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ORIGINAL
E n d o c r i n e
ARTICLE
R e s e a r c h
Dysregulation of the Autonomic Nervous System
Predicts the Development of the Metabolic Syndrome
Carmilla M. M. Licht, Eco J. C. de Geus, and Brenda W. J. H. Penninx
Department of Psychiatry (C.M.M.L., B.W.J.H.P.), Vrije Universiteit (VU) University Medical Center
Amsterdam, The Netherlands; Extramural Medicine Research⫹ Institute (C.M.M.L., E.J.C.d.G., B.W.J.H.P.)
for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department
of Biological Psychology (E.J.C.d.G.), VU University, Amsterdam, The Netherlands; Neuroscience Campus
Amsterdam (E.J.C.d.G., B.W.J.H.P.), VU University Medical Center, Amsterdam, The Netherlands;
Department of Psychiatry (B.W.J.H.P.), Leiden University Medical Center, Leiden, The Netherlands; and
Department of Psychiatry (B.W.J.H.P.), University Medical Center Groningen, Groningen, The
Netherlands
Context: Stress is suggested to lead to metabolic dysregulations as clustered in the metabolic
syndrome. Although dysregulation of the autonomic nervous system is found to associate with the
metabolic syndrome and its dysregulations, no longitudinal study has been performed to date to
examine the predictive value of this stress system in the development of the metabolic syndrome.
Objective: We examined whether autonomic nervous system functioning predicts 2-year development of metabolic abnormalities that constitute the metabolic syndrome.
Design: Data of the baseline and 2-year follow-up assessment of a prospective cohort: the Netherlands Study of Depression and Anxiety was used.
Setting: Participants were recruited in the general community, primary care, and specialized mental health care organizations.
Participants: A group of 1933 participants aged 18 – 65 years.
Main outcome measures: The autonomic nervous system measures included heart rate (HR), respiratory sinus arrhythmia (RSA; high RSA reflecting high parasympathetic activity), pre-ejection
period (PEP; high PEP reflecting low sympathetic activity), cardiac autonomic balance (CAB), and
cardiac autonomic regulation (CAR). Metabolic syndrome was based on the updated Adult Treatment Panel III criteria and included high waist circumference, serum triglycerides, blood pressure,
serum glucose, and low high-density lipoprotein (HDL) cholesterol.
Results: Baseline short PEP, low CAB, high HR, and CAR were predictors of an increase in the number
of components of the metabolic syndrome during follow-up. High HR and low CAB were predictors
of a 2-year decrease in HDL cholesterol, and 2-year increase in diastolic and systolic blood pressure.
Short PEP and high CAR also predicted a 2-year increase in systolic blood pressure, and short PEP
additionally predicted 2-year increase in diastolic blood pressure. Finally, a low baseline RSA was
predictive for subsequent decreases in HDL cholesterol.
Conclusion: Increased sympathetic activity predicts an increase in metabolic abnormalities over
time. These findings suggest that a dysregulation of the autonomic nervous system is an important
predictor of cardiovascular diseases and diabetes through dysregulating lipid metabolism and
blood pressure over time. (J Clin Endocrinol Metab 98: 2484 –2493, 2013)
ISSN Print 0021-972X ISSN Online 1945-7197
Printed in U.S.A.
Copyright © 2013 by The Endocrine Society
Received August 16, 2012. Accepted March 26, 2013.
First Published Online April 3, 2013
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Abbreviations: ANS, autonomic nervous system; ATC, World Health Organization Anatomical Therapeutic Chemical classification; BP, blood pressure; CAB, cardiac autonomic
balance; CAR, cardiac autonomic regulation; CI, confidence interval; ECG, electrocardiogram; HDL, high-density lipoproteins; HR, heart rate; IBI, interbeat interval; LV, left ventricle; NESDA, Netherlands Study of Depression and Anxiety study; OR, odds ratio; PEP,
pre-ejection period; RSA, respiratory sinus arrhythmia.
