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.
Microbiome Prescription has a rich collection of annotated samples from different labs (uBiome, Ombre, Biomesight). The samples are annotated with self declared symptoms from a list of 548 different symptoms/diagnosis. 328 symptoms had statistically significant associations.
Biomesight: 4169 samples
Ombre: 1514 samples
uBiome: 795 samples
There are several possibility of associations to these symptoms, including:
Bacteria Association
Enzyme Association
Metabolite Association which we can decompose into
Production
Substrate (Consumers)
Net Metabolite (Production – Consumer)
For each of these 5 vectors, we use these three statistical methods and set out criteria to p < 0.005:
Fisher’s exact test on prevalence of bacteria
Mann Whitney Wilcoxon Test
t-Test on Means
We used KEGG.JP data as a poor man method of compute metabolites.
Below we have counts of the associations found. It is clear that bacteria associations are weaker(fewer) than Enzymes by a factor of 4-10. With metabolites, the net metabolite appears a poorer estimator than either producers or substrates.
As would be expected, large population, we find more associations as the population increases.
This post deals with lab results that can not be uploaded for anyone of dozens of reasons. the current list is shown below. I am going to do a walkthru in 3 manners for the latest addition SynLab (EU):
The usual best practice is to PRINT the report from the lab and have a pen handy.
First step is to go thru and circle the high and low.
For High, if you are more than DOUBLE the high of the range, put 2 up arrow
For Low, if you are less than HALF the high of the range, put 2 up arrow
The next step can become a little confusing because the same bacteria may have multiple names – your lab uses one, Microbiome Prescription uses another name. We use the standardized names from the NCBI Taxonomy Browser because those names are used by most labs.
To help resolve this issue, we often list the bacteria in the same sequence as on the report.
Some Reports will list one bacteria at many places which can add to confusion
Some bacteria do not have matches…
CAG names are produced by an alternative naming schema that do not map to any NCBI Ids
Often strains are given, since they do not precisely match, we ignore them and go with the species or genus instead (“closest match”)
For some genus, the alternative schema breaks things down into _A, _B, _C, _D subgroups. We ignore those
Since we are entering ONLY abnormal, then use an that are abnormal when there are many to choose from!
We also give some of the alternative names to the right side. If it is a 2 part name, the second part is usually key to making a match
Next we indicate whether the lab says too high or too low. If normal, do nothing. This is made easier if you have a printed copy that has been marked up.
Once you finished entering the data, fill in the bottom. and then clock Do Analysis. You do not need to enter any emails if you wish, but if you want to explore options later, it saves having to re-enter the data.
The Do Analysis will take you to a page to select what type of modifiers you want, etc.
Video using a PC
This is a long video (40 minutes) that does the entire long test results.
We have two self reported symptoms with sufficient samples to explore associations:
Comorbid: Histamine or Mast Cell issues
Official Diagnosis: Mast Cell Dysfunction
I have done simplified tables below. One item that was very interesting is that some Bifidobacterium was too high and others too low. Of the four low bacteria, only Bifidobacterium breve is available commercially. Low Lactobacillus was not reported anywhere and high Lactobacillales is reported
Too High
Bifidobacteriaceae
Bifidobacteriales
Bifidobacterium
Bifidobacterium adolescentis
Bifidobacterium adolescentis JCM 15918
Bifidobacterium angulatum
Bifidobacterium gallicum
Too Low
Bifidobacterium breve
Bifidobacterium catenulatum PV20-2
Bifidobacterium catenulatum subsp. kashiwanohense
Bifidobacterium cuniculi
Everything below is P < 0.005 (or 1 in 200 of happening at random).
Official Diagnosis: Mast Cell Dysfunction
Biomesight
Bacteria
Rank
Shift
Anaerofustis
genus
Too High
Anaerofustis stercorihominis
species
Too High
Luteibacter
genus
Too Low
Luteibacter anthropi
species
Too Low
Ombre
Bacteria
Rank
Shift
Actinomycetes incertae sedis
no rank
Too High
Comamonadaceae
family
Too High
Deinococci
class
Too High
Deinococcota
phylum
Too High
Desulfocella
genus
Too High
Desulfocella halophila
species
Too High
Emticicia
genus
Too High
Hungateiclostridiaceae
family
Too High
Hungateiclostridium
genus
Too High
