Puzzles have emerged from research on the intersection of cannabis smoking, obesity, and type 2 diabetes mellitus. Obesity–diabetes mellitus associations have causal mechanisms involving insulin resistance.1 Increased appetite and obesity are plausible cannabis smoking outcomes, given preclinical evidence on central activation of cannabinoid-1 receptors that promote hyperphagia, as well as activation of cannabinoid-1 receptors in liver, increased de-novo fatty acid synthesis, decreased lipolysis, and induced insulin resistance.2,3 Against this backdrop of plausible harms from cannabis smoking, risk estimates from epidemiologic studies run in the direction of protective effects: cannabis smoking is associated with lower obesity prevalence, lower levels of biomarkers that indicate impaired glucose metabolism, and lower diabetes mellitus prevalence.4–6
Given the increased prevalence of cannabis smoking and diabetes mellitus in the United States (US) and elsewhere, there is good reason to study cannabis smoking–diabetes mellitus linkages.7,8 To shed new light on these puzzles, we derived meta-analysis summary estimates of the association between cannabis smoking and diabetes mellitus from multiple recent independent nationally representative replication samples in the United States (US)—namely, National Health and Nutrition Examination Surveys (NHANES), 2005–2012, and National Drug Use and Health Surveys (NSDUH), 2005–2012.
NHANES and NSDUH are designed to be nationally representative for the US noninstitutionalized civilian population, via area probability sample survey approaches, using IRB-approved recruitment and audio computer-assisted self- interview assessment protocols, with participation levels ≥ 70% in 2005–2012. NHANES adds standardized clinical and lab measurements.9,10 NHANES and NSDUH details can be found in the eAppendix, with eFigure 1 as a flow chart for each survey’s sample size and eTable 1 presentation of unweighted marginal sample totals for cannabis smoking and diabetes mellitus (http://links.lww.com/EDE/A916).
Diabetes, as the key NSDUH response variable for this study, is from standardized computer-assisted self-report items about physician-diagnosed health conditions. NHANES also uses diabetes mellitus self-report items, but adds information on current insulin and/or oral hypoglycemic medicine use, plus lab-derived glycohemoglobin levels, for a composite diabetes mellitus indicator.11
Cannabis smoking assessment is via a separate standardized audio computer-assisted self-interview assessment module. Cannabis smoking items enable distinctions between recently active cannabis smokers, past smokers, and never smokers.
Comparably measured covariates in NHANES and NSDUH assessments include age, sex, ethnic self- identification, education, and income–poverty ratio. Use of tobacco and/or alcoholic beverages also is assessed, and NHANES examinations yield body mass index (BMI) values.
In our statistical approach, Tukey-style exploratory analyses were used to shed light on univariate distributions of each variable, with no exploration of the cannabis smoking–diabetes mellitus relations under study. In subsequent analysis/estimation steps, multiple logistic regressions produced crude and covariate adjusted estimates for odds ratios (OR) of diabetes mellitus across cannabis smoking categories, with Stata “svy” software (StataCorp. 2013. Stata Statistical Software: Release 13. StataCorp LP, College Station, TX) for complex survey data analysis, analysis weights, and Taylor series variance estimation. Via Stata “metan” software, the meta- analysis step yields a summary estimate from OR estimates of the eight independent replication samples.
These primary analysis/estimation steps motivated us to examine the temporal relation between cannabis smoking and diabetes mellitus. These extra analyses used NHANES standardized item data about ages of onset for diabetes mellitus and for cannabis smoking, plus time since last cannabis smoking, which were asked for a subset of NHANES participants (n = 378); accordingly, statistical power and precision are constrained. Here, time to diabetes mellitus onset is modeled as a function of cannabis smoking onset using discrete time survival analysis.12 Then, another discrete-time survival analysis model was fit, with time to cannabis smoking onset modeled as a function of diabetes mellitus onset, to check whether diabetes mellitus diagnosis might be prompting reduced incidence of cannabis smoking. Finally, time to cannabis smoking cessation was modeled as a function of diabetes mellitus diagnosis, as a check on whether diabetes mellitus diagnosis prompts cannabis smoking cessation. The eAppendix (http://links.lww.com/EDE/A916) provides details about these data analysis steps.
A final postestimation exploration step probed potential subgroup variation of the cannabis smoking–diabetes mellitus association. Subgroups considered were defined by age, sex, ethnic self-identification, and BMI, as well as use of tobacco or alcohol.
In general, cannabis smoking preceded diabetes mellitus. Estimated mean age of “first diagnosis” of diabetes mellitus was 40 years in the aggregate NHANES samples; mean age for “first cannabis smoking” was earlier, at 17 years. NSDUH did not assess age at onset for diabetes mellitus age at onset, but its mean age at onset for cannabis smoking was 18 years. Appendix eFigure 2 (http://links.lww.com/EDE/A916) shows a distribution of individual age differences in participants who reported both cannabis smoking and diabetes obtained via subtraction of the age of onset, indicating 93% with cannabis smoking preceding diabetes mellitus.
The “never cannabis smoking” serve as reference subgroup for OR estimates presented in Table, which disclose consistent inverse cannabis smoking–diabetes mellitus associations in each replication sample and in the covariate-adjusted meta-analytic summary estimate (OR = 0.7; 95% confidence interval [CI] = 0.6, 0.8). Analyses restricted to NSDUH and NHANES self-reported diabetes mellitus diagnosis produced a slightly wider CI (OR = 0.7; 95% CI = 0.6, 0.9; results not tabulated). Adjustment for BMI did not shift point estimates appreciably, and postestimation exploratory steps disclosed no appreciable variations in OR estimates across covariate- specified subgroups (eTable 2; (http://links.lww.com/EDE/A916)).
NHANES analyses (results not tabulated) of age-at-onset had the above-mentioned constraints on statistical power and precision. They disclosed: (1) the discrete-time survival analysis hazard ratio point estimate for time to diabetes mellitus as a function of cannabis smoking onset was inverse, but imprecise (hazard ratio [HR] = 0.9; 95% CI = 0.7, 1.1); (2) the DTSA point estimate for time to cannabis smoking as a function of diabetes mellitus onset could not be estimated; too few NHANES participants had diabetes mellitus preceding cannabis smoking; and (3) HR point estimates for diabetes mellitus diagnosis age and subsequent cannabis smoking cessation were inverse but imprecise (HR = 0.8; 95% CI = 0.6, 1.1).
We excluded diabetes mellitus-diagnosed NHANES cases, and regressed glucose metabolism biomarker levels on cannabis smoking status. As shown in eTable 3 (http://links.lww.com/EDE/A916), these estimates indicate possible cannabis smoking-associated lower biomarker levels even when diabetes mellitus has not been diagnosed.
In our judgment, these inverse cannabis smoking– diabetes mellitus associations are important because they have behind them the strength of NHANES and NSDUH research approaches across multiple independent replication samples. In meta-analysis, they disclose a statistically robust inverse association of cannabis smoking and diabetes mellitus. For the most part, our postestimation analysis steps did not contradict what main analyses disclosed, and tended to be supportive. Nonetheless, due to limitations of the type listed below, we also judge that we have not yet solved the cannabis smoking–diabetes mellitus puzzle; the inverse cannabis smoking–diabetes mellitus association might yet be spurious, and more definitive research is needed.
Notwithstanding this study’s important strengths, we will be the first to admit that cross-sectional field survey data are generally not designed to support causal inferences, even when they can be very useful for motivating future studies with more probing and definitive approaches. In addition to this limitation, for the most part, we assume validity and nondifferential diagnostic classification for type 2 diabetes mellitus, although, in both NSDUH and NHANES, the diabetes mellitus assessments are based on self-report, not on carefully standardized clinical-laboratory workups by expert physicians blind to cannabis smoking histories. Alternatively, because both diabetes mellitus and cannabis smoking were measured by self-report, there might be a bias due to shared methods co-variation of a type that can be avoided in multi-wave longitudinal research. These two limitations, by themselves, are enough to prompt caution.
Moreover, there are other limitations of note. These include self-selection of cannabis smoking exposures in processes not readily controlled in observational studies and incomplete control over other sources of spurious association, such as the possibility that cannabis smoking might be more toxic to individuals with incipient diabetes mellitus, with side effects of cannabis smoking contributing to its cessation during the diabetes mellitus prodrome before diagnosis. (We note that dry or “cotton” mouth is a well-known side effect of cannabis smoking, and might exacerbate this facet of the diabetes mellitus prodrome.) In addition, there might be a healthy cannabis smoker effect or some other process that promotes fewer physician care visits, perhaps delaying onset of physician-diagnosed diabetes mellitus in active cannabis smoking versus never users. Alternatively, some individuals with incipient diabetes mellitus might become more conscientious about health, avoiding cannabis smoking among other potentially risky behaviors.
If cannabis smoking truly reduces diabetes mellitus incidence, we have more puzzles to solve. With caution, we can speculate about underlying mechanisms. For example, chronic low-grade inflammation is implicated in diabetes mellitus pathogenesis.13–15 Cannabinoids inhibit release of many inflammation mediators, some of them implicated in pathological processes leading toward insulin resistance and diabetes mellitus, plausibly via CB-2 receptors in the immune system.16,17 Administering cannabidiol to nonobese mice inhibits destructive insulitis and inflammatory cytokine production, which might reduce diabetes mellitus incidence.18 Clinical trials also have disclosed anti-inflammatory cannabinoid effects.19–22
In conclusion, this epidemiologic evidence from eight independently drawn replication samples tends to confirm what prior research found—namely, an inverse, possibly protective, but also possibly spurious link between active cannabis smoking and occurrence of type 2 diabetes mellitus. We have not solved the cannabis smoking–diabetes mellitus puzzle, and we have offered reasons for caution before any firm cause-effect inference is drawn. Before these cannabis smoking–diabetes mellitus puzzles are solved, more probing experimentation of a clinical translational character is needed, including research on potential mechanisms.
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