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PERSPECTIVE
Understanding Nutritional Epidemiology and Its
Role in Policy1,2
3
Department of Nutrition and 4Department of Epidemiology, Harvard School of Public Health, Boston, MA; and 5Channing
Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA
ABSTRACT
Nutritional epidemiology has recently been criticized on several fronts, including the inability to measure diet accurately, and for its reliance
on observational studies to address etiologic questions. In addition, several recent meta-analyses with serious methodologic flaws have arrived
at erroneous or misleading conclusions, reigniting controversy over formerly settled debates. All of this has raised questions regarding the
ability of nutritional epidemiologic studies to inform policy. These criticisms, to a large degree, stem from a misunderstanding of the
methodologic issues of the field and the inappropriate use of the drug trial paradigm in nutrition research. The exposure of interest in
nutritional epidemiology is human diet, which is a complex system of interacting components that cumulatively affect health. Consequently,
nutritional epidemiology constantly faces a unique set of challenges and continually develops specific methodologies to address these.
Misunderstanding these issues can lead to the nonconstructive and sometimes naive criticisms we see today. This article aims to clarify common
misunderstandings of nutritional epidemiology, address challenges to the field, and discuss the utility of nutritional science in guiding policy
by focusing on 5 broad questions commonly asked of the field. Adv Nutr 2015;6:5–18.
Keywords:
dietary assessment, food policy, meta-analysis, nutritional epidemiology, randomized controlled trials, prospective cohort studies
Introduction
Epidemiology has long had its share of skeptics, with Taubes’
1995 article being the most well-known (1). However, more
recent commentaries have attacked nutritional epidemiology on several fronts. Ioannidis (2) criticizes the observational nature of epidemiologic studies and small trials,
stating that “definitive solutions won’t come from another
million observational papers or small randomized trials.”
He refers to an article by Archer et al. (3), which calls into
question the validity of data from the NHANES and suggests
that “the ability to estimate population trends in caloric
intake and generate empirically supported public policy relevant to diet-health relations from US nutritional surveillance is extremely limited.” Furthermore, questionably
designed and executed meta-analyses have disseminated
conflicting messages about nutrition and health, such as
the conclusion that being overweight lowers the risk of allcause mortality (4) and that replacing saturated fat with
polyunsaturated fats has no substantial impact on cardiovascular risk (5). Such conclusions are not only confusing but
also dangerous because they can be perceived as misleading
messages, or can lead to the communication of misleading
messages to the public by popular media and the consequent
adoption of unhealthy practices by the population at large.
For instance, after the publication of the latter meta-analysis,
New York Times columnist Mark Bittman told his readers
that they “can go back to eating butter” (6).
Many authors have suggested that large randomized controlled trials (RCTs)6 are the only solution to circumventing
the problems in observational research. In reality, RCTs are
far from being the panacea in the study of diet and chronic
disease, and the results of such trials can be misleading. A
key reason is that the exposure of interest in nutritional
epidemiology—dietary intake—is complex, with interactions and synergies across different dietary components,
which can be difficult to study with use of a linear drug trial
approach. A complex behavioral exposure such as diet also
makes other aspects important in pharmacologic RCTs,
such as high compliance and blinding, difficult and infeasible in most dietary intervention trials. Consequently,
1
6
Supported by NIH grants HL60712, DK58845, P30 DK46200, and U54CA155626.
Author disclosures: A Satija, E Yu, WC Willett, and FB Hu, no conflicts of interest.
* To whom correspondence should be addressed. E-mail: nhbfh@channing.harvard.edu.
2
ã2015 American Society for Nutrition. Adv. Nutr. 6: 5–18, 2015; doi:10.3945/an.114.007492.
Abbreviations used: CVD, cardiovascular disease; DLW, doubly labeled water; IHD, ischemic
heart disease; NTD, neural tube defect; RCT, randomized controlled trial; SSB,
sugar-sweetened beverage; TFA, trans FA; WHI, Women’s Health Initiative.
