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We emphasise the existence of two distinct neurophysiological subtypes in schizophrenia, characterised by different sites of initial grey matter loss. We review evidence for potential neuromolecular mechanisms underlying these subtypes, proposing a biologically based disease classification approach to unify macro- and micro-scale neural abnormalities of schizophrenia.
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Resilience to emotional disorders is critical for adolescent mental health, especially following childhood abuse. Yet, brain signatures of resilience remain undetermined due to the differential susceptibility of the brain's emotion processing system to environmental stresses. Analyzing brain's responses to angry faces in a longitudinally large-scale adolescent cohort (IMAGEN), we identified two functional networks related to the orbitofrontal and occipital regions as candidate brain signatures of resilience. In girls, but not boys, higher activation in the orbitofrontal-related network was associated with fewer emotional symptoms following childhood abuse, but only when the polygenic burden for depression was high. This finding defined a genetic-dependent brain (GDB) signature of resilience. Notably, this GDB signature predicted subsequent emotional disorders in late adolescence, extending into early adulthood and generalizable to another independent prospective cohort (ABCD). Our findings underscore the genetic modulation of resilience-brain connections, laying the foundation for enhancing adolescent mental health through resilience promotion.
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Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.
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Potenciais de Ação , Biologia Computacional , Modelos Neurológicos , Neurônios , Neurônios/fisiologia , Humanos , Potenciais de Ação/fisiologia , Redes Neurais de Computação , Rede Nervosa/fisiologia , Aprendizagem/fisiologia , Animais , Simulação por Computador , Encéfalo/fisiologiaRESUMO
The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.
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Encéfalo , Eletroencefalografia , Imageamento por Ressonância Magnética , Humanos , Encéfalo/crescimento & desenvolvimento , Encéfalo/diagnóstico por imagem , Feminino , Masculino , Adulto , Criança , Rede Nervosa/crescimento & desenvolvimento , Rede Nervosa/diagnóstico por imagem , Adulto Jovem , Adolescente , Mapeamento Encefálico/métodosRESUMO
A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.
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AIMS: Altered brain functional connectivity has been proposed as the neurobiological underpinnings of attention-deficit/hyperactivity disorder (ADHD), and the default mode interference hypothesis is one of the most popular neuropsychological models. Here, we explored whether this hypothesis is supported in adults with ADHD and the association with high-risk genetic variants and treatment outcomes. METHODS: Voxel-based whole-brain connectome analysis was conducted on resting-state functional MRI data from 84 adults with ADHD and 89 healthy controls to identify functional connectivity substrates corresponding to ADHD-related alterations. The candidate genetic variants and 12-week cognitive behavioral therapy data were leveraged from the same population to assess these associations. RESULTS: We detected breakdowns of functional connectivity in the precuneus and left middle temporal gyrus in adults with ADHD, with exact contributions from decreased connectivity within the default mode, dorsal and ventral attention networks, as well as increased connectivity among them with the middle temporal gyrus serving as a crucial 'bridge'. Additionally, significant associations between the altered functional connectivity and genetic variants in both MAOA and MAOB were detected. Treatment restored brain function, with the amelioration of connectivity of the middle temporal gyrus, accompanied by improvements in ADHD core symptoms. CONCLUSIONS: These findings support the interference of default mode on attention in adults with ADHD and its association with genetic risk variants and clinical management, providing insights into the underlying pathogenesis of ADHD and potential biomarkers for treatment evaluation.
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Transtorno do Deficit de Atenção com Hiperatividade , Conectoma , Rede de Modo Padrão , Imageamento por Ressonância Magnética , Humanos , Transtorno do Deficit de Atenção com Hiperatividade/genética , Transtorno do Deficit de Atenção com Hiperatividade/fisiopatologia , Transtorno do Deficit de Atenção com Hiperatividade/diagnóstico por imagem , Masculino , Feminino , Adulto , Rede de Modo Padrão/diagnóstico por imagem , Rede de Modo Padrão/fisiopatologia , Resultado do Tratamento , Adulto Jovem , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Atenção/fisiologia , Variação Genética/genética , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiopatologia , Terapia Cognitivo-Comportamental/métodosRESUMO
The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1-V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1-3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1-3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T.
