The impact of traumatic brain injury (TBI) on subsequent risk of schizophrenia (SCZ) or bipolar disorder (BD) remains contested. Possible genetic and environmental confounding effects have also been understudied. Therefore, we aim to investigate the impact of TBI on the risk of SCZ and BD and whether the effect varies by injury severity, age at injury, and sex. We identified 4,184 SCZ and 18,681 BD cases born between 1973 and 1998 in the Swedish National Registers. Case-control samples matched (1:5) on birth year, sex, and birthplace were created along with a family design study, with cases matched to non-case full siblings. TBI was associated with higher risk of SCZ and BD (IRR=1.33 for SCZ, IRR=1.78 for BD). The association remained significant in the sibling comparison study. Moderate or severe TBI was associated with higher risk for both SCZ and BD compared to mild TBI. Older age at injury was associated with higher risk of SCZ and BD, and the effect of TBI was stronger in women than men. Findings indicate that TBI is a risk factor for both SCZ and BD with differential impact by age, severity and sex and that this association cannot be explained by familial confounding alone.
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http://dx.doi.org/10.1016/j.psychres.2024.115990 | DOI Listing |
Background: Psychotic symptoms may manifest in Alzheimer's disease (AD), especially in advanced disease stages and in patients with higher polygenic risk scores for schizophrenia (SCZ-PRS). Such genetic risk seems also to influence grey matter volume (GMV) alterations in patients with psychosis. Since multiple neurotransmitter systems, namely dopamine (DA) and serotonin (5-HT), have been implicated in psychosis, the aim of this study was to investigate whether a SCZ-PRS may explain variance in the association between GMV and the cerebral distribution of DA and 5-HT.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.
View Article and Find Full Text PDFSchizophr Bull
January 2025
Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390, United States.
Background: Investigations of causal pathways for psychosis can be guided by the identification of environmental risk factors. A recently developed composite risk tool, the exposome score for schizophrenia (ES-SCZ), which controls for intercorrelations between risk factors, has shown fair to good performance. We tested the transdiagnostic psychosis classifier performance of the ES-SCZ with the Bipolar-Schizophrenia Network for Intermedial Phenotypes data and examined its relationship with clinical-level outcomes.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, PR China (J.H.L.); Department of Social medicine, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China (J.H.L.); Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Soochow University, Suzhou, Jiangsu 215123, PR China (J.H.L.).
Rationale And Objectives: To systematically review the diagnostic efficacy of abbreviated magnetic resonance imaging sequence (AMRI) screening for hepatocellular carcinoma (HCC).
Materials And Methods: Medline (via PubMed), EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, and VIP databases were electronically searched to collect studies on the diagnostic efficacy of AMRI screening for HCC from inception to August 10th, 2024. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2), then, the meta-analysis with a bivariate mixed-effects regression model was performed by using Stata 14.
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