Familial factors and the risk of borderline personality pathology: genetic and environmental transmission.

BACKGROUND: Parental characteristics and practices predict borderline personality disorder (BPD) symptoms in children. However, it is difficult to disentangle whether these effects are genetically or environmentally mediated. The present study examines the contributions of genetic and environmental influences by comparing the effects of familial risk factors (i.e. parental psychopathology and borderline traits, maladaptive parenting, marital discord) on child BPD traits in genetically related (biological) and non-related (adoptive) families. METHODS: Data are from 409 adoptive and 208 biological families who participated in the Siblings Interaction and Behavior Study (SIBS) and 580 twin families the Minnesota Twin Family Study (MTFS). Parent characteristics and practices included parental psychopathology (measured via structured clinical interviews), parental BPD traits, parenting behaviors, and marital discord. A series of multi-level regression models were estimated to examine the relationship of familial risk factors to child BPD traits and to test whether children's adoptive status moderated the association. RESULTS: Symptom counts of parents' conduct disorder, adult antisocial behavior, nicotine, alcohol, and illicit drug dependence, and paternal BPD traits substantially predicted child BPD traits only in biological offspring, implying genetic transmission. Maternal BPD traits and both maternal and paternal conflict, lack of regard, and lack of involvement predicted offspring BPD traits regardless of the adoptive status, implying environmental transmission. CONCLUSIONS: Parental externalizing psychopathology and father's BPD traits contribute genetic risk for offspring BPD traits, but mothers' BPD traits and parents' poor parenting constitute environmental risks for the development of these offspring traits.

Symptoms of borderline personality and related pathologies behave as temporal and contemporaneous networks.

In contrast to latent variable models suggesting a common etiology, network theory proposes that symptoms of psychopathology co-occur because of direct, dynamic associations among them. We examined how symptoms associated with borderline personality disorder, depression, and anxiety mutually reinforce one another over time, forming a network. We further identified symptoms that drove the network by exerting the most influence on other symptoms. Participants were 37 undergraduate students aged 18 to 26. Following baseline assessment, participants were prompted to answer a Qualtrics-based survey of current symptoms of BPD, depression, and anxiety twice daily for 40 days. Multilevel time-series network analyses were conducted with (a) BPD symptoms alone and (b) BPD, depressive and anxiety symptoms. In the network of BPD symptoms, momentary interpersonal difficulties predicted later dissociation, which predicted later affective fluctuation at the within-person level. Dissociation exerted the strongest influence on the overall symptom network. When depressive and anxiety symptoms were included, the networks identified several cross-disorder connections, such as anhedonia and feeling tense, which highlight potential pathways that describe the comorbidity of BPD with anxiety and depressive syndromes. Overall, cognitive symptoms and dissociation were identified as the most influential symptoms across the networks. This study indicates that BPD, depression, and anxiety symptoms may mutually reinforce one another concurrently and over time. Cognitive symptoms exert the highest influence on the cross-disorder networks, such that they influence BPD, depressive, and anxiety symptoms. Our results support the need of targeting cognitions in the treatment of comorbid BPD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data.

With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.

Comorbidity in borderline personality: understanding dynamics in development.

Borderline personality disorder is marked by high levels of comorbidity in both adolescent and adult populations. However, the mechanisms involved in the development of comorbidity in BPD remain unclear. To address this issue, the current paper proposes the use of dynamic mutualism theory as a valuable and underexplored framework for investigating comorbidity in BPD from a developmental perspective. Specifically, we discuss how predictions of dynamic mutualism can be extended to better understand the onset, maintenance, and interplay of BPD symptoms with other forms of psychopathology over time. Moreover, we suggest that mutualistic interactions among internalizing and externalizing features throughout early development may foster the emergence of BPD symptoms in adolescence and beyond. Next, we discuss methodological approaches for testing mutualism and review indirect evidence that supports the role of mutualistic processes in the emergence and maintenance of BPD and its comorbidities. We conclude with methodological cautions and recommendations for future studies.

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations.

Co-occurrence of psychiatric disorders is well documented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the general psychopathology or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that bifactor models, when properly applied and interpreted, can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight 3 types of research questions for which bifactor models are well suited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models with other analytic approaches and note that no statistical model is a panacea to rectify limitations of the research design used to gather data.