A reader on Facebook requested this data (since I am likely the only one that has the data that can speak of it). Here’s the charts – Have fun interpreting
What the Kefir is in Kefir?
A reader message me about Kefir. My usual response is “You do not know what you are getting”. While for a person with near normal health, it likely does some good (keeping with the concept of hygiene hypothesis), this is not so clear for more severe dysbiosis of the gut.
“Kefir grains consist of complex symbiotic mixtures of bacteria and yeasts, and are reported to impart numerous health-boosting properties to milk and water kefir beverages. ” [2022]
Which bacteria could be in Kefir
There is no legal requirement to report the name of the bacteria in the kefir, nor the genus, species or strains. Each batch may have a significantly different mixture of bacteria. You want live and active cultures? Go to the forest and eat a spoonful of dirt!!
What has been seen in Kefir?
From this study: A Big World in Small Grain: A Review of Natural Milk Kefir Starters, 2020 we see the following reported:
- Acetobacter
- pasteurianus
- not classified species
- Dekkera
- anomalus
- bruxellensis [2022]
- Enterobacter
- not classified species
- Kazachstania
- exigua
- not classified species
- Kluyveromyces
- marxianus
- Lactobacillus
- Lactococcus
- lactis
- Lentilactobacillus
- hilgardii [2022]
- Leuconostoc
- pseudomesenteroides [2022]
- not classified species
- Liquorilactobacillus
- satsumensis [2022]
- Nauvomozyma
- not classified species
- Oenococcus
- sicerae [2022]
- Saccharomyces
- cerevisiae
- not classified species
And the list grows every year (look at the number of 2022 citations above).
Water vs Milk Kefir?
” in this study, the variety of WK grain microbial consortia was wider than that of MK grains, and this significantly affected the resultant WK products.” Water kefir grains vs. milk kefir grains: Physical, microbial and chemical comparison [2022]
And from A comparison of milk kefir and water kefir: Physical, chemical, microbiological and functional properties [2021] “The two different fermented beverages produced from these grains have different physical and chemical characteristics and different microbiological composition.”
From Milk Kefir to Water Kefir: Assessment of Fermentation Processes, Microbial Changes and Evaluation of the Produced Beverages [2022] ” It is indeed reported that kefir grains may adapt to new available carbon sources affecting their granulation and the microbial growth on them, as well as the microbial characteristic of the final beverage.”
Bottom Line
Kefir is not a precise product, even in the vaguest terms. I am bias to juggling as few balls at a time as possible… Kefir feels like working with all of the balls in an IKEA kids ball room. It’s spinning the barrel of a bacteria roulette — especially since the same product from the same manufacturer may be different in the next batch. For grown at home kefir, the variability will be a lot higher.
Follow up Analysis for ME/CFS (After COVID)
Foreword – and Reminder
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.
The Next Episode of the Story
This person’s early analysis is at IBS + BioNTech COVID Vaccine -> ME/CFS? He forwarded these notes:
- I have done everything as planned since your first review
- I maybe went up from 15% to 20%. I was able to reintroduce some new activities, but still am lying in bed most of the time. Also taking piracetam seems to help.
- I still won’t be able to do the analysis myself.
- COMMENT: ME/CFS patients are a priority for me because I personally understand their brain fog and cognitive impairments from past experiences.
- I have had COVID in the meantime in case that matters for your analysis, but I did not notice any changes afterwards.
Analysis
Given the recent post for another ME/CFS person who had COVID too, with the result that their microbiome became a good match for long COVID and a poor match for ME/CFS, this was my first question. Fortunately, the sample was done via Biomesight, he did not needed with FASTQ files and transferring them. To keep the story short, I looked at his shifts compared to annotated sampled and compare to literature from the US National Library of Medicine nothing shifted between the samples. There is no shift towards Long COVID from ME/CFS in this case.
Comparing Samples
I do not know the answers. I have a model. Models often need adjustments so comparing samples (for better or worst) in a consistent manner is part of my learning process.
First thing we see a dramatic change with rare bacteria being seen much more often and common bacteria less often. There are more genus seen (184 vs 141) and more Species (230 vs 161) but this may be due the better sample reads in the latest sample (82,102 reads versus 55,117 reads).
Percentile | Latest Genus | Latest Species | Earlier Genus | Earlier Species |
0 – 9 | 47 | 65 | 2 | 4 |
10 – 19 | 23 | 27 | 10 | 16 |
20 – 29 | 19 | 16 | 16 | 11 |
30 – 39 | 13 | 17 | 12 | 13 |
40 – 49 | 15 | 18 | 13 | 14 |
50 – 59 | 16 | 17 | 23 | 32 |
60 – 69 | 15 | 22 | 14 | 19 |
70 – 79 | 17 | 22 | 13 | 14 |
80 – 89 | 13 | 16 | 24 | 22 |
90 – 99 | 6 | 10 | 14 | 16 |
Average | 18.4 | 23.0 | 14.1 | 16.1 |
Std Dev | 11.0 | 15.4 | 6.2 | 7.4 |
- Hawrelak’s criteria was 95.6%ile for both samples.
- Potential Medical Condition dropped from 7 to 1. With Obesity being in common.
- Bacteria deemed Unhealthy increased from 9 to 17. With Collinsella, Dorea, Prevotella bivia, Prevotella copri, Rickettsia, Serratia and various Streptococcus being added and Mogibacterium disappearing. This may be a side-effect of the better sample.
- Kegg Probiotics
- The maximum value went down from 18.45 to 4.61 (indicating less compound are an extremely low level).
- There were 6 compound listed before and it dropped to just 1 (Aromatic aldehyde) when using 1% filtering level.
- There were 18 compound listed before and it dropped to just 1 when using 5% filtering level.
- There were 19 compound listed before and it dropped to just 3 when using 10% filtering level.
- The maximum value went down from 18.45 to 4.61 (indicating less compound are an extremely low level).
Was there improvement? Despite the potential confusion because of sample quality we had 3 indicators of improvement:
- Significantly less matches to known medical conditions profiles
- Significantly less compound that the person appears to be low in (using data derived from Kyoto Encyclopedia of Genes and Genomes )
- The person feeling subjectively better and doing more activities
Most of the other measures are the same or difficult to interpret. There is one possible concern, the high levels of Prevotella copri is an indicator of mycotoxin, typically from moulds and fungi. Considering that the time between the samples was winter with close windows and heating — there could be an environment issues here – so lots of fresh air may be good.
Over to Suggestions
There are various algorithms to suggesting probiotics, the strongest results are for:
- From KEGG
- At 10% Filtering: Prescript-Assist®/SBO Probiotic – and I would be find substituting Equilibrium as an alternative here.
