A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

BRD3 Regulates the Inflammatory and Stress Response in Rheumatoid Arthritis Synovial Fibroblasts.

BACKGROUND: Individual functions of members of the bromodomain (BRD) and extra-terminal (BET) protein family underlying the anti-inflammatory effects of BET inhibitors in rheumatoid arthritis (RA) are incompletely understood. Here, we aimed to analyze the regulatory functions of BRD3, an understudied member of the BET protein family, in RA synovial fibroblasts (FLS). METHODS: BRD3 was silenced in FLS prior to stimulation with TNF. Alternatively, FLS were treated with I-BET. Transcriptomes were analyzed by RNA sequencing (RNAseq), followed by pathway enrichment analysis. We confirmed results for selective target genes by real-time PCR, ELISA, and Western blotting. RESULTS: BRD3 regulates the expression of several cytokines and chemokines in FLS, and positively correlates with inflammatory scores in the RA synovium. In addition, RNAseq pointed to a profound role of BRD3 in regulating FLS proliferation, metabolic adaption, and response to stress, including oxidative stress, and autophagy. CONCLUSIONS: BRD3 acts as an upstream regulatory factor that integrates the response to inflammatory stimuli and stress conditions in FLS and executes many functions of BET proteins that have previously been identified using pan-BET inhibitors.

The histone acetyl transferases CBP and p300 regulate stress response pathways in synovial fibroblasts at transcriptional and functional levels.

The activation of stress response pathways in synovial fibroblasts (SF) is a hallmark of rheumatoid arthritis (RA). CBP and p300 are two highly homologous histone acetyl transferases and writers of activating histone 3 lysine 27 acetylation (H3K27ac) marks. Furthermore, they serve as co-factors for transcription factors and acetylate many non-histone proteins. Here we showed that p300 but not CBP protein expression was down regulated by TNF and 4-hydroxynonenal, two factors that mimic inflammation and oxidative stress in the synovial microenvironment. We used existing RNA-sequencing data sets as a basis for a further in-depth investigation of individual functions of CBP and p300 in regulating different stress response pathways in SF. Pathway enrichment analysis pointed to a profound role of CBP and/ or p300 in regulating stress response-related gene expression, with an enrichment of pathways associated with oxidative stress, hypoxia, autophagy and proteasome function. We silenced CBP or p300, and performed confirmatory experiments on transcriptome, protein and functional levels. We have identified some overlap of CBP and p300 target genes in the oxidative stress response pathway, however, with several genes being regulated in opposite directions. The majority of stress response genes was regulated by p300, with a specific function of p300 in regulating hypoxia response genes and genes encoding proteasome subunits. Silencing of p300 suppressed proteasome enzymatic activities. CBP and p300 regulated autophagy on transcriptome and functional levels. Whereas CBP was indispensable for autophagy synthesis, silencing of p300 affected late-stage autophagy. In line with impaired autophagy and proteasome function, poly-ubiquitinated proteins accumulated after silencing of p300.

Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study.

Given the polygenic nature of antipsychotic-induced weight gain (AIWG), we investigated whether polygenic risk scores (PRS) for various psychiatric and metabolic traits were associated with AIWG. We included individuals with schizophrenia (SCZ) of European ancestry from two cohorts (N = 151, age = 40.3 ± 11.8 and N = 138, age = 36.5 ± 10.8). We investigated associations of AIWG defined as binary and continuous variables with PRS calculated from genome-wide association studies of body mass index (BMI), coronary artery disease (CAD), fasting glucose, fasting insulin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, type 1 and 2 diabetes mellitus, and SCZ, using regression models. We observed nominal associations (uncorrected p < 0.05) between PRSs for BMI, CAD, and LDL-C, type 1 diabetes, and SCZ with AIWG. While results became non-significant after correction for multiple testing, these preliminary results suggest that PRS analyses might contribute to identifying risk factors of AIWG and might help to elucidate mechanisms at play in AIWG.

Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis.

