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[Music]
okay for our next presentation here in
zalza I'm very happy to introduce to you
sopia
Kon um with her ah okay sorry uh so
sopia is a computational biology
researcher in Berlin I was about to say
here in Berlin but we're in Hamburg uh
in Berlin and we're really happy to uh
have sopia with us and she's giving us
the presentation gut feelings can we
optimize lifestyle diet and medication
according to our respective micro biota
sopia your stage
wonderful thank you so much uh I am
properly miked and you can hear me in
the back and so on uh excellent it is
very very much an honor to speak with
you and this has been my first uh visit
to the Congress I am blown away this
feels like home this feels like my
people I will look into ways I can
participate and assist this movement in
whatever way I can anyway enough with
the gushing
um what I want going to talk about
basically
so I approach my re Arch and the
research of my lab into the gut
microbiome from a perspective of
personalized medicine what do I mean by
that well all of us want to be healthy
we want to live long we want to thrive
we want to be happy um and there are a
lot of ways in which we can fail to do
that as we age as we experience various
internal and and external perations so
we've developed all sorts of
interventions nutritional medication
wise lifestyle to try to
prevent disease try to revert disease
try to improve the efficacy of ways of
treating disease in different ways uh
however none of these work consistently
for everyone uh anyone who's tried out a
bunch of medications trying to fix
something we know how difficult that can
be anyone who trying to lose weight will
know how difficult that can be the
problem being the best tools we know of
still do not work consistently in every
person in most cases so the goal of
personal ized prevention or personalized
intervention is to try to see can we
work out what works best for a given
person based on what we know about them
what we know about their life and the
circumstances of their life uh what we
know about their body and the processes
going on in there whether they are
genetic or or shaped
otherwise uh can we make informed
conclusions based on this hideously
complex system that will let us tailor
what a person should eat what drugs they
should take what interventions they
should undergo in their lifestyle to be
able to stay healthy for as long as
possible in as many ways as possible of
course reflecting their personal
definition of of health and disease as
what's an desirable outcome for them so
this is what we want to try to achieve
and there are a few different aspects we
can draw on when we try to do this um
one subfield of this is the idea of
personal association which is what I'll
mainly focus on in this talk the idea
being that perhaps what we eat will be
processed differently in different
individuals and that will lead to
different outcomes on even the same diet
is this true and can we in that case
determine what's best for a given person
to eat to be able to have their body mov
in the direction they
want uh so that's a question I think
that is extremely important not only for
for sort of quality of life as we live
but also for how long our lives
are I approached this
from the perspective of microbiota um
there are no sterile surfaces more or
less in the world certainly none on the
surface or in the mucosal uh components
of the human body all those surfaces are
colonized by billions and billions of
bacteria the most dense uh prevailance
being in intestines but also elsewhere
um throughout the body I'll Focus
primarily on intestine here because that
is most relevant for the topic here here
but we are also researching other
microbiomes and the interconnectedness
between
them these bacterial communities um do
involve pathogens that invade us for a
while cause trouble and then hopefully
afford off or become dormant uh sleeper
Invaders uh but most of them are either
commensals or neutrals so they are can
kind of helpful for us under some
circumstances or their opportunists
waiting for a moment to strike at mostly
kept down by their fellows the typical
human gut has maybe about a 100
different species of bacteria present at
different concentrations but they very
widely between different
individuals we may or may not get some
of them already in utero but we
definitely colonized shortly off the
birth during the process of birth
there's a succession different bacteria
take over that moves forward changes a
bit during puberty it changes definitely
during weaning changes a bit during Age
changes a bit with some things happening
in your lifetime such as a severe viral
infection sometimes or antibiotics
treatment
Etc and it's very very variable
Community between different
individuals we have co-evolved with
these microbes so they have adapted to
us we have adapted to them even though
they will work in some other species
many human bacteria work best in humans
and vice versa uh others are sort of
more
generalists and we have delegated a
bunch of functions to them or relied on
them in our Evolution we are dependent
on them for training our immune system
we dependent on them to keep some other
bacteria away so they sort of policing
