Social isolation and loneliness in adults over 50 with high autistic traits.

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Gavin Stewart, Radvile Medeisyte, Eleanor Nuzum, Aphrodite Eshetu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6838818/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 Social isolation and loneliness are major public health issues, especially in old age, with serious health impacts. Some populations, such as autistic people, have been found to report high rates of social isolation and loneliness compared to their non-autistic peers. Among 9,979 participants aged 50+, 672 met the AQ-10 cut-off for probable autism, while 9,307 formed a comparison group. Those above the AQ-10 cut-off reported greater social isolation and loneliness. Path analysis showed that sex, depression, anxiety, and trauma partly mediated this association. Males reported more social isolation, while females experienced more loneliness. Linear regression highlighted specific autism-related traits were associated with social isolation and loneliness; aloof personality correlated with social isolation, while rigid personality and pragmatic language difficulties were linked to loneliness. These findings underscore the urgent need for autism-aware support systems to reduce social isolation and loneliness among autistic adults in midlife and older age. Scientific community and society/Social sciences/Psychology Biological sciences/Neuroscience/Social neuroscience/Agency autistic traits autism midlife old age social isolation loneliness Figures Figure 1 Figure 2 INTRODUCTION Autism is a set of neurodevelopmental conditions characterised by differences in social communication and rigid and repetitive behaviours and interests (APA, 2013). Autism is increasingly viewed as a lifelong condition, with a global prevalence of 1% (Santomauro 2025). However, due to many changes in diagnostic criteria over the past several decades (Happé and Frith 2020), disparities in rates of diagnosis are common, particularly for people now in midlife and old age. Analysis of UK healthcare records suggest that only over 90% of autistic adults over the age of 50 are likely undiagnosed (O'Nions 2023). This high rate of underdiagnosis has resulted, in part, to a lack of awareness about ageing in autistic populations, with only 0.4% of indexed autism research between 1980 and 2022 focusing on autism in old age (Mason 2022). To account for this problem, researchers often leverage autism being a spectrum of traits by utilising a dimensional approach to studying autism in overlooked groups (Happe and Frith 2020). Utilising this dimensional trait-wise approach and examining high autistic traits has been particularly effective in the study of high priority topics in ageing (Mason et al., 2021). A major current concern in ageing is social isolation ( objectively having fewer social contacts and networks; Gardiner et al., 2018) and loneliness (subjectively experiencing a negative perception of lack or loss of companionship; ONS, 2018); while distinct experiences, both have been found to be globally elevated in adults aged 50 and older, relative to younger peers (Surkalim et al., 2022). Both are also important issues for society (in terms of social dislocation and cohesion) and also public health, with both social isolation and loneliness being linked to higher odds of having a mental health difficulty and poorer physical health (Coyle & Dugan, 2012), and with links to increased premature mortality also becoming apparent (Blazer, 2020). Consequently, social isolation and loneliness have been identified as critical public health challenges, and it is important to understand their causes in order to tackle them (OSG, 2023). Autistic people of all ages have also been found to be at high risk of social isolation and loneliness (Grace, K. et al 2022), however, few studies have examined these experiences in midlife and older age. The very limited quantitative research suggests that middle-aged and older autistic people are at greatest risk of being lonelier and less socially connected than their non-autistic peers (Stewart et al., 2024). Some qualitative studies have also found a similar pattern, with autistic people describing their lived experience of being socially isolated and desiring social connections with others (Hickey et al., 2018; Francis et al., 2025; Viner et al., 2024). However, critically, much of this work does not look at what potential variables might underpin these associations, and doing so is critical for interventions to prevent and remediate social isolation and loneliness in this group. Several risk factors for social isolation and loneliness have been identified in general middle-aged and older adult populations, including poor mental health (e.g., depression, anxiety) and negative/traumatic life events (Victor et al., 2000). These experiences have also been found to be elevated in autistic populations in midlife and old age (Stewart et al., 2024). While risk factors for social isolation and loneliness were not explicitly examined in their study, Stewart et al. (2024) found associations between social isolation, loneliness and symptoms of poor mental health in their middle-aged and older autistic and non-autistic sample, with their group differences surviving correction for these problems. They also found that autistic women may be more likely to experience loneliness than autistic men, although this may change as men get older (Stewart et al., 2024). Additionally, Lo et al. (2025) found that better theory of mind (the socio-cognitive ability of understanding another person’s perspective, which many autistic people have difficulties with) also partially mediated the relationship between autistic traits and social isolation in a sample of middle-aged and older adults. While these studies have begun to examine factors that may be influencing the high rates of social isolation and loneliness in middle-aged and older autistic populations, these studies have been narrow in approach and have not considered the wider (and possibly compounded) influence of the multiple risk factors known to influence isolation and loneliness in the general population. Additionally, we know little about whether traits and characteristics associated with autism (e.g., aspects of the broad autism phenotype, such as aloof and rigid personality, pragmatic language problems) are autism-specific risk factors and are predictive of social isolation and loneliness. Understanding the contributions of these risk factors and autistic characteristics on the experiences of social isolation and loneliness in autistic populations in midlife and older age will provide information about how help and support can be tailored towards individuals, to mitigate the negative consequences of being socially isolated and lonely in old age. The aim of the current study is to examine the unique contributions of risk factors to social isolation and loneliness (as separate constructs) in a large sample of middle-aged and older adults using path analysis. To account for the high rates of undiagnosed autistic people in midlife and old age, a dimensional approach will be used by classifying our sample as having high or low autistic traits. It is hypothesised that: 1) higher rates of social isolation and loneliness will be reported by those with high autistic traits compared to those with low autistic traits; 2) known risk factors (e.g., poor mental health, negative/traumatic experiences, and sex) will be associated with social isolation and loneliness; 3) associations between autistic traits and social isolation and loneliness will be mediated by these risk factors; and 4) different aspects of the broad autism phenotype will be predictive of social isolation and loneliness. METHODS Study Design This study uses cross-sectional data from the PROTECT study, a UK-wide research study launched in 2015 ( www.protectstudy.org.uk ). Participants complete annual online questionnaires centred around lifestyle, health and cognitive tests. Participants were recruited to the study via advert in charities, the press and social media. Inclusion criteria for the PROTECT study were adults aged over 50 years, resident in the United Kingdom, with a good understanding of English, and able to use a computer with internet access. Participants who had an established diagnosis of dementia at baseline were excluded. All participants gave written, informed consent. Further details can be found at http://www.protectstudy.org.uk . The PROTECT study received ethical approval from the U.K. London Bridge National Research Ethics Committee (Ref: 13/LO/1578). A steering group facilitated by the UK’s National Autistic Society provided feedback on the current study. Participants From a total sample of over 20,000 PROTECT participants, 9,979 participants (75% female) aged 50–97 years old had complete data and were included in the current study. Using the standard cut-off of ≥ 6 on the Autism Spectrum Quotient 10-item scale (AQ-10), 672 (6.7%) of participants were identified as having high autistic traits (High AST group), with the remaining 9,307 participants forming a low autistic traits (Low AST) group. Some differences were observed between groups; notably, the High AST group were older (mean age = 68.2 years vs. 67.0 years) and more often male (48% vs. 20%) than the Low AST group. Groups were broadly similar in ethnicity (90% white) and highest educational attainment (< 62% with university-level qualifications). See Table 1. Table 1 HERE. Measures Demographic variables Demographic information was collected using PROTECT’s online survey platform, including age, sex, marital status, education history, and employment status. Autistic Traits Autistic traits were measured using the Autism Spectrum Quotient 10-item scale (AQ-10; Allison, Auyeung & Baron-Cohen, 2012 ). Scores range from 0–10 (low-to-high traits), with a cut-off score of ≥ 6 being used for probable autism. The AQ-10 has a sensitivity of 0.88, specificity of 0.91, and a positive predictive value of 0.85 for correctly identifying those with autism (Allison et al, 2012 ). Specific subscales of autistic traits were measured using the Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al., 2007). The BAPQ is comprised of three 12-item subscales (aloof personality, rigid personality, pragmatic language problems). The BAPQ uses a six-point response scale (1 = very rarely; 6 = very often). Scores are averaged and range from 1–6 for each subscale. The BAPQ has good reliability and a robust three-factor structure in biological parents of autistic children (Sasson et al., 2013 ). In this study, the three subscales were shown to have moderately high to high internal consistency (aloof personality: self-report α = .92, informant α = .93; rigid behaviour: self α = .86, informant = α .89; pragmatic language difficulties: self α = .80, informant α = .76). Additionally, the informant version of the BAPQ was used for a sensitivity analysis on the subset who also completed the informant-report BAPQ, as averaging across both scores is thought to produce the best estimate (Sasson et al., 2013 ). Social Isolation and Loneliness Social isolation, or poor social connectedness, was measured using the Lubben Social Networking Scale (LSNS-6; Lubben et al., 2006 ). The LSNS-6 is a six-item questionnaire (rated on a 6-point scale [‘zero’ to ‘nine or more’ social contacts], maximum score = 30) examining the frequency and quality of contact with family members and