World Journal of Cardiovascular Diseases, 2013, 3, 361-370 WJCD Published Online August 2013 (
Opiate exposure increases arterial stiffness, advances
vascular age and is an independent cardiovascular risk
factor in females: A cross-sectional clinical study*
Albert Stuart Reece#, Gary Kenneth Hulse
School of Psychiatry and Clinical Neurosciences, University of Western Australia, Crawley, Australia
Received 11 May 2013; revised 11 June 2013; accepted 15 July 2013
Copyright © 2013 Albert Stuart Reece, Gary Kenneth Hulse. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Background: Whilst several studies have demonstra-
ted poor cardiovascular health in opiate dependence,
its role as a cardiovascular risk factor has not been
considered. Methods: Pulse wave analysis was under-
taken by radial arterial tonometry (SphygmoCor) in
female control and opiate-dependent patients and
compared to lifetime opiate use. Results: 222 opiate
dependent women were compared to 175 controls.
Opiate dependent patients were receiving treatment
with buprenorphine (83.3%), methadone (13.5%), or
naltrexone (3.2%). Non log transformed chronologic
age (CA) for the two groups was 33.58 ± 0.57 (opiate)
vs. 32.62 ± 0.96 (controls) years (mean ± S.E.M.; P =
0.39). Vascular Reference Age (RA) 39.30 ± 1.28, vs.
35.03 ± 1.41 the RA-CA difference (5.73 ± 1.02 vs.
2.41 ± 0.91) and the RA/CA ratio (1.16 ± 0.03 vs. 1.07
± 0.02; all P < 0.02), and all measurements of central
arterial stiffness (P < 0.02) were significantly worse
for opiates compared to controls. When adjusted for
CA, RA and central augmentation pressure and index
were all worse by themselves and in interaction with
CA (all P < 0.005). At 60 years the modelled RA’s
were 83.79 and 67.52 years respectively. The opiate
dose-duration interaction showed a dose-response
effect with RA (P = 0.0033). After full adjustment for
established cardiovascular risk factors, the dose-du-
ration interaction remained significant (P = 106), was
included in 10 other terms, and dose or duration was
included in 15 other interactions. Conclusion: These
data show that lifetime opiate use is significantly as-
sociated with increased arterial stiffness and vascular
age and suggest a dose-response relationship. This
relationship is robust and persists after full multi-
variate adjustment. These findings carry far-reaching
implications for opiate-induced generalized accelera-
tion of organismal ageing.
Keywords: Arterial Stiffness; Heroin; Opiates;
Vascular Ageing; Dependence; Human Ageing
Opiate dependence which can arise from illegal drug use,
inappropriate use of medically derived opiates, or iatro-
genically as in the course of chronic pain management, is
a major public health issue in modern western nations.
Chronic pain is experienced by 90 million Americans
over a lifetime, and is responsible for $100 billion in
annual healthcare costs [1]. In 2004, it was estimated that
235 million scripts for opiates were prescribed. Indeed, it
has been noted that in the USA in recent years overdoses
with legal opiates outnumber those from heroin and co-
caine combined [1]. Data from the Drug Abuse Warning
Network suggested that from 1998-2009 non-heroin non-
methadone opiate overdoses presenting to US Emer-
gency Rooms rose from 2.2% of all ER presentations to
11.5% [2].
Whilst cardiovascular complications of amphetamine
and cocaine addiction are well recognized, a small but
increasing amount of empirical data exist linking long
term opiate dependence with heart and vascular disease
[3-5]. 17% of heroin dependent patients of 44 or more
years of age in one study had coronary stenoses of more
than 75% [6]. High rates of myocardial fibrosis, ven-
tricular hypertrophy, interstitial fibrosis, perivascular fi-
brosis and severe coronary disease were identified in
*Conflict of Interest: Nil declared.
Sources of Funding: Nil.
Statement of Authorship: This work is the work of the authors in its
#Corresponding author.
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370
another study; all worse in those patients treated with
methadone [4]. In Australia, large state-wide autopsy
population based study reviewing in excess of 20 years
of opiate maintenance treatments from Sydney, showed a
relative risk of cardiovascular disease of 2.2 (95% C.I.
1.8 - 2.7, Appendix 6) [7]. A careful angiographic study
of 2405 patients showed that opium use was significantly
related to coronary disease with odds ratio 1.8 (C.I. 1.8 -
4.7, P = 0.01), including a dose response effect with dis-
ease severity (P = 0.002) [8]. This relationship persisted
when only non-smokers were considered (P < 0.001).
These workers further showed that the coronary disease
presents four years earlier in opium users [9]. Most of
this literature however does not report on gender specific
The results of various genome wide association studies
(GWAS) in coronary disease [10-13] have identified a
“hotspot” on the senescence locus at chromosome
9p21.3, a site which is adjacent to genes CDKN2A and
CDKN2B which code for P16INK4A, P15INK4B and
P19ARF, and actually maps to the long non-protein cod-
ing senescence-associated RNA called ANRIL [14].
ANRIL has been shown to interact in cis with the pro-
moter for CDKN2A (coding for P16), and to act via
γ-interferon [15,16]. P16 activation is known to be asso-
ciated with senescence induction in many tissues [17,18].
