Available online at www.sciencedirect.com
ScienceDirectNutritional metabolomics: Recent developments and
future needs
Maaria Kortesniemi1, Stefania Noerman2, Anna Kårlund1,
Jasmin Raita1, Topi Meuronen1, Ville Koistinen1,3,
Rikard Landberg2 and Kati Hanhineva1,3Abstract
Metabolomics has rapidly been adopted as one of the key
methods in nutrition research. This review focuses on the recent
developments and updates in the field, including the analytical
methodologies that encompass improved instrument sensitivity,
sampling techniques and data integration (multiomics). Metab-
olomics has advanced the discovery and validation of dietary
biomarkers and their implementation in health research.
Metabolomics has come to play an important role in the un-
derstanding of the role of small molecules resulting from the
diet–microbiota interactions when gut microbiota research has
shifted towards improving the understanding of the activity and
functionality of gut microbiota rather than composition alone.
Currently, metabolomics plays an emerging role in precision
nutrition and the recent developments therein are discussed.
Addresses
1 Food Sciences Unit, Department of Life Technologies, University of
Turku, FI-20014 Turun yliopisto, Finland
2 Division of Food and Nutrition Science, Department of Life Sciences,
Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
3 Institute of Public Health and Clinical Nutrition, School of Medicine,
University of Eastern Finland, FI-70211 Kuopio, Finland
Corresponding author: Kortesniemi, Maaria (mkkort@utu.fi)Current Opinion in Chemical Biology 2023, 77:102400
This review comes from a themed issue on Omics - Metabolomics
(2023)
Edited by James McCullagh and Hector Keun
For a complete overview see the Issue and the Editorial
Available online xxx
https://doi.org/10.1016/j.cbpa.2023.102400
1367-5931/© 2023 The Author(s). Published by Elsevier Ltd. This is an
open access article under the CC BY license (http://creativecommons.
org/licenses/by/4.0/).
Keywords:
Metabolomics, Nutrition, Biomarker, Dietary pattern, Endogenous
metabolite, Gut microbiota, Precision prevention.
Introduction
Metabolomics has become a keymethodological approach
in nutrition research. It allows the characterization of the
molecular phenotypes of individuals, their metabolicwww.sciencedirect.comresponsiveness to various foods and diets, and compre-
hensive assessment of their mechanistic and predictive
role in health. Ultimately, this may pave the way for the
development of novel precision prevention approaches
[1]. The use of metabolomics in nutritional research also
helps to gain a mechanistic understanding of the role of
dietary compounds in nutritional status and metabolism,
provides a read-out from gut microbiota composition and
function, and may reflect other external exposures and
their interactions with the host with implications for
human health. Metabolomics may also provide novel,
objective biomarkers that reflect specific dietary and
lifestyle exposures. Single metabolites or metabolite
profiles reflecting specific exposures could be used to
assess individual responses to dietary interventions
eventually enabling improved disease risk prediction. In
precision nutrition, metabolomics is emerging as an
important technique to assess the impact of foods and
diets on an individual level [1] and to determine metab-
otypes, i.e., subgroups of individuals with a similar meta-
bolic response to diet [2]. Self-sampling and the use of
non-invasive sample materials are emerging and could
further enhance practical applications for precision
nutrition and precision medicine, where frequent sam-
pling is warranted, but comprehensive metabolomics
techniques integrated and validated with the self-
sampling techniques are yet lacking [3]. Advances in
instrumentation, spectral databases, and computational
tools have promoted the understanding of the complexity
related to nutrition due to the wealth of biochemicals
derived from foods, especially those produced fromwhole
plants, although this “darkmatter” of food remainsmostly
unresolved and poorly acknowledged [4*,5]. In this
review, we highlight the most recent developments and
challenges in nutritional metabolomics including meth-
odological aspects and their application in nutrition
research to reflect dietary and life-style exposures, mo-
lecular responses, dietemicrobiota interactions, eventu-
ally offering possibilities for tailored precision nutrition.Methodological advancements and
challenges
The primary analytical techniques used in nutritional
metabolomics are MS- and NMR-based methods.Current Opinion in Chemical Biology 2023, 77:102400
2 Omics - Metabolomics (2023)Coupled with UHPLC, and more recently with ion
mobility, the MS techniques provide broad metabolite
coverage and high sensitivity [6,7]. Although ion
mobility greatly enhances the separation and identifi-
cation of metabolite and lipid isomers by providing an
additional dimension to the measurements, its utiliza-
tion in nutritional research has thus far been limited [8].
