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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.
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, outcome and risk factors in bipolar disorder.
After discharge from a psychiatric community hospital, 300 consecutively admitted ICD-10 bipolar I (n=158) and II (n=142) patients were followed-up naturalistically during for 4 years. Patients were assessed as to time to relapse, relapse in relation to index episode, prophylactic effects of prescribed medication, and risk factors for relapses such as prescribing attitudes, medication adherence, life events and alcohol use disorders.
204 (68%) of 300 patients 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. Using a Kaplan survival analysis, only lithium delayed time to the next affective relapse in a statistically significant way. Survival was reduced in a statistically significant manner when prophylactic medication was replaced by the psychiatrist or stopped by the patient. In a sub-analysis of this cohort life events (n=222) and alcohol use disorders (n=284) were associated with more depressive episodes in bipolar I patients.
Even though lithium seems more protective than other commonly used drugs, bipolar patients still suffer from a high relapse rate. Bad adherence, life events and alcohol use disorders are hereby major risk factors. The results urge for further and more holistic treatment approaches in bipolar disorder.
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).
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.
The non-Li group had smaller hippocampal volumes than the controls or the Li group (F2,102 = 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 (t51 = 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 (t43 = 2.62, corrected p = 0.02).
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.
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