J Clin Endocrinol Metab, June 2013, 98(6):2484 –2493
doi: 10.1210/jc.2012-3104
doi: 10.1210/jc.2012-3104
t has often been hypothesized that stress leads to the
metabolic syndrome (1–3). Dysregulation of one of the
main stress systems—the autonomic nervous system—
could lead to insulin resistance, altered lipid metabolism,
and increased blood pressure (BP) (4 – 8). Results of a large
cross-sectional study indeed indicated that dysregulation
of the autonomic nervous system (ANS) is associated with
several metabolic alterations. Increased heart rate (HR)
with decreased respiratory sinus arrhythmia (RSA), indicative of low parasympathetic activity, and decreased preejection period (PEP), indicative of high sympathetic activity, were found to associate with high BP, serum
triglycerides, serum glucose, and waist circumference and
with the presence of the metabolic syndrome and the number of its components (9). The metabolic syndrome consists of a cluster of these metabolic abnormalities and is
thought to be one of the most important risk factors for
cardiovascular diseases (10, 11) and diabetes (12). Our
findings were in line with most other cross-sectional studies investigating the association between metabolic abnormalities and ANS functioning (3, 13, 14). Elevated
sympathetic nervous system activity and diminished parasympathetic nervous system activity were found in subjects with metabolic syndrome (15–19). As far as we
know, only one longitudinal study has been performed
that investigated the predictive value of metabolic syndrome factors for changes in HR variability (20). However, no longitudinal studies have been performed to
test the reverse causality. Therefore, it remains unclear
whether autonomic dysregulation, as a marker of biological stress activation, leads to metabolic dysregulations
and the metabolic syndrome (21).
To examine the relation between (multiple) measures of
ANS and metabolic components in a large cohort study,
we explored whether and which baseline autonomic measures predicted worsening of metabolic syndrome components over a 2-year time period, while considering possible important covariates.
I
Materials and Methods
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participating universities and all respondents provided written
informed consent. Two years after baseline, a face-to-face follow-up assessment was conducted with a response of 2596 of the
2981 respondents (87%). Nonresponders were younger, more
often of non-northern European ancestry, and less educated and
more often had major depressive disorder (23).
Of the total follow-up sample, 340 participants had no data
on metabolic abnormalities on 1 of the 2 time points and an
additional 107 had missing data on all baseline ANS measures.
Because of the known effects of antidepressants on the ANS (24)
and the metabolic syndrome (25), we excluded 131 subjects who
changed antidepressant use during the follow-up period (ie, subjects who started, stopped, or switched to another antidepressant) because in these individuals changes in metabolic syndrome
also could be due to changes in antidepressant medication. Use
of antidepressants was considered present when taken for at least
1 month and 50% of the time. Using the World Health Organization Anatomical Therapeutic Chemical (ATC) classification, medications were classified. Tricyclic antidepressants (ATC
code N06AA), serotonergic and noradrenergic working antidepressants (ATC code N06AF/N06AX), and selective serotonin
reuptake inhibitors (ATC code N06AB) were included. Similarly, because of the impact of ␤-blockers on the ANS as well as
on metabolic factors (26 –31), subjects who stopped or started
the use of a ␤-blocker (ATC code C07, used for at least a month
and daily or more than 50% of the time) were also excluded (n ⫽
85). Subjects who consistently used ␤-blockers or antidepressants during the 2-year follow-up remained included. The present study sample therefore consisted of 1933 participants.
Outcome measures
Metabolic syndrome
The metabolic syndrome was defined according to the American Heart Association and National Heart, Lung, and Blood
Institute’s update of the US National Cholesterol Education Program–Adult Treatment Panel III criteria (32). The US National
Cholesterol Education Program–Adult Treatment Panel III
guidelines define metabolic syndrome as a presence of 3 or more
of the following criteria: 1) waist circumference ⱖ102 cm in men
and ⱖ88 cm in women; 2) triglycerides ⱖ1.7 mmol/L (150 mg/
dL) or medication for hypertriglyceridemia; 3) high-density lipoprotein (HDL) cholesterol ⬍1.03 mmol/L (40 mg/dL) in men
and ⬍1.30 mmol/L (50 mg/dL) in women or medication for
reduced HDL cholesterol; 4) BP: systolic ⱖ130 and/or diastolic
ⱖ85 mm Hg or antihypertensive medication; 5) fasting plasma
glucose ⱖ5.6 mmol/L (100 mg/dL) or antidiabetic medication.
The number of metabolic syndrome components was used as an
indicator of severity of metabolic abnormalities (25).
Study sample
Data are from the Netherlands Study of Depression and Anxiety (NESDA), a large longitudinal cohort study among 2981
adults (18 – 65 y), 95.2% of North-European ancestry (see [22]).
