Another ME/CFS after 19 years

This is actually a referral from a person from a previous blog, Another ME/CFS person has gone to Firmicutes!. He shared his experience (see that post) “ My energy levels are perhaps the best they’ve been in the last 5 years.  I’ve still got a very long way to go but the results thus far are promising!” and a friend decided to try

Back Story

I became symptomatic around 19, progressively got worse so that by the age of 23 I had fully crashed with classic CFS symptoms. Severe symptoms persisted for 5 years during which time I was unable to work due to unrelenting fatigue. Slowly got about 50% better through an extremely low stress lifestyle and dietary/food changes. I’ve tested positive for a few Lyme co infections, chronically low cortisol and pretty much anything else the chronic illness community tests for, I’ve tested and treated. I’m now 38 and my recovery seems to hang at about 40% of optimal capacity. I wake up unrefreshed and have lagging energy all day long. I have to live an extremely low stress life, if I don’t, my sympathetic system kick into high gear. I seem to have issues with histamines (though I can ingest them, I just get flushed, puffy and hot at times), and my fatigue gets profoundly worse around my cycle. I don’t have any significant digestive issues that I’m aware of.

Analysis

The first thing that I should mention is that I recall a study finding that the duration of ME/CFS does not impact the probability of remission. So 19 years with ME/CFS is not a factor.

Dr. Jason Hawrelak Recommendations came in at 99.9%ile, so no pro-forma general issues. That is not unusual, most labs on ME/CFS patients report normal. Looking at the Potential Medical Conditions Detected list, nothing of concern.

Looking at specific bacteria, a few bacteria stand out:

Looking at bacteria distributions we see a good pattern except with the rare bacteria which appear under represented. An ideal microbiome would have the same count in this range. This suggests a well established microbiome, perhaps with a touch of “inbreeding”.

Diving into the species of Lactobacillus interests me. We find just one species dominates,  Lactobacillus rogosae. A 2018 review,”Occurrence and Dynamism of Lactic Acid Bacteria in Distinct Ecological Niches: A Multifaceted Functional Health Perspective“, states “All in all, no consistent marker for any pathology or a healthy state is simply defined by a specific proportion of Lactobacillus“. This strain being classified as a Lactobacillus has been challenged [1974] with the suggestion that they may belong to Propionibacterium, a family associated with Acne. We are back to the fuzziness of 16s lab software as well as challenge of RNA/DNA being exchanged between different bacteria.

We have irony here, because the friend was high in Firmicutes and we have 77% of the microbiome in this sample also being Firmicutes with heavy domination of several ones as shown in the Krona chart below

I am inclined to do the customary ones PLUS one just trying to reduce Lactobacillus to build the consensus.

Take Suggestions

The top items had one little surprise – Cadium! There is a source for this that is also known to be good for ME/CFS – dark chocolate! See Dark chocolate is high in cadmium and lead. Prior studies on ME/CFS found that dark chocolate/ cacao improves ME/CFS symptoms.

The full consensus and simple consensus (with some dosages) are below

Items to Reduce or Avoid

The avoid list containing many popular items claimed to help the microbiome (which it does in some cases). Some are obvious with high lactobacillus — i.e. avoid lactobacillus probiotics. lactulose is a key food for lactobacillus.

Vitamin A is omitted because one form helps and the other form hurts.

Probiotics

Only one probiotic had all 4 saying take: bacillus coagulans (probiotics). KEGG suggestions had #1 being Escherichia coli (Mutaflor or Symbioflor2) which was also on the take list with a variety of Bacillus probiotic on the list ( Bacillus amyloliquefaciens, Bacillus velezensis, Bacillus subtilis, Bacillus licheniformis, Bacillus subtilis subsp. natto) with all of the same ones on the take list (but not recommended by all 4 sets of suggestions).