Such tissues secrete various factors including pro-in-
flammatory cytokines which further maintain and induce
the senescent state [19]. It is of great interest therefore to
note that opiates have long been known to interfere with
tissue growth [20,21] by an effect which has been shown
to be mediated by P16 and P21 [22,23]. Indeed, it has
been shown that most tissues are under a normal tonic
endorphin/enkephalin mediated negative growth sup-
pression, which can be unmasked pharmacologically by
opiate antagonists, or mechanistically with appropriate
targeted siRNA’s directed against the perinuclear recep-
tor which mediates these activities called the opiate
growth factor receptor [24].
Pulse Wave Analysis (PWA) by radial arterial tono-
metry is a technique which has been widely used in re-
cent years to ascertain central arterial function, arterial
stiffness and vascular compliance. It is able to differenti-
ate the forward pressure wave originating from the heart
from the backward projected pressure wave originating
from peripheral resistance sites. The speed and amplitude
of the reflected wave are a function of the stiffness of the
arterial system, which in turn has been related to age in
large population based studies. The SphygmoCor system
automatically generates a vascular reference age (VA or
RA) from the data output. The technique has several ad-
vantages in research into vascular health in opiate de-
pendent persons, particularly, that it becomes sensitive to
changes early in life in the third and fourth decade, prior
to the time when many other vascular function tests be-
come discriminative and is the age of most of our drug
dependent cohort. PWA is also rapid, and can readily be
repeated on subjects who re-present at a later time.
Importantly, cardiovascular ageing has been found to
account for more than half the effect of aging in western
populations [25]. For this reason, the importance of such
studies extends well beyond its implications for cardio-
vascular medicine, and suggests that a demonstration of
advanced vascular age is actually a surrogate marker for
generalized organismal ageing.
As this clinic sees both general medical and opiate
dependent patients and has experience with the PWA
technique, we were ideally placed to formally test whe-
ther long term opiate dependence is associated with in-
creased vascular stiffness and central arterial ageing.
Data in males, in longitudinal studies and by pharma-
cological treatment types is presented in companion pa-
Patient Selection and Treatment. Control females (N =
175) were recruited from patients having insurance or
employment medical examinations, patients with minor
physical health problems, such as ear blockages or minor
psychological presentations (N = xx), university students
(N = xx) and community volunteers (N = xx). 222 heroin
dependent females maintained on methadone (13.5%),
buprenorphine (83.3%), or implant naltrexone (3.2%)
were recruited and sampled opportunistically at the time
of their presentation to the clinic. Pharmacotherapy
treatment was in accordance with established clinical
practices either at this clinic or by their usual treating
physicians. Naltrexone implants were not performed as
part of this study, but patients who had them previously
inserted for management of heroin dependence were
studied on the occasion of their visit to the clinic.
Naltrexone implants are not a registered therapeutic good
in Australia, but are available to patients on a compass-
sionate access scheme within the Special Access scheme
of the Therapeutic Goods Administration. These techni-
ques have been previously described [26]. Implants were
obtained from Go Medical Industries in Perth, Western
Australia, were of 1.7 g, and designed to last about five
PWA Measurements. Patients were not permitted to
talk or sleep during the performance of PWA studies.
Access to food, drink, and tobacco was not restricted. If
patients identified use of alcohol prior to the testing, the
studies from that day were discarded. PWA was per-
formed with the Miller microtonometer, the Sphygmo-
Cor software and the AtCor preamplifier and hardware,
obtained from AtCor in Sydney. Studies were performed
over the right radial artery unless it was not available.
The brachial blood pressure was taken from the contra-
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370 363
lateral arm to the study side using an Omron HEM 907
oscillometric device. Studies were done in quintuplicate
wherever possible. A history of recent and lifetime drug
use was taken from the patient at the time of study, and
entered into the software’s database. If patients’ main
opiate of abuse was not heroin, it was converted into
morphine equivalents, and then into heroin equivalents,
at a conversion rate of 500 mg of morphine per gram of
street heroin (REF). The heroin dose was the dose usu-
ally taken at the time of study. Length of heroin use was
the total period of opiate use from the time of first use to
the present. Cardiovascular parameters were generated
automatically by the software and outputted from it.
Major indices calculated from this technique included
the Vascular or Reference Age (VA, RA), Central Sys-
tolic Pressure (C_SP), Central Diastolic Pressure (C_DP),
the Chronologic Age (CA), the Central Augmentation
Pressure at Heart Rate 75 (C_AP_HR75), the Central
Augmentation Pressure/Pulse Height Ratio at Heart Rate
75 (C_AGPH_HR75) also known as the Augmentation
Index, Peripheral-Central Pulse Pressure Amplification
Ratio (PPAmpRatio), Central Pulse Height (C_PH),
Central Mean Pressure (C_MEANP), Central End Sys-
tolic Pressure (C_ESP), the Central Diastolic Time Index
(C_DTI), the Central Tension Time index (C_TTI), the
Central Diastolic Duration (C_DD), and an index of sub-
endocardial perfusion known variously as the Central
Stroke Volume Index (C_SVI), the Subendocardial Per-
fusion Ratio (SEVR) or the Buckberg ratio, which is
defined as the C_TTI/C_DTI.