- At 1%, 5% – any of the following (remember we are short of one compound only and all of there produces it)
- I ran a few ways of picking bacteria based on Bacteria (and not genes) and lactobacillus casei kept was the top in the consensus report (overall and in terms of probiotics)
Doing my usual consensus building
- Use JasonH (15 Criteria) – 7 bacteria
- Standard Lab Ranges (+/- 2 Std Dev) – 6 bacteria
- Box Plot Whisker – 48 bacteria
- Kaltoft-Moltrup Normal Ranges – 56 bacteria
- Percentile in top or bottom 10 % – 66 bacteria
Among the top items are ones that are supported for ME/CFS in studies, including:
- Cacao [2010]
- lactobacillus casei [2009] [2018]
- resveratrol (grape seed/polyphenols/red wine) [2011]
- magnesium [many, going back to the Lancet in 1991 ]
- Cyanocobalamin (Vitamin B-12) [2015 et al]
- folic acid,(supplement Vitamin B9) [2015 et al]
- zinc [2021]
- melatonin supplement [2021]
Unfortunately, some of the items have no studies. Given that the suggestions are based solely on bacteria with no knowledge of the diagnosis, the convergence with the literature suggests that the suggestions are very appropriate. Two different roads came to the same conclusion. In data science this is sometimes called “cross validation”. In Scotland, “O ye’ll tak’ the high road, and I’ll tak’ the low road,
And I’ll be in Scotland a’fore ye,”
I looked at the antibiotic list for the latest sample and the top two are typically used for ME/CFS:
- metronidazole [British Medical Journal] [Patient Survey]
- macrolide antibiotics [2006] [2011]
And interesting that several others often used are NOT recommended: azithromycin (which is a macrolide ?!?), minocycline [2021], fluoroquinolone, doxycycline.
ME/CFS is a heterogeneous condition with a wide variety of microbiome dysfunctions. I believe that using the microbiome to target the best candidate antibiotics is the rational way to proceed.
Questions and Answers
- Question: Sadly I do not tolerate chocolate, but I will try it out again.
- Answer: These are suggestions, do only what you are comfortable with. Nothing is required. The chocolate issue is interesting, my daughter does not tolerate most chocolates, she discovered that it was the type of sugar (i.e. made with liquid sugar / liquid glucose — adverse reaction) made with solid sugar — happiness. See Health effects of glucose syrup
- If you try again, you may wish to determine the type of sugar actually being used first.
- Question: Is there no avoid list?
- Answer: Yes, in the download, any item with a NEGATIVE value in the priority is an avoid
- Question: Is 1 capsule of Equilibrium per day really enough?
- Answer: I honestly do not know. There is no literature to work from. If you take more, than separate them (i.e. 12 hrs apart)
- Question: It seemed whenever I took turmeric that I was getting more nervous and anxious. Still take it now and then?
- Answer: As above, do only what you are comfortable with — there are hundreds of items listed. Anxiety is contrary to the effects of turmeric / curcumin reported in the literature [2021] [2019] [2018] [2017]. If turmeric is causing die-off of bacteria that causes vascular constriction, that would result in anxiety. If you tolerate aspirin or niacin (flushing type), then try taking those with the turmeric.
ME/CFS x COVID :- Long COVID instead
Foreword – and Reminder
I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”. I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.
I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships.
Backstory of Latest Sample
In light of your recent few blog posts about uploads without many microbiome shifts to work with, I was thinking this could be a beneficial walkthrough video for what seems to be the opposite.
I was doing pretty well on my antibiotic rotations (mainly tetracycline two weeks on, two weeks off since Aug of 2021) until Feb or so when I had a major crash / flare that I’m still suffering from.
I did have a very mild case of Covid in mid January that felt no worse than a regular cold.
But from what little I can parse from this sample, it seems I may be struggling with long Covid. I say little, because my brain fog is extremely dense.
And all of the results I’m getting for this sample via your site seem so drastically different from what has been going on over the last 7 years (my oldest sample is from 2015).
Comparison of samples
This person has samples going back to 2015 using uBiome. Unfortunately for comparison we need to keep to the same lab (why? read The taxonomy nightmare before Christmas…).
Jason Hawrelak Criteria etc
We finally see an improvement with Jason’s criteria. We also may be seeing more diversity with the increase of Genus and Species found. I say may because this could be a side-effect of a low raw count in some samples.
Date | Percentile | Unhealthy Bacteria | Genus | Species |
2022-04-11 | 98.8 %ile | 8 | 220 | 303 |
2022-01-11 | 89 %ile | 11 | 89 | 141 |
2021-03-09 | 89 %ile | 8 | 108 | 153 |
2020-05-27 | 89% ile | 7 | 153 | 223 |
I decided to look at the raw reads (which are captured from Thryve and Biomesights)
Sample Date | Raw Reads |
5/27/2020 | 43311 |
3/9/2021 | 29247 |
1/11/2022 | 17630 |
4/11/2022 | 153194 |
This lead me to look at what typical raw counts are from Ombre/Thryve
To find the raw counts for your sample, open the csv and look for this line
taxon_id,rank,name,parent,count,...
2,kingdom,Bacteria,,45341,...
What is the consequences? It means that rarer bacteria may be ghost-like, appearing or disappearing from sample to sample. This adds let one more layer of fuzziness to doing analysis and generating suggestions.
First Question: ME/CFS or Long COVID microbiome or both?
This person uploaded the Ombre FASTQ files to BiomeSight so I may used data from the Long COVID study there. Both condition present similarly, I am curious to see if we have sufficient reference data to decide which condition is a better match.
Rank | Name ( 👍 match National Library of Medicine Citations for Long COVID) | Your value | Percentile |
---|---|---|---|
clade | FCB group | 25060 | 5.9 |
class | Bacteroidia 👍 | 22700 | 4.1 |
class | Betaproteobacteria 👍 | 1510 | 13.3 |
class | Spirochaetia | 140 | 86.1 |
family | Bacteroidaceae 👍 | 20690 | 5.4 |
family | Eubacteriaceae 👍 | 650 | 30.9 |
genus | Caloramator 👍 [family] | 1520 | 68.5 |
genus | Nostoc | 20 | 30.6 |
genus | Roseburia 👍 | 12230 | 34.6 |
norank | Eubacteriales incertae sedis 👍 [family] | 60 | 11.9 |
order | Bacteroidales 👍 | 22700 | 4.1 |
order | Burkholderiales | 1470 | 12.9 |
phylum | Spirochaetes | 140 | 86 |
species | Butyrivibrio proteoclasticus 👍[genus] | 10 | 3.6 |
species | Faecalibacterium prausnitzii 👍 | 301950 | 98.5 |
species | Roseburia faecis 👍 [family] | 620 | 24.3 |
Rank | Name (👍 matches National Library of Medicine Citations for Chronic Fatigue Syndrome | Your value | Percentile |
---|---|---|---|
family | Halanaerobiaceae | 20 | 37 |
genus | Anaerovibrio | 570 | 65.1 |
genus | Finegoldia | 20 | 7 |
genus | Halanaerobium | 20 | 31.8 |
genus | Leuconostoc | 10 | 3.2 |
genus | Pediococcus | 10 | 3.9 |
order | Syntrophobacterales | 10 | 3.9 |
species | Anaerotruncus colihominis | 850 | 60.2 |
species | Anaerovibrio lipolyticus | 570 | 65.4 |
species | Bacteroides acidifaciens 👎[sibling] | 10 | 0.9 |
species | Bacteroides fluxus 👎[sibling] | 20 | 9.8 |
species | Clostridium akagii 👎[sibling] | 10 | 5.5 |
species | Clostridium cadaveris 👎[sibling] | 10 | 3.8 |
species | Finegoldia magna | 10 | 1.8 |
species | Odoribacter denticanis 👍[sibling] | 10 | 2.5 |
species | Prevotella copri 👍[sibling] | 10 | 0.6 |
We have concurrent matches for both both conditions
- Finegoldia magna, which is not reported in the literature
- The table above hints that he is at present much closer to Long COVID than ME/CFS.