Limosilactobacillus
genus
Too High
Limosilactobacillus fermentum
species
Too High
Listeria
genus
Too High
Listeriaceae
family
Too High
Methylococcaceae
family
Too High
Methylococcales
order
Too High
Microbacter
genus
Too High
Neisseriaceae
family
Too High
Neisseriales
order
Too High
Oscillatoriales incertae sedis
no rank
Too High
Paracoccaceae
family
Too High
Pseudoscillatoria
genus
Too High
Pseudoscillatoria coralii
species
Too High
Rickettsia
genus
Too High
Slackia heliotrinireducens
species
Too High
Sphingobacterium
genus
Too High
Staphylococcus
genus
Too High
unclassified Burkholderiales
family
Too High
unclassified Clostridiales
family
Too High
Varibaculum
genus
Too High
Comorbid: Histamine or Mast Cell issues
We have a lot more annotated samples on this self-reported symptoms. There is fuzziness between a pure histamine issue and a mast cell issue
Ombre
Bacteria
Rank
Shift
Absiella tortuosum
species
Too High
Actinomycetes incertae sedis
no rank
Too High
Actinopolysporales
order
Too High
Agaribacter
genus
Too High
Agaribacter marinus
species
Too High
Anaeromicropila
genus
Too High
Anaeromicropila populeti
species
Too High
Blastocatellia
class
Too High
Cerasicoccus frondis
species
Too High
Clostridium grantii
species
Too High
Comamonadaceae
family
Too High
Cryomorphaceae
family
Too High
Deinococci
class
Too High
Deinococcota
phylum
Too High
Desulfitobacteriaceae
family
Too High
Desulfitobacterium
genus
Too High
Desulfobacteriaceae
family
Too High
Desulfocella
genus
Too High
Desulfocella halophila
species
Too High
Desulfofarcimen acetoxidans
species
Too High
Desulfosporosinus
genus
Too High
Desulfuromonadaceae
family
Too High
Desulfuromonadia
class
Too High
Emticicia
genus
Too High
Fusibacter
genus
Too High
Gammaproteobacteria incertae sedis
no rank
Too High
Halopolyspora
genus
Too High
Halopolyspora alba
species
Too High
Holdemania massiliensis
species
Too High
Hydrogenibacillus
genus
Too High
Hydrogenibacillus schlegelii
species
Too High
Limosilactobacillus
genus
Too High
Limosilactobacillus fermentum
species
Too High
Listeria
genus
Too High
Listeriaceae
family
Too High
Mesomycoplasma conjunctivae
species
Too High
Methylococcaceae
family
Too High
Microbacter
genus
Too High
Microbacter margulisiae
species
Too High
Mzabimycetaceae
family
Too High
Neisseriaceae
family
Too High
Neisseriales
order
Too High
Nostocales
order
Too High
Odoribacter laneus
species
Too High
Oscillatoriales incertae sedis
no rank
Too High
Oscillibacter valericigenes
species
Too High
Paracoccaceae
family
Too High
Parasporobacterium
genus
Too High
Pedobacter
genus
Too High
Planctomycetales
order
Too High
Planctomycetia
class
Too High
Planctomycetota
phylum
Too High
Pontibacillus
genus
Too High
Pontibacillus halophilus
species
Too High
Porphyromonas somerae
species
Too High
Propioniferax
genus
Too High
Propioniferax innocua
species
Too High
Proteinivorax tanatarense
species
Too High
Pseudoramibacter
genus
Too High
Pseudoramibacter alactolyticus
species
Too High
Pseudorhodobacter
genus
Too High
Pseudoscillatoria
genus
Too High
Pseudoscillatoria coralii
species
Too High
Rhodocyclaceae
family
Too High
Rhodocyclales
order
Too High
Rickettsia
genus
Too High
Rickettsiaceae
family
Too High
Rickettsiales
order
Too High
Rickettsieae
tribe
Too High
Saccharofermentans
genus
Too High
Saccharofermentans acetigenes
species
Too High
Sedimentibacter
genus
Too High
Sphingobacterium
genus
Too High
spotted fever group
species group
Too High
Stackebrandtia nassauensis
species
Too High
Stomatobaculum
genus
Too High
Texcoconibacillus
genus
Too High
Texcoconibacillus texcoconensis
species
Too High
Thiohalobacter
genus
Too High
Thiohalobacter thiocyanaticus
species
Too High
Thiohalobacteraceae
family
Too High
Thiohalobacterales
order
Too High
Thiohalorhabdaceae
family
Too High
Thiohalorhabdales
order
Too High
Verrucomicrobiaceae
family
Too High
Weeksellaceae
family
Too High
Biomesight
Bacteria
Rank
Shift
Acidaminococcus
genus
Too Low
Acidaminococcus fermentans
species
Too Low
Actinomycetes
class
Too High
Actinomycetota
phylum
Too High
Amedibacillus
genus
Too High
Amedibacillus dolichus
species
Too High
Anaerobranca
genus
Too High
Anaerobranca zavarzinii
species
Too High
Anaerolinea
genus
Too High
Anaerolinea thermolimosa
species
Too High
Anaerolineaceae
family
Too High
Anaerolineales
order
Too High
Anaerotruncus
genus
Too Low
Anaerotruncus colihominis
species
Too Low
Archaea
superkingdom
Too Low
Atopobium fossor
species
Too Low
Azoarcus
genus
Too High
Bacteroidaceae
family
Too Low
Bacteroides
genus
Too Low
Bacteroides acidifaciens
species
Too Low
Bacteroides cellulosilyticus
species
Too Low
Bacteroides fluxus
species
Too Low
Bacteroides uniformis
species
