Latest / Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast. / Healthspan, Sleep, and Innovations

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Healthspan, Sleep, and Innovations · Deep Dive Ortho — Orthopaedic Surgery, AI & Digital Health Podcast.

0:00
OK, let's get into this. We're taking a deep
0:03
dive today into some, well, some really cutting
0:06
edge areas that are fundamentally changing how
0:08
we think about health and medicine. Absolutely.
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We're pulling insights from some fascinating
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conversations Eric Tupel hosted recently. That's
0:16
right. First up, we'll be digging into the latest
0:18
science on sleep with Professor Matthew Walker.
0:21
And we're talking updates that go... quite a
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bit beyond just counting hours. Then we'll switch
0:27
gears a bit, look at the bigger picture, the
0:29
future landscape of life science. Things like
0:32
genomics, AI, and our understanding of the immune
0:37
system, which is evolving so rapidly. And for
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that, we're drawing on insights from Sir John
0:41
Bell, who's a real visionary in those fields.
0:44
Exactly. So the mission for this deep dive, really,
0:47
is to pull out the most impactful bits of knowledge
0:50
from what these experts are saying. Yeah, translating
0:52
some quite complex research into to hopefully
0:54
surprising facts and insights that are actually
0:56
relevant, especially for professionals, working
0:59
in industries touched by all these changes. Because
1:01
understanding this stuff isn't just academic
1:03
curiosity anymore, is it? It feels key to navigating,
1:07
well, both our personal health futures and the
1:09
professional world in the next few years. Couldn't
1:11
agree more. It really is. Right then. Let's jump
1:14
straight in. We'll start with the science of
1:16
sleep, which is often, I think, underappreciated,
1:18
and maybe begin with something quite unexpected
1:21
at social side. Yes. It's become increasingly
1:25
clear that sleep's influence goes way beyond
1:28
just us as individuals. Recent work has really
1:32
shown a light on its crucial role in how we interact
1:35
socially. Which is surprising, isn't it? Because
1:38
you tend to think of sleep deprivation effects
1:39
as your mood, your concentration, maybe physical
1:43
health. That's the traditional view, yes. But
1:46
it's about more than just how you feel when you
1:48
haven't slept. It's actually about how that lack
1:52
of sleep affects the people around you. Okay,
1:54
tell me more about that social dimension. What's
1:56
the new thinking? Well, there's some really compelling
1:58
research, particularly from Dr. Eddie Ben Simon
2:01
over at Berkeley. Her work demonstrates pretty
2:04
clearly that sleep deprivation makes people measurably
2:08
more asocial. Asocial. Yes. Essentially, not
2:13
getting enough sleep seems to make you, well,
2:15
she used the term, socially repellent. Socially
2:17
repellent? That sounds quite harsh. It is a strong
2:19
term, yes. But the data suggests that people
2:22
who are under -stumped tend to withdraw socially.
2:25
They report feeling lonelier. And crucially,
2:28
they seem to project something that makes other
2:30
people less inclined to want to engage with them.
2:33
So people can actually sense it, even if they
2:35
don't know you pulled an all -nighter. It seems
2:37
that way, yes. The research showed that even
2:40
when observers didn't know the person was sleep
2:42
-deprived, they rated them as less socially appealing,
2:45
less attractive to interact with. And here's
2:48
where it gets, well, frankly, a bit alarming.
2:51
The studies also found that after interacting
2:53
with someone who was sleep deprived, a person
2:55
who was actually well -rested walked away feeling
2:57
more lonely themselves. No way. So a kind of
3:00
social loneliness contagion. Your bad night's
3:03
sleep can actually make someone else feel lonely
3:05
just by talking to them. Exactly. It's like a
3:08
ripple effect. A sort of viral transmission of
3:11
that feeling. And the work also uncovered another
3:14
really significant social impact, a reduction
3:18
in what they call helping behavior. Meaning?
3:20
Meaning, when you're insufficiently slept, you're
3:23
simply less likely to offer help to others, even
3:26
strangers. Your sort of default setting shifts
3:29
towards being less altruistic, less pro -social.
3:32
And I remember reading there was population level
3:34
data on this too, something about daylight savings.
3:36
That's right, yes. The daylight sevens time study
3:38
is a really striking example. They looked at
3:41
charitable donations across the U .S. And in
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the days immediately after the clocks go forward
3:45
in spring, when everyone loses that hour of sleep
3:48
opportunity, there was a statistically significant
3:51
drop. A drop in donations. A big dent, as Professor
3:54
Walker described it, in national charitable giving.
3:56
Just from losing one hour of potential sleep
3:59
across the population, it shows this reduced
4:01
helping behavior playing out on a massive scale.
4:04
That's incredible. It really speaks volumes,
4:06
doesn't it, about how fundamental sleep is for
4:09
just Well, basic human cooperation and empathy.
4:13
It does. And you can immediately see the professional
4:15
implications. Teamwork, collaboration, effective
4:18
leadership. They all rely so heavily on positive
4:22
social interaction, on that willingness to help
4:25
each other out. Absolutely. If your team or even
4:28
just key people in it are chronically under -slept,
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it's bound to have a tangible, negative effect
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on how everyone functions together. It's not
4:37
just about individual performance, it's about
4:38
the collective. It really reframes sleep health,
4:41
doesn't it? Not just a personal wellness thing,
4:43
but almost a social or organizational imperative.
4:48
I think that's exactly right. A social imperative.
4:50
OK, so that's the surprising social angle. What
4:51
about the underlying biology? There's been a
4:54
lot of talk about genetic short sleepers. Is
4:55
that a real thing? Are some people genuinely
4:58
OK on much less sleep? Yes, it is real. But,
5:02
and this is a huge, but it's incredibly exceedingly
5:05
rare, for the vast, vast majority of us, that
5:08
need for seven to nine hours is biologically
5:10
baked in. The evidence for impairment below that
5:13
is just overwhelming. But there are some people.
5:16
There are. Science has identified this tiny,
5:18
tiny subset of individuals who seem to genuinely
5:21
thrive on significantly less sleep. Maybe five
5:25
hours. Maybe even a bit less. And Critically,
5:28
they show no apparent signs of impairment. No
5:31
impairment at all. That sounds like a superpower.
