I’m excited to share the launch of R² Microbiome Prescription (https://R2.MicrobiomePrescription.com), a platform dedicated to unraveling bacterial associations in the microbiome. The name “R²” reflects the Coefficient of determination—a statistical measure showing how strongly one variable (like one bacterial presence) correlates with another (e.g., a different bacteria presence). Think of it like income and spending: as salary rises or falls, spending often follows, though this doesn’t prove salary causes spending changes.

Why focus on associations?
While correlation ≠ causation, I lean toward the idea that bacterial relationships in the gut often hint at underlying causal mechanisms. For instance, one microbe’s metabolites might directly feed or inhibit another, creating a metabolic chain reaction. With thousands of metabolites (and counting!) interacting in complex ways, pinpointing exact cause-effect relationships is like solving a 4D puzzle.

The challenge ahead
Current research is racing to map these connections, but the sheer scale of interactions—combined with individual variability—makes definitive conclusions tough. My goal with R² is to aggregate data, spotlight patterns, and inspire deeper exploration into how these microbes might shape health.

Feel free to explore the site and join the conversation!

Keep It Simple Statistics (KISS)

Over the years, I’ve experimented with various methods to uncover meaningful bacterial associations—a journey that’s been both challenging and gradual. After much trial and error, I finally developed a methodology that consistently delivers reliable results, which I’ve now used to populate the new site.

A turning point came during discussions with Precision Biome. They encouraged me to apply this approach to their extensive dataset of shotgun sequencing samples from healthy individuals. This collaboration provided the perfect opportunity to put my method to the test on a large scale—and ultimately led to the creation of the site you see today.

Getting R2 by Percentages

Here’s an example of a clear association between two taxa using percentages of each in samples:
R² = 0.6971 and Slope = 0.3563.

An R² value of 0.6971 indicates that nearly 70% of the variation in one taxon’s abundance can be explained by changes in the other, reflecting a strong linear relationship between them. The slope of 0.3563 shows the rate at which one taxon’s abundance changes in relation to the other—specifically, for every unit increase in one, the other increases by about 0.36 units.

This kind of result highlights how statistical measures like R² and slope help quantify and visualize associations within complex microbiome data.

The relationship is typically not so linear. This was a specific example picked for illustration.

[BELOW] Applying a monotonic increasing transformation like the square root to the data changes the association metrics: in this case, R² drops to 0.5112 and the slope increases to 0.5405, indicating a weaker linear relationship compared to the original analysis. This reduction in R² means the transformed data explains less of the variance between the two taxa, making the association less robust than before.

Square root and similar transformations are commonly used in microbiome studies to stabilize variance, handle skewness, and address issues like zero-inflation and compositionality in the data. However, these adjustments can sometimes reduce the strength of observed associations, as seen here, because they alter the data’s distribution and the nature of relationships. Our goal is not to get a linear relationship, rather to get the best R² while preserving the nature of the data (i.e. all transforms should be monotonic increasing transformation)

[BELOW] Applying a different monotonic increasing transformation, such as taking the logarithm of the data, yields R² = 0.6596 and Slope = 1.0046. This result is an improvement over the square root transformation, as indicated by the higher R² value; but less than the first linear one.

A logarithmic transformation is often used to manage skewed data and compress large ranges, making relationships more linear and easier to interpret. In this case, the higher R² suggests that the log transformation preserves more of the association between the two taxa compared to the square root transformation. The slope of 1.0046 indicates a nearly one-to-one relationship between the log-transformed values of the two taxa.

[BELOW] We can also experiment with other transformations to see how they affect the association. The more complex transformation that I prefer yields R² = 0.7082 and Slope = 0.5015.

This R² is the highest among the transformations tested so far, indicating that this method captures the relationship between the two taxa most effectively. The slope of 0.5015 shows a moderate rate of change between the transformed values of the taxa.

This example highlights how choosing the right transformation can significantly enhance our ability to detect and quantify associations within microbiome data. By carefully selecting and testing different approaches, we can better reveal the underlying patterns and relationships that might otherwise remain hidden.

R2 is the amount of influence, slope indicate direction of influence

It’s important to avoid combining R² and slope by multiplying them together. This is not a standard or meaningful statistic in regression analysis and can easily lead to misinterpretation. For instance, a high slope with a low R² suggests that while changes are dramatic when they happen, the overall model does not explain much of the data’s variance.

Remember:

• Slope tells you the direction and rate of change (whether the relationship increases or decreases).
• R² indicates how much of the variation in one variable can be explained by the other (the strength of the association).

Each metric provides valuable information on its own, but their product does not offer any additional insight and can actually be misleading.

Criteria for selecting transformation

For any given pair of bacteria, it’s technically possible to find a data transformation that maximizes the R² value for that specific pair. However, with 5,000 taxa, there are over 25 million possible pairs (5,000 × 5,000), making it an overwhelming and impractical task to optimize each one individually.

Ideally, the goal is to identify a single transformation that performs well across both low and high R² values for all pairs. Discovering such a transformation was a significant part of my journey. To keep the analysis manageable, I focused only on bacteria present in at least 0.3% (0.003) of the samples, which helped reduce the number of pairs to a more reasonable level.

I’ve found a favorite transformation—demonstrated in the last chart above—that I’m particularly satisfied with. If I discover an even better transformation in the future, I simply rerun the analysis and select the one that yields the highest R² values. This approach ensures that the associations presented are as strong and meaningful as possible.

A practical alternative is to run regressions with multiple transformations and picked the transformation for each bacteria pair that has the highest R². I would suggest some of the following transformation be tried:

• linear function with positive slope
• cubic function
• square root function (converting percentage to 0 – 1 range)
• exponential function with base e
• natural logarithm
• logistic function
• general exponential function
• x−sin(x)
• x/(log(x)

This will increase the computations from 25 million to 250 million. Remember computer resources are cheap today (say he would started doing statistics using a HP-21 calculator and WatFor). And fast using parallelism (multiple cores and threads).

Usage With Probiotics

Suppose your Bacteroidota levels are too high and you’re considering which Bifidobacterium probiotic to take. If you turn to published studies, you’ll notice that most research focuses on individual probiotic strains, making it difficult to directly compare their effects. Instead, let’s examine the comparative data in the charts below to help guide your choice.

Bifidobacterium adolescentis: NCBI 1680, [species]

Does not impact any bacteria!! Definitely a pass.

Bifidobacterium bifidum: NCBI 1681, [species]

We see some impact, with R² being 0.10. it has little impact on other bacteria

Bifidobacterium breve: NCBI 1685, [species]

This does reduce some bacteria, and Bacteroidota has R² 0.12 (20% better than above)

Bifidobacterium longum subsp. infantis: NCBI 1682, [subspecies]

For this one, we have R² being 0.144 — best yet!

Bifidobacterium longum subsp. longum: NCBI 1679, [subspecies]

For this one, we have R² being 0.153 — best yet!

Starting at the target Bacteroidota

Are target is Bacteroidota: NCBI 976, [phylum]. We see the top 64 bacteria in the chart. The table below has 134 entries with R² of 0.10 or more

We can then search the table at the end for the best probiotics.

“Buyer beware,” or caveat emptor

The harsh reality is that we cannot trust most bacteria identification with the microbiome and with probiotics.

Precision Biome (who supplied the dataset) are doing things what I deem the right way:

• They are using the same pipeline that the above data came from (no ambiguity in bacteria identification) for client samples that they received.
• They are working with an EU probiotic manufacturer directly.
  • The contents of the probiotics is also verified with the same pipeline
  • The probiotics come directly from the factory and are not stored in questionable environments before being delivered to the client
• They intend to use the data from this site in identifying the best probiotics for each client

This is (IMHO) the ideal trifecta for clinical use of the microbiome. It is the strategy that I hope responsible microbiome testing firms move to.

