This is a follow up post on ME/CFS x COVID :- Long COVID instead from June, 2022. The person on seeing the results stated “New Sample Looks Worse”. I am curious because so far all subsequent analysis showed objective improvements (and subjective improvements too!) for people with ME/CFS. I am prepared to be humbled. He used Ombre for the data processing.
- Lifestyle-wise, I’ve definitely been experiencing a big increase in stress due to working a full-time job for the first time in almost a decade. The job is remote, I couldn’t do it otherwise, but it still requires some late nights and lots of childcare complications since my wife also works full-time and then some.
- Diet-Wise: Not much has changed in terms of my diet. I remain 95% Gluten Free with the occasional slip-up or cheat. What’s interesting is a lot has flip-flopped in this sample, so maybe I was overdoing it on the last round of food suggestions, especially in terms of adding fiber to my smoothies, mostly resistant starch.
- Supplementation-wise, I had been taking the recommended probiotics from my last sample at a pretty high and aggressive dose to very mixed reactions. I probably wasn’t rotating them often enough.
- Paxlovid Experience: As I mentioned in a previous email, I had a pretty bad case of Covid around Christmas time. And to my surprise Paxlovid not only helped my acute Covid symptoms, but it overall made me feel much better than baseline. I was able to confirm this experiment in mid-January when another member of my family got COVID, but couldn’t tolerate their Paxlovid. So as an experiment, I took the remaining three-day course, and again almost immediately my brain fog, executive function, and most neurological symptoms lifted.
Also, and this is where I think we might be able to confirm some of Dr. AI’s suggestions instead of you being humbled by them, I was getting desperate when starting the new job in January and coming off of my case of Covid. So I started throwing pretty much any “energy” and “anti-viral” supplement I had on hand or had short-term success in the past with, to try and get well enough to do a good job at my work. A lot of those herbs ended up on the avoid list on this sample, especially Baicalin, Oregano Oil, and Resveratrol (which is usually mostly Japanese Knotweed).
Other things on the avoid list now I had frequently been taking before this sample: salt (salty electrolyte packets added to water), fish oil, pulses (lots of beans, especially navy beans since those had been a strong Take for a few samples going), and Culturelle (lactobacillus rhamnosus gg) for diarrhea, because I’ve been experiencing more frequent and urgent loose stools since Covid.Reader
First, I did a recalculated to make sure all samples used the same reference sets. The reference data is recomputed weekly so we have a living analysis system. The data has been processed thru both Ombre Labs (OL) and BiomeSight (BS), so the details are below. It is the same three FASTQ files processed by two different software packages (for backstory see: Different Microbiome Results from Different Providers on Same Sample, Same Raw Data via Ombre Labs(Thryve) and Biomesight, The taxonomy nightmare before Christmas…)
|Lab Read Quality||5.3||8.6||15.3||5.3||8.6||15.3|
|Bacteria Reported By Lab||531||567||493||671||746||687|
|Bacteria Over 99%ile||35||19||8||16||11||3|
|Bacteria Over 95%ile||75||46||37||49||24||22|
|Bacteria Over 90%ile||133||71||57||86||47||50|
|Bacteria Under 10%ile||55||193||280||51||161||329|
|Bacteria Under 5%ile||26||173||245||23||50||289|
|Bacteria Under 1%ile||2||145||197||3||5||83|
|Rarely Seen 1%||7||5||6||15||7||8|
|Rarely Seen 5%||24||30||32||61||58||70|
|Outside Range from JasonH||4||4||4||5||5||5|
|Outside Range from Medivere||12||12||12||15||15||15|
|Outside Range from Metagenomics||8||8||8||7||7||7|
|Outside Range from MyBioma||3||3||3||8||8||8|
|Outside Range from Nirvana/CosmosId||23||23||23||25||25||25|
|Outside Range from XenoGene||28||28||28||43||43||43|
|Outside Lab Range (+/- 1.96SD)||34||21||9||24||14||17|
|Condition Est. Over 99%ile||7||0||7||0||0||4|
|Condition Est. Over 95%ile||21||1||17||0||3||18|
|Condition Est. Over 90%ile||35||2||34||2||5||31|
|Enzymes Over 99%ile||167||17||10||32||42||0|
|Enzymes Over 95%ile||338||52||102||157||85||40|
|Enzymes Over 90%ile||481||109||134||245||183||142|
|Enzymes Under 10%ile||110||311||638||126||221||441|
|Enzymes Under 5%ile||19||258||432||48||112||314|
|Enzymes Under 1%ile||2||188||162||1||2||64|
|Compounds Over 99%ile||208||24||47||95||169||36|
|Compounds Over 95%ile||461||94||128||356||502||158|
|Compounds Over 90%ile||639||304||235||584||694||329|
|Compounds Under 10%ile||598||629||691||610||663||726|
|Compounds Under 5%ile||592||617||668||604||628||710|
|Compounds Under 1%ile||584||591||628||600||606||680|
My initial impression is positive using Enzymes. The number of under production of Enzymes has been reduced significantly – between 50% reduction to 99% reduction. I am not that concerned with over production, typically the body discard surplus of chemicals (with a very few exceptions). I tend to be more concern over enzyme starvation. Special studies found that Compounds tend to have much weaker relationships than Enzymes. While included, I usually do not over-read the compound significance.
Outside of ranges were interesting — my preferred range Kaltoft-Moldrup, had less in the latest sample (with the numbers dropping with each sample). All of the 3rd party lab results were unchanged. To remind readers on the 3 suggested ranges assumptions:
- Outside Lab Range (+/- 1.96SD) – forces on the bacteria analysis the belief that data is a bell curve — very false
- Box-Plot-Whiskers – this method was created to deal better with data that is not a bell curve, but with the underlying assumptions of a skewed bell curve. – better but not ideal
- Kaltoft-Moldrup – is the bacteria whisperer. It listens to the data and looks for atypical patterns. IMHO it produces the best identification of data of concern.
