Personality as a Predictor of Treatment Response to Escitalopram in Adults With Body Dysmorphic Disorder

Universal personality dimensions and dysfunctional obsessional beliefs in the DSM-5's OCD and related disorders (OCRDs)

This study aimed to determine the extent to which personality and cognitive factors contribute to the identification of shared associations between the DSM-5's OCD and Related Disorders (OCRDs). Participants (n = 239) were treatment-seeking outpatients with a principal diagnosis of obsessive-compulsive disorder (OCD), body dysmorphic disorder (BDD), hoarding disorder (HD), trichotillomania (TTM), or excoriation disorder (EXC), as compared to healthy community controls (n = 100). Analyses examined the relationships between diagnostic group, personality dimensions, and obsessive beliefs. Results demonstrated that compared to non-clinical controls, all diagnostic groups scored significantly higher on neuroticism and lower on extraversion and conscientiousness. Few significant differences were found across diagnostic groups: extraversion was higher in the TTM group (vs. all OCRDs), conscientiousness was lower in the HD group (vs. OCD, TTM, EXC), and openness to experience was higher in the TTM and EXC groups (vs. OCD, HD). Obsessional beliefs were significantly elevated in all clinical conditions (vs. controls) except for beliefs surrounding responsibility and threat estimation, which were only significantly higher in OCD and BDD groups. These results highlight shared personality and cognitive vulnerability in the OCRDs as well as unique disorder-specific vulnerabilities related to OCD.

Unmet Needs in Psychodermatology: A Narrative Review

Psychodermatology, the multidisciplinary field that explores the intricate interplay between the mind and the skin, has gained increasing recognition over the past decade. However, several knowledge gaps and unmet needs persist in the field. The objective of this narrative review was to investigate the unmet needs in the field of psychodermatology as they pertain to medical training, treatment, research, and care access. PubMed was searched from inception through December 2023 to identify articles related to psychodermatology. Findings revealed several unmet needs within the field of psychodermatology. First, there is a need for further investigation into the pathophysiology that links psychological stress to cutaneous disease including the development of novel therapies targeting key neuropeptides. Second, the existing literature focuses primarily on the pharmacologic treatment of body dysmorphic disorder and body-focused repetitive behaviors, as well as delusional parasitosis, for which the first-line agents are selective serotonin reuptake inhibitors and atypical antipsychotics, respectively. However, additional research into the efficacy and safety of the remaining psychotropic medications and the treatment of other common psychocutaneous diseases is required. Finally, there exists a significant gap in knowledge amongst clinicians tasked with treating psychocutaneous diseases. Dermatologists report low rates of training in psychodermatology and discomfort with prescribing psychotropic medications. In conclusion, increasing resources for dermatologist education on psychotropic agent use, development of new drugs targeting stress-induced skin conditions, and research on the psychocutaneous applications of current medications may greatly improve the quality and access of psychodermatology care.

Comparative Analysis of Personality Traits in Major Depressive Disorder and Bipolar Disorder: Impact, Differences, and Associations with Symptoms

Purpose

This cross-sectional study aimed to compare the personality traits of patients with major depressive disorder (MDD) and bipolar disorder (BD) with those of healthy individuals. The goal was to gain insight into the potential impact of personality traits on the development and manifestation of mood disorders.

Methods

One hundred seventy-eight patients with mood disorders were analyzed as either MDD or BD, with each group containing euthymic and depressive members: e-MDD, d-MDD, e-BD, and d-BD. Mood status was assessed using the Young Mania Rating Scale (YMRS), and the 17-item Hamilton Depression Rating Scale (HAMD-17). Ninety-five healthy individuals served as controls. Personality traits were assessed with the Eysenck Personality Questionnaire.

Results

The scores for neuroticism in the patient groups were comparable, but each group had higher scores compared to the control group (P < 0.001). Each patient group exhibited significantly lower scores for extraversion compared to the control group, with e-MDD, d-MDD, and d-BD showing particularly notable differences (P < 0.001); these groups scored significantly lower than the e-BD (P = 0.041, 0.009, 0.038). In patients with BD, there was an inverted association between extraversion score and HAMD total score (P = 0.010, r = -0.27), and a positive association with the YMRS total score (P = 0.022, r = 0.24). In the MDD group, there was a positive association between the neuroticism score and HAMD total score (P = 0.021, r = 0.25).

Conclusion

Patients with mood disorders are characterized by lower extraversion and higher neuroticism. Level of neuroticism associated with depression severity in MDD. Patients with BD may be more extraverted, but their extraversion can be affected by depressive episodes. Extraversion may be a feature of BD, and may differentiate BD from MDD. Personality traits are related to disease diathesis and state, and shaped by symptom manifestations.

Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach

BACKGROUND

Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes.

METHODS

The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response.

RESULTS

SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive.

CONCLUSIONS

The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.

Body Dysmorphic Disorder

This article summarizes current knowledge of body dysmorphic disorder across the life span. An overview of the epidemiology and phenomenology of this condition is provided, as well as clinical perspectives on assessment and treatment. Barriers to accessing treatment are considered, along with recent developments to improve access. Future directions in research and clinical care for this population are summarized.

The impact of patient and partner personality traits on learning success for a cognitive rehabilitation intervention for patients with MCI

The Memory Support System (MSS) is the memory compensation tool used in the HABIT Healthy Action to Benefit Independence and Thinking^® Program. People diagnosed with mild cognitive impairment (pwMCI; n = 153) participated in this cognitive rehabilitative programme with a partner. We first aimed to determine if prior research on the positive impact of higher baseline cognitive status on successful MSS learning would be replicated in a new sample. We further evaluated the impact of the pwMCI's and partner's personality traits, as measured by the Ten Item Personality Inventory, on successful learning. Better global cognitive status was again shown to increase the odds for MSS learning success. In terms of personality, the highest odds of learning success occurred when the pwMCI was high in Openness to Experience (OR = 5.43), followed by high partner Openness (OR = 2.53) or high Openness in both the pwMCI and partner (OR = 2.31). In sum, when the pwMCI possessed both better cognitive status and openness to new experience they were better able to master a cognitive rehabilitation tool for MCI.