Category Archives: Conditions

Unknown's avatar lassesen 10:23 am on January 1, 2025  

Statistically Significant Bacteria shifts seen in ME/CFS

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 ME/CFS.

  • Total Population: 3525
  • ME/CFS: 280

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_nameTAX
RANK		Chi2ObservedExpectedShift
Segatella oulorumspecies13.91638Under-Represented
Bifidobacterium cuniculispecies10.83357Under-Represented
Brenneriagenus10.5112Under-Represented
Bifidobacterium adolescentis strain10.35077Under-Represented
Prevotella veroralisspecies10.3620Under-Represented
Aggregatibactergenus10.14672Under-Represented
Hoylesella shahiispecies10.11228Under-Represented
Segatella paludivivensspecies9.64064Under-Represented
Actinobacillus pleuropneumoniaespecies8.63354Under-Represented
Slackia heliotrinireducensspecies8.2820Under-Represented
Prevotella micansspecies8.1313Under-Represented
Actinobacillusgenus8123157Under-Represented
[Actinobacillus] rossiispecies7.179105Under-Represented
Pasteurellaceae incertae sedisno rank7.179105Under-Represented
[Pasteurella] aerogenes-[Pasteurella] mairii-[Actinobacillus] rossii complexspecies group7.179105Under-Represented
Aggregatibacter aphrophilusspecies7413Under-Represented
Mitsuokella multacidaspecies6.8312Under-Represented

Seen too Often (Want to decrease)

We see 118 bacteria over a Chi2 of 6.635 ( P < 0.01 or 1 change in 100 of being a false detection). The list below are for genus and higher taxonomy orders.

Tax NameRankChi2ObservedExpectedShift
Dethiobacteraceaefamily20.97545Over-Represented
Hungateiclostridiaceaefamily16.17145Over-Represented
Tepidimicrobiaceaefamily14.6167Over-Represented
unclassified Burkholderialesfamily12.62916Over-Represented
Corynebacteriaceaefamily11.2159123Over-Represented
Halanaerobiaceaefamily10.17654Over-Represented
Alcanivoracaceaefamily9.65335Over-Represented
Halanaerobialesorder9.68158Over-Represented
Desulfonatronovibrionaceaefamily9.15840Over-Represented
Acetobacteraceaefamily8.712496Over-Represented
Hyphomonadaceaefamily82918Over-Represented
Tissierellaceaefamily7.52011Over-Represented
Rhizobiaceaefamily7.19876Over-Represented
Nannocystineaesuborder6.93624Over-Represented
Rubritaleaceaefamily6.912499Over-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.

Unknown's avatar lassesen 11:40 am on December 29, 2024  

Visual Exploration of Odds Ratios

This is a “scribe notes” post. I am working on implementing odds ratio as a forecaster for ME/CFS and encountered some issues. In my work experience, this means taking a significant step backwards to look at the data better. I will look at the bacteria with a high frequency of being reported in tests first. The genus of greatest interest from preliminary work are:

  • Blautia
  • Faecalibacterium
  • Clostridium
  • Lachnospira
  • Streptococcus
  • Collinsella
  • Anaerostipes
  • Parabacteroides
  • Anaerotruncus
  • Bifidobacterium
  • Pseudobutyrivibrio

The data is from BiomeSight samples. The red line is what would be expected with no influence. The population percentile included those with ME/CFS. Removing them would increase the differences more (I went lazy).

Charts

Blautia

Lower levels are clearly significant.

Faecalibacterium

Lower levels are some significance.

Clostridium

Lower levels are clearly significant.

Lachnospira

Higher levels are some significance for low values.

Streptococcus

Does not appear to have much significance

Collinsella

Higher levels are some significance for below average values

Anaerostipes

Lower levels are significance for all values.

Parabacteroides

Does not appear to have much significance

Anaerotruncus
Lower levels are significance for all values.

Bifidobacterium

Higher levels are significance for all values.

Pseudobutyrivibrio

Higher levels are significance for all values.

Next Steps

We can visually see how bacteria is shifted for these genus. The challenge is converting it to a formula to forecast that cross validates. Stay tune.

Unknown's avatar lassesen 8:37 am on December 28, 2024
Tags: , , lactobacillus-probiotics, , , ,   

ME/CFS: The Evils of Lactobacillus Probiotics?

