This is derived from “Asymptomatic: No Health Issues” Odd-Ratio Model (See this post). These ranges should apply to any test. It applies to only bacteria that are reported by the test.

If you are interested in the mathematics see my prior posts.

Tax_Name	Tax_Rank	(No column name)
Bacteroidota/Chlorobiota group	clade	Above 10.6%ile
delta/epsilon subdivisions	clade	Above 9.4%ile
Bacteria candidate phyla	clade	Above 54%ile
PVC group	clade	Above 6.7%ile
FCB group	clade	Above 10.6%ile
Terrabacteria group	clade	Above 10.6%ile
Cyanobacteriota/Melainabacteria group	clade	Above 13.5%ile
Methanomada group	clade	Above 5.9%ile
Balneolia	class	Above 49.4%ile
Chitinophagia	class	Above 15%ile
Desulfobacteria	class	Above 26.7%ile
Cyanophyceae	class	Above 12.3%ile
Desulfovibrionia	class	Above 8.9%ile
Ignavibacteria	class	Above 59.5%ile
Cytophagia	class	Above 15.8%ile
Negativicutes	class	Above 10.5%ile
Lentisphaeria	class	Above 25%ile
Tissierellia	class	Above 6.3%ile
Bacteroidia	class	Above 10.4%ile
Verrucomicrobiia	class	Above 2.3%ile
Fibrobacteria	class	Above 1.1%ile
Dehalococcoidia	class	Above 8.9%ile
Caldilineae	class	Above 4%ile
Erysipelotrichia	class	Above 10.6%ile
Holophagae	class	Above 3.4%ile
Synergistia	class	Above 0.8%ile
Acidimicrobiia	class	Above 2.1%ile
Coriobacteriia	class	Above 10.3%ile
Bacilli	class	Above 10.5%ile
Flavobacteriia	class	Above 6.1%ile
Sphingobacteriia	class	Above 9.6%ile
Methanobacteria	class	Above 5.9%ile
Clostridia	class	Above 10.4%ile
Gammaproteobacteria	class	Above 9.4%ile
Actinomycetes	class	Above 10.6%ile
Alphaproteobacteria	class	Above 5.7%ile
Betaproteobacteria	class	Above 9.9%ile
Deltaproteobacteria	class	Above 8.6%ile
Myxococcia	class	Above 1.6%ile
Lysobacteraceae	family	Above 3.9%ile
Bifidobacteriaceae	family	Above 7.7%ile
Veillonellaceae	family	Above 9.7%ile
Clostridiaceae	family	Above 10.5%ile
Lactobacillaceae	family	Above 9.1%ile
Halomonadaceae	family	Above 5.3%ile
Actinomycetaceae	family	Above 5.2%ile
Mycoplasmataceae	family	Above 8.3%ile
Spiroplasmataceae	family	Above 48.8%ile
Acholeplasmataceae	family	Above 9.6%ile
Methanobacteriaceae	family	Above 5.9%ile
Streptococcaceae	family	Above 10%ile
Corynebacteriaceae	family	Above 7.4%ile
Moraxellaceae	family	Above 0.8%ile
Enterobacteriaceae	family	Above 6.9%ile
Pasteurellaceae	family	Above 3.9%ile
Bacteroidaceae	family	Above 10.4%ile
Chromatiaceae	family	Above 4.5%ile
Lachnospiraceae	family	Above 10.7%ile
Peptostreptococcaceae	family	Above 10.5%ile
Eubacteriaceae	family	Above 3.7%ile
Peptococcaceae	family	Above 7.1%ile
unclassified Clostridiales	family	Above 49.8%ile
Thermoanaerobacteraceae	family	Above 2.7%ile
Bacillaceae	family	Above 1.7%ile
Planococcaceae	family	Above 15.8%ile
Paenibacillaceae	family	Above 3.1%ile
Thermoactinomycetaceae	family	Above 9.5%ile
Piscirickettsiaceae	family	Above 1.3%ile
Rikenellaceae	family	Above 26.8%ile
Porphyromonadaceae	family	Above 7.4%ile
Prevotellaceae	family	Above 6.9%ile
unclassified Burkholderiales	family	Above 22.7%ile
Cytophagaceae	family	Above 12.4%ile
Erysipelotrichaceae	family	Above 10.6%ile
Pseudomonadaceae	family	Above 15.7%ile
Acidimicrobiaceae	family	Above 2.4%ile
Nocardioidaceae	family	Above 18.2%ile
Microbacteriaceae	family	Above 18.8%ile
Sphingobacteriaceae	family	Above 9.8%ile
Coriobacteriaceae	family	Above 10.1%ile
Syntrophomonadaceae	family	Above 3.6%ile
Hyphomonadaceae	family	Above 37.4%ile
Rhodocyclaceae	family	Above 1.5%ile
Enterococcaceae	family	Above 1.4%ile
Rhodospirillaceae	family	Above 4.1%ile
Sphingomonadaceae	family	Above 12.2%ile
Flavobacteriaceae	family	Above 6.2%ile
Halobacteroidaceae	family	Above 36.6%ile
Holophagaceae	family	Above 3.3%ile
Kiloniellaceae	family	Above 2.8%ile
Bacillales Family X. Incertae Sedis	family	Above 10.2%ile
Gemellaceae	family	Above 0.6%ile
Ruminococcaceae	family	Above 11.1%ile
Eubacteriales Family XII. Incertae Sedis	family	Above 2.6%ile
Eubacteriales Family XIII. Incertae Sedis	family	Above 10.1%ile
Marinilabiliaceae	family	Above 57.3%ile
Cyclobacteriaceae	family	Above 29%ile
Chitinophagaceae	family	Above 15.6%ile
Caldilineaceae	family	Above 4%ile
Desulfarculaceae	family	Above 23.8%ile
Victivallaceae	family	Above 35.8%ile
Verrucomicrobiaceae	family	Above 1.4%ile
Desulfovibrionaceae	family	Above 8.5%ile
Desulfomicrobiaceae	family	Above 9%ile
Desulfobacteraceae	family	Above 25.9%ile
Oscillospiraceae	family	Above 10.4%ile
Akkermansiaceae	family	Above 3.5%ile
Peptoniphilaceae	family	Above 6.5%ile
Eggerthellaceae	family	Above 10.1%ile
Melioribacteraceae	family	Above 71.4%ile
Proteinivoraceae	family	Above 4.2%ile
Acidaminococcaceae	family	Above 6.4%ile
Christensenellaceae	family	Above 11.5%ile
Sutterellaceae	family	Above 9.7%ile
Desulfurisporaceae	family	Above 11.3%ile
Coprobacillaceae	family	Above 5.6%ile
Turicibacteraceae	family	Above 6.8%ile
Tepidimicrobiaceae	family	Above 48%ile
Kribbellaceae	family	Above 2.6%ile
Azospirillaceae	family	Above 8.6%ile
Rhodovibrionaceae	family	Above 17.8%ile
Thalassospiraceae	family	Above 19.6%ile
Thermohalobacteraceae	family	Above 80.4%ile
Metamycoplasmataceae	family	Above 1.9%ile
Spirosomataceae	family	Above 1.6%ile
Desulfitobacteriaceae	family	Above 11%ile
Desulfotomaculaceae	family	Above 1.9%ile
Tissierellaceae	family	Above 34.5%ile
Sporomusaceae	family	Above 6.7%ile
Balneolaceae	family	Above 49.5%ile
Hungateiclostridiaceae	family	Above 5.6%ile
Odoribacteraceae	family	Above 8.7%ile
Pectobacteriaceae	family	Above 25.9%ile
Morganellaceae	family	Above 20%ile
Barnesiellaceae	family	Above 24.7%ile
Dysgonomonadaceae	family	Above 10.8%ile
Tannerellaceae	family	Above 9.6%ile
Desulfofarcimen	genus	Above 9.3%ile
Desulfohalotomaculum	genus	Above 35.9%ile
Clostridioides	genus	Above 45.8%ile
Hungateiclostridium	genus	Above 37.3%ile
Mediterraneibacter	genus	Above 18%ile
Schaalia	genus	Above 6%ile
Anaerocolumna	genus	Above 66.8%ile
Hathewaya	genus	Above 11%ile
Segatella	genus	Above 15.4%ile
Hoylesella	genus	Above 0.7%ile
Leyella	genus	Above 5.5%ile
Paracholeplasma	genus	Above 71.1%ile
Oleidesulfovibrio	genus	Above 54.4%ile
Limosilactobacillus	genus	Above 13.2%ile
Thomasclavelia	genus	Above 23.3%ile
Christensenella	genus	Above 11.9%ile
Pseudoflavonifractor	genus	Above 24.6%ile
Melioribacter	genus	Above 73.3%ile
Murdochiella	genus	Above 34.1%ile
Alloprevotella	genus	Above 4.3%ile
Flavonifractor	genus	Above 31.8%ile
Phocaeicola	genus	Above 10.1%ile
Cellulosilyticum	genus	Above 71.6%ile
Romboutsia	genus	Above 25.9%ile
Terrisporobacter	genus	Above 32.4%ile
Intestinibacter	genus	Above 41.9%ile
Erysipelatoclostridium	genus	Above 34%ile
Ruminiclostridium	genus	Above 13.6%ile
Brassicibacter	genus	Above 83.9%ile
Fretibacterium	genus	Above 13.3%ile
Candidatus Soleaferrea	genus	Below 47.5%ile
Kosakonia	genus	Above 14.3%ile
Coprobacter	genus	Above 26.9%ile
Intestinimonas	genus	Above 30.4%ile
Fusicatenibacter	genus	Above 35.9%ile
Acetatifactor	genus	Below 52.3%ile
Eisenbergiella	genus	Above 31.1%ile
Aestuariispira	genus	Below 24.8%ile
Dielma	genus	Above 23.1%ile
Senegalimassilia	genus	Above 26.6%ile
Peptoclostridium	genus	Above 53.5%ile
Holdemanella	genus	Above 9.7%ile
Ezakiella	genus	Above 44.4%ile
Aminicella	genus	Below 30.1%ile
Hungatella	genus	Above 66.5%ile
Herbinix	genus	Above 36.5%ile
Fenollaria	genus	Above 71.9%ile
Pseudoscardovia	genus	Above 75.6%ile
Casaltella	genus	Above 49.4%ile
Faecalibacterium	genus	Above 10.3%ile
Alistipes	genus	Above 29%ile
Akkermansia	genus	Above 3.1%ile
Gemmiger	genus	Below 45.3%ile
Anaerostipes	genus	Above 10.6%ile
Acetoanaerobium	genus	Above 49.4%ile
Dorea	genus	Above 10.7%ile
Sedimentibacter	genus	Above 2.8%ile
Turicibacter	genus	Above 7.4%ile
Marvinbryantia	genus	Above 31.9%ile
Ethanoligenens	genus	Above 25.8%ile
Hespellia	genus	Above 27.1%ile
Anaerotruncus	genus	Above 10.8%ile
Anaerospora	genus	Above 54.1%ile
Acetanaerobacterium	genus	Above 8.6%ile
Odoribacter	genus	Above 8.7%ile
Tepidimicrobium	genus	Above 50%ile
Oribacterium	genus	Above 20.6%ile
Subdoligranulum	genus	Above 35.2%ile
Gracilibacter	genus	Above 77.7%ile
Henriciella	genus	Above 42.8%ile
Oscillibacter	genus	Above 29.1%ile
Lactonifactor	genus	Above 30.5%ile
Adlercreutzia	genus	Above 8.4%ile
Pelosinus	genus	Above 36.2%ile
Parabacteroides	genus	Above 9.7%ile
Niabella	genus	Above 18.1%ile
Barnesiella	genus	Above 21.5%ile
Howardella	genus	Above 6.6%ile
Proteiniborus	genus	Below 35.9%ile
Moryella	genus	Above 22.2%ile
Desulfurispora	genus	Above 11.3%ile
Blautia	genus	Above 10.7%ile
Butyricimonas	genus	Above 4.2%ile
Parasutterella	genus	Above 29.4%ile
Enterorhabdus	genus	Above 27.3%ile
Robinsoniella	genus	Above 8.4%ile
Gordonibacter	genus	Above 26.9%ile
Anaerosporobacter	genus	Above 40.3%ile
Propioniferax	genus	Below 52.3%ile
Holdemania	genus	Above 8.2%ile
Acidaminobacter	genus	Above 0.5%ile
Hallella	genus	Above 1.4%ile
Anaerofilum	genus	Above 3.5%ile
Pantoea	genus	Above 21.1%ile
Pseudobutyrivibrio	genus	Above 11.2%ile
Anaerobranca	genus	Above 1.2%ile
Johnsonella	genus	Above 13.6%ile
Sutterella	genus	Above 6.9%ile
Catonella	genus	Above 17.5%ile
Paenibacillus	genus	Above 10.5%ile
Caloramator	genus	Above 12.9%ile
Filifactor	genus	Above 7%ile
Geothrix	genus	Above 1.5%ile
Sporobacter	genus	Above 23.4%ile
Weissella	genus	Above 13.3%ile
Facklamia	genus	Above 60.7%ile
Brenneria	genus	Above 31.6%ile
Slackia	genus	Above 3.2%ile
Eggerthella	genus	Above 1.6%ile
Pedobacter	genus	Above 3.6%ile
Halochromatium	genus	Above 3%ile
Mogibacterium	genus	Above 1.8%ile
Papillibacter	genus	Above 31.1%ile
Coprobacillus	genus	Above 1%ile
Thermicanus	genus	Above 9.8%ile
Collinsella	genus	Above 12.2%ile
Olsenella	genus	Above 26.9%ile
Granulicatella	genus	Above 6%ile
Parasporobacterium	genus	Above 19.5%ile
Oscillospira	genus	Above 10.7%ile
Victivallis	genus	Above 35.1%ile
Anaerococcus	genus	Above 10.1%ile
Thalassospira	genus	Above 17.9%ile
Lachnobacterium	genus	Above 14.5%ile
Finegoldia	genus	Above 10.6%ile
Dysgonomonas	genus	Above 10.4%ile
Peptoniphilus	genus	Above 4.4%ile
Varibaculum	genus	Above 5.8%ile
Kribbella	genus	Above 2.6%ile
Porphyromonas	genus	Above 4%ile
Prevotella	genus	Above 5.3%ile
Syntrophomonas	genus	Above 3%ile
Desulfovibrio	genus	Above 4%ile
Desulfomicrobium	genus	Above 9.5%ile
Acidaminococcus	genus	Above 7.3%ile
Megasphaera	genus	Above 3.9%ile
Shigella	genus	Above 42.9%ile
Butyrivibrio	genus	Above 4.9%ile
Roseburia	genus	Above 10.5%ile
Oxalobacter	genus	Above 5%ile
Bacteroides	genus	Above 10.3%ile
Haemophilus	genus	Above 5.7%ile
Kluyvera	genus	Above 70.8%ile
Proteus	genus	Above 9.1%ile
Azospirillum	genus	Above 8.4%ile
Campylobacter	genus	Above 0.7%ile
Flavobacterium	genus	Above 40.1%ile
Pseudomonas	genus	Above 15.7%ile
Actinomyces	genus	Above 6.9%ile
Bifidobacterium	genus	Above 8.1%ile
Corynebacterium	genus	Above 6.9%ile
Eubacterium	genus	Above 0.9%ile
Bacillus	genus	Above 4.8%ile
Clostridium	genus	Above 10.5%ile
Desulfotomaculum	genus	Above 11.5%ile
Sporosarcina	genus	Above 34.2%ile
Lactobacillus	genus	Above 7%ile
Erysipelothrix	genus	Above 9.2%ile
Streptococcus	genus	Above 10%ile
Ruminococcus	genus	Above 18.2%ile
Gemella	genus	Above 0.7%ile
Atopobium	genus	Above 1.6%ile
Methanobrevibacter	genus	Above 3.3%ile
Peptococcus	genus	Above 4.5%ile
Acholeplasma	genus	Above 1.1%ile
Sarcina	genus	Above 17.7%ile
Spiroplasma	genus	Above 49%ile
Mycoplasma	genus	Above 4.9%ile
Halomonas	genus	Above 25%ile
Sphingomonas	genus	Above 20.6%ile
Lachnospira	genus	Above 9.6%ile
Microbacterium	genus	Above 1.3%ile
Acetobacterium	genus	Above 5%ile
Dialister	genus	Above 6.1%ile
Acetivibrio	genus	Above 17.1%ile
Bilophila	genus	Above 10.6%ile
Acetitomaculum	genus	Above 8.8%ile
Veillonella	genus	Above 7.5%ile
Coprococcus	genus	Above 18.3%ile
Phascolarctobacterium	genus	Above 6.7%ile
Bacillota sensu stricto incertae sedis	no rank	Above 64.6%ile
cellular organisms	no rank	Above 1.3%ile
Bacteria incertae sedis	no rank	Above 33.5%ile
Eubacteriales incertae sedis	no rank	Above 8%ile
Oscillospiraceae incertae sedis	no rank	Above 68.7%ile
Peptostreptococcaceae incertae sedis	no rank	Above 4.4%ile
Tissierellia incertae sedis	no rank	Above 11.6%ile
unclassified Streptococcus	no rank	Above 81%ile
unclassified Alistipes	no rank	Above 72.1%ile
unclassified Veillonella	no rank	Above 87.9%ile
unclassified Peptoniphilus	no rank	Above 73.6%ile
unclassified Bacteroides	no rank	Above 76.7%ile
unclassified Blautia	no rank	Above 61.5%ile
unclassified Parabacteroides	no rank	Above 36.5%ile
Chitinophagales	order	Above 15%ile
Balneolales	order	Above 49.4%ile
Acidaminococcales	order	Above 6.5%ile
Veillonellales	order	Above 9.7%ile
Marinilabiliales	order	Above 45.9%ile
Mycoplasmoidales	order	Above 1.8%ile
Hyphomonadales	order	Above 42.3%ile
Moraxellales	order	Above 0.8%ile
Tissierellales	order	Above 6.5%ile
Cellvibrionales	order	Above 9.7%ile
Eggerthellales	order	Above 10.1%ile
Ignavibacteriales	order	Above 60.3%ile
Selenomonadales	order	Above 5.7%ile
Cytophagales	order	Above 15.8%ile
Rhodospirillales	order	Above 3.5%ile
Rhodobacterales	order	Above 25.9%ile
Sphingomonadales	order	Above 17.4%ile
Flavobacteriales	order	Above 5.9%ile
Sphingobacteriales	order	Above 9.6%ile
Desulfovibrionales	order	Above 8.9%ile
Desulfobacterales	order	Above 26.9%ile
Fibrobacterales	order	Above 1.1%ile
Victivallales	order	Above 25.9%ile
Erysipelotrichales	order	Above 10.6%ile
Holophagales	order	Above 3.3%ile
Synergistales	order	Above 0.8%ile
Caldilineales	order	Above 4%ile
Desulfarculales	order	Above 24.3%ile
Verrucomicrobiales	order	Above 2.3%ile
Halanaerobiales	order	Above 21.5%ile
Coriobacteriales	order	Above 10.4%ile
Bifidobacteriales	order	Above 7.7%ile
Mycobacteriales	order	Above 4.3%ile
Streptosporangiales	order	Above 1.6%ile
Acidimicrobiales	order	Above 2.1%ile
Thiotrichales	order	Above 1.8%ile
Pseudomonadales	order	Above 10%ile
Chromatiales	order	Above 8%ile
Lysobacterales	order	Above 7%ile
Alteromonadales	order	Above 6.1%ile
Aeromonadales	order	Above 7.9%ile
Pasteurellales	order	Above 3.9%ile
Burkholderiales	order	Above 9.8%ile
Enterobacterales	order	Above 6.1%ile
Bacteroidales	order	Above 10.4%ile
Eubacteriales	order	Above 10.4%ile
Entomoplasmatales	order	Above 13.1%ile
Acholeplasmatales	order	Above 10.2%ile
Lactobacillales	order	Above 10.4%ile
Oceanospirillales	order	Above 11.5%ile
Mycoplasmatales	order	Above 12.4%ile
Actinomycetales	order	Above 7%ile
Methanobacteriales	order	Above 5.9%ile
Bacillales	order	Above 6.2%ile
Oscillatoriales	order	Above 17.5%ile
Nostocales	order	Above 2%ile
Hyphomicrobiales	order	Above 8.3%ile
Myxococcales	order	Above 1.8%ile
Bacteroidota	phylum	Above 10.4%ile
Cyanobacteriota	phylum	Above 13.6%ile
Pseudomonadota	phylum	Above 10.3%ile
Bacillota	phylum	Above 10.5%ile
Euryarchaeota	phylum	Above 4.2%ile
Candidatus Saccharibacteria	phylum	Above 28.8%ile
Verrucomicrobiota	phylum	Above 6.3%ile
Synergistota	phylum	Above 0.8%ile
Lentisphaerota	phylum	Above 24.1%ile
Chloroflexota	phylum	Above 11%ile
Thermodesulfobacteriota	phylum	Above 6.5%ile
Actinomycetota	phylum	Above 10.3%ile
Ignavibacteriota	phylum	Above 57.9%ile
Myxococcota	phylum	Above 1.6%ile
Balneolota	phylum	Above 42.4%ile
Melioribacter roseus	species	Above 65.1%ile
Fusicatenibacter saccharivorans	species	Above 33.2%ile
Intestinimonas butyriciproducens	species	Above 8.4%ile
Ruminococcus champanellensis	species	Above 63.9%ile
Parabacteroides faecis	species	Above 59.4%ile
Dielma fastidiosa	species	Above 21.5%ile
Coprobacter fastidiosus	species	Above 21.4%ile
Tepidimicrobium xylanilyticum	species	Above 52.3%ile
Bacteroides reticulotermitis	species	Above 85.9%ile
Anaerostipes sp. 5_1_63FAA	species	Above 86.3%ile
Casaltella massiliensis	species	Above 51.8%ile
Anaerococcus provencensis	species	Above 26.2%ile
Blautia stercoris	species	Above 13.6%ile
Blautia faecis	species	Above 35.3%ile
[Lactobacillus] rogosae	species	Below 62.3%ile
Phocaeicola paurosaccharolyticus	species	Above 15.9%ile
Brassicibacter mesophilus	species	Above 80.3%ile
Gemmiger formicilis	species	Below 45.8%ile
Eisenbergiella tayi	species	Above 30.1%ile
Dysgonomonas macrotermitis	species	Above 45.5%ile
Butyricimonas faecihominis	species	Above 31.8%ile
Aestuariispira insulae	species	Below 29.5%ile
Oleidesulfovibrio vietnamensis	species	Above 57%ile
Oribacterium sinus	species	Above 18.4%ile
Akkermansia muciniphila	species	Above 3.9%ile
Dialister invisus	species	Above 1.3%ile
Bifidobacterium longum	species	Above 8.5%ile
Subdoligranulum variabile	species	Above 31.5%ile
Alistipes finegoldii	species	Above 28.7%ile
Phocaeicola massiliensis	species	Above 4.2%ile
