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):

• Written Description
• Video using a PC

Test Name	Bacteria Reported
All Bacteria [Family] Reported	128 Bacteria
All Bacteria [Genus] Reported	720 Bacteria
All Taxonomies from tests	178 Bacteria
Biomefx	79 Bacteria
Bioscreen (cfu/gm)	30 Bacteria
Biovis Microbiome Plus (cfu/g)	40 Bacteria
Chuckling Goat	40 Bacteria
DayTwo	76 Bacteria
Diagnostic Solution GI-Map (cfu/gm)	72 Bacteria
Estudio de Disbiosis: Intestinal + Parasitos	13 Bacteria
GanzImmun Diagnostic A6 (cfu/gm)	76 Bacteria
GanzImmun Diagnostics AG Befundbericht	25 Bacteria
Genova Gi Effects (cfu/g)	69 Bacteria
Genova Parasitology (cfu/g)	7 Bacteria
GI EcologiX (Invivo)	55 Bacteria
GI360 Stool (UK)	66 Bacteria
Gut Zoomer (vibrant-wellness)	152 Bacteria
HealthPath	60 Bacteria
InVitaLab (cfu/gm)	23 Bacteria
Kyber Kompakt (cfu/g)	11 Bacteria
Laboratorio Teletest	202 Bacteria
Medivere Mikrobiom Plus Stuhlanalyse	55 Bacteria
Medivere: Darm Mikrobiom Stuhltest (16s limited)	16 Bacteria
Medivere: Darn Magen Diagnostik (16s Limited)	16 Bacteria
Medivere: Gesundsheitscheck Darm (16s Limited)	17 Bacteria
Metagenomics Stool (De Meirleir) (16s Limited)	53 Bacteria
Microbiome Healthpath Maxi	73 Bacteria
MyBiota (Austria)	133 Bacteria
Nordic Laboratories	56 Bacteria
NutriPATH	51 Bacteria
Randox Health	33 Bacteria
Smart Gut (16s – Limited Taxonomy)	23 Bacteria
SynLab (EU)	111 Bacteria
Tarmkollen Mega	50 Bacteria
TinyHealth	140 Bacteria
Verisana (cfu/ml) aka (kbe/ml)	11 Bacteria
Viome (Latest Reports Fail to provide ANY measurements)	30 Bacteria

Written Description

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]
  • Bacteria Shifts that are Statistically Significant for ME/CFS

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.

Long COVID appears similar to ME/CFS, so comparing results below to those in this post: Bacteria Shifts that are Statistically Significant for ME/CFS, may provide further insight.

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]
  • Bacteria Shifts that are Statistically Significant for ME/CFS
  • Bacteria Shifts that are Statistically Significant for Long COVID

Not all symptoms have many bacteria associated

A few examples (using Biomesight data). All samples are P < 0.01 (1 in 1000)

• Myalgia (pain)
  • Mobiluncus — too high
  • Peptoniphilus asaccharolyticus — too high
  • Campylobacter ureolyticus — too high
• Headaches
  • Microbacterium — too high
  • Anaerococcus hydrogenalis — too high
  • Eubacterium limosum — too high
  • Peptoniphilus asaccharolyticus — too high
• Recurrent flu-like symptoms
  • Sphingomonas — too high
  • Chromatium — too high
  • Chromatium weissei — too high
• Excessive adrenaline
  • unclassified Bacteroidetes Order II — too low
  • Bifidobacterium adolescentis — too low. Implies that Bifidobacterium adolescentis probiotics may help
• Difficulty reading
  • Bifidobacterium indicum — too low
• Upset stomach
  • Streptococcus anginosus – too high
  • Viridiplantae (kingdom) – green plants! – too high (how this shows up in results, I will leave to Biomesight to explain)
• Tingling feeling
  • Bifidobacterium indicum – too high
  • Prevotella bivia – too low
• Need to nap during each day
  • Kushneria – too low
  • Prevotella bivia – too low
• Difficulty falling asleep
  • Alkalithermobacter thermoalcaliphilus – too low
  • Paraprevotella xylaniphila – too high
• Absent-mindedness
  • Corynebacterium aurimucosum — too low
  • Streptococcus gordonii — too low
  • Catenibacterium mitsuokai – too low
• Mood swings
  • Glaciecola – too high
• Acne
  • Mogibacterium vescum — too low
  • Listeria — too low
  • Listeria innocua — too low
  • Mogibacterium vescum — too low
• Dry Mouth
  • Prevotella bivia — too low
  • Prevotella disiens — too low
  • Clostridium malenominatum — too low

The reasons that there may be few bacteria associated may originate in symptoms being self-declared and there is a wide variety of actual shifts.

