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Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.

To examine cytokine receptor biomarkers in bipolar disorder (BD), we recruited 133 well-phenotyped BD patients and 50 normal controls and measured serum levels of soluble interleukin 1 receptor antagonist (sIL-1RA), soluble interleukin-2 receptor (sIL-2R), sIL-6R, and tumor necrosis factor receptor 60 and 80 kDa (sTNFR60/80). sIL-1RA and sTNFR80 are significantly higher in BD than in controls and sTNFR80 and higher in melancholic than in non-melancholic patients and controls. Kapczinski’s stages 3 + 4 are characterized by lowered sIL-2R and increased sTNFR80 levels. Acute phase depression is characterized by increased sTNFR80 levels as compared with controls, manic, and euthymic patients. Both sTNFR60 and sTNFR80 levels are significantly and positively related with severity of depression but not mania. Logistic regression analysis showed that the significant predictors for BD are increased sIL-1RA levels, nicotine dependence and a family history of depression and alcoholism. The risk factors for stages 3 + 4 are lowered sIL-2R levels and nicotine dependence. Melancholia is predicted by higher sTNFR80 levels and female sex. Severity of depression is predicted by female sex, nicotine dependence, and increased sTNFR60 and sTNFR80 levels. Cell-mediated immunity is activated during a current episode of depression but not (hypo)mania or the euthymic state. There are no associations between the biomarkers and age at onset, duration of illness, severity of mania, bipolar (BP)2 or BP1 subtypes, rapid cycling, atypical depression, psychotic or suicidal symptoms, and a family history of psychiatric disease. The results show that increased sIL-1RA may be a trait marker of BD, increased sTNFR80 a state marker of the depressive phase, especially melancholia, while lower sIL-2R but higher sTNFR80 may be staging biomarkers.

Background: Lowered antioxidant defense systems and increased oxidative stress are implicated in bipolar disorders (BD). Early and late stages of BD may present different biological features (including the level of oxidative stress) and may therefore require different treatment strategies. The aim of this study was to analyze serum levels of lipid peroxidation [measured as thiobarbituric acid-reactive substances (TBARS), a derivative of malondialdehyde] in BD patients at various stages and phases of the illness and compare their TBARS levels with those of healthy controls. Method: A total of 129 patients (58 in the depressive episode, 23 in the manic episode and 48 in remission) diagnosed with type I (n = 69) or type II (n = 60) BD and 50 healthy volunteers (control group) were enrolled in the study. The level of lipid peroxidation was measured in blood serum using a TBARS assay kit. Results: TBARS levels in the acute episode of mania/hypomania and depression (but not in remission) were significantly higher than in healthy controls. With regard to the BD stage, both early- and late-stage BD TBARS levels were significantly increased in patients in the depressive episode. In late-stage BD, the TBARS level in patients in remission remained elevated compared with controls. A multiple regression model confirmed the association between the TBARS level and BD stage or acute BD. Conclusion: Our findings indicate that TBARS levels reflect the oxidative stress state which increases both in the acute phase of BD (mania/hypomania and depression) and with BD progression (stage).

Objectives Zinc may be involved in the pathophysiology and treatment of depressive disorder. However, data on this issue in bipolar disorder (BD) are limited. The aim of the study was to assess zinc concentrations in the blood serum of patients at various phases and stages of bipolar disorder. Methods The study included 129 patients with a diagnosis of bipolar disorder type I (n=69) or type II (n=60). Fifty-eight were in a depressive episode, 23 in a manic episode and 48 in remission. Fifty healthy volunteers made a control group. Zinc concentration was measured using flame atomic absorption spectrometry. Results Serum zinc level in patients diagnosed with BD type I in the depressive phase was significantly reduced as compared with mania, remission and healthy subjects. In the BD type II, serum zinc level in hypomania, depression or remission phase was not significantly different from the control group. In the whole group, lower level of zinc in depression compared to remission and control subjects was found during late stage of the illness but not in the early stage. Zinc concentration was not dependent on the severity of manic or depressive symptoms and subtype of depression but correlated positively with the number of manic/hypomanic relapses in the past year. Limitations Lack of prospective model, heterogeneity of pharmacological treatment, small number of subgroups presenting specified clinical features. Conclusions Decreased serum zinc concentration occurs in depression in BD type I and probably in depression in the late stage of BD.