Respondents were recruited from the community, in primary
care, through a screening procedure conducted among 65 general practitioners, and in specialized mental health care when
newly enrolled at 1 of the 17 participating mental health organization locations. The baseline assessment comprised a face-toface interview, written questionnaires, and biological measurements (among which was a blood draw in fasting state). The
research protocol was approved by the Ethical Committee of
Metabolic syndrome components
In addition to metabolic syndrome, associations with continuous levels of individual metabolic components were examined,
to investigate consistency across components. Waist circumference was measured with a measuring tape at the central point
between the lowest front rib and the highest front point of the
pelvis, upon light clothing. HDL cholesterol, triglycerides, and
glucose levels were determined from the fasting blood samples
using routine standardized laboratorial methods. As has been
proposed and applied before (9), the continuous measures were
adjusted for medication use based on the estimated effects of the
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Dysregulated ANS Predicts Metabolic Syndrome
medication. According to the standards of medical care in diabetes, the goal of antidiabetic medication should be to lower the
fasting glucose level to ⬍7.0 mmol/L (34). In agreement with
these standards, for persons using antidiabetic medication when
glucose level was less than 7.0 mmol/L (126 mg/dL), a value of
7.0 mmol/L (126 mg/dL) was assigned. According to the average
decline in triglycerides and increases in HDL cholesterol in fibrate trials (35– 40), 0.10 mmol/L (3.8 mg/dL) was subtracted
from the HDL cholesterol level and 0.67 mmol/L (60 mg/dL) was
added to the triglyceride level of persons using fibrates. Similarly,
for persons using nicotinic acid, 0.15 mmol/L (5.8 mg/dL) was
subtracted from the HDL cholesterol and 0.19 mmol/L (17 mg/
dL) was added to the triglycerides (41– 45). Systolic BP and diastolic BP were measured twice during supine rest on the right
arm with the Omron M4-I, HEM 752A (Omron, Healthcare
Europe BV, Hoofddorp, The Netherlands) and were averaged
over the 2 measurements. For persons using antihypertensive
medication, 10 mm Hg was added to the systolic BP and 5 mm
Hg to the diastolic BP according to the average decline in BP in
antihypertensive trials (46 – 48). All metabolic variables were
measured at baseline assessment as well as at 2-year follow-up.
Measurements
Autonomic nervous system
During the visit to the research centers, NESDA subjects were
wearing the VU Ambulatory Monitoring System. The VU-Ambulatory Monitoring System is a lightweight, unobtrusive device
that records the electrocardiogram (ECG) and changes in thorax
impedance (dZ) from 6 surface electrodes placed at the chest and
back of the subjects (49, 50). The interbeat interval (IBI) time
series was extracted from the ECG signal to obtain HR, an indicator of combined cardiac sympathetic and parasympathetic
activity. To index the cardiac effects of both ANS branches separately, PEP (high PEP reflects low sympathetic activity) and
RSA (high RSA reflects high parasympathetic activity) were extracted from the combined dZ and ECG signals.
The PEP reflects noradrenergic inotropic drive to the left ventricle (LV) and was obtained from the ECG and dZ/dt signals,
with the latter ensemble averaged across 1-minute periods timelocked to the R-wave of the ECG. Three time points can be scored
in impedance cardiography ensemble averages: the upstroke or
B-point, the dZ/dt(min) point, and the incisura or X-point. The
PEP is defined as the interval from the Q-wave onset in the ECG,
which is the onset of the LV electrical activity, to the B-point in
the impedance cardiography that indicates the beginning of the
blood ejection through the aortic valve. As a more reliably assessed proxy for the Q-wave onset, the R-wave peak minus a
fixed interval of 48 ms was used (50, 51). The RSA reflects cardiac parasympathetic activity and was obtained by combining
the IBI time series with the filtered (0.1– 0.4 Hz) dZ signal, which
corresponds to the respiration signal. RSA was obtained by subtracting the shortest IBI during HR acceleration in the inspirational phase from the longest IBI during deceleration in the expirational phase for all breaths, as described in detail elsewhere
(49). Automated scoring of IBI, RSA, and PEP was checked by
visual inspection, and valid data were averaged over approximately 90 minutes to create a single PEP, RSA, and HR value.
