genus	Above 7.2816 %ile
Roseburia	genus	Above 9.4187 %ile
Hathewaya histolytica	species	Below 51.6193 %ile
Caloramator mitchellensis	species	Below 81.2403 %ile
Bifidobacterium boum	species	Above 57.9041 %ile
Collinsella aerofaciens	species	Above 4.329 %ile
Anaplasmataceae	family	Below 12.4088 %ile
Sporobacterium olearium	species	Above 84.1248 %ile
Hespellia stercorisuis	species	Above 88.634 %ile
Terrisporobacter petrolearius	species	Above 82.1981 %ile
Thiomonas thermosulfata	species	Above 54.5358 %ile
Bacteroides denticanum	species	Above 92.336 %ile
Bifidobacterium scardovii	species	Above 33.3333 %ile
Oscillospira guilliermondii	species	Below 59.8165 %ile
Lactobacillaceae	family	Above 6.5217 %ile
Sporolactobacillus putidus	species	Above 84.2342 %ile
Chlorobiia	class	Below 71.5658 %ile
Atopobiaceae	family	Above 17.8974 %ile
Chlorobiaceae	family	Below 71.624 %ile
Blautia coccoides	species	Below 69.1216 %ile
Chlorobiota	phylum	Below 71.5367 %ile
Anoxynatronum sibiricum	species	Above 55.1773 %ile
Rhodothermus clarus	species	Below 64.2594 %ile
Candidatus Amoebophilus	genus	Below 5.216 %ile
Candidatus Amoebophilus asiaticus	species	Below 5.216 %ile
Anoxynatronum	genus	Above 54.8936 %ile
Stomatobaculum	genus	Above 57.1429 %ile
Pseudobutyrivibrio xylanivorans	species	Below 6.4199 %ile
Anaerovibrio	genus	Below 44.4533 %ile
Entomoplasmataceae	family	Above 69.2719 %ile
Chlorobiales	order	Below 71.8823 %ile
Hungatella hathewayi	species	Above 63.9344 %ile
Desulfofalx	genus	Above 73.865 %ile
Desulfofalx alkaliphila	species	Above 73.865 %ile
Sporolactobacillus	genus	Above 68.4685 %ile
Geopsychrobacteraceae	family	Above 67.4395 %ile
Amoebophilaceae	family	Below 5.216 %ile
Desulfuromusa	genus	Above 67.4395 %ile
Anaerotignum	genus	Above 72.6027 %ile
Guggenheimella bovis	species	Above 69.7337 %ile
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	Above 62.2426 %ile
Erysipelothrix	genus	Above 9.2072 %ile
Pseudoclostridium	genus	Below 35.8599 %ile
Clostridium thermosuccinogenes	species	Below 35.8599 %ile
Erysipelothrix muris	species	Below 56.0898 %ile
Butyrivibrio hungatei	species	Above 50 %ile
Streptococcus australis	species	Above 80.5556 %ile
Bifidobacterium choerinum	species	Above 60.9756 %ile
FCB group	clade	Above 8.6426 %ile
Ruminococcaceae	family	Above 9.2529 %ile
Acholeplasmatales	order	Above 7.6923 %ile
Acetobacterium	genus	Above 4.9703 %ile
Intestinibacter bartlettii	species	Above 77.7778 %ile
Bacteroides pyogenes	species	Above 76.3825 %ile
Sarcina maxima	species	Above 71.8652 %ile
Coriobacteriia	class	Above 8.4291 %ile
Blautia stercoris	species	Above 70.4819 %ile
Emticicia oligotrophica	species	Above 88.5117 %ile
[Clostridium] hylemonae	species	Above 66.9763 %ile
Pectinatus cerevisiiphilus	species	Above 91.7887 %ile
Streptococcus oralis	species	Above 57.0136 %ile
Howardella ureilytica	species	Above 60.3774 %ile
Shewanellaceae	family	Below 49.7147 %ile
Shewanella	genus	Below 49.7147 %ile
Howardella	genus	Above 45.9016 %ile
Agathobacter rectalis	species	Above 71.3043 %ile
[Clostridium] symbiosum	species	Above 73.595 %ile
Bacillota sensu stricto incertae sedis	no rank	Above 56.1265 %ile
Falseniella	genus	Above 59.4221 %ile
Falseniella ignava	species	Above 59.4221 %ile
Alistipes onderdonkii	species	Above 64.4737 %ile
Sedimentibacter hydroxybenzoicus	species	Below 25.5814 %ile
Escherichia albertii	species	Above 85.7263 %ile
Selenomonas infelix	species	Below 41.4716 %ile
Aphanizomenonaceae	family	Below 28.3032 %ile
Bifidobacterium adolescentis	species	Above 56.4593 %ile
Rhodothermus	genus	Below 64.2613 %ile
Enterocloster citroniae	species	Above 77.193 %ile
Hespellia porcina	species	Above 75.8751 %ile
Carboxydocella ferrireducens	species	Below 32.8112 %ile
Nostocaceae	family	Below 53.6647 %ile
Mediterraneibacter glycyrrhizinilyticus	species	Below 28.6822 %ile
Limnobacter	genus	Below 51.6404 %ile
Sutterella sanguinus	species	Above 86.9959 %ile
Natronincola peptidivorans	species	Above 68.3226 %ile
Sphingobacterium bambusae	species	Below 24.5188 %ile
[Clostridium] leptum	species	Below 36.3636 %ile
Dolichospermum	genus	Below 28.0601 %ile
Veillonella montpellierensis	species	Above 52.2388 %ile
Limnobacter litoralis	species	Below 51.6087 %ile
Streptococcus thermophilus	species	Above 54.8936 %ile
Natronincola	genus	Above 67.8571 %ile

(c) 2026 Lassesen Consulting, LLC. ALL RIGHTS RESERVED

This work is licensed under the Creative Commons Attribution‑NonCommercial 4.0 International License (CC BY‑NC 4.0). Commercial use is prohibited without prior written permission. See https://creativecommons.org/licenses/by-nc/4.0/.

This is derived from “Asymptomatic: No Health Issues” Odd-Ratio Model (See this post). These ranges should apply to any test. It applies to only bacteria that are reported by the test. The current matrix of Odds ratio is available at Key Bacteria for Symptoms using Percentiles (Lab Agnostic) – R2.

If you are interested in the mathematics see my prior posts.

