Background: Adolescent self-reported psychotic experiences are associated with mental illness and could help guide prevention strategies. The Community Assessment of Psychic Experiences (CAPE) was developed over 20 years ago. In a rapidly changing society, where new generations of adolescents are growing up in an increasingly digital world, it is crucial to ensure high reliability and validity of the questionnaire. Methods: In this observational validation study, we used unique transgenerational questionnaire and health registry data from the Norwegian Mother, Father, and Child Cohort, a population-based pregnancy cohort. Adolescents, aged ~14 years, responded to the CAPE-16 (n = 18,835) and fathers to the CAPE-9 questionnaire (n = 28,793). We investigated the psychometric properties of CAPE-16 through factor analyses, measurement invariance testing across biological sex, response before/ during the COVID-19 pandemic, and generations (comparison with fathers), and examined associations with later psychiatric diagnoses. Outcomes: One third (33·4%) of adolescents reported lifetime psychotic experiences. We confirmed a three-factor structure (paranoia, bizarre thoughts, and hallucinations) of CAPE-16, and observed good scale reliability of the distress and frequency subscales (ω = ·86 and ·90). CAPE-16 measured psychotic experiences were invariant to biological sex and pandemic status. CAPE-9 was non-invariant across generations, with items related to understanding of the digital world (electrical influences) prone to bias. CAPE-16 sum scores were associated with a subsequent psychiatric diagnosis, particularly psychotic disorders (frequency: OR = 2·06; 97·5% CI = 1·70-2·46; distress: OR = 1·93; 97·5% CI = 1·63-2·26). Interpretation: CAPE-16 showed robust psychometric properties across sex and pandemic status, and sum scores were associated with subsequent psychiatric diagnoses, particularly psychotic disorders. These findings suggest that with certain adjustments, CAPE-16 could have value as a screening tool for adolescents in the modern, digital world. Funding: European Union's Horizon 2020 Programme, Research Council of Norway, South-Eastern Norway Regional Health Authority, NIMH, and the KG Jebsen Stiftelsen.
Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.
BACKGROUND: BIPCOM aims to (1) identify medical comorbidities in people with bipolar disorder (BD); (2) examine risk factors and clinical profiles of Medical Comorbidities (MC) in this clinical group, with a special focus on Metabolic Syndrome (MetS); (3) develop a Clinical Support Tool (CST) for the personalized management of BD and medical comorbidities. METHODS: The BIPCOM project aims to investigate MC, specifically MetS, in individuals with BD using various approaches. Initially, prevalence rates, characteristics, genetic and non-genetic risk factors, and the natural progression of MetS among individuals with BD will be assessed by analysing Nordic registers, biobanks, and existing patient datasets from 11 European recruiting centres across 5 countries. Subsequently, a clinical study involving 400 participants from these sites will be conducted to examine the clinical profiles and incidence of specific MetS risk factors over 1 year. Baseline assessments, 1-year follow-ups, biomarker analyses, and physical activity measurements with wearable biosensors, and focus groups will be performed. Using this comprehensive data, a CST will be developed to enhance the prevention, early detection, and personalized treatment of MC in BD, by incorporating clinical, biological, sex and genetic information. This protocol will highlight the study's methodology. DISCUSSION: BIPCOM's data collection will pave the way for tailored treatment and prevention approaches for individuals with BD. This approach has the potential to generate significant healthcare savings by preventing complications, hospitalizations, and emergency visits related to comorbidities and cardiovascular risks in BD. BIPCOM's data collection will enhance BD patient care through personalized strategies, resulting in improved quality of life and reduced costly interventions. The findings of the study will contribute to a better understanding of the relationship between medical comorbidities and BD, enabling accurate prediction and effective management of MetS and cardiovascular diseases. TRIAL REGISTRATION: ISRCTN68010602 at https://www.isrctn.com/ISRCTN68010602 . Registration date: 18/04/2023.