To investigate additionally whether patterns of cardiac sympathetic and parasympathetic coactivation or parallel reciprocity were related to the metabolic syndrome, 2 measures of autonomic balance were acquired after the approach of Berntson et
J Clin Endocrinol Metab, June 2013, 98(6):2484 –2493
al (52). Normalized values of PEP and RSA were computed by
dividing the individual raw scores minus the mean of the population by the standard deviation of the group. Cardiac autonomic balance (CAB) was calculated as the difference between
normalized values of RSA (zRSA) and PEP (zPEP). The formula
is CAB ⫽ zRSA ⫺ (⫺zPEP) [because increased sympathetic activity is associated with shortened PEP values, PEP was multiplied by ⫺1], such that low values reflect high sympathetic and
low vagal cardiac activity (unfavorable cardiac pattern) and high
values reflect low sympathetic and high vagal cardiac activity
(favorable cardiac pattern). Cardiac autonomic regulation
(CAR) was calculated as the sum of the normalized values of RSA
and PEP (formula ⫽ zRSA ⫹ (⫺zPEP)) and low values represent
coinhibition (low sympathetic and low vagal activity) and high
values represent coactivation (high sympathetic and high vagal
activity) of the 2 cardiac branches.
Because ANS measures served as predictors, only baseline
values were used.
Covariates
Sociodemographic factors included sex, age, and years of attained education. Health confounders included cardiovascular
diseases, heart medication, and smoking. Cardiovascular disease
(including coronary disease, cardiac arrhythmia, angina, heart
failure, and myocardial infarction) was ascertained by self-report. Furthermore, it was determined whether subjects were using heart medication by copying the names of medicines from the
containers brought in by the subjects. Use of heart medication
other than ␤-blockers was ascertained (ATC-codes C01 [cardiac
therapy], C02 [antihypertensives], C03 [diuretics], C04 [peripheral vasodilators], C05 [vasoprotectives], C08 [calcium channel
blockers], C09 [renin and angiotensin agents], and C10 [lipidmodifying agents]), and the change in use in any of these medications over the 2-year period was captured in a categorical
variable (persistent nonusers, persistent users, discontinuing users, and new users). Smoking was addressed using a continuous
variable measuring the mean number of tobacco consumptions
a day. RSA as a proxy for individual differences in cardiac vagal
activity suffers from potential confounding by individual differences in respiratory behavior (53, 54). Accordingly, it has often
been suggested that studies investigating RSA should take respiration rate into account (49, 56). Therefore, respiration rate
was included as a covariate as number of breaths per minute.
Statistical analyses
Mean baseline characteristics and mean 2-year changes in
metabolic components were calculated for the whole sample.
Multiple linear regression analyses were used to analyze the relationship between baseline ANS measures and the changes in
number of metabolic syndrome components and changes in continuous individual metabolic syndrome components during the
2-year follow-up period. All changes in metabolic syndrome
components were normally distributed. Adjustment for confounding was done in the following 2 steps: basic adjustment
(demographic factors and baseline metabolic values) and additional adjustment for health factors. To exclude potential effects
of persistent antidepressant medication use or psychopathology
status, additional sensitivity analyses were performed with additional adjustment for antidepressant medication (yes/no stable
use during follow-up period) and for psychopathology status
(yes/no current [6-mo recency] depression at baseline, yes/no
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Table 1. Sample Characteristics (n ⫽ 1933)
Sociodemographics
Age, y (mean ⫾ SD)
% Female
Education, y (mean ⫾ SD)
Health factors
Smoker (mean ⫾ SD)
% Nonsmoker
