This study aims to understand borderline personality disorder (BPD) features by employing the Personality Psychopathology Five (PSY-5) scales from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2).
A total of 156 psychiatric patients completed PSY-5 scales of MMPI-2 and Personality Assessment Inventory-Borderline Subscale (PAI-BOR). Pearson’s partial correlation analysis was conducted to control the impact of age and gender and to determine the relationship between PSY-5 scales and BOR. A hierarchical multiple regression analysis was implemented to examine whether PSY-5 scales predicted the BOR-total, and a path analysis was performed to determine whether PSY-5 scales predicted each PAI-BOR subscale.
The BOR-total score had a significant correlation with all PSY-5 scores, even after controlling for age and gender. However, only aggressiveness (AGGR), disconstraint (DISC), negative emotionality/neuroticism (NEGE), and introversion/low positive emotionality (INTR), excluding psychoticism (PSYC), significantly predicted BOR-total. The path analysis indicates that PSYC did not predict any BOR subscale, while NEGE predicted all BOR subscales.
The study findings indicate that NEGE best reflects BPD features, while PSYC is far from the core domain that describes BPD. In addition, the influence of age should be considered when understanding BPD, since age predicted the BOR-total and two BOR subscales.
Borderline personality disorder (BPD), commonly known as a clinically severe and impairing disorder, is characterized with “a pervasive pattern of instability of interpersonal relationships, self-image, and affects, and marked impulsivity that begins by early adulthood and is present in a variety of contexts” according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) [
To solve these issues, DSM-5 proposed an alternative model of personality disorder (AMPD) with dimensional domains [
Studies examining relationship between the MMPI-2 PSY-5 scales and BPD utilized various measurements such as the Structured Interview for DSM-IV Personality (SIDP-R) [
Therefore, the purpose of this study is to understand BPD features with PSY-5 dimensional domains in MMPI-2 in Korean clinical samples.
Participants were in- and out-patients who visited the psychiatry department of a university hospital in Iksan, Korea, from March 2015 to March 2019, and their data were collected anonymously. Individuals with severe psychotic symptoms assessed by clinicians as well as individuals with invalid MMPI-2 results (>80 T-scores on the VRIN and TRIN scales, or >30 points on the “cannot-say” scale) were excluded. Final samples consisted of 101 (64.7%) men and 55 (35.3%) women with a mean age of 34.15 years (SD=15.79). Participants represented a wide range of single and comorbid psychiatric discharge diagnoses, including depressive disorders (40.4%), psychotic disorders (12.2%), anxiety disorders (11.5%), adjustment disorder (11.5%), post-traumatic stress disorder (8.3%), bipolar disorders (3.8%), substance use disorder (3.2%), and other disorders (8.3%). The study was conducted after obtaining the approval of the Institutional Review Board of the Wonkwang University Hospital (IRB No. WKUH 2021-02-003).
The PSY-5 scale in the Korean version of MMPI-2 [
The Korean version of PAI-BOR [
Descriptive statistics including frequency, mean, and standardized deviation were performed. Normality was tested for each variable before further analysis. Skewness over 2.0 and kurtosis over 7.0 were considered to reflect a moderately nonnormal distribution [
The Pearson’s partial correlation analysis was performed with MMPI-2 PSY-5 scales and PAI-BOR subscales while controlling for the effect of age and gender with 1,000 times of bootstrapping process.
Based on the correlation results, the hierarchical multiple regression analysis was performed to discover predictors for PAI-BOR among PSY-5 scales. The enter method was used with 1,000 times of bootstrapping process for the hierarchical regression analysis. Age and gender were included as covariates to control for the contaminating effect.
In addition, path analyses were performed to discover predictors of PAI-BOR subscales among PSY-5 scales using maximum likelihood estimation. Age and gender were also included as covariates. Chi-square test (
The demographic characteristics of participants are provided in
The correlation analysis was performed with all variables including demographic characteristics. Since age and gender were significantly correlated with the BOR-total score (r=-0.295, p<0.00; r=-0.170, p=0.034, respectively), the partial correlation analysis was performed to control the effect of age and gender on the BOR score. Correlation coefficients, mean, and standard deviation of all variables are presented in
The BOR-total score was significantly correlated with all MMPI-2 PSY-5 scales even after controlling for the effect of age and gender. BOR-A and BOR-N showed significant relationship with all PSY-5 scales. On the other hand, BOR-I showed significant correlation with PSYC, NEGE, and INTR, but not with AGGR (r=0.122, p=0.132) nor DISC (r=0.072, p=0.037). BOR-S also showed significant correlation with AGGR, PSYC, DISC, and NEGE, but not with INTR (r=0.137, p=0.089).
