A pilot study examining the impact of lithium treatment and responsiveness on mnemonic discrimination in bipolar disorder.

INTRODUCTION: Mnemonic discrimination (MD), the ability to discriminate new stimuli from similar memories, putatively involves dentate gyrus pattern separation. Since lithium may normalize dentate gyrus functioning in lithium-responsive bipolar disorder (BD), we hypothesized that lithium treatment would be associated with better MD in lithium-responsive BD patients. METHODS: BD patients (N = 69; NResponders = 16 [23 %]) performed the Continuous Visual Memory Test (CVMT), which requires discriminating between novel and previously seen images. Before testing, all patients had prophylactic lithium responsiveness assessed over ≥1 year of therapy (with the Alda Score), although only thirty-eight patients were actively prescribed lithium at time of testing (55 %; 12/16 responders, 26/53 nonresponders). We then used computational modelling to extract patient-specific MD indices. Linear models were used to test how (A) lithium treatment, (B) lithium responsiveness via the continuous Alda score, and (C) their interaction, affected MD. RESULTS: Superior MD performance was associated with lithium treatment exclusively in lithium-responsive patients (Lithium x AldaScore ß = 0.257 [SE 0.078], p = 0.002). Consistent with prior literature, increased age was associated with worse MD (ß = -0.03 [SE 0.01], p = 0.005). LIMITATIONS: Secondary pilot analysis of retrospectively collected data in a cross-sectional design limits generalizability. CONCLUSION: Our study is the first to examine MD performance in BD. Lithium is associated with better MD performance only in lithium responders, potentially due to lithium's effects on dentate gyrus granule cell excitability. Our results may influence the development of behavioural probes for dentate gyrus neuronal hyperexcitability in BD.

Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis.

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

Correction of depression-associated circadian rhythm abnormalities is associated with lithium response in bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is characterized by episodes of depression and mania and disrupted circadian rhythms. Lithium is an effective therapy for BD, but only 30%-40% of patients are fully responsive. Preclinical models show that lithium alters circadian rhythms. However, it is unknown if the circadian rhythm effects of lithium are essential to its therapeutic properties. METHODS: In secondary analyses of a multi-center, prospective, trial of lithium for BD, we examined the relationship between circadian rhythms and therapeutic response to lithium. Using standardized instruments, we measured morningness, diurnal changes in mood, sleep, and energy (circadian rhythm disturbances) in a cross-sectional study of 386 BD subjects with varying lithium exposure histories. Next, we tracked symptoms of depression and mania prospectively over 12 weeks in a subset of 88 BD patients initiating treatment with lithium. Total, circadian, and affective mood symptoms were scored separately and analyzed. RESULTS: Subjects with no prior lithium exposure had the most circadian disruption, while patients stable on lithium monotherapy had the least. Patients who were stable on lithium with another drug or unstable on lithium showed intermediate levels of disruption. Treatment with lithium for 12 weeks yielded significant reductions in total and affective depression symptoms. Lithium responders (Li-Rs) showed improvement in circadian symptoms of depression, but non-responders did not. There was no difference between Li-Rs and nonresponders in affective, circadian, or total symptoms of mania. CONCLUSIONS: Exposure to lithium is associated with reduced circadian disruption. Lithium response at 12 weeks was selectively associated with the reduction of circadian depressive symptoms. We conclude that stabilization of circadian rhythms may be an important feature of lithium's therapeutic effects. CLINICAL TRIALS REGISTRY: NCT0127253.

The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes.

BACKGROUND: Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. RESULTS: There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. CONCLUSIONS: The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short.

Clinical predictors of non-response to lithium treatment in the Pharmacogenomics of Bipolar Disorder (PGBD) study.

