Pub Date : 2026-01-16DOI: 10.1038/s44220-025-00576-3
Giuseppe Riva, Giulia Brizzi, Clara Rastelli, Antonino Greco
Psychedelic-assisted therapy shows promise for mental health treatment but faces regulatory, methodological and safety challenges. In this Comment, we propose using artificial intelligence and virtual reality to simulate similar experiences to those produced by traditional psychedelic compounds for use in psychedelic-assisted therapy modalities.
{"title":"AI-generated virtual psychedelics bridge digital and therapeutic frontiers in mental health research","authors":"Giuseppe Riva, Giulia Brizzi, Clara Rastelli, Antonino Greco","doi":"10.1038/s44220-025-00576-3","DOIUrl":"10.1038/s44220-025-00576-3","url":null,"abstract":"Psychedelic-assisted therapy shows promise for mental health treatment but faces regulatory, methodological and safety challenges. In this Comment, we propose using artificial intelligence and virtual reality to simulate similar experiences to those produced by traditional psychedelic compounds for use in psychedelic-assisted therapy modalities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 2","pages":"183-185"},"PeriodicalIF":8.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148318","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 : 2026-01-15DOI: 10.1038/s44220-025-00580-7
Zheng Zhang, Chuantao Zhou, Runjia Zhang, Yangyang Tang, Yange Zhang, Pengmin Qin, Binyuan Su, Yuanyuan Wang
Depression is a major mental health concern among adolescents, and adverse childhood experiences (ACEs) are known risk factors. However, how depression affects the recall of ACEs remains unclear. Using three waves of data from 6,260 Chinese adolescents in the Developmental & Emotional Pathways in Transition to Adulthood Study, we examine the bidirectional relationship between depression and ACE recall. Depression was assessed with the Beck Depression Inventory-II and ACEs with an adapted ACE scale, controlling for sociodemographic factors. Random intercept cross-lagged panel model analyses show that, within individuals, baseline depressive symptoms predict increased subsequent recall of ACEs, whereas ACE recall did not predict later depression. Cross-lagged panel network analysis identified punishment feelings, fatigue and emotional neglect as key nodes linking depression and ACE recall. These findings indicate that depression can reshape autobiographical memory of adversity, probably via negative emotional processing and memory bias. This highlights the need to account for depression-driven distortions when assessing trauma history, and suggests that alleviating depressive symptoms may reduce trauma-related distress. In this longitudinal cohort study, Wang et al. examine how depression and recall of adverse childhood experiences interact over time among Chinese university students.
{"title":"Depression shapes the recall of adverse childhood experiences: evidence from a three-wave longitudinal study of 6,260 Chinese adolescents","authors":"Zheng Zhang, Chuantao Zhou, Runjia Zhang, Yangyang Tang, Yange Zhang, Pengmin Qin, Binyuan Su, Yuanyuan Wang","doi":"10.1038/s44220-025-00580-7","DOIUrl":"10.1038/s44220-025-00580-7","url":null,"abstract":"Depression is a major mental health concern among adolescents, and adverse childhood experiences (ACEs) are known risk factors. However, how depression affects the recall of ACEs remains unclear. Using three waves of data from 6,260 Chinese adolescents in the Developmental & Emotional Pathways in Transition to Adulthood Study, we examine the bidirectional relationship between depression and ACE recall. Depression was assessed with the Beck Depression Inventory-II and ACEs with an adapted ACE scale, controlling for sociodemographic factors. Random intercept cross-lagged panel model analyses show that, within individuals, baseline depressive symptoms predict increased subsequent recall of ACEs, whereas ACE recall did not predict later depression. Cross-lagged panel network analysis identified punishment feelings, fatigue and emotional neglect as key nodes linking depression and ACE recall. These findings indicate that depression can reshape autobiographical memory of adversity, probably via negative emotional processing and memory bias. This highlights the need to account for depression-driven distortions when assessing trauma history, and suggests that alleviating depressive symptoms may reduce trauma-related distress. In this longitudinal cohort study, Wang et al. examine how depression and recall of adverse childhood experiences interact over time among Chinese university students.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 2","pages":"231-242"},"PeriodicalIF":8.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148383","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 : 2026-01-15DOI: 10.1038/s44220-025-00572-7