friends. The LSNS-6 has been shown to have high levels of internal consistency, stable factor structures, and high correlations with criterion variables (Lubben et al., 2006 ). It has good internal consistency in autistic adults (Cronbach’s alpha = 0.83; Stewart at al., 2024). The measure was reverse scored for consistency with other variables, whereby higher values indicated fewer social contacts i.e. greater social isolation. Feelings of current loneliness were measured using the UCLA 3-item Loneliness Scale (UCLA-3LS; Hughs et al., 2004). The UCLA-3LS is a three-item questionnaire (rated on a 3-point scale, maximum score = 9) examining subjective dissatisfaction with social relationships, with higher scores indicating loneliness. The UCLA-3LS has been found to have good internal consistency and reliability (Cronbach’s alpha = 0.72; Hughs et al., 2004).The 20-item version is the preferred loneliness measure among autistic adults (Grace et al., 2023) and the 3-item version is recommended by the Office for National Statistics (ONS, 2018). Potential mediator variables Symptoms of current depression were measured using the nine-item Patient Health Questionnaire (PHQ-9; Kroenke et al., 2001 ). The PHQ-9 uses a four-point response scale (0 = not at all, 3 = nearly every day). Scores are totalled, ranging 0–27. The PHQ-9 has excellent psychometric properties, with a cut-off score of ≥ 10 having 88% sensitivity and 88% specificity for major depressive disorder (Kroenke et al., 2001 , 2009). The PHQ-9 has been found to have excellent psychometric properties for assessing depression symptoms in autistic populations (Cassidy et al., 2018 ; Arnold et al., 2020 ). Symptoms of current anxiety were measured with the seven-item Generalised Anxiety Disorder Questionnaire (GAD-7; Spitzer et al., 2006 ). The GAD-7 uses a four-point response scale (0 = not at all, 3 = nearly every day). Scores are totalled, ranging 0–24. The GAD-7 has excellent psychometric properties, with a cut-off score of ≥ 10 having 89% sensitivity and 82% specificity for generalised anxiety disorder. While not formally validated for use in autistic populations, the GAD-7 has been found to have very good psychometric properties in autistic adults (e.g., Cronbach’s alpha = 0.78; Stewart et al., 2024 ). Negative/traumatic life experiences were measured using the five-item Childhood Trauma Screener (CTS-5; Grabe et al., 2012 ) and five-item Adult Trauma Screener (ATS-5; Khalifeh et al., 2015). Both questionnaires ask about the frequency of physical abuse/neglect, emotional abuse/neglect, and sexual abuse experienced in childhood and adulthood, respectively. The CTS-5 and ATS-5 both use a five-point scale (0 = never true, 4 = very often true), with scores ranging from 0–20. In the general population, the CTS-5 has acceptable internal consistency (α = 0.76; Grabe et al., 2012 ). The specific psychometrics for the ATS-5 are not formally reported in the literature. Both the CTS-5 and ATS-5 have been previously used in middle-aged and older adult populations, including in those with high autistic traits (Stewart et al., 2022a ). Statistical Analyses All analyses were conducted in RStudio v4.3.0. The ‘mice’ package was used for assumption testing and to analyse missingness of data. Statistical power was considered good as per various guidelines for structural equation modelling (SEM; Fritz and MacKinnon, 2007 ; Nunnally, 1967; Wang & Wang, 2019 ). Maximum likelihood was used to estimate parameters and full information maximum likelihood (FIML) was used to address missing data (Enders, 2001 ). Chi-square and t-test analyses were used to examined group differences (High/Low AST) group in demographic characteristics. Mann-Whitney U tests and rank biserial correlations were used to examine group differences (High/Low AST) in social isolation and loneliness scores. SEM, specifically path modelling, examined whether the relationship between AST group (predictor) and social isolation and loneliness (outcomes) was mediated by current symptoms of depression and anxiety, negative/traumatic life events, and sex (male/female). The direct path (AST group to social isolation/loneliness) and indirect paths (AST group to social isolation/loneliness through mediators) were modelled using the ‘lavaan’ package add-on. Model fit was checked by examining Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). A standardised regression value (β) between 0.10–0.29 was interpreted as a small effect size, 0.30–0.49 as a medium effect size, and ≥0.50 as a large effect size (Cohen, 1998). Path Analysis - Model Fit A path model was designed with high- vs low-AT group as the predictor (a binary predictor based on whether a person met the cut-off on the AQ-10 or not); with outcomes of loneliness (score on the UCLA-3LS ) and social isolation (reversed score on the LSNS-6, so that a higher score indicates fewer social contacts and relationships); with mediators of sex, mental health factors (continuous scores on measures of depression and anxiety), and negative experience factors (continuous scores on childhood and adulthood trauma screeners). Using cut-offs proposed by Hu & Bentler ( 1999 ), the Comparative Fit Index (CFI = 0.981) indicated a reasonably good fit of the user model to the data; the Tucker-Lewis Index (TLI = 0.869) and Root Mean Square Error of Approximation (RMSEA = 0.072) indicated an acceptable fit of the model; the Standardized Root Mean Square Residual (SRMR = 0.033) suggested a good fit of the model. Taken together, these results suggest that the proposed path model adequately fits the observed data. Linear regressions were used to investigate whether subscales of the broad autism phenotype predict social isolation and loneliness in the high AST group. A Cohen’s f 2 = 0.02 was interpreted as a small effect size, 0.15 as medium effect, and ≥0.35 as a large effect size (Cohen, 1998). RESULTS Experiences of social isolation and loneliness The High AST group (compared to the Low AST group) reported significantly higher scores of social isolation ( W = 4,304,525, p < .0001) and loneliness ( W = 4,028,518, p < .0001). See Table 2 . Table 2 HERE. Mental Health and Negative Experiences The High AST group (compared to the Low AST group) reported significantly higher scores of current symptoms depression ( W = 2,523,577, p < .0001 ) and anxiety ( W = 2,451,314, p < .0001), childhood trauma ( W = 2,031,675, p < .0001), and adulthood trauma ( W = 1,765,687, p < .0001 ). See Table 2 . Path Analysis Direct paths from Autistic Trait groups to Social Isolation and Loneliness In the full model, the direct paths between AST group and social isolation (β = 2.44, p < .001, large effect) and loneliness (β = 0.38, p < .001, medium effect size) were statistically significant, suggesting that differences between High/Low AST groups in social isolation and loneliness are not fully mediated by the indirect factors. FIGURE 1 HERE . FIGURE 2 HERE. Indirect Paths through Current Mental Health, Negative/Traumatic Life Events and Sex High AST group membership was significantly associated with symptoms of depression (β = 1.61, p < .001, large effect), anxiety (β = 1.00, p < .001, large effect), childhood trauma (β = 0.65, p < .001, large effect), adult trauma (β = 0.37, p = .003, medium effect), and male sex (β = -0.30, p < .001, medium effect). Social isolation was significantly associated with current symptoms of depression (β = 0.26, p < .001, small effect), childhood trauma (β = 0.21, p < .001, small effect), adult trauma (β = 0.26, p < .001, small effect), and male sex (β = -1.82, p < .001, large effect), but not current symptoms of anxiety (β = -0.02, p = .433). The relationship between High AST group membership and social isolation was partially mediated by current symptoms of depression (β = 0.42, p < .001; medium effect size), childhood trauma (β = 0.14, p < .001; small effect), and adulthood trauma (β = 0.10, p = .004; small effect). Loneliness was significantly associated with current symptoms of depression (β = 0.14, p < .001, small effect), anxiety (β = 0.05, p < .001, very small effect), childhood trauma (β = 0.02, p = .007, very small effect), adult trauma (β = 0.04, p < .001, very small effect), and female sex (β = 0.08, p = .021, very small effect). The relationship between High AST group membership and loneliness was partially mediated by current symptoms of depression (β = 0.22, p < .001, small effect size), anxiety (β = 0.05, p < .001, very small effect), childhood trauma (β = 0.01, p = .015, very small effect), adult trauma (β = 0.01, p = .008, very small effect), and female sex (β = 0.08, p = .020, very small effect). There was a significant overall effect of all indirect paths between high autistic traits and social isolation and loneliness (Σ = 1.45, p < .001, large effect). Broad Autism Phenotype subscales as predictors of social isolation and loneliness For social isolation, the regression model was statistically significant ( F (3, 668) = 52.51 p < .0001). Aloof personality was a significant predictor of social isolation (β 1 = 2.78, SE = 0.27, t (668) = 10.46, p < .0001), while rigid personality (β 2 = 0.05, SE = 0.29, t (668) = 0.16, p = 0.871) and pragmatic language difficulties (β 3 = 0.09, SE = 0.36, t (668) = 0.26, p = 0.795) were not. The full regression model accounted for 19.1% of the variance in social isolation in the High AST group (Intercept: β 0 = 6.76, SE = 1.13, t (668) = 5.97, p < .0001). For loneliness, regression model was statistically significant (, F (3, 668) = 33.75, p < .0001). Rigid personality (β 2 = 0.33, SE = 0.11, t (668) = 3.10, p = .002) and pragmatic language difficulties (β 3 = 0.85, SE = 0.13, t (668) = 6.41, p < .0001) were significant predictors of loneliness, while aloof personality was not (β 1 = 0.05, SE = 0.10, t (668) = 0.50, p = .614). The full regression model accounted for 13.2% of the variance in social isolation in the High AST group (Intercept: β 0 = 1.17, SE = 0.40, t (668) = 2.97, p = .003). Sensitivity analyses were conducted using BAPQ informant-report scores and using the full sample (High and Low AST groups), with a similar pattern of results being found. DISCUSSION This is the first study to examine the combined influence of several known risk factors for social isolation and loneliness in a sample of high- and low-autistic trait adults aged 50+. Participants with high autistic traits reported significantly higher levels of social isolation and loneliness than a comparison group with low autistic traits. The association between high autistic traits and social isolation was partially mediated by depression (with medium effect) and childhood and adulthood trauma (with small effects) but was not mediated by anxiety. The association between high autistic traits and loneliness was partially mediated by depression (with small effect) as well as anxiety and childhood and adulthood trauma (with very small effects). Male sex was associated with a higher rate of social isolation in comparison to female sex with a large effect, and female sex was associated with a higher rate of loneliness with a very small effect. These findings highlight that while middle-aged and older adults with high autistic traits are more likely to experience social isolation and loneliness than their peers, this may partly be a consequence of elevated mental health difficulties and, to a lesser extent, adverse life experiences in this population. The presence of large effect size direct pathways between high autistic traits and social isolation and