Pub Date : 2026-01-28DOI: 10.1001/jamapsychiatry.2025.4372
Alicia Walker,Brittany L Mitchell,Tian Lin,Jacob J Crouse,Clara Albiñana,Chloe X Yap,Mary Ellen Lynall,Penelope A Lind,Andrea Cipriani,Enda M Byrne,Sarah E Medland,Nicholas G Martin,Maxime Taquet,Ian B Hickie,Naomi R Wray
ImportanceAntidepressant treatment remains a trial-and-error process: one-third of people with major depressive disorder (MDD) report inefficacy of first-line medications. Predictors of prescription patterns are needed to improve prescribing precision.ObjectiveTo investigate phenotypic and genetic heterogeneity of MDD subgroups defined by antidepressant prescription patterns.Design, Setting, and ParticipantsThis was a retrospective cohort study of Australian Genetics of Depression Study (2017-2018) adult participants (aged ≥18 years) with lifetime MDD who filled 1 or more prescriptions of the 10 most commonly used antidepressants across 4.5 years (2013-2017). Data were analyzed from August 2024 to October 2025.ExposuresTreatment complexity was assessed as number of different antidepressant classes in prescriptions filled in 4.5 years. Sustained-use 360 groups were defined as 360 or more cumulative days (in 4.5 years) of a single antidepressant. Participants with genome-wide genotypes were contrasted across mutually exclusive sustained-use 360 groups.Main Outcomes and MeasuresAssociations of 44 self-reported phenotypes and polygenic scores (PGSs) for 15 traits with sustained-use 360 subgroups. Genome-wide association studies (GWASs) were conducted for selective serotonin reuptake inhibitor (SSRI) or SSRI/serotonin-norepinephrine reuptake inhibitor sustained use contrasted to other participants.ResultsOf 12 074 participants (9041 [75%] female with a mean [SD] age of 41.8 [14.6] years; 3022 [25%] male with a mean [SD] age of 47.7 [14.6] years) with 1 or more prescriptions and lifetime MDD, 8898 had genotyping data. High treatment complexity was significantly associated with 37 of 44 self-reported phenotypes (eg, higher rates of smoking, recurrent MDD, suicidal ideation, chronic pain, and circadian and atypical depression subtypes) and higher PGSs for psychiatric traits (MDD PGS: β, 0.04; 95% CI, 0.03-0.06; P = 1.2 × 10-8; ADHD PGS: β, 0.03; 95% CI, 0.02-0.05; P = 2.1 × 10-5 ; bipolar disorder PGS: β, 0.03; 95% CI, 0.01-0.04. P = 1.2 × 10-4 ; neuroticism PGS: β, 0.02; 95% CI, 0.01-0.04; P = 1.3 × 10-3). A total of 5453 (61%) met criteria for an exclusive antidepressant sustained-use 360 group. These groups had distinct phenotypic profiles, including associations with body mass index, suicidal ideation, and co-occurring conditions. GWASs identified novel loci, including an immune-related gene SLAMF3/LY9, for which single-nucleotide variant rs4656934 was associated with reduced odds of sustained SSRI use (G allele; odds ratio, 0.81; 95% CI, 0.75-0.87; P = 3.5 × 10-8).Conclusions and RelevanceThis study found that phenotypic factors were associated with sustained antidepressant use and treatment complexity. PGSs for traits studied were associated with treatment complexity but showed little association with sustained-use 360 groups. These findings support further research to guide treatment selection and to identify patients at risk of difficult-to-treat
{"title":"Genetic and Phenotypic Associations With Sustained Antidepressant Use in Major Depressive Disorder.","authors":"Alicia Walker,Brittany L Mitchell,Tian Lin,Jacob J Crouse,Clara Albiñana,Chloe X Yap,Mary Ellen Lynall,Penelope A Lind,Andrea Cipriani,Enda M Byrne,Sarah E Medland,Nicholas G Martin,Maxime Taquet,Ian B Hickie,Naomi R Wray","doi":"10.1001/jamapsychiatry.2025.4372","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4372","url":null,"abstract":"ImportanceAntidepressant treatment remains a trial-and-error process: one-third of people with major depressive disorder (MDD) report inefficacy of first-line medications. Predictors of prescription patterns are needed to improve prescribing precision.ObjectiveTo investigate phenotypic and genetic heterogeneity of MDD subgroups defined by antidepressant prescription patterns.Design, Setting, and ParticipantsThis was a retrospective cohort study of Australian Genetics of Depression Study (2017-2018) adult participants (aged ≥18 years) with lifetime MDD who filled 1 or more prescriptions of the 10 most commonly used antidepressants across 4.5 