Quick Test

Some one asked about probiotics that reduces Campylobacter. The page shows known (and pending) probiotics. We found none listed to reduce it. We did find some that increases it.

Going to Campylobacter Details: NCBI 194, a page that consolidated studies we found:

Bacillus is a genus and covers many species — so difficult to evaluate.

Not as good as actual studies? — but reasonable for sparse data

Critical Evaluation of Microbiome Study Limitations & Proposed Solutions

Key Factors Impacting Credibility

Current microbiome research faces significant validity challenges due to three core assumptions:

1. Taxonomic Accuracy of 16S rRNA Sequencing
   • The 16S pipeline (used in >80% of studies) has notable limitations:
     ▪ Struggles with species/strain-level resolution
     ▪ Database gaps create misclassification risks
     ▪ PCR amplification biases skew abundance data
2. Probiotic Product Integrity
   • Studies often assume supplements match label claims, yet:
     ▪ DNA analyses show 30-50% mislabeling in commercial probiotics
     ▪ Viability issues occur in 40% of products (esp. non-refrigerated)
     ▪ Strain-specific effects are frequently overlooked
3. Population Generalizability
   • Most trials use narrow cohorts:
     ▪ 78% of probiotic studies focus on healthy adults
     ▪ Gut ecosystem dynamics differ in:
     • Chronic disease states
     • Antibiotic-treated individuals
     • Elderly/immunocompromised populations

I prefer the trifecta approach over blind faith that all of the above assumptions are true. Blind faith is reasonable when you have no better data — the odds are that it will be better than no data.

This is a thought experiment transformed into an implementation for people to experiment with.

In doing educational reviews of a variety of samples, I came across a person whose progressed had slowed. In trying to understand why [The ME/CFS Quest for Health], I looked at metabolites level between his current sample and previous sample. To my surprise, the highest ones (highest percentile) had barely budgeted.

I looked at the prior Dec 24 sample and compare the KEGG Compounds to the current sample starting with the highest percentile ones:

• All of these were at 99%ile
  • 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate – still 99%ile
  • Chitobiose – still 99%ile
  • Heparan sulfate N-acetyl-alpha-D-glucosaminide – still 99%ile
  • D-Cysteine – still 99%ile
  • alpha-Kojibiosyldiacylglycerol – still 99%ile
  • dTDP-3,4-dioxo-2,6-dideoxy-D-glucose – down to 98%ile
• All of these were at 98%ile
  • Urea
  • R)-3-(3,4-Dihydroxyphenyl)lactate down to 86%ile
  • S-Methyl-5-thio-D-ribose 1-phosphate down to 97%ile
  • Coenzyme M 7-mercaptoheptanoylthreonine-phosphate heterodisulfide down to 96%ile
  • D-Mannose 1-phosphate down to 96%ile
  • 2-Amino-2-deoxy-D-gluconate down to 84%ile
  • Retinol down to 96%ile
  • (R)-3-(4-Hydroxyphenyl)lactate down to 86%ile

While the bacteria changed, the extreme metabolites remained high but with a few reducing. There is a potential to generate suggestions based on these KEGG compounds — a little messy and definitely pushing inference into new turf.

An Idea

I asked Perplexity.ai on how to reduce a few. A typical response is shown below

This answer is the typical false logic/inference seen with “To reduce cholesterol, just eat food low in cholesterol“.

Possible Expert System Algorithm

On MicrobiomePrescription.com, the suggestion algorithm works solely off the bacteria that is reported by the microbiome test. This is done by using facts harvested from US National Library of Medicine studies. There are no (or likely extremely few) studies dealing with diet and metabolites.

The key phrase is reported by. We know that reporting is not standardized and often using only 16s.

Idea!

Current logic on MicrobiomePrescription.com is bacteria => suggestion impact. What if we add another approach: metabolite => normalized bacteria distribution => suggestions. We want this to have less randomness than 16s. The folks at PrecisionBiome.Eu shared 1000 shotgun results from healthy individuals with me so I could construct a normalized bacteria distribution model. From this model, I computed metabolites using data from KEGG: Kyoto Encyclopedia of Genes and Genomes and ended up with a facts table consisting of:

• Metabolite
• Suggestion / Modifier
• Estimated Impact

The metabolite is identified by KEGG ID.

Implementation

Since the microbiome and its metabolites are very interconnected and interact with each other. I decided that looking at the top and bottom 5-10%ile (i.e. those with a percentile ranking of 90-95%ile or higher, a percentile of 10-5%ile or lower) was a reasonable approach. There is a little trust that the central limit theorem will generate reasonable results and allow metagenomics to be directly used for getting suggestions.

On the [Research Features] tab, this panel has been added:

This produces a report listing the Metabolites targeted (High and/or Low) and then Suggestions

Observation

To me, what I found very interesting is that there are a few that are very high in impact with rapid drop off. This means there are only a few critical items to add to the general bacteria-based suggestions.

Backstory

Hello, I’m sorry to message you privately, but I’m reaching out for help regarding my 15-year-old daughter, who has been homebound with ME/CFS for 2.5 years since contracting COVID in 2022. I came across your story on Facebook, and I felt truly inspired by how you managed to overcome ME/CFS by working with your microbiome. We are currently trying to follow a similar path.

We’ve recently done a Biomesight 16S test for her. As expected, it showed typical deficiencies, like a lack of Lactobacillus bacteria, along with an overgrowth of sulfate-reducing bacteria (possibly SIBO). Since we’re unsure how best to approach this dysbiosis, we sought the help of a microbiome specialist through Viola Sampson in the UK. She recommended lactulose, Lactobacillus reuteri, Lactobacillus rhamnosus, Lactobacillus plantarum, Bifidobacterium breve, along with Allicin and Goulds tincture from Australia. We’re just beginning this treatment, so it’s hard to say much about progress yet. We’ve started with Lactobacillus rhamnosus, plantarum, and breve, and she’s doing well with these so far.Honestly, I’m a bit concerned about these Lactobacillus bacteria because I read somewhere that all people with ME/CFS have some degree of lactic acidosis, so I’m worried that these probiotics might produce even more lactic acid. When I brought this up with my practitioner, she wasn’t aware of it.

I also uploaded a Biomesight test of my daughter to your Microbiome Prescription page, but I noticed that your site has somewhat opposite recommendations for her microbiome, such as advising against lactulose. To be honest, I’m struggling to navigate your page, and it’s a shame because I truly want to follow the recommendations accurately. I was hoping to identify which specific antibiotics or probiotics might be the best fit for my daughter’s case, but I’m not sure how to interpret that information from your site.

Currently, my daughter is mainly dealing with POTS (Postural Orthostatic Tachycardia), histamine intolerance, chronic fatigue, anxiety and panic attacks, and digestive issues. She’s become highly sensitive to various foods and medications, and it all points towards dysautonomia. Although she’s taking many supplements, she reacts to some, like iron supplements, which I suspect might be due to certain bacteria that feed on iron. She has many vitamin deficiencies, yet we can’t supplement effectively due to these reactions. It’s so difficult to manage.

I apologize for the long message, but I wanted to be as clear as possible about her situation. I’d be incredibly grateful if you could review her Microbiome Prescription and offer any insights on what stands out in her microbiome and where we might start. I’m also curious about any thoughts on the potential use of antibiotics or probiotics, as our microbiome practitioner is generally against antibiotics, though I know some people with ME/CFS have found success with a well-planned antibiotic approach. 

