Studies Quality Assurance Launch

Over the last two weeks, there has been a couple of email pointing out possible errors in some citations. I am not surprised. I expect 90-95% correctness (i.e. 1 in 10 or 1 in 20) may be incorrectly entered. To improve the quality, we need independent review of the data. In one amusing case, I quoted my source correctly but that review study incorrectly cited it’s source. The data entry was right, the source document was wrong.

The articles are technical studies which often require advance reading skills and knowledge of this topic. Some of the sources are available in full on the web for free, others are behind a paywall. If you are connected with a university or college, you may have access thru your institution.

If you cannot access the full text of the source, then skip it. Extracts and summaries can contain errors.


Just email me from the email account that you logged in with and I will add auditor or QA permissions to your account.

Doing it

When you logged in, you should see:

When you look at citations, you will see the ⚖️ icon (or a ✅ if someone has already checked) beside the citation.

Example for a list of citations

Note that there may be more in the study then what the titles implies. Often data is from Appendix and tables.

Clicking ⚖️ will take you to a page showing what was extracted and gives you an opportunity to correct it,

Click [Report above Issue] will send emails to me and to your self. If all of the information is correct, then click [I have verified..] and the next time you see the citation, there will be a ✅ beside it. Your email is stored beside the citation as the reviewer.

You will get an email confirming stuff

If there is information that was missed (more likely) please include the TAXON numbers of the bacteria. This speeds up the process. Often information was missed because of alternative spelling.

That’s the process. Short, simple and with the ability for me to quickly make corrections.

Will Inulin have a good impact?

Response differences of gut microbiota in oligofructose and inulin are determined by the initial gut Bacteroides/Bifidobacterium ratios [2023] Indicates difference of response.

Bacteroides/Bifidobacterium (Ba/Bi) ratios “with Low Ratio showing a significant increase in propionic acid and being enriched in glycolysis functions, whereas High was enriched in amino acids and aminoglycolysis functions”

Of course, the question becomes what are low and high ratio? To answer that question, I pulled some data by lab

Ubiome Ratios

Ombre Ratios

BiomeSight Ratios

Bottom Line

I would suggest a ratio > 500 to 1000 is High, Below 200 is low

Bacteria Difference by Age, Gender, Blood Type

The following is based on Biomesight data uploaded with some annotated by uploaders. Technically, interpretation of microbiome results should factor these issues in. At least 10 samples had to have Symptom Name to be included. The numbers shown are averages (on count per million) on samples that reported the bacteria.

As a spot check, we see multiple Bifidobacterium in the 0-10 range which agrees with the literature.

In general, the symptom has more than the population because we filtered by using a high T Score to keep the list shorter for illustration purposes. A variety of other factors are not shown, for example: FUT2 non-secretor or FUT2 secretor.

Dropping the T-Score filtering to 1.96 resulted in 3600 rows.