In NMR-based metabolomics, increased sensitivity of
probes, optimized NMR excitation pulse schemes,
hybrid NMR approaches and faster spectra preprocess-
ing are important recent developments [9].
The main methodological advantages during the past
few years have come from merging the metabolomics
data with other omics, clinical, and dietary data along-
side with developments of tools and pipelines for the
purpose [10e13*,14**,15e21]. Since the plasma
metabolome represents a snap-shot read-out modified
by the host genetics, gut microbiota, diet and the
exposome, recent research is increasingly focused on
elucidating their interactions [11,22]. The most widely
used multiomics approaches in current nutritional
studies include combining metabolomics data and
metagenomics data on gut microbial composition with a
variety of available computational methods, such as
metabolic networks, Spearman’s correlation networks,
and Sparse Generalized Canonical Correlation Analysis
(SGCCA) [10e12,16,18e20]. Eventually, multiomics
and data integration will help to unravel interactions
between diet, gut microbiota and health [14**]. To
promote this, novel multiomics databases are being
introduced [14**,23]. Furthermore, the use of new
sampling techniques, such as dried blood spot by finger-
prick lancet [24], allowing for the first time quantitative
measurement of metabolites in small volume samples
[25], as well as alternative biofluids, including saliva
[26], sweat [27], and breast milk [12], broaden the
possibilities of metabolomics in human nutritional
studies because of their non-invasive and less labor-
intensive nature, allowing the samples to be collected
at home. So far, these applications have been only used
to a limited extent in nutritional studies. The variation
of the reported metabolites in different biological
matrices and the question of their representativeness of
the food intake are challenging the field.
One of the biggest challenges in metabolomics is also
to comprehensively characterize and annotate the vast
number of compounds measured. This is particularly
challenging in the case of nutritional metabolomics, as
the immense pool of compounds gained from foods
adds a level of complexity that needs to be addressed
[4*,28]. In the case of lipidomics, essential improve-
ments within the analytical methods as well as identi-
fication of lipids have been accomplished recently
[29,30]. Besides the advancements in analytical tech-
nology, both open-access and commercial spectralCurrent Opinion in Chemical Biology 2023, 77:102400databases are continuously expanding, and software
utilizing machine learning and molecular networking
have emerged to assist with the laborious process of
metabolite identification [14**,15,17,31]. To fully un-
derstand the nutritional and eventually health proper-
ties of foods and diets, remarkable advancements are
still required in our capacity to identify the individual
metabolites to track down their metabolism and effects
in the body. Randomized controlled trials represent the
gold standard for establishing a causal relationship be-
tween food/diets and the metabolome of the collected
sample material. Considering the inter- and intra-
individual variability is vital and identifying responders
and non-responders and their underlying determinants
(i.e., metabotypes) will provide new possibilities to
tailored diets to improve the efficacy of in-
terventions [1,32*].
Biomarkers of food intake and dietary
patterns
Dietary biomarkers are metabolites objectively depict-
ing the intake of certain foods or dietary patterns.
Biomarkers can be classified based on the level of evi-
dence [33**]. Recently, biomarker development and
validation frameworks have been advanced [34] and the
most promising biomarkers across important foods cat-
egories have been recently comprehensively reviewed
[35**]. There are currently few validated food intake
biomarkers, such as alkylresorcinols for whole grain
wheat and rye intake, proline betaine for citrus fruits,
and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid
(CMPF) for fish [33**,34,36*]. The search of novel
biomarkers brings constantly emerging candidates for
various food categories as summarized for the past 2e3
years in Table 1. For example, a study conducted by
following the protocols of the Food Biomarker Alliance
(FoodBAll) project aiming for the systematic coverage
and validation of food biomarkers, shows promise for
novel volatile biomarkers for dairy, cheese and soy-based
drink intake [37]. Sex-specific differences in metabolic
outcomes portray as one important aspect of biomarker
discovery. Langenau et al. [38], for example, have re-
ported differential biomarkers between men and
women for coffee and fish.
In addition to individual foods, various studies have
suggested biomarker candidates also for dietary pat-
terns, as summarized for the past 2e3 years in Table 2.
Mediterranean and Nordic diets are frequently referred
as healthy dietary patterns with e.g. fish and whole-grain
consumption as common elements. For example,
Gu¨rdeniz et al. [36*] reported urinary DHBA-glycine,
CMPF and CMPF-glucuronide being significant me-
tabolites associated with Healthy Nordic Diet. To
accommodate both human and planetary health, plant-
rich diets are increasingly important, which also shows
in the recent research (Table 2).www.sciencedirect.com
Table 1
Most recently suggested biomarkers of food intake (reported in 2020–2023).
