Category: Uncategorized

Example of How To Research: Circulating inflammatory cytokines

In my weekly review of new studies on PubMed of potential interest (50-150/week) I came across this one

Exploring the casual association between gut microbiome, circulating inflammatory cytokines and chronic pancreatitis: A Mendelian randomization analysis [2024]

I suspected that this may be connected to Mast Cell Activation Syndrome (MCAS), a condition that many people struggle with. I thought it would be good to do a walk-through of how I currently research things that are of interest but not explicit in the literature.

My first step is to see if it is worth while exploration. At present, I use Perplexity because it provides references for what it answers. You need to check them, one reference that I got on a different question came from [sarcasm]”the definitive medical resource: Amazon Product Information“.

The [answer] was: “Circulating inflammatory cytokines can have a significant impact on Mast Cell Activation Syndrome (MCAS)…“. If you click on [answer] you will see the full response.

Phrase I Tracking immediate leads

Returning to this new article we read:

In terms of compounds we have items which we lack microbiome data for, BUT as you will see later, we have herb data for!

  • CCL23 increases risk [Chemokine (C-C motif) ligand 23 (CCL23) is a small cytokine belonging to the CC chemokine family that is also known as Macrophage inflammatory protein 3 (MIP-3) Wikipedia ] 
  • SIRT2, ADA and Caspase 8 decreases risk

We now have a bunch of scents to follow. Some may bear fruit.

Next step was to look up the above bacteria (and their parents) on our citizen science site.

I inspected these lists (which are at the genus level), and then look for matches. There were none 🙁

I went over to a sample from a MCAS person done with shotgun (Thorne) and a parallel Biomesight and Ombre test done at the same time. For the positively associated there were only one, reported by Ombre at 39%ile). For negatively:

So the person appears to be deficient in bacteria that inhibits things. Incidentally, this person takes Mutaflor and SymbioFlor-2 (Escherichia probiotics) and have reported that it seems to moderate histamine issues.

Checking KEGG for scents:

  • SIRT2 – we see NAD-dependent protein deacetylase sirtuin-2 isoform X1, this corresponds to this person feeling better from taking (flushing) niacin. Perplexity clarifies it with “Niacin (also known as vitamin B3) is a precursor for the coenzyme nicotinamide adenine dinucleotide (NAD).” and five references!
  • ADA – adenosine deaminase connected to [EC:3.5.4.4] which we can seen on the site, this person BiomeSight was at 98%ile by Biomesight and 43%ile by Ombre, so situation is fine
  • Caspase 8 [EC:3.4.22.61] has no associated bacteria to estimate from.

Take Away

For this person, the study and experience has reasonable agreement with this person’s positive response to Escherichia probiotics and (flushing) niacin appearing to be explained.

Phrase 2

This starts by using Perplexity to find branches of the above to explore.

Now, if we go to Microbiome Prescription for these, example Sellimonas we have many dozens of substances with some being explicit. Clearly, Perplexity is inferior for bacteria information.

Next, we go and look at things not on microbiome prescription.

  • What are foods and herbs that will increase NAD? The response does include the hallucination cited above.
    • “Niacin-rich foods: Niacin (vitamin B3) is a precursor for NAD biosynthesis in the body. Therefore, consuming foods rich in niacin can provide the raw material for NAD production.”
    • “Tryptophan-rich foods: The amino acid tryptophan can also be converted to NAD in the body, albeit at a lower efficiency than niacin.2 Foods rich in tryptophan include:Turkey, Oats, Bananas, Milk, Yogurt” The quality of references were poor (very poor), so I did a PubMed search and found:
      • “Underlying these benefits [of moderate excise] were transcriptional changes in enzymes driving the conversion of tryptophan to NAD(+), this leading to increased hepatic NAD(+) and elevated activity of the NAD(+)-” [2021]

So tryptophan rich food plus moderate exercise would be an alternative for people who cannot use (flushing) niacin.