Statistics. Data are presented as mean ± S.E.M. Epi-
Info from CDC Atlanta, Georgia was used to per-
form Chi Squared tests to compare categorical variables.
“Statistica” 7.1 from Statsoft, Oklahoma was used to
compare continuous variables using student’s t-tests. Se-
parate variances were employed were Levene’s test was
significant. Data was log transformed as indicated by the
Shapiro test in the interests of normality assumptions
with the sole exception of CRP. CRP was transformed by
the arcsinh transformation which is similar to log trans-
formation, but it also accepts negative and zero argu-
ments. Model appropriateness was determined by the
outcome of Anova tests and Akiane Information Criteria
(A.I.C.). Multiple-regression was performed in “R”
2.13.1 obtained from the Central “R” Archive Network
mirror at the University of Melbourne. Graphs were
drawn with the aid of Ggplot 2 software. P < 0.05 was
considered significant.
Ethical Approval. Informed consent was obtained
prior to the performance of the study in all patients. Pa-
tients undergoing naltrexone implants also gave infor-
med written consent. This study was approved by the
Human Research Ethics Committee (HREC) of South-
city Medical Centre, a recognized HREC by the National
Health and Medical Research Council. All procedures
complied with the Declaration of Helsinki. Relevant re-
gulatory requirements were met throughout.
As shown in Table 1 there were 175 control and 222
opiate dependent female patients. The chronological
mean age (CA) of the two groups was 32.62 ± 0.96 and
33.58 ± 0.57 years (mean ± S.E.M.), respectively, was
not statistically different (t = 0.85, dF = 292.37, P = 0.39).
Significant differences between substance exposure and
some laboratory values were also documented and have
been previously reported [27]. 83.33% of the opiate de-
pendent patients were treated with buprenorphine,
13.51% were treated with methadone, and 3.15% were
treated with naltrexone. The mean dose of buprenorphine
used was 6.83 ± 0.36 mg, and the mean dose of metha-
done used by these patients was 55.80 ± 6.04 mg.
Table 2 presents the results of the direct comparison
of the two groups for central and peripheral cardiovascu-
lar parameters. The quality index (Operator Index) in the
two groups was uniformly and similarly high. The vas-
cular age, the difference between the vascular and chro-
nologic ages, and the RA/CA ratio were all significantly
elevated in the opiate dependent group. All five cited
measures of arterial stiffness were elevated amongst ad-
dicted patients, except the pulse pressure amplification
ratio, where depression is associated with age related
Figures 1-4 present various plots of age, arterial stiff-
ness, pressure and timing indices respectively against
Judged by the AIC, the best way to model age related
changes is the semi-log model. Using this technique,
when patients achieve a CA of 60 years, the controls
(intercept = 2.4868, slope = 0.0287) have a predicted
modelled age of 67.52 years, and the opiate dependent
patients (intercept = 2.4267, slope = 0.00333) of 83.79
years. This represents an elevation in the modelled age of
16.27 years or 24.10%.
When the (log) RA is regressed against the CA and
addictive status, the addictive status is significantly pre-
dictive both as a factor in an additive model, and in in-
teraction with CA. Details of these results and other re-
sults for a similar age dependent analysis of major cen-
tral cardiovascular parameters are given in Table 3.
Possible dose-response relationship with lifetime opi-
ate exposure in opiate exposed individuals was examined
in an interactive model of the log of the RA/CA ratio
against both heroin dose and duration. In the final model
(Adj. R2 = 0.193, F = 8.73, dF = 1391, P = 0.0033) the
only remaining significant variable was the dose-dura-
tion interaction (est. = 0.0057 ± 0.0019, t = 2.954, P =
Persistence of a dose-response effect after adjustment
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A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370
Copyright © 2013 SciRes.
Table 1. Socio-demographic parameters.
Parameter Controls Addiction P Value
No. 175 222
Chronologic_Age* 32.62 (0.96) 33.58 (0.57) 0.0439
Height (m) 165.74 (0.51) 165.52 (0.44) 0.7429
Weight (kg) 66.33 (0.93) 63.99 (0.82) 0.0583
BMI (kg/m2) 24.17 (0.34) 23.35 (0.28) 0.0635
Substance Abuse
Smokers, No. (%) 29 (16.57%) 200 (90.09%) 0.0000
Cigarettes/d 2.18 (0.45) 14.66 (0.62) 0.0000
Minutes Post-Cigarette 150.51 (27.76) 106.41 (20.26) 0.1976
Heroin Dose (g) 0 (0) 0.57 (0.06) 0.0000
Heroin Duration (Years) 0.11 (0.11) 12.05 (0.6) 0.0000
Heroin Dose-Duration (g-Years) 0.01 (0.01) 8.78 (1.79) 0.0000
Laboratory Values
Cholesterol (mmol/l) 4.72 (0.1) 4.55 (0.07) 0.1956
Triglyceride (mmol/l) 1.17 (0.08) 1.27 (0.05) 0.3057
HDL (mmol/l) 1.45 (0.06) 1.38 (0.04) 0.3830
LDL (mmol/l) 2.72 (0.14) 2.55 (0.07) 0.2360
ALT (IU/l) 25.96 (1.57) 54.58 (7.55) 0.0003
AST (IU/l) 25.01 (1.12) 46.44 (5.52) 0.0002
Glucose (mmol/l) 4.73 (0.11) 5.27 (0.21) 0.0915
Creatinine (mcmol/l) 71.33 (1.59) 70.33 (0.95) 0.5756
Urea (mmol/l) 4.9 (0.12) 4.35 (0.11) 0.0030
Albumin (g/l) 44.28 (0.32) 43.49 (0.26) 0.0798
Globulin (g/l) 29.83 (0.41) 31.57 (0.35) 0.0033
CRP (mg/l) 2.73 (0.6) 5.75 (0.74) 0.0017
ESR (mm/hr) 9.03 (0.83) 15.47 (1.01) 0.0000
Lymphocytes (×109/l) 2.37 (0.08) 2.47 (0.06) 0.3538
Monocytes (×109/l) 0.51 (0.03) 0.54 (0.02) 0.3117
*—Statistics for log transformed data presented; data presented as mean (S.E.M.).
