Back Story

I have severe /very severe ME/CFS and have noticed partially dramatic changes (although short lived) when taking a probiotic, especially Miyarisan[Clostridium butyricum].

Analysis

Sample Comparison

My general impression is that this person has lost some ground in terms of reference ranges(more found at extremes), but has gained ground with Kegg Compounds and Enzymes (less ones at extremes).

To get better insights, I added a Pattern Matching Comparison. Only symptoms marked in either samples are compared. We see some improvement happened.

Going Forward

My updated starting point with the new UI when the person has one or more conditions picked to [Beginner-Symptoms: Select bacteria connected with symptoms]. As shown below, we have a large number of symptoms matching the patterns from our data analysis. This suggests that we are likely to pick the right bacteria to focus on (based on statistical evidence – which any skilled person can reproduce using data on https://citizenscience.microbiomeprescription.com/).

The top suggestions are below

As well as the top avoids

Probiotics

The top probiotics using published studies on PubMed were:

• Lactobacillus gasseri {L.gasseri}
• Lactobacillus reuteri
• aor / probiotic-3 (contains miyarisan probiotic)
• miyarisan (jp) / miyarisan

With the new UI, we also have probiotics computed from RNA/DNA of your microbiome and probiotics. Usually I select only low compounds (i.e. some bacteria will be inhibited from starvation).

As is typical with ME/CFS, the top ones are E.Coli probiotics.

I checked each of the PubMed suggestion to see their relative impact and put in [ ] below.

• Lactobacillus gasseri {L.gasseri} [14]
• Lactobacillus reuteri [22]
• aor / probiotic-3 (contains miyarisan probiotic) [28]
• miyarisan (jp) / miyarisan [24] a.k.a. Clostridium butyricum

I would start with aor / probiotic-3, then one of the Lactobacillus reuteri , then Lactobacillus gasseri and end with miyarisan. While aor and miyarisan both contain the same bacteria species, they are different strains.

• Food avoid list is high in food containing fiber (in agreement with diet style)

Update using new Simple UI

We see our forecast symptoms being accurate very often as shown below, so I did [Beginner-Symptoms: Select bacteria connected with symptoms]. The intent is to focus solely on the bacteria likely causing the symptoms. There were 141 symptoms associations use which resulted in just 50 bacteria being picked. Often the same group of bacteria will cause multiple symptoms (depending on a person’s DNA etc).

The result was dominated by antibiotics and other off-label prescription items (needing a cooperative MD).

Swinging to Probiotics — the usual starting point for many people. The top choices were:

• Lacticaseibacillus casei {L. casei} [one source]
• Lacticaseibacillus paracasei {L.paracasei} – Yakult is the easiest one to get
• Lentilactobacillus kefiri {Kefibios} – Available only in Italy
• Lactobacillus gasseri {L.gasseri} [one source]

These are based on probiotics that has had clinical studies done on them. In other words, those likely to inhibit or encourage desired bacteria shifts. An alternative approach to look for probiotics that produce metabolites and enzymes that the person appears to be low on. The goal is reduce the dysbiosis caused by starvation. The top suggestions are:

• Escherichia coli – Mutaflor, Symbioflor2 [48 / 103] and on the to take list above.
• Bacillus clausii [34 / 72]
• Bacillus subtilis [32 / 68]
• Lacticaseibacillus casei [25 /54]
• Lacticaseibacillus paracasei [25 / 55]
• Lactobacillus gasseri [14 / 38]

The safest trinity of probiotics is: Escherichia coli, Lacticaseibacillus casei, and Lacticaseibacillus paracasei. Taking each for 1-2 weeks and then rotate to the next. With Lactobacillus gasseri and the two bacillus being worth an experiment afterwards

Important Note: The reader updated their symptoms and this is with the updated symptoms. Changing the selection of bacteria will usually cause shifts of suggestions (see this post)

Bottom Line

The user report of improvement with miyarisan and the suggestions are a nice agreement to see. The issue of being short termed is not atypical to see when there is no rotation of probiotics and antibiotics.

IMHO, probiotics should be viewed as natural antibiotics. As with all antibiotics, antibiotic resistance (probiotic resistance) may developed from continuous use. For Lactobacillus reuteri we have Reuterin; for Clostridium butyricum we have: CBP22, Butyricin 7423, Butyricum M588, Perfringocin 1105. (see Effects of Clostridium butyricum as an Antibiotic Alternative [2023]).

