This weekend I hope to finish making changes so that throat, nose and other ubiome samples may also be investigated.

Symptoms will be linked to the email + sample date, so there is no need to re-enter symptoms. This would allow people to see the impact of changes in mouth health by changes in habits they implemented.

Recommendations will continue to be ONLY for gut — there are not enough studies to venture into other areas.  BUT, if you start chewing mastic gum (something that I have suggested before), you should see the impact on your throat and nose bacteria.

Also simplified recommendations are being worked on.

Many items appear under 3-6 different names in the literature. The citation used the term used in the study. The simplification will replace things like:

•  resveratrol,
• red wine
• red wine polyphenol
• red wine polyphenols

with just resveratrol (whatever the common name of a supplement is).

This will impact some recommendations because we will stop double counting or under counting some modifiers.

In my earlier post, COMPARING REPEATED UBIOME RESULTS, I provided a tool but little guidance on interpreting. The key question is always whether the symptoms improved, not whether some numbers change. We do not know which numbers are significant and which numbers are just natural variation.

Model Of Onset

People see progression of symptoms since onset. Each progression is likely associated with a set of changes in the uBiome. This can be represented in the chart below (which likely have a lot more items than shown).

Model Of Recovery

Our Wishful Model

This appears to happen in two scenarios:

• Chemotherapy for Cancer in some cases
• Fecal Matter Transplants — but usually do not persist longer than a few weeks

Probable reality

This model comes from the model that we may have more than a dozen bacteria groups involved in the symptoms. For depression alone, we see 28!!. Being able to correct all of them in one-go is not likely. You may correct one and those not effected adapt to try entrenching themselves better (like filling the empty space of the bacteria reduced).

Interpreting results of Comparisons

I am looking at a person with two samples. I have not automated the comparisons shown below, instead I selected a sample and then went to the page and did a simple count.

• Depression Profile
  • Early Sample: 11 matches
  • Later Sample: 8 matches
• Chronic Fatigue Profile
  • Early Sample: 8 matches
  • Later Sample: 9 matches
• Inflammatory Bowel Disease
  • Early Sample: 7 matches
  • Later Sample:  6 matches
• Irritable Bowel Syndrome
  • Early Sample:  3 matches
  • Late Sample: 2 matches
• Mood Disorders
  • Early Sample: 2 matches
  • Later Sample: 2 matches

Over all, we went from 31 matches to 27 matches. One increased and three decreased — I wish it was possible to do decrease across all items…

Comparison Report – Summary

The original detail was done as a quickly and dirty report comparing numbers and attempts to keep people from becoming excessively focus on numbers (which we are not certain of significance – some are likely important and some are not… we do not know for sure which ones are).

From feedback, it is clear that a simpler report, the summary, would be easier to understand and likely more beneficial

Time between samples: 10 months

Metabolism is likely the most significant

Like the profiles used above, this filters ‘noise’ and looks at the net result of a lot of different bacteria.  I have just added a new item to the report

Average Metabolism (Ideal healthy would be 1.0)

• Early sample 1.32
• Later Sample 1.18

In short, the metabolism is improving greatly.

New and Updated Pages

There are now two buttons available for comparison

The new report is Summary, and an example is shown below. The most significant ones are high lighted.

• Profiles – this means the total matches for all of the conditions we have profiles on
  • We see a decrease from 59 matches to 53.
  • The individual profiles are listed in plain text
  • A decrease suggests moving away from an autoimmune state
  • This is an objective measurement
• Symptoms:
  • Symptoms are subjective, ideally we would like to see a decrease
  • Go to the detail reports to see what symptoms changed.
• Metabolism Average
  • This is the average of the values transcribed from uBiome.
  • A perfect (healthy) score is 1.0
  • This is an objective measurement
• Metabolism Standard Deviation
  • This is a measure of how scattered from a healthy score you are
  • A perfect (healthy) score is 0.0
  • This is an objective measurement

Bottom Line

The new summary page give you three objective measures of your progress. For the reader above, all three objective measures improved. The subjective measure (number of symptoms) increased — we do not record severity of symptoms because that is so-so-so very subjective, just a count.

