Pub Date : 2025-09-22DOI: 10.1038/s44220-025-00506-3
Audrey R. Stromberg, Anastasia K. Yocum, Melvin G. McInnis, Ivy F. Tso, Sarah H. Sperry
Increasing evidence suggests that bipolar disorders are associated with mood instability even outside the context of mood episodes. Here we use data from the Prechter Longitudinal Study of Bipolar Disorder to identify subgroups of individuals with bipolar disorders based on mood instability, identify biopsychosocial predictors of mood instability and determine whether mood instability predicts future outcomes. In a total of 481 participants, mood was assessed every 2 months (Patient Health Questionnaire and Altman Self-Rating Mania Scale) over 5 years, and clinical and functioning outcomes were assessed in year 6. Low, moderate and high mood instability classes were identified. Neuroticism, sleep quality, childhood emotional neglect and physical abuse, stimulant abuse, hypomania age of onset and number of depressive episodes were the most influential predictors of mood instability. Being in the high instability class (based on mood from years 1 to 5) predicted greater suicidal ideation and functional impairment in year 6. In summary, we show that mood instability represents a core phenotype of bipolar disorder with distinct predictors and long-term implications. Routine assessment may improve personalization in bipolar disorder treatment and research. Sperry et al. used machine learning approaches to investigate profiles of mood instability and create a prediction model for clinical and functioning outcomes using data from the Prechter Longitudinal Study of Bipolar Disorder.
{"title":"Modeling and predicting mood instability in a longitudinal cohort of bipolar disorder","authors":"Audrey R. Stromberg, Anastasia K. Yocum, Melvin G. McInnis, Ivy F. Tso, Sarah H. Sperry","doi":"10.1038/s44220-025-00506-3","DOIUrl":"10.1038/s44220-025-00506-3","url":null,"abstract":"Increasing evidence suggests that bipolar disorders are associated with mood instability even outside the context of mood episodes. Here we use data from the Prechter Longitudinal Study of Bipolar Disorder to identify subgroups of individuals with bipolar disorders based on mood instability, identify biopsychosocial predictors of mood instability and determine whether mood instability predicts future outcomes. In a total of 481 participants, mood was assessed every 2 months (Patient Health Questionnaire and Altman Self-Rating Mania Scale) over 5 years, and clinical and functioning outcomes were assessed in year 6. Low, moderate and high mood instability classes were identified. Neuroticism, sleep quality, childhood emotional neglect and physical abuse, stimulant abuse, hypomania age of onset and number of depressive episodes were the most influential predictors of mood instability. Being in the high instability class (based on mood from years 1 to 5) predicted greater suicidal ideation and functional impairment in year 6. In summary, we show that mood instability represents a core phenotype of bipolar disorder with distinct predictors and long-term implications. Routine assessment may improve personalization in bipolar disorder treatment and research. Sperry et al. used machine learning approaches to investigate profiles of mood instability and create a prediction model for clinical and functioning outcomes using data from the Prechter Longitudinal Study of Bipolar Disorder.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1267-1275"},"PeriodicalIF":8.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1038/s44220-025-00498-0
Srinivas Kamath, Elysia Sokolenko, Scott R. Clark, Courtney B. Cross, Jacqui Scott, Hannah R. Wardill, Kara G. Margolis, Paul Forsythe, Philip W. J. Burnet, Timothy G. Dinan, John F. Cryan, Christopher A. Lowry, Paul Joyce
The gut microbiota, the dynamic orchestrator of physiological and neuroimmune processes, influences mental health via the bidirectional microbiota–gut–brain axis. Although distinct microbial signatures are linked with psychiatric disorders such as depression, anxiety, post-traumatic stress disorder and schizophrenia, whether this relationship is causative, correlative or represents a complex interplay remains unresolved. This Review examines this trichotomy, highlighting key mechanistic pathways including microbial metabolites, immune modulation and neural signaling, alongside challenges in disentangling causation from correlation. Clarifying this distinction elevates the gut microbiota from a curiosity to a cornerstone of personalized medicine. Furthermore, emphasis is placed on advancing methodological frameworks, fostering interdisciplinary collaboration and addressing research disparities that bias insights toward specific populations. Clearer understanding of the microbiota’s role in mental health could yield new therapies and predictive biomarkers, ultimately charting paths toward more equitable and evidence-based approaches. This work outlines the transformative potential of clarifying the microbiota–gut–brain axis in addressing global mental health burden. This Review provides a critical assessment of current mechanistic and clinical evidence on the interaction between the gut microbiota and mental health to differentiate causative, correlative and bidirectional roles of the gut microbiota in psychiatric disorders. It highlights current priority questions and provides recommendations for the standardization of future studies.
{"title":"Distinguishing the causative, correlative and bidirectional roles of the gut microbiota in mental health","authors":"Srinivas Kamath, Elysia Sokolenko, Scott R. Clark, Courtney B. Cross, Jacqui Scott, Hannah R. Wardill, Kara G. Margolis, Paul Forsythe, Philip W. J. Burnet, Timothy G. Dinan, John F. Cryan, Christopher A. Lowry, Paul Joyce","doi":"10.1038/s44220-025-00498-0","DOIUrl":"10.1038/s44220-025-00498-0","url":null,"abstract":"The gut microbiota, the dynamic orchestrator of physiological and neuroimmune processes, influences mental health via the bidirectional microbiota–gut–brain axis. Although distinct microbial signatures are linked with psychiatric disorders such as depression, anxiety, post-traumatic stress disorder and schizophrenia, whether this relationship is causative, correlative or represents a complex interplay remains unresolved. This Review examines this trichotomy, highlighting key mechanistic pathways including microbial metabolites, immune modulation and neural signaling, alongside challenges in disentangling causation from correlation. Clarifying this distinction elevates the gut microbiota from a curiosity to a cornerstone of personalized medicine. Furthermore, emphasis is placed on advancing methodological frameworks, fostering interdisciplinary collaboration and addressing research disparities that bias insights toward specific populations. Clearer understanding of the microbiota’s role in mental health could yield new therapies and predictive biomarkers, ultimately charting paths toward more equitable and evidence-based approaches. This work outlines the transformative potential of clarifying the microbiota–gut–brain axis in addressing global mental health burden. This Review provides a critical assessment of current mechanistic and clinical evidence on the interaction between the gut microbiota and mental health to differentiate causative, correlative and bidirectional roles of the gut microbiota in psychiatric disorders. It highlights current priority questions and provides recommendations for the standardization of future studies.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1137-1151"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-18DOI: 10.1038/s44220-025-00495-3
Sonali N. Reisinger, Anthony J. Hannan
This Perspective outlines developmental origins of mental health and disorders (DOMHaD) as a proposed theoretical framework, building on the concept of the developmental origins of health and disease (DOHaD). DOMHaD is supported by accumulating evidence that all major psychiatric disorders, including those with adult onset, have their origins in development, including neurodevelopmental trajectories and spatiotemporal regulation of brain–body interactions. DOMHaD incorporates the understanding that each individual’s genome, determined at conception, makes them predisposed or resilient to one or more disorders. Overlaid on these genetic factors are environmental and epigenetic factors with origins before conception, via parental exposures, and ongoing in utero, postnatal, childhood and adolescent exposures. These genetic and environmental factors interact over time, revealing healthy and dysfunctional developmental paths as two sides of the same complex ‘coin’. Implications of the DOMHaD conceptual framework for the understanding, prevention and treatment of psychiatric disorders are discussed. This Perspective presents the developmental origins of mental health and disorders (DOMHaD) framework, emphasizing genetic, environmental and epigenetic influences on psychiatric conditions. It highlights interactions over time, revealing pathways for understanding, preventing and treating these complex disorders effectively.
{"title":"Developmental origins of mental health and disorders (DOMHaD): an approach to understanding, preventing and treating psychiatric disorders","authors":"Sonali N. Reisinger, Anthony J. Hannan","doi":"10.1038/s44220-025-00495-3","DOIUrl":"10.1038/s44220-025-00495-3","url":null,"abstract":"This Perspective outlines developmental origins of mental health and disorders (DOMHaD) as a proposed theoretical framework, building on the concept of the developmental origins of health and disease (DOHaD). DOMHaD is supported by accumulating evidence that all major psychiatric disorders, including those with adult onset, have their origins in development, including neurodevelopmental trajectories and spatiotemporal regulation of brain–body interactions. DOMHaD incorporates the understanding that each individual’s genome, determined at conception, makes them predisposed or resilient to one or more disorders. Overlaid on these genetic factors are environmental and epigenetic factors with origins before conception, via parental exposures, and ongoing in utero, postnatal, childhood and adolescent exposures. These genetic and environmental factors interact over time, revealing healthy and dysfunctional developmental paths as two sides of the same complex ‘coin’. Implications of the DOMHaD conceptual framework for the understanding, prevention and treatment of psychiatric disorders are discussed. This Perspective presents the developmental origins of mental health and disorders (DOMHaD) framework, emphasizing genetic, environmental and epigenetic influences on psychiatric conditions. It highlights interactions over time, revealing pathways for understanding, preventing and treating these complex disorders effectively.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1116-1136"},"PeriodicalIF":8.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-18DOI: 10.1038/s44220-025-00499-z
