ME/CFS Continues Improvement + Lab Read Quality Issues

Prior Posts


A summary of his seven results are below. The Lab Read Quality bounces around, and with that, other values may echo these shifts (i.e. up to 20% shifts for some measures). A low read quality means less bacteria are reported, for example, when it was low, the Outside Kaltoft-Moldrup has low, when it was high, the value became high.

Another way to view it is this: If 10% are out of range and 400 are reported then we have 40. If we have 660 in another report then we would expect 66. This could be misread as a 66/40 or a 65% increase in out of range bacteria when the same percentage is out of range. Technically, it is more complicated but that should explain the problem.

Looking only at high read quality ( 1/22/2024, 2/22/2023, 8/31/2021) we see improvements where there are 🙂 below. This is an unfortunate aspect of 16s tests.

I have added at the bottom Forecast Symptoms compared to actual.

Lab Read Quality7.
Outside Range from GanzImmun Diagostics16161515171720
Outside Range from JasonH7777446
Outside Range from Lab Teletest20 🙂202424222225
Outside Range from Medivere16161515151519
Outside Range from Metagenomics7799778
Outside Range from Microba Co-Biome2277111
Outside Range from MyBioma5 🙂577778
Outside Range from Nirvana/CosmosId20202323181821
Outside Range from Thorne (20/80%ile)198 🙂198223223217217246
Outside Range from XenoGene24 🙂243232363639
Outside Lab Range (+/- 1.96SD)5 🙂1510119914
Outside Box-Plot-Whiskers54564236425942
Outside Kaltoft-Moldrup123 🙂70139567859140
Bacteria Reported By Lab511399666478613456572
Bacteria Over 90%ile20 🙂542624265746
Bacteria Under 10%ile108418248442999
Shannon Diversity Index1.3681.181.0381.2871.5610.8950.903
Simpson Diversity Index0.1150.0630.050.0420.0580.0220.02
Chao1 Index760350571253480531323455639209
Pathogens26 🙂253023392430
Condition Est. Over 90%ile0000000
Actual Symptoms in top 10 Forecasted581088109
Max Forecast Symptom Factor38.522.325.316.915.826.433.1

Explaining the new Symptom Forecast Algorithm

This algorithm is similar to the Eubiosis algorithm. We compute the expected number of matches to bacteria shifts associated with the symptoms. The expected theoretical threshold by randomness is 16%. A higher number indicate increased odds, a lower number decreased odds. This is based on the existing annotated samples uploaded. It is not definitive and often there can be multiple subsets of bacteria associated with a symptom. The match is on too much or too few of a collection of bacteria

The checkmarks are the entered symptoms, the list are the predictions from most likely to lesser.

This data actually clarifies that the ideal 16+ for a factor is dependent on the Lab Read Quality and that 16 may apply to shotgun results but for 16s results, some flexibility with the 16 is warranted.

As a general FYI, hitting 80-100% correct prediction of symptoms implies that the algorithm performs well and the change of algorithm was appropriate.

It also implies that we are successfully identifying the bacteria associated with the symptoms..

The drop of matches with this sample is difficult to clearly interpret. It was not intended to be an indicator but a tool to correctly identify the bacteria of concern. Getting suggestions solely from the symptoms have been added. See the video below.

Going Forward

Again, using Just give me suggestions include Symptoms is how we are going to proceed. And then add in the two Special Studies. This results in 7 packages of suggestions.

Thresholds: High is 524 thus 260 or higher, Low is -346 this -170 or lower

For our first pass, we are going to look items that all 7 agrees upon, the list is very short

The two take list is very short. Prescription items dominates the list with metronidazole (antibiotic)s[CFS] at 524(the TOP), followed by amoxicillin (antibiotic)s[CFS], ciprofloxacin (antibiotic)s[CFS]. The top NOT-PRESCRIPTION is 232, so we will drop the threshold to 116

The avoid list is much bigger

For myself, I would try to obtain and rotate the antibiotics listed above and use Splenda where practical.

Bottom Line

This analysis has been both challenging and informative. We see that 16s Lab Read Quality can confuse analysis because it will alter many measures significantly. Care must be taken when comparing two or more samples with different Read Quality. Additionally, having the top suggestions full of prescription items means that we needed to adjust the threshold based on the top non-prescription item.

On the positive side, we see that the revised symptom forecasts appear to perform well, actually better than I was expecting.

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 result 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 anyone. Always review with your knowledgeable medical professional.