Quick start to 2 blogs and an analysis site

My primary concern for the last 20 years was been the condition known as Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). I deduced some seven+ years ago that the simplest explanation of the multitude of symptoms and abnormalities reported was a stable microbiome dysfunction. This explanation can also be applied to many other conditions. My focus is still on ME/CFS but I wish to make the data and algorithms available to people with any conditions. My old home page is here (dry technical).

The basic model that is supported by studies is:

  • DNA Snps that results in increased risk
  • Environmental changes of DNA (epigenetics) that further increase risk
  • Microbiome function that acts as a catalyst to the risk.

The microbiome is the simplest to alter technically — but very complex to alter because there are thousands of bacteria that interact with each other in the human body. DNA can also encourage some bacteria and discourage others. Example: Typhoid Mary is an excellent example of some one whose DNA and a nasty bacterial infection co-existed nicely.

Does changing the microbiome work for ME/CFS?

Answer is yes:

Open-label pilot for treatment targeting gut dysbiosis in myalgic encephalomyelitis/chronic fatigue syndrome: neuropsychological symptoms and sex comparisons , 2018

Recommended Site For Testing

With ME/CFS, there is always a nasty cost factor for testing. My usual recommendation is for the cheapest, high quality provider that provides information for upload to my analysis site. Some sites provide a mountain more of information — but the benefit from that extra information is almost nothing (and it adds $$$$ and complexity).

  • uBiome.com is shutting down. This had been my personal usual site because using a variety of techniques, the cost was $25/sample. Don’t order from there.
  • BiomeSight.com (EU based but serves the world) – discount code “MICRO” has integrated with my analysis site with automatic data transfer. For most people it is likely the best deal.
  • Thryve (US Based) is what I have used. Their reports may be processed here for independent suggestions. I would also recommend

Who am I?

I am a citizen-scientist with reasonable scientist credentials: taught Chemistry and Physics at College Level; Master of Science, accepted for the PhD program, certified data scientist with R, one of the top mathematics and physics competition students in Canada during my university years, etc.

I am a closet academic — so I give links to my source of information everywhere and usually keep them to the highest quality sources (PubMed, professional journals). I have even had a letter of mine published in the Lancet.

The Sites

  • This site — over 1200 blog posts published over the last 5 years. This is where I publish most. You can subscribe to get new posts by email.
  • Microbiome Prescription site – started in 2018. This is a massive data store with a variety of artificial intelligence algorithms applied to it. Almost 800 people have uploaded their microbiome results to it and many annotated it with their symptoms.
  • Microbiome Prescription Word Press – started recently. This is intended as a reference to the above site. Just essential pages and a bunch of homemade videos taking you through some features.
  • Facebook Site: Where I usually post new blog entries and the occasional odd note that is not worth a blog post. Make sure that you like it so you get notices of new posts.

Findings to Date

The assumption that bacteria shifts connect to symptoms appears confirmed using the upload microbiomes.

  • We have found statistically significant patterns of some bacteria to symptoms, see this post
  • We appear to have a high probability of correctly predicting symptoms from a microbiome report. See this post.

These findings can be independently confirmed by using the public shared data at: http://lassesen.com/ubiome/

Tools to Help

The Microbiome Prescription site is a theoretical site, that is, it works from the logical application of data and is not based on actual human experience. It does have the ability to create suggestions of things to take and to avoid to try reducing abnormalities in your microbiome. It supports multiple models and algorithms because we do not know which actually works best.

The site states that the suggestions should be reviewed by a medical professional. The source of the information is provided by links (hundreds of articles are cited).

Evolving Story

As more data comes in, and more insight happens, there will be more posts and more features (some labelled experimental — because I am unsure of their accuracy) will be added. This is citizen science.

Video to kickstart using your microbiome use

Overview of this Blog and the Microbiome

My ideas on this blog have evolved, as more and more information becomes available. This post is an attempt to bring readers up to date with my current thinking. I am striving to be transparent in my logic — showing the evidence I am working from, and my thought processes.


Notes to Treating Physicians     Quick Self Start on treating CFS


Analysis of Microbiome/stool with recommendations

Site: has moved to http://microbiomeprescription.azurewebsites.net

The data is available in an online collaborative python workbook for analysis. See this post.


Microbiome Definition of CFS/FM/IBS

A coarse condition that results from:

  • Low or no Lactobacillus, AND/OR
  • Low or no Bifidobacteria , AND/OR
  • Low or no E.Coli , AND/OR
  • A marked increase in number of bacteria genus (as measured by uBiome) to the top range
    • Most of these genus are hostile to/suppress Lactobacillus, Bifidobacteria, E.Coli
    • Several are two or more times higher than normally seen
    • The number of bacteria genus goes very high (using uBiome results), but most of them are low amounts.
      (“Death by a thousand microbiome cuts” and not “Death by a single bacteria blow”)
  • The appearance of rarely seen bacteria genus in uBiome Samples.

A finer definition would be a condition with a significant number of abnormalities in the ‘Autoimmune profiles see this page for the current criteria (i.e. over 25%).

The specific genus and their interactions determine the symptoms seen — likely due to the over- or under-production of metabolites (chemicals). Other autoimmune conditions may share these core shifts. The specific high and low bacteria determine the symptoms if the person was the DNA/SNP associated with the symptoms.

Replace the metabolites produced by the missing bacteria

Replacing the metabolites should result in the reduction of symptoms associated with a deficiency of these metabolites.

See this post for the study references. These items should/could be done continuously.