Gangliang Zhong, Tianzhen Chen, Hang Su, Xiaotong Li, Na Zhong, Haifeng Jiang, Yuzheng Hu, Marc N. Potenza, Jiang Du, Min Zhao
Methamphetamine use disorder (MUD) is a substantial public health crisis characterized by neurobiological abnormalities. Although neurofunctional variations across abstinence stages are documented, brain connectivity patterns associated with abstinence duration remain poorly characterized. In this cross-sectional study, we characterize brain connectivity patterns associated with abstinence duration in MUD. We hypothesize that whole-brain functional connectivity patterns would covary with abstinence duration in MUD. Applying connectome-based predictive modeling with leave-one-out cross-validation to resting-state functional connectivity data from participants with MUD stratified by abstinence duration (<1 month, 1–3 months, 3–6 months, 6–24 months; total N = 85), we identified patterns significantly associated with abstinence duration (r = 0.51, P < 0.001), validated in an independent cohort (N = 48, r = 0.41, P < 0.004). These patterns comprised positive components showing strengthened within-network connectivity in motor/sensory, subcortical and medial frontal networks, and enhanced between-network connectivity involving motor/sensory, cerebellum/brainstem and subcortical networks, and negative components demonstrating reduced connectivity between motor/sensory and default mode networks, as well as among motor/sensory, medial frontal and visual association networks. Exploratory analyses revealed systematic variation in strength, with healthy comparison individuals exhibiting intermediate connectivity relative to individuals who were short-term (<1 month) versus prolonged (6–24 months) MUD-abstinent. Our findings reveal cross-sectional associations between abstinence duration and brain connectivity in MUD. This study identifies brain connectivity patterns linked to the duration of methamphetamine abstinence in individuals with methamphetamine use disorder. The identified networks were validated in an out-of-sample replication.
甲基苯丙胺使用障碍是一种以神经生物学异常为特征的重大公共卫生危机。尽管在禁欲阶段的神经功能变化被记录在案，但与禁欲持续时间相关的大脑连接模式仍然缺乏特征。在这项横断面研究中，我们描述了MUD中与戒断持续时间相关的大脑连接模式。我们假设全脑功能连接模式与MUD的戒断持续时间相关。将基于连接体的预测模型与留一交叉验证应用于根据戒断时间（1个月、1 - 3个月、3-6个月、6-24个月，总N = 85）对MUD参与者的静息状态功能连接数据进行分层，我们发现了与戒断时间显著相关的模式（r = 0.51, P < 0.001），并在独立队列中进行了验证（N = 48, r = 0.41, P < 0.004）。这些模式包括积极成分，显示运动/感觉网络、皮层下网络和内侧额叶网络的网络内连通性增强，运动/感觉网络、小脑/脑干网络和皮层下网络的网络间连通性增强，消极成分显示运动/感觉网络和默认模式网络之间以及运动/感觉网络、内侧额叶网络和视觉关联网络之间的连通性降低。探索性分析揭示了强度的系统性差异，健康对照个体相对于短期（1个月）和长期（6-24个月）戒断mud的个体表现出中间的连通性。我们的研究结果揭示了戒断持续时间与MUD患者大脑连通性之间的横断面关联。这项研究确定了与甲基苯丙胺使用障碍患者戒断甲基苯丙胺持续时间有关的大脑连接模式。识别的网络在样本外复制中得到验证。
{"title":"Brain connectivity patterns associated with duration of abstinence in methamphetamine use disorder","authors":"Gangliang Zhong, Tianzhen Chen, Hang Su, Xiaotong Li, Na Zhong, Haifeng Jiang, Yuzheng Hu, Marc N. Potenza, Jiang Du, Min Zhao","doi":"10.1038/s44220-025-00499-z","DOIUrl":"10.1038/s44220-025-00499-z","url":null,"abstract":"Methamphetamine use disorder (MUD) is a substantial public health crisis characterized by neurobiological abnormalities. Although neurofunctional variations across abstinence stages are documented, brain connectivity patterns associated with abstinence duration remain poorly characterized. In this cross-sectional study, we characterize brain connectivity patterns associated with abstinence duration in MUD. We hypothesize that whole-brain functional connectivity patterns would covary with abstinence duration in MUD. Applying connectome-based predictive modeling with leave-one-out cross-validation to resting-state functional connectivity data from participants with MUD stratified by abstinence duration (<1 month, 1–3 months, 3–6 months, 6–24 months; total N = 85), we identified patterns significantly associated with abstinence duration (r = 0.51, P < 0.001), validated in an independent cohort (N = 48, r = 0.41, P < 0.004). These patterns comprised positive components showing strengthened within-network connectivity in motor/sensory, subcortical and medial frontal networks, and enhanced between-network connectivity involving motor/sensory, cerebellum/brainstem and subcortical networks, and negative components demonstrating reduced connectivity between motor/sensory and default mode networks, as well as among motor/sensory, medial frontal and visual association networks. Exploratory analyses revealed systematic variation in strength, with healthy comparison individuals exhibiting intermediate connectivity relative to individuals who were short-term (<1 month) versus prolonged (6–24 months) MUD-abstinent. Our findings reveal cross-sectional associations between abstinence duration and brain connectivity in MUD. This study identifies brain connectivity patterns linked to the duration of methamphetamine abstinence in individuals with methamphetamine use disorder. The identified networks were validated in an out-of-sample replication.