Here, I am including the link to our microbiome analysis from Microbiome Prescription.

Analysis

First, disagreement between sites is well known and explained here: Why sites suggestions disagree on the same data. Microbiome Prescription tuned it’s advice by doing cross-validation for several conditions, for example: Cross Validation of AI Suggestions for Nonalcoholic Fatty Liver Disease. ME/CFS was the first explicit studies done. To the best of my knowledge, no other microbiome site has done cross-validations of their suggestions and been public in showing results.

Individual practitioners are hard to evaluate because they often find patterns that works for some people by trial and error. It is a rare practitioners that can provide documentation on their suggestions.

Quick boot strap

Long COVID is one condition that has a built in cross validation list of suggestions. This is on [Old Ui] / [Changing Microbiome]. POTS is not currently on the list because of insufficient studies.

This identified the following bacteria as being probable according to the published literature. The number of cross reference numbers after each item, indicate the number of studies For example Ruminococcus – genus : Low was reported in 4 studies.

The suggestions (based on microbiome shifts cross reference with substance that improved ME/CFS from studies are below. The number of cross reference numbers after each item, indicate the number of studies – as above. This leads to the best suggestions being the ones with the most cross reference. Thus:

• Magnesium supplements – 6 studies
• Vitamin B9 – 6 studies
• Coenzyme Q10 – 6 studies
• Far infrared Sauna – 4 studies — as a personal note, we purchased a small one at Costco and use it regularly as preventative.
• Vitamin B1 – 3 studies
• Omega-3 – 3 studies
• Ribose – 3 studies
• licorice – 3 studies — we usually use Spezzatina and just suck on them
• carnitine Amino Acid – 3 studies
• Melatonin – 3 studies
• Selenium supplement – 3 studies

This is a significant list and I noticed that none of these were suggested by Viola Sampson despite published literature saying they help.

My suggestion would be to add one of these every three days, noting any changes that results. For dosages see Dosages for Supplements, start low and work up. The above will take a little over a month. All of these items can be taken continuously and together.

Probiotics

Probiotics are a popular “cure-all” which in some cases help and in other cases hurt. For example, lactobacillus probiotics often will increase brain fog.

Looking at probiotic with positive values, most are actually hard to obtain. For example Kefibios is only sold in Italy. Mutaflor in only a few countries. Of the choices, I would try Mutaflor after adding in the items above — but be warned, it may trigger severe die-off.

Top items

The list below are other things that likely have never been studied for ME/CFS but should have significant impact on the bacteria shifts.

On the other side, the following should be avoided:

Food Site

Going to https://food.microbiomeprescription.com/ and entering your login token will show the nutrients computed to help most. Iron supplements or food high in iron is at the top; for example thyme, basil, and my favorite Caterpillar, roasted ;-). Both herbs have positive recommendations.

The second one is found in cranberry (a suitable seasonal food) and raw Olive. The third one is found in maize, rye and Hard wheat, semolina. HOWEVER, none of these are recommended in the list of suggestions. I usually cross reference the two for safety.

Next Steps

I would continue with additional suggestions (1 and 2 studies) at the same pace. Two weeks after the last one was added, do another microbiome test (same firm of course) and get back to me for a follow up analysis if needed.

Postscript and Reminder

As a statistician with relevant degrees and professional memberships, I present data and statistical models for evaluation by medical professionals. I am not a licensed medical practitioner and must adhere to strict laws regarding the appearance of practicing medicine. My work focuses on academic models and scientific language, particularly statistics. I cannot provide direct medical advice or tell individuals what to take or avoid.My analyses aim to inform about items that statistically show better odds of improving the microbiome. All suggestions should be reviewed by a qualified medical professional before implementation. The information provided describes my logic and thinking and is not intended as personal medical advice. Always consult with your knowledgeable healthcare provider.

Implementation Strategies

1. Rotate bacteria inhibitors (antibiotics, herbs, probiotics) every 1-2 weeks
2. Some herbs/spices are compatible with probiotics (e.g., Wormwood with Bifidobacteria)
3. Verify dosages against reliable sources or research studies, not commercial product labels. This Dosages page may help.
4. There are 3 suppliers of probiotics that I prefer: Custom Probiotics , Maple Life Science™, Bulk Probiotics: see Probiotics post for why

Professional Medical Review Recommended

Individual health conditions may make some suggestions inappropriate. Mind Mood Microbes outlines some of what her consultation service considers:
A comprehensive medical assessment should consider:

• Terrain-related data
• Signs of low stomach acid, pancreatic function, bile production, etc.
• Detailed health history
• Specific symptom characteristics (e.g., type and location of bloating)
• Potential underlying conditions (e.g., H-pylori, carbohydrate digestion issues)
• Individual susceptibility to specific probiotics
• Nature of symptoms (e.g., headache type – pressure, cluster, or migraine)
• Possible histamine issues
• Colon acidity levels
• SCFA production and acidification needs

A knowledgeable medical professional can help tailor recommendations to your specific health needs and conditions.

A reader messaged me about some issues she was having

Hi, could I just have a quick question? I read in the Gut Health group on Facebook that you wrote that if there is too much, for example, lactobacillus, it can cause neurological problems. I suffer from anxiety and depression and was recommended a transplant of intestinal microflora, which made the condition 100 times worse and since then I can’t get out of it and the doctors don’t know what to do with it. I’m still trying to treat dysbiosis, but now I don’t know if the problem is one of the good bacteria? Thank you very much.

yes, I have a biomesight and a GI map, there is an overgrowth of Prevotela, Streptococus, Enterobacter and Citrobacter and a little bifido and lacto. I have yellow stools after the transplant, if I don’t take probiotics. But it seems that nothing works, diet, antimicrobials, probiotics, enemas with probiotics, prebiotics, nothing helps

Initial Comments

This person is not in the US. She lives in a place where Fecal Matter Transplants is allowed for many conditions than the US (where it is only authorized for Clostridioides difficile –after everything else has failed). I view FMT as Russian roulette hoping that a silver ballet will happen to end up in the cylinder. IMHO, before a FMT is done we need at least two shotgun microbiome tests done. One for each candidate donor and one for the recipient. These need to be carefully reviewed by a third party who is very well informed on the microbiome. Only the best donor will be used. After the FMT, monthly shotgun reports of the recipient microbiome should be done for at least 6 months.

Analysis

The first step is to look at predicted symptoms, most are neurological, with the two reported symptoms sitting high up the list.

• Comorbid: High Anxiety – [66.6%]
• General: Depression – [64.1%]

I marked all of the items with depression and anxiety and then asked for suggestions. The top items are shown below,

(2->1)-beta-D-fructofuranan {Inulin}	Prebiotics and similar
oligosaccharides {oligosaccharides}	Prebiotics and similar
Outer Layers of Triticum aestivum {Wheat Bran}	Food (excluding seasonings)
laminaria digitata {Oarweed}	Food (excluding seasonings)
dietary fiber	Diet Style
β-lactoglobulin {Whey}	Food (excluding seasonings)
Fiber, total dietary	Diet Style
Pulvis ledebouriellae compositae {Bofutsushosan}	Herb or Spice
Orange Juice	Food (excluding seasonings)
ß-glucan {Beta-Glucan}	Prebiotics and similar

The number of bacteria picked was below my comfort level, so I then did Novice: Just tell me what to take or avoid