The same applies to herbs and spices with antibiotic characteristics… resistance will often develop from continuous use.

Postscript and Reminder

As a statistician with relevant degrees and professional memberships, I present data and statistical models for evaluation by medical professionals. I am not a licensed medical practitioner and must adhere to strict laws regarding the appearance of practicing medicine. My work focuses on academic models and scientific language, particularly statistics. I cannot provide direct medical advice or tell individuals what to take or avoid.My analyses aim to inform about items that statistically show better odds of improving the microbiome. All suggestions should be reviewed by a qualified medical professional before implementation. The information provided describes my logic and thinking and is not intended as personal medical advice. Always consult with your knowledgeable healthcare provider.

Implementation Strategies

1. Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
2. Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
3. Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
4. There are 3 suppliers of probiotics that I prefer: Custom Probiotics , Maple Life Science™, Bulk Probiotics: see Probiotics post for why
5. My preferred provider for herbs etc is Maple Life Science™ – they are all organic, fresh, without fillers, and very reasonably priced.

Professional Medical Review Recommended

Individual health conditions may make some suggestions inappropriate. Mind Mood Microbes outlines some of what her consultation service considers:
A comprehensive medical assessment should consider:

• Terrain-related data
• Signs of low stomach acid, pancreatic function, bile production, etc.
• Detailed health history
• Specific symptom characteristics (e.g., type and location of bloating)
• Potential underlying conditions (e.g., H-pylori, carbohydrate digestion issues)
• Individual susceptibility to specific probiotics
• Nature of symptoms (e.g., headache type – pressure, cluster, or migraine)
• Possible histamine issues
• Colon acidity levels
• SCFA production and acidification needs

A knowledgeable medical professional can help tailor recommendations to your specific health needs and conditions.

Back Story

Started feeling slightly tired in 2014, but I didn’t pay much attention to it. Around 2016 I am told the fatigue I am suffering from is likely caused by depression and so I take various SSRIS for 4 years. They made me anhedonic[inability to feel pleasure] and actually caused fatigue to somewhat worsen.

In 2022 after recovering from covid, I take aj immune boosting supplement to try and finally break free from the fatigue I was suffering. It actually worked and brought me back to life, which is when I decided ro come off my SSRI.

This was a mistake. It made me even more anhedonic and caused me to crash. I have not recovered since.

Lately, I have been dealing with actinic acid build up which is very weird for me as I was a professional athlete. 

Analysis

This has been sitting in my backlog (waiting for feedback from reader). I just discovered that he has since done a second sample, so this is a revision and update.

Comparisons

I do not know how many of the suggestions made in the earlier draft was done. Note that we went from 760 bacteria down to 447 (just 58%). So for most of the numbers below, we need to see at least a 50% drop in bacteria for something to be an improvement. Most of these measures failed to make this criteria.

We have added a new comparison table of changes of fit to reported symptoms. This also show a general loss of ground.

With the new UI appearance, I am also trying to keep the analysis simple by not obfuscating with too many measures.

Going Forward

I am going to do [Beginner-Symptoms: Select bacteria connected with symptoms] and then [Probiotic computed from Kyoto Encyclopedia of Genes and Genomes compounds].

We ended up with 8 bacteria being selected. The top suggestions are shown below

With best probiotics being: CustomProbiotics.com / L. Salivarius Probiotic Powder and Bulk Probiotics / L. Helveticus Probiotic Powder.

I decided to also try [Novice: Just tell me what to take or avoid] which increased the selected bacteria to 23. There are some similarities and differences (to be expected from the targeted bacteria increasing from 8 to 23)

The probiotics suggested were the same.

Going to KEGG Probiotics

We have a very different list. One jumps out: E.Coli probiotics. The number is the number of low compounds that it increases.

Checking with the earlier suggestions we see

• CustomProbiotics.com / L. Salivarius Probiotic Powder is a 11
• Bulk Probiotics / L. Helveticus Probiotic Powder. is a 11

My Probiotics Bottom Line

I would run with these 4 probiotics (taking each for one week and then rotating to the next)

• Mutaflor
• Prescript-Assist®/SBO Probiotic or Maple Life Science™ / Bacillus clausii
• L. Salivarius
• L. Helveticus

Why did I go with two from KEGG? The reason is simple — this is computed across the entire microbiome and does not depend on someone doing studies. The two other ones are based on published studies.

