=Paper=
{{Paper
|id=Vol-2969/paper7-IFOW
|storemode=property
|title=ONS Modeling of Diet Concepts:
Further Development Required by the Objective Definition of “Western Diet”
|pdfUrl=https://ceur-ws.org/Vol-2969/paper7-IFOW.pdf
|volume=Vol-2969
|authors=Ana Reis-Costa,Francesco Vitali,Agnese Gori,Giovanni Bacci,Carlotta De Filippo,Duccio Cavalieri
|dblpUrl=https://dblp.org/rec/conf/jowo/Reis-CostaVG0FC21
}}
==ONS Modeling of Diet Concepts:
Further Development Required by the Objective Definition of “Western Diet”==
https://ceur-ws.org/Vol-2969/paper7-IFOW.pdf
ONS Modeling of Diet Concepts: Further Development Required
by the Objective Definition of “Western Diet”
Ana Reis-Costaa, Francesco Vitalib, Agnese Goria, Giovanni Baccia,Carlotta De Filippob
and Duccio Cavalieria
a
Department of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Italy
b
Institute of Agricultural Biology and Biotechnology (IBBA) - National Research Council (CNR), Via Moruzzi
1, 56124,Pisa, Italy
Abstract
The industrial revolution was the main driver for the “westernization” of human lifestyle,
increasing the intake of highly industrially processed foods, rich in simple sugars and
saturated fats. This new dietary pattern is often referred to as the “western diet”. The issue of
using this term is that it lacks a standardized definition. Moreover, the dietary pattern that
characterizes this diet is not entirely restricted to the West anymore. In this way, we propose
a new definition of the diet that is heavily rooted on the process of food choice management,
which is influenced by several documented socio-economic factors, such as geography,
income and education. Moreover, we suggest that “Globalized diet” should be preferred as a
label for the underlying concept of the “Western diet” since developed countries share more
dietary traits among themselves than with developing countries, where the industrial
revolution was less impactful, and where the globalized lifestyle has yet to be established.
We are currently reaching out to as many researchers in the food nutrition field to achieve
consensus in our proposed definition as we recognize the importance of standardizing terms
for the generation of new knowledge through meta-analysis on broad multi-center nutritional
studies.
Keywords 1
Diet model, dietary pattern, western diet, food choice management, fermented food,
microbiota
1. Introduction
The gene pool of contemporary species is the result of their ancestors’ adaptation to the
environment they lived in. Current research suggests that the agricultural revolution, with the
beginning of farming and animal husbandry (approximately, 10 000 years ago), and later on the
industrial revolution (approximately, 200 years ago) are too recent in History for the human genome
to fully adapt, especially in western societies [1].
The Industrial Revolution started in Great Britain at the end of the 18th century and rapidly spread
to the rest of Europe and the United States. This event deeply affected the lifestyle of western
countries in positive and negative ways, leading to a clear socioeconomic advantage that the rest of
the world gradually aspired to. That was the beginning of Globalization, which was inevitably center
on the lifestyle of western countries. On one hand, sanitation, housing, and medical care improved
IFOW 2021: 2nd Integrated Food Ontology Workshop, held at JOWO 2021: Episode VII The Bolzano Summer of Knowledge, September
11-18, 2021, Bolzano, Italy
EMAIL: anamaria.reiscosta@unifi.it (A. Reis-Costa); francesco.vitali@ibba.cnr.it (F. Vitali); agnese.gori@unifi.it (A. Gori);
giovanni.bacci@unifi.it (G. Bacci); carlotta.defilippo@ibba.cnr.it (C. D. Filippo); duccio.cavalieri@unifi.it
(D. Cavalieri)
ORCID: 0000-0002-0892-7164 (A. Reis-Costa); 0000-0001-9125-4337 (F. Vitali); 0000-0002-0619-1894 (A. Gori); 0000-0003-4406-7816
(G. Bacci); 0000-0002-2222-6524 (C. D. Filippo); 0000-0001-6426-5719 (D. Cavalieri)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Wor
Pr
ks
hop
oceedi
ngs
ht
I
tp:
//
ceur
-
SSN1613-
ws
.or
0073
g
CEUR Workshop Proceedings (CEUR-WS.org) Proceedings
human quality of life by decreasing the incidence of infectious disease and overall mortality. On the
other hand, the dietary patterns of industrialized regions became more caloric, less energy-dense and
less nutrient-rich, whereas physical activity drastically decreased leading to the rise of the so-called
diseases of civilization, such as cancer, asthma, obesity, type 2 diabetes mellitus Inflammatory bowel
disease (IBD) and atherosclerosis, among others [2]. This hypothesis is highly supported by studies
comparing the lifestyle and health of western countries to rural areas in developing countries, where
industrialization and globalization didn’t cause a huge effect on people’s lives [3].