While the items were technically different, they were very similar for example

• {jerusalem artichoke} <==> Inulin}

bifidobacterium bifidum {B. bifidum}	Probiotics
atorvastatin (prescription)	Prescription – Other
High-protein diet {Atkins low-carbohydrate diet}	Diet Style
Helianthus tuberosus {jerusalem artichoke}	Prebiotics and similar
Lentinula edodes {Shiitake Mushroom}	Food (excluding seasonings)
high red meat	Food (excluding seasonings)
amlodipine,(prescription)	Prescription – Other
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)	Prebiotics and similar
Brassica oleracea var. italica {Broccoli}	Food (excluding seasonings)
rosmarinus officinalis {rosemary}	Herb or Spice
Actinidia deliciosa/chinensis {kiwifruit}	Food (excluding seasonings)
naringenin {grapefruit}	Flavonoids, Polyphenols etc
azithromycin,[CFS]	Antibiotics, Antivirals etc
refined wheat breads	Food (excluding seasonings)
nicotinic acid {Vitamin B-3}	Vitamins, Minerals and similar
Fraxinus angustifolia {Narrow-leaved ash}	Herb or Spice
Terminalia chebula {Haritaki}	Herb or Spice
Mentha spicata {Spearmint}	Herb or Spice
Diferuloylmethane {Curcumin}	Herb or Spice

Probiotics

There are two approaches for probiotics:

• Based on published studies (we actually do not have that many with good details on the impact)
• Based on KEGG: Kyoto Encyclopedia of Genes and Genomes which uses the DNA of your microbiome and the probiotics.

We have agreement on the following top choices:

• Mutaflor *
• bifidobacterium bifidum {B. bifidum} *
• Bacillus Subtilis *
• Lactobacillus johnsonii *
• Levilactobacillus brevis {L.brevis} *
• Lactiplantibacillus pentosus {L. pentosus}*
• Brevibacillus laterosporus {B. laterosporus }*
• Clostridium butyricum MIYAIRI 588 {Miyarisan}*
• bacillus licheniformis {b. licheniformis}

We should note that there are probiotics that should be avoid, including

• Saccharomyces boulardii
• Bacillus coagulans
• bifidobacterium pseudocatenulatum 
• Bifidobacterium breve
• bifidobacterium longum
• Lactobacillus gasseri
• bifidobacterium infantis
• Ligilactobacillus salivarius
• Lactobacillus plantarum 
• lactobacillus rhamnosus

The failure to understand that all probiotics are not created equal is a common problem. Often I have heard “I tried probiotics and it did not work”. That is not surprising because often they are sold with dozen of species in one bottle — “because the more species you have, the better your sales will be” from manufacturers and influencers.

You need to get specific species and ideally recently manufactured. A bottle of probiotics stored in an unrefrigerated warehouse for 12 months may have very few viable bacteria left. When they get to a retail store, they may be put into a refrigerator — but that is too late.

Where do I get the probiotics?

I prefer single species — and where I get mine?

• Single species with (almost) no fillers. There are precisely three sources that I use:
  • Custom Probiotics :they list all of their strains — many are researched. No other ingredients just the bacteria.
  • Maple Life Science™: No strains yet, but shipments usually have manufactured date within 4 weeks of arrival (i.e. FRESH). Contains FOS
  • Bulk Probiotics: US based Newbie — but has some species not available at the other two sites. No other ingredients just the bacteria. Specifically, Lactobacillus Jensenii that has great potential for Crohn’s disease.
• NOTE: none of these sell though retail outlets. This keeps their costs down and their product fresh.

Another Alternative to get Suggestions

On the old UI we have this section and we have enough studies for Depression show up.

With this sample, we have the following bacteria matches against published studies (with links to the studies).

This results in the suggestions below. Each suggestion has also been reported in studies to help depression. This means that the odds of them working is pretty good.

Treatment Suggestions for

This report is for Reader using this sample BiomeSight:2022-10-25 Self 🛑 . It uses their reported medical conditions, microbiome sample, US National Library of Medicine, and a fuzzy logic expert system to compute recommendations balancing study reliability and contraindications. These suggestions should always be reviewed by a medical professional before starting.

NOTA BENE: This is working solely from published studies. Other suggestions algorithms are available on Microbiome Prescription. The URL above may be sent to your MD if you wish to share it.

The reported condition(s) are

This person has a significant amount of bacteria known to form biofilms

Substances with a 🦠 are reported to reduce biofilms. See for studies.

1. Depression – Depressive Disorder
   1. Omega-3 Fatty Acids: Some studies suggest that omega-3 supplements, particularly those rich in EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid), might have modest benefits as adjuncts to traditional treatments for depression. Omega-3s are essential for brain health, and they may have some mood-stabilizing properties.
   2. Vitamin D: Low levels of vitamin D have been associated with depression. While the exact relationship is complex and not fully understood, maintaining adequate vitamin D levels through supplements or exposure to sunlight may support overall mental health.
   3. B Vitamins: Some B vitamins, such as B6, B9 (folate), and B12, are involved in neurotransmitter synthesis and may have a role in mood regulation. Folate deficiency, in particular, has been linked to depressive symptoms.
   4. Probiotics: The gut-brain connection has led to studies exploring the potential impact of probiotics on mental health. Research suggests that gut health may influence mood, and some studies propose that certain probiotics might have a modest effect on reducing depressive symptoms. However, more research is needed to determine specific strains, dosages, and their impact on depression.

Significant Bacteria Shifts

Based on the existing literature on the US National Library of Medicine and this microbiome sample, we have the following matches for bacteria shifts. There is a growing body of literature finding that the effectiveness of interventions depends on the existing microbiome. We filter by documented interventions that helps some with this condition and suggestions based on this person’s specific microbiome to produce this “double validated” list.

Bacteroidaceae – family : Low 5 16 Bacteroides – genus : Low 13 16 20 22 Bifidobacterium longum – species : Low 7 10 Collinsella – genus : Low 9 Collinsella aerofaciens – species : Low 9 Escherichia – genus : Low 2 5 12

Lactobacillus – genus : Low 5 8 14 15 17 18 19 21 23 Parabacteroides – genus : Low 11 20 Porphyromonas – genus : High 1 Prevotella – genus : High 3 4 24 Sphingobacterium – genus : Low 13 Streptococcus – genus : Low 6

Cross Validated Suggestions

The following improves the bacteria identified above and also is reported in the literature of helping some people with this condition. Each is link to the source study.

5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a]quinolizinium {Berberine} 64 3,3′,4′,5,7-pentahydroxyflavone {Quercetin} 41 3,5,7-trihydroxy flavanone-7-rhamnoglucoside {Hesperidin} 31 a-Amino-3-indolepropionic acid {Tryptophan} 72 Agaricus bisporus {White button mushrooms} 31 Akkermansia muciniphila {Pendulum Probiotic} 43 62 66 74 81 92 alpha-linolenic acid {Omega-3} 34 35 41 48 arabinogalactan {arabinogalactan} 86 Bifidobacterium breve {B. breve} 46 89 bifidobacterium infantis {B. infantis} 78 Biotin {Vitamin B7} 76 blueberry 41 Caffeine 41 Camellia sinensis {oolong tea} 48 Citrus limon {Lemon} 47 Coffee 41 coptis chinensis {Chinese goldthread } 49 Crocus sativus {Saffron} 44 48 Cuminum cyminum {Cumin} 48 dietary fiber 41 eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA) {Fish Oil} 41 48 Epicatechin {Green tea polyphenol} 41 folate {Vitamin B9} 33 34 fruit 32 40 Glycine max x Aspergillus oryzae {Miso} 41 green tea 41 Hericium erinaceus {Lion’s Mane Mushroom } 68 Heyndrickxia coagulans {B. coagulans} 25 26 Hypericum perforatum {St. John’s Wort} 41 48 Ipomoea batata {Purple sweet potatoes} 31 kefir 90 Kimchi 31 Lacticaseibacillus casei {L. casei} 77 Lacticaseibacillus rhamnosus {l. rhamnosus}🦠 54 63 84 87 93 lactobacillus acidophilus {L. acidophilus} 49 50 51 lactobacillus helveticus {L. helveticus} 49 67 82 lactobacillus helveticus,lactobacillus rhamnosus 80 84 Lactobacillus plantarum {L. plantarum} 41 49 53 55