5:33
It almost defies everything else we know about
5:35
sleep debt. It certainly seems that way, and
5:37
researchers have actually pinpointed specific
5:39
genes linked to this. The first one identified
5:41
was called the DEC2 gene. A specific mutation
5:45
in that gene seems strongly connected to this
5:47
natural short sleep ability. But you stressed
5:49
how rare it is. How rare are we talking? Professor
5:52
Walker put it very starkly. He said, you are
5:54
statistically more likely to be struck by lightning
5:56
in your lifetime than to have this specific genetic
5:59
makeup that allows you to genuinely function
6:00
perfectly well on very little sleep. Struck by
6:03
lightning. OK. It puts it in perspective. So
6:06
most people who think they're fine on five hours
6:07
probably aren't. That's the overwhelming likelihood,
6:10
yes. They might feel okay, or they've adapted
6:13
subjectively, but objectively they're likely
6:15
accumulating significant sleep debt with all
6:18
the downstream negative consequences for their
6:20
health and cognitive function, even if they don't
6:23
realize it. Right. So why is this tiny group
6:27
so interesting to science, then, if it's that
6:30
uncommon? Because it poses this fundamental biological
6:34
puzzle. How has Mother Nature, through just random
6:37
genetic mutation, found a way in these few individuals
6:41
to essentially, well, ZIP file sleep? ZIP file
6:45
sleep? I like that analogy. They somehow compress
6:47
all the necessary functions that normally take
6:49
seven to nine hours into maybe five or six and
6:51
still seem to get all the benefits. The huge
6:53
question is, how? What are the molecular tricks?
6:56
What are the mechanisms that let them do that?
6:58
And if we could figure that out? If we could
7:00
figure that out. It could unlock profound secrets
7:03
about what sleep is actually for at a fundamental
7:05
level, and potentially lead to new ways to help
7:09
people with sleep problems, or maybe even enhance
7:12
sleep efficiency for everyone else. Which I suppose
7:15
immediately raises thoughts about future applications,
7:18
maybe even trying to replicate it artificially.
7:20
Exactly. And that's where it gets complicated,
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ethically and societally. While the main scientific
7:26
goal is understanding, the knowledge could lead
7:28
down paths like developing drugs or even genome
7:32
editing to try and induce this short sleep ability.
7:36
And Professor Walker raised concerns about that.
7:38
He did. He talked about the risk of an arms race
7:40
of de -escalation and sleep. A scenario where
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society, chasing productivity gains, pushes people
7:47
towards less and less sleep, potentially overriding
7:49
other vital functions of sleep that we don't
7:51
even fully understand yet. It highlights why
7:54
we need to really grasp the why and how before
7:58
we start thinking about artificial manipulation.
8:00
Which is a perfect segue to another critical
8:02
biological mechanism, the glymphatic system.
8:05
This is the brain's cleaning crew, right? That's
8:07
a great way to put it, yes. It's basically the
8:09
brain's equivalent of the lymphatic system that
8:11
cleans waste from the rest of the body. The glymphatic
8:14
system's job is to flush out metabolic byproducts
8:18
that accumulate in the brain while we're awake.
8:20
Including things like the proteins linked to
8:22
Alzheimer's. Precisely. Things like beta amyloid
8:25
and tau proteins. They're essentially the molecular
8:28
garbage that the brain produces through its normal
8:30
activity. And it needs to be cleared out regularly,
8:33
otherwise it builds up and causes problems. And
8:35
the established view, the overwhelming evidence,
8:37
has pointed to sleep being absolutely crucial
8:40
for this process. Yes, that's been the prevailing
8:42
understanding for some time now, based on a huge
8:44
amount of evidence from many different research
8:47
groups across different animal species and increasingly
8:51
in humans too. The idea being that deep sleep
8:54
is the prime time for this brainwashing. Exactly.
8:57
The data strongly suggested that sleep, particularly
9:00
that deep, non -REM sleep, stage 3 sleep, provides
9:03
the optimal window for this glymphatic system
9:06
to really kick into high gear. Why then? Why
9:09
not when we're awake? Well, the thinking is that
9:11
when we're awake, the brain is just too busy
9:13
processing information. There's less space between
9:17
the brain cells, and the fluid flow needed for
9:20
cleansing seems to be significantly reduced.
9:23
Some researchers even describe wakefulness as
9:25
a state of sort of low -level biochemical damage,
9:28
and sleep provides the essential sanitary salvation.
9:31
But wasn't there a study recently that caused
9:34
a bit of a stir, suggesting maybe clearance was
9:36
higher when we were awake? That's correct. A
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study published I think around 2022 or 2023 used
9:43
some specific imaging techniques in humans and
9:46
reported findings suggesting higher fluid flow
9:49
and potentially higher clearance during wakefulness
9:52
compared to sleep, which naturally seemed to
9:55
contradict quite a lot of the previous work.
9:56
And what was the reaction from other scientists?
9:58
Were there criticisms of that study? Yes, researchers
10:01
who'd been proponents of the sleep -dependent
10:03
model raised several points about the methodology.
10:05
They noted the use of imaging methods that weren't
10:08
standard, or at least different from those used
10:10
in many of the animal studies. There were also
10:12
questions about an artificial method used to
10:14
perfuse fluid through the brain, rather than
10:17
observing the natural flow. And importantly,
10:19
concerns about the sampling rate, whether it
10:21
was fast enough to capture the crucial slow brainwave
10:25
oscillations that happen during deep sleep. These
10:28
slow waves being important for driving the fluid
10:30
flow. That's the hypothesis, yes, that these
10:32
very slow, large amplitude brain waves physically
10:36
help to pump the cerebral spinal fluid through
10:39
the brain tissue, facilitating that waste removal.
10:42
So if the measurement technique missed those,
10:44
it could skew the results. So weighing everything
10:46
up, what's the current sort of nuanced understanding?
10:49
Is it all about sleep or is it more complicated?
10:52
I think the picture is becoming a bit more nuanced
10:54
than just saying it only happens during sleep.