Bruna Santos da Silva, Claiton Henrique Dotto Bau, Humberto Nicolini, Maria Eduarda de Araujo Tavares, Victor Fernandes de Oliveira, Yago Carvalho Lima, Eduardo Schneider Vitola, Alma Genis-Mendoza, Isabella Folego-Temoteo, Gabriela Ariadna Martínez-Levy, Lucia Spangenberg, Gabriel Barg, Cibele Edom Bandeira, Rodrigo Sosa, Zuriel Ceja, Luciana Tovo-Rodrigues, Marina Xavier Carpena, Julia Pasqualini Genro, Iago Junger-Santos, Hugo Naya, Nicolás Garzón Rodríguez, María Fernanda Quiroz-Padilla, Nicolas Pereira Ciochetti, Ricardo Laube, Gustavo Melo de Andrade, Mario Rodrigues Louzã, Luis Augusto Rohde, Eugenio Horacio Grevet, Diego Luiz Rovaris, On behalf of the Brazilian ADHD Research Network, RoADHD Uruguay–Brasil cooperation, the ADHD Working Group of the Latin American Genomics Consortium
Genomic studies of attention-deficit/hyperactivity disorder (ADHD) have advanced the understanding of its neurobiology but are still constrained by one of the most pronounced Eurocentric biases in psychiatric genetics. Expanding ADHD genomics to under-represented populations, particularly in Latin America, offers a unique opportunity to yield transformative discoveries by capturing the genetic diversity of admixed individuals. We call for a global, coordinated effort to prioritize diversity in ADHD research, not only to foster innovation in precision psychiatry but also to ensure that these advancements benefit all populations equitably.
{"title":"Shaping the future of ADHD genetic research through ancestral diversity","authors":"Bruna Santos da Silva, Claiton Henrique Dotto Bau, Humberto Nicolini, Maria Eduarda de Araujo Tavares, Victor Fernandes de Oliveira, Yago Carvalho Lima, Eduardo Schneider Vitola, Alma Genis-Mendoza, Isabella Folego-Temoteo, Gabriela Ariadna Martínez-Levy, Lucia Spangenberg, Gabriel Barg, Cibele Edom Bandeira, Rodrigo Sosa, Zuriel Ceja, Luciana Tovo-Rodrigues, Marina Xavier Carpena, Julia Pasqualini Genro, Iago Junger-Santos, Hugo Naya, Nicolás Garzón Rodríguez, María Fernanda Quiroz-Padilla, Nicolas Pereira Ciochetti, Ricardo Laube, Gustavo Melo de Andrade, Mario Rodrigues Louzã, Luis Augusto Rohde, Eugenio Horacio Grevet, Diego Luiz Rovaris, On behalf of the Brazilian ADHD Research Network, RoADHD Uruguay–Brasil cooperation, the ADHD Working Group of the Latin American Genomics Consortium","doi":"10.1038/s44220-025-00572-7","DOIUrl":"10.1038/s44220-025-00572-7","url":null,"abstract":"Genomic studies of attention-deficit/hyperactivity disorder (ADHD) have advanced the understanding of its neurobiology but are still constrained by one of the most pronounced Eurocentric biases in psychiatric genetics. Expanding ADHD genomics to under-represented populations, particularly in Latin America, offers a unique opportunity to yield transformative discoveries by capturing the genetic diversity of admixed individuals. We call for a global, coordinated effort to prioritize diversity in ADHD research, not only to foster innovation in precision psychiatry but also to ensure that these advancements benefit all populations equitably.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 2","pages":"186-189"},"PeriodicalIF":8.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148375","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 : 2026-01-14DOI: 10.1038/s44220-025-00566-5
We used artificial intelligence (AI) to map pan-disease dimensions — disease subtypes across an array of organ-specific disorders — from imaging data of the brain, eye and heart that captured shared and organ-specific heterogeneity. We then showed how these AI-derived dimensions can predict future risks of disease and mortality, provide insights into clinical trials, and inform potential drug targets.
