Statistics is fun because there many paths. Most studies using the microbiome uses the easy, but naïve, path of computing averages and standard deviation. As my dataset has grown, I have been travelling some less traveled path, for example: Visual Exploration of Odds Ratios, and a patent pending method termed “Kaltoft-Moltrup”.

One of the frequent decisions that I see in studies is to limit examination of bacteria that have a high frequency in the samples. This allows the researchers to keep to familiar and classic statistics. Using frequency of observation in the control group and the condition group is one of these much less travelled paths. It usually require big sample sizes and many studies have a sample size of 30 (sufficient for the mean and standard deviation approach).

I just completed code to compute Chi2 using Biomesight data for users reporting Autism.

• Control Population: 3525
• Autism: 88

Chi2 can be converted to probability (p) of happening at random with the following table

Seen too Rarely(Want to increase)

We see one bacteria available as a probiotic Bifidobacterium adolescentis. The rest would need to be altered by diet.

tax_name	TAX_RANK	Chi2	Observed	Expected	Shift
Butyricimonas synergistica	species	10	17	36	Under-Represented
Bifidobacterium adolescentis JCM 15918	strain	9.8	9	24	Under-Represented
Dehalobacterium	genus	9.7	18	37	Under-Represented
Pelotomaculum isophthalicicum	species	8	17	33	Under-Represented
Ammonifex thiophilus	species	7.3	17	32	Under-Represented

Seen too Often (Want to decrease)

We see 32 bacteria over a Chi2 of 6.635 ( P < 0.01 or 1 change in 100 of being a false detection). One very striking feature is that there are many, many different species of Bifidobacterium that are over represented while one species is under represented. This is not a simple situation to address.

tax_name	TAX_RANK	Chi2	Observed	Expected	Shift
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	43.5	54	23	Over-Represented
Bifidobacterium angulatum	species	33	39	16	Over-Represented
Staphylococcus pseudolugdunensis	species	23.6	20	7	Over-Represented
Clostridium cellulovorans	species	22.9	20	7	Over-Represented
Bifidobacterium catenulatum PV20-2	strain	19.5	58	33	Over-Represented
Streptococcus mutans	species	19	23	10	Over-Represented
Hungateiclostridium	genus	18.3	30	14	Over-Represented
Hungateiclostridiaceae	family	18.1	30	14	Over-Represented
Streptococcus intermedius	species	17.5	23	10	Over-Represented
Bifidobacterium catenulatum	species	16.9	58	34	Over-Represented
Absiella	genus	16.7	22	10	Over-Represented
Clostridium chartatabidum	species	16.7	43	23	Over-Represented
Bifidobacterium gallicum	species	15.4	73	47	Over-Represented
Prevotella veroralis	species	13	15	6	Over-Represented
Corynebacterium durum	species	12.6	14	6	Over-Represented
Bifidobacterium thermacidophilum	species	11.3	18	8	Over-Represented
Parascardovia	genus	10.5	32	18	Over-Represented
Klebsiella oxytoca	species	10.5	27	15	Over-Represented
Bifidobacterium scardovii	species	10	30	17	Over-Represented
Bifidobacterium cuniculi	species	9.9	31	18	Over-Represented
Candidatus Blochmanniella camponoti	species	9.8	21	11	Over-Represented
Abiotrophia	genus	9.2	12	5	Over-Represented
Enterococcus gilvus	species	9.1	14	6	Over-Represented
Megamonas funiformis	species	8.8	21	11	Over-Represented
Segatella oulorum	species	8.6	22	12	Over-Represented
Ralstonia	genus	7.9	14	7	Over-Represented
Bifidobacterium indicum	species	7.7	67	48	Over-Represented
Candidatus Blochmanniella	genus	7.2	35	22	Over-Represented
ant endosymbionts	clade	7.2	35	22	Over-Represented
unclassified Bacteroidetes Order II.	order	7.2	75	55	Over-Represented
Enterobacter hormaechei	species	6.9	36	23	Over-Represented
Moorella group	norank	6.7	66	48	Over-Represented

Bottom Line

The next step is to compute similar tables for all symptoms and incorporate these findings into a new algorithm. I say new, because I do not know if it is better than the existing ones. Conceptually, it would be added as a 5th set of suggestions to the existing consensus view on Microbiome Prescription.