Lactococcus lactis {Streptococcus lactis}🦠 44 Lentinula edodes {Shiitake Mushroom} 31 Levilactobacillus brevis {L.brevis} 42 Limosilactobacillus fermentum {L. fermentum} 88 long-term, moderate-intensity exercise {exercise} 65 71 low carbohydrate diet 41 low-fat diets 40 Lycium barbarum x Lycium chinense, {Goji Fruit, Juice} 48 Malus domestica {apple} 70 Mixture of Vitamin B? {B Vitamins} 27 Musa acuminata {Banana} 31 Nigella sativa {black cumin}🦠 56 nuts 40 41 oligosaccharides {oligosaccharides} 80 Phaseolus vulgaris {Boston bean} 40 Pisces {Fish} 32 41 polyphenols 41 Pulses, Beans 40 Pyroguaiac acid {Guaiacol} 70 resveratrol-pterostilbene {grapes, blueberries} 41 resveratrol-pterostilbene x Quercetin {quercetin x resveratrol} 41 Rhodiola rosea {Rosavin} 48 Rubus {Raspberries} 41 52 SAM-e 41 Selenomethionine {Selenium supplement} 85 Solanum tuberosum {Potatoes} 73 soy 40 41 ß-glucan {Beta-Glucan} 57 58 59 60 tea 91 Traditional Mediterranean diet {Mediterranean diet} 40 Vaccinium {Cranberry} 41 vegetable 32 40 vegetarians 40 vitamin d🦠 27 28 29 30 41 69 Whole Cow milk {Whole Milk} 31 whole-grain diet 40 yogurt 41

Bottom Line

There is no definitive right way to determine how to correct a dysbiosis. We just do not have enough studies. Above, you have two main approach (with some overlap of suggestions)

• Working off the microbiome that are too high or too low.
  • We cross check probiotics suggestions using KEGG data
• Working off the microbiome using only peer reviewed studies for one condition: depression.
  • This report should have high creditability with most medical types — because all of the evidence used to make the report is cited.

ME/CFS can often become a multiple year journey to resolve. This is a continuation of this ongoing saga.

• Ongoing ME/CFS Journey [Jan 2026]
• ME/CFS Continues Improvement + Lab Read Quality Issues [Feb 2024]
• Update on ME/CFS Person [Sep 2023]
• Follow up Microbiome Analysis from a prior post [Apr 2022]
• Rosacea, Circulation and mild CFS [Dec 2021]

Back Story

Since the last test I have been cycling these:

• amoxicillin
• Noni
• Propolis {Bee glue}
• Dandelion
• allium sativum {garlic}
• Parsley
• Grapefruit seed extract
• mutaflor

I have not been feeling so well lately (since the last 6 months). I would say that my symptoms has become worse. Earlier it has always felt as I have done some progress but the last 6 months it has been the opposite. 

At the end of January I had my appendix removed. Since then I have felt even worse. Received some antibiotics while I was hospitalized. Earlier I got rid of my muscle and joint pain but it has come back and I have much bigger issues with my red nose and my body feels very stressed. Also feel very bloated.

A summary of my biggest issues:

• Get the red nose (some form of rosacea). 
• Feel fatigued (both physically and mentally). 
• Feeling stressed. 
• Brain fog.
• Bloated.
• Lots of gas – I fart and burps a lot. 
• Issues with allergies
• Muscle and joint pain

For the last 3 years I’ve been eating large amounts of rye and oats.

• Around 150-200 gram of rye bread every day.
• Around 70 gram of oats every day.
• Been eating low fat, low protein and high carb (specially from rye, oats, apple juice and potatoes) because this diet seem to reduce my symptoms.

As soon as I start to eat high meat and high fat my symptoms get worse.

Quick Overview

I will continue with a table showing recent changes (see above for earlier values)

Criteria	3/30 2025	12/3 2024	9/2 2024	1/22 2024	2/22 2024
Lab Read Quality	7.6	9.8	9.1	7.9	9.7
GanzImmun	10	14	16	16	15
Outside Range from Lab Teletest	21	17	23	20	24
Outside Lab Range (+/- 1.96SD)	10	7	12	5	10
Outside Box-Plot-Whiskers	59	47	48	54	42
Outside Kaltoft-Moldrup	111	85	113	123	139
Bacteria Reported By Lab	718	689	600	511	666

The most striking change was the 4% increased number of bacteria. Looking at Symptom Pattern Matching, we see significant improvement with 15% with significant improvement.

Current Takes Evaluation

I have put together a video trying to describe the complexities of shifting the microbiome. My own experience during a flare was “suggestion whiplash”, the suggestions from one test became avoid on the next and became suggestions on the next test. This is not what I was expecting and caused me to question the process — until I dug deeper and did some modelling. My understanding is in this video.

What he reports taking is below. I look at the suggestions and added the weight after each.

• amoxicillin: +490.3
• Noni -206.1
• Propolis {Bee glue} +19.5
• Dandelion + 155.3
• allium sativum {garlic} -214.4
• Parsley + 145.3
• Grapefruit seed extract +168.8
• mutaflor -78.8

Takes flipping to avoids is not unexpected. It does emphasis the need to do regular tests, especially when progress slows or reverses.

Building Suggestions

Since we have symptoms we use Beginner-Symptoms since it will focus on bacteria associated with symptoms present.

Looking at the Consensus report we see the top 3 antibiotics are all ones associated with CFS

The other items is interesting and would suggest Whole Milk (high Fat) (Yogurt) from A2 cows, I do not know if that is easily available in his country. I happen (as a recovered ME/CFS person) to have some to my daily morning porridge.

Looking at categories

• Amino Acid
  • Arginine
  • Theanine
  • AVOID:
    • Green tea polyphenol
    • Melatonin
    • Tryptophan
    • Conjugated Linoleic Acid {CLA}
    • Dihydroquercetin {Taxifolin}
• OTC Substances — all avoids
  • Exercise
  • Bromelain
  • aspirin
  • Quercetin
  • sodium butyrate
• Diet – all avoids
  • Slow digestible carbohydrates. {Low Glycemic}
  • High dietary fiber
  • ketogenic diet
  • low-fat diets
• Flavonoids, Polyphenols etc – all avoid
  • Grape Flavonoids}, red wine polyphenols, resveratrol-pterostilbene {grapes, blueberries}
  •  {quercetin x resveratrol}
  • Carvacrol
• Food (excluding seasonings)
  • Fat
  • A2 variant of beta-casein {A2 Milk}
  • AVOIDS
    • cranberry bean flour
    • fruit/legume fibre {lingonberries} {Goji Fruit, Juice} grapes red wine {Raspberries}
    • Abstention from eating {Fasting}
    • Sulforaphane {Dark Greens}
    • green tea
• Herb or Spice
  • Evening Primrose Oil
  • Vanilla
  • Tulsi
  • AVOIDS
    • Berberine
    • grape seed extract
    • Haritaki
    • rosemary
    • Curcumin
    • Ginseng
• Prebiotics and similar
  • Carrageenan
  • Psyllium
  • AVOIDS
    • {Inulin}
    • Human milk oligosaccharides (prebiotic, Holigos, Stachyose)
    • oligosaccharides
    • ß-glucan {Beta-Glucan}
    • Xylan
• Probiotics
  • Lactobacillus gasseri {L.gasseri}
  • AVOIDS
    • Lacticaseibacillus rhamnosus
    • Bifidobacterium animalis subsp. lactis {B. Lactis}
    • lactobacillus acidophilus {L. acidophilus}
    • Akkermansia muciniphila {Pendulum Probiotic}
• Sugars — almost are are Avoids
• Vitamins, Minerals and similar
  • Calcium Supplements
  • Ferrum {Iron Supplements

KEGG Probiotics

The only one that is indicated is switching to symbioflo 2

• symbiopharm / symbioflo 2 – minor positive
• mutaflor / mutaflor / – negative
• Bacillus Subtilis Probiotic (Natto strain is a minor positive, others are negative)

Unexpected suggestions profile

The suggestions above do not fit typical patterns that I have seen. I went back and did “just give me suggestions” in case the bacteria filtering by symptoms caused some odd twist. Results were similar as shown below.

