Metagenomics Classifications Across Human And Cat Samples

As previously described, part of the reason why I collected a 2nd Whole Genome Sequencing (WGS) sample from Basepaws in order to have raw data to re-analyze for 2 time points (which include Dental Health Test reports).

One of the points raised in that blog post was to improve coverage for germline variants.  I do see some evidence for improvement in Bastu's heterozygous dilute variant.  However, I primarily wanted to better understand and assess the metagenomics classifications (related to the Oral/Dental Health Test), and those are the primary focus for this blog post.

The exact format for the FASTQ files for the Basepaws raw data was different for the 2nd WGS sample than the 1st WGS sample.  The 1st WGS sample had a format more similar to what was provided by other companies, so I wrote a script to confirm the file format for the 2nd WGS samples.  This requires some familiarity with programming (even if mostly to comment or uncomment lines to run), so I am not sure how much it will help with reformatting for all customers.  Nevertheless, you can see the code and details for the file reformatting and the analysis below here.  A summary of data that I am sharing (and I would be interested to see from others) is provided here.

There are different strategies for metagenomics analysis.  For Basepaws, I believe that either regular coverage WGS or low-coverage WGS (lcWGS) is being used for oral microbiome classifications.  There are some human fecal samples where I describe the details of the sequencing design for samples collected from multiple companies here.  The human oral samples are also a mix of different companies and different sequencing design (such as WGS/lcWGS versus 16S Amplicon-Seq).  All cat fecal samples come from PetQCheck.

While I have not looked in as much depth to the sequencing type and re-analysis details for the other raw data, I have at least one oral and at least one fecal sample for myself and my cat Bastu.

The oral samples for myself are from uBiome, Bristle Health, and any WGS data collected for any purpose (Veritas, Sequencing.com, Nebula, etc.).  The oral samples for my cat Bastu only come from Basepaws.

The fecal samples with raw data for myself come from uBiome, Psomagen/Kean, and thryve/Ombre.  The fecal samples for my cat Bastu only come from PetQCheck.

For previous human comparisons, the company / sequencing design had a noticeable effect on the results.

However, if I go back and use a method that is at least capable of providing some classification for all of the sequencing types (Kraken2/Bracken), then I can re-analyze all of those data types together and visualize how they cluster:

I have added clustering for the species (human or cat) and the collection site (oral or fecal).  If Pearson Dissimilarity (1 - Pearson Correlation Coefficient) is used as the distance metric for clustering, then samples cluster primarily by sample site (oral versus fecal).  For example, all fecal samples intended for microbiome analysis do tend to have Bacteroides classification rates.  

Additionally, Veritas only provided reads aligned to human chromosomes in a BAM (Binary Alignment Mapping) alignment file that I converted to paired-end FASTQ files (the reads used for the classifications above), so I expect all accurate assignments should be relatively close to 0% for that sample.  So, this may be one reason to be skeptical about differences in the Basepaws WGS samples with this classification method, which show differences from each other and vary at a level similar to the Veritas samples should really close to 100% true human reads.

I also created another heatmap, only looking at assignments with the Top 20 genera assignments with highest average read fractions (calculated as percentages after removing Eukaryotic reads, which is also how the values for the first heatmap was calculated):

In that view, it looks like there might be additional Prevotella classifications for the cat fecal samples.  However, I have not looked into the details of the PetQCheck sequencing design, to make sure it matches at least one set of human samples.

Additionally, independent of overall clustering, I think it is encouraging that there is greater detection of Faecalibacterium in the fecal samples than the oral samples (for both species).

Also, unlike the human oral sample that should have only contained human (hg19) reads, the human oral samples showed some similarities that were noticeable beyond the differences in sequencing design.

For advanced readers, you can see some additional testing for alternate normalizations here.  However, I think the main conclusions are the same, and (if anything) there is possibly a less clear distinction for the negative control approximation (and the Basepaws samples) with the other normalizations.

Also more for the advanced readers, but you can also see some troubleshooting where I looked for even coverage in representative sequence alignments here.  If a classification is made for a large fraction of reads but coverage is uneven, then I would say those classifications are more likely to be false positives.  For example, in the above images (or here), you can see some higher reads given Staphylococcus and Pseudomonas assignments in the negative control approximation.  Those representative genomes also have uneven coverage in these Bowtie2 alignments or these BWA-MEM alignments.

