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Heart-rate variability: a biomarker to study the influence of
nutrition on physiological and psychological health?
Hayley A. Young and David Benton
As the influence of diet on health may take place over a
period of decades, there is a need for biomarkers that help to
identify those aspects of nutrition that have either a positive
or a negative influence. The evidence is considered that heart-
rate variability (HRV) (the time differences between one beat
and the next) can be used to indicate the potential health
benefits of food items. Reduced HRV is associated with the
development of numerous conditions for example, diabetes,
cardiovascular disease, inflammation, obesity and psychiatric
disorders. Although more systematic research is required,
various aspects of diet have been shown to benefit HRV
acutely and in the longer term. Examples include a
Mediterranean diet, omega-3 fatty acids, B-vitamins,
probiotics, polyphenols and weight loss. Aspects of diet that
are viewed as undesirable, for example high intakes of
saturated or trans-fat and high glycaemic carbohydrates,
have been found to reduce HRV. It is argued that the
consistent relationship between HRV, health and morbidity
supports the view that HRV has the potential to become a
widely used biomarker when considering the influence of diet
on mental and physical health. Behavioural Pharmacology
29:140–151 Copyright © 2018 The Author(s). Published by
Wolters Kluwer Health, Inc.
Behavioural Pharmacology 2018, 29:140–151
Keywords: diet, disease, health, heart-rate variability, nutrition
Department of Psychology, University of Wales Swansea, Swansea, Wales, UK
Correspondence to David Benton, DSc, Department of Psychology, University of
Wales Swansea, Swansea SA2 8PP, Wales, UK
E-mail: d.benton@swansea.ac.uk
Received 19 May 2017 Accepted as revised 14 January 2018
Introduction
Biomarkers are important as proxy measures when
studying health or disease states that develop over long
periods. As a disease can develop over decades, this is an
area where there is a need for biomarkers that identify
aspects of life style that are potentially beneficial or
problematic. The case will be made for using heart-rate
variability (HRV) as an indicator of the physiological
response to food by those interested in the association
between diet and various health outcomes, and by
manufacturers developing functional foods with potential
health benefits. HRV is of interest as a wide range of
diseases are associated with decreased variability,
including diabetes, cardiovascular disease and psychiatric
disorders. In addition, there is an increasing literature
that reports that HRV responds to various aspects of the
diet, raising the possibility that HRV offers a convenient
measure of potential benefit.
Traditionally, heart rate (HR) has been considered a
product of emotional response or stress, but it is
becoming apparent that the interval between beats is a
marker of the capacity to regulate internal and external
demands. The intervals are not constant, but differ from
beat to beat: essentially a higher HRV indicates better
general health (Jarczok et al., 2015). In essence, the
multitude of ways in which different physiological
mechanisms modulate each other ensures that studying
one aspect of the body in isolation limits our
understanding.
In 1948, the WHO defined health as ‘a state of complete
physical, mental and social well-being and not merely the
absence of disease or infirmity’. As complete well-being
is practically impossible, more recent definitions have
emphasized resilience, the capacity to cope and the
ability to maintain a sense of well-being. To assess the
level of existing health and to evaluate dietary inter-
ventions, such definitions need to be operationalized.
With this in mind, HRV may serve as a biomarker
relating to the above definition, and thus serve as an
indicator of the response to diet. In this review, the
control of HR and the measurement of HRV are first
outlined and then associations between HRV, health and
aspects of diet are considered.
Control of heart rate
Figure 1 shows an ECG trace from which the R-to-R
intervals are measured, although it is the variability in the
differences between these intervals that is of interest. In
the brain stem, the medulla oblongata controls HR
through the vagus, the tenth cranial nerve: vagal tone
reduces HR by inhibiting the sinoatrial node, the heart’s
pacemaker. Although there is a tendency to view HR as
an involuntary mechanism, the medulla oblongata
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140 Review article
0955-8810 Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. DOI: 10.1097/FBP.0000000000000383
receives information from the rest of the brain: including
the central autonomic network (CAN) (Benarroch, 1993),
with the prefrontal cortex playing a leading role (Thayer
and Lane, 2009). Indeed, the cortical regulation of the
CAN has been well described; there are both direct and
indirect pathways (involving the cingulate and insula
cortices, amygdala, hypothalamus and medulla oblon-
gata) linking the frontal cortex to autonomic motor cir-
cuits responsible for both the excitatory and inhibitory
effects on the heart (see Thayer and Lane, 2009 for a
review). Therefore, the moment-to-moment control of
HR reflects complex interactions between physiology,
emotion and cognition (Benarroch, 1993; Thayer and
Lane, 2009), encompassing the integration of a wide
variety of information. Thus, it has been suggested that
HRV reflects the overall capacity of the body to deal with
on-going demands (Young and Benton, 2015). ‘HRV may
serve as a proxy for the ‘vertical integration’of the brain
mechanisms that guide flexible control over behaviour
with peripheral physiology, and as such provides an
important window into understanding stress and health’
(Thayer et al., 2012). In this way, HRV may act as a
biomarker when considering the influence of diet on
health-related mechanisms.
