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Soremekun O, Musanabaganwa C, Uwineza A, Ardissino M, Rajasundaram S, Wani AH, Jansen S, Mutabaruka J, Rutembesa E, Soremekun C, Cheickna C, Wele M, Mugisha J, Nash O, Kinyanda E, Nitsch D, Fornage M, Chikowore T, Gill D, Wildman DE, Mutesa L, Uddin M, Fatumo S. A Mendelian randomization study of genetic liability to post-traumatic stress disorder and risk of ischemic stroke. Transl Psychiatry 2023; 13:237. [PMID: 37391434 PMCID: PMC10313806 DOI: 10.1038/s41398-023-02542-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/02/2023] Open
Abstract
Observational studies have shown an association between post-traumatic stress disorder (PTSD) and ischemic stroke (IS) but given the susceptibility to confounding it is unclear if these associations represent causal effects. Mendelian randomization (MR) facilitates causal inference that is robust to the influence of confounding. Using two sample MR, we investigated the causal effect of genetic liability to PTSD on IS risk. Ancestry-specific genetic instruments of PTSD and four quantitative sub-phenotypes of PTSD, including hyperarousal, avoidance, re-experiencing, and total symptom severity score (PCL-Total) were obtained from the Million Veteran Programme (MVP) using a threshold P value (P) of <5 × 10-7, clumping distance of 1000 kilobase (Mb) and r2 < 0.01. Genetic association estimates for IS were obtained from the MEGASTROKE consortium (Ncases = 34,217, Ncontrols = 406,111) for European ancestry individuals and from the Consortium of Minority Population Genome-Wide Association Studies of Stroke (COMPASS) (Ncases = 3734, Ncontrols = 18,317) for African ancestry individuals. We used the inverse-variance weighted (IVW) approach as the main analysis and performed MR-Egger and the weighted median methods as pleiotropy-robust sensitivity analyses. In European ancestry individuals, we found evidence of an association between genetic liability to PTSD avoidance, and PCL-Total and increased IS risk (odds ratio (OR)1.04, 95% Confidence Interval (CI) 1.007-1.077, P = 0.017 for avoidance and (OR 1.02, 95% CI 1.010-1.040, P = 7.6 × 10-4 for PCL total). In African ancestry individuals, we found evidence of an association between genetically liability to PCL-Total and reduced IS risk (OR 0.95 (95% CI 0.923-0.991, P = 0.01) and hyperarousal (OR 0.83 (95% CI 0.691-0.991, P = 0.039) but no association was observed for PTSD case-control, avoidance, or re-experiencing. Similar estimates were obtained with MR sensitivity analyses. Our findings suggest that specific sub-phenotypes of PTSD, such as hyperarousal, avoidance, PCL total, may have a causal effect on people of European and African ancestry's risk of IS. This shows that the molecular mechanisms behind the relationship between IS and PTSD may be connected to symptoms of hyperarousal and avoidance. To clarify the precise biological mechanisms involved and how they may vary between populations, more research is required.
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Affiliation(s)
- Opeyemi Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Discipline of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | - Annette Uwineza
- Department of Biochemistry, Molecular Biology and Genetics, CMHS, University of Rwanda, Kigali, Rwanda
- Center for Human Genetics at the College of Medicine and Health Sciences-University of Rwanda, Kigali, Rwanda
| | - Maddalena Ardissino
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK
| | - Skanda Rajasundaram
- Centre for Evidence-Based Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Agaz H Wani
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Stefan Jansen
- Directorate of Research and Innovation, University of Rwanda, Kigali, Rwanda
| | - Jean Mutabaruka
- Department of Clinical Psychology, University of Rwanda, Kigali, Rwanda
| | - Eugene Rutembesa
- Department of Clinical Psychology, University of Rwanda, Kigali, Rwanda
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Cisse Cheickna
- The African Center of Excellence in Bioinformatics, University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali
| | - Mamadou Wele
- The African Center of Excellence in Bioinformatics, University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali
| | | | - Oyekanmi Nash
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | - Dorothea Nitsch
- Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Austin, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Austin, USA
| | - Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK
| | - Derek E Wildman
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Leon Mutesa
- Center for Human Genetics at the College of Medicine and Health Sciences-University of Rwanda, Kigali, Rwanda
| | - Monica Uddin
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- MRC/UVRI and LSHTM, Entebbe, Uganda.
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Liu Q, Zhong R, Ji X, Law S, Xiao F, Wei Y, Fang S, Kong X, Zhang X, Yao S, Wang X. Decision-making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART). Depress Anxiety 2022; 39:845-857. [PMID: 36329675 DOI: 10.1002/da.23291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/30/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND In the last decade, suicidality has been increasingly theorized as a distinct phenomenon from major depressive disorder (MDD), with unique psychological and neural mechanisms, rather than being mostly a severe symptom of MDD. Although decision-making biases have been widely reported in suicide attempters with MDD, little is known regarding what components of these biases can be distinguished from depressiveness itself. METHODS Ninety-three patients with current MDD (40 with suicide attempts [SA group] and 53 without suicide attempts [NS group]) and 65 healthy controls (HCs) completed psychometric assessments and the balloon analog risk task (BART). To analyze and compare decision-making components among the three groups, we applied a five-parameter Bayesian computational modeling. RESULTS Psychological assessments showed that the SA group had greater suicidal ideation and psychological pain avoidance than the NS group. Computational modeling showed that both MDD groups had higher risk preference and lower ability to learn and adapt from within-task observations than HCs, without differences between the SA and NS patient groups. The SA group also had higher loss aversion than the NS and HC groups, which had similar loss aversion. CONCLUSIONS Our BART and computational modeling findings suggest that psychological pain avoidance and loss aversion may be important suicide risk factor that are distinguishable from depression illness itself.
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Affiliation(s)
- Qinyu Liu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Runqing Zhong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xinlei Ji
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Samuel Law
- Department of Psychiatry, University of Toronto, Ontario, Toronto, Canada
| | - Fan Xiao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Yiming Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shulin Fang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xinyuan Kong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xiaocui Zhang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.,China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, China
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Emotion socialization in mothers with mood disorders: Affective modeling and recollected responses to childhood emotion. Dev Psychopathol 2020; 33:1156-1169. [PMID: 32672147 DOI: 10.1017/s0954579420000395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Growing evidence suggests that emotion socialization may be disrupted by maternal depression. However, little is known about emotion-related parenting by mothers with bipolar disorder or whether affective modeling in early childhood is linked to young adults' recollections of emotion socialization practices. The current study investigates emotion socialization by mothers with histories of major depression, bipolar disorder, or no mood disorder. Affective modeling was coded from parent-child interactions in early childhood and maternal responses to negative emotions were recollected by young adult offspring (n = 131, 59.5% female, M age = 22.16, SD = 2.58). Multilevel models revealed that maternal bipolar disorder was associated with more neglecting, punishing, and magnifying responses to children's emotions, whereas maternal major depression was associated with more magnifying responses; links between maternal diagnosis and magnifying responses were robust to covariates. Young adult recollections of maternal responses to emotion were predicted by affective modeling in early childhood, providing preliminary validity evidence for the Emotions as a Child Scale. Findings provide novel evidence that major depression and bipolar disorder are associated with altered emotion socialization and that maternal affective modeling in early childhood prospectively predicts young adults' recollections of emotion socialization in families with and without mood disorder.
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