Prior research examining secondary effects of alcohol BMIs have noted a decrease in marijuana use when there was also a decrease in alcohol consumption . It could be that factors that result in students’ experiencing fewer alcohol-related consequences without changing their drinking differ from ones that would lead to reductions in alcohol or marijuana use. Although our study did not include a measure of marijuana-related consequences, future research should examine changes in marijuana consequences to investigate whether changes in alcohol-related consequences correspond with changes in marijuana consequences following alcohol-focused BMIs. Second, a lack of effects may be due to the fact that our BMI was focused solely on changing alcohol-related behaviors and did not discuss the participant’s marijuana use. Future research should examine process coding in BMIs that do discuss marijuana use to explore possible in-session processes that may be related to changes in marijuana use and can be targeted in future interventions3 . Similarly, although alcohol and marijuana use share similar predictors , they may differ in their mechanisms of change. For example, the underlying motives that drive these two behaviors may vary so changing one will not ultimately lead to changes in the other and existing BMIs may not be targeting or altering both. Third, the referral incident in this study may not have been severe enough to warrant an overall re-evaluation of substance use, as may have been the case for those who required a visit to the ED as a result of their alcohol use . Marijuana users may require a more focused intervention or a supplemental session that targets alternative substance free activities to facilitate changes in marijuana use . Finally,cannabis grow tray with growing trends in decriminalization and legalization of marijuana in the US, the perceived risk of marijuana has decreased among college students .
Marijuana use may be more entrenched in the college social environment and more difficult to change without a targeted marijuana specific intervention. The results of this study should be interpreted within the context of its limitations. First, our study is restricted by our measure of marijuana use, which was limited to frequency and did not assess for marijuana-related consequences. Future studies may include assessments of quantity, days smoked, and consequences to get a better of understanding of the severity of participants’ marijuana use. Although daily marijuana use is on the rise, with almost 6% of college students reporting daily use , marijuana users in our study were using about 13.7 times in the past month. This is fairly low compared to those seeking treatment for marijuana use or being seen in an emergency department. Findings may be different in those populations where marijuana use is greater. For example, Metrik et al. found that compared to lighter users, those who reported weekly marijuana use demonstrated a significant decrease in use following treatment. Furthermore, our measure of pBAC was derived from participants’ reported heaviest drinking event and may not be the best way to capture peak BAC levels. Additionally, the study sample was predominantly white which may limit our ability to generalize findings to other populations of interest. Finally, we relied on self-reported data collection that did not include corroborating measures. Research using collateral informants indicated that mandated students may under-report alcohol use . Despite these limitations, this study adds to the existing literature on the secondary effects of alcohol-focused BMIs. To our knowledge it is the first study to examine the influence of two different alcohol interventions on marijuana use in the context of stepped care. Furthermore, findings indicate that heavy drinking college students who also use marijuana may still benefit from alcohol treatment especially in reducing their alcohol related consequences.
From a theoretical perspective, our results suggest that changing one behavior does not necessarily mean changes in another will occur, at least with respect to marijuana. However, future work should examine other health behaviors that might change as a result of reducing alcohol consequences. For example, it may be that increases in substance free activities like exercising, volunteering, or academic related behaviors occur alongside changes in alcohol-related behaviors . Future research examining marijuana focused interventions of different intensity implemented in a stepped care approach may enhance our understanding of which interventions are most effective for college students with varying levels of involvement with marijuana.Marijuana is the third most commonly used drug after alcohol and tobacco, with approximately half of US adults having ever used marijuana and 10% having used marijuana in the past month. Marijuana has been subject to ongoing legal and social debates, including its use for medical therapies and recreational use. As a medical therapy, marijuana is used to reducechemotherapy-induced nausea and vomiting and chronic neuropathic pain, although it increases risk of cardiovascular disease, respiratory illness and metabolic disorders. Marijuana use has also increased over the past several decades, coincident with laws and regulations. Due to the increase in use and increasing number of states legalizing recreational marijuana, studies are needed to evaluate its health effects, in particular its cumulative effects on health. While previous studies observed associations between marijuana and age-related health outcomes, the effect of marijuana on the aging process at a molecular level has not received suffcient attention. Several molecular markers have been proposed to quantify biological age, including epigenetic age as estimated from age-related DNA methylation biomarkers . Moreover, the discrepancy between chronological age and epigenetic age is used to calculate epigenetic age acceleration , where a higher value represents an older epigenetic age relative to one’s chronological age and vice versa.