Indices of heart-rate variability
Three approaches are used to monitor HRV: time-
domain and frequency-domain measures are linear in
nature, whereas complexity measures offer a nonlinear
approach. The simplest approach is to summarize dif-
ferences in the interbeat interval as a mean and SD, so-
called time-domain measures (Fig. 2). Second, a spectral
analysis allows frequency domain indices to be calculated
and describes the interbeat intervals as a complex sum
of waveforms. The variance in HR is distinguished in
terms of the underlying rhythms that occur at different
frequencies. There is growing evidence that frequency-
domain indices measure the functioning of the autonomic
nervous system. The high-frequency (HF) measure
(power in the range 0.15–0.4 Hz) is more easily under-
stoodasitissaidtoreflectthe parasympathetic nervous
systems (PNSs) (Katona et al., 1970; Appel et al., 1989;
Billman and Hoskins 1989; Camm et al., 1996; Thayer
et al., 2010). In contrast, low-frequency (LF) (power in
the range: 0.04–0.15 Hz) is less readily interpreted as it
is influenced by both the PNS and also baroreceptor
activity (Houle and Billman, 1999; Billman, 2013). A
frequently considered index is the LF/HF ratio, which
is said to reflect the balance between PNS and the
sympathetic nervous system activity (Malik, 1998),
although this contention has been challenged recently
(Billman, 2013).
More recently, nonlinear indices have been developed:
as one example, approximate entropy provides an indi-
cation of the irregularity or randomness in a data set. The
time series 1, 2, 3, 1, 2, 3 has the same variability as the
time series 1, 1, 2, 2, 3, 3 and 3, 1, 2, 2, 3, 1, but different
underlying patterns. Nonlinear analysis enables the
quantification of this extra information. Essentially, the
nonlinear approach measures the complexity of the sig-
nal; how frequently do similar patterns reoccur? Although
frequency measures in particular have been studied
widely, it has become apparent that nonlinear indices of
complexity reflect different underlying mechanisms
(Young and Benton, 2015). For example, a recent study
reported that nonlinear measures of HRV were better
able to predict the performance of cognitive tests than
linear measures (Young and Benton, 2015). In addition, a
meta-analysis of the impact of depression and anti-
depressant treatment on HRV found that the effects
were most apparent with nonlinear measures of HRV
(Kemp et al., 2010). There is an impression that those
capable of generating more complex signals have a
greater ability to respond to environmental demands in a
more subtle manner (de la Torre-Luque et al., 2016).
Heart-rate variability, inflammation and
disease
Inflammation is part of the immune response, with too
much and too little inflammation indicating, or leading to,
a wide range of diseases. Hotamisligil (2006) noted that
metabolic regulation and the immune system are highly
integrated: he suggested that this interaction reflects a
central homeostatic mechanism that, if dysfunctional,
results in chronic metabolic disorders, particularly obe-
sity, type 2 diabetes and cardiovascular disease. An
inadequate response creates immunodeficiency that can
result in infection or cancer (Mellemkjaer et al., 2002). As
the inflammatory response is in part regulated by the
autonomic nervous system, HRV is of interest.
The vagus plays a role in the inflammatory reflex that
controls innate immune responses when tissue is injured
Fig. 1
A heart-rate trace. A typical electrocardiogram trace is illustrated. R is
the peak of the QRS complex (electrocardiogram trace) and heart-rate
variability is measured by considering consecutive R–R intervals. The
R–R interval is not constant, but varies within a normal range of
0.6–1.2 s. It is the degree of this R–R variability that is of interest as
greater variability is associated with better health.
HRV: a biomarker for health Young and Benton 141
or there is a pathogen invasion (Pavlov and Tracey, 2012).
The inflammatory reflex has an afferent component that
is activated by cytokines that relay information to the
hypothalamus. This input can then initiate an anti-
inflammatory response that prevents the release of
inflammatory products into the blood stream (Tracey,
2002). It is becoming increasingly clear that the nervous
system, through the vagal nerve, both monitors
Fig. 2
Linear domain - Time indices
Index Interpretation
Mean RR interval (ms) The average of NN intervals. A longer NN interval indicates a
lower heart rate.
SDNN (ms) Standard deviation of the NN intervals. Indicates total variability
with a high value is thought to associate with better health.
RMSSD (ms) The square root of the mean of the sum of the squares of
differences between adjacent NN intervals (RMSSD) is thought
to indicate parasympathetic nervous system activity.
Linear domain - Frequency indices
Index Interpretation
Total power (ms2) Variance of all inter-beat intervals ( ≤0.4 Hz). Indicates total
variability with a high value is thought to associate with better
health.
ULF (ms2) Power in the ultra-low frequency range ( ≤0.003 Hz). Might
reflect circadian, neuroendocrine, activity, other unknown
rhythms.
VLF (ms2) Power in the very low frequency range (0.003–0.04 Hz). Might
reflect vasomotor changes, thermoregulatory and possibly
parasympathetic influences on heart rate.
LF (ms2) Power in the low frequency range (0.04–0.15 Hz). Thought to
reflect a combination of parasympathetic and baroreceptor
activity.
HF (ms2) Power in the HF range (0.15–0.4 Hz). Reflects parasympathetic
activity.
LF/HF Ratio LF (ms2)/HF (ms2). Frequently used index of autonomic
balance although this has been debated.
Non-linear analysis
Index Interpretation
ApEn Approximate Entropy measures irregularity or randomness in a
set of data, such that smaller values reflect a greater regularity.
Sample entropy (SampEn) is a similar measure that is more
suitable for short recordings.
DFA Detrended Fluctuation Analysis quantifies the fractal like
correlation properties of a time series and reveals short-range
(DFA1) and long-range (DFA2) correlations within a series.
Various measures of heart-rate variability. The variability in consecutive differences in the R–R interval can be expressed using a number of different
approaches that are one of three types: time or frequency based or nonlinear. HF, high-frequency; LF, low-frequency.