Clostridium frigoris	species	Above 2.2%ile
Ethanoligenens harbinense	species	Above 49.2%ile
Bifidobacterium thermacidophilum	species	Above 20%ile
Bacteroides cellulosilyticus	species	Above 14.3%ile
Bacteroides helcogenes	species	Above 34.5%ile
Eubacterium coprostanoligenes	species	Above 82%ile
Anaerosporobacter mobilis	species	Above 31.2%ile
Acholeplasma hippikon	species	Above 6.2%ile
Desulfurispora thermophila	species	Above 11.5%ile
Flavonifractor plautii	species	Above 29.5%ile
Gracilibacter thermotolerans	species	Above 77.2%ile
Mediterraneibacter glycyrrhizinilyticus	species	Below 27%ile
Oscillibacter valericigenes	species	Above 6.2%ile
Phocaeicola dorei	species	Above 7.2%ile
Roseburia inulinivorans	species	Above 26.1%ile
Leyella stercorea	species	Above 5.5%ile
Roseburia faecis	species	Above 8.4%ile
Dialister propionicifaciens	species	Above 25.3%ile
Phocaeicola plebeius	species	Above 11.1%ile
Phocaeicola coprocola	species	Above 14.8%ile
Bacteroides pyogenes	species	Above 78.9%ile
Acetivibrio alkalicellulosi	species	Above 15.2%ile
Parabacteroides goldsteinii	species	Above 1.7%ile
Alistipes onderdonkii	species	Above 64.3%ile
Alistipes shahii	species	Above 26.4%ile
Bacteroides finegoldii	species	Above 7.9%ile
Lactonifactor longoviformis	species	Above 21%ile
Bacteroides graminisolvens	species	Above 11%ile
Barnesiella intestinihominis	species	Above 25.1%ile
Parasutterella excrementihominis	species	Above 29.4%ile
Porphyromonas bennonis	species	Above 6.8%ile
Butyricicoccus pullicaecorum	species	Below 66.3%ile
Alistipes sp. EBA6-25cl2	species	Below 67.6%ile
Bacteroides sp. EBA5-17	species	Below 82.8%ile
Aminicella lysinilytica	species	Below 43.8%ile
Bacteroides sp. 2_2_4	species	Above 59%ile
Gordonibacter pamelaeae	species	Above 29.5%ile
Bifidobacterium bombi	species	Above 27.1%ile
Sutterella parvirubra	species	Above 14%ile
Segatella maculosa	species	Above 1.5%ile
Adlercreutzia equolifaciens	species	Above 6.1%ile
Blautia wexlerae	species	Above 10.7%ile
Howardella ureilytica	species	Above 4.9%ile
Niabella aurantiaca	species	Above 8.3%ile
[Bacteroides] pectinophilus	species	Above 58.7%ile
Phocaeicola coprophilus	species	Above 23.4%ile
Phocaeicola salanitronis	species	Above 11.8%ile
Bacteroides gallinarum	species	Above 13%ile
Pelosinus fermentans	species	Above 38.9%ile
Bacteroides xylanisolvens	species	Above 1.4%ile
Anaerostipes hadrus	species	Above 16%ile
Phocaeicola sartorii	species	Above 12.4%ile
Bacteroides faecis	species	Above 7.9%ile
Bacteroides rodentium	species	Above 16.8%ile
Mediterraneibacter faecis	species	Below 41.2%ile
Bacteroides fluxus	species	Above 10.2%ile
Alistipes indistinctus	species	Above 18.1%ile
Slackia piriformis	species	Above 8.5%ile
Phascolarctobacterium succinatutens	species	Above 1.2%ile
Slackia isoflavoniconvertens	species	Above 12.8%ile
Blautia glucerasea	species	Above 0.5%ile
Collinsella aerofaciens	species	Above 8.4%ile
Thomasclavelia cocleata	species	Below 52.6%ile
[Clostridium] methylpentosum	species	Above 82.7%ile
Segatella albensis	species	Above 21%ile
Bifidobacterium boum	species	Above 48.8%ile
Kluyvera georgiana	species	Above 68.1%ile
Paracholeplasma vituli	species	Above 72.7%ile
Eggerthella lenta	species	Above 4.8%ile
Bacteroides acidifaciens	species	Above 3.7%ile
[Ruminococcus] lactaris	species	Below 67.3%ile
Acidaminobacter hydrogenoformans	species	Above 0.5%ile
Lachnoclostridium phytofermentans	species	Above 50%ile
Holdemania filiformis	species	Above 34.5%ile
Anaerococcus octavius	species	Above 11%ile
Desulfofarcimen acetoxidans	species	Above 15.8%ile
Parabacteroides merdae	species	Above 6.8%ile
Bacteroides stercoris	species	Above 2.1%ile
[Clostridium] viride	species	Above 53.5%ile
Bacteroides caccae	species	Above 9.5%ile
Sporobacter termitidis	species	Above 58%ile
Catonella morbi	species	Above 7.6%ile
Succiniclasticum ruminis	species	Above 24.8%ile
Johnsonella ignava	species	Above 9.9%ile
Clostridium putrefaciens	species	Above 33.3%ile
Anaerostipes caccae	species	Above 6%ile
Pseudoflavonifractor capillosus	species	Above 26.8%ile
Coprobacillus cateniformis	species	Above 7%ile
Catenibacterium mitsuokai	species	Above 9.7%ile
Dorea longicatena	species	Above 32.1%ile
Blautia luti	species	Above 33.3%ile
[Clostridium] hylemonae	species	Above 68.2%ile
Coprococcus catus	species	Above 20.4%ile
Filifactor alocis	species	Above 16.4%ile
Lactobacillus iners	species	Above 13.9%ile
Segatella copri	species	Above 15%ile
Hungatella hathewayi	species	Above 40.6%ile
Turicibacter sanguinis	species	Above 7.3%ile
Anaerotruncus colihominis	species	Above 3.8%ile
Roseburia intestinalis	species	Above 28.9%ile
Marvinbryantia formatexigens	species	Below 32.2%ile
Victivallis vadensis	species	Above 11.9%ile
Blautia schinkii	species	Above 12%ile
Robinsoniella peoriensis	species	Above 58.3%ile
Varibaculum cambriense	species	Above 29.4%ile
[Clostridium] cellulosi	species	Above 65.3%ile
Caloramator fervidus	species	Above 5.4%ile
Alkalithermobacter thermoalcaliphilus	species	Above 8.3%ile
[Clostridium] scindens	species	Above 61.5%ile
Thomasclavelia spiroformis	species	Below 51.4%ile
Clostridium chartatabidum	species	Above 28.7%ile
[Clostridium] polysaccharolyticum	species	Below 17.3%ile
Gemella morbillorum	species	Above 23%ile
Lachnospira pectinoschiza	species	Above 7.2%ile
Bacteroides ovatus	species	Above 5.7%ile
Alistipes putredinis	species	Above 28.6%ile
Odoribacter splanchnicus	species	Above 29.6%ile
Prevotella corporis	species	Above 2.1%ile
Bifidobacterium animalis	species	Above 3.6%ile
Bifidobacterium pseudocatenulatum	species	Above 18.5%ile
Peptococcus niger	species	Above 5.5%ile
Methanobrevibacter smithii	species	Above 3.4%ile
Ruminococcus bromii	species	Above 21.6%ile
Ruminococcus callidus	species	Above 14.7%ile
Blautia obeum	species	Above 18.8%ile
Bilophila wadsworthia	species	Above 8.9%ile
Terrisporobacter glycolicus	species	Above 17.5%ile
Clostridium intestinale	species	Above 63.5%ile
Dorea formicigenerans	species	Above 10%ile
[Eubacterium] siraeum	species	Above 68.9%ile
Veillonella atypica	species	Above 0%ile
Veillonella dispar	species	Above 7.7%ile
Phascolarctobacterium faecium	species	Above 5.8%ile
Peptoniphilus lacrimalis	species	Above 0.7%ile
Blautia producta	species	Above 20.9%ile
Anaerococcus vaginalis	species	Above 11.2%ile
Mediterraneibacter gnavus	species	Above 17.2%ile
[Ruminococcus] torques	species	Above 21.6%ile
Coprococcus eutactus	species	Above 20.1%ile
Veillonella parvula	species	Above 0.4%ile
Finegoldia magna	species	Above 5.9%ile
Ruminococcus albus	species	Above 19.2%ile
Ruminococcus flavefaciens	species	Above 38.8%ile
Streptococcus sanguinis	species	Above 1.3%ile
Streptococcus thermophilus	species	Above 3.5%ile
Streptococcus parasanguinis	species	Above 9%ile
Blautia hansenii	species	Above 3.4%ile
Streptococcus anginosus	species	Above 10.5%ile
Enterococcus faecalis	species	Above 9.1%ile
Lactococcus lactis	species	Above 4.3%ile
Clostridioides difficile	species	Above 53.7%ile
Clostridium kluyveri	species	Above 24.5%ile
[Clostridium] leptum	species	Above 74.7%ile
Thomasclavelia ramosa	species	Above 31%ile
Faecalispora sporosphaeroides	species	Above 89.2%ile
Eubacterium oxidoreducens	species	Below 14.9%ile
Holdemanella biformis	species	Above 10.1%ile
Eubacterium limosum	species	Above 1.3%ile
Propioniferax innocua	species	Below 52%ile
Bifidobacterium indicum	species	Above 6.3%ile
Bifidobacterium magnum	species	Above 54%ile
Schaalia odontolytica	species	Above 8%ile
Hoylesella loescheii	species	Above 6.2%ile
Butyrivibrio fibrisolvens	species	Above 61.2%ile
Oxalobacter formigenes	species	Above 4.5%ile
Faecalibacterium prausnitzii	species	Above 9.9%ile
Proteus mirabilis	species	Above 11.1%ile
Haemophilus parainfluenzae	species	Above 5.5%ile
Aggregatibacter aphrophilus	species	Above 9.8%ile
Bacteroides fragilis	species	Above 6.1%ile
Bacteroides thetaiotaomicron	species	Above 9.2%ile
Bacteroides uniformis	species	Above 8.8%ile
Phocaeicola vulgatus	species	Above 9.3%ile
Parabacteroides distasonis	species	Above 9.4%ile
Lactobacillus casei group	species group	Above 23.9%ile
Streptococcus anginosus group	species group	Above 4.4%ile
Oscillatoriophycideae	subclass	Above 6.3%ile
Archaea	superkingdom	Above 7%ile

