The Straight Dope on Cholesterol

eatingacademy.com

The straight dope on cholesterol – Part

I

I’ve been planning to write at length about this topic for a few

months, but I’ve been hesitant to do so for several reasons:

To discuss it properly requires great care and attention (mine

and yours, respectively).

1.

My own education on this topic only really began about 9

months ago, and I’m still learning from my mentors at a

geometric pace.

2.

This topic can’t be covered in one post, even a Peter-Attia-

who-can’t-seem-to-say-anything-under-2,000-word post.

3.

I feel a bit like an imposter writing about lipidology because my

mentors on this topic (below) have already addressed this topic

so well, I’m not sure I have anything to add.

4.

But here’s the thing. I am absolutely – perhaps pathologically –

obsessed with lipidology, the science and study of lipids.

Furthermore, I’m getting countless questions from you on this

topic. Hence, despite my reservations above, I’m going to give this

a shot.

A few thoughts before we begin.

I’m not even going to attempt to cover this topic entirely in this

post, so please hold off on asking questions beyond the

scope of this post.

1.

Please resist the urge to send me your cholesterol2.

The straight dope on cholesterol – Part I

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numbers. I get about 30 such requests per day, and I cannot

practice medicine over the internet. By all means, share your

story with me and others, but understand that I can’t really

comment other than to say what I pretty much say to everyone:

standard cholesterol testing (including VAP) is largely irrelevant

and you should have a lipoprotein analysis using NMR

spectroscopy (if you don’t know what I mean by this, that’s ok…

you will soon).

This topic bears an upsettingly parallel reality to that of nutrition

“science” in that virtually all health care providers have no

understanding of it and seem to only reiterate conventional

wisdom (e.g., “LDL is bad,” “HDL is good”). We’ll be blowing

the doors off this fallacious logic.

3.

By the end of this series, should you choose to internalize this

content (and pick up a few homework assignments along the way),

you will understand the field of lipidology and advanced lipid testing

better than 95% of physicians in the United States. I am not being

hyperbolic.

One last thing before jumping in: Everything I have learned and

continue to learn on this topic has been patiently taught to me by

the words and writings of my mentors on this subject: Dr. Tom

Dayspring, Dr. Tara Dall, Dr. Bill Cromwell, and Dr. James Otvos. I

am eternally in their debt and see it as my duty to pass this

information on to everyone interested.

Are you ready to start an exciting journey?

Concept #1 – What is cholesterol?

Cholesterol is a 27-carbon molecule shown in the figure below.

Each line in this figure represents a bond between two carbon

atoms. Sorry, I’ve got to get it out there. That’s it. Mystery over.

All this talk about “cholesterol” and most people don’t actually know

what it is. So there you have it. Cholesterol is “just” another


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organic molecule in our body.

I need to make one important distinction that will be very important

later. Cholesterol, a steroid alcohol, can be “free” or “unesterified”

(“UC” as we say, which stands for unesterified cholesterol)

which is its active form, or it can exist in its “esterified” or storage

form which we call a cholesterol ester (“CE”). The diagram above

shows a free (i.e., UC) molecule of cholesterol. An esterified

variant (i.e., CE) would have an “attachment” where the arrow is

pointing to the hydroxyl group on carbon #3, aptly named the

“esterification site.”

Since cholesterol can only be produced by organisms in the Animal

Kingdom it is termed a zoosterol. In a subsequent post I will write

about a cousin of cholesterol called phytosterol, but at this time I

think the introduction would only confuse matters. So, if you have a

question about phytosterols, please hang on.

Concept #2 – What is the relationship between the cholesterol

we eat and the cholesterol in our body?

We ingest (i.e., take in) cholesterol in many of the foods we eat and

our body produces (“synthesizes”) cholesterol de novo from various

precursors. About 25% of our daily “intake” of cholesterol –

roughly 300 to 500 mg — comes from what we eat (called


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exogenous cholesterol), and the remaining 75% of our “intake” of

cholesterol — roughly 800 to 1,200 mg – is made by our body

(called endogenous production). To put these amounts in context,

consider that total body stores of cholesterol are about 30 to 40 gm

(i.e., 30,000 to 40,000 mg) and most of this resides within our cell

membranes. Every cell in the body can produce cholesterol and

thus very few cells actually require a delivery of cholesterol.

Cholesterol is required by all cell membranes and to produce

steroid hormones and bile acids.

Of this “made” or “synthesized” cholesterol, our liver synthesizes

about 20% of it and the remaining 80% is synthesized by other cells

in our bodies. The synthesis of cholesterol is a complex four-step

process (with 37 individual steps) that I will not cover here (though I

will revisit), but I want to point out how tightly regulated this process

is, with multiple feedback loops. In other words, the body works

very hard (and very “smart”) to ensure cellular cholesterol levels are

within a pretty narrow band (the overall process is called

cholesterol homeostasis). Excess cellular cholesterol will crystalize

and cause cellular apoptosis (programmed cell death). Plasma

cholesterol levels (which is what clinicians measure with standard

cholesterol tests) often have little to do with cellular cholesterol,

especially artery cholesterol, which is what we really care about.

For example, when cholesterol intake is decreased, the body will

synthesize more cholesterol and/or absorb (i.e., recycle) more

cholesterol from our gut. The way our body absorbs cholesterol is

so amazing, so I want to spend a bit of time discussing it.

In medical school, whenever we had to study physiology or

pathology I always had a tendency to want to anthropomorphize

everything. It’s just how my brain works, I guess, and understanding

cholesterol absorption is a great example of this sort of thinking.

The figure below shows a cross-section of a cell in our small

intestine (i.e., our “gut”) called an enterocyte that governs how stuff

in our gut actually gets absorbed. The left side with the fuzzy

border is the side facing the “lumen” (the inside of the “tube” that

makes up our gut). You’ll notice two circles on that side of the cell,


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a blue one and a pink one.

[What follows is a bit more technical than I would have liked, but I

think it’s very important to understand how this process of

cholesterol absorption works. It’s certainly worth reading this a few

times to make sure it sinks in.]

The blue circle represents something called a Niemann-Pick

C1-like 1 protein (NPC1L1). It sits at the apical surface of

enterocytes and it promotes active influx (i.e., bringing in) of gut

luminal unesterified cholesterol (UC) as well as unesterified

phytosterols into the enterocyte. Think of this NPC1L1 as the

ticket-taker at the door of the bar (where the enterocyte is the

“bar”); he lets most cholesterol (“people”) in. However, NPC1L1

cannot distinguish between cholesterol (“good people”) and

phytosterol (“bad people” – I will discuss these guys later, so no

need to worry about it now) or even too much cholesterol (“too

many people”). [I can’t take any credit for this

anthropomorphization – this is how Tom Dayspring explained it

to me!]

The pink circle represents an adenosine triphosphate (ATP)-

binding cassette (ABC) transporters ABCG5 and ABCG8.

This complex promotes active efflux (i.e., kicking out) of

unesterified sterols (cholesterol and plant sterols – of which over

40 exist) from enterocytes back into the intestinal lumen for


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excretion. Think of ABCG5,G8 as the bouncer at the bar; he

gets rid of the really bad people (e.g., phytosterols as they serve

no purpose in humans) you don’t want in the bar who snuck

past the ticket-taker (NPC1L1). Of course in cases of

hyperabsorption (i.e., in cases where the gut absorbs too much

of a good thing) they can also efflux out un-needed cholesterol.

Along this analogy, once too many “good people” get in the bar,

fire laws are violated and some have to go. The enterocyte has

“sterol-excess sensors” (a nuclear transcription factor called

LXR) that do the monitoring and these sensors activate the

genes that regulate NPC1L1 and ABCG5,G8).

There is another nuance to this, which is where the CE versus UC

distinction comes in:

Only free or unesterified cholesterol (UC) can be absorbed

through gut enterocytes. In other words, cholesterol esters

(CE) cannot be absorbed because of the bulky side chains they

carry.

Much (> 50%) of the cholesterol we ingest from food is esterified

(CE), hence we don’t actually absorb much, if any,

exogenous cholesterol (i.e., cholesterol in food). CE can be

de-esterified by pancreatic lipases and esterolases – enzymes

that break off the side branches and render CE back to UC —

so some ingested CE can be converted to UC.

Furthermore, most of the unesterified cholesterol (UC) in our gut

(on the order of about 85%) is actually of endogenous origin

(meaning it was synthesized in bodily cells and returned to the

liver), which ends up in the gut via biliary secretion and

ultimately gets re-absorbed by the gut enterocyte. The liver is

only able to efflux (send out via bile into the gut) UC, but not CE,

from hepatocytes (liver cells) to the biliary system. Liver CE

cannot be excreted into bile. So, if the liver is going to excrete

CE into bile and ultimately the gut, it needs to de-esterify it using

enzymes called cholesterol esterolases which can convert liver


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CE to UC.

Also realize that the number one way for the liver to rid itself of

cholesterol is to convert the cholesterol into a bile acid, efflux

that to the bile (via a transporter called ABCB11) and excrete

the bile acids in the stool (typically most bile acids are

reabsorbed at the ileum).

Concept #3 – Is cholesterol bad?

One of the biggest misconceptions out there (maybe second only to

the idea that eating fat makes you fat) is that cholesterol is “bad.”

This could not be further from the truth. Cholesterol is very

good! In fact, there are (fortunately rare) genetic disorders in which

people cannot properly synthesize cholesterol. Once such disease

is Smith-Lemli-Opitz syndrome (also called “SLOS,” or

7-dehydrocholesterol reductase deficiency) which is a metabolic

and congenital disorder leading to a number of problems including

autism, mental retardation, lack of muscle, and many others.

Cholesterol is absolutely vital for our existence. Let me repeat:

Cholesterol is absolutely vital for our existence. Every cell in

our body is surrounded by a membrane. These membranes are

largely responsible for fluidity and permeability, which essentially

control how a cell moves, how it interacts with other cells, and how

it transports “important” things in and out. Cholesterol is one of the

main building blocks used to make cell membranes (in particular,

the ever-important “lipid bilayer” of the cell membrane).

Beyond cholesterol’s role in allowing cells to even exist, it also

serves an important role in the synthesis of vitamins and steroid

hormones, including sex hormones and bile acids. Make sure you

take a look at the picture of steroid hormones synthesis and

compare it to that of cholesterol (above). If this comparison doesn’t

convince you of the vital importance of cholesterol, nothing I say

will.

One of the unfortunate results of the eternal need to simplify


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everything is that we (i.e., the medical establishment) have done

the public a disservice by failing to communicate that there is no

such thing as “bad” cholesterol or “good” cholesterol. All

cholesterol is good!

The only “bad” outcome is when cholesterol ends up inside of the

wall of an artery, most famously the inside of a coronary artery or

a carotid artery, AND leads to an inflammatory cascade which

results in the obstruction of that artery (make sure you check out

the pictures in the links, above). When one measures cholesterol in

the blood – we really do not know the final destination of those

cholesterol molecules!

And that’s where we’ll pick it up next time – how does “good”

cholesterol end up in places it doesn’t belong and cause “bad”

problems? If anyone is looking for a little extra understanding on

this topic, please, please, please check out my absolute favorite

reference for all of my cholesterol needs, LecturePad. It’s designed

primarily for physicians, but I suspect many of you out there will find

it helpful, if not now, certainly once we’re done with this series.

To summarize this somewhat complex issue

Cholesterol is “just” another fancy organic molecule in our body,

but with an interesting distinction: we eat it, we make it, we

store it, and we excrete it – all in different amounts.

1.

The pool of cholesterol in our body is essential for life. No

cholesterol = no life.

2.

Cholesterol exists in 2 forms – UC and CE – and the form

determines if we can absorb it or not, or store it or not

(among other things).

3.

Most of the cholesterol we eat is not absorbed and is excreted

by our gut (i.e., leaves our body in stool). The reason is it not

only has to be de-esterified, but it competes for absorption with

the vastly larger amounts of UC supplied by the biliary route.

4.


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Re-absorption of the cholesterol we synthesize in our body is

the dominant source of the cholesterol in our body. That is,

most of the cholesterol in our body was made by our body.

5.

The process of regulating cholesterol is very complex and

multifaceted with multiple layers of control. I’ve only

touched on the absorption side, but the synthesis side is also

complex and highly regulated. You will discover that synthesis

and absorption are very interrelated.

6.

Eating cholesterol has very little impact on the cholesterol

levels in your body. This is a fact, not my opinion. Anyone

who tells you different is, at best, ignorant of this topic. At worst,

they are a deliberate charlatan. Years ago the Canadian

Guidelines removed the limitation of dietary cholesterol. The

rest of the world, especially the United States, needs to catch

up.

7.

(To Part II »)

25

APR


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eatingacademy.com


II

In this post I’m going to tackle the next set of logical (at least in my

mind) questions to follow up on last week’s post, Part I in this

series.

Last week we addressed these 3 concepts:

#1 — What is cholesterol?

#2 — What is the relationship between the cholesterol we eat

and the cholesterol in our body?

#3 — Is cholesterol bad?

This week we’ll address the following concept:

#4 — How does cholesterol move around our body?

I want to thank folks for doing their best to resist the following two

urges:

Please resist asking me questions beyond the scope of this

post. If it’s not in here, it will probably be in a subsequent post

in this series.

1.

Please resist sending me your cholesterol numbers. Share

your story with me and others, but understand that I can’t really

comment other than to say what I pretty much say to everyone:

standard cholesterol testing (including VAP) is of limited value

and you should have a lipoprotein analysis using NMR

spectroscopy (if you don’t know what I mean by this, that’s ok…

2.

The straight dope on cholesterol – Part II

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you will soon). I can’t practice medicine over the internet.

Remember last week’s take away messages:

Cholesterol is “just” another fancy organic molecule in our body



1.



2.

Cholesterol exists in 2 forms – unesterified or “free” (UC) and

esterified (CE) – and the form determines if we can absorb it

or not, or store it or not (among other things).

3.

Much of the cholesterol we eat is in the form of CE. It is not

absorbed and is excreted by our gut (i.e., leaves our body in

stool). The reason this occurs is that CE not only has to be

de-esterified, but it competes for absorption with the vastly

larger amounts of UC supplied by the biliary route.

4.

Re-absorption of the cholesterol we synthesize in our body

(i.e., endogenous produced cholesterol) is the dominant source

of the cholesterol in our body. That is, most of the cholesterol

in our body was made by our body.

5.






6.







7.


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up. To see an important reference on this topic, please look

here.

Concept #4 – How does cholesterol move around our body?

To understand how cholesterol travels around our body requires

some understanding of the distinction between what is hydrophobic

and hydrophilic. A molecule is said to be hydrophobic (also called

nonpolar) if it repels water, while a molecule is said to be

hydrophilic (also called polar) if it attracts water. I could spend a lot

of time getting in to the nuances of these properties, but I think it’s

best to just focus on the major issues. Think of your veins, arteries,

and capillaries as the “waterways” or rivers of your body.

BONUS concept: Another important concept is that cell

membranes are lipid bilayers (which are hydrophobic) as I wrote

about last week. Hence, a hydrophilic substance cannot pass

through lipid membranes. Substances that can pass through lipid

membranes are said to be lipophilic. A substance that has both

polar (hydrophilic) and nonpolar (hydrophobic) properties is called

amphipathic. The fact that unesterified cholesterol (UC) is an

amphipathic molecule is a crucial property for its location in cell

membranes. CE in which the –OH group has been replaced by a

long chain fatty acid is a very nonpolar or hydrophobic molecule.

If a molecule needs to travel from your gastrointestinal tract (A) to,

say, a cell in your quadriceps muscle (B) it needs to get on the river

and travel from point A to point B. Because blood is effectively

water, (the “water” part of blood is called plasma, an aqueous

solution with a bunch of “stuff” in it (e.g., red blood cells, white blood

cells, other proteins, ions) there are two ways to move down the

river – swim or hitch a ride on a boat.

If a molecule is hydrophilic, it can be transported in our bloodstream

without any assistance – sort of like swimming freely in the river –

because it is not repelled by water. Conversely, if a molecule is

hydrophobic, it must have a “transporter” to move about the river

because the plasma (water) wants to repel it. I know this seems


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like a strange concept, but if you think about it, you’ve already seen

great examples in your day-to-day life:

Sugar and salt will easily dissolve in water. They are, therefore,

hydrophilic. Oil does not dissolve in water. It is, therefore,

hydrophobic.