BACKGROUND: Lithium is regarded as a first-line treatment for bipolar disorder (BD), but partial response and non-response commonly occurs. There exists a need to identify lithium non-responders prior to initiating treatment. The Pharmacogenomics of Bipolar Disorder (PGBD) Study was designed to identify predictors of lithium response. METHODS: The PGBD Study was an eleven site prospective trial of lithium treatment in bipolar I disorder. Subjects were stabilized on lithium monotherapy over 4 months and gradually discontinued from all other psychotropic medications. After ensuring a sustained clinical remission (defined by a score of ≤3 on the CGI for 4 weeks) had been achieved, subjects were followed for up to 2 years to monitor clinical response. Cox proportional hazard models were used to examine the relationship between clinical measures and time until failure to remit or relapse. RESULTS: A total of 345 individuals were enrolled into the study and included in the analysis. Of these, 101 subjects failed to remit or relapsed, 88 achieved remission and continued to study completion, and 156 were terminated from the study for other reasons. Significant clinical predictors of treatment failure (p < 0.05) included baseline anxiety symptoms, functional impairments, negative life events and lifetime clinical features such as a history of migraine, suicidal ideation/attempts, and mixed episodes, as well as a chronic course of illness. CONCLUSIONS: In this PGBD Study of lithium response, several clinical features were found to be associated with failure to respond to lithium. Future validation is needed to confirm these clinical predictors of treatment failure and their use clinically to distinguish who will do well on lithium before starting pharmacotherapy.

Exemplar scoring identifies genetically separable phenotypes of lithium responsive bipolar disorder.

Predicting lithium response (LiR) in bipolar disorder (BD) may inform treatment planning, but phenotypic heterogeneity complicates discovery of genomic markers. We hypothesized that patients with "exemplary phenotypes"-those whose clinical features are reliably associated with LiR and non-response (LiNR)-are more genetically separable than those with less exemplary phenotypes. Using clinical data collected from people with BD (n = 1266 across 7 centers; 34.7% responders), we computed a "clinical exemplar score," which measures the degree to which a subject's clinical phenotype is reliably predictive of LiR/LiNR. For patients whose genotypes were available (n = 321), we evaluated whether a subgroup of responders/non-responders with the top 25% of clinical exemplar scores (the "best clinical exemplars") were more accurately classified based on genetic data, compared to a subgroup with the lowest 25% of clinical exemplar scores (the "poor clinical exemplars"). On average, the best clinical exemplars of LiR had a later illness onset, completely episodic clinical course, absence of rapid cycling and psychosis, and few psychiatric comorbidities. The best clinical exemplars of LiR and LiNR were genetically separable with an area under the receiver operating characteristic curve of 0.88 (IQR [0.83, 0.98]), compared to 0.66 [0.61, 0.80] (p = 0.0032) among poor clinical exemplars. Variants in the Alzheimer's amyloid-secretase pathway, along with G-protein-coupled receptor, muscarinic acetylcholine, and histamine H1R signaling pathways were informative predictors. This study must be replicated on larger samples and extended to predict response to other mood stabilizers.

The association between lithium use and neurocognitive performance in patients with bipolar disorder.

Lithium remains the gold standard for the treatment of bipolar disorder (BD); however, its use has declined over the years mainly due to the side effects and the subjective experience of cognitive numbness reported by patients. In the present study, we aim to methodically test the effects of lithium on neurocognitive functioning in the largest single cohort (n = 262) of BD patients reported to date by harnessing the power of a multi-site, ongoing clinical trial of lithium monotherapy. At the cross-sectional level, multivariate analysis of covariance (MANCOVA) was conducted to examine potential group differences across neurocognitive tests [California Verbal Learning Test (CVLT trials 1-5,CVLT delayed recall), Wechsler Digit Symbol, Trail-making Test parts A and B (TMT-A; TMT-B), and a global cognition index]. At the longitudinal level, on a subset of patients (n = 88) who achieved mood stabilization with lithium monotherapy, we explored the effect of lithium treatment across time on neurocognitive functioning. There were no differences at baseline between BD patients that were taking lithium compared with those that were not. At follow-up a significant neurocognitive improvement in the global cognitive index score [F = 31.69; p < 0.001], CVLT trials 1-5 [F = 29.81; p < 0.001], CVLT delayed recall [F = 15.27; p < 0.001], and TMT-B [F = 6.64, p = 0.012] was detected. The cross-sectional and longitudinal (on a subset of 88 patients) investigations suggest that lithium may be beneficial to neurocognitive functioning in patients with BD and that at the very least it does not seem to significantly impair cognition when used therapeutically.

