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.

Autism Factors – Part A

I am planning to update my knowledge of Autism by reviewing recent literature. Taking different scopes in each set.

Before birth factors

  • Prenatal exposure to per- and polyfluoroalkyl substances increases risk [2021]
    • “People are most likely exposed to these chemicals by consuming PFAS-contaminated water or food, using products made with PFAS, or breathing air containing PFAS.” [National Institute of Health]
    • “High maternal exposure to PFAS was consistently associated with increased abundance of Methanobrevibacter smithii in maternal stool. ” with some association to the infant microbiome. [2023]
  • “Valproic acid (VPA) was reported to increase the prevalence of ASD in humans as a consequence of its use during pregnancy. ” [2020] [2017]
  • “Maternal prenatal antifungal use and frequent prenatal antibiotic use are associated with an increased risk of ADHD in offspring” [2023] [2023] [2021] [2019]
  • “Children of mothers with hypertensive disorders of pregnancy (HDP) have high rates of preterm-birth (gestational age < 37 weeks) and small-for-gestational-age (SGA), both of which are risk factors of autism spectrum disorder (ASD). ” [2023]
  • “maternal prenatal exposure to lithium from naturally occurring drinking water sources in Denmark was associated with an increased ASD risk in the offspring.” [2023] [2023]
  • “We found the previously reported relationship between precipitation and autism in a county was dependent on the amount of drinking water derived from surface sources in the county.” [2012]
    • Comment: PFAS etc. is more likely to be present in surface sources
  • In animal studies:
    • Arsenic [2020]
    • Glyphosate [2021] – a widely used herbicide, “Roundup”
    • Environmental Pollutants [2015]
    • Water chlorination byproducts [2011]

After birth – early factors

  • How was the infant fed? [2023], [2021] [2023]
    • Exclusive breastfeeding had lowest incidence
    • Partial breastfeeding had medium incidence
    • Exclusive formula feeding had highest incidence
  • “women who gave birth by caesarean delivery were more likely to stop exclusive breastfeeding in the first 4 months, and those children who were not exclusively breastfed at 4 months were more likely to have autism-like behaviours “[2022]
  • “Children with ASD have a shorter duration of breastfeeding, a later introduction of complementary foods, and poorer acceptance of complementary foods than typical children…The research suggests that continued breastfeeding for longer than 12 months may be beneficial in reducing ASD symptoms” [2023]
  • “Difficulties during breastfeeding, breast milk refusal and avoidance of taking solids have been linked to ASD. Infants with ASD have been referred to as picky eaters. Problematic mealtime behaviour during infancy has also been associated with ASD.” [2022]
  • “Constipation in early childhood was correlated with a significantly increased risk of ASD.” [2023]
  • “Compared with children who did not use antibiotics during the first year of life, those who received antibiotics had a reduced risk of ASD ” [2018]
    • Comment: Could the antibiotics inhibit microbiome shifts contributing to autism?

Opportunity to use Artificial Intelligence for Autism?

As a FYI, I have taught Artificial Intelligence for Chapman University, worked as a AI/Software developer for Amazon and Microsoft. Artificial Intelligence is composed of a large number of very different techniques, at one end you have “ponies” and at the other end “Space Shuttles”. One technique, expert system with fuzzy logic (ESFL), appears to be the best. Why? Most of the techniques require vast quantity of training data and usually produce black box suggestions/predictions which lacks explanations of why. ESFL provides a complete logic/evidence trail for each suggestions/predictions which can be walked. If one piece of evidence is disputed, it can be removed easily and a new set of suggestions/predictions produced.

Build our own AI Autism Model?

This post comes from the success of using AI on Microbiome Prescription. It would be nice to see if we can do the same with Autism. The goal is to use all available knowledge from studies.

I would suggest using SWI-PROLOG as the main engine. Prolog is a language that does not compute numbers but compute logic. The following is actual program code. It is almost english-like and prolog can resolved what you should take or not take.