A reader wrote me today with the following question

I read your article on microbial involvement, can you explain in more detail why you recommended cutting out Lactobacillus? If I interpreted your analysis, you said lactobacillus was rare in ME/CFS, doesn’t that mean increasing it could be beneficial? “..lactobacillus shows up barely in only one result..”.Meaning lactobacillus is rare for ME/CFS patients?

I meant that there is no clear evidence of a lactobacillus deficiency with ME/CFS patients microbiomes. There are reasons to believe that it may be harmful and helps maintain ME/CFS state.

History

I am a facts/study based individual that have been reading studies, conference reports since 1990’s. Back in 1999, on eGroups CFSFM-Experimental, taking probiotics were often suggested. Why? Because probiotics has been promoted as a cure-all for all conditions. A influencer snake oil. Reported results on CFSFM-Experimental were disappointing.

My mind proceeded logically. So ask the US National Library of Medicine (PubMed), “Which probiotics have been helpful for ME/CFS? Given that there were 2400 studies on ME/CFS then, I expected to find a few dozen by then — after all, it would likely be one of the first choices by naturopaths who would rush to publish their results!! There were none that used lactobacillus probiotics. Even today, we have just 32 studies mentioning “chronic fatigue syndrome” probiotics in the 8500 studies posted. [Sarcasm] “Surely, there would have been a rush with all of the ME/CFS specialists to use lactobacillus probiotics given all of this evidence”.

Being a scientist, I know that what gets published are positive results — not no result nor negative results.

Reading conference papers presented by specialist on ME Research UK, I came across a report of a conference panel by active practitioners where the consensus was no benefit. I have worked as a professional technical writer and very “phrasing aware”, I read the wording to indicate that probiotics likely did harm in some of their patients. Slamming probiotics tend to be view as a heresy with many health influencers.

There Appears No Significant Objective Evidence that lactobacillus helps!

Yes, you will find testimonials — but that is not objective evidence. They may have helped because the person did not have ME/CFS (self diagnosis) or a different condition. It is incomprehensible that there have not been dozens (or hundreds) of studies trying lactobacillus — studies that are unpublished because of unfavorable results.

Why may it be EVIL?

Again, conference papers from Australia’s Alison Hunter Memorial Foundation play an important role here. From the Way-Back machine I retrieved items no longer on their site and pasted it into 1998 Was a very good year…. The key finding was “The mean distribution of E.coli as percentage of the total aerobic microbial flora for the control subjects and CFS patients was 92.3% and 49% ” Not a little drop, but almost half the level!

NOTA BENA: The typical (cheap) 16s tests used for most modern microbiome studies effectively ignore E.Coli. Shotgun testing (much more expensive) finds E.Coli in almost every sample. Some 16s finds it in 1 in a thousand samples as shown by the table below. Modern studies not repeating these results is a direct consequence of their methodologies!!

This was the motivation for my trying Mutaflor Probiotics (E.Coli Nissle 1917) which I happen to have in the house because my wife has Crohn’s and it made a huge difference for her (with lots of studies reporting it too!!!). I had a severe Jarisch–Herxheimer reaction for two weeks and a rapid recovery from ME/CFS afterwards.

If you look at Odds Ratios for Metabolites and ME/CFS, you will see that E.Coli probiotics has the biggest impact on the metabolite imbalance with ME/CFS

Going over to the E.Coli page on Microbiome Prescription we see that Lactobacillus constantly reduces E.Coli. So we are moving from levels that are 50% of normal levels to even lower levels.

IMHO, for ME/CFS, Lactobacillus probiotics are EVIL

Yes there are a few lactobacillus that will help some symptoms (and likely make other symptoms worse). Unless you are very sure that it has the actual probiotic strain used in studies, don’t do it. See Probiotics — what is advertised may not be what you get.

IMHO, for brain fog, Lactobacillus probiotics are EVIL

Interesting study relative to ME/CFS and brain fog. Lactobacillus can trigger “thick blood”, decreasing oxygen delivery (hypo perfusion). The aggregation of human platelets by Lactobacillus species

This extends to a few other Conditions

Bottom line, checking for clinical studies if a probiotics clearly helps is recommended. This search engine may help.

Bifidobacterium also?