Finally, as yet another option for more advanced readers, you can see some alternative methods of running Kraken2/Bracken.  Perhaps of note, there is supposed to be an implementation of KrakenUniq within later version of Kracken2 (referred to as "Kraken2Uniq", where I added the parameters "--report-minimizer-data --minimum-hit-groups 3" based upon this protocol):

Without adding the minimizer parameter for the KrakenUnique implementation within Kraken2, increasing the minimum number of hit groups to 10 ("--minimum-hit-groups 10") also reduces similarity between the approximate human negative control and the Basepaws oral Whole Genome Sequencing samples (at least with the latest versions of Kraken2 and Bracken):

Using a larger database can substantially reduce the run-time.  However, there is some additional information uploaded on GitHub for that option, if using a reduced set of total reads for some samples.  You can see a large difference in the Komagataeibacter classification rate for the larger database versus the more stringent requirements for the smaller "MiniKraken2 database.

In other words, I think these are important take-home messages:

• Human and cat fecal samples form a cluster for that collection site, and they separate by species within that cluster (although PetQCheck is the only source for any cat fecal samples).
• The Veritas sample was provided to me as chromosome alignments, rather than reads.  By definition, this means that the reads were aligned to the human genome, so I consider "Vertias_WGS" to be a negative control approximation.
• Both the human fecal and oral samples contain a mix of Whole Genome Sequencing (WGS) and targeted sequencing for the 16S bacterial gene (16S Amplicon-Seq).  So, when these samples cluster together, I consider that a relatively robust result (even if there are also additional factors contributing to the exact abundance values, such as the company and the sequencing design).
• On the contrary, the 2 Basepaws samples are less similar to each other than the different companies / libraries for my human oral samples.  Additionally, if there no no additional troubleshooting, then there is relative clustering between the Basepaws oral cat samples (particularly Basepaws_WGS2) and the human negative control approximation.

In term of trying to understand the Basepaws cat oral samples, there is a noticeable negative shift in the fragment size estimated by Picard (particularly for the first sample):

If the classifications can be reproducibly run in the same way, then I hope sharing reprocessed data might help with helping develop more of an opinion of the cat Oral/Dental Health Test from Basepaws.  I have started a discussion thread here.  In the Basepaws oral health test preprint, I also have additional comments, and I would be interested to hear feedback from others.  My understanding is that Kraken2/Bracken was also used in the preprint, but I think something about the exact way that the program was run was different.  Also, if I understand correctly, then I don't think the program can take the paired-end read information into consideration if the file is in an interleaved format (as provided for my cat's 2nd Basepaws WGS sample); however, I am not sure how important that is.  Within the GitHub discussion, I also provide some information for my human oral microbiome report from Bristle Health.

I would say the Basepaws Oral/Dental Health Test risk assessments were not a great match for my cat's individual dental health assessed by the vet.  However, I understand that the risk assessment is not meant to provide a diagnosis, and I am therefore still not completely sure what I think about the results.

That said, I feel comfortable saying that I wish raw data was provided for all of the Oral/Dental Health Test results from Basepaws (not only for the Whole Genome Test).

So, I hope this contributes some interest and encourages discussion on the topic!  I have some notes for the Basepaws Oral/Dental Health Test here, but I would be interested in seeing re-processed raw data with similar classifications (if possible).

Change Log:

12/20/2022 - public post date.

1/1/2023 - add content related to Kraken2/Bracken troubleshooting + revised take-home messages.

1/4/2022 - add note for Komagataeibacter assignments in Basepaws WGS1.

Human Metagenomics Comp 2021/2022: Overall Summary

Inspired by an earlier post that I don't believe is currently accessible (from several years ago), I tested collecting stool samples at the same time for multiple companies.  This sometimes includes the same sample submitted the same company at the same time.

The sample collection can be summarized as follows:

	Psomagen	Thryve	Viome
Stool 1 (3/11/21)	1 Gene & GutBiome 1 GutBiome+		2 samples
Stool   2 (5/3/21)	1 GutBiome+	1 sample	1 sample
Stool   3 (6/27/21)	1 GutBiome+	2 samples	1 sample
Stool   4 (10/6/21)	1 GutBiome+	1 sample	1 sample
Stool   5 (12/11/21)	1 Kean Gut	1 Ombre
Stool   6 (5/6/22)	1 Kean Gut 1 Kean Gut+	1 Ombre	1 sample

 There are additional details (including the reports and data) on GitHub.

