Example of how Lab Quality Can make Intrepretation Difficult + Oral Bacteria Issue

This person is a long time ME/CFSer. I should start by pointing out that the Eubiosis measure is easy to misinterpret. This person has a good 90.2% eubiosis. This indicates that things are more even (balance is likely a poor choice of word, too many meanings attached) — this is not a health measure as such. It’s a statistical measure whether the microbiome is matching the ideal pattern (not health specific).

Health Analysis is health orientated. Looking at those values we see a loss of health indicators while the evenness has improved.

  1. “Leaked” from Oral Cavity
    • By Taxa is 70%ile, was 48%ile (7/2023), 70% (09/2022)
    • By Volume 49%ile, was 19% (7/2023) , 38.5 (09/2022)
  2. Jason Criteria: 17%ile was 17%ile the same
  3. Key Symptoms bacteria went to 18 was 12

The Oral Cavity stands out as a concerning factor for this person.

Analysis

We have a historic trend of 6 samples. We need to be careful of our interpretation because of differences in Lab Read Quality. The Lab Read Quality bounces around, and with that, other values may echo these shifts (i.e. up to 20% shifts for some measures). A low read quality means less bacteria are reported, for example, when it was low, the Outside Kaltoft-Moldrup has low, when it was high, the value became high.

Another way to view it is this: If 10% are out of range and 850 are reported then we have 85. If we have 500 in another report then we would expect 50. This could be misread as a 85/50 or a 70% increase in out of range bacteria. Technically, it is more complicated but that should explain the problem.

We have only one item šŸ™‚ that is a clear improvement and one item suggesting a loss :-(.

Criteria1/5/20247/3/20239/26/20221/9/20223/8/20217/26/2020
Eubiosis91.568.841.165.631.543.5
Lab Read Quality7.94.4113.33.63
Outside Range from GanzImmun Diagostics151519191313
Outside Range from JasonH445566
Outside Range from Lab Teletest212118182727
Outside Range from Medivere161617171313
Outside Range from Metagenomics557788
Outside Range from Microba Co-Biome222222
Outside Range from MyBioma10103399
Outside Range from Nirvana/CosmosId222218182020
Outside Range from Thorne (20/80%ile)215215224224228228
Outside Range from XenoGene363635353737
Outside Lab Range (+/- 1.96SD)7142101126
Outside Box-Plot-Whiskers5549467392103
Outside Kaltoft-Moldrup114 šŸ™7284473886
Bacteria Reported By Lab749545838495660483
Bacteria Over 90%ile23 šŸ™‚47346669100
Bacteria Under 10%ile49282111244
Shannon Diversity Index1.9581.1721.5951.4911.970.933
Simpson Diversity Index0.0410.2340.0750.1280.0450.012
Chao1 Index165547002186146127111127513
Pathogens322534292823
Condition Est. Over 90%ile000000
Actual Symptoms in top 10 Forecastedn/a5n/a358

We have a good number of bacteria strongly statistically associated with symptoms

The difference between the two symptom forecast methods is shown below. The newer method (genus only) appears to predict symptoms better

Going Forward

With the revised algorithm (see Algorithm for ā€œJust Give Me Suggestionsā€ with symptoms we get the following. Unlike many samples, there were few antibiotics at the top of the list.  

To Take

Spices and herbs can be done as capsules, teas, oils or just putting on food, but given the Oral leakage — as teas should be considered.

Modifier
whey
raffinose(sugar beet)
lactobacillus casei (probiotics)
fructo-oligosaccharides (prebiotic)
rosmarinus officinalis,rosemary
garlic (allium sativum)
lactobacillus paracasei (probiotics)
vitamin b2,Riboflavin
Lactobacillus Johnsonii (probiotic)
peppermint (spice, oil)
bifidobacterium longum bb536 (probiotics)
Cacao
trametes versicolor(Turkey tail mushroom)
lactobacillus reuteri (probiotics)
chondrus crispus,red sea weed
Dangshen
jerusalem artichoke (prebiotic)
lactobacillus sakei (probiotics)
lauric acid(fatty acid in coconut oil,in palm kernel oil,)
lactobacillus kefiri (NOT KEFIR)
green tea
foeniculum vulgare,fennel

Looking at probiotics, we have a good number that would allow easy rotation of probiotics. Two are usually difficult to obtain: lactobacillus kefiri and lactobacillus sakei . Most are available at my usual two preferred sources: Custom Probiotics and Indian Bulk Exporter (Maple Life Sources). See this list for sources not available there. By rotation, I mean 20-50 BCFU daily of one probiotic for 2 weeks and then change to another probiotic.

lactobacillus kefiri (NOT KEFIR)
lactobacillus casei (probiotics) [CB,MLS]
lactobacillus sakei (probiotics)
lactobacillus reuteri (probiotics) [CB,MLS]
bifidobacterium infantis,(probiotics) [CB,MLS]
bifidobacterium longum bb536 (probiotics)
lactobacillus paracasei (probiotics) [CB,MLS]
Lactobacillus Johnsonii [MLS]
Modifier To Avoid
saccharin
Ferric citrate (iron)
stevia
polydextrose
Pork
Slippery Elm
l-citrulline
nuts
Psyllium (Plantago Ovata Husk)
vegetarians Top of Form Bottom of Form
fibre-rich macrobiotic ma-pi 2 diet
Pulses
xylan (prebiotic)

Oral Bacteria Issue

This may be a likely contributor to ongoing issues. Why? the sinus and oral cavity repopulates the gut and undo prior improvements. My best suggestions are to address this are:

  • When possible, drink teas with the any of the suggested herbs.
  • Do not take probiotics as capsules, instead as powder dissolved in warm water and hold in the mouth before swallowing. See SymbioflorĀ® 1 – Respiratory diseases | SymbioPharm, this may help.
  • Address any dental issues

Postscript ā€“ and Reminder

I am not a licensed medical professional and there are strict laws where I live about ā€œappearing to practice medicineā€.  I am safe when it is ā€œacademic modelsā€ 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.

Key Bacteria Genus Associated to Patients Symptoms

This is a follow up to Technical Note: Identifying Key Bacteria to Address, part of this series Technical Notes on Microbiome Analysis. I am working with a new EU based start-up Precision Biome and in refining their algorithms they asked me to identify the key genus that should be considered (and other information).

The table below shows the top results that may be of interest in a clinical setting.