This is a preview of the next generation of analysis. I described a mathematical model in Microbiome Guilds, Metabolites and Enzymes. I mentioned a concept in it and over the weekend tried the concept out. It worked and is very sweet.

To explain it, look at the chart below. The blue line is for those that have a symptom and the orange line is what is expected. If you divide observed by expected for different percentiles, you get an odds ratio. Most people know odds ratio (OR) from things like:

For current male smokers consuming >30 cigarettes daily:

• Squamous Cell Carcinoma (SqCC): OR = 103.5
• Small Cell Lung Cancer (SCLC): OR = 111.3
• Adenocarcinoma (AdCa): OR = 21.91

This pattern does not determine that you will absolutely get it. It means that your are more likely — odds. (My native environment as a statistican)

This means that we move from a vague hand-waving “Too high” or “Too Low” to actual numbers (percentiles to be precise, not percentages).

Biomesight Bacteria

The genus bacteria listed below, each have at least an odds ratio of 1.5 for general fatigue using Biomesight data if your percentile is below the amount show. I stopped listing at 10%ile items. Compared to my earlier post for Bacteria Associated with General Fatigue, we have some really string candidates – 90!. 96 means 96%ile.

If you have 10 of them then 1.5 ^ 10 = 57x greater odds of having general fatigue. It is NOT one bacteria causing it, or even a specific group of bacteria, but different combinations of possible bacteria.

I should mention that these numbers only applies to Biomesight data. “results from one pipeline cannot be safely applied to another“. For background see: The taxonomy nightmare before Christmas.

. It potentially allows a screening test to be done for autism from a microbiome sample (and also hints at what specifically needs to be corrected).

• Chlorobaculum >= 96
• Roseococcus >= 94.9
• Marichromatium >= 93.8
• Ectothiorhodospira >= 93.8
• Aquimonas >= 93.5
• Xenophilus >= 92.5
• Neorickettsia >= 90.9
• Syntrophobacter >= 90
• Pelomonas >= 88.5
• Trichococcus >= 88
• Steroidobacter >= 86.5
• Desulfofrigus >= 65
• Hathewaya <= 47.6
• Pseudoclostridium <= 42.2
• Desulfovibrio <= 37.9
• Anaerovibrio <= 37.1
• Ehrlichia <= 36.1
• Phocaeicola <= 36
• Johnsonella <= 30
• Acetobacterium <= 27.6
• Candidatus Amoebophilus <= 27.2
• Oscillospira <= 25.2
• Anaerotruncus <= 25.1
• Erysipelothrix <= 24.9
• Bacteroides <= 24.6
• Porphyromonas <= 21.2
• Selenomonas <= 21
• Pedobacter <= 20.1

Ombre Equivalent Bacteria

If you have Ombre’s microbiome results, these are the critical bacteria. It is a much smaller list then above (and as expected, very few bacteria names in common — “the nightmare”)

• Cystobacter >= 95.8
• Cellulomonas >= 95.2
• Tannerella <= 39.6
• Erysipelatoclostridium <= 34.7
• Alistipes <= 29.1
• Alloprevotella <= 28.8
• Phocaeicola <= 28.6
• Oleidesulfovibrio <= 27.6
• Pseudoflavonifractor <= 27.5
• Odoribacter <= 25.6
• Ethanoligenens <= 24.9
• Pedobacter <= 24.3
• Leyella <= 23.8

uBiome Equivalent Bacteria

There was not sufficient data to compute bacteria odds ration

Metabolites

I did a follow up post using Odds Ratio with Metabolites in the context of ME/CFS.

Metabolite-Centric Analysis

Bacterial Metabolic Activity: Bacteria produce and consume various metabolites, which can significantly impact the host’s metabolic environment¹³.Metabolic Imbalances: Different bacterial compositions can lead to similar metabolite imbalances, making metabolite profiles potentially more informative than bacterial species profiles alone^7 8.

