The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.
The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.
I’m writing because 8 months ago I got Covid and since then I have been very sick. My main symptoms are fatigue, exercise intolerance/pem, many histamine issues slash food intolerances, upset GI with alternating diarrhea and constipation, weight loss, headaches, anxiety and depression, panic attacks….the list goes on.
I know something is wrong with my gut but I’m having trouble fixing it because my diet is so limited and I have so many reactions to things. I know a limited diet is not good but I also feel so much worse when I eat certain foods especially carbs. I think I might have SIBO. I uploaded my profile to you site and would love any help. I’m giving permission to share.
Analysis
This person has added symptoms and we see a good match of bacteria shifts to reported symptoms
Further down, we have many more matches
Immune Manifestations: new food sensitivities ✅ – [86.7%]
We have 69 symptoms marked resulting in 44 bacteria flagged. This is common and shows that there is often bacteria overlap between symptoms. The other factor with symptoms is a person’s DNA.
The best suggestion is walnuts. Looking at the probiotics, I was not surprised at the top ones:
Why am I not surprised…. because my own post COVID symptoms cleared rapidly when I did high dosages of fresh Bifidobacterium (manufacture date was the month before). The top of the list is below.
On the avoid list are many items that appear related to carbs (fiber) — what this person reacts to
My take away for no known-risk probiotics are these items suggested
Various Bifidobacterium above were on the list too.
Foods
The “many histamine issues slash food intolerances” causes me to suggest looking at the foods suggested above, especially those that are not in a person’s typical diet. I.e. Walnuts, Acai, Burdock Root, Asparagus, Rye bread (100% – not wheat+rye mixture), Beets, papaya, etc.
But wait! Those are based on studies of those explicit foods. When we go to the associated food sites, we see 116 nutrients identified as to take or avoid
The top to take are:
And to avoid:
With a quick list of food to take:
And to Avoid
My Approach if this was me
I would see about getting a bottle of only Bifidobacterium species probiotic as soon as possible to try to kick start things (i.e. a local health food store, or online with quick delivery). There is a risk that there may be no living or barely living bacteria in this bottle (background). So fingers crossed. At the same time I would order bottles of the following (which may take 3-4 weeks to arrive). Direct links to Maple Life Science’s Ebay site are linked below.
Those prices include shipping, so $44.00 total (which may be close to the price of the local purchase bottle). They ship worldwide! Why this source? My experience has been very good with them. Manufacture date is usually within a few weeks of shipping. Everyone that I have tried has had “kick”, that is, I see changes of stools (shape, size, frequency) and changes of fart smells within days of starting. I would start with just one, one capsule only and then work up to 5/day. Once the first bottle is empty, start the next bottle with the same pattern.
Next, I will try to incorporate as many of the above things — especially items that are not usually in your diet. With that, check the to avoid and reduce as much as is practical.
After 2-3 months, do another sample with the same firm — things are expected to change significantly and a new set of suggestions should occur.
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.
Born premature 25 weeks ivf pregnancy on tons of hormones for myself. Vaccines for her. Can’t poop on her own. Gi maps test showed clostridia, strep, entero faec etc. Mycotox urine kit showed 2 most toxic molds citrinin ocratoxin a, fatty acid oxidation issues, methylation issues, mthfr, double slow comt gene, reactions to most foods (behaviors),restless sleep. Autism diagnosis. She is 6 years old now.
Analysis
I always approach under 15 y.o. with caution because they are very understudied, and the existing studies show major changes from adults.
It will be just a “give me suggestions” plus some suggestions that are typical for autism. In general, I try to cross validate the suggestions with the current literature on Autism. Example: Go to https://pubmed.ncbi.nlm.nih.gov/, enter the item and autism and see if there is any literature.
In this case, one result was returned (a bit of a heavy and twisted read).
“luteolin and diosmin inhibited neuronal JAK2/STAT3 phosphorylation both in vitro and in vivo following IL-6 challenge as well as significantly diminishing behavioral deficits in social interaction. Importantly, our results showed that diosmin (10mg/kgday) was able to block the STAT3 signal pathway; significantly opposing MIA-induced abnormal behavior and neuropathological abnormalities in MIA/adult offspring.”