  • Occurrences is the number of times it was detected as statistically significant.
  • We averaged Chi2 to provide more generic detection.
  • [Shift] indicates if Chi2 is for the low range test or the high range test.
    • Low with High Count indicates that the absence of cells is more severe in the low range than than the abundance of cells in the top range.
    • Often there is a shift to upper, resulting in over population of the top range and almost complete disappearance of the low range.
  • Majority of the results were from 16s processing (with the inherent risk of misidentification)
  • Contrary to common belief, low levels seems to be rarely the apparent cause, they may be a consequence of multiple highs.
  • The data below can be replicated from data at https://citizenscience.microbiomeprescription.com/ (Open data) with the processes referred to above.
taxonTax NameShiftOccurrencesAverage Chi2Density
84567PedobacterLow355127.9High Count
114627AlkaliphilusLow356127.7High Count
33951AcetobacteriumLow358126.6High Count
190972SedimentibacterLow353126.4High Count
1769729HathewayaLow362126.4High Count
100883CoprobacillusLow360125.4High Count
28453SphingobacteriumLow350123High Count
970SelenomonasLow353120.1High Count
44000CaldicellulosiruptorLow352120High Count
1378GemellaLow353119.3High Count
82373AnaerovibrioLow350117.6High Count
281119Candidatus AmoebophilusLow352117.2High Count
29548RhodothermusLow348115.2High Count
69894TindalliaLow347110.1High Count
864PectinatusLow351108.1High Count
33926Candidatus PhytoplasmaLow340107.1High Count
256319ChlorobaculumLow348106.4High Count
89958NatronincolaLow332105.1High Count
2304693PseudoclostridiumLow346104.9High Count
561EscherichiaLow345103.6High Count
1647ErysipelothrixLow354103.6High Count
2093MycoplasmaLow345103.5High Count
46205PseudobutyrivibrioLow587103.4High Count
94008ThermicanusLow338103.2High Count
156973DysgonomonasLow345102.1High Count
119852OscillospiraLow569101.6High Count
1263RuminococcusLow669100.8High Count
2316020MediterraneibacterLow670100.7High Count
33042CoprococcusLow670100.5High Count
42447AnaerobrancaLow32899.7High Count
1350EnterococcusLow26499.3High Count
35829AcetivibrioLow66497.6High Count
1730EubacteriumLow66796.6High Count
44260MoorellaLow25496.3High Count
376469OlivibacterLow19896.1High Count
2740PeptococcusLow54395.2High Count
131079LimnobacterLow15195.2High Count
52784AnaerofilumLow52694.4High Count
44258CaloramatorLow62993.8High Count
572511BlautiaLow89793.3High Count
816BacteroidesLow89793.2High Count
841RoseburiaLow89793High Count
1485ClostridiumLow89692.8High Count
43994JohnsonellaLow61892.8High Count
265975OribacteriumLow66192.7High Count
216851FaecalibacteriumLow89792.7High Count
909656PhocaeicolaLow89592.6High Count
1301StreptococcusLow89492.4High Count
189330DoreaLow89792.4High Count
28050LachnospiraLow88492.3High Count
35832BilophilaLow55891.5High Count
375288ParabacteroidesLow88791.5High Count
906MegasphaeraLow55990.5High Count
140625LachnobacteriumLow63488.6High Count
1678BifidobacteriumLow86988High Count
244127AnaerotruncusLow86787.3High Count
207244AnaerostipesLow89386.4High Count
838PrevotellaLow85985.9High Count
40544SutterellaLow83985.2High Count
84108SlackiaLow50883.8High Count
29465VeillonellaLow84283.6High Count
1578LactobacillusLow87082.2High Count
162289PeptoniphilusLow85281.8High Count
283168OdoribacterLow85780.6High Count
61170HoldemaniaLow86079.5High Count
102106CollinsellaLow85379.3High Count
33024PhascolarctobacteriumLow85377.6High Count
447020AdlercreutziaLow47376.5High Count
1508657RuminiclostridiumLow43675.5High Count
724HaemophilusLow45174.5High Count
239934AkkermansiaLow82474.3High Count
86331MogibacteriumLow84173.5High Count
872DesulfovibrioLow80672.6High Count
836PorphyromonasLow81370.3High Count
84111EggerthellaLow76769.6High Count
574697ButyricimonasLow77969High Count
1266SarcinaLow22868.8High Count
1407607FusicatenibacterLow53568.2High Count
1506553LachnoclostridiumLow31468.1High Count
292632SubdoligranulumLow53768High Count
1505663ErysipelatoclostridiumLow54067.7High Count
1506577TyzzerellaLow31467.5High Count
577309ParaprevotellaLow41267.4High Count
946234FlavonifractorLow54067.3High Count
239759AlistipesLow54066.9High Count
1654ActinomycesLow71866.9High Count
43996CatonellaLow31266.5High Count
459786OscillibacterLow53766.3High Count
1769729HathewayaHigh27066.2High Count
1392389IntestinimonasLow53165.7High Count
84567PedobacterHigh26765.7High Count
1649459HungatellaLow31165.5High Count
33951AcetobacteriumHigh27065.5High Count
114627AlkaliphilusHigh27265.3High Count
1164882LachnoanaerobaculumLow31365.3High Count
2048137AgathobaculumLow31265.3High Count
698776CellulosilyticumLow30864.8High Count
2039240AnaerotignumLow31164.6High Count
100883CoprobacillusHigh27764.4High Count
190972SedimentibacterHigh26864.1High Count
52784AnaerofilumHigh29164High Count
2569097AnaerobutyricumLow30963.6High Count
1774128MurimonasLow30363.5High Count
2740PeptococcusHigh28263.4High Count
2719313EnteroclosterLow30963.3High Count
28453SphingobacteriumHigh26763.1High Count
970SelenomonasHigh25962.7High Count
1427378AcetatifactorLow30462.5High Count
44000CaldicellulosiruptorHigh25661.7High Count
1378GemellaHigh26961.4High Count
204475GemmigerLow30061.2High Count
119852OscillospiraHigh33361.1High Count
29548RhodothermusHigh25860.8High Count
281119Candidatus AmoebophilusHigh25360.5High Count
82373AnaerovibrioHigh25759.8High Count
1505657IntestinibacterLow48859.7High Count
248744MarvinbryantiaLow50259.4High Count
253238EthanoligenensLow30359.1High Count
46205PseudobutyrivibrioHigh37158.9High Count
241189HespelliaLow51958.3High Count
69894TindalliaHigh25257.7High Count
864PectinatusHigh25957.5High Count
89958NatronincolaHigh25357.4High Count
43994JohnsonellaHigh34657.4High Count
131079LimnobacterHigh14057.3High Count
1843210AnaerocolumnaLow29356.8High Count
44249PaenibacillusLow29756.7High Count
1481960PeptoclostridiumLow20856.7High Count
94008ThermicanusHigh25356.6High Count
84108SlackiaHigh27156.6High Count
577310ParasutterellaLow50156.5High Count
830ButyrivibrioLow49956.4High Count
906MegasphaeraHigh31556.4High Count
44258CaloramatorHigh35556.2High Count
256319ChlorobaculumHigh24756.1High Count
437755MoryellaLow49756.1High Count
1647ErysipelothrixHigh25556High Count
33926Candidatus PhytoplasmaHigh24456High Count
561EscherichiaHigh24755.9High Count
2304693PseudoclostridiumHigh24955.9High Count
33042CoprococcusHigh43855.5High Count
1505652TerrisporobacterLow50455.5High Count
1569SporosarcinaLow29455.5High Count
35829AcetivibrioHigh41055.3High Count
2316020MediterraneibacterHigh44155.2High Count
376469OlivibacterHigh18155.1High Count
2093MycoplasmaHigh25154.6High Count
588605RobinsoniellaLow29454.6High Count
156973DysgonomonasHigh25854.6High Count
35832BilophilaHigh31954.5High Count
1263RuminococcusHigh45654.5High Count
1562DesulfotomaculumLow28354.5High Count
838PrevotellaHigh44754.3High Count
140625LachnobacteriumHigh34854.1High Count
1350EnterococcusHigh22154.1High Count
44748SporobacterLow49354High Count
42447AnaerobrancaHigh24253.8High Count
40544SutterellaHigh44453.3High Count
162289PeptoniphilusHigh42853.1High Count
1485ClostridiumHigh53653High Count
909656PhocaeicolaHigh54152.9High Count
572511BlautiaHigh54552.8High Count
816BacteroidesHigh54852.8High Count
44260MoorellaHigh21252.6High Count
189330DoreaHigh54652.6High Count
841RoseburiaHigh55252.5High Count
1017280PseudoflavonifractorLow49052.4High Count
39948DialisterLow49852.3High Count
265975OribacteriumHigh41952.3High Count
216851FaecalibacteriumHigh56352.3High Count
1678BifidobacteriumHigh49252.1High Count
29465VeillonellaHigh45052.1High Count
66831FacklamiaLow27552High Count
244127AnaerotruncusHigh48052High Count
1730EubacteriumHigh46251.9High Count
375288ParabacteroidesHigh53951.9High Count
1301StreptococcusHigh56451.9High Count
28050LachnospiraHigh55551.6High Count
1501226RomboutsiaLow48051.3High Count
379899NiabellaLow29251.3High Count
1432051EisenbergiellaLow48550.5High Count
579KluyveraLow20850High Count
1578LactobacillusHigh47549.9High Count
283168OdoribacterHigh45649.6High Count
653683AnaerosporobacterLow20949.6High Count
724HaemophilusHigh26649.4High Count
207244AnaerostipesHigh54949.3High Count
61170HoldemaniaHigh44649.3High Count
404402HowardellaLow26849.2High Count
872DesulfovibrioHigh39548.7High Count
1433991BrassicibacterLow27048.6High Count
86331MogibacteriumHigh39648.6High Count
836PorphyromonasHigh39048.4High Count
33024PhascolarctobacteriumHigh43948.2High Count
1508657RuminiclostridiumHigh28548.1High Count
2282743DesulfohalotomaculumLow26947.9High Count
239934AkkermansiaHigh40747.7High Count
397864BarnesiellaLow47647.6High Count
447020AdlercreutziaHigh24747.3High Count
150022FinegoldiaLow48447.3High Count
1470353Candidatus SoleaferreaLow20147.1High Count
102106CollinsellaHigh47846.7High Count
577309ParaprevotellaHigh24346.4High Count
420345LactonifactorLow45146.3High Count
596767HydrogenoanaerobacteriumLow44145.8High Count
2529408SchaaliaLow20245.4High Count
574697ButyricimonasHigh38145High Count
1654ActinomycesHigh36145High Count
84111EggerthellaHigh39444.3High Count
177971ShuttleworthiaLow43442.5High Count
1870884ClostridioidesLow19342.5High Count
644652GordonibacterLow20041.7High Count
191303TuricibacterLow19041.6High Count
292632SubdoligranulumHigh25840.2High Count
1716CorynebacteriumLow20240High Count
1407607FusicatenibacterHigh26039.9High Count
946234FlavonifractorHigh25639.9High Count
459786OscillibacterHigh25139.8High Count
1392389IntestinimonasHigh24739.3High Count
1505663ErysipelatoclostridiumHigh27939.1High Count
1506577TyzzerellaHigh16139.1High Count
44748SporobacterHigh16338.5High Count
239759AlistipesHigh28338.4High Count
830ButyrivibrioHigh18138.2High Count
1164882LachnoanaerobaculumHigh15838.1High Count
43996CatonellaHigh16237.9High Count
1649459HungatellaHigh15537.9High Count
698776CellulosilyticumHigh15437.8High Count
241189HespelliaHigh20537.6High Count
2048137AgathobaculumHigh15137.4High Count
1774128MurimonasHigh14537.4High Count
100175PapillibacterLow19437.3High Count
2039240AnaerotignumHigh14837.3High Count
2569097AnaerobutyricumHigh14237.2High Count
1506553LachnoclostridiumHigh19137High Count
2719313EnteroclosterHigh14536.8High Count
596767HydrogenoanaerobacteriumHigh14836.8High Count
237FlavobacteriumLow16836.3High Count
1505657IntestinibacterHigh21036.3High Count
437755MoryellaHigh17436.2High Count
1427378AcetatifactorHigh14736.1High Count
577310ParasutterellaHigh19135.9High Count
253238EthanoligenensHigh13235.8High Count
165779AnaerococcusLow19435.7High Count
248744MarvinbryantiaHigh20435.4High Count
204475GemmigerHigh14534.9High Count
39948DialisterHigh16134.9High Count
397864BarnesiellaHigh12434.6High Count
420345LactonifactorHigh11534.5High Count
1017280PseudoflavonifractorHigh15734.4High Count
1505652TerrisporobacterHigh19034.2High Count
177971ShuttleworthiaHigh12034.2High Count
588605RobinsoniellaHigh11634High Count
1432051EisenbergiellaHigh16734High Count
1569SporosarcinaHigh12233.7High Count
44249PaenibacillusHigh12633.7High Count
1843210AnaerocolumnaHigh12833.6High Count
168934ThalassospiraLow17633.6High Count
1501226RomboutsiaHigh16533High Count
150022FinegoldiaHigh13032.8High Count
1562DesulfotomaculumHigh10932.6High Count
66831FacklamiaHigh10431.6High Count
1348911CoprobacterLow19431.4High Count
379899NiabellaHigh11031.3High Count
135858CatenibacteriumLow16928.9High Count
1472649DielmaLow18028High Count
117563GranulicatellaLow16226.3High Count
172900VictivallisLow15726.3High Count
194CampylobacterLow18126.1High Count
109326AnaerovoraxLow15525.7High Count
184869VaribaculumLow17824.7High Count
580024EnterorhabdusLow16724.5High Count
963HerbaspirillumLow15924.4High Count
1357LactococcusLow15723.8High Count
1161127MurdochiellaLow15422.2High Count
189326GelriaLow14920.7High Count
1473205SenegalimassiliaLow13520.4High Count
(c) 2024 Lassesen Consulting LLC, DBA Microbiome Prescription

