More recently, these features have been reported among individuals who are at increased risk for psychosis due to clinical features and/or genetic liability . Moreover, at-risk individuals who later develop full psychosis show even greater increases in basal cortisol and pituitary volume , suggesting that increased HPA axis activity may signal risk for worsening illness. In parallel with this research, studies show that at-risk individuals report greater exposure and sensitivity to a range of psychosocial stressors, including major life events, childhood trauma, and minor daily stressors . However, there has been a paucity of studies examining the concordance between psychosocial stress or exposure/distress and HPA axis function; as such, the extent to which individuals on the psychosis spectrum exhibit ‘abnormal’ HPA axis responses to psychosocial stressors is unclear. That is, the increases in basal cortisol observed in those with, and at-risk for, psychosis may represent eithera ‘normal/ adaptive’ response to the high levels of psychosocial stressors reported in these populations , orhyper responsivity of the HPA axis , characterised by an increase in cortisol greater than that expected in a healthy individual . Alternatively, the elevated basal cortisol levels observed may be partially independent of psychosocial stress exposure/distress , and instead reflect individual-level factors such as genetic predisposition to HPA axis hyperactivity or metabolic abnormalities ,cannabis grow tent the latter being more common among individuals at clinical high-risk for psychosis , who present features consistent with the prodromal phase of illness.

Two recent studies of at-risk individuals support the ‘increased concordance’ hypothesis: Using the experience sampling method, siblings of psychosis patients showed more pronounced increases in salivary cortisol in response to unpleasant events relative to controls , whilst a further study reported a stronger association between retrospectively-reported stressful life events and basal cortisol in CHR youth compared to controls . In contrast, lower cortisol responses during psychosocial stress or tasks have observed in CHR individuals and young adults with high schizotypy traits relative to controls; a pattern consistent with that observed in patients with chronic schizophrenia . Together, these findings tentatively suggest that naturally-occurring psychosocial stressors are associated with greater cortisol increases in at-risk individuals compared to healthy controls, whereas the response to experimentally-induced psychosocial stressors is blunted. However, the degree to which HPA axis responses to laboratory-based stressor tasks are relevant to psychosis aetiology is unclear. Studying the effect of naturally-occurring stressors on HPA axis function is methodologically complex. Unlike studies using experimentally-induced stressor tasks, the lapse of time between stressor exposure and cortisol measurement may be considerable. Whilst elevations in cortisol levels following stressor exposure appear to decrease over time , early life events and trauma exposure are associated with HPA dys regulation later in life, suggesting long term effects of stress exposure . A related issue is that stress measures and cortisol samples may not be collected on the same day, particularly when studies have large assessment batteries spanning several days. It is possible that day-to-day variations in perceived stress might influence both retrospective reporting of stressful events and cortisol levels, such that greater concordance is observed when measures are collected on the same day.

However, to our knowledge, this has yet to be investigated. Determining the extent to which HPA axis responsivity in at-risk youth predicts clinical outcome is important, as such work might ultimately help to identify individuals at increased risk of illness progression by virtue of being more sensitive to the effects of psychosocial stress, enabling targeted interventions. Utilising data from the North American Prodrome Longitudinal Study 2 [NAPLS 2, ] we investigated whether psychosocial stressors, basal cortisol levels, and stressor-cortisol concordanceat the baseline assessment differed across healthy controls and CHR subgroups defined on the basis of their clinical presentation at the two-year follow-up . Based on previous studies, we hypothesised that CHR youth who later converted to psychosis would show greater exposure and distress in relation to psychosocial stressors,elevated basal cortisol, and higher stressor-cortisol concordance relative to healthy controls; we also anticipated that CHR non-converters would be intermediate to CHR converters subgroups and healthy controlson these measures. In all analyses we controlled for a range of potential confounders , and additionally explored the effect of lapse-of-time between assessments on stressor-cortisol concordance.NAPLS 2 is a consortium of eight research sites examining CHR youth, the aims and recruitment methods for which are detailed elsewhere . Briefly, CHR subjects were help-seeking individuals who met criteria for one or more prodromal syndromes:attenuated psychotic symptoms;brief intermittent psychotic symptoms; or substantial functional decline combined with a first degree relative with a psychotic disorder, or schizotypal personality disorder in individuals younger than 18 years. Prodromal syndromes were assessed using the Criteria of Prodromal Syndromes , based on the Structured Interview for Prodromal Syndromes [SIPS ], conducted by clinically-trained interviewers; psychiatric diagnoses were determined via the Structured Clinical Interview for DSM-IV . CHR individuals who had met criteria for an Axis I psychotic disorder were not eligible for inclusion; treatment with antipsychotic medication was permitted provided that full psychotic symptoms were not present at the time of medication commencement.

