Barbara König

Neuroprotective effect of lithium on hippocampal volumes in bipolar disorder independent of long-term treatment response.

BACKGROUND Neuroimaging studies have demonstrated an association between lithium (Li) treatment and brain structure in human subjects. A crucial unresolved question is whether this association reflects direct neurochemical effects of Li or indirect effects secondary to treatment or prevention of episodes of bipolar disorder (BD). METHOD To address this knowledge gap, we compared manually traced hippocampal volumes in 37 BD patients with at least 2 years of Li treatment (Li group), 19 BD patients with <3 months of lifetime Li exposure over 2 years ago (non-Li group) and 50 healthy controls. All BD participants were followed prospectively and had at least 10 years of illness and a minimum of five episodes. We established illness course and long-term treatment response to Li using National Institute of Mental Health (NIMH) life charts. RESULTS The non-Li group had smaller hippocampal volumes than the controls or the Li group (F 2,102 = 4.97, p = 0.009). However, the time spent in a mood episode on the current mood stabilizer was more than three times longer in the Li than in the non-Li group (t(51) = 2.00, p = 0.05). Even Li-treated patients with BD episodes while on Li had hippocampal volumes comparable to healthy controls and significantly larger than non-Li patients (t(43) = 2.62, corrected p = 0.02). CONCLUSIONS Our findings support the neuroprotective effects of Li. The association between Li treatment and hippocampal volume seems to be independent of long-term treatment response and occurred even in subjects with episodes of BD while on Li. Consequently, these effects of Li on brain structure may generalize to patients with neuropsychiatric illnesses other than BD.

A prospective 4-year naturalistic follow-up of treatment and outcome of 300 bipolar I and II patients.

BACKGROUND Because randomized clinical trials in bipolar disorder include restricted study populations, the possibilities for generalizing to real-world bipolar patients are limited. Naturalistic long-term data can add valuable information about the diversity of treatment and outcome in bipolar disorder. METHOD After discharge from a psychiatric community hospital, 300 consecutively admitted patients with ICD-10 bipolar I (n = 158) and II (n = 142) disorder were followed up naturalistically for 4 years. Patients were assessed with regard to time to relapse, relapse polarity in relation to index episode, prophylactic effects of prescribed medication, prescribing behaviors, and medication adherence. Drugs were chosen by the treating psychiatrists on the basis of clinical judgment. Prescribed medications included lithium, carbamazepine, valproate, lamotrigine, antidepressants, and atypical antipsychotics, all of which were compared as a single mood stabilizer or in combination with at least 2 prophylactic agents. The study was conducted from 2000 through 2008. RESULTS 204 of 300 patients (68%) relapsed within 4 years, with a mean of 208 days (SD = 356.2) until the next affective episode. Relapses correlated in a statistically significant manner with the index episode (χ²₄ = 57.48, P = .000; bipolar I: χ²₄ = 20.19, P = .000; bipolar II: χ²₄ = 106.82, P = .000). A Kaplan survival analysis showed that lithium in monotherapy statistically significantly delayed time to the next affective relapse (P = .002). Survival (time to relapse) was also statistically significantly reduced when prophylactic medication was changed by the psychiatrist (P = .000) or stopped by the patient (P = .001). In general, no differences in tested parameters were seen between the bipolar I and II groups. CONCLUSIONS Our data confirm a high risk of relapse in a naturalistic setting. Lithium seems to offer some advantage over other medication over the long-term treatment of bipolar I and II disorder. Patients tend to relapse with the same polarity as their index episode; this emphasizes the importance of the polarity concept. Changing of medications by the psychiatrist and stopping of medication by the patient appear to be risk factors for an earlier affective relapse. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01792128.