Why are Metabolites different on same sample from different analysis?

A reader forward this image from BiomeSight to me:

Apart from wording being questionable (former technical writer speaking) because many people will read it as needing to be increased whereas the chart on the right clearly shows that it needs to be decreased.

Same sample on MP reports:

  • D-Lactic Acid 18%ile. Thus much lower than the mid point! A Contradiction! D-Lactic acid is the bad form of lactic acid. L-Lactic acid is the very good form!

A brief discussion of how metabolites are estimated. There are several approaches used by retail providers:

  • Based on clinical studies reporting that a certain bacteria produces this compound.
    • Then just total up the count of all such bacteria
  • Based on clinical studies reporting that a certain bacteria produces this compound AND the amount that each produces.
    • Then just total up the count times the amount of all such bacteria
  • Based on KEGG: Kyoto Encyclopedia of Genes and Genomes and derive the average amount per species (and/or genus)
    • Then just total up the count times the amount of all such bacteria
    • This is the method that Microbiome Prescription [MP] uses.

There are additional methods but most are significantly more expensive. Working from clinical studies always have the issue of some bacteria not being studies – hence estimates may be off.

Example of one metabolite (Reuterin) showing different amounts per bacteria according to strains

From prediction to function using evolutionary genomics: human-specific ecotypes of LactobacillusreuteriĀ have diverse probiotic functions[2014].

Bottom Line

There is no “right” answer. You should ask the provider of the estimates exactly how are metabolites computed (and data sources). In the case of MP, the source and method are clearly identified above. Additionally, the site should provide some education on the significance of each. Often metabolites are requested by customers and the provider does an “economical” analysis and implementation to satisfy the customer request; the results may be less than ideal or complete.

Often education is needed. The reader that emailed me, asked “should I not stop all lactate/lactic acid producing probiotics“. NO…. because L-Lactic acid is good and inhibits bad bacteria. So I checked L-Lactate (commonly just called Lactate) – see below.

Description on MP of this Metabolite

  1. D-Lactic Acidosis: Elevated levels of D-lactic acid in the blood can lead to a condition known as D-lactic acidosis. This occurs when the body’s ability to metabolize D-lactic acid is impaired or overwhelmed. It’s often associated with specific conditions such as short bowel syndrome (SBS) or other gastrointestinal disorders where there’s an increase in the production and absorption of D-lactic acid.
  2. Neurological Symptoms: D-lactic acidosis can lead to neurological symptoms, including confusion, impaired cognitive function, difficulty concentrating, and altered mental status. Severe cases may even lead to coma.
  3. Gastrointestinal Symptoms: Symptoms such as abdominal pain, diarrhea, and bloating can occur in individuals with D-lactic acidosis. These gastrointestinal symptoms are often related to the underlying conditions causing D-lactic acid accumulation.
  4. Metabolic Acidosis: Elevated D-lactic acid levels can contribute to metabolic acidosis, an imbalance in the body’s acid-base equilibrium, leading to a decrease in blood pH. This can have systemic effects and affect various organs and bodily functions.
  5. Impaired Energy Production: D-lactic acid can interfere with cellular metabolism and energy production, potentially contributing to fatigue and weakness.

