The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.
The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.
The three episodes of ME/CFS that I have had were all caused by stress:
In University days — Doing triple honors and a life threatening condition hit my father
At Microsoft — Assigned to a bad boss that created endless non-productive stress (he was “asked” to leave shortly after I went off sick — co-workers also has issues)
At Amazon — similar to the above, except no co-operation from fellow employees — everyone was trying to have other team members be who was cut on the next review cycle.
Society and family often expects you to put up with stress. In some cases, it may be needing a job and stress is “just a normal aspect of that“. In other cases, there may be “drama queens”, “gas lighters” and other personality types that uses stress to manipulate people.
There have been many studies finding that ME/CFS is associated with prior stress. Some literature:
One study is particularly interesting:Distinctive personality profiles of fibromyalgia and chronic fatigue syndrome patients. [2016] and likely applies to many people with microbiome dysfunctions. For many years, “Personality Type A are more prone to get ME/CFS” that is people with a pattern of behavior and personality associated with high achievement, competitiveness, and impatience. This study refines that paint brush more. Please read this study.
“Well, I can’t do anything about it”
Most people can do many things. For myself, I have (finally) accepted this simple rule: “If the stress cannot be resolved in X weeks, promptly exit stage left/abandon ship”. In terms of work, if I quit a job, take a financial hit for a while, and get a less stressful job — that is a better alternative than risking a ME/CFS relapse and being unable to work at all. In terms of family, decline contact with stressful members of the family. The latter often require a smoke screen so you agree to come to family gathering and then car troubles, work emergency, prevents you attending.
In other cases, it may take help from others to identify and unlearn habits/conditionings from childhood or prior relationships.
Society and friends can be a major source of stress. This can come from many sources “high expectations of you” to “keeping up with the Jones” to “you are not doing your duty”.
IMHO, your first duty is for your own health. How can you support and care for others when your health is blocking things.
Stress is a co-factor
Other factors are involved, diet, past virus, DNA. Stress is a significant factor and for many people it is the hardest to change because people often resign to their current stress level. For myself, I often surprise my wife by being hyper-reactive to issues. I avoid procrastination and attempt to deal with issues promptly — it is an effective way of reducing stress. Procrastination on the grounds of having to think thing over (or hoping things will resolve themselves) adds to your stress queue in small amounts. If you can’t do anything about it, then accept that reality and don’t worry about it.
Bottom Line
Take a hard look at stress in your life and then resolve them. Your health is the cost of not resolving them.
This is a series of samples from a long time suffer from Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). In looking at the samples, we must keep in mind two factors:
Lab Read Quality. A low read quality reports much fewer bacteria than a high read quality.
1.8 reports 300, a high read quality can triple the bacteria counts selected.in a category
This usually does not impact the broad stroke criteria such as Dr. Jason Hawrelak (JasonH)
A sickness (COVID, Flu) or even vaccination will alter the microbiome and create the appearance of lost ground. This was seen and covered in the post below
My usual practice is compare the latest with the prior only. Trying to weave a dialog covering all prior tests is contra productive with going forward. What is past, is water under the bridge.
This reader had one major question — the suggestions seem to have changed a lot. That is the focus of this post. Have there been a dramatic change, or are the changes likely connected to a few bacteria dropping off the list and a few new ones being added.
Person’s Subjective Report: “Symptom-wise and treatment-wise, I don’t have much to report that is new or an outlier to previous years of samples and treatments.”
Review over Multiple Samples
Data from the same lab (using Ombre data, Biomesight version is also available).
Compared to the prior sample, we see:
Less extreme percentiles than prior sample
Less rarely seen bacteria
Less pathogens
Less outside of:
Outside Lab Range (+/- 1.96SD)
Outside Box-Plot-Whiskers
Outside Kaltoft-Moldrup
No change using a multitude of 3rd party criteria.