Impact Of Sunlight on the Age Of Onset Of Bipolar Disorder

Bauer M, Glenn T, Alda M, Andreassen OA, Ardau R, Bellivier F, Berk M, Bjella TD, Bossini L, Del Zompo M, Dodd S, Fagiolini A, Frye MA, Gonzalez‐Pinto A, Henry C, Kapczinski F, Kliwicki S, König B, Kunz M, Lafer B, Lopez‐Jaramillo C, Manchia M, Marsh W, Martinez‐Cengotitabengoa M, Melle I, Morken G, Munoz R, Nery FG, O’Donovan C, Pfennig A, Quiroz D, Rasgon N, Reif A, Rybakowski J, Sagduyu K, Simhandl C, Torrent C, Vieta E, Zetin M, Whybrow PC. Impact of sunlight on the age of onset of bipolar disorder. Bipolar Disord 2012: 14: 654–663.

Relationship between sunlight and the age of onset of bipolar disorder: an international multisite study.

BACKGROUND The onset of bipolar disorder is influenced by the interaction of genetic and environmental factors. We previously found that a large increase in sunlight in springtime was associated with a lower age of onset. This study extends this analysis with more collection sites at diverse locations, and includes family history and polarity of first episode. METHODS Data from 4037 patients with bipolar I disorder were collected at 36 collection sites in 23 countries at latitudes spanning 3.2 north (N) to 63.4 N and 38.2 south (S) of the equator. The age of onset of the first episode, onset location, family history of mood disorders, and polarity of first episode were obtained retrospectively, from patient records and/or direct interview. Solar insolation data were obtained for the onset locations. RESULTS There was a large, significant inverse relationship between maximum monthly increase in solar insolation and age of onset, controlling for the country median age and the birth cohort. The effect was reduced by half if there was no family history. The maximum monthly increase in solar insolation occurred in springtime. The effect was one-third smaller for initial episodes of mania than depression. The largest maximum monthly increase in solar insolation occurred in northern latitudes such as Oslo, Norway, and warm and dry areas such as Los Angeles, California. LIMITATIONS Recall bias for onset and family history data. CONCLUSIONS A large springtime increase in sunlight may have an important influence on the onset of bipolar disorder, especially in those with a family history of mood disorders.

Influence of light exposure during early life on the age of onset of bipolar disorder

BACKGROUND Environmental conditions early in life may imprint the circadian system and influence response to environmental signals later in life. We previously determined that a large springtime increase in solar insolation at the onset location was associated with a younger age of onset of bipolar disorder, especially with a family history of mood disorders. This study investigated whether the hours of daylight at the birth location affected this association. METHODS Data collected previously at 36 collection sites from 23 countries were available for 3896 patients with bipolar I disorder, born between latitudes of 1.4 N and 70.7 N, and 1.2 S and 41.3 S. Hours of daylight variables for the birth location were added to a base model to assess the relation between the age of onset and solar insolation. RESULTS More hours of daylight at the birth location during early life was associated with an older age of onset, suggesting reduced vulnerability to the future circadian challenge of the springtime increase in solar insolation at the onset location. Addition of the minimum of the average monthly hours of daylight during the first 3 months of life improved the base model, with a significant positive relationship to age of onset. Coefficients for all other variables remained stable, significant and consistent with the base model. CONCLUSIONS Light exposure during early life may have important consequences for those who are susceptible to bipolar disorder, especially at latitudes with little natural light in winter. This study indirectly supports the concept that early life exposure to light may affect the long term adaptability to respond to a circadian challenge later in life.

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.

The prevalence and effect of life events in 222 bipolar I and II patients: A prospective, naturalistic 4 year follow-up study

BACKGROUND Life events may very well increase the likelihood of affective episodes in bipolar disorder, but prospective data on survival are inconsistent. METHODS The authors examined the prevalence of negative and goal-attainment life events within 6 months prior to the index episode and after the index episode and their impact on the risk of relapse. Two hundred twenty-two consecutively admitted ICD-10 bipolar I (n=126) and II (n=96) patients were followed-up naturalistically over a period of 4 years. RESULTS One-hundred thirty-eight (62.2%) of the patients had at least one life event 6 month before the index episode. Seventy patients (31.5%) experienced one, 48 (21.6%) two, and 20 (9.0%) three (or more) life events. Regarding life events after the index episode, 110 (49.5%) patients had at least one life event. Fifty-four patients (24.3%) experienced one, 31 (14.0%) two, and 25 (11.3%) three (or more) life events. The number of life events was larger in patients with bipolar II disorder than in patients with bipolar I disorder (p=0.004). Using a Cox regression analysis, the risk of a depressive relapse in bipolar I patients was associated with the number of life events after the index episode (p=0.002). This was independent of the quality of the life event. LIMITATIONS Standardized life event scales, defined dosages of drugs or blood sampling during all visits were not performed. CONCLUSIONS Our data suggest a high and continuous number of life events prior to affective episodes. Life events after the index episode worsened the course of bipolar I patients with more depressive episodes. This underlines the importance of detection and treatment of emerging life events.