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1256-1266"},"PeriodicalIF":8.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-18DOI: 10.1038/s44220-025-00502-7
Carlos Coronel-Oliveros, Sebastián Moguilner, Hernan Hernandez, Josephine Cruzat, Sandra Baez, Vicente Medel, Jhosmary Cuadros, Hernando Santamaria-Garcia, Pedro A. Valdes-Sosa, Francisco Lopera, John Fredy Ochoa-Gómez, Alfredis González-Hernández, Jasmín Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Ebru Yıldırım, Renato Anghinah, Agustina Legaz, Sol Fittipaldi, Görsev G. Yener, Javier Escudero, Claudio Babiloni, Susanna Lopez, Robert Whelan, Alberto Fernández, David Huepe, Gaetano Di Caterina, Marcio Soto-Añari, Raul Gonzalez-Gomez, Eduar Herrera, Daniel Abasolo, Kerry Kilborn, Nicolás Rubido, Ruaridh Clark, Rubén Herzog, Deniz Yerlikaya, Bahar Güntekin, Gustavo Deco, Pavel Prado, Mario A. Parra, Patricio Orio, Enzo Tagliazucchi, Brian Lawlor, Agustin Ibanez
Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here we combine source space connectivity (via electroencephalography) with generative brain modeling in healthy controls from the global south and north, alongside patients with Alzheimer’s disease and behavioral variant frontotemporal dementia (bvFTD) (N = 1,399). BAGs in aging were influenced by geography (south > north), income (low > high), sex (female > male) and education (low > high), with larger BAGs in patients, especially females, with Alzheimer’s disease. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations. Brain age gaps (BAGs) highlight deviations from healthy brain aging, yet their biophysical underpinnings in aging and dementia are not well understood. Here, the authors use EEG connectivity and generative modeling across diverse populations to reveal that BAGs are influenced by geography, income, sex and education, with implications for understanding accelerated aging and dementia.
{"title":"Diversity-sensitive brain clocks linked to biophysical mechanisms in aging and dementia","authors":"Carlos Coronel-Oliveros, Sebastián Moguilner, Hernan Hernandez, Josephine Cruzat, Sandra Baez, Vicente Medel, Jhosmary Cuadros, Hernando Santamaria-Garcia, Pedro A. Valdes-Sosa, Francisco Lopera, John Fredy Ochoa-Gómez, Alfredis González-Hernández, Jasmín Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Ebru Yıldırım, Renato Anghinah, Agustina Legaz, Sol Fittipaldi, Görsev G. Yener, Javier Escudero, Claudio Babiloni, Susanna Lopez, Robert Whelan, Alberto Fernández, David Huepe, Gaetano Di Caterina, Marcio Soto-Añari, Raul Gonzalez-Gomez, Eduar Herrera, Daniel Abasolo, Kerry Kilborn, Nicolás Rubido, Ruaridh Clark, Rubén Herzog, Deniz Yerlikaya, Bahar Güntekin, Gustavo Deco, Pavel Prado, Mario A. Parra, Patricio Orio, Enzo Tagliazucchi, Brian Lawlor, Agustin Ibanez","doi":"10.1038/s44220-025-00502-7","DOIUrl":"10.1038/s44220-025-00502-7","url":null,"abstract":"Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here we combine source space connectivity (via electroencephalography) with generative brain modeling in healthy controls from the global south and north, alongside patients with Alzheimer’s disease and behavioral variant frontotemporal dementia (bvFTD) (N = 1,399). BAGs in aging were influenced by geography (south > north), income (low > high), sex (female > male) and education (low > high), with larger BAGs in patients, especially females, with Alzheimer’s disease. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations. Brain age gaps (BAGs) highlight deviations from healthy brain aging, yet their biophysical underpinnings in aging and dementia are not well understood. Here, the authors use EEG connectivity and generative modeling across diverse populations to reveal that BAGs are influenced by geography, income, sex and education, with implications for understanding accelerated aging and dementia.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1214-1229"},"PeriodicalIF":8.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-16DOI: 10.1038/s44220-025-00486-4