INTRODUCTION: Adequate antipsychotic treatment intensity is required before diagnosing resistant schizophrenia and initiating clozapine treatment. We aimed to investigate potential rapid drug metabolism underlying low dose-adjusted serum concentration (CD) of non-clozapine atypical antipsychotics preceding clozapine treatment. METHODS: Patients using non-clozapine, atypical antipsychotics (aripiprazole, risperidone, olanzapine, or quetiapine) within 1 year before starting clozapine were included in this study from a therapeutic drug monitoring service in Oslo, Norway, between 2005 and 2023. Patients were assigned into low CD (LCD) and normal CD (NCD) subgroups. Using a reference sample with 147,964 antipsychotic measurements, LCD was defined as CDs below the 25th percentile, while patients with NCD exhibited CDs between the 25th and 75th percentile of the respective reference measurements. Metabolic ratios, doses, and frequency of subtherapeutic levels of non-clozapine antipsychotics were compared between LCD and NCD groups. RESULTS: Preceding clozapine treatment, 110 out of 272 included patients (40.4%) were identified with LCD. Compared with the NCD group, LCD patients exhibited higher metabolic ratios of olanzapine (1.5-fold; p < 0.001), quetiapine (3.0-fold; p < 0.001), and risperidone (6.0-fold; p < 0.001). Metabolic ratio differences were independent of smoking and CYP2D6 genotype for olanzapine (p = 0.008) and risperidone (p = 0.016), respectively. Despite higher doses of olanzapine (1.25-fold; p = 0.054) and quetiapine (1.6-fold; p = 0.001) in LCD versus NCD patients, faster metabolism among the former was accompanied by higher frequencies of subtherapeutic levels of olanzapine (3.3-fold; p = 0.044) and quetiapine (1.8-fold; p = 0.005). CONCLUSION: LCD and associated rapid metabolism of non-clozapine antipsychotics is frequent before starting clozapine treatment. For olanzapine and quetiapine, this is associated with significantly increased risk of having subtherapeutic concentrations.
Objective: Cognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG). Methods: We applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples. Results: Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10-14; 'all epilepsy': p = 5.6 × 10-3). Significance: Our study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.
Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
BACKGROUND: The timing of puberty may have an important impact on adolescent mental health. In particular, earlier age at menarche has been associated with elevated rates of depression in adolescents. Previous research suggests that this relationship may be causal, but replication and an investigation of whether this effect extends to other mental health domains is warranted. METHODS: In this Registered Report, we triangulated evidence from different causal inference methods using a new wave of data (N = 13,398) from the Norwegian Mother, Father, and Child Cohort Study. We combined multiple regression, one- and two-sample Mendelian randomisation (MR), and negative control analyses (using pre-pubertal symptoms as outcomes) to assess the causal links between age at menarche and different domains of adolescent mental health. RESULTS: Our results supported the hypothesis that earlier age at menarche is associated with elevated depressive symptoms in early adolescence based on multiple regression (ß = - 0.11, 95% CI [- 0.12, - 0.09], pone-tailed < 0.01). One-sample MR analyses suggested that this relationship may be causal (ß = - 0.07, 95% CI [- 0.13, 0.00], pone-tailed = 0.03), but the effect was small, corresponding to just a 0.06 standard deviation increase in depressive symptoms with each earlier year of menarche. There was also some evidence of a causal relationship with depression diagnoses during adolescence based on one-sample MR (OR = 0.74, 95% CI [0.54, 1.01], pone-tailed = 0.03), corresponding to a 29% increase in the odds of receiving a depression diagnosis with each earlier year of menarche. Negative control and two-sample MR sensitivity analyses were broadly consistent with this pattern of results. Multivariable MR analyses accounting for the genetic overlap between age at menarche and childhood body size provided some evidence of confounding. Meanwhile, we found little consistent evidence of effects on other domains of mental health after accounting for co-occurring depression and other confounding. CONCLUSIONS: We found evidence that age at menarche affected diagnoses of adolescent depression, but not other domains of mental health. Our findings suggest that earlier age at menarche is linked to problems in specific domains rather than adolescent mental health in general.
Menarche , Mental Health , Child , Female , Adolescent , Humans , Cohort Studies , Causality , Mendelian Randomization Analysis
Oxytocin is a neuropeptide associated with both psychological and somatic processes like parturition and social bonding. Although oxytocin homologs have been identified in many species, the evolutionary timeline of the entire oxytocin signaling gene pathway has yet to be described. Using protein sequence similarity searches, microsynteny, and phylostratigraphy, we assigned the genes supporting the oxytocin pathway to different phylostrata based on when we found they likely arose in evolution. We show that the majority (64%) of genes in the pathway are 'modern'. Most of the modern genes evolved around the emergence of vertebrates or jawed vertebrates (540 - 530 million years ago, 'mya'), including OXTR, OXT and CD38. Of those, 45% were under positive selection at some point during vertebrate evolution. We also found that 18% of the genes in the oxytocin pathway are 'ancient', meaning their emergence dates back to cellular organisms and opisthokonta (3500-1100 mya). The remaining genes (18%) that evolved after ancient and before modern genes were classified as 'medium-aged'. Functional analyses revealed that, in humans, medium-aged oxytocin pathway genes are highly expressed in contractile organs, while modern genes in the oxytocin pathway are primarily expressed in the brain and muscle tissue.