I decided to dive into PubMed for Caspase 8 with herbs and got 91 results. Some interesting ones are below. WARNING: Some of the herbs below are prohibited to be sold in the US because of risks. They are listed here for illustration only:

  • “The expressions of caspase–8 and caspase-9 were significantly elevated by the Kerra extract ” [2023]
  • “Furthermore, it[ leaf extract of Acorus calamus] also increased expressions of caspase-3, caspase-9, caspase-8,” [2023] FYI, available for sale. [WARNING from Web MD]
  • “Tribulus terrestris (TT), a herb belonging to Zygophyllaceae family …caspase 8 genes were also upregulated” [2019] FYI, it can be purchased as a powder in bulk
  • “extracted from the Chinese herb Tripterygium wilfordii Hook…was associated with activation of both caspase-3 and caspase-8” [2006]
  • “isolated from Solanum incanum herb … activation of caspase-8” [2004]

Of special interest is that most of these studies were in the context of cancer with caspase-8 inhibiting the cancer. MCAS and cancer are associated: ” Our data support the view that mast cells may promote development of certain malignant tumors. These findings indicate a need for increased surveillance of certain types of cancer in MCAS patients irrespective of its individual clinical presentation.” [2017]

I did a second dive into CCL23 (and checked it alternative names too!)

  • “CCL23 is produced predominantly by mast cells in systemic mastocytosis, and CCL23 plasma levels are associated with disease severity, correlating positively with established markers of disease burden, thus suggesting that CCL23 is a specific SM biomarker.” [2023]
  • “Human neutrophils produce CCL23 in response to various TLR-agonists and TNFα. ” [2017]
    • This means that reducing TNFα should reduce CCL23. [perplexity gives a list with reference including: Trikatu, Allium sativum, Capsaicin, Carvacrol,Thymol. ]
    • TLR-agonists are prescription medicines… and also Fungi, Seaweed

Take Away

Usually I work on microbiome modifications. The reason is simple — it is pretty time consuming to maintain the database! Above, we see a secondary approach — targeting specific enzymes and compounds using PubMed. I would love to get funding to put this extra information into an equivalent (and connected) expert system.

Bottom Line

The above illustrates how AI (Large Language Models – ChatGPT and Perplexity) can assist in researching — but beware of hallucinations! and check the sources of the information.

What is interesting is that we cascaded from Circulating inflammatory cytokines to MCAS to specific compound and then to herbs. The herbs are often atypical herbs that should be researched for safety and any other known issues. Many appear to be used traditionally in eastern medicine. This approach gives new options to consider when conventional options fail. We do not know the microbiome impact of these herbs — but we do know the impact on specific compounds and enzymes of interest. I would love to build a fuzzy logic expert system in this area so that contraindications are visible and the results are sounder; unfortunately that takes skilled people and time.

As always, before starting any herbs, spices and supplements, you should review your plan with a licensed medical professional.

Technical Note: Bacteria Influencing Bacteria

There are many approaches that can be taken. With the same data, different methods can reap a huge variation of harvest (i.e. the number of statistically significant relationships found).

I will use the Pearson’s Chi2 to determine significance and a simple “does the presence of bacteria A results in a shift of bacteria B”. By a shift, I mean either an increase/decrease of the number of bacteria above a threshold or below count. The threshold for bacteria count varies from bacteria to bacteria, and lab to lab.

To illustrate:

Bacteria PresentBacteria ImpactedLow ZoneHigh Zone
LactobacillusRickettsieae528185

The naive “expected number” is (528+185)/2=356.5, The actual expected number is lower 198 for both zones, yielding a chi2 of 647! The middle values have shifted lower. The conclusion that having Lactobacillus reduces the risk of Rickettsieae appears confirmed in publications such as Use of Lactobacillus to prevent infection by pathogenic bacteria [2002].

Looking in the opposite direction, we have a reduced Chi2 of just 29 with the apparent intrepretaion being that the prescience of Rickettsieae reduces Lactobacillus slightly (‘Odds ratio’ of 1.35, versus 2.66 going the other direction).

Bacteria PresentBacteria ImpactedLow ZoneHigh Zone
RickettsieaeLactobacillus268202

IMHO, this approach yields more significant findings than looking at the differences of averages using standard deviations. Some people will attempt to find a linear regression between the counts of Lactobacillus and Rickettsieae. An example using the same data is below. I will leave it to the reader to reconcile.

A key philosophical question is whether we need to use all data, or only data that is significant. My resolution of this question is that the high and low levels are what is significant and the middle data is effectively just noise. Determining the cutoff points with backing mathematics/statistics is essential.

This week’s presentation from The Institute for Systems Biology was the inspiration for this post and data extraction.

In Action

I have put interactions between bacteria of the same rank into Look up a bacteria taxa web site. Just search for a bacteria of interest and click the link.

