Nutrients 2022, 14, 5361. https://doi.org/10.3390/nu14245361 www.mdpi.com/journal/nutrients
Short Chain Fatty Acid Metabolism in Relation to Gut
Microbiota and Genetic Variability
Guilherme Ramos Meyers 1,2, Hanen Samouda 1 and Torsten Bohn 1,*
1 Nutrition and Health Research Group, Department of Precision Health, Luxembourg Institute of Health,
1 A-B, Rue Thomas Edison, 1445 Strassen, Luxembourg
2 Doctoral School in Science and Engineering, University of Luxembourg, 2, Avenue de l’Université,
4365 Esch-sur-Alzette, Luxembourg
* Correspondence: email@example.com; Tel.: +352-621216637
Abstract: It is widely accepted that the gut microbiota plays a significant role in modulating inflam-
matory and immune responses of their host. In recent years, the host-microbiota interface has gained
relevance in understanding the development of many non-communicable chronic conditions, in-
cluding cardiovascular disease, cancer, autoimmunity and neurodegeneration. Importantly, dietary
fibre (DF) and associated compounds digested by the microbiota and their resulting metabolites,
especially short-chain fatty acids (SCFA), were significantly associated with health beneficial effects,
such as via proposed anti-inflammatory mechanisms. However, SCFA metabolic pathways are not
fully understood. Major steps include production of SCFA by microbiota, uptake in the colonic ep-
ithelium, first-pass effects at the liver, followed by biodistribution and metabolism at the host’s cel-
lular level. As dietary patterns do not affect all individuals equally, the host genetic makeup may
play a role in the metabolic fate of these metabolites, in addition to other factors that might influence
the microbiota, such as age, birth through caesarean, medication intake, alcohol and tobacco con-
sumption, pathogen exposure and physical activity. In this article, we review the metabolic path-
ways of DF, from intake to the intracellular metabolism of fibre-derived products, and identify pos-
sible sources of inter-individual variability related to genetic variation. Such variability may be in-
dicative of the phenotypic flexibility in response to diet, and may be predictive of long-term adap-
tations to dietary factors, including maladaptation and tissue damage, which may develop into dis-
ease in individuals with specific predispositions, thus allowing for a better prediction of potential
health effects following personalized intervention with DF.
Keywords: nutrigenetics; nutrigenomics; dietary fibre; short chain fatty acids; microbiome;
synergies; sustainable development; holobiont; SNPs; translational research
The human organism is composed of eukaryotic cells, as well as of an assembly of
microbes collectively termed the microbiota, including archaea, bacteria, fungi and eukar-
yota. These may outnumber human cells, although a 1:1 ratio seems more likely, accord-
ing to more recent estimates . Regardless of the quantity of genes within individual
microbial cells, the microbiome (the whole genome of the microbiota) encompasses over
1000 microbial species. Thus, the microbiome complements the human genome in func-
tionality, such as enhancing digestion or protecting from pathogenic invasion [2,3]. The
largest fraction of microbiota is found in the colon, and is termed, together with a smaller
fraction residing in the stomach and small intestine, the gut microbiota . Indeed, evo-
lutionary biology proposes an analogous eukaryon-mitochondrion symbiosis that oc-
curred between multicellular eukaryotes and prokaryotes millions of years ago, the so-
called holobiont theory .
Citation: Ramos Meyers, G.;
Samouda, H.; Bohn, T. Short Chain
Fatty Acid Metabolism in Relation to
Gut Microbiota and Genetic
Variability. Nutrients 2022, 14, 5361.
Academic Editor: Stavroula Kanoni
Received: 26 November 2022
Accepted: 13 December 2022
Published: 16 December 2022
Publisher’s Note: MDPI stays neu-
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This article is an open access article
distributed under the terms and con-
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tribution (CC BY) license (https://cre-
Nutrients 2022, 14, 5361 2 of 55
Evidence is mounting that the gut microbiota (GM) plays a fundamental role in reg-
ulating metabolic, immune and endocrine functions, as well as priming the immune re-
sponse against pathogens. Indeed, GM alterations such as total abundance of or ratios
between different species or families have been associated with many different health is-
sues , specifically those of non-communicable chronic diseases (NCDs) such as obesity
, cardiovascular disease and atherosclerosis , type 2 diabetes (T2D) [6,8], autoim-
mune disorders such as rheumatoid arthritis , ageing conditions, e.g., osteoporosis and
sarcopenia , neurodegenerative diseases [11,12] including Parkinson’s  and Alz-
heimer’s disease , as well as several types of cancer [15,16]. In addition to GM changes,
the majority of these conditions is characterized by a low-grade chronic inflammation [17–
20], concurring with increased levels of oxidative stress [21,22].
Research has highlighted the significant and strong relationship between dietary pat-
terns and the development of NCDs, such as CVD, depression, cognitive decline, multiple
sclerosis, Parkinson’s disease, osteoarthritis and gastrointestinal conditions such as irrita-
ble bowel syndrome (IBD) [7,23–34], with much attention being dedicated to dietary fibre
(DF) . Overall, a higher DF intake has been associated with reduced all-cause mortal-
ity, e.g., in the Asian population , and hypotheses on its role as a health protective
factor have been existing for several decades . Studies have demonstrated improved
health outcomes with higher fiber intake in conditions ranging from C. difficile infection
 to paediatric kidney disease , showing its wide applicability in health mainte-
nance. Regrettably, in most countries, it appears that DF intake has been on the decline.
In Japan, where data are available since the 1950s, a 30% drop in DF intake was observed
between the 1950s and 1970s, and then stabilized—though this may be subject to change,
as younger generations report far less DF intake than their elders . A review by the
Nutrition Society , as assessed by national surveys in the UK, revealed a DF intake of
approximately 14.8 g/d in adults, men and women, in 1999 , and about 13.6 g/d in
2009–2012 . In the USA, DF intakes remained stable from 1999 to 2008, but well below
recommendations, at around 15 g/d . Concurrently, the highest consumption of DF in
Europe was found in Germany (25 g/d for males and 23 g/d for females), based on a tele-
phone-survey performed in 2005–2006 , being in line with EFSA recommendations.
DF may be at the centre of the symbiotic relationship between the GM and the human
host [35,44–49]. DF is not absorbed or broken down to a significant degree by human di-
gestive enzymes, and can, at least in part, be used as an energy substrate by the GM. De-
pending on the nature of DF, it is predominantly metabolized into short chain fatty acids
(SCFA), including butyrate, acetate, and propionate . Butyrate, acetate and propionate
cross the enterocyte layer and are absorbed, while lactate and succinate appear to be in-
termediate products of DF fermentation . Immediately, butyrate acts as the main en-
ergy source for colonocytes and controls maturation of mucosa associated lymphoid tis-
sue (MALT) [51–63], characterized by a high presence of immune cells such as macro-
phages, B and T cells and that plays an important role in antigen sensing. Only a fraction
of the produced SCFA enter the host’s systemic circulation, with acetate corresponding to
around 75% of total peripheral SCFA [64,65]. However, these values have shown a high
degree of inter-individual variation, as well as intra-individual variation such as dose–
response, time-course and circadian variance . SCFA may act as pleiotropic immuno-
modulators, i.e., having different functions in different tissues [35,51,67]. SCFA appear to
be strong influencers of immune regulation, as seen in studies regarding asthma and
atopy in infants, as well as in mice models [68–72], or gastrointestinal health in adults
[35,48,51,52,73–75]. As described in the following chapters, SCFA production and concen-
trations were associated with disease risk. In addition to SCFA, DF acts as a vehicle for
antioxidants in the upper gastrointestinal tract [76,77], as it is associated with a large num-
ber of phenolic compounds [44,78–81] and other secondary plant metabolites such as ca-
rotenoids [44,80]. Especially phenolic compounds may likewise be turned into bioactive
metabolites by the GM [77,82], and synergies between these food derived compounds may
exist, further highlighting their importance [83–87].
Nutrients 2022, 14, 5361 3 of 55
Apart from drugs, age, delivery method, medication intake, alcohol and tobacco con-
sumption, pathogen exposure, besides diet in general, and dietary secondary plant me-
tabolites in particular, are known to be significant modulators of the GM [44,88–94]. Die-
tary antioxidants can alter GM composition and thus its products . However, the ge-
netic background also modulates bacterial colonization [3,96]. In particular, genetic vari-
ants such as single nucleotide polymorphisms (SNPs), may further explain some of the
inter-personal variability observed following fibre intake, such as circulating levels of
SCFA [53,72,97,98]. Variations in genes such as GPR41, GPR43 or GPR109A (G-protein
coupled receptors for SCFA)  could have substantial impact on the immunometabo-
lism of certain tissues in particular, and the organism in general. Furthermore, transporter
genes of the SLC16A family (monocarbohydrate transporters), effector genes such as
MUC2 (for mucus layer production in the colon) or regulatory genes such as NRF2 (regu-
lating the expression of proteins involved in the bodies’ antioxidant defence mechanism
such as superoxide dismutase (SOD)), could have important downstream effects on health
outcomes (Figure 1) due to impaired absorption of SCFA or by impacting their functions
Figure 1. Host-driven variability in SCFA metabolism and distribution may lead to different disease
outcomes. ADME (sub-) steps may explain the variability in SCFA effects. The enterotype influences
the amount of SCFA produced, while human digestive enzymatic activity may regulate microbial
communities; (1) Absorption: SNPs in mucin, MCTs or tight junction function could impair SCFA
bioavailability. Butyrate is the main energy source for colonocytes. (2) In the portal circulation SCFA
undergo first-pass effects, where a majority of propionate is metabolized via GPR109A, GPR43 and
GPR41, having gluconeogenic or lipogenic effects. Distribution In the systemic circulation: although
at present at low concentrations, butyrate and propionate are still detectable; acetate is now the most
abundant SCFA. (3) Acetate inhibits lipolysis at the adipose tissue level. (4) Acetate can cross the
“blood-brain-barrier” (BBB). Metabolism: SCFA have showed to be effective against microglial oxi-
dative stress responses. SCFA may also have cellular signalling properties, as evidenced by its con-
trol of centrally released insulin (6) or its impact on the hypothalamic-pituitary-adrenal axis in leptin
and cortisol responses, which may ultimately lead into maladaptive health conditions across the
body (7). Finally, gluconeogenic, lipogenic and insulinogenic signals impact ghrelin, leptin and pep-
tide YY release, leading to appetite suppression and satiety (8), improved insulin sensitivity and
glucose metabolism, as well as reduction of serum lipids. (9) Excretion: in the kidney, SCFA can be
re-absorbed by MCT1. Note: the intracellular effect of SCFA e.g., on HDAC or NF-κB are not dis-
played. Created with BioRender.com.
In this review, we aim to relate the relationship between the metabolism of short
chain fatty acids (SCFA) and the host-genetic background. In particular, we will investi-
gate the genetics associated with differences in terms of SCFA production at the GM level
and its metabolism at the host level and relationship to health.
Nutrients 2022, 14, 5361 4 of 55
2. Dietary Fibre and Short Chain Fatty Acids
2.1. Dietary Fibre (DF)
Westernized types of diet are characterized by a relatively low intake in DF, despite
attempts to increase its intake since the 1970s. Most European countries have established
recommendations on daily intake for DF, e.g., 25–35 g for adults. Concretely, 25–32 g/d
for adult women and 30–35 g/d for adult men, while recommendations for children and
older adults depend on age, being approximately 3–4 g/MJ . The Physicians Commit-
tee for Responsible Medicine (PCRM) of the US recommends even a considerably higher
intake of 40 g/d for an optimal health .
The European Food Security Authority (EFSA) has recommended an adequate intake
(AI) of 25 g/d for DF, mostly based on its association with improved bowel function (as
per defecation frequency and transit time), and the reduction of gastro-intestinal symp-
toms such as constipation . DF refers to total fibre occurring naturally in foods such
as fruits, vegetables, pulses and cereal grains [40,102]. Grain products are at present the
largest source for DF intake worldwide, providing approx. 32% of total dietary fibre in-
take in the USA and 48% in the Netherlands. Other sources vary widely in European coun-
tries, e.g., vegetables (12–21%), potatoes (6–19%) and fruits (8–23%) . Lack of DF intake
has been emphasized as one of the major dietary factors associated with the increased
incidence of NCDs [103–106]. A recent systematic review and meta-analysis suggested
that high DF consumption was associated with a 15–30% decrease in cardiovascular-re-
lated mortality, T2D and colorectal cancer, when compared with low-fibre consumption
. Concurring dietary factors such as increased sugar consumption, increased satu-
rated fat consumption and low nutrient density, among others, and their possible rela-
tionship to metabolic and neurophysiological disorders, may be present and are expected
to play a role [40,108]. However, as human lifespan has expanded during the past decades
[109,110], we expect to face an increase of NCDs, as these are rather associated with age-
related chronic inflammation (i.e., inflammageing ). Therefore, it is paramount to fully
understand the pathophysiology of NCDs, and how to counteract them with affordable
and efficient strategies, including improved dietary patterns and healthy food items
[18,110–117]. In this respect, fiber intake could be increased both within a low-fat diet a
low-carbohydrate diet. A randomized controlled trial aiming at weight reduction over a
period of 12 months assessed sources of DF in a balanced low-fat diet vs. a balanced low-
carbohydrate diet. A large proportion of DF for both diets was from non-starchy vegeta-
bles. While the low-fat group mainly increased DF intake from whole grains and fruits,
the low-carbohydrate one obtained DF rather from vegetables and plant protein sources.
This was further reflected in gut microbiota alterations throughout the intervention, and
such dietary adaptations may constitute an important factor for precision nutrition .
A variety of definitions has been proposed to classify DF; most were dependent on
the methods used to extract DF. This led to difficulties in defining the term, as most non-
starch polysaccharides (NSP) were retrieved by such methods, which often did not in-
clude resistant (i.e., non-digestible) starches (RS). DF can further be categorized based on
its solubility, fermentability or viscosity, which often caused distinctions within the
group. While soluble fibres can be fermented to different degrees, and are the main sub-
strate for colonic fermenters (e.g., β-glucans), insoluble fibres mainly serve a stool bulking
function (e.g., cellulose). Both types of DF have beneficial health properties, and as such,
the dichotomy of soluble-insoluble may no longer play a main role in terms of public
To date, definitions have reached a certain consensus [119,120]. DF is composed of
carbohydrate polymers with three or more monomeric units (MU), which are neither hy-
drolysed by human digestive enzymes nor absorbed in the human intestine, and include
NSPs from fruits, vegetables, grains and tubers, whether intrinsic or extracted, either
chemically, enzymatically, or in physically modified forms. Polymers with more than 10
Nutrients 2022, 14, 5361 5 of 55
MU, e.g., cellulose, hemicelluloses, pectins, hydrocolloids (i.e., gums, β-glucans, muci-
lages); resistant oligosaccharides, e.g., fructo-oligosaccharides (FOS), galacto-saccharides
(GOS) with 3–9 MU; and RS with 10 or more MU  are included. Furthermore, some
constituents produced by micro-organisms (e.g., xanthan) and polysaccharide constitu-
ents of crustaceans and fungi (e.g., chitin, chitosan, chondroitin sulphate), are resistant to
digestion and are included in the DF definition, according to some national agencies .
Furthermore, it has been proposed that proteins resistant to digestion exist, and may re-
produce similar effects as DF, namely improved bowel function and improved immunity
[121–123], but these are typically not included in the DF definition.
Thus, DF is any polymeric carbohydrate not digested in the small intestine. DF gen-
erally also includes substances associated with, or linked to plant cell walls, but that are
not carbohydrates, such as lignin or polyphenols. Often, these distinctions are not re-
ported in food tables, where only the sum of DF is given. In 2002, the French Agency for
Food Security (ANSES), included in its definition all of the above polymeric carbohydrates
(MU ≥ 3) as DF, while excluding animal-based sources and lactulose, a non-absorbable
sugar, to prevent its incorporation into foods (as it is a strong laxative) as a fibre source
Within this manuscript, DF is considered as any polymeric compound, which is not
digestible by human enzymes and which mainly travels through the gut to reach the co-
lonic milieu, where it is either fermented by colonic bacteria (i.e., broadly, soluble fibres)
into smaller molecules such SCFA, or can act as a bulking agent during stool production
(i.e., generally insoluble fibres). This broader definition would thus also include non-car-
bohydrate compounds such as lignin and resistant proteins, as well as compounds asso-
ciated with plant-based carbohydrates, such as polyphenols. These compounds may also
be substrates for bacteria, such as Akkermansia, Lactobacillus and Bifidobacterium, which
produce metabolites such as SCFA, which in turn induce various beneficial effects on the
host, including reduction in: appetite, insulin resistance, lipid accumulation, and inflam-
mation . However, the effects of phytochemicals are likely to vary according to the
composition of the gut microbiota and host genetic polymorphisms, which affect absorp-
tion, detoxification, and overall bioactivities . One such example is equol, produced
form the isoflavone daidzein, which may bind to β-oestrogen receptors, and has been as-
sociated with the incidence of various types of hormone-associated cancers . This is
in line with the definition proposed by Jones , and may overcome the matter of “func-
tionality” often discussed regarding DF, as previously pointed out .
Fibre fermentation relies on its chemical and physical structure, as well as the com-
position of the colonic microflora. Digestion of DF by the GM may vary or fluctuate de-
pending on which fibres are consumed, and thus the amounts of SCFA produced too. For
example, lignin and cellulose are rather lost through the stool, being insoluble bulking
fibres; polysaccharides from extremely hard plant tissue areas are also less well digestible
because physical encrustation and chemical bonding to lignin can occur . Oligosaccha-
rides, RS and pectins are the DF compounds thought to contribute the most to SCFA pro-
duction in the colon .
2.2. Short Chain Fatty Acids (SCFA)
Recent studies on DF, GM and probiotics have emphasized the role of SCFA. Indeed,
SCFA may be a good example of microbiota-derived modulator molecules, i.e., a nutrient
that can modulate the host, acting as communicating molecules between the GM and the
host . Provided that SCFA metabolism may have a broad range of implications for
human health, many studies are being conducted to understand their effects (Table 1).
Sakata  recently pointed out relevant pitfalls in the study of these molecules. SCFA are
defined as volatile fatty acids with a skeleton of six or less carbons in straight (C1, formate;
C2, acetate; C3, propionate; C4, butyrate; C5, valerate; C6, caproate), or branched-chain
conformation (C4, isobutyrate; C5, isovalerate and 2-methyl-butanoate). Acetate (C2), pro-
Nutrients 2022, 14, 5361 6 of 55
pionate (C3) and butyrate (C4) amount for 90–95% of total GM SCFA output and are de-
rived from carbohydrate fermentation [129,130]. Until recently, caproate  and val-
erate  were considered dietary food components. However, recent studies have
demonstrated that these may also be GM products, with caproate being significantly in-
creased in faecal samples of volunteers with severe obesity (BMI ≥40) .
Branched-chain SCFA (BCFA), mainly isobutyrate, isovalerate and 2-methylbuta-
noate, contribute to as much as 5% of total SCFA production, and arise from the metabo-
lism of the amino acids valine, leucine, and isoleucine, respectively [129,131]. BCFA levels
in faecal samples show an inverse correlation with fibre consumption, especially insoluble
fibre [131,133]. BCFA levels in stool have also been related to depression [32,34] and other
psychiatric conditions , possibly through vagal afferent nerve signalling . Fur-
thermore, BCFA were found to be increased in subjects with hypercholesterolemia com-
pared to normocholesterolemic individuals, with isobutyrate being associated with worse
serum lipid profiles . It is likely that such elevated BCFA correspond to high protein
intake, such as from meat-based diet and a reduced DF intake, which are likewise associ-
ated with negative health outcomes and ageing related health complications .
Recently, products of DF fermentation have been termed post-biotics . In human
adults, the principal products of DF fermentation are SCFA together with certain gases
(CO2, CH4, and H2), which may be taken up by the host, or excreted . Production of
SCFA in the colon accompanies the bacterial consumption of ammonia, H2S and BCFA in
the synthesis of protein components for the microbial cell. Therefore, the reduction of
these metabolites may also be, at least in part, responsible for the health benefits attributed
to SCFA , as in addition to BCFA also ammonia  has been related to negative
health outcomes such as neurotoxicity and hepatotoxicity, as well as increased intestinal
permeability, loss of tight junction proteins and increase in pro-inflammatory cytokines
as found in animal studies . H2S, hydrogen disulphide, may be associated with neu-
rological, cardiovascular and metabolic diseases, when abnormally produced .
In this review, SCFA describes, “saturated unbranched alkyl group monocarboxylic
acids of 2 to 4 carbon atoms”, referring to acetate (C2), propionate (C3) and butyrate (C4).
We will briefly mention valerate (C5) and caproate (C6). It excludes BCFA, as well as suc-
cinate and lactate, which are rather intermediate products in GM metabolism, and there-
fore their concentrations in human serum are related rather to human metabolism, and
not influenced considerably by GM or intestinal absorption.
Nutrients 2022, 14, 5361 7 of 55
Table 1. Identified effects of SCFA in human interventional, observational, and animal studies.
Human interventional studies
C2 H (n =32) Case-control
Immunopharmacological effects of
Increased polyclonal antibody production and NK cell activ-
ity in healthy and cancer subjects 
C3 H (n = 6) Cross-over Serum and stool
Blood lipids and glucose, stool bulk
C3 supplementation lowers blood glucose. Lipid changes
not significant; increase in stool bulk and Bifidobacteria after
1 week intervention
C4 H (n = 16) Cross-over
Sigmoid colon biopsies and plasma
Oxidative stress markers in colon;
CRP, calprotectin; histological in-
Rectal administration significantly reduced uric acid and in-
creased GSH. No significant changes in other parameters 
Human Observational studies
C2-C6 H (n = 232) Observation
Stool Levels of faecal SCFA and BCFA
association with BMI and age
BCFA strongly correlated with age, but not with BMI;
BCFA negatively associated with fibre consumption;
BMI ≥ 40 showed significantly higher production of SCFA,
total BCFA, isobutyrate, isovalerate and caproate
SCFA production decreases with age
Animal (interventional) studies
C2, C3 M (n = 15) Knock-out Adipose tissue Effects of GPCR43 activation
Reduction of lipolysis, reduced plasma free fatty acids levels
without flushing associated with GPCR109A 
C2, C3 M (n = 12) Case-control
Adipose, gut, vascular and mesen-
GPCR41 and GPCR43 mRNA ex-
GPCR43 activation promoted adipose differentiation via
PPARγ2. No effects on GPCR41 
C2, C3, C4
S (n = 10) Case-control
Portal and peripheral blood, liver Food intake following SCFA infu-
dependent depression in food intake, explained by C3
content in portal vein, which resolved with portal plexus de-
C3 R (n = 20)
P (n = 12, 60) Case-control
Portal blood and liver Cholesterol synthesis and distribu-
Supplemented C3 likely absorbed in the stomach
Dose-dependent hypocholesterolemic effect likely due to re-
distribution of cholesterol from plasma to liver, as opposed
to synthesis inhibition
C3 R (n = 74, 114) Case-control
Brain, intracerebral ventricles
neuropathological, and biochemical
C3 intraventricular infusion impaired social behaviours,
similar to those seen in human ASD; induced neuroinflam-
mation and oxidative stress; Alteration of brain phospho-
lipid and acylcarnitine1 profiles
Nutrients 2022, 14, 5361 8 of 55
C4 R (n = 22) Case-control
Duodenum, jejunum, cecum and
PYY and proglucagon gene expres-
sion in gut epithelial cells
Up-regulation of local peptide YY and proglucagon expres-
sion via colonocyte sensing following a RS diet in vivo,
proved by in vitro incubation with butyrate
C4 M (n = 16–20) Case-control
Whole-body autopsy Insulin sensitivity and energy me-
tabolism, mitochondrial function
C4 supplementation prevented diet-induced insulin re-
sistance and reduced adiposity in high-fat model, without
intake. Attributed to enhanced mitochondrial
activity and thermogenesis
In Vitro Studies
C2-C6 M (n = 18) N/A mouse adipocyte cell line and adi-
pose primary culture Leptin expression C2-C6 stimulate leptin expression via GPCR41
Acute administration of C3 increased leptin levels 
C2, C4 R, B N/A Anterior pituitary, fat and liver as-
Leptin and leptin-
C2 and C4 enhanced leptin expression in bovine pituitary
and fat cells, however C4 inhibited leptin expression in rat
anterior pituitary cells; while C4 suppressed leptin receptor
expression in both rat and bovine pituitaries; probable spe-
cies specific nutrient sensing
C2, C3, C4
R, H N/A Colonic stimulation Effects on colon functions, inc. mo-
C3 and C4 induced phasic and tonic contractions of circular
muscle via GPCR41 and GPCR43 in mucosae, C2 did not 
C2, C3, C4
M (n= 4)
H (n= 3) N/A
Human blood samples, colon cul-
tures (colo320DM) and mice with
Anti-inflammatory properties of
All SCFA decreased neutrophil TNF-α release without af-
fecting IL-8; all decreased IL-6 release; all inhibited NF-κB
activity in colon cells; C4 > C3 > C2
C3 H (n = 5–9) N/A
Human umbilical vein endothelial
Expression of endothelial leukocyte
adhesion molecules and leukocyte
recruitment by cytokine-stimula-
Significant inhibition of TNF-α and NF-κB, reducing expres-
sion of VCAM-1 and ICAM-1 in a time- and dose-
manner; significantly increased PPARα expression
C3 H (n = 28) N/A Omental and subcutaneous adi-
pose tissue Adipokine expression Significant leptin induction and secretion; no effect on adi-
ponectin; Reduction of resistin mRNA expression 
C3 R, H (n = 1) N/A Human blood and rat mesenteric
T and B lymphocyte proliferation
Inhibition of lipid
synthesis as a possible mechanism leading
to reduction of lymphocyte proliferation 
C3 R (n = 9) N/A Isolated hepatocytes Hepatic lipidogenesis Inhibits hepatic cholesterol and fatty acid synthesis in a
dose-dependent manner, possibly by competition with C2 
ASD, autism spectrum disorder; B, bovine; H, human,; M, mice; P, pigs; R, rat; S, sheep; C2, acetate; C3, propionate; C4, butyrate; C5, valerate; C6, caproate;
HUVEC, human umbilical vein endothelial cells; TNF-α, tumour necrosis factor alpha; VCAM-1, vascular cell adhesion molecule-1; ICAM-1, intracellular adhesion
molecule-1; RS, resistant starch; GSH, glutathione peroxidase; PYY, peptide YY; SCFA, short chain fatty acids ; BCFA, branched-chain fatty acids; BMI, body mass
index; GPCR, G-protein coupled receptor; TNF-α, tumour necrosis factor alpha; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells N/A, not
Nutrients 2022, 14, 5361 9 of 55
3. Inter-Individual Variability
DF intake does not appear to produce equal results in all individuals . Indeed,
this is observed for most nutritional components, and a limitation of conventional nutri-
tional studies . Both host-related factors, but also food matrix related aspects, may
play a role. As for vitamins, DF-metabolite bioavailability may be influenced by the
SLAMENGHI factors (i.e., molecular species, linkage, amount, matrix, effectors of absorp-
tion, nutrition status, genetics, host-related factors, and the interaction of these) . An
additional problem regarding DF, at least when comparing results across studies is the
variability of DF definitions. A systems analysis approach, currently recommended in
clinical oncology , and taking its place in other biomedical disciplines [162,165–167],
may be required to better understand factors explaining inter-individual variability of DF
Foremost, identification of different levels of variability in human populations is re-
quired. Until recently, the basal level of variation of the effects of DF consumption on
health outcomes had not been significantly studied, i.e., the different GM found in hu-
mans. One may refer to this level as the enterotype [168,169]. A matter of debate among
the scientific community, the enterotype level, attempts to stratify populations according
to GM prevalence and abundance. Indeed, depending on each individual’s GM composi-
tion, the impact of DF intake on its metabolism and related outcomes may change signif-
icantly . DF is associated with reduced transit times [35,171], increased frequency of
bowel movements [172,173] and overall improvement of bowel health [35,75,129,174].
