1
00:00:00,000 --> 00:00:05,640
[Music]

2
00:00:05,640 --> 00:00:08,640
Welcome to the Proteomics and Proximity Podcast.

3
00:00:08,640 --> 00:00:14,200
We are co-hosts, Dale Yuzuki, Cindy Lawley, and Sarantis Chlamydas from Olink Proteomics

4
00:00:14,200 --> 00:00:18,640
talk about the intersection of proteomics with genomics for drug target discovery,

5
00:00:18,640 --> 00:00:22,080
the application of proteomics to reveal disease biomarkers,

6
00:00:22,080 --> 00:00:27,040
and current trends in using proteomics to unlock biological mechanisms.

7
00:00:27,040 --> 00:00:31,360
Here we have your hosts, Dale, Cindy, and Sarantis.

8
00:00:31,360 --> 00:00:38,800
Hello, everybody! Welcome to our episode dedicated to biomarkers of heart failure.

9
00:00:38,800 --> 00:00:44,800
I welcome with me today Dale and Cindy. Hey. Hey there.

10
00:00:44,800 --> 00:00:52,480
And today the paper that we will discuss is about protein biomarkers of new onset heart failure,

11
00:00:52,480 --> 00:00:57,520
and is published in the Circulation Heart Failure journal.

12
00:00:57,520 --> 00:01:04,000
The first author is Nicolas Girerd, and the last outer is Professor Faiez Zannad.

13
00:01:04,000 --> 00:01:09,840
The most important finding of this paper is their use proteomics and Olink proteomics

14
00:01:09,840 --> 00:01:15,360
in order to identify biomarkers related to heart failure,

15
00:01:15,360 --> 00:01:18,560
and it is a great, it is an amazing study

16
00:01:18,560 --> 00:01:25,040
looking in three independent cohorts, ARIC, FHS and HOMAGE,

17
00:01:25,040 --> 00:01:28,480
and finally it was dedicated to the

18
00:01:28,480 --> 00:01:34,240
secretome, proteome, secretome, and biomarker discovery connected to heart failure.

19
00:01:34,240 --> 00:01:41,040
Cindy, would you like to give us a little bit of background of these cohorts and some of the

20
00:01:41,040 --> 00:01:44,640
characteristics of these cohorts that they have studied? Thank you.

21
00:01:44,640 --> 00:01:51,120
Sure, absolutely. I'll just summarize them in the order that they are in the title.

22
00:01:51,120 --> 00:01:56,960
So the heart, omics, and aging cohort is the HOMAGE consortium,

23
00:01:56,960 --> 00:02:03,120
an EU-funded program, and they're looking for personalized strategies for

24
00:02:03,120 --> 00:02:11,040
understanding cardiovascular disease. This is a consolidated effort among eight European countries.

25
00:02:11,040 --> 00:02:16,880
I mean, this is just a massive undertaking, right? So they have eight different countries looking at

26
00:02:16,880 --> 00:02:26,560
heart omics, and aging, so the two are together? So both, yes, together. But within this subset,

27
00:02:26,560 --> 00:02:33,120
there's three represented groups. So there's something called predictor, health ABC, and prosper.

28
00:02:33,120 --> 00:02:40,640
And that's from subset of samples from within 20,000 that are this combined

29
00:02:40,640 --> 00:02:46,640
consortium that represents HOMAGE. Yeah, when you start digging into

30
00:02:46,640 --> 00:02:54,640
these cohorts, it's just awe-inspiring what is able to be done bringing collaboration together,

31
00:02:54,640 --> 00:03:01,760
right? So, HOMAGE is roughly 20,000 large or even larger? Oh, it is. That's right, 20,000 large.

32
00:03:01,760 --> 00:03:06,960
Yep. And then there's a subset that is represented within this study. I see.

33
00:03:06,960 --> 00:03:14,560
So I think the HOMAGE, I think they had 562 individuals in this study that were cases.

