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Legal or not, let’s stop calling Cannabis healthy.

The picture being painted by the ever expanding body of literature regarding Cannabis’ adverse effects on health continues to look bleaker with a new paper to be released next month in the Journal of The American College of Cardiology. This paper is set to deal another blow to this narrative by detailing substantial adverse effects on the heart including an almost 600% greater risk of heart attack in young Cannabis users versus non users .

Already there is an exponentially growing encyclopedia of studies supporting the adverse neuropsychiatric impact of even modest Cannabis use that seems to grow by the week. And while there are still many questions, no one that has been paying attention should be surprised by the steady stream of papers supporting even modest Cannabis use can lead to serious adverse neuropsychiatric outcomes, major and minor mental illness and structural and functional changes to the brain (in essence brain damage.)

Now In recent months there has been numerous papers that continue to support adverse Cardiovascular outcomes caused by use of the recently legalized in many places drug. The yet to be officially released paper titled "Cannabis Use and Risk of Myocardial Infarction" adds even more strength to the growing literature showing that Cannabis use is as bad for the heart as it is for the brain.

Researchers retrospectively looked at data for almost 5 million healthy individuals under 50 for 8 years and after excluding those with known confounders (like hypertension, diabetes, high cholesterol and to***co use) found that Cannabis users had over 600% greater chance of having a heart attack compared to a cohort of similar non cannabis users. There was also a 430% greater chance of having an ischemic stroke as well as substantially higher rates of Major Adverse Cardiovascular Events, Heart Failure and All Cause Mortality.

The authors based out of Boston Medical Center comment that the study supports that Cannabis “appears to pose a substantial and independent risk” for the noted outcomes even in a young population without traditional risk factors for heart disease.

The findings in this paper continue to debunk the absurd and unscientific notion that Cannabis use somehow promotes health. Any substance with a body of literature detailing these sort of harms on the heart (and also the brain) should not be mass marketed to the public as being healthy or supporting a healthy lifestyle. Individuals that choose to legally use Cannabis should do so being well aware of the potential harms just like with to***co and alcohol.

A few interesting observations about this paper:

(1) The authors identified Cannabis use based on the presence of ICD-10 codes in patient charts for Cannabis use disorder. This could lead to something called exposure misclassification, a phenomenon where the exposure in a study is underestimated . The authors comment on this in the discussion. It is my experience (and practice in clinic) that only extremely heavy cannabis users would get this diagnosis added. It is quite possible that many users in the non Cannabis group used Cannabis, but not to the level to merit the diagnosis from their doctor. This type of misclassification would bias to the Null and UNDERESTIMATE the strength of the association because it Is likely the non Cannabis group included some Cannabis users . (You can read more about when doctors would code for CUD here: https://www.aafp.org/pubs/fpm/blogs/inpractice/entry/marijuana_use.html)

(2) The study does not attempt to quantify cannabis use only define it as a yes or no, so no dose response relationship can be ascertained based on these methods. It is safe to say that these strong associations are most applicable to regular, heavy users (individuals who use several times a week). While it is clear in this paper that individuals who use Cannabis daily or near daily appear to have substantially higher risk of stroke or heart attack, it remains unclear how these findings might relate to less frequent users. In addition to being unable to ascertain if there is a dose response relationship, it is also unclear based on this paper if there is a threshold effect (i.e a level of frequency of use that does not pose risk to the heart).

(3) The strength of the observed risk in this paper is jaw dropping. The take home from this paper is that regular Cannabis use leads to a 6x greater risk of heart attack and a 4.5x greater risk of stroke. To put this in context it is widely held that that relative risk for to***co use for heart attack is somewhere around 2.5-3.0. Will further studies support that regular Cannabis use is twice as dangerous for the heart as to***co? It is improbable to explain away an association this strong with confounding, bias or methodologic flaws.

(4) The study does not distinguish between smoking Cannabis or other delivery methods, such as edible products. It seems easy to imagine why smoking Cannabis would cause heart disease, but is harder to conceptualize that eating it would do so. Could THC itself directly manifest atherosclerosis? This study does not distinguish whether eating or smoking is any safer. Individuals should assume the risk applies to all delivery methods.