Looking up Lactate Value

Clicking on this tab button shows the thousands of metabolites that can be estimated on MP from KEGG data which we can search over.

and we find Lactate (C3H6O3) listed at 27%. IMHO that is low and you want to increase it. The description provided on this is below.

  1. Energy Production: Lactate is produced as a result of the breakdown of glucose during anaerobic metabolism when the body’s demand for energy exceeds its oxygen supply. It serves as an alternative fuel source, particularly for muscles and red blood cells, and can be converted back into glucose (gluconeogenesis) in the liver.
  2. Exercise and Muscle Function: During intense physical activity, the body produces lactate as muscles work vigorously, leading to temporary increases in blood lactate levels. Contrary to earlier beliefs associating lactate with muscle fatigue and soreness, lactate is not the primary cause of muscle fatigue but is rather utilized as a fuel by muscles and other tissues.
  3. Lactate Threshold: The lactate threshold is the exercise intensity at which lactate begins to accumulate in the blood more rapidly than it can be cleared. Athletes often train to improve their lactate threshold, as it correlates with performance and endurance in certain sports.
  4. Clinical Implications: Elevated lactate levels in the blood (lactic acidosis) can occur due to various conditions, such as sepsis, shock, hypoxia, liver disease, certain medications, or metabolic disorders. Severe lactic acidosis can lead to symptoms such as rapid breathing, nausea, abdominal pain, and, in severe cases, it can be life-threatening if left untreated.
  5. Health Conditions: While lactate plays essential roles in energy metabolism, excessive accumulation due to underlying health conditions or imbalances can lead to health issues. Conditions associated with lactic acidosis require proper medical evaluation and treatment.
  6. Diagnostic Tool: Blood lactate levels are sometimes measured in clinical settings to assess tissue oxygenation, especially in emergency and critical care situations, as elevated lactate levels can indicate tissue hypoperfusion and metabolic stress.

2 year follow up: ME/CFS back to work

This is a follow up to this analysis ME/CFS after trying suggestions from Dr.A.I. from 2 years ago. The goal of MicrobiomePrescription is not to cure any condition — rather to generate suggestions that have a high probability of mitigate a person’s symptoms. Mitigate means reduce severity or eliminate. With some condition, it could result in a technical remission (i.e. no longer matches a diagnostic criteria). The term remission and not cure should be used always — because the microbiome could return to a dark place due to future events or inadequate ongoing care.

Update Story

I’ve been doing MUCH better for the last two years. I feel nowhere near the “normal” levels of energy that a 36 years old man should have and a lot of things are difficult, but I’ve been better and I’m able to work full time as a web developer, remotely. I know live with my significant other, and working + living with someone takes absolutely all of my energy, so I usually have no resources left to meet friends or do extra activities, but the fact of being able to work full time is a huge improvement compared to two years ago. Most of the brain fog has lifted. It comes back on my bad days, but it’s really not as bad as it used to be

I feel that my energy levels are going worse these days and it’s getting harder to work full time though. That’s why I’ve done another microbiome analysis, and it seems to be even “worse” than 2 years ago, according to Biomesight’s score. 

I still have trouble digesting things (even though it’s been a bit better the last two years, it has never been “normal” I think) and get diarrhea quite often. I usually have to go to the toilets 5-6 times a day. I’m still very skinny.

I take much less dietary supplements than I used to, but I’m still taking :

  • Acetyl L-Carnitine
  • Ubiquinole
  • R Lipoic Acid
  • B12
  • Vitamin D
  • Nattokinase

I’ve also done another round of “Spezzata”[Licorice] recently, but it definitely makes my blood pressure go up. I also eat a lot of dark chocolate and drink a lot of coffee.

My diet has changed a bit since I’ve started living with my Significant Other about a year ago. I eat a lot more rice than I used to now.

Analysis

The first thing that I noticed was the lab quality is down a lot. Lower quality means less bacteria types (793 dropped to 334) are identify and the estimated costs have greater uncertainty. On the plus side, all measures are the same or are significantly better.

CriteriaCurrent Sample
2024
Old Sample
2022
Lab Read Quality25.4
Lab Quality Adjustment Percentage85.9100
Outside Range from GanzImmun Diagostics1919
Outside Range from Lab Teletest2525
Outside Range from Medivere1515
Outside Range from Metagenomics99
Outside Range from Microba Co-Biome66
Outside Range from MyBioma88
Outside Range from Nirvana/CosmosId2121
Outside Range from Thorne (20/80%ile)327327
Outside Range from XenoGene4141
Outside Lab Range (+/- 1.96SD)710
Outside Box-Plot-Whiskers43118
Outside Kaltoft-Moldrup2468
Bacteria Reported By Lab334793
Bacteria Over 90%ile099
Bacteria Under 10%ile031
Lab: BiomeSight
Pathogens1641
Condition Est. Over 90%ile03
Standard Items

Looking at new features added. Note that the drop in number of bacteria reported impacts many of these values)

Measure20242022
“Leaked” from Oral Cavity [LOW is better]21.1%ile65.7%ile
General Health Predictors [More is better]617
Jason Hawrelak Ratio 10%ile14%ile
Butyrate9%ile29%ile
Potential Medical Conditions Detected03
Bacteria deemed Unhealthy031
Eubiosis8%71.6%ile

The charts show similar patterns but with a group of bacteria dominating (80-90%ile) range

Conclusion: He is better than 2022 in many measures but is worse in a few (especially newer ones).

Let us look at some recent feature additions. First key bacteria — we see improvement we a lot of key bacteria dropping down below the threshold. (see Technical Note: Identifying Key Bacteria to Address for background)

“Leaked” from Oral Cavity is based on this study Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes, 2024. As with above, we see improvement.

  • 2024: 21.1 Percentile.
  • 2022: 65.7 Percentile

Going Forward

As usual, I start with “Just give me Suggestions include Symptoms” with the new refactored algorithm (see Algorithm for ā€œJust Give Me Suggestionsā€). The items were similar but with just a change of order.

First, let us look at what is currently being take. Microbiome changes over time. The highest for non-prescription items is 327 and lowest -516.

  • Acetyl L-Carnitine: 152
  • Ubiquinole: AKA COq 10, Not on current suggestions
  • R Lipoic Acid: Not on current suggestions
  • B12: 82
  • Vitamin D: -152
  • Nattokinase: looking at bacillus subtilis natto -39
  • Spezzata [glycyrrhizic acid (licorice)]: -42
  • chocolate : 11.5
  • coffee/ Trigonelline (found in Coffee): 152

None of the above is clearly a bad choice. The worst, vitamin D tend to be a slow acting supplement. This illustrates that doing a retest every 6 months to reset your course to recovery is likely good practice.

Some observations on suggestions:

AVOID List

Postscript ā€“ and Reminder

I am not a licensed medical professional and there are strict laws where I live about ā€œappearing to practice medicineā€.  I am safe when it is ā€œacademic modelsā€ 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.

Question: Finding the best probiotics for a condition

A reader wrote:

Hi Ken – I’m attempting to use MBP. I recall you linked (a long time ago) to a section that showed a list of probiotics useful in colitis, with each probiotic having the studies. How would I arrive to that page?

There is a multiple step/layer approach that I would use. The sequence is below:

#1 Researched Probiotics Available Retail

Go to https://microbiomeprescription.com/library/ProbioticSearch and type in the name of the condition.

You will see the list of probiotics with the ones with the most studies listed first.

Below it are the links to the studies. You should read at least some of them – some may report “no effect”. The list is generated by a data gathering algorithm on the species and the condition and the condition may be mentioned in passing and not directly referenced or tested.