Symptom NameNameRankPopulation
per million
per million
Age: 0-10Listeriagenus323617.2
Age: 0-10Bifidobacterium magnumspecies2123016.4
Age: 0-10Bifidobacterium pseudocatenulatumspecies377091214.7
Age: 0-10Paenibacillusgenus373305.5
Age: 0-10Cystobacterineaesuborder191156.1
Age: 0-10Desulfovibrio vietnamensisspecies232264127
Age: 0-10Anaerostipesgenus1581217015.3
Age: 0-10Ethanoligenens harbinensespecies39157130.4
Age: 0-10Bifidobacterium bombispecies346747.3
Age: 0-10Eubacteriales incertae sedisnorank30090979.5
Age: 10-20Anaerococcus octaviusspecies10317387.2
Age: 10-20Bifidobacterium boumspecies34144120
Age: 10-20Puniceicoccalesorder8220917.2
Age: 10-20Bifidobacterium bombispecies34207223.1
Age: 10-20Eubacteriales incertae sedisnorank30091189.5
Age: 10-20Ruminiclostridiumgenus696103965.1
Age: 20-30Aggregatibacter aphrophilusspecies3544511.1
Age: 20-30Listeriagenus322675.2
Age: 20-30Bifidobacterium magnumspecies211207.8
Age: 20-30Halomonasgenus3082618.5
Age: 20-30Bifidobacterium pseudocatenulatumspecies37185455.3
Age: 20-30Halomonadaceaefamily404708.5
Age: 20-30Lactobacillus crispatusspecies356215838.1
Age: 20-30Fibrobacteresphylum2246602832.3
Age: 20-30Brenneriagenus53132110.7
Age: 20-30Dietziaceaefamily11316275.4
Age: 20-30Piscirickettsiaceaefamily241376.7
Age: 20-30Desulfovibrio vietnamensisspecies23162590.8
Age: 20-30Ethanoligenens harbinensespecies393085.3
Age: 20-30Saccharospirillaceaefamily18972110.2
Age: 20-30Prevotella maculosaspecies107298017.5
Age: 20-30Eubacteriales incertae sedisnorank30053405.4
Age: 20-30Eubacteriales Family XII. Incertae Sedisfamily3871511.2
Age: 20-30Pectobacteriaceaefamily53125710.7
Age: 20-30Balneolaeotaphylum2620413791.7
Age: 30-40Francisellagenus183459456.5
Age: 30-40Enterobacter cloacaespecies9813058.4
Age: 30-40Aggregatibacter aphrophilusspecies35124732.7
Age: 30-40Aggregatibacter segnisspecies8416009.4
Age: 30-40Halomonasgenus303818.2
Age: 30-40Bifidobacterium pseudocatenulatumspecies376838207
Age: 30-40Francisellaceaefamily183239427.3
Age: 30-40Halochromatium salexigensspecies231256
Age: 30-40Cystobacterineaesuborder191095.7
Age: 30-40Dietziaceaefamily11318566.2
Age: 30-40Halochromatiumgenus241879.4
Age: 30-40Desulfovibrio vietnamensisspecies233094174
Age: 30-40Ethanoligenens harbinensespecies393646.5
Age: 30-40Saccharospirillaceaefamily1856763.4
Age: 30-40Prevotella maculosaspecies1071239474.7
Age: 30-40Eubacteriales incertae sedisnorank30053055.4
Age: 30-40Eubacteriales Family XII. Incertae Sedisfamily383525.2
Age: 30-40Balneolaeotaphylum26223685.8
Age: 30-40Rhodovibrionaceaefamily119402222.4
Age: 40-50Aggregatibacter aphrophilusspecies3577119.9
Age: 40-50Bifidobacterium pseudocatenulatumspecies374210127
Age: 40-50Francisellaceaefamily1862680.7
Age: 40-50Fibrobacteresphylum2242933014.3
Age: 40-50Cystobacterineaesuborder191387.6
Age: 40-50Desulfovibrio vietnamensisspecies233100174.3
Age: 40-50Ethanoligenens harbinensespecies3985716.3
Age: 40-50Saccharospirillaceaefamily1820922
Age: 40-50Prevotella pleuritidisspecies52220217.1
Age: 40-50Megamonas funiformisspecies1344337547.2
Age: 40-50Prevotella maculosaspecies107453526.9
Age: 40-50Eubacteriales Family XII. Incertae Sedisfamily3885813.6
Age: 40-50Balneolaeotaphylum2612551486.4
Age: 40-50Rhodovibrionaceaefamily11912496.5
Age: 50-60Desulfomicrobiumgenus342768.3
Age: 50-60Bifidobacterium pseudocatenulatumspecies377205218.1
Age: 50-60Hathewaya proteolyticaspecies19896.6
Age: 50-60Paenibacillusgenus373706.2
Age: 50-60Fibrobacteresphylum224175118.5
Age: 50-60Sporotomaculum hydroxybenzoicumspecies37230136.9
Age: 50-60Cystobacterineaesuborder1917810.2
Age: 50-60Olsenellagenus13142759.8
Age: 50-60Desulfovibrio vietnamensisspecies233624204
Age: 50-60Desulfomicrobiaceaefamily342788.4
Age: 50-60Ethanoligenens harbinensespecies39190737.1
Age: 50-60Saccharospirillaceaefamily1836540.1
Age: 50-60Tepidimicrobiumgenus271587
Age: 50-60Opitutaeclass17335325.3
Age: 50-60Puniceicoccalesorder82362712.8
Age: 50-60Eubacteriales incertae sedisnorank30057555.9
Age: 50-60Eubacteriales Family XII. Incertae Sedisfamily385418.3
Age: 50-60Clostridium amylolyticumspecies211318.4
Age: 50-60Atopobiaceaefamily11329457.2
Age: 50-60Balneolaeotaphylum265457210.9
