Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life.

The microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome's composition, which can change during the host's lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm's lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function. IMPORTANCE: The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

Measuring maladaptive personality traits with the Structured Clinical Interview for DSM-IV Axis II Screening Questionnaire using a common metrics approach.

The classification of personality disorder (PD) is undergoing a paradigm shift in which categorically defined specific PDs are being replaced by dimensionally defined maladaptive trait domains. To bridge the classificatory approaches, this study attempts to use items from the categorical PD model in DSM-IV to measure the maladaptive trait domains described in DSM-5 Section III/ICD-11. A general population sample comprising 1228 participants completed the Screening Questionnaire of the Structured Clinical Interview for DSM-IV Axis II (SCID-II-SQ), the Personality Inventory for DSM-5 (PID-5), and the anankastia scale of the Personality Inventory for ICD-11 (PiCD). Using item response theory models and a psychometric linking technique, SCID-II-SQ items were evaluated for their contribution to measuring maladaptive trait domains. The best discriminating items were then selected to derive proxy scales. We found that convergent validity of these proxy scales was in a similar range to that of other self-report measures for PD, except for the proxy scale for PiCD anankastia. However, only the proxy scale for negative affectivity showed acceptable reliability that would allow its application in research settings. Future studies should seek to establish a common metric between specific PDs and maladaptive trait domains using self-report measures with higher specificity or semi-structured interviews.

Changes in Bacterial Gut Composition in Parkinson's Disease and Their Metabolic Contribution to Disease Development: A Gut Community Reconstruction Approach.

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with the major symptoms comprising loss of movement coordination (motor dysfunction) and non-motor dysfunction, including gastrointestinal symptoms. Alterations in the gut microbiota composition have been reported in PD patients vs. controls. However, it is still unclear how these compositional changes contribute to disease etiology and progression. Furthermore, most of the available studies have focused on European, Asian, and North American cohorts, but the microbiomes of PD patients in Latin America have not been characterized. To address this problem, we obtained fecal samples from Colombian participants (n = 25 controls, n = 25 PD idiopathic cases) to characterize the taxonomical community changes during disease via 16S rRNA gene sequencing. An analysis of differential composition, diversity, and personalized computational modeling was carried out, given the fecal bacterial composition and diet of each participant. We found three metabolites that differed in dietary habits between PD patients and controls: carbohydrates, trans fatty acids, and potassium. We identified six genera that changed significantly in their relative abundance between PD patients and controls, belonging to the families Lachnospiraceae, Lactobacillaceae, Verrucomicrobioaceae, Peptostreptococcaceae, and Streptococcaceae. Furthermore, personalized metabolic modeling of the gut microbiome revealed changes in the predicted production of seven metabolites (Indole, tryptophan, fructose, phenylacetic acid, myristic acid, 3-Methyl-2-oxovaleric acid, and N-Acetylneuraminic acid). These metabolites are associated with the metabolism of aromatic amino acids and their consumption in the diet. Therefore, this research suggests that each individual's diet and intestinal composition could affect host metabolism. Furthermore, these findings open the door to the study of microbiome-host interactions and allow us to contribute to personalized medicine.

Mindreading measures misread? A multimethod investigation into the validity of self-report and task-based approaches.

Mindreading ability-also referred to as cognitive empathy or mentalizing-is typically conceptualized as a relatively stable dimension of individual differences in the ability to make accurate inferences about the mental states of others. This construct is primarily assessed using self-report questionnaires and task-based performance measures. However, the validity of these measures has been questioned: According to rival interpretations, mindreading tasks may capture general cognitive ability, whereas mindreading self-reports may capture perceived rather than actual mindreading ability. In this preregistered multimethod study involving 700 participants from the U.S. general population, we tested the validity of mindreading measures by examining the nomological network of self-reports and task-based methods using structural equation modeling. Specifically, we contrasted the empirical associations with theoretical predictions that assume mindreading measures are valid versus invalid. More consistent with rival interpretations, mindreading tasks showed a negligible latent correlation with mindreading self-reports (.05) and a large one with general cognitive ability (.85), whereas mindreading self-reports were specifically associated with perceived performance in mindreading tasks (.29). Also more consistent with rival interpretations, neither mindreading self-reports nor task-based measures showed positive unique associations with psychosocial functioning when controlling for general cognitive ability and general positive self-evaluation. Instead, negative unique associations emerged for both methods, although this effect was not robust for tasks. Overall, the results cast doubt on the validity of commonly used mindreading measures and support their rival interpretations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