Just a quick note on some bacteria are associated with long life,

Bacteria	Shift	Sources
Sutterella	Low	1
Megamonas	Low	1,9
Oscillospira	High	2
Collinsella	High	2
Odoribacter	High	2
Turicibacter	High	2
Campylobacter	High	2
Anaerofustis	High	2
Faecalibacterium	Low	2
Burkholderiales	Low	2
Akkermansia muciniphila	High	3,4,6,9,11
Eggerthella lenta	High	3
B. uniformis	Low	3
Eubacterium rectale	High	3, Low 8,12
Methanobrevibacter smithii,	High	3,8,12
Escherichia coli	High	3,6,12
Faecalibacterium prausnitzii	Low	3,6,8,12
C. comes	Low	3
Bifidobacterium	High	4,6,12
Christensenellaceae	High	4,6, 12
Bifidobacterium adolescentis	High	8
Lactobacillus	High	9
Ruminococceae	High	12
Proteobacteria	High	12
Anaerotruncus colihominus	High	12
Porphyromonaceae	High	12
Rikenellaceae	High	12
Roseburia	High	12
Bacteroidetes	High	12

Sources

1. Does Microbiome Contribute to Longevity? Compositional and Functional Differences in Gut Microbiota in Chinese Long-Living (>90 Years) and Elderly (65-74 Years) Adults 2024
2. Gut Microbiota Composition and Metabolic Potential of Long-Living People in China 2022
3. Shotgun Metagenomics of Gut Microbiota in Humans with up to Extreme Longevity and the Increasing Role of Xenobiotic Degradation 2022
4. Gut Microbiota and Extreme Longevity 2016
5. Gut microbiota and old age: Modulating factors and interventions for healthy longevity 2020
6. Gut microbiota and aging-A focus on centenarians – ScienceDirect 2020
7. The Human Gut Resistome up to Extreme Longevity 2021
8. A Cross-Sectional Study of Compositional and Functional Profiles of Gut Microbiota in Sardinian Centenarians 2019
9. Structural characteristics of gut microbiota in longevity from Changshou town, Hubei, China 2024
10. Comparative analysis of gut microbiota in elderly people of urbanized towns and longevity villages 2015
11. Causal relationship between gut microbiota and ageing: A multi-omics Mendelian randomization study 2025
12. Exploring the Relationship between the Gut Microbiota and Ageing: A Possible Age Modulator 2023

Other studies of interest

• The Power of Environment: A Comprehensive Review of the Exposome’s Role in Healthy Aging, Longevity, and Preventive Medicine—Lessons from Blue Zones and Cilento 2025

A reader sent an email with some good questions. He is pleased with the results. Looking at repeat users of Microbiome Prescription (tried suggestions and came back within a year for more suggestion) is almost 69%. This suggests happy users.

That system works great!

1. 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.
2. 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.
3. 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?
4. 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?
5. 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?
6. 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:

• Over and Under Represented for Symptoms by Lab
• Symptom Association Studies which can be based on many approaches
  • Difference of Average or Difference of Median
  • Chi-square values between top and bottom X%iles
  • etc

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.

A reader wrote:

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:

1. 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.
2. 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.
3. 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

1. Shannon index: Measures both richness and evenness of species in a community.
2. Simpson’s index: Reflects the probability that two randomly selected individuals belong to different species.
3. Chao1 index: Estimates species richness, particularly useful for data sets skewed toward low-abundance classes.
4. Observed number of Amplicon Sequence Variants (ASVs): Counts the number of unique sequences in a sample.
5. Phylogenetic Diversity (PD): Considers the evolutionary relationships between species.
6. ACE (Abundance-based Coverage Estimator) index: Estimates species richness, accounting for rare species.

Beta Diversity Indices

1. Bray-Curtis dissimilarity: Considers both the presence/absence and abundance of species.
2. UniFrac:
   • Unweighted UniFrac: Considers presence/absence of species and their phylogenetic relationships.
   • Weighted UniFrac: Incorporates abundance information along with phylogenetic relationship.
3. 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.

A reader posted on Facebook:

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:

• Reference Ranges are usually suspect from labs (see Using Ranges based on Averages is BOGUS) and compute ranges in a more valid way.
• 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:

1. 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.
2. 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.
3. 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:

1. 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.
2. 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:

1. 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.
2. 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.
3. 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.
4. 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:

1. 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.
2. 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.
3. 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.