However, in individuals with typically low DF intake to whom a rich DF diet has been
imposed, adverse effects may arise, such as bloating and intestinal discomfort . Thus,
depending on the host’s pre-existent microbiota, the degree of DF fermentation, and there-
fore SCFA production and their uptake will vary. In the following sub-chapters, we will
summarize the three strata of inter-individual variability (enterotypes, genotypes and
phenotypes), while focusing on adults free from disease. We will leave aside known dif-
ferences found in this topic between geographical regions, i.e., countries and continents
3.1. Enterotypes—SCFA Production and Relation to Disease
Gut bacterial composition is determined by a myriad of factors. On one hand, host
factors (age, genetics, digestive secretion and physiology, immune status, use of medica-
tion, e.g., metformin or antibiotics)  and environment (geography, diet, environmen-
tal pollutants) do play a role [178,179]. On the other hand, microbial factors (substrate
competition, metabolic cooperation or species antagonism), as well as microbial environ-
ment (local pH, redox potential, quorum sensing) drive the GM composition. Firmicutes,
Bacteroidetes, Proteobacteria, Verrucobacteria, Actinobacteria and Fusobacteria are found ubiq-
uitously in the GM, with 99% of the species falling into the phyla Bacteroidetes or Firmicutes
in adulthood. These two phyla represent 70% of the total GM . In the first years of
life, the GM is mainly composed of Proteobacteria and Actinobacteria, although this de-
pends largely on delivery mode and feeding mode in infancy. A recent study studying
over 2700 families found that around 6.6% of taxa are heritable (especially Proteobacteria,
A. muciniphila, Bifidobacterium longum) consistent with previous twin studies , while
around 48.6% of taxa is significantly explained by cohabitation.
SCFA producing bacteria are known as DF fermenters (Table 2). The effect of DF in-
terventions on the GM of healthy adults has been reviewed elsewhere , as well as
SCFA production by the microbiota  (Figure 2). Whereas a meta-analysis revealed
considerable heterogeneity in results, significant relationships between specific DF inter-
ventions, GM communities and SCFA production, could be made. Particularly, glycans
and GOS led to significantly greater abundance of both Bifidobacterium spp. and Lactoba-
cillus spp. compared with placebo and low fibre diet comparators. Faecal butyrate con-
Nutrients 2022, 14, 5361 10 of 55
centration was significantly increased when compared to placebo/low-fibre regimens, alt-
hough heterogeneously across studies . In short, acetate and propionate were mainly
produced by Bacteroidetes, whereas the Firmicutes phylum tended to produce butyrate
. This can have repercussions on the inflammatory state of the host (see following
chapter), as especially butyrate has been related to anti-inflammatory properties.
Figure 2. SCFA-producing microbiota. Different bacterial taxa are associated with the production of
different SCFA. Of note, the Clostridium family is not associated with a particular SCFA. This may
reflect the abundance of different species of the Clostridia genus in the human gut. Adapted from
Macfarlane and Macfarlane .
Because bacteria tend to organize based on interspecies metabolic relationships, the
notion of enterotypes has been proposed . Enterotypes do not occur as discrete clus-
ters, but instead in gradients, with groups tending towards preferred genus level compo-
sition . The abundance distribution of different microbial taxa is thus complex. Nev-
ertheless, networks of co-occurring microbes have been described, whose regulator
(driver) taxon could be identified, i.e., a taxon that best correlates among bacterial group
tendencies . These are:
Enterotype 1, or ET-B, presenting Bacteroides as the taxon driver;
Enterotype 2, or ET-P has Prevotella genus as common denominator—abundance of
Prevotella is inversely correlated with Bacteroides;
Enterotype 3, or ET-F is characterized by an abundance of Firmicutes, namely Rumi-
ET-F displays a positive association with Akkermansia spp., a known mucin-degrader,
and with Methanobrevibacter smithii, the most abundant and prevalent methane-producer
in the human gut , which in turn are negatively associated with Prevotella .
In a recent review, growth performance and diarrheal states in pigs and Prevotella
spp. abundance were investigated . In pigs, ET-P was positively associated with lu-
minal IgA secretion as well as increased body weight. Compared to ET-B, ET-P was asso-
ciated with 2–3 times more propionate production, following a reduction in butyrate. ET-
P was found to associate with chronic inflammation and colitis in pigs, possibly due to
reduced IL-18 production. This finding contrasts with mechanistic mice models, where
Prevotella (an acetate-producer) was found to cross-feed Roseburia and Faecalibacterium
spp. (butyrate-producers), regulating the host’s immunity via increased IL-10 production
and receptor-dependent repression of claudin-2, important for tight junction integrity
. Furthermore, Prevotella copri was found to modulate Listeria monocytogenes infection
in piglets. In humans, high Prevotella abundance was associated with autism spectrum
disorders (ASD), rheumatoid arthritis and HIV in individual studies. However, following
a meta-analysis, Duvallet et al. have found no association between Prevotella, ASD and
Nutrients 2022, 14, 5361 11 of 55
rheumatoid arthritis. In the case of HIV, the association with Prevotella was likely due de-
mographic factors unrelated to disease . Gacesa et al. also found that in humans,
seemingly unrelated diseases did share a common microbiome signature independently
of comorbidities . This study identified Prevotella copri as driving two distinct clusters,
where P. copri abundance positively associated with general health. In this cohort, micro-
bial disease signatures were consistently related to increases in Anaerotruncus, Ruminococ-
cus, Bacteroides, Holdemania, Flavonifractor, Eggerthella and Clostridium species and de-
creases in Faecalibacterium, Bifidobacterium, Butyrivibrio, Subdoligranulum, Oxalobacter, Eu-
bacterium and Roseburia. The differences found across studies may reflect the duration of
the study or outcomes studied.
Another not fully understood host-microbe relationship is that of the well-known
bacteria Akkermansia muciniphila , which in absence of glycan DF , degrades the
host’s mucosa-associated mucus layer, thus regulating mucus layer thickness. While Ak-
kermansia is a mucin-degrader and a producer of propionate, acetate and ethanol , it
is overrepresented in faecal samples from healthy individuals when compared to disease
cohorts . Akkermansia up-regulates the Muc2 gene in human enterocytes, increasing
the amount of mucus produced , which may lead to a thicker mucus layer in the pres-
ence of glycan DF, thus assuring optimal barrier properties. By modulating the fucosyla-
tion status of mucus , A. muciniphila further regulates how other mucus-degraders
such as B. thethaitaomicron, digest the protective mucus layer when dietary glycans are
unavailable. Abundance of Akkermansia seems to decrease with age . Interestingly,
Akkermansia was similarly abundant in young adults when compared to elderly free from
disease or centenarians in Italy . Butyrate-producing bacteria were positively associ-
ated with age, with Eubacteriium limosum overexpressed in centenarians when compared
to the other arms of this cohort . Depletion of Akkermansia muciniphila has also been
associated with mass translocation of endotoxin-activated CCR2+ monocytes, leading to
pancreatic injury and type 1 diabetes (T1D) . The examples of Akkermansia and E.
limosum may reflect an intricate symbiont homeostasis driven by diet.
Thus, stratification of human populations based on their relative microbiota abun-
dance species is challenging. In this regard, the concepts of eubiosis and dysbiosis have
been introduced. Eubiosis refers to a still undefined, but balanced and adequate GM pop-
ulation. Dysbiosis corresponds to a dysfunctional GM, which may start developing as
early as during the neonatal period . Whether dysbiosis can be a reaction to disease
or instead, drive disease, is yet to be determined . In a meta-analysis by Duvallet et
al., it was found that dysbiosis can be further categorized, i.e., a dysbiotic state relating to
an increase of pathogenic bacteria, vs. a dysbiotic state in which health-associated bacteria
are reduced or missing . One may refer to these states as inflammatory dysbiosis and
hypotrophic dysbiosis, respectively. Microbial signatures have previously been shown to
be disease-unspecific, i.e., there does not seem to be a direct association between specific
bacteria and concrete pathologies . In a dysbiotic state associated with disease (e.g.,
T2D, IBD), A. muciniphila is typically reduced in number . This may result from a
positive feedback loop, where lack of mucus (firstly due to possible lack of DF as alterna-
tive energy source, secondly due to increased microbial competition and/or decreased
mucus production ) impedes A. muciniphila to reproduce, which in turn diminishes
the Akkermansia-derived mucugenic and tolerogenic signals. This may further downregu-
late mucus production, allowing for pathobiont invasion of gut laminae and the pro-in-
flammatory milieu predisposing to disease development. Thus, during a prolonged time-
trajectory, hypotrophic dysbiosis may be a gateway for inflammatory states . For ex-
ample, the infection by the parasite Giardia lamblia, endemic in several regions of the
world, alters the GM due to its metabolites. Hypotrophic dysbiosis, and not directly
through eliciting an inflammatory response, may be the cause of diarrheal states associ-
ated with giardiasis .
Nutrients 2022, 14, 5361 12 of 55
Table 2. Identified microbiome signatures (DF fermenters) in health and disease (i.e., eubiosis and dysbiosis). In eubiosis, mean relative abundance (~98% bacteria
retrieved) of phyla: 60% (58–88%) Firmicutes (F); 22% (8.5–28%) Bacteroidetes (B); 5% (2.5–7%) Actinobacteria (A); 5% (0.1–8%) Proteobacteria (P).
SCFA(s) Bacterial Genera (Phy-
Representative Bacterial Spe-
cies Observed Effects References
Clostridiales cluster I-II
(F) Clostridium histolyticum
Identified as a potential tumour
regression therapy (via collagenase production) as
well as being associated with gas gangrene in diverticular disease and trauma (via
Clostridiales XIV, Rumi-
noccacea (F) R. bromii Taxon driver of enterotype 3; Believed to be the main resistant starch fermenter
into butyrate, was significantly increased following RS diet in men with obesity [168,201,202]
Clostridiales XIV (F) Clostridium symbiosum A SCFA producer, was shown to improve post stroke disability in aged mice 
Clostridiales IV, Lachno-
Can rescue intestinal epithelium autophagy and mitochondrial respiration insuffi-
ciency, are associated with reduced colorectal cancer; Lachnospiraceae phylotypes
increased on an NSP diet with strong cross-feeding interactions
Clostridiales IV (F) F. prausnitzii Produce butyrate in 1 step reaction; Influences Muc2 and goblet cell differentia-
tion; depleted in IBD and Crohn’s disease [52,205,206]
Eubacteriae (F) E. rectale, E. hallii, E. ventrio-
Together with F. prausnitzii
, are the major butyrate producers; growth is promoted
by low colonic pH, which also inhibits pH-sensitive pathogenic bacteria [207,208]
Propionibacteria (F) P. acidipropionici Propionate producer, induces colorectal cancer apoptosis through mitochondrial
adenine nucleotide translocator (ANT) [57,63,209,210]
Bacteroides (B) B. thetaiotaumicron
Driver of enterotype 1; is a mucus-forager with lack of DF
B. thetaiotaumicron regenerates NAD+; reduced S-BCAA and alleviated diet-in-
duced weight-gain and obesity in mice. Influences Muc2 and goblet cell differenti-
ation. Produces butyrate via the succinate pathway
Negativicutes (F) N. succinicivorans Produce propionate via succinate pathway [211,212]
Veillonellaceae (F) V. parvula Produce propionate via acrylate pathway (lactate) and/or acetate. Have been asso-
ciated with osteomyelitis, hypertension and endocarditis [211,213,214]
Lachnospiraceae (F) Blautia hydrogenotrophica Produce propionate via acrylate pathway (lactate) and propanodiol pathways (de-
Nutrients 2022, 14, 5361 13 of 55
Christensenellaceae (F) C. minuta
Regarded as the most heritable taxon, forming the hub of a co-occu
composed of other heritable taxa; is enriched in lean subjects; in mice, reduced adi-
posity gain in GF model
Bacteroides (B) B. fragilis
Ferment xyloglucans, C3 directly inhibited Salmonella overgrowth by pH modula-
tion in vitro. Bacteroidetes relative abundance has been linked to faecal propionate
concentration. Decreased in ASD; in contrast, C3 administration led to ASD behav-
iour in rodent models via altered mitochondrial metabolism
Prevotella (B) P. intestinalis
Driver of Enterotype 2; significant high prevalence of Prevotella
in healthy African
Americans 50–65 y, while decreased in Western populations. P. intestinalis admin-
istration in mice led to reductions of overall SCFA production and increased mu-
cosal inflammation which abated with IL-18 supplementation
Methanobrevibacter (F) Methanobrevibacter smithii
Found to be highly inherited, methanogens are inconclusively associated with in-
creased BMI and reduced transit time in humans, as well as with leanness in mice.
Metabolizers of formate, which can result in decreased blood pressure. Co-
with R. intestinalis and B. hydrogenotrophica
decreased H2 and produced CH4 and
acetate, reducing pH
Bifidobacterium (A) B. adolescentis
FOS, GOS fermenter. High inheritability LF diet with prebiotic supp. increased
Bifidobacteria abundance, which ameliorates the allergic phenotype and inhibited
the growth of enteropathogenic bacteria.
Bifidobacteria seems to be reduced in obese-derived faecal cultures as well as in
ASD; Significantly decreased with a weight-loss diet given to men with obesity
Lactobacillus (B) L. johnsonii
Lactobacillus is a lactate producer commonly found in the upper gastrointestinal
tract. FOS, GOS fermenter. May protect against diet-induced obesity and
asthma incidence in children. However, is increased in ASD. Probiotic supplemen-
tation impact revealed to be dependent on basal microbiota between individuals
with obesity and normal weight
Nutrients 2022, 14, 5361 14 of 55
3.2. Genotypes—Interactions with Gut Microbiota
The interplay between the host genome and the microbiome is complex and dynamic
. While the enterotype may be subject to change over a lifetime [5,115], being affected
not just by diet , but also by lifestyle factors such as smoking status [232,233], exercise
or geographical location , the host genome is considerably more stable [3,234,235],
although epigenetic modifications may occur in relation to environmental exposure.
Nutrigenetics focuses on how individual genetic profiles, such as copy number vari-
ation (CNVs) and single nucleotide polymorphisms (SNPs) may influence fates of differ-
ent food items through, e.g., absorption, distribution, metabolism or excretion (ADME)
patterns (Table 3). In recent years, CNVs and SNPs in coding and non-coding regions of
the genome were identified as drivers of phenotypical differences among individuals. Pol-
ygenic risk scores have been associated with phenotypes across various human patholo-
gies [236,237]. When describing homeostasis on the holobiont level (this is, when taking
the host genome and microbiome together) [230,238], polygenic risk is thus relevant .
Twin studies have indicated that certain SNPs may be affecting microbiota coloniza-
tion since implantation in the first days of life [96,240], acting as a matrix where optimal
homeostasis and adaptation to environment will be grounded. A number of SNPs that
may interact with SCFA, such as G-protein coupled cellular receptors (GPCR41, GPCR43,
GPCR109A) , transporter encoding genes such as MCT, SMCT (monocarbohydrate
transporters), effector genes such as MUC2 (for mucus layer production in colon) or reg-
ulatory genes such as NRF2 (regulating the expression of proteins involved in the bodies’
antioxidant defence mechanism such as superoxide dismutase (SOD)) may, inde-
pendently or in polygenic aggregation, predispose to different health outcomes in human
In studying elderly populations, genes responsible for inflammatory response such
as IL-6, IL-10 and the IL-1 cluster, genes involved in the insulin/IGF1 pathway and genes
involved in oxidative stress management (PON1) were correlated with extreme old age
. Indeed, while bacterial colonization in early life is essential for the correct develop-
ment of MALT germinal centres, NK cell maturation and Treg differentiation, establishing
a balance between pro- and anti-inflammatory T cell subpopulations in the mucosa as
reviewed elsewhere , the host’s genetic background was shown to modulate the extent
of bacterial effects. Protein programmed cell death (PD1) knockout mice models have de-
veloped certain modified forms of immunoglobulin A (IgA), which led to altered micro-
biota profiles, specifically by reducing the numbers of bacteria from the genera Bifidobac-
terium and Bacteroides and increasing the bacteria belonging to the family Enterobacteriaceae
. Adequate secretion of IgA is essential for the colonization of certain “good” bacteria,
while at the same time targeting “bad” bacteria, further deepening our understanding of
the interdependency of host genome and microbiome. Commensal bacteria require IgA
coating for colonization, while the same coating leads to immune responses towards path-
ogenic bacteria [242,243]. Interestingly, it was found that microbial acetate in the gut reg-
ulated IgA reactivity to commensal bacteria, thus selecting microbiota species and its col-
onization of the colon .
Furthermore, antimicrobial peptides (AMPs), such as α-defensins and β-defensin 1,
were produced by intraepithelial cells (IEC) in the gut after stimulation by IL-22 and IL-
17 as a way of quickly inactivating breaching microorganisms. AMPs not only helped to
sustain host–microorganism segregation, but affected microbial composition . Mice
deficient in MYD88 (an important member of the Toll/IL-1 receptor family ), NOD2
 (a gene associated with intestinal homeostasis and IBD ), or mice transgenic for
α-defensin 5 , exhibited an altered microbiota composition. In regard to the genotype
of Alzheimer’s disease, strongly linked to the APOε4 allele, associations between higher
levels of Erysipelotrichaceae, a family including pro-inflammatory bacteria was found,
while the protective APOε2 allele was positively correlated with family Ruminococcaceae
(SCFA producers). It was further shown that SCFAs are able to inhibit amyloid β (Aβ)
Nutrients 2022, 14, 5361 15 of 55
aggregation—the histopathologic hallmark of Alzheimer’s disease—in vitro. Mutations in
MEFV (leading to familial Mediterranean fever)  have also demonstrated the ability
of the host to modulate microbial composition. This suggests that the host genome and
microbiome structure may be functionally linked, striving for homeostasis. The host gen-
otype would thus constitute another level of inter-individual variability towards DF ef-
fects, both directly (i.e., metabolism of SCFA) and indirectly (i.e., by modulating GM)
Nutrients 2022, 14, 5361 16 of 55
Table 3. Identified possible host genetic variability related to SCFA-ADME steps and effects in blood and tissues.
(Tissue) Gene (Protein) SNP/CNV * Observed Statistically Significant Association from GWAS References
Impacts oral and gut microbiome due to bioavailability of starches; altered blood
protein measurements [251,252]
Lactase persistence allows for dairy product consumption in adult life and in-
creased expression of Bifidobacterium in the gut; altered lung function and leu-
Modulated by butyrate. Variations associated with decrease gastric cancer pro-
gression, enhanced gastric lesion regression, asthma [61,256–259]
FUT2 rs516246, rs601338,
Mucus fucosylation status. Predisposition to Crohn’s disease and dysbiosis; al-
tered blood protein measurements [252,258,260]
α-defensins modulate microbial populations; copy number gain identified as
pathogenic; altered white blood cell counts; susceptibility to paediatric leukae-
MMP7 rs11568818 Involved in antimicrobial processes; prostate cancer
SCFA uptake; variations have been associated to exercise-induced hyperinsuline-
mia (EIHI); microglial activation, refractive errors of the eye, blood pressure dis-
rs79297227 SCFA uptake (hepatocytes); BMI trajectories, development of non-
small cell lung
rs1004763 Cerebral white matter microstructure; cognitive function 
rs4239020 Adipose tissue distribution, BMI 
(SLC16A11) rs13342232 Associated with the risk of paediatric-onset T2D in Mexican families 
rs7094971 Carnitine transporter, associated with reversible ASD and mitochondrial abnor-
SCFA uptake by colonocytes; in absence of microbiota, marked down-
of SLC5A8, which acts as a tumour suppressor protein in the presence of butyr-
ate; variation decreases BMI-adjusted waist-hip ratio; decreased IL-2 levels
rs10835056 SCFA uptake; decreased MIP-1α levels 
Nutrients 2022, 14, 5361 17 of 55
Butyrate receptor in enterocytes and MALT, regulating dendritic cell and Treg
diff, also present in microglia. Ligand niacin is used to treat dyslipidaemia; vari-
ant associate with blood lipid measurements
Acetate and propionate receptor
, leading to NLRP3 assembly. Regulation of Treg
population in colon, ROS production and neutrophil chemotaxis. KO models
showed increased arthritis, colitis and allergic disease; regulates adipogenesis
and GLP-1 release; associated white and blood cell variance
Regulation of SCFA-dependent energy homeostasis. Activation by propionate,
butyrate and valerate results in inhibition of NF-κB
activation; induce chemokine
and cytokine expression; associated with gastrointestinal motility and stool fre-
Recently reclassified as functioning gene; Propionate affinity; polymorphisms as-
sociated with strong pharmacokinetic variation 
LEP CNV, rs7799039,
40–70% estimated heritability for BMI; SNPs associated with CVD and MetS, in-
creased HbA1c, insulin and increased fat mass, among other clinical phenomes.
KO mice had higher susceptibility to dysbiosis
Same as above, variations associate with blood lipids, proteins, cytokines and
cell counts [280,282–284]
Associated with obesity, insulin sensitivity and abundance levels of Akkermansia
* selected examples of SNPs, p < 5 × 10−6 as reported by the GWAS catalogue, not extensive. ASD, autism spectrum disorders; BMI, body mass index;
CNVs, copy number variations; GWAS, genome-wide association studies; MIP-1α, macrophage inflammatory protein-1 alpha; SNPs, single nucleo-
tide polymorphisms; LPS, lipopolysaccharide; GLP-1, glucagon like peptide 1; GLP-2, intestinotrophic proglucagon-derived peptide; CVD, cardio-
vascular disease; MetS, metabolic syndrome; KO, knock-out mice model.
Nutrients 2022, 14, 5361 18 of 55
3.3 Phenotypes, Epigenetic Aspects of SCFA
Complementary to nutrigenetics, nutrigenomics refers to how nutrients influence
gene expression. In this regard, SCFA may directly influence genetic expression via his-
tone deacetylase modulation [54,57,286,287]. Accumulated epigenetic variations such as
histone (de-)acetylation, may translate into an individual’s phenotype over time. The phe-
notype refers to the observable, apparent properties resulting from the interplay between
genetics, lifestyle and environment. As a result, disease and health status tend to be cate-
gorized according to interpretable anthropometric, clinical or laboratorial parameters,
e.g., age, body mass index (BMI) or blood counts, respectively. Such outcomes might have
different associations with the health status, according to the study conducted: epidemio-
logical versus clinical and mechanistic studies. The variations observed in certain epide-
miological studies may be due to genetic predisposition and phenotypical flexibility of
the individuals, i.e., the capacity to maintain homeostasis and how to deal with environ-
mental stressors [288–290]. Such flexibility may, on one hand, be epigenetically deter-
mined, via DNA methylation, histone acetylation or imprinting, which may be deter-
mined as early as during prenatal development , as seen in cases such as the Dutch
Winter Hunger . On the other hand, such flexibility may be related to genetic factors
(i.e., nutrigenetics), as we will emphasize for the specific case of SCFA utilization and reg-
ulation of immunometabolism.
It must be noted, however, that such associations at the level of the phenotype (such
as the relation between BMI and metabolic abnormalities, or the levels of glycated haemo-
globin and diabetes progression), are possibly driven by genome, microbiome and diet
interactions, which entail environmental, neurophysiological and hormonal factors. As
some authors notice, a genotype presenting with variations leading to an increased level
of inflammatory function (which may be relevant to defend against infections), may have
deleterious effects when a low-grade chronic inflammation is not desired, such as hap-
pening with NCDs  and ageing , as shown in the association of IL-6 levels and
T2D incidence . Furthermore, even genes not related to immune function may have
a relevant impact in attaining holobiont homeostasis, through interactions still not fully
understood, which may lead to NCD and ageing progression, such as proposed for FUT-
2  and AMY1 .