142 Behavioural Pharmacology 2018, Vol 29 No 2&3
peripheral inflammation and dampens the immune
response (Boeckxstaens, 2013). The sympathetic arm of
the autonomic nervous system also plays a role: it releases
noradrenaline, which appears to act at sites other than the
synapse, as many immune cells, including lymphocytes
and macrophages, have adrenergic receptors (Kavelaars
et al., 1999).
HRV is associated with the levels in the blood of
C-reactive protein (CRP), a protein that is produced by
the liver as a response to inflammation, with the levels
increasing following the secretion of interleukin (IL)-6
from macrophages and T cells. The role of the protein is
to bind to lysophosphatidyl choline on the surface of
dead cells and to enhance the ability of antibodies and
phagocytic cells to remove pathogens. Higher levels of
CRP are associated with a greater risk of hypertension,
diabetes and cardiovascular disease (Dehghan et al.,
2007). Jarczok et al. (2014) reported that the HF com-
ponent of HRV predicted the level of CRP when it was
measured 4 years later; this is important as both inflam-
mation and HRV have been implicated in conditions
such as diabetes and cardiovascular disease (Kudat et al.,
2006; Thayer et al., 2010).
Madsen et al. (2007) related low-grade inflammation to
autonomic dysfunction in those with suspected coronary
heart disease. The mean SD of normal to normal R–R
(SDNN) was higher in those with lower levels of CRP,
with the association being greater in those with a pre-
vious history of myocardial infarction. However, CRP
remained associated with HRV independent of disease, a
finding interpreted as a relationship occurring between
low-grade inflammation and autonomic dysfunction.
Experimental evidence is beginning to emerge and
support these associations. For example, a recent study
found that vagus nerve-stimulation in epilepsy patients
inhibited peripheral blood production of tumour necrosis
factor, IL-1βand IL-6 (Koopman et al., 2016).
Whether by influencing inflammation or by other
mechanisms, HRV has been associated with a range of
diseases, in some cases predicting subsequent problems
and in others being an index of disease progression
(RenuMadhavi and Ananth, 2012). As a generalization, a
healthy biological system tends to be both variable and
complex, characteristics that decline with disease.
Irrespective of disease, HRV declines with age, reaching,
in some older than 65 years of age, levels that are a risk
factor for mortality (Umetani et al., 1998).
The American Heart Association states that diabetes
increases the risk of heart disease or a stroke and thus
early diagnosis of complications is the key to decreasing
mortality. A systematic review of those with diabetes
concluded that HRV can help to predict cardiac mor-
bidity and mortality, and that it can be used at an early
stage to indicate the future risk of complications
(Fakhrzadeh et al., 2012). As one example, Yoshioka and
Terasaki (1994) reported that the HF component, which
indicates parasympathetic nervous activity, was lower in
diabetics rather than controls, and there were inverse
correlations between the LF and HF. It was concluded
that these measures of HRV are useful when evaluating
diabetic autonomic and peripheral neuropathies. The
Atherosclerosis Risk in Communities study (Schroeder
et al., 2005) also found that diabetics differenced in HF
power and that HF in nondiabetics was greater in those
with lower levels of fasting insulin. Thus, there was a
relationship between insulin resistance, as indicated by
higher levels of fasting insulin, and lower HRV. In
addition, after a 9-year follow–up, there was a general
decline in HRV. Such findings suggest that an impair-
ment of the functioning of the autonomic nervous sys-
tem, reflected in HRV, occurs during the early stages of
diabetes and becomes progressively worse over time.
A more general question is the association between HRV
and blood glucose levels. The Framingham Heart Study
(Singh et al., 2000) related HRV to fasting levels of blood
glucose: LF and HF power was reduced in those with
diabetes or impaired levels of fasting glucose. In addition,
a recent study found that resting HRV (average R–R
interval, HF power, sample entropy) predicted the
increase in blood glucose following a high glycaemic load
drink (Young and Watkins, 2016). Overall, HRV appears
to be associated inversely with plasma glucose levels. It is
plausible that the links between insulin resistance, blood
glucose and HRV are attributable to deficits in the
inflammatory reflex (Pavlov and Tracey, 2012). Vinik
(2012) noted that activation of inflammatory cytokines in
newly diagnosed type 2 diabetes correlated with changes
in sympatho–vagal balance. As, in type 2 diabetes,
changes in the autonomic nervous system predict sudden
death, such measures offer the chance of an early
intervention.
As mentioned above, a reduction in HRV predicts mac-
rovascular disease, for example carotid artery athero-
sclerosis (Gottsäter et al., 2006). Indeed, a recent study
found that insulin resistance mediated the association
between HF-HRV and carotid intima–media thickness
(Kemp et al., 2016). In fact, it has been known for many
years that following myocardial infarction, HRV is related
to the risk of consequent morbidity and mortality (La
Rovere et al., 1998). In addition, the rate at which con-
gestive heart failure and arrhythmias occur has been
related to a reduced HRV (Sandercock and Brodie, 2006)
and lower HRV complexity is associated with a worse
outcome in cardiac patients (Souza et al., 2015). Although
fewer studies have considered those without a history of
coronary heart disease, a meta-analysis examined those
without any problem at baseline (Hillenbrand et al.,
2013). A lower HRV, measured as SDNN, was associated
with a subsequent 40% increase in the risk of suffering a
first cardiovascular event.