Too Low
Bifidobacteriaceae
family
Too High
Bifidobacteriales
order
Too High
Bifidobacterium
genus
Too High
Bifidobacterium adolescentis
species
Too High
Bifidobacterium adolescentis JCM 15918
strain
Too High
Bifidobacterium angulatum
species
Too High
Bifidobacterium breve
species
Too Low
Bifidobacterium catenulatum PV20-2
strain
Too Low
Bifidobacterium catenulatum subsp. kashiwanohense
subspecies
Too Low
Bifidobacterium cuniculi
species
Too Low
Bifidobacterium gallicum
species
Too High
Bilophila
genus
Too Low
Bilophila wadsworthia
species
Too Low
Blautia
genus
Too Low
Caloramator mitchellensis
species
Too High
Candidatus Tammella caduceiae
species
Too High
Catenibacterium
genus
Too High
Catenibacterium mitsuokai
species
Too High
Cetobacterium
genus
Too High
Chloroflexota
phylum
Too High
Coprococcus
genus
Too High
Coprococcus eutactus
species
Too High
Coraliomargarita
genus
Too High
Coraliomargarita
genus
Too Low
Coraliomargarita akajimensis
species
Too High
Coraliomargarita akajimensis
species
Too Low
Coraliomargaritaceae
family
Too High
Coraliomargaritaceae
family
Too Low
Deferribacter
genus
Too High
Deferribacter autotrophicus
species
Too High
Deferribacteraceae
family
Too High
Deferribacterales
order
Too High
Deferribacteres
class
Too High
Deferribacterota
phylum
Too High
Desulfitobacterium
genus
Too Low
Desulfomonilaceae
family
Too High
Desulfomonilales
order
Too High
Desulfomonilia
class
Too High
Desulforamulus
genus
Too High
Ectothiorhodospira imhoffii
species
Too High
Entomoplasmataceae
family
Too Low
Entomoplasmatales
order
Too Low
Eubacterium limosum
species
Too High
Euryarchaeota
phylum
Too Low
Faecalibacterium
genus
Too High
Fusobacterium nucleatum
species
Too High
Hathewaya
genus
Too Low
Hathewaya histolytica
species
Too Low
Helicobacter
genus
Too High
Helicobacter
genus
Too Low
Helicobacteraceae
family
Too High
Helicobacteraceae
family
Too Low
Holdemanella
genus
Too High
Holdemanella biformis
species
Too High
Holdemania
genus
Too Low
Hoylesella loescheii
species
Too High
Hyphomicrobiales
order
Too High
Hyphomicrobiales
order
Too Low
Johnsonella
genus
Too Low
Johnsonella ignava
species
Too Low
Lachnobacterium
genus
Too High
Lactobacillales
order
Too High
Lactococcus
genus
Too High
Limosilactobacillus
genus
Too Low
Luteibacter
genus
Too High
Luteibacter anthropi
species
Too High
Lysobacter deserti
species
Too High
Mesoplasma
genus
Too Low
Mesoplasma entomophilum
species
Too Low
Methanobacteria
class
Too Low
Methanobacteriaceae
family
Too Low
Methanobacteriales
order
Too Low
Methanobrevibacter
genus
Too Low
Methanobrevibacter smithii
species
Too Low
Methanomada group
clade
Too Low
Mogibacterium vescum
species
Too High
Mollicutes
class
Too High
Mycobacteriaceae
family
Too High
Mycobacterium
genus
Too High
Mycoplasmatota
phylum
Too High
Myxococcales
order
Too High
Myxococcia
class
Too High
Myxococcota
phylum
Too High
Natranaerobiales
order
Too High
Pedobacter
genus
Too Low
Phascolarctobacterium faecium
species
Too Low
Phocaeicola
genus
Too Low
Phocaeicola massiliensis
species
Too High
Phocaeicola paurosaccharolyticus
species
Too Low
Polyangia
subclass
Too High
Prevotella dentasini
species
Too High
Prevotellaceae
family
Too High
Prosthecobacter
genus
Too High
Proteinivoraceae
family
Too High
Ruminococcus callidus
species
Too High
Schaalia naturae
species
Too High
Segatella
genus
Too High
Segatella copri
species
Too High
Segatella paludivivens
species
Too High
Shewanella upenei
species
Too High
Slackia
genus
Too High
Slackia isoflavoniconvertens
species
Too Low
Sphingobium
genus
Too High
Sutterella stercoricanis
species
Too High
Syntrophales
order
Too High
Syntrophia
class
Too High
Syntrophomonadaceae
family
Too High
Thermus
genus
Too High
Thiothrix ramosa
species
Too High
Bottom Line
The above data will eventually be incorporated into the expert system suggestions on Microbiome Prescription.
The process is very simple, for a condition like ME/CFS, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.
Actual example:
Tetragenococcus halophilus – Species reported by Biomesight
Expected to see 15
Actually seen 59
In other words almost 4x more common than expected. The probability is
1.68054690853052E-30
or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]
Biomesight and Ombre identifies bacteria using different methodologies so often give different names and amounts. For background on this lack of standardization, see The taxonomy nightmare before Christmas…
The data below is for samples marked with “Official Diagnosis: COVID19 (Long Hauler)”. Only Biomesight had sufficient data to get patterns.
Unlike some conditions shown below, it is not just one bacteria involved but combinations.
