Gender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population

Gender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population Agnieszka Siedler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5497260/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Autism Spectrum Disorder (ASD) often presents comorbidity with depression, sharing similar characteristics between the two disorders with respect to social interaction, regulation of emotions, and flexibility in cognition. The current study investigates the relationship between autistic traits related to BAP and depressive symptoms in a general population sample, considering possible differences according to gender. In a sample of 239 adults, the results indicated that autistic traits, especially in the domains of communication and social skills, showed a significant association with depressive symptoms, with even more robust associations specifically in women. These results emphasize the presence of important sex differences in the associations found between autistic traits and specific depressive symptoms. In women, significant positive correlations were observed for autistic traits related to communication, social skills, and difficulties with attention-shifting, with depressive symptoms regarding thoughts of death, feelings of pessimism, experiences of alienation, cognitive impairments, and psychosomatic presentations. In contrast, males showed fewer significant associations, with only attention to detail significantly related to depressive symptoms such as cognitive deficits and decreased energy levels. It thus appears that there might be sex differences in the way the different dimensions of the autism spectrum relate to the various dimensions of depressive symptomatology. Furthermore, moderation analysis showed that gender influences the strength of these relationships, which highlights the need for gender-sensitive approaches both in research and clinical practice when assessing and targeting depressive symptoms in subclinical ASD populations. The implications for clinical practice as well as the limitations of the study are discussed. autism spectrum dosorders depression broader autism phenotype Introduction Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and comorbid psychopathological traits. Recent studies have extensively explored the intricate relationship between autism and various psychopathological profiles. ASD frequently coexists with other psychiatric conditions, adding complexity to its clinical presentation. Research shows that 68% of children with ASD have comorbid psychiatric disorders, predominantly anxiety and ADHD (Zoromba et al., 2022 ). Additionally, higher rates of mental health comorbidities were reported among adults with ASD compared to those without, indicating the need for lifelong mental health support for this population (Radwan & Mallik, 2020 ). Autistic traits often overlap with other psychopathological characteristics, suggesting a continuum across different psychiatric conditions. Cluster analysis showed intermediate levels of autistic traits in disorders such as bipolar disorder and eating disorders, indicating a shared underlying spectrum across various psychopathologies (Dell’Osso et al., 2023 ). Moreover, studies emphasized the importance of considering both genetic and environmental factors when assessing the etiology of autism, further complicating the interplay of psychopathological traits (Guney & Işeri, 2013 ). Autism Spectrum Disorder (ASD) and depression are two prevalent conditions that often co-occur in adults, complicating the clinical profile and treatment strategies for these individuals. Research has consistently shown that adults on the autism spectrum experience higher rates of depression compared to the general population. Studies found that 46% of adults diagnosed with ASD reported moderate to severe depression, with factors such as age, gender, and severity of autism symptoms significantly influencing these levels (Murray et al., 2019 ). The overlapping characteristics of ASD and depression, such as social withdrawal and communication challenges, often complicate the diagnostic process (Stewart et al., 2006 ). Several factors contribute to the high prevalence of depression among adults with autism. Research emphasized the role of underlying cognitive impairments and challenging life experiences in exacerbating depressive symptoms in this group (Ghaziuddin et al., 2002 ). Moreover, some adults with ASD may have an increased awareness of their functional difficulties, leading to emotional distress and heightened depressive symptoms (Einabad et al., 2017 ). The Broader Autism Phenotype (BAP) refers to a range of subclinical traits of autism that are present in family members of individuals with Autism Spectrum Disorder (ASD) and the general population. These traits can include social communication difficulties, rigid thinking, and a focus on details, all of which may predispose individuals to psychological challenges. Autistic traits related to the broader autism phenotype have been shown to correlate with increased levels of depression. Research found that aspects such as social skills deficits and challenges with attention switching contribute significantly to depressive symptoms. This is particularly evident in individuals who possess a greater awareness of their functional problems, which can lead to a negative self-image and emotional distress (Einabad et al., 2017 ). Moreover, previous studies highlights that both autism and depression share common difficulties, such as perceived worries and a sense of lacking control. While these overlapping symptoms do not entirely explain their co-occurrence, they suggest that factors like mastery perception may play a critical role in linking BAP with depression (van Heijst et al., 2019 ). The intersection between BAP and depression poses significant implications for mental health care. Misdirected social communication and empathy challenges, which mirror those observed in chronic depression, may necessitate tailored therapeutic interventions that address these overlapping features (Domes et al., 2016 ). The relationship between the broader autism phenotype and depression is complex and multifaceted. It highlights the need for a comprehensive approach to diagnosis and treatment that accounts for the spectrum of autistic traits and their psychological implications. The primary objective of the current study was to investigate the association between depression symptoms and traits from the broader autism phenotype (BAP) within the general population. Recent research has identified an overlap between depressive symptoms and autistic traits, particularly among individuals who exhibit subclinical levels of autism spectrum disorder (ASD) characteristics. This overlap is especially relevant given the unique challenges faced by individuals with heightened ASD traits, such as difficulties in social communication, emotional regulation, and cognitive flexibility. These areas of functioning are also commonly impacted in depression, suggesting potential shared mechanisms that may increase the risk of depressive symptoms in individuals with ASD traits. The rationale for this study lies in understanding specific ASD traits that might elevate the likelihood of experiencing depressive symptoms. Identifying these traits could enhance diagnostic assessments and lead to more tailored interventions for individuals displaying both autistic and depressive characteristics. Additionally, by examining gender differences, this research addresses an important gap: while ASD and depression present differently across genders, most studies have focused on either clinical ASD populations or have not stratified data by gender. This study aims to clarify whether certain ASD traits, such as communication difficulties or social skill deficits, are more strongly associated with depression symptoms in females compared to males in the general population. Based on these considerations, we formulated several hypotheses. First, we hypothesized that individuals with elevated BAP traits would report higher levels of depressive symptoms than those with lower levels of BAP traits. Second, we expected gender differences, with females showing a stronger correlation between specific ASD traits (e.g., deficits in social communication and emotional regulation) and depressive symptoms. Our third hypothesis posited that particular subscales, such as AQ Communication and Social Skills, would be most predictive of depression symptoms, especially in the female group. Our primary research questions were thus directed at understanding (1) the extent to which elevated BAP traits predict depressive symptoms in a non-clinical population, (2) the role of gender in moderating these associations, and (3) which specific ASD traits are most strongly linked to depressive symptomatology. By answering these questions, this study aims to contribute to a more nuanced understanding of the interaction between autistic traits and depression, offering insights that could inform clinical practice and guide future research on comorbid mental health challenges in individuals with subclinical ASD traits. Methods and materials Participants The study recruited 239 adults (121 females and 108 males) from the general population, aged 19 to 48 (mean age: 35.44), with no prior diagnoses of ASD or depression to maintain a focus on subclinical ASD traits and depressive symptoms. Participants were selected through random sampling and completed the assessments independently. People who reported having diagnosed mental health problems (e.g. depressive episode, bipolar disorder, ADHD, autism, anxiety disorders) were excluded from the study group. The data collection was carried out by two trained psychologists, ensuring standardized administration and guidance when necessary. Participants provided informed consent and completed a demographic questionnaire covering basic information such as age, education, and employment status. The study was approved by the local institutions’ Research Ethics Committee and was in accordance with the Declaration of Helsinki. The sample size aligns with those in previous studies examining gender differences in autism and depression. Table 1 Sociodemographic data Variable Total Sample Females Males N 239 121 108 Age M (SD) 35.44(8.01) 35.08 (8.22) 35.85(7.80) Age range in years (min.-max.) 19–68 19–68 19–62 Place of living Village n(%) 47(20.05) 22(18.2) 25(23.1) City (50 thousand inhabitants) n(%) 35(15.3) 20(16.5) 15(13.9) City (50–100 thousand inhabitants) n(%) 72(31.4) 41(33.9) 31(28.7) City (100 thousand inhabitants) n(%) 75(32.8) 38(31.4) 37(34.3) Employment Student n(%) 12(5.2) 9(7.4) 3(2.8) Unemployed n(%) 22(9.6) 19(15.7) 3(2.8) Employed n(%) 190(83.0) 90(74.4) 100(92.6) Retired n(%) 5(2.2) 3(2.5) 2(1.9) Education Primary education n(%) 8(3.5) 0(0) 8(7.4) Junior high n(%) 10(4.4) 3(2.5) 7(6.5) Vocational education n(%) 59(25.8) 33(27.3) 26(24.1) High school education n(%) 40(17.5) 18(14.9) 22(20.4) Masters / bachelor studies graduation n(%) 91(39.7) 56(46.3) 35(32.4) Currently studying n(%) 20(8.7) 11(9.1) 9(8.3) Measures Autism Spectrum Quotient (AQ) is the self-report questionnaire The Autism–Spectrum Quotient (AQ) (Baron-Cohen et al., 2001 ) was used in its Polish version (Pisula et al., 2013) to measure the intensity of autistic features. The questionnaire consists of 50 statements, to which the participants respond on a 4-point Likert scale. Total score (the sum of the scores in all items) ranges from 0 to 50 points. In this study, the reliability of the AQ measured by Cronbach's alpha coefficient was α = 0.89 for the Total score. In addition to the total score, it is possible to calculate scores on 5 subscales, each consisting of 10 items: Social skills (Ss, α = 0.84), Attention switching (At_s; α = 0.83), Attention to detail (At_d; α = 0.70); Communication (Com; α = 0.79), and imagination (Imm; α = 0.72). The Depression Measurement Questionnaire (DMQ)(Łojek, Stańczak i Wójcik, 2015) is a measure of the intensity of depressive effects and those resulting from self-regulation, It consists of 75 indicators relating to individual thoughts, feelings and behaviours of the examined person and uses a four-point scale to provide answers. The results are presented on five scales: Cognitive deficits and loss of energy (DPUE), Thoughts about death, pessimism and alienation (MSPA), Guilt and anxiety (PWNL), Psychosomatic symptoms and loss of interests (OPSZ), Self-regulation (SR). In addition, a total score is calculated, which is the sum of the results obtained in the DPUE, MSPA, PWNL and OPSZ scales, which is an overall indicator of the intensity of depressive symptoms. The questionnaire has high and very high internal consistency coefficients and very high absolute stability coefficients. The tool also has confirmed construct validity through correlations with other measures examining symptoms of depression, measures examining personality, mood, self-esteem and cognitive functioning (Łojek, Stańczak i Wójcik, 2015). In this study, the reliability of the questionnaire measured by Cronbach's alpha coefficient was α = 0.93 for the Total score. The Reading Minds In Eyes test (RME) (Baron-Cohen et al., 2001 ) is a commonly used assessment of mentalizing abilities. It comprises 36 monochromatic images of an individual's ocular region and one practice item. The participant must identify the mental state depicted in the image and select one of four forced-choice replies provided. Each item receives a score of 1 for a valid response and 0 for an erroneous answer, resulting in a total score range of 0 to 36. Besides demonstrating strong test–retest reliability, it remains unconfounded by gender when evaluated in large-scale populations (Baron-Cohen. Et al., 2001; Gallant & Good, 2020 ; Elamin et al., 2012 ). Analysis Pearson correlation analysis were conducted to examine the relationships between AQ total scores, DMQ total scores, RME scores, and the subscales within each measure. This