Advantages of This Approach

1. Net Effect: By examining metabolites, we can assess the overall impact of the microbiome on the host, regardless of the specific bacterial species present⁵.
2. Consistency: Metabolite imbalances may be more consistent across patients than bacterial species composition, which can vary widely⁷.
3. Functional Insight: This approach provides insight into the functional consequences of microbiome dysbiosis in ME/CFS^3 8.

KEGG Application

Using the KEGG: Kyoto Encyclopedia of Genes and Genomes,(KEGG) allows for:

• Mapping of metabolites to specific pathways
• Identification of key metabolic alterations in ME/CFS patients
• Potential discovery of new biomarkers or therapeutic targets⁷

Metabolite Profiling in ME/CFS

Recent studies have identified several metabolic alterations in ME/CFS patients:

• Disruptions in energy metabolism and mitochondrial function^2 5
• Alterations in lipid metabolism, including changes in ceramides and complex lipids⁴
• Disturbances in amino acid metabolism⁸

Clinical Implications

Understanding metabolite profiles in ME/CFS could lead to:

• Improved diagnostic tools
• Identification of potential therapeutic targets
• Personalized treatment approaches based on individual metabolic profiles58

I am showing the numbers for Biomesight sample below. Conclusions across Ombre, uBiome and Biomesight are at the bottom.

Warning: These are the chemical names — a few are available as supplements with more common name.

Looking for Metabolites shared between Ombre and Biomesight samples, only a single metabolite was flagged by both for low: Hydroquinone (1.6%ile for Ombre, 26%ile for Biomesight).

Ombre flagged some 510 metabolites, while Biomesight flagged 542 metabolites. Too much to drill down into. So let us look at the shared one above in more detail.

Based on the Perplexity search results, hydroquinone has shown some interesting connections to cognitive functions, particularly in the context of brain injury and neuroprotection:

Neuroprotective Effects

Hydroquinone (HQ) has demonstrated significant neuroprotective properties in experimental models of brain injury:

1. In a rat model of transient focal cerebral ischemia, HQ treatment strongly alleviated ischemic brain injury^{3 4}.
2. The neuroprotective effect of HQ was associated with the prevention of blood-brain barrier (BBB) disruption³. This is crucial because the BBB plays a vital role in maintaining brain homeostasis and protecting cognitive functions.
3. HQ treatment maintained the expression of tight junction proteins in the ischemic cortex, which are essential for BBB integrity³.

Potential Cognitive Benefits

While not directly tested for cognitive enhancement, the neuroprotective effects of HQ suggest potential cognitive benefits:

1. By preventing BBB disruption, HQ may help maintain normal brain function and protect against cognitive decline associated with ischemic events³.
2. Both pre- and post-treatment with HQ showed protective effects against ischemic damage in experimental models⁶. This suggests potential applications in both preventive and therapeutic contexts for cognitive protection.

Considerations and Limitations

It’s important to note some limitations and considerations:

1. Most studies on HQ’s neuroprotective effects have been conducted in animal models, and more research is needed to confirm these effects in humans^{3 4} .
2. The typical use of HQ as a skin-lightening agent is unrelated to its potential cognitive effects⁵. Its primary application remains in dermatology.
3. High doses or long-term use of HQ may have adverse effects. A study on percutaneous drug delivery showed that high doses of HQ could impair hippocampal structure and induce behavioral disorders in mice¹.

In conclusion, while hydroquinone shows promising neuroprotective effects that could potentially benefit cognitive functions, especially in the context of brain injury, more research is needed to fully understand its impact on human cognition and to determine safe and effective applications beyond its current use in dermatology.

Bottom Line

Adding this to the website to allow individual analysis of individual microbiome is high on my backlog. One of the benefits is the ability to focus on specific bacteria being at specific levels which conceptually should result in better suggestions.