Xueyi Shen, Miruna Barbu, Doretta Caramaschi, Ryan Arathimos, Darina Czamara, Friederike S. David, Anna Dearman, Evelyn Dilkes, Marisol Herrera-Rivero, Floris Huider, Luise Kühn, Kuan-Chen Lu, Teemu Palviainen, Alicia M. Schowe, Gemma Shireby, Antoine Weihs, Chloe C. Y. Wong, Eleanor Davyson, Hannah Casey, Mark J. Adams, Antje-Kathrin Allgaier, Michael Barber, Joe Burrage, Avshalom Caspi, Ricardo Costeira, Erin C. Dunn, Lisa Feldmann, Josef Frank, Franz J. Freisleder, Danni A. Gadd, Ellen Greimel, Eilis Hannon, Sarah E. Harris, Georg Homuth, David M. Howard, Stella Iurato, Tellervo Korhonen, Tzu-Pin Lu, Nicholas G. Martin, Jade Martins, Edel McDermott, Susanne Meinert, Pau Navarro, Miina Ollikainen, Verena Pehl, Charlotte Piechaczek, Aline D. Scherff, Frederike Stein, Fabian Streit, Alexander Teumer, Henry Völzke, Jenny van Dongen, Rosie M. Walker, Natan Yusupov, Louise Arseneault, Jordana T. Bell, Klaus Berger, Elisabeth Binder, Dorret I. Boomsma, Simon R. Cox, Udo Dannlowski, Kathryn L. Evans, Helen L. Fisher, Andreas J. Forstner, Hans J. Grabe, Jaakko Kaprio, Tilo Kircher, Johannes Kopf-Beck, Meena Kumari, Po-Hsiu Kuo, Qingqin S. Li, Terrie E. Moffitt, Hugh Mulcahy, Therese M. Murphy, Gerd Schulte-Körne, Jonathan Mill, Cathryn M. Lewis, PGC MDD Working Group, Naomi R. Wray, Andrew M. McIntosh
Major depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation may be associated with the condition. However, previous DNA methylation studies have, so far, not been widely replicated, suggesting a need for larger meta-analysis studies. Here we conducted a meta-analysis of methylome-wide association analysis for lifetime MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1,846 controls). We identified 15 CpG sites associated with lifetime MD with methylome-wide significance. The methylation score created using the methylome-wide association analysis summary statistics was significantly associated with MD status in an out-of-sample classification analysis (area under the curve 0.53). Methylation score was also associated with five inflammatory markers, with the strongest association found with tumor necrosis factor beta. Mendelian randomization analysis revealed 23 CpG sites potentially causally linked to MD, with 7 replicated in an independent dataset. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system. The authors report a meta-analysis of methylome-wide association studies, identifying 15 significant CpG sites linked to major depression, revealing associations with inflammatory markers and suggesting potential causal relationships through Mendelian randomization analysis.
{"title":"A methylome-wide association study of major depression with out-of-sample case–control classification and trans-ancestry comparison","authors":"Xueyi Shen, Miruna Barbu, Doretta Caramaschi, Ryan Arathimos, Darina Czamara, Friederike S. David, Anna Dearman, Evelyn Dilkes, Marisol Herrera-Rivero, Floris Huider, Luise Kühn, Kuan-Chen Lu, Teemu Palviainen, Alicia M. Schowe, Gemma Shireby, Antoine Weihs, Chloe C. Y. Wong, Eleanor Davyson, Hannah Casey, Mark J. Adams, Antje-Kathrin Allgaier, Michael Barber, Joe Burrage, Avshalom Caspi, Ricardo Costeira, Erin C. Dunn, Lisa Feldmann, Josef Frank, Franz J. Freisleder, Danni A. Gadd, Ellen Greimel, Eilis Hannon, Sarah E. Harris, Georg Homuth, David M. Howard, Stella Iurato, Tellervo Korhonen, Tzu-Pin Lu, Nicholas G. Martin, Jade Martins, Edel McDermott, Susanne Meinert, Pau Navarro, Miina Ollikainen, Verena Pehl, Charlotte Piechaczek, Aline D. Scherff, Frederike Stein, Fabian Streit, Alexander Teumer, Henry Völzke, Jenny van Dongen, Rosie M. Walker, Natan Yusupov, Louise Arseneault, Jordana T. Bell, Klaus Berger, Elisabeth Binder, Dorret I. Boomsma, Simon R. Cox, Udo Dannlowski, Kathryn L. Evans, Helen L. Fisher, Andreas J. Forstner, Hans J. Grabe, Jaakko Kaprio, Tilo Kircher, Johannes Kopf-Beck, Meena Kumari, Po-Hsiu Kuo, Qingqin S. Li, Terrie E. Moffitt, Hugh Mulcahy, Therese M. Murphy, Gerd Schulte-Körne, Jonathan Mill, Cathryn M. Lewis, PGC MDD Working Group, Naomi R. Wray, Andrew M. McIntosh","doi":"10.1038/s44220-025-00486-4","DOIUrl":"10.1038/s44220-025-00486-4","url":null,"abstract":"Major depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation may be associated with the condition. However, previous DNA methylation studies have, so far, not been widely replicated, suggesting a need for larger meta-analysis studies. Here we conducted a meta-analysis of methylome-wide association analysis for lifetime MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1,846 controls). We identified 15 CpG sites associated with lifetime MD with methylome-wide significance. The methylation score created using the methylome-wide association analysis summary statistics was significantly associated with MD status in an out-of-sample classification analysis (area under the curve 0.53). Methylation score was also associated with five inflammatory markers, with the strongest association found with tumor necrosis factor beta. Mendelian randomization analysis revealed 23 CpG sites potentially causally linked to MD, with 7 replicated in an independent dataset. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system. The authors report a meta-analysis of methylome-wide association studies, identifying 15 significant CpG sites linked to major depression, revealing associations with inflammatory markers and suggesting potential causal relationships through Mendelian randomization analysis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1152-1167"},"PeriodicalIF":8.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00486-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1038/s44220-025-00501-8
