As mentioned previously, the relationship between dietary fat
intake and the risk of developing breast cancer was investigated
in a variety of epidemiologic studies. These studies included case series
reports, ecologic analyses, case–control studies, and cohort
studies. To date, no randomized controlled clinical trial of the
relationship between consumption of a low-fat diet and prevention of
breast cancer has been published.
Ecologic or correlation studies have demonstrated a consistently
strong relationship between dietary habits, as estimated by per-capita
consumption of dietary fat, and breast cancer occurrence in different
countries. Plots of these data have yielded a linear relationship,
with increasing fat consumption associated with higher breast cancer
occurrence. The problem with such studies is that they do not demonstrate
that increased dietary fat in individuals is
associated with breast cancer occurrence in the same individuals
(ie, the ecologic fallacy may be involved).
For example, in industrialized countries in which fat consumption
and breast cancer mortality tend to be higher than in developing
countries, it may not be the high fat consumers who are developing
breast cancer. The comparatively high mortality rates of breast
cancer in industrialized countries may be attributable to other
factors, such as earlier menarche, delayed childbearing, or other
Case–control studies have yielded conflicting results
on the question of diet and breast cancer. The reasons for these
conflicting results are not clear. One criticism is that dietary
data collected retrospectively are inaccurate (ie, dietary exposure
is misclassified). General difficulties in recalling diet could
contribute to nondifferential misclassification, and case–control
differences in ability or motivation to recall dietary fat could
result in differential misclassification. Another potential explanation
is that the influence of dietary fat could have been exerted many
years prior to the diagnosis of breast cancer. Most case–control
studies have included diet information from the recent but not from
the remote past. Some reviewers have concluded that none of the
studies, when viewed individually, had a large enough sample size
to provide adequate statistical power. In other words, a true relationship
between dietary fat intake and risk of developing breast cancer might
be missed because of inadequate discriminatory ability (ie, a type
II error may be involved).
Some of these problems can be avoided with the cohort study design.
Prospective cohort studies eliminate the potential for distortion
of results from differential recall of dietary history. This type of
study was used to investigate the question of fat in the diet and
the risk of developing breast cancer. Again, the published results
of cohort studies of this question have yielded conflicting results.
Some studies have produced evidence of a relatively weak association,
with risk ratios in the range of 1.2 to 1.7. Other cohort studies,
however, have indicated little or no association. In general, the
dose–response relationship suggested by the ecologic studies
has not been borne out in the case–control and cohort studies.
Some reviewers have argued that the range of dietary fat intake
in the analytic studies is smaller than in the international ecologic
studies. Within a single country, there may be too little variation
in fat intake to demonstrate a dose–response relationship in
case–control and cohort studies. Others have argued that
factors other than diet (ie, confounders) that could not be controlled
easily in ecologic studies were the real explanation for the association
between fat intake and development of breast cancer observed in
Was there biological plausibility to the relationship? What pathophysiologic
mechanism might be involved? Were there animal models that showed
the relationship between fat in the diet and development of breast
cancer? In fact, animal studies have found an association between
fat intake and the development of mammary cancers in mice bearing
the mammary tumor virus. Similar findings have been reported in
other animal models. The pathophysiology involved in this process
is less clear, but potential mechanisms have been discussed. It
has been speculated that mammary neoplasms are controlled by endocrine
balance, which in turn is affected by dietary factors, including
fat intake. For example, women consuming high-fat diets have been
shown to have more circulating estrogen than women on low-fat diets.
In postmenopausal women, adipose tissue has been demonstrated to
be a contributor to the production of estrogen. Dietary fat intake
may also have modified DNA synthesis and cell duplication. If this
were found to be true for breast tissue, this could be relevant
to breast carcinogenesis. In animal studies, certain types of fatty
acid in the diet modulate mammary tumor growth and metastasis. The
evidence is strongest for a promotional effect of polyunsaturated
fat. The potential differential effect of various types of fatty
acid on development and progression of cancer might explain some
of the variation observed in studies of fat intake and breast cancer
risk in humans. Most epidemiologic studies have evaluated total fat
intake. Some studies have used adipose tissue levels of fat as a
surrogate measure of fat intake. Adipose tissue levels, however,
do not provide an indication of dietary intake of fatty acids that can
be synthesized internally. The Nurses Health Study did include a
prospective evaluation of different types of fatty acid intake.
In this study, none of the various types of dietary fat was associated
with the risk of developing breast cancer.
In Chapter 7: Clinical Trials
, we introduced the concept of a meta-analysis.
meta-analysis is a type of quantitative systematic review in which
the results of multiple studies that are considered combinable are
aggregated together to obtain a precise, and hopefully unbiased,
estimate of the relationship in question. As already illustrated
in the dietary fat–breast cancer question, single studies
rarely provide the definitive answer on a topic. A systematic review
helps in two specific ways:
- 1. By combining a series
of smaller studies, each with a statistically imprecise estimate
of effect, a larger sample size is obtained, with a corresponding
increase in statistical precision.
