Claire Slaney

Clinical Features of Bipolar Disorder with and without Comorbid Diabetes Mellitus

Objective: Several papers have reported higher prevalence of diabetes mellitus (DM) type 2 in patients suffering from bipolar disorder (BD). The possible links between these 2 disorders include treatment, lifestyle, alterations in signal transduction, and possibly, a genetic link. To study this relation more closely, we investigated whether there are any differences in the clinical characteristics of BD patients with and without DM. Method: We compared the clinical data of 26 diabetic and 196 nondiabetic subjects from The Maritime Bipolar Registry. Subjects were aged 15 to 82 years, with psychiatric diagnoses of BD I (n = 151), BD II (n = 65), and BD not otherwise specified (n = 6). The registry included basic demographic data and details on the clinical course of bipolar illness, its treatment, and physical comorbidity. In a subsequent analysis using logistic regression, we examined the variables showing differences between groups, with diabetes as an outcome variable. Results: The prevalence of DM in our sample was 11.7% (n = 26). Diabetic patients were significantly older than nondiabetic patients (P < 0.001), had higher rates of rapid cycling (P = 0.02) and chronic course of BD (P = 0.006), scored lower on the Global Assessment of Functioning Scale (P = 0.01), were more often on disability for BD (P < 0.001), and had higher body mass index (P < 0.001) and increased frequency of hypertension (P = 0.003). Lifetime history of treatment with antipsychotics was not significantly associated with an elevated risk of diabetes (P = 0.16); however, the data showed a trend toward more frequent use of antipsychotic medication among diabetic subjects. Conclusions: Our findings suggest that the diagnosis of DM in BD patients is relevant for their prognosis and outcome.

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.

Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report

Objective The assessment of response to lithium maintenance treatment in bipolar disorder (BD) is complicated by variable length of treatment, unpredictable clinical course, and often inconsistent compliance. Prospective and retrospective methods of assessment of lithium response have been proposed in the literature. In this study we report the key phenotypic measures of the “Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder” scale currently used in the Consortium on Lithium Genetics (ConLiGen) study. Materials and Methods Twenty-nine ConLiGen sites took part in a two-stage case-vignette rating procedure to examine inter-rater agreement [Kappa (κ)] and reliability [intra-class correlation coefficient (ICC)] of lithium response. Annotated first-round vignettes and rating guidelines were circulated to expert research clinicians for training purposes between the two stages. Further, we analyzed the distributional properties of the treatment response scores available for 1,308 patients using mixture modeling. Results Substantial and moderate agreement was shown across sites in the first and second sets of vignettes (κ = 0.66 and κ = 0.54, respectively), without significant improvement from training. However, definition of response using the A score as a quantitative trait and selecting cases with B criteria of 4 or less showed an improvement between the two stages (ICC1 = 0.71 and ICC2 = 0.75, respectively). Mixture modeling of score distribution indicated three subpopulations (full responders, partial responders, non responders). Conclusions We identified two definitions of lithium response, one dichotomous and the other continuous, with moderate to substantial inter-rater agreement and reliability. Accurate phenotypic measurement of lithium response is crucial for the ongoing ConLiGen pharmacogenomic study.

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.

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.

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.

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.

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.

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 (β = .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.