Less compounds at high levels, more compounds at low levels
General impression, there has been objective improvement
Criteria
4/18/2023
2/2/2023
11/1/2022
4/11/2022
1/11/2022
03/09/2021
5/27/2020
Lab Read Quality
4.8
5.3
8.6
15.3
1.8
2.9
4.3
Bacteria Reported By Lab
638
734
800
750
312
394
544
Bacteria Over 99%ile
4
14
12
3
9
3
4
Bacteria Over 95%ile
36
42
24
21
30
10
12
Bacteria Over 90%ile
56
79
46
49
46
25
36
Bacteria Under 10%ile
25
46
154
329
16
26
32
Bacteria Under 5%ile
10
21
54
287
10
15
11
Bacteria Under 1%ile
2
1
7
84
1
5
0
Lab: Thryve
Rarely Seen 1%
10
15
7
8
0
2
9
Rarely Seen 5%
36
61
58
70
2
4
18
Pathogens
27
34
32
30
21
26
30
Outside Range from JasonH
4
4
4
4
7
7
7
Outside Range from Medivere
13
13
19
19
14
14
14
Outside Range from Metagenomics
10
10
7
7
9
9
9
Outside Range from MyBioma
13
13
10
10
8
8
8
Outside Range from Nirvana/CosmosId
22
22
22
22
17
17
17
Outside Range from XenoGene
51
51
48
48
42
42
42
Outside Lab Range (+/- 1.96SD)
18
24
14
17
8
5
5
Outside Box-Plot-Whiskers
67
97
63
63
58
28
45
Outside Kaltoft-Moldrup
143
218
290
400
89
102
141
Condition Est. Over 99%ile
0
0
0
5
2
1
0
Condition Est. Over 95%ile
0
0
2
24
6
5
1
Condition Est. Over 90%ile
0
7
3
38
11
9
2
Enzymes Over 99%ile
13
47
44
4
1
41
56
Enzymes Over 95%ile
114
179
99
72
7
311
465
Enzymes Over 90%ile
469
266
242
152
54
683
512
Enzymes Under 10%ile
133
123
219
430
70
141
106
Enzymes Under 5%ile
58
47
108
310
47
88
53
Enzymes Under 1%ile
7
1
1
64
6
14
5
Compounds Over 99%ile
11
31
22
3
1
27
53
Compounds Over 95%ile
37
122
67
46
9
305
276
Compounds Over 90%ile
146
190
231
93
38
488
365
Compounds Under 10%ile
954
809
823
998
530
617
800
Compounds Under 5%ile
906
789
788
961
521
599
787
Compounds Under 1%ile
872
783
757
892
515
582
776
Comparing Suggestions
The reader noticed a significant change of suggestions between the samples. So I am going to document out how to verify or see the results. Also, remember that you can merge consensus from two different samples. Minor items like taking samples at different time of days, unusual food, significant dosages of herbs, probiotics or supplements within 48 hours of taking a sample can cause temporal shifts.
Step by step:
Do “Just Give Me Suggestions” for 1st sample
Click “For more technical details”
Click Download, save the file
Repeat for 2nd sample
Open Excel or equivalent on the .csv file downloads
Each sample will be in a difference instance of Excel
Add a new work sheet to one
Copy one set of data to the other
Rename worksheets as “Current Consensus” and “Prior Consensus”
Copy the Mid2 column to the first column in the Prior Consensus”
Insert a blank column after the Mid2 column,
Insert into this new blank column: =VLOOKUP(I2,’Prior Consensus’!A:J,2,FALSE)
This brings the net value over.
You can brink other values over by changing “2”
I have done a video below of the process.
Wait! This is more for your mobile phone users
Go to Multiple samples, then pick your samples as shown below
You will now see a summary at the top
In this case we have 80% agreement on suggestions between samples
Bottom Line
I have not done further analysis etc. The reader has acquired those skills already. I have address only the issue of shifting suggestions. My best advice is simple: Do an uber consensus between the present and the last sample. The logic is simple — samples has some volatility based on time of day that the sample was taken as well as substances consumed in the prior 48 hours. Remember we are dealing with a lot of fuzzy data — both in what is reported from your sample, and what changes what from the literature.
The result of an uber consensus is a continuation of the prior suggestions (with a little pruning) and incorporation of the current suggestions.
This is a continuation of a nerdy technical series with Predicting Conditions from PubMed Studies earlier. I continue using depression as my canary. We use two data sets from two different retail providers: Ombre Labs (USA) and Biomesight (UK).
Means, T-Scores – Walking Two Paths
There are two paths available, with no clear best choice:
Compare averages from samples that reported some amount (thus excluding zero values)
Lower degree of freedom and thus T-Scores
Compare averages from samples with a zero being used when an item is not reported.
Higher degrees of freedom and higher T-Scores
Picking one or the other of these paths seems to be done a priori by most researchers. To get a feel for the differences, I processed data from Ombre Labs and Biome Sight by both methods. Ombre Labs is almost solely from American Samples. BiomeSight is from around the world. The data suggests that Americans are more prone to depression.