Peptic ulcer disease: Helicobacter pylori
Tetanus: Clostridium tetani
Typhoid fever: Salmonella typhi
Diphtheria: Corynebacterium diphtheriae
Syphilis: Treponema pallidum
Cholera: Vibrio cholerae
Leprosy: Mycobacterium leprae
Tuberculosis: Mycobacterium tuberculosis
Sinusitis: Corynebacterium tuberculostearicum
Biomesight Data
We have more data from Biomesight which means better (more) detection of significant bacteria. The data is very different from ME/CFS. We have 16 bacteria too high and 61 bacteria too low. With ME/CFS and the same lab, we have 12 bacteria that are too low and 116 bacteria that are too high.
We have some commonalities
Bifidobacterium adolescentis is too low for both Long COVID and ME/CFS
Lactobacillus crispatus is too high
Another probiotic genus, Lactococcus, is also too high
Tax_Name
Tax_Rank
Expected
Observed
Shift
Probability
50 kb inversion clade
clade
77.3
54
Too Low
0.008002
Acinetobacter antiviralis
species
13.7
24
Too High
0.00524
Acinetobacter johnsonii
species
18.1
30
Too High
0.004944
Actinopolyspora
genus
62.3
35
Too Low
0.001477
Actinopolysporaceae
family
62.3
35
Too Low
0.001477
Actinopolysporales
order
62.3
35
Too Low
0.001477
Aeromonadaceae
family
81.8
57
Too Low
0.006169
Alkalibacterium
genus
112.5
81
Too Low
0.005041
Anaerococcus lactolyticus
species
23.2
38
Too High
0.002205
Anaerococcus prevotii
species
20.1
33
Too High
0.003987
ant, tsetse, mealybug, aphid, etc. endosymbionts
clade
82.7
58
Too Low
0.006624
Bifidobacterium adolescentis
strain
103.5
65
Too Low
0.002509
Chromatium
genus
61.3
34
Too Low
0.00355
Chromatium weissei
species
61.2
34
Too Low
0.00355
Chromobacterium group
no rank
15.3
26
Too High
0.006127
Citrobacter
genus
64.1
41
Too Low
0.003939
Clostridium neonatale
species
13.7
25
Too High
0.002196
Cohnella
genus
108.6
78
Too Low
0.005067
Coraliomargarita
genus
96.4
70
Too Low
0.00718
Coraliomargarita akajimensis
species
96.3
70
Too Low
0.007357
core genistoids
clade
77.3
54
Too Low
0.008002
Corynebacterium striatum
species
16.9
28
Too High
0.006887
Crotalarieae
tribe
77.3
54
Too Low
0.008002
Deferribacteraceae
family
98.2
71
Too Low
0.006129
Deferribacterales
order
98.2
71
Too Low
0.006129
Deferribacteres
class
98.2
71
Too Low
0.006129
Deferribacterota
phylum
98.2
71
Too Low
0.006129
Desulfallaceae
family
148.6
108
Too Low
0.001472
Enterobacter cloacae complex
species group
86.4
60
Too Low
0.004516
Enterobacter hormaechei
species
85.4
57
Too Low
0.002134
Enterobacteriaceae incertae sedis
no rank
82.7
58
Too Low
0.006624
Erysipelothrix inopinata
species
54.2
21
Too Low
4.45E-05
Fabaceae
family
77.3
54
Too Low
0.008002
Fabales
order
77.3
54
Too Low
0.008002
fabids
clade
77.3
54
Too Low
0.008002
genistoids sensu lato
clade
77.3
54
Too Low
0.008002
Granulicella
genus
16.4
29
Too High
0.001841
Granulicella tundricola
species
16.2
29
Too High
0.00148
Hallella bergensis
species
20.1
33
Too High
0.003987
Lactobacillus crispatus
species
26.5
43
Too High
0.001406
Lactococcus
genus
161.5
201
Too High
0.001877
Leptospira
genus
89.5
61
Too Low
0.002559
Leptospira licerasiae
species
89.4
61
Too Low
0.002701
Leptospiraceae
family
89.5
61
Too Low
0.002559
Leptospirales
order
89.5
61
Too Low
0.002559
Lysinibacillus
genus
51.5
32
Too Low
0.006618
Lysinibacillus parviboronicapiens
species
50.4
29
Too Low
0.002564
Macrococcus
genus
118.9
89
Too Low
0.006111
Microbacteriaceae
family
99.5
72
Too Low
0.005912
Moorella group
norank
152.6
188
Too High
0.004132
Oxalobacter
genus
130.9
99
Too Low
0.005356
Oxalobacter vibrioformis
species
94.9
65
Too Low
0.007793
Papilionoideae
subfamily
77.3
54
Too Low
0.008002
Peptoniphilus lacrimalis
species
51.8
72
Too High
0.004884
Piscirickettsiaceae
family
51.5
29
Too Low
0.007262
Psychrobacter
genus
138.9
99
Too Low
0.001332
Psychrobacter glacialis
species
75.1
51
Too Low
0.00545
rosids
clade
77.3
54
Too Low
0.008002
Rothia
genus
77.3
54
Too Low
0.008002
Rothia mucilaginosa
species
64.1
40
Too Low
0.002631
Sporotomaculum
genus
148.6
108
Too Low
0.001472
Sporotomaculum syntrophicum
species
146.7
107
Too Low
0.001751
Streptococcus massiliensis
species
53.6
34
Too Low
0.007353
Syntrophobacteraceae
family
118.3
83
Too Low
0.00291
Tetragenococcus halophilus
species
18.0
59
Too High
3.63E-22
Thiomicrospira
genus
43.7
26
Too Low
0.007396
Tolumonas
genus
80.7
55
Too Low
0.004169
Tolumonas auensis
species
79.9
54
Too Low
0.003748
Trabulsiella
genus
59.1
37
Too Low
0.004074
Vagococcus
genus
99.2
72
Too Low
0.00718
Varibaculum cambriense
species
17.3
30
Too High
0.002302
Bottom Line
My personal view is that this pattern is not unexpected. ME/CFS microbiome is typically after years of the dysbiosis microbiome evolving. With Long COVID, we have the microbiome still trying to stabilize.