You will see two charts, impacts and impacted by. For our example bacteria, we see that three other genus increases it (and looking at the names, no surprise!)

  • The size of the circles reflect the relative average count scaled
  • The width of the lines reflect the relative chi2 (significance), thicker lines implies more impact.
  • Green indicates increases/feed
  • Red indicates decreases/reduces

And a ton of bacteria that is reduced by its presence.

Applying to a Sample

Many of the bacteria above are rare. With an explicit sample, we filter to what is shown in the sample. We display them with their percentile ranking (i.e. a good indicator of relative amount)

Data Availability

The data (over 400K statistically significant impacts) will be available at MicrobiomePrescription Citizen Science for anyone wishing to compare against their own data. The data used was from Biomesight, a 16s provider that ships worldwide.

Post COVID: Bifidobacterium with low levels for both in a family

Back story

For me it’s still LongCovid > ME/CFS (thanks to SarsCov2) and unfortunately, in February, I had to take a 14-day course of antibiotics (amoxicillin 100 mg 3 times a day) because of Helicobacter Pylori

and also Pantoprazole 40 mg twice daily), which my micorobiome certainly didn’t like.

My PEM is less frequent and not as terrible as it used to be. My baseline has also improved, but I’m still at Bell 40 and pacing a lot. You already know the rest of my story.

Unfortunately, my daughter Carlotta looks as if she is slowly moving from the LongCovid control group to the LongCovid affected group. Which of course makes me particularly worried about my ME/CFS. She is also quite hypermobile.

Regarding her history, it should be mentioned that she developed bad migraines when she was around 5 years old (now 17). Which was triggered by certain foods. On a test it showed at 60 out of 80

Food intolerance. This could be almost completely remedied through a strict diet. The migraines were now rare, but have become more frequent recently. (maybe also interesting, there is a suspicion of Asperger’s Syndrome / Autism Level 1)

Mother

It seems that the microbiome has gone downhill over the year.

US National Library of Medicine Pattern Matching

  • 2023: multiple chemical sensitivity [MCS], SIBO, Graves’ disease, Acne, hypertension
  • 2024: hypertension, Menopause

Symptoms

  • 2023: Not entered
  • 2024: 101 Symptoms …
  • Dr. Jason Hawrelak Criteria: 66%ile

Daughter

The same pattern of the microbiome going downhill over the year.is seen here.

US National Library of Medicine Pattern Matching

  • 2023: Nothing
  • 2024: Chronic Lyme, NonCeliac Gluten Sensitivity, Insomnia, Halitosis, Acne,hypertension

Symptoms:

  • 2023: Nothing entered (entering long afterwards is discouraged for the sake of accuracy)
  • 2024: Neurocognitive: Can only focus on one thing at a time, Neurological: Joint hypermobility, Need to nap during each day,  Impaired Memory & concentration, Onset: Gradual, Headaches, Migraine, Viral infections with prolonged recovery periods, Joint: Tenderness, Official Diagnosis: COVID19 (Fully Recovered), Acne, Difficulty falling asleep, Easily irritated, Tinnitus (ringing in ear)
  • Dr. Jason Hawrelak Criteria: 13%ile (i.e. bad)

Going Forward

The daughter’s Bifidobacterium was at 5%ile (extremely low with few species) and the mother’s at 29%ile with many species at low levels. This leads directly to my next observation.

My personal experience post-COVID was that a mixture of Bifidobacterium probiotics cleared a lot of symptoms in less than 2 weeks. I tried that based on the first published study below, with support from other studies:

  • A synbiotic preparation (SIM01) for post-acute COVID-19 syndrome in Hong Kong (RECOVERY): a randomised, double-blind, placebo-controlled trial [2023]
  • “Overall, Bifidobacterium was associated with both protective effects and reduced abundance in relation to the disease. The genus has been found to be abundant in some cases and linked to disease severity.  The studies evaluating the use of Bifidobacterium as probiotics have demonstrated the potential of this genus in reducing symptoms, improving pulmonary function, reducing inflammatory markers, alleviating gastrointestinal symptoms, and even contributing to better control of mortality. In summary,” [2023]
  • “Growing evidence demonstrate that gut microbiota alteration is associated with COVID-19 progress and severity, and post-COVID-19 syndrome, characterized by decrease of anti-inflammatory bacteria like Bifidobacterium” [2023]
  • “Specifically, it suggests an association of anti-inflammatory bacteria, including Bifidobacteria species and Eubacterium rectale, with lower severity, and pro-inflammatory bacteria such as Prevotella copri with higher severity. ” [2022]
  • “Although the mortality rate was 5% in the [Bifidobacterium] probiotic group, it was 25% in the non-probiotic group. ” [2021]
  • “positive patients overall had lower relative abundances of Bifidobacterium ” [2022]