% Former smoker
% Current smoker
% Use of heart medication
% Cardiovascular disease
% Diabetic medication
Body mass index, kg/m2 (mean ⫾ SD)
Underweight, %
Normal weight, %
Overweight, %
Obese, %
Current depressive disorder, %
Current anxiety disorder, %
Current antidepressant use, %
Autonomic indices
RSA, ms (mean ⫾ SD)
HR, bpm (mean ⫾ SD)
PEP, ms (mean ⫾ SD)
CAB (mean ⫾ SD)
CAR (mean ⫾ SD)
Measures of metabolic syndrome
No. of metabolic components (mean ⫾ SD)
Elevated waist circumference, %
Elevated blood pressure, %
Elevated fasting glucose, %
Reduced HDL cholesterol, %
Elevated triglycerides, %
Waist circumference, cm (mean ⫾ SD)
Systolic BP, mm Hg (mean ⫾ SD)
Diastolic BP, mm Hg (mean ⫾ SD)
Glucose, mmol/L (mean ⫾ SD)
HDL cholesterol, mmol/L (mean ⫾ SD)
Triglycerides, mmol/L (mean ⫾ SD)
Baseline
Follow-up
42.0 ⫾ 13.3
67.2
12.5 ⫾ 3.3
—
—
—
4.3 ⫾ 8.2
29.7
35.7
34.6
9.9
6.1
2.6
25.3 ⫾ 4.8
2.2
53.0
30.0
14.8
30.5
36.7
16.3a
4.1 ⫾ 8.0
31.3
35.1
33.6
12.0
7.8
3.6
25.6 ⫾ 4.8
1.9
51.1
31.0
15.9
19.5
24.1
16.3a
44.7 ⫾ 25.3
72.0 ⫾ 9.6
119.5 ⫾ 17.7
⫺0.022 ⫾ 1.46
0.050 ⫾ 1.33
42.8 ⫾ 22.7
72.6 ⫾ 9.5
119.7 ⫾ 17.1
0.062 ⫾ 1.49
0.037 ⫾ 1.33
1.42 ⫾ 1.3
30.1
58.5
20.6
14.3
19.2
88.4 ⫾ 13.8
135.6 ⫾ 19.6
81.0 ⫾ 10.9
5.15 ⫾ 0.9
1.64 ⫾ 0.4
1.29 ⫾ 0.9
1.48 ⫾ 1.3
32.9
53.5
25.9
17.9
20.0
89.0 ⫾ 13.7
133.1 ⫾ 19.0
79.2 ⫾ 10.9
5.31 ⫾ 1.0
1.55 ⫾ 0.4
1.31 ⫾ 0.9
⌬
0.07 ⫾ 0.9
2.8
⫺5.0
5.3
3.6
0.8
0.7 ⫾ 6.0
⫺2.6 ⫾ 12.3
⫺1.9 ⫾ 7.5
0.17 ⫾ 0.6
⫺0.09 ⫾ 0.2
0.02 ⫾ 0.6
Data indicate that since data of the two time points are based on the same sample and follow-up measurement is exactly two years after the
baseline assessment for all participants, %female sex, age and education do not change or only in absolute value (⫹ 2 year). Only persistent
antidepressant users were included in the present study. Therefore the percentage of users is equal for baseline and follow-up.
current anxiety disorder at baseline, yes/no current depression at
follow-up, yes/no current anxiety disorder at follow-up).
To investigate linear relationships, fully corrected logistic regression analyses were conducted with quartiles of the baseline
ANS measure as a predictor of the new onset of metabolic syndrome at follow-up. To make sure incident cases were predicted,
subjects with the metabolic syndrome at baseline were excluded
(n ⫽ 352). A P value of ⱕ.05 was regarded as statistically significant. All analyses were conducted using SPSS version 20.0
(SPSS, Chicago, Illinois).
Results
In our sample, 18.2% met the criteria for the metabolic
syndrome at baseline and 21.9% met the criteria at 2-year
follow-up. Of the respondents with metabolic syndrome
at baseline, 75.3% still met the criteria at follow-up and of
the respondents without the metabolic syndrome 10.0%
developed a new onset of metabolic syndrome. Sample
characteristics are presented in Table 1. In general, a mean
decrease in BP and HDL cholesterol, and a mean increase
in waist circumference, glucose, and triglyceride levels and
number of metabolic components were seen over the
2-year follow-up period, although absolute changes were
rather small.
Table 2 shows the results of the predictive value of ANS
measures for a 2-year increase in the number of metabolic
components. Low baseline CAB (indicating high sympathetic and/or low parasympathetic activity), short baseline
PEP, and high baseline HR predicted a 2-year increase in
the number of metabolic components. Fully adjusted anal-
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Dysregulated ANS Predicts Metabolic Syndrome
P ⫽ .04, respectively) and short PEP and low CAB additionally for the 2-year change in diastolic BP (␤ ⫽ ⫺.052,
P ⫽ .02, and ␤ ⫽ ⫺.053, P ⫽ .02, respectively). No autonomic measure significantly predicted 2-year changes in
waist circumference, glucose, or triglyceride levels. Sensitivity analyses additionally adjusting for persistent antidepressant use and depressive and/or anxiety disorders at
baseline and at follow-up did not alter our findings.
