A Longitudinal Transdiagnostic Cognitive Model of Depression and Anxiety in Chronic Inflammatory Disease: Evidence from Multiple Sclerosis and Endometriosis

A Longitudinal Transdiagnostic Cognitive Model of Depression and Anxiety in Chronic Inflammatory Disease: Evidence from Multiple Sclerosis and Endometriosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Longitudinal Transdiagnostic Cognitive Model of Depression and Anxiety in Chronic Inflammatory Disease: Evidence from Multiple Sclerosis and Endometriosis Rebekah Allison Davenport, Isabel Krug, Litza Kiropoulos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9195106/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 Objective: Clinical levels of depressive and anxiety symptoms are prevalent in Multiple Sclerosis (MS) and Endometriosis (EMS), yet their underlying mechanisms remain poorly understood. Guided by a transdiagnostic cognitive framework (Nolen-Hoeksema & Watkins, 2011), this study examined whether anxiety sensitivity, intolerance of uncertainty, distress tolerance, and rumination mediate the pathways between neuroticism and depressive and anxiety symptoms. Further, pain severity in EMS and gait-disability may represent contextual moderators of factor-symptom relationships. Methods: Population-based cohorts of individuals with MS (T1, baseline: n= 229; T2, 6-month follow-up: n= 134) and EMS (T1, n= 399; T2, n= 130) completed online surveys at two-time points. Adopting a path-analytic approach, the relationships between neuroticism, cognitive factors, and depressive and anxiety symptoms were evaluated cross-sectionally and prospectively within an integrated model. Gender and age were included as covariates. Pain severity in EMS, and gait disability in MS, were examined as moderating factors. Structural invariance testing explored the plausibility of a common model among diseases. Results: Cross-sectionally, distress tolerance and rumination emerged as the most consistent mediators, with the largest effect for distress tolerance in the relationship between neuroticism and depressive symptoms in the MS group ( ß =.17, 95% CI: .02, .33). Anxiety sensitivity and intolerance of uncertainty demonstrated symptom- and disease-specific associations. Longitudinally, lower distress tolerance mediated the association between neuroticism and later anxiety symptoms in MS ( ß =.10, 95% CI: .04, .17), whereas in EMS, distress tolerance and anxiety sensitivity showed small indirect effects in the depressive pathway. No interaction effects were detected. Although structural non-invariance was observed, substantial convergence in trait–factor and factor–symptom associations was evident across diseases. Conclusions: The principal finding identified distress tolerance as a transdiagnostic cognitive factor linking neuroticism with affective symptoms, underscoring its therapeutic relevance and suggesting shared psychological mechanisms across MS and EMS. Findings extend an emerging evidence-base of cross-disease parallels into the psychological domain. Multiple sclerosis Endometriosis Transdiagnostic science Personality Cognitive mechanisms Figures Figure 1 Figure 2 Figure 3 Introduction Multiple sclerosis (MS) and endometriosis (EMS) are chronic inflammatory diseases and the most prevalent conditions affecting young adults in neurology and gynecology, respectively. MS and EMS commonly co-occur (Shigesi et al., 2019), with women diagnosed with EMS reported to have a 7-fold greater prevalence for MS (Sinaii et al., 2002) and a 20-60% increased risk compared to female controls (Nielsen et al., 2011). Further, MS and EMS share substantial parallels (Hart et al., 2015), including a common immune background (e.g., Katiyar et al., 2018; Zizolfi et al., 2023). Depressive and anxiety symptoms are significantly elevated in both diseases compared to the general population, and rank among the most common comorbidities within these groups (Feinstein et al., 2004; Verket et al., 2018). Depression and anxiety are predictive of self-directed violence (i.e., suicide, self-inflicted injury) in EMS (Estes, et al., 2021), poor quality of life in MS (Marrie et al., 2015), and are associated with negative prognostic factors, including fatigue and pain in EMS (Laganà et al., 2017; Ramin-wright et al., 2018) and MS (O’Connor et al., 2008; Strober & Arnett, 2005), as well as MS disability and relapse (Bruce et al., 2010). While treatment guidelines for MS (Marchesi et al., 2022) and EMS (Mardon et al., 2022) emphasise the importance of addressing depression and anxiety as part of standard care, symptoms remain underdiagnosed and undertreated (Carbone et al., 2021; Marrie et al., 2009). This clinical problem is compounded by the absence of an evidence-based framework explaining depression and anxiety in either disease population, along with a lack of specific cognitive therapies for treating these symptoms in MS (Sesel et al., 2018; Gromisch et al., 2018) and EMS (Evans et al., 2019). Understanding the personality and cognitive factors that contribute to depression and anxiety in MS and EMS is essential for developing evidence-based models to guide tailored cognitive therapies. A body of evidence suggests that depression and anxiety fit into a broader category of internalising disorders (e.g., Caspi et al., 2014; Wright et al., 2013), underpinned by common personality and cognitive factors (referred to as ‘transdiagnostic factors’ ). Nolen-Hoeksema and Watkins (2011) constructed a heuristic for developing cognitive models, outlining potential factors underlying the co-occurrence of depression and anxiety. They propose that symptoms arise from (a) distal individual characteristic factors (e.g., personality traits); (b) proximal cognitive factors that mediate the influence of distal factors; and (c) biological or environmental circumstances (e.g., stress, illness) which moderate proximal factors to produce common or specific symptom expressions (Nolen-Hoeksema & Watkins, 2011). Research conducted in community samples (Norton & Mehta, 2007; Sexton et al., 2003) and in individuals with depressive and anxiety disorders (Norton et al., 2005; Paulus et al., 2015; 2016), (referred hereafter as ‘non-medically ill populations’ ) provides evidence for the relationships between personality, cognitive factors, and depression and anxiety symptoms. Limited research exists on these relationships in MS and EMS (e.g., see systematic reviews by Davenport et al., 2024a; 2024b), and no studies to date have explored these factors within an explanatory model or utilised longitudinal data to clarify the nature and direction of these relationships. Neuroticism as a distal factor Nolen-Hoeksema and Watkins (2011) describe distal factors as stable characteristics that shape beliefs and thought processes, thereby creating the conditions for psychopathology. Neuroticism represents one of the most robust predictors of depression and anxiety, accounting for large variance in symptoms in individuals with clinical disorders (Kotov et al., 2010), as well as those with MS (Davenport et al., 2024a) and EMS (Marschall et al., 2021). For example, in a meta-analysis of 77 studies examining personality and cognitive factors associated with depressive and anxiety symptoms in MS (Davenport et al., 2024a), neuroticism was found to have a large effect on both depression and anxiety, which exceeded the effects of the other Big Five traits. In contrast, a review with comparable aims but in EMS (Davenport et al., 2024b) identified only 13 studies, with just one examining the relationship between neuroticism and depressive symptoms (Marschall et al., 2021), and none addressing anxiety. Marschal et al. (2021) reported that neuroticism accounted for over half of the variance in depressive symptoms in a sample of 120 individuals with EMS. Understanding the role of neuroticism in depressive and anxiety in MS and EMS is an important direction for future research. However, considering the stability of distal factors like neuroticism (Hampson & Goldberg, 2006), it is argued that research should focus on elucidating related, but modifiable cognitive factors which are proximally related to symptoms (Nolen-Hoeksema, 2011; Dalgeish et al., 2020). Proximal cognitive factors Cognitive factors refer to patterns of encoding and processing of information that directly influence psychological symptoms such as depression and anxiety (Nolen-Hoeksema & Watkins, 2011). They pertain to thoughts, ruminative narratives, beliefs, and assumptions about oneself, others and the world, which for the basis of Beck's negative cognitive triad (Beck et al., 1979). The relationships between anxiety sensitivity, intolerance of uncertainty, rumination, distress tolerance and neuroticism, depressive and anxiety symptoms have been extensively discussed in relation to non-medically ill populations (e.g., Mathews & MacLeod, 2005; Norton and Mehta, 2007; Norton et al., 2005; Paulus et al., 2015; 2016; Sexton et al., 2003). Anxiety sensitivity refers to a cognitive bias in which anxiety-related arousal cues are interpreted as signals of serious harm (McNally, 1994). Intolerance of uncertainty is characterized by the appraisal of uncertain situations as threatening and intolerable (Carleton et al., 2007; Koerner & Dugas, 2008). Rumination