Whiplash on Recommendations

The reader found that the recommendations changed a lot between the two samples. A change is expected if progress is made. Conceptually, the number of items with a high (or low — for avoid) should decrease if progress is made.  We see this in the samples of this reader, as shown below.

Take Lists

For Example 1, we had the > 4 take

amoxicillin
Azithromycin
florfenicol
Cranberry bean flour
Bacillus subtilis natto
Gallate
Flaxseed
High Fat
Plant-rich diet
ß-lactam antibiotics
Tannin
Trimethoprim-sulfamethoxazole
Vitamin D
Polymannuronic acid

For Example 2, the list has grown smaller, with some new items appearing

Polymannuronic acid
Ketogenic diet
Lactobacillus kefiri
Low fat diets
L-Taurine
Lactobacillus plantarum
Bacillus subtilis natto

Avoid Lists

For Exampl1, the < -4 list

Arabinoxylans
black raspberries
Acetic acid
Chrysanthemum morifolium
ibuprofen
Isobutyric acid
Isovaleric acid
Sunflower Oil
High fruit intake
barley
Saccharin
Saccharomyces boulardii
animal-based protein
High meat diet
Fraxinus angustifolia
berberine

For Example 2, the list is again smaller for < -4, with a few items in common

Walnuts
Navy bean
berberine
High protein diet
Carboxymethyl cellulose
Polysorbate 80
ku ding cha tea
barley
Fraxinus angustifolia

I have done a few posts on depression over the years

• CFS Depression, Diet and the Microbiome
• Depression, Confusion, Fear – the protocol disrupters
• Depression: “Natural”, CFS, and other forms
• Psychoactive Probiotics!

A friend asked me to revisit it, especially now that I have a recommendation program working.

Depression and the Microbiome – PubMed

From The role of microbiota in the pathogenesis of schizophrenia and major depressive disorder and the possibility of targeting microbiota as a treatment option [2017].

• over-presentation of the orders of Bacteroidales and
• under-presentation of the orders of  Lachnospiraceae,
• Higher level of Oscillibacter and Alistipes had higher depression
•  increased levels of Enterobacteriaceae and Alistipes
•  reduced levels of Faecalibacterium 

Altered fecal microbiota composition in patients with major depressive disorder [2015]. adds that “Bacterial diversity was significantly higher ” and also reported additionally (for two types of depression):

• higher Bacteroidaceae,
• higher Butyricimonas,
• higher Clostridium XlVb
• higher Enterobacteriaceae,
• higher Parabacteroides,
• higher Phascolarctobacterium,
• higher Porphyromonadaceae,
• higher Rikenellaceae
• higher Roseburia
• higher Acidaminococcaceae,
• higher Flavonifractor,
• higher Proteobacteria
• lower Actinobacteria
• lower Fusobacteria,
• lower Ruminococcaceae,
• lower Veillonellaceae 
• lower Lachnospiraceae,
• lower Dialister,
• lower Escherichia/Shigella,
• lower Firmicutes,
• lower Haemophilus,
• lower Prevotella,
• lower Ruminococcus

Additionally, Isovaleric acid in stool correlates with human depression.[2016] hence isovaleric acid producing bacteria appear to be another facet.

Isovaleric acid producing bacteria includes [ref]:

• Bacillus subtilis natto
• Prevotella
• Porphyromonas

Automated Analysis of your Ubiome

After you logged on to http://microbiomeprescription.com/  This Depression link will show your results against the above list. As a reminder, there are other comparisons available:

• Autism
• Autoimmune Disease
• Chronic Fatigue Syndrome
• Crohn’s Disease
• Depression
• High Blood Pressure
• Histamine Issues
• Inflammatory Bowel Disease
• Irritable Bowel Syndrome
• Metabolic Syndrome
• Mood Disorders
• Rheumatoid arthritis
• Type 2 Diabetes
• Ulcerative colitis

The result is a report such as shown below

At the bottom is a link to recommendations for the ‘Match’ items above only. That is, the recommendations are specific to the depression symptom.

For the first one on the list, I did a google and found lots of pages saying that pomegranate helps depression!

• Take Pomegranate for Depression and Reduce Symptoms
• 9 Pomegranate Healing Benefits – Dr. Mercola
• Foods That Can Help Anxiety & Depression | LIVESTRONG.COM

Imagine that! With the microbiome approach, you are getting truly individualized suggestions and not proforma suggestions.