Healthy controls were recruited from the community and had no personal history or first-degree relative with psychosis and did not meet criteria for any prodromal syndrome. All participants were aged between 12 – 35 years at recruitment. Exclusion criteria for both groups included substance dependence in the past six months, neurological disorder, or full-scale IQ < 70.Ethical approval was provided by Institutional Review Boards at each NAPLS site , all participants provided informed consent or assent. The current sample includes 662 participants for whom variables of interest at baseline and clinical status at follow-up were available. At baseline, participants provided information on sociodemographic factors and potential confounders, completed stress measures, and collected saliva samples. Where possible, saliva was collected on the same day as daily stressor, life event and childhood trauma measures . However, in some in cases , the baseline assessment was interrupted that lead to a substantial delayin the completion of all measures. In such instances, the remaining baseline measures were collected when the participant was able to return and complete the schedule, with clinical assessments repeated to confirm CHR status. All participants were included in the analysis which accounted for timelapse between assessments. Prodromal symptoms were assessed via the SIPS at 12- and 24-month follow-up assessments and used to categorise CHR subgroups [see Table 1 for details ].Participant date of birth, sex, and ethnicity were assessed via self report, the latter was subsequently collapsed to a four-level variable . Cannabis use was assessed via a structured interview . For the purposes of the current investigation we created a binary variable indexing current use . Details of all prescribed psychotropic medications were obtained at the baseline assessment via self-report, pharmacy records,grow lights for cannabis and/or medical records. Binary variables were created for current antipsychotic use and current psychotropic use , irrespective of type, dose, or data source.The 58-item, Daily Stress Inventory , was used to determine the presence of minor stressors occurring within the past 24 -hs. Participants indicated whether they experienced each stressor and the level of distress elicited by each endorsed stressor . Total distress scores were then divided by the total exposure scoreto obtain an average distress per item score . Life events were assessed via the Psychiatric Epidemiology Research Interview Life Events Scale , modified to exclude life events of lesser relevance to youth. The 59 events can be classified as independent or dependent . Interviewers recorded how often each of the 59 events had occurred in the participant’s lifetime and the associated level of distress ; participants could report multiple exposures to the same event , where the maximum occurrence for any single life event in the NAPLS cohort was four. An average life event distress score was derived by dividing the total distress score by the total exposure score . Participants additionally completed the Childhood Trauma and Abuse Scale , a semi-structured interview examining experiences of physical, sexual, and psychological abuse, and emotional neglect, occurring prior to age 16 . Each trauma type was scored as absent/present with a binary variable indexing any form of trauma derived.At the research session, participants provided three saliva samples with a mean salivary cortisol value subsequently derived when two or more samples were available . The median time of collection for the three samples was 1107 h , 1207 h , and 1300 h , respectively. The mean cortisol value, which is highly correlated with area under the curve values , was computed to provide consistency with previous publications.

