Over the last two weeks, there has been a couple of email pointing out possible errors in some citations. I am not surprised. I expect 90-95% correctness (i.e. 1 in 10 or 1 in 20) may be incorrectly entered. To improve the quality, we need independent review of the data. In one amusing case, I quoted my source correctly but that review study incorrectly cited it’s source. The data entry was right, the source document was wrong.

The articles are technical studies which often require advance reading skills and knowledge of this topic. Some of the sources are available in full on the web for free, others are behind a paywall. If you are connected with a university or college, you may have access thru your institution.

If you cannot access the full text of the source, then skip it. Extracts and summaries can contain errors.

Process

Just email me from the email account that you logged in with and I will add auditor or QA permissions to your account.

Doing it

When you logged in, you should see:

When you look at citations, you will see the ⚖️ icon (or a ✅ if someone has already checked) beside the citation.

Example for a list of citations

Note that there may be more in the study then what the titles implies. Often data is from Appendix and tables.

Clicking ⚖️ will take you to a page showing what was extracted and gives you an opportunity to correct it,

Click [Report above Issue] will send emails to me and to your self. If all of the information is correct, then click [I have verified..] and the next time you see the citation, there will be a ✅ beside it. Your email is stored beside the citation as the reviewer.

You will get an email confirming stuff

If there is information that was missed (more likely) please include the TAXON numbers of the bacteria. This speeds up the process. Often information was missed because of alternative spelling.

That’s the process. Short, simple and with the ability for me to quickly make corrections.

Response differences of gut microbiota in oligofructose and inulin are determined by the initial gut Bacteroides/Bifidobacterium ratios [2023] Indicates difference of response.

Bacteroides/Bifidobacterium (Ba/Bi) ratios “with Low Ratio showing a significant increase in propionic acid and being enriched in glycolysis functions, whereas High was enriched in amino acids and aminoglycolysis functions”

Of course, the question becomes what are low and high ratio? To answer that question, I pulled some data by lab

Ubiome Ratios

Ombre Ratios

BiomeSight Ratios

Bottom Line

I would suggest a ratio > 500 to 1000 is High, Below 200 is low

The following is based on Biomesight data uploaded with some annotated by uploaders. Technically, interpretation of microbiome results should factor these issues in. At least 10 samples had to have Symptom Name to be included. The numbers shown are averages (on count per million) on samples that reported the bacteria.

As a spot check, we see multiple Bifidobacterium in the 0-10 range which agrees with the literature.

In general, the symptom has more than the population because we filtered by using a high T Score to keep the list shorter for illustration purposes. A variety of other factors are not shown, for example: FUT2 non-secretor or FUT2 secretor.

Dropping the T-Score filtering to 1.96 resulted in 3600 rows.