is characterized by a repetitive and passive focus on the symptoms, causes, and consequences of distress (Nolen-Hoeksema, 2008), while distress tolerance reflects an individual's perceived ability to endure negative emotional, cognitive, or physical states (Bardeen et al., 2013). Although these cognitive factors are intercorrelated (Carleton et al., 2007; Hong & Cheung, 2015), each account for unique variance in depressive and anxiety symptoms (Hong & Cheung, 2015). Increasing evidence from randomised controlled trials in non-medically ill populations further underscores the amenability of these factors. Cognitive therapies targeting rumination (Spinhoven et al., 2018), intolerance of uncertainty (Van der Heiden et al., 2012; Wilson et al., 2023), anxiety sensitivity (Fitzgerald et al., 2021), and distress tolerance (Sherman & Ehrenreich-May, 2020) have demonstrated concomitant reductions in both these cognitive vulnerabilities and depressive and anxiety symptoms. Previously described meta-analytic reviews conducted in MS (Davenport et al., 2024a) and EMS (Davenport et al., 2024b) reveal the scarcity and low quality of research on these cognitive factors in connection to depressive and anxiety symptoms. In Davenport et al. (2024a), a significant and large pooled effect of three studies indicated a strong relationship between rumination and depression in EMS (Davenport et al., 2024b). In the MS population (Davenport et al., 2024a), intolerance of uncertainty was not associated with depressive or anxiety symptoms, though the reliability of findings was undermined limited available data and large heterogeneity. Systematic reviews of the EMS and MS literature revealed no existing research on the effects of anxiety sensitivity or distress tolerance on depressive and anxiety symptoms. Furthermore, the prospective effects of any cognitive factors on depressive or anxiety symptoms remain unknown, with no longitudinal studies identified in either review. Future longitudinal research is required to develop an evidence-based model that describes the relationships between these cognitive factors and depressive and anxiety symptoms in MS and EMS, which can guide the development of targeted cognitive therapies. Moderators of proximal factors Nolen-Hoeksema and Watkins (2011) propose that biological and environmental moderators (e.g., stress) shape symptom trajectories by raising concerns that proximal transdiagnostic factors act upon, altering the reinforcement value of certain factors over others. Both MS and EMS are characterised by inherent uncertainty and threat (Alschuler & Beier, 2015), with EMS-related pain and MS-related gait disability disrupting identity, bodily control, and daily functioning (Cole et al., 2021; Courts et al., 2004). EMS-related pain (Lagana et al., 2017) and gait disability in MS (Butler et al., 2016; Hassan et al., 2023) are associated with increased depressive and anxiety symptoms, though their role in moderating the relationships between cognitive factors and depressive and anxiety symptoms remains unclear. Research suggests that EMS-related pain and MS gait disability intensify cognitive biases in threat perception, impairing the ability to experience the body as a source of calm and disrupting emotional regulation (Bruce & Arnott, 2009; Spinoni et al., 2024). EMS-related pain is highly distressing and difficult to tolerate (Young et al., 2015), often precipitating rumination about its origins (Denny et al., 2007) and increasing hypervigilance to bodily sensations, a core feature of anxiety sensitivity (Sayer-Jones & Sherman, 2023). While these cognitive strategies may provide an initial sense of symptom management and safety, they exacerbate depressive and anxiety over time (Zhao & Zhou, 2024). Cognitive theories of pain (Eccleston & Crombez, 1999; Chapman, 1978) suggest that illness symptoms, such as pain, disrupt attentional resources, redirecting focus toward understanding that its causes and consequences, thereby intensifying emotional distress. Misinterpreting ambiguous sensations as indicators of worsening pain in EMS (Pickup et al., 2023) and or progressing disability in MS (De Gier et al., 2024) is common. Fear of worsening MS-related disability, particularly the uncertainty of wheelchair dependence, is among the most distressing aspects of living with MS (Boeije & Janssens, 2004; Pearce & Meyer, 2020). As perceived severity increases and the anticipated timeline for wheelchair use shortens, individuals report heightened rumination, intrusive thoughts, and worsening depressive and anxiety symptoms, regardless of clinical disability status (Janssens et al., 2004; Malivoire et al., 2018). The current study The heuristic for developing transdiagnostic models of psychopathology (Nolen-Hoeksema & Watson, 2011), together with evidence from non-medically ill populations, supports the proposed relationships between neuroticism, cognitive factors (i.e., anxiety sensitivity, distress tolerance, rumination, intolerance of uncertainty), and depressive and anxiety symptoms depicted in Figure 1. However, these relationships remain largely unexplored in MS and EMS. This omission is significant given the centrality of these factors in cognitive models of depression and anxiety (Beck et al., 1979; Norton & Mehta, 2007; Norton et al., 2005; Paulus et al., 2015; 2016; Sexton et al., 2003) and the high rates of underdiagnosis and undertreatment of these symptoms in MS and EMS. This study aimed to evaluate the conceptual model proposed in Figure 1, within MS and EMS samples at two time points (T1: Baseline; T2: 6-months follow-up). Following cross-sectional evaluation of the model, longitudinal analyses examined whether baseline associations prospectively predicted later depressive and anxiety symptoms. Three hypotheses (HYP1-3) were formulated. First, it was hypothesised that neuroticism would predict increases in depressive and anxiety symptoms, mediated by maladaptive levels of cognitive factors (higher rumination, anxiety sensitivity, intolerance of uncertainty, and lower distress tolerance) (HYP1). In the longitudinal model, outcomes at T2 were adjusted for baseline symptom levels, permitting examination of prospective associations between baseline predictors and symptom outcomes. Secondly, it was hypothesised that higher pain severity in EMS and gait-disability in MS may act as moderators of the effects of cognitive factors on depressive and anxiety symptoms (HYP2). Considering the substantial parallels among these diseases (e.g., Hart et al., 2015; Shigesi et al., 2019), this study explored the plausibility of a common model among disease groups through structural invariance testing. It was hypothesised that models would be identified as structurally invariant (i.e., sharing a similar model structure), reflecting a shared psychological basis for depressive and anxiety symptoms (HYP3). Comparing findings across MS and EMS provides an opportunity understand the utility of treating depressive and anxiety symptoms in similar ways for individuals with one or both conditions. Further, findings contribute to knowledge regarding the unique and shared etiologies of depressive and anxiety symptoms in MS and EMS. Methods Participants and Procedure Participants were drawn from a larger cohort enrolled in the [INSTITUTION RESEARCH PROGRAM]. Ethical approval for this project was obtained from the [INSTITUTION ETHICS COMMITTEE] (Project Number: XXXXXXXX). Data were collected between August 2021 and 2023 via international advertisements distributed through multiple sclerosis, endometriosis, and cancer society websites, social media platforms, and [LOCAL PUBLIC HOSPITAL] noticeboard and the [INSITUTION UNDERGRADUATE RESEARCH PROGRAM]. Participants were invited complete an online survey hosted on the Qualtrics platform hosted at two time points: baseline (T1; Baseline) and time two (T2; 6-month follow-up). Informed consent was obtained from participants at both time points. The following inclusion applied: (1) individual had a self-report of a physician’s medical diagnosis of MS or EMS; (2) were at least 18-years old; (3) provided signed consent prior to participating in the study; and (3) could read and complete English language surveys. Individuals of all genders were eligible, acknowledging MS affects men, and recognising the presence of endometriosis among transmasculine individuals (Ferrando, 2022). Measured described below demonstrated good-to-excellent reliability and test-retest reliability across disease samples in the current study (Table S1). Outcome variables Depressive symptoms were measured using the 20-item scale Center for Epidemiologic Studies Depression Scale Revised (CESD-R-20; Eaton et al., 2014) and anxiety symptoms by the 7-item Generalised Anxiety Disorder scale (GAD-7; Spitzer et al., 2006). Respective scale items are summed to reflect global depression and anxiety scores, with higher scores reflecting higher symptomatology. The CESD-R and GAD-7 demonstrate adequate psychometric properties, including excellent concurrent validity when compared to scores to DSM-IV clinical criteria for major depressive disorder (Eaton et al., 2014) and generalised anxiety disorder (Terrill et al., 2015). Explanatory variables Neuroticism was measured by The