Tax_Name	Tax_Rank	(No column name)
Bacteroidota/Chlorobiota group	clade	Above 10.6%ile
delta/epsilon subdivisions	clade	Above 9.4%ile
Bacteria candidate phyla	clade	Above 54%ile
PVC group	clade	Above 6.7%ile
FCB group	clade	Above 10.6%ile
Terrabacteria group	clade	Above 10.6%ile
Cyanobacteriota/Melainabacteria group	clade	Above 13.5%ile
Methanomada group	clade	Above 5.9%ile
Balneolia	class	Above 49.4%ile
Chitinophagia	class	Above 15%ile
Desulfobacteria	class	Above 26.7%ile
Cyanophyceae	class	Above 12.3%ile
Desulfovibrionia	class	Above 8.9%ile
Ignavibacteria	class	Above 59.5%ile
Cytophagia	class	Above 15.8%ile
Negativicutes	class	Above 10.5%ile
Lentisphaeria	class	Above 25%ile
Tissierellia	class	Above 6.3%ile
Bacteroidia	class	Above 10.4%ile
Verrucomicrobiia	class	Above 2.3%ile
Fibrobacteria	class	Above 1.1%ile
Dehalococcoidia	class	Above 8.9%ile
Caldilineae	class	Above 4%ile
Erysipelotrichia	class	Above 10.6%ile
Holophagae	class	Above 3.4%ile
Synergistia	class	Above 0.8%ile
Acidimicrobiia	class	Above 2.1%ile
Coriobacteriia	class	Above 10.3%ile
Bacilli	class	Above 10.5%ile
Flavobacteriia	class	Above 6.1%ile
Sphingobacteriia	class	Above 9.6%ile
Methanobacteria	class	Above 5.9%ile
Clostridia	class	Above 10.4%ile
Gammaproteobacteria	class	Above 9.4%ile
Actinomycetes	class	Above 10.6%ile
Alphaproteobacteria	class	Above 5.7%ile
Betaproteobacteria	class	Above 9.9%ile
Deltaproteobacteria	class	Above 8.6%ile
Myxococcia	class	Above 1.6%ile
Lysobacteraceae	family	Above 3.9%ile
Bifidobacteriaceae	family	Above 7.7%ile
Veillonellaceae	family	Above 9.7%ile
Clostridiaceae	family	Above 10.5%ile
Lactobacillaceae	family	Above 9.1%ile
Halomonadaceae	family	Above 5.3%ile
Actinomycetaceae	family	Above 5.2%ile
Mycoplasmataceae	family	Above 8.3%ile
Spiroplasmataceae	family	Above 48.8%ile
Acholeplasmataceae	family	Above 9.6%ile
Methanobacteriaceae	family	Above 5.9%ile
Streptococcaceae	family	Above 10%ile
Corynebacteriaceae	family	Above 7.4%ile
Moraxellaceae	family	Above 0.8%ile
Enterobacteriaceae	family	Above 6.9%ile
Pasteurellaceae	family	Above 3.9%ile
Bacteroidaceae	family	Above 10.4%ile
Chromatiaceae	family	Above 4.5%ile
Lachnospiraceae	family	Above 10.7%ile
Peptostreptococcaceae	family	Above 10.5%ile
Eubacteriaceae	family	Above 3.7%ile
Peptococcaceae	family	Above 7.1%ile
unclassified Clostridiales	family	Above 49.8%ile
Thermoanaerobacteraceae	family	Above 2.7%ile
Bacillaceae	family	Above 1.7%ile
Planococcaceae	family	Above 15.8%ile
Paenibacillaceae	family	Above 3.1%ile
Thermoactinomycetaceae	family	Above 9.5%ile
Piscirickettsiaceae	family	Above 1.3%ile
Rikenellaceae	family	Above 26.8%ile
Porphyromonadaceae	family	Above 7.4%ile
Prevotellaceae	family	Above 6.9%ile
unclassified Burkholderiales	family	Above 22.7%ile
Cytophagaceae	family	Above 12.4%ile
Erysipelotrichaceae	family	Above 10.6%ile
Pseudomonadaceae	family	Above 15.7%ile
Acidimicrobiaceae	family	Above 2.4%ile
Nocardioidaceae	family	Above 18.2%ile
Microbacteriaceae	family	Above 18.8%ile
Sphingobacteriaceae	family	Above 9.8%ile
Coriobacteriaceae	family	Above 10.1%ile
Syntrophomonadaceae	family	Above 3.6%ile
Hyphomonadaceae	family	Above 37.4%ile
Rhodocyclaceae	family	Above 1.5%ile
Enterococcaceae	family	Above 1.4%ile
Rhodospirillaceae	family	Above 4.1%ile
Sphingomonadaceae	family	Above 12.2%ile
Flavobacteriaceae	family	Above 6.2%ile
Halobacteroidaceae	family	Above 36.6%ile
Holophagaceae	family	Above 3.3%ile
Kiloniellaceae	family	Above 2.8%ile
Bacillales Family X. Incertae Sedis	family	Above 10.2%ile
Gemellaceae	family	Above 0.6%ile
Ruminococcaceae	family	Above 11.1%ile
Eubacteriales Family XII. Incertae Sedis	family	Above 2.6%ile
Eubacteriales Family XIII. Incertae Sedis	family	Above 10.1%ile
Marinilabiliaceae	family	Above 57.3%ile
Cyclobacteriaceae	family	Above 29%ile
Chitinophagaceae	family	Above 15.6%ile
Caldilineaceae	family	Above 4%ile
Desulfarculaceae	family	Above 23.8%ile
Victivallaceae	family	Above 35.8%ile
Verrucomicrobiaceae	family	Above 1.4%ile
Desulfovibrionaceae	family	Above 8.5%ile
Desulfomicrobiaceae	family	Above 9%ile
Desulfobacteraceae	family	Above 25.9%ile
Oscillospiraceae	family	Above 10.4%ile
Akkermansiaceae	family	Above 3.5%ile
Peptoniphilaceae	family	Above 6.5%ile
Eggerthellaceae	family	Above 10.1%ile
Melioribacteraceae	family	Above 71.4%ile
Proteinivoraceae	family	Above 4.2%ile
Acidaminococcaceae	family	Above 6.4%ile
Christensenellaceae	family	Above 11.5%ile
Sutterellaceae	family	Above 9.7%ile
Desulfurisporaceae	family	Above 11.3%ile
Coprobacillaceae	family	Above 5.6%ile
Turicibacteraceae	family	Above 6.8%ile
Tepidimicrobiaceae	family	Above 48%ile
Kribbellaceae	family	Above 2.6%ile
Azospirillaceae	family	Above 8.6%ile
Rhodovibrionaceae	family	Above 17.8%ile
Thalassospiraceae	family	Above 19.6%ile
Thermohalobacteraceae	family	Above 80.4%ile
Metamycoplasmataceae	family	Above 1.9%ile
Spirosomataceae	family	Above 1.6%ile
Desulfitobacteriaceae	family	Above 11%ile
Desulfotomaculaceae	family	Above 1.9%ile
Tissierellaceae	family	Above 34.5%ile
Sporomusaceae	family	Above 6.7%ile
Balneolaceae	family	Above 49.5%ile
Hungateiclostridiaceae	family	Above 5.6%ile
Odoribacteraceae	family	Above 8.7%ile
Pectobacteriaceae	family	Above 25.9%ile
Morganellaceae	family	Above 20%ile
Barnesiellaceae	family	Above 24.7%ile
Dysgonomonadaceae	family	Above 10.8%ile
Tannerellaceae	family	Above 9.6%ile
Desulfofarcimen	genus	Above 9.3%ile
Desulfohalotomaculum	genus	Above 35.9%ile
Clostridioides	genus	Above 45.8%ile
Hungateiclostridium	genus	Above 37.3%ile
Mediterraneibacter	genus	Above 18%ile
Schaalia	genus	Above 6%ile
Anaerocolumna	genus	Above 66.8%ile
Hathewaya	genus	Above 11%ile
Segatella	genus	Above 15.4%ile
Hoylesella	genus	Above 0.7%ile
Leyella	genus	Above 5.5%ile
Paracholeplasma	genus	Above 71.1%ile
Oleidesulfovibrio	genus	Above 54.4%ile
Limosilactobacillus	genus	Above 13.2%ile
Thomasclavelia	genus	Above 23.3%ile
Christensenella	genus	Above 11.9%ile
Pseudoflavonifractor	genus	Above 24.6%ile
Melioribacter	genus	Above 73.3%ile
Murdochiella	genus	Above 34.1%ile
Alloprevotella	genus	Above 4.3%ile
Flavonifractor	genus	Above 31.8%ile