(c) 2026 Lassesen Consulting, LLC. ALL RIGHTS RESERVED

This work is licensed under the Creative Commons Attribution‑NonCommercial 4.0 International License (CC BY‑NC 4.0). Commercial use is prohibited without prior written permission. See https://creativecommons.org/licenses/by-nc/4.0/.

Past analyses have explored the prediction of symptoms using odds ratios derived from populations that utilized the same microbiome test and data-processing pipeline. A colleague recently inquired whether percentile-based comparisons could potentially be applied across different microbiome testing platforms.

The core issue, as well documented in the literature, stems from a severe lack of standardization in microbiome data processing (See discussion here). This report presents results derived from all available data encompassing multiple test sources.

An important methodological concern arises from potential double-counting when odds ratios are calculated simultaneously for a strain, its parent species, and its broader taxonomic family. To address this, I apply odds ratios at distinct taxonomic ranks to ensure proper separation of these hierarchies.

A secondary consideration involves the subjective nature of symptom reporting. As symptoms are self-reported, respondents tend to record only the most severe experiences, resulting in an expected underreporting of minor or moderate symptoms. This introduces reporting bias that likely reduces overall agreement levels.

Forecast agreement with Report

The table below provides insight into which taxonomic ranks may be most predictive. It is truncated at a 10% agreement threshold for clarity.

Rank	Predicted	Agreement	Percentage
species subgroup	126	55	43.7
subkingdom	42	17	40.5
superphylum	1124	379	33.7
superfamily	2187	438	20.0
infraclass	669	126	18.8
cohort	692	130	18.8
superorder	1034	193	18.7
suborder	18539	3147	17.0
subphylum	8339	1373	16.5
subfamily	5424	834	15.4
kingdom	19385	2779	14.3
strain	28091	3855	13.7
subspecies	49169	6714	13.7
species group	149291	17646	11.8
superkingdom	114214	12513	11.0

Reported with Forecast Odds < 1.0

Looking at symptoms reported where the odds was less than 1.0 (i.e. not elevated odds), we have a much shorter table. Looking at some ranks we see that we have around a 2:1 ratio between the tables, i.e. 66% (2/3)

species subgroup	30	7	23.3
superphylum	1782	311	17.5
subkingdom	22	3	13.6
suborder	14242	1707	12.0
subphylum	7540	872	11.6
infraclass	787	85	10.8

Clinical Implications

A More Effective Intake Process

Using forecast results as an interview checklist for clinicians could significantly improve patient assessment. Early data suggest that approximately two-thirds of the checklist items will correspond to reported symptoms. Directly asking patients about specific symptoms is typically more reliable than relying on spontaneous disclosure, as individuals tend to report only their most severe issues.

Improved Identification of Symptom-Associated Bacteria

When a patient confirms the presence of a symptom, this information can be leveraged to identify bacterial taxa most likely contributing to it. This approach contrasts with the traditional method of labeling deviations greater than two standard deviations from the mean as problematic. Instead, it establishes distinct, data-driven thresholds, which sometimes produce counterintuitive but statistically supported findings.

SymptomName	Tax_Name	Critical Percentile	Odds Ratio
Comorbid: Histamine or Mast Cell issues	Cystobacterineae	46.4	0.04
Comorbid: Histamine or Mast Cell issues	Actinomycineae	58	3.46
Comorbid: Histamine or Mast Cell issues	Nannocystineae	22.4	0.1
Sleep: Waking up early in the morning (e.g. 3 AM)	Cystobacterineae	54.2	5.33
Sleep: Waking up early in the morning (e.g. 3 AM)	Nannocystineae	22.4	0.13
Sleep: Waking up early in the morning (e.g. 3 AM)	Coriobacterineae	65.3	0.32
Post-exertional malaise: Next-day soreness after everyday activities	Sternorrhyncha	31.9	0.11
Post-exertional malaise: Next-day soreness after everyday activities	Cystobacterineae	10.6	8.49
Post-exertional malaise: Next-day soreness after everyday activities	Nannocystineae	16.7	0.18

For example, in cases of post-exertional malaise (next-day soreness following ordinary activities):

• Individuals above the 31st percentile for Sternorrhyncha have an 18% reduction in odds.
• Those above the 16th percentile for Cystobacterineae experience an 849% increase in odds.
• Individuals above the 16th percentile for Nannocystineae show an 11% reduction in odds.

These relationships indicate that increasing two bacterial groups (if their abundances are below threshold) and decreasing one (if above threshold) could normalize the microbiome toward a healthier state. For the other two conditions presented, the decision points cluster near the median rather than at the extremes, reinforcing that not all microbial influences occur at outlier levels.

Implications for Generating Therapeutic Suggestions

To refine microbial intervention strategies, I employ the Monte Carlo method. In essence, multiple algorithms independently select bacterial targets and propose compensatory actions. The aggregate of these independent results identifies interventions with high consensus and robustness.

Furthermore, using different taxonomic ranks yields multiple independent bacterial subsets—ideal for Monte Carlo modeling—providing a more reliable foundation for therapeutic recommendation generation.

Caveat: The best odds-ratios are based by the same microbiome processing that is used for odds ratio. The intent of this was to see if reasonable results may be obtain at a generic level. IMHO, the answer is yes.

Try it yourself

I have exposed the data on this page. Just paste (or call by API) your data in the format shown. No charge. No limit on use of this Api.

The samples used for illustrations are obtained randomly from samples from a different lab (no data from this lab was used to compute the odds ratios) that are from “healthy individuals”. These samples tend to have the top symptom being “Asymptomatic: No Health Issues” which is consistent with the claim of “healthy individuals”.

• Freq: Is the number of taxonomic ranks that predicted this symptom
• Estimate: Indicates the magnitude of the prediction.

Email me at Research@MicrobiomePrescription.com with comments and opinions.

My exploration of microbiome modification began with reading studies archived in the U.S. National Library of Medicine. As someone who has been developing expert systems since the 1990s, my instinct was to encode the findings from these studies as facts within an expert system—letting logic determine the optimal course of action.

In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code — Wikipedia

Some Difficult Discoveries

As I built the knowledge base, some major problems quickly emerged:

• Results from different studies often contradicted one another.
• Some results were replicated consistently, while others produced conflicting outcomes.
• Certain findings were reported only once and never replicated.
• There was significant uncertainty about bacterial identification due to non-standardized testing methods (see this explanation).
• Studies tended to report results at a single taxonomy rank—often not the rank relevant to my analysis.

To address the first issue, I incorporated fuzzy logic into the expert system, allowing it to handle ambiguity and partial truths rather than rigid yes/no classifications.

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty. — Wikipedia

The second issue required a different approach. I began using bacterial association data (available here) to infer relationships between taxa. For example, if a compound influenced the genus Bifidobacterium, I could reasonably infer a similar effect for its species. This two-way relationship also works in reverse: if you want to increase Bifidobacterium overall, the species Bifidobacterium longum—a readily available probiotic—shows the strongest positive association.

Species Name	Estimate Percentage Inference
Bifidobacterium actinocoloniiforme	18.8
Bifidobacterium adolescentis	54.8
Bifidobacterium angulatum	26.6
Bifidobacterium animalis	14.5
Bifidobacterium asteroides	40.2
Bifidobacterium avesanii	34.2
Bifidobacterium bifidum	25.5
Bifidobacterium bohemicum	52.7
Bifidobacterium bombi	57.7
Bifidobacterium boum	64.1
Bifidobacterium breve	52.4
Bifidobacterium catenulatum	33.9
Bifidobacterium choerinum	66.6
Bifidobacterium commune	45.4
Bifidobacterium cuniculi	21.8
Bifidobacterium dentium	23.2
Bifidobacterium gallicum	30.8
Bifidobacterium indicum	52.9
Bifidobacterium lemurum	50.4
Bifidobacterium longum	73.7
Bifidobacterium magnum	62.5
Bifidobacterium minimum	27.5
Bifidobacterium mongoliense	31.9
Bifidobacterium pseudocatenulatum	31.2
Bifidobacterium pullorum	30.2
Bifidobacterium ruminantium	20.4
Bifidobacterium scardovii	16.9
Bifidobacterium subtile	38.8
Bifidobacterium thermacidophilum	44.5
Bifidobacterium thermophilum	29.8
Bifidobacterium tsurumiense	11.7

With fuzzy logic, study findings indicating increases or decreases could be translated into numerical values. Using bacterial association data, I could then adjust those values to create a more accurate estimate of impact.

Why Do the Mathematics?

Modern AI models, particularly Large Language Models (LLMs), operate differently. They generate responses by finding text that resembles the question rather than reasoning from factual relationships. LLMs do not distinguish whether data comes from a single study or many, nor do they analyze hierarchical relationships within bacterial taxonomy. As a result, taxonomic nuances—such as the difference between Lactobacillus reuteri and Limosilactobacillus reuteri—are often overlooked because the evolution of bacterial naming conventions is ignored.

At the other end of the treatment spectrum is a “whole health” influencer who might recall a single study about Bifidobacterium dentium and use it to infer a complete treatment plan. My approach is simpler: I prefer actions grounded in probability—ones that have the best odds of success.

This is a bit of the engine used on my free site for individuals, Microbiome Prescription. At present we have 14,388,582 million facts over 1,882 modifiers.

Recently on my Microbiome Sites I added a table of metabolites whose presence or absence is associated with various symptoms. Technical details is give in this post: Metabolites and Symptoms. The volume of data is overwhelming.

Today I realized that a good solution is to have the page frame a question that people can paste into their favorite AI.

These metabolites have been identified statistically: their levels, estimated from the microbiome, are abnormally low with P < 0.001 for people reporting this symptom pattern (for example, ME/CFS without IBS). They are hypothesis-generating signals only and have not been validated in clinical trials. In my opinion, they highlight candidates that could be explored in formal clinical research or cautiously by an experienced physician with individual patients. None of these AI-assisted suggestions should be used without review and approval by an appropriate medical professional.