Other Supplements Reported to Help

Bootstrapping Bifidobacterium and Lactobacillus

The items below were found in studies to increase bifidobacterium and lactobacillus:

Unless the bifidobacterium and lactobacillus (B&L) are human sourcedthere is almost zero chance of taking up residency. Taking probiotics will not allow B&L to get established. In fact, there are grounds to believe that most commercial probiotics actually reduce your  native B&L. You want to encourage your native B&L. See this post for citations.

Bootstrapping E.Coli

The E.Coli probiotics below are human sourced and known to take up residency in the human gut.

  • Core: D-Ribose a preferred food that it uses
  • Mutaflor probiotics — E.Coli Nissle 1917
  • Symbioflor 2 — multiple strains

Dealing with the other microbiome shifts

The other microbiome shifts appear to be in different clusters of microbiome shifts. This 2017 paper by Peterson, Klimas, Komaroff, Lipkin (and a stack of other CFS researchers) makes that clear in its title: “Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome”.

The best way at present to proceed is to order an analysis from uBiome. (Disclosure: I have no financial interest in this company.) When your get your results back, log in, click on the “Compare” tab, then go to “Genus,” and click on “ratio” twice, so the results are in descending order.

This is the “hit list” of what you are trying to reduce. DataPunk provides a nice summary of what we know about these. See, for example, Alistipes:

At this point, we run into a logistical challenge.  You want to avoid items that are “Enhanced By” (which is in common across all of the high items) and take the items that are “Inhibited By” (which are not on any of the “Enhanced By” lists).  You may also wish to reduce foods that are high in items listed in “Nutrients/Substrates.”  It becomes a jig-saw puzzle! I have done this exercise for many readers’ uBiome results:

I have discovered that DataPunk is not absolutely current, and have started creating posts based on its data, and then added studies from 2016 and 2017 to the page. Past pages are below, for current list MicrobiomePrescription site.

nihms-731256-f0001

Src: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754147/

General Suggestions (no uBiome results)

Some of these items are contraindicated with a few uBiomes that I have reviewed. This likely is why person B reports no results while person A reports improvement. Example: Magnesium is usually very helpful — but there are a few cases where it encourages overgrowth of undesired  bacteria.

Probiotics

Most probiotics do not take up residency. They are “here today, gone tomorrow”. Their primary role in my model is producing natural antibiotics against other bacteria. For example:

Probiotics should be rotated: 2 weeks on a specific one, then several weeks off. As a general rule, you want about  6-12 B CFU taken three times a day (or 2-3 times the recommended dosage) — but work up slowly because you may get be a major herx! In general, do not take Lactobacillus with Bifidobacteria or with E.Coli etc. Keep to one family per cycle. You do not want them to kill off one another!

Why 3x per day? Because almost none of them are detected after 12-24 hrs. So to keep them — and the production of natural antibiotics — going, you need to keep taking them during the day. See this post for citations.

The following probiotics commonly seem to help people with CFS/Lyme/Fibro:

Some probiotics, however, may make your symptoms worse! And, unfortunately, most commercial probiotics contains some of these. At the moment Bifidobacterium animalis, Saccharomyces boulardii and Lactobacillus acidophilus are on my best to totally avoid list.

  • “. The findings show that the six species of Bifidobacterium differed in their ability to relieve constipation. B. longum, B. infantis and B. bifidum were the most effective in relieving constipation, B. adolescentis and B. breve were partially effective and B. animalis was not effective. Furthermore, edible Bifidobacterium treated constipation by increasing the abundance of Lactobacillus and decreasing the abundance of Alistipes, Odoribacter and Clostridium. .” [2017]

On my neutral list (no clear benefit) is Lactobacillus Plantarum.

Teas

Some teas can also be antibiotics (among other roles). There are two teas that seem to produce significant results quickly:

Again, rotate and, if practical, change brands too. Their antibiotic compounds are different from different sources.

Herbs and Spices

The best choice needs examination of your microbiome (i.e. uBiome results) and doing the work cited above.  Survey results found:

  1. Neem and Oregano with 80% improving
  2. Olive Leaf and Licorice with 56% improving
  3. Thyme with 50% improving
  4. Wormwood and Tulsi with 33% improving

Other things

If you do not know your microbiome, then see https://cfsremission.com/reader-surveys-of-probiotics-herbs-etc/  for suggestions. Your results will vary because your microbiome vary.

Thick blood is an issue also — but here things gets more complicated and not suitable for this recap.

Antibiotics can have a role — but getting prescriptions for the right ones can be a major challenge.

Metabolism Shifts

From volunteered data, we can identify some distinctive shifts, see Metabolism Explorer Summary

Bottom Line

Working with the microbiome and autoimmune is like working with fragments of the dead sea scrolls. For many bacteria we can identify it — what inhibits or encourages it is not known to modern medical science.  We have extremely thin slices of knowledge –Almonds enhances Bifidobacterium, Lactobacillus (B&L)  as do sesame seeds. What about sunflower seeds? Peanuts? Cashews? We find that Walnuts help the bacteria that inhibits B&L — so we cannot safely generalize to “all seeds/nuts are helpful”.

In many cases, we find that healthy diet or supplements demonstrated to work for normal people have the opposite effect on CFS and other altered microbiome conditions. This is made even worst because most of the studies were done on males and most people with CFS are females. We end up having to swim up-stream thru good and valid suggestions — that are just wrong for us.

