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The MA+ groups had higher rates of all other lifetime substance use disorders than the MA-groups

Further, poorer sleep quality among PWH with comorbid lifetime MA use disorder was associated with a number of neurobehavioral functional outcomes, including decreased physical and mental life quality, IADL dependence, unemployment and clinician-rated functional disability. As expected, lifetime MA use disorder was negatively associated with sleep quality; however, this finding was isolated to PWH and independent of recent MA use. In addition, MA use characteristics did not differ by HIV serostatus, suggesting sleep among PWH may be specifically related to the effects of nonrecent MA use. Prior studies have demonstrated detrimental effects of MA on neurobehavioral health specific to PWH, including neurocognitive impairment and associated everyday life consequences such as unemployment and difficulties performing activities of daily living . It is possible that disrupted sleep may mediate the link between MA and functional outcomes, although longitudinal studies are needed to determine causality. Depressive symptoms in the HIV+/MA+ group are also consistent with prior research . While depressive symptoms were also associated with global PSQI scores, as expected, this did not attenuate the relationship between MA and global PSQI scores in PWH, suggesting additional mechanisms underlying MA-related sleep disturbance independent of mood. One explanation for our findings is the combined, long-term CNS effects of excessive MA use and HIV on brain structures and/or pathways responsible for sleep regulation. While MA’s major mechanism of action is through increased activity of the mesolimbic dopamine system , emerging evidence supports that GABA-ergic dysfunction results from abuse of amphetamines . Projection systems of GABA include the reticular nucleus of the thalamus to the rostral brainstem reticular formation, a structure critical for sleep regulation. Further, GABA also promotes sleep via hypothalamic projections that inhibit serotonergic, noradrenergic, histaminergic,vertical grow racks and cholinergic arousal systems . Future studies linking GABA to MA use and sleep quality are necessary to establish this theoretical mechanism of action. Also, while the lack of evidence of sleep disturbance in the very small HIV−/MA+ group would not support long-term effects of MA use on CNS mechanisms important for sleep, a much larger subject sample would be needed to draw any confident conclusions about HIV−/MA+ individuals.

Prior literature on the prevalence of sleep disturbance in PWH is variable and comparisons between demographically matched, HIV serostaus groups on sleep quality is lacking. In a meta-analysis of self-reported sleep disturbance in PWH, the overall prevalence was 58% . No comparisons have been made with HIV-uninfected individuals from the same population to determine whether this prevalence is higher than in this type of comparison group. The current findings suggest HIV status alone may not elicit poor perception of sleep, however, fragmented sleep has been identified in chronic health conditions even without the patient’s perception of poor sleep . Consistent with prior literature , detectable HIV RNA was associated with poorer perceived sleep quality in our multiple regression analyses, but the specific mechanism for this association could not be established. Other literature has suggested that HIV infection is linked to objective sleep measurements, including reduced slow wave sleep and reduced rapid eye movement latency . However, studies have failed to detect similar associations between HIV disease severity and objective sleep measurements , highlighting the uncertainty to which HIV infection, by itself, may contribute to reductions in sleep quality. The study has several limitations. First, the data are cross-sectional and cannot determine causality. Lifetime MA use disorder is suspected to precede self-reported poor sleep within the last 30 days, however, such self-reported sleep disturbances may be longstanding and could even have served as a precursor to problematic substance use . Thus, future longitudinal evaluations or with increased sample size, the use of structural equation modeling, would be helpful in better determining the timing, duration, and directionality of associations between MA use disorders and sleep. This goes alongside our report of neurobehavioral outcomes associated with problematic sleep within PWH with a history of MA use disorder. While theoretically, sleep should have some influence on function, it is also possible that there is some unique third variable quality within the HIV+/MA+ group that leads to both poor sleep and poor neurobehavioral outcomes. Again, a longitudinal research design or a larger sample size may help in teasing out the directionality of our findings. Second, the small sample size of the HIV−/MA+ group hinders our ability to detect statistically significant associations between MA use and other findings with the HIV− participants.