5
Downloaded from advances.nutrition.org at FORT VALLEY STATE UNIVERSITY on January 9, 2018
Ambika Satija,3,4 Edward Yu,3 Walter C Willett,3–5 and Frank B Hu3–5*
nutritional epidemiology has design and analysis issues
unique to the field, and understanding the details of nutritional epidemiologic studies requires a deep knowledge of
nutritional science and its methodologic background.
The purpose of this article is to clarify common misunderstandings of nutritional epidemiology, address the challenges to the field, and discuss the utility of nutritional
science in guiding policy. In particular, we address 5 broad
questions that have been commonly raised about nutritional
epidemiologic studies.
Can We Reliably Measure Dietary Intakes in
Individuals and Populations?
6 Satija et al.
Downloaded from advances.nutrition.org at FORT VALLEY STATE UNIVERSITY on January 9, 2018
Measuring diet in free-living populations is challenging because individual diets are complex exposures with innumerable and sometimes poorly characterized components that
are consumed in varying amounts and combinations by different individuals. Dietary variables are rarely dichotomous;
often, but not always, the entire population is “exposed” to
some degree. Diet is also a time-varying exposure, with individual dietary habits and food composition changing
over time. It is not surprising, then, that most dietary assessment methods have a component of error, which could be
random day-to-day, diurnal, and seasonal variation in an individual’s diet over time, or because of systematic mechanisms, such as omission of foods when collecting data.
Nonetheless, several techniques have been developed to ascertain dietary intake from free-living populations, and
these methods have shown good validity with use of multiple criteria. Although each assessment method comes with
its own set of limitations, strengths unique to each method
make it appropriate for use in specific applications (7–10).
Multiple-week diet records, which require participants to
record everything they eat or drink over the course of several
weeks, are regarded as the gold standard for ascertaining dietary information because, unlike other methods, they do
not rely on memory. The high participant burden and cost
of keeping diet records has limited their use in large-scale
epidemiologic studies; however, their ability to accurately
ascertain detailed dietary information makes them useful
in validation studies of other diet assessment methods,
and in monitoring compliance in trials. Another limitation
of diet records is that the process of recording can change
an individual’s diet, rendering the data atypical of usual intake, although estimated intakes from diet records have been
found to correlate reasonably well with those from multiple
24-h recalls (11). Repeated 24-h recalls involve a respondent
reporting all foods consumed in the previous 24 h or calendar day to a trained interviewer in person or over the phone.
Although reliance on the participant’s memory leaves room
for measurement error, a skilled interviewer can produce
highly detailed and useful nutritional data comparable to a
diet record (11, 12). This method has been widely employed
in dietary intervention trials. It is also used in national surveys to monitor trends in nutritional intake.
A potential source of error common to these methods is
in the estimation of nutrients with use of food composition
tables. The nutrient content of a food varies with season, location of production, growing conditions, storage, processing, and cooking techniques, and many of these factors are
unaccounted for in food composition tables. The degree to
which this is problematic differs from nutrient to nutrient.
Although for some nutrients, such as dietary FAs, it is reasonable to assume that these variations do not substantially
affect calculated intakes, for others, such as selenium, the
variation can result in calculated intakes that are substantially different from true intakes (7). In general, however,
this source of error does not substantially compromise the
ability to rank individuals with respect to nutrient intake
so as to evaluate associations with health outcomes (7,
13). Nevertheless, estimating nutrient composition from
food intake data is a challenge, especially given the changing
food landscape, and it is crucial that we continue to improve
the accuracy of food composition databases.
When participants provide biological specimens, researchers can additionally measure intake by assaying biomarkers. Examples of biomarkers include doubly labeled
water (DLW) (for total energy intake), urinary nitrogen
(for protein intake), 24-h urinary sodium and potassium,
blood lipid profiles, serum and plasma folate, and selenium
and other trace minerals in toenails. Biomarkers allow for
objective measurement of intake without any bias because
of self-reporting. The limitations of biomarkers, however,
have prevented their wider use. In particular, many foods
and nutrients lack sensitive or specific biomarkers, their
assessment always includes error from multiple sources,
they may not be indicators of individual long-term intake,
and obtaining and testing for biomarkers is expensive and
burdensome. Thus, use of biomarkers to investigate nutrientdisease relations has been mostly confined to nested casecontrol studies and small trials. Biomarkers are also useful
in assessing the validity of less-expensive, self-reported assessments of diet, such as FFQs.