Big Factor Inventory (BFI; John & Srivastava, 1999) 8-item neuroticism subscale. The following self-report measures were included to measure cognitive factors: anxiety sensitivity based on The 36-item Anxiety Sensitivity Index Revised (ASI-R; Taylor et al., 1998); intolerance of uncertainty based on the 27-item Intolerance of Uncertainty Scale (IUS; Freeston et al., 1994); distress tolerance based on the 15-item self-report Distress Tolerance Scale (DTS; Simons & Gaher, 2005); and rumination based on The 10-item Ruminative Response Scale (RRS; Treynor et al 2003). Higher scores reflected greater maladaptive cognitive processes, except for distress tolerance, which was scored per scale conventions such that lower scores indicated lower tolerance. Disease-related moderators The 19-item Pain Outcomes Questionnaire Short-Form (POQ-SF; Clark et al., 2003), established for pain populations, was used to assess pain severity and interference in the endometriosis group. The POQ yields a summed score from 0-190, with higher scores reflecting greater pain severity and interference. The Self-administered Expanded Disability Status Scale (SRDSS; Kaufmann et al., 2020) was used to assess MS-related gait disability. The SRDSS is frequently used as a proxy measure of overall MS-related disability when clinician-rated EDSS scores are not available or feasible, as in large-scale survey (Rodgers et al., 2021; Stanikic et al., 2022). Scores are derived from three mobility-related items and categorised according to established EDSS cut-points (≤3.5, 4–6.5, ≥7), with higher categories reflecting greater physical disability and reduced ambulation. The SRDSS demonstrates excellent accuracy and comparability to clinician-rated EDSS categories (Kaufmann et al., 2020). Data analytic strategy Analyses were conducted in R (R Core Team, 2013). The initial sample compromised 677 individuals. A subgroup of 49 individuals reported comorbid MS and endometriosis and were excluded, yielding a final analytic sample of 628 (MS T1, n= 229; T2, n= 134; EMS T1, n= 399; T2, n= 130). Although MS and endometriosis frequently co-occur (Shigesi et al., 2019), the elevated prevalence observed may also reflect the broader recruitment context of the parent study. Exploring how this multimorbidity impacts model parameters was not feasible due to limited sample size and was beyond the scope of this study. All other medical comorbidities were present at prevalence rates below 5%. Data inspection for missingness revealed <10% missing values at baseline across all groups (range: 0-9.98%). Little’s test (1988) confirmed that data were missing completely at random within all-time points and between groups for model variables ( p =.03). Missing data were handled using the Multiple Imputation by Chained Equations (MICE) technique with predictive mean matching, applied within subgroups across time points to preserve temporal dependencies and ensure precise imputation tailored to group characteristics (van Buuren, 2018). Convergence and density plots, and diagnostics (SMD.05) indicated stable estimates, strong observed-imputed agreement, and minimal bias. Although skewness and kurtosis were within acceptable limits, visual inspection suggested non-normal distributions, supporting the use of non-parametric methods. Two-tailed Spearman’s rho correlations were used to examine bivariate correlations between model variables, adjusted by gender, age, gait-disability (MS group), and pain (EMS group). Between-group differences on sociodemographic, clinical, and model variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables, with effect sizes reported as Hedges’ g . Categorical variables were analysed using Pearson’s chi-square tests, with effect sizes reported as Cramér’s V . The path model (Figure 1) was analysed using Full Information Maximum Likelihood Estimation, both cross-sectionally at T1 and longitudinally from T1-T2 to examine stable patterns and temporal dynamics. Gender and age were included as covariates to account for group differences. Covariate adjustment performs well compared against matching methods (Elze et al., 2017), which have been criticised for artificially constraining samples and reducing power (King & Nielsen, 2019). Gait-disability (MS group) and pain severity (EMS group) were evaluated as moderators of the relationships between cognitive factors and dependent variables. Standardised regression coefficients were reported to index effect sizes, and the proportion mediation statistic was included as a supplementary index. Robust standard error-based 95% confidence intervals were computed for direct and total effects, while indirect effects were evaluated using 95% Monte Carlo confidence intervals based on 20,000 draws (Preacher & Selig, 2012). Multi-group analyses were conducted to assess structural non-invariance across groups. A sequential model comparison approach was employed, where path constraints were progressively added to the initially unconstrained model until a significant worsening of fit was observed. Model fit was evaluated against established criteria for global fit indices: non-normed Tucker-Lewis Index (N[NFI] TLI; >.90 acceptable), Comparative Fit Index (CFI; >.90 acceptable), the Root Mean Square Error of Approximation (RMSEA; <.06), and the Standardised Root Mean Square Residual (SRMR; <.08) (Byrne, 2016; Hu & Bentler, 1999). Model non-invariance was determined by a significant Chi-square difference test (Δ X 2 , p <.05) and changes in fit indices, specifically ΔRMSEA ≥.15 or SRMR ≥ .025, supplemented by a change of ≥.01 in ΔCFI/ΔTLI (Chen, 2007). An a-priori power analysis using the pwr R package indicated that a minimum sample size of n = 123 was required to detect a medium effect (.30) in the planned moderated mediation outcome equation, assuming a two-tailed alpha of .05 and 80% power. Sample size recommendations for detecting moderated mediation effects vary widely, ranging from 50-900 (e.g., Bauer et al., 2006; Xu et al., 2024). Results Descriptive statistics and clinical characteristics for the final analytic samples are summarised in Table 1. Summary statistics for explanatory and outcome variables are provided in Table 2. Attrition rates were high at 6-month follow-up (EMS drop-out rate: 67.42%; MS: 41.48%), though consistent with previous longitudinal research in these populations (Gete et al., 2023; Weiland et al., 2018). Attrition analyses indicated largely comparable baseline characteristics between retained and non-retained participants, with only a small set of differences (Table S2). Notably, depressive and anxiety symptoms did not differ by retention in either group. The baseline MS sample comprised 229 neurologist-confirmed cases, predominantly relapsing–remitting phenotype (70.74%) with moderate gait disability (48.91%). The EMS sample included 399 gynaecologist-confirmed cases, most commonly presenting with cystic lesions within the ovary (endometriomas) (37.84%). Aside from one participant identifying outside the gender binary, all reported concordant sex and gender identity. Men were under-represented, consistent with rates of female predominance in MS (The Multiple Sclerosis International Federation, 2020) and EMS (Parazzini et al., 2020). The majority identified as Caucasian (T1: MS 80.79%; EMS 75.69%) and resided in Australia (47.16%; 88.63%). Group differences on study variables Several between-group differences were observed across sociodemographic, clinical, and explanatory variables, predominantly at T1 rather than T2 (Table 2). At T1, the EMS group reported significantly higher depressive and anxiety symptoms, with the largest effect observed for anxiety (hedges’ g = .43, p <.0001). These differences were not observed at T2, although the incidence of probable MDE (cut-off ≥16 CESD-R; [Eaton et al., 2014]) and GAD (cut-off ≥8 GAD score; [Terrill et al., 2015]) was elevated with small effect ( g = .14 and.15, p <.05, respectively). Further, the EMS group demonstrated greater maladaptive scores on explanatory variables at T1 (e.g., higher neuroticism, anxiety sensitivity, intolerance of uncertainty, rumination, and lower distress tolerance). Similar trends were observed at T2 but not reach significance. Attrition analyses suggested baseline differences between retained and non-retained participants (Table S2). In the EMS group, retained participants reported lower baseline intolerance of uncertainty, anxiety sensitivity, and pain severity. In the MS group, retained participants reported lower baseline intolerance of uncertainty, but no other differences were noted among model variables. Partial correlations among study variables Explanatory variables were moderately-to-strongly correlated with depressive and anxiety symptoms in expected directions, controlling for the effects of age, gender, pain (EMS group) and gait disability (MS group) (Figures S1-2). Model invariance across disease groups Structural non-invariance was observed in both cross-sectional and longitudinal modelling, justifying the separate reporting of path analyses for each group (Table S3). The unconstrained cross-sectional model (MC1), inclusive of interactions effects, produced fit indices converging on adequate fit [ χ 2 (40)= 62.84, p <.05, CFI= .98, RMSEA= .06, SRMR= .06, (N[NFI] TLI)= 