Die Zhang, Yingji Fu, Chenye Shen, Chaoqiang Liu, Nanguang Chen, Hua Cao, Kui Kai Lau, Anqi Qiu
Body mass index (BMI) is commonly used to assess obesity, but it fails to capture the complexities of regional adiposity, which can have varying effects on brain health. This study analyzed data from over 18,000 UK Biobank participants to investigate the relationship between regional adiposity, measured using dual-energy X-ray absorptiometry, and brain health, evaluated through multimodal brain imaging and cognitive tests. Adiposity in the arm, leg, trunk and visceral regions was differentially associated with brain morphology, functional connectivity and white-matter integrity in the sensorimotor, limbic, default mode and subcortical–cerebellar–brainstem systems. The aging of these four brain systems was indexed by brain age gap (BAG), with cortical-related BAGs (sensorimotor, limbic, default mode) mediating relationships between visceral adiposity and cognitive performance in reasoning, executive function, processing speed and memory. These results highlight the importance of considering regional adiposity, beyond BMI, in characterizing its associations with brain and cognitive aging. In this large-scale study, the authors used multimodal neuroimaging and cognitive data from UK Biobank participants to examine the relationship between regional adiposity and brain health.
{"title":"Regional adiposity shapes brain and cognition in adults","authors":"Die Zhang, Yingji Fu, Chenye Shen, Chaoqiang Liu, Nanguang Chen, Hua Cao, Kui Kai Lau, Anqi Qiu","doi":"10.1038/s44220-025-00501-8","DOIUrl":"10.1038/s44220-025-00501-8","url":null,"abstract":"Body mass index (BMI) is commonly used to assess obesity, but it fails to capture the complexities of regional adiposity, which can have varying effects on brain health. This study analyzed data from over 18,000 UK Biobank participants to investigate the relationship between regional adiposity, measured using dual-energy X-ray absorptiometry, and brain health, evaluated through multimodal brain imaging and cognitive tests. Adiposity in the arm, leg, trunk and visceral regions was differentially associated with brain morphology, functional connectivity and white-matter integrity in the sensorimotor, limbic, default mode and subcortical–cerebellar–brainstem systems. The aging of these four brain systems was indexed by brain age gap (BAG), with cortical-related BAGs (sensorimotor, limbic, default mode) mediating relationships between visceral adiposity and cognitive performance in reasoning, executive function, processing speed and memory. These results highlight the importance of considering regional adiposity, beyond BMI, in characterizing its associations with brain and cognitive aging. In this large-scale study, the authors used multimodal neuroimaging and cognitive data from UK Biobank participants to examine the relationship between regional adiposity and brain health.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1168-1180"},"PeriodicalIF":8.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00501-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1038/s44220-025-00500-9
Robert J. Jirsaraie, Deanna M. Barch, Ryan Bogdan, Scott A. Marek, Janine D. Bijsterbosch, Aristeidis Sotiras, Nicole R. Karcher
A key challenge in predicting a person’s state of mind is that a wide range of contributing factors each has a subtle, yet meaningful, influence on mental health. Here we applied data mining techniques to identify the most important risk factors for predicting current symptoms and longitudinal outcomes from the Adolescent Brain Cognitive Developmental study (n = 11,552). Our results consistently revealed that social conflicts were the strongest predictors of psychopathology, especially family fighting and reputational damage between peers. Sex differences also emerged as a critical factor for predicting long-term mental health outcomes. Neuroimaging-derived metrics were consistently the least informative. Although these findings provide novel insight into the developmental origins of psychopathology, our best-performing models could explain only up to 40% of the variation between individuals. Future research is needed to obtain a more complete understanding of all the factors that meaningfully contribute to mental health. In this study, Jirsaraie et al. analyze data from the Adolescent Brain Cognitive Developmental study and use machine learning to predict both current and future psychological symptoms and to determine rates of change in symptom severity over time.