For example ME/CFS without IBS link as an example or to look at other symptoms go to that page and change the drop down. In terms of PROBIOTIC TRIALS, you want to see which of these substances are produced by bacteria.

Instead of “Are any of the following available as supplements or prescriptible from MDs:” ask,
“Are any of the following available produced by species uses as probiotics” or more explicit “According to KEGG are any produced by lactobacillus species”

Perplexity Response

None of the listed KEGG compounds appear to be commonly prescribable by MDs as pharmaceuticals. A few are available or derivable as dietary supplements, primarily those related to natural pigments and vitamins found in plants or bacteria.

Available Supplements

Chlorophyll a (KEGG: C05306) is widely sold as a supplement in forms like chlorophyllin for odor control, detoxification, and general health support, often derived from alfalfa or algae.

Ectoine (KEGG: C06231) is offered in over-the-counter supplements, eye drops, and skincare for dry eye relief, skin protection, and anti-inflammatory effects.

Vitamin E Forms

Alpha-tocopherol (KEGG: C02477), beta-tocopherol (KEGG: C14152), alpha-tocotrienol (KEGG: C14153), and beta-tocotrienol (KEGG: C14154) are standard vitamin E components available in tocopherol/tocotrienol supplements for antioxidant and cardiovascular support.

Unavailable Compounds

All others, including bacteriochlorophylls (e.g., C11242, C18155, C21217), complex lipids, CoA derivatives, and specialized bacterial metabolites like archaeal lipids or mycothiol conjugates, show no evidence of commercial supplements or prescriptions; they remain research or biochemical intermediates.

Claude AI Response

Based on my research, I can provide you with information about which compounds from your list are available as supplements or prescribable by physicians. Here’s a summary:

Compounds Available as Supplements:

Chlorophyll a – Available as dietary supplements, typically sold as “chlorophyllin” (a water-soluble derivative with copper instead of magnesium). Available in liquid drops, capsules, and tablets at most health food stores and drug stores WebMDWebMD.

Arachidonate (Arachidonic acid) – Marketed as a dietary supplement, particularly for bodybuilding and muscle growth Wikipedia. Available over-the-counter in capsule form.

Raffinose – Not typically taken as a standalone supplement, though it occurs naturally in foods and may have prebiotic properties Nutrients ReviewWikipedia. Used primarily in research and cryopreservation applications.

Alpha-Tocopherol (Vitamin E) – Widely available as a dietary supplement in capsule, gel capsule, and liquid forms, and can also be prescribed by doctors for certain conditions MedlinePlusWikipedia. In 2023, it was the 290th most commonly prescribed medication in the United States Wikipedia.

Beta-Tocopherol, Alpha-Tocotrienol, Beta-Tocotrienol – Available in mixed vitamin E supplements containing various tocopherols and tocotrienols.

Compounds with Medical/Prescribable Uses:

Bilirubin – Not available as a supplement. Medications like phenobarbital can be prescribed to reduce bilirubin levels in conditions like Crigler-Najjar syndrome and Gilbert’s syndrome Medscape, but bilirubin itself is not supplemented.

D-Xylose – Used primarily as a diagnostic tool in medical testing for malabsorption.

Important Notes:

The vast majority of the compounds on your list are:

• Biochemical intermediates in metabolic pathways
• Research chemicals
• Bacterial/plant-specific compounds
• Not available as supplements or prescription medications

Only a very small number (primarily the vitamin E forms, chlorophyll, and arachidonic acid) are commercially available as dietary supplements. I’m not a medical professional, so if you’re considering any of these supplements for health purposes, please consult with a healthcare provider.

This is intended for MD and Researchers

None of the listed KEGG compounds have robust clinical trial validation for therapeutic use. They represent microbiome-derived metabolites with statistically low levels (P < 0.001), suggesting potential biological roles worth exploring in formal trials or under MD supervision.

Key Caveat

Always consult a medical professional before trying any supplement or intervention based on these findings. AI suggestions are not medical advice.

In recent discussions with microbiome startup teams, a key question emerged: which metabolites correlate with specific symptoms? I recommended odds ratios as the optimal analytical approach, and one team is now considering integrating this into their product.

My prior analysis of KEGG-derived metabolite data from various labs revealed stronger consistency in metabolite patterns than bacterial profiles. Symptoms likely arise from adverse metabolite combinations circulating systemically—one metabolite can stem from hundreds of bacteria, and one bacterium can influence hundreds of metabolites—creating a complex web akin to an oversized Gordian knot.

Humans naturally gravitate toward simple “sound bites.” Asked for the highest odds of criminality, people might cite race, city neighborhood, or age range (with 0-5-year-olds showing near-zero risk). True predictive power comes from aggregating all statistically significant odds ratios—in this case, all reported metabolites with meaningful associations.

Using 4500 symptom-annotated samples from BiomeSight, this post explores that approach.

Go to Site

Computing the Odds Ratio

The process is simple:

• Take the Biomesight samples and compute the different metabolites using the KEGG: Kyoto Encyclopedia of Genes and Genomes data.
  • This produced 2,690 different metabolites
• Convert the amount for each metabolite to Percentile ranking. This allows the results to be applied to other pipeline data that may produce different values.
• Compute the Chi2 for each integer Percentile rank(100) for each metabolite and symptom (with at least 30 reports, i.e. 207)
  • 2,690 x 100 x 207 = 55,683,000 Chi2 computations
• Take the most significant vector with P < 0.001 or Chi2 > 10.83.
  • Then compute the odds ratio for it

The calculations were brutal with the CPU pegged for days (with some overclocking). If you are running on a cloud service, I trust you have a fat bitcoin wallet.

The results were over 585,094 significant metabolites for our 207 symptoms, or about 1% of the cases has a significant association found.

Using Odds Ratios

Human nature likes simplicity. “Give me just one factor to determine if a person is likely an illegal resident in the USA.” A 2025 report cites there are 150,000 illegal Irish citizens in the US, why is an Irish accent not used as a flag by a certain paramilitary group? With the microbiome data, we suffer a similar bias for simplicity with exclusion of inconvenient facts.

Looking at the odds ratios in detail, we may see large numbers. We should avoid using just one numberin isolation. The table for General: Fatigue is below. For example for the first two, metabolites if one is above and one is below, the resulting Odds ratio is about 1.09 (62.60 * 0.0174), i.e. no major risk. In short all available metabolites should be used, not just one or two.

CompoundName	Percentile	Odds Ratio Above	Odds Ratio Below
Pseudouridine 5′-phosphate	39	62.60	0.0160
N-Acetylmuramic acid 6-phosphate	37	57.32	0.0174
Uridine	43	74.51	0.0134
1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide	34	84.22	0.0119
GDP-4-amino-4,6-dideoxy-alpha-D-mannose	40	109.80	0.0091
beta-L-Arabinofuranose	29	0.62	1.6092

There is another interesting pattern that arise. Every metabolite is most significant for being present or ot present. This is a natural pattern to use for various machine learning and AI methods, for example

• Logistic Regression: Outputs probabilities for binary decisions via sigmoid.
• Support Vector Machines (SVM): Finds hyperplanes separating binary classes.
• Decision Trees: Splits data into binary paths leading to class labels.
• Naive Bayes: Probabilistic classifier assuming feature independence for binary outcomes.
• Perceptrons: Single-layer neural nets for linearly separable binary problems.
• Random Forests: Ensemble of trees voting on binary predictions

Clinical Use No, Research Use Yes

With a few exceptions, clinical use is limited. For most metabolites there are no easy ways to explicitly, safely, increase or decrease it.

One interesting exception was for C06570: Tetracycline. If this is not seen, then with Chronic Fatigue Syndrome (ME/CFS), the odds of having it is 22x higher. Tetracycline family of probiotics have had significant positive effects on ME/CFS patients. Other similar metabolites include:

• Oxytetracycline (C06571)
• Chlortetracycline (C11453)
• ​Penicillin G (C06925)
• Streptomycin (C04282)
• Erythromycin (C06911)

In other words, detecting the absence of naturally occurring antibiotics in a patient with a matching symptom suggests specific antibiotics could be tried.

For items like probiotics, the impact is very strain specific. Very few probiotics are sold by strain (and those that do, often lack data). A simple example for Limosilactobacillus reuteri is illustrated below.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic functions[2014].

Technical Note

With bacteria Odds ratio I did a “double odds ratio”. A Double Odds ratio means:

• Determining Odds ratio for Detected or Not Detected.
• Determining the Optimal Odds Ratio when it is Detected.

This was done because I was focused on computing suggestions on individual samples.

These are coarse (single) odds tables because we are not evaluating individual samples but a collection of samples. It is very possible to do a double odds ratio for metabolites — but at present, it appears to have low utilization because of the extremely low number of possible interventions. If interested, contact me at Research at MicrobiomePrescription (US decimal point) com

The reader with Multiple Chemistry Sensitivity(MCS) also has a diagnosed Mast Cell Activation Syndrome (MCAS). We will add this anchorite request. On the symptom list, I will be using a more generic :

• Comorbid: Histamine or Mast Cell issues

The two earlier posts are here:

• Multiple Chemistry Sensitivity Exploration
• Light Sensitivity Exploration

Not as Expected

After seeing results, I said to myself “This is NOT what I was expecting”. The first thing that I noted was that the number of significant bacteria was at 98%ile, i.e. 98 of 100 symptoms have less significant bacteria. There are a lot of bacteria that are statistically associated with this symptom!

What are we dealing with

Mast Cell Activation Syndrome (MCAS) is thought to stem from abnormally sensitive or dysregulated mast cells that release mediators too easily, often in response to everyday triggers, with genetics, immune dysregulation, and environmental factors all suspected contributors [2024]

My KISS model for symptoms that are statistically significant is that the metabolites (chemicals) released into the body have become unbalanced because the bacteria in the microbiome has become unbalanced. There are several thousands possible metabolites and we lack any in depth studies for the mixtures seen with MCAS.

Existing Common Treatments

All of these are reducing symptom severity and not curing the cause.

1. Histamine blockade (H1 + H2 antihistamines)
   • Non‑sedating H1 blockers (e.g., cetirizine, loratadine) plus H2 blockers (e.g., famotidine) are widely used as first‑line and help a large proportion of patients by blocking a key mediator, histamine.
   • Doses are often higher and more frequent than standard allergy dosing, under medical supervision.
2. Mast‑cell stabilizers
   • Cromolyn sodium (especially for GI symptoms) and ketotifen (dual mast‑cell stabilizer + H1 blocker) reduce mediator release and can markedly improve flushing, GI, and some neurologic symptoms over weeks to months.
   • These are usually added when antihistamines alone are insufficient.​
3. Targeted add‑ons (often leukotriene blockers or biologics) plus trigger control
   • Leukotriene receptor antagonists (e.g., montelukast) are commonly used to dampen mediator‑driven inflammation, especially in respiratory or systemic symptoms.
   • In more refractory or IgE‑linked cases, omalizumab (Xolair) is increasingly used off‑label and has helped some patients by reducing IgE‑mediated activation.
   • Systematic trigger identification and avoidance (food, temperature, chemicals, stress) is considered a core part of effective management in all major reviews.