Measure
Biomesight
Ombre Labs
Bacteria Taxon Seen
1,878
2,586
With Depression
87
101
Without Depression
639
343
Percentage with Depression Reported
12%
23%
Highest T-Score with reported only (No Zero values)
95
60
ABs(T-Score) over 3.2 (No Zero values)
1135
1185
Highest T-Score with Zero if missing
282
176
Abs(T-Score) over 3.2 (with Zero if missing)
1391
1492
Highest Composite T-Score (Sqrt of the above t-score multiple by each other)
158
92
Composite T-Score over 3.2 ( < 0.001)
1193
1306
Percentage of Bacteria with T-Score over 3.2
63%
50%
Larger total sample size results in higher t-scores
We then did a composite T-Score between the two labs by bacteria to see the best indicators (in decreasing order of significance). We then compared to results from studies on the US National Library of Medicine
Bacteria
Depression
PubMed
Blautia wexlerae
Increased
Genus has 50% high and 50% low
Catonella
Increased
Catonella morbi
Increased
Pseudobutyrivibrio
Increased
Clostridiaceae
Decreased
Sutterella wadsworthensis
Decreased
Match (genus)
Slackia
Increased
Match
Anaerobranca
Decreased
Actinobacillus
Decreased
Haemophilus parainfluenzae
Decreased
Match (genus)
Proteinivoraceae
Decreased
Bacteroides ovatus
Decreased
Thermoclostridium
Decreased
Eggerthellales
Increased
Match
Eggerthellaceae
Increased
Thermoanaerobacterales
Increased
Prevotella
Decreased
General Match at genus
Veillonella dispar
Decreased
Match (genus)
Blautia glucerasea
Increased
Genus has 50% high and 50% low
Blautia hansenii
Increased
Genus has 50% high and 50% low
Lachnospira
Decreased
General Match at genus
Alphaproteobacteria
Increased
[Ruminococcus] torques
Increased
Disagree
Desulfallaceae Watanabe et al. 2020
Increased
Bifidobacterium bifidum
Decreased
Disagrees, but the genus was a match
Where there was a study available, we had our results in good agreement. Note that our sample sizes far exceed those of any of the studies we found.
We had about 20% less significant bacteria by eliminating those samples with no/zero counts. By requiring both methods to be significant reduces the risk of false positives. In this case, we have an abundance of significant bacteria and such a criteria is viable.
Many of the items deemed very significant were of low frequency of being seen in samples
Erwinia tasmaniensis: seen in 7.2% of Biomesight samples, not reported in Ombre.
With Condition: Seen in 3% of samples with count of 353 /million
Without Condition: Seen in 7.7% of samples with count of 128 / million
Significant with excluding (11.2), significant with deeming zero (23)
This takes us to the phrase of the analysis: Given a random sample, what is the probability of correctly predicting depression. The end goal is to determine the most influential bacteria responsible for depression. One approach is using random forest on the data trimmed to those bacteria deemed significant or very significant. Another approach is use splines mixed with logistic regression. We must be careful not to shove this problem into an existing square or round hole, instead we need to allow the data to speak to us.
Remember, bacteria do not operate independent of each other. They are highly dependent on the metabolites of each other. Some of my earlier explorations found the KEGG Enzymes estimators were better predictors than bacteria. To be continued.
It all started in February 2018, I am a nurse by profession and after having worked in various cities in my country. I finally got a stable job in my birthplace , my work always caused me stress, and I always thought that sooner or later stress would affect me. I would go to work and notice how the anxiety was going to my digestive system but when my working day ended it was as if a deep sensation of peace would come to me as if a valve opened and let out the gas from a pot..
I have seen that stress is one of the factors that can cause a change in the intestinal microbiota. I was finally diagnosed with a Generalized Anxiety Disorder and Major Depression in September 2018. In September of that year I was also diagnosed with ME/CFS since my fatigue was palpable (non-refreshing sleep). My wisdom teeth were extracted since I was diagnosed with temporomandibular joint dysfunction and I have to say that my instability problems dissipated over the months but everything else remained. Throughout these years I have been struggling with this, without knowing what I really had: CFS/ME, depression, generalized anxiety disorder, irritable bowel syndrome…
But what I do know for sure is that I was a happy person, with a partner, with economic stability, without any debt problem, nor work problems and from one day to the next my life changes to such an extent that I end up with depression and suicidal ideas.
Analysis
This person did the results with Xenogene which gives more information than 16s tests. Unfortunately, they do not provide percentile rankings so we have to borrow those estimates from elsewhere.
The pattern matches that of most people with ME/CFS: over representation of the 0-9%ile. An ideal microbiome should have the number in each 10%ile range being the same.
Percentile
Genus
Species
0 – 9
23
70
10 – 19
4
13
20 – 29
8
16
30 – 39
10
17
40 – 49
7
9
50 – 59
4
13
60 – 69
6
16
70 – 79
6
12
80 – 89
2
17
90 – 99
8
5
Looking at other health issues:
Dr. Jason Hawrelak Recommendations is at 89%ile, with the following of most concern:
Faecalibacterium prausnitzii – too low (lab agrees)
E.Coli — is too high, (lab agrees)
Roseburia – too low (Lab identifies one species too low, but two normal)
These levels of Salmonella and Shigella are of conventional medical concern. Potentially, you could negotiate with the MD for antibiotics that are effective for them and which will also help your microbiome.
Going Forward
As is becoming my norm, “Just give me Suggestions” button runs 4 sets of suggestion that will usually produce good suggestions.
And add this to make 4 sets of suggestion. While these may already be selected in the other suggestions, this additional run will emphasis those suggestions more.
