Lactobacillus Jensenii [store] – suggestions are neutral for this
Details
The reader does not appear to have added their symptoms, but the top current symptoms is cited under Current Symptoms. In fact, there are 245 symptoms forecasts at 80% or higher…
Lyme at 84%
COVID at 90.1%
Diarrhea at 90.7%
Joint pain at 87.9
I am just going to do Novice: Just tell me what to take or avoid, to start. We have 43 bacteria identified as being too high or too low. The top suggestions are shown below
Typically I look at Probiotics first — mainly because of the common belief that they should be “cure all” In this case we have the following being usually available, I added some online stores after a few of them:
My usual suggestion is to do one at a time starting with a low dosage and then increase (double every second day) until you are at known effective dosages listed here. Keep at the maximum dosage for a week (or bottle is empty), and then proceed to the next Take notes on any changes seen (stool shape, farts, moods, etc). Some will have dramatic positive effects, others little.
The above were computed based on the complex interactions across the microbiome using literature from the US National Library of Medicine. An alternative approach for probiotic selection is using KEGG data to supplement with enzymes and metabolites that you appear to be low in.
Typical for Lyme, Long COVID and ME/CFS, Escherichia coli is the top choice (Mutaflor and Symbioflor2) at 109/293. With this method, {B. coagulans} and Clostridium butyricum would be the next choices.
Other Items
Vitamin B-3 (explicit, not a B-Complex). B1 and B2 are to be avoided.
There were no strong diet suggestions to take, but avoid high carbohydrate diet and ketogenic diet. Foods to avoid are atypical items (cranberry bean flour, cranberry bean flour, {black currant},lychee fruit etc). Also to avoid: {Ginkgo}, a-Gluco-oligosaccharides {GOS}, Carrageenans {Carrageenan}, {Stevia}.
Cross Validated for Long COVID
This is a more restricted identification of bacteria to shift. The changes found that agreed with literature is shown below.
The suggestions are shorter and includes antibiotics.
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
Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
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)
I’m writing today to get your guidance. I recently got back my 5th microbiome sample and I haven’t made significant progress. I suspect it’s because of the food I eat, and I’m hoping you can help advise me of what to eat.
My hypothesis as to why i’m not getting better results is that I haven’t followed the dietary recommendations very closely.
The recommendations are to basically eat a carnivore diet. My main diet… Fruit, Oats, Rice, Potatoes, Nuts, Wheat and cheese are all hugely negative. Thus, I think this is what’s holding me back. It’s nice to identify the problem, but the solution isn’t easy.
The only thing for me to eat on the recommendation list is Meat. Even vegetables are mostly disallowed because I’m supposed to eat low fiber.
Is that your understanding as well from the recommendations – that I should be nearly 100% meat (and possibly eggs), with some low fiber vegetables? It’s hard for me because i prefer to be vegan. But i’ll do whatever I need to fix my gut.
Note: 4 months ago I completely eliminated wheat and cheese and I started eating beef once a day. That helped but it doesn’t seem to be enough, which is why I’m reaching out to inquire what to eat.
I think I can withdraw my question. I didn’t know what I would eat when meat showed up on my Suggestions list as basically the only option. But I’m having miso or bone broth soup for breakfast, eggs for lunch, and meat for dinner… and that feels sustainable for me. So far I think it’s working. Time will tell.
Over the last 18 months i’ve had some good days and even a good week here and there, which has given me hope that your approach holds the solution i’ve dedicated a lot of my life to over the last 15 years. However I always gravitate back to my baseline of feeling crummy.
I thought that taking all the top suggested pills, and cutting out the foods with very negative Priority score would be enough, but I now believe that my diet of mostly oatmeal, nuts, beans, (and previously wheat) has been holding me back. All those items have a negative Priority scores, and my recommendation list very clearly points to red meat, low fiber, low carb.
First Step: Compare Last to First Sample
This immediately ran into issues with sample quality. One sample produced 723 bacteria versus 434, i.e. 66% more. It makes comparing the numbers very difficult.
Criteria
Current Sample
Old Sample
Lab Read Quality
11.8
4.3
Outside Kaltoft-Møldrup
220
86
Bacteria Reported By Lab
723
435
Bacteria Over 85%ile
84
66
Bacteria Under 15%ile
268
58
Lab: Thryve
Pathogens
33
20
Condition Est. Over 85%ile
2
10
Kegg Compounds Low
238
331
Kegg Compounds High
196
309
Kegg Enzymes Low
83
479
Kegg Enzymes High
322
237
A second approach is to compare forecast symptoms
Current Sample
First Sample
The surprising thing is how close both sets of forecasts are to each other, often with less than 1% difference. The reader’s “I haven’t made significant progress” appears to be right on. This is the first repeat analysis that there was not objective improvement all. The expert system is not perfect.
Going Forward — let us try getting religious on suggestions!
I am going to do three approaches to try helping this person:
Stock suggestions from the Expert System using the simplified UI. This used only studies on PubMed to try to shift the microbiome.
Using the Food Site – This use the nutrients identified as significant and tries to map them to a wide variety of foods (infer from nutrients to food)
Probiotics using KEGG – this uses no explicit studies, rather identify what substances that the microbiome may be starving for, then identify any probiotics that produces those substances. The reasoning is that the starvation contributes to the dysfunction.
Remember, everything are estimates of likely to help (or hurt). Estimates can be wrong. In some cases, it may be worthwhile to do a short term trial to see if there are any immediate changes. This applies especially to anything that is a mixture because the good may overpower the bad (or the reverse).
Simplify UI
We get 137 (out of 723, i.e. ) or 19%. The top diet types are shown below
With inulin, the basic form is a big -1040, while oligofructose-enriched inulin is a positive but different forms of oligofructose are all negatives — so would definitely avoid all inulin
I had the same takeaway about inulin! The basic form was super negative but the oligofructose enriched was very positive. Not going to risk touching it.
Similar thing with “low-fat high-complex carbohydrate” diet, which has a positive Priority score of 237. However, “Slow digestible carbohydrates” are negative 634 AND a “restricted-fiber diet” is hugely positive. It would seem to me that eating low-fat high complex carbs is way too risky 🙂
Reader Feedback
The hard part – AVOIDS
The list contain many items that are often seen as to take.
Over to Food Helper
The food site, Microbiome Prescription Food And Nutrients is designed to bridge the gap between some nutrient tested in studies and foods it is found in. The food are often not tried in studies – so this is an inference because of sparse data. It should always be used with a grain of sodium chloride (salt).
Take translates to American cranberry, raw black olives, a variety of fish
And these are the to avoid
Avoids translates to Vinegar, Almond, Red wine, chocolate
I will leave it to the reader to dig more on the food site. It may reveal what may have gone wrong.
KEGG Probiotics
This is an alternative way to determine probiotics working off compounds and enzymes produced (or not produced) compare to an estimated norm.