An FFQ consists of a structured food list and a frequency
response section on which the participant indicates his/her
usual frequency of intake of each food over a certain period
of time in the past, usually 1 y. This is the most common
choice for measuring intake in large observational studies
owing to its ease of use, low participant burden, and ability
to capture usual long-term dietary intake. These features
make possible repeated assessments over time, which is important to capture longer term variation in diets. Table 1
presents a comparative summary of the advantages, disadvantages, and applications of these dietary assessment
methods.
Thus, a collection of diverse diet assessment methods is
available; their appropriate application, alone or in combination, allows for a reasonably comprehensive assessment
of the diet of free-living populations. Nevertheless, recent
critiques of these dietary assessment methods have called
into question their utility in examining diet-disease relations
and informing policy. A recent example is the article by Archer et al. (3), which criticizes the use of 24-h dietary recall
data periodically collected in the NHANES. Archer et al.
TABLE 1
Comparison of diet assessment methods
Several day/week
diet records
Advantages
Needs literate, motivated participants;
participant burden is
very high when done
over several days.
Could also alter usual
eating habits.
Expensive and resource-intensive diet
assessment method.
Potential for errors in
nutrient estimation
from food composition tables.
Applications
Validation of other diet
assessment methods.
Monitoring compliance
in dietary intervention trials.
Multiple 24-h recalls
A single 24-h recall
Validated FFQ
Biomarkers
Provides fairly accurate,
detailed, openended data on dietary intake, without
reliance on longterm memory.
Provides detailed,
open-ended data on
dietary intake, without reliance on longterm memory.
Provides time-integrated data that represents usual longterm intake. Can assess past dietary
intake.
Has lower respondent
burden and is less
expensive than diet
records, and works
well in low-literacy
contexts.
There is scope for shortterm recall error,
omissions, and errors
in portion size
estimation.
Has lower respondent
burden and is less
expensive than diet
records and multiple
recalls; works in lowliteracy contexts.
There is scope for shortterm recall error,
omissions, and errors
in portion size estimation. Has high
random within-person error.
The least expensive and
most easily administered diet assessment method, with
the lowest respondent burden.
There is scope for longterm recall error.
Omissions possible
because of fixedfood list.
FFQs need to be culture- and populationspecific.
Provides an objective
assessment of intake.
Represents bioavailable dose, which is
relevant when it is
used in etiologic
analyses.
May be available in retrospect (analysis of
stored specimens).
Has high interviewer
burden and is more
expensive than a
single recall and
FFQs.
Potential for errors in
nutrient estimation
from food composition tables.
Validation of other diet
assessment methods.
Has high interviewer
burden and is more
expensive than FFQs.
Potential for errors in
nutrient estimation
from food composition tables.
Semi-quantitative.
Potential for errors in
nutrient estimation
from food composition tables.
National surveillance of
mean population
intake.
Assessment of trends in
dietary intake (earlier
NHANES).
Association analyses in
large epidemiologic
studies.
Assessing past dietary
intake.
Monitoring compliance
in dietary intervention trials.
Assessment of trends
in dietary intake
(current NHANES).
compared reported energy intake as assessed by the 24-h recalls with expected basal metabolic rate and concluded that
recalled energy intake data were implausibly low and recommended that NHANES data be eliminated in considering
public policy. This finding represents the danger of misunderstanding methodologic issues and making inferences
with use of faulty logic. A recent article by Hébert et al.
(13) comprehensively refutes the conclusions drawn from
this study. The following section discusses key points from
this article while providing an overview of measurement error assessment and correction in nutritional epidemiology.
Nutritional epidemiology has advanced considerably over
the last 50 y with respect to understanding types and sources
of measurement error in dietary intake data (7, 14). An
insufficient appreciation of this can lead to erroneous conclusions like those of Archer et al. (3). Because of the considerable day-to-day variation in dietary intake among
Biomarker may not be
sensitive to intake,
may have low specificity, may not be
time-integrated, may
not represent usual
long-term intake, and
is subject to laboratory errors and other
sources of bias.