The increase in food accessibility and industrial processing led to higher consumption of simple
carbohydrates and saturated fats, whereas decreasing the ingestion of complex carbohydrates, such as
fiber [1, 4]. Most of the diseases of civilization are correlated with inflammation and an association
with the so-called “western diet” has been demonstrated [5, 6, 3, 7]. Among other detrimental
consequences, these alterations highly impacted gut microbial profiles by decreasing the variety of
bacterial lineages capable of fermenting complex carbohydrates into short-chain fatty acids (SCFAs)
[5], which are correlated with decreased inflammation. Currently, wealth of studies in the gut
microbiota field is elucidating the intertwined connections between the diet, gut microbiota, the
metabolites that the community produces from the elements of the diet, and human health.
Although the “western diet” concept has recently gained much popularity in the scientific
community, in particular, due to the wide use of this term in microbiome studies, it lacks a
standardized definition or any defined quantitative measurement of an associated dietary pattern. This
problem causes constraints into cohesion, integration and interoperability of (meta)data, hence
affecting the meta-analysis on broad multi-center nutritional studies [7]. To tackle this issue, many
international initiatives joined researchers, clinicians and the industry in an effort to standardize
current data to generate better knowledge in the future. The ultimate goal of those research efforts is
enabling the re-analysis of data obtained in nutritional studies, heterogeneous and often multi-omics
in nature.
Examples of this joint effort are the European Nutritional Phenotype Assessment and Data Sharing
Initiative (ENPADASI) under the Joint Programming Initiative - A Healthy Diet for a Healthy Life
(JPI-HDHL) and, more recently, the Food and Nutrition Science Cloud Project (FNS-Cloud) funded
by the EU in the H2020 program. Both ENPADASI (http://www.enpadasi.eu/) and FNS-Cloud
(https://www.fns-cloud.eu/) objectives are to make available big nutritional data through open access
nutritional databases and a distributed infrastructure. The Ontology for Nutritional Studies (ONS -
https://github.com/enpadasi/Ontology-for-Nutritional-Studies), born inside ENPADASI and growing
into FNS-Cloud, has the ambitious aim of enabling the harmonization of biochemical, genetic,
clinical, and nutritional concepts. The main goal of harmonization is to translate data according to the
FAIR principles (Findable, Accessible, Interoperable and Reusable) [8]. In this way, we can integrate
already available data in the analysis of broad multi-center nutritional studies to improve current
knowledge. Thanks to their increasing cost-effectiveness and widespread use, “omics” techniques are
increasingly used in biomedical research. Moreover, nutritional studies, standing at the intersection of
various scientific disciplines, are frequently characterized by multi-omics data, including targeted or
untargeted metagenomics, metabolomics or microbiomics. If harmonization is not performed and
meta-data is not openly shared, information is condemned to remain underused in data-silos. In this
light, harmonization of dietary terms such as “western diet”, and optimally their quantitative
definition, might ease this process, allowing for correct subject stratification among different studies,
hence reducing the occurrence of uncontrollable bias in analysis.