1.00]. The chi-square test was significant [ χ 2 (40)= 62.84, p <.05], as is expected with smaller sample sizes. The unconstrained longitudinal model demonstrated adequate fit [ML1 χ 2 (60)= 175.99, p <.01, CFI= .85, RMSEA= .10, SRMR= .09 (N[NFI] TLI)= 1.00]. The exclusion of interaction effects in an unconstrained model (ML2) did not result in any consensus on significant differences in fit, suggesting they may not enhance the explanatory power of the hypothesised model. The partially unconstrained longitudinal model (ML3) demonstrated superior fit indices compared to an unconstrained model (ML2), however, the Chi-square difference test and most changes in fit indices were non-significant. When fully constrained models (MC4, ML4) were compared to partially constrained models where select paths were freely estimated to loosen the invariance assumption (MC3, ML3), models differed significantly across groups (MC4 vs MC3: Δ X 2 = 62.81, p <.0001, ΔCFI= -.03, ΔSRMR= .02; ML4 vs ML3: Δ X 2 = 19.19, p <.05, ΔCFI= -.04). Findings suggest that forcing equality constraints leads to worsening fit when attempting a single model for MS and EMS. Inspection of modification indices revealed significant differences in specific direct cross-sectional and longitudinal paths (see MC3: Figure 2; ML3; Figure 3). Initial unconstrained models (MC1, ML1) were reported due to alignment with the hypothesised model (Figure 1), however differences in the magnitude of direct effects between groups are also reported (MC3: Figure 2; ML3; Figure 3). Path Analysis Model pathways are illustrated in Figures 2-3. Complete regression information is provided in Tables S4-5. Cross-sectional model pathways across diseases. Controlling for age and gender, neuroticism had a large total effect on both depression ( ß =.36, 95% CI: .24, .47) and anxiety in the MS group ( ß =.56, 95% CI: .47, .65). Following the inclusion of cognitive mediators, the direct effect on depressive symptoms remained significant but attenuated ( ß =.12, 95% CI: .01, .23). Comparable total effects were observed in the EMS group for depression ( ß =.33, 95% CI: .23, .42) anxiety ( ß =.51, 95% CI: .41, .60), with smaller reductions in directs effects (depression ß =.22, 95% CI: .13, .33; anxiety ß =.39, 95% CI: .29, .48). Regarding hypothesis one, model findings provided partial support for mediation in the MS group, demonstrating that lower distress tolerance ( ß =.17, 95% CI: .02, .33) and higher rumination ( ß =.11, 95% CI: .07, .15) mediated the relationship between neuroticism and depression. Although indirect effects were small, mediators collectively accounted for 46% of the total effect. Lower distress tolerance ( ß =.05, 95% CI: .03, .09) and higher rumination ( ß =.05, 95% CI: .02, .08) mediated the relationship between neuroticism and anxiety symptoms, along with higher anxiety sensitivity ( ß =.05, 95% CI: .02, .08) and intolerance of uncertainty ( ß =.07, 95% CI: .03, .11). Cognitive mediators carried 29% of the total effect of neuroticism on anxiety. In the EMS group, higher rumination had a small but significant indirect effect in the relationship between neuroticism and depression ( ß =.05, 95% CI: .02, .09), accounting for 17% of the total effect. Higher intolerance of uncertainty ( ß =.03, 95% CI: .01, .06) and lower distress tolerance ( ß =.04, 95% CI: .02, .08) mediated the anxiety pathway, accounting for 14% of the total effect of neuroticism. Contrary to hypothesis two, there was no interaction effects identified for pain in EMS, or gait disability in MS. Visual inspection of interaction plots (Figures S3-18) suggested trends in the effects of increasing gait disability in the on the relationships between distress tolerance, intolerance of uncertainty, rumination, and depression in the MS group (Figures S8-10). Longitudinal model pathways across diseases. Against expectations, the total and direct effects of neuroticism on depression and anxiety were non-significant in the MS group. Regarding hypothesis one, no cognitive factors were found to mediate the depression pathway. In contrast, lower distress was found to have an indirect effect in the relationship between neuroticism and anxiety ( ß =.10, 95% CI: .04, .17) and accounted for 76% of the total effect. This pattern suggests that neuroticism primarily exerts its influence on anxiety through distress tolerance, though the overall effect is negligible. In the EMS group, neuroticism had significant total ( ß = .20, 95% CI: .05, .35) and direct effects on anxiety symptoms ( ß = .20, 95% CI: .04, .36). Neuroticism did not show a significant total effect on depressive symptoms, yet significant indirect effects were observed via distress tolerance (ML3: ß =-.05, 95% CI: -.12, -.03) and anxiety sensitivity (ML3: ß = -.04, 95% CI: -.08, -.01), though effects were small. Findings suggest inconsistent mediation (VanderWeele, 2015), with indirect effects opposing the non-significant direct effect of neuroticism on depressive symptoms (ML1: ß = .01, 95% CI: -.14, .16). Modification indices revealed that the direct effect between anxiety sensitivity and depressive symptoms in the EMS group was significantly different from that in the MS group, both in direction and magnitude. No cognitive factors significantly mediated the relationship between neuroticism and anxiety in the EMS group, also indicated by significant but highly similar direct and total effects. No interactions effects for pain (EMS group) or gait disability (MS group) were observed (Figures S19-34), providing no support for hypothesis two, which predicted moderated mediation. Table 1. Sociodemographic and clinical characteristics of participants with MS and EMS at T1 and T2, with between-group comparisons. T1 T2 MS (n= 229) EMS (n= 399) Test statistic p Effect size MS (n= 134) EMS (n= 130) Test statistic p Effect size Age (M±SD), median (IQR) 44.83 (12.97), 43 (19) 32.70 (7.56), 32 (10) -14.80 <.0001 -1.23 46.88 (12.35), 47.50 (18) 33.82 (7.46), 32 (11) -10.31 <.0001 -1.27 Gender (females, %) 178 (77.73%) 391 (97.99%) 119.84 <.0001 .23 109 (81.34%) 125 (96.15%) 13.51 <.01 .23 Employment (yes, %) 138 (60.26%) 342 (85.71%) 55.94 <.0001 .30 82 (61.19%) 96 (73.85%) 8.53 <.01 .18 Highest level of education (yes, %) 1.74 .42 - Primary 0 0 Secondary 29 (12.70%) 51 (12.78%) Certificate level 53 (23.14%) 75 (18.79%) Tertiary 147 (64.19%) 273 (68.42%) Years since formal diagnosis (M±SD), median (IQR) 9.71 (8.34), 6.83 (9.42) 4.34 (4.66), 3.25 (4.25) -8.53 <.0001 -.83 11.60 (8.94), 8.50 (12.20) 6.22 (7.14), 3.29 (6.73) -5.60 <.0001 -.71 MS relapse (yes, %) - - Current 41 (17.90%) 20 (14.93%) Preceding 12mths 125 (54.59%) 72 (53.73%) SRDSS categories ( n , %) - - ≤3.5 82 (35.81%) 30 (22.39%) 4-6.5 112 (48.91%) 85 (63.43%) ≥7 35 (15.28%) 19 (14.18%) Pain severity (M±SD), median (IQR) 88.30 (29.48), 87.80 (45.70) 96.32 (26.87), 94.00 (35) 3.01 <.01 .29 80.16 (30.93), 75.70 (44.80) 81.09 (24.98), 77.50 (31.80) . 05 .96 .03 MS phenotype (yes, %) - - RRMS 162 (70.74%) 93 (69.40%) Progressive MS 59 (25.76%) 39 (29.10%) Unsure 8 (3.49%) 2 (1.49%) Endometriosis phenotype (yes, %) - - DIE - 135 (33.83%) 50 (38.47%) Endometrioma(s) - 151 (37.84%) 39 (30%) Superficial Peritoneal Endometriosis - 70 (17.54%) 31 (23.84%) Abdominal wall endometriosis - 74 (18.54%) 34 (26.15%) Not sure - 127 (31.83%) 52 (40%) Other - 44 (11.03%) 23 (17.69%) Disease-modifying therapies for MS (yes, %) 156 (68.12%) - 114 (85.07%) - Receiving treatment for Endo (yes, %) a - 345 (86.37%) - 110 (84.62%) Depressive disorder (yes, %) 121 (52.84%) 217 (54.39%) .14 .71 34 (25.37%) 44 (33.85%) 1.95 .16 .06 Anxiety disorder (yes, %) 92 (40.17%) 234 (58.65%) 19.89 <.001 .18 39 (29.10%) 68 (50.31%) 14.74 <.001 .24 Comorbid conditions (yes, %) b 119 (51.97%) 253 (63.41%) 7.88 <.05 .11 57 (42.44%) 72 (55.38%) 4.36 <.05 .12 Note. (-) variable is not relevant to characterising the disease group or was not assessed at T2. In all inferential analyses, the MS group were used as the reference group for comparisons by disease type. One participant reported ‘Other’ gender, with no descriptor of gender identity in the accompanying open-ended question. Variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables, with effect sizes reported as Hedges’ g for significant effects. Categorical variables were analysed using Pearson’s chi-square tests, with effect sizes reported as Cramér’s V . Abbreviations: MS= Multiple sclerosis; SRDSS= Self-Administered Expanded Disability Scale; RRMS= relapsing-remitting multiple sclerosis; DIE= deep-infiltrating endometriosis. a Treatment refers to any medical or allied health intervention for the management of endometriosis. b See Table S6. for full list of physical and psychiatric comorbidities (excluding depressive or anxiety disorders). Table 2. Summary statistics for explanatory and outcome variables among participants with MS and EMS at T1 and T2, with between-group comparisons. T1 T2 MS (n= 229) EMS (n= 399) Test statistic p Effect size MS (n= 134) EMS (n= 130) Test statistic p Effect size Depressive symptoms (M±SD), median (range) 27.84 (16.30), 25 (25) 33 (17.50), 34 (28.80) 3.64 16 ( n , %) 158 (69%) 298 (35.60%) 2.53 .11 82 (61.19%) 96 (71.64%) 5.33 <.05 .14 Anxiety symptoms (M±SD), median (range) 7.66 (5.76), 7 (10) 10.10 (5.61), 