Polarity at illness onset in bipolar I disorder and clinical course of illness.

OBJECTIVES: Studies have suggested that episode polarity at illness onset in bipolar disorder may be predictive of some aspects of lifetime clinical characteristics. We here examine this possibility in a large, well-characterized sample of patients with bipolar I disorder. METHODS: We assessed polarity at onset in patients with bipolar I disorder (N = 553) recruited as part of our ongoing studies of affective disorders. Lifetime clinical characteristics of illness were compared in patients who had a depressive episode at first illness onset (n = 343) and patients who had a manic episode at first illness onset (n = 210). RESULTS: Several lifetime clinical features differed between patients according to the polarity of their onset episode of illness. A logistic regression analysis showed that the lifetime clinical features significantly associated with a depressive episode at illness onset in our sample were: an earlier age at illness onset; a predominantly depressive polarity during the lifetime; more frequent and more severe depressive episodes; and less prominent lifetime psychotic features. CONCLUSIONS: Knowledge of pole of onset may help the clinician in providing prognostic information and management advice to an individual with bipolar disorder.

Clinical characteristics of unipolar disorder and bipolar disorder according to the lifetime presence of recurrent panic attacks.

OBJECTIVES: The frequent comorbidity of panic and affective disorders has been described in previous studies. However, it is not clear how panic disorder comorbidity in unipolar disorder and bipolar disorder is related to illness course. METHODS: We compared lifetime clinical characteristics of illness and items of symptomatology in samples of individuals with bipolar I disorder (n = 290) and unipolar disorder (n = 335) according to the lifetime presence of recurrent panic attacks. RESULTS: We found significant differences in clinical course of illness characteristics that were shared across the unipolar and bipolar samples according to the lifetime presence of panic attacks. We also found a number of differences according to the presence of panic attacks that may be specific to the diagnostic group. CONCLUSIONS: Distinguishing patients who have mood disorder diagnoses, especially bipolar I disorder, according to the lifetime presence of panic attacks may not only be of use in clinical practice, but may also be informative for aetiological research, such as molecular genetic studies.

Operation of the schizophrenia susceptibility gene, neuregulin 1, across traditional diagnostic boundaries to increase risk for bipolar disorder.

CONTEXT: Family and twin data suggest that, in addition to susceptibility genes specific for bipolar disorder or schizophrenia, genes exist that contribute to susceptibility across the traditional kraepelinian divide. Several studies have provided evidence that variation at the neuregulin 1 (NRG1) gene on chromosome 8p12 influences susceptibility to schizophrenia. The most consistent finding has been that one particular haplotype (the "core" haplotype) is overrepresented in cases compared with control subjects. OBJECTIVE: To investigate the possible role of NRG1 in bipolar disorder. DESIGN: Genetic case-control association analysis. SETTING: Subjects were unrelated and ascertained from general psychiatric inpatient and outpatient services. PARTICIPANTS: Five hundred twenty-nine patients with DSM-IV bipolar I disorder and 1011 controls from the United Kingdom (100% white). METHODS: We genotyped the markers constituting the NRG1 core haplotype in cases and controls and reanalyzed our existing data from 573 DSM-IV schizophrenia cases with this larger set of controls. RESULTS: We found a significant difference in haplotype distribution between bipolar cases and controls globally (P = .003) and specifically for the core haplotype. Frequencies were 10.2% for bipolar cases and 7.8% for controls (effect size, as measured by odds ratio [OR], 1.37; 95% confidence interval [CI], 1.03-1.80; P = .04). The effect size in our bipolar sample was similar to that in our schizophrenia sample (OR, 1.22; 95% CI, 0.92-1.61). In the bipolar cases with predominantly mood-incongruent psychotic features (n = 193), the effect was greater (OR, 1.71; 95% CI, 1.29-2.59; P = .009), as was the case in the subset of schizophrenia cases (n = 27) who had experienced mania (OR, 1.64; 95% CI, 0.54-5.01). CONCLUSIONS: Our findings suggest that neuregulin 1 plays a role in influencing susceptibility to bipolar disorder and schizophrenia and that it may exert a specific effect in the subset of functional psychosis that has manic and mood-incongruent psychotic features.

Bipolar disorder and polymorphisms in the dysbindin gene (DTNBP1).