Symptom Name	Name	Rank	Population per million	Sample per million	T-Score
Age: 0-10	Listeria	genus	32	361	7.2
Age: 0-10	Bifidobacterium magnum	species	21	230	16.4
Age: 0-10	Bifidobacterium pseudocatenulatum	species	37	7091	214.7
Age: 0-10	Paenibacillus	genus	37	330	5.5
Age: 0-10	Cystobacterineae	suborder	19	115	6.1
Age: 0-10	Desulfovibrio vietnamensis	species	23	2264	127
Age: 0-10	Anaerostipes	genus	1581	21701	5.3
Age: 0-10	Ethanoligenens harbinense	species	39	1571	30.4
Age: 0-10	Bifidobacterium bombi	species	34	674	7.3
Age: 0-10	Eubacteriales incertae sedis	norank	300	9097	9.5
Age: 10-20	Anaerococcus octavius	species	103	1738	7.2
Age: 10-20	Bifidobacterium boum	species	34	1441	20
Age: 10-20	Puniceicoccales	order	82	2091	7.2
Age: 10-20	Bifidobacterium bombi	species	34	2072	23.1
Age: 10-20	Eubacteriales incertae sedis	norank	300	9118	9.5
Age: 10-20	Ruminiclostridium	genus	696	10396	5.1
Age: 20-30	Aggregatibacter aphrophilus	species	35	445	11.1
Age: 20-30	Listeria	genus	32	267	5.2
Age: 20-30	Bifidobacterium magnum	species	21	120	7.8
Age: 20-30	Halomonas	genus	30	826	18.5
Age: 20-30	Bifidobacterium pseudocatenulatum	species	37	1854	55.3
Age: 20-30	Halomonadaceae	family	40	470	8.5
Age: 20-30	Lactobacillus crispatus	species	356	21583	8.1
Age: 20-30	Fibrobacteres	phylum	224	66028	32.3
Age: 20-30	Brenneria	genus	53	1321	10.7
Age: 20-30	Dietziaceae	family	113	1627	5.4
Age: 20-30	Piscirickettsiaceae	family	24	137	6.7
Age: 20-30	Desulfovibrio vietnamensis	species	23	1625	90.8
Age: 20-30	Ethanoligenens harbinense	species	39	308	5.3
Age: 20-30	Saccharospirillaceae	family	18	972	110.2
Age: 20-30	Prevotella maculosa	species	107	2980	17.5
Age: 20-30	Eubacteriales incertae sedis	norank	300	5340	5.4
Age: 20-30	Eubacteriales Family XII. Incertae Sedis	family	38	715	11.2
Age: 20-30	Pectobacteriaceae	family	53	1257	10.7
Age: 20-30	Balneolaeota	phylum	26	20413	791.7
Age: 30-40	Francisella	genus	18	3459	456.5
Age: 30-40	Enterobacter cloacae	species	98	1305	8.4
Age: 30-40	Aggregatibacter aphrophilus	species	35	1247	32.7
Age: 30-40	Aggregatibacter segnis	species	84	1600	9.4
Age: 30-40	Halomonas	genus	30	381	8.2
Age: 30-40	Bifidobacterium pseudocatenulatum	species	37	6838	207
Age: 30-40	Francisellaceae	family	18	3239	427.3
Age: 30-40	Halochromatium salexigens	species	23	125	6
Age: 30-40	Cystobacterineae	suborder	19	109	5.7
Age: 30-40	Dietziaceae	family	113	1856	6.2
Age: 30-40	Halochromatium	genus	24	187	9.4
Age: 30-40	Desulfovibrio vietnamensis	species	23	3094	174
Age: 30-40	Ethanoligenens harbinense	species	39	364	6.5
Age: 30-40	Saccharospirillaceae	family	18	567	63.4
Age: 30-40	Prevotella maculosa	species	107	12394	74.7
Age: 30-40	Eubacteriales incertae sedis	norank	300	5305	5.4
Age: 30-40	Eubacteriales Family XII. Incertae Sedis	family	38	352	5.2
Age: 30-40	Balneolaeota	phylum	26	2236	85.8
Age: 30-40	Rhodovibrionaceae	family	119	4022	22.4
Age: 40-50	Aggregatibacter aphrophilus	species	35	771	19.9
Age: 40-50	Bifidobacterium pseudocatenulatum	species	37	4210	127
Age: 40-50	Francisellaceae	family	18	626	80.7
Age: 40-50	Fibrobacteres	phylum	224	29330	14.3
Age: 40-50	Cystobacterineae	suborder	19	138	7.6
Age: 40-50	Desulfovibrio vietnamensis	species	23	3100	174.3
Age: 40-50	Ethanoligenens harbinense	species	39	857	16.3
Age: 40-50	Saccharospirillaceae	family	18	209	22
Age: 40-50	Prevotella pleuritidis	species	52	2202	17.1
Age: 40-50	Megamonas funiformis	species	1344	33754	7.2
Age: 40-50	Prevotella maculosa	species	107	4535	26.9
Age: 40-50	Eubacteriales Family XII. Incertae Sedis	family	38	858	13.6
Age: 40-50	Balneolaeota	phylum	26	12551	486.4
Age: 40-50	Rhodovibrionaceae	family	119	1249	6.5
Age: 50-60	Desulfomicrobium	genus	34	276	8.3
Age: 50-60	Bifidobacterium pseudocatenulatum	species	37	7205	218.1
Age: 50-60	Hathewaya proteolytica	species	19	89	6.6
Age: 50-60	Paenibacillus	genus	37	370	6.2
Age: 50-60	Fibrobacteres	phylum	224	17511	8.5
Age: 50-60	Sporotomaculum hydroxybenzoicum	species	37	2301	36.9
Age: 50-60	Cystobacterineae	suborder	19	178	10.2
Age: 50-60	Olsenella	genus	131	4275	9.8
Age: 50-60	Desulfovibrio vietnamensis	species	23	3624	204
Age: 50-60	Desulfomicrobiaceae	family	34	278	8.4
Age: 50-60	Ethanoligenens harbinense	species	39	1907	37.1
Age: 50-60	Saccharospirillaceae	family	18	365	40.1
Age: 50-60	Tepidimicrobium	genus	27	158	7
Age: 50-60	Opitutae	class	173	3532	5.3
Age: 50-60	Puniceicoccales	order	82	3627	12.8
Age: 50-60	Eubacteriales incertae sedis	norank	300	5755	5.9
Age: 50-60	Eubacteriales Family XII. Incertae Sedis	family	38	541	8.3
Age: 50-60	Clostridium amylolyticum	species	21	131	8.4
Age: 50-60	Atopobiaceae	family	113	2945	7.2