Food Biomarker HMDB ID Sample; method Reference
Almond alpha-Tocopherol HMDB0001893 Feces; GC–MS [39]
5-Hydroxyindoleacetic acid (5-HIAA) HMDB0000763
Beer L-Cysteine HMDB0000574 Plasma; GC–MS
Urine; GC–MS
[40]
D-Lactose HMDB0041627
D-Psicose HMDB0250793
Bell pepper Six capsanthin- and
capsorubin-derived glucuronides
(see ref. for exact structures)
n/a Urine; LC-MS, structural
elucidation with NMR
[41]
Butter Undecylenic acid (11:1n−1) HMDB0033724 Serum; LC-MS/MS [38]
Cheese 2-Heptanone HMDB0003671 Plasma; GC–MS [37]
2-Undecanone HMDB0033713
Coffee Glutamic acid HMDB0000148 Plasma; GC–MS
Serum; LC-MS/MS
Urine; GC–MS
[38,40]
Quinic acid HMDB0003072
Paraxanthine (in men) HMDB0001860
Catechol HMDB0000957
Dimethyluric acid HMDB0001857
Methyluric acid HMDB0003099
Niacin HMDB0001488
D-Psicose HMDB0250793
Dairy 3,5-Dimethyloctan-2-one n/a Plasma; GC–MS [37]
Fish EPA (20:5n−3) (in men) HMDB0001999 Serum; LC-MS/MS [38]
Milk 3-Ethylphenol HMDB0059873 Urine; GC–MS [37]
Poultry 3-Methylhistidine HMDB0000479 Serum; LC-MS/MS [38]
Spinach Des-aminoarginine pentenol ester* n/a Urine; LC-MS [42]
D/L-Malic acid-p-coumarate HMDB0303755
Walnut 5-HIAA HMDB0000763 Feces; GC–MS [39]
Uric acid HMDB0000289
White bread Dodecanoic acid HMDB0000638 Plasma; GC–MS [40]
Whole-grain (wheat/rye) 5-Aminovaleric acid betaine (5-AVAB) HMDB0240732 Serum; LC-MS [43,44**]
Pipecolic acid betaine HMDB0304559
Tetradecanedioic acid HMDB0000872
Wine Erythritol HMDB0002994 Plasma; GC–MS
Urine; GC–MS
[40]
Xylitol HMDB0242149
Cinnamoylglycine HMDB0011621
Citramalate HMDB0000426
Erythritol HMDB0002994
D-Gluconic acid HMDB0000625
Tartaric acid HMDB0000956
* Tentative identification, novel structure not verified
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Table 2
Examples of biomarker candidates of dietary patterns (reported in 2021–2022). Most of the biomarkers remain to be validated.
Food intake pattern Associated foods Associated metabolites HMDB ID Reference
Dietary approaches
to stop
hypertension
(DASH)
Rich in fruits,
vegetables, low-fat
dairy products;
moderate in meat,
fish, poultry, nuts,
and beans; low in
sugar and red meat
2-Methylserine n/a [45–47]
4-Allylphenol sulfate HMDB0170765
beta-Cryptoxanthine HMDB0033844
CAR 3:0 HMDB0000824
DG 18:2/18:3 HMDB0007249
DG 18:2/22:6 HMDB0007266
N-Methylproline HMDB0094696
Ornithine HMDB0000214
Panthotenate HMDB0000210
Proline betaine HMDB0004827
S-Allylcysteine HMDB0034323
Threonate HMDB0245425
Healthy Nordic diet Berries, fish, fruits,
low/non-fat dairy,
vegetables, whole-
grain
3,5-Dihydroxybenzoic acid (3,5-DHBA) HMDB0013677 [36*,48]
3,5-DHBA-glycine n/a
CEHC n/a
3-Carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) HMDB0061112
CMPF-glucuronide n/a
CAR 8:1 n/a
CAR 10:3 n/a
(Glycine) betaine HMDB0000043
Hippuric acid HMDB0000714
Indole-3-propionic acid HMDB0002302
Linoleamide HMDB0062656
Loliolide HMDB0302428
Methylimidazoleacetic acid HMDB0002820
Phenylalanine HMDB0000159
Proline betaine HMDB0004827
Healthy plant-based
diet (hPDI)
Coffee, fruits,
legumes, nuts, plant
protein, tea,
vegetables, whole-
grain
1-Methylnicotinamide HMDB0000699 [49,50]
1-Methyluric acid HMDB0003099
3-Hydroxypyridine sulfate HMDB0240652
4-Pyridoxic acid HMDB0000017
4-Vinylphenol sulfate HMDB0062775
Glyceric acid HMDB0000139
Hippuric acid HMDB0000714
Hydrocinnamic acid (3-phenylpropionic acid) HMDB0000764
Indole-3-propionic acid HMDB0002302
Lenticin (tryptophan betaine) HMDB0061115
myo-Inositol HMDB0000211
N1-methyl-2-pyridone-5-carboxamide HMDB0004193
N-Acetylornithine n/a