Digging into our data

I am going to skip the explorations that I did in the earlier posts. As with prior post, Odds Ratio has better fine level identification. We also see a lot more bacteria are identified than with the prior two. There are several thousands possible metabolites and we lack any in depth studies for the mixtures seen with MCAS.

	Classic	Odds Ratio
Bacteria Considered	213	298
Bacteria In Common	52	52
Species	20	108
Genus	33	76
Family	54	46
Order	36	34
Class	23	16

What about MCAS occurring with other symptoms? I just put a page up on the odds. Multiple Chemical Sensitivity is reported just 18% of the time, and light sensitivity at 37% of the time. There is not a strong association.

I did a comparison of the net Log(odd ratio) between people. A person without these issues is expected to have a Log(Odds Ratio) < 0. This Post Person reports moderate light sensitivity.

Person	Light Sensitivity	MCS	MCAS
Last Post Person	11.8	17.3	26.1
Anchorite	5.7	16.4	13.8

Odds based Suggestions are now available

See this video on how to do it. The following suggestions were computed for the Anchorite. There were 24 bacteria deemed significant with Biomesight data, with the top high one being Mycobacterium

Probiotics to take and avoid

There are no really outstanding recommendations, but three are available and likely worth an experiment.

• Lactobacillus helveticus – this keeps appearing on multiple samples…..
• Bacillus subtilis
• Clostridium butyricum

And the to-avoid list. Lactobacillus acidophilus implies that yogurt should be avoided.

The suggestions are based on studies from the US National Library of Medicine (PubMed). First, I should point out that Lactobacillus plantarum {L. plantarum} is not in the R2 list because there was not sufficient impact computed. PubMed data is less precise for multiple reasons.

The avoid list is almost 4x longer. I noticed that a lot of herbs and spices are listed as avoid.

Second Opinion using Ombre

The person had done testing with Ombre, then uploaded their FastQ files to Biomesight — so we have two different reports on the bacteria and the amounts. We also have two different sets of Odds Ratio — each is based on a specific testing lab. 101 bacteria were deemed significant with three being especially too high:

• Lacrimispora sphenoides
• Paucilactobacillus vaccinostercus
• Oribacterium parvum

In this case, we had much stronger probiotic recommendations. Lactobacillus helveticus and Bacillus subtilis probiotics occurs on both.

We have similarity with to take suggestions. None of the probiotics suggested were on the above list; this is not unexpected because we are using data from the US National Library of Medicine (PubMed) that is a lot less precise and more fuzzy.

As always, when there is disagreement – go with items that there is agreement with.

The to avoid list is shorter here.

Summary

I discussed some of these finding with folks at PrecisionBiome.Eu and they pointed out that herbs are often avoided because of the belief that they contain histamines. An alternative explanation is that they shift the microbiome; in some cases, they may promote sudden cell death with a release of metabolites into the system (i.e. see Jarisch–Herxheimer reaction).

Why herbs are tricky in MCAS

• MCAS mast cells are hypersensitive, and many different chemicals, including plant constituents, can trigger degranulation, especially in multi‑ingredient herbal mixes.
• Case experience and MCAS guidance note that herbal and “natural” supplements can provoke flares (e.g., some antimicrobial herbs, moringa with high oxalates, concentrated essential oils), so they should be approached like medications, not “automatically safe” foods.

The algorithm that computed suggestions about things to avoid knows nothing about histamines. The calculations are done solely on the bacteria increases and decreases they cause.

This cause the following interpretation speculation of these results:

• The likely cause is not enough of good bacteria to keep the bad ones in balance/control
  • The key word is “balance”
• Unless a herb or spice is a strong recommendation, avoid them as much as is practical. There is the appearance that they contribute to inbalance.
  • The same may be said of antibiotics
• Probiotics are unlikely to be a “cure all” with low benefit for most and adverse effects with others

Last item, remember the Microbiome Prescription suggestions are best efforts given the lack of standardization of microbiome tests reporting your results, the lack of standardization of microbiome tests used in published studies, etc. It is a fuzzy logic expert system.

If you have multiple samples, compute suggestions for each one and look for commonality. MCAS is a long duration symptom that persists across multiple samples. Increase your odds by looking for consensus in the To Take, and also aggregate the to avoid into a master list of avoids.

A reader with Multiple Chemistry Sensitivity(MCS) read my Light Sensitivity Exploration post and asked me to look at her sample because her MCS has been getting worse and she is hoping to slow and ideally reverse it. She does not want to become an anchorite with complete isolation from people. On her symptom list it is:

• Comorbid: Multiple Chemical Sensitivity
• Mast Cell Activation Syndrome (Next Post)

Looking at Comorbidity from our contributed data

• 14% of people with Photo Sensitivity have MCS, but 89% have MCAS/Histamine issues
• 17% of people with MCAS/Histamine issues have MCS, but 51% have Photo Sensitivity

	Photo Sensitivity	MCS	MCAS
Photo Sensitivity	427	60	383
MCS		238	135
MCAS			753

I am going to skip the explorations that I did in the earlier post. As with prior post, Odds Ratio has better fine level identification.

	Classic	Odds Ratio
Bacteria Considered	85	103
Bacteria In Common	17	15
Species	6	37
Genus	16	35
Family	24	13
Order	17	10
Class	10	4

Since Light Sensitivity and MCS tends to go hand in hand, I did a comparison of the net Log(odd ratio) between people. A person without these issues is expected to have a Log(Odds Ratio) < 0. This Post’s anchorite has moderate light sensitivity in reality.

Person	Light Sensitivity	MCS
Last Post Person	11.8	17.3
Anchorite	5.7	16.4

This feature is now available on the web site for samples from Biomesight, Ombre, Thorne and uBiome. How to get to it and use it is shown below.

Probiotics Suggestions for MCS

The full list is below (remember only probiotic bacteria reported by Biomesight are included). The list for the Last Post person for MCS was very similar.

Tax_name	Impact
Bifidobacterium longum	2.22
Bifidobacterium adolescentis	1.92
Enterococcus faecalis	1.89
Bifidobacterium breve	1.83
Clostridium butyricum	1.78
Faecalibacterium prausnitzii	1.73
Streptococcus thermophilus	1.54
Bifidobacterium bifidum	0.89
Bifidobacterium catenulatum	0.8
Ruminobacter amylophilus	0.55
Bifidobacterium animalis	0.51
Bifidobacterium pseudocatenulatum	0.51
Enterococcus durans	0.47
Lactobacillus johnsonii	0.4
Leuconostoc mesenteroides	0.33
Roseburia faecis	0.32
Veillonella atypica	0.23
Lacticaseibacillus paracasei	0.2
Phocaeicola coprophilus	0.18
Bacillus velezensis	0.08
Lactobacillus acidophilus	-0.05
Bacteroides thetaiotaomicron	-0.23
Bacteroides uniformis	-0.3
Limosilactobacillus reuteri	-0.3
Bacillus amyloliquefaciens group	-0.3
Lentilactobacillus parakefiri	-0.31
Phocaeicola dorei	-0.32
Bacillus subtilis group	-0.32
Limosilactobacillus vaginalis	-0.32
Enterococcus faecium	-0.33
Bacillus subtilis	-0.7
Lactobacillus helveticus	-0.85
Lactobacillus jensenii	-0.85
Pediococcus acidilactici	-1.05

I should note that Pediococcus acidilactici is a high take for Light Sensitivity and a take for MCS for the light sensitive person. It is a to be avoided for the Anchorite in both cases. This goes back to the old saying “No probiotics can serve two people with the same symptoms” (Matt 6:24, Microbiome Translation).

Take Suggestions

These match the general pattern seen for Long COVID and ME/CFS

Modifier	Net	Take	Avoid
(2->1)-beta-D-fructofuranan {Inulin}	133	137	4
dietary fiber	82	106	25
oligosaccharides {oligosaccharides}	78	90	12
Slow digestible carbohydrates. {Low Glycemic}	77	106	29
Fiber, total dietary	69	91	21
fruit	60	80	21
Lactobacillus plantarum {L. plantarum}	50	67	18
fruit/legume fibre	48	67	19
fructo-oligosaccharides	48	51	3
synthetic disaccharide derivative of lactose {Lactulose}	46	48	2
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)	38	46	8
Cichorium intybus {Chicory}	36	39	3
wheat	35	40	6
Hordeum vulgare {Barley}	34	44	11
whole-grain diet	33	46	13
ß-glucan {Beta-Glucan}	33	38	5
High-fibre diet {Whole food diet}	32	48	16
Bovine Milk Products {Dairy}	32	46	13
resistant starch	32	40	8

Avoid Suggestions

We have a few herbs or spices showing up as an avoid. When we look at MCAS, we see a very atypical avoid list.

Modifier	Net	Take	Avoid
Ferrum {Iron Supplements}	-26	4	30
high-fat diets	-15	9	24
Ethyl alcohol {Grain alcohol}	-8	3	10
high red meat	-7	0	8
5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a]quinolizinium {Berberine}	-7	11	18
Nitrogen Oxide x Particulate Matter {Urban air pollutant}	-6	2	8
High-protein diet {Atkins low-carbohydrate diet}	-6	4	10
vegetarians	-6	4	10
low fodmap diet	-6	8	15
Azadirachta indica {Neem}	-4	0	4
Silver nanoparticles {Colloidal silver}	-4	0	4

Summary

The new offering is easy to use, just follow the video above. Remember, most symptoms are caused by combinations of bacteria that alters the metabolites (chemicals) that the body gets. There are many distinct combinations that can produce a symptom. Above is NOT a general guidance, it shows the results for a specific person using their microbiome. The suggestions for your microbiome may be different. Testing is not optional if you want to make progress.

This morning I got this email:

My daughter’s light sensitivity is now so bad, she’s screaming in pain at daylight  and won’t let her flatmate put up the blinds! Of course it’s related to her autism. Now we’ve uploaded her new sample, is there anything implicated in her current dysbiosis that might lessen this?
She is tormented by this..

I believe we just have enough data to get some traction. I will first use the new Odds Ratio because it give an objective measurement of the importance of each bacteria. Second, I will use the older methodology to simply get a second opinion of which bacteria (unfortunately, this does not indicate importance of each bacteria).

There are three symptom choices related. The difference in count is a reflection of when the symptom was added (the earliest one had the highest count).

• Neurological-Vision: photophobia (Light Sensitivity) 431 samples
• DePaul University Fatigue Questionnaire : Abnormal sensitivity to light 259 samples
• Other:Light sensitivity (photophobia) 5

The sample above was done using biomesight and we have 148 different bacteria using Odds that are statistically significant for increasing or reducing the odds.

