Intent and Purpose

Be yourself; Everyone else is already taken.

— Oscar Wilde.

I am a high function ASD person in the late 60’s. I am a data scientist, artificial intelligence engineer, former high school science teacher etc. Needless to say, the term autism or ASD was unknown while I was growing up. The classic delay in speech (I did not start talking until I was almost 9 y.o.) and other characteristics were ascribed to some form of brain damage. Three causes were speculated: forceps delivery, German Measles at 18 months and the medication that my mother was give to keep from miscarrying (she had 6 miscarriages before me).

Today, I know that I was high risk because my father was 44 when I was born (Parental Age at Conception and the Relationship with Severity of Autism Symptoms. 2019). My childhood was not fun, because I understood enough about my situation that I was in terror for most of it. The terror caused me to work hard and I found success in a very non-social activity: mathematics and mathematics competitions. I placed in the top 3 repeatedly in both my Province and in Canadian Mathematics Competitions. That’s enough of my story.

Purpose of this Blog

Over the last few years I have became focused (the typical uber focus of an ASD person) on the microbiome to deal with family health issues. My primary focus has been on myalgic encephalomyelitis on which I have written some 1300 posts here. Out of that, I developed an analysis site using reference site and citizen science site called Microbiome Prescription. I have also became active in a Facebook group The Gut Club: Stool Test Discussion Group. This group had resulted in contact with many mothers with autistic children. Needless to say, I have both empathy for the mother and for the children (been there myself before there was support!).

This site is very open to guest posts. I do request that they be well researched with links to source studies. I hate to be ‘anti-social’ and ignoring chat-board opinions and consensus — but what do you expect from someone with ASD? 😉

As interesting notes comes across my desktop, I will explore and attempt to write up posts on what we know today.

I will start this blog by copying across some blog posts that I have done on Autism elsewhere.

Suggested watching and subscribing to: https://www.youtube.com/c/AutismResearchCoalition

Help Needed to Improve Suggestions for Autism.

I am working with a startup PrecisionBiome.eu and create a demo report exploring one of the features they want to consider on their pending offering. The draft feature used brain trauma as a test case. This caused me to think of Autism.

The draft report is designed to be a document to be used by Medical Doctors and to educate them on the latest studies.

All data contributed will be freely available for personal use on Microbiome Prescription, in keeping with its open data policy.

Example Report

Bacteria Identification

The person’s sample is examined and compared to the literature

Validated Suggestions

There are suggestions that the literature reports that some people in studies improved taking.

Not Validated Suggestions

These are items that will improved the microbiome but do not have any studies for them being tested with brain trauma / Autism. Conceptually, some researchers should conduct trials with them.

How Can You Help

We need to get a comprehensive list of items that help autism. This means YOU DOING RESEARCH, FILL OUT A SPREADSHEET and then send to me Ken@lassesen

How to do it

Best Lab for Autism Microbiome?

Using the five methods described in Technical Note: The Four Winds of Microbiome Analysis, I ran these method on all of the data on the citizen science site of Microbiome Prescription testing for all symptoms that have been self-reported from users of Ombre Labs and Biomesight retail microbiome tests. The data from each lab was done is insolation (you cannot mix data from different processions flows, see The taxonomy nightmare before Christmas… for how the results from the same FASTQ files are reported by 4 different processing flows).

My criteria for deeming a genus significant was:

  • At least one method reported P < 0.01
  • At least two methods reported P < 0.05

The 2 @ P < 0.05 is a bit of shooting from the hip; I expect some correlation between methods but not sufficient to have that adjusted P value to be outside of the range 0.0025 and 0.01. Looking at the statistics on significant genus, we found the 2 @ P < 0.05 produce only a small contributions,

See Citizen Science Symptoms To Genus Special Studies

When I looked at associations for autism, I noticed a striking contrast between the two most common labs.

There are two possible causes:

  • Less annotated samples on Biomesight uploads
  • The algorithm being used on Biomesight is a poor match for the RNA used in 16s that are associated with autism (and other neurocognitive issues).

Looking at some other neurocognitive issues, we see the same pattern — Ombre identifies more significant genus.

How to Fix This Issue

The first issue may disappear if all biomesight samples with autism are annotated. HINT HINT

The second issue would be addressed by having the 16s FastQ files processed by OmbreLabs (At one time they offered that for free).

Example of Using

In the sample below, we see for Bifidobacterium that average amount is 2.6 x of the values of people without this symptom. The percentile rankings are 36% higher, and this genus is seen 3% more often.