10 (9) 5.18 8 a 101 (44.10%) 226 (56.65%) 9.98 <.05 .13 59 (44.03%) 78 (60%) 5.81 <.05 .15 Minimal 5 to <10 49 (21.40%) 90 (22.56%) 23 (17.16%) 31 (23.85%) Moderate ≥10 to <15 56 (24.45%) 112 (28.07%) 36 (26.87%) 37 (28.46%) Severe ≥15 29 (12.66%) 91 (%) 15 (11.19%) 34 (26.15%) Neuroticism (M±SD), median (range) 25 (7.03), 25 (9.25) 28.80 (5.80), 29 (8) 7.41 <.0001 .62 - - Intolerance of uncertainty (M±SD), median (range) 68.40 (22.50), 69.50 (33) 76.40 (20), 79 (30) 4.50 <.001 .38 58.40 (22.70), 55 (37) 71.80 (21), 72 (29.50) 4.78 .12 Distress tolerance (M±SD), median (range) 48.50 (14.40), 47 (22.50) 43.50 (12.10), 44 (19) -4.42 <.001 -.39 49.70 (14.30), 50 (20) 44.10 (12.80), 45 (17) -2.76 .33 Rumination (M±SD), median (range) 21.70 (6.40), 22 (9) 24.20 (5.85), 24 (8) 4.58 <.01 .40 21 (6.49), 20 (10) 23.40 (5.54), 23 (7.50) 2.41 .40 Anxiety sensitivity (M±SD), median (range) 49.40 (31.60), 53 (57) 61.10 (30.60), 65 (40) 4.46 <.001 .38 42 (29.10), 41 (44.50) 53.40 (29.50), 54 (44) 2.40 .40 Note. (-) variable is not relevant to characterising the disease group. Variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables with Hedges’ g effect sizes. Categorical variables were analysed using Pearson’s chi-square tests Cramér’s V effect sizes. Abbreviations: MS= Multiple sclerosis; EMS= Endometriosis; CESD-R= Center for Epidemiologic Studies Depression Scale Revised; GAD-7= Generalised Anxiety Disorder scale. a ‘Probable GAD’ category is not mutually exclusive as it is defined as >8, overlapping with mild-to-severe categories. Discussion This study aimed to examine the mediating roles of specific cognitive factors (rumination, anxiety sensitivity, intolerance of uncertainty, distress tolerance) in the relationships between neuroticism and depressive and anxiety symptoms in individuals with EMS or MS. Pathways were examined both cross-sectionally and prospectively within an integrated model, enabling differentiation between cognitive factors associated with symptoms at a single time point and those predictive of symptom trajectories over time. Additionally, clinical indicators of disease severity – pain severity in EMS and gait disability levels in MS – were explored as moderators of the effects of cognitive factors on depression and anxiety. Overall, findings provide partial support for the model outlined in Figure 1, and the heuristic for transdiagnostic cognitive models of psychopathology more broadly (Nolen-Hoeksema & Watson, 2011). Concerning mediation hypotheses, the most robust evidence was for distress tolerance as a potential transdiagnostic cognitive factor underlying the relationships between neuroticism and depression and anxiety. Rumination, anxiety sensitivity, and intolerance of uncertainty were also implicated, though with notable symptom-specific variations within and across disease groups and time points. Path findings are discussed in detail below. Contrary to the moderated mediation hypotheses, neither pain nor gait disability moderated factor-symptoms relationships. Structural invariance testing indicated that increased model complexity was necessary to account for differences in the effects of explanatory variables on dependent variables. However, aside from a distinct pathway in the EMS group – where the effects of neuroticism on high depressive symptoms were partially transmitted through lower baseline anxiety sensitivity levels – groups exhibited shared trait-factor and factor-symptom relationships, supporting the robustness of the hypothesised model. Cognitive mediators at a single time-point Lower distress tolerance mediated the cross-sectional relationships between neuroticism and anxiety in both groups, and between neuroticism and depressive symptoms in the MS group. Findings extend existing evidence for distress tolerance as a transdiagnostic cognitive factor in depression and anxiety beyond in non-medically ill populations (e.g., Allan et al., 2014; Li et al., 2023) to chronic inflammatory disease contexts. Further, higher rumination mediated the relationship between neuroticism and anxiety in MS and was a common cognitive mediator in depression pathways in both groups though with a more pronounced effect in the MS group. These findings are consistent with meta-analytic evidence linking rumination with depression in both MS (Davenport et al., 2024b) and EMS (Davenport et al., 2024a). Rumination is frequently observed in MS samples (e.g., Malivoire et al., 2018; Sauder et al., 2021) and is thought to partly arise from neurobiological changes affecting emotion regulation (e.g., lesion load in the amygdala-prefrontal tracts), especially in individuals with clinical levels of depressive symptoms (Meyer-Arndt, 2022). Intolerance of uncertainty was a common mediator in anxiety pathways across both groups, alongside higher anxiety sensitivity in the MS group. These findings replicate prior work identifying anxiety sensitivity as a predictor of anxiety in MS (Yazar et al., 2021) and support broader evidence linking intolerance associations and anxiety sensitivity with depression in MS (Alschuler et al., 2021; Khatibi et al., 2020; Fahy, 2023). Moreover, the observed mediation patterns are consistent with literature in non-medically ill populations, demonstrating that the relationships between neuroticism and depressive and anxiety symptoms are partly explained by intolerance of uncertainty, anxiety sensitivity, and rumination (Clarke & Kiropoulos, 2021; Paulus et al., 2015; Norton & Mehta, 2007; Merino et al., 2016). The direct effects of several cognitive factors on depressive and anxiety symptoms remained significant after controlling for neuroticism, suggesting the influence unexamined distal factors. Adverse childhood experiences (ACEs) represent one such candidate. A body of evidence suggests that ACEs may shape maladaptive cognitive processes implicated in depression and anxiety, including intolerance of uncertainty, rumination, and emotion regulation difficulties in non-medically ill populations (Mares et al., 2023; Sarin et al., 2010; Watkins, 2009), as well as negatively framed illness appraisals among individuals with asthma (Traino et al., 2023). Given the high prevalence of ACEs in individuals with MS (Polick et al., 2022) and EMS (Harris et al., 2018), and their associations with disease onset and severity (Liebermann et al., 2018; Pust et al., 2020), examining ACEs as distal determinants of cognitive pathways represents an important direction for future research. The predictive value of distress tolerance as a transdiagnostic process The principal longitudinal findings implicated distress tolerance in prospective symptom trajectories. The longitudinal association between lower distress tolerance and increased anxiety symptoms in the MS group is consistent with findings from non-medically ill populations (e.g., Hashoul Andary et al., 2016; Lin et al., 2018). Similarly, the mediating role of distress tolerance in the association between neuroticism and anxiety symptoms is supported by prior research in non-medically ill populations (Ranney et al., 2022). Against expectations, the direct effects of neuroticism on depressive symptoms in both groups and on anxiety symptoms in MS were non-significant, suggesting that distress tolerance may enhance the predictive validity of neuroticism on symptoms (MacKinnon et al., 2000). Attrition analyses provide support for the stability of longitudinal findings for distress tolerance in MS, with no significant baseline differences between retained and non-retained participants on this cognitive factor. By contrast, the absence of a prospective indirect effect via intolerance of uncertainty may reflect selective retention, with lower baseline intolerance of uncertainty in the retained MS sample potentially attenuating this pathway. The direct effect of neuroticism on anxiety symptoms in the EMS group remained significant and comparable in magnitude to the total effect, highlighting the need for exploration of this pathway and its underlying processes. The longitudinal model showed a negative indirect effect of neuroticism on later depressive symptoms through distress tolerance, alongside a positive prospective path from baseline distress tolerance to follow-up depression. The directionality of this effect diverges from the cross-sectional pattern observed in the EMS group and is inconsistent with prior longitudinal mediation findings in non-medically ill populations (Ranney et al., 2022). Considering the adjustment for baseline depressive symptoms, this pattern is unlikely to reflect simple symptom continuity and is more consistent with inconsistent mediation, suggesting that distress tolerance may capture a more complex process once shared variance with other cognitive factors is accounted for. One explanation for the prospective association between higher distress tolerance and depressive symptoms in the EMS group is that distress tolerance may reflect an acquired capacity to endure distress, rather than adaptive emotional functioning. This aligns the concept of distress over-tolerance (Lynch & Mizon, 2011), which is increasingly conceptualised as a mechanism underpinning experience avoidance strategies, including non-suicidal self-injury (NSSI) (Chung et al., 2025). While such strategies are often enacted to alleviate negative affect in the short term (Zvolensky et al., 2010), they confer longer term risk for depressive symptoms (Chapman et al., 2006; Anestis & Joiner, 2012; Faura Garcia et al., 2023). This interpretation may be particularly relevant in EMS, where the chronicity of pain, diagnostic delay, and ongoing symptom management demands may reinforce avoidant coping (Williams et al., 2024). Consistent with this, women with EMS, particularly those experiencing chronic pain, demonstrate greater reliance on avoidant coping strategies that facilitate emotional suppression relative to non-medically ill populations (Thomas et al., 2006). Moreover, rates of NSSI are elevated in this population and are strongly associated with depressive symptoms (Estes et al., 2021). Future research should distinguish between adaptive distress tolerance and endurance-based forms of distress persistence and examine whether coping strategies moderate associations with depressive symptoms over time in the EMS population. Mediation effects independent of pain and gait disability Neither pain in the EMS group nor gait disability levels in the MS group moderated the effects of cognitive factors on depressive or anxiety symptoms. Cognitive factors were moderately-to-strongly associated with depressive and anxiety symptoms, while gait disability and pain levels were weakly associated or non-significant. This aligns with broader findings that psychological and cognitive factors account for unique variance in depressive and anxiety symptoms beyond the effects of over and above the pain intensity in EMS (Facchin et al., 2017) or disability in MS (Podda et al., 2020). Cognitive-affective theories of pain (Eccleston & Crombez, 1999; Chapman, 1978) suggest that the novelty of a threat, such as pain, compared to one’s prior experiences and knowledge of its onset, determines its impact on cognitive biases and distress. Thus, in the contexts where pain in EMS and increasing gait disability in MS are expected, the impact of these variables on cognitive factors and depressive and anxiety symptoms may be reduced. Limitations Several limitations warrant consideration. Firstly, substantial attrition reduced the samples available for longitudinal modelling, constraining power and likely limiting sensitivity to detect smaller indirect and moderated mediation effects, while also increasing uncertainty around parameter estimates. This is particularly relevant to the EMS group in which retained and non-retained participants varied on several characteristics. Notably, attrition rates are comparable to prior research in these populations (Gete et al., 2023; Weiland et al., 2018) and should be considered in the context of the coronavirus pandemic – a period associated with increased respondent burden and lowered response rates (De Koning et al., 2021). Secondly, although a subgroup of participants reported comorbid MS and EMS, the sample size was insufficient to examine multimorbidity as a distinct analytic subgroup. Future research should consider the cumulative allostatic load of these two chronic conditions, including the effects of multimorbidity on cognitive factors, and depressive and anxiety symptoms. Finally, while a two-wave design was appropriate for examining prospective associations, future research would benefit from multi-wave designs to test bidirectional influences between cognitive factors and symptoms, including autoregressive and cross-lagged paths. Clinical and theoretical implications Present findings highlight the potential clinical value of targeting distress tolerance to reduce depressive and anxiety symptoms in individuals with MS and EMS. Interest in the role of distress tolerance in depression and anxiety is paralleled by the dissemination of cognitive therapies designed to promote it, namely dialectical behaviour therapy (DBT; Linehan, 1993). Meta-analytic evidence confirms the efficacy of DBT for reducing depression and anxiety in non-medically ill samples (Delaquis et al., 2022). In MS, however, evidence remains limited; a scoping review identified only two randomised controlled trials, both demonstrating improvements in depressive and anxiety symptoms (Blair et al., 2017; Sepehri et al., 2017; Zarotti et al., 2023). Although DBT has not yet been evaluated in EMS, qualitative evidence suggests it may offer benefits for both the physical and psychological sequelae of the condition (Dowding et al., 2024). Findings also suggest potential utility in targeting rumination, anxiety sensitivity, and intolerance of uncertainty to reduce depressive and anxiety symptoms, particularly for short-term symptom reduction. This aligns with interventional evidence demonstrating that cognitive therapies focused on anxiety sensitivity (Fitzgerald et al., 2021) and intolerance of uncertainty (Wilson et al., 2023) produce reductions in depression and anxiety, though these effects are rarely maintained long-term. Similarly, cognitive behavioural approaches targeting rumination (e.g., Spinhoven et al., 2018) may reduce depressive symptoms in EMS and confer transdiagnostic benefits across both depressive and anxiety symptoms in MS. To the authors’ knowledge, this study is among the first to translate transdiagnostic frameworks to MS and EMS, examining neuroticism and core cognitive factors implicated in depression and anxiety. Findings partially support Nolen-Hoeksema and Watson’s (2011) heuristic, with the model performing well in capturing the underlying patterns and relationships across both groups. While a distinct pathway involving anxiety sensitivity and depressive symptoms was observed in the EMS group, and some effects varied in magnitude, groups exhibited substantial shared trait-factor and factor-symptom relationships. This suggests a shared psychological background underlying depressive and anxiety symptoms, extending an emerging evidence-base of biological parallels between these conditions into the psychological domain. Conclusion Findings provide partial support for the model outlined, and the heuristic for transdiagnostic cognitive models of psychopathology more broadly (Nolen-Hoeksema & Watson, 2011). The most robust evidence was for distress tolerance as a potential transdiagnostic cognitive factor underlying the relationships between neuroticism and depression and anxiety, highlighting potential clinical utility in examining therapeutic modalities targeting distress tolerance. Rumination, anxiety sensitivity, and intolerance of uncertainty were also implicated, with notable symptom-specific variations within and across disease groups and time points. Pain severity in the EMS group and gait disability levels in the MS group were not found to moderate the factor-symptom relationships. Structural non-invariance was detected between groups at each time-point, though groups exhibited substantial shared trait-factor and factor-symptom relationships. Declarations Author contributions LEAD AUTHOR NAME (Conceptualization-Lead, Data curation- Lead, Formal analysis- Lead, Investigation- Lead, Methodology- Lead, Visualization- Lead, Funding acquisition-Supporting, Writing–original draft- Lead, Project administration- Lead, Writing–review & editing- Lead). CO-AUTHOR NAME (Conceptualisation-Equal, Investigation-Supporting, Methodology-Supporting, Visualization-Supporting, Validation-Equal, Writing–review & editing-Supporting, Supervision-Supporting). CO-AUTHOR NAME (Conceptualisation-Equal, Investigation-Supporting, Methodology- Supporting, Supervision-Lead, Visualization-Supporting, Validation-Equal, Writing–original draft-Supporting, Writing–review & editing-Supporting, Funding Acquisition-Lead). Funding statement The first author was supported by [RESEARCH FUNDING INSTITUTION]; and the [GOVERNMENT FUNDING BODY] while undertaking this work. Funding sources had no direct involvement in the conduct of the research and/or preparation of the article. This research was partly funded by [UNIVERSITY DEPARTMENT NAME] research incentives grant (recipient CO-AUTHOR 2). Conflicts of interest None of the authors have conflicts of interest. Data availability De-identified data related to the results of this study can be requested from the corresponding author upon reasonable request. A data sharing agreement will be developed with the corresponding author and researchers. Compliance with Ethical Standards Ethical approval was obtained from the [UNIVERSITY ETHICS COMMITTEE] (Project No.XXX). Data were collected between August 2021 and 2023 via international advertisements distributed through multiple sclerosis, endometriosis, and cancer society websites, social media platforms, and [LOCAL PUBLIC HOSPITAL] noticeboard and the [UNIVERSITY RESEARCH PROGRAM]. Participants completed an online survey hosted on the Qualtrics platform. 