The results are shown below (first compounds and then enzymes). Suggestions are similar with the exception of the E.Coli probiotics. I would suggest these two:
Note: Prices include shipping anywhere in the world. These are typically manufactured weeks before shipping (have manufactured date) and thus alive. If you double the number of capsules, the costs usually go up by 20% only (see example below) and the price is awesome!!
$.29 per capsule
$.20 per capsule
$.15 per capsule
I just ordered the bacillus clausii and the bacillus mesentericus you recommended. Thanks for making it so easy and linking it to an eBay seller!..
[Update] I just cancelled the bacillus mesentericus and the bacillus clausil you linked to. They both contain fructooligosaccharids, which are a negative 193.
Question: Why did I skip Mutaflor?
Items that are on both list are more likely (better odds) of having a positive impact. I am an odds-person (i.e. a statistician).
Also, Mutaflor has a large number of additives: The other ingredients include maltodextrin, talc, methacrylic acid-methylmethacrylate copolymer, macrogol, triethyl citrate, glycerol, titanium dioxide, iron(III)-oxide, white beeswax, carnauba wax, shellac, and purified water
Probiotics Solutions when additives are a problems
There are two providers with no declared additives that I know of:
Bulk Probiotics: US based Newbie — but has some species not available at the other two sites. No other ingredients just the bacteria.
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
Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
I’m now 35. As a child I contracted a bacterial gut infection from water that had cow poop in it (broken pipe through a field), and was unable to eat sugar without being sick. As a teenager I had relatively bad insomnia, acne and eczema. I was prescribed months of antibiotics as treatment and put on a contraceptive to address the acne. I also had a spinal injury that I was prescribed painkillers including ibuprofen and codeine, and a painkiller that’s no longer approved for distribution that I forget the name of. By the time I was 19 I was in a high stress job, sleeping well, but taking guarana and caffeine pills on top of coffee to stay functional due to fatigue.
Doctors took blood tests, and everything was in the normal range so no diagnosis.
At 22 I had sever food poisoning in the Netherlands.
At 25, in 2016, I took a trip to California – I’m from New Zealand – and contracted giardia, during a heatwave, and ended up in hospital in hypovolemic shock. I was prescribed fluids which where intravenous and an anti-psychotic, which as a New Zealander I didn’t take. My sister flew over to help me get home. Once back in NZ, I was found to have irregular heartbeat, giardia, and neck injury from severe vomiting. I had an allergic reaction to the first antibiotic to treat giardia, was given a course of prednisone then a different antibiotic which killed off the giardia.
During this time, I had extreme fatigue, brain fog, heart palpitations, insomnia, panic attacks, tension headache and the worst, internal vibrations or tremors that could not be seen. I developed a dependency on sleeping pills, which quickly stopped working all together. Doctors couldn’t find anything physically wrong with me, and eventually one decided to try b12 injections. This, along with osteopathy and rest, resolved most symptoms, leaving only tinnitus. I have had b12 injections 1-3 monthly ever since, and this mostly controls the vibrations. I resolved my dependence on sleeping pills by using natural sleep aids and pushing through several sleepless nights. I was left with an inability to eat gluten and dairy.
By 2021 I was in good health – able to regularly exercise and hold down a high pressure job. I had my first Covid vaccine no issues, the second vaccine I became violently ill for 48 hours with fevers, fatigue, and body aches. I took the recommendation to get the third shot, still Pfizer, and was bedbound for 2 days with fevers, rigours, vomiting, headache, weakness and fatigue. In December 2022 I contracted Covid. My symptoms included sore throat, swollen lymph nodes, vomiting, fevers, rigours, fatigue, elevated heart rate. By day 7 I was weak but recovering. On day 8, internal vibrations started with severity, followed by blurred and double vision (I required a new prescription following this), breathlessness walking across a room, heart palpitations, tension headache, brain fog, PEMS and extreme fatigue. I was diagnosed after 3 months with long Covid.
I tried every treatment suggested by the internet and then some, from HBOT to magnesium floats to ozone sauna, thousands of dollars of supplements, osteopathy, massage, and more. After almost 2 years I’m working full time again, and most symptoms are managed enough to wake up, work, clean up a bit, and sleep again. However they are not fully resolved – I can’t exercise without an extreme energy crash, and I have to continually practise meditation and other calm down methods to regulate. It’s certainly not a life lived well, and one in constant fear of catching so much as a cold.
I am hesitant to accept a ‘long Covid’ diagnosis, despite it being the only thing on offer. This is because I experienced the same set of symptoms I after having giardia and dehydration, neither of which are viral so not a “post viral syndrome”. My lifelong battle with fatigue indicates to me that serious illness triggers a specific set of symptoms, and my Biomesight results strongly indicates a gut issue. I have tried various diets, each resulting in my becoming more fatigued – keto, Mediterranean, low carb, vegetarian, carnivore, low formal, and high protein.
I find the microbiome prescription results a little overwhelming and difficult to sort through, so your advice would be so appreciate
Analysis
First, about “overwhelming”. I agree! Just like “No man can serve two masters”, “No site can server all users”. My primary focus has been correctness, depth of coverage and other technical (nerd) aspects. This came out of seeing failure after failure of simplified and naïve approaches. The “new UI” has been an attempt to simplify the process while maintaining a reasonable level of accuracy.
You have had a long history of microbiome insults causing dysbiosis. Some are known to cause ME/CFS, see IBS/CFS/Long Covid Insight from Bergen’s Giardia Infection [2018]. Rather than retracing past events, let us focus on your current state and go forward.
Looking at predicted symptoms versus reported symptoms, we see a high number of matches.
This usually indicate that “Beginner-Symptoms: Select bacteria connected with symptoms” is likely the best start, This will focus on the probable bacteria that are causing most of the symptoms (and ideally ignore bacteria shifts that are likely just noise).
Usually the number of bacteria identified is a fraction of the symptoms. In this case we have 63 bacteria identified for 55 symptoms. The suggestions are shown below. I usually suggest keeping to those above 50% of the highest weight (1706 x 50% =853) and avoiding to those below 50% of lowest value (-1375 x 50% = -865). The reason is that the higher (or lower) the weight, the more probable it will have an impact).
I retried checking all types of microbiome modifiers (usually overwhelming). Mainly to see if antibiotics often used for ME/CFS rank high. Going to the Consensus Report and filtering to antibiotics we see that none made it over the threshold – which means that they are likely to be really helpful.
At this point, I typically decompose suggestions into types, first with probiotics. VSL#3 is rarely a clean choice because there were changes in the formula and other issues.
I would go for the following probiotics (two weeks of one at a high dosage – 50-150 BCFU) and then rotate to the next. Given that you are “down under” , I would suggest getting them from my favorite Indian Manufacturer (usually manufactured just weeks before shipping and best prices) – I linked to the sight below. Note that doubling the capsules in a bottle often costs just 20% more and a lot cheaper than ordering two bottles!!
P.S. spirulina was a coding error – just fixed it.
Your inability to handle gluten or diary means that some suggestions must be excluded. I would be tempted to try an experiment with oats flakes, then barley flakes, then 100% rye bread — mainly to see if it is wheat gluten that is the issue (there are many types of gluten).