I am not sure about the political correctness of saying “Congrads! You no longer have ME/CFS, you have Long COVID!” is what the microbiome reads like.
What is interesting is that the microbiome constantly shifts/evolves, with Long COVID the infection is constant and the duration since the infection is short — hence less evolution of the microbiome over all patients. With ME/CFS the triggering infection possibilities are huge with 20, 30, 40 years of evolution of the microbiome — hence patterns are diffused by time and original infection.
Looking at deficiency of compounds produced, we see a dramatic drop from the previous sample suggesting that bacteria are getting the needed inputs for correct functioning.
Sample Date | 1%ile | 5%ile | 10%ile |
5/27/2020 | 4 | 14 | 60 |
3/9/2021 | 2 | 14 | 16 |
1/11/2022 | 197 | 233 | 244 |
4/11/2022 | 6 | 28 | 52 |
Where do we go from here
I am going to do consensus, but do only 3 items:
- Hand Picked Bacteria using the study in progress data using BiomeSight (16 bacteria)
- Using US National Library of medicine filter to Long COVID using BiomeSight and Box-Whiskers (14 bacteria)
- Using US National Library of medicine filter to Long COVID using Ombre and Box-Whiskers (14 bacteria)
The consensus is below as a download. Since antibiotics are being prescribed at present, I included that in the suggestions criteria.
Some highlights
- linseed(flaxseed) is #1 by a wide margin
- thiamine hydrochloride (vitamin B1), pyridoxine hydrochloride (vitamin B6), vitamin b3 (niacin), vitamin b7 biotin (supplement) (vitamin B7), Vitamin C (ascorbic acid)
- Cyanocobalamin (Vitamin B-12) and folic acid,(supplement Vitamin B9) has less predicted impact
- B Complex appears negative, as does Vitamin E, D, K2 and A
- glycyrrhizic acid (licorice) – which means to me, usually means Spezzatina
- Then a stack of antibiotics and prescription items (checking items listed on this page)
- I checked the typical ME/CFS ones
- minocycline (antibiotic)s -250 AVOID
- tetracycline (antibiotic)s -207 AVOID
- fluoroquinolone (antibiotic)s -29 AVOID
- rifaximin (antibiotic)s – 134 AVOID
- azithromycin,(antibiotic)s +49
- fludrocortisone acetate,(prescription) +353
- gabapentin,(prescription) +353
- naltrexone hydrochloride dihydrate,(prescription) +353
- nimodipine,(prescription) + 353
- pentoxifylline,(prescription) + 353
- magnesium -180.2 If supplementing, pause
- melatonin supplement + 266.3
- I checked the typical ME/CFS ones
Why did I focus on the ME/CFS ones? Path of least resistance for the prescribing MD – the MD accepts ME/CFS and thus will have low resistance to prescriptions often used for ME/CFS. Asking for them for Long COVID could get rolling of eyes…. As always, we are using these off-label for their computed microbiome effect. For the prescription items, I would suggest rotation (one item for 10 days, then a 0-10 day break, then another item (or repeat if limited to one item).
Long Covid Study – VERY early data
This post is intended for researchers by pointing to bacteria whose genetics are likely significant for long COVID. The raw data is below. Preliminary z-scores indicated that they are significant (Pr < 0.01) and no filtering has occurred for False Detection Rate. Users are advised to perform their own statistics.
Note: These results are lab-specific, using the data provided by BiomeSight.
The reference site for the study is: http://longcovid.microbiomeprescription.com/, the raw data is available at: http://citizenscience.microbiomeprescription.com/
Symptom Obs | No Symptom Obs | Lab | SymptomName |
152 | 998 | biomesight | Official Diagnosis: COVID19 (Long Hauler |
Bacteria | tax_rank | No Symptom Count | Symptom Count | No Symptom Frequency % | Symptom Frequency % |
Lactococcus | genus | 620 | 136 | 62.1 | 89.5 |
Negativicoccus | genus | 461 | 86 | 46.2 | 56.6 |
Pedobacter kwangyangensis | species | 354 | 76 | 35.5 | 50 |
Hydrogenophilaceae | family | 480 | 90 | 48.1 | 59.2 |
Nostoc | genus | 344 | 68 | 34.5 | 44.7 |
Veillonella montpellierensis | species | 517 | 94 | 51.8 | 61.8 |
Rhodothermales | order | 24 | 36 | 2.4 | 23.7 |
Gillisia limnaea | species | 586 | 114 | 58.7 | 75 |
Tetragenococcus | genus | 601 | 107 | 60.2 | 70.4 |
Gillisia | genus | 592 | 114 | 59.3 | 75 |
Paenibacillaceae | family | 771 | 133 | 77.3 | 87.5 |
Tetragenococcus halophilus | species | 101 | 55 | 10.1 | 36.2 |
Rhodothermia | class | 24 | 36 | 2.4 | 23.7 |
Hydrogenophilalia | class | 479 | 90 | 48 | 59.2 |
Bifidobacterium breve | species | 431 | 82 | 43.2 | 53.9 |
Thermosediminibacterales | order | 72 | 55 | 7.2 | 36.2 |
Bifidobacterium choerinum | species | 654 | 117 | 65.5 | 77 |
Bifidobacterium longum | species | 715 | 129 | 71.6 | 84.9 |
Hydrogenophilales | order | 480 | 90 | 48.1 | 59.2 |
Hydrogenophilus | genus | 430 | 81 | 43.1 | 53.3 |
Rhodothermaeota | phylum | 24 | 36 | 2.4 | 23.7 |
Bacteria | tax_rank | No Symptom Mean | Symptom Mean | No Symptom StdDev | Symptom Std Dev | Symptom Obs | No Symptom Obs |
Porphyromonas bennonis | species | 317 | 713 | 1435.9 | 2361.1 | 43 | 288 |
Clostridia | class | 618307 | 487672 | 199278.7 | 150542.1 | 152 | 995 |
Insolitispirillum peregrinum | species | 8259 | 12198 | 15534.5 | 21217.5 | 96 | 593 |
Sphingobacterium | genus | 1125 | 1590 | 1432.3 | 1764.9 | 150 | 938 |