loneliness indicated that additional factors may also be important in understanding the relationship. Specific aspects of the broad autism phenotype appeared to be differentially associated with loneliness and social isolation. Specifically, rigid personality and pragmatic language difficulties were associated with higher rates of loneliness, with small effect sizes. Aloof personality was associated with higher rates of social isolation with a medium effect. Our findings are in line with individual strands of evidence in the existing literature: of elevated rates of social isolation and loneliness in diagnosed autistic adults in midlife and older age (Stewart et al., 2024 ); of heightened anxiety, depression, and negative experiences being some of the main predictors of loneliness in autistic adults (Grace et al., 2022 ) and in the general population (Victor et al., 2000 ). Ours is the first study to identify this in adults with high autistic traits over 50, with a large sample, differentiating between social isolation and loneliness, and investigating discreet autistic traits. Findings aligning with the diagnosed literature further strengthens the rationale for implementing the autistic traits approach to studying autism in older age. The findings have serious implications because of the strong evidence in the general population for social isolation and loneliness being associated with negative outcomes for physical and mental health (Coyle & Dugan, 2012 ). Conversely, research finds that social support is a key predictor of quality of life for autistic people in older age (Charlton et al., 2023). This highlights the need for supporting autistic adults with addressing issues of social isolation and loneliness, to prevent negative health outcomes and to ensure good quality of life in midlife and older age. Importantly, we were able to differentiate between the more objective measure of social isolation and the more subjective measure of loneliness: constructs that are often used interchangeably in the literature (Grace et al., 2022 ). It is first important to note that both were significant. Social isolation was most strongly associated with depression, and to a smaller extent negative past experiences. Loneliness was associated with depression to a smaller extent, and to a smaller extent with anxiety and negative past experiences. The findings suggest that treating depression might be an effective strategy for reducing both social isolation and loneliness. Our study also addresses a gap in the literature noted by Grace et al. ( 2022 ), who found female gender to be a predictor of decreased loneliness in autistic adults but noted that isolation and loneliness were treated interchangeably in most of the literature. In differentiating between the two, we found that female sex was in fact associated with higher levels of subjective loneliness, despite being associated with lower levels of social isolation in comparison to male sex. This is partly similar to findings of Stewart et al. ( 2024 ) who found that autistic women in midlife and older age were lonelier than men, but found no gender difference for social isolation. Further research is needed with autistic adults to elucidate the association, but initial evidence suggests that males with high autistic traits may be in particular need of support with establishing social contacts and confiding relationships, whereas females may benefit more from support with subjective feelings of loneliness. There remained a direct path from high autistic traits to social isolation with a large effect size, and to loneliness with a medium effect size. Either autistic traits themselves confer higher risk of loneliness and even more so of social isolation (discussed below) or there are mediators unaccounted for that need to be considered, such as societal factors (lack of autism awareness and acceptance from others) or adaptations to such societal inequalities (sensory avoidance, camouflaging); these are all linked to increased levels of social isolation and loneliness in autistic adults (Grace et al., 2022 ). Our study is the first, to our knowledge, to identify that the association between autistic traits and social isolation and loneliness may depend on specific traits of the broad autism phenotype. For people with high autistic traits, rigid personality and pragmatic language difficulties were associated with more loneliness (with a small effect), whereas aloof personality was associated with more social isolation (with a medium effect). This suggests that lack of interest or enjoyment in social interaction is associated with being socially isolated, but it is not necessarily associated with subjective feelings of loneliness for people with autistic traits. On the other hand, struggling with change and experiencing difficulties with the social aspects of language might not be linked to social isolation but might put a person at risk of feeling lonely. Autism is a highly heterogeneous condition and these findings highlight that autistic traits should not just be treated as one variable, recognising that there is variation in the phenotype and these differences can influence the association with social isolation and isolation. This may help with identifying people who would benefit from targeted support for either increasing social connectedness or reducing subjective feelings of loneliness. This is the largest study to date of autistic traits in middle-aged and older adults using two widely used autistic trait measures. We found a similar pattern of results regarding social isolation and loneliness in this trait-based study as is found in the autism literature, which is further evidence that studies of this type can be used a proxy for diagnosed autism when grappling with the issue of under-diagnosis in midlife and older age. We used standardised measures of social isolation, loneliness, depression and anxiety which have good psychometric properties in autistic and non-autistic populations. Importantly, we were able to differentiate between objective and subjective experiences of social isolation and loneliness, which is a gap in the current literature. It is also the first study to investigate pathways from different subscales of the broad autism phenotype to social isolation and loneliness. Looking at demographics, the high autistic trait group had a good gender balance, and the mean sample age was older than most other ageing studies in the literature. Regarding how the sample was selected, the PROTECT study was not a targeted study of social isolation and loneliness so it is less likely to have had sampling bias. However, there are several limitations which impact the generalisability of our findings. It is a cross-sectional study that was exploratory in nature and causality cannot be inferred. Reverse causality (for example, social isolation or loneliness being the origin of depression) cannot be ruled out and further research is needed using longitudinal designs to clarify the causality behind the findings. A longitudinal design could also allow for exploration of how social isolation and loneliness change with age for adults with high autistic traits, as old age is a known risk factor for social isolation and loneliness (Newmyer et al., 2022 ). Furthermore, there were missing data for measures of depression, anxiety and negative past experiences, and these data were not missing completely at random. There might be unmeasured variables that are associated with the participants who had missing data, so the analysis might not be accounting for the experiences of a certain type of participant. There are also limitations regarding the sample and demographic data. Participation in the PROTECT study is voluntary and conducted entirely online, which can introduce sampling biases, particularly for older adults who might not have access or confidence in using technology. The recruited cohort was almost entirely White British and predominantly well-educated White British women, which greatly limits the generalisability of our findings to the wider population. The demographic data for gender were also missing, which limited our analyses to a binary variable of sex which might not match up to the gender identity of participants. Having diverse demographics in a study sample is particularly important when trying to understand risk factors for autistic people, who are more likely to face social disadvantages such as underemployment (Sonido et al., 2020 ), as well as to have individual differences moderated by sex and gender identity (Bölte et al., 2023 ). Designing accessible studies which can recruit participants from a wide range of demographics as well as capture diverse demographic characteristics is important for building our understanding of people’s experiences. Future research should also consider external factors that are strongly linked to negative outcomes for autistic people in the literature, such as lack of autism awareness and acceptance. Conclusion This study provided new insight into the relationship between autistic traits and social isolation and loneliness in adults over 50. It found that high autistic traits were associated with higher levels of social isolation and loneliness, and that the associations were partially and differentially mediated by mental health and negative past experiences. Females with high autistic traits had lower levels of social isolation but higher levels of loneliness than males. Different subscales of the broad autism phenotype had different associations with social isolation and loneliness. It is important to differentiate between subjective loneliness and objective social isolation, and to consider sex and different aspects of the broad autism phenotype when researching autistic traits. Most importantly, we must ensure that autistic people receive help and support with addressing risk factors and negative consequences of social isolation and loneliness, to help ensure that they can live long happy lives. References Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). 