November 12, 2024 • 56 mins
Welcome to the Olink® Proteomics in Proximity podcast!
Below are some useful resources mentioned in this episode:
Olink tools and software
Olink® Explore 3072, the platform utilized by the UK Biobank to measure ~3000 proteins in plasma: https://olink.com/products-services/explore/
Olink® Explore HT, Olink’s most advanced solution for high-throughput biomarker discovery, measuring 5400+ proteins simultaneously with a streamlined workflow and industry-leading specificity: https://olink.com/products-services/exploreht/
UK Biobank Pharma Proteomics Project (UKB-PPP), one of the world’s largest scientific studies of blood protein biomarkers conducted to date, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/uk-biobank-launches-one-of-the-largest-scientific-studies
Research articles
The support of human genetic evidence for approved drug indications
Matthew R Nelson et al, Nature Genetics 2015
https://www.nature.com/articles/ng.3314
Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations
M. Austin Argentieri et al, Nature Medicine 2024
https://www.nature.com/articles/s41591-024-03164-7
Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems
Ludger J.E. Goeminne et al, Cell Metabolism 2024, in press
https://www.sciencedirect.com/science/article/abs/pii/S1550413124004017?via%3Dihub
Plasma proteomic associations with genetics and health in the UK Biobank
Benjamin B. Sun et, Nature 2023
https://www.nature.com/articles/s41586-023-06592-6
Rare variant associations with plasma protein levels in the UK Biobank
Ryan S. Dhindsa; Nature 2023
https://www.nature.com/articles/s41586-023-06547-x
Disease prediction with multi-omics and biomarkers empowers case–control genetic discoveries in the UK Biobank
Manik Garg, Nature Genetics, 2024
https://www.nature.com/articles/s41588-024-01898-1
China Kadoorie Biobank: https://www.ckbiobank.org/publications
Publications: https://www.ckbiobank.org/publications
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:05):
Welcome to the Proteomics in Proximity podcast,
where your co-hosts Cindy Lawley and Sarantis Chlamydas from Oink proteomics.
Talk about the intersection of proteomicswith genomics for drug target discovery,
the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
Here we have your host,Cindy, and Sarantis.
Hey there.
Welcome to Proteomics in Proximitywhere Sarantis, and I will be talking to
Cornelia today.
I'll have Sarantisintroduce her in a moment.
But first I wanted to announcea very exciting advance,
in Olink where we have now mergedwith Thermo Fisher Scientific.
(00:46):
So we're part of the Proteomics Servicesdivision within, Thermo Fisher.
And we're definitely going to be talking about
the ability to sort of sequencethe proteome as well as genotype
the proteome in future episodes,because these technologies are
incredibly complementaryunder this umbrella of this exciting
(01:09):
Thermo Fisher Scientific parent company.
And with that, I'm going to allow Sarantisto introduce our guest for today.
We're super excited to have Cornelia here.
Sarantis, please.
Thank you very much, Cindy for the introduction.
Thank you very much, Cornelia
for coming with us, it’sthe last episode before summer holidays.
We are really excited to have with us,Professor Cornelia van Duijn.
(01:30):
She's a professor of epidemiology in the Population Health Department of Oxford University.
And today, we’regoing to talk about your exciting work
and mainly dedicated to aging and age-related diseases.
Cornelia,
would you like to start
telling us a little bitabout your background
and your scientific interestand expertise?
Thank you very much for joining our group.
Sure it's my pleasure to be here.
(01:52):
It's the great pleasure.
But, yeah, my background, I think.
I work in epidemiology again,I studied there, there's an epidemiologist
now one of 30 years agoworking on dementia,
which was in that timestill a forgotten epidemic.
I think everybody swarmed with dementia
in their families, I guess,particularly parents and grandparents.
(02:14):
But in those days, people hadn't heard of the disease, hardly.
Definitely not of Alzheimer's disease
and had difficultygrabbing what Parkinson's disease is.
But, I started out doing the epidemiology,
but then figured that outpretty soon, that the only risk factor
that we could find inthose days was just family history.
(02:38):
So I switched to genetics. And for long.
I did my PhDjust waiting for the good old markers,
the genetic markers,to do the linkage analysis in the family.
It's finding the genes.
So this was waiting for monthsfor a six RFLPs to arrive.
And then two had failed and I went intoanother cycle of waiting and waiting.
(03:02):
So in the end of the day we found genes,and in the end of the day, I was more
than happy that at the technologyemerged to do larger based studies.
And then I went into the genetic associations studies genome wide.
And that was millions of millions
of genetic variantsto study in millions of people
(03:24):
and now finally arrived for metabolomicsfor the age of proteomics.
So that's the backgroundand back to epidemiology, not anymore in
Rotterdam but now in Oxford.
So in epidemiology, I think about thisas a challenging field
because oftenyou're dealing with population level data
sets of community datathat are imperfect, that are messy,
(03:48):
that aren't as clean as I imagine
some of the genetic data sets enable.
Is that a factor in how you'vehow you've evolved your career in bringing
in these omics that now you have something
to associate with that maybe is more
I don't know, it's just really hardto collect environmental data, right?
(04:12):
And epidemiology is plagued with this.
Well, I totally agree with you because I think
if you look at epidemiologyand I'm not only the data analyst,
I also have been able to set up
the different epidemiological studies
and, one of them was a Rotterdam study,
(04:32):
with theelderly people really followed over time.
And it's hard. It's a lot of effort.
And sometimes I wonder that people,young people
who are dealing with all these data nowthink why haven't they done it better?
But it's a huge effort.
Not only the Rotterdam study,we set up large family
(04:53):
based studies, like the Erasmus Rettfeld study
and last but not least Generation R
Wasn’t the leading in there,but I was working on there, setting up
and a study of, little children,followed from utero.
And it is hard.
It's hard to get really a graspon how you capture,
(05:16):
to what people are exposed to.
And then, of course,if you think about people,
the exposures that you have overtime are changing, they’re ever changing.
Your smoking habits,your alcohol habits.
What your weight is and what you'reeating, incredibly changes over time.
(05:37):
And, I think it's the availability,the cost, but also definitely,
what you know is healthy and unhealthy.
So, they're growing inside,but what we’re learning to know now
that it's important to do these studies.
And they have been incredibly helpfulin making the genetics study happen.
(06:03):
It has enabled itthat it would have not been at the states
where it is now without it, but definitely
also, a lot of the future of proteomics will be
in these studiesso we’re depending on them.
That's a great.
No, go a head, Sarantis.
I just wanted to follow up
on this question that you have posed,know, for genetics and proteomics.
(06:27):
Nowadays, for example, these complicateddiseases like Alzheimer's,
do you think one omic is enoughand how you see multi omics in this field?
How you see the challenge that peoplethat are facing of data integration.
What is your feeling on this?
Well, I think, we learn a lot fromgenetics, and I think you can't deny that.
So people have had troubles with itthat once you start doing at scale
(06:50):
as genome wideassociation studies,
we’re just going to the moon,
And then beyond,we were almost going to Mars,
just finding new pathwaysin the disease process.
And then of course, people said,
“well, we knew this, we always get this.”But we finally have it established.
(07:11):
And that is what you do with genomics.
I mean, you can hypothesizethat the complement,
system as one of the immune systems,
that is one of the defenses against that
invasive swarm of bacteria and virusesthat you can have the hypothesis.
And it was there already before,the theory was, that it’s implicated
(07:33):
in your pathogenesis,the development of dementia.
But, you know, genetics nailed it.
It benchmarked it.
It says, well,
if we have genes, I'm not sufficientthere, you’re not going to make it.
In genetics of dementia,we went through the whole series of
we though it's a neuronal diseasebecause your neurons don’t
(07:54):
function anymoreand therefore you're demented.
You forget things,you can’t even comprehend things,
how to put your shoes onand where you should put them.
You put them on your head.
All the things for your brain to work,
of course it's the neurons that dieand that, give you the disease
that will make you forget thingsand not understand things.
(08:17):
But then in the end of the day,what we learned from GWAS
is that the microglia, the helper cellsof your neurons, were much more important.
So definitely we learned a lot of it,what we did not learn.
And that's always
as the scientist, for young scientist,that's even more important, right?
It wasn't the endpoint because whatwe learned from genetics, for instance,
(08:39):
that the apolipoprotein E4 variant
more or less splitsthe population in half,
who gets the disease and determineswho gets the disease early or late.
But you know, it doesn't tell youwhether you get it that 16, 17 or 18.
That is so important for peopleand for that, you need these proteins
(09:02):
or the metabolites, that will tell you.
And that's what we seenow that B tau is telling you that.
But we see also, that other proteins
like GFAP and that NFL
that you can measureeasily that there's also doing that.
And that is incredibly importantand that is what we need to know.
(09:25):
And that is what we need to take further.
So I’ll ask a question now along that genetics line.
Along with Rotterdam study,Generation R, certainly CHARGE initiatives.
And all the cohortsthat are involved in that.
You have been involvedin a lot of really pivotal work
in that population health area.
One of the other BiobanksI've seen you involved with
(09:48):
is the China Kadoorie Biobank.
That's incredibly importantfor our understanding
of East Asian populationsand how they're very different from
what we see in the UK Biobankas just another example.
And I just saw in Oxford
a presentation given by,I believe it was Alfred,
who talked about, GWAS
(10:09):
leveraging proteomicsin the context of genomics
with the clinical data that arethat are available for these cohorts.
Can you talk a little bit about the outliersand liars that we talked about there?
And just explain how proteins are showing signals about lifestyle factors
that I think is pretty compelling.
(10:33):
Yeah, sure I think what has beena breakthrough in that, not with my head
as a geneticist, but with the other headas an epidemiologist,
because after all, I'm a genetic epidemiologist by training.
Is that what the proteomics is giving us
is really the mirror of what happensif you have an exposure that is,
(10:56):
in the case of smoking,I think nobody doubts anymore
that that is shortening your lifespan,is giving you increased risk of cancer,
but also lung diseases,cardiovascular diseases,
and definitely in the end of the timealso it's related to
many a neurological diseases
and neurodegenerative diseases like dementia.
(11:19):
But measuringthese exposures is a nightmare.
And it's difficult for smoking.
And there’s people specializedin how to asses how much you smoke.
But it's quite a difficult task.
So you have to ask,when did you start smoking?
When did you stop smoking?
How much the smoke over time.
(11:39):
Because that everybody thinks, oh,I smoke half a packets or a packet today.
I only have smoke today, 24 cigarettes.
I do have to take another one, would I?
So it's approximation.
Nobody will live like that.
People stop smoking when they're pregnantor the first child is born.
You think I'd have to be more healthy now?
(12:00):
It's quite an effort.
And don't get usstarted as epidemiologists
on something more complex like,alcohol use.
Because alcohol use, we have the month
that we're all asked to be sober Octoberor dry January.
And that becomes even more difficult.
Definitely there is the pregnancy issue.
(12:22):
Definitely there is,once you start being older,
you can't deal with itanymore as well as before.
So what do we do now?
Well,we really ventured out targeted smoking
because it is the major determinantof your life expectancy
and all the diseases that you'll encounter with the old age.
(12:43):
So the question was, what is really
the proteomic profileassociated with smoking?
And see how [---]
really ventured out on this he had an interesting cancer.
And of course lung cancer,very well known as the major outcome.
And what we didsee in the very simple experiment,
(13:07):
seeing whether we could discriminatethose who were never smokers
or told us that were never smokers,
and those who were currently smokingand had been honest about that.
We saw that we would set the data to,I mean, really quite well
or using the proteomics,and then you really talking about
the discrimination of 0.95,
(13:29):
you hardly see that in any epidemiological setting.
Well, that was fantastic.
But we still saw overlapbetween the two groups.
And I know that is the major question.
So if you are a never smoker,you declare yourself as a never smoker,
and then you still have a proteome profile
(13:49):
that looks like
you are quite a heavy smoker.
It raises questionsand that is the fantastic thing.
So we thought, if may be that these people have not been fully honest
or they forgot that they ever smoked
or they didn't want to be remindedof the fact that they ever smoked.
And that is certainly the case.
(14:11):
And we noticed for instance of alcoholthat people say, I’m not drinking alcohol.
And they turned out to be ex usersthat have to stop because some problem
that was related to alcohol,for instance, the liver.
But there's alsoalternative explanations.
And that was the important thingthat, we really soon found out
(14:34):
that if you look at this profile,it's really determined
at least half of itin the general population by smoker.
I used smoker that determineshow high your score is in
what we call P -SIN, how much you've seenin terms of your smoking habits.
But, if you really, look at to other factors
(14:58):
that may determine this score,
how can it beif we talked to a genetic epidemiologist,
we looked at the genes and there's somecontribution of the genes but not big.
If you'll look at the exposures.
Well see all the exposures.
So one of the most fantastic thing is thatwe found that your maternal smoker,
(15:19):
whether you're not a smoker,was popping up.
Whether you were passively smoking,popped up.
How much air pollution was around you, popped up.
But there's also all these factors
that we thought, hey,also obesity pops up.
And if you know a little bitabout smoking, it's
(15:41):
it’s one of the strange things is if you smoke you'll
usually have a lower weightthan nonsmokers.
If you stop smoking, a lot of peoplesay I go obese and I don't want that,
I don't fit in my dress anymore, andI don't look as beautiful as I did before.
So that is affected.
That did not surprise us.
(16:01):
And if think about how to explain this,we also started seeing that
there are probably common pathways whichgo to aging and age related diseases.
With overlapfor instance for obesity and smoking.
That is really what you expectalso because and we don't think
that smoking has a unique pathway.
(16:23):
It may be in your lungs,
I mean, in direct exposure.
The oesophagus also, right.
We all know, that is a problem.
But really if you start thinkinghow it causes aging, of course,
we all knowthat if you ask your pathologist, well,
you will not ask your own pathologist,but that of a another person.
(16:49):
And if you look at the skin,
really,if you look at the in the microscope,
you really see somethingawkward in the smokers.
The skin ages and we all see thatyour throat, you're voice.
Usually, people who are 80 years andhave smoked all their life,
you hear, oh, this is a course voice.
(17:11):
So we do see differences.
But the processesthat are ongoing in your body overlap.
So we also saw that of coursewe think some people don't tell us anymore
whether they smoke.And how much they smoke.
But, we alsothink that there are other reasons.
But some of the reasons are, you know,we can't put our finger on it.
(17:34):
But the other common ones, like obesity,it's the major problem worldwide, so
we see it.
I’ll also correct myself.
It wasn’t Alfred.
Alfred talked about GWAS in the China Kadoorie Biobank,
but it was Sihao that actuallypresented this.
Sihao is a PhD student
(17:54):
who has been working with these dataand looking in the UK B data as well
as corroborating in China KadoorieBiobank, B data, super, super interesting.
So that that piece and that this ideaof having a smoking signature
and an ability to determineand maybe it's, you know,
secondhand smoking and heavy secondhandsmoking or something like that.
(18:14):
But I think being able to parse this outand corroborate the genetics
and the proteomics in any way with,
the epidemiological dataand vice versa is super exciting.
And then, of course, we've talkedon this podcast before about using
genetics to corroborate proteomicsand proteomics to corroborate,
what we're seeing in the, in the geneticsthat have maybe supported
(18:37):
drug programs, for example.
So can we, and this is Sarantis’ absolute area of expertise,
if we could transition to aging,
That's a great point, actually.
You know, I’m intriguing for the factwe say the mothers when they are pregnant
and they're smoking,you see effects on the babies.
There are a lot of studies like that.
That means apart genetics,there are a lot of other factors,
probably epigeneticsthat may influence all of this transition.
(19:01):
And we know for measuring the aging
epigenetic clocks are reallythe gold standard so far.
But proteomics takes a really big
attention and really go to nail
down the details of agingand aging related disease.
Right.
And you have seen these
with your own data and with amazing workwe had with Austin together.
(19:23):
And It would be soon published.
Would you like to say a few wordsabout the biological age
and how proteomics clock enablethe study of biological age?
That'd be great.
Yeah.
I think one of the the golden grailswe're all looking for is how to live long
and how to not to become olderlooking than you are, right.
(19:45):
And it's a it's a golden grail.
And I think this longevityresearch, has been
what has baffled me for
always and I’ve been really working on aging
and dementia now already 30 or something more.
That there was a lot of progress
in the field of animalbased experimental studies.
(20:07):
And they had wonderful findings,whether it was telomeres.
Whether it was on
the basis of protein homeostasis or metabolites.
IGF 1 was a notorious one.
And all these things seem to fit, right.
All the animals,if you look at the animal kingdom
(20:27):
except for the birds, but the smalleranimals live longer than the other animals
and the wonderful study,
dogs in science with undercovera big dog life expectancy 6 to 8 years,
if it’s a Danish dog or a big pointer,
a small dog with a very long lifeexpectancy of 15 years, 20 years.
(20:55):
But it never translated to humans,and that has bothered me forever.
So even something like telomeres again,the Nobel Prize, right.
So as a Nobel Prize on it,it works the most well ever.
And in the animal it works.
Except in humans you do see associations,you do see suggestions.
You don't see a lot, a lot, a lot,
(21:18):
if you translate it to diseaseshas been the breakthrough.
If we look at the proteomics clock now,
and if you look how it
predicts, projectsto diseases, it's phenomenal.
And in that sense if you compare itwith the methylation clock.
Well the first thing I didyou say well whatever we're going to do,
(21:42):
compare first what the overlap iswith the methylation clock.
And I was really understanding that
whatever you find in methylationalso very much goes to this.
I was already up to date
that, you know, a lot on the cancer fieldand methylation, huge progress,
(22:03):
it' seen as a very helpfuland promising field.
But I was actually surprised how few evidence there is
for direct links between theproteomics group and diseases,
and definitely, as with so many diseases
as we see now with the proteomics.
So we were talking a lotthe methylation folks,
(22:26):
and we were just arguing like, okay,we worked a bit on it,
and definitely [---] worked on it
in relation to psychiatric diseases.
But and we were a little bit amazedthat the
overlap between the proteomics and themethylation clocks isn't big.
But what you also sawthat in the methylation clocks
(22:49):
what you usually have to tweak that the
the methylation clocksonly associate to disease.
If you are any focusing all
coding proteins at the methylationthat is related
to genes that are known to be involvedin diseases.
It's not so strangebecause if you really start thinking
(23:11):
what the what methylation does,it will be agnostic.
It's just going all over the genome.
The CPT unit.
And what we know of the genome,only a small fraction
is involved in coding protein.
Now of coursewe all think that in a translation
(23:32):
and RNA regulation is importantin the development of the disease.
But in the end of the day,it's still the protein
who does a lot of the job.
Exactly.
In Alzheimer'sand dementia and vascular dementia,
it's the most important the proteins there.
But I think what we are seeing thatthe proteins are also mentioned in
(23:55):
cardiovascular disease.
And it's not unexpected, is it?
It's it's more I expect that the,
the metabolome for instance, did
much less than the proteome.
And that that brings us back to work thatthat this is probably the field to be in.
It feels like it'sthe druggable aspect of the omics as well.
(24:17):
So the fact that we do have antibody
therapies that are able to targetpathways, I think means that
the translation feels likeit will be more straightforward.
But I thinkwe're only scratching the surface.
I think well, what I tell anyall young people in my group,
(24:37):
and also others that I come across now,is that you really has to invest in this.
And I confess to youand to the world, I always was
a metabolomics fan and I thoughtthat is going to make it happen.
And that is the place to be
because it's the active compounds, it'sthe activated part
(24:57):
and if you compare that nowto the development in proteomics,
I do agree with you, Cindy, it's morethe druggable part in it,
but it's also the part that explainsfor us, the thing is, and that makes you
wonder a little bit what's happening.
It's the phenotype, right?
The proteins are really depicting the real phenotype.
Yeah, definitely.
(25:19):
If you go to CPGs, they are, like, more upstream,like more going to the mechanistic.
That will be other factorsthat may influence.
But at the end, end point is the protein.
The real phenotype is what happened at the protein level, right?
And that's the real picture.
What worries me also a little bit if you are looking at
expression data in the brain, now
and there's oftennot a correlation between the two.
(25:40):
And they often go opposite direction.
So that makes us worry a little bitwhat's going on there.
I mean you should ask ourselves
what will be the height of the dayin five years.
But the idea now is that,it's the proteomics
that matters more than anything else.
Exactly.
It's nice to hear that it's adding value to the data sets we've got already.
(26:05):
I think there's
the in -depth pathway analysis trying to dig into why RNA would go one direction
and proteins would go the other direction.
If we can at least come up with some hypotheses
for any given system why that would be,
for example, maybe the
products are being cleared out to move to a different place where they're being used.
(26:27):
Maybe they're in vesicles or something like that.
Being able to sort of dig in to provide hypotheses for testing the mechanism is exciting.
And it means that if people are listening to this podcast thinking they wanna go do their PhD,
there are so many questions to answer and they should consider going to Oxford,
(26:51):
I will say.
Definitely, definitely.
So I echo that.
I think that, I, I noticed that, and it really is the same as genetics.
I mean, we weren't doing the genetic,the genome wide association study
that we had found three genes for diabetes.
And then people said, oh,we got to find out what these
(27:12):
genes do, and this is probably it.
There's no other genes to be found.
Well, afterwards we found hundreds more.
I mean, that is what we areat the stage with the proteomics.
I mean, this is the start.
It looks fantastic. It looks great.
But we are at the start,this will be an effort of 10, 15 years
(27:35):
like it was with genome association studies
We’ve been working on it,
and we still haven't finalized it,
but we have now, genetic risk factorsthat we all add together,
the picture is becomingcompletely more and more clear.
And in is work in progress.
I mean, we know that from the genetics.
(27:56):
We were staring at the genome-wide association studies.
We said, oh, we don't see amyloidat all in working in the genome
Five years later we go into GWAS andthat was the first pathway was amyloid.
The second pathway,
the third one was pathway.
And we asked, what is happening here?
I asked my friend's colleagueand he said, well, we looked at it too,
(28:18):
but what happened is that the peopledoing more research in the biochemistry
and start linking those genes to amyloid completely.
Now we can go the reverse way.
We we can look at the proteinsassociated with the disease.
And of course with now checkingwhether they also associate
to the genes of the disease
and the exposures related to the disease.
(28:41):
So it's one of the most exciting tangles,
if you are interested in the disease
and understanding disease,but also predicting disease,
it's the breaking point,but don't see it as end points yet.
We are still on the way.
It's a journey. We’re moving up.
I think, you know,I think genetics pay off in the
(29:02):
pharma space has been pretty clear.
I think it's, Matthew Wilson.
I shouldn't say the name,but I think his publication outlined that.
When you have genetic evidencegoing into a program,
you're more than twice as likely to havea successful exit of that of that target.
So I think we're still early dayswith proteomics, but I'm very optimistic
(29:24):
that having proteomicsevidence will further help us with
with demonstrating that, a program is likely to be successful.
So it's
then we'll have to be able to juggleall these hugely successful programs
and get them out into the marketwith the health care system
that maybe unprepared topay for them. But we'll see.
But that's, that's
(29:46):
different problems fordifferent health care systems.
But yeah.
So so both of you, I'd love to understandwhere you see
an ability to have a subset of proteinsthat really help us understand
biological age and how biological agemay not be reflective
of chronological age,how might that actually be useful
(30:08):
in the future as a clinical tool,
It's a great point.
as a direct to consumer tool?
If the ancestry.coms or 23andMe's of the world
build something like this, how might people use it?
What are your thoughts there?
And also to add something here before
Cornelia, you're of course the best person to answer this,
but also to add the fact that now we're not talking about single proteins or single genes,
(30:29):
we're talking about pathways, we're talking about signatures at the end.
And, we see a lot of inflammationcoming with aging.
And I think probably
we have to deep dive a little bit more in inflammation mechanism to understand aging.
But yeah, I'm happy to hear your thoughtshow you see going to the clinics
or how do you see go to the prognosis,for example, from your prospective.
Well, I think well, again,we learn from the genetics.
(30:52):
I think the 23andMe people are interested in
in their genes,either at the risk of the disease,
but it was also in their heritage.
I think if you look in, the UK,we have the ZOE program
where people I'm very much interestedin their microbiome.
Again, it's a field in action.
(31:14):
I can't believe that,people getting the tools
and the final tools in there,but they get an impression
how well their gut microbiome is functioning
based on the state of the artand and the truth on that.
So I, I definitely thinkthat in the direct consumer field,
(31:36):
this is exciting.
This will be interesting.
I can imagine that if you linkyour microbiome to
your aging profile that,that it's even going be more interesting.
And that is where I seethe field also going.
What we trying to dois starting out with the smoking data
(31:59):
What we have to try out now is
to what extent you can revert back your aging profile.
And to me,
based on what my gut feeling is
in there specifically,
is that you probably can hold the processes
(32:19):
as long as you intervene early.
And old age, it's not clear,but I think we have to find that out now.
We don't know. Does it pay the price?
If you are 90 plus to start doingphysical activity.
Well, you ask me,there's also dangers associated with it.
I mean, we all know thatif your hip breaks,
(32:42):
you have a broken hip after the age of 85,
it's one of the strongest predictors of dying.
But I think thatis what we are facing at the
I think, well, the beauty is ofour analysis,
it will give you a readout of interventionsthat we always missed.
I mean, if one of the interventionsthat has been
(33:03):
well pursuited is of course,chlorectristration.
Now, we all know thatthat is quite a harsh job,
because you really have to eatless than you're supposed to eat
Lika a third or something.
It is quite harsh.
And it really goes to this ideathat small animals live longer,
(33:26):
than large animals.
Really small men and women live longer,
than tall men than women.
And, there is a point to that and,that is really targeted at this system.
It's IGF one signaling.
And in all animalsthat is a problem
for living long.
(33:47):
So I think that is one of the outcomes.
But I think it gives us hands and feet now,
to have a readout that
think about the monkey studies,
in caloric restriction.
There's only three, four done.
You have to wait for ages before these monkeys age.
And now we have a readoutthat that is a little bit closer
(34:11):
The readout seems to work already by age 40,
and probably also age 20, 30.
So hey, that must accelerate research also.
And it must give us an insight whether intervention
stopping smoking, don't wait for it just do it.
Too much alcohol.Stop that too.
(34:35):
But physical activity wasif you talk to people in the aging field,
some people are saying, well,maybe good, but wait a minute,
if you're doing other physical activity,also generating a lot of oxidative stress
is that not also cause of aging?
So I think we read it out now.
We can read it out.
(34:56):
It doesn't look that way in our hands.
So it means that totally, you know,some physical activity is good, and
at least also for not only for vascularbut also for the brain.
And I think that kind of opportunities,the multitude to use it now as an outcome.
We have to prove it but it looks that way that it is working.
(35:18):
Well, you hear it here.
Smoking, stop smoking,
drink less alcohol, eat less food,
and do exercise, but not to the extreme, right?
Well,but going back to the point of Sarantis,
I think that inflammation we're all interested in it.
But we also get now other proteins.
(35:40):
That's also interesting and,
what is the other thingthat is pushing us.
And I definitely think that this was the startfor a lot of diseases
and aging,but also age-related diseases,
but also exposures,you know, the plastic exposure.
Nobody knows what it does.
I used to
(36:01):
like if you have a readout for that,that will inform us a little bit
what goes on in the bodyand how worried we should be.
Yeah, PFOS, PFAS,these sort of forever molecules.
Would you.
Would you like to comment,
a little bit about the drug interventionsI mean old drugs.
Old dog, new tricks, like rapamycin for example.
Hg2 inhibitors, now we hearthat they are player or...
(36:23):
What is your feeling about that?
Targeting everything is targeting aging actually?
Or vice versa?
Why do you mention this, Sarantis?
Because we won’t need to study that.
So we have this week a break for what is our low hanging fruit.
Because I knew if you join this field it'snot for the faint hearted.
There's big competition, stiff competitionthat we usually,
(36:45):
we've always been reasonable about it,
that we say, okay, if we see already apublication.
What is our lease?What is are what is the low hanging fruit?
And we definitely haveeverything lined up there
with Sihaoand Austin to do this aging clock.
So but one of the thingsthat we are getting moving to
as a field, of interestis also the clinical application.
(37:08):
We have already done a study that liver and alcohol are big problem.
A big problem is alsothat people don't know
how much alcohol they use, and they don'twant to know how much alcohol they use.
And the produce.
So can we just, distinguished for liver diseases
(37:30):
can we not use this profile for that,then predict how long we will do this?
And I of course,it's used lots of alcohol
and you get the usual diseases,but you also get the cirrhosis
and you get liver cancer.
so here you goso that is what we take as a benchmark.
The other benchmark we definitely we're to use is
(37:51):
how to, serve the certian drugs,how do they act what we know that.
But also what is that unexpected actions.
So this will be negative side effects.
But we all know that some drugs,think about statins you know,
there was time this is, we are working here in the group
that did the most statins research
and you know except that
(38:13):
some people get some muscle painand some very severe ones
there is quite an argument to almostput it in the drinking water, right.
So of course you shouldn’t do that.
But there's also positive effects, sideeffects of the drugs which were never in
the notes you get if you take the drugsbut it's very interesting.
It's very interesting on this act for instance on inflammation and how
(38:37):
so definitelythat is in part our target
and that's also with the waywe're working population health and
we should really resolve these issues.
There's so much opportunity to understand mechanism,
rapamycin,like Serhant has mentioned,
we don't really understandmetformin has some beneficial effects,
(38:58):
but it can also alterhow exercise is, is benefiting us too.
So understanding the mechanism of that,
G... what are the GLP 1s?
I mean, those are acting in the brain.
That's fascinating. Right?
We're really just parsing all that outand it's already almost in the water
for many.
For many populations. Right.
(39:19):
There's just so much opportunitythat I hope proteins can help.
At least,like I said, point to some hypotheses
that can then be testedby groups like yours, Cornelia
So definitely that is a field of interestand I.
But on the other hand,
the exposures of two exposuresto that shouldn’t be there.
The plastics that are built,the pesticides.
(39:42):
I think we see them.
We see that the, you know, that.
And, the fact that there's air pollution in the region,
pops upas having the similar effect of smoking.
And that is not good.
So I think there's a lot of opportunitiesand we need a lot of hands,
but also a lot of brains to do that.
(40:02):
And technologies.
And technologies to do that.
and that definitely,we need more of the protein.
We know that there’s lots more proteins.
We need more, the different isoforms.
We need to know moreabout the phosphorylation and the,
processes of processing of these proteins.
But it it isn't that a fieldthat, you know, we
(40:25):
I'm not I'm not young anymore,but I think yeah, I think we definitely
the future will tell a lot about whatwe always have been wondering about.
To that.
To the point aroundthe needs for this area.
What are the cohorts that come to mind
that are collectingenvironmental information
(40:46):
that you think are oneswe want to highlight and promote?
Because it's like I said, it'shard to collect these sort
of environmental variables.
Are there ones that you particularlylike that you want to make sure,
are successful in the future, continueto collect data, that sort of thing?
I think that there are many cohorts now.
(41:06):
That,of course, has, has really dedicated
their life to look at multiple exposures.
I really favorthe epidemiological setting.
And the reason for that is that,
what you probably, if you single outone exposure,
right, it's unlikely that in your life
(41:27):
you only have one exposureyou need a broader picture.
So I, I'm brought up ina department
in Rotterdamwhere we always, looked and try
to look at the complete picturewith the view that in the end of the day,
you're asking yourselfwhat is the effect of smoking?
(41:49):
Oh, but,you know, if you smoke, you often
more likely to drink a lot of coffee,a lot of alcohol.
You're more likely to use oral contraceptives.
Hey, there’s a lot more things you do.
And, I think that these studieshave been incredibly powerful.
And incredibly important, the UK Biobankis a is a fantastic example on that,
(42:13):
that also data have been gathered,you know, they been adding of data
stacked onto each other.
And that allows you to domulti-omics studies
in a very valid way,but also weigh in exposures.
Now, one of the examplesI would give that convinced me totally
is that you have to look at, multi-omics.
(42:35):
Is that what, we didis look at metabolomics,
and we started thinking, why?
Well, the idea is metabolomicsis genetically determined, but so environment,
is the active component?
And you're really getting quite overwhelmed
(42:57):
if you look at the how strong medicationalso influences metabolomics.
We're now going back the same as Sarantis on proteomics
and for some it’s really overwhelminghow it's medication is, influencing your proteome.
Now look in the most of the epidemiologists
(43:18):
have been wiseand have been gathering data
of a lot of exposuresand that will be helpful.
And definitely the medicationyou need to take that into account.
But, on the other hand,they should look at medication.
The smokers also turned off to be, a confounding factor for that.
(43:39):
But, you know,the fact that both metabolomics
but also proteomicseven more is associated to medication,
suggests what we already have hypothesized
that a lot of medicationis somehow targeting proteome.
Yeah. It's.
It's the messy part of the data. Right?
But it's because we are collecting it across
(44:02):
ideally large numbers of peoplethat signal can emerge
even even though there's challengesin collecting those data.
I think more and more we should include
it also proteomics in in trials.
We should do that.
And it's in clinical trialsin which you test medication.
But please if we dothese intervention trials also show me
(44:25):
that you have an effect of the proteinsthat develop the disease.
and there is our aging work is important,but there's a lot of more,
profiles that we need for dementiain the early phase.
So not the fact that you have P tau,which is just a signal that your head is
full of tau and if your head if full of tau...
It’s one only biomarker, right?
(44:48):
We need something earlier.
We need more.
I don't think that if, physical activity
protects you against dementia,you shouldn’t start with it at age 85.
You should start with that early.
And we've I've read out of studiesthat show death.
That convinced me. And of course, the...
Yeah, a little adviceis that we have on the exposures
(45:10):
are interesting, but we need much more.
We need much more.
On nowadays whatwe are exposed to that even the fact that
our sleep is different, thatwe are exposed to light at night,
that we never were exposed to.
There's a lot to be learned.
And I think that type of trials,
there’s two things on trials for exposures,is the first of all,
(45:34):
they have to be big,even for caloric restrictions.
You see all these smaller studies,people lose weight.
I mean,we have the better outcome, right?
of course we lose weightif you don't eat the calories.
it is obviousthat that will happen.
But we need the readouts of that,that shows us
that it takesreally that it stops aging.
(45:58):
And the trials, I came to Oxford
to the Oxford department, partly
because the trials are so big,but partly I like the spirit
about the trials here, that they haveto be big in order to show things,
because that affects sometimes I, I mean,
(46:18):
are still subtle. I think we got usedto that in the genetics too.
Of course you have genes with big effects,
but a lot of themwill not have that big effect.
It's the aggregate of all the genesand if it's the aggregate of the genes,
it has to bethe aggregate of the proteins to.
Otherwise the effect of theseall these genes don't make sense.
(46:39):
So I think that is what we’re facing that,
we have to start thinking of trialswith complex outcomes.
And we have had a lot of benefit
that coming to Oxford I really wantedto start looking at machine learning too.
And that gave us also a
(47:00):
very much of a boost, I should say.
I'm not saying that machine learningsolves everything, and a definitely not.
You don't hear me say that.
But if you look at the in a simple,even simple
machine learning models, it can dealwith the complexity a bit easier.
And I think we we nailed that down.
(47:22):
And for strong associationslike the proteomics age group,
it really doesn't matter what you takea more classical approach
like elastic net or gradient boosting,which is kind of a random forest
or you take a neural network,but in the end of the day,
it may be that that some of these methodsmay be more powerful
(47:44):
to pick up these aggregates and alsotranslate it back that you get into your hands.
Which plotting is doing what?
If it becomes completely obscurein the neural network,
what has done what?Are you really going to invest
hundreds of millionsto develop in therapy for that?
No, you want to first know,
(48:05):
not too many proteins,
and what is doing what, tell me. Right?
And that is when you have to be able to start out.
And machine learning, it's giving us a lot.
Yeah, yeah. And.
But we have to be carefulabout overtraining.
But that's where having this
growing field of machine learning is informing us.
But I think parsing out, what's the genetic contribution from ancestry?
(48:29):
What's the contribution from gender?
Yeah.
What are the signals in the proteins that confer gender
that you can then use to stratify that?
There's so much complexity that machine learning is helping us to parse out.
Yeah, and that we were lucky.
I think we've been wonderful here in Oxford.
That we have multipleall these cohorts
We have the China Kadoorie Biobank,
(48:51):
Yeah.
you saw that it’s fantasticwhat they are setting up.
We have bigger cohorts in the millionwomen study, but we have also,
the large trials that have been done.
And, you know, even in a trial,you can do now, start thinking
of a silico experimentsthat if the trial has been done
(49:12):
with a certain drugthat you want to repurpose,
you can just measure in that trialwhat the effects of the proteins are.
I think you really have to go.
We have to be intelligible.
And more intelligenton how to repurpose,
and reuse the studies that we had.
But the fact I,I totally agree with people that say,
(49:34):
if you split two data in the trainingand the test set,
if there's structure in your data,
then it’s in your trainingand your test set and then
in my early days using machine learning
in team discovery is that,we figured out the hard way.
We had the test setand training set replicated,
(49:57):
but when we finally looked,
what the neural network was using,
it was using missing data to predictthings like, how is it possible?
How is that possiblethat you can predict with missing data?
There must be something that you can’t input
in that range region well,or there must be a reason for that.
But if the problem with the missingdata is in your training set
(50:21):
it’s also in your test set.
So for us, it'sso important that you can use
data across studies that we could use UKBiobank as a powerhouse
and a powerful tool,
but that we can replicate it in other studies
that are completely independentand that will be important in genetics.
(50:41):
It will also be important in exposures.
And there are more and more studiesthat are integrating, I know of,
Olink proteomics,of course, that are integrating these data
with genetic data that will offeropportunities for collaboration.
Yeah. Awesome.
Well, so I think this is a great placefor us to sort of wrap up.
(51:02):
I'd love to give you a chance to say anylast thoughts that you'd like to share?
Cornelia.
No. Anything that it’s...
The only take home is
well, I already mentionedthe race is not the realm.
I think it's not technology.
You guys are still finding,better ways to quantify the protein.
(51:23):
You are finding better ways to describe the proteome.
That will be ongoing.
I think it's quite excitingto be in this field.
The other way around is that for us, in the data science and
the epidemiology,I think there's a lot of work to do.
A lot of thinking to do how to analyze the data,
(51:45):
how to integrate it over the exposome, the genome.
And then, it'sgoing to be very exciting on that field
telling people how to prevent the diseasebetter, giving them tools to monitor.
And nobody would have thought, 20 years ago
first of all, that we were going not outside
(52:05):
of or housewithout taking the phone.
But, you know,that we would be having Apple Watches.
and Fitbits
Yeah, how many computers do we carry?
For them it was difficult to understandthat we landed on the moon.
And to accept that.
But you know nowadaysthis is the field that’s going to develop
(52:27):
and we’re going to be boosted also on the data analytics,
on the integration of data,
the use of machine learning,the abuse, but also correct that again
And how to translate that back.
You know, it's not only the data sciencethat is relevant.
In the end of the day, it's relevant,
what you do, the impact that you have inin curing people,
(52:52):
in presenting the disease
because you know, if anything in your lifeyou don't will to become diseased,
you want to prevent it. Definitely with dementia,
but also with many other diseases.
It's that translational that counts.
And that is important.
And it's importantthat we all keep that in mind.
(53:13):
Couldn't agree more.
And you all heard it here,
this is the place to go,
Oxford is the place to go for largedata sets.
They're amazing cohorts here and amazing scientists to work with.
So for those of you who are thinkingabout post-docs or PhDs, think about that.
Sarantis, I'll give you a chance to please
It was great.
Any last thought?
It was great to hear from Cornelia, about the aging and aging-related diseases
(53:36):
I think proteins play a really important role on that.
Plasma proteome is on spot now and we can,
using this plasma proteome,we can understand the biology of disease
from different tissue types.
We've also we can also understandof different tissue types, phenotypes
screening only for plasma proteomics.
I think that's the take home message here.
(53:56):
And really niceto have you, Cornelia.
Great, I enjoyed it a lot.
Thank you very much, Cindy.
And yeah I mean thethe last word is for you, Cindy.
Super fun. Super fun!
So thenI'll just go back and double click on.
So I mentioned, the study thatthat demonstrated that having genetic data
going into a clinical trial helpsimprove success by at least two times.
(54:18):
That was actually Matt Nelson.
So apologies for that, 2015,
and that was a Nature Genetics paperreally pivotal paper.
And then of courseAstraZeneca has also published
on their ways of filtering and leveraginggenetic data in different ways.
Gives them a seven times
improvement in clinical trial outcomes,which is which I just wanted to highlight.
And then we also mentioned Austin Argentieri.
(54:41):
We mentioned, Sihao Zhao, a PhD studentand then we also talked
about China Kadoorie Biobank,but we didn't mention Zhengming.
So I want to I want to, give a shout outto the amazing biobank that he's built.
As I understand it, really,a lot of the UK Biobank structure,
was founded in how Zhengming, built out the China Kadoorie Biobank.
(55:05):
So those two are great ones.
And people use them a lotfor corroboration and combining data.
And Austin is a great exampleof someone who's done that so well.
Well, in the future. Get him.
Get him on the podcast.
Perhaps once that paper comes outand that paper will probably be out
by the time we get this podcast,published, I hope so.
(55:26):
We can use this.
This is an opportunityto promote that important work.
And so with with all of that exciting, content that we've talked about today.
And I want to thank you, Cornelia,so much for agreeing to
to come on and trust us withsome of your story.
Thank you very much.
Thank you for having me.
(55:51):
Well, that wraps upthis episode of Proteomics in Proximity.
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
(56:12):
In addition, if you'd be willingto head over to Apple or Spotify
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
So we have a dedicated email addresspip@olink.com
(56:32):
Please reach out.
Let us know what you're interestedin hearing about, what you care about,
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you like to hear about.
Thank you so much and we'll see you soon.
Welcome to the Proteomics in Proximity podcast,
where your co-hosts Cindy Lawley and Sarantis Chlamydas from Oink proteomics.
Talk about the intersection of proteomicswith genomics for drug target discovery,
the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
Here we have your host,Cindy, and Sarantis.
Hey there.
Welcome to Proteomics in Proximitywhere Sarantis, and I will be talking to
Cornelia today.
I'll have Sarantisintroduce her in a moment.
But first I wanted to announcea very exciting advance,
in Olink where we have now mergedwith Thermo Fisher Scientific.
(00:46):
So we're part of the Proteomics Servicesdivision within, Thermo Fisher.
And we're definitely going to be talking about
the ability to sort of sequencethe proteome as well as genotype
the proteome in future episodes,because these technologies are
incredibly complementaryunder this umbrella of this exciting
(01:09):
Thermo Fisher Scientific parent company.
And with that, I'm going to allow Sarantisto introduce our guest for today.
We're super excited to have Cornelia here.
Sarantis, please.
Thank you very much, Cindy for the introduction.
Thank you very much, Cornelia
for coming with us, it’sthe last episode before summer holidays.
We are really excited to have with us,Professor Cornelia van Duijn.
(01:30):
She's a professor of epidemiology in the Population Health Department of Oxford University.
And today, we’regoing to talk about your exciting work
and mainly dedicated to aging and age-related diseases.
Cornelia,
would you like to start
telling us a little bitabout your background
and your scientific interestand expertise?
Thank you very much for joining our group.
Sure it's my pleasure to be here.
(01:52):
It's the great pleasure.
But, yeah, my background, I think.
I work in epidemiology again,I studied there, there's an epidemiologist
now one of 30 years agoworking on dementia,
which was in that timestill a forgotten epidemic.
I think everybody swarmed with dementia
in their families, I guess,particularly parents and grandparents.
(02:14):
But in those days, people hadn't heard of the disease, hardly.
Definitely not of Alzheimer's disease
and had difficultygrabbing what Parkinson's disease is.
But, I started out doing the epidemiology,
but then figured that outpretty soon, that the only risk factor
that we could find inthose days was just family history.
(02:38):
So I switched to genetics. And for long.
I did my PhDjust waiting for the good old markers,
the genetic markers,to do the linkage analysis in the family.
It's finding the genes.
So this was waiting for monthsfor a six RFLPs to arrive.
And then two had failed and I went intoanother cycle of waiting and waiting.
(03:02):
So in the end of the day we found genes,and in the end of the day, I was more
than happy that at the technologyemerged to do larger based studies.
And then I went into the genetic associations studies genome wide.
And that was millions of millions
of genetic variantsto study in millions of people
(03:24):
and now finally arrived for metabolomicsfor the age of proteomics.
So that's the backgroundand back to epidemiology, not anymore in
Rotterdam but now in Oxford.
So in epidemiology, I think about thisas a challenging field
because oftenyou're dealing with population level data
sets of community datathat are imperfect, that are messy,
(03:48):
that aren't as clean as I imagine
some of the genetic data sets enable.
Is that a factor in how you'vehow you've evolved your career in bringing
in these omics that now you have something
to associate with that maybe is more
I don't know, it's just really hardto collect environmental data, right?
(04:12):
And epidemiology is plagued with this.
Well, I totally agree with you because I think
if you look at epidemiologyand I'm not only the data analyst,
I also have been able to set up
the different epidemiological studies
and, one of them was a Rotterdam study,
(04:32):
with theelderly people really followed over time.
And it's hard. It's a lot of effort.
And sometimes I wonder that people,young people
who are dealing with all these data nowthink why haven't they done it better?
But it's a huge effort.
Not only the Rotterdam study,we set up large family
(04:53):
based studies, like the Erasmus Rettfeld study
and last but not least Generation R
Wasn’t the leading in there,but I was working on there, setting up
and a study of, little children,followed from utero.
And it is hard.
It's hard to get really a graspon how you capture,
(05:16):
to what people are exposed to.
And then, of course,if you think about people,
the exposures that you have overtime are changing, they’re ever changing.
Your smoking habits,your alcohol habits.
What your weight is and what you'reeating, incredibly changes over time.
(05:37):
And, I think it's the availability,the cost, but also definitely,
what you know is healthy and unhealthy.
So, they're growing inside,but what we’re learning to know now
that it's important to do these studies.
And they have been incredibly helpfulin making the genetics study happen.
(06:03):
It has enabled itthat it would have not been at the states
where it is now without it, but definitely
also, a lot of the future of proteomics will be
in these studiesso we’re depending on them.
That's a great.
No, go a head, Sarantis.
I just wanted to follow up
on this question that you have posed,know, for genetics and proteomics.
(06:27):
Nowadays, for example, these complicateddiseases like Alzheimer's,
do you think one omic is enoughand how you see multi omics in this field?
How you see the challenge that peoplethat are facing of data integration.
What is your feeling on this?
Well, I think, we learn a lot fromgenetics, and I think you can't deny that.
So people have had troubles with itthat once you start doing at scale
(06:50):
as genome wideassociation studies,
we’re just going to the moon,
And then beyond,we were almost going to Mars,
just finding new pathwaysin the disease process.
And then of course, people said,
“well, we knew this, we always get this.”But we finally have it established.
(07:11):
And that is what you do with genomics.
I mean, you can hypothesizethat the complement,
system as one of the immune systems,
that is one of the defenses against that
invasive swarm of bacteria and virusesthat you can have the hypothesis.
And it was there already before,the theory was, that it’s implicated
(07:33):
in your pathogenesis,the development of dementia.
But, you know, genetics nailed it.
It benchmarked it.
It says, well,
if we have genes, I'm not sufficientthere, you’re not going to make it.
In genetics of dementia,we went through the whole series of
we though it's a neuronal diseasebecause your neurons don’t
(07:54):
function anymoreand therefore you're demented.
You forget things,you can’t even comprehend things,
how to put your shoes onand where you should put them.
You put them on your head.
All the things for your brain to work,
of course it's the neurons that dieand that, give you the disease
that will make you forget thingsand not understand things.
(08:17):
But then in the end of the day,what we learned from GWAS
is that the microglia, the helper cellsof your neurons, were much more important.
So definitely we learned a lot of it,what we did not learn.
And that's always
as the scientist, for young scientist,that's even more important, right?
It wasn't the endpoint because whatwe learned from genetics, for instance,
(08:39):
that the apolipoprotein E4 variant
more or less splitsthe population in half,
who gets the disease and determineswho gets the disease early or late.
But you know, it doesn't tell youwhether you get it that 16, 17 or 18.
That is so important for peopleand for that, you need these proteins
(09:02):
or the metabolites, that will tell you.
And that's what we seenow that B tau is telling you that.
But we see also, that other proteins
like GFAP and that NFL
that you can measureeasily that there's also doing that.
And that is incredibly importantand that is what we need to know.
(09:25):
And that is what we need to take further.
So I’ll ask a question now along that genetics line.
Along with Rotterdam study,Generation R, certainly CHARGE initiatives.
And all the cohortsthat are involved in that.
You have been involvedin a lot of really pivotal work
in that population health area.
One of the other BiobanksI've seen you involved with
(09:48):
is the China Kadoorie Biobank.
That's incredibly importantfor our understanding
of East Asian populationsand how they're very different from
what we see in the UK Biobankas just another example.
And I just saw in Oxford
a presentation given by,I believe it was Alfred,
who talked about, GWAS
(10:09):
leveraging proteomicsin the context of genomics
with the clinical data that arethat are available for these cohorts.
Can you talk a little bit about the outliersand liars that we talked about there?
And just explain how proteins are showing signals about lifestyle factors
that I think is pretty compelling.
(10:33):
Yeah, sure I think what has beena breakthrough in that, not with my head
as a geneticist, but with the other headas an epidemiologist,
because after all, I'm a genetic epidemiologist by training.
Is that what the proteomics is giving us
is really the mirror of what happensif you have an exposure that is,
(10:56):
in the case of smoking,I think nobody doubts anymore
that that is shortening your lifespan,is giving you increased risk of cancer,
but also lung diseases,cardiovascular diseases,
and definitely in the end of the timealso it's related to
many a neurological diseases
and neurodegenerative diseases like dementia.
(11:19):
But measuringthese exposures is a nightmare.
And it's difficult for smoking.
And there’s people specializedin how to asses how much you smoke.
But it's quite a difficult task.
So you have to ask,when did you start smoking?
When did you stop smoking?
How much the smoke over time.
(11:39):
Because that everybody thinks, oh,I smoke half a packets or a packet today.
I only have smoke today, 24 cigarettes.
I do have to take another one, would I?
So it's approximation.
Nobody will live like that.
People stop smoking when they're pregnantor the first child is born.
You think I'd have to be more healthy now?
(12:00):
It's quite an effort.
And don't get usstarted as epidemiologists
on something more complex like,alcohol use.
Because alcohol use, we have the month
that we're all asked to be sober Octoberor dry January.
And that becomes even more difficult.
Definitely there is the pregnancy issue.
(12:22):
Definitely there is,once you start being older,
you can't deal with itanymore as well as before.
So what do we do now?
Well,we really ventured out targeted smoking
because it is the major determinantof your life expectancy
and all the diseases that you'll encounter with the old age.
(12:43):
So the question was, what is really
the proteomic profileassociated with smoking?
And see how [---]
really ventured out on this he had an interesting cancer.
And of course lung cancer,very well known as the major outcome.
And what we didsee in the very simple experiment,
(13:07):
seeing whether we could discriminatethose who were never smokers
or told us that were never smokers,
and those who were currently smokingand had been honest about that.
We saw that we would set the data to,I mean, really quite well
or using the proteomics,and then you really talking about
the discrimination of 0.95,
(13:29):
you hardly see that in any epidemiological setting.
Well, that was fantastic.
But we still saw overlapbetween the two groups.
And I know that is the major question.
So if you are a never smoker,you declare yourself as a never smoker,
and then you still have a proteome profile
(13:49):
that looks like
you are quite a heavy smoker.
It raises questionsand that is the fantastic thing.
So we thought, if may be that these people have not been fully honest
or they forgot that they ever smoked
or they didn't want to be remindedof the fact that they ever smoked.
And that is certainly the case.
(14:11):
And we noticed for instance of alcoholthat people say, I’m not drinking alcohol.
And they turned out to be ex usersthat have to stop because some problem
that was related to alcohol,for instance, the liver.
But there's alsoalternative explanations.
And that was the important thingthat, we really soon found out
(14:34):
that if you look at this profile,it's really determined
at least half of itin the general population by smoker.
I used smoker that determineshow high your score is in
what we call P -SIN, how much you've seenin terms of your smoking habits.
But, if you really, look at to other factors
(14:58):
that may determine this score,
how can it beif we talked to a genetic epidemiologist,
we looked at the genes and there's somecontribution of the genes but not big.
If you'll look at the exposures.
Well see all the exposures.
So one of the most fantastic thing is thatwe found that your maternal smoker,
(15:19):
whether you're not a smoker,was popping up.
Whether you were passively smoking,popped up.
How much air pollution was around you, popped up.
But there's also all these factors
that we thought, hey,also obesity pops up.
And if you know a little bitabout smoking, it's
(15:41):
it’s one of the strange things is if you smoke you'll
usually have a lower weightthan nonsmokers.
If you stop smoking, a lot of peoplesay I go obese and I don't want that,
I don't fit in my dress anymore, andI don't look as beautiful as I did before.
So that is affected.
That did not surprise us.
(16:01):
And if think about how to explain this,we also started seeing that
there are probably common pathways whichgo to aging and age related diseases.
With overlapfor instance for obesity and smoking.
That is really what you expectalso because and we don't think
that smoking has a unique pathway.
(16:23):
It may be in your lungs,
I mean, in direct exposure.
The oesophagus also, right.
We all know, that is a problem.
But really if you start thinkinghow it causes aging, of course,
we all knowthat if you ask your pathologist, well,
you will not ask your own pathologist,but that of a another person.
(16:49):
And if you look at the skin,
really,if you look at the in the microscope,
you really see somethingawkward in the smokers.
The skin ages and we all see thatyour throat, you're voice.
Usually, people who are 80 years andhave smoked all their life,
you hear, oh, this is a course voice.
(17:11):
So we do see differences.
But the processesthat are ongoing in your body overlap.
So we also saw that of coursewe think some people don't tell us anymore
whether they smoke.And how much they smoke.
But, we alsothink that there are other reasons.
But some of the reasons are, you know,we can't put our finger on it.
(17:34):
But the other common ones, like obesity,it's the major problem worldwide, so
we see it.
I’ll also correct myself.
It wasn’t Alfred.
Alfred talked about GWAS in the China Kadoorie Biobank,
but it was Sihao that actuallypresented this.
Sihao is a PhD student
(17:54):
who has been working with these dataand looking in the UK B data as well
as corroborating in China KadoorieBiobank, B data, super, super interesting.
So that that piece and that this ideaof having a smoking signature
and an ability to determineand maybe it's, you know,
secondhand smoking and heavy secondhandsmoking or something like that.
(18:14):
But I think being able to parse this outand corroborate the genetics
and the proteomics in any way with,
the epidemiological dataand vice versa is super exciting.
And then, of course, we've talkedon this podcast before about using
genetics to corroborate proteomicsand proteomics to corroborate,
what we're seeing in the, in the geneticsthat have maybe supported
(18:37):
drug programs, for example.
So can we, and this is Sarantis’ absolute area of expertise,
if we could transition to aging,
That's a great point, actually.
You know, I’m intriguing for the factwe say the mothers when they are pregnant
and they're smoking,you see effects on the babies.
There are a lot of studies like that.
That means apart genetics,there are a lot of other factors,
probably epigeneticsthat may influence all of this transition.
(19:01):
And we know for measuring the aging
epigenetic clocks are reallythe gold standard so far.
But proteomics takes a really big
attention and really go to nail
down the details of agingand aging related disease.
Right.
And you have seen these
with your own data and with amazing workwe had with Austin together.
(19:23):
And It would be soon published.
Would you like to say a few wordsabout the biological age
and how proteomics clock enablethe study of biological age?
That'd be great.
Yeah.
I think one of the the golden grailswe're all looking for is how to live long
and how to not to become olderlooking than you are, right.
(19:45):
And it's a it's a golden grail.
And I think this longevityresearch, has been
what has baffled me for
always and I’ve been really working on aging
and dementia now already 30 or something more.
That there was a lot of progress
in the field of animalbased experimental studies.
(20:07):
And they had wonderful findings,whether it was telomeres.
Whether it was on
the basis of protein homeostasis or metabolites.
IGF 1 was a notorious one.
And all these things seem to fit, right.
All the animals,if you look at the animal kingdom
(20:27):
except for the birds, but the smalleranimals live longer than the other animals
and the wonderful study,
dogs in science with undercovera big dog life expectancy 6 to 8 years,
if it’s a Danish dog or a big pointer,
a small dog with a very long lifeexpectancy of 15 years, 20 years.
(20:55):
But it never translated to humans,and that has bothered me forever.
So even something like telomeres again,the Nobel Prize, right.
So as a Nobel Prize on it,it works the most well ever.
And in the animal it works.
Except in humans you do see associations,you do see suggestions.
You don't see a lot, a lot, a lot,
(21:18):
if you translate it to diseaseshas been the breakthrough.
If we look at the proteomics clock now,
and if you look how it
predicts, projectsto diseases, it's phenomenal.
And in that sense if you compare itwith the methylation clock.
Well the first thing I didyou say well whatever we're going to do,
(21:42):
compare first what the overlap iswith the methylation clock.
And I was really understanding that
whatever you find in methylationalso very much goes to this.
I was already up to date
that, you know, a lot on the cancer fieldand methylation, huge progress,
(22:03):
it' seen as a very helpfuland promising field.
But I was actually surprised how few evidence there is
for direct links between theproteomics group and diseases,
and definitely, as with so many diseases
as we see now with the proteomics.
So we were talking a lotthe methylation folks,
(22:26):
and we were just arguing like, okay,we worked a bit on it,
and definitely [---] worked on it
in relation to psychiatric diseases.
But and we were a little bit amazedthat the
overlap between the proteomics and themethylation clocks isn't big.
But what you also sawthat in the methylation clocks
(22:49):
what you usually have to tweak that the
the methylation clocksonly associate to disease.
If you are any focusing all
coding proteins at the methylationthat is related
to genes that are known to be involvedin diseases.
It's not so strangebecause if you really start thinking
(23:11):
what the what methylation does,it will be agnostic.
It's just going all over the genome.
The CPT unit.
And what we know of the genome,only a small fraction
is involved in coding protein.
Now of coursewe all think that in a translation
(23:32):
and RNA regulation is importantin the development of the disease.
But in the end of the day,it's still the protein
who does a lot of the job.
Exactly.
In Alzheimer'sand dementia and vascular dementia,
it's the most important the proteins there.
But I think what we are seeing thatthe proteins are also mentioned in
(23:55):
cardiovascular disease.
And it's not unexpected, is it?
It's it's more I expect that the,
the metabolome for instance, did
much less than the proteome.
And that that brings us back to work thatthat this is probably the field to be in.
It feels like it'sthe druggable aspect of the omics as well.
(24:17):
So the fact that we do have antibody
therapies that are able to targetpathways, I think means that
the translation feels likeit will be more straightforward.
But I thinkwe're only scratching the surface.
I think well, what I tell anyall young people in my group,
(24:37):
and also others that I come across now,is that you really has to invest in this.
And I confess to youand to the world, I always was
a metabolomics fan and I thoughtthat is going to make it happen.
And that is the place to be
because it's the active compounds, it'sthe activated part
(24:57):
and if you compare that nowto the development in proteomics,
I do agree with you, Cindy, it's morethe druggable part in it,
but it's also the part that explainsfor us, the thing is, and that makes you
wonder a little bit what's happening.
It's the phenotype, right?
The proteins are really depicting the real phenotype.
Yeah, definitely.
(25:19):
If you go to CPGs, they are, like, more upstream,like more going to the mechanistic.
That will be other factorsthat may influence.
But at the end, end point is the protein.
The real phenotype is what happened at the protein level, right?
And that's the real picture.
What worries me also a little bit if you are looking at
expression data in the brain, now
and there's oftennot a correlation between the two.
(25:40):
And they often go opposite direction.
So that makes us worry a little bitwhat's going on there.
I mean you should ask ourselves
what will be the height of the dayin five years.
But the idea now is that,it's the proteomics
that matters more than anything else.
Exactly.
It's nice to hear that it's adding value to the data sets we've got already.
(26:05):
I think there's
the in -depth pathway analysis trying to dig into why RNA would go one direction
and proteins would go the other direction.
If we can at least come up with some hypotheses
for any given system why that would be,
for example, maybe the
products are being cleared out to move to a different place where they're being used.
(26:27):
Maybe they're in vesicles or something like that.
Being able to sort of dig in to provide hypotheses for testing the mechanism is exciting.
And it means that if people are listening to this podcast thinking they wanna go do their PhD,
there are so many questions to answer and they should consider going to Oxford,
(26:51):
I will say.
Definitely, definitely.
So I echo that.
I think that, I, I noticed that, and it really is the same as genetics.
I mean, we weren't doing the genetic,the genome wide association study
that we had found three genes for diabetes.
And then people said, oh,we got to find out what these
(27:12):
genes do, and this is probably it.
There's no other genes to be found.
Well, afterwards we found hundreds more.
I mean, that is what we areat the stage with the proteomics.
I mean, this is the start.
It looks fantastic. It looks great.
But we are at the start,this will be an effort of 10, 15 years
(27:35):
like it was with genome association studies
We’ve been working on it,
and we still haven't finalized it,
but we have now, genetic risk factorsthat we all add together,
the picture is becomingcompletely more and more clear.
And in is work in progress.
I mean, we know that from the genetics.
(27:56):
We were staring at the genome-wide association studies.
We said, oh, we don't see amyloidat all in working in the genome
Five years later we go into GWAS andthat was the first pathway was amyloid.
The second pathway,
the third one was pathway.
And we asked, what is happening here?
I asked my friend's colleagueand he said, well, we looked at it too,
(28:18):
but what happened is that the peopledoing more research in the biochemistry
and start linking those genes to amyloid completely.
Now we can go the reverse way.
We we can look at the proteinsassociated with the disease.
And of course with now checkingwhether they also associate
to the genes of the disease
and the exposures related to the disease.
(28:41):
So it's one of the most exciting tangles,
if you are interested in the disease
and understanding disease,but also predicting disease,
it's the breaking point,but don't see it as end points yet.
We are still on the way.
It's a journey. We’re moving up.
I think, you know,I think genetics pay off in the
(29:02):
pharma space has been pretty clear.
I think it's, Matthew Wilson.
I shouldn't say the name,but I think his publication outlined that.
When you have genetic evidencegoing into a program,
you're more than twice as likely to havea successful exit of that of that target.
So I think we're still early dayswith proteomics, but I'm very optimistic
(29:24):
that having proteomicsevidence will further help us with
with demonstrating that, a program is likely to be successful.
So it's
then we'll have to be able to juggleall these hugely successful programs
and get them out into the marketwith the health care system
that maybe unprepared topay for them. But we'll see.
But that's, that's
(29:46):
different problems fordifferent health care systems.
But yeah.
So so both of you, I'd love to understandwhere you see
an ability to have a subset of proteinsthat really help us understand
biological age and how biological agemay not be reflective
of chronological age,how might that actually be useful
(30:08):
in the future as a clinical tool,
It's a great point.
as a direct to consumer tool?
If the ancestry.coms or 23andMe's of the world
build something like this, how might people use it?
What are your thoughts there?
And also to add something here before
Cornelia, you're of course the best person to answer this,
but also to add the fact that now we're not talking about single proteins or single genes,
(30:29):
we're talking about pathways, we're talking about signatures at the end.
And, we see a lot of inflammationcoming with aging.
And I think probably
we have to deep dive a little bit more in inflammation mechanism to understand aging.
But yeah, I'm happy to hear your thoughtshow you see going to the clinics
or how do you see go to the prognosis,for example, from your prospective.
Well, I think well, again,we learn from the genetics.
(30:52):
I think the 23andMe people are interested in
in their genes,either at the risk of the disease,
but it was also in their heritage.
I think if you look in, the UK,we have the ZOE program
where people I'm very much interestedin their microbiome.
Again, it's a field in action.
(31:14):
I can't believe that,people getting the tools
and the final tools in there,but they get an impression
how well their gut microbiome is functioning
based on the state of the artand and the truth on that.
So I, I definitely thinkthat in the direct consumer field,
(31:36):
this is exciting.
This will be interesting.
I can imagine that if you linkyour microbiome to
your aging profile that,that it's even going be more interesting.
And that is where I seethe field also going.
What we trying to dois starting out with the smoking data
(31:59):
What we have to try out now is
to what extent you can revert back your aging profile.
And to me,
based on what my gut feeling is
in there specifically,
is that you probably can hold the processes
(32:19):
as long as you intervene early.
And old age, it's not clear,but I think we have to find that out now.
We don't know. Does it pay the price?
If you are 90 plus to start doingphysical activity.
Well, you ask me,there's also dangers associated with it.
I mean, we all know thatif your hip breaks,
(32:42):
you have a broken hip after the age of 85,
it's one of the strongest predictors of dying.
But I think thatis what we are facing at the
I think, well, the beauty is ofour analysis,
it will give you a readout of interventionsthat we always missed.
I mean, if one of the interventionsthat has been
(33:03):
well pursuited is of course,chlorectristration.
Now, we all know thatthat is quite a harsh job,
because you really have to eatless than you're supposed to eat
Lika a third or something.
It is quite harsh.
And it really goes to this ideathat small animals live longer,
(33:26):
than large animals.
Really small men and women live longer,
than tall men than women.
And, there is a point to that and,that is really targeted at this system.
It's IGF one signaling.
And in all animalsthat is a problem
for living long.
(33:47):
So I think that is one of the outcomes.
But I think it gives us hands and feet now,
to have a readout that
think about the monkey studies,
in caloric restriction.
There's only three, four done.
You have to wait for ages before these monkeys age.
And now we have a readoutthat that is a little bit closer
(34:11):
The readout seems to work already by age 40,
and probably also age 20, 30.
So hey, that must accelerate research also.
And it must give us an insight whether intervention
stopping smoking, don't wait for it just do it.
Too much alcohol.Stop that too.
(34:35):
But physical activity wasif you talk to people in the aging field,
some people are saying, well,maybe good, but wait a minute,
if you're doing other physical activity,also generating a lot of oxidative stress
is that not also cause of aging?
So I think we read it out now.
We can read it out.
(34:56):
It doesn't look that way in our hands.
So it means that totally, you know,some physical activity is good, and
at least also for not only for vascularbut also for the brain.
And I think that kind of opportunities,the multitude to use it now as an outcome.
We have to prove it but it looks that way that it is working.
(35:18):
Well, you hear it here.
Smoking, stop smoking,
drink less alcohol, eat less food,
and do exercise, but not to the extreme, right?
Well,but going back to the point of Sarantis,
I think that inflammation we're all interested in it.
But we also get now other proteins.
(35:40):
That's also interesting and,
what is the other thingthat is pushing us.
And I definitely think that this was the startfor a lot of diseases
and aging,but also age-related diseases,
but also exposures,you know, the plastic exposure.
Nobody knows what it does.
I used to
(36:01):
like if you have a readout for that,that will inform us a little bit
what goes on in the bodyand how worried we should be.
Yeah, PFOS, PFAS,these sort of forever molecules.
Would you.
Would you like to comment,
a little bit about the drug interventionsI mean old drugs.
Old dog, new tricks, like rapamycin for example.
Hg2 inhibitors, now we hearthat they are player or...
(36:23):
What is your feeling about that?
Targeting everything is targeting aging actually?
Or vice versa?
Why do you mention this, Sarantis?
Because we won’t need to study that.
So we have this week a break for what is our low hanging fruit.
Because I knew if you join this field it'snot for the faint hearted.
There's big competition, stiff competitionthat we usually,
(36:45):
we've always been reasonable about it,
that we say, okay, if we see already apublication.
What is our lease?What is are what is the low hanging fruit?
And we definitely haveeverything lined up there
with Sihaoand Austin to do this aging clock.
So but one of the thingsthat we are getting moving to
as a field, of interestis also the clinical application.
(37:08):
We have already done a study that liver and alcohol are big problem.
A big problem is alsothat people don't know
how much alcohol they use, and they don'twant to know how much alcohol they use.
And the produce.
So can we just, distinguished for liver diseases
(37:30):
can we not use this profile for that,then predict how long we will do this?
And I of course,it's used lots of alcohol
and you get the usual diseases,but you also get the cirrhosis
and you get liver cancer.
so here you goso that is what we take as a benchmark.
The other benchmark we definitely we're to use is
(37:51):
how to, serve the certian drugs,how do they act what we know that.
But also what is that unexpected actions.
So this will be negative side effects.
But we all know that some drugs,think about statins you know,
there was time this is, we are working here in the group
that did the most statins research
and you know except that
(38:13):
some people get some muscle painand some very severe ones
there is quite an argument to almostput it in the drinking water, right.
So of course you shouldn’t do that.
But there's also positive effects, sideeffects of the drugs which were never in
the notes you get if you take the drugsbut it's very interesting.
It's very interesting on this act for instance on inflammation and how
(38:37):
so definitelythat is in part our target
and that's also with the waywe're working population health and
we should really resolve these issues.
There's so much opportunity to understand mechanism,
rapamycin,like Serhant has mentioned,
we don't really understandmetformin has some beneficial effects,
(38:58):
but it can also alterhow exercise is, is benefiting us too.
So understanding the mechanism of that,
G... what are the GLP 1s?
I mean, those are acting in the brain.
That's fascinating. Right?
We're really just parsing all that outand it's already almost in the water
for many.
For many populations. Right.
(39:19):
There's just so much opportunitythat I hope proteins can help.
At least,like I said, point to some hypotheses
that can then be testedby groups like yours, Cornelia
So definitely that is a field of interestand I.
But on the other hand,
the exposures of two exposuresto that shouldn’t be there.
The plastics that are built,the pesticides.
(39:42):
I think we see them.
We see that the, you know, that.
And, the fact that there's air pollution in the region,
pops upas having the similar effect of smoking.
And that is not good.
So I think there's a lot of opportunitiesand we need a lot of hands,
but also a lot of brains to do that.
(40:02):
And technologies.
And technologies to do that.
and that definitely,we need more of the protein.
We know that there’s lots more proteins.
We need more, the different isoforms.
We need to know moreabout the phosphorylation and the,
processes of processing of these proteins.
But it it isn't that a fieldthat, you know, we
(40:25):
I'm not I'm not young anymore,but I think yeah, I think we definitely
the future will tell a lot about whatwe always have been wondering about.
To that.
To the point aroundthe needs for this area.
What are the cohorts that come to mind
that are collectingenvironmental information
(40:46):
that you think are oneswe want to highlight and promote?
Because it's like I said, it'shard to collect these sort
of environmental variables.
Are there ones that you particularlylike that you want to make sure,
are successful in the future, continueto collect data, that sort of thing?
I think that there are many cohorts now.
(41:06):
That,of course, has, has really dedicated
their life to look at multiple exposures.
I really favorthe epidemiological setting.
And the reason for that is that,
what you probably, if you single outone exposure,
right, it's unlikely that in your life
(41:27):
you only have one exposureyou need a broader picture.
So I, I'm brought up ina department
in Rotterdamwhere we always, looked and try
to look at the complete picturewith the view that in the end of the day,
you're asking yourselfwhat is the effect of smoking?
(41:49):
Oh, but,you know, if you smoke, you often
more likely to drink a lot of coffee,a lot of alcohol.
You're more likely to use oral contraceptives.
Hey, there’s a lot more things you do.
And, I think that these studieshave been incredibly powerful.
And incredibly important, the UK Biobankis a is a fantastic example on that,
(42:13):
that also data have been gathered,you know, they been adding of data
stacked onto each other.
And that allows you to domulti-omics studies
in a very valid way,but also weigh in exposures.
Now, one of the examplesI would give that convinced me totally
is that you have to look at, multi-omics.
(42:35):
Is that what, we didis look at metabolomics,
and we started thinking, why?
Well, the idea is metabolomicsis genetically determined, but so environment,
is the active component?
And you're really getting quite overwhelmed
(42:57):
if you look at the how strong medicationalso influences metabolomics.
We're now going back the same as Sarantis on proteomics
and for some it’s really overwhelminghow it's medication is, influencing your proteome.
Now look in the most of the epidemiologists
(43:18):
have been wiseand have been gathering data
of a lot of exposuresand that will be helpful.
And definitely the medicationyou need to take that into account.
But, on the other hand,they should look at medication.
The smokers also turned off to be, a confounding factor for that.
(43:39):
But, you know,the fact that both metabolomics
but also proteomicseven more is associated to medication,
suggests what we already have hypothesized
that a lot of medicationis somehow targeting proteome.
Yeah. It's.
It's the messy part of the data. Right?
But it's because we are collecting it across
(44:02):
ideally large numbers of peoplethat signal can emerge
even even though there's challengesin collecting those data.
I think more and more we should include
it also proteomics in in trials.
We should do that.
And it's in clinical trialsin which you test medication.
But please if we dothese intervention trials also show me
(44:25):
that you have an effect of the proteinsthat develop the disease.
and there is our aging work is important,but there's a lot of more,
profiles that we need for dementiain the early phase.
So not the fact that you have P tau,which is just a signal that your head is
full of tau and if your head if full of tau...
It’s one only biomarker, right?
(44:48):
We need something earlier.
We need more.
I don't think that if, physical activity
protects you against dementia,you shouldn’t start with it at age 85.
You should start with that early.
And we've I've read out of studiesthat show death.
That convinced me. And of course, the...
Yeah, a little adviceis that we have on the exposures
(45:10):
are interesting, but we need much more.
We need much more.
On nowadays whatwe are exposed to that even the fact that
our sleep is different, thatwe are exposed to light at night,
that we never were exposed to.
There's a lot to be learned.
And I think that type of trials,
there’s two things on trials for exposures,is the first of all,
(45:34):
they have to be big,even for caloric restrictions.
You see all these smaller studies,people lose weight.
I mean,we have the better outcome, right?
of course we lose weightif you don't eat the calories.
it is obviousthat that will happen.
But we need the readouts of that,that shows us
that it takesreally that it stops aging.
(45:58):
And the trials, I came to Oxford
to the Oxford department, partly
because the trials are so big,but partly I like the spirit
about the trials here, that they haveto be big in order to show things,
because that affects sometimes I, I mean,
(46:18):
are still subtle. I think we got usedto that in the genetics too.
Of course you have genes with big effects,
but a lot of themwill not have that big effect.
It's the aggregate of all the genesand if it's the aggregate of the genes,
it has to bethe aggregate of the proteins to.
Otherwise the effect of theseall these genes don't make sense.
(46:39):
So I think that is what we’re facing that,
we have to start thinking of trialswith complex outcomes.
And we have had a lot of benefit
that coming to Oxford I really wantedto start looking at machine learning too.
And that gave us also a
(47:00):
very much of a boost, I should say.
I'm not saying that machine learningsolves everything, and a definitely not.
You don't hear me say that.
But if you look at the in a simple,even simple
machine learning models, it can dealwith the complexity a bit easier.
And I think we we nailed that down.
(47:22):
And for strong associationslike the proteomics age group,
it really doesn't matter what you takea more classical approach
like elastic net or gradient boosting,which is kind of a random forest
or you take a neural network,but in the end of the day,
it may be that that some of these methodsmay be more powerful
(47:44):
to pick up these aggregates and alsotranslate it back that you get into your hands.
Which plotting is doing what?
If it becomes completely obscurein the neural network,
what has done what?Are you really going to invest
hundreds of millionsto develop in therapy for that?
No, you want to first know,
(48:05):
not too many proteins,
and what is doing what, tell me. Right?
And that is when you have to be able to start out.
And machine learning, it's giving us a lot.
Yeah, yeah. And.
But we have to be carefulabout overtraining.
But that's where having this
growing field of machine learning is informing us.
But I think parsing out, what's the genetic contribution from ancestry?
(48:29):
What's the contribution from gender?
Yeah.
What are the signals in the proteins that confer gender
that you can then use to stratify that?
There's so much complexity that machine learning is helping us to parse out.
Yeah, and that we were lucky.
I think we've been wonderful here in Oxford.
That we have multipleall these cohorts
We have the China Kadoorie Biobank,
(48:51):
Yeah.
you saw that it’s fantasticwhat they are setting up.
We have bigger cohorts in the millionwomen study, but we have also,
the large trials that have been done.
And, you know, even in a trial,you can do now, start thinking
of a silico experimentsthat if the trial has been done
(49:12):
with a certain drugthat you want to repurpose,
you can just measure in that trialwhat the effects of the proteins are.
I think you really have to go.
We have to be intelligible.
And more intelligenton how to repurpose,
and reuse the studies that we had.
But the fact I,I totally agree with people that say,
(49:34):
if you split two data in the trainingand the test set,
if there's structure in your data,
then it’s in your trainingand your test set and then
in my early days using machine learning
in team discovery is that,we figured out the hard way.
We had the test setand training set replicated,
(49:57):
but when we finally looked,
what the neural network was using,
it was using missing data to predictthings like, how is it possible?
How is that possiblethat you can predict with missing data?
There must be something that you can’t input
in that range region well,or there must be a reason for that.
But if the problem with the missingdata is in your training set
(50:21):
it’s also in your test set.
So for us, it'sso important that you can use
data across studies that we could use UKBiobank as a powerhouse
and a powerful tool,
but that we can replicate it in other studies
that are completely independentand that will be important in genetics.
(50:41):
It will also be important in exposures.
And there are more and more studiesthat are integrating, I know of,
Olink proteomics,of course, that are integrating these data
with genetic data that will offeropportunities for collaboration.
Yeah. Awesome.
Well, so I think this is a great placefor us to sort of wrap up.
(51:02):
I'd love to give you a chance to say anylast thoughts that you'd like to share?
Cornelia.
No. Anything that it’s...
The only take home is
well, I already mentionedthe race is not the realm.
I think it's not technology.
You guys are still finding,better ways to quantify the protein.
(51:23):
You are finding better ways to describe the proteome.
That will be ongoing.
I think it's quite excitingto be in this field.
The other way around is that for us, in the data science and
the epidemiology,I think there's a lot of work to do.
A lot of thinking to do how to analyze the data,
(51:45):
how to integrate it over the exposome, the genome.
And then, it'sgoing to be very exciting on that field
telling people how to prevent the diseasebetter, giving them tools to monitor.
And nobody would have thought, 20 years ago
first of all, that we were going not outside
(52:05):
of or housewithout taking the phone.
But, you know,that we would be having Apple Watches.
and Fitbits
Yeah, how many computers do we carry?
For them it was difficult to understandthat we landed on the moon.
And to accept that.
But you know nowadaysthis is the field that’s going to develop
(52:27):
and we’re going to be boosted also on the data analytics,
on the integration of data,
the use of machine learning,the abuse, but also correct that again
And how to translate that back.
You know, it's not only the data sciencethat is relevant.
In the end of the day, it's relevant,
what you do, the impact that you have inin curing people,
(52:52):
in presenting the disease
because you know, if anything in your lifeyou don't will to become diseased,
you want to prevent it. Definitely with dementia,
but also with many other diseases.
It's that translational that counts.
And that is important.
And it's importantthat we all keep that in mind.
(53:13):
Couldn't agree more.
And you all heard it here,
this is the place to go,
Oxford is the place to go for largedata sets.
They're amazing cohorts here and amazing scientists to work with.
So for those of you who are thinkingabout post-docs or PhDs, think about that.
Sarantis, I'll give you a chance to please
It was great.
Any last thought?
It was great to hear from Cornelia, about the aging and aging-related diseases
(53:36):
I think proteins play a really important role on that.
Plasma proteome is on spot now and we can,
using this plasma proteome,we can understand the biology of disease
from different tissue types.
We've also we can also understandof different tissue types, phenotypes
screening only for plasma proteomics.
I think that's the take home message here.
(53:56):
And really niceto have you, Cornelia.
Great, I enjoyed it a lot.
Thank you very much, Cindy.
And yeah I mean thethe last word is for you, Cindy.
Super fun. Super fun!
So thenI'll just go back and double click on.
So I mentioned, the study thatthat demonstrated that having genetic data
going into a clinical trial helpsimprove success by at least two times.
(54:18):
That was actually Matt Nelson.
So apologies for that, 2015,
and that was a Nature Genetics paperreally pivotal paper.
And then of courseAstraZeneca has also published
on their ways of filtering and leveraginggenetic data in different ways.
Gives them a seven times
improvement in clinical trial outcomes,which is which I just wanted to highlight.
And then we also mentioned Austin Argentieri.
(54:41):
We mentioned, Sihao Zhao, a PhD studentand then we also talked
about China Kadoorie Biobank,but we didn't mention Zhengming.
So I want to I want to, give a shout outto the amazing biobank that he's built.
As I understand it, really,a lot of the UK Biobank structure,
was founded in how Zhengming, built out the China Kadoorie Biobank.
(55:05):
So those two are great ones.
And people use them a lotfor corroboration and combining data.
And Austin is a great exampleof someone who's done that so well.
Well, in the future. Get him.
Get him on the podcast.
Perhaps once that paper comes outand that paper will probably be out
by the time we get this podcast,published, I hope so.
(55:26):
We can use this.
This is an opportunityto promote that important work.
And so with with all of that exciting, content that we've talked about today.
And I want to thank you, Cornelia,so much for agreeing to
to come on and trust us withsome of your story.
Thank you very much.
Thank you for having me.
(55:51):
Well, that wraps upthis episode of Proteomics in Proximity.
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
(56:12):
In addition, if you'd be willingto head over to Apple or Spotify
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
So we have a dedicated email addresspip@olink.com
(56:32):
Please reach out.
Let us know what you're interestedin hearing about, what you care about,
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you like to hear about.
Thank you so much and we'll see you soon.
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