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• Response to Lane
  Albert S. Reece, Gary K. Hulse
  Published on: 26 March 2020
• Re: Cannabis exposure as an interactive cardiovascular risk factor and accelerant of organismal ageing: A longitudinal study
  Ian A Lane
  Published on: 9 March 2020

• Published on: 26 March 2020

  Response to Lane

  • Albert S. Reece, Professor University of Western Australia
  • Other Contributors:
    • Gary K. Hulse, Professor

  We wish to thank Dr Lane for his interest in our study. We are pleased to see statistical input to the issues of cannabis medicine as we feel that sophisticated statistical methodologies have much to offer this field.

  Most of the concerns raised are addressed in our very detailed report. As described our research question was whether, in our sizeable body of evidence (N=13,657 RAPWA studies), we could find evidence for the now well-described cannabis vasculopathy and what such implications might be. As this was the first study of its type to apply formal quantitative measures of vascular stiffness to these questions it was not clear at study outset if there would be any effect, much less an estimate of effect size. In the absence of this information power calculations would be mere guesswork. Nor indeed are they mandatory in an exploratory study of this type. Similarly the primary focus of our work was on whether cannabis exposure was an absolute cardiovascular risk factor in its own right, and how it compared to established risk factors. Hence Table 2 contains our main results. The role of Table 1 is to illustrate the bivariate (uncorrected) comparisons which can be made, show the various groups involved, and compare the matching of the groups. It is not intended to be a springboard for effect-size-power calculations which are of merely esoteric interest. Calculations detailing the observed effect size are clearly described in our text being 11.84% and 8....

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  We wish to thank Dr Lane for his interest in our study. We are pleased to see statistical input to the issues of cannabis medicine as we feel that sophisticated statistical methodologies have much to offer this field.

  Most of the concerns raised are addressed in our very detailed report. As described our research question was whether, in our sizeable body of evidence (N=13,657 RAPWA studies), we could find evidence for the now well-described cannabis vasculopathy and what such implications might be. As this was the first study of its type to apply formal quantitative measures of vascular stiffness to these questions it was not clear at study outset if there would be any effect, much less an estimate of effect size. In the absence of this information power calculations would be mere guesswork. Nor indeed are they mandatory in an exploratory study of this type. Similarly the primary focus of our work was on whether cannabis exposure was an absolute cardiovascular risk factor in its own right, and how it compared to established risk factors. Hence Table 2 contains our main results. The role of Table 1 is to illustrate the bivariate (uncorrected) comparisons which can be made, show the various groups involved, and compare the matching of the groups. It is not intended to be a springboard for effect-size-power calculations which are of merely esoteric interest. Calculations detailing the observed effect size are clearly described in our text being 11.84% and 8.35% age advance in males and females respectively.

  Mixed-effects models are the canonical way to investigate longitudinal data given a usual random error structure 1. We agree with Lane that unusual error structures can affect significance conclusions. Diagnostic tests run on our models confirm that the residuals had the usual spheroidal error structure so that the application of mixed-effects models in the classical way is quite satisfactory. Another way to investigate this issue is that of incremental model building comparing models with and without cannabis exposure terms. If one considers regression equations from our data with cannabis use treated either as a categorical (RA/CA ~ Days_Post-Cannabis * BMI + * Cannabis_Category) or a continuous (RA/(CA*BMI) ~ Cigs*SP + * Cannabis_Use +Chol+DP+HDL+HR+CRH) variable one notes firstly that terms including cannabis use remain significant in final models (after model reduction) and secondly that models which include cannabis exposure are significantly better than ones without (Categorical: AIC = 1088.56 v. 1090.22, Log.Ratio = 19.62, P = 0.0204; Continuous: AIC = 412.33 v. 419.73, Log.Ratio = 9.37, P = 0.0022). Unfortunately formatting rules for BMJ Rapid Responses do not allow us to include a detailed table of regression results in each model in the present reply.

  We also note that AIC’s are little used in our report, and simply indicate the direction of the ANOVA results comparing models linear, quadratic and cubic in chronological age. They also appear routinely in the display of mixed-effects model results. Their use in such contexts is methodologically unremarkable. Control groups are also spelled out in fine detail in Table 1, in all our Figures and in the text.