Brain age in bipolar disorders: Effects of lithium treatment.

OBJECTIVE: Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk of dementia. However, until now no study has specifically investigated the effects of Li on brain age. METHODS: We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03. CONCLUSIONS: Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.

Early-onset and very-early-onset bipolar disorder: distinct or similar clinical conditions?

OBJECTIVE: This study aimed to examine differences in the clinical presentation of very-early-onset (VEO) and early-onset (EO) bipolar disorder (BD) not fully explored previously. METHODS: We selected two groups of subjects with BD from the Maritime Bipolar Registry based on age at onset of first major mood episode (VEO with onset prior to age 15 years; EO ranging from 15 to 18 years) and compared them with a reference group (onset after 18 years of age). There were 363 subjects (240 with bipolar I disorder and 123 with bipolar II disorder; mean age 44.2 ± 12.8 (SD) years), with 41 subjects in the VEO and 95 in the EO groups. RESULTS: In comparison with the EO and reference groups, more subjects in the VEO group developed major depression as an index episode (88% for the VEO group versus 61% for the EO group and 54% for the reference group), and had an unremitting clinical course (65% versus 42% and 42%, respectively), rapid cycling (54% versus 34% and 28%, respectively), and comorbid attention-deficit hyperactivity disorder (17% versus 1% and 3%, respectively); a higher proportion of the VEO group had first-degree relatives with affective disorders compared with the EO and reference groups (0.41 versus 0.32 and 0.29, respectively), and they had lower scores on the Global Assessment of Functioning scale (mean scores of 64 versus 70 and 70). Overall, the EO group was similar to the reference group on most measures, except for increased suicidal behavior VEO 53%, EO 44% and reference group 25%). The results of polychotomous logistic regression also support the view that VEO BD represents a rather specific subtype of BD. CONCLUSIONS: Our results suggest the recognized correlates of early-onset BD may be driven by subjects at the lowest end of the age at onset spectrum.

Nonlinear dynamics of mood regulation in bipolar disorder.

OBJECTIVES: We sought to study the underlying dynamic processes involved in mood regulation in subjects with bipolar disorder and healthy control subjects using time-series analysis and to then analyze the relation between anxiety and mood using cross-correlation techniques. METHODS: We recruited 30 healthy controls and 30 euthymic patients with bipolar disorder. Participants rated their mood, anxiety, and energy levels using a paper-based visual analog scale; and they also recorded their sleep and any life events. Information on these variables was provided over a three-month period on a daily basis, twice per day. We analyzed the data using Box-Jenkins time series analysis to obtain information on the autocorrelation of the series (for mood) and cross-correlation (mood and anxiety series). RESULTS: Throughout the study, we analyzed 10,170 data points. Self-ratings for mood, anxiety, and energy were normally distributed in both groups. Autocorrelation functions for mood in both groups were governed by the autoregressive integrated moving average (ARIMA) (1,1,0) model, which means that current values in the series were related to one previous point only. We also found a negative cross-correlation between mood and anxiety. CONCLUSIONS: Mood can be considered a memory stochastic process; it is a flexible, dynamic process that has a 'short memory' both in healthy controls and euthymic patients with bipolar disorder. This process may be quite different in untreated patients or in those acutely ill. Our results suggest that nonlinear measures can be applied to the study of mood disorders.

Insulin resistance and outcome in bipolar disorder.

BACKGROUND: Little is known about the impact of insulin resistance on bipolar disorder. AIMS: To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in bipolar disorder. METHOD: We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment. RESULTS: Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses. CONCLUSIONS: Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.

Type 2 diabetes mellitus: a potentially modifiable risk factor for neurochemical brain changes in bipolar disorders.

