This is part 2A of Comparing Microbiomes from Three Different Providers – Part 1. I decided to do each lab separately and then do an overview at the end.

• Biomesight Suggestions Analysis – Good Results -2B
• Thorne Suggestions Analysis – INCOMPLETE / FAILED – 2C
• Microbiome Prescription Uber-Consensus Analysis – Excellent – 2D
• Comparing standalone suggestions – 2E, a reader wanted to know how similar Microbiome Prescription suggestions were using different data

Process

It is very simple, look at their suggestions, look at any references they provided. Then look at Microbiome Prescription evidence trail for the same substances. My usual “Just the facts, ma’am” approach. This data was retrieved on 24 Aug 2023.

They recommended their own brand mixture

Then we asked for suggests with no diet and no allergies.

• Oyster, citing below: This word was not found in the full text of either
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4622128/
  • gut.bmj.com/content/64/1/93.long
• Zucchini, citing
  • www.ncbi.nlm.nih.gov/pubmed/10395608/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4622128/ – not found
  • gut.bmj.com/content/64/1/93.long – not found
  • insights.ovid.com/pubmed – Page not found…
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4855163/ – not found
• Tagliatelle (Wheat Pasta), citing
  • www.ncbi.nlm.nih.gov/pubmed/10395608/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4622128/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4855163/ – not found
  • insights.ovid.com/pubmed – Page not found…
• Yuzu (Yuzu is a citrus fruit and plant in the family Rutaceae of East Asian origin ), citing
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4488768/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4365176/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4772042/ – not found
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4080166/ – not found
• Yellow beans
  • www.ncbi.nlm.nih.gov/pubmed/24053022 – nothing on yellow bean, cites soy and bean tempeh (which is fermented)
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC3770567/ – cites cooked navy beans consumed by dogs
  • www.ncbi.nlm.nih.gov/pubmed/26878790 – cites cranberry beans!!
• Wild garlic, cites a stack — I really doubt that wild garlic is mentioned in them
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC3705355/ – cites  Dietary Guidelines for Americans  recommend them, nothing bacteria specific
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4622128/ cites used in high FODMAP, nothing bacteria specific
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4855163/ – not mentioned
  • www.ncbi.nlm.nih.gov/pubmed/8777017/ – not mentioned
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC5470704/ – mention as part of a diet plan — nothing bacteria specific
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4960240/ – not mentioned
  • www.ncbi.nlm.nih.gov/pubmed/23280537/ – not mentioned
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC3832963/ – mention prebiotics that may be in it, — nothing bacteria specific
  • www.ncbi.nlm.nih.gov/pmc/articles/PMC4896924/ – mention prebiotics that may be in it, — nothing bacteria specific
  • www.ncbi.nlm.nih.gov/pubmed/23823585/ – not mentioned

At this point, I deem their evidence is grossly insufficient. Not one solid piece of evidence in almost 30 “scientific evidence”. I will say myself frustration (and the reader boredome) .

Using Microbiome Prescription Evidence

Most of the studies cited above were references to FODMAP, either low or high with this being ignored.

• Multivariate modelling of faecal bacterial profiles of patients with IBS predicts responsiveness to a diet low in FODMAPs. Gut (Gut ) Vol: 67 Issue 5 Pages: 872-881 Pub: 2018 May Epub: 2017 Apr 17 Authors Bennet SMP , Böhn L , Störsrud S , Liljebo T , Collin L , Lindfors P , Törnblom H , Öhman L , Simrén M ,

For Tagliatelle (Pasta), we find the following study with adverse effect on many bacteria (it was an avoid)

• Carbohydrate Staple Food Modulates Gut Microbiota of Mongolians in China. Frontiers in microbiology (Front Microbiol ) Vol: 8 Issue Pages: 484, Pub: 2017 Epub: 2017 Mar 21 Authors Li J , Hou Q , Zhang J , Xu H , Sun Z , Menghe B , Zhang H ,

Yuzu (Yuzu is a citrus fruit). This is not in the database, but another citric food is there, and it is a take suggestion

• Analysis of Temporal Changes in Growth and Gene Expression for Commensal Gut Microbes in Response to the Polyphenol Naringenin. Microbiology insights (Microbiol Insights ) Vol: 11 Issue Pages: 1178636118775100 Pub: 2018 Epub: 2018 May 30 Authors Firrman J , Liu L , Argoty GA , Zhang L , Tomasula P , Wang M , Pontious S , Kobori M , Xiao W 
•  The inhibitory effect of polyphenols on human gut microbiota. Journal of physiology and pharmacology : an official journal of the Polish Physiological Society (J Physiol Pharmacol ) Vol: 63 Issue 5 Pages: 497-503 Pub: 2012 Oct Epub: Authors Duda-Chodak A ,

For beans — we have the same citation to bean and soy tempeh 

For Garlic, we have it as an avoid with the following studies being the basis

•  Effect of garlic powder on the growth of commensal bacteria from the gastrointestinal tract.
  Phytomedicine : international journal of phytotherapy and phytopharmacology (Phytomedicine ) Vol: 19 Issue 8-9 Pages: 707-11 Pub: 2012 Jun 15 Epub: 2012 Apr 4 Authors Filocamo A , Nueno-Palop C , Bisignano C , Mandalari G , Narbad A ,
• Dietary prophage inducers and antimicrobials: toward landscaping the human gut microbiome.
  Gut microbes (Gut Microbes ) Vol: Issue Pages: 1-14 Pub: 2020 Jan 13 Epub: 2020 Jan 13 Authors Boling L , Cuevas DA , Grasis JA , Kang HS , Knowles B , Levi K , Maughan H , McNair K , Rojas MI , Sanchez SE , Smurthwaite C , Rohwer F
• Inhibitory activity of garlic (Allium sativum) extract on multidrug-resistant Streptococcus mutans.
  Journal of the Indian Society of Pedodontics and Preventive Dentistry (J Indian Soc Pedod Prev Dent ) Vol: 25 Issue 4 Pages: 164-8 Pub: 2007 Oct-Dec Epub: Authors Fani MM , Kohanteb J , Dayaghi M 
•  Effects of garlic polysaccharide on alcoholic liver fibrosis and intestinal microflora in mice.
  Pharmaceutical biology (Pharm Biol ) Vol: 56 Issue 1 Pages: 325-332 Pub: 2018 Dec Epub: Authors Wang Y , Guan M , Zhao X , Li X 
• Black garlic melanoidins prevent obesity, reduce serum LPS levels and modulate the gut microbiota composition in high-fat diet-induced obese C57BL/6J mice. Food & function (Food Funct ) Vol: 11 Issue 11 Pages: 9585-9598 Pub: 2020 Nov 18 Epub: Authors Wu J , Liu Y , Dou Z , Wu T , Liu R , Sui W , Jin Y , Zhang M 

Their recommended probiotic contains the following species (with the evidence based suggestions count after):

• L. Acidophilus – Mixed impact (2 plus, 1 minus)
• E. Faecium – Avoid (3 minus)
• L. Paracasei – MAJOR Avoid ( 4 minus)
• L. Helveticus – Avoid ( 3 minus, 1 plus)
• L. Rhamnosus – Mixed (2 plus, 2 minus)
• L. Plantarum – MAJOR Avoid (4 minus)
• B. Lactis – MAJOR Avoid (4 minus)
• S. Boulardi – Mixed (2 plus, 2 minus)

IMHO, this is NOT a good mixture to take.