Consensus – antibiotics
The topic antibiotics suggested (considering everything) are:
Cross checking with Special Reports (which computes a different way), we find that both lists have many in agreement. This list is full of the antibiotics often prescribed by ME/CFS specialists.
There are many antibiotics that are listed in the avoid list. If the MD is willing, I would suggest following the Cecile Jadin’s approach for antibiotics, single course of one antibiotic, a break, then a different antibiotic (preferable a different family).
Consensus – Other
The top ones of the non-antibiotics list are also familiar supplements to many ME/CFS patients. In decreasing priority:
I would suggest (after discussing with your medical profession) to keep to the above for 2-3 months and then do a retest. You are sailing your microbiome to safer waters though an archipelago. The winds and the charts will often require many course corrections.
Reader Feedback
From what I have seen of your recommendations, the conventional antibiotic treatment would be shorter than that of products such as oregano, thyme…
There is no doubt that I will talk to my doctor, precisely in a few days I have an appointment with my internal medicine doctor (infections) and I will take my test result so that if he wishes he can prescribe antibiotics, although I am afraid that taking them will further imbalance my microbiota but there is no doubt that possibly my digestive problems (abdominal pain) do not have their origin in irritable bowel syndrome or irritable colon and are produced by these bacteria that I may infect myself having worked in an infectious unit before falling ill .
I know of several MDs that specialized in treating ME/CFS patients who after a few years also came down with ME/CFS. Bacteria is easily transferred by skin contact, inhalation – in most cases, each exposure compound the very low odds of contagion from a single contact. Over time, the risk increases.
“Is CFS contagious? Because the cause of CFS remains unknown, it is impossible to answer this question with certainty. However, there is no convincing evidence” Wisconsin Department of Health
” The chronic phase of ME/CFS does not appear to be particularly infective. Some healthy patient-contacts show immune responses similar to patients’ immune responses, suggesting exposure to the same antigen (a pathogen).” [2015]
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.
We’ve had COVID over half a dozen times since early 2020. My primary symptoms are fatigue, brain fog, and breathlessness/lack of endurance. But the full list of symptoms is long… It’s gotten so bad that I’m out of work.
I felt brief but significant improvement with low-dose naltrexone, and with low-dose nicotine patches (wasn’t a fan of using that, but it was a successful experiment).
We’re on a ton of supplements with the guidance of our doctors, but our symptoms are worse than ever, and getting worse with time. We think our microbiomes have been damaged, which the Biomesight results seem to agree with.
Looking at what is reported for Long COVID, we have few matches: (5 %ile) 24 of 212, but wtih Coronary artery disease (93 %ile) 6 of 18. This hints at a different direction which the literature indicating that it is possible.
Patients with COVID-19 were at increased risk of a broad range of cardiovascular disorders including cerebrovascular disorders, dysrhythmias, ischemic and non–ischemic heart disease, pericarditis, myocarditis, heart failure, and thromboembolic disease.
”As with ongoing symptomatic COVID-19, multiple Prevotella species (38) are associated with long COVID. Prevotella species are overrepresented in patients with COVID-19 and are thought to produce proteins that can promote SARS-CoV-2 infection and increase clinical severity of COVID-19 (42).” which may account for the multiple COVID.
Dr. Jason Hawrelak Recommendations is at 99.9%ile with low Akkermansia and Bifidobacterium being main item of concern.
We have the typical over representation of bacteria in the 0-9%ile.
I am inclined to suggest diet changes with rotating probiotics every 2 weeks (making sure that you have therapeutic dosages). Retest after 8 weeks and see where we moved to.
Bottom Line
The assumption of typical long COVID or ME/CFS were reasonable assumptions with no microbiome data. You may wish to review other reviews of Long COVID patients. see Analysis Posts on Long COVID and ME/CFS. However, when we add in microbiome data we do not find a match — instead we have indicators of possible cardiac issues arising out of COVID. I would suggest asking for an in depth analysis by your medical professional in this direction.
For the last decade I have been citing literature for the appropriate dosage levels of Vitamin D. The RDA level is not sufficient for a healthy microbiome — it is sufficient only to stop rickets from occurring.
“Several diseases have been linked to vitamin D deficiency, such as hypertension, diabetes, depression, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and chronic pain syndromes such as fibromyalgia. ” [2013]
“Vitamin D deficiency was defined as a serum 25-hydroxyvitamin D concentrations ≤20 ng/mL (50 nmol/L). The overall prevalence rate of vitamin D deficiency was 41.6%,” [2011]
“Older adults are advised to maintain serum 25(OH)D concentrations >75 nmol/L.” [2006]
Dr. Mercola recommends 45-50 ng/ml or 115-128 nmol/l [source]
“Vitamin D intakes required to maintain serum 25(OH)D concentrations of >80 nmol/L in 97.5% of the sample[of men and women aged 20-40 y] were … 41.1μg/d (1640 IU), respectively.” [2008]
“The clinical trial evidence shows that a prolonged intake of 250 mug (10,000 IU)/d of vitamin D(3) is likely to pose no risk of adverse effects in almost all individuals in the general population; this meets the criteria for a tolerable upper intake level.” [2007]
“Evidence from clinical trials shows, with a wide margin of confidence, that a prolonged intake of 10,000 IU/d of vitamin D(3) poses no risk of adverse effects for adults,” [2009]
NOTE: there was no evidence going 50% higher has any risks. The studies just tested the 10,000IU level.