Centenarians are individuals belonging to a group of people who reach ages over 100
years, without significant present chronic disease. Although research on those groups is
in its early steps, some studies suggest that centenarians present with distinct GM signa-
tures, when compared with young adults (20–40 years old) and elderly in general (60–80
years old) [110,114,115], although geographical differences were noticed across studies,
regarding specific bacterial species’ abundances. Although other factors for variability ex-
ist, it has been argued that these results may reflect specific gene polymorphisms. The
FUT2 gene, encoding for fucosyltransferase 2, a protein present at the Golgi membrane
that is associated with regulating the composition and function of secreted glycans in mu-
cosal tissues of the gut and other tissues, being a predisposing factor for Crohn’s disease
 is an example. Enterotyping further revealed that the (mucus) secretor phenotype
was more likely to cluster in ET-3, or ET-F (associated with Firmicutes, Akkermansia and
Ruminococcus spp.) . This goes with the evidence that genes not directly involved in
immune function may be responsible for several health outcomes, including the develop-
ment of NCDs and survival into old age . For example, copy-number variations
(CNVs) in the salivary α-amylase (AMY1) seems to correlate with oral and GM composi-
tion , possibly via its role in carbohydrate digestion. In mice, the impact of the geno-
type in microbial colonization is well recognized . On the other hand, the time of meals
(i.e., chrononutrition) may also lead to significant changes in GM composition, as well as
immune and metabolic conditions, including T2D [296,297], CVD  and psychological
well-being . While assessing the effect of DF intake timing on postprandial and 24 h
glucose levels, stronger reductions in both Ruminococcus and 24 h glucose levels were
Nutrients 2022, 14, 5361 19 of 55
found with morning DF ingestion, whereas the reduction of these phenotypes in the even-
ing was less pronounced . Another study found that diurnal oscillations of oral mi-
crobiota composition were linked to salivary cytokine levels, particularly IL-1β and
Prevotella, and IL-6 with Prevotella, Neisseria and Porphyromonas .
Recent studies have emphasized the interplay of diet and GM in persons with genetic
predisposition regarding neurodegeneration , cancer , menopause symptoms 
and non-alcoholic fatty liver disease (NAFLD) , all of which are characterized by
chronic inflammation, locally or systemically , although further research is needed
 (Figure 1).
As mentioned above, Goodrich et al. [3,217] also found microbiota heritability in hu-
mans through studying homo- and dizygotic twins. As the microbiome is associated with
health status and fitness (Table 4), the host may benefit from interactions between their
genomic makeup and microbiome composition, which are modulated by dietary patterns
among other environmental exposures . A degree of inter-individual variability exists,
in what concerns SCFA production. In a longitudinal study, while acetate was generally
the most abundant SCFA in faeces of all individuals, one individual presented with a 10-
fold decrease in propionate and butyrate when compared with other participants. One
individual presented with high caproate concentrations across the observation. Traces of
valerate were consistently detected in all individuals . In this study, microbiota pro-
files remained stable, and the only unpredictable variable was ammonium concentration
In the following chapters, we will attempt to elucidate further the host-microbe
driven interactions associated with SCFA.
Nutrients 2022, 14, 5361 20 of 55
Table 4. Identified dysbiosis signatures in disease.
Condition(s) Increased Bacteria Decreased Bacteria Opportunistic spp. or Additional Findings References
ratio, Blautia, Dorea, Pro-
Akkermansia, F. praust-
nizii, B. thetaiotaumicron
Ratio seems to be higher in women with ↑BMI
Diversity and richness is crucial for responding or not to dietary inter-
vention aiming at improving metabolic parameters (insulin sensitivity,
lipid and inflammation markers); increased propionate production
compared to normal weight microbiota
ratio, Blautia, Dorea,
BCFA are associated with obesity, insulin resistance and development
of T2D; Bacteroides spp. may improve the efficiency of BCFA degrada-
Ass. With ↑faecal SCFA, plasma BCFA, plasma TMAO, plasma total
bile acids and plasma LPS. MetS and NAFLD seem to occur via intesti-
Collinsella, Rothia, Desul-
Similar findings with obesity enterotype, may remain postpartum
P. copri and B. vulgatus identified as the main species leading the bio-
synthesis of BCFAs and insulin resistance; prebiotic supp. increased
Bifidobacteria and led to reduction of faecal SCFA and serum fasting
glucose and insulin
tes, Dorea, Escherichia,
Overall diversity re-
duced; R. intestinalis,
Similar findings with obesity and MetS enterotypes, although some
studies find ↑Bacteroidetes:Firmicutes ratio. Opportunistic infections
with B. caccae, C. hathewayi, C. ramosum, C. symbiosum, E. lenta and E.
coli. Butyrate is beneficial for pancreatic B-cell function, whereas propi-
onate has shown to be detrimental. Metformin therapy increases A. mu-
Opportunistic overgrowth of Ruminococcus gnavus and Streptococcus in-
. T1D may be related to delivery method, feeding method and
antibiotic use in infancy
rium, B. vulgatus
Increased intestinal permeability associated with the degree of steato-
sis, affects up to 70% of patients with T2D and 90% of obese, possibly
due to intestinal inflammation and permeability dysfunction, bile acid
metabolism (FXR), anaerobic fermentation, and LPS activation
leading to insulin resistance
Nutrients 2022, 14, 5361 21 of 55
Prevotella seems to be reduced in advanced stages of NAFLD, i.e.,
NASH; the levels of serum LPS and TNF-α correlated with disease se-
verity. Synbiotic supp. of B. longum and FOS reduced disease severity
of NAFLD and NASH progression
E. faecalis, E. coli, Proteo-
Only 40% of patients had dysbiosis. E. faecalis
correlated with mortality
rates in alcohol-induced steatohepatitis; supp. with B. subtilis and E.
faecium improved symptoms and microbiome
Firmicutes, esp F.
Clostridium ; Pepto-
Increase in fungal Candida albicans, Aspergillus clavatus, and Cryptococ-
cus neoformans, decreased Saccharomyces cerevisiae. IBD can arise from
genetic susceptibility or from disruption of commensal bacteria such as
SCFA-producing bacteria, reduction in tryptophan metabolism (pro-
moting mucus barrier function and reduces inflammatory responses),
Proteobacteria may represent 20% of overall diversity
S. bovis, H. Pylori, E, fae-
calis, E. coli, B. fragilis, F.
fucleatum, C. septicum,
cilli, Bacteroidetes, Fir-
micutes, F. prausnitzii,
S. bovis is increased in neoplastic milieu and may forage tumour metab-
olites, inducing inflammation. Some bacterial strains may propel CRC
development, while others are only found in late stages of CRC, arising
as opportunistic pathogens, which may deplete symbionts by substrate
competition and lead to tumour survival by immune evasion mecha-
Overall reduced microbial diversity, similar to IBD and other autoim-
mune phenotypes such as skin psoriasis; however, Akkermansia and
Ruminococcus were uniquely decreased in psoriatic arthritis. Rheuma-
toid arthritis presents with increased P. copri
tis and asthma
tes, C. difficile, Enterobac-
tericeae, E. coli
cilli, Clostridia, Bac-
Supp. L. rhamnosus GG and L. fermentum to mothers in the prenatal and
early postnatal periods or to young children may be effective in reduc-
ing symptoms, treatment and prevention of early atopic disease in off-
tes, Lactobacillus, Calo-
ramator, Sarcina, Propi-
Increased production of propionate due to dysbiosis may be a cause of
reversible ASD, also leading to GI symptoms in a majority of cases,
which ameliorated by supp. strains of Bifidobacteria and Lactobacilli.
Children with ASD show increased levels of opportunistic Candida albi-
Nutrients 2022, 14, 5361 22 of 55
C. perfringens, E. timonen-
microbial richness, di-
versity and evenness
S-TMAO (microbial-derived choline metabolite) levels were dose-de-
pendent associated with CVD outcomes and other indicators such as
serum cholesterol, glycaemic indices (HbA1c, fasting
inflammation biomarkers (IL-
6, CRP), overall cardiovascular risk, and
Odoribacter is a butyrate-
producer negatively correlated with systolic
blood pressure, like other SCFA producers, although SCFAs increase
crobia (A. muciniphila), Bi-
rium, Roseburia, Blau-
tia, C. coccoides, B. fragilis
Paraprevotella mainly decreased in females; Bilophila abundance asso-
ciated with disease severity; Blautia associated with disease onset/du-
ration; neurotransmitters such as serotonin, dopamine and GABA are
produced by microbiota; E. coli producing amyloid protein Curli cross-
seeds with α-synuclein and stimulates protein aggregation in gut (pre-
sent in 65–85% of cases), with gut-to-brain transport demonstrated.
Microbial sulphur metabolism is profoundly changed in PD, mainly as-
sociated with A. muciniphila and B. wadsworthia
tes, E. coli, Shigella, Heli-
cillus, Firmicutes, Ac-
crobia, Roseburia, Eu-
bacterium, F. prausnitzii
Similar dysbiosis in MCI as in AD; amyloid protein Curli produced by
E. coli and S. typhimurium enhances colonization and biofilm develop-
ment; E. rectale
and Shigella taxon in the faecal samples of patients with
advanced AD correlated well to the amyloidosis and level of proin-
flammatory cytokines in the brain. TMAO induced synaptic impair-
ment in AD model with deposition of Aβ plaques and neurofibrillary
tangles. Aβ plaques found in gut vessels prior to disease onset, accom-
panied with systemic inflammation
Abbreviations: Aβ, amyloid-β; AD, Alzheimer’s disease; BMI, body mass index; BCFA, branched-chain fatty acids; CNS, central nervous system;
CVD, cardiovascular disease; FXR, Farnesoid X receptor; GABA, γ-aminobutyric acid; GI, gastrointestinal; HDL, high density lipoprotein; IBD, in-
flammatory bowel disease; MCI, mild cognitive impairment; MetS, metabolic syndrome; LPS, lipopolysaccharide; SCFA, short chain fatty acids;
Supp., supplementation; T2D, type 2 diabetes mellitus; T1D, type 1 diabetes mellitus; TMAO, trimethylamine N-oxide.
Nutrients 2022, 14, 5361 23 of 55
4. The Holobiont and Short Chain Fatty Acids
4.1. Host-Microbe Interface
The human gut is composed of several tissues with specific characteristics. Whereas
the relatively short passage time (up to 2–3 h) and low pH of the stomach (1.5–2 in fasting
state, up to 5 with meal ) are associated with low numbers of bacteria (<102/mL) ,
the slower passage and more stable pH progressively attained in the small intestine may
allow for an increase of >108/mL microbes at the ileal-cecal valve. In the colon, the bacterial
community increases gradually from proximal to distal, with viable cell counts in faecal
samples, reaching 1011 to 1012 cells/g, the majority being obligate anaerobes [4,63].
Of interest is the change in luminal pH, which modulates significantly which bacte-
rial species can colonize different gut territories. The increase in luminal pH occurs mostly
due to neutralization of gastric acids and pancreatic secretions in the small intestine. Food
intake is the main determinant for availability of DF, and its composition as a substrate
will influence which fermentation products will be formed, depending on redox capacity
[78,344]. An increased number of DF fermenters proximal in the colon will reduce luminal
pH (down to pH 5.5–7.5) , increasing GM diversity in the colon, as individual species
of microbiota use each other’s complex carbohydrate breakdown products (substrate
cross-feeding) [346,216], or even end-products (metabolic cross-feeding) in mutualistic in-
teractions promoted by anoxic conditions common in the colon . Den Besten and col-
leagues  showed that bacterial cross feeding occurs mainly with conversion of acetate
to butyrate, to a lower extent from butyrate to propionate, and virtually no metabolic flux
exists between propionate and acetate. Of note, F. prausnitzii is able to derive butyrate
from acetate produced by B. thetaiotaumicron. This could have a significant impact on the
intestinal barrier , as butyrate increases mucin production, resulting in increased
MUC3, MUC4 and MUC12 gene expression, potentially through mitogen-activated pro-
tein kinase (MAPK) signalling pathways , as well as up-regulating the assembly of
tight junctions through activation of AMP-activated protein kinase (AMPK) . Both
processes would enhance barrier functionality. While acetate produced by Bifidobacteria
may play a role in inhibiting enteropathogenic microbial growth such as E. coli , an-
other colitis mice model revealed that inoculation with Lactobacillus rhamnosus L34 prof-
ited both local gut inflammation (reduced leaky gut and faecal dysbiosis), as well as sys-
temic inflammation, possibly due to reduced translocation of lipopolysaccharides (LPS)
. Further, L. reuteri GroEL protein administration also reduced markers of inflamma-
tion (TNFα, IL-1β, IFNγ) induced by LPS via TLR-4 both in vivo and in vitro .
SCFA derived from fermentation are at highest concentrations in the proximal colon,
and their concentrations decrease towards the distal colon, as reviewed by Topping and
Clifton . Colonocytes progressively absorb acetate, propionate and butyrate, which
enter the bloodstream. Their rate of absorption, as well as effects on crypt proliferation
are dependent on luminal pH, as demonstrated by Ichikawa . Specifically, butyrate
is virtually absent in the portal circulation, and it has been found to be a main substrate
for colonocyte energy requirements, affecting colonocyte proliferation and differentiation,
as well as mucus production . Blood butyrate may thus not be a good measure of bu-
tyrate production in the colon. Butyrate is at present regarded as a protective factor in the
development of colorectal carcinoma, one of the leading causes of morbidity in developed
countries [51,354]. Sometimes referred to as the “butyrate paradox” , it induces pro-
liferation of healthy colonocytes, but terminates differentiation and triggers apoptosis in
metaplastic cells via the Warburg effect . SCFA concentrations change across the lu-
men, and tend to be less prevalent in sites of highest absorption, i.e., the colonic crypts
. Contrarily, GM-derived formate has recently been found to promote colorectal car-
cinoma progression .
Nutrients 2022, 14, 5361 24 of 55
4.2. Digestive Enzymes
Poole et al.  demonstrated that the oral and gut microbiotae of individuals with
normal BMI and no chronic disease may be related to the copy-number of the AMY1 gene,
encoding salivary α-amylase. Copy-number variations (CNVs) in AMY2, encoding pan-
creatic α-amylase, were positively correlated with salivary amylase copy-numbers. Look-
ing to attain homeostasis, individuals with a low copy-number of AMY1 (which facilitates
starch digestion) had microbiomes with enhanced capacity to digest carbohydrates, in-
cluding increased members of Lachnospiraceae, as well as Akkermansia and Bifidobacteria,
and presented increased faecal CAZyme activities of the glycoside hydrolase and poly-
saccharide lyase classes, in line with more complex carbohydrates reaching the distal gut
. In contrast, individuals with a high copy-number AMY1 did display a higher abun-
dance of DF fermenters (increased abundance of Ruminococcus, Oscillospira and F.
prausnitzii), possibly to counterbalance for the absence of non-digested carbohydrates due
to more optimal host digestion, as shown by increased SCFA concentrations in the stool.
Using the enterotype stratification, both genotypes would fall into ET-3 (Firmicutes), alt-
hough with substantial species differences. Why high copy-number AMY1 individuals’
stool had increased concentrations of SCFA remains elusive, assuming that the rate of
absorption across individuals is comparable. Possibly, bacterial cross feeding [67,216,346]
is enhanced in low AMY1 subjects, which could imply a more intense utilization of digest-
ible starch as opposed to indigestible fibres as a source of microbial energy, which would
in turn be related to lower overall rate of SCFA production. After faecal transplantation
to germfree mice, mice receiving high AMY1 individuals’ stool showed increased weight
gain, although dietary intakes and gut inflammation parameters between mice were not
significantly different. This may suggest that a high AMY1 CNV predisposes the GM to
more specialized digestion of RS, and possibly other types of fibre. It also suggests that an
increased functional diversity between mutualistic bacteria (in subjects in which a higher
proportion of complex carbohydrates reaches the colon, or low AMY1) was protective
against weight gain, at least in mice . This finding contrasts with human studies,
which found an inverse association between AMY1 numbers and overweight/obesity in
elementary school aged children in the USA . We can hypothesise that in this case,
the microbiota of children with low AMY1 may have already suffered adaptations regard-
ing carbohydrate digestion capability.
Investigating the relationship between single nucleotide polymorphisms (SNP) and
microbiota, Blekhman et al. , found that variants in the LCT gene (encoding lactase)
were significantly correlated with Bifidobacterium in the gut. This correlation is interesting,
as lactase persistence may permit individuals to continue consuming dairy products into
adulthood, and certain products may contain Bifidobacteria. Indeed, in another study the
LCT locus associations to GM composition seemed modulated by lactose intake, whereas
others associations could be explained by secretor status as determined by the participant
FUT2 genotype . This relationship makes the study of nutrigenetics and nutri-
genomics all the more relevant, as it may help understand human dietary habits, as well
as comprehend inter-individual variability of the GM. Furthermore, the human responses
to medications containing lactose moieties, such as alprazolam, lorazepam, carvedilol or
cetirizidine hydrochloride may be affected by such SNPs , which may result in dif-
ferent medication responses.
4.3. Genetic Diversity and Physical Barriers
Microbial communities can regulate the expression of the host’s physical barrier in
the gut, particularly that of mucin production . Mucin is the main glycoprotein com-
ponent of the mucus layer that separates enterocytes and microbiota in the lumen; the
mucin family is composed of 21 members. While mucin acts as a fundamental part of the
mucosal barrier throughout the gut, different types are expressed in different gut tissues
Nutrients 2022, 14, 5361 25 of 55
. Recently, in order to understand the development of gastric cancer, the third leading
cause of cancer-related deaths worldwide, polymorphisms in mucin genes were explored
. The gastric mucosa normally expresses Muc1, Muc5AC and Muc6. However, dur-
ing gastric carcinogenesis, these were differently expressed, and Muc2 was concomitantly
activated and secreted. It must be noted that MUC2 is a major mucin gene in the intestine,
particularly the colon, where the environment is profoundly different (pH, bacterial colo-
nies) than that of the stomach. Furthermore, gastric carcinoma is associated with H. pylori
infections; however, only 1–3% of infected persons develop gastric carcinoma, suggesting
that SNPs in MUC genes may confer protection or risk for cancer. In effect, Muc2 is up-
regulated in intestinal metaplasia. Marín et al. reported that three SNPs (rs10794293,
rs3924453 and rs4077759) at the 3′ moiety in MUC2 were associated with a decreased risk
of lesion progression. Furthermore, four SNPs (rs10902073, rs10794281, rs2071174 and
rs7944723) at the 5′ moiety of MUC2 were significantly associated with regression of gas-
tric lesions .
4.3.2. Tight Junction Proteins (TJPs)
As a main source of energy for colonocytes, SCFA also help maintain the gut barrier
integrity (as well as the blood-brain barrier) , by upregulating tight junction proteins
(TJPs) such as claudin-5 and occludin . Enteric bacteria with pathogenic potential can
interrupt the impermeability of the gut, allowing for pathogen invasion of intestinal tis-
sues and possible translocation into the host’s system. Such tissue invasion triggers an
inflammatory cascade that has been associated with obesity and insulin resistance .
It has been recently shown that SCFA, particularly butyrate, promote recovery of tight
junctions during gastrointestinal infections , possibly through mediation of different
kinases (e.g., PKC , MAPK, PKA ), leading to phosphorylation of zonula oc-
cludens-1 (ZO-1) and inhibition of zonulin, increased expression of claudin-1 and oc-
cludin redistribution [366,367], reducing intestinal permeability . Bacterial lipopoly-
saccharide (LPS) triggers a toll-like receptor 4 (TLR4) mediated pro-inflammatory cascade
in mucosal immune cells, leading to the activation of signalling pathways, such as nuclear
factor κ B (NF-κB) and MAPK, which promotes inflammation driven by cytokines such as
tumour necrosis factor α (TNF-α) and IL-6. Inhibition of HDACs by butyrate results in
reduction of LPS-induced activation of the NLRP3 inflammasome and autophagy and al-
leviates disruptions of ZO-1 and occludin, thus enhancing intestinal barrier function ,
as well as through repression of claudin-2 formation . Butyrate may thus aid in coun-
teracting negative effects of LPS induced pro-inflammatory cascades.
A permeable intestinal barrier has been associated with coeliac disease, inflammatory
bowel disease, obesity and food allergies , apart from low-grade chronic inflamma-
tion and systemic disease development such as arthritis [9,371]. This mechanism was
shown to be targetable in order to prevent the onset of arthritis, reduce disease progres-
sion and associated low-grade chronic inflammation, using butyrate or larazotide acetate
. Furthermore, it is recognized that the onset of Parkinson’s disease may occur in the
gut, with α-synuclein aggregation upon LPS binding, eliciting a potent inflammatory re-
sponse by specifically activating TLR4/NLRP3 inflammasome pathway. In addition, TLR2
has been shown to be important for the regulation of intestinal barrier integrity, being
activated by different bacterial amyloid peptides. Through vagal axonal transport, these
amyloid peptides are hypothesized to serve as a scaffold for the development of cerebral
amyloid β aggregation (seen in Alzheimer’s disease) and α-synuclein aggregates (of Par-
kinson’s disease). TLR2 is also involved in neuroinflammatory process of clearance of
amyloids such as α-synuclein and amyloid β [11,334].
4.3.3. Immune Cell Populations in the Gut
Butyrate further impacts intestinal macrophages differentiating into M2 type macro-
phages (tolerant macrophages), which induce dampened responses to LPS stimulation
Nutrients 2022, 14, 5361 26 of 55
and suppressed pro-inflammatory cytokine (IL-6 and IL-12) responses, via histone
deacetylases (HDACs) .
Natural killer (NK) cell differentiation requires IL-23 produced by activated myeloid
and epithelial cells, as well the presence of intestinal microbiota, as evidenced in germ-
free vs. conventional mice studies . NK cells produce IL-22, promoting a rather im-
permeable intestinal barrier  via signal transducer and activation of transcription 3
(STAT3). The GM also modulates the abundance of invariant NK T cells, a pro-inflamma-
tory subset of T cells that secretes T helper 1 (TH1)- and TH2-type chemokines and cyto-
kines, including interferon-γ, IL-2, IL-4, IL-13, IL-17A, IL-21 and TNFα . The colon of
germ-free mice is rich in invariant NK cells, further suggesting the immune-tolerant role
of the microbiome through SCFA [57,59,286,375]. In human populations, evidence is
mounting that antibiotic exposure in early ages  can predict risk of asthma develop-
ment several years later. An opposite exposure, the so-called “farm effect” where many
microorganisms may colonize the infant, are linked with reduced risks of asthma, atopy
and possibly even autism spectrum disorder [95,377]. A study assessing the impact of
prebiotic GOS in the elderly population found that GOS significantly increased the abun-
dance of Bifidobacteria, at the expense of less beneficial taxa compared with the baseline
and placebo arm. Phagocytosis, NK cell activity, and the production of IL-10 were signif-
icantly increased, whereas the production of pro-inflammatory cytokines (IL-6, IL-1β, and
TNF-α) were reduced. GOS did not alter total cholesterol or HDL-cholesterol production,
4.3.4. Transporter Genetics
SCFA are taken up by colonocytes, using proton and Na+-coupled monocarboxylate
transporters (MCTs and SMCTs, respectively). Fourteen transporters belonging to the
SLC16 transporter family have been identified, of which four (SLC16A1, SLC16A3,
SLC16A7 and SLC16A8), encoding for MCT1, MCT4, MCT2 and MCT3 respectively, have
shown to mediate proton-linked transport of monocarboxylates [379,380]. They allow for
the uptake of carboxylated pharmaceuticals, as well as monocarboxylate transfer through
tissues. The two members of SMCTs, SLC5A8 and SLC5A12, are present in the gastroin-
testinal tract, kidney, thyroid, brain and retina .
MCTs 1–4 have distinct properties and tissue distribution, making them involved in
a myriad of metabolic functions such as energy metabolism (specifically in the intestines,
brain, skeletal muscle, heart and tumour cells), drug transport, thyroid hormone metabo-
lism (SLC16A2 or MCT8), and T-lymphocyte activation. This family of transporters has
been studied in recent years, and recently reviewed . Exercise-induced hyperinsu-
linemia, an autosomal dominant condition, has been attributed to a mutation in the pro-
moter region of MCT1 in β-cells in the islets of Langerhans, leading to inappropriate in-
sulin expression . MCT2 appears to be an early indicator of prostate malignancies
and MCT4 was associated with poor prognosis of prostate cancer, as reviewed elsewhere
. Nuclear localization of MCT1 in soft tissue sarcomas is instead associated with
lower neoplastic scores and longer survival rates -. In retinal tissue, MCT3 and
MCT4 seem to be drivers of correct cellular differentiation upon healing , and may
thus be relevant in age-related retinal pathologies. Ongoing studies suggest that other
members of SLC16A, such as MCT9, being associated with carnitine efflux , are a
potential cause of reversible autism spectrum disorders . Four missense SNPs, i.e.,
nucleotide polymorphisms leading to incorrect amino acid expression and one synony-
mous variant (Leu > Leu) on MCT11 were significantly associated with the risk of adult
and paediatric T2D . Thus, it appears that SCFA uptake and distribution is potentially
intertwined with, and influenced by, a number of genetic variations that relate to disease
In the intestine, MCT1 present in the basolateral membrane of colonocytes allows for
passive transmembrane transfer of SCFA into the bloodstream [271,386], following its ac-
Nutrients 2022, 14, 5361 27 of 55
tive transport into the cell by the SMCT SL5A8, expressed in the apical membrane of epi-
thelia. Like MCT1 , SLC5A8 may act as a tumour suppressor as it mediates the uptake
of butyrate, propionate and pyruvate. Butyrate is converted to acetyl-CoA in normal co-
lonocytes, providing energy and up-regulating histone acetylases (HATs). Conversely, in
metaplastic processes, cells turn to aerobic glycolysis due to the Warburg effect. In such
conditions, butyrate and propionate accumulate, leading to reduced genetic transcription
(through decreased HDAC activity) [356,388]. However, if high metabolism cancerous
cells become deprived of glucose, oxidation of fatty acids is activated, converting butyrate
or propionate into acetyl-CoA. This could result in the rescue of metaplastic cells, through
upregulating HATs. This mechanism may potentially be involved in the metaplastic-to-
anaplastic process seen in cancer .
