2026 ECNP Congress

PS03-3193

Clinical state and personality dimensions in unipolar and bipolar depression: a latent class analysis

Mauro Scala — I. de la Osa Subtil, G. Fanelli, M. Colizzi, A. Serretti · Universidad Europea de Madrid, Faculty of Biomedical and Health Sciences, Madrid, Spain

2026 ECNP Congress

Introduction

Depressive episodes define major depressive disorder (MDD) and are the most prevalent mood state across the course of bipolar disorder (BD). Patients with the same diagnosis show heterogeneity in symptom severity, illness course, and treatment response. This variability partly explains the limited effectiveness of standard treatments [1] and is not fully captured by traditional diagnostic frameworks, as it may reflect interactions between clinical and psychological dimensions [2]. Dimensional approaches may better characterize inter-individual differences beyond diagnostic labels. For instance, personality traits contribute to differences in symptom expression, functional outcomes, and illness course across psychiatric conditions, supporting their potential role as transdiagnostic factors contributing to clinical heterogeneity [3].

Aims

To identify clinically homogeneous subgroups among patients with unipolar and bipolar depression based on clinical characteristics and dimensional personality traits.

Methods

Latent class analysis (LCA) to identify homogeneous subgroups of patients was performed using clinical indicators, including current clinical status (euthymic vs. depressive), remission (i.e., MDD: HDRS ≤7; BD: HDRS ≤7 with no concurrent hypomanic episode), treatment resistance (i.e., MDD: ≤50% reduction or HDRS ≥17 after ≥2 adequate treatments; BD: ≤50% reduction after ≥2 adequate treatments in the absence of concurrent hypomania), number of depressive episodes, and melancholic features. The Bayesian Information Criterion (BIC) supported a two-class solution.Variables showing an association with class membership at the univariate level (p < 0.05), together with relevant covariates, including clinical variables and Temperament and Character Inventory (TCI)-derived personality traits, were included in the multivariable model [4].

Results

The sample included 412 patients (27.4% male; mean age 46.4 ± 12.7 years). LCA identified two subgroups with comparable sociodemographic profiles but different in clinical and personality features. Class 1 (n=341, 26.1% male; mean age 46.2 ± 11.9 years) was characterized by a high probability of depressive status (0.96), very low probability of remission, and greater illness burden, including treatment resistance (0.33), recurrent depressive episodes (0.58), and melancholic features (0.77). In contrast, Class 2 (n=71, 33.8% male; mean age 46.1 ± 16.7 years) showed a high probability of euthymic status (0.79), very high probability of remission, lower treatment resistance (0.01), fewer depressive episodes (0.49), and fewer melancholic features (0.51). In multivariable analysis, higher self-directedness (OR=1.06, p<0.001), bipolar diagnosis versus unipolar (OR=3.11, p=0.011), and day-care setting versus inpatient care (OR=9.73, p=0.004) were associated with a higher odds of Class 2 membership.

Conclusions

Higher self-directedness may mark greater self-regulation, treatment adherence, and coping abilities associated with a more stable clinical profile. The association with BD should be interpreted cautiously, as it may reflect differences in clinical phase at assessment rather than intrinsic differences between disorders. The relationship with treatment setting is consistent with the distribution of patients across levels of care, with less severe cases more frequently managed in day-care settings. These findings suggest that diagnostic categories alone may be insufficient to describe clinically relevant heterogeneity in depression, and that personality traits, less dependent on transient phases such as euthymia or depression, provide additional insights to tailor clinical assessment and treatment strategies.

References

  1. Balestri, M., Porcelli, S., Souery, D., Kasper, S., Dikeos, D., Ferentinos, P., et al. 2019. Temperament and character influence on depression treatment outcome. J Affect Disord 252:464–474.
  2. Serretti, A., Kasper, S., Bartova, L., Zohar, J., Souery, D., Montgomery, S., et al. 2025. Clinical predictors of treatment resistant depression. Eur Neuropsychopharmacol 98:26–34.
  3. Scala, M., Gori, D., Roca, P., Fabbri, C., Arroyo Iturra, R., Calvo García, S.F., et al. 2026. Personality domains in early stages of psychosis: a systematic review and meta-analysis. Eur Arch Psychiatry Clin Neurosci 276(3):1111–1135.
  4. Hansenne, M., Reggers, J., Pinto, E., Kjiri, K., Ajamier, A., Ansseau, M. 1999. Temperament and character inventory (TCI) and depression. J Psychiatr Res 33(1):31–36.