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Autism . https://doi.org/10.1177/13623613241241202 Tables Table 1. Descriptive Statistics across the full sample, High AST and Low AST groups. Variable Full sample N= 9,979 High AST N= 672 (6.7%) Low AST N= 9,307 (93.3%) Group Difference Effect Size Sex Male : Female 2223 : 7017 (22.3% : 70.3%) 325 : 298 (48.4% : 44.3%) 1898 : 6719 (20.4% : 72.2%) X2(1)=287.24, p <.001** v = 0.18 Age (years) M(SD) 67.12 (7.17) 68.22 (7.86) 67.04 (7.12) t(692.27),=-3.61, p <.001** d = 0.17 Range 50-97 50-92 50-97 Ethnicity White 9069 608 (90.5%) 8461 (90.9%) X2(17)=20.35, p =.260 v = 0.03 Mixed 60 4 (0.6%) 56 (0.6%) Black 10 - 10 (0.1%) Asian 75 10 (1.5%) 65 (0.7%) Other 26 1 (0.1%) 25 (0.3%) Marital Status Married 6232 405 (60.3%) ⱡ 5827 (62.6%) ⱡ X2(6) = 20.05, p =.003* v = 0.05 Widowed 706 45 (6.7%) 661 (7.1%) Separated 133 8 (1.2%) 125 (1.3%) Divorced 1010 57 (8.5%) ⱡ 953 (10.2%) ⱡ Civil Partnership 51 1 (0.1%) 50 (0.5%) Co-habiting 519 48 (7.1%) ⱡ 471 (5.1%) ⱡ Single 587 59 (8.8%) ⱡ 528 (5.7%) ⱡ Education School to 16 1092 71 (10.6%) 1021 (11.0%) X2(5)=7.29, p =.200 v = 0.03 School to 18 2822 190 (28.3%) 2632 (28.3%) Undergraduate 3171 215 (32.0%) 2956 (31.8%) Postgraduate 2155 147 (21.9%) 2008 (21.6%) Current Employment Employed 3315 185 (27.5%) ⱡ 3130 (33.6%) ⱡ X2(4) =23.612, p <.001** v = 0.05 Retired 5718 414 (61.6%) ⱡ 5304 (57.0%) ⱡ Unemployed 200 22 (3.3%) ⱡ 178 (1.9%) ⱡ Current Voluntary Work Yes : No 4393 : 4774 (44.0% : 47.8%) 304 : 314 (45.2% : 46.7%) 4089 : 4460 (43.9% : 47.9%) X2(5)= 3.19, p =.67 v = 0.02 Note. M=mean, SD=standard deviation. Specific ethnicities were collapsed to create broader groups. Effect size measured using Cohen’s d and Cramer’s v. *p<.05, **p<.001, ***p<.001. ⱡ significant difference in adjusted residual values between cells. Table 2. Mental Health Scores and Negative Experiences of High AST and Low AST groups. Variable H igh AST ( n = 672) L ow AST ( n = 9,307) Group difference Effect size Social isolation (max score = 30) M ( SD ) 16.40 (5.35) 12.78 (5.17) W = 4,304,525 p <.0001*** r = 0.38 Loneliness (max score = 9) M ( SD ) 5.05 (1.86) 4.14 (1.50) W = 4,028,518 p <.0001*** r = 0.29 Depression (max score = 27) M ( SD ) 4.06 (4.71) 2.41 (3.07) W = 2,523,577 p <.0001*** r = 0.19 Anxiety (max score = 21) M ( SD ) 2.49 (3.87) 1.47 (2.57) W = 2,451,314 p <.0001*** r = 0.22 Childhood trauma (max score = 20) M ( SD ) 2.48 (3.00) 1.82 (2.55) W = 2,031,675 p <.0001*** r = 0.35 Adulthood trauma (max score = 20) M ( SD ) 2.85 (2.73) 2.46 (2.67) W = 1,765,687 p <.0001*** r = 0.44 Note. W = Mann-Whitney U test. Effect size measured using rank-biserial correlation. Depression measured by PHQ-9. Anxiety measured by GAD-7. Childhood trauma measured by CTS-5. Adulthood trauma measured by ATS-5. Loneliness measured by UCLA-3LS. Social isolation measured by reversed score on the LSNS-6. *p<.05, **p<.001, ***p<.0001. Additional Declarations There is NO Competing Interest. Supplementary Files SUPPLEMENTARYMATERIALS.docx Supplementary File 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:39:33","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194960,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6838818/v1/3159a6a18f577e999bb02fda.html"},{"id":92479971,"identity":"0c521102-5af0-4408-8d06-80ea34d07dd2","added_by":"auto","created_at":"2025-09-30 07:39:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePath Model of the Relationship Between High Autistic Traits and Social Isolation, Partially Mediated by Mental Health, Negative Experiences, and Sex\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6838818/v1/295612e442187b74f4600dfd.png"},{"id":92479979,"identity":"db4d2471-79d1-4734-b8e8-ec3264ddd57d","added_by":"auto","created_at":"2025-09-30 07:39:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePath Model of the Relationship Between High Autistic Traits and Loneliness, Partially Mediated by Mental Health, Negative Experiences, and Sex\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eAll reported numbers are standardised regression values. Solid lines indicate significant\u003cem\u003e \u003c/em\u003eeffects at a significance level of \u003cem\u003ep \u003c/em\u003e\u0026lt;.05. A dotted line indicates a non-significant effect. For sex, male was coded as 1 and female as 2, therefore a negative effect from sex to an outcome indicates that male sex is associated with an increase in the outcome. High autistic traits as measured by meeting cut-off on AQ-10. Depression as measured by PHQ-9. Anxiety as measured by GAD-7. Loneliness as measured by UCLA-3LS. Social isolation as measured by reversed score on LSNS-6. Child trauma and adult trauma as measured by the CTS-5 and ATS-5, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6838818/v1/388dd88b3a9ef23436198837.png"},{"id":92481199,"identity":"d6c54c02-6561-4979-81e5-87d398d223ad","added_by":"auto","created_at":"2025-09-30 07:47:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1068442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6838818/v1/acbaa784-cae7-4c6a-b490-b7eacd4b8ad9.pdf"},{"id":92479981,"identity":"ea7271a1-5205-44e6-8b9f-0c7d1416584c","added_by":"auto","created_at":"2025-09-30 07:39:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15323,"visible":true,"origin":"","legend":"Supplementary File","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-6838818/v1/15e1ec2efc5fffeb543ef2e9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Social isolation and loneliness in adults over 50 with high autistic traits.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAutism is a set of neurodevelopmental conditions characterised by differences in social communication and rigid and repetitive behaviours and interests (APA, 2013). Autism is increasingly viewed as a lifelong condition, with a global prevalence of 1% (Santomauro 2025). However, due to many changes in diagnostic criteria over the past several decades (Happ\u0026eacute; and Frith 2020), disparities in rates of diagnosis are common, particularly for people now in midlife and old age. Analysis of UK healthcare records suggest that only over 90% of autistic adults over the age of 50 are likely undiagnosed (O\u0026apos;Nions 2023). This high rate of underdiagnosis has resulted, in part, to a lack of awareness about ageing in autistic populations, with only 0.4% of indexed autism research between 1980 and 2022 focusing on autism in old age (Mason 2022). To account for this problem, researchers often leverage autism being a spectrum of traits by utilising a dimensional approach to studying autism in overlooked groups (Happe and Frith 2020). Utilising this dimensional trait-wise approach and examining high autistic traits has been particularly effective in the study of high priority topics in ageing (Mason et al., 2021).\u003c/p\u003e\n\u003cp\u003eA major current concern in ageing is social isolation \u003cu\u003e(\u003c/u\u003eobjectively having fewer social contacts and networks; Gardiner et al., 2018) and loneliness (subjectively experiencing a negative perception of lack or loss of companionship; ONS, 2018); while distinct experiences, both have been found to be globally elevated in adults aged 50 and older, relative to younger peers (Surkalim et al., 2022). Both are also important issues for society (in terms of social dislocation and cohesion) and also public health, with both social isolation and loneliness being linked to higher odds of having a mental health difficulty and poorer physical health (Coyle \u0026amp; Dugan, 2012), and with links to increased premature mortality also becoming apparent (Blazer, 2020). Consequently, social isolation and loneliness have been identified as critical public health challenges, and it is important to understand their causes in order to tackle them (OSG, 2023). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAutistic people of all ages have also been found to be at high risk of social isolation and loneliness (Grace, K. et al 2022), however, few studies have examined these experiences in midlife and older age. The very limited quantitative research suggests that middle-aged and older autistic people are at greatest risk of being lonelier and less socially connected than their non-autistic peers (Stewart et al., 2024). Some qualitative studies have also found a similar pattern, with autistic people describing their lived experience of being socially isolated and desiring social connections with others (Hickey et al., 2018; Francis et al., 2025; Viner et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, critically, much of this work does not look at what potential variables might underpin these associations, and doing so is critical for interventions to prevent and remediate social isolation and loneliness in this group. Several risk factors for social isolation and loneliness have been identified in general middle-aged and older adult populations, including poor mental health (e.g., depression, anxiety) and negative/traumatic life events (Victor et al., 2000). These experiences have also been found to be elevated in autistic populations in midlife and old age (Stewart et al., 2024). While risk factors for social isolation and loneliness were not explicitly examined in their study, Stewart et al. (2024) found associations between social isolation, loneliness and symptoms of poor mental health in their middle-aged and older autistic and non-autistic sample, with their group differences surviving correction for these problems. They also found that autistic women may be more likely to experience loneliness than autistic men, although this may change as men get older (Stewart et al., 2024). Additionally, Lo et al. (2025) found that better theory of mind (the socio-cognitive ability of understanding another person\u0026rsquo;s perspective, which many autistic people have difficulties with) also partially mediated the relationship between autistic traits and social isolation in a sample of middle-aged and older adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile these studies have begun to examine factors that may be influencing the high rates of social isolation and loneliness in middle-aged and older autistic populations, these studies have been narrow in approach and have not considered the wider (and possibly compounded) influence of the multiple risk factors known to influence isolation and loneliness in the general population. Additionally, we know little about whether traits and characteristics associated with autism (e.g., aspects of the broad autism phenotype, such as aloof and rigid personality, pragmatic language problems) are autism-specific risk factors and are predictive of social isolation and loneliness. Understanding the contributions of these risk factors and autistic characteristics on the experiences of social isolation and loneliness in autistic populations in midlife and older age will provide information about how help and support can be tailored towards individuals, to mitigate the negative consequences of being socially isolated and lonely in old age. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe aim of the current study is to examine the unique contributions of risk factors to social isolation and loneliness (as separate constructs) in a large sample of middle-aged and older adults using path analysis. To account for the high rates of undiagnosed autistic people in midlife and old age, a dimensional approach will be used by classifying our sample as having high or low autistic traits. It is hypothesised that: 1) higher rates of social isolation and loneliness will be reported by those with high autistic traits compared to those with low autistic traits; 2) known risk factors (e.g., poor mental health, negative/traumatic experiences, and sex) will be associated with social isolation and loneliness; 3) associations between autistic traits and social isolation and loneliness will be mediated by these risk factors; and 4) different aspects of the broad autism phenotype will be predictive of social isolation and loneliness.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis study uses cross-sectional data from the PROTECT study, a UK-wide research study launched in 2015 (\u003cspan class=\"ExternalRef\"\u003ewww.protectstudy.org.uk\u003c/span\u003e\u003cspan address=\"http://www.protectstudy.org.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Participants complete annual online questionnaires centred around lifestyle, health and cognitive tests. Participants were recruited to the study via advert in charities, the press and social media. Inclusion criteria for the PROTECT study were adults aged over 50 years, resident in the United Kingdom, with a good understanding of English, and able to use a computer with internet access. Participants who had an established diagnosis of dementia at baseline were excluded. All participants gave written, informed consent. Further details can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.protectstudy.org.uk\u003c/span\u003e\u003cspan address=\"http://www.protectstudy.org.