Pending Work

Integrating this data with the algorithms on Microbiome Prescription to generate suggestions to reduce these shifts.

Also see Technical Note: Yield of Applying Different Statistical Methods for more information

Tool for getting suggestions for your specific Autism Symptoms

After I posted List of Bacteria significant for ME/CFS from the shared samples uploaded to Microbiome Prescription, several readers asked “How do I use this”. This person has a child with autism. This took me a few days to come up with, code and implement an answer.

I wanted this to go beyond just one condition because there is a huge variety of symptoms and co-morbidity seen with different conditions. After testing and tuning the algorithm, I am pleased with the current results.

The process is show below.

The Steps

  • Return to “My Profile”
  • A new button will appear

Clicking it will move to the page below. YOU MAY FIND THAT IT TAKES UP TO A MINUTE (We are doing a massive number of computation)

This will show a tree of the bacteria involved. The Species are under the genus they below to. In the example below we see the ENTIRE phylum that Bifidobacterium is in are low (none found) of 9 species whose presence would likely reduce your symptoms.


Elsewhere you may see highs with certain bacteria species desired to higher. Often the symptom key is at the species level.

At the bottom you will see a button to get suggestions

The next page shows the symptoms being targeted to and choices of what you want to consider.

Make any changes desired and click show suggestions

REMEMBER these are suggestions for ONE person using their Symptoms and their microbiome profile. It is intended for them only. Your own suggestions may be very different with many items exchanged between ADD and REMOVE.

Technical Methodology Details are described here Technical Note: Prevalence, Average and Not Reported.

Post-Script

This approach sidestep the proforma process often drilled into researchers (you must have a health control group and a verified, criteria matching target population) and keeps to rigorous statistical analysis while ignoring these constraints which are philosophical in nature. We used the available data and set our significance level to P < 0.005; instead of the typical research level of P < 0.05. In other words, we are 10 times more certain about our results.

Autism and Fungi

This is intended to be used with reports from Thorne or Xenogene. A shotgun microbiome report is needed that reports Fungi. Most microbiome do not report fungi in detail.

Most microbiome reports use 16s technology that do not report on fungi. Fungi produces mycotoxins

Fungi are listed from lowest taxonomy level upwards.

CAUTION: Some test results may reflect foods (mushrooms) or supplements that you are consuming and could result in false high levels.

As a personal note, I am a high function autism; the first three years of my life I lived in an area known as “Asthma flats” and this early life exposure to high level of fungi may have been a factor for me.

Diagram from World Health Organization fungal priority pathogens list to guide research, development and public health action

Possible Medical Plants are covered in this article: A Review: Antifungal Potentials of Medicinal Plants [2015] . e.g. Garlic and other wonder herbs do not work on all fungi. 

Smoking Guns of the Autism Microbiome

This post presents solid evidence of items that are statistically significant based on 226 samples of people with Autism. The intent of this post is show the guns. Getting fingerprints and other “why” detective work is not included. This is just the statistical facts… painting a narrative is for others to do.

What we know about Autism Microbiome and Related issues – YouTube

The Excel File used above

What’s next?

Simple, develop a suggestion algorithm that is a superior match for this data than the default on Microbiome Prescription.

Bacteria Associated with Autism

The following bacteria association with Autism is P < 0.01 or more significant. This is using the current 218 contributed samples.

When the Frequency seen is higher than Control, then there is too many. Additionally, the average amount seen in the list below is also higher than that seen in the Control. Same logic applies to those that are lower.

The probability of significance is using Chi2. A value of 6 is about P < 0.01, higher values are even more significant. As you will quickly note: Bifidobacterium for many species is too high.