This post started with the post below on Facebook

I was curious if by chance some bacteria produced it. To find such information you must start with KEGG: Kyoto Encyclopedia of Genes and Genomes. On that site we find a description of the condition, we also find information about this enzyme glucose-6-phosphate dehydrogenase (NADP+) EC 1.1.1.49

Since it is deficiency, we look for an alternative supply from bacteria in the microbiome. There are many bacteria that has the capacity of producing it, but the enzyme may not be turned on (epigenetics). I was directed by perplexity to Wikipedia. This identifies a bacteria that is likely a high (actual) producer.

Leuconostoc mesenteroides: This bacterium has been shown to possess a G6PD enzyme that is reactive toward 4-hydroxynonenal, in addition to glucose-6-phosphate

This bacteria is available as a probiotic (one source).

General Information about Autism Enzymes and Compound Production

From the hundreds of donated microbiome samples annotated with Autism on Microbiome Prescription, I have done some statistical analysis (using a patent pending method for partitioning samples), “poor man metagenomics”, and have produce a summary on those who have an official diagnosis of autism.

First, I checked if EC 1.1.1.49 was on the list. It was not, which implies that is not a very common item across all autism patients.

Browsing the lists I did find two familiar items being high:

• (R)-Lactate – also known as d-lactic acid, a common cause of brain fog and other neurological conditions see this for a list.
• L-Histidine which is likely a protective feature (more information)

The amount of (R)-Lactate reported above was computed from the microbiome data which implies that reducing its level by microbiome manipulation is a viable path.

Modelling on an individual sample

With recent revisions of the UI, I have built an algorithm to select the probiotics that supply the maximum amount of these KEGG compounds and Enzymes that most meet the deficiencies detected.

To illustrate this feature, I took one of the autism samples upload and ask for the suggestions.

Below is the results of probiotics to increase the low KEGG compound in this sample.

Below is the results of probiotics to increase the low KEGG Enzymes in this sample. They are reasonably close to each other.

Bottom Line

This is all theoretical, but as probiotics are usually deemed to be safe and without significant risks, it is a possible experiment to try. Always take detail notes and report benefits and problems as comments on this post

It is interesting to note that the common Lactobacillus and Bifidobacterium are not need the top of either list.

An older video on the process

Postscript and Reminder

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

Implementation Strategies

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

Professional Medical Review Recommended

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

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

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

I am doing a normalization and update of data on Microbiome Prescription. There are many items to review and items that have been reviewed have { } in their name. The pattern is:

• Scientific Name
• {Common Name}
• Other Information

and not reviewed (YET)

So far in this review, I have come across two substances (more likely to come) where there has been many or interesting studies for Autism

Sulforaphane

This is found in broccoli sprouts,cauliflower, kale, cole crops, cabbage, collards, mustard, and cress

• Sulforaphane Treatment in Children with Autism: A Prospective Randomized Double-Blind Study.
• Therapeutic efficacy of sulforaphane in autism spectrum disorders and its association with gut microbiota: animal model and human longitudinal studies
• Current understanding and future directions of cruciferous vegetables and their phytochemicals to combat neurological diseases.
• Exploring sulforaphane as neurotherapeutic: targeting Nrf2-Keap & Nf-Kb pathway crosstalk in ASD.
• A Review on Autism Spectrum Disorder: Pathogenesis, Biomarkers, Pharmacological and Non-Pharmacological Interventions.
• Emerging drugs for the treatment of irritability associated with autism spectrum disorder.