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The PROTECT study received ethical approval from the U.K. London Bridge National Research Ethics Committee (Ref: 13/LO/1578). A steering group facilitated by the UK\u0026rsquo;s National Autistic Society provided feedback on the current study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eFrom a total sample of over 20,000 PROTECT participants, 9,979 participants (75% female) aged 50\u0026ndash;97 years old had complete data and were included in the current study. Using the standard cut-off of \u0026ge;\u0026thinsp;6 on the Autism Spectrum Quotient 10-item scale (AQ-10), 672 (6.7%) of participants were identified as having high autistic traits (High AST group), with the remaining 9,307 participants forming a low autistic traits (Low AST) group. Some differences were observed between groups; notably, the High AST group were older (mean age\u0026thinsp;=\u0026thinsp;68.2 years vs. 67.0 years) and more often male (48% vs. 20%) than the Low AST group. Groups were broadly similar in ethnicity (90% white) and highest educational attainment (\u0026lt;\u0026thinsp;62% with university-level qualifications). See Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable\u0026nbsp;1 HERE.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eDemographic variables\u003c/h2\u003e\u003cp\u003eDemographic information was collected using PROTECT\u0026rsquo;s online survey platform, including age, sex, marital status, education history, and employment status.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAutistic Traits\u003c/h3\u003e\n\u003cp\u003eAutistic traits were measured using the Autism Spectrum Quotient 10-item scale (AQ-10; Allison, Auyeung \u0026amp; Baron-Cohen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Scores range from 0\u0026ndash;10 (low-to-high traits), with a cut-off score of \u0026ge;\u0026thinsp;6 being used for probable autism. The AQ-10 has a sensitivity of 0.88, specificity of 0.91, and a positive predictive value of 0.85 for correctly identifying those with autism (Allison et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecific subscales of autistic traits were measured using the Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al., 2007). The BAPQ is comprised of three 12-item subscales (aloof personality, rigid personality, pragmatic language problems). The BAPQ uses a six-point response scale (1\u0026thinsp;=\u0026thinsp;very rarely; 6\u0026thinsp;=\u0026thinsp;very often). Scores are averaged and range from 1\u0026ndash;6 for each subscale. The BAPQ has good reliability and a robust three-factor structure in biological parents of autistic children (Sasson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, the three subscales were shown to have moderately high to high internal consistency (aloof personality: self-report α\u0026thinsp;=\u0026thinsp;.92, informant α\u0026thinsp;=\u0026thinsp;.93; rigid behaviour: self α\u0026thinsp;=\u0026thinsp;.86, informant\u0026thinsp;=\u0026thinsp;α .89; pragmatic language difficulties: self α\u0026thinsp;=\u0026thinsp;.80, informant α\u0026thinsp;=\u0026thinsp;.76). Additionally, the informant version of the BAPQ was used for a sensitivity analysis on the subset who also completed the informant-report BAPQ, as averaging across both scores is thought to produce the best estimate (Sasson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSocial Isolation and Loneliness\u003c/h3\u003e\n\u003cp\u003eSocial isolation, or poor social connectedness, was measured using the Lubben Social Networking Scale (LSNS-6; Lubben et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The LSNS-6 is a six-item questionnaire (rated on a 6-point scale [\u0026lsquo;zero\u0026rsquo; to \u0026lsquo;nine or more\u0026rsquo; social contacts], maximum score\u0026thinsp;=\u0026thinsp;30) examining the frequency and quality of contact with family members and friends. The LSNS-6 has been shown to have high levels of internal consistency, stable factor structures, and high correlations with criterion variables (Lubben et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It has good internal consistency in autistic adults (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.83; Stewart at al., 2024). The measure was reverse scored for consistency with other variables, whereby higher values indicated fewer social contacts i.e. greater social isolation.\u003c/p\u003e\u003cp\u003eFeelings of current loneliness were measured using the UCLA 3-item Loneliness Scale (UCLA-3LS; Hughs et al., 2004). The UCLA-3LS is a three-item questionnaire (rated on a 3-point scale, maximum score\u0026thinsp;=\u0026thinsp;9) examining subjective dissatisfaction with social relationships, with higher scores indicating loneliness. The UCLA-3LS has been found to have good internal consistency and reliability (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.72; Hughs et al., 2004).The 20-item version is the preferred loneliness measure among autistic adults (Grace et al., 2023) and the 3-item version is recommended by the Office for National Statistics (ONS, 2018).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePotential mediator variables\u003c/h2\u003e\u003cp\u003eSymptoms of current depression were measured using the nine-item Patient Health Questionnaire (PHQ-9; Kroenke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The PHQ-9 uses a four-point response scale (0\u0026thinsp;=\u0026thinsp;not at all, 3\u0026thinsp;=\u0026thinsp;nearly every day). Scores are totalled, ranging 0\u0026ndash;27. The PHQ-9 has excellent psychometric properties, with a cut-off score of \u0026ge;\u0026thinsp;10 having 88% sensitivity and 88% specificity for major depressive disorder (Kroenke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, 2009). The PHQ-9 has been found to have excellent psychometric properties for assessing depression symptoms in autistic populations (Cassidy et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arnold et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSymptoms of current anxiety were measured with the seven-item Generalised Anxiety Disorder Questionnaire (GAD-7; Spitzer et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The GAD-7 uses a four-point response scale (0\u0026thinsp;=\u0026thinsp;not at all, 3\u0026thinsp;=\u0026thinsp;nearly every day). Scores are totalled, ranging 0\u0026ndash;24. The GAD-7 has excellent psychometric properties, with a cut-off score of \u0026ge;\u0026thinsp;10 having 89% sensitivity and 82% specificity for generalised anxiety disorder. While not formally validated for use in autistic populations, the GAD-7 has been found to have very good psychometric properties in autistic adults (e.g., Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.78; Stewart et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNegative/traumatic life experiences were measured using the five-item Childhood Trauma Screener (CTS-5; Grabe et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and five-item Adult Trauma Screener (ATS-5; Khalifeh et al., 2015). Both questionnaires ask about the frequency of physical abuse/neglect, emotional abuse/neglect, and sexual abuse experienced in childhood and adulthood, respectively. The CTS-5 and ATS-5 both use a five-point scale (0\u0026thinsp;=\u0026thinsp;never true, 4\u0026thinsp;=\u0026thinsp;very often true), with scores ranging from 0\u0026ndash;20. In the general population, the CTS-5 has acceptable internal consistency (α\u0026thinsp;=\u0026thinsp;0.76; Grabe et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The specific psychometrics for the ATS-5 are not formally reported in the literature. Both the CTS-5 and ATS-5 have been previously used in middle-aged and older adult populations, including in those with high autistic traits (Stewart et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted in RStudio v4.3.0. The \u0026lsquo;mice\u0026rsquo; package was used for assumption testing and to analyse missingness of data. Statistical power was considered good as per various guidelines for structural equation modelling (SEM; Fritz and MacKinnon, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nunnally, 1967; Wang \u0026amp; Wang, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Maximum likelihood was used to estimate parameters and full information maximum likelihood (FIML) was used to address missing data (Enders, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChi-square and t-test analyses were used to examined group differences (High/Low AST) group in demographic characteristics. Mann-Whitney U tests and rank biserial correlations were used to examine group differences (High/Low AST) in social isolation and loneliness scores.\u003c/p\u003e\u003cp\u003eSEM, specifically path modelling, examined whether the relationship between AST group (predictor) and social isolation and loneliness (outcomes) was mediated by current symptoms of depression and anxiety, negative/traumatic life events, and sex (male/female). The direct path (AST group to social isolation/loneliness) and indirect paths (AST group to social isolation/loneliness through mediators) were modelled using the \u0026lsquo;lavaan\u0026rsquo; package add-on. Model fit was checked by examining Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). A standardised regression value (β) between 0.10\u0026ndash;0.29 was interpreted as a small effect size, 0.30\u0026ndash;0.49 as a medium effect size, and \u0026ge;0.50 as a large effect size (Cohen, 1998).\u003c/p\u003e\n\u003ch3\u003ePath Analysis - Model Fit\u003c/h3\u003e\n\u003cp\u003eA path model was designed with high- vs low-AT group as the predictor (a binary predictor based on whether a person met the cut-off on the AQ-10 or not); with outcomes of loneliness (score on the UCLA-3LS ) and social isolation (reversed score on the LSNS-6, so that a higher score indicates fewer social contacts and relationships); with mediators of sex, mental health factors (continuous scores on measures of depression and anxiety), and negative experience factors (continuous scores on childhood and adulthood trauma screeners).\u003c/p\u003e\u003cp\u003eUsing cut-offs proposed by Hu \u0026amp; Bentler (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the Comparative Fit Index (CFI\u0026thinsp;=\u0026thinsp;0.981) indicated a reasonably good fit of the user model to the data; the Tucker-Lewis Index (TLI\u0026thinsp;=\u0026thinsp;0.869) and Root Mean Square Error of Approximation (RMSEA\u0026thinsp;=\u0026thinsp;0.072) indicated an acceptable fit of the model; the Standardized Root Mean Square Residual (SRMR\u0026thinsp;=\u0026thinsp;0.033) suggested a good fit of the model. Taken together, these results suggest that the proposed path model adequately fits the observed data.\u003c/p\u003e\u003cp\u003eLinear regressions were used to investigate whether subscales of the broad autism phenotype predict social isolation and loneliness in the high AST group. A Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 was interpreted as a small effect size, 0.15 as medium effect, and \u0026ge;0.35 as a large effect size (Cohen, 1998).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eExperiences of social isolation and loneliness\u003c/h2\u003e\n \u003cp\u003eThe High AST group (compared to the Low AST group) reported significantly higher scores of social isolation (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4,304,525, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) and loneliness (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4,028,518, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). See Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eHERE.