Foreword

This post started out with a title of “Post-Acute COVID-19 Syndrome vs Myalgic Encephalomyelitis – Similarities and Differences“. It scope was pretty obvious — compare microbiome shifts from these two sibling conditions. Expectations was a bland informational review.

The result was calling into question the typical assumption that we could isolate symptoms and conditions to specific bacteria. I follow the statistics and discovered that you can get a magnitude better statistical significance by moving beyond bacteria. In coming weeks, I hope to code up suggestions AI based on this enlightenment.

My starting point

In my last post, Long COVID – an update, I did a comparison between the citizen science data and the literature published on the US Library of Medicine. In terms of symptoms, these two appear the same — but at the microbiome (and enzyme levels) how similar are they?

• Post-Acute COVID-19 Syndrome (PCAS), also known as Long COVID
• Myalgic Encephalomyelitis (ME), also known as Chronic Fatigue Syndrome (CFS)

One frustrating aspect of many studies on the US Library of Medicine for many conditions is simple: results are not replicated in subsequent studies for the same condition. Historically I have viewed this as a result of different equipment and different reference libraries. In many cases the bacteria deemed significant are often different and when they did report the same bacteria, they report opposite shifts!

This post explores some of these issues, and came to an interesting conclusion.

Study Caveats

The studies on the US Library of Medicine compare people with the condition to healthy controls. With the citizen science data that is almost impossible to do. If a person has gotten a microbiome test, they likely have some condition(s) and thus are not healthy controls!!

This is not all bad. It means that when we find things that are statistically significant they are differentiators against other people with microbiome issues. That is, how are people with ME different than people with FM and IBS. Conceptually, we are more likely to identify the key features for these conditions and not key features for auto-immune conditions or a gut disturbance in general. It is a nuisance difference, but may be a very important nuisance.

Comparison that we will review are from:

• US Pubmed — bacteria reported by both with direction
  • List for ME
  • List for PACS
• KEGG Enzymes shifts from Citizen Science (using only Biomesight data)
• Bacteria shifts from Citizen Science (using only Biomesight data)

For citizen science we may have many uploaded samples annotated both with PCAS and ME. To resolve this conflict, ME will contain only samples with ME and without PCAS. Both ME and PCAS have many, many comorbid symptoms which may also come into play. Many of the pure ME samples are before COVID swept the world, hence relatively clean. PCAS are more recent samples.

For PACS citizen science data, we have only significance difference identified from Biomesight data, hence we will compare those only.

Scope	ME	PACS	Same
US National Library of Medicine	68	233	25
Enzymes – Citizen Science with p < 0.001	228	199	31
Bacteria – Citizen Science	109	36	0

I must admit that finding no bacteria in common with the same lab and the same reference library was a little bit of a surprise. One explanation is that microbiome dysfunctions evolve over time. People with PACS have had it less then 3 years, likely an average of just 1 year. People with ME has had it often for 30+ years. Comparing the two may be similar to comparing a one bottle of grape juice to a bottle of vintage wine.

Details for Common Bacteria from US National Library of Medicine

In the table below: H indicates High, L indicates Low.

Note that Bacteroides are reported high and low in different studies, suggesting there are subsets of each condition

tax_rank	tax_Name	Direction
class	Bacteroidia	H
family	Bacteroidaceae	H
family	Clostridiaceae	H
family	Lachnospiraceae	L
genus	Anaerostipes	L
genus	Bacteroides	H
genus	Bacteroides	L
genus	Bifidobacterium	L
genus	Coprobacillus	H
genus	Coprococcus	L
genus	Dorea	L
genus	Eggerthella	H
genus	Enterococcus	H
genus	Faecalibacterium	L
genus	Lactobacillus	L
genus	Streptococcus	H
genus	Turicibacter	H
order	Eubacteriales	L
phylum	Bacteroidetes	H
phylum	Firmicutes	L
phylum	Fusobacteria	H
species	Anaerobutyricum hallii	L
species	Enterocloster bolteae	H
species	Faecalibacterium prausnitzii	L
species	Ruminococcus gnavus	H

Details for Shared Enzymes with p < 0.001

In recent posts for conditions comorbid with ME, PACS, I found that enzyme analysis had greater statistical significance than bacteria. All of these posts reported higher enzyme levels were significant with these conditions.

The result for items shared that had p < 0.001 was almost overwhelming!

ECKey	EnzymeName
1.1.1.292	1,5-anhydro-D-mannitol:NADP+ oxidoreductase
1.12.98.4	H2:polysulfide oxidoreductase
1.7.2.2	ammonia:ferricytochrome-c oxidoreductase
1.8.7.3	CoB,CoM:ferredoxin oxidoreductase
1.8.98.4	CoB,CoM,ferredoxin:coenzyme F420 oxidoreductase
1.8.98.5	CoB,CoM,ferredoxin:H2 oxidoreductase
1.8.98.6	coenzyme B,coenzyme M,ferredoxin:formate oxidoreductase
2.3.1.201	acetyl-CoA:UDP-2-acetamido-3-amino-2,3-dideoxy-alpha-D-glucuronate N-acetyltransferase
2.7.1.227	1-phosphatidyl-1D-myo-inositol:a very-long-chain (2’R)-2′-hydroxy-phytoceramide phosphoinositoltransferase
2.7.8.12	CDP-glycerol:4-O-[(2R)-glycerophospho]-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol glycerophosphotransferase
2.7.8.36	UDP-N,N’-diacetylbacillosamine:tritrans,heptacis-undecaprenyl-phosphate N,N’-diacetylbacillosamine transferase
3.1.1.114	methyl acetate acetohydrolase
3.1.3.27	phosphatidylglycerophosphate phosphohydrolase
3.1.6.6	choline-sulfate sulfohydrolase
3.1.6.8	cerebroside-3-sulfate 3-sulfohydrolase
3.10.1.1	N-sulfo-D-glucosamine sulfohydrolase
3.2.1.11	6-alpha-D-glucan 6-glucanohydrolase
3.2.1.152	mannosylglycoprotein endo-beta-mannosidase
3.2.1.197	beta-1,2-D-mannoside mannohydrolase
3.2.1.24	alpha-D-mannoside mannohydrolase
3.4.21.26	prolyl oligopeptidase
4.1.99.1	L-tryptophan indole-lyase (deaminating; pyruvate-forming)
4.2.2.20	chondroitin-sulfate-ABC endolyase
4.2.2.21	chondroitin-sulfate-ABC exolyase
4.2.2.3	alginate beta-D-mannuronate—uronate lyase
4.2.2.8	heparin-sulfate lyase
4.3.1.7	ethanolamine ammonia-lyase (acetaldehyde-forming)
5.1.1.20	L-alanyl-D-glutamate epimerase
5.1.3.11	cellobiose 2-epimerase
5.3.1.22	hydroxypyruvate aldose-ketose-isomerase
6.1.1.13	D-alanine:poly(phosphoribitol) ligase (AMP-forming)

One of them caught my eye, heparin-sulfate lyase, because micro-clots and “thick blood” are associated with these conditions with good results reported from the use of heparin for some patients.

 HSGAGs are widely distributed on the cell surface and extracellular cell matrix of virtually every mammalian cell type and play critical role in regulating numerous functions of blood vessel wall, blood coagulation, inflammation response and cell differentiation.

Microbial heparin/heparan sulphate lyases: potential and applications [2012]

Bacteria – Citizen Science

This blew me away — we have over 150 people with PCAS and over 250 with ME giving us superior sample sizes. We have 145 bacteria deemed significant for one or the other. We had NONE that was in common. This gut punch gives two main options: denial and look for an excuse to disregard. or roll with the punch and roll with enzymes.

Bottom Line

The enzyme aspect is the strongest association between PACS and ME. The count is higher, but more important, we are dealing with p < 0.001 data making false positives unlikely. This leads to a model that there is no ‘magical list of bacteria involved’ but a diverse array of bacteria that may be different for each person — but united in the over production of enzymes. This shifts the microscope of research into a different light spectrum. This is very interesting and may require some new brain cells to be used.

Using this information to improve..

If Enzymes estimate trumps bacteria levels (in a statistical sense), then we need to look at the enzyme levels and deduce for each one of concern, which collection of bacteria needs reduction — even when they are in the normal range. It is the aggregation of bacteria where the issue arises, not individual bacteria or specific subsets of bacteria.

A few examples may illustrate things a little

Example: (S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming) a.k.a. EC 4.1.3.4, which was the most significant for PACS in the post: Long COVID – an update. There are some 2000+ taxon associated with it. We look at the averages for these below.

Rank	Tax_Name	With PACS	Without PACS	TScore	DF
species	Faecalibacterium prausnitzii	138151	109604	3.796775	667
species	Pseudomonas viridiflava	53	25	2.628108	32
species	Comamonas kerstersii	125	40	2.600388	54
species	Pseudomonas aeruginosa	62	31	1.824644	43
species	Emticicia oligotrophica	2303	967	1.727619	455
species	Denitratisoma oestradiolicum	42	24	1.650657	22
species	Granulicella tundricola	29	21	1.61225	48
species	Bacillus subtilis	40	19	1.374431	17
species	Niabella soli	31	24	0.963943	16
species	Ralstonia insidiosa	53	38	0.914874	36
species	Oligella ureolytica	51	32	0.89768	19
species	Glaciecola nitratireducens	27	24	0.67899	66
species	Bacillus halotolerans	32	28	0.371219	58
species	Acidaminococcus intestini	749	624	0.357674	146
species	Acinetobacter guillouiae	67	63	0.098956	18

For another one, we see the pattern stronger. Below we see the difference of Faecalibacterium prausnitzii is around 30,000 units. Looking at the other contributors, we see an additional 40,000 units. These extra units doubles the shift (and thus significance) of the enzyme above that of a single bacteria. Some of these are deemed healthy usually, for example: Akkermansia muciniphila which was at the 78%ile for Without PACS and 83%ile with PCAS. Neither would be deem to be outliers.