34
00:03:14,560 --> 00:03:19,760
And again, those were identified, as Sarantis mentioned, incident heart failure. So,

35
00:03:19,760 --> 00:03:27,600
the characteristic was that they were diagnosed and hospitalized, but not necessarily,

36
00:03:27,600 --> 00:03:32,400
or they would have been excluded if they had been identified with heart failure coming into

37
00:03:32,400 --> 00:03:41,040
the study. So the importance there is to be able to more likely identify the markers that are

38
00:03:41,040 --> 00:03:48,320
linked to predictive value in predicting heart failure, the likelihood. And we're trying to,

39
00:03:48,320 --> 00:03:56,560
you know, tease out the pathogenetic biomarkers, right? Over time, with these studies, we're trying

40
00:03:56,560 --> 00:04:04,640
to understand what's predictive and what's incident, what happens after you have this diagnosis.

41
00:04:04,640 --> 00:04:10,560
So for the HOMAGE study, then they ended up taking healthy and non-healthy individuals of a certain age

42
00:04:10,560 --> 00:04:15,680
and then follow them over time? Yeah, that's right. All three of these are longitudinal studies.

43
00:04:15,680 --> 00:04:22,160
As, you know, my understanding is that the exclusion criteria was if they had heart failure

44
00:04:22,880 --> 00:04:28,800
in their diagnosis in advance that they were excluded. So here, they're really trying to get at

45
00:04:28,800 --> 00:04:34,640
incident heart failure. You know, what are the predictive markers that help us identify

46
00:04:34,640 --> 00:04:40,640
that heart failure is going to happen? And so as far as it skews older, right? So we're getting,

47
00:04:40,640 --> 00:04:46,640
that's right. We're getting an older group of individuals, regardless, I mean, meaning they're

48
00:04:46,640 --> 00:04:52,640
nominally healthy, but oftentimes they have just any different kinds of conditions other than heart

49
00:04:52,640 --> 00:05:01,200
conditions? So it depends upon the cohort, right? So the other two cohorts, as Sarantis mentioned,

50
00:05:01,200 --> 00:05:09,520
are the extremely famous Framingham Heart Study, right? And again, that was a community study.

51
00:05:11,360 --> 00:05:18,160
It was based on residents in Framingham and that group is actually in their seventh exam. That has been

52
00:05:18,160 --> 00:05:24,720
going since 1948. So I remember when they were first using genetics in that study and some of those

53
00:05:24,720 --> 00:05:30,240
first publications that came out there, there's some pretty impactful stuff that comes out with Framingham.

54
00:05:30,240 --> 00:05:34,880
If I understood correctly, chances are that there was not any diagnostic method, right? At the time,

55
00:05:34,880 --> 00:05:40,160
the point zero is entering the hospital with heart failure, right? If I understood correctly,

56
00:05:40,160 --> 00:05:46,000
and then they got diagnosed with heart failure. For the subset of samples in this study, right?

57
00:05:46,000 --> 00:05:54,960
But not the full Framingham study. Okay. But yeah, so in Framingham, there were 191 cases and a matched

58
00:05:54,960 --> 00:06:00,960
set of controls. So they would refer to this as a nested case control design because it's nested

59
00:06:00,960 --> 00:06:07,760
within the larger cohorts that these samples were identified and then matched. And again,

60
00:06:08,240 --> 00:06:17,200
what a phenomenal resource to be able to have so many cases and so many controls that are so many

61
00:06:17,200 --> 00:06:23,200
individuals in a cohort to be able to match those controls as well as they were able to. And then

62
00:06:23,200 --> 00:06:29,760
the third cohort that you mentioned was the ARIC study. And this, I think, is really exciting

63
00:06:29,760 --> 00:06:38,160
included in here because it's over 30% of African diaspora individuals. So individuals that

64
00:06:38,160 --> 00:06:47,120
are declaring their ethnicity in the study as black, which I think this is incredibly important for

65
00:06:47,120 --> 00:06:53,280
us to understand what we've been missing by the over-representation of northern European populations

66
00:06:53,280 --> 00:07:00,800
in studies around heart disease. And the data for heart disease among the black community is

67
00:07:00,800 --> 00:07:08,800
much higher, right? As far as heart failure goes? Yes, that's my understanding and certainly,

68
00:07:08,800 --> 00:07:16,800
you know, we have lots of findings that we would never have seen. PCSK9 is one example of them

69
00:07:16,800 --> 00:07:21,760
that we wouldn't have seen had we continued to look at European populations. So this,

70
00:07:21,760 --> 00:07:28,240
you know, this just double clicks on that. What we've said several times is this importance to not lag

71
00:07:28,240 --> 00:07:37,760
behind in understanding omics around these important diverse populations as we go forward.