My model is simple to understand and allows us to filter many suggestions and candidates. With the availability of uBiome testing (without needing a prescription!) we have entered the age of explicit treatment based on your unique microbiome. We do not know the role of many bacteria involved. We do not know what will inhibit or enhanced all of these bacteria. Frustrating little knowledge!

On the flip side, many readers have reported significant improvement, reduction of prescription medication, etc. so the model and suggestions have potential and thus hope of remission! Microbiome studies are exploding on PubMed, a lot of research is being done and we can often borrow their results.

This is an education post to facilitate discussing this approach with your medical professionals. It is not medical advice for the treatment of any medical condition. Always consult with your medical professional before doing any  changes of diet, supplements or activity. Some items cites may interfere with prescription medicines.

Follow up Analysis for ME/CFS (After COVID)

Foreword – 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 Next Episode of the Story

This person’s early analysis is at IBS + BioNTech COVID Vaccine -> ME/CFS? He forwarded these notes:

  • I have done everything as planned since your first review
  • I maybe went up from 15% to 20%. I was able to reintroduce some new activities, but still am lying in bed most of the time. Also taking piracetam seems to help.
  • I still won’t be able to do the analysis myself.
    • COMMENT: ME/CFS patients are a priority for me because I personally understand their brain fog and cognitive impairments from past experiences.
  • I have had COVID in the meantime in case that matters for your analysis, but I did not notice any changes afterwards.

Analysis

Given the recent post for another ME/CFS person who had COVID too, with the result that their microbiome became a good match for long COVID and a poor match for ME/CFS, this was my first question. Fortunately, the sample was done via Biomesight, he did not needed with FASTQ files and transferring them. To keep the story short, I looked at his shifts compared to annotated sampled and compare to literature from the US National Library of Medicine nothing shifted between the samples. There is no shift towards Long COVID from ME/CFS in this case.

Comparing Samples

I do not know the answers. I have a model. Models often need adjustments so comparing samples (for better or worst) in a consistent manner is part of my learning process.

First thing we see a dramatic change with rare bacteria being seen much more often and common bacteria less often. There are more genus seen (184 vs 141) and more Species (230 vs 161) but this may be due the better sample reads in the latest sample (82,102 reads versus 55,117 reads).

PercentileLatest
Genus
Latest
Species
Earlier
Genus
Earlier
Species
0 – 9476524
10 – 1923271016
20 – 2919161611
30 – 3913171213
40 – 4915181314
50 – 5916172332
60 – 6915221419
70 – 7917221314
80 – 8913162422
90 – 996101416
Average18.423.014.116.1
Std Dev11.015.46.27.4
  • Hawrelak’s criteria was 95.6%ile for both samples.
  • Potential Medical Condition dropped from 7 to 1. With Obesity being in common.
  • Bacteria deemed Unhealthy increased from 9 to 17. With CollinsellaDoreaPrevotella biviaPrevotella copriRickettsiaSerratia and various Streptococcus being added and Mogibacterium disappearing. This may be a side-effect of the better sample.
  • Kegg Probiotics
    • The maximum value went down from 18.45 to 4.61 (indicating less compound are an extremely low level).
      • There were 6 compound listed before and it dropped to just 1 (Aromatic aldehyde) when using 1% filtering level.
      • There were 18 compound listed before and it dropped to just 1 when using 5% filtering level.
      • There were 19 compound listed before and it dropped to just 3 when using 10% filtering level.

Was there improvement? Despite the potential confusion because of sample quality we had 3 indicators of improvement:

  • Significantly less matches to known medical conditions profiles
  • Significantly less compound that the person appears to be low in (using data derived from Kyoto Encyclopedia of Genes and Genomes )
  • The person feeling subjectively better and doing more activities

Most of the other measures are the same or difficult to interpret. There is one possible concern, the high levels of Prevotella copri is an indicator of mycotoxin, typically from moulds and fungi. Considering that the time between the samples was winter with close windows and heating — there could be an environment issues here – so lots of fresh air may be good.

Over to Suggestions

There are various algorithms to suggesting probiotics, the strongest results are for:

Doing my usual consensus building

Among the top items are ones that are supported for ME/CFS in studies, including:

Unfortunately, some of the items have no studies. Given that the suggestions are based solely on bacteria with no knowledge of the diagnosis, the convergence with the literature suggests that the suggestions are very appropriate. Two different roads came to the same conclusion. In data science this is sometimes called “cross validation”. In Scotland, “O ye’ll tak’ the high road, and I’ll tak’ the low road,
And I’ll be in Scotland a’fore ye,”

I looked at the antibiotic list for the latest sample and the top two are typically used for ME/CFS:

And interesting that several others often used are NOT recommended: azithromycin (which is a macrolide ?!?), minocycline [2021], fluoroquinolone, doxycycline.

ME/CFS is a heterogeneous condition with a wide variety of microbiome dysfunctions. I believe that using the microbiome to target the best candidate antibiotics is the rational way to proceed.