10:57
The glymphatic system is clearly dynamic. It's
11:00
possible there's a kind of low battery mode clearance
11:02
that can happen during quiet wakefulness. But
11:05
sleep is still king. But the overwhelming weight
11:08
of evidence, when you look across all the different
11:10
studies, different species, different methods,
11:12
still points very strongly towards sleep, especially
11:15
deep and REM sleep. providing the optimal opportunity
11:18
for a really thorough cleanse. A full biochemical
11:22
reboot. Like the difference between putting your
11:25
phone on low power mode versus actually switching
11:27
it off for a proper update and refresh. That's
11:30
a great analogy, yes. You might get some background
11:33
tidying in low power mode, but the deep clean,
11:36
the system overhaul, really happens when it's
11:38
properly off. The bulk of evidence still firmly
11:41
supports sleep as the preferential most effective
11:44
state for this vital brain maintenance. It just
11:47
makes intuitive sense, doesn't it, that such
11:48
a complex, active organ needs dedicated downtime
11:52
for housekeeping? And you mentioned a link between
11:54
this glymphatic system and other parts of the
11:57
body, like the heart. Yes, that was fascinating.
12:00
A paper in a cardiology journal reported that
12:02
patients with atrial fibrillation, or AFib, a
12:05
common heart rhythm problem, actually showed
12:07
less active glymphatic clearance during their
12:09
sleep. Really? How does that connect? It highlights
12:12
this idea of cardiorespiratory neurophysiological
12:15
coupling. It suggests it's not just the brain's
12:19
electrical activity driving this fluid flow.
12:21
The heart and the lungs, their rhythms during
12:23
sleep, seem to synchronize with the brain's slow
12:27
waves to help drive the pulsations needed for
12:29
efficient clearance. So it's the whole system
12:31
working together heart, lungs, brain to wash
12:34
the brain during sleep. Exactly. It's a beautiful
12:36
illustration of how sleep benefits the entire
12:39
embodied organism, as Professor Walker put it.
12:42
Your body and your brain are deeply interconnected
12:44
in this process. good cardiovascular and respiratory
12:47
health might actually contribute to better brain
12:50
cleansing overnight. That link is quite profound.
12:53
A heart condition potentially impacting brain
12:55
maintenance. Wow. And speaking of things that
12:58
impact sleep and potentially this glymphatic
13:00
function, what about sleep medications? Ah, yes.
13:03
A critical area. And the research here has thrown
13:06
up some quite surprising and frankly concerning
13:09
findings, especially regarding the older, more
13:12
established classes of sleeping pills. You mean
13:14
things like Ambien, the Z drugs, benzodiazepines?
13:17
Exactly. Those drugs, the ones often called sedative
13:20
notics, zolpidem, ezolpaclone, zeleplon, and
13:24
the older benzodiazepines like diazepam or temezepam,
13:27
they primarily work by, well... broadly sedating
13:31
the brain's cortex. They essentially just dampen
13:33
down overall brain activity. And they do help
13:35
people sleep or at least feel like they're sleeping
13:37
more. They certainly make you fall asleep faster
13:40
and they can increase the total time spent apparently
13:42
asleep. On a standard sleep study, they might
13:45
even show an increase in deep sleep minutes.
13:48
However, and this is crucial, they seem to significantly
13:51
alter the electrical quality or the signature
13:54
of that deep sleep. Alter the signature. What
13:56
does that mean in practice? It means they seem
13:58
to create a substantial dent, as Professor Walker
14:00
termed it, in those very large, very slow brain
14:04
waves that are the hallmark of truly restorative
14:06
deep NREM sleep. They suppress those critical
14:10
slow oscillations. The same oscillations thought
14:12
to drive the glymphatic system. Precisely. So
14:16
by suppressing those key brain waves, even if
14:19
the sleep looks like deep sleep on a superficial
14:21
level, the underlying functional machinery needed
14:23
for optimal brain cleansing seems to be impaired.
14:27
So even if your sleep tracker says you got more
14:29
deep sleep after taking an older sleeping pill,
14:32
you might not actually be getting the brain cleaning
14:34
benefits. That's exactly what the evidence suggests.
14:38
Pioneering work, particularly from making Natagard's
14:40
lab using animal models, found that despite increasing
14:43
sleep time and apparent deep sleep time, the
14:46
actual brain glymphatic cleansing was reduced
14:48
perhaps by 30 % to 40 % when these older sedative
14:51
hypnotics were used. Reduced? That's incredibly
14:54
counterintuitive. You take something to help
14:55
you sleep better. And it hinders one of sleep's
14:58
key functions. It is profoundly counterintuitive,
15:01
yes. But it strongly suggests these drugs might
15:03
be generating what's been called junk sleep,
15:06
sleep that ticks the boxes on basic duration
15:08
measures but lacks some of the crucial functional
15:10
properties, like efficient waste clearance. That's
15:13
a really significant finding, given how widely
15:16
prescribed these medications are and have been
15:18
for decades. Is there a newer approach, a different
15:21
class of drug that might avoid this problem?
15:24
Yes, thankfully there is. There's a newer class
15:26
of medications called DORAZ. That stands for
15:29
dualorexin receptor antagonists. DORAZ? Okay.
15:32
How do they work differently? They represent
15:35
a much more... well, elegant and targeted way
15:38
to promote sleep. Instead of just blanketing
15:40
the cortex with sedation, they target a very
15:43
specific chemical system in the brain, the orexin
15:46
system. Orexin. Orexin, sometimes called hypocretin,
15:49
is a key neurotransmitter that acts like a wakefulness
15:52
switch or volume control. Keeps us alert. Doors
15:55
work by blocking the receptors that erect and
15:58
binds to, so they essentially just turn down
16:00
the volume on wakefulness. So less forcing sedation,
16:03
more allowing sleep to happen naturally. Exactly.
16:06
It doesn't push the brain into an artificial
16:07
state, but rather removes the wakeup signal,
16:10
allowing the natural process of sleep onset and
16:12
maintenance to emerge more easily when conditions
16:15
are right. It's a much more subtle mechanism.
16:17
And have these newer Dora drugs been tested for
16:20
their effects on glymphatic activity or brain
16:23
cleansing? Yes. And there's a very exciting and
16:26
promising study that Professor Walker highlighted.