for established cardiovascular risk factors was also in-
vestigated. Table 4 shows the result of a multiple linear
regression of (log) RA against interactive terms in CA,
heroin dose and duration, cigarette consumption, HDL,
and CRP and additive terms in BMI, brachial systolic
pressure, cholesterol and height. A factor related to time
since cigarette consumption was found not to be signfi-
cant in exploratory modelling and so was omitted. The
parameters for this model were Adj. R2 = 0.5053, F =
4.652, dF = 33.85, P = 5.97 × 109.
The table arranges the results in ascending order of P
values. Amongst the final 33 terms remaining in the
model, the heroin dose: duration interaction is noted to
occur in 11 terms, and the heroin dose and duration
separately in a further 15 terms. Indeed, the first such
heroin dose: duration interaction is that with CA, which
has an est. = 0.1196 ± 0.0228, t = 5.300, and P = 9.00 ×
107. Interactions involving CA, tobacco consumption,
CRP and HDL are prominent. CA, CRP, tobacco and
brachial systolic pressure are independently predictive.
Study data indicated dramatic differences in central arte-
rial function, stiffness and vascular age in opiate exposed
compared to non-exposed women, on direct bivariate
comparison, and when corrected for chronologic age
(CA). In a fully adjusted multivariate model including all
major cardiovascular risk factors the dose: duration in-
teraction is independently significant and it is also inter-
actively significant with other major risk factors such as
age, tobacco consumption reby demon- and HDL, the
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370 365
Table 2. Selected cardiovascular parameters.
Parameter Controls Addiction P Value
Operator Index 86.23 (0.53) 87.1 (0.45) 0.2138
Vascular_Age* 35.03 (1.41) 39.30 (1.28) 0.0083
Difference 2.41 (0.91) 5.73 (1.02) 0.0156
RA/CA 1.07 (0.02) 1.16 (0.03) 0.0197
Log (RA/CA) 0.02 (0.02) 0.08 (0.02) 0.0732
Arterial Stiffness
C_AP_HR75 4.29 (0.4) 5.96 (0.34) 0.0016
C_AGPH_HR75 11.66 (1.07) 15.81 (0.83) 0.0023
C_PH (mmHg) 33.49 (0.59) 35.8 (0.5) 0.0029
PPAmpRatio 149.62 (1.59) 144.32 (1.38) 0.0120
P_AI 64.35 (1.48) 69.7 (1.29) 0.0066
HR (bpm) 69.42 (0.84) 70.63 (0.71) 0.2681
Ejection_Duration (msec) 331.06 (1.51) 326.27 (1.34) 0.0179
C_SVI 141.15 (2.21) 138.86 (1.71) 0.4066
C_DTI 2976.29 (29.29) 2971.69 (27.11) 0.9089
C_TTI 2175.61 (30.12) 2191.48 (25.16) 0.6841
C_Diastolic Duration (msec) 558.05 (10.1) 544.46 (8.02) 0.2866
SP 118.11 (0.9) 118.68 (0.77) 0.6320
DP 69.03 (0.7) 68 (0.69) 0.3029
Central_SP 103.82 (0.93) 105.21 (0.81) 0.2575
Central_DP 70.35 (0.72) 69.5 (0.69) 0.3967
Central_ESP 92.86 (0.89) 94.12 (0.8) 0.2929
Central_MEANP 85.93 (0.76) 86.17 (0.7) 0.8161
*—Statistics for log transformed data presented; abbreviations as in methods; data presented as mean (S.E.M.).
Table 3. Age dependent multiple regression of central CVS parameters.