By extension, a molecule of glucose (sugar) or sodium and chloride

ions (salt), because of their chemical properties which I won’t detail

here, will travel through plasma without assistance. A lipid will not.

All of this is a long way of saying that sterol lipids (of which

cholesterol ester is the predominant form in plasma), because they

are hydrophobic, need to be carried around our bloodstream.

They can’t move from one place to the next without a protein

transporting molecule.

In other words, cholesterol doesn’t exist in our bloodstream

without something to carry it from point A to point B.

So what are these “transporting molecules” called?

The proteins that traffic collections of lipids are called apoproteins.

Once bound to lipids they are called apolipoproteins, and the

protein wrapped “vehicle” that transports the lipids are called

lipoproteins. Many of you have probably heard this term before,

but I’d like to ensure everyone really understands their important

features. A crucial concept is that, for the most part, lipids go

nowhere in the human body unless they are a passenger inside a

protein wrapped vehicle called a lipoprotein. As their name

suggests lipoproteins are part lipid and part protein. They are

mostly spherical structures which are held together by a

phospholipid membrane (which, of course, contains free

cholesterol). The figure below shows a schematic of a lipoprotein.


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You will also notice variable-sized proteins on the surface of the

lipid membrane that holds the structure together. The most

important of these proteins are called apolipoproteins, as I alluded

to above. The apolipoproteins on the surface of lipoprotein

molecules serve several purposes including:

Assisting in the structural integrity and solubility of the

lipoprotein;

1.

Serving as co-factors in enzymatic reactions;2.

Acting as ligands (i.e., structures that help with binding) for

situations when the lipoprotein needs to interact with a receptor

on a cell.

3.

Apolipoproteins come in different shapes and sizes which

determine their “class.” Without getting into the details of protein

structure and folding, let me focus on two important classes:

apolipoprotein A-I and apolipoprotein B. Apoprotein A-I

(abbreviated apoA-I), which is composed of alpha-helicies, form

lipoproteins which are higher in density. (The “A” class designation

stems from the fact that apoA’s migrate with alpha-proteins in an

electrophoretic field). Conversely, apoprotein B (abbreviated

apoB), which is predominantly composed of beta-pleated-sheets,

form lipoproteins which are lower in density. (The “B” class

designation stems from the fact that apoB’s migrate with

beta-proteins in an electrophoretic field.)


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Virtually all apoB in our body is found on low-density lipoprotein –

LDL, while most apoA-I in our body is found on high-density

lipoprotein – HDL. Going one step further, the main structural

apoprotein on the LDL is called apoB100 (though we often shorten

this to just “apoB”), and there is only one apoB molecule per

particle. It’s starting to come together now with “high” and “low”

density lipoproteins, isn’t it?

But there’s actually more to it.

Everything I just described above deals with the structure and

surface of the lipoprotein molecule – sort of the like the hull of the

ship. But, what about the cargo? Remember what started this

discussion. It’s all about transporting cholesterol (and lipids) which

can’t freely travel in the bloodstream. The “cargo” of these ships,

what they actually carry both on their surface [molecules of

cholesterol and phospholipids] and in their core [cholesteryl esters

(CE) and triglycerides (TG, or triacylglycerols)] is what we’ll now

turn our attention to.

The ratio of lipid-to-protein in the lipoprotein structure determines its

density – which is defined as mass per unit volume. Something

that has a high density is heavier for a given volume than

something with a low density. The table in this link (which I’ve also

included below) shows the relative density of the five main classes

of lipoproteins (from most dense to least dense) as they were

originally discovered using ultracentrifugation: high density

lipoprotein (HDL), low density lipoprotein (LDL), intermediate

density lipoprotein (IDL), very low density lipoprotein (VLDL), and

chylomicron.

Note the very subtle difference in density between the most and

least dense lipoprotein – about 10 or 15%. Conversely, note the

very large difference in diameter between each lipoprotein – as

much as 2 orders of magnitude. Later in this series, when we start

to talk about the volume of a lipoprotein particle, this difference will

be amplified 1,000 times (because volume is calculated to the third


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power of diameter).

Below is a figure I’ve borrowed graciously from one of Tom

Dayspring’s remarkable lectures which gives you a sense of the

diversity of each of these classes of lipoproteins as well as the

subclasses within each class. If this topic wasn’t confusing enough,

there are actually multiple nomenclatures for the HDL subparticles.

Originally, nomenclature was based on their buoyancy. Today

nomenclature is based on the following methods, dependent on the

technology used to measure them:

Particle separation using gradient gel electrophoretic

fractionation (deployed by Berkeley Heart Lab).

1.

Magnetic resonance assaying of lipid terminal methyl groups,

called Nuclear Magnetic Resonance, or NMR (deployed by

Liposcience).

2.

Two-dimensional gradient gel electrophoresis and apoA-I

staining (deployed by Boston Heart Lab).

3.

We’ll cover this later, but I want to point this out now to avoid

(unnecessary) confusion in the figure below, which uses the first

two of these.


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A few things probably jump out as you look at this figure:

ApoA-I lipoproteins (i.e., HDLs) are tiny compared to ApoB

lipoproteins (i.e., VLDL’s, IDL’s, and LDL’s) [this figure is not

actually to scale – the “real” difference is even more

pronounced.]

1.

As a general rule (with pathological exceptions), as particles

move from being larger to smaller, the relative content of

triglycerides (TG) goes down while the relative content of

protein goes up, hence the density change.

2.

Actual cholesterol mass is greatest in the LDL particle.3.

Each specific lipoprotein has a different core make up –

meaning the variable ratio of TG to cholesterol ester changes. A

particle of VLDL has 5 times more TG than CE whereas a

particle of LDL typically has 4 or more times more CE than TG

(i.e., ratio > 4:1), and an HDL has 90-95% CE and < 10% TG in

its core.

4.

The TG trafficking lipoproteins are chylomicrons from the

intestine and VLDLs from the liver.

5.

Deep breath. Anyone left wondering why this topic is NOT

covered in medical school? I think I can conservatively say

95% to 99% of physicians do not know what you have just

learned — not because they aren’t “smart,” but because this

topic is simply not covered in medical school, and the pace at

which the field is developing is too great for most doctors to

keep up with.


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Why is cholesterol concentration increasing and triglyceride

concentration decreasing as lipoproteins progress from larger

to smaller?

The liver exports VLDL which, after chylomicrons (used to get

triglycerides to muscles and adipocytes and cholesterol from the

gut to the liver) are the largest of the lipoprotein particles. VLDL

particles “give up” some of their triglycerides in the form of free fatty

acids and shrink as they also release surface phospholipids. Once

a certain size or buoyancy is reached it is called a “VLDL remnant”

and ultimately an IDL. Some (though not all) of the IDL particles

undergo continued lipolysis to reduce in size and become the

famous (or infamous) LDL particles. However, most of the IDL

particles are actually cleared by liver LDL receptors and do not

become LDL particles.

All along this process, the larger particles “shed” phospholipids and

fatty acids and thus become cholesterol-rich. It is the LDL particle

that is the ultimate delivery vehicle of cholesterol back to the liver in

a process now called “indirect reverse cholesterol transport.”

However, under certain circumstances the LDL will penetrate and

deliver its cholesterol load to the artery walls. THIS IS EXACTLY

WHAT WE DON’T WANT TO HAPPEN. (Sorry for the bold ALL

CAPS – I know some of you may have fallen asleep by now, but I

didn’t want anyone missing the punch line.) Because almost all

cells in the body de-novo synthesize all the cholesterol they need,

LDLs are not actually needed to deliver cholesterol to most cells.

The final important point I want to make about cholesterol transport

is that it goes BOTH ways. Lipoprotein particles carry triglycerides

and cholesterol from the gut and liver to the periphery (muscles

and adipocytes – fat cells) for energy, cellular maintenance, and

other functions like steroid creation (called “steroidogenic” purposes

– remember the figure last week showing a cholesterol molecule

and steroid molecule). Historically this process of returning

cholesterol to the liver was thought to be performed only by HDL’s

and has been termed reverse cholesterol transport, or RCT (you’ll


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need to subscribe — for free — to lecturepad.org to access this last

link, which is well worth the time).

This RCT concept is outdated as we now know LDL’s actually

perform the majority of RCT. While the HDL particle is a crucial part

of the immensely complex RCT pathway, a not-so-well-known fact

is that apoB lipoproteins (i.e., LDL’s and their brethren) carry most

of the cholesterol back to the liver. In other words, the “bad”

lipoprotein, LDL, does more of the cleaning up (i.e., taking

cholesterol back to the liver) than the “good” lipoprotein, HDL!

The problem, as we’ll discuss subsequently, is that LDL’s actually

do the bad stuff, too – they dump cholesterol into artery walls.

Let’s put this all together to summarize how cholesterol gets

around our body

Cholesterol and triglycerides are not soluble in plasma (i.e.,

they can’t dissolve in water) and are therefore said to be

hydrophobic.

1.

To be carried anywhere in our body, say from your liver to your

coronary artery, they need to be carried by a special protein-

wrapped transport vessel called a lipoprotein.

2.


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As these “ships” called lipoproteins leave the liver they undergo

a process of maturation where they shed much of their

triglyceride “cargo” in the form of free fatty acid, and doing so

makes them smaller and richer in cholesterol.

3.

Special proteins, apoproteins, play an important role in moving

lipoproteins around the body and facilitating their interactions

with other cells. The most important of these are the apoB

class, residing on VLDL, IDL, and LDL particles, and the apoA-I

class, residing on the HDL particles.

4.

Cholesterol transport occurs in both directions, towards the

periphery and back to the liver.

5.

The major function of the apoB-containing particles is to traffic

energy (triglycerides) to muscles and phospholipids to all

cells. Their cholesterol is trafficked back to the liver. The apoA-I

containing particles traffic cholesterol to steroidogenic tissues,

adipocytes (a storage organ for cholesterol ester) and

ultimately back to the liver, gut, or steroidogenic tissue.

6.

All lipoproteins are part of the human lipid transportation system

and work harmoniously together to efficiently traffic lipids. As

you are probably starting to appreciate, the trafficking pattern is

highly complex and the lipoproteins constantly exchange their

core and surface lipids. This is a big reason why measuring

how much cholesterol is within various lipoprotein species

will in many circumstances be so misleading, as we’ll

discuss subsequently in this series.

7.

This was a bit of a tough one, so let’s stop there. Next week we’ll

discuss how to actually measure cholesterol levels. In other words,

if you’re looking at the river, with all its floating ships carrying their

cargo, how do we measure the amount of cargo actually contained

within the ships? Furthermore, is this the most important thing to

be measuring? Ironically, it’s easier to measure the cargo in the

ships, but more important to know the number of ships in the river.


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But now I’m getting ahead of myself.

P.S. Happy Birthday Dad (now I’ll know if you’re reading my

blog!)

(To Part III »)

3

MAY


12 of 12

eatingacademy.com


III

Previously, in Part I and Part II of this series, we addressed 4

concepts:


#2 — What is the relationship between the cholesterol we eat and

the cholesterol in our body?



This week we’ll address the following concept:

#5 – How do we measure cholesterol?

Quick refresher on take-away points from previous posts,

should you need it




1.



2.




3.

The straight dope on cholesterol – Part III

1 of 15






4.




in our body is made by our body.

5.




complex and highly regulated. You will see that synthesis and

absorption are very interrelated.

6.








here.

7.



hydrophobic.

8.




9.




10.


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class, residing for the most part on the HDL particles.

11.

Cholesterol transport in plasma occurs in both directions,

from the liver and small intestine towards the periphery and

back to the liver and small intestine (the “gut”).

12.







13.





core and surface lipids. This is a big reason why measuring

how much cholesterol is within various lipoprotein species

will in many circumstances be so misleading, as we’ll

discuss subsequently in this series.

14.

Concept #5 – How do we measure cholesterol?

All this talk about cholesterol probably has some of you wondering

how one actually measures the stuff. Much of the raw content I’m

going to present here is actually material I’ve had to learn recently.

One of the best resources I’ve found on this topic is the text book

Contemporary Cardiology: Therapeutic Lipidology, in particular,

chapter 14 by Tom Dayspring and chapter 15 by Bill Cromwell and

Jim Otvos. Anyone aspiring to be a lipid savant like these three

pioneers probably ought to get a copy. The other book that tells


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this story well is The Cholesterol Wars: The Skeptics versus the

Preponderance of Evidence. For most folks, however, I’m hoping

this series is sufficient and I’ll do my best to get the important points

across.

As far back as the 1940’s scientists understood that cholesterol and

lipids could not simply travel freely within the bloodstream without

something to carry them and obscure their hydrophobicity, but it

certainly wasn’t clear what these carriers looked like.

The initial breakthrough came during the Second World War when

two researchers, E.J. Cohn and J.L. Oncley at Harvard developed a

complex and elaborate technique to fractionate (i.e., separate)

human serum (serum is blood, less the cells and clotting factors)

into two “classes” of lipoproteins: those with alpha mobility and

those with beta mobility. [“Alpha” versus “beta” mobility describes a

pattern of movement seen by different particles, relative to fluid,

under a uniform electric field, which is the essence of

electrophoresis.]

You’ll recall that LDL particles are also called “beta” particles and

HDL particles are also called “alpha” particles. Now you see why.

This work set the stage for subsequent work, by a physicist named

John Gofman, using the techniques of preparative and analytic

ultracentrifugation to fully classify the major classes of human

lipoproteins. The table below summarizes what was gleaned by

these experiments.


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Cool, huh? Well, sort of. While this was an enormous

breakthrough scientifically, it didn’t really have an inexpensive and

quick test that could be used clinically the way, say, one could

measure glucose levels or hemoglobin levels in patients routinely.

What became crucial with Gofman’s discovery is that lipoproteins

were now a recognized entity and they got their names according to

their buoyancy: very low density, intermediate density, low density

and high density.

There is more interesting history to this tale, but let’s fast-forward to

where we are today. When you go to your doctor to have your

cholesterol levels checked, what do they actually do?

Let’s start at the finish line. What do they report? The figure below

is a representative result. It reports serum cholesterol (in total),

serum triglycerides, HDL cholesterol (i.e., HDL-C), LDL cholesterol

(i.e., LDL-C) and sometimes non-HDL-C (i.e., LDL-C + VLDL-C).

But where do these numbers come from?

Blood is drawn into a tube called a serum separator tube (SST) and

immediately spun in centrifuge to separate the blood from “whole

blood” into serum (normally clear yellow, top) and blood cells (dark

red, bottom). A gel film, from the SST, separates the serum and

blood cells, as shown below. The tube is kept cool and sent from

the phlebotomy lab to the processing lab.


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As early as the 1950’s scientists figured out clever chemical tricks

to directly measure the content of total cholesterol in the serum.

The chemical details probably are not interesting to non-chemists,

but I was able to find a great paper from 1961 that details the

methodology. The point is this: initially it was only possible to

measure the total content of cholesterol (TC), or concentration to

be technically correct, in plasma. By that I mean it is the total mass

(weight of all the cholesterol molecules) of cholesterol trafficked

within all of the lipoprotein species that exist in a specified unit of

volume: in the United States, we measure this in milligram of

cholesterol per deciliter of plasma abbreviated as mg/dL, or in the

rest of the world as mmol/Liter or mmol/L. Why? Think back to our

analogy from last week:

Cholesterol is a passenger on a ship — the “ship,” of course, being

a lipoprotein particle. The early methods of measuring cholesterol

had to break apart the hull of the ship to quantify the cargo. The

assays to do so, like the one described above, were pretty harsh. If

you had a bunch of LDL ships, HDL ships, VLDL ships, and IDL

ships, these assays ripped them all apart and told you the sum total

of the cargo. Obviously this was a great breakthrough in the day,

but it was limited. From this assay, one could conclude, for

example, that a person had 200 mg/dL of cholesterol hiding out in

all their lipoprotein particles.