#2 Use Perplexity AI

Go to https://www.perplexity.ai and just ask “What is the most effective probiotic for…”. I use this AI because it provided links to data sources so I can verify the response

A typical response is below

#3 Probiotic Via Published Microbiome Shifts

In this case we are using the MP engine to generate suggestions. It is inferior in reliability to the above methods but is a reasonable fall back. Go to Medical Conditions with Microbiome Shifts from US National Library of Medicine ( https://microbiomeprescription.com/Library/PubMed ) and search for the condition

You can click the PDF download (which report TOP items only) or click Candidates (all items)

Surprise, surprise! #1 and #2 agree with Perplexity!!! vsl#3 is not listed because we do not have strain information for this mixture of probiotics. Remember – these are computed SOLELY from the microbiome shifts without reference to any studies on ulcerative colitis!

#4 Probiotic via Citizen Science

Goto Symptoms associated with conditions reported for Samples https://microbiomeprescription.com/Library/Symptoms Enter the name — in this case it was not found (not enough samples that people have annotated with ulcerative colitis). In that case, you may want to go over to symptoms you have; for example Diarrhea. This data is conditioned on the lab that the contributor was using and layered by taxa level.

I will be adding a suggestions icon to this page shortly, and you can then review those suggestions. Clicking on the bowl, produces suggestions that can be filtered to probiotics.

#1 and #2 above are #1 and #3 with the most studied one show in above!

Clicking on the symptom name takes you to the symptom page listing all of the bacteria

This will then use the expert system to generate suggestions which can be filtered to probiotics

This is ranked not by the number of studies published but by the ones with the greatest estimated impact by the expert system. We see Mutaflor, L. rhamnosus – and many familiar ones in the researched strain list.

Bottom Line

We have 4 ways of selecting probiotics. For the specific question asked, all 4 converge to the same items. In terms of clinical use, we see that the suggestions based solely on bacteria shifts going through the expert system appear to be cross-validated. IMHO, this explicit expert system works better than the typical MD would.

Chronic sinus infections & Chronic diarrhea

Backstory

I’ve found your work through PR forum and then your website, it’s really interesting reading all that information and feeling so related to the issues.

For the last year I’ve been house bounded and sometimes bed bounded, all coming from ignoring one year of daily sandy-yellowish diarrhea along with a series of chronic sinus infections that one after another left me in a worst health state to finally being isolated fearing further infections.

I’ve been reading and investigating, trying methylation protocols and probiotics. The probiotic I took was fairly simple (bifidobacterium bifidum and lactobacillus acidophilus) but gave me pretty bad reaction which got me thinking about histamine release, one Google search showed me that those strains are not histamine releases but what it does (acidophilus) is increase D-lactic production and what other bacteria could do this? Streptococous, the one which probably caused my chronic sinusitis.

This is how I ended up thinking that I really need to know if I have those kind of bacterial overgrowth in my guts and reading your work thougth that you could have some advice to start learning how to do it.

Analysis

Given that sinus is significant, this study may be worth a read: Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes, 2024.

I looked at “Leaked” from Oral Cavity and while the count places him at 49%ile, the number of bacteria was much longer than usual — a lot of different bacteria at low levels which suggests a new statistics to add in the next revision.

Going Forward

Since symptoms are entered, I am just using the Just Give Me Suggestions with Symptoms button.

The suggestions are below, trimmed a little

  • sucralose
  • lauric acid(fatty acid in coconut oil,in palm kernel oil,)
  • enterococcus faecium (probiotic)
  • garlic (allium sativum)
  • momordia charantia(bitter melon, karela, balsam pear, or bitter gourd)
  • Tudca – Tauroursodeoxycholic acidĀ , a supplement
  • lactobacillus casei (probiotics)
  • whole-grain barley
  • fruit/legume fibre
  • bifidobacterium infantis,(probiotics)
  • dates
  • grape polyphenols
  • naringenin(grapefruit) (Flavonoid)
  • Cacao
  • rosa rugosa (Rosavin – an adaptogen)
  • Hesperidin (polyphenol)
  • Nicotine, Nicotine Patch
  • galla chinensis (herb)
  • Grapefruit seed extract
  • lactobacillus gasseri (probiotics)
  • thyme (thymol, thyme oil)
  • foeniculum vulgare,fennel
  • Turmeric
  • N-Acetyl Cysteine (NAC),
  • Onion

I will create two extracts: one for probiotics and one for teas (i.e. herbs).

Looking at probiotics, we have a good number that would allow easy rotation of probiotics. Two are usually difficult to obtain are removed:Ā lactobacillus kefiriĀ andĀ lactobacillus sakeiĀ . Most are available at my usual two preferred sources:Ā Custom ProbioticsĀ andĀ Indian Bulk Exporter (Maple Life Sources). SeeĀ this listĀ for sources not available there. By rotation, I mean 20-50 BCFU daily of one probiotic for 2 weeks and then change to another probiotic.

  • enterococcus faecium
  • lactobacillus casei [CB,MLS]
  • bifidobacterium infantis, [CB,MLS]
  • lactobacillus gasseri [CB,MLS]
  • lactobacillus jensenii [MS]
  • lactobacillus salivarius [CB,MLS]
  • pediococcus acidilactic
  • bifidobacterium breve [CB,MLS]
  • bifidobacterium pseudocatenulatum,
  • bifidobacterium longum [CB,MLS]
  • bacillus coagulans

While taking herbs as capsules or oils is very viable, herbal teas has the advantage of effecting the oral cavity (and thus sinus) and extends through the entire system.

  • thyme
  • fennel
  • anise
  • chamomile
  • eugenol (from clove oil, nutmeg, cinnamon, basil,bay leaf.)
  • peppermint
  • sage
  • tulsi
  • neem
  • oregano
  • ginger

Items to avoid or reduce

  • Slippery Elm
  • wheat bran, rice bran
  • xylan (prebiotic)
  • inulin (prebiotic)
  • arabinogalactan (prebiotic)
  • stevia, saccharin
  • resistant starch, resistant maltodextrin, slow digestible carbohydrates
  • Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
  • red wine
  • pectin
  • l-citrulline (supplement)
  • berberine (supplement)
  • Pulses
  • apples, nuts, banana
  • galacto-oligosaccharides (prebiotic)
  • low-fat diets, high beef diet, high red meat, animal-based diet, hypocaloric hyperproteic diet
  • iron
  • Pork
  • kefir
  • bacillus licheniformis,(probiotics)
  • fasting
  • galactose (milk sugar)
  • propionate
  • Guaiacol (polyphenol)

Postscript ā€“ and Reminder

I am not a licensed medical professional and there are strict laws where I live about ā€œappearing to practice medicineā€.  I am safe when it is ā€œacademic modelsā€ 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: Identifying Key Bacteria to Address

After a good night sleep after doing the video below, I realized the next step in this journey. With hundreds of bacteria in the microbiome the question arises which ones to focus on if the condition is not a known single bacteria condition. I.e. Crohn’s, Asthma vs Tuberculosis, Ulcers.

Recently I had major success is identifying bacteria associated to hundreds of symptoms. This was done by using the percentile ranking of each microbiome sample over the population and then examining the counts of the top and bottom 15%ile (simplified version). These should be equal if there is no relationship of the bacteria to the symptom. The difference allows a Chi2 value and probability to be computed for significance. Comparing the averages perform very poorly for finding equivalent statistical significance.

The result was a list of bacteria where the high in these end 15%ile was associated with symptoms / diagnosis. This allows us to filter out the noise from the significance in a sample. The question arises, should all of these be used or is there a critical limit? The current count is 1,960 taxa-shifts with one or more

To address this issue, I decided to plot the counts.

Applying a log function found a region became a straight line. Two apparent inflection points are suggested. Applying different function kept showing the same inflection points.

Bacteria with high and low associated to Symptoms

There are two species in this list. Both are reported in published clinical studies often.