analysis aimed to identify whether higher levels of autistic traits were associated with more severe depressive symptoms or reduced mentalizing abilities. Additionally, correlations were calculated separately for each gender to explore possible gender-specific patterns in these relationships, given prior evidence suggesting that the expression of autistic traits and related mental health impacts can vary significantly by gender. Moderation analyses were conducted using PROCESS v4.2 to examine whether gender moderates the relationship between autistic traits (measured by AQ) and depressive symptoms (measured by DMQ). The goal of these analyses was to determine if the association between autistic traits and depression differs by gender, providing insights into how gender may influence mental health dynamics in individuals with varying levels of autistic traits. PROCESS v4.2 was also employed to conduct mediation analyses to examine whether RME (Reading the Mind in the Eyes Test) serves as a mediator in the relationship between AQ (Autism Spectrum Quotient) and DMQ (depressive symptoms). The t-test was performed to investigate potential gender differences across key variables in the study. The multiple regression analysis was conducted to determine the impact of various predictors on the severity of depressive symptoms. Statistical tests were performed using SPSS for Windows Version 24. For all tests, the significance level was set to p < .05. Results Correlation analysis The correlation analysis presented in Table 2 reveals significant associations between DMQ subscales and AQ scores. Generally, higher levels of autistic traits, as measured by the total AQ score, were positively correlated with depressive symptoms (r = 0.431, p < 0.001). Among the specific AQ subscales, the Communication subscale showed the strongest positive correlation with depressive symptoms (r = 0.470, p < 0.001), indicating that difficulties in communication are particularly associated with increased depressive symptoms. Additionally, the MSPA subscale of the KPD, which measures pessimism, thoughts of death, and feelings of alienation, demonstrated robust positive correlations with several AQ subscales, notably AQ Social Skills (r = 0.603, p < 0.001), AQ Communication (r = 0.631, p < 0.001), AQ Attention Switching (r = 0.610, p < 0.001), and AQ Total (r = 0.700, p < 0.001). Table 2 Correlations between DMQ Subscales and AQ Scores in the whole sample DMQ Scores AQ_Attention_to_Detail AQ_Imagination AQ_Communication AQ_Attention_Switch AQ_Social_Skills AQ_Total SR r=-0.011 (p = 0.916) r = 0.468*** (p < 0.001) r = 0.291** (p < 0.01) r = 0.342** (p < 0.01) r = 0.350*** (p < 0.001) r = 0.381*** (p < 0.001) OPSZ r = 0.197 (p = 0.051) r = 0.445*** (p < 0.001) r = 0.509*** (p < 0.001) r = 0.566*** (p < 0.001) r = 0.452*** (p < 0.001) r = 0.580*** (p < 0.001) PWNL r = 0.294** (p < 0.01) r = 0.317** (p < 0.01) r = 0.537*** (p < 0.001) r = 0.591*** (p < 0.001) r = 0.443*** (p < 0.001) r = 0.585*** (p < 0.001) MSPA r = 0.270** (p < 0.01) r = 0.496*** (p < 0.001) r = 0.631*** (p < 0.001) r = 0.610*** (p < 0.001) r = 0.603*** (p < 0.001) r = 0.700*** (p < 0.001) DPUE r = 0.184 (p = 0.068) r = 0.412*** (p < 0.001) r = 0.516*** (p < 0.001) r = 0.537*** (p < 0.001) r = 0.490*** (p < 0.001) r = 0.571*** (p < 0.001) Total DMQ (depressive symptoms) r = 0.013 (p = 0.845) r = 0.198** (p < 0.01) r = 0.470*** (p < 0.001) r = 0.316*** (p < 0.001) r = 0.463*** (p < 0.001) r = 0.431*** (p < 0.001) Note : *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale. Results reveal notable gender differences in the correlations between AQ and DMQ subscales (Table 3 ). For females, AQ Communication and AQ Social Skills were strongly correlated with DMQ subscales SR, OPSZ, and MSPA, indicating more robust relationships in these areas than observed in the male group. Males displayed fewer significant correlations overall, particularly with AQ Attention to Detail, which showed limited associations. Depression severity correlated significantly with AQ Social Skills and Communication among females, while in males, these relationships were weaker and more variable across subscales. Table 3 Correlations between DMQ Subscales and AQ Scores for males and females DMQ Scores Gender AQ Total AQ Communication AQ Social Skills AQ Attention Switching AQ Attention to Detail AQ Imagination DMQ Subscale SR Females r = 0.464*** (p < 0.001) r = 0.398** (p < 0.01) r = 0.416*** (p < 0.001) r = 0.495*** (p < 0.001) r = -0.030 (p = 0.826) r = 0.573*** (p < 0.001) Males r = 0.170 (p = 0.275) r = 0.066 (p = 0.674) r = 0.236 (p = 0.128) r = -0.045 (p = 0.776) r = 0.003 (p = 0.984) r = 0.257 (p = 0.096) DMQ Subscale OPSZ Females r = 0.687*** (p < 0.001) r = 0.649*** (p < 0.001) r = 0.661*** (p < 0.001) r = 0.698*** (p < 0.001) r = 0.010 (p = 0.943) r = 0.657*** (p < 0.001) Males r = 0.339* (p < 0.05) r = 0.273 (p = 0.076) r = 0.057 (p = 0.715) r = 0.327* (p < 0.05) r = 0.473*** (p < 0.001) r = 0.067 (p = 0.669) DMQ Subscale MSPA Females r = 0.790*** (p < 0.001) r = 0.755*** (p < 0.001) r = 0.770*** (p < 0.001) r = 0.729*** (p < 0.001) r = 0.158 (p = 0.245) r = 0.687*** (p < 0.001) Males r = 0.515*** (p < 0.000) r = 0.437** (p < 0.01) r = 0.327* (p < 0.05) r = 0.398** (p < 0.01) r = 0.410** (p < 0.01) r = 0.176 (p = 0.258) DMQ Subscale DPUE Females r = 0.689*** (p < 0.001) r = 0.667*** (p < 0.001) r = 0.679*** (p < 0.001) r = 0.652*** (p < 0.001) r = 0.091 (p = 0.503) r = 0.612*** (p < 0.001) Males r = 0.349* (p < 0.05) r = 0.289 (p = 0.060) r = 0.199 (p = 0.200) r = 0.354* (p < 0.05) r = 0.271 (p = 0.079) r = 0.109 (p = 0.486) Total DMQ (Depression Symptoms) Females r = 0.628*** (p < 0.001) r = 0.636*** (p < 0.001) r = 0.639*** (p < 0.001) r = 0.434*** (p < 0.001) r = 0.162 (p = 0.077) r = 0.422*** (p < 0.001) Males r = 0.241* (p < 0.05) r = 0.343*** (p < 0.001) r = 0.289** (p < 0.01) r = 0.212* (p < 0.05) r = -0.081 (p = 0.407) r = -0.006 (p = 0.954) Note : *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale. The correlation analysis results in Table 4 show significant relationships between the Reading the Mind in the Eyes (RME) test scores and various DMQ variables. RME scores were negatively correlated with several DMQ subscales, including MSPA (r=-0.237, p < 0.05), DPUE (r=-0.205, p < 0.05), and the total depression score (r=-0.211, p < 0.01), indicating that lower mentalizing abilities may be associated with more severe depressive symptoms. Table 4 Correlations between DMQ Subscales and RME Scores for the whole sample DMQ Subscale SR DMQ Subscale OPSZ DMQ Subscale PWNL DMQ Subscale MSPA DMQ Subscale DPUE Total DMQ Depression Symptoms RME r=-0.135 (p = 0.184) r=-0.119 (p = 0.242) r=-0.163 (p = 0.108) r=-0.237* (p < 0.05) r=-0.205* (p < 0.05) r=-0.211** (p < 0.01) Note : *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale. The results indicate a stronger negative correlation between RME and all DMQ subscales in females than in males, with significant correlations observed for females across multiple subscales. Specifically, in females, significant correlations were found for DMQ OPSZ, DMQ PWNL, DMQ MSPA, DMQ DPUE, and overall DMQ Depression Symptoms. For males, significant associations were limited, with only DMQ PWNL and DMQ Depression Symptoms showing significant correlations. All correlations are shown in Table 5 . Table 5 Correlations between DMQ Subscales and RME Scores – males and females DMQ Subscale SR DMQ Subscale OPSZ DMQ Subscale PWNL DMQ Subscale MSPA DMQ Subscale DPUE Total DMQ Depression Symptoms Females r = -0.249 (p = 0.064) r = -0.328* (p < 0.05) r = -0.475** (p < 0.01) r = -0.446** (p < 0.01) r = -0.393** (p < 0.01) r = -0.324** (p < 0.01) Males r = -0.133 (p = 0.397) r = -0.248 (p = 0.109) r = -0.312* (p < 0.05) r = -0.241 (p = 0.120) r = -0.296 (p = 0.054) r = -0.287** (p < 0.01) Note : *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale. Moderation analysis The moderation analysis aimed to examine whether gender moderates the relationship between autistic traits and depressive symptoms. Table 6 provides a summary of the results. The model explained approximately 29.4% of the variance in depressive symptoms (R 2 = 0. F (3,225) = 31.20, p < 0.001). The interaction term (AQ_Total x Gender) was significant (t = − 3.67, p < 0.00), indicating that the relationship between AQ_Total and DMQ_Total differs by gender (see Table 7 ). The conditional effects indicate that the relationship between autistic traits and depressive symptoms is stronger in females than in males. Table 6 Model Summary and Interaction Effects Predictor Coefficient SE t p LLCI ULCI AQ_Total 45778 0.86 45510 < 0.001 20149 34851 Gender 20.63 13.68 18629 0.1329 -6.32 47.59 AQ_Total x Gender -2.07 0.56 -3.67 < .001 -3.18 -0.96 Table 7 Conditional Effects of AQ_Total on DMQ_Total by Gender Gender Effect SE t p LLCI ULCI Female 43160 0.38 15919 < 0.001 16103 34029 Male 45627 0.42 24504 < 0.01 0.29 34335 Mediation analysis A mediation analysis was conducted to test whether RME mediated the relationship between AQ and DMQ. However, the analysis showed no significant indirect effect, indicating no mediation. T-tests T -tests were conducted to identify gender differences across various scales. No significant gender differences were observed within the AQ scale results. However, significant gender differences were found in several subscales of the DMQ. For instance, significant differences emerged in RME, DMQ total score, and DMQ subscales including DPUE, MSPA, PWNL, and OPSZ, indicating notable gender-based distinctions in these areas. These results highlight areas where depressive symptomatology and other related measures vary significantly between males and females. There were no significant gender differences in the mean AQ total scores or its subscales. However, the Imagination and Attention to Detail subscales showed differences at a statistically trending level. Table 6 Gender Differences in t-tests Variable t df p Females Males RME 4.635 227 0.000 M = 26.40 (SD = 4.32) M = 23.19 (SD = 6.07) Total DMQ (Depression Symptoms) 3.400 227 0.001 M = 127.19 (SD = 50.99) M = 105.54 (SD = 44.63) DMQ Subscale DPUE 2.065 97 0.042 M = 41.95 (SD = 9.73) M = 38.05 (SD = 8.74) DMQ Subscale MSPA 2.283 97 0.025 M = 27.63 (SD = 9.64) M = 23.56 (SD = 7.51) DMQ Subscale PWNL 3.202 97 0.002 M = 35.04 (SD = 8.94) M = 29.00 (SD = 9.74) DMQ Subscale OPSZ 2.920 97 0.004 M = 20.34 (SD = 5.54) M = 17.44 (SD = 3.89) AQ_Imagination -1.808 227 0.072 M = 3.78 (SD = 2.53) M = 4.39 (SD = 2.59) AQ_AttentionToDetail -1.857 227 0.065 M = 4.96 (SD = 2.89) M = 5.76 (SD = 3.62) Note : DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ - Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale. Regression analysis The regression analysis was conducted to determine the impact of various predictors on the severity of depressive symptoms. The dependent variable was the total score for depressive symptoms, while the independent variables included AQ subscales (social skills, attention switching, imagination, attention to detail, and communication), as well as sociodemographic factors (education, employment status, place of residence), and the Reading the Mind in the Eyes (RME) test score. The regression model produced an R value of 0.510, indicating a moderate correlation between the predictors and depressive symptoms. The R-squared value was 0.260, meaning that approximately 26% of the variance in depressive symptoms was explained by the model. The analysis of variance (ANOVA) for this regression model showed a significant F (9) = 8.572 (p < 0.001), indicating that the model as a whole provides a statistically significant explanation of the variance in depressive symptoms. Only two predictors, AQ Communication and AQ Social Skills, demonstrated significant positive effects on depressive symptoms. This suggests that higher difficulties in communication and poorer social skills are associated with greater severity of depressive symptoms, whereas other predictors in the model did not show statistically significant impacts. Table 6 Model summary Model Statistics Value R 0.510 R-squared 0.260 Adjusted R-squared 0.230 Standard Error of Estimate 43.168 Durbin-Watson Statistic 1.094 F-value 8.572 p-value < 0.001 Table 7 Regression coefficients Predictor B Standard Error Beta t p-value AQ_Communication 4.795 1.661 0.286 2.887 0.004 AQ_Attention_Switch 1.250 1.116 0.078 1.121 0.264 AQ_Social_Skills 3.964 1.598 0.237 2.481 0.014 AQ_Imagination -1.035 1.386 -0.054 -0.746 0.456 AQ_Attention_to_Detail -1.032 0.933 -0.069 -1.106 0.270 RME -0.073 0.626 -0.008 -0.117 0.907 Employment Status 0.399 6.061 0.004 0.066 0.948 Place of Residence 1.412 2.707 0.032 0.522 0.602 Education -0.160 0.459 -0.021 -0.348 0.728 Discussion Relationship between autistic traits and depressive symptoms The study findings are in line with previous research reporting associations between autism traits, as measured by the Autism Spectrum Quotient (AQ), and symptoms of depression. Previous literature has indicated that individuals with elevated autistic traits, especially those who experience difficulties in social interactions, often report increased depressive symptoms (Murray et al., 2019 ). The current study found a significant relationship between the AQ subscales related to communication and social competencies and depressive symptoms, which is in line with previous studies that highlight the significance of social challenges in both clinical and non-clinical groups (Stewart et al., 2006 ). These challenges are often related to the lack of social support, giving rise to social isolation and emotional distress (Ghaziuddin et al., 2002 ). It has been suggested, based on various research findings, that higher levels of autism often co-occur with impairments in social communication, which may heighten one's vulnerability to depression. Difficulties in social communication might contribute to higher depressive symptom scores, for example; the link between these experiences actually remains constant from childhood into early adulthood because of bullying (Rai et al., 