Dietary and health biomarkers—time for an update
Dragsted et al. Genes & Nutrition
Dietary and health biomarkers-time for an update
Lars O. Dragsted 0
Qian Gao 0
Giulia Praticò 0 2
Claudine Manach 1
David S. Wishart 5
Augustin Scalbert 4
Edith J. M. Feskens 3
0 Department of Nutrition, Exercise and Sports, University of Copenhagen , Copenhagen , Denmark
1 INRA, Human Nutrition Unit, Université Clermont Auvergne , F63000 Clermont-Ferrand , France
2 Department of Food Science, University of Copenhagen , Copenhagen , Denmark
3 Division of Human Nutrition, Wageningen University & Research , Wageningen , The Netherlands
4 Nutrition and Metabolism Section, Biomarkers Group, International Agency for Research on Cancer (IARC) , Lyon , France
5 Department of Biological Sciences, University of Alberta , Edmonton , Canada
In the dietary and health research area, biomarkers are extensively used for multiple purposes. These include biomarkers of dietary intake and nutrient status, biomarkers used to measure the biological effects of specific dietary components, and biomarkers to assess the effects of diet on health. The implementation of biomarkers in nutritional research will be important to improve measurements of dietary intake, exposure to specific dietary components, and of compliance to dietary interventions. Biomarkers could also help with improved characterization of nutritional status in study volunteers and to provide much mechanistic insight into the effects of food components and diets. Although hundreds of papers in nutrition are published annually, there is no current ontology for the area, no generally accepted classification terminology for biomarkers in nutrition and health, no systematic validation scheme for these biomarker classes, and no recent systematic review of all proposed biomarkers for food intake. While advanced databases exist for the human and food metabolomes, additional tools are needed to curate and evaluate current data on dietary and health biomarkers. The Food Biomarkers Alliance (FoodBAll) under the Joint Programming Initiative-A Healthy Diet for a Healthy Life (JPI-HDHL)-is aimed at meeting some of these challenges, identifying new dietary biomarkers, and producing new databases and review papers on biomarkers for nutritional research. This current paper outlines the needs and serves as an introduction to this thematic issue of Genes & Nutrition on dietary and health biomarkers.
Metabolomics; Biomarker; Nutrition; Ontology; Food intake; Classification; Validation; Databases; Review
Introduction—biomarkers in nutrition research
Dietary and health biomarkers have been addressed in
several recent reviews [
]. These reviews cover
various applications of biomarkers in food, nutrition, and
health research as well as aspects of their identification,
measurement, and validation. The definition of the term
“biomarker” varies considerably. While definitions in
these papers cover specific aspects of food intake or
health effects, biomarkers are more generally defined as
“chemical or biological test results in an analysed
biological material related to a certain exposure,
susceptibility, or biological effect” [
]. In the Ontobee subsection
on Chemical Entities of Biological Interest (ChEBI) [
biomarker is defined as “A substance used as an
indicator of a biological state,” clearly reflecting biomarkers as
a subcategory of “indicators.” “Indicators” are, in turn,
defined as “anything used in a scientific experiment to
indicate the presence of a substance or quality, change in a
body, etc.” The ChEBI ontology therefore reflects
experimental science and measurement of chemical substances as
prerequisites for the use of the term, “biomarker.” However,
in nutrition research, there is widespread use of
observational studies and of markers that cannot be characterized
as a substance, e.g., blood pressure, waist circumference, or
a host antibody response. While discriminating between
the terms “indicators” and “biomarkers” may be useful in
some areas of research, the overlap in their definitions and
use make this distinction less useful in nutrition research
underlining the need for a specific ontology for nutritional
The distinction between different categories of
biomarkers has been underlined in several reviews in the
area. Jenab et al. [
] subdivides them into recovery,
predictive, concentration, and replacement biomarkers, based
on their biokinetics and intended use. As already
mentioned, another classification divides them into exposure,
effect, and susceptibility biomarkers, thereby focusing only
on their use. These classifications may cause ambiguity
and a unifying classification scheme may therefore be
needed. This is particularly important since the discovery
of new biomarkers and their validation is clearly needed
to advance nutritional science as outlined in several recent
reviews of this area [
4, 7, 9
Biomarker validation is particularly important in order
to improve the quality of nutritional studies. However,
the reliability of a biomarker may depend on the
application, biological sample, sample collection strategy (time,
frequency), and study design. A clear distinction of
validation criteria for the different classes of biomarkers
is therefore needed.