ME2_Consensus Download

Questions and Answers

  • Question: Sadly I do not tolerate chocolate, but I will try it out again.
    • Answer: These are suggestions, do only what you are comfortable with. Nothing is required. The chocolate issue is interesting, my daughter does not tolerate most chocolates, she discovered that it was the type of sugar (i.e. made with liquid sugar / liquid glucose — adverse reaction) made with solid sugar — happiness. See Health effects of glucose syrup
    • If you try again, you may wish to determine the type of sugar actually being used first.
  • Question: Is there no avoid list?
    • Answer: Yes, in the download, any item with a NEGATIVE value in the priority is an avoid
  • Question: Is 1 capsule of Equilibrium per day really enough?
    • Answer: I honestly do not know. There is no literature to work from. If you take more, than separate them (i.e. 12 hrs apart)
  • Question: It seemed whenever I took turmeric that I was getting more nervous and anxious. Still take it now and then?
    • Answer: As above, do only what you are comfortable with — there are hundreds of items listed. Anxiety is contrary to the effects of turmeric / curcumin reported in the literature [2021] [2019] [2018] [2017]. If turmeric is causing die-off of bacteria that causes vascular constriction, that would result in anxiety. If you tolerate aspirin or niacin (flushing type), then try taking those with the turmeric.

ME/CFS x COVID :-> Long COVID instead

Foreword – 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.

Backstory of Latest Sample

In light of your recent few blog posts about uploads without many microbiome shifts to work with, I was thinking this could be a beneficial walkthrough video for what seems to be the opposite.

I was doing pretty well on my antibiotic rotations (mainly tetracycline two weeks on, two weeks off since Aug of 2021) until Feb or so when I had a major crash / flare that I’m still suffering from.

I did have a very mild case of Covid in mid January that felt no worse than a regular cold.

But from what little I can parse from this sample, it seems I may be struggling with long Covid. I say little, because my brain fog is extremely dense. 

And all of the results I’m getting for this sample via your site seem so drastically different from what has been going on over the last 7  years (my oldest sample is from 2015).

Comparison of samples

This person has samples going back to 2015 using uBiome. Unfortunately for comparison we need to keep to the same lab (why? read The taxonomy nightmare before Christmas…).

Jason Hawrelak Criteria etc

We finally see an improvement with Jason’s criteria. We also may be seeing more diversity with the increase of Genus and Species found. I say may because this could be a side-effect of a low raw count in some samples.

DatePercentileUnhealthy BacteriaGenusSpecies
2022-04-1198.8 %ile8220303
2022-01-1189 %ile1189141
2021-03-0989 %ile8108153
2020-05-2789% ile7153223

Finally, we have a significant improvement

Expected values ar 10% for each line

I decided to look at the raw reads (which are captured from Thryve and Biomesights)

Sample DateRaw Reads
5/27/202043311
3/9/202129247
1/11/202217630
4/11/2022153194

The cause of the jumps above may be the number of reads from the sample

This lead me to look at what typical raw counts are from Ombre/Thryve

To find the raw counts for your sample, open the csv and look for this line

taxon_id,rank,name,parent,count,...2,kingdom,Bacteria,,45341,...

What is the consequences? It means that rarer bacteria may be ghost-like, appearing or disappearing from sample to sample. This adds let one more layer of fuzziness to doing analysis and generating suggestions.

First Question: ME/CFS or Long COVID microbiome or both?

This person uploaded the Ombre FASTQ files to BiomeSight so I may used data from the Long COVID study there. Both condition present similarly, I am curious to see if we have sufficient reference data to decide which condition is a better match.

Long Covid matches against Biomesight 154 Samples
ME/CFS matches against Biomesight 62 Samples

We have concurrent matches for both both conditions

  • Finegoldia magna, which is not reported in the literature
  • The table above hints that he is at present much closer to Long COVID than ME/CFS.

I am not sure about the political correctness of saying “Congrads! You no longer have ME/CFS, you have Long COVID!” is what the microbiome reads like.

What is interesting is that the microbiome constantly shifts/evolves, with Long COVID the infection is constant and the duration since the infection is short — hence less evolution of the microbiome over all patients. With ME/CFS the triggering infection possibilities are huge with 20, 30, 40 years of evolution of the microbiome — hence patterns are diffused by time and original infection.

Looking at deficiency of compounds produced, we see a dramatic drop from the previous sample suggesting that bacteria are getting the needed inputs for correct functioning.

Sample Date1%ile5%ile10%ile
5/27/202041460
3/9/202121416
1/11/2022197233244
4/11/202262852

Kegg Compounds below %ile shown

Where do we go from here

I am going to do consensus, but do only 3 items:

  • Hand Picked Bacteria using the study in progress data using BiomeSight (16 bacteria)
  • Using US National Library of medicine filter to Long COVID using BiomeSight and Box-Whiskers (14 bacteria)
  • Using US National Library of medicine filter to Long COVID using Ombre and Box-Whiskers (14 bacteria)

The consensus is below as a download. Since antibiotics are being prescribed at present, I included that in the suggestions criteria.

MECOVID_Consensus Download

Some highlights

Why did I focus on the ME/CFS ones? Path of least resistance for the prescribing MD – the MD accepts ME/CFS and thus will have low resistance to prescriptions often used for ME/CFS. Asking for them for Long COVID could get rolling of eyes…. As always, we are using these off-label for their computed microbiome effect. For the prescription items, I would suggest rotation (one item for 10 days, then a 0-10 day break, then another item (or repeat if limited to one item).

ME/CFS caused by breast augmentation?

Foreword – 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.