O-methoxycatechol-O-sulfate HMDB0060013
Pantothenic acid HMDB0000210
Pipecolic acid HMDB0000070
Pyrocatechol sulfate HMDB0059724
Quinic acid HMDB0003072
Threitol HMDB0004136
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Threonic acid HMDB0000943
Trigonelline HMDB0000875
Mediterranean diet Fruits, legumes, olive
oil, red wine,
seafood, vegetables,
whole-grain
5-Hydroxyindole HMDB0059805 [10,51,52]
Deoxycholic acid glucuronide HMDB0002596
Hydroxyperoxyeicosapentaenoic acid n/a
L-Aspartylphenylalanine HMDB0000706
PC 35:1 n/a
PC 40:6 n/a
Succinic acid HMDB0000254
TMA HMDB0000906
Plant-based diet
(overall PDI)
Plant foods in
general
4-Vinylphenol sulfate HMDB0062775 [49,50]
gamma-CEHC HMDB0001931
gamma-Glutamyl peptides n/a
(Glycine) betaine HMDB0000043
Cinnamoylglycine HMDB0011621
Glutamine HMDB0000641
Glycerate HMDB0000139
Glycine HMDB0000123
Hippuric acid HMDB0000714
Indole-3-propionic acid HMDB0002302
Lenticin HMDB0061115
myo-Inositol HMDB0000211
N-Acetylornithine n/a
N-Methylproline HMDB0094696
O-methoxycatechol-O-sulfate HMDB0060013
Paraxanthine HMDB0001860
Pipecolic acid HMDB0000070
Proline betaine HMDB0004827
Pyrocatechol sulfate HMDB0059724
scyllo-Inositol HMDB0006088
Threonic acid HMDB0000943
Trigonelline HMDB0000875
Sugar-rich diet E.g. sugar-
sweetened
beverages
Chenodeoxycholic acid HMDB0000518 [53]
Glycocholic acid HMDB0000138
Glutamic acid HMDB0000148
Lithocholic acid HMDB0000761
Phenylglycine HMDB0002210
Tyrosine HMDB0000158
Unhealthy plant-
based diet (uPDI)
Desserts, fruit juices,
potatoes, refined
grains, sugar-
sweetened and
artificially sweetened
beverages, sweets
1,5-Anhydrosorbitol (1,5-anhydroglucitol) HMDB0002712 [49]
3-Methyl-2-oxovaleric acid HMDB0000491
gamma-CEHC HMDB0001931
Bilirubin (Z,Z) HMDB0000054
Bradykinin HMDB0004246
Hydroxyphenyllactic acid HMDB0000755
N2,N2-Dimethylguanosine HMDB0004824
Proline HMDB0000162
S–N-Methylcysteine HMDB0302211
Ultra-processed
foods
Industrially
processed meat-
containing products,
instant noodles,
3-Methyl-2-oxovaleric acid HMDB0000491 [54,55]
4-Methylsyringol sulfate n/a
Bradykinin HMDB0004246
Elaidic acid HMDB0000573
(continued on next page)
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6 Omics - Metabolomics (2023)
Current Opinion in Chemical Biology 2023, 77:102400Host and gut metabolites reflecting diet
Gut microbiota is an essential modulator of the meta-
bolic response to diet and resulting health effects
[10,53,58**,59,60]. Indeed, gut microbiota has been
shown to contribute to approx. 13% of the total variation
in plasma metabolites [58**]. The importance of
microbiota is evidenced by the recent cataloguing of the
metabolites related to dietemicrobiota interactions
[14**,59]. Indolepropionic acid is a microbial conver-
sion product from dietary tryptophan linked with better
insulin as well as lower risk of type 2 diabetes (T2D) and
metabolic syndrome [61*], while indolelactic acid is
associated with obesity, insulin resistance, and higher
T2D risk [62]. Indolepropionic acid levels in blood were
associated with a lower intake of animal-based food and
a higher intake of fiber-rich plant-based foods [62], and
partially explained by indolepropionic acid-associated
gut bacteria, such as Firmicutes and Bifidobacterium. The
interaction between diet, gut microbiota, and metabo-
lite profiles was particularly shown by the association
between higher milk intake, higher levels of bifidobac-
teria and serum indolepropionic acid (detected only
among lactase non-persistent individuals) [62].
