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 following looks at a holisitic approach to generate suggestions for microbiome dysfunctions, symptoms (that may be microbiome associated) and diagnosis (that have microbiome patterns).
This model (or variation there of) is being used by several microbiome testing companies today. See the bottom for example of clinical success.
This post illustrate the process and is not a precise match for current implemenation on Microbiome Prescription (which continuously evolves over time).
Native taxa weights
The first step is to get a weight for each taxa in a sample to identify what should be altered and the importance of each. With shotgun samples, there may be over 7000 different taxa.
The simple first step is to just do a lookup compare to ranges for each taxa (assuming there is sufficient data to compute ranges). Then assign weights based on the sample positioning in the ranges. The key function (tax_range) is often a complex function which may incorporate percentage, percentile, gender, age, diet style, and bacteria hierarchy. For example, Lachnospiraceae bacterium GAM79 may dominate and result in Lachnospiraceae being given no weight and thus expert system rules may be involved.
Conceptually, it is the importance of a bacteria to be shifted with the desired direction of shift converted to a numeric value or vector of values.
This is called a native taxa weights .
Presentation taxa weight.
These native taxa weights are then modified by the presence or absences of diagnosis and symptoms. Conditions are not either/or. A good example is Autism which has a wide spectrum of levels. A bacteria known associated with a condition will likely have an increase weight. A bacteria with no known associations will have a decreased or no weight. This is called a presentation taxa weight. As above, it may be a single value or a vector of values.
Modifier Matrix
We drop the taxa weight into our grid as show below. We show the weigh as a single value below. With a positive weight indicating something to increase and a negative weight indicating something to decrease. The “-1 to 1” indicates a factor.
We now want to maximize the value of the suggestions, i.e.
Sum Over All Bacteria( FactorVit B1 * AmountVit B1 +FactorVit B2 * AmountVit B2 + etc)
Amount often becomes a 1 or 0 (take or do not take) when there is no dosage related data. Factormodifier may be multidimension function on occasion. For example, it values may depend on other factors being selected. This can result in iterations that was the goal the Simula programming language. That is, you get the first naive suggestions(no dependencies), then feed the results into the next iteration.
We can rotate our focus to obtain lists of “to take” and “to avoid”
Sum Over All Bacteria( FactorVit B1 * AmountVit B1)
Factors are often computed from a variety of factors, a few examples:
the number of studies reporting a shift (often studies disagree),
the magnitude of the shift (and/or P value),
the modifier (a specific probiotic strain, a probiotic mixture, a species)
context of the studies (humans, mice, pigs, fish, fouls).
Then We enter the Casino…
Rather than arguing over exactly which formulae for weights are correct. We make use of multiple reasonable formulae. Each is run independently and we then apply Monte Carlo modelling to these results.
Linearity is Dangerous To Assume
Our experience is that assuming linearity produces poor results. We found that doing cross validation allows this host of functions to be tuned.
Inferences should also be factored in, i.e. if a modifier alters Lactobacillus genus without details on individual species, most people will assume that it will alter some of the species — unfortunately, there are many studies reporting that lactobacillus increased with some species decreasing and other increasing.
The key issue is dealing with very sparse data that is often heavily conditioned, i.e.
This may explain why wieghts can be vectors of values.
This is where the art of microbiome manipulation comes in.
Clinical Success
Personal Experiences
Via our free for personal use (not commercial/medical office use) we have had many people have done a sample with one of many supported labs, obtained suggestions from the above model and implemented some, and then done a second sample. For everyone that has done this, there has been OBJECTIVE and SUBJECTIVE improvement. I was expecting > 50% only, but we are running 90+%. For example analysis from those who consented to share, see this collection dealing with Long COVID and Chronic Fatigue Syndrome.
A recent example is shown below using multiple “measuring sticks” from different labs. We see clear improvement.
