Modernist-Cuisine-Vol.-1-Small

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Go ahead and eat high-fiber, bran-rich
cereal if you like it—just don’t expect it to
lower your risk of cancer.
Testing a Dietary System
Science has developed a rigorous process that can,
in principle, determine whether a food contributes
to (or helps prevent) a particular disease. In
practice, this scientific process sometimes breaks
down, largely because this kind of science
known as nutritional epidemiologyis a blend
of human physiology and sociology, both of which
are tremendously complex and difficult to control
experimentally.
The first step in scientifically testing a dietary
system is to express it in the form of a hypothesis,
which is a statement about the relationship
between measurable quantities whose veracity
can be supportedor, more important, contradictedby
experimental evidence. Burkitt’s
hypothesis, for example, was “A diet rich in fiber
reduces the risk of colon cancer.”
Epidemiologists test their hypotheses in several
ways (see Types of Nutritional Epidemiology
Studies, page 221). The most rigorous is a prospective
randomized, controlled clinical trial. Burkitt
was a surgeon, however, not an epidemiologist,
and he based his enthusiasm for his high-fiber
dietary system on anecdotal evidence from an
ecological study. Many years after his idea caught
on, large, randomized, controlled trials proved his
hypothesis wrongan unfortunately common
fate for hypotheses about diet and health.
The first hurdle a new nutritional hypothesis
must clear is usually a small-scale study. Relatively
cheap, fast, and easy to run, small-scale studies are
useful for selecting dietary systems that are worth
testing in a more rigorous way.
Small studies do not usually definitively settle
a scientific question, however, because they suffer
from various kinds of errors and bias that undermine
the reliability of the results. Sampling error
is familiar from opinion pollsit reflects the fact
that whenever you choose a subset of people to
represent a larger group, or humanity as a whole,
sheer coincidence might give you a group that
produces a misleading answer.
Bias comes in several varieties. Recall bias
often plagues nutritional studies when researchers
ask people to remember how frequently they have
eaten certain foods in recent months or to keep
a food diary. In either case, people may subconsciously
suppress memories of eating certain foods
and exaggerate their consumption of others.
Prospective clinical studies that actually measure
or control subjects’ diets can eliminate recall bias,
but these are relatively rare.
Observation bias occurs when the act of
studying a person changes his or her behavior.
Weight-loss intervention studies frequently
overestimate the benefit of the proposed diet, for
example, because participants stick to the diet
only as long as the scientists track their progress.
Once the study ends, the subjects tend to slip off
the diet and regain their weight.
In another form of observation bias, researchers
tend to treat patients receiving an intervention
differently from the “control” patients, who
receive only a placebo. Double-blinded trials
in which neither the doctor nor the patient knows
who is getting the interventionsignificantly
reduce this bias. But they are hard to do when it
comes to food.
Selection bias afflicts nearly every nutritional
study because it is so hard to recruit a group of
participants that mimics the composition of the
population overall. Almost always, one arm of the
study ends up with more men, for example, or
fewer African-Americans, or more tall people than
the other arm has. As a result, it is rarely possible
to know whether the findings of the study will
apply to groups that differ from the study cohorts.
Randomizing participants into different arms of
the study helps reduce selection bias. But randomization
cannot overcome the limits of a study that
includes only men (as some have done) or only
female nurses (such as the Nurses’ Health Study).
Selection bias sometimes occurs in a more
insidious form, when researchers deliberately try
to skew the outcome. Ancel Keys, M.D., the initial
champion of a link between dietary fat and heart
disease, was accused of such intentional selection
bias by other scientists.
Even if a study is large enough to reduce
sampling error and careful enough to avoid
significant bias, confounding effects can produce
misleading results. Confounding occurs when two
unrelated characteristics, such as gray hair and
colon cancer, appear strongly connected because
both are affected by the actual causal factor (age,
in this example).
When the studies have been done and the
papers have been written, publication bias can
come into play. A recent study shows that clinical
trials with positive results are more likely to be
published in scientific journals than studies that
show that a treatment did not work. This means
that negative results often disappear unheeded.
Imagine if your local newspaper published only
good news and never informed you about murders,
break-ins, and assaults. You would imagine
that your local police force was 100% effective.
Publication bias similarly deprives doctors and
their patients of all the relevant facts about the risk
of disease as they consider the relative merits of
a particular treatment or dietary system.
When scientists compare the risks experienced
in various arms of a study, they often use the terms
hazard ratio, odds ratio, or relative risk. These
numbers have a similar interpretation; namely,
whether the risk of getting a disease was higher or
lower in the intervention group than in the control
group. A hazard ratio close to 1.0 tells us that there
was little difference between the intervention and
control groups, so the intervention did not work.
In the Women’s Health Initiative study, for
example, the women who ate a low-fat, high-fiber
diet were slightly more likely to get colorectal
cancer than were the women who ate their normal
diets. The hazard ratio was 1.08, meaning those in
the low-fat, high fruit-and-grain group were 8%
more likely to get cancer (see page 217).
Very strong statistical associations—such
as the observation that smokers are about
10 times as likely to get lung cancer as
nonsmokers are—can persuasively link
a behavior to a disease even if scientists
are uncertain of the detailed causal
mechanisms at work. But in nutritional
epidemiology, associations between diet
and health outcomes are generally far
weaker—closer to 10% than to 1,000%—
so the links are much less clear.
218 VOLUME 1 · HISTORY AND FUNDAMENTALS
FOOD AND HEALTH 219