Age: 60-70Achromobactergenus282466.7
Age: 60-70Salmonellagenus7418707.4
Age: 60-70Aggregatibacter aphrophilusspecies353819.3
Age: 60-70Desulfomicrobiumgenus342266.6
Age: 60-70Deinococcus-Thermusphylum83217212.4
Age: 60-70Lacticaseibacillus paracaseispecies1141074825.3
Age: 60-70Limosilactobacillus fermentumspecies632015978.5
Age: 60-70Bifidobacterium magnumspecies2130222.1
Age: 60-70Sphingomonasgenus49531737.2
Age: 60-70Bifidobacterium pseudocatenulatumspecies37387361177.7
Age: 60-70Deltaproteobacteriaclass5224402765
Age: 60-70Salmonella entericaspecies7414165.6
Age: 60-70Burkholderiagenus99414624
Age: 60-70Sphingomonadaceaefamily57461521
Age: 60-70Paenibacillusgenus373746.3
Age: 60-70Abiotrophia defectivaspecies356606.1
Age: 60-70Fibrobacteresphylum224209585102.7
Age: 60-70Sporotomaculum hydroxybenzoicumspecies37157125
Age: 60-70Bifidobacterium boumspecies346699
Age: 60-70Cystobacterineaesuborder191266.8
Age: 60-70Burkholderiaceaefamily27230985.4
Age: 60-70Turicibacter sanguinisspecies89509219.1
Age: 60-70Deinococciclass83217212.4
Age: 60-70Chloroflexiphylum11020618.6
Age: 60-70Desulfovibrio vietnamensisspecies232883162.1
Age: 60-70Sphingomonadalesorder58444620.8
Age: 60-70Desulfomicrobiaceaefamily342176.3
Age: 60-70Ethanoligenens harbinensespecies395199.5
Age: 60-70Saccharospirillaceaefamily1828030.2
Age: 60-70Acholeplasma equifetalespecies241147.8
Age: 60-70Prevotella pleuritidisspecies52240018.7
Age: 60-70Prevotella maculosaspecies107576134.4
Age: 60-70Bifidobacterium bombispecies34125213.8
Age: 60-70Synergistetesphylum3014899719.8
Age: 60-70Eubacteriales incertae sedisnorank30080638.3
Age: 60-70Eubacteriales Family XII. Incertae Sedisfamily385568.6
Age: 60-70Slackia piriformisspecies16630215.9
Age: 60-70Lactobacillus casei groupspecies group319107509.8
Age: 60-70Ruminiclostridiumgenus696162128.1
Age: 60-70Balneolaeotaphylum2611534446.9
Age: 60-70Limosilactobacillusgenus245143899.2
Age: 60-70Rhodovibrionaceaefamily119261614.3
Age: 70-80Eubacteriales incertae sedisnorank30070647.3
Age: 70-80Ruminiclostridiumgenus696111415.5
Blood Type: A NegativeButyrivibriogenus46760875.3
Blood Type: A PositiveAggregatibacter aphrophilusspecies35115030.1
Blood Type: A PositiveBifidobacterium pseudocatenulatumspecies3710314312.8
Blood Type: A PositiveHathewaya proteolyticaspecies19836
Blood Type: A PositiveLactobacillus crispatusspecies356182136.8
Blood Type: A PositiveFibrobacteresphylum2244128720.1
Blood Type: A PositiveDesulfovibrio vietnamensisspecies232546143
Blood Type: A PositiveEthanoligenens harbinensespecies393656.5
Blood Type: A PositiveSaccharospirillaceaefamily1829732.2
Blood Type: A PositivePrevotella pleuritidisspecies52378529.7
Blood Type: A PositivePrevotella maculosaspecies107822849.4
Blood Type: A PositiveEubacteriales incertae sedisnorank30071207.3
Blood Type: A PositiveEubacteriales Family XII. Incertae Sedisfamily3866610.4
Blood Type: A PositiveRuminiclostridiumgenus696103225
Blood Type: A PositiveRhodovibrionaceaefamily11912146.3
Blood Type: B PositiveEthanoligenens harbinensespecies39378274.4
Blood Type: B PositiveMoryellagenus15852546.4
Blood Type: FUT2 non-secretorEubacteriales incertae sedisnorank3001161112.2
Blood Type: O NegativeHalomonadaceaefamily40108820.8
Blood Type: O NegativeAlteromonadaceaefamily414885.4
Blood Type: O NegativeEthanoligenens harbinensespecies394508.2
Blood Type: O NegativeEubacteriales incertae sedisnorank30059096
Blood Type: O NegativeEubacteriales Family XII. Incertae Sedisfamily38246240.1
Blood Type: O NegativeSymbiobacteriaceaefamily11315365.3
Blood Type: O NegativeRuminiclostridiumgenus696153287.6
Blood Type: O NegativeMorganellaceaefamily685328746
Blood Type: O PositiveAchromobactergenus282276.1
Blood Type: O PositiveKlebsiella pneumoniaespecies11314317.1
Blood Type: O PositiveSalmonellagenus7413345.2
Blood Type: O PositiveAggregatibacter aphrophilusspecies353388.2
Blood Type: O PositiveDeinococcus-Thermusphylum8316139.1
Blood Type: O PositiveLacticaseibacillus paracaseispecies114820619.3
Blood Type: O PositiveLimosilactobacillus fermentumspecies631572961.2
Blood Type: O PositiveBifidobacterium magnumspecies2123216.6
Blood Type: O PositiveSphingomonasgenus49383126.7