A handful of almonds and walnuts everyday is an obvious item. Do not do a B-Complex — some B vitamins will help and other hurt. Keep to Vitamin B7, B2. Hopefully you can tolerate dark chocolate (Cacao). Lime is suggested (as is Vitamin C).
The avoids should be pretty clear.
Alternative Paths
At this point, we branch into two additional paths — one by using Kyoto Encyclopedia of Genes and Genomes (KEGG) data on compound being produce and consumed by your microbiome. With KEGG we look for probiotics that can provide enzymes etc that you are deficient in. Why these alternative paths? Simple, there is not enough data available so we use inference.
The second path is going to the food site to identify foods rich in suggested nutrients. The foods may not have been used in studies, but the dominant nutrients in the foods may have been studied.
KEGG
The top suggestions are below. #1 item is common for ME/CFS, Mutaflor is available in Australia, I warn people that it may cause a major die-off / herx reaction. Symbioflor 2 is the same probiotic species but at ~ 1/100 of the BCFU and unlikely to cause die off. To the above list I would add these two
We see that you may be eating a lot of beans – they appear to be providing some of the nutrients that caused wheat to be suggested above.
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
Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
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)
For a number of years, I taught a 3rd year Artificial Intelligence survey course for Chapman University. Career wise while my “bread and butter” came from software engineering, I have in that profession often done data science, statistics and artificial intelligence for various employers.
Looking at AI systems for the microbiome back in 2015, I went with a model that I had used professionally: Expert System with Fuzzy Logic. An expert system use logic. The term fuzzy logic means that probability is used at decision points.
History Lesson
The first significant use of an expert system in medicine was the development and application of MYCIN in the early 1970s at Stanford University. MYCIN was designed to diagnose bacterial infections, particularly blood diseases, and to recommend appropriate antibiotics, taking into account factors such as patient weight and the specific infection identified. The system was based on a set of about 500 cause-and-effect rules and could explain its reasoning, as well as suggest additional laboratory tests if necessary.
MYCIN demonstrated the potential of artificial intelligence to support clinical decision-making, often matching or even exceeding the diagnostic accuracy of human specialists—achieving around 70% accuracy in controlled tests. Despite its success, MYCIN was never implemented in routine clinical practice, largely due to legal and accountability concerns regarding the use of AI in critical medical decisions.
Other expert systems were developed around the same time, but MYCIN is widely recognized as the first major, influential expert system specifically designed for medical diagnosis and treatment, marking a pivotal milestone in the history of AI in healthcare.
(Doctor) Watson Healthcare Application
IBM Watson’s entry into healthcare began in earnest around 2011, when IBM started developing healthcare-specific applications leveraging Watson’s natural language processing and machine learning capabilities. The system was designed to analyze large volumes of medical data—including electronic health records, medical literature, and clinical guidelines—to assist physicians in making more informed treatment decisions, especially in complex cases like cancer.
Key milestones and applications include:
2013: Watson’s first commercial healthcare application was for utilization management decisions in lung cancer treatment at Memorial Sloan-Kettering Cancer Center. This marked the beginning of Watson’s deployment in real-world clinical settings.
2016: IBM launched “Watson for Oncology,” a product designed to provide personalized, evidence-based cancer care options to physicians and patients.
Watson was positioned as a tool to bridge knowledge gaps, keep clinicians updated on the latest evidence, and support personalized care by tailoring recommendations to individual patient profiles. However, the initiative faced significant challenges, including high costs, privacy concerns, regulatory hurdles, and mixed adoption by healthcare providers. Notably, the partnership with MD Anderson Cancer Center was discontinued after substantial investment, and Watson for Oncology faced criticism for inconsistent recommendations and limited adaptability to local clinical practices.
Large Language Models
The first significant large language model (LLM) is generally considered to be GPT-1, released by OpenAI in 2018. GPT-1 was relatively small by today’s standards, with only 117 million parameters.
The release of GPT-2 in 2019 (with 1.5 billion parameters), and especially GPT-3 in 2020 (with 175 billion parameters), brought LLMs to much greater prominence and capability. These models, especially GPT-3 and later, are what most consider the foundation for today’s advanced LLM technologies.
Large language models (LLMs) present several well-documented problems and limitations when applied to medicine:
Hallucinations and Incorrect Information: LLMs can generate plausible-sounding but incorrect or fabricated medical advice, a phenomenon known as “hallucination.” This poses serious risks, especially when users do not verify outputs or lack medical expertise.
Lack of Medical Domain Optimization: General-purpose LLMs are often not fine-tuned with sufficient medical data, leading to misinterpretations of clinical terminology and context-specific nuances.
Transparency and Interpretability: The reasoning behind LLM outputs is often opaque (“black box” nature), making it difficult for clinicians and patients to understand or trust the basis for recommendations.
Algorithmic and Data Bias: Biases in training data or model design can result in unfair or inaccurate recommendations, especially for underrepresented patient groups.
Automation Bias and Overreliance: Clinicians may become overly reliant on LLM outputs, leading to uncritical acceptance and reduced independent judgment, a phenomenon known as automation bias.
Limited Regulatory Oversight: There is a lack of clear legal and ethical guidelines governing the use of LLMs in clinical settings, raising concerns about accountability and patient consent.
Information Completeness and Consistency: LLMs may provide incomplete or inconsistent answers, particularly when faced with complex or rare medical scenarios.
Privacy and Data Security: The use of sensitive patient data to train or fine-tune LLMs raises concerns about privacy, data security, and compliance with regulations like HIPAA.
Inequity of Access: Differences in technology access and digital literacy can exacerbate healthcare disparities, limiting the benefits of LLMs for certain populations.
Bottom line: Large Large Models are very questionable for use with the microbiome; while convenient, cheap and heavily hyped – there are so many issues that I view it has having huge legal liability risk dealing with the microbiome in a clinical setting.
Going Forward
A long time Ph.D. friend that attended multiple National Institutes of Standards and Technology conferences with me shared a post with me below. He, like me, have worked in senior positions as Architect and Strategist for firms such as Intel, VMWare, RSA and DELL, while I did time at Microsoft, Amazon, Starbucks and Verizon.
I responded with doing 7 times more data with 1/22 of the memory using my expert system!!!
He added: These LLM-ish technologies cannot approach the efficiency or trustability of an expert system or knowledge based system implementation, and LLMs fall apart under significant ambiguity.
LeCun ( world models (e.g. V-Jepa)), Pearl (Causal Models) and a few others are pointing the way to LLM alternatives (not an extension of LLMs).
The next step is at the intersection of Knowledge Representation and Reinforcement Learning , giving us a place to hang prior knowledge, ground truth, dependencies and real logics ( reasoning as opposed to chain of thought).
For those not familiar with these concepts and naively believe that AI is only Large-Language-Models.