72
00:07:37,760 --> 00:07:42,160
You mentioned something about the Framingham cohort that this was the seventh examination.

73
00:07:42,160 --> 00:07:45,920
Right. So it's actually the kids, the offspring of the originals.

74
00:07:45,920 --> 00:07:49,600
That's what I was getting at. 1948 is getting pretty old.

75
00:07:49,600 --> 00:07:52,640
Not many people are still around.

76
00:07:53,600 --> 00:07:59,040
Yeah, that's a very important... Yeah, the progeny of that original group...

77
00:07:59,040 --> 00:08:04,960
Framingham heart study then is on the second generation of the original people.

78
00:08:04,960 --> 00:08:08,400
Is that correct? Or did they take... That's my understanding.

79
00:08:08,400 --> 00:08:12,240
So they have the genetics of the parents and now they're looking at the offspring.

80
00:08:12,240 --> 00:08:20,800
Yeah. Wow. And then the seventh examination was what 1998 to 2001 so that they were

81
00:08:22,160 --> 00:08:26,000
taking blood samples and full medical workup of all the children.

82
00:08:26,000 --> 00:08:31,040
Yeah, that's right. And actually there's a comment in the paper about the Framingham

83
00:08:31,040 --> 00:08:35,520
participants being a little younger than the other two cohorts.

84
00:08:35,520 --> 00:08:38,800
By about 10 years, I think.

85
00:08:38,800 --> 00:08:43,040
I'm trying to imagine what it would be like to be the son or daughter of a Framingham volunteer.

86
00:08:43,040 --> 00:08:48,560
It's like, mom, you did what? I have to do this because you signed up in 1948?

87
00:08:50,880 --> 00:08:53,360
Well, are they doing the next generation after that?

88
00:08:53,360 --> 00:08:55,920
Yeah, it's a good question. I don't know.

89
00:08:55,920 --> 00:09:01,280
I think there is. It's one of these... There's so much interesting data, right?

90
00:09:01,280 --> 00:09:11,200
Yeah. And having them nested within families tells us so much about what's passed on

91
00:09:11,200 --> 00:09:17,840
from parent to offspring, and genetics we think of as kind of static and then these other biomarkers

92
00:09:17,840 --> 00:09:24,880
dynamic over time. I think if we could just run all samples in that Framingham Heart Study from

93
00:09:24,880 --> 00:09:32,240
the beginning to now, imagine what we might learn just with the broadest look at the proteome.

94
00:09:32,240 --> 00:09:37,680
Speaking of which, let's talk about the proteins that were looked at in this study.

95
00:09:37,680 --> 00:09:43,120
That's a good point. I mean, I would have expected to look a little bit broader to be honest in

96
00:09:43,120 --> 00:09:47,440
something that they mentioned in the Discussion [section], right? That they should have gone more in the

97
00:09:47,440 --> 00:09:55,920
exploratory. I think they used the allocated panel. I think that was what was available at the time.

98
00:09:55,920 --> 00:10:01,600
Right? So over time, you know, just like I always compare these genetics, you know, like my frame

99
00:10:01,600 --> 00:10:10,880
of mind is genetics, right? But over time, you know, the first broad scale tools to look at SNPs were

100
00:10:10,880 --> 00:10:18,480
around 1536, right? So being able to expand that to a million or five million on these chips

101
00:10:18,480 --> 00:10:27,120
that are available today is just a representation of advances in innovation. And I think we

102
00:10:27,120 --> 00:10:36,240
seen that obviously, us here at Olink in proteomics. So I would love to see these, a publication around

103
00:10:36,480 --> 00:10:41,760
an even broader look at the proteins. And I think with what they've found here, especially with the

104
00:10:41,760 --> 00:10:48,720
value of this cohort, that perhaps that's something that they're thinking about, it would be great to

105
00:10:48,720 --> 00:10:54,320
talk to them and ask them. Yeah, I think the [Olink Explore] HT [proteomics platform] would be great to be able to do that.