Among the most recently emerged compounds related
to diet, gut microbiota and health is 5-aminovaleric acid
betaine (5-AVAB) [44**,63,64]. It can be directly
absorbed from its dietary sources, such as whole-grain
cereals (Table 1), or conversion of other compounds
with trimethyl groups potentially mediated by gut
microbiota. Despite its anti-inflammatory, anticancer,
and antioxidant properties, associations with fatty liver
disease and cognitive decline have been reported, call-
ing for further studies to disentangle if the association
between 5-AVAB and health outcomes is depending on
certain constraints or individual factors [44**].
Another controversial metabolite in terms of health
relevance is trimethylamine N-oxide (TMAO), that has
been linked with a higher risk of cardiovascular disease
[65e67]. TMAO can enter circulation either directly
from dietary sources such as fish, or via the activity of
gut bacteria and liver metabolism from dietary pre-
cursors such as carnitine and choline rich in for example
red meat [66,68,69]. Interestingly, females seem to have
a higher abundance of bacteria producing trimethyl-
amine, despite generally higher consumption of meat in
males [68]. Even when TMAO levels can be associated
with foods such as fish, whole-grains, poultry and eggs,
these dietary items per se are not linked with adverse
health outcomes, but merely the opposite, which high-
lights the fact that we have not yet fully understood the
metabolic role of TMAO [66,67,70].
Short-chain fatty acids (SCFAs; formate, acetate, pro-
pionate, and butyrate) have been shown to benefit gut
health e.g. by protecting epithelial integrity andwww.sciencedirect.com
Nutritional Metabolomics – Recent Developments Kortesniemi et al. 7suppressing pro-inflammatory pathways [71]. SCFAs
have also been shown to play roles in the regulation of
intestinal hormones, appetite, and blood pressure, as
well as glucose and lipid homeostasis [52]. Consumption
of fiber-rich foods has been shown to increase SCFA
levels, e.g. in vegan and Mediterranean diets [10,16].
Other determinants of SCFAs are intestinal gases, iron
abundance, and colonic pH, all connected to both diet
and gut microbial composition and activity [72]. Recent
studies have suggested that plasma SCFA concentra-
tions are of greater importance than fecal SCFA
concerning metabolic risk factors and diseases [73e75].
Glycerophospholipids have been suggested to provide a
pool of early biomarker candidates for metabolic syn-
drome, cardiometabolic diseases [13*,76,77*,78] and
adherence to healthy eating patterns [36*]. Lysophos-
phatidylcholine (LPC) species may be the most inter-
esting group of glycerophospholipids regarding the
discovery of dietary biomarkers for metabolic health. For
example, decreased LPC 18:2 levels have been sug-
gested to indicate metabolic syndrome risk [13*,76]
whereas a positive association has been found between
LPC 18:1 and LPC 18:2 and adherence to dietary rec-
ommendations of The World Cancer Research Fund and
American Institute for Cancer Research (WCRF/AICR)
[79]. As LPC 18:2 may play a role as a robust biomarker
for healthy dietary patterns and metabolic health, acyl-
carnitine (CAR) with an 18:2 fatty acid tail has been
found to correlate positively with the intake of linoleic
acid (18:2), as well as with circulating ornithine and
male sex [80]. In general, circulating acylcarnitines are
often suggested as markers of impaired lipid and/or
glucose metabolism [80].