We also have associations of symptoms to bacteria using our 5000+ donated samples annotated with symptoms. Often the associations exceed P < 0.001 on a lab specific basis. From this data we can give percentage estimates on pattern matching to symptoms. Below is an example for the person shown above.
We see improvement across all of the top symptoms.
We do not look at “cure” (that does happen sometimes), but reduction of symptoms as our criteria.
We have had incidental reports of it appearing to improve the success rate and speed of remission for some cancers.
AI Cross Validation
Additionally we have done cross validation against the literature. We take the microbiome shifts reported for a condition across multiple studies, run those shifts through the engine, then see how many of the top suggestions have been found to improve this condition according to published studies using those suggestions. An example is here: Cross Validation of AI Suggestions for Nonalcoholic Fatty Liver Disease .
While not a clinical study as such, it shows that our suggestions appear to agree with results from third party clinical studies.
Here we hit a philosophy crossroad (and often a zebra crossing/speed bump of medical practitioner ego and/or arrogance).
The road most travelled is focusing on the bacteria most heard about and trying to address them one by one.
It keeps the microbiome simple, naively simple. “All you have to do to raise your lactobacillus and bifidobacterium by taking my preferred probiotic mixture [which I will sell to you].”
It ignore the need to keep current on recent studies. Chart below is from PubMed. There are almost 25,000 new studies a year or 68 new studies a day.
The road that I take is to ignore this chatter, and aim to adjust everything in one pass using mathematical models. No favorites bacteria to focus on (without firm evidence from studies that it is critical for a symptom or diagnosis).
I view this approach is most likely to cause desired changes and not chasing this bacteria or that bacteria is isolation.
It is accepting microbial interdependence in all of it’s complexity (see below)
Using KEGG: Kyoto Encyclopedia of Genes and Genomes data for Metabolites and Enzymes, I do not go down the rabbit hole of some substance being produced by just one bacteria or small set of bacteria. I accept the full width of the microbiome.
Gut Microbiome
The human gut hosts a diverse and complex microbial community:
Over 10,000 microbial species have been identified in the human ecosystem, with the majority residing in the gut.
Gut bacteria contribute about 8 million unique protein-coding genes, which is 360 times more than human genes. These bacterial genes are critical for human survival, as they enable us to:
Digest foods and absorb nutrients that we cannot process on our own
Produce beneficial compounds like vitamins and anti-inflammatories
Microbial Interdependence
Microbial interdependence refers to the complex relationships and interactions between different microorganisms in a community, where they rely on each other for survival and functioning. Here are some key aspects of microbial interdependence.
This study illustrates some interactions, one bacteria reduced a lot of other bacteria. Taking a probiotic that reduces this bacteria, and restore other bacteria.
“[Heyndrickxia coagulans] supplementation improved the gut microbiota imbalance by reversing the decreased numbers [caused by E Coli] of Enterococcus, Clostridium and Lactobacillus in jejunum and Bifidobacterium and Lactobacillus “
Many microbes cannot produce all the nutrients they need and depend on other microbes to obtain essential compounds:
The vast majority of microorganisms require nutrients like amino acids and vitamins that they cannot synthesize themselves.
Corrinoids (vitamin B12 and related compounds) are an important example – while most microbes use corrinoids, only a subset can produce them.
Metabolic Cross-Feeding
Microbes often exchange metabolic products in mutually beneficial relationships:
Some bacteria break down complex molecules that other species then use as food sources.
Waste products from one species may serve as nutrients for another.
Symbiotic Relationships
Many microbes form close, interdependent associations with other organisms:
Corals have symbiotic relationships with algal cells living within them.
Lichens are symbiotic associations between fungi and algae or cyanobacteria.
Gut bacteria in animals help digest plant material the host cannot break down alone.
Community Assembly and Function
Microbial interdependence shapes how communities form and operate:
Public goods sharing drives adaptive function loss and the rise of metabolic cross-feeding over evolutionary time.
Interdependent patterns that emerge through reductive evolution can make communities more resistant to environmental perturbations.