2018 ). Furthermore, autism-related characteristics, such as impairments in social perception, interaction, and communication, resemble symptoms seen in chronic depression, suggesting an intersection point that could contribute to the persistence of depressive symptoms (Radtke et al., 2019 ). In the current study, problems with communication and social abilities were significantly associated with certain depressive features in the general population sample. High levels of communication difficulties were especially related to expressions of pessimism, thoughts about death, and feelings of alienation, while social skills deficits had a significant relationship to the cognitive symptoms of depression, namely, low energy and loss of interest. Only two predictors—AQ Communication and AQ Social Skills—showed significant positive relationships to depressive symptoms, according to the regression analysis. This would suggest that more severe depressive symptoms are associated with greater difficulties in communication and reduced social abilities, as opposed to the other variables in the model that were not statistically significant predictors. Gender differences in the relationship between autistic traits and depression The moderation analysis showed that gender significantly moderated the strength of association between autistic traits and depressive symptoms. This would suggest that females with higher level of autistic traits might experience depressive symptoms more intensely, possibly related to greater expectations from society in respect to functioning (van Heijst et al., 2019 ). The difficulties related to social communication and engagement in women seem to predict depressive symptoms more consistently than in men, which underlines even more the need for clinical strategies sensitive to sex differences. Significant gender differences emerged in the relationship between AQ subscales and depressive symptoms, with females showing stronger associations between communication difficulties, social skills deficits, and depressive symptoms than males. Depression is strongly correlated with social communication impairments. Previous literature has indicated that these impairments might disproportionately affect autistic females because of social norms and expectations around communication and social interaction (Rai et al., 2018 ). Increased vulnerability to difficulties in social communication could consequently worsen depressive symptoms. Moreover, autistic females tend to have greater awareness of their struggle with social functioning, which may contribute to more prominent emotional distress (Einabad et al., 2017 ). In the present study, males showed fewer significant correlations across AQ subscales, particularly for attention to detail, which demonstrated little association with depression. This contrast would suggest that social difficulties bear differently on females, and further research into gender-based approaches in both assessment and intervention is therefore justified. Moreover, specific gender-related differences in depressive symptoms related to autismtic traits were established. In the female sample, strong and significant associations were found between the severity of autismtic traits and difficulties with self-regulation. By contrast, this association was not found in the male sample, indicating a gender-specific pattern in the relationship between autistic traits and difficulties with self-regulation. It may be explained by the differences in strategies of emotional regulation and their corresponding consequences for emotional experiences. According to several studies, females with more autistic traits are more likely to engage in maladaptive strategies of emotional regulation—for example, suppression—enhancing negative emotions. This is less observed in males and provides specific evidence of gender in relation to the impact of autism traits on emotional and self-regulation (Zhao et al., 2020 ). Moreover, autistic traits—like rigidities in ways of thinking and difficulties in switching attention—may be subjectively experienced as being "stuck" in negative thinking, a hallmark of depressive cognition. Individuals with high autistic traits, especially women, may have considerable difficulty generating alternative solutions or possibilities of positive outcomes to the situation at hand, thus reinforcing feelings of hopelessness and negativity (Ishizuka et al., 2021 ; South et al., 2019 ). After analyzing the gender differences in the DMQ subscales, some significant differences were identified. The OPSZ subscale measures psychosomatic symptoms and loss of interest, the MSPA subscale assesses thoughts about death, pessimism, and alienation, and the DPUE subscale assesses cognitive deficits and loss of interest. The DMW total score was significantly correlated with AQ traits in both males and females, although slightly stronger for the female group in Total AQ, Communication, Social Skills, and Attention Switching. This pattern might indicate that autistic traits could more strongly exacerbate depressive symptoms in women, especially those concerning cognitive deficits, psychosomatic symptoms, and pessimism. This could mean that depressive symptomatology in females with elevated autistic traits might be more extensively interconnected with each other, possibly due to greater sensitivity to social and communication difficulties. Females with more autistic traits internalize stress and emotional struggles, possibly resulting in physical symptoms such as tiredness or even somatic pain. These same traits are associated with a preference for social masking or camouflage, which could result in a rise in emotional and mental distress and, thereby, psychosomatic symptoms. On the other hand, males with corresponding autistic traits may experience stress in different ways and in a more external manner, potentially alleviating the impact on their physical and psychosomatic health. It is also worth noting that according to most research, autistic traits manifest differently between males and females. Females with autism have fewer stereotyped behaviors and restricted interests compared to their male counterparts. This may contribute to underrecognition and misdiagnosis in females (Edwards et al., 2023 ). The underrecognition in turn contributes to higher psychosomatic symptoms and a loss of interest, as the females are not afforded the proper support and interventions. Furthermore, research conducted on subthreshold autism in adolescents indicated that both male and female individuals with autistic traits showed significant relations with dimensions of arousal, although the nature of these relations differed. In females, they found strong positive correlations between all arousal dimensions and autismtic traits. This shows that individuals with autistic traits may experience high levels of stress and anxiety, which can manifest in psychosomatic symptoms and a reduced inclination toward activities (Ianuzzo et al., 2022). Women with strong autistic traits often struggle with social interaction and in-person communication, leading to severe social isolation and reduced social support. Social isolation of this kind is very characteristic of ASD and can be combined with persistent depression, which can magnify feelings of loneliness and hopelessness, thereby amplifying depressive symptoms (Domes et al., 2016 ; Radtke et al., 2019 ). Interestingly, males show a positive relation between MSPA and OPSZ subscales of DMQ and Attention to Detail subscale of AQ, whose interrelation is not observed in the female group.This relationship would then imply that in men, a stronger attention to detail could be related to an increase in negative thinking and somatic symptoms of depression. This suggests that having a high attention to detail may increase the likelihood of rumination or experiencing emotional pain, a phenomenon that affects men more than women. People with high autistic traits often demonstrate significant impairment in their ability to switch attention and have intense interest in details. These features may lead to sustained rumination and negative cognitive patterns, which are core aspects of depression. For example, a study discovered that high scores in "attention switching" and "attention to detail" predicted persistent depressive symptoms in adults with depression (Ishizuka et al., 2021 ). This would suggest that the cognitive rigidity and perseverative attention associated with autistic traits might exacerbate depressive symptoms by supporting a negative cognitive framework. Males generally score higher than females on the AQ, including the "Attention to Detail" subscale (Steward & Austin, 2009; Zhang et al., 2016 ).In the present investigation, males scored higher compared to females on the Attention to Detail subscale that was statistically significant at a trend level. The attention to detail trait is related to better performance on visual working memory (Richmond et al., 2013 ) and less multisensory temporal adaptation (Stevenson et al., 2017 ). People who score high in Attention to Detail also tend to show higher sensory sensitivity along with heightened perception, as evidenced by superior performance in visual perception tasks and odor discrimination tasks (Baros et al., 2020; Ward et al., 2017 ). Higher scores in Attention to Detail are correlated with a higher chance of looking at the eyes and superior facial recognition abilities, which reflect the complex interrelationship between attention to detail and social cognitive processes (Davis et al., 2017 ). Although increased sensory sensitivity and improved perceptual abilities can have some advantages, they can also lead to cognitive overload, making individuals more prone to anxiety, rumination, and sensory overload, all of which are often associated with symptoms of depression. Furthermore, people with detailed visual perception abilities may struggle with adapting to complex or unpredictable social environments, which could amplify the sense of isolation and lead to depressive thinking styles in more social or highly dynamic contexts. In males, higher scores for Attention to Detail may be associated with the expression of depressive symptoms, due to gender differences in cognitive processing and expectations from society. High detail orientation may place men at greater cognitive load and sensory sensitivity, but societal role expectations of them usually remind them to withhold expression of their emotions. These discrepancies may elevate the risk for depression by limiting the available repertoire of coping strategies. In females, all subscales of the DMQ and total DMQ score are positively correlated with the AQ Imagination subscale, whereas in males, this relationship is not found. This particular subscale measures difficulties related to imaginative activities, which can be linked to a tendency toward rigid thinking and a lack of flexibility in thought processes (Maryam & Khawar, 2022 ). In addition, a study looking at sub-threshold autistic traits in college students found that the Imagination subscale of the Autism Quotient was a significant predictor for negative affect, including depressive thoughts and feelings of alienation. This was mediated by experiential avoidance, such that people who scored higher on the Imagination subscale are more likely to engage in avoidance behaviors, which in turn negatively influence their mood and mental health (Maryam & Khawar, 2022 ). The positive association of the AQ Imagination subscale with depressive symptoms, observed in the current study, may be interpreted by the social difficulties and rigid ways of thinking associated with high autistic traits, in combination with experiential avoidance as a mediating factor in the enhancement of negative emotional experiences. This would suggest that, in women, negative thinking and feelings of detachment may be linked with reduced ability in creative thinking or flexibility in seeing from different perspectives. Indeed, the subscale of imagination in the AQ has shown to be particularly meaningful, demonstrating the strongest male bias of the AQ subscales in non-clinical samples (Crespi et al., 2016 ), but it implies diminished imaginative ability may be more common in men without necessarily deteriorating their psychical state. In women, imaginative impairments associated with depressive symptoms may signal that their overall level of autism traits has escalated to a point where it is significantly impacting their mental health. The observation suggests that in women, high levels of autistic traits, especially when combined with certain depressive features such as pessimism or feelings of alienation, may have a cumulative effect that results in a more challenging emotional experience. Further study is needed to explore whether a threshold effect exists for autistic traits in non-clinical populations—thereby surpassing this threshold might elevate susceptibility to mental health problems differentially across genders. The role of mentalizing abilities in depression Statistical analyses using T-tests revealed that females achieved higher scores on the RME, suggesting enhanced social-cognitive competencies, which may be indicative of gender-based disparities in emotional and social processing. Regarding depressive symptoms, females demonstrated a greater severity—a finding consistent with existing literature that posits a higher prevalence and manifestation of depression in women, potentially attributable to a combination of biological and psychosocial influences. An analysis of the Reading the Mind in the Eyes test (RME) scores revealed significant inverse correlations with symptoms of depression. Specifically, in females only, RME scores were negatively correlated with a range of subscales of the DMQ, indicating that increased emotion recognition is associated with lower levels of cognitive impairments (DPUE), pessimism and alienation (MSPA), guilt and anxiety (PWNL), as well as psychosomatic symptoms (OPSZ). This suggests that women who have