106
00:10:54,320 --> 00:11:00,640
That's right. Yeah, you want to tell our audience a little bit about our latest, broadest look at the proteome?

107
00:11:00,640 --> 00:11:05,760
I think Dale would be the best person to answer that. Yeah, he's actually developing all the content.

108
00:11:05,760 --> 00:11:12,240
I'd really interested, Sarantis and Cindy, to hear your sort of input on this, right? Because I was a

109
00:11:12,240 --> 00:11:18,080
person behind the curtain rolling it out. I'd like to hear both of you describe it in your own words.

110
00:11:18,080 --> 00:11:23,680
Okay, Sarantis, you first. I mean, I would say it in a very simple word. I mean, it's like a

111
00:11:23,680 --> 00:11:29,920
revolution, right? Of high-through proteomics, right? This is the ultimate tool and

112
00:11:29,920 --> 00:11:37,360
assay to go exploratory. We offer like more than 5,000 biomarkers that were selected from artificial

113
00:11:37,360 --> 00:11:43,120
intelligence, from the public research, from the opinion leaders, voice of customers. And now we are

114
00:11:43,120 --> 00:11:50,000
really at the age of proteomics. And then we offer this great tool for people to explore and

115
00:11:50,000 --> 00:11:55,760
identify biomarkers in many digits. Now, I think it's the ultimate assay in the field of proteomics.

116
00:11:56,320 --> 00:12:02,000
And also we offer it in a very high-throughput and efficient manner.

117
00:12:02,000 --> 00:12:09,600
It's really adjustable to the new generation sequencing capabilities, right? And this is like

118
00:12:09,600 --> 00:12:15,040
makes it the ultimate platform. I would say that's the I'm really proud. I'm really proud. I'm looking

119
00:12:15,040 --> 00:12:20,080
forward to see the first data that comes out of this platform. Not that I see it

120
00:12:20,080 --> 00:12:24,320
from the outside. But that's great.  Cindy, you want to add to that?

121
00:12:25,280 --> 00:12:32,080
Yeah, sure. So what I'll add is that one of the elements that Olink has

122
00:12:32,080 --> 00:12:41,760
integrated into this new product is the ability to ease the workflow and the way we've done that

123
00:12:41,760 --> 00:12:48,560
is by, instead of having a very diseased focused organization of the panels,

124
00:12:50,240 --> 00:12:55,200
And when I say panels, I mean, you know, subsets of proteins that make up, for example, our

125
00:12:55,200 --> 00:13:02,800
Explore 3072 [platform], rather than being disease focused, we instead are focusing - And we've talked in here

126
00:13:02,800 --> 00:13:08,400
before about how many proteins we cover in the low abundant proteome. So those are ones where we're

127
00:13:08,400 --> 00:13:14,400
measuring them in a neat fashion, meaning one-to-one, we don't dilute them at all in order to

128
00:13:14,400 --> 00:13:22,000
discriminate between diseased and healthy individuals. And so we've organized them, rather than organizing

129
00:13:22,000 --> 00:13:27,680
them by cardiovascular, oncology, inflammation, or neurology, we're organizing them by dilution

130
00:13:27,680 --> 00:13:34,480
block, right? And so we've been able to really streamline the workflow as a result of that. And

131
00:13:34,480 --> 00:13:40,960
groups that I'm talking to that are doing large numbers of samples in this population health space

132
00:13:41,440 --> 00:13:50,960
can triple their throughput or their capacity with a very modest investment in automation.

133
00:13:50,960 --> 00:13:57,120
And of course, you know, at this scale, we're an automated platform, controlling beginning

134
00:13:57,120 --> 00:14:04,000
to end the processing of the samples in an Olink Explore environment. That said, I would not expect us to

135
00:14:04,000 --> 00:14:10,240
sit back on our laurels in trying to cover as much of the proteome as we can in this targeted fashion.

136
00:14:10,240 --> 00:14:20,560
Of course, we launched the the [next-generation sequencing] NGS platform in 2020. We doubled the number of proteins in 2021.