  • increases(Modifier_A,Bacteria_31979).
  • decreases(Modifier_X,Bacteria_1236).
  • low(“person”,Bacteria_1239).
  • high(“person”,Bacteria_1234).
  • helps(Person,Modifier) :- high(Person,Taxon),decreases(Modifier,Taxon).
  • helps(Person,Modifier) :- low(Person,Taxon),increases(Modifier,Taxon).
  • hurts(Person,Modifier) :- low(Person,Taxon),decreases(Modifier,Taxon).
  • hurts(Person,Modifier) :- high(Person,Taxon),increases(Modifier,Taxon).
  • take(Person,Modifiler) :- remove(helps(Person,Modifier),is,hurts(Person,Modifier)).
  • contradict(Modifier,Taxon) :- decreases(Modifier,Taxon),increases(Modifier,Taxon).

“take” above says get the list of items that helps and then removes all items that hurts. I.e. only items without contradictions.

The number of lines (statements) to explicitly write Microbiome Prescription in SWI-PROLOG is about 15 million statements. That code code be licensed (for free or nominal cost) to a project of this type so building such a microbiome resource is not needed; rather just augment that data with autism specific data.

One key difference between ESFL is the ability to infer and not just parrot (typical Machine Learning). Bacteria A increases IL 10. A person with SNP ABC has decreased IL 10, thus we can desired to increase Bacteria A for people with SNP ABC.

Vision – Do it For Autism!

I have a web based data entry system (that is being used commercially) that can be made available to the project (with hosting of site and database). We have an almost ready to run system.

The Rub

This is a time consuming process to enter the data and then have the data reviewed (to insure correctness). Typically the people doing the entry and reviews are M.Sc., Ph.D. or M.D. Using students working on their degrees (summer work, part time) to do entry is often a way of keeping costs down. These students typically have access to the full text of articles through their educational institutes.

  • Typical time per study/article is 30 minutes to read and enter, 10 minutes to review
  • There are 72,000 studies citing Autism on the US National Library of Medicine.
    • A complete coverage would be 36,000 hours or 900 man-weeks or a team of 18 people working full time for a year.
  • The expected number of facts will likely be around 120,000.

Facts should be put in a Public Domain Type of License

This allows other groups to continue the work and prevents the need to duplicate the effort of encoding the same studies multiple times. The terms of license should require people that uses and extends it to also make their additional data available under the same terms.

Benefits

One of the key benefit is the identification of gaps in the knowledge base as well as identifying areas where there are contrary results. This allows better funding of research to fill gaps and not duplicate existing work.

A secondary benefit is that it could always be kept current and provide far more specific data for a patient based on all of the information available. The issue of MD’s knowledge being stale or bias is reduced.

What is needed — FUNDING AND MANAGEMENT

The Rub above takes money to happen — even if you are paying students the minimum wage, we quickly get a significant cost. Ideally one or more existing Autism Organizations can be persuaded to partially or fully fund this project.

My own role would be at most, a process consultant. Working Pro Bono.

Some Video on Expert Systems

A model for aggressive response to some substances

Any ideas why olive oil would cause severe aggression tantrums?

From a reader

This is an interesting question.. When I see “severe aggression tantrums”, my mind goes to hypoxia (shortage of oxygen delivery to the brain).

The most significant impact of short-term memory loss for a person with hypoxia is that it impairs the ability to retain and recall new or unfamiliar information. Behavioural changes. The person may become more verbally and physically aggressive. They may also have issues with disinhibition

Hypoxia – – common causes, symptoms and treatment

I know that  olea europaea (olive leaf)   is an antimicrobial, that is, it kills off various bacteria and thus could result in Jarisch Herxheimer Reaction (Herx). Many of the symptoms associated with a herx could be explained from hypoxia. This is cited in the literature [Recurrent Jarisch-Herxheimer reaction: case report 2013].