Bottom Line

Ombre’s suggestions for both probiotic and diet style leaves great opportunity to be made better. Their scientific citations is almost an embarrassment. I suspect that they were contracted out to a dietician and had no or poor quality control/review.

The videos below shows how you can see the evidence for the suggestions on Microbiome Prescription.

A reader did Ombre and Thorne from the same physical sample and then processed the Ombre results via Biomeisght resulting in three different reports on the same shit. That is:

• Ombre (16s) – same digital data used
• Biomesight (16s) – same digital data used
• Thorne (MSS)

This is the first of two parts — the bacteria numbers as percentiles and percentages, The second part will look at suggestions from each (and provided documentation for why it was suggested when available).

• Ombre Suggestions Analysis – Failing Grade – 2A
• Biomesight Suggestions Analysis – Good Results – 2B
• Thorne Suggestions Analysis – INCOMPLETE / FAILED – 2C
• Microbiome Prescription Uber-Consensus Analysis – Excellent – 2D
• Comparing standalone suggestions – 2E, a reader wanted to know how similar Microbiome Prescription suggestions were using different data

As a reminder of issues, see the chart below and this earlier post: The taxonomy nightmare before Christmas… [2019]

From The gut microbiome and thromboembolism [2022]

From Standards seekers put the human microbiome in their sights, 2019

My intent is not to suggest/render judgements on the different test results, just show the differences. It should be noted that most studies are done with 16s and not MSS. Conclusions from one study may not be reproduceable using a different lab/software (even with the same digital data).

Pass #1: Percentile Rankings

These are the most likely to be similar between samples. I only looked at those bacteria reported by all three samples.

phylum	Thryve	BiomeSight	Thorne
Acidobacteria	76	10	52
Actinobacteria	38	46	65
Bacteroidetes	16	6	79
Chloroflexi	99	88	88
Cyanobacteria	96	96	56
Firmicutes	72	86	6
Fusobacteria	14	10	78
Proteobacteria	27	14	29
Synergistetes	46	5	42
Tenericutes	77	61	37
Verrucomicrobia	81	83	64

class	Thryve	BiomeSight	Thorne
Actinomycetia	46	54	62
Alphaproteobacteria	35	52	58
Anaerolineae	33	84	53
Bacilli	84	62	58
Bacteroidia	16	6	80
Betaproteobacteria	24	13	15
Caldilineae	100	79	1
Clostridia	63	90	7
Coriobacteriia	76	81	73
Cytophagia	20	58	77
Deltaproteobacteria	81	78	81
Epsilonproteobacteria	68	71	95
Erysipelotrichia	18	28	36
Fusobacteriia	14	10	78
Gammaproteobacteria	47	39	60
Mollicutes	78	61	35
Negativicutes	90	88	3
Opitutae	59	78	60
Synergistia	46	5	42
Tissierellia	96	99	88
Verrucomicrobiae	81	81	63

order	Thryve	BiomeSight	Thorne
Acholeplasmatales	77	62	4
Acidaminococcales	20	21	17
Actinomycetales	93	95	45
Alteromonadales	16	95	86
Anaerolineales	33	26	61
Bacillales	90	47	75
Bacteroidales	16	6	80
Bifidobacteriales	30	25	37
Burkholderiales	24	13	14
Caldilineales	100	79	1
Campylobacterales	68	71	96
Chromatiales	45	92	72
Coriobacteriales	47	84	11
Corynebacteriales	91	94	96
Cytophagales	21	58	77
Desulfovibrionales	81	78	80
Eggerthellales	92	94	96
Enterobacterales	29	22	67
Erysipelotrichales	18	28	36
Eubacteriales	64	89	7
Fusobacteriales	14	10	78
Halanaerobiales	94	100	67
Hyphomicrobiales	18	74	94
Lactobacillales	83	67	61
Micrococcales	39	9	92
Micromonosporales	74	19	85
Mycoplasmatales	87	86	54
Nostocales	72	97	25
Oceanospirillales	57	10	89
Oscillatoriales	95	80	28
Puniceicoccales	62	16	1
Rhodospirillales	8	50	41
Rickettsiales	29	10	49
Selenomonadales	85	86	29
Streptosporangiales	56	78	76
Synergistales	47	5	42
Thermoanaerobacterales	22	18	48
Tissierellales	95	99	82
Veillonellales	96	92	10
Verrucomicrobiales	81	81	63

family	Thryve	BiomeSight	Thorne
Acholeplasmataceae	77	62	4
Acidaminococcaceae	21	21	17
Actinomycetaceae	93	95	66
Aerococcaceae	92	98	60
Akkermansiaceae	82	85	69
Anaerolineaceae	35	26	61
Atopobiaceae	90	16	71
Bacillaceae	76	98	97
Bacteroidaceae	23	15	55
Bifidobacteriaceae	32	25	41
Bradyrhizobiaceae	34	25	81
Caldilineaceae	100	79	1
Campylobacteraceae	68	66	95
Carnobacteriaceae	52	10	67
Clostridiaceae	87	94	12
Coprobacillaceae Verbarg et al. 2014	76	42	1
Coriobacteriaceae	18	84	7
Corynebacteriaceae	90	92	95
Desulfovibrionaceae	83	78	80
Dysgonomonadaceae	60	4	79
Eggerthellaceae	92	94	96
Enterobacteriaceae	58	23	67
Enterococcaceae	60	62	89
Erysipelotrichaceae	19	25	39
Eubacteriaceae	92	77	29
Eubacteriales Family XIII. Incertae Sedis	95	96	66
Lachnospiraceae	80	93	26
Lactobacillaceae	89	74	64
Micrococcaceae	16	13	91
Microcoleaceae	91	77	1
Micromonosporaceae	74	20	85
Mycoplasmataceae	87	86	54
Odoribacteraceae	97	94	77
Oscillospiraceae	34	54	17
Paenibacillaceae	95	47	96
Peptococcaceae	98	90	39
Peptoniphilaceae	95	99	82
Peptostreptococcaceae	47	84	83
Planococcaceae	30	80	78
Porphyromonadaceae	55	27	36
Prevotellaceae	58	52	50
Puniceicoccaceae	63	58	1
Rhodospirillaceae	10	48	35
Rickettsiaceae	35	13	38
Selenomonadaceae	81	87	26
Sporomusaceae	86	38	46
Streptococcaceae	55	66	73
Streptosporangiaceae	68	20	52
Sutterellaceae	24	14	9
Syntrophomonadaceae	96	58	6
Tannerellaceae	29	20	76
Thermoactinomycetaceae	85	88	43
Turicibacteraceae Verbarg et al. 2014	26	27	22
Veillonellaceae	96	92	10