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eMental Health and Negative Experiences\u003c/h2\u003e\n \u003cp\u003eThe High AST group (compared to the Low AST group) reported significantly higher scores of current symptoms depression (\u003cem\u003eW\u0026thinsp;=\u0026thinsp;2,523,577, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001\u003c/em\u003e) and anxiety (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,451,314, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001), childhood trauma (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,031,675, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001), and adulthood trauma (\u003cem\u003eW\u0026thinsp;=\u0026thinsp;1,765,687, p\u0026thinsp;\u0026lt;\u0026thinsp;.0001\u003c/em\u003e). See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePath Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003eDirect paths from Autistic Trait groups to Social Isolation and Loneliness\u003c/h2\u003e\n \u003cp\u003eIn the full model, the direct paths between AST group and social isolation (\u0026beta;\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;2.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect) and loneliness (\u0026beta;\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, medium effect size) were statistically significant, suggesting that differences between High/Low AST groups in social isolation and loneliness are not fully mediated by the indirect factors.\u003c/p\u003e\n \u003cp\u003eFIGURE \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cstrong\u003eHERE\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eFIGURE \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eHERE.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eIndirect Paths through Current Mental Health, Negative/Traumatic Life Events and Sex\u003c/h2\u003e\n \u003cp\u003eHigh AST group membership was significantly associated with symptoms of depression (\u0026beta;\u0026thinsp;=\u0026thinsp;1.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect), anxiety (\u0026beta;\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect), childhood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect), adult trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003, medium effect), and male sex (\u0026beta; = -0.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, medium effect).\u003c/p\u003e\n \u003cp\u003eSocial isolation was significantly associated with current symptoms of depression (\u0026beta;\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, small effect), childhood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, small effect), adult trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, small effect), and male sex (\u0026beta; = -1.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect), but not current symptoms of anxiety (\u0026beta; = -0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.433). The relationship between High AST group membership and social isolation was partially mediated by current symptoms of depression (\u0026beta;\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; medium effect size), childhood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; small effect), and adulthood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004; small effect).\u003c/p\u003e\n \u003cp\u003eLoneliness was significantly associated with current symptoms of depression (\u0026beta;\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, small effect), anxiety (\u0026beta;\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, very small effect), childhood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007, very small effect), adult trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, very small effect), and female sex (\u0026beta;\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.021, very small effect). The relationship between High AST group membership and loneliness was partially mediated by current symptoms of depression (\u0026beta;\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, small effect size), anxiety (\u0026beta;\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, very small effect), childhood trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.015, very small effect), adult trauma (\u0026beta;\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008, very small effect), and female sex (\u0026beta;\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020, very small effect).\u003c/p\u003e\n \u003cp\u003eThere was a significant overall effect of all indirect paths between high autistic traits and social isolation and loneliness (\u0026Sigma;\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, large effect).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eBroad Autism Phenotype subscales as predictors of social isolation and loneliness\u003c/h2\u003e\n \u003cp\u003eFor social isolation, the regression model was statistically significant (\u003cem\u003eF\u003c/em\u003e(3, 668)\u0026thinsp;=\u0026thinsp;52.51 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Aloof personality was a significant predictor of social isolation (\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.78, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;10.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001), while rigid personality (\u0026beta;\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.871) and pragmatic language difficulties (\u0026beta;\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.795) were not. The full regression model accounted for 19.1% of the variance in social isolation in the High AST group (Intercept: \u0026beta;\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6.76, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;5.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e\n \u003cp\u003eFor loneliness, regression model was statistically significant (, \u003cem\u003eF\u003c/em\u003e(3, 668)\u0026thinsp;=\u0026thinsp;33.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). Rigid personality (\u0026beta;\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;3.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) and pragmatic language difficulties (\u0026beta;\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.85, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;6.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) were significant predictors of loneliness, while aloof personality was not (\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;0.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.614). The full regression model accounted for 13.2% of the variance in social isolation in the High AST group (Intercept: \u0026beta;\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.17, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003et\u003c/em\u003e(668)\u0026thinsp;=\u0026thinsp;2.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003).\u003c/p\u003e\n \u003cp\u003eSensitivity analyses were conducted using BAPQ informant-report scores and using the full sample (High and Low AST groups), with a similar pattern of results being found.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first study to examine the combined influence of several known risk factors for social isolation and loneliness in a sample of high- and low-autistic trait adults aged 50+. Participants with high autistic traits reported significantly higher levels of social isolation and loneliness than a comparison group with low autistic traits. The association between high autistic traits and social isolation was partially mediated by depression (with medium effect) and childhood and adulthood trauma (with small effects) but was not mediated by anxiety. The association between high autistic traits and loneliness was partially mediated by depression (with small effect) as well as anxiety and childhood and adulthood trauma (with very small effects). Male sex was associated with a higher rate of social isolation in comparison to female sex with a large effect, and female sex was associated with a higher rate of loneliness with a very small effect.\u003c/p\u003e\u003cp\u003eThese findings highlight that while middle-aged and older adults with high autistic traits are more likely to experience social isolation and loneliness than their peers, this may partly be a consequence of elevated mental health difficulties and, to a lesser extent, adverse life experiences in this population. The presence of large effect size direct pathways between high autistic traits and social isolation and loneliness indicated that additional factors may also be important in understanding the relationship.\u003c/p\u003e\u003cp\u003eSpecific aspects of the broad autism phenotype appeared to be differentially associated with loneliness and social isolation. Specifically, rigid personality and pragmatic language difficulties were associated with higher rates of loneliness, with small effect sizes. Aloof personality was associated with higher rates of social isolation with a medium effect.\u003c/p\u003e\u003cp\u003eOur findings are in line with individual strands of evidence in the existing literature: of elevated rates of social isolation and loneliness in diagnosed autistic adults in midlife and older age (Stewart et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); of heightened anxiety, depression, and negative experiences being some of the main predictors of loneliness in autistic adults (Grace et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and in the general population (Victor et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Ours is the first study to identify this in adults with high autistic traits over 50, with a large sample, differentiating between social isolation and loneliness, and investigating discreet autistic traits. Findings aligning with the diagnosed literature further strengthens the rationale for implementing the autistic traits approach to studying autism in older age.\u003c/p\u003e\u003cp\u003eThe findings have serious implications because of the strong evidence in the general population for social isolation and loneliness being associated with negative outcomes for physical and mental health (Coyle \u0026amp; Dugan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conversely, research finds that social support is a key predictor of quality of life for autistic people in older age (Charlton et al., 2023). This highlights the need for supporting autistic adults with addressing issues of social isolation and loneliness, to prevent negative health outcomes and to ensure good quality of life in midlife and older age.\u003c/p\u003e\u003cp\u003eImportantly, we were able to differentiate between the more objective measure of social isolation and the more subjective measure of loneliness: constructs that are often used interchangeably in the literature (Grace et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is first important to note that both were significant. Social isolation was most strongly associated with depression, and to a smaller extent negative past experiences. Loneliness was associated with depression to a smaller extent, and to a smaller extent with anxiety and negative past experiences. The findings suggest that treating depression might be an effective strategy for reducing both social isolation and loneliness.