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.

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.

Background Hyperprolactinemia is frequent in patients with schizophrenic psychoses. It is usually regarded as an adverse effect of antipsychotics but has recently also been shown in patients without antipsychotic medication. Our objective was to test whether hyperprolactinemia occurs in antipsychotic-naive first-episode patients (FEPs). Method In the framework of the European First Episode Schizophrenia Trial (EUFEST), 249 out of 498 FEPs were eligible for this study, of whom 74 were antipsychotic naive. All patients were investigated regarding their serum prolactin levels with immunoassays standardized against the 3rd International Reference Standard 84/500. Results Twenty-nine (39%) of the 74 antipsychotic-naive patients showed hyperprolactinemia not explained by any other reason, 11 (50%) of 22 women and 18 (35%) of 52 men. Conclusions Hyperprolactinemia may be present in patients with schizophrenic psychoses independent of antipsychotic medication. It might be stress induced. As enhanced prolactin can increase dopamine release through a feedback mechanism, this could contribute to explaining how stress can trigger the outbreak of psychosis

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. (C) 2014 Elsevier Masson SAS. All rights reserved.

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. (C) 2014 Elsevier Masson SAS. All rights reserved.

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 (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). 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.

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 (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). 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.

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 (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). 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.

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 (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). 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.

Objective: The risk-benefit profile of antidepressant medications in bipolar disorder is controversial. When conclusive evidence is lacking, expert consensus can guide treatment decisions. The International Society for Bipolar Disorders (ISBD) convened a task force to seek consensus recommendations on the use of antidepressants in bipolar disorders.Method: Anexpert task force iteratively developed consensus through serial consensusbased revisions using the Delphi method. Initial survey items were based on systematic review of the literature. Subsequent surveys included new or reworded items and items that needed to be rerated. This process resulted in the final ISBD Task Force clinical recommendations on antidepressant use in bipolar disorder. Results: There is striking incongruity between the wide use of and the weak evidence base for the efficacy and safety of antidepressant drugs in bipolar disorder. Few well-designed, long-term trials of prophylactic benefits have been conducted, and there is insufficient evidence for treatment benefits with antidepressants combined with mood stabilizers. A major concern is the risk for mood switch to hypomania, mania, and mixed states. Integrating the evidence and the experience of the task force members, a consensus was reached on 12 statements on the use of antidepressants in bipolar disorder. Conclusions: Because of limited data, the task force could not make broad statements endorsing antidepressant use but acknowledged that individual bipolar patients may benefit from antidepressants. Regarding safety, serotonin reuptake inhibitors and bupropion may have lower rates of manic switch than tricyclic and tetracyclic antidepressants and norepinephrine-serotonin reuptake inhibitors. The frequency and severity of antidepressant-associated mood elevations appear to be greater in bipolar I than bipolar II disorder. Hence, in bipolar I patients antidepressants should be prescribed only as an adjunct to moodstabilizing medications.

Background Hyperprolactinemia is frequent in patients with schizophrenic psychoses. It is usually regarded as an adverse effect of antipsychotics but has recently also been shown in patients without antipsychotic medication. Our objective was to test whether hyperprolactinemia occurs in antipsychotic-naive first-episode patients (FEPs). Method In the framework of the European First Episode Schizophrenia Trial (EUFEST), 249 out of 498 FEPs were eligible for this study, of whom 74 were antipsychotic naive. All patients were investigated regarding their serum prolactin levels with immunoassays standardized against the 3rd International Reference Standard 84/500. Results Twenty-nine (39%) of the 74 antipsychotic-naive patients showed hyperprolactinemia not explained by any other reason, 11 (50%) of 22 women and 18 (35%) of 52 men. Conclusions Hyperprolactinemia may be present in patients with schizophrenic psychoses independent of antipsychotic medication. It might be stress induced. As enhanced prolactin can increase dopamine release through a feedback mechanism, this could contribute to explaining how stress can trigger the outbreak of psychosis