Sphingobacteriia | class | 29634 | 42219 | 29577.8 | 39150.6 | 152 | 995 |
Lelliottia amnigena | species | 922 | 205 | 2676.6 | 592.2 | 38 | 301 |
Roseburia faecis | species | 13441 | 7122 | 19243.9 | 8796.5 | 152 | 978 |
Leptolyngbyaceae | family | 67 | 266 | 152.6 | 1051.5 | 35 | 232 |
cellular organisms | norank | 994054 | 988395 | 9012.5 | 5355.3 | 152 | 996 |
Opitutae | class | 142 | 315 | 447.2 | 759.3 | 79 | 604 |
Caloramator mitchellensis | species | 8162 | 13580 | 19886.9 | 27582.2 | 145 | 925 |
Leptolyngbya | genus | 67 | 266 | 152.2 | 1051.5 | 35 | 230 |
Sphingobacteriaceae | family | 25959 | 37362 | 26540.8 | 35832.1 | 152 | 994 |
Cytophagia | class | 948 | 2232 | 2899 | 11229.4 | 150 | 960 |
Aphanizomenonaceae | family | 222 | 124 | 374.4 | 152.8 | 108 | 653 |
Betaproteobacteria | class | 28271 | 21480 | 26518.8 | 17465.6 | 151 | 993 |
Pseudanabaenales | order | 110 | 230 | 274 | 973.3 | 41 | 297 |
Pseudanabaenaceae | family | 68 | 230 | 141.9 | 973.4 | 41 | 297 |
Spirosomaceae | family | 730 | 2282 | 3089.7 | 12178.6 | 127 | 791 |
Tenericutes | phylum | 2507 | 6395 | 5922.1 | 16841.5 | 147 | 957 |
Puniceicoccaceae | family | 139 | 301 | 443 | 741.7 | 79 | 599 |
Sutterella wadsworthensis | species | 6418 | 10491 | 10990.4 | 13485.8 | 108 | 636 |
Eubacteriales | order | 614586 | 482370 | 199230.3 | 150329.8 | 152 | 995 |
Dolichospermum | genus | 217 | 125 | 371.1 | 153.2 | 107 | 646 |
Dialister invisus | species | 4796 | 8035 | 9328.1 | 10909.7 | 105 | 622 |
Mollicutes | class | 2507 | 6395 | 5922.1 | 16841.5 | 147 | 957 |
Coprococcus catus | species | 1256 | 868 | 1371 | 850 | 136 | 804 |
Treponemataceae | family | 1604 | 5448 | 13832.3 | 32843.8 | 37 | 235 |
Dorea formicigenerans | species | 1434 | 914 | 1591 | 1112.9 | 142 | 902 |
Bacteroides eggerthii | species | 8545 | 15385 | 21533.5 | 30777.7 | 57 | 394 |
Cytophagales | order | 948 | 2232 | 2899 | 11229.4 | 150 | 960 |
Spirochaetales | order | 1551 | 5448 | 13579.1 | 32843.8 | 37 | 244 |
Terrabacteria group | clade | 721047 | 521084 | 231350.3 | 165314.8 | 152 | 996 |
Cytophagaceae | family | 785 | 2189 | 2917.9 | 11452.3 | 144 | 907 |
Caloramator | genus | 8859 | 14126 | 20084.9 | 27641.6 | 151 | 972 |
Spirochaetes | phylum | 903 | 3743 | 10120 | 27201.3 | 54 | 441 |
Emticicia | genus | 784 | 2553 | 3286.6 | 12937.6 | 112 | 693 |
Leptolyngbya laminosa | species | 66 | 266 | 152.8 | 1051.5 | 35 | 228 |
Burkholderiales | order | 27963 | 21305 | 26363.5 | 17380.9 | 151 | 993 |
Pedobacter | genus | 8642 | 13041 | 10106 | 17703.6 | 151 | 980 |
Burkholderiaceae | family | 361 | 197 | 598.7 | 409.8 | 135 | 886 |
Eubacteriales incertae sedis | norank | 375 | 161 | 837.1 | 256.6 | 139 | 895 |
Butyrivibrio proteoclasticus | species | 496 | 203 | 1108.1 | 350.5 | 89 | 657 |
Dolichospermum curvum | species | 201 | 97 | 393.5 | 137.7 | 87 | 505 |
Desulfovibrio fairfieldensis | species | 720 | 263 | 1789.3 | 508 | 43 | 284 |
Bacteroidaceae | family | 300654 | 238983 | 181167 | 141573.4 | 152 | 995 |
Bacteroidia | class | 426327 | 369089 | 188681.8 | 168273.3 | 152 | 995 |
Acidaminococcus | genus | 984 | 2304 | 4664.6 | 9773.8 | 105 | 707 |
Bacteroidales | order | 426327 | 369089 | 188681.8 | 168273.3 | 152 | 995 |
Spirochaetaceae | family | 1598 | 5448 | 13805.7 | 32843.8 | 37 | 236 |
Porphyromonas asaccharolytica | species | 314 | 1553 | 970.4 | 8744.8 | 43 | 238 |
Erysipelotrichaceae | family | 6385 | 3475 | 10831.8 | 4414.9 | 152 | 993 |
Roseburia | genus | 30033 | 19201 | 34050.4 | 17905.1 | 152 | 991 |
FCB group | clade | 448905 | 391426 | 199421.4 | 186334 | 152 | 996 |
Dialister | genus | 4668 | 7912 | 9234.3 | 10868.1 | 107 | 646 |
Lachnospiraceae | family | 219269 | 176420 | 109155.3 | 81234.8 | 152 | 995 |
Cerasicoccus | genus | 249 | 650 | 650.7 | 1037.4 | 34 | 250 |
Sphingobacteriales | order | 29634 | 42219 | 29577.8 | 39150.6 | 152 | 995 |
Eubacteriaceae | family | 3543 | 1555 | 7588.1 | 4328.8 | 152 | 989 |
Synechococcaceae | family | 68 | 230 | 141.6 | 973.4 | 41 | 299 |
Acidaminococcus intestini | species | 300 | 799 | 1051.3 | 2292.1 | 37 | 222 |
Acholeplasma hippikon | species | 426 | 812 | 1052.5 | 2058.9 | 35 | 260 |
Treponema | genus | 1604 | 5448 | 13832.3 | 32843.8 | 37 | 235 |
Firmicutes | phylum | 657764 | 502820 | 205065.5 | 155846.7 | 152 | 997 |
Caloramator indicus | species | 373 | 1065 | 2091.5 | 3540.5 | 44 | 373 |
Faecalibacterium prausnitzii | species | 100109 | 141766 | 77192.1 | 87778 | 152 | 986 |
Spirochaetia | class | 903 | 3743 | 10119.8 | 27201.3 | 54 | 441 |
Prevotella stercorea | species | 5077 | 10010 | 18648.5 | 25185.5 | 54 | 406 |
Insolitispirillum | genus | 8259 | 12198 | 15534.5 | 21217.5 | 96 | 593 |
Emticicia oligotrophica | species | 785 | 2553 | 3291.1 | 12937.7 | 112 | 691 |
Lelliottia | genus | 922 | 205 | 2676.6 | 592.2 | 38 | 301 |
Sphingobacterium bambusae | species | 316 | 506 | 452.1 | 1018.8 | 140 | 825 |
Bacteria to Hand-Pick for Autism with Ombre/Thryve samples
Some recent work has identified bacteria that are associated with Autism. For a summary of method, see this post. The following are the list of bacteria seen with Ombre/Thryve samples that are annotated with Autism. There are not sufficient samples yet for specific autism characteristics – so please check your uploaded samples and update the symptoms.
