# Refining and Validating Borderline Personality Disorder Phenotypes Through Factor Mixture Modeling

> **NIH NIH F31** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $25,358

## Abstract

The proposed research seeks to clarify the symptomatic heterogeneity of borderline personality disorder (BPD)
by examining BPD phenotypes through advanced latent variable modeling. A second, innovative aim is to
validate these findings through intensive longitudinal assessment in daily life. BPD is associated with high rates
of emergency room visits and costly healthcare service utilization, affecting 10-20% of psychiatric outpatients
and 20-40% of psychiatric inpatients. BPD also contributes to impaired social and occupational functioning and
significant suicide risk, with 1 in 10 individuals with BPD completing suicide. Recent research has aimed to
enhance treatment effectiveness for BPD by identifying prototypical patterns of symptom manifestation that
may suggest ideographic treatment targets. However, no research has simultaneously included: a) a
sufficiently large patient sample; b) ecologically sound validation of results; and c) use of appropriate statistical
techniques. The proposed project builds on this research through two aims. Aim 1: Utilize a model comparison
approach to identify BPD phenotypes in a large psychiatric outpatient sample assessed via semi-structured
diagnostic interviews (Study 1). Aim 2: Validate the results of Study 1 by applying phenotype classification
algorithms produced in Study 1 to a smaller sample of patients who have completed 21 days of momentary
surveys on symptoms and clinical outcomes (Study 2). To address Aim 1, factor mixture modeling (FMM)—a
novel, flexible, and integrative latent variable modeling approach—will be compared to standard factor analysis
and latent class analysis in order to evaluate the dimensional and categorical structure of BPD. We expect a
single-factor, multi-class FMM will best explain heterogeneity in BPD, over and above other sources of
heterogeneity (e.g., gender, comorbidity). To address Aim 2, we will use a prototype-matching approach to
algorithmically assign patients in the validation sample to phenotypes identified in Aim 1 and determine their
predictive validity in terms of daily clinical outcomes. Results of this project will provide empirically grounded
personalized prediction tools for BPD intervention and treatment development, in line with the NIMH’s goal of
“developing, testing, and refining tools and methodologies… for personalized risk and trajectory prediction and
intervention.” This fellowship will allow the applicant to receive tailored consultation from experts in
methodology, data analysis, and BPD theory and assessment, as well as advanced statistical training and
grantsmanship courses and workshops. This training will be enhanced by the resource-rich environment and
explicit support of student research and funding provided by the Pennsylvania State University, as well as the
support of Dr. Kenneth Levy and his lab. This promising young researcher will gain training in computational
modeling, proficiency in working with “big data,” increased understanding of...

## Key facts

- **NIH application ID:** 9911299
- **Project number:** 1F31MH121020-01A1
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Benjamin Norman Johnson
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $25,358
- **Award type:** 1
- **Project period:** 2020-01-10 → 2020-07-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9911299

## Citation

> US National Institutes of Health, RePORTER application 9911299, Refining and Validating Borderline Personality Disorder Phenotypes Through Factor Mixture Modeling (1F31MH121020-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9911299. Licensed CC0.

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