“Vitamin D, important for maintaining bone health in Crohn Disease[CD], may have potential as a treatment for the core inflammatory disease process. There is plausible evidence in favour of vitamin D as an anti-inflammatory from animal models, epidemiological and cross sectional studies of CD.”[2015]
“Active Crohn’s disease was associated with low serum 25-OH vitamin D.”[2013]
“In addition, low vitamin D has been associated with disease activity in CD patients, and supplementation appears to be beneficial in improving clinical scores and reducing inflammation.” [2014]
“Vitamin D is an inexpensive supplement which has been shown to improve IBD outcomes.”[2014]
“people with IBD may remain in remission longer when treated with oral vitamin D…suggest that vitamin D may modify the immune response in IBD.” [2015]
“Our findings showed that the high-dose supplementation of vitamin D[9 of 50000 IU cholecalciferol capsules for 3 months taken at weekly intervals] affects measures of systemic inflammation: reductions in High-Sensitivity C-Reactive Protein level and Neutrophil-to-lymphocyte ratio (NLR) distribution.” [2017]
“The results of the meta-analysis of 10 trials involving a total of 924 participants showed that vitamin D supplementation significantly decreased the circulating hs-CRP level by 1.08 mg/L” [2014]
I mentioned a formula from 2010 in the first post. Fortunately, there are several Vitamin D calculator available. I put in the numbers from the above post below
Males 15% drop in the ability to absorb once you get over 50.
Females 45% drop in the ability to absorb once you get over 50.
This suggests that the dosage computed by the calculator needs to be doubled for a female over 50.
In addition to classic role of vitamin D in musculoskeletal health over the last decade it was shown that low blood serum concentrations of 25(OH)D are associated with a number of non-skeletal disorders including cancer, high blood pressure, age-related cognitive decline, disorders of the immune and reproductive systems, etc. The prevention of the development of these diseases is reached under considerably higher concentrations of the vitamin in the blood serum, than is necessary to maintain the normal state of the bone tissue, to regulate calcium absorption and homeostasis
“Vitamin D was first discovered as the curative agent for nutritional c, and its classical actions are associated with calcium absorption and bone health. However, vitamin D exhibits a number of extra-skeletal effects, particularly in innate immunity. Notably, it stimulates production of pattern recognition receptors, anti-microbial peptides, and cytokines, which are at the forefront of innate immune responses. They play a role in sensing the microbiota, in preventing excessive bacterial overgrowth, and complement the actions of vitamin D signaling in enhancing intestinal barrier function. Vitamin D also favours tolerogenic rather than inflammogenic T cell differentiation and function. Compromised innate immune function and overactive adaptive immunity, as well as defective intestinal barrier function, have been associated with IBD. Importantly, observational and intervention studies support a beneficial role of vitamin D supplementation in patients with Crohn’s disease, a form of IBD. This review summarizes the effects of vitamin D signaling on barrier integrity and innate and adaptive immunity in the gut, as well as on microbial load and composition. Collectively, studies to date reveal that vitamin D signaling has widespread effects on gut homeostasis, and provide a mechanistic basis for potential therapeutic benefit of vitamin D supplementation in IBD.”
Vitamin D signaling in intestinal innate immunity and homeostasis [2017]
There is no appropriate dosage to take without lab results. I know females over 50 that freaked out their physician when they stated that they were taking 20,000 IU of vitamin D3 daily for the last 2 years. Lab tests were quickly ordered!! The lab tests showed her just above the middle of the normal range. The ability to absorb is significantly reduced with IBS, IBD, UC, Crohn’s disease, etc. It’s a matter of getting the labs, take an aggressive dosage for 6 months, retest and then adjust the dosage according to the new results.
“The biggest problem is that MDs are often behind the times and do not work from the latest literature. For example a single dosage of 600,000 IU of vitamin D is deemed safe on medscape, or 10,000 IU/day for months [source]. One of the studies above cited an average of 60,000 IU/day for 3 months! [2017]”
From my 2017 post
Target Dosages?
At least 128 nmol/l or 50 ng/mL. You want to be at the TOP of the normal range. You will get push back from MD with higher level because of medical myth that above this is toxic. I say myth because there is no evidence in the literature. There were issues with Vitamin D2 supplements which was sold for a while because it was cheap to produce and is chemically different.