Olive Oil does not have this reputation. When I checked the known impact of Olive Oil, I see that it increases Bilophila which is reported to increase cognitive impairment [2021], cognitive impairment is strongly associated with hypoxia. Olive Oil also decreases/inhibits/kills Prevotella bivia, Prevotella intermedia,Prevotella melaninogenica,Prevotella oris,Prevotella timonensis and Prevotella buccalis. A common by-product of a bacteria being inhibited is a dumping of toxins into the body, triggering inflammation (thus narrowing blood vessels and reducing oxygen delivery).

A continuous monitoring with a Saturated O2 monitor may reveal this shift (it may not, if the inflammation is in the brain — the monitor is unlikely to record it). For myself, I use a relatively cheap smart watch that records SO2 every 10-15 minutes as part of my normal proactive health monitoring, see Monitoring watch for CFS and other Conditions.

This is just a model, with some ability to check the mechanism.

From Social Media

5 yo Autistic Boy – Analysis

Backstory

  • My son is 5.4 years old , he was diagnosed with autism at 22 months of age .
  • We have been strict gluten free casein free sugar free no processed food since his diagnosis .
  • For almost 2 years we followed the ne chek protocol with olive oil, fish oil and 1/32 spoon of inulin.
  • My son was always verbal with alphabets , numbers colors , planets shapes , colors etc and the odd one / two word request sentences but never conversational.
  • His receptive language was very poor until maybe the last year where he is able to follow simple one step instructions.
  • He now repeats what is said to him and can answer to what is your name and other v simple questions .
  • He is not potty trained and does not understand social norms . He interacts with family members and other adults ( other adults mostly for his needs ) 
  • He eats mainly veg and chicken , fish or lamb . Occasionally rice but mostly carb free.
  • He consumes a lot of bananas in a day (5-6).

Foreword – 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.

Reminder: the purpose of these posts is show the process. Every individual will get different suggestions, the suggestions should not be used viewed as suggestions for any symptom or condition.

For more posts on Autism and the Microbiome

Proforma Overview

The percentile overview shows a moderate bias toward rarer bacteria.

PercentileGenusSpecies
0 – 92237
10 – 192221
20 – 291713
30 – 39812
40 – 491114
50 – 5946
60 – 69912
70 – 791117
80 – 891215
90 – 991116

Potential Medical Conditions Detected has two very interesting items over 90%ile matches, namely:

  • Brain Trauma (99%ile)
  • Schizophrenia (98%ile)
  • For Autism he is down at the 81%ile

Other items over 90% tend to be age related, i.e. Alopecia (Hair Loss), hypertension (High Blood Pressure)

Dr. Jason Hawrelak Recommendations came in at 95.6%ile healthy, so no issue there

Since the interpretation of the microbiome data was done thru biomesight, we see that the top matches include:

  • Depression
  • Autism
  • Easily Irritated
  • Brain Fog
  • Neurocognitive: Attention, Memory, Focus

We have 8 significant pattern matches above and will use each to build a consensus of suggestions. A pattern match means that both of the following appear correct: microbiome pattern matches, the patients symptoms or issue are a reasonable match.

Speculation: The brain trauma and schizophrenia being a much stronger match than autism hints that this child issue may be more related to the microbiome than autism proper.

Consensus Overview

Probiotics

The top item using KEGG data was, Escherichia coli, the prescript assist/equilibrium bacteria and then Bacillus thuringiensis, Bacillus subtilis, Bacillus subtilis subsp. natto, Bacillus amyloliquefaciens, Bacillus licheniformis – further down the list, Akkermansia muciniphila.

Consensus suggestions

We have agreement between two very different paths of picking them (i.e. one by using the genomics of the microbiome and looking at the genomics of the probiotics; the other was using clinical studies reporting desired shifts for the bacteria that was selected). To put some specific brands (when not named above) see this list.

Supplements and Food

Iron (Ferric citrate), Magnesium, l-citrulline, propionate, Reduce choline (Beef, Chicken Eggs) – i.e. more fish, cranberry bean flour or cranberries, blackcurrant, blueberry hydrogenated palm oil, Pulses (pea (fiber, protein), navy bean), Cottage Cheese, berberine, stevia, d-ribose, galactose (milk sugar), lactose.

