The Journey Begins with your microbiome

Thanks for joining me!

This is a companion site to the analysis site at: https://microbiomeprescription.com/

The intent of this site to assist people with health issues that are, or could be, microbiome connected. There are MANY conditions known to have the severity being a function of the microbiome dysfunction, including Autism, Alzheimer’s, Anxiety and Depression. See this list of studies from the US National Library of Medicine. Individual symptoms like brain fog, anxiety and depression have strong statistical association to the microbiome. A few of them are listed here.

The base rule of the site is to avoid speculation, keep to facts from published studies and to facts from statistical analysis(with the source data available for those wish to replicate the results). Internet hearsay is avoid like the plague it is.

The Microbiome as a Key to Health

Continue reading “The Journey Begins with your microbiome”

Transcribing Lab Results 2025 Update

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 NameBacteria Reported
All Bacteria [Family] Reported128 Bacteria
All Bacteria [Genus] Reported720 Bacteria
All Taxonomies from tests178 Bacteria
Biomefx79 Bacteria
Bioscreen (cfu/gm)30 Bacteria
Biovis Microbiome Plus (cfu/g)40 Bacteria
Chuckling Goat40 Bacteria
DayTwo76 Bacteria
Diagnostic Solution GI-Map (cfu/gm)72 Bacteria
Estudio de Disbiosis: Intestinal + Parasitos13 Bacteria
GanzImmun Diagnostic A6 (cfu/gm)76 Bacteria
GanzImmun Diagnostics AG Befundbericht25 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
HealthPath60 Bacteria
InVitaLab (cfu/gm)23 Bacteria
Kyber Kompakt (cfu/g)11 Bacteria
Laboratorio Teletest202 Bacteria
Medivere Mikrobiom Plus Stuhlanalyse55 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 Maxi73 Bacteria
MyBiota (Austria)133 Bacteria
Nordic Laboratories56 Bacteria
NutriPATH51 Bacteria
Randox Health33 Bacteria
Smart Gut (16s – Limited Taxonomy)23 Bacteria
SynLab (EU)111 Bacteria
Tarmkollen Mega50 Bacteria
TinyHealth140 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.

Bacteria Shifts that are Statistically Significant for Mast Cell Issues

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

BacteriaRankShift
AnaerofustisgenusToo High
Anaerofustis stercorihominisspeciesToo High
LuteibactergenusToo Low
Luteibacter anthropispeciesToo Low

Ombre

BacteriaRankShift
Actinomycetes incertae sedisno rankToo High
ComamonadaceaefamilyToo High
DeinococciclassToo High
DeinococcotaphylumToo High
DesulfocellagenusToo High
Desulfocella halophilaspeciesToo High
EmticiciagenusToo High
HungateiclostridiaceaefamilyToo High
HungateiclostridiumgenusToo High
LimosilactobacillusgenusToo High
Limosilactobacillus fermentumspeciesToo High
ListeriagenusToo High
ListeriaceaefamilyToo High
MethylococcaceaefamilyToo High
MethylococcalesorderToo High
MicrobactergenusToo High
NeisseriaceaefamilyToo High
NeisserialesorderToo High
Oscillatoriales incertae sedisno rankToo High
ParacoccaceaefamilyToo High
PseudoscillatoriagenusToo High
Pseudoscillatoria coraliispeciesToo High
RickettsiagenusToo High
Slackia heliotrinireducensspeciesToo High
SphingobacteriumgenusToo High
StaphylococcusgenusToo High
unclassified BurkholderialesfamilyToo High
unclassified ClostridialesfamilyToo High
VaribaculumgenusToo 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