Parameter Variable Estimate (Std. Error)t ValuePr (> |t|)Adjusted R2F DF1, DF2 Model P
RA Opiate. Status 0.095 (0.034) 2.82000.0050 0.4912 192.10 2394 <2.0E16
RA CA: Opiate. Status 0.003 (0.001) 3.21400.0014 0.4942 194.40 2394 <2.0E16
C_AP_HR75 Opiate. Status 1.3344 (0.3697) 3.610 0.0003 0.5158 211.90 2394 <2.0E16
C_AP_HR75 CA: Opiate. Status 0.041 (0.0106) 3.869 0.0001 0.5181 213.80 2394 <2.0E16
C_AGPH_HR75 Opiate. Status 3.3319 (0.9692) 3.438 0.0006 0.4820 185.30 2394 <2.0E16
C_AGPH_HR75 CA: Opiate. Status 0.0999 (0.0278) 3.588 0.0004 0.4834 186.30 2394 <2.0E16
C_SVI Opiate. Status 28.371 (9.274) 3.0590.0024 0.0159 3.13 3393 0.0255
C_SVI CA: Opiate. Status 0.4766 (0.2121) 2.247 0.0252 0.0159 3.13 3393 0.0255
C_SP/CA Opiate. Status 0.0513 (0.027) 1.9000.0582 0.0065 3.61 1395 0.0582
Abbreviations as in Methods.
strating a positive dose-response. Opiate dose or duration
exposure featured in 26 of the terms in the final regres-
sion model, which accounted for 50.5% of the variance
in (log) RA. The most powerful interaction we demon-
strated was that between the dose-duration interaction
and age, which had a P value < 106 (Table 4). Based on
the regression models established, at a CA of 60 years,
controls would have a mean vascular age of 67.52 years
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370
Table 4. Final model of CVS risk factors.
Variable Parameter Estimates
Estimate_(S. E.) t Value Pr (> |t|)
CA 2.244 (0.3621) 6.196 0.000000
Cigarettes: CRP 0.0548 (0.0102) 5.359 0.000001
CA: H. Dose: H. Dura. n 0.1196 (0.0226) 5.3 0.000001
H. Dura. n: Cigarettes: CRP 0.0046 (0.0009) 5.091 0.000002
CA: H. Dose: Cigarettes: HDL: CRP 0.1412 (0.0278) 5.076 0.000002
CA: H. Dose: HDL: CRP 3.056 (0.6055) 5.047 0.000003
CA: H. Dose: Cigarettes: HDL 1.685 (0.3351) 5.028 0.000003
CRP 1.111 (0.2237) 4.965 0.000004
CA: H. Dose: H. Dura. n: HDL: CRP 0.2134 (0.0449) 4.758 0.000008
CA: H. Dose: H. Dura. n: Cigarettes 0.0057 (0.0012) 4.755 0.000008
H. Dose: Cigarettes: HDL 5 (1.055) 4.74 0.000009
CA: H. Dose: HDL 29.4 (6.708) 4.383 0.000033
CA: H. Dose: H. Dura. n: HDL 2.533 (0.5862) 4.321 0.000042
H. Dose: HDL 84.36 (20.76) 4.063 0.000107
CA: H. Dose: H. Dura. n: Cigarettes: HDL: CRP 0.0086 (0.0022) 3.989 0.000140
H. Dose: H. Dura. n: HDL 7.555 (1.969) 3.837 0.000239
CA: H. Dose: H. Dura. n: Cigarettes: HDL 0.1146 (0.0304) 3.772 0.000298
CA: H. Dose: CRP 1.309 (0.3512) 3.727 0.000349
H. Dose 2.164 (0.5981) 3.617 0.000504
H. Dose: CRP 4.644 (1.297) 3.58 0.000572
CA: H. Dose: H. Dura. n: CRP 0.1494 (0.0419) 3.571 0.000589
CA: Cigarettes: HDL 0.2205 (0.0638) 3.454 0.000864
H. Dura. n: Cigarettes: HDL: CRP 0.0061 (0.0018) 3.395 0.001046
Cigarettes: HDL 0.7377 (0.2177) 3.388 0.001069
H. Dose: H. Dura. n: Cigarettes: HDL 0.341 (0.1024) 3.332 0.001279
Cigarettes 0.0447 (0.0134) 3.328 0.001296
CA: H. Dose: Cigarettes 0.0297 (0.0091) 3.277 0.001520
H. Dose: H. Dura. n: CRP 0.4326 (0.146) 2.962 0.003964
H. Dura. n: CRP 0.1944 (0.0669) 2.905 0.004681
CA: H. Dose: H. Dura. n: Cigarettes: CRP 0.0009 (0.0003) 2.673 0.009015
SP 0.9822 (0.3713) 2.645 0.009717
CA: H. Dura. n: HDL: CRP 0.014 (0.0054) 2.595 0.011135
CA: H. Dura. n: CRP 0.0361 (0.0171) 2.115 0.037373
CA—Chronologic Age; H. Dose—Usual Heroin Dose; H. Dura. n—Lifetime Duration of Heroin Use.
and opiate dependent patients a mean vascular age of
83.79 years, an elevation of 16.27 years or 24.1%. These
results demonstrate that long term opiate dependence is
an independent and interactive cardiovascular risk factor
in females.
Figures 1, 2, and 4 show a very clear separation be-
tween the age, arterial stiffness and timing data for the
opiate exposed and control groups. Even when the mean
CA of the two groups was assessed as significantly dif-
ferent using log-transformed data, the magnitude of this
is less than one year, which is much less than the demon-
strated differences in the various measures of vascular
age which was 4.2 years. The changes in vascular age
were paralleled by significant changes in measures of
central arterial stiffness, subendocardial perfusion and
systolic pressure (Table 3).