The sequencing performed can be described as follows:

Psomagen GutBiome (within combined "Gene & Gutbiome"): 16S (V3+V4 region, PE300 reads)

Kean Gut: 16S (V3+V4 region, PE300 reads)

thyrve/Ombre: 16S (V4 region, PE150 reads)

Psomagen GutBiome+: "Shotgun" Metagenomics (DNA-Seq, PE150 reads)

Kean GutBiome+: "Shotgun" Metagenomics (DNA-Seq, PE150 reads)

Viome: Metatranscriptomics (RNA-Seq, unknown read length)

As verified by Ombre technical support, you can view an alignment from my 5th paired sample here.  The other V4 sequences were downloaded from the GitHub repository for code associated with Johnson et al. 2019.

I have a couple paired posts (here and here), but I hope this can provide a fairly good summary of my overall experiences:

Individual assignments are not provided within the web interface for Kean Gut, but assignments can be made with re-analysis of the raw data and you can see some of such analysis with mothur here and Kraken2/Bracken here.

Raw Data Return:

1) thryve/Ombre - automatically provides raw FASTQ files as well as table with classifications at various levels

2) Psomagen/Kean - provides FASTQ files if e-mailed (but not automatically)

3) Viome - does not currently provide raw data (even if e-mailed) and classifications have discrete assignments (not percentages of reads)

When raw data was available (either automatically or by request), I have uploaded the data in public links on Google Cloud.  You can see a table of files to download here.

The samples and companies / organizations are different.  However, if it might help to see metagenomics samples that were collected before any of the samples described in this blog post, then you can see links to download raw data here.

Post-Collection Bacterial Growth Suppression:

1) thryve/Ombre - A liquid is included, but I have not yet verified the contents of that liquid.

2) Viome - liquid with preservative ("[bacteria] are not being killed nor growing").  I am not 100% sure about the implications for using RNA to study "active" bacteria, but I think it should help over adding nothing.

3) Psomagen/Kean - no liquid in collection tube to prevent / suppress bacterial growth

I only collected 1 BIOHM sample (10/6/2021), but there was no liquid (and therefore nothing to prevent / suppress post-collection bacterial growth)

Sample Collection Options:

1) Viome - originally provided 2 sizes of stool collector, but I think this is now reduced to 1 stool collector.

2a) Psomagen - 1 stool collector

2b) Kean -  tissue paper (0 stool collectors, if collected by itself)

3) thryve/Ombre - tissue paper (0 stool collectors, if collected by itself)

Cost:

1) Psomagen - $149 (with discounts - I paid $83.49, including $8.99 shipping, for 1 of my samples)

Kean splits options into ability to purchase separate Gut Health (for $99) and Gut+ Health (for $169)

2) thryve/Ombre - $199 (with discounts - I paid $99 before taxes, with free shipping, for at least 1 of my samples)

If you count the re-test discount (and that is still offered through Ombre), then I paid as low as $74.32 for 1 kit (with taxes).

3) Viome -  $299 (with discounts - I paid $129 before taxes, with free shipping, for at least 1 of my samples)

Result Turn-Around Time:

I think there is a limitation or lowest raking for each company, depending upon whether you define "turn-around time" for the kit, the results, the raw data, or for answering questions.

If it takes weeks to receive results that I think should mostly be considered hypotheses, then I don't think the slightly faster arrival of materials is really helping very much.  However, I consider returning raw data for re-analysis and answering questions from consumers to be very important.

Robustness of Results:

1) thryve/Ombre

2) Psomagen/Kean

3) Viome - noticeable variability results for samples collected at the same time

The explanation here is somewhat complicated.

As explained in a paired post, the signature/scores for collections from the same sample were better for thryve than Viome.  There are also some extra examples related to variability in the Viome results in this other paired post, but that is only for Viome.

Viome does not provide raw data and the data collected is different. So, only 2 signature/scores were available for thryve.  The Psomagen Gene & GutBiome kit used a different library design than the Psomagen GutBiome+ kit, so I don't have the replicates from the same sample that I intended.

There is also some information that I manually extracted from the 3 reports here (as well as on the highest level subfolder).  As mentioned earlier, you can also see some re-analysis of raw data (for Psomagen/Kean and thryve/Ombre, here and here).