Many of the above are clustered, i.e. d-ribose and e.coli probiotics (one feeds the other); high saturated milk fat diet and (cottage cheese, galactose, lactose).

Diet style

The Avoids

The avoidance list is rich in “you should be taking items” suggested on the social media on the internet. In fact, they are contrary to the pattern that I typically see with ME/CFS or Long COVID. Everyone is different and it is good to do an evidence based approach for suggestions.

Questions and Answers

Readers usually get a chance to provide more information and ask questions before postings.

Q: we have been on a strict diet for our son, mainly we have not been doing GF/CF diet. Do you recommend moving to a full diet, i.e giving him milk and Gluten.

A: Milk and milk products – yes. Chocolate milk made with real Cacao is supported by the suggestions. 
I checked gluten items:

  • Gluten Free was a mild negative (-25) but checking for specific gluten foods, they were all more negative
  • Wheat was also a negative – 103
  • Oats was also a negative -32
  • Barley was also a negative -146

    So, I would suggest keeping him gluten free

Q: (From Social Media): “I wonder what the effect is/was of 5 to 6 bananas a day”

A: In the suggestions, bananas was listed as a take in terms of the microbiome. Potassium is a factor for some autism (not all):

Daughter with ASD Level-III with Gut Issues

The message from the parent was clear and with lots of documentation

 My 5 y.o. daughter is having major gut issues for which I am seeking your help. She is having these issues since her birth and we just stand helpless while she cries most of the night.  I have made an excel sheet explaining all her issues for your reference. I am also attaching  her Biomesight.csv file, HTMA Test, GI Map test and her other medical test and stool test results for your kind reference. 

Gut IssuesSensory IssuesGeneral BehaviourSocializationSpeechHistory, Curent Supplements & Diet
Excessive Gas mostly in evening and night.Hitting Hard Objects/Plastic in the front teethNo aggression , no self injurious behavioursDoesn’t have age appropraite play behaviourNo true words. Only few nonsense syllablesBirth weight-3.45 kg. C-Section delivery.
BloatingCrashing & Jumping on couch & bed.Sweet frendly kid. Attention seeker.More afectionate to adults than kids.Started comprehending two word commands with gestures She had several doses of Antibiotics due to flu, fever on more than 5 occasions. She had a teeth infection for which she was given antibiotics. She also had some gut bug for which she vomited more than 12 times in a single night, in the past (at the age of 20 months)
Fatulence/BurpsLooking from the corner of the eye.Scratching othersNear Normal Eye Contact and Sitting behaviour.Child expresses her needs through hand leading and dragging parents towards the desired object. Child gained a lot of weight since childhood. She was 22kg at the age of 2.5 years. Now she is 40kg. Her height is 120 cm
Undigested food in stool mostly vegetables Rocking back & forth while standing.Biting otherPoor Focus & Attention Celiac test is negative. Low in Vitamin D & Iron. Ultrasound test of abdomen is normal
Constipation. Bristol Stool Chart No.-3/4Making sudden loud noisesPinching & Hitting othersShows no interest/high resistance  to any learning activity. Escape behaviour Supplement– SunFiber, Liver Sauce, Chamomile Tea, Sodium BiCarbonate to relieve gas issues.
There is immediate relief as soon as she passes the gas through fart or burps.Unusual laughter/ Teeth Grinding (Occassionally)Restlessness, Crying and Yelling  Diet -GFCFSF diet. Vegetables- Okra, potato, carrots, beans, tomato,snow peas. Other food- boiled chicken, low GI- rice, millet, gluten free flour, gluten/diary free cookies. Fruits- Banana, kiwi, apples.  
 Stifening of whole body and hitting with her finger tipsAll the above problematic behaviour is noticed when the kid has gut issues   
 Hitting her face on gym ball/mattress    
 Hand Twirling infront of her face.    
Slightly edited notes from parent

Initial Review

My expertise is statistics associated with the microbiome, not autism (although I am a high functioning ASD person).