Note the list is Ombre/Thryve specific and cannot be applied to other microbiome reports. There is also Bacteria to Hand-Pick for Autism with Biomesight samples
These are bacteria that you want to reduce (with one caveat — the suggestions algorithm requires the percentile to be 50%ile or more). How to hand pick them? See below the list.
Note: you may only have a few of these. They are shown in the same sequence as seen on Microbiome Tree. The LAST item is what was found to be statistically significant.
- Proteobacteria Gammaproteobacteria Gammaproteobacteria incertae sedis
- Pectobacteriaceae Brenneria Brenneria alni
- Enterobacterales Enterobacteriaceae Klebsiella/Raoultella group
- Enterobacteriaceae Klebsiella/Raoultella group Klebsiella
- Alphaproteobacteria Hyphomicrobiales Devosiaceae
- Alphaproteobacteria Hyphomicrobiales Hyphomicrobiaceae
- Betaproteobacteria Neisseriales Neisseriaceae
- Betaproteobacteria Rhodocyclales Zoogloeaceae
- Burkholderiales Burkholderiaceae Burkholderia
- Sutterellaceae Sutterella Sutterella wadsworthensis
- Bacteria Proteobacteria delta/epsilon subdivisions
- Proteobacteria delta/epsilon subdivisions Deltaproteobacteria
- Desulfovibrionaceae Desulfovibrio Desulfovibrio piger
- PVC group Verrucomicrobia Opitutae
- Flavobacteriia Flavobacteriales Cryomorphaceae
- Flavobacteriales Cryomorphaceae Cryomorpha
- Cryomorphaceae Cryomorpha Cryomorpha ignava
- Bacteroidetes/Chlorobi group Bacteroidetes Sphingobacteriia
- Bacteroidetes Sphingobacteriia Sphingobacteriales
- Sphingobacteriia Sphingobacteriales Sphingobacteriaceae
- Bacteroidales Porphyromonadaceae Porphyromonas
- Clostridia Eubacteriales Defluviitaleaceae
- Eubacteriales Defluviitaleaceae Defluviitalea
- Clostridia Eubacteriales Proteinivoraceae
- Eubacteriales Proteinivoraceae Anaerobranca
- Eubacteriales Lachnospiraceae Butyrivibrio
- Lachnospiraceae Butyrivibrio Butyrivibrio crossotus
- Lachnospiraceae Butyrivibrio Butyrivibrio fibrisolvens
- Eubacteriaceae Eubacterium Eubacterium coprostanoligenes
- Eubacteriales Peptococcaceae Desulfosporosinus
- Eubacteriales Clostridiaceae Alkaliphilus
- Clostridiaceae Clostridium Clostridium oryzae
- Clostridiaceae Clostridium Clostridium lundense
- Clostridiaceae Clostridium Clostridium intestinale
- Eubacteriales Clostridiaceae Lactonifactor
- Eubacteriales Clostridiaceae Caloramator
- Eubacteriales Eubacteriales incertae sedis Natranaerovirga
- Eubacteriales Eubacteriales incertae sedis [Bacteroides] pectinophilus
- Clostridia Thermosediminibacterales Thermosediminibacteraceae
- Terrabacteria group Firmicutes Firmicutes sensu stricto incertae sedis
- Firmicutes Firmicutes sensu stricto incertae sedis Hydrogenispora
- Selenomonadales Sporomusaceae Anaerospora
- Sporomusaceae Anaerospora Anaerospora hongkongensis
- Selenomonadales Selenomonadaceae Megamonas
- Bacilli Bacillales Listeriaceae
- Bacillales Listeriaceae Listeria
- Bacilli Bacillales Bacillales incertae sedis
- Bacillales Bacillales incertae sedis Bacillales Family X. Incertae Sedis
- Bacillales incertae sedis Bacillales Family X. Incertae Sedis Thermicanus
- Lactobacillales Lactobacillaceae Limosilactobacillus
- Lactobacillaceae Limosilactobacillus Limosilactobacillus fermentum
- Lactobacillales Lactobacillaceae Liquorilactobacillus
- Lactobacillaceae Liquorilactobacillus Liquorilactobacillus vini
- Cyanobacteria/Melainabacteria group Cyanobacteria Oscillatoriophycideae
- Cyanobacteria Oscillatoriophycideae Oscillatoriales
- Bacteria Terrabacteria group Chloroflexi
- Terrabacteria group Chloroflexi Chloroflexia
- Bacteria Terrabacteria group Actinobacteria
- Terrabacteria group Actinobacteria Actinomycetia
- Actinobacteria Actinomycetia Bifidobacteriales
- Actinomycetia Bifidobacteriales Bifidobacteriaceae
- Bifidobacteriales Bifidobacteriaceae Bifidobacterium
- Bifidobacteriaceae Bifidobacterium Bifidobacterium bifidum
- Bifidobacteriaceae Bifidobacterium Bifidobacterium asteroides
- Bifidobacteriaceae Bifidobacterium Bifidobacterium subtile
- Actinomycetia Propionibacteriales Nocardioidaceae
- Actinobacteria Coriobacteriia Coriobacteriales
- Coriobacteriia Coriobacteriales Coriobacteriaceae
- Coriobacteriales Coriobacteriaceae Senegalimassilia
- Coriobacteriaceae Senegalimassilia Senegalimassilia anaerobia
- Tenericutes Mollicutes Entomoplasmatales
- Mollicutes Entomoplasmatales Spiroplasmataceae
- Entomoplasmatales Spiroplasmataceae Spiroplasma
- Tenericutes Mollicutes Anaeroplasmatales
- Mollicutes Anaeroplasmatales Anaeroplasmataceae
- Anaeroplasmatales Anaeroplasmataceae Anaeroplasma
- Acidobacteria Acidobacteriia Acidobacteriales
- Acidobacteriia Acidobacteriales Acidobacteriaceae
- cellular organisms Bacteria Elusimicrobia
- Bacteria Elusimicrobia Elusimicrobia
To make a selection, just check the appropriate checkboxes.
Using Frequency of Detection in Samples
This is a technical note. Recently I came across this doing analysis of Long COVID data.
Thermosediminibacterales(order)
- With Long COVID: 55/152 samples or 36.2%
- Reference (excluding Long COVID samples): 72/996 or 7.2%
This present an interesting insight on possible blinkered thinking when seeing such data. Some examples are:
- Don’t brother looking —
- It’s a rare bacteria (just 7% of people have it…)
- It does not occur in most Long COVID patients, not interesting
- I computed the means and standard deviations, and the difference is not sufficiently significant, so do not mention
My take is simple, it occurs FIVE times more often. I view microbiome dysbiosis are the result of the “perfect storm” or should I say “imperfect storm”. The wrong concentrations of compounds and enzymes coming together from a host of bacteria. With that dysbiosis view, a rare bacteria oddity like this, hints at a subset. This is contrary to the common view that dysbiosis is caused by a single or small group of bacteria and you can make simple either/or decisions based on their presence or lack of presence.
In the case of the long COVID data, I observed some odd (by traditional thinking) situation. A few examples:
- A 10 fold difference of frequency with the higher frequency having a higher average – the traditional expectation. More of this bacteria is growing, hence we find more often.