Based on the correlation analysis, hierarchical multiple regression analysis was performed to discover significant predictors of BOR-total score among PSY-5 scales. Age and gender were entered at step one as covariates, and all PSY-5 scales were entered at step two. The result of hierarchical multiple regression is shown in
The hierarchical multiple regression revealed that at step one, age and gender contributed significantly to the regression model (F(2,153)=8.09, p<0.001) and accounted for 10% of the variance in the BOR-total score. Adding PSY-5 scales at step two explained an additional 57% of variance in the BOR-total score and this change in R2 was significant (F(7,148)=67.24, p<0.001). When all seven variables were included in step two of the regression model, neither gender nor the PSYC scale were significant predictors of the BOR-total score (t=-0.418, p=0.677; t=0.628, p=0.531, respectively).
To examine detailed relationships among PSY-5 scales with BOR subscales, the path analysis was performed. A hypothesized model was driven based on the significant relationships from the correlation analysis.
The result of the path analysis revealed that some paths were not significant in the hypothesized model. The path between AGGR and BOR-N (β=0.136, p=0.073), AGGR and BOR-S (β= 0.007, p=0.922), PSYC and BOR-I (β=0.085, p=0.252), INTR and BOR-I (β=0.100, p=0.106), PSYC and BOR-A (β=-0.015, p=0.831), PSYC and BOR-N (β=0.055, p=0.514), INTR and BOR-N (β=0.137, p=0.076), PSYC and BOR-S (β=0.041, p= 0.583), INTR and BOR-S (β=0.045, p=0.507), age and BOR-A (β=-0.077, p=0.155), age and BOR-N (β=-0.085, p=0.204), gender and BOR-N (β=-0.065, p=0.318), and gender and BOR-S (β=0.008, p=0.895) were excluded from the hypothesized model. In addition, the correlation path between AGGR and NEGE (r=0.146, p=0.072), PSYC and DISC (r=0.141, p=0.079), DISC and NEGE (r=0.086, p=0.262), DISC and INTR (r=-0.144, p=0.063), and the error of BOR-N and the error of BOR-S (r=0.110, p=0.175) were also excluded from the hypothesized model.
The final model with significant paths and correlations was analyzed. This model showed great fit since
Based on the PSY-5 domain theory, this study aimed to predict BPD features by utilizing MMPI-2 PSY-5 scales as an extension of the AMPD. Therefore, this study employed MMPI-2, which is commonly used in clinical practices, and PAI-BOR, which is a reliable indicator reflecting BPD features.