Zinc

• Dietary zinc supplementation rescues fear-based learning and synaptic function in the Tbr1(+/-) mouse model of autism spectrum disorders.
• Potential importance of supplementation with zinc for autism spectrum disorder.
• The Assessment of Selenium, Aluminum, and Zinc in Children with Autism Spectrum Disorder.
• Differential effectiveness of dietary zinc supplementation with autism-related behaviours in Shank2 knockout mice.
• Zinc Water Prevents Autism-Like Behaviors in the BTBR Mice.
• xploring potential mechanisms for zinc deficiency to impact in autism spectrum disorder: a narrative review.
• Zinc deficiency and supplementation in autism spectrum disorder and Phelan-McDermid syndrome.
• Zinc Status and Autism Spectrum Disorder in Children and Adolescents: A Systematic Review.
• The association of vitamin A, zinc and copper levels with clinical symptoms in children with autism spectrum disorders in Jilin Province, China.
• Neurodevelopmental Consequences of Dietary Zinc Deficiency: A Status Report.
• The Role of Zinc and NMDA Receptors in Autism Spectrum Disorders.
• Zinc is a key regulator of gastrointestinal development, microbiota composition and inflammation with relevance for autism spectrum disorders.
• Impact of Zinc Oxide Nanoparticles on the Composition of Gut Microbiota in Healthy and Autism Spectrum Disorder Children.
• The role of zinc supplementation on the metallothionein system in children with autism spectrum disorder.
• In vitro zinc supplementation alters synaptic deficits caused by autism spectrum disorder-associated Shank2 point mutations in hippocampal neurons.
• Dietary Zinc Supplementation Prevents Autism Related Behaviors and Striatal Synaptic Dysfunction in Shank3 Exon 13-16 Mutant Mice.

and more studies…

Glyphosate

Studies from US National Library of Medicine

“Furthermore, it has been reported that most infant formulas are contaminated with glyphosate. One study reported levels between 0.03 mg kg−1 and 1.08 mg kg−1. This could potentially further exacerbate the problem of Bifidobacterium reduction in the infant gut.” This may be a factor for increasing Autism and ADHD rates.

Impact of glyphosate (Roundup^TM) on the composition and functionality of the gut microbiome

• Is autism a PIN1 deficiency syndrome? A proposed etiological role for glyphosate.
• Maternal exposure of mice to glyphosate induces depression- and anxiety-like behavior in the offspring via alterations of the gut-brain axis.
• Autism-like Behaviors in Male Juvenile Offspring after Maternal Glyphosate Exposure.
• Pesticides used in Europe and autism spectrum disorder risk: can novel exposure hypotheses be formulated beyond organophosphates, organochlorines, pyrethroids and carbamates? – A systematic review.
• The relationship between pesticide exposure during critical neurodevelopment and autism spectrum disorder: A narrative review.
• Maternal glyphosate exposure causes autism-like behaviors in offspring through increased expression of soluble epoxide hydrolase.
• Gut microbiota and neurological effects of glyphosate.
• Inflammatory state and autism-like behavioral phenotype of offspring induced by maternal exposure to low-dose chemical mixtures during pregnancy in mice.
• Clostridium Bacteria and Autism Spectrum Conditions: A Systematic Review and Hypothetical Contribution of Environmental Glyphosate Levels.
• Evidence the U.S. autism epidemic initiated by acetaminophen (Tylenol) is aggravated by oral antibiotic amoxicillin/clavulanate (Augmentin) and now exponentially by herbicide glyphosate (Roundup).
• Elevated Urinary Glyphosate and Clostridia Metabolites With Altered Dopamine Metabolism in Triplets With Autistic Spectrum Disorder or Suspected Seizure Disorder: A Case Study.

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.

• Current Studies for Brain Trauma
• Current Studies for Autism

Example Report

Example for a (real) person with Autism, OCD and Chronic Fatigue Syndrome

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

• Google Sheets
• Excel Spread Sheet below:

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

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.

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

• Case Study: Rapid Complete Recovery From An Autism Spectrum Disorder After Treatment of Aspergillus With The Antifungal Drugs Itraconazole And Sporanox [2020]
• Could Candida Overgrowth Be Involved in the Pathophysiology of Autism? [2022]
• Dysbiotic microbiota in autistic children and their mothers: persistence of fungal and bacterial wall-deficient L-form variants in blood [2019]
• Role of mycotoxins in the pathobiology of autism: A first evidence [2017]
• Mycotoxin Exposure and Autism: A Systematic Review of the Molecular Mechanism [2021]
• Effects of Mycotoxins on Neuropsychiatric Symptoms and Immune Processes [2018]
• New evidences on the altered gut microbiota in autism spectrum disorders [2017]
  “We also observed that the relative abundance of the fungal genus Candida was more than double in the autistic than neurotypical subjects,”
• See also: CDC provides a list of known fungal diseases: Types of Fungal Diseases.