{"title":"Pan-disease dimensions in the brain, eye and heart capture shared and specific heterogeneity","authors":"","doi":"10.1038/s44220-025-00566-5","DOIUrl":"10.1038/s44220-025-00566-5","url":null,"abstract":"We used artificial intelligence (AI) to map pan-disease dimensions — disease subtypes across an array of organ-specific disorders — from imaging data of the brain, eye and heart that captured shared and organ-specific heterogeneity. We then showed how these AI-derived dimensions can predict future risks of disease and mortality, provide insights into clinical trials, and inform potential drug targets.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 2","pages":"195-196"},"PeriodicalIF":8.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148346","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 : 2026-01-14DOI: 10.1038/s44220-025-00574-5
Arianna Zuanazzi, Michael P. Milham, Gregory Kiar
Data science competitions offer a collaborative, inclusive approach to tackling the complexity of brain health research. This Comment explores the challenges faced by competition organizers and how they can harness diverse expertise to address data heterogeneity, assess modeling strategies and translate findings into practice.
{"title":"How data science competitions accelerate brain health discovery","authors":"Arianna Zuanazzi, Michael P. Milham, Gregory Kiar","doi":"10.1038/s44220-025-00574-5","DOIUrl":"10.1038/s44220-025-00574-5","url":null,"abstract":"Data science competitions offer a collaborative, inclusive approach to tackling the complexity of brain health research. This Comment explores the challenges faced by competition organizers and how they can harness diverse expertise to address data heterogeneity, assess modeling strategies and translate findings into practice.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 2","pages":"190-192"},"PeriodicalIF":8.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148328","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 : 2026-01-09DOI: 10.1038/s44220-025-00584-3
Behavioral health and mental health are distinct but overlapping concepts. Behavioral health is a systems-oriented framework to address complex mental health conditions through integrated, continuous care. Although it holds promise for improving access and outcomes, its potential remains constrained by fragmented delivery systems and social inequities.
{"title":"Bolstering behavioral health","authors":"","doi":"10.1038/s44220-025-00584-3","DOIUrl":"10.1038/s44220-025-00584-3","url":null,"abstract":"Behavioral health and mental health are distinct but overlapping concepts. Behavioral health is a systems-oriented framework to address complex mental health conditions through integrated, continuous care. Although it holds promise for improving access and outcomes, its potential remains constrained by fragmented delivery systems and social inequities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"1-2"},"PeriodicalIF":8.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00584-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931240","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 : 2026-01-09DOI: 10.1038/s44220-025-00562-9
Ella Arensman, Anvar Sadath, Aileen Callanan, Almas Khan, Mallorie Leduc, Grace Cully, Niall McTernan, Katharina Schnitzspahn, Kahar Abdulla, Simge Celik, Pia Hauck, Carolina Pina, Giancarlo Giupponi, Michela Roberti, Andreas Conca, Vargiu Nuhara, Marco Lazzeri, Serena Trentin, Manuela Tosti, Aurora Belfanti, Camilla Ferrara, Victor Perez Sola, Saiko Allende, Azucena Justicia Diaz, András Székely, Diana Ruzsa, Éva Zsák, András Székely Jr, Piotr Toczyski, Chantal Van Audenhove, Evelien Coppens, Giota Fexi, Panagiota Deredini, Nikoletta Konsta, Thanasis Arabatzis, Beky Pasho, Eleni Tsagaraki, Tsvety Naydenova, Albena Drobachka, Peeter Värnik, Agnes Sirg, Merike Sisask, Lenne Lillepuu, Rainer Mere, Ulrich Hegerl
The Global Burden of Disease studies have consistently highlighted the persisting burden of mental disorders worldwide. Public health emergencies such as the COVID-19 pandemic, war and conflict, and climate change have exacerbated many determinants of poor mental health, resulting in an increased prevalence of anxiety and depression worldwide. Despite substantial advancements in intervention and prevention programs, treatment gaps in depression and suicidal behavior persist. Addressing these gaps requires a multi-level approach involving both community and health services. This Perspective addresses the urgent need to strengthen mental health systems globally. The primary purpose of this Perspective is to discuss the four-level community-based approaches of the European Alliance Against Depression program, including evidence in support of its four-level intervention as a sustainable model for community-based mental health care that can be effectively adapted to various contexts, including current and future public health emergencies. In this Perspective, the authors provide an overview of the four-level community-based intervention by the European Alliance Against Depression and highlight the need for improved public mental health care for depression and suicide risk.