Tax_NameTax_rankFrequency seen in AutismFrequency seen in ControlFrequency Chi2
Senegalimassiliagenus28.913.831.7
Bifidobacterium pseudocatenulatumspecies17.97.329
Megamonasgenus30.317.319.2
unclassified Clostridialesfamily46.330.117.4
Hungateiclostridiaceaefamily44.528.717.1
Abiotrophiagenus14.7716.4
Nitriliruptoriaclass17.433.315.6
Adlercreutzia equolifaciensspecies27.545.715
Euzebyaceaefamily17.432.814.9
Euzebyalesorder17.432.814.9
Euzebyagenus17.432.814.9
Megamonas funiformisspecies15.67.814.8
Intestinibacter bartlettiispecies36.223.214.6
Adlercreutziagenus31.750.314.4
Euzebya tangerinaspecies17.432.414.3
Brochothrixgenus17.431.813.4
Olivibactergenus22.938.312.8
Pseudoclostridiumgenus27.143.412.7
Pseudoclostridium thermosuccinogenesspecies27.143.412.7
Eggerthella sinensisspecies15.128.412.7
Symbiobacteriaceaefamily25.240.912.5
Odoribacter denticanisspecies19.333.212.1
Brochothrix thermosphactaspecies1730.312.1
Erysipelothrix murisspecies26.641.911.6
Hymenobactergenus20.233.811.4
Hymenobacteraceaefamily26.140.911.1
Dolichospermumgenus20.233.611.1
Corynebacterium durumspecies179.611
Acidaminococcus fermentansspecies21.635.110.7
Selenomonasgenus37.253.710.6
Aphanizomenonaceaefamily21.134.410.6
Bifidobacterium angulatumspecies17.410.110.5
Ruminiclostridium cellobioparum subsp. termitidissubspecies21.634.810.4
Carboxydocella ferrireducensspecies16.127.910.4
Ruminiclostridium cellobioparumspecies22.936.410.3
Dysgonomonas wimpennyispecies26.640.910.3
Caldicellulosiruptorgenus34.450.110.1
Clostridiales Family XVI. Incertae Sedisfamily18.831.110.1
Carboxydocellagenus18.831.110.1
Senegalimassilia anaerobiaspecies16.59.510
Streptococcus mutansspecies20.212.310
Pseudobutyrivibrio xylanivoransspecies33.949.39.9
Hathewaya histolyticaspecies35.350.89.8
Hymenobacter xinjiangensisspecies14.725.79.7
Porphyromonas canisspecies21.133.69.6
Oscillospiragenus45.962.99.6
Nostocaceaefamily29.8449.4
Prevotella timonensisspecies22.535.19.3
Intestinibactergenus51.438.19.3
Butyrivibrio proteoclasticusspecies24.837.89.3
Propionispora hippeispecies21.133.39.2
Thermicanusgenus42.758.89.1
Amoebophilaceaefamily31.745.89.1
Thermoclostridium caenicolaspecies20.2329
Oscillospira guilliermondiispecies31.245.29
Clostridium cellulovoransspecies15.69.18.9
Hungateiclostridiumgenus32.122.28.7
Blautia coccoidesspecies37.251.88.6
Candidatus Amoebophilus asiaticusspecies31.745.48.5
Finegoldia magnaspecies37.251.78.5
Bacteroides stercorirosorisspecies37.652.28.5
Amoebophilusgenus31.745.48.5
Bifidobacterium catenulatum subsp. kashiwanohensesubspecies30.320.88.5
Phascolarctobacterium succinatutensspecies36.250.68.4
Peptoniphilus methioninivoraxspecies24.336.58.4
Planifilum fimeticolaspecies21.132.68.3
Bifidobacterium catenulatumspecies31.7228.3
Tindallia magadiensisspecies27.540.28.3
Anaerovibrio lipolyticusspecies31.745.18.3
Acholeplasmagenus41.756.78.2
Hathewayagenus52.368.78.2
Thiothrixgenus25.237.48.2
Propionisporagenus24.336.28.1
Alkaliphilus crotonatoxidansspecies34.448.18.1
Pseudoramibactergenus14.223.98
Acidaminococcusgenus40.855.17.7
Sphingobacterium bambusaespecies29.842.37.6
Thiotrichaceaefamily26.1387.6
Anaerovibriogenus32.645.57.6
Bifidobacterium boumspecies24.816.77.6
Shewanellagenus25.737.37.5
Shewanellaceaefamily25.737.37.5
Caulobacteraceaefamily18.328.57.4
Planifilumgenus2232.97.4
Rhodothermusgenus33.946.97.4
Tetragenococcus doogicusspecies18.328.57.4
Bifidobacterium thermacidophilumspecies21.614.27.4
Sphingobacterium shayensespecies31.243.77.4
Caulobacteralesorder18.328.57.4
Selenomonas infelixspecies33.946.97.4
Oscillatoriaceaefamily17.427.27.3
Bilophila wadsworthiaspecies45.459.97.3
Anaerostipes hadrusspecies5038.47
Bacteroides faecisspecies38.551.87
Caloramator mitchellensisspecies36.749.46.7
Streptococcus fryispecies18.828.46.7
Acetobacteraceaefamily1726.16.5
Moorella groupnorank30.742.26.4
Chlorobaculumgenus30.742.26.4
Butyricimonas virosaspecies28.439.56.4
Sphingobacteriumgenus39.952.46.2
Sedimentibactergenus43.156.16.2
Rhodothermus clarusspecies33.945.76.2
Sedimentibacter hydroxybenzoicusspecies38.550.96.2
Thiotrichalesorder36.248.36.2