Of 20,308 measurements, 8 percent of the people who had their vitamin D measured had levels greater than 50 ng/mL, and less than 1 percent had levels over 100 ng/mL.
“We found that even in those with high levels of vitamin D over 50 ng/mL, there was not an increased risk of hypercalcemia, or elevated serum calcium, with increasing levels of vitamin D,” says study co-author Thomas D. Thacher, M.D., a family medicine expert at Mayo Clinic. [2015]
Of course, there are some rare DNA mutations that could cause problems with that level. One person’s experience with the details. If you have adverse reaction — get the additional tests described below.
“The evidence is clear that vitamin D toxicity is one of the rarest medical conditions and is typically due to intentional or inadvertent intake of extremely high doses of vitamin D (usually in the range of >50,000-100,000 IU/d for months to years).”
If the US had a unified medical system recording information from birth until the grave, then such questions can be answered by some database queries by a skilled statistician. Unfortunately, that is not the case and access to such data is often challenging because of privacy laws.
There is an approach that may hint at answers. Look at the bacteria and direction of shifts reported for various conditions and see if there are good matches. For example, you have 10 of the shifts seen for being afraid of dogs and there are 15 shifts seen in people that are afraid of pets. You would appear to be 10/15 = 66% of the way to that other condition.
The challenge is sparse data. For some conditions we have had lots of microbiome studies and for others almost none. We can view it as shown below.
The more the bacteria are in common, the more likely you may drift from one condition to another — often added as a co-morbid condition.
Clicking will take you to a page that lists the percentage overlap and show a visualization with arrows thickness reflecting the percentage overlap. Remember that many conditions also require DNA mutations to cause the condition. This is NOT predictive, merely a factor to consider.
At the top, you can pick a different condition, set the percentage association to show (the lower the amount, the more items will appear and you may get a very busy page). Or, go over to every thing on one page.
Looking at what could cause Asthma, we see the key contributors.
When you hover over the line, you will get more information, for example Chronic Lyme. We see that we only have 3 bacteria types/taxon listed for Chronic Lyme (at this time),
Looking at Sleep Apnea, we see more bacteria are known about it.
You must balance the number of bacteria (taxons) with the percentage. High taxon counts with high percentage are likely significant. The issue keeps coming down to few studies on microbiome shifts.
Note: The numbers and charts will change over time as more data is found and entered.
The coordinator of a UK Long COVID group asked me to research and post on this topic. Endometriosis is one form of infertility that has a prevalence around 7%. When I checked the US National Library of Medicine for Endometriosis and microbiome, I found 88 studies most published in 2021 and later. It is a newly discovered association. I have added it to my PubMed Study page with bacteria listed here and a priori changes of diet to counter these shifts listed here.
I repeated searching the US National Library of Medicine for Endometriosis and microbiome for endometriosis and COVID. I found 66 studies — a volume that surprised me! A few studies that appear interesting:
Association of laparoscopically-confirmed endometriosis with long COVID–19: a prospective cohort study. [2023]. We know that long COVID has an altered microbiome and it would not be unexpected that some alterations would predispose some people to endometriosis by the microbiome alterations.
From the database I compared shifts between COVID and endometriosis, with the shift in common shown below.
Tax Name
Tax Rank
Shift
Coriobacteriaceae
family
High
Enterobacteriaceae
family
High
Atopobium
genus
High
Bacteroides
genus
High
Bifidobacterium
genus
High
Blautia
genus
High
Campylobacter
genus
High
Candida
genus
High
Corynebacterium
genus
High
Dialister
genus
Low
Escherichia
genus
High
Faecalibacterium
genus
High
Lachnospira
genus
Low
Lactobacillus
genus
Low
Odoribacter
genus
Low
Parabacteroides
genus
High
Paraprevotella
genus
Low
Prevotella
genus
High
Pseudomonas
genus
High
Ruminococcus
genus
Low
Shigella
genus
High
Streptococcus
genus
High
Eubacteriales
order
Low
Actinobacteria
phylum
High
Firmicutes
phylum
High
Proteobacteria
phylum
High
Verrucomicrobia
phylum
High
Can COVID cause Endometriosis
There is no clear evidence of that, but the table of common shifts implies that it is a reasonable hypothesis. Looking at numbers from Long COVID groups, you may see over representation (above the 7% expected). Endometriosis increased the odds (almost double) of getting COVID then seeing 14% of Long COVID population with Endometriosis could be explain by the increase risk of getting COVID.
On the brighter side, we now see that endometriosis has a significant microbiome dimension and thus treatment by microbiome manipulation becomes an option worth exploring.
I’m in my early thirties and I caught COVID in November 2022. I’ve had dizziness from the beginning which is slowly going away, shortness of breath which is going away, weakness and pains all across my body, numbness, and this general feeling of derealization. Some days it felt I couldn’t even walk up the stairs. After extensive testing it was revealed that i had EBV reactivated, toxic mold, and whatever damage was left from long COVID. The big symptom that I’m still dealing with to this day are some sort of MCAS presentation where when i eat high histamine foods, exercise, sauna, or go for too long of a walk my throat will get tight, which is pretty scary. I also get dizzier upon anaerobic exercise.