{"title":"Mapping multimodal risk factors to mental health outcomes","authors":"Robert J. Jirsaraie, Deanna M. Barch, Ryan Bogdan, Scott A. Marek, Janine D. Bijsterbosch, Aristeidis Sotiras, Nicole R. Karcher","doi":"10.1038/s44220-025-00500-9","DOIUrl":"10.1038/s44220-025-00500-9","url":null,"abstract":"A key challenge in predicting a person’s state of mind is that a wide range of contributing factors each has a subtle, yet meaningful, influence on mental health. Here we applied data mining techniques to identify the most important risk factors for predicting current symptoms and longitudinal outcomes from the Adolescent Brain Cognitive Developmental study (n = 11,552). Our results consistently revealed that social conflicts were the strongest predictors of psychopathology, especially family fighting and reputational damage between peers. Sex differences also emerged as a critical factor for predicting long-term mental health outcomes. Neuroimaging-derived metrics were consistently the least informative. Although these findings provide novel insight into the developmental origins of psychopathology, our best-performing models could explain only up to 40% of the variation between individuals. Future research is needed to obtain a more complete understanding of all the factors that meaningfully contribute to mental health. In this study, Jirsaraie et al. analyze data from the Adolescent Brain Cognitive Developmental study and use machine learning to predict both current and future psychological symptoms and to determine rates of change in symptom severity over time.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 10","pages":"1230-1240"},"PeriodicalIF":8.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1038/s44220-025-00504-5
Despite progress, suicide remains one of the leading causes of death worldwide. Changing the narrative means moving beyond stigma and embracing a collective agenda across communities, health systems and policy to implement solutions and to prevent avoidable deaths.
{"title":"Changing the narrative in suicide prevention","authors":"","doi":"10.1038/s44220-025-00504-5","DOIUrl":"10.1038/s44220-025-00504-5","url":null,"abstract":"Despite progress, suicide remains one of the leading causes of death worldwide. Changing the narrative means moving beyond stigma and embracing a collective agenda across communities, health systems and policy to implement solutions and to prevent avoidable deaths.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 9","pages":"957-958"},"PeriodicalIF":8.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00504-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-08DOI: 10.1038/s44220-025-00478-4
Mette Lise Lousdal, Sonja LaBianca, Esben Agerbo, Clara Albiñana, Bjarni J. Vilhjálmsson, John J. McGrath, Andrew J. Schork, Oleguer Plana-Ripoll
During recent decades, the incidence of several psychiatric disorders has increased, but no previous study has investigated whether the polygenic burden based on common variants for psychiatric disorders in diagnosed individuals has changed over time. Here we aimed to explore changes in polygenic scores for schizophrenia, depression, autism and attention deficit hyperactivity disorder (ADHD) in the general population and in case populations according to birth cohorts. The iPSYCH2015 is a Danish population-based case–cohort study, including individuals born between 1981 and 2008, who were followed for a psychiatric diagnosis between 1994 and 2015. We included 41,132 individuals from the random subcohort and 60,293 individuals diagnosed with schizophrenia spectrum disorders, depression, autism or ADHD. We estimated changes in polygenic scores across birth years based on linear regression. The average polygenic score was stable in the random subcohort but decreased across birth years in case populations, most predominantly for schizophrenia (per 10 years: −0.13 s.d., 95% confidence interval (CI) −0.18 to −0.07) but also for depression (−0.06 s.d., 95% CI −0.10 to −0.03) and autism (−0.08 s.d., 95% CI −0.13 to −0.04) and to a limited degree for ADHD (−0.03 s.d., 95% CI −0.08 to 0.02). Moreover, we estimated how the hazard ratio for being diagnosed given a 1 s.d. increase in polygenic score changed according to birth year, which decreased for schizophrenia but remained stable for the other disorders. Finally, we estimated the number of additional cases per 1 s.d. increase in polygenic score according to birth year, which decreased for both schizophrenia and depression, whereas autism and ADHD showed increases. In conclusion, the polygenic burden for psychiatric disorders changed across two decades among diagnosed individuals in Denmark. For schizophrenia, the polygenic score itself and its predictive ability decreased over time, whereas depression, autism and ADHD showed diverse changes. Lousdal et al. investigate the changes in polygenic scores for schizophrenia, depression, autism and attention deficit hyperactivity disorder using data from a Danish population-based case–cohort study that includes individuals born between 1981 and 2008.
