For other analysis for ME/CFS see this list.

Concerns

“For the Ombre/Thryve sample data, dated 12/31/24, I found the following:

• Rickettsiales – order – 31%ile High
• Rickettsiaceae – family – 34%ile High
• Rickettsieae – tribe – 34%ile High
• Parasutterella excrementihominis – species – 69%ile High

The reason these bacteria stand out to me are because my CFS journey,
like many, began with a Lyme diagnosis 20 years ago.
And as testing and research improved, Lyme became synonymous with
“co-infections” like Rickettsia and Ehrlichia and a whole host of
others in the “Lyme Soup”.”

Parasutterella excrementihominis has been present in all samples, varying from 61-89%ile. For Rickettsiaceae, we see in much older samples

• 2022-04-11 at 6%ile
• 2022-11-01 at 18%ile

All bacteria wax and wane over time. Personally, I would not be concerned unless it is constantly above 75%ile for pathogenic. It is is over, then take your concerns to your MD for appropriate testing to confirm..

For this person we have a lot of symptom forecasts[new algorithm] matching reported symptoms

Analysis

Ombre Lab Processing

We are looking at the Ombre Lab Report data below. These are a lot of swings over the last 4 sample. Looking at the prior (likely the worse)

• Eubiosis was the worse
• Bacteria Under 10%ile was the highest
• Outside Kaltoft-Moldrup was the highest
• Chao1 Index was the highest

A lot of other measures had little or no change (i.e. JasonH, Microba Co-Biome,,Nirvana/CosmosId etc) suggesting low sensitivity to detect shifts. Some hits at a continuous shift (Simpson Diversity Index.

The wide variety of lab read quality makes reliable comparisons difficult. 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 660 are reported then we have 66. If we have 940 in another report then we would expect 94. This could be misread as a 94/66 or a 43% increase in out of range bacteria. Technically, it is more complicated but that should explain the problem.

Criteria	12/31/2023	10/2/2023	7/28/2023	4/18/2023
Lab Read Quality	5.6	12.6	8	4.9
Eubiosis	53.4	8.2	38.8	83.1
Rickettsiaceae	34%ile	0	0	0
Outside Range from GanzImmun Diagostics	17	17	14	14
Outside Range from JasonH	5	5	5	5
Outside Range from Lab Teletest	32	32	25	25
Outside Range from Medivere	17	17	17	17
Outside Range from Metagenomics	8	8	7	7
Outside Range from Microba Co-Biome	5	5	6	6
Outside Range from MyBioma	16	16	9	9
Outside Range from Nirvana/CosmosId	24	24	26	26
Outside Range from Thorne (20/80%ile)	310	310	210	210
Outside Range from XenoGene	51	51	41	41
Outside Lab Range (+/- 1.96SD)	36	20	7	19
Outside Box-Plot-Whiskers	114	97	71	69
Outside Kaltoft-Moldrup	164	239	141	79
Bacteria Reported By Lab	765	941	762	663
Bacteria Over 90%ile	101	82	42	58
Bacteria Under 10%ile	60	302	102	28
Shannon Diversity Index	2.953	3.099	2.987	2.62
Simpson Diversity Index	0.043	0.053	0.079	0.098
Chao1 Index	23041	38183	26135	15707
Pathogens	28	25	23	15
Condition Est. Over 90%ile	1	1	1	1
Top 10 forecast symptoms matching	9	9	10	n/a

Rickettsiaceae was seen in earlier samples:

• 2022-04-11 at 6%ile
• 2022-11-01 at 18%ile

Biomesight Reporting

One sample could not be successfully processed thru Biomesight. Biomesight indicated that there are ongoing technical issues with their processing of Thryve FastQ files.

Criteria	12/12/2023	7/28/2023	4/18/2023
Lab Read Quality	5.6	8	4.9
Eubiosis	42.8	80.8	74.3
Outside Range from GanzImmun Diagostics	13	14	14
Outside Range from JasonH	3	5	5
Outside Range from Lab Teletest	16	23	23
Outside Range from Medivere	12	13	13
Outside Range from Metagenomics	5	8	8
Outside Range from Microba Co-Biome	1	2	2
Outside Range from MyBioma	8	4	4
Outside Range from Nirvana/CosmosId	11	23	23
Outside Range from Thorne (20/80%ile)	144	250	250
Outside Range from XenoGene	20	23	23
Outside Lab Range (+/- 1.96SD)	38	22	25
Outside Box-Plot-Whiskers	149	97	81
Outside Kaltoft-Moldrup	99	123	60
Bacteria Reported By Lab	664	696	618
Bacteria Over 90%ile	122	93	61
Bacteria Under 10%ile	38	46	9
Shannon Diversity Index	1.565	1.242	1.507
Simpson Diversity Index	0.037	0.057	0.078
Chao1 Index	17542	14092	10705
Shannon Diversity Percentile	41.2	10.8	32.6
Simpson Diversity Percentile	32.4	55.9	72
Chao1 Percentile	88.4	75.6	53.8
Lab: BiomeSight
Pathogens	25	23	19
Condition Est. Over 90%ile	1	1	1
Top 10 forecast symptoms matching	7	n/a	9

Bottom line, the variability of 16s Lab Quality leaves too much fogginess for interpretations 🙁

Revised Symptom Forecasting

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.

As seen in another post over multiple samples, we have a good accuracy in predicting symptoms from the microbiome.

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 for each lab. Since we have two different interpretations (Biomesight and Thryve) of the FastQ files. We then do an Uber Consensus merging 14 packages of suggestions!

And the merge (on [Multiple Samples] tab)

Thresholds: High is 383 thus 190 or higher, Low is -462 this -231 or lower

First we are going to look on what ALL 14 sets of suggestions agreed upon

• linseed(flaxseed)
• high red meat
• high animal protein diet
• lactobacillus rhamnosus (probiotics)
• low-fat high-complex carbohydrate diet

So low-fat animal protein. There were no avoids that everyone agreed upon.

Usual High Level Review

• Amino Acids and flavonoids
  • fumarate
  • propionate
  • l-taurine
  • l-glutamine
  • glutamate
  • melatonin supplement
  • luteolin (flavonoid)
  • Arbutin (polyphenol)
  • diosmin,(polyphenol)
  • Hesperidin (polyphenol)
• Diet and Food
  • vegetable/fruit juice-based diets
  • high animal protein diet
  • gluten-free diet
  • linseed(flaxseed)
  • high red meat
  • Kale
• Probiotics
  • bifidobacterium longum bb536 (probiotics)
  • lactobacillus rhamnosus (probiotics)
• Vitamins
  • Vitamin B1,thiamine hydrochloride
  • Vitamin B9,folic acid
  • Vitamin B6,pyridoxine hydrochloride
  • vitamin B7, biotin
  • Vitamin C (ascorbic acid)
  • Vitamin B-12
  • vitamin d

Remember to check suggested dosages here. A common issue is taking token amounts which are unable to effect changes.

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.