\u003c/p\u003e\u003cp\u003eOur study also addresses a gap in the literature noted by Grace et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found female gender to be a predictor of decreased loneliness in autistic adults but noted that isolation and loneliness were treated interchangeably in most of the literature. In differentiating between the two, we found that female sex was in fact associated with higher levels of subjective loneliness, despite being associated with lower levels of social isolation in comparison to male sex.\u003c/p\u003e\u003cp\u003eThis is partly similar to findings of Stewart et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who found that autistic women in midlife and older age were lonelier than men, but found no gender difference for social isolation. Further research is needed with autistic adults to elucidate the association, but initial evidence suggests that males with high autistic traits may be in particular need of support with establishing social contacts and confiding relationships, whereas females may benefit more from support with subjective feelings of loneliness.\u003c/p\u003e\u003cp\u003eThere remained a direct path from high autistic traits to social isolation with a large effect size, and to loneliness with a medium effect size. Either autistic traits themselves confer higher risk of loneliness and even more so of social isolation (discussed below) or there are mediators unaccounted for that need to be considered, such as societal factors (lack of autism awareness and acceptance from others) or adaptations to such societal inequalities (sensory avoidance, camouflaging); these are all linked to increased levels of social isolation and loneliness in autistic adults (Grace et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study is the first, to our knowledge, to identify that the association between autistic traits and social isolation and loneliness may depend on specific traits of the broad autism phenotype. For people with high autistic traits, rigid personality and pragmatic language difficulties were associated with more loneliness (with a small effect), whereas aloof personality was associated with more social isolation (with a medium effect).\u003c/p\u003e\u003cp\u003eThis suggests that lack of interest or enjoyment in social interaction is associated with being socially isolated, but it is not necessarily associated with subjective feelings of loneliness for people with autistic traits. On the other hand, struggling with change and experiencing difficulties with the social aspects of language might not be linked to social isolation but might put a person at risk of feeling lonely.\u003c/p\u003e\u003cp\u003eAutism is a highly heterogeneous condition and these findings highlight that autistic traits should not just be treated as one variable, recognising that there is variation in the phenotype and these differences can influence the association with social isolation and isolation. This may help with identifying people who would benefit from targeted support for either increasing social connectedness or reducing subjective feelings of loneliness.\u003c/p\u003e\u003cp\u003eThis is the largest study to date of autistic traits in middle-aged and older adults using two widely used autistic trait measures. We found a similar pattern of results regarding social isolation and loneliness in this trait-based study as is found in the autism literature, which is further evidence that studies of this type can be used a proxy for diagnosed autism when grappling with the issue of under-diagnosis in midlife and older age. We used standardised measures of social isolation, loneliness, depression and anxiety which have good psychometric properties in autistic and non-autistic populations. Importantly, we were able to differentiate between objective and subjective experiences of social isolation and loneliness, which is a gap in the current literature. It is also the first study to investigate pathways from different subscales of the broad autism phenotype to social isolation and loneliness.\u003c/p\u003e\u003cp\u003eLooking at demographics, the high autistic trait group had a good gender balance, and the mean sample age was older than most other ageing studies in the literature. Regarding how the sample was selected, the PROTECT study was not a targeted study of social isolation and loneliness so it is less likely to have had sampling bias.\u003c/p\u003e\u003cp\u003eHowever, there are several limitations which impact the generalisability of our findings. It is a cross-sectional study that was exploratory in nature and causality cannot be inferred. Reverse causality (for example, social isolation or loneliness being the origin of depression) cannot be ruled out and further research is needed using longitudinal designs to clarify the causality behind the findings. A longitudinal design could also allow for exploration of how social isolation and loneliness change with age for adults with high autistic traits, as old age is a known risk factor for social isolation and loneliness (Newmyer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, there were missing data for measures of depression, anxiety and negative past experiences, and these data were not missing completely at random. There might be unmeasured variables that are associated with the participants who had missing data, so the analysis might not be accounting for the experiences of a certain type of participant.\u003c/p\u003e\u003cp\u003eThere are also limitations regarding the sample and demographic data. Participation in the PROTECT study is voluntary and conducted entirely online, which can introduce sampling biases, particularly for older adults who might not have access or confidence in using technology. The recruited cohort was almost entirely White British and predominantly well-educated White British women, which greatly limits the generalisability of our findings to the wider population. The demographic data for gender were also missing, which limited our analyses to a binary variable of sex which might not match up to the gender identity of participants.\u003c/p\u003e\u003cp\u003eHaving diverse demographics in a study sample is particularly important when trying to understand risk factors for autistic people, who are more likely to face social disadvantages such as underemployment (Sonido et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as to have individual differences moderated by sex and gender identity (B\u0026ouml;lte et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Designing accessible studies which can recruit participants from a wide range of demographics as well as capture diverse demographic characteristics is important for building our understanding of people\u0026rsquo;s experiences. Future research should also consider external factors that are strongly linked to negative outcomes for autistic people in the literature, such as lack of autism awareness and acceptance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provided new insight into the relationship between autistic traits and social isolation and loneliness in adults over 50. It found that high autistic traits were associated with higher levels of social isolation and loneliness, and that the associations were partially and differentially mediated by mental health and negative past experiences. Females with high autistic traits had lower levels of social isolation but higher levels of loneliness than males. Different subscales of the broad autism phenotype had different associations with social isolation and loneliness. It is important to differentiate between subjective loneliness and objective social isolation, and to consider sex and different aspects of the broad autism phenotype when researching autistic traits. Most importantly, we must ensure that autistic people receive help and support with addressing risk factors and negative consequences of social isolation and loneliness, to help ensure that they can live long happy lives.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllison, C., Auyeung, B., \u0026amp; Baron-Cohen, S. (2012). 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A., \u0026amp; Happ\u0026eacute;, F. (2022b). Self-harm and suicidality experiences of middle-age and older adults with vs. without high autistic traits. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e. https://doi.org/10.1007/s10803-022-05595-y\u003c/li\u003e\n\u003cli\u003eStewart, G. R., Luedecke, E., Mandy, W., Charlton, R. A., \u0026amp; Happ\u0026eacute;, F. (2024). Experiences of social isolation and loneliness in middle-aged and older autistic adults. \u003cem\u003eNeurodiversity, 2\u003c/em\u003e. https://doi.org/10.1177/27546330241245529\u003c/li\u003e\n\u003cli\u003eSurkalim, D. L., Luo, M., Eres, R., Gebel, K., van Buskirk, J., Bauman, A., \u0026amp; Ding, D. (2022). The prevalence of loneliness across 113 countries: Systematic review and meta-analysis. \u003cem\u003eBMJ, 376\u003c/em\u003e, e067068. https://doi.org/10.1136/bmj-2021-067068\u003c/li\u003e\n\u003cli\u003eTabachnick, B. G., \u0026amp; Fidell, L. S. (2013). \u003cem\u003eUsing multivariate statistics\u003c/em\u003e (6th ed.). Pearson.\u003c/li\u003e\n\u003cli\u003eTaylor, E. C., Livingston, L. A., Clutterbuck, R. A., \u0026amp; Shah, P. (2020). Psychometric concerns with the 10-item Autism-Spectrum Quotient (AQ10) as a measure of trait autism in the general population. \u003cem\u003eExperimental Results, 1\u003c/em\u003e. https://doi.org/10.1017/exp.2019.3\u003c/li\u003e\n\u003cli\u003evan Steensel, F. J. A., B\u0026ouml;gels, S. M., \u0026amp; Perrin, S. (2011). Anxiety disorders in children and adolescents with autistic spectrum disorders: A meta-analysis. \u003cem\u003eClinical Child and Family Psychology Review, 14\u003c/em\u003e(3), 302\u0026ndash;317. https://doi.org/10.1007/s10567-011-0097-0\u003c/li\u003e\n\u003cli\u003eVictor, C., Scambler, S., Bond, J., \u0026amp; Bowling, A. (2000). Being alone in later life: loneliness, social isolation and living alone. \u003cem\u003eReviews in Clinical Gerontology, 10\u003c/em\u003e(4), 407-417. https://doi.org/10.1017/s0959259800104101\u003c/li\u003e\n\u003cli\u003eViner (2014). https://doi.org/10.1038/s44271-024-00142-0\u003c/li\u003e\n\u003cli\u003eWang, J., \u0026amp; Wang, X. (2019). The rules of thumb for sample size needed for SEM. In \u003cem\u003eStructural equation modeling: Applications using Mplus\u003c/em\u003e (2nd ed.). Standards Information Network.\u003c/li\u003e\n\u003cli\u003eZeidan, J., Fombonne, E., Scorah, J., Ibrahim, A., Durkin, M. S., Saxena, S., Yusuf, A., Shih, A., \u0026amp; Elsabbagh, M. (2022). Global prevalence of autism: A systematic review update. \u003cem\u003eAutism Research, 15\u003c/em\u003e(5), 778\u0026ndash;790. https://doi.org/10.1002/aur.2696\u003c/li\u003e\n\u003cli\u003eZajic, M. C., \u0026amp; Gudknecht, J. (2024). Person- and identity-first language in autism research: A systematic analysis of abstracts from 11 autism journals. \u003cem\u003eAutism\u003c/em\u003e. https://doi.org/10.1177/13623613241241202\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable 1. Descriptive Statistics across the full sample, High AST and Low AST groups.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 9,979\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh AST\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 672 (6.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow AST\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN= 9,307 (93.