Overall, I submitted an FDA MedWatch report Viome (for the currently commercially available tests described in this blog post), where the full draft is available to view here.  You can see the de-identified version in the MAUDE database under MW5106218.

To be fair, thryve/Ombre also have some food predictions that I would not place too much emphasis on.  However, Viome clearly has less consistency than the thryve/Ombre (positive and negative) recommendations.  If you download the thryve/Ombre PDF summary tables, then you should be able to access the links to the reports on Google Cloud (because they were too large for GitHub).

If you specifically purchase Kean Gut+, then there are some food recommendations.  I have not made changes based upon the results from any company, and I am not making any changes based upon any specific feedback from Kean either.  However, there were at least no recommendations to avoid eating food that I already find helpful or favorable.  I think the food recommendations also seem like mostly good ideas, regardless of any particular metagenomic result.  On the other hand, I am not certain what to say about the supplement recommendations for either Kean Gut or Kean Gut+.

A Note About Probiotic Detection:

The reason that I ordered a Viome sample for my 6th (but not 5th) paired sample is that I better understood the difference between the Kean Gut and Kean Gut+ kits, and I wondered if it was possibly useful to have a paired sample with untargeted DNA-Seq (for Kean Gut+) along with a Viome sample.

Viome still does not provide raw data, and I believe they only provide discrete specific assignments.  The Viome interface has changed recently, but I was still able to see those in a PDF file when I e-mailed myself to "Share My Results"  (under "Scores," and then at the bottom of the details for one of the scores)

Nevertheless, I could compare Kean Gut+ assignments and the "Active" status for Viome, and most assignments matched in that sense (everything above 1% listed in the table on this page match).

The one exception where I can have both Kean Gut and Kean Gut+ information for individual bacteria is for probiotics.  Lactobacillus was copied over from earlier collections, but I thought that might be interesting in that Ombre and Viome reported detection when both Kean Gut and Kean Gut+ reported a lack of detection.

I thought this was somewhat interesting because I have Activia with lunch on Monday to Friday.  The Wikipedia page mentions that other common probiotics are also present, but Bifidobacterium is mentioned as something specific to Activia.  So, I went back to check the original reports and update the GitHub table for the 6th paired collection, and all 3 companies report Bifidobacterium detection.

There are non-zero mothur read fraction assignments for Bifidobacterium for all samples except Psomagen for the 3rd paired collection (Psomagen3), and I can tell that Kraken2/Braken is also capable of making those assignments at the genus level.  I don't think this indicates what is present is specifically the proprietary strain, but I might guess eating yogurt on a regular basis might have some level of contribution.  I also don't think the bacteria has to be that proprietary strain in order to be helpful.

Closing Thoughts:

In general, my opinion is that is better to focus on a smaller number of things that are truly predictive than attempt to make a large number of claims (and have a lot of them not be valid).  I also think having access to raw data for re-analysis is very important.

I selected Psomagen because it was the company that purchased assets from uBiome.  However, there are notable differences in what Psomagen provides (such as no longer using a liquid to prevent post-collection bacterial growth).  Even though there were other ethical problems for uBiome, I think stopping post-collection growth was a good idea.  I also previously had the ability to sample multiple sites from uBiome, but that was not currently an option.  So, I don't think these company purchases/acquisitions necessarily mean that you can expect the same product.

I think I might have purchased a Viome kit because of an advertisement, but I believe that I am less likely to make future Viome purchases (if similar to what is currently provided).  I also do not plan to make additional BIOHM purchases.  My overall impression for Psomagen/Kean was somewhere in the middle, but I would guess that I am most likely to purchase Ombre in future.  For example, I posted Trustpilot reviews for all 3 companies: 4 stars for Ombre, 3 stars for Kean, and 2 stars for Viome.  However, to be clear, that is largely because of the automatic return of the raw data and the presence of some sort of liquid that I assume helps reduce post-collection growth.  That does not mean that I like or approve everything within the Ombre results.

For example, I am not sure if I support the Ombre supplement recommendations, and I don't remember seeing anything that I considered particularly helpful among the food recommendations.