Blood Type: O PositiveBifidobacterium pseudocatenulatumspecies37330841005.7
Blood Type: O PositiveBurkholderiagenus99258214.8
Blood Type: O PositiveFrancisellaceaefamily182597342.1
Blood Type: O PositiveSphingomonadaceaefamily57332615.1
Blood Type: O PositivePaenibacillusgenus373746.3
Blood Type: O PositiveFibrobacteresphylum22417055583.5
Blood Type: O PositiveSporotomaculum hydroxybenzoicumspecies37110517.4
Blood Type: O PositiveBifidobacterium boumspecies344546
Blood Type: O PositiveCystobacterineaesuborder191216.5
Blood Type: O PositiveTuricibacter sanguinisspecies89358313.3
Blood Type: O PositiveDeinococciclass8316139.1
Blood Type: O PositiveChloroflexiphylum11014666
Blood Type: O PositiveDesulfovibrio vietnamensisspecies232704151.9
Blood Type: O PositiveSphingomonadalesorder58306714.3
Blood Type: O PositiveEthanoligenens harbinensespecies39101119.3
Blood Type: O PositiveSaccharospirillaceaefamily1829131.5
Blood Type: O PositivePrevotella pleuritidisspecies52136310.4
Blood Type: O PositivePrevotella maculosaspecies1071250075.3
Blood Type: O PositiveBifidobacterium bombispecies34109012
Blood Type: O PositiveSynergistetesphylum3013487814
Blood Type: O PositiveEubacteriales incertae sedisnorank30072117.4
Blood Type: O PositiveEubacteriales Family XII. Incertae Sedisfamily385268.1
Blood Type: O PositiveSlackia piriformisspecies16627535.3
Blood Type: O PositiveLactobacillus casei groupspecies group319107519.8
Blood Type: O PositiveRuminiclostridiumgenus696115015.6
Blood Type: O PositiveBalneolaeotaphylum264614178.2
Blood Type: O PositiveLimosilactobacillusgenus245116187.4
Blood Type: O PositiveRhodovibrionaceaefamily11913467.1
Gender: FemaleFrancisellagenus181666218.6
Gender: FemaleSerratia marcescensspecies12414497.2
Gender: FemaleDesulfomicrobiumgenus341805
Gender: FemaleBifidobacterium magnumspecies2115710.7
Gender: FemaleCarnobacteriumgenus292506.3
Gender: FemaleBifidobacterium pseudocatenulatumspecies379394284.8
Gender: FemaleFrancisellaceaefamily181575206.6
Gender: FemaleFibrobacteresphylum2243498417
Gender: FemaleCystobacterineaesuborder191377.5
Gender: FemaleDietziaceaefamily11316875.6
Gender: FemalePlanomicrobiumgenus201067.3
Gender: FemaleDesulfovibrio vietnamensisspecies233181178.9
Gender: FemaleEthanoligenens harbinensespecies3984316
Gender: FemaleSaccharospirillaceaefamily1841045.3
Gender: FemalePrevotella pleuritidisspecies529006.7
Gender: FemaleMegamonas funiformisspecies1344244565.2
Gender: FemalePrevotella maculosaspecies107339820
Gender: FemaleEubacteriales incertae sedisnorank30057755.9
Gender: FemaleEubacteriales Family XII. Incertae Sedisfamily3891114.4
Gender: FemaleBalneolaeotaphylum269664374.3
Gender: FemaleRhodovibrionaceaefamily119390421.7
Gender: MaleFrancisellagenus181379180.6
Gender: MaleEnterobacter cloacaespecies9813268.5
Gender: MaleKlebsiella pneumoniaespecies113488925.9
Gender: MaleAggregatibacter aphrophilusspecies3594524.5
Gender: MaleDesulfomicrobiumgenus341975.6
Gender: MaleBifidobacterium magnumspecies2123717
Gender: MaleSphingomonasgenus49258817.9
Gender: MaleBifidobacterium pseudocatenulatumspecies379099275.8
Gender: MaleFrancisellaceaefamily181071139.7
Gender: MaleSphingomonadaceaefamily5718198.1
Gender: MaleFibrobacteresphylum2247124934.8
Gender: MaleSporotomaculum hydroxybenzoicumspecies3786013.4
Gender: MaleBifidobacterium boumspecies344145.4
Gender: MaleCystobacterineaesuborder191146.1
Gender: MaleHalochromatiumgenus241185.4
Gender: MaleMitsuokella jalaludiniispecies4002194329.5
Gender: MaleDesulfovibrio vietnamensisspecies233011169.3
Gender: MaleSphingomonadalesorder5816977.8
Gender: MaleDesulfomicrobiaceaefamily341935.5
Gender: MaleEthanoligenens harbinensespecies3963411.8
Gender: MaleSaccharospirillaceaefamily1843848.5
Gender: MaleBifidobacterium tsurumiensespecies2931113.7
Gender: MalePrevotella pleuritidisspecies52140910.8
Gender: MalePrevotella maculosaspecies107918255.2
Gender: MaleBifidobacterium bombispecies345195.5
Gender: MaleSynergistetesphylum301167056.7
Gender: MaleEubacteriales incertae sedisnorank30061116.2
Gender: MaleEubacteriales Family XII. Incertae Sedisfamily384246.4
Gender: MaleRhodovibrionaceaefamily11911045.7