HRV: a biomarker for health Young and Benton 143
Heart-rate variability, obesity and weight loss
Decreased HRV is associated with a significantly
increased risk of death from cardiovascular disease
(Bigger et al., 1993; Makikallio et al., 1999). Therefore,
Kim et al. (2005) considered whether the high rate of
cardiovascular disease in obese individuals might be
associated with changes in HRV. They found that the
RMSSD (a frequently used index of parasympathetic
activity) was correlated negatively with fat mass and the
hip-to-waist ratio. Similar effects were noted for LF
power, although as noted above, this index is not easily
interpretable. Nonetheless, it seems that obesity can
alter HRV.
For example, Mouridsen et al. (2013) examined the
impact of weight loss on HR and HRV in overweight
postmenopausal women. An average weight loss of 3.9 kg
was associated with a decrease in HR and increased
HRV, as indicated by SDNN and the interbeat interval.
Previously, Karason et al. (1999) had considered obese
patients, who, after surgery, had lost 28% of their body
weight, on average 32 kg. SDNN and HF power were
attenuated in those with obesity compared with lean
patients; HRV improved with weight loss. Adachi et al.
(2011) examined the influence of obesity on autonomic
activity during sleep. Volunteers were allocated randomly
to a standard diet or one that over 8 weeks increased
weight by 4 kg. After weight gain, HF power decreased
both when awake and when asleep, changes that resolved
with weight loss. A related finding was that caloric
restriction may reverse the autonomic changes that occur
as we age. A decline in HRV with age is well described:
for example, Zulfiqar et al. (2010) compared HRV in
patients of ages ranging from 10 to 99 years. Several
measures of HRV all decreased rapidly from the second
to fifth decades, although the decline then slowed. They
concluded that a healthy long life depends on the pre-
servation of autonomic functioning, more specifically, the
influence of the PNS on HRV. Stein et al. (2012) found
that in patients who had practiced caloric restriction, for
on average 7 years, HR was lower and several measures of
HRV values were significantly higher. In fact, HRV was
comparable to the norms for those 20 years younger. The
overall impression is that weight gain adversely influ-
ences HRV, although this effect may be reversible with
weight loss and/or dietary restriction.
Heart-rate variability and eating behaviour
HR variables have also been related to dieting and eating
behaviour. A recent review concluded that most,
although not all, studies investigating HRV in those with
anorexia nervosa find parasympathetic dominance
(Mazurak et al., 2011). Similarly, those with bulimia
nervosa are characterized by higher vagal activity, parti-
cularly HF-HRV (Peschel et al., 2016). In a healthy
sample, Meule et al. (2012b) found that restrained eating,
which is the intentional restriction of food intake to
prevent weight gain or to promote weight loss, was
associated with low cardiac vagal control (Table 1). The
restraint score and the extent to which weight fluctuated
predicted HF-HRV negatively. In a further study, Meule
et al. (2012a) qualified this finding by considering suc-
cessful versus unsuccessful dieters: success was asso-
ciated positively with HF-HRV. They concluded that
vagal–cardiac control reflected the strength of self-
regulation such that successful restrained eaters were
characterized by higher cardiac vagal control. These
findings are in line with two studies that found reduced
HR variability and complexity (R–R interval, HF power,
sample entropy) in those with a propensity towards dis-
inhibited eating, which is a tendency to overeat in the
presence of palatable foods or other disinhibiting stimuli,
such as emotional stress (Young and Watkins, 2016;
Young et al., 2017a). Importantly, these effects remained
after controlling for the healthiness of the diet. Thus, it
appears that tonic HRV might index individual differ-
ences in the capacity to self-regulate in the face of
temptation. Indeed, Geisler et al. (2016) reported that
restrained eaters who suppressed their feelings about a
distressing film had higher vagally mediated HRV
(RMSSD) during subsequent exposure to a palatable
food (jelly beans). In an earlier study, Segerstrom and
Nes (2007) found an increase in RMSSD when partici-
pants were told to resist warm cookies and instead eat
carrots. A caveat is that caloric restriction and intermittent
fasting, behaviours commonly observed in restrained
eaters, have been shown to increase HF oscillatory
components in HR (Mager, 2006). Thus, the higher HF-
HRV observed in this population may be because of
lifestyle factors rather than differences in self-regulatory
capacity. Future research should explore this possibility.
That said, these associations between eating behaviour
and HRV offer the possibility of examining the associa-
tion between the ability of different types of food to
influence HRV and their ability to reduce or prevent
pathological eating behaviours.
Heart-rate variability and psychiatric disorders
A dysfunctional autonomic nervous system, with an
associated reduction in HRV, has been found in a wide
range of psychiatric disorders, a contention supported by
a number of recent meta-analyses. Kemp et al. (2010)
examined the association between depression and HRV
in those without cardiovascular disease. When depressed
patients and healthy controls were compared, the former
had a lower HRV; it was particularly lower in those with
more severe symptoms. Similar meta-analytic reviews
have shown HRV reductions in a range of psychiatric
disorders: bipolar disorder (Faurholt-Jepsen et al., 2017),
anxiety disorders (Chalmers et al., 2014), post-traumatic
stress disorder (Nagpal et al., 2013) and schizophrenia
(Clamor et al., 2016). Notably, such effects are most
consistently examined using HF-HRV or other vagally
mediated HRV indices (e.g. RMSSD), although on
144 Behavioural Pharmacology 2018, Vol 29 No 2&3
occasion, larger effect sizes have been noted with non-
linear indices (Kemp et al., 2010). Importantly, HRV
predicted the onset of psychological illness 10 years later
(Jandackova et al., 2016). Given the links between HRV,
emotion regulation and executive functioning (Williams
et al., 2015; Zahn et al., 2016; Holzman and Bridgett,
2017), it has been proposed that HRV is a transdiagnostic
biomarker of mental illness (Beauchaine and Thayer,
2015).