Phocaeicola	genus	Above 10.1%ile
Cellulosilyticum	genus	Above 71.6%ile
Romboutsia	genus	Above 25.9%ile
Terrisporobacter	genus	Above 32.4%ile
Intestinibacter	genus	Above 41.9%ile
Erysipelatoclostridium	genus	Above 34%ile
Ruminiclostridium	genus	Above 13.6%ile
Brassicibacter	genus	Above 83.9%ile
Fretibacterium	genus	Above 13.3%ile
Candidatus Soleaferrea	genus	Below 47.5%ile
Kosakonia	genus	Above 14.3%ile
Coprobacter	genus	Above 26.9%ile
Intestinimonas	genus	Above 30.4%ile
Fusicatenibacter	genus	Above 35.9%ile
Acetatifactor	genus	Below 52.3%ile
Eisenbergiella	genus	Above 31.1%ile
Aestuariispira	genus	Below 24.8%ile
Dielma	genus	Above 23.1%ile
Senegalimassilia	genus	Above 26.6%ile
Peptoclostridium	genus	Above 53.5%ile
Holdemanella	genus	Above 9.7%ile
Ezakiella	genus	Above 44.4%ile
Aminicella	genus	Below 30.1%ile
Hungatella	genus	Above 66.5%ile
Herbinix	genus	Above 36.5%ile
Fenollaria	genus	Above 71.9%ile
Pseudoscardovia	genus	Above 75.6%ile
Casaltella	genus	Above 49.4%ile
Faecalibacterium	genus	Above 10.3%ile
Alistipes	genus	Above 29%ile
Akkermansia	genus	Above 3.1%ile
Gemmiger	genus	Below 45.3%ile
Anaerostipes	genus	Above 10.6%ile
Acetoanaerobium	genus	Above 49.4%ile
Dorea	genus	Above 10.7%ile
Sedimentibacter	genus	Above 2.8%ile
Turicibacter	genus	Above 7.4%ile
Marvinbryantia	genus	Above 31.9%ile
Ethanoligenens	genus	Above 25.8%ile
Hespellia	genus	Above 27.1%ile
Anaerotruncus	genus	Above 10.8%ile
Anaerospora	genus	Above 54.1%ile
Acetanaerobacterium	genus	Above 8.6%ile
Odoribacter	genus	Above 8.7%ile
Tepidimicrobium	genus	Above 50%ile
Oribacterium	genus	Above 20.6%ile
Subdoligranulum	genus	Above 35.2%ile
Gracilibacter	genus	Above 77.7%ile
Henriciella	genus	Above 42.8%ile
Oscillibacter	genus	Above 29.1%ile
Lactonifactor	genus	Above 30.5%ile
Adlercreutzia	genus	Above 8.4%ile
Pelosinus	genus	Above 36.2%ile
Parabacteroides	genus	Above 9.7%ile
Niabella	genus	Above 18.1%ile
Barnesiella	genus	Above 21.5%ile
Howardella	genus	Above 6.6%ile
Proteiniborus	genus	Below 35.9%ile
Moryella	genus	Above 22.2%ile
Desulfurispora	genus	Above 11.3%ile
Blautia	genus	Above 10.7%ile
Butyricimonas	genus	Above 4.2%ile
Parasutterella	genus	Above 29.4%ile
Enterorhabdus	genus	Above 27.3%ile
Robinsoniella	genus	Above 8.4%ile
Gordonibacter	genus	Above 26.9%ile
Anaerosporobacter	genus	Above 40.3%ile
Propioniferax	genus	Below 52.3%ile
Holdemania	genus	Above 8.2%ile
Acidaminobacter	genus	Above 0.5%ile
Hallella	genus	Above 1.4%ile
Anaerofilum	genus	Above 3.5%ile
Pantoea	genus	Above 21.1%ile
Pseudobutyrivibrio	genus	Above 11.2%ile
Anaerobranca	genus	Above 1.2%ile
Johnsonella	genus	Above 13.6%ile
Sutterella	genus	Above 6.9%ile
Catonella	genus	Above 17.5%ile
Paenibacillus	genus	Above 10.5%ile
Caloramator	genus	Above 12.9%ile
Filifactor	genus	Above 7%ile
Geothrix	genus	Above 1.5%ile
Sporobacter	genus	Above 23.4%ile
Weissella	genus	Above 13.3%ile
Facklamia	genus	Above 60.7%ile
Brenneria	genus	Above 31.6%ile
Slackia	genus	Above 3.2%ile
Eggerthella	genus	Above 1.6%ile
Pedobacter	genus	Above 3.6%ile
Halochromatium	genus	Above 3%ile
Mogibacterium	genus	Above 1.8%ile
Papillibacter	genus	Above 31.1%ile
Coprobacillus	genus	Above 1%ile
Thermicanus	genus	Above 9.8%ile
Collinsella	genus	Above 12.2%ile
Olsenella	genus	Above 26.9%ile
Granulicatella	genus	Above 6%ile
Parasporobacterium	genus	Above 19.5%ile
Oscillospira	genus	Above 10.7%ile
Victivallis	genus	Above 35.1%ile
Anaerococcus	genus	Above 10.1%ile
Thalassospira	genus	Above 17.9%ile
Lachnobacterium	genus	Above 14.5%ile
Finegoldia	genus	Above 10.6%ile
Dysgonomonas	genus	Above 10.4%ile
Peptoniphilus	genus	Above 4.4%ile
Varibaculum	genus	Above 5.8%ile
Kribbella	genus	Above 2.6%ile
Porphyromonas	genus	Above 4%ile
Prevotella	genus	Above 5.3%ile
Syntrophomonas	genus	Above 3%ile
Desulfovibrio	genus	Above 4%ile
Desulfomicrobium	genus	Above 9.5%ile
Acidaminococcus	genus	Above 7.3%ile
Megasphaera	genus	Above 3.9%ile
Shigella	genus	Above 42.9%ile
Butyrivibrio	genus	Above 4.9%ile
Roseburia	genus	Above 10.5%ile
Oxalobacter	genus	Above 5%ile
Bacteroides	genus	Above 10.3%ile
Haemophilus	genus	Above 5.7%ile
Kluyvera	genus	Above 70.8%ile
Proteus	genus	Above 9.1%ile
Azospirillum	genus	Above 8.4%ile
Campylobacter	genus	Above 0.7%ile
Flavobacterium	genus	Above 40.1%ile
Pseudomonas	genus	Above 15.7%ile
Actinomyces	genus	Above 6.9%ile
Bifidobacterium	genus	Above 8.1%ile
Corynebacterium	genus	Above 6.9%ile
Eubacterium	genus	Above 0.9%ile
Bacillus	genus	Above 4.8%ile
Clostridium	genus	Above 10.5%ile
Desulfotomaculum	genus	Above 11.5%ile
Sporosarcina	genus	Above 34.2%ile
Lactobacillus	genus	Above 7%ile
Erysipelothrix	genus	Above 9.2%ile
Streptococcus	genus	Above 10%ile
Ruminococcus	genus	Above 18.2%ile
Gemella	genus	Above 0.7%ile
Atopobium	genus	Above 1.6%ile
Methanobrevibacter	genus	Above 3.3%ile
Peptococcus	genus	Above 4.5%ile
Acholeplasma	genus	Above 1.1%ile
Sarcina	genus	Above 17.7%ile
Spiroplasma	genus	Above 49%ile
Mycoplasma	genus	Above 4.9%ile
Halomonas	genus	Above 25%ile
Sphingomonas	genus	Above 20.6%ile
Lachnospira	genus	Above 9.6%ile
Microbacterium	genus	Above 1.3%ile
Acetobacterium	genus	Above 5%ile
Dialister	genus	Above 6.1%ile
Acetivibrio	genus	Above 17.1%ile
Bilophila	genus	Above 10.6%ile
Acetitomaculum	genus	Above 8.8%ile
Veillonella	genus	Above 7.5%ile
Coprococcus	genus	Above 18.3%ile
Phascolarctobacterium	genus	Above 6.7%ile
Bacillota sensu stricto incertae sedis	no rank	Above 64.6%ile
cellular organisms	no rank	Above 1.3%ile
Bacteria incertae sedis	no rank	Above 33.5%ile
Eubacteriales incertae sedis	no rank	Above 8%ile
Oscillospiraceae incertae sedis	no rank	Above 68.7%ile
Peptostreptococcaceae incertae sedis	no rank	Above 4.4%ile
Tissierellia incertae sedis	no rank	Above 11.6%ile
unclassified Streptococcus	no rank	Above 81%ile
unclassified Alistipes	no rank	Above 72.1%ile
unclassified Veillonella	no rank	Above 87.9%ile
unclassified Peptoniphilus	no rank	Above 73.6%ile
unclassified Bacteroides	no rank	Above 76.7%ile
unclassified Blautia	no rank	Above 61.5%ile
unclassified Parabacteroides	no rank	Above 36.5%ile
Chitinophagales	order	Above 15%ile
Balneolales	order	Above 49.4%ile