genus	Thryve	BiomeSight	Thorne
Acetivibrio	66	86	14
Acetobacterium	98	85	13
Acholeplasma	77	48	6
Actinobaculum	38	83	3
Actinomyces	74	79	53
Actinotignum	51	71	62
Akkermansia	82	85	69
Alloscardovia	93	93	1
Anaerococcus	94	96	81
Anaerostipes	90	73	41
Arcanobacterium	97	97	5
Bacillus	21	93	96
Bacteroides	26	16	71
Bifidobacterium	19	18	44
Bilophila	59	61	2
Blautia	84	94	4
Brevibacillus	41	85	87
Butyricimonas	73	56	82
Butyrivibrio	94	87	74
Caloramator	65	60	7
Campylobacter	67	67	96
Clostridium	78	87	16
Coprobacillus	87	49	1
Coprococcus	27	4	22
Corynebacterium	90	92	95
Desulfotomaculum	82	75	60
Desulfovibrio	69	84	87
Dialister	97	93	45
Dorea	90	69	1
Dysgonomonas	60	4	40
Eggerthella	75	78	86
Enterococcus	26	67	88
Erysipelothrix	76	67	17
Escherichia	72	32	52
Ethanoligenens	99	37	47
Eubacterium	92	54	38
Faecalibacterium	33	32	21
Filifactor	78	97	50
Finegoldia	90	84	75
Gemella	65	97	18
Hathewaya	88	77	1
Helcococcus	99	87	6
Lachnospira	5	93	88
Lactobacillus	89	60	64
Ligilactobacillus	58	15	81
Limosilactobacillus	24	23	49
Mediterraneibacter	86	81	46
Megasphaera	91	97	41
Mobiluncus	18	16	3
Mogibacterium	98	96	31
Mycoplasma	88	86	47
Mycoplasmopsis	35	94	0
Negativicoccus	75	92	24
Odoribacter	98	98	58
Paenibacillus	95	99	96
Parabacteroides	29	20	76
Pectinatus	23	19	2
Peptococcus	82	90	1
Peptoniphilus	95	99	69
Phocaeicola	3	25	55
Porphyromonas	94	95	97
Prevotella	67	64	58
Pseudobutyrivibrio	40	43	28
Roseburia	69	60	63
Ruminiclostridium	76	46	5
Ruminococcus	91	84	8
Schaalia	36	10	26
Slackia	37	81	10
Streptococcus	56	70	75
Sutterella	46	14	16
Syntrophomonas	97	20	2
Thermoclostridium	60	38	34
Turicibacter	26	27	22
Varibaculum	86	86	2
Veillonella	60	84	20
Weissella	87	13	31

Pass #2: Percentage

phylum	Thryve	BiomeSight	Thorne
Acidobacteria	0.011	0.002	0.005
Actinobacteria	0.652	0.364	2.117
Bacteroidetes	12.526	12.633	60.723
Chloroflexi	0.183	0.02	0.02
Cyanobacteria	0.123	0.374	0.024
Firmicutes	82.61	82.717	27.458
Fusobacteria	0.002	0.002	0.02
Proteobacteria	1.163	1.502	1.894
Synergistetes	0.004	0.002	0.005
Tenericutes	0.049	0.048	0.015
Verrucomicrobia	1.994	1.954	0.627

class	Thryve	BiomeSight	Thorne
Actinomycetia	0.641	0.351	1.093
Alphaproteobacteria	0.013	0.085	0.171
Anaerolineae	0.002	0.011	0.003
Bacilli	2.931	0.729	0.765
Bacteroidia	12.522	10.238	60.036
Betaproteobacteria	0.266	0.263	0.175
Caldilineae	0.182	0.009	0.001
Clostridia	73.508	81.689	25.405
Coriobacteriia	0.665	0.527	0.989
Cytophagia	0.004	0.036	0.047
Deltaproteobacteria	0.723	0.735	0.875
Epsilonproteobacteria	0.011	0.009	0.275
Erysipelotrichia	0.281	0.191	0.336
Fusobacteriia	0.002	0.002	0.02
Gammaproteobacteria	0.152	0.198	0.359
Mollicutes	0.049	0.048	0.013
Negativicutes	3.839	3.4	0.069
Opitutae	0.004	0.008	0.004
Synergistia	0.004	0.002	0.005
Tissierellia	1.468	3.352	0.386
Verrucomicrobiae	1.993	1.946	0.621

order	Thryve	BiomeSight	Thorne
Acholeplasmatales	0.023	0.033	0.002
Acidaminococcales	0.008	0.007	0.007
Actinomycetales	0.092	0.321	0.021
Alteromonadales	0.002	0.065	0.021
Anaerolineales	0.002	0.002	0.003
Bacillales	0.231	0.147	0.233
Bacteroidales	12.567	10.238	60.01
Bifidobacteriales	0.164	0.03	0.123
Burkholderiales	0.267	0.259	0.127
Caldilineales	0.182	0.009	0.001
Campylobacterales	0.011	0.009	0.273
Chromatiales	0.004	0.03	0.013
Coriobacteriales	0.143	0.527	0.025
Corynebacteriales	0.196	0.202	0.81
Cytophagales	0.004	0.036	0.047
Desulfovibrionales	0.716	0.727	0.825
Eggerthellales	0.517	0.525	0.963
Enterobacterales	0.011	0.012	0.157
Erysipelotrichales	0.282	0.191	0.336
Eubacteriales	73.717	80.554	25.38
Fusobacteriales	0.002	0.002	0.02
Halanaerobiales	0.013	0.239	0.004
Hyphomicrobiales	0.002	0.008	0.07
Lactobacillales	2.71	0.388	0.525
Micrococcales	0.006	0.002	0.063
Micromonosporales	0.004	0.002	0.006
Mycoplasmatales	0.021	0.015	0.007
Nostocales	0.006	0.357	0.005
Oceanospirillales	0.008	0.002	0.02
Oscillatoriales	0.117	0.009	0.002
Puniceicoccales	0.004	0.002	0
Rhodospirillales	0.002	0.069	0.021
Rickettsiales	0.002	0.002	0.004
Selenomonadales	0.096	0.392	0.02
Streptosporangiales	0.004	0.008	0.007
Synergistales	0.004	0.002	0.005
Thermoanaerobacterales	0.002	0.007	0.019
Tissierellales	0.99	3.424	0.267
Veillonellales	3.749	3.4	0.041
Verrucomicrobiales	2	1.946	0.621

