Tobacco smoking and other substance use disorders associated with recurrent suicide attempts in bipolar disorder.

BACKGROUND: Suicide attempts (SA) are more frequent in bipolar disorder (BD) than in most other mental disorders. Prevention strategies would benefit from identifying the risk factors of SA recurrence in BD. Substance use disorders (SUD) (including tobacco-related) are strongly associated with both BD and SA, however, their specific role for the recurrence of SA in BD remains inadequately investigated. Thus, we tested if tobacco smoking - with or without other SUDs - was independently associated with recurrent SA in BD. METHODS: 916 patients from France and Norway with ascertained diagnoses of BD and reliable data about SA and SUD were classified as having no, single, or recurrent (≥2) SA. Five SUD groups were built according to the presence/absence/combination of tobacco, alcohol (AUD) and cannabis use disorders. Multinomial logistic regression was used to identify the correlates of SA recurrence. RESULTS: 338 (37%) individuals reported at least one SA, half of whom (173, 51%) reported recurrence. SUD comorbidity was: tobacco smoking only, 397 (43%), tobacco smoking with at least another SUD, 179 (20%). Regression analysis showed that tobacco smoking, both alone and comorbid with AUD, depressive polarity of BD onset and female gender were independently associated with recurrent SA. LIMITATIONS: Lack of data regarding the relative courses of SA and SUD and cross-national differences in main variables. CONCLUSION: Tobacco smoking with- or without additional SUD can be important risk factors of SA recurrence in BD, which is likely to inform both research and prevention strategies.

Solar insolation in springtime influences age of onset of bipolar I disorder.

OBJECTIVE: To confirm prior findings that the larger the maximum monthly increase in solar insolation in springtime, the younger the age of onset of bipolar disorder. METHOD: Data were collected from 5536 patients at 50 sites in 32 countries on six continents. Onset occurred at 456 locations in 57 countries. Variables included solar insolation, birth-cohort, family history, polarity of first episode and country physician density. RESULTS: There was a significant, inverse association between the maximum monthly increase in solar insolation at the onset location, and the age of onset. This effect was reduced in those without a family history of mood disorders and with a first episode of mania rather than depression. The maximum monthly increase occurred in springtime. The youngest birth-cohort had the youngest age of onset. All prior relationships were confirmed using both the entire sample, and only the youngest birth-cohort (all estimated coefficients P < 0.001). CONCLUSION: A large increase in springtime solar insolation may impact the onset of bipolar disorder, especially with a family history of mood disorders. Recent societal changes that affect light exposure (LED lighting, mobile devices backlit with LEDs) may influence adaptability to a springtime circadian challenge.

Cannabis use disorder is associated with greater illness severity in tobacco smoking patients with bipolar disorder.

OBJECTIVE: Cannabis use disorders (CUD) may influence the course of bipolar disorder (BD), but key confounding factors such as tobacco smoking have not been adequately addressed. This study examined whether CUD was associated with a more severe illness course in tobacco smoking BD patients. METHODS: A sample of French and Norwegian tobacco smoking patients with BD I and II (N=642) was investigated. DSM-IV diagnoses and other characteristics were obtained through personal interviews using structured questionnaires. The association between CUD and illness course was assessed in regression analyses. RESULTS: In bivariate analyses, CUD was associated with earlier BD onset, higher frequency of manic (in BD I) and depressive episodes and hospitalizations per illness year, and a higher occurrence of psychotic episodes. After controlling for potential confounders, the relationships with earlier BD onset (B=-5.60 95% CI=-7.65 to -3.64), and increased rates of manic episodes (OR=1.93, 95% CI: 1.15 to 3.23) and hospitalizations (OR=2.93, 95% CI: 1.85 to 4.64) remained statistically significant. LIMITATIONS: Despite the multivariate approach, differences between the two samples may lead to spurious findings related to hidden confounders. Substance use and mood episode information was collected retrospectively, and potential birth cohort effects could not be controlled for. CONCLUSION: Studies have found associations between tobacco smoking and poorer outcomes in BD. In this study on tobacco smoking BD patients we report an association between CUD and illness severity, suggesting that CUD exacerbates the disease evolution independently of tobacco smoking. Specific treatment and prevention programs addressing CUD in BD patients are warranted.

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.