Although both pathways result in hyperacetylation of DNA, different genes are af-
fected and expressed. For example, in colon metaplasia, HDACs regulate intestinal mac-
rophage activity  as well as inhibition of colonocyte proliferation, and induction of
apoptosis. Furthermore, while butyrate was the strongest influencer of colonic HDAC ex-
pression, propionate and valerate caused growth arrest and differentiation in human col-
orectal carcinoma cells. Acetate and caproate did not cause histone hyperacetylation in
this tissue . Drugs such as salicylates, γ-hydroxybutyrate, valproate or non-steroidal
anti-inflammatory drugs, such as ibuprofen, act as blockers of SLC5A8 function, and may
reduce SCFA uptake by colonocytes . Long-term use of these drugs may alter colonic
intracellular physiology. Furthermore, a number of naturally occurring inhibitors of
MCTs have been described, such as stilbene disulphonates (including DIDS and DBDS),
phloretin (a natural phenol) and bioflavonoids such as quercetin . Given that these
compounds occur together with DF and thus with SCFA, their effect may be negligible, or
instead provide low-grade stress known as xenohormesis. Indeed, these compounds are
generally known to have antioxidant, anti-inflammatory and therapeutic effects .
MCT1, in particular, is expressed in cells of the intestine, the colon and the blood-
brain barrier, and may be relevant in delivering pharmaceuticals and SCFA across these
membranes. MCTs 1–4 require the binding of a transmembrane glycoprotein (either
embigin or basigin) for their activity [386,391], otherwise they will accumulate in the Golgi
apparatus. MCTs 1–4 can mediate either cellular influx or efflux, depending on the pre-
vailing substrate and pH gradients. MCT2 has the highest affinity for monocarboxylates,
followed by MCT1 and MCT4 (MCT3 is less well characterized). MCTs further transport
lactate, pyruvate and ketone bodies .
MCT1, MCT2 and MCT4 expression was significantly altered with fasting, and was
tissue specific . MCT1 transcription in skeletal muscle and T lymphocytes may be up-
regulated following AMPK activation by SCFA. Other mechanisms may involve increased
cytosolic concentrations of calcium, which stimulates calcineurin to dephosphorylate and
activate NFAT (nuclear factor of activated T cells), resulting in up-regulation of target
genes influencing the cell cycle, apoptosis and angiogenesis. Other mechanisms seem to
regulate MCT1 expression in several tissues in response to obesity, diabetes and thyroid
dysfunction, however more research is needed .
These results suggest that MCTs play a critical role in modulating adequate energy
supply to different tissues of the organism, particularly dependent on the availability of
oxygen, glucose or ketone bodies. For example, MCT2 is upregulated in neurons follow-
ing food deprivation and recovery from ischemia. In contrast, hypoxia reduced MCT2 ex-
pression in adipose tissue. Furthermore, MCT2 expression is upregulated in the brain by
both insulin and IGF-1, through a post-transcriptional mechanism involving stimulation
of the phosphoinositide 3-kinase (PI3) pathway . MCT4 was reduced in all murine
tissues upon 48 h of fasting . Concurrently, MCT4 expression was increased in all
tissues in response to hypoxia via hypoxia-inducible factor 1α (HIF-1α), further support-
ing its role in glycolysis . MCTs may show preferential binding to different SCFA
. As MCTs are also used for drug delivery, their expression in different health and
nutritional states must be taken into account for optimal therapeutic results.
Nutrients 2022, 14, 5361 28 of 55
Concomitantly to MCTs being pleiotropic (i.e., different expression and different
downstream pathways) in mammals, SCFA concentrations also change across tissues.
How SCFA and MCTs interact in particular disease states is still not fully understood in
human populations. Such mechanistic insights are relevant in maintaining health as well
as for SCFA use as therapeutic agents.
4.4. SCFA Metabolism
As recently reviewed by Van der Hee and Wells , SCFA are estimated to con-
tribute to 10% of human energy requirements, where butyrate is the main source of energy
for colonocytes and propionate is partially converted to glucose in the liver [64,394]. It is
estimated that the GM produces 500–600 mmol/d SCFA, whereof 60% are acetate, 20%
propionate and 20% butyrate, amounting to about 37 mmol/kg body weight acetate, 13
mmol/kg body weight propionate and 12,4 mmol/kg body weight butyrate . In the
human descending colon, SCFA concentration may reach 69–91 mmol/kg luminal content,
with acetate accounting for 60–75% of total faecal SCFA. Methanobrevibacter smithii is pre-
sent in 70% of humans, and is considered the main methane producer in the GM. Methane
production in humans (assessed by breath test) has been significantly associated with
higher BMI scores in obesity, and was further associated with constipation and antide-
pressant use . In the presence of methane, elongation of propionate (a whole-body
energy regulator) can produce valerate , of which little is known regarding health
maintenance. Similarly, little is known for caproate, also present in small amounts in the
gut, which is generated from butyrate, acetate and lactate  under appropriate condi-
tions. Despite their small concentrations found in several studies, over time these SCFA
may alter the concentrations of acetate, butyrate and propionate reaching human cells,
with a cumulative impact for health status.
Colonic gut epithelia absorb more than 95% of SCFA produced by the GM. Butyrate
oxidation accounts for more than 70% of colonocyte energy production [58,356,397], alt-
hough colonocytes can also oxidise glucose and glutamine. Both butyrate and propionate
appeared to increase cellular proliferation rates, while acetate did not [130,398–400]. This
further highlights different SCFA utilization in particular tissues in the human body.
Butyrate may be essential for enterocyte differentiation, as previously reviewed .
Mature, but not progenitor enterocytes are strong butyrate metabolizers. It was a shown
that butyrate leads to an arrest of proliferation and induction of differentiation of entero-
cytes, primarily by FoxO3, but also by hypoxia-inducible factor α (HIF-α). FoxO3 is asso-
ciated with cellular homeostasis and longevity . In contrast, activation of FoxP3 in
naïve CD4+ T cells was associated with Treg differentiation and a tolerant profile [402,403].
This observation further suggests an important role of SCFA in the epigenetic modulation
of several transcription factors, such as FoxO3 and FoxP3, which may relate to cancer de-
velopment and tissue healing. Butyrate further inhibited DNA-damaged cell proliferation
via p53 . In intestinal crypts, a diffusion gradient allows for butyrate to be metabo-
lized by apical mature enterocytes, also leading to upregulation of zonula occludens 1 and
occludin, while down-regulating claudin 1 and 2. This would result in a net reduction on
intestinal permeability. Apical mature enterocytes also produce TGB-β promoting Treg
differentiation and a tolerogenic profile, with increased levels of circulating IL-10 .
Butyrate may thus directly and indirectly (through colonocyte paracellular signalling) im-
prove intestinal permeability and immune function. Here, butyrate may further suppress
TNF-α, IL-6, and myeloperoxidase activity by preventing NF-κB activation such as exem-
plified in Küpffer cells of the liver .
However, arterio-venous studies have demonstrated a relative indifferent usage of
SCFA by colonic tissue . Furthermore, acetate may be the strongest stimulant of in-
testinal blood flow, and appears to regulate the brain-pancreas axis regarding insulin-re-
lease regulation . Regarding propionate, evidence is mounting regarding its role on
Nutrients 2022, 14, 5361 29 of 55
phasic colonic motility . On a more systemic level, it has been previously demon-
strated that SCFA have dose-dependent effects in vitro and in silico, becoming inhibitory
of smooth muscle cell proliferation at non-physiological high doses . MCT1 and
MCT2 SNPs may lead to different absorption rates from the gut in individuals, and may
be predictive of colorectal cancer outcomes . Ketone bodies produced from β-oxida-
tion of SCFA serve as precursors for lipid synthesis in human cells. In the distal colon,
however, fewer ketone bodies are produced, which may suggest that SCFA enter predom-
inantly the tricarboxylic acid (TCA) cycle following oxidation. In parallel, glucose and
glutamine oxidation are more relevant for energy production in small intestine and prox-
imal colon enterocytes . Recently, it has been proposed that intestinal gluconeogene-
sis is crucial for metabolic health, by adjusting which SCFA (butyrate via c-AMP, or pro-
pionate via FFAR3/GPCR41) the colonocyte will utilize for energy production [410,411].
In vitro studies found that cultures with propionate increased the expression of FoxP3 and
IL-10, leading to colonic Treg proliferation via GPCR43, also known as FFAR2 [55,56], also
emphasizing its role for the immune system and potential anti-inflammatory aspects. In
human adults at increased risk of colorectal cancer, a dietary intervention with green leafy
vegetables reduced oxidative stress and inflammatory markers such as TNFα . As for
MCTs/SMCTs, SNPs in GPCRs can result in decreased potency of SCFA action . Sim-
ilarly, FFAR2 rs416633 was reported to decrease monocyte percentage and increase neu-
trophil counts in the European population , further supporting the role of polygenic
risk associated to complex traits regarding SCFA.
4.4.2. Liver and Adipose Tissue
Studies performed in human victims of sudden death have shown that butyrate ra-
tios decrease from 20% of total SCFA in the gut lumen, to 8% in portal blood, revealing
substantial epithelial uptake and usage, with a clearance rate by the colonic epithelium of
approximately 65% . SCFA are then taken up by the portal circulation and further
used as energy substrate by hepatocytes, particularly propionate . MCTs and SMCTs
are also the means of SCFA uptake in the liver. In cells that use lactic acid as a substrate
for lipogenesis and gluconeogenesis, specifically the liver, kidney tubules and adipose
tissue, MCT1 and MCT2 are primarily expressed . Certain authors underlined that
this loop will metabolize a majority of the absorbed SCFA. Indeed, only acetate is found
in measurable amounts in the systemic circulation (reaching 200 μM in venous serum)
, while butyrate and propionate show only vestigial concentrations [6,171,412]. In a
human study with 22 participants, portal concentrations of acetate, propionate and butyr-
ate were 263, 30.3 and 30.1 mmol/l, respectively. Arterial concentrations were 173, 3.6 and
7.5 mmol/l, for acetate, propionate and butyrate respectively. Consequently, the hepatic
clearance of SCFA was 4.2%, 9.8% and 5.1%, for acetate, propionate and butyrate respec-
tively. The authors performed a sub-group analysis to observe the impact of BMI (above
and below 25) and of colon resection in the production and utilization of SCFA, with no
significant differences being found .
Den Besten analysed the metabolism of SCFA by cecal infusion of stable isotope la-
belled SCFA in mice. In the liver, propionate appears to be gluconeogenic after its conver-
sion to succinate (62% used for whole body glucose production), while acetate and butyr-
ate are rather used for fatty acid and cholesterol synthesis. Low to absent contribution
from propionate to palmitate or cholesterol formation was noticed . Daily propionate
production from DF is estimated to be 29.5 mg/kg/day  for an individual of about 85
kg, and presumably makes a small contribution to endogenous glucose production
[366,413]. An inulin-propionate ester given orally induced appetite reduction through
peptide YY (PYY) and GLP-1 mediated mechanisms in adults with overweight, leading to
weight loss and reduced intrahepatocellular lipid contents . Indeed, acetate has been
linked to suppression of lipolysis in adipose tissue, thus reducing free fatty acid flux to
the liver and mitigating fatty liver in humans . Dietary SCFA supplementation re-
duced obesity and insulin resistance in animal models , which occurred via the
Nutrients 2022, 14, 5361 30 of 55
down-regulation of peroxisome proliferator activated receptor-γ (PPARγ)  in adi-
pose tissue, which has a distinct and complementary role to hepatic PPARγ, which pro-
motes a shift to lipid oxidation and increased energy expenditure. Interestingly, in a pre-
vious study, it was found that adipogenesis was stimulated in differentiating adipocytes
through PPARγ2 up-regulation responding to acetate and propionate concentrations in a
high vs low fat diet, up-regulating its receptor FFAR2, thus leading to adipose tissue ac-
cumulation . Concomitantly, SCFA stimulated leptin expression via FFAR2.
However, the potential role of SCFA as signalling molecules regulating hepatic glu-
cose homeostasis has not been fully elucidated in humans. SCFA appear to differentially
regulate hepatic lipid and glucose homeostasis in an AMPK-dependent manner, involv-
ing PPAR regulated effects on gluconeogenesis and lipogenesis , as found for ather-
osclerosis, steatosis and adiposity , as well as for the development of T2D .
4.4.3. Systemic Metabolism
The effects of SCFA are complex, diverse, sometimes indirect, and likely synergistic.
Both acetyl-CoA and pyruvate, close relatives of SCFA, are continuously feeding the TCA
cycle demonstrating the capacity of conversion and reconversion between SCFA, and the
ultimate importance of acetate in energy production. Acetate can quickly be converted to
acetyl-CoA and enter the TCA, increasing citrate concentrations. Propionate controls the
TCA though its conversion to succinate. Butyrate is first β-oxidised and then enters the
TCA as acetate. The impact of SCFA on the TCA and energy production may be further
regulated depending on the receptors present on the cell membranes, as MCTs regulate
influx and efflux of SCFA, as well as lactate, pyruvate and ketone bodies. Lactic acid and
ketone bodies are important respiratory substrates for tissues such as the myocardium or
red skeletal muscle (primarily mediated by MCT1) or the brain (mediated by MCT2 in
neurons, and MCT4 in astrocytes). In cells that rely on aerobic glycolysis, such as lympho-
cytes, astrocytes, tumour cells and white muscle fibres, MCT4 is more abundantly ex-
pressed than MCT1. As such, MCTs shuttle lactate from glycolysing cells to respiratory
cells. An example is the production of lactic acid by astrocytes, exported by MCT4 or
MCT1, and taken up by nearby neurons via MCT2 or MCT1. Probably due to MCT4 hav-
ing a very high affinity for pyruvate, this may reflect the need of converting pyruvate into
lactate in glycolysis, in order to regenerate cytoplasmic NADH from NAD+. MCT4 is up-
regulated in hypoxic conditions, where an increase in intracellular lactic acid is expected.
Increased concentrations of intracellular lactic acid may slow glycolysis and lead to mus-
cular fatigue . Therefore, concentrations of SCFA as well as ketone bodies and lactate
can regulate nutrient access to different cell tissues synchronously. In rats, this mechanism
was shown to impact long term hippocampal function, with loss of memory in case of
knockdown expression of MCTs. SNPs affecting MCTs can therefore have substantial im-
pacts in disease development in a tissue-specific manner, although human relevance and
therapeutic potential of SCFA are currently unknown .
A recent study combining genomic, metagenomic and metabolomic analysis showed
that plasma levels of acetate, rather than faecal levels of SCFA, were related to inflamma-
tory markers (IL-10, IL-6 IL-12p70, IL-18bp) and lipid subclasses (such as VLDL-C and
LDL-C), and metabolic risk score , given that 95% of SCFA are absorbed by colono-
cytes. Similarly, a recent study assessed the association between faecal and circulating
levels of SCFA and insulin sensitivity in human individuals. A large variability in circu-
lating SCFA was noticed (acetate 2.8–429.4 μmol/l, propionate 0.06–12.0 μmol/l, butyrate
0.07–6.7 μmol/l), but only circulating propionate could be predicted from faecal propio-
nate concentrations. The study proposed that circulating, but not faecal SCFA, were asso-
ciated with levels of fasting GLP-1 and lipid metabolites (acetate with fasting glycerol,
propionate with fasting TAG, and butyrate with free fatty acid concentrations). Circulat-
ing acetate negatively associated with insulin sensitivity, while propionate was positively
Nutrients 2022, 14, 5361 31 of 55
associated with insulin sensitivity in peripheral tissues. Regarding inflammatory param-
eters, the study did not find an association between serum SCFA and fasting PYY, IL-6,
IL-8 and TNFα .
Indeed, following hepatic metabolism, it is estimated that plasma concentrations of
acetate reach 100–150 μmol/L, propionate 4–5 μmol/L, and butyrate 1–3 μmol/L . In
vivo effects of SCFA are the sum of direct and indirect effects; these are dose-dependent
and vary between different SCFA . In order to assess the effects of SCFA on peripheral
cells, SCFA concentration measures of arterial blood should be the preferred method. Un-
fortunately, to our knowledge, studies on arterial concentration of SCFA are scarce [64,65].
Although only significant concentrations of acetate reach terminal organs (brain, lungs,
heart, pancreas), studies performed with blood-perfused liver and heart show that these
organs buffer blood acetate, with uptakes above a blood concentration of 0.25 mmol and
a net release below it. Thus, blood acetate is of little value as an indicator of total SCFA
circulating in plasma . Likewise, ratios of SCFA appear to vary substantially. In one
study, a portal acetate:propionate:butyrate ratio has been described to be 58:26:16 ,
whereas others reported a ratio of 78:15:7 . However, the ratio between serum propi-
onate and serum acetate may be the best determinant of the contribution of microbial-
derived SCFA to energy homeostasis in the host .
Regarding propionate, evidence is increasing on the important role for whole-body
energy homeostasis [142,423]. Aside from gluconeogenesis in the liver, propionate stimu-
lates intestinal lipolysis, and induces the release of GLP-1 and PYY, reducing food intake.
The route of administration of SCFA may, however, have different effects in vivo as de-
scribed in a recent review . While investigating the effects of SCFA in a mice model
of influenza infection, high fibre consumers displayed increased serum levels of all SCFA,
had reduced neutrophil-induced damage to lung tissue, and both butyrate and propionate
reduced pro-inflammatory molecules in the lung . In this study, high fibre consumers
had increases in acetate (1.82-fold over control), propionate (1.39-fold over control) and
butyrate (138.5-fold over control group) .
Acetate is the SCFA present at highest concentrations in arterial blood. It is estimated
that 0–171 μmol of acetate reach the brain, crossing the blood-brain-barrier (a rather high
concentration of acetate when compared to blood concentrations), as well as 0–6 μmol of
propionate (18.8 pmol/mg) and 0–2.8 μmol of butyrate (17 pmol/mg) due to different ex-
pression of GPCR41 and GPCR43 at the blood-brain-barrier.
Increased acetate production associated with dysbiosis in a high fat diet mice model
promoted insulin secretion via a gut-brain-pancreas axis using the parasympathetic nerv-
ous system, resulting in increased gastrin plasma levels. Chronically increased acetate
turnover appeared to induce metabolic syndrome, associated with hyperinsulinemia, in-
sulin resistance, increased triglyceride levels and over-expression of ghrelin. Both acute
and chronic effects of acetate were significantly diminished in vagotomised rats . This
axis was activated by acetate, but not by butyrate, which may be a negative consequence
of too much acetate production, without the counterbalance of propionate at the intestinal
and hepatic level. This would represent another link between dysbiosis and T2D devel-
opment. Furthermore, acetate levels were significantly higher in individuals with T2D and
obesity than in obese normoglycemic and healthy subjects. This study found significant
correlations between HbA1c, glucose, and acetate levels, but not between acetate and C-
peptide or insulin . In line with positive effects of fiber, in women with T2D who
received oligofructose-enriched inulin showed a significant decrease in the levels of fast-
ing plasma glucose, HbA1c, IL-6, TNF- α and plasma LPS, as compared with maltodex-
trin. Decreases in levels of interferon-γ and CRP as well as an increase in the level of IL-
10 were not significant between the oligofructose-enriched inulin group and the malto-
dextrin group. .
Nutrients 2022, 14, 5361 32 of 55
4.5. Signalling Pathways of Interest
SCFA interact with several G-protein coupled receptors (GPCRs), particularly in the
colon, skeletal muscle, liver, adipose tissue, lymphocytes and cells of the nervous system
(Figure 3). Here, the relevant effect depends on the cell population, as SCFA are merely
the activator of intracellular cascades. Activation of GPCR in the enteroendocrine cells
lead to increased secretion of GLP-1 and PYY , whereas the brain-mediated activation
of pancreatic β-cells leads to an increased insulin secretion . GPCR identification is on-
going, with pseudogenes being currently reclassified as novel receptors/encoding genes
Figure 3. Pleotropism of SCFA, acetate and butyrate. Each SCFA seem to have some organ specific-
ity; butyrate is mainly used for energy generation at the colonic level. In the liver, propionate is
principally metabolized, where pleotropic action is found, being lipogenic or gluconeogenic based
on its concentration. Acetate is found to affect the hypophysis-adrenal gland axis. However, the
same SCFA may impact different organs through different receptors, as receptors show preference
but are not restricted to a single SCFA, possibly leading to synergistic effects. Created with BioRen-
Signalling using pattern recognition receptors (PRR), JAK/Stat, CXCR4, chemokines,
inositol triphosphate and acylcarnitine shuttles may all be involved in SCFA-driven im-
munometabolism, which may have effects in overall homeostasis. For example, leptin, a
hormone structurally belonging to the cytokine superfamily and which can activate mon-
ocytes, neutrophils and macrophages, also regulates appetite and body weight and affects
basal metabolism by regulating insulin secretion. GWAS have found that SNPs of genes
involved in the leptin pathway were the greatest influencing factors of microbiota coloni-
zation in the nose, oral cavity and skin , possibly due to modulation of mucin expres-
sion . While rs7799039 and rs1137101 in leptin (LEP) and leptin receptor (LEPR)
genes, respectively, did not alter circulating leptin levels, these are associated with cardi-
ovascular disease and metabolic syndrome, with predisposed individuals presenting with
increased glycated haemoglobin, insulin and increased fat mass, among other clinical phe-
nomes . Leptin may be further associated with wound healing  and psoriasis
[431,432]. Butyrate and propionate also promote wound healing, by stimulation of epithe-
lial migration and differentiation through p21 activated kinase (PAK1) and milk fat glob-
ule-EGF factor 8 (MFGE8) .
Plasma leptin concentration is negatively correlated to Aβ levels in Alzheimer’s dis-
ease (AD). Indeed, AD animal models of AD treated with leptin showed a reduction in
Aβ and phosphorylated tau levels. SCFAs have therefore indirect but also direct possible
Nutrients 2022, 14, 5361 33 of 55
therapeutic potential in neurodegenerative diseases. Firstly, SCFA act as substrates for the
synthesis of serotonin. Further, butyrate acts as a HDAC inhibitor capable of restoring
fear learning, counteracting intraneuronal Aβ deposition, and butyrate, valerate and pro-
pionate have attenuated AD progression by inhibiting Aβ oligomerization .
5. Conclusions and Perspectives
5.1. Main Conclusions
Currently, it is estimated that the DF intake in Europe for adult males is around 18 to
24 g/d and for females 16 to 20 g/d, with little variation between countries, falling below
most national AI recommendations, i.e., an intake of 25–35 g for adults (25–32 g/d for adult
women and 30–35 g/d for adult men) . Sufficient intake of DF alone would contribute
to an estimated 15–30% reduction in NCDs . Overall, SCFA produced from dietary
fibres may exert profound systemic effects, although they are strongly associated with
secondary plant metabolites, which are likely concomitantly taken up by the host, and
may further contribute to observed health benefits.
SCFA are the major carbon flux shared between the GM and the host, and regulatory
roles in local and peripheral metabolism are emerging . Many of these aspects are
potentially related to the effect of SCFA on immune and inflammatory modulation path-
ways. It is not unreasonable to wonder whether SCFA represent a key molecular link be-
tween diet, the gut microbiome and health . However, the causality of microbiota-
derived metabolites in the aetiology of human disease remains unclear. Butyrate has re-
ceived much attention due to its effect on cell proliferation studied in vitro, but more re-
search is warranted in order to understand the roles of non-butyrate SCFA, i.e., propio-
nate, acetate, valerate and caproate.
Such research should be based preferably on human studies. Several authors have
warned about the implications of extrapolating in vitro (studies often performed in cancer
or immortalized cells) findings of SCFA, i.e., anti-inflammatory, anti-cancer or epigenetic
effects such as HDAC inhibition, to humans [100,434], as in vitro cell cultures may use
other than normal metabolic pathways. Indeed, butyrate and propionate concentrations
are higher in cancer cells due to the Warburg effect, leading to reduced genetic transcrip-
tion than could be expected in healthy cells in vivo . However, such mechanistic in-
sights provide knowledge on the association of genomes, disease phenotypes and micro-
bial taxa .
The enterotype, i.e., the clustering of gut bacterial communities, represents the first
level of inter-individual variability in vivo. A changed enterotype may result in substan-
tial gastrointestinal and systemic impact. In vivo effects of SCFA are the sum of direct,
and indirect effects; these are not only dose-dependent, but vary between different SCFA
. Colonocytes take up SCFA and derive a large extent of their energy requirement from
β-oxidation of SCFA. Non-metabolized SCFA enter the bloodstream and are metabolized
in the liver, skeletal muscle and brain, among others. As the effect of SCFA is pleiotropic,
eliciting intracellular cascades can be lipogenic or gluconeogenic, tolerogenic or immuno-
genic [62,98,156,375,435]. Butyrate appears to impinge on enterocyte proliferation in a
dose-dependent manner, inducing both cell proliferation in healthy intestinal crypts, as
well as apoptosis in metaplastic cells, via p53 . Acetate and propionate, thought to be
tolerogenic, are rather decreased (though not significantly) in a fibre-free diet, although
butyrate levels in faeces remain stable .