a better capacity to read emotions may experience reduced depressive symptoms across several domains, possibly due to their improved emotional understanding, which acts as a protective agent against depressive processes. By way of contrast, males show fewer significant correlations; only PWNL (guilt and anxiety) and total depression symptoms are significantly related to RME scores. This less strong association may indicate that the capacity for emotion recognition in males has less of a direct buffering effect against depressive processes in diverse domains compared with what has been observed in females. Possible explanations include gender-specific socialization, which often encourages the development of better emotional knowledge and empathy in females, thus possibly increasing the protective role of emotional intelligence. For men, however, societal expectations and different expectations about emotional experience might mean that the capacity to identify emotions has a weaker buffering effect on depressive symptoms, specifically in areas of self-regulation or loss of interest (OPSZ). Results indicate that there is a need to integrate gender-sensitive emotional and social schemes into intervention programs, since females might benefit more directly from improved skills in emotion recognition in coping with depression, while it might be more productive to target specific domains for males, such as guilt and anxiety. The results of this study would seem to support previous findings that impaired mentalizing skills, often seen in autism, can contribute to misunderstandings in social situations, which in turn could lead to further feelings of isolation (Domes et al., 2016 ). Impaired empathy and mentalizing faculties may overlap with symptoms found in enduring depression, especially in women. Gender differences in depression subscales T-tests demonstrated significant differences between genders regarding the severity of depression and the DMQ subscales (DPUE, MSPA, PWNL, and OPSZ), with female participants exhibiting higher mean scores across all measured dimensions. Specifically, in relation to the DMQ subscales, females achieved markedly elevated scores in DPUE (cognitive deficits and energy loss), MSPA (thoughts of death and pessimism), PWNL (guilt and tension), and OPSZ (psychosomatic symptoms), indicating that they may experience these symptoms associated with depression and anxiety with greater intensity. These disparities may result from differences in emotional expression, social roles, and coping behaviors linked to gender. In addition, societal pressures and expectations may shape the nature of depressive symptoms such that women may be more likely to internalize stress, leading to higher scores on these measures. A large body of research has found that females consistently score higher on depression scales (Salokangas et al., 2002 ) and are more prone to depression than their male counterparts (Salk, Hyde & Abramson, 2017 ). Men report fewer depressive symptoms than women, which means fewer men will meet the diagnosis threshold for having depression (Angst et al., 2002 ). Some depression measuring tools may even be gender-biased, with items like crying and loss of interest in sex being more culturally and biologically relevant to females, possibly inflating their scores (Salokangas et al., 2002 ). Implications These findings bear strong clinical implications, especially in the relationship between autistic traits and depressive symptoms. Specifically, the importance of autistic characteristics as significant predictors of depressive symptoms, even in subclinical groups, underscores the necessity for mental health professionals to incorporate these assessments into their evaluation of depressive symptoms. It would be beneficial for healthcare providers to utilize tools like the Autism Spectrum Quotient (AQ) when assessing these traits as it would help deepen their understanding of a patient's depressive symptomatology by investigating his/her autistic profile. Conclusion The findings are that adults with higher autistic traits are at an increased risk of having depressive symptoms regardless of their education level or whether they are employed or not. Early intervention programs, therefore, that target improving emotion regulation, development of social skills, and resilience are important in the subclinical symptoms of autistic traits that predispose individuals to major depressive disorders. The study further emphasizes interventions targeting specific autistic features, such as management of problems with social communication. Depressive symptoms of females with difficulties in social communication might require interventions that build resilience and also cope with social expectations in society. The association between autistic traits and self-regulation difficulties in women suggests that interventions targeting emotional regulation, such as cognitive-behavioral techniques or mindfulness-based interventions, may be especially beneficial. Additionally, considering the stronger association of perfectionism with depressive symptoms in men, clinical strategies targeting reduction of cognitive rigidity while at the same time promoting healthy ways to cope with stress might result in a decrease in depressive symptoms. This may mean greater tailoring of mental health interventions, with a view to how these characteristics manifest differently and have different impacts on emotional well-being in different genders. Limitations Some limitations of this study should also be considered. The cross-sectional design limits the ability to make causal inferences regarding the relationship between autistic traits and depressive symptoms. Longitudinal studies could provide insight into the causal mechanisms and changes that occur over time. Also, the use of self-report measures for both autistic traits and depression might introduce response biases, as participants might not fully identify or report some characteristics or symptoms. It should further be noted that, although the sample represents a non-clinical sample, it cannot be assumed to represent broader community diversity, especially across different cultural backgrounds. Excluding participants with clinically diagnosed ASD or major psychiatric disorders is important for increasing the generalizability of the findings to clinical populations, where autistic traits and depressive symptoms might interact differently. 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BMC Psychiatry 16. https://doi.org/10.1186/s12888-016-0915-5 Zhao X, Li X, Song Y, Li C, Shi W (2020) Autistic traits and emotional experiences in Chinese college students: Mediating role of emotional regulation and sex differences. Res Autism Spectr Disorders 77:101607. https://doi.org/10.1016/j.rasd.2020.101607 Zoromba MA, El-Gazar HE, Loutfy A, El-Sheikh OY, El-Monshed A (2022) Autistic Severity and Psychiatric Comorbidity Among Children with Autism Spectrum Disorder. Psychiatric Scan 11(4):568–581 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5497260","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380886936,"identity":"cf55990f-9d59-45bd-8f9b-f30c486bc125","order_by":0,"name":"Agnieszka Siedler","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8713-9464","institution":"Akademia Pedagogiki Specjalnej im. Marii Grzegorzewskiej","correspondingAuthor":true,"prefix":"","firstName":"Agnieszka","middleName":"","lastName":"Siedler","suffix":""}],"badges":[],"createdAt":"2024-11-21 11:01:28","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5497260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5497260/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69834516,"identity":"6ecfef7e-eb60-4782-b05e-8800a135712f","added_by":"auto","created_at":"2024-11-25 16:04:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":866074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5497260/v1/b75206c4-8d80-4b2c-96cf-0aea09707f57.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and comorbid psychopathological traits. Recent studies have extensively explored the intricate relationship between autism and various psychopathological profiles. ASD frequently coexists with other psychiatric conditions, adding complexity to its clinical presentation. Research shows that 68% of children with ASD have comorbid psychiatric disorders, predominantly anxiety and ADHD (Zoromba et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, higher rates of mental health comorbidities were reported among adults with ASD compared to those without, indicating the need for lifelong mental health support for this population (Radwan \u0026amp; Mallik, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Autistic traits often overlap with other psychopathological characteristics, suggesting a continuum across different psychiatric conditions. Cluster analysis showed intermediate levels of autistic traits in disorders such as bipolar disorder and eating disorders, indicating a shared underlying spectrum across various psychopathologies (Dell\u0026rsquo;Osso et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, studies emphasized the importance of considering both genetic and environmental factors when assessing the etiology of autism, further complicating the interplay of psychopathological traits (Guney \u0026amp; Işeri, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAutism Spectrum Disorder (ASD) and depression are two prevalent conditions that often co-occur in adults, complicating the clinical profile and treatment strategies for these individuals. Research has consistently shown that adults on the autism spectrum experience higher rates of depression compared to the general population. Studies found that 46% of adults diagnosed with ASD reported moderate to severe depression, with factors such as age, gender, and severity of autism symptoms significantly influencing these levels (Murray et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The overlapping characteristics of ASD and depression, such as social withdrawal and communication challenges, often complicate the diagnostic process (Stewart et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral factors contribute to the high prevalence of depression among adults with autism. Research emphasized the role of underlying cognitive impairments and challenging life experiences in exacerbating depressive symptoms in this group (Ghaziuddin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, some adults with ASD may have an increased awareness of their functional difficulties, leading to emotional distress and heightened depressive symptoms (Einabad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Broader Autism Phenotype (BAP) refers to a range of subclinical traits of autism that are present in family members of individuals with Autism Spectrum Disorder (ASD) and the general population. These traits can include social communication difficulties, rigid thinking, and a focus on details, all of which may predispose individuals to psychological challenges. Autistic traits related to the broader autism phenotype have been shown to correlate with increased levels of depression. Research found that aspects such as social skills deficits and challenges with attention switching contribute significantly to depressive symptoms. This is particularly evident in individuals who possess a greater awareness of their functional problems, which can lead to a negative self-image and emotional distress (Einabad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, previous studies highlights that both autism and depression share common difficulties, such as perceived worries and a sense of lacking control. While these overlapping symptoms do not entirely explain their co-occurrence, they suggest that factors like mastery perception may play a critical role in linking BAP with depression (van Heijst et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe intersection between BAP and depression poses significant implications for mental health care. Misdirected social communication and empathy challenges, which mirror those observed in chronic depression, may necessitate tailored therapeutic interventions that address these overlapping features (Domes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The relationship between the broader autism phenotype and depression is complex and multifaceted. It highlights the need for a comprehensive approach to diagnosis and treatment that accounts for the spectrum of autistic traits and their psychological implications.\u003c/p\u003e \u003cp\u003eThe primary objective of the current study was to investigate the association between depression symptoms and traits from the broader autism phenotype (BAP) within the general population. Recent research has identified an overlap between depressive symptoms and autistic traits, particularly among individuals who exhibit subclinical levels of autism spectrum disorder (ASD) characteristics. This overlap is especially relevant given the unique challenges faced by individuals with heightened ASD traits, such as difficulties in social communication, emotional regulation, and cognitive flexibility. These areas of functioning are also commonly impacted in depression, suggesting potential shared mechanisms that may increase the risk of depressive symptoms in individuals with ASD traits.\u003c/p\u003e \u003cp\u003eThe rationale for this study lies in understanding specific ASD traits that might elevate the likelihood of experiencing depressive symptoms. Identifying these traits could enhance diagnostic assessments and lead to more tailored interventions for individuals displaying both autistic and depressive characteristics. Additionally, by examining gender differences, this research addresses an important gap: while ASD and depression present differently across genders, most studies have focused on either clinical ASD populations or have not stratified data by gender. This study aims to clarify whether certain ASD traits, such as communication difficulties or social skill deficits, are more strongly associated with depression symptoms in females compared to males in the general population.