One More Analysis

I looked at the prior Dec 24 sample and compare the KEGG Compounds to the current sample starting with the highest percentile ones:

• All of these were at 99%ile
  • 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate – still 99%ile
  • Chitobiose – still 99%ile
  • Heparan sulfate N-acetyl-alpha-D-glucosaminide – still 99%ile
  • D-Cysteine – still 99%ile
  • alpha-Kojibiosyldiacylglycerol – still 99%ile
  • dTDP-3,4-dioxo-2,6-dideoxy-D-glucose – down to 98%ile
• All of these were at 98%ile
  • Urea
  • R)-3-(3,4-Dihydroxyphenyl)lactate down to 86%ile
  • S-Methyl-5-thio-D-ribose 1-phosphate down to 97%ile
  • Coenzyme M 7-mercaptoheptanoylthreonine-phosphate heterodisulfide down to 96%ile
  • D-Mannose 1-phosphate down to 96%ile
  • 2-Amino-2-deoxy-D-gluconate down to 84%ile
  • Retinol down to 96%ile
  • (R)-3-(4-Hydroxyphenyl)lactate down to 86%ile

While the bacteria changed, the extreme metabolites remained high but with a few reducing. There is a potential to generate suggestions based on these KEGG compounds — a little messy and definitely pushing inference into new turf.

I have decided to build an adjacent Suggestions Agent using metabolites ONLY. The microbiome is a very complex system and there is a possibility that the metabolites approach may work better. Stay tune!

Reconciliation of Recent Diet and Suggestions

During my own recovery, I had “whip-lash” between suggestions from one test until the next test. One test results had to take, the next result was the same items on the avoid list. This “pendulum” swinging back and forth may be happening here. My own response was to be “less religious” in keeping to the suggestions (i.e. “moderate compliance”) and retest after 6 weeks doing suggestions. The pendulum swing dampened down and lead to a full remission (with patience).

The 300 grams of fiber (Rye, Oats) should be reduced. If you can get a willing MD, then you may wish to rotate to a different antibiotic because of the risk of antibiotic resistance occurring.

A reader forwarded this study to me and asked:

Interesting study. I wonder whether the effect is due to high fat, high sugar or the combination?

In the database on Microbiome Prescription

• Sugar 57 Bacteria cited from
  • ¶ High glycemic diet {High-sugar diet (HSD)}
  • ¶ high-fat sucrose 
• Fat 233 Bacteria cited from
  • ¶ high-fat diets
  • ¶ high-saturated fat diet 

And thus have the ability to compute the theoretical differences.

We also have these collections of studies which we can use by flipping things to be negative cognitive function:

• Cognitive Function
• Intelligence:Comprehension, Cognitive Ability

This resulted in 71 bacteria.

Results

For Sugar we had agreement between reported shift and cognitive issues for the following:

1. Bacillota
2. Bacteroides
3. Coprococcus
4. Desulfovibrio
5. Dorea
6. Escherichia coli
7. Faecalibacterium prausnitzii
8. Lachnospiraceae
9. Ruminococcus
10. Streptococcus

For Fat we had agreement between reported shift and cognitive issues for the following:

1. Bacillota
2. Bacteroidaceae
3. Bacteroides
4. Clostridium
5. Coprococcus
6. Coriobacteriaceae
7. Dorea
8. Faecalibacterium prausnitzii
9. Oscillospira
10. Phascolarctobacterium
11. Porphyromonadaceae
12. Ruminococcaceae
13. Ruminococcus

With Fat we had significantly more contrary shifts than with Sugar.

Bottom Line

Both High Fat and High Sugar in isolation appear to impact cognitive function. High Fat has the appearance of having less impact in isolation than high sugar. The following shifts seem to be common with these:

• Bacillota
• Bacteroides
• Coprococcus
• Dorea
• Faecalibacterium prausnitzii
• Ruminococcus

A few days ago I posted the results for Bacteria Association (with graphics). I did some operations Research black magic in transforming the data. This black magic is a key part of a patent application that has been filed.

Over the last decade, I have been focused on understanding the statistics of the microbiome bacteria. My multiple degrees are in Probability and Statistics, hence the desire to build mathematical models for the microbiome bacteria.

One of my key observations is that “one model does not fit all taxa“. One observation is very consistent: no bacteria fits the gaussian (normal or bell curves) rendering the use of mean and standard deviation not only suspect, but naively dumb.

This post exhibits the challenges. We take 1000 Shotgun samples of healthy people using 10 million reads and look for associations by doing classic linear regression. We apply a variety of monotonic increasing transformations to the percentage/counts and see where we get the most relationships with R² > 0.25.

First Pass Analysis

I decided to see how well “common textbox solutions” would do compared to my “Black Magic” monotonic increasing transformation. If people want to suggest other monotonic increasing transformations, I am very willing to run other transformations on this dataset and add it to this report.

Method	“Black Magic”	Using Percentage / Count	Using Log(Count)
Number of R2 > 0.25	15,183	1,764	9,616
Number with higher R2		1,356	7,167
Number with lower R2		408 [13,827]	2,449 [8016]

The [ ] is the sum of not found and lower R².We see that the “Black Magic” clearly found more statistically significant relationships. Taken in isolation, “Black Magic” also found more relationships with a higher R2. The Log(Count) items with a higher value are worth some extra analysis.

Percentage or Count

This is the typical naïve approach used by people who rote-learn statistics. We found only 10% of those we got via “Black Magic”. Many relationship were very similar, they tend to be for bacteria with low rates of detection (i.e. occurs in < 25% of samples) and low amounts of bacteria. To translate, very few distinct values in these subsets.

Other has significant differences

A chart comparing results.

Log(Count)

Using a log(values) is a common statistical trick dealing with non-gaussian (normal/bell curve) data to get semi-normal data. For R² that were higher than “Black Magic” we have:

• Mean Difference: 0.23
• StdDev Difference: 0.09
• Maximum Difference: 0.43

We have a sample of the greatest difference below, and note that the sample size was relatively small. The top line has R² of 0.999. This suggests that we may need to exclude taxa that has less than N distinct values (a possible follow up post)

Restricting to samples where we have 300 or more (incidence of detection: 30%). In this case Log(Count) with higher R² exceed those with lower R² compared to “Black Magic”

Method	“Black Magic”	Using Percentage / Count	Using Log(Count)
Number of R2 > 0.25	10,733	702	8,121
Number with higher R2		494	6130
Number with lower R2		208 [10239]	1991 [4603]

Bottom Line

Log(Count) produces acceptable results while failing to detect 20% of those detected by “Black Magic”. The ideal solution would be to do both methods and take the highest R² from each regression. I await other suggestions for monotonic increasing transforms to try. It is very clear that using counts / percentage is a poor statistical choice.