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Hipocampo , Magnetoencefalografia , Córtex Visual , Humanos , Hipocampo/fisiologia , Hipocampo/diagnóstico por imagem , Masculino , Adulto , Feminino , Córtex Visual/fisiologia , Córtex Visual/diagnóstico por imagem , Adulto Jovem , Mapeamento Encefálico/métodosRESUMO
BACKGROUND: We aimed to investigate the association between OA and treatment with dementia risk and structural brain abnormalities. METHODS: We recruited a total of 466,460 individuals from the UK Biobank to investigate the impact of OA on the incidence of dementia. Among the total population, there were 63,081 participants diagnosed with OA. We subsequently categorised the OA patients into medication and surgery groups based on treatment routes. Cox regression models explored the associations between OA/OA treatment and dementia risk, with the results represented as hazard ratios (HRs) and 95% confidence intervals (95% CI). Linear regression models assessed the associations of OA/OA therapy with alterations in cortical structure. RESULTS: During an average of 11.90 (± 1.01) years of follow-up, 5,627 individuals were diagnosed with all-cause dementia (ACD), including 2,438 AD (Alzheimer's disease), and 1,312 VaD (vascular dementia) cases. Results revealed that OA was associated with the elevated risk of ACD (HR: 1.116; 95% CI: 1.039-1.199) and AD (HR: 1.127; 95% CI: 1.013-1.254). OA therapy lowered the risk of dementia in both medication group (HR: 0.746; 95% CI: 0.652-0.854) and surgery group (HR: 0.841; 95% CI: 0.736-0.960). OA was negatively associated with cortical area, especially precentral, postcentral and temporal regions. CONCLUSIONS: Osteoarthritis increased the likelihood of developing dementia, and had an association with regional brain atrophy. OA treatment lowered the dementia risk. OA is a promising modifiable risk factor for dementia.
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Demência , Osteoartrite , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Doença de Alzheimer/epidemiologia , Demência/epidemiologia , Demência Vascular/epidemiologia , Demência Vascular/diagnóstico , Incidência , Modelos Lineares , Imageamento por Ressonância Magnética , Osteoartrite/epidemiologia , Osteoartrite/terapia , Modelos de Riscos Proporcionais , Estudos Prospectivos , Fatores de Proteção , Medição de Risco , Fatores de Risco , Fatores de Tempo , Biobanco do Reino Unido , Reino Unido/epidemiologiaRESUMO
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.
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Progressão da Doença , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Doença de Parkinson , Índice de Gravidade de Doença , Doença de Parkinson/diagnóstico por imagem , Doença de Parkinson/patologia , Doença de Parkinson/metabolismo , Doença de Parkinson/líquido cefalorraquidiano , Humanos , Masculino , Feminino , Idoso , Pessoa de Meia-Idade , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Encéfalo/metabolismo , Biomarcadores/líquido cefalorraquidiano , Aprendizado ProfundoRESUMO
INTRODUCTION: The heritability of Alzheimer's disease (AD) is estimated to be 58%-79%. However, known genes can only partially explain the heritability. METHODS: Here, we conducted gene-based exome-wide association study (ExWAS) of rare variants and single-variant ExWAS of common variants, utilizing data of 54,569 clinically diagnosed/proxy AD and related dementia (ADRD) and 295,421 controls from the UK Biobank. RESULTS: Gene-based ExWAS identified 11 genes predicting a higher ADRD risk, including five novel ones, namely FRMD8, DDX1, DNMT3L, MORC1, and TGM2, along with six previously reported ones, SORL1, GRN, PSEN1, ABCA7, GBA, and ADAM10. Single-variant ExWAS identified two ADRD-associated novel genes, SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. The druggability evidence suggests that DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets. DISCUSSION: Our study contributes to the current body of evidence on the genetic etiology of ADRD. HIGHLIGHTS: Gene-based analyses of rare variants identified five novel genes for Alzheimer's disease and related dementia (ADRD), including FRMD8, DDX1, DNMT3L, MORC1, and TGM2. Single-variant analyses of common variants identified two novel genes for ADRD, including SLCO1C1 and NDNF. The identified genes were predominantly enriched in amyloid-ß process pathways, microglia, and brain regions like hippocampus. DDX1, DNMT3L, TGM2, SLCO1C1, and NDNF could be effective drug targets.