Age: 50-60	Balneolaeota	phylum	26	5457	210.9
Age: 60-70	Achromobacter	genus	28	246	6.7
Age: 60-70	Salmonella	genus	74	1870	7.4
Age: 60-70	Aggregatibacter aphrophilus	species	35	381	9.3
Age: 60-70	Desulfomicrobium	genus	34	226	6.6
Age: 60-70	Deinococcus-Thermus	phylum	83	2172	12.4
Age: 60-70	Lacticaseibacillus paracasei	species	114	10748	25.3
Age: 60-70	Limosilactobacillus fermentum	species	63	20159	78.5
Age: 60-70	Bifidobacterium magnum	species	21	302	22.1
Age: 60-70	Sphingomonas	genus	49	5317	37.2
Age: 60-70	Bifidobacterium pseudocatenulatum	species	37	38736	1177.7
Age: 60-70	Deltaproteobacteria	class	5224	40276	5
Age: 60-70	Salmonella enterica	species	74	1416	5.6
Age: 60-70	Burkholderia	genus	99	4146	24
Age: 60-70	Sphingomonadaceae	family	57	4615	21
Age: 60-70	Paenibacillus	genus	37	374	6.3
Age: 60-70	Abiotrophia defectiva	species	35	660	6.1
Age: 60-70	Fibrobacteres	phylum	224	209585	102.7
Age: 60-70	Sporotomaculum hydroxybenzoicum	species	37	1571	25
Age: 60-70	Bifidobacterium boum	species	34	669	9
Age: 60-70	Cystobacterineae	suborder	19	126	6.8
Age: 60-70	Burkholderiaceae	family	272	3098	5.4
Age: 60-70	Turicibacter sanguinis	species	89	5092	19.1
Age: 60-70	Deinococci	class	83	2172	12.4
Age: 60-70	Chloroflexi	phylum	110	2061	8.6
Age: 60-70	Desulfovibrio vietnamensis	species	23	2883	162.1
Age: 60-70	Sphingomonadales	order	58	4446	20.8
Age: 60-70	Desulfomicrobiaceae	family	34	217	6.3
Age: 60-70	Ethanoligenens harbinense	species	39	519	9.5
Age: 60-70	Saccharospirillaceae	family	18	280	30.2
Age: 60-70	Acholeplasma equifetale	species	24	114	7.8
Age: 60-70	Prevotella pleuritidis	species	52	2400	18.7
Age: 60-70	Prevotella maculosa	species	107	5761	34.4
Age: 60-70	Bifidobacterium bombi	species	34	1252	13.8
Age: 60-70	Synergistetes	phylum	301	48997	19.8
Age: 60-70	Eubacteriales incertae sedis	norank	300	8063	8.3
Age: 60-70	Eubacteriales Family XII. Incertae Sedis	family	38	556	8.6
Age: 60-70	Slackia piriformis	species	166	3021	5.9
Age: 60-70	Lactobacillus casei group	species group	319	10750	9.8
Age: 60-70	Ruminiclostridium	genus	696	16212	8.1
Age: 60-70	Balneolaeota	phylum	26	11534	446.9
Age: 60-70	Limosilactobacillus	genus	245	14389	9.2
Age: 60-70	Rhodovibrionaceae	family	119	2616	14.3
Age: 70-80	Eubacteriales incertae sedis	norank	300	7064	7.3
Age: 70-80	Ruminiclostridium	genus	696	11141	5.5
Blood Type: A Negative	Butyrivibrio	genus	467	6087	5.3
Blood Type: A Positive	Aggregatibacter aphrophilus	species	35	1150	30.1
Blood Type: A Positive	Bifidobacterium pseudocatenulatum	species	37	10314	312.8
Blood Type: A Positive	Hathewaya proteolytica	species	19	83	6
Blood Type: A Positive	Lactobacillus crispatus	species	356	18213	6.8
Blood Type: A Positive	Fibrobacteres	phylum	224	41287	20.1
Blood Type: A Positive	Desulfovibrio vietnamensis	species	23	2546	143
Blood Type: A Positive	Ethanoligenens harbinense	species	39	365	6.5
Blood Type: A Positive	Saccharospirillaceae	family	18	297	32.2
Blood Type: A Positive	Prevotella pleuritidis	species	52	3785	29.7
Blood Type: A Positive	Prevotella maculosa	species	107	8228	49.4
Blood Type: A Positive	Eubacteriales incertae sedis	norank	300	7120	7.3
Blood Type: A Positive	Eubacteriales Family XII. Incertae Sedis	family	38	666	10.4
Blood Type: A Positive	Ruminiclostridium	genus	696	10322	5
Blood Type: A Positive	Rhodovibrionaceae	family	119	1214	6.3
Blood Type: B Positive	Ethanoligenens harbinense	species	39	3782	74.4
Blood Type: B Positive	Moryella	genus	158	5254	6.4
Blood Type: FUT2 non-secretor	Eubacteriales incertae sedis	norank	300	11611	12.2
Blood Type: O Negative	Halomonadaceae	family	40	1088	20.8
Blood Type: O Negative	Alteromonadaceae	family	41	488	5.4
Blood Type: O Negative	Ethanoligenens harbinense	species	39	450	8.2
Blood Type: O Negative	Eubacteriales incertae sedis	norank	300	5909	6
Blood Type: O Negative	Eubacteriales Family XII. Incertae Sedis	family	38	2462	40.1
Blood Type: O Negative	Symbiobacteriaceae	family	113	1536	5.3
Blood Type: O Negative	Ruminiclostridium	genus	696	15328	7.6
Blood Type: O Negative	Morganellaceae	family	685	32874	6
Blood Type: O Positive	Achromobacter	genus	28	227	6.1
Blood Type: O Positive	Klebsiella pneumoniae	species	113	1431	7.1
Blood Type: O Positive	Salmonella	genus	74	1334	5.2
Blood Type: O Positive	Aggregatibacter aphrophilus	species	35	338	8.2
Blood Type: O Positive	Deinococcus-Thermus	phylum	83	1613	9.1
Blood Type: O Positive	Lacticaseibacillus paracasei	species	114	8206	19.3
Blood Type: O Positive	Limosilactobacillus fermentum	species	63	15729	61.2
Blood Type: O Positive	Bifidobacterium magnum	species	21	232	16.6