Good to know, but what next? It turns out there were two other

important factors that could be measured directly in blood:


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triglycerides and the cholesterol content within the HDL

particle, HDL-C. Early on laboratories could easily separate

apoA-I-containing particles (i.e., HDL) from the apoB-containing

particles (i.e., VLDLs, IDLs and LDLs), but they could not easily and

economically separate the various apoB-containing particles from

one another. A full description of these methods is not necessary

to appreciate this discussion, but for those interested,

methodologies can be found here (TG) and here (HDL-C).

Important digression for context

What becomes critical to understand for our subsequent

discussions is that the apoB particles have the potential to deliver

cholesterol into an artery wall (the problem we’re trying to avoid),

and 90-95% of the apoB particles are LDL particles. Hence, it is

LDL particle number (LDL-P or apoB) that drives the

particles into the artery wall. Thus, physicians need to be able

to quantify the number of LDL particles present in a given

individual. For decades there was no way of doing that. Then

LDL-C (read on) became available and it served as a way (not

entirely accurate, but nonetheless a way) of quantitating LDL

particles.

Back to the story

How can one figure out the concentration of cholesterol in the LDL

particle? As you may recall from last week, LDL is the “ship” that

carries the most cholesterol cargo. More importantly, as I

mentioned above, it is also the key ship that traffics cholesterol

directly into the artery wall. Thus, there has always been an

enormous interest in knowing how much cholesterol is trafficked

within LDL particles.

For a long time it was not possible to directly measure LDL-C, the

cholesterol content of an LDL particle. However, we did know the

following had to be true:


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TC = LDL-C + HDL-C + VLDL-C + IDL-C + chylomicron-C

+ remnant-C + Lp(a)-C

where X-C denotes the cholesterol content of a respective

cholesterol-carrying particle. There are 2 particles in the equation

above that I didn’t specifically mention last week, the remnant

particle and the Lp(a) particle (pronounced “EL – pee – little – a,”

which sounds less silly than, “Lip-a”). Lp(a) is an LDL-like particle

but with a special apoprotein attached to it, aptly called

apoprotein(a), which is actually “attached” to the apoB molecule of

a standard LDL particle. Think of Lp(a) as a “special” kind of LDL

particle. As we’ll learn later in this series, Lp(a) particles are bad

dudes when it comes to atherosclerosis.

“Remnants” are nearly-empty-of-triglyceride particles of

chylomicrons and VLDL. In essence they are larger TG-rich

particles that have lost a lot of their TG core content as well as

surface phospholipids and are thus smaller than, or remnants of,

their “parent particles.” Hence,they are cholesterol-rich particles.

Under fasting conditions, in a not-too-terribly-insulin-resistant

person, IDL-C, chylomicron-C, and remnant-C are negligible.

Furthermore, in most people Lp(a)-C does not exist or is not very

high.

So we’re left with this simplification:

TC ~ LDL-C + HDL-C + VLDL-C

which is clearly an improvement in convenience over the first

equation. But what to do about that pesky VLDL-C?

There are a number of variations, but essentially a breakthrough

(mid 1970s) formula, called the Friedewald Formula, estimates

VLDL-C as one-fifth the concentration of serum triglycerides (some

variants use 0.16 instead of one-fifth, or 0.20). This assumes all

TG are trafficked in one’s VLDL particles and that a normally

composed VLDL contains five times more TG than cholesterol.


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Rearranging the above simplified formula we have:

LDL-C ~ TC – HDL-C – TG/5

Let’s plug in the numbers from the above figure, as an example.

TC = 234 mg/dL; HDL-C = 48 mg/dL, and TG = 117 mg/dL. Hence,

LDL-C is approximately 234 – 48 – 117/5 = 163 mg/dL.

Kind of a long run for a short slide, huh? Perhaps, but it is important

to understand that when you go to your doctor and get a

“cholesterol test,” odds are this is exactly what you’re getting.

Therefore LDL-C can be estimated knowing just TC, HDL-C, and

TG, assuming LDL-C matters (hint: it doesn’t matter much in

many folks).

Furthermore, what if the LDL particle is cholesterol-depleted

instead of its normal state of being cholesterol-enriched?

Unfortunately, especially in an insulin resistant population (i.e., the

United States), both TG content within lipoproteins and the

exchange of TG for cholesterol esters between particles is very

common, and using this formula can significantly underestimate

LDL-C. Worse yet, LDL-C becomes less meaningful in predicting

risk, as I will address next week.

What about direct measurement of LDL-C?

To chronicle the entire history of direct LDL-C measurement is

beyond the scope of this post. Many companies have developed

proprietary techniques to measure LDL-C directly, along with apoB,

and ultimately LDL-P. I’ll address two “major players” here:

Atherotech and LipoScience.

Atherotech developed an assay, called a VAP panel (VAP stands

for Vertical Auto Profile) to do everything described above, but also

to directly measure the amount of cholesterol contained within the

LDL particle. Furthermore, they have developed assays to directly

measure the cholesterol in IDL particles, VLDL particles, and even


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Lp(a) particles. Below is a snapshot of how VAP reporting looks.

A couple of things are worth mentioning:

Subparticle cholesterol content information is also generated,

including 2 different classes of HDL particles (HDL-2, HDL-3)

and 4 different classes of LDL particles (LDL-1, LDL-2, LDL-3,

LDL-4).

1.

LDL particles, based on the subparticle information, are

classified as “pattern A,” “pattern B,” or “pattern A/B.” Pattern A

implies more large, buoyant LDL particles, while pattern B

implies more small, dense LDL particles.

2.

Remember, though, while cholesterol mass concentration numbers

may correlate with the number of particles, they often do not. They

only convey the mass concentration of cholesterol molecules within

all of the particle subtypes per unit of volume. VAP tests do not

report the number of LDL or HDL particles, but they do attempt to

estimate atherogenic particle number (apoB) using a proprietary

formula based on subparticle cholesterol concentration and particle

sizes. I should point out that the formula, to my knowledge, has not

been validated in any study and not published in a peer reviewed

journal.


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A high estimate of apoB100 (i.e., what the VAP reports) is said to

correlate with the actual measurement of apoB. Since apoB is

found on each LDL particle, this serves as a proxy of LDL-P. The

American Diabetic Associate and the American College of

Cardiology Consensus Statement on Lipoproteins and the new

National Lipid Association biomarker paper stipulates that apoB

must be done using a protein immunoassay, not an estimate, such

as that of VAP.

But how can one actually count the number of LDL particles

and HDL particles?

There are several methods of doing this, but only one company,

LipoScience, has the FDA approved technology to do so using

nuclear magnetic resonance spectroscopy, or NMR for short. The

other available methodologies are ion mobility transfer and

ultracentrifugation (by Quest) and separation of LDL particles with

particle staining (by Spectracell). Virtually all guidelines (e.g., ADA,

ACC, AACC and NLA) only advise LDL-P via NMR at this time.

NMR, which is the basis for not only how to count lipoprotein

particles, but also diagnostic tests such as MRI scans, is really one

of my favorite technical topics. In residency I wrote a surgical

handbook and on page145-146, if you’re interested, you can read a

brief description of how MRI technology works, which will explain

how NMR technology can actually count lipoprotein particles.

As an aside, and just to give you an idea of what a great sport my

wife is, I wrote this surgical handbook over the course of a year

while in residency. To do so, I had to read approximately 8,000

pages of surgical textbooks and try to distill them down to just this

160 page summary. Doing so required reading about 22 pages

every day while working about 110 hours per week, typical of a

surgical residency “back in the day.” Besides exercising, I spent

every single moment of my “free” time reading for and writing this

handbook. Finally, a few months into it, my wife asked, “Why the

hell are you doing this? You never watch TV, you never go out,


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you never do anything else!” I responded that it was the best way

I could learn this material, but also, that I wanted to have a legacy

when I left residency. Half joking, I asked her, “What’s your

legacy?” Blank stare. A few months later, for Valentine’s Day, she

gave me this t-shirt. I think it’s safe to say not a single person has

read this handbook. So much for my legacy…

A brief explanation of how NMR works to count (and measure)

particles can also be found here.

Below is a snapshot of how NMR reporting looks. This particular

report is from Health Diagnostics Laboratory (HDL), Inc.

LipoScience performs the actual NMR test, but HDL, Inc. runs a

number of complimentary biomarkers I will discuss in subsequent

posts. I now use the HDL, Inc. test exclusively for reasons I will

explain later.


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In addition to counting the actual total number of LDL particles

(LDL-P) and HDL particles (HDL-P) per liter, HDL, Inc. (not

LipoScience) directly measures apoB and apoA-I. Furthermore, the

size of each particle is measured using NMR in nanometers (to give

you a sense of how small these things are, and why we need to use

nanometers to measure them, about 1.3 million LDL particles

stacked side-by-side would measure only one inch).

The final point I’ll make about the value of NMR reported

subparticle sizes and diameters is particularly telling when it comes

to insulin resistance. In the panel below, you can see that this

person has small VLDL particles, small HDL particles, and LDL

particles. Why is this interesting? The presence of increased large

VLDL-P, large VLDL size, increased small LDL- P, small LDL size,

reduced large HDL-P, small HDL size are early markers for insulin

resistance, and such findings may actually precede more

conventional signs of insulin resistance (insulin levels, glycemic

abnormalities) by several years. In other words, the number and

size of the lipoprotein particles is perhaps the earliest warning

sign for insulin resistance.


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In summary

The measurement of cholesterol has undergone a dramatic

evolution over the past 70 years with technology at the heart of

the advance.

1.

Currently, most people in the United States (and the world for

that matter) undergo a “standard” lipid panel which only

directly measures TC, TG, and HDL-C. LDL-C can be

measured directly, but is most often estimated.

2.

More advanced cholesterol measuring tests do exist to directly

measure LDL-C (though none are standardized), along with the

cholesterol content of other lipoproteins (e.g., VLDL, IDL) or

lipoprotein subparticles.

3.

The most frequently used and guideline recommended test that

can count the number of particles is the NMR LipoProfile. In

addition to counting the number of particles – the most

important predictor of risk – NMR can also measure the size of

each lipoprotein particle, which is valuable for predicting

insulin resistance in drug naïve patients, before changes are

noted in glucose or insulin levels.

4.

I know some of you are getting antsy. I thank you for your patience,

and I hope you appreciate that it was a necessary step to get

through this somewhat technical material and nomenclature. Next

week we’ll get to the “fun” stuff – what does all of this cholesterol

have to do with heart disease?

In addition, we’ll get further into the importance of using LDL-P as

the best predictor of risk. If anyone wants to read up on another

very important topic, especially for understanding why LDL-P is

more important to know than LDL-C, get familiar with the concepts


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of discordant and concordant variables. You’ll be hearing a lot

about these.

(To Part IV »)

10

MAY


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eatingacademy.com


IV

Previously, in Part I, Part II and Part III of this series, we addressed

these 5 concepts:






#5 –How do we measure cholesterol?

In this post we’ll continue to build out the story with the next

concept:

#6 – How does cholesterol actually cause problems?

Asked another way, how does someone end up with a coronary

artery that looks like the one in the picture above?


should you need it:




1.

The pool of cholesterol in our body is essential for life. No2.

The straight dope on cholesterol – Part IV

1 of 13





3.






4.





5.






6.








here.

7.



hydrophobic.

8.

To be carried anywhere in our body, say from your liver to your9.


2 of 13







10.






11.




12.







13.





core and surface lipids.

14.



the advance.

15.


that matter) undergo a “standard” lipid panel which only

16.


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directly measures TC, TG, and HDL-C. LDL-C is measured or

most often estimated.





17.

The most frequently used and guideline-recommended test that

can count the number of LDL particles is either

apolipoprotein B or LDL-P NMR which is part of the NMR

LipoProfile. NMR can also measure the size of LDL and other

lipoprotein particles, which is valuable for predicting insulin

resistance in drug naïve patients (i.e., those patients not on

cholesterol-lowering drugs), before changes are noted in

glucose or insulin levels.

18.

Concept #6 – How does cholesterol actually cause problems?

If you remember only one factoid from the previous three posts on

this topic, remember this: If you were only “allowed” to know one

metric to understand your risk of heart disease it would be the

number of apoB particles (90-95% of which are LDLs) in your

plasma. In practicality, there are two ways to do this:

Directly measure (i.e., not estimate) the concentration of apoB

in your plasma (several technologies and companies offer such

an assay). Recall, there is one apoB molecule per particle;

1.

Directly measure the number of LDL particles in your plasma

using NMR technology.

2.

If this number is high, you are at risk of atherosclerosis.

Everything else is secondary.

Does having lots of HDL particles help? Probably, especially if they

are “functional” at carrying out reverse cholesterol transport, but it’s

not clear if it matters when LDL particle count is low. In fact, while


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many drugs are known to increase the cholesterol content of HDL

particles (i.e., HDL-C), not one to date has ever shown a benefit

from doing so. Does having normal serum triglyceride levels

matter? Probably, with “normal” being defined as < 70-100 mg/dL,

though it’s not entirely clear this is an independent predictor of low

risk. Does having a low level of LDL-C matter? Not if LDL-P (or

apoB) are high, or better said, not when the two markers are

discordant.

But why?

As with the previous topics in this series, this question is sufficiently

complex to justify several textbooks – and it’s still not completely

understood. My challenge, of course, is to convey the most

important points in a fraction of that space and complexity.

Let’s focus, specifically, on the unfortunately-ubiquitous clinical

condition of atherosclerosis – the accumulation of sterols and

inflammatory cells within an artery wall which may (or may not)

narrow the lumen of the artery.

Bonus concept: It’s important to keep in mind that this disease

process is actually within the artery wall and it may or may not

affect the arterial lumen, which is why angiograms can be normal

in persons with advanced atherosclerosis. As plaque progresses

it can encroach into the lumen leading to ischemia (i.e., lack of

oxygen delivery to tissues) as the narrowing approaches 70-75%,

or the plaque can rupture leading to a thrombosis: partial leading

to ischemia or total leading to infarction (i.e., tissue death).

To be clear, statistically speaking, this condition (atherosclerotic

induced ischemia or infarction) is the most common one that will

result in the loss of your life. For most of us, atherosclerosis (most

commonly within coronary arteries, but also carotid or cerebral

arteries) is the leading cause of death, even ahead of all forms of

cancer combined. Hence, it’s worth really understanding this

problem.


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In this week’s post I am going to focus exclusively on what I like to

call the “jugular issue” – that is, I’m going to discuss the actual

mechanism of atherosclerosis. I’m not going to discuss

treatment (yet). We can’t get into treatment until we really

understand the cause.

“It is in vain to speak of cures, or think of remedies, until

such time as we have considered of the causes . . . cures

must be imperfect, lame, and to no purpose, wherein the

causes have not first been searched.”

—Robert Burton, The Anatomy of Melancholy, 1621

The sine qua non of atherosclerosis is the presence of sterols in

arterial wall macrophages. Sterols are delivered to the arterial wall

by the penetration of the endothelium by an apoB-containing

lipoprotein, which transport the sterols. In other words, unless an

apoB-containing lipoprotein particle violates the border

created by an endothelium cell and the layer it protects, the

media layer, there is no way atherogenesis occurs.

For now, let’s focus only on the most ubiquitous apoB-containing

lipoprotein, the LDL particle. Yes, other lipoproteins also contain

apoB (e.g., chylomicrons, remnant lipoproteins such as VLDL

remnants, IDL and Lp(a)), but they are few in number relative to

LDL particles. I will address them later.

The endothelium is the one-cell-thick-layer which lines the lumen

(i.e., the “tube”) of a vessel, in this case, the artery. Since blood is

in direct contact with this cell all the time, all lipoproteins – including

LDL particles – come in constant contact with such cells.

So what drives an LDL particle to do something as sinister as to

leave the waterway (i.e., the bloodstream) and “illegally” try to park

at a dock (i.e., behind an endothelial cell)? Well, it is a gradient

driven process which is why particle number is the key driving

parameter.


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As it turns out, this is probably a slightly less important question

than the next one: what causes the LDL particle to stay there? In

the parlance of our metaphor, not only do we want to know why the

ship leaves the waterway to illegally park in the dock, but why

does it stay parked there? This phenomenon is called “retention.”

Finally, if there was some way an LDL particle could violate the

endothelium, AND be retained in the space behind the cell (away

from the lumen on the side aptly called the sub-endothelial side)

BUT not elicit an inflammatory (i.e., immune) response, would it

matter?