- A 10 fold difference of frequency with the higher frequency having a lower average, with statistical significance. This is what stopped me to re-examine my perspective, including the need to re-evaluate some blinkers.
The natural question: Determining Significance!
For most people dealing with biological data, presence or non-presence is typically a dependent factor. For example, here are some means for bacteria with the outcome being Crohn’s disease detected or not (the control case). The data will often be dropped into logistic regression.
I went back to flipping bias coins thinking and raise a beer to the memory of Bernoulli. In the above case, the expected bias is that 7.2% of the time the coin will land with a head. We try a new coin and toss it 152 times and get heads 36.2% of the time…
The hypothesis to test is whether the coin is equivalent?
- The standard deviation of the population is a simple calculation – except we need to change .50 to .362 in formula below. (P.S. The Std Dev of the population is about 1%, so a range of .342 to .382 could be tried safety)
The result is a z-score of -7.43, or clearly significant well beyond a 0.01 level. Thus the presence or lack of presence is statistically significant and should be included in any analysis (but rarely seems to be in most papers)
Using Diet Style Information
One of the goal of Microbiome Prescription is to stay true to source data / study. There are many studies that deal with a diet style or atypical food elements, like ‘high milk fat’. Below these wide sweeping terms may be concrete specific items that are reported in a different manner. A simple examples:
- Take Vitamin B2 (Riboflavin). Milk and beef are significant contributors
- Dietary intake of the water-soluble vitamins B1, B2, B6, B12 and C in 10 countries in the European Prospective Investigation into Cancer and Nutrition [2009]
- Dietary Intake and Food Sources of Niacin, Riboflavin, Thiamin and Vitamin B₆ in a Representative Sample of the Spanish Population. The Anthropometry, Intake, and Energy Balance in Spain (ANIBES) Study † [2018]
Underneath the covers of this complex microbiome engine in the human body, the impact of more beef or more milk is an increased availability of Vitamin B2.
Diets are complex concepts subject to regional interpretation. A high beef diet means more beef than a typical person… so how much is that [source]?
- If you are in China, it’s more than 1 pound of beef a month.
- If you are in Russia, it’s more than 2 pound of beef a month.
- If you are in USA, it’s more than 3.5 ounces of beef a day (so, more than a MacDonald’s Quarter Pounder every day).
- If you are in Uruguay or Argentina, it’s more than 5.5 ounces of beef a day.
When we go over to items like a Mediterranean Diet, often it can mean many things with a wide range of contents. Both of the following would meet that criteria for many people:
- One serving of cereal, two servings of citrus fruits, one servings each of eggplant, okra , green beans
- 13 servings of cereal and breads, one half apple, five servings of potatoes, 3 servings of carrots, 1 serving of onions.
The MedDiet contained three to nine serves of vegetables, half to two serves of fruit, one to 13 serves of cereals and up to eight serves of olive oil daily. It contained approximately 9300 kJ, 37% as total fat, 18% as monounsaturated and 9% as saturated, and 33 g of fibre per day.
Definition of the Mediterranean Diet; a Literature Review [2015]
The majority of studies emphasized the same key dietary components and principles: an increased intake of vegetables, wholegrains, and the preferential consumption of white meat in substitute of red and processed meat and abundant use of olive oil. However, the reporting of specific dietary recommendations for fruit, legumes, nuts, bread, red wine, and fermentable dairy products were less consistent or not reported
Differences in the interpretation of a modernized Mediterranean diet prescribed in intervention studies for the management of type 2 diabetes: how closely does this align with a traditional Mediterranean diet? [2019]
To me, a medDiet is eating traditional Greek — stuffed grape leaves, Tomato Fritters, etc with a glass of Ouzo [example] – in my younger days while I was teaching, I would have this 3-4 nights of the week.
At this point, we find that most studies involving diet deteriorates into vague hand-waving.
Can you use diet style?
This is a two sided coin. If you take recommendation for items like Luteolin, it can be translated into diet such as more celery seed, olives, blueberries. Quercetin into Cranberries and Blueberries. etc. While a high meat diet is vague — does it mean beef? pork? chicken? fish? – how much?
A logical solution is to decompose the diet into an itemized list of what the diet means by component. Then using the wonderful databases at the US Department of Agriculture develop a profile of what you are getting with this style of diet. Usually there are multiple diet suggestions, so you need to intersect them to get the true bottom line on what the diet changes should be.
Bottom Line — Use Diet Style with caution!
IMHO, it is so close to saying “Buy tech stocks for your retirement”. Without doing due diligence, you may end up with a worthless portfolio. At the bottom of the suggestions is a Flavonoid section which could be translated into food specific items.
Antibiotic Apocalypse on the Microbiome
This is an interesting case which appears to illustrate well that microbiome-agnostic prescription of antibiotics can produce horrible results. Doing a yearly 16s microbiome test will allow you to potentially negotiate with your MD to pick antibiotics that both address the MD concerns and potentially improve your microbiome as a side effect. See this post: Negotiations with your Medical Professional
My backstory:
I have used FQ antibiotics many times in the last 15 years for Chronic bacterial prostatitis..
During the last few years I was diagnosed with diverticula and had an episode of diverticulitis 3 years ago which also required antibiotics.. In the last 2 years my bloating was so severe that I was like a pregnant woman.. I am a male 40 years old.. So last July I went to the beach and caught E.Coli once again from the water or the beach.. This gave me acute infection with fever the next day.. This is where the drama starts as I ended up going to 4 different labs giving me different results and switching antibiotics for 5 months.. My gut was so bad that I’ve spend one night at the WC and another day I was stuck in traffic and I didn’t come back in time.. So embarrassing..
So January I stopped the FQs since I got a severe reaction with a set of symptoms that almost took my life.. My calf tore while being in bed, not even walking, swollen joints with fluid, tinnitus, diarrhea for 1 month, stomach ache and spasms, neuropathy, brain fog, insomnia and more..
I was sure that everything started from my gut, something triggered auto-immune along with toxicity from the drugs.. 2 months in bed.. 4 months and I barely walk with many symptoms.. What saved me initially I think was homemade Kefir I had and making myself..
Then I did the test at Biomesight and understood why and what happened.. Now I know very well that life or death starts from the gut..
Current State
First, I like to get a feel for where the microbiome is at from a high level. Looking at the usual health measures:
- Dr. Jason Hawrelak Recommendations guidance puts the person at the 35%ile, definitely in the concerning space
- On the Potential Medical Conditions Detected, 14 items were flagged, again concerning
- In the Bacteria deemed Unhealthy list, the following stood out
Name | Rank | Percentile | Count | Comment | More Info |
---|---|---|---|---|---|
[Ruminococcus] gnavus | species | 95.2 | 27410 | Not Healthy Predictor | Citation |
Anaerotruncus colihominis | species | 97.9 | 5200 | Not Healthy Predictor | Citation |
Bacteroides fragilis | species | 86.3 | 17220 | H02076 Bacteroides infection | Citation |
Looking at the distribution by frequency, nothing really stands out.