years (2013-2017). Data were analyzed from August 2024 to October 2025.ExposuresTreatment complexity was assessed as number of different antidepressant classes in prescriptions filled in 4.5 years. Sustained-use 360 groups were defined as 360 or more cumulative days (in 4.5 years) of a single antidepressant. Participants with genome-wide genotypes were contrasted across mutually exclusive sustained-use 360 groups.Main Outcomes and MeasuresAssociations of 44 self-reported phenotypes and polygenic scores (PGSs) for 15 traits with sustained-use 360 subgroups. Genome-wide association studies (GWASs) were conducted for selective serotonin reuptake inhibitor (SSRI) or SSRI/serotonin-norepinephrine reuptake inhibitor sustained use contrasted to other participants.ResultsOf 12 074 participants (9041 [75%] female with a mean [SD] age of 41.8 [14.6] years; 3022 [25%] male with a mean [SD] age of 47.7 [14.6] years) with 1 or more prescriptions and lifetime MDD, 8898 had genotyping data. High treatment complexity was significantly associated with 37 of 44 self-reported phenotypes (eg, higher rates of smoking, recurrent MDD, suicidal ideation, chronic pain, and circadian and atypical depression subtypes) and higher PGSs for psychiatric traits (MDD PGS: β, 0.04; 95% CI, 0.03-0.06; P = 1.2 × 10-8; ADHD PGS: β, 0.03; 95% CI, 0.02-0.05; P = 2.1 × 10-5 ; bipolar disorder PGS: β, 0.03; 95% CI, 0.01-0.04. P = 1.2 × 10-4 ; neuroticism PGS: β, 0.02; 95% CI, 0.01-0.04; P = 1.3 × 10-3). A total of 5453 (61%) met criteria for an exclusive antidepressant sustained-use 360 group. These groups had distinct phenotypic profiles, including associations with body mass index, suicidal ideation, and co-occurring conditions. GWASs identified novel loci, including an immune-related gene SLAMF3/LY9, for which single-nucleotide variant rs4656934 was associated with reduced odds of sustained SSRI use (G allele; odds ratio, 0.81; 95% CI, 0.75-0.87; P = 3.5 × 10-8).Conclusions and RelevanceThis study found that phenotypic factors were associated with sustained antidepressant use and treatment complexity. PGSs for traits studied were associated with treatment complexity but showed little association with sustained-use 360 groups. These findings support further research to guide treatment selection and to identify patients at risk of difficult-to-treat","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"87 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1001/jamapsychiatry.2025.4447
Mark Abie Horowitz, James O'Neill, David Taylor
{"title":"Interpretation of the HAMLETT Study.","authors":"Mark Abie Horowitz, James O'Neill, David Taylor","doi":"10.1001/jamapsychiatry.2025.4447","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4447","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":17.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1001/jamapsychiatry.2025.4444
Lex Wunderink
{"title":"Interpretation of the HAMLETT Study.","authors":"Lex Wunderink","doi":"10.1001/jamapsychiatry.2025.4444","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4444","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":17.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1001/jamapsychiatry.2025.4450
Iris E Sommer, Shiral S Gangadin, Franciska de Beer
{"title":"Interpretation of the HAMLETT Study-Reply.","authors":"Iris E Sommer, Shiral S Gangadin, Franciska de Beer","doi":"10.1001/jamapsychiatry.2025.4450","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4450","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":17.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146063598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1001/jamapsychiatry.2025.4344
Jacob T Kannarkat,Noah N Smith,Andrew D Carlo
{"title":"Driving Access in Commercial Behavioral Health Networks.","authors":"Jacob T Kannarkat,Noah N Smith,Andrew D Carlo","doi":"10.1001/jamapsychiatry.2025.4344","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4344","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"31 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1001/jamapsychiatry.2025.4303
Victor M Castro, Thomas H McCoy, Pilar Verhaak, Anudeepa Ramachandiran, Roy H Perlis
Importance: Despite increasingly widespread use of artificial intelligence (AI)-driven ambient scribes in medicine, the extent to which they are associated with clinician practice is not well studied.
