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Redox Biology 71 (2024) 103095
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nc-nd/4.0/).
Factors driving the inter-individual variability in the metabolism and 
bioavailability of (poly)phenolic metabolites: A systematic review of 
human studies 
Claudia Favari a,*,1, José Fernando Rinaldi de Alvarenga a,1, Lorena Sánchez-Martínez a,b, 
Nicole Tosi a, Cristiana Mignogna a, Eleonora Cremonini c,d, Claudine Manach e, 
Letizia Bresciani a, Daniele Del Rio a,f, Pedro Mena a,f 
a Human Nutrition Unit, Department of Food and Drugs, University of Parma, Parma, Italy 
b Department of Food Technology, Food Science and Nutrition, Faculty of Veterinary Sciences, Regional Campus of International Excellence ‘Campus Mare Nostrum’, 
Biomedical Research Institute of Murcia (IMIB-Arrixaca-UMU), University Clinical Hospital ‘Virgen de La Arrixaca’, Universidad de Murcia, Espinardo, Murcia, Spain 
c Department of Nutrition, University of California, Davis, CA, USA 
d Department of Environmental Toxicology, University of California, Davis, CA, USA 
e Université Clermont Auvergne, INRAE, Human Nutrition Unit, Clermont-Ferrand, France 
f Microbiome Research Hub, University of Parma, 43124, Parma, Italy 
A R T I C L E I N F O 
Keywords: 
Bioavailability 
Gut microbiota 
Inter-person variation 
Metabolite 
Metabotype 
(poly)phenol 
A B S T R A C T 
This systematic review provides an overview of the available evidence on the inter-individual variability (IIV) in 
the absorption, distribution, metabolism, and excretion (ADME) of phenolic metabolites and its determinants. 
Human studies were included investigating the metabolism and bioavailability of (poly)phenols and reporting 
IIV. One hundred fifty-three studies met the inclusion criteria. Inter-individual differences were mainly related to 
gut microbiota composition and activity but also to genetic polymorphisms, age, sex, ethnicity, BMI, (patho) 
physiological status, and physical activity, depending on the (poly)phenol sub-class considered. Most of the IIV 
has been poorly characterised. Two major types of IIV were observed. One resulted in metabolite gradients that 
can be further classified into high and low excretors, as seen for all flavonoids, phenolic acids, prenylflavonoids, 
alkylresorcinols, and hydroxytyrosol. The other type of IIV is based on clusters of individuals defined by qual-
itative differences (producers vs. non-producers), as for ellagitannins (urolithins), isoflavones (equol and O- 
DMA), resveratrol (lunularin), and preliminarily for avenanthramides (dihydro-avenanthramides), or by quali- 
quantitative metabotypes characterized by different proportions of specific metabolites, as for flavan-3-ols, 
flavanones, and even isoflavones. Future works are needed to shed light on current open issues limiting our 
understanding of this phenomenon that likely conditions the health effects of dietary (poly)phenols. 
1. Introduction 
Phenolic compounds or (poly)phenols are plant secondary metabo-
lites commonly found in fruits, vegetables, and beverages like tea, cof-
fee, and wine, widely consumed within the human diet [1–3]. In recent 
years, epidemiological studies have associated (poly)phenol intake with 
beneficial health effects on cardiovascular diseases, metabolic diseases, 
and some types of cancer [4–6]. However, divergent responses have led 
to controversial results in clinical trials, implying that a “one-size--
fits-all” approach is not adequate to explore the potential beneficial 
effects attributed to dietary phenolic compounds [7,8]. In addition, it is 
not clear yet whether the health effects attributed to (poly)phenol intake 
are influenced by the ingested (poly)phenols themselves, which might 
exert their effects systemically through their derived bioavailable me-
tabolites and catabolites, and/or by a modification of the gut microbial 
ecology associated with (poly)phenol absorption and metabolism [9]. 
The existing inter-individual variability (IIV) in (poly)phenol meta-
bolism and response makes solving these key points difficult, blurring 
the potential that (poly)phenol consumption may have on human 
health. 
* Corresponding author. 
E-mail address: claudia.favari@unipr.it (C. Favari). 
1 Equal contribution. 
Contents lists available at ScienceDirect 
Redox Biology 
journal homepage: www.elsevier.com/locate/redox 
https://doi.org/10.1016/j.redox.2024.103095 
Received 24 November 2023; Received in revised form 16 February 2024; Accepted 18 February 2024 
mailto:claudia.favari@unipr.it
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Since phenolic metabolites might be mediators of the health effects 
attributed to dietary (poly)phenols, it is essential to assess the IIV in 
their production, as well as to understand the main factors affecting this 
phenomenon. In this sense, the different production of metabolites with 
different biological activities might condition the response to the con-
sumption of (poly)phenol-rich foods. The molecular mechanisms by 
which phenolic metabolites may confer their potential health benefits 
include diverse actions within intra- and inter-cellular signalling path-
ways, including regulation of nuclear transcription factors and fat 
metabolism, modulation of the synthesis of inflammatory mediators as 
cytokines tumour necrosis factor α, interleukin (IL)-1β, and IL-6, and 
other direct and indirect antioxidant activities protecting cells and tis-
sues [10–14]. The direct antioxidant effect was the primary mechanism 
of action attributed to (poly)phenols decades ago by means of in vitro 
studies, then no longer considered to be as relevant in vivo, due to the 
low concentrations reached by these compounds in most tissues, not 
enough to have a significant free radical scavenging effect [10]. How-
ever, a recent study has proposed that the reaction rate between 
phenolic compounds and free radicals is much higher than previously 
reported, and the antioxidant action does not necessarily require 
phenolic groups, but only a carbon-centred free radical and an aromatic 
molecule, restoring an important direct antioxidant role not only for 
dietary (poly)phenols, but also for their metabolites [12]. In addition, 
several indirect antioxidant actions have recently been suggested for 
(poly)phenols and their metabolites. For example, certain dietary (poly) 
phenols and their metabolites originated in the gut seem to affect 
aquaporin expression and the intracellular uptake of the oxidant mole-
cule H2O2 mediated by this enzyme, resulting in a H2O2-triggered 
intracellular down-stream signalling mechanism that protect against 
excessive generation of reactive oxygen species and the onset of oxida-
tive stress [14]. Also, both native compounds and phenolic metabolites 
at physiological concentrations might act on NADPH-oxidases, modi-
fying their expression and consequently their capacity to generate 
oxidant species, potentially mitigating events that are closely associated 
with inflammation [13]. 
An effort by researchers involved in the COST Action POSITIVe was 
made to define IIV in absorption, metabolism, distribution, and excre-
tion (ADME) of (poly)phenols and to assess its impact on cardiovascular 
health outcomes [8,15–27]. Determinants such as age, sex, genotype, 
gut microbiota, and (patho)physiological status could lead to differences 
among subjects in (poly)phenol metabolism and bioavailability, gener-
ating a heterogeneous responseacid and 3-(3ʹ,5ʹ-dihydrox-
yphenyl)propanoic acid [161]. On the contrary, men showed higher 
AUCs for conjugated metabolites of hydroxytyrosol after intake of an 
olive leaf extract, independently of the oleuropein dose and the 
administration format [162]. 
Last, the colonic metabolism of oat avenanthramides into dihydro- 
avenanthramides may not be completed in all individuals as this trans-
formation depends on the presence of Faecalibacterium prausnitzii [163]. 
This preliminary observation was achieved in a small sample set of 13 
healthy subjects, but was supported by both human faecal fermentations 
and gnotobiotic mice experiments. 
3.3.3. Determinants of inter-individual variability in (poly)phenol 
bioavailability 
The scientific literature considered so far highlighted that even if the 
inter-individual differences in the metabolism and absorption of 
phenolic compounds are frequently observed and reported, the factors 
underlying these differences are mainly undefined, particularly for some 
sub-classes of flavonoids and phenolic acids (Fig. 4). 
An influence of genetic polymorphisms in phase I/II enzymes and 
transporters has been reported for flavanone metabolites [44,109,110], 
and suggested for flavan-3-ol metabolites [33,35,42], based on different 
excretion patterns of sulfate and glucuronide derivatives among in-
dividuals. In this latter case, different data pre-treatment methods 
proved fundamental to unravel inter-individual differences in the 
Fig. 4. Factors driving the inter-individual variability in the ADME of main phenolic classes. The size of the circle accounts for the number of studies. BMI, body 
mass index. 
C. Favari et al. 
Redox Biology 71 (2024) 103095
15
production of phase II flavan-3-ol metabolites [42]. These results sug-
gested that diverse pre-treatments, as well as clustering methods (den-
sity- versus distance-based), are fundamental to explore inter-individual 
differences and their potential causes [42]. 
The gut microbiota composition and its functionality emerged as the 
primary determinant for IIV in the metabolism and bioavailability of 
isoflavones, ellagitannins, and lignans (Fig. 4). The microbiota un-
doubtedly plays a key role also in the inter-person differences in the 
production of gut microbiota-derived catabolites of all other flavonoid 
classes, phenolic acids, stilbenes and other minor (poly)phenol sub- 
classes, although the specific bacteria and conditions responsible for 
the observed differences still have to be clarified. Indeed, many works 
where the source of IIV was classified as “undefined” are likely related to 
differences in the gut microbiota composition, but evidence of this still 
has to be provided. Faecal fermentation studies may be of help in clas-
sifying individuals according to the microbial metabolism of specific 
phenolics, as recently done for some flavan-3-ol monomers and ave-
nanthramides [163–165]. 
Sex, age, ethnicity, BMI and nutritional status, physical activity, di-
etary habits, and (patho)physiological status may condition the meta-
bolism of some phenolic classes, but, in general, they do not seem to 
influence too much phenolic metabolism. Data are lacking for most 
classes of phenolics, but for some, scattered data are available. For 
instance, for ellagitannins, age, sex, nutritional and health status have 
been described to be relevant sources of IIV in the production of uroli-
thins [48,49,149], while ethnicity may be involved in a different pro-
duction of equol/O-DMA from isoflavones [131]. (Fig. 4). Sex may also 
drive the metabolism of flavanones [75,76]. Studies comparing different 
classes of phenolic metabolites may help to better understand the drivers 
of the inter-person variation in the ADME of (poly)phenols. An example 
is the work by Hidalgo-Liberona et al. [77], who showed that a healthy 
intestinal barrier favours phenolic bioavailability compared to impaired 
intestinal permeability. 
3.4. Study limitations, gaps, and recommendations 
Although more than 150 studies were found and examined in this 
systematic review, it is clear that the way in which IIV in ADME of (poly) 
phenols has been studied so far has major limitations and that future 
studies on the topic should certainly adopt new methodological 
practices. 