First looking at distributions we see a predominance of rare bacteria. Usually that hints at herbs to reduce their numbers. Dr. Jason Hawrelak Recommendations placed her at the 95.6%ile, so a main stream solution is unlikely.

Looking at the unhealthy bacteria, several stands out as items of concern:

  • Escherichia coli – which are likely the unhealthy ones. This immediately causes me to suggest either Mutaflor (available where she lives) or Symbioflor-2, the good E.Coli probiotics. Given her age, I would start with Symbioflor-2 because it is given in drops and thus we can slowly ramp up (to avoid severe adverse effects). The other option would be to repackage one capsules of Mutaflor in eight capsules and start with lower dosages. Given the symptoms above — this would be my first course of action if it was my child.
  • Bacteroides fragilis is high. In terms of non-prescription, pomegranate, lactobacillus reuteri (probiotics), N-Acetyl Cysteine (NAC), Cacao and Mangosteen are the most documented. REMEMBER: There is no literature on relative effects, the Confidence on suggestions is based on the number of studies showing desired effects on the number of bacteria we selected to alter.
  • Serratia is also high. Usually associated with urinary tract infections. For this, we have less literature. I would suggest neem tea and perhaps  lactobacillus casei (probiotics)

At this point we have a little dilemma — Escherichia coli and Lactobacillus are hostile to each other, so it’s an either/or. I would usually resolve it by 2 weeks on one and then 2 weeks on the other, recording any changes seen.

Given the history of needing antibiotics in the past, I looked at the computed suggestions. If the needs arise again, see if your MD is willing to use any of the following:

  1. macrolide ((antibiotic)s)   (0.444)
  2. imipenem (antibiotic)s   (0.382)
  3. fluoroquinolone (antibiotic)s   (0.337)
  4. ciprofloxacin (antibiotic)s   (0.305)
  5. clindamycin (antibiotic)s   (0.294)
  6. erythromycin (antibiotic)   (0.274)

Please make sure that you check risk factors for the above, especially given her age. For example, fluoroquinolone has many! The above were calculated solely on the microbiome impact, not risk factors.

Other Medical Reports

After the first impressions above, I went to look at the other test results

  • Hair shows slightly out of range for Arsenic (common for ASD)
  • Celiac tests: negative
  • Cortisol: in range
  • Lipid Profile: normal
  • Serum Free T3+Free T4+TSH: normal
  • Ultrasound: normal
  • Vitamin D + Iron: normal
  • Gastrointestinal Pathogen PCR (Stool): Negative
  • Liver Function: normal except for
    • Alkaline Phosphatase: high (2x upper limit)
    • Sodium and Potassium – slightly high (similar on hair above)
  • GI Map
    • Bacteroides fragilis: High, as above
    • Enterococcus: High (versus 18%ile, low on Biomesight results)
    • Escherichia species: At top of reference range (97%ile on Biomesight results)
    • Akkermansia Munciniphila: none detected (none detected on Biomesight results)
    • Zonulin [literature] was very high indicating leaky gut.
From GI Map.

Looking at Biomesight results

  • Bacillus was not high
  • None of the Enterococcus were reported and the family was a little low
  • Morganella was not reported
  • S. Aureus was not reported
  • Streptococcus was low

I will make the assumption that at least one round of antibiotics were done between the GI Map (Mar 29,2022) results and the Biomesight results — although I have read studies questioning the reliability of some GI Map results.

Probable Symptoms

This was recently revised, and seems to match — especially the time since offset.

SymptomMatches
less than 04 years since onset77
Frequently loose train of thought75
Lyme74
Less Avoidance of Eye Contact or Poor Eye Contact73

Where Do we go from here?

I picked the following to build the consensus report shown below:

It was interesting to note that the methods that selected the most bacteria, were the special studies, KM and the top/bottom 10%.

