Late preterm antenatal corticosteroids in singleton and twin gestations: a retrospective cohort study.

BACKGROUND: In 2016, the American College of Obstetricians and Gynecologists recommended antenatal corticosteroids in the late preterm period for women at risk for preterm delivery. Limited real-world evidence exists on neonatal outcomes, particularly for twin gestations, following the guideline change. The study objective is to determine the association of antenatal corticosteroids in late preterm singleton and twin pregnancies with respiratory complications and hypoglycemia in a real-world clinical setting. METHODS: This is a retrospective cohort study comprising late preterm deliveries (4,341 mother-child pairs) within the Mount Sinai Health System, 2012-2018. The exposure of interest is antenatal corticosteroid administration of betamethasone during pregnancy between 34 0/7 and 36 6/7 weeks. Our primary outcomes are neonatal respiratory complications and hypoglycemia. Multivariable logistic regression was used to estimate the association between antenatal corticosteroid exposure and these two outcomes. We stratified the study population by singleton gestations and twins to minimize the potential confounding from different obstetric management between the two groups. RESULTS: Among a total of 4,341 mother-child pairs (3,309 singleton and 1,032 twin mother-child pairs), 745 mothers received betamethasone, of which 40.94% (305/745) received the full course. Relative to no treatment, a full course of betamethasone was associated with reduced odds of respiratory complications (OR = 0.53, 95% CI:[0.31-0.85], p < 0.01) and increased odds of hypoglycemia (OR = 1.86, 95%CI:[1.34-2.56], p < 0.01) in singletons; however, the association with respiratory complications was not significant in twins (OR = 0.42, 95% CI:[0.11-1.23], p = 0.16), but was associated with increased odds of hypoglycemia (OR = 2.18, 95% CI:[1.12-4.10], p = 0.02). A partial course of betamethasone (relative to no treatment) was not significantly associated with any of the outcomes, other than respiratory complications in twins (OR = 0.34, 95% CI:[0.12-0.82], p = 0.02). CONCLUSIONS: Exposure to antenatal corticosteroids in singletons and twins is associated with increased odds of hypoglycemia. Among singletons, exposure to the full dosage (i.e. two doses) was associated with decreased odds of respiratory complications but this was only the case for partial dose among twins. Twin gestations were not studied by the Antenatal Late Preterm Steroids trial. Therefore, our study findings will contribute to the paucity of evidence on the benefit of antenatal corticosteroids in this group. Health systems should systematically monitor guideline implementations to improve patient outcomes.

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study.

BACKGROUND: Physical activity is increasingly recognized as an important modifiable factor for depression. However, the extent to which individuals with stable risk factors for depression, such as high genetic vulnerability, can benefit from the protective effects of physical activity, remains unknown. Using a longitudinal biobank cohort integrating genomic data from 7,968 individuals of European ancestry with high-dimensional electronic health records and lifestyle survey responses, we examined whether physical activity was prospectively associated with reduced risk for incident depression in the context of genetic vulnerability. METHODS: We identified individuals with incident episodes of depression, based on two or more diagnostic billing codes for a depressive disorder within 2 years following their lifestyle survey, and no such codes in the year prior. Polygenic risk scores were derived based on large-scale genome-wide association results for major depression. We tested main effects of physical activity and polygenic risk scores on incident depression, and effects of physical activity within stratified groups of polygenic risk. RESULTS: Polygenic risk was associated with increased odds of incident depression, and physical activity showed a protective effect of similar but opposite magnitude, even after adjusting for BMI, employment status, educational attainment, and prior depression. Higher levels of physical activity were associated with reduced odds of incident depression across all levels of genetic vulnerability, even among individuals at highest polygenic risk. CONCLUSIONS: Real-world data from a large healthcare system suggest that individuals with high genetic vulnerability are more likely to avoid incident episodes of depression if they are physically active.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Development and Multi-Site External Validation of a Generalizable Risk Prediction Model for Bipolar Disorder.

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

A comprehensive digital phenotype for postpartum hemorrhage.