BACKGROUND: Neuroimaging changes in bipolar disorder (BD) may be secondary to the presence of certain clinical factors. Type 2 diabetes mellitus (T2DM) damages the brain and frequently co-occurs with BD. Studying patients with both T2DM and BD could help identify preventable risk factors for neuroimaging changes in BD. METHODS: We used 1.5T magnetic resonance spectroscopy to measure prefrontal N-acetylaspartate (NAA), which is mainly localized in neurons, and total creatine (tCr), an energy metabolite, in 19 BD patients with insulin resistance/glucose intolerance (BD + IR/GI), 14 BD subjects with T2DM (BD + T2DM), 15 euglycemic BD participants, and 11 euglycemic, nonpsychiatric control. RESULTS: The levels of NAA and tCr were lowest among BD + T2DM, intermediate in the BD + IR/GI, and highest among the euglycemic BD and control subjects (F3,55 = 4.57, p = .006; F3,55 = 2.92, p = .04, respectively). Even the BD + IR/GI subjects had lower NAA than the euglycemic participants (t43 = 2.13, p = .04). Total Cr was associated with NAA (ß = .52, t56 = 5.57, p = .000001). Both NAA and tCr correlated with Global Assessment of Functioning scores (r46 = .28, p = .05; r46 = .48, p = .0004, respectively). CONCLUSIONS: T2DM, but also prediabetes, may be risk factors for prefrontal neurochemical alterations in BD. These changes were associated with poor psychosocial functioning and could indicate impaired energy metabolism. The findings emphasize the importance of improving diabetes care in BD and suggest potential options for treatment of neuroimaging alterations.

Insulin resistance, diabetes mellitus, and brain structure in bipolar disorders.

Type 2 diabetes mellitus (T2DM) damages the brain, especially the hippocampus, and frequently co-occurs with bipolar disorders (BD). Reduced hippocampal volumes are found only in some studies of BD subjects and may thus be secondary to the presence of certain clinical variables. Studying BD patients with abnormal glucose metabolism could help identify preventable risk factors for hippocampal atrophy in BD. We compared brain structure using optimized voxel-based morphometry of 1.5T MRI scans in 33 BD subjects with impaired glucose metabolism (19 with insulin resistance/glucose intolerance (IR/GI), 14 with T2DM), 15 euglycemic BD participants and 11 euglycemic, nonpsychiatric controls. The group of BD patients with IR, GI or T2DM had significantly smaller hippocampal volumes than the euglycemic BD participants (corrected p=0.02) or euglycemic, nonpsychiatric controls (corrected p=0.004). Already the BD subjects with IR/GI had smaller hippocampal volumes than euglycemic BD participants (t(32)=-3.15, p=0.004). Age was significantly more negatively associated with hippocampal volumes in BD subjects with IR/GI/T2DM than in the euglycemic BD participants (F(2, 44)=9.96, p=0.0003). The gray matter reductions in dysglycemic subjects extended to the cerebral cortex, including the insula. In conclusion, this is the first study demonstrating that T2DM or even prediabetes may be risk factors for smaller hippocampal and cortical volumes in BD. Abnormal glucose metabolism may accelerate the age-related decline in hippocampal volumes in BD. These findings raise the possibility that improving diabetes care among BD subjects and intervening already at the level of prediabetes could slow brain aging in BD.

An admixture analysis of the age at index episodes in bipolar disorder.