Thus, I was interested in what the KEGG Probiotics Suggestions came up with:

These KEGG suggestions appear to agree with the literature. See Explanation of the methodology if you are interested in the mechanics of these suggestions. I also looked at the revised supplements from KEGG (just done). Remember, using KEGG is not trying to fix individual bacteria, rather to make sure all of the nutrients needed in the “microbiome soil” are there in the hope of producing a bountiful healthy crop.

Note: Both Clostridium butyricum and Bacillus mesentericus are rarely used in clinical studies. Personally, I have taken both with positive effects.

The Regular Path Forward

Since symptoms (lots of them) have been entered for both, then a simple “Just give me suggestions with symptoms” for both

What I found very interesting is the great similarity between suggestions of the mother and the child. Same DNA, similar diet, and likely similar time since COVID. I will leave them to review the avoid list. I will point out that the other B-Vitamins are on the avoid list. I should also point out that NADH and Niacin are closely related so we have agreement between the KEGG data and our usual expert system. Vitamin K is not often on our expert system list (little data to work from).

Key Take Away:

Depending on finances, retest after being on it for 6-12 weeks. Correcting the microbiome is usually a long list of course corrections.

  • Rotate and change Probiotics (maximum time on any one should be two weeks).
    • At least 10 BCFU for each probiotic species
  • Have Barley Porridge each morning with some butter
  • There was no strong converge in diet style (diet is very subjective with most studies and usually problematic to interpret). The following are specific items you should consider

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.

Identifying Bacteria associated with a cluster of symptoms

There are two approaches to identifying bacteria associated with a group of symptoms:

  • UNION — you just join the bacteria associated with each symptom into a single list. This is often done when there is not sufficient data. It’s simple to do.
  • INTERSECTION — this identifies all people with the same combination of symptoms and then identify what is associated. This requires statistical computations to be done each time.

Using the technic for statistical significance describe in my prior post, Symptom associated Bacteria, Compounds and Enzymes, I have successfully implemented it for samples contributed to my Citizen Science web site. The site is open data so you can replicate results.

The video below is a quick walkthrough. What is interesting to note is that the number of significant bacteria can increase as more symptoms are added. Why? because you are filtering out noise from the bacteria.

You can also have bacteria appearing that were not in the prior list by adding one more symptom. Example below.

Bottom Line

With a large enough sample and enough characteristics recorded, you can drill down into a lot more data using the appropriate statistical techniques.

Technical Note: Using KEGG Enzymes to Pick Probiotics

We have the Enzymes produces by a wide variety of strains on the KEGG: Kyoto Encyclopedia of Genes and Genomes. We can aggregate(i.e. average) this data up to the species level and then estimate the enzymes that a probiotic species or retail probiotic mixture may produce.

The next step is to identify the enzymes that a person is deficient in. I use the patent pending Kaltoft-Moltrup (KM) method to determine the bottom boundaries applying to percentile values of a significant population.

Some visual examples, with this point being around the 8th percentile.

Another example with this point being between 2 and 7%ile

A sample with the cutoff being close to 20%ile.

Thus it is possible to determine:

  • If a person is likely deficient enough that supplementing enzymes via probiotic may be helpful
  • We could infer dosages by the distance from the KM cutoff point.

Then we can proceed to apply this to a collection of retail probiotics products

For example, Bifidobacterium Lactis was estimated to produce some 458 different enzymes.

Bottom Line

This approach does not try to “fix bacteria”, rather it tries to make sure that the fuel and oxygen need for the microbiome fire are there. Thus the bacteria issues resolve themselves! A very different way of trying to address microbiome dysfunction.

Symptom associated Bacteria, Compounds and Enzymes

On April 14th, this new/revised feature was released. It determines shifts in these items by looking at the top and bottom 15% of people with these symptoms compared to the annotated sample population. This makes it easy to understand how significance is determined.