For example, our group has been studying three populations with different levels of westernization
in Burkina Faso, having demonstrated that the level of westernization of one’s diet affects the gut
microbiota compositions. Our next step is to show that the change in gut microbiota can affect human
health, taking into consideration that these populations are also related to different incidences of
inflammatory diseases (i.e. Inflammatory Bowel Disease, Crohn’s Disease) [3,10]. Moreover, we
intend to associate those differences to the difference in lifestyles, particularly to the differences in
terms of dietary patterns (i.e. fiber intake, consumption of ultra-processed foods or simple sugar-rich
foods and ingestion of fermented foods). Hence, on a first level, we need to define in a quantitative
manner the “western” or “rural” diet, to be able to understand how much rural or how much western
the diet of a group is (i.e. as those countries develop, the rural village may now have a less rural diet
than once before). On a second level, we usually produce a huge amount of data from those single
subjects (i.e. clinical, anthropometric and dietary data, bacterial and fungi microbiota with targeted
metagenomics, citokyne, metabolites like short-chain fatty acids (SCFAs), occasionally untargeted
microbiomics - providing the genes of the community - or metabolomics - providing often unknown
metabolites), that we try to ultimately connect to subjects groups, optimally over this known dietary
gradient. Omics data in these settings are often not straightforward to analyze, and the problem gets
even bigger if a meta-analysis is performed, for example, if we want to include other cohorts from big
European or American studies for comparison. This is where we are expecting to get the most out of
these efforts: quantitatively defining the various dietary patterns would allow for better classification
of subjects based on dietary surveys, which in turn would allow for hopefully unbiased diet-
microbiota-metabolites associations.
2. The ONS Model for the Diet Paradigm
The modeling of diet-related concepts has always been a central aspect for ONS. We defined
“diet” [ONS:1000001] as an information artifact under the “information content entity” class
[IAO:0000030]. The key vision behind this is that the diet concept is not general/material, or even
stable through time or with respect to different individuals. Rather, diet is denoted by the observation
and recording of the actual foods that are consumed by an individual during meals, that is by a certain
“dietary pattern” [ONS:0000094]. The latter is another information content entity that would be
related to a “data item” with records on food consumption, which, in the context of nutritional studies,
typically results from various assays (i.e. Food Frequency Questionnaire) or food diaries. In this way,
and if dietary patterns are quantitatively defined, we could infer the diet of an individual by some
similarity matching/measurement of a set of known dietary patterns. Various conceptually similar
indices were already developed to measure adherence to specific diets, for example, the MEDI-LITE
score [11], which was developed with the aim of measuring adherence to the Mediterranean diet
based on literature data.
In this model, the “dietary pattern” (and consequently the sub-type of diet that an individual
adheres to) is the result of individual food choices, which are affected by a multitude of factors. Some
of those factors could be easily identified and described. For instance, a dietary pattern excluding or
exclusively including some kind of food (for example “dietary pattern by food organism”
[ONS:2000019]) can be straightforwardly defined with the aid of FOODON [15] as eating or not
eating some specific food product under the “food product by organism” class in FOODON
[FOODON:00002381]. (i.e. the “pescetarian dietary pattern” would be annotated, among others, with
[“eats” some “fish food product”] and with [not (“eats” some “avian food product”)]) (Figure 1).
Various nuances of the dietary pattern and diet concept were defined and are included in the current
version of ONS.
Some other factors influencing the dietary pattern are more general, vague, and require further
modeling to be disclosed. As introduced, we have identified various issues related to the inconsistent
definition of the concept of ”western diet”. In an attempt to solve these problems, we readily realized
that the diet model in ONS had to be further detailed to allow the modeling of complex dietary
patterns, such as the one referred to with the concept of “western diet”.
Figure 1: Schematic representation of the rationale behind the definition of a specific dietary pattern
which is determined by eating or avoiding specific food products. In this figure, the colors indicate
the ontology from which the term was imported. Dark blue indicates BFO, lilac indicates IAO, beige
indicates FOODON and green indicates ONS. Dashed lines indicate a ”is a” relation, while solid lines
indicate other types of relations, specified as text.