Objective: To characterize differences in documentation and treatment of psychiatric symptoms in primary care outpatient notes generated using ambient scribes compared with human or no scribes.
Design, setting, and participants: This cohort study used a matched retrospective case-control design to evaluate primary care annual visit notes from the Massachusetts General and Brigham and Women's Hospital systems between February 2023 and February 2025. A random sample of notes from 4 types of visits, matched 1:1 using sociodemographic and clinical features, was used: those using an ambient scribe, those using a human scribe, those occurring during the same period without a scribe (contemporaneous), and those occurring prior to scribe deployment. Data analysis was performed from April 25 to May 1, 2025.
Exposure: Use of an AI ambient scribe.
Main outcomes and measures: Neuropsychiatric symptom documentation, in terms of estimated Research Domain Criteria (RDoC), using a Health Insurance Portability and Accountability Act-compliant large language model (GPT-4o version gpt-4o-11-20; OpenAI); antidepressant prescriptions and diagnostic codes; and referral for mental health follow-up.
Results: Among 20 302 notes, the mean (SD) age of the patients was 48 (14) years and 11 960 (59%) were for visits by female patients; 1026 (5%) met criteria for moderate or greater depressive symptoms by Patient Health Questionnaire-9 score. Estimated levels of RDoC symptoms in all 6 domains were significantly greater in the AI-scribed notes compared with other groups. In a multiple logistic regression model, likelihood of a psychiatric intervention (referral, new diagnosis, or antidepressant prescription) was significantly lower among AI-scribed visits compared with contemporaneous unscribed visits (adjusted odds ratio, 0.83; 95% CI, 0.72-0.95), but not for human-scribed visits compared with contemporaneous unscribed visits (adjusted odds ratio, 0.97; 95% CI, 0.85-1.11).
Conclusions and relevance: In this retrospective cohort study using a matched case-control design examining outpatient primary care notes, incorporation of AI ambient scribes in primary care was associated with greater levels of neuropsychiatric symptom documentation but lesser likelihood of documented management of psychiatric symptoms. Further study will be required to determine whether these changes are associated with differential outcomes.
{"title":"Psychiatric Documentation and Management in Primary Care With Artificial Intelligence Scribe Use.","authors":"Victor M Castro, Thomas H McCoy, Pilar Verhaak, Anudeepa Ramachandiran, Roy H Perlis","doi":"10.1001/jamapsychiatry.2025.4303","DOIUrl":"10.1001/jamapsychiatry.2025.4303","url":null,"abstract":"<p><strong>Importance: </strong>Despite increasingly widespread use of artificial intelligence (AI)-driven ambient scribes in medicine, the extent to which they are associated with clinician practice is not well studied.</p><p><strong>Objective: </strong>To characterize differences in documentation and treatment of psychiatric symptoms in primary care outpatient notes generated using ambient scribes compared with human or no scribes.</p><p><strong>Design, setting, and participants: </strong>This cohort study used a matched retrospective case-control design to evaluate primary care annual visit notes from the Massachusetts General and Brigham and Women's Hospital systems between February 2023 and February 2025. A random sample of notes from 4 types of visits, matched 1:1 using sociodemographic and clinical features, was used: those using an ambient scribe, those using a human scribe, those occurring during the same period without a scribe (contemporaneous), and those occurring prior to scribe deployment. Data analysis was performed from April 25 to May 1, 2025.</p><p><strong>Exposure: </strong>Use of an AI ambient scribe.