BacteriaRankShift
Absiella tortuosumspeciesToo High
Actinomycetes incertae sedisno rankToo High
ActinopolysporalesorderToo High
AgaribactergenusToo High
Agaribacter marinusspeciesToo High
AnaeromicropilagenusToo High
Anaeromicropila populetispeciesToo High
BlastocatelliaclassToo High
Cerasicoccus frondisspeciesToo High
Clostridium grantiispeciesToo High
ComamonadaceaefamilyToo High
CryomorphaceaefamilyToo High
DeinococciclassToo High
DeinococcotaphylumToo High
DesulfitobacteriaceaefamilyToo High
DesulfitobacteriumgenusToo High
DesulfobacteriaceaefamilyToo High
DesulfocellagenusToo High
Desulfocella halophilaspeciesToo High
Desulfofarcimen acetoxidansspeciesToo High
DesulfosporosinusgenusToo High
DesulfuromonadaceaefamilyToo High
DesulfuromonadiaclassToo High
EmticiciagenusToo High
FusibactergenusToo High
Gammaproteobacteria incertae sedisno rankToo High
HalopolysporagenusToo High
Halopolyspora albaspeciesToo High
Holdemania massiliensisspeciesToo High
HydrogenibacillusgenusToo High
Hydrogenibacillus schlegeliispeciesToo High
LimosilactobacillusgenusToo High
Limosilactobacillus fermentumspeciesToo High
ListeriagenusToo High
ListeriaceaefamilyToo High
Mesomycoplasma conjunctivaespeciesToo High
MethylococcaceaefamilyToo High
MicrobactergenusToo High
Microbacter margulisiaespeciesToo High
MzabimycetaceaefamilyToo High
NeisseriaceaefamilyToo High
NeisserialesorderToo High
NostocalesorderToo High
Odoribacter laneusspeciesToo High
Oscillatoriales incertae sedisno rankToo High
Oscillibacter valericigenesspeciesToo High
ParacoccaceaefamilyToo High
ParasporobacteriumgenusToo High
PedobactergenusToo High
PlanctomycetalesorderToo High
PlanctomycetiaclassToo High
PlanctomycetotaphylumToo High
PontibacillusgenusToo High
Pontibacillus halophilusspeciesToo High
Porphyromonas someraespeciesToo High
PropioniferaxgenusToo High
Propioniferax innocuaspeciesToo High
Proteinivorax tanatarensespeciesToo High
PseudoramibactergenusToo High
Pseudoramibacter alactolyticusspeciesToo High
PseudorhodobactergenusToo High
PseudoscillatoriagenusToo High
Pseudoscillatoria coraliispeciesToo High
RhodocyclaceaefamilyToo High
RhodocyclalesorderToo High
RickettsiagenusToo High
RickettsiaceaefamilyToo High
RickettsialesorderToo High
RickettsieaetribeToo High
SaccharofermentansgenusToo High
Saccharofermentans acetigenesspeciesToo High
SedimentibactergenusToo High
SphingobacteriumgenusToo High
spotted fever groupspecies groupToo High
Stackebrandtia nassauensisspeciesToo High
StomatobaculumgenusToo High
TexcoconibacillusgenusToo High
Texcoconibacillus texcoconensisspeciesToo High
ThiohalobactergenusToo High
Thiohalobacter thiocyanaticusspeciesToo High
ThiohalobacteraceaefamilyToo High
ThiohalobacteralesorderToo High
ThiohalorhabdaceaefamilyToo High
ThiohalorhabdalesorderToo High
VerrucomicrobiaceaefamilyToo High
WeeksellaceaefamilyToo High