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Current Directions in Psychological Science, 19 (6), 406-410. https://doi.org/10.1177/0963721410388642 Additional Declarations No competing interests reported. Supplementary Files FigureS1.Heatmap.docx.docx FigureS2.Heatmap.docx.docx FiguresS334.Interactionplots.docx.docx TableS1.Reliabilitycoefficients.docx.docx TableS2.Attritionanalyses.docx TableS3.Invariancetesting.docx TableS4.MSpaths.docx TableS5.EMSpaths.docx TableS6.Comorbidities.docx 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9195106","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612069349,"identity":"499067b9-5679-4d8a-8a66-7f526c5d7a31","order_by":0,"name":"Rebekah Allison Davenport","email":"data:image/png;base64,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","orcid":"","institution":"University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Rebekah","middleName":"Allison","lastName":"Davenport","suffix":""},{"id":612069350,"identity":"06066240-f028-4dc9-96c4-ec5fffa1f2c6","order_by":1,"name":"Isabel Krug","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Krug","suffix":""},{"id":612069351,"identity":"305e1d4c-91dc-453d-8266-3c804c8c18cb","order_by":2,"name":"Litza Kiropoulos","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Litza","middleName":"","lastName":"Kiropoulos","suffix":""}],"badges":[],"createdAt":"2026-03-23 03:39:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9195106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9195106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105780158,"identity":"afb54dce-394b-412c-9eb3-ea811d7831e6","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103869,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of depression and anxiety, super-imposed on the heuristic for cognitive models of psychopathology by Nolen-Hoeksema and Watkins (2011). According to this model, neuroticism (distal factor) contributes to depression and anxiety partially through mediating proximal cognitive factors (anxiety sensitivity, intolerance of uncertainty, distress tolerance, rumination). Pain in EMS, and gait disability in MS are proposed as moderators of the effects of cognitive factors on depressive and anxiety symptom outcomes. \u003cem\u003eNote: \u003c/em\u003ePlausible direct relationships between neuroticism and depression and anxiety, and correlations among cognitive factors, are not presented here for ease of interpretation.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/dac1bf2995edbd11741aa21b.png"},{"id":105904297,"identity":"05888388-0578-4bf1-868f-104c70c7bc1e","added_by":"auto","created_at":"2026-04-01 10:07:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128962,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts estimates of tested cross-sectional unconstrained model inclusive of interaction effects across samples (MC1), separated by disease type (MS vs. EMS).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Values to the left side (first) of divided forward slashes indicate coefficients for the MS sample, and values to the right represent values for the EMS sample. Dashed line indicates non-significant interaction effects between all mediators and pain severity (EMS) or gait disability level (MS) and depression and anxiety outcomes. Bold type indicates significant effects (i.e., 95% confidence intervals not containing zero). (*) indicates paths in the partially unconstrained model (MC3) with significantly larger coefficients in one group compared to the other.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/36ee421a0ca895f196616f43.png"},{"id":105780161,"identity":"408be7a9-ed91-4d51-92a6-7b8b2636a20b","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127199,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts estimates of tested longitudinal unconstrained model inclusive of interaction effects across samples (ML1), separated by disease type (MS vs. EMS).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Values to the left side (first) of divided forward slashes indicate coefficients for the MS sample, and values to the right represent values for the EMS sample. Dashed line indicates non-significant effects. Bold type indicates significant effects (i.e., 95% confidence intervals not containing zero). (*) indicates paths in the partially unconstrained model (ML3) with significantly larger coefficients in one group compared to the other.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/d3b44d9b2a722f2001161828.png"},{"id":106093006,"identity":"a163707a-9920-4f29-8992-717e7cb986de","added_by":"auto","created_at":"2026-04-03 11:32:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522117,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/573bc25c-4046-45c8-9197-6eff6194dd2e.pdf"},{"id":105904388,"identity":"997f8737-260c-4474-86fb-a67f5553555e","added_by":"auto","created_at":"2026-04-01 10:07:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":127250,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.Heatmap.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/ba55af055a81878246734b28.docx"},{"id":105780160,"identity":"c4ee9db0-4976-40f8-a9e9-f731a206d6e3","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":148977,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.Heatmap.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/a9b21fdd6877ae536c66baab.docx"},{"id":105904450,"identity":"9bde8554-fe5f-4ec9-98e6-d05c2b9779cd","added_by":"auto","created_at":"2026-04-01 10:08:41","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1246772,"visible":true,"origin":"","legend":"","description":"","filename":"FiguresS334.Interactionplots.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/79241468f0137dcee2f1e9ab.docx"},{"id":105780163,"identity":"b4825fdc-d678-4f1a-a833-8fbfe67b40a4","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17713,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.Reliabilitycoefficients.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/0cec55f490297d27e2d32391.docx"},{"id":105904237,"identity":"a61478d7-a8c8-4808-9715-6a0b5821f39b","added_by":"auto","created_at":"2026-04-01 10:06:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":26080,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.Attritionanalyses.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/ec9c328cf6ff8c6f8f722fee.docx"},{"id":105780165,"identity":"5172019f-1f08-4515-b726-e06e03b686ef","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17894,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.Invariancetesting.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/00c840130f1a19d1be95a36e.docx"},{"id":105904185,"identity":"ac474997-20c4-4a9e-99e5-e6c39f2980f4","added_by":"auto","created_at":"2026-04-01 10:05:55","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":30315,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.MSpaths.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/3737980d77d81f3fee3652b6.docx"},{"id":105780167,"identity":"b75f710e-8767-4314-b5ca-5a52dd034405","added_by":"auto","created_at":"2026-03-31 04:44:01","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":28032,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.EMSpaths.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/73c781b2355c6b351f97d536.docx"},{"id":105904557,"identity":"75e860a0-86b9-49af-a9d1-d53685ccfbfe","added_by":"auto","created_at":"2026-04-01 10:09:31","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":20791,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.Comorbidities.docx","url":"https://assets-eu.researchsquare.com/files/rs-9195106/v1/c2425a4fd2deebc41375d061.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Longitudinal Transdiagnostic Cognitive Model of Depression and Anxiety in Chronic Inflammatory Disease: Evidence from Multiple Sclerosis and Endometriosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) and endometriosis (EMS) are chronic inflammatory diseases and the most prevalent conditions affecting young adults in neurology and gynecology, respectively. MS and EMS commonly co-occur (Shigesi et al., 2019), with women diagnosed with EMS reported to have a 7-fold greater prevalence for MS (Sinaii et al., 2002) and a 20-60% increased risk compared to female controls (Nielsen et al., 2011). Further, MS and EMS share substantial parallels (Hart et al., 2015), including a common immune background (e.g., Katiyar et al., 2018; Zizolfi et al., 2023). Depressive and anxiety symptoms are significantly elevated in both diseases compared to the general population, and rank among the most common comorbidities within these groups (Feinstein et al., 2004; Verket et al., 2018). Depression and anxiety are predictive of self-directed violence (i.e., suicide, self-inflicted injury) in EMS (Estes, et al., 2021), poor quality of life in MS (Marrie et al., 2015), and are associated with negative prognostic factors, including fatigue and pain in EMS (Lagan\u0026agrave; et al., 2017; Ramin-wright et al., 2018) and MS (O\u0026rsquo;Connor et al., 2008; Strober \u0026amp; Arnett, 2005), as well as MS disability and relapse (Bruce et al., 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile treatment guidelines for MS (Marchesi et al., 2022) and EMS (Mardon et al., 2022) emphasise the importance of addressing depression and anxiety as part of standard care, symptoms remain underdiagnosed and undertreated (Carbone et al., 2021; Marrie et al., 2009). This clinical problem is compounded by the absence of an evidence-based framework explaining depression and anxiety in either disease population, along with a lack of specific cognitive therapies for treating these symptoms in MS (Sesel et al., 2018; Gromisch et al., 2018) and EMS (Evans et al., 2019). Understanding the personality