For more information on this project see Health Rising post. Both participants has granted me to do a review with their real names. For information about Tess see the Wired Story: How Chronic Illness Patients Are ‘Hacking’ Their Wearables (See Objective evidence — the smart watch dimension for my take). This is the first of a series of posts on this project.

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

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

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

Overview of results

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

Criteria	2-Mar	9-Mar	21-Mar	3-Apr	16-Jun
Shannon Diversity Index (Percentile)	85.2	79.0	99.8	72.4	16.0
Simpson Diversity Index (Percentile)	72.6	63.4	64.4	77.0	91.4
Chao1 Index (Percentile)	65.7	31.2	77.2	62.9	7.9
Lab Read Quality	8.9	8.7	6	4.5	4.4
Bacteria Reported By Lab	654	533	725	585	389
Bacteria Over 99%ile	2	4	30	11	24
Bacteria Over 95%ile	13	10	70	33	38
Bacteria Over 90%ile	25	23	103	63	55
Bacteria Under 10%ile	287	213	123	113	140
Bacteria Under 5%ile	253	173	66	48	96
Bacteria Under 1%ile	219	135	17	8	46
Lab: BiomeSight
Rarely Seen 1%	13	1	38	5	6
Rarely Seen 5%	28	9	107	17	29
Pathogens	36	31	50	40	36
Outside Range from JasonH	6	6	9	9	8
Outside Range from Medivere	15	15	22	22	22
Outside Range from Metagenomics	7	7	9	9	9
Outside Range from MyBioma	5	5	6	6	6
Outside Range from Nirvana/CosmosId	26	26	18	18	15
Outside Range from XenoGene	28	28	46	46	42
Outside Lab Range (+/- 1.96SD)	6	8	56	17	27
Outside Box-Plot-Whiskers	44	36	124	70	67
Outside Kaltoft-Moldrup	233	169	207	137	170
Condition Est. Over 99%ile	0	0	13	10	48
Condition Est. Over 95%ile	1	0	24	35	75
Condition Est. Over 90%ile	2	2	34	55	84
Enzymes Over 99%ile	0	0	347	606	957
Enzymes Over 95%ile	7	0	635	970	1145
Enzymes Over 90%ile	24	28	746	1159	1228
Enzymes Under 10%ile	506	515	561	237	245
Enzymes Under 5%ile	378	360	422	143	174
Enzymes Under 1%ile	194	166	120	52	94
Compounds Over 99%ile	0	0	225	215	617
Compounds Over 95%ile	2	0	355	410	732
Compounds Over 90%ile	8	11	441	520	778
Compounds Under 10%ile	1243	986	1105	902	822
Compounds Under 5%ile	1156	926	1032	856	796
Compounds Under 1%ile	1115	875	936	828	767

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

	2-Mar	2-Mar	9-Mar	9-Mar	21-Mar	21-Mar	3-Apr	3-Apr	16-Jun	16-Jun
Percentile	Genus	%	Genus	%	Genus	%	Genus	%	Genus	%
0 – 9	65	40%	51	36%	28	15%	29	19%	35	36%
10-19	13	8%	16	11%	36	19%	23	15%	9	9%
20 – 29	11	7%	16	11%	20	11%	23	15%	14	14%
30 – 39	5	3%	9	6%	17	9%	17	11%	4	4%
40 – 49	13	8%	10	7%	14	8%	11	7%	5	5%
50 – 59	12	7%	8	6%	10	5%	8	5%	3	3%
60 – 69	12	7%	9	6%	6	3%	14	9%	1	1%
70 – 79	12	7%	3	2%	10	5%	6	4%	5	5%
80 – 89	10	6%	10	7%	15	8%	9	6%	7	7%
90 – 99	11	7%	8	6%	30	16%	15	10%	15	15%
Total	164		140		186		155		98

	2-Mar	2-Mar	9-Mar	9-Mar	21-Mar	21-Mar	3-Apr	3-Apr	16-Jun	16-Jun
Percentile	%	Species	%	Species	%	Species	%	Species	%	Species
0 – 9	41%	94	43%	79	13%	40	17%	36	21%	31
10-19	6%	14	9%	16	15%	44	13%	28	9%	13
20 – 29	8%	18	9%	16	13%	39	12%	27	15%	22
30 – 39	3%	7	5%	10	10%	29	10%	22	8%	11
40 – 49	7%	17	4%	7	7%	20	8%	17	6%	9
50 – 59	9%	21	5%	9	7%	22	6%	12	5%	7
60 – 69	6%	14	8%	14	3%	10	6%	14	6%	9
70 – 79	8%	18	4%	7	6%	19	7%	15	6%	9
80 – 89	6%	13	8%	15	8%	23	8%	17	10%	14
90 – 99	5%	12	6%	11	19%	57	13%	29	14%	21
Total		228		184		303		217		146

The Events Around the above Samples

• 2023-03-02 (baseline)
• 2023-03-09 (during a stomach bug)
• 2023-03-21 (during a “Level 1” remission while taking Amoxiclav. Level 1 = all symptoms gone. Felt great. Had started Amoxiclav on the morning of 3/20, this sample was taken after 3 doses around 3pm)
• 2023-04-03 (4 days after stopping Amoxiclav, was on Doxycycline)
• 2023-06-16 (During 2nd round of Amoxiclav when I felt really bad)

The microbiome results definitely reflected the Level 1 remission on 3-21. The low percentile genus and species percentage almost made it to the target level of 10% from the prior 40%! The Shannon Diversity Index was awesome (the higher the better!).