Coagulation and Autism

I am a high functioning autistic person (which is a major factor in microbiome prescription being created — some autism characteristics allowed me to be super focused and not bored creating it), I know part of my issues was low grade coagulation issues — not usually deemed clinical significant usually. Coagulation impacts oxygen flow to the brain… which impacts behaviors. Hypoxia (low oxygen – i.e. from being at altitude without oxygen) have the following symptoms:

  • Euphoria
  • Headache
  • Increased response time
  • Impaired judgment
  • Drowsiness
  • Confusion or foggy decision making

And also speed of acquiring learning.

I did a little searching and found most of the items were very recent publications:

Where do we go from here?

If you can get piracetam — it could be an experiment to try (800 mg with each meal for a few days).  see https://cfsremission.com/2021/04/20/piracetam-for-me-cfs-long-haul-covid-brain-fog-2/ . It works extremely well for me (but your mileage may vary)

For more items, see my old post in a different context for Thick Blood Supplements easily available.

Remember, systematic (one at a time) trials with (hopefully ) some way of objectively evaluating changes.

One possible way to evaluate, is to monitor Saturated O2 levels. There are [cheap] smart watches that will do that constantly.

An Autistic Child’s Microbiome

Autism is a variety of conditions caused by DNA mutations, environmental influences and a host of other factors. A significant contributor can be the microbiome. This impact can be further amplified because many children with autism are picky eaters shifting the microbiome further. What is discussed in this post applies to this child and not autistic children in general.

Back Story

A son with autism. He had COVID in April 2021. With autism, it can be challenging to identify long COVID symptoms from autism symptoms. “We have seen marginal  improvements in his receptive language and command following. His social skills and emotional understanding is poor . His diet has largely remained the same , vegetables and chicken , lamb, beef or fish and spices. He is verbal but not conversational, does not sleep well at night, does stimming throughout the day, his understanding is minimal He has very good energy levels and is playing till he sleeps on most days. He has very good memory and learn preferred topics quickly but is unable to focus on any task , he is unable to write or hold pencil for long . He cannot always reply to questions and has ecolalia[unsolicited repetition of utterances made by others] . ” 

Analysis

Lookin at Percentages of Percentiles, I see a different pattern than seen with ME/CFS and Long COVID — my most frequent analysis types. He has statistically (between 2 and 5%) significant abnormalities, but far less than people with ME/CFS and Long COVID.

Looking at Potential Medical Conditions Detected we see that ADHD and Mood Disorders patterns are there. Everything is reasonably in range for Dr. Jason Hawrelak Recommendations with two significantly out of range (too low) is Akkermansia (which is available as a probiotic) and Faecalibacterium prausnitzii is too high (27%). This pattern is seen across all of his samples.

Going over to our Citizen Science Special studies, the top three pattern matches are for:

  • COVID19 (Long Hauler)
  • Autism
  • Brain Fog

These also are seen with an earlier sample from 2020.

Plan for Suggestions

Since this is a persistent state with reasonable continuity across samples, I am going to go the Uber-Consensus route. By this I mean we will do for Each Sample:

  • “Just Give Me Suggestions” which executes 4 algorithms
  • Citizen Science using Autism

Then we combine the suggestions from each sample into one, an uber suggestion consensus. The advantage of this approach is to minimize minor fluctuations of the microbiome over time. This means that we have 20 sets of suggestions combined.

I was disappointed with the results — nothing was consistently suggested. I experimented and found that the last two samples gave more consistent results. This implies that there has been significant changes in the microbiome over the last two years.

The top suggestions from the PDF are below

As a FYI, in terms of how many times things were suggested:

I should talk a bit about the apparent contradiction with low-fat diets vs lard and fat. These come from the terms that clinical studies used. Low fat diet tends towards fish and poultry, lard is a pork product – I speculate that the type of fat may be significant.

On the flip side, we have these avoids. One item seems to be to suggest gluten free (despite wheat being a to-take):

As an experiment/learning activity — I looked at some of the suggested prescription items and checked if any are used for autism. We are matching these items impact on the microbiome and the shifts that this person has (autism as a diagnosis was not considered).

