A reader requested a straightforward video on how to use the Microbiome Prescription (MP) site for her autistic child. While there isn’t an ultra-simple way, there are two main approaches:

• One method is to compare your child’s results to samples from other autistic children, tested at the same lab. MP identifies which key bacteria (KB#1) to target for changes this way.
• The second method uses scientific literature about autism from the US National Library of Medicine. These studies use many different labs, which are not standardized—so results can vary (KB#2).

With either method, MP suggests probiotics based on limited research. The lists of bacteria identified (KB#1 and KB#2) help determine using the novel R² algorithm which probiotics might help, using a very large data pool. This creates four sets of probiotic recommendations, which you’ll need to manually review—look for overlaps in suggestions or options that none of the sets disagree with.

MP can also use identified nutrients to suggest which foods to include or avoid in the diet.

• National Library of Medicine Citations for Autism
• Symptom Association for Autism
• Example of Cross Validated Suggestions.
• Recommended Testing Lab (Most samples with Autism in Database) Discount code: “MICRO”

Note: Suggestions are specific to an individual — bacteria shifts are different from one person to the next.

I am a statistician by training and experience. A common problem with people is to project causation on a random association (often of thousands possible) that agrees with ideological beliefs or doctrines. In this post, I will look at the dramatic increase of autism through the eyes of statistican. I will not be documenting the shyte out of things, just hitting highlights with many borrowed graphics.

The Reality

Before the 1960s, autism was considered a very rare condition, with prevalence estimates around 0.05% (1 in 2,000 children). This contrasts with recent estimates, which are significantly higher. Some studies from the 1960s and 70s reported prevalence rates between 2 and 4 cases per 10,000 children. 

Vaccines

First, a 2025 study Large Danish Study: No link between vaccines and autism or 49 other health conditions using data from a national medical system (i.e. uniformity of treatment and records) with a high degree of uniformity in nutrition and other compounding analysis issues — effectively should end this red-herring that is popular in MAGA groups

Simple Culture Changes

A few years ago I wrote Autism Factors where I reviewed what was found to be very statistically significant including the following:

• Age of mother
• Caesarean delivery
• Breast Feeding

Marrying Older means more Autism

The increase of age shown below follow the above curve well!

We also see that in age of birthing parents. Notice that both above and below, we have a flatening around 2010.

Looking at C-Sections, we seem more matching of patterns. Also breastfeeding decreases risk of autism.

Less Kids –> More Autism

Studies have revealed that the odds of a first child having autism is 160% of the chance of a second child. As the number of kids decrease, the number of kids with autism will increase. Assuming that everyone has one kid and 16/1000 has autism. With an average of 4 kids, it should drop to 11/1000. That is a 32% drop in the rate of autism

“Inbreeding”

Additional studies found increase incidence when both parents are involved with mathematics and computers. It has been suggested that DNA mutation that allows people to be successful in those occupations effectively increased the odd of an “in-breeding like” child. One mutation is fine, matching mutations is horrible.

Environmental Factors

The chart below makes this potential clear. “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]

Looking at some factors

PFAS-contaminated water or food is strongly associated: “Longitudinal study links PFAS contamination with teas, processed meats and food packaging“. This leads to this report: Autism rates declining among wealthy whites, escalating among poor. Wealthy whites are more prone to eat organic, avoid fast and processed foods.

The study, published Thursday in the Journal of Autism and Developmental Disorders, raises the possibility that parents in wealthier counties are successfully reducing environmental exposures that may contribute to autism risk, or taking other steps to curb its severity early on.

This leads on a last factor, the microbiome: Bacteria Associated with Autism from Microbiome Prescription, or if you prefer, Studies on the US National Library of Medicine. Bacteria is strongly associated to diet, i.e. processed meats and food additives to name a few.

