lassesen 3:26 am on May 4, 2018

Contradicting Antibiotics and Prescription Impact on the Microbiome

I am just finishing a course of high dosage antibiotics for cellulitis that sent me to the hospital last week. With the http://microbiomeprescription.azurewebsites.net website data, I realized that I do not need to do random things to recover from the antibiotics.

First, the antibiotic that I was using was a cephalosporin i antibiotics that antibiotics and many many antibiotics and prescription drugs are listed at http://microbiomeprescription.com/Library/GutModifiers.

Clicking on it showed what changed:

cephalosporin i antibiotics

beta-lactam antibiotics

The following are impacted

Taxonomy	Rank	Effect	Citations	Links
Enterobacteriaceae	family	Increases	Studies	Also modified by
Pseudomonadaceae	family	Increases	Studies	Also modified by
Streptococcaceae	family	Increases	Studies	Also modified by
Citrobacter	genus	Increases	Studies	Also modified by
Kluyvera	genus	Increases	Studies	Also modified by
Pseudomonas	genus	Increases	Studies	Also modified by
Streptococcus	genus	Increases	Studies	Also modified by

I just added a link below the table:

Recommendations to contradict the shifts

When I click this, it takes me to the recommendations page.

Recommendations from cephalosporin i antibiotics Lab Results

Item	Action	Confidence value
bacillus probiotics	Take	14.341
herb	Take	8.041
gallic acid and tannins	Take	3.507
fruit	Take	3.396
antiseptics	Take	2.911
triphala herb mixture	Take	2.842
vitamin d cholecalciferol	Take	2.773
lactobacillus casei probiotics	Take	2.585
fungus	Take	2.585
lactobacillus rhamnosus probiotics	Take	2.585
low/no meat diet	Take	2.495
neem herb	Take	2.426
lactobacillus plantarum probiotics	Take	2.426

etc,

Bottom Line

I have recommendations of what to take and to avoid that are specific to the antibiotic / prescription drug that I took! No random picking of stuff from the air.

In the near future, I will extend this to allow microbiome information from ubiome to be incorporated (i.e. if you are low, and the prescription item increases it — then we attempt no correction).

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.

MaximilianKohler 4:03 am on May 4, 2018

I don’t see a way to message you directly, but I can’t help but say that while in theory what you’re doing here (with this website) seems highly desirable, the current methods/data is extremely flawed since it’s all based on way too high up in the phylogenetic tree. See:

Studies which use phylum-level percentage comparisons are completely useless from a microbiological point of view. Species level should be bare minimum: https://archive.is/O39RL

Gut microbiota assembly is based on functions encoded in bacterial genomes provided by a consortium of bacteria with different growth characteristics that adapt to environmental factors rather than on specific species: https://archive.is/Np2Im

Moving forward we need to appreciate compositional profile vs functionality of the gut microbiota. It is now appreciated that it is not just which bacteria inhabit the gut but also their genetic make up and the capability of these different species to produce different neuroactive and influential metabolites: https://archive.is/j3g8d

The importance of species identity and interactions on multifunctionality depends on how ecosystem functions are valued http://onlinelibrary.wiley.com/doi/10.1002/ecy.1954/abstract

Interactions between species introduce spurious associations in microbiome studies: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005939
- lassesen 1:35 pm on May 4, 2018
  
  Messaging via https://www.facebook.com/CFSRemission/ or via email ken@lassesen.com
  
  I am in complete agreement over the lack of sufficiently detailed data — i.e. insufficient instead of flawed.
  
  The question becomes simple: Is there sufficient data to increase the possibility of helping better than with no data?
  
  IMHO, the answer is yes — a definite yes. I have often stated that this is based on fuzzy logic applications in artificial intelligence. We are dealing with partial information with less than desired reliability. The bottom line criteria is simple: Does this approach improve the treatment odds over ignoring the limited flawed data we have to work with.
  - MaximilianKohler 6:06 pm on May 5, 2018
    
    Does this approach improve the treatment odds over ignoring the limited flawed data we have to work with
    
    Are you sure that’s even answerable based on the feedback you get from people trying this stuff? The stuff that gets recommended both here and from test results (IE: ubiome) are all “generally beneficial/healthy” things, so you would expect a decent percentage of people to benefit regardless of any “personalization”. And perhaps there is not a balanced number of people who report back “harm vs help” results. Perhaps people who benefit are more likely to report back.
    
    I strongly support the overall goal of this site, I just think it’s ahead of its time due to current limitations in gut microbiome knowledge. My one complaint is that the suggestions/conclusions often seem too authoritative/confident, and are based on very questionable data. The “condition that results from” on the main page overview for example.
    
    As someone with CFS who’s been solely learning/experimenting for more than a decade, and following the microbiome research closely for a few years now, I think intestinal permeability might be one of the most major factors contributing to CFS, and it’s not even mentioned on that overview page. For example, some people seem to have significant dysbiosis yet they don’t present major symptoms because they don’t suffer from permeability.
    
    Jarrow’s s.boulardii seems to help massively with intestinal permeability, but I’ve gotten little to no feedback from other’s who’ve used it.
    
    The focus on “Lactobacillus, Bifidobacteria, E.Coli” is a tragedy to me. I think these are generally way over-emphasized in the current microbiome ethos due to detection/sequencing biases/limitations and the ease of producing them for probiotic products.
    - lassesen 5:22 am on May 6, 2018
      
      There are literally hundred of threads such as EBV, HHV6, mouse leukemia virus, occult ricketesia infection, mycoplasma infection, thyroid issues, vitamin D deficiency, vitamin 1,25D saturation, etc which are also omitted.
      
      I focus on what I deem to be the most probable model — one that meets the criteria of a good scientific model. The ability to predict. For example: If an antiviral greatly helps CFS, it is very likely that it also greatly modifies the microbiome.
      
      See my latest post to see apparent confirmation of that prediction.
      https://cfsremission.com/2018/05/06/adding-common-name-to-drugs-and-ebv-reflection/
      
      The challenge would be to find an antiviral that greatly helps CFS which causes no alteration in the microbiome. Until such is found, whether the antiviral helped because it was an antiviral OR because it was a microbiome modifier, is up for debate.
      - MaximilianKohler 5:42 am on May 6, 2018
        
        I don’t disagree, but that seems unrelated to what I wrote 🙂
        
        lassesen 3:11 pm on May 6, 2018
        
        With CFS there are hundreds of theories proposed — many deal with items that may be consequences of other items. For example: a bacteria shift that cause EBV re-activation which then result in leaky gut which then result in bacteria translocation and a ton of other items.
        
        To me the key question is to not chase the downstream symptoms. If there is an item that
        #1 explains the symptom and metabolic cascades
        #2 have direct actual studies demonstrating each step of the cascade (NOT Speculation or “educated opinion”– “may be due to”)
        #3 Is the simplest model that does not have any contradictory evidence
        
        This is the classic scientific method, Osler Principle, etc. I am still waiting for a uBiome result that does not have significant bacteria shift. The typical score across all profiles is 120-200. After altering diet, etc, there is often a 20 item drop with symptom improvement.