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup Difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eMale : Female \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2223 : 7017\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(22.3% : 70.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e325 : 298\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48.4% : 44.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1898 : 6719\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(20.4% : 72.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(1)=287.24, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years) \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eM(SD) \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e67.12 (7.17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e68.22 (7.86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e67.04 (7.12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003et(692.27),=-3.61, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003ed = 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eRange \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e50-97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e50-92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e50-97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eWhite \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9069\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e608 (90.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8461 (90.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(17)=20.35,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e=.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eMixed \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e56 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eBlack \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eAsian \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10 (1.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e65 (0.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eOther \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e25 (0.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eMarried \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6232\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e405 (60.3%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5827 (62.6%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(6) = 20.05,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e=.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eWidowed \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e706\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e45 (6.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e661 (7.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eSeparated \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e133\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e8 (1.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e125 (1.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eDivorced \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e57 (8.5%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e953 (10.2%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eCivil Partnership \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e50 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eCo-habiting \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e519\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e48 (7.1%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e471 (5.1%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eSingle \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e587\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e59 (8.8%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e528 (5.7%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eSchool to 16 \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1092\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e71 (10.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1021 (11.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(5)=7.29,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e=.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eSchool to 18 \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2822\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e190 (28.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2632 (28.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eUndergraduate \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3171\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e215 (32.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2956 (31.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003ePostgraduate \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2155\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e147 (21.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2008 (21.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Employment \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eEmployed \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3315\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e185 (27.5%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3130 (33.6%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(4) =23.612,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eRetired \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5718\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e414 (61.6%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5304 (57.0%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eUnemployed \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e200\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e22 (3.3%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e178 (1.9%) ⱡ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent Voluntary Work \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eYes : No \u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4393 : 4774\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(44.0% : 47.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e304 : 314\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(45.2% : 46.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4089 : 4460\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(43.9% : 47.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eX2(5)= 3.19,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e=.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ev = 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e M=mean, SD=standard deviation. Specific ethnicities were collapsed to create broader groups. Effect size measured using Cohen\u0026rsquo;s d and Cramer\u0026rsquo;s v. *p\u0026lt;.05, **p\u0026lt;.001, ***p\u0026lt;.001. ⱡ significant difference in adjusted residual values between cells.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 2. \u003cem\u003eMental Health Scores and Negative Experiences of High AST and Low AST groups.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003cstrong\u003eigh AST\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 672)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003cstrong\u003eow AST\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;= 9,307)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial isolation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e16.40 (5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e12.78 (5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 4,304,525\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoneliness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e5.05 (1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4.14 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 4,028,518\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4.06 (4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.41 (3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u003c/em\u003e = 2,523,577\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e = 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.49 (3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.47 (2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 2,451,314\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e = 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChildhood trauma\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.48 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.82 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 2,031,675\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e = 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdulthood trauma\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(max score = 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.85 (2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.46 (2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eW\u0026nbsp;\u003c/em\u003e= 1,765,687\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;.0001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e = 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNote. W = Mann-Whitney U test. Effect size measured using rank-biserial correlation. Depression measured by PHQ-9. Anxiety measured by GAD-7. Childhood trauma measured by CTS-5. Adulthood trauma measured by ATS-5. Loneliness measured by UCLA-3LS. Social isolation measured by reversed score on the LSNS-6. *p\u0026lt;.05, **p\u0026lt;.001, ***p\u0026lt;.0001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"autistic traits, autism, midlife, old age, social isolation, loneliness","lastPublishedDoi":"10.21203/rs.3.rs-6838818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6838818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial isolation and loneliness are major public health issues, especially in old age, with serious health impacts. Some populations, such as autistic people, have been found to report high rates of social isolation and loneliness compared to their non-autistic peers. Among 9,979 participants aged 50+, 672 met the AQ-10 cut-off for probable autism, while 9,307 formed a comparison group. Those above the AQ-10 cut-off reported greater social isolation and loneliness. Path analysis showed that sex, depression, anxiety, and trauma partly mediated this association. Males reported more social isolation, while females experienced more loneliness. Linear regression highlighted specific autism-related traits were associated with social isolation and loneliness; aloof personality correlated with social isolation, while rigid personality and pragmatic language difficulties were linked to loneliness. These findings underscore the urgent need for autism-aware support systems to reduce social isolation and loneliness among autistic adults in midlife and older age.\u003c/p\u003e","manuscriptTitle":"Social isolation and loneliness in adults over 50 with high autistic traits.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:39:27","doi":"10.21203/rs.3.rs-6838818/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":"718d4649-3fc9-4d83-bddd-adafda7abf4f","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50134129,"name":"Scientific community and society/Social sciences/Psychology"},{"id":50134130,"name":"Biological sciences/Neuroscience/Social neuroscience/Agency"}],"tags":[],"updatedAt":"2025-09-30T07:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:39:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6838818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6838818","identity":"rs-6838818","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}