Alternative AI Models
Yann LeCun
Yann LeCun is a Turing Award-winning computer scientist, Meta’s Chief AI Scientist, and a professor at New York University. He is best known for pioneering Convolutional Neural Networks (CNNs) in the late 1980s and early 1990s, which revolutionized computer vision and laid the foundation for modern deep learning systems. His work, especially LeNet-5, enabled breakthroughs in handwritten digit recognition and influenced countless AI applications, from facial recognition to autonomous driving.
LeCun is a vocal critic of the current generation of large language models (LLMs), arguing that they lack true reasoning, understanding of the physical world, persistent memory, and planning capabilities. He advocates for the development of world models—AI systems that can observe, interact with, and reason about the world, aiming for human-level intelligence.
V-JEPA (Joint Embedding Predictive Architecture)
JEPA (Joint Embedding Predictive Architecture) is a new paradigm in AI research championed by LeCun. Unlike traditional LLMs, which predict the next token in a sequence, JEPA aims to develop systems that can reason, plan, and interact with the physical world by learning from observation and experience.
JEPA’s core idea is to move beyond mere language processing, focusing instead on building AI architectures that can:
Understand and model the world (not just text or tokens)
Reason and plan based on learned representations
Maintain persistent memory for long-term understanding and context7
This approach is seen as a step toward more robust, general-purpose AI systems that could eventually surpass the limitations of current LLMs.
Judea Pearl (Causal Models)
Judea Pearl is a computer scientist and philosopher renowned for his foundational work on probabilistic and causal reasoning. He introduced the concepts of Bayesian networks and, most notably, the do-calculus for causal inference, which allows researchers to distinguish correlation from causation in complex systems.
Pearl’s causal models provide a mathematical framework for understanding how interventions (e.g., treatments in medicine) affect outcomes, enabling more accurate predictions and explanations in fields like epidemiology, economics, and machine learning. His work has had a profound impact on AI, particularly in areas where understanding cause-and-effect relationships is critical.
Hybrid AI Systems:
Combining symbolic AI (rule-based reasoning) with neural networks for more robust and interpretable A
Basic Criteria to evaluate an AI Model
Ability to Accurately forecast symptoms and conditions from a sample
A recent educational analysis that I did had a high rate of correctly predicting symptoms. The illustration below are from developing a predictor on a collection of 4000+ samples processed through the same pipeline. The checkmarks are the symptoms that the user confirm having (i.e. a correct prediction).
The same person’s earlier sample ( when they had more severe issues) was even more impressive:
This can also be done using published studies but this has a challenge because of a severe lack of standardization in studies. This translates usually into rare repeatability of results.
Last week I had a nice session discussing this issue with the Scott whom I cited below.
Ability to provide objective evidence of improvement
The ability to forecast gives a natural mechanism of evaluation. If the suggestions from the AI improves the microbiome, then one would expect the values for the forecasts to reduce. An example is shown below (full details). In this example, every single forecast value was decreased from implementing the suggestions from the expert system.
Access to data used above
The microbiome sample data annotated with symptoms that my expert system uses to build algorithms is freely available at my CitizenScience site.
Cheap and sloppy OR Expensive and accurate
Large Language Models (LLMs) are often considered relatively inexpensive to develop compared to earlier AI systems, since they can be trained by scraping vast amounts of text from the internet to identify statistical patterns. In effect, the knowledge these models acquire is based on the aggregate of publicly available information—much of which is uncorroborated or unverified, analogous to hearsay in legal terms.
Legal systems do not accept hearsay as reliable evidence, and this foundational limitation raises significant concerns when LLMs are used in medicine. If an LLM’s output leads to patient harm, its reliance on potentially unreliable or unverified sources could make it difficult for clinicians to defend their actions in malpractice lawsuits, as the standard of care in medicine requires decisions to be based on rigorously validated, evidence-based knowledge.
Expert Systems are typically costly to develop because their construction relies heavily on human expertise to define and encode logic rules. This process involves painstakingly translating medical knowledge and clinical guidelines into a structured set of rules that the system can follow. Furthermore, encoding the necessary facts into the system demands a thorough review of medical literature by subject-matter experts, which is both time-consuming and expensive.
These challenges are compounded by the nature of medical information itself. Critical data—such as detailed clinical findings, research outcomes, or supplementary evidence—are often found in tables, charts, and appendices within medical articles. Traditional expert systems can be engineered to process these structured formats, but this again requires manual effort and expert intervention. In contrast, large language models (LLMs) struggle to reliably extract and interpret information from such tabular data, as their training is primarily based on unstructured text and is less adept at handling complex, structured formats.
As a result, while expert systems offer the advantage of transparent, rule-based reasoning that can be clearly explained and audited, their development remains resource-intensive. This is due to the need for ongoing expert involvement, meticulous data encoding, and specialized handling of non-textual information that LLMs currently manage less effectively.
Bottom Line
My choice (before LLM days) of using Expert Systems appears to still be the most appropriate. While I currently use some Bayesian mathematics in the model (the “Fuzzy”). I will be digging into Judea Pearl’s Causal Models to see what may be effectively incorporated into the existing model.
In September 2023 I suffered a series of gut insults (food poisoning, antibiotics and gastritis) in a very short period. This gave me unrelenting brain fog and cognitive issues, fatigue, tinnitus, sleep difficulties, and a slew of other issues.
In an attempt to recover from this I went hard on prebiotics and probiotics, and seemingly without reason these would trigger worsening of symptoms. Looking back at my November 2023 sample (closest after the insult) on your website, I can see some of the items that made me worse, like slippery elm, were at the top of the avoid list! I wish I had an understanding of the website back then...
I have undergone multiple killing phases with antimicrobials, which did nothing but make me worse. I now see that building up the good bugs and crowding out the bad is the better technique for my situation.
After getting enough of understanding of your website and suggestions I have made some objective improvements in my most recent sample and across the board symptom matches are down. Unfortunately, this hasn’t resulted in symptom improvements (yet).
Today
My worst symptoms remain brain fog (cognitive issues, fuzzy thinking, memory issues, etc), fatigue and tinnitus. I remain bedbound since the event in 2023.
I believe your approach of focusing on enzymes and compounds are more likely to result in improvements for me than just targeting bacteria changes.
Upon reviewing my enzyme and compound movements, pre and post 2023, there are some that are likely to be causing my cognitive issues and maybe fatigue (such as very low L-LDH, high H2S, etc). What do you think about this?
Based on the most recent sample suggestions I am using apple cider vinegar, BB536, cannabinoids and dandelion.
Preliminary Analysis
Using [Old Menu] / [Multiple Samples] is my usual start point when there are multiple sample.
I did a compare of the latest sample [2025-05-20] with [2025-03-25] and everything was better!
This person has a series of samples, so I am going to compare each against the prior to see the trend over time. The first sample marked with symptoms was 2023-11-30. Total is the count of distinct symptoms reported from prior and current sample.
Sample
Better
Worse
Total
2024-02-02
7
1
15
2024-09-05
78
0
78
2024-10-30
0
79
79
2025-02-20
0
1
70
2025-03-25
1
0
77
2025-05-20
66
0
66
There was a dramatic reversal in September 2024 which appears to be starting to correct itself in May 2025.