Acidaminococcales	order	Above 6.5%ile
Veillonellales	order	Above 9.7%ile
Marinilabiliales	order	Above 45.9%ile
Mycoplasmoidales	order	Above 1.8%ile
Hyphomonadales	order	Above 42.3%ile
Moraxellales	order	Above 0.8%ile
Tissierellales	order	Above 6.5%ile
Cellvibrionales	order	Above 9.7%ile
Eggerthellales	order	Above 10.1%ile
Ignavibacteriales	order	Above 60.3%ile
Selenomonadales	order	Above 5.7%ile
Cytophagales	order	Above 15.8%ile
Rhodospirillales	order	Above 3.5%ile
Rhodobacterales	order	Above 25.9%ile
Sphingomonadales	order	Above 17.4%ile
Flavobacteriales	order	Above 5.9%ile
Sphingobacteriales	order	Above 9.6%ile
Desulfovibrionales	order	Above 8.9%ile
Desulfobacterales	order	Above 26.9%ile
Fibrobacterales	order	Above 1.1%ile
Victivallales	order	Above 25.9%ile
Erysipelotrichales	order	Above 10.6%ile
Holophagales	order	Above 3.3%ile
Synergistales	order	Above 0.8%ile
Caldilineales	order	Above 4%ile
Desulfarculales	order	Above 24.3%ile
Verrucomicrobiales	order	Above 2.3%ile
Halanaerobiales	order	Above 21.5%ile
Coriobacteriales	order	Above 10.4%ile
Bifidobacteriales	order	Above 7.7%ile
Mycobacteriales	order	Above 4.3%ile
Streptosporangiales	order	Above 1.6%ile
Acidimicrobiales	order	Above 2.1%ile
Thiotrichales	order	Above 1.8%ile
Pseudomonadales	order	Above 10%ile
Chromatiales	order	Above 8%ile
Lysobacterales	order	Above 7%ile
Alteromonadales	order	Above 6.1%ile
Aeromonadales	order	Above 7.9%ile
Pasteurellales	order	Above 3.9%ile
Burkholderiales	order	Above 9.8%ile
Enterobacterales	order	Above 6.1%ile
Bacteroidales	order	Above 10.4%ile
Eubacteriales	order	Above 10.4%ile
Entomoplasmatales	order	Above 13.1%ile
Acholeplasmatales	order	Above 10.2%ile
Lactobacillales	order	Above 10.4%ile
Oceanospirillales	order	Above 11.5%ile
Mycoplasmatales	order	Above 12.4%ile
Actinomycetales	order	Above 7%ile
Methanobacteriales	order	Above 5.9%ile
Bacillales	order	Above 6.2%ile
Oscillatoriales	order	Above 17.5%ile
Nostocales	order	Above 2%ile
Hyphomicrobiales	order	Above 8.3%ile
Myxococcales	order	Above 1.8%ile
Bacteroidota	phylum	Above 10.4%ile
Cyanobacteriota	phylum	Above 13.6%ile
Pseudomonadota	phylum	Above 10.3%ile
Bacillota	phylum	Above 10.5%ile
Euryarchaeota	phylum	Above 4.2%ile
Candidatus Saccharibacteria	phylum	Above 28.8%ile
Verrucomicrobiota	phylum	Above 6.3%ile
Synergistota	phylum	Above 0.8%ile
Lentisphaerota	phylum	Above 24.1%ile
Chloroflexota	phylum	Above 11%ile
Thermodesulfobacteriota	phylum	Above 6.5%ile
Actinomycetota	phylum	Above 10.3%ile
Ignavibacteriota	phylum	Above 57.9%ile
Myxococcota	phylum	Above 1.6%ile
Balneolota	phylum	Above 42.4%ile
Melioribacter roseus	species	Above 65.1%ile
Fusicatenibacter saccharivorans	species	Above 33.2%ile
Intestinimonas butyriciproducens	species	Above 8.4%ile
Ruminococcus champanellensis	species	Above 63.9%ile
Parabacteroides faecis	species	Above 59.4%ile
Dielma fastidiosa	species	Above 21.5%ile
Coprobacter fastidiosus	species	Above 21.4%ile
Tepidimicrobium xylanilyticum	species	Above 52.3%ile
Bacteroides reticulotermitis	species	Above 85.9%ile
Anaerostipes sp. 5_1_63FAA	species	Above 86.3%ile
Casaltella massiliensis	species	Above 51.8%ile
Anaerococcus provencensis	species	Above 26.2%ile
Blautia stercoris	species	Above 13.6%ile
Blautia faecis	species	Above 35.3%ile
[Lactobacillus] rogosae	species	Below 62.3%ile
Phocaeicola paurosaccharolyticus	species	Above 15.9%ile
Brassicibacter mesophilus	species	Above 80.3%ile
Gemmiger formicilis	species	Below 45.8%ile
Eisenbergiella tayi	species	Above 30.1%ile
Dysgonomonas macrotermitis	species	Above 45.5%ile
Butyricimonas faecihominis	species	Above 31.8%ile
Aestuariispira insulae	species	Below 29.5%ile
Oleidesulfovibrio vietnamensis	species	Above 57%ile
Oribacterium sinus	species	Above 18.4%ile
Akkermansia muciniphila	species	Above 3.9%ile
Dialister invisus	species	Above 1.3%ile
Bifidobacterium longum	species	Above 8.5%ile
Subdoligranulum variabile	species	Above 31.5%ile
Alistipes finegoldii	species	Above 28.7%ile
Phocaeicola massiliensis	species	Above 4.2%ile
Clostridium frigoris	species	Above 2.2%ile
Ethanoligenens harbinense	species	Above 49.2%ile
Bifidobacterium thermacidophilum	species	Above 20%ile
Bacteroides cellulosilyticus	species	Above 14.3%ile
Bacteroides helcogenes	species	Above 34.5%ile
Eubacterium coprostanoligenes	species	Above 82%ile
Anaerosporobacter mobilis	species	Above 31.2%ile
Acholeplasma hippikon	species	Above 6.2%ile
Desulfurispora thermophila	species	Above 11.5%ile
Flavonifractor plautii	species	Above 29.5%ile
Gracilibacter thermotolerans	species	Above 77.2%ile
Mediterraneibacter glycyrrhizinilyticus	species	Below 27%ile
Oscillibacter valericigenes	species	Above 6.2%ile
Phocaeicola dorei	species	Above 7.2%ile
Roseburia inulinivorans	species	Above 26.1%ile
Leyella stercorea	species	Above 5.5%ile
Roseburia faecis	species	Above 8.4%ile
Dialister propionicifaciens	species	Above 25.3%ile
Phocaeicola plebeius	species	Above 11.1%ile
Phocaeicola coprocola	species	Above 14.8%ile
Bacteroides pyogenes	species	Above 78.9%ile
Acetivibrio alkalicellulosi	species	Above 15.2%ile
Parabacteroides goldsteinii	species	Above 1.7%ile
Alistipes onderdonkii	species	Above 64.3%ile
Alistipes shahii	species	Above 26.4%ile
Bacteroides finegoldii	species	Above 7.9%ile
Lactonifactor longoviformis	species	Above 21%ile
Bacteroides graminisolvens	species	Above 11%ile
Barnesiella intestinihominis	species	Above 25.1%ile
Parasutterella excrementihominis	species	Above 29.4%ile
Porphyromonas bennonis	species	Above 6.8%ile
Butyricicoccus pullicaecorum	species	Below 66.3%ile
Alistipes sp. EBA6-25cl2	species	Below 67.6%ile
Bacteroides sp. EBA5-17	species	Below 82.8%ile
Aminicella lysinilytica	species	Below 43.8%ile
Bacteroides sp. 2_2_4	species	Above 59%ile
Gordonibacter pamelaeae	species	Above 29.5%ile
Bifidobacterium bombi	species	Above 27.1%ile
Sutterella parvirubra	species	Above 14%ile
Segatella maculosa	species	Above 1.5%ile
Adlercreutzia equolifaciens	species	Above 6.1%ile
Blautia wexlerae	species	Above 10.7%ile
Howardella ureilytica	species	Above 4.9%ile
Niabella aurantiaca	species	Above 8.3%ile
[Bacteroides] pectinophilus	species	Above 58.7%ile