Rank	Taxon Name	With PACS	Without PACS	t-score	DF
species	Faecalibacterium prausnitzii	138151	109604	3.796775	667
species	Sutterella wadsworthensis	9626	6772	2.380718	452
species	Aliarcobacter skirrowii	3756	21	2.223602	17
species	Akkermansia muciniphila	19096	12290	1.896922	547
species	Desulfovibrio desulfuricans	1423	469	1.769772	32
species	Emticicia oligotrophica	2303	967	1.727619	455
species	Enterococcus casseliflavus	1965	81	1.635666	38
species	Porphyromonas asaccharolytica	1350	254	1.59988	186
species	Bacteroides fragilis	8080	5595	1.523991	489
species	Bifidobacterium dentium	1454	461	1.433823	239
species	Phocaeicola dorei	35482	29075	1.396731	649
species	Corynebacterium aurimucosum	1105	407	1.275234	96
species	Bacteroides eggerthii	14379	10345	1.108857	263
species	Corynebacterium jeikeium	1897	723	0.858282	70
species	Phocaeicola coprophilus	6496	3642	0.856783	152
species	Desulfovibrio piger	2032	1534	0.848976	141
species	Megamonas funiformis	1677	1130	0.620283	90
species	Hathewaya histolytica	2890	2729	0.467066	660
species	Haemophilus parainfluenzae	1343	1250	0.282656	500
species	Mesoplasma entomophilum	1182	1069	0.230055	294
species	Phocaeicola vulgatus	51403	51213	0.034398	665

Going Forward

The logical approach is simple to describe. For a person with the symptom, determine the enzymes which are abnormal. Determine the bacteria that are too high (even if only a little). Then use the suggestions AI Engine to determine the substances that will affect the greatest number of these bacteria to shift in the desired direction without encouraging other bacteria that could contribute to these enzymes to increase.

Now, the mathematics and complexities of this computation is a different matter but well within the power of today’s computer.

This request came from the person discussed in Follow up to: A German CFS Patient Experience and Analysis.

I don’t know, if you remember me, we did two reports together, and your suggestions really helped to get my microbiome back on track (which shows in the samples).

And then I got COVID in November 2021-December 2021. But I felt better with it, but unfortunately I couldn’t give up my sample while having COVID.

Anyway I wanted to ask you whether you may be interested in my case, because I had a huge, irreversible it seems, crash from 20-30 on Bell CFIDS disability scale to now under 10 and my microbiome crashed along with me . (The crash also resulted in a high number of Lorazepam intake from which I’m slowly withdrawing now. But I didn’t get a clear idea of the effect of Lorazepam on the microbiome, other then they make the slowed gut motility worse of course.)

I have a very severe and have a progressive form of ME/CFS in the way that whenever I really crash I always go down to a lower baseline and do not recover. And with most crashes I loose about 50 % of my functionality, so it just took me one year to go from very mild to very severe.

From my lowest point onwards I’ve always had to take about 4 Lorazepam to guarantee a minimum of a bearable quality of life in bed. I succeeded for 4 years not to have a major crash and did did not build up a tolerance towards Lorazepam in that time.

A lot of things seem to have reversed, what I should take before are often things that I now should avoid. What Biomesight says seems to contradict slightly from what I can gather from your site. (Yes, I know you explained why there can be contradictory results).

And I have difficulties getting the suggestions for the handpicked criteria to show. Of course I would be super glad, if you could help, but I understand if you have more interesting projects to work on. (I would of course donate for your effort, as this is the only or easiest way to say thank you),

I believe one of the differences between Microbiome Prescription(MP) and Biomesight is simply the number of studies used to make suggestions. At present, we have over 11,000 studies coded into MP, I do not know the number that Biomesight uses, but I expect less than 1%. Also, MP suggestions was written by a person that has worked professionally in Artificial Intelligence. I suspect Biomesight lacks that skill set for development. Regardless, put items not in disagreement as first priority.

Analysis — The Numbers

There is no magic number that answers questions about the microbiome. Usually, I look for abnormalities. Since the earlier post, she had 6 more microbiome samples done periodically and shown below. She is wise to regularly monitor and ideally take moderate steps (diet and supplements) to counter any concerning trends.

Criteria	Sep21	Mar22	May22	Sep22	Jan23	May23
Shannon Diversity Index	78.2	94.3	67.1	53.9	98.9	84.70
Simpson Diversity Index	30.7	40.7	44.4	17.5	42.9	48.90
Chao1 Index	53.6	66.8	81.5	36.7	65.1	61.90
Lab Read Quality	4.8	7.3	7.7	5.2	5	6.5
Bacteria Reported By Lab	612	653	717	536	636	642
Bacteria Over 99%ile	2	15	9	6	8	4
Bacteria Over 95%ile	4	20	50	33	32	12
Bacteria Over 90%ile	29	44	69	50	68	38
Bacteria Under 10%ile	44	181	181	40	53	43
Bacteria Under 5%ile	12	164	165	9	20	14
Bacteria Under 1%ile	1	140	148	1	3	0
Rarely Seen 1%	5	3	7	0	2	2
Rarely Seen 5%	16	11	21	10	19	15
Pathogens	28	29	38	31	32	34
Outside Range from JasonH	5	5	6	6	8	8
Outside Range from Medivere	12	12	19	19	19	19
Outside Range from Metagenomics	9	9	10	10	6	6
Outside Range from MyBioma	6	6	6	6	9	9
Outside Range from Nirvana/CosmosId	20	20	14	14	21	21
Outside Range from XenoGene	36	36	36	36	39	39
Outside Lab Range (+/- 1.96SD)	2	12	24	16	18	9
Outside Box-Plot-Whiskers	67	83	106	94	106	58
Outside Kaltoft-Moldrup	64	183	218	87	106	75
Condition Est. Over 99%ile	0	0	0	0	0	0
Condition Est. Over 95%ile	0	2	0	0	0	0
Condition Est. Over 90%ile	0	3	5	0	0	0
Enzymes Over 99%ile	0	0	21	0	0	0
Enzymes Over 95%ile	19	0	66	15	17	36
Enzymes Over 90%ile	68	13	119	34	27	118
Enzymes Under 10%ile	30	285	203	94	200	80
Enzymes Under 5%ile	13	225	130	41	81	27
Enzymes Under 1%ile	1	164	80	2	2	11
Compounds Over 99%ile	1	0	17	0	0	0
Compounds Over 95%ile	18	0	35	3	10	18
Compounds Over 90%ile	49	5	73	13	17	64
Compounds Under 10%ile	789	876	965	1124	1135	998
Compounds Under 5%ile	779	848	927	1092	1057	959
Compounds Under 1%ile	773	832	904	1069	1018	930