</p><p><strong>Main outcomes and measures: </strong>Neuropsychiatric symptom documentation, in terms of estimated Research Domain Criteria (RDoC), using a Health Insurance Portability and Accountability Act-compliant large language model (GPT-4o version gpt-4o-11-20; OpenAI); antidepressant prescriptions and diagnostic codes; and referral for mental health follow-up.</p><p><strong>Results: </strong>Among 20 302 notes, the mean (SD) age of the patients was 48 (14) years and 11 960 (59%) were for visits by female patients; 1026 (5%) met criteria for moderate or greater depressive symptoms by Patient Health Questionnaire-9 score. Estimated levels of RDoC symptoms in all 6 domains were significantly greater in the AI-scribed notes compared with other groups. In a multiple logistic regression model, likelihood of a psychiatric intervention (referral, new diagnosis, or antidepressant prescription) was significantly lower among AI-scribed visits compared with contemporaneous unscribed visits (adjusted odds ratio, 0.83; 95% CI, 0.72-0.95), but not for human-scribed visits compared with contemporaneous unscribed visits (adjusted odds ratio, 0.97; 95% CI, 0.85-1.11).</p><p><strong>Conclusions and relevance: </strong>In this retrospective cohort study using a matched case-control design examining outpatient primary care notes, incorporation of AI ambient scribes in primary care was associated with greater levels of neuropsychiatric symptom documentation but lesser likelihood of documented management of psychiatric symptoms. Further study will be required to determine whether these changes are associated with differential outcomes.</p>","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":17.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12824846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146010646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1001/jamapsychiatry.2025.4308
Mehdi Farokhnia,Lorenzo Leggio
ImportanceGlucagon-like peptide-1 (GLP-1) therapies have revolutionized the management of chronic conditions like obesity and diabetes. Consistent with the overlap between feeding and metabolic pathways and those mediating addictive behaviors, growing evidence suggests that GLP-1 therapies may also be beneficial for treating alcohol and other substance use disorders (ASUDs). This review discusses the current landscape of GLP-1 therapies in the context of ASUDs, mental health considerations, and gaps and opportunities in this field.ObservationsPreclinical evidence across several experimental models and species consistently shows that GLP-1 receptor agonists (GLP-1RAs) reduce drug intake and other addictive behaviors. Research to date has primarily focused on alcohol; however, nicotine, opioids, and psychostimulants have also been studied. Observational cohort studies using electronic health records suggest improvements in ASUD-related outcomes among people treated with GLP-1RAs for other indications. Randomized clinical trials (RCTs) have been limited, yielding mixed results but overall promising signals. Several RCTs are ongoing or about to start. Despite some early pharmacovigilance alarms, GLP-1RAs do not seem to cause or increase the risk of psychopathology (eg, depression, suicidal ideation and/or behavior). Some recent studies suggest beneficial effects of GLP-1RAs on mental health outcomes, but more work is needed.Conclusions and RelevanceThe rationale for studying GLP-1 therapies for ASUDs is supported by preclinical and observational clinical evidence. RCTs are emerging and critically needed at this juncture to determine the safety and efficacy of GLP-1 therapies in people with ASUDs. Pending results from RCTs, GLP-1 therapies have the potential to be repurposed for ASUDs. However, there are several relevant questions in need of further investigation, including the specifics of treatment with GLP-1 therapies in the context of addiction (eg, dose, duration, tachyphylaxis, impact of discontinuation), individual differences and potential predictors of response, mechanisms of action, intersection with mental health and medical comorbidities, cost, and fair access to these treatments.