In short, avoid Lactobacillus and Bifidobacterium probiotics. We want to greatly reduce the Lactobacillus from the 94%ile – it is very likely that d-lactic acid from lactobacillus is responsible for many symptoms. Bifidobacterium INCREASES Lactobacillus which is the opposite of what we want to do. We want to get lactobacillus down as a first priority, later we look at increasing bifidobacterium once it is down far enough.

https://microbiomeprescription.com/library/modifier?mid2=1753

Questions and Answers

  • Q: Is there any reason you chose to focus on the lowering Lactobacillus, as opposed to the Bifidobacterium being low?  Is it because the “bacteria deemed unhealthy” table is a more important focus to you than the jason hawrelak recommendations?
    • A: The medical condition of ME/CFS and Lactobacillus has a long connection to each other. Both “bacteria deemed unhealthy” and Hawrelak are general criteria. Being at the top of Hawrelak’s rating (99+% of people are worst), would imply no issues — you have issues.
  • Q:  Why not focus on the Mogibacterium that’s in the 100 percentile?
    • A: We could — it was a factor included in the bacteria picked to modify. The list of items is here.
      Some people go after a single bacteria with a “all other factors be ignored”. For example, Fennel reduces Mogibacterium BUT it also increases Lactobacillus!! The AI algorithm attempts to balance the dozens or substances impact on dozens (or thousands) of bacteria. You are welcome to do it by hand for the 123 bacteria flagged and the several thousand modifiers.
  • Q: the last question is about interpreting Krona charts. The Krona chart doesn’t seem to provide standard ranges
    • A: This form of visual display will get extremely busy and confusing with ranges. If you attempt to draw a high range line it will sit over a different bacteria.
  • Q:  So is there any easy way to know when a bacteria is high or low by looking at that chart?
    • A: Use the hierarchy chart. You can pick one set of ranges at the top of the page. Items that are high (by the selected ranges) are in blue, and low in pink.
      You can also hand pick bacteria to alter.
On My Profile Tab
Checking the checkboxes, then clicking Create… allows a hand picked set of targets.

Different Microbiome Results from Different Providers on Same Sample

This is part 2 of Unclassified Bacteria, Fungi and Virus and I will continue with this analogy

So doing a microbiome test is like collecting the DNA from a bunch of people at a major airport and then asking: Which country did this person originated from according to their DNA. You will find some people that are good matches to an ethnic origin and some people that are “Heinz 57” aka “Mutts”. These people are unclassified — just like some living components are unclassified.

Let us look at my own DNA to illustrate the issue better. The same DNA file was used for ALL of the following charts. Why do they not agree? Simple — thing are done by matching patterns. The reference library determines where matches are done. Every provider use difference reference libraries. There is no universal reference for Human DNA, nor for the microbiome.

From 23 and Me
From Ancestry

and one more site, this one almost causes whip lapse!

FamilyTreeDNA

One site offers the choice of reference library to use and how to match

GEDMatch

GEDMATCH show where your DNA matches historical samples

I look very Irish here

When we drill down to the next level, we see different “species names”

From 23 and me
Ancestry

Wait — we are talking about where

The above patterns are based on matching to current populations. We really would like older populations. There is a site that does that! My True Ancestry. The same DNA file suggests more UK or southern Germany. We could view this as an illustration between 16s and shotgun reports.

It is interesting to note that this seems closer to Family Tree DNA results shown above.

In some cases, it is not where the ancestors came from BUT where ancestor siblings settled, as in Iceland and the Shetland Island. The same can happen with bacteria identification. All that we know is that some components are shared.

Unclassified Bacteria, Fungi and Virus

Some people get concerned about finding unclassified stuff in their microbiome sample. This does not happen with some labs because they elected not to report what is not classified. Why? It leads to support calls asking for explanations (which means $$$$ spent for the company).

Labs could create synthetic proprietary names for the unclassified to make the issue disappear — that causes the issue to disappear but really does not help.

There is a part 2: Different Microbiome Results from Different Providers on Same Sample

An Analogy

Bacteria is like the population of a country or the world. They interbred to some extent

Genetic exchanges among bacteria occur by several mechanisms. In transformation, the recipient bacterium takes up extracellular donor DNA. In transduction, donor DNA packaged in a bacteriophage infects the recipient bacterium. In conjugation, the donor bacterium transfers DNA to the recipient by mating.

Medical Microbiology. 4th edition., Chapter 5

So doing a microbiome test is like collecting the DNA from a bunch of people at a major airport and then asking: Which country did this person originated from according to their DNA. You will find some people that are good matches to an ethnic origin and some people that are “Heinz 57” aka “Mutts”. These people are unclassified — just like some living components are unclassified.

If I was picked, the answer is pretty clear for the “genus” that fits me

Which agrees with paper records back to 1500

If we want to get a more precise name, the “species”, then some fuzziness appears

There is a possible misidentification, my father’s ancestors (all lines) records go back to 1600 on an island that is low probability.

Lolland/Falster

The process of giving names to bacteria in the microbiome is extremely similar. For some bacteria (like me) we fit into a nice box. For the person below, the box is not so clear

Would this person be classified as Irish or English

Bottom Line

A wise man knows what he does not know, and what cannot be known.

Another Long COVID story

Back Story

COVID in February 2021. 37 y.o. Male at the time, athletic/fit. Crossfit x 3 a week, playing football weekly Only mild gastritis prior to Covid. No other health issues.

Moderate severity Covid, lots of symptoms.

And then Long COVID and CFS/ME type of symptoms mostly fatigue, PEM and GI problems (pain, food intolerance, bloating..etc) I’d say it’s a moderate/mild case of CFS/ME. But after 18 months still not back to previous levels, can’t walk too long otherwise i crash. I’d say i am around %75.

For other analysis of Long COVID see Analysis Posts on Long COVID and ME/CFS

Analysis

We have two samples available, one early in Long COVID and one more recent

  • 2021-10-01
  • 2022-08-17

With this type of information, let us start by comparing them. We are fortunate that both samples are similar read quality which reduces fuzziness. Unfortunately, it appears that the microbiome dysfunction has increased in many aspects. One aspect that it has improved in terms of bacteria with very low counts. We went from 74% of bacteria with low counts down to 51% with low count. Ideally we would love to see the low count to drop to a modelled 15%.

I should note that the increase in some Outside Ranges is likely because many of the ranges are 0 to some amount, hence the older sample had less because it was full of different trace amounts. The same apply to many other criteria, the low abundance of many bacteria skewed the criteria to appear better.

CriteriaCurrent SampleOld Sample
Lab Read Quality5.85.9
Bacteria Reported By Lab393399
Bacteria Over 99%ile81
Bacteria Over 95%ile2621
Bacteria Over 90%ile4235
Bacteria Under 10%ile135160
Bacteria Under 5%ile56108
Bacteria Under 1%ile929
Lab: BiomeSight
Rarely Seen 1%10
Rarely Seen 5%98
Pathogens3033
Outside Range from JasonH1010
Outside Range from Medivere1515
Outside Range from Metagenomics1010
Outside Range from MyBioma88
Outside Range from Nirvana/CosmosId2222
Outside Range from XenoGene3434
Outside Lab Range (+/- 1.96SD)168
Outside Box-Plot-Whiskers4943
Outside Kaltoft-Moldrup147108
Condition Est. Over 99%ile180
Condition Est. Over 95%ile380
Condition Est. Over 90%ile500
Enzymes Over 99%ile561115
Enzymes Over 95%ile793282
Enzymes Over 90%ile866680
Enzymes Under 10%ile289294
Enzymes Under 5%ile149149
Enzymes Under 1%ile2923
Compounds Over 99%ile48050
Compounds Over 95%ile600214
Compounds Over 90%ile677298
Compounds Under 10%ile581315
Compounds Under 5%ile563242
Compounds Under 1%ile54891

I next went to the Krona Charts to try to understand the shifts better. We see a massive increase of unclassified Bacteroides

2021-10-01 Sample
2022-08-17 Sample

Going to sample comparison, we see that (genus) Bacteroides was at the 99%ile on both samples. Looking at members of this genus, we see several of the identified species at high levels

Going Forward

We have a good idea of the issue: lots of bacteria at token levels, lots of unidentified bacteria.

My approach is to try the following, looking ONLY at Restrict to Bacteria with Low Levels, do the following three

Then create a handpicked bacteria focused on Bacteroides and the high species under it. To express another way: Feed the weak and destitute, Bring down the mighty.