family	Thryve	BiomeSight	Thorne
Acholeplasmataceae	0.023	0.033	0.002
Acidaminococcaceae	0.008	0.007	0.007
Actinomycetaceae	0.096	0.086	0.021
Aerococcaceae	0.096	0.084	0.007
Akkermansiaceae	2.07	1.903	0.616
Anaerolineaceae	0.002	0.002	0.003
Atopobiaceae	0.141	0.002	0.018
Bacillaceae	0.014	0.07	0.085
Bacteroidaceae	8.206	7.869	30.336
Bifidobacteriaceae	0.17	0.03	0.123
Bradyrhizobiaceae	0.002	0.002	0.011
Caldilineaceae	0.189	0.009	0.001
Campylobacteraceae	0.012	0.009	0.267
Carnobacteriaceae	0.004	0.002	0.007
Clostridiaceae	2.456	11.827	0.304
Coprobacillaceae Verbarg et al. 2014	0.051	0.138	0
Coriobacteriaceae	0.006	0.527	0.007
Corynebacteriaceae	0.195	0.192	0.776
Desulfovibrionaceae	0.743	0.721	0.822
Dysgonomonadaceae	0.006	0.002	0.023
Eggerthellaceae	0.537	0.525	0.963
Enterobacteriaceae	0.012	0.012	0.117
Enterococcaceae	0.004	0.015	0.06
Erysipelotrichaceae	0.293	0.138	0.336
Eubacteriaceae	8.637	0.25	0.054
Eubacteriales Family XIII. Incertae Sedis	0.377	0.188	0.047
Lachnospiraceae	35.558	38.817	15.522
Lactobacillaceae	2.488	0.047	0.09
Micrococcaceae	0.002	0.002	0.027
Microcoleaceae	0.121	0.007	0.001
Micromonosporaceae	0.004	0.002	0.006
Mycoplasmataceae	0.022	0.015	0.006
Odoribacteraceae	1.028	0.955	0.588
Oscillospiraceae	1.604	21.973	5.153
Paenibacillaceae	0.105	0.007	0.085
Peptococcaceae	0.295	0.178	0.03
Peptoniphilaceae	1.028	3.413	0.261
Peptostreptococcaceae	0.162	0.047	0.81
Planococcaceae	0.006	0.006	0.015
Porphyromonadaceae	1.701	0.886	1.001
Prevotellaceae	0.654	0.507	0.285
Puniceicoccaceae	0.004	0.004	0
Rhodospirillaceae	0.002	0.065	0.012
Rickettsiaceae	0.002	0.002	0.002
Selenomonadaceae	0.078	0.388	0.016
Sporomusaceae	0.022	0.004	0.004
Streptococcaceae	0.18	0.165	0.344
Streptosporangiaceae	0.004	0.002	0.003
Sutterellaceae	0.277	0.255	0.028
Syntrophomonadaceae	0.016	0.034	0.001
Tannerellaceae	0.389	0.545	2.972
Thermoactinomycetaceae	0.01	0.015	0.004
Turicibacteraceae Verbarg et al. 2014	0.004	0.004	0.004
Veillonellaceae	3.892	3.4	0.041

genus	Thryve	BiomeSight	Thorne
Acetivibrio	0.019	0.473	0.007
Acetobacterium	0.015	0.196	0.003
Acholeplasma	0.023	0.007	0.001
Actinobaculum	0.002	0.006	0
Actinomyces	0.015	0.014	0.009
Actinotignum	0.004	0.004	0.005
Akkermansia	1.995	1.903	0.616
Alloscardovia	0.009	0.009	0
Anaerococcus	0.414	0.407	0.073
Anaerostipes	4.839	0.144	0.173
Arcanobacterium	0.03	0.03	0.001
Bacillus	0.002	0.019	0.052
Bacteroides	7.908	7.869	30.336
Bifidobacterium	0.017	0.019	0.121
Bilophila	0.295	0.292	0.002
Blautia	13.004	18.727	1.315
Brevibacillus	0.002	0.004	0.004
Butyricimonas	0.147	0.142	0.374
Butyrivibrio	0.276	0.108	0.066
Caloramator	0.023	0.241	0.003
Campylobacter	0.011	0.009	0.263
Clostridium	1.006	3.204	0.188
Coprobacillus	0.051	0.138	0
Coprococcus	0.196	0.035	0.277
Corynebacterium	0.188	0.192	0.761
Desulfotomaculum	0.017	0.007	0.006
Desulfovibrio	0.414	0.429	0.813
Dialister	2.448	2.075	0.019
Dorea	1.94	0.535	0.001
Dysgonomonas	0.006	0.002	0.006
Eggerthella	0.077	0.046	0.139
Enterococcus	0.002	0.013	0.056
Erysipelothrix	0.008	0.019	0.003
Escherichia	0.011	0.01	0.03
Ethanoligenens	0.756	0.004	0.007
Eubacterium	8.214	0.039	0.051
Faecalibacterium	6.494	8.261	4.065
Filifactor	0.008	0.043	0.004
Finegoldia	0.154	0.054	0.041
Gemella	0.006	0.099	0.003
Hathewaya	0.013	0.349	0.001
Helcococcus	0.024	0.006	0.001
Lachnospira	0.004	7.122	4.473
Lactobacillus	2.397	0.017	0.043
Ligilactobacillus	0.006	0.002	0.029
Limosilactobacillus	0.002	0.002	0.003
Mediterraneibacter	5.661	0.813	0.456
Megasphaera	0.036	0.196	0.009
Mobiluncus	0.002	0.002	0.001
Mogibacterium	0.143	0.188	0.007
Mycoplasma	0.021	0.015	0.006
Mycoplasmopsis	0.002	0.015	0
Negativicoccus	0.008	0.06	0.002
Odoribacter	0.844	0.813	0.202
Paenibacillus	0.098	0.017	0.072
Parabacteroides	0.389	0.545	2.955
Pectinatus	0.002	0.004	0.001
Peptococcus	0.019	0.145	0.001
Peptoniphilus	0.395	2.946	0.09
Phocaeicola	0.002	3.922	10.611
Porphyromonas	0.352	0.335	1.001
Prevotella	0.63	0.507	0.158
Pseudobutyrivibrio	0.004	0.076	0.022
Roseburia	2.675	2.71	3.37
Ruminiclostridium	1.995	0.008	0.002
Ruminococcus	8.972	9.736	0.374
Schaalia	0.004	0.002	0.003
Slackia	0.008	0.107	0.003
Streptococcus	0.171	0.165	0.34
Sutterella	0.211	0.255	0.028
Syntrophomonas	0.015	0.002	0
Thermoclostridium	0.004	0.006	0.004
Turicibacter	0.004	0.004	0.004
Varibaculum	0.036	0.034	0.001
Veillonella	0.023	0.556	0.01
Weissella	0.017	0.002	0.003

Bottom Line….

For some bacteria we have the numbers being close, and for others very, very different. Often we see percentage being 10x different between tests! For Percentile, we see one test reforming 91%ile and a different test reporting 1%.

A human analogy: You pick a person off the street and ask “What is this person?” A Canadian? A Swede?

There is usually no clear answer —

• Their name may indicate Iceland — Guðrún Jónsdóttir
• Their passport may indicate that they are a Canadian Citizen
• Their skin color may indicate that they are from Africa
• Their nose may suggest that are from the Mediterranean
• Their DNA may suggest that they are part Nigerian, Finnish, Thai, Irish
  • They are mitochondrial Haplogroup K, very common among Jewish People
• Their eating habits suggests they may be Hindu
• The way they speak English suggests that they are Haida Gwaii (native tribe on Canada’s west coast)
• Their music preference (opera) suggests Italian

Bacteria exchange RNA etc constantly — just like our humans! We may want to have definitive answers — we should fix ourselves before complaining about bacteria! ;-). Different tests use different characteristics to give a bacteria a name.

What I am curious about is whether these changes make dramatic changes in suggestions.

These are notes in progress. Use them at your own risk

In terms of KEGG data, one enzyme stands out: ATP:L-threonine O3-phosphotransferase (2.7.1.177). This is referred to as L-threonine in simpler terms. The people with this sensitivity has 10% of the levels of people not reporting it and there is a value for almost every sample.