Most of the studies were conducted with a limited number of par-
ticipants, and IIV was usually a post-hoc observation rather than an a 
priori outcome. The lack of focus on this phenomenon clearly driving the 
overall bioavailability of (poly)phenols and likely conditioning their 
health effects at the individual level, may hamper the achievement of 
robust findings for these plant bioactives. This highlights the need to 
include an adequate number of participants in intervention studies and 
look for specific associations between ADME/bioavailability of (poly) 
phenolic metabolites and potential IIV determinants, such as age, sex, 
genetic polymorphisms and gut microbiota, among others. 
The works conducted for some classes like ellagitannins and iso-
flavones may be a benchmark for designing future initiatives with other 
phenolic classes, even if their presence in a limited number of foods 
occasionally consumed helps to conduct intervention studies that are 
feasible for volunteers (i.e., that not imply to refrain from eating plant- 
based foods and beverages for days). Differently, it becomes more 
difficult to carry out such intervention studies for major dietary phe-
nolics like flavan-3-ols and hydroxycinnamic acids, as they are wide-
spread in plant-based foods and beverages and this implies adhering to 
strict (poly)phenol-free diets that are often a burden for study partici-
pants. Also for this reason, studies that consider many (poly)phenol sub- 
classes at the same time and assess IIV in the production of their me-
tabolites/catabolites, trying to define broader phenolic metabotypes and 
the determinants involved, are missing. Just a few examples have been 
published in the literature, focused on cranberry [55] or wine [56,57] 
phenolic metabolites, in which volunteers were clustered on the base of 
the type and/or amount of phenolics excreted beyond the metabolic 
pathway of a single family of dietary (poly)phenols. Another very recent 
example is the administration of a supplement containing ellagi-
tannins/ellagic acid, resveratrol, and isoflavones to 127 healthy adults, 
where up to 10 different combinations of urolithin-, lunularin- and 
equol-producing and non-producing metabotypes were found to coexist 
in the studied population [130]. This kind of comprehensive approach 
has also been recently applied in a study conceived by Mena and col-
leagues [166]. Briefly, 300 healthy volunteers (18–74 y) have been 
asked to perform a standardised oral (poly)phenol challenge test, con-
sisting in an acute supplementation of several classes of dietary (poly) 
phenols. To assess the individual urinary excretion of phenolic metab-
olites, urine samples have been collected in fasting conditions the day 
after the challenge and, for some participants, for 24 h. This will allow to 
define aggregate phenolic metabotypes and assessing the associations in 
metabotype formation among different classes of (poly)phenols. Sub-
jects’ information on dietary habits, smoking, physical activity, 
sleeping, anthropometric measures, health status, cardiometabolic risk, 
genetics, and gut microbiota composition have been collected to un-
derstand the determinants of the inter-individual variability [166]. Once 
published, this study may help to shed light on many of the questions 
arisen in this review. 
The use of data-driven approaches to decipher the main de-
terminants explaining IIV may be in fact very useful when dealing with 
complexdatasets. In this sense, machine learning, like in the case of the 
study by Hernández-Prieto and colleagues [75,76], and other statistics 
or artificial intelligence approaches will have to be explored. They may 
be particularly relevant when dealing with samples and data from large 
intervention trials with massive data collection. 
We should also be aware of the potential lack of relevance of the 
identified metabotypes in the future, as they may depend on instru-
mental limits of detection or the statistical approaches followed for their 
definition in a specific sample population. Moreover, what still needs to 
be proven for many families of (poly)phenols is the replication of the 
observed metabotypes and their stability over time, as these data are 
missing (except for isoflavones and ellagitannins) [9,52,130,138]. 
Lastly, adequate data reporting is essential to properly demon-
strating and handling IIV. Due to the lack of guidelines in the field, the 
COST Action POSITIVe provided some recommendations for better 
designing and reporting studies dealing with IIV [41]. They also 
developed the “POSITIVe quality index”, a valid, reliable, and respon-
sive score including 11 reporting criteria (sample size-power calcula-
tion, data distribution, p value, effect size, general characteristics of the 
subgroups where IIV was evaluated, data reporting for end-points by 
subgroups, measures of central tendencies and dispersion parameters, 
outliers, tables, graphs, presentation of full data and population char-
acteristics) to assist the research community while describing IIV. The 
use of this index is quite anecdotal to date, but the adherence to it is 
encouraged to improve the quality of the evidence published. 
4. Conclusions 
IIV in the metabolism and bioavailability of (poly)phenols is a fact. 
Its specific determinants are somehow clear for isoflavones and ellagi-
tannins, while they have not yet been defined, but only hypothesized, or 
insufficiently characterised for the majority of phenolic sub-classes. The 
gut microbiota plays a major role in the inter-individual differences 
existing in the ADME of most (poly)phenols. Genetic polymorphisms for 
enzymes associated with (poly)phenol metabolism may also play a role 
in some phenolic sub-classes. The information on the contribution of sex, 
age, ethnicity, BMI, nutritional status, physical activity, dietary habits, 
and (patho)physiological status to the inter-person variability is scarce 
and scattered among phenolic sub-classes. 
Two major types of IIV in the bioavailability of (poly)phenols were 
observed: continuous or cluster-based metabolite production. The most 
C. Favari et al. 
Redox Biology 71 (2024) 103095
16
common patterns of IIV are associated with continuous production of 
metabolites (all the subjects produce the same metabolites but in 
different amounts), where some groups of low, medium or high excre-
tors can be identified using various criteria (below/above median, top 
10%, etc.). These patterns have been reported for all flavonoids, 
phenolic acids, prenylflavonoids, alkylresorcinols, and hydroxytyrosol. 
Cluster production can be achieved discriminating subjects based on their 
different ability of producing or not specific metabolites (qualitative 
differences, producers vs. non-producers), as in the case of ellagitannins 
-urolithins-, isoflavones -equol and O-DMA-, resveratrol -lunularin-, or 
preliminarily for avenanthramides -dihydro-avenanthramides-, or based 
on quali-quantitative metabotypes (when subjects produce different 
proportions of the same pool of metabolites), as defined for flavan-3-ols 
-phenyl-γ-valerolactones and 3-HPPA-, flavanones -ratio phenolic acid/ 
phase II conjugates-, and isoflavones -daidzein, genistein and their 
colonic metabolites-. In most of the cases, what still needs to be verified 
is the replication of metabotypes for many families of (poly)phenols and 
their stability over time. 
IIV is usually assessed in relation to individual phenolic sub-classes 
and hardly ever takes into account the whole spectrum of phenolic 
metabolites produced from a foodstuff or a diet, precluding the inves-
tigation of different patterns of (poly)phenol metabolism and limiting 
the possibility of drawing sound conclusions on its determinants. Future 
studies, designed to comprehensively assess the metabolism of (poly) 
phenols, collecting as much information as possible about individuals 
(dietary and lifestyle habits, microbiota composition, genetics data, etc.) 
and taking into account the whole set of phenolic metabolites found in 
biofluids, may help to identify and define broader phenolic metabotypes 
and their determinants. The actual relevance of these phenolic metab-
otypes will then have to be determined through specific associations 
with health-related outcomes. 
Funding 
This work was supported by the European Research Council (ERC) 
under the European Union’s Horizon 2020 research and innovation 
programme (PREDICT-CARE project, grant agreement No 950050) and 
the National Recovery and Resilience Plan (NRRP), Mission 4 Compo-
nent 2 Investment 1.3 - Call for tender No. 341 of March 15, 2022 of 
Italian Ministry of University and Research funded by the European 
Union – NextGenerationEU; Award Number: Project code PE00000003, 
Concession Decree No. 1550 of October 11, 2022 adopted by the Italian 
Ministry of University and Research, CUP D93C22000890001, Project 
title“ON Foods - Research and innovation network on food and nutrition 
Sustainability, Safety and Security – Working ON Foods”. C.M. received 
funding from the French Agency of National Research (ANR, grant 19- 
HDH2-0002-01) and D.D.R. from the Ministero delle Politiche Agricole 
Alimentari e Forestali (MIPAAF, ID 1160) as part of the FoodPhyt 
project, under the umbrella of the European Joint Programming Initia-
tive “A Healthy Diet for a Healthy Life” (JPI HDHL) (2019–02201) and 
of the ERA-NET Cofund HDHL INTIMIC (GA N◦ 727565 of the EU Ho-
rizon 2020 Research and Innovation Programme). 
CRediT authorship contribution statement 
Claudia Favari: Writing – original draft, Visualization, Investiga-
tion. José Fernando Rinaldi de Alvarenga: Writing – original draft, 
Visualization, Investigation. Lorena Sánchez-Martínez: Investigation, 
Writing – original draft. Nicole Tosi: Investigation. Cristiana Migno-
gna: Investigation. Eleonora Cremonini: Writing – review & editing. 
Claudine Manach: Funding acquisition, Writing – review & editing. 
Letizia Bresciani: Writing – review & editing. Daniele Del Rio: Writing 
– review & editing, Funding acquisition. Pedro Mena: Writing – review 
& editing, Supervision, Funding acquisition, Conceptualization. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
No data was used for the research described in the article. 
Acknowledgements 
The authors want to thank Antonella Rosaria Guzzi and Denise 
Barone for their valuable work within this project. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.redox.2024.103095. 
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Fig. 1. Schematic representation of the possible existing metabolic profiles. A) Metabotypes based on a qualitative criterion are characterized by the production vs. 
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term “producers”). D) Quali-quantitative metabolic profiles are characterized by different proportions of specific metabolites belonging to the same metabolic 
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be a concentration or excretion measure. 
C. Favari et al. 
Redox Biology 71 (2024) 103095
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glucuronides or the proportion of methylated derivatives have been pro-
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(poly)phenols, attributable to variable enzymatic activities and perhaps 
also to genotypic diversity [33–35]. Therefore, we consider that phenolic 
metabotypes can be reported when the variability is well characterized 
from a metabolomics/statistical point of view, even when the causes 
behind their formation are unknown. This concept is fully in line with the 
broad definition for “metabotype” commonly adopted in the nutrition 
[36] and medical [37] fields, where metabotypes are defined as subgroups 
of individuals sharing similar combinations of specific metabolites or 
metabolic profiles. These metabolic profiles can be qualitative (production 
vs. non-production of specific metabolites), quantitative (high- vs. 
low-production of metabolites), or quali-quantitative (different produc-
tion proportions of specific metabolites) (Fig. 1). Although the term 
“metabotype” can be applied in sensu lato to these three cases, it is better to 
use it just for qualitative and quali-quantitative metabolic profiles as the 
metabolic capacities are clearly different, while in the case of quantitative 
profiles the term high/low producers/excretors is preferred. Of note, if 
robustly managed from a metabolomics and statistics point of view, all 
these ways of classifying individuals may help understand the factors 
driving the inter-individual variability in the metabolism and bioavail-
ability of phenolic compounds. 