137
00:14:20,560 --> 00:14:26,720
And now we're almost doubling it again. So it's like you say, Sarantis, I'm very excited and

138
00:14:26,720 --> 00:14:34,320
very proud of of what our R&D team has been able to accomplish in a very short time from my

139
00:14:34,320 --> 00:14:42,240
perspective. And building for that scale, even this particular paper, right? The HOMAGE cohort had

140
00:14:42,240 --> 00:14:50,640
560 cases and 870 controls. That's 1300 samples right there. And people's heads are

141
00:14:50,640 --> 00:14:57,520
just kind of like, wow, that's a lot of work to do, right? 13 micro-titer plates. And then ARIC had

142
00:14:57,520 --> 00:15:03,920
250 cases, 250 controls. That's another 500 on top of the 1300. And the Framingham had about

143
00:15:03,920 --> 00:15:14,000
400. And so that's, okay, what are we now at? We're at 1400 plus 500 is 1900 plus 400 is, you know,

144
00:15:14,000 --> 00:15:21,360
2500 samples, right? Just say that's a very - that's a lot of work for someone. And yet for the

145
00:15:21,360 --> 00:15:29,760
Olink Explore HT, right? It's built for this kind of scale in terms of scaling the number of

146
00:15:29,760 --> 00:15:37,120
samples and being able to finish projects really efficiently with an NGS readout. So super exciting,

147
00:15:37,120 --> 00:15:43,600
right? In terms of what our customers and what the scientific community will be able to discover.

148
00:15:43,600 --> 00:15:54,560
With this particular paper, it was only using Three 96-Plex Olink Target 96 panels. And you say,

149
00:15:54,560 --> 00:16:02,000
well, it was only ten years ago, it was amazing to do 260 to 270 proteins

150
00:16:02,000 --> 00:16:11,920
at a time. But then you think, well, we'll up that by a factor of 20. They go from 270 to over 5,300.

151
00:16:11,920 --> 00:16:18,160
Then it's just, wow, what can you do with 20-fold times the biomarkers?

152
00:16:18,160 --> 00:16:24,400
Yeah, or the potential biomarkers to then narrow it down to the ones that are the pathways

153
00:16:24,400 --> 00:16:29,920
of interest, right? Because that's the dream. The dream is to be able to make discoveries as broadly

154
00:16:29,920 --> 00:16:37,680
as possible and then narrow down to a panel, a subset of proteins that are going to really improve

155
00:16:37,680 --> 00:16:44,960
your ability to predict heart failure above and beyond clinical factors being used today,

156
00:16:44,960 --> 00:16:50,560
like NT-proBNP that they talked about in this paper. Yep. And that is what the whole

157
00:16:50,560 --> 00:16:56,880
goal of the research was, right? The whole goal was to improve prediction. So, Sarantis, how can you

158
00:16:56,880 --> 00:17:08,320
comment then on what they found? I mean, they found 142 proteins, like potential biomarkers connected

159
00:17:08,320 --> 00:17:12,720
to heart failure disease. Of course, in order to be classified as biomarkers, they need a

160
00:17:12,720 --> 00:17:16,320
lot of proteomic studies to follow up. They mentioned that they don't know

161
00:17:16,320 --> 00:17:20,880
if some of the proteins that are caused by or affect heart failure, right? They need to do some

162
00:17:20,880 --> 00:17:26,960
studies in mouse or probably some follow-up studies. But the important thing that this,

163
00:17:26,960 --> 00:17:32,000
among these 142 proteins, there are eight proteins that are common in all of these

164
00:17:32,000 --> 00:17:36,800
three cohorts. And among these eight proteins, of course,

165
00:17:36,800 --> 00:17:43,840
natriuretic peptides were there, something that was expected to be. And some that

166
00:17:43,840 --> 00:17:49,920
pop up to my mind and we were discussing before, it was the eukaryotic translation initiation factor

167
00:17:49,920 --> 00:17:56,160
4E-BP1 protein. It's a really interesting protein because it's involved in stress and

168
00:17:56,160 --> 00:18:05,600
metabolic stress and actually binds at the 5' of mRNA and blocks the translation

169
00:18:05,600 --> 00:18:12,880
of mRNA when we have a metabolic stress. And it's also regulated by mTOR. And I know that

170
00:18:12,880 --> 00:18:18,880
David had a great story about tropomyosin. And I think it would be great because mTOR

171
00:18:18,880 --> 00:18:22,880
 is a target of tropomyosin, and tropomyosin is a really famous drug.