Recent research shows that diet quality follows complex socio-economic gradients defined by
geography, income and education. The higher socio-economic class is more prone to choose higher-
quality diets rich in micronutrients, due to higher education and wealth; whereas lower socio-
economic groups are most likely to choose energy-dense, micronutrient-poor diets due to their
decreased cost and higher satiety [12, 13]. Another important factor is religion since many religious
beliefs lead to temporal abstinence or complete removal of certain foods from one’s diet. [14].
Finally, stress is also an important feature of the western lifestyle, which promotes behaviors
mediated by the dopamine reward pathway, causing an increase in the demand for palatable foods,
usually rich in fats and sugars [15].
With this premise, we present the latest addition to the previous diet and dietary pattern model
(Figure 2) in ONS. The model envisioned a “food choice management process” [ONS:0000128]
defined as “the complex decision-making process an organism carries out to make choices about
which food to be consumed, and to the choices regarding quantity and mode of consumption of
foods”, which determines the individual “food choice” [ONS:0000132]. The “food choice” class
“refers to any decision an organism makes regarding food and diet. It both refers to the selection of
specific foods to be consumed by an organism, from a variety of available options, and to the choices
regarding quantity and mode of consumption of foods. Food choice is a direct consequence of the
food management process, and depends on a variety of determinants of diet”. The “food management
process” is divided into two subclasses: “healthy management of food choice” [ONS:0000129], which
is defined as “a food choice management process resulting in healthy food choice”, or
“mismanagement of food choice” [ONS:0000130], which is defined as “a food choice management
process resulting in unhealthy food choice”. These decisions are driven by the interaction of a
multitude of factors that we define to be “determinant of diet” [ONS:0000131]. The “determinant of
diet” class virtually includes all the individual factors that drive the “food choice” of an organism,
including, for example, ethnography [GSSO:010236], education [ExO:0000041], income
[NCIT:C41150], geography [NCIT:C16633], religion [SCDO:0000981] and palatability
[ONS:0000133]. These will determine the high, moderate or low consumption of certain foods, which
can be assessed by some “food intake measurement”, that will impact the (im)balance of macro- and
micronutrients that characterize the “western diet” dietary pattern [16]. It is important to note that the
“determinant of diet” class is very broad, thus there might be the need to include more terms to this
class in the future.
In this way, the diet associated with a specific dietary pattern is the result of the “food choice”
which is a direct consequence of the food management process and depends on a myriad of
determinants of diet. Moreover, it is possible to have a healthy diet despite sometimes opting for the
“mismanagement of food choice”.
For example, red wine consumption is part of the Mediterranean diet, which could be regarded as a
healthy dietary pattern. A mismanagement of alcohol consumption, which might be related to alcohol
addiction, leads to an unhealthy food choice regarding the number of wine servings consumed. This
produces a specific dietary pattern and diet, with unfavorable health consequences, diverging from the
Mediterranean dietary pattern even though all the other requirements are met. In opposition, a healthy
management of wine consumption would not negatively impact the dietary pattern of an individual,
which would continue to adhere to the Mediterranean dietary pattern, and would meet different health
outcomes. Thereafter, the health implication of the “healthiness” of foods strictly depends on the
dosage of consumption concept, and capturing this into the connection with the health-related
ontological term is thus crucial. [17]
Figure 2: Schematic representation of the most recent update on the definition of diet according to
the individual’s dietary pattern model. In this figure, the colors indicate the ontology from which the
term was imported. Lilac indicates IAO, grey indicates CMO, while green indicates ONS. Dashed lines
indicate a ”is a” relation, while solid lines indicate other types of relations, specified as text.