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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.

I was messaged by someone expecting a baby who complained about this constant barrage of advertisements on Facebook and in Email for post natal supplements, etc. She wanted to know what actually have science behind it. I approach this in two ways:

• Documented supplements with studies on Pub Med (BEST) focus on human clinical studies (lots of vet studies)
• Microbiome shifts seen from delivery and suggestions to mitigate them

Dietary supplements do not require extensive pre-marketing approval from the U.S. Food and Drug Administration. Manufacturers are responsible to ensure the safety, but do not need to prove the safety and effectiveness of dietary supplements before they are marketed. Dietary supplements may contain multiple ingredients, and differences are often found between labeled and actual ingredients or their amounts. 

Drugs and Lactation Database (LactMed®) – [2023]

There are a number of prenatal studies of interest, i.e. Prenatal Gut Microbiota Predicts Temperament in Offspring at 1-2 Years [2024] but that is out of scope. “Our findings support the maternal-fetal GM axis in the setting of fetal-placental development with subsequent postnatal neurocognitive developmental outcomes, and suggest that early childhood temperament is in part associated with specific GM in the prenatal setting.”

Documented Supplements

• From Nutritional Gaps and Supplementation in the First 1000 Days – [2019] we have the following table (I deleted no change rows)

Nutrient	DRI (Pregnancy) DRI (Lactation)	DRI (Non-Pregnancy)	Examples of Common Dietary Sources (Listed Alphabetically)
Carbohydrate	175 g/day 210 g/day	130 g/day	Fruits, legumes, low-fat dairy products, vegetables (starch and non-starchy), whole grains
Total Fiber	28 g/day * 29 g/day *	25 g/day *	Fruits, legumes, vegetables, whole grains
Protein	71 g/day 71 g/day	46 g/day	Animal sources: Beef, chicken, dairy products, eggs, pork, seafood, turkey Plant sources: Legumes, nuts, quinoa, seeds, soy
Linoleic Acid (Omega-6)	13 g/day * 13 g/day *	12 g/day *	Nuts, seeds, vegetable oils (including soybean, safflower and corn oil)
alpha-Linolenic Acid (Omega-3)	1.4 g/day * 1.3 g/day *	1.1 g/day *	Fatty fish, oils (including flax seed). Smaller amounts found in poultry, meats and eggs
Vitamin A	770 μg RAE/day 1300 μg RAE/day	700 μg RAE/day	Apricots, broccoli, carrots, fortified milk and eggs, kale, mangoes, margarine, sweet potatoes
Vitamin C	85 mg/day 120 mg/day	75 mg/day	Citrus fruits, kiwifruit, strawberries, vegetables (red pepper, green pepper, broccoli, Brussels sprouts, cabbage)
Vitamin E	15 mg/day 19 mg/day	15 mg/day	Nuts, plant-based oils, seeds
Vitamin B6	1.9 mg/day 2.0 mg/day	1.3 mg/day	Fish, meat, poultry and whole grains including oats
Vitamin B12	2.6 μg/day 2.8 μg/day	2.4 μg/day	Dairy products, eggs, meat, poultry, seafood
Choline	450 mg/day * 550 mg/day *	425 mg/day *	Beef and chicken, eggs (with yolk), mushrooms, salmon, wheat germ
Folate	600 μg/day 500 μg/day	400 μg/day	Beans, dark green vegetables (including spinach and asparagus), fortified cereals, fortified juices (including orange juice), nuts
Iodine	220 μg/day 290 μg/day	150 μg/day	Dairy products, fish, iodized salt, seaweed

• Selenium – 100 microgram (µg) selenium yeast tablets
  • Selenium Intake and Postnatal Depression—A Short Review – [2024]
    • a significant inverse association between selenium intake and the risk of postpartum depression [2022]
• Beneficial Effects of Limosilactobacillus reuteri PBS072 and Bifidobacterium breve BB077 on Mood Imbalance, Self-Confidence, and Breastfeeding in Women during the First Trimester Postpartum – PMC (nih.gov) [2023]
  • The Relationship between the Infant Gut Microbiota and Allergy. The Role of Bifidobacterium breve and Prebiotic Oligosaccharides in the Activation of Anti-Allergic Mechanisms in Early Life – [2020]
  • Ability of Bifidobacterium breve 702258 to transfer from mother to infant: the MicrobeMom randomized controlled trial [2023] “The presence of the supplemented strain was detected through at least 2 methods (polymerase chain reaction and culture) in 2 infants in the intervention group (n=2/65; 3.1%) and none in the control group (n=0; 0%; P=.230).”
• Effects of Varied Omega-3 Fatty Acid Supplementation on Postpartum Mental Health and the Association between Prenatal Erythrocyte Omega-3 Fatty Acid Levels and Postpartum Mental Health – [2023] “consumption of α-linolenic acid during pregnancy may stabilize postpartum mental health.”
• Nutritional experiences of postpartum mothers – A qualitative study – [2024] “Most of the practices were beneficial to the mother and a few of them were harmful like avoiding protein-rich foods, few vegetables, most fruits, and night meals. “
• Maternal Diet during Pregnancy and Lactation and Risk of Child Food Allergies and Atopic Allergic Diseases: A Systematic Review [Internet] – [2020] found no significant evidence post natal