Finally, we graphically displayed the association between baseline ANS indicators and the development of
new onset of metabolic syndrome to check for linearity of
associations. Figure 1 shows the results of multivariable
logistic regression analyses associating baseline quartiles
of RSA, HR, PEP, CAB, and CAR with new onset of the
metabolic syndrome among subjects without the metabolic syndrome at baseline (n ⫽ 1581). Compared to subjects in the lowest quartile of HR, subjects in 1 of the upper
2 quartiles had an increased risk for developing the metabolic syndrome during the 2-year follow-up period (odds
ratio, OR [95% confidence interval, CI] ⫽ 1.67 [1.01–
2.86], P ⫽ .05 and OR ⫽ 1.96 [1.14 –3.37], P ⫽ .008,
respectively). A similar but reversed pattern was seen for
PEP and CAB. Compared to the subjects with the lowest
quartile of PEP (the highest cardiac sympathetic activity),
those with higher values of PEP had lower odds of developing the metabolic syndrome over time (Second quartile
: OR [95%CI] ⫽ 0.65 [0.40 –1.05], P ⫽ .08; third quartile:
OR ⫽ 0.64 [0.39 –1.05], P ⫽ .08, and fourth quartile:
OR ⫽ 0.46 [0.26 – 0.82], P ⫽ .009). Having a higher CAB
(indicating low sympathetic and/or high parasympathetic
activity) was a protective factor against the new onset of
the metabolic syndrome (Second quartile: OR [95%CI] ⫽
0.63 [0.38 –1.05], P ⫽ .08; third quartile: OR ⫽ 0.56
[0.33– 0.94], P ⫽ .03, and fourth quartile: OR ⫽ 0.57
[0.33– 0.98], P ⫽ .04).
Table 2. Adjusted Associations Between Baseline
Cardiac Autonomic Control and 2-y Change in Number
of Metabolic Syndrome Components (n ⫽ 1933)
2-y Change in Number of
Metabolic Syndrome
Components, per 1
Component Increase
Baseline ANS
␤
P
␤a
Pa
RSA, per 10 ms increase
HR, per 10 bpm increase
PEP, per 10 ms increase
CAB, per 1 U increase
CAR, per 1 U increase
⫺.016
.057
⫺.065
⫺.066
.041
.57
.02
.005
.007
.10
⫺.016
.056
⫺.079
⫺.077
.054
.59
.02
⬍.001
.002
.03
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Abbreviation: ␤, standardized ␤-coefficient. Based on linear regression
analyses adjusted for age, sex, education, and baseline number of
metabolic syndrome components (RSA was additionally adjusted for
respiration rate).
a
Additionally adjusted for cardiovascular disease, smoking, and
(change in) use of heart medication (other than ␤-blocking agents).
yses showed a similar pattern of results as basic adjusted
analyses, but added a significant positive association with
CAR. Predicting 2-year changes in the continuous measures of individual metabolic syndrome components (Table 3) indicated that all relationships pointed in the direction that low-parasympathetic and high-sympathetic
activity predicted increases in metabolic risk factors over
time. However, only some of the predictions were significant after adjustment. High HR predicted decreased HDL
cholesterol (␤ ⫽ ⫺.056, P ⫽ .008), increased diastolic BP
(␤ ⫽ .089, P ⬍ .001), and increased systolic BP (␤ ⫽ .056,
P ⫽ .009). Low RSA predicted a 2-year decrease in HDL
cholesterol (␤ ⫽ .063, P ⫽ .02). Also, low baseline CAB
was predictive of a decrease in HDL cholesterol over time
(␤ ⫽ .056, P ⫽ .02). Short PEP, low CAB, and high CAR
were also predictors of the 2-year change in systolic BP
(␤ ⫽ ⫺.055, P ⫽ .009, ␤ ⫽ ⫺.044, P ⫽ .05, and ␤ ⫽ .047,
Table 3. Adjusted Associations Between Baseline Autonomic Indices and 2-y Changes in Individual Components of
the Metabolic Syndrome (n ⫽ 1933)
⌬Waist
Circumference,
per 1 cm
⌬Triglycerides,
per 1 mmol/L
⌬HDL
cholesterol,
per 1 mmol/L
⌬SBP, per
1 mm Hg
ANS BL
␤
P
␤
P
␤
P
␤
P
␤
P
␤
P
RSA, ms
RSA, msa
HR, bpm
HR, bpma
PEP, ms
PEP, msa
CAB
CABa
CAR
CARa
.010
.010
.003
.004
.002
⫺.005
.005
.000
.002
.009
.71
.73
.88
.86
.92
.84
.83
.99
.94
.73
⫺.008
⫺.008
.020
.020
⫺.006
⫺.010
⫺.011
⫺.014
.002
.002
.78
.77
.39
.38
.81
.67
.65
.57
.92
.95
.063
.063
⫺.052
⫺.056
.024
.029
.052
.056
.016
.011
.02
.02
.02
.008
.29
.20
.03
.02
.52
.65
⫺.001
⫺.002
.053
.056
⫺.047
⫺.055
⫺.037
⫺.044
.041
.047
.98
.94
.01
.009
.03
.009
.10
.05
.07
.04
⫺.014
⫺.019
.082
.089
⫺.046
⫺.052
⫺.046
⫺.053
.030
.033
.60
.47
⬍.001
⬍.001
.03
.02
.05
.02
.19
.15
⫺.025
⫺.023
.025
.020
.009
.006
⫺.009
⫺.009
⫺.025
⫺.021
.38
.40
.28
.39
.70
.79
.73
.71
.31
.40
⌬DBP, per
1 mm Hg
⌬Glucose, per
1 mmol/L
Abbreviations: ␤, standardized ␤-coefficient; DBP, diastolic BP; SBP, systolic blood pressure. Based on linear regression analyses adjusted for
baseline values of the metabolic component, age, sex, and education (RSA was additionally adjusted for respiration rate).