- 2. By identifying the differences
in findings across different studies, sensitivity analyses can be
conducted that may lead to greater insight into the sources of heterogeneity.
The steps in a systematic review should follow a clear sequence.
The first step is to formulate a clear and meaningful question to
be addressed. Elements to be considered in the formulation of this
question are (1) the type of person(s) involved, (2) the type of
exposure that the person(s) experiences (eg, a risk factor, a prognostic
factor, a diagnostic procedure, or a therapeutic intervention),
(3) the type of control with which the exposure is compared, and
(4) the outcomes to be addressed. In the context of the patient
profile, we might specify the question in the following way: For premenopausal women with a family history
of breast cancer, is reduction of dietary fat consumption substantially
below levels typical of the American diet likely to reduce the risk
of developing breast cancer?
Note that even with this level of specification, there is some
ambiguity. The characteristics of the population could have been
delimited further, by noting other characteristics of the patient,
such as her age of 40, a history of having her first child after
age 30, and a recent normal mammogram. The precise level of dietary
fat reduction was characterized as substantial, but not quantified
further. Although greater specificity is desirable in formulating
the question, if the question becomes too specific, there may be
an insufficient amount of pertinent literature available to address
it. With insufficient data, it may be impossible to answer the question.
As a general rule, features of the patient or the intervention that
are thought in advance to affect the answer should be considered.
Once the question is formulated, the next step is to decide what
studies to include. A pragmatic approach is to limit the source
studies to those published in the English language. There is a possibility
that this will introduce some bias, as studies with positive findings
may be submitted preferentially to journals with wider circulation,
which tend to be the English language journals. A further consideration
is whether to limit the review to published studies, as these are
peer reviewed and presumably of higher quality. As noted in Chapter 7: Clinical Trials, however, a publication bias might
result, as studies with positive findings may be more likely to
be submitted and accepted for publication. Of course, the identification
of unpublished work may be incomplete.
The next step is to search for the studies of interest. As suggested
earlier in this chapter, an automated search of an electronic database,
such as Medline, is an efficient way
to identify the relevant published literature. Such a search, however,
is limited to the articles contained in the database, which will
tend to exclude the proceedings of meetings that are not published
in journals, doctoral dissertations, and journals that are not indexed.
Once the articles for potential inclusion are identified, they
must be reviewed one at a time. Specific eligibility criteria for
inclusion must be specified. The included studies should be directly relevant
to the question under consideration. In the present context, they
must include, at a minimum, a study sample including healthy premenopausal
women, information on dietary consumption of fats, comparison of
higher and lower fat intakes, and estimation of breast cancer risk.
If a qualitative review is under consideration, a wide range of
studies, including those involving laboratory animals, human case
series, cross-sectional, case–control, and cohort studies,
as well as clinical trials might be included. If there is interest
in a statistical integration of data from the various studies, the
analysis may be limited to studies in which the data are combinable
(eg, case–control and cohort studies and clinical trials).
The next step is to extract the key data elements from
the included studies. A pretested standard abstract form should
be used to collect the relevant information from each source paper.
To ensure reliability, it is advisable to have two independent reviewers
collect information from each paper.
The actual analysis of the data begins with an estimation of
the effect of interest in each of the included studies. It is useful
to display the individual study results as shown in Figure 13–5.
In this graph, the results of five separate hypothetical studies
of the relationship between reduced dietary fat intake and risk
of developing breast cancer are presented. The findings of each
investigation are summarized with a point estimate of effect and
a corresponding confidence interval around that estimate. The point
estimate is represented as a solid circle, with a horizontal line stretching
in both directions to the upper and lower bounds of the confidence
interval. By convention, the effect typically is measured in terms
of either an odds ratio or a relative risk (risk ratio).
Meta-analysis of five hypothetical epidemiologic studies
(A–E) of the relationship between reduced dietary fat intake
and the risk of developing breast cancer.
In Figure 13–5, the results are displayed in terms of
estimated relative risk of developing breast cancer associated with
a reduced level of dietary fat intake. If reducing fat in the diet
decreases the risk of developing breast cancer, a relative risk
less than 1 would be expected. The point estimates for the relative
risks in these hypothetical studies ranged from a low of 0.6 (Study
E) to a high of 1.3 (Study A). Four of the five studies (Studies
B through E) had point estimates lower than 1, consistent with the
possibility of a reduction in breast cancer risk. On the other hand,
most of these estimates are close to the null value of 1 (no association
between dietary fat intake and risk of developing breast cancer).
It is possible, therefore, that these small benefits suggested by
a reduction in fat consumption may have arisen by chance.
Examination of the corresponding confidence intervals for the
individual studies provides some insight into the statistical precision
of the results and whether they are statistically significant. By convention,
95% confidence limits typically are calculated. The odds
or risk ratio often is displayed on a logarithmic scale as in Figure
13–5, so as to make the confidence intervals symmetrical
around the point estimate.