In Visual Exploration of Odds Ratios, we see that ME/CFS people have higher then general population amounts of Bifidobacterium. On the flip side, the average amount is reported lower on several studies. This compounds issues with several things that needs to be investigated.

  • Did the lower bifidobacterium count not found in their average as zero? We use the values only when detected. Looking at the dots, we see that the dots are sparse/rare for lower values suggesting a lower detection rate. This suggests a threshold behavior of bifidobacterium.

Looking at impact on E.Coli, we see most studies say that it decreases E.Coli

There is not enough data to come to a safe conclusion.

Unknown's avatar lassesen 6:18 am on December 25, 2024
Tags: , , , ,   

Odds Ratios for Metabolites and ME/CFS

This post extends the analysis of microbial involvement in ME/CFS pathophysiology by focusing on metabolites produced or consumed by bacteria, rather than on individual bacterial species seen in the earlier post Microbial involvement in myalgic encephalomyelitis/chronic fatigue syndrome pathophysiology. . This shift in perspective is valuable because:

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

  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/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.

BiomeSight Results

I did three slice-and-dice

  • Producers
  • Consumers
  • Net metabolites (Producers – Consumers) – this is like the most important

Remember: results may be different for different labs. Also, these are estimates of the metabolites

Metabolite Producers

  • DNA N4-methylcytosine <= 31.1
  • Cytidine 5′-diphosphoramidate <= 28
  • Pyridoxal <= 24.3
  • Allantoate <= 23.6
  • [L-Glutamate:ammonia ligase (ADP-forming)] <= 22.9
  • Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)] <= 22.9
  • Uridylyl-[protein-PII] <= 22.6
  • 4-Hydroxybenzoate <= 19.6
  • 5-Phospho-D-xylonate <= 18.5
  • 5-Phospho-L-arabinate <= 18.5
  • Formyl-CoA <= 18
  • Aminoacrylate <= 17.3
  • Methylaminoacrylate <= 17.3
  • Acetoacetate <= 17.3
  • 5-Carboxyamino-1-(5-phospho-D-ribosyl)imidazole <= 17.3
  • UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate <= 17
  • N-Acyl-L-homoserine <= 16.7
  • (R)-Piperazine-2-carboxylate <= 16.1
  • 2-[(2-Aminoethylcarbamoyl)methyl]-2-hydroxybutanedioate <= 16
  • D-Mannitol 1-phosphate <= 16
  • D-Erythritol 1-phosphate <= 15.6
  • 2-Acetylphloroglucinol <= 15.5
  • 3-Dehydrocarnitine <= 15.2
  • Deoxynucleoside <= 14.9
  • Formaldehyde <= 14.5
  • (S)-3-Acetyloctanal <= 14.4
  • Pyrrole-2-carbonyl-[pcp] <= 14.4
  • (L-Prolyl)adenylate <= 14.4
  • (L-Arginyl)adenylate <= 14.4
  • 2”-Nucleotidylgentamicin <= 13.9
  • 4-O-(beta-L-Arabinofuranosyl)-(2S,4S)-4-hydroxyproline <= 13.3
  • beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose <= 13.3
  • Polysulfide <= 13.2
  • 6-Deoxy-6-sulfo-D-fructose <= 13
  • 1-Phosphatidyl-1D-myo-inositol 5-phosphate <= 12.9
  • 2-Dehydro-3-deoxy-D-galactonate <= 12.8
  • beta-L-Arabinofuranose <= 12.8
  • Maltose 6′-phosphate <= 12.7
  • Cytidine <= 12.5
  • [beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-6-N-(beta-D-Asp)-L-Lys-D-Ala-D-Ala)]n <= 12.5
  • O-Phospho-L-homoserine <= 12.3
  • 2,5-Diamino-6-(5-phospho-D-ribosylamino)pyrimidin-4(3H)-one <= 12.3
  • Butanoyl-CoA <= 12.2
  • 4-Amino-5-hydroxymethyl-2-methylpyrimidine <= 12.1
  • Oxalyl-CoA <= 12.1
  • 5-(2-Hydroxyethyl)-4-methylthiazole <= 11.8
  • 2-Hydroxyornithine lipid <= 11.5
  • N3-Acetyl-2-deoxystreptamine antibiotic <= 11.3
  • Protein histidine <= 11.3
  • Protein N6-acetyl-L-lysine <= 11.2
  • Protoporphyrinogen IX <= 11
  • Divinylprotochlorophyllide <= 10.9

Metabolite Consumers (Substrates)

We have a shorter list with 5 metabolites bubbling to the surface as excessive metabolites.