Doing the first step, we see at the top of the consensus:

The hand picked collection is below with percentiles

  • genus  Bacteroides 99
  • species  Bacteroides faecis 93
  • species  Bacteroides graminisolvens 86
  • species  Bacteroides ovatus 99
  • species  Bacteroides rodentium 95
  • species  Bacteroides stercorirosoris 91
  • species  Bacteroides thetaiotaomicron 93
  • species  Bacteroides uniformis 97
  • species  Bacteroides xylanisolvens 100

The suggestions for just these are shown below. The pattern is similar to other peoples suggestions with ME/CFS – lots of specific B-Vitamins, dark chocolate etc:

  • whole-grain barley
  • sucralose
  • Caffeine
  • Hesperidin (polyphenol)
  • polymannuronic acid
  • momordia charantia(bitter melon, karela, balsam pear, or bitter gourd)
  • walnuts
  • folic acid,(supplement Vitamin B9)
  • garlic (allium sativum)
  • vitamin a
  • lactobacillus casei (probiotics)
  • diosmin,(polyphenol)
  • Arbutin (polyphenol)
  • pyridoxine hydrochloride (vitamin B6)
  • retinoic acid,(Vitamin A derivative)
  • thiamine hydrochloride (vitamin B1)
  • Vitamin B-12
  • vitamin b3 (niacin)
  • vitamin b7 biotin (supplement) (vitamin B7)
  • Vitamin C (ascorbic acid)
  • melatonin supplement
  • luteolin (flavonoid)
  • lauric acid(fatty acid in coconut oil,in palm kernel oil,) – Monolaurin
  • Cacao

The avoid list (items that help bacteroides grow) included some items from our earlier to take list.

  • Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
  • inulin (prebiotic)
  • saccharin
  • resistant starch
  • red wine
  • stevia
  • xylan (prebiotic)
  • berberine
  • arabinoxylan oligosaccharides (prebiotic)
  • apple
  • high red meat
  • l-citrulline
  • low-fat diets
  • schisandra chinensis(magnolia berry or five-flavor-fruit)
  • lactobacillus plantarum (probiotics)
  • Slippery Elm
  • triphala
  • Pulses
  • wheat bran

This is not unexpected, every substance/modifier has multiple impact.

Looking at the resulting consensus and items that are agreed to by both analysis, we have (in descending order):

  • bacillus subtilis (probiotics)
  • walnuts
  • high fiber diet
  • fruit/legume fibre
  • lactobacillus reuteri (probiotics)
  • saccharomyces cerevisiae (probiotics)
  • Nicotine
  • glycine
  • oregano (origanum vulgare, oil) |
  • pediococcus acidilactic (probiotic)
  • lactobacillus rhamnosus gg (probiotics)
  • rhubarb
  • bifidobacterium pseudocatenulatum,(probiotics)

Going over the KEGG based suggestions we see Escherichia coli at the top (typical for ME/CFS) and the next regular probiotic being Bacillus subtilis (in agreement with the above), followed by other Bacillus. Pediococcus acidilactici was listed. Further down the list we see Clostridium butyricum, Lacticaseibacillus casei (i.e. L. Casei above), Lacticaseibacillus rhamnosus. These were a pleasant surprise to see the same probiotics suggested from different models.

As a FYI, clostridium butyricum (probiotics) was on the consensus list, but mutaflor escherichia coli nissle 1917 (probiotics) was on the avoid for the consensus.

Bottom line for probiotics to try (add just one new one a week so you can see the response to each). See Simple Suggestions download below for suggested dosages or look them up on 📏🍽️ Dosages for Supplements. Using too small (almost homeopathic) dosages is a common error – the dosages on bottles are determined for profit margin (repeat business) and not from effective dosages from clinical studies.

The two downloads of the final consensus are attached below.

I would suggest getting another sample 6 weeks after implementing the above to see what the progress is.

As always, review with your medical professional before implementing.

Bottom Line

This patient history and their microbiome are in agreement. The antibiotics suggestions (off label usage) matches the history.

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.

Contribute your time to Citizen Science to find Gut Changers!

On Microbiome Prescription we have Look up a modifier of bacteria with many entries. It would be good for everyone if we increase the number of entries especially for atypical items.

YOU can help make it happen!

  • Find a herb, spice, food, drug of special interest to you
  • Go to https://pubmed.ncbi.nlm.nih.gov/
  • Type in the name with ” 16s microbiome “, for example, with “Vitamin K”

Click Search. you will hopefully get a few dozen (or hundreds) of studies.

Next is the long haul part. Go thru them (Example URL).

Ruminococcus ASV, a Lachnospiraceae Anerostipes ASV, two Lachnospiraceae NK4A136 group ASVs, and a Muribaculaceae ASV were enriched in the vitamin K deficient group, whereas a Bacteroides ASV was enriched in the MK4 group, and a Lactobacillus ASV in the MK9 group. 