The findings indicate that AGGR, DISC, NEGE, and INTR were significant predictors of the PAI-BOR total scores. Moreover, NEGE was the most significant predictor of the BOR-total (β=0.569, p<0.001), followed by DISC (β=0.237, p<0.001), INTR (β=0.189, p=0.001), and AGGR (β=0.132, p=0.001), whereas PSYC did not significantly predict the PAI-BOR total score (β=0.039, p=0.531). These results are in line with previous findings [
For each PSY-5 scale, the findings from path analyses suggest that AGGR, DISC, NEGE, and INTR predicted BOR-A, NEGE and age predicted BOR-I, DISC and NEGE predicted BOR-N, and NEGE, DISC, and age predicted BOR-S. In other words, NEGE significantly predicted all the PAI-BOR subscales, while PSYC did not significantly predict any subscale. Similarly, DISC significantly predicted three subscales: BOR-A, BOR-N, and BOR-S; however, AGGR or INTR significantly predicted only BOR-A. In summary, PSY-5 domains NEGE and DISC are key predictors associated with BPD features. Neuroticism, represented as NEGE, is the characteristic trait reflecting BPD’s core symptoms [
In contrast, PSYC was the only scale that exhibited no significant prediction of BPD features among all PSY-5 domains. Although some studies have demonstrated the association between PSYC and BPD [
The study added age and gender as covariates to see the pure predictive effects of PSY-5 on BOR-total and BOR scales. As a result, age significantly predicted the BOR-total, which revealed that the younger the patient is, the more intensive is the BPD symptom. Moreover, age significantly predicted BOR-I and BOR-S, which illustrates that the younger the patient, the greater is the intensity with which they experience these episodes. According to previous studies, BPD showed advanced symptoms in early adulthood and they gradually stabilized with aging [
Gender showed significant correlations with BOR-total and BOR subscales, but not in the regression nor path analyses. In fact, many studies have reported that the gender difference of BPD is not significant [
This study is meaningful in that it attempted to find a personality domain for predicting BPD and various BPD sub-symptoms in South Korean clinical samples. Various problems with existing diagnostic criteria relating to BPD have been reported and they present challenges in making diagnoses in clinical practice [
This study has several limitations. First, different diagnostic groups were represented together. Additional studies to explain the diagnosis and symptoms using PSY-5 by recruiting patients with only BPD should be performed. Furthermore, expanding the study to non-clinical samples could contribute to the early detection and support of BPD vulnerabilities in a community. In addition, since this study used MMPI-2 and PAI, which are self-evaluated tests, it is necessary to attempt to understand BPD objectively using other biological tests such as EEG, biochemical markers, and structural brain indices.
In conclusion, this study sought to determine whether MMPI-2 PSY-5 scales significantly predict BPD features among South Korean clinical samples. The results indicate that NEGE, DISC, AGGR, and INTR significantly predict the BOR-total and BOR subscales, and of these, NEGE is the key predictor that best describes BPD features. Finally, age also predicts the BOR-total, BOR-I, and BOR-S, revealing that young age plays a key role in understanding BPD features.
Data sharing not applicable to this article as no datasets were generated or analyzed during the study.
The authors have no potential conflicts of interest to disclose.
Conceptualization: Kyu-Sic Hwang. Data curation: Min Jin Jin. Formal analysis: Min Jin Jin. Investigation: Min Jin Jin. Methodology: Min Jin Jin. Project administration: Chan-Mo Yang, Seung-Ho Jang, Sang-Yeol Lee. Resources: Kyu-Sic Hwang, Chan-Mo Yang, Seung-Ho Jang, Sang-Yeol Lee. Software: Min Jin Jin. Supervision: Sang-Yeol Lee. Validation: Kyu-Sic Hwang. Visualization: Min Jin Jin. Writing—original draft: Min Jin Jin. Writing—review & editing: Min Jin Jin, Hye-Jin Lee, Kyu-Sic Hwang, Jae-Hee Lee.
None
A hypothesized model based on the correlation analysis for the path analysis.
Path analysis final model and standardized estimates.
Demographic characteristics of participants (N=156)
Variables | Group | Frequency (%) |
---|---|---|
Gender | Male | 101 (64.7) |
Female | 55 (35.3) | |
Age | 20s | 82 (52.6) |
30s | 20 (12.8) | |
40s | 22 (14.1) | |
50s | 18 (11.5) | |
60s | 9 (5.8) | |
>70s | 4 (2.5) | |
Marital status | Married | 43 (27.5) |
Unmarried | 104 (66.7) | |
Separation | 1 (0.6) | |
Bereaved | 2 (1.3) | |
Divorced | 6 (3.8) | |
Education years | <6 | 7 (4.4) |
7–9 | 15 (9.7) | |
10–12 | 64 (41.0) | |
13–16 | 70 (44.9) |
Partial correlation coefficients of MMPI-2-PSY-5 scales and PAI-BOR subscales after controlling for age and gender effect (N=156)
AGGR | PSYC | DISC | NEGE | INTR | BOR-A | BOR-I | BOR-N | BOR-S | BOR total | |
---|---|---|---|---|---|---|---|---|---|---|
AGGR | - | |||||||||
PSYC | 0.24 |
- | ||||||||
DISC | 0.42 |
0.17 |
- | |||||||
NEGE | 0.15 | 0.63 |
0.09 | - | ||||||
INTR | -0.38 |
0.18 |
-0.16 | 0.35 |
- | |||||
BOR-A | 0.23 |
0.45 |
0.23 |
0.68 |
0.39 |
- | ||||
BOR-I | 0.12 | 0.46 |
0.07 | 0.66 |
0.32 |
0.72 |
- | |||
BOR-N | 0.26 |
0.39 |
0.28 |
0.52 |
0.20 |
0.59 |
0.53 |
- | ||
BOR-S | 0.27 |
0.40 |
0.48 |
0.53 |
0.14 | 0.59 |
0.50 |
0.45 |
- | |
BOR total | 0.27 |
0.52 |
0.32 |
0.73 |
0.32 |
0.90 |
0.84 |
0.76 |
0.78 |
- |
M (SD) | 46.22 (10.79) | 56.74 (15.01) | 47.01 (11.63) | 60.41 (15.00) | 62.49 (17.46) | 58.20 (14.12) | 59.82 (14.19) | 59.24 (14.51) | 55.98 (15.37) | 60.62 (15.36) |
p<0.05;
p<0.01;
p<0.001.