To the best of our knowledge, no systematic approaches gathering 
data on several (poly)phenol classes are available on the IIV in (poly) 
phenol metabolism and its main determinants. Therefore, this system-
atic review aims to provide an overview of the available scientific evi-
dence on inter-individual differences in phenolic metabolite and 
catabolite production and bioavailability. In order to define the factors 
affecting individual differences, only papers that reported IIV were 
considered. Furthermore, suggestions for taking IIV into account in 
future studies assessing the health-related consequences of (poly)phenol 
intake were provided. 
2. Methods 
2.1. Search strategy and study selection 
This systematic review was developed in line with PRISMA 
(Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 
statement guidelines [38,39]. The systematic literature search was 
conducted in July 2022 using Scopus (https://www.scopus.com) and 
the Web of Science (http://apps.webofknowledge.com) databases, using 
the syntaxes reported in Supplemental Table 1. The literature search was 
updated in June 2023. No temporal or spatial filters were applied to the 
search. Reference lists of included manuscripts were also examined for 
any additional studies not previously identified. Studies were included 
in the present systematic review if they (i) were human studies sup-
plying to subjects sources of (poly)phenols (either a food, an extract or a 
pure compound), (ii) investigated the ADME/bioavailability of (poly) 
phenols, and (iii) reported IIV in (poly)phenol ADME/bioavailability. 
Exclusion criteria included studies published in a non-European lan-
guage. No restrictions for the characteristics of study participants (e.g., 
age, sex, ethnicity and health condition) were applied. 
2.2. Data extraction 
The systematic literature search was independently conducted by 2 
authors (CF and JRA), who independently assessed the studies for their 
inclusion. Discrepancies between authors were resolved through 
consultation with a third independent reviewer (PM). Data from each 
study were extracted using a standardized form, and the following in-
formation was collected: name of the first author; year of publication; 
description of the study population; (poly)phenol source; (poly)phenol 
class or specific compound(s) administered; specific metabolite(s) pro-
duced for which IIV was reported; type of biofluid(s) in which metab-
olites were detected and/or quantified; the outcome of ADME/ 
bioavailability (e.g. AUC, urinary excretion, plasma concentration …); 
type of IIV (continuous production or cluster production); clustering 
method and data normalization strategy, if reported; explanation of IIV; 
and potential IIV source. To clarify, continuous production indicates that 
all the subjects are able to produce a metabolite but in different 
amounts, so that, in certain cases, it is possible to classify subjects as 
high- vs. low-producers/excretors. Cluster production instead describes a 
production of metabolite(s) characterized by qualitative (when subjects 
are producers vs. non-producers of a specific metabolite or pool of me-
tabolites) or quali-quantitative differences (when subjects produce 
different proportions of the same pool of metabolites). A detailed 
description of the collected paper characteristics has been listed in the 
supplemental material. When studies reported several pharmacokinetic 
parameters (i.e., Tmax, Cmax, AUC, and t1/2) as bioavailability outcomes, 
only the AUC value was taken into consideration to calculate the per-
centage of the coefficient of variation (CV%), as this datum was 
considered more representative of the bioavailability of a metabolite. 
2.3. Data analysis 
Chemical names of circulating metabolites and catabolites were 
standardized according toL.) leaf extract, Mol. Nutr. Food Res. 57 (2013) 
2079–2085, https://doi.org/10.1002/mnfr.201200795. 
[163] P. Wang, S. Zhang, A. Yerke, C.L. Ohland, R.Z. Gharaibeh, F. Fouladi, A.A. Fodor, 
C. Jobin, S. Sang, Avenanthramide metabotype from whole-grain oat intake is 
influenced by Faecalibacterium prausnitzii in healthy adults, J. Nutr. 151 (2021) 
1426–1435, https://doi.org/10.1093/JN/NXAB006. 
[164] Q. Li, F. Van Herreweghen, S.O. Onyango, M. De Mey, T. Van De Wiele, In vitro 
microbial metabolism of (+)-Catechin reveals fast and slow converters with 
individual-specific microbial and metabolite markers, J. Agric. Food Chem. 70 
(2022) 10405–10416, https://doi.org/10.1021/ACS.JAFC.2C00551. 
[165] C. Liu, S. Boeren, I.M.C.M. Rietjens, Intra- and inter-individual differences in the 
human intestinal microbial conversion of (-)-Epicatechin and bioactivity of its 
major colonic metabolite 5-(3′,4′-Dihydroxy-Phenyl)-γ-Valerolactone in 
regulating nrf2-mediated gene expression, Front. Nutr. 9 (2022) 910785, https:// 
doi.org/10.3389/FNUT.2022.910785/BIBTEX. 
[166] P. Mena, C. Mignogna, N. Tosi, E. Monica, V. Agulló, A. Rosi, L. Narduzzi, 
V. Spigoni, R. Aldigeri, A. Bianchera, L. Bresciani, E. Balestreri, E. Eletto, 
G. Frigeri, R. Musa, P. Cerati, R. Aloe, D. Martorana, A. Gil-Izquierdo, A.- 
M. Minihane, F. Scazzina, R. Bettini, F. Brighenti, M. Ventura, A. Acharjee, A. Dei 
Cas, R. Bonadonna, D. Del Rio, Development of an oral (Poly)Phenol challenge 
test (Opct) to identify aggregate metabotypes for dietary (Poly)Phenols and their 
drivers: a study protocol, Curr. Dev. Nutr. 6 (2022) 1148, https://doi.org/ 
10.1093/cdn/nzac072.020. 
C. Favari et al. 
https://doi.org/10.3945/AJCN.2009.28290
https://doi.org/10.1002/mnfr.201200795
https://doi.org/10.1093/JN/NXAB006
https://doi.org/10.1021/ACS.JAFC.2C00551
https://doi.org/10.3389/FNUT.2022.910785/BIBTEX
https://doi.org/10.3389/FNUT.2022.910785/BIBTEX
https://doi.org/10.1093/cdn/nzac072.020
https://doi.org/10.1093/cdn/nzac072.020
	Factors driving the inter-individual variability in the metabolism and bioavailability of (poly)phenolic metabolites: A sys ...
	1 Introduction
	2 Methods
	2.1 Search strategy and study selection
	2.2 Data extraction
	2.3 Data analysis
	3 Results and discussion
	3.1 Study selection
	3.2 Characteristics of the included studies
	3.3 Inter-individual variability in the metabolism and bioavailability of phenolic metabolites and driving factors
	3.3.1 Flavonoids
	3.3.1.1 Flavan-3-ols
	3.3.1.2 Flavonols
	3.3.1.3 Flavones
	3.3.1.4 Flavanones
	3.3.1.5 Anthocyanins
	3.3.1.6 Isoflavones
	3.3.2 Non-flavonoids
	3.3.2.1 Phenolic acids
	3.3.2.2 Hydrolysable tannins (mainly ellagitannins)
	3.3.2.3 Lignans
	3.3.2.4 Stilbenes
	3.3.2.5 Other (poly)phenols
	3.3.3 Determinants of inter-individual variability in (poly)phenol bioavailability
	3.4 Study limitations, gaps, and recommendations
	4 Conclusions
	Funding
	CRediT authorship contribution statement
	Declaration of competing interest
	Data availability
	Acknowledgements
	Appendix A Supplementary data
	ReferencesKay et al. [40]. Several strategies were 
adopted to report IIV as described in the selected studies, in accordance 
with the type of data presented, type of IIV observed, and statistical 
analysis performed. Only statistically significant results were considered 
based on the reported p values or VIP values. When the type of IIV 
observed was continuous, i.e. all subjects produced the same metabolite 
(s) or catabolite(s) but in different amounts, as expressed by a high 
standard deviation (SD) or standard error of the mean (SEM) presented 
together with the mean value or by a high CV%, this latter index was 
considered as representative of IIV [41]. If not reported, CV% was 
calculated using the SD or SEM using the formula CV% = 100 ×
SD/mean or CV% = 100 X (SEM × √n)/mean, where n is the number of 
volunteers. CV% higher than 50% were considered relevant descriptors 
of IIV, according to Almeida et al. [15]. In a few cases, CV% was 
impossible to calculate, and fold-variation between low and high pro-
ducers was reported. When IIV was characterized by producers and 
non-producers of specific metabolite(s) or catabolite(s), the number of 
producers and non-producers was reported as a percentage with respect 
to the study population. 
3. Results and discussion 
3.1. Study selection 
The study selection process is shown in Fig. 2. A total of 3730 records 
were collected through database searches. After removing 1032 dupli-
cates, up to 2698 studies were screened, of which 2485 were excluded 
based on title or abstract. A total of 213 eligible records underwent the 
full-text screening, after which 74 records were excluded. Thirteen 
studies were identified through manual search in reference lists. Finally, 
153 publications met the eligibility criteria and were included in the 
data analysis. 
3.2. Characteristics of the included studies 
The main characteristics of the studies that met all inclusion criteria 
are reported in Table 1 and Supplemental Tables 2–8. 
Of the 153 intervention studies reporting IIV in the metabolism and 
absorption of phenolic metabolite(s) and catabolite(s), 24 papers dealt 
with more than one (poly)phenol sub-class [33,54,55,58–78]. Iso-
flavones and ellagitannins were the most investigated (poly)phenol 
sub-classes, covering about 26% and 21% of selected papers, respec-
tively. Phenolic acids accounted for 19% of extracted studies, whereas 
flavan-3-ols covered 16%. The remaining ADME/bioavailability papers 
included pharmacokinetics or urinary data on flavonols (7%), flavones 
C. Favari et al. 
https://www.scopus.com
http://apps.webofknowledge.com
Redox Biology 71 (2024) 103095
4
(3%), flavanones (11%), anthocyanins (4%), lignans (7%), stilbenes 
(4%), other minor phenolic sub-classes like flavononols, prenyl-
flavonoids, alkylresorcinols, tyrosols and oleuropeins, avenathramides, 
phlorotannins and benzene triols (7%) and (poly)phenols in general 
(2%) (Fig. 3). 
Most papers were intervention studies with (poly)phenol-rich ex-
tracts or foods. Only a few studies presenting observational data were 
included [79–87]. 
3.3. Inter-individual variability in the metabolism and bioavailability of 
phenolic metabolites and driving factors 
3.3.1. Flavonoids 
3.3.1.1. Flavan-3-ols. Flavan-3-ol metabolites for which IIV has been 
reported are both native ones, as proanthocyanidins and (epi)(gallo) 
catechins, and gut microbiota-derived catabolites, such as tri/di/mono- 
hydroxyphenyl-γ-valerolactones (PVLs) and phenylvaleric acids (PVAs), 
either described as aglycones or as conjugates (Table 1 and Supple-
mental Table 2). 3-(Hydroxyphenyl)propanoic acids (3-HPPAs) and 
Fig. 2. Flowchart of the study selection process. 