I have done a few, but the reader should check each one. Items that cross-validate should be choice #1, other items as a secondary choice.
Probiotics
bifidobacterium infantis,(probiotics) was high on the list, which is to be expected with autism. “participants on both treatments saw a reduction in the frequency of certain GI symptoms, as well as reduced occurrence of particular aberrant behaviors.” [2019]
lactobacillus reuteri (probiotics) – “However, L. reuteri combination yields significant improvements in social functioning [in autism] that generalized across different measure” [2023]
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.
Let us start with a more real world example: Dogs.
Take a vaccine against Rabies tested on dogs in a pound (Canis Lupis). It was successful. Inference means that there is a high probability that it would work for Welsh Pembroke Corgis — although there was none in the pound. This is a child inference.
There is a high probability that this vaccine would also work for the Genus Canis, which include wild dogs such as Jackals (Africa), Wolves, Coyote and Dingos (Australia). This is a parent inference.
There is a reasonable probability that this vaccine would also work for the Family Canidea which includes Foxes. This is a grandparent inference.
The key thing to remember is that each layer of the taxonomy hierarchy has significant DNA shared with those above and below. It is likely (not guaranteed) that the layer above or below will respond similarly.
In the last 20 years, different bacteria has been sequenced resulting in a more correct hierarchy based on DNA. For example, Lactocaseibacillus casei was originally Bacillus casei, then Lactobacillus casei. A short table of a few others is shown below.
Current name
New name
Lactobacillus casei
Lacticaseibacillus casei
Lactobacillus paracasei
Lacticaseibacillus paracasei
Lactobacillus rhamnosus
Lacticaseibacillus rhamnosus
Lactobacillus plantarum
Lactiplantibacillus plantarum
Lactobacillus brevis
Levilactobacillus brevis
Lactobacillus salivarius
Ligilactobacillus salivarius
Lactobacillus fermentum
Limosilactobacillus fermentum
Lactobacillus reuteri
Limosilactobacillus reuteri
We do not do sibling inference. Studies on Limosilactobacillus fermentum are not inferred to Limosilactobacillus reuteri, we do parent inference to Limosilactobacillus with no inference to Levilactobacillus, Lactiplantibacillus, Lactobacillus, nor Lacticaseibacillus (i.e. uncle inferences).
The recent reorganization of the bacteria hierarchy based on DNA makes inferences more probable.
Avoiding Inferences
It is technically possible to avoid inferences for some bacteria. For other bacteria, for example Propionibacterium freudenreichii subsp. shermanii, you may find just one study and that decreases only — when you want to increase it! Looking at Propionibacterium freudenreichii and inferences, you have over thirty studies. We do not know if these substances will work. There is a good probability that it may work
“Who you gonna Call? Call Sparse Data Busters!”
Using inference allows us to get suggestions with a reasonable chance of working. We give direct citations a high weight. We give inferences a diminished weight.
Microbiome Prescription works off probability estimators when using inference.
It’s your choice on Microbiome Prescription
Using inference is the user’s choice. You may agree or disagree on inference — if you disagree than please be consistent and only use the strains of probiotics cited in studies.
First things first — no vaccination, herb, supplement is absolutely safe for every person. To get approved for use, a vaccinated persons must have better outcomes (as a group) than an unvaccinated person. I am of the early vaccinated generation. A class mate got Polio as a child recovered, and then later in life developed Post-Polio syndrome. I got the Polio shots and was fine. A vaccine for whopping cough was not available when I was born, I got it and suffered some brain damage to my speech center. I once met someone my age that suffered major brain damage after whopping cough. Taking a shot for whopping cough has much less risk of life long adverse effects than getting it. I am pro-vaccination, being of the generation that saw disease after disease ripple through the population causing much harm. I do not want those times to return…..