Bottom Line

Autism is not purely genetic in a strict sense (if may be a significant factor), but age of birth, birth order, mother’s environmental issues are significant factors. All of these are “before the fact” issue. The best options for “after the fact” appear to be:

• Environment
• Microbiome manipulation

Concerning drugs, most drugs alter the microbiome and it is unclear if the drug is directly causing improvement, or indirectly by altering the microbiome.

The data that tend to follow or correlate alongside autism rate—meaning demographics, diagnostic trends, or related variables often tracked or reported with autism prevalence—include:

• Age: Autism diagnoses are most commonly made in early childhood, especially between ages 2 and 8. Prevalence estimates are often reported for specific age cohorts, such as age 4 or 8, which the CDC uses for tracking trends.
• Sex: A consistent male predominance is reported, usually about a 4-to-1 male-to-female ratio in childhood diagnoses. However, recognition of autism in girls lags behind, with many women diagnosed much later, suggesting under-identification in females.
• Race/Ethnicity: Rates of diagnosis have historically been higher in white children, but recent data show increases among children of color as diagnostic access and awareness grow.
• Socioeconomic Status: Diagnosis rates may connect to family income, healthcare access, and parental education, but increasing prevalence in different demographics may reflect better awareness and diagnostic efforts rather than true increases in incidence.
• Geography: Autism prevalence varies by region and state, partly due to differences in healthcare systems, special education reporting, and diagnostic practices.
• Method of Case Finding: Surveillance approaches (e.g., school/education records, medical billing data, or direct assessment) influence reported rates. Records-review surveillance and direct assessment generally yield higher prevalence than administrative counts.
• Comorbid Conditions: Many studies note higher rates of intellectual disability, ADHD, anxiety, or other neurodevelopmental issues reported in those diagnosed with autism.
• Time: Time trends are a key data point. Autism prevalence has risen substantially since the 2000s, largely attributed to broader diagnostic criteria, greater awareness, and increased screening—not necessarily a true rise in underlying incidence.
• Diagnosis Age and Early Identification: Data often track the percentage of children diagnosed by a certain age, as early diagnosis is important for access to services.

This post uses this May 2025 study for selecting bacteria desired to be shifted.

Perturbations in gut microbiota in autism spectrum disorder: a systematic review [May 2025]

Bacteria	Direction
Eubacteriales, Klebsiella, and Clostridium	Statistically High
Oscillospira, Dorea, and Collinsella	Enriched
Streptococcus, Akkermansia, Coprococcus, and Dialiste	Depleted

Our approach is simple, we look up each on Microbiome Taxa R² Site. We then look at probiotics bacteria that are associated with desired shifts.

• Too High — rarely do probiotics reduce most bacteria
  • Eubacteriales [ order } – nothing found
  • Klebsiella [ genus } – nothing found
  • Clostridium [ genus } – nothing found
  • Oscillospira – nothing found
  • Dorea [ genus } – nothing found
  • Collinsella [ genus } – nothing found
• Too Low
  • Streptococcus [ genus }
    • Streptococcus thermophilus
    • Lactococcus lactis
    • Lacticaseibacillus paracasei
    • Enterococcus faecalis
    • Bifidobacterium breve
    • Bifidobacterium catenulatum
  • Akkermansia [ genus }
    • Akkermansia muciniphila
    • Enterococcus faecium
    • Lactococcus cremoris
    • Bacteroides thetaiotaomicron
  • Coprococcus [ genus }
    • Enterococcus faecium
    • Lactococcus cremoris
    • Akkermansia muciniphila
    • Lactococcus lactis
    • Bacteroides thetaiotaomicron
    • Levilactobacillus brevis
    • Ligilactobacillus salivarius
    • Latilactobacillus sakei
    • Lactiplantibacillus plantarum
    • Bifidobacterium adolescentis
    • Bifidobacterium catenulatum
      

What is Microbiome Taxa R² Site

It based on associations determined from 1000 healthy individuals using shotgun analysis,

This is followed by a table of the bacteria associations. This table has probiotics identified (including several that are pending).