Consensus Suggestions

As always, the full consensus report is attached.

In reviewing the list, the ones that I would be most inclined to use are:

I should note that symbioflor 2 e.coli probiotics is on the avoid list — this often happens when there is a need to increase lactobacillus which was sitting at 6%ile. When lactobacillus is low, I tend to ignore the avoid on E.Coli probiotics and point out the mutual hostility they have – so do one and then the other, noticing any changes (the one that gives the best positive change, do more of — but keep rotating). I usually suggest starting with the E.Coli probiotics because they are known to persist. This is rarely the case for Lactobacillus probiotics.

Special Studies: Easily irritated

People who have uploaded their (or their children) samples have annotated some with easily irritated. On my main blog, I have been doing a series of deep statistical dives on my main microbiome blog site, and checked it the data available reaches the threshold for inclusion as defined in A new specialized selection of suggestions links (A summary table of various studies has been added there).

I am posting here to since it is a blog focused on Autism and easily irritated is a common symptom.

Study Populations:

SymptomReferenceStudy
Easily irritated110855
  • Bacteria Detected with z-score > 2.6: found 146 items, highest value was 9.9
  • Enzymes Detected with z-score > 2.6: found 896 items, highest value was 5.9
  • Compound Detected with z-score > 2.6: found ZERO items

For the number of samples, the z-score for bacteria are unusually high compared to other studies in the series. A high z-score means strong statistical significance. Enzymes has a huge numbers detected without any extreme values – this is interesting but unsure on how to interpret that.

Interesting Significant Bacteria

  • All bacteria found significant had too low levels

The most significant ones are listed below. I should point out that these bacteria may not be the cause, rather they may be ‘the canaries in the coal mine’ of the microbiome. These studies’ methodology determines association and not causality.

BacteriaReference MeanStudyZ-Score
Prevotella oulorum (species)63179.9
Prevotella copri (species)6552290898.8
Clostridium malenominatum (species)57226.3
Pediococcus (genus)112486.1
Phocaeicola coprocola (species)7544456.1
Actinobacillus pleuropneumoniae (species)57196
Opitutae (class)169435.8
Puniceicoccaceae (family)163405.8
Lactiplantibacillus pentosus (species)122175.8
Bifidobacterium cuniculi (species)81255.7
Cerasicoccus arenae (species)552875.5
Prevotella (genus)73346266815.5
Puniceicoccales (order)114355.4
Veillonella (genus)400419455.4
Cerasicoccus (genus)319565.3
Tenericutes (phylum)313810535.1

Interesting Enzymes

The first 100 enzymes (of 800+) found significant had too low levels.

EnzymeReference MeanStudy MeanZ-Score
propanoyl-CoA:NADP+ oxidoreductase (1.3.1.84)29105.9
S-methyl-5′-thioadenosine:phosphate S-methyl-5-thio-alpha-D-ribosyl-transferase (2.4.2.28)365314265.8
3-oxo-Delta1-steroid:acceptor 1-oxidoreductase (1.17.99.11)28105.7
cholest-4-en-3-one:acceptor oxidoreductase (25-hydroxylating) (1.17.99.10)28105.7
n/a (3.4.24.20)40165.2
malonyl-CoA:acetate malonyltransferase (tetracenomycin-F2-forming) (2.3.1.235)41115.2
malonyl-CoA:malonamoyl-[OxyC acyl-carrier protein] malonyltransferase (2.3.1.260)41115.2
NADPH:acceptor oxidoreductase (1.6.99.1)572623824.8

I will leave it to the reader to go to Kyoto Encyclopedia of Genes and Genomes to learn about these enzymes (a steep learning curve).