Blood Type: O Positive	Sphingomonas	genus	49	3831	26.7
Blood Type: O Positive	Bifidobacterium pseudocatenulatum	species	37	33084	1005.7
Blood Type: O Positive	Burkholderia	genus	99	2582	14.8
Blood Type: O Positive	Francisellaceae	family	18	2597	342.1
Blood Type: O Positive	Sphingomonadaceae	family	57	3326	15.1
Blood Type: O Positive	Paenibacillus	genus	37	374	6.3
Blood Type: O Positive	Fibrobacteres	phylum	224	170555	83.5
Blood Type: O Positive	Sporotomaculum hydroxybenzoicum	species	37	1105	17.4
Blood Type: O Positive	Bifidobacterium boum	species	34	454	6
Blood Type: O Positive	Cystobacterineae	suborder	19	121	6.5
Blood Type: O Positive	Turicibacter sanguinis	species	89	3583	13.3
Blood Type: O Positive	Deinococci	class	83	1613	9.1
Blood Type: O Positive	Chloroflexi	phylum	110	1466	6
Blood Type: O Positive	Desulfovibrio vietnamensis	species	23	2704	151.9
Blood Type: O Positive	Sphingomonadales	order	58	3067	14.3
Blood Type: O Positive	Ethanoligenens harbinense	species	39	1011	19.3
Blood Type: O Positive	Saccharospirillaceae	family	18	291	31.5
Blood Type: O Positive	Prevotella pleuritidis	species	52	1363	10.4
Blood Type: O Positive	Prevotella maculosa	species	107	12500	75.3
Blood Type: O Positive	Bifidobacterium bombi	species	34	1090	12
Blood Type: O Positive	Synergistetes	phylum	301	34878	14
Blood Type: O Positive	Eubacteriales incertae sedis	norank	300	7211	7.4
Blood Type: O Positive	Eubacteriales Family XII. Incertae Sedis	family	38	526	8.1
Blood Type: O Positive	Slackia piriformis	species	166	2753	5.3
Blood Type: O Positive	Lactobacillus casei group	species group	319	10751	9.8
Blood Type: O Positive	Ruminiclostridium	genus	696	11501	5.6
Blood Type: O Positive	Balneolaeota	phylum	26	4614	178.2
Blood Type: O Positive	Limosilactobacillus	genus	245	11618	7.4
Blood Type: O Positive	Rhodovibrionaceae	family	119	1346	7.1
Gender: Female	Francisella	genus	18	1666	218.6
Gender: Female	Serratia marcescens	species	124	1449	7.2
Gender: Female	Desulfomicrobium	genus	34	180	5
Gender: Female	Bifidobacterium magnum	species	21	157	10.7
Gender: Female	Carnobacterium	genus	29	250	6.3
Gender: Female	Bifidobacterium pseudocatenulatum	species	37	9394	284.8
Gender: Female	Francisellaceae	family	18	1575	206.6
Gender: Female	Fibrobacteres	phylum	224	34984	17
Gender: Female	Cystobacterineae	suborder	19	137	7.5
Gender: Female	Dietziaceae	family	113	1687	5.6
Gender: Female	Planomicrobium	genus	20	106	7.3
Gender: Female	Desulfovibrio vietnamensis	species	23	3181	178.9
Gender: Female	Ethanoligenens harbinense	species	39	843	16
Gender: Female	Saccharospirillaceae	family	18	410	45.3
Gender: Female	Prevotella pleuritidis	species	52	900	6.7
Gender: Female	Megamonas funiformis	species	1344	24456	5.2
Gender: Female	Prevotella maculosa	species	107	3398	20
Gender: Female	Eubacteriales incertae sedis	norank	300	5775	5.9
Gender: Female	Eubacteriales Family XII. Incertae Sedis	family	38	911	14.4
Gender: Female	Balneolaeota	phylum	26	9664	374.3
Gender: Female	Rhodovibrionaceae	family	119	3904	21.7
Gender: Male	Francisella	genus	18	1379	180.6
Gender: Male	Enterobacter cloacae	species	98	1326	8.5
Gender: Male	Klebsiella pneumoniae	species	113	4889	25.9
Gender: Male	Aggregatibacter aphrophilus	species	35	945	24.5
Gender: Male	Desulfomicrobium	genus	34	197	5.6
Gender: Male	Bifidobacterium magnum	species	21	237	17
Gender: Male	Sphingomonas	genus	49	2588	17.9
Gender: Male	Bifidobacterium pseudocatenulatum	species	37	9099	275.8
Gender: Male	Francisellaceae	family	18	1071	139.7
Gender: Male	Sphingomonadaceae	family	57	1819	8.1
Gender: Male	Fibrobacteres	phylum	224	71249	34.8
Gender: Male	Sporotomaculum hydroxybenzoicum	species	37	860	13.4
Gender: Male	Bifidobacterium boum	species	34	414	5.4
Gender: Male	Cystobacterineae	suborder	19	114	6.1
Gender: Male	Halochromatium	genus	24	118	5.4
Gender: Male	Mitsuokella jalaludinii	species	400	21943	29.5
Gender: Male	Desulfovibrio vietnamensis	species	23	3011	169.3
Gender: Male	Sphingomonadales	order	58	1697	7.8
Gender: Male	Desulfomicrobiaceae	family	34	193	5.5
Gender: Male	Ethanoligenens harbinense	species	39	634	11.8
Gender: Male	Saccharospirillaceae	family	18	438	48.5
Gender: Male	Bifidobacterium tsurumiense	species	29	311	13.7
Gender: Male	Prevotella pleuritidis	species	52	1409	10.8
Gender: Male	Prevotella maculosa	species	107	9182	55.2
Gender: Male	Bifidobacterium bombi	species	34	519	5.5
Gender: Male	Synergistetes	phylum	301	16705	6.7
Gender: Male	Eubacteriales incertae sedis	norank	300	6111	6.2
Gender: Male	Eubacteriales Family XII. Incertae Sedis	family	38	424	6.4
Gender: Male	Rhodovibrionaceae	family	119	1104	5.7

