Briefly
There were always two types of cholesterol, the good and the
bad. Until now. A large new study tells us that good cholesterol might have
been an impostor. That's food for the media types. For those who think before
they type, the real news is that we are finally getting closer to uncovering
the impostors. Thanks to the genetics revolution which seems to be paying off
in an unexpected area.
HDL - The Knight in Shining Armor
In the cholesterol universe there are two camps: good
cholesterol, also known as HDL, and bad cholesterol, often referred to as
non-HDL cholesterol. The latter comes in a variety of flavors, of which LDL is
the most prominent and best known. From many large observational studies we
know that high levels of LDL and low levels of HDL associate with an elevated
risk for heart disease and stroke. Certain limits have been derived from these
studies, above which your LDL shouldn't rise and below which your HDL shouldn't
fall. The magic level for HDL is 60 mg/dL blood. Above that limit, we are
assured, HDL will even offset some other risk factor, such as age or being of
the male persuasion. Given that a large percentage of people fail to achieve
these desirable levels, researchers have been eagerly sourcing for
pharmaceutical means to increase HDL. Now a new study tells us, that HDL might
have to be stripped off its White-Knight title, much for the same reason as
"Dr." Karl-Theodor zu Guttenberg, the former German defense minister,
had to be stripped off his doctorate last year: for being an impostor.
Epidemiology 101
If you have been following biomedical research for a couple
of years, you will have noticed that results are often conflicting. So, you
might discount the findings of one study if hundreds of others come to a
different conclusion. Only in this case you should pay closer attention,
because what Voight and colleagues have produced strikes at the foundation of
how we do research in epidemiology, the science which studies the health of
populations [1].
To appreciate the gravity of the
situation, I need to familiarize you with a basic concept of epidemiological
studies: Confounding. I'll use a very simple and hypothetical example.
Let's say we are interested to know the causes of health and
disease in children in the hypothetical and impoverished state of Maladipore.
The figure to the left represents our astonishing finding that children growing
up in a household which owns a TV are significantly less likely to die during
childhood than children growing up without the boob tube. The correlation
between TV ownership status and survival are very strong and compelling.
On the
face of it we could now recommend the prime minister to improve the health of
the nation by simply installing a TV in every household in which there are
children. If we know that this is nonsense, we take our epidemiology tools and
look for another factor which has an influence on TV ownership AND on survival
rate.
And so we discover that wealth is this third factor. We call it a
confounder. Wealth has confounded our original finding because the wealthy can
afford a TV and they can also afford medical care and immunization for their
children. Whereas the inability to buy a TV certainly reflects the inability to
buy medical care, too. When we repeat our analysis of the data, which we
gathered during our observational study, we find that the link between TV
ownership and survival disappears once we bring the third variable, wealth,
into the equation. Clearly, providing every household with a TV wouldn't have reduced
the rate of child deaths. Greater wealth however will.
In the case of Maladipore, common sense is all it takes to
suspect and find the confounder. In real life it is almost never as simple.
When we find an association between cholesterol and heart disease, then we
typically have some idea about the way cholesterol might contribute to heart
disease. At that stage our ideas are merely hypothetical. The classic way of
investigating them is through clinical trials in which we randomize
participants into 2 groups, one in which we lower (bad) cholesterol and another
in which we don't, the control group. Then we observe them for a period and
note the rate at which people in both groups develop heart disease or die from
it. If we find that the control group, the one which didn't receive the benefit
of having its cholesterol lowered, has a significantly higher rate of falling
ill, we conclude that lowering cholesterol is the way to go. Sounds easy, but
it isn't. For several reasons. In the case of cholesterol, the time between
developing high bad, or low good, cholesterol and suffering a heart attack or
stroke is measured in decades rather than in years. We also cannot just
experiment with people as we would like to in the name of science. Ethics
boards look very closely at the potential risks and benefits associated with
what we do in trials. We cannot simply withhold treatment from a control group,
with scientific curiosity as the motivation. With these obstacles, we had to draw our conclusions from
observational studies, which tell us a lot about associations but nothing about
cause and effect. Until now, we simply had no other choice. But not any more:
It's Mendel All Over Again
With larger and larger databases being
developed from genetic research we can now do something else: Mendelian randomization
studies. Which is what Voight and colleagues did. The concept behind it is
amazingly simple and elegant, though not as brand new as you might think. It
has been named after Gregor Mendel, the father of modern genetics, who first
observed and described how traits are inherited. As
always, a concept is best understood using an example. In the 1980s some
researchers thought that very low cholesterol levels might increase the risk of
cancer. There was definitely an association being observed between cancer and
low cholesterol, but nobody knew which was the cause and which the effect. Or
whether there was a third confounding variable, as yet unknown. Now, you can't
make a study in which you lower the cholesterol in some people, just to see
whether they will develop cancer. Go
and find volunteers for that one.