	Sep21	Sep21	Mar22	Mar22	May22	May22	Sep22	Sep22	Jan23	Jan23	May23	May23
Percentile	Genus	%	Genus	%	Genus	%	Genus	%	Genus	%	Genus	%
0 – 9	7	4%	46	26%	43	23%	7	5%	11	6%	9	6%
10-19	19	11%	13	7%	9	5%	17	12%	27	15%	21	13%
20 – 29	26	15%	14	8%	15	8%	16	11%	14	8%	23	14%
30 – 39	13	8%	13	7%	16	8%	12	8%	15	9%	18	11%
40 – 49	14	8%	13	7%	14	7%	17	12%	18	10%	15	9%
50 – 59	14	8%	16	9%	14	7%	10	7%	15	9%	16	10%
60 – 69	22	13%	20	11%	18	9%	10	7%	17	10%	18	11%
70 – 79	23	13%	15	8%	22	12%	19	13%	16	9%	13	8%
80 – 89	23	13%	19	11%	18	9%	18	12%	21	12%	15	9%
90 – 99	11	6%	11	6%	22	12%	19	13%	21	12%	13	8%
Total	172		180		191		145		175		161

	Sep21	Sep21	Mar22	Mar22	May22	May22	Sep22	Sep22	Jan23	Jan23	May23	May23
Percentile	%	Species	%	Species	%	Species	%	Species	%	Species	%	Species
0 – 9	5%	10	28%	60	27%	70	7%	14	10%	22	8%	16
10-19	13%	26	5%	11	5%	13	12%	23	15%	33	16%	34
20 – 29	13%	27	8%	18	6%	16	9%	18	10%	23	12%	25
30 – 39	7%	15	4%	8	6%	15	12%	23	10%	22	11%	23
40 – 49	9%	19	8%	16	9%	24	8%	15	8%	18	10%	21
50 – 59	12%	25	9%	20	12%	30	9%	18	10%	22	12%	25
60 – 69	8%	17	12%	25	8%	21	7%	14	8%	17	6%	12
70 – 79	10%	20	9%	19	7%	19	11%	22	9%	20	10%	21
80 – 89	13%	27	9%	19	7%	19	16%	30	9%	21	9%	19
90 – 99	7%	15	8%	17	12%	30	8%	15	12%	28	7%	15
		201		213		257		192		226		211

We lack any data on Lorazepam and other Benzodiazepines impacts on the microbiome which complicates interpretations. I did a search on the US National Library of Medicine and found nothing useful.

More History of Patient

I discovered Pregabalin in March 22 which brought me from Bell CFIDS disability scale below 10 up to nearly bell 20-30. The ditch in the curve around May 22nd is probably me taking too much Lorazepam and Pregabalin, as I for the first time I could take care of things that needed to betaken care of.

At that time I took about 500mg metformin (which did give me energy) most of the day and stayed with my Thorne Fibre mend, Inulin (in the beginning Inulin from the Argave helped dramatically with nausea and headaches)and Acacia Fibre, sometime an Amino Acid complex, but they make me jittery. Usually completely constipated I suddenly developed a strong diarrhea along with an unbearable itching of my whole skin in August / September for which Famotidine(Pepcid) and Cromoglicic acid (Cromolyn – prescription in US) worked best. That was a time where I ate lots of cake and carbohydrates and would take Metformin (I am always hovering around the entrance point to prediabetic) afterwards. That seemed to be too much sugar, my body couldn’t deal with. After I stopped the cake , eat more vegetables again, it went away. 

I have got restless legs, which are kind of turned on or off with every mayor crash. Now unfortunately they are turned on, and the only thing apart from medication that helps is when I eat complex carbohydrates lie brown rice, whole food, pasta, oat flakes etc, when I don’t I use Pramipexole.

Pregabalin been used with Fibromyalgia, a sibling condition for some, and suggested by the American Family Physician journal in 2023. Pregabalin with Lorazepam has known interactions: ” increase side effects such as dizziness, drowsiness, confusion, and difficulty concentrating.”[Src] so she is right about her loss of effectiveness.

Of the many items cited, we know what a few of them likely shifts. Others we lack data.