From a technical standpoint, my critiques were mostly for Viome.  Of course, anybody can submit FDA MedWatch reports for a technical issue with a diagnostic (as I did).  However, if other consumers do plan on taking supplements recommended by the company, then I hope that they use FDA MedWatch if they encounter any adverse events.  I did this for an earlier human genomic test, but I am hesitant to try recommendations from the multiple other companies.  So, I hope that customers for all 3 companies are aware of resources like this and take the time to provide important feedback as relevant.

Unfortunately, I don't think have enough specialized background to gauge the relative importance of various options for specific diseases (such as those offered by a given company, versus other options that might even be free).  So, if you are well aware of the common best practices (as either a physician or possibly as a patient with a chronic condition), then please provide feedback if you notice any claims that might either over-emphasize the benefit of a supplement from a given company or if enough emphasis on other available/established options is not adequately described.  I think there are reporting systems for advertising, but I am not sure of those can be recommended by somebody else.  I think critical evaluation of claims is important, and I think e-mails to the company and discussion with your general or specialized physician are probably a good starting place.

Again, I did not make any changes based upon any results from any of the companies.  I only used this for research purposes to gain a better appreciation for the bacterial metagenomic analysis, which would allow me to do things like study microbiome changes over time.  In the future, I might also make changes based upon independent physician advice and see what happens in my metagenome (mostly as a matter of curiosity), but I consider that different than starting from recommendations based upon the metagenomic data.

I hope others find this helpful, and I would certainly encourage feedback! I think the blog post comments section has been OK in the past.  However, if there is any possible benefit to using the GitHub discussion (which can include images and code), then that is also an option.

Change Log:

12/12/2021 - public post date

12/13/2021 - various wording revisions/corrections

12/14/2021 - change wording for last change log entry; additional revisions

12/19/2021 - add Viome response + additional links

12/29/2021 - thryve/Ombre food recommendations

1/22/2022 - add FDA MedWatch report

5/2/2022 - change title and add additional sample information

6/18/2022 - after receiving results from all companies, add collection date for 6th sample.

6/19/2022 - add links for raw data

6/27/2022 - revise closing thoughts and add some additional details (such as re-analysis and Bifidobacterium detection)

6/28/2022 - add link to GitHub disucssion

7/16/2022 - add Trustpilot review links, and re-arrange content.

For example, I have moved the content below out of the main post for brevity, but I am keeping the comments below for reference:

When I asked Viome for feedback regarding the extra discordance that I believe I saw in my samples (including those collected at the same time), I was directed to this page.  I am not sure if I see the assessments currently being provided to consumers.  However, if that represents the response, then perhaps it can be mentioned that I think it is important to emphasize that the "FDA Breakthrough Device Designation" for a different application that I believe is not available to consumers is not in fact FDA approved (matching the bar chart in the provided link, if you look carefully - however, it was an issue that I separately reported for regulatory misconduct, with draft available here that is a follow-up from discussion with other FDA that informed me about the regulatory misconduct reporting system). That said, I wish to emphasize that it is important that each claim be evaluated independently (within and between tests).

...

I believe this article references low consumer reviewers for Viome.   As of 12/19/2021, I see 1.28 out of 5 from the BBB, and 3.2 out of 5 from Trustpilot.

Human Metagenomics Comp 2021/2022: Additional Viome Comparisons

To be clear, I did not take any action based upon these reports.

In this post, I present some additional measures that you can see compare between my Viome stool collections, under the following categories: i) dietary recommendations and ii) supplement recommendations.

Viome "Foods to Avoid":

	Stool 1a	Stool 1b	Stool 2	Stool 3	Stool 4
Vegetables to Avoid	Bell Pepper Broccoli Brussels Sprouts Cabbage Mustard Greens Tomato	Bell Pepper Tomato	Bell Pepper Sauerkraut Tomato	Bell Pepper Tomato	Bell Pepper Cucumber Tomato
Proteins and Fats to Avoid	Almonds Chicken Egg Yolk Pistachios	Almonds Pistachios	Kefir (Cow Milk) Yogurt (Cow Milk, Plain)	Almonds Pistachios	Shrimp (Domestic)
Fruits and Grains to Avoid	None	None	Barley Blueberry	None	Watermelon
Other Food Items to Avoid	None	None	Coffee	Turmeric	None

I do drink tea instead of coffee, since coffee can irritate my eyes (and, at least to some extent, my stomach).