3. Western Diet through Its Determinants
Finally, we applied the above outline and detailed model to attempt to define the “western dietary
pattern” (and, consequently, the “western diet”). First of all, despite this term’s vast popularity, its
connection to some geographically defined concept (i.e. “western”) might be misleading. We believe,
in fact, that most of the time the concept is used to indicate the diet of industrialized/globalized
countries in opposition to that of rural areas in developing countries, or more precisely in opposition
to a rural/slow lifestyle, thus a “rural diet”. In this way, the west is just a portion of the regions that
would qualify for the “western diet”; hence we decided to favor the use of “globalized diet” as a label
for the concept referring to “western diet”. In general, the assumption is that westernized foods are
industrially produced foods that adhere to standards defined by law. Industrialized foods are often
blamed for the unhealthy outcomes of the western diet. The NOVA food classification organized
foods into 4 groups according to both the extent and purpose of the processing they endured:
unprocessed and minimally processed (group 1), processed culinary ingredients (group 2), processed
foods (groups 3) and ultra-processed foods (group 4). This classification acknowledges that only
ultra-processed foods pose a threat to human health. These are energy-dense, rich in unhealthy types
of fat, sugars and salt, and poor in protein and fiber. Thus, the industrial processing of foods does not
make them necessarily unhealthy, but its excessive processing, which may include hydrolysis,
hydrogenation, addition of several additives like food colorings, emulsifiers, etc., is “intrinsically
unhealthy” and also characteristic of ultra-processed foods. Indeed, minimal processing might
improve some interesting food properties, including bioavailability of micronutrients, digestibility and
food safety [18–23]. Furthermore, food processing is not exclusive to the western diet. People living
in rural isolated areas also process their food, even though they use very rudimental methods to do so,
such as fermentation [23]. Even though those forms of processing are also performed by the industry,
they are executed in a very well controlled and repetitive way, which is very different from what is
done in rural countries [10, 24].
In January 2021, a panel of experts defined fermented foods as “foods made through desired
microbial growth and enzymatic conversions of food components”. In this way, these foods endure
chemical transformations promoted by microorganisms, which are usually followed by microbial
inactivation or removal before being available for sale (i.e. wine, beer, bread and dairy). Most
fermented foods available in the market have undefined microbiota communities at variable levels
and their potential health benefits are usually not known [25]. Fermentation has always been a way to
preserve foods, by production of ethanol, acetic acid, lactic acid, or other products of fermentation
that inhibit bacterial growth. Interestingly the ability to preserve foods and extend their shelf-life is
another typical feature of globalized foods that have to be available in areas of the world often far
away from their production site.
In Europe, the Protected Designation of Origin imposes geographical, manufacturing and quality
requirements to the producers who want to get this seal of quality. Despite not imposing any
guidelines affecting the microbiota used, the physicochemical criteria imposed can dictate the type or
nature of the cultures used in the foods. Even though very few microorganisms survive industrial
processing, some manufacturers supplement fermented foods with microorganisms after heat
treatment, possibly adding some benefits for gut microbiome health [25]. Industrialization and, as a
consequence, Globalization had an important impact on fermented foods by the introduction of the
starter cultures, homogenizing the microbial community present in the products. However, traditional
regional fermented foods are still produced at home using local microbial communities through
traditional methods, especially in the non-globalized world [26]. Consequently, the microbial
composition of a food product can be drastically different in a rural area compared to a globalized
region, leading to several differences in the properties of the food itself.
4. Conclusion
With this work, we aimed at spotlighting a problem we frequently face in the microbiota literature;
the non-standard definition of the dietary pattern of individuals, hampering our ability for unbiased
data analysis. Semantic web technologies and ontologies represent the most promising technology to
solve those problems, making data silos effectively available for integration, highly improving the
evolution of knowledge. We have worked to the best of our abilities in attempting to shape
information, but we are aware that we present the vision of a small collective of researchers.
Furthermore, we are not sure if creating a single class with a unique definition for “western diet” is a
reachable goal, as we still are not fully aware of the different definitions we could obtain from the
input of different fields. We started from the microbiome use case as this is our background, and we
are well aware of how this term is commonly used in the articles of the field and of how it is now a
fact that there is a relationship between a certain type of diet and the microbiota, although the
boundaries of this diet are still to be fully disclosed.