Participants were instructed to avoid consumption of caffeine, alcohol, or dairy products after 1900 h on the day before sampling; individuals who reported non-compliance with these instructions were not excluded as previous analyses performed on a subset of the cohort found no association with these variables and cortisol levels . Use of nonprescription medications over the past 24 hours was assessed via self-report. Samples were stored at -20 °C, and rapidly thawed and centrifuged prior to assay using a highly sensitive enzyme immuno assay . All samples were assayed in duplicate with intra- and inter-assay coefficients of variation less than 10% and 15%, respectively.All analyses were performed using Stata Version 15 . The number of days between stress measure completion and cortisol collection could not be computed for ∼6% of the sample due to missing assessment dates. In such cases, missing values for the three time-lapse variables were imputed using the median number of days across the entire sample. The imputed data variables were used in all subsequent analyses. Ladder and gladder commands were used to identify transformations yielding normally distributed continuous variables. Subsequently, age, cortisol, daily stressor average distress scores, and life event exposure scores were log-transformed, daily stressor exposure scores were square-root transformed, whilst life event average distress scores did not require transformation. There were no transformations that could improve the distribution of the assessment time-lapse variable, therefore a five-level categorical variable was created cortisol collected before stress measurement;assessments completed on the same day;cortisol 1-10 days after stress measurement;cortisol 11-30 days after; and cortisol > 30 days after. Next, we examined correlations between salivary cortisol and sampling variables, namely, time of first sample collection, number of samples collected , and use of non-prescription medications in past 24 hours. To remove the influence of relevant factors , cortisol values for the entire sample were regressed on sampling time, cough/cold medication use, and corticosteroid use to obtain standardised residuals. The resulting variable was used for all subsequent analyses. Group differences in demographic variables were examined using one-way analysis of variance, Kruskall Wallis, and chi-squared tests. To identify potential confounders, associations among demographic factors, cortisol, and psychosocial stress measures were examined using within-group Pearson’s correlations , biserial correlations , and chi-squared tests . Associations of group status with basal cortisol and psychosocial stressors were next examined, with adjustment for factors that were found in the above steps to be significantly associated with basal cortisol and/or any stressor in any group. Analyses of covariance , were employed for basal cortisol and continuous stressor measures, with estimated means and standard errors derived from these models. For trauma exposure, a logistic regression model was used to test the association with group status ; pairwise comparisons and adjusted trauma prevalence rates and associated SEs for each group were derived from the logistic model. All pairwise comparisons were performed with Sidak correction for multiple testing. To test whether stressor-cortisol concordance was moderated by time-lapse between assessments, correlations between cortisol and psychosocial stressors were examined within each time-lapse category. Linear regression models were used to test associations between individual psychosocial stressors and salivary cortisolin each group. Owing to multicollinearity, each stressor was examined in a separate model; all models were adjusting for potential confounders identified in the above steps. To facilitate comparison of stressor-cortisol concordance across groups, from these adjusted models, we obtained standardised beta coefficients for each psychosocial stress measure and computed SEs for these coefficients [SEStβ = SEβ ]. Stβ coefficients for each stressor were then pooled using the ‘meta’ command to obtain an overall measure of stressor-cortisol concordance.Of the 457 CHR individuals included in the current study,showed a remission of CHR symptoms,remained symptomatic, experienced a progression of positive symptoms, and 69converted to psychosis.

Older homeless adults are more likely to utilize inpatient health care as a result of SUDs: older homeless adults visit the Emergency Department for substance use at similar rates as younger homeless adults, but older homeless adults are more likely to be admitted.Older homeless adults with SUDs interact frequently with the health care system, yet there are inadequate numbers of substance use and mental health treatment providers who have the expertise in treating older adults to meet the demand for services.Older substance-using homeless adults will challenge both homeless services and substance use treatment programs, which have traditionally targeted younger adults and may be unprepared to deliver treatment to older adults with co-morbid conditions such as cognitive impairment or ADL difficulties.Many shelters exclude participants with active substance use, requiring sobriety as a condition for entrance.The high prevalence of chronic illness and functional disabilities of older homeless adults will increase the likelihood they will need services from long term care facilities, but some facilities exclude older adults with SUDs or do not have sufficient SUD treatment resources.Providers that provide care to older homeless adults should screen for substance use and follow positive screens with brief interventions and referral to treatment. Long term care facilities that care for homeless clients will need resources for treatment of substance use. Long term care facilities, such as skilled nursing facilities, should reevaluate policies that exclude older adults with substance use given the need for services for this high risk population. Given the shortage of geriatric substance use and mental health providers, policymakers seeking to address substance use in homeless adults will need to provide incentives for expansion of training programs for geriatrics, geriatric psychiatry, and geriatric addiction medicine.