Phocaeicola coprophilus	species	Above 23.4%ile
Phocaeicola salanitronis	species	Above 11.8%ile
Bacteroides gallinarum	species	Above 13%ile
Pelosinus fermentans	species	Above 38.9%ile
Bacteroides xylanisolvens	species	Above 1.4%ile
Anaerostipes hadrus	species	Above 16%ile
Phocaeicola sartorii	species	Above 12.4%ile
Bacteroides faecis	species	Above 7.9%ile
Bacteroides rodentium	species	Above 16.8%ile
Mediterraneibacter faecis	species	Below 41.2%ile
Bacteroides fluxus	species	Above 10.2%ile
Alistipes indistinctus	species	Above 18.1%ile
Slackia piriformis	species	Above 8.5%ile
Phascolarctobacterium succinatutens	species	Above 1.2%ile
Slackia isoflavoniconvertens	species	Above 12.8%ile
Blautia glucerasea	species	Above 0.5%ile
Collinsella aerofaciens	species	Above 8.4%ile
Thomasclavelia cocleata	species	Below 52.6%ile
[Clostridium] methylpentosum	species	Above 82.7%ile
Segatella albensis	species	Above 21%ile
Bifidobacterium boum	species	Above 48.8%ile
Kluyvera georgiana	species	Above 68.1%ile
Paracholeplasma vituli	species	Above 72.7%ile
Eggerthella lenta	species	Above 4.8%ile
Bacteroides acidifaciens	species	Above 3.7%ile
[Ruminococcus] lactaris	species	Below 67.3%ile
Acidaminobacter hydrogenoformans	species	Above 0.5%ile
Lachnoclostridium phytofermentans	species	Above 50%ile
Holdemania filiformis	species	Above 34.5%ile
Anaerococcus octavius	species	Above 11%ile
Desulfofarcimen acetoxidans	species	Above 15.8%ile
Parabacteroides merdae	species	Above 6.8%ile
Bacteroides stercoris	species	Above 2.1%ile
[Clostridium] viride	species	Above 53.5%ile
Bacteroides caccae	species	Above 9.5%ile
Sporobacter termitidis	species	Above 58%ile
Catonella morbi	species	Above 7.6%ile
Succiniclasticum ruminis	species	Above 24.8%ile
Johnsonella ignava	species	Above 9.9%ile
Clostridium putrefaciens	species	Above 33.3%ile
Anaerostipes caccae	species	Above 6%ile
Pseudoflavonifractor capillosus	species	Above 26.8%ile
Coprobacillus cateniformis	species	Above 7%ile
Catenibacterium mitsuokai	species	Above 9.7%ile
Dorea longicatena	species	Above 32.1%ile
Blautia luti	species	Above 33.3%ile
[Clostridium] hylemonae	species	Above 68.2%ile
Coprococcus catus	species	Above 20.4%ile
Filifactor alocis	species	Above 16.4%ile
Lactobacillus iners	species	Above 13.9%ile
Segatella copri	species	Above 15%ile
Hungatella hathewayi	species	Above 40.6%ile
Turicibacter sanguinis	species	Above 7.3%ile
Anaerotruncus colihominis	species	Above 3.8%ile
Roseburia intestinalis	species	Above 28.9%ile
Marvinbryantia formatexigens	species	Below 32.2%ile
Victivallis vadensis	species	Above 11.9%ile
Blautia schinkii	species	Above 12%ile
Robinsoniella peoriensis	species	Above 58.3%ile
Varibaculum cambriense	species	Above 29.4%ile
[Clostridium] cellulosi	species	Above 65.3%ile
Caloramator fervidus	species	Above 5.4%ile
Alkalithermobacter thermoalcaliphilus	species	Above 8.3%ile
[Clostridium] scindens	species	Above 61.5%ile
Thomasclavelia spiroformis	species	Below 51.4%ile
Clostridium chartatabidum	species	Above 28.7%ile
[Clostridium] polysaccharolyticum	species	Below 17.3%ile
Gemella morbillorum	species	Above 23%ile
Lachnospira pectinoschiza	species	Above 7.2%ile
Bacteroides ovatus	species	Above 5.7%ile
Alistipes putredinis	species	Above 28.6%ile
Odoribacter splanchnicus	species	Above 29.6%ile
Prevotella corporis	species	Above 2.1%ile
Bifidobacterium animalis	species	Above 3.6%ile
Bifidobacterium pseudocatenulatum	species	Above 18.5%ile
Peptococcus niger	species	Above 5.5%ile
Methanobrevibacter smithii	species	Above 3.4%ile
Ruminococcus bromii	species	Above 21.6%ile
Ruminococcus callidus	species	Above 14.7%ile
Blautia obeum	species	Above 18.8%ile
Bilophila wadsworthia	species	Above 8.9%ile
Terrisporobacter glycolicus	species	Above 17.5%ile
Clostridium intestinale	species	Above 63.5%ile
Dorea formicigenerans	species	Above 10%ile
[Eubacterium] siraeum	species	Above 68.9%ile
Veillonella atypica	species	Above 0%ile
Veillonella dispar	species	Above 7.7%ile
Phascolarctobacterium faecium	species	Above 5.8%ile
Peptoniphilus lacrimalis	species	Above 0.7%ile
Blautia producta	species	Above 20.9%ile
Anaerococcus vaginalis	species	Above 11.2%ile
Mediterraneibacter gnavus	species	Above 17.2%ile
[Ruminococcus] torques	species	Above 21.6%ile
Coprococcus eutactus	species	Above 20.1%ile
Veillonella parvula	species	Above 0.4%ile
Finegoldia magna	species	Above 5.9%ile
Ruminococcus albus	species	Above 19.2%ile
Ruminococcus flavefaciens	species	Above 38.8%ile
Streptococcus sanguinis	species	Above 1.3%ile
Streptococcus thermophilus	species	Above 3.5%ile
Streptococcus parasanguinis	species	Above 9%ile
Blautia hansenii	species	Above 3.4%ile
Streptococcus anginosus	species	Above 10.5%ile
Enterococcus faecalis	species	Above 9.1%ile
Lactococcus lactis	species	Above 4.3%ile
Clostridioides difficile	species	Above 53.7%ile
Clostridium kluyveri	species	Above 24.5%ile
[Clostridium] leptum	species	Above 74.7%ile
Thomasclavelia ramosa	species	Above 31%ile
Faecalispora sporosphaeroides	species	Above 89.2%ile
Eubacterium oxidoreducens	species	Below 14.9%ile
Holdemanella biformis	species	Above 10.1%ile
Eubacterium limosum	species	Above 1.3%ile
Propioniferax innocua	species	Below 52%ile
Bifidobacterium indicum	species	Above 6.3%ile
Bifidobacterium magnum	species	Above 54%ile
Schaalia odontolytica	species	Above 8%ile
Hoylesella loescheii	species	Above 6.2%ile
Butyrivibrio fibrisolvens	species	Above 61.2%ile
Oxalobacter formigenes	species	Above 4.5%ile
Faecalibacterium prausnitzii	species	Above 9.9%ile
Proteus mirabilis	species	Above 11.1%ile
Haemophilus parainfluenzae	species	Above 5.5%ile
Aggregatibacter aphrophilus	species	Above 9.8%ile
Bacteroides fragilis	species	Above 6.1%ile
Bacteroides thetaiotaomicron	species	Above 9.2%ile
Bacteroides uniformis	species	Above 8.8%ile
Phocaeicola vulgatus	species	Above 9.3%ile
Parabacteroides distasonis	species	Above 9.4%ile
Lactobacillus casei group	species group	Above 23.9%ile
Streptococcus anginosus group	species group	Above 4.4%ile
Oscillatoriophycideae	subclass	Above 6.3%ile
Archaea	superkingdom	Above 7%ile

