Pub Date : 2026-01-05DOI: 10.1038/s44220-025-00561-w
Adam Benzekri, Marco Thimm-Kaiser, Francis Kwadwo Amankwah, Vincent Guilamo-Ramos
A behavioral healthcare workforce — concordant in race, ethnicity, lived experience, language, and geography with the populations it serves — is urgently needed to end the US behavioral health crisis.
{"title":"The need for a representative workforce to address the US behavioral health crisis","authors":"Adam Benzekri, Marco Thimm-Kaiser, Francis Kwadwo Amankwah, Vincent Guilamo-Ramos","doi":"10.1038/s44220-025-00561-w","DOIUrl":"10.1038/s44220-025-00561-w","url":null,"abstract":"A behavioral healthcare workforce — concordant in race, ethnicity, lived experience, language, and geography with the populations it serves — is urgently needed to end the US behavioral health crisis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"6-8"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931213","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 : 2026-01-05DOI: 10.1038/s44220-025-00564-7
Stephen V. Faraone, Jeffrey H. Newcorn
Stimulant medications are the first-line treatment for ADHD, with non-stimulants often used if stimulants are ineffective. Here, by reinterpreting randomized controlled trials, addressing heterogeneity of treatment effects, and considering societal impact, we argue for equal consideration of stimulant and non-stimulants as first-line treatment options.
{"title":"Rethinking the role of non-stimulants in ADHD treatment","authors":"Stephen V. Faraone, Jeffrey H. Newcorn","doi":"10.1038/s44220-025-00564-7","DOIUrl":"10.1038/s44220-025-00564-7","url":null,"abstract":"Stimulant medications are the first-line treatment for ADHD, with non-stimulants often used if stimulants are ineffective. Here, by reinterpreting randomized controlled trials, addressing heterogeneity of treatment effects, and considering societal impact, we argue for equal consideration of stimulant and non-stimulants as first-line treatment options.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"9-12"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931250","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 : 2026-01-05DOI: 10.1038/s44220-025-00555-8
Yunfei Luo, Iman Deznabi, Bhanu Teja Gullapalli, Mark Tuomenoksa, Madalina Brostean Fiterau, Eric L. Garland, Tauhidur Rahman
Fluctuations in pain, stress and craving are thought to contribute to opioid misuse. Developing accurate prediction models is vital for intervention and prevention efforts. In this work, we leverage physiological data and semantic analysis of electronic health records to tackle the challenge of detecting opioid misuse. Utilizing personalized hierarchical deep-learning models, we analyze trajectories of predicted pain, stress and craving states with 10,140 hours of heart-rate data collected by wearables from patients on long-term opioid therapy. From these trajectories, we extract entropy features from nonlinear dynamical analysis and develop a novel relevance-based temporal fusion model of opioid misuse risk. We incorporate clinical data into a large language model to enhance opioid misuse risk detection. We then fuse these modalities to achieve an accurate opioid misuse risk assessment with area under the precision-recall curve of 0.94 ± 0.05. This study marks a substantial advancement in personalized prediction of addictive behavior by elucidating the entropic nature of underlying affective state dynamics. This study addresses opioid misuse prediction by integrating physiological data and electronic health records. Utilizing personalized deep-learning models, it achieves a high accuracy in risk assessment through entropy feature extraction and relevance-based temporal fusion, demonstrating effective intervention potential.