C. Favari et al. 
RedoxBiology71(2024)103095
5
Table 1 
Selection of studies reporting inter-individual variability for different classes of phenolic metabolites. 
Reference Population (Poly)phenol source (Poly)phenol 
class/specific 
compound(s) 
Specific metabolite(s) Biofluid Outcome of ADME/ 
bioavailability 
IIV 
type 
Clustering method IIV explanation IIV source 
Flavan-3-ols 
Mena et al., 2022 
[75] 
(a) n = 10 (0F/10 M); 
Age: 18.0– – 35.0 y/o; 
Healthy 
(b) n = 22 (0F/22 M); 
Age: 18.0– – 45.0 y/o; 
Healthy 
(a) Cranberry drinks containing 
716, 1131, 1396 and 1741 mg 
total flavan-3-ols 
(b) Cranberry powder containing 
0.5 mg of flavan-3-ol monomers 
and 374 mg of proanthocyanidins 
Flavan-3-ols 1. 3ʹ-HPVLs 
5-Phenyl- 
γ-valerolactone-3ʹ-sulfate 
5-Phenyl- 
γ-valerolactone-3ʹ- 
glucuronide 
2. 4ʹ-HPVLs 
5-Phenyl- 
γ-valerolactone-4ʹ- 
glucuronide 
3. 3ʹ, 4ʹ-dHPVLs 
5-(3ʹ,4ʹ- 
Dihydroxyphenyl)- 
γ-valerolactone 
5-(Hydroxyphenyl)- 
γ-valerolactone-sulfate 
(3ʹ,4ʹ isomers) 
5-(4ʹ-Hydroxyphenyl)- 
γ-valerolactone-3ʹ- 
glucuronide 
5-(3ʹ-Hydroxyphenyl)- 
γ-valerolactone-4ʹ- 
glucuronide 
5-Phenyl- 
γ-valerolactone- 
methoxy-sulfate isomer 
5-Phenyl- 
γ-valerolactone-sulfate- 
glucuronide isomer 
4. 3-HPPAs 
3-(Phenyl)propanoic 
acid-sulfate 
3-(Phenyl)propanoic 
acid-glucuronide 
U 24-h cumulative 
excretion (μμmol) 
ClP Final Consensus 
K-means 
Expectation- 
maximization 
PCA-based, forming 
2 clusters 
PCA-based, forming 
3 clusters 
Normalization: 
centering + unit 
variance scaling 
Ratio of sulfate/ 
glucuronide 
derivatives 
↑ vs ↓ PVLs producers 
↑ vs ↓ PVLs producers 
↑ vs ↓ 4ʹ-HPVLs and 3- 
HPPAs producers 
Cluster 1: ↑ 4ʹ-HPVLs, ↑ 3- 
HPPAs and ↓ 3ʹ, 4ʹ-dHPVLs 
producers 
Cluster 2: ↑ 3ʹ, 4ʹ-dHPVLs 
and ↓ 4ʹ-HPVLs, ↓ 3-HPPAs 
producers 
Cluster 1: ↑ 3ʹ-HPVLs, ↑ 4ʹ- 
HPVLs, ↑ 3-HPPAs and ↓ 3ʹ, 
4ʹ-dHPVLs producers 
Cluster 2: ↑ 3ʹ, 4ʹ-dHPVLs 
and ↓ 3ʹ-HPVLs, ↓ 4ʹ- 
HPVLs, ↓ 3-HPPAs 
producers 
Cluster 3: ↓↓ PVLs 
producers 
↑ excretors of sulfate 
derivatives vs ↑ excretors 
of glucuronide derivatives 
Undefined 
Mena et al., 2019 
[74] 
n = 11 (9F/2 M); Age: 
18.0– -45.0 y/o; BMI: 
18.5– - 24.9 kg/m2; 
Healthy 
Tablets of green tea exctract Flavan-3-ols 1. TrihydroxyPVLs 
2. DihydroxyPVLs 
3. 3-(3′-hydroxyphenyl) 
propanoic acids (as sum 
of all the derivatives 
belonging to the same 
aglycone moiety) 
4. (+)-Catechin 
5. (Epi)catechin- 
glucuronide 
6. (Epi)catechin-sulfate 
7. Methoxy-(epi) 
catechin-glucuronide 
8. Methoxy-(epi) 
gallocatechin-sulfate- 
glucuronide 
9. Methoxy-(epi) 
gallocatechin- 
U Excretion (μμmol/ 
24h) 
ClP 
CoP 
PLS-DA + one-way 
ANOVA with post 
hoc Dunnett’’s T3 
test n.a. 
Metabotype 1 (36.3%): ↑ 
triydroxyPVLs, ↑ 
dihydroxyPVLs, ↓ 3- 
(hydroxyphenyl)propionic 
acid 
Metabotype 2 (45.5%): 
medium dihydroxyPVLs, ↓ 
trihydroxyPVLs, ↓ 3- 
(hydroxyphenyl)propionic 
acid 
Metabotype 3 (18.2%): ↓ 
trihydroxyPVLs, ↓ 
dihydroxyPVLs, ↑ 3- 
(hydroxyphenyl)propionic 
acid 
4. CV (%) = 60 
5. CV (%) = 51 
6. CV (%) = 53 
Undefined 
(continued on next page) 
C. Favari et al. 
RedoxBiology71(2024)103095
6
Table 1 (continued ) 
Reference Population (Poly)phenol source (Poly)phenol 
class/specific 
compound(s) 
Specific metabolite(s) Biofluid Outcome of ADME/ 
bioavailability 
IIV 
type 
Clustering method IIV explanation IIV source 
glucuronide 
10. 5-(Phenyl)- 
γ-valerolactone-3′- 
glucuronide 
11. 5-(Phenyl)- 
γ-valerolactone-3′-sulfate 
12. 5-(4′- 
Hydroxyphenyl)- 
γ-valerolactone 
7. CV (%) = 83 
8. CV (%) = 55 
9. CV (%) = 133 
10. CV (%) = 115 
11. CV (%) = 121 
12. CV (%) = 122 
Flavanones 
Fraga etal., 2022 
[90] 
n = 46 (26F/20 M); Age: 
19–-38 y/o; BMI: 
23.23 ± 2.54 kg/m2; 
Healthy 
Pasteurized orange juice (500 mL- 
Citrus sinensis L. Osbeck var. Pera) 
Flavanones 1. Phase II flavanone 
metabolites (total) 
U Excretion (ng 
equivalents/24h) 
CoP Defined thresholds ↑ vs ↓ excretors 
↑ excretors are 
associatedassocitaed with 
GGCC and GCTG 
haplotypes 
for ABCC2_rs8187710/ 
SULT1A1_rs3760091/ 
SULT1A1_rs4788068/ 
SULT1C4_rs1402467 
Genetic 
polymorphisms 
Fraga et al., 2021 
[91] 
n = 74 (F/M); Age: 
19.0– - 40.0 y/o; 
Eutrophic (n = 53); Age: 
26.5 y/o; BMI: 22.0 kg/ 
m2;HealthyOverweigh 
(n = 21); Age: 26.5 y/o; 
BMI: 27.0 kg/m2; 
Healthy 
Pasteurized orange juice (500 mL- 
Citrus sinensis L. Osbeck var. Pera) 
Flavanones Flavanone phase II 
conjugates 
1. Hesperetin-7- 
glucuronide 
2. Hesperetin-3′- 
glucuronide 
3. Hesperetin- 
glucuronyl-sulfate 
4. Hesperetin 
-diglucuronide 
5. Hesperetin-sulfate 
6. Naringenin-7- 
glucuronide 
7. Naringenin-isomer 
8. Naringenin- 
glucuronyl-sulfate 
9. Hippuric acid 
10. 3,4-Dihydroxyben-
zoic acid 
11. 3′- 
Hydroxyphenylacetic 
acid 
12. 4′- 
Hydroxyphenylacetic 
acid 
13. 3-(4′- 
Hydroxyphenyl) 
propionic acid 
U Total excretion 
(mg/24 h) 
CoP oPLS-DA 
ContinuousRatio 
(phenolic acids 
/flavanone phase II 
conjugates) 
34% ↑ excretors 
38% medium excretors 
21% ↓ excretors 
Low ratio: flavanone phase 
II conjugates (↑) vs 
phenolic acid (↓) 
Intermediate ratio: 
flavanone phase II 
conjugates (=) vs phenolic 
acid (=) 
High ratio: flavanone 
phase II conjugates (↓) vs 
phenolic acid (↑) 
Undefined 
Isoflavones 
Soukup et al., 2023 
[92] 
n = 59 (59F/0 M); 
Healthy, being post 
menopause 
Isoflavone-enriched soy extract Isoflavones 1. Daidzein 
2. Genistein 
3. Dihydrodaidzein 
4. Dihydrogenistein 
5. O-DMA, 
U Excretion (μμmol) ClP HCA with Ward 
method 
Normalization by 
the total amount of 
isoflavone 
Metabotype 1 (12%): 
equol producer, ↑ 
Genistein, ↓ 4-Ethylphenol 
Metabotype 2 (17%): 
equol producer, ↓ 
Gut microbiota 
(continued on next page) 
C. Favari et al. 
RedoxBiology71(2024)103095
7
Table 1 (continued ) 
Reference Population (Poly)phenol source (Poly)phenol 
class/specific 
compound(s) 
Specific metabolite(s) Biofluid Outcome of ADME/ 
bioavailability 
IIV 
type 
Clustering method IIV explanation IIV source 
6. 6-OH-O-DMA 
7. Equol 
8. 4-Ethylphenol 
equivalents excreted 
(%) 
Genistein, ↑ 4-Ethylphenol 
Metabotype 3 (25%): ↑↑ 4- 
Ethylphenol 
Metabotype 4 (39%): ↑↑ 
Daidzein, ↑↑ Genistein 
Metabotype 5 (7%): ↑↑ 
Dihydrodaidzein, ↑↑ 
Dihydrogenistein 
de Oliveira Silva 
et al., 2020 [93] 
n = 18 (10F/8 M); Age: 
19.0– -32.0 y/o; BMI: 
23.2 ± 2.4 kg/m2; 
Healthy 
Soybean meal (SBM) and 
fermented soybean meal (FSBM) 
biscuits 
Isoflavones 1. Total aglycones 
(glycitein, genistein, 
daidzein) 
2. Total colonic 
metabolites 
(dihydrodaidzein, 
dihydrogenistein, O- 
DMA, 6-OH-O-DMA, 
equol) 
3.O-DMA 
4. Equol 
U Excretion (μμmol) CoP 
CoP 
ClP 
n.a 
n.a. 