Oxytocin , Receptors, Oxytocin , Animals , Humans , Aged , Oxytocin/metabolism , Receptors, Oxytocin/genetics , Signal Transduction , Brain/metabolism
Trace elements are important for human health but may exert toxic or adverse effects. Mechanisms of uptake, distribution, metabolism, and excretion are partly under genetic control but have not yet been extensively mapped. Here we report a comprehensive multi-element genome-wide association study of 57 essential and non-essential trace elements. We perform genome-wide association meta-analyses of 14 trace elements in up to 6564 Scandinavian whole blood samples, and genome-wide association studies of 43 trace elements in up to 2819 samples measured only in the Trøndelag Health Study (HUNT). We identify 11 novel genetic loci associated with blood concentrations of arsenic, cadmium, manganese, selenium, and zinc in genome-wide association meta-analyses. In HUNT, several genome-wide significant loci are also indicated for other trace elements. Using two-sample Mendelian randomization, we find several indications of weak to moderate effects on health outcomes, the most precise being a weak harmful effect of increased zinc on prostate cancer. However, independent validation is needed. Our current understanding of trace element-associated genetic variants may help establish consequences of trace elements on human health.
Selenium , Trace Elements , Male , Humans , Trace Elements/metabolism , Genome-Wide Association Study , Zinc , Selenium/analysis , Manganese
BACKGROUND: Dysmyelination could be part of the pathophysiology of schizophrenia spectrum (SCZ) and bipolar disorders (BPD), yet few studies have examined myelination of the cerebral cortex. The ratio of T1- and T2-weighted magnetic resonance images (MRI) correlates with intracortical myelin. We investigated the T1w/T2w-ratio and its age trajectories in patients and healthy controls (CTR) and explored associations with antipsychotic medication use and psychotic symptoms. METHODS: Patients with SCZ (n = 64; mean age = 30.4 years, s.d. = 9.8), BPD (n = 91; mean age 31.0 years, s.d. = 10.2), and CTR (n = 155; mean age = 31.9 years, s.d. = 9.1) who participated in the TOP study (NORMENT, University of Oslo, Norway) were clinically assessed and scanned using a General Electric 3 T MRI system. T1w/T2w-ratio images were computed using an optimized pipeline with intensity normalization and field inhomogeneity correction. Vertex-wise regression models were used to compare groups and examine group × age interactions. In regions showing significant differences, we explored associations with antipsychotic medication use and psychotic symptoms. RESULTS: No main effect of diagnosis was found. However, age slopes of the T1w/T2w-ratio differed significantly between SCZ and CTR, predominantly in frontal and temporal lobe regions: Lower T1w/T2w-ratio values with higher age were found in CTR, but not in SCZ. Follow-up analyses revealed a more positive age slope in patients who were using antipsychotics and patients using higher chlorpromazine-equivalent doses. CONCLUSIONS: While we found no evidence of reduced intracortical myelin in SCZ or BPD relative to CTR, different regional age trajectories in SCZ may suggest a promyelinating effect of antipsychotic medication.
BACKGROUND: Endothelial inflammation may be involved in the pathogenesis of schizophrenia, and cellular adhesion molecules (CAMs) on endothelial cells may facilitate leukocyte binding and transendothelial migration of cells and inflammatory factors. The aim of the present study was to assess levels of soluble cellular adhesion molecules, including intercellular adhesion molecule (ICAM)-1, vascular adhesion molecule (VCAM)-1, mucosal addressin cell adhesion molecule (MADCAM), junctional adhesion molecule (JAM-A) and neural cadherin (N-CAD) in patients with schizophrenia compared to healthy controls. METHODS: The study population consists of 138 patients with schizophrenia-spectrum disorder, of whom 54 were drug-naïve, compared to 317 general population controls. The potential confounders age, gender, smoking and body mass index (BMI) were adjusted for in linear regression models. RESULTS: The total patient group showed significantly higher levels of ICAM-1 (p < 0.001) and VCAM-1 (p < 0.001) compared to controls. Previously medicated patients showed higher ICAM-1 levels compared to drug-naïve patients (p = 0.042) and controls (p < 0.001), and elevated VCAM-1 levels compared to controls (p < 0.001). Drug-naive patients had elevated levels of VCAM-1 (p = 0.031) compared to controls. CONCLUSIONS: In our study, patients with schizophrenia - including the drug-naïve - have higher levels of soluble CAMs compared to healthy controls. These findings suggest activation of the endothelial system as in inflammation.