• cromolyn disodium salt
• famotidine
• metformin
• inulin

This missing data illustrates the challenge of trying to manipulate the microbiome — an absence of data. For antibiotics we have a reasonable amount of information, thus we can negotiate with MDs between their desired goal for the antibiotic and our goal of improving the microbiome to find a mutually acceptable compromise.

Going Forward

As part of my learning process, I evaluated each against the “Just Give Me Suggestions” consensus to see it that provide any insight. I also looked at the top items in three other classes.

Criteria	Sep21	Mar22	May22	Sep22	Jan23	May23
cromolyn disodium salt	275.5	243.9	368.9	504.3	391.1	393.3
famotidine	275.5	248.8	378.5	504.3	379.7	393.3
metformin	146.4	163.8	234.1	249.9	293	-17
inulin	-231.6	-79.1	-207.5	-203.3	-333	-157
Total	399	587	725	806	631	491
Best Probiotic	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei	lactobacillus casei
Best Amino Acid	polymannuronic acid	polymannuronic acid	melatonin supplement	melatonin supplement	melatonin supplement	polymannuronic acid
Best Vitamin/Mineral	Vitamin B7	Vitamin C	Vitamin C	Vitamin B1	Vitamin B1	Vitamin B-12

This helps us evaluate possible (we do not know for certain) impact on various microbiome.

I am not a medical professional and have no clinical experience, so picking items tend to be arbitrary in most cases. I am familiar with the literature for ME/CFS and if the person has ME/CFS, I will tend to pick items that studies reporting helping.

My preference is simple.

My suggestion (given all of the fuzziness and items being taken) is to persist with the prescription items — they help both her symptoms and her microbiome! I would suggest adding the following items (see Dosages for Supplements for literature on dosage):

• lactobacillus casei – at least 48 BCFU/day — this is the suggested serving size from Custom Probiotics product. Or a Yakult bottle with each meal (each bottle is 20 BCFU). Depending on availability and cost.
• melatonin – 10 mg/d – in three dosages, i.e. one with each meal.
• Vitamin B1, B12, C7 and C. (see above for dosages)

One additional item that I would suggest, being prediabetic is to take the Pendulum Akkermansia muciniphila probiotic. This may be a challenge to obtain in Germany (if someone is visiting the US, that may be a backdoor to get it).

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.

As a result of doing an analysis for a 19 month old toddler, I added a new option that can also be used with Transcribed tests. This post applied to the following tests:

• Bioscreen
• Biovis Microbiome Plus
• DayTwo
• Diagnostic Solution GI-Map
• GanzImmun Diagnostic A6
• GanzImmun Diagnostics AG Befundbericht
• Genova Gi Effects
• Genova Parasitology
• GI EcologiX (Invivo)
• GI360 Stool (UK)
• Gut Zoomer
• InVitaLab
• Kyber Kompakt
• Medivere Mikrobiom Plus Stuhlanalyse
• Medivere: Darm Mikrobiom Stuhltest
• Medivere: Darn Magen Diagnostik
• Medivere: Gesundsheitscheck Darm
• Metagenomics Stool (De Meirleir)
• Nordic Laboratories
• NutriPATH
• Smart Gut
• Verisana

You must save your input.

Process

Here’s an example

When you logged in, you will see your saved tests, CLICK ON Review.

And then we have the details you entered below with an important column, taxon number.

Below this are conditions where your pattern matches at least 5 shifts reported in Published Studies.

There may be many items listed. This is by pattern matching and is not predictive.

If you have any of these conditions, or suspect you may have. Just click the appropriate button.

An example is below. These are tuned safest-suggestions for the matches. What do I mean by safest? It means the items are not reported in any study in the database to adversely impact any of bacteria listed. Many substances have contradictory reports on shifts — this substances are excluded.

Not Listed Condition?

This person believes they may have Autoimmune, so going to https://microbiomeprescription.com/Library/PubMed we find that it is listed.

If it is not listed, search for bacteria shifts reported and use those (please send me the studies so I may add them).

The bacteria are shown in a tree. You have to manually match between the two.

In this we have:

• Escherichia     ⬇️  but our sample is high,
• Roseburia intestinalis   ⬆️  - we are high on Roseburia, we will include it

We have only one match — this tests with limited reporting is not a good fit for this condition. Doing a test like Biomesight, Xenogene, Thorne or Ombre is likely the best choice.

We just copy the taxon number into the form at the bottom of the page, and then click suggestions.

In this case, we get a short list. Remember, doing a single bacteria means you are ignoring a lot of interactions and factors. The suggestions could feed other bacteria that are too high.

• 
  

Backstory

A sample result dated 29/10/2023 it’s for 19month old son born via C section and having lots of ongoing tummy pain since birth.

Fully breastfed for well over 12 months but the microbiome doesn’t appear that way.

Analysis

This is a very much “flying by the seats of my pants” analysis. Why? From birth for the next 10-20 years the microbiome has dramatic natural changes. The Fuzzy Logic Expert System on Microbiome Prescription is tuned for adults and not these age ranges. If you are dealing with a child, the approach below is suggested.

I am going to use ChatGPT selectively to make analysis easier, checking that it’s answers agree with my memories from reading studies..

So, first some literature to frame this analysis in. There are 400+ studies on C Sections and Infant microbiome.