{"title":"Personalized entropy-informed deep learning for identifying opioid misuse","authors":"Yunfei Luo, Iman Deznabi, Bhanu Teja Gullapalli, Mark Tuomenoksa, Madalina Brostean Fiterau, Eric L. Garland, Tauhidur Rahman","doi":"10.1038/s44220-025-00555-8","DOIUrl":"10.1038/s44220-025-00555-8","url":null,"abstract":"Fluctuations in pain, stress and craving are thought to contribute to opioid misuse. Developing accurate prediction models is vital for intervention and prevention efforts. In this work, we leverage physiological data and semantic analysis of electronic health records to tackle the challenge of detecting opioid misuse. Utilizing personalized hierarchical deep-learning models, we analyze trajectories of predicted pain, stress and craving states with 10,140 hours of heart-rate data collected by wearables from patients on long-term opioid therapy. From these trajectories, we extract entropy features from nonlinear dynamical analysis and develop a novel relevance-based temporal fusion model of opioid misuse risk. We incorporate clinical data into a large language model to enhance opioid misuse risk detection. We then fuse these modalities to achieve an accurate opioid misuse risk assessment with area under the precision-recall curve of 0.94 ± 0.05. This study marks a substantial advancement in personalized prediction of addictive behavior by elucidating the entropic nature of underlying affective state dynamics. This study addresses opioid misuse prediction by integrating physiological data and electronic health records. Utilizing personalized deep-learning models, it achieves a high accuracy in risk assessment through entropy feature extraction and relevance-based temporal fusion, demonstrating effective intervention potential.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"112-124"},"PeriodicalIF":8.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931211","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 : 2026-01-02DOI: 10.1038/s44220-025-00558-5
Isabella Goodwin, Kelly M. J. Diederen, Emily J. Hird, Veith Weilnhammer, Marta I. Garrido, Franziska Knolle
Predictive processing has revolutionized cognitive neuroscience, offering a comprehensive computational framework for understanding normative behavior and psychiatric illness. This narrative Review evaluates the role of predictive processing in understanding psychosis, revisiting the seminal work of Sterzer and colleagues. It consolidates recent experimental evidence on the alteration of priors and sensory likelihoods across different stages of psychosis in an attempt to reconcile top-down (that is, overly precise priors/noisy sensations) and bottom-up (that is, noisy priors/overly precise sensations) accounts. It evaluates predictive processing alterations across the continuum of psychosis, from non-clinical psychotic experiences to high-risk and first-episode psychosis to schizophrenia, exploring the explanatory potential of predictive processing as a transdiagnostic framework. We discuss the translational potential of predictive processing, including its use as a biomarker and in therapeutic interventions. We emphasize the need for standardized paradigms and longitudinal studies to advance predictive processing theories in clinical practice. By offering a unified theoretical perspective, this Review aims to inspire further research into the neuro-computational mechanisms underlying psychosis and enhance our understanding of psychiatric disorders. In this Review the authors integrate the latest evidence on predictive processing alterations across the continuum of psychosis and discuss its potential applications as a biomarker and in therapeutic interventions.
{"title":"Predictive processing accounts of psychosis: bottom-up or top-down disruptions","authors":"Isabella Goodwin, Kelly M. J. Diederen, Emily J. Hird, Veith Weilnhammer, Marta I. Garrido, Franziska Knolle","doi":"10.1038/s44220-025-00558-5","DOIUrl":"10.1038/s44220-025-00558-5","url":null,"abstract":"Predictive processing has revolutionized cognitive neuroscience, offering a comprehensive computational framework for understanding normative behavior and psychiatric illness. This narrative Review evaluates the role of predictive processing in understanding psychosis, revisiting the seminal work of Sterzer and colleagues. It consolidates recent experimental evidence on the alteration of priors and sensory likelihoods across different stages of psychosis in an attempt to reconcile top-down (that is, overly precise priors/noisy sensations) and bottom-up (that is, noisy priors/overly precise sensations) accounts. It evaluates predictive processing alterations across the continuum of psychosis, from non-clinical psychotic experiences to high-risk and first-episode psychosis to schizophrenia, exploring the explanatory potential of predictive processing as a transdiagnostic framework. We discuss the translational potential of predictive processing, including its use as a biomarker and in therapeutic interventions. We emphasize the need for standardized paradigms and longitudinal studies to advance predictive processing theories in clinical practice. By offering a unified theoretical perspective, this Review aims to inspire further research into the neuro-computational mechanisms underlying psychosis and enhance our understanding of psychiatric disorders. In this Review the authors integrate the latest evidence on predictive processing alterations across the continuum of psychosis and discuss its potential applications as a biomarker and in therapeutic interventions.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"60-84"},"PeriodicalIF":8.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931251","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 : 2026-01-02DOI: 10.1038/s44220-025-00563-8