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Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.
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Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.
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Bancos de Espécimes Biológicos , Herança Multifatorial , Tentativa de Suicídio , Humanos , Tentativa de Suicídio/estatística & dados numéricos , Reino Unido/epidemiologia , Fatores de Risco , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Estudos de Casos e Controles , Idoso , Análise da Randomização Mendeliana , Aprendizado de Máquina , Estudo de Associação Genômica Ampla , Biobanco do Reino UnidoRESUMO
BACKGROUND: Varicose veins (VV) are one of the common human diseases, but the role of genetics in its development is not fully understood. METHODS: We conducted an exome-wide association study of VV using whole-exome sequencing data from the UK Biobank, and focused on common and rare variants using single-variant association analysis and gene-level collapsing analysis. FINDINGS: A total of 13,823,269 autosomal genetic variants were obtained after quality control. We identified 36 VV-related independent common variants mapping to 34 genes by single-variant analysis and three rare variant genes (PIEZO1, ECE1, FBLN7) by collapsing analysis, and most associations between genes and VV were replicated in FinnGen. PIEZO1 was the closest gene associated with VV (P = 5.05 × 10-31), and it was found to reach exome-wide significance in both single-variant and collapsing analyses. Two novel rare variant genes (ECE1 and METTL21A) associated with VV were identified, of which METTL21A was associated only with females. The pleiotropic effects of VV-related genes suggested that body size, inflammation, and pulmonary function are strongly associated with the development of VV. CONCLUSIONS: Our findings highlight the importance of causal genes for VV and provide new directions for treatment.
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Sequenciamento do Exoma , Exoma , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Varizes , Humanos , Varizes/genética , Feminino , Masculino , Exoma/genética , Polimorfismo de Nucleotídeo Único , Enzimas Conversoras de Endotelina/genética , Pessoa de Meia-Idade , Variação Genética , Adulto , Canais IônicosRESUMO
Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.
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BACKGROUND AND OBJECTIVES: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. METHODS: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. RESULTS: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. DISCUSSION: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.
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Biomarcadores , Proteínas Sanguíneas , Doença de Parkinson , Humanos , Doença de Parkinson/sangue , Doença de Parkinson/diagnóstico , Doença de Parkinson/epidemiologia , Feminino , Masculino , Pessoa de Meia-Idade , Proteínas Sanguíneas/análise , Idoso , Estudos Longitudinais , Estudos de Casos e Controles , Biomarcadores/sangue , Reino Unido/epidemiologia , Aprendizado de Máquina , Progressão da Doença , Valor Preditivo dos TestesRESUMO
Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.
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Substância Cinzenta , Imageamento por Ressonância Magnética , Humanos , Substância Cinzenta/diagnóstico por imagem , Adolescente , Feminino , Masculino , Adulto Jovem , Encéfalo/diagnóstico por imagem , Encéfalo/crescimento & desenvolvimento , Adulto , Estudos Longitudinais , Tamanho do Órgão , Neuroimagem , Cognição/fisiologia , Longevidade , Pessoa de Meia-Idade , Reino UnidoRESUMO
The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.
