The typical results is ‘bio-forming’ your guts to be more friendly to the cause. This can result in bacteria shifts across multiple taxonomies which may persist after the root cause has been eliminated.
Excluding antibiotic induced diarrhea
“However, the quantity of Enterococcus and Bifidobacterium groups were significantly higher in healthy controls than in diarrheal group (p = 0.000076 and 0.001, respectively).” https://www.ncbi.nlm.nih.gov/pubmed/31062602
AUTHORS׳ CONCLUSIONS: Moderate quality evidence suggests a protective effect of probiotics in preventing AAD (in children). [2016]
Based on high-dose probiotics, the number needed to treat (NNT) to prevent one case of diarrhea is seven (NNT 7; 95% CI 6 to 10). However, a GRADE analysis indicated that the overall quality of the evidence for the primary endpoint (incidence of diarrhea) was low [2011] – that is 1 person in 7 showed benefit.
While Bacillus lecheniformis significantly reduced the incidence of AAD in patients older than 65 relative to no treatment, L. acidophilus, L. casei Shirota, S. cerevisiae (boulardii) lyo, mixture of L. acidophilus and B. bifidum, and mixture of L. acidophilus CUL60, CUL21, B. bifidum CUL20 and B. lactis CUL34 were not effective in reducing AAD in this population… a mixture of L. rhamnosus R011 and L. helveticus R052 did not demonstrate efficacy in preventing AAD in adults of all ages
Probiotics did not have a significant effect on incidence of gastrointestinal disorders when compared with placebo only;17 on incidence of gastric pain when compared with no treatment only[18 on incidence of cramping, nausea, fever, soft stools, flatulence, and taste disturbance when compared with placebo or usual care;10 and on incidence of undisclosed adverse events when compared with placebo or no treatment.
This is usually caused by bacterial infection. See this paper on treatment.
Bottom Line
These results are contrary to my expectation, i.e. urban-myth about the effectiveness of probiotics. Zinc, Enterococcus faecalis probiotics , and high dosages bifidobacterium appears to be best course.
Human nature likes simple answers with simpler solutions. If you state that SIBO is caused by an overgrowth of Enterococcus faecalis and that berberine kills this bacteria – a lot of people will take it as gospel even when faced with dozens of studies listing over a dozen different bacteria involved.
Many people using my site are looking for simple, easy, effortless analysis and resolutions. The reality is that we are dealing with an extremely complex and interacting system that is constantly changing.
Your microbiome is constantly changing — usually at a slow rate. The association of microbiome dysfunction with symptoms and medical conditions is getting stronger and stronger. It has already open treatment options for some almost untreatable conditions.
Purpose of Microbiome Comparison Pages
These pages purpose are two fold:
To more easily identify what is changing as a result of probiotics, diet changes, and even work stress.
To try to identify which of thousands of bacterias may be responding to these changes. If the changes are bad, then we want to know how to change things.
The microbiome comparison pages have gotten a face lift. They give more data and are easier to use. If you have two or more samples uploaded, you will see the button shown below on the Available Samples page
Clicking it will take you to a new page showing some high level statistics. In general, the lower these numbers, the better you are. These are the high level simplification into just 4 numbers for each of your microbiome samples.
How many “Match Taxonomy” should you expect?
The chart below shows what is in the uploaded sample. Some key statistics:
Average is 142 (It is 146 for those deeming themself to be healthy)
Median is 147
Bottom 10% is 114 or less
Top 10% is 167 or more
Symptom Matches
This uses the data from our Eureka discover that some symptoms have a statistically significant match to some bacteria. See this post for more information. For some 200 bacterias that matched to some combination of bacteria we do the following:
Count how many of these bacteria that you have (Match Taxonomy)
Count how often your values matches the pattern discovered, then convert it to a percent.
For a totally representative microbiome, we would expect to see 25%. We used 4 “bins” to get the Eureka data. We put the same number of samples for a specific bacteria in each these bins. This means that we would expect to have 25% of our bacteria match up if things are random.
We see 75% on one sample — Think of a four sided die that comes up 75% of the time — does that sound like a fair die? No. Neither does this sound like a healthy microbiome.
A second dimension of this measure is the number of matches. Above we see as low as 136, below, another person has 173. This is a proxy for measuring microbiome diversity — the higher your count, the more diverse you are.