  • Linalool >= 96.4
  • 6-Oxocyclohex-1-ene-1-carbonyl-CoA >= 96.4
  • 2-epi-5-epi-Valiolone >= 96.2
  • Lupanine >= 95.2
  • 4′-Hydroxyacetophenone >= 95.2
  • DNA cytosine <= 31.1
  • Xylitol <= 29.4
  • N5-(Cytidine 5′-diphosphoramidyl)-L-glutamine <= 28
  • 6-Hydroxynicotinate <= 27.6
  • Pyridoxine <= 24.1
  • [L-Glutamate:ammonia ligase (ADP-forming)] <= 22.9
  • Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)] <= 22.9
  • 2-Amino-2-deoxyisochorismate <= 22.7
  • [Protein-PII] <= 22.6
  • Formyl-CoA <= 20.4
  • D-Erythrulose 4-phosphate <= 20 alpha-Maltose 1-phosphate <= 18.8
  • L-Arabino-1,4-lactone 5-phosphate <= 18.5
  • D-Xylono-1,4-lactone 5-phosphate <= 18.5
  • N-Acyl-L-homoserine lactone <= 18.1
  • Oxalyl-CoA <= 18
  • Methylureidoacrylate <= 17.3
  • Ureidoacrylate <= 17.3
  • UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate <= 17
  • D-arabino-Hex-3-ulose 6-phosphate <= 17
  • beta-Alaninamide <= 16.1
  • (R)-Piperazine-2-carboxamide <= 16.1
  • 2-[(L-Alanin-3-ylcarbamoyl)methyl]-2-hydroxybutanedioate <= 16
  • Erythritol <= 15.6
  • alpha-L-Rhamnopyranosyl-(1->3)-N-acetyl-alpha-D-glucosaminyl-diphospho-trans,octacis-decaprenol <= 15.5 2,4-Diacetylphloroglucinol <= 15.5
  • (S)-Allantoin <= 15.1
  • L-Prolyl-[pcp] <= 14.4
  • trans-2-Octenal <= 14.4
  • (L-Prolyl)adenylate <= 14.4
  • (L-Arginyl)adenylate <= 14.4
  • (5-L-Glutamyl)-L-amino acid <= 13.6
  • 4-O-(beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranosyl-(1->2)-beta-L-arabinofuranosyl)-(2S,4S)-4-hydroxyproline <= 13.3
  • Sulfoquinovose <= 13
  • 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate <= 12.9
  • D-Galactonate <= 12.8
  • beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose <= 12.8
  • Oxalate <= 12.7
  • N4-Acetylcytidine <= 12.5
  • D-Aspartate <= 12.5
  • [beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n <= 12.5
  • 3′-Phosphoadenylyl sulfate <= 12.4
  • 2,5-Diamino-6-(5-phospho-D-ribitylamino)pyrimidin-4(3H)-one <= 12.3
  • D-Xylulose 5-phosphate <= 12.2
  • 4-Amino-5-aminomethyl-2-methylpyrimidine <= 12.1
  • Deoxynucleoside 5′-phosphate <= 11.9
  • Thiosulfate <= 11.6
  • Ornithine lipid <= 11.5
  • 2-Deoxystreptamine antibiotic <= 11.3
  • UDP-alpha-D-galactofuranose <= 11.2
  • 3′,5′-Cyclic AMP <= 11.1
  • [Sulfatase]-L-serine <= 11
  • trans-2,3-Dehydroacyl-CoA <= 10.9
  • D-Serine <= 10.6
  • 5,6-Dihydrothymine <= 10.4
  • Electron-transferring flavoprotein <= 10.3
  • D-Mannose <= 10.1
  • Ethanol <= 10.1