This lead to this interest quote (dealing with plants)

• “Salicylic acid[SA] .. in the rhizosphere may be strongly reduced, or completely abolished, due to the presence of histidine and threonine in the root exudates.” [2014] in other threonine may be a key chemical is processing SA.

Checking for the bacteria known to produce the EC2.7.1.177 ATP:L-threonine O3-phosphotransferase the top 3 are:

• Clostridium difficile [species] at 639
• Veillonella dispar [species] at 554
• Megamonas funiformis [species] at 534
• Megasphaera elsdenii [species] at 392

There are no probiotics that produces it. It is available as a supplement, with WebMd suggests this dosage of “Early research suggests that taking 1.5 grams to 2 grams of threonine by mouth three times daily might improve some symptoms in people with familial spastic paraparesis. But the improvement does not seem to be very significant.”

I was unable to find anyone trying this for Salicylate Sensitivity (Hint: any volunteers?)

Salicylate Issues

I did an analysis looking at the frequency percentage of a bacteria being found for Biomesight samples (the largest collection). The key items are below. The higher the Chi² value, the more likely it is.

Tax_Name	Tax_Rank	WithCondition	NoCondition	Chi2
Oceanospirillaceae	family	51.7 (Over)	24.9	8.18
Ruminococcus callidus	species	24.1 (Under)	56.3	5.3
Macrococcus (in Staphylococcaceae family)	genus	58.6 (Over)	35.7	4.16
Amedibacillus	genus	96.6 (Over)	66.7	3.81

There was not sufficient data on Ombre samples

The substances that reduces these overgrowths are: Cacao, green tea, kefir, lychee fruit,  papaya, rhubarb, rosehip tea,  trametes versicolor(Turkey tail mushroom), Slippery Elm. Many of these are low in salicylate (check each one before doing).

Histamine Issues

I did an analysis looking at the frequency percentage of a bacteria being found for Biomesight samples (the largest collection). The statistically significant items are below, with 1 overgrowth “Streptococcus oralis” and the rest as undergrowth . The higher the Chi² value, the more likely it is.

Tax_Name	Tax_Rank	WithCondition	NoCondition	Chi2
Clostridium chartatabidum	species	16.5	29.4	6.54
Euryarchaeota	phylum	14.9	26.9	6.29
Methanobacteriaceae	family	14.9	26.3	5.75
Streptococcus oralis subsp. tigurinus	subspecies	57.9	43.7	5.13
Heliobacteriaceae	family	30.6	44.1	4.8
Ruminococcus callidus	species	42.1	56.7	4.31
Bifidobacterium asteroides	species	20.7	31.4	4.29
Thermosediminibacterales	order	27.3	38.5	3.82

Tax_Name	Tax_Rank	WithCondition	NoCondition	Chi2
Actinopolysporales	order	38.8	24.1	8.83
Anaeroplasmataceae	family	50.9	34.4	7.77
Desulfocella	genus	43.1	28.6	7.26
Desulfocella halophila	species	42.2	28.3	6.8
Rhodocyclales	order	45.7	31.8	6
Burkholderiaceae	family	42.2	29.3	5.77
Spirosomaceae	family	74.1	56.8	5.35
Rothia mucilaginosa	species	19	31.4	5.33
Ruminococcus gauvreauii	species	47.4	34	5.28
Rhodocyclaceae	family	44	31.2	5.19
Ezakiella	genus	39.7	28	4.85
Holdemania massiliensis	species	44	31.7	4.71
Limosilactobacillus	genus	38.8	27.6	4.62
Cytophagaceae	family	85.3	68.1	4.47
Bacteroides fluxus	species	69	53.6	4.45
Halobacteroidaceae	family	44.8	32.9	4.44
Oscillibacter valericigenes	species	44.8	32.9	4.37
Senegalimassilia	genus	44.8	32.9	4.35
Flammeovirgaceae	family	42.2	31.1	4.03
Hungateiclostridium	genus	56.9	43.8	3.95

This is a hot new area is a speculation, first suggested in Postbiotics: what else?[2013] which states “Recent work on relevant probiotic strains has also led to the isolation and characterization of certain probiotic-produced, soluble factors, here called postbiotics, which were sufficient to elicit the desired response.”. To translate, culture probiotic and separate out the chemicals they produce (for example, lactic acid for lactobacillus cultures), you do not alter their composition. It appears that marketing types are using the same term for something different that they are trying to sell.

There are just two clinical studies in progress. All from 2022 or later, a few examples:

• Effect of POSTbiotics Supplementation on Microbiome in OBese Children: the POST-OB Study (POST-OB) [2022] using “immunofos from Lactobacillus paracasei CNCM I-5220).”
• Oral Postbiotics in Patients With Macular Atrophy (REVERS-GAPilot Study to Evaluate the Safety and Efficacy of Oral Therapy With Postbiotics in Patients…. using postbiotics (IGENH35.3A). NOTE the SAFETY in the title!

There have been no results published.

Some Key Word

Lysate Probiotics: See Lysis – Wikipedia. This is caused by gently breaking down a bacteria (probiotic) often using bacteriophages.  It keeps all of the components intact, but the cell is no longer alive. This has had clinical studies, for example

• Bacterial lysates in food allergy prevention [2013] “heat-killed Bifidobacterium breve and Streptococcus thermophilus; … food allergy prevalence could not be reduced by these interventions.”
• Oral Bacterial Extract for the Prevention of Wheezing Lower Respiratory Tract Illness [2014]
  • No results posted, after 9 years it indicated failure usually.
• A Nonviable Probiotic in Irritable Bowel Syndrome: A Randomized, Double-Blind, Placebo-Controlled, Multicenter Study [2022] “a nonviable probiotic lysate is not effective across all IBS subtypes”
• Probiotic Lysate (Postbiotic and Metabiotic) Supplementation for Adults NAFLD Patients [2023] “
• Bacterial Lysates on Respiratory Tract Microecology  [2022]

Sterilized: This is cooking the bacteria to kill it. It changes the factors or metabolites that would be there if the bacteria had been killed by a bacteriophages.

The new Snake Oil

A reader asked me about one new product, Thaenabiotic being pushed by Flora Medicine in Portland, OR. This is described as:

ThaenaBiotic® is a fecal-derived, sterilized, full-spectrum postbiotic that contains metabolites from a unique, healthy ecosystem of microbes originating from special, hand-picked donors.

https://www.floramedicine.com/thaenabiotic

This is the second time in a month that I have been asked about sterilized fecal matter postbiotics (or similar names). I roll my eyes for several reasons:

• Being sterilized means “He’s dead Jim”. Not just changed but well cooked (perhaps very char-boiled!). This is a clever way to attempt to get around the FDA limits on the matter of Fecal Matter Transplants — it’s dead material!
• Even if some metabolites survives, whether it has any results beyond placebo effects is very questionable. At best, the effect may last one or two days — hence you will need to keep reordering! An excellent business model!

The metabolites may cause effects, but the persistence of the effects is the key question. With appropriate living probiotics (or live FMT), sufficient bacteria takes up residence — not possible with killed bacteria.

“This suggests that it is the host response to probiotics, rather than microbial metabolism that facilitates the molecular changes in the brain and downstream behaviours.”