Your Microbiome determines how effective the Vaccine is
“the abundance of Prevotella copri and two Megamonas species were enriched in individuals with fewer adverse events” [2021]
“Bifidobacterium adolescentis was enriched in high-responders while Bacteroides vulgatus, Bacteroides thetaiotaomicron and Ruminococcus gnavus were more abundant in low-responders ” [2021]
“At 1 month after second dose of vaccination, seven species including B. adolescentis, A. equolifaciens and A. celatus were more abundant whereas B. vulgatus remained less abundant in high responders” [2021]
Lactobacillaceae, Rumen family, and Clostridium bacteria were associated with vaccine efficacy [2021]
The abundance of Clostridium and Lactonemae was positively correlated with vaccine efficacy [2020]
“Of the species altered following vaccination, 79.4% and 42.0% in the CoronaVac and BNT162b2 groups, respectively, recovered at 6 months.” [2023]
“Bilophila abundance was associated with better serological response, while Streptococcus was associated with poorer response.'[2023]
“vaccination can also change the composition of the gut microbiome. We found that 1 month after a second vaccine dose, the relative abundances of Bacteroides caccae increased significantly” [2023]
“This study demonstrated a statistically significant reduction in alpha diversity and a shift in gut microbiota composition following vaccination, characterised by reductions in Actinobacteriota, Blautia, Dorea, Adlercreutzia, Asacchaobacter, Coprococcus, Streptococcus, Collinsella and Ruminococcus spp and an increase in Bacteroides cacaae and Alistipes shahii. ” [2022]
“Bifidobacterium and Faecalibacterium appeared to be microbial markers of individuals with higher spike IgG titers, while Cloacibacillus was associated with low spike IgG titers. ” [2023]
“vaccine responders were associated with an increased abundance of Streptococcus Bovis and decreased abundance of Bacteroides phylum;’ [2017]
“Responders were associated with increased Streptococcus Bovis abundance and decreased Bacteroides phylum abundance” [2018]
“Proteus and Egella abundance were positively correlated with vaccine efficacy, and Fusobacterium and Enterobacteriaceae were negatively correlated with vaccine efficacy” [2020]
“The abundance of Bifidobacterium longum subspecies was positively correlated ; Clostridium, Enterobacteriaceae, and Pseudomonas abundance were inversely correlated with vaccine efficacy [2019]
The Specific Vaccine and Your Microbiome
It is possible that the microbiome alteration caused by a vaccination will interact with an existing microbiome dysbiosis and cause adverse effects. The adverse effect could move the microbiome into a stable and more severe dysbiosis — the claims of a child developing autism after a vaccination is viable. The vaccination may be just a contributing cause to an existing disposition. The literature below suggests that there is no statistically significant evidence supporting some people beliefs.
A 2024 study found “Rates of early childhood vaccine receipt did not differ between autistic and non-autistic cohorts.” as well as “Notice of Retraction: Measles, Mumps, Rubella Vaccination and Autism” indicating early studies claiming association was questionable, if not outright ideological. “At the same time, other environmental factors, such as vaccination, maternal smoking, or alcohol consumption, are not linked to the risk of ASD. ” [2024]
I have severe /very severe ME/CFS and have noticed partially dramatic changes (although short lived) when taking a probiotic, especially Myorisan [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:
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.
Food avoid list is high in food containing fiber (in agreement with diet style)
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
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.
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)
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
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.
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.
Some supplements that I have been taking since the last test:
Tetracycline
Clove
Holy basil (Neem)
Augmentin + Bromelain
Grapefruit seed extract
Monolaurin
Apple peel powder
Thyme
My symptoms:
Still get the red nose (some form of rosacea).
Still feel fatigued (both physically and mentally). But it is better than before.
Feeling stressed. But it is better than before.
Brain fog.
Bloated.
Lots of gas – I fart and burps a lot.
