I will not look at the differences of what is reported. Many people have done blogs and articles complaining about differences. What I care about is the differences between analysis and suggestions — the actual application of the reports. Reader reports: “same sample, same time.”

Comparison tools

Reader Beware: There is no gold standard for matching rDNA to bacteria.- firms use different algorithms. Second, analysis is done using box-plot. The base data for box-plat is at present: 731 ubiome, 44 Thryve so there is a bias to ubiome’s processing.

1-15 is the Thryve sample, 7-11 is the uBiome sample

Thrye had more condition matches (113) compared to ubiome (88) – but is more bias to the low levels.

This may be explained by the number of bacteria reported, a major difference. The number for ubiome seemed very low (bad sample taken or processing issue).

• ubiome: 335
• thryve: 657

Let us look at what is reported by each.

So we are at the 72%ile for Thryve samples and 96%ile for uBiome samples. The median counts: 234 for ubiome, 560 for thryve.

Conclusion: Thryve on average provide more bacteria reported. I look forward to comparing SunGenomics.com tests which should have even a higher count than Thryve.

Details

Looking at the highest level, Phylum, we see very great differences, especially in the more uncommon phylum.

Suggestions

This is what most interests me. The suggestions prediction algorithms are not based on any lab results but on pubmed studies. The data used may be lab-dependent.

We have just one commonality: niacin.

Bottom Line

This has been interesting. Which is more accurate? I do not know; clearly results are very different (as reported by many other bloggers). Is the difference because of the computer algorithm used by each lab (known to be different), natural variation in a stool sample, sample collection process, etc.

Although the literature is generally conflicting with regard to sampling methodology, it is important to consider that comparisons of data obtained using different approaches should be avoided.

The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies

All steps of the complex analysis workflow significantly influenced microbiome profiles, but the magnitude of variation caused by PCR primers for 16S rDNA amplification was clearly the largest. In order to advance microbiome research to a more standardized and routine medical diagnostic procedure, it is essential to establish uniform standard operating procedures throughout laboratories and to initiate regular proficiency testing.

Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability

Before everyone goes to Thryve — be aware of the following:

• Box-Plot detection is based largely on ubiome samples. When I get 100 (at present 44, half-way there), I will split the reference numbers into a Thryve and a uBiome set.
• Averages were all reversed engineered from ubiome values.
• Symptom matching is based on submitted samples (94% from ubiome)
• Condition Templates are based on published research which uses a wide variety of tests methodologies.

So, the reality is that the site favors uBiome data.

There is a natural desire to get absolute, precise information. We are dealing with methods that are evolving. My attitude is to take the best data currently available (factoring in costs) and use it. It is better than working from no information.

This post comes out of a 2018 article: Chronic fatigue syndrome patients have alterations in their oral microbiome composition and function. [Sep 2018]. The basic findings was:

Of these, Veillonella abundance was significantly different between CFS patients and healthy controls, with 9.81 ± 8.26% and 13.97 ± 8.91% 

In addition, many of the genera with a relative abundance greater than 1% were significantly different in CFS patients compared to healthy controls. For example, Fusobacterium, Prevotella, Leptotrichia, and Campylobacter had increased abundance in CFS patients compared to healthy controls, while Haemophilus, Porphyromonas, and Moraxella had decreased abundance in CFS patients compared to healthy controls.

In the past, I have written that oral/mouth bacteria was likely a reserve for ME/CFS bacteria. This study appears to confirm my earlier inference.

• A mouth full – for better or worst (2014)
• Treatment: Where do I go from Here #1 (2015)
• Your mouth can trigger flares (2017)

Constructing a Mouth Wash Conceptually

This is done by using the Genus Lab report page, The results of which summarize below (Some are in some commercial alternative mouth washes):

• ku ding cha tea
• Cumin / Tumeric
• Coconut water (or apple juice) with following dissolved:
  • folic acid (supplement Vitamin B9)
  • Cyanocobalamin (Vitamin B-12)
  • NAC (N-Acetyl Cysteine)
  • vitamin b7 biotin (supplement) (vitamin B7)
• Rosemary, Neem, Oregano

In terms of probiotics, the following should be dissolved in the mouth (capsules open) and not swallowed:

Bottom Line

The above is an a priori formulation based on the report cited above. It is interesting to note that saccharin is a to avoid (and found in some commercial mouth washes)

Self Experiment: Of the above probiotics, Shin Biofermin (JP) /S seemed the best to try — small hard tablets (Normal dosage is 18) that lends itself to dissolving in the mouth. At bedtime, I slowly dissolved two in the mouth. That night I slept the hardest that I have in months. (I have added a link to a world wide shipper on the above probiotic link)