While numerous health-beneficial interactions between host and microbiota have been identified, there is still a lack of targeted approaches for modulating these interactions. Thus, we here identify precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In the first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we use metabolic modeling to identify precision prebiotics for a two-member Caenorhabditis elegans microbiome community comprising the immune-protective target species Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. We experimentally confirm four of the predicted precision prebiotics, L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid, to specifically increase the abundance of MYb11. L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.IMPORTANCEWhile various mechanisms through which the microbiome influences disease processes in the host have been identified, there are still only few approaches that allow for targeted manipulation of microbiome composition as a first step toward microbiome-based therapies. Here, we propose the concept of precision prebiotics that allow to boost the abundance of already resident health-beneficial microbial species in a microbiome. We present a constraint-based modeling pipeline to predict precision prebiotics for a minimal microbial community in the worm Caenorhabditis elegans comprising the host-beneficial Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71 with the aim to boost the growth of MYb11. Experimentally testing four of the predicted precision prebiotics, we confirm that they are specifically able to increase the abundance of MYb11 in vitro and in vivo. These results demonstrate that constraint-based modeling could be an important tool for the development of targeted microbiome-based therapies against human diseases.

Impairments in Cognitive and Emotional Empathy as Markers of General versus Specific Personality Pathology.

INTRODUCTION: The alternative model for personality disorders (AMPD) of the Diagnostic and Statistical Manual of Mental Disorders - 5th edition (DSM-5) considers impairments in empathy a basic feature of personality disorders (PDs). In contrast, the AMPD pathological personality trait model and the categorical DSM-5 Section II PD model associate deficits in empathy to specific forms of personality pathology. The present study investigated to what extent impairments in cognitive and emotional empathy are markers of general versus specific personality pathology. METHODS: In a clinical sample (n = 119), the Multifaceted Empathy Test was used to assess cognitive empathy, emotional empathy for positive emotions, and emotional empathy for negative emotions. Personality functioning, pathological personality traits, and DSM-5 Section II PDs were assessed via interviews and self-reports. Confirmatory factor analyses were applied to associate the three empathy facets with the three personality pathology approaches, each modeled with general personality pathology (common factor) and specific personality pathology (residuals of indicators). RESULTS: Impairments in cognitive empathy and emotional empathy for positive emotions were significantly correlated with general personality pathology. All three empathy facets were also correlated to specific personality pathology when controlling for general personality pathology, respectively. Impairments in cognitive empathy were incrementally associated with identity and empathy (personality functioning), psychoticism (pathological personality traits), and paranoid and dependent PD (DSM-5 Section II PDs). Deficits in emotional empathy for positive emotions were incrementally associated with self-direction and intimacy (personality functioning) and detachment (pathological personality traits). Impairments in emotional empathy for negative emotions were incrementally associated with antagonism (pathological personality traits) and antisocial PD (DSM-5 Section II PDs). CONCLUSION: The results suggest that impairments in cognitive empathy and emotional empathy for positive emotions, but not for negative emotions, are markers of general personality pathology, while deficits in the three empathy facets are also markers for specific personality pathology.

Developmental trajectories and cooperating genomic events define molecular subtypes of BCR::ABL1-positive ALL.