I don’t know. But it seems that not long after an LDL particle gets

into the sub-endothelial space and takes up “illegal” residence (i.e.,

binds to arterial wall proteoglycans), it is subject to oxidative forces

and as one would expect an inflammatory response is initiated.

The result is full blown mayhem. Immunologic gang warfare breaks

out and cells called monocytes and macrophages and mast cells

show up to investigate. When they arrive, and find the LDL particle,

they do all they can to remove it. In some cases, when there are

few LDL particles, the normal immune response is successful. But,

it’s a numbers game. When LDL particle invasion becomes

incessant, even if the immune cells can remove some of them, it

becomes a losing proposition and the actual immune response to

the initial problem becomes chronic and maladaptive and expands

into the space between the endothelium and the media.

The multiple-sterol-laden macrophages or foam cells coalesce,

recruit smooth muscle cells, induce microvascularization, and

before you know it complex, inflamed plaque occurs.

Microhemorrhages and microthrombus formations occur within the

plaque. Ultimately the growing plaque invades the arterial lumen or

ruptures into the lumen inducing luminal thrombosis. Direct luminal

encroachment by plaque expansion or thrombus formation causes

the lumen of the artery to narrow, which may or may not cause

ischemia.


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Before we go any further, take a look at the figure below from an

excellent review article on this topic from the journal Circulation –

Subendothelial Lipoprotein Retention as the Initiative Process in

Atherosclerosis. This figure also discuss treatment strategies, but

for now just focus on the pathogenesis (i.e., the cause of the

problem).

In this figure you can see the progression:

LDL particles (and a few other particles containing apoB) enter

the sub-endothelium

1.

Some of these particles are retained, especially in areas where

there is already a bit of extra space for them (called pre-lesion

susceptible areas)

2.

“Early” immune cells initiate an inflammatory response which

includes the direct interaction between the LDL particle and

proteins called proteoglycans.

3.

The proteoglycans further shift the balance of LDL particle

movement towards retention. Think of them as “cement”

keeping the LDL particles and their cholesterol content in the

sub-endothelial space.

4.

More and more apoB containing particles (i.e., LDL particles

and the few other particles containing apoB) enter the

sub-endothelial space and continue to be retained, due to the

existing “room” being created by the immune response.

5.

As this process continues, an even more advanced form of

immune response – mediated by cells called T-cells – leads to

further retention and destruction of the artery wall.

6.

Eventually, not only does the lumen of the artery narrow, but a

fibrous cap develops and plaques take form.

7.

It is most often these plaques that lead to myocardial infarction,8.


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or heart attacks, as they eventually dislodge and acutely

obstruct blood flow to the portion of muscle supplied by the

artery.

Another way to see this progression is shown in the figure below,

which shows the histologic progression of atherosclerosis in the

right coronary artery from human autopsy specimens. This figure is

actually a bit confusing until you understand what you’re looking at.

Each set of 3 pictures shows the same sample, but with a different

kind of histological stain. Each row represents a different

specimen.

The column on the left uses a stain to highlight the distinction

between the intimal and media layer of the artery call. The

intima, recall, is the layer just below the endothelium and should

not be as thick as shown here.

The middle column uses a special stain to highlight the

presence of lipids within the intimal layer.

The right column uses yet a different stain to highlight the


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presence of macrophages in the intima and the media.

Recall, macrophages are immune cells that play an important

role of the inflammatory cascade leading to atherosclerosis.

What is particularly compelling about this sequence of figures is

that you can see the trigger of events from what is called diffuse

intimal thickening (“DIT”), which exacerbates the retention of

lipoproteins and their lipid cargo, only then to be followed by the

arrival of immune cells, which ultimately lead the inflammatory

changes responsible for atherosclerosis.

Why LDL-P matters most

You may be asking the chicken and egg question:

Which is the cause – the apoB containing LDL particle OR the

immune cells that “overreact” to them and their lipid cargo?


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You certainly wouldn’t be alone in asking this question, as many

folks have debated this exact question for years. The reason, of

course, it is so important to ask this question is captured by the

Robert Burton quote, above. If you don’t know the cause, how can

you treat the disease?

Empirically, we know that the most successful pharmacologic

interventions demonstrated to reduce coronary artery disease are

those that reduce LDL-P and thus delivery of sterols to the artery.

(Of course, they do other things also, like lower LDL-C, and maybe

even reduce inflammation.)

Perhaps more compelling is the “natural experiment” of people with

genetic alterations leading to elevated or reduced LDL-P. Let’s

consider an example of each:

Cohen, et al. reported in the New England Journal of Medicine

in 2006 on the cases of patients with mutations in an enzyme

called proprotein convertase subtilisin type 9 or PCSK9 (try

saying that 10 times fast). Normally, this proteolytic enzyme

degrades LDL receptors on the liver. Patients with mutations

(“nonsense mutations” to be technically correct, meaning the

enzyme is somewhat less active) have less destruction of

hepatic LDL receptors. Hence, they have more sustained

expression of hepatic LDL receptors, improved LDL clearance

from plasma and therefore fewer LDL particles. These patients

have very low LDL-P and LDL-C concentrations (5-40 mg/dL)

and very low incidence of heart disease. Note that a reduction

in PCSK9 activity plays no role in reducing inflammation.

1.

Conversely, patients with familial hypercholesterolemia (known

as FH) have the opposite problem. While there are several

variants and causes of this disease, the common theme is

having decreased clearance of apoB-containing particles, often

but not always due to defective or absent LDL receptors,

leading to the opposite problem from above. Namely, these

patients have a higher number of circulating LDL particles, and

2.


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as a result a much higher incidence of atherosclerosis.

So why does having an LDL-P of 2,000 nmol/L (95th percentile)

increase the risk of atherosclerosis relative to, say, 1,000 nmol/L

(20th percentile)? In the end, it’s a probabilistic game. The more

particles – NOT cholesterol molecules within the particles and not

the size of the LDL particles – you have, the more likely the chance

a LDL-P is going to ding an endothelial cell, squeeze into the

sub-endothelial space and begin the process of atherosclerosis.

What about the other apoB containing lipoproteins?

Beyond the LDL particle, other apoB-containing lipoproteins also

play a role in the development of atherosclerosis, especially in an

increasingly insulin resistant population like ours. In fact, there is

some evidence that particle-for-particle Lp(a) is actually even more

atherogenic than LDL (though we have far fewer of them). You’ll

recall that Lp(a) is simply an LDL particle to which another protein

called apoprotein(a) is attached, which is both a prothrombotic

protein as well as a carrier of oxidized lipids – neither of which you

want in a plaque. The apo(a) also retards clearance of Lp(a) thus

enhancing LDL-P levels. It may foster greater penetration of the

endothelium and/or greater retention within the sub-endothelial

space and/or elicit an even greater immune response.

In summary

The progression from a completely normal artery to an

atherosclerotic one which may or may not be “clogged” follows a

very clear path: an apoB containing particle gets past the

endothelial layer into the sub-endothelial space, the particle and

its cholesterol content is retained and oxidized, immune cells

arrive, an initially-beneficial inflammatory response occurs that

ultimately becomes maladaptive leading to complex plaque.

1.

While inflammation plays a key role in this process, it’s the

penetration of the apoB particle, with its sterol passengers, of

the endothelium and retention within the sub-endothelial space

2.


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that drive the process.

The most numerous apoB containing lipoprotein in this process

is certainly the LDL particle, however Lp(a) (if present) and

other apoB containing lipoproteins may play a role.

3.

If you want to stop atherosclerosis, you must lower the LDL

particle number.

4.

(To Part V »)

17

MAY


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eatingacademy.com


V

In Part I, Part II, Part III and Part IV of this series, we addressed

these 6 concepts:









concept:

#7 – Does the size of an LDL particle matter?


should you need it:




1.



2.

The straight dope on cholesterol – Part V

1 of 12




3.






4.





5.






6.








here.

7.



hydrophobic.

8.




9.


2 of 12





10.






11.




12.







13.






14.



the advance.

15.


that matter) undergo a “standard” lipid panel, which only



16.


3 of 12





17.



apolipoprotein B or LDL-P NMR, which is part of the NMR



resistance in drug naïve patients, before changes are noted in


18.

The progression from a completely normal artery to a “clogged”

or atherosclerotic one follows a very clear path: an apoB

containing particle gets past the endothelial layer into the

subendothelial space, the particle and its cholesterol content is

retained, immune cells arrive, an inflammatory response ensues

“fixing” the apoB containing particles in place AND making more

space for more of them.

19.


penetration of the endothelium and retention within the

endothelium that drive the process.

20.

The most common apoB containing lipoprotein in this process is

certainly the LDL particle. However, Lp(a) and apoB containing

lipoproteins play a role also, especially in the insulin resistant

person.

21.


particle number.

22.

Concept #7 – Does the size of an LDL particle matter?

There are few, if any, topics in lipidology that generate more

confusion and argument that this one. I’ve been leading up to it all

month, so I think the time is here to address this issue head on.


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I’ve read many papers and seen many lectures on this topic, but

the one that stole my heart was a lecture given by Jim Otvos at the

ADA 66th Scientific Sessions in Washington, DC. Some of the

figures I am using in this post are taken directly or modified from his

talk or subsequent discussions.

At the outset of this discussion I want to point out two clinical

scenarios to keep in mind:

The most lethal lipoprotein disorder is familial

hypercholesterolemia, which I have discussed in previous

posts. Such patients all have large LDL particles, but most of

these patients die in childhood or early adulthood if not treated

with medications to reduce particle number.

1.

Conversely, diabetic patients and other patients with

advanced metabolic syndrome have small LDL particles, yet

often live well into their 50s and 60s before succumbing to

atherosclerotic diseases.

2.

The common denominator is that both sets of patients in (1) and

(2) have high LDL-P. What I’m going to attempt to show you today

is that once adjusted for particle number, particle size has no

statistically significant relationship to cardiovascular risk. But first,

some geometry.

“Pattern A” versus “Pattern B” LDL

The introduction of gradient gel electrophoresis about 30 years ago

is what really got people interested in the size of LDL particles.

There is no shortage of studies of the past 25 years demonstrating

that of the following 2 scenarios, one has higher risk, all other

things equal. [This is a big disclaimer and I went back and forth

for a while before deciding to include this point. It is an

uncharacteristic oversimplification. If you’ve been reading this

blog for a while, you’ll know I’m rarely accused of that sin – but I’m

about to be].


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Here’s the example: Consider 2 patients, both with the same total

content of cholesterol in their LDL particles, say, 130 mg/dL.

Furthermore, assume each has the “ideal” ratio of core cholesterol

ester-to-triglyceride (recall from Part I and III of this series, this ratio

is 4:1). I’m going to explain in a subsequent post why this

assumption is probably wrong as often as it’s right, but for the

purpose of simplicity I want to make a geometric point.

LDL-C = 130 mg/dL, Pattern A (large particles) – person on the

left in the figure below

1.

LDL-C = 130 mg/dL, Pattern B (small particles) – person on the

right in the figure below

2.

Under the set of assumptions I’ve laid out, case #2 is the higher risk

case. In other words, at the same concentration of cholesterol

within LDL particles, assuming the same ratio of CE:TG, it is

mathematically necessary the person on the right, case #2, has

more particles, and therefore has greater risk.

Bonus concept: What one really must know is how many

cholesterol molecules there are per LDL particle. It always

requires more cholesterol-depleted LDL particles than

cholesterol-rich LDL particles to traffic cholesterol in plasma, and

the number of cholesterol molecules depends on both size and

core TG content. The more TG in the particle, the less the

cholesterol in the particle.

So why does the person on the right have greater risk? Is it

because they have more particles? Or is it because they have

smaller particles?

This is the jugular question I want to address today.


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If you understand that the person on the right, under the very

careful and admittedly overly simplified assumptions I’ve given, is at

higher risk than the person on the left, there are only 4 possible

reasons:

Small LDL particles are more atherogenic than large ones,

independent of number.

1.

The number of particles is what increases atherogenic risk,

independent of size.

2.

Both size and number matter, and so the person on the right is

“doubly” at risk.

3.

Neither feature matters and these attributes (i.e., size and

number) are markers for something else that does matter.

4.

Anyone who knows me well knows I love to think in MECE terms

whenever possible. This is a good place to do so.

I’m going to rule out Reason #4 right now because if I have not yet

convinced you that LDL particles are the causative agent for

atherosclerosis, nothing else I say matters. The trial data are

unimpeachable and there are now 7 guidelines around the world

advocating particle number measurement for risk assessment. The

more LDL particles you have, the greater your risk of

atherosclerosis.

But how do we know if Reason #1, #2, or #3 is correct?


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This figure (one of the most famous in this debate) is from the

Quebec Cardiovascular Study, published in 1997, in Circulation.

You can find this study here.

This is kind of a complex graph if you’re not used to looking at

these. It shows relative risk – but in 2 dimensions. It’s looking at

the role of LDL size and apoB (a proxy for LDL-P, you’ll recall from

previous posts). What seems clear is that in patients with low

LDL-P (i.e., apoB < 120 mg/dl), size does not matter. The relative

risk is 1.0 in both cases, regardless of peak LDL size. However, in

patients with lots of LDL particles (i.e., apoB > 120 mg/dl), smaller

peak LDL size seems to carry a much greater risk – 6.2X.

If you just looked at this figure, you might end up drawing the

conclusion that both size and number are independently

predictive of risk (i.e., Reason #3, above). Not an illogical

conclusion…

What is not often mentioned, however, is what is in the text of the

article:

“Among lipid, lipoprotein,and apolipoprotein variables, apo B

[LDL-P] came out as the best and only significant predictor of

ischemic heart disease (IHD) risk in multivariate

stepwiselogistic analyses (P=.002).”


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“LDL-PPD [peak LDL particle diameter] — as a continuous

variable did not contribute to the risk of IHD after the contribution

of apo B levels to IHD risk had been considered.”

What’s a continuous variable? Something like height or weight,

where the possible values are infinite between a range. Contrast

this with discrete variables like “tall” or “short,” where there are only

two categories. For example, if I define “tall” as greater than or

equal to 6 feet, the entire population of the world could be placed in

two buckets: Those who are “short” (i.e., less than 6 feet tall) and

those who are “tall” (i.e., those who are 6 feet tall and taller). This

figure shows LDL size like it’s a discrete variable – “large” or

“small” – but obviously it is not. It’s continuous, meaning it can

take on any value, not just “large” or “small.” When this same

analysis is done using LDL size as the continuous variable it is,

the influence of size goes away and only apoB (i.e., LDL-P)

matters.

This effect has been observed subsequently, including the famous

Multi-Ethnic Study of Atherosclerosis (MESA) trial, which you can

read here. The MESA trial looked at the association between

LDL-P, LDL-C, LDL size, IMT (intima-media thickness – the best

non-invasive marker we have for atherosclerosis), and many other

parameters in about 5,500 men and women over a several year

period.

This study used the same sort of statistical analysis as the study

above to parse out the real role of LDL-P versus particle size, as

summarized in the table below.


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This table shows us that when LDL-P is NOT taken into account

(i.e., “unadjusted” analysis), an increase of one standard deviation

in particle size is associated with 20.9 microns of LESS

atherosclerosis, what one might expect if one believes particle size

matters. Bigger particles, less atherosclerosis.

However, and this is the important part, when the authors adjusted

for the number of LDL particles (in yellow), the same phenomenon

was not observed. Now an increase in LDL particle size by 1

standard deviation was associated with an ADDITIONAL 14.5

microns of atherosclerosis, albeit of barely any significance

(p=0.05).

Let me repeat this point: Once you account for LDL-P, the

relationship of atherosclerosis to particle size is abolished (and

even trends towards moving in the “wrong” direction – i.e., bigger

particles, more atherosclerosis).

Let me use another analysis to illustrate this point again. If you

adjust for age and sex, but not LDL-P [left graph, below], changes

in the number of LDL particles (shown in quintiles, so each group

shows changes by 20% fractions) seem to have no relationship

with IMT (i.e., atherosclerosis).

However, when you adjust for small LDL-P [right graph, below], it

becomes clear that increased numbers of large LDL particles

significantly increase risk.


