summarize this "It’s important, obviously, that our strategy be based on evidence.
Unfortunately, the pursuit of longevity is where the most powerful tool of
Medicine 2.0, the randomized clinical trial in humans, runs into a brick wall.
Randomized controlled trials are used to determine cause and effect in
relatively simple, short-term situations. It’s fairly easy, for example, to run a
study showing that sunscreen prevents sunburn. But such studies are of
limited use in our quest for longevity.
This is where my approach may ruffle some people’s feathers. The purists
of evidence-based medicine demand data from randomized controlled trials
(RCTs) before doing anything. Those trials are the gold standard of medical
evidence, yet they also reinforce some major limitations of Medicine 2.0,
beginning with its short time horizon. In general, the types of clinical
questions that are best resolved by RCTs are those involving simple
interventions such as a vaccine, or a medication to lower cholesterol. We give
this treatment over a relatively short period, from six months up to maybe five
or six years at the longest and look for its effect on a specified outcome. Does
the vaccine reduce the rate of serious illness and death? Does this drug lower
cholesterol and prevent cardiac death, or at least heart attacks, in highly
susceptible individuals?
This type of study is the foundation of evidence-based medicine. But if
our goal is longevity, the situation becomes more complicated. A one-year
clinical trial, or even a five-year study, will not tell us everything we need to
know about disease processes that take decades to unfold. There will never be
a clinical trial to guide a cardiovascular prevention strategy for a healthy
forty-year-old. It would simply take too long to do the study. Furthermore,
outside of pharmacology, the interventions are very complex, particularly if
they involve exercise, nutrition, and sleep. Studying longevity itself in this
way is almost impossible—unless we could somehow take a hundred
thousand babies, randomize them to four or five different interventions, and
follow them throughout their lifetimes. That would (hopefully) yield a rocksolid, evidence-based prescription for maximizing lifespan and healthspan.
But the obstacles to doing this are insurmountable, not least because it would
require a century to complete.
Option B is to look at the different types of data that we do have and then
develop a strategy that triangulates between them. This might not definitively
solve the problem, but it can at least point us in the right direction. Our
Option B strategy is based on combining insights from five different sources
of data that, viewed separately, probably aren’t strong enough to act on. When
taken together, however, they can provide a solid foundation for our tactics.
But our supporting framework must shift, from exclusively evidence-based to
evidence-informed, risk-adjusted precision medicine.
Our first source of data comes from studies of centenarians, people who
have lived to the age of one hundred and beyond, often in good health. These
are the extreme outliers, the tiny sliver of the population who have outlived
our usual life expectancy by two decades or more. By and large, they have
delayed or evaded the diseases that kill most of the rest of us, and many of
them have remained in fairly good shape. We would like to know how they
accomplished this feat. What do centenarians have in common? What genes
do they share that might give them an advantage over noncentenarians? What
explains their survival and their apparently slower rate of aging? And most of
all, what can the rest of us do to emulate their good fortune?
This evidence is made stronger by the fact that centenarians represent our
“species of interest”—that is, they are human. Unfortunately, centenarian
data are almost entirely observational rather than experimental, so we can’t
truly infer cause and effect. Centenarians’ life histories and habits tend to be
idiosyncratic, to say the least, and the fact that their numbers are relatively
small means that it can be difficult to draw firm conclusions at all. (We will
discuss centenarians in more detail in the next chapter.)
Next, we turn to lifespan data from animal “models,” such as laboratory
mice. It is obviously much easier, ethically and logistically, to test lifespanaltering tactics in mice, which typically only live about two or three years,
than in humans. We have a huge amount of data about how different sorts of
interventions, both dietary and in the form of exogenous molecules, affect
mouse lifespan. The limitation, obviously, is that mice are not human; many
drugs have succeeded in mice only to fail spectacularly in human studies.
There are other types of animal models, including a tiny species of nematode
worm called C. elegans that is often used in research, as well as fruit flies,
dogs, primates, and even lowly yeast cells. All of these have strengths and
weaknesses. My rule of thumb is that if a given intervention can be shown to
extend lifespan or healthspan in multiple species spanning a billion years of
evolution, for example, from worms to monkeys, then I am inclined to take it
seriously.