Presence vs absence 
of specific 
metabolites 
1. 29% ↑ in M 
2. 51% ↑ in F 
64% O-DMA-producers, 
25% non-producers, 11% 
equol-producers 
Sex 
Sex 
Gut microbiota 
Ellagitannins 
Cortés-Martín et al., 
2018 [94] 
n = 839 (442F/397 M); 
Age: 5.0– – 90.0 y/o; 
Normoweight, 
overweight, obese; 
Healthy and with 
colorectal cancer, 
metabolic syndrome, 
prostate cancer 
Walnuts (peeled), pomegranate 
juice or pomegranate extract 
Ellagitannins 1. Urolithin A and 
conjugates 
2. Urolithin B and 
conjugates 
3. Isourolithin A and 
conjugates 
U Excretion (n.a.) ClP Presence vs absence 
of specific 
metabolites 
Metabotype 0 constant in 
the age range (mean 
10.4 ± 1.9%) 
Metabotype A ↓ with ↑ age 
(from 80.8 ± 9.3% to 
52.1 ± 5.3%) 
Metabotype B ↑ with ↑ age 
(from 8.2 ± 8.5% to 
40.7 ± 4.6%) 
Metabotype A ↓ with ↑ BMI 
in the age range 5– – 40 y/ 
o 
Metabotype B ↑ with ↑ BMI 
in the age range 5– – 40 y/ 
o 
Gut microbiota 
Age 
BMI (nutritional 
status) 
Selma et al., 2018 
[95] 
n = 94 (36F/58 M); 
(a) Healthy, 
normoweight: n = 20 
(9F/11 M); BMI: 
27.0 kg/ 
m2; 
(c) With MetS: n = 25 
(10F/15 M) 
Nuts (peeled) or pomegranate 
extract 
Ellagitannins 1. Urolithin A and 
conjugates 
2. Urolithin B and 
conjugates 
3. Isourolithin A and 
conjugates 
U Excretion (n.a.) ClP Presence vs absence 
of specific 
metabolites 
Metabotype B ↑ in MetS 
(41%) than overweight- 
obese (31%) than 
normoweight (20%) 
Metabotype A ↑ in 
normoweight (70%) than 
overweight-obese (57%) 
than MetS (50%) 
Metabotype 0 constant 
around 10% 
Gut microbiota 
Nutritional 
status (Patho) 
physiological 
state 
Lignans 
Mullens et al., 2022 
[96] 
n = 42 (22F/20 M); Age: 
(F): 31.4 ± 8.0 (M): 
33.2 ± 8.7 y/o; 
BMI: (F): 27.7 ± 7.4 (M): 
26.0 ± 3.9 kg/m2; 
Healthy 
Flaseed lignan extract Lignans 1. Enterolactone 
2. Secoisolariciresinol 
3. Enterodiol 
U Excretion 0–-24h 
(μμmol/24h) 
CoP Defined threshold n. 
a. 
n.a. 
1. ↓vs ↑ producers 
2. ↑ for ↑ ENL producers 
3. ↑ for ↑ ENL producers 
Gut microbiota 
Undefined 
(continued on next page) 
C. Favari et al. 
RedoxBiology71(2024)103095
8
Table 1 (continued ) 
Reference Population (Poly)phenol source (Poly)phenol 
class/specific 
compound(s) 
Specific metabolite(s) Biofluid Outcome of ADME/ 
bioavailability 
IIV 
type 
Clustering method IIV explanation IIV source 
Navarro et al., 2020 
[97] 
n = 46 (23F/23 M); 
Age: 32.1 ± 8.4 y/o; 
BMI: 26.7 ± 5.8 kg/m2; 
(a) ↓ENL = 23 (11F/ 
12 M); 
Age: 33.1 ± 9.3; 
BMI: 27.2 ± 7.1 kg/m2; 
(b) ↑ENL = 23 (12F/ 
11 M); 
Age: 31.1 ± 7.6 y/o; 
BMI: 26.2 ± 4.3 kg/m2 
Flexseed lignan extract Lignans 1. Enterolactone 
2. Secoisolariciresinol 
3. Enterodiol 
U Excretion 0–-24h 
(μμmol/24h) 
CoP Defined threshold n. 
a. 
n.a. 
1. ↓vs ↑ producers 
2. CV (%) = 120 (↓ENL), 
133 (↑ENL) 
3. CV (%) = 250 (↓ENL), 
218 (↑ENL) 
Gut microbiota 
Undefined 
Stilbenes 
Iglesias-Aguirre 
et al., 2022 [98] 
n = 195 (124F/71 M); 
Age: 18.0– - 81.0 y/o; 
BMI: 25.7 ± 5.0 kg/m2; 
Healthy 
Hard gelatine capsule Resveratrol 1. Lunularin and 
conjugates 
2. 4-Hydroxydibenzyl 
3. 3,4ʹ-Dihydroxy-trans- 
stilbene 
U Excretion (n.a.) ClP Presence vs absence 
of specific 
metabolites 
Sex 
Presence vs absence 
of specific 
metabolites 
74.4% producers, 25.6% 
non-producers 
↑ vs medium vs ↓ lunularin 
producers 
30.6% F vs 16.9% M 
lunularin producers 
Present only in lunularin 
producers 
Gut microbiota 
Sex 
Bode et al., 2013 
[99] 
n = 12 (F/M); Age: 
19.0– - 28.0 y/o; BMI: 
20.0– - 26.0 kg/m2; 
Healthy 
Colloid formulation (0.5 mg 
transtrans-resveratrol/kg body 
weight) 
trans-resveratrol 1. Dihydroresveratrol 
2. 3,4′’-dihydroxy-trans- 
stilbene 
3. 3,4′’- 
dihydroxybibenzyl 
(lunularin) 
U Urinary excretion 
(% of the oral dose 
of trans-resveratrol) 
ClP n.a. Cluster 1: 3,4′’- 
dihydroxybibenzyl 
production 
Cluster 2: 
Dihydroresveratrol 
production 
Cluster 3: Mixed 
production of 
dihydroresveratrol and 
3,4′’-dihydroxybibenzyl 
Gut microbiota 
Prenylflavonoids 
Bolca et al., 2009 
[53] 
n = 50 (50F/0 M); 
Age: 44.0– - 76.0; 
BMI: 24.4 ± 4.0 g/m2; 
Healthy, being post 
menopause 
Hop extract Prenylflavonoids 1. 8-prenylnaringenin 
2. Isoxanthohumol 
U Excretion [ratio 8- 
prenylnaringenin 
/8- 
prenylnaringenin +
Isoxanthohumol)] 
CoP TwoStep cluster 
analysis protocol 
Normalization: 
calculated ratios 8- 
prenylnaringenin 
/(8- 
prenylnaringenin +
Isoxanthohumol) 
14% Strong producers 
24% Moderate producers 
62% Poor producersGut microbiota 
(Poly)phenols 
Tosi et al., 2023 [50] n = 60 (F/M); Age: 50– – 
80 y/o; Healthy 
Free-living diet with cranberry 
powder supplementation in the 
treated group 
(Poly)phenols 1. 3ʹ-HPVLs 
2. 3ʹ,4ʹ-dHPVLs 
3. 3ʹ-HCAs 
4. 3-HPPAs 
5. 3-HBAs 
U Excretion (μμmol/ 
mmol creatinine) 
ClP PCA- and PLS-DA- 
based, forming 3 
clusters 
Normalization: 
centering + unit 
variance scaling 
Metabotype 1: ↑ 3ʹ-HPVLs 
and ↑ 3ʹ, 4ʹ-dHPVLs 
producers Metabotype 2: ↑ 
3ʹ-HCAs, ↑ 3-HPPAs, ↑ 3- 
HBAs producers 
Metabotype 3: ↓↓ 
metabolite producers 
Undefined 
Muñoz-González 
et al., 2013 [100]; 
Belda et al., 2021 
[101] 
n = 33 (F/M); Age: 
20.0– – 65.0 y/o 
Red wine Benzoic acids 
Phenols 
Phenylacetic 
acids 
Phenylpropionic 
acids 
Total polyphenols 
metabolites 
Fe Excretion (μμg/g) CoP Tertiles of low 
(1000) 
polyphenol 
excretion 
21% ↑ excretors 
39% medium excretors 
39% ↓ excretors 
Undefined 
Gut microbiota 
(continued on next page) 
C. Favari et al. 
RedoxBiology71(2024)103095
9
Table 1 (continued ) 
Reference Population (Poly)phenol source (Poly)phenol 
class/specific 
compound(s) 
Specific metabolite(s) Biofluid Outcome of ADME/ 
bioavailability 
IIV 
type 
Clustering method IIV explanation IIV source 
Cinnamic acids 
Silbenes 
Flavan-3-ols 
Flavonols 
Anthocyanins 
ADME, Absorption, Distribution, Metabolism and Excretion; IIV, inter-individual variability; F, females; M, males. 
3ʹ-HPVLs, 3ʹ-(hydroxyphenyl)-γ-valerolactones; 4ʹ-HPVLs, 4ʹ-(hydroxyphenyl)-γ-valerolactones; 3ʹ,4ʹ-dHPVLs, 3ʹ,4ʹ-(dihydroxyphenyl)-γ-valerolactones; 3ʹ-HCAs, 3ʹ-hydroxycinnamic acids; 3-HPPAs, 3-(3′-hydroxyphenyl) 
propanoic acids; 3-HBAs, 3-hydroxybenzoic acids; ENL, enterolactone. 
U, urine; Fe, feces; ClP, cluster production; CoP, continuous production; CV%, coefficient of variation (in percentage); n.a., not available. 
PCA, principal component analysis; PLS-DA, partial least squares-discriminant analysis; oPLS-DA, orthogonal partial least squares-discriminant analysis; HCA, hierarchical clustering analysis; BMI, body mass index; MetS, 
metabolic syndrome. 
C. Favari et al. 
Redox Biology 71 (2024) 103095
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their conjugates, important catabolites of flavan-3-ols, as well as com-
mon metabolic end-products of several (poly)phenol sub-classes have 
been discussed in the Phenolic acids section (3.3.2.1.) and presented in 
Supplemental Table 6, except for four studies in which these metabolites 
were inextricably considered for the definition of flavan-3-ol metab-
otypes and are thus presented here, in Tables 1 and in Supplemental 
Table 2 [33,43,42]. 
In general, high and low producers of flavan-3-ol metabolites have 
been observed (continuous production), as reported by authors or high-
lighted by high CV% and fold-variations [34,60,63,66,70,72,74,78, 
88–93]. However, high producers of native compounds are not neces-
sarily high or low producers of gut microbiota-derived catabolites, as the 
inter-relationship between these two metabolite classes has rarely been 
assessed [89]. 