• “There is certainly a transient difference in the gut microbiota of infants born by Cesarean delivery compared to their VD counterparts. While this difference appears to be corrected after weaning, it may have lifelong impacts on the development of the immune system. ” [2018]
• “When comparing the gut microbiota composition of CSD babies with vaginally delivered (VD) babies, the former show a microbiome that closely resembles that found in the environment and the mother’s skin, while VD babies show a microbiome more similar to the vaginal microbiome. Although these alterations of normal gut microbiota establishment tend to disappear during the first months of life, they still affect host health in the mid–long term since CSD has been correlated with a higher risk of early life infections and non-transmissible diseases, such as inflammatory diseases, allergies, and metabolic diseases.” [2021]
  • Too late, but important for any future babies “Lab analysis showed that the microbiota of the C-section babies swabbed with their mother’s vaginal fluids was close to that of vaginally born babies” [2021]

Bifidobacteria and Firmicutes Dominance: In healthy infants and toddlers, the gut microbiome often shows dominance of beneficial bacteria like Bifidobacteria and Firmicutes. These bacteria play crucial roles in digestion, immune system development, and protection against pathogens.

Generally, Bifidobacterium can comprise anywhere from 10% to 40% or more of the total gut microbial population in toddlers.

In healthy toddlers, Firmicutes can typically constitute a substantial portion of the gut microbiota, often ranging from around 30% to 60% or more of the total bacterial population

From ChatGpt

Looking at Bifidobacterium, the numbers are lower (57%ile) than expected for a child of this age but with a rich diversity of species. Bifidobacterium is 2.4%, well below the expected 10-40%. Bacillota (formerly known as Firmicutes at 83%ile) is 79.4%, well above expected 30-60%.

Switch to consequence, ChatGpt reports:
“Some of the health implications of low Bifidobacterium levels in toddlers may include:

1. Digestive Issues: Bifidobacterium species contribute to the breakdown of complex carbohydrates, aiding in the digestion of certain fibers and sugars. Low levels of these bacteria may result in digestive problems such as constipation, diarrhea, or irregular bowel movements.
2. Weakened Immune Function: Bifidobacterium species are involved in regulating and supporting the immune system. Insufficient levels of these beneficial bacteria may compromise the immune response, making toddlers more susceptible to infections and illnesses.
3. Increased Risk of Allergies and Asthma: Some research suggests that alterations in the gut microbiota, including low levels of Bifidobacterium, might be associated with a higher risk of developing allergies and asthma in children.
4. Potential Weight and Metabolic Effects: The gut microbiome, including Bifidobacterium, can influence metabolism and energy regulation. Low levels of these beneficial bacteria in toddlers’ gut microbiota might potentially be linked to metabolic issues or an increased risk of obesity later in life, although this connection is still an area of ongoing research.
5. Impact on Neurodevelopment: Emerging studies suggest a possible link between the gut microbiota and brain development. Some evidence indicates that alterations in gut bacteria, including Bifidobacterium, might influence cognitive development and behavior in children.
6. Susceptibility to Infections and Antibiotics: A decrease in beneficial bacteria like Bifidobacterium can leave the gut more vulnerable to colonization by harmful bacteria. Moreover, toddlers with low levels of these beneficial bacteria may face a higher risk of complications after antibiotic treatments, as antibiotics can further disrupt the delicate balance of the gut microbiota.
7. Digestive Imbalances: In some cases, an overabundance of certain Firmicutes strains might affect the balance of gut microbiota and contribute to digestive problems, including irregular bowel movements, bloating, or discomfort.”

At this point, using the Fuzzy-Logic Expert System on Microbiome Prescription is not a good choice to get suggestions. It is tuned for adults and not toddlers. All of the values are in the normal range for an adult, but definitely out of range for a toddler.

What we want is to increase one bacteria and decrease another bacteria without looking at the percentile. I just added a subsection on the Research Features tab to make that available. It requires the the taxon numbers be entered. In this case: Decrease: 1239, Increase 1678 (Bifidobacterium).

See this video for a walk thru of the process.

This results in this page

You can click on each modifier to verify that it only impacts the bacteria named by taxon in the desired way.

In toddlers, several Bifidobacterium species are commonly found in their gastrointestinal tract. Among these species, Bifidobacterium longum, Bifidobacterium breve, and Bifidobacterium infantis are frequently observed in the gut microbiota of toddlers. These species play essential roles in maintaining gut health, aiding in digestion, and supporting the immune system during early childhood.

ChatGPT

We can take this one step further, picking specific children :

• Increase: 1678,1680,1682,1685 (the usual toddler species)
• Decrease: 1239, 186801 (Clostridia), 186802 (Eubacteriales), 186803 (Lachnospiraceae) the biggest ones under Bacillota

With a deeper set of suggestions:

Feed Back

I usually send drafts to the person for comments, concerns etc. This was the response:

I wondered whether prevotella/segatella buccae was a concern as it was the highest species in the sample and bacteroides was extremely low. The practitioner we saw prescribed HMO and lactulose after reviewing Biomesight raw data.

Mother of child

The HMO suggestion is reasonable if you do not check all of the literature. We have contradictory results from studies for HMO. Remember Bacillota is the modern name for Firmicutes.

Similarly, we have some contradiction in results with Bifidobacterium — so it was not deemed ultra safe.

This suggests adding segatella buccae (NCBI 28126) be added.