Several epigenetic age and EAA metrics have been developed, including those by Horvath, Hannum, Levine, and Lu, and have been associated with multiple age-related outcomes, such as disease, physical functionality, and mortality. Lifestyle factors, such as alcohol consumption, tobacco smoking, physical activity, and diet, have been shown to accelerate or decelerate epigenetic aging relative to chronological age. For example, cumulative alcohol consumption was positively associated with EAA, whereas recent consumption exhibited inverse associations, suggesting possible difference in effects of cumulative and recent exposures on EAA. However, studies examining the effect of marijuana, both cumulative and recent use, on epigenetic aging remain limited. Given the limited data on marijuana age-related epigenetic changes, we investigated the association between marijuana and EAA in the Coronary Artery Risk Development in Young Adults Study, in which marijuana has been longitudinally collected.Marijuana use was obtained at baseline and at each follow-up examination by asking participants “Have you ever used marijuana?”, “About how many times in your lifetime have you used marijuana?”, and “During the last 30 days, on how many days did you use marijuana?” We considered four variables to capture cumulative and recent use of marijuana at Y15 and Y20. Two binary marijuana variables indicated if a participant has ever used marijuana and used in the last 30 days . A continuous variable quantified the number of days of marijuana use in the last 30 days . We also estimated a continuous variable capturing cumulative marijuana use, i.e. ‘marijuana-years’,vertical grow systems for sale as previously described. Briefly, we assumed marijuana use in the last 30 days refflected use during the time period between examinations, where a marijuana-year is equivalent to 365 days of marijuana use. We then estimated cumulative marijuana-years by summing the total number of days of marijuana use from baseline to Y15 and Y20 separately and dividing by 365.Lastly, Lu’s age, GrimAge acceleration , was estimated from 1,030 CpGs and is associated with lifespan. The DNA-methylation epigenetic age estimates were calculated using the publicly available online calculator . EAA was calculated from the residuals from a linear regression model for each epigenetic age regressed on chronological age.We observed positive associations between cumulative and recent marijuana use and GAA in young adults. We observed ever use of marijuana and each additional marijuana-year were associated with a 6-month and 2.5- month higher GAA average, respectively. Additionally, any recent use, which exhibited the largest effect estimate, and each additional day of recent use were associated with a 20-month and 1-month higher GAA average, respectively. We also observed statistical interactions between cumulative and recent marijuana use and alcohol consumption on GAA, with nondrinkers exhibiting a higher average in GAA compared to heavy drinkers. These findings provide novel insights into the association between marijuana use and epigenetic age acceleration as estimated by GAA. As a DNA-methylation-based measure of biological age, GrimAge is a composite biomarker of seven DNA methylation surrogates. Several of these surrogates of GAA have been associated with components of the endocannabinoid system, including leptin, GDF1, cystatin C, and PAI1. We observed similar, albeit weak, correlations between several GrimAge surrogate biomarkers of blood plasma proteins and marijuana in our study, suggesting the association between marijuana and GAA may occur through DNA methylation changes related to these specific plasma proteins. When comparing correlations between the GrimAge surrogate biomarkers of blood plasma proteins and marijuana use and cumulative packs of cigarettes, we note despite only a modest correlation between these variables , their correlations with surrogate biomarkers were generally consistent in direction but smaller in magnitude for marijuana use. This suggests marijuana and tobacco use may operate via similar pathways.
The associations between marijuana and GAA remained robust even after adjustment for cumulative packs of cigarettes, suggesting epigenetic age-related changes are independent of cigarette smoking. Additionally, the observed variation in associations between the four EAA metrics may be due to the methodological differences in the development of these measures, which capture different aspects of the aging process. Together, the current and previous results demonstrate marijuana may modulate DNA methylation-based surrogate biomarkers associated with lifespan and may negatively impact the aging process. Given the movement to legalize marijuana, interventions to limit marijuana use may aid in slowing the aging process and potentially, hinder age-related conditions and improve longevity. However, further studies examining marijuana and its effect on GAA and corresponding blood plasma proteins may provide new mechanistic insight into the molecular effects of this health-related behavior and its effects on disease risk. The magnitude of effect of marijuana on age-related epigenetic changes appear to differ by the period of exposure to marijuana. Although recent use of marijuana exhibited a three times greater gain in GAA compared to ever use of marijuana during GEE analysis, marijuana years exhibited a greater gain in GAA compared to the number of days of recent use, suggesting the large effect of recent exposure is also transient . This may reflect the pharmacokinetics of cannabis where plasma concentrations of metabolites, such as tetrahydrocannabinol, are highest after use and decrease over time. The higher concentration and rapid decline in blood tetrahydrocannabinol concentration with recent use may result in temporary epigenetic alterations that subsequently resolve over time. However, prolonged use may lead to the accumulation of marijuana metabolites in adipose tissue that are released into the blood and subsequently, exert sustained effects on blood DNA methylation. As such, behavioral modifications to limit use of marijuana may aid in limiting both short- and long-term impacts on the aging process as captured through DNA methylation. Marijuana is the most commonly used controlled substance among those who consume alcohol. We observed cumulative and recent marijuana use were associated with a higher GAA among nondrinkers compared to drinkers, who exhibited a smaller GAA gain with increasing alcohol intake. While these findings suggest statistical interactions between marijuana and alcohol, the biological mechanism for this interaction remains unclear. Alcohol consumption has previously been shown to increase cytokine production and subsequent peripheral inflammation and damage to organ, and cannabis may exert anti-inflammatory properties and mitigate inflammation from alcohol consumption, suggesting opposing effects of cannabis and alcohol on inflammatory pathways. Inflammatory marker IL-6 was previously found to be positively associated with alcohol consumption and further analysis identified a statistical interaction between alcohol consumption and marijuana use, where a significant positive association was observed among non-users and a non-significant negative association was observed among users, demonstrating marijuana may modulate inflammatory cytokines induced by alcohol. Studies have also observed cannabinoids may reduce alcohol-induced oxidative stress and autophagy related damage. In sum, our statistical findings are consistent with proposed mechanisms and findings demonstrating opposing effects of marijuana use in the context of alcohol consumption.