The Odds of her having light sensitivity is quite high: log(Odds)=11.8,

These notes document ongoing work on this issue. The goal is both to address her request and to deepen our understanding of how the MP classic method compares to the newer Odds Ratio approach. The MP classic method has produced good results so far, and Odds Ratios may further improve them. For details on how Odds Ratios are calculated, see this related post: Odds Ratio for the Microbiome 101.

In subsequent posts I will look at two symptoms that are very often seen with light sensisitivy:

• Multiple Chemical Sensitivity
• Mast Cell Activation Syndrome

Comparison of “MP Classic” and Odds Ratio Algorithms

Across all symptoms, using Biomesight data, we see consistent patterns in which bacterial levels are involved. The Odds Ratio analysis focuses on more specific bacterial taxa and is therefore more targeted. For example, instead of simply indicating low Lactobacillus, the Odds Ratio can highlight a particular species such as Lactobacillus reuteri. This higher resolution enables more precise selection of probiotics.

Taxonomy Rank	MP Classic	Odds Ratio
Species	1727	13541
Genus	5130	10040
Family	8463	6158
Order	5860	3269
Class	3663	1437

Overview of all Samples

The list of bacteria that DOUBLES or more the odds when present in larger amounts

Bacteria	Rank	Odds Ratio
Salidesulfovibrio	genus	5.9
Salidesulfovibrio brasiliensis	species	5.9
Ethanoligenens	genus	4.9
Peptoniphilus lacrimalis	species	4.3
Slackia faecicanis	species	4.2
Collinsella tanakaei	species	3.8
Finegoldia magna	species	3.5
Viviparoidea	superfamily	3.5
Architaenioglossa	order	3.5
Rivularia	genus	3.5
Viviparidae	family	3.5
Rivularia atra	species	3.5
Rivularia	genus	3.5
Finegoldia	genus	3.4
Lysobacter	genus	3.4
Desulfovibrio fairfieldensis	species	3.3
Aerococcaceae	family	3.3
Anaerococcus	genus	3.2
Streptococcus anginosus	species	3.1
Luteolibacter	genus	3
Luteolibacter algae	species	3
Anaerotruncus colihominis	species	3
Odoribacter denticanis	species	3
Filifactor	genus	2.8
Lactobacillus gallinarum	species	2.8
Peptoniphilus asaccharolyticus	species	2.8
Selenomonas infelix	species	2.7
Corynebacterium striatum	species	2.7
Adlercreutzia equolifaciens	species	2.6
Streptococcus anginosus group	species group	2.6
Glutamicibacter soli	species	2.6
Anaerotruncus	genus	2.5
Rubritaleaceae	family	2.5
Rubritalea	genus	2.5
Gardnerella	genus	2.4
Oscillatoriales	order	2.3
Amedibacillus dolichus	species	2.3
Amedibacillus	genus	2.3
Glutamicibacter	genus	2.2
Anaerococcus prevotii	species	2.2
Azospirillum palatum	species	2.2
Eggerthella sinensis	species	2.2
Sphingomonas abaci	species	2.2
Alcanivorax	genus	2.1
Alcanivoracaceae	family	2.1
Haploplasma	genus	2.1
Haploplasma cavigenitalium	species	2.1
Isoalcanivorax	genus	2.1
Isoalcanivorax indicus	species	2.1
Oscillatoriaceae	family	2.1
Selenomonadales	order	2.1
Nisaea nitritireducens	species	2.1
Anaerococcus tetradius	species	2.1
Selenomonadaceae	family	2.1
Lactobacillus acidophilus	species	2.1
Anaerococcus lactolyticus	species	2.1

On the other end, the bacteria that reduces the odds when present in higher amounts are:

Propionibacteriales	order	0.1
Dyadobacter	genus	0.3
Herbaspirillum magnetovibrio	species	0.3
Calditrichia	class	0.4
Calditrichales	order	0.4
Calditrichaceae	family	0.4
Caldithrix	genus	0.4
Calditrichota	phylum	0.4
Desulfitobacteriaceae	family	0.4
Bifidobacterium adolescentis	species	0.4
Bifidobacterium longum	species	0.4

In terms of probiotics, we see some quick observations: good and bad.

• Two Lactobacillus probiotics significantly increases the odds — i.e. AVOID, especially yogurts!
• Two Bifidobacterium species (and the genus as a whole) significantly decreases the odds — TAKE A LARGER DOSAGE.

Looking at this specific sample

We found no lactobacillus at all, and Bifidobacterium adolescentis is too low. Bifidobacterium longum was found but the amount was significant for reducing the risk.

Getting best probiotics via modelling

This is done using the Correlation Coefficient between bacteria from the R2 site (using the lab specific numbers). We focused solely on the bacteria that increased the odds significantly, and then compute the probiotics (based on only the species what Biomesight reports) that will shift them in the right direction.

Tax_name	Impact
Pediococcus acidilactici	4.28
Bacillus amyloliquefaciens group	3.89
Limosilactobacillus vaginalis	2.95
Bifidobacterium	2.5
Enterococcus faecalis	1.73
Bifidobacterium pseudocatenulatum	1.6
Leuconostoc mesenteroides	1.6
Heyndrickxia coagulans (bacillus coagulans)	1.53
Bifidobacterium longum	1.49
Clostridium butyricum	1.46
Lacticaseibacillus paracasei	1.35
Lactococcus lactis	1.33
Bifidobacterium breve	1.28
Lactobacillus helveticus	1.27
Enterococcus faecium	1.24
Bacillus subtilis group	1.16
Lactiplantibacillus plantarum	1.08
Bifidobacterium bifidum	0.96
Bifidobacterium adolescentis	0.84

Taking these same bacteria using the odds ratios and our usual suggestions engine, we get the following as the top suggestions.

Modifier	Net	Take	Avoid
Slow digestible carbohydrates. {Low Glycemic}	37	52	16
dietary fiber	29	45	16
Fiber, total dietary	24	38	14
fruit	22	34	12
fruit/legume fibre	20	32	12
(2->1)-beta-D-fructofuranan {Inulin}	20	23	3
High-fibre diet {Whole food diet}	19	32	13
oligosaccharides {oligosaccharides}	19	26	6
whole-grain diet	18	25	7
Lactobacillus plantarum {L. plantarum}	17	29	12
bifidobacterium	15	16	1
wheat	12	14	2

The Avoids. I noticed that Bofutsushosan is an avoid. This is a promoter of Akkermansia — which was on our avoid probiotics list. There appears to be reasonable consistency although we are using two different sources and mechanism to get these suggestions.

Modifier	Net	Take	Avoid
high-fat diets	-8	3	11
Ganoderma sichuanense {Reishi Mushroom}	-5	1	6
Pulvis ledebouriellae compositae {Bofutsushosan}	-4	0	5
2-aminoacetic acid {glycine}	-4	0	4
Bacteriophages LH01,T4D,LL12,LL5 {PreforPro}	-4	0	4
laminaria hyperborea {Cuvie}	-4	0	4
low protein diet	-4	1	6
D-glucose {Glucose}	-4	1	6
Ferrum {Iron Supplements}	-4	1	5
Ulmus rubra {slippery elm}	-4	2	6
Honey {Honey }	-4	2	6

Going Old School Suggestions

This is done the usual way but we temporarily clear all of the symptoms and then just marked this single symptom. We are wanting to focus solely on this one horrible symptom.

Clicking on this one symptom, we then get 10 bacteria associated

And also suggestions. I note some agreements between the methods:

• Avoids: Honey, Ganoderma sichuanense {Reishi Mushroom},laminaria hyperborea {Cuvie}, etc
• Takes: whole-grain diet, oligosaccharides
• Disagreement: Bifidobacterium Longum – this gets interesting because the Odds Ratio indicate that the amount of Bifidobacterium Longum present was sufficient to reduce the odds to below 1.0

Summary

I generally favor a consensus of recommendations as the safest course. In this case, my impression is that using Odds Ratios leads to better identification of the bacteria involved (10 versus 24 for this sample), with the added benefit of indicating the relative importance of each bacterium. With Odds Ratios, the thresholds for being too high or too low are symptom-specific, rather than some magical universal cutoff that applies to all conditions.

Believing that there is one magic reference range for any bacteria is simply naive and ignoring the data.

I need to do some more refining of the code as well as enhancement to handle multiple symptoms concurrently; in time, this will be added to the sight.

Using Odds Ratio is now available on the site. The video below shows how to access it.

Technical Notes

Doing a low level comparison between the “classic forecast method” and the “Odds Ratio method I generated the table below. The Odds Ratio identified bacteria at a much more at a finer level (species) and most people would interpret that as being more targeted and likely better outcomes.

Measure	Classic	Odds Ratio
Bacteria Considered	115	148
Bacteria in common	20	20
Species	8	57
Genus	22	51
Family	33	21
Order	23	10
Class	14	3

This also implies that only Genus and Species should be considered with Odds Ratio. Statistically this is preferred to reduce the amount of double counting.

Revisiting Suggestions using only Genus and Species with Odds Ratio

The R2 Probiotics are similar. Most probiotics are more challenging to obtain — see this page for known sources. The avoids are:

• Lactobacillus johnsonii
• Akkermansia muciniphila
• Bacillus subtilis

Note: Pediococcus acidilactici and L.Plantarum (positive) mixtures is likely the easiest to obtain.

Tax_name	Impact	Possible Source
Pediococcus acidilactici	4.28	Imagilin / NutriLots
Bacillus amyloliquefaciens group	3.1	only in big mixtures 🙁
Limosilactobacillus vaginalis	1.79	n/a
Bifidobacterium pseudocatenulatum	1.6	only in big mixtures 🙁
Leuconostoc mesenteroides	1.6	Bulk Probiotics / Leuconostoc Mesenteroides Probiotic Powder
Clostridium butyricum	1.46	Many sources
Lacticaseibacillus paracasei	1.35	danactive drink and many others
Lactococcus lactis	1.33	Bulk Probiotics / Lactococcus Lactis Probiotic Powder
Bifidobacterium	1.04

The To Take List

Modifier	Net	Take	Avoid
Slow digestible carbohydrates. {Low Glycemic}	34	47	12
dietary fiber	29	40	11
Fiber, total dietary	23	35	11
fruit	20	30	10
oligosaccharides {oligosaccharides}	20	24	4
High-fibre diet {Whole food diet}	19	29	10
fruit/legume fibre	19	28	9
whole-grain diet	18	24	5
(2->1)-beta-D-fructofuranan {Inulin}	17	18	1
bifidobacterium	12	12	0
Lactobacillus plantarum {L. plantarum}	11	22	11
wheat	11	12	1
3,3′,4′,5,7-pentahydroxyflavone {Quercetin}	10	11	1
Bovine Milk Products {Dairy}	9	13	4
Human milk oligosaccharides (prebiotic, Holigos, Stachyose)	9	10	1
polyphenols	8	12	4

The To Avoid List

high-fat diets	-6	1	7
Honey {Honey }	-5	1	6
Pulvis ledebouriellae compositae {Bofutsushosan}	-4	0	5
2-aminoacetic acid {glycine}	-4	0	4
laminaria hyperborea {Cuvie}	-4	0	4
Vaccinium myrtillus {Bilberry}	-4	0	4
D-glucose {Glucose}	-4	1	6
Sodium Chloride {Salt}	-4	1	5
Ferrum {Iron Supplements}	-4	1	5
Ulmus rubra {slippery elm}	-4	2	6
2-Amino-5-(carbamoylamino)pentanoic acid {Citrulline}	-3	0	3
Lactotransferrin {Lactoferrin}	-3	0	3
Sus domesticus {Pork}	-3	0	3
Ganoderma sichuanense {Reishi Mushroom}	-3	1	4
low protein diet	-3	1	3
Theobroma cacao {Cacao}	-3	2	5

By Kenneth Lassesen, B.Sc.(Statistics), M.Sc.(Operations Research)

Odds Ratio and Chi2 are two sides of the same coin. The worth of this coin is far more than the fourrées seen with studies using averages.