Bif. Adolescentis
And all Lactobacillus and Lactococcus probiotics should be avoided.
The above information will be eventually integrated into Microbiome Prescription suggestions expert system. The purpose is to first identify the bacteria of concern.
The following bacteria were reported by 2 or 3 of the ME/CFS analysis and the same shift seen with Long COVID.
Anaerococcus murdochii
species — sibling high in ME/CFS
Peptoniphilus lacrimalis
species – HIGH EVERYWHERE
Varibaculum
genus – HIGH EVERYWHERE
Varibaculum, particularly Varibaculum cambriense, has been identified as a potential pathogen associated with various human infections, especially in skin and soft tissues26. This anaerobic, gram-positive bacterium was first described in 2003 and has since been isolated from several clinical cases2.
A new species, Varibaculum timonense, has been isolated from human stool samples, indicating that the genus Varibaculum may have a broader presence in the human microbiome than previously recognized3.
While Varibaculum species are not yet widely known pathogens, their isolation from various infection sites suggests they may play a more significant role in human health than currently understood. Further research is needed to fully elucidate the pathogenic potential and clinical importance of these bacteria.
The process is very simple, for a condition like Long COVID, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.
Actual example:
Tetragenococcus halophilus – Species reported by Biomesight
Expected to see 15
Actually seen 59
In other words almost 4x more common than expected. The probability is
1.68054690853052E-30
or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]
Could you explain the main differences between the OLD UI and NEW UI? Sometimes the data doesn’t seem to match up well, and I’m unsure which one I should use.
I’d like to understand the symptoms sections better, as they look very different in both UIs. The old UI symptoms make much more sense for me.
I have a Biomesight test for a friend with many gut symptoms, but when I analyze the data, I’m not seeing much in terms of actionable recommendations for things to add or remove. I do see a little more in terms of statistical significance in the OLD UI. What would be the most accurate way to read the data in a case like this?
I primarily use the full consensus reporting feature in the database. Are there any advanced features or sections you think I should familiarize myself with?
What is the main difference between symptoms and medical conditions in the database? My understanding is that medical conditions are more supported by proven data, while symptoms are based more on self-reporting. Is that correct?
If possible, I’d love to see your workflow for analyzing a test.
Could you explain the main differences between the OLD UI and NEW UI? Sometimes the data doesn’t seem to match up well, and I’m unsure which one I should use.
The site evolves as I keep getting better insight on data and different ways of getting statistically significant data. In general, when I get a new insight it is added as a new feature while keeping the older approaches. The older approaches appear to work well, but I want to keep pressing forward finding “better ways”. Actually, the way may not be better for everyone — rather better for some cases. It is the classic “no algorithm works for every one”.
Using Monte Carlo Model that builds consensus suggestions, my hope is that these various approaches will net better suggestions.
I avoid dropping methods. It upsets some people. Also these older methods work well for some.
Suggestion: Use both and work with the Consensus
I’d like to understand the symptoms sections better, as they look very different in both UIs. The old UI symptoms make much more sense for me.
At present there are at least three different ways of forecasting symptoms. Most of the methods pick slightly different sets of bacteria with different weights. Forecasting symptoms depends on which regression / modelling is used. Some examples:
I have in my backlog to test each of these methods to evaluate their ability forecast symptoms. This also require tuning each of these to try to get the best accuracy in forecasting. That is likely at least a month of work (once I get the cycles).
In short, different methods were tried to detect statistical significance using both parametric and non-parametric methods. When there were a sufficient number of bacteria found significant, then a forecaster is built.
When I get time to do comparison before forecasting accuracy, the number of choices will likely be reduced.
I have a Biomesight test for a friend with many gut symptoms, but when I analyze the data, I’m not seeing much in terms of actionable recommendations for things to add or remove. I do see a little more in terms of statistical significance in the OLD UI. What would be the most accurate way to read the data in a case like this?
IMHO, the most accurate is checking the symptoms they have and use that for suggestions. It is the most likely way to pick the significant bacteria to focus on.
I usually use the metabolites and enzymes approach to select probiotics. Typically this will be the same probiotics in suggestions but in a different order. I give the probiotics suggested based on KEGG data a higher value because the suggestions above are based on what has been studied (which tends to be erratic). The KEGG data is based on the DNA/RNA of the microbiome and far less sensitive to what has been studied in clinical studies.