{"title":"The European Alliance Against Depression approach: an evidence-based program to reduce depression and suicidal behavior","authors":"Ella Arensman, Anvar Sadath, Aileen Callanan, Almas Khan, Mallorie Leduc, Grace Cully, Niall McTernan, Katharina Schnitzspahn, Kahar Abdulla, Simge Celik, Pia Hauck, Carolina Pina, Giancarlo Giupponi, Michela Roberti, Andreas Conca, Vargiu Nuhara, Marco Lazzeri, Serena Trentin, Manuela Tosti, Aurora Belfanti, Camilla Ferrara, Victor Perez Sola, Saiko Allende, Azucena Justicia Diaz, András Székely, Diana Ruzsa, Éva Zsák, András Székely Jr, Piotr Toczyski, Chantal Van Audenhove, Evelien Coppens, Giota Fexi, Panagiota Deredini, Nikoletta Konsta, Thanasis Arabatzis, Beky Pasho, Eleni Tsagaraki, Tsvety Naydenova, Albena Drobachka, Peeter Värnik, Agnes Sirg, Merike Sisask, Lenne Lillepuu, Rainer Mere, Ulrich Hegerl","doi":"10.1038/s44220-025-00562-9","DOIUrl":"10.1038/s44220-025-00562-9","url":null,"abstract":"The Global Burden of Disease studies have consistently highlighted the persisting burden of mental disorders worldwide. Public health emergencies such as the COVID-19 pandemic, war and conflict, and climate change have exacerbated many determinants of poor mental health, resulting in an increased prevalence of anxiety and depression worldwide. Despite substantial advancements in intervention and prevention programs, treatment gaps in depression and suicidal behavior persist. Addressing these gaps requires a multi-level approach involving both community and health services. This Perspective addresses the urgent need to strengthen mental health systems globally. The primary purpose of this Perspective is to discuss the four-level community-based approaches of the European Alliance Against Depression program, including evidence in support of its four-level intervention as a sustainable model for community-based mental health care that can be effectively adapted to various contexts, including current and future public health emergencies. In this Perspective, the authors provide an overview of the four-level community-based intervention by the European Alliance Against Depression and highlight the need for improved public mental health care for depression and suicide risk.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"42-51"},"PeriodicalIF":8.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931256","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 : 2026-01-07DOI: 10.1038/s44220-025-00568-3
Robert Y. Chen, Tiffany A. Greenwood, David L. Braff, Laura C. Lazzeroni, Neal R. Swerdlow, Monica E. Calkins, Robert Freedman, Michael F. Green, Ruben C. Gur, Raquel E. Gur, Keith H. Nuechterlein, Allen D. Radant, Jeremy M. Silverman, William S. Stone, Catherine A. Sugar, Ming T. Tsuang, Bruce I. Turetsky, Gregory A. Light, Debby W. Tsuang
The development of neurocognitive biomarkers for schizophrenia (SCZ) has relied on lengthy test batteries that are infeasible to deploy in clinical settings. Using machine learning, we sought to identify a subset of neurocognitive domains that could distinguish between patients with SCZ and healthy comparison subjects (HCS). Leveraging data from 559 patients with SCZ or schizoaffective disorder and 745 HCS who completed 15 neurocognitive assessments spanning a diverse range of neurocognitive domains, we developed a machine learning model that could accurately separate SCZ from HCS (area under the receiver operating characteristic curve of 0.899), and was replicated in an independent cohort. Recursive feature elimination revealed that just two neurocognitive domains—verbal learning and emotion identification—were sufficient to achieve the same classification accuracy. These findings support a ‘less-is-more’ approach to efficient neurocognitive profiling across the schizophreniform spectrum and highlight what may be the most impaired neurocognitive domains in this debilitating disorder. This study identifies key neurocognitive domains that distinguish patients with schizophrenia from healthy individuals using machine learning. Analyzing data from 1,304 participants, it demonstrates that verbal learning and emotion identification effectively classify conditions, promoting efficient neurocognitive profiling strategies.