  • Then comes the thinking part. Trying to describe the results.
    • Can you write a sentence such as “Vitamin K may increase Bacteroides  and Lactobacillus and a reduce Ruminococcus,  Anerostipes ,Muribaculaceae ” 
    • If so, include that in the file sent. (I will verify and it will serve as a double check the reading)
  • If the study was done on a person (or mouse) with a specific condition, we still include it.
  • Sometimes you will find that a substance with a common name may have multiple breakdowns.
  • Ideally, find other names of the substance and search each one.

Sending the Information to Me

I would suggest putting the links (i.e. like https://pubmed.ncbi.nlm.nih.gov/36471554/ ) in a text file or excel and sending to me. Where practical, include a sentence on the impact.

The information sent (when added to database) is available to everyone! This is citizen science! As I remarked to someone earlier today “I do not have a business model, I have a pro bono model“.

Clicking on 📚 PubMed Citations  will show the citations

The encoded data can be used to evaluate yours and others microbiome against the patterns reported.

The data is extended on entry to it’s children and it’s parent (with reduced confidence)

That is it!!! You spend the time so others do not have to and can act on their challenges with better information!

Email the lists to Research@microbiomeprescription.com

Contribute your time to Citizen Science!

On Microbiome Prescription we have Medical Conditions with Microbiome Shifts from US National Library of Medicine with some 91 entries. It would be good for everyone if we increase the number of entries.

YOU can help make it happen!

  • Find a condition of concern to you or someone you know.
  • Go to https://pubmed.ncbi.nlm.nih.gov/
  • Type in the name with ” 16s microbiome “, for example, with “pregnancy”

Click Search. you will hopefully get a few dozen (or hundreds) of studies.

Next is the long haul part. Go thru them (Example URL).

And compared with the placebo group, the GOS group had a higher abundance of Paraprevotella and Dorea, but lower abundance of LachnospiraceaeUCG_001.

  • Then comes the thinking part. Trying to describe the results. In this case, we see that the focus is “gestational diabetes mellitus (GDM)”
  • If not a good fit for your focus, add it to a new list to come back to later.

In the above case, a new search (gestational diabetes mellitus 16s microbiome) returned 70+ studies. This is a good candidate for a new collection to add.

Sending the Information to Me

I would suggest putting the links (i.e. like https://pubmed.ncbi.nlm.nih.gov/36471554/ ) in a text file or excel and sending to me.

The information sent (when added to database) is available to everyone! This is citizen science! As I remarked to someone earlier today “I do not have a business model, I have a pro bono model“.

Clicking on the book stack will list the studies (and provide links!)

The encoded data can be used to evaluate yours and others microbiome against the patterns reported.

Click Taxon to get the list. Or Candidates to get an abstract list of recommendations without a sample

That is it!!! You spend the time so others do not have to and can act on their challenges with better information!

Email the lists to Research@microbiomeprescription.com

Some probiotics or antibiotics makes you feel worse…

A reader wrote about feeling worse

Have you ever heard from anyone who felt significantly worse on antibiotics?  Since 2020 when I had ebv and iv antibiotics for an infection I can’t tolerate antibiotics like I used to.  Low dose doxy 50mg makes me feel very unwell within a few hours, clarithromycin is better tolerated but if I take that I get severe abdominal pain after meals and reduced physical function which only pro and prebiotics fix.  If I take a broad spectrum like metronidazol that completely fucks me up (pardon my french!) and I have to get into bed as I cannot stay awake.

There can be multiple causes, the most likely is a Jarisch–Herxheimer reaction a.k.a. “die-off”. (see this article). I have written about these for many years with a few prior posts.

There are other possible causes, for example, something is being feed and more chemicals are being dumped into your system. That is best determines with lab tests.

So to see explore possibilities (I deal with probabilities, not certainty), For your sample go to My Profile. You will see the new link there under Special Reports. Other common possible causes are probiotics that produce d-lactic acid (often causing brain-fog) or histamines.

Enter what causes you to feel worse on the left size, click compute and see which bacteria are likely being lowered.