BACKGROUND: Several studies support the dysbindin (dystrobrevin binding protein 1) gene (DTNBP1) as a susceptibility gene for schizophrenia. We previously reported that variation at a specific 3-locus haplotype influences susceptibility to schizophrenia in a large United Kingdom (UK) Caucasian case-control sample. METHODS: Using similar methodology to our schizophrenia study, we have investigated this same 3-locus haplotype in a large, well-characterized bipolar sample (726 Caucasian UK DSM-IV bipolar I patients; 1407 ethnically matched controls). RESULTS: No significant differences were found in the distribution of the 3-locus haplotype in the full sample. Within the subset of bipolar I cases with predominantly psychotic episodes of mood disturbance (n = 133) we found nominally significant support for association at this haploptype (p < .042) and at SNP rs2619538 (p = .003), with a pattern of findings similar to that in our schizophrenia sample. This finding was not significant after correction for multiple testing. CONCLUSIONS: Our data suggest that variation at the polymorphisms examined does not make a major contribution to susceptibility to bipolar disorder in general. They are consistent with the possibility that DTNBP1 influences susceptibility to a subset of bipolar disorder cases with psychosis. However, our subset sample is small and the hypothesis requires testing in independent, adequately powered samples.

Age-at-onset in bipolar-I disorder: mixture analysis of 1369 cases identifies three distinct clinical sub-groups.

BACKGROUND: To assess whether bipolar disorder type I segregates into three clinically distinct sub-groups defined by age-at-onset. METHODS: Clinical data were available on 1369 individuals with DSM-IV bipolar I disorder. Mixture analysis was performed on the age-at-onset (AAO) data to determine whether they were composed of more than one normal distribution. Individuals were allocated to groups according to the results of the mixture analysis. Categorical logistic regression was then used to investigate relationships between AAO and nine clinical characteristics. RESULTS: The distribution of AAOs in our sample comprised a mixture of three normal distributions with means of 18.7 (SD=3.7), 28.3 (SD=5.5) and 43.3 (SD=9.1) years, with relative proportions of 0.47, 0.39 and 0.14 respectively. Individuals were allocated into three groups dependent on their AAO: < or = 22; 25-37; and > or = 40 years, producing a sample of 1225 individuals (144 with borderline values were excluded). Eight out of the nine clinical characteristics showed evidence for a statistical association with AAO group. LIMITATIONS: Systematic and non-systematic recruitment of participants. Some data relied on retrospective recall. CONCLUSIONS: Our results provide further robust evidence to suggest that the AAO distribution of individuals affected with bipolar disorder is composed of three normal distributions. Substantial clinical heterogeneity between the three AAO groups may reflect genetic heterogeneity within bipolar I disorder. Future genetic studies should consider AAO grouping as potential sub-phenotypes.

Clinical differences between bipolar and unipolar depression.

It is commonly -- but wrongly -- assumed that there are no important differences between the clinical presentations of major depressive disorder and bipolar depression. Here we compare clinical course variables and depressive symptom profiles in a large sample of individuals with major depressive disorder (n=593) and bipolar disorder (n=443). Clinical characteristics associated with a bipolar course included the presence of psychosis, diurnal mood variation and hypersomnia during depressive episodes, and a greater number of shorter depressive episodes. Such features should alert a clinician to a possible bipolar course. This is important because optimal management is not the same for bipolar and unipolar depression.

Genetic variation of brain-derived neurotrophic factor (BDNF) in bipolar disorder: case-control study of over 3000 individuals from the UK.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) influences neuronal survival, proliferation and plasticity. Three family-based studies have shown association of the common Valine (Val) allele of the Val66Met polymorphism of the BDNF gene with susceptibility to bipolar disorder. AIMS: To replicate this finding. METHOD: We genotyped the Val66Met polymorphism in our UK White bipolar case-control sample (n=3062). RESULTS: We found no overall evidence of allele or genotype association. However, we found association with disease status in the subset of 131 individuals that had experienced rapid cycling at some time (P=0.004). We found a similar association on re-analysis of our previously reported family-based association sample (P < 0.03, one-tailed test). CONCLUSIONS: Variation at the Val66Met polymorphism of BDNF does not play a major role in influencing susceptibility to bipolar disorder as a whole, but is associated with susceptibility to the rapid-cycling subset of the disorder.

Cognitive style in bipolar disorder.

BACKGROUND: Abnormalities of cognitive style in bipolar disorder are of both clinical and theoretical importance. AIMS: To compare cognitive style in people with affective disorders and in healthy controls. METHOD: Self-rated questionnaires were administered to 118 individuals with bipolar I disorder, 265 with unipolar major recurrent depression and 268 healthy controls. Those with affective disorder were also interviewed using the Schedules for Clinical Assessment in Neuropsychiatry and case notes were reviewed. RESULTS: Those with bipolar disorder and those with unipolar depression demonstrated different patterns of cognitive style from controls; negative self-esteem best discriminated between those with affective disorders and controls; measures of cognitive style were substantially affected by current levels of depressive symptomatology; patterns of cognitive style were similar in bipolar and unipolar disorder when current mental state was taken into account. CONCLUSIONS: Those with affective disorder significantly differed from controls on measures of cognitive style but there were no differences between unipolar and bipolar disorders when current mental state was taken into account.