Ecosystem Roles
Microbial interactions contribute to important ecosystem processes:
Soil microbes like mycorrhizal fungi and nitrogen-fixing bacteria form symbioses with plant roots.
Microbial communities in oceans, soil, etc. carry out crucial nutrient cycling.
Understanding these complex webs of microbial interdependence is crucial for fields like ecology, medicine, and biotechnology. It highlights how cooperation and mutualism, not just competition, shape biological communities.
Microbial interdependence occurs when different bacterial species rely on each other for growth or survival. This can happen through various mechanisms:
Metabolic cross-feeding: One species produces metabolites that another species uses for growth.
Signaling interactions: Chemical signals from one species trigger responses in another.
Modification of the environment: One species alters the local environment in ways that benefit another species.
Metabolic Interdependence
Different bacterial species in the gut perform complementary metabolic functions. For example, some bacteria break down complex molecules that other species then use as food sources.
Colonization and Development
Infants acquire their initial microbiome from their mother and other caregivers. Even one-day-old pre-term infants have unique microbiomes that differ from each other and their mothers.
The developing infant microbiome is shaped by factors like genetics, environment, and immune system interactions.
Community Dynamics
Microbial communities in the human body demonstrate properties like stability (resistance to change) and resilience (ability to return to initial state after perturbation).
These dynamics can be studied through longitudinal sampling, for example, before, during, and after events like surgery or antibiotic treatment.
Site-Specific Communities
Different body sites host distinct microbial communities adapted to those environments. For instance, the skin, gut, and mouth each have their own characteristic microbiota.
Examples from Research
Several studies have documented cases where the abundance of one bacterial species depends on the presence or amount of another:
In the human gut microbiome, researchers have observed that the growth of certain Bacteroides species depends on the presence of specific Ruminococcus species.
Bottom Line
My approach is a holistic approach that attempts to use all of the facts to be considered. At present, over 2.5 million facts or rules. This is based on almost 13,000 studies. The suggestions may not be perfect, but they seem to be both reasonable (strong cross validation is common) and effective for many people.
The alternative paths often is based on “it worked for John Doe, so it should work for you”, or reading a handful of studies (often just one is sufficient for some people to claim being an expert).
When someone tries to “sell you” on their approach ask them:
How many of the 10,000+ known bacteria do you consider? What is the evidence for excluding bacteria from consideration?
How many of the thousands of metabolites do you consider? What is the evidence for excluding metabolites from consideration?
How many studies do you review each month? For myself, it is close to 600 new studies that are identified as worth manual review.
Remember the old analogy of the broad path full of people taking the easy and popular way, versus the narrow path with very few on it.
In a clinical setting, a practitioner may conceptually believe that a patient would benefit from a probiotic. The problem is which one(s). Often the advice is a generic “take a good probiotic”; a suggestion bordering on magical thinking. Video version below.
Level 1: Using Published Studies
In general, published studies use specific strains of probiotics. Those strains may not be readily available. Often, the suggestion would be to take the same species (with fingers crossed).
For those that wish to avoid this wishful thinking, we have a page listing Research Probiotics available Retail. This allows you to do a quick search. For example, for ADHD we have just two strains listed as shown below. For some conditions, nothing will be found. These are links to studies or reviews that need to be reviewed by the practitioner.
The basic issue is a lack of studies. Comparison studies are usually non-existant.
Level 2: Identifying cause of condition(s) and targeting taxa
Often this is done by using microbiome analysis looking for abnormal levels of bacteria and seeing what will alter them. For example, multiple studies report low levels of Faecalibacterium and high levels of Bifidobacterium for ADHD. As above, we have a search page that links to studies of the impact of different probiotics (and supplements) on each bacteria.
Level 3: Identifying cause using Enzymes and Metabolites
At this point we enter into the Citizen Science world at Microbiome Prescription. Thousands of people have uploaded their microbiome samples from a host of different providers and then annotated the samples with their symptoms and conditions. The data is at MicrobiomePrescription Citizen Science.
