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. 2023 Dec 18;14(1):47.
doi: 10.1186/s13229-023-00580-3.

The biosocial correlates and predictors of emotion dysregulation in autistic adults compared to borderline personality disorder and nonclinical controls

Affiliations

The biosocial correlates and predictors of emotion dysregulation in autistic adults compared to borderline personality disorder and nonclinical controls

Doha Bemmouna et al. Mol Autism. .

Abstract

Background: Emotion dysregulation (ED) is a core symptom of borderline personality disorder (BPD), whose aetiology has been attributed to biosocial factors. In autism spectrum condition (ASC), although ED is prevalent and is associated with decreased well-being (e.g. self-harm, suicidality), it has been understudied, especially in adults. It is therefore crucial to further understand ED in autistic adults to improve its treatment. Our study investigates ED, its behavioural correlates (e.g. self-harm, suicidality) and biosocial predictors in autistic adults relative to BPD and nonclinical controls (NC).

Methods: A total of 724 participants (ASC = 154; BPD = 111; NC = 459) completed 11 self-reported questionnaires assessing ED, ASC and BPD traits, co-occurring disorders, alexithymia, emotional vulnerability and invalidating experiences (e.g. bullying, autistic camouflaging). The occurrence of ED behavioural correlates (i.e. self-harm, history of suicide attempts, and psychiatric hospitalizations) was collected. In addition, between-groups analyses, linear regressions and machine learning (ML) models were used to identify ED predictors in each group.

Results: ED and its behavioural correlates were higher in ASC compared to NC, but milder than in BPD. While gender did not predict ED scores, autistic women had increased risk factors to ED, including sexual abuse and camouflaging. Interestingly, BPD traits, emotional vulnerability and alexithymia strongly predicted ED scores across the groups. Using ML models, sensory sensitivity and autistic camouflaging were associated with ED in ASC, and ADHD symptoms with ED in BPD.

Limitations: ASC and BPD diagnoses were self-reported, which did not allow us to check their accuracy. Additionally, we did not explore the transactional and the moderating/mediating relationships between the different variables. Moreover, our research is cross-sectional and cannot draw conclusions regarding the direction and causality of relationships between ED and other clinical dimensions.

Conclusions: ED and its behavioural correlates are heightened in BPD compared to ASC and nonclinical controls. In the ASC group, there were no gender differences in ED, despite the heightened exposure of autistic women to ED risk factors. BPD traits, emotional vulnerability, and alexithymia are core to ED regardless of diagnosis. Although less central, sensory sensitivity and autistic camouflaging seem to be specific predictors of ED in autistic adults.

Keywords: Aetiology; Autism spectrum condition; Biosocial; Borderline personality disorder; Emotion dysregulation; Non-suicidal self-injury; Suicidality.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Study flow chart. Note: ASC = Autism spectrum condition; BPD = Borderline personality disorder, NC = Nonclinical controls
Fig. 2
Fig. 2
Shapley values plots illustrate how explanatory variables contribute to ED in each group (ASC/BPD/NC). The feature list down the y-axis is in order of contribution to the model (most to least). On the x-axis, the SHAP values for each observation are presented—negative SHAP values are interpreted as reduced ED, while positive SHAP values are interpreted as increased ED. Each dot represents an individual respondent; hence, the number of dots against each feature reflects the sample size of the training set. The dot’s position along the x-axis is the feature’s impact on the model’s prediction for that respondent. The colour indicates whether the value of the characteristic considered is high or low in relation to the range of values (red refers to high values and blue to low values). When multiple dots arrive at the same coordinate in the plot, they pile up to show the density of effect sizes. The graph has a median line and the farther the point is from the median line, the stronger is the influence on the output, with the points on the right correlating positively with ED and the points on the left negatively)

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