Going Forward
My approach is a three step approach.
Use the symptom patterns to identify the most likely bacteria involved. Then look for the best probiotics to address them
Use the symptom patterns to get a list of suggestions
Do a generic (not using symptoms) to get a 2nd set of suggestions
First step, using the symptoms to identify bacteria of interest and then get suggestions. We have a high rate of match for forecast symptoms and actual symptoms — which is a good indicator.
The result was just 6 bacteria identified as off.
Bacteria
Rank
Shift
Dysgonomonadaceae
family
Low
Erysipelotrichaceae
family
High
Erysipelotrichales
order
High
Erysipelotrichia
class
High
Hathewaya
genus
Low
Negativicoccus
genus
High
For comparison, I did the same for the prior sample and instead of 6 bacteria, we had 18. Four are in common with the above.
Focus on Probiotics ONLY
We have the most information on probiotics. We have the following sets of data to work from:
Clinical Studies: Certain probiotics are heavily studies, others are not. Often reporting of changes is on a few bacteria. Studies populations are small resulting in only strong associations.
KEGG Data on Metabolites and Enzymes: Complete coverage of all probiotic bacteria. Ignores epigenetics and related issues, i.e. we assume everything is “turned on”. Yeasts (i.e. aspergillus oryzae, Saccharomyces) not included.
Taxa R2 Site: Complete coverage of all probiotic bacteria and full taxonomy. Yeasts (i.e. aspergillus oryzae, Saccharomyces) not included.
Our goal is determine the bacteria probiotics that every diverse methods agree upon. That is the “consensus” or conservative Monte Carlo Model.
Experiment R2 approach
I went to Microbiome Taxa R2 Site to see if there are any probiotics that would be suggested based on these. I have only listed those currently available (not pending)
For Erysipelotrichia, Erysipelotrichales, Erysipelotrichaceae
It appears that E.Coli Probiotics are the only ones with a slim chance of reducing (Mutaflor, SymbioFlor-2) but that they appear adverse to the low bacteria above.
Clinical Studies
I checked probiotics and found the top 2 were Bifidobacterium and thus should be avoided? Why do I trust R2 over clinical studies… simple — clinical studies are sparse for data. Just bits and pieces of the puzzle. R2 is far more complete.
And then Experimental Using Metabolites also had them on the take list. So we have the three key probiotics that are available retail with consensus for all of the above methods.
Other Suggestions
I am going to start with the Food Menu Planner. This look at some 111 nutrients identified and then at what various foods contain.
The site presents all recent results
The simplest way is to just click the “Quick” lists. It uses food nutrients databases from all over the world, so some items are likely not on your local café menu.
My general impression is try to get a single course of each of the two specific antibiotics above (after each course, do probiotics, then the next course, followed by a different probiotic). I see that Rye is positive and wheat is negative… so we will likely be eating 100% rye bread likely with liver paste (that is one of my favorite foods by the way!) and Pizza (light on the cheese).
Remember these are suggestions — not a protocol. suggestions are items you should take a bit more of or a bit less of! The goal is to persuade the microbiome to shift in the right direction. I have only highlighted items of interest, a review of the details should always be done.
Looping back to Brain Fog — typically caused by excessive d-lactic acid. I have several past post on this issue:
Checking your microbiome tree, I see high levels of Veillonella but with a gotcha, Veillonella is mainly Veillonella montpellierensis (99%ile), a novel species. 1/6 of studies on pubmed has this species causing issues like Polymicrobial bacteremia with little known about it. It is not listed on R2 site for Veillonella.
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.
For a time, the answer to this question was straightforward: uBiome.com was the go-to choice for microbiome testing. However, uBiome went out of business in 2019 following legal and regulatory issues, including an FBI investigation and bankruptcy13. As a result, uBiome is no longer an option.
Today, for most individuals—especially those not working directly with a specialist—the best available test is offered by BiomeSight.com (There is a discount code “MICRO” that may still be available). Unlike many other platforms, BiomeSight allows users full access to their raw data, supports data uploads from other providers, and offers advanced tools for those who want to explore their results in depth. Critically, they have the easiest transfer of data to Microbiome Prescription.
The reasons for this recommendation are detailed below.
Declaration of Interests
I do not sell any products or offer any services, nor do I have any financial interest in any microbiome testing companies.
My experience with microbiome dysbiosis, both personally and within my family, motivated me to develop Microbiome Prescription. Initially, I wrote code for my own use on my personal computer. Over time, I transitioned the platform to the web so that it could be accessible to everyone.
I offer data on Microbiome Prescription freely to individuals out of compassion for the suffering of others and as part of religious obligation-duty. Feel not! I will not preach.
An extreme shortage of expertise
The typical medical doctor or naturopath receives surprisingly little training on how to influence or manipulate the microbiome—often less than what you could learn from a focused day of watching YouTube videos. Most of their education is based on memorizing standard treatment protocols, which they then apply to patients in a “cookbook” fashion. For example:
If a patient has Crohn’s disease, prescribe these drugs.
If someone struggles with insomnia, suggest these herbs.
The core issue is that treatments are usually prescribed based on the diagnosis alone, rather than being tailored to the individual’s unique microbiome. In effect, practitioners are relying on broad stereotypes rather than personalized care.
Many healthcare practitioners have limited familiarity with the vast diversity of gut bacteria, often focusing narrowly on well-known genera like Lactobacillus and Bifidobacterium due to gaps in training and the complexity of microbiome science. Practitioners will often focus on these because they know no better. They do not know the significance of high Sphingobacterium bambusae or Dolichospermum curvum.
First Question: Who will interpret the results?
There are common paths that people take:
The lab themselves.
If they sell any product, there is a huge conflict of interest between their financial health and your personal health!
Often their suggestions are based off trolling the internet or using existing AI search engines
Most AI is prone to hallucinations
Often they may be based on a single study found on the US National Library of Medicine
I know one startup in this area that has a team of 6 Ph.D.s (or candidates) building a superior database than what Microbiome Prescription gas. They are barely 10% done. That is a very significant cost and needs significant financial backing to do. It requires a strongcommitment to not do a “good enough” to keep investors happy.
Word of warning: uBiome.com was one of the earliest labs and dominated the market. They filed for bankruptcy, IMHO due to investor pressure to turn a profit. Many labs keep expenses to the minimum and do not do due-diligence.
The healthcare practitioners that you are using and a specific test that they recommend. This assumes that they have been trained on the test. Buyer Beware: many will keep to cook book recipes.
You will go off and find a consultant to interpret the test. Conceptually that sound wise. The problem is that there are so many tests out there and finding someone with experience and successful track record is likely very hard.
You take ownership and education of the issue using selective resources, for example Microbiome Prescription free site. You should review your plans with your regular medical professional to insure no clear adverse issues with you medical history. In short, you are not asking them to develop a plan — you are asking them to make sure this plan is reasonable with no known health risks.