{"title":"Machine learning enables efficient neurocognitive profiling in patients with schizophrenia","authors":"Robert Y. Chen, Tiffany A. Greenwood, David L. Braff, Laura C. Lazzeroni, Neal R. Swerdlow, Monica E. Calkins, Robert Freedman, Michael F. Green, Ruben C. Gur, Raquel E. Gur, Keith H. Nuechterlein, Allen D. Radant, Jeremy M. Silverman, William S. Stone, Catherine A. Sugar, Ming T. Tsuang, Bruce I. Turetsky, Gregory A. Light, Debby W. Tsuang","doi":"10.1038/s44220-025-00568-3","DOIUrl":"10.1038/s44220-025-00568-3","url":null,"abstract":"The development of neurocognitive biomarkers for schizophrenia (SCZ) has relied on lengthy test batteries that are infeasible to deploy in clinical settings. Using machine learning, we sought to identify a subset of neurocognitive domains that could distinguish between patients with SCZ and healthy comparison subjects (HCS). Leveraging data from 559 patients with SCZ or schizoaffective disorder and 745 HCS who completed 15 neurocognitive assessments spanning a diverse range of neurocognitive domains, we developed a machine learning model that could accurately separate SCZ from HCS (area under the receiver operating characteristic curve of 0.899), and was replicated in an independent cohort. Recursive feature elimination revealed that just two neurocognitive domains—verbal learning and emotion identification—were sufficient to achieve the same classification accuracy. These findings support a ‘less-is-more’ approach to efficient neurocognitive profiling across the schizophreniform spectrum and highlight what may be the most impaired neurocognitive domains in this debilitating disorder. This study identifies key neurocognitive domains that distinguish patients with schizophrenia from healthy individuals using machine learning. Analyzing data from 1,304 participants, it demonstrates that verbal learning and emotion identification effectively classify conditions, promoting efficient neurocognitive profiling strategies.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"146-156"},"PeriodicalIF":8.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931257","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 : 2026-01-07DOI: 10.1038/s44220-025-00554-9
Stephen Murtough, Daisy Mills, Noushin Saadullah Khani, Marius Cotic, Lauren Varney, Alvin Richards-Belle, Rosemary Abidoph, Nicholas Bass, Dharmisha Chauhan, Sarah Curran, Yogita Dawda, Jana de Villiers, Frances Elmslie, Robert J. Howard, Sophie E. Legge, Alexander Martin, Andrew McQuillin, Daniele Panconesi, Antonio F. Pardiñas, Suzanne Reeves, Maria Richards-Brown, Jane Sarginson, Anna Skowronska, Oriella Stellakis, James TR Walters, Jessica Woodley, Beverley Chipp, Shreyans Gandhi, Sara Stuart-Smith, Dyfrig A. Hughes, Munir Pirmohamed, Huajie Jin, Olubanké Dzahini, Elvira Bramon
Clozapine is the most effective therapy for treatment-resistant schizophrenia, although it can cause neutropenia. In many countries, neutrophil count monitoring is mandatory for people taking clozapine, who must remain above a minimum threshold to start and continue treatment. Some people have low neutrophil counts without increased infection risk, caused by a homozygous variant in ACKR1 and termed ACKR1/DARC-associated neutropenia (ADAN). When ADAN is confirmed, reduced neutrophil count thresholds are applied to allow people to start and continue clozapine. However, ADAN diagnoses are often missed, resulting in reduced access to clozapine and unnecessary discontinuation. We review the evidence for ACKR1 genetic testing to rapidly identify ADAN in people taking clozapine. With multidisciplinary input, we recommend internationally relevant test eligibility criteria, comprising pre-emptive and reactive testing strategies, and we conduct a health economic analysis, estimating total cost savings between £42,732 and £727,990 for the UK healthcare system during the first year of testing. Finally, we propose how to integrate these criteria into clinical practice to enable equitable access to clozapine. This Perspective considers the addition of ACKR1 genetic testing for identifying ACKR1/DARC-associated neutropenia in patients receiving clozapine, recommending eligibility criteria and testing strategies while estimating substantial cost savings for the UK healthcare system and enhancing equitable treatment access.