A third and important source of information to support our strategy
comes from human studies of the Horsemen: cardiovascular and
cerebrovascular disease, cancer, Alzheimer’s disease and related
neurodegenerative conditions, and type 2 diabetes and related metabolic
dysfunction. How do these diseases begin? How do they progress? What risk
factors help to cause them, or fuel them? What underlying factors do they
share? What are the cutting-edge treatment modalities for those with
“advanced” disease—and what do they tell us about developing a strategy for
prevention? We want to know each of these diseases inside and out,
understanding their weaknesses and vulnerabilities, just as Ali scrutinized
Foreman before their match.
Fourth, we consider the molecular and mechanistic insights derived from
the study of aging in both humans and animal models. We have learned an
enormous amount about the cellular changes that occur during the aging
process and in specific diseases. From this, we also have developed some
ideas for how to manipulate these changes, via exogenous molecules (e.g.,
drugs) or behavioral changes (e.g., exercise).
Our final source of insights is a very clever method of analysis called
Mendelian randomization, or MR for short. MR helps bridge the gap between
randomized controlled trials, which can establish causality, and pure
epidemiology, which often cannot. We’ll talk about epidemiology in more
detail later, but while it has proved useful in certain situations, such as
determining the link between smoking and lung cancer, it has been less useful
in more complex scenarios. Mendelian randomization helps tease out causal
relationships between modifiable risk factors (e.g., LDL cholesterol) and an
outcome of interest (e.g., cancer) in situations where actual randomized
experiments cannot easily be done. It accomplishes this by letting nature do
the randomization.
[*2] By considering the random variation in relevant genes
and comparing them against the observed results, it eliminates many of the
biases and confounders that limit the usefulness of pure epidemiology.
For example, some epidemiologic studies have suggested an inverse
relationship between LDL cholesterol and cancer risk. That is, people with
lower LDL cholesterol appear to have a higher risk of cancer. But is the
relationship causal? That’s a tricky but important question. If true, it would
imply that lowering LDL cholesterol, such as with statins, increases the risk
of cancer, which would obviously be bad news. Epidemiology does not tell us
the direction of causality, so we turn to MR.
With MR, we can look at genetic variations that result in low, medium,
and high levels of LDL cholesterol. These genes are randomly occurring, so
they serve as a proxy for a randomized natural experiment. By examining the
relationship between the resulting LDL cholesterol levels and cancer
incidence, we can answer the question without the usual confounders that
plague traditional epidemiology. And lo and behold, it turns out that low LDL
cholesterol does not cause cancer or increase its risk. If we use the same
technique to look at the effect of LDL levels on cardiovascular disease (our
dependent variable), it turns out that higher LDL cholesterol is causally linked
to the development of cardiovascular disease (as we’ll discuss in chapter 7).
An astute reader will notice that one concept has been conspicuously
absent from this chapter so far: absolute certainty. This took me a little while
to grasp when I transitioned from mathematics to medicine, but in biology we
can rarely “prove” anything definitively the way we can in mathematics.
Living systems are messy, and confounding, and complex, and our
understanding of even fairly simple things is constantly evolving. The best we
can hope for is reducing our uncertainty. A good experiment in biology only
increases or decreases our confidence in the probability that our hypothesis is
true or false. (Although we can feel fairly certain about some things, such as
the evidence supporting the idea that your doctor should wash her hands and
put on sterile gloves before operating on you.)
In the absence of multiple, repeated, decades-long randomized clinical
trials that might answer our questions with certainty, we are forced to think in
terms of probabilities and risk. In a sense it’s a bit like charting an investment
strategy: we are seeking the tactics that are likeliest, based on what we know
now, to deliver a better-than-average return on our capital, while operating
within our own individual tolerance for risk. On Wall Street, gaining an
advantage like this is called alpha, and we’re going to borrow the idea and
apply it to health. I propose that with some unorthodox but very reasonable
lifestyle changes, you can minimize the most serious threats to your lifespan
and healthspan and achieve your own measure of longevity alpha.
My aim here is to equip you with a set of tools that you can apply to your
own specific situation—whether you need to pay attention to your glucose
regulation, your weight, your physical condition, your Alzheimer’s disease
risk, and so on. Your personal tactics should never be static, but will evolve as
needed, as you journey through life with all its uncertainties—and as we learn
more about the science of aging and the workings of diseases like cancer. As
your own situation changes, your tactics can (and must) change, because as
the great philosopher Mike Tyson once put it, “Everyone has a plan until they
get punched in the mouth.”
Advice George Foreman could have used.
"