BacteriaRank
Faecalibacterium prausnitziispecies
Dorea formicigeneransspecies
Weissellagenus
Faecalibacteriumgenus
Acidaminococcaceaefamily
Acidaminococcalesorder

The Short List of Bacteria

We see many bacteria not usually associated plus two familiar ones. Increases means more are in the 85-100%ile, decrease means more are in the 0-15%ile

BacteriaRankDirectionCount of Associations
Thermodesulfobacteriotaphylumincreases460
Rhodothermotaphylumincreases247
Rhodothermiaclassincreases247
Rhodothermalesorderincreases247
Actinomycetotaphylumdecrease146
Holdemaniagenusincreases135
Faecalibacteriumgenusdecrease133
Coriobacteriaceaefamilydecrease125
Coriobacterialesorderdecrease120
[Clostridium] thermoalcaliphilumspeciesincreases116
Coriobacteriiaclassdecrease115
Lactobacillaceaefamilydecrease114
Bifidobacteriumgenusdecrease113
Sphingobacteriaceaefamilyincreases113

Longer List of Bacteria

BacteriaRankShiftCount
Oscillospiraceaefamilydecrease108
Anaerotruncus colihominisspeciesincreases107
Actinomycetesclassdecrease106
Bacteroidesgenusincreases106
Hathewayagenusincreases105
Sharpea azabuensisspeciesincreases105
Bacillotaphylumdecrease105
Sharpeagenusincreases105
Anaerotruncusgenusincreases104
Sphingobacteriiaclassincreases104
Sphingobacterialesorderincreases104
Lactobacillalesorderdecrease101
Ruminococcaceaefamilydecrease101
Erysipelothrix murisspeciesincreases100
Erysipelothrixgenusincreases96
Veillonellaceaefamilyincreases94
Bacteroides caccaespeciesincreases92
Clostridiaclassdecrease90
Bacteroides fluxusspeciesincreases89
Eubacterialesorderdecrease89
Ethanoligenensgenusincreases89
Finegoldiagenusincreases88
Hathewaya histolyticaspeciesincreases87
Hymenobacter xinjiangensisspeciesincreases87
Roseburia faecisspeciesdecrease85
Blautia obeumspeciesdecrease84
[Clostridium] leptumspeciesincreases83
Hymenobactergenusincreases82
Negativicutesclassincreases80
Terrabacteria groupcladedecrease79
Bifidobacteriaceaefamilydecrease79
Bacilliclassdecrease78
Bifidobacterium adolescentisspeciesdecrease78
Marvinbryantiagenusincreases77
Phocaeicola vulgatusspeciesincreases77
FCB groupcladeincreases76
Odoribacter denticanisspeciesincreases75
Mediterraneibactergenusincreases74
Faecalibacterium prausnitziispeciesdecrease73
Eggerthellagenusincreases72
Bifidobacterialesorderdecrease72
Lachnoclostridiumgenusincreases70
Acidaminococcalesorderincreases70
Odoribactergenusincreases70
Acidaminococcaceaefamilyincreases70
Blautia schinkiispeciesincreases69
Dorea formicigeneransspeciesincreases69
Collinsellagenusdecrease69
Bacteroides stercorisspeciesdecrease69
Lactobacillusgenusdecrease68
Pseudomonadotaphylumincreases67
Odoribacteraceaefamilyincreases67
Peptoniphilaceaefamilyincreases67
Fusobacteriaceaefamilydecrease66
Dorea formicigeneransspeciesdecrease66
Amedibacillus dolichusspeciesincreases65
Amedibacillusgenusincreases65
Candidatus Glomeribactergenusincreases64
Eggerthella lentaspeciesincreases63
Finegoldia magnaspeciesincreases63
Oscillatoria corallinaespeciesincreases62
Ruminiclostridium cellobioparum subsp. termitidissubspeciesdecrease62
Oscillatoriagenusincreases62
Tissierellalesorderincreases62
Ruminiclostridium cellobioparumspeciesdecrease62
Hymenobacteraceaefamilyincreases62
Pseudoflavonifractorgenusincreases61
Mycobacterialesorderincreases61
Tissierelliaclassincreases60
Lachnoanaerobaculumgenusdecrease60
Coprococcusgenusdecrease59
Blautiagenusincreases59
Collinsella aerofaciensspeciesdecrease59
Verrucomicrobiotaphylumincreases59
Veillonellalesorderincreases58
[Ruminococcus] gnavusspeciesincreases58
Selenomonas infelixspeciesincreases58
Eubacterium ventriosumspeciesdecrease58
Lactonifactorgenusincreases57
Bacteroides cellulosilyticusspeciesincreases57
Enterococcaceaefamilydecrease57
Fusobacteriumgenusdecrease57
Alistipes putredinisspeciesincreases57
Sutterellagenusdecrease56
Anaerotignum lactatifermentansspeciesincreases56
Anaerotignumgenusincreases56
Clostridium akagiispeciesincreases56
Burkholderiaceaefamilyincreases55
Akkermansiaceaefamilyincreases55
Turicibactergenusdecrease55
Butyricimonasgenusincreases55
Segatella coprispeciesdecrease55
Mycoplasmoidalesorderincreases55
Turicibacteraceaefamilydecrease55
Turicibacter sanguinisspeciesdecrease55
Akkermansia muciniphilaspeciesincreases54
Sporomusaceaefamilyincreases54
Limnobactergenusincreases54
Limnobacter litoralisspeciesincreases54
Lactobacillus rogosaespeciesdecrease54
PVC groupcladeincreases54
Thiocapsagenusincreases53
Sphingobacterium bambusaespeciesincreases53
Enterobacteriaceaefamilyincreases53
Pseudoflavonifractor capillosusspeciesincreases53
Blautia hanseniispeciesincreases53
Akkermansiagenusincreases53
Marvinbryantia formatexigensspeciesincreases52
Enterococcusgenusdecrease52
Corynebacteriaceaefamilyincreases52
Lachnospiragenusincreases52
Lachnospira eligensspeciesincreases52
Corynebacteriumgenusincreases52
Mycoplasmataceaefamilyincreases52
delta/epsilon subdivisionscladedecrease52
Bacteroides faecisspeciesincreases52
Bacteroides gallinarumspeciesincreases52
Veillonellagenusincreases51
Weissellagenusincreases51
Dialister invisusspeciesincreases51
Blautia hydrogenotrophicaspeciesincreases51
Lactonifactor longoviformisspeciesincreases50
Dorea longicatenaspeciesdecrease50

Best Test/Lab?

The above is based on percentile and not the typical percentage. The results from Clinical Microbiomics A/S (a.k.a. CosmosId) provides percentile ranking again a regionalized healthy population. In the US, their tests are available retail from Thorne. They will soon be available retail from Precision Biome in the EU.

A walk thru using Microbiome Prescription

Probiotics impacting diamine oxidase (DAO)

People with mast cell issues typical treatment approach is:

  • Reduce foods high in histamine
  • Avoid probiotics that produces histamine
  • Use DAO supplements.

There is a fourth leg that should be added to this three legged stool. Probiotics (and other substances) that impacts DAO levels. Some could argue that they impact DAO because they produce histamines. That may be true in some rare cases, but many of the items listed below are not known to produce histamine.

More information on PubMed: Histamine Intolerance-The More We Know the Less We Know. A Review [2021].

The production and activity of DAO can be regulated by various factors, including hormones, inflammatory mediators, and the presence of substrates like histamin. Difference probiotics produces different enzymes which may inhibit or assist with the production of DAO.

The probiotics, impact and study used are listed below:

Reviewing the studies is recommended. In some cases DAO levels were raised due to infection, i.e. Escherichia coli K88; and the probiotic may be inhibiting the increase due to the infection though reducing the infection and not actually impacting DAO levels.

Other Common Supplements and DAO or Histamine

Niacin

What is needed are control studies measuring DAO levels in humans with different probiotics. The above illustrates that probiotics are likely to make good or bad differences.