their own for us trying to be excellent
to their host and a few other sort of
varying functions including a bunch of
metabolic functions and digestion
functions I am sort of on time
good how do we study these complex
microbiomes well in the old days you
culture stuff look at Aug plates in
microscopes but many of these bacteria
cannot grow on their own they need their
friends around others are basically
intolerant to
oxygen so what we've learned to do once
Next Generation sequencing came online
and became uh less costly and requiring
like the entire budget of a small
country or something for the human
genome um is what is called metagenomic
sequencing so in the case for example
intestinal microbiota we typically take
stool samples they're accessible they do
not necessarily represent the small
intestine super well but it's harder to
get volunteers so let us like go in with
a long colonoscopy you know Etc uh we
get the samples we treat them in a way
to minimize batch effects we use
sometimes automated now setups to
extract bacterial DNA put it into a
sequence and we get out loss and losss
and lots of short text strings of DNA
that we then align interesting
computational problems along that use
that related to our reference databases
to quantify what bacteria are present in
what relative amounts we can sometimes
tell how fast to divide we can tell what
functional capacities with genes en code
and we can get a readout of the
functionality and composition of a given
microbiome which we can summarize
statistically in different
ways uh and that's basically how we try
to do this in most cases when we study
it now um I primarily approach this from
a perspective of clinically relevant
microbiomes which means analyzing
samples from Human donors one way or the
other in many cases this is a
cross-sectional analysis so for example
You' compare say diabetics and
non-diabetics or old and young people or
something like that get the samples
quantify the microbiota do statistical
analysis to check are the differences
between these
groups uh this is challenging in some
ways
because the microbiota in our guts is so
much more different between individuals
than it is between groups so they with
between individual variability is far
larger than any variability between any
two conditions we can still often find
signals but it it necessitates some
interesting sort of noise correction
this is one reason why I really like
longitudinal analysis instead so you
compare each person to themselves over
time before and after an intervention
for example that gives us more
statistical power to achieve this and it
allows us to circumvent also some
confounding influences which I'll get
back to in a
moment I will say however that all of
this is just Association mining so we
end up with uh connection statistically
between one state and another we still
need to prove that there's a relevant
causality there and one obstacle here
comes about specifically in the human
setting because a human being is not
just a body it's a body in a life it's a
body in A Life That's treated by I so
when someone gets sick of course the
physician will prescribe the medications
those medications will also change the
microbiome or there might be indirect
Association someone who has a certain
disease might come from a background
that that makes more likely and that
would also have different effects on the
microbiome one way or the other so it's
hard from just the cross-sectional
comparisons to teleport what is a direct
effect versus an indirect effect or a
bystander so we are working on
statistical methods to get around this
some of it is resolved through the
longitudal rather and cross-sectional
setting uh but it remains something that
has to be born in mind when interpreting
now one thing I really find fascinating
are some of these
medication Compounders that actually
seem to be are potential mediators you
might have a bacterium that's depleted
in the gut of people with diabetes but
that's increased when someone takes an
anti-diabetic medication that would be
an indication perhaps this is both part
of the path mechanism of the disease and
something which we can intervene against
with with a medication but we can pick
up statistically from mining this kind
of combined or mixed patient data so
that's generally exciting to me at least
um still associations are what we have
and until we can prove that something
works it may be risky to put it into
practice in some rare cases one might go
directly to humans and try something but
generally that that
is difficult for legal and ethical
reasons I know many people feel it's
also difficult for for ethical reasons
to do animal experimentation and we
trying to minimize that as far as we can
but these are extremely complicated
systems with a whole community of
different living things inside another
living thing from birth to death it's
very hard to reduce that to just a slice
of cells for example so to prove that a
given microbiome component such as
presence of a bacteria or the depletion
of bacteria causes a clinical phenotype
what we typically have to do is this
kind of colonization experiment where
we'll raise animals in say an incubator
as completely germfree astronaut style
then we might take for example gut
microbiota from someone before a
treatment and after a treatment we put
it into two batches of animals that we
keep apart and we see that's the one
gets sicker than the other for example
can we replicate a positive or negative
phenotype this demonstrates causality
and after that we can typically try to
move into clinical
trials um and this is also an
interesting potentially useful
intervention that some people actually
do and it's sometimes work that you put
stool from one person into another
person and they might get better is kind
of uncontrolled it's not approved for
sort of legal purposes but there are
states where it seems to work that's a
side note that it's not a nutritional
intervention per se um instead the
question I want to ask ask so the sort
of main point of this talk that I've
been trying to lead up to is I want to
ask the question and answer it to the
extent that it is as yet possible on
whether or not we can use understanding
of a person's gut microbiome to
determine what nutrition and also what
medication is good for that person for
them to reach their personal health
goals um and uh there are a few ways
that we know that this can work I'm not
going to talk so much about medications
but there are a few examples I want to
mention for example there are a bunch of
drugs that will be degraded by the acid
in the stomach so you make them in a
modified more resilient form they pass
through the stomach as a prod drug and
then rely on the presence of bacteria in
the intestines to be converted back into
active form now some people have less of
those bacteria for that particular
formulation and we have a lower
concentration of the drug and lower
efficacy this is one example where no
someone's gut microbiota might help in
deciding what medication to prescribe
other similar cases exist for the heart
medicine dioxin for example for typ
diabetes medication AC acros we know of
bacteria present in some people but not
others that degrade the medication so
that it doesn't work as well in those
people either again something that's
good to know then there are exciting
cases like for some psychiatric
medications and for statins that lower
blood lipids
where some gut microbiome compositional
states make the drug work worse or
better uh we don't always know how we
are trying to explore that further as
another example we again we getting
closer and closer some of this and then
there are these super exciting cases
where a drug might actually change the
gut microbiota and that contributes to
Better Health or in some cases
contributes to side effects we showed
this couple years ago most common
anti-diabetic drug met forming for
example that increases carriages on
bacteria that promote metabolic health
and it increases the carriage in some
people or bacteria that may be driving
some of the S side effects like bloating
Etc so there may be ways of optimizing
this of force multiplying it by
targeting the bacteria directly or by
targeting it in Tandem and it might be
that is possible to Prime people for
optimal efficacy or to choose
medications these these are all isolated
examples however we will learn more of
those by studying larger and larger
patient cohorts where we have gut
microbiome data medication track records
and outcome of treatment if we do this
we will be mining this for more things
that can become directions and
guidelines but so far it's only known
for a few cases I anticipate that this
can be very useful in the future at some
point um going however to the uh
nutritional the sort of key nutritional
area
I want to introduce the concept of Prost
prandial surgery so after we eat
something uh we end up having these
blood sugar spikes right so also blood
lipid spikes uh this is heavily
clinically relevant even though what
usually used for diagnosis of diseases
is more often the fasting glucose or
fasting lipid levels uh the spikes will
represent the substantial part of a day
and the spikes might reach far more
dangerous levels too much blood sugar
too much lipid all these are dangerous
all these make the body sort of
overreact and try to sort of over adapt
sugar can actually be toxic to some
areas and membranes and so it's lots of
weird associations that is good reasons
to try to curb these spikes if we can
even though we still need to explore the
epidemiology to know exactly when it
matters the most uh what is known of
course and this is from a study but I'll
talk a little bit more on the next slide
just shows that uh for the same for
different the the same person might have
very different
spikes what they did however and these
are colleagues in Israel who were doing
this for 800 people they had blood uh
glucose sensors on the arm you know
these ones that have become also
possible in the last few years which is
why this is now possible to do all this
has
been possible through wearable
sequencing Tech and computational
Technologies um you can see that the
same person might eat uh no two
different people might the same meal and
have really opposite blood sugar spikes
it's not necessarily common but it does
happen so what they did was they were
able to show that they can in principle
predict the postprandial surges of blood
glucose in this population also transfer
some of it to another population sort of
training and test data uh and they can
trace some of this to uh either the gut
microbiome to the meal or to a
combination of the meal of the gut
microbiome the question is what fraction
or are of those because of course what's
just there for anyone eating this meal
that's not so useful for personalizing
and what's there
for just the microbiome regardless of
what you eat is
also doesn't affect what you what you
should eat it affects how you should
change your microbiome but the
combination is relevant some follow-up
studies were done in the UK and the US
and they still haven't proven that this
always works but this always works super
strongly but we have gone with an
approximate idea that maybe 20% of the
post brandal Spike variability is
derivable to the specifics of the meals
alone about 15% is attributable to the
microbiome and that's about 5% that's
combinatorial which would actually guide
you so okay your microbiome in the case
you should eat this or that this is
still ongoing work they are doing more
trials I'm sure they'll put something
out on the Market but there is nothing
yet where we sort of know that this will
have a Major Impact for your life if you
do it and here it's available so this is
again something that's going to come up
uh and it's a little bit of an open
question how often it's important but
even if it's just a small fraction for
the population as a whole there might
still exist some fraction of people for
which it really does have a lot of
impact so I'm quite hopeful this two can
be
useful um finally I'm interested in ways
in which we we can alter the microbiome
two things uh nutritional interventions
of different kind and this is relevant
because we have identified the number of
associations between microbiome and
health using the methods I told you
before with cross-section longitudinal
code comparisons and in many cases they
have also been validated as causal in
animal model so we can show or we can
induce some diseases by putting stool
from a sick versus healthy person into
different groups of mice meaning we have
a Good Grounds for believing they are
also part of why some people have a
higher risk of some diseases this these
are not radical risk changes these are
increases of risk but those add together
uh and it's also a lot of General
mechanisms that have one affecting one
person one affecting another a lot of
these disease associations that you see
are traceable to sort of underlying
component pathologies increases in blood
pressure increases in systemic
inflammation increases in blood lipids
uh leaky guts so increasing permeability
where bacteria go out into circulation
and Trigger inflammation various
systemic inflammation processes the
immune system learning maladaptive
behaviors a traumatized immune system
starting to develop autoimmunity Etc all
these various things where hopefully the
more we know the more things we can
Target and overall we can decrease these
risks uh by decisions we can take
so when we know that a gut microbi is
associated with disease how can we try
to change it well one thing we do know
is
that nice um it certainly does respond
to diet both habitual long-term diet and
acute immediate changes in diet and
we've got data on this it's not
entirely uh consistent but there's
clearly an effect and the more data we
collect the more exactly we can start to
determine what is true in what
person Beyond this more specifically
Beyond this sort of example of what I
just said is sort of micronutrient
composition change like eat more fiber
eat less cholesterol eat more protein
eat less uh sugar eat more
uh what else eat more
of vegetables whatever uh you get
it
uh my point being that certainly has an
effect it might not be an individualized
effect so much like it might be
something that all of us should do uh to
some degree but where there is an
individualized problem we can also use
various biotic approaches and by that I
mean on one hand Prebiotic so taking
indigestible fibers of different kind we
can't digust them our bacteria can
inulin uh collagen Etc various various
things that are sometimes converted into
helpful anti-inflammatory metabolites
sometimes act in different ways but what
they also do is that they make the
bacteria that likes these for their
nutrition multiply and become a larger
fraction we got community so we can give
food for the bacteria we want to we can
subsidize the bacteria we prefer to
prebiotics
probiotics is adding live bacteria so
yogurts and other forms this certainly
can do a lot of things we don't know yet
exactly when they will stay resident or
when you have to keep on taking them
transiently there might be sort of
complicated core uh Corner cases but
it's definitely one one instrument in
this and it's very clear to understand
how it works and then there's what has
been called postbiotic so things that
are made by bacteria like the products
that they make when they digest these
fibers uh we benefit from those but also
other bacteria that are friends of those
first fermentors may like that for their
nutrition and they will increase numbers
and this is another way to change the
gut community and this is basically why
fermented foods generally seem to be
good for us how is this personalizable
aside from sort of recognizing you might
have a bad starting point and want to
change it well um there's been some
interesting studies in giving people
high fiber diet or Prebiotic diets
including some things from traditional
Chinese medicine and various other sort
of interesting plant compounds that have
been perhaps underexplored
uh they definitely work they improve
metabolic Health in the randomized
control trials that they've done
in but they do so better for the people
who have more of the bacteria at
Baseline that can benefit from theg
grading those compounds and multiply so
the more bacteria you already have who
will uh have a chance to gain dominance
or at least increase in number if you
give them these prebiotics the better
are your chances of achieving also
overall health impact from a treatment
like this like reduced
inflammation uh however even if we find
that sorry you don't actually have so
many FAL bacterium PR that could degrade
this fiber Baseline you are not screwed
because we have ways of increasing that
as well uh one study we did a while back
took uh
hypertensive metabolic syndrome patients
in the south of Berlin and randomly put
them in two groups both started a
metabolic no Mediterranean diet high
fiber Mediterranean diet known to reduce
hypertension but half of them we
randomized into first fasting for a week
and it turns out this primed them so the
fasting changed the microbin composition
in such a way that they were better able
to uh take up uh the changes from this
diet change and thereby reducing
inflammation substantially more more to
a point and this is super relevant the
people for whom this treatment really
made a difference were the ones who
didn't have those bacteria in very high
abundances at Baseline so we can
identify those who lack particular
commensals and in some cases a treatment
like fasting and this also actually
works for intermittent fasting uh that
can change their microbiome to better be
able to benefit from a given nutritional
intervention and we can see Downstream
effect on the population of immune cells
and so on for how this works uh in
principle we can predict this from
what's available uh in these
measurements we did machine learning on
for example immune cell population that
worked
well still this was like 75 people uh
it's still a large study by the
standards of this particular
subfield uh but after we did this I
looked up what has other people done in
similar things where they put people on
some kind of fasting diet and measure
the microbiome before and after and
there's been some studies but many of
them quite few individuals uh we do see
that the bacteria that we were seeing as
the main drivers of the Improvement and
these are this kind of ferments fiber
intering fatty acids particularly BC
acid which is calming the immune system
down and propionic acid which we've
shown reduces blood pressure um we see
those most consistently but there's a
lot of difference between studies in how
often each signal comes up many of these
other earlier studies were smaller still
uh but this is consistently a problem
because what we're trying to do is we're
trying to discover we not very early
dating hypothesis so if we are not to
cheat with our statistics we have to
correct for the fact that we're testing
every possible hypothesis so statistical
power is generally low uh and this
frustrates me I feel that sometimes it
looks like you're looking at entirely
different beasts when you're looking
into these microbiome associations of
different diseases or different
treatments like how can this hang
together how can this be part of the
same Hole uh and the main thing I I I
have to conclude is for that the ways we
currently study it we looking only at a
small part at a time and we see that
part only dependent on the specifics of
our study and specific or study
population and what kind of is closest
to the surface there so the hope would
be that with more data and seeing it
from more angles and integrating that
and also finding out how our particular
Viewpoint biases what we see uh
especially in M analysis and sort of
pulling data together and actually
pulling the raw data together the
sequence reads from movie studies not
just the uh not just the sort of results
uh we might be able to get closer and
get a clearer view of an underlying D
theory of host microbiome symbiosis and
it's its possible modification to
nutritional means so what are the
obstacles for us I'll I'll say that this
is not just true for fasting and the
microbiome this is true for any kind of
omix biology any kind of omix medicine
anything that tries to look at the human
body with all its environmental and host
complexities using hrit methods
replication is difficult and it
shouldn't be we need to get around that
we need to be more sa secure so we can
actually get healthc care providers to
use the recommendations we make we need
uh to be able to prove it to health
insurances and so on so there are a few
hurdles that I would like to overcome
what I believe could help us overcome
this current uncertainty on the
specifics of the mechanisms by which for
example nutritional intervention into
the microbiome
works the main problem is lack of data
this is actually not big data I mean
it's big data in the the level that we
have millions and millions millions and
millions of bacteria from each person or
billions of sequencing reads from from
anyone's transcriptomic experiment but
we typically don't have more than a few
hundred of patients for which we have
high resolution omix data this is
because these techniques are are
expensive but people are hacking these
platforms and building cheaper sequences
sequencers and building cheaper MOS
specs Etc so we're getting there and
hopefully that also means that the uh
restriction of these things for
Diagnostic purposes to just Posh uh
research hospitals might expand and we
might get it all in in in in wider
Scopes also ways of sort of overcoming
Logistics problems we beginning to get a
point where we can send samples by post
for example and still have them
preserved um there's another problem
however in that my view is that with
humans being so utterly complicated the
only way we'll be able to sort of get
across this
elephant problem if you will is to be
able to observe humans not just when we
recruit them for co-ords but every time
they interact with the Healthcare System
consensually Of course uh we need some
way of getting omix data from the
Healthcare System regularly and medical
uh track records like we tried this
medication it didn't work we tried this
medication it didn't work here are
properties of this person that might be
associated with it this is a major
integrity problem and a logistics
problem and a legal problem and there's
been super interesting talks earlier on
the conference on sort of hurdles and
issues in some ways of trying to get
around that and I think this is a
dialogue that I'm hoping we'll keep on
working on so we'll find some ways of
accountably safely effectively let all
of us contribute when we choose to for
research purposes with the data
generated by our actual day-to-day
Healthcare or entirely botom up like
trace your step counters or your glucose
monitors and upload it to some
crowdsourced tool but this is
legislative work also so I'm I'm hoping
to get ideas on Congress I'm already
emerged on how this could be
improved uh another area that's a main
problem in this camera replication
circumstances is when we're trying to
put together what's been published
before on different things because
there's a lot of articles that go out
there and a lot of them are garbage but
even when they're not in many cases they
don't share the data in any standardized
way if they share the data is or free
text in abstract sometimes hidden
between pay walls we can't even get to
the papers so this ties into the wider
like open science movement and the
possibilities of making the work that
actually went into so many people's PE
diseases out there Etc available and
useful for
application uh and for pooling
together I think one of the problems
here is lack of
standardization uh especially of medical
records or of omix Records or details of
experiments there are a bunch of
attempts are like let's make a new
standard let's make this standard and
everyone you use it and sure that's good
when it happens if people do it but
people are selfish and want attention
and they will use their own weird [ __ ]
because that's more fun for them well
maybe we have somewhere but until we do
I am very curious now uh on the
possibility of using something like the
large language models or similar types
of machine learning to look at what
human beings have published and to
compile it in a systematic way so from
the perspective of taking like the
equivalent of me telling a master
student read this paper download the
data and put it into the same format as
those other articles we have for the
metadata file if I could ask like my my
chaty P assistant or whatever to do with
on a systematic scale we could overcome
a lot of these issues and I think that
would be super interesting i' be curious
again during the Congress what ideas
might come up to do this
um what else did I see as a main problem
I think those were kind of my main
problems at that level but there's also
another level and that's the purely
scientific one because I think this is
not all of us there I think the elephant
is more complex in the sense
that perhaps there isn't a simple
biology where this thing does that under
all circumstances it might be this thing
or that thing or that thing to give a
balancing by this thing if this thing is
not in place that determines the actual
outcome and the different studies will
sample different populations where this
or that factor is or isn't there so we
need some way of representing from the
data that we do have and the data that
we will get these kind of oppositional
forces or these kind of balancing forces
I suspect there is some way at looking
at these data matrices where we would
see this as some sort of latent
structure that would show more
consistent signal across data sets so
this is something I would love to work
more on and ideally find more people who
are interested in trying to solve I'm
sure you guys know so much more
mathematics and computer science when I
do so like I'm hoping for ideas hit me
up later
uh and similarly I guess this is kind of
the same idea
that a healthy microbiome seems to look
different between cohorts from different
places different
ages maybe the truth is you don't
actually need this or that you need
either this this or this and then
something from this and then enough from
this and avoid all of these things but
what we have are count data we have
233,000 copies of this bacterium in this
count sample I suppose in this sample
count data behaves
weirdly I believe it should be possible
to mind some sort of assembly rules from
that kind of data when there's enough
data assembled I don't know how but I
would love to find people who have ideas
and explore them because that could
again perhaps give us sort of more
transferable consistent
signatures uh that's kind of what I
wanted to go through so let me again
thank you guys for listening and you
guys for inviting me this was absolutely
glorious I uh adore you uh I want to
thank of course all the people that I
have worked with in some of the work
here what came from me our various
collaborators the various participants
in those studies also the colleagues and
Rivals elsewhere that have done great
work that I have also presented uh I
draw a lot of inspiration from not
inspiration a lot of artwork if I were
taken from the Mo yashim anime you
should go watch it it's an absolutely
amazing anime about microbiomes it's
also got a transition plot that's really
really well done which I know several of
you will appreciate as much as I did and
I use some illustrations from Adventure
Time finally uh we have an international
PhD program that people can apply to uh
if you do apply there and you're
successful you could do a project with
me about something like this or if you
don't collaborate Us in any other way
contact us at any point hit me up at the
Congress hit me up after
and we'll see we'll take it thank
[Applause]
you great so we have uh only a couple of
minutes time um we will do it as follows
because people are already lining up we
are doing uh internet one too so
internet is
first uh the internet is interested to
know your opinion on uh systems where
people share uh biome sample to be
Implement implanted in an other
patient I know there are attempts at
making stool Banks of like healthy
donors uh what's not known yet is to
what extent this should be personalized
there's some indications from a study
done in by my postto lab and their
collaborators at some point that diet
shapes the overall structure but immune
system shapes the fine structure if the
latter is true you might have benefits
changing the overall structure but not
the fine structures of a particular
strains unless you choose a donor that's
related or that you have been around for
a long time so you have some immune
tolerance for but we really don't know
this yet it needs more exploration so
please keep making these Banks please
keep trying it out out and please store
that data and we can try to mine it and
see if we can learn more on when it will
versus will not
work so for everybody who's leaving
please be quiet doing so so Mark for one
next
question I have a question regarding
some specific research on microb
regarding the low car high fet or
sometimes called metabolic U diet that
is known to reduce the glucose levels so
like not to have them and also the
insulin levels that inflects diabetes 2
case cardiovascular disease and in
particular I would be interested in the
effect on microbiome if it was some
research and is there anything you can
tell in short there has been research I
need to read up more on the detail um
the low carb thing here is probably more
a shift in which carbs so avoiding sort
of simple carbs I don't think the
fibrous will be a problem for
example uh there is some risk of high
fat even in a low carp high fat diet
under some circumstances but I think
that in turn might be mitigated by the
change in carbs leading to a change in
inflamat status and therefore reducing
intestinal permeability it certainly has
empirical support and there's been an
unnecessary fear of fat long in dietary
track records but important thing if you
do this is consider which corbs and what
fat uh other than that there's
definitely microbiome associations but I
don't have it off the top of my head
which the O we've tried in a bunch of
things
uh happy to discuss it further and sort
of guide it in some direction thank
you okay microphone two quick concise
we're running out of time um do you see
any difference between supplementing
vitamins with uh pills or just adjusting
them with the food that you choose to
eat we haven't tested this and I'm not
aware if this literature there might be
there probably is but it's not something
I looked into it's a good question and
we should start tracking that more
closely in Co we do
recruit okay I see interest is very high
so uh sopia where can people find you
here on Congress uh I'll be hanging
around here I'll be hanging around
tomorrow uh you
could have I way oh yeah um you can hit
me up on
like uh I mean Google me and you'll find
like my uh usernames I'm no longer on
Twitter but I still use the same
username there and on telegram or signal
Etc I'm sort of at inore analytica on
all platforms I can be you can message
me or like uh do we have deck
numbers not yet okay we don't have deck
numbers yet but we will okay uh thank
you so much super happy to speak with
you guys thanks
Sophia
[Music]
I