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Cosentino L, Witt SH, Dukal H, Zidda F, Siehl S, Flor H, De Filippis B. Methyl-CpG binding protein 2 expression is associated with symptom severity in patients with PTSD in a sex-dependent manner. Transl Psychiatry 2023; 13:249. [PMID: 37419878 DOI: 10.1038/s41398-023-02529-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/09/2023] Open
Abstract
Traumatic events may lead to post-traumatic stress disorder (PTSD), with higher prevalence in women. Adverse childhood experiences (ACE) increase PTSD risk in adulthood. Epigenetic mechanisms play important roles in PTSD pathogenesis and a mutation in the methyl-CpG binding protein 2 (MECP2) in mice provide susceptibility to PTSD-like alterations, with sex-dependent biological signatures. The present study examined whether the increased risk of PTSD associated with ACE exposure is accompanied by reduced MECP2 blood levels in humans, with an influence of sex. MECP2 mRNA levels were analyzed in the blood of 132 subjects (58 women). Participants were interviewed to assess PTSD symptomatology, and asked to retrospectively report ACE. Among trauma-exposed women, MECP2 downregulation was associated with the intensification of PTSD symptoms linked to ACE exposure. MECP2 expression emerges as a potential contributor to post-trauma pathophysiology fostering novel studies on the molecular mechanisms underlying its potential sex-dependent role in PTSD onset and progression.
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Affiliation(s)
- Livia Cosentino
- Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Roma, Italy
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Helene Dukal
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Francesca Zidda
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Siehl
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Bianca De Filippis
- Center for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Roma, Italy.
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Schultebraucks K, Chang BP. The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae. Exp Neurol 2021; 336:113526. [PMID: 33157093 PMCID: PMC7856033 DOI: 10.1016/j.expneurol.2020.113526] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/28/2020] [Accepted: 10/30/2020] [Indexed: 11/25/2022]
Abstract
Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this narrative review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.
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Affiliation(s)
- Katharina Schultebraucks
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, NY, United States of America; Data Science Institute, Columbia University, New York, NY, United States of America.
| | - Bernard P Chang
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, NY, United States of America
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Schultebraucks K, Sijbrandij M, Galatzer-Levy I, Mouthaan J, Olff M, van Zuiden M. Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study. Neurobiol Stress 2021; 14:100297. [PMID: 33553513 PMCID: PMC7843920 DOI: 10.1016/j.ynstr.2021.100297] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/22/2020] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
The necessary requirement of a traumatic event preceding the development of Posttraumatic Stress Disorder, theoretically allows for administering preventive and early interventions in the early aftermath of such events. Machine learning models including biomedical data to forecast PTSD outcome after trauma are highly promising for detection of individuals most in need of such interventions. In the current study, machine learning was applied on biomedical data collected within 48 h post-trauma to forecast individual risk for long-term PTSD, using a multinominal approach including the full spectrum of common PTSD symptom courses within one prognostic model for the first time. N = 417 patients (37.2% females; mean age 46.09 ± 15.88) admitted with (suspected) serious injury to two urban Academic Level-1 Trauma Centers were included. Routinely collected biomedical information (endocrine measures, vital signs, pharmacotherapy, demographics, injury and trauma characteristics) upon ED admission and subsequent 48 h was used. Cross-validated multi-nominal classification of longitudinal self-reported symptom severity (IES-R) over 12 months and bimodal classification of clinician-rated PTSD diagnosis (CAPS-IV) at 12 months post-trauma was performed using extreme Gradient Boosting and evaluated on hold-out sets. SHapley Additive exPlanations (SHAP) values were used to explain the derived models in human-interpretable form. Good prediction of longitudinal PTSD symptom trajectories (multiclass AUC = 0.89) and clinician-rated PTSD at 12 months (AUC = 0.89) was achieved. Most relevant prognostic variables to forecast both multinominal and dichotomous PTSD outcomes included acute endocrine and psychophysiological measures and hospital-prescribed pharmacotherapy. Thus, individual risk for long-term PTSD was accurately forecasted from biomedical information routinely collected within 48 h post-trauma. These results facilitate future targeted preventive interventions by enabling future early risk detection and provide further insights into the complex etiology of PTSD.