Prior Post: Another ME/CFS person has gone to Firmicutes!

This just came in on Dec 4, 2023

I wanted to share some good news with you.  In the last 3 weeks I’ve noticed my time going to the bathroom has decreased in half and my gut has been less irritable.  Over the last 2 weeks, my mood has steadily improved and I’ve enjoyed more energy than usual.  It appears that your guidance has pointed me in the right direction! 

Note:  I didn’t end up performing the FMT.  

I thought about why my Firmicutes would get to 97%+.  It’s most likely because the majority of the foods I eat are continually pushing my gut towards Firmicutes.  And it could also explain why after adding 5+ daily supplements to push my gut the other direction, it hasn’t worked.   You noted that my being a vegetarian may be acting as a significant counter balance to the direction we’re trying to go.

Besides adding a small amount of meat in the morning, I cut out the foods I eat most frequently.  Bananas, raspberries, blueberries, nut bars.  And then I added in the Seaweed you recommended.  I think the seaweed has made a massive difference and with all these changes implemented collectively, the boat has begun to turn around!
I’m hoping this continues

Backstory

I’m 39 and have suffered from moderate CFS since i was an early teenager.  My major 3 symptoms are low energy, brain fog, and IBS.  My CFS didn’t affect me as much when I was 18, but combined with the effects of aging, I’ve been feeling the fatigue more impactfully the last two years.  

Journey over last 10 months

When I first began working on this in January, my samples showed my Firmicutes at 98%.  That seemed to be the smoking gun as you described it, and I was eager to begin shifting my microbiome.  Over the next 6 weeks I felt markedly better but unfortunately I now believe that was merely a placebo effect.  Once I started to believe the benefits I had received were from a placebo, I rapidly returned to baseline.  Over this time period, I cut out many of the foods that pushed my gut in the wrong direction, and I was taking 4 supplements 2-3x a day.  By my second test 2 mos later, my firmicutes adjusted downwards from 98% to 93%.  In terms of how I felt, it was difficult to assess whether it was better than my baseline.  I was hopeful that it was, but I couldn’t say for sure.

Over the last 6 mos my Firmicutes has reduced from 93% to 89%.  During this time period I continued to cut out foods that were counter-recommended.  I ordered 4 more vitamins & supplements that were in my consensus list, and I was taking 4 supplements 1X/ day, while also rotating the supplements every 4 weeks to prevent resistance.

Once again, it’s difficult to assess now how i feel versus my baseline.  I don’t feel significantly better, that I know.  And while I’m disappointed my sample isn’t improving drastically, the upshot from my perspective is that at least my sample results match how i’ve been feeling.

Reader Addendum After reading

“I’m mostly vegetarian… which may help to explain why after 9 months of supplements I’m partially moving in the wrong direction.  I’ll incorporate the seaweed and increase my red meat.”

Analysis

We have three samples to compare.

Percentage of Percentiles

We see significant shifts between samples with chi² values increasing (meaning more abnormal) instead of decreasing. What is interesting is that the two earlier samples does not the typical ME/CFS or Long COVID pattern, but the third sample shifted to the pattern of spikes in the 0-9%ile range. This is open to many interpretations; some good and some concerning.

Evaluation Criteria

The numbers below are mixed, some showing improvement, others showing loss.

Criteria	1/6/2023	3/29/2023	9/12/2023
Lab Read Quality	4.3	5.9	8.7
Outside Range from JasonH	3	3	6
Outside Range from Medivere	18	18	20
Outside Range from Metagenomics	8	8	7
Outside Range from MyBioma	9	9	15
Outside Range from Nirvana/CosmosId	23	23	24
Outside Range from XenoGene	38	38	43
Outside Lab Range (+/- 1.96SD)	18	15	10
Outside Box-Plot-Whiskers	54	53	56
Outside Kaltoft-Moldrup	85	125	181
Bacteria Reported By Lab	431	553	591
Bacteria Over 90%ile	49	32	34
Bacteria Under 10%ile	43	72	174
Shannon Diversity Index	2.852	2.937	2.49
Simpson Diversity Index	0.083	0.098	0.101
Chao1 Index	8592	11807	15969
Pathogens	26	35	40
Condition Est. Over 90%ile	11	7	2
Kegg Compounds Low	821	731	1190
Kegg Compounds High	134	251	99
Kegg Enzymes Low	359	204	259
Kegg Enzymes High	208	311	243
Kegg Products Low	201	124	170
Kegg Products High	130	199	158
Kegg Substrates Low	195	114	156
Kegg Substrates High	149	216	162

Forecast symptoms

The top 3 forecasted items are below. See this post: Post Exertional Malaise (PEM) with diminished ME/CFS for more information on forecast symptoms in use. The earliest sample had no forecasts being reliable (i.e. > 60% match). What we also see in that the symptom patterns are becoming stronger. Again, this is usually not desired.

• 2023-01-06
  • 54.3 % match for Neurological-Vision: Blurred Vision on 35 taxa
  • 52.4 % match for General: Headaches on 42 taxa
  • 50 % match for Immune Manifestations: new food sensitivities on 56 taxa
• 2023-03-29
  • 65.9 % match for Pain: Pain or aching in muscles on 44 taxa
  • 56.2 % match for Immune Manifestations: Diarrhea on 89 taxa
  • 56 % match for Neurocognitive: Brain Fog on 50 taxa
• 2023-09-12
  • 65.7 % match for Neurocognitive: Difficulty paying attention for a long period of time on 70 taxa
  • 62.3 % match for Neurocognitive: Can only focus on one thing at a time on 53 taxa
  • 61 % match for Neurological-Vision: Blurred Vision on 41 taxa

Impression and Possible Model

This is the first follow up sample where there was neither clear objective or subject improvement. What we see clearly above was that the microbiome has changed. Subjectively, there was no deterioration reported.