Prevalence and impact of comorbid alcohol use disorder in bipolar disorder: A prospective follow-up study

Objective: Alcohol use disorder may very well increase the likelihood of affective episodes in bipolar disorder, but prospective data on survival are inconsistent. Method: The authors examined the prevalence of alcohol use disorders and their impact on the risk of relapse. A total of 284 consecutively admitted International Classification of Diseases-10 bipolar I (n = 161) and II (n = 123) patients were followed up naturalistically over a period of 4 years. Results: The prevalence of alcohol use disorders was higher in bipolar II disorder than in bipolar I disorder (26.8% vs 14.9%; χ2 = 5.46, p = 0.019), with a global prevalence of alcohol use disorders of 20.1% in the whole sample. A total of 8.7% of bipolar I patients suffered from alcohol abuse and 6.2% from alcohol dependency, whereas 13% bipolar II patients had alcohol abuse and 13.8% alcohol dependency. Male bipolar subjects had a higher prevalence of alcohol use disorders than female patients (38.3% vs 12.8%; χ2 = 21.84, p-value < 0.001). The presence of alcohol use disorders was associated with an increased risk of depressive relapse in bipolar I patients (Cox regression analysis hazard ratio = 2.7, p = 0.005). The increased risk was not modulated by medication. Conclusion: Our data underline the negative long-term impact of alcohol use disorders on bipolar disorder with more depressive bipolar I episodes and the importance of its detection and treatment.

Psychiatric and physical comorbidities and their impact on the course of bipolar disorder: A prospective, naturalistic 4‐year follow‐up study

The aim of the present study was to increase the available evidence on how physical and psychiatric comorbidities influence the long‐term outcome in bipolar I and II disorder.

Impulsivity predicts illness severity in long-term course of bipolar disorder: A prospective approach:

Background: Bipolar disorder is a common, severe and chronic mental illness. Despite this, predictors of illness severity remain poorly understood. Impulsivity is reported to be associated with bipolar disorder and aggravating comorbidities. This study therefore sought to examine the predictive value of impulsivity for determining illness severity in euthymic bipolar disorder patients. Methods: Baseline trait impulsivity of 120 bipolar euthymic patients (81 bipolar disorder I [68%], 80 female [67%]) and 51 healthy controls was assessed using Barratt Impulsiveness Scale 11. The impact of impulsivity on illness severity (measured with morbidity index) was prospectively tested in 97 patients with sufficient follow-up data (average observation time: 54.4 weeks), using linear regression analysis. Results: Barratt Impulsiveness Scale 11 total (β = 0.01; p < 0.01) and in particular Barratt Impulsiveness Scale 11 attentional subscale scores (β = 0.04; p < 0.001) predicted illness severity in bipolar disorder, while controlling for other clinical variables. Only age at onset persisted as an additional, but less influential predictor. Barratt Impulsiveness Scale 11 total scores and Barratt Impulsiveness Scale 11 attentional subscale scores were significantly higher in euthymic patients compared to controls. This was not observed for the motor or non-planning subscale scores. Limitations: The average year-long observation time might not be long enough to account for the chronic course of bipolar disorder. Conclusion: Trait impulsivity and particularly attentional impulsivity in euthymic bipolar patients can be strong predictors of illness severity in bipolar disorder. Future studies should explore impulsivity as a risk assessment for morbidity and as a therapeutic target in bipolar disorder patients.