It should also be noted that although this study has
considered all the opiate dependent patients together, in
fact this group is heterogeneous when judged by treat-
ment type (methadone (13.5%), buprenorphine (83.3%),
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370 367
Figure 1. Ageing indices by chronologic age by opiate de-
pendency status.
Figure 2. Arterial stiffness by chronologic age by opiate de-
pendency status.
or implant naltrexone (3.2%)). Unpublished data from
this project (manuscript submitted) shows that the type
of pharmacotherapy used to treat opiate dependence has
a very material impact on the central cardiovascular out-
comes. Other studies in the literature are consistent with
this view [4,8]. 83.3% of our patients were treated with
buprenorphine with a mean dose of 6.83 ± 0.36 mg
which is an unusually low dose judged by literature stan-
dards. As a partial µ-agonist buprenorphine is a rela-
tively mild intoxicant compared to other treatments. For
this reason we consider that the results reported herein
represent a best case scenario for opiate dependence and
likely a lower bound of their cardiovascular toxicological
The strength of association and the uniformity of the
Figure 3. Central pressures by chronologic age by opiate de-
pendency status.
Figure 4. Central timing indices by chronologic age by opiate
dependency status.
results presented raise the question as to possible mecha-
nisms of action by which opiates might be impacting the
cardiovasculature. At the outset it is important to note
that opiate use has been associated with exacerbation of
most of the major cardiovascular risk factors including
smoking [8], cholesterol [28], poor dietary habits, weight
gain [29], hypertension [30,31], and hyperglycaemia and
diabetes [32]. Higher levels of immune activity have also
been shown [27]. Indeed the present study also demon-
strated elevated levels of ESR, CRP and globulins in our
patients (Table 1).
The pro-senescence activities of opiates have been
noted in the Introduction. This effect is likely com-
pounded by the affects of opiates to stimulate or prime
apoptosis [33] and to induce immune responses both
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370
through toll-like receptor 4 [34] and chemokines [35].
Senescent tissues have also been shown to secrete vari-
ous substances including interleukins -6 and -8 [19]
which are highly toxic to most stem cells niches [36] and
perpetuate the senescent phenotype. From a mechanistic
point it is possible that in opiate dependent patients, there
is a pro-senescence stimulus mediated via ANRIL and
P16, compounded by apoptotic activities, further im-
pacted by heightened immune activity, which is further
exacerbated by the interactive effect of immune stimula-
tion on stem cell vulnerability to produce the observed
phenotype of accelerated ageing in all organ beds exam-
ined. The relative hypercalcaemia likely exacerbates
these changes and contributes to the increased vascular
The importance of cardiovascular age as a surrogate
marker for generalized organismal ageing was also noted
in the introduction [25]. The present findings are there-
fore consistent with other data which shows acceleration
of age dependent changes in bone, hair, teeth and stem
cells [37-39], and with the hypothesis that opiate de-
pendent patients are ageing in a more accelerated manner
across all tissue beds [4,6,30] (see also Appendix 6 [7]).
This study had a number of limitations, primarily re-
lated to its observational design. Design of a randomised
study however has ethical concerns with the randomisa-
tion of opiate or non-opiate dependent persons to treat-
ment or non treatment by an opiate pharmacotherapy.
Additionally when log as opposed to non-log trans-
formed data was used there was a significant difference
between the control and drug dependent group. Further, a
systematic drug history was not collected in a format
which facilitated easy statistical analysis: a key feature
that would be required in future prospective work. Future
iterations of this study would also need to give careful
consideration to the treatment make-up of the opiate de-
pendent condition. Ideally the numbers in the treatment
groups would be broadly comparable, and treatment as-
signment might be randomized between the various con-
ditions. Further studies might also consider investigating
the mechanism of these changes with prospectively col-
lected immune, stem cell and senescence related pa-
rameters of circulating cells able to be quantified and
In summary, this study provides evidence suggesting
an increased vascular age including central arterial stiff-
ness in opiate dependent patients, and demonstrated a
dose-response relationship between these features and
lifetime opiate exposure. This relationship is robust and
persists after adjustment for other cardiovascular risk
factors. At age 60, this is equivalent to a 24.1% ad-
vancement in cardiovascular age above controls. These
findings are consistent with the observations of other
workers, and have the advantage that by studying a sub-
clinical endophenotype, they are performed on living
patients. These results are also consistent with an emerg-
ing body of evidence showing accelerated ageing in all
body systems in opiate dependent patients. Whilst the
present study has not presented evidence for likely
mechanisms of these changes, they are consistent with
the known pro-senescence, pro-apoptotic, immunostimu-
latory activities of opiates, and the interactions of these
effects. Opiates are also known to exacerbate classical
cardiovascular risk factors. Various suggestions for fur-
ther research are made.
The authors would like to thank Dr Mervyn Thomas of Emphron for
assistance with the statistics and graphical design.
[1] Jamison, R.N., Serraillier, J. and Michna, E. (2011) As-
sessment and treatment of abuse risk in opioid prescribe-
ing for chronic pain. Pain Research and Treatment,
[2] Drug Abuse Warning Network (DAWN). (2009) Treat-
ment Episode Data Set—Admissions (Teds-A)—Concate-
nated, 1992 to 2009 (Computer file). Prepared by Synec-
tics for Management Decisions, Incorporated. ICP-
[3] Darke, S., Degenhardt, L. and Mattick, R. (2007) Mortal-
ity amongst illicit drug users: Epidemiology, causes and
intervention. Cambridge University Press, Sydney, 2007.