All of the above are typically deficient in samples (or assumed by some medical practitioners to be the cause of issues). This is not the case, and suggestions reflect this.

Items to Take

I would work off the two lists above – there is a reasonable amount of agreement. I note that fiber and high fiber foods are common on both of to-avoid list as is wheat, gluten (and bifidobacterium probiotics).

General Guidance

For items to take, remember that goal is to disrupt the dysbiosis. This means subjecting it to constantly changing “shocks” so it is unable to adapt. This has been shown to be effective when dealing with antibiotics (i.e. rotating between different antibiotics with breaks is more effective than taking the same antibiotic continuously). It likely applies to probiotics and herbs.

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.

The cartoon below illustrates what 6 different microbiome testing companies report on a person’s microbiome. This is not talking about 6 different samples from a stool — but from a single FASTQ digital file from a stool. In other words, all of them got the identical digital data.

For background, see The taxonomy nightmare before Christmas….

There are parallels between Hans Christian Andersen’s “The Emperor’s New Clothes” and the certainty of correct identification of bacteria often expressed by many microbiome researchers should be noted. “Andersen altered the source tale to direct the focus on courtly [academic] pride and intellectual vanity “

Attached you will find a PowerPoint PDF with a YouTube presentation. The target is treating Medical Practitioners. Despite these issues, the microbiome test data can be very useful after some data manipulation and with a suitable reference data set.

Above is a detailed walk through targeted for Medical Practitioners on using the Microbiome to treat Long COVID and ME/CFS. New findings on strong associations (P less than 0.001) derived from the microbiome to these conditions. Discussion of how these finding can lead to treatment suggestions on an individual basis (instead of generic suggestions). Associations listed in full at:

• Long COVID: https://microbiomeprescription.com/sa…
• and for ME/CFS https://microbiomeprescription.com/sa…
• Associated Links:
  • https://cfsremission.com
  • https://microbiomeprescription.com
  • https://blog.microbiomeprescription.com

The following looks at a holisitic approach to generate suggestions for microbiome dysfunctions, symptoms (that may be microbiome associated) and diagnosis (that have microbiome patterns).

This model (or variation there of) is being used by several microbiome testing companies today. See the bottom for example of clinical success.

This post illustrate the process and is not a precise match for current implemenation on Microbiome Prescription (which continuously evolves over time).

Native taxa weights

The first step is to get a weight for each taxa in a sample to identify what should be altered and the importance of each. With shotgun samples, there may be over 7000 different taxa.

The simple first step is to just do a lookup compare to ranges for each taxa (assuming there is sufficient data to compute ranges). Then assign weights based on the sample positioning in the ranges. The key function (tax_range) is often a complex function which may incorporate percentage, percentile, gender, age, diet style, and bacteria hierarchy. For example, Lachnospiraceae bacterium GAM79 may dominate and result in Lachnospiraceae being given no weight and thus expert system rules may be involved.

Conceptually, it is the importance of a bacteria to be shifted with the desired direction of shift converted to a numeric value or vector of values.

This is called a native taxa weights .

Presentation taxa weight.

These native taxa weights are then modified by the presence or absences of diagnosis and symptoms. Conditions are not either/or. A good example is Autism which has a wide spectrum of levels. A bacteria known associated with a condition will likely have an increase weight. A bacteria with no known associations will have a decreased or no weight. This is called a presentation taxa weight. As above, it may be a single value or a vector of values.

Modifier Matrix

We drop the taxa weight into our grid as show below. We show the weigh as a single value below. With a positive weight indicating something to increase and a negative weight indicating something to decrease. The “-1 to 1” indicates a factor.

We now want to maximize the value of the suggestions, i.e.

Sum _{Over All Bacteria}( Factor_{Vit B1} * Amount_{Vit B1} +Factor_{Vit B2} * Amount_{Vit B2} + etc)

Amount often becomes a 1 or 0 (take or do not take) when there is no dosage related data. Factor_modifier may be multidimension function on occasion. For example, it values may depend on other factors being selected. This can result in iterations that was the goal the Simula programming language. That is, you get the first naive suggestions(no dependencies), then feed the results into the next iteration.

We can rotate our focus to obtain lists of “to take” and “to avoid”

Sum _{Over All Bacteria}( Factor_{Vit B1} * Amount_{Vit B1})

Factors are often computed from a variety of factors, a few examples:

• the number of studies reporting a shift (often studies disagree),
• the magnitude of the shift (and/or P value),
• the modifier (a specific probiotic strain, a probiotic mixture, a species)
• context of the studies (humans, mice, pigs, fish, fouls).