Since the non-prescription items above should cause similar shifts (and likely with less risk of side effects), it appears that the algorithms are making reasonable suggestions.

The process of checking suggestions derived exclusively from the microbiome against clinical studies for a condition is called cross-validation. When there is a high percentage of agreement, it implies that the mechanism may be via the microbiome and generating candidate substance from the microbiome may produce good results.

KEGG Based Suggestions

These use data from Kyoto Encyclopedia of Genes and Genomes to try to identify substances that the microbiome and the body may be short of which can be obtain via supplements or probiotics.

  • Probiotics (in decreasing priority)
    • Escherichia coli – which can be Mutaflor (recommended above) or Symbioflor-2 (which is easier for people in the US to get).
    • There were several items that are counter-indicated from the suggestions – when there is disagreement, don’t gamble — ignore
    • Akkermansia muciniphila – low positive score but also identify as low on Dr. Jason Hawrelak Recommendations
  • Supplements (again double checking across suggestions and keeping only that both agree with)

Questions

Q:  His gut according to the test is in good condition. I have heard in the past from one of his doctors that his Gut results were one of the best that he has seen in Autistic children, but we have not been able to make a considerable shift in his symptoms in the last few years.

  • A: My working hypothesis is simple: symptoms are associated to microbiome shifts. He has bacteria shifts that are matches to autism drugs (see above); so I believe further improvement of the gut and behaviors are possible and probable. He may be good; I believe he can be better.

Q: Faecalibacterium prausnitzii is high in my son , I have read it works as anti-inflammatory , but on the contrary I have heard that children with ASD have an inflamed Brian ,I would have thought this would have worked in his favor.

  • A: Excellent question! Faecalibacterium prausnitzii is anti-inflammatory for Crohn’s disease[2008], colitis [2013]. I was unable to find any clear literature on its effect on the brain. I did found some information that cause me to suspect that it does not impact the brain significantly.
    • “A 15kDa protein with anti-inflammatory properties is produced by F. prausnitzii, a commensal bacterium involved in CD pathogenesis. This protein is able to inhibit the NF-κB pathway in intestinal epithelial cells and to prevent colitis in an animal model.” [2017] – the size of this is very important.
    • “Most proteins in the plasma are not able to cross the blood—brain barrier because of their size and hydrophilicity.” [Basic Neurochemistry]
    • “does not have a barrier against molecules less than 1 kDa.. may form a barrier against molecules larger than 4 kDa” [2020]
  • Bottom Line — it appears the chemical produced by Faecalibacterium prausnitzii may be too big to reach the brain.
  • We also find the following reported, suggesting we want to reduce it to a normal rangeyou should independently research this
    • Faecalibacterium predicted social deficit scores in children with ASD” [2018]
    • Faecalibacterium prausnitzii … were also found to be highly correlated with Autism Treatment Evaluation Checklist (a measure of Autism severity )” 
    • On the flip side, it reduces abdominal pain and improved bowel movement in ASD [2018].
    • “Gut microbiome data revealed Akkermansia sp. and Faecalibacterium prausnitzii to be statistically lower in abundance in autistic children than their neurotypical peers with a five and two-fold decrease” [2021] — which may account for the gut issues.
      • “Compared with healthy controls, Faecalibacterium,..were more abundant in ASD patients” [2021]
      • Your son’s range is thus very atypical being many, many times higher than expected.
  • I have caution hereFaecalibacterium and cognitive issues have inconsistent reports [20212023 ], Faecalibacterium is implicated in cognitive issues[2018]. IMHO, encouraging it to the normal ranges may be the wisest course.

Q: “His results are over all satisfactory ,same as last year about – Gut wellness score – 89.52.  I have noticed that Clostridia is about 79.7 % could this be the reason, would appreciate your help.”

Searching for Faecalibacterium + autism on PubMed resulted in 29+ studies. There was nothing found for Oscillospiraceae + autism. Looking at the latest sample, only 10% of the organisms in Oscillospiraceae could be identified in the sample — no smoking gun for which genus. Doing a Metagenomic Shotgun Sequencing test would like provide more information (for example, Thorne) — but it is unlikely that will produce more actionable item — just give names.

Looking at what reduces Oscillospiraceae, we see Bumetanide, cycloserine, cefixime and chlorpromazine in that list (as well as many of the above suggestions).

Some visuals: Clostridia is not that extreme, but two of it’s children are.