16:28
It involved older adults, age 65 and up, using
16:32
a Dora drug called SuverXant brand name Balsamra.
16:35
What did that study involve? It was a proper,
16:38
rigorous, randomized placebo -controlled trial.
16:41
They gave the participants either SuverXant or
16:44
a placebo. They measured not only how much sleep
16:46
they got and how much deep NREM sleep, but crucially,
16:50
they also measured the levels of those Alzheimer's
16:52
biomarkers, beta amyloid and tau in the bloodstream
16:55
before and after the night of sleep. Okay, and
16:58
what did they find regarding those biomarkers?
17:00
Did the Dora help clear them? They found that
17:02
on the night the participants took the Dora drug,
17:04
Suvarexan, there was significantly greater clearance
17:07
of both beta amyloid and tau protein from the
17:10
brain into the bloodstream compared to when they
17:12
took the placebo. The opposite effect to the
17:14
older drugs. Exactly the opposite. It wasn't
17:17
just increasing sleep duration or deep sleep
17:19
minutes, it seemed to be generating genuinely
17:21
functional sleep. sleep that was actually facilitating
17:25
the removal of these potentially harmful proteins
17:29
linked to Alzheimer's disease. That's a massive
17:31
development. It really is. Professor Walker framed
17:34
it as potentially the first evidence we have
17:36
of a sleep medication that not only helps you
17:39
sleep in a more naturalistic way, but also seems
17:42
to enhance the brain's own restorative cleansing
17:45
processes. That could have huge implications
17:47
for brain health, especially as we age. It really
17:50
drives home that not all sleep is created equal,
17:52
especially when medication is involved. So quantity
17:55
is important, obviously, but it sounds like we
17:57
absolutely have to focus just as much, if not
18:00
more, on sleep quality and also sleep regularity.
18:03
Absolutely. For years, the public health message,
18:06
the whole conversation, was heavily skewed towards
18:09
just getting enough hours The quantity. And don't
18:12
get me wrong, sufficient quantity is non -negotiable.
18:14
You need that foundation. But it's not the whole
18:16
story. Not by a long shot. Research, especially
18:19
over the last decade or so, has made it increasingly
18:22
clear that sleep quality and sleep regularity
18:25
are equally, and in some cases more, predictive
18:28
of critical health outcomes. Like what kind of
18:31
outcomes? Things like all -cause mortality, your
18:34
overall risk of dying from any cause, also cardiovascular
18:38
disease risk, metabolic health, think diabetes
18:41
risk, and potentially even cancer mortality as
18:44
well. Quality and regularity are powerful predictors
18:47
across the board. OK, let's unpack quality first.
18:50
How is that usually defined or measured? A common
18:53
and useful metric for sleep quality is sleep
18:55
efficiency. It's actually quite simple. It's
18:58
the percentage of time you spend actually asleep
19:00
while you're in bed trying to sleep. Right. So
19:02
if you're in bed for eight hours, but tossing
19:04
and turning for two, your efficiency isn't great.
19:06
Exactly. Generally, a sleep efficiency of 85
19:09
% or higher is considered good, indicating solid,
19:12
consolidated sleep. When efficiency drops much
19:15
below that, usually because your sleep is fragmented
19:17
with lots of awakenings, that's when you start
19:19
seeing problems. And what kind of problems are
19:22
linked specifically to poor quality fragmented
19:25
sleep? Pretty much system -wide impairments,
19:28
increased risk of those metabolic and cardiovascular
19:30
issues we mentioned, hormonal imbalances. But
19:33
perhaps most strikingly, the effects on mood
19:36
and mental health are profound. How so? It's
19:39
a remarkable finding, but sleep is abnormal in
19:42
every single psychiatric condition that's ever
19:44
been studied. Depression, anxiety, PTSD, bipolar
19:48
disorder, schizophrenia, you name it, sleep is
19:52
disrupted. And very often, the key feature isn't
19:55
necessarily just short sleep duration, but severely
19:57
fragmented poor quality sleep. Poor sleep efficiency
20:00
is a major red flag for mental health issues.
20:03
That really highlights how fundamental good quality
20:05
sleep is for just, well, keeping everything running
20:08
smoothly mentally and physically. Okay, what
20:10
about regularity then? You said that's also vital,
20:12
maybe even underappreciated. Hugely underappreciated,
20:14
I think. but absolutely vital. There was a really
20:18
stunning study using data from the UK Biobank,
20:21
this massive data set with hundreds of thousands
20:23
of people. What did they look at in that study?
20:25
They tracked not just how much sleep people were
20:27
getting on average, but also how regular their
20:30
sleep schedules were over many years. So how
20:33
consistent were their bedtimes and wake times
20:35
from day to day, including weekends? And what
20:38
did they find about regularity versus quantity?
20:40
The results were quite remarkable. People who
20:43
maintained high sleep regularity, basically going
20:47
to bed and waking up around the same time every
20:49
day, had a significantly lower risk of dying
20:52
from any cause, from cancer and from cardiovascular
20:55
disease, compared to those with very irregular
20:57
schedules. Even accounting for how much sleep
20:59
they got overall. Yes. And here's the kicker.
21:01
When they statistically compared the predictive
21:04
power of regularity versus quantity for all -cause
21:07
mortality, regularity almost beat quantity. Really?
21:11
Almost more important than total hours slept.
21:13
That feels counterintuitive if we've always been
21:15
told to get your eight hours. It does, doesn't
21:17
it? But it highlights something crucial. While
21:20
you absolutely need sufficient quantity, being
21:23
perfectly regular but only sleeping four hours
21:25
a night isn't going to work. Regularity acts
21:28
as a powerful anchor for your entire internal
21:31
biological clock, your 24 -hour circadian rhythm.
21:34
So keeping a consistent schedule helps keep your
21:37
body clock stable. Exactly. It sends strong,
21:40
reliable timing signals to your brain and body
21:42
every day. And having a well -aligned, robust
21:45
circadian rhythm doesn't just directly benefit
21:47
health. It also feeds back to improve both your
21:50
sleep quantity and your sleep quality. They all
21:52
reinforce each other quantity quality and regularity
21:55
They're the three pillars of healthy sleep and
21:57
we need to pay attention to all of them Okay,
21:59
so with all this detail stages quality efficiency
22:02
regularity It's no surprise people are turning
22:04
to technology sleep trackers rings watches even
22:07
smart mattresses now Are these things genuinely
22:10
helpful or can they sometimes make things worse?