a
Additionally adjusted for cardiovascular disease, smoking, and (change in) use of heart medication (other than ␤-blocking agents).
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Figure 1. Odds ratios (ORs) for incident onset of the metabolic syndrome at follow-up for all autonomic indices (n ⫽ 1581). Circles represent
ORs; lines represent the 95% confidence intervals. ORs and P values are for comparison with the first quartile. Based on multinomial logistic
regression adjusted for age, sex, education, cardiovascular diseases, smoking, and (change in) use of heart medication (other than ␤-blocking
agents).
Discussion
In this large longitudinal study, we found that short baseline PEP, low CAB, high HR, and high CAR were associated with an increase in number of metabolic syndrome
components over a 2-year time period. These findings suggest that increased sympathetic nervous system activity
predicts an increase in number of metabolic components.
Results on CAB and CAR (52) showed us that this holds
true in situations in which parasympathetic nervous system activity is reciprocally decreased but also when it is
coactivated. In other words, increased sympathetic activity predicts an increase in number of metabolic compo-
nents irrespective of vagal activity. Lower RSA with higher
HR was found to predict a decrease in HDL cholesterol.
These findings suggest that diminished parasympathetic
activity was predictive for future HDL cholesterol dysregulation. To our knowledge, we are the first to report
this. Increases in systolic BP over a 2-year period were
mainly predicted by high baseline sympathetic activity,
reflected by high HR and short PEP. High diastolic BP was
also predicted by high sympathetic control, reflected by
high HR and short PEP. For prediction of 2-year changes
in other measures of the metabolic syndrome (waist circumference, triglycerides, and glucose levels), autonomic
2490
Licht et al
Dysregulated ANS Predicts Metabolic Syndrome
nervous system measures appeared to be less firm predictors. In addition, tests for linearity in relations showed that
for HR, PEP, and CAB a “dose-response effect” was seen
in the prediction of new onset of the metabolic syndrome.
In other words, for higher baseline values of HR and lower
baseline values of PEP and CAB, higher odds for new onset
of the metabolic syndrome at follow-up were seen.
These findings extend the previous findings by adding
a prospective design, which gives a good indication of
which autonomic indices are predictive for later metabolic
abnormalities. Our longitudinal findings are consistent
with our prior cross-sectional findings that indicated a
negative association between the number of metabolic
syndrome components and RSA, PEP, and CAB (and a
positive association with HR) (9). Results are also largely
congruent with other cross-sectional studies such as Koskinen et al, Liao et al, and Min et al, who found that
diminished parasympathetic and increased sympathetic
activity were associated with higher numbers of metabolic
abnormalities (17–19). BP findings were rather consistent
with our previous cross-sectional results that indicated an
association between high systolic BP and high sympathetic
activity. These findings are not unexpected because the
role of the sympathetic nervous system in the control of BP
has already been known for decades (57, 58). However,
the lack of a relationship between parasympathetic activity and BP is in contrast with our cross-sectional results.
Our results are also in disagreement with some other studies that suggested vagal involvement in the development of
hypertension (59 – 61). A likely explanation is the follow-up duration of 2 years only (in contrast to 4 y in the
Framingham study), which may have been too short to
allow lasting effects of decreased vagal tone on BP. Although the predictive value for HDL cholesterol is not
entirely in line with our previous results—we only found
a cross-sectional relationship between HDL cholesterol
and HR—it does match with other cross-sectional results
(17, 19, 62). Our BP and HDL cholesterol findings perfectly match those of Palatini et al. (6). In a study on hypertension and lipid abnormalities, they reported that subjects with sympathetic predominance (high sympathetic
activity relative to low parasympathetic activity) showed
increased BP (systolic as well as diastolic) and total cholesterol levels at 6-year follow-up compared to the subjects
without autonomic dysregulations.