I primarily use the full consensus reporting feature in the database. Are there any advanced features or sections you think I should familiarize myself with?
Nothing at the moment, On the [Changing Your Microbiome] under “Suggestions for building general consensus”. These are the four most promising methods. New methods will likely be added at the bottom of this list as they are added.
What is the main difference between symptoms and medical conditions in the database? My understanding is that medical conditions are more supported by proven data, while symptoms are based more on self-reporting. Is that correct?
Medical conditions are those reported in the literature — unfortunately every study uses different processing. If the same study samples processed through a different process, different bacteria will be found significant (See The taxonomy nightmare before Christmas…). These are “best efforts” selection when we do not have sufficient data for a condition or symptoms for the specific processing lab that you are using.
The “inhouse” associations are always done using data from the same processing lab, so the identification of the “lab named bacteria” are consistent. This is the most likely to pick the right bacteria (according to the lab). One major difference is that Medical conditions are often based on 30-60 samples alone. For Biomesight data, we often have 600 samples and thus better ability to identify.
If possible, I’d love to see your workflow for analyzing a test.
After uploading my sample, it gave a chi-square score of 1116 (image attached). Does this warrant any change in treatment approach (just asking as most of the scores I’ve seen posted on your blog are below 100)?
The short answer is no. This indicate that dysbiosis is likely happening. It is likely that is already known (hence getting a test).
The Simple Logic
We look at different bacteria at the genus level. Naively, this should be the equivalent of having independent variables. For each bacteria, we get the percentile ranking (in terms of a reference population). The odds of any bacteria being in the 1-10%ile range is 1 in 10. The same applies to every other range and bacteria.
This becomes a simple statistics problems. We would expect every range to have about 10% of the genus in it.We can then calculate whether the actual distribution conforms to this expectation using Chi-Square. If there is no dysbiosis, we would expect the significance to be 0.95 or less. Many users have significance being 0.9999 or higher; that is, very strong indicator of dysbiosis.
In the above example, we have definite dysbiosis. We have a large number of bacteria that are too high percentile. We do not know the precise ones that are problematic, we have a list of possible bacteria that we would want to reduce.
Since we do not know the explicit bacteria to focus on (only a collection of candidates), we cannot generate suggestions explicitly from this information.
Technical Note: The Percentile is computed from those reporting some of each genus. The percentile could be done across all tests (i.e. not found included); that approach results in a much more complicated computation.
I view Chi-Square as a better alternative to Diversity Indices. Most diversity indices apply to only certain condition. IMHO, it is a more robust measure because it is based purely on statistics and uses a reference set.
The Shannon index has been studied in relation to:
Septic shock: A study found that low bacterial diversity (Shannon index <3.0) was associated with higher 28-day mortality rates in septic shock patients1.
General health status: The Gut Microbiome Health Index (GMHI), which incorporates the Shannon index, was used to distinguish between healthy and non-healthy individuals across various conditions2.
Parkinson’s disease: However, a study found that the Shannon index was not significantly associated with Parkinson’s disease or other neurological disorders6.
In microbiome studies, several diversity indices are frequently used to analyze the composition and structure of microbial communities. These indices can be broadly categorized into two types: alpha diversity (within-sample diversity) and beta diversity (between-sample diversity).
Alpha Diversity Indices
Shannon index: Measures both richness and evenness of species in a community.
Simpson’s index: Reflects the probability that two randomly selected individuals belong to different species.
Chao1 index: Estimates species richness, particularly useful for data sets skewed toward low-abundance classes.
Observed number of Amplicon Sequence Variants (ASVs): Counts the number of unique sequences in a sample.
Phylogenetic Diversity (PD): Considers the evolutionary relationships between species.
ACE (Abundance-based Coverage Estimator) index: Estimates species richness, accounting for rare species.
Beta Diversity Indices
Bray-Curtis dissimilarity: Considers both the presence/absence and abundance of species.
UniFrac:
Unweighted UniFrac: Considers presence/absence of species and their phylogenetic relationships.
Weighted UniFrac: Incorporates abundance information along with phylogenetic relationship.
Jaccard index: Measures the similarity between sample sets based on presence/absence of species.
These diversity indices provide different perspectives on microbial community structure and are often used in combination to gain a comprehensive understanding of microbiome diversity36.
Ken Lassesen Hi Ken, maybe you can explain this: based on my latest biomesight test, one suggestions recurs in most of the suggestions on MicrobiomePerscripitons.com: Sucralose. Sucralose is not regarded as particularly beneficial for the gut or overall health, actually it is associated with leaky gut and can decrease the diversity of bacteria. But I guess it comes up as it can modulate certain bacteria short term in a way that can potentially be beneficial for me?
Common Paths Starting Points
I have seen the following being very common:
[A] You complain about symptoms and a friend speculate that you have X, for example “Acid Stomach”
[B] You see a medical professional, often a naturopath, and the say “You appear to have T” example, “Gluten Issues”
[C] You see a medical professional, who perform an extensive list of tests. These tests results precisely match a known condition. example: Heliobacter pylori causing peptic ulcer.