Biomesight

BacteriaRankShift
AcidaminococcusgenusToo Low
Acidaminococcus fermentansspeciesToo Low
ActinomycetesclassToo High
ActinomycetotaphylumToo High
AmedibacillusgenusToo High
Amedibacillus dolichusspeciesToo High
AnaerobrancagenusToo High
Anaerobranca zavarziniispeciesToo High
AnaerolineagenusToo High
Anaerolinea thermolimosaspeciesToo High
AnaerolineaceaefamilyToo High
AnaerolinealesorderToo High
AnaerotruncusgenusToo Low
Anaerotruncus colihominisspeciesToo Low
ArchaeasuperkingdomToo Low
Atopobium fossorspeciesToo Low
AzoarcusgenusToo High
BacteroidaceaefamilyToo Low
BacteroidesgenusToo Low
Bacteroides acidifaciensspeciesToo Low
Bacteroides cellulosilyticusspeciesToo Low
Bacteroides fluxusspeciesToo Low
Bacteroides uniformisspeciesToo Low
BifidobacteriaceaefamilyToo High
BifidobacterialesorderToo High
BifidobacteriumgenusToo High
Bifidobacterium adolescentisspeciesToo High
Bifidobacterium adolescentis JCM 15918strainToo High
Bifidobacterium angulatumspeciesToo High
Bifidobacterium brevespeciesToo Low
Bifidobacterium catenulatum PV20-2strainToo Low
Bifidobacterium catenulatum subsp. kashiwanohensesubspeciesToo Low
Bifidobacterium cuniculispeciesToo Low
Bifidobacterium gallicumspeciesToo High
BilophilagenusToo Low
Bilophila wadsworthiaspeciesToo Low
BlautiagenusToo Low
Caloramator mitchellensisspeciesToo High
Candidatus Tammella caduceiaespeciesToo High
CatenibacteriumgenusToo High
Catenibacterium mitsuokaispeciesToo High
CetobacteriumgenusToo High
ChloroflexotaphylumToo High
CoprococcusgenusToo High
Coprococcus eutactusspeciesToo High
CoraliomargaritagenusToo High
CoraliomargaritagenusToo Low
Coraliomargarita akajimensisspeciesToo High
Coraliomargarita akajimensisspeciesToo Low
CoraliomargaritaceaefamilyToo High
CoraliomargaritaceaefamilyToo Low
DeferribactergenusToo High
Deferribacter autotrophicusspeciesToo High
DeferribacteraceaefamilyToo High
DeferribacteralesorderToo High
DeferribacteresclassToo High
DeferribacterotaphylumToo High
DesulfitobacteriumgenusToo Low
DesulfomonilaceaefamilyToo High
DesulfomonilalesorderToo High
DesulfomoniliaclassToo High
DesulforamulusgenusToo High
Ectothiorhodospira imhoffiispeciesToo High
EntomoplasmataceaefamilyToo Low
EntomoplasmatalesorderToo Low
Eubacterium limosumspeciesToo High
EuryarchaeotaphylumToo Low
FaecalibacteriumgenusToo High
Fusobacterium nucleatumspeciesToo High
HathewayagenusToo Low
Hathewaya histolyticaspeciesToo Low
HelicobactergenusToo High
HelicobactergenusToo Low
HelicobacteraceaefamilyToo High
HelicobacteraceaefamilyToo Low
HoldemanellagenusToo High
Holdemanella biformisspeciesToo High
HoldemaniagenusToo Low
Hoylesella loescheiispeciesToo High
HyphomicrobialesorderToo High
HyphomicrobialesorderToo Low
JohnsonellagenusToo Low
Johnsonella ignavaspeciesToo Low
LachnobacteriumgenusToo High
LactobacillalesorderToo High
LactococcusgenusToo High
LimosilactobacillusgenusToo Low
LuteibactergenusToo High
Luteibacter anthropispeciesToo High
Lysobacter desertispeciesToo High
MesoplasmagenusToo Low
Mesoplasma entomophilumspeciesToo Low
MethanobacteriaclassToo Low
MethanobacteriaceaefamilyToo Low
MethanobacterialesorderToo Low
MethanobrevibactergenusToo Low
Methanobrevibacter smithiispeciesToo Low
Methanomada groupcladeToo Low
Mogibacterium vescumspeciesToo High
MollicutesclassToo High
MycobacteriaceaefamilyToo High
MycobacteriumgenusToo High
MycoplasmatotaphylumToo High
MyxococcalesorderToo High
MyxococciaclassToo High
MyxococcotaphylumToo High
NatranaerobialesorderToo High
PedobactergenusToo Low
Phascolarctobacterium faeciumspeciesToo Low
PhocaeicolagenusToo Low
Phocaeicola massiliensisspeciesToo High
Phocaeicola paurosaccharolyticusspeciesToo Low
PolyangiasubclassToo High
Prevotella dentasinispeciesToo High
PrevotellaceaefamilyToo High
ProsthecobactergenusToo High
ProteinivoraceaefamilyToo High
Ruminococcus callidusspeciesToo High
Schaalia naturaespeciesToo High
SegatellagenusToo High
Segatella coprispeciesToo High
Segatella paludivivensspeciesToo High
Shewanella upeneispeciesToo High
SlackiagenusToo High
Slackia isoflavoniconvertensspeciesToo Low
SphingobiumgenusToo High
Sutterella stercoricanisspeciesToo High
SyntrophalesorderToo High
SyntrophiaclassToo High
SyntrophomonadaceaefamilyToo High
ThermusgenusToo High
Thiothrix ramosaspeciesToo High

Bottom Line

The above data will eventually be incorporated into the expert system suggestions on Microbiome Prescription.