Amount of information in Test Result
The table below gives the possible test types. Typically:
qPCR gives 30-100 bacteria
16S gives 600 bacteria
Shotgun gives 6000 bacteria
IMHO 16S is the best give what we know about how to change the microbiome.
Method
What It Measures
Strengths
Limitations
16S rRNA Sequencing
Bacterial taxonomy (genus level)
Cost-effective, widely used
Limited resolution, misses non-bacteria
Shotgun Metagenomics
All DNA (all microbes, functions)
High resolution, functional insights
Expensive, complex analysis
qPCR
Specific DNA targets
Rapid, quantitative, targeted
Limited scope, requires known targets
Culture-Based
Viable, culturable microbes
Functional studies, viability assessment
Misses unculturable microbes
Metatranscriptomics
Active gene expression (RNA)
Reveals microbial activity
Technically demanding, expensive
Metaproteomics
Proteins produced
Functional protein insights
Complex, needs advanced equipment
Metabolomics
Metabolites (small molecules)
Functional metabolic readout
Source of metabolites often ambiguous
Specialized Sampling
Microbes from specific locations
Spatial resolution, targeted sampling
Invasive, technically challenging
What test gives the most actionable items using Microbiome Prescription database and expert system?
As a statistician, sample size determines the ability to detect patterns, make forecasts and thus identify the key bacteria connected to your symptoms.
BiomeSight has 308 Symptoms – thus we know which bacteria of the 600 to focus on
OmbreLabs/Thryve has 86 Symptoms
uBiome has 67 symptoms
There are a few things that 16s does not detect which shotgun does (virus, phages, antibiotic resistance etc). A small number of people may need that information … unfortunately, the ability to detect which bacteria are responsible for your symptoms is lost.
For more background, you may want to watch this video.
This recent discussion with a Long COVID patient that used Biomesight and the data from Microbiome Prescription may be helpful.
Fecal Transplants has shown the obesity is very microbiome controlled. With no change of diet, a lean mouse with a fecal matter transplant from a fat mouse, became fat.
Like the song about White Rabbit by Jefferson Airplane (referencing Alice in Wonderland book), we need to make sure we take the right (probiotic) pill.
One pill makes you larger And one pill makes you small And the ones that mother gives you Don’t do anything at all
Severity of Issue
Human Studies
When used as single-strain, all probiotic interventions showed positive effects in decreasing body weight, BMI, waist circumference, body fat mass or fat percentage. These strains belonged to the genera Lactobacillus (L. rhamnosus CGMCC1.3724 (LPR) [25], L. gasseri BNR17 [38], L.gasseri SBT2055 [27], L. sakei CJLS03 [46] and L. plantarum Dad-13 [49]), Bifidobacterium (B. lactis Bb-12 [36], B. animalis ssp. Lactis 420 (B420) [51], B. animalis CECT8145 [39]) and Pediococcus (Pediococcus pentosaceus LP28 [34])…. a maximum dose of 5 × 1010 (50 BCFU)
many of the studies the probiotic/synbiotic intervention was accompanied by dietary or physical activity interventions, which may have hidden the real effect of the probiotic strain(s) used.
Probiotic supplementation may also cause weight gain. Jones et al. [122] conducted trial in 19 obese adolescents, administering three packets per day of a mixture probiotics (L. acidophilus BA05, L. plantarum BP06, L. paracasei BP07, L. delbrueckii subsp. bulgaricus BD08, B. breve BB02, B. longum BL03, B. infantis BI04, and S. thermophilus BT01) for 16 weeks. Compared to placebo, observed a statistically significant increase in body weight in people using VLS#3.
“The case from Rhode Island (or mom gets fecal transplant and gains weight).. When Stacy returned to clinic 16 months later she had gone from 136 pounds to 170 pounds” [2015]
From Perplexity: “Systematic reviews and meta-analyses highlight that combinations of various Bifidobacterium and Lactobacillus strains tend to be more effective than single-strain probiotics for reducing body weight, BMI, waist circumference, and body fat mass”
Vet Studies
These appear to lack dietary or physical activity interventions and thus may be preferred combinations.
My model is simple, events pushed someone into a obesity dysbiosis where it become “stuck”. The dysbiosis has a distinct metabolite mixture that alters the human brain to prefer foods that feeds the dysbiosis. The metabolites results in higher efficiency of fat and protein absorption which the body then stores.
If you can alter the bacteria (and thus metabolites) to less efficiency then weight loss may occur naturally. There is some evidence that this may be possible with appropriate probiotics.
Without dietary or physical activity interventions, I believe that the Vet/Dog Studies probiotic pairs are likely the best (study evidence based) with the suggestions from the above exploration being very reasonable.
I have personally tried those combination and slowly loss weight with no clear dietary or physical activity interventions. The source that I used (I trust Maple Life Science for being correctly identified and fresh from the fermentation vat): Click to go to ordering page [No financial interests].
I’m excited to share the launch of R² Microbiome Prescription (https://R2.MicrobiomePrescription.com), a platform dedicated to unraveling bacterial associations in the microbiome. The name “R²” reflects the Coefficient of determination—a statistical measure showing how strongly one variable (like one bacterial presence) correlates with another (e.g., a different bacteria presence). Think of it like income and spending: as salary rises or falls, spending often follows, though this doesn’t prove salary causes spending changes.
Why focus on associations? While correlation ≠ causation, I lean toward the idea that bacterial relationships in the gut often hint at underlying causal mechanisms. For instance, one microbe’s metabolites might directly feed or inhibit another, creating a metabolic chain reaction. With thousands of metabolites (and counting!) interacting in complex ways, pinpointing exact cause-effect relationships is like solving a 4D puzzle.
The challenge ahead Current research is racing to map these connections, but the sheer scale of interactions—combined with individual variability—makes definitive conclusions tough. My goal with R² is to aggregate data, spotlight patterns, and inspire deeper exploration into how these microbes might shape health.
Feel free to explore the site and join the conversation!
Keep It Simple Statistics (KISS)
Over the years, I’ve experimented with various methods to uncover meaningful bacterial associations—a journey that’s been both challenging and gradual. After much trial and error, I finally developed a methodology that consistently delivers reliable results, which I’ve now used to populate the new site.
A turning point came during discussions with Precision Biome. They encouraged me to apply this approach to their extensive dataset of shotgun sequencing samples from healthy individuals. This collaboration provided the perfect opportunity to put my method to the test on a large scale—and ultimately led to the creation of the site you see today.
Getting R2 by Percentages
Here’s an example of a clear association between two taxa using percentages of each in samples: R² = 0.6971 and Slope = 0.3563.
An R² value of 0.6971 indicates that nearly 70% of the variation in one taxon’s abundance can be explained by changes in the other, reflecting a strong linear relationship between them. The slope of 0.3563 shows the rate at which one taxon’s abundance changes in relation to the other—specifically, for every unit increase in one, the other increases by about 0.36 units.
This kind of result highlights how statistical measures like R² and slope help quantify and visualize associations within complex microbiome data.
The relationship is typically not so linear. This was a specific example picked for illustration.