[4] Darke, S., Duflou, J. and Torok, M. (2010) The compara-
tive toxicology and major organ pathology of fatal meth-
adone and heroin toxicity cases. Drug and Alcohol De-
pendence, 106, 1-6.
[5] Oviedo-Joekes, E., Brissette, S., Marsh, D.C., Lauzon, P.,
Guh, D., Anis, A. and Schechter, M.T. (2009) Diacetyl-
morphine versus methadone for the treatment of opioid
addiction. The New England Journal of Medicine, 361,
777-786. doi:10.1056/NEJMoa0810635
[6] Darke, S., Kaye, S. and Duflou, J. (2006) Systemic dis-
ease among cases of fatal opioid toxicity. Addiction, 101,
1299-1305. doi:10.1111/j.1360-0443.2006.01495.x
[7] Degenhardt, L., Randall, D., Hall, W., Law, M., Butler, T.
and Burns, L. (2009) Mortality among clients of a state-
wide opioid pharmacotherapy program over 20 years:
Risk factors and lives saved. Drug and Alcohol Depend-
ence, 105, 9-15. doi:10.1016/j.drugalcdep.2009.05.021
[8] Sadeghian, S., Darvish, S., Davoodi, G., Salarifar, M.,
Mahmoodian, M., Fallah, N. and Karimi, A.A. (2007)
The association of opium with coronary artery disease.
European Journal of Cardiovascular Prevention & Re-
habilitation, 14, 715-717.
[9] Sadeghian, S., Dowlatshahi, S., Karimi, A. and Tazik, M.
(2011) Epidemiology of opium use in 4398 patients ad-
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370 369
mitted for coronary artery bypass graft in Tehran Heart
Center. Journal of Cardiothoracic Surgery (Torino), 52,
[10] Feero, W.G., Guttmacher, A.E. and McCarthy, M.I. (2010)
Genomics, type 2 diabetes, and obesity. New England
Journal of Medicine, 363, 2339-2350.
[11] O’Donnell, C.J. and Nabel, E.G. (2011) Genomics of
Cardiovascular Disease. New England Journal of Medi-
cine, 365, 2098-2109. doi:10.1056/NEJMra1105239
[12] Helgadottir, A., Thorleifsson, G., Manolescu, A., Gre-
tarsdottir, S., Blondal, T., Jonasdottir, A., Sigurdsson, A.,
Baker, A., Palsson, A., Masson, G., et al. (2007) A com-
mon variant on chromosome 9p21 affects the risk of my-
ocardial infarction. Science, 316, 1491-1493.
[13] McPherson, R., Pertsemlidis, A., Kavaslar, N., Stewart,
A., Roberts, R., Cox, D.R., Hinds, D.A., Pennacchio,
L.A., Tybjaerg-Hansen, A., Folsom, A.R., et al. (2007) A
common allele on chromosome 9 associated with coro-
nary heart disease. Science, 316, 1488-1491.
[14] Pasmant, E., Sabbagh, A., Vidaud, M. and Bieche, I.
(2010) ANRIL, a long, noncoding RNA, is an unexpected
major hotspot in GWAS. The FASEB Journal, 25,
444-448. doi:10.1096/fj.10-172452
[15] Visel, A., Zhu, Y., May, D., Afzal, V., Gong, E., Attana-
sio, C., Blow, M.J., Cohen, J.C., Rubin, E.M. and Pen-
nacchio, L.A. (2010) Targeted deletion of the 9p21
non-coding coronary artery disease risk interval in mice.
Nature, 464, 409-412. doi:10.1038/nature08801
[16] Harismendy, O., Notani, D., Song, X., Rahim, N.G.,
Tanasa, B., Heintzman, N., Ren, B., Fu, X.D., Topol, E.J.,
Rosenfeld, M.G. and Frazer, K.A. (2011) 9p21 DNA
variants associated with coronary artery disease impair
interferon-gamma signalling response. Nature, 470, 264-
268. doi:10.1038/nature09753
[17] Bazarov, A.V., Van Sluis, M., Hines, W.C., Bassett, E.,
Beliveau, A., Campeau, E., Mukhopadhyay, R., Lee, W.J.,
Melodyev, S., Zaslavsky, Y., et al. (2010) p16INK4a -me-
diated suppression of telomerase in normal and malignant
human breast cells. Aging Cell, 9, 736-746.
[18] Coppe, J.P., Rodier, F., Patil, C.K., Freund, A., Desprez,
P.Y. and Campisi, J. (2011) Tumor suppressor and aging
biomarker p16INK4 a induces cellular senescence without
the associated inflammatory secretory phenotype. The
Journal of Biological Chemistry, 286, 36396-36403.
[19] Bhaumik, D., Scott, G.K., Schokrpur, S., Patil, C.K.,
Orjalo, A.V., Rodier, F., Lithgow, G.J. and Campisi, J.
(2009) MicroRNAs miR-146a/b negatively modulate the
senescence-associated inflammatory mediators IL-6 and
IL-8. Aging (Albany NY), 1, 402-411.