{"title":"ACKR1 genetic testing should be offered before starting clozapine treatment","authors":"Stephen Murtough, Daisy Mills, Noushin Saadullah Khani, Marius Cotic, Lauren Varney, Alvin Richards-Belle, Rosemary Abidoph, Nicholas Bass, Dharmisha Chauhan, Sarah Curran, Yogita Dawda, Jana de Villiers, Frances Elmslie, Robert J. Howard, Sophie E. Legge, Alexander Martin, Andrew McQuillin, Daniele Panconesi, Antonio F. Pardiñas, Suzanne Reeves, Maria Richards-Brown, Jane Sarginson, Anna Skowronska, Oriella Stellakis, James TR Walters, Jessica Woodley, Beverley Chipp, Shreyans Gandhi, Sara Stuart-Smith, Dyfrig A. Hughes, Munir Pirmohamed, Huajie Jin, Olubanké Dzahini, Elvira Bramon","doi":"10.1038/s44220-025-00554-9","DOIUrl":"10.1038/s44220-025-00554-9","url":null,"abstract":"Clozapine is the most effective therapy for treatment-resistant schizophrenia, although it can cause neutropenia. In many countries, neutrophil count monitoring is mandatory for people taking clozapine, who must remain above a minimum threshold to start and continue treatment. Some people have low neutrophil counts without increased infection risk, caused by a homozygous variant in ACKR1 and termed ACKR1/DARC-associated neutropenia (ADAN). When ADAN is confirmed, reduced neutrophil count thresholds are applied to allow people to start and continue clozapine. However, ADAN diagnoses are often missed, resulting in reduced access to clozapine and unnecessary discontinuation. We review the evidence for ACKR1 genetic testing to rapidly identify ADAN in people taking clozapine. With multidisciplinary input, we recommend internationally relevant test eligibility criteria, comprising pre-emptive and reactive testing strategies, and we conduct a health economic analysis, estimating total cost savings between £42,732 and £727,990 for the UK healthcare system during the first year of testing. Finally, we propose how to integrate these criteria into clinical practice to enable equitable access to clozapine. This Perspective considers the addition of ACKR1 genetic testing for identifying ACKR1/DARC-associated neutropenia in patients receiving clozapine, recommending eligibility criteria and testing strategies while estimating substantial cost savings for the UK healthcare system and enhancing equitable treatment access.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"30-41"},"PeriodicalIF":8.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931254","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 : 2026-01-06DOI: 10.1038/s44220-025-00569-2
Maximilian Oscar Steininger, Jonas Paul Nitschke, Mathew Philip White, Claus Lamm
Pain is a global health issue with substantial individual, societal and economic impacts. Given the risks of pharmacological treatments, complementary approaches to pain management are essential. Nature exposure has emerged as a promising nonpharmacological strategy, but evidence of its effectiveness is inconclusive. Here in this systematic review and meta-analysis we examined 62 studies (96 effects) across 21 countries, including 4,439 participants, to assess the impact of nature exposure on self-reported pain. The results indicate a significant small-to-moderate reduction in pain associated with nature exposure (standardized mean difference of 0.53), but studies exhibited moderate-to-high risk of bias and substantial heterogeneity. Studies evaluating nature against matched comparators reported effects roughly half the size of those using nonmatched controls and multisensory stimuli tended to show stronger effects. These findings support nature as a promising complementary pain management strategy. However, high heterogeneity and risk of bias warrant caution and highlight the need for more rigorous research. The authors conducted a systematic review and meta-analysis of 62 studies, including more than 4,400 participants across 21 countries, to investigate the effects of nature exposure on self-reported pain.
{"title":"Nature exposure reduces self-reported pain: a systematic review and meta-analysis","authors":"Maximilian Oscar Steininger, Jonas Paul Nitschke, Mathew Philip White, Claus Lamm","doi":"10.1038/s44220-025-00569-2","DOIUrl":"10.1038/s44220-025-00569-2","url":null,"abstract":"Pain is a global health issue with substantial individual, societal and economic impacts. Given the risks of pharmacological treatments, complementary approaches to pain management are essential. Nature exposure has emerged as a promising nonpharmacological strategy, but evidence of its effectiveness is inconclusive. Here in this systematic review and meta-analysis we examined 62 studies (96 effects) across 21 countries, including 4,439 participants, to assess the impact of nature exposure on self-reported pain. The results indicate a significant small-to-moderate reduction in pain associated with nature exposure (standardized mean difference of 0.53), but studies exhibited moderate-to-high risk of bias and substantial heterogeneity. Studies evaluating nature against matched comparators reported effects roughly half the size of those using nonmatched controls and multisensory stimuli tended to show stronger effects. These findings support nature as a promising complementary pain management strategy. However, high heterogeneity and risk of bias warrant caution and highlight the need for more rigorous research. The authors conducted a systematic review and meta-analysis of 62 studies, including more than 4,400 participants across 21 countries, to investigate the effects of nature exposure on self-reported pain.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"165-180"},"PeriodicalIF":8.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00569-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931210","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：