Yinxian Chen, Qingyue Yuan, Lina Dimitrov, Benjamin Risk, Benson Ku, Anke Hüls
The genetic risk of persistent distressing psychotic-like experiences (PLE) in the multiancestral population is underinvestigated. The gene–neighborhood environment interaction in persistent distressing PLE is also unknown. This study included 6,449 participants from the Adolescent Brain and Cognitive Development Study. The genetic risk was measured by a multiancestral schizophrenia polygenic risk score (SCZ-PRS). The multidimensional neighborhood-level exposures were used to form the neighborhood exposome (NE). SCZ-PRS was not statistically significantly associated with odds of persistent distressing PLE (odds ratio (OR) of 1.04, 95% confidence intervals (CI) 0.97 to 1.13, P = 0.280), whereas the NE score was (OR of 1.15, 95% CI 1.05 to 1.26, P = 0.003). A significant negative multiplicative interaction between SCZ-PRS and NE was found (estimate of −0.08, 95% CI −0.15 to −0.00, P = 0.039). The additive interaction followed the same direction but was statistically insignificant (estimate of −0.06, 95% CI −0.15 to 0.03, P = 0.189). Persistent distressing PLE in children may be driven by detrimental neighborhood exposures in multiancestral populations, particularly among those with low genetic risk. Here the findings provide important evidence on persistent distressing PLE etiology attributed to genetic and environmental risks and identify potential susceptible populations for targeted interventions. Chen et al. examined how genetic risk interacts with neighborhood environmental exposures to influence psychotic-like experiences in children from the ABCD cohort study.
{"title":"Interaction between neighborhood exposome and genetic risk in persistent distressing psychotic-like experiences in children","authors":"Yinxian Chen, Qingyue Yuan, Lina Dimitrov, Benjamin Risk, Benson Ku, Anke Hüls","doi":"10.1038/s44220-025-00563-8","DOIUrl":"10.1038/s44220-025-00563-8","url":null,"abstract":"The genetic risk of persistent distressing psychotic-like experiences (PLE) in the multiancestral population is underinvestigated. The gene–neighborhood environment interaction in persistent distressing PLE is also unknown. This study included 6,449 participants from the Adolescent Brain and Cognitive Development Study. The genetic risk was measured by a multiancestral schizophrenia polygenic risk score (SCZ-PRS). The multidimensional neighborhood-level exposures were used to form the neighborhood exposome (NE). SCZ-PRS was not statistically significantly associated with odds of persistent distressing PLE (odds ratio (OR) of 1.04, 95% confidence intervals (CI) 0.97 to 1.13, P = 0.280), whereas the NE score was (OR of 1.15, 95% CI 1.05 to 1.26, P = 0.003). A significant negative multiplicative interaction between SCZ-PRS and NE was found (estimate of −0.08, 95% CI −0.15 to −0.00, P = 0.039). The additive interaction followed the same direction but was statistically insignificant (estimate of −0.06, 95% CI −0.15 to 0.03, P = 0.189). Persistent distressing PLE in children may be driven by detrimental neighborhood exposures in multiancestral populations, particularly among those with low genetic risk. Here the findings provide important evidence on persistent distressing PLE etiology attributed to genetic and environmental risks and identify potential susceptible populations for targeted interventions. Chen et al. examined how genetic risk interacts with neighborhood environmental exposures to influence psychotic-like experiences in children from the ABCD cohort study.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"136-145"},"PeriodicalIF":8.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931246","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 : 2025-12-31DOI: 10.1038/s44220-025-00559-4
Claire Houtsma, Chris J. Kennedy, Howard Liu, Emily R. Edwards, Nancy A. Sampson, Joe C. Geraci, Brian P. Marx, Matthew K. Nock, James Wagner, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler
US veterans are significantly more likely than civilians to die by suicide. Machine-learning models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most veteran suicides, and firearm-specific preventions exist. We used data from US Army veterans from 2010 to 2019 (N = 800,579) to develop and compare firearm-specific machine-learning models with a method-agnostic model to predict firearm suicides among transitioning Army veterans up to 10 years after discharge. The models performed comparably overall (area under the receiver operating characteristic curve = 0.710–0.708; integrated calibration index = 0.0003–0.0005% for firearm-specific and method-agnostic models, respectively), with the best model depending on the intervention threshold. Results from this study show the method-agnostic model was better at predicting firearm suicides at the highest intervention threshold, whereas the firearm-specific model was better at lower thresholds. When considering fairness with respect to sex and race/ethnicity, the firearm-specific model was best across all thresholds. Thus, model choice depends on weighing numerous factors, and optimal thresholds might differ for coordinated firearm-specific and method-agnostic interventions. This research developed and compared firearm-specific and method-agnostic machine-learning models using data from 800,579 Army veterans, revealing that model choice and intervention thresholds impact predictive accuracy and fairness, guiding tailored suicide prevention efforts.
