Adverse Childhood Life Events and Postpartum Psychosis in Bipolar Disorder

Background Women with bipolar disorder are at increased risk of postpartum psychosis. Adverse childhood life events have been associated with depression in the postpartum period, but have been little studied in relation to postpartum psychosis. In this study we investigated whether adverse childhood life events are associated with postpartum psychosis in a large sample of women with bipolar I disorder. Methods Participants were 432 parous women with DSM-IV bipolar I disorder recruited into the Bipolar Disorder Research Network (www.BDRN.org). Diagnoses and lifetime psychopathology, including perinatal episodes, were obtained via a semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry; Wing et al., 1990) and case-notes. Adverse childhood life events were assessed via self-report and case-notes, and compared between women with postpartum psychosis (n=208) and those without a lifetime history of perinatal mood episodes (n=224). Results There was no significant difference in the rate of any adverse childhood life event, including childhood sexual abuse, or in the total number of adverse childhood life events between women who experienced postpartum psychosis and those without a lifetime history of perinatal mood episodes, even after controlling for demographic and clinical differences between the groups. Limitations Adverse childhood life events were assessed in adulthood and therefore may be subject to recall errors. Conclusions We found no evidence for an association between adverse childhood life events and the occurrence of postpartum psychosis. Our data suggest that, unlike postpartum depression, childhood adversity does not play a significant role in the triggering of postpartum psychosis in women with bipolar disorder.

Epilepsy in Bipolar Disorder: Impact on Clinical Features, Course and Outcome

Background and Aims: It is well recognized that mood disorders and epilepsy commonly co-occur. However, the relationship between epilepsy and the clinical features and course of illness in bipolar disorder (BD) is currently unknown. Here we explore the rate of epilepsy within a large sample of individuals with BD and examine bipolar illness characteristics according to the presence or absence of epilepsy. Methods: 1596 participants recruited to the Bipolar Disorder Research Network; a well-defined sample of UK subjects with a diagnosis of BD, completed a self-report questionnaire to assess lifetime history of epilepsy (Ottman et al., 2010). A subset of participants (n = 29) completed a telephone interview assessment to determine expert-confirmed epilepsy status. Lifetime clinical characteristics of illness were compared between BD subjects with and without a history of epilepsy. Results: 127 individuals (8%) screened positively for lifetime history of epilepsy. Bipolar subjects with epilepsy experienced higher rates of: suicide attempt (64.2% vs. 47.4%, p = 0.000367); panic disorder (29.6% vs. 16.1%, p = 0.001); phobias (13.6% vs. 5.7%, 0.004); alcohol abuse (18.6% vs. 10.6%, p = 0.017); and other substance abuse (10.2% vs. 4%, p = 0.009). History of suicide attempt (OR = 1.79, p = 0.013) remained significant within a multivariate model. Similar trends were observed within bipolar subjects with well-defined, expert-confirmed epilepsy (n = 29). Conclusions: Results demonstrate an increased rate of self-reported epilepsy in the BD sample, compared to the general population, and suggest differences in the clinical course of BD according to the presence of epilepsy. Comorbid epilepsy within BD may provide an attractive opportunity for subcategorising for future genetic studies, potentially identifying common underlying mechanisms.

History of Premenstrual Mood Change and Postpartum Episodes are Associated with Perimenopausal Episodes in Women with Bipolar Disorder

Background and Aims: Reproductive life events are potential triggers of mood episodes in women with bipolar disorder. We aimed to establish whether a history of premenstrual mood change and postpartum episodes are associated with perimenopausal episodes in women who have bipolar disorder. Methods: Participants were 339 post-menopausal women with DSM-IV bipolar disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Women self-reported presence (N = 200) or absence (N = 139) of an illness episode during the perimenopausal period. History of premenstrual mood change was measured using the self-report Premenstrual Symptoms Screening Tool (PSST), and history of postpartum episodes was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry, SCAN) and inspection of case-notes. Results: History of a postpartum episode within 6 months of delivery (OR = 2.13, p = 0.03) and history of moderate/severe premenstrual syndrome (OR = 6.33, p < 0.001) were significant predictors of the presence of a perimenopausal episode, even after controlling for demographic factors. When we narrowed the definition of premenstrual mood change to premenstrual dysphoric disorder, it remained significant (OR = 2.68, p = 0.007). Conclusions: Some women who have bipolar disorder may be particularly sensitive to reproductive life events. Previous mood episodes in relation to the female reproductive lifecycle may help clinicians predict individual risk for women with bipolar disorder approaching the menopause. There is a need for prospective longitudinal studies of women with bipolar disorder providing frequent contemporaneous ratings of their mood to overcome the limitations of retrospective self-report data.

The Influence of Borderline Personality Traits on the Course of Bipolar Disorder

Background and Aims: Bipolar disorder and borderline personality disorder are commonly comorbid. Borderline personality disorder is diagnosed categorically, but personality pathology may be better characterised dimensionally. The impact of borderline personality traits (not diagnosis) on the course of bipolar disorder is unknown. We examined the presence and severity of borderline personality traits in a large UK sample of bipolar disorder, and the impact of these traits on illness course. Methods: Borderline Evaluation of Severity over Time (BEST) was used to measure presence and severity of borderline traits in 1447 individuals with DSM-IV bipolar I disorder (n = 1008) and bipolar II disorder (n = 439) recruited into the Bipolar Disorder Research Network (www.bdrn.org). Clinical course was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry) and case-notes. Results: BEST score was higher in bipolar II than bipolar I (36 v 27, p < 0.001) and 9/12 individual BEST traits were significantly more common in bipolar II than bipolar I. Within both bipolar I and bipolar II higher BEST score was associated with younger age of bipolar onset (p < 0.001), history of alcohol misuse (p < 0.010), and history of suicide attempt (p < 0.001). Conclusions: Borderline personality traits are common in bipolar disorder, and more severe in bipolar II than bipolar I disorder. Borderline trait severity was associated with more severe bipolar illness course; younger age of onset, alcohol misuse and suicidal behaviour. Clinicians should be vigilant for borderline personality traits irrespective of whether criteria for diagnosis are met, particularly in those with bipolar II disorder and younger age of bipolar onset.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.