ME/CFS Person: 6 mos since last test / not much movement – IMPROVEMENT!!!

Prior Post: Another ME/CFS person has gone to Firmicutes!

This just came in on Dec 4, 2023

I wanted to share some good news with you.  In the last 3 weeks I’ve noticed my time going to the bathroom has decreased in half and my gut has been less irritable.  Over the last 2 weeks, my mood has steadily improved and I’ve enjoyed more energy than usual.  It appears that your guidance has pointed me in the right direction! 

Note:  I didn’t end up performing the FMT.  

I thought about why my Firmicutes would get to 97%+.  It’s most likely because the majority of the foods I eat are continually pushing my gut towards Firmicutes.  And it could also explain why after adding 5+ daily supplements to push my gut the other direction, it hasn’t worked.   You noted that my being a vegetarian may be acting as a significant counter balance to the direction we’re trying to go.

Besides adding a small amount of meat in the morning, I cut out the foods I eat most frequently.  Bananas, raspberries, blueberries, nut bars.  And then I added in the Seaweed you recommended.  I think the seaweed has made a massive difference and with all these changes implemented collectively, the boat has begun to turn around!
I’m hoping this continues


I’m 39 and have suffered from moderate CFS since i was an early teenager.  My major 3 symptoms are low energy, brain fog, and IBS.  My CFS didn’t affect me as much when I was 18, but combined with the effects of aging, I’ve been feeling the fatigue more impactfully the last two years.  

Journey over last 10 months

When I first began working on this in January, my samples showed my Firmicutes at 98%.  That seemed to be the smoking gun as you described it, and I was eager to begin shifting my microbiome.  Over the next 6 weeks I felt markedly better but unfortunately I now believe that was merely a placebo effect.  Once I started to believe the benefits I had received were from a placebo, I rapidly returned to baseline.  Over this time period, I cut out many of the foods that pushed my gut in the wrong direction, and I was taking 4 supplements 2-3x a day.  By my second test 2 mos later, my firmicutes adjusted downwards from 98% to 93%.  In terms of how I felt, it was difficult to assess whether it was better than my baseline.  I was hopeful that it was, but I couldn’t say for sure.

Over the last 6 mos my Firmicutes has reduced from 93% to 89%.  During this time period I continued to cut out foods that were counter-recommended.  I ordered 4 more vitamins & supplements that were in my consensus list, and I was taking 4 supplements 1X/ day, while also rotating the supplements every 4 weeks to prevent resistance.

Once again, it’s difficult to assess now how i feel versus my baseline.  I don’t feel significantly better, that I know.  And while I’m disappointed my sample isn’t improving drastically, the upshot from my perspective is that at least my sample results match how i’ve been feeling.

Reader Addendum After reading

“I’m mostly vegetarian… which may help to explain why after 9 months of supplements I’m partially moving in the wrong direction.  I’ll incorporate the seaweed and increase my red meat.”


We have three samples to compare.

Percentage of Percentiles

We see significant shifts between samples with chi2 values increasing (meaning more abnormal) instead of decreasing. What is interesting is that the two earlier samples does not the typical ME/CFS or Long COVID pattern, but the third sample shifted to the pattern of spikes in the 0-9%ile range. This is open to many interpretations; some good and some concerning.

Evaluation Criteria

The numbers below are mixed, some showing improvement, others showing loss.

Lab Read Quality4.35.98.7
Outside Range from JasonH336
Outside Range from Medivere181820
Outside Range from Metagenomics887
Outside Range from MyBioma9915
Outside Range from Nirvana/CosmosId232324
Outside Range from XenoGene383843
Outside Lab Range (+/- 1.96SD)181510
Outside Box-Plot-Whiskers545356
Outside Kaltoft-Moldrup85125181
Bacteria Reported By Lab431553591
Bacteria Over 90%ile493234
Bacteria Under 10%ile4372174
Shannon Diversity Index2.8522.9372.49
Simpson Diversity Index0.0830.0980.101
Chao1 Index85921180715969
Condition Est. Over 90%ile1172
Kegg Compounds Low8217311190
Kegg Compounds High13425199
Kegg Enzymes Low359204259
Kegg Enzymes High208311243
Kegg Products Low201124170
Kegg Products High130199158
Kegg Substrates Low195114156
Kegg Substrates High149216162

Forecast symptoms

The top 3 forecasted items are below. See this post: Post Exertional Malaise (PEM) with diminished ME/CFS for more information on forecast symptoms in use. The earliest sample had no forecasts being reliable (i.e. > 60% match). What we also see in that the symptom patterns are becoming stronger. Again, this is usually not desired.

