Physical activity was assessed using self-report to several questions . For the physical activity variable, subjects were classified as inactive if they did not report engaging in any of the following activities during the previous month: walking, jogging, bike riding, swimming, aerobics, dancing, calisthenics, gardening, lifting weights or other physical activity outside their occupation. Individuals were considered to fulfil national recommendations for physical activity if they reported five or more episodesper week of moderate-intensity physical activity or three or more episodes per week of vigorous-intensity physical activity.Descriptive statistics were used to characterise the subjects . To test the statistical difference between the groups, we used c2 test for categorical variables and two-sided t tests for continuous variables. A p value of <0.05 was considered significant. Univariate and multivariate logistic regression analyses were used to determine the relationship between DM and marijuana use. We used multivariate logistic regression to adjust for confounding variables and reported the OR and the 95% CI. Variables considered as possible confounders in the multivariate analysis were age, gender, race/ ethnicity, BMI, education level, cigarette smoking, alcohol use, physical activity, serum total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, vitamin D, CRP, ferritin, fibrinogen, WBC count and uric acid. In order to confirm that marijuana use was associated with DM and not due to confounders, we analysed how each potential confounder changed the OR of having DM. Variables that changed the OR by $10% were considered as confounders and included in the multivariate model. We performed stratified analysis to test for effect modification. For effect modifier variable, multivariate logistic regression model was constructed for each subgroup. In addition, to help adjust for selection bias, we analysed the data using the propensity score matching and estimated the average treatment effect for the treated, cannabis racks bootstrap SE and t statistics. We added the propensity score to the logistic regression model as inverse weight, blocks that satisfy the balancing property and quartiles.

Data were analysed using SAS and the survey module of STATA . Sample weights, provided by the National Center for Health Statistics, were used to correct for differential selection probabilities and to adjust for non-coverage and non-response.13Our analyses of adults aged 20e59 years in the NHANES III database showed that participants who used marijuana had lower prevalence of DM and had lower odds of DM relative to non-marijuana users. We did not find an association between the use of marijuana and other chronic diseases, such as hypertension, stroke, myocardial infarction and heart failure. This could be due to the smaller prevalence of stroke, myocardial infarction and heart failure in the examined age group. We noted the lowest prevalence of DM in current light marijuana users, with current heavy marijuana users and past users also having a lower prevalence of DM than non-marijuana users. The finding that past marijuana users had lower odds of prevalent DM than non-users suggests that early exposure to marijuana may affect the development of DM and a window of time of marijuana exposure earlier in life could be a factor to study. Similarly, our findings of a significant association between marijuana use and DM was only found in those aged $40 years suggest that the possibility of some protection from marijuana use may require many years before they become manifested. By contrast, it could reflect the increased prevalence of DM with age and the ability to detect an association with a lesser sample size when there is a greater cohort at risk for DM. The possible association of light marijuana use with decreased DM is similar to that of alcohol on DM and the metabolic syndrome, in which mild alcohol use was associated with lower prevalence of DM and the metabolic syndrome, and higher alcohol use associated with higher prevalence of DM and the metabolic syndrome. Smit and Crespo9 used the NHANES III population to examine dietary factors of non-marijuana users and marijuana users among adults aged 20e59 years. Similar to our data, they found that 45% reported used marijuana in their lifetime and 8.7% used marijuana in the past month.

Current marijuana users had higher intakes of energy and nutrients and consumed more soft drinks but had slightly lower BMI than non-current marijuana users. Thus, it is unlikely that a healthier diet contributed to the decreased prevalence of DM among marijuana users found in our study. In our study, all marijuana users had lower BMI than non-users, with heavy marijuana users having the lowest BMI. The lower BMI may be protective for DM, although when we controlled for BMI, the prevalence of DM was not significantly changed suggesting additional BMI-independent pathways. Smit and Crespo9 did not record glycaemic parameters or prevalence of DM. Using NHANES III data, marijuana users had lower rates of obesity and lower mean BMI, with current heavy marijuana users having the lowest BMI, in agreement with a recent report using National Epidemiologic Survey on Alcohol and the National Comorbidity Surveye Replication databases. Correcting for the effect of BMI, the association between marijuana use and DM was reduced by 17% but remained highly significant. We postulate that the decreased prevalence of DM and marijuana use may be due to the anti-inflammatory properties of marijuana. CBs found in marijuana favourably modify inflammation probably through the inhibitory actions on prostaglandins and COX-2.18 Hu and colleagues reported that CRP, but not interleukin-6 and tumour necrosis factor-a receptor-2, was associated with the risk of developing DM. In our study, serum level of CRP, fibrinogen ferritin, uric acid and WBC counts revealed varied associations with marijuana use. Of note, the CRP assay used in NHANES III was not a highly sensitive assay and is unlikely to pick up small changes in an inflammatory state in a single individual; however, it is still a robust measure of inflammation and is useful in population studies. However, we did find a U-shaped association between the CRP levels and marijuana use groups. Rodent studies using CBs have shown significant benefits against diabetic complications and atherosclerosis.19 20 Additionally, lower doses of CBs appear to be anti-inflammatory in rodents.19 CBs, including the non-psychoactive cannabidiol, have also been shown to attenuate progression of type 1 DM in animal models. We have not identified any study in human subjects or animals examining marijuana or its active ingredients and the incidence of type 2 DM, although one study found similar glucose levels in marijuana users as non-users.8 In a prospective study using a cannabis based medicinal extract compared to placebo to treat diabetic neuropathy, glycaemic indices were not mentioned. We examined physical activity in patients using marijuana and found that it did not confound the association between marijuana and DM. Although the CB1 antagonist, rimonabant has been used successfully to treat DM, we are not surprisedat the association between marijuana use and decreased prevalence of DM. Marijuana contains a variety of CBs, of which some, such as cannabidiol and delta9- tetrahydrocannabivarin, have antagonist properties that may mediate the anti-inflammatory properties of marijuana.