Then We enter the Casino…

Rather than arguing over exactly which formulae for weights are correct. We make use of multiple reasonable formulae. Each is run independently and we then apply Monte Carlo modelling to these results.

Linearity is Dangerous To Assume

Our experience is that assuming linearity produces poor results. We found that doing cross validation allows this host of functions to be tuned.

Inferences should also be factored in, i.e. if a modifier alters Lactobacillus genus without details on individual species, most people will assume that it will alter some of the species — unfortunately, there are many studies reporting that lactobacillus increased with some species decreasing and other increasing.

The key issue is dealing with very sparse data that is often heavily conditioned, i.e.

• Shift(“Wormwood”, “Bifidobacterium” | Autism) < 0
• Shift(“Wormwood”, “Bifidobacterium” | Diabetes) > 0
• Shift(“Fasting”, BacteriaA | “Chinese” ) > 0 (see Ramadan Fasting Leads to Shifts in Human Gut Microbiota Structured by Dietary Composition – PMC (nih.gov) )
• Shift(“Fasting”, BacteriaA |”Pakistan”) < 0

This may explain why wieghts can be vectors of values.

This is where the art of microbiome manipulation comes in.

Clinical Success

Personal Experiences

Via our free for personal use (not commercial/medical office use) we have had many people have done a sample with one of many supported labs, obtained suggestions from the above model and implemented some, and then done a second sample. For everyone that has done this, there has been OBJECTIVE and SUBJECTIVE improvement. I was expecting > 50% only, but we are running 90+%.  For example analysis from those who consented to share, see this collection dealing with Long COVID and Chronic Fatigue Syndrome.

A recent example is shown below using multiple “measuring sticks” from different labs. We see clear improvement.

We also have associations of symptoms to bacteria using our 5000+ donated samples annotated with symptoms. Often the associations exceed P < 0.001 on a lab specific basis. From this data we can give percentage estimates on pattern matching to symptoms. Below is an example for the person shown above.

We see improvement across all of the top symptoms.

We do not look at “cure” (that does happen sometimes), but reduction of symptoms as our criteria.

We have had incidental reports of it appearing to improve the success rate and speed of remission for some cancers.

AI Cross Validation

Additionally we have done cross validation against the literature.  We take the microbiome shifts reported for a condition across multiple studies, run those shifts through the engine, then see how many of the top suggestions have been found to improve this condition according to published studies using those suggestions.  An example is here: Cross Validation of AI Suggestions for Nonalcoholic Fatty Liver Disease .

While not a clinical study as such, it shows that our suggestions appear to agree with results from third party clinical studies.

Here we hit a philosophy crossroad (and often a zebra crossing/speed bump of medical practitioner ego and/or arrogance).

• The road most travelled is focusing on the bacteria most heard about and trying to address them one by one.
  • It keeps the microbiome simple, naively simple. “All you have to do to raise your lactobacillus and bifidobacterium by taking my preferred probiotic mixture [which I will sell to you].”
  • It ignore the need to keep current on recent studies. Chart below is from PubMed. There are almost 25,000 new studies a year or 68 new studies a day.

• The road that I take is to ignore this chatter, and aim to adjust everything in one pass using mathematical models. No favorites bacteria to focus on (without firm evidence from studies that it is critical for a symptom or diagnosis).
  • I view this approach is most likely to cause desired changes and not chasing this bacteria or that bacteria is isolation.
  • It is accepting microbial interdependence in all of it’s complexity (see below)
  • Using KEGG: Kyoto Encyclopedia of Genes and Genomes data for Metabolites and Enzymes, I do not go down the rabbit hole of some substance being produced by just one bacteria or small set of bacteria. I accept the full width of the microbiome.

Gut Microbiome

The human gut hosts a diverse and complex microbial community:

• Over 10,000 microbial species have been identified in the human ecosystem, with the majority residing in the gut.
• Gut bacteria contribute about 8 million unique protein-coding genes, which is 360 times more than human genes. These bacterial genes are critical for human survival, as they enable us to:
  • Digest foods and absorb nutrients that we cannot process on our own
  • Produce beneficial compounds like vitamins and anti-inflammatories

Microbial Interdependence

Microbial interdependence refers to the complex relationships and interactions between different microorganisms in a community, where they rely on each other for survival and functioning. Here are some key aspects of microbial interdependence.