Objectively it seems that the microbiome has been de-noised. This person has had ME/CFS for 20+ years and thus the microbiome dysfunction will evolve. His latest sample changed to the typical pattern for ME/CFS for percentage/percentile chart above. The forecasted symptoms values are increasing for what are likely correct forecasts. The bacteria associated with ME/CFS and IBS are showing themselves better and other bacteria causing noise are diminished.

Going Forward

I first looked at the US National Library of Medicine studies for bacteria reported for the conditions he reported.

• Chronic Fatigue Syndrome   (1 %ile) 9 of 64
• ME/CFS with IBS   (9 %ile) 4 of 18

Symptom matching on these is a clear miss. I did note some high matches that are typical symtpoms so I add in the results from these selections:

The result was just 84 unique taxa and we have 5 sets of suggestions in our consensus.

So we fall back to the “Just give me suggestions”. The high priority value was 300 and the low priority value was -405. Adding in special studies suggestions moderated the ranges but most items stayed the same.

• Antibiotics: There was only 1 over the 150 (300/2) threshold. None are associated/studied with his conditions so I would pass one them. This antibiotic is usually used for tuberculosis.
• Amino Acids: propionate, l-citrulline, GABA
• Diet Style: high beef diet, high-protein diet, animal-based diet, low carbohydrate diet etc give a pretty consistent message
• Foods: high red meat, laminaria hyperborea( tangle/cuvie – seaweed), red alga Laurencia tristicha, ascophyllum nodosum (sea weed), linseed(flaxseed), Far infrared Sauna, nuts
• Sugars: stevia, polydextrose, saccharin
• Prebiotics: Only carboxymethyl cellulose (prebiotic) which is typically added to most probiotics
• Probiotics: NOTHING
• Vitamins: NOTHING
• Misc items:
  • isobutyric acid
  • isovaleric acid(fatty acid)

It looks the a modified Surf And Turf diet, i.e. Steak and Seaweed (our local Costco does sell a nice seaweed salad that is a regular for me)

The AVOIDS

The avoid values are so high compared to the takes that we need to review them to try reducing or excluding them. The threshold value is -300 (-600/2).

• Antibiotics: ampicillin trihydrate (antibiotic)
• Diet: high fiber diet, dietary fiber
• Flavonoids: polyphenols
• Spices: oregano (origanum vulgare, oil), rosmarinus officinalis,rosemary, noni
• Prebiotics:
  • inulin (prebiotic),
  • jerusalem artichoke (prebiotic),
  • fructo-oligosaccharides (prebiotic),
  • arabinogalactan (prebiotic),
  • chicory (prebiotic)
• Probiotics (the majority of probiotics were negative)
  • bacillus,lactobacillus,streptococcus,saccharomyces probiotic
  • lactobacillus reuteri (probiotics)
  • pediococcus acidilactic (probiotic)
  • bacillus subtilis (probiotics)
  • clostridium butyricum (probiotics),Miya,Miyarisan
  • lactobacillus plantarum,xylooligosaccharides (probiotics)
• Misc Items:
  • polymannuronic acid

Bottom Line

This has been a challenging set of samples to do an analysis on. I have often used the analogy of going from the port of sickness to the port of health in a sailing ship along a rugged coast. There may be a long series of course corrections needed.

The suggests above are very atypical. Given that there appear to be a lot of noise in his microbiome, we may have some more denoising to do (bring out more the ME/CFS and IBS bacteria into the light). I would advocate attempting to get a course of rifaximin. It has been well used for his IBS diagnosis so there should be little push back from his MD.

Questions and Answers

Q: I’ve read through my new Simplified Suggestions List, and The suggestions of what I need to take and avoid are the same as before…. but the impact score of each was adjusted.  Although you’ve noted in the past that a higher impact doesn’t indicate it works more effectively, it looks from your suggestions that you stick to the highest impact items as there are the most studies to support them, right?

• Correct. One study may report honey increases a bacteria, and another study report that it decreases the same bacteria. To determine the confidence of a suggestion we look at all reports. If you have 10 honey studies (8 increases and 2 decreases) and one study alone for roasted whale increasing the bacteria; most people will have greater confidence that honey is a better choice to try.

Q: One initial inquiry comes to mind- for complex cases, have you given any additional consideration to (probiotic) enemas as a way to make a big impact, and then to adjust with oral supplements from there?

• I work from published clinical studies and not from influencers opinion. There is a good summary on WebMD, What to know about probiotic enemas that gives pros and cons.
• I found Clinical trial: probiotic treatment of acute distal ulcerative colitis with rectally administered Escherichia coli Nissle 1917 (EcN) which had no results (” the number of responders was not significantly higher in the EcN group than in the placebo group”) or results depending on the statistical method used. Thus there is uncertainty about it’s effectiveness.
• Using commercial/retail probiotics have two significant risks:
  • Very often the declared species is not what is in the bottle, the exception is for those strains that are owned by someone.
  • Often they contain fillers that will be well consumed before they reaches the nether regions. These fillers being inserted here may give a feast to some bacteria that usually do not get much, sparking an unexpected overgrowth.

Q: Below are the supplements I’ve been taking each day.  My plan is to cut out everything that doesn’t have an impact score north of a 150.  Does that sound like a reasonable approach?