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Affiliation(s)
- Katharina Schultebraucks
- Vagelos School of Physicians and Surgeons, Department of Emergency Medicine, Columbia University Medical Center, New York, NY, United States of America; Data Science Institute, Columbia University, New York, New York, USA
| | - Marit Sijbrandij
- Vrije Universiteit, Department of Clinical, Neuro- and Developmental Psychology; Amsterdam Public Health Research Institute, World Health Organization Collaborating Centre for Research and Dissemination of Psychological Interventions, Amsterdam, the Netherlands
| | - Isaac Galatzer-Levy
- Department of Psychiatry, New York University School of Medicine, New York, New York, USA
| | - Joanne Mouthaan
- Department of Clinical Psychology, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, the Netherlands
| | - Miranda Olff
- ARQ National Psychotrauma Centre, Diemen, the Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam, the Netherlands
| | - Mirjam van Zuiden
- Department of Psychiatry, Amsterdam University Medical Centers, Location Amsterdam Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience Research Institute, Amsterdam, the Netherlands
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Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males. Mol Psychiatry 2021; 26:4300-4314. [PMID: 33339956 PMCID: PMC8550967 DOI: 10.1038/s41380-020-00966-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 02/10/2020] [Accepted: 11/18/2020] [Indexed: 12/31/2022]
Abstract
Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.
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A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nat Med 2020; 26:1084-1088. [PMID: 32632194 DOI: 10.1038/s41591-020-0951-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 05/22/2020] [Indexed: 12/23/2022]
Abstract
Annually, approximately 30 million patients are discharged from the emergency department (ED) after a traumatic event1. These patients are at substantial psychiatric risk, with approximately 10-20% developing one or more disorders, including anxiety, depression or post-traumatic stress disorder (PTSD)2-4. At present, no accurate method exists to predict the development of PTSD symptoms upon ED admission after trauma5. Accurate risk identification at the point of treatment by ED services is necessary to inform the targeted deployment of existing treatment6-9 to mitigate subsequent psychopathology in high-risk populations10,11. This work reports the development and validation of an algorithm for prediction of post-traumatic stress course over 12 months using two independently collected prospective cohorts of trauma survivors from two level 1 emergency trauma centers, which uses routinely collectible data from electronic medical records, along with brief clinical assessments of the patient's immediate stress reaction. Results demonstrate externally validated accuracy to discriminate PTSD risk with high precision. While the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.
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Wiss DA. A Biopsychosocial Overview of the Opioid Crisis: Considering Nutrition and Gastrointestinal Health. Front Public Health 2019; 7:193. [PMID: 31338359 PMCID: PMC6629782 DOI: 10.3389/fpubh.2019.00193] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 06/26/2019] [Indexed: 12/11/2022] Open
Abstract
The opioid crisis has reached epidemic proportions in the United States with rising overdose death rates. Identifying the underlying factors that contribute to addiction vulnerability may lead to more effective prevention strategies. Supply side environmental factors are a major contributing component. Psychosocial factors such as stress, trauma, and adverse childhood experiences have been linked to emotional pain leading to self-medication. Genetic and epigenetic factors associated with brain reward pathways and impulsivity are known predictors of addiction vulnerability. This review attempts to present a biopsychosocial approach that connects various social and biological theories related to the addiction crisis. The emerging role of nutrition therapy with an emphasis on gastrointestinal health in the treatment of opioid use disorder is presented. The biopsychosocial model integrates concepts from several disciplines, emphasizing multicausality rather than a reductionist approach. Potential solutions at multiple levels are presented, considering individual as well as population health. This single cohesive framework is based on the interdependency of the entire system, identifying risk and protective factors that may influence substance-seeking behavior. Nutrition should be included as one facet of a multidisciplinary approach toward improved recovery outcomes. Cross-disciplinary collaborative efforts, new ideas, and fiscal resources will be critical to address the epidemic.
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Affiliation(s)
- David A. Wiss
- Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
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