(c) 2026 Lassesen Consulting, LLC. ALL RIGHTS RESERVED

This work is licensed under the Creative Commons Attribution‑NonCommercial 4.0 International License (CC BY‑NC 4.0). Commercial use is prohibited without prior written permission. See https://creativecommons.org/licenses/by-nc/4.0/.

Past analyses have explored the prediction of symptoms using odds ratios derived from populations that utilized the same microbiome test and data-processing pipeline. A colleague recently inquired whether percentile-based comparisons could potentially be applied across different microbiome testing platforms.

The core issue, as well documented in the literature, stems from a severe lack of standardization in microbiome data processing (See discussion here). This report presents results derived from all available data encompassing multiple test sources.

An important methodological concern arises from potential double-counting when odds ratios are calculated simultaneously for a strain, its parent species, and its broader taxonomic family. To address this, I apply odds ratios at distinct taxonomic ranks to ensure proper separation of these hierarchies.

A secondary consideration involves the subjective nature of symptom reporting. As symptoms are self-reported, respondents tend to record only the most severe experiences, resulting in an expected underreporting of minor or moderate symptoms. This introduces reporting bias that likely reduces overall agreement levels.

Forecast agreement with Report

The table below provides insight into which taxonomic ranks may be most predictive. It is truncated at a 10% agreement threshold for clarity.

Rank	Predicted	Agreement	Percentage
species subgroup	126	55	43.7
subkingdom	42	17	40.5
superphylum	1124	379	33.7
superfamily	2187	438	20.0
infraclass	669	126	18.8
cohort	692	130	18.8
superorder	1034	193	18.7
suborder	18539	3147	17.0
subphylum	8339	1373	16.5
subfamily	5424	834	15.4
kingdom	19385	2779	14.3
strain	28091	3855	13.7
subspecies	49169	6714	13.7
species group	149291	17646	11.8
superkingdom	114214	12513	11.0

Reported with Forecast Odds < 1.0

Looking at symptoms reported where the odds was less than 1.0 (i.e. not elevated odds), we have a much shorter table. Looking at some ranks we see that we have around a 2:1 ratio between the tables, i.e. 66% (2/3)

species subgroup	30	7	23.3
superphylum	1782	311	17.5
subkingdom	22	3	13.6
suborder	14242	1707	12.0
subphylum	7540	872	11.6
infraclass	787	85	10.8

Clinical Implications

A More Effective Intake Process

Using forecast results as an interview checklist for clinicians could significantly improve patient assessment. Early data suggest that approximately two-thirds of the checklist items will correspond to reported symptoms. Directly asking patients about specific symptoms is typically more reliable than relying on spontaneous disclosure, as individuals tend to report only their most severe issues.

Improved Identification of Symptom-Associated Bacteria

When a patient confirms the presence of a symptom, this information can be leveraged to identify bacterial taxa most likely contributing to it. This approach contrasts with the traditional method of labeling deviations greater than two standard deviations from the mean as problematic. Instead, it establishes distinct, data-driven thresholds, which sometimes produce counterintuitive but statistically supported findings.

SymptomName	Tax_Name	Critical Percentile	Odds Ratio
Comorbid: Histamine or Mast Cell issues	Cystobacterineae	46.4	0.04
Comorbid: Histamine or Mast Cell issues	Actinomycineae	58	3.46
Comorbid: Histamine or Mast Cell issues	Nannocystineae	22.4	0.1
Sleep: Waking up early in the morning (e.g. 3 AM)	Cystobacterineae	54.2	5.33
Sleep: Waking up early in the morning (e.g. 3 AM)	Nannocystineae	22.4	0.13
Sleep: Waking up early in the morning (e.g. 3 AM)	Coriobacterineae	65.3	0.32
Post-exertional malaise: Next-day soreness after everyday activities	Sternorrhyncha	31.9	0.11
Post-exertional malaise: Next-day soreness after everyday activities	Cystobacterineae	10.6	8.49
Post-exertional malaise: Next-day soreness after everyday activities	Nannocystineae	16.7	0.18

For example, in cases of post-exertional malaise (next-day soreness following ordinary activities):

• Individuals above the 31st percentile for Sternorrhyncha have an 18% reduction in odds.
• Those above the 16th percentile for Cystobacterineae experience an 849% increase in odds.
• Individuals above the 16th percentile for Nannocystineae show an 11% reduction in odds.