Given the health risks of continued substance use, the low prevalence of treatment of older homeless adults with SUDs,cannabis plant growing and the rising proportion of mortality of older homeless adults attributable to substance use,older homeless adults will benefit from targeted treatment programs and geriatric substance use workforce development. The misuse of opiates is a serious problem worldwide, is increasing in young adults, and has substantial individual and societal consequences. In 2014 in the United States alone, approximately 1.9 million people had an opiate use disorder, including 586,000 heroin users. Neuroimaging in opiate dependence indicates both altered brain structure, particularly in the anterior cingulate cortex , and brain function involving dorsolateral prefrontal cortex and ACC. Magnetic resonance spectroscopy allows the non-invasive quantitation of brain metabolites that provide information on the neurophysiologic integrity of brain tissue. The few 1H MRS studies in opiate dependence to date revealed lower concentration of N-acetylaspartate , a marker of neuronal integrity, in the medial frontal cortex, including the ACC, as well as lower glutamate , a primary excitatory neurotransmitter, or glutamate+glutamine concentration in some but not all studies. The discrepant MRS findings may relate to differences among study participants regarding the prevalence and severity of comorbid substance use , the type, dose and duration of replacement therapy for heroin users , and/or participant age. The ACC, DLPFC and orbitofrontal cortexare important components of the brain reward/executive oversight system, a neural network critically involved in the development and maintenance of addictive disorders. Structural brain imaging in opiate dependence revealed generally lower gray matter volume or density in frontal regions, including the DLPFC, with thinner frontal cortices related to longerduration of opiate misuse. Functional MR imaging showed that the DLPFC, OFC and ACC are involved in decision making, and in opiate dependent individuals, lower task-based fMRI activity in the ACC related to compromised behavioural control of cognitive interference. Furthermore, smaller frontal gray matter volume in opiate dependence related to higher impulsivity on the Barratt Impulsivity Scale .

Correspondingly, opiate dependence is associated with cognitive deficits, primarily in executive functioning and self-regulation . Thus, the neuroimaging literature in opiate dependence suggests altered frontal brain structure as well as compromised neuronal integrity and glutamatergic metabolism. Few if any studies however investigated their relationships to opioid and other substance use behaviour or cognition. Further research into specific regional brain effects and their potential cognitive and behavioural correlates may inform better targeted treatment of individuals with opioid use disorders. We measured in opiate dependent individuals’ metabolite concentrations from the ACC and previously unexplored DLPFC and OFC and related them to quantitative measures of neurocognition, self-regulation, and substance use. Specifically, we compared opiate dependent individuals on buprenorphine maintenance to controls . We also included another control group, a substance-dependent ‘control’ group of 3 week abstinent alcohol dependent individuals , a commonly investigated treatment-seeking group to differentiate opiate dependence from not only control individuals but also individuals with a substance dependence . Our primary hypotheses were that:OD have lower NAA and Glu concentrations than CON in ACC, DLPFC, and OFC,these frontal cortical NAA and Glu deficits are associated with the level of opiate use and cigarette-smoking severity,the frontal NAA and Glu deficits in OD relate to higher impulsivity, poorer executive function, and lower decision making skills, and OD have more pronounced metabolite concentration deficits than ALC. The results of this study will contribute to a better understanding of the neurobiology and neuropsychology in OD, helping to identify novel targets for the treatment of opiate dependence. All participants provided informed consent according to the Declaration of Helsinki and underwent procedures approved by the University of California, San Francisco and San Francisco VA Medical Center00000068. Twenty-one chronic cigarette smoking OD, stable on buprenorphine maintenance therapy for at least 3 months, met DSM-IV criteria for dependence on opiates; they were allowed to meet DSMIV criteria for current abuse or dependence on cocaine, amphetamines, and/or cannabis, but dependence on alcohol or benzodiazepines was exclusionary.