The top item S-adenosyl-L-methionine (SAMe), had significant literature associated with it, a few samples:

  • “S-adenosyl methionine prevents ASD like behaviors triggered by…” [2019]
  • “S-Adenosyl-Methionine alleviates sociability aversion and reduces changes in gene expression in a mouse model of social hierarchy” [2022]
  • “Blood biomarker levels of methylation capacity in autism spectrum disorder: a systematic review and meta-analysis” [2020] – decrease SAMe in blood
EnzymeReference MeanStudy MeanZ-Score
S-adenosyl-L-methionine:23S rRNA (guanine2535-N1)-methyltransferase (2.1.1.209)151707.6
GDP-alpha-D-rhamnose:NAD(P)+ 4-oxidoreductase (1.1.1.281)4811206.4
ATP:D-galacturonate 1-phosphotransferase (2.7.1.44)5061205.7
NAD+:poly(ADP-D-ribosyl)-acceptor ADP-D-ribosyl-transferase (2.4.2.30)5051205.6
beta-D-galactosyl-(1->4)-L-rhamnose:phosphate 1-alpha-D-galactosyltransferase (2.4.1.247)5051205.6
delta/gamma-tocopherol lyase (ring-opening) (5.5.1.24)7032135.6
all-trans-heptaprenyl-diphosphate diphosphate-lyase (cyclizing, tetraprenyl-beta-curcumene-forming) (4.2.3.130)3041574.7
CDP-diacylglycerol:myo-inositol 3-phosphatidyltransferase (2.7.8.11)133884.6
Hg:NADP+ oxidoreductase (1.16.1.1)7741954.6
ferredoxin:dinitrogen oxidoreductase (ATP-hydrolysing, molybdenum-dependent) (1.18.6.1)293014784.4
myo-inositol-hexakisphosphate 4-phosphohydrolase (3.1.3.26)216310744.2
phenol,NADPH:oxygen oxidoreductase (2-hydroxylating) (1.14.13.7)326514.1
1,2-beta-D-glucan:phosphate alpha-D-glucosyltransferase (2.4.1.333)2641284
acetyl-CoA:2-oxoglutarate C-acetyltransferase (thioester-hydrolysing, carboxymethyl-forming) (2.3.3.14)9363414

Cross Validation To Literature

The term “irritability” on PubMed often links to  irritable bowel syndrome (IBS), making this difficult. The closest proxy appears to be stress (most people that are stressed tend to be irritable…) and there was some matches. We have agreement on the following being low.

  • Atopobium
  • Lactiplantibacillus plantarum
  • Anaeroplasma
  • Coriobacterium
  • Tenericutes

Bottom Line

Lactiplantibacillus plantarum is the modern name for Lactobacillus Plantarum, a commonly available probiotic.

Special Studies: Autism

People who have uploaded their (or their children) samples have annotated some with Autism. On my main blog, I have been doing a series of deep statistical dives on my main microbiome blog site, and checked it the data available reaches the threshold for inclusion as defined in A new specialized selection of suggestions links (A summary table of various studies has been added there).

I am posting here to since it is a blog focused on Autism.

On the [Changing Microbiome] tab

Study Populations:

SymptomReferenceStudy
Bloating108267
  • Bacteria Detected with z-score > 2.6: found 196 items, highest value was 8.2
  • Enzymes Detected with z-score > 2.6: found 203 items, highest value was 7.6
  • Compound Detected with z-score > 2.6: found ZERO items

For the number of samples, the z-score are unusually high compared to other studies in the series. A high z-score means strong statistical significance.

Interesting Significant Bacteria

All bacteria found significant had too low levels with the exception of the following that was too high:

  • Prevotellaceae (family)
    • Prevotella copri (species)
    • Prevotella veroralis (species)
  • Bifidobacterium gallicum (species)
  • Peptococcaceae (family)

The most significant ones are listed below. I should point out that these bacteria may not be the cause, rather they may be ‘the canaries in the coal mine’ of the microbiome. These studies’ methodology determines association and not causality.