[20] Zagon, I.S. and McLaughlin, P.J. (1977) Morphine and
brain growth retardation in the rat. Pharmacology, 15,
276-282. doi:10.1159/000136699
[21] McLaughlin, P.J., Zagon, I.S. and White, W.J. (1978)
Perinatal methadone exposure in rats. Effects on body
and organ development. Biology of the Neonat, 34, 48-54.
[22] Cheng, F., Zagon, I.S., Verderame, M.F. and McLaughlin,
P.J. (2007) The opioid growth factor (OGF)-OGF recep-
tor axis uses the p16 pathway to inhibit head and neck
cancer. Cancer Research, 67, 10511-10518.
[23] Cheng, F., McLaughlin, P.J., Verderame, M.F. and Zagon,
I.S. (2009) The OGF-OGFr axis utilizes the p16INK4a
and p21WAF1/CIP1 pathways to restrict normal cell pro-
liferation. Molecular Biology of the Cell, 20, 319-327.
[24] Zagon, I.S., Verderame, M.F. and McLaughlin, P.J. (2002)
The biology of the opioid growth factor receptor (OGFr).
Brain Research Reviews, 38, 351-376.
[25] Chien, K.R. and Karsenty, G. (2005) Longevity and line-
ages: Toward the integrative biology of degenerative dis-
eases in heart, muscle, and bone. Cell, 120, 533-544.
[26] Reece, A.S. (2007) Psychosocial and treatment correlates
of opiate free success in a clinical review of a naltrexone
implant program. Substance Abuse Treatment, Prevention,
and Policy, 2, 35-49. doi:10.1186/1747-597X-2-35
[27] Reece, A.S. (2007) Evidence of Accelerated Ageing in
Clinical Drug Addiction from Immune, Hepatic and Me-
tabolic Biomarkers. Immunity & Ageing, 4, 6-15.
[28] Cooper, O.B., Brown, T.T. and Dobs, A.S. (2003) Opiate
drug use: A potential contributor to the endocrine and
metabolic complications in human immunodeficiency vi-
rus disease. Clinical Infectious Diseases, 37, S132-S136.
[29] Kolarzyk, E., Pach, D., Wojtowicz, B., Szpanowska-Wo-
hn, A. and Szurkowska, M. (2005) Nutritional status of
the opiate dependent persons after 4 years of methadone
maintenance treatment. Przegl Lek, 62, 373-377.
[30] Rosen, D., Smith, M.L. and Reynolds, C.F. (2008) The
prevalence of mental and physical health disorders among
older methadone patients. The American Journal of Geri-
atric Psychiatry, 16, 488-497.
[31] Hser, Y.I., Gelberg, L., Hoffman, V., Grella, C.E., Mc-
Carthy, W. and Anglin, M.D. (2004) Health conditions
among aging narcotics addicts: Medical examination re-
sults. Journal of Behavioral Medicine, 27, 607-622.
[32] Ceriello, A., Giugliano, D., Passariello, N., Quatraro, A.,
Dello Russo, P., Torella, R. and D’Onofrio, F. (1987)
Impaired glucose metabolism in heroin and methadone
users. Hormone and Metabolic Research, 19, 430-433.
[33] Cunha-Oliveira, T., Rego, A.C., Garrido, J., Borges, F.,
Macedo, T. and Oliveira, C.R. (2007) Street heroin in-
duces mitochondrial dysfunction and apoptosis in rat cor-
tical neurons. Journal of Neurochemistry, 101, 543-554.
[34] Hutchinson, M.R., Zhang, Y., Shridhar, M., Evans, J.H.,
Copyright © 2013 SciRes. OPEN ACCESS
A. S. Reece, G. K. Hulse / World Journal of Cardiovascular Diseases 3 (2013) 361-370
Copyright © 2013 SciRes.
Buchanan, M.M., Zhao, T.X., Slivka, P.F., Coats, B.D.,
Rezvani, N., Wieseler, J., et al. (2010) Evidence that opi-
oids may have toll-like receptor 4 and MD-2 effects.
Brain, Behavior, and Immunity, 24, 83-95.
[35] Adler, M.W., Geller, E.B., Chen, X. and Rogers, T.J.
(2005) Viewing chemokines as a third major system of
communication in the brain. AAPS Journals, 7, E865-
E870. doi:10.1208/aapsj070484
[36] Anderson, J.E. (2000) A role for nitric oxide in muscle
repair: Nitric oxide-mediated activation of muscle satel-
lite cells. Molecular Biology of the Cell, 11, 1859-1874.
[37] Reece, A.S. (2011) Differing age related trajectories of
dysfunction in several organ systems in opiate depend-
ence. Aging Clinical and Experimental Research, 24, 85-
[38] Kim, T.W., Alford, D.P., Malabanan, A., Holick, M.F.,
Samet, J.H. (2006) Low bone density in patients receiv-
ing methadone maintenance treatment. Drug and Alcohol
Dependence, 85, 258-262.
[39] Reece, A.S. and Davidson, P. (2007) Deficit of circulat-
ing stem—Progenitor cells in opiate addiction: A pilot
study. Substance Abuse Treatment, Prevention, and Poli-
cy, 2, 19-28. doi:10.1186/1747-597X-2-19