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I’ve only covered a small amount of the work addressing this

question, but this issue is now quite clear. A small LDL particle is

no more atherogenic than a large one, but only by removing

confounding factors is this clear. So, if you look back at the figure I

used to address this question, it should now be clear that Reason

#2 is the correct one.

This does not imply that the “average” person walking around with

small particles is not at risk. It only implies the following:

The small size of their particles is probably a marker for

something else (e.g., metabolic derangement due to higher

trafficking of triglycerides within LDL particles);

1.

Unless you know their particle number (i.e., LDL-P or apoB),

you actually don’t know their risk.

2.

Let’s wrap it up here for this week. Next week we’ll address

another question that’s probably been on your mind: Why do we

need to measure LDL-P or apoB? Isn’t the LDL-C test my doctor

orders enough to predict my risk?

Summary

At first glance it would seem that patients with smaller LDL

particles are at greater risk for atherosclerosis than patients with

large LDL particles, all things equal. Hence, this idea that

Pattern A is “good” and Pattern “B” is bad has become quite

popular.

To address this question, however, one must look at changes in


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cardiovascular events or direct markers of atherosclerosis (e.g.,

IMT) while holding LDL-P constant and then again holding

LDL size constant. Only when you do this can you see that

the relationship between size and event vanishes. The only

thing that matters is the number of LDL particles – large, small,

or mixed.

“A particle is a particle is a particle.” If you don’t know the

number, you don’t know the risk.

(To Part VI »)

23

MAY


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eatingacademy.com


VI

Previously, in Part I, Part II, Part III, Part IV and Part V of this

series, we addressed these 7 concepts:










concept:

#8 – Why is it necessary to measure LDL-P, instead of just

LDL-C?

(Not so) quick refresher on take-away points from previous

posts, should you need it:




1.

The straight dope on cholesterol – Part VI

1 of 17



2.




3.






4.





5.






6.








here.

7.



hydrophobic.

8.


2 of 17




9.





10.






11.




12.







13.






14.



the advance.

15.

Currently, most people in the United States (and the world for16.


3 of 17








17.








18.








19.




20.




person.

21.


particle number.

22.


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large LDL particles, all things equal.

23.



24.







or mixed.

25.

Concept #8 – Why is it necessary to measure LDL-P, instead of

just LDL-C?

In the growing list of reasons why I used to refer to myself as

“chick-repellant” in college, I have a confession to make: I find the

topic of statistical concordance and discordance to be so

exciting, I sometimes have a hard time containing myself. This may

explain the paucity of girlfriends in college. Let me use an example

to illustrate the distinction between these terms. Let’s say you want

to predict the change in home prices in the following year (I used to

model this for a living). There are at least a dozen parameters

linked to this, including: GDP growth, unemployment, interest rates

(both short term and long term, though to different degrees),

housing inventory (i.e., how many houses are on the market),

housing absorption (i.e., how quickly houses go from being on the

market to being sold), major stock indices, and consumer

confidence. Historically, from the mid-1990’s until about the fourth

quarter of 2006, this worked like clockwork. While each of these

variables had differing strengths of predicting changes in home

prices, they all moved together. For example, when GDP growth

was robust, unemployment was low, interest rates were modest,

housing inventories were about 60 to 90 days, etcetera. All of


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these variables pointed to a predictable change in home values.

Around Q42006 (i.e., last 3 months of 2006), one of these variables

began to deviate from the others. The details aren’t important, but

the point is one variable began to suggest home prices would fall

while the others all pointed to a continued rise. Prior to Q42006

these parameters were said to be concordant – they all predicted

the same thing – either up or down. By 2007, they became

discordant – one variable said the sky was falling while others said

everything was fine.

This was true on the “micro” level, too. [What I described above is

called “macro” level.] As a lender, it should be very important to

know the risk of each and every loan you make (clearly this was

part of the root problem in the age of mass securitization). Will this

person pay the loan back or will they default?

Same game here, but now a new set of even greater variables. As

a lender, if I want to know if YOU will default, I will want to know a

lot of things about you, such as your agency credit risk scores, your

bank account activity, payroll activity, how much you’re borrowing

relative to the value of your house, where your house is located,

and about 50 other things (literally).

Not surprisingly, the same thing that happened on the macro side

happened on the micro side. It became difficult to predict who

would default and would not default because there were so many

variables to consider and lenders didn’t know which ones were still

predictive. The models that predict default are very sensitive to the

balance of these inputs. When all of the variables are concordant,

their accuracy is prophetic, as was the case from the mid-1990s

until late 2006. When some variables become discordant with each

other, especially variables that were historically concordant with

each other, really bad stuff happens, as became evident to me,

personally, one Thursday afternoon in November 2007. It became

clear the sky was about to fall. And, of course, it did.


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What does real estate have to do with atherosclerosis?

Fortunately, predicting heart disease is a little easier than predicting

changes in home prices. It’s not perfect, of course, but it’s pretty

good. Why is it not perfect? For one thing, we can’t do the

“perfect” experiment. The “perfect” experiment would look

something like this:

Take 100,000 people and randomize them into four matched

groups, A, B, C, and D. Wave a magic wand (you can see why

this experiment hasn’t been and won’t be done) and give the folks

in Group A an LDL particle concentration of, say, 700 nmol/L;

those in Group B you give 1,200 nmol/L; those in Group C you

give 1,600 nmol/L; and those in Group D get 2,000 nmol/L.

In our dream world, due to the randomization process, these four

groups would be statistically identical in every way except one –

they would, thanks to our magic wand, have a different number of

LDL particles. We would follow them without further intervention for

10 years and then compare their rates of heart disease, stroke, and

death.

There are some areas in medicine where we can do such

experiments. But, we can’t do this experiment for this question.

Even when we do the next best thing — give people a drug that

lowers their LDL-P and measure the impact of this intervention —

there is always a chance we’ve done something in addition to “just”

lowering LDL-P. If you’ve been reading this series, you no doubt

know my thoughts on this: while other factors are likely to be

involved the pathogenesis of atherosclerosis (e.g., endothelial

“health”, normal versus abnormal inflammatory response) the

primary driver of atherosclerosis is the number of apoB

trafficking lipoproteins in circulation, of which LDL particles

are the vast majority.

The data below should further clarify this association.

What do concordant LDL-C and LDL-P values look like?


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Among the two largest studies tracking the association between

cholesterol and atherosclerotic mortality are the Framingham study

and the MESA trial (the two largest trials were AMORIS and

INTERHEART). The figure below, which I’ve graciously borrowed

from Jim Otvos, shows the risk stratification of LDL-C (top) and

LDL-P (bottom) from the Framingham study and MESA trial,

respectively. As you can see, conveniently, LDL-C values in mg/dL

are about 10x off from LDL-P values in nmol/L. In other words, in

the Framingham population, the 20th percentile value of LDL-C was

100 mg/dL, while the MESA trial found the 20th percentile of the

population to have an LDL-P concentration of 1,000 nmol/L. As

you will see by the end of this post, this “rule of the thumb” should

never be used to infer LDL-P from LDL-C.

If this were always the case – that is, if LDL-C and LDL-P were

always concordant – we could conclude that LDL-C and LDL-P

would be of equal value in predicting heart disease. Obviously this

is not the case, or I wouldn’t be making such a fuss over the

distinction. But how bad is it?

What do discordant LDL-C and LDL-P values look like?

The figure below, from the Journal of Clinical Lipidology, shows the


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cumulative incidence of cardiovascular events (e.g., myocardial

infarction, death) over time in three sub-populations:

Those with concordant LDL-P and LDL-C (black line);1.

Those with discordant LDL-P and LDL-C (LDL-P>LDL-C,

shown by the red line);

2.

Those with discordant LDL-P and LDL-C (LDL-P<LDL-C,

shown by the blue line).

3.

This analysis was done using a Cox proportional hazard model and

was adjusted for age, sex, and race. The steeper the line the more

people in that sub-population died or experienced adverse cardiac

events relative to other sub-populations. In other words, the folks in

the red group had the worst outcomes, followed by the folks in the

black group, followed by the folks in the blue group.

What can we infer from these data?

First, we confirm what I alluded to above. Namely, that a non-zero

percent of the population do not have LDL-C and LDL-P values that


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predict the same level of risk. However, and perhaps more

importantly, we get another look at an important theme of this

series: LDL-P is driving atherosclerotic risk, not LDL-C. If

LDL-P and LDL-C were equally “bad” – even when discordant –

you would expect the blue line to be as steep as the red line (and

both to be steeper than the black line). But this is not the case.

Let’s look at these data parsed out another way. Below we see the

four possible subgroups, from the top:

Not low LDL-P, low LDL-C (red line);1.

Not low LDL-P, not low LDL-C (yellow line);2.

Low LDL-P, low LDL-C (black line); and3.

Low LDL-P, not low LDL-C (blue line).4.

Note that “low” is defined below the 30th percentile and “not low” is

defined as greater than 30th percentile for each variable. This

figure is even more revealing than the one above. Again, it

demonstrates the frequency of discordance (about 20% in this

population with these cut-off points), and it shows the importance of

LDL-P’s predictive power, relative to that of LDL-C.

In fact, though not statistically significant, the highest risk group

has high LDL-P and actually has low LDL-C (I’ll give you a hint of

why, below) while the lowest risk group has low LDL-P and

not-low LDL-C. *This is not a typo.


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The highest risk and lowest risk groups are those with discordant

LDL-C and LDL-P. The high risk group has high LDL-P and low

LDL-C, while the lowest risk group has high LDL-C with low

LDL-P. Only a minority of physicians would know that there is a

segment of the population with elevated LDL-C who are at low risk!

The same conclusion will be drawn from the next study.

Let’s look at an even longer-term follow up study, below. This study

followed a Framingham offspring cohort of about 2,500 patients

over a median time period of almost 15 years in each of the four

possible groups (i.e., high-high, high-low, low-high, and low-low)

and tracked event-free survival. In this analysis the cut-off points

for LDL-P and LDL-C were the median population values of 1,414

nmol/L and131 mg/dL, respectively. So “high” implies above these

values; “low” implies below these values. Kaplan-Meier survival

curves are displayed over a 16 year period – the steeper the slope

of the line the worse the outcome (survival).


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The same patterns are observed:

LDL-P is the best predictor of adverse cardiac events.1.

LDL-C is only a good predictor of adverse cardiac events when

it is concordant with LDL-P; otherwise it is a poor predictor of

risk.

2.

Amazingly the persons with the worst survival had low (below

median) LDL-C but high LDL-P. The patients most likely to have

high LDL-P with unremarkable or low LDL-C are those with either

small LDL particles, or TG-rich / cholesterol poor LDL particles, or

both (e.g., insulin resistant patients, metabolic syndrome patients,

T2DM patients). This explains why small LDL particles, while

no more atherogenic on a per particle basis than large

particles, are a marker for something sinister.

Populations where LDL-P and LDL-C discordance are even

more prevalent

As I described above, the discordance between LDL-P and LDL-C

is exacerbated in patients with metabolic syndrome. The figure

below, MESA data, again borrowed from Jim Otvos, presents this

difference in an elegant way. The horizontal axes show LDL-P

concentration in the usual units, nmol/L.


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Patients with LDL-C between 100 and 118 mg/dL (i.e., second

quartile of risk: 25th to 50th percentile) are shown without metabolic

syndrome (top) and with metabolic syndrome (bottom). In the

patients without metabolic syndrome, LDL-C under-predicts

cardiac risk 22% of the time, consistent with the population data I

have shown you earlier. However, when you look at the patients

with metabolic syndrome, you can see that 63% of the time their

risk of cardiac disease is under-predicted. Again, not a typo.

There are so many subsets and cut-off points that I could devote

ten more posts to showing you every one of these analyses. Let

me finish this point with the most recent, hot-off-the-press (actually,

still in press in the American Journal of Cardiology, though you can

get a preprint here) analysis of which Tom Dayspring is one of the

authors.


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These data were collected from nearly 2,000 patients with diabetes

who presented with “perfect” standard cholesterol numbers: LDL-C

< 70 mg/dL; HDL-C > 40 mg/dL; TG <150 mg/dL. However, only

in 22% of cases were their LDL-P concordant with LDL-C. That is,

in only 22% of cases did these patients have an LDL-P level below

700 nmol/L.

Remember, LDL-C < 70 mg/dL is considered VERY low risk – the

5th percentile. Yet, by LDL-P, the real marker of risk, 35% of these

patients had more than 1,000 nmol/L and 7% were high risk. When

you do this analysis with the same group of patients stratified by

less stringent LDL-C criteria (e.g., <100 mg/dL) the number of

patients in the high risk group is even higher.

The real world tragedy: 90-95% of physicians, including

cardiologists, would bet their own lives that persons with an

LDL-C < 70 mg/dL have no atherosclerotic risk.

Tim Russert, shortly before his death, had his LDL-C level

checked. It was less than 70 mg/dL. Sadly, his doctors didn’t

realize they should also have been checking his LDL-P or apoB.

The figure below, which is from one of Tom Dayspring’s

presentations, shows data from this study of nearly 137,000

patients hospitalized for coronary artery disease between 2000 and

2006. As you can see, LDL-C fails to even reasonably predict

cardiovascular disease in a patient population sick enough to show

up in the hospital with chest pain or outright myocardial infarction.


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Why are LDL-C and LDL-P so often discordant?

Think back to what you learned in a previous post in this series.

LDL particles traffic not only cholesterol ester but also triglycerides.

Each and every LDL particle has a variable number of cholesterol

molecules which, because of constant particle remodeling, is

constantly changing. In other words, of the several quadrillion LDL

particles floating in your plasma, no two are carrying the exact

same number of cholesterol molecules. It takes many more

cholesterol-depleted LDL particles than cholesterol-rich LDL

particles to traffic a given cholesterol mass (i.e., number of

cholesterol molecules) per volume of plasma (i.e., per dL). Core

cholesterol mass is related to both LDL particle size (the volume

of a sphere is a third power of the radius — it can take 40-70%

more small particles than large LDL particles to traffic a given

cholesterol mass) and the number of TG molecules per LDL

particle.

TG molecules are larger than cholesterol ester molecules, so as the

number of TG molecules per particle increases, the number of

cholesterol molecules will be less – in a very non-linear manner.

Regardless of size it takes many more TG-rich LDL particles (which

are necessarily cholesterol-depleted) to traffic a given cholesterol

mass than TG-poor LDL particles. The persons with the highest

LDL particles typically (though not always) have small LDL particles

that are TG-rich. These are incredibly cholesterol-depleted LDL

particles.

Summary

Take a look at this figure below from the 2011 Otvos et al. paper I

referenced above. It’s a scatterplot of each data point (i.e., patient)

in the study. The solid red line shows perfect concordance between

LDL-P and LDL-C. The dashed red lines show a +/- 12% margin


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on each side. Look at how many dots (remember: each dot

represents a person) lie OUTSIDE of the dashed red lines. Now

look again.

When people argue with me about why it’s unnecessary to check

LDL-P or apoB because it’s much easier and cheaper to check

LDL-C, I like to remind them of what Clint Eastwood would probably

say in such a situation: “You’ve got to ask yourself one

question: Do I feel lucky? Well, do ya, punk?”

With respect to laboratory medicine, two markers that have a

high correlation with a given outcome are concordant – they

equally predict the same outcome. However, when the two tests

do not correlate with each other they are said to be discordant.

1.

LDL-P (or apoB) is the best predictor of adverse cardiac events,

which has been documented repeatedly in every major

cardiovascular risk study.

2.


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risk.

3.

There is no way of determining which individual patient may

have discordant LDL-C and LDL-P without measuring both

markers.

4.

Discordance between LDL-C and LDL-P is even greater in

populations with metabolic syndrome, including patients with

diabetes. Given the ubiquity of these conditions in the U.S.

population, and the special risk such patients carry for

cardiovascular disease, it is difficult to justify use of LDL-C,

HDL-C, and TG alone for risk stratification in all but the most

select patients.

5.

This raises the question: if indeed LDL-P is always as good and

in most cases better than LDL-C at predicting cardiovascular

risk, why do we continue to measure (or calculate) LDL-C at all?

6.

(To Part VII »)

30

MAY


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eatingacademy.com


VII

Previously, in Part I, Part II, Part III, Part IV, Part V ,and Part VI of

this series, we addressed these 8 concepts:









#8 – Why is it necessary to measure LDL-P, instead of just

LDL-C?


concept:

#9 – Does “HDL” matter after all?