{"title":"Prospects of GLP-1 Therapies for Addiction and Mental Health Comorbidities-Quo Vadis?: A Review.","authors":"Mehdi Farokhnia,Lorenzo Leggio","doi":"10.1001/jamapsychiatry.2025.4308","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4308","url":null,"abstract":"ImportanceGlucagon-like peptide-1 (GLP-1) therapies have revolutionized the management of chronic conditions like obesity and diabetes. Consistent with the overlap between feeding and metabolic pathways and those mediating addictive behaviors, growing evidence suggests that GLP-1 therapies may also be beneficial for treating alcohol and other substance use disorders (ASUDs). This review discusses the current landscape of GLP-1 therapies in the context of ASUDs, mental health considerations, and gaps and opportunities in this field.ObservationsPreclinical evidence across several experimental models and species consistently shows that GLP-1 receptor agonists (GLP-1RAs) reduce drug intake and other addictive behaviors. Research to date has primarily focused on alcohol; however, nicotine, opioids, and psychostimulants have also been studied. Observational cohort studies using electronic health records suggest improvements in ASUD-related outcomes among people treated with GLP-1RAs for other indications. Randomized clinical trials (RCTs) have been limited, yielding mixed results but overall promising signals. Several RCTs are ongoing or about to start. Despite some early pharmacovigilance alarms, GLP-1RAs do not seem to cause or increase the risk of psychopathology (eg, depression, suicidal ideation and/or behavior). Some recent studies suggest beneficial effects of GLP-1RAs on mental health outcomes, but more work is needed.Conclusions and RelevanceThe rationale for studying GLP-1 therapies for ASUDs is supported by preclinical and observational clinical evidence. RCTs are emerging and critically needed at this juncture to determine the safety and efficacy of GLP-1 therapies in people with ASUDs. Pending results from RCTs, GLP-1 therapies have the potential to be repurposed for ASUDs. However, there are several relevant questions in need of further investigation, including the specifics of treatment with GLP-1 therapies in the context of addiction (eg, dose, duration, tachyphylaxis, impact of discontinuation), individual differences and potential predictors of response, mechanisms of action, intersection with mental health and medical comorbidities, cost, and fair access to these treatments.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"63 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1001/jamapsychiatry.2025.4347
Michael J C Bray,Jacob S Shaw,Christopher B Morrow,Chiadi U Onyike,
{"title":"Alzheimer Disease-Relevant Biomarker Elevations in Psychosis and Broad Neuropsychiatric Impairment.","authors":"Michael J C Bray,Jacob S Shaw,Christopher B Morrow,Chiadi U Onyike, ","doi":"10.1001/jamapsychiatry.2025.4347","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4347","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"12 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1001/jamapsychiatry.2025.4253
Arnav Gupta,Tushar Tejpal,Chanhee Seo,Nicholas Fabiano,Selina Zhao,Stanley Wong,Yuan Qiu,Jenna MacNeil,Dain R Kim,Natasha Aleksova,Sara Siddiqi,Marco Solmi,Jess G Fiedorowicz
ImportanceMental disorders have been associated with traditional cardiovascular risk factors that may mediate the risk of acute coronary syndrome (ACS).ObjectiveTo estimate the association of ACS among patients with mental disorders, as compared with patients without mental disorders.Data SourcesMEDLINE, Embase, and PubMed were searched for studies between July 1, 2025, and date of database inception.Study SelectionStudy screening was performed in duplicates with conflicts resolved upon consensus. Inclusion criteria were as follows: (1) observational or randomized study, (2) measured association with ACS (incident events, risk ratio, odds ratio, hazard ratio [HR]), and (3) investigated any clinical mental disorder (based on DSM and International Classification of Diseases) before ACS events.Data Extraction and SynthesisThis systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Data extraction was performed in duplicate and resolved on consensus. Data were quantitatively synthesized through random-effects meta-analysis. The National Institutes of Health Study Quality Assessment Tools were used to assess the quality of included studies. Studies were analyzed from January 1966 to October 2021.Main Outcomes and MeasuresAssociation and/or risk of ACS.ResultsAmong 3616 initially identified studies, 25 full-text articles met inclusion criteria with 22 048 504 participants of median (IQR) age 48.0 (34.5-56.1) years, with 13 019 897 males (59.1%). Depressive disorder (HR, 1.40; 95% CI, 1.11-1.78; P = .01; Grading of Recommendations Assessment, Development, and Evaluation [GRADE] certainty = very low), anxiety disorder (HR, 1.63; 95% CI, 1.40-1.89; P < .001; GRADE certainty = low), sleep disorder (HR, 1.60; 95% CI, 1.22-2.10; P < .001; GRADE certainty = low), and posttraumatic stress disorder (PTSD; HR, 2.73; 95% CI, 1.94-3.84; P < .001; GRADE certainty = moderate) were associated with increased risk of ACS. Bipolar (HR, 1.48; 95% CI, 0.47-4.61; P = .28; GRADE certainty = very low) and psychotic (HR, 0.97; 95% CI, 0.01-178.30; P = .06; GRADE certainty = very low) disorders were not significantly associated with increased risk of acute myocardial infarction, although they had similar point estimates to some other mental disorders.Conclusions and RelevanceResults of this systematic review and meta-analysis suggest that depressive disorders, anxiety disorders, PTSD, and sleep disorders were associated with an increased risk of ACS. Particularly, PTSD and sleep disorders emerged as significant risk factors for ACS, indicating the potential impact of sleep quality on cardiovascular outcomes. Future research addressing these limitations could provide more nuanced insights into the association between mental health and ACS.