{"title":"Predicting firearm suicide among US Army veterans transitioning from active service","authors":"Claire Houtsma, Chris J. Kennedy, Howard Liu, Emily R. Edwards, Nancy A. Sampson, Joe C. Geraci, Brian P. Marx, Matthew K. Nock, James Wagner, Murray B. Stein, Robert J. Ursano, Ronald C. Kessler","doi":"10.1038/s44220-025-00559-4","DOIUrl":"10.1038/s44220-025-00559-4","url":null,"abstract":"US veterans are significantly more likely than civilians to die by suicide. Machine-learning models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most veteran suicides, and firearm-specific preventions exist. We used data from US Army veterans from 2010 to 2019 (N = 800,579) to develop and compare firearm-specific machine-learning models with a method-agnostic model to predict firearm suicides among transitioning Army veterans up to 10 years after discharge. The models performed comparably overall (area under the receiver operating characteristic curve = 0.710–0.708; integrated calibration index = 0.0003–0.0005% for firearm-specific and method-agnostic models, respectively), with the best model depending on the intervention threshold. Results from this study show the method-agnostic model was better at predicting firearm suicides at the highest intervention threshold, whereas the firearm-specific model was better at lower thresholds. When considering fairness with respect to sex and race/ethnicity, the firearm-specific model was best across all thresholds. Thus, model choice depends on weighing numerous factors, and optimal thresholds might differ for coordinated firearm-specific and method-agnostic interventions. This research developed and compared firearm-specific and method-agnostic machine-learning models using data from 800,579 Army veterans, revealing that model choice and intervention thresholds impact predictive accuracy and fairness, guiding tailored suicide prevention efforts.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"125-135"},"PeriodicalIF":8.7,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931212","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 : 2025-12-22DOI: 10.1038/s44220-025-00552-x
Evidence from national medical records of over 8 million people in the Netherlands shows that autism is associated with increased risk of cardiometabolic conditions. These associations emerged in adolescents and young adults, suggesting earlier onset of such conditions in individuals with autism than in individuals without it.
{"title":"People with autism are at increased risk of cardiometabolic conditions","authors":"","doi":"10.1038/s44220-025-00552-x","DOIUrl":"10.1038/s44220-025-00552-x","url":null,"abstract":"Evidence from national medical records of over 8 million people in the Netherlands shows that autism is associated with increased risk of cardiometabolic conditions. These associations emerged in adolescents and young adults, suggesting earlier onset of such conditions in individuals with autism than in individuals without it.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"13-14"},"PeriodicalIF":8.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931249","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 : 2025-12-22DOI: 10.1038/s44220-025-00553-w
Leah S. Richmond-Rakerd, Barry J. Milne, Renate M. Houts, Gabrielle Davie, Stephanie D’Souza, Sidra Goldman-Mellor, Lara Khalifeh, Avshalom Caspi, Terrie E. Moffitt, Fartein Ask Torvik
Mental health conditions are associated with an increased risk of chronic physical diseases, but their implications for other physical health outcomes, including injuries, are less established. In this prospective cohort study, we tested whether mental health conditions antedate unintentional as well as self-harm and assault injuries, using administrative data from Norway (N = 2,753,646) and New Zealand (N = 2,238,813). In Norway, after accounting for pre-existing injuries, individuals with a primary care encounter for a mental health condition had an elevated risk of subsequent primary care-recorded injury. In New Zealand, as expected, individuals with a mental health-related inpatient hospital admission had an elevated risk of subsequent inpatient hospital-recorded self-harm injury, as well as assault injury. However, they also had an elevated risk of unintentional injuries. Associations extended to injury insurance claims. Associations were evident across mental health conditions, sex, age and after accounting for indicators of socioeconomic status. Risk was particularly increased for brain and head injuries. Patients with mental health conditions are an important group for injury prevention. In this two-nation administrative register study (~5 million individuals), mental health conditions were linked to subsequent unintentional, self-harm and assault injuries. These results highlight the need for targeted injury prevention strategies.