These relationships indicate that increasing two bacterial groups (if their abundances are below threshold) and decreasing one (if above threshold) could normalize the microbiome toward a healthier state. For the other two conditions presented, the decision points cluster near the median rather than at the extremes, reinforcing that not all microbial influences occur at outlier levels.

Implications for Generating Therapeutic Suggestions

To refine microbial intervention strategies, I employ the Monte Carlo method. In essence, multiple algorithms independently select bacterial targets and propose compensatory actions. The aggregate of these independent results identifies interventions with high consensus and robustness.

Furthermore, using different taxonomic ranks yields multiple independent bacterial subsets—ideal for Monte Carlo modeling—providing a more reliable foundation for therapeutic recommendation generation.

Caveat: The best odds-ratios are based by the same microbiome processing that is used for odds ratio. The intent of this was to see if reasonable results may be obtain at a generic level. IMHO, the answer is yes.

Try it yourself

I have exposed the data on this page. Just paste (or call by API) your data in the format shown. No charge. No limit on use of this Api.

The samples used for illustrations are obtained randomly from samples from a different lab (no data from this lab was used to compute the odds ratios) that are from “healthy individuals”. These samples tend to have the top symptom being “Asymptomatic: No Health Issues” which is consistent with the claim of “healthy individuals”.

• Freq: Is the number of taxonomic ranks that predicted this symptom
• Estimate: Indicates the magnitude of the prediction.

Email me at Research@MicrobiomePrescription.com with comments and opinions.

My exploration of microbiome modification began with reading studies archived in the U.S. National Library of Medicine. As someone who has been developing expert systems since the 1990s, my instinct was to encode the findings from these studies as facts within an expert system—letting logic determine the optimal course of action.

In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code — Wikipedia

Some Difficult Discoveries

As I built the knowledge base, some major problems quickly emerged:

• Results from different studies often contradicted one another.
• Some results were replicated consistently, while others produced conflicting outcomes.
• Certain findings were reported only once and never replicated.
• There was significant uncertainty about bacterial identification due to non-standardized testing methods (see this explanation).
• Studies tended to report results at a single taxonomy rank—often not the rank relevant to my analysis.

To address the first issue, I incorporated fuzzy logic into the expert system, allowing it to handle ambiguity and partial truths rather than rigid yes/no classifications.

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty. — Wikipedia

The second issue required a different approach. I began using bacterial association data (available here) to infer relationships between taxa. For example, if a compound influenced the genus Bifidobacterium, I could reasonably infer a similar effect for its species. This two-way relationship also works in reverse: if you want to increase Bifidobacterium overall, the species Bifidobacterium longum—a readily available probiotic—shows the strongest positive association.

Species Name	Estimate Percentage Inference
Bifidobacterium actinocoloniiforme	18.8
Bifidobacterium adolescentis	54.8
Bifidobacterium angulatum	26.6
Bifidobacterium animalis	14.5
Bifidobacterium asteroides	40.2
Bifidobacterium avesanii	34.2
Bifidobacterium bifidum	25.5
Bifidobacterium bohemicum	52.7
Bifidobacterium bombi	57.7
Bifidobacterium boum	64.1
Bifidobacterium breve	52.4
Bifidobacterium catenulatum	33.9
Bifidobacterium choerinum	66.6
Bifidobacterium commune	45.4
Bifidobacterium cuniculi	21.8
Bifidobacterium dentium	23.2
Bifidobacterium gallicum	30.8
Bifidobacterium indicum	52.9
Bifidobacterium lemurum	50.4
Bifidobacterium longum	73.7
Bifidobacterium magnum	62.5
Bifidobacterium minimum	27.5
Bifidobacterium mongoliense	31.9
Bifidobacterium pseudocatenulatum	31.2
Bifidobacterium pullorum	30.2
Bifidobacterium ruminantium	20.4
Bifidobacterium scardovii	16.9
Bifidobacterium subtile	38.8
Bifidobacterium thermacidophilum	44.5
Bifidobacterium thermophilum	29.8
Bifidobacterium tsurumiense	11.7

With fuzzy logic, study findings indicating increases or decreases could be translated into numerical values. Using bacterial association data, I could then adjust those values to create a more accurate estimate of impact.

Why Do the Mathematics?

Modern AI models, particularly Large Language Models (LLMs), operate differently. They generate responses by finding text that resembles the question rather than reasoning from factual relationships. LLMs do not distinguish whether data comes from a single study or many, nor do they analyze hierarchical relationships within bacterial taxonomy. As a result, taxonomic nuances—such as the difference between Lactobacillus reuteri and Limosilactobacillus reuteri—are often overlooked because the evolution of bacterial naming conventions is ignored.

At the other end of the treatment spectrum is a “whole health” influencer who might recall a single study about Bifidobacterium dentium and use it to infer a complete treatment plan. My approach is simpler: I prefer actions grounded in probability—ones that have the best odds of success.

This is a bit of the engine used on my free site for individuals, Microbiome Prescription. At present we have 14,388,582 million facts over 1,882 modifiers.

Recently on my Microbiome Sites I added a table of metabolites whose presence or absence is associated with various symptoms. Technical details is give in this post: Metabolites and Symptoms. The volume of data is overwhelming.

Today I realized that a good solution is to have the page frame a question that people can paste into their favorite AI.

These metabolites have been identified statistically: their levels, estimated from the microbiome, are abnormally low with P < 0.001 for people reporting this symptom pattern (for example, ME/CFS without IBS). They are hypothesis-generating signals only and have not been validated in clinical trials. In my opinion, they highlight candidates that could be explored in formal clinical research or cautiously by an experienced physician with individual patients. None of these AI-assisted suggestions should be used without review and approval by an appropriate medical professional.

For example ME/CFS without IBS link as an example or to look at other symptoms go to that page and change the drop down. In terms of PROBIOTIC TRIALS, you want to see which of these substances are produced by bacteria.

Instead of “Are any of the following available as supplements or prescriptible from MDs:” ask,
“Are any of the following available produced by species uses as probiotics” or more explicit “According to KEGG are any produced by lactobacillus species”

Perplexity Response

None of the listed KEGG compounds appear to be commonly prescribable by MDs as pharmaceuticals. A few are available or derivable as dietary supplements, primarily those related to natural pigments and vitamins found in plants or bacteria.

Available Supplements

Chlorophyll a (KEGG: C05306) is widely sold as a supplement in forms like chlorophyllin for odor control, detoxification, and general health support, often derived from alfalfa or algae.

Ectoine (KEGG: C06231) is offered in over-the-counter supplements, eye drops, and skincare for dry eye relief, skin protection, and anti-inflammatory effects.

Vitamin E Forms

Alpha-tocopherol (KEGG: C02477), beta-tocopherol (KEGG: C14152), alpha-tocotrienol (KEGG: C14153), and beta-tocotrienol (KEGG: C14154) are standard vitamin E components available in tocopherol/tocotrienol supplements for antioxidant and cardiovascular support.

Unavailable Compounds

All others, including bacteriochlorophylls (e.g., C11242, C18155, C21217), complex lipids, CoA derivatives, and specialized bacterial metabolites like archaeal lipids or mycothiol conjugates, show no evidence of commercial supplements or prescriptions; they remain research or biochemical intermediates.