However, I think these results were problematic overall:

• It looks like the variation for the same stool is at least similar to the variation between stools.
• I drink almond milk every morning, which I believe helps some with digestion (compared to diary milk).  I don't think my reaction to dairy is severe, but I am not going to stop drinking almond milk.  This was in multiple reports.
• I also think it is helpful for me to eat Activia with lunch.  I am not completely sure how much eating regular yogurt helps, but I am certainly not going to stop easing any yogurt.
• Shrimp and watermelon are among my favorite foods.  I don't typically encounter serious problems, and I think they might even help a bit with digestion (at least if the shrimp is cooked and fresh).  So, again, these are examples of food that I am definitely not going to stop eating.

I think that the formatting in the blog post is not great, but you can also see this in a PDF format here.

To be fair, thryve/Ombre also have some food predictions that I would not place too much emphasis on.  However, Viome clearly has less consistency than the thryve/Ombre (positive and negative) recommendations.

Viome Supplement Recommendations:

I was not certain the best way to show these results in this blog post.

You can download the full Excel table as "raw" from the GitHub page.

You can also view the table in PDF format here.  If you want to see all 5 columns of results, then this is my recommended option.

I think that you can probably only clearly view the full content of the first 3 columns of results below (1a/1b and 2).  However, the 2 highlighted columns are for the collections from the same sample (for both this table as well as the "Food to Avoid" table above).  So, I think it is particularly useful to note when those are different.

Supplement	Stool 1a	Stool 1b	Stool 2	Stool 3	Stool 4
Acacia Fiber			1000 mg
Alpha-Lipoic Acid (ALA)		153 mg
Amylase			20 mg
Angelica Root Extract			87 mg
Astaxanthin					149 mg
B. animalis ssp lactis B420		270 million CFU		270 million CFU	500 million CFU
B. animalis ssp lactis BL-04			1.5 million CFU
B. animalis ssp lactis VK2	2 billion CFU				2.5 billion CFU
B. bifidum Bb-06	500 million CFU			1.4 billion CFU	500 million CFU
B. breve Bb-03	2 billion CFU	1 billion CFU			2.5 billion CFU
Bacillus coagulans SANK 70258	500 million CFU		1.3 billion CFU		1 billion CFU
Bacillus subtillis DE111	1 billion CFU		750 million CFU
Beet Root Juice	700 mg		700 mg	600 mg
Benfotiamine			101 mg		122 mg
Berberine		515 mg
Beta-Glucan		1000 mg	250 mg	750 mg
Bilberry Extract		79 mg
Boswellia Serrata Gum Extract			140 mg	120 mg
Butterbur Root Extract	62 mg	60 mg	59 mg		59 mg
Capsicum Extract		24 mg
Caraway Seed Extract			161 mg
Cellulase			74 mg
Chamomile Flower Extract			45 mg
Chromium		2 mg
Citicoline	149 mg		124 mg		149 mg
Curcumin	200 mg	200 mg			107 mg
Dandelion Root Extract			454 mg
Deglycyrrhizinated Licorice (DGL) Root Extract			31 mg
Elderberry Extract					208 mg
Feverfew Extract			239 mg		239 mg
Fisetin	70 mg			60 mg
Forskohilli Root Extract				21 mg
Fructo-oligosaccharides (FOS)			3250 mg
Garlic Extract					306 mg
Greater Celandine			76 mg
Inulin	1900 mg				2400 mg
L. acidophilus DDS-1			1.3 billion CFU
L. acidophilus NCFM			200 million CFU
L. casei Lc-11			200 million CFU
L. delbrueckii ssp bulgaricus Lb-87	500 million CFU
L. delbrueckii ssp bulgaricus LE	500 million CFU				500 million CFU
L. fermentum LF61			400 million CFU
L. gasseri Lg-36				270 million CFU	500 million CFU
L. helveticus VPro13				1.4 billion CFU
L. paracasei Lpc-37				270 million CFU
L. plantarum 299v			750 million CFU	1.4 billion CFU
L. plantarum LM	500 million CFU			270 million CFU	500 million CFU
L. plantarum Lp-115	500 million CFU				500 million CFU
L. reuteri 1E1	500 million CFU			270 million CFU	500 million CFU
L. rhamnosus GG (ATCC 53103)			1.4 billion CFU
L. rhamnosus LB3	500 million CFU				500 million CFU
L. rhamnosus Lr-32			200 million CFU
L. salivarius Ls-33			200 million CFU
Lactase			1 mg
Lactobacillus acidophilus La-14	500 million CFU		200 million CFU		500 million CFU
L-Arginine				124 mg
Lemon Balm Extract			76 mg
L-Glycine	152 mg		126 mg	177 mg	152 mg
L-Lysine					406 mg
L-Tyrosine	152 mg		126 mg	177 mg	152 mg
Lutein	59 mg		49 mg		59 mg
Magnesium	758 mg	735 mg	727 mg	727 mg
Mastic Gum Extract	257 mg			257 mg
Mulberry Leaf Extract		1000 mg		1000 mg
N-Acetyl-L-Cysteine (NAC)	509 mg			509 mg
Nettle Extract					209 mg
Olive Leaf Extract			299 mg
Oregano Leaf					74 mg
Panax Ginseng Root Extract			99 mg	139 mg	119 mg
Phosphatidylcholine			304 mg
Phosphatidylserine	92 mg		76 mg		92 mg
Phygeum Bark Extract	99 mg
Psyllium Husk Fiber	2000 mg				2000 mg
Pumpkin Seed	385 mg	330 mg	385 mg	330 mg
Pygeum Bark Extract				99 mg
Rhodiola Root Extract	149 mg		124 mg	174 mg	149 mg
Saccharomyces boulardii DBVPG 6763	500 million CFU			279 million CFU	500 million CFU
Sage Leaf Extract		209 mg
Saw Palmetto Berry Extract	224 mg	192 mg	224 mg
Schisandra Berry	239 mg		199 mg	279 mg	239 mg
Serrapeptase			17 mg
Spirulina Extract				34 mg
Streptococcus thermophilus St-21	1 billion CFU	470 million CFU		470 million CFU	1 billion CFU
Thyme Leaf Extract					79 mg
Tinospora Cordifolia Extract					419 mg
Tribulus Terrestris Extract	356 mg			356 mg
Turkey Tail Fruit Body Extract	124 mg			124 mg
Vitamin B5 (Panthoenic Acid)				20 mg
Vitamin B7 (Inositol)				257 mg
Vitamin B8 (Biotin)		99 mg
Vitamin C (Ascorbic Acid)					353 mg
Xylo-oligoaccharides (XOS)	1550 mg			750 mg	1000 mg
Zeaxanthin	59 mg			69 mg
Zinc					59 mg
Total Supplements	28	30	37	34	30
Total Probiotics	19	18	18	18	19