This is largely a visual post — showing the results of a reader who has taken frequent ubiome sample, uploaded them and then started following the suggestions generally. It appears to confirm that the microbiome may be manipulated. Dates of samples

• 3/11/2018 – first set of suggestions tried
• 8/25/2018 –
• 3/26/2019 – symptoms got bad enough that ‘coasting’ was no longer acceptable. Regular testing started.
• 5/7/2019
• 6/12/2019

Bottom Line

Correcting the microbiome is not a one step-dance or a direct root. This person had things drop to avoid and then re-appear as take on a later sample. The WHY charts are a nice visual representation of the known dependencies. For example, we need to reduce most of the bacteria slowing Bifidobacterium (as shown below)

A reader asked me to look at their ubiome results. They were having trouble interpreting the data (Brain Fog: 1, Site: 0)

Preferred Approach

This reader has enter their symptoms AND we have some matches in terms of patterns associated with symptoms and their microbiome.

So on the Custom Suggestion page, we select this and exclude things that we are not interested in:

When we get the suggestions, we see that no Flavonoids are listed.

We also have a list of supplements etc showing up

There is a long list of Postive Impact Probiotics. We want to always go for the highest impact ones that are available to you. The best is available in Germany: Probiotic PUR (DE) / RealDose Nutrition : impact 5.4

At the bottom we have Flavonoid Foods

I checked Almonds, Walnuts and Oregano to see if they had any flavonoids in common and high. No luck.

Approach 2 – Outliers

This looks at outliers compared to other samples uploaded. In this case we see one family that is massively high — Eubacteriaceae. Being focused on this alone is in Approach 3 below (targeted).

We change to this method of selecting the bacteria to be concerned about.

In this case, we get all three links at the top

Our probiotic list is much smaller.

For Flavonoid foods we see cumin and rice

The interesting list is the Flavonoids list

Curcumin is found only in turmeric(22.14) and curry (2.85). Catechol only in coffee… However, checking the Flavoid page, we see there are three other forms of Catechol.

4-Methylcatechol 4-Ethylcatechol 3-Methylcatechol

At this point, we enter fuzzy territory. Did the study include all of these different forms under Catechol or not? Peeking into the database, it appears that all four variations are the same or similar. They are associated with the following foods:

Approach 3 – Targeted

We see above that Eubacteriaceae was very very high. We could just go to it’s page and look at what modifies it. This often results in saturation.

An easier path is to go to the [Other Lab Analysis] and select: All Bacteria [Family] Reported. On that page, select only this one (add more if you wish)

This gives a filtered list (Sorry, I have not updated this page to the above style yet).

The risk with this approach is that you may be missing the bacteria that supports this very high bacteria. Unfortunately, we do not know what feeds this bacteria (clicking on WHY on the sample page). What we do see are odd unexpected relationships which hints at a specific strain being the issue.

Looking at the drop down, we see a lot of species that are overgrown in this family.

We counld return to the Other Lab analysis page and pick: All Bacteria [Genus] Reported This may produce a different report because some studies reported on the family impact and other on the genus impact.

We get a similar list to the above, with a few variations.

Bottom Line

I know people want things to be ultra simple — having multiple ways of looking at stuff can be a challenge. Looking at commonality in the above reports, I would suggest discussing the following with your medical professional.

• Vitamins B9,B12,B1,B7, B6
• Probiotics: Bifidobacterium Bifidum and possibly include other Bifidobacterium (avoid all Lactobacillus)
• Berberine
• Melatonin
• Rice with turmeric

A few readers have severe diet restrictions (mast cells, etc), which contributed to my adding flavonoids and polyphenols data. Once added, I have been connecting them up (a slow manual process).

New Sections on Suggestions Page

At the top, there may be up to 3 onpage jumps such as shown below.

We have already cover probiotics in a prior post, so let us look at the two new one:

Flavonoid Foods

This is a match up with all of the positive modifiers (2x more good risk than harm risk) that are also in our flavonoid database.

Note that all of them are hyperlinked. Clicking on one, for example bananas — takes us to the details for banana.

We see that ONE of the items is very high. This may be what is causing the benefits from banana. Click it to see other foods with this flavonoid.

So, if you can’t eat bananas — this is a list of possible alternatives that may help. Some, like Carob, we have no studies on… but this suggests it may be logical to try it.

Flavonoids Suggested

These have been studied and are on your list above. A common problem is getting them. You can buy them as supplements (with huge markups) or you can get them from fresh food — but which foods?

Just click on one of them, say Quercetin,

Items like Capers have not been studied for it’s impact on the microbiome. Again, it is a logical suggestion.

Bottom Line

The early users of these new features have been delighted. It has open up new choices that are logical and reasonable. Whether they work — that is to be determined.