For example, the difference between HIV+/MA+ and HIV−/MA+ groups on global PSQI was not statistically significant , yet the effect size suggests a nontrivial difference . While our sample did not demonstrate an interaction between HIV and MA possibily due to this limitation, this relationship may exist. Further, while lifetime MA use disorder independently contributed to sleep quality in PWH, we did not observe a recent MA use effect on sleep. We should note that this too may be due to low power, with very few participants reporting use in the last 30 days. It is also important to highlight the complexity of poly substance use in the context of a cross-sectional, retrospective study. Despite this, lifetime MA use disorder was retained in the multiple regression model, while the other substances did not. Due to limited data on participants who met criteria for a current substance use disorder or other measurements of current substance use parameters, our finding cannot speak to other potential factors associated with poly substance use that may explain differences in sleep between MA+ and MA− groups. Future studies to formally investigate poly substance use in more detail is needed to futher confirm our findings. In addition, we did not find associations between age, sex, or sexual orientation on sleep quality, which is contrary to well established literature on these topics . We suspect that the presence of other clinical risk factors for poor sleep, including those identified in this study , may be masking the detection of these variables traditionally known to impact sleep quality. There also remains the possibility that other unmeasured factors such as homelessness and/or SES may account for the observed relationship that MA was related to sleep in PWH that should be explored further in future studies. Lastly, the PSQI questionnaire is based on self-report, which is subject to recall and reporting bias. While there is merit in characterizing perceived sleep quality in vulnerable populations, as even the perception of poor sleep can influence mood and physical health , subjective measurements are just one facet of sleep quality and the inclusion of objective measurements such as actigraphy would enhance understanding of sleep in PWH and substance using populations. Importantly, the global PSQI score demonstrates strong sensitivity and specificity in distinguishing good from poor sleepers among the general population . While the sensitivity in detecting an insomnia diagnosis in PWH remains high , the specificity drops considerably . This suggests that the PSQI may not just be capturing sleep quality in PWH and raises the question as to whether items such as “trouble staying awake during the day” or “trouble keeping enthusiasm” are purely a function of poor sleep or a result of HIV-infection, prescribed medications, and/or associated psychosocial factors. Studies investigating the quality of the PSQI sub-components in capturing sleep quality within PWH using factor analyses may be a natural next step for future research. For people with substance use disorders,vertical grow rack system denial of untoward consequences from their actions is common and can affect commitment to treatment. In 2019, 96% of untreated individuals with a substance use disorder in the previous year denied needing treatment.