OBJECTIVE: We aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased and ignore complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contributes to ongoing challenges in tracking PPH outcomes, developing more accurate risk assessments, and identifying novel interventions. MATERIALS AND METHODS: We constructed a cohort of 71 944 deliveries from the Mount Sinai Health System. Estimates of postpartum blood loss, shifts in hematocrit, administration of uterotonics, surgical interventions, and diagnostic codes were combined to identify PPH, retrospectively. Clinical features were extracted from EMRs and mapped to common data models for maximum interoperability across hospitals. Blinded chart review was done by a physician on a subset of PPH and non-PPH patients and performance was compared to alternate PPH phenotypes. PPH was defined as clinical diagnosis of postpartum hemorrhage documented in the patient's chart upon chart review. RESULTS: We identified 6639 PPH deliveries (9% prevalence) using our phenotype-more than 3 times as many as using blood loss alone (N = 1,747), supporting the need to incorporate other diagnostic and intervention data. Chart review revealed our phenotype had 89% accuracy and an F1-score of 0.92. Alternate phenotypes were less accurate, including a common blood loss-based definition (67%) and a previously published digital phenotype (74%). CONCLUSION: We have developed a scalable, accurate, and valid digital phenotype that may be of significant use for tracking outcomes and ongoing clinical research to deliver better preventative interventions for PPH.

Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records.

OBJECTIVE: Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice. MATERIALS AND METHODS: We used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries from our delivery cohort (N = 70 948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model with 3 clinical risk-assessment tools and 1 previously published model. RESULTS: Our 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 0.55 [95% CI, 0.54-0.56] to 0.61 [95% CI, 0.59-0.62]). Five features were novel, including red blood cell indices and infection markers measured upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH. CONCLUSIONS: We developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data.

Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

An Exposure-Wide and Mendelian Randomization Approach to Identifying Modifiable Factors for the Prevention of Depression.

OBJECTIVE: Efforts to prevent depression, the leading cause of disability worldwide, have focused on a limited number of candidate factors. Using phenotypic and genomic data from over 100,000 UK Biobank participants, the authors sought to systematically screen and validate a wide range of potential modifiable factors for depression. METHODS: Baseline data were extracted for 106 modifiable factors, including lifestyle (e.g., exercise, sleep, media, diet), social (e.g., support, engagement), and environmental (e.g., green space, pollution) variables. Incident depression was defined as minimal depressive symptoms at baseline and clinically significant depression at follow-up. At-risk individuals for incident depression were identified by polygenic risk scores or by reported traumatic life events. An exposure-wide association scan was conducted to identify factors associated with incident depression in the full sample and among at-risk individuals. Two-sample Mendelian randomization was then used to validate potentially causal relationships between identified factors and depression. RESULTS: Numerous factors across social, sleep, media, dietary, and exercise-related domains were prospectively associated with depression, even among at-risk individuals. However, only a subset of factors was supported by Mendelian randomization evidence, including confiding in others (odds ratio=0.76, 95% CI=0.67, 0.86), television watching time (odds ratio=1.09, 95% CI=1.05, 1.13), and daytime napping (odds ratio=1.34, 95% CI=1.17, 1.53). CONCLUSIONS: Using a two-stage approach, this study validates several actionable targets for preventing depression. It also demonstrates that not all factors associated with depression in observational research may translate into robust targets for prevention. A large-scale exposure-wide approach combined with genetically informed methods for causal inference may help prioritize strategies for multimodal prevention in psychiatry.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems.

OBJECTIVE: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. METHODS: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. RESULTS: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. CONCLUSIONS: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

Complement Gene Expression Correlates with Superior Frontal Cortical Thickness in Humans.

Recent work suggests that genes encoding complement proteins that are active in the innate immune system may confer risk for schizophrenia by disrupting typical synaptic pruning in late adolescence. Alterations in the complement pathway may contribute to aberrant cortical thinning in schizophrenia prodromes and reduced prefrontal cortical thickness in chronic schizophrenia patients; however, this theory needs to be translated to humans. We conducted a series of analyses in a sample of adult Swedish twins enriched for schizophrenia (N=129) to assess the plausibility of a relationship between complement gene expression and cortical thickness that could go awry in the etiology of schizophrenia. First, we identified that peripheral mRNA expression levels of two complement genes (C5, SERPING1) made unique contributions to the variance in superior frontal cortical thickness among all participants. Vertex-wise maps of the association between gene expression levels and thickness across the cortex suggested that this relationship was especially strong with SERPING1 in the superior frontal region, consistent with the pattern of disruption in cortical thickness observed in schizophrenia. Additional analyses identified that these genes are expressed in the human superior frontal cortex, that heritable genetic factors influence SERPING1 gene expression levels, and that these associations are observed regardless of case status. These findings provide initial evidence linking the complement system with cortical thinning in humans, a process potentially involved in the pathogenesis of schizophrenia.

Association between physical exercise and mental health in 1·2 million individuals in the USA between 2011 and 2015: a cross-sectional study.

BACKGROUND: Exercise is known to be associated with reduced risk of all-cause mortality, cardiovascular disease, stroke, and diabetes, but its association with mental health remains unclear. We aimed to examine the association between exercise and mental health burden in a large sample, and to better understand the influence of exercise type, frequency, duration, and intensity. METHODS: In this cross-sectional study, we analysed data from 1 237 194 people aged 18 years or older in the USA from the 2011, 2013, and 2015 Centers for Disease Control and Prevention Behavioral Risk Factors Surveillance System survey. We compared the number of days of bad self-reported mental health between individuals who exercised and those who did not, using an exact non-parametric matching procedure to balance the two groups in terms of age, race, gender, marital status, income, education level, body-mass index category, self-reported physical health, and previous diagnosis of depression. We examined the effects of exercise type, duration, frequency, and intensity using regression methods adjusted for potential confounders, and did multiple sensitivity analyses. FINDINGS: Individuals who exercised had 1·49 (43·2%) fewer days of poor mental health in the past month than individuals who did not exercise but were otherwise matched for several physical and sociodemographic characteristics (W=7·42 × 1010, p<2·2 × 10-16). All exercise types were associated with a lower mental health burden (minimum reduction of 11·8% and maximum reduction of 22·3%) than not exercising (p<2·2 × 10-16 for all exercise types). The largest associations were seen for popular team sports (22·3% lower), cycling (21·6% lower), and aerobic and gym activities (20·1% lower), as well as durations of 45 min and frequencies of three to five times per week. INTERPRETATION: In a large US sample, physical exercise was significantly and meaningfully associated with self-reported mental health burden in the past month. More exercise was not always better. Differences as a function of exercise were large relative to other demographic variables such as education and income. Specific types, durations, and frequencies of exercise might be more effective clinical targets than others for reducing mental health burden, and merit interventional study. FUNDING: Cloud computing resources were provided by Microsoft.

Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study.

OBJECTIVE: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need. METHODS: Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment. RESULTS: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all). CONCLUSIONS: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.

Interaction of childhood urbanicity and variation in dopamine genes alters adult prefrontal function as measured by functional magnetic resonance imaging (fMRI).

Brain phenotypes showing environmental influence may help clarify unexplained associations between urban exposure and psychiatric risk. Heritable prefrontal fMRI activation during working memory (WM) is such a phenotype. We hypothesized that urban upbringing (childhood urbanicity) would alter this phenotype and interact with dopamine genes that regulate prefrontal function during WM. Further, dopamine has been hypothesized to mediate urban-associated factors like social stress. WM-related prefrontal function was tested for main effects of urbanicity, main effects of three dopamine genes-catechol-O-methyltransferase (COMT), dopamine receptor D1 (DRD1), and dopamine receptor D2 (DRD2)-and, importantly, dopamine gene-by-urbanicity interactions. For COMT, three independent human samples were recruited (total n = 487). We also studied 253 subjects genotyped for DRD1 and DRD2. 3T fMRI activation during the N-back WM task was the dependent variable, while childhood urbanicity, dopamine genotype, and urbanicity-dopamine interactions were independent variables. Main effects of dopamine genes and of urbanicity were found. Individuals raised in an urban environment showed altered prefrontal activation relative to those raised in rural or town settings. For each gene, dopamine genotype-by-urbanicity interactions were shown in prefrontal cortex-COMT replicated twice in two independent samples. An urban childhood upbringing altered prefrontal function and interacted with each gene to alter genotype-phenotype relationships. Gene-environment interactions between multiple dopamine genes and urban upbringing suggest that neural effects of developmental environmental exposure could mediate, at least partially, increased risk for psychiatric illness in urban environments via dopamine genes expressed into adulthood.

The NDE1 genomic locus can affect treatment of psychiatric illness through gene expression changes related to microRNA-484.

Genetic studies of familial schizophrenia in Finland have observed significant associations with a group of biologically related genes, DISC1, NDE1, NDEL1, PDE4B and PDE4D, the 'DISC1 network'. Here, we use gene expression and psychoactive medication use data to study their biological consequences and potential treatment implications. Gene expression levels were determined in 64 individuals from 18 families, while prescription medication information has been collected over a 10-year period for 931 affected individuals. We demonstrate that the NDE1 SNP rs2242549 associates with significant changes in gene expression for 2908 probes (2542 genes), of which 794 probes (719 genes) were replicable. A significant number of the genes altered were predicted targets of microRNA-484 (p = 3.0 × 10-8), located on a non-coding exon of NDE1 Variants within the NDE1 locus also displayed significant genotype by gender interaction to early cessation of psychoactive medications metabolized by CYP2C19. Furthermore, we demonstrate that miR-484 can affect the expression of CYP2C19 in a cell culture system. Thus, variation at the NDE1 locus may alter risk of mental illness, in part through modification of miR-484, and such modification alters treatment response to specific psychoactive medications, leading to the potential for use of this locus in targeting treatment.

The Role of microRNA Expression in Cortical Development During Conversion to Psychosis.

In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with non-converters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Here we used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Our cortical thinning classifier included nine microRNAs, p=3.63 × 10-08, R2=0.358, permutation-based p=0.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (p<0.05, Benjamini-Hochberg corrected). The classifier was also related to proinflammatory cytokine levels in serum (p=0.038). Furthermore, miRNAs that predicted conversion status were found to do so in a manner partially mediated by rate of cortical thinning (point estimate=0.078 (95% CIs: 0.003, 0.168), p=0.03). Many of the miRNAs identified here have been previously implicated in brain development, synaptic plasticity, immune function and/or schizophrenia, showing some convergence across studies and methodologies. Altered intracellular signaling within the immune system may interact with cortical maturation in individuals at high risk for schizophrenia promoting disease onset.

Cognitive endophenotypes inform genome-wide expression profiling in schizophrenia.

OBJECTIVE: We performed a whole-genome expression study to clarify the nature of the biological processes mediating between inherited genetic variations and cognitive dysfunction in schizophrenia. METHOD: Gene expression was assayed from peripheral blood mononuclear cells using Illumina Human WG6 v3.0 chips in twins discordant for schizophrenia or bipolar disorder and control twins. After quality control, expression levels of 18,559 genes were screened for association with the California Verbal Learning Test (CVLT) performance, and any memory-related probes were then evaluated for variation by diagnostic status in the discovery sample (N = 190), and in an independent replication sample (N = 73). Heritability of gene expression using the twin design was also assessed. RESULTS: After Bonferroni correction (p < 2.69 × 10-6), CVLT performance was significantly related to expression levels for 76 genes, 43 of which were differentially expressed in schizophrenia patients, with comparable effect sizes in the same direction in the replication sample. For 41 of these 43 transcripts, expression levels were heritable. Nearly all identified genes contain common or de novo mutations associated with schizophrenia in prior studies. CONCLUSION: Genes increasing risk for schizophrenia appear to do so in part via effects on signaling cascades influencing memory. The genes implicated in these processes are enriched for those related to RNA processing and DNA replication and include genes influencing G-protein coupled signal transduction, cytokine signaling, and oligodendrocyte function.