[BELOW] Applying a monotonic increasing transformation like the square root to the data changes the association metrics: in this case, R² drops to 0.5112 and the slope increases to 0.5405, indicating a weaker linear relationship compared to the original analysis. This reduction in R² means the transformed data explains less of the variance between the two taxa, making the association less robust than before.
Square root and similar transformations are commonly used in microbiome studies to stabilize variance, handle skewness, and address issues like zero-inflation and compositionality in the data. However, these adjustments can sometimes reduce the strength of observed associations, as seen here, because they alter the data’s distribution and the nature of relationships. Our goal is not to get a linear relationship, rather to get the best R² while preserving the nature of the data (i.e. all transforms should be monotonic increasing transformation)
[BELOW] Applying a different monotonic increasing transformation, such as taking the logarithm of the data, yields R² = 0.6596 and Slope = 1.0046. This result is an improvement over the square root transformation, as indicated by the higher R² value; but less than the first linear one.
A logarithmic transformation is often used to manage skewed data and compress large ranges, making relationships more linear and easier to interpret. In this case, the higher R² suggests that the log transformation preserves more of the association between the two taxa compared to the square root transformation. The slope of 1.0046 indicates a nearly one-to-one relationship between the log-transformed values of the two taxa.
[BELOW] We can also experiment with other transformations to see how they affect the association. The more complex transformation that I prefer yields R² = 0.7082 and Slope = 0.5015.
This R² is the highest among the transformations tested so far, indicating that this method captures the relationship between the two taxa most effectively. The slope of 0.5015 shows a moderate rate of change between the transformed values of the taxa.
This example highlights how choosing the right transformation can significantly enhance our ability to detect and quantify associations within microbiome data. By carefully selecting and testing different approaches, we can better reveal the underlying patterns and relationships that might otherwise remain hidden.
R2 is the amount of influence, slope indicate direction of influence
It’s important to avoid combining R² and slope by multiplying them together. This is not a standard or meaningful statistic in regression analysis and can easily lead to misinterpretation. For instance, a high slope with a low R² suggests that while changes are dramatic when they happen, the overall model does not explain much of the data’s variance.
Remember:
Slope tells you the direction and rate of change (whether the relationship increases or decreases).
R² indicates how much of the variation in one variable can be explained by the other (the strength of the association).
Each metric provides valuable information on its own, but their product does not offer any additional insight and can actually be misleading.
Criteria for selecting transformation
For any given pair of bacteria, it’s technically possible to find a data transformation that maximizes the R² value for that specific pair. However, with 5,000 taxa, there are over 25 million possible pairs (5,000 × 5,000), making it an overwhelming and impractical task to optimize each one individually.
Ideally, the goal is to identify a single transformation that performs well across both low and high R² values for all pairs. Discovering such a transformation was a significant part of my journey. To keep the analysis manageable, I focused only on bacteria present in at least 0.3% (0.003) of the samples, which helped reduce the number of pairs to a more reasonable level.
I’ve found a favorite transformation—demonstrated in the last chart above—that I’m particularly satisfied with. If I discover an even better transformation in the future, I simply rerun the analysis and select the one that yields the highest R² values. This approach ensures that the associations presented are as strong and meaningful as possible.
A practical alternative is to run regressions with multiple transformations and picked the transformation for each bacteria pair that has the highest R². I would suggest some of the following transformation be tried:
linear function with positive slope
cubic function
square root function (converting percentage to 0 – 1 range)
exponential function with base e
natural logarithm
logistic function
general exponential function
x−sin(x)
x/(log(x)
This will increase the computations from 25 million to 250 million. Remember computer resources are cheap today (say he would started doing statistics using a HP-21 calculator and WatFor). And fast using parallelism (multiple cores and threads).
Usage With Probiotics
Suppose your Bacteroidota levels are too high and you’re considering which Bifidobacterium probiotic to take. If you turn to published studies, you’ll notice that most research focuses on individual probiotic strains, making it difficult to directly compare their effects. Instead, let’s examine the comparative data in the charts below to help guide your choice.
The harsh reality is that we cannot trust most bacteria identification with the microbiome and with probiotics.
Precision Biome (who supplied the dataset) are doing things what I deem the right way:
They are using the same pipeline that the above data came from (no ambiguity in bacteria identification) for client samples that they received.
They are working with an EU probiotic manufacturer directly.
The contents of the probiotics is also verified with the same pipeline
The probiotics come directly from the factory and are not stored in questionable environments before being delivered to the client
They intend to use the data from this site in identifying the best probiotics for each client
This is (IMHO) the ideal trifecta for clinical use of the microbiome. It is the strategy that I hope responsible microbiome testing firms move to.
Quick Test
Some one asked about probiotics that reduces Campylobacter. The page shows known (and pending) probiotics. We found none listed to reduce it. We did find some that increases it.
Bacillus is a genus and covers many species — so difficult to evaluate.
Not as good as actual studies? — but reasonable for sparse data
Critical Evaluation of Microbiome Study Limitations & Proposed Solutions
Key Factors Impacting Credibility
Current microbiome research faces significant validity challenges due to three core assumptions:
Taxonomic Accuracy of 16S rRNA Sequencing
The 16S pipeline (used in >80% of studies) has notable limitations: ▪ Struggles with species/strain-level resolution ▪ Database gaps create misclassification risks ▪ PCR amplification biases skew abundance data
Probiotic Product Integrity
Studies often assume supplements match label claims, yet: ▪ DNA analyses show 30-50% mislabeling in commercial probiotics ▪ Viability issues occur in 40% of products (esp. non-refrigerated) ▪ Strain-specific effects are frequently overlooked
Population Generalizability
Most trials use narrow cohorts: ▪ 78% of probiotic studies focus on healthy adults ▪ Gut ecosystem dynamics differ in:
Chronic disease states
Antibiotic-treated individuals
Elderly/immunocompromised populations
I prefer the trifecta approach over blind faith that all of the above assumptions are true. Blind faith is reasonable when you have no better data — the odds are that it will be better than no data.
Illustration of a different pair
With our special monotonic transformation: R2 =0.23
This is a thought experiment transformed into an implementation for people to experiment with.
In doing educational reviews of a variety of samples, I came across a person whose progressed had slowed. In trying to understand why [The ME/CFS Quest for Health], I looked at metabolites level between his current sample and previous sample. To my surprise, the highest ones (highest percentile) had barely budgeted.
I looked at the prior Dec 24 sample and compare the KEGG Compounds to the current sample starting with the highest percentile ones:
While the bacteria changed, the extreme metabolites remained high but with a few reducing. There is a potential to generate suggestions based on these KEGG compounds — a little messy and definitely pushing inference into new turf.
An Idea
I asked Perplexity.ai on how to reduce a few. A typical response is shown below
On MicrobiomePrescription.com, the suggestion algorithm works solely off the bacteria that is reported by the microbiome test. This is done by using facts harvested from US National Library of Medicine studies. There are no (or likely extremely few) studies dealing with diet and metabolites.
