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Wolfe EC, Snorrason I, Laky ZE, Miyares P, Klare D, Fang A, Summers B, Phillips KA, Wilhelm S, Greenberg JL. Clinical characteristics among sexual minority and heterosexual women with body dysmorphic disorder. Body Image 2024; 49:101687. [PMID: 38471234 PMCID: PMC11139585 DOI: 10.1016/j.bodyim.2024.101687] [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: 10/06/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
Body dysmorphic disorder (BDD) is a common disorder associated with substantial comorbidity, impairment, and poor quality of life. Research on subcultural variations of BDD is limited but may impact assessment and treatment of the disorder. The current study examined clinical features in a sample of sexual minority (SM; n = 43) and heterosexual (n = 155) women with diagnosed BDD. Participants completed self-report and clinician-administered measures of demographic and clinical characteristics. Results indicated largely similar clinical features across groups with some exceptions: compared to non-SM women, SM women were younger (M = 25.50 vs 31.96 years, p < .001), had better BDD-related insight (M = 14.51 vs 16.26, p = .01), endorsed a greater number of disliked body parts, and were more likely to express preoccupation with body build (OR = 4.6, 95% CI [2.0, 10.9]), chin/jaw (OR = 4.7, 95% CI [2.1, 10.3]), and shoulders (OR = 10.1, 95% CI [2.7, 37.9]), possibly reflecting nuanced beauty ideals within the SM community. There were no significant group differences in other body parts of concern, BDD severity, or depression. Future studies are needed in larger, more inclusive samples to explore the relationship between diverse identities on BDD and its associated features.
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Affiliation(s)
- Emma C Wolfe
- University of Virginia, 485 McCormick Road, Charlottesville, VA 22903, USA.
| | - Ivar Snorrason
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
| | - Zoë E Laky
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA; American University, 4400 Massachusetts Avenue, NW, Washington, DC 20016, USA
| | - Peyton Miyares
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
| | - Dalton Klare
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
| | - Angela Fang
- University of Washington, 3751 West Stevens Way NE, Seattle WA 98195, USA
| | - Berta Summers
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
| | - Katharine A Phillips
- Rhode Island Hospital and Alpert Medical School of Brown University, 222 Richmond St, Providence, RI 02903, USA; New York-Presbyterian Hospital and Weill Cornell Medical College, 315 East 62nd Street, New York, NY 10065, USA
| | - Sabine Wilhelm
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
| | - Jennifer L Greenberg
- Massachusetts General Hospital and Harvard Medical School, 185 Cambridge Street, Suite 2000, Boston MA 02114, USA
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Krebs G, Clark BR, Ford TJ, Stringaris A. Epidemiology of Body Dysmorphic Disorder and Appearance Preoccupation in Youth: Prevalence, Comorbidity and Psychosocial Impairment. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00126-6. [PMID: 38508411 DOI: 10.1016/j.jaac.2024.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVE Little is known about how common and impairing body dysmorphic disorder (BDD) is in the general population of youth. We evaluated the prevalence, comorbidity, and psychosocial impairment associated with BDD and more broadly defined appearance preoccupation in young people. METHOD Data were drawn from the 2017 Mental Health of Children and Young People in England survey. BDD and psychiatric comorbidity were assessed in individuals 5 to 19 years of age (N = 7,654) according to DSM-5 criteria, using a clinician-rated standardized diagnostic assessment. Psychosocial impairment was measured with a quantitative scale and was indexed by reported self-harm and suicide attempts, as well as service use, assessed using structured interviews. RESULTS The point prevalence of BDD was 1.0% (95% CI = 0.8%-1.3%). BDD was significantly more common among adolescents than children (1.9 vs 0.1%; OR = 22.5, p < .001), and among female than male participants (1.8% vs 0.3%; OR = 7.3, p < .001). Approximately 70% of young people with BDD had psychiatric comorbidity, most commonly internalizing disorders. BDD was associated with self- and parent-reported psychosocial impairment, self-harm and suicide attempts, and service utilization. Appearance preoccupation was more common than full-syndrome BDD, but showed similar age and sex effects, patterns of comorbidity, and associated impairment. CONCLUSION BDD and appearance preoccupation are relatively common, especially among adolescent girls, and are associated with substantial co-occurring psychopathology, impairment, and risk. Improved screening is needed to increase detection and diagnosis of BDD, and to facilitate access to evidence-based treatment. STUDY PREREGISTRATION INFORMATION The epidemiology of body dysmorphic disorder the youth: prevalence, comorbidity and psychosocial impact; https://osf.io/g83jy.
