Differences in schizophrenia treatments by race and ethnicity-analysis of electronic health records.

Racial disparities in prescriptions of anti-psychotics have been highlighted before. However, (i) the evidence on other medications, including anti-depressant or mood stabilizing medications is lacking, and (ii) the role of potentially confounding factors and (iii) specificity of such disparities to schizophrenia (SCZ), are still unknown. We used electronic health records (EHRs) from 224,212 adults to estimate the odds ratios of receiving a prescription for different nervous system medications among patients with SCZ of different race/ethnicity, and analogous linear models to investigate differences in prescribed medication doses. To verify specificity of the observed patterns to SCZ, we conducted analogous analyses in depression and bipolar disorder (BD) patients. We found that Black/African American (AA) and Hispanic patients with SCZ were more likely to be prescribed haloperidol (Black/AA: OR = 1.52 (1.33-1.74); Hispanic: OR = 1.32 (1.12-1.55)) or risperidone (Black/AA: OR = 1.27 (1.11-1.45); Hispanic: OR = 1.40 (1.19-1.64)), but less likely to be prescribed clozapine (Black/AA: OR = 0.40 (0.33-0.49); Hispanic: OR = 0.45 (0.35-0.58)), compared to white patients. There were no race/ethnicity-related differences in the prescribed medication doses. These patterns were not specific to SCZ: Asian, Hispanic and Black/AA patients with BD or depression were more likely to be prescribed anti-psychotics, but less likely to be prescribed antidepressants or mood-stabilizers. In conclusion, we found racial/ethnic disparities in the medications prescribed to patients with SCZ and other psychiatric conditions. We discuss the potential implications for the quality of care for patients of diverse races/ethnicities.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Sex differences in predictors and regional patterns of brain age gap estimates.

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia.

Background: Accelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms. Methods: Global and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16-35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16-37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters. Results: HYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE. Discussion: Accelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth.

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.

Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function.

Objective: Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning. Methods: We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition. Results: Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (PFDR = 0.004) and crystallized intelligence (PFDR = 0.01), cognitive flexibility (PFDR = 0.02), inhibitory control (PFDR = 0.01), working memory (PFDR = 0.0005), and processing speed (PFDR = 0.04). Conclusions: We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.

Neural signatures of data-driven psychopathology dimensions at the transition to adolescence.

BACKGROUND: One of the challenges in human neuroscience is to uncover associations between brain organization and psychopathology in order to better understand the biological underpinnings of mental disorders. Here, we aimed to characterize the neural correlates of psychopathology dimensions obtained using two conceptually different data-driven approaches. METHODS: Dimensions of psychopathology that were either maximally dissociable or correlated were respectively extracted by independent component analysis (ICA) and exploratory factor analysis (EFA) applied to the Childhood Behavior Checklist items from 9- to 10-year-olds (n = 9983; 47.8% female, 50.8% white) participating in the Adolescent Brain Cognitive Development study. The patterns of brain morphometry, white matter integrity and resting-state connectivity associated with each dimension were identified using kernel-based regularized least squares and compared between dimensions using Spearman's correlation coefficient. RESULTS: ICA identified three psychopathology dimensions, representing opposition-disinhibition, cognitive dyscontrol, and negative affect, with distinct brain correlates. Opposition-disinhibition was negatively associated with cortical surface area, cognitive dyscontrol was negatively associated with anatomical and functional dysconnectivity while negative affect did not show discernable associations with any neuroimaging measure. EFA identified three dimensions representing broad externalizing, neurodevelopmental, and broad Internalizing problems with partially overlapping brain correlates. All EFA-derived dimensions were negatively associated with cortical surface area, whereas measures of functional and structural connectivity were associated only with the neurodevelopmental dimension. CONCLUSIONS: This study highlights the importance of cortical surface area and global connectivity for psychopathology in preadolescents and provides evidence for dissociable psychopathology dimensions with distinct brain correlates.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Hippocampal volume reduction is associated with direct measure of insulin resistance in adults.

Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23-61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m2], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) µg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as risk factor for this phenotype.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Impact of opioid agonist treatment on mental health in patients with opioid use disorder: a systematic review and network meta-analysis of randomized clinical trials.

Background: There is a knowledge gap in systematic reviews on the impact of opioid agonist treatments on mental health.Objectives: We compared mental health outcomes between different opioid agonist treatments and placebo/waitlist, and between the different opioids themselves.Methods: This meta-analysis of randomized clinical trials (RCTs) was pre-registered at PROSPERO (CRD42018109375). Embase, MEDLINE, PsychInfo, CINAHL Complete, and Web of Science Core Collection were searched from inception to May 2020. RCTs were included if they compared opioid agonists with each other or with placebo/waitlist in the treatment of patients with opioid use disorder and reported at least one mental health outcome after 1-month post-baseline. Studies with psychiatric care, adjunct psychotropic medications, or unbalanced psychosocial services were excluded. The primary outcome was overall mental health symptomatology, e.g. Symptom Checklist 90 total score, between opioids and placebo/waitlist. Random effects models were used for all the meta-analyses.Results: Nineteen studies were included in the narrative synthesis and 15 in the quantitative synthesis. Hydromorphone, diacetylmorphine (DAM), methadone, slow-release oral morphine, buprenorphine, and placebo/waitlist were among the included interventions. Based on the network meta-analysis for primary outcomes, buprenorphine (SMD (CI95%) = -0.61 (-1.20, -0.11)), DAM (-1.40 (-2.70, -0.23)), and methadone (-1.20 (-2.30, -0.11)) were superior to waitlist/placebo on overall mental health. Further direct pairwise meta-analysis indicated that overall mental health improved more in DAM compared to methadone (-0.23 (-0.34, -0.13)).Conclusions: Opioid agonist treatments used for the treatment of opioid use disorder improve mental health independent of psychosocial services.

Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis.

BACKGROUND: Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. METHODS: Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. RESULTS: There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. CONCLUSIONS: Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.

Multivariate Patterns of Brain-Behavior-Environment Associations in the Adolescent Brain and Cognitive Development Study.

BACKGROUND: Adolescence is a critical developmental stage. A key challenge is to characterize how variation in adolescent brain organization relates to psychosocial and environmental influences. METHODS: We used canonical correlation analysis to discover distinct patterns of covariation between measures of brain organization (brain morphometry, intracortical myelination, white matter integrity, and resting-state functional connectivity) and individual, psychosocial, and environmental factors in a nationally representative U.S. sample of 9623 individuals (aged 9-10 years, 49% female) participating in the Adolescent Brain and Cognitive Development (ABCD) study. RESULTS: These analyses identified 14 reliable modes of brain-behavior-environment covariation (canonical rdiscovery = .21 to .49, canonical rtest = .10 to .39, pfalse discovery rate corrected < .0001). Across modes, neighborhood environment, parental characteristics, quality of family life, perinatal history, cardiometabolic health, cognition, and psychopathology had the most consistent and replicable associations with multiple measures of brain organization; positive and negative exposures converged to form patterns of psychosocial advantage or adversity. These showed modality-general, respectively positive or negative, associations with brain structure and function with little evidence of regional specificity. Nested within these cross-modal patterns were more specific associations between prefrontal measures of morphometry, intracortical myelination, and functional connectivity with affective psychopathology, cognition, and family environment. CONCLUSIONS: We identified clusters of exposures that showed consistent modality-general associations with global measures of brain organization. These findings underscore the importance of understanding the complex and intertwined influences on brain organization and mental function during development and have the potential to inform public health policies aiming toward interventions to improve mental well-being.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study.

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all PFDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|ρ| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|ρ| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|ρ| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.

Parsing out the role of dopamine D4 receptor gene (DRD4) on alcohol-related phenotypes: A meta-analysis and systematic review.

Genetics account for moderate variation of individual differences in developing alcohol use disorder (AUD), but it is unclear which genetic variations contribute to AUD risk. One candidate gene investigated due to its association with AUD is the dopamine D4 receptor gene (DRD4), which contains a 48-base pair variable number tandem repeat (VNTR) in exon 3 of its coding region. To date, no quantitative synthesis of the published literature on the effects of DRD4 VNTR variation on alcohol-related phenotypes has been conducted. MEDLINE, Embase, Web of Science, and PsycInfo were searched for studies that reported on alcohol craving, alcohol consumption, severity of AUD, and case-control (AUD versus no diagnosis of AUD) studies in DRD4L (seven repeats or more) carriers compared with DRD4S (six repeats or less) homozygotes. Random-effects meta-analysis was used for all analyses. A pooled sample size of 655 to 13,360 of 28 studies were included. Compared with DRD4S homozygotes, DRD4L carriers had increased number of drinking days (SMD: 0.205; 95% CI: 0.008 to 0.402), binge drinking days (SMD: 0.217; 95% CI: 0.0532 to 0.380), and severity of AUD (SMD: 0.143; 95% CI: 0.028 to 0.259). There was no difference between DRD4 VNTR genotypes on drinks per drinking day, largest number of drinks per day/occasion, and case-control analysis. It was not possible to conduct a meta-analysis of the craving data, but a systematic review of this literature found mixed results on DRD4 VNTR genotype effect. The present meta-analysis suggests DRD4 VNTR variation may be a risk factor for problematic alcohol use. Our findings are limited, however, by the absence of ancestry data from studies included in our analysis, precluding our ability to adjust for population stratification. Due to the likelihood of type I error in candidate gene approaches, our work highlights the critical need for studies with larger and more inclusive samples that account for sex and genetic ancestry to fully understand this relationship.

Parental Age and Differential Estimates of Risk for Neuropsychiatric Disorders: Findings From the Danish Birth Cohort.

OBJECTIVE: Parental age at birth has been shown to affect the rates of a range of neurodevelopmental disorders, but the understanding of the mechanisms through which it mediates different outcomes is still lacking. A population-based cohort was used to assess differential effects of parental age on estimates of risk across pediatric-onset neuropsychiatric disorders: autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), and Tourette's disorder/chronic tic disorder (TD/CT). METHOD: The study cohort included all singleton births in Denmark from 1980 through 2007 with full information on parental ages (N = 1,490,745) and was followed through December 31, 2013. Cases of ASD, ADHD, OCD, and TD/CT were identified in the Danish Psychiatric Central Register and the National Patient Register. Associations with parental age were modeled using a stratified Cox regression, allowing for changes in baseline diagnostic rates across time. RESULTS: Younger parental age was significantly associated with increased estimates of risk for ADHD and TD/CT, whereas older parental age was associated with ASD and OCD. Except for OCD, no evidence for differential effects of parental ages on male versus female offspring was observed. CONCLUSION: This study provides novel evidence for the association between age at parenthood and TD/CT and OCD and for the first time shows in a population-based sample that parental age confers differential risk rates for pediatric-onset psychiatric disorders. These results are consistent with a model of shared and unshared risk architecture for pediatric-onset neuropsychiatric conditions, highlighting unique contributions of maternal and paternal ages.

Hemorrhage, Seizures, and Dynamic Changes of Familial versus Nonfamilial Cavernous Malformation: Systematic Review and Meta-analysis.

BACKGROUND: Cerebral cavernous malformations (CCMs) may be familial or nonfamilial. This systematic review compared the natural history of CCMs in familial compared with nonfamilial cases. METHODS: We searched MEDLINE, Web of Science, and EMBASE for natural history studies on CCMs up to September 2018. We included studies that followed at least 20 untreated patients. Primary outcomes were hemorrhage, seizures, and neuroimaging changes in familial and nonfamilial cases. Incidence rate per person-year (PY) or lesion-year (LY) of follow-up were used to pool the data using fixed-effects or random-effects models. We used the incidence rate ratio for comparison. RESULTS: We could not compare hemorrhage rates between familial and nonfamilial cases mainly owing to mixtures of subgroups of patients. The seizure rate was similar in familial and nonfamilial cases with pooled incidence rate of 1.5%/PY (95% confidence interval 1.1%-2.2%). The reseizure rate was higher than the seizure rate (P < 0.001). New lesion development was higher in familial cases (32.1%/PY vs. 0.7%/PY, P < 0.001). Signal change on neuroimaging ranged from 0.2%/LY to 2.4%/LY in familial cases. In familial cases, incidence rate of size change was 8%/PY (95% confidence interval 5.2%-12.2%) and 1.1%/LY (95% confidence interval 0.6%-1.6%). CONCLUSIONS: Familial CCMs show higher dynamic changes than nonfamilial cases. However, the presence of actual dynamic changes needs further assessment in nonfamilial cases. CCMs demonstrate a low incidence of seizure. First-time seizure increases the chance of recurrent seizure. Seizure rate based on the location and type of the lesion should be investigated further.

Apgar score and risk of autism.

Low Apgar score has been associated with higher risk for several neurological and psychiatric disorders, including cerebral palsy and intellectual disability. Studies of the association between Apgar score and autism spectrum disorder (ASD) have been inconsistent. We aimed to investigate (1) the association between low Apgar score at 5 min and risk for ASD, and (2) the modifying effects of gestational age and sex on this association in the largest multinational database of ASD. We included prospective data from 5.5 million individuals and over 33,000 cases of ASD from Norway, Sweden, Denmark and Western Australia who were born between 1984 and 2007. We calculated crude and adjusted risk ratios (RR) with 95% confidence intervals (95% CIs) for the associations between low Apgar score and ASD. All analyses for ASD were repeated for autistic disorder (AD). We used interaction terms and stratified analysis to investigate the effects of sex, gestational age, and birth weight on the association. In fully adjusted models, low Apgar scores (1-3) (RR, 1.42; 95% CI, 1.16-1.74), and intermediate Apgar scores (4-6) (RR, 1.50; 95% CI, 1.36-1.65) were associated with a higher RR of ASD than optimal Apgar score (7-10). The point estimates for low (RR, 1.88; 95% CI, 1.41-2.51) and intermediate Apgar score (RR, 1.54; 95% CI, 1.32-1.81) were larger for AD than for ASD. This study suggests that low Apgar score is associated with higher risk of ASD, and in particular AD. We did not observe any major modifying effects of gestational age and sex, although there seems to be substantial confounding by gestational age and birth weight on the observed association.

Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses.

BACKGROUND: According to recent evidence, up to 40-50% of variance in autism spectrum disorder (ASD) liability might be determined by environmental factors. In the present paper, we conducted a review of systematic reviews and meta-analyses of environmental risk factors for ASD. We assessed each review for quality of evidence and provided a brief overview of putative mechanisms of environmental risk factors for ASD. FINDINGS: Current evidence suggests that several environmental factors including vaccination, maternal smoking, thimerosal exposure, and most likely assisted reproductive technologies are unrelated to risk of ASD. On the contrary, advanced parental age is associated with higher risk of ASD. Birth complications that are associated with trauma or ischemia and hypoxia have also shown strong links to ASD, whereas other pregnancy-related factors such as maternal obesity, maternal diabetes, and caesarian section have shown a less strong (but significant) association with risk of ASD. The reviews on nutritional elements have been inconclusive about the detrimental effects of deficiency in folic acid and omega 3, but vitamin D seems to be deficient in patients with ASD. The studies on toxic elements have been largely limited by their design, but there is enough evidence for the association between some heavy metals (most important inorganic mercury and lead) and ASD that warrants further investigation. Mechanisms of the association between environmental factors and ASD are debated but might include non-causative association (including confounding), gene-related effect, oxidative stress, inflammation, hypoxia/ischemia, endocrine disruption, neurotransmitter alterations, and interference with signaling pathways. CONCLUSIONS: Compared to genetic studies of ASD, studies of environmental risk factors are in their infancy and have significant methodological limitations. Future studies of ASD risk factors would benefit from a developmental psychopathology approach, prospective design, precise exposure measurement, reliable timing of exposure in relation to critical developmental periods and should take into account the dynamic interplay between gene and environment by using genetically informed designs.