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 alone7 8.
Advantages of This Approach
- Net Effect: By examining metabolites, we can assess the overall impact of the microbiome on the host, regardless of the specific bacterial species present5.
- Consistency: Metabolite imbalances may be more consistent across patients than bacterial species composition, which can vary widely7.
- Functional Insight: This approach provides insight into the functional consequences of microbiome dysbiosis in ME/CFS3 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 function2 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:
- In a rat model of transient focal cerebral ischemia, HQ treatment strongly alleviated ischemic brain injury3 4.
- 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.
- 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:
- By preventing BBB disruption, HQ may help maintain normal brain function and protect against cognitive decline associated with ischemic events3.
- 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:
- Most studies on HQ’s neuroprotective effects have been conducted in animal models, and more research is needed to confirm these effects in humans3 4 .
- The typical use of HQ as a skin-lightening agent is unrelated to its potential cognitive effects5. Its primary application remains in dermatology.
- 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.