  • 2023-01-06
    • 54.3 % match for Neurological-Vision: Blurred Vision on 35 taxa
    • 52.4 % match for General: Headaches on 42 taxa
    • 50 % match for Immune Manifestations: new food sensitivities on 56 taxa
  • 2023-03-29
    • 65.9 % match for Pain: Pain or aching in muscles on 44 taxa
    • 56.2 % match for Immune Manifestations: Diarrhea on 89 taxa
    • 56 % match for Neurocognitive: Brain Fog on 50 taxa
  • 2023-09-12
    • 65.7 % match for Neurocognitive: Difficulty paying attention for a long period of time on 70 taxa
    • 62.3 % match for Neurocognitive: Can only focus on one thing at a time on 53 taxa
    • 61 % match for Neurological-Vision: Blurred Vision on 41 taxa

Impression and Possible Model

This is the first follow up sample where there was neither clear objective or subject improvement. What we see clearly above was that the microbiome has changed. Subjectively, there was no deterioration reported.

Objectively it seems that the microbiome has been de-noised. This person has had ME/CFS for 20+ years and thus the microbiome dysfunction will evolve. His latest sample changed to the typical pattern for ME/CFS for percentage/percentile chart above. The forecasted symptoms values are increasing for what are likely correct forecasts. The bacteria associated with ME/CFS and IBS are showing themselves better and other bacteria causing noise are diminished.

Going Forward

I first looked at the US National Library of Medicine studies for bacteria reported for the conditions he reported.

  • Chronic Fatigue Syndrome   (1 %ile) 9 of 64
  • ME/CFS with IBS   (9 %ile) 4 of 18

Symptom matching on these is a clear miss. I did note some high matches that are typical symtpoms so I add in the results from these selections:

The result was just 84 unique taxa and we have 5 sets of suggestions in our consensus.

So we fall back to the “Just give me suggestions”. The high priority value was 300 and the low priority value was -405. Adding in special studies suggestions moderated the ranges but most items stayed the same.

It looks the a modified Surf And Turf diet, i.e. Steak and Seaweed (our local Costco does sell a nice seaweed salad that is a regular for me)


The avoid values are so high compared to the takes that we need to review them to try reducing or excluding them. The threshold value is -300 (-600/2).

Bottom Line

This has been a challenging set of samples to do an analysis on. I have often used the analogy of going from the port of sickness to the port of health in a sailing ship along a rugged coast. There may be a long series of course corrections needed.

The suggests above are very atypical. Given that there appear to be a lot of noise in his microbiome, we may have some more denoising to do (bring out more the ME/CFS and IBS bacteria into the light). I would advocate attempting to get a course of rifaximin. It has been well used for his IBS diagnosis so there should be little push back from his MD.

Questions and Answers

Q: I’ve read through my new Simplified Suggestions List, and The suggestions of what I need to take and avoid are the same as before…. but the impact score of each was adjusted.  Although you’ve noted in the past that a higher impact doesn’t indicate it works more effectively, it looks from your suggestions that you stick to the highest impact items as there are the most studies to support them, right?

  • Correct. One study may report honey increases a bacteria, and another study report that it decreases the same bacteria. To determine the confidence of a suggestion we look at all reports. If you have 10 honey studies (8 increases and 2 decreases) and one study alone for roasted whale increasing the bacteria; most people will have greater confidence that honey is a better choice to try.

Q: One initial inquiry comes to mind- for complex cases, have you given any additional consideration to (probiotic) enemas as a way to make a big impact, and then to adjust with oral supplements from there?

  • I work from published clinical studies and not from influencers opinion. There is a good summary on WebMD, What to know about probiotic enemas that gives pros and cons.
  • I found Clinical trial: probiotic treatment of acute distal ulcerative colitis with rectally administered Escherichia coli Nissle 1917 (EcN) which had no results (” the number of responders was not significantly higher in the EcN group than in the placebo group”) or results depending on the statistical method used. Thus there is uncertainty about it’s effectiveness.
  • Using commercial/retail probiotics have two significant risks:
    • Very often the declared species is not what is in the bottle, the exception is for those strains that are owned by someone.
    • Often they contain fillers that will be well consumed before they reaches the nether regions. These fillers being inserted here may give a feast to some bacteria that usually do not get much, sparking an unexpected overgrowth.

Q: Below are the supplements I’ve been taking each day.  My plan is to cut out everything that doesn’t have an impact score north of a 150.  Does that sound like a reasonable approach?