Post Concussion Syndrome Microbiome Shifts

Back Story

  • 47yo Male
  • 1976-2012 – Sustained 7 concussions playing contact sports, minor short term symptoms and fully recovered.  Fit, active, and employed as adult. Satisfying family/social life. Higher intensity physical training 5-6 days per week outside or in gym. No drugs/alcohol since 2003, no medical issues or medications. 
  • 2012 – Post Concussion Syndrome – Fell and hit head skiing, diagnosed with concussion, symptoms did not resolve. No detectable damage on MRI. 

Symptoms

  • Fatigue, brain fog, intense and unpredictable nerve pain in head 
  • Cognitive and visual processing issues
  • Sensitivity to light, loud noise, busy environments
  • GI – constipation
  • Multiple food, chemical, and environmental sensitivities
  • Overall functionality reduced, still able to socialize, drive, complete tasks, problem solve, etcā€¦ sporadically and in short increments. Physical fitness mostly unaffected, continued to exercise, incorporated yoga/gyrotonic.
  • Divorced
  • Received disability benefits in 2019
  • chronic sinus issues that started after concussion and worsened with vaccine. 
  • diagnosed w nasal mold colonization, nasal biofilms, and chronic staph  aureus.

2021 – mRNA Vaccine Injury (2 shots Pfizer)

Symptoms:

  • Total exercise intolerance, sympathetic activation, anxiety and confusion
  • Exacerbation of pre-existing symptoms – severe fatigue, brain fog, food/chemical/medication/supplement sensitivities, constipation
  • Neuropathy – pain, tingling, numbness in legs and feet. 
  • Minimal activity, short walks, occasional driving short distances, most time spent resting

Tests

  • Microclotting confirmed by visual sample analysis
  • Elevated Leukotiene 4 + typical symptoms suggests MCAS
  • Reactivated EBV – elevated latent IFN-gamma, lytic IFN-gamma, lytic IL-2 (Infectolabs)
  • Autoantibodies (Celltrends)
  • Spike protein present in non-classical monocytes 5 months post-vax
  • OATS – apergillus, clostridia, metabolic dysfunction

Research

We appear to have two happenstances cascading:

  • Concussion
  • COVID Shots – which build immunity by causing a reaction similar to COVID. This reaction includes some microbiome shifts.

Concussion

There is some literature here:

Reductions in Bacteroides have been associated with irritable bowel syndrome (IBS) development and identified after stroke [87]. Bacteroides are imperative for the maintenance of intestinal barrier integrity, with supplementation being associated with increased tight junction proteins [88]. Reductions in Clostridium sensu stricto have been associated with reduced butyrate production and Alzheimerā€™s disease [89,90]. 

Age matters: Microbiome depletion prior to repeat mild traumatic brain injury differentially alters microbial composition and function in adolescent and adult rats [2022]

Treatment

  • Involvement of Microbiome Gut-Brain Axis in Neuroprotective Effect of Quercetin in Mouse Model of Repeated Mild Traumatic Brain Injury [2023]
  • “probiotic supplementation (Lactobacillus acidophilus or Clostridium butyricum) improved neurologic function.” [2022]
  • ” Findings from this trial support the feasibility, acceptability, and safety of supplementation with an anti-inflammatory/immunoregulatory probiotic, Lactobacillus reuteri DSM 17938, among Veterans with PPC and PTSD symptoms.” [2020]

COVID Shots

Explicit studies are rare. What we do have is a variety of studies between high and low responder which enumerates the difference of bacteria. We do not clearly know which goes in any specific direction. We do know the bacteria that are likely to change.

The high response group were primarily characterized by a predominance of Enterococcus faeciumPrevotella biviaActinomyces massiliensisVeillonella disparVeillonella_sp_T11011_6Eubacterium_sp_CAG_38Ruminococcus torquesActinomyces odontolyticus, while Alistipes putredinisAllisonella histaminiformansBacteroides clarusClostridium lavalenseClostridium asparagiformeBacteroides eggerthiiCoprobacter fastidiosusSutterella parvirubra, and Blautia coccoides are more abundant in the low response group 

Plasma and urine proteomics and gut microbiota analysis reveal potential factors affecting COVID-19 vaccination response [2024]

Analysis

We have two results to work from: Biomesight report and an OATS (Organic Acid) report.

Looking at Forecasted symptoms we have a high rate of pattern matches. This is hopeful because it implies we now have strongly suspected bacteria.

We also see a significant shift of bacteria that are atypically over represented.

The reader noticed the dominance of some unusual bacteria (species and the genus they belong to). These amount to be 28.4% of the microbiome. These two bacteria usually average 3.5% of the microbiome.

For Caloramator, the probiotics most documented to reduce are: lactobacillus plantarum and lactobacillus casei; fructo-oligosaccharides (prebiotic) (BUT inulin increases).

As a starting point, we will do [Just give me Suggestions include Symptoms]. Since we have some condition specific suggestions above, let us see where they rank.

This type of cross validation is nice to see — everything known to help TBI/concussion is in the recommended to take list. It builds confidence in the suggestions being generated.

The next thing is to see which of the bacteria shifts cited in the above literature. We go over the the Bacteria tree. We filter out those not usually reported by Biomesight and ended with just one (which illustrates the benefit of shot-gun tests).

  • reductions in Bacteroides: was at 32%ile

Other incidental measures of note:

  • Anti Inflammatory Ratio: 20% (so inflammation is likely)

Other Issues

Last, do we see Staphylococcus aureus in the sample? No.

Elevated Leukotriene E4 (LTE4)  is an accepted indicator of Mast Cell Activation Syndromes, see Diagnosis, Classification and Management of Mast Cell Activation Syndromes (MCAS) in the Era of Personalized Medicine[2020].  I will look at LTE4 only and not the much bigger MCAS. LTE4 depends on  5-lipoxygenase (EC:1.13.11.34) [ Leukotrienes and inflammation [1990]]. This gives us some possible paths to explore:

Unfortunately, there are no reported bacteria associated with 5-lipoxygenase (EC:1.13.11.34) . What we find is:

Again, we have cross-validations happening, i.e. the expert system suggestions and clinical studies are in agreement.

OATS Tests

Next we see what the OATS results produces. Only one was out of range where we have data

I should point out that something is very funky with the OATS report. Every value below is within their declared range but their graphics show it is out of range.

The result was 326 probiotics — specifically, probiotics that are known to consume Malate. We have no significant additions.

Action Plan

First item of note, above we have 100% cross validation on what the fuzzy logic expert system says should help with clinical studies of what helps given his particulars. This hints that other suggestions are far more likely to help than hurt.

The easy set of suggestions are quercetin, clostridium butyricum (probiotics),lactobacillus acidophilus, lactobacillus reuteri (probiotics) because they are double recommended: both clinical studies and the microbiome. Since with this history, getting any prescription drugs is unlikely, the consensus suggestion leads me to consider the following (filtered for availability etc):

Ideally, the reader will use all of the details and avoid all of the negative values ones if possible

The high-fat diets / high-saturated fat diet suggestion takes a little figuring. I deduce that being high in fat from:

  • Coconut oil 1 2 3 4
  • Palm oil and palm kernel oil

IMHO, This is the general problem with diet data — they contain vast baskets of substances: some good and some bad; with a wide variety of definitions. I prefer working substance by substance.

Retest

I would suggest a retest with Biomesight in 3 months (if the reader consents, I would be glad to do a follow up post). Remember these are not generic suggestions for anyone with a concussion but based on the individual’s microbiome with cross referencing to the literature to develop a clean consensus of what may help.

Questions and Answers

Q: What does with “Symptoms” mean?

  • Using “With Symptoms” uses the bacteria associations found from donated samples (i.e. seeĀ Ā Citizen Science Symptoms To Bacteria Studies (microbiomeprescription.com))
  • Ideally, the user will do the time consuming process of checking the suggestions against the literature (which I did above for illustration).
  • There is NO direct linkage using studies of symptoms to supplements by the engine. That is technically possible, but would require major funding to hire qualified people to enter the data.
    • Experience have found that 85-95% of suggestions that has studies for a conditions are in agreement.