The key phrase is reported by. We know that reporting is not standardized and often using only 16s.
Idea!
Current logic on MicrobiomePrescription.com is bacteria => suggestion impact. What if we add another approach: metabolite => normalized bacteria distribution => suggestions. We want this to have less randomness than 16s. The folks at PrecisionBiome.Eu shared 1000 shotgun results from healthy individuals with me so I could construct a normalized bacteria distribution model. From this model, I computed metabolites using data from KEGG: Kyoto Encyclopedia of Genes and Genomes and ended up with a facts table consisting of:
Metabolite
Suggestion / Modifier
Estimated Impact
The metabolite is identified by KEGG ID.
Implementation
Since the microbiome and its metabolites are very interconnected and interact with each other. I decided that looking at the top and bottom 5-10%ile (i.e. those with a percentile ranking of 90-95%ile or higher, a percentile of 10-5%ile or lower) was a reasonable approach. There is a little trust that the central limit theorem will generate reasonable results and allow metagenomics to be directly used for getting suggestions.
On the [Research Features] tab, this panel has been added:
This produces a report listing the Metabolites targeted (High and/or Low) and then Suggestions
Observation
To me, what I found very interesting is that there are a few that are very high in impact with rapid drop off. This means there are only a few critical items to add to the general bacteria-based suggestions.
Hello, I’m sorry to message you privately, but I’m reaching out for help regarding my 15-year-old daughter, who has been homebound with ME/CFS for 2.5 years since contracting COVID in 2022. I came across your story on Facebook, and I felt truly inspired by how you managed to overcome ME/CFS by working with your microbiome. We are currently trying to follow a similar path.
We’ve recently done a Biomesight 16S test for her. As expected, it showed typical deficiencies, like a lack of Lactobacillus bacteria, along with an overgrowth of sulfate-reducing bacteria (possibly SIBO). Since we’re unsure how best to approach this dysbiosis, we sought the help of a microbiome specialist through Viola Sampson in the UK. She recommended lactulose, Lactobacillus reuteri, Lactobacillus rhamnosus, Lactobacillus plantarum, Bifidobacterium breve, along with Allicin and Goulds tincture from Australia. We’re just beginning this treatment, so it’s hard to say much about progress yet. We’ve started with Lactobacillus rhamnosus, plantarum, and breve, and she’s doing well with these so far.Honestly, I’m a bit concerned about these Lactobacillus bacteria because I read somewhere that all people with ME/CFS have some degree of lactic acidosis, so I’m worried that these probiotics might produce even more lactic acid. When I brought this up with my practitioner, she wasn’t aware of it.
I also uploaded a Biomesight test of my daughter to your Microbiome Prescription page, but I noticed that your site has somewhat opposite recommendations for her microbiome, such as advising against lactulose. To be honest, I’m struggling to navigate your page, and it’s a shame because I truly want to follow the recommendations accurately. I was hoping to identify which specific antibiotics or probiotics might be the best fit for my daughter’s case, but I’m not sure how to interpret that information from your site.
Currently, my daughter is mainly dealing with POTS (Postural Orthostatic Tachycardia), histamine intolerance, chronic fatigue, anxiety and panic attacks, and digestive issues. She’s become highly sensitive to various foods and medications, and it all points towards dysautonomia. Although she’s taking many supplements, she reacts to some, like iron supplements, which I suspect might be due to certain bacteria that feed on iron. She has many vitamin deficiencies, yet we can’t supplement effectively due to these reactions. It’s so difficult to manage.
I apologize for the long message, but I wanted to be as clear as possible about her situation. I’d be incredibly grateful if you could review her Microbiome Prescription and offer any insights on what stands out in her microbiome and where we might start. I’m also curious about any thoughts on the potential use of antibiotics or probiotics, as our microbiome practitioner is generally against antibiotics, though I know some people with ME/CFS have found success with a well-planned antibiotic approach.
Here, I am including the link to our microbiome analysis from Microbiome Prescription.
Analysis
First, disagreement between sites is well known and explained here: Why sites suggestions disagree on the same data. Microbiome Prescription tuned it’s advice by doing cross-validation for several conditions, for example: Cross Validation of AI Suggestions for Nonalcoholic Fatty Liver Disease. ME/CFS was the first explicit studies done. To the best of my knowledge, no other microbiome site has done cross-validations of their suggestions and been public in showing results.
Individual practitioners are hard to evaluate because they often find patterns that works for some people by trial and error. It is a rare practitioners that can provide documentation on their suggestions.
Quick boot strap
Long COVID is one condition that has a built in cross validation list of suggestions. This is on [Old Ui] / [Changing Microbiome]. POTS is not currently on the list because of insufficient studies.
This identified the following bacteria as being probable according to the published literature. The number of cross reference numbers after each item, indicate the number of studies For example Ruminococcus – genus : Low was reported in 4 studies.
The suggestions (based on microbiome shifts cross reference with substance that improved ME/CFS from studies are below. The number of cross reference numbers after each item, indicate the number of studies – as above. This leads to the best suggestions being the ones with the most cross reference. Thus:
Magnesium supplements – 6 studies
Vitamin B9 – 6 studies
Coenzyme Q10 – 6 studies
Far infrared Sauna – 4 studies — as a personal note, we purchased a small one at Costco and use it regularly as preventative.
Vitamin B1 – 3 studies
Omega-3 – 3 studies
Ribose – 3 studies
licorice – 3 studies — we usually use Spezzatina and just suck on them
carnitine Amino Acid – 3 studies
Melatonin – 3 studies
Selenium supplement – 3 studies
This is a significant list and I noticed that none of these were suggested by Viola Sampson despite published literature saying they help.
My suggestion would be to add one of these every three days, noting any changes that results. For dosages see Dosages for Supplements, start low and work up. The above will take a little over a month. All of these items can be taken continuously and together.
Probiotics
Probiotics are a popular “cure-all” which in some cases help and in other cases hurt. For example, lactobacillus probiotics often will increase brain fog.
Looking at probiotic with positive values, most are actually hard to obtain. For example Kefibios is only sold in Italy. Mutaflor in only a few countries. Of the choices, I would try Mutaflor after adding in the items above — but be warned, it may trigger severe die-off.
Top items
The list below are other things that likely have never been studied for ME/CFS but should have significant impact on the bacteria shifts.
On the other side, the following should be avoided:
Food Site
Going to https://food.microbiomeprescription.com/ and entering your login token will show the nutrients computed to help most. Iron supplements or food high in iron is at the top; for example thyme, basil, and my favorite Caterpillar, roasted ;-). Both herbs have positive recommendations.
The second one is found in cranberry (a suitable seasonal food) and raw Olive. The third one is found in maize, rye and Hard wheat, semolina. HOWEVER, none of these are recommended in the list of suggestions. I usually cross reference the two for safety.
Next Steps
I would continue with additional suggestions (1 and 2 studies) at the same pace. Two weeks after the last one was added, do another microbiome test (same firm of course) and get back to me for a follow up analysis if needed.
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
Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
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)
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