172
00:18:22,880 --> 00:18:32,240
In the aging space, yeah. It was about a year ago, I was at a user group meeting

173
00:18:32,240 --> 00:18:39,680
for Olink. And a professor from Institute of Systems Biology named Nathan Price was there

174
00:18:39,680 --> 00:18:47,520
and gave a really interesting talk about the early or the wellness and the whole markers of wellness

175
00:18:47,520 --> 00:18:53,840
and the markers of sort of illness and that transition phase. They're mining data from a startup

176
00:18:53,840 --> 00:18:58,800
company called Arivale from years ago that they published several different papers using Olink

177
00:18:58,800 --> 00:19:05,200
technology and they continue to. Well anyway, about a few months later, he and Lee Hood,

178
00:19:05,200 --> 00:19:11,600
the founder of ISB, published a book called, "The Age of Scientific Wellness." And so, me being curious,

179
00:19:11,600 --> 00:19:16,880
I wonder if there's Olink data in this particular book on longevity and wellness.

180
00:19:16,880 --> 00:19:22,720
And I went ahead and read it, and it just blew my mind, right, with the different research on

181
00:19:22,720 --> 00:19:28,240
sirtuins on mTOR, and mTOR in particular was interesting because they talked a little bit about

182
00:19:28,240 --> 00:19:35,600
rapamycin and the ability of rapamycin as sort of an anti-aging longevity compound. I mean,

183
00:19:35,600 --> 00:19:42,320
I'm like, what? I remember reading about mTOR as being this sort of master regulator, or what have

184
00:19:42,320 --> 00:19:47,120
you, but to include it in aging? Well, fast forward here to just a few months ago,

185
00:19:47,120 --> 00:19:54,000
and I'm listening to a podcast by Paul M. Cooper called, "The Fall of Civilizations." And it's about

186
00:19:54,000 --> 00:20:00,640
history and it's about different places and different peoples that I'm not very familiar with.

187
00:20:00,640 --> 00:20:07,120
And there was one podcast on Rapa Nui, which was Easter Island. And they basically call it a

188
00:20:07,120 --> 00:20:17,280
collapse or a contact between civilizations. But it was on Rapa Nui that rapamycin was isolated from.

189
00:20:17,280 --> 00:20:26,400
It's inside a volcano on Easter Island in the Pacific Ocean, where scientists collected an

190
00:20:26,400 --> 00:20:33,280
unusual specimen, right? They thought, okay, well, this looks like an antibiotic in terms of the

191
00:20:33,280 --> 00:20:39,760
way it was growing. So they went, took it back. I think it was a Canadian group that ended up isolating

192
00:20:39,760 --> 00:20:48,160
rapamycin. And then naturally, right, the biology of it, mTOR is the mammalian target of rapamycin.

193
00:20:48,160 --> 00:20:55,360
And it's completely off-label, right, for people to start taking rapamycin just for longevity

194
00:20:55,360 --> 00:21:00,400
benefits. It's a crazy world once you get into the whole longevity, aging, you know, wellness

195
00:21:00,400 --> 00:21:07,040
of business. But the science is rapidly catching up to do away with all charlatanry

196
00:21:07,040 --> 00:21:15,440
that is going on or has gone on. But nonetheless, when I saw, you know, 4E-BP1 in this

197
00:21:15,440 --> 00:21:22,880
particular paper as being a significant biomarker and then, Sarantis, you tying it back to mTOR,

198
00:21:22,880 --> 00:21:28,720
what mTOR being an effector, right? A regulator. Yeah, it phosphorylates.

199
00:21:28,720 --> 00:21:36,160
Yeah, phosphorylates 4E-BP1. Yep, biology is a system. And it's all connected.