Percentile | Genus | Species |
---|---|---|
0 – 9 | 14 | 18 |
10 – 19 | 19 | 34 |
20 – 29 | 19 | 19 |
30 – 39 | 12 | 14 |
40 – 49 | 15 | 7 |
50 – 59 | 9 | 15 |
60 – 69 | 13 | 13 |
70 – 79 | 8 | 15 |
80 – 89 | 10 | 9 |
90 – 99 | 15 | 18 |
Looking at the antibiotics list taken, I went over to the Antibiotics List for MDs page for this sample. We are using this to see which antibiotics likely helped the dysbiosis of the gut to happen.
The following were the antibiotics that he had been prescribed. I put after each the positive and negative estimates from the above page. We see a -.266 for something taken for 84 days…
- IV Cipro 1 time in hospital
- Cipro oral cycles (21 days) : 5x – (0.194)
- Norfloxacin cycles (14 days) 6x (0.282)
- Levofloxacin : 10 days – no data
- Fosfomycin: 8 sachets – no impact
- Cefaclor cycle (14 days): 12x – negative impact (Take Estimate: 35.1, Avoid Estimate: 39) (0.079)
- Amoxicillin / Clavulanic Acid cycles (21 days): 4x ( – 0.266 )
- Cefixime cycle (24 days) : 1x ( – 0.114 )
- Trimethoprim / Sulfamethoxazole : 3 days (0.128) / ( – 0.604 )
- Doxycycline: 2 days ( – 0.173 )
In this case, it is clear from the data above that the antibiotics were a factor for his problems. if he must take antibiotics again (or can persuade his MD to do a trial), the best ones suggested by the Artificial Intelligence algorithms are:
- rifaximin (antibiotic)s (1)
- metronidazole (antibiotic)s (0.887)
- ampicillin trihydrate (antibiotic) (0.834)
Action Plan Going Forward
The KEGG AI Computed Probiotics had the HIGHEST VALUES that I have ever seen with the top items being, I would go for three of these (2 weeks of one, then rotate to the next, repeat): Something that lists bacillus subtilis as the first ingredient, miyarisan (jp) / miyarisan, something that is just lactobacillus plantarum (i.e. 299v)
For supplements, we have (even at 20%) a short list. Usually supplements can be taken consistently.
- beta-alanine – Percentile: 5.2
- Glycine – Percentile: 3
- L-Cysteine – Percentile: 10.4
- L-glutamine – Percentile: 15.5
- Magnesium – Percentile: 3.7
- Molybdenum – Percentile: 0.9
Building Consensus Suggestions
Remember, no one knows how to pick the best bacteria to target. We apply multiple criteria and then work from what is agreed upon with the different approaches (i.e. consensus).
- Use JasonH (15 Criteria) – 11 bacteria picked (and the same for the other ones at the top of this list)
- Standard Lab Ranges (+/- 2 Std Dev) – 24 bacteria picked
- Box Plot Whisker – 58 bacteria picked
- Kaltoft-Moltrup Normal Ranges – 89 bacteria picked
- Percentile in top or bottom 10 % – 101 bacteria picked
The consensus list is long with 534 items (typical). My main take away
- Eliminate/Avoid the following:
- high red meat / high-protein diet / high animal protein diet /high saturated milk fat diet/ milk-derived saturated,fat / animal-based diet / rare meat / fat
- Dairy related: Cottage Cheese / Kombucha (home-made) / enriched butter diet
- low energy diet/ calorie restriction / low carbohydrate diet / gluten-free diet
- stevia / saccharin / chitosan,(sugar) / high sugar diet
- broccoli / Sauerkraut
- alcoholic beverages
- high red meat / high-protein diet / high animal protein diet /high saturated milk fat diet/ milk-derived saturated,fat / animal-based diet / rare meat / fat
- Take or Increase:
- inulin (prebiotic) / chicory (prebiotic) / oligofructose (prebiotic)
- high fiber diet / fruit/legume fibre / brown rice / whole-grain barley /whole-grain wheat / barley,oat
- walnuts
- Cacao
- Vitamins and Supplements
- Vitamin B-12 (Cyanocobalamin)
- grape polyphenols / grapes (but no wine!) / polyphenols
- from above: magnesium (found in consensus too with positive value), molybdenum and from consensus: selenium and zinc. But no iron supplements.
- Probiotics
- All of the items cited above are on the to take list (and many more), but the ones above are IMHO, “double blessed”
- A reminder, the items are based on the term that various studies used. In some cases, there can appear to be contradictions. In some cases this could be due to what was measure or not measured in the study as well as sample size. We do not know what is “right” when this happens, it drops into a state called “indeterminate”. There are some of those here, but also some very clear items like high fat beef.
Bottom Line
Given the severity of this person, I suggest trying suggestions for 2-3 months and then gets retested. I expect significant changes — but that is likely just a course correction. We need to do more course corrections to get back to a safe harbor.
ALWAYS REVIEW WITH YOUR MEDICAL PROFESSIONAL BEFORE STARTING
Bacteria Shifts Seen in Chronic Fatigue Syndrome
Using novel technics for my earlier post Bacteria Shifts Seen in Long COVID caused me to look at it’s sibling: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Since we have a much large sample size, we can get more rigorous and be lab specific (see The taxonomy nightmare before Christmas…). The result are the three tables below. The criteria for shift was a difference of 4 percentile or more.