重要的是，精神疾病与传统的心血管危险因素有关，这些因素可能介导急性冠脉综合征（ACS）的风险。目的评估精神障碍患者与无精神障碍患者ACS的相关性。数据来源medline， Embase和PubMed检索了2025年7月1日至数据库建立日期之间的研究。研究选择研究筛选按重复进行，冲突在一致意见下解决。纳入标准如下：(1)观察性或随机研究，(2)测量与ACS的相关性（事件事件、风险比、优势比、危险比[HR]），(3)调查ACS事件发生前的任何临床精神障碍（基于DSM和国际疾病分类）。本系统评价遵循系统评价和荟萃分析首选报告项目（PRISMA） 2020指南。数据提取一式两份，协商一致解决。通过随机效应荟萃分析定量合成数据。使用美国国立卫生研究院研究质量评估工具评估纳入研究的质量。研究分析了从1966年1月到2021年10月的研究。主要结局和测量ACS的关联和/或风险。结果在最初确定的3616项研究中，25篇全文文章符合纳入标准，22 048 504名参与者中位（IQR）年龄为48.0（34.5-56.1）岁，其中13 019 897名男性（59.1%）。抑郁症（HR, 1.40; 95% CI, 1.11-1.78; P = 0.01；建议分级评估、发展和评价[GRADE]确定性=极低）、焦虑症（HR, 1.63; 95% CI, 1.40-1.89; P < 0.001； GRADE确定性=低）、睡眠障碍（HR, 1.60; 95% CI, 1.22-2.10; P < 0.001； GRADE确定性=低）和创伤后应激障碍（PTSD； HR, 2.73; 95% CI, 1.94-3.84; P < 0.001； GRADE确定性=中等）与ACS风险增加相关。双相障碍（HR, 1.48; 95% CI, 0.47-4.61; P = 0.28； GRADE确定性=极低）和精神障碍（HR, 0.97; 95% CI, 0.01-178.30; P = 0.06； GRADE确定性=极低）与急性心肌梗死风险增加没有显著相关，尽管它们与其他一些精神障碍有相似的点估计。结论和相关性本系统综述和荟萃分析的结果表明，抑郁症、焦虑症、创伤后应激障碍和睡眠障碍与ACS风险增加相关。特别是，PTSD和睡眠障碍成为ACS的重要危险因素，表明睡眠质量对心血管结局的潜在影响。针对这些局限性的未来研究可以为心理健康和ACS之间的关系提供更细致的见解。
{"title":"Mental Disorders as a Risk Factor of Acute Coronary Syndrome: A Systematic Review and Meta-Analysis.","authors":"Arnav Gupta,Tushar Tejpal,Chanhee Seo,Nicholas Fabiano,Selina Zhao,Stanley Wong,Yuan Qiu,Jenna MacNeil,Dain R Kim,Natasha Aleksova,Sara Siddiqi,Marco Solmi,Jess G Fiedorowicz","doi":"10.1001/jamapsychiatry.2025.4253","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4253","url":null,"abstract":"ImportanceMental disorders have been associated with traditional cardiovascular risk factors that may mediate the risk of acute coronary syndrome (ACS).ObjectiveTo estimate the association of ACS among patients with mental disorders, as compared with patients without mental disorders.Data SourcesMEDLINE, Embase, and PubMed were searched for studies between July 1, 2025, and date of database inception.Study SelectionStudy screening was performed in duplicates with conflicts resolved upon consensus. Inclusion criteria were as follows: (1) observational or randomized study, (2) measured association with ACS (incident events, risk ratio, odds ratio, hazard ratio [HR]), and (3) investigated any clinical mental disorder (based on DSM and International Classification of Diseases) before ACS events.Data Extraction and SynthesisThis systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Data extraction was performed in duplicate and resolved on consensus. Data were quantitatively synthesized through random-effects meta-analysis. The National Institutes of Health Study Quality Assessment Tools were used to assess the quality of included studies. Studies were analyzed from January 1966 to October 2021.Main Outcomes and MeasuresAssociation and/or risk of ACS.ResultsAmong 3616 initially identified studies, 25 full-text articles met inclusion criteria with 22 048 504 participants of median (IQR) age 48.0 (34.5-56.1) years, with 13 019 897 males (59.1%). Depressive disorder (HR, 1.40; 95% CI, 1.11-1.78; P = .01; Grading of Recommendations Assessment, Development, and Evaluation [GRADE] certainty = very low), anxiety disorder (HR, 1.63; 95% CI, 1.40-1.89; P < .001; GRADE certainty = low), sleep disorder (HR, 1.60; 95% CI, 1.22-2.10; P < .001; GRADE certainty = low), and posttraumatic stress disorder (PTSD; HR, 2.73; 95% CI, 1.94-3.84; P < .001; GRADE certainty = moderate) were associated with increased risk of ACS. Bipolar (HR, 1.48; 95% CI, 0.47-4.61; P = .28; GRADE certainty = very low) and psychotic (HR, 0.97; 95% CI, 0.01-178.30; P = .06; GRADE certainty = very low) disorders were not significantly associated with increased