\u003c/p\u003e \u003cp\u003eBased on these considerations, we formulated several hypotheses. First, we hypothesized that individuals with elevated BAP traits would report higher levels of depressive symptoms than those with lower levels of BAP traits. Second, we expected gender differences, with females showing a stronger correlation between specific ASD traits (e.g., deficits in social communication and emotional regulation) and depressive symptoms. Our third hypothesis posited that particular subscales, such as AQ Communication and Social Skills, would be most predictive of depression symptoms, especially in the female group.\u003c/p\u003e \u003cp\u003eOur primary research questions were thus directed at understanding (1) the extent to which elevated BAP traits predict depressive symptoms in a non-clinical population, (2) the role of gender in moderating these associations, and (3) which specific ASD traits are most strongly linked to depressive symptomatology. By answering these questions, this study aims to contribute to a more nuanced understanding of the interaction between autistic traits and depression, offering insights that could inform clinical practice and guide future research on comorbid mental health challenges in individuals with subclinical ASD traits.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study recruited 239 adults (121 females and 108 males) from the general population, aged 19 to 48 (mean age: 35.44), with no prior diagnoses of ASD or depression to maintain a focus on subclinical ASD traits and depressive symptoms. Participants were selected through random sampling and completed the assessments independently. People who reported having diagnosed mental health problems (e.g. depressive episode, bipolar disorder, ADHD, autism, anxiety disorders) were excluded from the study group. The data collection was carried out by two trained psychologists, ensuring standardized administration and guidance when necessary. Participants provided informed consent and completed a demographic questionnaire covering basic information such as age, education, and employment status. The study was approved by the local institutions\u0026rsquo; Research Ethics Committee and was in accordance with the Declaration of Helsinki. The sample size aligns with those in previous studies examining gender differences in autism and depression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSociodemographic data\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.44(8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.08 (8.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.85(7.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge range in years\u003c/p\u003e \u003cp\u003e(min.-max.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026ndash;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u0026ndash;62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePlace of living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillage n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(20.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity (50 thousand inhabitants) n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity (50\u0026ndash;100 thousand inhabitants) n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(28.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity (100 thousand inhabitants) n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38(31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190(83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100(92.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVocational education n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school education n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMasters / bachelor studies graduation n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(32.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrently studying n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eAutism Spectrum Quotient (AQ) is the self-report questionnaire The Autism\u0026ndash;Spectrum Quotient (AQ) (Baron-Cohen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) was used in its Polish version (Pisula et al., 2013) to measure the intensity of autistic features. The questionnaire consists of 50 statements, to which the participants respond on a 4-point Likert scale. Total score (the sum of the scores in all items) ranges from 0 to 50 points. In this study, the reliability of the AQ measured by Cronbach's alpha coefficient was α\u0026thinsp;=\u0026thinsp;0.89 for the Total score. In addition to the total score, it is possible to calculate scores on 5 subscales, each consisting of 10 items: Social skills (Ss, α\u0026thinsp;=\u0026thinsp;0.84), Attention switching (At_s; α\u0026thinsp;=\u0026thinsp;0.83), Attention to detail (At_d; α\u0026thinsp;=\u0026thinsp;0.70); Communication (Com; α\u0026thinsp;=\u0026thinsp;0.79), and imagination (Imm; α\u0026thinsp;=\u0026thinsp;0.72).\u003c/p\u003e \u003cp\u003eThe Depression Measurement Questionnaire (DMQ)(Łojek, Stańczak i W\u0026oacute;jcik, 2015) is a measure of the intensity of depressive effects and those resulting from self-regulation, It consists of 75 indicators relating to individual thoughts, feelings and behaviours of the examined person and uses a four-point scale to provide answers. The results are presented on five scales: Cognitive deficits and loss of energy (DPUE), Thoughts about death, pessimism and alienation (MSPA), Guilt and anxiety (PWNL), Psychosomatic symptoms and loss of interests (OPSZ), Self-regulation (SR). In addition, a total score is calculated, which is the sum of the results obtained in the DPUE, MSPA, PWNL and OPSZ scales, which is an overall indicator of the intensity of depressive symptoms. The questionnaire has high and very high internal consistency coefficients and very high absolute stability coefficients. The tool also has confirmed construct validity through correlations with other measures examining symptoms of depression, measures examining personality, mood, self-esteem and cognitive functioning (Łojek, Stańczak i W\u0026oacute;jcik, 2015). In this study, the reliability of the questionnaire measured by Cronbach's alpha coefficient was α\u0026thinsp;=\u0026thinsp;0.93 for the Total score.\u003c/p\u003e \u003cp\u003eThe Reading Minds In Eyes test (RME) (Baron-Cohen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) is a commonly used assessment of mentalizing abilities. It comprises 36 monochromatic images of an individual's ocular region and one practice item. The participant must identify the mental state depicted in the image and select one of four forced-choice replies provided. Each item receives a score of 1 for a valid response and 0 for an erroneous answer, resulting in a total score range of 0 to 36. Besides demonstrating strong test\u0026ndash;retest reliability, it remains unconfounded by gender when evaluated in large-scale populations (Baron-Cohen. Et al., 2001; Gallant \u0026amp; Good, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Elamin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003ePearson correlation analysis were conducted to examine the relationships between AQ total scores, DMQ total scores, RME scores, and the subscales within each measure. This analysis aimed to identify whether higher levels of autistic traits were associated with more severe depressive symptoms or reduced mentalizing abilities. Additionally, correlations were calculated separately for each gender to explore possible gender-specific patterns in these relationships, given prior evidence suggesting that the expression of autistic traits and related mental health impacts can vary significantly by gender. Moderation analyses were conducted using PROCESS v4.2 to examine whether gender moderates the relationship between autistic traits (measured by AQ) and depressive symptoms (measured by DMQ). The goal of these analyses was to determine if the association between autistic traits and depression differs by gender, providing insights into how gender may influence mental health dynamics in individuals with varying levels of autistic traits. PROCESS v4.2 was also employed to conduct mediation analyses to examine whether RME (Reading the Mind in the Eyes Test) serves as a mediator in the relationship between AQ (Autism Spectrum Quotient) and DMQ (depressive symptoms). The t-test was performed to investigate potential gender differences across key variables in the study. The multiple regression analysis was conducted to determine the impact of various predictors on the severity of depressive symptoms. Statistical tests were performed using SPSS for Windows Version 24. For all tests, the significance level was set to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eThe correlation analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals significant associations between DMQ subscales and AQ scores. Generally, higher levels of autistic traits, as measured by the total AQ score, were positively correlated with depressive symptoms (r\u0026thinsp;=\u0026thinsp;0.431, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the specific AQ subscales, the Communication subscale showed the strongest positive correlation with depressive symptoms (r\u0026thinsp;=\u0026thinsp;0.470, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that difficulties in communication are particularly associated with increased depressive symptoms. Additionally, the MSPA subscale of the KPD, which measures pessimism, thoughts of death, and feelings of alienation, demonstrated robust positive correlations with several AQ subscales, notably AQ Social Skills (r\u0026thinsp;=\u0026thinsp;0.603, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AQ Communication (r\u0026thinsp;=\u0026thinsp;0.631, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AQ Attention Switching (r\u0026thinsp;=\u0026thinsp;0.610, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and AQ Total (r\u0026thinsp;=\u0026thinsp;0.700, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations between DMQ Subscales and AQ Scores in the whole sample\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMQ Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAQ_Attention_to_Detail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAQ_Imagination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAQ_Communication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAQ_Attention_Switch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAQ_Social_Skills\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAQ_Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er=-0.011 (p\u0026thinsp;=\u0026thinsp;0.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.468*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.291** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.342** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.350*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.381*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.197 (p\u0026thinsp;=\u0026thinsp;0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.445*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.509*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.566*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.452*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.580*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.294** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.317** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.537*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.591*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.443*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.585*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.270** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.496*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.631*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.610*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.603*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.700*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.184 (p\u0026thinsp;=\u0026thinsp;0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.412*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.516*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.537*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.490*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.571*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal DMQ (depressive symptoms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.013 (p\u0026thinsp;=\u0026thinsp;0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.198** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.470*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.316*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.463*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.431*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults reveal notable gender differences in the correlations between AQ and DMQ subscales (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For females, AQ Communication and AQ Social Skills were strongly correlated with DMQ subscales SR, OPSZ, and MSPA, indicating more robust relationships in these areas than observed in the male group. Males displayed fewer significant correlations overall, particularly with AQ Attention to Detail, which showed limited associations. Depression severity correlated significantly with AQ Social Skills and Communication among females, while in males, these relationships were weaker and more variable across subscales.