OD was part of a buprenorphine treatment program focusing on smoking cessation and they were studied before smoking cessation. For group comparisons of metabolite concentrations specifically in the ACC, DLPFC, and POC and when correlated with neuropsychological variables, there were data from thirty-five cigarette smoking ALC recruited from local treatment programs of the VA and Kaiser Permanente and 28 cigarette smoking CON recruited from the community. The ALC group met DSM-IV criteria for alcohol dependence and was abstinent from alcohol for 21 ± 11 days at time of study. For group comparisons of metabolite concentrations in the OFC and when correlated with neuropsychological variables , smokers and non-smokers were included in the ALC and CON groups: 21 ALC and 19 CONdue to a lack of sufficient data in smokers. All participants were studied with structural MRI, 1H MRS, and neuropsychological testing,cannabis drying racks all were fluent in English and they were allowed to smoke ad libitum before assessment and during breaks. Table 1 contains demographics, tobacco and alcohol use variables, mood measures, and laboratory variables for the three groups. Further exclusion criteria for ALC and CON are described elsewhere. In brief, ALC and CON participants were excluded for neurological disorders , psychiatric disorders , and medical and vascular risk factors , known to affect neurobiology or cognition as well as for MRI contraindications. In OD and ALC, hepatitis C, type-2 diabetes, hypertension, unipolar mood disorder, or generalized anxiety disorders were not exclusionary due to their high prevalence in addiction. Six OD, 4 ALC and 1 CON had hepatitis C , while 4 OD and 13 ALC had medically-controlled hypertension. All OD were on buprenorphine maintenance therapy averaging 15 ± 9 mg/day. Table 2 depicts their recent and lifetime substance use histories. Overall, OD as a group were all cigarette smokers and had comorbid stimulant and marijuana use over lifetime, which they reduced during the year before study. Only a few OD individuals had drug use within the last 30 days: 3 used opiates and/or cocaine but only 1 used opiates for 20 days, 1 other OD used amphetamines daily, and about one-third of the sample used marijuana. The majority of OD individuals were moderate alcohol drinkers over their lifetime, but they reduced their alcohol consumption during the last year before study; only 3 had consumed alcohol on more than 10 days within the last 30 days. The ALC group for the ACC, DLPFC, and POC VOI analyses were cigarette smokers abstinent from alcohol for about 3 weeks and used other drugs occasionally . Thus, the ALC group for the majority of the analyses and the entire OD group were cigarette-smoking treatment seekers, abstinent from their main drug of abuse for several weeks and they had similarly low levels of drug use within the last month before study.OD and ALC completed the Structured Clinical Interview for DSM-IV Axis I disorders Patient Edition, v2.0, CON were administered the corresponding screening module. The clinical and neurocognitive assessments of ALC and CON are detailed elsewhere. In all groups, alcohol consumption was estimated with the lifetime drinking history interview, nicotine dependence was assessed with the Fager strom Tolerance Test for Nicotine Dependence, and lifetime substance use history was assessed with an in-house questionnaire.

A neurocognitive battery assessed the major domains affected by opioid and alcohol use disorders and Z-scores were calculated based on corresponding normative data. Cognitive domains were formed from specific neurocognitive tasks . The cognitive domain scores in ALC and CON were calculated according to the shortened neurocognitive battery of tests administered to the OD group and therefore, the constituent measures for cognitive domains in this study are different from our previous publications. All participants completed self-regulation measures, which included the BIS to assess self reported impulsivity, the Balloon Analogue Risk Task to assess risk taking, and the Iowa Gambling Task to assess decision making. Laboratory tests within 2–3 days of the MR scan evaluated the nutritional status and alcohol-related or other hepatocellular injury in OD and ALC. See Table 1 for laboratory variables, cognitive domain and self-regulation measures for the three groups. Univariate analyses of covariance tested for group differences on demographic and clinical variables. All statistical analyses were performed with SPSS version 22. Separate ANCOVAs were performed for the four VOIs and each metabolite, followed by planned pairwise comparisons to test for group differences in metabolite concentrations between OD, ALC and CON. Given the participants’ wide age range and as age correlates with metabolite concentrations , age was used as a covariate in group comparisons. As GM, WM, and CSF contributions to the VOIs affect brain metabolite levels and as tissue content in ACC and OFC VOIs differed between groups , we included these variables as predictors in the ANCOVAs. Each a priori hypothesis was tested with an alpha level of 0.05. In pairwise group comparisons of metabolite levels without a specific a priori hypothesis, we used corrected alpha levels to account for the multiplicity of metabolites in each VOI via a modified Bonferroni procedure, which yielded adjusted alpha levels for each VOI separately by using the number of metabolites under investigation and their average inter-correlation coefficients . OFC spectra often did not have a well-defined mI resonance and therefore, OFC mI was not analysed. Effect sizes were calculated via Cohen’s d. Correlations between outcome measures were corrected for age , except for correlations with cognitive domains , and reported as Pearson coefficients. Age and years of education did not differ between OD and CON . ALC were equivalent on age to OD and CON, but had fewer years of education. OD had lower hemoglobin and hematocrit than both CON and ALC. There were no significant differences in blood tests of liver function in individuals with and without Hepatitis C within the ALC group and also within the OD group. In addition, none of the individuals with Hepatitis C were taking medications at the time of study for Hepatitis C. Furthermore, the individuals taking hypertension medication did have controlled blood pressure by self report but blood pressure levels at time of study were not measured. Nicotine dependence scores were higher in OD than ALC; OD and CON also smoked significantly more cigarettes per day than ALC, but all groups were equivalent on cigarette smoking duration. Gender did not contribute to any group difference or correlation. See Table 1 for drinking severity measures in OD, CON and ALC. This study compared cortical metabolite concentrations, neurocognition, and self-regulation between cigarette-smoking opiate dependent individuals on buprenorphine maintenance therapy, treatment-seeking alcohol dependent smokers, and smoking controls.

MOR antagonists increase somatic withdrawal symptoms and aversion in nicotine-dependent mice and rats, and decrease nicotine self-administration, nicotine preference, and cue induced reinstatement of nicotine seeking.However, a small number of conflicting studies report no significant differences in nicotine reward, self-administration, or withdrawal following administration of a MOR antagonist, possibly as a result of differences in route of administration, dose, duration, or pharmacodynamics of the antagonist used.Moreover, morphine exhibits significant functional interactions with nAChRs.Chronic nicotine treatment in mice enhances the effect of morphine on striatal dopaminergic pathways, thereby influencing locomotor activity and reinforcement.Although there are minimal data on nicotine and opioid interactions during adolescence, increasing evidence supports a role of the KOR system in modulating nicotine-associated behaviors. Rodent studies suggest that teen susceptibility to nicotine use is likely due to adolescents finding nicotine more rewarding and less aversive than adults.These differences in sensitivity to nicotine reward and aversion may be due, in part, to the KOR system, as activation of KORs increases aversive effects and withdrawal signs of nicotine in adult rodents, but not adolescents.Furthermore, KOR antagonists increase concurrent nicotine and alcohol self-administration in adult, but not adolescent, male rats.Given the interactions between the cholinergic and opioidergic systems in reward regulation and the alarming increases in opioid-related deaths, it is important to recognize and understand risk factors of opioid addiction,hydroponic system for cannabis including adolescent nicotine exposure.Bipolar disorderis a severe, chronic, and disabling mental illness characterized by recurrent episodes of hypomania or mania and depression.

It is a clinically defined nosological entity with multi-factorial but poorly understood etiologic mechanisms. The evidence from twin, family, and adoption studies provide compelling evidence for a strong genetic predisposition to BPD with heritability estimated to be as high as ≥80%. Given BPD is a heterogeneous disease with substantial phenotypic and genetic complexities, the identification for BPD risk loci has proven to be difficult. Some researchers have proposed that dissecting BPD into clinical subgroups with distinct sub-phenotypes may result in genetically homogeneous cohorts to facilitate the mapping of BPD susceptibility genes. Among the subphenotypes, early-onset BPD is of particular interest as several independent cohort studies have demonstrated their existence. Comparing to the non-early-onset BPD, the early-onset subtype is associated with a more severe form of clinical manifestations characterized by frequent psychotic features, more mixed episodes, greater psychiatric comorbidity such as drug and alcohol abuse and anxiety disorders, higher risk of suicide attempt, worse cognitive performance, and poorer response to prophylactic lithium treatment. In addition, the pattern of disease inheritance seems to differ between early‐ and late‐onset BPD families, with the former involving greater heritability. These observations indicate that early-onset BPD may be a genetically homogenous subset and thus could be used for genetic study to identify its susceptibility genes. A number of BPD genes identified by genome-wide association study have been widely replicated and intensively studied, but these studies did not include early-onset BPD. Over the past two decades, a host of studies have investigated genetic loci responsible for early-onset BPD through linkage-analyses, candidate–gene association, analyses of copy number variants , and GWAS, but findings are inconclusive.

Candidate–gene association studies have identified a number of genes potentially associated with early-onset BPD, including glycogen synthase kinase 3-β gene, circadian clock gene Per 3, serotonin transporter gene, brain-derived neurotrophic factor gene, and gene coding synaptosomal-associated protein SNAP25. However, very few positive findings of these studies have been replicated independently. Findings from linkage studies suggested chromosomal regions harboring the susceptibility genes at 3p14 and 21q22, plus the loci at 18p11, 6q25, 9q34 and 20q11 with nominal significance. Studies of CNVs in early-onset BPD were based on relatively small effect sizes and were irreproducible, suggesting that CNVs are unlikely the major source of liability. Finally, GWAS failed to find any risk variant with genome-wide statistical significance in Caucasian populations, despite some variants showed suggestive significance. In previous genetic studies, the definition of early-onset in BPD typically ranged from 15 to 25 years of age. Most of them compared early-onset BPD vs. healthy control. Such a case–control design is more likely to identify susceptibility gene for BPD per se, but not for the early-onset sub-type. The optimal strategy to identify gene for the early-onset BPD is to include the non-early-onset BPD group for comparison.In this paper, we reported findings from a GWAS with high-density SNP chips on early-onset, defined as ≤20 years of age, BPI patients of Han Taiwanese descent.The clinical phenotype assessment of manic and depressive episodes was performed by well-trained psychiatric nurses and psychiatrists using a cross culturally validated and reliable Chinese version of the Schedules for Clinical Assessment in Neuropsychiatry, supplemented by available medical records. All of them were diagnosed according to the DSM-IV criteria for BPI disorder with recurrent episodes of mania with or without depressive episode. The assessment of onset age was based on a life chart prepared with graphic depiction of lifetime clinical course for each of the BPI patient recruited. This life chart included largely all the mood episodes with date of onset , duration, and severity.

The construction of this life chart was based on integrated information gathered from direct interview with patients and their family members, interviews with in-charge psychiatrists, and a thorough medical chart review. Different definitions for early onset of BPI have been proposed in previous work. Our selection of 20 as the age threshold was based on a systematic review for pediatric BPD. The age at onset was defined by the first mood episode . Of all patients, 1306with genotyping data available were included in the discovery group for GWAS and the rest 473without genotyping data were included in the replication group.In this paper, we have reported one of the largest GWASs to investigate genetic susceptibility to early-onset BPI with the first mood episode occurring ≤20 years of age. We employed standardized psychiatric interview and constructed a life chart with detailed clinical history to ensure the accuracy and homogeneity of phenotype for genotyping. Our GWAS with high-density SNP chips identified the SNP rs11127876 in CADM2 gene to be associated with early-onset BPI in both discovery and replication groups, and meta-analysis for the association was close to genome-wide significance . The gene CADM2 on chromosome 3 encodes a synaptic cell adhesion molecule that is prominently expressed in neurons, and plays key roles in the development, differentiation, and maintaining synaptic circuitry of the central nervous system. In previous GWASs, CADM2 has been found to be associated with a number of mental health related traits, including alcohol consumption, cannabis use, reduced anxiety, neuroticism and conscientiousness, and increased risk-taking behavior. CADM2 was also reported to be associated with executive functioning and processing speed, general cognitive function, and educational attainment. Though there have been no investigations examining the risk-taking phenotype in early-onset relative to nonearly-onset BPD, Homes et al. showed that BPD patients with a past history of alcohol abuse or dependence had a higher risk-taking propensity,indoor hydroponics cannabis suggesting a relationship between early-onset BPD and risk-taking propensity. Of note, Morris et al. suggested that CADM2 variants may not only link with psychological traits, but also influence metabolic-related traits, such as body mass index, blood pressure, and C-reactive protein. In addition, they found that CADM2 variants had genotype specific effects on CADM2 expression levels in adult brain and adipose tissues. The finding highlights the potential pleiotropy of CADM2 gene, i.e., the genetic variants may influence multiple traits, and shared biological mechanisms across brain and adipose tissues through the regulation of CADM2 expression. Given that the metabolic comorbidities are prevalent in patients with early-onset BPD, it is conceivable that CADM2 variants may influence both psychologicaland physicaltraits, further contributing to a more severe clinical subtype of BPD with accompanying risk of metabolic adversities. In addition, an association between risk-taking and obesity has also been implicated in previous research, which suggests that risk takers tend to overlook health-related outcomes and are prone to aberrant reward circuitry predisposing them to poor dietary choices and excessive intake.

Collectively, in line with the characteristics found to be associated with CADM2 variants, it is likely that CADM2 may exert an effect on the constellation of clinical features related to early-onset BPD with greater symptom severity . Therefore, our findings suggest that CADM2 genetic variants may have significant effects on a subtype of BPD with early-onset. Two previous GWASs comparing early-onset BPD patients with healthy controls did not find any genetic variants reaching genome-wide significance. Our study included a larger sample of early-onset BPI patients to conduct GWAS using high-density genotyping . The statistical power was calculated using Post-hoc Power Calculator , combining the allelic frequencies of both discovery and replication groups. In this study of two independent samples of BPI with dichotomous endpoint, the power reached 99.4% and 18.2% under type I error= 0.05 and = 5 × 10−8 , respectively. Results of our study are also likely to be under powered under the stringency setting of type I error. However, the frequency of risk allele T was higher in patients with onset ≤20 than in patients with onset >20 in both discovery and replication groups. We believe all these have provided strong evidence to confirm the association of this SNP with early onset BPD. In Table 2, the minor allele frequencies differ quite a bit between the discovery and replication cohorts. In the NCBI SNP database, minor allele frequency of rs11127876 is 0.08in Han Chinese in Beijing, close to our results and suggest that the different allele frequencies observed in Table 2 may mainly result from our sampling. The over-representative minor allele frequency in replication group may have come from random sampling or effects of hidden characters of our patients recruited. Genetic variant of CADM2 has been reported to be associated with behavioral and metabolic traits, which were not assessed in this study. Though the minor allele frequencies of rs11127876 were different in discovery and replication groups, the same direction of ORs of rs11127876 minor allele supports the reliability of our findings. The SNP rs75928006 located in the upstream of MIR522 reached genome-wide significance in discovery group but failed to show statistical significance in replication group. MIR522 promotes glioblastoma cell proliferation, but there was no evidence to suggest its association with any psychiatric disorders. One major limitation of this study is the possibility of recall bias about the exact onset age of the first mood episode of BPI, particular when there was a long history of the illness. Previous studies have however suggested that age at onset can be assessed reliably. The preparation of life chart containing detailed clinical course and treatment based on a semi-structured clinical interview plus a thorough medical chart review for individual patients should have overcome this potential limitation satisfactorily. In summary, we have identified a genetic locus rs11127876 in CADM2 gene to be associated with early onset BPI. The finding has reflected the co-sharing genetic features of psychiatric disorders and behavioral traits. Further investigations of the CADM2 biological function in BPI are warranted.HEAVY DRINKING IS common during college years and is associated with potentially serious consequences. About 65% of students who have drunk so much became ill ; half consumed at least 4 to 5 drinks per occasion ; a similar proportion experienced alcohol related blackouts ; 10% had an alcohol-related injury; while 2,000 students died each year from alcohol-related causes . These experiences are relatively ubiquitous across colleges and universities and relate to a wide range of preexisting characteristics . Demographic items associated with higher drinking intensities include being European American , sex and, in adolescents, an older age . Heavier past substance use patterns also predict college alcohol use and problems, as do several alcohol related genetically influenced characteristics such as how a person responds to alcohol . The latter includes a low level of response , or the need for higher blood alcohol concentrations to experience the effects of this drug, which predicts heavier alcohol intake in a wide range of situations . Environment/attitude characteristics related to heavy drinking include substance use among peers, using alcohol to cope with stress, higher positive expectancies of alcohol’s effects, beliefs that people have positive views about alcohol use and drunkenness , and a person’s belief that people around them are heavy drinkers.