This brief overview illustrates that HRV is a risk for, or a
marker of, a wide range of disorders. As such, any inter-
vention that impacted positively on HRV has the
potential to benefit health. Evidence suggests that life-
style may be one such factor. Young et al. (2017a)
reported that smoking cigarettes and drinking alcohol
negatively influenced HRV, measured using linear (HF
power) and nonlinear (sample entropy) measures. In
addition, these aspects of the HR time series were
increased in those who took regular exercise and con-
sumed a healthy diet. Notably, diet quality at least par-
tially explained the association between mood,
disinhibition and HRV (Young et al., 2017a); thus, dietary
modification may improve HRV in those at risk of psy-
chological disorders.
The nature of the diet and heart-rate variability
In an early study, Lu et al. (1999) examined the post-
prandial changes in HRV following a 500 kcal test meal
(turkey sandwich: 32.4% fat, 17.5% protein and 50.1%
carbohydrate). HF power decreased during the first and
second 30 min following consumption, suggesting a
reduction in vagal tone. Similarly, after a mixed meal of
501.8 kcal that comprised 31% protein, 18% lipids and
51% carbohydrates, a decrease in HF power was
observed from 40 to 120 min after the meal, an effect that
correlated negatively with ghrelin concentrations (Chang
et al., 2010). In relation to specific macronutrients,
Kanaley et al. (2007) studied the influence of an acute
glucose load on HRV in obese women with and without
diabetes. Total power decreased in response to the glu-
cose challenge compared with what was observed in the
fasted state. In addition, a high-carbohydrate meal was
reported to augment HRV reactivity (in response to
mental stress) compared with a high-protein meal: HR
decreased more quickly poststress after the carbohydrate
meal (Uijtdehaage et al., 1994). Over a 2-day period,
Lima-Silva et al. (2010) examined the impact of a low-
carbohydrate diet in those who had been exercising.
Compared with a high-carbohydrate diet, the low-
carbohydrate diet increased LF and decreased HF
power. There were, however, no differences in HR or
R–R interval. As this study considered those who had
been exercising, there is a need to look at those simply
going about their everyday life.
Nagai et al. (2005) reported that a high-fat meal increased
the VLF component of HRV. Although the physiological
processes underlying VLF-HRV are unclear, Nagai et al.
(2005) interpreted this as an indication of thermo-
regulatory sympathetic nervous system activity. Other
components of HRV may also be influenced by the
nature of dietary fat, at least in the longer term:
Soares-Miranda et al. (2012) reported that in two cohorts,
either 19 or 72 years of age, a greater consumption of
trans-fats was associated with a less favourable HRV;
SDNN, RMSSD and total power were lower.
Importantly, trans-fat consumption predicted lower HRV
5 years later.
When considering the influence of diet, by far the
majority of work has involved supplementation with
Table 1 The relation between heart-rate variability and eating behaviour
References Sample Design Outcome
Segerstrom and
Nes (2007)
168 students aged
19 years
Asked to resist eating palatable food and consume
less palatable. Then, they performed an anagram
task that needed persistence
Increase in RMSSD when eating cookies was resisted,
and carrots consumed. HRV seen as an index of self-
regulatory strength
Meule et al.
(2012a)
47 females aged
24 years
BMI 22
Compared those who were and were not retrained
eaters
A high HF associated with less weight fluctuation. Weight
fluctuation predicted LH/HF
Meule et al.
(2012b)
50 females aged
18–40 years
BMI between
17.5 and 25
Compared those who were or were not currently
dieting
Successful dieting positively associated with HF and
negatively with LH/H F
Geisler et al.
(2016)
111 students aged
23 years
Participants took on a task that did or did not require
self-control. Then,, they were exposed to palatable
food. Effortable self-control measures as H RV
Restrained eaters, who suppressed feelings, had higher
RMSSD when exposed to a palatable food, but not
when self-control had not been exercised. Suggested
dieting required self-control as indicated by HRV
Young and Watkins
(2016)
66 females between
18 and 29 years
BMI 17–31
Studied the relation between HRV and glucose
tolerance
Disinhibited eaters had reduced HRV (HF and sample
entropy). Those with low HRV had poorer glucose
tolerance –the levels remained higher for longer
Young et al.
(2017a)
156 healthy adults
from 18 to 34 years,
122 healthy adults
From 18 to 30 years
Cross sectional study asked whether diet mediated
the association between H RV and mood
Diet shown to mediate the relation between HRV (H F and
sample entropy) and mood
HF, high-frequency; HRV, heart-rate variability; LF, low-frequency.
HRV: a biomarker for health Young and Benton 145
omega-3 fatty acids. For example, in a secure forensic
inpatient facility, Hansen et al. (2014) randomly allocated
patients to a diet containing salmon three times a week
compared with meat in the control group. The changes in
HRV associated with fish consumption correlated nega-
tively with sleep latency and positively with a measure of
daily functioning. Table 2 lists a representative sample of
studies that show that research has been driven by an
interest in disease states, particularly heart disease, and
has typically involved the examination of older adults.