The chart below shows the process. The number of abnormal bacteria (too high or too low) is much larger than published studies — not unexpected given the much larger sample size.
Abstraction
We take the microbiome data and transformed it with data from KEGG: Kyoto Encyclopedia of Genes and Genomes to get estimates of enzymes and metabolites or compounds. This data is processed thru a variety of methods to determine associations of the enzymes and metabolites to condition.
What we observe is that at the metabolite level we often have agreement across the three most common providers
At the enzyme level, we do not get this strong pattern
Nor do we get it by the bacteria associated.
Apparent Conclusion
The cause of the symptom or diagnosis appears to be an imbalance of the metabolites. Metabolites levels are the results of multiple bacteria and not a specific bacteria.
Monte Carlo Selection of Probiotics
As a proof of concept, I applied algorithms to the above with the following being the top items suggested (in descending priority). Play with it on Symptom Association Studies.
Taxa Based — Select probiotics based on abnormal bacteria shifts
Enzyme Based — Select probiotics based on enzymes that are deficient in the condition, but know to be produced by the probiotic
Metabolite Based — Select probiotics based on metabolites that are deficient in the condition, which the probiotic impacts
Some probiotics are high on all three lists, for example: E.Coli. Others are not. I am inclined to using enzymes as the preferred abstraction. Metabolites have a very nice clustering, but at present deriving probiotics is not as clean and simple as desired. A more complex model is needed.
What have we learnt:
There may not be studies on probiotics for a specific condition
There are studies on probiotics that shifts some taxa. Things can become complex when there are multiple taxa in scope (as well as reliability of taxa identification)
From the KEGG Enzymes estimated from a sample, we can derive the enzyme producing probiotics that may conceptually help
Note: Organic Acid Test (OATS) report on many of these enzymes and can be used to validate estimates. Additionally, OATS tests can be used to select probiotics for the reported deficiencies
From the KEGG metabolites estimated from a sample, we can supplement with the deficiency where practical, or look for probiotics that produces deficient metabolites.
The Enzymes and Metabolite approaches should produce reasonable candidates for future clinical studies.
Patient Specific Suggestions
The above exploration analysis was done ignoring the amount of bacteria in a specific example (and thus enzymes and metabolites). It also ignored whether there is duplication of enzymes and metabolites in the probiotics. Ideally, you want a full coverage of enzymes and metabolites.
I have known for years that conventional MD knowledge often trail science by generations. It means that patients need to educate themselves (and in some cases, their MD). The first example is simple:
Stress being the Cause of Ulcers
The discovery that ulcers are caused by bacteria, specifically Helicobacter pylori, was made by Australian researchers Barry Marshall and Robin Warren in 1982. They identified H. pylori as a major cause of gastritis and peptic ulcer disease, challenging the prevailing belief that stress and lifestyle factors were the primary causes of ulcers.[23 years of the discovery of Helicobacter pylori: Is the debate over?]. In 1994, the National Institutes of Health (NIH) held a consensus meeting that concluded the key to treating gastric and duodenal ulcers was the detection and eradication of H. pylori. Then it spent the next decade educating MDs.
But wait! John Lykoudis, MD. after treating himself for peptic ulcer disease with antibiotics in 1958 and finding the treatment effective, Lykoudis began treating patients with antibiotics. After experimenting with several combinations of antibiotics he eventually arrived at a combination which he termed Elgaco and which he patented in 1961. So it took 4 decades from demonstration to acceptable practice.
“One BMI will rule them all”. Except, if we are talking about health — this is wrong
Older adults with BMI <25 and >35 kg/m2 were at a higher risk of a decrease in functional capacity, and experienced gait and balance problems, fall risk, decrease in muscle strength, and malnutrition. Data from this study suggest that the optimum range of BMI levels for older adults is 31–32 and 27–28 kg/m2 for female and male, respectively.