22:13
It's a great question and the answer is Well,
22:15
it depends For the average person, someone who
22:18
doesn't already have clinical insomnia or a lot
22:20
of anxiety around sleep, tracking can generally
22:23
be pretty useful. Why is that? Because sleep
22:26
is notoriously difficult for us to judge subjectively.
22:30
We're often quite bad at estimating how long
22:32
it took us to fall asleep, how many times we
22:34
woke up, how much deep sleep we got. Unlike,
22:37
say, counting steps or tracking calories, which
22:39
are more concrete. So the old adage, what gets
22:42
measured gets managed, can definitely apply.
22:45
Can you give an example of how seeing the data
22:47
might help someone change their behavior? A classic
22:50
one is alcohol. Many people are quite surprised,
22:53
maybe even shocked, when they see the objective
22:55
data from their tracker showing just how consistently
22:58
even moderate alcohol consumption close to bedtime
23:01
absolutely tanks their sleep quality. How does
23:03
it show up in the data? Typically you'll see
23:06
reduced REM sleep. Increased heart rate throughout
23:08
the night, more awakenings, poor sleep efficiency.
23:11
Seeing that pattern night after night can be
23:14
a really powerful motivator to, say, cut back
23:17
on evening drinking or stop drinking earlier.
23:19
Right, makes sense. If someone is thinking about
23:21
getting a tracker, any tips on what to look for?
23:24
I think the key things are low friction and reasonable
23:27
accuracy. Low friction just means it needs to
23:30
be easy and comfortable enough that you'll actually
23:33
wear it or use it every single night without
23:35
it being a hassle. So something like a ring might
23:38
be lower friction than a watch for some people.
23:41
Potentially, yes. Or a smart mattress is perhaps
23:44
the ultimate in low friction. It's just there.
23:47
You don't wear anything. In terms of accuracy,
23:50
especially for sleep staging, the technology
23:53
is getting better all the time. Data generally
23:55
suggests devices like the Aura Ring are currently
23:58
among the more accurate wearables compared to
24:00
gold standard sleep lab polysomnography. But
24:03
others like Fitbit, Apple Watch, Whoop are also
24:05
pretty good and constantly improving. Full disclosure,
24:08
I should mention I have previously done some
24:10
advisory work for Aura in the past. Okay, useful
24:12
context. But you mentioned a potential downside,
24:15
particularly for some people. Yes, and this is
24:17
really important. For individuals who are already
24:19
prone to anxiety, especially anxiety about sleep
24:22
or maybe have perfectionistic tendencies, these
24:25
trackers can sometimes backfire. There's a recognized
24:28
phenomenon now called orthosomia. Orthosomnia?
24:32
What's that? It's basically where someone becomes
24:34
so fixated, so obsessed with achieving the perfect
24:37
sleep score or hitting ideal numbers on their
24:40
sleep tracker app. That the anxiety about it
24:42
actually makes their sleep worse. Exactly. The
24:45
pressure to perform to get that perfect data
24:47
creates stress. And stress is the absolute enemy
24:50
of sleep. It becomes this vicious cycle. You
24:53
worry about the score. That makes you sleep poorly.
24:55
You get a bad score. You worry more. So what
24:58
should someone do if they suspect they might
25:00
be falling into that trap? Well, the first thing
25:02
might be to simply take a break from tracking
25:04
altogether for a while. See if that reduces the
25:07
anxiety. If the sleep problems persist, especially
25:11
if it's chronic insomnia, the gold standard treatment
25:14
is cognitive behavioral therapy for insomnia,
25:17
or CBTI. That directly addresses the unhelpful
25:20
thoughts and behaviors around sleep. Another
25:23
strategy could be to just check the data much
25:25
less frequently. maybe only once a week, looking
25:28
for broad trends rather than getting caught up
25:30
in the nightly numbers. It's a really valuable
25:33
reminder, isn't it, that the tracker data is
25:35
just a tool. It's information. It's not the ultimate
25:39
judge of your sleep health. How you feel is still
25:42
paramount. Precisely. You always need to integrate
25:44
the objective data with your subjective experience.
25:48
How rested do you actually feel during the day?
25:50
How's your energy, your mood? Does it align with
25:52
what the tracker says? Sometimes the data might
25:55
look bad, but you feel fine or vice versa. Your
25:57
subjective feeling of restfulness is incredibly
26:00
important. And just finally, on tech, you mentioned
26:02
smart mattresses. How are they different? What's
26:04
the promise there? This is where the technology
26:06
starts to move beyond just tracking sleep to
26:10
actively augmenting or improving it. Devices
26:14
like the eight -sleep mattress, for example,
26:16
they don't just measure your sleep stages, heart
26:18
rate, breathing rate. They actually do something
26:20
with that data. Yes. They use it to intelligently
26:23
adjust the temperature of the mattress surface,
26:26
zone by zone, throughout the night. Temperature.
26:28
Why is that important? Because our body temperature
26:31
regulation is incredibly tightly linked to our
26:34
sleep cycle. To fall asleep easily, your core
26:37
body temperature needs to drop slightly. To maintain
26:40
deep sleep, you generally need to stay slightly
26:42
cool. and then your temperature needs to rise
26:44
again towards morning to help you wake up naturally.
26:47
And the mattress can manage that automatically
26:49
based on your individual sleep. That's the idea.
26:52
It learns your patterns, senses your current
26:54
sleep stage and physiology minute by minute,
26:57
and then subtly adjusts the heating or cooling
26:59
of the mattress surface to keep you in your optimal
27:02
thermal zone for each stage of sleep. It's a
27:04
really elegant use of technology, creating this
27:07
constant personalized thermal feedback loop to
27:10
improve sleep quality and maybe even quantity.