Back story 

I’ve had ME/CFS 14 years (symptoms came on stronger after breast augmentation 2008) but the last 9 yrs I’ve been mostly housebound & bed bound 21 hours per day. I’d say I’m moderate/severe. (I live in the Uk) 

My diet:- 

  • Brekkie :- muesli (take raisins out) with coconut milk or almond milk  Or poached egg on 1 seeded brown bread Or scrambled egg with cumin, coconut milk, salt & black pepper on 1 seeded brown bread Or porridge with coconut milk, frozen blueberries or dried goji berries 
  • Lunch:-A banana, nuts (walnuts, pecans, almonds) & mixed seeds 
  • Dinner:-Salad (bistro salad leaves with a little shredded beetroot) cucumber, celery, grated carrot, with jacket potato (or sweet potato) & vegan cheese on top.. I’ve just started to add a sprinkle of organic olive oil & black pepper (tasty)  Or Steamed veg – carrots, green beans, broccoli, stringless beans & sugar snap peas (or mangetout) with sweet potato & usually something out the freezer I pop in oven… which I know I need to stop as it’s processed. I will begin to change that for chicken or salmon (it’s just been easier) 

More information on diet and supplements was also sent.

Analysis

Looking at Bacteria Deemed Unhealthy, the sole item that stands out was moderately high Streptococcus oralis. Dr. Jason Hawrelak Recommendations came out to 90%ile — so better than most people.

For this person, we have a shift towards rarer bacteria with those seen in only 30-50% of samples being over represented.

PercentileGenusSpecies
0 – 922
10 – 1985
20 – 29119
30 – 391020
40 – 491119
50 – 59913
60 – 6999
70 – 7959
80 – 8946
90 – 9956

Since ME/CFS is the condition, I checked the list of antibiotics often prescribed with the following being the top of the list. It is interesting to note that the literature finds that they do improve some ME/CFS patients

  1. gentamicine sulfate (antibiotic)   (1) [See 2021] [2017]
  2. rifaximin (antibiotic)s   (0.949) [Health Rising] [2011] and many more
  3. amikacin hydrate (antibiotic)   (0.845) – no significant literature
  4. gentamicin (antibiotic)s   (0.745) [2017]
  5. metronidazole (antibiotic)s   (0.727) “Metronidazole/Flagyl is reported to have 75% of CFS patients improving ” [Src] also [BMJ]

On the avoid list are some antibiotics that are also prescribed for ME/CFS

  1. rifampicin (antibiotic)s   ( – 0.423 )
  2. tetracycline (antibiotic)s   ( – 0.291 )
  3. azithromycin,(antibiotic)s   ( – 0.289 )
  4. rifampicin (antibiotic)   ( – 0.289 )
  5. doxycycline (antibiotic)s   ( – 0.191 )

She provided some of the items that she is taking so I looked at the estimated impact on her microbiome

  • sertraline,(prescription) which she is reducing. This is likely a good course of action
    • Take Estimate:  20.4, Avoid Estimate:  24.7
  • dopamine (prescription) – this is a bit of a concern to me because it is likely to contribute to microbiome dysfunction
    • Take Estimate:  13.2, Avoid Estimate:  23.1
  • Vitamin C, — keep taking
    • Take Estimate:  22.3, Avoid Estimate:  13.9
  • Cetirizine:
    • Take Estimate:  20.4, void Estimate:  29.1

Note: the above are estimates on whether they will improve or contribute to the microbiome dysfunction. Medical concerns should always dominate.

Suggestions

Doing my usual pick the bacteria to change using different criteria we have:

The number of bacteria selected for each of the above was lower than I often seen.

Going to the Consensus

The following suggestions stand out:

Dropping down to the avoid list, we see

The full list is attached.

Trying out an Experimental feature

I have used this feature of the several recent blogs on autism (Bacteria to Hand-Pick for Autism with Ombre/Thryve samples, Bacteria to Hand-Pick for Autism with Biomesight samples). I have posted the bacteria identified here. Unfortunately sample size are small (1/3 of the Long COVID samples)

Using the 61 samples annotated with CFS and processed thru the same lab (Biomesight.com). We found 11 bacteria identified as statistically associated with CFS. Three were too high and 8 were too low.

RankNameYour valuePercentile
family Actinomycetaceae8025.7
genus Actinomyces4020.2
genus Adlercreutzia7014.4
species Adlercreutzia equolifaciens7021.3
species Anaerotruncus colihominis156079.7
species Bacteroides acidifaciens7027.7
species Bacteroides rodentium185073.6
species Bacteroides uniformis2116061.8
species Bifidobacterium longum30032.8
species Finegoldia magna4028.4
species Prevotella copri708.9

The results from this custom picked set are shown below.

Modifier – Suggestion to takeConfidence
  Human milk oligosaccharides (prebiotic, Holigos, Stachyose)1  📏
  whole grain diet0.576
  inulin (prebiotic)0.541
  blueberry0.4
  vitamin k20.4  📏
  bacillus subtilis,lactobacillus acidophilus (probiotics)0.38
  lactobacillus fermentum (probiotics)0.38  📏
  lactobacillus gasseri (probiotics)0.38  📏
  lactobacillus reuteri (probiotics)0.38  📏
  maltitol0.38
  oats0.38
  navy bean0.379
  ketogenic diet0.379
  vitamin a0.345
  arabinoxylan oligosaccharides (prebiotic)0.328
  chondrus crispus (red sea weed)0.328
  green tea0.328
Modifier to avoidConfidence
 saccharin0.533
 Slippery Elm0.382
 triphala0.382
 glycyrrhizic acid (licorice)0.382
 sugar0.38
 Cacao0.245
 refined wheat breads0.214
Probiotics that are best to take NameImpact
  Bromatech (IT) / Rotanelle plus7.9
  probiotic pur (de) / realdose nutrition6.18
  Realdose6.18
  Jetson (US) / Immunity Probiotics6.09
  douglas laboratories / multi probiotic 40 billion6.09
  Thryve L.PCasei Th1, L.PCasei Th2,L.Ferm IBF1, L.acidoph6.09
Flavonoid Foods Flaxseed, meal Nuts, almonds Almond
Flavonoid / Supplements   Vitamin K (phylloquinone)  Resveratrol   Magnesium, Mg

We have some contradictions between these suggestions and the ones above. Why? The data we are using is very very incomplete. It is the best that is available. In keeping with my principle of less risk, when suggestions are in disagreement (for example Nuts and almonds – recommended here but to avoid above), then omit them. There are a lot of possible items, keeping to positive items where there are no disagreement is the best path.