So, Martjin Katan had another idea [2]. In 1986 he pointed out that
there existed a certain variation in one gene (the gene which encodes the apolipoprotein
E), which, if you had that variation, would give you extremely low cholesterol
levels. He also knew, of course, that we inherit our genes from our mother and
our father in a random way. That means, your hodgepodge of genes and my
hodgepodge of genes are not systematically different from each other. Both are
just random assemblies of genes from among all possible variations. In case you
inherited that low-cholesterol gene, and I didn't, then it was just the luck of
the draw. The important point is, that there is no room for confounding the
random selection of genes.
Unfortunately, in 1986 it was impossible to realize this
study design. The required genetic data were not yet available. That is changing.
While, to the best of my knowledge, Katan's proposal has not been carried out yet, Voight and
colleagues used his proposed design to investigate the HDL-heart
disease theory.
The Death of Cholesterol?
They looked at a rare gene variant, which, as far as we know
today, correlates strongly with HDL concentration, but not with any other
cholesterol type. That's important, because we need to disentangle the effects
of HDL from those of LDL. In their analysis, using data from 21,000 heart
disease patients and 95,000 controls (people free of heart disease), the
researchers could not find any association between HDL level and risk of heart
disease. But Voight and colleagues didn't leave it at that. They also
formulated a genetic risk score using 14 common gene variants with known
effects on HDL (but not on LDL) and examined the score's association with heart
disease in over 12,000 patients and over 41,000 controls. Again, nothing.
Elevated HDL did not show up as the cherished knight in shining armor.
What do we make of this? First, that raising HDL cholesterol
may not be a way to reduce the risk of heart disease. Therefore, secondly,
let's not think that treating a so-called risk factor will reduce risk (more on
that in my post "when risk factors for heart disease really suck").
Third, let's hope Pfizer & Co. get this message, too. Because drugs, which
treat risk factors but not risk, are like impostors: they never deliver.
Voight, B., Peloso, G., Orho-Melander, M., Frikke-Schmidt, R., Barbalic, M., Jensen, M., Hindy, G., Hólm, H., Ding, E., Johnson, T., Schunkert, H., Samani, N., Clarke, R., Hopewell, J., Thompson, J., Li, M., Thorleifsson, G., Newton-Cheh, C., Musunuru, K., Pirruccello, J., Saleheen, D., Chen, L., Stewart, A., Schillert, A., Thorsteinsdottir, U., Thorgeirsson, G., Anand, S., Engert, J., Morgan, T., Spertus, J., Stoll, M., Berger, K., Martinelli, N., Girelli, D., McKeown, P., Patterson, C., Epstein, S., Devaney, J., Burnett, M., Mooser, V., Ripatti, S., Surakka, I., Nieminen, M., Sinisalo, J., Lokki, M., Perola, M., Havulinna, A., de Faire, U., Gigante, B., Ingelsson, E., Zeller, T., Wild, P., de Bakker, P., Klungel, O., Maitland-van der Zee, A., Peters, B., de Boer, A., Grobbee, D., Kamphuisen, P., Deneer, V., Elbers, C., Onland-Moret, N., Hofker, M., Wijmenga, C., Verschuren, W., Boer, J., van der Schouw, Y., Rasheed, A., Frossard, P., Demissie, S., Willer, C., Do, R., Ordovas, J., Abecasis, G., Boehnke, M., Mohlke, K., Daly, M., Guiducci, C., Burtt, N., Surti, A., Gonzalez, E., Purcell, S., Gabriel, S., Marrugat, J., Peden, J., Erdmann, J., Diemert, P., Willenborg, C., König, I., Fischer, M., Hengstenberg, C., Ziegler, A., Buysschaert, I., Lambrechts, D., Van de Werf, F., Fox, K., El Mokhtari, N., Rubin, D., Schrezenmeir, J., Schreiber, S., Schäfer, A., Danesh, J., Blankenberg, S., Roberts, R., McPherson, R., Watkins, H., Hall, A., Overvad, K., Rimm, E., Boerwinkle, E., Tybjaerg-Hansen, A., Cupples, L., Reilly, M., Melander, O., Mannucci, P., Ardissino, D., Siscovick, D., Elosua, R., Stefansson, K., O'Donnell, C., Salomaa, V., Rader, D., Peltonen, L., Schwartz, S., Altshuler, D., & Kathiresan, S. (2012). Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study The Lancet DOI: 10.1016/S0140-6736(12)60312-2
Everyone used to believe HDL cholesterol protect a person from coronary heart disease. It may not be true after all.
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