27:13
That sounds quite sophisticated. It is. And they're
27:15
adding other features, too, like detecting snoring
27:17
and then maybe subtly adjusting the head elevation
27:20
to help open the airway. Yeah. These kinds of
27:22
smart beds, I think, represent a really promising
27:24
new direction for actually enhancing sleep, not
27:27
just measuring it. OK. That brings us to the
27:29
end of our deep dive into sleep science. Some
27:32
truly surprising stuff there. The social impacts,
27:35
the genetic quirks, the brain's cleaning system,
27:38
how meds affect it, the importance of quality
27:41
and regularity, and how technology fits in. It
27:44
certainly shows how much more we're learning
27:46
about this fundamental biological need and just
27:49
how fast the science is moving on so many different
27:51
fronts at once. Right. So... Let's shift gears
27:54
now. We're staying with cutting edge science,
27:57
but broadening the view to the whole future of
28:00
life science, drawing on that conversation with
28:02
Sir John Bell. Yes, a really influential figure,
28:06
long distinguished career in genetics, immunology,
28:09
formerly the Regis Professor of Medicine at Oxford.
28:11
And now he's heading up this significant new
28:13
venture called the Ellison Institute of Technology,
28:16
the EIT, based over here in the UK. OK, tell
28:19
us about the EIT. What's its mission? What makes
28:21
it different? Well, it's incredibly ambitious.
28:24
It's funded by Larry Ellison, the tech entrepreneur.
28:27
And his mission is really broad, using state
28:30
-of -the -art science and technology, especially
28:31
AI and machine learning, to tackle some of the
28:34
world's biggest problems. Not just health? Not
28:36
just health, no. Health care and disease are
28:38
major focuses, obviously, but also critical areas
28:41
like food security, climate change, even things
28:45
like global governance. It's a very wide scope.
28:47
And you mentioned a unique approach or model.
28:49
Yes, that's a key difference. The vision is that,
28:52
where possible, the solutions they develop should
28:55
actually be commercially viable and sustainable.
28:57
It's structured more like a company than a traditional
28:59
academic foundation. The aim is to create tangible
29:02
products, platforms, services that have real
29:05
-world impact and can sustain themselves, rather
29:08
than relying solely on grants or donations. It's
29:11
a more entrepreneurial take on Philanthropic
29:14
science, perhaps. And the scale sounds pretty
29:17
huge. Oh, absolutely massive. They're building
29:19
this enormous campus, potentially millions of
29:21
square feet eventually, and planning to recruit
29:23
around 5 ,000 people. Really top talent from
29:26
across the globe. 5 ,000 people, wow. Yeah. The
29:29
focus is on getting the absolute best minds,
29:32
the top 1 % in their fields, and giving them
29:34
the resources and freedom to take risks on really
29:37
novel, potentially disruptive ideas that might
29:40
struggle to get funding through more traditional
29:41
channels. And it works closely with Oxford University.
29:44
How does that relationship function? Yes, there's
29:46
a very strong synergistic link. Oxford's been
29:49
very supportive. The idea is that the EIT complements
29:53
Oxford's strengths and the wider UK life science
29:56
ecosystem. One specific gap Sir John mentioned
29:59
EIT aims to fill is helping promising UK science
30:02
scale up. Historically the UK has been great
30:05
at discovery but less good at growing those discoveries
30:08
into large impactful companies. EI key structure
30:11
and funding are designed to allow them to build
30:13
full stack solutions taking things right through
30:15
development. That makes sense. And speaking of
30:17
the UK's life science landscape, Sir John Bell
30:20
himself was hugely influential in establishing
30:22
the UK as a world leader in human genomics, wasn't
30:25
he? Absolutely pivotal. His influence was immense.
30:29
There were really three massive, successive initiatives
30:32
that built that leadership in large -scale human
30:35
genomics data. Which were? The UK Biobank, then
30:37
Genomes England, and the newest one, our Future
30:40
Health. He was kind of built on the last tackling
30:42
different aspects of using genomic information.
30:44
Let's start with UK Biobank. That's become this
30:47
incredible global resource for researchers. How
30:49
did that come about? Well, it really grew out
30:51
of Oxford's traditional strength in large -scale
30:54
epidemiology, studying disease patterns in big
30:56
populations. The key insight was realizing you
31:00
needed enormous numbers of people, huge cohorts,
31:03
to reliably detect the often small effects of
31:05
individual genetic variations on common diseases.
31:08
So the idea was born to recruit a massive group.
31:11
Exactly. The incredibly ambitious plan was to
31:14
recruit half a million people across the UK,
31:16
collect loads of health data questionnaires,
31:18
physical measures but critically, also collect
31:20
biological samples, like DNA, and get consent
31:23
to link all that to their ongoing NHS health
31:26
records. Half a million people linked to health
31:28
records. That sounds like a logistical nightmare
31:30
to set up. Was it difficult? Extremely difficult.
31:33
Sir John talked about facing massive resistance
31:35
initially from peer reviewers. People doubted
31:38
it was even feasible. Could you recruit that
31:40
many? Get reliable data, manage the NHS linkage,
31:44
even do the genetics on that scale back then.
31:46
But they persevered, got crucial funding from
31:48
the MRC and Wellcome Trust, and built this resource
31:52
that is now genuinely used by tens of thousands
31:55
of researchers around the world every single
31:58
day. It completely changed the game for genetic
32:00
epidemiology. Amazing achievement. And then came
32:04
Genomes England. That shifted the focus more
32:06
towards using genomics in actual clinical care.
32:09
Yes, exactly. As the cost of sequencing a whole
32:12
genome just plummeted thanks to new technology,
32:15
the idea emerged. Could we use this powerful
32:18
tool right now within the NHS to help diagnose
32:21
rare diseases? Because many rare diseases have
32:23
a genetic cause, but finding it is hard. Precisely.
32:27
It can be like finding a needle in a genetic
32:28
haystack. Whole genome sequencing gives you the
32:31
whole code. This project got really important
32:33
backing from the then Prime Minister, David Cameron,
32:36
partly driven apparently by his own family's
32:38
experience with a rare condition. And did that
32:40
face resistance, too? Oh, yes. So John mentioned
32:43
massive resistance, again, this time from parts
32:45
of the medical establishment, perhaps understandably
32:47
wary of the complexity, the cost, the interpretation
32:49
challenges. But again, they pushed through, sequenced
32:53
100 ,000 genomes from patients with rare diseases
32:56
and their families, made huge numbers of diagnoses.