The simplest case is how often is a specific bacteria reported with the control versus study groups. This is easy computed and can be placed in a table such as the one below

	Control (without Symptom)	Study (or with Symptom)
Bacteria Seen	300	90
Bacteria Not Seen	600	700

Just looking at the table, it is obvious that this bacteria is less likely to be seen in a study group. We can just drop these numbers in a page like this one, and get the results.

Converting to odds ratio is simple:

• Compute odds for study group: $\frac{300}{90}=3.333$90300=3.333.
• Compute odds for control group: $\frac{600}{700}\approx 0.857$700600≈0.857.
• Odds ratio: $OR=\frac{3.333}{0.857}\approx 3.89$ that seeing this bacteria put you likely not in the study group
  • Or 1/3.89 = 0.257 if seeing this bacteria, places you in the study group

Second Tier: The amount

This is identical to the above, except there is a little mathematics needed to compute the best range of bacteria for odds ratio.

At 0.04%	Control (without Symptom)	Study (or with Symptom)
Above or Equal	100	60
Below	200	30

Again a simple computation with great statistical significant.

And again the Odds Ratio is calculated the same as above.

• 100/60 = 1.66
• 200/30 = 6.66
• OR = 1.66 / 6.66 = 0.25 (or 4.00 for the reverse.

We have a tri-state odds ratio

• Bacteria not seen: 0.257 of having symptom (i.e. bacteria is rarely seen with symptom)
• Bacteria see but above or equals to 0.04%: 3.89 * 4 =15.56
• Bacteria see but below 0.04%: 3.89 * .25 = 0.9725, almost no effect.

In this example, we used above or below 0.04%; we could have also used in the range (0.03 to 0.07) or not in the range.

Key points

• Use only bacteria with P < 0.001 or better
  • Check Present or not Present
  • There is a finite enumeration of possible ranges when a bacteria present.
    • With today’s powerful computers, this is not a challenge
• Check all bacteria that satisfies the minimum size constraint for the function used for the 2×2 table

For some symptoms we have:

• over 450 bacteria with significant odds ratios for some conditions.
• Highest Odds ratio over 92 for some bacteria

Performance

This data is based on self-declared symptoms from users. Often the symptoms entered are incomplete (some users had over 100 symptoms entered). While not rigorous, this appears to work for getting sample annotations entered in a citizen science context and for demonstration of the concept. There was enough consistency of data to get results.

The best news: The following had the Odds Ratio > 1.0, over a dozen in the sampling and agreement with entered symptoms.

Source	SymptomName	Accurate %
BiomeSight	Official Diagnosis: Mood Disorders	100
Thryve	DePaul University Fatigue Questionnaire : Frequently get words or numbers in the wrong order	100
Thryve	Autism: More Repetitive Movements	100
Thryve	Autonomic Manifestations: cardiac arrhythmias	100
Thryve	Condition: Acne	100
Thryve	DePaul University Fatigue Questionnaire : Pain in Multiple Joints without Swelling or Redness	100
Thryve	DePaul University Fatigue Questionnaire : Feeling like you have a temperature	100
Thryve	Official Diagnosis: Diabetes Type 1	100
Thryve	Neurological: Spatial instability and disorientation	100
Thryve	Condition: Type 1 Diabetes	100
Thryve	Neuroendocrine Manifestations: abnormal appetite	100
BiomeSight	Autonomic Manifestations: delayed postural hypotension	100
Thryve	Physical: Long term antibiotics(over 6 months)	100
Thryve	Comorbid: Electromagnetic Sensitivity (EMF)	100
BiomeSight	Physical: Bad Air Quality	100
BiomeSight	Neuroendocrine Manifestations: marked diurnal fluctuation	100
Thryve	Physical: Amalgam fillings	100
BiomeSight	Comorbid: Reactive Hypoglicemia	100
Thryve	Comorbid: Sugars cause sleep or cognitive issues	100
BiomeSight	Official Diagnosis: Dermatitis (all types)	100
Thryve	Physical: Steps Per Day 2000-4000	100
Thryve	Neuroendocrine Manifestations: Painful menstrual periods	100
Thryve	General: Anhedonia (inability to feel pleasure)	100
BiomeSight	Virus: Parvovirus positive (B19)	100
BiomeSight	Blood Type: FUT2 secretor	100
Thryve	Official Diagnosis: High Blood Pressure (Hypertension)	100
Thryve	DePaul University Fatigue Questionnaire : Poor hand to eye coodination	100
Thryve	Infection: Coxsackie	100
Thryve	Neuroendocrine Manifestations: marked diurnal fluctuation	100

Looking at the biggest sets. we see very good performance for some symptoms and poor performance for items like gender. Unrefreshing Sleep is interesting:

• Unrefreshed sleep: 88.6% accurate
• Unrefreshing Sleep, that is waking up feeling tired: 36.7% accurate

Is the cause, the fineness of definition (and lack of clarity by users entering) or some other issues?

Source	Symptom	% Correct	Size
BiomeSight	General: Fatigue	98.70317	694
BiomeSight	Neurocognitive: Brain Fog	98.18182	660
BiomeSight	Sleep: Unrefreshed sleep	88.57616	604
BiomeSight	Neurocognitive: Difficulty paying attention for a long period of time	75.54113	462
BiomeSight	Immune Manifestations: Bloating	90.13761	436
BiomeSight	DePaul University Fatigue Questionnaire : Fatigue	85.96491	399
BiomeSight	Gender: Male	59.79644	393
BiomeSight	Comorbid: Histamine or Mast Cell issues	88.0102	392
BiomeSight	Official Diagnosis: COVID19 (Long Hauler)	97.87798	377
BiomeSight	DePaul University Fatigue Questionnaire : Unrefreshing Sleep, that is waking up feeling tired	36.66667	360
BiomeSight	Neurocognitive: Can only focus on one thing at a time	63.76404	356
BiomeSight	Neuroendocrine Manifestations: worsening of symptoms with stress.	70.26239	343
BiomeSight	Neurological-Audio: Tinnitus (ringing in ear)	60.71429	336
BiomeSight	Neurocognitive: Problems remembering things	47.00599	334
BiomeSight	Age: 30-40	97.14286	315
BiomeSight	DePaul University Fatigue Questionnaire : Post-exertional malaise, feeling worse after doing activities that require either physical or mental exertion	92.33227	313
BiomeSight	Neurocognitive: Absent-mindedness or forgetfulness	62.7907	301
BiomeSight	Sleep: Daytime drowsiness	69.33333	300
BiomeSight	Post-exertional malaise: General	85.95318	299
BiomeSight	Immune Manifestations: Constipation	83.22148	298

Lab Performance

Identification by Age exhibits the reality of all labs are not equal. If Odds Ratios from the microbiome was not statistically significant for estimating age, we would see 14% for accuracy. We far exceed that.

Lab	Symptom	Accuracy	Size
BiomeSight	Age: 0-10	86.2	29
Ombre	Age: 0-10	76.3	59
BiomeSight	Age: 10-20	80	25
Ombre	Age: 10-20	94.7	19
BiomeSight	Age: 20-30	58.5	135
Ombre	Age: 20-30	64.7	34
BiomeSight	Age: 30-40	97.1	315
Ombre	Age: 30-40	66.3	104
BiomeSight	Age: 40-50	22.2	203
Ombre	Age: 40-50	71.4	63
BiomeSight	Age: 50-60	29.7	111
Ombre	Age: 50-60	61.7	47
BiomeSight	Age: 60-70	52.5	59
Ombre	Age: 60-70	18.1	83
BiomeSight	Age: 70-80	90	20

This difference of labs is seen with other symptoms — some of which has associations reported in the literature.

Source	SymptomName	Ratio	Size
BiomeSight	General: Depression	67.7	195
Ombre	General: Depression	13.9	108
BiomeSight	General: Fatigue	98.7	694
Ombre	General: Fatigue	20.8	149
BiomeSight	General: Headaches	71.6	197
Ombre	General: Headaches	15.5	103

Summary

The use of odds ratios provides statistically significant evidence for identifying probable symptoms. While not definitive—acknowledging that few diagnostic tests achieve complete certainty—the results demonstrate that both the selected testing method and its interpretation (for example, in relation to bacterial associations) materially influence diagnostic accuracy.

In clinical contexts, reliance on odds ratios offers greater methodological rigor than studies reporting merely “higher or lower levels of certain bacteria with $P<0.05$.” A notable clinical strength of this approach lies in its capacity to generate a structured list of potential symptoms for further inquiry, including those that patients may not have initially disclosed.

Nota Bene: It should be noted that the observed error rate is likely attributable, at least in part, to underreporting of symptoms. Patients often disclose only the symptoms they perceive as most severe, thereby introducing reporting bias into the dataset.

The table below shows the accuracy from 4 different labs. It is not a surprise that Shotgun data is more accurate than 16s tests.

Source	Ratio	Size
BiomeSight – 16s	60.8	45069
Thorne – Shotgun	80.7	491
Ombre/Thryve – 16s	40.8	17123
uBiome – 16s	47.6	13071

I just pushed a new feature that is shown in the video below

The differences of the two methods are:

• Old method looks at the level of bacteria that you have only. This is ideal for making suggestions because we want to alter what is there.
• New, Odds Ratio method, looks at which bacteria were found (or missing) and their levels. Addressing missing bacteria is not trivial — unless it is a known probiotics species (99% are not)

Odds Ratio is likely more accurate because it considers what is there, the amount and what is missing. It is not an ideal choice for computing suggestions.

Remember Odds Ratios do not say you will have a symptom, it merely indicates increased odds of having the symptom.

Example below of two samples taken 5 years apart. Earlier sample, patient was 68, later sample 73. In general reflects well the symptoms reported.