Net Modifiers

Here we have shorter list with

  • (R)-3-(4-Hydroxyphenyl)lactoyl-CoA >= 98.9
  • 14alpha-Formylsteroid >= 98.7
  • (E)-2-Methylgeranyl diphosphate >= 98.3
  • Harderoheme III >= 97
  • D-Erythritol 1-phosphate >= 83.4
  • 1-(5-O-Phospho-beta-D-ribofuranosyl)-5-(sulfanylcarbonyl)pyridin-1-ium-3-carbonyl adenylate >= 56.8
  • 8-Oxo-GDP <= 32.5 8-Oxo-dGDP <= 32.5
  • 2,4-Diketo-3-deoxy-L-fuconate <= 27.2
  • S-(Hercyn-2-yl)-L-cysteine S-oxide <= 27
  • L-Formylkynurenine <= 24.6
  • 2-[(2-Aminoethylcarbamoyl)methyl]-2-hydroxybutanedioate <= 23.5
  • alpha-Oxo-benzeneacetic acid <= 23.4
  • trans-o-Hydroxybenzylidenepyruvate >= 22.8
  • 2-(alpha-D-Mannosyl)-3-phosphoglycerate <= 22
  • Reduced FMN <= 20.6
  • Deamino-NAD+ <= 19.7
  • 5-(5-Phospho-D-ribosylaminoformimino)-1-(5-phosphoribosyl)-imidazole-4-carboxamide <= 18.5
  • cis-2,3-Dihydroxy-2,3-dihydro-p-cumate >= 18.2
  • Phthalate <= 17.9
  • alpha-Ribazole <= 17.7
  • beta-D-Fructose 6-phosphate <= 17.5
  • Reduced electron-transferring flavoprotein <= 16.5
  • GDP-L-fucose <= 16.4
  • 3-Hydroxy-5,9,17-trioxo-4,5:9,10-disecoandrosta-1(10),2-dien-4-oate <= 15.4
  • 2-Keto-D-gluconic acid <= 14.6
  • 6-(Hydroxymethyl)-7,8-dihydropterin <= 14.4
  • Formaldehyde <= 13.9
  • Adenosyl cobyrinate hexaamide <= 13.6
  • 3-Deoxy-D-manno-octulosonate <= 12.9
  • L-Fuculose 1-phosphate <= 12.8
  • D-Glutamate <= 12.5
  • L-Tyrosyl-tRNA(Tyr) <= 12.1
  • Maltose 6′-phosphate <= 11.8
  • O-Phospho-L-serine <= 11.8
  • 4-Guanidinobutanal <= 11.7
  • 7-Carboxy-7-carbaguanine <= 11.7
  • CDP-diacylglycerol <= 11.1
  • Protoporphyrinogen IX <= 11.1
  • 5-Guanidino-2-oxopentanoate <= 11
  • N5-Phospho-L-glutamine <= 10.9
  • D-1-Aminopropan-2-ol O-phosphate <= 10.5
  • Thymine <= 10.5
  • (2-Amino-1-hydroxyethyl)phosphonate <= 10.5
  • Hydrogenobyrinate a,c diamide <= 10.2
  • 2-Amino-3-carboxymuconate semialdehyde <= 10

Across Labs Consolidation

The analysis of metabolites across multiple microbiome testing platforms (Ombre, Biomesight, and uBiome) reveals a more consistent pattern of metabolite imbalances compared to bacterial species identification.

Potential Consequences of Low GDP-L-fucose (the top one)

The deficiency in GDP-L-fucose could have several implications:

  • Altered Immune Response: It may affect the proper functioning of the immune system, potentially impacting inflammatory processes12.
  • Cancer-Related Changes: Low levels might influence tumor progression or immune evasion mechanisms, as fucosylation is often altered in cancer24.
    • First-degree relatives: A clinic-based study reported that first-degree relatives of ME/CFS patients had a significantly higher(four times) prevalence of any cancer compared to controls (OR 4.06) [2022]
  • Cellular Communication: It could disrupt normal cell-cell interactions and signaling pathways dependent on fucosylated glycans3.
MetaboliteThresholdLowHigh
GDP-L-fucose16.512.121.2
Holo-[citrate (pro-3S)-lyase]12.71.823.4
N,N’-Diacetyllegionaminate12.15.422
alpha-Oxo-benzeneacetic acid11.81.923.4
Oxalyl-CoA11.3222.4
S-Methyl-5-thio-D-ribose 1-phosphate11.21.330.6
Malonyl-CoA10.62.526.3
1,2-Diacyl-3-alpha-D-glucosyl-sn-glycerol10.55.420

Translate Low Metabolites to Probiotics

Many of these metabolites are produced by probiotics, so in terms of highest importance and reasonably available probiotics, I produced the list below.