Psychodynamic approaches toward addiction encourage accountability and minimizing denial; and 12-step programs, such as Alcoholics Anonymous, target denial by encouraging clients to acknowledge that they have lost control over addictive behavior, with a focus on accountability-centered goals. Among participants who had polysubstance misuse and attended Alcoholics Anonymous or Narcotics Anonymous, the number of days in attendance was associated with decreased self-deception measured in a followup assessment.The transtheoretical model of behavior change likewise posits that changing addictive behavior relies on a transition from lack of recognition that a problem exists to increased awareness and motivation to change.The rostral anterior cingulate cortex , which participates in self-related processing, including self-awareness, has been implicated in personal relevance of drug-related stimuli, as is the ventromedial prefrontal cortex, which contributes to decision making.In an fMRI study, denial of methamphetamine-related problems was negatively related to resting-state connectivity between the rACC and precuneus.Among participants who met diagnostic criteria for Methamphetamine Dependence ,denial of methamphetamine-related problems correlated negatively with overall cognitive function and with rACC connectivity to frontal lobe regions, including the precentral gyri, left ventromedial prefrontal cortex, and left orbitofrontal cortex.These data implicate the rACC and its connections in a person’s ability to acknowledge problematic aspects of their substance use. One of the most important clinical measurements, the diagnosis of a substance use disorder, involves clinical judgment, but self-reports are very important. Structured diagnostic interviews, such as the Structured Clinical Interview for DSM-IV or Mini-International Neuropsychiatric Interview , query self-reports of symptoms indicating craving, tolerance, withdrawal, and interference with activities of daily living. Although interview guidelines encourage the use of referral notes, records, and observations of friends and family,diagnosis often relies on interview with the client alone. In these interviews, denial of problems related to substance use is common and can alter diagnosis. This study sought to clarify how a diagnostic measure of Methamphetamine Dependence that relies on self-report is related to a participant’s denial of his or her addiction problem. Participants comprised a sample of 69 individuals who acknowledged enough symptoms on the SCID to meet criteria for the diagnosis of Methamphetamine Dependence. They also completed the University Rhode Island Change Assessment Scale , which assesses motivation for change by providing scores on 4 stages of change: Precontemplation, Contemplation, Action and Maintenance. The Precontemplation score measures the respondent’s denial that their drug problem warrants change and is based on a transtheoretical model of addiction.In a prior study, the Precontemplation score was positively related to years of heavy methamphetamine use and arrests for drug offenses, supporting the notion that high scores reflect denial rather than the absence of problems. We hypothesized the Precontemplation score would correlate negatively with symptom severity, confounding the diagnosis.A quasi-experimental, non-intervention design was employed using secondary data analysis. Other studies of the parent dataset have been published.Participants, recruited using internet and local newspaper advertisements, provided written informed consent, following the guidelines of the UCLA Office for Protection of Research Subjects. This analysis included data from 69 participants. Detailed inclusion/exclusion criteria are published.In brief, participants were fluent in English, met criteria for Methamphetamine Dependence but not diagnoses related to drugs other than methamphetamine, cannabis, or tobacco; or for any Axis-I psychiatric disorders other than those related to drug abuse . They had a positive urine test for methamphetamine at screening but were not seeking treatment and were otherwise healthy. Participants received monetary payment for their time.The opioid crisis has had a substantial effect on women who are pregnant and parenting, focusing both public health and policymaker attention on opioids and on other substance use in pregnancy and postpartum. The number of pregnant women with an opioid use disorder diagnosis at delivery quadrupled from 1999 to 2014,1 and the incidence of neonatal opioid withdrawal syndrome increased nearly seven-fold from 2000 to 2014. Alcohol use remains common, with 1 of 9 pregnant women endorsing past 30 day use, one third of whom reported binge drinking.Cannabis use is increasing, with daily or near-daily cannabis use in pregnancy increasing from <1% in 2002 to nearly 3.5% in 2017.Stimulant use, specifically methamphetamine, doubled in pregnancy from 2008 to 2015.These trends have contributed to an increase in drug-related deaths among women in general and during pregnancy and postpartum in particular, with overdose among the leading causes of maternal death in the US today.Furthermore, the child welfare system response to substance use in pregnancy is straining already-limited resources. From 2011 to 2017, the number of infants entering the U.S. foster care system grew by almost 10,000, and at least half of infant placements are associated with parental substance use.Below, we review the change over time in state-level policy environments around substance use in pregnancy and contrast the policy response with the principles and guidance from professional societies and federal agencies.

The results also include the effect size using partial ƞ2 as a measure of the strength of the independent effects

This is important because a better understanding of the underpinnings for phenotypes that contribute to an enhanced vulnerability to heavy drinking and alcohol problems can lead to prevention approaches that diminish that vulnerability . The concept of tolerance is broad and has several components. These include pharmacodynamic, or functional, tolerance where the body develops less response, or more resistance, to a given level of the drug . Functional tolerance can be further characterized based on the duration and intervals between alcohol exposure. Acute tolerance, which develops during a single exposure to alcohol and is sometimes labeled as within-session tolerance or the Mellanby effect , refers to the phenomenon whereby in a single drinking session one experiences less alcohol effect at a given blood level at falling alcohol concentrations as compared to an identical alcohol concentration at rising levels . Repeated bouts of alcohol exposure can also produce chronic, or intersession, tolerance to the drug which might reflect both the pharmacodynamic and pharmacokinetic effects and is the usual tolerance definition that applies to the AUD criterion item in the recent versions of the Diagnostic and Statistical Manuals of the American Psychiatric Association.Acute tolerance in humans can be measured in a research laboratory by either having subjects ingest alcohol-containing beverages or by infusing ethanol intravenously . While each method of administration has its strengths and limitations for a critical review, systematic reviews of the acute tolerance literature find that 60% to 80% of these alcohol challenge studies yield evidence for acute tolerance to at least some of alcohol’s effects. The reviews also find more consistent evidence of acute tolerance when subjective measures of intoxication are assessed at rising and falling alcohol concentrations as opposed to more objective measurements such as performance on neuropsychological tests or driving simulation.

In summary,trimming weed plants some studies have used alcohol challenges to document acute tolerance and, and others have used alcohol challenges to evaluate the type and intensity of reaction to alcohol in individuals at higher risk for AUDs before repeated binge drinking or multiple alcohol problems develop. However, few, if any, studies have evaluated both acute tolerance and LR in the same population. When the relatively lower intensity of response to alcohol was first identified in young adult light-to-moderate drinking non-AUD offspring of individuals with AUDs, the phenomenon was labeled as a “low LR” because it was not possible to determine if the measure related to innate sensitivity or was the consequence of the development of some form of tolerance. Thus, there is a need to add evaluations of acute tolerance to alcohol challenge studies focusing on the low LR phenotype.This paper presents the results of secondary data analyses from one of our prior alcohol challenge studies to directly test whether moderate drinking low and high LR individuals differ in the development of acute tolerance. The data compare alcohol challenge scores at similar breath alcohol concentrations along the ascending and descending limbs of the BrAC curve. Data are available on changes in scores for subjective responses to alcohol and alterations in the amount of body sway. Our Hypothesis 1 is that low LR individuals, who have been shown to demonstrate less intense subjective feelings and body sway during the alcohol challenge, will also demonstrate greater levels of acute tolerance than their sex- and age-matched high LR counterparts. In addition, Hypothesis 2 predicts that, the relationship of LR to acute tolerance will be similar across the sexes .As described in detail in our prior work , participants in the present secondary data analysis were 18- to 25-yearold Anglo and white Hispanic students enrolled at the University of California, San Diego who took part in a multistage experiment examining fMRI differences in subjects with low and high responses to alcohol. Following approval by the UCSD Human Research Protection Program, a random cohort of students was first asked to respond to an email survey requesting information on demography, physical health, drinking and other drug use characteristics, as well as their family history of alcohol and other drug related problems.

Their survey responses were used to identify an initial cohort of healthy, right-handed students who had experience with alcohol but who never met criteria for an alcohol use or illicit substance use disorder; were not pregnant; and to be eligible for this functional Magnetic Resonance Imaging study, had no irremovable body metal and no history of traumatic brain injury.The survey also included the Self-Report of the Effects of Alcohol questionnaire, a retrospective measure of LR, as a preliminary screen for the low LR phenotype . The SRE uses 12-items that ask individuals to recall the number of standard drinks it took to feel four effects of alcohol across three time frames. The effects are: first feeling any effect; feeling as if speech was slurred; feeling unsteady walking; and unwanted falling asleep . The three time periods included the first five times one ever consumed at least a full drink, most recent three months where drinking at least once a month, and during one’s period of heaviest drinking. The score for each period was the sum of the number of drinks for the effects actually experienced with alcohol for that time frame, divided by the number of the up to four experiences reported to generate the average drinks needed per effect. In the present analysis, the First-5 metric was used to preliminarily categorize participants into low and high LR subgroups . Each low LR individual was matched to a high LR subject on other characteristics that might affect LR including age, sex, recent six-month pattern of intake of alcohol, nicotine use and their use of other drugs . Respondents who completed the survey, met the initial inclusion criteria, and who completed the SRE were contacted by phone to confirm their continued interest in participating in the laboratory portion of the protocol. Selected participants were invited to come to the laboratory where a trained interviewer administered the Semi-Structured Assessment for the Genetics of Alcoholism  interview to review their personal and family history of psychiatric and substance use disorders. Participants who still met the recruitment criteria were instructed to fast overnight before coming to the laboratory at 8AM and to refrain from using alcohol or other drugs for at least 48 hours prior to their first alcohol challenge session in our laboratory as part of the final screen for the subsequent fMRI placebo and alcohol challenges. The data reported here came from that laboratory-based alcohol challenge as the fMRI-based sessions did not include the full usual laboratory measures.Upon arrival at the laboratory, participants underwent a breathalyzer test to confirm a zero-breath alcohol concentration .

They were seated in a recliner, allowed to acclimate to the lab environment, and fed an isocaloric snack. After approximately one hour, they were given 10 minutes to imbibe an alcoholic beverage mixed as a 20% by volume solution in a carbonated, non-caffeinated sugar-free soda flavored to their choice. Male participants received 0.75 mL/kg ethanol while female participants ingested a drink containing 0.70 mL/kg to adjust for sex differences in body water . The average resulting BrAC peak was approximately 60 milligrams/dL at about 60 minutes postingestion as shown in Table 1 . As per the standard procedure performed in our lab over the years, the beverage was consumed through a straw extending from a thermos that obscured the actual beverage offered. At baseline prior to administering the drink, and at 30-minute intervals thereafter for up to 210 minutes, participants completed the Subjective High Assessment Scale . For these secondary analyses,vertical growing system to assess SHAS items most comparable to subjective measures used in other labs that perform human laboratory alcohol research , we focused on the SHAS-7 items of feeling High, Clumsy, Confused, Dizzy, Drunk, Alcohol’s Effects, and Difficulty Concentrating. Notably, the SHAS-7 score correlates highly with the complete 13-item measure that the Schuckit lab has used widely in their research and it uses the same visual analog marking scales to measure an individual’s subjective responses to alcohol . To compare our results more directly with reports from other human laboratories that measure subjective responses to alcohol and that use Biphasic Alcohol Effects Scale , we also analyzed the feeling Sleepy sub-scale of the SHAS which corresponds best with the Sedation sub-scale of the BAES. BrACs were also obtained every 30 minutes. Body sway, or standing ataxia, was recorded using a harness attached to the participant at the level of the axilla, from which ropes extended to the front and side at an approximate 90- degree angle from one another. Each rope passed over a pulley and anterior-posterior and lateral sway were recorded as the total number of centimeters of back-and-forth movement of the rope. Subjects completed three 1-minute trials at each time point with eyes open, feet together, and hands at their sides, with scores recorded as the mean values of the three trials. This is the same approach that has been used in our laboratory since about 1980. Body sway scores were adjusted for baseline differences before analyses were conducted. In keeping with NIAAA guidelines, participants were released from the laboratory when their BrAC fell below 0.01 g %. Following the completion of the laboratory-based alcohol challenge individuals went on to participate in the fMRI portion of the study the results of which have been reported previously .The following paradigm was used to compare low and high LR participants on their patterns of within-session acute tolerance. Using the methods of Plawecki et al. , the half-peaks on the ascending and descending BrAC arms, as well as the peak of the individual’s BrAC curve, were calculated.

Specifically, we used the Spline function in MATLAB® to determine the latencies corresponding to a session’s peak BrAC and to the same half-peak BrAC on the ascending and descending arms of the BrAC curve. We then computed corresponding subjective responses on the SHAS-7, Sleepy sub-scale, and Body Sway measures at those latencies, using linear interpolation between the nearest data collection time points. In keeping with procedures used in our lab for decades, participants were instructed to rate their subjective feelings on the SHAS visual analog scale as “none” prior to consuming the beverage. Thus, the baseline SHAS value was always a score of zero. The combined SHAS-7 total of scores were calculated by summing the scores for the seven individual items that comprise the scale that included the feeling High, Clumsy, Confused, Dizzy, Drunk, Alcohol’s Effects, and Difficulty Concentrating sub-scales. SHAS-7 total and individual item scores, the Sleepy sub-scale score, and baseline-corrected anterior-posterior and lateral body sway data were analyzed using a series of two-way, 3 within-subjects factors-by-2 groups mixed effects analysis of covariance , with Greenhouse-Geisser corrections for sphericity violations. The 3-level within-subjects factor was Timeand the 2-level between-subjects factor was either LR group or Sex . Separate analyses examining acute tolerance were performed utilizing one-way ANCOVAs between LR and Sex groups. Here, we defined the dependent variable, acute tolerance, as the difference score for each SHAS item at half-peak BrACs. In both sets of analyses, we covaried for the usual number of drinks per typical drinking occasion for the prior 6 months given that the low- and high-LR groups differed on this measure of recent drinking history prior to the alcohol challenge. The covariate was centered around the population mean before entry into the ANCOVA models as a main effect and as an interaction term with Time . All analyses were done in SPSS version 26 .Table 1 displays the demographic and physical characteristics as well as the drinking and other drug use patterns of the 60 pairs of low and high LR participants categorized based on their scores on the SRE-5. Consistent with prior reports on subsets of this sample , the two groups were well matched on demographic and physical characteristics and most measures of drinking and other drug use frequency occurring in the past six months.