ABSTRACT: Distinct diagnostic entities within BCR::ABL1-positive acute lymphoblastic leukemia (ALL) are currently defined by the International Consensus Classification of myeloid neoplasms and acute leukemias (ICC): "lymphoid only", with BCR::ABL1 observed exclusively in lymphatic precursors, vs "multilineage", where BCR::ABL1 is also present in other hematopoietic lineages. Here, we analyzed transcriptomes of 327 BCR::ABL1-positive patients with ALL (age, 2-84 years; median, 46 years) and identified 2 main gene expression clusters reproducible across 4 independent patient cohorts. Fluorescence in situ hybridization analysis of fluorescence-activated cell-sorted hematopoietic compartments showed distinct BCR::ABL1 involvement in myeloid cells for these clusters (n = 18/18 vs n = 3/16 patients; P < .001), indicating that a multilineage or lymphoid BCR::ABL1 subtype can be inferred from gene expression. Further subclusters grouped samples according to cooperating genomic events (multilineage: HBS1L deletion or monosomy 7; lymphoid: IKZF1-/- or CDKN2A/PAX5 deletions/hyperdiploidy). A novel HSB1L transcript was highly specific for BCR::ABL1 multilineage cases independent of HBS1L genomic aberrations. Treatment on current German Multicenter Study Group for Adult ALL (GMALL) protocols resulted in comparable disease-free survival (DFS) for multilineage vs lymphoid cluster patients (3-year DFS: 70% vs 61%; P = .530; n = 91). However, the IKZF1-/- enriched lymphoid subcluster was associated with inferior DFS, whereas hyperdiploid cases showed a superior outcome. Thus, gene expression clusters define underlying developmental trajectories and distinct patterns of cooperating events in BCR::ABL1-positive ALL with prognostic relevance.

Evaluating the Psychometric Properties of the German Self and Interpersonal Functioning Scale (SIFS).

The Self and Interpersonal Functioning Scale (SIFS) is a 24-item self-report questionnaire assessing personality functioning according to the alternative DSM-5 model for personality disorders. We evaluated the German SIFS version in a total sample of 886 participants from Germany and Switzerland. Its factor structure was investigated with confirmatory factor analysis comparing bifactor models with two specific factors (self- and interpersonal functioning) and four specific factors (identity, self-direction, empathy, and intimacy). The SIFS sum and domain scores were tested for reliability and convergent validity with self-report questionnaires and interviews for personality functioning, -organization, -traits, -disorder categories, and well-being. None of the bifactor models yielded good model fit, even after excluding two items with low factor loadings and including a method factor for reverse-keyed items. Based on a shortened 22-item SIFS version, models suggested that the g-factor explained 52.9-59.6% of the common variance and that the SIFS sum score measured the g-factor with a reliability of .68-.81. Even though the SIFS sum score showed large test-retest reliability and correlated strongly with well-established self-report questionnaires and interviews, the lack of structural validity appears to be a serious disadvantage of the SIFS compared to existing self-reports questionnaires of personality functioning.

Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability.

Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Evaluating a multimodal, clinical and work-directed intervention (RTW-PIA) to support sustainable return to work among employees with mental disorders: study protocol of a multicentre, randomised controlled trial.