Because this person suffers from brain fog, they may wish to sit down with a nutritionist to go thru the download list above to craft a diet plan. The suggestions are in no way a balanced nor complete diet. For example increase food rich in magnesium on the take suggestions, or using supplements.

As always, this information is computed from the best available data and is not based on clinical experience. Always review with your medical professional for appropriateness of suggestions before starting.

Bottom Line

I check with this person and the breast augmentation has not been undone. In other words, there is a reasonable chance that it is a significant contributor to the ongoing symptoms. I expect some progress from microbiome manipulation, but I suspect the side effects from augmentation will limit it. The literature seems to confirm it.

  • “Subclinical bacterial infections (biofilms) are strongly implicated in breast augmentation” [2021]
  • “The most common cause of surgical readmission after breast implant surgery remains infection. Six causative organisms are principally involved: Staphylococcus epidermidis and S. aureus, Escherichia, Pseudomonas, Propionibacterium, and Corynebacterium.” [2015]
  • “The researchers found that the women with silicone gel-filled breast implants were significantly more likely to be diagnosed with autoimmune or rheumatic disorders, such as Sjögren syndrome, systemic sclerosis, and sarcoidosis, compared with women without breast implants of a similar age and socioeconomic status.” [2022]
  • “Silicone breast implants(SBI)  are associated in a proportion of patients with complaints such as fatigue, cognitive impairment, arthralgias, myalgias, pyrexia, dry eyes and dry mouth. Silicones can migrate from the implant through the body and can induce a chronic inflammatory process. Explantation[Removal] of SBI results in the majority of patients in an amelioration of the symptoms.” [2017]

ME/CFS bacteria shifts for Ombre/Thryve tests

This post is intended for researchers by pointing to bacteria whose genetics are likely significant for ME/CFS. The microbiome specific raw data is below. Preliminary z-scores indicated that they are significant (Pr < 0.01) and no filtering has occurred for False Detection Rate. Users are advised to perform their own statistics. For bacteria associated using BiomeSight tests, see this post.

Note: These results are lab-specific, using the data from OmbreLabs / formerly known as Thryve.

The raw data is available at: http://citizenscience.microbiomeprescription.com/.

NOTE: The sample size is very low (less than 1/3 of the size of current Long COVID Study)