Excellent tools and guidance exist for producing
systematic reviews and meta-analyses such as the
Cochrane handbook [
]. In addition, the PRISMA
] has been developed to assist
researchers in conducting systematic reviews of
randomized trials and interventions. When it comes
to biomarkers used as tools for measuring food intake
or assessing nutritional status, there is a need for
another paradigm because several of the steps
described for the current procedures do not apply.
Also, when it comes to sharing all of this information
in databases and associated online tools, there is a
need to build upon several of the tools already
mentioned. These include ontologies for the subject area,
a classification scheme for biomarkers, validation
tools, and high quality reviews of the current state of
knowledge, see Fig. 1. As a project launched under
the JPI-HDHL, the FoodBAll consortium aims to
close some of these gaps through a series of reviews
in this thematic issue of the journal.
An ontology for the dietary and health biomarker area
Ontologies exist for several nutrition-related areas,
including biological chemistry [
] and environmental [
], and biomedical investigations [
is even some initial work on an ontology for nutritional
] and an ontology for food [
most terms and relationships related to nutrition and
biomarkers are not yet covered at any of these sites.
Creating a network of defined terms with connections to
some of these ontologies is therefore a potential way
forward. It is not the intention here to formally develop
full nutrition ontology, only to define terms that can
serve as classes and subclasses in developing ontology
for this field. Figure 2 contains suggestions for terms
that could be included in such nutrition ontology and at
the same time outlines the definitions of terms used in
this thematic issue of Genes and Nutrition.
The connection of the term “food” to the simple
definition in ChEBI (“Any material that can be ingested by an
organism”) is useful. However, it is a bit too broad by
not excluding ingestion of drugs or non-food objects. It
is further complicated because this ontology has
organized the term as a subclass of “food component,” while
it would be more useful to have food as class term, with
food components and food compounds as subclasses as
we suggest here. In ENVO (the Environment Ontology),
the term “food product” is defined as “A substance,
usually composed primarily of carbohydrates, fats, water
and/or proteins, that can be eaten or drunk by an animal
or human being for nutrition or pleasure” [
food products solely as substances may be confusing in
chemistry-related fields such as nutrition and food
chemistry, so using the term “material” to define food
should be preferred. Since many non-foods such as
drugs could be ingested “for pleasure,” the definition is
also a bit too broad and the complex practices,
frequency, or reasons for ingesting foods should be
avoided in the definition. Therefore, in simplicity, any
material may be considered a food as long as it is
inherently able to sustain nutrition to some extent. The
preferred definition of food for nutrition and health
science would therefore be “Any material or substance
that can be ingested by an animal or human for
nutrition.” The subclasses of “food products” in ENVO
should also be subclasses of “food” and “food group”
thereby linking downstream to the various single foods
and food components for which biomarkers of intake
should ideally be found.
Nutrition, as such, is not found as a term in any
ontology yet while “nutritional science” is an undefined
class term with no subclasses under biomedical science
in EMBRACE [
]. A broad definition of nutritional
science suggested here is “The science of all processes
by which organisms take in and utilise nutrients or
other food components,” while nutrients could be
defined as “Food compounds needed to maintain a living
organism.” Note that nutritional science as defined here
also embraces non-nutrient components in the food
since these compounds may have considerable
influence on the health effects of foods. This is also the case
for public definitions such as the one found in
Wikipedia, “Nutrition is the science that interprets the
interaction of nutrients and other substances in food in
relation to maintenance, growth, reproduction, health
and disease of an organism” [
]. Food intake and
nutrition are closely related therefore making “diet” a
natural link between the food science terms and the
nutrition area. In this case, “diet” may therefore in this
context be defined as “The combination of foods
consumed by an individual or a group within a certain
time period.” By defining these terms and linking them
with existing ontologies, dietary and health biomarkers
can now be discussed on the basis of a coherent set of
Ambiguity of biomarker classifications
There are a number of biomarkers that may belong to
several of the classes described by Biesalski et al. [
they can be used as exposure, effect, or susceptibility
biomarkers, depending on the study purpose and design.
Several of the exposure biomarkers measured in plasma,
which are termed concentration and replacement
biomarkers by Jenab et al. [
], may be used to assess
exposures to nutrients or contaminants. However, for some
of these compounds such as vitamins, minerals, or heavy
metals, there are also established thresholds for minimal
or maximal concentrations beyond which there is an
increased risk of deficiency (for vitamins and minerals) or
toxicity. When biomarkers are used to compare sample
concentrations with such limits, they are actually used
to assess the status, vulnerability, or even risk of an
individual and, hence, should be classified as susceptibility
biomarkers. Clearly, the classification in these cases is
more dependent on the intended application of the
measurements than on the methodology as such. Exposure
biomarkers in urine have been termed recovery
biomarkers if the full dose may be recovered. Alternately,
they are called predictive biomarkers if only a fraction is
]. If a dietary treatment is used to improve
absorption of a nutrient, then this marker becomes
related to response rather than exposure or susceptibility.