Live or heat-killed probiotic administration reduces anxiety and central cytokine expression in BALB/c mice, but differentially alters brain neurotransmitter gene expression [2023]

This is being run out of a Naturopath office with the three sole people that can “prescribe” appearing to be members of that same office. You must pay for a consult with them before you can order.

“Fecal Matter Transplants” and “Post-Biotics” are hot words in trade magazines. If you want to make money fast, you create a product wrapped in those words without needing any evidence that they work or are safe. It will be at least two years before the FDA will shut you down.

And not surprising, they are looking for investors and venture capitalists. To paraphrase John Paul Jones, “Give me Research or Give me Money”

P.S. I have emailed them asking for “Can you provide published clinical studies on the use of your ThaenaBiotic product? As well as details on the composition… which metabolites and chemicals are in it and the amount of each.” – I expect stonewalling or no answer back.

As a result of some readers asking for a PDF that they could shared with their medical professional I have created two reports and deployed the first versions today. The readers requested these features:

• Simplify the suggestions into shorter list without extra data like Priority.
• Provide some of the literature used to generate the suggestions
• Provide the bacteria being targeted.

The reports are based on the consensus reports (so you can build them as you like).

See this update: Updates on PDF Reports for Professionals

For the automatic emailing of the PDF, we run “Just Give Me Suggestions” — which executes 4 different algorithms (Mean +/- 2 Standard Deviation, Box-Whiskers, Kaltoft-Moltrup and top/bottom 5%ile) and to obtain a consensus report. From the consensus report, we pick those that are at least 50% of the highest value to take, and below 50% of the lowest value to avoid. We then sort the items alphabetically for the lists.

The suggestions are from Microbiome Prescription and may disagree with suggestions from the lab used. To see how we get suggestions (tracing back to source studies), see this video. You will need to ask the lab how they do theirs — in some cases, it is opinion from a dietician.

Links are on various pages, for example, changing Microbiome tab.

First Page is an introduction to what the report is

Second Page is the bacteria being targeted, group by taxonomy rank

Third page are suggestions to take. Where dosages from clinical studies are available, they are shown

The next page are things to avoid

The last page are a partial listing of citations explicitly used. The newest studies are listed first to pre-emptively answer the question about how old the data being used is.

This is in Beta Testing Mode

Open for suggestions and improvements. Remember this is targeted for the typical medical professional with limited knowledge and understanding of the microbiome.

Based on several online meeting that I had, I thought a series of online meeting for people to ask questions, be shown features, etc would benefit many. All meetings were recorded and then posted on YouTube. After viewing these, you may wish to view this from Jan 2023 also.

2023 Walk thru of Personal Microbiomes Suggestions

The scope of these meetings

Link to Calendar Click to go directly to Meeting

• Aug 12th — Looking at information that you can just lookup. Includes getting suggestions for specific conditions.
• Aug 19th — Working with transcribed data from a test and simple suggestions from uploaded tests
• Aug 26th — Working with data from one test alone
• September 2nd — Working with multiple test results (comparison, evaluating progress). Direct links
• https://www.youtube.com/watch?v=7fizV1GZuJ4
• https://www.youtube.com/watch?v=e0RFCvRj5VI
• https://www.youtube.com/watch?v=jr7cBPqBAtc
• https://www.youtube.com/watch?v=tjE1-artKXg
• https://www.youtube.com/watch?v=owhzeVfrapo

One link was broken in the demo. It is now fixed and a walk through is done below.

A reader message me and sent over two files. One was familiar and my advise has been to transcribe the data; the other was a new format and it was very possible to code an uploader for in less than a day.

The small one, bacteria-count is what can be uploaded.

The analysis one has less taxonomy data than the bacteria count one and starts up with the type of page shown below. Dissecting it to get the data would likely be six full man-weeks of development. My attitude is for clients to hassle the provider to make a more friendly format (i.e. tab or comma delimited text files).

The Sweet Bacteria Count File

This file looks like below and is relatively easy to extract the data for (i.e. less than one developer day).

How to Upload

Go to MicrobiomePrescription : Microbiome Sample Uploads and select the highlighted link.

This will open MicrobiomePrescription : Ganzimmun Diagnostics Uploads, with the usual format.

Some Caveat’s

We have only a hundred bacteria at different levels reported. If you go to the microbiome tree, you will see a lot more! Why, we build all of the missing levels of the bacteria hierarchy from the missing data. The upload has no family, order or class information … so we make a best effort attempt to estimate them.

We also apply percentile based on all samples uploaded (until we get 200 samples for a specific test) … again best efforts. See The taxonomy nightmare before Christmas… for background.

For more information on this project see Health Rising post. Both participants has granted me to do a review with their real names. This is the second of a series of posts on this project, the first one was on The Remission Biome Project: Tess Falor.

Connected with review, you may wish to read Dr. Jadin’s Current Protocol for ME/CFS – Microbiome Prescription Blog, parts are being consider for incorporation into the Remission Biome Projects

The earliest use of antibiotics for treating ME/CFS that I am aware of, dates from the late 1990’s with articles in  Journal of Chronic Fatigue Syndrome (and conference reports prior)

• Role of Rickettsiae and Chlamydiae in the Psychopathology of Chronic Fatigue Syndrome (CFS) Patients by Philippe Bottero
• Common Clinical and Biological Windows on CFS and Rickettsial Diseases by C. L. Jadin
• Role of Mycoplasmal Infections in Fatigue Illnesses by G.L. Nicolson

My remission from ME/CFS was done by combining C.L. Jadin protocol with Dave Berg anticoagulant protocol.

A big thanks to BiomeSight.com for donating some testing kits to the project. If interested in using their kits, there is a discount code (“micro”).

Overview of results

First, let us show the numbers and then talk about them. It is clear that there are significant changes. There are a lot of dimensions to consider. Some highlights:

• The number of bacteria with abnormally high representation has gone from 123 down to 29
• The number of bacteria with abnormally low representation has gone from 222, dropping down to as low as 19, before rebounding to 162 (still better than the start)
• Most measure showed great improvement and then some relapse.

