There is a dearth of data on cannabis use in relation to cardiometabolic factors in the aboriginal peoples of North America. According to the Nunavik Inuit Health Survey (Canada) of 2004, prevalence of cannabis use is very high ( 8 ). In this population, despite high obesity prevalence (49%), very high tobacco‐smoking rates (84%), and high prevalence of sedentary lifestyle (>55%), type 2 diabetes incidence is low compared to other indigenous populations (4.7% versus 20%) and remains comparable with values in the rest of Canada (5.5%) ( 9 ). We analyzed the data on this specific population to ascertain the association of cannabis use with obesity, insulin resistance, and inflammatory markers.

Cannabis is the world's most widely used illicit drug with at least 119 million cannabis users worldwide ( 1 ). From a public health perspective, cannabis is associated with adverse mental and physical health effects ( 2 ). However, studies that analyzed its relationship with metabolic status are scarce. Frequent cannabis use is associated with higher caloric intake ( 3 ), but investigations into overweight/obesity have yielded inconsistent results ( 4 , 5 ). Furthermore, cannabis use is associated with low prevalence and decreased likelihood of diabetes ( 6 ). Cannabinoids are now being considered as one way of increasing sensitivity to insulin ( 7 ).

Age‐adjusted prevalences were estimated by contrasts, whereas adjusted means and 95% confidence interval (95% CI) for anthropometric and metabolic measures were obtained by least square ANCOVA. As sensitivity analyses, we further assessed whether the relationship between cannabis use and outcomes varied by BMI categories, age groups, and smoking status, by including their multiplicative terms in ANCOVA models and applying the likelihood ratio test with 0.05 cut off. We undertook multivariate logistic regression to estimate odds ratios (ORs, 95% CI) of obesity (BMI ≥30 kg/m 2 ). Outcomes with non‐normal residual distribution were log‐transformed for analysis, and geometric means reported. Finally, mediation analysis was conducted to determine whether the effect of past‐year cannabis use on insulin resistance was mediated by its impact on BMI. This test was performed with SAS MACRO “PROCESS” ( 13 ). All analyses were done with SAS software (version 9.3). All P values were 2‐sided.

A fasting blood sample was drawn from all participants. Methods implemented for lipid analysis have been described previously ( 9 , 10 ). Plasma fasting glucose was quantified by hexokinase enzymatic assay, and fasting insulin was measured by double‐antibody radioimmunoassay (LINCO Research, St. Louis, MO, USA). Plasma ferritin levels were measured with the Elecsys‐2010 system from Roche, as described elsewhere ( 10 ). Homeostasis model assessment of insulin resistance (HOMA‐IR) was undertaken as described by Matthews and colleagues ( 11 ). Plasma high‐sensitivity C‐reactive protein (hs‐CRP) concentrations were ascertained by highly sensitive CRP assay, and plasma interleukin‐6 (IL‐6) and tumor necrosis factor‐alpha (TNF‐α) concentrations were measured with commercial enzyme‐linked immunosorbent assay kits, as described elsewhere ( 12 ).

The frequency of cannabis use was assessed from the confidential questionnaire, by the question: “In the past 12 months, have you used or tried pot, marijuana, grass, or hashish? (yes/no)”; participants were thus categorized as cannabis users or nonusers. We also created a dichotomous variable from the use of at least 1 drug other than cannabis in the past 12 months, which included solvents, cocaine, and its derivatives, hallucinogens (i.e. Ecstasy, PCP, LSD), and likely injectable drugs (i.e. opioids).

The Nunavik Inuit Health Survey (2004) and its design have been described elsewhere ( 9 , 10 ). Briefly, the survey was based on interviewer‐administered and self‐completed confidential questionnaires. Weight was taken on a beam scale, and height was quantified with a rigid square and measuring tape. BMI was calculated as the ratio of weight (kg) to squared height (m 2 ). Sampling strategies for the present study have been described in detail previously ( 10 ). Of the 914 Inuit adults (aged 18‐74 years) intended for clinical examination, 128 were excluded [nonInuit ( n = 20), pregnancy ( n = 26), missing information on cannabis use ( n = 82)]. Data on 786 Inuit were available for analysis. The study and consent form were approved by the Université Laval and Quebec Public Health research ethics committee.

Of the 786 adults studied, the prevalence of cannabis use (57.4%) was significantly higher among younger subjects. Table 1 presents the adjusted characteristics of cannabis users and nonusers. Cannabis use in the past year was statistically associated with lower insulin, HOMA‐IR, and LDL‐cholesterol, but not with ferritin, hs‐CRP, IL‐6, or TNF‐α (Table 2 ). Further adjustment for BMI rendered these associations nonsignificant. There was no interaction between past‐year cannabis use and BMI category for all metabolic measures (Table 3 ). Excepted for glucose ( P interaction = 0.01 and 0.02), no significant interactions between cannabis use and age in relation to BMI, % fat mass, glucose, and HOMA‐IR were observed (Table 3 ). There was no interaction between cannabis use and smoking status in relation to metabolic measures. The OR for obesity was 0.56 (95% CI: 0.37‐0.84) for past‐year cannabis users compared to nonusers; this association was consistent across age strata ( P interaction = 0.11) and was not influenced by tobacco‐smoking status ( P interaction = 0.33). Total energy intake was not significantly different between past‐year cannabis users and nonusers across age strata, BMI categories, and smoker status (data not reported). In a mediation analysis, the total effect (β = −9.46 [95% CI: −8.58, −0.35], P = 0.04) of past‐year cannabis use on insulin was significant. The direct effect was not significant (β = −3.94 [95% CI: −12.63, 4.75], P = 0.37), while the indirect effect was highly significant (β = −5.52 [95% CI: −9.41, 2.33], P = 0.001).

Discussion

In this large cross‐sectional adult survey with high prevalence of both substance use and obesity, cannabis use in the past year was associated with lower BMI, lower % fat mass, lower fasting insulin, and HOMA‐IR. However, further adjustment for BMI rendered fasting insulin and HOMA‐IR differences statistically nonsignificant between past‐year cannabis users and nonusers. In our multivariate analysis, we noted that obesity prevalence among past‐year cannabis users was about half that among nonusers. The association between past year cannabis use and insulin mediated by BMI was highly significant.

Previous research on the association between cannabis use and BMI yielded inconsistent results. The inverse association observed in our work supports evidence from a larger proportion of previous cross‐sectional (5, 7, 14) and follow‐up investigations (15). However, none of these studies strengthened their observations on several metabolic, lifestyle behavior (especially smoking) and inflammatory parameters, thus limiting their search for possible explanatory mechanisms.

While studies looking into possible associations between cannabis use and insulin/glucose parameters are scarce, our findings are consistent with a recent investigation by Penner et al. (7) who observed that current use was associated with lower fasting insulin levels and HOMA‐IR compared to abstinence. The fading of such differences between users and nonusers after adjusting for BMI in our study suggests that the possible effect of cannabis on body weight occurs somewhere other than through glucose metabolism. Blood concentrations of inflammatory markers were similar between users and nonusers. This is in contrast with findings on adults aged 20‐59 years from the US where ferritin was significantly higher among frequent cannabis users (6). This may be explained by the quite different cigarette‐smoking profiles of these 2 groups.

Several other hypotheses on how cannabis may influence body weight and fat are plausible. As suggested by Muniyappa et al. (16), cannabis might affect adipose tissue metabolism, depending on the duration of exposure or impact circulating appetite hormones, which they failed to observe. We cannot also exclude the potential influence of CB1 receptor gene polymorphism in our population as at least 1 type is associated with significantly lower BMI (17). Most importantly, cannabis‐smoking may also result in similarly increased energy expenditure through various mechanisms as with tobacco‐smoking. Cannabis use also augments heart rate in a dose‐dependent manner (18) and smoking it imposes a respiratory burden from carbon monoxide and tar but in a greater proportion than smoking a similar quantity of tobacco (19). This may explain why the influence of cannabis on BMI was only seen among never and former smokers. Unfortunately, no data regarding metabolic expenditure rate were available from our participants.

Some caveats must be considered when interpreting our results. Cannabis use data were collected as a dichotomous variable, and we failed to assess degree and duration of exposure. Such information would have allowed us to evaluate a possible dose‐response between cannabis use and BMI. More refined and precise measures of exposure would be desirable in future work. In addition, the Inuit population may present a particular metabolic profile or reaction to cannabis that may limit the generalizability of our results.

There is growing interest in the endocannabinoid system and its possible influence on appetite, metabolism, and weight. As a result, cannabinoids from cannabis may be viewed as an interesting avenue for research on obesity and associated conditions. At the same time, adverse health consequences from cannabis use are numerous, yet it is prevalent. In our study, past‐year cannabis use was associated with lower BMI—noted mainly among never and former tobacco smokers. Mediation analysis showed that cannabis use effect on insulin resistance was mediated solely by BMI. Further data are needed to address this relevant question. Future studies should look into the possibility of acting on the cannabinoid system and the possible effect on weight.