The interaction between polarity at onset (PAO) and age at onset (AAO) appears to be important for interpreting results of previous analyses of AAO in bipolar disorder (BD). Using an admixture analysis, we examined independently the distributions of age at first depressive and hypomanic/manic episodes in 379 BD I and II patients. Subsequently, we examined the association of PAO and AAO with specific clinical variables, using parametric and nonparametric analyses. Both depressive and manic onsets showed bimodal distributions. For depressive episodes, the means were: 18.5±4.1 (early onset) and 33.6±10.4 (late onset) years; and for manic episodes 18.9±3.3 (early onset) and 34.8±10.9 (late onset) years. For the overall AAO the best fit was for a mixture of three lognormal distributions (mean±S.D.): 15.5±2.0, 22.8±4.6, and 36.1±10.1years. Overall, an early onset was significantly associated with a chronic course of the disorder, a stronger family history of affective disorder, higher rates of rapid cycling, suicidal behavior, psychotic symptoms, and co-morbid anxiety disorders. Early onset depressive episodes were associated with higher rates of suicidal behavior and anxiety disorders, whereas early onset manic episodes were associated with psychotic symptoms and rapid cycling. Our results suggest the presence of a bimodal distribution of age at onset in BD according to the polarity of the index episode, and denote that an early onset BD, irrespective of polarity, may be a more serious subtype of the disorder.

Cross-prevalence of migraine and bipolar disorder.

OBJECTIVE: In two related studies, we explored the prevalence of migraine and its associated clinical characteristics in patients with bipolar disorder (BD) as well as psychiatric morbidity in patients treated for migraine. METHOD: The first study included 323 subjects with BD type I (BD I) or BD type II (BD II), diagnosed using the Schedule for Affective Disorders and Schizophrenia, Lifetime version (SADS-L) format, or the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Migraine history was assessed by means of a structured questionnaire. In a second sample of 102 migraine patients, we investigated current and lifetime psychiatric morbidity using the SADS-L. Statistical analyses were conducted using nonparametric analysis and log-linear models. RESULTS: A total of 24.5% of BD patients had comorbid migraine; those with BD II had a higher prevalence (34.8%) compared to BD I (19.1%) (p < 0.005). BD patients with comorbid migraine had significantly higher rates of suicidal behaviour, social phobia, panic disorder, generalized anxiety disorder, and obsessive-compulsive disorder (all p < 0.05). In the sample of migraine patients, 34.3% had a current psychiatric diagnosis, and 73.5% had a lifetime psychiatric diagnosis. The prevalence of BD I was 4.9%, and 7.8% for BD II. DISCUSSION: Migraine is prevalent within the BD population, particularly among BD II subjects. It is associated with an increased risk of suicidal behaviour and comorbid anxiety disorders. Conversely, migraine sufferers have high rates of current and lifetime psychopathology. A greater understanding of this comorbidity may contribute to our knowledge of the underlying mechanisms of BD.

Amygdala and hippocampal volumes in relatives of patients with bipolar disorder: a high-risk study.

OBJECTIVE: Bipolar disorders (BD) have a strong genetic underpinning, yet no biological vulnerability markers for BD have yet been identified. To test whether amygdala or hippocampal volumes represent an endophenotype for BD, we measured mesiotemporal volumes in young affected and unaffected relatives of patients with BD (high-risk design). METHOD: High-risk participants (aged 15 to 30 years) were recruited from families multiply affected with BD. They included 20 affected and 26 unaffected offspring of parents with primary mood disorders, matched by age and sex with 31 control subjects without a personal or family history of psychiatric disorders. Amygdala and hippocampal volumes were measured on 1.5 Tesla 3-dimensional anatomical magnetic resonance images using standard methods. RESULTS: We found comparable amygdala and hippocampal volumes among unaffected relatives, affected high-risk patients, and control subjects. The exclusion of 6 medicated patients did not change the results. There were no differences between participants with family history of BD I, compared with participants with family history of BD II, or between subjects with family history of BD with psychotic symptoms, compared with subjects with family history of BD without psychotic symptoms. CONCLUSIONS: Hippocampal and amygdala volume abnormalities were absent in unaffected and affected relatives of patients with BD and thus did not meet criteria for endophenotype.

Can body mass index help predict outcome in patients with bipolar disorder?