Even though most literature agrees that the energy obtained on a Western diet is roughly derived
from 49% carbohydrates, 35% fats and 16% protein (resembling the United States of America
lifestyle), both macro- and micronutrient compositions of western diets used in animal and human
nutritional studies vary and might not be representative of all the western societies [1,4–7]. In this
way, we acknowledge that the problem is way more ample, and accordingly our main goal with this
paper was to raise awareness so that more people get engaged in this discussion and we can, as a
collective of experts from different fields, reach consensus in time.
We are currently defining “western diet”, using the preferred label of “globalized diet”, as a diet
resulting from the lifestyle adaptation following the industrial revolution. It is often associated with
western countries, even though it can be verified in other geographic areas. It is known to be rich in
red and processed meats, sweets, fried foods, and refined grains while being poor in fruits, vegetables,
legumes, fish, poultry, and whole grains. In terms of micronutrients, this diet is usually rich in salt, yet
poor in other minerals and vitamins, whereas in terms of fats, it is high in saturated and trans-fatty
acids, and low in mono- and polyunsaturated fatty acids. Its fiber and complex carbohydrate contents
are also very low due to the high consumption of refined sugars instead of whole-grain foods [27, 28].
In terms of food stability this diet is indeed poor in fresh foods and rich in foods that contain
stabilizers or preservatives.
As the diet model in ONS is acquiring a higher level of complexity, we plan to reach out to the
broader nutritional scientific community to obtain consensus and enrichment in modeling the different
flavors of diet and dietary patterns. To do this, we plan to leverage our involvement in the H2020
presently funded project “FNSCloud” (https://www.fns-cloud.eu/), whose goal is to create the first-
generation “food cloud”, uniting existing and emerging datasets, while offering new services to
support long term use and re-use of the available knowledge, and to the previous JPI-HDHL funded
project “ENPADASI”. In addition, we intend to reach out and involve specific scientific communities
in nutrition (i.e. the Italian Society for Human Nutrition - SINU), while still participating in the
international initiative Joint Food Ontology Workgroup (https://github.com/FoodOntology/joint-food-
ontology-wg), which was really fruitful for ONS curation.
5. Acknowledgements
Funding to this project was provided by the H2020 FNS-Cloud project (Grant agreement ID:
863059), by European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI)
and its infrastructure as part of the Joint Programming Initiative “A Healthy Diet for a Healthy Life”
(JPI-HDHL), and by the Italian Ministry of Agriculture, Food and Forestry Policies (MiPAAF, DM
36954/7303/18), within the trans-national project INTIMIC of the JPI-HDHL initiative (Expression of
Interest 868 and 895)
6. References
[1] P. Carrera-Bastos, M. Fontes-Villalba, J. H. O’Keefe, S. Lindeberg, and L. Cordain. The western
diet and lifestyle and diseases of civilization. Research Reports in Clinical Cardiology. Volume
2, 15-35 (2011). https://doi.org/10.2147/RRCC.S16919
[2] S. B. Eaton, M. Konner, and M. Shostak. Stone agers in the fast lane: chronic degenerative
diseases in evolutionary perspective. The American Journal of Medicine. Volume 84, Issue 4,
739–749 (1988). https://doi.org/10.1016/0002-9343(88)90113-1
[3] C. De Filippo, D. Cavalieri, M. Di Paola, M. Ramazzotti, J. B. Poullet, S. Massart, S. Collini, G.
Pieraccini, P. Lionetti. Impact of diet in shaping gut microbiota revealed by a comparative study
in children from Europe and rural Africa. Proceedings of the National Academy of Sciences of
the United States of America. Volume 10, Issue 33, 14691–14696 (2010).
https://doi.org/10.1073/pnas.1005963107
[4] L. Cordain, S. B. Eaton, A. Sebastian, N. Mann, S. Lindeberg, B. A. Watkins, J. H. O'Keefe, J.
Brand-Miller. Origins and evolution of the Western diet: Health implications for the 21st
century. American Journal of Clinical Nutrition. Volume 81, Issue 2, 341–354 (2005).
https://doi.org/10.1093/ajcn.81.2.341
[5] L. Chan, N. Vasilevsky, A. Thessen, J. McMurry, M. Haendel, The landscape of nutri-
informatics: a review of current resources and challenges for integrative nutrition research.
