Population Neuroscience and Public Health

Transcript

• 00:05Good afternoon, everyone, and welcome
• 00:07to Grand Ryans and especially to
• 00:09everyone joining us on Zoom.
• 00:11And I'd like to remind you that for the Q&A,
• 00:13please feel free to put on your video
• 00:15cameras and we'll project you here on
• 00:17our screens here in the Cohen Auditorium
• 00:20and we'll hope for a lively discussion.
• 00:22Now as usual, we just want to preview
• 00:23a couple of our presentations that are
• 00:25coming up over the next couple of weeks.
• 00:26And so next Tuesday,
• 00:28we will hear from Doctor Jessica Cardena.
• 00:30And this is a very special by
• 00:32Ola Barnard lecture series.
• 00:33And so Doctor Cardeno will be talking to
• 00:36us about what we can learn from Latino
• 00:38mothers and what Latino mothers can
• 00:40teach clinicians about trauma and recovery.
• 00:43And then a special date for
• 00:45your diary on Monday.
• 00:46And we have Doctor Tracy Bale
• 00:48coming to give a seminar in the
• 00:51Division of Reproductive Sciences.
• 00:53So that's in the department of OB GYN,
• 00:55my other home department.
• 00:56And so on Monday from 12:00 to 1:00,
• 00:59Doctor Bale will be coming to talk
• 01:01to us about extracellular vesicles
• 01:02as a novel form of communication
• 01:05between the mother and the fetus.
• 01:07And as you'll all know,
• 01:07Doctor Bale has done some seminal
• 01:09work trying to uncover the molecular
• 01:11mechanisms that underpin the
• 01:13intergenerational transmission of stress.
• 01:15And now to our speaker for today,
• 01:17it is my distinct pleasure to welcome
• 01:19Doctor Hennington Meyer to the Child
• 01:21Study Center for the very first time,
• 01:22I'm told.
• 01:23And we did a little bit of history of
• 01:25the Child Study Center earlier on and a.
• 01:26Tour So Doctor Timmeyer is joining us
• 01:29from the Harvard School of Public Health,
• 01:31where he is the Professor of Social
• 01:33and Behavioral Science and holds
• 01:35the Sumner and Esther Feldberg
• 01:36Chair of Maternal and Child Health,
• 01:38where he also directs the Maternal
• 01:40and Child Center for Excellence at
• 01:42Harvard School of Public Health.
• 01:44And, of course, Dr.
• 01:45Timmeyer also holds a professorship at
• 01:47the Erasmus University in Rotterdam,
• 01:50where, as many of you know,
• 01:51he set up the Generation Rotterdam cohort,
• 01:54the Gen. R cohort.
• 01:56Which has made a tremendous
• 01:58contribution to our understanding of
• 02:00how the environment shapes individual
• 02:02differences in child development.
• 02:03And I hope we'll hear a little
• 02:04bit about that today,
• 02:05as well as many of the other
• 02:07initiatives that Doctor Tiamar is
• 02:09involved in since moving to Harvard.
• 02:11And of course,
• 02:12he has published prolifically and is
• 02:14regarded as a ISI highly cited researcher.
• 02:16So please join me in giving
• 02:18a warm child study.
• 02:19Welcome to Doctor Tiameyer.
• 02:20Thank
• 02:24you. Thank you very much.
• 02:27Let me put on my mic and thank
• 02:29you very much for the kind,
• 02:31very kind and warm introduction
• 02:34and the invitation to come here.
• 02:36Indeed, I'm quite proud to talk here.
• 02:38I should say that because just teach
• 02:41currently again the the course child
• 02:44Psychiatric EPI at Harvard and on
• 02:47my third slide there I show the Yale
• 02:49Study Center and the work of gazelle,
• 02:52which I think shaped longitudinal studies.
• 02:56More than many others or anybody
• 02:59else was that introduction.
• 03:00For those that are also interested in
• 03:03more recent work I'm doing or more other
• 03:05work on the maternal child space space,
• 03:07I must disappoint you or focus on
• 03:10generation R still doing much of my work.
• 03:12What I did is I okay is I selected work
• 03:18from ongoing studies or older studies even
• 03:22because I do much population or imaging.
• 03:24I'll show you.
• 03:25And the theme I thought was answering
• 03:28an all discussion saying this work
• 03:30should do now that you're at the
• 03:31School of public health is not really
• 03:33relevant to public health at all.
• 03:35And after 20 years or 30 years of imaging
• 03:38research, it's still not relevant.
• 03:41And that doesn't insult me.
• 03:43I think it's a fair critique,
• 03:44but at least I have to live with
• 03:45it and address it.
• 03:46And that's what I'm trying to do
• 03:47with you today. Discuss it with you.
• 03:49Could it be relevant?
• 03:50It's not so obvious.
• 03:53So yes,
• 03:54they asked me to do learning objectives.
• 03:56So here you are a bit,
• 03:57it's a bit about the prenet exposures,
• 03:59which I'll Kieran is working on.
• 04:01So I'll focus on that.
• 04:02And the question really is,
• 04:04is it identified?
• 04:05I don't think that's the
• 04:07learning objective to be honest.
• 04:08It would be discussed with me how
• 04:10child imaging might possibly in
• 04:13theory a bit impact public health.
• 04:16What am I talking about?
• 04:18I see Euroscience population of science
• 04:20not as broad as somebody like Thomas Powells.
• 04:23Thomas Powers, I would see.
• 04:24It's really the intersection of,
• 04:26if you wish,
• 04:28population research or etymology
• 04:29and neuroscience.
• 04:30Essentially that's what happened in genetics,
• 04:32that genetics has been now 1520 years really
• 04:35infused with genetics as we just talked,
• 04:38epidemiology,
• 04:38but now also influences
• 04:40epidemiology with new methods.
• 04:43And then I'll focus on prenatal exposures,
• 04:45psychosocial or chemicals.
• 04:46I've got one more chemical
• 04:48exposure pull that up after I met.
• 04:51Somebody yesterday night,
• 04:53I thought that's a good one,
• 04:57how that impacts child development.
• 04:59I'll start with what I think
• 05:01is not public health relevant.
• 05:02So I thought I'll start with
• 05:04something where I think it's not
• 05:08what imaging research is not and I
• 05:09start with not other people's work.
• 05:11That's not very cool.
• 05:12I start with my own work,
• 05:14so I'll show you my.
• 05:16Were my best publication last
• 05:17year or one of my nicest,
• 05:19but I don't think it is any
• 05:21public health relevance.
• 05:22It's answering the question child psychiatry.
• 05:26Really it's giving you an example
• 05:27of that because much of my work
• 05:29or all of my work was funded under
• 05:32the premise that it will inform in
• 05:34the prediction and the causality
• 05:36of child psychiatric disorders.
• 05:37And now 20, not 15 years later,
• 05:41what have we delivered?
• 05:43It's this type of work.
• 05:45Can we really predict adolescent
• 05:47hallucinations with imaging?
• 05:49Does it add anything?
• 05:50So last year we published work on
• 05:53this question, Public Health Relevant.
• 05:55You ask yourself,
• 05:56can we predict adolescent hallucinations
• 05:58would be very, very important.
• 06:00We measured that in Generation
• 06:02R at 10 and 14 years.
• 06:05It's actually quite easy to measure.
• 06:07You can ask the adolescents themselves,
• 06:09You can ask them to hear voices.
• 06:12You have strange thoughts.
• 06:15I don't know if anybody here has an idea
• 06:17how prevalent that is at age 10, At 14,
• 06:20Any idea if it's a fringe thing happening
• 06:22at 2% of the population or 10 or 15%?
• 06:25But actually if you ask them,
• 06:29do you hear voices, it's 25% easily.
• 06:32And that is not just waking up
• 06:35after dreaming, it is really work of
• 06:39Keleha and Mary Cannon in Ireland.
• 06:42Has shown it's somewhat less frequent,
• 06:44so it goes down to 15% if you wish.
• 06:46If you really get them bothered by voices,
• 06:5029 is really what you get with these
• 06:52population assessments if you do it crudely.
• 06:54But trust me, it is easily 15% at
• 06:57age 10 and then it drops to 12%.
• 06:59And again, if you do it more carefully,
• 07:01it would probably be six, 7% at age 14.
• 07:03That hear voices,
• 07:05which is huge, don't forget.
• 07:07Don't confuse that with schizophrenia.
• 07:09That's nowhere near schizophrenia.
• 07:10Actually, if you know their work,
• 07:12it predicts depression, anxiety,
• 07:15borderline much more than schizophrenia.
• 07:18And we did,
• 07:19so that's the special thing.
• 07:20We did repeated imaging at age 10 and age
• 07:2414 so we can show does the brain change.
• 07:27We can also say, can we predict it?
• 07:30In the paper in Biological
• 07:32Psychiatry last year we showed
• 07:33something after all lots of studies,
• 07:37different approaches,
• 07:38different work with the brains.
• 07:40What we found really is if you hire voices,
• 07:44then the typical decline,
• 07:47this is sort of exaggerated.
• 07:48This is a bad curve.
• 07:49It should be much more than sort
• 07:50of trajectory curve.
• 07:51But forgive me for that that the
• 07:53decline in Gray matter which.
• 07:55Originates probably much earlier than age 10,
• 07:57probably age 6 onwards is a tiny
• 08:00bit far faster in those that have
• 08:09new onset hallucinations at age 14.
• 08:15I'm showing this.
• 08:17It is an association.
• 08:18It has a tiny effect size.
• 08:21You need a few thousands 2000s to
• 08:25find it as a tiny effect size.
• 08:28It is a specific,
• 08:30it is much of your Gray matter,
• 08:33and actually it also maps on
• 08:35other psychiatric problems,
• 08:36so it would not be that very
• 08:39specific for hallucinations.
• 08:40You can also zoom in and find other
• 08:42structures, of course we did that.
• 08:43And the hippocampus,
• 08:46that's what the small A says
• 08:48it's The effect is again small.
• 08:51It survives multiple testing,
• 08:52correction for other structures.
• 08:53It's a tiny effect.
• 08:54Again, it is unspecific.
• 08:56The conclusion here is useless.
• 08:59As a predictor,
• 09:00I have little doubt over and above any
• 09:03prediction model which we published.
• 09:06These brain imaging does nothing.
• 09:07You can better do predict
• 09:10with socioeconomic factors,
• 09:12better predict with clinical factors.
• 09:14You can better predict with well-being.
• 09:17It does not predict and this is
• 09:19the biggest imaging study so far.
• 09:21So it may be that one day all of
• 09:23you will search for more specific
• 09:25markers and we'll do resting
• 09:26state analysis and whatever.
• 09:28But we had this unique data
• 09:30set with repeated imaging and
• 09:32repeated hallucinations over
• 09:33the really relevant period.
• 09:35I would give this a one out of five
• 09:38in population public health relevance.
• 09:41It does not add to any child
• 09:43psychiatric clinicians.
• 09:47Addiction, therapeutic,
• 09:49understanding model.
• 09:50I would say we've done
• 09:53lots of this type of work.
• 09:54It's fascinating, it's fun.
• 09:55I think it's important to understand
• 09:58that the brain can predict,
• 10:00but it is not clinically useful.
• 10:02Let me go on with transition to the work.
• 10:05I'm going to show where I think we can
• 10:08discuss public health relevance And again,
• 10:12this is a crude analysis, I know that.
• 10:14Actually more fine grain didn't predict,
• 10:16more if you do multiple testing
• 10:18correction and the prediction was small.
• 10:21This is a paper I'm not going to discuss,
• 10:22I'm just going to recommend it for your read.
• 10:24From last year we said thought
• 10:26it harder that the population of
• 10:28science is the best paper of the year
• 10:30and so it got our prize for that,
• 10:33whoever cares.
• 10:35And what it does is it uses the biggest
• 10:40databases like the UK Biobank and
• 10:42the ABCD studies and others to show.
• 10:44That for a
• 10:49if you don't zoom in on our ones
• 10:52but you take a broader approach
• 10:55for resting state and for volumes
• 10:59that you need to find anything.
• 11:02They say it's three to 6000 people in
• 11:05the general population to find anything
• 11:08you can argue in your clinical sample
• 11:10it's different there's a letter or.
• 11:12An answer to nature arguing that very
• 11:15recently I actually fundamentally
• 11:16disagree with that letter.
• 11:18I think they have it right.
• 11:20It's my own experience too,
• 11:23and the only thing I'm not so sure,
• 11:26and that's the judgment that's out.
• 11:28This analysis is clearly only cross-sectional
• 11:33and actually I'm not so interested
• 11:36in cross-sectional prediction.
• 11:37So we would have to move to longitudinal
• 11:39and if you've got repeated brain measures,
• 11:41I would argue because you control
• 11:44for quite a bit and you have change
• 11:47that could be different, although we
• 11:49don't know what's the interval change.
• 11:51Secondly, they have very poor phenotypes.
• 11:54You could argue,
• 11:55I think they should have used multiple
• 11:57informant and other approaches, but anyway,
• 11:59it was all that critique I think.
• 12:02It's very humbling that all of a sudden
• 12:04after so many years where we had studies
• 12:06of 1520 people and found big effects,
• 12:09we now have people that say if we want
• 12:11to do it well, we need 3 to 5000.
• 12:15So I would argue in child psychiatry so far
• 12:18without very few exceptions you can think of,
• 12:21you know, but very rare syndromes,
• 12:24not so sure OCD, there's some debate that
• 12:26that's quite specific, but otherwise.
• 12:29I think it's poor discrimination,
• 12:31poor specificity, poor sensitivity.
• 12:33We've done machine learning.
• 12:35I'm not talking about that.
• 12:36To overcome that,
• 12:37what we find is a very small signal and
• 12:39actually something we already knew.
• 12:41It's quite broad changes in
• 12:44externalizing behaviors,
• 12:45nothing very specific either.
• 12:47So I think in sharp psychiatry,
• 12:49my research has not contributed
• 12:51that much for public health.
• 12:56That does not mean it's useless, of course.
• 12:58I would like to discuss prenatal exposures,
• 13:00some old work and then zoom in more
• 13:02recent work, ongoing work even.
• 13:05And we identify important introduction
• 13:07influences on the Turtle Shine house.
• 13:09And we've done a lot over the years.
• 13:11We sort of in generation are measured as
• 13:14much as we could and we were quite creative.
• 13:17We've got environmental toxins,
• 13:18we've got thyroid poverty.
• 13:20That's the recent thing that I added to
• 13:22the list because I was interested in that.
• 13:24Depression.
• 13:25So the bold ones are zooming on today.
• 13:27One or two of you will know earlier work,
• 13:30but poverty is very recent and the
• 13:33environmental stuff is just out last year.
• 13:37So that's cool.
• 13:38Discuss with me what you think is the
• 13:41role of imaging, which is so well funded.
• 13:43You know, if you take the European
• 13:45funding in the neuroscience,
• 13:47probably 1/2 goes to brain imaging,
• 13:50which is shoot.
• 13:52Not as much in the US
• 13:54interestingly relatively speaking,
• 13:55but a lot Okay just a bit about
• 13:59Generation R as a prospective cohort.
• 14:00It started in early fetal life but
• 14:02early the inclusion we promised
• 14:04and we had funding for 10,000,
• 14:06I don't know for whatever reason the
• 14:07end of the year came and we had to stop.
• 14:08So we didn't manage the 10,000
• 14:10but we got close,
• 14:11it's 10,000 if you know who's active.
• 14:13It's still more than 5000 are
• 14:15contributing participating 6000 which
• 14:17is very good if you start prenatally.
• 14:19I think it's much better in a way
• 14:21than ABCD because they have a 15%
• 14:23response rate at baseline or lower.
• 14:25So this is a 62% response rate
• 14:28and then the Dutch majority group,
• 14:30it's actually 70%.
• 14:31So it's more selective in minorities,
• 14:34it's urban and multiethnic and I do
• 14:36because I have a slide later on this
• 14:38ethnicity normally I sort of gloss over it.
• 14:40Note that if you're on Rotterdam,
• 14:42it's not much different than in many of
• 14:46the Americans cities that about half.
• 14:48Of the population is Dutch means
• 14:53that has Dutch ancestry origin
• 14:5610% would be other Europeans,
• 14:57So that's expats largely.
• 15:00And then you've got both migrant
• 15:02or guest worker I should say,
• 15:04which are the Turkish for example,
• 15:06and the Moroccans.
• 15:07And then you've got colonial history,
• 15:09people like tsunamis, Cape roses,
• 15:11also guest workers,
• 15:12but Dutch Antilles.
• 15:13Are ex colonies of the Netherlands where
• 15:16people could migrate easily into meaning.
• 15:19It's a very dangerous city.
• 15:20And yeah, that's important because we'll
• 15:24talk about poverty just about the measures.
• 15:28I have no pointer,
• 15:29but I have a cursor,
• 15:30I'm told,
• 15:31so I don't want to go through measures.
• 15:33Nothing is more boring than telling
• 15:34you what we measured in the study,
• 15:36but we measured a lot ultrasound
• 15:37in the beginning questionnaires,
• 15:39lots of motor development was exciting.
• 15:41We have IQ measures but also actually
• 15:43of the parents which are the mother.
• 15:44It's very important to control
• 15:46for baseline confounding.
• 15:48If you have intrauterine infectors,
• 15:49what is sort of genetic
• 15:52background and then the imaging,
• 15:54I'll focus much of my talk.
• 15:56On the imaging at age 9 to
• 15:5810 which is at 4000 people.
• 16:01I have one study later where we do a
• 16:03follow up of the imaging which we have.
• 16:05This is actually a typo.
• 16:06It shouldn't be 4050.
• 16:08This should be 3 thousands
• 16:10and 52 hundred 3200.
• 16:11Just copy pasted the wrong thing here.
• 16:14So we have now three wave
• 16:16completed and the 4th wave ongoing.
• 16:17In total it would be 6000 different
• 16:20individuals that have been scanned
• 16:22of 5500 and the overlap is not that
• 16:24big but it is there to do nice multi
• 16:27level analysis over three waves already.
• 16:30I would like to start with my
• 16:31classical one of my classical
• 16:33papers maternal depression.
• 16:34So I think there we can learn a
• 16:36bit about public health relevance
• 16:37and actually I'm saying that also
• 16:40because it informed a study or
• 16:42work that I'm doing currently.
• 16:43Maternal depression from fetal life forward.
• 16:46What I'm trying to show you is
• 16:48that we've measured maternal
• 16:50depressive symptoms at three time
• 16:53points during pregnancy.
• 16:54This would have been forgive me
• 16:56that the error is not quite good.
• 16:58It should be after birth at two months,
• 17:00after birth at three years.
• 17:03We didn't use that and we used
• 17:04it at 9 to 10 years.
• 17:06So 4 measures of maternal depression 1/2
• 17:09just after birth in the early childhood and.
• 17:13At 10,
• 17:14why is 10 interesting?
• 17:15That is interesting because that
• 17:16is cross-sectional if you wish.
• 17:17Was the brain imaging,
• 17:19focusing on the brain imaging at
• 17:2110 years when we measured 4000
• 17:23children and not all in study
• 17:24at the end there will always be
• 17:26only 2000 or 3000 in the study,
• 17:28but that's at that time a very big study.
• 17:31Certainly the biggest study was
• 17:34prenatal exposure assessment,
• 17:36prospective prenatal exposure assessment.
• 17:40And I always ask when I see the
• 17:42slides and those who have not seen it,
• 17:44what time is there a strongest relation
• 17:49of maternal depressive symptoms to
• 17:51the brain of a child measured at age 10?
• 17:57So we've got it at during pregnancy,
• 18:00just after birth, early childhood
• 18:04and cross-sectional with the MRI.
• 18:08And the question is when?
• 18:11Is there a relation between the
• 18:15maternal depressive symptoms
• 18:17and the volume and connectivity
• 18:21of the child brain at age 10?
• 18:24So is there a long term influence
• 18:27from prenatal life forward?
• 18:29Is there an influence of early
• 18:32after birth perinatal depression?
• 18:36Is an influence of childhood depression
• 18:39or an influence of cross-sectional just
• 18:44concurrent depression to the mother?
• 18:47Talk about structure of the
• 18:49brain of the child at age 10.
• 18:55So I'm not, as I sometimes do,
• 18:56pull somebody up and say what
• 18:57do you think I'll do it myself.
• 18:59You can think many people would think
• 19:03it's either prenatal depression.
• 19:05That has a big effect because that's
• 19:07when the child is in the womb.
• 19:09So you would think that the mother's
• 19:12depression influences her Physiology,
• 19:15and that impacts the child.
• 19:18You could argue for just after the birth,
• 19:21because that's a key period of attachment.
• 19:25You could even argue somewhat
• 19:26less for the childhood,
• 19:28but you could argue for that because it's a
• 19:30long period of childhood upbringing anyway.
• 19:33If you look at this,
• 19:35this is just very broad.
• 19:36Total measures, Total white measure.
• 19:38Total Gray measure.
• 19:39Because we start with
• 19:41hierarchical approaches,
• 19:41doing big parts of the brain
• 19:43and then zooming in on specific
• 19:45regions if we find something.
• 19:47You can look at these small effects.
• 19:49They're actually translatable in centimeters.
• 19:52Cubic.
• 19:55You can see nothing was
• 19:57the white matter much.
• 19:58And if you look at the Gray matter.
• 20:01There is a period,
• 20:02two months where there is an effect and
• 20:04again that survives multiple testing.
• 20:06So if you want an answer from this,
• 20:09it is not.
• 20:10And I've said that many times,
• 20:11for me, this is one of the
• 20:12big it's a few years old now,
• 20:14four years ago we published it.
• 20:16It's
• 20:19not the prenatal exposure that is most
• 20:22important and we see that in some of this,
• 20:24it is actually just after birth.
• 20:28Where we see an effect and
• 20:30that's actually very consistent.
• 20:32So there's two ways to look at the data.
• 20:36One is prenatal is not everything and
• 20:39sometimes the Doha people would tell you.
• 20:42Secondly, effects are small and if anything
• 20:47that is a small effect postnatal depression,
• 20:50which makes sense if you know
• 20:53the literature and attachment,
• 20:55maternal bonding and how important it is.
• 20:57To have and how that is impacted
• 20:59in clinically depressed mothers.
• 21:01If you look at and I'll show later some,
• 21:03I think some more DTI.
• 21:04This is a slide of how we look at DTI.
• 21:06We sort of don't integrate it all.
• 21:08We look at different tracts
• 21:10which we then sometimes sum.
• 21:11So this would be the connectivity
• 21:13in the white matter.
• 21:14You measure that with two measures FA or MD,
• 21:17but it essentially shows you
• 21:18the integrity of the in these.
• 21:21Different tracts.
• 21:22We measured that. Well,
• 21:23that was just the global brain measures.
• 21:26And I can tell you this effect is
• 21:28quite broad across parts of the brain.
• 21:31So it's not just in the temporal
• 21:32lobe or the frontal lobe.
• 21:33We find it a global effect.
• 21:35And then we also looked at the
• 21:37DTI and what is interesting,
• 21:39that's not so surprising.
• 21:40Well, there was nothing in the white matter.
• 21:42We saw that the tracts, the general tracts,
• 21:46the integrity of the tracts.
• 21:48Again, depression at 2:00.
• 21:51Months Postnatally there was
• 21:54less integrity of these tracts
• 21:56together and trust me there's not
• 21:58a single track that does it.
• 21:59These global integrity of tracts is less,
• 22:03is less clear is there's less
• 22:06integrity in these tracts.
• 22:08And then in fact was the depression at
• 22:11two months on the child brain of 10 years.
• 22:14So it's different exposure times was
• 22:16one outcome time always at 10 years.
• 22:18So you see the effect and there's
• 22:19nothing Again,
• 22:20there's prenatal
• 22:25If we discuss public health relevance,
• 22:29you will not want me to say we now
• 22:33found that maternal depression is
• 22:35important because there's fifty years
• 22:37or 100 years of research to show that.
• 22:39You might want to say, wow, he has a
• 22:44way of finding sensitive periods.
• 22:48And that's why I would think perhaps,
• 22:51but really, honestly, I don't think so.
• 22:54And I'll tell you why I tell you that.
• 22:58And I know people in everywhere think
• 23:01differently that you can with Social
• 23:03adversity study sensitive periods.
• 23:07I actually have tried to do that
• 23:10now with measure of homelessness
• 23:11and other work in my group.
• 23:13We feel that is largely flawed.
• 23:17Because of the following thing,
• 23:19depression in mothers does not
• 23:21occur in isolate meaning over time.
• 23:24What I mean is that is a mother
• 23:26that is depressed at two months
• 23:28after birth has likely some elevated
• 23:31symptoms already during pregnancy.
• 23:33Not only likely, very likely,
• 23:36meaning that all these poverty, abuse,
• 23:41depression, all these risk factors,
• 23:43all these social adversities are studying.
• 23:46Have a high carry over and we cannot
• 23:50validly or have seen very little studies
• 23:54to validly study the period specific
• 23:56exposure because then you would have
• 23:58to have people that have it only in
• 24:00this period and not in the others.
• 24:01And if you see how carefully they
• 24:04account for the other periods,
• 24:06I can tell you in most models I've
• 24:08seen that is flawed, including my own.
• 24:11So I'll show you why it's flawed
• 24:13and this is the trajectories.
• 24:16It's flawed because the mothers who
• 24:17have that peak of depressive symptoms
• 24:19at two months were actually those
• 24:21that were on average as a group.
• 24:23If we just do these trajectories
• 24:25and we classify them in groups
• 24:26and we forget about that,
• 24:27this is of course a continuum, this,
• 24:29this series of continuum on that level.
• 24:31But if we do them in four groups,
• 24:32we see this group that actually I
• 24:34can tell you carries the results,
• 24:36has high levels here,
• 24:37super high levels here and then keeps
• 24:39on in the all these ten years after.
• 24:44Assessments to be reasonably high because
• 24:46this is 0.7 is exactly where the clinical
• 24:49line of clinical severity would have been,
• 24:51meaning that there is a group that has
• 24:53clinical symptoms but they're high all over.
• 24:55And of course there are
• 24:56some that have only high.
• 24:57When the children get older, only in
• 24:59sort of childhood life they develop it.
• 25:01It's a small group actually,
• 25:03but the important thing is that.
• 25:07These are so tied,
• 25:08So to say that this is the unique effect
• 25:11of this episode when they're far be above
• 25:14clinical levels and others makes no sense.
• 25:17It is because it's not like an infection.
• 25:19It's not like a COVID infection where
• 25:21you can say that during pregnancy
• 25:23because you don't have continuous COVID,
• 25:25well, not the infection probably
• 25:27over 10 years is different.
• 25:29I think it doesn't work.
• 25:30We've done it with homelessness and then we
• 25:32have a set where people experience only.
• 25:34Short time and then find housing again
• 25:36and if you have very detailed data I
• 25:38think you can do that with poverty.
• 25:40But people who are really below the
• 25:43poverty line will have been mostly
• 25:45in a tough spot a year or two later
• 25:49or a year or two before.
• 25:51So indeed that was consistent.
• 25:53So there is this carry over effects,
• 25:55there is these.
• 25:57Perhaps there's a biological
• 25:59rapid development post natally
• 26:00there are sensitive peers,
• 26:02there's good ideas.
• 26:02I think we might be able to do that.
• 26:04I'll show you later something with
• 26:06the thyroid hormones where we managed
• 26:07to do that with sensitive peers.
• 26:08I think with social adversities
• 26:11we cannot do that.
• 26:12So if you judge this study
• 26:15against public health relevance,
• 26:17give me a two out of five because I
• 26:20think the carry sort of the sensitive
• 26:23period effects which I marketed as.
• 26:25Don't convince me myself,
• 26:28and I hope I don't convince you.
• 26:31It's interesting,
• 26:32but I don't think it should guide.
• 26:35It did for a while,
• 26:36influenced me that I thought, you know,
• 26:37I have to put more of my research
• 26:40time into very early depression.
• 26:41I think that's still valid,
• 26:43but I'm not so sure that we need
• 26:46imaging research to show that.
• 26:48I'll show you because it's very
• 26:49popular now to do imaging and poverty.
• 26:52I'll show you a bit of that result
• 26:54and then an angle I tried to take
• 26:56and I'm trying to hear your thoughts
• 26:58or at least look at you whether it
• 27:00might convince you what we did there.
• 27:02So household income has been
• 27:05associated with brain morphology.
• 27:07We had this data prospectively from it.
• 27:10Life again that's a sort of marketing trick.
• 27:13So we show you that we did that and.
• 27:16I was interested in two things, the timing.
• 27:18So is it different if it's prenatal or later?
• 27:21And I was also interested if it's
• 27:23different in minority majority
• 27:24and I'll come to that why I'm so
• 27:27interested in that in a minute.
• 27:28So if we have 2000 children against
• 27:30imaging at 10 years poverty defined as
• 27:33national low income threshold in the
• 27:35Netherlands, that's nicely defined.
• 27:36So you get different analyses, you can do it.
• 27:40Never low income and ever low income.
• 27:42Note that we have repeatedly assessed income,
• 27:44So what people? You can just simply do it.
• 27:46Have you ever in any period been
• 27:49poor and we can do that chronic
• 27:51or for example in pregnancy only.
• 27:53And what you see is just the
• 27:57distribution which made it for me,
• 27:59made this a very complicated distribution
• 28:03because in the Netherlands and you
• 28:07see a very similar pattern in the US,
• 28:09it's just not immigrant.
• 28:12Or non western,
• 28:14it's just classified as white and non white.
• 28:17You would see a very similar pattern
• 28:20that those that are poor are very often
• 28:24from here from a non western background.
• 28:26So there is a racial ethnic patterning
• 28:29of poverty in the Netherlands.
• 28:31There's a racial ethnic pattern
• 28:34of poverty in the in America.
• 28:38So you see that of the four hundreds,
• 28:40quite a few that were poor,
• 28:41so 20% were poor at one time a
• 28:45majority would have been from
• 28:47long Western and then we have,
• 28:49you can see the numbers 100 people
• 28:50that were poor in pregnancy,
• 28:52100 and 200 that were poor at any one time.
• 28:54So you can see the breakdown
• 28:56of these numbers.
• 28:59Here is so how it looks at truth.
• 29:02You can see all the different
• 29:03results that you know.
• 29:04If you analyze, you get,
• 29:05even if you take this broad approach of
• 29:07total brain volume and Gray volume and
• 29:09then the typical hippocampus, amygdala.
• 29:11If you do this mix of global and
• 29:14to specific areas, researchers,
• 29:16regions of interest, you see with
• 29:19these many poverty categorizations,
• 29:21you see all these patterns and then you
• 29:24can look where there's significance.
• 29:27And honestly, you could find,
• 29:29that's why I had it in red,
• 29:31some association between the
• 29:32amygdala volume and poverty.
• 29:34And if you look at it carefully,
• 29:35this is the reference group.
• 29:37Never. Then you see what's this?
• 29:38This is the low income childhood only.
• 29:40There seems to be no effect.
• 29:41But if you're chronically poor,
• 29:43if you're chronic poor, or if you're.
• 29:47So ever low income is cost,
• 29:48a combination,
• 29:48but it's really by low income
• 29:50and pregnancy or chronic force.
• 29:51So it really seems to be, if anything,
• 29:53the pregnancy that might drive it,
• 29:56the amygdala.
• 29:57But I can truthfully tell you that this
• 29:59does not survive multiple testing.
• 30:01So there would be,
• 30:03if anything overall in the total group,
• 30:05no real.
• 30:11Convincing or strong consistent effect?
• 30:13Not on the global measures for sure
• 30:15and on these regions of interest.
• 30:16Well, if you find it somewhere
• 30:18just borderline significant,
• 30:20you should probably discount it.
• 30:23However, we had very good data
• 30:25from Child IQ that certainly the
• 30:28pregnancy was very different in
• 30:30minority groups and majority groups,
• 30:33so we had reason from that
• 30:35paper to stratify a sample in.
• 30:38Let me call it Western or Nonwestern.
• 30:40That's the Dutch, Dutch language in America.
• 30:42Western on Western is not
• 30:43really cool anymore,
• 30:44So I'd rather should say
• 30:48it's not immigrants,
• 30:49it's people whose ancestors were born
• 30:52in probably not high income countries
• 30:55and came to the Netherlands for colonial
• 30:57history reasons or work reasons,
• 30:59and the Dutch and the Dutch and European.
• 31:04Community on the other hand,
• 31:07and why do I think that's a very
• 31:09important difference in poverty?
• 31:11Not only did we have some prior results,
• 31:13but also we know that if you're
• 31:15financially strained and you
• 31:16have a network in the country,
• 31:18that's a different thing if your
• 31:20family lives there than if you come
• 31:22as an immigrant from the Cape Verin
• 31:24Islands to work in the harbour.
• 31:25If you're then out of job then you're really,
• 31:28it really is tough.
• 31:30So that's why we stratified
• 31:32for these groups and then.
• 31:34We see if we do that and
• 31:36this is only the Dutch,
• 31:37we actually all of a sudden saw
• 31:39very broad effects on cerebral and
• 31:42other broad parameters of the brain.
• 31:44So taking out this big group of
• 31:48non Dutch ancestry participants,
• 31:50let's call it that way, not Dutch ancestry,
• 31:54but taking them out shoulders all of
• 31:56a sudden we had a strong effect in.
• 32:00The overall brain volume.
• 32:02But what was perhaps more interesting
• 32:04in the numb Dutch.
• 32:05So this was the Dutch.
• 32:06This is the numb Dutch.
• 32:08We didn't see any global parameters,
• 32:09but we see very consistently
• 32:14the effects of in pregnancy or chronic,
• 32:17which also means in pregnancy
• 32:18and later on as well.
• 32:20If you pull that group to sort of
• 32:22ever in pregnancy, we get a very.
• 32:24Very significant effect on consistent because
• 32:27it's significant in both of the subgroupings.
• 32:29If you pull it, it gets very
• 32:31significant in effect on the amygdala.
• 32:33So we get a very different pattern.
• 32:35So we get a much more stress related
• 32:38grain poverty pattern in the non Dutch
• 32:41ancestry group and a very global effect.
• 32:44It's very hard to think what that means.
• 32:47Does that valid?
• 32:47I can tell you I immediately
• 32:49looked at the ABCD data.
• 32:50Does it also fall apart in similar
• 32:52patterning and again of course that would be.
• 32:54Would be none, white and white,
• 32:56probably what you could do.
• 32:58And it's interesting,
• 32:59we saw the same similar different complicated
• 33:01pattern for the behavioral outcomes,
• 33:02but not so much for the brain.
• 33:04So there is some reason to think that if
• 33:07poverty comes with different stresses,
• 33:10it could have a different
• 33:11meaning for the brain.
• 33:12We see that very clearly for
• 33:14the behaviour also in ABCD,
• 33:15but not for the brain.
• 33:17And I haven't looked at the amygdala.
• 33:19As they make that and actually in the
• 33:21Dutch this really predicted school
• 33:22performance so it was meaningful.
• 33:24So if you summarize early in life poverty
• 33:27and pre adolescent brain morphology,
• 33:30there is an association but they really
• 33:32differ from majority and minority groups.
• 33:34And was all the caveats that you
• 33:36hate this subtyping of majority and
• 33:37minority that's up to you to dislike it.
• 33:40I think that is some evidence that we do it.
• 33:42In America,
• 33:43I would say we should do it to
• 33:44some extent because poverty and
• 33:46discrimination go together,
• 33:47which makes a very different terrible mix.
• 33:50In the Netherlands it is also discrimination
• 33:52and stress of surviving financial strength.
• 33:54So there is some reason to do that
• 33:56and this I think what that reflects,
• 33:59I'll be very,
• 33:59very careful to speculate about that.
• 34:01I think it could also be
• 34:03genetically associated,
• 34:04we don't know,
• 34:06but in the in the minority groups
• 34:09as I call them here or.
• 34:11If you want the real nice terminology,
• 34:13I think the exact terminology the
• 34:16non Dutch and ancestry group.
• 34:18I think it is likely stressed
• 34:19by discrimination and because we
• 34:21have that variable in the model I
• 34:23can tell you pull it in and it's
• 34:26substantially weakened the association.
• 34:28So it's not a real mediation analysis,
• 34:30but there is.
• 34:31About 30% of the association and
• 34:33that was a very crude measure of
• 34:35discrimination disappeared once
• 34:36we put that in the model.
• 34:37So I think there's real reason to
• 34:39think that could be different and we
• 34:41have to think more carefully about
• 34:43our neurodevelopmental measures.
• 34:44I'll do the thyroid and then another,
• 34:47I'll do that quickly.
• 34:49I've presented that for many times.
• 34:51So what would that be in if you give me.
• 34:56My sort of scale,
• 34:57my own scales is rating your
• 34:59own work. But let's do it critical.
• 35:01I think we're still only at a three out
• 35:03of five of public health because to
• 35:06think that poverty measures mentioned
• 35:09poverty matters for the brain,
• 35:11I don't think we need too much brain imaging.
• 35:14But, you know, to carefully dissect the
• 35:17effects of minority groups again, Well,
• 35:20really, do we need the imaging for that?
• 35:24Although I think that it has
• 35:26lasting effects on child brains,
• 35:27that it what is it affects is
• 35:30associated with child brains may be
• 35:32very different How it associates
• 35:34with the brain where you come from,
• 35:37It's at least makes us think so give
• 35:39me a three out of five perhaps we want
• 35:42to go to four out of five, don't we?
• 35:44So here's thyroid where I think
• 35:46we can manage thyroid.
• 35:48Old work of mine and it was one recent
• 35:51update which is I think quite spectacular.
• 35:53Thyroid of the brain,
• 35:55So note that the maternal thyroid brain.
• 35:57So maternal thyroid hormones are very
• 35:59important for the brain development.
• 36:01Animal work has shown convincingly
• 36:03that actually it's fascinating
• 36:04that the neurogenesis and it's
• 36:07particularly the neuro neuromigration
• 36:08which actually comes from around,
• 36:10you know the central,
• 36:12the ventricles and then the neurons
• 36:15migrate out to your cortex.
• 36:17Obviously that's where they are
• 36:19in our brains that is guided by.
• 36:22Thyroid hormones that happens in
• 36:26early embryonic life when the embryo
• 36:30is reliant on the maternal thyroid.
• 36:35So much of the neurodevelopment.
• 36:37So the nature is seem sort of
• 36:39very pasimonious.
• 36:40It has only 10 mechanisms
• 36:41and what does you know,
• 36:43vitamin D does something and
• 36:45serotonin do something very different
• 36:46in the fetal life they're much
• 36:49more neurodevelopmental than in
• 36:50us where they have.
• 36:51Very new endocrine function,
• 36:52but they have very new
• 36:54developmental functions.
• 36:55All these systems and in particular
• 36:57thyroid in pregnancy and only in
• 37:00week 14 does then the fetus produce
• 37:03its own thyroid and only by week 20,
• 37:06so sometime later does it produce
• 37:08somewhat sufficient levels and takes over.
• 37:11So in that time the mother
• 37:13supplies the thyroid.
• 37:15In that time many women who have a low
• 37:17thyroid function actually become a bit
• 37:19hyperthyroid because they need more.
• 37:21There's very good graphs.
• 37:22I haven't got them with me because
• 37:24I do a short version of this talk.
• 37:25But trust me, there's very good work,
• 37:27mostly animal work,
• 37:28very consistent that we need the
• 37:30thyroid levels for a brain development.
• 37:33And what we showed in the very early
• 37:35publications, nearly ten years ago now,
• 37:37is that if you take the total sample,
• 37:39and this is the measure of s s.
• 37:44SRS,
• 37:44the social responsiveness of good
• 37:46population trait measure of autism,
• 37:48you see that the people with
• 37:49good levels of the mothers,
• 37:51the offspring of mothers with normal
• 37:53levels of thyroid hormone have much
• 37:56lower levels than those that have subautimal.
• 37:59And this is subclinical,
• 38:00we're not talking about a clinical,
• 38:01this is untreated hypothics,
• 38:04thyroxinemia, you can do severe,
• 38:05you can do less severe,
• 38:06but it's all subclinical,
• 38:08so it's just low levels.
• 38:10Of thyroid hormone in the mother
• 38:12and you saw that association which
• 38:14we showed and then we move on
• 38:16to more recent work,
• 38:19a first Lancet endocrinology paper where
• 38:22we showed that if we take the levels
• 38:26continuous now FT-4 that's the thyroid.
• 38:29So this means more thyroid,
• 38:30this means less thyroid.
• 38:32We showed actually a a
• 38:34curvilinear association with IQ.
• 38:36It's most robust in the
• 38:38low thyroid levels here.
• 38:40And then this is a quite
• 38:41a wide confident role,
• 38:41but you see some significant down decline.
• 38:44So there is a tightly regulated level
• 38:46and that's where most mothers are.
• 38:48If you see the distribution of hormones,
• 38:50it would be just most people are in
• 38:52this space, some are in the low,
• 38:55some are in the high.
• 38:56And we saw a very robust relation with IQ.
• 39:00And later we've replicated this in 2-3
• 39:02other cohorts where I must be honest,
• 39:04this ups this low levels of and the
• 39:08relation to low IQ is extremely robust.
• 39:10This in other cars looks more like this,
• 39:12going sort of much more flat.
• 39:13There's not such a decline,
• 39:15but there is a very robust association
• 39:19between prenatal thyroid hormones and I Q.
• 39:23And then we move to another
• 39:26hormone thyroid parameter.
• 39:28So be careful.
• 39:28This is now thyroid,
• 39:29thyroid stimulating hormone.
• 39:31This means that now you'd beware that
• 39:33higher levels of the stimulating hormones
• 39:36means lower levels of thyroid hormone.
• 39:38It's flipped.
• 39:39I think you have to be a doctor or
• 39:41an endocrinologist or physiologist
• 39:42who immediately get it.
• 39:44But trust me whereas we had easy more
• 39:47hormone is we thought better but this
• 39:49is not the case because it gets worse here.
• 39:51But this is more hormones.
• 39:53This is less hormones and
• 39:55less hormones means lower IQ.
• 39:56Here it is two different things modeled.
• 39:59It's not IQ, it's Gray matter.
• 40:01So it's not a brain parameter.
• 40:02And you see this is essentially flipped.
• 40:06So this means.
• 40:07Less hormones.
• 40:08This would mean more hormones,
• 40:10but I'm presenting at the stimulating
• 40:12axis hormone and what you see is
• 40:15the same similar inverted U-shaped
• 40:18curve tightly regulated on all
• 40:21levels of the thyroid between the
• 40:24brain and the between the brain.
• 40:27And the thyroid hormone and
• 40:29it's highly significant.
• 40:31So it's 2000 children at age 10,
• 40:33it's their prenatal,
• 40:35their mothers in the early
• 40:37mostly around week 10 to 14,
• 40:39it's their thyroid hormone levels.
• 40:41And this has led to some guidelines
• 40:43and discussion and guidelines.
• 40:44Should we measure more thyroid
• 40:46hormones in women that have no
• 40:48symptoms and no history of and there
• 40:51have been trials based on this work
• 40:52which it have to have been largely
• 40:54negative or very small effects.
• 40:55So they're sort of.
• 40:57Equivocal trials have been done,
• 40:59so we don't know,
• 41:00but there is some evidence that it is
• 41:02a very important parameter to regulate.
• 41:04And now comes the recent work.
• 41:07I don't know,
• 41:08I don't have a date when that
• 41:09was published 2 years or so ago,
• 41:11which is very fascinating.
• 41:13We did that.
• 41:14We just realized this data because
• 41:16we had the idea what actually we
• 41:19included the women at different ages.
• 41:21So we can model always in about 200 women.
• 41:25The curve essentially continuously
• 41:28moving the curve with a time interaction
• 41:32variable across the inclusion period.
• 41:34So the first women came to
• 41:37generation out to be included and
• 41:39we took the blood at week seven.
• 41:41The latest that we included
• 41:43were week eighteen.
• 41:44Note these are not the same women.
• 41:46This is the first blood assessment we
• 41:49had where we did the thyroid hormones.
• 41:52So what we modeled it as a
• 41:55sort of continuous model,
• 41:56but then cut it for the doing
• 41:59essentially the intercept for
• 42:01the different week 7 to 18.
• 42:03And what we see is that this curvy linear
• 42:08pattern which is very remarked up to age,
• 42:11then sort of disappears at the
• 42:13end of this inclusion period.
• 42:15And this was still 200 women on average.
• 42:20Time period per week and what this
• 42:22shows you I think is convincingly
• 42:25a sensitive period because it
• 42:27is in the same study measured at
• 42:31different time points specifically.
• 42:33And why is that so credible?
• 42:34Because the reviews they sort
• 42:36of were extremely excited.
• 42:37I've never got anything in that sort
• 42:39of Lancet like paper that easily
• 42:41because as in chronologist said,
• 42:43I've done animal work and I showed
• 42:45by week 15 the child produces
• 42:47on thyroid and thus the mother.
• 42:49Thyroid is just not informative anymore.
• 42:53So while I marketed as a final we
• 42:56got their sensitive period study,
• 43:00the reviewer toned it down to saying
• 43:02it's really showing that the measure
• 43:04is not informative At age 15.
• 43:06It may still influence the brain,
• 43:08but you're measuring the wrong parameter.
• 43:10So this is getting closer to the
• 43:12sensitive period. Holy Grail.
• 43:14That's all these Doha epinologists
• 43:15want to get to, but even there,
• 43:17a very careful reviewer can tell.
• 43:19Tell you you're not there.
• 43:21It just means that from week 14 onwards
• 43:23you're measuring the wrong person,
• 43:25essentially like having the wrong informant.
• 43:27But what does it tell you?
• 43:30It tells you what.
• 43:32I think that this is valid because how
• 43:36could it's if you then have the right
• 43:39measure and you find what you expected,
• 43:41perhaps that's sort of a circumvential say.
• 43:44I don't think it proves causality.
• 43:46But it's getting better that
• 43:48this is quite credible.
• 43:49So I do think in all honesty there
• 43:51is a true curvilinear relationship
• 43:54between thyroid hormone and the brain.
• 43:57I do think given the biology
• 43:59it is likely to be causal.
• 44:00Whether that's amenable for
• 44:02intervention is another study.
• 44:04I've got the wrong slides.
• 44:06I was going to ask you so
• 44:08transition to new results.
• 44:10I missed my transition slide because I
• 44:12pulled it up yesterday night after the.
• 44:15Chemical exposure for the colleague.
• 44:17I hope she's there on the zoom.
• 44:21Does anybody know what trans fatty acids are?
• 44:25Take a sip of coffee while you tell me.
• 44:31Is that forgotten? You're not bisphosals and
• 44:38organophosphates?
• 44:39Which are about what?
• 44:40Does anybody still know
• 44:41what trans fatty acids are?
• 44:47I'll tell you, trans fatty assets
• 44:51in the Netherlands were a big scandal,
• 44:55a public health scandal of big proportions.
• 44:57Why? Because in the 1990s eighties,
• 45:01I don't know, to that time,
• 45:03your grandparents wouldn't have eaten butter.
• 45:06Which they would have
• 45:06lived in these countries.
• 45:07And then there comes the introduction of
• 45:09margarines which is better for public health?
• 45:11Okay, it's better for your fat because
• 45:14it's unsaturated and saturated.
• 45:16Fatty Acids in butter versus margarine.
• 45:18And these are people that eat,
• 45:19you know bread butter and I don't know cheese
• 45:24on twice a day.
• 45:27And so the problem was that these
• 45:30margarines where fatty acids,
• 45:32but they also had trans fatty acids.
• 45:35Meaning these are industrial fatty
• 45:37acids which come with the production
• 45:39of fat and essentially if you produce
• 45:41fat and if you have sort of a if you
• 45:44fry your French fries and you have very
• 45:46poor fat in one of these, I don't know,
• 45:49I don't want to point at any cart here,
• 45:51but if you have very poor,
• 45:52you get trans fatty acids in them.
• 45:54And those were really,
• 45:56it turned out to be terrible
• 45:58for cardiovascular health,
• 46:00actually so bad so that the whole
• 46:02benefit of eating margarine was offset
• 46:05by the effect of trans fatty assets.
• 46:08It was a real scandal in the 1990s.
• 46:11OK, It's sort of forgotten.
• 46:13And I don't know,
• 46:14and I don't know anything much about America.
• 46:15My work is mostly from Europe.
• 46:17So there what happened is
• 46:19there were countries.
• 46:20It's already interesting to see what
• 46:21happens in countries once that's,
• 46:23you know.
• 46:23Detected that Hans very as a mouse
• 46:26models and humans and observational and
• 46:28is really bad and sort of kills you
• 46:31the there's countries that forbid it.
• 46:33Okay Denmark said gone two years
• 46:35and we phase it out of production.
• 46:38It's easy you can just make
• 46:39a bit more expensive oils.
• 46:42The Dutch you might not know them
• 46:45are sort of compromising country
• 46:47so they say to the industry
• 46:49you know it would be good.
• 46:52If you reduced it in your
• 46:53products in the next five years,
• 46:55we do that on a voluntary basis and we
• 46:57will also do a bit of shaming and naming.
• 46:59So there is some pressure.
• 47:01That's the Dutch approach.
• 47:02Now you would laugh about the Dutch,
• 47:04but they do get it done so slowly, by slowly.
• 47:07Uni Lever,
• 47:08whom you know from the Dove products,
• 47:10is a real big you know you Lever
• 47:12is the modern maker in that time.
• 47:15I don't know if they still do it and
• 47:17they phased it out, which leaves.
• 47:19Other products like um,
• 47:22cheap bakery products where it's still
• 47:24used because they couldn't care less.
• 47:26You know, that's the fringe market
• 47:28and they couldn't care.
• 47:28It's cheap to do that.
• 47:30So what we found, um, so here is.
• 47:34If you want to know trans and sis
• 47:36fatty acids, so this is the big difference.
• 47:38Industrial fatty acids like trans would have
• 47:41the hydrogen here instead of like this.
• 47:43Wow, you think that's the difference?
• 47:45That's it.
• 47:45Yes, that's the difference.
• 47:47That's it.
• 47:47And where are they?
• 47:48They're found in fried foods,
• 47:50commercial bakers and processions.
• 47:51But what happens in the statcha pros?
• 47:53There was not a law to stop them,
• 47:55but really,
• 47:56in the early 2000s in this country, this.
• 47:59And the changes in the Netherlands went down
• 48:02in vegetable oils and fat in those years.
• 48:05The production went down dramatically.
• 48:08So without,
• 48:09I'm not saying that's the best approach,
• 48:10but both in the Netherlands
• 48:12and Denmark and other countries
• 48:14in Europe, they reduce these fatty acids.
• 48:17And why is that? Why am I telling
• 48:19you all this in an imaging study?
• 48:21I'll tell you why.
• 48:23It's really fascinating.
• 48:24I saw this once and I thought these are.
• 48:28The inclusion years of the Generation R study
• 48:32we included from 2003 to actually to 2007,
• 48:40we included in exactly the years when
• 48:43trans fatty acids disappeared in the in
• 48:49the food industry in the Netherlands.
• 48:52That means we can look at.
• 48:55The blood levels of women who came
• 48:58at different times in those years,
• 49:00and we did. And you know,
• 49:01you'd think you'd see the same exact curve,
• 49:03But you know,
• 49:04we saw a 10/10 a quarter of decline,
• 49:09which for anything in
• 49:10biology is quite dramatic.
• 49:12So in 2000,
• 49:14the people who included in 2005 had only 3/4
• 49:17of the levels of those included in 2002.
• 49:21So it indeed related to a.
• 49:25Reduction in the blood of a women and
• 49:29I don't know if they knew and they
• 49:30didn't change their eating behavior,
• 49:32they just ate the same bread
• 49:34and French fries as before,
• 49:36but they got less of this.
• 49:38Which means if we can relate
• 49:41this to a child outcome,
• 49:43we have something which we call
• 49:46instrumental barrel approach
• 49:47because it is a policy change.
• 49:49That is related to biology
• 49:51in the blood of people.
• 49:53And so we published that last
• 49:55year after sort of after I had
• 49:56what I don't know how I came.
• 49:57I come from a Baker's family to be honest,
• 49:59I was, I don't know,
• 50:00reading this in the sort of Baker thing
• 50:02digest and I was quite fascinated
• 50:04and I thought, yes, you can do that.
• 50:06And so we did the.
• 50:08Trans fatty acids in the blood and
• 50:10then we showed and you know you
• 50:12this is a very bad slides taken
• 50:14directly from the publication.
• 50:16But you can see a highly,
• 50:17highly significant association of
• 50:20trans fatty acids with fetal head growth.
• 50:24And this is true head growth.
• 50:25This is the change from fetal head
• 50:27size from one point to the other.
• 50:29It's not just growth and you say birth
• 50:31weight is a measure of fetal growth.
• 50:33This is really fetal growth
• 50:34as it is a change from.
• 50:362nd to 3rd trimester,
• 50:38there was no effect when the head is very,
• 50:41very small, but when it expands,
• 50:42when it gets big,
• 50:43that's where all the growth is.
• 50:44And that second to third end of
• 50:47trimester and 6000 children.
• 50:48So that's a good inclusion.
• 50:50We see super significant associations and
• 50:52then we can actually do the same trick.
• 50:54We can only do the, the,
• 50:56the, the TFA measures.
• 50:58We can do that with the high
• 51:00and see very clear patterns,
• 51:01but we can see that this calendar time.
• 51:05There is an association of
• 51:08calendar time with fetal growth,
• 51:11meaning that in the course of
• 51:15that studies the the heads of
• 51:18the children became a tiny bit.
• 51:21I must admit it's a tiny bit,
• 51:22but fetal measures in 6000 are very precise,
• 51:25bigger and we think and we can show that
• 51:28was an instrumental viral approach,
• 51:30which is a different sort
• 51:32of statistical technique.
• 51:33We can show that the association is
• 51:37driven by the reduction and the policy
• 51:41change and that is something I've been
• 51:44working 20 years towards and never got done.
• 51:46So that we show that policy translates
• 51:50into biology and sad thing is we didn't
• 51:54get it to behaviour, so bigger heads.
• 51:55And I'm not really much related to behavior
• 51:57and then it becomes very messy and noisy.
• 51:59But you know,
• 52:00the journal loved it that it was.
• 52:02And why does it have
• 52:04clinical health relevance?
• 52:06Well, first of all, it does.
• 52:07This is causality,
• 52:08not only policy,
• 52:10it is quite a causal approach,
• 52:11but really interesting.
• 52:12If you look at the production
• 52:14of East Europe and South Asia,
• 52:15that's the Indian region and
• 52:17the East European region where
• 52:19there's nobody cares about this,
• 52:21The levels are still shockingly high.
• 52:24So I think it's still relevant,
• 52:25although for us it's a historic
• 52:28study to be honest.
• 52:30And do I do one more or should I do
• 52:32for questions. This is a good ending.
• 52:34So I could do a physical activity
• 52:36in the brain,
• 52:37but
• 52:41good, then I'll wrap up.
• 52:42So I'll leave away that
• 52:44there is an association with.
• 52:46Brain change that I should
• 52:47I just do one slide?
• 52:49No, I don't do one slide.
• 52:50It doesn't work.
• 52:50I do the, I do the IT doesn't work.
• 52:53I just tell you it is we show that
• 52:56would have been the last one.
• 52:57I sort of did too much
• 52:58fatty acids carried away.
• 53:02I was going to show you that
• 53:04we can show that physical
• 53:06activity is related not just to
• 53:08brain size and brain volume,
• 53:10but it is related to the volume
• 53:13change over adolescence.
• 53:15Which is quite a bit more and that's
• 53:17essentially an answer to the,
• 53:18you know we need bigger studies
• 53:20or we need studies of change.
• 53:22We've now got the first studies of change.
• 53:24If you want to show,
• 53:24just show the result,
• 53:28it's total physical activity really,
• 53:30not just the the also quite a
• 53:33bit of the hippocampus grows or.
• 53:37Grows a bit faster if you do physical
• 53:39activity and it's interesting
• 53:41because it's consistent across
• 53:43parent and child reported physical
• 53:45activity reports Okay I'll wrap up.
• 53:47So the dominant force in research is
• 53:51the is the know you're imaging a lot
• 53:54in autism and a DHDI would challenge
• 53:57or like to discuss with people who say
• 54:00it's made a change in our clinical
• 54:02treatment or in our public health
• 54:04understanding of autism and brain I think.
• 54:06It did a lot for understanding the disease.
• 54:09I'm not so sure it did a lot for how we
• 54:12treat disease, which is a big difference.
• 54:15I would say The effect sizes are often
• 54:17small and often correlational and not causal.
• 54:20There's a real problem which I didn't show
• 54:22you, but we've struggled with that a lot.
• 54:24Can we reproduce imaging results?
• 54:28Anybody who might talk today was talking
• 54:30about the heterogeneity of populations.
• 54:31That's the same.
• 54:32And I showed you that was the
• 54:35minority majority is one example.
• 54:37I think we have to, and that was my
• 54:40first talk this morning was Kerim.
• 54:41I think he's there in the back row.
• 54:43We should really go to developmental
• 54:45approaches and longitudinal trajectories.
• 54:46I think that's the only way forward.
• 54:49I fell short of showing you
• 54:50that was the physical activity,
• 54:52but I think that's what matters.
• 54:55I'd like to wrap up,
• 54:56it's not a diagnostic or prognostic tool.
• 54:58It does have some public health relevance,
• 55:01but I would say.
• 55:03Occasionally,
• 55:03and sometimes even sort of coincidentally,
• 55:07but it does as many other fancy techniques.
• 55:11These are the students that helped
• 55:13Ryan Mitzler.
• 55:14I want to mention him because he does
• 55:15much of my imaging in the Netherlands
• 55:17and students who did these papers,
• 55:19and of course the participants.
• 55:20Thank you very much.
• 55:27Thank you so much, honey.
• 55:28I will just say that we do have
• 55:30time after the presentation.
• 55:31So if anyone would like to stay in the
• 55:33room and continue the conversation,
• 55:35we're free until 2:30.
• 55:36And but any burning
• 55:38questions for Doctor Tamar
• 55:49that was that was pretty interesting to me.
• 55:51And I just wonder your thoughts
• 55:53about how far do you go on
• 55:56restrictive public policy to?
• 56:00Get the good for for young children
• 56:04who can't protect themselves.
• 56:06So for instance you know it's good to
• 56:10keep lead away from babies and and young
• 56:13children but when you start talking
• 56:15you know dietary and cultural things,
• 56:18just your thoughts.
• 56:19How how far do you go with this?
• 56:20Do you do you, you know say that's it,
• 56:23fruits and vegetables and
• 56:25Mediterranean diet for everyone or.
• 56:28That's an interesting one.
• 56:29So that's sort of the whole public health
• 56:32school of Harvard debates that every day
• 56:36and seriously does if it's good to to
• 56:38zoom in on an example because otherwise
• 56:40I'm going to give a sort of overreaching,
• 56:43I would be struggling.
• 56:44That's a little evening thing debate.
• 56:46If you take the dietary example,
• 56:48I am in favor. Of restricting
• 56:52soda and sweet drinks in schools.
• 56:55We have seen now that
• 56:57that is really so obesity,
• 57:00making so much diabetes down the road.
• 57:02I think we should go there.
• 57:05Many of the others like no sweets,
• 57:07which are also, you know,
• 57:08sugar is bad, but I would be very,
• 57:10very hesitant.
• 57:12I think the best way to do it
• 57:15is to think carefully with the
• 57:17schools should sell them but.
• 57:19To forbid them, perhaps a sugar tax.
• 57:21But other than that I think
• 57:23very little is evidence based.
• 57:24So much of these things are not causal.
• 57:26We changes.
• 57:27You know look at the history of
• 57:29the Harvard schools of department
• 57:31of petition advice for diet.
• 57:32You know that's a funny changing thing.
• 57:34You know nuts and that and oils
• 57:36and meat and alcohol.
• 57:38Just look at the alcohol.
• 57:39You know 20 years ago I was
• 57:41taught in Rotterdam alcohol is
• 57:42better than any concentration.
• 57:44You come to Harvard and they say no,
• 57:45but of alcohol is very good.
• 57:47Now they have to sort of change that,
• 57:49but it took them 15 years to
• 57:52really come to a conclusion there.
• 57:54So that encouraging of your
• 57:57glass of red wine
• 57:58is now gone. You know, you have to
• 58:00have it was a bad conscience tonight,
• 58:03but I think still think,
• 58:04still think you should.
• 58:05So I'd be very, very restrictive,
• 58:07very, very cautious,
• 58:09but I wouldn't shy away from a few measures,
• 58:12very, very cautious.
• 58:13But sometimes I think soda, we got it.
• 58:16Sugars, we've got it.
• 58:17So restrict the sugars in a
• 58:19creative way and for not forbid
• 58:21but tax it and don't have it. Yeah,
• 58:25just really quickly. Perhaps relatedly,
• 58:28you know when you talk about your
• 58:30trans fatty acid decline over time,
• 58:31I was thinking about PER and
• 58:33polyfluoroloco substances.
• 58:34You know, these forever chemicals and and.
• 58:36You know, what we've seen in with
• 58:38some pilot data there is that
• 58:40there's a patterning by income level,
• 58:41a patterning by income level per country.
• 58:44I'm just wondering the decline in
• 58:46trans fatty acids that you described,
• 58:48was there a patterning by SES?
• 58:50Did you observe A steeper? No.
• 58:52We see much less of that patterning
• 58:53in the US than in the US.
• 58:55In the US, every environmental exposure
• 58:58is highly socially patterned to an
• 59:00extent that sometimes escapes me.
• 59:02I don't quite know, you know,
• 59:03why are they having so much more?
• 59:05And then I hear they have different
• 59:06hair products and this and that.
• 59:08It's very hard for me to understand.
• 59:10In the Netherlands, for example,
• 59:11I'll tell you, organo phosphates,
• 59:14which is pesticides,
• 59:15were higher in the high SES
• 59:18because they ate more fruit.
• 59:19So in in the US we looked at the
• 59:22same thing and lo and behold,
• 59:24organo phosphates are lower in high SES.
• 59:29I don't understand the US
• 59:30enough to understand why that is
• 59:32such a ubiquitous pattern.
• 59:34In the Netherlands,
• 59:35it's much less so people live.
• 59:39I don't know as many reasons.
• 59:42I don't quite understand that.
• 59:43So in the Netherlands?
• 59:44No, not always,
• 59:45although some of some of the chemicals,
• 59:48yes, very much so,
• 59:49but not as not as dramatic as here.
• 59:53I think you're trans fatty policy
• 59:55example is one of the most profound
• 59:58statements in support of integrating
• 01:00:00the research and policy says
• 01:00:01Thank you so much for sharing.
• 01:00:03I definitely want to find out more
• 01:00:04about that and track that
• 01:00:06and try to replicate that.
• 01:00:07My question for you is building
• 01:00:09on all that you've done,
• 01:00:10especially in the area of policy,
• 01:00:12what do you see next?
• 01:00:13What do you see is the next area that
• 01:00:16you could be pursuing building out?
• 01:00:19What does policy mean?
• 01:00:20Because when I when I looked at the data,
• 01:00:22I thought back to let.
• 01:00:24Because in the United States there's
• 01:00:26definitely an association with with
• 01:00:28low income and and lead in your
• 01:00:30pipes and in your drinking water.
• 01:00:32So what is on your horizon
• 01:00:34next in the space of of poverty
• 01:00:38and research and policy?
• 01:00:42Yeah there's there's in my school and in
• 01:00:45my world thinking too 2 lines of research.
• 01:00:48One is always which we have.
• 01:00:50You know can you dissect. Why poverty?
• 01:00:56What makes poverty relate to behavioral
• 01:00:58and new developmental cognitive school
• 01:01:00achievement problems or do you just
• 01:01:02think you know it's money. That's it.
• 01:01:08You know I am it's I think those
• 01:01:12two are are totally separate.
• 01:01:14I think we should fight LED
• 01:01:17and environmental things really
• 01:01:18more better and full force.
• 01:01:22Lead is just I can't yeah we've
• 01:01:23discussed that I don't need to say that
• 01:01:24no none of us can believe that it's
• 01:01:26still around as a public house hazard.
• 01:01:28It should be gone.
• 01:01:29It's just not acceptable.
• 01:01:31At the same time I think make very
• 01:01:34clear that as long as we have these
• 01:01:37substantial poverty gradients that
• 01:01:41that is a policy taxing and that's you
• 01:01:44know beyond me to to do much about the
• 01:01:46but it's clearly something that has
• 01:01:48to be addressed because I think with.
• 01:01:50Addressing LED, you will not
• 01:01:53substantially address the poverty
• 01:01:54inequality in this country as
• 01:01:56much as I think it's important,
• 01:01:58but it's completely different thing.
• 01:02:00And you see that in
• 01:02:04we all know that, you know homelessness.
• 01:02:08I'm yeah, the the the extent of homelessness
• 01:02:11in Boston and other areas is just so
• 01:02:14dramatic and such a health hazard.
• 01:02:17I don't know why that's not addressed.
• 01:02:18I really fail to see that could
• 01:02:20easily be addressed. Wonderful.
• 01:02:22Well, just in the interest
• 01:02:23of everyone's time,
• 01:02:23if anyone would like to stay on,
• 01:02:25please do wait in the room.
• 01:02:26We can continue this conversation.
• 01:02:28And but just please do join
• 01:02:29me again in thanking Dr.
• 01:02:31Kmar for his presentation.
• 01:02:34Yeah. Sorry to talk so
• 01:02:36long and see you later.