Regarding native metabolites, most studies simply showed IIV in 
their plasma concentrations or urinary excretion without determining 
specific factors responsible for the observed IIV. Actis-Goretta et al. [90] 
found large IIV in the absorption of (− )-epicatechin after perfusion of 
purified (− )-epicatechin into an isolated jejunal segment of 6 volunteers, 
as determined by inter-subject different recovery of (− )-epicatechin in 
intestinal perfusates. Similarly, the amount of (− )-epicatechin phase II 
metabolites detected in bile and urine varied considerably among in-
dividuals, with one subject excreting higher amounts of (− )-epicatechin 
metabolites in bile than the rest of the volunteers and, consequently, 
presenting lower amounts of these metabolites in urine. Despite the 
small number of volunteers prevented from reaching any firm conclu-
sion, authors assumed that different rates of metabolism could be one of 
the most important variables determining the overall absorption of 
(− )-epicatechin [90]. 
In another study, age was hypothesized as a potential cause for IIV in 
the ADME of native flavan-3-ol metabolites [94]. Authors only found 
slight differences between young and elderly when the highest dose of 
cocoa flavan-3-ols foreseen in the study was administered, while no 
significant difference was observed between age groups when the lowest 
cocoa flavan-3-ol dose was consumed. In detail, differences were 
observed in plasma levels of sulfated and glucuronidated (epi)catechin 
metabolites and were explained by differences in the elimination rate, 
suggesting that the expected decline of renal function with age may 
influence the plasma levels of phase II (epi)catechin metabolites in 
elderly [94]. 
When a green tea extract was administered to 84 healthy participants 
[93], IIV in the pharmacokinetics of (− )-epigallocatechin, (− )-epi-
gallocatechin-gallate, and (− )-epicatechin-gallate was not associated 
with sex, age, weight, height, body mass index (BMI), lifestyle param-
eters (such as smoking, alcohol consumption or habitual consumption of 
green tea), nor with polymorphisms in the genes coding p-glycoprotein 
(Pgp) and sulfotransferase 1A1 (SULT1A1). However, inter-person dif-
ferences were associated with the use of contraceptives and inherent 
genetic variations in genes coding for multidrug resistance-associated 
protein 2 (MRP2) and organic anion-transporting polypeptide 1B1 
(OATP1B1) for (− )-epigallocatechin and (− )-epigallocatechin-gallate 
[93]. 
Andreu Fernández et al. [95] reported greater IIV differences in AUC 
values for epigallocatechin-gallate in males than females, suggesting a 
potential influence of sex. Unfortunately, further studies that corrobo-
rate all these findings are missing. 
Concerning gut microbiota catabolites, attempts to define flavan-3-ol 
metabotypes based on the differential quali-quantitative urinary excre-
tion of PVLs and 3-HPPAs (cluster production) have been performed in 
recent years. Firstly, three putative metabotypes were proposed after 
consumption of an (epi)gallocatechin-rich green tea extract: metabotype 
1 was characterized by high excretion of tri- and dihydroxyPVLs and 
reduced excretion of 3-HPPAs; metabotype 2 was distinguished by a 
medium excretion of dihydroxyPVLs and a limited excretion of trihy-
droxyPVLs and 3-HPPAs; and metabotype 3 was characterized by a 
limited production of PVLs and high amounts of 3-HPPAs [43]. Later 
works reported similar findings, although flavan-3-ol precursors differed 
from those of green tea and did not include trihydroxylated compounds, 
namely (epi)gallocatechin derivatives [33,55,42]. The resulting picture 
was more straightforward but consistent, as authors reported at least 
two urinary flavan-3-ol metabotypes after consumption of cranberry and 
nuts: one cluster characterized by a high excretion of 3-HPPAs and 
monohydroxyPVLs, and a reduced excretion of dihydroxyPVLs, whereas 
the other cluster by a high excretion of dihydroxyPVLs and low excre-
tions of smaller catabolites (monohydroxyPVLs and 3-HPPAs) [33,42]. 
At the same time, depending on the clustering technique used, a third 
cluster, characterised by a low excretion of all the metabolites, was also 
observed [33,55,42]. Indeed, a particular influence of the normalization 
method, as well as the clustering algorithm used (i.e., distance- or 
density-based), has emerged when defining metabotypes originating 
from quali-quantitativedifferences in metabolite production [42]. 
Despite a certain robustness of the evidence for colonic metabotypes 
of flavan-3-ols, further ad hoc confirmatory research is required to 
consolidate this observation and fully understand the factors behind 
these differences, as well as their possible health-related consequences. 
Among the factors to be considered, gut microbiota composition and 
bioconversion capacity are likely the main variables determining the 
observed IIV, but efforts are needed to identify the microbial species 
responsible for the different catabolism of flavan-3-ols. Information on 
specific bacterial strains responsible for the bioconversion of flavan-3- 
ols into PVLs and low molecular weight phenolics is currently scarce. 
Van Velzen et al. [66] positively correlated dihydroxyPVL production 
with members of Clostridia and Actinobacteria, including Clostridium 
leptum, Flavonifractor plautii, Ruminococcus bromii, Sporobacter termitidis, 
and Eubacterium ramulus, and the genus Propionibacterium. Lactobacillus 
plantarum IFPL935, Eggerthella lenta, Adlercreutzia equolifaciens, and 
Fig. 3. The number of selected studies reporting IIV in the bioavailability of the different phenolic sub-classes. Each square represents one study and different colours 
indicate different phenolic sub-classes, so the higher the number of squares having the same colour, the higher the studies available for that sub-class. (For inter-
pretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 
C. Favari et al. 
Redox Biology 71 (2024) 103095
11
Flavonifractor plautii are the only bacteria identified being able to 
convert flavan-3-ols into 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone and 
5-(3′-hydroxyphenyl)-γ-valerolactone, but no microorganisms respon-
sible for further catabolism into PVAs and 3-HPPAs have yet been 
identified [96,97]. On the other hand, transit time should also be 
considered as it may drive the metabotypes elucidated so far. 
Lastly, a certain IIV in phase II metabolism of gut-microbiota ca-
tabolites has been reported, mainly related to dihydroxyPVLs, with 
subjects excreting higher amounts of glucuronide than sulfate de-
rivatives, or vice versa [33,35,42]. This fact has also been observed for 
(epi)catechin derivatives [35]. This variability could be related to ge-
netic polymorphisms in phase II enzymes [98–100], but also to the dose 
of flavan-3-ols consumed. It has been reported that the sulfonation 
pathway has higher affinity but lower efficiency than the glucur-
onidation one, and when the ingested amount of flavan-3-ols increases, a 
shift from sulfation toward glucuronidation might occur [101]. Future 
studies will elucidate this and further document the stability of the 
phenotype over time and ingested doses, to finally evaluate its potential 
relevance for human health. 
3.3.1.2. Flavonols. Data on IIV in the ADME of flavonol metabolites 
have been mainly reported for kaempferol, quercetin, and myricetin, 
either as aglycones or phase II conjugates, and for two methylated de-
rivatives of quercetin, namely isorhamnetin and tamarixetin (Supple-
mental Table 3). Unspecific phenolic acids that can originate from gut 
microbiota catabolism of flavonols in the large intestine have been re-
ported in Supplemental Table 6. 
For flavonol native metabolites, IIV in their plasma concentration 
and urinary excretion has been observed, as expressed by the high CV% 
(continuous production), but no specific factor has been associated to the 
observed inter-individual differences (Supplemental Table 3) [59,63,74, 
78,102–105]. One study performed on just 7 healthy volunteers re-
ported a correlation between the percentage of kaempferol excreted 
upon bean consumption and the BMI of volunteers, with higher 
kaempferol excretion observed for lower BMI [106]. Sex and gut 
microbiota were also investigated as potentially responsible for the IIV 
observed in quercetin AUC values after consumption of quercetin and 
rutin (quercetin-rutinoside) by 16 healthy subjects [107]. While a small 
level of IIV was observed after quercetin aglycone intake, rutin con-
sumption by females led to higher quercetin AUC and Cmax values, with 
subjects using oral contraceptives having the highest values. It was hy-
pothesized that women’s microbiota was able to hydrolyze sugar moi-
eties more efficiently than men’s, and this might be linked to the use of 
oral contraceptives [107]. 
Despite these interesting results, 8 out of 10 studies included herein 
did not specify any possible cause of the observed IIV [59,63,74,78, 
102–105]. Accordingly, Almeida et al. [15] recently reviewed the IIV in 
quercetin bioavailability in humans and did not manage to establish the 
causes behind the reported IIV, although suggesting a potential impli-
cation of genetic polymorphisms, dietary adaptation, composition of gut 
microbiota, drug exposure, and other subject characteristics such as BMI 
and health status. These factors may be common to many other phenolic 
families. 
3.3.1.3. Flavones. Scarce IIV data were found for flavones, mainly 
related to luteolin and apigenin phase II metabolites, which are char-
acterized by high CV% in urinary excretion (Supplemental Table 3) [63, 
65,108]. Phenolic acid catabolites that can originate from gut micro-
biota catabolism of flavones have been reported in Supplemental 
Table 6. 
A recent study by Tomás-Navarro et al. [65] reported different levels 
of urinary excretion of phase II derivatives of 
hydroxy-polymethoxyflavones, demethylated metabolites of specific 
flavones found in orange juice. Upon definition of excretion levels as 
high (if above 10% of the intake), medium (between 5 and 10% of the 
intake), and low (if below 5% of the intake), the possible existence of 
three quantitative groups of high, medium and low excretors of these 
polymethoxyflavone metabolites was suggested (continuous production) 
[65]. In addition, in the study, three quantitative groups of high, me-
dium and low excretors of phase II metabolites of flavanones (flavonoids 
also present in orange juice) were reported (see the paragraph “Flava-
nones” below). However, no correlations between the suggested quan-
titative phenotypes for polymethoxyflavone metabolites and those 
observed in the excretion of flavanone phase II metabolites were 
observed. The potential factors responsible for the presence of the 
quantitative phenotypes for polymethoxyflavone metabolites observed 
could not be elucidate, but the involvement of gut microbiota and 
CYP1A1 polymorphisms in the demethylation of polymethoxyflavones 
was hypothesized [65]. 
3.3.1.4. Flavanones. Regarding flavanones, IIV has been observed for 
unchanged compounds and phase II metabolites (Table 1 and Supple-
mental Table 4) and gut microbiota-derived phenolic acids described in 
the Phenolic acid section and in Supplemental Table 6. For unchanged 
and phase II metabolites, most of the studies reported a continuous pro-
duction [65,44,109–111], sometimes reporting the existence of two/-
three groups, depending on the amount of flavanone metabolites 
excreted and thresholds considered, thus classifying subjects into high, 
medium and low/no producers [65,45,109,110,112] or high and 
low/no producers [44,111,113]. 