BACKGROUND: Mental disorders (MDs) are one of the leading causes for workforce sickness absence and disability worldwide. The burden, costs and challenges are enormous for the individuals concerned, employers and society at large. Although most MDs are characterised by a high risk of relapse after treatment or by chronic courses, interventions that link medical-psychotherapeutic approaches with work-directed components to facilitate a sustainable return to work (RTW) are rare. This protocol describes the design of a study to evaluate the (cost-)effectiveness and implementation process of a multimodal, clinical and work-directed intervention, called RTW-PIA, aimed at employees with MDs to achieve sustainable RTW in Germany. METHODS: The study consists of an effectiveness, a health-economic and a process evaluation, designed as a two-armed, multicentre, randomised controlled trial, conducted in German psychiatric outpatient clinics. Sick-listed employees with MDs will receive either the 18-month RTW-PIA treatment in conjunction with care as usual, or care as usual only. RTW-PIA consists of a face-to-face individual RTW support, RTW aftercare group meetings, and web-based aftercare. Assessments will be conducted at baseline and 6, 12, 18 and 24 months after completion of baseline survey. The primary outcome is the employees´ achievement of sustainable RTW, defined as reporting less than six weeks of working days missed out due to sickness absence within 12 months after first RTW. Secondary outcomes include health-related quality of life, mental functioning, RTW self-efficacy, overall job satisfaction, severity of mental illness and work ability. The health-economic evaluation will be conducted from a societal and public health care perspective, as well as from the employer's perspective in a cost-benefit analysis. The design will be supplemented by a qualitative effect evaluation using pre- and post-interviews, and a multimethod process evaluation examining various predefined key process indicators from different stakeholder perspectives. DISCUSSION: By applying a comprehensive, multimethodological evaluation design, this study captures various facets of RTW-PIA. In case of promising results for sustainable RTW, RTW-PIA may be integrated into standard care within German psychiatric outpatient clinics. TRIAL REGISTRATION: The study was prospectively registered with the German Clinical Trials Register ( DRKS00026232 , 1 September 2021).

Piezoresistive Free-standing Microfiber Strain Sensor for High-resolution Battery Thickness Monitoring.

Highly sensitive microfiber strain sensors are promising for the detection of mechanical deformations in applications where limited space is available. In particular for in situ battery thickness monitoring where high resolution and low detection limit are key requirements. Herein, the realization of a highly sensitive strain sensor for in situ lithium-ion (Li-ion) battery thickness monitoring is presented. The compliant fiber-shaped sensor is fabricated by an upscalable wet-spinning method employing a composite of microspherical core-shell conductive particles embedded in an elastomer. The electrical resistance of the sensor changes under applied strain, exhibiting a high strain sensitivity and extremely low strain detection limit of 0.00005 with high durability of 10 000 cycles. To demonstrate the accuracy and ease of applicability of this sensor, the real-time thickness change of a Li-ion battery pouch cell is monitored during the charge and discharge cycles. This work introduces a promising approach with the least material complexity for soft microfiber strain gauges.

Phosphoproteomics Profiling Defines a Target Landscape of the Basophilic Protein Kinases AKT, S6K, and RSK in Skeletal Myotubes.

Phosphorylation-dependent signal transduction plays an important role in regulating the functions and fate of skeletal muscle cells. Central players in the phospho-signaling network are the protein kinases AKT, S6K, and RSK as part of the PI3K-AKT-mTOR-S6K and RAF-MEK-ERK-RSK pathways. However, despite their functional importance, knowledge about their specific targets is incomplete because these kinases share the same basophilic substrate motif RxRxxp[ST]. To address this, we performed a multifaceted quantitative phosphoproteomics study of skeletal myotubes following kinase inhibition. Our data corroborate a cross talk between AKT and RAF, a negative feedback loop of RSK on ERK, and a putative connection between RSK and PI3K signaling. Altogether, we report a kinase target landscape containing 49 so far unknown target sites. AKT, S6K, and RSK phosphorylate numerous proteins involved in muscle development, integrity, and functions, and signaling converges on factors that are central for the skeletal muscle cytoskeleton. Whereas AKT controls insulin signaling and impinges on GTPase signaling, nuclear signaling is characteristic for RSK. Our data further support a role of RSK in glucose metabolism. Shared targets have functions in RNA maturation, stability, and translation, which suggests that these basophilic kinases establish an intricate signaling network to orchestrate and regulate processes involved in translation.

Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.