There is a general impression that omega-3 supple-
mentation resulted in greater parasympathetic activity,
although greater confidence in this conclusion comes
from a meta-analysis. Xin et al. (2013) integrated fifteen
studies. The time-domain measures, SDNN and
RMSSD, did not differ after supplementation. However,
although there was no effect on the frequency measure
LF, fish oil significantly increased HF. The authors
concluded that the ‘enhancement of vagal tone may be
an important mechanism underlying the antiarrhythmic
and other clinical effects of fish oil’. Similarly, the review
of Christensen and Schmidt (2007) concluded that: ‘In
most (Billman et al., 1994; Christensen et al., 1999;
Christensen and Schmidt 2007; Billman and Harris, 2011;
Christensen, 2011), although not all, studies, dietary n-3
PUFA levels and n-3 PUFA supplementation are related
to improved HR variability’. The findings suggested that
an increased parasympathetic regulation of cardiac func-
tioning occurred.
More generally, the Mediterranean diet, which has fish as
a component, is also associated with HRV. Dai et al.
(2010), using a food-frequency questionnaire, established
the extent to which middle-aged male twins ate a
Mediterranean diet. After adjusting for genetic con-
tributions, those who conformed to the Mediterranean
diet had higher HRV across a range of indices: SDNN,
RMSSD, ULF, VLF, HF and LF. Although it was
unclear which aspect of the diet was beneficial, it is
tempting to suggest that one factor is eating fish and in
this way increasing the intake of omega-3 fatty acids.
Billman (2013) reviewed the evidence that the intake of
omega-3 fatty acids influenced cardiac rhythms. He
concluded that supplementation with n3-PUFAs affects
ion channels and calcium-regulatory proteins, although
these depended on the route of administration.
Immediately, there is a direct effect of the fatty acids on
ion channels, although over the longer term, after the
incorporation of the fatty acids into the cell membrane,
cardiac electrical activity changes. HR is reduced and
HRV increases, reflecting alterations in the intrinsic
pacemaker rather than regulation by the activity of the
autonomic nervous system.
Other contributions to a Mediterranean diet have also
been found to influence HRV. In a randomized trial, the
influence of a multivitamin-mineral preparation on HRV
was assessed (Pomportes et al., 2015). Faster reactions in a
test of the ability to inhibit responses were associated
with a stable HF, indicative of PNS activity. In those
taking micronutrients, HF remained stable, whereas in
the placebo condition, HF decreased over time. Jaatinen
et al. (2014) studied the influence of yoghurt enriched
with α-lactalbumin, bioactive peptides and B vitamins in
individuals with high-trait anxiety. In the active group,
vagally mediated HRV (RMSSD) was higher. They
concluded that this yoghurt combination may aid coping
with stress. As nut consumption is associated with a lower
risk of cardiovascular disease, Sauder et al. (2014) studied
its influence on HRV. The pistachio diet was associated
with a higher HF and greater total variation in beat-to-
beat intervals. Similar effects have been observed in
relation to the consumption of polyphenol-rich red wine.
Intake of wine, but not of spirits or beer, is positively and
independently (after controlling for other health beha-
viours) associated with HRV (SDNN, total power, VLF
power, LF power and HF power) in women with CHD
(Janszky et al., 2005).
There is also some evidence that particular nutrients
influence HRV. Sucharita et al. (2014) considered HRV in a
healthy elderly Indian sample with either a high or a low
vitamin B
12
status. The LF was lower in those with a
poorer vitamin B
12
status, although no effects on HF power
or HR were observed. Supplementation for 3 months
increased LF to levels comparable to those with an initially
good vitamin B
12
status. Sodium intake is also linked to
HRV. In a randomized-controlled trial, Allen et al. (2014)
asked participants, with normal blood pressure, to consume
for 5 days a diet with low (10 mmol/day), normal
(150 mmol) or high (400 mmol) levels of sodium. The
response to low sodium was consistent with sympathetic
activation and reduced vagal activity; LFnu (normalized)
increased and SDNN, RMSSD and HFnu (normalized)
decreased compared with both normal and high sodium
conditions.
In summary, although there has been limited systematic
study, there is a series of reports that relate various
indices of HRV to the intake of a range of food items. As
such, there is good reason to support the further con-
sideration of HRV as a biomarker with the potential to
indicate the potential influence of food on health.
Discussion
There is growing evidence that a range of diseases are
accompanied by a decrease in HRV, including, amongst
others, diabetes (Yoshioka and Terasaki, 1994), cardio-
vascular disease (Gottsäter et al., 2006) and psychiatric
disorders (Kemp et al., 2014). Although indices of HRV
do not distinguish between types of disorder, there is a
consistent pattern of a reduced variability in HR being
associated with disease: a higher HRV is associated with
psychological flexibility and allostatic resilience. Indeed,
longitudinal studies support the view that reduced HRV
predicts psychological and physiological morbidity years
146 Behavioural Pharmacology 2018, Vol 29 No 2&3
Table 2 The influence of fish oil supplementation and eating fish on heart-rate variability
References Sample Design Intervention Outcome
Christensen et al. (1996) 49 patients with CAD aged 63 years Parallel 4.3 g EPA/DHA a day or placebo for
12 weeks
SDNN and mean RR increased from baseline in the active, but not the control
condition. No significant difference between active and control
Christensen et al. (1998) 17 patients with RF aged 52 years Parallel 4.7 g EPA/DHA a day or placebo for
12 weeks
High cell fatty acids associated with higher SDNN
Those with CRF had lower HRV
Christensen et al. (1999) 40 healthy individuals aged 38 years Parallel 1.68, 5.9 g EPA/DHA a day or
placebo for 12 weeks
Baseline positive correlation between SDN N, RMSSD and levels of DHA in cell.