In some instances, polymorphisms in phase II enzymes and trans-
porters have been associated with the observed quantitative metab-
otypes. For example, Fraga et al. [44] associated high excretion of phase 
II flavanone metabolites with GGCC and GCTG haplotypes for the 
ABCC2_rs8187710, SULT1A1_rs3760091, SULT1A1_rs4788068, and 
SULT1C4_rs1402467 enzymes involved in the efflux of flavanone me-
tabolites back to the intestinal lumen(which may limit the bioavail-
ability of these compounds) and their conjugation with a sulfate moiety 
(which favours their elimination in urine). In some other cases, the in-
fluence of polymorphisms in phase I enzymes (in particular CYP1A1 and 
CYP2A6) has been suggested as a potential explanation of the observed 
IIV, although no confirmatory results have been found [65,109]. 
Nishioka et al. [110] observed two different flavanone metabotypes, 
one characterized by a lower excretion, a slower excretion rate and a 
lower glucuronidation rate (profile A), and the other one conversely 
showing higher excretion, a faster excretion rate, and a higher glucur-
onidation rate (profile B). These differences were attributed to a possible 
involvement of polymorphic variants of the phase II enzyme 
UDP-glucuronosyltransferase (UGT). These metabolic profiles (A and B) 
did not correlate with quantitative metabotypes defined in the same 
studies for total flavanone metabolites excretion (high, medium and low 
excretors of total flavanone metabolites). Moreover, subjects classified 
as profile B showed a greater abundance of species responsible for the 
O-deglycosylation of flavanones (Bacteroides uniformis and Bifidobacte-
rium bifidum), while a Clostridium sp., related to the C-ring fission, was 
exclusively found in profile A individuals. However, the meaning of 
these observations still need to be clarified [110]. 
The urinary excretion of phase II flavanones and their ratio with 
some phenolic acids (mainly hippuric acid, 4ʹ-hydroxyphenylacetic acid, 
and 3-(4ʹ-hydroxyphenyl)propanoic acid)) was used by Fraga et al. [45] 
to classify individuals consuming orange juice into three different 
metabolic patterns. Note that subjects with a low or intermediate ratio 
(i.e., higher production of phase II metabolites) saw a significant 
reduction in body fat percentage and blood pressure after 60 days of 
orange juice consumption [45]. 
In other studies, authors did not define quantitative metabotypes for 
flavanone phase II metabolites, but consistently observed high CV% in 
their plasma concentrations or excreted amounts in urine, suggesting 
that some individuals are producing largely more phase II metabolites 
than other individuals [62,63,68,75,76,114]. Pereira-Caro et al. [62] 
C. Favari et al. 
Redox Biology 71 (2024) 103095
12
reported CV% up to 300% in the urinary excretion of phase II flavanone 
metabolites in a study investigating the effect of exercise on flavanone 
metabolism after consumption of orange juice. Exercise did not signifi-
cantly modify the mean amount of phase II flavanones in circulation. 
Recently, machine learning has been used to better understand the 
contribution of sex to the pool of flavanones and phenolic acids in cir-
culation, both in plasma and urine, after chronic consumption of a 
maqui-lemon beverage [75,76]. Results accounted for an effect of sex on 
some metabolites,as naringenin-glucoside, homoeriodyctiol-glucur-
onide, eriodyctiol and its sulfate derivative, for certain individuals. 
3.3.1.5. Anthocyanins. Data on IIV in the ADME of anthocyanidin me-
tabolites have been mainly reported for cyanidin, delphinidin, malvidin, 
pelargonidin, and petunidin, either as aglycones or phase II conjugates 
(Supplemental Table 3). Unspecific phenolic acids, such as phenyl-
propanoic, phenylacetic and benzoic acid derivatives, which can be 
originated by gut microbiota catabolism of anthocyanidins, have been 
reported in Supplemental Table 6. Regarding native anthocyanidin 
metabolites, a substantial variation in their plasma concentration and 
urinary excretion among individuals has been observed, as expressed by 
high CV% (continuous production), but to date no specific cause has been 
identified to explain the reported IIV (Supplemental Table 3). An 
attempt to review the factors that affect anthocyanidin bioavailability in 
humans has been recently done by Eker et al. [21], without reaching any 
firm conclusion due to a lack of literature addressing this topic. The 
authors assumed that differences in enzymes involved in anthocyanidin 
metabolism and transport and in gut microbiota composition and ac-
tivity, which in turn can be affected by further factors such as genetics, 
age, and diet, may be the main responsible for IIV in anthocyanidin 
ADME [21]. However, more studies are needed to support these 
assumptions. 
3.3.1.6. Isoflavones. Regarding isoflavones,IIV has been suggested for 
both unchanged compounds, mainly daidzein, genistein, glycitein, for-
mononetin, and biochanin A, and for gut microbiota-derived catabolites, 
namely dihydrodaidzein, dihydrogenistein, equol, O-DMA, and 6ʹ-hy-
droxy-O-DMA (Table 1 and Supplemental Table 5). Equol has been the 
most studied compound for long and likely the first catabolite for which 
IIV was hypothesized in 1984 [115]. 
Differences among subjects have been described mainly in the pro-
duction or non-production of equol and O-DMA (cluster production), the 
two main microbial-derived catabolites of daidzein. In fact, for both 
metabolites, two distinct metabotypes have generally been identified, 
clustering subjects between producers and non-producers. These 
observed metabotypes are determined by a specific gut microbiota 
composition and activity. It was reported that the gut microbes involved 
in equol production are more than ten, including Slackia equolifaciens, 
Slakia isoflavoniconvertens and Adlercreutzia equolifaciens, while, unfor-
tunately, there are fewer reports on the bacterial species specifically 
linked to O-DMA production [9]. According to Zheng et al. [116], sub-
jects with an equol-producing metabotype have a higher abundance of 
the equol-producing bacteria Adlercreutzia equolifaciens and Bifido-
bacterium bifidum, and a different microbial composition enriched in the 
genera Prevotella, Megamonas, Allistipes, Desulfovibrio, Collinsella, and 
Eubacterium. This is in agreement with the previous findings of Nakatsu 
et al. [117], who reported that post-menopausal women able to produce 
equol presented an enrichment in Bifidobacterium and Eubacterium 
compared to non-producers after one-week consumption of soy bars. 
Interestingly, Yoshikata et al. [81] identified equol-producing bacteria 
in 97% of women participating in their study, although only 22% of 
them were equol producers, suggesting that equol production might not 
depend on the quantity, but on the quality of equol-producing bacteria 
consortiums. In addition, equol producers presented higher microbiota 
diversity than non-producers, which might favour equol production. 
The equol-producing and O-DMA-producing metabotypes are 
unrelated: the capacity to harbour equol-producing bacteria is not 
associated with the ability to harbour O-DMA-producing bacteria. The 
studied populations have displayed a distinct distribution of these 
metabotypes, but, in most cases, the proportion of equol-producers was 
below 50%, while that of O-DMA producers was above 50% of the 
population [79,80,82,83,47,118–129]. Nevertheless, a couple of recent 
reports seem to deny the existence of a specific O-DMA-producing 
phenotype, as O-DMA was found in urine of the 95% [46] and 100% 
[130] of German postmenopausal women and Spanish adults, respec-
tively. Future studies will confirm whether O-DMA production is char-
acterized by quantitative differences (high vs. low production, i.e., 
continuous production), instead of qualitative differences (cluster pro-
duction), as previously believed. Ethnicity may also play an important 
role in the distribution of equol- and O-DMA-producing metabotypes, as 
observed for example by Song et al. [131] in a population of more than 
300 Korean American and Caucasian-American female subjects. 
Equol-producerphenotype was significantly more prevalent in Korean 
American than in Caucasian American women and girls and, conversely, 
the O-DMA-producer phenotype was significantly less prevalent in 
Korean American than in Caucasian American females [131]. However, 
further investigation is needed to evaluate the role of ethnicity and di-
etary habits in the observed differences between sub-groups of 
individuals. 
Some studies looked for associations between the incidence of the 
equol- or O-DMA-producing metabotypes and sociodemographic char-
acteristics of the population other than ethnicity, including age and 
education level, anthropometric values (height, weight and BMI, among 
others) and lifestyle factors (as dietary habits) [54,80,81,83,120, 
125–127,132–134]. No clear associations with any particular factor 
were consistently reported across all studies. 
An increasing number of studies have associated equol- or O-DMA- 
producing metabotypes with potential effects on health, mainly related 
to protection against menopausal symptoms [79,133,135] and cardio-
vascular risk, as comprehensively reviewed by Frankenfeld (2017). 
Equol- and/or O-DMA-producers seemed to have better cardiovascular 
health profiles than non-producers [82,116,134,136]. In addition, acute 
vascular benefits of equol in men have been reported only in equol 
producers upon soy consumption. Interestingly, the same benefits were 
not observed in equol non-producers after intake of synthetic equol, 
suggesting that the microbial ecology associated with the 
equol-producing capacity is crucial for determining health benefits 
[137]. However, these results were not fully supported by other scien-
tific evidence: the study by Usui et al. [121] described a significant 
improvement of cardiometabolic risk biomarkers particularly in equol 
non-producer females, after chronic daily oral administration of S-equol. 
Clarifying whether the health effects exerted by equol and/or O-DMA 
are due to the presence of a specific microbial ecology associated with 
their production or to the direct activity of the microbial metabolites 
themselves is needed, as well as understanding whether the health status 
has an impact on the production of these metabolites or are the me-
tabolites that condition the health status [138]. 
Other isoflavone metabolites are produced by all individuals, 
although in different amounts (continuous production), as evidenced by 
high CV% values [58,117,124,129,132,139–141]. The factors respon-
sible for the observed IIV have not been determined yet, and further 
studies are needed to elucidate them. Interestingly, unchanged isofla-
vone metabolites (in particular daidzein, genistein, and glycitein) are 
excreted in higher amounts in equol non-producers [119,134]. A recent 
work reported that the AUC0-24h and Cmax values of 5-hydroxyequol, but 
not of other isoflavone metabolites changed according to the enterotype, 
with subjects belonging to Prevotella-rich enterotype presenting higher 
values than in the Bacteroides and Ruminococcaceae enterotypes [142]. 
Recently, Soukup et al. [46] assessed the metabolic profile of iso-
flavones considering both daidzein and genistein, in 59 
post-menopausal women consuming a soy supplement for 12 weeks. The 
study population was classified into 5 isoflavone metabotypes (cluster 
C. Favari et al. 
Redox Biology 71 (2024) 103095
13
production), characterised by different proportions of daidzein, genis-
tein, equol, dihydrodaidzein, dihydrogenistein, and 4-ethylphenol. 
Insterestingly, clusters were associated with estrogenic potencies. This 
study opened the door to more comprehensive metabotyping ap-
proaches for soy isoflavones, avoiding reductionist approaches focused 
on just equol or O-DMA. 