Cross Validation of AI Suggestions for Nonalcoholic Fatty Liver Disease

A friend got this diagnosis from a naturopath examining blood cells using a microscope. Visual inspection of blood lacks the degree of objectivity that I would prefer (AI imaging of the microscope slides is what I would like to see things evolve to). I went to Microbiome Prescription’s Medical Conditions with Microbiome Shifts from US National Library of Medicine and then look at the candidate suggestions for Nonalcoholic Fatty Liver Disease (nafld) Nonalcoholic .

For information about the Artificial Intelligence approach, see AI Generated Diet Suggestion for Medical Conditions. Note: This is not machine learning but old school fuzzy expert systems.

Out of curiosity, I did a few cross validations for the highest to take and highest to avoid. Everything was in agreement. The few suggested items that I checked were shown to have the desired impact on NAFLD.

The purpose of cross validation is to see how well the Artificial Intelligence Logic (and assumptions) are performing.

I decided to do a deeper cross validation because I found that the treatment literature was relatively abundant. REMEMBER the AI only knows the bacteria shifts and nothing about the diagnosis or treatments.

Items to Take

In priority order, the top items with AI Weight of 20 or more

Net ImpactLinkModifier
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35678936/soy
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36698477/lactobacillus plantarum (probiotics)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35662935/resistant starch  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36278802/barley
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35101633/vitamin d
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35782914/Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35052765/bifidobacterium bifidum (probiotics)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/34292103/inulin (prebiotic)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/24475018/lactobacillus rhamnosus gg (probiotics)
n/a resistant maltodextrin  
n/a saccharin  
n/a fibre-rich macrobiotic ma-pi 2 diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/32718371/lactobacillus casei (probiotics)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/33317254/lactobacillus acidophilus (probiotics)
RIGHThttps://pubmed.ncbi.nlm.nih.gov/23026517/potatoes  
n/a Goji (berry,juice)  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/31017556/chicory (prebiotic)  
WRONGhttps://pubmed.ncbi.nlm.nih.gov/34773093/salt (sodium chloride)  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36433820/low protein diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35485931/apple  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36235752/vegetarians  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/30166633/red wine  
n/a fasting  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/31804025/Burdock Root  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/32158345/oregano (origanum vulgare, oil) |  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35843540/wheat  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/26915720/Guaiacol (polyphenol)  
WRONGhttps://pubmed.ncbi.nlm.nih.gov/34773093/high salt  
n/a merbromin  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/34482357/bacillus subtilis (probiotics)

Items to Avoid

Values with AI Weight of -14 or less

RIGHThttps://pubmed.ncbi.nlm.nih.gov/36565558/lard  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36682412/low carbohydrate diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36223657/l-glutamine  
n/a thiamine hydrochloride (vitamin B1)
n/a melatonin supplement
WRONGhttps://pubmed.ncbi.nlm.nih.gov/29081885/linseed(flaxseed)
n/a camelina seed  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/34534894/dairy  
WRONGhttps://pubmed.ncbi.nlm.nih.gov/35269912/Caffeine  
complex high animal protein diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/29984415/gluten-free diet  
n/a mannooligosaccharide (prebiotic)  
n/a low fodmap diet  
n/a sodium stearoyl lactylate  
Complex high-protein diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36565558/fat  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36670457/smoking  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/35896521/triclosan  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36771448/high sugar diet  
RIGHThttps://pubmed.ncbi.nlm.nih.gov/36445049/Vitamin C (ascorbic acid)

Discussion

There is a strong bias to publish positive results, hence finding many n/a in the to avoid list is expected. A study show an adverse effect is unlikely to see publication. We see the following ratios:

  • To Take: 22 Right to 2 Wrong, i.e. 92% correct
  • To Avoid: 10 Right to 2 Wrong i.e. 83% correct

This suggests that the n/a ones above are likely to be correct.

Thorne Microbiome Tests Are Now Supported

A reader forwarded his results and ask if they could be uploaded. It was a CSV file which was a good sign. Inspecting the file I noticed two things:

  • No NCBI Taxonomy numbers were included 🙁
  • The report gave percentile numbers for every bacteria — a wonderful thing to see.