[D] You go the “self-serve” approach using microbiome tests and ‘heal thyself’. Borrowing from Hippocrates: ‘First do no harm‘, ‘Let food be thy medicine and medicine be thy food’, ‘Walking is the best medicine’ and ‘All diseases begin in the gut‘.
Often this is the result of disappointment or non-availability of [A],[B] and [C].
Typical Treatment Path for [A]
This is usually done by following friends suggestions or random searching of the internet for solutions. In short, it is an influencer treatment plan. Sometimes these treatment will work; the majority of people will get short term relief at best, if any,
Typical Treatment Path for [B]
This is usually done by the medical professionals working off their clinical experience and suggesting what they perceived to work. This is rarely objective, rather subjective. Their decisions are based on their view through rose-color glasses.
A simple example: “Jill Muller came to see me and said she would follow my advice. She did not come for a follow-up appointment –hence my treatment advice worked!” Reality, Jill followed the advice and became much worse, she concluded that this medical practitioner does not know what they are talking about and went elsewhere. To the practitioner, the lack of more appointments is proof that their treatment plan worked very well.
Sometimes these treatment will work; the majority of people will get short term relief at best, if any,
Typical Treatment Path for [C]
This is usually done by the medical professionals working off their clinical experience influenced by clinical studies and pharmaceutical sales representatives. For many conditions, these treatment will work to either cure or slow progression. These practitioner knows exactly what their target is. There can often be failure or less than desired progress because the current body of approved treatments is insufficient.
Two examples that I am personally familiar with are Mast Cell Activation Syndrome and Crohn’s Disease. Many other conditions like Autism, Depression, Anxiety, Alzheimer’s Disease, etc.
As with all of the above, when the treatment fails or is insufficient, path [D] is often taken
Typical Treatment Path for [D]
Following Hippocrates, All diseases begin in the gut. The problem is that despite having microbiome test results, we do not have clarity on what the target is.
From personal experience, I took a uBiome test and downloaded their FASTQ file and then processed it through:
Thryve/Ombre
BiomeSight
Sequentia Biotech
I got 4 sets of interpretations of this digital microbiome sample. This was not taking 4 samples and sending it to these labs (hence differences could be ascribed to where the sample was taken in the stool), but one sample, processed into a single digital description and then processed.
There was less than 5% agreement on which bacteria were out of range. In one case, a bacteria was too high according to one lab and too log according to another lab. For more information see: The taxonomy nightmare before Christmas…
Influencer versus Single Study of Single Bacteria versus Holistic
I view an influencer as a person who looks at a single microbiome test (from random lab) and say “It looks like …. and you should take ….”. The basis of the recommendation is often internet legend. When an influencer is asked to provide studies supporting their recommendation, there is typically no answer. IMHO, quod erat demonstrandum on this being internet legend.
A medical professional, often a naturopath, or a well-educated self-learner may provide a study supporting their suggestions. Often this study will show that one and only one bacteria is changed by their suggestions. If you ask these people for a study showing that the same suggestions will help a different bacteria that is out of reference ranges, it is very unlikely that they can provide one.
I term this as the “bacteria tunnel vision” version.
The Holistic or Microbiome Prescription approach is to accept two things:
Decisions should be based on all available studies (at present 98,841direct citations generating 2,301,058 expert system rules)
This volume of data to consider require software and not brain-ware
The suggestions generated should always be reviewed by a knowledgeable and experienced medical professional before doing. Dosages are often a critical factor (more is not better always). There can be significant medical conditions that would exclude some suggestions. The suggestions are not “this is a protocol”, rather this is a list of things that are more likley to help than hurt — do whatever you feel comfortable with.
Returning to the question
To speed an accurate response, I used Perplexity and ask for studies.
Perplexity on sucralose consumption and leaky gut
From perplexity:
Recent studies have shown a potential association between sucralose consumption and leaky gut syndrome, although the evidence is not yet conclusive from clinical studies in humans.
Laboratory and Animal Studies
Several laboratory and animal studies have demonstrated concerning effects of sucralose on gut health:
A study led by North Carolina State University found that sucralose and its metabolite sucralose-6-acetate damaged the junctions between intestinal cells, potentially causing leaky gut17.
In mice, sucralose administration for 6 months altered gut microbiome composition and increased pro-inflammatory gene expression in the liver, which could contribute to inflammation and gut permeability4.
Another mouse study showed that even low doses of sucralose altered gut microbiome composition, affecting intestinal barrier function8.
Human Studies
While direct clinical evidence in humans is limited, some studies suggest potential effects:
A randomized, placebo-controlled study found that acute intake of sucrose, but not sucralose, resulted in post-prandial endotoxemia (increased blood endotoxin levels). However, this study did not directly measure gut permeability5.
Researchers have called for revisiting the safety and regulatory status of sucralose based on its potential genotoxicity and effects on gut health7.
It’s important to note that more comprehensive clinical studies in humans are needed to definitively establish the link between sucralose consumption and leaky gut syndrome. The current evidence, while concerning, is primarily based on laboratory and animal studies, which may not directly translate to human health outcomes.