{"title":"Mental health conditions are associated with increased risk of subsequent self-harm, assault and unintentional injuries in two nations","authors":"Leah S. Richmond-Rakerd, Barry J. Milne, Renate M. Houts, Gabrielle Davie, Stephanie D’Souza, Sidra Goldman-Mellor, Lara Khalifeh, Avshalom Caspi, Terrie E. Moffitt, Fartein Ask Torvik","doi":"10.1038/s44220-025-00553-w","DOIUrl":"10.1038/s44220-025-00553-w","url":null,"abstract":"Mental health conditions are associated with an increased risk of chronic physical diseases, but their implications for other physical health outcomes, including injuries, are less established. In this prospective cohort study, we tested whether mental health conditions antedate unintentional as well as self-harm and assault injuries, using administrative data from Norway (N = 2,753,646) and New Zealand (N = 2,238,813). In Norway, after accounting for pre-existing injuries, individuals with a primary care encounter for a mental health condition had an elevated risk of subsequent primary care-recorded injury. In New Zealand, as expected, individuals with a mental health-related inpatient hospital admission had an elevated risk of subsequent inpatient hospital-recorded self-harm injury, as well as assault injury. However, they also had an elevated risk of unintentional injuries. Associations extended to injury insurance claims. Associations were evident across mental health conditions, sex, age and after accounting for indicators of socioeconomic status. Risk was particularly increased for brain and head injuries. Patients with mental health conditions are an important group for injury prevention. In this two-nation administrative register study (~5 million individuals), mental health conditions were linked to subsequent unintentional, self-harm and assault injuries. These results highlight the need for targeted injury prevention strategies.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"102-111"},"PeriodicalIF":8.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00553-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931248","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 : 2025-12-15DOI: 10.1038/s44220-025-00546-9
Yiran Li, Tian Xie, Lin Li, Jing Lin, Melissa Vos, Zheng Chang, Harold Snieder, Catharina A. Hartman
Autism has been associated with cardiometabolic conditions mainly in cross-sectional studies of children, but evidence in adults remains limited. Here we conducted the largest cohort study, using Dutch register data of 8,690,286 individuals aged 12–65 years. These individuals were followed up from 1 January 2014 to their first incidence of cardiometabolic conditions, emigration, death or 31 December 2020. Cox proportional hazards models indicated that autism was associated with higher risks of cardiometabolic conditions (hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.18–1.23), specifically hypertension (HR 1.16, CI 1.14–1.19), dyslipidemia (HR 1.17, CI 1.12–1.23), diabetes (HR 1.22, CI 1.14–1.30), stroke (HR 1.23, CI 1.14–1.34) and heart failure (HR 1.28, CI 1.07–1.53). Sex-stratified findings were similar. Associations were observed in adolescent, young and middle-aged but not older individuals (41–65 years), indicating earlier onset in individuals with autism compared with those without. Our results underscore the need for monitoring and treatment of cardiometabolic conditions among individuals with autism. In this study analyzing data from 8,690,286 individuals in the Netherlands, autism significantly increased the risks for various cardiometabolic conditions. Cox proportional hazards models demonstrated heightened hazard ratios, emphasizing the importance of monitoring these health issues in people with autism.