  • ground flaxseed.  I took this every day for 4 months and had to stop because it began to make me nauseous. — Try it again at low dosages after one month
  • Luteolin – low positive
  • carboxymethyl cellulosedefinite take
  • Dietary Fiber Cellulose definite take
  • partially Hydrolyzed Guar Gum (SunFiber)remove
  • licorice rootremove
  • Vit B1, B6, B7 — low positive
  • Gaba definite take
  • Vit C remove
  • Melatonin — low positive, suggest you try removing for a week then trying for a week to see if it still help sleep
  • Diosmin – low positive
  • Astragalus – low positive

Answer: The logic is good but I would restrict to only items that are negative. You may wish to revisit the reason that you started each; if it was symptom related and improved — then monitor that symptom and return it if the symptom returns. I have annotated the list above


Suggestions could also be described as influencers. There is no need to get religious about taking everything. Take what you are comfortable with is my usual advice. In this case, the person being a vegetarian may have significantly counter-indicated the other influencers. Once the microbiome is normalize, a return to be a vegetarian is viable.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Post Exertional Malaise (PEM) with diminished ME/CFS

This is from a person that have been using Microbiome Prescription for a while with significant success. He messaged me about a new sample and asked me to take a look. It was good timing because I have over the last week refactor forecast symptoms as well as allowing them to be used to generate suggestions. This gave me an opportunity to test the analysis and suggestions.

My impression is that they are both working well with better sensitivity than the older method. Prior posts include:

Immediate Back Story

I had covid and an awful toothache a week after.

-Fatigue on and off specially in the morning. Lowest energy point 
-Some brain fog. If I take a binder my brain fog is much better. Guessing that could be related to mold/fungus issues?
-unrefreshing sleep
-hair shedding unless I take Lactoferrin 
-difficulty losing weight 

– Reader

Review of Test Results

I am skipping the sample comparison table because the forecast symptoms seem more useful for between samples comparisons.

Percentages of Percentiles

These are helpful overview to see if there are problems. At the top is a chi2 value which a healthy person should have a value below 0.90 . All of his values were .99999…. indicating abnormal shifts.

Prior to 2023

2023, the impact of COVID in the latest sample is quite apparent.

Forecast Symptoms

What shocked (and delighted) me was how accurate the forecasts were, especially with the main issue, PEM being near the top on every sample. The percentage match ebbed and flowed with improvements and set backs (typically from COVID).

  • 2023-11-02 – After recent COVID
    • 74.2 % match for Neurological: Cognitive/Sensory Overload on 31 taxa
    • 63.5 % match for Immune Manifestations: new food sensitivities on 52 taxa
    • 61.5 % match for Neuroendocrine Manifestations: cold extremities on 65 taxa
    • 60.9 % match for Neurological-Vision: Blurred Vision on 87 taxa
    • 59.6 % match for Condition: ME/CFS with IBS on 52 taxa
    • 58.3 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 36 taxa
  • 2023-07-07
    • 57.1 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 42 taxa
    • 54.2 % match for Condition: ME/CFS with IBS on 59 taxa
    • 48.1 % match for Post-exertional malaise: Post-exertional malaise on 54 taxa
  • 2023-05-10
    • 63.2 % match for Post-exertional malaise: Post-exertional malaise on 38 taxa
    • 59.3 % match for Post-exertional malaise: Muscle fatigue after mild physical activity on 59 taxa
    • 59.1 % match for Pain: Pain or aching in muscles on 44 taxa
    • 57.1 % match for Condition: ME/CFS with IBS on 42 taxa
  • 2022-12-05
    • 82.4 % match for Official Diagnosis: Autoimmune Disease on 51 taxa
    • 81.9 % match for Comorbid: Histamine or Mast Cell issues on 83 taxa
    • 79.5 % match for Post-exertional malaise: Next-day soreness after everyday activities on 39 taxa
    • 71.8 % match for Post-exertional malaise: Physically tired after minimum exercise on 39 taxa
  • 2022-10-29
    • 64.4 % match for Neurological-Sleep: Inability for deep (delta) sleep on 45 taxa
    • 62.7 % match for Official Diagnosis: Mast Cell Dysfunction on 75 taxa
    • 60.9 % match for Post-exertional malaise: Next-day soreness after everyday activities on 46 taxa
    • 60 % match for Condition: ME/CFS with IBS on 40 taxa
    • 55 % match for Post-exertional malaise: Muscle fatigue after mild physical activity on 60 taxa
    • 54.5 % match for Post-exertional malaise: Post-exertional malaise on 44 taxa
  • 2022-03-23
    • 85.4 % match for Neurological-Sleep: Inability for deep (delta) sleep on 41 taxa
    • 82.1 % match for Immune Manifestations: Inflammation (General) on 67 taxa
    • 77.5 % match for Immune Manifestations: Inflammation of skin, eyes or joints on 111 taxa
    • 75 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 32 taxa
    • 71.4 % match for Post-exertional malaise: Next-day soreness after everyday activities on 42 taxa

Drilling down on PEM

With the revised symptom-taxa association tool, I went to the 2022-12-05 sample because it had very high values for PEM in forecasting. I then went to [Special Studies] and selected all of the PEM items

Some 78 bacteria were selected with the following being the top suggestions:

While this is not his current sample, it is reasonable guidance for dealing with the bacteria associated with PEM.