Microbiome Suggestions for Autism — Cross Validation

Microbiome Prescription uses over a million rules to generate suggestions on improving the microbiome and hopefully reduce or moderate autism behavior. All of the sources of the rules are studies on the US National Library of Medicine. Microbiome Prescription also can provide the complete evidence trail for every suggestion! That is, where — precisely– is all of the information coming from — none of it is private personal opinion or speculation.

Cross Validation

Cross validation is the process of taking one set of information to generate forecasts or suggestions and then look at a totally independent source of information to see if the forecasts and suggestions are valid, reasonable, and appear to help individuals with the condition. I have done that for several conditions with very good results. NOTE: Everything is generated by code — code that I prefer to improve or correct. I have no personal stake in the suggestion, nothing to defend.

Source of Suggestions

Simple, 🥣 Candidates which is based on bacteria shifts reported from studies for autism: 🦠 Taxons. These two links are on the Medical Conditions with Microbiome Shifts from US National Library of Medicine page

The process is simple: some items may have been tests in trials for autism, some have not. If it has been tried, we see what the result in and provide a link to the study (open data!! no “trust me, I am an expert” hype)

Score?

  • Five items with no information
  • Two items with weak information (not PubMed)
  • Twelve items with confirmed information (PubMed)
  • One item that is complex/questionable – depends on the child’s DNA

The goal / objective of Microbiome Prescription is to make suggestions that are more likely to help than to hurt. That goal seems to be accompanied. A secondary goal is to suggest items that have not been studied but modelling suggests that it may be of benefit to try. We have 5 such items above.

Remember these are GENERIC Suggestions for GENERIC Autism

The results of an individuals microbiome will be different — there are many variants and subsets for Autism. Each variant will tend towards their own set of variations for the microbiome. Using an individual’s microbiome sample will get suggestions unique for them.

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.

Autism Microbiome Treatments – Part B

A good starting point is: Interconnection between Microbiota-Gut-Brain Axis and Autism Spectrum Disorder Comparing Therapeutic Options: A Scoping Review [2023] a few quotes (worth reading the whole thing!)

 Kang and colleagues created a modified FMT procedure called MTT for autistic youngsters [29]. This therapy alleviated ASD behavioral symptoms to some extent, with excellent tolerance indicated and improvements lasting 2 years after treatment ceased….Fascinatingly, microbial metabolic genes for folate biosynthesis, oxidative stress defense, and sulfur metabolism were dissimilar from those found in normally developing (TD) patients at ASD baseline but mirrored those found in TD and/or donors following MTT [56].

Several investigations in recent years have indicated qualitative and quantitative changes in the gut flora in a variety of neuropsychiatric illnesses, supporting the role of Gut Microbiome (GM) in the maintenance of physiological condition in the CNS. Within neurobehavioral disorders, it appears that at least a portion of ASD instances are linked to, and maybe reliant on, the health and wellbeing of the GM.

Related FMT Studies:

Caution: FMT can be tricky — determine the appropriate FMT sample to use is still being investigated. The donor microbiome should exhibits none of the shifts documented below, ideally the opposite direction.

Other Recent Studies

Reported Shifts

Based on the literature cited here. We find the most frequently reported listed below. Note that Lactobacillus is high 8 times and low 5 times. My usual attitude when there are such contrary results is to ignore this species entirely.

This leads to the following probiotic suggestions:

Tax_NameDirection
L – Low
H – High
Studies
BifidobacteriumL13
LactobacillusH8
CoprococcusL7
PrevotellaL7
StreptococcusL6
VeillonellaL6
DialisterL6
BacteroidesL5
LactobacillusL5
ParabacteroidesL5
SutterellaH5
ClostridiumH5
CandidaH4
Clostridium perfringensH4
CollinsellaH4
DesulfovibrioH4
SarcinaH4
MegamonasH4
FusobacteriumH4
LachnospiraceaeL4
VeillonellaceaeL4
BilophilaL4
BlautiaL4
Akkermansia MuciniphilaL3

Using Microbiome Prescription

For getting a microbiome sample, it is suggested that Thorne be used because it reports Clostridium perfringens levels in almost every sample. Ombre and Biomesight only report finding it between 11–16% of the time.

Then use this to generate suggestions specific for the autism associated bacteria shifts.

Next Post — Cross Validation

Cross Validation means checking the a priori suggestions (based on above bacteria shifts) against the literature to see if they agree. Three examples are above where studies and suggestions are in agreement.