Most metabolites measured in urine may therefore
qualify in each of the major classes, depending on the
purpose of the measurements and study design. For
example, p-cresol sulfate along with other metabolites
would result from environmental exposure to p-cresol;
however, this compound is also formed endogenously by
our microbiota. Formation is clearly affected by the
composition of the diet in terms of omnivorous and
vegetarian diets and hence may be said to reflect dietary
]. On the other hand, p-cresol formation may
also affect sulfation capacity so its sulfate ester may be a
marker of altered metabolism (effect) or residual
capacity (susceptibility) [
]. Additionally, p-cresol sulfate
has been shown to be a susceptibility marker related to
risk of progressing kidney disease [
]. In other words,
p-cresol sulfate as a biomarker could have at least three
different classifications, depending on the intended use.
Many other exposure biomarkers, including omega-3 fatty
acids, beta-carotene, and choline metabolites also reflect
some degree of functional change or host factor capacity
leading to similar biomarker classification ambiguity.
Other examples, some of which will be discussed below,
include measurements of blood pressure, blood glucose,
and hippuric acid. Biomarker ambiguity, whether
biochemical, anthropometric, or physiological, is therefore
quite common, as many combine elements of two or three
of the exposure, effect, and susceptibility marker classes.
Biomarkers are typically affected by a combination of
exposures and host factors and consequently complex to
interpret, resulting potentially in controversy. Blood
pressure may serve as an illustrative example. It is well
established today that blood pressure is influenced by genetic
(host) factors and genetic variation may be involved in 50
% of the population variability [
]. Blood pressure is also
affected by dietary and lifestyle exposures, including
], smoking [
], and healthy eating [
relationships with risk of stroke and coronary disease is
quite clear, health-related effects of blood pressure within
the normal range from 120/80 to − 90/60 mmHg are not
equally clear and a large variation in what constitutes an
optimal blood pressure may exist on an inter-individual
]. Moreover, the measurement is very sensitive to
the protocol and repeated measurements should be done
by the same person. Care must therefore be exercised in
study planning and in interpretation when blood pressure
is used as a marker, and it should be clear whether it is
used for determining risk or effect. In analogy to blood
pressure, there is a range of biochemical and physiological
biomarkers where only the high and low ends of the
outcome scale are readily interpreted in terms of individual
risk, e.g., most anthropometric measures, hormones,
micronutrients, intermediary metabolites, and cognitive scales.
An important consequence of this ambiguity is that
validation of a biomarker may depend on its use. Most
validation schemes can roughly be subdivided into
analytical performance and biological interpretability. The
analytical performance of a biomarker may often be
independent of the study design and purpose. However,
this is clearly not the case when the measurement of
extremes is more important than the normal range for
biological reasons. For instance, the detection limit or
linear range of a method may suffice for an assessment
of baseline characteristics but not for the assessment of
an extreme response or vice versa. For instance, the use
of glucose monitors may reflect variation with sufficient
precision to follow the change in response in individuals
during an OGTT or dietary test (i.e., used as an effect
biomarker), while the accuracy of the same method
would not suffice to determine fasting glucose levels for
diagnostic purposes (i.e., as a risk or susceptibility
biomarker). Validation of biomarker measurements may
therefore depend on the biomarker class, which in turn
may depend mainly on its intended use. Validation
schemes taking into consideration the intended
applications of dietary and health biomarkers are therefore
needed in order to help validate the large number of
new potential biomarkers resulting from the many
explorative (“omics”) investigations on diet and health.
Analysis of the literature for assessing biomarker validity
Putative new biomarkers of dietary exposures and of
dietary effects on health are being published at a rapid
pace as a result of recent developments in metabolomics
], but previous work through the last 40+ years has
also pointed to a number of potentially important
dietary biomarker compounds identified by more traditional
approaches. Some of these have been “re-discovered” by
metabolomics. This calls for standards for doing
systematic literature searches and for evaluating biomarker
candidates. Standards for systematic reviews and
metaanalyses already exist for effect markers, including the
Cochrane guidelines . The PRISMA statement [
also helps to assess many aspects of individual study
quality in order to weigh their importance for an overall
conclusion. These aspects relate to the strength of the
experimental or observational designs, the quality of
recruitment, the randomization procedures, etc. The aim
of these guidelines are to critically assess effects reported
in human studies and they are not aimed at assessing
methodological studies or performing systematic reviews
for food and dietary intake biomarkers. Guidelines
developed specifically for assessing the literature on
biomarkers are therefore needed. The aim should be to find
previously suggested biomarkers and to critically assess
their quality. Moreover, the evaluation of each biomarker
candidate should be supported by the literature search
strategy by including different quality aspects. The vision
for this work on intake biomarkers would be:
(a) to identify and evaluate existing putative intake
biomarkers for all food groups based on the
(b)to validate the more promising candidates using a
coherent quality assessment scheme, and
(c) to create a database including all suggestive food
intake biomarkers along with their current level of
validity for assessing exposure.