The reader approached Thorne support about getting the NCBI Taxonomy numbers added — with no success. After a few days of work I ended up with 99.9% success of matching their bacteria names to NCBI Taxonomy numbers. The import worked… but wait! The price is about the same as some 16s tests, but you get MORE DATA and more accurate data! See this study for the difference between 16s and Shotgun

This is whole-genome shotgun metagenomics which is more accurate. It provides percentiles against a much larger sample than I could hope to get. My site is focused on percentiles — so most thing flows nicely – even when there is just one sample!!!

There are items that will not work until we hit 100+ samples from Thorne (i.e. KEGG Percentile Ranking, Pub Med Condition Percentile, etc).

We use Thorne’s Percentiles
Insufficient Data To Use these options

We substitute Percentage Match for Percentile in this section (since we are less than 100 samples)

KEGG is based on Percentiles

Bottom Line

I have ordered Thorne for my next test and expect to keep using them if the pricing stays the same. These test costs are driven by technology — which keep dropping cost over time. I recall spending $1000 to get 1 Meg of RAM many decades ago, today for the same amount, I can get 320 GB of memory — that’s 320,000 x more! The same thing is happening with microbiome and DNA testing technologies.

The taxonomy nightmare — Episode II

See also:

Several readers have emailed this article (or news story on it) Current microbiome analyses may falsely detect species that are not actually present. or The virtual microbiome: A computational framework to evaluate microbiome analyses. This is not a surprise, it was reported earlier

Common approaches to analyzing DNA from a community of microbes, called a microbiome, can yield erroneous results, in large part due to the incomplete databases used to identify microbial DNA sequences.

The process is equivalent to naming a person’s last name from a random DNA sample of a person.

 To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information and optimizing the use of this information.

The analogy of “The process is equivalent to naming a person’s last name from a random DNA sample of a person” is a good description of the issue. If you get more people DNA is the database, the odds of correctly identifying the person’s birth last name increases…. naming the bacteria species or strain has the same issue.

For the purposes of Microbiome Prescription, it is not a significant factor because the Artificial Intelligence is based on odds and probability (just like finding the name). For a human, you may identify that it is likely a Norwegian or Dane and thus the last name likely ends with a “sen” with 4.6% odds of being a Jensen (see more here). It is significant if your ideology requires absolute answers.

The script of the first Episode from 2019 is repeated below.

The taxonomy nightmare before Christmas…

This post is intended to educate people more on the technical aspects of the microbiome. I am not talking about taking 4 samples from one stool and sending it to 4 different testing company. I am talking about one sample sent to one testing company which then provided their analysis and a FASTQ file. The raw data.

What is a FASTQ file (besides being megabytes big)? It is the DNA (technically the RNA) of the bacteria in the stool. It looks like this (using the 4 letters that DNA has):

CCGGACTACACGGGTTTCTAATCCTGTTTGATACCCACTCTTTCGAGCATCAGTGTCAGTTGCAGTCCAGTGAGCAGCCTTCGCAATCGGAGTTCATCGTTATATCTAAGCATTTCACCGCTACACAACGAATTCCGCACACCTCTA

The file that I am using as text would be around 16 megabytes. This data comes from a lab machine. The company then processes it through their software to match up sequences to bacteria.

In this post, I am using the FASTQ from uBiome and getting reports on the bacteria from:

  • ubiome
  • thryve inside
  • biomesight
  • sequentia biotech.

Naively, one would expect almost identical results. What I got is shown in detail below. At a high level we had the following taxa counts reported

  • ubiome – 253
  • thryve inside – 632
  • biomesight – 558
  • sequentia biotech 366

I did a more technical post on my other blog. From some providers, a taxonomy may be 40% on another 2% or even none… ugly!

Standards seekers put the human microbiome in their sights, 2019

The headaches!

Number One Issue: You cannot, repeat cannot, compare a taxonomy report from one lab with another. EVER!

  • I have 8 uBiome reports and 2 Thryve reports. I can compare the uBiome to each other and the Thryve to each other. I can never mix their direct taxonomy reports !

Number Two Issue: If I wish to compare different lab reports, I MUST obtain the FastQ files from each lab and process them thru the same provider. The FastQ files are the raw data! For me, I prefer to push them through multiple providers which means that the 10 reports suddenly become 40 or 50 different reports in my site.

For more details with examples, see The problem with “official” ranges from labs

My Headaches

I have revised my site to show data by specific provider (while keeping the across all provider data still available). A lot of pages to revise and test.

Share this: