The Yale LISTEN Study Town Hall: October 2023

Transcript

• 00:04We can jump into this,
• 00:05and I think the first thing I want to do
• 00:08is just present to you a little bit of
• 00:10information that we've been looking at.
• 00:12This is an interesting mixed group.
• 00:14There's some people in this group
• 00:16who have had a syndrome that
• 00:17emerged after the vaccination.
• 00:19There's some people in this group who
• 00:21have had a family of symptoms emerge
• 00:23after having been infected with COVID.
• 00:26And of course the people who who have
• 00:29symptoms emerged after the vaccination.
• 00:31We, you know, we think that that's
• 00:33that's not viral persistence,
• 00:34that's something else that's going on.
• 00:37And and for people along COVID,
• 00:38we think they're at least some
• 00:39people that you know emerging thing
• 00:41that's about viral persistence.
• 00:42So one of the questions has been well
• 00:45but a lot of the symptoms sound similar.
• 00:48You know we've now got a group that's
• 00:50substantial with both groups together.
• 00:52You know they each have different
• 00:54interests and their different challenges,
• 00:56but I think in the in the minds of people
• 01:00in society then they sometimes get them
• 01:02confused. Are these the same thing?
• 01:03Are they different? What?
• 01:04What is it?
• 01:05And one thing we want to ask was
• 01:07if you look at the responses,
• 01:09particularly on the symptoms,
• 01:11could we differentiate the groups?
• 01:13You know it it.
• 01:14Do the groups all look the same
• 01:16or is this are these two different
• 01:18groups based on the symptom complex,
• 01:19the self reported symptoms?
• 01:21So I wanted to also say that our team,
• 01:23we have some remarkable medical
• 01:25students who have joined us,
• 01:26which I think is really great,
• 01:27you know medical students who are
• 01:29contributing so importantly to this and
• 01:30and aren't afraid of the challenges
• 01:34of trying to learn in an area that's
• 01:37that's so formative so early still.
• 01:39And so I wanted to and an undergraduate,
• 01:43Rishi Shaw's an undergraduate actually
• 01:45who's joined us a very talented people
• 01:46who've been are sort of part of this.
• 01:48And so let me just turn it over to you,
• 01:49Rishi,
• 01:50and maybe can set up this study.
• 01:52And then Lula Wu's actually Biostatistics
• 01:59concentration in the school,
• 02:00public health, getting a master's degree
• 02:01on her way to then going to get a PhD.
• 02:03She'll present a little bit and then out
• 02:06of the room who's is a medical student is
• 02:10is going to present a little bit also.
• 02:11Then we'll have a little bit of a discussion.
• 02:13But we're trying to keep this maybe like
• 02:1415 minutes just to kind of give you guys
• 02:16a taste of something we were looking at.
• 02:18So Rishi, why don't you go ahead.
• 02:20Yeah, definitely.
• 02:21Thank you so much. Yeah.
• 02:24So like Doctor Kamal's mentioned,
• 02:27there are a lot of ongoing efforts
• 02:29right now in the listen project
• 02:30and this is just one of those.
• 02:31And so we're going to be talking
• 02:33about today is this project where
• 02:35we've been trying to compare the
• 02:36phenotypes between listen participants
• 02:38that have long COVID and listen
• 02:40participants have something that we've
• 02:42termed post vaccination syndrome,
• 02:43which you may remember as a vaccine injury.
• 02:47And so the question driving and
• 02:49motivating this project is what are
• 02:50the similarities and differences among
• 02:52listen participants with either long
• 02:55COVID or post vaccination syndrome.
• 02:57And so we've explored this by first
• 03:00talking about how we define long
• 03:02COVID and post vaccination syndrome.
• 03:04And so you may remember from
• 03:05your listen survey that we asked
• 03:07the self reported question,
• 03:08do you think you have long COVID?
• 03:10And these are symptoms of of COVID
• 03:13infection that persist 4 weeks or longer.
• 03:16This new term that you may not be
• 03:18familiar with this post vaccination
• 03:19syndrome and post vaccination syndrome
• 03:21is synonymous to vaccine injury.
• 03:23So you may have seen this term
• 03:25vaccine injury on your survey and
• 03:26so this was defined by the self
• 03:29reported response to the question,
• 03:30Do you think that you were
• 03:32injured by the vaccine
• 03:33And and just to say, I mean we've
• 03:35sort of using this as a term of art,
• 03:36but we're not, I don't know
• 03:38whether it'll be picked up or not,
• 03:39but we're just using it as a as a as a way
• 03:41to express what this group is right now.
• 03:43Go ahead, keep going. Sorry.
• 03:47So we've in our participants from Listen,
• 03:50we found similar characteristics
• 03:52in demographics between the long
• 03:54COVID groups and the PBS or post
• 03:57vaccination syndrome group.
• 03:58And the medium age for the long COVID
• 04:01participants was 46 years old with
• 04:0474% identified themselves as females
• 04:07and 86% of them identified as white,
• 04:10while in the PBS group the median
• 04:13age was 46 years old with 80% of
• 04:17them identified as females and
• 04:2087% identified as white.
• 04:21We did not find any significant
• 04:24difference in age, race and ethnicities.
• 04:27We did not find any significant
• 04:30differences either in marital status,
• 04:32pre pandemic employment status or
• 04:35pre pandemic household income status.
• 04:40So we found many similarities and
• 04:42differences in some differences in
• 04:45the pre pandemic comorbidities.
• 04:46In the two groups,
• 04:48we found that the participants with
• 04:51long COVID were more likely to have
• 04:53MECFS or My Myelogic and Syphilis
• 04:58chronic fatigue syndrome with 9019%
• 05:03in the long COVID groups versus 12% in
• 05:07the post vaccination syndrome groups.
• 05:10And they are all they were also more
• 05:13likely to have depressive disorders
• 05:15with 28% in the long COVID groups
• 05:18while in with 20% in the PBS groups.
• 05:21On the other hand,
• 05:22the post vaccine syndrome participants
• 05:25were more likely to have the M
• 05:27cast or in the full name is mass
• 05:30mast cell activation syndromes.
• 05:32It was 12% while only while 7%
• 05:37of the participants in long COVID
• 05:39had this comodibilities.
• 05:40The participants with prospects
• 05:42and syndrome syndrome were also
• 05:45more likely to have neurological
• 05:48conditions include including a
• 05:50spectrums of medical conditions
• 05:53including dementia dementia and almost
• 05:561/3 of the participants with PBS
• 05:59had this pre pandemics comorbidity
• 06:04and and just just to say on this again
• 06:05I think the main point was these groups
• 06:07were very similar on their demographics.
• 06:09Imagine, I I don't know that you could
• 06:11have even designed it like this.
• 06:13Exactly the same mean age and
• 06:16and pretty much, you know,
• 06:17very similar sex distribution
• 06:20and very similar comorbidities,
• 06:22except for the few that she's mentioning.
• 06:24But but actually the groups look pre,
• 06:27Pandemic, Pre,
• 06:28all the stuff that's been going
• 06:30on look to be pretty similar.
• 06:32That was interesting.
• 06:37Speaking of more, there was
• 06:38a question I was asked on
• 06:39the survey regarding health status,
• 06:41and this was assessed using the Euro
• 06:44quality of life visual analog score.
• 06:46So participants were asked to rank
• 06:48on a scale of zero to 100 how how
• 06:50they would best characterize their
• 06:52health at the time of survey where
• 06:540 indicated the worst health status
• 06:56and 100 was the best health status.
• 06:57So again we've shown here the curves
• 07:00for both LC which is long COVID
• 07:02and VI which is vaccine injury
• 07:04or post vaccination syndrome.
• 07:06And again the point to emphasize here
• 07:08is that both groups exhibited similar
• 07:11health status on the medians were
• 07:14very close to 5054 the vaccine injury
• 07:16group and 49 for the long COVID group.
• 07:18And so again,
• 07:19both group had similar health status.
• 07:23Another metric that we used to kind of
• 07:25assess health status was symptom severity.
• 07:27And so this was asked as a question
• 07:29of we were trying to understand
• 07:31how your symptoms are,
• 07:33how bad your symptoms are on the
• 07:34worst day that you feel them.
• 07:36And so this is sort of a flipped scale
• 07:39where zero meant your your condition
• 07:41was a trivial illness and 100 meant
• 07:44that your symptoms were unbearable.
• 07:46And so we can kind of see
• 07:48the skewed distribution here.
• 07:49And again,
• 07:49the point to emphasize here is that
• 07:51both participants from the long
• 07:53COVID cohort and the vaccine injury
• 07:56cohort have similarly high levels of
• 07:58symptom severity around the 8082 mark.
• 08:04So this is a plot which in which all the
• 08:07significant significant differences in
• 08:10the percentage of participants experience
• 08:14symptoms between long COVID groups and
• 08:17the post vaccination syndrome groups.
• 08:20The the the direction towards the right
• 08:23means more participants with long COVID were
• 08:26more likely to have the death syndromes,
• 08:29and the directions toward the left
• 08:31means that participants with the
• 08:33post vaccination syndrome were more
• 08:35likely to have the symptoms the most.
• 08:38Like the most significant,
• 08:40significantly different syndrome for the
• 08:43long COVID participants were memory problems,
• 08:47change, sense of smell, brain fog,
• 08:49shortness of breath and cough.
• 08:51While people with the post vaccination
• 08:56syndrome were more likely to have
• 08:58burning sensations, neuropathy,
• 09:00internal vibration,
• 09:01numbness, and tinnitus
• 09:07we have these are some implications
• 09:10we have from our study results.
• 09:13The first one is that the listen
• 09:15participants with either long COVID
• 09:17or post vaccination syndrome have
• 09:20similar demographic characteristics.
• 09:22Both groups reported a spectrum
• 09:25of symptoms and overall lower
• 09:27and poor health status and lower
• 09:30the quality of life scores.
• 09:32And the most important symptoms
• 09:34that can differentiate the group
• 09:36seem to be the brain frog,
• 09:38the changed sense of smell,
• 09:40cough and burning sensations.
• 09:45Do you wanna talk a little bit about
• 09:47when we really tried to figure out
• 09:48whether we could differentiate
• 09:52so kind of as Lilo had pointed out that
• 09:56there are these differences between
• 09:58folks who who who sort of had post
• 10:00vaccination syndrome and long COVID.
• 10:02And and one of the questions is is
• 10:03sort of can we differentiate and and
• 10:05what are the symptoms you know that
• 10:08sort of identify whether someone
• 10:09has long COVID or someone someone
• 10:11has post vaccination syndrome.
• 10:12And and sort of to get at this we put
• 10:15together a machine learning model
• 10:17that sort of identified you know a
• 10:19set of of eight symptoms that together
• 10:22when you sort of consider their their
• 10:24overall pattern in in a given patient
• 10:26are able to sort of predict whether
• 10:29someone is sort of experiencing long
• 10:31COVID or post vaccination syndrome and
• 10:33so kind of high and so so so these
• 10:35eight symptoms that that that sort
• 10:37of come together as this group are
• 10:39are are are people who are brain fog,
• 10:42palpitations memory problems sore
• 10:44throat cough neuropathy burning
• 10:46sensations and a changed sense of smell.
• 10:49And so I I I think the important thing
• 10:52to to take away from here is that
• 10:54you know we're we're able to sort
• 10:55of have this combination of symptoms
• 10:58that indicate whether someone is sort
• 11:00of you know distinguishes whether
• 11:02someone is experiencing long COVID
• 11:03or post vaccination syndrome.
• 11:05And and so the metric that we
• 11:07use to quantify this is,
• 11:08is it something called the area under the
• 11:10curve And and that says that the metric
• 11:12that here is that it has a value of .8.
• 11:15And so it ranges from zero to 10.5,
• 11:18meaning that we sort of randomly say
• 11:19that someone has long COVID or post
• 11:21vaccination syndrome and one being that
• 11:23we're able to perfectly distinguish and
• 11:24.8 means that we're doing a pretty good job.
• 11:26And so I think practically
• 11:28that means that you know,
• 11:29given someone's symptom profile and and
• 11:31looking at these eight symptoms and sort
• 11:33of combining them together in their totality,
• 11:35there's an 80% chance that we're able
• 11:37to sort of say that someone has long
• 11:40COVID or post vaccination syndrome.
• 11:41And I I,
• 11:42I think this is just the beginning of
• 11:45sort of us exploring kind of you know,
• 11:47different modeling techniques and and
• 11:49and different representations of of of
• 11:52understanding folks who who have long
• 11:54COVID or post vaccination syndrome.
• 11:56Yeah.
• 11:59Yeah. I'll just, I'll just add
• 12:00a little color to that too.
• 12:02Thank you, Allison.
• 12:03And you really did a great job on
• 12:06many of the analysis that we did.
• 12:07So just imagine, you know,
• 12:09we've got this survey that you guys,
• 12:11many of you filled out that you
• 12:13know goes from top to bottom.
• 12:15I think there are 99 symptoms.
• 12:16And the question would be based
• 12:19on the pattern of your responses,
• 12:21if we didn't know which group you were in,
• 12:23could we predict which group
• 12:25you were in and and how how well
• 12:29could we be able to do that.
• 12:32And so it just turned out that that
• 12:36the model that we did based on the
• 12:38pattern of responses and and what
• 12:40some of what others is saying is
• 12:41what what were some of the most
• 12:43influential symptoms in being able to
• 12:45move people But but this takes into
• 12:48account the entire pattern of the responses.
• 12:51You know could we be able to differentiate
• 12:52and tell one group from the other.
• 12:54And in .8 it turns out like you
• 12:56said .5 is means it's coin flip.
• 12:58One is you perfectly can predict .8
• 13:02in in our in the world of prediction
• 13:04it's not bad it's pretty good.
• 13:07It mean it's much more likely than
• 13:08chance you know that you can tell
• 13:10which group somebody was in just
• 13:11by the pattern of the responses.
• 13:13Now we have to dig deeper to really try
• 13:15to understand what are those patterns,
• 13:17what might they mean?
• 13:19You know,
• 13:20I think my view of this is that they're
• 13:25probably people within the living
• 13:27with long COVID who have different
• 13:29mechanisms of their long COVID.
• 13:31And you know,
• 13:32this goes back to Kiko's and I'm going
• 13:34to refer over to her in a second.
• 13:35You know her,
• 13:36her theories of of long COVID and and that,
• 13:40you know,
• 13:40it's probably not all one or the other.
• 13:42There's probably underneath that
• 13:43there's some people whose mechanism
• 13:44might be viral persistence.
• 13:46There might be other people that
• 13:47has to do with the spike protein
• 13:49or the other various different ways
• 13:51or reactivation of other viruses
• 13:53and and so forth.
• 13:54And so,
• 13:55you know that means that there are
• 13:57some mechanisms that might cross
• 13:58over the groups and and be similar
• 14:01because they're actually having the
• 14:03same underlying mechanism and some that are,
• 14:05are different.
• 14:06And that's why the work that's being
• 14:08done in the lab is so important
• 14:10because if we can now begin to start
• 14:12adding nuance and and clarity to like well,
• 14:15which one are you,
• 14:16you know what what is it that's
• 14:18causing it you which can provide a
• 14:20target for the intervention that
• 14:22might help to help you feel better.
• 14:24And so to me this is this is the
• 14:26first time you know what first I
• 14:27think it's one of the first studies
• 14:29that actually has both groups in
• 14:31together and they're both suffering and
• 14:34and in this population you all are in,
• 14:37those two groups are about the
• 14:39same Asian demographic and about
• 14:40the same before all this started.
• 14:42And now we've got the ability to
• 14:45actually we can say with advanced
• 14:47analytics based on on just your personal
• 14:50responses to a survey, we can pretty
• 14:53much tell which group you're in.
• 14:55And I think that starts to help us understand
• 14:57that that that where is that overlap.
• 14:58It's not perfect prediction and that
• 15:00tells me it's maybe because there's
• 15:02some people who are very similar and
• 15:04there's some people who are are different.
• 15:06But when you just look at it based
• 15:08on responses to the symptoms,
• 15:10you don't see it because everyone looks like,
• 15:12yeah, they're all,
• 15:13isn't everyone reporting more or
• 15:15less a lot of overlap of symptoms?
• 15:17They are.
• 15:18But when you look at the pattern
• 15:20of the symptoms that are reported
• 15:22by the individuals,
• 15:23you can begin to see patterns
• 15:26that are humanized,
• 15:28don't necessarily perceive.
• 15:29But that we have to be able to use some
• 15:31of these analytic approaches to tease out.
• 15:33And I would say that we're
• 15:35still trying to learn on this,
• 15:36but a lot of the folks,
• 15:37so you've you've heard from the
• 15:39students who are great but they're
• 15:40also backed up by you know very
• 15:42accomplished scientists who have
• 15:44a lot of experience with these
• 15:47things so that you know they're not,
• 15:49they're not out by themselves but
• 15:51they're being supported and and helped.
• 15:52So they're,
• 15:53you know I wanted to let them present today,
• 15:55but just know that that they've got,
• 15:57you know we've got outstanding
• 15:59data scientists behind them.
• 16:00And then I think that some of the
• 16:03interesting pieces will be also as as
• 16:05we're working in the lab to see whether
• 16:07or not of the deep immune phenotyping.
• 16:09Can we also predict based on the
• 16:11on the patterns of responses,
• 16:13you know which group would be which
• 16:15based on on the signals that are
• 16:17coming out of the immune phenotyping.
• 16:18But I I don't know,
• 16:20Akiko if you want to comment on what
• 16:21what you think about what you've
• 16:23heard or what's going on in the lab.
• 16:24I know Bernali's also been working
• 16:26very hard on, on some of this work.
• 16:30Yeah. Thank you Harlan and thank you Rishi,
• 16:32Lilo and Adith for this great presentation.
• 16:37I think what what you're presenting
• 16:39basically points to the fact that there
• 16:43are combinations of symptoms that can be
• 16:46helpful to distinguish these two diseases.
• 16:48But we we know that you know there are
• 16:52so many overlapping symptom between
• 16:54these two conditions and that is why
• 16:57I think we need to do a deep dive on
• 17:00the biology and biological factors that
• 17:03that might be overlapping or different,
• 17:06we don't know yet.
• 17:08We have collected some samples from many
• 17:12of you that are attending today and we
• 17:15have done some preliminary analysis.
• 17:18We still need to do a lot more
• 17:20different sort of analysis angles,
• 17:24but we're gearing towards looking
• 17:26at head to
• 17:36Just to kind of I know that many questions
• 17:39on the chat is really addressing the
• 17:42similarity and differences and what might
• 17:44be the hypothesis if I could just take
• 17:47one minute to maybe present a slide which
• 17:52I shared with you a few town halls ago.
• 17:55But I think it's the same concept
• 17:58still which is I don't know if you
• 18:03can see this but there is OK great,
• 18:05there is this remarkable overlap of
• 18:08symptoms and sex ratios for long COVID
• 18:10and this isn't even you know this is
• 18:13the prior way prior to the the nice
• 18:15work that was just presented today.
• 18:16But you know just by looking at the
• 18:19the the frequency of these symptoms,
• 18:22there are many overlapping ones.
• 18:24And so you know there are currently the
• 18:27hypothesis that I shared with you before,
• 18:30but I wanted to go over again because they
• 18:34they still stand as far as I'm concerned.
• 18:37So as you know that they're like
• 18:41these different hypothesis that
• 18:43are trying to explain long COVID,
• 18:46those include sort of persistent virus
• 18:49which may be overlapping with post
• 18:52vaccine with respect to persistent spike
• 18:55protein or persistent RNA presence that
• 18:58leads to innate immune activation and so on.
• 19:01There's also the possibility of autoimmunity
• 19:05and that can be triggered by both
• 19:09vaccination and COVID psoroscopy 2 infection.
• 19:12There's could be tissue damage,
• 19:15microclots,
• 19:16endothelial dysfunction.
• 19:18Again, you could theoretically,
• 19:22you know, conceive of both of
• 19:24these agents leading to that.
• 19:27And then of course EBV reactivation
• 19:30and other things that we've already
• 19:32seen in long COVID may be happening
• 19:35also in post vaccine syndrome.
• 19:37So you know,
• 19:39these are still the hypothesis and
• 19:42we should be able to test these
• 19:45with the biospecimen that you have
• 19:48generously provided, for instance,
• 19:50a virus,
• 19:51a vaccine,
• 19:52Stimulation of innate immune
• 19:54responses or tissue damage can be
• 19:58inferred to a large extent by looking
• 20:01at circulating immune factors.
• 20:05And we know what exactly the immune
• 20:08factors are downstream of inflamosome
• 20:10activation or RNA sensors which could
• 20:12be triggered by the vaccination.
• 20:14And it's interesting that we are
• 20:17seeing the onset of post vaccine
• 20:20syndrome very clustered around
• 20:22the first week of vaccination.
• 20:24Of course,
• 20:25there are some participants
• 20:26that develop it much later,
• 20:28but there is this large number of
• 20:31participants reporting post vaccine
• 20:32syndrome around the first week.
• 20:34So this leads us to think that it's
• 20:37not necessarily sort of antibody
• 20:40mediated because that's too early
• 20:42to develop these antibodies,
• 20:44but it could be innate immune
• 20:46activation or T cell mediated.
• 20:49The other possibilities that are
• 20:51listed here are the more dependent on
• 20:55adaptive immune responses and the only
• 20:58shared adaptive antigen is the spike.
• 21:01So it's possible that antibodies
• 21:03against a spike protein could
• 21:05form immune complex that could
• 21:07deposit onto different tissues or
• 21:10endothelial cells or form micro clots.
• 21:12And we are directly testing this in
• 21:15our laboratory right now looking at
• 21:17micro clots and platelet activation
• 21:20from people with post vaccine syndrome.
• 21:22We also have the the possibility that
• 21:26anti spike antibodies or T cells that
• 21:29attack spike expressing host cells
• 21:31and that could be of course the muscle
• 21:33cells are the major ones but there
• 21:36could be endothelial cells if the
• 21:38vaccine is circulates systemically
• 21:40there may be other source of sort
• 21:43of the cells expressing the spike
• 21:45protein which could be attacked by the
• 21:48adaptive immune cells and antibodies.
• 21:51There's also the possibility of molecular
• 21:53mimicry which is that the antibodies
• 21:56that are generated against the spike
• 21:59protein might also cross react to self
• 22:02antigens and that can also be a common
• 22:05pathology between post vaccine and
• 22:09COVID post COVID syndromes and it's
• 22:12not just antibody but T cells could
• 22:15also detect overlapping peptides from
• 22:18spike protein and and self protein
• 22:22and then you know vaccine induced
• 22:25reactivation of latent viruses could
• 22:27also be contributing and microbiome
• 22:29dysbiosis and and many others.
• 22:32So I'm sorry,
• 22:34I'm sorry I'm talking so much.
• 22:35But I also wanted to raise this other
• 22:37thing that was just published this
• 22:40week in Cell about the serotonin
• 22:43levels that are that are found to
• 22:45be reduced in people with on COVID.
• 22:47And and they were able to sort of
• 22:51link persistent virus and reduction in
• 22:54trypsin uptake within the intestine.
• 22:57That's a tryptophan uptake in
• 22:59the intestine that might lead to
• 23:01reduction in serotonin levels.
• 23:03And so you know there are many
• 23:06groups looking at the sort of
• 23:08link between persistent virus,
• 23:10persistent viral antigen and sort of
• 23:13chronic issues that can result all the
• 23:16way from the intestine to the to the brain.
• 23:19So you know it is encouraging to see
• 23:22lots of other sort of groups reporting
• 23:26interesting potential pathways and of
• 23:28course we'll be looking at the same
• 23:31pathways in post vaccine syndromes as well.
• 23:34So there's a lot to sort of go from
• 23:36you know like lots of emerging data
• 23:39happening and we're very excited about that.
• 23:42So I'm going to stop here.
• 23:45I think that was really important
• 23:47and and and I think isn't it one of
• 23:50the ideas that Kiko that by being
• 23:52able to look at at the vaccine group,
• 23:55it can provide insights to both groups,
• 23:58right. It's like because it in a
• 24:00way they they shouldn't have the
• 24:02vaccine persistent I mean the viral
• 24:04persistence but but that many of
• 24:06these mechanisms you're talking about
• 24:08could be crossing over to long COVID
• 24:09mechanisms as well all of them could.
• 24:12Yeah exactly. So the two different
• 24:15syndromes can sort of inform each
• 24:18other that the more we look deeper
• 24:20in the biology, the more we will
• 24:23find similarity and differences.
• 24:24And that's what we're excited about.
• 24:30Mitsu, what should we do about the questions?
• 24:32Because there are also questions
• 24:33coming up on the chat and and
• 24:35some people submitted questions.
• 24:37I don't know how we want to manage this,
• 24:39but I think lots of people want to hear
• 24:41from Akiko around a lot of things.
• 24:43Maybe even somebody asked about the
• 24:45study that found T cell dysfunction,
• 24:48EBV reactivation and low cortisol
• 24:49as a trifecta biomarker of sorts.
• 24:51So that's sort of what what you want
• 24:54to just mention because it's yeah,
• 24:58absolutely. So that that's our like
• 25:00first big study based on the Mount
• 25:03Sinai Yale long COVID Group which I'm
• 25:08part of and that was finally published.
• 25:11It was on the by Medarchive for
• 25:13almost a actually more than a year,
• 25:16but usually it takes about a
• 25:17year to get something published.
• 25:19But anyway, yes,
• 25:19that's the trifecta that you're
• 25:21talking about and that's something
• 25:23that we were able to demonstrate with
• 25:25that group and obviously with the,
• 25:26yeah, listen,
• 25:27we want to be able to validate
• 25:29some of these findings and of
• 25:32course cross reference that to
• 25:34the post vaccine syndrome.
• 25:35So yeah,
• 25:36there's a lot happening and we're
• 25:38very excited to be part of it.
• 25:43I don't know, Mitsu, how who's,
• 25:45how do you want to do the questions?
• 25:51We can go through the chat
• 25:52if that would be better.
• 25:54We also have other questions,
• 25:5515 or so questions that have been
• 25:59submitted before the meeting started. I
• 26:02think you could just pick off
• 26:03if you just see any of that.
• 26:04Some of the questions are are actually
• 26:06similar in theme, so just keep,
• 26:07let's just see if we can have rapid
• 26:10responses from Akiko around a bunch of
• 26:12different questions because I think
• 26:14people would like to have have a lot
• 26:16of curiosity about a lot of things.
• 26:19This one's for Akiko,
• 26:20Maybe a question on micro clots,
• 26:22latest research on micro clots,
• 26:24What if any treatments do you
• 26:27recommend for micro micro clots?
• 26:29What's the latest research
• 26:31like this on long COVID?
• 26:35Yeah, thank you so much.
• 26:36So we just completed our analysis
• 26:41on micro clots and I I don't want
• 26:45to prematurely state anything that
• 26:47that may still change over time.
• 26:50But there there there is a lot
• 26:53of new ones to studying micro
• 26:55clots because there isn't like a
• 26:58standard way that a scientist,
• 27:00scientist all over the world are
• 27:01using to sort of determine micro
• 27:05clots and also you know quantify.
• 27:08So David Petrino and I,
• 27:11we've spent a lot of time and then
• 27:14with Reciproatorius and many others
• 27:16who are working on this area.
• 27:18I've tried to kind of come up
• 27:20with a normalized standardized
• 27:21way of measuring micro clots.
• 27:23So that's something we're working towards,
• 27:26but we are certainly seeing so.
• 27:29So we did a lot of different analysis,
• 27:30again micro clots, platelet activation,
• 27:34also mass spectrometry to see what
• 27:36factors are being elevated in the blood.
• 27:40And all of these combined seem
• 27:42to suggest definitely platelet
• 27:44hyperactivation and many people with
• 27:47long COVID that's accompanied by
• 27:51things like neutrophil activation
• 27:54and potential endothelial damage.
• 27:57This we just started to wrap this up.
• 28:01So you know I don't want to prematurely
• 28:03conclude anything but these are some
• 28:05of the insights we're getting and
• 28:07but but you know again we don't know
• 28:10what actionable things we can suggest
• 28:13because you know anticoagulation
• 28:15therapies and things like that
• 28:17are you know come with some danger
• 28:20and we don't want to be promoting
• 28:22everyone to get those things either.
• 28:25So very prematurely we are seeing
• 28:29some signs but I I just want to
• 28:31be also also cautious that things
• 28:33can change after further analysis.
• 28:37Thanks. We have a question. One
• 28:41thing I just somebody was asking about
• 28:43number of people and listen and and the
• 28:45percentages of both groups actually
• 28:46I don't know the most recent numbers
• 28:48but we were we see you may know but
• 28:50something like we were up to like 2500
• 28:52people listen overall and I thought
• 28:54it was something like 30% or or maybe
• 28:58you you know receives 30% vaccine,
• 29:01right. Are you referring to like how
• 29:03many participants in each cohort
• 29:05or well there's we have the like larger
• 29:07study and then in what in the analysis
• 29:10you've presented how many people
• 29:11were in each of the groups, right.
• 29:12So we had 443 participants that
• 29:15had just long COVID only and
• 29:17this was as of July of this year.
• 29:20And then in the A vaccine injury
• 29:23only group we had 241 participants.
• 29:25Yeah. And you know we had some
• 29:27restrictions in order to get
• 29:28those groups together like the not
• 29:30overlapping syndromes which we still
• 29:32think is interesting and important.
• 29:33Many of you are suffering and and from both,
• 29:36but it was just trying to get, you know,
• 29:39distinctive cohorts that people
• 29:40are only reporting one one thing.
• 29:42Another thing by
• 29:43the question on potential bio markers
• 29:46for long COVID, this was for Akiko Fall.
• 29:51Yeah. So all of them to Kiko,
• 29:53'cause she's got all of this
• 29:55about this. No. No. Yeah.
• 29:57So that that that's probably
• 29:58referring to the, you know,
• 30:00study that we just published in Nature
• 30:02about the different distinguishing
• 30:04factors we've found with people,
• 30:07with people with long COVID
• 30:09versus people who recovered or
• 30:11people who were never infected.
• 30:13And so yes, we did find several
• 30:17distinct features including lower
• 30:19levels of cortisol ebb reactivation
• 30:23and some level of differences
• 30:27in T cell and B cell features.
• 30:30Particularly you know activated B
• 30:32cells and exhausted T cells were
• 30:35elevated in in some people with long
• 30:37COVID and elevated antibody levels
• 30:39to SARS COVID 2 spike protein.
• 30:42So these things all together to us
• 30:44suggest that the four hypothesis that I
• 30:47mentioned prior may all be happening.
• 30:49And we're now doing a more deeper dive
• 30:52on sex differences in long COVID.
• 30:55And there we're finding for instance
• 30:58elevated levels of auto antibodies
• 31:01and particularly in female patients
• 31:03as well as this ebb reactivation
• 31:06being also more dominant in female
• 31:08patients compared to the male patients.
• 31:11So there's a lot of nuances to
• 31:14the biomarker discussion.
• 31:16One has to do with obviously different
• 31:19SX differences in these biomarkers.
• 31:21The other has to do with endotypes
• 31:23of long COVID.
• 31:25We believe that long COVID is likely
• 31:28representing multiple different
• 31:30diseases under one umbrella and
• 31:33based on the driver of disease,
• 31:36we will likely have distinct sort
• 31:39of biomarkers to describe it.
• 31:42And that's not only important
• 31:44for medical sort of purposes
• 31:47of diagnosing patients,
• 31:48but also for potential therapy down the road.
• 31:52For instance,
• 31:53if persistent virus is found in a
• 31:56subset of long COVID patients which
• 31:58we will uncover using the Paxlovid
• 32:01biological study that we're doing
• 32:04in conjunction with the the trial,
• 32:07we we should be able to say OK
• 32:09here are the five markers that
• 32:11correspond with a Pacslovid response,
• 32:14positive response to Pacslovid and
• 32:17that by definition should sort of
• 32:19define a subset of patients that
• 32:22have persistent virus and therefore
• 32:24might benefit in the future with
• 32:27Pacslovid treatment in in the
• 32:29group the larger population.
• 32:30So that's just one example of a
• 32:33biomarker that we are going after
• 32:36based on the the four different
• 32:38drivers of disease and we can also
• 32:41have similar biomarkers for say
• 32:43auto antibody mediated conditions
• 32:45or EBV dependent conditions.
• 32:47And so, so you know we're,
• 32:50we're still at the very early phase,
• 32:52but we're hopeful to be able to find
• 32:55distinct features that correspond
• 32:57to these different drivers and
• 33:00therefore diagnose and potentially
• 33:02treat patients with these markers.
• 33:13Thank you, Akiko. Related to that,
• 33:16there's a question on T lymphocytopenia
• 33:20and what can you tell us about that?
• 33:24Yeah. So during the acute
• 33:27severe COVID investigation,
• 33:29we did early in the pandemic,
• 33:32we saw as long as well as many
• 33:35other studies significant depletion
• 33:37of T cells in circulation.
• 33:39So this is AT lymphopenia is a clear
• 33:42marker for acute severe disease.
• 33:45And in fact the more severe the disease,
• 33:47the less T cells we found
• 33:49in the in the patients.
• 33:51However, most patients eventually
• 33:54recovered the level of T cells.
• 33:57Now that's talking about acute COVID.
• 33:59In long COVID we didn't find a
• 34:02significant reduction in T cell number,
• 34:05naive T cell number for example in
• 34:08people with with or without long COVID.
• 34:11So so we don't we don't know
• 34:13if T lymphopenia is really a
• 34:15key feature of long COVID.
• 34:17But again,
• 34:18as I mentioned that there
• 34:19may be different subtypes of
• 34:21long COVID and and you know,
• 34:22we can't rule out that that is one
• 34:25feature that's associated with the subtype.
• 34:27So, but in our My long COVID study,
• 34:30we didn't see a huge reduction in T cells.
• 34:38Thank you, Geko. And we also had
• 34:41a question on children's immunity
• 34:43towards COVID and Lung COVID.
• 34:47Do 4 year olds, 5 year old kids have any
• 34:49kind of immune advantage over adults?
• 34:55Yeah. So there's been several key studies
• 34:59done to compare children's immune
• 35:01response to source COVID 2 versus adults.
• 35:04And I think the consensus in the field
• 35:08is that children do develop strong
• 35:12innate immune response that prevents
• 35:15the spread of the virus much more
• 35:19quickly than adults and therefore
• 35:22their disease tend to be milder.
• 35:25But that doesn't mean that children
• 35:27are spared from developing long COVID.
• 35:30We know that even though the rate
• 35:33of conversion to long COVID may
• 35:35be lower than adults,
• 35:37children do develop long COVID.
• 35:39And in fact one of the pediatric
• 35:42infectious disease fellow in my
• 35:44laboratory is looking at children with
• 35:47long COVID and we're starting to think
• 35:50about how do we provide a medical
• 35:53care for people in that category.
• 35:56But anyway, yes,
• 35:57so in in general children do tend to
• 36:02develop this very robust innate immune
• 36:04resistance against the virus and
• 36:07therefore are spared from severe disease.
• 36:09But there are sort of Missy as
• 36:12well as like cases of long COVID
• 36:15that can happen in children.
• 36:16So that that is even less
• 36:18study than adult long COVID.
• 36:25Thank you, Akiko. And we are
• 36:29nearing yeah 10 more minutes for
• 36:32the until the end of this meeting.
• 36:35Colin would you like to. I
• 36:37just want to ask Akiko one more question.
• 36:39Just so sometimes I'm looking at the
• 36:44literature and I'm seeing like even
• 36:46studies that are at odds with each
• 36:49other and and it seems sometimes that,
• 36:51you know, it's there's a it's
• 36:53slow to have a full consensus.
• 36:55And I I sometimes even worry that some
• 36:58studies come out and we, you know,
• 36:59we get excited about the next study.
• 37:01The one thing about the serotonin
• 37:02state that was also an animal's right,
• 37:03they were using an animal study model.
• 37:05So I thought that was that was also
• 37:07an advance like because they could
• 37:08control more things and and and
• 37:10show things what what is it that
• 37:12makes you most optimistic about
• 37:13like what's going to happen in this
• 37:16field in the next six months just
• 37:20because I think people need hope,
• 37:21you know that that there actually
• 37:23is something that that will happen.
• 37:25What gives you the most optimism about
• 37:26that we're going to make progress.
• 37:30So you know, even though it
• 37:33seems very slow to patients,
• 37:36the scientific community is working
• 37:39very hard around this problem and
• 37:42the discoveries that are being
• 37:44made in this field is quite rapid.
• 37:48You know as you all know things like HIV,
• 37:52it took many, many years to decades
• 37:55to come up with the standardized
• 37:58diagnostic criteria treatment,
• 38:00but eventually it happened, right.
• 38:02So now we can treat with
• 38:05antiretrovirals and the the,
• 38:06the disease can be managed to a very,
• 38:09very large extent.
• 38:10We still don't have vaccines
• 38:12against HIV but that's you know
• 38:15the disease can be managed and
• 38:17diagnosed properly and our dream is
• 38:19to get there as soon as possible.
• 38:22Can we have a diagnostic criteria and
• 38:25can we have a treatment that works
• 38:27for these different and the types of
• 38:30won't COVID so and of course before
• 38:33post vaccine syndrome as well we would
• 38:35love to come up with a diagnostic
• 38:38tool as well as therapeutic tools.
• 38:42And so I'm you know I I think it's
• 38:46it's hard to sort of forecast how
• 38:48long it's going to take to get there.
• 38:49But I am hopeful the serotonin study,
• 38:53yeah,
• 38:54that that one used a combination
• 38:57of metabolomic data along with a
• 39:00different animal models to model this
• 39:03distinct aspect of infection And you
• 39:06know people are you know using clever
• 39:09approaches like that too to tackle this.
• 39:11And and again I I I don't
• 39:13think it's one disease.
• 39:15So I don't think one thing is 1 drug
• 39:17is going to be able to treat everyone.
• 39:20But the the more we make these
• 39:22discoveries and have hypothesis to test
• 39:25the more we can do so with you know
• 39:28clinical trial coupled with biomarker
• 39:31analysis like what we're doing with
• 39:33you know the the the Paxlovit trial.
• 39:36So I'm hopeful and we're
• 39:40working really hard and we just
• 39:43we just need to keep going.
• 39:44I
• 39:46think that's great.
• 39:48I I I appreciate that I and I
• 39:50agree with you 100% you know but I
• 39:53know we also know that for people
• 39:55who are experiencing this you
• 39:57know living with these conditions
• 39:59it it's never fast enough and we
• 40:01know that and so we never want to
• 40:03diminish you know that but but but.
• 40:06We are seeing progress.
• 40:10The one thing I know somebody
• 40:11keeps somebody put up twice this
• 40:12thing about Vinny Prasad and
• 40:13that kind of thing that he wrote.
• 40:15I'll just make a comment on it.
• 40:18You know, there are people out there
• 40:21who want to dismiss or diminish what
• 40:23some people are experiencing and that's
• 40:26unfortunate but maybe not unexpected.
• 40:29Think of the best we can do is
• 40:31to continue to drive forward the
• 40:32science and to amplify the voices
• 40:34of what people are are living with
• 40:35so that people know it's real.
• 40:38Actually been a who I've known for a
• 40:42long time I throughout most of the pandemic.
• 40:44I mean, I what I I know is being recorded.
• 40:47But I'll just say it, you know,
• 40:48a lot of the stuff I read, you know,
• 40:50it's like he's thinks he's the
• 40:51smartest person in the world.
• 40:52And and like he what he says is like
• 40:54he sees things nobody else sees.
• 40:56And especially retrospectively
• 40:57he seems really accurate.
• 40:58You know, when he can look back.
• 41:01There are some things in what he
• 41:03wrote that that aren't wrong really
• 41:04that we do need to be able to get
• 41:06more standardized definitions.
• 41:07We need to be able to understand this better.
• 41:09And there's a risk that until we
• 41:12get there that that it's fuzzy
• 41:15but like that shouldn't deter us.
• 41:17And it doesn't say that it doesn't
• 41:18exist and it doesn't say that,
• 41:20you know,
• 41:20we shouldn't be pushing forward
• 41:21to try to improve.
• 41:22And I I mean the thing about this
• 41:25is he's sort of on this bandwagon
• 41:27about you know this when people say
• 41:29that 25% of people have along COVID,
• 41:31I don't I think that's too high.
• 41:32I think I don't think that's probably
• 41:34that high because what I'm talking
• 41:36about are people like people on this on
• 41:38our study who are severely suffering.
• 41:40I mean it may be that a lot of people
• 41:43are experiencing increased risk.
• 41:44A lot of people post COVID post vaccine
• 41:48are experiencing different symptoms
• 41:50but severe debilitating illness.
• 41:52I think that's probably a smaller
• 41:53number but that you what's the truth?
• 41:54We don't know.
• 41:55There aren't good population
• 41:57based epidemiologic studies.
• 41:58So I'm ignorant about that.
• 42:00I mean,
• 42:00I don't know what the right number is.
• 42:02I can have my own beliefs,
• 42:03but I shouldn't be like standing
• 42:05on a pedestal telling you what the
• 42:07answer is when I don't really know it.
• 42:09So that's how my my feelings,
• 42:10I've got my feelings about what
• 42:12the numbers might be but but we
• 42:14don't know for sure.
• 42:15What I do know is that the people
• 42:17in this study are people whose
• 42:19lives have been severely affected
• 42:21by what they're experiencing.
• 42:22And and there's a significant
• 42:24number of people who are in this
• 42:27situation and demands our attention.
• 42:29So that Vinay Prasad thing,
• 42:30I just thought it was unfortunate.
• 42:33But you know to engage him is only
• 42:35to amplify his message because you
• 42:36give him attention. So you know what?
• 42:38Akiko and I were sort of
• 42:39talking about this and the answer so
• 42:40like rather than just respond to it,
• 42:42then that just gets more people talking
• 42:44about him and that's what he wants.
• 42:46And so I just like we just should keep
• 42:48going with what what what we're doing
• 42:50until we're forced unless we're forced
• 42:52to to be in position after responsive.
• 42:54I I wanted to tell you that what we're
• 42:57we're going to pre print meaning we're
• 42:59going to put out before going through
• 43:00that year of peer review but just for
• 43:02people to be able to see and every so
• 43:05forth a piece on a post vaccination
• 43:07syndrome or or vaccine tree that just
• 43:09talks about the the people that are in
• 43:12listen and and just describing them and
• 43:14their experience and trying to get voice
• 43:16to what they're what's they're going through.
• 43:18We're going to do the
• 43:19same thing for long COVID.
• 43:20On the long COVID side,
• 43:21it won't be nearly as novel because
• 43:23other people have used surveys and
• 43:25questionnaires to talk about the group.
• 43:26But but for everyone and listen,
• 43:28we just want to honor the fact that
• 43:30people took time to fill this stuff
• 43:31out and and put it out there.
• 43:33You know,
• 43:33this is what the listen cord is.
• 43:35And whenever we do this,
• 43:36we're going to let all of you
• 43:37know that this is up there.
• 43:38You can see it, you can pull it down.
• 43:40We can have more town halls talk about
• 43:41them and we're going to the the analysis
• 43:43you just saw about the comparison.
• 43:44We're also going to post.
• 43:46So for people to see and read and to
• 43:49and comment on, when it's a preprint,
• 43:52it means it's put up on the web
• 43:54as an enduring artifact,
• 43:56meaning it's it's going to be permanent,
• 43:58but that there are plenty of comments people
• 44:01can make and we can revise it and version it.
• 44:03So you know,
• 44:04the point is that it's work
• 44:06in progress there.
• 44:07Here it is and all of you should
• 44:09read it and give us feedback and
• 44:11then we can continue to refine
• 44:13the contribution over time.
• 44:14We also want to do a piece where
• 44:16we're going to take advantage of
• 44:18people who have linked their medical
• 44:19records so that we can give some
• 44:22insight into healthcare utilization,
• 44:23what kind of and mostly like how
• 44:24people have had to bounce around,
• 44:26what kind of testing has been done
• 44:27or what's in just sort of describe
• 44:28that we would like to be able to
• 44:30do that we'd like to be able to
• 44:31do something with with wearables.
• 44:33Somebody sent in a question about
• 44:35heart rate variability.
• 44:36I don't think we really know it's
• 44:39very tied to autonomic function,
• 44:41more variability is thought to be good.
• 44:43This sort of chaos of the variability,
• 44:46the meaning,
• 44:47the this sort of fact that it's
• 44:49not a predictable,
• 44:50but it's actually not so predictable.
• 44:52It's meant to be kind of
• 44:53the complexity of it.
• 44:54It's how people talk about it,
• 44:55of your heart rate variability,
• 44:57which means you've got a heart rate,
• 44:59but actually there are many
• 45:00variations in that heart rate.
• 45:01Even if your rate is 90 beats per minute,
• 45:04each beat is not exactly the same
• 45:06as distance from the last beat.
• 45:08There's minor variations and
• 45:09the heart rate variability
• 45:11picks up those minor variations,
• 45:12which is associated with your
• 45:15autonomic health in essence.
• 45:17And that more complexity is better,
• 45:20less complex is worse.
• 45:20But I can tell you, like,
• 45:21I have a bunch of wearables and I don't know,
• 45:24like sometimes it says my
• 45:25heart rate variability is low,
• 45:26sometimes it says it's high.
• 45:26I don't even know what that means.
• 45:28And I don't have a chronic condition.
• 45:30So I I there's a lot to learn about this.
• 45:33But you know,
• 45:34if if it may be that enough people at
• 45:36some point will have connected their
• 45:38wearables and we can start looking at
• 45:39some of the data and correlating it
• 45:41to symptoms and how people are doing.
• 45:42But the last thing I wanted to say
• 45:45was that we're going to try to do a
• 45:47longitudinal study and see how many
• 45:48people entered. Listen and got better.
• 45:50And who were those people and and what?
• 45:53What? What can we learn from them?
• 45:54So we want to launch a longitudinal study
• 45:57where there's going to be a second.
• 45:59We'll contact everyone and try to get
• 46:01everyone to fill out a second survey
• 46:03and try to figure out who got better,
• 46:04who got worse, you know what,
• 46:06what kind of differences are there?
• 46:07And see if we can learn something
• 46:10from that change.
• 46:11And so there'll be more
• 46:12information about that,
• 46:13but that's what what we're seeing.
• 46:15And then we're going to,
• 46:18I mean the most important work we're doing.
• 46:20Well,
• 46:20let me say a very important
• 46:21work that we're doing.
• 46:22I actually think the most important
• 46:23work is what that we're doing
• 46:25in collaboration with the lab.
• 46:26And as Akiko said,
• 46:28the insights will come out of the
• 46:30fact of trying to draw patterns of
• 46:32what you guys are experiencing with
• 46:34what's being reflected through the
• 46:36biology in trying to understand
• 46:38what how that can translate into
• 46:40diagnostics and therapeutics.
• 46:41So we're going to try to double down
• 46:42on what we call extreme phenotypes.
• 46:43So maybe the people experiencing
• 46:45the most extreme symptoms within
• 46:47a within a thing like internal
• 46:49vibrations and and and tremors or
• 46:52or you know POTS or something,
• 46:55you know look at a couple different
• 46:57groups and see do they have are there
• 46:59signals in their immune signatures
• 47:01that are that are differentiating
• 47:03them based on that and including
• 47:04you know people in both groups
• 47:06are having those symptoms.
• 47:07So.
• 47:07So we'd be able to try to do that but
• 47:09that that's sort of on the horizon
• 47:11what we're thinking trying to do.
• 47:14So with that I know we're at time.
• 47:16Kiko,
• 47:16let me hand it over to you for final words.
• 47:18And also to thank our team
• 47:20and our students and
• 47:22all the people who have been involved
• 47:24on the listen side and particularly
• 47:26all of you who are pioneering this
• 47:28kind of new approach with us and
• 47:30have have trusted that we're trying
• 47:33to get this right and recognize that
• 47:35we're not always going to be right.
• 47:36So we need your help to to learn as we go.
• 47:39So, Akiko. Yeah.
• 47:41Thank you, Harlan.
• 47:42So as someone who's, you know,
• 47:45experienced being minimized or dismissed
• 47:48and discriminated against in as a
• 47:51woman in science throughout my life,
• 47:54I find that the best defense against
• 47:57minimizers is irrefutable science.
• 47:59So we're here to provide that
• 48:02irrefutable evidence of disease
• 48:04and markers and therapies.
• 48:06And so you know,
• 48:07collectively we will be able to get there.
• 48:09And I'm really grateful for my partnership
• 48:12with Harlan and his team without
• 48:14whom none of this can be happening.
• 48:16So really appreciate all of you.
• 48:18And thank you, some of you for showing
• 48:20your face and being brave out here.
• 48:22And really appreciate being
• 48:23able to see who you are.
• 48:25And really grateful.
• 48:27Thank you so much.
• 48:29Yeah. Making a mistake.
• 48:31Akiko's our captain. Thank you.
• 48:34Thank you so much. Bye, bye. Thank you all.