Symptom ObsNo Symptom ObsLabSymptomName
51935thryveOfficial Diagnosis: Chronic Fatigue Syndrome (CFS/ME)
Basic Data
Bacteriatax_rankNo Symptom CountSymptom CountNo Symptom Frequency %Symptom Frequency %
Burkholderiales Genera incertae sedisnorank2682028.739.2
Anaerococcusgenus5303756.772.5
Vibrionalesorder89109.519.6
Porphyromonas bennonisspecies4183544.768.6
Anaeroglobus geminatusspecies4743350.764.7
Parvimonas micraspecies178171933.3
Insolitispirillum peregrinumspecies1691718.133.3
Rickettsia honeispecies91139.725.5
Levyella massiliensisspecies327253549
Ezakiellagenus243252649
Synergistaceaefamily3432536.749
Odoribacter splanchnicusspecies7024675.190.2
Mucinivoransgenus5095.317.6
Prevotella buccalisspecies4703550.368.6
Enorma massiliensisspecies80118.621.6
Microbactergenus1711718.333.3
Flavobacterialesorder4993453.466.7
Porphyromonas someraespecies1821619.531.4
Parvimonasgenus178171933.3
Anaeroplasmatalesorder3262834.954.9
Desulfurispora thermophilaspecies5073554.268.6
Ruminococcus gauvreauiispecies2852230.543.1
Synergistetesphylum3442536.849
Prevotella disiensspecies2372025.339.2
Holdemania filiformisspecies6414168.680.4
Prevotella baroniaespecies346243747.1
Corynebacterium jeikeiumspecies5185.515.7
Corynebacterialesorder5854162.680.4
Campylobacter hominisspecies1771818.935.3
Peptostreptococcaceae incertae sedisnorank5233655.970.6
Campylobacteralesorder4012942.956.9
Clostridium frigidicarnisspecies659717.6
Pseudomonadaceaefamily3362635.951
Dialister propionicifaciensspecies299233245.1
Absiella tortuosumspecies2091722.433.3
Epsilonproteobacteriaclass439314760.8
Ezakiella coagulansspecies2352225.143.1
Anaeroglobusgenus4753350.864.7
Tannerellagenus2702228.943.1
Gordonibacter pamelaeaespecies346293756.9
Bacteroides ovatusspecies8125086.898
Schaalia suimastitidisspecies1991721.333.3
Lacrimispora sphenoidesspecies1511416.127.5
Propioniferax innocuaspecies1281413.727.5
Rickettsiagenus1421615.231.4
Corynebacteriumgenus5293656.670.6
Prevotella loescheiispecies2022021.639.2
Corynebacteriaceaefamily5343657.170.6
Desulfovibrio idahonensisspecies2312024.739.2
Thiomonasgenus1911620.431.4
Peptoniphilus lacrimalisspecies2231823.935.3
Lactonifactor longoviformisspecies6073964.976.5
Synergistiaclass3432536.749
Fulvitaleagenus4985.215.7
Alistipes shahiispecies7024475.186.3
Anaerococcus octaviusspecies1251513.429.4
Levyellagenus3322735.552.9
unclassified Burkholderialesfamily1831619.631.4
Enormagenus81118.721.6
Sphingomonas abacispecies5285.615.7
Anaerococcus vaginalisspecies3022432.347.1
Rickettsialesorder2142122.941.2
Thermoanaerobacteraceaefamily262212841.2
Flavobacteriiaclass4993453.466.7
Eubacterium pyruvativoransspecies5223455.866.7
Anaeroplasmataceaefamily3252834.854.9
Peptostreptococcus anaerobiusspecies89149.527.5
Mogibacterium timidumspecies168161831.4
Alistipes obesispecies5533659.170.6
Tissierellia incertae sedisnorank5644060.378.4
Dielma fastidiosaspecies5023353.764.7
Clostridium chartatabidumspecies6384168.280.4
Desulfocellagenus2662128.441.2
Mucinivorans hirudinisspecies4995.217.6
Rickettsieaetribe1932020.639.2
Syntrophomonas sapovoransspecies88109.419.6
Rickettsiaceaefamily1952020.939.2
Butyricimonas faecihominisspecies3142333.645.1
Ectothiorhodospiraceaefamily2131722.833.3
Anaeroplasmagenus1411415.127.5
Gordonibactergenus4973453.266.7
Fulvitalea axinellaespecies4885.115.7
Dehalobactergenus1721518.429.4
Hydrogenisporagenus4233145.260.8
Fenollariagenus4703150.360.8
Anaerococcus murdochiispecies1211612.931.4
Anaerococcus senegalensisspecies112171233.3
Porphyromonas asaccharolyticaspecies2282124.441.2
Mogibacteriumgenus664427182.4
Campylobactergenus3522537.649
Firmicutes sensu stricto incertae sedisnorank4173144.660.8
spotted fever groupspecies group1101511.829.4
Acinetobactergenus2562027.439.2
Synergistalesorder3432536.749
Lactonifactorgenus6134065.678.4
Persicobacteraceaefamily59136.325.5
Desulfurisporagenus5083554.368.6
Holdemaniagenus7794883.394.1
Desulfocella halophilaspecies2632028.139.2
Ezakiella peruensisspecies2582.715.7
Tannerella forsythiaspecies2652128.341.2
Campylobacteraceaefamily3662639.151
Absiellagenus3522537.649
Flavobacteriaceaefamily4533048.458.8
Tepidibactergenus83108.919.6
Dehalobacter restrictusspecies1721518.429.4
Peptoniphilus coxiispecies2702428.947.1
Hydrogenispora ethanolicaspecies4163044.558.8
Pseudomonasgenus327233545.1
Insolitispirillumgenus1731718.533.3
Thermoanaerobacteralesorder3902841.754.9
Odoribactergenus7374678.890.2
Microbacter margulisiaespecies168171833.3
Propioniferaxgenus1291413.827.5
Items deem significant based on Bernoulli distribution


Bacteriatax_rankNo Symptom MeanSymptom MeanNo Symptom StdDevSymptom Std DevSymptom ObsNo Symptom Obs
Alistipes onderdonkiispecies48591162215348.323577.447819
Natranaerovirgagenus1749831892.43879.331578
Rikenellaceaefamily169812825824648.130780.651920
Enterobacteralesorder31161563510315.978732.246856
Blautia gluceraseaspecies123734464110.711731.940727
Desulfobacteraceaefamily91279155.3988.132561
Tenericutesphylum119831693795.417659.146824
Mollicutesclass117831693773.817659.146824
Chromatialesorder1024914952392.333625
Gammaproteobacteriaclass65151765516956.987886.151933
Bacteroides intestinalisspecies199274328144.430306.343779
Alistipesgenus166852749124264.130694.651920
Desulfobacteralesorder94256155.3933.736585
Items deemed significant based on mean and standard deviation

ME/CFS bacteria shifts for BiomeSight tests

This post is intended for researchers by pointing to bacteria whose genetics are likely significant for ME/CFS. The microbiome specific raw data is below. Preliminary z-scores indicated that they are significant (Pr < 0.01) and no filtering has occurred for False Detection Rate. Users are advised to perform their own statistics. For Ombre/Thryve bacteria see this post.

Note: These results are lab-specific, using the data from BiomeSight.