Where as CDC shows this evaluation and would encourage older people to move to an unhealthy BMI. As a FYI, I am at 31.7 and working to reduce to 28 using probiotics see Probiotics, Obesity and Diabetes.
Among older Japanese adults with isolated systolic hypertension and baseline SBP values ≥160 mm Hg, the on-treatment SBP level at which CVD event risks and all-cause mortality were minimized was 130 to < 145 mmHg. On-treatment SBP values of < 130 or ≥145 mmHg were associated with increased CVD event risk and all-cause mortality.
Isolated systolic hypertension (ISH) is a condition characterized by an elevated systolic blood pressure (the top number) of 130 mm Hg or higher, while the diastolic blood pressure (the bottom number) remains below 80 mm Hg
Bottom Line
The reality is that your MD knowledge may be stale and not in agreement with the latest research. Use the US National Library of Medicine to find the latest research — be specific for age and gender in your research. Share it with your MD.
Long COVID [Post COVID Syndrome] is likely an immature variant of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). I use the term immature because typically a ME/CFS diagnosis comes 2-10 years after on-set. Between onset and a later static state, the microbiome is a state of constant transition attempting to reach a new stable state.
Doing a search on Pub Med for “Long COVID hypoxia”, we get over 200 hits. Searching for “Long COVID coagulation”, we get over 300 hits! The following are quick notes to sketch out avenues for people treating Long COVID, especially patients with brain fog. I have cited classic ME/CFS literature and matching literature on Long COVID. This is a page in progress and may be updated periodically.
How can Hypoxia happen?
There are many ways, here’s a recap:
Coagulation Issues: Often it is an inherited coagulation defect that flares as a side-effect of post COVID changes (likely of the microbiome). See Thick Blood, Clots dimension of CFS.
Coagulation is a complex process and “taking a baby aspirin” is NOT a cure all.
The defect may be in just one or several of the Roman Numeral items shown below. (From Coagulation Cascade)
Contributing factor: Small heart syndrome [2008] [2009] [2011] [2012] or heart damage [NCBI]
Hemoglobin issues (Hemoglobin, a form of iron, is what carries oxygen). Some bacteria are iron greedy, reducing the iron available.
“Iron homeostasis disturbances may persist for more than two months after the onset of COVID-19, which may lead to reduced iron bioavailability, hypoferremia, hyperferritinemia, impaired hemoglobin, and red blood cell synthesis.” [2022]
“Research has shown that long COVID may be associated with low iron levels and anemia.”
Some Signs of the Above
Objectively measured abnormalities of blood pressure variability in CFS[2012]
A few years ago I wrote A Frugal List of Supplements for ME/CFS using knowledge at that time trying to rank order supplements that may help by best cost. Today a similar question came up. I am retired (72 y.o.) and working part time with a variety of complex conditions in the household so getting the right stuff at reasonable cost is a priority.
In this post I will share what our current strategies are and illustrate cost savings. For making our own capsules, I have ignored the cost (since it is low).
Example #1 Supplement Hesperidin
Choice #1: Off the shelf: 13.57 / ( 0.500 g x 60) = $0.45 per gram
Choice #1: Off the shelf: 30 capsules with 10 BCFU: $12.42 / (30 x 10) = $0.04 / BCFU
Choice #2: Bulk Probiotics as powder: 169.17 / (400 x 100) = $0.004 per BCFU. Lower package sizes available at slightly higher cost per BCFU.