Slow intraluminal SCFA diffusion rate, rapid mucosal absorption (>95%) and enter-
ocyte metabolism of SCFA, are some of the factors in vivo that cause the estimation of
SCFA production from faecal samples rather imprecise [129,436]. This measurement only
allows drawing conclusions based on the approximately 5% of SCFA that remain unab-
sorbed following their colonic passage. Furthermore, luminal concentrations of SCFA
measured in animals were criticized for not reflecting purely their rate of production, but
instead their rate of epithelial absorption, which is even further modulated by luminal
Nutrients 2022, 14, 5361 34 of 55
lactate concentrations and pH . With regard to circulating concentrations, blood ace-
tate seems to be of little value as an indicator of total SCFA circulating in plasma. The ratio
between serum propionate and serum acetate may be the best determinant of the SCFA
microbial-contribution to whole-organism homeostasis .
SCFA production has clearly been shown to be related to the amount of DF intake,
as well as to the composition of the GM. While the majority of GM are obligate anaerobes
[437,438], making their study difficult with traditional methods, metagenomic approaches
may allow the complete taxonomy of the GM soon to be known. However, understanding
the metabolic complexity of the holobiont will undoubtedly require more time and efforts.
Depending on the host’s GM, the degree of DF fermentation and therefore SCFA produc-
tion and their uptake will vary. Bacteroidetes spp. mainly produce acetate and propionate,
Prevotella is an acetate producer, and the Firmicutes phylum tend to produce butyrate
. Many cross-feeding interactions exist between these phyla, which are the most
abundant in the human gut. Given that the GM composition has not been found to have
characteristic changes in relation to specific phenotypes, we can hypothesize that some
genotypes will benefit from one or the other enterotype for improved health status. Nev-
ertheless, in vivo studies of total DF consumption, GM composition, circulating concen-
trations of SCFA and genetic analysis in healthy humans are scarce and present heteroge-
neous results, possibly due to study design or the use of various types of dietary fiber
The second level of variability is the individual’s host genome. Recent studies have
found significant correlations between different cellular signalling pathways and specific
microbial colonization in individuals . The feedback between the GM and the ge-
nome is reflected in the regulation of the mucus layer (responding to DF intake), potency
of enzymatic activity (as seen for AMY1 and LCT), or reactivity of receptors and transport-
ers present in colonic strata, such as SMCTs and MCTs. SNPs in genes such as AMY1,
MUC2, FUT2, SCFA receptors (MCT1-4, GPCR41, GPCR43, GPCR109A) and tight junction
proteins, to name a few, may strongly modulate the gut microbiota composition [3,61,251].
Acetate, propionate and butyrate are taken up by specific MCTs in the gut epithelium and
are widely distributed in human tissues. Furthermore, both SCFA and their receptors
GCPR109A, GPCR41 and GPCR43 have been previously associated with specific disease
phenotypes such as metabolic syndrome, Parkinson disease, cancer, gastrointestinal dis-
orders and T2D [6,35,202,439,440]. However, authors note that much larger sample sizes
are needed to elucidate the remaining effects of host genetics on the gut microbiome .
The (combined host-microbe) metabolic steps of DF reveal the deep symbiosis exist-
ing between the human host and their microbiome. In this context, DF and the mucus
barrier in the colon, seem to be the strongest mechanism linking both genomes . Ac-
cording to the holobiont theory, host and microbe genomes, including individual genes,
are selected if advantageous for the holobiont. This implies that the microbiome and the
host are attempting to achieve homeostasis through cooperative mechanisms. Blekhman
 and Bonder , among others, have emphasized the importance of genetic varia-
tion and associated microbiota, with innate immunity genes being highly conserved and
correlated with microbial taxa.
The third level of variability, the phenotypical level, refers to the resultant interplay
of the genome, the microbiome and lifestyle factors, of which diet is thought to be a sig-
nificant contributor [44,442]. The mentioned examples of Prevotella, Akkermansia, and E.
limosum may reflect an intricate symbiont homeostasis associated with dietary patterns,
resulting in observable phenotype trajectories. DF and produced SCFA may influence all
phenotypes to a certain degree, whether by maintaining homeostatic balance , by mod-
ulating the GM via substrate competition , by decreasing the rate of disease progres-
sion as a result of reduced mean arterial pressure and heart rate , by reducing disease
symptoms due to anti-inflammatory actions , or by modulating pharmacological re-
sponses, potentially by interacting with the further transport of drugs [240,286,379]. How-
ever, the impact of various DF-associated compounds acting as transport inhibitors must
Nutrients 2022, 14, 5361 35 of 55
also be taken into account , as the same receptors are used for drug delivery. These
and other microbial derived metabolites represent a new frontier in understanding path-
ogenesis and physiology .
Understanding the inter-individual differences regarding the effect of DF, including
the metabolism of SCFA and potential health outcomes, is complex. In specific organs, a
balance between aerobic and anaerobic cells is achieved, with SCFA at its core. Some stud-
ies further delegate SCFA a dominant role in cell differentiation, as seen for macrophages
and colonocytes. Indeed, as close relatives of members of the TCA or the mitochondrial
respiration chain, SCFA concentrations may strongly impact these energy systems, both
at the cellular level, as well as at the systemic level (e.g., insulin and glucose control)
Thus, microbial metabolites including SCFA may be suitable biomarkers in clinical
practice, as their detection in blood plasma and possibly also in faeces is usually afforda-
ble, and may precede the onset of clinically manifest disease symptoms. Furthermore, nat-
urally occurring DF is also covalently bound to other phytochemicals, such as polyphe-
nols , and other compounds such as carotenoids can be entrapped . These second-
ary plant metabolites would therefore reach the colon, where they may further exert direct
and indirect antioxidant and anti-inflammatory effects by, e.g., its redox potential, upreg-
ulation of Nrf2 or downregulating NF-κB respectively, and may contribute to the positive
health effects of DF [44,341]. A number of health effects may be attributed to DF-bound
phytochemicals, with or without microbial bioconversion, presenting with potential in-
terferences on the hormonal level, such as lignans . However, little is known on fac-
tors explaining inter-individual differences in response to DF-bound phytochemicals, and
their levels may vary drastically, depending on the type of DF consumed.
5.2. Future Perspectives
Studies are consensual in demonstrating that the GM can shift dramatically upon
dietary changes, but also that it is quite resilient and will quickly return to a stable baseline
. Precision dietary interventions, faecal microbiota transplantation and complemen-
tary or synergistic synbiotics (formulations of pre- and probiotics) are all technologies un-
dergoing rapid evolution. These are expected to have a beneficial impact on a broad range
of diseases, from paediatric diseases such as autism spectrum disorders or atopy and al-
lergy, to age-associated conditions such as neurodegeneration and cancer, as well as food
Some authors have observed that the increased prevalence of NCDs in the last fifty
years may not be related to “losing microbes” or “proliferation of individual pathogens”,
but instead to the evolution of environmental exposures, including the increased con-
sumption of ultra-processed food and decreased DF consumption . These authors
suggested a relationship between NCDs and a different interplay between our “new” mi-
crobiota with our “old” genes; i.e., as human diet has shifted in the last 50 years, e.g., to
more sugar, more saturated fatty acids and less fibre intake, so has the microbiome. The
authors postulate that the low-grade chronic pro-inflammatory status associated with
NCDs is the result of dietary changes and incomplete adaptation by the holobiont, includ-
ing the microbiota .
Highly complex interactions involving the human host, GM and dietary patterns re-
sult in the creation of large databases with numerous variables, many of which, e.g., im-
munity or human genetics, may not be yet fully characterized . Wolter et al. pointed
out that future research must have as an aim to identify both general and subpopulation-
specific biomarkers, in order to understand the underlying mechanisms behind varied
responses to standardized interventions . While microbiota-focused treatment op-
tions, such as through DF and modulation of SCFA production may become highly rele-
vant in the future, consensual “core optimal” or “healthy” microbiome needs further de-
fining, in a highly individualized and self-regulated system. This has led to several au-
Nutrients 2022, 14, 5361 36 of 55
thors affirming that translational research—in an attempt to complement fundamental re-
search, animal models and mechanistic studies with epidemiological studies, human in-
tervention studies, deep phenotyping and longitudinal study designs—may be the neces-
sary next step to gain insight into the nutritional interactions taking place in vivo, in order
to explain DF variations among individuals [445,446].
Although investigating the associations between dietary patterns, GM and host ge-
netics is a promising field of study, the specific mechanisms providing phenotypical dif-
ferences and specific disease trajectories over lifetime remain unknown. Deep phenotyp-
ing and adapted study designs, such as N-of-1 methods performed in the frame of ran-
domised controlled trials (RCTs) may be required for such insights . Longitudinal
follow-up studies of dietary interventions and observation across many years may be nec-
essary to evaluate the real polygenic risk impact of common SNPs related to SCFA metab-
olism in the population. In addition, longitudinal population-based studies are necessary
to confirm the relationship between polygenic risk and common SNPs in the general pop-
ulation. Precision, patient-tailored therapies combined with measuring SCFA may be pos-
sible in the future following pharmacological or nutritional intervention, potentially with-
out strong interference in overall homeostasis and wellbeing .
Enterotyping and genetic sequencing will have to be complementary to each other,
as to understand the impact of such metabolites in individual persons. In future research,
polygenic risk scores of common polymorphisms, using novel tools, should be scruti-
nized.While further research is needed before drawing any conclusions, this review elu-
cidated the potential of SCFA as biomarkers for future healthcare, while taking into ac-
count potential factors explaining individual variability of responses.
Author Contributions: G.R.M. was involved in conceptualizing the manuscript and wrote the ma-
jority of the article. T.B. was involved in the conceptualization, writing, and revision of the article.
H.S. was involved in the revision of the article. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by the Fonds National de la Recherche Luxembourg, grant num-
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
1. Sender, R.; Fuchs, S.; Milo, R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016,
14, e1002533. https://doi.org/10.1371/journal.pbio.1002533.
2. Zilber-Rosenberg, I.; Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory
of evolution. FEMS Microbiol. Rev. 2008, 32, 723–735. https://doi.org/10.1111/j.1574-6976.2008.00123.x.
3. Goodrich, J.K.; Davenport, E.R.; Clark, A.G.; Ley, R.E. The Relationship Between the Human Genome and Microbiome
Comes into View. Annu. Rev. Genet. 2017, 51, 413–433. https://doi.org/10.1146/annurev-genet-110711-155532.
4. Donaldson, G.P.; Lee, S.M.; Mazmanian, S.K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 2016, 14, 20–
5. Sommer, F.; Bäckhed, F. The gut microbiota—Masters of host development and physiology. Nat. Rev. Microbiol. 2013, 11,
6. Perry, R.J.; Peng, L.; Barry, N.A.; Cline, G.W.; Zhang, D.; Cardone, R.L.; Petersen, K.F.; Kibbey, R.G.; Goodman, A.L.; Shul-
man, G.I. Acetate mediates a microbiome-brain-β cell axis promoting metabolic syndrome. Nature 2016, 7606, 213–217.
7. McRae, M.P. Dietary Fiber is Beneficial for the Prevention of Cardiovascular Disease: An Umbrella Review of Meta-analyses.
J. Chiropr. Med. 2017, 16, 289–299. https://doi.org/10.1016/j.jcm.2017.05.005.
8. Akshintala, V.S.; Talukdar, R.; Singh, V.K.; Goggins, M. The Gut Microbiome in Pancreatic Disease. Clin. Gastroenterol. Hepa-
tol. 2018, 17, 290–295. https://doi.org/10.1016/j.cgh.2018.08.045.
Nutrients 2022, 14, 5361 37 of 55
9. Diamanti, A.P.; Rosado, M.M.; Laganà, B.; D’Amelio, R. Microbiota and chronic inflammatory arthritis: An interwoven link.
J. Transl. Med. 2016, 14, 233. https://doi.org/10.1186/s12967-016-0989-3.
10. Kang, L.; Li, P.; Wang, D.; Wang, T.; Hao, D.; Qu, X. Alterations in intestinal microbiota diversity, composition, and function
in patients with sarcopenia. Nature 2021, 11, 4628. https://doi.org/10.1038/s41598-021-84031-0.
11. Gentile, F.; Doneddu, P.E.; Riva, N.; Nobile-Orazio, E.; Quattrini, A. Diet, Microbiota and Brain Health: Unraveling the
Network Intersecting Metabolism and Neurodegeneration. Int. J. Mol. Sci. 2020, 21, 7471.
12. Friedland, R.P. Mechanisms of Molecular Mimicry Involving the Microbiota in Neurodegeneration. J. Alzheimer’s Dis. 2015,
45, 349–352. https://doi.org/10.3233/JAD-142841.
13. Baldini, F.; Hertel, J.; Sandt, E.; Thinnes, C.C.; Neuberger-Castillo, L.; Pavelka, L.; Betsou, F.; Krüger, R.; Thiele, I. Parkinson’s
disease-associated alterations of the gut microbiome predict disease relevant changes in metabolic functions. BMC Biol. 2020,
18, 62. https://doi.org/10.1186/s12915-020-00775-7.
14. Goyal, D.; Ali, S.A.; Singh, R.K. Emerging role of gut microbiota in modulation of neuroinflammation and neurodegenera-
tion with emphasis on Alzheimer's disease. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021, 106, 9.
15. Lazar, V.; Ditu, L.-M.; Pircalabioru, G.G.; Gheorghe, I.; Curutiu, C.; Holban, A.M.; Picu, A.; Petcu, L.; Chifiriuc, M.C. Aspects
of Gut Microbiota and Immune System Interactions in Infectious Diseases, Immunopathology, and Cancer. Front. Immunol.
2018, 9, 1830. https://doi.org/10.3389/fimmu.2018.01830.
16. Kunzmann, A.T.; Coleman, H.G.; Huang, W.-Y.; Kitahara, C.M.; Cantwell, M.M.; Berndt, S.I. Dietary fiber intake and risk
of colorectal cancer and incident and recurrent adenoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening
Trial. Am. J. Clin. Nutr. 2015, 102, 881–890. https://doi.org/10.3945/ajcn.115.113282.
17. Kotas, M.E.; Medzhitov, R. Homeostasis, Inflammation, and Disease Susceptibility. Cell 2015, 160, 816–827.
18. Cevenini, E.; Monti, D.; Franceschi, C. Inflamm-ageing. Curr. Opin. Clin. Nutr. Metab. Care 2013, 16, 14–20.
19. Hotamisligli, G.S. Inflammation and metabolic disorders. Nature 2006, 444, 870–867. https://doi.org/10.1038/nature05485.
20. Hotamisligil, G.S. Inflammation, metaflammation and immunometabolic disorders. Nature 2017, 542, 177–185.
21. Kaulmann, A.; Bohn, T. Carotenoids, inflammation, and oxidative stress—Implications of cellular signaling pathways and
relation to chronic disease prevention. Nutr. Res. Rev. 2014, 34, 907–929. https://doi.org/10.1016/j.nutres.2014.07.010.
22. Menzel, A.; Samouda, H.; Dohet, F.; Loap, S.; Ellulu, M.S.; Bohn, T. Common and Novel Markers for Measuring Inflamma-
tion and Oxidative Stress Ex Vivo in Research and Clinical Practice—Which to Use Regarding Disease Outcomes? Antioxi-
dants 2021, 10, 414. https://doi.org/10.3390/antiox10030414.
23. Pan, A.; Lin, X.; Hemler, E.; Hu, F.B. Diet and Cardiovascular Disease: Advances and Challenges in Population-Based Stud-
ies. Cell Metab. 2018, 27, 489–496. https://doi.org/10.1016/j.cmet.2018.02.017.
24. Christ, A.; Latz, E. The Western lifestyle has lasting effects on metaflammation. Nat. Rev. Immunol. 2019, 19, 267–268.
25. Devore, E.E.; Kang, J.H.; Breteler, M.M.B.; Grodstein, F. Dietary intake of berries and flavonoids in relation to cognitive
decline. Ann. Neurol. 2012, 72, 135–143. https://doi.org/10.1002/ana.23594.
26. Darmadi-Blackberry, I.; Wahlqvist, M.L.; Kouris-Blazos, A.; Steen, B.; Lukito, W.; Horie, Y.; Horie, K. Legumes: The most
important dietary predictor of survival in older people of different ethnicities. Asia Pac. J. Clin. Nutr. 2004, 13, 217–220.
27. Katagiri, R.; Goto, A.; Sawada, N.; Yamaji, T.; Iwasaki, M.; Noda, M.; Iso, H.; Tsugane, S. Dietary fiber intake and total and
cause-specific mortality: The Japan Public Health Center-based prospective study. Am. J. Clin. Nutr. 2020, 111, 1027–1035.
28. Szic, K.S.V.; Declerck, K.; Vidaković, M.; Berghe, W.V. From inflammaging to healthy aging by dietary lifestyle choices: Is
epigenetics the key to personalized nutrition? Clin. Epigenetics 2015, 7, 33. https://doi.org/10.1186/s13148-015-0068-2.
29. Fitzgerald, K.C.; Tyry, T.; Salter, A.; Cofield, S.S.; Cutter, G.; Fox, R.; Marrie, R.A. Diet quality is associated with disability
and symptom severity in multiple sclerosis. Neurology 2018, 90, e1–e11. https://doi.org/10.1212/WNL.0000000000004768.
30. Yoo, J.Y.; Kim, S.S. Probiotics and Prebiotics: Present Status and Future Perspectives on Metabolic Disorders. Nutrients 2016,
3, 173. https://doi.org/10.3390/nu8030173.
31. Mischley, L.K.; Lau, R.C.; Bennett, R.D. Role of Diet and Nutritional Supplements in Parkinson’s Disease Progression. Oxi-
dative Med. Cell. Longev. 2017, 2017, 6405278. https://doi.org/10.1155/2017/6405278.
32. Lucas, M.; Chocano-Bedoya, P.; Shulze, M.B.; Mirzaei, F.; O’Reilly, É.J.; Okereke, O.I.; Hu, F.B.; Willett, W.C.; Ascherio, A.
Inflammatory dietary pattern and risk of depression among women. Brain Behav. Immun. 2014, 36, 46–53.
33. Rayman, M.P. Diet, nutrition and osteoarthritis. BMC Musculoskelet. Disord. 2015, 16, S1–S7. https://doi.org/10.1186/1471-
34. Martínez-González, M.A.; Sánchez-Villegas, A. Food patterns and the prevention of depression. Proc. Nutr. Soc. 2016, 75, 139–146.
35. Gill, S.K.; Rossi, M.; Bajka, B.; Whelan, K. Dietary fibre in gastrointestinal health and disease. Nat. Rev. Gastroenterol. Hepatol.
2021, 18, 101–116. https://doi.org/10.1038/s41575-020-00375-4.
Nutrients 2022, 14, 5361 38 of 55
36. Trowell, H. Definition of dietary fiber and hypotheses that it is a protective factor in certain diseases. Am. J. Clin. Nutr. 1976,
29, 417–427. https://doi.org/10.1093/ajcn/29.4.417.
37. Wu, Z.; Xu, Q.; Wang, Q.; Chen, Y.; Lv, L.; Zheng, B.; Yan, R.; Jiang, H.; Shen, J.; Wang, S.; et al. The impact of dietary fibers
on Clostridioides difficile infection in a mouse model. Front. Cell. Infect. Microbiol. 2022, 12, 1028267.
38. Snauwaert, E.; Paglialonga, F.; Vande Walle, J.; Wan, M.; Desloovere, A.; Polderman, N.; Renken-Terhaerdt, J.; Shaw, V.;
Shroff, R. The benefits of dietary fiber: The gastrointestinal tract and beyond. Pediatr. Nephrol. 2022, 2022, 1–10.
39. Nakaji, S.; Sugawara, K.; Saito, D.; Yoshioka, Y.; Macauley, D.; Bradley, T.; Kernohan, G.; Baxter, D. Trends in dietary fiber
intake in Japan over the last century. Eur. J. Nutr. 2002, 41, 222–227. https://doi.org/10.1007/s00394-002-0379-x.
40. Stephen, A.M.; Champ, M.M.-J.; Cloran, S.J.; Fleith, M.; Lieshout, L.V.; Mejborn, H.; Burley, V.J. Dietary fibre in Europe:
Current state of knowledge on definitions, sources, recommendations, intakes and relationships to health. Nutr. Res. Rev.
2017, 30, 149–190. https://doi.org/10.1017/S095442241700004X.
41. Prynne, C.J.; McCarron, A.; Wadsworth, M.E.; Stephen, A.M. Dietary fibre and phytate—A balancing act: Results from three
time points in a British birth cohort. Br. J. Nutr. 2010, 103, 274–280. https://doi.org/10.1017/S0007114509991644.
42. Public Health England; Food Standards Agency. National Diet and Nutrition Survey: Results from Years 1, 2, 3 and 4 (Combined)
of the Rolling Programme (2008/2009–2011/2012); Public Health England: London, UK, 2014.
43. King, D.E.; Mainous, A.G., 3rd; Lambourne, C.A. Trends in dietary fiber intake in the United States, 1999–2008. J. Acad. Nutr.
Diet. 2012, 112, 642–648. https://doi.org/10.1016/j.jand.2012.01.019.
44. Dingeo, G.; Brito, A.; Samouda, H.; Iddir, M.; Frano, M.R.L.; Bohn, T. Phytochemicals as modifiers of gut microbial commu-
nities. Food Funct. 2020, 11, 8444–8471. https://doi.org/10.1039/d0fo01483d.
45. Makki, K.; Deehan, E.C.; Walter, J.; Bäckhed, F. The Impact of Dietary Fiber on Gut Microbiota in Host Health and Disease.
Cell Host Microbe 2018, 23, 705–716. https://doi.org/10.1016/j.chom.2018.05.012.
46. Jha, S.K.; Singh, H.R.; Prakash, P. Dietary Fiber and Human Health: An Introduction; Elsevier: Amsterdam, The Netherlands,
2017; pp. 1–22. https://doi.org/10.1016/b978-0-12-805130-6.00001-x.
47. Desai, M.S.; Seekatz, A.M.; Koropatkin, N.M.; Kamada, N.; Hickey, C.A.; Wolter, M.; Pudlo, N.A.; Kitamoto, S.; Terrapon,
N.; Muller, A.; et al. A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen
Susceptibility. Cell 2016, 167, 1339–1353. https://doi.org/10.1016/j.cell.2016.10.043.
48. Tan, J.; McKenzie, C.; Vuillermin, P.J.; Goverse, G.; Vinuesa, C.G.; Mebius, R.E.; Macia, L.; Mackay, C.R. Dietary Fiber and
Bacterial SCFA Enhance Oral Tolerance and Protect against Food Allergy through Diverse Cellular Pathways. Cell Rep. 2016,
15, 2809–2824. https://doi.org/10.1016/j.celrep.2016.05.047.
49. Kuo, S.-M. The interplay between fiber and the intestinal microbiome in the inflammatory response. Adv. Nutr. 2013, 4, 16–
50. Topping, D.L.; Clifton, P.M. Short-Chain Fatty Acids and Human Colonic Function: Roles of Resistant Starch and Nonstarch
Polysaccharides. Physiol. Rev. 2001, 81, 1031–1064. https://doi.org/10.1152/physrev.2001.81.3.1031.
51. Gill, P.A.; Zelm, M.C.V.; Muir, J.G.; Gibson, P.R. Review article: Short chain fatty acids as potential therapeutic agents in
human gastrointestinal and inflammatory disorders. Aliment. Pharmacol. Ther. 2018, 48, 15–34.
52. Louis, P.; Hold, G.L.; Flint, H.J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 2014, 12,
53. Singh, N.; Gurav, A.; Sivaprakasam, S.; Brady, E.; Padia, R.; Shi, H.; Thangaraju, M.; Prasad, P.D.; Manicassamy, S.; Munn,
D.H.; et al. Activation of Gpr109a, Receptor for Niacin and the Commensal Metabolite Butyrate, Suppresses Colonic Inflam-
mation and Carcinogenesis. Immunity 2014, 40, 128–139. https://doi.org/10.1016/j.immuni.2013.12.007.
54. Chang, P.V.; Hao, L.; Offermanns, S.; Medzhitov, R. The microbial metabolite butyrate regulates intestinal macrophage
function via histone deacetylase inhibition. Proc. Natl. Acad. Sci. USA 2014, 111, 2247–2252.
55. Furusawa, Y.; Obata, Y.; Fukuda, S.; Endo, T.A.; Nakato, G.; Takahashi, D.; Nakanishi, Y.; Uetake, C.; Kato, K.; Kato, T.; et
al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 2013, 504, 446–450.
56. Smith, P.M.; Howitt, M.R.; Panikov, N.; Michaud, M.; Gallini, C.A.; Bohlooly-Y.M.; Glickman, J.N.; Garrett, W.S. The Micro-
bial Metabolites, Short-Chain Fatty Acids, Regulate Colonic Treg Cell Homeostasis. Science 2013, 341, 569–573.
57. Zimmerman, M.A.; Singh, N.; Martin, P.M.; Thangaraju, M.; Ganapathy, V.; Waller, J.L.; Shi, H.; Robertson, K.D.; Munn,
D.H.; Liu, K. Butyrate suppresses colonic inflammation through HDAC1-dependent Fas upregulation and Fas-mediated
apoptosis of T cells. Am. J. Physiol.-Gastrointest. Liver Physiol. 2012, 302, G1405–G1415. https://doi.org/10.1152/aj-
58. Donohoe, D.; Garge, N.; Zhang, X.; Sun, W.; O’Connel, T.; Bunger, M.; Bultman, S. The Microbiome and Butyrate Regulate
Energy Metabolism and Autophagy in the Mammalian Colon. Cell Metab. 2011, 13, 517–526.