Issues with allergies (itching eyes, stuffed nose and itchy skin)
Video
Analysis
We will start with the high-level comparison. Note that some numbers will change with time. There are no major changes. Since the latest sample reports 20% more bacteria, many counts are expected to be 20% higher – for example: Thorne Ranges: old: 230 + 20% = 276, with the seen count being 253 (so an apparent improvement although the number went up)
Criteria
9/2/2024
1/22/2024
9/12/2023
2/22/2023
8/11/2022
3/25/2022
12/3/2021
8/31/2021
Lab Read Quality
9.1
7.9
3.5
9.7
5.5
6.2
3.6
7.8
Outside Range from GanzImmun Diagostics
16
16
16
15
15
17
17
20
Outside Range from Lab Teletest
23
20
20
24
24
22
22
25
Outside Range from Medivere
14
16
16
15
15
15
15
19
Outside Range from Metagenomics
6
7
7
9
9
7
7
8
Outside Range from Microba Co-Biome
3
2
2
7
7
1
1
1
Outside Range from MyBioma
6
5
5
7
7
7
7
8
Outside Range from Nirvana/CosmosId
21
20
20
23
23
18
18
21
Outside Range from Thorne (20/80%ile)
253
230
198
223
223
217
217
246
Outside Range from XenoGene
32
32
24
32
32
36
36
39
Outside Lab Range (+/- 1.96SD)
12
5
15
10
11
9
9
14
Outside Box-Plot-Whiskers
48
52
56
42
36
42
59
42
Outside Kaltoft-Moldrup
113
123
70
139
56
78
59
140
Bacteria Reported By Lab
600
508
399
666
478
613
456
572
Bacteria Over 85%ile
48
52
Bacteria Under 15%ile
118
157
Pathogens
23
26
25
30
23
39
24
30
Condition Est. Over 85%ile
2
5
There is a new comparison table added that compares sets of symptoms bacteria for symptoms reported in either sample. This is a thought experiment on a different way of evaluating the microbiome, i.e. are symptom bacteria reducing. Remembering that we have 20% more bacteria reported, the improvement may be slightly under-reported.
Going Forward
My current preference is to use symptom associations suggestions with KEGG suggested suggestions. This assumes that the person has added their symptoms.
Using Entered symptoms
Since this person has access to antibiotics, I opted to include all classes of modifiers. We have 38 bacteria selected — a reasonable number
The suggests were a nice mixture for ME/CFS. Typically, I see the top being just antibiotics, in this case we have several probiotics there.
And suggested retail probiotics are:
Using Diagnosis and PubMed
Using a diagnosis provides less precise filtering compounded by different labs (with different identification of bacteria). If the person is using a lab that lacks a large number of annotated samples from that lab, then it is the best path.
The suggested path is to go down the list and pick the ones that has the highest value(s) that agrees with one or more of the diagnoses that you have.
In this case we have only 4 bacteria in the selection, so the suggestions will be likely more generic than specific.
There are no antibiotics in this list
The probiotic list is below. It has some similarities to the above list.
Using KEGG Derived Probiotics
This is an experimental approach that attempts to do a metagnòmia approach from the available data. We estimate which compounds are too high or too low. Then we match them to probiotics which produce or consumes them. Postbiotics can be used for items that are too low. There is no filtering of any type; we look at the entire microbiome.
The results are different — as to be expected. Why expected? The prior ways depended on studies being done what each probiotics bacterium does. Often there are no studies. This way uses the DNA/RNA sequences of everything and thus we do not need studies.
I usually focus on too low, with the assumption that a surplus will just be ignored or has less impact (i.e. starvation versus obesity) We can see where there is agreement between the lists.
This can be made more complex by using consensus / Monte Carlo Model
Reader Plan
Microbiome Prescription produces suggestions, the weights/priorities are the odds of causing a change and not the amount of change (there is simply no objective data to compute the amount). This reader did their own evaluation of what they felt comfortable with (excellent idea).
I have also bought 2 more tests so I will do them with max 3 months apart as you said in the video.
I came up with this protocol by using the “Beginner-Symptoms: Select bacteria connected with symptoms”:
Week 1-2: Gum arabic
Week 3-4: Monolarin (lauric acid)
Week 5-6: Psyllium
Week 7-8: Rosemary
Week 9-10: Parsley
Week 11-12: SymbioFlor-2
I found that I get best results from herbs, prebiotics and antibiotics. The only probiotic I’ve got good results from is Symbioflor 2 (an E.Coli probiotic) [Editor: E.Coli probiotics also worked best for me]
A lot of probiotics that I’ve tested I’ve got bad results from.
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.
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)
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.
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:
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( FactorVit B1 * AmountVit B1 +FactorVit B2 * AmountVit B2 + etc)
Amount often becomes a 1 or 0 (take or do not take) when there is no dosage related data. Factormodifier 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( FactorVit B1 * AmountVit 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.
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 “
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.
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