The MACH 14 cohort is a dataset pooled from 16 studies conducted at 14 sites across 12 states. Each study in MACH14 used electronic data monitoring pillcaps to objectively measure participants’ adherence to antiretroviral medication. The focus of this study was on non-methadone substance abuse treatment so studies conducted in methadone maintenance programs were not considered in this analysis. From the 1579 participants in the MACH14 dataset, we identified 215 from two studies based outside methadone clinics because only these two studies’ participants had both EDM and substance abuse treatment status data. Written informed consent was obtained for participation in the parent studies, and the Yale Institutional Review Board approved the secondary analyses. Patients were asked about engagement in substance abuse treatment and use of specific substances for varying preceding time frames: one of the two studies asked about participation in substance abuse treatment during the past 90 days and use of specific substances over the past 30 days, while the other study asked about treatment over the past 30 days and substance use over past 14 days. To aggregate substance use data across studies, variables representing use of specific substances were defined as the proportion of days within the asked-about time frame the person had used each of several substances. This analysis used data collected at the first time point at which participants had EDM data for the preceding four weeks, had also been asked about being recently enrolled in substance abuse treatment, and were not enrolled in a methadone-clinic-based study. To estimate the effect of substance abuse treatment on adherence, adherence was calculated for the four weeks up to and including the date recent substance abuse treatment enrollment was assessed,grow rack as well as for the four weeks after the substance abuse treatment determination. Adherence in each week was calculated by dividing the weekly number of doses taken by the weekly number of prescribed doses for each medication, with adherence to each medication capped at 100%.
Adherence for a patient on multiple antire trovirals was calculated by averaging across prescribed medications.The effects of substance abuse treatment on adherence were determined in multivariate analyses that included a grouping variable denoting whether the patient was enrolled in substance abuse treatment and a variable reflecting substance abuse treatment over time. The analyses were conducted controlling for sociodemographic characteristics that might differ between patients in, and not in, substance abuse treatment. To control for the anticipated finding that patients in substance abuse treatment would have more active drug use than a reference group including people who had never had significant substance use, analyses included a measure representing the largest proportion of days during which participants had used either cocaine, opiates, or stimulants. Cannabis use was not included in this measure of illicit drug use because in a separate analysis of the MACH14 dataset and in an earlier study recent cannabis use was not associated with worse adherence. Analyses were run with SAS 9.2. The model included random effects for intercept and slope as this model had better fit to the data than models with fixed effects only.Although the analyses controlled forillicit drug use, it is possible that our self-report measures of substance use understated the impact of substance abuse treatment on substance abuse and that it is in fact abstinence that facilitates adherence. In one of the few randomized controlled studies of HIV-positive drug users in which abstinence was the target outcome, there was a trend towards a significant correlation between consecutive weeks of toxicology-tested abstinence during the intervention and reductions in viral load. There is also evidence from a naturalistic longitudinal cohort study that attendance at HIV treatment, a sine qua non for adherence, appears to improve with newly-achieved abstinence. Substance abuse treatment might improve adherence by mechanisms other than facilitating abstinence from using drugs. Substance abuse treatment typically involves case management to address the unstable housing characteristic of drug users. Stable housing arrangements during substance abuse treatment would be expected to foster adherence, in that stable routines have been associated with better adherence.
Substance abuse treatment also focuses patients on future goals, an orientation that has been described as fostering adherence, and substance abuse treatment can involve re-arranging social networks in ways that also might foster better adherence. It is possible that enrollment in substance abuse treatment reflects a lurking un-measured variable associated with both being in substance abuse treatment and better adherence. The finding of better adherence among people in substance abuse treatment was not buttressed by finding better adherence over time among patients in treatment. However, it might have been difficult to detect the time course of benefit from substance abuse treatment because the data did not specify when patients were entering, continuing, or finishing substance abuse treatment.Substance abuse was measured by self-report, and it is possible that substance abuse was disproportionately under-reported by people out of substance abuse treatment, thus exaggerating the impact of substance abuse treatment on adherence. The type of substance abuse treatment was not specified and the findings may not apply to all types of substance abuse treatment. Finally, the sample size was modest, and the number of participants in substance abuse treatment was small. It is noteworthy that although adherence decreased on average over time, the course of adherence varied significantly by person. Further analyses should test variables that may account for individual differences in adherence over time. These findings lend some support to the clinical practice of addressing substance use in an effort to improve adherence. The crucial next step is to develop and prospectively test substance abuse-focused interventions for patients with both substance abuse and adherence problems.Marijuana has been used for hundreds of years for mystical and religious ceremonies, for social interaction, greenhouse grow tables and for therapeutic uses. The primary active ingredient in marijuana is delta-9 tetrahydrocannabinol,one of some 60 21-carbon terpenophenolic compounds knows as cannabinols, which exerts its actions via cannabinoid receptors referred to as CB1 and CB2 receptors. Endogenous cannabinoids have been isolated from peripheral and nervous tissue. Among these, N-arachidonoylethanolamine and 2-arachydonoylglycerol are the best-studied examples.Behaviorally, AEA increases food intake and induces hypomotility and analgesia.
Anandamide also induces sleep in rats.Cannabinoid stimulation stabilizes respiration by potently suppressing sleep apnea in all sleep stages.In humans, marijuana and ∆9-THC increase stage, or deep, sleep.The mechanism by which the cannabinoids induce sleep is not known, hampering the development of this drug for possible therapeutic use. The sleep-inducing effects of cannabinoids could be linked to endogenous sleep factors, such as adenosine . There is substantial evidence that AD acts as an endogenous sleep factor. Extracellular levels of AD measured by microdialysis are higher in spontaneous waking than in sleep in the basal forebrain and not in other brain areas such as thalamus or cortex.Given this evidence, we hypothesized that the soporific effects of AEA could be associated with increased AD levels. In the present study, extracellular AD levels were assessed in the basal forebrain via the microdialysis method. The basal forebrain was sampled because this region is particularly sensitive to AD.The cholinergic neurons located in the basal forebrain are implicated in maintaining waking behavior, and it is hypothesized that sleep results from the accumulating AD, which then inhibits the activity of the wake-active cholinergic neurons.Our results show that systemic application of AEA leads to increased AD levels in the basal forebrain during the first 3 hours after injection, and total sleep time is increased in the third hour. These findings identify a possible mechanism by which the endocannabinoid system influences sleep.Previous reports have found that application of AEA directly to the brain increases total sleep time and SWS.We have now shown that systemic administration of AEA also has the same effect. More importantly, the increased sleep is associated with increased extracellular levels of AD in the basal forebrain. The increase in sleep induced by AEA occurred during the third hour after injection of the compound and was associated with peak levels of AD. In each of the first 2 hours, AD levels were significantly higher compared to vehicle injections, with a peak in AD occurring in the third hour. Increased sleep was not evident in the first 2 hours, suggesting that a threshold accumulation of AD might be necessary to drive sleep. In the fourth hour, AD decreased dramatically relative to the third hour, and the levels were not different from those observed after vehicle administration. There was no significant difference in delta power between AEA and DMSO, even though the percentage of SWS was higher with AEA.Sleep is hypothesized to result from accumulating AD levels, and then there is a decline as a result of sleep.
This effect is present in the basal forebrain and not in other brain areas.Thus, this purine is hypothesized to act as a homeostatic regulator of sleep; its buildup increases the sleep drive, and as AD levels decline, sleep drive also diminishes. The present data are consistent with this hypothesis in that peak sleep levels occur with peak AD levels, and then as a result of sleep, AD levels also decline . The CB1-receptor antagonist blocked the AEA-induced induction of AD as well as the sleep-inducing effect. The CB1-receptor antagonist SR141716A has been tested in diverse behavioral paradigms, and it blocks the effects induced by AEA.Santucci and coworkers demonstrated that administration of SR141716A increases W and decreases SWS.Here we replicated these effects but also demonstrated that the increase in AD levels after injection of AEA were blocked by the CB1- receptor antagonist. The AEA exerts its effect via the CB1 receptor and hyperpolarizes the neuron.The CB1 receptors are coupled to the Gi/Go family of G protein heterotrimers. Activation of the CB1 receptor inhibits adenylate cyclase and decreases synthesis of cAMP.In rats, the CB1 receptor is localized in the cortex, cerebellum, hippocampus, striatum, thalamus, and brainstem.The CB1 receptor is also present on basal-forebrain cholinergic neurons as determined by immunocytochemistry.The CB1- receptor mRNA is present in the basal forebrain.This receptor is also present in the brainstem where the cholinergic pedunculopontine tegmental region is implicated in W.Microinjection of AEA into this region decreases W and increases REM sleep.The CB1 receptors are also localized in the thalamus,an area implicated in producing slow waves in the EEG.Activation of these receptors in the pedunculopontine tegmentum, basal forebrain, and thalamus may decrease the firing of wake-active neurons, resulting in sleep. Additionally, accumulation of AD in the basal forebrain may inhibit the cholinergic neurons and also increase sleep. Direct injections of the AEA into the basal forebrain were not possible since AEA dissolves only in DMSO and alcohol. Moreover, delivery of the AEA dissolved in DMSO clogs the microdialysis membrane. The mechanism by which AEA increases AD in the basal forebrain is not known, even though both AD and AEA could directly inhibit the wake-active neurons given the inhibitory action of these agents on their receptors. Nevertheless, there is evidence of an interaction between the adenosinergic and the endocannabinoid systems.For example, the motor impairment induced by the principal component of cannabis, ∆9- THC, is enhanced by adenosine A1-receptor agonists.We now show that stimulation of the endocannabinoid system via the CB1 receptor increases AD in the basal forebrain. The endocannabinoids and AD may regulate sleep homeostasis via second and third messengers, as we have hypothesized.Previously, investigators have shown that stage 4 sleep in humans is increased in response to administration of ∆9-THC or smoking of marijuana cigarettes.We now show that such a soporific effect is associated with an increase in AD levels in the basal forebrain. Cannabinoid stimulation suppresses sleep apnea in rats,and A1-receptor stimulation also has the same effect.The endocannabinoid system also influences other neurotransmitter systems,in particular, inhibiting the glutamatergic system.It would be important to determine whether endogenous levels of specific neurotransmitter systems are changed as a result of AEA-induced activation of the CB1 receptor. Irrespective of the mechanism involved, our studies underscore the importance of endocannabinoid-AD interactions in sleep induction and open new perspectives for the development of soporific medications. The discovery of the cannabinoid receptors and endocannabinoid ligands has generated a great deal of interest in identifying opportunities for the development of novel cannabinergic therapeutic drugs. Such an effort was first undertaken three decades ago by a number of pharmaceutical industries, but was rewarded with only modest success.