3.3.2. Non-flavonoids 
3.3.2.1. Phenolic acids. Phenolic acid metabolites can originate from 
human metabolism or from resident microbiota catabolism of phenolic 
acids occurring in foods, namely hydroxycinnamic and benzoic acid 
derivatives, as well as from gut microbial catabolism of dietary flavo-
noids [20]. It is noteworthy that some of them can also derive from the 
metabolism of aromatic amino acids, or from a few endogenous me-
tabolites such as dopamine. Phenolic acids can also undergo subsequent 
phase II metabolism. In this class, data on IIV has been reported, mainly 
for cinnamic, phenylpropanoic, phenylacetic, benzoic and hippuric acid 
derivatives, either as aglycones or phase II conjugates (Supplemental 
Table 6). Their plasma concentration and urinary levels are affected by a 
substantial IIV, as represented by the high CV%, up to 301%, as reported 
in Supplemental Table 6 (continuous production) [59,63,68,72,74,78,84, 
91,143]. This may indicate that some factors can dramatically affect 
their production in every single individual, or that high and low pro-
ducers of these metabolites exist. Indeed, some studies focused on fla-
vonoids and reporting on phenolic acids have discussed the role of 
phenolic acids for classifying subjects according to metabolic patterns 
(cluster production) [55,43,42,45]. As an example, phenylpropanoic 
acids, in particular 3-HPPAs, have proved to be fundamental for the 
determination of colonic metabotypes of flavan-3-ols, showing different 
production patterns associated with PVL production [33,55,43,42]. 
Similarly, when subjects following a free-diet supplemented or not with 
cranberry powders were clustered according to their phenolic metabo-
lite profiles, three metabotypes characterized by quali-quantitative dif-
ferences in the excretion of some metabolites were identified: one 
metabotype presented a high excretion of PVLs; another metabotype 
showed high excretion of several low molecular weight phenolic me-
tabolites belonging to cinnamic, phenylpropanoic, phenylacetic, and 
benzoic acids, benzaldehydes and benzene diols (catechols); and the 
third metabotypes was characterized by a low phenolic metabolite 
excretion [55]. Whether these phenolic profiles may be related to spe-
cific gut microbiota profiles or other drivers of IIV is unknown. Such 
metabotyping approach considering several classes of phenolic com-
pounds pointed out the importance of phenolic acids when classifying 
individuals consuming phenolic-rich products or not following any 
specific dietary treatment. 
The excretion of a few phenolic acids (mainly hippuric acid, 4ʹ- 
hydroxyphenylacetic acid, and 3-(4ʹ-hydroxyphenyl)propanoic acid)) 
served, together with their excretion ratio to phase II flavanones, to 
classify orange juice consumers into three groups (low, medium, and 
high excretors) and explore the benefits of orange juice on subjects 
presenting different patterns of metabolism [45]. As phenolic acids 
contribute to a large extent to the total amount of (poly)phenols in 
circulation, investigating different phenolic acid production patterns 
may help better understand the role of (poly)phenols on human health. 
Specific factors influencing IIV related to phenolic acid metabolites 
have not been clearly identified, although a certain role of the gut 
microbiota composition and microbial activity have been suggested [66, 
69,84,85,144]. The impact of age, BMI, and lifestyle factors, including 
dietary habits, alcohol consumption, and physical activity, are still not 
robustly characterised in available studies [33,75,76,85,145,146]. 
Bento-Silva et al. [20] recently reviewed the variables affecting IIV 
associated to phenolic acids, suggesting a major contribution of gut 
microbiota and genetic polymorphisms, however, without reaching any 
firm conclusion. 
3.3.2.2. Hydrolysable tannins (mainly ellagitannins). Regarding hydro-
lysable tannins, no literature dealing with IIV in the ADME/bioavail-
ability of gallotanninswas found, probably due to their rare occurrence 
in plant-based foods. Conversely, much information was found about 
ellagitannins, high molecular weight ellagic acid derivatives, with 32 
studies reporting a marked IIV (Table 1 and Supplemental Table 7). This 
is likely related to the fact that metabotypes involved in the metabolism 
of ellagitannins/ellagic acid, characterized by the production or not 
(cluster production) of specific gut microbiota-derived metabolites, 
namely urolithins, were first described many years ago and have sub-
sequently been the subject of in-depth studies [48,49,147–153]. Three 
metabotypes have been consistently described: urolithin metabotype A 
includes producers of only urolithin A (3,8-dihydroxy-urolithin), uroli-
thin metabotype B includes producers of isourolithin-A (3,9-dihydrox-
y-urolithin) and urolithin-B (3-hydroxy-urolithin) in addition to 
urolithin-A, whereas urolithin metabotype 0 identifies non-producers. 
All the available information on IIV in the production of ellagi-
tannin/ellagic acid metabolites, that mainly concerns urolithin metab-
otypes, the microbial species involved in urolithin production 
(Gordonibacter pamelaeae, Gordonibacter urolithinfaciens, and Ellagibacter 
isourolithinifaciens), and other determinants (such as age, BMI, and 
(patho)physiological status) involved in their distribution, as well as the 
potential relationships with human health, has already comprehensively 
been reviewed and updated by Tomás-Barberán et al. [152] and Gar-
cía-Villalba et al. [154], thus it is not further discussed in this review. 
3.3.2.3. Lignans. The main lignan catabolites enterolactone and enter-
odiol, and in a few cases the native compound secoisolariciresinol, are 
the compounds for which an IIV in their production has been observed 
(Table 1 and Supplemental Table 8). Enterolactone is the most 
frequently assessed catabolite. Two quantitative phenotypes have been 
mostly described, namely high and low enterolactone producers 
(continuous production) [86,50,51,155] and different criteria (top 10% or 
above median) have been used to define high producers. 
Hålldin et al. [22] recently reviewed the factors that may affect 
human plasma concentration of enterolactone and concluded that the 
composition and activity of the intestinal microbiota appear to be the 
most critical determinants associated with IIV, followed by the use of 
antibiotics. Enterolactone is produced by the gut microbiota, starting 
from plant lignans, through a series of deglycosylation, demethylation, 
dehydroxylation, and dehydrogenation reactions, catalysed by a con-
sortium of bacteria. Some bacterial genera have been identified and 
associated with each step: Bacteroides and Clostridium with deglycosy-
lation, Eubacterium, Butyribacterium and Blautia for demethylation, 
Clostridium and Eggerthella for dihydroxylation, and the species Lacto-
nifactor longoviformis for dehydrogenation. Consequently, the lack of 
certain bacteria, their interaction, or an inappropriate intestinal envi-
ronment could decrease enterolactone production, determining the 
different metabotypes [22]. Intake of lignan-rich food, constipation, 
BMI, sex and other lifestyle factors, as smoking, could also influence the 
total variability among subjects [22,156]. Regarding the health impli-
cations of different levels of enterolactone production, not much has 
been reported yet [22]. A high enterolactone production may be related 
to better anthropometric parameters [86]. Interestingly, a gene 
expression analysis in fecal exfoliated cells has recently revealed that 
high enterolactone production was associated with a suppressed in-
flammatory status [50]. 
Besides enterolactone, enterodiol is a peculiar microbial catabolite of 
lignans. Nowadays, it is not clear if high and low producer phenotypes 
can be clearly discriminated for enterodiol as well, but undoubtedly its 
production, as well as secoisolariciresinol metabolism, is affected by a 
high IIV, as shown by a high reported CV% (continuous production) 
(Table 1 and Supplemental Table 8). Whether high enterolactone pro-
ducers are also high enterodiol and secoisolariciresinol producers re-
mains to be established. 
C. Favari et al. 
Redox Biology 71 (2024) 103095
14
3.3.2.4. Stilbenes. IIV has been reported for unchanged compounds, 
namely trans-resveratrol (3,5,4ʹ-trihydroxy-trans-stilbene) and piceid 
(resveratrol-3-glucoside), and for glucuronide derivatives of trans- 
resveratrol and its gut microbiota-originated catabolites (Table 1 and 
Supplemental Table 8). Trans-resveratrol is a metabolite that can be 
found in plasma of all subjects, but in different amounts, as indicated by 
a high CV% [157,158]. At the same time, its glucuronide derivatives 
seem to be selectively produced only by some subjects, as described in 
the study by Vitaglione et al. [159]. However, this observation would 
need further assessment. 
Regarding gut microbiota-derived catabolites, Bode et al. [53] 
described different routes of microbial conversion of trans-resveratrol: 
one leading to the production and excretion of dihydroresveratrol, 
another one producing lunularin and a third one leading to an equiva-
lent production of both catabolites. In detail, lunularin, also known as 3, 
4ʹ-dihydroxybibenzyl, is a trans-resveratrol catabolite obtained by its 
reduction to dihydroresveratrol and subsequent dehydroxylation at the 
5-position. Almost all lunularin producers also excreted 3,4ʹ-dihydrox-
y-trans-stilbene [53]. Thanks to additional in vitro experiments within 
the same study, Bode and colleagues found an association between 
dihydroresveratrol production and an intestinal microbiota enriched in 
Slackia equolifaciens and Adlercreutzia equolifaciens [53]. The 
non-production of dihydroresveratrol was associated with a greater 
presence of Bacteroidetes, Actinobacteria, Verrucomicrobia and Cyano-
bacteria [53]. 
Iglesias-Aguirre et al. [52] have recently described the consistent 
observation of two metabotypes associated with resveratrol catabolism: 
195 healthy individuals have been clusterized as lunularin producers 
and non-producers, similarly to what Bode et al. [53] previously 
observed both in vitro and in vivo in a smaller population of 12 in-
dividuals. In the population studied by Iglesias-Aguirre et al. [52], 
74.4% of individuals were lunularin producers and among them, high, 
medium and low producers were defined. In addition, lunularin pro-
ducers selectively excreted two additional catabolites, namely 4-hydrox-
ydibenzyl and 3,4ʹ-dihydroxy-trans-stilbene. The lunularin 
non-producer metabotype was significantly more prevalent in females, 
but independent of individuals’ BMI and age. Even if these are inter-
esting results, further research is needed to confirm lunularin metab-
otypes, the role of gut microbiota composition in its production, and the 
possible impact on health. 
3.3.2.5. Other (poly)phenols. Concerning other (poly)phenol sub- 
classes, it is worth mentioning IIV in the metabolism of hop prenyl-
flavonoids, particularly isoxanthohumol. Isoxanthohumol microbial 
catabolism allows the classification of individuals into poor, moderate 
and strong producer phenotypes (continuous production) of 8-prenylnar-
ingenin [58,54,160]. These three quantitative metabotypes could be 
linked to differences in gut microbiota composition (Table 1 and Sup-
plemental Table 8). 
Regarding IIV in alkylresorcinol and oleuropein metabolism, sex may 
influence it, based on available studies [161,162]. In particular, for both 
classes, continuous production of catabolites was observed, with women 
presenting higher AUCs for the two main end products of alkylresorcinol 
catabolism, namely 3,5-dihydroxybenzoic