BacteriaReference MeanStudyZ-Score
Acholeplasma (genus)14212158.2
Pseudoclostridium (genus)146707
Pseudoclostridium thermosuccinogenes (species)145707
Bacteroides rodentium (species)337114816.9
Anaerovibrio lipolyticus (species)13283556.7
Anaerovibrio (genus)13443686.7
Anaerotruncus (genus)192911506
Butyrivibrio (genus)5191205.8
Xanthomonadaceae (family)8202665.8
Blautia hydrogenotrophica (species)252735.7
Thiotrichaceae (family)179755.6
Thiothrix (genus)179755.6
Butyrivibrio proteoclasticus (species)4981205.6
Thiotrichales (order)179785.5
Adlercreutzia equolifaciens (species)232905.5
Anaerotruncus colihominis (species)183311405.4
Rubritalea (genus)61275.3

Interesting Enzymes

All enzymes found significant had too low levels.

I will leave it to the reader to go to Kyoto Encyclopedia of Genes and Genomes to learn about these enzymes (a steep learning curve).

The top item S-adenosyl-L-methionine (SAMe), had significant literature associated with it, a few samples:

  • “S-adenosyl methionine prevents ASD like behaviors triggered by…” [2019]
  • “S-Adenosyl-Methionine alleviates sociability aversion and reduces changes in gene expression in a mouse model of social hierarchy” [2022]
  • “Blood biomarker levels of methylation capacity in autism spectrum disorder: a systematic review and meta-analysis” [2020] – decrease SAMe in blood
EnzymeReference MeanStudy MeanZ-Score
S-adenosyl-L-methionine:23S rRNA (guanine2535-N1)-methyltransferase (2.1.1.209)151707.6
GDP-alpha-D-rhamnose:NAD(P)+ 4-oxidoreductase (1.1.1.281)4811206.4
ATP:D-galacturonate 1-phosphotransferase (2.7.1.44)5061205.7
NAD+:poly(ADP-D-ribosyl)-acceptor ADP-D-ribosyl-transferase (2.4.2.30)5051205.6
beta-D-galactosyl-(1->4)-L-rhamnose:phosphate 1-alpha-D-galactosyltransferase (2.4.1.247)5051205.6
delta/gamma-tocopherol lyase (ring-opening) (5.5.1.24)7032135.6
all-trans-heptaprenyl-diphosphate diphosphate-lyase (cyclizing, tetraprenyl-beta-curcumene-forming) (4.2.3.130)3041574.7
CDP-diacylglycerol:myo-inositol 3-phosphatidyltransferase (2.7.8.11)133884.6
Hg:NADP+ oxidoreductase (1.16.1.1)7741954.6
ferredoxin:dinitrogen oxidoreductase (ATP-hydrolysing, molybdenum-dependent) (1.18.6.1)293014784.4
myo-inositol-hexakisphosphate 4-phosphohydrolase (3.1.3.26)216310744.2
phenol,NADPH:oxygen oxidoreductase (2-hydroxylating) (1.14.13.7)326514.1
1,2-beta-D-glucan:phosphate alpha-D-glucosyltransferase (2.4.1.333)2641284
acetyl-CoA:2-oxoglutarate C-acetyltransferase (thioester-hydrolysing, carboxymethyl-forming) (2.3.3.14)9363414

Cross Validation To Literature

Using microbiome studies from published studies is often difficult because the results often vary greatly due to the lab equipment used and the software used to interpret the data.

The taxonomy nightmare before Christmas…

Checking against the literature we found:

The purpose of these studies is to enable apple-to-apple-to-apple analysis and suggestions. The same lab is used for the raw read (Ombre and Biomesight uses the same lab) and all of the data is processed thru the same software (BiomeSight). Your sample (if you use the website) is processed thru the same process.

A final comment on most published studies, typically there are 25 Autism patients and 25 control patients. This actually results in much lower sensitivity in those studies than we have with over 1000 as the control.

Bottom Line

The SAMe “shouting out” in the enzymes being in agreement with published studies causes me to have considerable confidence in the analysis. Dr. Artificial Intelligence suggestions on Microbiome Prescription look like they are likely to have positive results.

P.S. If anyone does more drill down into the enzymes above in the literature, feel free to add your findings as comments.