The microbiome is increasingly receiving attention as an important modulator of host health and disease. However, while numerous mechanisms through which the microbiome influences its host have been identified, there is still a lack of approaches that allow to specifically modulate the abundance of individual microbes or microbial functions of interest. Moreover, current approaches for microbiome manipulation such as fecal transfers often entail a non-specific transfer of entire microbial communities with potentially unwanted side effects. To overcome this limitation, we here propose the concept of precision prebiotics that specifically modulate the abundance of a microbiome member species of interest. In a first step, we show that defining precision prebiotics by compounds that are only taken up by the target species but no other species in a community is usually not possible due to overlapping metabolic niches. Subsequently, we present a metabolic modeling network framework that allows us to define precision prebiotics for a two-member C. elegans microbiome model community comprising the immune-protective Pseudomonas lurida MYb11 and the persistent colonizer Ochrobactrum vermis MYb71. Thus, we predicted compounds that specifically boost the abundance of the host-beneficial MYb11, four of which were experimentally validated in vitro (L-serine, L-threonine, D-mannitol, and γ-aminobutyric acid). L-serine was further assessed in vivo, leading to an increase in MYb11 abundance also in the worm host. Overall, our findings demonstrate that constraint-based metabolic modeling is an effective tool for the design of precision prebiotics as an important cornerstone for future microbiome-targeted therapies.

Applying multimodal social relations analyses in personality pathology research.

In this article, we introduce multimodal social relations analysis as a powerful tool for studying personality pathology that tackles several important limitations of existing research. By implementing a design in which groups of participants provide repeated ratings as they interact, researchers can gather data on individuals' mutual perceptions, affective experiences, and interpersonal behaviors in naturalistic social contexts. We demonstrate how the social relations model can be used to analyze and make conceptual sense of these complex, dyadic data and showcase how this may be used to address not only the experiences and behaviors of individuals diagnosed with a personality disorder but also the reactions these individuals evoke in others. We provide recommendations as to what settings and measures might be best suited when designing a study that applies multimodal social relations analysis, and we discuss practical and theoretical implications as well as possible extensions to this method. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adverse childhood experiences and personality functioning interact substantially in predicting depression, anxiety, and somatization.

Etiological theories on the development of psychopathology often incorporate adverse childhood experiences (ACE) as an important contributing factor. Recent studies suggest personality functioning (PF; i.e., stability of the self and interpersonal relationships) as an important transdiagnostic construct that could be useful in better understanding when persons with ACE do (not) develop psychopathological symptoms. A representative sample of N = 2363 was assessed by questionnaires on ACE, PF (Level of Personality Functioning Scale-Brief Form 2.0), and current symptoms of depression, anxiety, and somatization (Brief Symptom Inventory 18). The interaction between ACE and PF on symptoms was investigated using multiple group models and Bayesian structural equation modeling. ACE were positively associated with psychopathology and PF impairments. The interaction effect between ACE and PF explained incremental variance in current symptoms, ranging from 26% for somatization to 49% for depression with the complete model explaining up to 91% of the latent variance in psychopathology. Our findings indicate a diathesis-stress model with PF as a resource or resilience that may buffer against the development of symptoms in the face of adversity. Treatments of depression and anxiety targeting self and interpersonal functioning therefore may lead to improvements in resilience and relapse prevention. [Correction added on 15 March 2023, after first online publication: Level of Personality Functioning Scale-Brief Form has been replaced to Level of Personality Functioning Scale-Brief Form 2.0 ].

Pathological personality in relation to multiple domains of quality of life and impairment: Evidence for the specific relevance of the maladaptive poles of major trait domains.

The current study examined whether personality domains have nonmonotonic relationships with functional outcomes, specifically in relation to quality of life and impairment. Four samples were utilized, which were drawn from the United States and Germany. Personality trait domains were measured via the IPIP-NEO and PID-5; quality of life (QoL) was measured with the WHOQOL-BREF, and impairment was measured using the WHODAS-2.0. The PID-5 was analyzed in all four samples. Two-line testing, which fits two spline regression lines separated at a break point, was conducted to evaluate potential nonmonotonicity of the relationship between personality traits and quality of life. Overall, results demonstrated little support for nonmonotonic relationships in the PID-5 and IPIP-NEO dimensions. Rather, our results indicate that there is one clear pathological pole of major domains of personality that is associated with lower quality of life and increased impairment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).