Criteria	7-Mar	23-Mar	15-Apr	22-Apr	29-Apr
Shannon Diversity Index	33.8	78.0	97.1	76.5	77.1
Simpson Diversity Index	0.7	65.1	58.6	60.3	73.4
Chao1 Index	91.3	61.6	72.0	89.4	14.8
Chi-Square (Lower is better)	55	47	46	50	30
Lab Read Quality	8.6	7.1	5.4	2.2	6.9
Bacteria Reported By Lab	755	638	628	765	461
Bacteria Over 99%ile	27	11	13	56	5
Bacteria Over 95%ile	72	25	30	105	18
Bacteria Over 90%ile	132	46	63	178	29
Bacteria Under 10%ile	222	218	62	19	162
Bacteria Under 5%ile	191	195	18	12	144
Bacteria Under 1%ile	177	179	0	3	112
Lab: BiomeSight
Rarely Seen 1%	8	4	3	66	2
Rarely Seen 5%	22	24	27	128	14
Pathogens	39	32	33	31	29
Outside Range from JasonH	8	8	8	8	8
Outside Range from Medivere	18	18	16	16	16
Outside Range from Metagenomics	9	9	6	6	6
Outside Range from MyBioma	10	10	6	6	6
Outside Range from Nirvana/CosmosId	18	18	12	12	12
Outside Range from XenoGene	52	52	39	39	39
Outside Lab Range (+/- 1.96SD)	43	15	17	36	7
Outside Box-Plot-Whiskers	146	51	83	227	43
Outside Kaltoft-Moldrup	251	189	105	212	158
Condition Est. Over 99%ile	1	5	0	0	3
Condition Est. Over 95%ile	2	14	0	5	13
Condition Est. Over 90%ile	11	28	2	11	21
Enzymes Over 99%ile	7	68	5	19	37
Enzymes Over 95%ile	22	281	209	123	250
Enzymes Over 90%ile	58	435	361	317	409
Enzymes Under 10%ile	219	354	59	48	201
Enzymes Under 5%ile	173	265	34	24	144
Enzymes Under 1%ile	138	94	13	12	79
Compounds Over 99%ile	3	44	1	13	16
Compounds Over 95%ile	15	186	82	68	87
Compounds Over 90%ile	27	297	154	153	183
Compounds Under 10%ile	882	889	985	987	875
Compounds Under 5%ile	862	859	959	963	841
Compounds Under 1%ile	845	802	935	952	820

As with Tess, the percentages by percentile which I noticed tend to have over representation with ME/CFS and Long COVID in the 0-9 percentile. We see this pattern at the start, with improvement and then a bounce back to high numbers in the last sample

Tamara suggested that I convert the tables below to charts. Both are now available on the site.

Pretty Pictures

Tamara suggested that I convert the tables below to charts. Both are now available on the site.

First, an old sample that she happened to have where we see Chi-Square at 6. The first of the recent samples had it jumping to 55, A normal microbiome is expected to have a Chi-square < 13. A higher value indicates a statistically significant, abnormal microbiome.

The next three show the changes with antibiotics. Chi-square went from 46 to 50 with a dramatic shift and then drifted down to 30.

The latest sample increased upward again, with the pronounced spikes that are common with ME/CFS being there.

The raw numbers are also shown. I will spare your eyes by omitting them.

The Events Around the above Samples

• 7 Mar – Before
• 23 Mar – Day 4 AmoxClav
• 15 Apr – More
• 22 Apr – Final Day of AmoxClav (30 days of AC)
  • This sample has a low Lab Read Quality, this may account for the number of spikes in its report.
• 29 April – After 3 days of Aprepitant + Erythromycin (this was a BIG difference from Tess and was the intervention that seemed to give me the baseline increase this time).

As with Tess, let us see how these items rank in each sample. As with Tess, imipenem is the most common best suggestion.

Criteria	7-Mar	23-Mar	15-Apr	22-Apr	29-Apr	22-May
Amoxicillin	-104	95	276	296	432	402
Erythromycin	59	253	340	236	222	228
Aprepitant	393	297	260	320	280	80
Highest	632	497	635	610	650	594
	cefaclor hydrate	imipenem	imipenem	imipenem	cefoxitin	cefoxitin

Going Forward

As a result of a conference call with some of the Remission Biome Project, and Dr. Jadin’s Current Protocol for ME/CFS. I annotated all of the antibiotics used in studies for ME/CFS, Lyme, and related conditions with [CFS]. This allows us to quickly see the “consensus” antibiotics (i.e. used in studies and suggested by microbiome prescription algorithms).

The top ones are shown before (Just enter “CFS” in the Search dialog)

Only two of these were negative for her (doxycycline and ampicillin) with docycline sibling, minocycline being just 21).

I would suggest using this list to pick 2 antibiotics to do a one week course and then take a 3 week break. After the course, then do some of these probiotics. I am inclined to omit L.Casei because the strain used in Yakult is a negative. Thus we end up with these three as top suggestions. P.S.

• bacillus subtilis (probiotics) : 342 @ 1 – 10 BCFU/day
• bifidobacterium breve (probiotic) :328 @ 2-15 BCFU/day
• bifidobacterium infantis,(probiotics): 292 @ 1 – 10 BCFU/day

Note the weight of these are above many of the antibiotics above. I usually advocate single species. The Bifido is available from Custom Probiotics with their recommended dosages above the amount listed above.

Part Deux — More Samples!

Her description of subjective changes: generally keep improving in terms of PEM, function etc. (was definitely a dip around the 2nd ‘constipation’ sample)

• 1st, [2023-06-14] in the series just a temporal sample, no additional treatments
• 2nd, [2023-07-15] in the series I had a major episode of constipation – wanted to catch that 
• 3rd, [2023-07-20]last one was was post my 2nd treatment of aprepitant+erythromycin

Sample Comparison

We include the prior one above for easy reference). The key change items are:

• The new Anti inflammatory Bacteria Score has seen a dramatic increase from 17%ile to 73%ile. The four prior samples were 7.6%ile, 8.2%ile, 3.9%ile and 6.9%ile
• Outside Kaltoft-Moldrup is dropping. In terms of %age of reported: 32% -> 28% -> 29% ->16%
• The high and low Enzymes also seem to be dropping
• The last sample had a Chi-Square of 9, that is a probability of 0.54 instead of the .9999999… for all other samples. Unfortunately, the poor read quality makes this fuzzy.
• Note: The last sample has a low read quality (thus less bacteria types are being reported)

Criteria	22-May	14-Jun	15-Jul	22-Jul
Shannon Diversity Index	69.4	29.20	43.00	15.00
Simpson Diversity Index	54.7	7.60	27.50	60.00
Chao1 Index	72.4	0.87	21.40	8.30
Anti inflammatory Bacteria Score	17.0	30.90	43.60	73.20
Chi-Square Score	49	51	32	9
Lab Read Quality	7.2	10.9	6.6	2.3
Bacteria Reported By Lab	659	752	512	375
Bacteria Over 99%ile	10	12	1	4
Bacteria Over 95%ile	22	24	2	20
Bacteria Over 90%ile	45	41	13	39
Bacteria Under 10%ile	199	229	189	19
Bacteria Under 5%ile	186	208	184	3
Bacteria Under 1%ile	167	165	166	0
Lab: BiomeSight
Rarely Seen 1%	25	13	0	0
Rarely Seen 5%	49	37	7	1
Pathogens	32	34	36	21
Outside Range from JasonH	4	7	7	4
Outside Range from Medivere	14	19	19	14
Outside Range from Metagenomics	6	7	7	6
Outside Range from MyBioma	4	6	6	4
Outside Range from Nirvana/CosmosId	18	19	19	18
Outside Range from XenoGene	33	34	34	33
Outside Lab Range (+/- 1.96SD)	11	12	1	13
Outside Box-Plot-Whiskers	56	75	23	50
Outside Kaltoft-Moldrup	209	212	150	61
Condition Est. Over 99%ile	0	0	1	0
Condition Est. Over 95%ile	0	0	1	0
Condition Est. Over 90%ile	1	0	3	0
Enzymes Over 99%ile	62	1	5	2
Enzymes Over 95%ile	130	3	18	11
Enzymes Over 90%ile	215	12	99	33
Enzymes Under 10%ile	429	211	171	304
Enzymes Under 5%ile	310	146	142	211
Enzymes Under 1%ile	152	85	73	47
Compounds Over 99%ile	31	0	3	1
Compounds Over 95%ile	64	2	9	11
Compounds Over 90%ile	106	7	48	24
Compounds Under 10%ile	959	1096	1009	1015
Compounds Under 5%ile	909	1041	981	971
Compounds Under 1%ile	860	1009	956	922

Since we had a symptom of constipation, let us see how well the samples match that reported from Studies on PubMed — there were no matched. When we went to our Special Studies, we see that the microbiome followed the reported symptoms. We then look at the top value from Special Studies — which was Long COVID for all samples. We see the lost of ground around the constipation and then regaining the progress.