Microbiome Changes

• The Postpartum Maternal and Newborn Microbiomes – 2017
  • “the vaginal microbiome dramatically changes composition with an increase in alpha diversity characterized by a decrease in Lactobacillus species.” This hints at these probiotics:
    • L. Crispatus Probiotic Powder (Vagina Support)
    • L. Jensenii Probiotic Powder (Vagina Support)
  • “Human studies have found individuals with major depressive disorder had significantly lower counts of Bifidobacterium and Lactobacillus and variations in bacterial diversity (Aizawa et al., 2016; Jiang et al., 2015).” Hints at the above 3 probiotics being beneficial: L. Crispatus, L.Jensenii and Bifidobacterium breve
  • “When used as a preventative measure, probiotics can help prevent overgrowth of virulent microbial species that contribute to mastitis” [2012]
  • The preventive and therapeutic effects of probiotics on mastitis: A systematic review and meta-analysis – PMC (nih.gov) “The incidence of mastitis in women taking probiotics was significantly lower (49%) than that in women taking the placebo ” lists: Lactobacillus salivarius and Lactobacillus fermentum
    • Ligilactobacillus salivarius PS2 Supplementation during Pregnancy and Lactation Prevents Mastitis: A Randomised Controlled Trial – PMC (nih.gov) “supplementation of L. salivarius PS2 during late pregnancy and early lactation was safe and effective in preventing mastitis, which is one of the main barriers for continuing breastfeeding.”
      

Asthma Risks

One RCT showed that early Lactobacillus rhamnosus GG (LGG) led to a reduction in the cumulative incidence rate of asthma. Another study demonstrated that mixed strains of Lactobacillus paracasei and Lactobacillus fermentum could support clinical improvement in children with asthma while one trial reported a significant reduction in the frequency of asthma exacerbations using a mixture of Ligilactobacillus salivarius and Bifidobacterium breve. 

Postnatal probiotic supplementation can prevent and optimize treatment of childhood asthma and atopic disorders: A systematic review of randomized controlled trials [2022]

• Allergic Patients with Long-Term Asthma Display Low Levels of Bifidobacterium adolescentis [2016]
• “breastfeeding is associated with lower incidence of allergic and autoimmune diseases”[2020]…The microbiome of breastfed infants is dominated by species of Bifidobacterium and Lactobacillus which are known to have immunomodulatory properties and protect against allergies. In contrast, the microbiome of formula-fed infants contains a lower abundance of these beneficial bacteria and is dominated by the Clostridium, Granulicatella, Citrobacter, Enterobacter, and Bilophila species
  • My take is that if the mother is supplementing with Bifidobacterium and Lactobacillus (especially if using powder dissolved in water), then there will be transference to the infant through typical actions (kissing, infant putting fingers in mother’s mouth, etc) see below (UBC study!)
  • Breastmilk Feeding Practices Are Associated with the Co-Occurrence of Bacteria in Mothers’ Milk and the Infant Gut: the CHILD Cohort Study [2020] note:
    • This co-occurrence is reduced when breastmilk is pumped and fed from a bottle
      

Bottom Line

The following probiotics would appear to have benefit post partum

• Lactobacillus Salivarius
• Lactobacillus Fermentum
• Lactobacillus Crispatus
• Lactobacillus Jensenii
• Bifidobacterium Breve

Additionally: Bifidobacterium species associated with breastfeeding produce aromatic lactic acids in the infant gut [2021] identifies Bifidobacterium longum, Bifidobacterium breve and Bifidobacterium bifidum and cites “important for controlling intestinal homoeostasis and immune responses.” in the infant

• Role of Bifidobacteria on Infant Health – [2021] given some reports of adverse reaction from directly giving probiotics to infants and the study cited above finding that the mother taking them can transfer them to the infant, I favor the maternal route.