risk of acute myocardial infarction, although they had similar point estimates to some other mental disorders.Conclusions and RelevanceResults of this systematic review and meta-analysis suggest that depressive disorders, anxiety disorders, PTSD, and sleep disorders were associated with an increased risk of ACS. Particularly, PTSD and sleep disorders emerged as significant risk factors for ACS, indicating the potential impact of sleep quality on cardiovascular outcomes. Future research addressing these limitations could provide more nuanced insights into the association between mental health and ACS.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"120 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1001/jamapsychiatry.2025.4116
Roy H Perlis
ImportanceThe potential of tools using artificial intelligence (AI) to address the many challenges in delivery of mental health care has been widely discussed. However, the possible negative consequences of AI for such care have received less attention.ObservationsIntegrating AI with mental health care has the potential to expand access and improve quality of care. It may also contribute to improvements in diagnosis, risk stratification, and development of novel therapeutics. At the same time, availability of AI chatbots and stratification algorithms may diminish access to human-delivered care. Reliance on AI tools may have other unanticipated adverse consequences on clinical practice, including diminished human clinician skill. The probabilistic nature of many of these tools, including large language models, makes their capacity to cause harm difficult to determine.Conclusions and RelevanceThe likely benefits of AI for psychiatric care delivery must be balanced against substantial risks. Strategies to mitigate this risk may require regulation to enhance transparency and systematically evaluate the impact of AI in practice, as well as clinician training to make optimal use of these emerging methods.
{"title":"Artificial Intelligence and the Potential Transformation of Mental Health.","authors":"Roy H Perlis","doi":"10.1001/jamapsychiatry.2025.4116","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.4116","url":null,"abstract":"ImportanceThe potential of tools using artificial intelligence (AI) to address the many challenges in delivery of mental health care has been widely discussed. However, the possible negative consequences of AI for such care have received less attention.ObservationsIntegrating AI with mental health care has the potential to expand access and improve quality of care. It may also contribute to improvements in diagnosis, risk stratification, and development of novel therapeutics. At the same time, availability of AI chatbots and stratification algorithms may diminish access to human-delivered care. Reliance on AI tools may have other unanticipated adverse consequences on clinical practice, including diminished human clinician skill. The probabilistic nature of many of these tools, including large language models, makes their capacity to cause harm difficult to determine.Conclusions and RelevanceThe likely benefits of AI for psychiatric care delivery must be balanced against substantial risks. Strategies to mitigate this risk may require regulation to enhance transparency and systematically evaluate the impact of AI in practice, as well as clinician training to make optimal use of these emerging methods.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"216 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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