Condition Matches
From medical literature studies we have gathered a list of bacteria reported to statistically significant over or under representation in some study. We do not know what would be expected on a healthy population, nor can we compute odds. Comparing independent microbiome studies is very difficult to do. These numbers are a general feel set of numbers. A downward movement of the numbers is usually deem to be good — you are moving closer to “control” values.
My Own History
The example above is from a reader that showed some contrasts. Let us look at mine since I was in remission for 2 samples and then have a flare that is on-going (but improving) – with 3 samples (a 4th was mailed today)
The first two numbers above were my being in remission from ME/CFS. The first set showed increasing microbiome diversity and less symptom matches (good); the second showed more matches to conditions (bad). I would deem the change between the first two as being inconclusive.
With the flare, we had dramatic changes: Diversity dropped and symptom matches increased (bad). On the condition front, it seems like we are improving but the change is very dramatic (bad).
Looking at the three flare microbiome samples, we see the second one being the worst with the third one showing sign of recovery (i.e. moving back closer to the pattern seen during remission.
At onset, the number of bacteria shifting towards symptom association increased by almost 10%.
At the same time, various low bacteria associated with conditions increased considerably.
Lots of changing symptoms
At the 2nd sample, even more symptoms hits occurred
We had the appearance of instability — high bacterias dropped by 80% and low ones started to increase.
At the third sample, we seem to be heading back to restoration of the former (pre-flare) values:
A 11% drop in bacteria matches suggests that bio-diversity was taking a hit.
Condition Bacteria continue their restoration to pre-flare levels.
All of this comes out of 4 numbers for each sample.
Eureka Page
This page compares your values to those seen with statistical significance. Remember, all of the samples are divided into 4 bins with the same number of samples out in each. If one bin has many excessive samples for a certain condition, it may be marked as such (after testing for statistical significance). This page shows which bacteria you have matches for — you are expected to have some by random chance (25% of the total) – in this case 160/4 = 40. So we can not say any item is significant by itself.
Very High: Match the top 25%
High shift: above median but not in very high
Low Shift: below median but not in very low
Very Low: Match the bottom 25%
A bacteria may be Very High for one symptom, and High shift for a different symptom.We can see instability of the microbiome on the 2nd flare sample. Some moved into very high for a short period and then disappeared.
My suggestion would be to start crossing out Low-Shift and High-Shift until you get the expected number (40) – working downwards from Phylus -> Class -> Family -> Genus (I may mechanize that in a future update). What is left over are likely the main bacteria that contributes to health issues.
Conditions
This compares against templates for various conditions based on multiple studies. We see a major jump in bacteria associated with various conditions in the first microbiome done during the flare – i.e. almost 50% increase of bacteria with a high association, this quieted down in subsequent weeks – but these overgrowths likely contributed to the flare.
Do not read in too much – it is NOT predictive
Comparing Symptoms
This is effectively a symptom tracker. The usefulness depends on the accuracy of information entered. It also allows us to do citizen science.
Comparing Bacteria
This done old school — using averages. It may be revised in the future to use BoxPlot methods (which should quiet down the noise).
Comparing Metabolism
If you have entered metabolism data from ubiome, then you can see how these functions have altered. The terms are those used by Ubiome and you may need to google to understand them and what they mean.
If you want to know how to enter this data, look at this post (which now has a rough video included).
Bottom Line
Everyone is different and there can be no one path fits all formula. We can provide information of what changes — and potentially offer suggestions of how to correct things. These are suggestions only — because we do not know, nor can we expect to know in the next century.
I had a chat with a work colleague this week, his wife like mine, has suffered from Transient Ischemic Attack induced by stress. This post explores what we know from medical literature about these two things.
At the genus and species levels, significant enrichment in the Social defeat [stressed – SD] group feces included Oscillospira, Ruminococcus (FDR = 0.003), and Dehalobacterium (P value = 0.028) (Firmicutes, 2- to 3-fold), Mucispirillum schaedleri (FDR = 0.004) (Deferribacteres, ∼3-fold), and Bilophila (FDR = 0.0004) (Proteobacteria, ∼6-fold) …. fourteen days after the last exposure of the mice to the aggressor, the structure of the SD-associated bacterial community nearly returned to homeostasis without any significant change
Genera/species that were significantly enriched in at least one of the experiments included Adlercreutzia (P value = 0.0259; Actinobacteria), Brevundimonas diminuta (P value = 0.044; Alphaproteobacteria; an opportunistic pathogen in immunocompromised hosts [32]), and Helicobacter (P value = 0.022; Epsilonproteobacteria; highly prevalent pathobiont that can lead to gastritis, peptic ulcer disease, and possibly autoimmune diseases [33]), whereas genera/species that were relatively less abundant in SD group MLNs included Sutterella (P value = 0.0163; Betaproteobacteria; reduced in the gut of human patients with MS) and Prevotella (P value = 0.010;
We show that an environmental trigger such as social stress affects the bacterial composition and transcriptional patterns in a way that enforces an immune response with potential deleterious consequences to self-tolerance. This might be a hit-and-run effect since although the gut microbial community recovered after the cessation of the stress, the microbial alterations and the immune response in the MLNs persisted, highlighting the consequences of an early stress-inducible disturbance on the homeostasis later in life. In that aspect, many of the stress-responsive bacteria that we found are known to be associated with autoimmunity and other diseases.