The top one is one is one that had very dramatic positive effect for me when I relapsed into ME/CFS (after the worse herxheimer reaction that I have ever experienced): Mutaflor (E.Coli Nissle 1917). I took it as a result of a 1999 study in Australia reporting very low levels of E.Coli in CFS patients [As a FYI, 16s tests do a very poor reporting on E.Coli].

The retail product microbiome labs/ megasporebiotic has several of the next on the list.

  1. Escherichia coli (Mutaflor, SymbioFlor-2) : 100%
  2. Bacillus thuringiensis: 70%
  3. Bacillus licheniformis: 67%
  4. Bacillus subtilis: 66%
  5. Bacillus subtilis subsp. natto: 67%
  6. Clostridium butyricum: 57% of the top
  7. Heyndrickxia coagulans (a.k.a. Bacillus coagulans): 65%
  8. Lactiplantibacillus plantarum: 59%
  9. Enterococcus faecalis: 57%
  10. Akkermansia muciniphila: 54%

I asked perplexity, which foods may increase any of the above metabolites. The following were reported:

  • Spinach
  • Rhubarb
  • Beets
  • Nuts
  • Chocolate
  • Tea
  • Wheat bran
  • Strawberries

Bottom Line

While the metabolite-focused approach provides valuable insights into the biochemical imbalances associated with ME/CFS, its immediate clinical applications are somewhat limited. The probiotics and the food suggestions are reasonable and I see several of the items appearing on suggestions from the expert system for ME/CFS patients.

Unknown's avatar lassesen 12:18 pm on December 23, 2024
Tags: , , , ,   

Microbial involvement in myalgic encephalomyelitis/chronic fatigue syndrome pathophysiology

This is the title of a new publication in Microbes & Immunity, available here.

Microbiome fluctuations or metabolic endotoxemia are proposed as possible disorder biomarkers. Based on the fact that gut microbiota dysbiosis reverts to a state of eubiosis in long-term patients with this condition, it may be hypothesized that disease progression begins with the loss of beneficial gut microorganisms, particularly short-chain fatty acid producers, leading to more widespread gastrointestinal phenotypes that are subsequently reflected in plasma metabolite levels. These alterations, specific of each individual, thereby result in metabolic and phenotypic shifts and in ME/CFS.

Microbial involvement in myalgic encephalomyelitis/chronic fatigue syndrome pathophysiology 6 Sep 2024

This has been my primary hypothesis for many years and lead to my writing Microbiome Prescription to normalize the dysbiosis. The bacteria shifts will be reported differently from different labs because of a lack of standardization of microbiome tests (See The taxonomy nightmare before Christmas… ).

Recently, I implemented an Odds Ratio analysis of the 5000+ sample microbiomes that have been upload to Microbiome Prescription. A quick overview is here: Bacteria Associated with General Fatigue.

Below I have pulled the lab specific dysbiosis shifts for ME/CFS at thhe genus level.

Biomesight Tests

The number are the percentile ranking for the bacteria listed. Each match increases the odds of ME/CFS by 1.5.

  • Methylonatrum >= 99
  • Planifilum >= 98.9
  • Thiohalorhabdus >= 98.9
  • Granulicatella >= 98.7
  • Amedibacillus >= 98.6
  • Enterococcus >= 98
  • Methylobacillus >= 98
  • Emticicia >= 97.7
  • Candidatus Tammella >= 97.7
  • Dokdonella >= 97.6
  • Runella >= 97.4
  • Collinsella <= 18
  • Pseudobutyrivibrio <= 16.4
  • Calothrix <= 13.8
  • Lachnospira <= 11.4
  • Veillonella <= 10.8
  • Shuttleworthia <= 8.5
  • Parabacteroides <= 8.4
  • Bifidobacterium <= 8.4
  • Faecalibacterium <= 7.5
  • Actinobacillus <= 7.5
  • Pedobacter <= 7.2
  • Coprococcus <= 4.6
  • Moorella <= 3.5
  • Natronincola <= 2.9
  • Mediterraneibacter <= 2.5
  • Oscillospira <= 1.8
  • Anaerovibrio <= 1.7
  • Sphingobacterium <= 1.6