This study illustrates some interactions, one bacteria reduced a lot of other bacteria. Taking a probiotic that reduces this bacteria, and restore other bacteria.

“[Heyndrickxia coagulans] supplementation improved the gut microbiota imbalance by reversing the decreased numbers [caused by E Coli] of Enterococcus, Clostridium and Lactobacillus in jejunum and Bifidobacterium and Lactobacillus “

Bacillus coagulans prevents the decline in average daily feed intake in young piglets infected with enterotoxigenic Escherichia coli K88 by reducing intestinal injury and regulating the gut microbiota [2023]

Nutrient Sharing

Many microbes cannot produce all the nutrients they need and depend on other microbes to obtain essential compounds:

• The vast majority of microorganisms require nutrients like amino acids and vitamins that they cannot synthesize themselves.
• Corrinoids (vitamin B12 and related compounds) are an important example – while most microbes use corrinoids, only a subset can produce them.

Metabolic Cross-Feeding

Microbes often exchange metabolic products in mutually beneficial relationships:

• Some bacteria break down complex molecules that other species then use as food sources.
• Waste products from one species may serve as nutrients for another.

Symbiotic Relationships

Many microbes form close, interdependent associations with other organisms:

• Corals have symbiotic relationships with algal cells living within them.
• Lichens are symbiotic associations between fungi and algae or cyanobacteria.
• Gut bacteria in animals help digest plant material the host cannot break down alone.

Community Assembly and Function

Microbial interdependence shapes how communities form and operate:

• Public goods sharing drives adaptive function loss and the rise of metabolic cross-feeding over evolutionary time.
• Interdependent patterns that emerge through reductive evolution can make communities more resistant to environmental perturbations.

Ecosystem Roles

Microbial interactions contribute to important ecosystem processes:

• Soil microbes like mycorrhizal fungi and nitrogen-fixing bacteria form symbioses with plant roots.
• Microbial communities in oceans, soil, etc. carry out crucial nutrient cycling.

Understanding these complex webs of microbial interdependence is crucial for fields like ecology, medicine, and biotechnology. It highlights how cooperation and mutualism, not just competition, shape biological communities.

Microbial interdependence occurs when different bacterial species rely on each other for growth or survival. This can happen through various mechanisms:

• Metabolic cross-feeding: One species produces metabolites that another species uses for growth.
• Signaling interactions: Chemical signals from one species trigger responses in another.
• Modification of the environment: One species alters the local environment in ways that benefit another species.

Metabolic Interdependence

• Different bacterial species in the gut perform complementary metabolic functions. For example, some bacteria break down complex molecules that other species then use as food sources.

Colonization and Development

• Infants acquire their initial microbiome from their mother and other caregivers. Even one-day-old pre-term infants have unique microbiomes that differ from each other and their mothers.
• The developing infant microbiome is shaped by factors like genetics, environment, and immune system interactions.

Community Dynamics

• Microbial communities in the human body demonstrate properties like stability (resistance to change) and resilience (ability to return to initial state after perturbation).
• These dynamics can be studied through longitudinal sampling, for example, before, during, and after events like surgery or antibiotic treatment.

Site-Specific Communities

• Different body sites host distinct microbial communities adapted to those environments. For instance, the skin, gut, and mouth each have their own characteristic microbiota.

Examples from Research

Several studies have documented cases where the abundance of one bacterial species depends on the presence or amount of another:

• In the human gut microbiome, researchers have observed that the growth of certain Bacteroides species depends on the presence of specific Ruminococcus species.

Bottom Line

My approach is a holistic approach that attempts to use all of the facts to be considered. At present, over 2.5 million facts or rules. This is based on almost 13,000 studies. The suggestions may not be perfect, but they seem to be both reasonable (strong cross validation is common) and effective for many people.

The alternative paths often is based on “it worked for John Doe, so it should work for you”, or reading a handful of studies (often just one is sufficient for some people to claim being an expert).

When someone tries to “sell you” on their approach ask them:

• How many of the 10,000+ known bacteria do you consider? What is the evidence for excluding bacteria from consideration?
• How many of the thousands of metabolites do you consider? What is the evidence for excluding metabolites from consideration?
• How many studies do you review each month? For myself, it is close to 600 new studies that are identified as worth manual review.

Remember the old analogy of the broad path full of people taking the easy and popular way, versus the narrow path with very few on it.