Q: Do you use studies on my conditions to pick bacteria?

  • If there are sufficient studies, then yes. In your case there is not. Clinical studies on conditions often have contradictory results for a vast number of reasons: the lab and software used; the diet of the people in the studies; often low significance (often P < 0.05 is cited, with our lab specific analysis we typically use P < 0.001 as a criteria).

Q: The high Bilophila on my Biomesight got my attention. Is that something that MP.com does not identify specifically to address but rather it just corrects as part of the overall microbiome rebalancing?

  • MP does a holistic analysis — so things that may reduce Bilophila but also shifts others bacteria in the wrong direction may be eliminated.
    • “The typical MP matrix to solve is around 60 taxa (up to 430 in some cases) by 2092 possible modifiers – thus an array of some 12,000 to 800,000 elements to consider. The Monte Carlo method typically uses 5 runs resulting in 60,000 to 4,000,000 elements evaluated.” [blog]

Q: The prebiotic that shows up as #1 rec for me is Prefor Pro, but you have chicory. Is that due to availability?

  • The retailed probiotic selection is based on the species in the probiotics ignoring additives and relative amounts (often not declared). If there are issues with these additives, then just move down the list.

Q: Do you think using the condition specific bacteria shifts is a better approach than the bacteria identified by the AI as “likely to be causing my symptoms”?

  • Definitely, my observations from feedback is that targeting those bacteria do moderate or eliminate symptoms.

Postscript ā€“ and Reminder

I am not a licensed medical professional and there are strict laws where I live about ā€œappearing to practice medicineā€.  I am safe when it is ā€œacademic modelsā€ 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.

Microbiome Results and AI – An Incidence of marketing hype

This is part of a series of technical notes. If this interests you, you may wish to view others in the series.

A colleague wrote me:

I am currently researching companies who are using machine learning and LLM approaches for Microbiome analysis I wanted to know your opinion why this approach fails/ is less precise than your [MicrobiomePrescription MP] Fuzzy Logic Expert System with Monte Carlo method.

I have been doing a wide variety of Artificial Intelligence development professionally since 1988 for firms including Microsoft, Amazon, Verizon and Starbucks. I also have a reasonable science background including High School General Science teacher and College Chemistry and Physics instructor.

In the early days of Microbiome Prescription I tried a panacea of technics such as Random Forest, linear and non linear regression, supervised learning, etc. The results were less than acceptable. A friend, Richard Sprague, also ex-Microsoft, who worked as Citizen Scientist in Residence for uBiome for a while set up a series of meeting with the teams of the Allen Institute for AI [AI2]. The consensus working with those experts was that my direction of using the expert system model was far superior than what they could come up with.

How does Machine Learning and LLM work?

The core mechanism is pattern recognition of key words and phrases. This allows a numeric representation of the literature to be created, i.e. [subject #,verb #, object #]. When someone asks about subject, a set of equivalent subject # is obtained, a set of equivalent verb # is obtained and we just lookup the data. The number of records may be used to determine priority or most probable outcome.

This leads to the problem with many implementations, phrase recognition. To illustrate consider two articles, one mentions: Limosilactobacillus reuteri and the other mentions for Lactobacillus reuteri. A microbiologist knows that these are the same, but the typical ML or LLM does not. Do we end up with two collections of information? Since both Limosilactobacillus and Lactobacillus are also taxological units, do we get this species information incorrectly applied to the genus?

This is further complicated going to studies dealing with strains. Compare these two species: Escherichia coli O157:H7 and Escherichia coli Nissle 1917. One causes food poisoning, and the other is a probiotic shown to improves Crohn’s disease. Strains often have multiple identifiers and retail names. For example, Enterococcus faeciumĀ SF68Ā is also known as Enterococcus faecium NCIMB 10415 and sold retail as Bioflorin for humans and multiple brands in veterinary practice.

So the number 1 issue is correct identification. We also have some name collisions: Bacteroidetes is the name of both a class [Taxon 200643] and a phylum [Taxon 976] (which is now called Bacteroidota). A knowledgeable reader can reason out what is meant.

Taxonomy Hierarchy Inference

Information on the microbiome is sparse. A microbiome practitioner asked about what would reduce Lactobacillus balticus may discover that there is no literature on it. This practitioner would then infer that whatever reduces Lactobacillus would reduce it. This is an inference which MP does (remember it is an expert system mimicking the behavior of a human expert). MP takes it one step further by recognizing that is now classified as Limosilactobacillus balticus. Instead of items impacting Lactobacillus, it will use items impacting Limosilactobacillus. The suggestions are more probable to be correct.

What to address question

The various AI systems may well scrap ranges from studies to apply to a microbiome sample. Results are not consistent from lab to lab [the back story is this 2019 post: The taxonomy nightmare before Christmasā€¦] . This means that reference ranges are more inconsistent as a consequence. Below are some range examples from commercial tests

The reference range dilemma is why MP generates suggestions based on reference ranges determine by multiple methods: Average +/- Std Dev, Box Plot Whiskers, ranges from specific sources (including the processing lab in some cases), and patent pending algorithms. Each reference range is determined from a large collection of sample processed by the same lab. The suggestions using each of these reference ranges are then aggregated into a “consensus” report (i.e. Monte Carlo method).

“We are using AI to get Suggestions” – marketing hype!

A few years ago, microbiome testing companies would attempt to get creditability by claiming their suggestions were created by registered dietitian. Today, “AI” is the replacement. If you ask about what AI methodology is being used, the size and scope of the data behind, etc. 99% of the time you will be given a “It’s proprietary! We cannot disclose it“.

They may be truthful that is coming from AI, for example, someone asked https://www.perplexity.ai/, “Which foods reduces Fusobacterium” (Example answer – Fusobacterium nucleatum is what is cited). They copy the answer into their database to show their customers. Thus it is true that the answer is coming from AI; but it is a one-dimensional blinkered answer that will often leaves their customer worse. This AI is ignorant that Fusobacterium prausnitzii [The bacteria formerly known as Faecalibacterium prausnitzii] belongs to it. This bacteria has lots of studies that are ignored by the AI! The AI appears ignorant that two studies report both quercetin, mastic gum (prebiotic) reduces it. It likely has the data but has misclassified it.

My observations of reviewing many sites is that suggestions are scoped to a single bacteria and ignores side-effects on other bacteria that would also be impacted. MP uses holistic algorithms [which is what would be expected when someone has done Ph.D. courses on Integer and Non-Integer Programming Optimization]. The typical MP matrix to solve is around 60 taxa (up to 430 in some cases) by 2092 possible modifiers – thus an array of some 12,000 to 800,000 elements to consider. The Monte Carlo method typically uses 5 runs resulting in 60,000 to 4,000,000 elements evaluated.

Examples of LLM gone bad

Most experts know that Mutaflor is Escherichia coli Nissle 1917 and is clearly names as such in publicly accessible papers on the National Library of Medicine. So this response is one of AI’s famous hallucinations; hallucinations are not possible from expert systems.

Bottom Line

Microbiome testing firms may correctly claim[in a legal sense] they are AI based. If they refuse to fully disclose the methodology being used (ideally on their site), then the safest assumption is that they got a summer intern to ask one of the LLM’s the questions and just copied the answers into the database. Without full disclosure, they simply cannot be trusted.

If you are considering using AI because some “hot shot evangelist or venture capitalist” is pushing for it; then — look at the above issues and insists on documentation on how each of these issues will be addressed. Until there is clear, understandable documentation on these issues, “The suitability of AI has not been shown for the proposed AI implementation” and stop wasting time and money!

MP uses a very old model of AI that requires manual data curation being feed to the expert system. This a hallmark of expert systems.