(No so) Quick refresher on take-away points from previous

posts, should you need it:



1.

The straight dope on cholesterol – Part VII

1 of 17




2.




3.






4.





5.






6.








here.

7.



8.


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hydrophobic.




9.





10.






11.




12.







13.






14.



the advance.

15.


3 of 17





16.





17.








18.








19.




20.




person.

21.


particle number. Period.

22.


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23.



24.





25.




26.



risk.

27.



markers.

28.







select patients.

29.






30.


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or mixed.

Concept #9 – Does “HDL” matter after all?

Last week was the largest annual meeting of the National Lipid

Association (NLA) in Phoenix, AZ. The timing of the meeting could

not have been better, given the huge buzz going around on the

topic of “HDL.” (If you’re wondering why I’m putting HDL in quotes,

I’ll address it shortly.)

What buzz, you ask? Many folks, including our beloved health

columnists at The New York Times, are talking about the death of

the HDL hypothesis – namely, the notion that HDL is the “good

cholesterol.”

Technically, this “buzz” started about 6 years ago when Pfizer made

headlines with a drug in their pipeline called torcetrapib.

Torcetrapib was one of the most eagerly anticipated drugs ever,

certainly in my lifetime, as it had been shown to significantly raise

plasma levels of HDL-C. You’ll recall from part II of this series,

HDL particles play an important role in carrying cholesterol from the

subendothelial space back to the liver via a process called reverse

cholesterol transport (RCT). Furthermore, many studies and

epidemiologic analyses have shown that people with high plasma

levels of HDL-C have a lower incidence of coronary artery disease.

In the case of torcetrapib, there was an even more compelling

reason to be optimistic. Torcetrapib blocked the protein

cholesterylester transfer protein, or CETP, which facilitates the

collection and one-to-one exchange of triglycerides and cholesterol

esters between lipoproteins. Most (but not all) people with a

mutation or dysfunction of this protein were known to have high

levels of HDL-C and lower risk of heart disease. Optimism was very

high that a drug like torcetrapib, which could mimic this effect and

create a state of more HDL-C and less LDL-C, would be the biggest

blockbuster drug ever.


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The past month or so has seen this discussion intensify, which I’ll

quickly try to cover below.

The data

Torcetrapib

After several smaller clinical trials showed that patients taking

torcetrapib experienced both an increase in HDL-C and a reduction

in LDL-C, a large clinical trial pitting atorvastatin (Lipitor) against

atorvastatin + torcetrapib was underway. This trial was to be the

jewel in the crown of Pfizer. It was already known that Lipitor

reduced coronary artery disease (and reduced LDL-C, though this

may have been a bystander effect and real reduction in mortality

may be better attributed to the reduction in LDL-P).

I still remember exactly where I was standing, on the corner of

Kerney St. and California St. in the heart of San Francisco’s

financial district, on that December day back in 2006 when it was

announced the trial had been halted because of increased mortality

in the group receiving torcetrapib. In other words, adding

torcetrapib actually made things worse. I was shocked.

Many reasons were offered for this, including the notion that

torcetrapib was, indeed, helpful, but because of unanticipated

side-effects, (raising blood pressure in some patients and altering

electrolyte balance in others), the net impact was harmful. Some

even suggested that the drug could be useful in the “right” patients

(e.g., those with low HDL-C, but normal blood pressure).

Furthermore, in two subsequent studies looking at carotid IMT

(thickening of the carotid arteries) and intravascular ultrasound,

there was no reduction in atherosclerosis.

This was a big strike against the HDL hypothesis and work on

torcetrapib was immediately halted.

Niacin


7 of 17

Niacin has long been known to raise HDL-C and has actually been

used therapeutically for this reason for many years. The AIM-HIGH

trial (Atherothrombosis Intervention in Metabolic Syndrome with

Low HDL/High Triglycerides – you can’t have trials in medicine

without catchy names!) sought to test this. The trial randomly

assigned over 3,000 patients with known and persistent, but stable

and well treated cardiovascular risk, to one of two treatments:

Simvastatin (40-80 mg/day), +/- ezetimibe (10 mg/day) as

necessary to maintain LDL-C below 70 mg/dL + placebo (a tiny

dose of crystalline niacin to cause flushing);

1.

As above, but instead of a placebo, patients were given 1,500 to

2,000 mg/day of extended-release niacin.

2.

Both arms of the study had their LDL-C < 70 mg/dL, non-HDL-C <

100 md/dL and apoB < 80 mg/dL, but despite the statin or statin +

ezetimibe treatment still had low HDL-C. So, if niacin raised HDL-C

and reduced events, the HDL raising hypothesis would be proven.

Simvastatin, as its name suggests, is a statin which primarily works

by blocking HMG-CoA reductuse, an enzyme necessary to

synthesize endogenous cholesterol. Ezetimibe works on the other

end of problem, by blocking the NPC1L1 transporter on gut

enterocytes and hepatocytes at the hepatobiliary junction (for a

quick refresher, go back to part I of this series and look at the

second figure – ezetimibe blocks the “ticket taker” in the bar).

After two years the niacin group, as expected, had experienced a

significant increase in plasma HDL-C (along with some other

benefits like a greater reduction in plasma triglycerides). However,

there was no improvement in patient survival. The trial was futile

and the data and safety board halted the trial. In other words, for

patients with cardiac risk and LDL-C levels at goal with medication

niacin, despite raising HDL-C and lowering TG, did nothing to

improve survival. This was another strike against the HDL

hypothesis.


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Dalcetrapib

By 2008, as the AIM-HIGH trial was well under way, another

pharma giant, Roche, was well into clinical trials with another drug

that blocked CETP. This drug, a cousin of torcetrapib called

dalcetrapib, albeit a weaker CETP-inhibitor, appeared to do all the

“right” stuff (i.e., it increased HDL-C) without the “wrong” stuff (i.e., it

did not appear to adversely affect blood pressure). It did nothing to

LDL-C or apoB.

This study, called dal-OUTCOMES, was similar to the other trials in

that patients were randomized to either standard of care plus

placebo or standard of care plus escalating doses of dalcetrapib.

A report of smaller safety studies (called dal-Vessel and

Dal-Plaque) was published a few months ago in the American

Heart Journal, and shortly after Roche halted the phase 3 clinical

trial. Once again, patients on the treatment arm did experience a

significant increase in HDL-C, but failed to appreciate any clinical

benefit. Another futile trial.

Currently, two additional CETP inhibitors, evacetrapib

(manufactured by Lilly) and anacetrapib (manufactured by Merck)

are being evaluated. They are much more potent CETP inhibitors

and, unlike dalcetrapib, also reduce apoB and LDL-C and Lp(a).

Both Lilly and Merck are very optimistic that their variants will be

successful where Pfizer’s and Roche’s were not, for a number of

reasons including greater anti-CETP potency.

Nevertheless, this was yet another strike against the HDL

hypothesis because the drug only raised HDL-C and did nothing to

apoB. If simply raising HDL-C without attacking apoB is a viable

therapeutic strategy, the trial should have worked. We have been

told for years (by erroneous extrapolation from epidemiologic data)

that a 1% rise in HDL-C would translate into a 3% reduction in

coronary artery disease. These trials would suggest otherwise.

Mendelian randomization


9 of 17

On May 17 of this year a large group in Europe (hence the spelling

of randomization) published a paper in The Lancet, titled, “Plasma

HDL cholesterol and risk of myocardial infarction: a mendelian

randomisation study.” Mendelian randomization, as its name sort of

suggests, is a method of using known genetic differences in large

populations to try to “sort out” large pools of epidemiologic data.

In the case of this study, pooled data from tens of studies where

patients were known to have myocardial infarction (heart attacks)

were mapped against known genetic alterations called SNPs

(single nucleotide polymorphisms, pronounced “snips”). I’m not

going to go into detail about the methodology because it would take

3 more blog posts., But, the reason for doing this analysis was to

ferret out if having a high HDL-C was (only) correlated with better

cardiovascular outcome, which has been the classic teaching, or if

there was any causal relationship. In other words, does having a

high HDL-C cause you to have a lower risk of heart disease or is it

a marker for something else?

This study found, consistent with the trials I’ve discussed above,

that any genetic polymorphism that seems to raise HDL-C does not

seem to protect from heart disease. That is, patients with higher

HDL-C due to a known genetic alteration did not seem to have

protection from heart disease as a result of that gene. This

suggests that people with high or low HDL-C who get coronary

artery disease may well have something else at play.

Oh boy. This seems like the last nail in the casket of the entire

“HDL” hypothesis, as evidenced by all of the front page stories

worldwide.

The rub: the difference between HDL-C and HDL-P

The reason I’ve been referring to high density lipoprotein as “HDL,”

unless specifically referring to HDL-C, is that HDL-P and HDL-C are

not the same thing. Just as you are now intimately familiar with the

notion that LDL-C and LDL-P are not the same thing, the same is

true for “HDL” which simply stands for high density lipoprotein, and


10 of 17

like LDL is not a lab assay. In fact, unpublished data from the

MESA trial found that the correlation between HDL-C and HDL-P

was only 0.73, which is far from “good enough” to say HDL-C is a

perfect proxy for HDL-P.

HDL-C, measured in mg/dL (or mmol/L outside of the U.S.), is the

mass of cholesterol carried by HDL particles in a specified volume

(typically measured as X mg of cholesterol per dL of plasma).

HDL-P is something entirely different. It’s the number of HDL

particles (minus unlipidated apoA-I and prebeta-HDLs: at most 5%

of HDL particles) contained in a specified volume (typically

measured as Y micromole of particles per liter).

As you can see in the figure below (courtesy of Jim Otvos’

presentation at the NLA meeting 2 weeks ago), the larger an HDL

particle, the more cholesterol it carries. So, an equal number of

large versus small HDL particles (equal HDL-P) can carry very

different amounts of cholesterol (different HDL-C). Of course, it’s

never this simple because HDL particles, like their LDL

counterparts, don’t just carry cholesterol. They carry triglycerides,

too. Keep in mind, HDL core CE/TG ratio is about 10:1 or greater –

if the large HDL carries TG, it will not be carrying very much

cholesterol.


11 of 17

So, the important point is that HDL-C is not the same as HDL-P

(which is also not the same as apoAI, as HDL particles can carry

more than one apoAI).

But there’s something else going on here. If you look at the figure

below, from the Framingham cohort, you’ll note something

interesting. As HDL-C rises, it does so not in a uniform or “across

the board” fashion. A rise in HDL-C seems to disproportionately

result from an increase in large HDL particles. In other words, as

HDL-C rises, it doesn’t necessarily mean HDL-P is rising at all, and

certainly not as much.


12 of 17

As you can see, for increases in HDL-C at low levels (i.e., below 40

mg/dL) the increase in small particles seems to account for much of

the increase in total HDL-P, While for increases over 40 mg/dL, the

increase in large particles seems to account for the increase in

HDL-C. Also note that as HDL-C rises above 45 mg/dL, there is

almost no further increase in total HDL-P – the rise in HDL-C is

driven by enlargement of the HDL particle – more cholesterol per

particle – not the drop in small HDL-P. This reveals to us that the

small HDL particles are being lipidated.

Is there a reason to favor small HDL particles over large ones?

In the 2011 article, “Biological activities of HDL subpopulations and

their relevance to cardiovascular disease,” published in Trends in

Molecular Medicine, the authors describe in great detail some of

protective mechanisms imparted by HDL particles.

Large HDL particles may be less protective and even dysfunctional

in certain pathological states, whereas small to medium-sized HDL

particles seem to confer greater protection through the following

mechanisms:

Greater antioxidant activity

Greater anti-inflammatory activity

Greater cholesterol efflux capacity

Greater anti-thrombotic properties

In other words, particle for particle, it seems a small HDL particle


13 of 17

may be better at transporting cholesterol from the subendothelial

space (technically, they acquire cholesterol from cholesterol-laden

macrophages or foam cells in the subedothelial space) elsewhere,

better at reducing inflammation, better at preventing clotting, and

better at mitigating the problems caused by oxidative free radicals.

Of course, reality is complicated. If there was no maturation from

small to large HDL particles (i.e., the dynamic remodeling of HDL),

the system would be faulty. So, the truth is that all HDL sizes are

required and that HDL particles are in a constant dynamic state (or

“flux”) of lipidating and delipidating, and the real truth is no

particular HDL size can be said to be the best. If the little HDLs do

not enlarge, the ApoA-I mediated lipid trafficking system is broken.

The truth about the old (and overly simplistic) term called

reverse cholesterol transport (RCT)

HDL particles traffic cholesterol and proteins and last in plasma on

average for 5 days. They are in a constant state of acquiring

cholesterol (lipidation) and delivering cholesterol (delipidation).

There are membrane receptors on cells that can export cholesterol

to HDL particles (sterol efflux transporters) or extract cholesterol

or cholesterol ester from HDL particles (sterol influx transporters).

The vast majority of lipidation occurs (in order): 1) at the liver, 2) the

small intestine, 3) adipocytes and 4) peripheral cells, including

plaque if present. The liver and intestine account for 95% of this

process. The amount of cholesterol pulled out of arteries (called

macrophage reverse cholesterol transport) is critical to disease

prevention but is so small it has no effect on serum HDL levels.

Even in patients with extensive plaque, the cholesterol in that

plaque is about 0.5% of total body cholesterol. HDL particles

circulate for several days as a ready reserve of cholesterol: almost

no cell in humans require a delivery of cholesterol as cells

synthesize all they need. However, steroidogenic hormone

producing tissues (e.g., adrenal cortex and gonads) do require

cholesterol and the HDL particle is the primary delivery truck.


14 of 17

If, as is the case in a medical emergency, the adrenal gland must

rapidly make a lot of cortisone, the HDL particles are there with the

needed cholesterol. This explains the low HDL-C typically seen in

patients with severe infections (e.g., sepsis) and severe

inflammatory conditions (e.g., Rheumatoid Arthritis).

Sooner or later HDL particles must be delipidated, and this takes

place at: 1) the adrenal cortex or gonads 2) the liver, 3) adipocytes,

4) the small intestine (TICE or transintestinal cholesterol efflux) or

give its cholesterol to an apoB particle (90% of which are LDLs) to

return to the liver. A HDL particle delivering cholesterol to the liver

or intestine is called direct reverse cholesterol transport (RCT),

whereas a HDL particle transferring its cholesterol to an apoB

particle which returns it to the liver is indirect RCT. Hence, total

RCT = direct RCT + indirect RCT.

The punch line: a serum HDL-C level has no known relationship to

this complex process of RCT. The last thing a HDL does is lose its

cholesterol. The old concept that a drug or lifestyle that raises

HDL-C is improving the RCT process is wrong; it may or may not

be affecting that dynamic process. Instead of calling this RCT, it

would be more appropriately called apoA-I trafficking of cholesterol.

Why do drugs that specifically raise HDL-C seem to be of little

value?

As I’ve argued before, while statins are efficacious at preventing

heart disease, it’s sort of by “luck” as far as most prescribing

physicians are concerned. Most doctors use cholesterol lowing

medication to lower LDL-C, not LDL-P. Since there is an overlap

(i.e., since the levels of LDL-P and LDL-C are concordant) in many

patients, this misplaced use of statins seems to work “ok.” I, and

many others far more knowledgeable, would argue that if statins

and other drugs were used to lower LDL-P (and apoB), instead of

LDL-C, their efficacy would be even greater. The same is true for

dietary intervention.

Interestingly, (and I would have never known this had Jim Otvos not


15 of 17

graciously spent a hour on the phone with me two weeks ago giving

me a nuanced HDL tutorial), a study that went completely

unnoticed by the press in 2010, published in Circulation, actually

did a similar analysis to the Lancet paper, except that the authors

looked at HDL-P instead of HDL-C as the biomarker and looked at

the impact of phospholipid transfer protein (PLTP) on HDL

metabolism. In this study, though not the explicit goal, the authors

found that an increase in the number of HDL particles and smaller

HDL particles decreased the risk of cardiovascular disease. The

key point, of course, is that the total number of HDL particles rose,

and it was driven by increased small HDL-P. The exact same thing

was seen in the VA-HIT trial: the cardiovascular benefit of the

treatment (fibrate) was related to the rise in total HDL-P which was

driven by the fibrates’ ability to raise small HDL-P.

