Cannabis exposure as an interactive cardiovascular risk factor and accelerant of organismal ageing: a longitudinal study
INTRODUCTION
With increasing availability of cannabis derivatives
in many parts of North America, and
intensifying research on the physiology and
pharmacology of the endocannabinoid
system, cannabinoids are becoming increasingly
prominent on the public and research
agenda. The Global Burden of Disease
project identified that cannabis abuse had a
global prevalence of 13 625 000 and was associated
with 396 000 years of life lived with disability
(YLD), a figure which has increased
by 22% from 1990 to 2013.1 Moreover, as
substance abuse and mental illness were
Strengths and limitations of this study
▪ Study strengths include its design features
including combined cross-sectional and longitudinal
structure, the detailed annotation of the
database including consideration of multiple clinical,
pathological and cardiovascular variables
incorporating information on time from exposure
to consider exclude cannabinoid effects.
▪ Advanced conceptual understanding and statistical
modelling employed.
▪ Significant cannabis exposure in contrast to
many previously published studies.
▪ Study limitations included that only 11
cannabis-only patients could be identified of the
125 cannabis-exposed patients.
▪ Significant coefficient of variation was found
with the biomarker of cardiovascular–organismal
age employed; use of an alternative parameter
such as epigenetic age based on DNA methylation
would allow more refined and detailed
studies in smaller patient groups.
Reece AS, et al. BMJ Open 2016;6:e011891. doi:10.1136/bmjopen-2016-011891 1
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some of the five major causes of increasing YLD globally1
and with the role of cannabis now established as a
gateway drug to various drug dependency syndromes2–5
with several serious psychological disorders,2–5 it is likely
that its impact on the global YLD may be larger than is
usually measured.
While cannabinoid toxicology is well established in
the respiratory and neurological–psychiatric literature, it
is less well known that a variety of fascinating studies also
exist which portray its effects on the cardiovascular
system. The effects of cannabinoids on the cardiovascular
system are currently believed to be mediated by
several signalling systems and intracellular transduction
pathways. These include the cannabinoid receptor type
1 (CB1R), cannabinoid receptor type 2 (CBR2), vanilloid,
prostanoid, lysophospholipid and unidentified
endocannabinoid pathways, among others,6 which interact
in complex ways with immune active cells and cytokines,
7–9 all of which are subject to increasingly complex
levels of epigenetic regulation.10 Case reports exist of
serious adverse effects including supraventricular and
ventricular arrhythmias, coronary thrombosis, sudden
cardiac death, asystole, angina, epicardial coronary
spasm and microvascular mediated no-flow phenomena
frequently in very young patients or patients without
other cardiovascular risk factors which have been
recently collated.11 12 A threefold to fivefold elevation of
all causes and cardiac death has been shown within
1 hour of cannabis use in cross-sectional13 and longitudinal
studies.14 A large longitudinal study of 1913 individuals
showed a dose–response relationship between
cannabis exposure and cardiovascular mortality.14 Both
acute strokes and reversible cerebral vasoconstriction
syndrome have been reported in a number of case
reports particularly from France, with the mean age of
the patients much younger than usual at 32–33 years of
age.12 Moreover the very complexity of endocannabinoid
vascular physiology implies that it is both nuanced
and interactive as CB1- mediated effects are often proand
CB2- effects anti -vasculitic and -arteriopathic.15 16
Cannabis is now believed to contain 104 cannabinoid
compounds.12 As cannabis use becomes more widespread
a more complete appreciation of its clinical presentations
becomes an increasing imperative.
Implicit within its diverse multi-system toxicological
profile, which also includes an association with cancers
of several sites,4 17–19 is the distinct possibility that it may
be altering the underlying rate of ageing of the whole
organism. Immune modulation–oxidative stress20 and
epigenetic change21 are believed to be major drivers of
the ageing process, and cannabinoids are now known to
be involved in both.22 Cannabinoids have also been
linked with stem cell physiology23 24 as well as increased
mitochondrial uncoupling and oxyradical flux.25 26
Moreover, it is established in cardiovascular medicine
that since the majority of deaths in western nations are
due to cardiovascular causes, one’s cardiovascular age is
a powerful surrogate for organismal or biological
age.27 28 Many stem cell niches have a vascular component.
29 30 It follows therefore that if one could measure
cardiovascular age, a surrogate for organismal age could
be established and one could test the hypothetical link
between cannabis use and the ageing process.
Indeed, just such an opportunity was afforded recently
in our clinic with the secondary analysis of a longitudinal
cardiovascular database. Encoded cannabis use
details in text format were available. The AtCor
SphygmoCor system measures arterial stiffness and links
it algorithmically to vascular–biological age. As we see
both general and drug-addicted patients and as cannabis
use is common among the latter group, it was decided
to undertake the present analysis.
METHODS
Patient selection
Patients were not selected. Patients presenting to the
clinic were studied in consecutive order in accordance
with the dictates of workflow on the day of presentation.
Patients were restudied, again opportunistically, on presentation
to the clinic at approximately the 2-year and
5-year marks. Opioid-dependent patients were prescribed
buprenorphine both at presentation and
throughout their care.
Our clinic sees 250–350 patients weekly. We have
worked in addiction medicine since 1998. We have seen
more than 2699 of the ∼5500 known registered opioiddependent
patients in Queensland.
Radial arterial pulse wave tonometry (RAPWT)
Radial arterial pulse wave tonometry (RAPWT) was performed
with the Atcor SphygmoCor (Sydney, Australia)
system V.7.0 as previously described.31 Patients were positioned
supine on a bed and the radial arterial pulse
wave was sampled using a probe containing a Millar
micromanometer sensor. Input biophysical data were
analysed by the SphygmoCor software. Accepted studies
were required to have an Operator Index >70% and to
be technically satisfactory. All studies were performed in
quintuplicate. The central waveform was standardised
against the brachial blood pressure obtained sphygmanometrically
using an Omron HEM-907 automated
blood pressure device (Tokyo, Japan). Many indices
were collected from this system including central and
peripheral pressure augmentation, timing indices and
pressure indices. The vascular and reference ages (VA,
- RA) were calculated internally by the software from an
algorithm matching the degree of arterial stiffening with
height, age and sex. Patients were allowed to eat, drink
and smoke prior to study.
Demographic and laboratory data
At the time RAPWT was performed, patients were asked
about drug use and the duration for which these drugs
had been used. Patients usually quantified cannabis use
as cones/day, which equates to ∼0.1 g/day. They were
2 Reece AS, et al. BMJ Open 2016;6:e011891. doi:10.1136/bmjopen-2016-011891
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also asked when they had last used drugs, including
tobacco, as this can affect the RAPWT result. This information
was entered as notes into a RAPWT database.
The RAPWT data were linked with our clinical pathology
database. Clinical pathology testing of our patients was
performed by Queensland Medical Laboratories, which
are accredited to both the Australian Standard AS-15189
and the International Laboratory Standard ISO- 9001.
Data are listed as mean±SEM. Blood was drawn at initial
presentation and as clinically indicated thereafter and
also on an approximately annual basis to update their
clinical profiles. Laboratory data from the time of their
RAPWT was combined with the clinical and RAPWT
data for analysis.
Statistics
Data were held in Microsoft Excel spreadsheets
(Redmond, Washington, USA). All data shown are listed
as mean (±SEM). Categorical data were compared using
EpiInfo 7.1.4.0 from Centres for Disease Control,
Atlanta, Georgia, USA. Bivariate analysis was conducted
using Statistica V.7.1 (Statsoft Tulsa, Oklahoma, USA).
All t-tests were two-tailed. Linear regression was performed
in ‘R’ V.3.2.3 from the Cloud Central R Archive
Network (CRAN) mirror using the base, reshape,
ggplot2, and nlme packages. In order to comply with
normality assumptions, continuous variables were log
transformed as indicated by the Shapiro test.
Time-dependent analyses were conducted using
repeated measures non-linear mixed effects restricted
maximum likelihood estimator (REML) models with
unity and the patient’s unique identifier as random
effects. Models were fitted as suggested by loess plots
and quantitated using analysis of variance (ANOVA)
models. Repeated measures models were compared by
maximum likelihood (ML) methods. Model reduction
was conducted classically, with the progressive elimination
of the least significant term. Missing data were
casewise deleted. To calculate effect sizes, mean dependent
variable parameters (age, BMI and time) were used
together with the coefficient estimates obtained from
the final regression models. Standard abbreviations relating
to statistical models such as degrees of freedom
(DF), Akaike Information Criterion (AIC), Bayesian
Information Criterion (BIC) and Log-likelihood ratio
(Log.Lik) are used. p<0.05 was considered significant.
Ethics
All patients gave informed consent to the performance
of the RAPWT and the inclusion of their anonymised
data in the present analysis. The study was approved by
the Human Research Ethics Committee of South City
Medical Centre, which is registered with the National
Health and Medical Research Council of Australia. The
study was compliant with the Declaration of Helsinki.
RESULTS
Data from 13 657 RAPWT studies were collected from
1553 patients. Three hundred and ninety cases were
excluded because of exposure to conditions (hypertension,
drug withdrawal) or medications (amphetamine,
alcohol, heroin), which were known to interfere with
central cardiovascular status. Cocaine use was not
reported by our patients. Cocaine use in Australia is very
uncommon outside of Sydney and outside of certain
sociodemographically restricted subgroups. Methadone
has also been shown by our group to perturb cardiovascular
status32 and was therefore also excluded. This left
1163 patients including 817 (70.25%) males. The breakdown
by study group is shown in figure 1, and by age,