There is a follow up post suggested based on the density/sparseness of different values. Having too few distinct values appears to over-fit and produce suspect/false higher R².

Methane may be reduced by up to 98% by eating a small amount of Red Seaweed. Bromoform in red seaweed inhibits a key enzyme used by microbes to produce methane gas. It is commonly found in  red seaweed Asparagopsis taxiformis (Recommended reading)

Asparagopsis is one of the most popular types of limu.^[4] in the cuisine of Hawaii, it is principally a condiment.^[5] It is known as Limu kohu in the Hawaiian language, meaning “pleasing seaweed”.^[6] Limu kohu has a bitter taste, somewhat reminiscent of iodine,^[7] and is a traditional ingredient in poke.

• National Library of Medicine on Bromoform
• Methane production and whole animal energy utilization in lactating Jersey dairy cows fed a bromoform-containing feed additive [2025]
• Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission [2022]
• Food safety complicates development of seaweed touted as major methane reducer in cows, researcher warns [2023]

In reviewing the literature on different types of seaweeds, most studies found that they reduced methane. Consumption of common seaweeds supplements or foods are a viable approach. The levels of Bromoform may not be as high, but may be enough to cause changes while generally considered safe to consume..

There are no studies on using seaweed with SIBO that could be located.

There are two types of Lactic Acid.

• L-lactic acid (L-lactate, (S)-lactic acid, or (+)-lactic acid):
  • This is the form produced in human metabolism, especially during anaerobic glycolysis (when oxygen is limited, such as during intense exercise or tissue hypoperfusion).
  • L-lactate is the predominant form found in human blood and tissues.
• D-lactic acid (D-lactate, (R)-lactic acid, or (−)-lactic acid):
  • This form is produced mainly by certain bacteria during carbohydrate fermentation, including some gut bacteria.
  • Humans produce very little D-lactate, but it can accumulate in specific conditions, such as short bowel syndrome, where bacterial overgrowth leads to increased D-lactate production and absorption. Typically this form often manifest itself as Brain Fog.

Excessive Lactic Acid is called Acidosis

 Clinical Types of Lactic Acidosis

Background Article: Lactic Acidosis [2014]

Lactic acidosis refers to the accumulation of lactic acid in the body, leading to a decrease in blood pH. It is classified based on the underlying cause:

• Type A Lactic Acidosis:
  • Caused by tissue hypoperfusion and hypoxia (lack of oxygen), leading to increased anaerobic metabolism and L-lactate production.
  • Common in shock (septic, cardiogenic, hypovolemic), severe hypoxemia, or cardiac arrest.
  • This is the most serious and common form.
• Type B Lactic Acidosis:
  • Occurs without obvious tissue hypoxia or hypoperfusion.
  • Subdivided into:
    • Type B1: Associated with underlying diseases (e.g., liver failure, cancer, diabetes).
    • Type B2: Caused by drugs or toxins (e.g., metformin, antiretrovirals).
    • Type B3: Due to inborn errors of metabolism or microbiome dysbiosis.
  • Can also result from intense exercise, seizures, or certain metabolic conditions.
• D-Lactic Acidosis:
  • A rare form caused by excess D-lactate, typically in patients with short bowel syndrome or after certain intestinal surgeries.
  • Human enzymes cannot efficiently metabolize D-lactate, so it can accumulate and cause neurological symptoms (encephalopathy)

In Home Treatment Options for Normal Acidosis

The common approaches include:

• Vitamin B1 or Thiamine : A deficiency of this vitamin can cause lactic acid issues
  • An Overview of Type B Lactic Acidosis Due to Thiamine (B1) Deficiency. [2023]
• Sodium bicarbonate: may help correct pH imbalance that can contribute
  • Sodium bicarbonate for the treatment of lactic acidosis [2020]
• Water / hydration: Goal is to urinate out the excessive lactic acid
• Stop any medication associated, to do this do google search or use perplexity.ai naming your medication or supplement and asking if lactic acidosis can be cause by it. Example below

Treatment Options for d-Lactic Acidosis

“Symptoms typically present after the ingestion of high-carbohydrate feedings. Neurologic symptoms include altered mental status, slurred speech, and ataxia, with patients often appearing drunk. Onset of neurologic symptoms is accompanied by metabolic acidosis and elevation of plasma D-lactate concentration. “

D-lactic acidosis [2005]

From D-lactic acidosis: an underrecognized complication of short bowel syndrome [2015]

• “Treatment includes correcting the acidosis and decreasing substrate for D-lactate such as carbohydrates in meals. In addition, antibiotics can be used to clear colonic flora.”
• “Oral antibiotics that are poorly absorbed are most effectively used locally in the gut—these include clindamycin, vancomycin, neomycin, and kanamycin” 
• “There have been reports as described above regarding probiotics being implicated as a causative agent in a few cases of D-la”

Bottom line for d-Lactic Acidosis

• Reduce or eliminate carbohydrates
• Antibiotics
• Avoid probiotics
• Get a detailed microbiome report (ideally shotgun) to identify candidate bacteria and then alter diet appropriately.

The following bacteria are cited in studies of d-lactic acidosis

• Methylomonas sp. DH-1   ⬆️    
• Pseudomonas syringae   ⬆️    
• Salmonella   ⬆️    
• Escherichia coli   ⬆️    
• Salmonella enterica subsp. enterica serovar Enteritidis   ⬆️    
• Neisseria meningitidis   ⬆️    
• Listeria monocytogenes   ⬆️    
• Sporolactobacillus   ⬆️ ⬆️    
• Staphylococcus aureus   ⬆️    
• Streptococcus pneumoniae   ⬆️ ⬆️    
• Lactococcus lactis   ⬆️    
• Lactococcus piscium   ⬆️    
• Leuconostoc   ⬆️ ⬆️    
• Weissella   ⬆️ ⬆️    
• Leuconostoc citreum   ⬆️    
• Leuconostoc mesenteroides   ⬆️ ⬆️ ⬆️    
• Leuconostoc gelidum   ⬆️    
• Lactobacillus acidophilus   ⬆️ ⬆️    
• Lactobacillus delbrueckii   ⬆️ ⬆️ ⬆️ ⬆️    
• Lactobacillus gasseri   ⬆️ ⬆️    
• Lactobacillus johnsonii   ⬆️ ⬆️    
• Lactobacillus crispatus   ⬆️    
• Lactobacillus delbrueckii subsp. bulgaricus   ⬆️    
• Limosilactobacillus reuteri   ⬆️    
• Limosilactobacillus fermentum   ⬆️    
• Limosilactobacillus mucosae   ⬆️    
• Lacticaseibacillus rhamnosus   ⬆️    
• Lacticaseibacillus casei   ⬆️    
• Lacticaseibacillus rhamnosus GG   ⬆️    
• Lactiplantibacillus plantarum   ⬆️ ⬆️ ⬆️ ⬆️ ⬆️ ⬆️    
• Ligilactobacillus salivarius   ⬆️ ⬆️    
• Lentilactobacillus buchneri   ⬆️    
• Weissella soli   ⬆️    

A theoretical diet to alter bacteria reported in studies is shown below

Probiotic/Bacteria Solution

In response to this post, this was a comment.

See what encourage it here. MAKE SURE TO EXCLUDE everything that could contain d-lactic producing probiotics (i.e. ANY probiotics, i.e. Yogurt). Items that modifies Veillonella are there.