BACKGROUND: Radiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis ("radiomics") for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD. METHODS: We investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis. RESULTS: Radiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation. CONCLUSIONS: Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

Individual functions of the histone acetyl transferases CBP and p300 in regulating the inflammatory response of synovial fibroblasts.

Chromatin remodeling, and a persistent histone 3 lysine 27 acetylation (H3K27ac) in particular, are associated with a sustained inflammatory response of synovial fibroblasts (SF) in rheumatoid arthritis (RA). Here we investigated individual functions of the writers of H3K27ac marks, the homologues histone acetyl transferases (HAT) CBP and p300, in controlling the constitutive and inflammatory gene expression in RA SF. We applied a silencing strategy, followed by RNA-sequencing and pathway analysis, complemented with the treatment of SF with inhibitors targeting the HAT (C646) or bromo domains (I-CBP) of CBP and p300. We showed that CBP and p300 undertook overlapping and, in particular at gene levels, distinct regulatory functions in SF. p300 is the major HAT for H3K27ac in SF and regulated more diverse pathways than CBP. Whereas both factors regulated genes associated with extracellular matrix remodeling, adhesion and proliferation, p300 specifically controlled developmental genes associated with limb development. Silencing of CBP specifically down regulated the TNF-induced expression of interferon-signature genes. In contrast, silencing of p300 resulted in anti- and pro-inflammatory effects. Integration of data sets derived from RNA-sequencing and chromatin immunoprecipitation sequencing for H3K27ac revealed that changes in gene expression after CBP or p300 silencing could be only partially explained by changes in levels of H3K27ac. Inhibition of CBP/p300 using HAT and bromo domain inhibitors strongly mirrored effects obtained by silencing of p300, including anti- and pro-inflammatory effects, indicating that such inhibitors are not sufficient to be used as anti-inflammatory drugs.

Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response.

Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 (OR = 0.33 [0.21, 0.51], p = 1.42 × 10-6) and intergenic rs6916777 (Beta = 14.03 [8.47, 19.59], p = 1.25 × 10-6), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease (n = 25 of 190 genes, p = 8.03 × 10-6, FDR-corrected p = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer's disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p = 2.45 × 10-4). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction.

Contributions of cholinergic receptor muscarinic 1 and CYP1A2 gene variants on the effects of plasma ratio of clozapine/N-desmethylclozapine on working memory in schizophrenia.

BACKGROUND: Clozapine has heterogenous efficacy in enhancing working memory in schizophrenia. We have previously hypothesized that this is due to opposing effects of clozapine and its metabolite, N-desmethylclozapine, at the muscarinic M1 receptor and demonstrated that a lower clozapine/N-desmethylclozapine ratio is associated with better working memory than clozapine or N-desmethylclozapine levels alone. AIMS: In this study, we expanded the above hypothesis to explore whether genetic variation in the cholinergic receptor muscarinic 1 gene, encoding the M1 receptor, affects the relationship between clozapine/N-desmethylclozapine and working memory. Further, we explored whether CYP1A2 gene variants affect the ratio of clozapine/N-desmethylclozapine and by this, working memory performance. METHODS: We evaluated two functionally significant single nucleotide polymorphisms, rs1942499 and rs2075748, in cholinergic receptor muscarinic 1, with the haplotype T-A associated with lower transcriptional activity than the haplotype C-G. Further, we examined CYP1A2 *1F, with *1F/*1F conferring high inducibility in the presence of smoking. RESULTS: In a sample of 30 patients with schizophrenia on clozapine monotherapy, clozapine/N-desmethylclozapine was correlated with working memory only in non-carriers of the haplotype T-A of the cholinergic receptor muscarinic 1 gene. Interaction of CYP1A2 genotype and smoking status significantly affected clozapine concentrations, but there were no significant effects of CYP1A2 genotype and smoking status on the relationship between clozapine/N-desmethylclozapine on working memory. CONCLUSIONS: Our finding that the relationship between clozapine/N-desmethylclozapine and working memory is specific to patients with potentially higher transcription of M1 receptor (i.e. non-carriers of the haplotype T-A of cholinergic receptor muscarinic 1) supports a cholinergic mechanism underlying this relationship.