Essential tremor (ET) is a prevalent neurological disorder with a largely unknown underlying biology. In this genome-wide association study meta-analysis, comprising 16,480 ET cases and 1,936,173 controls from seven datasets, we identify 12 sequence variants at 11 loci. Evaluating mRNA expression, splicing, plasma protein levels, and coding effects, we highlight seven putative causal genes at these loci, including CA3 and CPLX1. CA3 encodes Carbonic Anhydrase III and carbonic anhydrase inhibitors have been shown to decrease tremors. CPLX1, encoding Complexin-1, regulates neurotransmitter release. Through gene-set enrichment analysis, we identify a significant association with specific cell types, including dopaminergic and GABAergic neurons, as well as biological processes like Rho GTPase signaling. Genetic correlation analyses reveals a positive association between ET and Parkinson's disease, depression, and anxiety-related phenotypes. This research uncovers risk loci, enhancing our knowledge of the complex genetics of this common but poorly understood disorder, and highlights CA3 and CPLX1 as potential therapeutic targets.
Essential Tremor , Genetic Predisposition to Disease , Genome-Wide Association Study , Essential Tremor/genetics , Humans , Polymorphism, Single Nucleotide , Genetic Loci
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
Aging , Machine Learning , Magnetic Resonance Imaging , Humans , Middle Aged , Aged , Adult , Male , Aging/physiology , Female , Aged, 80 and over , Brain/diagnostic imaging , Brain/physiology , Body Composition/physiology , Gray Matter/diagnostic imaging , Gray Matter/anatomy & histology , White Matter/diagnostic imaging , White Matter/anatomy & histology , Bayes Theorem
BACKGROUND: Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS: Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS: Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS: This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.
Arthritis, Rheumatoid , Cardiovascular Diseases , Coronary Artery Disease , Heart Failure , Stroke , Humans , Cardiovascular Diseases/epidemiology , Cardiovascular Diseases/genetics , Genome-Wide Association Study/methods , Genetic Predisposition to Disease/genetics , Arthritis, Rheumatoid/genetics , Arthritis, Rheumatoid/epidemiology , Coronary Artery Disease/genetics , Stroke/epidemiology , Stroke/genetics , Polymorphism, Single Nucleotide/genetics
The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.
Machine Learning , Magnetic Resonance Imaging , Schizophrenia , Transcranial Magnetic Stimulation , Humans , Schizophrenia/therapy , Schizophrenia/diagnostic imaging , Schizophrenia/physiopathology , Transcranial Magnetic Stimulation/methods , Female , Male , Adult , Workflow , Treatment Outcome , Middle Aged , Young Adult
Background: A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth. Methods: To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Results: Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. Conclusion: Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.
Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e-29). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e-31). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e-05, 1.7e-12)]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.
OBJECTIVE: There was more than a 10-fold increase in the incidence of narcolepsy type 1 (NT1) after the H1N1 mass vaccination in 2009/2010 in several countries. NT1 is associated with loss and increase of cell groups in the hypothalamus which may be associated with secondary affected sub-cortical and cortical gray matter. We performed a case-control comparison of MRI-based global and sub-cortical volume and cortical thickness in post-H1N1 NT1 patients compared with controls. METHODS: We included 54 post-H1N1 NT1 patients (51 with confirmed hypocretin-deficiency; 48 H1N1-vaccinated with Pandemrix®; 39 females, mean age 21.8 ± 11.0 years) and 114 healthy controls (77 females, mean age 23.2 ± 9.0 years). 3T MRI brain scans were obtained, and the T1-weighted MRI data were processed using FreeSurfer. Group differences among three global, 10 sub-cortical volume measures and 34 cortical thickness measures for bilateral brain regions were tested using general linear models with permutation testing. RESULTS: Patients had significantly thinner brain cortex bilaterally in the temporal poles (Cohen's d = 0.68, p = 0.00080), entorhinal cortex (d = 0.60, p = 0.0018) and superior temporal gyrus (d = 0.60, p = 0.0020) compared to healthy controls. The analysis revealed no significant group differences for sub-cortical volumes. CONCLUSIONS: Post-H1N1(largely Pandemrix®-vaccinated) NT1 patients have significantly thinner cortex in temporal brain regions compared to controls. We speculate that this effect can be partly attributed to the hypothalamic neuronal change in NT1, including loss of function of the widely projecting hypocretin-producing neurons and secondary effects of the abnormal sleep-wake pattern in NT1 or could be specific for post-H1N1 (largely Pandemrix®-vaccinated) NT1 patients.
Influenza A Virus, H1N1 Subtype , Narcolepsy , Female , Humans , Child , Adolescent , Young Adult , Adult , Orexins , Case-Control Studies , Narcolepsy/etiology , Magnetic Resonance Imaging , Brain
Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.