Biomesight
Tax_name | Tax_rank | Sample Frequency of Detection | Population Frequency of Detection | Shift |
Phocaeicola plebeius | species | 12 | 5.2 | Lower |
Oscillatoriales | order | 13.5 | 5.1 | Lower |
Bacteroides gallinarum | species | 13.5 | 4.5 | Lower |
Aerococcaceae | family | 14.1 | 4.5 | Lower |
Phocaeicola coprocola | species | 15.1 | 5.4 | Lower |
Desulfovibrio | genus | 22.9 | 8.8 | Lower |
Collinsella | genus | 25 | 8.7 | Lower |
Hathewaya | genus | 30.2 | 10.6 | Higher |
Bacteroides ovatus | species | 30.2 | 10.6 | Higher |
Anaerotruncus colihominis | species | 30.2 | 10.4 | Higher |
Bacteroides rodentium | species | 30.2 | 10.6 | Higher |
OmbreLabs / Thryve
Tax_name | Tax_rank | Sample Frequency of Detection | Population Frequency of Detection | Shift |
Alteromonadaceae | family | 9.4 | 3.4 | Lower |
Paenibacillus | genus | 12 | 6.6 | Lower |
Phocaeicola plebeius | species | 12 | 7.1 | Lower |
Bacteroides gallinarum | species | 13.5 | 7.3 | Lower |
Oscillatoriales | order | 13.5 | 4.8 | Lower |
Planococcaceae | family | 13.5 | 6.7 | Lower |
Aerococcaceae | family | 14.1 | 6.8 | Lower |
Phocaeicola coprocola | species | 15.1 | 7.5 | Lower |
Turicibacter sanguinis | species | 15.1 | 5.1 | Lower |
Sarcina | genus | 19.3 | 0.4 | Lower |
[Ruminococcus] torques | species | 19.8 | 8.1 | Lower |
Desulfovibrio | genus | 22.9 | 7.2 | Lower |
Ruminiclostridium | genus | 22.9 | 8.2 | Lower |
Peptostreptococcaceae | family | 24 | 8.2 | Lower |
Collinsella | genus | 25 | 7.2 | Lower |
Anaerostipes | genus | 26.6 | 8.3 | Lower |
Eubacterium | genus | 29.2 | 8.3 | Lower |
Eubacteriales incertae sedis | norank | 29.7 | 8 | Lower |
Clostridium | genus | 30.2 | 8.2 | Higher |
Sphingobacteriales | order | 30.2 | 4.8 | Higher |
Blautia hansenii | species | 30.2 | 5.3 | Higher |
Oscillospira | genus | 30.2 | 0 | Higher |
Bacteroides ovatus | species | 30.2 | 7.2 | Higher |
Anaerotruncus colihominis | species | 30.2 | 6.7 | Higher |
Blautia wexlerae | species | 30.2 | 8.3 | Lower |
Bacteroides rodentium | species | 30.2 | 7.4 | Higher |
Sphingobacteriia | class | 30.2 | 4.8 | Higher |
Sphingobacteriaceae | family | 30.2 | 4.8 | Higher |
Hathewaya | genus | 30.2 | 4.1 | Higher |
Anaerofilum | genus | 30.2 | 4.2 | Higher |
Actinobacteria | phylum | 30.2 | 8.3 | Lower |
uBiome
Tax_name | Tax_rank | Sample Frequency of Detection | Population Frequency of Detection | Shift |
Planococcaceae | family | 13.5 | 0.3 | Lower |
Bacteroides gallinarum | species | 13.5 | 0.1 | Lower |
Oscillatoriales | order | 13.5 | 0.1 | Lower |
Aerococcaceae | family | 14.1 | 0.6 | Lower |
Phocaeicola coprocola | species | 15.1 | 0.4 | Lower |
Turicibacter sanguinis | species | 15.1 | 3.3 | Lower |
Sarcina | genus | 19.3 | 5.4 | Lower |
Ruminiclostridium | genus | 22.9 | 0.2 | Higher |
Adlercreutzia equolifaciens | species | 22.9 | 3.8 | Lower |
Desulfovibrio | genus | 22.9 | 2.7 | Lower |
Peptostreptococcaceae | family | 24 | 5.6 | Lower |
Collinsella | genus | 25 | 5.5 | Lower |
Anaerostipes | genus | 26.6 | 5.6 | Lower |
Eubacterium | genus | 29.2 | 1.1 | Higher |
Eubacteriales incertae sedis | norank | 29.7 | 5.5 | Lower |
Oscillospira | genus | 30.2 | 4.2 | Higher |
Anaerotruncus colihominis | species | 30.2 | 2.1 | Higher |
Blautia wexlerae | species | 30.2 | 5.4 | Lower |
Bacteroides rodentium | species | 30.2 | 0.1 | Higher |
Sphingobacteriia | class | 30.2 | 0.1 | Higher |
Sphingobacteriaceae | family | 30.2 | 0 | Higher |
Anaerofilum | genus | 30.2 | 1.8 | Higher |
Actinobacteria | phylum | 30.2 | 5.6 | Lower |
Bacteroides ovatus | species | 30.2 | 2.9 | Higher |
Clostridium | genus | 30.2 | 5.5 | Higher |
Sphingobacteriales | order | 30.2 | 0.1 | Higher |
Blautia hansenii | species | 30.2 | 1.8 | Higher |
Common across all labs
Amount of Bacteria
For the amount of shift, the nightmare described in The taxonomy nightmare before Christmas… comes true!
Tax_name | Tax_rank | Ombre | Biomesight | uBiome |
Paenibacillus | genus | Lower | Higher | Higher |
Phocaeicola plebeius | species | Lower | Higher | Higher |
Oscillatoriales | order | Lower | Higher | Higher |
Planococcaceae | family | Lower | Higher | Higher |
Phocaeicola coprocola | species | Lower | Higher | Higher |
Turicibacter sanguinis | species | Lower | Higher | Higher |
Sarcina | genus | Lower | Higher | Higher |
Bacteroides gallinarum | species | Lower | Lower | Higher |
Frequency of Detection
Here we have agreement across all of the labs
Tax_name | Tax_rank | Ombre | Biomesight | uBiome |
Paenibacillus | genus | More | More | More |
Phocaeicola plebeius | species | More | More | More |
Bacteroides gallinarum | species | More | More | More |
Oscillatoriales | order | More | More | More |
Planococcaceae | family | More | More | More |
Phocaeicola coprocola | species | More | More | More |
Turicibacter sanguinis | species | More | More | More |
Sarcina | genus | More | More | More |
Aerococcaceae | family | More | More | |
Desulfovibrio | genus | More | More | |
Ruminiclostridium | genus | More | More | |
Peptostreptococcaceae | family | More | More | |
Collinsella | genus | More | More | |
Anaerostipes | genus | More | More | |
Eubacterium | genus | More | More | |
Eubacteriales incertae sedis | norank | More | More | |
Clostridium | genus | More | More | |
Sphingobacteriales | order | More | More | |
Blautia hansenii | species | More | More | |
Oscillospira | genus | More | More | |
Bacteroides ovatus | species | More | More | |
Blautia wexlerae | species | More | More | |
Sphingobacteriia | class | More | More |
What does all of this mean?
It means that the bacteria count may be a little bit of a red herring. It is the frequency of detection that may be a better criteria for what is significant.
To put this in human terms, for a political movement, looking at the bank account may not be the best way of detecting if it is significant; it is the number of different types of people that turns up at meetings!
The mathematics and number crunching becomes more complex… but we are dealing with a complex system. For example, if you are using uBiome and many of the following was detected, then the odds of having ME/CFS is significant. It suggests a different criteria for selecting bacteria to generate suggestions.
- Planococcaceae
- Bacteroides gallinarum
- Oscillatoriales
- Aerococcaceae
- Phocaeicola coprocola
- Turicibacter sanguinis
Returning to Long COVID
Below is NOT the amount of bacteria, it is the frequency that these bacteria were detected in the samples. In other words, there is a group of bacteria that blooms – they show up more frequently, not necessarily in larger numbers, just there — trouble makers!
Bacteria Identified in Long COVID | Ombre ME/CFS | Biomesight ME/CFS | Ubiome ME/CFS |
Micrococcaceae | More | More | More |
Peptostreptococcaceae | More | More | More |
Butyricimonas virosa | More | More | More |
Sarcina | More | More | More |
Enterobacter | More | More | More |
Lactobacillaceae | More | More | More |
Coriobacteriia | More | More | More |
Slackia faecicanis | More | More | |
Rhodovibrionaceae | More | More | |
Blautia wexlerae | More | More | |
Salinicoccus luteus | More | More | |
Staphylococcaceae | More | More | |
Bifidobacteriales | More | More | |
Holdemanella biformis | More | More | |
Coriobacteriales | More | More | |
Holdemanella | More | More | |
Eubacteriales incertae sedis | More | More | |
Fusobacteriia | More | More |
This analysis shows a very similar pattern in the microbiome between Long COVID and ME/CFS.
Recent Comments