Identifying Unstable and Empty Phenotypes of Borderline Personality Through Factor Mixture Modeling in a Large Nonclinical Sample

Benjamin N. Johnson, Kenneth N. Levy

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Borderline personality disorder (BPD) is serious, prevalent, and symptomatically heterogeneous. Identifying distinct phenotypes of BPD features promises useful diagnostic and treatment implications. Although a series of subtyping studies exist, only two have examined BPD symptom configurations while taking into account BPD severity. We used factor mixture modeling to identify discrete subtypes of BPD features, simultaneously considering symptom severity, in the largest nonclinical young adult sample to date. Undergraduates (N = 20,010; 63.86% women; Mage = 18.75, SD = 1.73) completed the McLean Screening Instrument for BPD, which was condensed to measure the 9 Diagnostic and Statistical Manual of Mental Disorders BPD criteria dichotomously. We used a model comparison approach to determine the optimal latent factor and class structure of BPD symptoms and validated classes via BPD-relevant constructs. The sample consisted of three subtypes: Asymptomatic (70%), Unstable (19%), and Empty (11%). The Unstable and Empty classes displayed elevated BPD symptomatology along a single continuum of BPD severity. Individuals in the Empty class displayed the highest levels of emptiness and dissociation, emotional distress, and attachment avoidance, whereas individuals in the Unstable class displayed a high frequency of reckless and self-damaging behaviors. Our results suggest the importance of a hybrid dimensional/categorical conceptualization of BPD as displayed in a nonclinical sample. Unstable and Empty classes may be associated with different treatment targets for subthreshold BPD presentations. The findings are discussed in terms of their clinical implications regarding diagnosis, treatment, and theoretical conceptualization of BPD.

Original languageEnglish (US)
JournalPersonality Disorders: Theory, Research, and Treatment
StateAccepted/In press - 2019

All Science Journal Classification (ASJC) codes

  • Clinical Psychology
  • Psychiatry and Mental health


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