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Affiliation(s)
- Georgina Krebs
- University College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, United Kingdom.
| | - Bruce R Clark
- South London and Maudsley NHS Foundation Trust, United Kingdom
| | - Tamsin J Ford
- University of Cambridge, United Kingdom, and Cambridge and Peterborough, NHS Foundation Trust, Cambridgeshire, United Kingdom
| | - Argyris Stringaris
- University College London, London, United Kingdom; National and Kapodistrian University of Athens, Athens, Greece
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Fang A, Baran B, Feusner JD, Phan KL, Beatty CC, Crane J, Jacoby RJ, Manoach DS, Wilhelm S. Self-focused brain predictors of cognitive behavioral therapy response in a transdiagnostic sample. J Psychiatr Res 2024; 171:108-115. [PMID: 38266332 PMCID: PMC10922639 DOI: 10.1016/j.jpsychires.2024.01.018] [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: 09/03/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre-to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample. METHODS Twenty-seven patients with primary social anxiety disorder (n = 14) and primary body dysmorphic disorder (n = 13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex of the default mode network. RESULTS At pre-treatment, stronger positive connectivity of the seed with the cerebellum, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre-to post-treatment, greater anticorrelation between the seed and postcentral gyrus, extending into the inferior parietal lobule and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding. CONCLUSIONS Pre-treatment functional connectivity with the default mode network was associated with CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA. CLINICAL TRIALS REGISTRATION ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.
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Affiliation(s)
- Angela Fang
- Department of Psychology, University of Washington, Seattle, WA, 98195-1525, USA.
| | - Bengi Baran
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242-1407, USA
| | - Jamie D Feusner
- Centre for Addiction and Mental Health, Brain Imaging Health Center, Ontario, Toronto, Canada, M5T1R8; Department of Psychiatry, University of Toronto, Ontario, Toronto, Canada, M5T1R8; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, 43210-1240, USA
| | - Clare C Beatty
- Department of Psychology, Stony Brook University, Stony Brook, NY, 11794-2500, USA
| | - Jessica Crane
- Department of Psychology, University of Washington, Seattle, WA, 98195-1525, USA
| | - Ryan J Jacoby
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129-2020, USA
| | - Sabine Wilhelm
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114-2696, USA
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Feng A, Feng Y, Zhi D, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional Imaging Derived ADHD Biotypes Based on Deep Clustering May Guide Personalized Medication Therapy. RESEARCH SQUARE 2023:rs.3.rs-3272441. [PMID: 37790426 PMCID: PMC10543279 DOI: 10.21203/rs.3.rs-3272441/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment. Here we proposed graph convolutional network plus deep clustering for ADHD biotype detection using functional network connectivity (FNC), resulting in two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were well replicated on independent ADHD adolescents undergoing longitudinal medication treatment (n=130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 treated with methylphenidate demonstrated significantly better recovery than biotype 2 treated with atomoxetine (p<0.05, FDR corrected). This imaging-driven, biotype-guided approach holds promise for facilitating personalized treatment of ADHD, exploring possible boundaries through innovative deep learning algorithms aimed at improving medication treatment effectiveness.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Yuan Feng
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 100049
| | - Michael Stevens
- Department of Psychiatry, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Healthcare Corporation, Hartford, CT, United States
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China, 100191
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, Georgia, United States, 30303
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