Future work could potentially benefit by further extending the present work to other health outcome variables such as ART medication adherence and HIV symptoms among persons with HIV/AIDS. It also may be advisable to explore how distress tolerance and emotion dysregulation relate to a broader array of symptoms and disorders. Finally, the present study was a secondary analysis from a larger study exploring the role of cannabis use among an HIV/AIDS population . Thus, as with any secondary analysis of data, it would be important to replicate and extend the findings in the future with an a priori research investigation. Overall, the results of the current study highlight the importance of perceived distress tolerance and emotion dysregulation in terms of elevated rates of anxiety and depression symptoms among an HIV/AIDS sample. specifically, findings indicated that perceived distress tolerance was significantly related to greater depressive and anxiety symptoms and that emotion dysregulation mediated this association. The present findings therefore suggest that emotion dysregulation may be important in better understanding the link between distress tolerance and certain negative emotional symptoms among people living with HIV. Indeed, it is possible that targeting emotion dysregulation among HIV? persons via strategies aimed at increasing self-efficacy over the ability to regulate affective states and gaining further control over affect-driven behaviors could be an integral step in efforts to promote greater degrees of psychological health among HIV? individuals.
Brain tissue volume losses in the frontal, temporal and select sub-cortical regions of individuals with alcohol use disorders have consistently been reported with volumetric magnetic resonance imaging , cannabis grow equipment and so have deficiencies in executive skills, learning and memory, processing speed, visuo spatial skills and working memory . Today, more than half of individuals with AUD who present for treatment also chronically abuse illicit substances . Substance use disorders have adverse effects on brain biology and function separate from those of AUD . Comorbid alcohol and substance use disorders have also been associated with brain morphological abnormalities. Liu et al. reported smaller normalized gray matter and white matter volumes of the prefrontal lobe in poly substance abusers abstinent from substance use for more than 2 weeks compared to controls. Tanabe et al. also reported smaller GM volumes of the bilateral medial orbito frontal cortex in long term abstinent individuals dependent on two or more substances compared to controls. As in AUD, the neurobiological abnormalities in individuals with PSUD are accompanied by cognitive deficiencies, particularly in visual and verbal memory, attention, psychomotor speed, visuomotor skills, problem solving and abstraction abilities . Thus, brain morphological abnormalities appear to occur in somewhat similar brain regions with similar neurocognitive deficits in AUD with and without comorbid substance abuse. To determine potential unique group differences, there is the need to directly contrast the magnitude and spatial distribution of structural brain abnormalities and their associated neurocognitive abnormalities in AUD with and without comorbid substance abuse. Directly contrasting structural brain abnormalities and their neurocognitive correlates in AUD with and without substance abuse will help design more efficacious treatment strategies tailored to individuals with PSUD or AUD. In this context, we showed recently that one-month-abstinent treatment-seeking PSUD individuals have prefrontal metabolite concentrations that were uniquely different from those of alcohol dependent individuals at similar abstinence duration, reflecting neuronal and glial dysfunction partly related to neurocognition . This quantitative volumetric magnetic resonance imaging study, contrasted differences in total and regional GM, WM and sub-cortical tissue volumes as well as ventricular and sulcal cerebrospinal fluid between abstinent alcohol dependent individuals without current illicit substance dependence and those with current psychostimulant dependence .
The functional relevance of our MRI measures was assessed by correlating them with neurocognitive measures. Since ALC recover brain tissue volume significantly but not completely within their first month of sobriety , while individuals with PSUD show regional GM tissue volume deficits even after many weeks and years of abstinence , we tested the following hypotheses in treatment seeking individuals after one month of abstinence from alcohol and other substances: PSU have smaller lobar GM, WM and sub-cortical tissue volumes as well as larger CSF volumes than light-drinking controls and ALC and in PSU and in ALC, smaller lobar GM and WM volumes correlate with worse measures of working memory, processing speed, visual-spatial learning and memory and auditory-verbal learning and general intelligence.Treatment-seeking PSU and ALC were recruited from the San Francisco VA Medical Center and Kaiser Permanente. For statistical reasons, we reduced our large ALC cohort to 40 individuals by matching them on age, education, smoking status and drinking variables to the smaller PSU group. Both ALC and PSU participants completed the structured clinical interview for the Diagnostic and Statistical Manual of Mental Disorders Axis I Disorder Patient Edition, Version 2.0 . Prior to enrollment, male participants consumed more than 150 alcoholic drinks per month for at least 8 years; females consumed more than 80 drinks per month for at least 6 years. PSU individuals were diagnosed with both alcohol dependence and dependence on at least one psychostimulant, with or without nicotine dependence and cannabis use disorder. All PSU met DSM-IV dependence criteria for at least one illicit substance and 10.5% met criteria for cannabis use disorder. specifically, 12 PSU met criteria for cocaine dependence and 2 of these were either abusing or dependent on cannabis; 2 other PSU met criteria for both cocaine and methamphetamine dependence ; and yet 2 others were dependent on both cocaine and opiates ; 2 other PSU met criteria for methamphetamine dependence only and 1 for opiate dependence only . In the PSU group, 32% were non-smokers, including 2 ex-smokers.
All ALC participants met DSM-IV criteria for alcohol dependence with or without nicotine dependence. 15 ALC participants were non-smokers, including 6 ex-smokers; the proportion of smokers did not differ among ALC and PSU.All ex-smokers among ALC and PSU individuals had stopped smoking for at least 5 years before the study. Within the ALC group, 2 individuals were currently abusing cannabis, while 3 had past cannabis abuse, currently in full remission. In addition, one ALC participant each showed past dependence on cocaine, amphetamines, or opioids, but all were currently in full remission. Thus, while the ALC participants were “clean” alcohol dependent individuals, the PSU participants were all dependent on alcohol and 84% on cocaine; only about 11% in both groups had a current or past cannabis use disorder diagnosis. Other non-substance-related inclusion and exclusion criteria were described previously . All ALC and PSU participants were tested daily with breathalyzers for alcohol consumption and randomly for substance use during outpatient treatment to ensure sobriety during the one-month-abstinence period. Twenty-seven non-substance-using LD, without histories of medical or psychiatric conditions known or suspected to influence brain structural outcome measures were recruited from the local community. Twenty-one of the LD individuals were never-smokers,mobile grow system and the proportion of non-smokers in the LD group was not significantly different from that in ALC or PSU .Within one day of the MR study, participants completed standardized questionnaires for alcohol withdrawal , depression and anxiety symptomatology . Alcohol consumption over lifetime was assessed with the lifetime drinking history . From the LDH, age of onset of heavy drinking [defined as consuming >100 alcoholic drinks per month or >80 drinks per month ] was derived and the average number of alcoholic drinks consumed per month over 1 year, 8 years before enrollment and over lifetime estimated. For PSU, substance use history was assessed with an in-house questionnaire based on the Addiction Severity Index , NIDA Addictive Drug Survey , drinking history, and Axis I disorders Patient Edition, Version 2.0 . The questionnaire probed for information on phases of drug use for each substance that a participant had a current or past disorder diagnosis on. The variables recorded included age of first and last use, number of total lifetime phases, duration of individual and total lifetime phases , frequency and quantity of use during each phase, and route of administration. Another variable recorded was money spent per day on a substance, which was then converted to one metric, using catchment area-specific conversion norms. Thus, monthly averages for grams of the substances over 1 year prior to enrolment and over lifetime were estimated.
To evaluate the nutritional status and alcohol-related or other hepatocellular injury, laboratory tests for serum, pre-albumin, alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase were obtained within three days of each MR scan. The values of these variables in the liver and the white blood cell counts were not significantly different between the groups. Table 1 shows demographics, alcohol consumption and select blood variables for LD, ALC and PSU.The neurocognitive domains and constituent measures evaluated were as follows : Executive skills: Short Categories Test, color-word portion of the Stroop Test, Trail Making Test part B, Wechsler Adult Intelligence Scale 3rd Edition Similarities, Wisconsin Card Sorting Test- 64: Computer Version 2-Research Edition non-perseverative errors, perseverative errors, and perseverative responses. Fine Motor Skills: Grooved Pegboard Test. General Intelligence: Ward-7 Full Scale IQ; based on WAIS-III Arithmetic, Block Design, Digit Span, Digit Symbol, Information, Picture Completion, and Similarities subtests. Learning and memory: Auditory-verbal: California Verbal Learning Test-II Immediate Recall trials 1–5 , Short and Long Delay Free Recall . Visuospatial: Brief Visuospatial Memory Test-Revised, Total Recall and Delayed Recall . Processing speed: WAIS-III Digit Symbol, Stroop Color & Word, WAIS-III Symbol Search Trail Making Test-A. Visuospatial skills: WAISIII Block Design; Luria-Nebraska Item 99. Working memory: WAIS-III Arithmetic, WAIS-III Digit Span. The raw scores for all neurocognitive measures were converted to age-adjusted or age-and-education-adjusted standardized scores. The standardized scores were then converted to z-scores for all measures. For the Luria-Nebraska Item 99 ratio, raw scores were converted to z-scores based on the performance of 27 non-smoking light drinking controls , since there are no published procedures available for this measure.Whole brain three-dimensional T1-weighted coronal images and two dimensional T2-weighted oblique-axial images were acquired on a 1.5 Tesla magnet , using Magnetization Prepared Rapid Acquisition Gradient Echo imaging sequence and spin-echo imaging sequence , respectively. After re-alignment of each subject’s T1- and T2-weighted images, the expectation maximization segmentation method was applied to the T1-weighted images to segment the brain into WM, GM, and CSF after coregistration and correction for bias field intensity variation of the T1-weighted images. The major lobes and sub-cortical regions were then parcellated by overlaying the tissue maps on a reference atlas containing landmarks of 36 brain regions , including the regions reported here. The volumes of total cortical GM and total lobar WM were calculated by summing the respective GM and WM values from the major lobes. The intracranial volume was estimated by summing all regional tissue and CSF volumes.Multivariate analysis of variance assessed group differences on age and education, differences between ALC and PSU on drinking and smoking severities, days of abstinence, anxiety and depression symptoms, as well as basic clinical laboratory measures . Multivariate analyses of covariance examined group differences on ICV-normalized volumes of 4 GM regions , 4 WM regions , 5 sub-cortical regions and 5 CSF regions separately . For total and regional cortical GM volumes and CSF, only age was a significant predictor of group variances and was therefore used as a covariate. For total and regional lobar WM volumes, only the body mass index contributed significantly to the variances and therefore was the only used covariate. Neither age nor BMI was a significant predictor of sub-cortical tissue volume variance. Participants’ cigarette smoking status was not a significant predictor of any tissue volume; however, this exploration has to be treated with caution, as the proportion of smokers in the LD sample tended to be smaller than in the patient groups. Given our a priori hypotheses, all MANCOVAs of tissue volumes were followed-up with post hoc analyses as well as pairwise and univariate t-tests . Also because of our a priori hypothesis, we did not correct for multiple comparisons. The reported p-values for GM are 1-tail, but those for WM and CSF volumes are 2-tail, because PSU unexpectedly had greater WM and smaller CSF volumes than LD and ALC. Although ALC and PSU did not differ significantly on the frequency of medical and psychiatric co-morbidities, these comorbidities were controlled for in all group comparisons. Effect sizes for pairwise comparisons were calculated using Cohen’s d .