It seems the problem with the “HDL hypothesis” is that it’s using the

wrong marker of HDL. By looking at HDL-C instead of HDL-P,

these investigators may have missed the point. Just like LDL, it’s

all about the particles.

Summary

HDL-C and HDL-P are not measuring the same thing, just as

LDL-C and LDL-P are not.

1.

Secondary to the total HDL-P, all things equal it seems smaller

HDL particles are more protective than large ones.

2.

As HDL-C levels rise, most often it is driven by a

disproportionate rise in HDL size, not HDL-P.

3.

In the trials which were designed to prove that a drug that raised

HDL-C would provide a reduction in cardiovascular events, no

benefit occurred: estrogen studies (HERS, WHI), fibrate studies

(FIELD, ACCORD), niacin studies, and CETP inhibition studies

(dalcetrapib and torcetrapib). But, this says nothing of what

happens when you raise HDL-P.

4.


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Don’t believe the hype: HDL is important, and more HDL

particles are better than few. But, raising HDL-C with a drug isn’t

going to fix the problem. Making this even more complex is that

HDL functionality is likely as important, or even more important,

than HDL-P, but no such tests exist to “measure” this.

5.

One last thing for San Diego residents…

On Wednesday June 20 I’ll be giving a talk at the UCSD School of

Medicine titled: The limits of scientific evidence and the ethics of

dietary guidelines.

The talk will be given at the UCSD School of Medicine in the

Medical Teaching Facility, Room 175 (map: http://maps.ucsd.edu

/mapping/viewer/default.htm) [It’s in the blue section of the map,

numbered building 830]. I’m told folks should show up 15 minutes

early to find parking and to get a seat.

The seminar runs from 4:30-6:30 pm and includes lots of time for

Q&A. Unfortunately, it won’t be filmed, so I hope you can make it.

I am actually really looking forward to this talk, as it covers material

I have not spoken on publicly before. I hope some of you can make

it.

13

JUN


17 of 17

eatingacademy.com


VIII

Last week the Journal of the American Medical Association (JAMA)

published an article titled Lipid-Related Markers and Cardiovascular

Disease Prediction, which you can download here. This is quite

timely as we are in the midst of our series on cholesterol and heart

disease risk factors.

I was planning to write a post on my interpretation of this report, as

I know many of you have questions about it, when I was reminded

of one of my favorite principles in life: never be afraid to outsource

to those more qualified.

While there are many folks more qualified than me to address this

entire topic of cholesterol and heart disease risk, there are a

handful who have always been very generous with their time and

insights on this subject and who I consider mentors on this topic.

This list includes Drs. Tom Dayspring, Tara Dall, Allan Sniderman,

and Jim Otvos.

Below are excerpts of comments from Drs. Dayspring and Dall,

followed by the comments of Dr. Sniderman, with my comments

interspersed for clarification.

Initial response by Drs. Dayspring and Dall

The authors from the The Emerging Risk Factors Collaboration

(ERFC) conclude:

In a study of individuals without known cardiovascular disease

(CVD), the addition of information on the combination of

apolipoprotein B and A-I, lipoprotein(a), or lipoprotein associated

The straight dope on cholesterol – Part VIII

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phospholipase A2 (Lp-PLA2) mass to risk scores containing total

cholesterol and HDL-C led to slight improvement in CVD

prediction.

In other words, the authors concluded that advanced lipid testing,

beyond “just” LDL-C, HDL-C, TG, and total cholesterol did little to

help predict heart disease in people without a known history of

heart disease.

The accompanying editorial by Dr. Scott Grundy (the former NCEP

chairman) raises several flaws of the analysis including old apoB

data where studies used primitive and non-standardized apoB

assays as well as the use of out-dated older risk assessment tools

established 20-30 years ago when cardiac disease manifestation

and presentation were very different than today.

These analyses are flawed with respect to examining the

atherogenic lipoprotein variables in patients in which cholesterol

measurements and lipoprotein concentration measurements are

not also examined in the patients where the variables are

discordant. These measures (cholesterol concentrations and apoB)

are correlated. However, in the many patients where the measures

are discordant, apoB and LDL-P are the proven better variables to

measure both risk prediction and therapeutic goals. It is also

unfortunate that this study provided no LDL-P analysis. Thus,

these analyses might be of some interest to epidemiologists who

look at entire populations, yet have little value to practicing

clinicians who treat people one at a time.

It is difficult to make the case for apoA-I by itself in routine

screening as it is not the most accurate way of quantifying total

HDL-P. However both AMORIS (which somehow was not included

in this analysis) and INTERHEART — two very large trials —

revealed that the best risk predictor was the apoB/apoA-I ratio. So,

in drug naive patients the ratio (which requires apoA-I

measurement) is validated. No ratio is likely valid in patients on lipid

modulating medications as drugs do not effect apoB and apoA-I


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equally nor do apoB and apoA-I have equal predictive abilities.

With respect to inflammation markers, such as highly sensitive

C-reactive protein (hs-CRP), their appropriate use (as discussed in

the recent NLA biomarker statement) is to be used not in place of

lipid or lipoprotein concentrations but afterwards to better fine tune

risk which several studies have shown they do. Their elevation,

based on current knowledge, should lead the clinician to obtain

more resolute lipid and lipoprotein goals of therapy, not per se any

(still nonexistent) inflammatory goals of therapy. However, current

studies do suggest further studies will be needed to show if it is

important to also normalize at least some inflammatory markers.

The JAMA study states:

The addition of the combination apolipoprotein B and A-I,

lipoprotein(a), or lipoprotein-associated phospholipaseA2

(Lp-PLA2) to risk scores containing total cholesterol and HDL-C

provided slight improvement in CVD prediction.

When you apply that slight improvement to 300 million Americans

you are talking about millions of persons who would indeed benefit.

Interestingly, last year the NLA reviewed all of these data, and

much more, and came to the conclusion that apoB, LDL-P, Lp-PLA2

and Lp(a) were indeed useful in almost all folks who have greater

than a 5% ten-year Framingham Risk score (most adults over 40

years of age).

Subsequent response by Drs. Dayspring and Dall

This study combined data from 37 prospective cohort studies where

plasma apolipoprotein levels were measured at baseline in patients

followed for an average of 10 years. They conducted 2 analyses:

One which used apolipoprotein B (apoB), apolipoprotein A-I

(apoA-I), lipoprotein(a) (Lp(a)), or Lp-PLA2 instead of total

cholesterol (TC) and HDL cholesterol (HDL-C), and

1.


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One using the alternative biomarkers in addition to TC and

HDL-C.

2.

They concluded that replacement of TC and HDL-C with

apolipoproteins or their ratios was not associated with improved

cardiovascular risk prediction, whereas adding lipoprotein factors to

TC and HDL-C was associated with slight improvement in risk

prediction.

Several previous epidemiologic studies have demonstrated that

apolipoproteins, including apoB, may be as good as, and often

better than, LDL-C , non-HDL-C and cholesterol ratios for

estimating coronary heart disease (CHD) risk. In a previous

meta-analysis assessing the association between baseline apoB

levels and CHD risk from 19 prospective studies with follow-up of 9

years, apoB was a significant predictor of CHD, with an overall

relative risk of about 2 (i.e., double the risk) for the upper tertile

(i.e., upper third of the population) compared with the lower tertile.

Non-HDL-C has been suggested as a potential surrogate for apoB.

However, while non-HDL-C and apoB are highly correlated they

can also be discordant in many patients, including those with and

without metabolic syndrome, as shown here and here.

Clinical trials showing that apoB was superior, even to non-HDL-C,

in predicting risk for CHD are numerous, including AMORIS, Leiden

Heart Study, AFCAPS/TexCAPS, LIPID, Health Professional

Follow-up Study, NHANES, The Chinese Heart Study, Framingham

Offspring Study, Cardiovascular Risk in Young Finns,

INTERHEART, and IDEAL (summarized here).

Strong evidence now also exists that cardiovascular disease risk

tracks with LDL-P/apoB (not LDL-C) in patients with discordant

levels of these markers. Discordance analyses in the MESA study

show that LDL-C over- or underestimated LDL-related risk in many

patients, leading to suboptimal LDL management. This recently

published study in JAMA did not account for specific groups that


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were discordant but looked only at the population as a whole.

Remember physicians do not treat populations; they treat

individual patients, one by one.

How can a physician know if a patient is discordant if they do not

measure apoB or LDL-P? To restate the point, another limitation of

this study is that it did not include studies that used LDL-P analysis.

Most physicians view it as their goal not to miss one patient who

could benefit from preventive therapies through lifestyle and

counseling interventions or medications proven to reduce

cardiovascular (CV) risk in the primary prevention setting.

Multiple organizations support the use of apoB level as both marker

of CV risk and treatment goal. Current Canadian lipid guidelines

have incorporated apoB as an alternate primary target of therapy

due to the wealth of data supporting apoB in CV risk prediction. The

American Diabetes Association and American College of

Cardiology consensus statement in 2008 also recommended apoB

as a target of therapy in those with high cardiometabolic risk.

Furthermore, as part of the comprehensive diabetes care treatment

goals, the American Association of Clinical Endocrinology

published recommendations for apoB as another target of therapy

in addition to LDL-C, Non HDL-C, HDL-C and triglycerides. The

recommendations from AACC Lipoproteins and Vascular Diseases

Division Working Group on Best Practices also list goals of therapy

for apoB and LDL-P.

The JAMA study in question included studies from 1968 to 2007.

ApoB assays have improved significantly over the years, as early

assays were more primitive, non-standardized, and therefore less

reliable. The study authors recognize this limitation in their

comment section and it was also addressed in the accompanying

editorial by Dr. Scott Grundy, the former NCEP chairman. He

highlighted several other flaws in the analysis including the use of

out-dated risk assessment tools established 20-30 years ago when

coronary artery disease presentation was very different to today. In


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addition, correct interpretation of the study findings is difficult

without consideration of treatment differences among the patients

included in the study (e.g., patients with multiple high risk markers

may have been treated more aggressively, resulting in fewer

events).

As a practicing physician, I have used apoB/LDL-P for more than a

decade in order not to miss any patient that could be at risk and

might benefit from preventive therapy. I do not want any of my

patients to become part of the national statistic:

50% of people with heart disease have normal traditional lipid

values.

Population studies have diluted relevant clinical meaning to

physicians treating individual patients. Clearly a better measure is

needed to understand risk in individual patients. Randomized

controlled clinical outcomes trials in children are rare, but does that

mean we don’t treat children or young women? ApoB and/or

LDL–P can help physicians target which of those primary

prevention patients need more aggressive lifestyle or medical

therapy.

Conversely, it is also important not to over-treat patients with high

cholesterol who in fact may not have apoB, LDL-P or lipoprotein

(a)-related risk. It may also not be cost-effective or even

reasonable to treat such patients based on cholesterol levels. The

key is early detection for effective prevention. After very careful

review of all the published studies to date, the National Lipid

Association’s published consensus on advanced biomarker testing

in 2011 recommends that, except in the lowest risk patients, apoB

and LDL-P should be considered in most patients for both risk

assessment as well as ongoing clinical management.

On the other hand, apoA-I is minimally useful as a test in isolation

as there is not a one-to-one relationship between each HDL particle

and apoA-1 (as there is for an LDL particle and apoB). As there


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may be several apoA-I apolipoproteins on each HDL particle,

measuring apoA-I alone will not accurately quantify HDL-P. HDL is

incredibly complex and the functionality of the HDL particle will

likely be the focus of future assays and studies. For example, the

use of apoA-I as a tool to diagnosis familial

hypoalphalipoproteinemia is very helpful. This condition is very

difficult to treat clinically but is an important secondary cause of low

HDL-C that should be ruled out. Additionally, in both the

INTERHEART and AMORIS studies the best predictor of

cardiovascular risk was the apoB/apoA-I ratio.

Lipoprotein associated phospholipase A2 (Lp-PLA2) is an

inflammatory marker, not intended for use as a “stand-alone”

marker to assess cardiovascular risk, but in combination with other

lipoprotein-based tools (e.g., apoB and LDL-P). It is well

recognized that inflammation plays a role in atherosclerosis.

Currently accepted methods of assessing inflammation such as

high sensitivity C reactive protein (hs-CRP) may be elevated in

many disease states including, but not limited to, vascular disease.

Furthermore, hs-CRP levels may also fluctuate greatly so multiple

measurements are typically required. I have always considered

Lp-PLA2 to be a superior marker of vascular disease or what I

consider “angry arteries.” When Lp-PLA2 is elevated, treatments

aimed at reducing inflammation (e.g., dietary modification, omega-3

fatty acid supplementation, smoking cessation) become important.

Clinically, high levels of Lp-PLA2 indicate that the disease process

has not been effectively halted — arterial plaque may still be

actively forming — and more aggressive treatment is required as

unstable plaque may be present. Lp-PLA2 is not meant to be used

as a marker in isolation or to replace other traditional methods of

risk assessment. However, it greatly augments the utility of the

latter, and is a very useful tool to guide us in ongoing treatment

decisions.

Until levels of a patient’s biomarkers lie within the optimal range, it

is not clear that their risk has been eliminated. If our goal is to

reduce the epidemics of cardiovascular disease and diabetes, we


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need to be aware of the role lipoproteins play in cardiovascular and

diabetes disease prediction and continue to carry out research to

find better ways of detecting disease at earlier and earlier stages.

Additional commentary by Dr. Sniderman

In the JAMA study the average non-HDL-C was 175 mg/dl, which is

the 82nd percentile of the U.S. population whereas the average

apoB was 110 mg/dl, which is the 67th percentile. They should

match, but they do not. Obviously, some populations have higher

non-HDL-C than Americans. The Swedes, for example, certainly

do. But the Swedes have higher apoB levels to match. No

population that I have ever seen has values this discordant, which

means their lipoprotein composition is different from any I have ever

seen. This raises the question as to how accurately apoB was

measured. From Table 1, of the 26 studies with data on apoB,

blood was collected in 1 starting in 1968, in 1 in 1970, in 9 in the

1980’s, in 8 in the early 1990’s.

When were the apoB’s measured in relation to when they were

obtained? We don’t know. Reading the original papers, in the

great majority, measuring apoB was not part of the protocol. For 13

of the studies, no methods are listed and another 5 are listed as

in-house assays (i.e., non-standardized). None of these can have

standardized results. Nor are they necessarily accurate. Even the

studies employing commercial assays were not necessarily

standardized. The actual average values for apoB are listed in

Table 1 and, not surprisingly, they are extraordinarily variable.

These are mainly European studies and the average apoB ranges

from 0.86 to 1.33 with many of values in the 1.0 to 1.2 range. This

variance exceeds anything I have ever seen and anything that I

think is epidemiologically possible.

The trends can be compared within studies but the problem is that

this is a patient level study, which means all of these different

results from all of these different assays are mixed together. How

do you mix apples and oranges and nuts and pineapples and

pretend they are all cherries? Obviously, you can’t. If you did not


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measure something accurately, if the results from the different

studies differ so radically, how can they be lumped together?

What ERFC claims is the strength of their study is actually its

weakness. Our meta-analysis was done at the study level. All the

studies in our meta-analysis were published and therefore all the

methods to measure apoB are listed. There are two sources of

assay error: imprecision (lack of reproducibility) and inaccuracy

(lack of standardization). Study level analyses are certainly affected

by imprecision but not so much by inaccuracy since the trends in

each study are what is quantitated. This means our design, in this

instance, is stronger than their design.

The irony of this analysis is that the assays for apoB have been

standardized and are precise accurate but require the use of a

standardized assay. LDL-C has not been standardized and the

errors in measuring LDL-C are much more substantial than the

errors in measuring apoB. ApoB is measured much more

accurately and precisely in clinical practice today than it was

measured in the research studies in ERFC. ApoB was evaluated in

this study based on methods that no one would use today.

If one accepts ERFC, then total cholesterol is just as good as

LDL-C, non-HDL-C and apoB. This is not a reasonable conclusion.