• ground flaxseed.  I took this every day for 4 months and had to stop because it began to make me nauseous. — Try it again at low dosages after one month
• Luteolin – low positive
• carboxymethyl cellulose — definite take
• Dietary Fiber Cellulose – definite take
• partially Hydrolyzed Guar Gum (SunFiber) — remove
• licorice root —remove
• Vit B1, B6, B7 — low positive
• Gaba — definite take
• Vit C — remove
• Melatonin — low positive, suggest you try removing for a week then trying for a week to see if it still help sleep
• Diosmin – low positive
• Astragalus – low positive

Answer: The logic is good but I would restrict to only items that are negative. You may wish to revisit the reason that you started each; if it was symptom related and improved — then monitor that symptom and return it if the symptom returns. I have annotated the list above

Reflection

Suggestions could also be described as influencers. There is no need to get religious about taking everything. Take what you are comfortable with is my usual advice. In this case, the person being a vegetarian may have significantly counter-indicated the other influencers. Once the microbiome is normalize, a return to be a vegetarian is viable.

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.

This is from a person that have been using Microbiome Prescription for a while with significant success. He messaged me about a new sample and asked me to take a look. It was good timing because I have over the last week refactor forecast symptoms as well as allowing them to be used to generate suggestions. This gave me an opportunity to test the analysis and suggestions.

My impression is that they are both working well with better sensitivity than the older method. Prior posts include:

• A review of a ME/CFS Microbiome Jan 2021
• Evidence of ME/CFS improving using Microbiome Data May 2022
• Comparing 4 ME/CFS Samples with New Tool Jul 2022
• A Microbiome Trek Continues thru the land of ME/CFS. Feb 2023
• Biomesight vs Thorne Tests – Differences Mar 2023

Immediate Back Story

I had covid and an awful toothache a week after.

-PEM 
-Fatigue on and off specially in the morning. Lowest energy point 
-Some brain fog. If I take a binder my brain fog is much better. Guessing that could be related to mold/fungus issues?
-unrefreshing sleep
-hair shedding unless I take Lactoferrin 
-difficulty losing weight 

– Reader

Review of Test Results

I am skipping the sample comparison table because the forecast symptoms seem more useful for between samples comparisons.

Percentages of Percentiles

These are helpful overview to see if there are problems. At the top is a chi² value which a healthy person should have a value below 0.90 . All of his values were .99999…. indicating abnormal shifts.

Prior to 2023

2023, the impact of COVID in the latest sample is quite apparent.

Forecast Symptoms

What shocked (and delighted) me was how accurate the forecasts were, especially with the main issue, PEM being near the top on every sample. The percentage match ebbed and flowed with improvements and set backs (typically from COVID).

• 2023-11-02 – After recent COVID
  • 74.2 % match for Neurological: Cognitive/Sensory Overload on 31 taxa
  • 63.5 % match for Immune Manifestations: new food sensitivities on 52 taxa
  • 61.5 % match for Neuroendocrine Manifestations: cold extremities on 65 taxa
  • 60.9 % match for Neurological-Vision: Blurred Vision on 87 taxa
  • 59.6 % match for Condition: ME/CFS with IBS on 52 taxa
  • 58.3 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 36 taxa
• 2023-07-07
  • 57.1 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 42 taxa
  • 54.2 % match for Condition: ME/CFS with IBS on 59 taxa
  • 48.1 % match for Post-exertional malaise: Post-exertional malaise on 54 taxa
• 2023-05-10
  • 63.2 % match for Post-exertional malaise: Post-exertional malaise on 38 taxa
  • 59.3 % match for Post-exertional malaise: Muscle fatigue after mild physical activity on 59 taxa
  • 59.1 % match for Pain: Pain or aching in muscles on 44 taxa
  • 57.1 % match for Condition: ME/CFS with IBS on 42 taxa
• 2022-12-05
  • 82.4 % match for Official Diagnosis: Autoimmune Disease on 51 taxa
  • 81.9 % match for Comorbid: Histamine or Mast Cell issues on 83 taxa
  • 79.5 % match for Post-exertional malaise: Next-day soreness after everyday activities on 39 taxa
  • 71.8 % match for Post-exertional malaise: Physically tired after minimum exercise on 39 taxa
• 2022-10-29
  • 64.4 % match for Neurological-Sleep: Inability for deep (delta) sleep on 45 taxa
  • 62.7 % match for Official Diagnosis: Mast Cell Dysfunction on 75 taxa
  • 60.9 % match for Post-exertional malaise: Next-day soreness after everyday activities on 46 taxa
  • 60 % match for Condition: ME/CFS with IBS on 40 taxa
  • 55 % match for Post-exertional malaise: Muscle fatigue after mild physical activity on 60 taxa
  • 54.5 % match for Post-exertional malaise: Post-exertional malaise on 44 taxa
• 2022-03-23
  • 85.4 % match for Neurological-Sleep: Inability for deep (delta) sleep on 41 taxa
  • 82.1 % match for Immune Manifestations: Inflammation (General) on 67 taxa
  • 77.5 % match for Immune Manifestations: Inflammation of skin, eyes or joints on 111 taxa
  • 75 % match for Post-exertional malaise: Worsening of symptoms after mild physical activity on 32 taxa
  • 71.4 % match for Post-exertional malaise: Next-day soreness after everyday activities on 42 taxa

Drilling down on PEM

With the revised symptom-taxa association tool, I went to the 2022-12-05 sample because it had very high values for PEM in forecasting. I then went to [Special Studies] and selected all of the PEM items

Some 78 bacteria were selected with the following being the top suggestions:

While this is not his current sample, it is reasonable guidance for dealing with the bacteria associated with PEM.