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations between DMQ Subscales and AQ Scores for males and females\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMQ Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAQ Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAQ Communication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAQ Social Skills\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAQ Attention Switching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAQ Attention to Detail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAQ Imagination\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale SR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.464*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.398** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.416*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.495*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er = -0.030 (p\u0026thinsp;=\u0026thinsp;0.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.573*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.170 (p\u0026thinsp;=\u0026thinsp;0.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.066 (p\u0026thinsp;=\u0026thinsp;0.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.236 (p\u0026thinsp;=\u0026thinsp;0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er = -0.045 (p\u0026thinsp;=\u0026thinsp;0.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.003 (p\u0026thinsp;=\u0026thinsp;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.257 (p\u0026thinsp;=\u0026thinsp;0.096)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale OPSZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.687*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.649*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.661*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.698*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.010 (p\u0026thinsp;=\u0026thinsp;0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.657*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.339* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.273 (p\u0026thinsp;=\u0026thinsp;0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.057 (p\u0026thinsp;=\u0026thinsp;0.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.327* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.473*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.067 (p\u0026thinsp;=\u0026thinsp;0.669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale MSPA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.790*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.755*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.770*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.729*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.158 (p\u0026thinsp;=\u0026thinsp;0.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.687*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.515*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.437** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.327* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.398** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.410** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.176 (p\u0026thinsp;=\u0026thinsp;0.258)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale DPUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.689*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.667*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.679*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.652*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.091 (p\u0026thinsp;=\u0026thinsp;0.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.612*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.349* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.289 (p\u0026thinsp;=\u0026thinsp;0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.199 (p\u0026thinsp;=\u0026thinsp;0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.354* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.271 (p\u0026thinsp;=\u0026thinsp;0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.109 (p\u0026thinsp;=\u0026thinsp;0.486)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal DMQ (Depression Symptoms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.628*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.636*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.639*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.434*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.162 (p\u0026thinsp;=\u0026thinsp;0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.422*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.241* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.343*** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.289** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;0.212* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er = -0.081 (p\u0026thinsp;=\u0026thinsp;0.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er = -0.006 (p\u0026thinsp;=\u0026thinsp;0.954)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e: *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation analysis results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show significant relationships between the Reading the Mind in the Eyes (RME) test scores and various DMQ variables. RME scores were negatively correlated with several DMQ subscales, including MSPA (r=-0.237, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), DPUE (r=-0.205, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the total depression score (r=-0.211, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that lower mentalizing abilities may be associated with more severe depressive symptoms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations between DMQ Subscales and RME Scores for the whole sample\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMQ Subscale SR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMQ Subscale OPSZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMQ Subscale PWNL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMQ Subscale MSPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDMQ Subscale DPUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal DMQ Depression Symptoms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er=-0.135 (p\u0026thinsp;=\u0026thinsp;0.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er=-0.119 (p\u0026thinsp;=\u0026thinsp;0.242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er=-0.163 (p\u0026thinsp;=\u0026thinsp;0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er=-0.237* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er=-0.205* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er=-0.211** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results indicate a stronger negative correlation between RME and all DMQ subscales in females than in males, with significant correlations observed for females across multiple subscales. Specifically, in females, significant correlations were found for DMQ OPSZ, DMQ PWNL, DMQ MSPA, DMQ DPUE, and overall DMQ Depression Symptoms. For males, significant associations were limited, with only DMQ PWNL and DMQ Depression Symptoms showing significant correlations. All correlations are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations between DMQ Subscales and RME Scores \u0026ndash; males and females\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMQ Subscale SR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMQ Subscale OPSZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMQ Subscale PWNL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDMQ Subscale MSPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDMQ Subscale DPUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal DMQ Depression Symptoms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.249 (p\u0026thinsp;=\u0026thinsp;0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.328* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.475** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er = -0.446** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er = -0.393** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er = -0.324** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er = -0.133 (p\u0026thinsp;=\u0026thinsp;0.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er = -0.248 (p\u0026thinsp;=\u0026thinsp;0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er = -0.312* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er = -0.241 (p\u0026thinsp;=\u0026thinsp;0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er = -0.296 (p\u0026thinsp;=\u0026thinsp;0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er = -0.287** (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ -Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModeration analysis\u003c/h2\u003e \u003cp\u003eThe moderation analysis aimed to examine whether gender moderates the relationship between autistic traits and depressive symptoms. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides a summary of the results. The model explained approximately 29.4% of the variance in depressive symptoms (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0. F\u003csub\u003e(3,225)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;31.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The interaction term (AQ_Total x Gender) was significant (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.00), indicating that the relationship between AQ_Total and DMQ_Total differs by gender (see Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The conditional effects indicate that the relationship between autistic traits and depressive symptoms is stronger in females than in males.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel Summary and Interaction Effects\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Total x Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eConditional Effects of AQ_Total on DMQ_Total by Gender\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eA mediation analysis was conducted to test whether RME mediated the relationship between AQ and DMQ. However, the analysis showed no significant indirect effect, indicating no mediation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eT-tests\u003c/h2\u003e \u003cp\u003e \u003cem\u003eT\u003c/em\u003e-tests were conducted to identify gender differences across various scales. No significant gender differences were observed within the AQ scale results. However, significant gender differences were found in several subscales of the DMQ. For instance, significant differences emerged in RME, DMQ total score, and DMQ subscales including DPUE, MSPA, PWNL, and OPSZ, indicating notable gender-based distinctions in these areas. These results highlight areas where depressive symptomatology and other related measures vary significantly between males and females. There were no significant gender differences in the mean AQ total scores or its subscales. However, the Imagination and Attention to Detail subscales showed differences at a statistically trending level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eGender Differences in t-tests\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRME\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;26.40 (SD\u0026thinsp;=\u0026thinsp;4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.19 (SD\u0026thinsp;=\u0026thinsp;6.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal DMQ (Depression Symptoms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;127.19 (SD\u0026thinsp;=\u0026thinsp;50.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;105.54 (SD\u0026thinsp;=\u0026thinsp;44.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale DPUE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;41.95 (SD\u0026thinsp;=\u0026thinsp;9.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;38.05 (SD\u0026thinsp;=\u0026thinsp;8.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale MSPA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;27.63 (SD\u0026thinsp;=\u0026thinsp;9.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;23.56 (SD\u0026thinsp;=\u0026thinsp;7.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale PWNL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;35.04 (SD\u0026thinsp;=\u0026thinsp;8.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;29.00 (SD\u0026thinsp;=\u0026thinsp;9.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMQ Subscale OPSZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;20.34 (SD\u0026thinsp;=\u0026thinsp;5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;17.44 (SD\u0026thinsp;=\u0026thinsp;3.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAQ_Imagination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;3.78 (SD\u0026thinsp;=\u0026thinsp;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;4.39 (SD\u0026thinsp;=\u0026thinsp;2.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAQ_AttentionToDetail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;4.96 (SD\u0026thinsp;=\u0026thinsp;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;=\u0026thinsp;5.76 (SD\u0026thinsp;=\u0026thinsp;3.