Nutrients 2022, 14, 5361 39 of 55
59. Usami, M.; Kishimoto, K.; Ohata, A.; Miyoshi, M.; Aoyama, M.; Fueda, Y.; Kotani, J. Butyrate and trichostatin A attenuate
nuclear factor κB activation and tumor necrosis factor α secretion and increase prostaglandin E2 secretion in human periph-
eral blood mononuclear cells. Nutr. Res. 2008, 28, 321–328. https://doi.org/10.1016/j.nutres.2008.02.012.
60. Hamer, H.M.; Jonkers, D.; Venema, K.; Vanhoutvin, S.; Troost, F.J.; Brummer, R.J. Review article: The role of butyrate on
colonic function. Aliment. Pharmacol. Ther. 2007, 27, 104–119. https://doi.org/10.1111/j.1365-2036.2007.03562.x.
61. Gaudier, E.; Jarry, A.; Blottière, H.M.; Coppet, P.D.; Buisine, M.P.; Aubert, J.P.; Laboisse, C.; Cherbut, C.; Hoebler, C. Butyr-
ate specifically modulates MUC gene expression in intestinal epithelial goblet cells deprived of glucose. Am. J. Physiol. 2004,
287, G1168–G1174. https://doi.org/10.1152/ajpgi.00219.2004.
62. Willemsen, L.E.M. Short chain fatty acids stimulate epithelial mucin 2 expression through differential effects on prostaglan-
din E1 and E2 production by intestinal myofibroblasts. Gut 2003, 52, 1442–1447. https://doi.org/10.1136/gut.52.10.1442.
63. Pryde, S.E.; Duncan, S.H.; Hold, G.L.; Stewart, C.S.; Flint, H.J. The microbiology of butyrate formation in the human colon.
FEMS Microbiol. Lett. 2002, 217, 133–139. https://doi.org/10.1111/j.1574-6968.2002.tb11467.x.
64. Boets, E.; Gomand, S.V.; Deroover, L.; Preston, T.; Vermeulen, K.; de Preter, V.; Hamer, H.M.; van den Mooter, G.; de Vuyst,
L.; Courtin, C.M.; et al. Systemic availability and metabolism of colonic-derived short-chain fatty acids in healthy subjects:
A stable isotope study. J. Physiol. 2017, 595, 541–555. https://doi.org/10.1113/jp272613.
65. Bloemen, J.G.; Venema, K.; van de Poll, M.C.; Olde Damink, S.W.; Buurman, W.A.; Dejong, C.H. Short chain fatty acids
exchange across the gut and liver in humans measured at surgery. Clin. Nutr. 2009, 28, 657–661.
66. Sakata, T. Pitfalls in short-chain fatty acid research: A methodological review. Anim. Sci. J. 2019, 90, 3–13.
67. Reichardt, N.; Vollmer, M.; Holtrop, G.; Farquharson, F.M.; Wefers, D.; Bunzel, M.; Duncan, S.H.; Drew, J.E.; Williams, L.M.;
Milligan, G.; et al. Specific substrate-driven changes in human faecal microbiota composition contrast with functional re-
dundancy in short-chain fatty acid production. ISME J. 2018, 12, 610–622. https://doi.org/10.1038/ismej.2017.196.
68. Durack, J.; Christophersen, C.T. Human Respiratory and Gut Microbiomes—Do They Really Contribute to Respiratory
Health? Front. Pediatrics 2020, 8, 528. https://doi.org/10.3389/fped.2020.00528.
69. Levan, S.R.; Stamnes, K.A.; Lin, D.L.; Panzer, A.R.; Fukui, E.; McCauley, K.; Fujimura, K.E.; McKean, M.; Ownby, D.R.;
Zoratti, E.M.; et al. Elevated faecal 12,13-diHOME concentration in neonates at high risk for asthma is produced by gut
bacteria and impedes immune tolerance. Nat. Microbiol. 2019, 4, 1851–1861. https://doi.org/10.1038/s41564-019-0498-2.
70. Durack, J.; Kimes, N.E.; Lin, D.L.; Rauch, M.; McKean, M.; McCauley, K.; Panzer, A.R.; Mar, J.S.; Cabana, M.D.; Lynch, S.V.
Delayed gut microbiota development in high-risk for asthma infants is temporarily modifiable by Lactobacillus supplemen-
tation. Nat. Commun. 2018, 9, 707. https://doi.org/10.1038/s41467-018-03157-4.
71. Fujimura, K.E.; Sitarik, A.R.; Havstad, S.; Lin, D.L.; Levan, S.; Fadrosh, D.; Panzer, A.R.; LaMere, B.; Rackaityte, E.; Lukacs,
N.W.; et al. Neonatal gut microbiota associates with childhood multi– sensitized atopy and T–cell differentiation. Nat. Med.
2016, 22, 1187–1191. https://doi.org/10.1038/nm.4176.
72. Maslowski, K.M.; Vieira, A.T.; Ng, A.; Kranich, J.; Sierro, F.; Di, Y.; Schilter, H.C.; Rolph, M.S.; Mackay, F.; Artis, D.; et al.
Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature 2009, 461, 1282–1286.
73. Skelly, A.N.; Sato, Y.; Kearney, S.; Honda, K. Mining the microbiota for microbial and metabolite-based immunotherapies.
Nat. Rev. Immunol. 2019, 19, 305–323. https://doi.org/10.1038/s41577-019-0144-5.
74. Tuck, C.J.; Vanner, S.J. Dietary therapies for functional bowel symptoms: Recent advances, challenges, and future directions.
Neurogastroenterol. Motil. 2017, 30, e13238. https://doi.org/10.1111/nmo.13238.
75. Blacher, E.; Levy, M.; Tatirovsky, E.; Elinav, E. Microbiome-Modulated Metabolites at the Interface of Host Immunity. J.
Immunol. 2017, 198, 572–580. https://doi.org/10.4049/jimmunol.1601247.
76. Saura-Calixto, F. Dietary Fiber as a Carrier of Dietary Antioxidants: An Essential Physiological Function. J. Agric. Food Chem.
2011, 1, 43–49. https://doi.org/10.1021/jf1036596.
77. Palafox-Carlos, H.; Ayala-Zavala, J.F.; González-Aguilar, G.A. The Role of Dietary Fiber in the Bioaccessibility and Bioavail-
ability of Fruit and Vegetable Antioxidants. J. Food Sci. 2011, 1, R6–R15. https://doi.org/10.1111/j.1750-3841.2010.01957.x.
78. Luca, S.V.; Macovei, I.; Bujor, A.; Miron, A.; Skalicka-Woźniak, K.; Aprotosoaie, A.C.; Trifa, A. Bioactivity of dietary poly-
phenols: The role of metabolites. Food Sci. Nutr. 2020, 60, 626–659. https://doi.org/10.1080/10408398.2018.1546669.
79. Dobson, C.C.; Mottawea, W.; Rodrigue, A.; Pereira, B.L.B.; Hammami, R.; Power, K.A.; Bordenave, N. Impact of molecular
interactions with phenolic compounds on food polysaccharides functionality. Adv. Food Nutr. Res. 2019, 90, 135–181.
80. Padayachee, A.; Day, L.; Howell, K.; Gidley, M.J. Complexity and health functionality of plant cell wall fibers from fruits
and vegetables. Food Sci. Nutr. 2017, 1, 59–81. https://doi.org/10.1080/10408398.2013.850652.
81. Bohn, T. Dietary factors affecting polyphenol bioavailability. Nutr. Rev. 2014, 72, 429–452.
82. Kaulmann, A.; Bohn, T. Bioactivity of Polyphenols: Preventive and Adjuvant Strategies toward Reducing Inflammatory
Bowel Diseases—Promises, Perspectives, and Pitfalls. Oxidative Med. Cell. Longev. 2016, 2016, 9346470.
Nutrients 2022, 14, 5361 40 of 55
83. Çelik, E.E.; Rubio, J.M.A.; Andersen, M.L.; Gökmen, V. Interactions of dietary fiber bound antioxidants with hy-
droxycinnamic and hydroxybenzoic acids in aqueous and liposome media. Food Chem. 2019, 278, 294–304.
84. Bermúdez-Oria, A.; Rodríguez-Gutiérrez, G.; Fernández-Prior, Á.; Vioque, B.; Fernández-Bolaños, J. Strawberry dietary fi-
ber functionalized with phenolic antioxidants from olives. Interactions between polysaccharides and phenolic compounds.
Food Chem. 2019, 280, 310–320. https://doi.org/10.1016/j.foodchem.2018.12.057.
85. Çelik, E.E.; Gökmen, V.; Skibsted, L.H. Synergism between Soluble and Dietary Fiber Bound Antioxidants. J. Agric. Food
Chem. 2015, 63, 2338–2343. https://doi.org/10.1021/acs.jafc.5b00009.
86. Doğan, E.; Gökmen, V. Mechanism of the interaction between insoluble wheat bran and polyphenols leading to increased
antioxidant capacity. Food Res. Int. 2015, 69, 189–193. https://doi.org/10.1016/j.foodres.2014.12.037.
87. Saura-Calixto, F.; Serrano, J.; Goni, I. Intake and bioaccessibility of total polyphenols in a whole diet. Food Chem. 2007, 101,
88. Johnson, A.J.; Vangay, P.; Al-Ghalith, G.A.; Hillmann, B.M.; Ward, T.L.; Shields-Cutler, R.R.; Kim, A.D.; Shmagel, A.K.;
Syed, A.N.; Students, P.M.C.; et al. Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans. Cell
Host Microbe 2019, 25, 789–802. https://doi.org/10.1016/j.chom.2019.05.005.
89. Filippis, F.D.; Pellegrini, N.; Vannini, L.; Jeffery, I.B.; Storia, A.L.; Laghi, L.; Serrazanetti, D.I.; Cagno, R.D.; Ferrocino, I.;
Lazzi, C.; et al. High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metab-
olome. BMJ Gut 2016, 65, 1812–1821. https://doi.org/10.1136/gutjnl-2015-309957.
90. Martínez, G.P.; Bäuerl, C.; Collado, M.C. Understanding gut microbiota in elderly’s health will enable intervention through
probiotics. Benef. Microbes 2014, 3, 235–246. https://doi.org/10.3920/BM2013.0079.
91. Wu, G.D.; Chen, J.; Hoffmann, C.; Bittinger, K.; Chen, Y.-Y.; Keilbaugh, S.A.; Bewtra, M.; Knights, D.; Walters, W.A.; Knight,
R.; et al. Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes. Science 2011, 334, 105–109.
92. Markiewicz, L.H.; Honke, J.; Haros, M.; Swiaztecka, D.; Wróblewska, B. Diet shapes the ability of human intestinal micro-
biota to degrade phytate—In vitro studies. J. Appl. Microbiol. 2013, 115, 247–259. https://doi.org/10.1111/jam.12204.
93. David, L.A.; Maurice, C.F.; Carmody, R.N.; Gootenberg, D.B.; Button, J.E.; Wolfe, B.E.; Ling, A.V.; Devlin, A.S.; Varma, Y.;
Fischbach, M.A.; et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014, 505, 559–563.
94. Merra, G.; Noce, A.; Marrone, G.; Cintoni, M.; Tarsitano, M.G.; Capacci, A.; de Lorenzo, A. Influence of Mediterranean Diet
on Human Gut Microbiota. Nutrients 2020, 13, 7. https://doi.org/10.3390/nu13010007.
95. Riaz Rajoka, M.S.; Thirumdas, R.; Mehwish, H.M.; Umair, M.; Khurshid, M.; Hayat, H.F.; Phimolsiripol, Y.; Pallarés, N.;
Martí-Quijal, F.J.; Barba, F.J. Role of Food Antioxidants in Modulating Gut Microbial Communities: Novel Understandings
in Intestinal Oxidative Stress Damage and Their Impact on Host Health. Antioxidants 2021, 10, 1563.
96. Esworthy, R.S.; Smith, D.D.; Chu, F.-F. A Strong Impact of Genetic Background on Gut Microflora in Mice. Int. J. Inflamm.
2010, 2010, 986046. https://doi.org/10.4061/2010/986046.
97. Nøhr, M.K.; Egerod, K.L.; Christiansen, S.H.; Gille, A.; Offermanns, S.; Schwartz, T.W.; Møller, M. Expression of the short
chain fatty acid receptor GPR41/FFAR3 in autonomic and somatic sensory ganglia. Neuroscience 2015, 290, 126–137.
98. Poul, E.L.; Loison, C.; Struyf, S.; Springael, J.-Y.; Lannoy, V.; Decobecq, M.-E.; Brezillon, S.; Dupriez, V.; Vassart, G.; Damme,
J.V.; et al. Functional Characterization of Human Receptors for Short Chain Fatty Acids and Their Role in Polymorphonu-
clear Cell Activation. J. Biol. Chem. 2003, 278, 25481–25489. https://doi.org/10.1074/jbc.M301403200.
99. Hudson, B.D.; Murdoch, H.; Milligan, G. Minireview: The Effects of Species Ortholog and SNP Variation on Receptors for
Free Fatty Acids. Mol. Endocrinol. 2013, 27, 1177–1187. https://doi.org/10.1210/me.2013-1085.
100. Van der Hee, B.; Wells, J.M. Microbial Regulation of Host Physiology by Short-chain Fatty Acids. Trends Microbiol. 2021, 29,
101. Medicine, P.C.F.R. Dietary Fibre Recommendations. Available online: https://www.pcrm.org/good-nutrition/nutrition-in-
formation/fiber (accessed on 22 October 2021).
102. EFSA Panel on Dietetic Products, Nutrition and Allergies. Scientific Opinion on Dietary Reference Values for carbohydrates
and dietary fibre. EFSA J. 2010, 8, 1462. https://doi.org/10.2903/j.efsa.2010.1462.
103. Alkerwi, A.A.; Sauvageot, N.; Donneau, A.-F.; Lair, M.-L.; Couffignal, S.; Beissel, J.; Delagardelle, C.; Wagener, Y.; Albert,
A.; Guillaume, M. First nationwide survey on cardiovascular risk factors in Grand-Duchy of Luxembourg (ORISCAV-LUX).
BMC Public Health 2010, 10, 468. https://doi.org/10.1186/1471-2458-10-468.
104. Alkerwi, A.A.; Donneau, A.-F.; Sauvageot, N.; Lair, M.-L.; Albert, A.; Guillaume, M. Dietary, behavioural and socio-eco-
nomic determinants of the metabolic syndrome among adults in Luxembourg: Findings from the ORISCAV-LUX study.
Public Health Nutr. 2012, 15, 849–859. https://doi.org/10.1017/S1368980011002278.
105. Alkerwi, A.A.; Pastore, J.; Sauvageot, N.; Coroller, G.L.; Bocquet, V.; d’Incau, M.; Aguayo, G.; Appenzeller, B.; Bejko, D.;
Bohn, T.; et al. Challenges and benefits of integrating diverse sampling strategies in the observation of cardiovascular risk
factors (ORISCAV-LUX 2) study. BMC Med. Res. Metholody 2019, 19, 27. https://doi.org/10.1186/s12874-019-0669-0.
Nutrients 2022, 14, 5361 41 of 55
106. Vangay, P.; Johnson, A.J.; Ward, T.L.; Al-Ghalith, G.A.; Shields-Cutler, R.R.; Hillmann, B.M.; Lucas, S.K.; Beura, L.K.;
Thompson, E.A.; Till, L.M.; et al. U.S. immigration westernizes the human gut microbiome. Cell 2018, 175, 962–972.
107. Reynolds, A.; Mann, J.; Cummings, J.; Winter, N.; Mete, E.; Te Morenga, L. Carbohydrate quality and human health: A series
of systematic reviews and meta-analyses. Lancet 2019, 393, 434–445. https://doi.org/10.1016/s0140-6736(18)31809-9.
108. Schättin, A.; Gennaro, F.; Egloff, M.; Vogt, S.; de Bruin, E.D. Physical Activity, Nutrition, Cognition, Neurophysiology, and
Short-Time Synaptic Plasticity in Healthy Older Adults: A Cross-Sectional Study. Front. Aging Neurosci. 2018, 10, 242.
109. Chesnais, J.-C. The Inversion of the Age Pyramid and the Future Population Decline in France: Implications and Policy Responses;
United Nations: New York, NY, USA, 2000.
110. Biagi, E.; Candela, M.; Turroni, S.; Garagnani, P.; Franceschi, C.; Brigidi, P. Ageing and gut microbes: Perspectives for health
maintenance and longevity Pharmacol. Res. 2013, 1, 11–20. https://doi.org/10.1016/j.phrs.2012.10.005.
111. Franceschi, C.; Capri, M.; Monti, D.; Giunta, S.; Olivieri, F.; Sevini, F.; Panourgia, M.P.; Invidia, L.; Celani, L.; Scurti, M.; et
al. Inflammaging and anti-inflammaging: A systemic perspective on aging and longevity emerged from studies in humans.
Mech. Ageing Dev. 2006, 128, 92–105. https://doi.org/10.1016/j.mad.2006.11.016.
112. Franceschi, C.; Olivieri, F.; Marchegiani, F.; Cardelli, M.; Cavallone, L.; Capri, M.; Salvioli, S.; Valensin, S.; Benedictis, G.D.;
Iorio, A.D.; et al. Genes involved in immune response/inflammation, IGF1/insulin pathway and response to oxidative stress
play a major role in the genetics of human longevity: The lesson of centenarians. Mech. Ageing Dev. 2005, 126, 351–361.
113. Dinan, T.G.; Cryan, J.F. Gut instincts: Microbiota as a key regulator of brain development, ageing and neurodegeneration.
J. Physiol. 2016, 595, 489–494. https://doi.org/10.1113/JP273106.
114. Biagi, E.; Nylund, L.; Candela, M.; Ostan, R.; Bucci, L.; Pini, E.; Nikkïla, J.; Monti, D.; Satokari, R.; Franceschi, C.; et al.
Through Ageing, and Beyond: Gut Microbiota and Inflammatory Status in Seniors and Centenarians. PLoS ONE 2010, 5,
115. Biagi, E.; Candela, M.; Fairweather-Tait, S.; Franceschi, C.; Brigidi, P. Ageing of the human metaorganism: The microbial
counterpart. Age 2012, 34, 247–267. https://doi.org/10.1007/s11357-011-9217-5.
116. Tiihonen, K.; Ouwehand, A.C.; Rautonen, N. Human intestinal microbiota and healthy ageing. Ageing Res. Rev. 2010, 2, 107–
117. Duncan, S.H.; Flint, H.J. Probiotics and prebiotics and health in ageing populations. Maturitas 2013, 1, 44–50.
118. Offringa, L.C.; Hartle, J.C.; Rigdon, J.; Gardner, C.D. Changes in Quantity and Sources of Dietary Fiber from Adopting
Healthy Low-Fat vs. Healthy Low-Carb Weight Loss Diets: Secondary Analysis of DIETFITS Weight Loss Diet Study. Nu-
trients 2021, 13, 3625. https://doi.org/10.3390/nu13103625.
119. Mccleary, B.V. Total Dietary Fiber (CODEX Definition) in Foods and Food Ingredients by a Rapid Enzymatic-Gravimetric
Method and Liquid Chromatography: Collaborative Study, First Action 2017.16. J. AOAC Int. 2019, 102, 196–207.
120. Codex Alimentarius Commission. Report of the 30th Session of the Codex Committee on Nutrition and Foods for Special Dietary
Uses; No. ALINORM 02/32/26; FAO: Rome, Italy; WHO: Geneva, Switzerland, 2009.
121. Kato, N.; Iwami, K. Resistant Protein; Its Existence and Function Beneficial to Health. J. Nutr. Sci. Vitaminol. 2002, 48, 1–5.
122. Wang, Z.; Liang, M.; Li, H.; Cai, L.; Yang, L. Rice Protein Exerts Anti-Inflammatory Effect in Growing and Adult Rats via
Suppressing NF-κB Pathway. Int. J. Mol. Sci. 2019, 20, 6164. https://doi.org/10.3390/ijms20246164.
123. Yang, L.; Chen, J.; Xu, T.; Qiu, W.; Zhang, Y.; Zhang, L.; Xu, F.; Liu, H. Rice Protein Extracted by Different Methods Affects
Cholesterol Metabolism in Rats Due to Its Lower Digestibility. Int. J. Mol. Sci. 2011, 12, 7594–7608.
124. Agence Française de Sécurité Sanitaire des Aliments (AFFSA). Dietary Fibre: Definitions, Analysis and Nutrition Claims; Agence
Française de Sécurité Sanitaire des Aliments (AFFSA): Paris, France, 2002.
125. Martel, J.; Ojcius, D.M.; Ko, Y.-F.; Young, J.D. Phytochemicals as Prebiotics and Biological Stress Inducers. Trends Biochem.
Sci. 2020, 45, 462–471. https://doi.org/10.1016/j.tibs.2020.02.008.
126. Fatima, A.; Khan, M.S.; Ahmad, M.W. Therapeutic Potential of Equol: A Comprehensive Review. Curr. Pharm. Des. 2020, 26,
127. Jones, J.M. CODEX-aligned dietary fiber definitions help to bridge the ‘fiber gap’. Nutr. J. 2014, 13, 34.
128. Williams, B.A.; Mikkelsen, D.; Flanagan, B.M.; Gidley, M.J. “Dietary fibre”: Moving beyond the “soluble/insoluble” classifi-
cation for monogastric nutrition, with an emphasis on humans and pigs. J. Anim. Sci. Biotechnol. 2019, 10, 45.
129. Macfarlane, G.T.; Macfarlane, S. Bacteria, Colonic Fermentation, and Gastrointestinal Health. J. AOAC Int. 2012, 95, 50–60.
130. Cummings, J.H.; Gibson, G.R.; Macfarlane, G.T. Quantitative estimates of fermentation in the hind gut of man. Acta Vet.
Scand. Suppl. 1989, 86, 76–82.
Nutrients 2022, 14, 5361 42 of 55
131. Rios-Covian, D.; González, S.; Nogacka, A.M.; Arboleya, S.; Salazar, N.; Gueimonde, M.; de Los Reyes-Gavilán, C.G. An
Overview on Fecal Branched Short-Chain Fatty Acids Along Human Life and as Related with Body Mass Index: Associated
Dietary and Anthropometric Factors. Front. Microbiol. 2020, 11, 973. https://doi.org/10.3389/fmicb.2020.00973.
132. McDonald, J.A.K.; Mullish, B.H.; Pechlivanis, A.; Liu, Z.; Brignardello, J.; Kao, D.; Holmes, E.; Li, J.V.; Clarke, T.B.; Thursz,
M.R.; et al. Inhibiting Growth of Clostridioides difficile by Restoring Valerate, Produced by the Intestinal Microbiota. Gas-
troenterology 2018, 155, 1495–1507.e1415. https://doi.org/10.1053/j.gastro.2018.07.014.
133. François, I.E.J.A.; Lescroart, O.; Veraverbeke, W.S.; Marzorati, M.; Possemiers, S.; Hamer, H.; Windey, K.; Welling, G.W.;
Delcour, J.A.; Courtin, C.M.; et al. Effects of Wheat Bran Extract Containing Arabinoxylan Oligosaccharides on Gastrointes-
tinal Parameters in Healthy Preadolescent Children. J. Pediatric Gastroenterol. Nutr. 2014, 58, 647–653.
134. Breit, S.; Kupferberg, A.; Rogler, G.; Hasler, G. Vagus Nerve as Modulator of the Gut-Brain Axis in Psychiatric and Inflam-
matory Disorders. Front. Psychiatry 2018, 9, 44. https://doi.org/10.3389/fpsyt.2018.00044.
135. Silva, Y.P.; Bernardi, A.; Frozza, R.L. The Role of Short-Chain Fatty Acids from Gut Microbiota in Gut-Brain Communica-
tion. Front. Endocrinol. 2020, 11, 25. https://doi.org/10.3389/fendo.2020.00025.
136. Granado-Serrano, A.B.; Martín-Garí, M.; Sánchez, V.; Riart Solans, M.; Berdún, R.; Ludwig, I.A.; Rubió, L.; Vilaprinyó, E.;
Portero-Otín, M.; Serrano, J.C.E. Faecal bacterial and short-chain fatty acids signature in hypercholesterolemia. Sci. Rep.
2019, 9, 1772. https://doi.org/10.1038/s41598-019-38874-3.
137. Nataraj, B.H.; Ali, S.A.; Behare, P.V.; Yadav, H. Postbiotics-parabiotics: The new horizons in microbial biotherapy and func-
tional foods. Microb. Cell Factories 2020, 19, 168. https://doi.org/10.1186/s12934-020-01426-w.
138. Dasarathy, S.; Mookerjee, R.P.; Rackayova, V.; Rangroo Thrane, V.; Vairappan, B.; Ott, P.; Rose, C.F. Ammonia toxicity:
From head to toe? Metab. Brain Dis. 2017, 32, 529–538. https://doi.org/10.1007/s11011-016-9938-3.
139. Ding, L.; Huang, Z.; Lu, Y.; Liang, L.; Li, N.; Xu, Z.; Zhang, J.; Shi, H.; Hong, M. Toxic effects of ammonia on intestinal health
and microbiota in red-eared slider (Trachemys scripta elegans). Chemosphere 2021, 280, 130630. https://doi.org/10.1016/j.chem-
140. Di Masi, A.; Ascenzi, P. H2S: A “double face” molecule in health and disease. BioFactors 2013, 39, 186–196.
141. Ishizaka, S.; Kikuchi, E.; Tsujii, T. Effects of acetate on human immune system. Immunopharmacol. Immunotoxicol. 1993, 15,
142. Todesco, T.; Rao, A.V.; Bosello, O.; Jenkins, D.J. Propionate lowers blood glucose and alters lipid metabolism in healthy
subjects. Am. J. Clin. Nutr. 1991, 54, 860–865. https://doi.org/10.1093/ajcn/54.5.860.
143. Hamer, H.M.; Jonkers, D.M.; Bast, A.; Vanhoutvin, S.A.; Fischer, M.A.; Kodde, A.; Troost, F.J.; Venema, K.; Brummer, R.J.