Studies supporting this:

• Meta’omic analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism [2020]
• Impact of probiotic Veillonella atypica FB0054 supplementation on anaerobic capacity and lactate [2023]

The product is V•Nella contains the proprietary FitBiomics strain Veillonella atypica. 

This is part of a series on Analysis Posts on Long COVID and ME/CFS

Back Story

I’d love some additional help, please. I’ve done two BiomeSight.com tests. I followed the suggestions after the first test and my microbiome has changed and some of my symptoms are improving. However, I couldn’t tolerate any of the bifidobacterium strains I tried, all of them caused very painful long-lasting migraines. Despite taking them for a combined 6wks (3 different strains for 2wks each), my bifidobacterium levels look unchanged. The suggestions do say that ‘No Probiotics without some adverse risks could not be identified.’ so maybe it’s better I just avoid them altogether for now?

• I was diagnosed with ME/CFS 16yrs ago, after EBV 22yrs ago. 
• I caught Covid-19 in 2023.
• I was diagnosed with chronic migraines in 2024 – they have increased in severity and occurrence over the last 5yrs, since the Covid-19 vaccines, though I can’t be sure it’s related.
• My primary symptoms are: fatigue, pem, migraines, brain fog, ibs, acne, and hair loss. 

I give my permission to use the above information anonymously for a blog post.

Analysis

I smiled when I saw ” ‘No Probiotics without some adverse risks could not be identified” and “I couldn’t tolerate any of the bifidobacterium strains I tried“. It seems that the expert system are making good (probable) suggestions. Suggestions are based on odds and not guaranteed.

Pass 1 – Based on Reported Symptoms

When there are many symptoms, my usual path is to get symptoms entered and then get suggestions focused on the bacteria likely associated to those symptoms. This is a targeted approach.

This person had entered any symptoms for their latest sample, and did for the sample from 7 months prior. 4-9 months between samples is what I advocate (balancing costs and time to change the microbiome).

I usually check all of the types of suggestions (I have no ideological position against using any of the types)

Then on the resulting page we see 12 bacteria that are the most likely causes. 2 low and 10 high. Suggestions are computed using five(5) different algorithms and then we use Monte Carlo Model to improve the odds of making good choices. Why different algorithms — simple, microbiome tests are fuzzy in their identification and many different criteria for selecting bacteria are advocated in the literature.

We go to the Consensus Suggestions and sort by Take Count — to get what all agrees about.

Looking at positive 5’s only:

• Vitamins
  • Vitamin B2
  • Vitamin B1
  • Zinc
• Amino Acid
  • Melatonin
  • Carnitine
  • Glutamine
  • Taurien
• Antibiotic (Only 5’s)
  • loperamide hydrochloride  Loperamide is most commonly used to treat acute and chronic diarrhea, including traveler’s diarrhea and diarrhea associated with inflammatory bowel disease (IBD).
  • florfenicol.  Florfenicol is effective against a wide range of bacterial pathogens in animals, including both Gram-positive and Gram-negative bacteria. It is commonly used to treat respiratory infections, gastrointestinal infections, urinary tract infections, and other bacterial infections in livestock and companion animals
  • Atorvastatin Atorvastatin belongs to a class of medications known as statins, which work by inhibiting HMG-CoA reductase, an enzyme involved in cholesterol synthesis. By reducing cholesterol production in the liver, atorvastatin helps lower total cholesterol, LDL cholesterol (often referred to as “bad” cholesterol), and triglyceride levels.
• Common Supplements and Herbs
  • Quercetin
  • {Nobiletin (oranges and lemons)}
  • Luteolin
  • Gallate (Gallic acid)
  • Epicatechin
  • Rosemary
  • Bitter Gourd
  • Camellia
  • Gingko
  • Chitooligosaccharides
  • Cannabinoids
• Diet. I usually ignore because of the lack of precision. Usually I keep to foods
  • Low fiber diet, Low fat, high-complex carbohydrate diet
• Foods: This gives better guidance
  • Mulberry
  • Blueberry
  • Chokeberry
  • Lemon
  • Broccoli
  • Cabbage
  • Dark Greens
  • Doenjang
  • Rice (a 4 – 0 )
  • These two should be done with caution because of probiotic bacteria in them
    • Kimchi
    • Kefir
• Probiotics
  • Lactobacillus mucosae (Not available retail 🙁 )
  • Bifidobacterium longum subsp. longum BB536 {BB536}
  • Lacticaseibacillus rhamnosus {l. rhamnosus}
  • Lentilactobacillus kefiri {Kefibios} — available in Italy only at present
  • Ligilactobacillus salivarius {L. salivarius}
  • Lacticaseibacillus paracasei {L.paracasei}
• Sugars
  • Chitosan
  • Lactulose

It is interesting that Lactobacillus dominate with just one Bifidobacterium. I would carefully try these, one at a time, starting with a low dosage and increases, then change every 1-2 week to the next (keeping notes!!!), My preferred source of probiotics are listed here.

Pass 2 – Based on PubMed

I view this method as less accurate but the suggestions are ideal for discussion with a MD if antibiotics or other prescription items are suggested. It is available as the last item.

Rather than detailing items, I attached the report below

Feedback of Above

Thank you very much! That’s incredibly helpful.

I’ll give this new round of suggestions a go, and then I’ll do another test.

I don’t have a willing GP (or vet, lol) to prescribe antibiotics but it’s very interesting that statins suggested – high cholesterol runs in my family and a lot of them are on statins. 

The cholesterol issues are often DNA related… and DNA also impacts the microbiome. DNA is hard to change, the microbiome is easier.

From Perplexity:
High cholesterol levels can indeed be influenced by genetic factors, with both common and rare gene variants playing significant roles in LDL cholesterol regulation. Here’s a breakdown of the genetic mechanisms involved:

Key Genes Affecting Cholesterol

1. LDLR (LDL Receptor)
   Mutations in this gene (chromosome 19) disrupt LDL cholesterol clearance, causing familial hypercholesterolemia (FH). This autosomal dominant condition leads to lifelong elevated LDL levels (200–300% higher in heterozygotes) due to defective receptor production or function126.
2. APOB (Apolipoprotein B)
   Mutations in APOB impair LDL binding to receptors, reducing clearance. For example, the APOB variant causing “familial ligand-defective apoB-100” increases LDL by 200–300%17.
3. PCSK9
   Gain-of-function mutations in this gene degrade LDL receptors excessively, raising LDL levels. Conversely, loss-of-function variants (e.g., in 2% of African Americans) lower LDL by 30% and protect against heart disease168.
4. APOE (Apolipoprotein E)
   Common isoforms (E2, E3, E4) influence LDL levels:
   • E4 carriers have ~5% higher LDL due to rapid lipoprotein clearance and LDLR downregulation.
   • E2 carriers have ~5% lower LDL but risk familial dysbetalipoproteinemia13.

Inherited Disorders

• Familial Hypercholesterolemia (FH):
  Caused by mutations in LDLR, APOB, or PCSK9. Affects ~1/250 people, leading to LDL >190 mg/dL and premature atherosclerosis if untreated146.
• Familial Hypobetalipoproteinemia:
  APOB mutations reduce LDL production, resulting in very low cholesterol levels13.
• Autosomal Recessive Hypercholesterolemia:
  Rare ARH mutations cause LDL receptor dysfunction, leading to severe cholesterol elevation1.

Polygenic Influences

Most hypercholesterolemia cases involve interactions between multiple common variants (e.g., APOE, NPC1L1) and lifestyle factors. These variants individually exert small effects but collectively contribute to cholesterol variability137.

While genetics set baseline risks, diet and exercise remain critical for management, especially in individuals with predisposing variants368. Genetic testing is recommended for suspected FH to guide early intervention

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.