ANS dysregulation is reported to have a direct effect on
BP regulation and lipid metabolism via circulating (nor)
epinephrine (63– 67). However, also more indirect ways
are reported, for instance, via insulin resistance and the
effects of adipokines. Increased sympathetic and decreased parasympathetic activity are (bidirectionally)
linked to increased levels of leptin and insulin (resistance),
J Clin Endocrinol Metab, June 2013, 98(6):2484 –2493
which are independent dysregulators of the lipid metabolism and BP (4, 6, 7, 13, 68 –73). In addition, HDL cholesterol has an antiatherogenic function and low HDL
cholesterol might deteriorate (diastolic) LV function. In
this way low HDL itself might also cause or worsen high
BP and hypertension (74 –77). Clearly, dysregulation of
the ANS can cause decreases in HDL cholesterol levels and
increases in BP in different ways.
An important contribution of the present study is the
finding that previous cross-sectional findings are now
(partly) confirmed in longitudinal analyses, making a
causal pathway more plausible. Our study has other
strengths as well: a large sample size and multiple measures of sympathetic as well as parasympathetic activity.
In addition to the presence of metabolic syndrome itself,
all separate components that constitute the metabolic syndrome were analyzed. Finally, our sample size enabled us
to consider important confounders. However, some limitations must be acknowledged as well. First, because the
follow-up period was rather short (2 y), time to develop a
new onset of the metabolic syndrome and observe clinically important changes in continuous measures might be
limited. This limitation might explain why we found little
predictive value for changes in waist circumference and
glucose levels, which might need more time to develop.
Future follow-up measurements will allow us to investigate these relationships over a more robust period. In addition, because several studies have indicated that autonomic dysregulation becomes apparent specifically during
stress, it would be valuable to investigate the predictive
value of autonomic stress reactivity (78 – 80). Finally, we
need to consider the fact that differences in adrenergic and
muscarinergic receptor sensitivity as well as differences in
cardiac afterload and preload can influence PEP and RSA,
independently of the actual cardiac ANS activity. These
parameters therefore do not unequivocally reflect sympathetic and parasympathetic activity. Nonetheless, various
studies have shown that PEP and RSA do reflect the expected individual differences in cardiac autonomic activity
across a wide range of paradigms including pharmacological blockade (81), chronic stress (82), or chronic exercise
exposure (83), making it useful parameters to answer our
research questions.
Taken together, these results suggest that increased
sympathetic activity is a predictor of an increase in the
number of metabolic syndrome components and high BP
and a decrease in HDL cholesterol over time, whereas
decreased parasympathetic activity only predicts a decrease in HDL cholesterol over time. Especially these metabolic factors have been associated with hypertension, arterial stiffness, diabetes, and stroke (77, 84 – 88). Because
several prospective studies indicated that decreased para-
doi: 10.1210/jc.2012-3104
sympathetic activity and increased sympathetic activity
are important risk factors for cardiovascular diseases (33,
55, 89 –95), our results suggest that part of this relationship may be explained by ANS effects on the development
of the metabolic syndrome.
Acknowledgments
Address all correspondence and requests for reprints to: Carmilla
M.M. Licht, PhD, Department of Psychiatry/Extramural Medicine Research⫹ Institute, VU University Medical Center, AJ
Ernststraat 1087, 1081 HL Amsterdam, The Netherlands. Email: [email protected].
The infrastructure for the NESDA study (www.nesda.nl) is
funded through the Geestkracht program of the Netherlands
Organisation for Health Research and Development (Zon-Mw,
Grant Number 10-000-1002) and is supported by participating
universities and mental health care organizations (VU University
Medical Center, Geestelijke Gezondheidszorg inGeest, Arkin,
Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ
Drenthe, Scientific Institute for Quality of Healthcare [IQ healthcare], Netherlands Institute for Health Services Research
[NIVEL], and Netherlands Institute of Mental Health and Addiction [Trimbos]). Data analyses were supported by NWO
Grant (Vidi, 917.66.320) (to B.W.J.H.P.) and an Extramural
Medicine Research⫹ fellowship (to C.M.M.L.).
Disclosure Summary: The authors have nothing to disclose.
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