Cluster analysis with MOODS-SR illustrates a potential bipolar disorder risk phenotype in young adults with remitted major depressive disorder.

OBJECTIVES

Delays in the diagnosis and detection of bipolar disorder can lead to adverse consequences, including improper treatment and increased suicide risk. The Mood Spectrum Self-Report Measure (MOODS-SR) was designed to capture the full spectrum of lifetime mood symptomology with factor scores for depression and mania symptom constellations. The utility of the MOODS-SR as a tool to investigate homogeneous subgroups was examined, with particular focus on a possible bipolar risk subgroup. Moreover, potential patterns of differences in MOODS-SR subtypes were probed using cognitive vulnerabilities, neuropsychological functioning, and ventral striatum connectivity.

METHODS

K-mean cluster analysis based on factor scores of MOODS-SR was used to determine homogeneous subgroupings within a healthy and remitted depressed young adult sample (N = 86). Between-group comparisons (based on cluster subgroupings) were conducted on measures of cognitive vulnerabilities, neuropsychological functioning, and ventral striatum rs-fMRI connectivity.

RESULTS

Three groups of participants were identified: one with minimal symptomology, one with moderate primarily depressive symptomology, and one with more severe manic and depressive symptomology. Differences in impulsivity, neuroticism, conscientiousness, facial perception accuracy, and rs-fMRI connectivity exist between moderate and severe groups.

CONCLUSIONS

Within a sample of people with and without depression histories, a severe subgroup was identified with potentially increased risk of developing bipolar disorder through use of the MOODS-SR. This small subgroup had higher levels of lifetime depression and mania symptoms. Additionally, differences in traits, affective processing, and connectivity exist between those with a more prototypic unipolar subgrouping and those with potential risk for developing bipolar disorder.