The basis is simple:

  • We count the number of people with symptoms that have a percentile ranking below 15%ile or above 85%ile.
  • If there is no association, then the numbers should be closed, i.e. 21 for each with the example below.
  • If the numbers are different, we compute Chi2 , a statistical measure on the odds of it happening at random.
    • A value of 6.635 means 1 in 100 (or P <.01)
    • A value of 10.8 means 1 in 1000 (or P < 0.001)
    • A value of 20 means 1 in 125,000 (or P <  0.000008)
    • A value of 40 means 1 in 4,000,000,000 aka 4 billion (or P < 2.539629e-10)
    • A Chi-Square Calculator for those interested.

Rather than get into statistics, we show the common sense counts.

How to get there?

Upon logging with samples you will see this new menu item.

The next screen will matched against annotated symptoms for this sample. If you have no symptoms, this will be shown. You should add your symptoms via the link on this

If you have symptoms entered, then you will be shown a summary of what has been associated (according to samples from the lab you used)

The right three columns are hyperlinked. The number of actual matches will be shown when you click the hyperlink. In some cases, many matches in other cases none.

A general description is on the right. For Enzymes, typing “vitamin” in the search box. If the percentile is low, then you should consider supplementing with the vitamins listed. Why? you appears to be deficient in one or more enzymes that produces or uses it (as always, seek an opinion from an expert first)

Video Walk Thru

P.S. all of the bugs identified has been fixed.

Data Availability?

See https://citizenscience.microbiomeprescription.com/ for data. Kegg data on compounds and enzymes needs to be obtained from http://kegg.jp/ (licensing issue).

Converting GI-Map to Percentage

First, some fundamentals:

Units of Measure

The notation “3.3e12” is scientific notation, which is a compact way of representing very large numbers. In this notation, the “e” stands for “exponent.” So, “3.3e12” means 3.3×10^12 .

Breaking it down:

  • 3.3 is the coefficient or mantissa.
  • 10 is the base.
  • 12 is the exponent or power of 10.

Therefore, “3.3e12” is equivalent to 3.3 multiplied by 10 raised to the power of 12 , which equals 3.3 followed by 12 zeros:

3.3×1012=3,300,000,000,000.

In other words, “3.3e12” represents 3.3 trillion.

Total Bacteria

Ideally, you would add up all of the phylums; unfortunately GI-MAP only provides two

So the total in this sample is 7.15e12 which is above the reference range max of 3.6e12. So for the purposes of this discussion, we will assume 7.15e12 (with the difference from reference being other phylums).

How does GI-MAP work?

The GI-MAP (Gastrointestinal Microbial Assay Plus) is a diagnostic tool used to assess the composition of bacteria, parasites, and other microorganisms present in the gastrointestinal tract. It utilizes a technique called quantitative polymerase chain reaction (qPCR) to measure the abundance of specific microbial DNA in a stool sample. Here’s how it works:

  1. Stool Sample Collection: A patient provides a stool sample, typically collected at home and sent to a laboratory for analysis.
  2. DNA Extraction: The laboratory extracts microbial DNA from the stool sample. This step involves breaking open the cells of the microorganisms to release their genetic material.
  3. Primer Design: Primers are short DNA sequences designed to bind specifically to the target DNA sequences of interest. For the GI-MAP, these primers target specific regions of the microbial DNA that are unique to certain bacterial species, parasites, or other microorganisms.
  4. qPCR Amplification: The extracted microbial DNA is mixed with the primers and other reagents in a reaction mixture. The qPCR machine then cycles through a series of temperature changes to amplify (copy) the target DNA sequences. Each cycle doubles the amount of DNA present, allowing for the exponential amplification of the target DNA.
  5. Fluorescent Detection: During the qPCR process, fluorescent reporter molecules are incorporated into the DNA as it is amplified. As the amount of amplified DNA increases, so does the fluorescence signal. This allows the qPCR machine to detect and quantify the amount of DNA present in the sample.
  6. Data Analysis: The fluorescence data collected during the qPCR process are analyzed to determine the abundance of specific microbial DNA targets in the stool sample. By comparing the fluorescence signals to standard curves generated from known concentrations of DNA, the laboratory can quantify the relative abundance of different microorganisms present in the sample.

Converting to Percentage

Now, how do we convert. The easy way is to use EXCEL or other spreadsheets. Example of the formula is below.

Then change the display from Scientific to Percentage

We now have the percentages

That is it!

You can then compare to Jason Hawrelak Criteria for Healthy Gut or other criteria.

A word of Caution

One word of caution…   to illustrate, take GI-MAP reference ranges and convert to  Percentiles.

this becomes 0.0007 – 0.28% 

then go to https://microbiomeprescription.com/library/details?taxon=841

Their measurement scale seems inconsistent with other scales (16s and Shotgun)

Revisions to the Health Analysis Page

The calculations were audited and several errors found and corrected.

Significant changes

Charts against samples from the same lab are shown by clicking on the chart icon.

For example:

NOTE: negative numbers means bacteria are consuming, thus less consumption means more gets to the body. We calculate both producers and consumers.

Information on what something means

There is a red question mark that will show more information about the measure

A walkthrough video is below.

Walkthru of new and revised features on the Health Indicators Page (youtube.com)

Saving Money with Probiotics

Probiotics are a HUGH profit margin. The cost to produce is pretty constant. The difference per BCFU from the cheapest to the most expensive is 650x (i.e. 65,000% difference). You can cut your supplement costs greatly with some careful shopping around and computing the cost per BCFU.

Below are some buying options. I will use Lactobacillus reuteri  to illustrate the price issues

BioGaia Gastrus

30 x 0.2 BCFU = 6 BCFU for $30.00 or 0.2 BCFU per dollar

Super Smart

60 x 5 BCFU = 300 BFU for $20 or 15 BCFU per dollar

Toniiq

They appear to have only one single strain probiotic for sale.

50 BCFU x 60 = 3000 BCFU for $22. or 136 BCFU per dollar

Custom Probiotics

They use a researched strain (different from the above). They are probiotics ONLY, no filler.

50/.8 * 60 BCFU = 3650 BCFU for $100 = 36.5 BCFU per dollar

Maple Life Sciences

50 grams at 20 BCFU/gram = 1000 BFCU for $15.00 = 66 BCFU per dollar
They are probiotics with fructooligosaccharides only, no other fillers. Certified Organic

Shotgun vs 16s OR Biomesight vs Xenogene

As a reminder, Microbiome Prescription is a “best efforts” site. We do the best that we can with the data that is available.

Request from Reader

Hello Ken, 

I would kindly like to ask your opinion on this. I did three BiomeSight tests in three years, then one Xenogene. The Xenogene and last BiomeSight were 10 days apart. 

BiomeSight was telling me for three years that my butyrate producers were awesome and my F.Prau was great. Then Xenogene told me the exact opposite. BiomeSight results being so good for so long, I always assumed I have no problem in the microbiome so I kept my diet (15g of fiber at max, lots of meat, veggiest mainly potatoes – no 30-40 different veg/fruit per week). Butyrate producers and F.Prau this high on this diet is a little hard to believe, so I came to the conclusion that BiomeSight is completely off, at least for me. 

I remember the Taxonomic nightmare article you wrote and I understand that I cant directly compare two test providers. But when one test tells you that your microbiome is a rockstar and the other tells you its a zombie, its hard to see the usefulness of biomeSight testing. I kindof hope there is some magic that Im forgetting and the biomeSight tests will not prove to be a waste of time and money. 

The Xenogene values are percentages of bacteria only (i.e. # F. Prau / # total bacteria), so its “the same thing” as in biomeSight results. Just to be clear that its not # F.Prau / (# bacteria + # protozoa + # archaea + # fungi).

Explanation

Numbers always need to be interpreted against reference ranges.

Standard Lab Reference Ranges

If you go to the Akkermansia reference page, we see the following:

LabMeanStandard DeviationShift in S.D.
Xenogene0.693 %1.074 %– 0.9
Biomesight1.393 %4.111 %+.83

In other words, both test results were within 1 standard deviation of the mean – that will usually be interpreted as in the normal/reference range. The formula is easy:

(Your Value - Mean) / Standard Deviation

You can do this for each of the bacteria in your report. If the resulting value is between -1.6 and +1.6, you are clearly in the reference ranges.

Lab Provided Ranges

Xenogene provides ranges — this means that over 70% of xenogene files uploaded to MP are below the reference range.

Biomesight reference ranges are below

Every value is within the reference ranges. We have a disagreement.

Dr. Jason Hawrelak Recommendations

His ranges are 1-5% for Akkermansia. These are much less than Biomesight and bigger than xenogene.
User Feedback: “Dr. Hawrelak’s ranges – how can Dr. Hawrelak have a general range on some bacterium w/o stating which lab he uses for this? Considering all labs report different numbers due to the taxonomy nightmare, I don’t understand how there can be “one range to rule them all”. “

I agree totally, some labs cites him as an authority because they lack the skilled resources to determine their lab specific ranges, Often I have seen ranges from a published studied applied to numbers from a totally different processing pipeline – when challenged they cite “it’s an authority“. Some more readings:

As a FYI: I include his ranges because people have requested it. I provide choices and not judgements.

Microbiome Prescription Ranges

We do not have enough data to independently compute xenogene. Xenogene samples are part of “Other Labs”. The Kaltoft-Moldrup ranges are:

  • Other Labs: 0 – 7%
  • Biomesight: 0 -9.6%

Similarly using BoxPlot methodology,

  • Other Labs: 0 – 3.8%
  • Biomesight: 0 -1.7%

Note that Zero (0) is in range for many of these.

The Symptoms Factor

Identifying bacteria associated with symptoms depends on the number of samples uploaded with annotated symptoms. We do not have sufficient results with Xenogene, we do have sufficient for Biomesight with some 289 associations at present. Note this is pattern matching. “It has the ears of a German Shepard, it has a double coat of a German Shepard, it eyes color matches a German Shepard…etc. ” It may be German Shepard or it may be a Wolf or a Welsh Pembroke Corgi

For the latest Biomesight, we see a lot of matches to existing patterns

For Xenogene (which uses “Other Labs”) we have less and weaker matched

If you want to include your symptom in the suggestions report, biomesight is a better choice.

Eubiosis

Eubiosis is not very comparable, because Xenogene is mixed with all of the other odd labs. We have just 19 xenogene, not sufficient to do that much data, We have 61 samples from Thorne – same issue.

Follow up Questions

So my question was how can BiomeSight tell me my butyrate producers are 60% when its highly unlikely, just as with F.Prau being 19%. And the answers that I could imagine getting would be

  • you having 60% butyrate producers is the result of taxonomic hell, in some cases the positive measurement errors can add up resulting in a hugely overestimated sum – judging the SCFA producers in the biomeSight report is unreliable and should not be taken into consideration at all
  • OR the only relevant marker for abundance of SCFA producers is stool pH or SCFA measured in stool – use that instead of the SCFA producers % reported by ANY test provider
  • OR something else if the former two are nonsense

Butyrate evaluation is a good illustration minefield. Some observations:

  • Labs will usually qualify with “It is important to note that this is not a measure of these metabolites found in the stool sample.“
  • One lab totals 9 Genus and 1 species.
  • Biomesight appears to total 21 genus
  • Another totals: 43 species
  • Microbiome Prescription is based on  KEGG: Kyoto Encyclopedia of Genes and Genomes :
    • 402 species that could produce butyrate
    • 515 species that could consume butyrate

MP is the only one that appears to include consumers (thus getting a net amount). The choice of genus and species is often based on the depth of research that each lab does. MP is based on the genetics of the bacteria as sequenced and aggregated by KEGG. We do not know if the genetics are activated or not (epigenetics).

What is missing are studies comparing various estimates from bacteria against actual directly determined levels of butyrate and other metabolites…

Second Issue: Percentage of WHAT?

Many labs pull a magical number out of the air, typically if you are over this number you are Satisfactory, below Not Satisfactory. MP gives a percentile ranking against others samples using the same lab. If you are over 30%ile, I would deem it to not need work — but that is a personal judgement call, the numbers are there for you.

Bottom Line

Microbiome reports are full of uncertainty aka fuzzy data. Fuzzy data is not a strong selling point for businesses. Claiming accuracy and correctness is a great marketing ploy. If there are no legal/financial consequences of these dubious claims then they will typically be made by some.

I have seen some labs that started by “just reporting the facts/numbers” and then drift into interpretations because marketing studies found that would increase business.

Xenogene (and Thorne) is good because of what else it reports that is not reported on Biomesight which may be part of your health issues. For items that seem very high, you should do some research on them and if any medical conditions are reported/associated with them.

On the other side, we have a lot of samples with annotated symptoms for Biomesight and Ombre. This means that suggestions to modify your microbiome (especially if symptoms are used) are likely better.

My usual advice is simple:

  • do one of Xenogene or Thorne to check for non bacteria issues (fungi, phages, etx)
  • use Biomesight or Ombre for regular testing and getting suggestions.