Butyrate modulates oxidative stress in the colonic mucosa of healthy humans. Clin. Nutr. 2009, 28, 88–93.
144. Ge, H.; Li, X.; Weiszmann, J.; Wang, P.; Baribault, H.; Chen, J.L.; Tian, H.; Li, Y. Activation of G protein-coupled receptor 43
in adipocytes leads to inhibition of lipolysis and suppression of plasma free fatty acids. Endocrinology 2008, 149, 4519–4526.
145. Hong, Y.-H.; Nishimura, Y.; Hishikawa, D.; Tsuzuki, H.; Miyahara, H.; Gotoh, C.; Choi, K.-C.; Feng, D.D.; Chen, C.; Lee, H.-
G.; et al. Acetate and Propionate Short Chain Fatty Acids Stimulate Adipogenesis via GPCR43. Endocrinology 2005, 146,
146. Anil, M.H.; Forbes, J.M. Feeding in sheep during intraportal infusions of short-chain fatty acids and the effect of liver de-
nervation. J. Physiol. 1980, 298, 407–414. https://doi.org/10.1113/jphysiol.1980.sp013090.
147. Thacker, P.A.; Bell, J.M.; Classen, H.L.; Campbell, G.L.; Rossnagel, B.G. The nutritive value of hulless barley for swine. Anim.
Feed Sci. Technol. 1988, 19, 191–196. https://doi.org/10.1016/0377-8401(88)90067-3.
148. Illman, R.J.; Topping, D.L.; McLntosh, G.H.; Trimble, R.P.; Storer, G.B.; Taylor, M.N.; Cheng, B.Q. Hypocholesterolaemic
Effects of Dietary Propionate: Studies in Whole Animals and Perfused Rat Liver. Ann. Nutr. Metab. 1988, 32, 97–107.
149. MacFabe, D.F.; Cain, D.P.; Rodriguez-Capote, K.; Franklin, A.E.; Hoffman, J.E.; Boon, F.; Taylor, A.R.; Kavaliers, M.; Ossen-
kopp, K.P. Neurobiological effects of intraventricular propionic acid in rats: Possible role of short chain fatty acids on the
pathogenesis and characteristics of autism spectrum disorders. Behav. Brain Res. 2007, 176, 149–169.
150. Shultz, S.R.; MacFabe, D.F.; Ossenkopp, K.P.; Scratch, S.; Whelan, J.; Taylor, R.; Cain, D.P. Intracerebroventricular injection
of propionic acid, an enteric bacterial metabolic end-product, impairs social behavior in the rat: Implications for an animal
model of autism. Neuropharmacology 2008, 54, 901–911. https://doi.org/10.1016/j.neuropharm.2008.01.013.
151. Zhou, J.; Hegsted, M.; McCutcheon, K.L.; Keenan, M.J.; Xi, X.; Raggio, A.M.; Martin, R.J. Peptide YY and Proglucagon mRNA
Expression Patterns and Regulation in the Gut. Obesity 2006, 14, 683–689. https://doi.org/10.1038/oby.2006.77.
152. Gao, Z.; Yin, J.; Zhang, J.; Ward, R.E.; Martin, R.J.; Lefevre, M.; Cefalu, W.T.; Ye, J. Butyrate Improves Insulin Sensitivity and
Increases Energy Expenditure in Mice. Diabetes 2009, 58, 1509–1517. https://doi.org/10.2337/db08-1637.
153. Xiong, Y.; Miyamoto, N.; Shibata, K.; Valasek, M.A.; Motoike, T.; Kedzierski, R.M.; Yanagisawa, M. Short-chain fatty acids
stimulate leptin production in adipocytes through the G protein-coupled receptor GPR41. Proc. Natl. Acad. Sci. USA 2004,
101, 1045–1050. https://doi.org/10.1073/pnas.2637002100.
Nutrients 2022, 14, 5361 43 of 55
154. Yonekura, S.; Senoo, T.; Kobayashi, Y.; Yonezawa, T.; Katoh, K.; Obara, Y. Effects of acetate and butyrate on the expression
of leptin and short-form leptin receptor in bovine and rat anterior pituitary cells. Gen. Comp. Endocrinol. 2003, 133, 165–172.
155. Tazoe, H.; Otomo, Y.; Kaji, I.; Tanaka, R.; Karaki, S.; Kuwahara, A. Roles of short-chain fatty acids receptors, GPR41 and
GPR43 on colonic functions. J. Physiol. Pharmacol. 2008, 59, 251–262.
156. Tedelind, S.; Westberg, F.; Kjerrulf, M.; Vidal, A. Anti-inflammatory properties of the short-chain fatty acids acetate and
propionate: A study with relevance to inflammatory bowel disease. World J. Gastroenterol. 2007, 13, 2826–2832.
157. Zapolska-Downar, D.; Naruszewicz, M. Propionate reduces the cytokine-induced VCAM-1 and ICAM-1 expression by in-
hibiting nuclear factor-kappa B (NF-kappaB) activation. J. Physiol. Pharmacol. 2009, 60, 123–131.
158. Al-Lahham, S.H.; Roelofsen, H.; Priebe, M.; Weening, D.; Dijkstra, M.; Hoek, A.; Rezaee, F.; Venema, K.; Vonk, R.J. Regula-
tion of adipokine production in human adipose tissue by propionic acid. Eur. J. Clin. Investig. 2010, 40, 401–407.
159. Curi, R.; Bond, J.A.; Calder, P.C.; Newsholme, E.A. Propionate regulates lymphocyte proliferation and metabolism. Gen.
Pharmacol. 1993, 24, 591–597. https://doi.org/10.1016/0306-3623(93)90216-k.
160. Wright, R.S.; Anderson, J.W.; Bridges, S.R. Propionate Inhibits Hepatocyte Lipid Synthesis. Proc. Soc. Exp. Biol. Med. 1990,
195, 26–29. https://doi.org/10.3181/00379727-195-43113.
161. Carlson, J.; Esparza, J.; Swan, J.; Taussig, D.; Combs, J.; Slavin, J. In vitro analysis of partially hydrolyzed guar gum fermen-
tation differences between six individuals. Food Funct. 2016, 7, 1833–1838. https://doi.org/10.1039/c5fo01232e.
162. Potter, T.; Vieira, R.; de Roos, B. Perspective: Application of N-of-1 Methods in Personalized Nutrition Research. Adv. Nutr.
2021, 12, 579–589. https://doi.org/10.1093/advances/nmaa173.
163. Jakobsen, J.; Melse-Boonstra, A.; Rychlik, M. Challenges to Quantify Total Vitamin Activity: How to Combine the Contri-
bution of Diverse Vitamers? Curr. Dev. Nutr. 2019, 3, nzz086. https://doi.org/10.1093/cdn/nzz086.
164. Yurkovich, J.T.; Tian, Q.; Price, N.D.; Hood, L. A systems approach to clinical oncology uses deep phenotyping to deliver
personalized care. Nature 2020, 17, 183–194. https://doi.org/10.1038/s41571-019-0273-6.
165. Subramanian, M.; Wojtusciszyn, A.; Favre, L.; Boughorbel, S.; Shan, J.; Letaief, K.B.; Pitteloud, N.; Chouchane, L. Precision
medicine in the era of artificial intelligence: Implications in chronic disease management. J. Transl. Med. 2020, 18, 472.
166. Linstow, C.U.V.; Gan-Or, Z.; Brundin, P. Precision medicine in Parkinson’s disease patients with LRRK2 and GBA risk
variants—Let’s get even more personal. Transl. Neurodegener. 2020, 9, 39. https://doi.org/10.1186/s40035-020-00218-x.
167. Kumar, M.; Garand, M.; Khodor, S.A. Integrating omics for a better understanding of Inflammatory Bowel Disease: A step
towards personalized medicine. J. Transl. Med. 2019, 17, 419. https://doi.org/10.1186/s12967-019-02174-1.
168. Arumugam, M.; Raes, J.; Pelletier, E.; le Paslier, D.; Yamada, T.; Mende, D.R.; Fernandes, G.R.; Tap, J.; Bruls, T.; Batto, J.-M.;
et al. Enterotypes of the human gut microbiome. Nature 2011, 473, 174–180. https://doi.org/10.1038/nature09944.
169. Christensen, L.; Roager, H.M.; Astrup, A.; Hjorth, M.F. Microbial enterotypes in personalized nutrition and obesity man-
agement. Am. J. Clin. Nutr. 2018, 108, 645–651. https://doi.org/10.1093/ajcn/nqy175.
170. Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.; Avnit-Sagi, T.; Lotan-
Pompan, M.; et al. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2015, 163, 1079–1094.
171. Wong, J.M.W.; de Souza, R.; Kendall, C.W.C.; Emam, A.; Jenkins, D.J.A. Colonic Health: Fermentation and Short Chain Fatty
Acids. J. Clin. Gastroenterol. 2006, 40, 235–243.
172. Mitsuhashi, S.; Ballou, S.; Jiang, Z.; Hirsch, W.; Nee, J.; Iturrino, J.; Cheng, V.; Lembo, A. Characterizing Normal Bowel
Frequency and Consistency in a Representative Sample of Adults in the United States (NHANES). Am. J. Gastroenterol. 2018,
1, 115–123. https://doi.org/10.1038/ajg.2017.213.
173. Sanjoaquin, M.A.; Appleby, P.N.; Spencer, E.A.; Key, T.J. Nutrition and lifestyle in relation to bowel movement frequency:
A cross-sectional study of 20 630 men and women in EPIC–Oxford. Public Health Nutr. 2003, 7, 77–83.
174. Chen, Y.; Zhou, J.; Wang, L. Role and Mechanism of Gut Microbiota in Human Disease. Front. Cell. Infect. Microbiol. 2021,
11, 625913. https://doi.org/10.3389/fcimb.2021.625913.
175. Christodoulides, S.; Dimidi, E.; Fragkos, K.C.; Farmer, A.D.; Whelan, K.; Scott, S.M. Systematic review with meta-analysis:
Effect of fibre supplementation on chronic idiopathic constipation in adults. Aliment. Pharmacol. Ther. 2016, 44, 103–116.
176. Li, J.; Jia, H.; Cai, X.; Zhong, H.; Feng, Q.; Sunagawa, S.; Arumugam, M.; Kultima, J.R.; Prifti, E.; Nielsen, T.; et al. An inte-
grated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 2014, 32, 834–841.
177. Spor, A.; Koren, O.; Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev.
Microbiol. 2011, 9, 279–290. https://doi.org/10.1038/nrmicro2540.
178. Benson, A.K.; Kelly, S.A.; Legge, R.; Ma, F.; Low, S.J.; Kim, J.; Zhang, M.; Oh, P.L.; Nehrenberg, D.; Hua, K.; et al. Individu-
ality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.
Proc. Natl. Acad. Sci. USA 2010, 107, 18933–18938. https://doi.org/10.1073/pnas.1007028107.
Nutrients 2022, 14, 5361 44 of 55
179. Rothschild, D.; Weissbrod, O.; Barkan, E.; Kurilshikov, A.; Korem, T.; Zeevi, D.; Costea, P.I.; Godneva, A.; Kalka, I.N.; Bar,
N.; et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 2018, 555, 210–215.
180. Iebba, V.; Totino, V.; Gagliardi, A.; Santangelo, F.; Cacciotti, F.; Trancassini, M.; Mancini, C.; Cicerone, C.; Corazziari, E.;
Pantanella, F.; et al. Eubiosis and dysbiosis: The two sides of the microbiota. New Microbiol. 2016, 39, 1–12.
181. Goodrich, J.K.; Davenport, E.R.; Waters, J.L.; Clark, A.G.; Ley, R.E. Cross-species comparisons of host genetic associations
with the microbiome. Science 2016, 352, 532–535. https://doi.org/10.1126/science.aad9379.
182. So, D.; Whelan, K.; Rossi, M.; Morrison, M.; Holtmann, G.; Kelly, J.T.; Shanahan, E.R.; Staudacher, H.M.; Campbell, K.L.
Dietary fiber intervention on gut microbiota composition in healthy adults: A systematic review and meta-analysis. Am. J.
Clin. Nutr. 2018, 107, 965–983. https://doi.org/10.1093/ajcn/nqy041.
183. Macfarlane, S.; Macfarlane, G.T. Regulation of short-chain fatty acid production. Proc. Nutr. Soc. 2003, 62, 67–72.
184. Costea, P.I.; Hildebrand, F.; Arumugam, M.; Bäckhed, F.; Blaser, M.J.; Bushman, F.D.; Willem, M.S.; Fraser, C.M.; Hattori,
M.; Huttenhower, C.; et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 2018, 3,
185. Le Chatelier, E.; Nielsen, T.; Qin, J.; Prifti, E.; Hildebrand, F.; Falony, G.; Almeida, M.; Arumugam, M.; Batto, J.M.; Kennedy,
S.; et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013, 500, 541–546.
186. Amat, S.; Lantz, H.; Munyaka, P.M.; Willing, B.P. Prevotella in Pigs: The Positive and Negative Associations with Production
and Health. Microorganisms 2020, 8, 1584. https://doi.org/10.3390/microorganisms8101584.
187. Zheng, L.; Kelly, C.J.; Battista, K.D.; Schaefer, R.; Lanis, J.M.; Alexeev, E.E.; Wang, R.X.; Onyiah, J.C.; Kominsky, D.J.; Colgan,
S.P. Microbial-Derived Butyrate Promotes Epithelial Barrier Function through IL-10 Receptor–Dependent Repression of
Claudin-2. J. Immunol. 2017, 199, 2976–2984. https://doi.org/10.4049/jimmunol.1700105.
188. Duvallet, C.; Gibbons, S.M.; Gurry, T.; Irizarry, R.A.; Alm, E.J. Meta-analysis of gut microbiome studies identifies disease-
specific and shared responses. Nat. Commun. 2017, 8, 1784. https://doi.org/10.1038/s41467-017-01973-8.
189. Gacesa, R.; Kurilshikov, A.; Vich Vila, A.; Sinha, T.; Klaassen, M.A.Y.; Bolte, L.A.; Andreu-Sánchez, S.; Chen, L.; Collij, V.;
Hu, S.; et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 2022, 604, 732–739.
190. Derrien, M.; Belzer, C.; de Vos, W.M. Akkermansia muciniphila and its role in regulating host functions. Microb. Pathog. 2017,
106, 171–181. https://doi.org/10.1016/j.micpath.2016.02.005.
191. Derrien, M.; Vaughan, E.E.; Plugge, C.M.; de Vos, W.M. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal
mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 2004, 54, 1469–1476. https://doi.org/10.1099/ijs.0.02873-0.
192. Kashyap, P.C.; Marcobal, A.; Ursell, L.K.; Smits, S.A.; Sonnenburg, E.D.; Costello, E.K.; Higginbottom, S.K.; Domino, S.E.;
Holmes, S.P.; Relman, D.A.; et al. Genetically dictated change in host mucus carbohydrate landscape exerts a diet-dependent
effect on the gut microbiota. Proc. Natl. Acad. Sci. USA 2013, 110, 17059–17064. https://doi.org/10.1073/pnas.1306070110.
193. Wang, F.; Yu, T.; Huang, G.; Cai, D.; Liang, X.; Su, H.; Zhu, Z.; Li, D.; Yang, Y.; Shen, P.; et al. Gut Microbiota Community
and Its Assembly Associated with Age and Diet in Chinese Centenarians. J. Microbiol. Biotechnol. 2015, 25, 1195–1204.
194. Bodogai, M.; O’Connell, J.; Kim, K.; Kim, Y.; Moritoh, K.; Chen, C.; Gusev, F.; Vaughan, K.; Shulzhenko, N.; Mattison, J.A.;
et al. Commensal bacteria contribute to insulin resistance in aging by activating innate B1a cells. Sci. Transl. Med. 2018, 10,
195. Parkin, K.; Christophersen, C.T.; Verhasselt, V.; Cooper, M.N.; Martino, D. Risk Factors for Gut Dysbiosis in Early Life.
Microorganisms 2021, 9, 2066. https://doi.org/10.3390/microorganisms9102066.
196. Vallianou, N.; Stratigou, T.; Christodoulatos, G.S.; Dalamaga, M. Understanding the Role of the Gut Microbiome and Mi-
crobial Metabolites in Obesity and Obesity-Associated Metabolic Disorders: Current Evidence and Perspectives. Curr. Obes.
Rep. 2019, 8, 317–332. https://doi.org/10.1007/s13679-019-00352-2.
197. Hiippala, K.; Jouhten, H.; Ronkainen, A.; Hartikainen, A.; Kainulainen, V.; Jalanka, J.; Satokari, R. The Potential of Gut
Commensals in Reinforcing Intestinal Barrier Function and Alleviating Inflammation. Nutrients 2018, 10, 988.
198. Barash, N.R.; Maloney, J.G.; Singer, S.M.; Dawson, S.C. Giardia Alters Commensal Microbial Diversity throughout the Mu-
rine Gut. Infect. Immun. 2017, 85, e00948-16. https://doi.org/10.1128/iai.00948-16.
199. Brunengraber, L.N.; Jayes, F.L.; Leppert, P.C. Injectable Clostridium histolyticum Collagenase as a Potential Treatment for
Uterine Fibroids. Reprod. Sci. 2014, 21, 1452–1459. https://doi.org/10.1177/1933719114553449.
200. Stevens, D.L.; Aldape, M.J.; Bryant, A.E. Life-threatening clostridial infections. Anaerobe 2012, 18, 254–259.
201. Ze, X.; Duncan, S.H.; Louis, P.; Flint, H.J. Ruminococcus bromii is a keystone species for the degradation of resistant starch in
the human colon. ISME J. 2012, 6, 1535–1543. https://doi.org/10.1038/ismej.2012.4.
202. Salonen, A.; Lahti, L.; Salojärvi, J.; Holtrop, G.; Korpela, K.; Duncan, S.H.; Date, P.; Farquharson, F.; Johnstone, A.M.; Lobley,
G.E.; et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese
men. ISME J. 2014, 8, 2218–2230. https://doi.org/10.1038/ismej.2014.63.
Nutrients 2022, 14, 5361 45 of 55
203. Lee, J.; D’Aigle, J.; Atadja, L.; Quaicoe, V.; Honarpisheh, P.; Ganesh, B.P.; Hassan, A.; Graf, J.; Petrosino, J.; Putluri, N.; et al.
Gut Microbiota–Derived Short-Chain Fatty Acids Promote Poststroke Recovery in Aged Mice. Circ. Res. 2020, 127, 453–465.
204. Chassard, C.; Bernalier-Donadille, A. H2 and acetate transfers during xylan fermentation between a butyrate-producing
xylanolytic species and hydrogenotrophic microorganisms from the human gut. FEMS Microbiol. Lett. 2006, 254, 116–122.
205. Wrzosek, L.; Miquel, S.; Noordine, M.-L.; Bouet, S.; Chevalier-Curt, M.; Robert, V.; Philippe, C.; Bridonneau, C.; Cherbuy,
C.; Robbe-Masselot, C.; et al. Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii influence the production of mucus
glycans and the development of goblet cells in the colonic epithelium of a gnotobiotic model rodent. BMC Biol. 2013, 11, 61.
206. Quévrain, E.; Maubert, M.A.; Michon, C.; Chain, F.; Marquant, R.; Tailhades, J.; Miquel, S.; Carlier, L.; Bermúdez-Humarán,
L.G.; Pigneur, B.; et al. Identification of an anti-inflammatory protein from Faecalibacterium prausnitzii, a commensal bacte-
rium deficient in Crohn’s disease. Gut 2016, 65, 415–425. https://doi.org/10.1136/gutjnl-2014-307649.
207. Louis, P.; Young, P.; Holtrop, G.; Flint, H.J. Diversity of human colonic butyrate-producing bacteria revealed by analysis of
the butyryl-CoA: Acetate CoA-transferase gene. Environ. Microbiol. 2010, 12, 304–314. https://doi.org/10.1111/j.1462-
208. Cerdó, T.; García-Santos, J.A.; Bermúdez, M.G.; Campoy, C. The Role of Probiotics and Prebiotics in the Prevention and
Treatment of Obesity. Nutrients 2019, 11, 635. https://doi.org/10.3390/nu11030635.
209. Jan, G.; Belzacq, A.S.; Haouzi, D.; Rouault, A.; Métivier, D.; Kroemer, G.; Brenner, C. Propionibacteria induce apoptosis of
colorectal carcinoma cells via short-chain fatty acids acting on mitochondria. Cell Death Differ. 2002, 9, 179–188.
210. Aguirre-Portolés, C.; Fernández, L.; Ramírez de Molina, A. Precision Nutrition for Targeting Lipid Metabolism in Colorectal
Cancer. Nutrients 2017, 9, 1076. https://doi.org/10.3390/nu9101076.
211. Ríos-Covián, D.; Ruas-Madiedo, P.; Margolles, A.; Gueimonde, M.; de los Reyes-Gavilán, C.G.; Salazar, N. Intestinal Short
Chain Fatty Acids and their Link with Diet and Human Health. Front. Microbiol. 2016, 7, 185.
212. Marchandin, H.; Teyssier, C.; Campos, J.; Jean-Pierre, H.; Roger, F.; Gay, B.; Carlier, J.P.; Jumas-Bilak, E. Negativicoccus suc-
cinicivorans gen. nov., sp. nov., isolated from human clinical samples, emended description of the family Veillonellaceae and
description of Negativicutes classis nov., Selenomonadales ord. nov. and Acidaminococcaceae fam. nov. in the bacterial phylum
Firmicutes. Int. J. Syst. Evol. Microbiol. 2010, 60, 1271–1279. https://doi.org/10.1099/ijs.0.013102-0.
213. Flint, H.J.; Duncan, S.H.; Scott, K.P.; Louis, P. Links between diet, gut microbiota composition and gut metabolism. Proc.
Nutr. Soc. 2015, 74, 13–22. https://doi.org/10.1017/s0029665114001463.
214. Pietropaoli, D.; del Pinto, R.; Ferri, C.; Ortu, E.; Monaco, A. Definition of hypertension-associated oral pathogens in
NHANES. J. Periodontol. 2019, 90, 866–876. https://doi.org/10.1002/jper.19-0046.
215. Duncan, S.H.; Holtrop, G.; Lobley, G.E.; Calder, A.G.; Stewart, C.S.; Flint, H.J. Contribution of acetate to butyrate formation
by human faecal bacteria. Br. J. Nutr. 2004, 91, 915–923. https://doi.org/10.1079/bjn20041150.
216. Flint, H.J.; Duncan, S.H.; Scott, K.P.; Louis, P. Interactions and competition within the microbial community of the human
colon: Links between diet and health. Environ. Microbiol. 2007, 9, 1101–1111. https://doi.org/10.1111/j.1462-2920.2007.01281.x.
217. Goodrich, J.K.; Davenport, E.R.; Beaumont, M.; Jackson, M.A.; Knight, R.; Ober, C.; Spector, T.D.; Bell, J.T.; Clark, A.G.; Ley,
R.E. Genetic Determinants of the Gut Microbiome in UK Twins. Cell Host Microbe 2016, 19, 731–743.
218. Jacobson, A.; Lam, L.; Rajendram, M.; Tamburini, F.; Honeycutt, J.; Pham, T.; van Treuren, W.; Pruss, K.; Stabler, S.R.; Lugo,
K.; et al. A Gut Commensal-Produced Metabolite Mediates Colonization Resistance to Salmonella Infection. Cell Host Mi-
crobe 2018, 24, 296–307.e297. https://doi.org/10.1016/j.chom.2018.07.002.
219. Larsbrink, J.; Rogers, T.E.; Hemsworth, G.R.; McKee, L.S.; Tauzin, A.S.; Spadiut, O.; Klinter, S.; Pudlo, N.A.; Urs, K.; Koropat-
kin, N.M.; et al. A discrete genetic locus confers xyloglucan metabolism in select human gut Bacteroidetes. Nature 2014, 506,
220. Xu, M.; Xu, X.; Li, J.; Li, F. Association between Gut Microbiota and Autism Spectrum Disorder: A Systematic Review and
Meta-Analysis. Front. Psychiatry 2019, 10, 473. https://doi.org/10.3389/fpsyt.2019.00473.
221. Macfabe, D.F. Enteric short-chain fatty acids: Microbial messengers of metabolism, mitochondria, and mind: Implications
in autism spectrum disorders. Microb. Ecol. Health Dis. 2015, 26, 28177. https://doi.org/10.3402/mehd.v26.28177.
222. Iljazovic, A.; Roy, U.; Gálvez, E.J.C.; Lesker, T.R.; Zhao, B.; Gronow, A.; Amend, L.; Will, S.E.; Hofmann, J.D.; Pils, M.C.; et
al. Perturbation of the gut microbiome by Prevotella spp. enhances host susceptibility to mucosal inflammation. Mucosal
Immunol. 2021, 14, 113–124. https://doi.org/10.1038/s41385-020-0296-4.
223. Tett, A.; Huang, K.D.; Asnicar, F.; Fehlner-Peach, H.; Pasolli, E.; Karcher, N.; Armanini, F.; Manghi, P.; Bonham, K.; Zolfo,
M.; et al. The Prevotella copri Complex Comprises Four Distinct Clades Underrepresented in Westernized Populations. Cell
Host Microbe 2019, 26, 666–679.e667. https://doi.org/10.1016/j.chom.2019.08.018.
224. Yang, G.; Hong, E.; Oh, S.; Kim, E. Non-Viable Lactobacillus johnsonii JNU3402 Protects against Diet-Induced Obesity. Foods
2020, 10, 1494. https://doi.org/10.3390/foods9101494.