Database. Volume 2021, baab003 (2021) https://doi.org/10.1093/database/baab003
[6] I. Sekirov, S. Russell, L. C. M. Antunes, B. Finlay. Gut microbiota in health and disease.
Physiological Reviews. Volume 90, Issue 3, 859–904 (2010).
https://doi.org/10.1152/physrev.00045.2009
[7] S. Carding, K. Verbeke, D. Vipond, B. Corfe, L. Owen. Dysbiosis of the gut microbiota in
disease. Microbial Ecology in Healyh and Disease. Volume 26, Issue 1, (2015).
https://doi.org/10.3402/mehd.v26.26191
[8] M. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg,
J. Boiten, L. B. da Silva Santos, P. Bourne, J. Bouwman, A. Brookes, T. Clark, M. Crosas, I.
Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. Gray, P. Groth,
C. Goble, J. S. Grethe, J. Heringa, P. A.C ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. Lusher,
M. Martone, A. Mons, A. L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A.
Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. Swertz, M. Thompson, J. van der
Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, B.
Mons. The FAIR Guiding Principles for scientific data management and stewardship. Scientific
Data. Volume 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
[9] F. Vitali, R. Lombardo, D. Rivero, F. Mattivi, P. Franceschi, A. Bordoni, A. Trimigno, F.
Capozzi, G. Felici, F. Taglino, F. Miglietta, N. De Cock, C. Lachat, B. De Baets, G. De Tré, M.
Pinart, K. Nimptsch, T. Pischon, J. Bouwman, D. Cavalieri and the ENPADASI consortium.
ONS: an ontology for a standardized description of interventions and observational studies in
nutrition. Genes and Nutrition. Volume 13, 12 (2018). https://doi.org/10.1186/s12263-018-0601-
y
[10] C. De Filippo, M. Di Paola, M. Ramazzotti, D. Albanese, G. Pieraccini, E. Banci, F. Miglietta,
D. Cavalieri, P. Lionetti. Diet, environments, and gut microbiota. A preliminary investigation in
children living in rural and Urban Burkina Faso and Italy. Frontiers in Microbiology. Volume 8,
1979 (2017). https://doi.org/10.3389/fmicb.2017.01979
[11] F. Sofi, M. Dinu, G. Pagliai, R. Marcucci, A. Casini. Validation of a literature-based adherence
score to Mediterranean diet: the MEDI-LITE score. International Journal of Food Sciences and
Nutrition. Volume 68, Issue 6, 757–762 (2017). https://doi.org/10.1080/09637486.2017.1287884
[12] D. Dooley, E. Griffiths, G. Gosal, P. Buttigieg, R. Hoehndorf, M. Lange, L. Schriml, F.
Brinkman, W. Hsiao. FoodOn: a harmonized food ontology to increase global food traceability,
quality control and data integration. npj Science Food. Volume 2, 23 (2018).
https://doi.org/10.1038/s41538-018-0032-6
[13] N. Darmon, A. Drewnowski. Does social class predict diet quality? American Journal of Clinical
Nutrition. Volume 87, Issue 5, 1107–1117 (2008). https://doi.org/10.1093/ajcn/87.5.1107
[14] J. Sabaté. Religion, diet and research. The British journal of nutrition. Volume 92, Issue 2, 199–
201 (2004). https://doi.org/10.1079/BJN20041229
[15] G. Leng, R. Adan, M. Belot, J. Brunstrom, K. de Graaf, S. Dickson, T. Hare, S. Maier, J.
Menzies, H. Preissl, L. Reisch, P. Rogers, P. Smeets. The determinants of food choice.