The bottom line about all this is that following the science means exactly that. An effective legal Cannabis system must be based on accurate scientific information regarding likely adverse health consequences available to the consumer. Otherwise we are creating another public health crisis.

Much like with to***co and alcohol years of clever product placement in popular culture has lead to wide misperceptions about the health effects of Cannabis use. The idea that Cannabis is a harmless plant continues to be refuted by science. The truth is legal or not we are seeing a substance with real health consequences to the brain and the heart, among other places. That information, just like with alcohol, to***co, tanning beds, hot dogs, PFAS and pretty much anything else that poses health risks must be conveyed to the public

Here is a link to the pre publication paper: https://www.sciencedirect.com/science/article/pii/S2772963X25001152?via%3D

www.sciencedirect.com

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Good evening. This is from my personal fb page about situation in Massachusetts. Thought I would share. My best to all- Dr. Teehan

A word about our future path. The average age of a covid death in Massachusetts in 82. The average age of a diagnosed covid case is 52. The average age of a Massachusetts resident is 40. What does this imply about our data about covid-19 and the future path? It tells us that we have disproportionately identified older and sicker cases from the spectrum of infections. To use an analogy I made many months ago, identified cases are the tip of the iceberg. We are seeing that this tip is the minority of infections (which I had long speculated) and now we see it is demographically skewed vs the general population, to such extent that the entire iceberg is probably skewed. The below link is a news article in Globe about a study that estimates the true prevalence of covid-19. It puts the number at about 900,000 cases, far above the 91,000 that have been diagnosed (I have consistently estimated we have underdiagnosed covid by a factor of 8-12). This equates to around 13% of the population in state. This tells us the infection fatality rate while still devastating is far lower than feared. However, To accurately estimate the true IFR and therefore make projections about the cost of herd immunity and the future is more complicated than simple multiplication to reach 70% of the population. The challenge is to account for the demographic differences in age between the observed cases (the iceberg now) and the general population (the iceberg when 4.5 million people are infected leading to herd immunity).

For example, the disease ravished our nursing homes and elderly and sick. Of the 91000 identified cases 19,000 came from nursing homes. Of the 6300 deaths a GHASTLY 3900 have come from long term care facilities. The 900,000 or so that have been infected thus far in a Massachusetts probably do not reflect demographics of the general population. For sure those 900,000 infections of the overall iceberg are far younger and healthier than the 90,000 identified cases that represent the tip of that ice berg. But those initial 90000 cases come from some of our oldest and sickest residents and there are only so many in that population. They have been hit so hard in the first cycle of the pandemic that they are probably weighing down the remaining 900000. It is likely that The 6 million Massachusetts residents who have not been infected are healthier and younger than the 900000 people who got the disease already, if only because the pandemic ravaged health care and l long term care facilities disproportionately.

These demographic differences have to be accounted for. How accurately a Model factors this in is a major contributor to the accuracy of its projections! Who those 4.5 million remaining infections are based on age general health and presence of chronic disease/known risk factors will be a major factor in number of deaths. This is because we know the risk of dying from Covid is literally orders of magnitude (aka severe multiples of ten) larger or smaller between different segments of the population. Hypothetically 4.5 million healthy non obese people under 60 without diabetes, hypertension or other serious medical problems may experience only a few dozen deaths to reach heard immunity. In Massachusetts there have been 310 deaths under 60 amongst 58,000 cases, which probably equates to around 600000 infections. Assuming that 98% of deaths have identified medical risk factors, a path to herd immunity in a totally healthy young population under 60 would probably kill around 30 or so people. Of course this is unrealistic because demographically any population in Massachusetts of that size has many older and sicker people (not to mention a third of us are obese and about 12-15% diabetic). Whereas a population of 4.5 million sick, elderly diabetic nursing home patients would probably experience several hundred thousand deaths. So How a model projects these demographic differences is critical to what it projects and Any estimate of the true IFR of covid-19 must account for these demographic differences. Since no one knows the true answer all projections are subject to be wrong.