Bottom Line

Looking on the impact, the following three probiotics would be my top choice:

• Enterococcus faecium – Source
• Lactococcus cremoris – Source
• Akkermansia muciniphila – Source or Source

Why not just use studies? There is nothing wrong with using studies. Pages listing studies result are linked to from each of the above pages. The difference is that with this approach you get the relative impact (R_²) between probiotics which is not available from the studies. Also, some probiotics have very few studies, i.e. Lactococcus cremoris. Using both published studies and this tool gives the maximum coverage.

Recently I have been updating the statistic processing for association though diverse methods. Everything below is P < 0.005 (or 1 in 200 of happening at random).

From Ombre data

Bacteria	Rank	Shift
Bifidobacterium bifidum	species	Too High
Bifidobacterium subtile	species	Too High
Hungateiclostridiaceae	family	Too High
Liquorilactobacillus	genus	Too High
Liquorilactobacillus vini	species	Too High
Neisseriaceae	family	Too High
Peptoniphilus obesi	species	Too Low
Senegalimassilia	genus	Too High
unclassified Clostridiales	family	Too High

From Biomesight Data

One note, we do 4 different computations and in general they agree with each other (the Monte Carlo or Consensus Model). For Bifidobacterium species, that is not the case, some computations suggests too high and other computations suggests too low. Bifidobacterium with Autism have a variety of disagreements between studies. In this case, we know that the disagreement is due to the statistical method used.

As seen below, there is over prevalence (seen more often), but the values are smaller than the reference population.

Bacteria	Rank	Statistical Test	P Value	Shift
Bifidobacterium angulatum	species	Chi-2 prevalence	3.21E-12	Too High
Bifidobacterium angulatum	species	Mann_Whitney_Wilcoxon	0.000456655	Too Low
Bifidobacterium angulatum	species	Averages	0.000464826	Too Low