Proceedings of the Nutrition Society. Volume 76, Issue 3, 316–327 (2017).
https://doi.org/10.1017/S002966511600286X
[16] T. Furst, M. Connors, C. Bisogni, J. Sobal, L. Winter Falk. Food choice: A conceptual model of
the process. Appetite. Volume 26, Issue 3, 247–266 (1996).
https://doi.org/10.1006/appe.1996.0019
[17] A. Giacosa, R. Barale, L. Bavaresco, M. A. Faliva, V. Gerbi, C. La Vecchia, E. Negri, A. Opizzi,
S. Perna, M. Pezzotti, M. Rondanelli. Mediterranean Way of Drinking and Longevity. Critical
reviews in food science and nutrition. Volume 56, Issue 4, 635–640 (2016).
https://doi.org/10.1080/10408398.2012.747484
[18] A. Ayton, A. Ibrahim. The Western diet: a blind spot of eating disorder research?-a narrative
review and recommendations for treatment and research. Nutrition reviews. Volume 78, Issue 7,
579–596 (2020). https://doi.org/10.1093/nutrit/nuz089
[19] H. Cena, P. Calder. Defining a Healthy Diet: Evidence for the Role of Contemporary Dietary
Patterns in Health and Disease. Nutrients. Volume 12, Issue 2, 334 (2020).
https://doi.org/10.3390/nu12020334
[20] F. Rizzello, E. Spisni, E. Giovanardi, V. Imbesi, M. Salice, P. Alvisi, M. C. Valerii, P.
Gionchetti. Implications of the Westernized Diet in the Onset and Progression of IBD. Nutrients.
Volume 11, Issue 5, 1033 (2019). https://doi.org/10.3390/nu11051033
[21] E. M. Leo, M. S. Campos. Effect of ultra-processed diet on gut microbiota and thus its role in
neurodegenerative diseases. Nutrition. Volume 71, 110609 (2020).
https://doi.org/10.1016/j.nut.2019.110609
[22] F. Melini, V. Melini, F. Luziatelli, A. G. Ficca, M. Ruzzi. Health-Promoting Components in
Fermented Foods: An Up-to-Date Systematic Review. Nutrients. Volume 11, Issue 5, 1189
(2019). https://doi.org/10.3390/nu11051189
[23] C. A. Monteiro, G. Cannon, J.-C. Moubarac, R. Bertazzi Levy, M. L. C Louzada, P. C. Jaime.
The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-
processing. Public health nutrition. Volume 21, Issue 1, 5–17 (2018).
https://doi.org/10.1017/S1368980017000234
[24] K. Zaheer, M Humayoun Akhtar. An updated review of dietary isoflavones: Nutrition,
processing, bioavailability and impacts on human health. Critical reviews in food science and
nutrition. Volume 57, Issue 6, 1280–1293 (2017).
https://doi.org/10.1080/10408398.2014.989958
[25] M. Marco, M. E. Sanders, M. Gänzle, M. C. Arrieta, P. Cotter, L. De Vuyst, C. Hill, W.
Holzapfel, S. Lebeer, D. Merenstein, G. Reid, B. Wolfe, R. Hutkins. The International Scientific
Association for Probiotics and Prebiotics (ISAPP) consensus statement on fermented foods.
Nature Reviews Gastroenterology and Hepatology. Volume 18, 196–208 (2021).
https://doi.org/10.1038/s41575-020-00390-5
[26] J. Prakash Tamang, P. Cotter, A. Endo, N. Soo Han, R. Kort, S. Quan Liu, B. Mayo, N.
Westerik, R. Hutkins. Fermented foods in a global age: East meets West. Comprehensive
reviews in food science and food safety. Volume 19, Issue 1, 184–217 (2020).
https://doi.org/10.1111/1541-4337.12520
[27] E. Lipski. Traditional Non-Western Diets. Nutrition in clinical practice. Volume 25, Issue 6,
585–593 (2010). https://doi.org/10.1177/0884533610385821
[28] K. J. Hintze, A. D. Benninghoff, R. E. Ward. Formulation of the Total Western Diet (TWD) as a
basal diet for rodent cancer studies. Journal of agricultural and food chemistry. Volume 60, Issue
27, 6736–6742 (2012). https://doi.org/10.1021/jf204509a