Conceptually the numbers under 10%ile and over 90%ile should be in the same ratio 1:1. What we see is shown below:
- 2/2/2023 – 2.4 or 1.7 ratio
- 11/1/2022 – 0.36 or 0.29
- 4/11/2022 – 0.2 or 0.15
There was a major change, a flip in ratio with the latest sample. This was seen by the reader.
So is he better? IMHO — yes, the objective evidence is not as strong as I would like to see but:
- None of the 3rd party ranges got worse (they remain unchanged)
- His microbiome is producing a much richer amount of enzymes — which should cascade into a more balance system
- The Kaltoft-Moldrup count continued to drop.
There are several events that adds noise to this analysis:
- Going back to work (the fact that he continues to work must be viewed as solid evidence)
- COVID and Paxlovid will alter the microbiome
- I was unable to find any studies on the impact of Paxlovid on the microbiome 😞
- Randomly tossing supplements into the mixture
- There were huge differences in lab quality between samples (from 5.3 to 15.3)
Additional Analysis Points
Potential Medical Conditions Detected
The improvement seen in earlier post have persisted
- 4/11/2022 : 26 items
- 11/1/2022 : 5 items
- 02/02/2023: 7 items
Bacteria Deemed Unhealthy
Nothing that is clear, randomness can explain the numbers well.
- 4/11/2022 : 10 items
- 11/1/2022 : 15 items
- 02/02/2023: 12 items
Below we see the extreme %iles (0-9) improving over time. He went from a multitude of bacteria with token amounts to a more appropriate number with token amounts.
|0 – 9||10||40||93||18||59||126|
|20 – 29||34||26||19||55||24||22|
|30 – 39||34||22||11||38||34||14|
|40 – 49||14||13||14||19||24||21|
|50 – 59||16||18||11||21||20||13|
|60 – 69||24||18||12||28||26||26|
|70 – 79||17||21||13||23||37||19|
|80 – 89||21||19||12||31||28||16|
|90 – 100||26||15||16||43||25||24|
First thing is that my own experience in a significant ME/CFS flare was lots of swings in the microbiome results as I altered supplements and diet. The analogy that I often use is that the trip from the Port of ME/CFS to the Port of Health is not a straight flight like hoping on a plane (“as the crow flags”) but similar to travelling by a sailing ship that needs to make a lot of tacks (changes of boat directions) because of winds, shoals and reefs. A bad microbiome happens as a result of a long series of minor changes, we need to undo those.
Doing the usual trio of suggestions to build a consensus report.
In addition to that, we do some of the “has conditions and matches published literature” where the matching is over 95%ile
- ME/CFS without IBS 12 of 18
- Neuropathy (all types) 11 of 18
- Hashimoto’s thyroiditis 8 of 17
- Long COVID 68 of 167
- COVID-19 39 of 96
This will produce a deep consensus report using 8 sets of suggestions. The downloads are attached
Looking at the consensus, we have 28 items that ALL of the suggestions agreed upon. These include:
- cranberry (flour, polyphenols), Cranberry, red wine polyphenols
- vegetable/fruit juice-based diets, low-fat diets
- lard – Note: high animal protein diet is recommended by 7 with none opposed. This is likely solely because of the B-Vitamins in it!
- Hesperidin (polyphenol), luteolin (flavonoid), Arbutin (polyphenol), diosmin,(polyphenol)
- melatonin supplement
- gluten-free diet – Already doing!
- red alga Laurencia tristicha
- thiamine hydrochloride (vitamin B1), pyridoxine hydrochloride (vitamin B6), retinoic acid,(Vitamin A derivative), Vitamin C (ascorbic acid), Vitamin B-12, vitamin b7 biotin (supplement) (vitamin B7)
- glycyrrhizic acid (licorice)
- chitooligosaccharides (prebiotic), oligofructose-enriched inulin (prebiotic)
A specific strain was recommended: Lactobacillus salivarius UCC118, but lactobacillus salivarius (probiotics) was a to-avoid — so I would skip that suggestion. The absence of any probiotics in this top list would inclined me to suggest skipping probiotics for the next round of doing and then testing.
The New Artificial Intelligence Diet (see More information) had #1 being … Cheerios!! — the reason was all of the fortifications added! Being gluten-free, then this is an easy to ignore.
I proceed to filter the diet suggestions by fruits first, and got the following:
When by Vegetables with this list:
- Leaves, cat’s whiskers, raw, Cleomegynadra, (Luni)
- Baobab (Adansonia digitata), leaves, dried (powder)
- Corn, yellow, canned
- Moringa, raw
- MUSHROOM,DRIED,RAW BOILED
Filtering by Roots, Tubers etc, had low values with only one nutrient contributing. Nuts etc had Sesame being the top item. Other items of note included: Avocado, Barleygrass, Coca (as in source of cocaine – have fun asking for that at your health food store!).
On the AVOID list, we have:
- Muesli, untoasted or natural style, added dried fruit, unfortified
- Sweets, honey, strained or extracted
- Fish, cod, Atlantic, dried and salted
- Raspberry juice concentrate
- Sweets, fancy molasses
- Ball, snack, date based
What I find interesting is that vegetable/fruit juice-based diets, is pretty broad. Using the AI Diet, we can likely isolate which food and vegetables are the best choices and may well explain the “why” for the general vague diet term.
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.