The raw data is available at: http://citizenscience.microbiomeprescription.com/

NOTE: The sample size is very low (less than 1/3 of the size of current Long COVID Study)

Symptom ObsNo Symptom ObsLabSymptomName
611095biomesightOfficial Diagnosis: Chronic Fatigue Syndrome (CFS/ME)
The data available

Bacteria that appear more or less often with statistical significance

Bacteriatax_rankNo Symptom CountSymptom CountNo Symptom Frequency %Symptom Frequency %
Syntrophobacteralesorder5713952.163.9
Chitinophagalesorder5073946.363.9
Anaerococcusgenus4763643.559
Porphyromonas bennonisspecies3082528.141
Rhodovibrio sodomensisspecies2311921.131.1
Novispirillumgenus6534359.670.5
Thiobacillusgenus1591614.526.2
Insolitispirillum peregrinumspecies6514359.570.5
Thiobacillus thiophilusspecies1231311.221.3
Amedibacillus dolichusspecies6844662.575.4
Adlercreutzia equolifaciensspecies6824662.375.4
Hymenobacteraceaefamily7374867.378.7
Streptococcus phocaespecies91128.319.7
Finegoldia magnaspecies4513541.257.4
Anaerococcus tetradiusspecies1231311.221.3
Leuconostocgenus4833544.157.4
Porphyromonas someraespecies2031918.531.1
Vagococcusgenus296232737.7
Carboxydocellagenus591405465.6
Clostridium cadaverisspecies3812834.845.9
Bacteroides acidifaciensspecies8395476.688.5
Thiomicrospira sibiricaspecies7711718
Desulfofrigusgenus107139.821.3
Chitinophagaceaefamily5103946.663.9
Eggerthellagenus7875071.982
Corynebacterium jeikeiumspecies80117.318
Corynebacterialesorder6434258.768.9
Corynebacterium aurimucosumspecies1211511.124.6
Blautia hydrogenotrophicaspecies7495168.483.6
Bacteroides heparinolyticusspecies3192529.141
Rickettsia marmionii Stenos et al. 2005species84137.721.3
Niabella aurantiacaspecies1581514.424.6
Carboxydocella ferrireducensspecies5773952.763.9
Bacteroides caccaespecies832537686.9
Desulfobacteraceaefamily2472022.632.8
Finegoldiagenus537394963.9
Phascolarctobacterium faeciumspecies6534359.670.5
Odoribacter denticanisspecies6564359.970.5
Corynebacterium amycolatumspecies83117.618
Pelotomaculumgenus3872835.345.9
Peptoniphilus asaccharolyticusspecies4183138.250.8
Corynebacteriumgenus5233747.860.7
Corynebacteriaceaefamily5233747.860.7
Pelotomaculum isophthalicicumspecies3872835.345.9
Thiobacillaceaefamily1591614.526.2
Rhodovibrionaceaefamily230192131.1
Bacteroides fluxusspecies8255575.390.2
Symbiobacteriaceaefamily6414258.568.9
Limnobacter litoralisspecies7705070.382
Clostridium akagiispecies5293648.359
Lactobacillus acidophilusspecies76116.918
Desulfofrigus oceanensespecies106139.721.3
Piscirickettsiaceaefamily1371412.523
Marinospirillumgenus2212020.232.8
Moryella indoligenesspecies78117.118
Roseospiragenus1832116.734.4
Halanaerobiaceaefamily2021818.429.5
Rhodovibriogenus2311921.131.1
Limnobactergenus7735070.682
Schaaliagenus5313748.560.7
Chitinophagiaclass5073946.363.9
Clostridiales Family XVI. Incertae Sedisfamily591405465.6
Symbiobacterium toebiispecies6414258.568.9
Anaerococcus hydrogenalisspecies1621614.826.2
Pediococcusgenus6444258.868.9
Symbiobacteriumgenus6414258.568.9
Adlercreutziagenus7865071.882
Actinomycesgenus7975272.885.2
Porphyromonas asaccharolyticaspecies2592323.737.7
Butyricimonas synergisticaspecies3972936.347.5
Bifidobacterium longumspecies7955272.685.2
spotted fever groupspecies group1391712.727.9
Actinomycetaceaefamily8665579.190.2
Phascolarctobacterium succinatutensspecies7865071.882
Contubernalisgenus1431613.126.2
Streptococcus oralis subsp. tigurinussubspecies4403140.250.8
Halanaerobiumgenus2021818.429.5
Shewanella upeneispecies263212434.4
Candidatus Contubernalis alkalaceticumspecies1431613.126.2
Oceanospirillaceaefamily2612223.836.1
Thiomicrospiragenus110131021.3
Roseospira mediosalinaspecies60115.518
Amedibacillusgenus6844662.575.4
Insolitispirillumgenus6514359.570.5
Psychrobacter glacialisspecies1901917.431.1
Ethanoligenensgenus5183547.357.4
Items deem significant based on Bernoulli distribution

Bacteria where the difference in counts is significant


tax_nametax_rankNo Symptom MeanSymptom MeanNo Symptom StdDevSymptom Std DevSymptom ObsNo Symptom Obs
Novispirillumgenus83051613815057.330286.743653
Insolitispirillum peregrinumspecies83271613315072.130272.343651
Streptococcus australisspecies196753434.53495.940739
Anaerovibriogenus12172540271910547.756968
Anaerotruncus colihominisspecies174924331677.82418.4611054
Prevotella coprispecies650825361148361.223886.1571000
Rhodospirillaceaefamily128142705725272.753923.251896
Bacteroides rodentiumspecies296762274910.210044.9611067
Anaerovibrio lipolyticusspecies120125372700.610546.956963
Bacteroides fluxusspecies22312131400.45587.455825
Bacteroides uniformisspecies259804074737165.540517.7601075
Streptococcus parasanguinisspecies192633387.92639.138685
Alphaproteobacteriaclass119392339426761.150681.6601062
Streptococcusgenus304861527181.526974611082
Burkholderiaceaefamily324578559.3817.556970
Rhodospirillalesorder127792613126212.853158.453933
Blautia obeumspecies58571075310528.122306.7601043
Streptococcus thermophilusspecies86321062185.98681.654905
Insolitispirillumgenus83271613315072.130272.343651
Items deemed significant based on mean and standard deviation