---

Bacteria	Rank	Shift
Absiella	genus	Too High
Acidaminococcus	genus	Too High
Actinomyces	genus	Too Low
Actinomycetaceae	family	Too Low
Actinomycetes	class	Too Low
Actinomycetota	phylum	Too Low
Agromyces	genus	Too High
Agromyces salentinus	species	Too High
Amoebophilaceae	family	Too High
Anaerotruncus	genus	Too High
Anaerotruncus colihominis	species	Too High
Anaerovibrio	genus	Too High
Anaerovibrio lipolyticus	species	Too High
Bacteroides	genus	Too High
Bacteroides cellulosilyticus	species	Too High
Bacteroides faecis	species	Too High
Bacteroides finegoldii	species	Too High
Bacteroides fragilis	species	Too Low
Bacteroides rodentium	species	Too High
Bacteroides stercorirosoris	species	Too High
Bacteroides thetaiotaomicron	species	Too High
Bacteroides uniformis	species	Too High
Bifidobacteriaceae	family	Too Low
Bifidobacteriales	order	Too Low
Bifidobacterium	genus	Too Low
Bifidobacterium adolescentis	species	Too Low
Bifidobacterium angulatum	species	Too High
Bifidobacterium angulatum	species	Too Low
Bifidobacterium bifidum	species	Too Low
Bifidobacterium catenulatum	species	Too High
Bifidobacterium catenulatum	species	Too Low
Bifidobacterium catenulatum PV20-2	strain	Too High
Bifidobacterium catenulatum PV20-2	strain	Too Low
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	Too High
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	Too Low
Bifidobacterium choerinum	species	Too Low
Bifidobacterium cuniculi	species	Too High
Bifidobacterium gallicum	species	Too High
Bifidobacterium gallicum	species	Too Low
Bifidobacterium indicum	species	Too High
Bifidobacterium indicum	species	Too Low
Bifidobacterium longum	species	Too Low
Bifidobacterium scardovii	species	Too High
Bifidobacterium subtile	species	Too Low
Burkholderia	genus	Too Low
Burkholderiales genera incertae sedis	no rank	Too Low
Caloramator fervidus	species	Too High
Caloramator indicus	species	Too High
Candidatus Amoebophilus	genus	Too High
Candidatus Amoebophilus asiaticus	species	Too High
Candidatus Blochmanniella	genus	Too High
Candidatus Blochmanniella camponoti	species	Too High
Clostridium	genus	Too High
Clostridium chartatabidum	species	Too High
Clostridium thermosuccinogenes	species	Too High
Collinsella aerofaciens	species	Too Low
Coriobacteriia	class	Too Low
Desulfotomaculaceae	family	Too High
Desulfovibrio	genus	Too High
Desulfuromonadaceae	family	Too Low
Desulfuromusa	genus	Too Low
Enterobacter	genus	Too Low
Enterobacter cloacae complex	species group	Too High
Enterobacter hormaechei	species	Too High
Enterobacterales	order	Too Low
Enterobacteriaceae	family	Too Low
Enterobacteriaceae incertae sedis	no rank	Too High
Erysipelothrix	genus	Too High
Escherichia	genus	Too Low
Escherichia coli	species	Too Low
Eukaryota	superkingdom	Too Low
Gammaproteobacteria	class	Too Low
Geopsychrobacteraceae	family	Too Low
Hathewaya	genus	Too High
Hathewaya histolytica	species	Too High
Hungateiclostridiaceae	family	Too High
Hungateiclostridium	genus	Too High
Hydrogenophilaceae	family	Too Low
Hydrogenophilales	order	Too Low
Hydrogenophilia	class	Too Low
Klebsiella oxytoca	species	Too High
Megamonas	genus	Too High
Megamonas	genus	Too Low
Megamonas funiformis	species	Too High
Megamonas funiformis	species	Too Low
Moorella group	norank	Too High
Oscillospira	genus	Too High
Parascardovia	genus	Too High
Parascardovia	genus	Too Low
Pedobacter	genus	Too High
Peptoniphilaceae	family	Too High
Peptoniphilus	genus	Too High
Phascolarctobacterium succinatutens	species	Too High
Phocaeicola	genus	Too High
Phocaeicola paurosaccharolyticus	species	Too High
Phocaeicola sartorii	species	Too High
Phocaeicola vulgatus	species	Too High
Porphyromonas	genus	Too High
Pseudoclostridium	genus	Too High
Rhodothermales	order	Too High
Rhodothermia	class	Too High
Rhodothermota	phylum	Too High
Sarcina	genus	Too Low
Sarcina maxima	species	Too Low
Segatella	genus	Too Low
Segatella albensis	species	Too Low
Segatella copri	species	Too Low
Segatella oulorum	species	Too High
Selenomonas	genus	Too High
Selenomonas infelix	species	Too High
Sphingobacteriaceae	family	Too High
Sphingobacteriales	order	Too High
Sphingobacteriia	class	Too High
Sphingobacterium bambusae	species	Too High
Streptococcus intermedius	species	Too High
Streptococcus mutans	species	Too High
Streptococcus thermophilus	species	Too Low
Thiomonas	genus	Too Low
Tissierellales	order	Too High
unclassified Bacteroidetes Order II.	order	Too High
Veillonella	genus	Too Low
Veillonella montpellierensis	species	Too Low

High Functioning Autism

We have smaller sample size, but do have some significant items

Ombre Data

Bacteria	Rank	Shift
Butyricimonas paravirosa	species	Too High
Hallella	genus	Too High
Hallella multisaccharivorax	species	Too High
Hoylesella oralis	species	Too High
Hoylesella timonensis	species	Too High
Leyella	genus	Too High
Leyella stercorea	species	Too High
Segatella baroniae	species	Too High
Segatella maculosa	species	Too High
Xylanibacter brevis	species	Too High

Biomesight Data

Bacteria	Rank	Shift
Bilophila wadsworthia	species	Too Low
Erysipelothrix	genus	Too Low
Erysipelothrix muris	species	Too Low
Holdemania	genus	Too Low

I will revisit as more data comes in.

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