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.

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 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 pro- and 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 opioid-dependent 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 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.