Cognition and Its Association with Psychosocial and Occupational Functioning during Treatment with Escitalopram in Patients with Major Depressive Disorder: A CAN-BIND-1 Report: La Cognition Et Son Association Avec Le Fonctionnement Psychosocial Et Professionnel Durant Le Traitement Par Escitalopram Chez Des Patients Souffrant De Trouble Dépressif Majeur: Une Étude Can-Bind-1.

OBJECTIVES: Major depressive disorder (MDD) is associated with impairments in both cognition and functioning. However, whether cognitive deficits significantly contribute to impaired psychosocial and occupational functioning, independent of other depressive symptoms, is not well established. We examined the relationship between cognitive performance and functioning in depressed patients before and after antidepressant treatment using secondary data from the first Canadian Biomarker Integration Network in Depression-1 study. METHODS: Cognition was assessed at baseline in unmedicated, depressed participants with MDD (n = 207) using the Central Nervous System Vital Signs computerized battery, psychosocial functioning with the Sheehan Disability Scale (SDS), and occupational functioning with the Lam Employment Absence and Productivity Scale (LEAPS). Cognition (n = 181), SDS (n = 175), and LEAPS (n = 118) were reassessed after participants received 8 weeks of open-label escitalopram monotherapy. A series of linear regressions were conducted to determine (1) whether cognitive functioning was associated with psychosocial and occupational functioning prior to treatment, after adjusting for overall depressive symptom severity and (2) whether changes in cognitive functioning after an 8-week treatment phase were associated with changes in psychosocial and occupational functioning, after adjusting for changes in overall symptom severity. RESULTS: Baseline global cognitive functioning, after adjusting for depression symptom severity and demographic variables, was associated with the SDS work/study subscale (ß = -0.17; P = 0.03) and LEAPS productivity subscale (ß = -0.17; P = 0.05), but not SDS total (ß = 0.19; P = 0.12) or LEAPS total (ß = 0.41; P = 0.17) scores. Although LEAPS and SDS scores showed significant improvements after 8 weeks of treatment (P < 0.001), there were no significant associations between changes in cognitive domain scores and functional improvements. CONCLUSION: Cognition was associated with occupational functioning at baseline, but changes in cognition were not associated with psychosocial or occupational functional improvements following escitalopram treatment. We recommend the use of more comprehensive functional assessments to determine the impact of cognitive change on functional outcomes in future research.

Association between the -2548G/A polymorphism of the leptin gene and antipsychotic-induced weight gain: Analysis of the CATIE sample and meta-analysis.

BACKGROUND: Antipsychotics, especially most of the second-generation antipsychotics, have a high risk for metabolic syndrome and antipsychotic-induced weight gain (AIWG). A promoter variant of the leptin (LEP) gene, -2548G/A (rs7799039), has been associated with AIWG in several studies. The aim of this study was to evaluate this association in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) sample, followed by meta-analysis. METHODS: We investigated the association between rs7799039 and AIWG in a sub-sample of European (N = 164) individuals from the CATIE study. Body mass index (BMI) change and weight gain (presence or absence) was analyzed using ANCOVA and logistic regression, respectively. For the meta-analysis, a literature search was conducted using MEDLINE, Embase, and PsycINFO up to October 2019. The pooled odds ratio was calculated for presence or absence of weight gain (≥7% weight change) using a random effects model. RESULTS: We did not detect an association between rs7799039 and BMI change or weight gain (presence or absence) in the CATIE sample. As for the meta-analysis, we included 12 studies. No significant associations between the LEP rs7799039 polymorphism and AIWG were observed under the allelic genetic model (allele A vs. allele G) (OR = 1.10 [0.71, 1.70], p = .68). In the subgroup analyses of first-episode schizophrenia patients, a significant association between the A-allele and weight gain was observed, respectively (OR = 2.32 [1.41, 3.82], p = .0009). CONCLUSIONS: The present meta-analysis showed no significant effect of rs7799039 on AIWG. However, this variant may influence AIWG in first-episode schizophrenia patients. Further investigation of a larger and more homogenous sample is required to elucidate the role of the LEP gene in AIWG.

Pharmacogenetic Implications for Antidepressant Pharmacotherapy in Late-Life Depression: A Systematic Review of the Literature for Response, Pharmacokinetics and Adverse Drug Reactions.

Affecting up to 15% of older adults, late-life depression (LLD) is characterized by the occurrence of depressive symptoms after the age of 50-65 years and maybe pathophysiologically distinct from depression in younger adults. Therefore, LLD is challenging to treat, and predictive genetic testing might be essential to improve treatment in this vulnerable population. The current review aims to provide a summary of the literature exploring genetic associations with antidepressant treatment outcomes in late-life. We conducted a systematic search of three integrated electronic databases. We identified 29 articles investigating genetic associations with antidepressant treatment outcomes, pharmacokinetic parameters, and adverse drug reactions in older adults. Given the small number of investigations conducted in older adults, it is difficult to conclude the presence or absence of genetic associations with the outcomes of interest. In sum, the most substantial amount of evidence exists for the CYP2D6 metabolizer status, SLC6A4 5-HTTLPR, and BDNF rs6265. These findings are consistent in the literature when not restricting to older adults, suggesting that similar treatment recommendations may be provided for older adults regarding genetic variation, such as those outlined for CYP2D6 by the Clinical Pharmacogenetics Implementation Consortium. Nonetheless, further studies are required in well-characterized samples, including genome-wide data, to validate if similar treatment adjustments are appropriate in older adults, given that there appear to be significant effects of genetic variation on antidepressant treatment factors.

Validation study of microRNAs previously associated with antidepressant response in older adults treated for late-life depression with venlafaxine.

BACKGROUND: MicroRNAs (miRNAs) are small 22 nucleotides long, non-coding RNAs that are potential biomarkers for antidepressant treatment response. We aimed to replicate previous associations of miRNAs with antidepressant treatment response in a sample of older adults diagnosed with late-life depression. METHODS: Our sample included 184 older adults diagnosed with moderately severe depression that received open-label venlafaxine (up to 300 mg/day) for approximately 12 weeks. We quantified miRNA expression levels at baseline and week 12 for miRNAs miR-1202, miR-135a-5p, miR-16-5p, miR-146a-5p, miR-146b-5p, miR-425-3p, and miR-24-3p to explore their association with remission status, response trajectories, and time-to-remission. RESULTS: At T0 and T12, there were no differences in miRNA expression levels between remitters and non-remitters. However, remitters showed a trend toward higher baseline miR-135a-5p (Median = 11.3 [9.9, 15.7], p = .083). Prior to correction, baseline miR-135a-5p expression levels showed an association with remission status (OR = 1.8 [1.0, 3.3], p = .037). Individuals with higher baseline miR-135a-5p showed better response trajectories (F = 4.5, FDR-corrected p = 4.4 × 10-4), particularly at weeks 10 and 12 (p < .05). In addition, individuals with higher miR-135a-5p expression reached remission faster than those with lower expression (HR = 0.6 [0.4, 0.9], FDR-corrected p = .055). LIMITATIONS: Although the sample size was relatively modest, our findings are consistent with the literature suggesting that higher miR-135a-5p levels may be associated with better antidepressant treatment response. CONCLUSIONS: However, the miRNA signature of antidepressant response in older adults may be different as compared to younger adults.

Genome-wide association study on antipsychotic-induced weight gain in Europeans and African-Americans.

BACKGROUND: Antipsychotic (AP) medications are the first line of treatment for schizophrenia. However, most conferr a risk of antipsychotic-induced weight gain (AIWG). The objective of this investigation was to conduct a genome-wide association study (GWAS) of AIWG, followed by comprehensive, post-GWAS approaches. METHODS: We investigated n = 201 schizophrenia or schizoaffective disorder patients of European and African American ancestry who were treated primarily with clozapine or olanzapine. We conducted a genome-wide association analysis for AIWG, defined primarily as a percentage of weight change from baseline. RESULTS: When examining Europeans (n = 147), we noticed an association between rs62097526 (ß = 0.39, p = 3.59 × 10-6, CADD = 2.213) variant, located downstream of the CIDEA gene, which is considered a risk factor for AIWG. In the entire sample, we observed a significant association between rs1525085 (ß = 0.411, p = 3.15 × 10-9) variant of the DGKB gene and AIWG. The association was nominally significant in Europeans (ß = 0.271, p = 0.002) and African Americans (ß = 0.579, p = 5.73 × 10-5) with the same risk allele. Our top genes (p < 5 × 10-5) were enriched in the GWAS catalog for the risk of obesity and interacted with the known risk factors for obesity (G6PD) and diabetes (IRS1). In addition, these genes are targeted by miRNAs related to schizophrenia (mir-34a) and obesity (mir-19b). However, our polygenic risk score analyses did not provide support for major genetic overlap between obesity and the risk of AIWG. CONCLUSIONS: In summary, we propose that the CIDEA and DGKB genes are risk factors for AIWG in transethnic populations. Additionally, our evidence suggests that the G6PD and IRS1 gene-related pathways might be involved in AIWG.

Genetic validation study of protein tyrosine phosphatase receptor type D (PTPRD) gene variants and risk for antipsychotic-induced weight gain.

Schizophrenia is a severe, debilitating disorder with a lifetime prevalence of 1% irrespective of gender or ethnicity and is typically treated with antipsychotic drugs. Antipsychotic-induced weight gain (AIWG) is a leading factor of patient non-compliance and has previously been shown to increase the risk of type 2 diabetes, metabolic syndrome, and cardiovascular events. The current study intends to replicate findings from a recent genome-wide association study in Han-Chinese patients implicating two gene variants (rs10977144 and rs10977154) of the protein tyrosine phosphatase receptor type D (PTPRD) in antipsychotic-induced weight gain (AIWG). We investigated a sample of European and African American ancestry (n = 201) and calculated percentage of weight change using linear regression corrected for type of antipsychotics, duration of treatment and principal components from ancestry checks. As secondary goal, we investigated additional gene variants of PTPRD previously not associated with AIWG. We found no association with rs10977144 and rs10977154. However, we found nominally significant results between PTPRD and AIWG with rs73398242 in Europeans (BETA = - 0.267, p = 0.002) and rs13294608 in African Americans (BETA = 0.423, p = 0.003). According to Haploreg, both SNPs are histone marks for enhancers and promoters across various brain regions including the cingulate gyrus and dorsolateral prefrontal cortex. In summary, our results tentatively suggest that PTPRD might be associated with AIWG although different SNPS might be involved in different ethnic groups.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction.

A comprehensive analysis of mitochondrial genes variants and their association with antipsychotic-induced weight gain.

Antipsychotic Induced Weight Gain (AIWG) is a common and severe side effect of many antipsychotic medications. Mitochondria play a vital role for whole-body energy homeostasis and there is increasing evidence that antipsychotics modulate mitochondrial function. This study aimed to examine the role of variants in nuclear-encoded mitochondrial genes and the mitochondrial DNA (mtDNA) in conferring risk for AIWG. We selected 168 European-Caucasian individuals from the CATIE sample based upon meeting criteria of multiple weight measures while taking selected antipsychotics (risperidone, quetiapine or olanzapine). We tested the association of 670 nuclear-encoded mitochondrial genes with weight change (%) using MAGMA software. Thirty of these genes showed nominally significant P-values (<0.05). We were able to replicate the association of three genes, CLPB, PARL, and ACAD10, with weight change (%) in an independent prospectively assessed AIWG sample. We analyzed mtDNA variants in a subset of 74 of these individuals using next-generation sequencing. No common or rare mtDNA variants were found to be significantly associated with weight change (%) in our sample. Additionally, analysis of mitochondrial haplogroups showed no association with weight change (%). In conclusion, our findings suggest nuclear-encoded mitochondrial genes play a role in AIWG. Replication in larger sample is required to validate our initial report of mtDNA variants in AIWG.

Norepinephrine Transporter Gene Variants and Remission From Depression With Venlafaxine Treatment in Older Adults.

OBJECTIVE: The primary objective of this study was to investigate five putatively functional variants of the norepinephrine transporter (SLC6A2, NET) and serotonin transporter (SLC6A4, SERT) genes and remission in depressed older adults treated with venlafaxine. A secondary objective was to analyze 17 other variants in serotonergic system genes (HTR1A, HTR2A, HTR1B, HTR2C, TPH1, TPH2) potentially involved in the mechanism of action of venlafaxine. METHOD: The sample included 350 adults age 60 or older with DSM-IV-defined major depressive disorder and a score of at least 15 on the Montgomery-Åsberg Depression Rating Scale (MADRS). Participants received protocolized treatment with open-label venlafaxine, up to 300 mg/day for approximately 12 weeks, as part of a three-site clinical trial. Each individual was genotyped for 22 polymorphisms in eight genes, which were tested for association with venlafaxine remission (a MADRS score ≤10) and changes in MADRS score during treatment. RESULTS: After adjusting for multiple comparisons, NET variant rs2242446 (T-182C) was significantly associated with remission (odds ratio=1.66, 95% CI=1.13, 2.42). Individuals with the rs2242446 C/C genotype were more likely to remit (73.1%) than those with either the C/T (51.8%) or the T/T genotype (47.3%). Individuals with the C/C genotype also had a shorter time to remission than those with the C/T or T/T genotypes and had a greater percentage change in MADRS score from baseline to end of treatment (up to week 12). CONCLUSIONS: NET rs2242446/T-182C may serve as a biomarker to predict the likelihood of remission with venlafaxine in older adults with major depression. These findings may help to optimize antidepressant outcomes in older adults.

Family association study of Transforming Growth Factor Beta1 gene polymorphisms in schizophrenia. / Badania rodzinne polimorfizmów genu transformujacego czynnika wzrostu beta1 (Transforming Growth Factor Beta1, TGFB1) w schizofrenii.

OBJECTIVES: Schizophrenia is a serious mental illness with chronic symptoms and significant impairment in psychosocial functioning. An etiopathological role for immunologic abnormalities in schizophrenia was hypothesized. Inflammatory markers are well-known etiological factors for psychiatric disorders, including schizophrenia. Several studies have investigated the possible effects of antipsychotics on inflammation and neurogenesis. Additionally, antiinflammatory adjuvant therapy has been under investigation as a treatment option for schizophrenia. Transforming Growth Factor Beta 1 (TGFB1) signaling is critical for many biological processes, including proliferation, development, differentiation and regeneration. Multiple members of the TGFB1 superfamily play a role in the developing nervous system and are regulated by neuronal activity. We conducted family-based study to assess whether TGFB1 gene is associated with susceptibility to schizophrenia in Polish population. METHODS: Two functional polymorphisms: rs1800469 (C-509T) and rs1800470 (T869C) of TGFB1 gene were analyzed within a group of 147 trios (patients diagnosed with schizophrenia and their healthy parents) using Transmission Disequilibrium Test (TDT). RESULTS: No association of these polymorphisms with schizophrenia was found in Polish population. CONCLUSIONS: Further studies on larger groups along with correlation with circulating protein levels are needed.

Family-based association study of interleukin 10 (IL10) and interleukin 10 receptor alpha (IL10RA) functional polymorphisms in schizophrenia in Polish population.

Schizophrenia is a heterogeneous disorder and its etiology remains incompletely elucidated. Among possible causes, immunological factors have been implicated in its pathogenesis and course. Interleukin-10 (IL10) and it's receptor IL10RA may play an important role for immunological aspects in etiologies of major psychiatric disorders including schizophrenia. The aim of this study was to perform a transmission disequilibrium test (TDT) on a group of 146 schizophrenia trios from the Polish population. Functional polymorphisms from IL10 (rs1800872, rs1800871, rs1800896, rs1800890, and rs6676671) and IL10RA (rs3135932 and rs2229113) genes were analyzed. A lack of association with schizophrenia was detected for IL10 and IL10RA single polymorphisms and haplotypes.