Dose-dependent increase in HRV
Effects found in men, but not women
Dyerberg et al. (2004) 49 healthy individuals aged 38 years Parallel 3.2 g EPA/DHA a day or placebo for
8 weeks
SDNN, RMSSD, NN50, NN6% were not significantly
Influenced
Holguin et al. (2005) 52 elderly individuals aged 77 years Parallel 1.55 EPA/DHA a day or placebo for
16 weeks
Supplementation increased LF, HF and RMSSD
O’Keefe et al. (2006) 36 patients with CAD aged 68 years Cross-over 0.8 EPA/DHA a day or placebo for
16 weeks
Supplement increased HF and reduced HR at rest
SDNN and LF not effected
Hamaad et al. (2006) 38 patients with CAD aged 60 years Parallel 0.8 EPA/DHA a day or placebo for
16 weeks
No significant influence on SDNN, RMSSD, LF, HF and LF/HF
Radaelli et al. (2006) 25 patients with CAD aged 60 years Parallel 1.68 EPA/DHA a day or placebo for
16 weeks
Supplementation increased LF, HF, SDNN, RMSSD
Ninio et al. (2008) 46 overweight patients aged 50 years Parallel 0.8 EPA/DHA a day or placebo for
12 weeks
Supplement increased HF but did not affect LF or LF/HF. Resting HR decreased after
supplement
Nodari et al. (2009) 41 patients with Idiopathic dilated
cardiomyopathy aged 63 years
Parallel 1.44 g E PA/DHA a day or placebo for
24 weeks
The LF/HF ratio increased after supplement when under mental stress. VLF
decreased in the placebo group, but not in the active group
Carney et al. (2010) 72 depressed patients with CAD aged
57 years
Parallel 1.68 g E PA/DHA a day or placebo for
10 weeks
No effect on LF or HF
Sjoberg et al. (2010) 67 overweight patients aged 52 years Parallel 0.64, 1.28, and 1.92 g EPA/DHA a
day or placebo for 12 weeks
There was a dose-dependent reduction in the LF/ HF, although neither LH or HF was
significantly influenced
Valera et al. (2014) 180 French Polynesian adults Cross-
sectional
The DHA in red cells was related to
HRV
Increasing DHA concentrations were associated with lower HR and greater HF,
RMSSD
Fish intake
Mozaffarian et al. (2008) 4263 ECGs 1152 24 h holter Cross-
sectional
Eating tuna and other fish assessed
using FFQ
Eating fish associated with greater RMSSD and HF and lower LF. A lower Poincaré
ratio and higher short-term fractal scaling exponent suggested less erratic sinoatrial
node firing
Hansen et al. (2014) 95 male forensic inpatients Parallel Ate either fish or meat meals three
times a week for 6 months
Fish group showed a significant increase in HF.
The increased variability associated with fish consumption correlated negatively with
sleep latency and positively with a measure of daily functioning
Grung et al. (2015) 49 male forensic inpatients Parallel Ate either fish or meat meals three
times a week for 6 months
Principal component analysis of HRV parameters produced a parasympathetic factor
that increased when fish was eaten
CAD, coronary artery disease; CRF, chronic renal failure; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; FFQ, food frequency questionnaire; HF, high-frequency; HR, heart rate; HRV, heart-rate variability; LF, low-
frequency; SDDN, SD of normal to normal R–R.
HRV: a biomarker for health Young and Benton 147
later (Carnethon et al., 2003; Jandackova et al., 2016).
Thus, HRV may serve as a biomarker for future health.
Various aspects of diet have been found to be associated
with HRV. In general, the types of diet and particular
foods that have been found to be associated with a
healthy life-style are associated with higher HRV. For
example, a Mediterranean diet (Mozaffarian et al., 2008;
Soares-Miranda et al., 2012), fish consumption
(Mozaffarian et al., 2008), multivitamins (Pomportes et al.,
2015) and losing weight (Zulfiqar et al., 2010) all
increased HRV. However, aspects of diet that are com-
monly viewed as undesirable, for example a high fat or
trans-fat diet, reduced HRV (Soares-Miranda et al., 2012).
Although it is clear that diet influences HRV, the
mechanisms and pathways underlying such effects are
multifactorial. In relation to physical health, it is clear that
vagal tone is central to the regulation of a number of
allostatic systems, including the cardiovascular system,
glucose regulation, the hypothalamic–pituitary–adrenal
axis function, and inflammatory processes (Thayer and
Sternberg, 2006; Young and Benton, 2015; Viljoen and
Claassen, 2017). Thus, reduced vagal tone may directly
contribute towards increased allostatic load (Koopman
et al., 2016). However, chronic diseases such as diabetes
contribute towards autonomic neuropathy (Vinik et al.,
2003), which is associated with reduced HRV
(Fakhrzadeh et al., 2012). Such bidirectional effects make
causality difficult to determine.