This should support further work on food intake
biomarker development and validation by pointing out the
studies needed to improve the assessment of validity.
Moreover, such a system should help researchers to
assess the quality of food intake biomarkers that are
considered for use in human studies on diet and health.
Similar literature search guidelines, quality assessment
tools, and validation schemes need also to be developed
for susceptibility and effect biomarkers.
Supporting databases for food intake biomarkers
Biomarker development for research in nutrition and
health is dependent on resources to quickly find
information on compounds in foods and on food intake
biomarkers proposed by others. The literature review and
validation of biomarkers for all major food groups
should therefore be entered into searchable database
structures along with unique identifiers. The most
comprehensive databases on food constituents and their
chemical and biological data are FooDB (www.foodb.ca)
], the expert-curated database PhytoHub
] focused on dietary phytochemicals, and
the Phenol-Explorer database on polyphenols [
These databases are currently being enriched to include
new data on food non-nutrients and their human
metabolites. The added metabolites will include the known
metabolites described in the literature as well as in silico
predicted metabolites, thereby covering large numbers
of potential biomarkers for food intake. In parallel, a
new database called Exposome-Explorer
(exposomeexplorer.iarc.fr) is being developed to include all known
dietary biomarkers and rich information on their
measurement in various populations [
will thereby supplement information in the human
metabolome database [
Adding mass spectral and other information is of
central importance to help researchers annotate findings
from metabolite profiling studies. In many cases, the
compounds measured as biomarkers are not commercially
available and information on their (bio)synthesis and
availability in non-commercial laboratories for sharing can be
found in FoodComEx (Food Compound Exchange,
]. FoodComEx is designed as an online
catalog of pure compounds, which have been made
available by academic laboratories. Exchange of compounds
with a provider depends on bilateral agreements on the
terms of collaboration. Rules for these collaborations have
been defined in a charter of good practices. FoodComEx is
a collaborative initiative widely open to new contributors
Another web resource developed in the FoodBAll
project is a web portal (foodmetabolome.org) [
portal is continuously updated to present links to the most
useful tools, databases, libraries of spectra, and software
for nutritional metabolomics as well as for dietary
biomarker discovery. The portal will be further developed
to present tutorials, webinars, and news related to the
food metabolome and to food intake biomarkers.
The current pace of biomarker discovery and biomarker
applications is higher than ever before due to the rapid
development of “omics” technologies and data
collection. This rapid development may reshape future
research in nutrition and health. In order to support this
development, there is a need to develop ontologies for
food, nutrition, and diet-related health areas. There is
also a need to classify biomarkers in such a way that
systematic attempts to validate them and develop them into
trusted research tools is possible according to
standardized criteria and according to their intended use. Finally,
there is a need for improved methods to systematically
search both older and more recent literature for the best
biomarkers for foods, food groups, and food constituents
and to develop and support database systems to include
updated information on the validity of biomarker
measurements for different applications. All of these aspects
are addressed in this special issue of Genes and
Nutrition by partners of the FoodBAll consortium.
FoodBAll is a project funded by the BIO-NH call under the Joint
Programming Initiative, “a Healthy Diet for a Healthy Life” (grant number 529051002).
The project is funded nationally by the respective Research Councils; the work
was funded in part by a grant from the Danish Innovation Foundation
(#420300002B) and a Semper Ardens grant from the Carlsberg Foundation to LOD, a
postdoc grant from the University of Rome La Sapienza (“Borsa di studio per la
frequenza di corsi o attività di perfezionamento all’estero” erogata ai sensi della
legge 398/89) to GP, a grant from the China Scholarship Council
(201506350127) to QG, a grant from the Agence Nationale de la Recherche
(#ANR-14-HDHL-0002-02) to CM, and a grant from the Canadian Institutes of
Health Research (CIHR) to DSW.
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This manuscript was drafted by LOD and QG. All other authors critically
commented the manuscript. All authors read and approved the final
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