Dataset Information

Toward the Definition of a Bipolar Prodrome: Dimensional Predictors of Bipolar Spectrum Disorders in At-Risk Youths.

ABSTRACT: The authors sought to assess dimensional symptomatic predictors of new-onset bipolar spectrum disorders in youths at familial risk of bipolar disorder ("at-risk" youths).Offspring 6-18 years old of parents with bipolar I or II disorder (N=359) and community comparison offspring (N=220) were recruited. At baseline, 8.4% of the offspring of bipolar parents had a bipolar spectrum disorder. Over 8 years, 14.7% of offspring for whom follow-up data were available (44/299) developed a new-onset bipolar spectrum disorder (15 with bipolar I or II disorder). Measures collected at baseline and follow-up were reduced using factor analyses, and factors (both at baseline and at the visit prior to conversion or last contact) were assessed as predictors of new-onset bipolar spectrum disorders.Relative to comparison offspring, at-risk and bipolar offspring had higher baseline levels of anxiety/depression, inattention/disinhibition, externalizing, subsyndromal manic, and affective lability symptoms. The strongest predictors of new-onset bipolar spectrum disorders were baseline anxiety/depression, baseline and proximal affective lability, and proximal subsyndromal manic symptoms (p<0.05). While affective lability and anxiety/depression were elevated throughout follow-up in those who later developed a bipolar spectrum disorder, manic symptoms increased up to the point of conversion. A path analysis supported the hypothesis that affective lability at baseline predicts a new-onset bipolar spectrum disorder in part through increased manic symptoms at the visit prior to conversion; earlier parental age at mood disorder onset was also significantly associated with an increased risk of conversion. While youths without anxiety/depression, affective lability, and mania (and with a parent with older age at mood disorder onset) had a 2% predicted chance of conversion to a bipolar spectrum disorder, those with all risk factors had a 49% predicted chance of conversion.Dimensional measures of anxiety/depression, affective lability, and mania are important predictors of new-onset bipolar spectrum disorders in at-risk youths. These symptoms emerged from among numerous other candidates, underscoring the potential clinical and research utility of these findings.

SUBMITTER: Hafeman DM

PROVIDER: S-EPMC4930393 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCurrent operational diagnostic systems have substantial limitations for lifetime diagnostic classification of bipolar spectrum disorders. Issues include: (1) It is difficult to operationalize the integration of diverse episodes of psychopathology, (2) Hierarchies lead to loss of information, (3) Boundaries between diagnostic categories are often arbitrary, (4) Boundaries between categories usually require a major element of subjective interpretation, (5) Available diagnostic categories are relatively unhelpful in distinguishing severity, (6) "Not Otherwise Specified (NOS)" categories are highly heterogeneous, (7) Subclinical cases are not accommodated usefully within the current diagnostic categories. This latter limitation is particularly pertinent in the context of the increasing evidence for the existence of a broader bipolar spectrum than has been acknowledged within existing classifications.MethodWe have developed a numerical rating system, the Bipolar Affective Disorder Dimension Scale, BADDS, that can be used as an adjunct to conventional best-estimate lifetime diagnostic procedures. The scale definitions were informed by (a) the current concepts of mood syndrome recognized within DSMIV and ICD10, (b) the literature regarding severity of episodes, and (c) our own clinical experience. We undertook an iterative process in which we initially agreed scale definitions, piloted their use on sets of cases and made modifications to improve utility and reliability.ResultsBADDS has four dimensions, each rated as an integer on a 0 - 100 scale, that measure four key domains of lifetime psychopathology: Mania (M), Depression (D), Psychosis (P) and Incongruence (I). In our experience it is easy to learn, straightforward to use, has excellent inter-rater reliability and retains the key information required to make diagnoses according to DSMIV and ICD10.ConclusionsUse of BADDS as an adjunct to conventional categorical diagnosis provides a richer description of lifetime psychopathology that (a) can accommodate sub-clinical features, (b) discriminate between illness severity amongst individuals within a single conventional diagnostic category, and (c) demonstrate the similarity between the illness experience of individuals who have been classified into different disease categories but whose illnesses both fall near the boundaries between the two categories. BADDS may be useful for researchers and clinicians who are interested in description and classification of lifetime psychopathology of individuals with disorders lying on the bipolar spectrum.

Project description:BackgroundDetecting prodromal symptoms of bipolar disorder (BD) has garnered significant attention in recent research, as early intervention could potentially improve therapeutic efficacy and improve patient outcomes. The heterogeneous nature of the prodromal phase in BD, however, poses considerable challenges for investigators. Our study aimed to identify distinct prodromal phenotypes or "fingerprints" in patients diagnosed with BD and subsequently examine correlations between these fingerprints and relevant clinical outcomes.Methods20,000 veterans diagnosed with BD were randomly selected for this study. K-means clustering analysis was performed on temporal graphs of the clinical features of each patient. We applied what we call "temporal blurring" to each patient image in order to allow clustering to focus on the clinical features, and not cluster patients based upon their varying temporal patterns in diagnosis, which lead to the desired types of clusters. We evaluated several outcomes including mortality rate, hospitalization rate, mean number of hospitalizations, mean length of stay, and the occurrence of a psychosis diagnosis within one year following the initial BD diagnosis. To determine the statistical significance of the observed differences for each outcome, we conducted appropriate tests, such as ANOVA or Chi-square.ResultsOur analysis yielded 8 clusters which appear to represent distinct phenotypes with differing clinical attributes. Each of these clusters also has statistically significant differences across all outcomes (p < 0.0001). The clinical features in many of the clusters were consistent with findings in the literature concerning prodromal symptoms in patients with BD. One cluster, notably characterized by patients lacking discernible prodromal symptoms, exhibited the most favorable results across all measured outcomes.ConclusionOur study successfully identified distinct prodromal phenotypes in patients diagnosed with BD. We also found that these distinct prodromal phenotypes are associated with different clinical outcomes.

Dataset Information

Toward the Definition of a Bipolar Prodrome: Dimensional Predictors of Bipolar Spectrum Disorders in At-Risk Youths.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets