scholarly journals Inflammation-related biomarkers in major psychiatric disorders: a cross-disorder assessment of reproducibility and specificity in 43 meta-analyses

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ning Yuan ◽  
Yu Chen ◽  
Yan Xia ◽  
Jiacheng Dai ◽  
Chunyu Liu
Keyword(s):  
Psychiatric Disorders ◽  
Effect Size ◽  
Statistical Power ◽  
Inflammatory Responses ◽  
Autism Spectrum ◽  
First Episode ◽  
Related Factor ◽  
Related Factors ◽  
Compulsive Disorder ◽  
Meta Analyses

Abstract Inflammation is a natural defence response of the immune system against environmental insult, stress and injury, but hyper- and hypo-inflammatory responses can trigger diseases. Accumulating evidence suggests that inflammation is involved in multiple psychiatric disorders. Using inflammation-related factors as biomarkers of psychiatric disorders requires the proof of reproducibility and specificity of the changes in different disorders, which remains to be established. We performed a cross-disorder study by systematically evaluating the meta-analysis results of inflammation-related factors in eight major psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), major depression disorder (MDD), post-trauma stress disorder (PTSD), sleeping disorder (SD), obsessive–compulsive disorder (OCD) and suicide. A total of 43 meta-analyses involving 704 publications on 44 inflammation-related factors were included in the study. We calculated the effect size and statistical power for every inflammation-related factor in each disorder. Our analyses showed that well-powered case–control studies provided more consistent results than underpowered studies when one factor was meta-analysed by different researchers. After removing underpowered studies, 30 of the 44 inflammation-related factors showed significant alterations in at least one disorder based on well-powered meta-analyses. Eleven of them changed in patients of more than two disorders when compared with the controls. A few inflammation-related factors showed unique changes in specific disorders (e.g., IL-4 increased in BD, decreased in suicide, but had no change in MDD, ASD, PTSD and SCZ). MDD had the largest number of changes while SD has the least. Clustering analysis showed that closely related disorders share similar patterns of inflammatory changes, as genome-wide genetic studies have found. According to the effect size obtained from the meta-analyses, 13 inflammation-related factors would need <50 cases and 50 controls to achieve 80% power to show significant differences (p < 0.0016) between patients and controls. Changes in different states of MDD, SCZ or BD were also observed in various comparisons. Studies comparing first-episode SCZ to controls may have more reproducible findings than those comparing pre- and post-treatment results. Longitudinal, system-wide studies of inflammation regulation that can differentiate trait- and state-specific changes will be needed to establish valuable biomarkers.

How to Detect Publication Bias in Psychological Research

2019 ◽  
Vol 227 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Frank Renkewitz ◽  
Melanie Keiner
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Psychological Research ◽  
Type I ◽  
True Effect Size ◽  
Questionable Research Practices ◽  
True Effect ◽  
Meta Analyses

Abstract. Publication biases and questionable research practices are assumed to be two of the main causes of low replication rates. Both of these problems lead to severely inflated effect size estimates in meta-analyses. Methodologists have proposed a number of statistical tools to detect such bias in meta-analytic results. We present an evaluation of the performance of six of these tools. To assess the Type I error rate and the statistical power of these methods, we simulated a large variety of literatures that differed with regard to true effect size, heterogeneity, number of available primary studies, and sample sizes of these primary studies; furthermore, simulated studies were subjected to different degrees of publication bias. Our results show that across all simulated conditions, no method consistently outperformed the others. Additionally, all methods performed poorly when true effect sizes were heterogeneous or primary studies had a small chance of being published, irrespective of their results. This suggests that in many actual meta-analyses in psychology, bias will remain undiscovered no matter which detection method is used.

scholarly journals Obsessive-compulsive disorder: does CBT with exposure and response (ERP) prevention work?

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S284-S285
Author(s):  
Jemma Reid ◽  
Naomi A Fineberg ◽  
Lynne Drummond ◽  
Keith Laws ◽  
Matteo Vismara ◽  
...  
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Effect Size ◽  
Randomised Controlled Trials ◽  
Meta Analysis ◽  
Obsessive Compulsive ◽  
Related Factors ◽  
Psychological Treatments ◽  
Compulsive Disorder ◽  
Control Intervention ◽  
Randomised Controlled

AimsSince the 1970s treatment for obsessive Compulsive Disorder (OCD) has consisted of the the application of drugs acting on the serotonin system of the brain or psychological treatments using graded exposure. Although there is a large number of studies on psychological treatments, they often are underpowered. Other major methodological issues include ignoring the effects of medication during the trial, using a variety of techniques and using waiting list data as controls.We decided to systematically review and perform a meta-analysis on randomised controlled trials (RCTs) of CBT with ERP (abbreviated to ERP)1.MethodThe study was preregistered in PROSPERO (CRD42019122311). RCTs incorporating ERP were examined. The primary outcome was the end-of-trial symptoms scores for OCD. In addition, factors which may have influenced the outcome including patient-related factors, type of control intervention, researcher allegiance and other potential forms of bias were examined. The moderating effects of patient-related and study-related factors including type of control intervention and risk of bias were also examined.ResultOverall, 36 studies were included in the analyses, involving 537 children/adolescents and 1483 adults (total 2020 subjects). A total of 1005 received ERP and the remainder a variety of control treatments. Initial results showed that ERP had a large effect size compared with placebo treatments. This was more marked in younger than older persons. However, whereas ERP was markedly more effective than waiting list or psychological control, this positive effect size disappeared when it was compared with other psychological treatments.When ERP was compared against psychopharmacological treatment it initially appeared significantly superior but this reduced to marginal benefit when compared with adequate doses of appropriate medication.The majority of studies were performed where there may be expected to be researcher allegiance to ERP and in these studies the effect size was large. In contrast, in the 8 studies considered to have low risk of researcher bias, ERP was found to be ineffective.ConclusionAlthough on initial sight CBT incorporating ERP seems to be highly efficacious in the treatment of OCD, further analysis revealed that this varied depending on the choice of comparator control. In addition there are considerable concerns about methodological rigour and reporting of studies using CBT with ERP. Further studies examining the role of researcher bias and allegiance are needed.Ref : 1 Jemma E Reid, Keith R Laws, Lynne Drummond, Matteo Vismara, Benedetta Grancini , Davis Mpavaenda, Naomi A Fineberg (2021) Cognitive Behavioural Therapy with Exposure and Response Prevention in the treatment of Obsessive-Compulsive Disorder: A systematic review and meta-analysis of randomised controlled trials. Comprehensive Psychiatry , in press.

scholarly journals Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders

Brain ◽  
2020 ◽  
Vol 143 (6) ◽  
pp. 1632-1650 ◽  
Author(s):  
Simon Ducharme ◽  
Annemiek Dols ◽  
Robert Laforce ◽  
Emma Devenney ◽  
Fiona Kumfor ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Social Cognition ◽  
Psychiatric Disorders ◽  
Frontotemporal Dementia ◽  
Clinical Assessment ◽  
Rating Scales ◽  
Diagnostic Delay ◽  
Autism Spectrum ◽  
Compulsive Disorder

Abstract The behavioural variant of frontotemporal dementia (bvFTD) is a frequent cause of early-onset dementia. The diagnosis of bvFTD remains challenging because of the limited accuracy of neuroimaging in the early disease stages and the absence of molecular biomarkers, and therefore relies predominantly on clinical assessment. BvFTD shows significant symptomatic overlap with non-degenerative primary psychiatric disorders including major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders and even personality disorders. To date, ∼50% of patients with bvFTD receive a prior psychiatric diagnosis, and average diagnostic delay is up to 5–6 years from symptom onset. It is also not uncommon for patients with primary psychiatric disorders to be wrongly diagnosed with bvFTD. The Neuropsychiatric International Consortium for Frontotemporal Dementia was recently established to determine the current best clinical practice and set up an international collaboration to share a common dataset for future research. The goal of the present paper was to review the existing literature on the diagnosis of bvFTD and its differential diagnosis with primary psychiatric disorders to provide consensus recommendations on the clinical assessment. A systematic literature search with a narrative review was performed to determine all bvFTD-related diagnostic evidence for the following topics: bvFTD history taking, psychiatric assessment, clinical scales, physical and neurological examination, bedside cognitive tests, neuropsychological assessment, social cognition, structural neuroimaging, functional neuroimaging, CSF and genetic testing. For each topic, responsible team members proposed a set of minimal requirements, optimal clinical recommendations, and tools requiring further research or those that should be developed. Recommendations were listed if they reached a ≥ 85% expert consensus based on an online survey among all consortium participants. New recommendations include performing at least one formal social cognition test in the standard neuropsychological battery for bvFTD. We emphasize the importance of 3D-T1 brain MRI with a standardized review protocol including validated visual atrophy rating scales, and to consider volumetric analyses if available. We clarify the role of 18F-fluorodeoxyglucose PET for the exclusion of bvFTD when normal, whereas non-specific regional metabolism abnormalities should not be over-interpreted in the case of a psychiatric differential diagnosis. We highlight the potential role of serum or CSF neurofilament light chain to differentiate bvFTD from primary psychiatric disorders. Finally, based on the increasing literature and clinical experience, the consortium determined that screening for C9orf72 mutation should be performed in all possible/probable bvFTD cases or suspected cases with strong psychiatric features.

scholarly journals Strength of functional signature correlates with effect size in autism

2016 ◽  
Author(s):  
Sara Ballouz ◽  
Jesse Gillis
Keyword(s):  
Study Design ◽  
Effect Size ◽  
Statistical Power ◽  
Fragile X ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Protein Interaction Data ◽  
Functional Convergence ◽  
Study Designs ◽  
The Impact

AbstractBackgroundDisagreements over genetic signatures associated with disease have been particularly prominent in the field of psychiatric genetics, creating a sharp divide between disease burdens attributed to common and rare variation, with study designs independently targeting each. Meta-analysis within each of these study designs is routine, whether using raw data or summary statistics, but combining results across study designs is atypical. However, tests of functional convergence are used across all study designs, where candidate gene sets are assessed for overlaps with previously known properties. This suggests one possible avenue for combining not study data, but the functional conclusions that they reach.MethodIn this work, we test for functional convergence in autism spectrum disorder (ASD) across different study types, and specifically whether the degree to which a gene is implicated in autism is correlated with the degree to which it drives functional convergence. Because different study designs are distinguishable by their differences in effect size, this also provides a unified means of incorporating the impact of study design into the analysis of convergence.ResultsWe detected remarkably significant positive trends in aggregate (p < 2.2e-16) with 14 individually significant properties (FDR<0.01), many in areas researchers have targeted based on different reasoning, such as the fragile X mental retardation protein (FMRP) interactor enrichment (FDR 0.003). We are also able to detect novel technical effects and we see that network enrichment from protein-protein interaction data is heavily confounded with study design, arising readily in control data.ConclusionsWe see a convergent functional signal for a subset of known and novel functions in ASD from all sources of genetic variation. Meta-analytic approaches explicitly accounting for different study designs can be adapted to other diseases to discover novel functional associations and increase statistical power.

scholarly journals Comprehensive exploration of the genetic contribution of the dopaminergic and serotonergic pathways to psychiatric disorders

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Judit Cabana-Domínguez ◽  
Bàrbara Torrico ◽  
Andreas Reif ◽  
Noèlia Fernàndez-Castillo ◽  
Bru Cormand
Keyword(s):  
Psychiatric Disorders ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Genetic Contribution ◽  
Obsessive Compulsive ◽  
Gene Set ◽  
Compulsive Disorder ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Psychiatric Conditions

AbstractPsychiatric disorders are highly prevalent and display considerable clinical and genetic overlap. Dopaminergic and serotonergic neurotransmission have been shown to play an important role in many psychiatric disorders. Here we aim to assess the genetic contribution of these systems to eight psychiatric disorders (attention-deficit hyperactivity disorder (ADHD), anorexia nervosa (ANO), autism spectrum disorder (ASD), bipolar disorder (BIP), major depression (MD), obsessive-compulsive disorder (OCD), schizophrenia (SCZ) and Tourette’s syndrome (TS)) using publicly available GWAS analyses performed by the Psychiatric Genomics Consortium that include more than 160,000 cases and 275,000 controls. To do so, we elaborated four different gene sets: two ‘wide’ selections for dopamine (DA) and for serotonin (SERT) using the Gene Ontology and KEGG pathways tools, and two’core’ selections for the same systems, manually curated. At the gene level, we found 67 genes from the DA and/or SERT gene sets significantly associated with one of the studied disorders, and 12 of them were associated with two different disorders. Gene-set analysis revealed significant associations for ADHD and ASD with the wide DA gene set, for BIP with the wide SERT gene set, and for MD with the core SERT set. Interestingly, interrogation of a cross-disorder GWAS meta-analysis of the eight psychiatric conditions displayed association with the wide DA gene set. To our knowledge, this is the first systematic examination of genes encoding proteins essential to the function of these two neurotransmitter systems in these disorders. Our results support a pleiotropic contribution of the dopaminergic and serotonergic systems in several psychiatric conditions.

An Examination of Effect Sizes and Statistical Power in Speech, Language, and Hearing Research

2020 ◽  
Vol 63 (5) ◽  
pp. 1572-1580
Author(s):  
Laura Gaeta ◽  
Christopher R. Brydges
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Effect Sizes ◽  
Medium Effect ◽  
Speech Language Pathology ◽  
Sample Sizes ◽  
Hearing Research ◽  
Language Pathology ◽  
Meta Analyses ◽  
Published Research

Purpose The purpose was to examine and determine effect size distributions reported in published audiology and speech-language pathology research in order to provide researchers and clinicians with more relevant guidelines for the interpretation of potentially clinically meaningful findings. Method Cohen's d, Hedges' g, Pearson r, and sample sizes ( n = 1,387) were extracted from 32 meta-analyses in journals in speech-language pathology and audiology. Percentile ranks (25th, 50th, 75th) were calculated to determine estimates for small, medium, and large effect sizes, respectively. The median sample size was also used to explore statistical power for small, medium, and large effect sizes. Results For individual differences research, effect sizes of Pearson r = .24, .41, and .64 were found. For group differences, Cohen's d /Hedges' g = 0.25, 0.55, and 0.93. These values can be interpreted as small, medium, and large effect sizes in speech-language pathology and audiology. The majority of published research was inadequately powered to detect a medium effect size. Conclusions Effect size interpretations from published research in audiology and speech-language pathology were found to be underestimated based on Cohen's (1988, 1992) guidelines. Researchers in the field should consider using Pearson r = .25, .40, and .65 and Cohen's d /Hedges' g = 0.25, 0.55, and 0.95 as small, medium, and large effect sizes, respectively, and collect larger sample sizes to ensure that both significant and nonsignificant findings are robust and replicable.

scholarly journals Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls

2018 ◽  
Vol 49 (07) ◽  
pp. 1166-1173 ◽  
Author(s):  
E. Pettersson ◽  
P. Lichtenstein ◽  
H. Larsson ◽  
J. Song ◽  
A. Agrawal ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Autism Spectrum ◽  
Psychiatric Research ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Hyperactivity Disorder ◽  
Unique Character ◽  
Psychiatric Conditions ◽  
Heritability Estimates ◽  
Study Designs

AbstractBackgroundMost studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.MethodsWe assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.ResultsHeritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.ConclusionsGiven the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.

scholarly journals Low statistical power in biomedical science: a review of three human research domains

2017 ◽  
Vol 4 (2) ◽  
pp. 160254 ◽  
Author(s):  
Estelle Dumas-Mallet ◽  
Katherine S. Button ◽  
Thomas Boraud ◽  
Francois Gonon ◽  
Marcus R. Munafò
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Average Power ◽  
Biomedical Science ◽  
Significant Finding ◽  
Biomedical Sciences ◽  
True Effect Size ◽  
Meta Analyses

Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result. We conducted a review of meta-analyses of studies investigating the association of biological, environmental or cognitive parameters with neurological, psychiatric and somatic diseases, excluding treatment studies, in order to estimate the average statistical power across these domains. Taking the effect size indicated by a meta-analysis as the best estimate of the likely true effect size, and assuming a threshold for declaring statistical significance of 5%, we found that approximately 50% of studies have statistical power in the 0–10% or 11–20% range, well below the minimum of 80% that is often considered conventional. Studies with low statistical power appear to be common in the biomedical sciences, at least in the specific subject areas captured by our search strategy. However, we also observe evidence that this depends in part on research methodology, with candidate gene studies showing very low average power and studies using cognitive/behavioural measures showing high average power. This warrants further investigation.

scholarly journals Cross-species machine learning improves diagnostic classification of human psychiatric disorders

2019 ◽  
Author(s):  
Yafeng Zhan ◽  
Jianze Wei ◽  
Jian Liang ◽  
Xiu Xu ◽  
Ran He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Diagnostic Classification ◽  
Imaging Data ◽  
Primate Model ◽  
Compulsive Disorder ◽  
Brain Imaging Data

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.

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