Expensive and more invasive. Biomarkers
are not available for
many nutrients.
Validation of other diet
assessment methods.
Association analyses in
epidemiologic studies and monitoring
compliance in intervention trials
individuals, a single recall, as was used by Archer et al. in
their analysis, will tend to capture extremes of dietary intake
as opposed to usual current intake, increasing the likelihood
that any individual’s single recall will be implausibly high or
low. This random variation adds noise to the data, overestimating the variance, and flattening the distribution, thereby
increasing the numbers of individuals in the extremes of the
distribution. Thus, repeated 24-h recalls on nonconsecutive
days are recommended to reduce within-person error. More
epidemiologic studies that use 24-h recalls to assess diet now
obtain multiple replicate measures on each participant, and
starting in 2002, a second 24-h recall was introduced in the
NHANES to address some of these issues (8).
However, as noted earlier, error in diet assessment need
not be completely random. Systematic sources of variation
include omission of foods consumed by individuals, errors
in estimating portion sizes, and over- or under-reporting
Understanding nutritional epidemiology 7
Downloaded from advances.nutrition.org at FORT VALLEY STATE UNIVERSITY on January 9, 2018
Disadvantages
Provides accurate, detailed, open-ended
data on dietary intake, with no reliance
on memory, and direct computation of
portion sizes.
Errors from omission,
portion size estimation, and recall are
least likely.
8 Satija et al.
widened with time. These data underscore the importance
of developing nutritional policies to improve diet quality
and reduce health disparities.
Because of its low cost and low participant burden, selfadministered computer-processed FFQs are the only option
in most large cohort studies to assess usual dietary intakes.
FFQs usually have lower random within-person variation
than other dietary assessment methods because they are designed to assess average usual intake over the past year. For
this reason, they are better equipped to assess long-term dietary intake, the exposure of etiologic interest for most diseases (7). Because of their reliance on memory, FFQs may
suffer from greater measurement error relative to recalls
and records if these methods are used for many days to reflect longer-term intakes (for certain nutrients, just a few
days of diet records or recalls might be enough, provided
the days are spread out over the entire reference period of
the FFQ). Nevertheless, FFQs have been shown to have acceptable validity when compared to reference measures
(29, 30), with typical correlation coefficients for individual
nutrients or foods ranging from 0.4 to 0.7 (7). Adjustment
for total energy intake, along with use of repeated FFQs in
long-term prospective cohort studies, further improves
these validity coefficients. Although extended dietary records are the most popular reference method, when biomarkers are available, triangulation methods can be used
to obtain improved estimates of correlations of FFQ intake
with true intake (31). These validity coefficients can be
used to correct for measurement error in epidemiologic
analyses, and the application of these measurement error
correction methods is increasingly being extended to more
complicated analyses (18–20). These techniques have allowed for valid inferences to be drawn from large cohort
studies with use of FFQ data.
Despite these developments in reducing measurement error in dietary intake data, continued improvements in dietary
assessment methodology and measurement error correction
are needed to advance the field. Nevertheless, the considerable progress made over the past few decades, especially the
use of repeated measures of diet over time, has enabled nutritional epidemiologists to reliably collect and use dietary information in both individuals and populations.
What Is the Role of Nutritional Epidemiology in
Inferring Causality?
One of the main criticisms leveled against nutritional epidemiology is that it relies predominantly on observational
data, which is deemed to be inferior to experimental data
in determining causality. Figure 1 illustrates the typical hierarchy of evidence from various study designs. While randomized trials with hard endpoints occupy the highest
position in this hierarchy, they are usually not the most appropriate or feasible study design to answer nutritional epidemiologic questions regarding long-term effects of specific
foods or nutrients (unless they can be packaged in a pill).
In the absence of evidence from large RCTs on hard
endpoints, nutritional epidemiologists typically rely on
Downloaded from advances.nutrition.org at FORT VALLEY STATE UNIVERSITY on January 9, 2018
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