New Comparison between 2 uBiome

Some one who been following the recommendations from http://microbiomeprescription.azurewebsites.net/ just uploaded their latest (march, 2018) results and asked me too look at the changes. I did this earlier when I first created the page to compare: Comparing repeated uBiome results  and the first study Simple Summary of Progress between uBiome Samples

Total for all AutoImmune like Profiles

Decreased from 127 to 116. Most significant was metabolic syndrome dropping from 8 factors to 3 factors. Chronic Fatigue Syndrome dropped from 18 factors to 15 factors.

On the other side, ADHD factors increased from 6 to 11, which agrees with the observed lost of focus.

Metabolism (KEGG) has improved

Std Dev is the measure of how scattered the results are around normal. If every result was 1.0 then Std Dev would be 0.0.

  • 0.47 would be like the red line below
  • 0.23 would be like the green line below

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Metabolism Average Sept 2017 March 2018
Metabolism Average 1.03 1.00
Metabolism Std Dev 0.47 0.23

Detail Changes

The following went up or down significantly

Secondary metabolite degradation: Fluorobenzoate degradation 0.575 0.3
Secondary metabolite biosynthesis: Stilbenoid, diarylheptanoid and gingerol biosynthesis [POST} 2.5 1.8
Secondary metabolite biosynthesis: Caffeine metabolism 1 0.575
Secondary metabolite biosynthesis: Betalain biosynthesis 0.3 1.425
Lipid metabolism: alpha-Linolenic acid metabolism 1.175 0.575
Lipid metabolism: Arachidonic acid metabolism 0.825 0.575

The above lead to some suggestions:

Bacteria Shifts

This is the percentage from the normal  ranges that I have been using. We see several phylum (high level of bacteria) has moved from very low to the normal range as the Bacteroides/Firmicutes ratio has also shifted.

(phylum)Actinobacteria -1 % -15 %
(phylum)Bacteroidetes 0 % 31 %
(phylum)Firmicutes 7 % -14 %
(phylum)Fusobacteria -72 % -97 %
(phylum)Proteobacteria 0 % 25 %
(phylum)Synergistetes -91 % 0 %
(phylum)Verrucomicrobia -97 % 0 %

Visuals

At the class level, we see that a lot of classes have increased in side, which I view as a good thing.

class

Recommendations

With the new recommendation engine,  we had 12 items on default recommendations with  the former sample, and only 1 item for the latest sample (high counts). When we switched to the low counts ONLY for current sample, we get a long list. A few examples below:

Item    Action    Confidence value
beta-glucan foods Take 5.734
mediterranean diet Take 5.353
barley Take 4.68
dairy milk fats Take 3.13
almonds Take 3.06
pulse / legumes Take 2.786
lactobacillus reuteri probiotics Take 2.742
arabinoxylan Take 2.556
raffinose foods Take 2.476
inulin prebiotics Take 2.467
coconut products Take 2.402
bacillus probiotics Take 2.331
high fiber diet Take 2.277
candida albicans yeast Take 2.131
bifidobacterium animalis lactis probiotic Take 1.98
bacillus subtilis probiotic Take 1.859
pomegranate fruit Take 1.804
fruit Take 1.702
whole grains Take 1.642
walnuts nuts Take 1.491
bacillus licheniformis probiotics Take 1.447
vitamin a retinol Take 1.425

Bottom Line

This is the second comparison that I have done [earlier one], and both had KEGG (Metabolites) improving and other improvements.

Modifying the microbiome is a complex area — do you reduce the high first, increase the low, try doing both at the same time?

The key take away is that “Yes Virginia, you can alter and improve your microbiome”. After altering it one way, the next ubiome sample may result in major changes of what to take. It is NOT a straight linear path.

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.