What does this imply about the studies that showed LDL-C was

better than total cholesterol (TC) and the studies that showed

non-HDL-C was better than LDL-C and the studies that showed

apoB was better than LDL-C and non-HDL-C? It’s hard to imagine,

based on the conclusions of the ERFC, that we should go back to

using TC as the screening tool for CV risk.

On page 2501, ERFC writes: “replacement of information on total

cholesterol (TC) and HDL-C with apolipoprotein B and A-I

significantly worsened risk discrimination and risk classification.”

However, look at Figure 1. What happens when TC and HDL-C are

replaced by the TC/HDL-C ratio? Taking out the numerator (TC)

and the denominator (HDL-C) and putting in the ratio (TC/HDL-C) is


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the worst thing one can do. The c-index change is -0.0098 — more

than three times worse than with apoB, and net reclassification is

much worse also. How can the way the same numbers are entered

make such a difference?

The current study published in JAMA does not create a

compelling case to abandon the use of advanced lipid testing

in favor of standard testing. It suffers from many

methodological flaws and, upon careful examination in the

context of the entire body of literature, actually reinforces the

need for lipoprotein testing in all but a select few patients.

26

JUN


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eatingacademy.com


IX

Previously, across 8 parts of this series we’ve laid the groundwork

to ask perhaps the most important question of all:

What should you eat to have the greatest chance of delaying

the arrival of cardiovascular disease?

Before we get there, since this series has been longer and more

detailed than any of us may have wanted, it is probably worth

reviewing the summary points from the previous posts in this series

(or you can just skip this and jump to the meat of this post).

What we’ve learned so far




1.



2.




3.






4.

The straight dope on cholesterol – Part IX

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5.






6.








here.

7.



hydrophobic.

8.




9.





10.





11.


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12.







13.






14.



the advance.

15.





16.





17.





18.


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19.




20.




person.

21.


particle number. Period.

22.




23.



24.





25.



26.


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risk.

27.



markers.

28.







select patients.

29.







or mixed.

30.

HDL-C and HDL-P are not measuring the same thing, just as

LDL-C and LDL-P are not.

31.

Secondary to the total HDL-P, all things equal it seems smaller

HDL particles are more protective than large ones.

32.

As HDL-C levels rise, most often it is driven by a

disproportionate rise in HDL size, not HDL-P.

33.

In the trials which were designed to prove that a drug that raised

HDL-C would provide a reduction in cardiovascular events, no

benefit occurred: estrogen studies (HERS, WHI), fibrate studies

34.


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(FIELD, ACCORD), niacin studies, and CETP inhibition studies

(dalcetrapib and torcetrapib). But, this says nothing of what

happens when you raise HDL-P.

Don’t believe the hype: HDL is important, and more HDL

particles are better than few. But, raising HDL-C with a drug isn’t

going to fix the problem. Making this even more complex is that

HDL functionality is likely as important, or even more important,

than HDL-P, but no such tests exist to “measure” this.

35.

Did you say “delay?”

That’s right. The question posed above did not ask how one could

“prevent” or eliminate the risk cardiovascular disease, it asked how

one could “delay” it. There is a difference. To appreciate this

distinction, it’s worth reading this recent publication by Allan

Sniderman and colleagues. Allan sent me a copy of this paper

ahead of publication a few months ago in response to a question I

had posed to him over lunch one day. I asked,

“Allan, who has a greater 5-year risk for cardiovascular disease, a

25 year-old with a LDL-P/apoB in the 99th percentile or a

75-year-old with a LDL-P/apoB in the 5th percentile?”

The paper Allan wrote is noteworthy for at least 2 reasons:

It’s an excellent reminder that age is a paramount risk factor for

cardiovascular disease.

1.

It provides a much better (causal) model for atherosclerosis

than the typical age-driven models, and explains why age is an

important risk factor.

2.

What do I mean by this? Most risk calculators (e.g., Framingham)

take their inputs (e.g., age, gender, LDL-C, HDL-C, smoking,

diabetes, blood pressure) and calculate a 10-year risk score. If

you’ve ever played with these models you’ll quickly see that age

drives risk more than any other input. But why? Is there something


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inherently “risky” about being older?

Sniderman and many others would argue (and I agree) that the

reason age is a strong predictor of risk has to do with exposure to

apoB particles — LDL, Lp(a), and apoB-carrying remnants. Maybe

it’s because I’m a math geek, but such models just seem intuitive to

me because I think of most things in life in terms of calculus,

especially integrals, the “area under a curve.”

[I once tried to explain to a girlfriend who thought I wasn’t spending

enough time with her that my interest in her should be thought of in

terms of the area under the curve, rather than any single point in

time. That is, think in terms of the integral function, not the point-

in-time function. Needless to say, she broke up with me on the spot

(in the middle of a parking lot!), despite me drawing a very cool

picture illustrating the difference, which I’ve re-created, below.]

The reason age is such a big driver of risk is that the longer your

artery walls are exposed to the insult of apoB particles, the more

likely they are to be damaged, for all the reasons we covered in

Part IV of this series. [This paper also reviews the clinical situation

of PCSK9 mutations which builds a very compelling case for the

causal model of apoB particles in the development of

atherosclerosis].


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What does eating have to do with cardiovascular risk?

So now that everyone is on the edge of their seat in anticipation of

this punch-line, let me provide two important caveats.

First, there are no long-term studies – either in primary or

secondary prevention – examining the exact question we all want

to know the answer to with respect to the role of dietary

intervention on cardiovascular disease. There are short-term

studies, some of which I will highlight, which look at proxies for

cardiovascular disease, but all of the long-term studies (looking at

secondary prevention), are either drug studies or multiple

intervention studies (e.g., cholesterol-lowering drug(s) + blood

pressure reducing drug(s) + dietary intervention + exercise + …).

In other words, the “dream” study has not been done and won’t be

done for a long time. The “dream” study would follow 2 randomized

groups for many years and only make one change between the

groups. Group 1 would consume a standard American diet and

group 2 would consume a very-low carbohydrate diet.

Furthermore, compliance within each group would be excellent

(many ways to ensure this, but none of them are inexpensive – part

of why this has not been done) and the study would be powered to

detect “hard outcomes” (e.g., death), instead of just “soft outcomes”

(e.g., changes in apoB, LDL-C, LDL-P, TG).

Second, everything we have learned to date on the risk

relationship between cardiovascular disease and risk markers is

predicated on the assumption that a risk maker of level X in a

person on diet A is the same as it would be for a person on diet

B.

Since virtually all of the thousands of subjects who have made up

the dozens of studies that form the basis for our understanding on

this topic were consuming some variant of the “standard American

diet” (i.e., high-carb), it is quite possible that what we know about

risk stratification is that this population is not entirely fit for

extrapolation to a population on a radically different diet (e.g., a


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very-low carbohydrate diet or a ketogenic diet). Many of you have

asked about this, and my comments have always been the same.

It is entirely plausible that an elevated level of LDL-P or apoB in

someone consuming a high-carb diet portends a greater risk than

someone on a ketogenic or low-carb diet. There are many reasons

why this might be the case, and there are many folks who have

made compelling arguments for this hypothesis.

But we can’t forget the words of Thomas Henry Huxley, who said,

“The great tragedy of science is the slaying of a beautiful

hypothesis by an ugly fact.” Science is full of beautiful hypothesis

slayed by ugly facts. Only time will tell if this hypothesis ends up in

that same graveyard, or changes the way we think about

lipoproteins and atherosclerosis.

The role of sugar in cardiovascular disease

Let’s start with what we know, then fill in the connections, with the

goal of creating an eating strategy for those most interested in

delaying the onset of cardiovascular disease.

There are several short-term studies that have carefully examined

the impact of sugar, specifically, on cardiovascular risk markers.

Let’s examine one of them closely. In 2011 Peter Havel and

colleagues published a study titled Consumption of fructose and

HFCS increases postprandial triglycerides, LDL-C, and apoB in

young men and women. If you don’t have access to this journal,

you can read the study here in pre-publication form. This was a

randomized trial with 3 parallel arms (no cross-over). The 3 groups

consumed an isocaloric diet (to individual baseline characteristics)

consisting of 55% carbohydrate, 15% protein, and 30% fat. The

difference between the 3 groups was in the form of their

carbohydrates.

Group 1: received 25% of their total energy in the form of glucose

Group 2: received 25% of their total energy in the form of fructose


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Group 3: received 25% of their total energy in the form of high

fructose corn syrup (55% fructose, 45% glucose)

The intervention was relatively short, consisting of both an inpatient

and outpatient period, and is described in the methodology section.

Keep in mind, 25% of total energy in the form of sugar is not as

extreme as you might think. For a person consuming 2,400

kcal/day this amounts to about 120 pounds/year of sugar, which is

slightly below the average consumption of annual sugar in the

United States. In that sense, the subjects in Group 3 can be

viewed as the “control” for the U.S. population, and Group 1 can be

viewed as an intervention group for what happens when you do

nothing more in your diet than remove sugar, which was the first

dietary intervention I made in 2009.

Despite the short duration of this study and the relatively small

number of subjects (16 per group), the differences brought on by

the interventions were significant. The figure below shows the

changes in serum triglycerides via 3 different ways of measuring

them. Figure A shows the difference in 24-hour total levels (i.e., the

area under the curve for serial measurements – hey, there’s our

integral function again!). Figure B shows late evening (post-

prandial) differences. Figure C shows the overall change in fasting

triglyceride level from baseline (where sugar intake was limited for

2 weeks and carbohydrate consumption consisted only of complex

carbohydrates).


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The differences were striking. The group that had all fructose and

HFCS removed from their diet, despite still ingesting 55% of their

total intake in the form of non-sugar carbohydrates, experienced a

decline in total TG (Figure A, which represents the daily integral of

plasma TG levels, or AUC). However, that same group

experienced the greatest increase in fasting TG levels (Figure C).

Post-prandial TG levels were elevated in all groups, but significantly

higher in the fructose and HFCS groups (Figure B). The question

this begs, of course, is which of these measurements is most

predictive of risk?

Historically, fasting levels of TG are used as the basis of risk

profiling (Figure C), and according to this metric glucose

consumption appears even worse than fructose or HFCS.

However, recent evidence suggests that post-prandial levels of TG

(Figure B) are a more accurate way to assess atherosclerotic risk,

as seen here, here, and here. One question I have is why did the

AUC calculations in Figure A show a reduction in plasma TG level

for the glucose group?

The figure below summarizes the differences in LDL-C, non-HDL-C,

apoB, and apoB/apoA-I.


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Again, the results were unmistakable with respect to the impact of

fructose and HFCS on lipoproteins, and by extension, the relative

lack of harm brought on by glucose in isolation. [Of course,

removal of glucose and fructose/HFCS would have been a very

interesting control group.]

One of the simultaneous strengths and weaknesses of this study

was the heterogeneity of its subjects, who ranged in BMI from 18 to

35, in age from18 to 40, and in gender. While this provided at least

one interesting example of age-related differences in carbohydrate

metabolism (older subjects had a greater increase in triglycerides in

response to glucose than younger subjects), it may have actually

diluted the results. There were also significant differences between

genders in the glucose group.

What was most interesting about this study was the clear difference

between the 3 groups that was not solely a function of fructose

load. In other words, the best outcome from a disease risk

standpoint was in the glucose group, while the worst outcome was

not in the all-fructose group, but in the 50/50 (technically 55/45)

mixed group. This is a very powerful indication that while glucose

and fructose alone can be deleterious in excess, their combination

seems synergistically bad.

The role of saturated fat in cardiovascular disease

In the next week or two I’ll be posting an hour-long comprehensive

lecture I gave at UCSD a few weeks ago on this exact topic. Rather

than repeat any of it here, I’ll highlight one study that I did not

include in that lecture. The study, Effect of a high saturated fat and

no-starch diet on serum lipid subfractions in patients with

documented atherosclerotic cardiovascular disease, published in


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2003, treated 23 obese patients (average BMI 39) with known

cardiovascular disease (status post coronary artery bypass surgery

and/or stent placement) with a high-fat ketogenic diet. Because the

study was free-living and relied on self-reporting, not all subjects

had documented levels of elevated serum B-OHB. However, the

subjects were instructed to avoid starch and consume 50% of their

caloric intake via saturated fat, primarily in the form of red meat and

cheese. There were no restrictions on fruits and vegetables, which

may have accounted for the observation that not all subjects were

ketotic during the 6-week intervention. In total, only 5 of the 23

patients achieved documented ketosis.

All of the subjects were on statins and entered the study at a goal

LDL-C level target of 100 mg/dL, which may have been the only

way the authors could get the IRB to approve such a study.

The table below shows the changes in lipoprotein fractions

following the intervention (there was no control group):


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This study was conducted during the height of the “outcry” over the

Atkins diet. While most doctors reluctantly agreed that Dr. Atkins’

diet could reduce body fat, most believed it was still very

dangerous. In the words of Dean Ornish, “Sure you can lose

weight on a low-carb diet, but you can also lose weight on heroin

and no one would recommend that!”

Fair point. In fact, the authors of this study acknowledged that they

“strongly expected” this dietary intervention to increase risk for

cardiovascular disease, which is why they only included subjects on

statins with low LDL-C. However, as you can see from the table

above, the authors were startled by the results. The subjects

experienced a significant reduction in plasma triglycerides and

VLDL triglycerides, without an increase in LDL-C or LDL-P. In fact,

LDL size and HDL size increased and VLDL size decreased – all

signs of improved insulin resistance. Furthermore, fasting glucose

and insulin levels also decreased significantly. The mean

HOMA-IR was reduced from 5.6 to 3.6 (normal is 1.0) and

TG/HDL-C from 3.3 to 2.0 (normal is considered below 3, but

“ideal” is probably below 1.0) in just 6 weeks. Taken together,

these changes, combined with the dramatic change in VLDL size,

suggest insulin resistance was dramatically improved while

consuming a diet of 50% saturated fat!

As all of these patients were taking statins, we’re really robbed of

seeing the impact of this diet on LDL-P, which did not change.

Also, CRP levels rose (though not clinically or statistically

significantly).

Putting it all together

It is very difficult to make the case that when carbohydrates in

general, and sugars in particular, are removed or greatly reduced in

the diet, insulin resistance is not improved, even in the presence of


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high amounts of saturated fats. When insulin resistance improves

(i.e., as we become more insulin sensitive), we are less likely to

have the signs and symptoms of metabolic syndrome. As we meet

fewer criteria of metabolic syndrome, our risk of not only heart

disease, but also stroke, cancer, diabetes, and Alzheimer’s disease

goes down.

Furthermore, as this study on the Framingham cohort showed us,

the more criteria you have along the spectrum of metabolic

syndrome, the more difficult it becomes to predict your risk, due to a

widening gap in discordant risk markers, as shown in this figure.

As I noted at the outset, the “dream” trial has not yet been done,

though we (NuSI) plan to change that. Until then each of us has to

make a decision several times every day about what we will and

won’t put in our mouths. Much of this blog is dedicated to

underscoring the impact of carbohydrate reduction on insulin

resistance and metabolic syndrome.

The results of the trials to date, combined with a nuanced

understanding of the lipoprotein physiology and their role on the

atherosclerotic disease process, bring us to the following

conclusions:

The consumption of sugar (sucrose, high fructose corn syrup)1.


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increases plasma levels of triglycerides, VLDL and apoB, and

reduces plasma levels of HDL-C and apoA-I.

The removal of sugar reverses each of these.2.

The consumption of fructose alone, though likely in

dose-dependent fashion, has a similar, though perhaps less

harmful, impact as that of fructose and glucose combined (i.e.,

sugar).

3.

The addition of fat, in the absence of sugar and starch, does not

raise serum triglycerides or other biomarkers of cardiovascular

disease.

4.

The higher the level of serum triglycerides, the greater the

likelihood of discordance between LDL-C and LDL-P (and

apoB).

5.

The greater the number (from 0 to 5) of inclusion criteria for

metabolic syndrome, the greater the likelihood of discordance

between LDL-C and LDL-P (and apoB).

6.

I would like to address one additional topic in this series before

wrapping it up – the role of pharmacologic intervention in the

treatment and prevention of atherosclerotic disease, so please hold

off on questions pertaining to this topic for now.

12

JUL


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The Straight Dope on Cholesterol

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Transcript of The Straight Dope on Cholesterol