Besides phospholipids, two other interesting categories
to mention are bile acids and ceramides. Sex-based
differences in fecal levels of bile acids have been
observed [81]. Adherence to certain dietary patterns,
such as vegan, Mediterranean or sugar-rich diet, has
been shown to modulate such bile acids profiles and
metabolic risk markers [10,16,52,53]. Fecal chenodeox-
ycholic acid, glycocholic acid, cholic acid, and hyodeox-
ycholic acid have been shown to be associated with
higher circulating glucose, insulin, triglycerides, and
LDL, especially in individuals with metabolic syndrome
[13*]. Ceramides (Cer) seem to associate with either a
higher or lower risk of T2D depending on the acyl chain
length. For example, Cer 16:0, Cer 18:0 and dihy-
droceramide dhCer 20:0 were associated with higher
risk, whereas Cer 20:0, dhCer 22:2 and dhCer 26:1 were
associated with lower T2D risk [82]. These ceramides
were also suggested to partly mediate the previously
reported adverse effect of high red meat consumption
on T2D risk. Similarly, Cer 22:2 was suggested to
mediate the positive effect of coffee consumption on
T2D risk. Ceramides may exemplify how metaboliteswww.sciencedirect.commay mediate the interaction between diet and cardio-
metabolic health [82].Towards precision nutrition and prevention
with metabolomics
It is well established that people vary in response to diet
[83], which makes it important to find metabolites
indicating or responsible for such variations in metabolic
phenotypes (metabotypes) [84]. Thus, profiling such
metabolites is promising in predicting dietary responses
and stratifying the individuals based on their predicted
responses. Once the metabolites are well characterized,
further attempts can be made to trace the dietary sources
or other factors responsible for such metabolites. This
knowledge will aid in precision prevention, either using
specific dietary components or lifestyle intervention to
target the alteration of such metabolites to achieve the
desired metabolite profiles. On the other hand, a preci-
sion intervention strategy specifically targeted to re-
sponders [47] will help improve the efficacy rate of the
intervention [85]. For example, giving dietary interven-
tion according to tissue-specific metabotypes has been
shown to enhance improvement in cardiometabolic
health markers in targeted participants [86]. The
PERSonalized Glucose Optimization Through Nutri-
tional Intervention (PERSON) study was one of the first
randomized clinical trials to test effects of a personalized
dietary intervention based on metabolism related phe-
notypes [87*]. Also in the PREVENTOMICS platform
individuals are classified into clusters for personalized
dietary plans [32*]. Although a personalized diet plan did
not show significant improvement in the endpoint
markers compared to a generic healthy diet, the
PREVENTOMICS study may work as a pioneer study to
further investigate the use of metabolomics together
with other omics in biomarker-guided dietary in-
terventions [32*].Concluding remarks
Metabolomics holds the potential for wider utilization in
all aspects related to nutrition as discussed in this review,
as long as the computational methods to process, mine,
and visualize the complex metabolomics data, including
the identification of the metabolites, continue to be
developed to provide reliable results and informative
interpretations of the data. This is also the prerequisite
in order to fully exploit the latest technological ad-
vancements and to have them more widely adopted.
Metabolomics-based frameworks keep accelerating the
discovery and validation of dietary biomarkers. Examples
mentioned here raise the importance of the examination
of metabolome profiles in investigating the links between
diet, gut microbiota and metabolic health outcomes.
Metabolomics has a key role in defining more detailed
and personalized nutritional recommendations and
dividing foods into more health-relevant categories.Current Opinion in Chemical Biology 2023, 77:102400
8 Omics - Metabolomics (2023)Author contributions
Conceptualization: KH, RL; Writing e Original Draft:
MK, SN, AK, JR, TM, VK; Writing e Review & Editing:
RL, KH, MK, SN, VK, AK, TM, JR.Declaration of competing interest
The authors declare the following financial interests/
personal relationships that may be considered as poten-
tial competing interests: Kati Hanhineva reports a rela-
tionship with Afekta Technologies that includes: board
membership, employment, and equity or stocks. Ville
Koistinen reports a relationship with Afekta Technologies
that includes: board membership, employment, and
equity or stocks. Topi Meuronen reports a relationship
with Afekta Technologies that includes: consulting or
advisory and paid expert testimony.Data availability
No data was used for the research described in
the article.
Acknowledgments
Funding from the Swedish Research Council (2019-01264; RL), Formas
(2019e02201) under the umbrella of the European Joint Programming
Initiative “A Healthy Diet for a Healthy Life” (RL, SN), ERA-NET
Cofund HDHL INTIMIC (GA N
 727565 of the EU Horizon 2020
Research and Innovation Programme; RL, SN), Jane and Aatos Erkko
Foundation (KH, AK, JR, TM, VK), Academy of Finland (no. 321716 and
334814; KH), EU Horizon 2020 (no. 874739; KH), EU Horizon Europe
(no. 101060247; KH, AK), and Lantma¨nnen Research Foundation (KH,
VK) are acknowledged. The funders had no contribution in study design,
data collection, data analysis, preparation of the manuscript, or decision
to publish.
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