33:00
And it continues today with things like a program
33:02
for sequencing newborns to spot treatable conditions
33:05
incredibly early. And the third and most recent
33:09
big initiative is our future health. What's the
33:12
ambition there? Our future health really represents
33:14
another shift in focus, moving towards prevention
33:17
and early diagnosis. It came out of the UK's
33:20
life science strategy back in 2017. A key push
33:23
came from the pharmaceutical industry, who basically
33:25
said the UK needs to focus on the future of health
33:27
care, stopping people getting sick in the first
33:29
place or catching diseases much, much earlier.
33:32
Moving away from just treating established disease.
33:35
Exactly. Moving decisively towards identifying
33:38
people at high risk before symptoms appear or
33:41
diagnosing conditions at stage zero or stage
33:43
one when treatments are often far more effective.
33:46
The scale here is just enormous. The goal is
33:50
to recruit five million adults in the UK. Five
33:53
million. That's about 10 % of the adult population.
33:56
It is. It's staggering. The idea is to track
33:59
their health over many years, combine genetic
34:01
data with lots of other health information, lifestyle
34:04
data, maybe even data from wearables. To do what,
34:06
exactly? Primarily to build incredibly detailed
34:09
models of who is likely to develop which diseases,
34:12
enabling much earlier identification of risk.
34:14
And secondly, to create a massive platform for
34:17
efficiently testing new preventative therapies
34:19
or early diagnostic tools on a huge scale. And
34:22
has recruitment going for that. Despite delays
34:24
due to the pandemic, it's been incredibly fast.
34:27
Millions have already registered and large numbers
34:29
have provided samples and data. It's going to
34:31
generate this unbelievably rich multiomics data
34:34
set. Multiomics. So beyond just the genome sequence.
34:38
Yes. much beyond. Looking at proteins, proteomics,
34:41
metabolites, metabolomics, maybe epigenetic markers,
34:44
even things like organ clocks, biological markers,
34:47
trying to estimate the functional age of different
34:49
organs. It's about building a much more holistic,
34:52
dynamic picture of health and disease risk over
34:55
time. That sounds like it could truly revolutionize
34:58
preventative medicine. But it must require new
35:00
ways of thinking, clinically and regulatory -wise.
35:04
Absolutely. You need new types of clinical trials
35:06
designed for prevention in healthy populations.
35:09
You need regulators to think about approving
35:11
diagnostics based on predicting future risk,
35:14
not just confirming present disease. It's a huge
35:17
shift. And driving a lot of this potential, you
35:19
mentioned AI is absolutely central to the Ellison
35:22
Institute's vision. Yes. AI is seen as a core
35:25
enabling platform, underpinning pretty much everything
35:27
they do, whether it's health, climate, food,
35:29
whatever. And they have a massive advantage here
35:31
because of the huge compute power funded by Larry
35:34
Ellison. Access to that level of computing being
35:37
essential for modern AI. Absolutely critical.
35:40
Training these incredibly complex machine learning
35:42
models on the vast data sets we're talking about
35:45
requires enormous computational resources. Few
35:49
universities or traditional institutes can compete
35:51
on that scale. And they're bringing in the talent
35:53
to use it? Yes, actively recruiting top AI and
35:56
machine learning talent from around the world,
35:58
including people from places like Google DeepMind.
36:00
So you have this potent combination, world -class
36:04
talent, massive compute, and access to the UK's
36:08
unique rich healthcare, and genomic data assets.
36:11
Those are the three essential pillars. Talent,
36:13
compute, data. Makes sense. Eric Topol's review
36:17
for the NHS actually highlighted the UK's potential
36:19
to lead in AI for health, didn't it? Building
36:22
on that genomic strength. It did, yes. The potential
36:25
is clearly there. And you mentioned a second
36:27
key technology platform at EIT working alongside
36:30
AI. That's right. Generative biology or synthetic
36:33
biology, as it's often called. That's the other
36:35
core platform. They've recruited a real leader
36:38
in the field, Professor Jason Chin from Cambridge,
36:40
to head that up. Generative or synthetic biology,
36:43
what does that involve? It's essentially about
36:44
designing and building new biological parts,
36:47
devices and systems, or redesigning existing
36:49
ones for useful purposes. So engineering life,
36:53
basically. What kind of applications are we talking
36:55
about? Things like being able to synthesize huge
36:58
stretches of DNA from scratch to encode new functions,
37:02
engineering cells or microbes to act as diagnostics
37:05
or therapeutics, or to produce valuable chemicals,
37:08
even potentially transforming plants to make
37:10
them more nutritious or resilient to climate
37:12
change. It's about using engineering principles
37:14
to manipulate biology in predictable ways. And
37:17
how does that connect with AI? They work synergistically.
37:21
AI can analyze vast amounts of biological data
37:24
to help design these new biological systems,
37:26
predict how they'll behave, and then generative
37:28
biology provides the tools to actually build
37:30
and test those designs in the real world. This
37:33
capability can then feed into all of EIT's main
37:35
focus areas, creating new health solutions, new
37:38
food production methods, new environmental technologies.
37:41
It's moving from just reading biology to actively
37:43
writing and editing it. Okay, let's move to another
37:46
area where Sir John has deep expertise and where
37:48
EIT is making a big push. Understanding the immune
37:52
system. You called it the next great frontier.
37:56
I think it really is. If you think about it,
37:58
our clinical tools for assessing someone's immune
38:01
status are still surprisingly crude. Often it
38:04
boils down to just counting different types of
38:06
white blood cells. Which doesn't tell you much
38:08
about how well they're actually functioning.
38:10
Exactly. The immune system is this incredibly
38:13
complex network of different cell types, T cells,
38:16
B cells, innate immune cells interacting through
38:19
countless molecular signals, antibodies, including
38:22
autoantibodies that can cause disease. It's vastly
38:25
complex. And without a really deep molecular
38:28
understanding of an individual's immune state,
38:30
making big breakthroughs in immune -related diseases
38:32
is really hard. Which covers a huge range. Infections,
38:36
cancer, autoimmune diseases. A massive range.