Similarly, it has been argued that vagal afferent signals
might mediate the influence that diet has on psycholo-
gical health (Kemp et al., 2017). Indeed, emerging evi-
dence suggests that cardiac interoceptive sensations
contribute towards one’s mental health (Craig, 2003;
Critchley et al., 2004; Seth, 2013; Young et al., 2017b);
however, it is difficult to isolate diet-related effects on
afferent vagal signals. In addition, influential models such
as the neurovisceral integration model (Thayer and Lane,
2009) consider HRV to be the product of the CAN, which
guides goal-directed behaviour; the prefrontal cortex
plays a central inhibitory role (Thayer and Lane, 2000;
Smith et al., 2017). Thus, HRV may capture primarily
efferent vagal activity. With this in mind, it is plausible
that the effects of diet on HRV operate indirectly through
changes in mental health. That is, diet influences brain
functioning, cognition and mood, which is then reflected
in changes in HRV. Indeed, a study supports the view
that diet influences psychological health not only acutely
(Young and Benton, 2014, 2015) but also chronically
(Jacka et al., 2010). However, whether these effects
mediate or are meditated by HRV remains to be
determined.
Furthermore, the link between HRV and pathological
eating behaviours (Young and Watkins, 2016) points
towards the possibility of mutual causation. That is,
reduced HRV, by virtue of its connections with the
prefrontal cortex (Dietrich et al.,, 2006; Chang et al., 2013;
Jennings et al., 2016), may indicate a predisposition to
make poor dietary choices. In turn, a poor-quality diet
could exacerbate the reduction in HRV. In support of this
proposition, Young et al. (2017a) found that disinhibited
eating and poor diet quality were associated indepen-
dently with deficits in HRV. Notably, HRV is related
positively to interoceptive sensitivity (Ainley et al., 2012),
raising the possibility that diet-related reductions in vagal
tone may diminish the ability to detect interoceptive
signals, including information about one’s current
homeostatic state (Attuquayefio et al., 2017, Smith et al.,
2017), and contribute towards mental illnesses (Paulus
and Stein, 2010). Irrespective of the mechanisms
involved, the association between HRV and so many
types of disease state suggests that it is a measure of
factors with a wide-spread significance. Thus, HRV may
serve as a useful biomarker for identifying potentially
beneficial or detrimental aspects of diet.
A distinction should be drawn between phasic and tonic
HRV as this will impinge upon interpretation of research
findings. In relation to emotion, demanding situations
may give rise to an increase or a decrease in phasic HRV.
The former might arise when an individual successfully
self-regulates to deal with the demands of the situation
(Park et al., 2014; Geisler et al., 2016) and the latter may
arise when the situation is perceived as threatening and
an individual shows an autonomic stress response
(Segerstrom and Nes, 2007).
The vast majority of research linking health and HRV has
focused on tonic resting-state HRV (Chalmers et al., 2014;
Kemp et al., 2014, 2016). Although tonic HRV predicts
phasic changes in vagal tone, lower tonic HRV was
associated with phasic HRV suppression and higher tonic
HRV was associated with phasic HRV enhancement
(Park et al., 2014). This is an important methodological
consideration when considering the effects of diet.
Phasic changes represent real-time adaptations to the
external or the internal environment; as such, a tempor-
ary decline in HRV need not necessarily be pathological.
An example can be considered in relation to the literature
on fluid consumption. Hypohydration necessitates a
counter-regulatory increase in HR to maintain blood
pressure: for every 1% decrease in body mass during
exercise, there is an increase in HR of 3.29 bpm (Adams
et al., 2014). Even in the absence of hypohydration, water
ingestion is followed by an increase in cardiac vagal
control (Helen et al., 2002). Under such circumstances, an
inability to flexibly alter the variability of HR may be
viewed as detrimental. Thus, whether tonic HRV influ-
ences one’s ability to effectively counter regulate during
the postprandial period is an important question. Young
and Watkins (2016) provided the first evidence that this
might be the case: individual differences in tonic vagal
tone moderated the postprandial response to drinks that
148 Behavioural Pharmacology 2018, Vol 29 No 2&3
differed in glycaemic load. Whether a higher resting-state
HRV confers greater metabolic resilience during the
post-prandial period is an important question for future
research.
Although we have argued that decreased HRV may serve
as a biomarker of future health, there are caveats that
need to be considered. For example, it has been
observed that black individuals have higher HRV, and
yet are at greater risk of future cardiovascular disease
(Hill et al., 2017). In addition, although most psychiatric
disorders are characterized by reduced HRV, those with
anorexia nervosa (Mazurak et al., 2011) or bulimia nervosa
(Peschel et al., 2016) tend to have parasympathetic
dominance (higher HF-HRV). Although both these
conundrums may be explained by dietary differences in
these populations –for instance, there are clear racial
differences in nutritional behaviour –the literature to
date has typically not explored this possibility. This will
be an important avenue for future research.
There are three types of approach by which HRV can be
measured: the linear time-based and frequency-based
approaches and also nonlinear methods. To date, limited
research has considered nonlinear methods when study-
ing diet, although there are reasons to believe that they
contribute additional information to that supplied by the
linear methods (Young and Benton, 2015). In addition,
there is some evidence that nonlinear HRV indices cap-
ture the influence of endogenous hormonal fluctuations
and thermoregulation (Bai et al., 2009; Young and
Benton, 2015), which may be pertinent when considering
the influence of diet. Thus, future studies should incor-
porate both linear and nonlinear approaches into their
designs.
In summary, the consistent relationship between HRV,
health and morbidity supports the view that HRV can be
considered an index of psychological and physiological
resilience. An increasing number of studies report that
particular foods, nutrients and dietary styles influence
HRV, supporting its future use when examining the
impact of what we eat. The insidious influence of diet
will be cumulative and take place over a period of many
decades (Benton, 2010). As such HRV may prove to be a
means for identifying beneficial or detrimental aspects of
diet.
Acknowledgements
Conflicts of interest
There are no conflicts of interest.
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