The results are similar, with less items on the to avoid.

Trying a different combinations, for example

We get different ordering and a few changes.

Bottom Line

We have various sets of suggestions, doing a consensus is likely the best path forward.

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.Posted on December 1, 2023 b

Back Story Section

April 2022 strong antibiotic treatment against another pathogen flared my chronic borrelia/babesia/bartonella. [I had Ceftriaxone iv and 1500mg of azithromycin as a single dose].

Shortly after this stinging started in the belly and burning when passing a stool and urinating. Its yeast symptoms. I have mthfr mutation and low bifido bacteria.

“chronic borrelia/babesia/bartonella” is also known as Chronic Lyme disease. See Lyme Disease Co-Infections | LymeDisease.org. It is a close sibling to ME/CFS, Long COVID and Occult Rickettsia. There are 77 samples uploaded marked with Lyme, 45 of these also indicate ME/CFS (58% overlap). There was no statistically significance difference in the microbiome between these two groups.

This person requested a video walkthrough due to cognitive issues with reading.

Analysis

Looking at the Percentile-Percentage distribution, we see the common pattern with ME/CFS and Long COVID: over representation of the 0-9%ile range. The numbers in each percentile range should be about the same. They are not.

Looking at the new Anti inflammatory Bacteria Score [Score: 12.56 or 16.9 %ile], we see that bacteria controlling inflammation appears to be very deficient. Dr. Jason Hawrelak Recommendations is at 89%ile with the following anti-inflammation bacteria being flagged as low: Roseburia, Bifidobacterium, Lactobacillus and Akkermansia.

Looking at the Potential Condition lists, we see many that we would expect to see

• Allergic Rhinitis (Hay Fever) 100%ile
• Hyperlipidemia (High Blood Fats) 97%ile
• Chronic Fatigue Syndrome 96%ile
• Allergies 95%ile
• Irritable Bowel Syndrome 94%ile
• Functional constipation / chronic idiopathic constipation 93%ile

Going Forward

I checked the KEGG suggested probiotics none of the suggestions were strong. On the other hand we have a good number of supplement suggestions from KEGG (shown below). The higher the Z-Score, the more important they are.

Looking at probiotics we see the best ones being bifidobacterium (which is good because many lactobacillus produce d-lactic acid that causes brain fog).

• bifidobacterium pseudocatenulatum,
• bifidobacterium infantis,
• bifidobacterium breve

There are some lactobacillus also suggested:

• lactobacillus casei — documented to be good for allergies and hay fever. Usually I suggest Yakult, one vial around each meal.
• lactobacillus reuteri — biogaia (reported not to produce d-lactic acid)

For supplements, checking the items from the KEGG list above, we found that all items suggested which we have data on, agreement that they should help:

• N-Acetyl Cysteine (NAC), +185
• l-proline + 161
• l-glutamine + 76
• l-arginine +45
• l-phenylalanine +40

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.

Back Story

• 38yr Old now, Issues started around the age of 24-27 i think [gradual onset]
• From the age of 17-35 my diet has been really bad ( Coca cola, pizza, burger, fries, candy and sweets etc)
• From 23-25 I started getting really tired everyday followed by pains in various locations
• Later, started loosing weight in the face, eyes started to sink deeper and deeper, my face become really gaunt. All my life i have been thin and could never gain weight nomatter how much i ate.
• I also startet getting extremly fatigue after eating.
• definitive stomach issues started around the age of 30-33, may have been before.
• Since I turned 34, i have been trying to figure out what is wrong with me.
• • Allof the standard checks at the docs Office(ultrasound of organs and stomach area, CT/MRI of stomach area, Colonscophy and gastroscophy)
  • Nothing found

So everything points towards gut dysbiosis or something like that

I started to change around with my diet July 2022. Details of various attempted changes (Gluten free, no dairy, no sugar, carnivore diet) — currently on Keto with resistant carbs.

But many symptoms are still there.

I have been taken multiple rounds of antibiotics from november 2022 until Jan. 2023 (80 days) because i had a sinus and deviated septum surgery. I have also taken 7 days of metrodinazole  and amoxicilin 12 weeks ago because of the H Pylori infection i had. Retest was negative for H. Pylori
Got diagnosed with methane SIBO via breath test in september 2023

I have been diagnosed by a GI Map test in May 2023 with:

• candida
• E coli overgrowth
• Streptococcus overgrowth
  by a gimap test in May 2023

I feel like my body is destroying itself.
A long list of symptoms was given

Analysis

Potential Medical Conditions Detected

Nothing stood out. By this I mean that the Percentile ranking is well into the Prevalence. The closest was SIBO where the borderline would be 100-52= 58%ile. He was reasonably over that. He wrote “Got diagnosed with methane SIBO via breath test in September 2023”, so this was a definite matching forecast from PubMed literature.

Bacteria deemed Unhealthy

The one item of interest was Faecalibacterium prausnitzii, which was 19% of his microbiome and associated with increased Candida risk (which he has had).

Dr. Jason Hawrelak Recommendations

• Akkermansia – too low
• Bifidobacterium – too low
• Lactobacillus – too low
• Roseburia – too low
• Proteobacteria – too high
• Escherichia coli – too high: in agreement with GI Map
• Faecalibacterium prausnitzii – too high

Percentages of Percentiles

This is my quick way to statistically determine if there is statistically significant dysfunction. The significance is 0.99999.. etc, so yes.