38:40
Sir John described our current approach in this
38:42
area as sometimes feeling like pinning the tail
38:44
on the donkey. A bit of guesswork in the dark.
38:47
We need a much more detailed map. So what's EIT
38:50
doing specifically to try and build that map
38:52
to get beyond the guesswork? They're establishing
38:54
a very significant long -term project focused
38:58
on deep immunophenotyping, basically characterizing
39:03
immune cells and responses at a really detailed
39:06
molecular level. And how are they going about
39:08
that? What's the methodology? A key part involves
39:11
using human challenge models in a very careful
39:14
and controlled way. Human challenge models? What
39:16
does that mean, exposing people to diseases?
39:18
Yes, essentially. It involves carefully selecting
39:22
healthy volunteers and exposing them to controlled,
39:25
safe doses of specific pathogens like common
39:28
cold viruses or bacteria, or giving them vaccines.
39:31
And the purpose is to watch the immune system
39:33
react in real time. Precisely. It allows researchers
39:36
to observe the entire immune response right from
39:39
the beginning in a controlled setting. They can
39:41
track exactly how different parts of the immune
39:43
system, different cell types, different molecules
39:45
respond over time at a very fine -grained molecular
39:48
level. It gives you much richer, more dynamic
39:51
information than just taking a snapshot blood
39:53
sample from someone who's already sick or even
39:56
someone healthy. And I'm guessing AI is absolutely
39:58
essential for making sense of all the data that
40:00
generates. Utterly essential. The sheer volume
40:04
and complexity of the data from these kinds of
40:06
studies is immense. You're looking at changes
40:09
in gene expression across thousands of genes
40:11
in different immune cells, sequencing the receptors
40:14
on T cells and B cells to understand their diversity,
40:18
measuring hundreds of thousands of different
40:20
proteins and metabolites in the blood. It sounds
40:23
overwhelming. It would be, without powerful computational
40:26
tools. AI and machine learning are critical for
40:31
finding the patterns in that complexity. Understanding
40:34
how all the different components, T cells, B
40:37
cells, innate immunity, antibodies interact.
40:40
Figuring out which interactions actually lead
40:41
to a protective immune response versus those
40:44
that might lead to disease or side effects. So
40:46
the ultimate goal is to build a kind of predictive
40:48
model of the immune system. Yes, to build a really
40:51
solid scientific foundation for predicting and
40:53
anticipating what kind of immune response you
40:56
need to generate for effective protection against
40:58
a specific infection or for effective therapy
41:00
against cancer or to calm down an autoimmune
41:03
attack. And if you can predict it, you can potentially
41:05
design interventions to achieve it. That's the
41:07
hope. If we understand exactly what a good immune
41:11
response looks like at the molecular level for,
41:13
say, flu, We can design much better vaccines
41:17
to elicit exactly that response. If we know how
41:20
cancer evades the immune system, we can design
41:23
immunotherapies to overcome those evasion tactics.
41:26
Or for autoimmune diseases, if we can pinpoint
41:29
the specific rogue immune cells or pathways,
41:32
we can develop much more targeted therapies.
41:34
Like the recent breakthroughs in treating diseases
41:36
like lupus you mentioned. Exactly. Treatments
41:39
targeting specific types of B cells, like those
41:42
expressing CD19, have shown remarkable results
41:44
in some severe autoimmune diseases, illustrating
41:47
the power of this kind of molecular process.
41:49
precision. It's about moving beyond broad immunosuppression
41:52
to highly targeted interventions. And Sir John
41:54
mentioned this major immunology project at EIT
41:57
is starting soon. Yes. He said they expect data
42:00
collection using these challenge models and deep
42:02
phenotyping to begin within the next six months
42:04
or so. It's a major undertaking, but the potential
42:07
payoff for understanding health and disease is
42:09
enormous. It really is exciting work. All right,
42:13
just finally, the conversation ended with a brief
42:15
reflection on the broader state of global science,
42:17
didn't it? Potential challenges ahead. Yes, there
42:20
was a brief, quite poignant exchange near the
42:23
end, alluding to potential headwinds facing science
42:26
globally, maybe political disruption, challenges
42:29
to international collaboration. Sir John shared
42:31
his perspective on that. And what was his take?
42:34
Was he pessimistic? Not entirely pessimistic,
42:37
but realistic perhaps. He strongly emphasized
42:39
the inherently global nature of science. That
42:42
despite national borders or politics, scientists
42:45
everywhere are fundamentally part of a shared
42:47
community working on common problems. He stressed
42:50
America's indispensable role as a scientific
42:52
powerhouse given its scale and impact. And his
42:55
hope for the future. His view was that the global
42:57
scientific community needs to stick together
42:59
to lean in collectively to support the scientific
43:02
enterprise, hoping that any political disruptions
43:05
are temporary. He made the point that the complex
43:09
structure supporting international science collaborations
43:11
take a long time to build but can be damaged
43:15
quite quickly, underlining the need for continued
43:17
vigilance and support from society and governments
43:20
everywhere. A thoughtful note to end on. Well,
43:23
this has been quite the journey from the unexpected
43:25
ways sleep affects our social lives and the intricate
43:28
mechanics of brain cleaning right through to
43:30
the future being forged by genomics, AI, synthetic
43:33
biology, and a deeper understanding of our own
43:36
immune systems at places like the EIT. It really
43:39
covers a vast landscape, doesn't it? And it makes
43:42
it clear these aren't just abstract topics for
43:44
scientists. They are fundamentally reshaping
43:46
our understanding of health. of aging and they
43:48
have profound implications for how we might live
43:50
and work in the future. The pace of change is
43:53
just incredible. It certainly gives you, the
43:56
listener, a lot to think about. Given these rapid
43:58
advances, this power to understand health at
44:01
such a deep level, how might our personal responsibility
44:04
for managing health change in the coming years?
44:07
And what does all this mean for you professionally,
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especially if you're in an industry touched by
44:12
biology, data, and technology? The ripple effects
44:15
are going to be immense. over the next decade.
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They really are. The future of health is being
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written right now in labs and data centers across
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the world. Absolutely. And if you found this
44:25
deep dive insightful and valuable, please do
44:27
consider taking a moment to rate and share the
44:29
show. It really helps others find it.