Choice #3: Buying direct from a manufacturer in bulk (Organic and typically manufactured within 2 weeks of shipping): $138.73 / (20 x 1000) = $0.007/BCFU. Lowest package is $0.02/BCFU
A key issue is probiotics is time since manufacture, abuse in storage (i.e. not kept is fridges in transit and storage — if you look “behind the scenes” at many health food stores, you will see boxes of probiotics just kept in the back, not refrigerated. They are then put it into the display refrigerator as needed). See Probiotics — what is advertised may not be what you get
Example #3 Herb Turmeric
Choice #1: Off the shelf: $12.49/(1 gm x 60) = $0.21 / gram
Choice #2: Bulk – from Amazon (note this is Organic, above is not): $14.99 / 907g = $0.016 / gram
Bottom Line: Up to 90% reduction in Supplement Costs is possible
There are always other factors — for example, some probiotics may only be available from just one supplier (i.e. L. Jensenii, E. Coli Nissle 1917). Do you want it to be Organic? Degree of trust in manufacturer, supply chain handling, seller’s handling (I deemed it very unlikely that Probiotics sold by Amazon are refrigerated, more likely just sits in their warehouses until sold).
Also, be pragmatic on likely duration of use. Don’t over buy to “save money” and have it sitting on the shelf forever…
Remember: Most supplements are high profit margins. At least one supplement seller who also sells microbiome testing kits is suspected to sell their kits at below cost because of the profit from selling the supplements to the same customers.
Our own experience with Maple Life Sciences probiotics have been awesome. We see changes in stools within 48 hours when we rotate between probiotics.
A study demonstrated Fecal Microbiota Transplantation (FMT) alone can change a skinny mouse into a fat one is detailed in the research published by the journal Science. In this study, researchers transplanted gut bacteria from human twins discordant for obesity into germ-free mice. The mice that received gut bacteria from obese twins grew fat, while those that received bacteria from lean twins remained lean. This experiment provided compelling evidence that gut microbiota can influence body weight and adiposity independently of diet or other factors. Visual below of mice feed identical diet.
My own experience is loosing 30 pounds from the addition of a specific probiotic (Akkermansia) over a year without a change of diet. IMHO, a change of diet along may not do it. Often it takes two things: changing the diet AND changing the bacteria in the gut.
Literature
Probiotics that have the best actual evidence
Note that some probiotics can result in weight gain, so taking random ones is not the way to do it. Many of the studies found effective using probiotics mixtures included Lactobacillus plantarum
My (incomplete) review of the many many studies suggests that four species of probiotics are the most likely to help with Obesity and Diabetes. Below are links to manufacturers that directly sell my preferred single strain probiotics. This means that you will get the probiotics within weeks (or days) of being manufactured (i.e. Very fresh and live probiotics).
Suggested daily dosage is a therapeutic 50 BCFU/day for the Lactobacillus, and likely a capsule of Akkermansia muciniphila (Pendulum is the established provider).
For the Lactobacillus, I find using powder dissolved in a glass of warm water works very well. It often has a side effect of inhibiting wanting to have a treat and gives a satisfied feeling in the gut. Taking them as capsules do not seem to have that effect.
We are likely the sole firm claiming the use of AI for microbiome analysis that is in conformity today. Most firms in this area that claim using AI, refuse to even disclose which type of AI that they are using. Since our founding, we have been OPEN DATA. The logic used for every suggestion is show and links to every data source. We are about to file patents for our proprietary, PATENT PENDING, algorithms – meaning that shortly even the algorithms will be available for inspection.
Our core AI model is an old classic: fuzzy logic expert systems.
The foundations of fuzzy logic were laid in 1965 by Lotfi Zadeh, a professor at the University of California, Berkeley. In his seminal paper “Fuzzy Sets”, Zadeh introduced the concept of fuzzy set theory, which allows for degrees of truth rather than the classical binary true or false [A brief History of Fuzzy Logic].
The concept of expert systems, which are computer programs designed to emulate the decision-making abilities of a human expert, began to take shape in the 1960s. One of the earliest and most notable examples was MYCIN, developed in the early 1970s at Stanford University. MYCIN was designed to diagnose bacterial infections and recommend antibiotics based on a set of if-then rules derived from expert knowledge. [Knowledge Discovery from Medical Data and Development of an Expert System in Immunology]
I have had a few emails asking if I am over-flagging these issues on MicrobiomePrescription.