自闭症主要在儿童的横断面研究中与心脏代谢状况有关，但在成人中的证据仍然有限。在这里，我们进行了最大的队列研究，使用荷兰登记的8,690,286名年龄在12-65岁之间的人的数据。从2014年1月1日起对这些人进行随访，直到他们首次出现心脏代谢疾病、移民、死亡或2020年12月31日。Cox比例风险模型显示，自闭症与心脏代谢疾病的高风险相关（风险比（HR） 1.20, 95%可信区间（CI） 1.18-1.23），特别是高血压（HR 1.16, CI 1.14-1.19）、血脂异常（HR 1.17, CI 1.12-1.23）、糖尿病（HR 1.22, CI 1.14-1.30）、中风（HR 1.23, CI 1.14-1.34）和心力衰竭（HR 1.28, CI 1.07-1.53）。性别分层的结果相似。在青少年、年轻人和中年人中观察到这种关联，但在老年人（41-65岁）中没有，这表明自闭症患者比非自闭症患者发病更早。我们的结果强调了监测和治疗自闭症患者心脏代谢状况的必要性。这项研究分析了荷兰8,690,286人的数据，自闭症显著增加了各种心脏代谢疾病的风险。Cox比例风险模型显示了更高的风险比，强调了在自闭症患者中监测这些健康问题的重要性。
{"title":"Cardiometabolic conditions in people with autism: a nationwide prospective cohort study from the Netherlands","authors":"Yiran Li, Tian Xie, Lin Li, Jing Lin, Melissa Vos, Zheng Chang, Harold Snieder, Catharina A. Hartman","doi":"10.1038/s44220-025-00546-9","DOIUrl":"10.1038/s44220-025-00546-9","url":null,"abstract":"Autism has been associated with cardiometabolic conditions mainly in cross-sectional studies of children, but evidence in adults remains limited. Here we conducted the largest cohort study, using Dutch register data of 8,690,286 individuals aged 12–65 years. These individuals were followed up from 1 January 2014 to their first incidence of cardiometabolic conditions, emigration, death or 31 December 2020. Cox proportional hazards models indicated that autism was associated with higher risks of cardiometabolic conditions (hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.18–1.23), specifically hypertension (HR 1.16, CI 1.14–1.19), dyslipidemia (HR 1.17, CI 1.12–1.23), diabetes (HR 1.22, CI 1.14–1.30), stroke (HR 1.23, CI 1.14–1.34) and heart failure (HR 1.28, CI 1.07–1.53). Sex-stratified findings were similar. Associations were observed in adolescent, young and middle-aged but not older individuals (41–65 years), indicating earlier onset in individuals with autism compared with those without. Our results underscore the need for monitoring and treatment of cardiometabolic conditions among individuals with autism. In this study analyzing data from 8,690,286 individuals in the Netherlands, autism significantly increased the risks for various cardiometabolic conditions. Cox proportional hazards models demonstrated heightened hazard ratios, emphasizing the importance of monitoring these health issues in people with autism.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"157-164"},"PeriodicalIF":8.7,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931244","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 : 2025-12-12DOI: 10.1038/s44220-025-00541-0
Xiaoyu Tong, Kanhao Zhao, Gregory A. Fonzo, Hua Xie, Nancy B. Carlisle, Corey J. Keller, Desmond J. Oathes, Yvette Sheline, Charles B. Nemeroff, Madhukar Trivedi, Amit Etkin, Yu Zhang
Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure–function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R2 = 0.31; placebo, R2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker’s generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure–function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure–function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094. Using a machine learning framework to predict antidepressant response in major depressive disorder by analyzing structural and functional connectivity, this study reveals distinct predictive patterns and highlights specific brain regions associated with treatment efficacy, advancing personalized therapeutic approaches.
{"title":"Generalizable structure–function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion","authors":"Xiaoyu Tong, Kanhao Zhao, Gregory A. Fonzo, Hua Xie, Nancy B. Carlisle, Corey J. Keller, Desmond J. Oathes, Yvette Sheline, Charles B. Nemeroff, Madhukar Trivedi, Amit Etkin, Yu Zhang","doi":"10.1038/s44220-025-00541-0","DOIUrl":"10.1038/s44220-025-00541-0","url":null,"abstract":"Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure–function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R2 = 0.31; placebo, R2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker’s generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure–function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure–function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094. Using a machine learning framework to predict antidepressant response in major depressive disorder by analyzing structural and functional connectivity, this study reveals distinct predictive patterns and highlights specific brain regions associated with treatment efficacy, advancing personalized therapeutic approaches.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"4 1","pages":"85-101"},"PeriodicalIF":8.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145931255","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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