COVID in February 2021. 37 y.o. Male at the time, athletic/fit. Crossfit x 3 a week, playing football weekly Only mild gastritis prior to Covid. No other health issues.
Moderate severity Covid, lots of symptoms.
And then Long COVID and CFS/ME type of symptoms mostly fatigue, PEM and GI problems (pain, food intolerance, bloating..etc) I’d say it’s a moderate/mild case of CFS/ME. But after 18 months still not back to previous levels, can’t walk too long otherwise i crash. I’d say i am around %75.
For other analysis of Long COVID see Analysis Posts on Long COVID and ME/CFS
We have two samples available, one early in Long COVID and one more recent
With this type of information, let us start by comparing them. We are fortunate that both samples are similar read quality which reduces fuzziness. Unfortunately, it appears that the microbiome dysfunction has increased in many aspects. One aspect that it has improved in terms of bacteria with very low counts. We went from 74% of bacteria with low counts down to 51% with low count. Ideally we would love to see the low count to drop to a modelled 15%.
I should note that the increase in some Outside Ranges is likely because many of the ranges are 0 to some amount, hence the older sample had less because it was full of different trace amounts. The same apply to many other criteria, the low abundance of many bacteria skewed the criteria to appear better.
|Criteria||Current Sample||Old Sample|
|Lab Read Quality||5.8||5.9|
|Bacteria Reported By Lab||393||399|
|Bacteria Over 99%ile||8||1|
|Bacteria Over 95%ile||26||21|
|Bacteria Over 90%ile||42||35|
|Bacteria Under 10%ile||135||160|
|Bacteria Under 5%ile||56||108|
|Bacteria Under 1%ile||9||29|
|Rarely Seen 1%||1||0|
|Rarely Seen 5%||9||8|
|Outside Range from JasonH||10||10|
|Outside Range from Medivere||15||15|
|Outside Range from Metagenomics||10||10|
|Outside Range from MyBioma||8||8|
|Outside Range from Nirvana/CosmosId||22||22|
|Outside Range from XenoGene||34||34|
|Outside Lab Range (+/- 1.96SD)||16||8|
|Condition Est. Over 99%ile||18||0|
|Condition Est. Over 95%ile||38||0|
|Condition Est. Over 90%ile||50||0|
|Enzymes Over 99%ile||561||115|
|Enzymes Over 95%ile||793||282|
|Enzymes Over 90%ile||866||680|
|Enzymes Under 10%ile||289||294|
|Enzymes Under 5%ile||149||149|
|Enzymes Under 1%ile||29||23|
|Compounds Over 99%ile||480||50|
|Compounds Over 95%ile||600||214|
|Compounds Over 90%ile||677||298|
|Compounds Under 10%ile||581||315|
|Compounds Under 5%ile||563||242|
|Compounds Under 1%ile||548||91|
I next went to the Krona Charts to try to understand the shifts better. We see a massive increase of unclassified Bacteroides
Going to sample comparison, we see that (genus) Bacteroides was at the 99%ile on both samples. Looking at members of this genus, we see several of the identified species at high levels
- (species) Bacteroides ovatus 97 –> 99%ile
- (species) Bacteroides rodentium 92 -> 95%ile
- (species) Bacteroides thetaiotaomicron 97 -> 93%ile
- (species) Bacteroides uniformis 96 -> 97%ile
- (species) Bacteroides xylanisolvens 84 -> 100%ile
- REMEMBER — we have lots of Bacteroides that are not identified.
We have a good idea of the issue: lots of bacteria at token levels, lots of unidentified bacteria.
My approach is to try the following, looking ONLY at Restrict to Bacteria with Low Levels, do the following three
Then create a handpicked bacteria focused on Bacteroides and the high species under it. To express another way: Feed the weak and destitute, Bring down the mighty.