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: DMQ subscales: DPUE - Cognitive deficits and loss of energy subscale, MSPA - Thoughts about death, pessimism and alienation subscale, PWNL - Guilt and anxiety subscale, OPSZ - Psychosomatic symptoms and loss of interests subscale, SR - Self-regulation subscale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegression analysis\u003c/h2\u003e \u003cp\u003eThe regression analysis was conducted to determine the impact of various predictors on the severity of depressive symptoms. The dependent variable was the total score for depressive symptoms, while the independent variables included AQ subscales (social skills, attention switching, imagination, attention to detail, and communication), as well as sociodemographic factors (education, employment status, place of residence), and the Reading the Mind in the Eyes (RME) test score. The regression model produced an R value of 0.510, indicating a moderate correlation between the predictors and depressive symptoms. The R-squared value was 0.260, meaning that approximately 26% of the variance in depressive symptoms was explained by the model. The analysis of variance (ANOVA) for this regression model showed a significant F\u003csub\u003e(9)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.572 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the model as a whole provides a statistically significant explanation of the variance in depressive symptoms. Only two predictors, AQ Communication and AQ Social Skills, demonstrated significant positive effects on depressive symptoms. This suggests that higher difficulties in communication and poorer social skills are associated with greater severity of depressive symptoms, whereas other predictors in the model did not show statistically significant impacts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel summary\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Error of Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin-Watson Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression coefficients\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Attention_Switch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Social_Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Imagination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQ_Attention_to_Detail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between autistic traits and depressive symptoms\u003c/h2\u003e \u003cp\u003eThe study findings are in line with previous research reporting associations between autism traits, as measured by the Autism Spectrum Quotient (AQ), and symptoms of depression. Previous literature has indicated that individuals with elevated autistic traits, especially those who experience difficulties in social interactions, often report increased depressive symptoms (Murray et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The current study found a significant relationship between the AQ subscales related to communication and social competencies and depressive symptoms, which is in line with previous studies that highlight the significance of social challenges in both clinical and non-clinical groups (Stewart et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These challenges are often related to the lack of social support, giving rise to social isolation and emotional distress (Ghaziuddin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It has been suggested, based on various research findings, that higher levels of autism often co-occur with impairments in social communication, which may heighten one's vulnerability to depression. Difficulties in social communication might contribute to higher depressive symptom scores, for example; the link between these experiences actually remains constant from childhood into early adulthood because of bullying (Rai et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, autism-related characteristics, such as impairments in social perception, interaction, and communication, resemble symptoms seen in chronic depression, suggesting an intersection point that could contribute to the persistence of depressive symptoms (Radtke et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the current study, problems with communication and social abilities were significantly associated with certain depressive features in the general population sample. High levels of communication difficulties were especially related to expressions of pessimism, thoughts about death, and feelings of alienation, while social skills deficits had a significant relationship to the cognitive symptoms of depression, namely, low energy and loss of interest. Only two predictors\u0026mdash;AQ Communication and AQ Social Skills\u0026mdash;showed significant positive relationships to depressive symptoms, according to the regression analysis. This would suggest that more severe depressive symptoms are associated with greater difficulties in communication and reduced social abilities, as opposed to the other variables in the model that were not statistically significant predictors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGender differences in the relationship between autistic traits and depression\u003c/h2\u003e \u003cp\u003eThe moderation analysis showed that gender significantly moderated the strength of association between autistic traits and depressive symptoms. This would suggest that females with higher level of autistic traits might experience depressive symptoms more intensely, possibly related to greater expectations from society in respect to functioning (van Heijst et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The difficulties related to social communication and engagement in women seem to predict depressive symptoms more consistently than in men, which underlines even more the need for clinical strategies sensitive to sex differences. Significant gender differences emerged in the relationship between AQ subscales and depressive symptoms, with females showing stronger associations between communication difficulties, social skills deficits, and depressive symptoms than males. Depression is strongly correlated with social communication impairments. Previous literature has indicated that these impairments might disproportionately affect autistic females because of social norms and expectations around communication and social interaction (Rai et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased vulnerability to difficulties in social communication could consequently worsen depressive symptoms. Moreover, autistic females tend to have greater awareness of their struggle with social functioning, which may contribute to more prominent emotional distress (Einabad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present study, males showed fewer significant correlations across AQ subscales, particularly for attention to detail, which demonstrated little association with depression. This contrast would suggest that social difficulties bear differently on females, and further research into gender-based approaches in both assessment and intervention is therefore justified. Moreover, specific gender-related differences in depressive symptoms related to autismtic traits were established. In the female sample, strong and significant associations were found between the severity of autismtic traits and difficulties with self-regulation. By contrast, this association was not found in the male sample, indicating a gender-specific pattern in the relationship between autistic traits and difficulties with self-regulation. It may be explained by the differences in strategies of emotional regulation and their corresponding consequences for emotional experiences. According to several studies, females with more autistic traits are more likely to engage in maladaptive strategies of emotional regulation\u0026mdash;for example, suppression\u0026mdash;enhancing negative emotions. This is less observed in males and provides specific evidence of gender in relation to the impact of autism traits on emotional and self-regulation (Zhao et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, autistic traits\u0026mdash;like rigidities in ways of thinking and difficulties in switching attention\u0026mdash;may be subjectively experienced as being \"stuck\" in negative thinking, a hallmark of depressive cognition. Individuals with high autistic traits, especially women, may have considerable difficulty generating alternative solutions or possibilities of positive outcomes to the situation at hand, thus reinforcing feelings of hopelessness and negativity (Ishizuka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; South et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter analyzing the gender differences in the DMQ subscales, some significant differences were identified. The OPSZ subscale measures psychosomatic symptoms and loss of interest, the MSPA subscale assesses thoughts about death, pessimism, and alienation, and the DPUE subscale assesses cognitive deficits and loss of interest. The DMW total score was significantly correlated with AQ traits in both males and females, although slightly stronger for the female group in Total AQ, Communication, Social Skills, and Attention Switching. This pattern might indicate that autistic traits could more strongly exacerbate depressive symptoms in women, especially those concerning cognitive deficits, psychosomatic symptoms, and pessimism. This could mean that depressive symptomatology in females with elevated autistic traits might be more extensively interconnected with each other, possibly due to greater sensitivity to social and communication difficulties. Females with more autistic traits internalize stress and emotional struggles, possibly resulting in physical symptoms such as tiredness or even somatic pain. These same traits are associated with a preference for social masking or camouflage, which could result in a rise in emotional and mental distress and, thereby, psychosomatic symptoms. On the other hand, males with corresponding autistic traits may experience stress in different ways and in a more external manner, potentially alleviating the impact on their physical and psychosomatic health. It is also worth noting that according to most research, autistic traits manifest differently between males and females. Females with autism have fewer stereotyped behaviors and restricted interests compared to their male counterparts. This may contribute to underrecognition and misdiagnosis in females (Edwards et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The underrecognition in turn contributes to higher psychosomatic symptoms and a loss of interest, as the females are not afforded the proper support and interventions. Furthermore, research conducted on subthreshold autism in adolescents indicated that both male and female individuals with autistic traits showed significant relations with dimensions of arousal, although the nature of these relations differed. In females, they found strong positive correlations between all arousal dimensions and autismtic traits. This shows that individuals with autistic traits may experience high levels of stress and anxiety, which can manifest in psychosomatic symptoms and a reduced inclination toward activities (Ianuzzo et al., 2022). Women with strong autistic traits often struggle with social interaction and in-person communication, leading to severe social isolation and reduced social support. Social isolation of this kind is very characteristic of ASD and can be combined with persistent depression, which can magnify feelings of loneliness and hopelessness, thereby amplifying depressive symptoms (Domes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Radtke et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Interestingly, males show a positive relation between MSPA and OPSZ subscales of DMQ and Attention to Detail subscale of AQ, whose interrelation is not observed in the female group.This relationship would then imply that in men, a stronger attention to detail could be related to an increase in negative thinking and somatic symptoms of depression. This suggests that having a high attention to detail may increase the likelihood of rumination or experiencing emotional pain, a phenomenon that affects men more than women. People with high autistic traits often demonstrate significant impairment in their ability to switch attention and have intense interest in details. These features may lead to sustained rumination and negative cognitive patterns, which are core aspects of depression. For example, a study discovered that high scores in \"attention switching\" and \"attention to detail\" predicted persistent depressive symptoms in adults with depression (Ishizuka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This would suggest that the cognitive rigidity and perseverative attention associated with autistic traits might exacerbate depressive symptoms by supporting a negative cognitive framework. Males generally score higher than females on the AQ, including the \"Attention to Detail\" subscale (Steward \u0026amp; Austin, 2009; Zhang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).In the present investigation, males scored higher compared to females on the Attention to Detail subscale that was statistically significant at a trend level. The attention to detail trait is related to better performance on visual working memory (Richmond et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and less multisensory temporal adaptation (Stevenson et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). People who score high in Attention to Detail also tend to show higher sensory sensitivity along with heightened perception, as evidenced by superior performance in visual perception tasks and odor discrimination tasks (Baros et al., 2020; Ward et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Higher scores in Attention to Detail are correlated with a higher chance of looking at the eyes and superior facial recognition abilities, which reflect the complex interrelationship between attention to detail and social cognitive processes (Davis et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although increased sensory sensitivity and improved perceptual abilities can have some advantages, they can also lead to cognitive overload, making individuals more prone to anxiety, rumination, and sensory overload, all of which are often associated with symptoms of depression. Furthermore, people with detailed visual perception abilities may struggle with adapting to complex or unpredictable social environments, which could amplify the sense of isolation and lead to depressive thinking styles in more social or highly dynamic contexts. In males, higher scores for Attention to Detail may be associated with the expression of depressive symptoms, due to gender differences in cognitive processing and expectations from society. High detail orientation may place men at greater cognitive load and sensory sensitivity, but societal role expectations of them usually remind them to withhold expression of their emotions. These discrepancies may elevate the risk for depression by limiting the available repertoire of coping strategies.