 The units for the supplements above are the dosage per day.

I am manually copying over the information from printed PDF files that I have saved (from the Viome web interface).  I think sometimes information might have gotten cut-off (and/or the screenshot did not contain all of the recommendations), since the sum don't exactly match the numbers that I recorded closer to the time when results were returned.  However, I did confirm the numbers themselves at the beginning of what was saved (and available on the GitHub page), which is what I report at the bottom of the table above.

If I test any of these recommendations and I have adverse reactions, then I will submit separate FDA MedWatch reports.  In my previous experience, when I tested a subset of a much smaller number of recommendations (blog post and MAUDE report for Theanine), I had some adverse reactions to supplements recommended based upon my genotypes.  However, I am currently hesitant to test any of these supplement recommendations from Viome.

I believe that I previously got headaches with high doses of zinc, and I noticed some amount of recommended zinc from my 4th stool sample (but not any of the other stool samples).  I noticed some other metals (like magnesium and chromium), but I hope somebody with more of a medical background might have more of an ability to guess the chances of causing harm before doing any testing.

Either way, you can certainly see noticeable changes in the recommendations, including the 1st 2 measurements from the same sample.

I believe somewhat more complicated machine-learning methodology is used for these predictions.  While I am not 100% certain, results like this wonder if there could be some sort of over-fitting.  If that is true, then the helpfulness/utility in independent samples may be lower for the machine learning model than a simpler model.  However, the only thing that I can say for certain is that there is noticeable variation without implementing any changes in my diet or medication,  and I am hesitant to test any of these predictions.

Please click here to return to the overall summary.

Change Log:

12/12/2021 - public post date

12/13/2021 - various wording revisions/corrections

12/14/2021 - change wording for last change log entry; additional revision

12/29/2021 - add thryve/Ombre food recommendations

5/2/2022 - modify title to match new main post (even though I am currently only planning to update GitHub with details for the Viome sample for my 6th paired colleciton)