200
00:21:36,160 --> 00:21:45,360
And as far as these biomarkers, though, what can you tell me then about, Sarantis, about the C-index

201
00:21:45,360 --> 00:21:51,600
that they refer to in the paper? Yeah, C-index is

202
00:21:51,600 --> 00:21:58,400
similar to AUC, the area under the curve. And it actually defines the

203
00:21:58,400 --> 00:22:04,240
prediction, actually, how accurate it would be a prediction for the model, right? And the

204
00:22:04,240 --> 00:22:10,480
really important thing that is here, and it's a fact that when they compare or when they add this

205
00:22:10,480 --> 00:22:19,920
proteomic signature to the clinical model, they see a delta C-index increase, and that was because

206
00:22:19,920 --> 00:22:24,800
there was more accurate the model. And actually it was increased even more,

207
00:22:26,240 --> 00:22:32,000
compared to when they add the typical factors, the typical biomarkers, like the

208
00:22:32,000 --> 00:22:39,520
naturietic peptides to the clinical factors, right? And there was an even larger increase.

209
00:22:39,520 --> 00:22:47,200
That means that adding proteomics to already existing, let's say, diagnostic tools enable

210
00:22:47,200 --> 00:22:53,120
and make the diagnosis a little bit more accurate than before. At the level of, I see the

211
00:22:53,120 --> 00:23:00,400
C-index increase from 5.9% to 11.1%, that's clearly significant.

212
00:23:00,400 --> 00:23:08,640
So it's a performance metric. Yeah. It sounds like, yeah, that's really helpful. Yeah, because I was

213
00:23:08,640 --> 00:23:21,200
looking for AUCs on here, and I see the delta C-I. The C-index, right?

214
00:23:21,200 --> 00:23:27,440
Yeah, looking at figure three, they show the increase in the C-index from what I understand.

215
00:23:27,440 --> 00:23:36,960
And concordance index, right? Yeah, okay. And it's roughly an increase of 10% from 77 to 88,

216
00:23:36,960 --> 00:23:46,080
from 73 to 79, from 74 to 81. So these are pretty nice jumps in the C-index value when you add the

217
00:23:46,080 --> 00:23:55,840
biomarkers. Now, a simple question, was it a lot of biomarkers? Or was it just the

218
00:23:55,840 --> 00:24:02,320
nine that they identified? No, there was these biomarkers, they were selected among these

219
00:24:02,320 --> 00:24:09,760
142 that they were identifying three cohorts, right? Oh, so it's all 142? I mean, they say that the

220
00:24:09,760 --> 00:24:15,040
excluded some of them in the analysis, but it was among these 142 for each of these cohorts, because you

221
00:24:15,040 --> 00:24:19,120
see that they have classified in figure three, they have classified the three cohorts. Yeah.

222
00:24:19,120 --> 00:24:23,760
And they have seen what is in each cohort, what is the difference, right? And then there are

223
00:24:23,760 --> 00:24:30,640
specific biomarkers in these cohorts. Yeah, I see. And they pointed to the inflammatory

224
00:24:30,640 --> 00:24:37,360
pathways as well as the remodeling pathways. Now, they call them mechanistic clusters related to

225
00:24:38,400 --> 00:24:46,000
these broad categories, which, you know, inflammation makes a lot of sense. I think this idea of

226
00:24:46,000 --> 00:24:52,720
extracellular matrix and apoptosis, I think that sort of points back to your

227
00:24:52,720 --> 00:25:00,480
linking this to mTOR, Sarantis. And remember, we talked about the EMPEROR study in a

228
00:25:00,480 --> 00:25:07,840
previous podcast, and some of those pathways they commented in their paper might be linked to some of

229
00:25:07,840 --> 00:25:20,000
these, you know, sirtuin pathways. So, I will say the complexity of proteomics is broad.

230
00:25:20,000 --> 00:25:27,040
And I find these anchors that I'm trying to hold onto to start to understand

231
00:25:27,040 --> 00:25:35,520
the mechanistic biology behind some of these diseases that are, frankly, the body is so good at

232
00:25:35,520 --> 00:25:43,600
using these chemicals in our various different organs, we don't have, you know, one smoking

233
00:25:43,600 --> 00:25:50,320
gun in any given disease. And so starting to try and put these things together is going to require

234
00:25:50,320 --> 00:25:58,240
some machine learning and some ... So this idea of predicting heart failure,

235
00:25:58,240 --> 00:26:03,200
you have underlying, I mean, the first take-home was inflammation, right? The inflammation,