Nutrients 2022, 14, 5361 46 of 55
225. Fonseca, W.; Lucey, K.; Jang, S.; Fujimura, K.E.; Rasky, A.; Ting, H.; Petersen, J.; Johnson, C.C.; Boushey, H.A.; Zoratti, E.; et
al. Lactobacillus johnsonii Supplementation Attenuates Respiratory Viral Infection via Metabolic Reprogramming and Im-
mune Cell Modulation. Mucosal Immunol. 2017, 10, 1569–1580. https://doi.org/10.1038/mi.2017.13.
226. Goh, Y.J.; Klaenhammer, T.R. Genetic Mechanisms of Prebiotic Oligosaccharide Metabolism in Probiotic Microbes. Annu.
Rev. Food Sci. Technol. 2015, 6, 137–156. https://doi.org/10.1146/annurev-food-022814-015706.
227. Fukuda, S.; Toh, H.; Hase, K.; Oshima, K.; Nakanishi, Y.; Yoshimura, K.; Tobe, T.; Clarke, J.M.; Topping, D.L.; Suzuki, T.; et
al. Bifidobacteria can protect from enteropathogenic infection through production of acetate. Nature 2011, 469, 543–547.
228. Nogacka, A.M.; de los Reyes-Gavilán, C.G.; Arboleya, S.; Ruas-Madiedo, P.; Martínez-Faedo, C.; Suarez, A.; He, F.; Harata,
G.; Endo, A.; Salazar, N.; et al. In vitro Selection of Probiotics for Microbiota Modulation in Normal-Weight and Severely
Obese Individuals: Focus on Gas Production and Interaction with Intestinal Epithelial Cells. Front. Microbiol. 2021, 12,
229. Basseri, R.J.; Basseri, B.; Pimentel, M.; Chong, K.; Youdim, A.; Low, K.; Hwang, L.; Soffer, E.; Chang, C.; Mathur, R. Intestinal
methane production in obese individuals is associated with a higher body mass index. Gastroenterol. Hepatol. 2012, 8, 22–28.
230. Bordenstein, S.R.; Theis, K.R. Host Biology in Light of the Microbiome: Ten Principles of Holobionts and Hologenomes.
PLoS Biol. 2015, 13, e1002226. https://doi.org/10.1371/journal.pbio.1002226.
231. Asnicar, F.; Berry, S.E.; Valdes, A.M.; Nguyen, L.H.; Piccinno, G.; Drew, D.A.; Leeming, E.; Gibson, R.; le Roy, C.; Khatib,
H.A.; et al. Microbiome connections with host metabolism and habitual diet from 1098 deeply phenotyped individuals. Nat.
Med. 2021, 27, 321–332. https://doi.org/10.1038/s41591-020-01183-8.
232. Bataineh, M.T.A.; Dash, N.R.; Elkhazendar, M.; Alnusairat, D.A.M.H.; Darwish, I.M.I.; Al-Hajjaj, M.S.; Hamid, Q. Revealing
oral microbiota composition and functionality associated with heavy cigarette smoking. J. Transl. Med. 2020, 18, 421.
233. Lee, S.H.; Yun, Y.; Kim, S.J.; Lee, E.-J.; Chang, Y.; Ryu, S.; Shin, H.; Kim, H.-L.; Kim, H.-N.; Lee, J.H. Association between
Cigarette Smoking Status and Composition of Gut Microbiota: Population-Based Cross-Sectional Study. J. Clin. Med. 2018,
7, 282. https://doi.org/10.3390/jcm7090282.
234. Telenti, A.; Jiang, X. Treating medical data as a durable asset. Nat. Genet. 2020, 52, 1005–1010. https://doi.org/10.1038/s41588-
235. Wainschtein, P.; Jain, D.; Zheng, Z.; Cupples, L.A.; Shadyab, A.H.; McKnight, B.; Shoemaker, B.M.; Mitchell, B.D.; Psaty,
B.M.; Kooperberg, C.; et al. Recovery of trait heritability from whole genome sequence data. bioRxiv 2019,
236. Billingsley, K.J.; Barbosa, I.A.; Bandrés-Ciga, S.; Quinn, J.P.; Bubb, V.J.; Deshpande, C.; Botia, J.A.; Reynolds, R.H.; Zhang,
D.; Simpson, M.A.; et al. Mitochondria function associated genes contribute to Parkinson’s Disease risk and later age at
onset. NPJ Parkinson's Dis. 2019, 5, 8. https://doi.org/10.1038/s41531-019-0080-x.
237. Natarajan, P.; Young, R.; Stitziel, N.O.; Padmanabhan, S.; Baber, U.; Mehran, R.; Sartori, S.; Fuster, V.; Reilly, D.F.; Butter-
worth, A.; et al. Polygenic Risk Score Identifies Subgroup with Higher Burden of Atherosclerosis and Greater Relative Ben-
efit from Statin Therapy in the Primary Prevention Setting. Circulation 2017, 135, 2091–2101. https://doi.org/10.1161/circula-
238. Levy, M.; Thaiss, C.A.; Elinav, E. Metabolites: Messengers between the microbiota and the immune system. Genes Dev. 2016,
30, 1589–1597. https://doi.org/10.1101/gad.284091.116.
239. Choi, S.W.; Mak, T.S.-H.; O’Reilly, P.F. Tutorial: A guide to performing polygenic risk score analyses. Nat. Protoc. 2020, 15,
240. Blekhman, R.; Goodrich, J.K.; Huang, K.; Sun, Q.; Bukowski, R.; Bell, J.T.; Spector, T.D.; Keinan, A.; Ley, R.E.; Gevers, D.; et
al. Host genetic variation impacts microbiome composition across human body sites. Genome Biol. 2015, 16, 191.
241. Kawamoto, S.; Tran, T.H.; Maruya, M.; Suzuki, K.; Doi, Y.; Tsutsui, Y.; Kato, L.M.; Fagarasan, S. The Inhibitory Receptor
PD-1 Regulates IgA Selection and Bacterial Composition in the Gut. Science 2012, 336, 485–489. https://doi.org/10.1126/sci-
242. Donaldson, G.P.; Ladinsky, M.S.; Yu, K.B.; Sanders, J.G.; Yoo, B.B.; Chou, W.C.; Conner, M.E.; Earl, A.M.; Knight, R.; Bjork-
man, P.J.; et al. Gut microbiota utilize immunoglobulin A for mucosal colonization. Science 2018, 360, 795–800.
243. Nakajima, A.; Vogelzang, A.; Maruya, M.; Miyajima, M.; Murata, M.; Son, A.; Kuwahara, T.; Tsuruyama, T.; Yamada, S.;
Matsuura, M.; et al. IgA regulates the composition and metabolic function of gut microbiota by promoting symbiosis be-
tween bacteria. J. Exp. Med. 2018, 215, 2019–2034. https://doi.org/10.1084/jem.20180427.
244. Takeuchi, T.; Miyauchi, E.; Kanaya, T.; Kato, T.; Nakanishi, Y.; Watanabe, T.; Kitami, T.; Taida, T.; Sasaki, T.; Negishi, H.; et
al. Acetate differentially regulates IgA reactivity to commensal bacteria. Nature 2021, 595, 560–564.
245. Liang, S.C.; Tan, X.-Y.; Luxenberg, D.P.; Karim, R.; Dunussi-Joannopoulos, K.; Collins, M.; Fouser, L.A. Interleukin (IL)-22
and IL-17 are coexpressed by Th17 cells and cooperatively enhance expression of antimicrobial peptides. J. Exp. Med. 2006,
203, 2271–2279. https://doi.org/10.1084/jem.20061308.
Nutrients 2022, 14, 5361 47 of 55
246. Kawai, T.; Adachi, O.; Ogawa, T.; Takeda, K.; Akira, S. Unresponsiveness of MyD88-deficient mice to endotoxin. Immunity
1999, 11, 115–122. https://doi.org/10.1016/s1074-7613(00)80086-2.
247. Petnicki-Ocwieja, T.; Hrncir, T.; Liu, Y.-J.; Biswas, A.; Hudcovic, T.; Tlaskalova-Hogenova, H.; Kobayashi, K.S. Nod2 is re-
quired for the regulation of commensal microbiota in the intestine. Proc. Natl. Acad. Sci. USA 2009, 106, 15813–15818.
248. Knights, D.; Silverberg, M.S.; Weersma, R.K.; Gevers, D.; Dijkstra, G.; Huang, H.; Tyler, A.D.; van Sommeren, S.; Imhann,
F.; Stempak, J.M.; et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 2014,
6, 107. https://doi.org/10.1186/s13073-014-0107-1.
249. Salzman, N.H.; Hung, K.; Haribhai, D.; Chu, H.; Karlsson-Sjöberg, J.; Amir, E.; Teggatz, P.; Barman, M.; Hayward, M.; East-
wood, D.; et al. Enteric defensins are essential regulators of intestinal microbial ecology. Nat. Immunol. 2010, 11, 76–82.
250. Khachatryan, Z.A.; Ktsoyan, Z.A.; Manukyan, G.P.; Kelly, D.; Ghazaryan, K.A.; Aminov, R.I. Predominant Role of Host
Genetics in Controlling the Composition of Gut Microbiota. PLoS ONE 2008, 3, e3064. https://doi.org/10.1371/jour-
251. Poole, A.C.; Goodrich, J.K.; Youngblut, N.D.; Luque, G.G.; Ruaud, A.; Sutter, J.L.; Waters, J.L.; Shi, Q.; El-Hadidi, M.; John-
son, L.M.; et al. Human Salivary Amylase Gene Copy Number Impacts Oral and Gut Microbiomes. Cell Host Microbe 2019,
25, 553–564.e557. https://doi.org/10.1016/j.chom.2019.03.001.
252. Sun, B.B.; Maranville, J.C.; Peters, J.E.; Stacey, D.; Staley, J.R.; Blackshaw, J.; Burgess, S.; Jiang, T.; Paige, E.; Surendran, P.; et
al. Genomic atlas of the human plasma proteome. Nature 2018, 558, 73–79. https://doi.org/10.1038/s41586-018-0175-2.
253. Troelsen, J.T. Adult-type hypolactasia and regulation of lactase expression. Biochim. Biophys. Acta BBA Gen. Subj. 2005, 1723,
254. Jackson, V.E.; Latourelle, J.C.; Wain, L.V.; Smith, A.V.; Grove, M.L.; Bartz, T.M.; Obeidat, M.; Province, M.A.; Gao, W.;
Qaiser, B.; et al. Meta-analysis of exome array data identifies six novel genetic loci for lung function. Wellcome Open Res 2018,
3, 4. https://doi.org/10.12688/wellcomeopenres.12583.3.
255. Wojcik, G.L.; Graff, M.; Nishimura, K.K.; Tao, R.; Haessler, J.; Gignoux, C.R.; Highland, H.M.; Patel, Y.M.; Sorokin, E.P.;
Avery, C.L.; et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019, 570, 514–518.
256. Gaudier, E.; Rival, M.; Buisine, M.P.; Robineau, I.; Hoebler, C. Butyrate enemas upregulate Muc genes expression but de-
crease adherent mucus thickness in mice colon. Physiol. Res. 2009, 58, 111–119. https://doi.org/10.33549/physiolres.931271.
257. Marin, F.; Bonet, C.; Munoz, X.; Garcia, N.; Pardo, M.L.; Ruiz-Liso, J.M.; Alonso, P.; Capella, G.; Sanz-Anquela, J.M.; Gon-
zalez, C.A.; et al. Genetic variation in MUC1, MUC2 and MUC6 genes and evolution of gastric cancer precursor lesions in a
long-term follow-up in a high-risk area in Spain. Carcinogenesis 2012, 33, 1072–1080. https://doi.org/10.1093/carcin/bgs119.
258. Caruso, R.; Lo, B.C.; Núñez, G. Host-microbiota interactions in inflammatory bowel disease. Nat. Rev. Immunol. 2020, 20,
259. Valette, K.; Li, Z.; Bon-Baret, V.; Chignon, A.; Bérubé, J.C.; Eslami, A.; Lamothe, J.; Gaudreault, N.; Joubert, P.; Obeidat, M.;
et al. Prioritization of candidate causal genes for asthma in susceptibility loci derived from UK Biobank. Commun. Biol. 2021,
4, 700. https://doi.org/10.1038/s42003-021-02227-6.
260. Tong, M.; McHardy, I.; Ruegger, P.; Goudarzi, M.; Kashyap, P.C.; Haritunians, T.; Li, X.; Graeber, T.G.; Schwager, E.; Hut-
tenhower, C.; et al. Reprograming of gut microbiome energy metabolism by the FUT2 Crohn’s disease risk polymorphism.
ISME J. 2014, 8, 2193–2206. https://doi.org/10.1038/ismej.2014.64.
261. Kachuri, L.; Jeon, S.; DeWan, A.T.; Metayer, C.; Ma, X.; Witte, J.S.; Chiang, C.W.K.; Wiemels, J.L.; de Smith, A.J. Genetic
determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia. Am. J. Hum. Genet.
2021, 108, 1823–1835. https://doi.org/10.1016/j.ajhg.2021.08.004.
262. Otonkoski, T.; Jiao, H.; Kaminen-Ahola, N.; Tapia-Paez, I.; Ullah, M.S.; Parton, L.E.; Schuit, F.; Quintens, R.; Sipilä, I.; Ma-
yatepek, E.; et al. Physical Exercise–Induced Hypoglycemia Caused by Failed Silencing of Monocarboxylate Transporter 1
in Pancreatic β Cells. Am. J. Hum. Genet. 2007, 81, 467–474. https://doi.org/10.1086/520960.
263. Felsky, D.; Roostaei, T.; Nho, K.; Risacher, S.L.; Bradshaw, E.M.; Petyuk, V.; Schneider, J.A.; Saykin, A.; Bennett, D.A.; de
Jager, P.L. Neuropathological correlates and genetic architecture of microglial activation in elderly human brain. Nat. Com-
mun. 2019, 10, 409. https://doi.org/10.1038/s41467-018-08279-3.
264. Hysi, P.G.; Choquet, H.; Khawaja, A.P.; Wojciechowski, R.; Tedja, M.S.; Yin, J.; Simcoe, M.J.; Patasova, K.; Mahroo, O.A.;
Thai, K.K.; et al. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing
to refractive error and myopia. Nat. Genet. 2020, 52, 401–407. https://doi.org/10.1038/s41588-020-0599-0.
265. Kichaev, G.; Bhatia, G.; Loh, P.R.; Gazal, S.; Burch, K.; Freund, M.K.; Schoech, A.; Pasaniuc, B.; Price, A.L. Leveraging Poly-
genic Functional Enrichment to Improve GWAS Power. Am. J. Hum. Genet. 2019, 104, 65–75.
266. You, D.; Wang, D.; Wu, Y.; Chen, X.; Shao, F.; Wei, Y.; Zhang, R.; Lange, T.; Ma, H.; Xu, H.; et al. Associations of genetic risk,
BMI trajectories, and the risk of non-small cell lung cancer: A population-based cohort study. BMC Med. 2022, 20, 203.
Nutrients 2022, 14, 5361 48 of 55
267. Smith, S.M.; Douaud, G.; Chen, W.; Hanayik, T.; Alfaro-Almagro, F.; Sharp, K.; Elliott, L.T. An expanded set of genome-
wide association studies of brain imaging phenotypes in UK Biobank. Nat. Neurosci. 2021, 24, 737–745.
268. Pulit, S.L.; Stoneman, C.; Morris, A.P.; Wood, A.R.; Glastonbury, C.A.; Tyrrell, J.; Yengo, L.; Ferreira, T.; Marouli, E.; Ji, Y.;
et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ances-
try. Hum. Mol. Genet. 2019, 28, 166–174. https://doi.org/10.1093/hmg/ddy327.
269. Miranda-Lora, A.L.; Cruz, M.; Molina-Díaz, M.; Gutiérrez, J.; Flores-Huerta, S.; Klünder-Klünder, M. Associations of com-
mon variants in the SLC16A11, TCF7L2, and ABCA1 genes with pediatric-onset type 2 diabetes and related glycemic traits
in families: A case-control and case-parent trio study. Pediatric Diabetes 2017, 18, 824–831. https://doi.org/10.1111/pedi.12497.
270. Suhre, K.; Shin, S.-Y.; Petersen, A.-K.; Mohney, R.P.; Meredith, D.; Wägele, B.; Altmaier, E.; Deloukas, P.; Erdmann, J.;
Grundberg, E.; et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 2011, 477, 54–60.
271. Ganapathy, V.; Thangaraju, M.; Gopal, E.; Martin, P.M.; Itagaki, S.; Miyauchi, S.; Prasad, P.D. Sodium-coupled Monocar-
boxylate Transporters in Normal Tissues and in Cancer. AAPS J. 2008, 10, 193–199. https://doi.org/10.1208/s12248-008-9022-
272. Christakoudi, S.; Evangelou, E.; Riboli, E.; Tsilidis, K.K. GWAS of allometric body-shape indices in UK Biobank identifies
loci suggesting associations with morphogenesis, organogenesis, adrenal cell renewal and cancer. Sci. Rep. 2021, 11, 10688.
273. Ahola-Olli, A.V.; Würtz, P.; Havulinna, A.S.; Aalto, K.; Pitkänen, N.; Lehtimäki, T.; Kähönen, M.; Lyytikäinen, L.-P.;
Raitoharju, E.; Seppälä, I.; et al. Genome-wide Association Study Identifies 27 Loci Influencing Concentrations of Circulating
Cytokines and Growth Factors. Am. J. Hum. Genet. 2017, 100, 40–50. https://doi.org/10.1016/j.ajhg.2016.11.007.
274. Chai, J.T.; Digby, J.E.; Choudhury, R.P. GPR109A and Vascular Inflammation. Curr. Atheroscler. Rep. 2013, 15, 325.
275. Richardson, T.G.; Leyden, G.M.; Wang, Q.; Bell, J.A.; Elsworth, B.; Davey Smith, G.; Holmes, M.V. Characterising metabo-
lomic signatures of lipid-modifying therapies through drug target mendelian randomisation. PLoS Biol. 2022, 20, e3001547.
276. Vuckovic, D.; Bao, E.L.; Akbari, P.; Lareau, C.A.; Mousas, A.; Jiang, T.; Chen, M.-H.; Raffield, L.M.; Tardaguila, M.; Huffman,
J.E.; et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell 2020, 182, 1214–1231.e1211.
277. Lührs, H.; Gerke, T.; Müller, J.G.; Melcher, R.; Schauber, J.; Boxberge, F.; Scheppach, W.; Menzel, T. Butyrate inhibits NF-
kappaB activation in lamina propria macrophages of patients with ulcerative colitis. Scand. J. Gastroenterol. 2002, 37, 458–
278. Bonfiglio, F.; Liu, X.; Smillie, C.; Pandit, A.; Kurilshikov, A.; Bacigalupe, R.; Zheng, T.; Nim, H.; Garcia-Etxebarria, K.; Bu-
janda, L.; et al. GWAS of stool frequency provides insights into gastrointestinal motility and irritable bowel syndrome. Cell
Genom. 2021, 1, 100069. https://doi.org/10.1016/j.xgen.2021.100069.
279. Puhl, H.L., III; Won, Y.-J.; Lu, V.B.; Ikeda, S.R. Human GPR42 is a transcribed multisite variant that exhibits copy number
polymorphism and is functional when heterologously expressed. Sci. Rep. 2015, 5, 12880. https://doi.org/10.1038/srep12880.
280. De Faria, A.P.; Ritter, A.M.; Sabbatini, A.R.; Modolo, R.; Moreno, H. Effects of leptin and leptin receptor SNPs on clinical-
and metabolic-related traits in apparent treatment-resistant hypertension. Blood Press. 2017, 26, 74–80.
281. Yaghootkar, H.; Zhang, Y.; Spracklen, C.N.; Karaderi, T.; Huang, L.O.; Bradfield, J.; Schurmann, C.; Fine, R.S.; Preuss, M.H.;
Kutalik, Z.; et al. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Diabetes
2020, 69, 2806–2818. https://doi.org/10.2337/db20-0070.
282. Park, K.S.; Shin, H.D.; Park, B.L.; Cheong, H.S.; Cho, Y.M.; Lee, H.K.; Lee, J.-Y.; Lee, J.-K.; Oh, B.; Kimm, K. Polymorphisms
in the leptin receptor (LEPR)—Putative association with obesity and T2DM. J. Hum. Genet. 2006, 51, 85–91.
283. Jeon, J.-P.; Shim, S.-M.; Nam, H.-Y.; Ryu, G.-M.; Hong, E.-J.; Kim, H.-L.; Han, B.-G. Copy number variation at leptin receptor
gene locus associated with metabolic traits and the risk of type 2 diabetes mellitus. BMC Genom. 2010, 11, 426.
284. Astle, W.J.; Elding, H.; Jiang, T.; Allen, D.; Ruklisa, D.; Mann, A.L.; Mead, D.; Bouman, H.; Riveros-Mckay, F.; Kostadima,
M.A.; et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 2016,
167, 1415–1429.e1419. https://doi.org/10.1016/j.cell.2016.10.042.
285. Davenport, E.R.; Cusanovich, D.A.; Michelini, K.; Barreiro, L.B.; Ober, C.; Gilad, Y. Genome-Wide Association Studies of
the Human Gut Microbiota. PLoS ONE 2015, 10, e0140301. https://doi.org/10.1371/journal.pone.0140301.
286. Park, J.; Kim, M.; Kang, S.G.; Jannasch, A.H.; Cooper, B.; Patterson, J.; Kim, C.H. Short-chain fatty acids induce both effector
and regulatory T cells by suppression of histone deacetylases and regulation of the mTOR–S6K pathway. Mucosal Immunol.
2015, 8, 80–93. https://doi.org/10.1038/mi.2014.44.
287. Tan, J.; McKenzie, C.; Potamitis, M.; Thorburn, A.N.; Mackay, C.R.; Macia, L. The Role of Short-Chain Fatty Acids in Health
and Disease. In Advances in Immunology; Elsevier: Amsterdam, The Netherlands, 2014; Volume 121, pp. 91–112.
Nutrients 2022, 14, 5361 49 of 55
288. Broek, T.J.V.D.; Bakker, G.C.M.; Rubingh, C.M.; Bijlsma, S.; Stroeve, J.H.M.; Ommen, B.V.; Erk, M.J.V.; Wopereis, S. Ranges
of phenotypic flexibility in healthy subjects. Genes Nutr. 2017, 12, 32. https://doi.org/10.1186/s12263-017-0589-8.
289. Stroeve, J.H.M.; Wietmarschen, H.V.; Kremer, B.H.A.; Ommen, B.V.; Wopereis, S. Phenotypic flexibility as a measure of
health: The optimal nutritional stress response test. Genes Nutr. 2015, 10, 12–33. https://doi.org/10.1007/s12263-015-0459-1.
290. Van Ommen, B.; van der Greef, J.; Ordovas, J.M.; Daniel, H. Phenotypic flexibility as key factor in the human nutrition and
health relationship. Genes Nutr. 2014, 9, 423. https://doi.org/10.1007/s12263-014-0423-5.
291. Zhu, Z.; Cao, F.; Li, X. Epigenetic Programming and Fetal Metabolic Programming. Front. Endocrinol. 2019, 10, 00764.
292. Lumey, L.; Stein, A.D.; Kahn, H.S.; van der Pal-De Bruin, K.M.; Blauw, G.; Zybert, P.A.; Susser, E.S. Cohort Profile: The
Dutch Hunger Winter Families Study. Int. J. Epidemiol. 2007, 36, 1196–1204. https://doi.org/10.1093/ije/dym126.
293. Duncan, B.B.; Schmidt, M.I.S.; Pankow, J.S.; Ballantyne, C.M.; Couper, D.; Vigo, A.; Hoogeveen, R.; Folsom, A.R.; Heiss, G.
Low-Grade Systemic Inflammation and the Development of Type 2 Diabetes. Diabetes 2003, 52, 1799–1805.
294. Wacklin, P.; Tuimala, J.; Nikkilä, J.; Sebastian, T.; Mäkivuokko, H.; Alakulppi, N.; Laine, P.; Rajilic-Stojanovic, M.; Paulin,
L.; de Vos, W.M.; et al. Faecal Microbiota Composition in Adults Is Associated with the FUT2 Gene Determining the Secretor
Status. PLoS ONE 2014, 9, e94863. https://doi.org/10.1371/journal.pone.0094863.
295. Barzilai, N.; Huffman, D.M.; Muzumdar, R.H.; Bartke, A. The Critical Role of Metabolic Pathways in Aging. Diabetes 2012,
61, 1315–1322. https://doi.org/10.2337/db11-1300.
296. Henry, C.J.; Kaur, B.; Quek, R.Y.C. Chrononutrition in the management of diabetes. Nutr. Diabetes 2020, 10, 6.
297. Papakonstantinou, E.; Oikonomou, C.; Nychas, G.; Dimitriadis, G.D. Effects of Diet, Lifestyle, Chrononutrition and Alter-
native Dietary Interventions on Postprandial Glycemia and Insulin Resistance. Nutrients 2022, 14, 823.
298. Katsi, V.; Papakonstantinou, I.P.; Soulaidopoulos, S.; Katsiki, N.; Tsioufis, K. Chrononutrition in Cardiometabolic Health. J.
Clin. Med. 2022, 11, 296. https://doi.org/10.3390/jcm11020296.
299. Mohd Azmi, N.A.S.; Juliana, N.; Mohd Fahmi Teng, N.I.; Azmani, S.; Das, S.; Effendy, N. Consequences of Circadian Dis-
ruption in Shift Workers on Chrononutrition and their Psychosocial Well-Being. Int. J. Environ. Res. Public Health 2020, 17,
300. Kim, H.-K.; Chijiki, H.; Nanba, T.; Ozaki, M.; Sasaki, H.; Takahashi, M.; Shibata, S. Ingestion of Helianthus tuberosus at Break-
fast Rather Than at Dinner is More Effective for Suppressing Glucose Levels and Improving the Intestinal Microbiota