We again see the typical pattern for Long COVID and ME/CFS. Over representation of the 0-9%ile. For more information see Background on using this approach
Percentile
Genus
Species
0 – 9
77
101
10 – 19
24
20
20 – 29
16
18
30 – 39
11
18
40 – 49
12
27
50 – 59
12
19
60 – 69
15
12
70 – 79
4
15
80 – 89
13
19
90 – 99
19
24
In this case we see a number of bacteria flagged as likely causes of the above.
Looking at the standard quick health overviews, we see a massive number of bacteria of concerns. While many are of low count, they are much higher than usually seen. This is in agreement with the over representation of the 0-9%ile range.
We see similar red flags with Dr. Jason Hawrelak Recommendations
Going Forward
There are so many items of concern that most practitioners would really not know where to start. Fortunately, the Artificial Intelligence engine was built to handle such complexities. Doing the quick route, I clicked the “Just Give Me Suggestions” and then click on the more technical details which takes us over to the consensus report.
The Consensus Report is done using all possible modifiers, including antibiotics and prescription drugs (that we have information on their microbiome impact). The quick suggests auto-picks items commonly used in treating ME/CFS and Long COVID.
The top suggestions are high in antibiotics, which is atypical.
Getting antibiotic prescribed off-label is a challenge.
Reader’s Question: “Any idea how to go about getting those prescription antibiotics? “
Ideally, you can get your primary care physician to be willing to prescribe one of those above. It does not need to be the first one. If the physician suggests something not on the list, use the filter feature to see it estimated benefit. Negotiate. I attach an article on Long COVID from this week’s edition of New Scientist.
Some practitioners may be uncooperative — there are several alternative approaches.
Look for a naturopath or MD that deals with Lyme infections. It is unlikely that you have Lyme BUT the gut disruption will often result in some Lyme tests returning a false positive. Getting that positive, even a weak one, will rationalize to that practitioner the prescription of antibiotics. Some of those listed above are used by Lyme physicians.
The following are not recommended but people have reported doing these approaches:
If you live near the Mexican border, many of the antibiotics are available there without prescription.
See if you can order directly from Vet Supply shops
If you have friends travelling to Mexico (or India or many 3rd world countries), they could buy at local pharmacies and bring it home to you.
The best way is always under medical supervision!
How to explain to your MD on how these suggestions are computed?
We have been developing an Artificial Intelligence program based on the pattern of the 1972 MYCIN system developed by Stanford University in California. Unlike the popular AI systems based on machine learning or large language models (Chat GPT), we use Probabilistic Inductive Logic Programming with over a million facts manually curated from the U.S. National Library of Medicine.
LInks to more information are in above quote
Not walking the Prescription Path
Fortunately, we also have some herbs and species listed near the top. These include the following
I have no financial interest in Custom Probiotics — they are just by far the cheapest per BCFU and advocate therapeutic dosages.
I would encourage you to look at the avoids and remove the high value items. See video below.
I would not be surprised if you have a Jarisch–Herxheimer reaction from some of the above. I usually advocate the Cecile Jadin approach which is to take one or two items at a time for up to 2 weeks and then rotate to other items. Start with a low dosage and slowly work up the dosage. This will usually reduce the risk of a strong herxheimer reaction. This approach reduces the risk of antibiotic (or equivalent) resistance happening. There will always be a bacteria mutations that will tolerate specific herbs, probiotics and antibiotics. The odds of the bacteria tolerating multiple substances is very low.
I would suggest doing the rotating suggestions for 8 weeks and then retest. Always use the same lab so comparisons are valid.
Video Walkthru
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.
I have an extensive collection of bacteria shifts reported from Studies on the US National Library of Medicine Studies for some 91 different conditions.
My past practice has been to deem over 75%ile to be a match if the study reported high and below 25%ile if the study reported a low. I then compute the number of hits for all samples and determine each person’s percentile. I then look at the reported incidence of the condition and see if there is a likely match.
For example, the condition is seen in 5% of the population: one person is at 45%ile – thus unlikely; another person is at 95%ile – thus borderline; a last person is at 99%ile – thus likely.
These 25%ile and 75%ile were arbitrary numbers point out of the air. I dislike arbitrary numbers. This attempts to find better numbers supported by evidence.
The root problem is that the study typically reports that the average of people with a condition is statistically different from the control group. This means that the only clear fact is whether the sample of people with this condition is higher or lower than the control group. This cannot be applied to an individual because the average is the average of a population with a wide range of values reported.
I looked at current available data and picked Depression to use for some test runs. We have 298 samples that are annotated with depression, and 226 different bacteria-shifts from studies). We divide our population into those that annotated with depression and those that annotated their sample but did not include depression.