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.

A reader sent these questions to me

USING ANTIBIOTICS IN THE TREATMENT OF CFS

An Interview with Ken Lassesen

• Antibiotics played a key role of all three of your remissions, is that correct?
  • Correct each time the working medical hypothesis was different
    • 1st time:  Antibiotic resistant walking pneumonia (this was before CFS existed as a syndrome, the 1970’s)
    • 2nd time: CFS proper
    • 3rd time: Lyme disease (which gave a rationale for prescribing the antibiotics – since there is more resistant to prescribe antibiotics)
• What antibiotics did you use each time?
  • The one’s in the 1970, i do not recall. I remember rotating thru several
  • 2nd time:  doxycycline, minocycline (with Olive leaf), Azithromycin
  • 3rd time: minocycline, Amoxicillian – with Olive leaf, monolaurin, neem, tulsi etc.
• How did you choose those particular antibiotics?
  • 1st time, because of the perception that we were dealing with an antibiotic resistant bacteria — the MD tried different ones
  • 2nd time, MD was following Dr. Jadin’s protocol 
  • 3rd time, ND was following a Lyme protocol (with some negotiation from me)
  • The non-prescription items were selected based on what would shift the bacteria dysfunction reported for CFS patients in 1998
• You’ve written at length about the Jadin protocol, though it was many years ago. [I’ll include a link to your PDF] Have your thoughts about that protocol changed any, in the years since? How many other CFS patients do you know, who’ve tried it?
  • Getting patientcounts is always difficult because when it works, the person tends to disappear and gets back to normal life fast!
  • The published rate was 70-85% went to remission. I believe it is at least 50%. A problem is that the 15+% that it did not work for are the ones in the CFS community saying it does not work! (Sample bias)
  • The protocol is still an excellent approach if you do not have solid microbiome data to work from. With that data, you can become more selective in which one(s) to use — ideally with the 15-30% of non-responders decreasing.
• The three times you developed CFS, were your symptoms largely the same, or was there some variability of symptoms?
  • Variability in symptoms
    • 1st time, “stress cough” and cognitive collapse.
    • 2nd time, cognitive and physical collapse, MCS for a while
    • 3rd time, digestive issues appeared with the above — this caused me to review the literature in this area and I re-discovered the 1998 papers (which I had dismissed in 1999 because there was no gut issues apparent!)
• Based on your surveys of readers, what antibiotics seem most effective, for someone with CFS?
  • See https://cfsremission.wordpress.com/2016/07/23/first-survey-results-on-antibiotics/
  • I just grab the latest numbers (below)

•   Ranked by odds of improving:
  • Metronidazole and Tinidazole
  • Azithromycin
  • Doxycycline
  • Minocycline
• From what you’ve seen and read, what are the biggest risks of taking antibiotics to treat these conditions?
  • Severe herx — both the patient and the MD should be aware it may happen and what to do
• If you had to take antibiotics again, would you combine them with probiotics? If so, which probiotics come to mind, for which antibiotics?
  • Yes, I would attempt to pair them appropriately (which may mean doing some research. For an example see Beware nullifying probiotics by other “good stuff”
    • i.e. Mutaflor and Symbioflor2 with Metronidazole and Azithromycin
• Your model posits that, for most CFS cases, an illness depleted their E. coli population, and without E. coli, pathogens were able to take up residence and kill the Bifidobacterium and Lactobacillus, among others. What studies led you down this path, about the key role played by E. coli?
  • Actually my model is a stable persistent dysfunction of the microbiome. This can have many favors. Deleted E.Coli is a presentation that fits the data and is simple for brain fogged CFS patients to understand. 
  • What lead me down the stable persistent dysfunction was finding that the microbiome shift reported in 1998 and the protocol of Jadin were a match. Her protocol would counter the typical microbiome shift seen. Trying that hypothesis on for size (i.e. seeing if it’s prediction agrees with studies), found more and more agreement.   Once the model has shown to hold water, then the non-responders made sense — they were different bacteria shifts that caused equivalent changes in the body’s metabolites but those bacteria may be resistant to these antibiotics.