Bacteria Shifts that are Statistically Significant for Long COVID

The process is very simple, for a condition like ME/CFS, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.

Actual example:

  • Tetragenococcus halophilus – Species reported by Biomesight
    • Expected to see 15
    • Actually seen 59
  • In other words almost 4x more common than expected. The probability is
    • 1.68054690853052E-30
    • or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
  • This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]

Biomesight and Ombre identifies bacteria using different methodologies so often give different names and amounts. For background on this lack of standardization, see The taxonomy nightmare before Christmas…

The data below is for samples marked with “Official Diagnosis: COVID19 (Long Hauler)”. Only Biomesight had sufficient data to get patterns.

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_NameTax_RankExpectedObservedShiftProbability
50 kb inversion cladeclade77.354Too Low0.008002
Acinetobacter antiviralisspecies13.724Too High0.00524
Acinetobacter johnsoniispecies18.130Too High0.004944
Actinopolysporagenus62.335Too Low0.001477
Actinopolysporaceaefamily62.335Too Low0.001477
Actinopolysporalesorder62.335Too Low0.001477
Aeromonadaceaefamily81.857Too Low0.006169
Alkalibacteriumgenus112.581Too Low0.005041
Anaerococcus lactolyticusspecies23.238Too High0.002205
Anaerococcus prevotiispecies20.133Too High0.003987
ant, tsetse, mealybug, aphid, etc. endosymbiontsclade82.758Too Low0.006624
Bifidobacterium adolescentis strain103.565Too Low0.002509
Chromatiumgenus61.334Too Low0.00355
Chromatium weisseispecies61.234Too Low0.00355
Chromobacterium groupno rank15.326Too High0.006127
Citrobactergenus64.141Too Low0.003939
Clostridium neonatalespecies13.725Too High0.002196
Cohnellagenus108.678Too Low0.005067
Coraliomargaritagenus96.470Too Low0.00718
Coraliomargarita akajimensisspecies96.370Too Low0.007357
core genistoidsclade77.354Too Low0.008002
Corynebacterium striatumspecies16.928Too High0.006887
Crotalarieaetribe77.354Too Low0.008002
Deferribacteraceaefamily98.271Too Low0.006129
Deferribacteralesorder98.271Too Low0.006129
Deferribacteresclass98.271Too Low0.006129
Deferribacterotaphylum98.271Too Low0.006129
Desulfallaceaefamily148.6108Too Low0.001472
Enterobacter cloacae complexspecies group86.460Too Low0.004516
Enterobacter hormaecheispecies85.457Too Low0.002134
Enterobacteriaceae incertae sedisno rank82.758Too Low0.006624
Erysipelothrix inopinataspecies54.221Too Low4.45E-05
Fabaceaefamily77.354Too Low0.008002
Fabalesorder77.354Too Low0.008002
fabidsclade77.354Too Low0.008002
genistoids sensu latoclade77.354Too Low0.008002
Granulicellagenus16.429Too High0.001841
Granulicella tundricolaspecies16.229Too High0.00148
Hallella bergensisspecies20.133Too High0.003987
Lactobacillus crispatusspecies26.543Too High0.001406
Lactococcusgenus161.5201Too High0.001877
Leptospiragenus89.561Too Low0.002559
Leptospira licerasiaespecies89.461Too Low0.002701
Leptospiraceaefamily89.561Too Low0.002559
Leptospiralesorder89.561Too Low0.002559
Lysinibacillusgenus51.532Too Low0.006618
Lysinibacillus parviboronicapiensspecies50.429Too Low0.002564
Macrococcusgenus118.989Too Low0.006111
Microbacteriaceaefamily99.572Too Low0.005912
Moorella groupnorank152.6188Too High0.004132
Oxalobactergenus130.999Too Low0.005356
Oxalobacter vibrioformisspecies94.965Too Low0.007793
Papilionoideaesubfamily77.354Too Low0.008002
Peptoniphilus lacrimalisspecies51.872Too High0.004884
Piscirickettsiaceaefamily51.529Too Low0.007262
Psychrobactergenus138.999Too Low0.001332
Psychrobacter glacialisspecies75.151Too Low0.00545
rosidsclade77.354Too Low0.008002
Rothiagenus77.354Too Low0.008002
Rothia mucilaginosaspecies64.140Too Low0.002631
Sporotomaculumgenus148.6108Too Low0.001472
Sporotomaculum syntrophicumspecies146.7107Too Low0.001751
Streptococcus massiliensisspecies53.634Too Low0.007353
Syntrophobacteraceaefamily118.383Too Low0.00291
Tetragenococcus halophilusspecies18.059Too High3.63E-22
Thiomicrospiragenus43.726Too Low0.007396
Tolumonasgenus80.755Too Low0.004169
Tolumonas auensisspecies79.954Too Low0.003748
Trabulsiellagenus59.137Too Low0.004074
Vagococcusgenus99.272Too Low0.00718
Varibaculum cambriensespecies17.330Too High0.002302

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 murdochiispecies — sibling high in ME/CFS
Peptoniphilus lacrimalisspecies – HIGH EVERYWHERE
Varibaculumgenus – 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.

A new set of Associations to Symptoms coming

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:

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.

Longevity and the microbiome

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

BacteriaShiftSources
Sutterella Low1
Megamonas Low1,9
OscillospiraHigh2
CollinsellaHigh2
OdoribacterHigh2
TuricibacterHigh2
CampylobacterHigh2
AnaerofustisHigh2
FaecalibacteriumLow2
Burkholderiales Low2
Akkermansia muciniphilaHigh3,4,6,9,11
Eggerthella lentaHigh3
B. uniformisLow3
Eubacterium rectaleHigh3, Low 8,12
Methanobrevibacter smithii,High3,8,12
Escherichia coliHigh3,6,12
Faecalibacterium prausnitziiLow3,6,8,12
C. comesLow3
BifidobacteriumHigh4,6,12
ChristensenellaceaeHigh4,6, 12
Bifidobacterium adolescentisHigh8
LactobacillusHigh9
RuminococceaeHigh12
ProteobacteriaHigh12
Anaerotruncus colihominusHigh12
PorphyromonaceaeHigh12
RikenellaceaeHigh12
RoseburiaHigh12
Bacteroidetes High12

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

Clinical Microbiome Issues: Questions from a Reader

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:

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.

Detecting Dysbiosis using Chi-Square

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.

Possible Paths to healing the Gut

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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Using Ranges based on Averages is BOGUS

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.

Post Natal Supplements and Microbiome

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

NutrientDRI (Pregnancy)
DRI (Lactation)
DRI (Non-Pregnancy)Examples of Common Dietary Sources (Listed Alphabetically)
Carbohydrate175 g/day
210 g/day
130 g/dayFruits, legumes, low-fat dairy products, vegetables (starch and non-starchy), whole grains
Total Fiber28 g/day *
29 g/day *
25 g/day *Fruits, legumes, vegetables, whole grains
Protein71 g/day
71 g/day
46 g/dayAnimal 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 A770 μg RAE/day
1300 μg RAE/day
700 μg RAE/dayApricots, broccoli, carrots, fortified milk and eggs, kale, mangoes, margarine, sweet potatoes
Vitamin C85 mg/day
120 mg/day
75 mg/dayCitrus fruits, kiwifruit, strawberries, vegetables (red pepper, green pepper, broccoli, Brussels sprouts, cabbage)
Vitamin E15 mg/day
19 mg/day
15 mg/dayNuts, plant-based oils, seeds
Vitamin B61.9 mg/day
2.0 mg/day
1.3 mg/dayFish, meat, poultry and whole grains including oats
Vitamin B122.6 μg/day
2.8 μg/day
2.4 μg/dayDairy products, eggs, meat, poultry, seafood
Choline450 mg/day *
550 mg/day *
425 mg/day *Beef and chicken, eggs (with yolk), mushrooms, salmon, wheat germ
Folate600 μg/day
500 μg/day
400 μg/dayBeans, dark green vegetables (including spinach and asparagus), fortified cereals, fortified juices (including orange juice), nuts
Iodine220 μg/day
290 μg/day
150 μg/dayDairy products, fish, iodized salt, seaweed

Microbiome Changes

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]

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 longumBifidobacterium 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.