The association between marijuana use and the prevalence of diabetes has also been examined in the National Health and Nutrition Examination Survey III population. The researchers reported that marijuana use was associated with a lower odds of diabetes after adjustment for manifold demographic, lifestyle and clinical characteristics. The study population was restricted to individuals aged 20– 59 years; excluded 25% of the potential analysis population because of missing laboratory data; and, when examining age-stratified analyses , found the association was restricted to the older age stratum. A recent meta-analysis of eight independent replications from NHANES and the National Surveys on Drug Use and Health found a summary adjusted OR of 0.7 for the association of current marijuana use and prevalent diabetes; however, both marijuana use and diabetes status were ascertained via self-report. These associations might result from the self-exclusion of unhealthy individuals who frequently use marijuana from study participation, resulting in an underestimation of diabetes cases in marijuana users, and from reverse causation where individuals with diabetes abstain from marijuana use in older age because of concerns about and awareness of their health status. Recent analysis of NHANES 2005–2010 participants found marijuana use to be associated with lower levels of fasting insulin and HOMA-IR, and a decreased WC compared with individuals who reported never using marijuana, after adjustment for relevant covariates; however, no association was found between current marijuana use and fasting glucose, HbA1c or BMI. In a small study of otherwise healthy individuals, after matching cannabis users to non-users by sex, ethnicity, age and BMI, adipocyte insulin sensitivity was found to be higher in cannabis users compared with non-users; however, skeletal muscle insulin sensitivity, insulin secretion,clone rack fasting insulin and glucose, and HbA1c were not different between the two groups. Significant differences in diet quality between the two groups were noted, and the effect of tobacco use on the results is unknown.
Previous epidemiological research has cited animal models investigating the potential mechanisms underlying the metabolic effects of marijuana. Potential anti-inflammatory effects and improved metabolism by actions through the cannabinoid receptors have been suggested to reduce the progression of type 1 diabetes, improve beta cell function and decrease weight in mouse models. However, no models have assessed this association in healthy mice, and these studies administered cannabis/cannabidiol via ingestion or intravenously. The mode of administration and the dose should be considered when extending these results to public health studies, as the most common modes of consuming cannabis among the general population are cigarettes, pipes and bongs, in which the user inhales the chemical compounds in smoke form and the quantity consumed varies from user to user. Given the potential of marijuana smoke to increase the production of reactive oxygen species and oxidative stress, any potential anti-inflammatory benefit might be countered by detrimental oxidative effects from intake by smoking. Research on the prospective evaluation of marijuana use on metabolic health is scant. It is unclear how marijuana use could place an individual at increased risk for prediabetes yet not diabetes. This is a potential study limitation, and may reflect a spurious prediabetes association. Similarly, it is possible that it is an artefact arising from our exclusion criteria disproportionately affecting those with higher levels of marijuana use and greater potential for the development of diabetes. Individuals excluded from our analysis generally had higher levels of marijuana use and less favourable levels of traditional diabetes risk factors and were, historically, more likely to develop diabetes. Alternatively, the effect of marijuana use might have a more noticeable impact on glucose metabolism in the prediabetes range compared with the diabetes range, when traditional diabetes risk factors are far less favourable and might dominate over any effect of marijuana. This might explain the differing results in the linear trend of fasting glucose level at censoring. There are plausible ways to reconcile the seemingly contradictory tendencies between this prospective analysis , animal and cellular models, and prior cross-sectional findings in which current marijuana use coincided with a lower prevalence of prediabetes and diabetes.
We speculate, for example, that some people in ill health might choose to quit marijuana as a result of a physician’s recommendation to abstain from tobacco and other substances or a general concern for their health, or because of more complicated associations between poor health, income and drug access. This speculation awaits confirmation. In addition, previous work has not accounted for the use of other illicit drugs. While illicit drug use per se might not cause a decline in metabolic health, it might be an indicator of the propensity to use drugs or overall deleterious health behaviour, or cause declines in overall health.Recreational stimulant use is a growing concern among young adults, with 4.4% and 5% to 35% of college students endorsing cocaine and recreational amphetamine use, respectively, and 16% of cocaine experimenters developing dependence within 10 years . To develop cost-effective prevention and intervention strategies, it is crucial to identify ultra–high risk recreational users. However, little is known about bio-behavioral markers forecasting trajectory of occasional stimulant use to stimulant use disorder . Previous stimulant use research is predominantly cross-sectional, comparing individuals with chronic stimulant use with healthy individuals; although findings from these studies highlight brain disruptions related to drug use, they cannot disentangle whether disruptions preceded or were a result of chronic use. Young adulthood is a period of increased independence, often providing more opportunities for risky behavior such as drug experimentation. Risky behavior can be defined as actions that may be subjectively desirable but are potentially harmful and is typically quantified in young adults by their degree of substance use, unprotected sex, health habits, and crime engagement . Risk taking often occurs in clusters of maladaptive behaviors, suggesting underlying impairments in decision making . Decision making involves several brain processes, including learning, inhibition, and outcome assessment, specifically appraising positive or negative valence of choices . Functional magnetic resonance imaging research indicates that individuals with SUD show impaired decision making associated with altered brain activation in executive control and reward processing regions . Decision making is thought to involve a cooperative relationship between an impulsive system activated by immediate rewards and aninhibitory control system. Through learning, the control network allows individuals to resist immediate attraction to rewards in favor of longer-term advantageous outcomes .
In SUD, bio-behavioral indices of risk taking suggest an underlying imbalance between the control and impulsive systems. The control system integral to decision making comprises prefrontal cortex , theorized as responsible for learning the relationship between stimuli and outcome, working memory, and inhibiting behavior . SUD samples exhibit frontal lobe impairments associated with compromised decision making and increased risk behavior . For example, cocaine abusers exhibit dorsolateral PFC hypoactivation during response inhibition and prediction of uncertain outcomes ; in cocaine dependence, orbitofrontal cortex and DLPFC attenuation are linked to reduced ability to differentiate between variable monetary gains . Similarly, methamphetamine users inaccurately process success or failure of available options, a pattern associated with orbitofrontal cortex/DLPFC hypoactivation . Working in conjunction with frontal regions is striatum, an area associated with reward processing , selecting and initiating actions , and learning . During the Iowa Gambling Task , healthy individuals show stronger striatal activation to wins than to losses ,4×8 tray grow but amphetaminedependent individuals demonstrate hypersensitive striatal responses to rewards . Cocaine and methamphetamine users also exhibit striatal hyperactivation but frontal hypoactivation during risky decision-making tasks such as the Iowa Gambling Task and the Balloon Analogue Risk Task that is linked to riskier behavioral performance .Evidence from fMRI studies has led researchers to theorize that frontal lobe and striatum form a functional circuit with insular cortex and anterior cingulate cortex ; these regions coordinate to integrate emotional and autonomic information about rewards into goal-oriented behavior . ACC is proposed to be involved in emotion and behavior management based on its neural connections to both the emotion processing limbic system and the cognitive control center, PFC . Insula is proposed to play a role in interoceptive processing, wherein individuals integrate physiological cues to differentiate between risky and safe decisions and transform these cues into conscious feelings and behaviors . ACC and insula hypoactivation is evident in chronic stimulant users in response inhibition and error monitoring during decision making . Evidence for aberrant activity in key components of the PFC-limbic network has led researchers to suggest that weakened ability to accurately process information about options and control behaviors leads to favoring choices that offer immediate, rather than delayed, rewards . Cross-sectional studies of occasional stimulant users report decision-making impairments that parallel findings in stimulant-dependent individuals, including 1) weakened inhibitory control and reduced cognitive flexibility ; 2) neuropsychological impairments in executive functions ; and 3) frontal, striatal, and insular attenuation during a Risky Gains Task paired with reduced ability to differentiate between safe and risky decisions . Several research groups have recognized limitations of cross-sectional addiction research and have shifted toward a longitudinal approach to understand the transition to problematic substance use . Structural MRI studies show that decreased brain volume in frontocentral regions at age 14 years predicts binge drinking at age 16 and that frontostriatal regions are linked to heightened stimulant use in OSUs 1 to 2 years later . However, fMRI has been less applied to predict the development of SUD.
The current longitudinal study used follow-up clinical and drug use data from OSUs 3 years after an fMRI scan to determine whether baseline behavioral and blood oxygen level–dependent responses during the RGT 1) differentiated young adults who became problem stimulant users from those who desisted from stimulant use during the 3-year interim and 2) predicted cumulative baseline and follow-up stimulant and marijuana use across OSUs, regardless of clinical status , to address concerns regarding significant rates of marijuana and stimulant co-use . Analyses compared BOLD activity related to specific task requirements: decision contrasts compared BOLD activity during risk-taking choice trials versus safe choice trials; outcome contrasts compared BOLD activity on trials where each subject took a risk and subsequently earned a win or a loss. Categorical hypotheses were tested based on prior bio-behavioral findings in stimulant- dependent individuals: 1) PSUs would exhibit riskier task performance than DSUs; 2) PSUs would show greater striatal BOLD signals than DSUs to outcomes, particularly in response to risky wins; and 3) PSUs would exhibit lower PFC, insular, and cingulate BOLD signals during decision making. Because dimensional analyses were exploratory, no a priori hypotheses were tested.The University of California, San Diego, Human Subjects Review Board approved the study protocol. Participants were recruited through newspapers, internet ads, and fliers mailed to college students. Figure 1 demonstrates participant recruitment and categorical/dimensional data analysis protocol. A total of 1025 individuals were phone screened, and 184 OSUs meeting study criteria provided written informed consent to participate. OSU inclusionary criteria were as follows: 1) within the last 6 months, two or more separate occasions of cocaine or prescription amphetamine use without a prescribed purpose; 2) no lifetime stimulant dependence; 3) no lifetime stimulant use for medical reasons; and 4) no drug treatment interest. Participants completed three sessions: 1) a baseline diagnostic interview to determine lifetime psychiatric diagnoses and current drug use patterns , 2) a neuroimaging session completing the RGT , and 3) a follow-up interview session 3 years later to determine changes in drug use and clinical diagnoses . The current study includes data from OSUs who completed all three sessions . No OSU reported using methamphetamines at baseline;all baseline stimulant use was of cocaine and prescription stimulants.Three hypotheses were tested. First, consistent with the prediction that PSUs would exhibit riskier task performance than DSUs, PSUs more frequently made a risky decision following a win compared with DSUs, while DSUs more frequently made a safe decision following a risky win. This pattern supports previous findings that PSUs are more reactive to rewards . Second, although it was predicted that PSUs would show greater activation in reward processing striatal regions to risky wins than to risky losses when compared with DSUs, our results demonstrated the opposite effect, with PSUs exhibiting lower striatal BOLD signals across outcomes than DSUs. However, this finding is consistent with a longitudinal study of sensation-seeking adolescents in which striatal hypoactivation predicted future problematic drug use; the authors theorized that lower striatal activity may lead to a compensatory mechanism in which one seeks out increased risk to gain greater stimulation, thereby balancing reward center hypoactivation .