The stress-associated virulent phenotype can explain, for example, how under healthy conditions, the relative abundance of Proteobacteria in the human gut can transiently increase from 2.5% to 45% without clinical signs, whereas under certain undefined circumstances, they do trigger inflammatory responses (55–57).
“Stroke and transient ischemic attack patients had more opportunistic pathogens, such as Enterobacter, Megasphaera, Oscillibacter, and Desulfovibrio, and fewer commensal or beneficial genera including Bacteroides, Prevotella, and Faecalibacterium. This dysbiosis was correlated with the severity of the disease.” [2015]
Note: higher Oscillibacter and lower Prevotella is seen with stress as reported above.
“a decreased amount of Roseburia, Bacteroides, Faecalibacterium prausnitzii and increased proportion of Enterobacteriaceae, Bifidobacteriaceae, and Clostridium difficile has been detected in gut samples of stroke patients, as compared to healthy volunteers, intensive care patients, patients with active ulcerative colitis or with irritable bowel syndrome [2012]. “
Bottom Line
The relationship seems self evident from the literature. The question arises, what can be done when under stress? Using the microbiome prescription site at the genus level, we can enter the above shifts and see what is suggested. Note: that if stress cause a condition, then there may be significant shifts as a response.
On the other side, we should avoid the following. Note that here we see some probiotics should be taken and others avoided. Probiotics are not a universal cure-all!
I have added a new page today that shows the relative production of various vitamins and a few metabolites compared to a healthy population. It is linked to from the samples page.
Each sample will give different results
The regular suggestions pages may list these vitamins because it is known that they will impact other bacteria that we wish to change. There is incomplete knowledge.
This new page computes for known producers, the amount that is likely being produced and compare it to those who declared themselves healthy in the uploads.
My own in a remission periodAt start of flare, GABA dropped a lot and B12 decreased2 weeks later, GABA declined more4 weeks later, GABA almost gone, Butyrate is increasing, Folate is climbing fast
There is a lot of uncertainty in modifying an ill microbiome. This starts with selecting the bacteria you want to change, see this post Steeing the species that may need correction to whether something actually modifies a bacteria without causing undesirable side-effects. You cannot be blinkered to a single bacteria because there are a large number of interactions between different bacteria (often across orders)
The Advance Suggestions page allows you to refine how suggestions are generated and what you are willing to do to modify your microbiome. For example, some people may not wish to do antibiotics (or have MDs that will not prescribe); others may be vegans.
The next item determines how fine you want the suggestions to be. Let me explain, some suggestions are based on reports of a suggestion impact on a Family or an Order as a reporting unit. If you go to a lower level, you will get less suggestions and they will be more specific to specific bacteria. It’s a trade-off. If you get no suggestion, try moving to a higher level.
The next step is whether you want to include literature reports that a specific bacteria was significant for a specific condition. If you have a condition, then this will increase the odds of the right bacteria being picked. The number of bacteria will likely decrease, and number of suggestions too. There is a long list of conditions:
The next is a list of categories of suggestions that you can filter to. Everything is checked by default. Uncheck the items that you are not interested in.
The last choices limit the items displayed to the best and worst suggestions. In some situation, more than a hundred items were reported with the old process. You can still see all if you want… but this is intended to just shown the best.
Last, when you click, the report will be shown in a NEW WINDOW OR TAB in your browser. This allows you to compare different combinations against each other.
Some sample reports are below:
Oops too many antibiotics, so I uncheck some choices.Antibiotics disappear and other things appear…What! No coffee!!!
You can also see how suggestions changes between sample. Once you have a good set of filters, just change the sample and you will see what the older sample would produce.
