Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.

The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.

There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.

These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

Comorbid anxiety disorders have been considered a risk factor for suicidal behavior in patients with mood disorders, although results are controversial. The aim of this two-year prospective study was to determine if lifetime and current comorbid anxiety disorders at baseline were risk factors for suicide attempts during the two-year follow-up.

We evaluated 667 patients with mood disorders (504 with major depression and 167 with bipolar disorder) divided in two groups: those with lifetime comorbid anxiety disorders (n = 229) and those without (n = 438). Assessments were performed at baseline and at 3, 12, and 24 months. Kaplan-Meier survival analysis and log-rank test were used to evaluate the relationship between anxiety disorders and suicide attempts. Cox proportional hazard regression was performed to investigate clinical and demographic variables that were associated with suicide attempts during follow-up.

Of the initial sample of 667 patients, 480 had all three follow-up interviews. During the follow-up, 63 patients (13.1%) attempted suicide at least once. There was no significant difference in survival curves for patients with and without comorbid anxiety disorders (log-rank test = 0.269; P = 0.604). Female gender (HR = 3.66, P = 0.001), previous suicide attempts (HR = 3.27, P = 0.001) and higher scores in the Buss-Durkee Hostility Inventory (HR = 1.05, P ≤ 0.001) were associated with future suicide attempts.

Our results suggest that comorbid anxiety disorders were not risk factors for suicide attempts. Further studies were needed to determine the role of anxiety disorders as risk factors for suicide attempts.

To compare the variability of patterns of depressive symptoms between two consecutive depressive episodes in patients with bipolar disorder type I.

Review of prospectively collected data from 136 subjects of an out-patient bipolar unit from 1997 to 2007. Binomial statistics was used for the analysis of Hamilton Depression Rating Scale (HDRS)-31 items of the first and second episodes, and the correlation of the HDRS-31 item scores of both episodes was determined using the Spearman coefficient.

Ten depressive symptoms showed a significant correlation between index and subsequent episodes: psychological anxiety, somatic anxiety, somatic symptoms, diurnal variation, paranoid symptoms, obsessive and compulsive symptoms, hypersomnia, loss of appetite and helplessness. Only four symptoms were stable in both statistical tests: paranoid symptoms, obsessive–compulsive symptoms, loss of appetite and hypersomnia.

Paranoid and obsessive–compulsive symptoms, loss of appetite and hypersomnia tended to be found in successive episodes. However, the moderate correlations of the symptoms across two depressive recurrences suggested that clinical presentations in bipolar depression may not be predicted by symptom profiles presented in previous episodes.

We aimed to determine the prevalence of obesity and metabolic syndrome (O/MetS) in a sample of Brazilian outpatients with bipolar disorder.

Eighty-four patients with bipolar disorder were evaluated. We used the definition of MetS established in the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults, modified by the American Heart Association (AHA). Patients were classified as obese if their body mass index (BMI) was ≥ 30 kg/m2.

We found that 28.6% of our sample met the AHA criteria for MetS and 35.7% were obese. The percentage of patients meeting each criterion of the AHA was as follows: 46% for abdominal obesity; 44% for hypertriglyceridemia or cholesterol-lowering medication use; 26% for low high-density lipoprotein cholesterol or being on a lipid-lowering medication; 45% for hypertension; and 20% for high fasting glucose or anti-diabetic medication use.

The prevalence of obesity in our sample of outpatients with bipolar disorder was higher than that observed for the general population of Brazil. The rate of MetS was similar to that observed for the general population. Our data indicate the need for prevention, early detection and treatment of O/MetS in patients with bipolar disorder.