LDL-cholesterol, this is a metric that's on any basic lipid panel and i would say that you know
within that basic lipid panel this is probably the most important value, the one that you should
be paying the most attention to, but this is debated within the nutrition space, especially
on social media and for that reason i i want you to lay out for the audience what are the
lines of evidence we can look to to evaluate the role that ldl-c
is playing in the risk of cardiovascular disease, i mean is there enough there from
different areas for us to look at this and say high ldl-c is causal in cardiovascular disease?
right, so let's just clarify just in case somebody's not clear on what LDL cholesterol is
versus LDL because they're very different things and they're very commonly confused, even by
scientists and doctors, it's very natural to confuse these things, the scientists did not
help us with the terminology, all these things sound the same, it's all confusing. so LDL is the
lipoprotein, the carrier of different types of fat, so LDL stands for low density lipoprotein
and LDL cholesterol is the cholesterol traveling inside that lipoprotein, so the analogy we use
in the videos is a bus and the passenger in the bus so LDL cholesterol is a type of passenger
the analogy needs to be elaborated a little bit, you need to have a couple different buses
and then different passengers within each bus to accommodate for the different types of fat
but basically the bus would be the LDL for now and the passengers would be the LDL cholesterol
and then triglycerides would be a different type of passenger. so the question is what evidence
points to or away from LDL cholesterol being causal in cardiovascular disease
so there's there's three basic pillars, and we're simplifying this a bit, there's a bit more, there's
a lot of work in animals but we'll gloss over that for now. in humans let's say, there's three
main pillars. the first pillar is observational, it's called epidemiology, so you
look at a population, you're not intervening, you're not telling them to do anything,
you're just looking at their metrics, in this case their lipid metrics and then you're recording
their outcomes, who gets heart attacks, who gets strokes, who dies of cardiovascular disease
and then you look for statistical correlations. and when you do that you
see a strong direct correlation between LDL cholesterol levels and cardiovascular risk
people who have higher LDL cholesterol tend to have higher risk
but that's just observational, we know that can be confounded by many different factors so
you want to confirm that with what's called an interventional trial, something where you go in
and you tell people to do this and do that, and ideally you do that randomized.
so later in time came the randomized controlled trials with cholesterol lowering drugs, statins
and then later other other classes of drugs and those drugs powerfully lower LDL cholesterol
and they powerfully lower cardiovascular risk. so that would look to confirm the causality
then later came genetic experiments, mendelian randomization sometimes it's called
and what you do there is you look at people who have genetically
determined lower or higher LDL cholesterol levels and you see if their risk is higher or lower
and there what you see is as well that people who have genetically high LDL cholesterol tend to
have more heart disease and vice versa so it all seems to fit together nicely. here's the problem
these metrics all affect each other, they're not all independent, they're all all hermetic. when
you change the number of passengers, typically you're changing the number of buses
as well and the number of different types of passengers affects each other so
in that analogy if you think about a city trying to manage their transportation system
if you lower the number of buses driving around there's there's also going
to be less passengers traveling in those buses because now you have less buses going around
and vice versa, if you have suddenly less passengers, probably you're going to cut back
on the number of buses because they're just going around empty, you don't need them. the same thing
happens with lipids. so those three pillars that we touched on where we see that nice correlation
between LDL cholesterol and risk, which by the way is dose-dependent, it's really
compelling. the problem is you also see a similar direct correlation with the number of lipoproteins
so the number of LDLs and this thing called ApoB which i guess we should define that for clarity
apoB is one protein that tags a class of these lipoproteins. so there's different
families of lipoproteins but LDL belongs to the apoB-carrying lipoproteins and those are the
ones that are atherogenic. and that tag, that apoB lipoprotein is really useful because there's only
one copy of apoB, one protein per lipoprotein, so it's really like a license plate, it's
unique and it's one copy per per bus which is really useful because then we can just measure
the number of apoB proteins and we know how many of these particles are traveling around
so the number of apoB, which reflects the number of lipoproteins, also correlates really well with
risk in those three pillars together with all the cholesterol, so just by looking at that
you can't tell which one is guilty, which one is the culprit, because you got these
two people that are always in the crime scenes together
so what you need to do is you need to go look for the exceptions where you can uncouple them,
you need to look for crime scenes where only one of the guys shows up and the other has an alibi
so in the observational evidence you do what's called a discordance analysis. fancy name
but all it means is you look at the exception, people where LDL-cholesterol and apoB are
not traveling together, where maybe they have high cholesterol, high LDL-cholesterol but normal apoB
or vice versa and when you do that you find that the risk tracks with apoB
rather than LDL cholesterol, tracks with the number of lipoproteins rather than the
passengers, the content. and then you do similarly for every pillar. with the second pillar, with
the cholesterol-lowering drugs, most of those famous drugs, the statins, the PCSK9 inhibitors, the
ezetimibe, they do lower LDL cholesterol but they also lower the number of lipoproteins
and what they do in fact is they modulate the function of the LDL receptor. when you increase the
number of LDL receptors, these little antennae on the surface of hepatocytes, of liver cells
they capture more LDL particles out of circulation and thereby you're reducing the number of
particles in circulation and you're reducing the the total LDL cholesterol in circulation as well
but there are some drugs that play with the concentration or with the amount of LDL
cholesterol content but don't do that, but do not modulate particle number, and those drugs don't
do much to risk. so there you uncouple that as well, so you see that lowering LDL cholesterol
is only beneficial and is only sufficient to improve risk to the extent that you've done that
through modulating the number of lipoproteins, not by modulating cholesterol by itself
and then the last pillar, the genetics, same thing, the people that tend to have
genetically determined higher LDL cholesterol also tend to have genetically determined
larger number of LDL lipoproteins and so there you also look for that
exception and you do something called multivariate analysis, where you ask statistically which metric
tracks best with risk, and again you find when you adjust for lipoprotein
number, for particle number, the effect of LDL cholesterol goes away
so when you put everything together what this is telling us is that LDL cholesterol
correlates well with risk, it's a useful proxy, one analogy we used in one of the
videos is that it's the check engine light on your dashboard, so when that comes on generally it's a
good idea to take a look at the engine, it's not a good idea to drive around for a year or more
with the light on, it spells trouble, but it's not perfect, you can imagine that
the circuitry to the light could be defective and the engine could be about to blow up
but the light is not on, and vice versa, the light could come on because
the circuitry is not working perfectly but the engine is fine so LDL cholesterol is like that,
most of the time, in most people, if there's a frank elevation, apoB will also be high
so it's a useful metric, a useful proxy, but it can fail
and those are the discordant cases which are about 25 percent of people
give or take depending on the study so it's good to keep in mind always that the real lever that
you want to be pulling on at the end of the day of causality is apoB, it's number of lipoproteins