近几十年来，几种精神疾病的发病率有所增加，但此前没有研究调查基于精神疾病常见变异的多基因负担是否随着时间的推移而改变。在这里，我们的目的是探讨精神分裂症、抑郁症、自闭症和注意力缺陷多动障碍（ADHD）的多基因评分在普通人群和病例人群中根据出生队列的变化。iPSYCH2015是一项基于丹麦人群的病例队列研究，包括1981年至2008年出生的个体，他们在1994年至2015年期间进行了精神病诊断。我们从随机亚队列中纳入了41132名个体和60293名被诊断患有精神分裂症谱系障碍、抑郁症、自闭症或多动症的个体。我们基于线性回归估计了多基因评分在出生年份的变化。平均多基因评分在随机亚队列中是稳定的，但在病例群中随着出生年份的不同而下降，主要是精神分裂症（每10年：- 0.13 s.d， 95%可信区间(CI) - 0.18至- 0.07），但也有抑郁症（- 0.06 s.d, 95% CI - 0.10至- 0.03）和自闭症（- 0.08 s.d, 95% CI - 0.13至- 0.04），ADHD （- 0.03 s.d, 95% CI - 0.08至0.02）。此外，我们估计了被诊断为多基因评分增加1 sd的风险比如何根据出生年份变化，精神分裂症的风险比下降，但其他疾病的风险比保持稳定。最后，我们根据出生年份估算了多基因评分每增加1 sd的额外病例数，精神分裂症和抑郁症的多基因评分都减少了，而自闭症和多动症的多基因评分则增加了。总之，在丹麦诊断个体中，精神疾病的多基因负担在20年间发生了变化。对于精神分裂症，多基因评分本身及其预测能力随着时间的推移而下降，而抑郁症、自闭症和多动症则表现出不同的变化。Lousdal等人调查了精神分裂症、抑郁症、自闭症和注意缺陷多动障碍的多基因评分变化，使用的数据来自丹麦基于人群的病例队列研究，其中包括1981年至2008年间出生的个体。
{"title":"Changes in polygenic burden for psychiatric disorders across two decades of birth cohorts","authors":"Mette Lise Lousdal, Sonja LaBianca, Esben Agerbo, Clara Albiñana, Bjarni J. Vilhjálmsson, John J. McGrath, Andrew J. Schork, Oleguer Plana-Ripoll","doi":"10.1038/s44220-025-00478-4","DOIUrl":"10.1038/s44220-025-00478-4","url":null,"abstract":"During recent decades, the incidence of several psychiatric disorders has increased, but no previous study has investigated whether the polygenic burden based on common variants for psychiatric disorders in diagnosed individuals has changed over time. Here we aimed to explore changes in polygenic scores for schizophrenia, depression, autism and attention deficit hyperactivity disorder (ADHD) in the general population and in case populations according to birth cohorts. The iPSYCH2015 is a Danish population-based case–cohort study, including individuals born between 1981 and 2008, who were followed for a psychiatric diagnosis between 1994 and 2015. We included 41,132 individuals from the random subcohort and 60,293 individuals diagnosed with schizophrenia spectrum disorders, depression, autism or ADHD. We estimated changes in polygenic scores across birth years based on linear regression. The average polygenic score was stable in the random subcohort but decreased across birth years in case populations, most predominantly for schizophrenia (per 10 years: −0.13 s.d., 95% confidence interval (CI) −0.18 to −0.07) but also for depression (−0.06 s.d., 95% CI −0.10 to −0.03) and autism (−0.08 s.d., 95% CI −0.13 to −0.04) and to a limited degree for ADHD (−0.03 s.d., 95% CI −0.08 to 0.02). Moreover, we estimated how the hazard ratio for being diagnosed given a 1 s.d. increase in polygenic score changed according to birth year, which decreased for schizophrenia but remained stable for the other disorders. Finally, we estimated the number of additional cases per 1 s.d. increase in polygenic score according to birth year, which decreased for both schizophrenia and depression, whereas autism and ADHD showed increases. In conclusion, the polygenic burden for psychiatric disorders changed across two decades among diagnosed individuals in Denmark. For schizophrenia, the polygenic score itself and its predictive ability decreased over time, whereas depression, autism and ADHD showed diverse changes. Lousdal et al. investigate the changes in polygenic scores for schizophrenia, depression, autism and attention deficit hyperactivity disorder using data from a Danish population-based case–cohort study that includes individuals born between 1981 and 2008.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 9","pages":"1037-1045"},"PeriodicalIF":8.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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