Claude AI Response

Based on my research, I can provide you with information about which compounds from your list are available as supplements or prescribable by physicians. Here’s a summary:

Compounds Available as Supplements:

Chlorophyll a – Available as dietary supplements, typically sold as “chlorophyllin” (a water-soluble derivative with copper instead of magnesium). Available in liquid drops, capsules, and tablets at most health food stores and drug stores WebMDWebMD.

Arachidonate (Arachidonic acid) – Marketed as a dietary supplement, particularly for bodybuilding and muscle growth Wikipedia. Available over-the-counter in capsule form.

Raffinose – Not typically taken as a standalone supplement, though it occurs naturally in foods and may have prebiotic properties Nutrients ReviewWikipedia. Used primarily in research and cryopreservation applications.

Alpha-Tocopherol (Vitamin E) – Widely available as a dietary supplement in capsule, gel capsule, and liquid forms, and can also be prescribed by doctors for certain conditions MedlinePlusWikipedia. In 2023, it was the 290th most commonly prescribed medication in the United States Wikipedia.

Beta-Tocopherol, Alpha-Tocotrienol, Beta-Tocotrienol – Available in mixed vitamin E supplements containing various tocopherols and tocotrienols.

Compounds with Medical/Prescribable Uses:

Bilirubin – Not available as a supplement. Medications like phenobarbital can be prescribed to reduce bilirubin levels in conditions like Crigler-Najjar syndrome and Gilbert’s syndrome Medscape, but bilirubin itself is not supplemented.

D-Xylose – Used primarily as a diagnostic tool in medical testing for malabsorption.

Important Notes:

The vast majority of the compounds on your list are:

• Biochemical intermediates in metabolic pathways
• Research chemicals
• Bacterial/plant-specific compounds
• Not available as supplements or prescription medications

Only a very small number (primarily the vitamin E forms, chlorophyll, and arachidonic acid) are commercially available as dietary supplements. I’m not a medical professional, so if you’re considering any of these supplements for health purposes, please consult with a healthcare provider.

This is intended for MD and Researchers

None of the listed KEGG compounds have robust clinical trial validation for therapeutic use. They represent microbiome-derived metabolites with statistically low levels (P < 0.001), suggesting potential biological roles worth exploring in formal trials or under MD supervision.

Key Caveat

Always consult a medical professional before trying any supplement or intervention based on these findings. AI suggestions are not medical advice.

In recent discussions with microbiome startup teams, a key question emerged: which metabolites correlate with specific symptoms? I recommended odds ratios as the optimal analytical approach, and one team is now considering integrating this into their product.

My prior analysis of KEGG-derived metabolite data from various labs revealed stronger consistency in metabolite patterns than bacterial profiles. Symptoms likely arise from adverse metabolite combinations circulating systemically—one metabolite can stem from hundreds of bacteria, and one bacterium can influence hundreds of metabolites—creating a complex web akin to an oversized Gordian knot.

Humans naturally gravitate toward simple “sound bites.” Asked for the highest odds of criminality, people might cite race, city neighborhood, or age range (with 0-5-year-olds showing near-zero risk). True predictive power comes from aggregating all statistically significant odds ratios—in this case, all reported metabolites with meaningful associations.

Using 4500 symptom-annotated samples from BiomeSight, this post explores that approach.

Go to Site

Computing the Odds Ratio

The process is simple:

• Take the Biomesight samples and compute the different metabolites using the KEGG: Kyoto Encyclopedia of Genes and Genomes data.
  • This produced 2,690 different metabolites
• Convert the amount for each metabolite to Percentile ranking. This allows the results to be applied to other pipeline data that may produce different values.
• Compute the Chi2 for each integer Percentile rank(100) for each metabolite and symptom (with at least 30 reports, i.e. 207)
  • 2,690 x 100 x 207 = 55,683,000 Chi2 computations
• Take the most significant vector with P < 0.001 or Chi2 > 10.83.
  • Then compute the odds ratio for it

The calculations were brutal with the CPU pegged for days (with some overclocking). If you are running on a cloud service, I trust you have a fat bitcoin wallet.

The results were over 585,094 significant metabolites for our 207 symptoms, or about 1% of the cases has a significant association found.

Using Odds Ratios

Human nature likes simplicity. “Give me just one factor to determine if a person is likely an illegal resident in the USA.” A 2025 report cites there are 150,000 illegal Irish citizens in the US, why is an Irish accent not used as a flag by a certain paramilitary group? With the microbiome data, we suffer a similar bias for simplicity with exclusion of inconvenient facts.

Looking at the odds ratios in detail, we may see large numbers. We should avoid using just one numberin isolation. The table for General: Fatigue is below. For example for the first two, metabolites if one is above and one is below, the resulting Odds ratio is about 1.09 (62.60 * 0.0174), i.e. no major risk. In short all available metabolites should be used, not just one or two.

CompoundName	Percentile	Odds Ratio Above	Odds Ratio Below
Pseudouridine 5′-phosphate	39	62.60	0.0160
N-Acetylmuramic acid 6-phosphate	37	57.32	0.0174
Uridine	43	74.51	0.0134
1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide	34	84.22	0.0119
GDP-4-amino-4,6-dideoxy-alpha-D-mannose	40	109.80	0.0091
beta-L-Arabinofuranose	29	0.62	1.6092

There is another interesting pattern that arise. Every metabolite is most significant for being present or ot present. This is a natural pattern to use for various machine learning and AI methods, for example

• Logistic Regression: Outputs probabilities for binary decisions via sigmoid.
• Support Vector Machines (SVM): Finds hyperplanes separating binary classes.
• Decision Trees: Splits data into binary paths leading to class labels.
• Naive Bayes: Probabilistic classifier assuming feature independence for binary outcomes.
• Perceptrons: Single-layer neural nets for linearly separable binary problems.
• Random Forests: Ensemble of trees voting on binary predictions

Clinical Use No, Research Use Yes

With a few exceptions, clinical use is limited. For most metabolites there are no easy ways to explicitly, safely, increase or decrease it.

One interesting exception was for C06570: Tetracycline. If this is not seen, then with Chronic Fatigue Syndrome (ME/CFS), the odds of having it is 22x higher. Tetracycline family of probiotics have had significant positive effects on ME/CFS patients. Other similar metabolites include:

• Oxytetracycline (C06571)
• Chlortetracycline (C11453)
• ​Penicillin G (C06925)
• Streptomycin (C04282)
• Erythromycin (C06911)

In other words, detecting the absence of naturally occurring antibiotics in a patient with a matching symptom suggests specific antibiotics could be tried.

For items like probiotics, the impact is very strain specific. Very few probiotics are sold by strain (and those that do, often lack data). A simple example for Limosilactobacillus reuteri is illustrated below.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic functions[2014].

Technical Note

With bacteria Odds ratio I did a “double odds ratio”. A Double Odds ratio means:

• Determining Odds ratio for Detected or Not Detected.
• Determining the Optimal Odds Ratio when it is Detected.

This was done because I was focused on computing suggestions on individual samples.

These are coarse (single) odds tables because we are not evaluating individual samples but a collection of samples. It is very possible to do a double odds ratio for metabolites — but at present, it appears to have low utilization because of the extremely low number of possible interventions. If interested, contact me at Research at MicrobiomePrescription (US decimal point) com