Forecast Symptoms

In the top ones we had the following agreements with reality:

• cold extremities
• Rapid muscular fatigability
• Joint pain
• Sinus issues with headaches 
• Onset: Gradual 
• Sinus issue
• Onset: 2010-2020 
• Gender: Male
• General: Headaches
• Post-exertional malaise

The ones that did not match were connected to cognitive issues.

Pattern appear to match a subset of myalgic encephalomyelitis/chronic fatigue syndrome. Many MDs will suspect it, but will not give a diagnosis if the person is not totally disabled. The reason is simple, no treatment plan and likely a negative psychological impact.

Going Forward

This looks likes a good candidate for a two stage building a consensis:

• “Just Give Me Suggestions”
• THEN using special studies (everything at once – skipping Gender) to add a fifth set of suggestions

The suggestions are short and tight. Barley porridge with Walnuts for breakfast for most days.

I would suggest taking Danish product Biogaia Lactobacillus Reuteri just before bed each night for two weeks, then switch to clostridium butyricum for two weeks. The other probiotics – do 1 at a time for 1-2 weeks, take them 1-2 hours after breakfast.

Akkermansia Muciniphila probiotics and Swedish Filmjölk (on your porridge?) are two probiotics with no known negative impact and some positive impact. The list above are the highest predicted impact.

What to avoid

Keep up the no alcohol but reduce/drop beef in your carnivore diet. Go for herring, eels and other fish product. It is interesting that the two E.Coli probiotics are listed as avoid (the logic does not look at E.Coli levels, but other bacteria levels to make that suggestion)

Prescription Items (if you have a willing MD)

Doing antibiotics is usually consider if the above do not cause sufficiently improvement over time. I mentioned that the history looks quasi-ME/CFS. I was not surprise to see many ME/CFS antibiotics on the list, including:

• AMOXICILLIN (ANTIBIOTIC)S[CFS]
• AMPICILLIN (ANTIBIOTIC)S[CFS]
• CIPROFLOXACIN (ANTIBIOTIC)S[CFS]

If you and your MD decide to try antibiotics, I would suggest on of those (using Dr. Jadin approach of pulsing).

Browsing the Details

High value was 701, low as -391. Usually these two numbers are about the same magnituded. Items spotted of note:

• Avoid
  • gluten-free diet: -215
  • low-fat diets: – 193
  • high sugar diet: -182
  • proton-pump inhibitors (prescription): – 157
  • mediterranean diet: -156
  • Beef: -210, high beef diet: -155
• Take
  • dietary fiber: 210
  • high fiber diet: 143
  • buckwheat: 305
  • brown rice 286
  • whey: 254
  • Herbs: fennel, tulsi, neem, cinnamon

Note that inulin (prebiotic), jerusalem artichoke (prebiotic) etc are of low (but positive) value.

Questions

Q: Regarding your sugestion of all the probiotics. Usually the probiotic comes in bottles where there is like 4-10 different strains. Should i avoid that and only buy single strains in each bottle of all the ones you mentioned?

A: Each strain impacts things in different ways. My preference is always single strains, ideally ones that have been researched with the ideal being ones researched for your condition or symptoms and found effective. See https://microbiomeprescription.com/library/ProbioticSearch , There are reports of some probiotics making people worse. A major issue is that probiotics are not well regulated Many “retail mixtures” have over 60% of their contents misidentified. See Deceptive Probiotic Labels or Assessment of commercial probiotic bacterial contents and label accuracy, When the bottle gives an explicit strain (not species), then the owner of that strain has motivation to insure quality control.

Looking at the challenges of getting probiotics in Denmark. What may be an acceptable compromise is to find a probiotic mixture that does not contain any probiotics with an estimated adverse risk. In your case these are:

• symbioflor 2 e.coli probiotics
• colinfant e.coli probiotics
• bacillus subtilis natto (probiotics)
• bifidobacterium longum,lactobacillus helveticus (probiotics)
• lactobacillus paracasei,lactobacillus acidophilus,bifidobacterium animalis (probiotics)
• General Biotics Equilibrium
• bifidobacterium (probiotics)
• lactobacillus rhamnosus gg,lactobacillus,rhamnosus,propionibacterium
• reudenreichii,bifidobacterium breve (probiotics)
• bifidobacterium bifidum (probiotics)
• bacillus licheniformis,(probiotics)
  Prescript Assist (2018 Formula)
• lactobacillus bulgaricus (probiotics)
• lactobacillus gasseri (probiotics)
• lactobacillus rhamnosus (probiotics)
• lactobacillus casei shirota (probiotics)
• lactobacillus fermentum (probiotics)
• lactobacillus sakei (probiotics)
• lactobacillus delbrueckii bulgaricus,bifidobacterium bifidum,enterococcus faecium,candida pintolopesii,aspergillus oryzae (probiotics)
• lactobacillus brevis (probiotics)
• bifidobacterium adolescentis,(probiotics)
• bifidobacterium lactis,streptococcus thermophilus probiotic

Example:  lactobacillus rhamnosus gg (probiotics) is an explicit strain (“GG”) is the second highest positive, while generic  lactobacillus rhamnosus is # 54 and negative.

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.

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.