236
00:26:03,920 --> 00:26:11,680
TNF and other inflammation-related proteins and interleukins. The other big take-home was

237
00:26:11,680 --> 00:26:16,800
remodeling, right? And this is what Cindy is talking about in terms of even before a person

238
00:26:16,800 --> 00:26:23,840
has heart failure, there is apoptosis happening. There is hints around autophagic flux, which is

239
00:26:23,840 --> 00:26:29,920
the SLG-T2, the Emperor study points out. And then, of course, is the possibility of

240
00:26:29,920 --> 00:26:38,480
this is with the lens of [analyzing] only 270 proteins, right? They only use three Target 96 panels. I say "only,"

241
00:26:38,480 --> 00:26:44,000
but still a lot of proteins. And still what, 2500 samples, so still a lot of samples,

242
00:26:44,000 --> 00:26:49,920
and some significant findings, which is what makes this paper so tantalizing, right? To say,

243
00:26:49,920 --> 00:26:56,960
okay, we've got a very good snapshot of inflammation. We have hints on remodeling, right?

244
00:26:57,600 --> 00:27:03,920
As well as, I think one of the things that they ended up - just remembering the figure we're just

245
00:27:03,920 --> 00:27:10,000
talking about - they're looking at all the protein specific to the cohort. It made me think,

246
00:27:10,000 --> 00:27:16,240
yeah, well, perhaps there just was enough, like, high signal when they narrowed it down, right? They

247
00:27:16,240 --> 00:27:24,000
lost that impact of predictive power. But nonetheless, this is the first snapshot. Any other good take-home

248
00:27:24,000 --> 00:27:32,080
messages here? I would say, you know, they talk about the promise of predictive

249
00:27:32,080 --> 00:27:39,760
tools in order to pave the way for design of predictive or preventative trials, right? Like

250
00:27:39,760 --> 00:27:47,200
the vision of being able to have these preventative trials, especially when we have imperfect

251
00:27:47,200 --> 00:27:54,240
therapies for heart failure today, which I really appreciate them clicking on that. That's

252
00:27:54,240 --> 00:27:59,440
one of the last points they make before they go through some of the limitations that

253
00:27:59,440 --> 00:28:07,040
Sarantis commented on earlier. So that, I think that's exciting. The world of prevention, right?

254
00:28:07,040 --> 00:28:15,920
If only. Yeah, so many different ways. So I've gone on this sort of hobby now of

255
00:28:15,920 --> 00:28:22,160
reading a lot of wellness and longevity books, really, really interesting stuff. Why? Because the

256
00:28:22,160 --> 00:28:29,200
science is catching up, right? And the science is now looking into longevity and aging as

257
00:28:29,200 --> 00:28:33,680
a separate discipline. So maybe we'll talk about the future. I was just noticing, yeah, I was just

258
00:28:33,680 --> 00:28:41,200
noticing that that Peter Attia's new book is now on the top 10 bestsellers list. And I was mentioning

259
00:28:41,200 --> 00:28:47,520
that in a group at a conference where I was at in Melbourne, a group of four people who were standing

260
00:28:47,520 --> 00:28:52,960
around saying goodbye, you know, good meeting, heading to the airport. And I mentioned that book

261
00:28:52,960 --> 00:28:57,680
and somebody reached into their backpack and pulled it out. It's funny you mentioned that book

262
00:28:57,680 --> 00:29:03,120
because I just finished it. It's called, "Outlive." Awesome. Highly recommended.

263
00:29:03,120 --> 00:29:07,840
Peter's podcast is fantastic as well. And he talks quite a bit about rapamycin as well.

264
00:29:07,840 --> 00:29:14,800
Yes. Now on that note: Hey, great to see you all. Thank you. That was great. Take care.

265
00:29:14,800 --> 00:29:19,840
Hope you're having a good summer. Enjoy your summer. Take care. All right. Bye-bye.

266
00:29:19,840 --> 00:29:28,160
Thank you for listening to The Proteomics in Proximity Podcast brought to you by Olink

267
00:29:28,160 --> 00:29:43,120
Proteomics. To contact the hosts or for further information, simply email info@olink.com.