It can be seen from Figure 13–5 that the precision of
the five study results varied, with the narrowest confidence intervals
(greatest precision) for Study D and the widest confidence intervals
(least precision) for Study A. Only one of the five studies had
a result that was statistically significant at the 5% level,
as indicated by the fact that its confidence interval did not cross
the dashed vertical line corresponding to the null value of one.
Statistical integration of the results of the individual studies
allows calculation of a summary estimate of effect. On the bottom
of Figure 13–5, the combined point estimate for the five
hypothetical studies is shown as a diamond. The vertical points
of the diamond are located at the point estimate of the summary
effect, and the horizontal points are located at the upper and lower bounds
of the 95% confidence interval, respectively. In this example,
the combined point estimate was 0.86, with a corresponding 95% confidence
interval from 0.70 to 1.05. The summary estimate is based on a larger
sample size than any of the individual study results. Accordingly,
the combined estimate is more precise, which is reflected in the
figure by comparatively narrow confidence intervals. In this example,
it can be seen that the combined effect is consistent with a small reduction
in risk of developing breast cancer, but even with the large sample
size of the five studies combined, chance cannot be excluded as
a possible cause of the findings.
Once the individual and combined estimates are obtained, it is
useful to consider the level of heterogeneity across the individual
results. A graphic display of the data, as in Figure 13–5,
provides some insight into this question. Although there is some
variation in the individual results, the impression is that there
is considerable overlap in the values of the relative risk that
are consistent with each result. This would suggest that heterogeneity
of findings is not a problem in summarizing these data. A formal
test of heterogeneity can be performed to address this issue from
a statistical perspective.
Sensitivity analysis can be performed to identify patterns of
results across the individual study results and potentially provide
insight into any heterogeneity that exists. For example, two questions
may be examined: do studies of a particular design (eg, case–control
studies) tend to yield results that differ from studies of other
designs (eg, cohort studies), and do investigations with younger
study populations tend to have results that differ from investigations
with older populations?
In the present hypothetical example, it is unclear that a sensitivity
analysis would yield great insights for two reasons. First, there
are a relatively modest number of studies involved, which limits
the ability to explore findings across subgroups. Any differences
that were observed might arise on the basis of chance, leading to
false-positive interpretations. Second, the results across studies
are reasonably consistent, further limiting the ability to find
different underlying patterns. From a descriptive point of view,
it might be useful to consider possible reasons (other than chance)
for an apparently different result in Study A.
Meta-analysis can be useful in achieving greater statistical
power, but it cannot overcome the limitations and potential biases
of individual studies. The investigator performing a meta-analysis must
also be careful that selection of some studies and exclusion of
others do not lead to a distorted conclusion.
A meta-analysis published in 2003 explored the relationship between
dietary fat intake and risk of breast cancer. This systematic review
included 45 studies (31 case–control and 14 cohort), with
a combined total of over 25,000 breast cancer patients and 580,000
control or comparison subjects. An overall small increase in risk
of breast cancer was associated with elevated total fat intake in both
the case–control (OR = 1.14) and cohort studies
(RR = 1.11). The combined association was statistically
significant and was higher in the studies judged to be of better
quality. Similar findings were observed in analyses of saturated
fat and meat intake.
In the Patient Profile, the physician wanted to determine whether
dietary fat intake is causally related to the development of breast
cancer. The findings of studies were inconsistent, and the strength
of association was weak at best, without clear evidence of a dose–response
relationship. The temporal sequence of dietary fat intake and development
of breast cancer appeared to be reasonable.
The physician in the Patient Profile, however, is left without
a firm answer to the question about the relationship between consumption
of dietary fat and risk of developing breast cancer. Even if research
has demonstrated a small increase in risk with high-fat diets, it
is unclear whether the patient can meaningfully reduce her risk
of breast cancer by changing her diet. The physician may believe,
however, that a low-fat diet is justifiable for other reasons, including
reduction of risk for cardiovascular disease. In addition, there
are other cancers, such as colon cancer, for which the protective
effect of a low-fat diet may be beneficial.
This type of uncertainty is common in clinical medicine. Physicians
often must weigh potential risks and benefits of an intervention
and make decisions without complete information. The goal of medical
research is to continue to provide better answers to important clinical
questions. The best way to resolve the issue of the relationship
between dietary fat intake and risk of developing breast cancer
is through large randomized controlled trials comparing occurrence
of breast cancer in women randomized to a low-fat diet with women
randomized to a high-fat diet. Clearly, this type of study would
provide the most definitive evidence about a causal relationship
between consumption of dietary fat and risk of developing breast
cancer. Such a study is underway, but until the results are available,
the best evidence is that which has been obtained through observational studies.
Much of the satisfaction derived from patient care relates to the
ability to incorporate new knowledge into the practice of medicine.
It is imperative to develop the skills required for a critical review
of the medical literature to keep current on the state of information.
This is a difficult task, but the reward is enormous when it results
in improved patient care.