Genes that are differentially methylated from fetal to neonatal life stage include DRD2, NOS1, SOX10 and DNMT1, all of which have been previously found to be associated with schizophrenia. We also found DNMT1, NOS1 and SOX10 to be differentially methylated in brain tissue from patients with schizophrenia. However, after adjusting for age and autolysis, DRD2 was not differentially methylated. The main limitation of our study was that patients were not free of antipsychotic medication, and antipsychotic medication has been shown to influence DNA methylation. A recent study reported that antipsychotic haloperidol was uniquely associated with higher global DNA methylation in patients with schizophrenia, but other antipsychotic drugs were not associated with changes in methylation.A further study observed that antipsychotics may have anti-inflammatory effects.However, as patients with chronic schizophrenia are almost certain to be treated with antipsychotic medications, future studies may only be able to adjust for antipsychotic medications in their analyses rather than eliminate these samples entirely. Our samples were obtained from a brain bank and had limited medication history collected. Although controls did not have schizophrenia, they were not screened for other psychiatric conditions such as depression. Antipsychotic medication use was not screened for among the control subjects, as these medications are not commonly used other than to treat psychosis. Another limitation is cell-type heterogeneity in the frontal cortex brain tissue used in our analysis. Our study did not account for differences in cell type seen in the frontal cortex,vertical grow lights and a previous study has shown that the two major cell types, neurons and glia, have different DNA methylation signatures.
Statistical methods that estimate brain cell types in gene expression studies and, more recently, in DNA methylation studies could be used in future brain DNA methylation studies. Our data indicate that studies of epigenetic changes in schizophrenia hold promise for the future development of diagnostic and prognostic biomarkers for schizophrenia, as well as therapeutic options that target causative epigenetic alterations. The key is to identify aberrant DNA methylation profiles in a functional tissue and determine if the results can be translated back into a diagnostically feasible tissue such as blood or saliva. Identifying when DNA methylation changes occur is also important in understanding the origins of schizophrenia. During the critical period of development between pregnancy and birth, altered DNA methylation occurs.If gene–environmental factors that affect DNA methylation status can be identified, then the incidence of schizophrenia could possibly be reduced by targeting the environmental triggers. To put together all the pieces of the schizophrenia puzzle, gene–environment interactions, as well as how they influence epigenetics, need to be identified. Over the years, there have been numerous studies on the effects of single nucleotide polymorphisms on mRNA expression in schizophrenia, but very few showing how single-nucleotide polymorphisms affect gene expression through DNA methylation. Investigating the involvement of single-nucleotide polymorphisms and their interaction with the environment, as well as their influence on epigenetics, will benefit our understanding of the pathophysiology of schizophrenia. The identification of enzymes that are capable of mediating DNA demethylation in mammalian cells as targets for therapeutic intervention is an exciting prospect that may hold the key to reversing this debilitating psychiatric illness.The global rise of methamphetamine use could jeopardize current intervention efforts to address the twin epidemics of opioid use disorder and HIV infection.
Use of methamphetamine is increasingly common among people with primary OUD. Prevalence of methamphetamine use disorders is increasing in Vietnam, raising concerns about increased risk of HIV infection and disruption of the substance use treatment systems, especially methadone programs. Methamphetamine use among people living with HIV could decrease retention in care, hinder medication adherence, accelerate viral replication, and further HIV disease progression. Other countries beyond South-East Asia encounter similar challenges. In low-and-middle-income countries, it is vital to identify cost-effective models of adapted evidence-based practices for addressing substance use disorders. Although there are no approved pharmacological treatments for methamphetamine use, evidence-based behavioral interventions such as motivational interviewing, contingency management, and cognitive behavioral therapy, including Matrix model, have shown efficacy in reducing methamphetamine use. However, we need to identify optimal combinations of EBI for effectiveness and cost effectiveness as many people in treatment face challenges to retention and sustained reductions in use.Motivational interviewing helps individuals to evaluate the pros and cons to change drug use and to develop personalized change behaviors. Motivational interviewing can be used in a single session or in multiple sessions. Polcin et al. compared two motivational interviewing conditions and found that both groups showed significant reductions in methamphetamine use without differences between the two groups. A greater reduction in psychiatric symptoms including anxiety and depression was found among those receiving more motivational interviewing sessions.Contingency management has shown the strongest evidence in treating methamphetamine use disorders. It is also effective in reducing other drug use including alcohol, cannabis, nicotine, and opioids.
Contingency management is based on the theory of operant conditioning where incentives are used to strengthen the target behavior such as abstinence, reduction of sexual risk behaviors, or other health-promoting behaviors like retention or adherence to treatment. Contingency management effects are enhanced in combination with other psychosocial interventions or education. A recent meta-analysis shows contingency management is more efficacious than other EBI up to 1 year following the discontinuation of reinforcers.The Matrix model has shown greater reduction in methamphetamine use, risky behaviors, and more days of abstinence compared to non-standardized outpatient treatment approaches. This intervention combines different elements of effective approaches including cognitive and behavioral treatment using accurate information on the effects of stimulants, relapse prevention skills training, 12-step program participation, and family education.Using SMS text messages with people who use methamphetamine has been shown to reduce methamphetamine use and HIV-related sexual transmission behaviors and increase retention in HIV care among some key populations. Scripted unidirectional texts outperform bidirectional interactive text-messaging conversations in reducing methamphetamine use and HIV sexual risk behaviors and are more cost-effective than in person therapies. Theory-driven messaging might better benefit people in the early stages of behavior change than people who are already seeking help.Despite some demonstrated efficacy, few studies have shown ways to optimize and combine treatment approaches for methamphetamine use disorders. Qualitative reports show patients found contingency management beneficial when combined with motivational interviewing and cognitive behavioral techniques for methamphetamine use disorders. Combined motivational interviewing and cognitive behavioral treatment show efficacy in reducing methamphetamine use in HIV-positive MSM. Evidence supports combining psychosocial treatment with medication-assisted treatment in people with OUD,vertical grow racks but it is unclear whether patients with comorbid methamphetamine use disorder will experience similar benefits. Integrating screening and brief interventions, contingency management or conditional cash transfer, and cognitive behavioral therapy for the management of substance use disorders requires trained health professionals. This is challenging in settings where human resource for mental health/substance use is scarce. Therefore, besides identifying optimal combination of EBI, it is essential to recognize potential barriers to the implementation of these strategies. Our study named “Screen, Treat and Retain people with opioid use disorders who use methamphetamine in methadone clinics” proposes to explore these questions.The study deploys a type-1 effectiveness-implementation hybrid design to evaluate the effectiveness of the proposed adaptive interventions and gather data on the implementation. To evaluate the effectiveness of the interventions, the study employs a Sequential Multiple Assignment Randomized Trial design. In the first phase, participants will be randomized into two front line interventions for 12 weeks.
Based on their outcome at the end of this phase, they will be placed or randomized into three adaptive strategies for another 12 weeks . The economic evaluation that addresses Aim 2 aims to weigh public health and societal costs against public health and societal benefits attributed to the interventions of different intensities with a time horizon of 12 months. To address Aim 3, we will conduct an ethnographic evaluation to identify the multi-level factors that influence the adoption and scale-up of the interventions in methadone clinics. The ethnographic evaluation is guided by the Consolidated Framework for Implementation Research. The CFIR assesses five domains of interventions, outer settings, inner settings, provider characteristics, and participant characteristics. The evaluation includes pre- and postintervention in-depth interviews with key informants who participate in the study and ethnographic observation with participants in their daily activities at the clinics and in the community settings.Measures of cost-effectiveness analysis corresponded to the outcomes of interest in Aim 1 including substance use, HIV risk behaviors among HIVnegative participants and HIV viral load, HIV adherence among HIV-positive participants, and Quality of life . The cost-effectiveness analysis will measure the increment in cost between contrasted interventions divided by the increment in effectiveness measures.In each cluster, we will interview 12 key informants including methadone providers, clinic managers, and participants under methadone treatment participating in the study. We will select at minimum 6 participants under treatment so that include old and young, employed and unemployed, and both responsive and non-responsive participants. All participants will receive VND 200,000 for their time in each interview. Interviews will be audio-recorded and transcribed verbatim.This activity is composed of two elements. The first element involves ethnographers spending time with participants with their consent in intervention sessions and other daily activities in the clinic. Such observations will build a rich picture of interventions and intervention settings, including interactions between various groups . The second element involves the study master counselors to observe random intervention sessions and assess the fidelity of intervention delivery using a checklist.In each selected clinic, a physician, two counselors, and one nurse will participate in the study as intervention providers. The physician will ensure referral to HIV and psychiatric services when necessary; two counselors will run motivational interviewing, group education sessions, and Matrix meetings; the nurse will collect urine twice a week and conduct contingency management based on the UDS results. Before the start of the intervention, to ensure the accuracy,integrity, and fidelity to the EBIs, all intervention staff at methadone clinics will receive didactic training on the theory behind the approach, evaluate their comprehension of the concepts within and behind the approach, watch a video of a Master Behavioral Counselor conducting intervention sessions and discuss the details of the session, and conduct at least two pilot intervention instances. All intervention sessions, except contingency management, will be audio-recorded, transcribed, and coded to ensure intervention fidelity. Intervention staff who have lower levels of intervention integrity or who have significant drift will be provided detailed feedback and supervision until there is parity with other staff.Sample sizes were chosen to compare primary outcomes based on first-stage randomization into one of two groups: high intensity or low intensity front line interventions. Sample size calculations are conducted in PASS 2008 for a two-group comparison of binary outcomes, a power of 80%, a 5% alpha level, and a conservative attrition rate of 20%. Using estimates from our prior work, we anticipate base rates of 80 to 90% for substance use and 60 to 70% for viral suppression. Based on these assumptions and a proposed sample of 200 HIV-positive participants , we can detect randomization group differences of 20% or more for binary outcomes, such as substance use and viralload suppression. We can detect even smaller group differences for substance use outcomes in the proposed sample of 400 HIV-negative participants and the combined sample of HIV-positive and HIV-negative participants. If estimated outcome probabilities are similar between first-stage randomization groups at 12 weeks, we will pool 12-week results for even greater power in evaluating second-stage randomization differences.We will use a time-varying mixed-effects model that will be fitted to the participants’ common outcome measures over time. The unadjusted model will include indicators of first-stage and second-stage intervention conditions, time of the assessment , and intervention indicators-by-time interaction terms. An additional interaction term of the two intervention indicators will be included to account for any interaction effect between the first and the second stage interventions. The adjusted model will include patients’ socio-demographic characteristics, drug use history, HIV-serostatus, and location as fixed effects. The mixed effects models will include a participant-level random effect to account for repeated observations of each participant, as well as a clinic-level random effect to account for the nested nature within the clinics. We will conduct subgroup analyses among HIV positive and HIV-negative participants. For the HIV positive subgroup, the specific outcomes of interest include HIV viral load suppression and adherence to antiretroviral treatment, and specific outcomes for HIV-negative subgroup include frequency of HIV testing and HIV seroconversion.