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Sequenciamento do Exoma , Proteínas Filagrinas , Humanos , Masculino , Feminino , Adulto , Predisposição Genética para Doença/genética , Pessoa de Meia-Idade , Doenças do Sistema Imunitário/genética , Análise da Randomização Mendeliana , Mutação , Proteômica , Variação Genética , Asma/genética , Asma/imunologia , Idoso , Dermatite Atópica/genética , Dermatite Atópica/imunologiaRESUMO
BACKGROUND: Shift work is associated with susceptibility to several neuropsychiatric disorders. This study aims to investigate the effect of shift work on the incidence of neuropsychiatric disorders, and highlighting how individual variability may influence the association. METHODS: UK Biobank participants with employment information were included. Cox survival was conducted in main and subgroup analyses. Correlation analyses explored the impact of shift work on brain structures, and mediation analyses were performed to elucidate the shared underlying mechanisms. Shift work tolerance was evaluated through survival analyses contrasting the risks associated with five neuropsychiatric disorders in shift versus non-shift workers across different demographic or occupational strata. RESULTS: The analysis encompassed 254,646 participants. Shift work was associated with higher risk of dementia (HR 1.29, 95 % CI 1.10-1.52), anxiety (1.08, 1.01-1.15), depression (1.29, 1.22-1.36), and sleep disorders (1.18, 1.09-1.28), but not stroke (p = 0.20). Shift work was correlated with decreasing volume of various brain regions, particularly in thalamus, lateral orbitofrontal, and middle temporal. Mediation analysis revealed that increased immune response and glucose levels are common pathways linking shift work to these disorders. We observed diversity in shift work tolerance across different individual characteristics, among which socioeconomic status and length of working hours were the most essential. LIMITATIONS: Self-reported employment information may cause misclassification and recall bias. And since we focused on the middle-aged population, the conclusions may not be representative of younger or older populations. CONCLUSIONS: Our findings indicated the need to monitor shift worker health and provide personalized management to help adapt to shift work.
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Jornada de Trabalho em Turnos , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Jornada de Trabalho em Turnos/efeitos adversos , Reino Unido/epidemiologia , Adulto , Incidência , Idoso , Demência/epidemiologia , Tolerância ao Trabalho Programado/fisiologia , Ansiedade/epidemiologia , Transtornos do Sono-Vigília/epidemiologia , Encéfalo/fisiopatologia , Transtornos Mentais/epidemiologia , Depressão/epidemiologiaRESUMO
Iron plays a fundamental role in multiple brain disorders. However, the genetic underpinnings of brain iron and its implications for these disorders are still lacking. Here, we conduct an exome-wide association analysis of brain iron, measured by quantitative susceptibility mapping technique, across 26 brain regions among 26,789 UK Biobank participants. We find 36 genes linked to brain iron, with 29 not being previously reported, and 16 of them can be replicated in an independent dataset with 3,039 subjects. Many of these genes are involved in iron transport and homeostasis, such as FTH1 and MLX. Several genes, while not previously connected to brain iron, are associated with iron-related brain disorders like Parkinson's (STAB1, KCNA10), Alzheimer's (SHANK1), and depression (GFAP). Mendelian randomization analysis reveals six causal relationships from regional brain iron to brain disorders, such as from the hippocampus to depression and from the substantia nigra to Parkinson's. These insights advance our understanding of the genetic architecture of brain iron and offer potential therapeutic targets for brain disorders.
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Encéfalo , Sequenciamento do Exoma , Ferro , Humanos , Ferro/metabolismo , Encéfalo/metabolismo , Masculino , Feminino , Análise da Randomização Mendeliana , Estudo de Associação Genômica Ampla , Doença de Parkinson/genética , Doença de Parkinson/metabolismo , Pessoa de Meia-Idade , Predisposição Genética para Doença/genética , Idoso , Proteínas do Tecido Nervoso/genética , Proteínas do Tecido Nervoso/metabolismo , Adulto , Doença de Alzheimer/genética , Doença de Alzheimer/metabolismoRESUMO
Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.