OBJECTIVE: Several studies have reported higher prevalence of obesity in patients suffering from bipolar disorder (BD). To study the relation of elevated body mass index (BMI) in patients with BD more closely, we investigated differences in sociodemographic, clinical, and medical characteristics with respect to BMI, with the hypothesis that BMI is related to prognosis and outcome. METHODS: We measured the BMI of 276 subjects of a tertiary care sample from the Maritime Bipolar Registry. Subjects were 16 to 83 years old, with psychiatric diagnoses of bipolar I disorder (n = 186), bipolar II disorder (n = 85), and BD not otherwise specified (n = 5). The registry included basic demographic data and details of the clinical presentation. We first examined the variables showing a significant association with BMI; subsequently, we modeled the relationship between BMI and psychiatric outcome using structural equation analysis. RESULTS: The prevalence of obesity in our sample was 39.1%. We found higher BMI in subjects with a chronic course (p < 0.001) and longer duration of illness (p = 0.02), lower scores on the Global Assessment of Functioning Scale (p = 0.02), and on disability (p = 0.002). Overweight patients had more frequent comorbid subthreshold social (p = 0.02) and generalized anxiety disorders (p = 0.05), diabetes mellitus type II (p < 0.001), and hypertension (p = 0.001). Subjects who achieved complete remission of symptoms on lithium showed significantly lower BMI (p = 0.01). CONCLUSIONS: Our findings suggest that BMI is associated with the prognosis and outcome of BD. Whether this association is causal remains to be determined.

Striatal volumes in affected and unaffected relatives of bipolar patients--high-risk study.

BACKGROUND: Striatal volume changes reported in bipolar disorders could represent artifacts of medication or comorbid conditions, or illness related changes, either biological predispositions or consequences of illness burden. We conducted volumetric high-risk study to investigate whether striatal volume changes represent primary biological risk factor for bipolar disorders. METHODS: High-risk (HR) participants (age range 15-30 years) were recruited from families multiply affected with bipolar disorders. They included 20 affected and 26 unaffected offspring of parents with primary mood disorders, matched by age and sex with 31 controls without a personal or family history of psychiatric disorders. Striatal volumes were measured on 1.5T 3D anatomical MRI images using standard methods. RESULTS: There was a significant difference between groups (affected, unaffected HR and control subjects) in caudate volumes (F=3.50, DF=2; 74 and p=0.04) in absence of putamen volume changes. The caudate volumes were largest in unaffected HR subjects without differences between affected and control or affected and unaffected HR subjects. The maximum changes were found in the head of the caudate. Controlling for non-independence of observations in multiple subjects per family yielded non-significant differences. CONCLUSIONS: Despite the biological plausibility, partial agreement with previous studies and nominal statistical significance, controlling for non-independence of observations within families changed the increased caudate volumes among unaffected subjects to non-significant. We thus present these findings as negative, pending further replication. Striatal volume abnormalities did not meet criteria for endophenotype in this study.

A comparison of affected and unaffected relatives of patients with bipolar disorder using proton magnetic resonance spectroscopy.

OBJECTIVE: Bipolar disorders have a strong genetic underpinning. Little is known about biological predispositions that convey vulnerability for the illness. We searched for biological vulnerability markers using proton magnetic resonance spectroscopy (MRS) in both affected and unaffected participants at high genetic risk for bipolar disorder. METHODS: We recruited high-risk participants aged 15-30 years from families in which multiple members were affected with bipolar disorder. Our primary sample included 14 affected and 15 unaffected relatives of probands with bipolar I disorder. Our extended sample comprised 19 affected and 21 unaffected participants with a family history of either bipolar I or bipolar II disorders. We matched both samples by age and sex with 31 control participants without a personal or family history of psychiatric disorders. We performed single voxel proton MRS at 1.5 T in bilateral dorsal and ventral medial prefrontal cortices with correction for grey matter proportion. RESULTS: We found comparable levels of choline, creatine, myo-inositol and N-acetylaspartate among the groups in both samples. There were no differences between regions of the medial prefrontal cortex or between hemispheres for any of the metabolites in any of the samples. The exclusion of 5 participants taking medication did not change our results. CONCLUSION: Neurochemical changes in the medial prefrontal cortex that are measurable using proton MRS do not appear to be antecedent to the onset of mood disorders in genetically susceptible individuals.