MMPI-2-PSY-5, Minnesota Multiphasic Personality Inventory-2 personality psychopathology five; PAI-BOR, Personality Assessment Inventory-Borderline Feature; AGGR, aggressiveness; PSYC, psychoticism; DISC, disconstraint; NEGE, negative emotionality/neuroticism; INTR, introversion/low positive emotionality; BOR-A, borderline features-affective instability; BOR-I, borderline features-identity diffusion; BOR-N, borderline features-negative relationships; BOR-S, borderline features-self harm; BOR, borderline features; M, mean; SD, standard deviation
Multiple regression analysis of MMPI-2-PSY-5 scales on PAI-BOR (N=156)
Step | B | SE | β | t | F | R2 | ∆R2 | |
---|---|---|---|---|---|---|---|---|
1 | 8.09 |
0.10 | 0.10 | |||||
Age | -0.26 | 0.08 | -0.27 | -3.36 |
||||
Gender | -3.06 | 2.57 | -0.10 | -1.19 | ||||
2 | 67.24 |
0.67 | 0.57 | |||||
Age | -0.15 | 0.05 | -0.16 | -3.14 |
||||
Gender | -0.70 | 1.69 | -0.02 | -0.42 | ||||
AGGR | 0.19 | 0.09 | 0.13 | 2.21 |
||||
PSYC | 0.04 | 0.06 | 0.04 | 0.63 | ||||
DISC | 0.31 | 0.07 | 0.24 | 4.25 |
||||
NEGE | 0.58 | 0.07 | 0.57 | 8.71 |
||||
INTR | 0.17 | 0.05 | 0.19 | 3.27 |
p<0.05;
p<0.01;
p<0.001.
MMPI-2-PSY-5, Minnesota Multiphasic Personality Inventory-2 personality psychopathology five; PAI-BOR, Personality Assessment Inventory-Borderline Feature; AGGR, aggressiveness; PSYC, psychoticism; DISC, disconstraint; NEGE, negative emotionality/neuroticism; INTR, introversion/low positive emotionality
Parameters estimates for the path analysis (N=156)
Endogeneous | Exogeneous | β | SE | p |
---|---|---|---|---|
BOR-A | AGGR | 0.14 | 0.07 | 0.012 |
DISC | 0.18 | 0.06 | <0.001 | |
NEGE | 0.58 | 0.05 | <0.001 | |
INTR | 0.23 | 0.04 | <0.001 | |
BOR-I | NEGE | 0.65 | 0.05 | <0.001 |
Age | -0.19 | 0.05 | <0.001 | |
BOR-N | DISC | 0.28 | 0.08 | <0.001 |
NEGE | 0.49 | 0.06 | <0.001 | |
BOR-S | DISC | 0.46 | 0.07 | <0.001 |
NEGE | 0.49 | 0.06 | <0.001 | |
Age | -0.14 | 0.05 | 0.016 |
BOR-A, borderline affective instability; BOR-I, borderline identity problems; BOR-N, borderline negative relationships; BOR-S, borderline self-harm; AGGR, aggressiveness; PSYC, psychoticism; DISC, disconstraint; NEGE, negative emotionality/neuroticism; INTR, introversion/low positive emotionality