Look at “acute intake of sucrose, but not sucralose” above. Microbiome Prescription does not give dosages except for a few items, and those are references to clinical studies only. My take would be moderate intake, not excessive dosages. The appropriate dosage should be coming from your knowledgeable and experienced medical professional
Can sucralose decrease the diversity of bacteria, with studies
From perplexity:
Yes, studies have shown that sucralose can decrease the diversity of gut bacteria:
A 2022 study on mice found that sucralose administration altered gut microbiome diversity. The number of bacterial features and Shannon index (a measure of diversity) showed a downward trend in groups given higher doses of sucralose compared to the control group1.
Research on rats demonstrated that a 1.5 mg/mL concentration of sucralose led to the lowest α-diversity in gut microbiota, indicating a reduction in bacterial diversity1.
A human study conducted over 10 weeks showed that sucralose consumption altered the abundance of certain bacterial phyla, specifically increasing Firmicutes while not affecting Actinobacteria or Bacteroidetes. It also led to a 3-fold increase in Blautia coccoides and a 0.66-fold decrease in Lactobacillus acidophilus2.
A 6-month study on mice revealed that sucralose altered the gut microbiome composition, affecting 14 different bacterial genera over time compared to the control group5.
While these studies provide evidence for sucralose’s impact on gut bacterial diversity, it’s important to note that more comprehensive clinical studies in humans are needed to fully understand the long-term effects of sucralose on gut microbiota diversity.
I agree that it changes bacteria — that is what the goal is. Again note “higher doses of sucralose“. The bacteria changed are listed here.
What is the appropriate dosage
Again from perplexity with reference. If you keep to these limits, you should be fine.
The healthy daily amount of Splenda (sucralose) is determined by regulatory bodies based on extensive safety studies. Here are the key points regarding the recommended daily intake:
The U.S. Food and Drug Administration (FDA) has established an Acceptable Daily Intake (ADI) for sucralose of 5 mg per kilogram of body weight per day37.
The European Food Safety Authority (EFSA) and the Joint FAO/WHO Expert Committee on Food Additives (JECFA) have set a higher ADI of 15 mg per kilogram of body weight per day12.
These ADI levels are considered conservative, representing an amount 100 times less than the quantity found to have no observed adverse effects in toxicology studies3.
To put this into perspective:
For a 150-pound (68 kg) person, the FDA’s ADI would be equivalent to consuming about 340 mg of sucralose daily3.
This translates to approximately 23 individual packets of Splenda per day, well above typical consumption levels37.
It’s important to note that:
Current estimated intake levels are well below these ADIs. A conservative mean estimate of sucralose intake from beverages among adults in the U.S. is about 1.6 mg/kg of body weight per day3.
The ADI applies to all population groups, including children, pregnant women, and individuals with medical conditions1.
While these guidelines suggest that Splenda is safe when consumed within these limits, recent recommendations from the World Health Organization (WHO) advise against the use of non-sugar sweeteners for weight control4. As with any dietary component, moderation is key, and individuals should consider their overall diet and health goals when consuming artificial sweeteners.
Bottom Line
A lot of the answers came from https://www.perplexity.ai/. I use this resource heavily to get summaries with references to data sources and then always check the source to make sure that this AI did not misread the study.
To illustrate this, we use our collection of distinct microbiome samples processed through BiomeSight (N: 3656).
Species: Phocaeicola massiliensis
Basic Statistics;
Minimum: 0.001 %
Maximum: 89.1%
Median: 0.254%
Mean / Average: 7.6%
Mode: 12.4%
Standard Deviation: 14.6%
5 Percentile: 0.009%
95 Percentile: 43.7%
Harmonic Mean: 0.035%
Geometric Mean: 0.445%
Skew: 1.5
Kurtosis: 0.035
When we apply Stats Class 101 methods, we get:
Mean +/- 1.95 SD ==> (-21% to 36.2%)
Box-Plot-Whiskers ==> (-9.4%, 15.8%)
WAIT: Having negative amount of bacteria!!! That is absurd!
What we should see if data was normal
Wait, Mean, Median and Mode should be next door to each other!!!
What do we see when we chart this data. The charts are identical — NOT!
What should be used to compute range?
There are many better suited statistical methods. A few are:
Kolmogorov-Smirnov test
Kruskal-Wallis test
Wilcoxon signed-rank test
Mann-Whitney U test
Bothe/Z-scores
Median Absolute Deviation
My Preference: Patent Pending Kaltoft Moldrup Algorithm
The basis of it is doing a data transformation, then taking derivates to get an almost straight line. When the data leaves the line is where it is deemed to be abnormal. The following diagrams illustrates the process.
Example: Original Data
2nd derivative line
3rd derivate line
4th derivative line (where we see the desired straight line in purple)
An example with real data. Most of the abnormal data is at the bottom in this example
Another more complex example indicating more complexity in the bacteria behavior in situ of the microbiome.
Another example showing both high and low abnormal areas
Bottom Line
Many suggested ranges are based on mean and never tests if methods that apply to a normal distribution/ bell curve applies. A small number of ranges are based on percentiles, i.e. over 95%ile or below 5%ile. Using percentiles is better but as suggested by the last curves above, this does not suggest evidence of being abnormal.
The patent pending Kaltoft Moldrup Algorithm appears to identify abnormal values in the classic sense of abnormal. It does require significant mathematical and statistical skills.
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