Going Forward

I am going to do [Just give me suggestions] and then special studies selecting only PEM items that are shown. We thus ended up with 5 packages. Looking at the details we have high at 555 -> 277 for high threshold and -542 –> -271 for low threshold

In general, it looks very pro-forma ME/CFS. He may wish to check the PEM suggestions against the details and include anything that is positive.

Bottom Line

I have often used the analogy of going from the port of sickness to the port of health in a sailing ship along a rugged coast. There may be a long series of course corrections needed. While there are still ME/CFS taxa shifts seen in his samples, subjectively the only symptom left of concern is a PEM after playing basketball for a while.

This is a good illustration that taxa/bacteria does not rule — DNA and other factors are involved with symptoms and the ability to tolerate microbiome dysfunctions.

Questions and Answers

Q: I may do another Thorne test if they’re back in stock. Wondering how much aspergillus is holding back healing/gut shifts 

  • Aspergillus usually impacts lungs and breathing [CDC] (thus ability to get oxygen in). An alternative mechanism for oxygen issues than with hypercoagulation.

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.

Technical Note: Examples of Problematic Bacteria

Part of this series of Technical Notes.

The following are bacteria that have very unusual statistical behaviors in uploaded samples. This hints that they may be connected to some disease or symptom state.

The common pattern seen with most bacteria is shown below. You can view each bacteria pattern by clicking on the chart icon after searching for a bacteria on

Prevotella oryzae

Phascolarctobacterium succinatutens

Bacteroides eggerthii

Paraprevotella clara

Phascolarctobacterium faecium


Performance of Microbiome-Symptom Prediction Model

Preliminary evaluations have shown strong and accurate forecasting ability. I am working with a Ph.D. in Molecular Genetics and processing his microbiome sample the top forecasts were correct:

  • He was a male
  • He had no Health Issues
  • He was blood type O-positive
  • He snored
  • He woke up early

This was not from a blood sample, but from a stool sample. The methodology is described in my earlier posts.

The methodology is computing intensive. Unlike many contemporary artificial intelligence engines, it is easy to understand:

  • In the entire population(n:10,000), a bacteria like unclassified Clostridiales has 10% of samples reported this bacteria with a mean of 0.3%.
  • In the condition / sample population(n:1000) we find that 30% of the samples reported this bacteria with a mean of 4.8%

I used Chi2 to determine significance because significance using means assumes a normal distribution which is a false assumption.

With this methodology it is good to get some indication of what sample sizes for conditions / symptoms is needed (assuming the control is at least the same size). A quick plot informs us that 200 looks like a minimum size with 300+ being desired.

One of the labs that I work with reports percentile ranking on their reports. This makes doing an exploration economical (i.e. cheap). Testing of the symptom/condition group is only needed because the control numbers are gifted you.

This also illustrates that the symptom is not with just one taxa, but the influence of many taxa working in unison to influence the symptom (or be influenced by the condition).

Samples of Symptoms / Conditions

The ones with the most predictors are below.

  • Neurocognitive: Absent-mindedness or forgetfulness
  • Neurological: Impairment of concentration
  • Neurocognitive: Difficulty paying attention for a long period of time
  • General: Fatigue
  • Autonomic Manifestations: irritable bowel syndrome
  • Official Diagnosis: COVID19 (Long Hauler)
  • Sleep: Problems staying asleep
  • Neurocognitive: Slowness of thought
  • Neurological-Sleep: Insomnia
  • Sleep: Daytime drowsiness
  • Immune Manifestations: Bloating
  • Neurocognitive: Problems remembering things

For blood types:

Only two were significant and we have the importance of sample size apparent.

Symptom NameSample NForecast Variables
Blood Type: O Positive284295
Blood Type: A Positive1263

For Age:

Symptom NameSample NForecast Variables
Age: 60-70206143
Age: 0-109842
Age: 50-6016433
Age: 20-3015212
Age: 30-4036911
Age: 40-502448

For Gender

Symptom NameSample NForecast Variable
Gender: Female415214
Gender: Male554200

Bottom Line

From the individual forecast variables, we can compute odds of a match. Consider a simplistic example of three factors

  • Factor A uses 5%ile
  • Factor B uses 10%ile
  • Factor C uses 20%ile

With A,B,C all being true has a 99% chance of being a taxa match (1 – .05 * .1 * .2). This does not mean the person has it, it indicate a significant risk of having it (depending on other factors, DNA, age, gender etc).

Of course, the more forecasting variables, the better the estimate becomes and also the more tolerant the forecast is to variations in the microbiome.