I used Perplexity.AI to get some numbers…. here is what was reported (with references to sources). Given the typical reasons that people get microbiome samples, the rates appear reasonable.
The National Institute of Health (NIH) statistics indicate that biofilm formation is present in about 65% of all bacterial infections and approximately 80% of all chronic infections.
In the context of human health, biofilms are responsible for about 80% of bacterial infections
D-lactic acidosis is considered rare in humans overall, but it may be underdiagnosed. Some experts suggest it should be looked for more often in cases of unexplained metabolic acidosis.
It is most commonly associated with short bowel syndrome (SBS). The incidence of SBS is estimated at approximately 2 persons per million per year. While not all SBS patients develop D-lactic acidosis, they are at higher risk.
In patients with short bowel syndrome, D-lactic acidosis appears to be a relatively frequent complication. One study found that all 29 SBS patients examined had experienced neurologic symptoms associated with D-lactic acidosis at some point [NOTE: SBS have a very high incidence of SIBO [src], so SIBO likely have an increased risk of d-lactic].
Histamine intolerance is estimated to affect approximately 1-3% of the general population. However, some experts suggest this number could be higher as the condition is often underdiagnosed.
Among people with digestive symptoms or conditions like IBS, IBD, and Crohn’s disease, a surprisingly high 30-55%may have histamine intolerance.
One study found that diamine oxidase (DAO) deficiency, which is associated with histamine intolerance, was present in up to 44% of the control population.
A more dramatic estimate suggests that histamine intolerance may affect 50-60% of the population, according to one source. However, this figure seems significantly higher than other estimates and may need further verification.
In people with digestive symptoms, one study showed that 30-55% also have histamine intolerance.
A reader asked, Which genus should I give highest priority in general?
This is an easy answer using the Conditions populated from studies on the US National Library of Medicine on 127 different conditions. The results are below for those that are seen in at least 10% of conditions.
Below that is a table showing the direction of shifts.
Taxa Name
Percentage Of Conditions with Shifts
Bifidobacterium
55
Prevotella
54
Bacteroides
52
Faecalibacterium
51
Lactobacillus
47
Blautia
44
Ruminococcus
43
Streptococcus
41
Roseburia
40
Escherichia
40
Clostridium
38
Parabacteroides
37
Coprococcus
34
Alistipes
33
Eubacterium
31
Shigella
31
Veillonella
30
Akkermansia
29
Fusobacterium
27
Dorea
27
Enterococcus
27
Anaerostipes
26
Dialister
25
Collinsella
25
Haemophilus
25
Klebsiella
25
Odoribacter
25
Megamonas
23
Bilophila
22
Desulfovibrio
22
Subdoligranulum
22
Lachnospira
21
Turicibacter
21
Phascolarctobacterium
20
Eggerthella
20
Enterobacter
20
Butyricicoccus
18
Oscillibacter
18
Porphyromonas
18
Megasphaera
17
Lachnoclostridium
16
Sutterella
16
Staphylococcus
16
Butyricimonas
16
Actinomyces
16
Oscillospira
15
Romboutsia
14
Parasutterella
14
Barnesiella
14
Campylobacter
14
Anaerotruncus
14
Paraprevotella
14
Methanobrevibacter
14
Catenibacterium
13
Butyrivibrio
13
Flavonifractor
13
Citrobacter
13
Coprobacillus
12
Adlercreutzia
12
Parvimonas
12
Rothia
12
Pseudomonas
12
Acidaminococcus
11
Fusicatenibacter
11
Gemella
11
Corynebacterium
11
Agathobacter
11
Ruminiclostridium
11
Lactococcus
11
Weissella
11
Slackia
10
Alloprevotella
10
Eisenbergiella
10
Direction Of Shifts for each bacteria
For some it is balanced, for others only one direction is significant.
“H” means that this genus for medical conditions are abnormally high for people with a condition
“L” means that this genus for medical conditions are abnormally low for people with a condition
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