A limitation of our study was its cross-sectional nature. Despite the efforts of NHANES to enrol a random representative sample of the US population, persons attending the study visits may differ from those not attending in subtle ways that may affect the results of this study. We are unable to conclude that marijuana use does not lead to DM nor do we suggest that marijuana should be a treatment for DM. Although we controlled for major confounders, it is possible that non-marijuana users and subjects with DM share some, as yet unknown, cannabis drying rack ideas characteristic accounting for the relationship between DM and non-marijuana use. An additional limitation is that the marijuana use was based on self-report and self-report of illicit substances is often underestimated on self-reports. Self-report is subjected to recall bias. However, we expect that recall bias would be similar in those with DM as those without DM and would be unlikely to bias our results. Although current marijuana users were divided into heavy and light users based on the number of times they reported using marijuana per month, the amount of marijuana consumed, route of consumption , duration of use and time when they quit were not reported. A potential limitation was that most patients with DM were identified by self-report, with a smaller number of patients identified by having an elevated fasting blood glucose levels. Because some patients with DM receiving treatment are euglycaemic, blood glucose levels alone cannot be used to identify those patients with DM. However, the percentage of marijuana user was similar in those patients with DM identified by self-report as that of those with DM identified by fasting glucose testing. While we analysed all patients with DM together, we estimated that over 98% of the patients had type 2 DM, and therefore, our results are likely to apply only to patients with type 2 DM. Another limitation is the possibility of a cohort effect since those who use marijuana may have other factors that may predispose decreased prevalence of diabetes compared to non-users besides lower BMI. In conclusion, marijuana use was associated with a decreased prevalence of DM. Prospective studies in rodents and humans are needed to determine a potential causal relationship between cannabinoid receptor activation and DM. Until those studies are performed, we do not advocate the use of marijuana in patients at risk for DM.Marijuana is the most commonly used “illicit” drug in the U.S. with approximately 12 million people over age 12 reporting past month use , and marijuana use disorder nearly doubling from 2001– 2002 to 2012–2013 . The addictiveness of marijuana continues to be debated as the landscape regarding marijuana legalization changes , but the evidence largely indicates that excessive use can lead to adverse consequences and diagnoses of MUD . In 2014, 4.1 million people 12 years of age or older met the DSM-IV criteria for MUD nationally . In addition, most people who develop MUD have comorbid conditions that can worsen prognosis and contribute to poor health outcomes . Regular and heavy marijuana use is associated with increased risk of anxiety, depression, and psychoses, although causality has not been established . In addition, heavy use, high potency, and exposure at younger ages can all negatively affect the course of mental illness . Marijuana use among adolescents also predicts increased risk of MUD in adulthood , which, in turn predicts, high risk of other drug use and escalation to co-occurring substance use disorder . Marijuana frequently is used by persons who drink in excess and use other illicit drugs , which compounds risk to health and safety. Marijuana is associated with increased risk of several medical conditions. Regular and heavy marijuana use can contribute to respiratory deficits such as airway resistance, large airway inflammation, lung hyperinflation, and can lead to chronic bronchitis . Marijuana use is also related to a high risk of respiratory infections and pneumonia , vascular conditions that raise the risk of cardio/ cerebrovascular events, such as stroke and myocardial infarction , and an increased risk of lung and digestive track cancers . Not surprisingly, these medical conditions are even more prominent among those with MUDs and contribute considerably to the burden of disease . Marijuana has also been associated with increased risk of motor vehicle accidents, and other acute health events . Despite the adverse health effects of MUD, few studies have examined the relationship of MUD to emergency and inpatient service utilization. These are among the most costly health services, and may indicate inappropriate use of health care and/or unmet need. The few studies in this area have focused on any marijuana use rather than on higher severity users with MUD; although MUD patients are likely at the highest risk for utilizing ED and inpatient resources, given the disorder’s consistent association with adverse outcomes and poor health. One recent study found marijuana, either used alone or in combination with other drugs, is often reported by those who have ED visits, and this number has increased over the past decade . Results are mixed regarding the effect of substance use on inpatient use ; and one study found no evidence of an association between frequency of marijuana use and hospital admissions . The degree to which such findings are specific to marijuana use or persist over time in persons with MUD, who likely have more complex clinical presentation and service needs than those with subdiagnostic use, is largely unknown. This study addresses this important question by examining emergency and hospitalization utilization trends and trajectories in a large sample of 2,752 patients with MUD and 2,752 healthy controls in a large integrated health care system.