I have often described correcting the microbiome as being similar to sailing a sailboat along a coast line. Depending on wind and tide, there may be a lot of course changes required. I am curious on the ranking of Amoxiclav[amoxicillin] and Doxycycline with these samples. The numbers below suggests that going on to Doxycycline may have been a poor choice. On the 2nd round of Amoxiclav, it was at just 35% of the highest value recommendation versus 70% on the 1st round.

Criteria	2-Mar	9-Mar	21-Mar	3-Apr	16-Jun
Amoxicillin	280	159	210	560	384
Doxycycline	3.7	67	260	-27	-141
Highest	402	354	1010	912	1050
Best Antibioitic	rifaximin	triclosan	rifampicin	imipenem	imipenem

Note that rifaximin and rifampicin are in the same family and used by some ME/CFS specialists. Imipenem shows up often with ME/CFS samples — but since it is intravenous, not the easiest to get on an ongoing basis.

Take Away and Reflections

This project main purpose was to show that remission can be triggered by antibiotics to another generation of people suffering from ME/CFS. The remission on 21-Mar demonstrates this to be correct both subjectively and objectively (Microbiome data — specifically Shannon Diversity Index and Percentages in different percentiles for Species and Genus). I should emphasis that both of these measures are multi-bacteria measures and do not support the common myth that ME/CFS is caused by a single bacteria.

The critical issue is maintaining remission. How do keep the microbiome where it should be. I often use analogies of cities because we are talking about a city of bacteria. To stop riots and looting in the streets you send in the National Guard (antibiotic). The city is back to normal. The causes of the the riots are still there. Typically lack of opportunities, neglect, etc. in these same ghettos. You must address these other issues. A repeat of sending in the National Guard may have the same effect as the first time OR very different effects – the same can be said for using the same antibiotics.

With bacteria we have two “radical organizations in the ghetto” — The Antibiotic resistant and the Resistors. For example, the bacteria that survive learnt how to avoid the National Guard and proceed to share that skill to other bacteria. Instead of a wall of shields pushing the rioters back, Molotov cocktails rain down on the troops from above.

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Another analogy is that the gut is a pendulum. The antibiotics, probiotics and other items pushes the gut towards the equilibrium position! There is great joy! The gut is centered… At this point, many people go down the wrong reasoning path — “Keep doing more of the same to stay in remission!“. Remission is lost and the momentum pushes the guts out to the other side!!! You pushed out one devil and seven more devils return! (Math 12:43-45)

The key is to dampen down the swing so there is less and less swing. My personal opinion is that Cecile Jadin’s protocol using antibiotics for only 7 days each month is a very effective way of dampening the pendulum gained from decades of experience.

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My approach with Microbiome Prescription is to try to address the whole city. Before taking action, we get intelligence on the nature of the city and decide on the appropriate action for a period of time (typically 4-10 weeks). We then update our intelligence (i.e. microbiome test) and pick the next course of action. Actions may include “food kitchens”, “safe injection sites”, “training programs”, “home repairs”, “on the beat policing”, etc.

There are many people who will state that antibiotics gave me ME/CFS. I do not disagree. Antibiotics can also trigger remission — the problem is WHICH antibiotics. Since every person’s microbiome is unique, there can be no universal “best antibiotic” for ME/CFS. Antibiotics must be selected based on the individuals microbiome (which is the path that Microbiome Prescription took) for the best odds of making the right choice.

My Suggestions Going Forward

I am working with C.L. Jadin, M.D., on documenting her current protocol from almost 30 years of experience using antibiotics for ME/CFS. It will be out soon. I am hoping to have that available within a month. One key aspect of her protocol has been pulsing antibiotics using two at a time. There are a few studies showing that pulsing is more effective. Pulsing often means 7 days on and 21 days off.

Looking at Tess’s last sample, I would propose the following to be considered (items that I picked from the consensus suggestions):

• Take a 3 week break from antibiotics (Ideally at the end take another sample)
• Do a single course of gentamicin or ciprofloxacin
• Take the following probiotics (rotating them every week)
  • lactobacillus reuteri (at least 10 BCFU/day)
  • clostridium butyricum AKA Miyarisan (500-1000 mg/day)
• Take the following herbs
  • neem
  • syzygium aromaticum (clove)
  • oregano (origanum vulgare, oil) |
• Have barley porridge with walnuts each day

In terms of things to stop (if doing)

• gluten-free diet
• alcoholic beverages

Use the Dosages for Supplements for guidance on dosages!

The Human Behavior Problem

A common pattern of people is “Just give me a pill to fix me, I want to keep my current life style and I am not prepared to make significant life style and diet changes”. My impression is that making those changes is often required. Inertia is a real maintainer of ME/CFS, especially the pendulum inertia when trying to heal.

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.