Ombre reports 166 of the 226 bacteria from studies
BiomeSight reports 147 of the 226 bacteria from studies
uBiome reported 154 of the 226 bacteria from studies
This implies that Ombre may produce the best results, uBiome with intermediate, and Biomesight the worst.
The method is easy, finding the number of matches per sample for with depression and without depression. Then get the ratio between them. If the percentage is below 100, then we have false positives. We want the numbers to be over 100%. After going through the numbers, I came up with 109% as being a good threshold.
My first run was aggregating all lab data. We see good discrimination using 6%ile/94%ile. This drops almost in half at 10%ile/90%ile and disappears at 40%ile/60%ile. Aggregating all samples together has usually resulted in reduced statistical significance.
Percentile
With
Without
Ratio
1
1.28
1.14
112%
2
2.55
2.31
111%
3
3.70
3.39
109%
4
4.89
4.44
110%
5
6.05
5.54
109%
6
7.13
6.57
109%
7
8.12
7.63
106%
8
9.25
8.75
106%
9
10.32
9.80
105%
10
11.41
10.80
106%
15
17.11
16.48
104%
19
21.29
20.75
103%
29
32.16
31.58
102%
30
33.11
32.70
101%
The next runs are being lab specific:
Biomesight:
78 with depression,
637 without depression.
We use a criteria of a ratio of 109% or better, so 6%ile/94%ile
Percentile
With
Without
Ratio
1
1.26
1.10
115%
2
2.86
2.48
115%
3
3.99
3.57
112%
4
5.22
4.68
111%
5
6.47
5.82
111%
6
7.68
6.87
112%
7
8.51
7.96
107%
Ombre Labs:
78 sampleswith depression,
340 samples without depression
The results blew me away! I give a possible explanation below.
Percentile
With
Without
Ratio
1
0.75
0.87
86%
2
2.09
1.05
199%
3
3.42
1.70
202%
4
5.05
2.32
217%
5
6.60
3.00
220%
6
8.19
3.61
227%
7
9.59
4.27
225%
8
10.97
4.93
222%
9
12.44
5.57
223%
10
14.05
6.20
226%
11
15.54
6.85
227%
12
17.04
7.57
225%
13
18.88
8.23
229%
48
74.23
31.98
232%
And going to the defunct uBiome:
With: 103 Samples
Without: 376 Samples
It looks like using 22%ile and 78%ile yields a 109% ration or better.
Percentile
With
Without
Ratio
1
1.75
1.32
132%
2
3.09
2.06
150%
3
4.30
2.89
149%
4
5.30
3.73
142%
5
6.32
4.64
136%
6
7.17
5.59
128%
7
8.20
6.48
127%
8
9.32
7.37
126%
9
10.35
8.30
125%
10
11.19
9.20
122%
11
12.17
10.18
119%
12
13.12
11.11
118%
13
14.13
12.01
118%
14
15.14
12.91
117%
15
16.13
13.83
117%
16
16.97
14.76
115%
17
17.82
15.74
113%
18
18.76
16.63
113%
19
19.64
17.52
112%
20
20.43
18.45
111%
21
21.36
19.44
110%
22
22.16
20.35
109%
23
23.00
21.28
108%
24
24.00
22.31
108%
25
24.93
23.24
107%
26
25.92
24.18
107%
uBiome
Why does Ombre Labs blow away other Labs?
First, you need to understand the back story read The taxonomy nightmare before Christmas…[2019]. There is NO STANDARDIZATION. Different labs use different reference libraries. I would speculate that the reference libraries typically used for studies on the US National Library of Medicine is much closer to the reference libraries used by Ombre than those used by Biomesight.
It could be roughly compared to the studies using metric Bolts. Ombre nuts are metric (cm) and thus fit well. Biomesight nuts are imperial (inches), they do not fit as well, and on occasion may need to be forced. That is the likely nuts and bolts of it.
What does this mean for Predictions using PubMed Studies?
At the simplest level it means that my criteria for matching is different for each lab.
High is over 60%ile and Low is under 40%ile for Ombre
High is over 94%ile and Low is under 6%ile for Biomesight
High is over 78%ile and Low is under 22%ile for uBiome
Other labs: High is over 94%ile and Low is under 6%ile for Biomesight
We ended up with the same best to worst order for labs as predicted at the start — except the differences is a lot larger than I expected. It will likely take a week for me to modify the build processes and have numbers on Microbiome Prescription updated.
What about to Shotgun analysis (Thorne, Xenogene)? Answer is simple, we need a lot more samples from both of them.
Testing Discrimination Performance
I charted the with versus without depression for various labs below to see how strong the discrimination was. The charts are below with uBiome showing the best discrimination. At higher percentiles, ubiome is 3-4x more likely to detect depression over a not depression.
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