  We are aware that various algorithms for vascular age have been reported in the literature. The list proposed by Lane is correct but non-exhaustive. Such algorithms are generally derived from known cardiovascular risk factors. As clearly stated in our report the algorithm for vascular age we employed is derived from the proprietary software used. As such its details have not been publicized and indeed are commercially protected information. We have however been assured by AtCor on many occasions that it includes measures of chronological age, sex, arterial stiffness and height (which is important as it dictates distance and thus speed parameters for the reflected and augmented central arterial pressure waves) and is very well validated and tested. AtCor recently advised that their algorithm is based on a very large series of studies done with arterial stiffness published in 2005 2. As such it has distinct advantages over algorithms which do not include indices of arterial stiffness. The AtCor website includes a very interesting, informative and educative animated loop which clearly illustrates the complex relationship between chronological and vascular age as a function of arterial stiffness and vascular tone 3.

  We are keen to see advanced statistical methods applied to such questions. We are becoming interested in geospatial and spacetime analyses and its application to the important questions of cannabis epidemiology 4. We find the very breadth of the organ systems impacted by cannabis to be quite remarkable with effects on the brain, cardiovasculature, liver, lungs, testes, ovaries, gastrointestinal, endocrine, reproductive and immune systems being well described and constituting most of the body’s major systems 5 6. Testicular and several pediatric cancers have also been described as being cannabis-associated 5. Such a multisystem generality of toxicity suggests to us that some basic cellular functions may be deleteriously affected – as implied by its well described mitochondriopathy 7, its heavy epigenetic footprint 8, accelerated aging as described in our present report 9 or some multi-way interaction between these and other processes. Given that the cannabis industry is presently entering a major commercialization growth phase, and given the multigenerational implications of mitochondriopathy-epigenotoxicity (by direct: substrate supply including ATP, NAD+ and acetate; and indirect: RNA transfer and malate-aspartate and glycerol-3-phosphate shuttle; pathways 10) further study and elucidation of these points is becoming an increasingly imperative international research priority.

  Apropos of the recent Covid-19 pandemic emergency it is also worth noting that since cannabis is immunosuppressive, is known to be damaging to lungs and airways and often carries chemical, microbial and fungal contaminants cannabis use and cannabis vaping is also likely to have a deleterious effect on the coronavirus epidemic. Such data implies an untoward convergence of two public health epidemics. Appropriate controls on cannabis use imply improved public health management of SARS-CoV-2.

  References

  1. Pinheiro J.C., Bates DM. Mixed-Effects Models in S and S-Plus: Springer 2000:1-527.
  2. McEniery CM, Yasmin, Hall IR, et al. Normal vascular aging: differential effects on wave reflection and aortic pulse wave velocity: the Anglo-Cardiff Collaborative Trial (ACCT). Journal of the American College of Cardiology 2005;46(9):1753-60. doi: 10.1016/j.jacc.2005.07.037 [published Online First: 2005/11/01]
  3. AtCor Medical Corporation. AtCor Research Conceptual Animations USA2020 [cited 2020 27th March 2020]. Available from: https://atcormedical.com/solutions/research/ accessed 27th March 2020 2020.
  4. Reece A. S., Hulse G.K. Canadian Cannabis Consumption and Patterns of Congenital Anomalies: An Ecological Geospatial Analysis. Journal of Addiction Medicine 2020;In Press
  5. Reece AS. Chronic toxicology of cannabis. Clin Toxicol (Phila) 2009;47(6):517-24. doi: 10.1080/15563650903074507
  6. Volkow ND, Baler RD, Compton WM, et al. Adverse Health Effects of Marijuana Use. New England Journal of Medicine 2014;370(23):2219-27. doi: doi:10.1056/NEJMra1402309
  7. Rossato M, Ion Popa F, Ferigo M, et al. Human sperm express cannabinoid receptor Cb1, the activation of which inhibits motility, acrosome reaction, and mitochondrial function. The Journal of clinical endocrinology and metabolism 2005;90(2):984-91. doi: 10.1210/jc.2004-1287
  8. Reece AS, Hulse GK. Impacts of Cannabinoid Epigenetics on Human Development: Reflections on Murphy et. al. 'Cannabinoid Exposure and Altered DNA Methylation in Rat and Human Sperm' Epigenetics 2018; 13: 1208-1221. Epigenetics 2019:1-16. doi: 10.1080/15592294.2019.1633868
  9. Reece A.S., Norman A, Hulse G.K. Cannabis Exposure as an Interactive Cardiovascular Risk Factor and Accelerant of Organismal Ageing – A Longitudinal Study. BMJ - Open 2016;6(11):e011891-e900. doi: http://dx.doi.org/10.1136/bmjopen-2016-011891 [published Online First: 7th November 2016]
  10. Canto C, Houtkooper RH, Pirinen E, et al. The NAD(+) precursor nicotinamide riboside enhances oxidative metabolism and protects against high-fat diet-induced obesity. Cell Metab 2012;15(6):838-47. doi: 10.1016/j.cmet.2012.04.022

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  Conflict of Interest:
  None declared.

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• Published on: 9 March 2020

  Re: Cannabis exposure as an interactive cardiovascular risk factor and accelerant of organismal ageing: A longitudinal study

  • Ian A Lane, Clinical & Population Health Researcher University of Massachusetts Medical School

  I have read with interest Reece and colleagues (2016) paper, but some questions remain.

  First, I think the authors have done a commendable job detailing most of their statistical methodology. However, some things are left to be desired, such as a description of the Statistical Power analysis. Using the Benjamini-Hochberg Procedure (a modified Bonferroni Procedure) on the data listed in their Supplementary materials, one can conclude that the authors' results were indeed statistically significant. What remains to be seen, however, is what the magnitude of these effects were, precisely, and what happened to the Power as these multiple comparisons were assessed, since Power decreases with increasing univariate statistical tests. One might assume Power to be sufficient given the N = 1,553, but these data have been parsed in many different ways, and it would be helpful to know the authors’ anticipated effect sizes and any Power analyses for these comparisons that were conducted prior to the start of the study.

  Second, due to issues with boundary conditions and computational modeling, the method used in this paper for the mixed-effects linear model may not be quite right [1, 2]. There is often a misapplication of traditional AIC selection criteria in linear mixed effects (LME) modeling, owing to poor justification for use in longitudinal data analysis, due in part to error variance estimates [2], which is partially how this seemed to have been used here, in th...

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  I have read with interest Reece and colleagues (2016) paper, but some questions remain.

  First, I think the authors have done a commendable job detailing most of their statistical methodology. However, some things are left to be desired, such as a description of the Statistical Power analysis. Using the Benjamini-Hochberg Procedure (a modified Bonferroni Procedure) on the data listed in their Supplementary materials, one can conclude that the authors' results were indeed statistically significant. What remains to be seen, however, is what the magnitude of these effects were, precisely, and what happened to the Power as these multiple comparisons were assessed, since Power decreases with increasing univariate statistical tests. One might assume Power to be sufficient given the N = 1,553, but these data have been parsed in many different ways, and it would be helpful to know the authors’ anticipated effect sizes and any Power analyses for these comparisons that were conducted prior to the start of the study.

  Second, due to issues with boundary conditions and computational modeling, the method used in this paper for the mixed-effects linear model may not be quite right [1, 2]. There is often a misapplication of traditional AIC selection criteria in linear mixed effects (LME) modeling, owing to poor justification for use in longitudinal data analysis, due in part to error variance estimates [2], which is partially how this seemed to have been used here, in the Reece et al. paper.

  Finally, and most importantly, the authors never detail the algorithm by which they have computed “vascular age” in this study. Cox proportional hazard estimates for vascular age have been proposed from Framingham Heart Study data, as well as the new “SCORE” method [3]. Should the authors have calculated their own VA score algorithm, this should have had some validity and reliability data published prior to the current article and should therefore be cited in the paper. Should they have used one of the other two methods, this should also be referenced, here, as their findings hinge entirely on ratio relations between VA and their other reference points, whether cannabis use or something else, such as BMI. If the VA calculations are inaccurate or the algorithm inappropriately selected for some reason, all the results drawn in this paper effectively become moot.

  In sum, although the study was generally well conducted, I do think, taking into account some of the broader limitations (e.g., with respect to limited female participants in the Cannabis-only group, etc.), as well as the aforementioned methodological questions that remain, the authors’ conclusions are too widely generalized from a fairly unique and non-randomized population with no controls. I will look forward to more work from this group in the future.

  1. Müller, S., Scealy, J. L., & Welsh, A. H. (2013). Model selection in linear mixed models. Statistical Science, 28(2), 135-167.
  2. Liang, H., Wu, H., & Zou, G. (2008). A note on conditional AIC for linear mixed-effects models. Biometrika, 95(3), 773-778.
  3. Cuende, J. I., Cuende, N., & Calaveras-Lagartos, J. (2010). How to calculate vascular age with the SCORE project scales: a new method of cardiovascular risk evaluation. European heart journal, 31(19), 2351-2358.

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  Conflict of Interest:
  None declared.

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