Doing the first step, we see at the top of the consensus:
- inulin (prebiotic), Pulses, high fiber diet,fruit/legume fibre
- arabinoxylan oligosaccharides (prebiotic), oligosaccharides (prebiotic)
- bacillus subtilis (probiotics), lactobacillus reuteri (probiotics), lactobacillus plantarum (probiotics)
- walnuts, fasting
The hand picked collection is below with percentiles
- genus Bacteroides 99
- species Bacteroides faecis 93
- species Bacteroides graminisolvens 86
- species Bacteroides ovatus 99
- species Bacteroides rodentium 95
- species Bacteroides stercorirosoris 91
- species Bacteroides thetaiotaomicron 93
- species Bacteroides uniformis 97
- species Bacteroides xylanisolvens 100
The suggestions for just these are shown below. The pattern is similar to other peoples suggestions with ME/CFS – lots of specific B-Vitamins, dark chocolate etc:
- whole-grain barley
- Hesperidin (polyphenol)
- polymannuronic acid
- momordia charantia(bitter melon, karela, balsam pear, or bitter gourd)
- folic acid,(supplement Vitamin B9)
- garlic (allium sativum)
- vitamin a
- lactobacillus casei (probiotics)
- Arbutin (polyphenol)
- pyridoxine hydrochloride (vitamin B6)
- retinoic acid,(Vitamin A derivative)
- thiamine hydrochloride (vitamin B1)
- Vitamin B-12
- vitamin b3 (niacin)
- vitamin b7 biotin (supplement) (vitamin B7)
- Vitamin C (ascorbic acid)
- melatonin supplement
- luteolin (flavonoid)
- lauric acid(fatty acid in coconut oil,in palm kernel oil,) – Monolaurin
The avoid list (items that help bacteroides grow) included some items from our earlier to take list.
- Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
- inulin (prebiotic)
- resistant starch
- red wine
- xylan (prebiotic)
- arabinoxylan oligosaccharides (prebiotic)
- high red meat
- low-fat diets
- schisandra chinensis(magnolia berry or five-flavor-fruit)
- lactobacillus plantarum (probiotics)
- Slippery Elm
- wheat bran
This is not unexpected, every substance/modifier has multiple impact.
Looking at the resulting consensus and items that are agreed to by both analysis, we have (in descending order):
- bacillus subtilis (probiotics)
- high fiber diet
- fruit/legume fibre
- lactobacillus reuteri (probiotics)
- saccharomyces cerevisiae (probiotics)
- oregano (origanum vulgare, oil) |
- pediococcus acidilactic (probiotic)
- lactobacillus rhamnosus gg (probiotics)
- bifidobacterium pseudocatenulatum,(probiotics)
Going over the KEGG based suggestions we see Escherichia coli at the top (typical for ME/CFS) and the next regular probiotic being Bacillus subtilis (in agreement with the above), followed by other Bacillus. Pediococcus acidilactici was listed. Further down the list we see Clostridium butyricum, Lacticaseibacillus casei (i.e. L. Casei above), Lacticaseibacillus rhamnosus. These were a pleasant surprise to see the same probiotics suggested from different models.
As a FYI, clostridium butyricum (probiotics) was on the consensus list, but mutaflor escherichia coli nissle 1917 (probiotics) was on the avoid for the consensus.
Bottom line for probiotics to try (add just one new one a week so you can see the response to each). See Simple Suggestions download below for suggested dosages or look them up on 📏🍽️ Dosages for Supplements. Using too small (almost homeopathic) dosages is a common error – the dosages on bottles are determined for profit margin (repeat business) and not from effective dosages from clinical studies.
- Bacillus subtilis
- lactobacillus reuteri (probiotics)
- lactobacillus rhamnosus gg (probiotics)
- Pediococcus acidilactici — source: Imagilin / NutriLots or alternatives listed here.
- clostridium butyricum (probiotics)
- saccharomyces cerevisiae (probiotics) WARNING: saccharomyces boulardii (probiotics) was a mild AVOID. — do this one last, because of the risk of mis-identification on many commercial probiotics.
The two downloads of the final consensus are attached below.
I would suggest getting another sample 6 weeks after implementing the above to see what the progress is.
As always, review with your medical professional before implementing.
This patient history and their microbiome are in agreement. The antibiotics suggestions (off label usage) matches the history.
Postscript – 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 answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.