To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Lithium is generally regarded as a first-line option for the long-term treatment (ie, maintenance treatment, prophylactic treatment) of bipolar disorders. However, there is a substantial amount of uncertainty regarding the most efficacious plasma concentration of this drug for this indication.
To allow clinical psychiatrists to practice evidence-based medicine when it comes to decide which lithium levels to target in the long-term treatment of their patients with bipolar disorders.
We will present the available evidence from randomized controlled trials (RCTs) explicitly addressing the issue of optimal lithium levels, show new data from post-hoc analyses of more recent approval-seeking RCTs including lithium as a comparator drug, discuss the methodological limitations and pitfalls inherent in all these studies and address open questions still waiting to be answered.
The available evidence suggests that lithium levels ≥0.6 mmol/L will be necessary for optimal protection against both manic/mixed and depressive episodes. For most patients an increase in lithium levels beyond 0.8 mmol/L will not be associated with superior efficacy against either manic/mixed or depressive episodes. In contrast, lithium levels between 0.4 - 0.6 mmol/L may be sufficient, at least for some patients, for optimal protection against pure depressive episodes.
Lithium levels between 0.6 - 0.8 mmol/L seem to be associated with optimal protection against both manic/mixed and depressive episodes in the long-term treatment of bipolar disorders.
Long-term lithium-treatment has been associated with deficits in several cognitive domains in euthymic bipolar patients. At the same time, long-term lithium treatment is also associated with an increase in parathyroid levels, often without a concomitant increase in calcium levels. Such an isolated increase in parathyroid levels has been linked to depressive symptoms and cognitive deficits in otherwise healthy individuals.
To investigate whether increased parathyroid levels are associated with cognitive deficits in euthymic bipolar patients.
We plan to recruit 30 euthymic bipolar patients on lithium treatment for this study. Patients will take part in several neuropsychological tests, covering executive functioning, memory and attention. In parallel, blood levels of lithium, parathyroid hormone, 25-hydroxyvitamin D, creatinine, calcium and phosphate will be assessed, besides clinical chemistry and blood cell count. In addition, to account for potential confounders, a variety of clinical variables will be recorded, including established mood rating scales and demographic variables as well as further parameters relevant to the course of the illness.
As the study is still ongoing results are not available yet at this moment.
Results will be discussed in the context of previous studies examining the impact of lithium and parathyroid hormone on mood and cognition in healthy individuals and patients with bipolar disorder, respectively. Dependent on the outcome of this study, potential future studies, including intervention trials aiming at lowering increased PTH levels in bipolar patients on lithium will be outlined.
In the multilayered treatment of bipolar patients, medication treatment is the basic component. The relevance of additional psychosocial treatment has been shown in several controlled and uncontrolled studies. In particular, psychoeducation and Cognitive Behavioural Therapy (CBT) seem to be effective concerning relapse prevention and symptom reduction. The aim of this 12 months randomised study was to examine the efficacy of a psychoeducation programme integrating cognitive behavioural elements in bipolar patients. We are presenting data at 3 months follow-up.
Medicated bipolar patients (CGI ≤ 3) were randomised to psychoeducation (intervention group) or waiting list (control group). The psychoeducation programme took place once a week over a period of 12 weeks, focusing on the following major topics: information on the origins of the illness, medical and psychological treatment options, how to detect symptoms and early warning signs, crisis management and how to maintain a regular lifestyle. The patients' knowledge of bipolar disorder, symptoms, social functioning, quality of life and medication compliance were assessed before and after the intervention and every three months for a total of 12 months. For the statistical analysis, mixed models were applied in order to evaluate group differences over time.
Forty patients have been randomised. On the outcome variables (symptoms, social functioning, knowledge), the patients receiving the early psychoeducation programme did numerically better on all variables than the control group. However, in terms of statistical significance, only trends could be detected.
Our findings support the benefits of psychoeducation in the management of bipolar disorder.
Affective disorders are associated with an increased risk of cardiovascular disease, which, at least partly, appears to be independent of psychopharmacological treatments used to manage these disorders. Reduced heart rate variability (SDNN) and a low Omega-3 Index have been shown to be associated with increased risk for death after myocardial infarction. Therefore, we set out to investigate heart rate variability and the Omega-3 Index in euthymic patients with bipolar disorders.
We assessed heart rate variability (SDNN) and the Omega-3 Index in 90 euthymic, mostly medicated patients with bipolar disorders (Bipolar-I, Bipolar-II) on stable psychotropic medication, free of significant medical comorbidity and in 62 healthy controls. Heart rate variability was measured from electrocardiography under a standardized 30 minutes resting state condition. Age, sex, BMI, smoking, alcohol consumption and caffeine consumption as potential confounders were also assessed.
Heart rate variability (SDNN) was significantly lower in patients with bipolar disorders compared to healthy controls (35.4 msec versus 60.7 msec; P < 0.0001), whereas the Omega-3 Index did not differ significantly between the groups (5.2% versus 5.3%). In a linear regression model, only group membership (patients with bipolar disorders versus healthy controls) and age significantly predicted heart rate variability (SDNN).
Heart rate variability (SDNN) may provide a useful tool to study the impact of interventions aimed at reducing the increased risk of cardiovascular disease in euthymic patients with bipolar disorders. The difference in SDNN between cases and controls cannot be explained by a difference in the Omega-3 Index.
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.
Email your librarian or administrator to recommend adding this to your organisation's collection.