Thryve

  • Collinsella <= 44.2
  • Bifidobacterium <= 41.1
  • Gemmiger <= 23
  • Dorea <= 19
  • Butyrivibrio <= 8
  • Fusicatenibacter <= 5.7
  • Lachnoclostridium <= 5.2
  • Gordonibacter <= 5.1
  • Eubacterium <= 4.8
  • Prevotella <= 4
  • Brassicibacter <= 3.9
  • Sporobacter <= 3.5
  • Johnsonella <= 3.5
  • Phascolarctobacterium <= 3.4
  • Dialister <= 3.4
  • Agathobacter <= 3.3
  • Lactonifactor <= 3.2
  • Murimonas <= 3.2
  • Moryella <= 3.1
  • Niabella <= 3.1
  • Ruminococcus <= 3
  • Hungatella <= 3
  • Anaerotruncus <= 2.8
  • Odoribacter <= 2.7
  • Lactobacillus <= 2.4
  • Eggerthella <= 2.3
  • Cellulosilyticum <= 2.1
  • Haemophilus <= 2
  • Holdemania <= 2
  • Terrisporobacter <= 2
  • Anaerobutyricum <= 1.9
  • Howardella <= 1.8
  • Hydrogenoanaerobacterium <= 1.8
  • Lachnobacterium <= 1.8
  • Hespellia <= 1.7
  • Agathobaculum <= 1.6
  • Pseudoflavonifractor <= 1.6
  • Desulfotomaculum <= 1.6
  • Intestinibacter <= 1.6
  • Barnesiella <= 1.5
  • Eisenbergiella <= 1.5
  • Facklamia <= 1.5
  • Acetatifactor <= 1.5
  • Ruminiclostridium <= 1.5
  • Natranaerovirga <= 1.5
  • Enterocloster <= 1.4
  • Caloramator <= 1.4
  • Thomasclavelia <= 1.4
  • Desulfovibrio <= 1.4
  • Parabacteroides <= 1.4
  • Shuttleworthia <= 1.3
  • Peptoniphilus <= 1.3
  • Akkermansia <= 1.3
  • Alistipes <= 1.3
  • Marvinbryantia <= 1.3
  • Acetivibrio <= 1.2
  • Paraprevotella <= 1.2
  • Veillonella <= 1.2
  • Bacteroides <= 1.2
  • Butyricimonas <= 1.2
  • Romboutsia <= 1.2
  • Ethanoligenens <= 1.2
  • Phocaeicola <= 1.2
  • Anaerocolumna <= 1.2
  • Blautia <= 1.1
  • Coprococcus <= 1
  • Lachnospira <= 1

uBiome

  • Veillonella <= 22.4
  • Hespellia <= 19
  • Bifidobacterium <= 15.1
  • Anaerosporobacter <= 7.2
  • Streptococcus <= 6.8
  • Eggerthella <= 6.4
  • Phascolarctobacterium <= 4.1
  • Eisenbergiella <= 3.9
  • Marvinbryantia <= 3.4
  • Holdemania <= 3.1
  • Dorea <= 3
  • Anaerostipes <= 3
  • Oscillibacter <= 2.7
  • Dialister <= 2.3
  • Kluyvera <= 2.1
  • Peptoclostridium <= 1.7
  • Erysipelatoclostridium <= 1.4
  • Intestinimonas <= 1.3
  • Sarcina <= 1.3
  • Roseburia <= 1.3
  • Phocaeicola <= 1.2
  • Terrisporobacter <= 1.2
  • Bacteroides <= 1.1

Bottom Line

Bifidobacterium is common across all of these, and lactobacillus shows up barely in only one result. Since there can be some hostility between Bifidobacterium and Lactobacillus, I would suggest cutting out lactobacillus probiotics.

I repeated this for Bifidobacterium species and one species shone.

  • Bifidobacterium longum <= 9.9

My personal preference for a source is Maple Life Science which sells direct from factory resulting in very fresh and alive probiotics.

Some examples of analysis people with Long COVID and ME/CFS are there. I am working on building an algorithm to build suggestions based on odds ratio which should improve the suggestions more.