Criteria	22-May	14-Jun	15-Jul	22-Jul
Special Studies	7%ile	14%ile	15%ile	7%ile
Top Item Long COVID	35 %	44 %	41 %	36 %

Next we go and look at aprepitant and erythromycin

Criteria	22-May	14-Jun	15-Jul	20-Jul
Aprepitant	80.4	113.7	205.8	169.9
Erythromycin	227.5	-76.8	226.7	194
Best CFS antibiotic Priority / Max Priority	metronidazole 430 /549	lymecycline 176 /307	amoxicillin 424 / 527	amoxicillin 351 / 484

The Percentage of Percentile show quite a shift — unfortunately, it is unclear if this is a temporary after effect of constipation, poor lab read quality, or the above aprepitant and erythromycin. The next sample may resolve this issue.

Is the Project working — YES

We are seeing both subjective improvement and object improvements.

Personally, I like what appears to be a shift towards Cecile Jadin’s approach — not continuous antibiotics but a course (7-10 days) followed by a break (ideally 3 weeks). Often I find that ME/CFS people tend to be impatient and just want to keep pressing on hard… which I have observed often result in tripping and rolling down the hill to where they were (or worst).

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 can compute items to take, those computations do not provide information on rotations etc.

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.

During the questions period of Jadin’s presentation: Dr. Jadin’s Current Protocol for ME/CFS; Questions were asked about her treatment in terms of it’s target and what microbiome prescription does. I thought a blog post may help people understand how microbiome prescription side-steps a Pandora box of theories.

A simple Premise: Fix the microbiome and symptoms will improve!

In Dr. Jadin’s presentation, she identifies a host of causes that could result in ME/CFS and similar conditions. For example Giardia — IBS/CFS/Long Covid Insight from Bergen’s Giardia Infection, Lyme, or my post from 2016, Post Infection Fatigue, virus: HHV6, EBV and of recent note: COVID. The question arises: Is the source still there? The pragmatic answer is likely yes, at low but significant levels (i.e. maintenance levels).

Not only is it there, but there is likely a half dozen low level infections associated with ME/CFS. WHY? When the microbiome goes off, the immune system follows it. Virus reactivation happens because the immune system is not firing on all cylinders. For example, your body may keep fungus well controlled in your living environment before; now you have fungi problems added to the mix.

Chasing Symptoms Trap

Often people will be focused on one symptom or lab result. For example: How do I reduce my methane levels, How do I reduce my Interleukin 6 levels, how do I get rid of brain fog, etc etc etc.

I do not have the answers for those questions, nor do I care. I review the body as a very complex dynamic organism with a great amount of interactions. My sole target is the microbiome and that is a big target — over 18000 different bacteria are reported from retail microbiome tests, 8,000+ different enzymes, 18,000 different compounds.

Let us take just one ME/CFS associated item: Epstein-Barr virus. Some of the interactions and interplays are shown in the chart below (See KEGG for full chart). So, you are wanting a single magic supplement?

My goal is very simple, normalize the microbiome and thus normalize the immune system and the levels of the 26,000+ substances involved. Then, and only then, will issues of substance appear (if any).

No matter what the infection (virus, fungi, bacteria) – they need “food”

Where does the food come from? The microbiome. Correcting the microbiome should starve the bad guys and feed the good guys is the simple concept. All of these infections are known to alter the microbiome — and we can reasonably assume that it is done to make the human body more friendly to the infection.

Now with 18,000 bacteria and 26,000 substances, no person can either read nor keep all of the available data in their brain. I happen to have the skill sets to encode much of the data and build a fuzzy logic expert system around the data – that’s my skill set.

Why does my recommendations often matches Jadin’s protocol?

If we assume the infection is still there, we likely have a strong association between the infection and the microbiome bacteria. What encourages the infection, likely also encourages a subset of microbiome bacteria; and the reverse. The microbiome may just be a proxy for the infection with the odd-characteristic that we may be able to determine effective antibiotics without needing to identify all of the infections present. The infection and the microbiome in one sense are mirrors of each other.

This is the philosophical basis that I am working from. As with best modelling practices, if a model predicts and the predictions are correct, you keep to the model (even if it is full of orcs and hobbits) until it breaks — then you work on building a better model.

The microbiome may be a sufficient proxy for the co-infections involved with ME/CFS.

It is easier, faster and cheaper to test – especially because it is available at a low cost, direct to consumers.

Nicotine often shows up in suggestions. On some people, quite high. It was included because we include everything we find studies for (including Round-Up!). Then I got a reader with ME/CFS who found that nicotine patches caused a major improvement. Then I saw someone mention that 30 years ago, smoking/nicotine was suggested by some MDs to stop UC’s Flares.

nicotine modulates the immune system, inhibits innate and acquired immunity and is used in treating many autoimmune diseases. It often stimulates the α7 receptor and causes an anti-inflammatory state in the body. This study is designed to evaluate the role of nicotine treatment on immune system. The results showed that nicotine affects many cells in immune system, alters the downstream intracellular mechanisms and changes lymphocytes polarization.

Effect of Nicotine on Immune System Function [2023]

Smoking tobacco is associated with a number of gastrointestinal disorders. In some, such as Crohn’s disease and peptic ulcer disease, it increases the risk of disease and has a detrimental effect on their course. In others, such as ulcerative colitis, it decreases the risk of disease and can have a favorable effect on disease course and severity.

Mechanisms of Disease: nicotine—a review of its actions in the context of gastrointestinal disease [2005]

Ulcerative colitis (UC) is predominantly a disease of non-smokers and treatment with transdermal nicotine improves symptoms in UC patients, whereas smoking seems to have a deleterious effect in patients with Crohn’s disease (CD). 

In-vivo effect of nicotine on cytokine production by human non-adherent mononuclear cells [1996]

Anecdotal reports suggest that smoking may be beneficial for patients with inflammatory bowel disease (IBD) as nicotine may act through inflammatory mediators within the colonic mucosa….Moreover, concentrations of IL-1beta and IL-8 were significantly reduced in smokers with UC compared with nonsmokers with UC.

The influence of cigarette smoking on cytokine levels in patients with inflammatory bowel disease [1999]

In this relatively small study of patients with active Crohn’s colitis, 6 mg nicotine enemas appeared to be of clinical benefit in most patients. They were well tolerated and safe.

Nicotine enemas for active Crohn’s colitis: an open pilot study [2008]

Bottom Line

It appears that smoking is bad for Crohn’s, but nicotine may be beneficial. Most studies are on smoking and not nicotine in isolation. IMHO, the suggestion of nicotine is probably a valid (and unusual) one.

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