Going Forward

I am going to do [Just give me suggestions] and then special studies selecting only PEM items that are shown. We thus ended up with 5 packages. Looking at the details we have high at 555 -> 277 for high threshold and -542 –> -271 for low threshold

• Antibiotics: the top 2 are used with ME/CFS
  • clarithromycin (antibiotic)s[CFS]
  • azithromycin,(antibiotic)s[CFS]
  • with an IBS antibiotic being close to the top: rifaximin (antibiotic)s
• Amino Acids
  • melatonin supplement
  • epicatechin
• Drugs (OTC etc)
  • acetaminophen,(prescription) Paracetamol in UK
• Flavonoids
  • resveratrol (grape seed/polyphenols/red wine)
  • quercetin,resveratrol
• Food
  • whey
  • Cranberry (just under… but seasonal)
• Herb Spices
  • aloe vera
  • garlic (allium sativum)
  • foeniculum vulgare,fennel
  • bacopa monnieri
  • cinnamomum tamala
  • cinnamomum zeylanicum
  • olea europaea,olive leaf
• Prebiotics
  • chitooligosaccharides (prebiotic)
• Probiotics
  • lactobacillus paracasei (probiotics)
• Sugars
  • saccharin
• Vitamins
  • Vitamin B1,thiamine hydrochloride
  • Vitamin B6,pyridoxine hydrochloride
  • vitamin B3,niacin
  • Vitamin B-12

In general, it looks very pro-forma ME/CFS. He may wish to check the PEM suggestions against the details and include anything that is positive.

Bottom Line

I have often used the analogy of going from the port of sickness to the port of health in a sailing ship along a rugged coast. There may be a long series of course corrections needed. While there are still ME/CFS taxa shifts seen in his samples, subjectively the only symptom left of concern is a PEM after playing basketball for a while.

This is a good illustration that taxa/bacteria does not rule — DNA and other factors are involved with symptoms and the ability to tolerate microbiome dysfunctions.

Questions and Answers

Q: I may do another Thorne test if they’re back in stock. Wondering how much aspergillus is holding back healing/gut shifts 

• Aspergillus usually impacts lungs and breathing [CDC] (thus ability to get oxygen in). An alternative mechanism for oxygen issues than with hypercoagulation.

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.

Part of this series of Technical Notes.

The following are bacteria that have very unusual statistical behaviors in uploaded samples. This hints that they may be connected to some disease or symptom state.

The common pattern seen with most bacteria is shown below. You can view each bacteria pattern by clicking on the chart icon after searching for a bacteria on https://microbiomeprescription.com/Library/Lookup

Prevotella oryzae

Phascolarctobacterium succinatutens

Bacteroides eggerthii

Paraprevotella clara

Phascolarctobacterium faecium

Paraprevotella

Preliminary evaluations have shown strong and accurate forecasting ability. I am working with a Ph.D. in Molecular Genetics and processing his microbiome sample the top forecasts were correct:

• He was a male
• He had no Health Issues
• He was blood type O-positive
• He snored
• He woke up early

This was not from a blood sample, but from a stool sample. The methodology is described in my earlier posts.

• Bacteria interacting with Bacteria
• Symptoms/Diagnosis association to Bacteria

The methodology is computing intensive. Unlike many contemporary artificial intelligence engines, it is easy to understand:

• In the entire population(n:10,000), a bacteria like unclassified Clostridiales has 10% of samples reported this bacteria with a mean of 0.3%.
• In the condition / sample population(n:1000) we find that 30% of the samples reported this bacteria with a mean of 4.8%

I used Chi² to determine significance because significance using means assumes a normal distribution which is a false assumption.

With this methodology it is good to get some indication of what sample sizes for conditions / symptoms is needed (assuming the control is at least the same size). A quick plot informs us that 200 looks like a minimum size with 300+ being desired.

One of the labs that I work with reports percentile ranking on their reports. This makes doing an exploration economical (i.e. cheap). Testing of the symptom/condition group is only needed because the control numbers are gifted you.

This also illustrates that the symptom is not with just one taxa, but the influence of many taxa working in unison to influence the symptom (or be influenced by the condition).

Samples of Symptoms / Conditions

The ones with the most predictors are below.

• Neurocognitive: Absent-mindedness or forgetfulness
• Neurological: Impairment of concentration
• Neurocognitive: Difficulty paying attention for a long period of time
• General: Fatigue
• Autonomic Manifestations: irritable bowel syndrome
• Official Diagnosis: COVID19 (Long Hauler)
• Sleep: Problems staying asleep
• Neurocognitive: Slowness of thought
• Neurological-Sleep: Insomnia
• Sleep: Daytime drowsiness
• Immune Manifestations: Bloating
• Neurocognitive: Problems remembering things

For blood types:

Only two were significant and we have the importance of sample size apparent.

Symptom Name	Sample N	Forecast Variables
Blood Type: O Positive	284	295
Blood Type: A Positive	126	3

For Age:

Symptom Name	Sample N	Forecast Variables
Age: 60-70	206	143
Age: 0-10	98	42
Age: 50-60	164	33
Age: 20-30	152	12
Age: 30-40	369	11
Age: 40-50	244	8

For Gender

Symptom Name	Sample N	Forecast Variable
Gender: Female	415	214
Gender: Male	554	200

Bottom Line

From the individual forecast variables, we can compute odds of a match. Consider a simplistic example of three factors

• Factor A uses 5%ile
• Factor B uses 10%ile
• Factor C uses 20%ile

With A,B,C all being true has a 99% chance of being a taxa match (1 – .05 * .1 * .2). This does not mean the person has it, it indicate a significant risk of having it (depending on other factors, DNA, age, gender etc).

Of course, the more forecasting variables, the better the estimate becomes and also the more tolerant the forecast is to variations in the microbiome.