\u003c/p\u003e \u003cp\u003eIn females, all subscales of the DMQ and total DMQ score are positively correlated with the AQ Imagination subscale, whereas in males, this relationship is not found. This particular subscale measures difficulties related to imaginative activities, which can be linked to a tendency toward rigid thinking and a lack of flexibility in thought processes (Maryam \u0026amp; Khawar, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, a study looking at sub-threshold autistic traits in college students found that the Imagination subscale of the Autism Quotient was a significant predictor for negative affect, including depressive thoughts and feelings of alienation. This was mediated by experiential avoidance, such that people who scored higher on the Imagination subscale are more likely to engage in avoidance behaviors, which in turn negatively influence their mood and mental health (Maryam \u0026amp; Khawar, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The positive association of the AQ Imagination subscale with depressive symptoms, observed in the current study, may be interpreted by the social difficulties and rigid ways of thinking associated with high autistic traits, in combination with experiential avoidance as a mediating factor in the enhancement of negative emotional experiences. This would suggest that, in women, negative thinking and feelings of detachment may be linked with reduced ability in creative thinking or flexibility in seeing from different perspectives. Indeed, the subscale of imagination in the AQ has shown to be particularly meaningful, demonstrating the strongest male bias of the AQ subscales in non-clinical samples (Crespi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but it implies diminished imaginative ability may be more common in men without necessarily deteriorating their psychical state.\u003c/p\u003e \u003cp\u003eIn women, imaginative impairments associated with depressive symptoms may signal that their overall level of autism traits has escalated to a point where it is significantly impacting their mental health. The observation suggests that in women, high levels of autistic traits, especially when combined with certain depressive features such as pessimism or feelings of alienation, may have a cumulative effect that results in a more challenging emotional experience. Further study is needed to explore whether a threshold effect exists for autistic traits in non-clinical populations\u0026mdash;thereby surpassing this threshold might elevate susceptibility to mental health problems differentially across genders.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eThe role of mentalizing abilities in depression\u003c/h2\u003e \u003cp\u003eStatistical analyses using T-tests revealed that females achieved higher scores on the RME, suggesting enhanced social-cognitive competencies, which may be indicative of gender-based disparities in emotional and social processing. Regarding depressive symptoms, females demonstrated a greater severity\u0026mdash;a finding consistent with existing literature that posits a higher prevalence and manifestation of depression in women, potentially attributable to a combination of biological and psychosocial influences.\u003c/p\u003e \u003cp\u003eAn analysis of the Reading the Mind in the Eyes test (RME) scores revealed significant inverse correlations with symptoms of depression. Specifically, in females only, RME scores were negatively correlated with a range of subscales of the DMQ, indicating that increased emotion recognition is associated with lower levels of cognitive impairments (DPUE), pessimism and alienation (MSPA), guilt and anxiety (PWNL), as well as psychosomatic symptoms (OPSZ). This suggests that women who have a better capacity to read emotions may experience reduced depressive symptoms across several domains, possibly due to their improved emotional understanding, which acts as a protective agent against depressive processes.\u003c/p\u003e \u003cp\u003eBy way of contrast, males show fewer significant correlations; only PWNL (guilt and anxiety) and total depression symptoms are significantly related to RME scores. This less strong association may indicate that the capacity for emotion recognition in males has less of a direct buffering effect against depressive processes in diverse domains compared with what has been observed in females. Possible explanations include gender-specific socialization, which often encourages the development of better emotional knowledge and empathy in females, thus possibly increasing the protective role of emotional intelligence. For men, however, societal expectations and different expectations about emotional experience might mean that the capacity to identify emotions has a weaker buffering effect on depressive symptoms, specifically in areas of self-regulation or loss of interest (OPSZ). Results indicate that there is a need to integrate gender-sensitive emotional and social schemes into intervention programs, since females might benefit more directly from improved skills in emotion recognition in coping with depression, while it might be more productive to target specific domains for males, such as guilt and anxiety. The results of this study would seem to support previous findings that impaired mentalizing skills, often seen in autism, can contribute to misunderstandings in social situations, which in turn could lead to further feelings of isolation (Domes et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Impaired empathy and mentalizing faculties may overlap with symptoms found in enduring depression, especially in women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eGender differences in depression subscales\u003c/h2\u003e \u003cp\u003eT-tests demonstrated significant differences between genders regarding the severity of depression and the DMQ subscales (DPUE, MSPA, PWNL, and OPSZ), with female participants exhibiting higher mean scores across all measured dimensions. Specifically, in relation to the DMQ subscales, females achieved markedly elevated scores in DPUE (cognitive deficits and energy loss), MSPA (thoughts of death and pessimism), PWNL (guilt and tension), and OPSZ (psychosomatic symptoms), indicating that they may experience these symptoms associated with depression and anxiety with greater intensity. These disparities may result from differences in emotional expression, social roles, and coping behaviors linked to gender. In addition, societal pressures and expectations may shape the nature of depressive symptoms such that women may be more likely to internalize stress, leading to higher scores on these measures. A large body of research has found that females consistently score higher on depression scales (Salokangas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and are more prone to depression than their male counterparts (Salk, Hyde \u0026amp; Abramson, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Men report fewer depressive symptoms than women, which means fewer men will meet the diagnosis threshold for having depression (Angst et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Some depression measuring tools may even be gender-biased, with items like crying and loss of interest in sex being more culturally and biologically relevant to females, possibly inflating their scores (Salokangas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThese findings bear strong clinical implications, especially in the relationship between autistic traits and depressive symptoms. Specifically, the importance of autistic characteristics as significant predictors of depressive symptoms, even in subclinical groups, underscores the necessity for mental health professionals to incorporate these assessments into their evaluation of depressive symptoms. It would be beneficial for healthcare providers to utilize tools like the Autism Spectrum Quotient (AQ) when assessing these traits as it would help deepen their understanding of a patient's depressive symptomatology by investigating his/her autistic profile. Conclusion The findings are that adults with higher autistic traits are at an increased risk of having depressive symptoms regardless of their education level or whether they are employed or not. Early intervention programs, therefore, that target improving emotion regulation, development of social skills, and resilience are important in the subclinical symptoms of autistic traits that predispose individuals to major depressive disorders. The study further emphasizes interventions targeting specific autistic features, such as management of problems with social communication. Depressive symptoms of females with difficulties in social communication might require interventions that build resilience and also cope with social expectations in society. The association between autistic traits and self-regulation difficulties in women suggests that interventions targeting emotional regulation, such as cognitive-behavioral techniques or mindfulness-based interventions, may be especially beneficial. Additionally, considering the stronger association of perfectionism with depressive symptoms in men, clinical strategies targeting reduction of cognitive rigidity while at the same time promoting healthy ways to cope with stress might result in a decrease in depressive symptoms. This may mean greater tailoring of mental health interventions, with a view to how these characteristics manifest differently and have different impacts on emotional well-being in different genders.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSome limitations of this study should also be considered. The cross-sectional design limits the ability to make causal inferences regarding the relationship between autistic traits and depressive symptoms. Longitudinal studies could provide insight into the causal mechanisms and changes that occur over time. Also, the use of self-report measures for both autistic traits and depression might introduce response biases, as participants might not fully identify or report some characteristics or symptoms. It should further be noted that, although the sample represents a non-clinical sample, it cannot be assumed to represent broader community diversity, especially across different cultural backgrounds. Excluding participants with clinically diagnosed ASD or major psychiatric disorders is important for increasing the generalizability of the findings to clinical populations, where autistic traits and depressive symptoms might interact differently. Future research should address these limitations by incorporating more diverse samples, representative samples, clinical populations, and alternative assessment methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngst J, Gamma A, Gastpar M, L\u0026eacute;pine J, Mendlewicz J, Tylee A (2002) Gender differences in depression. 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Res Autism Spectr Disorders 77:101607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rasd.2020.101607\u003c/span\u003e\u003cspan address=\"10.1016/j.rasd.2020.101607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoromba MA, El-Gazar HE, Loutfy A, El-Sheikh OY, El-Monshed A (2022) Autistic Severity and Psychiatric Comorbidity Among Children with Autism Spectrum Disorder. Psychiatric Scan 11(4):568\u0026ndash;581\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Akademia Pedagogiki Specjalnej im. M.Grzegorzewskiej","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"autism spectrum dosorders, depression, broader autism phenotype","lastPublishedDoi":"10.21203/rs.3.rs-5497260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5497260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutism Spectrum Disorder (ASD) often presents comorbidity with depression, sharing similar characteristics between the two disorders with respect to social interaction, regulation of emotions, and flexibility in cognition. The current study investigates the relationship between autistic traits related to BAP and depressive symptoms in a general population sample, considering possible differences according to gender. In a sample of 239 adults, the results indicated that autistic traits, especially in the domains of communication and social skills, showed a significant association with depressive symptoms, with even more robust associations specifically in women. These results emphasize the presence of important sex differences in the associations found between autistic traits and specific depressive symptoms. In women, significant positive correlations were observed for autistic traits related to communication, social skills, and difficulties with attention-shifting, with depressive symptoms regarding thoughts of death, feelings of pessimism, experiences of alienation, cognitive impairments, and psychosomatic presentations. In contrast, males showed fewer significant associations, with only attention to detail significantly related to depressive symptoms such as cognitive deficits and decreased energy levels. It thus appears that there might be sex differences in the way the different dimensions of the autism spectrum relate to the various dimensions of depressive symptomatology. Furthermore, moderation analysis showed that gender influences the strength of these relationships, which highlights the need for gender-sensitive approaches both in research and clinical practice when assessing and targeting depressive symptoms in subclinical ASD populations. The implications for clinical practice as well as the limitations of the study are discussed.\u003c/p\u003e","manuscriptTitle":"Gender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:55:56","doi":"10.21203/rs.3.rs-5497260/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c9c4753-b5e7-40d9-b272-7399321a393f","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T15:55:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 15:55:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5497260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5497260","identity":"rs-5497260","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}