Fungi are listed from lowest taxonomy level upwards.

• Fungi: Agaricales / Agaricomycetes – Eating button and baby bella before the test may result in a false high.
• Fungi: Aspergillus / Aspergillaceae / Eurotiales
• Fungi: Chaetomiaceae / Sordariales / Sordariomycetes
• Fungi: Clavicipitaceae / Hypocreales / Sordariomycetes
• Fungi: Debaryomycetaceae / Saccharomycetales / Saccharomycetes
• Fungi: Exophiala / Herpotrichiellaceae / Chaetothyriales – Loves Dishwashers!
• Fungi: Leotiomycetes / Ascomycota
• Fungi: Malassezia / Malasseziaceae / Malasseziales / Malasseziomycetes / Basidiomycota – associated with Inflammatory Bowel Disease and Crohn’s
• Fungi: Nectriaceae Fusarium / Hypocreales / Sordariomycetes – Loves garlic!
• Fungi: Pleosporales / Dothideomycetes
• Fungi: Saccharomycetaceae / Saccharomycetales / Saccharomycetes
• Fungi: Synchytrium / Synchytriaceae / Synchytriales / Chytridiomycetes / Chytridiomycota
• Fungi: Trichosporonaceae / Trichosporonales / Tremellomycetes / Basidiomycota

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. 

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.

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_Name	Tax_rank	Frequency seen in Autism	Frequency seen in Control	Frequency Chi2
Senegalimassilia	genus	28.9	13.8	31.7
Bifidobacterium pseudocatenulatum	species	17.9	7.3	29
Megamonas	genus	30.3	17.3	19.2
unclassified Clostridiales	family	46.3	30.1	17.4
Hungateiclostridiaceae	family	44.5	28.7	17.1
Abiotrophia	genus	14.7	7	16.4
Nitriliruptoria	class	17.4	33.3	15.6
Adlercreutzia equolifaciens	species	27.5	45.7	15
Euzebyaceae	family	17.4	32.8	14.9
Euzebyales	order	17.4	32.8	14.9
Euzebya	genus	17.4	32.8	14.9
Megamonas funiformis	species	15.6	7.8	14.8
Intestinibacter bartlettii	species	36.2	23.2	14.6
Adlercreutzia	genus	31.7	50.3	14.4
Euzebya tangerina	species	17.4	32.4	14.3
Brochothrix	genus	17.4	31.8	13.4
Olivibacter	genus	22.9	38.3	12.8
Pseudoclostridium	genus	27.1	43.4	12.7
Pseudoclostridium thermosuccinogenes	species	27.1	43.4	12.7
Eggerthella sinensis	species	15.1	28.4	12.7
Symbiobacteriaceae	family	25.2	40.9	12.5
Odoribacter denticanis	species	19.3	33.2	12.1
Brochothrix thermosphacta	species	17	30.3	12.1
Erysipelothrix muris	species	26.6	41.9	11.6
Hymenobacter	genus	20.2	33.8	11.4
Hymenobacteraceae	family	26.1	40.9	11.1
Dolichospermum	genus	20.2	33.6	11.1
Corynebacterium durum	species	17	9.6	11
Acidaminococcus fermentans	species	21.6	35.1	10.7
Selenomonas	genus	37.2	53.7	10.6
Aphanizomenonaceae	family	21.1	34.4	10.6
Bifidobacterium angulatum	species	17.4	10.1	10.5
Ruminiclostridium cellobioparum subsp. termitidis	subspecies	21.6	34.8	10.4
Carboxydocella ferrireducens	species	16.1	27.9	10.4
Ruminiclostridium cellobioparum	species	22.9	36.4	10.3
Dysgonomonas wimpennyi	species	26.6	40.9	10.3
Caldicellulosiruptor	genus	34.4	50.1	10.1
Clostridiales Family XVI. Incertae Sedis	family	18.8	31.1	10.1
Carboxydocella	genus	18.8	31.1	10.1
Senegalimassilia anaerobia	species	16.5	9.5	10
Streptococcus mutans	species	20.2	12.3	10
Pseudobutyrivibrio xylanivorans	species	33.9	49.3	9.9
Hathewaya histolytica	species	35.3	50.8	9.8
Hymenobacter xinjiangensis	species	14.7	25.7	9.7
Porphyromonas canis	species	21.1	33.6	9.6
Oscillospira	genus	45.9	62.9	9.6
Nostocaceae	family	29.8	44	9.4
Prevotella timonensis	species	22.5	35.1	9.3
Intestinibacter	genus	51.4	38.1	9.3
Butyrivibrio proteoclasticus	species	24.8	37.8	9.3
Propionispora hippei	species	21.1	33.3	9.2
Thermicanus	genus	42.7	58.8	9.1
Amoebophilaceae	family	31.7	45.8	9.1
Thermoclostridium caenicola	species	20.2	32	9
Oscillospira guilliermondii	species	31.2	45.2	9
Clostridium cellulovorans	species	15.6	9.1	8.9
Hungateiclostridium	genus	32.1	22.2	8.7
Blautia coccoides	species	37.2	51.8	8.6
Candidatus Amoebophilus asiaticus	species	31.7	45.4	8.5
Finegoldia magna	species	37.2	51.7	8.5
Bacteroides stercorirosoris	species	37.6	52.2	8.5
Amoebophilus	genus	31.7	45.4	8.5
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	30.3	20.8	8.5
Phascolarctobacterium succinatutens	species	36.2	50.6	8.4
Peptoniphilus methioninivorax	species	24.3	36.5	8.4
Planifilum fimeticola	species	21.1	32.6	8.3
Bifidobacterium catenulatum	species	31.7	22	8.3
Tindallia magadiensis	species	27.5	40.2	8.3
Anaerovibrio lipolyticus	species	31.7	45.1	8.3
Acholeplasma	genus	41.7	56.7	8.2
Hathewaya	genus	52.3	68.7	8.2
Thiothrix	genus	25.2	37.4	8.2
Propionispora	genus	24.3	36.2	8.1
Alkaliphilus crotonatoxidans	species	34.4	48.1	8.1
Pseudoramibacter	genus	14.2	23.9	8
Acidaminococcus	genus	40.8	55.1	7.7
Sphingobacterium bambusae	species	29.8	42.3	7.6
Thiotrichaceae	family	26.1	38	7.6
Anaerovibrio	genus	32.6	45.5	7.6
Bifidobacterium boum	species	24.8	16.7	7.6
Shewanella	genus	25.7	37.3	7.5
Shewanellaceae	family	25.7	37.3	7.5
Caulobacteraceae	family	18.3	28.5	7.4
Planifilum	genus	22	32.9	7.4
Rhodothermus	genus	33.9	46.9	7.4
Tetragenococcus doogicus	species	18.3	28.5	7.4
Bifidobacterium thermacidophilum	species	21.6	14.2	7.4
Sphingobacterium shayense	species	31.2	43.7	7.4
Caulobacterales	order	18.3	28.5	7.4
Selenomonas infelix	species	33.9	46.9	7.4
Oscillatoriaceae	family	17.4	27.2	7.3
Bilophila wadsworthia	species	45.4	59.9	7.3
Anaerostipes hadrus	species	50	38.4	7
Bacteroides faecis	species	38.5	51.8	7
Caloramator mitchellensis	species	36.7	49.4	6.7
Streptococcus fryi	species	18.8	28.4	6.7
Acetobacteraceae	family	17	26.1	6.5
Moorella group	norank	30.7	42.2	6.4
Chlorobaculum	genus	30.7	42.2	6.4
Butyricimonas virosa	species	28.4	39.5	6.4
Sphingobacterium	genus	39.9	52.4	6.2
Sedimentibacter	genus	43.1	56.1	6.2
Rhodothermus clarus	species	33.9	45.7	6.2
Sedimentibacter hydroxybenzoicus	species	38.5	50.9	6.2
Thiotrichales	order	36.2	48.3	6.2