and cognitive factors that contribute to depression and anxiety in MS and EMS is essential for developing evidence-based models to guide tailored cognitive therapies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA body of evidence suggests that depression and anxiety fit into a broader category of internalising disorders (e.g., Caspi et al., 2014; Wright et al., 2013), underpinned by common personality and cognitive factors (referred to as \u003cem\u003e\u0026lsquo;transdiagnostic\u0026nbsp;factors\u0026rsquo;\u003c/em\u003e). Nolen-Hoeksema and Watkins (2011) constructed a heuristic for developing cognitive models, outlining potential factors underlying the co-occurrence of depression and anxiety. They propose that symptoms arise from (a) distal individual characteristic factors (e.g., personality traits); (b) proximal cognitive factors that mediate the influence of distal factors; and (c) biological or environmental circumstances (e.g., stress, illness) which moderate proximal factors to produce common or specific symptom expressions (Nolen-Hoeksema \u0026amp; Watkins, 2011). Research conducted in community samples (Norton \u0026amp; Mehta, 2007; Sexton et al., 2003) and in individuals with depressive and anxiety disorders (Norton et al., 2005; Paulus et al., 2015; 2016), (referred hereafter as\u003cem\u003e\u0026nbsp;\u0026lsquo;non-medically ill populations\u0026rsquo;\u003c/em\u003e) provides evidence for the relationships between personality, cognitive factors, and depression and anxiety symptoms. Limited research exists on these relationships in MS and EMS (e.g., see systematic reviews by Davenport et al., 2024a; 2024b), and no studies to date have explored these factors within an explanatory model or utilised longitudinal data to clarify the nature and direction of these relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNeuroticism as a distal factor\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNolen-Hoeksema and Watkins (2011) describe distal factors as stable characteristics that shape beliefs and thought processes, thereby creating the conditions for psychopathology. Neuroticism represents one of the most robust predictors of depression and anxiety, accounting for large variance in symptoms in individuals with clinical disorders (Kotov et al., 2010), as well as those with MS (Davenport et al., 2024a) and EMS (Marschall et al., 2021). For example, in a meta-analysis of 77 studies examining personality and cognitive factors associated with depressive and anxiety symptoms in MS (Davenport et al., 2024a), neuroticism was found to have a large effect on both depression and anxiety, which exceeded the effects of the other Big Five traits. In contrast, a review with comparable aims but in EMS (Davenport et al., 2024b) identified only 13 studies, with just one examining the relationship between neuroticism and depressive symptoms (Marschall et al., 2021), and none addressing anxiety. Marschal et al. (2021) reported that neuroticism accounted for over half of the variance in depressive symptoms in a sample of 120 individuals with EMS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnderstanding the role of neuroticism in depressive and anxiety in MS and EMS is an important direction for future research. However, considering the stability of distal factors like neuroticism (Hampson \u0026amp; Goldberg, 2006), it is argued that research should focus on elucidating related, but modifiable cognitive factors which are proximally related to symptoms (Nolen-Hoeksema, 2011; Dalgeish et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProximal cognitive factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive factors refer to patterns of encoding and processing of information that directly influence psychological symptoms such as depression and anxiety (Nolen-Hoeksema \u0026amp; Watkins, 2011). They pertain to thoughts, ruminative narratives, beliefs, and assumptions about oneself, others and the world, which for the basis of Beck\u0026apos;s negative cognitive triad (Beck et al., 1979). The relationships between anxiety sensitivity, intolerance of uncertainty, rumination, distress tolerance and neuroticism, depressive and anxiety symptoms have been extensively discussed in relation to non-medically ill populations (e.g., Mathews \u0026amp; MacLeod, 2005; Norton and Mehta, 2007; Norton et al., 2005; Paulus et al., 2015; 2016; Sexton et al., 2003). Anxiety sensitivity refers to a cognitive bias in which anxiety-related arousal cues are interpreted as signals of serious harm (McNally, 1994). Intolerance of uncertainty is characterized by the appraisal of uncertain situations as threatening and intolerable (Carleton et al., 2007; Koerner \u0026amp; Dugas, 2008). Rumination is characterized by a repetitive and passive focus on the symptoms, causes, and consequences of distress (Nolen-Hoeksema, 2008), while distress tolerance reflects an individual\u0026apos;s perceived ability to endure negative emotional, cognitive, or physical states (Bardeen et al., 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough these cognitive factors are intercorrelated (Carleton et al., 2007; Hong \u0026amp; Cheung, 2015), each account for unique variance in depressive and anxiety symptoms (Hong \u0026amp; Cheung, 2015). Increasing evidence from randomised controlled trials in non-medically ill populations further underscores the amenability of these factors. Cognitive therapies targeting rumination (Spinhoven et al., 2018), intolerance of uncertainty (Van der Heiden et al., 2012; Wilson et al., 2023), anxiety sensitivity (Fitzgerald et al., 2021), and distress tolerance (Sherman \u0026amp; Ehrenreich-May, 2020) have demonstrated concomitant reductions in both these cognitive vulnerabilities and depressive and anxiety symptoms.\u003c/p\u003e\n\u003cp\u003ePreviously described meta-analytic reviews conducted in MS (Davenport et al., 2024a) and EMS (Davenport et al., 2024b) reveal the scarcity and low quality of research on these cognitive factors in connection to depressive and anxiety symptoms. In Davenport et al. (2024a), a significant and large pooled effect of three studies indicated a strong relationship between rumination and depression in EMS (Davenport et al., 2024b). In the MS population (Davenport et al., 2024a), intolerance of uncertainty was not associated with depressive or anxiety symptoms, though the reliability of findings was undermined limited available data and large heterogeneity. Systematic reviews of the EMS and MS literature revealed no existing research on the effects of anxiety sensitivity or distress tolerance on depressive and anxiety symptoms. Furthermore, the prospective effects of any cognitive factors on depressive or anxiety symptoms remain unknown, with no longitudinal studies identified in either review. Future longitudinal research is required to develop an evidence-based model that describes the relationships between these cognitive factors and depressive and anxiety symptoms in MS and EMS, which can guide the development of targeted cognitive therapies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModerators of proximal factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNolen-Hoeksema and Watkins (2011) propose that biological and environmental moderators (e.g., stress) shape symptom trajectories by raising concerns that proximal transdiagnostic factors act upon, altering the reinforcement value of certain factors over others. Both MS and EMS are characterised by inherent uncertainty and threat (Alschuler \u0026amp; Beier, 2015), with EMS-related pain and MS-related gait disability disrupting identity, bodily control, and daily functioning (Cole et al., 2021; Courts et al., 2004). EMS-related pain (Lagana et al., 2017) and gait disability in MS (Butler et al., 2016; Hassan et al., 2023) are associated with increased depressive and anxiety symptoms, though their role in moderating the relationships between cognitive factors and depressive and anxiety symptoms remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch suggests that EMS-related pain and MS gait disability intensify cognitive biases in threat perception, impairing the ability to experience the body as a source of calm and disrupting emotional regulation (Bruce \u0026amp; Arnott, 2009; Spinoni et al., 2024). EMS-related pain is highly distressing and difficult to tolerate (Young et al., 2015), often precipitating rumination about its origins (Denny et al., 2007) and increasing hypervigilance to bodily sensations, a core feature of anxiety sensitivity (Sayer-Jones \u0026amp; Sherman, 2023). While these cognitive strategies may provide an initial sense of symptom management and safety, they exacerbate depressive and anxiety over time (Zhao \u0026amp; Zhou, 2024). Cognitive theories of pain (Eccleston \u0026amp; Crombez, 1999; Chapman, 1978) suggest that illness symptoms, such as pain, disrupt attentional resources, redirecting focus toward understanding that its causes and consequences, thereby intensifying emotional distress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMisinterpreting ambiguous sensations as indicators of worsening pain in EMS (Pickup et al., 2023) and or progressing disability in MS (De Gier et al., 2024) is common. Fear of worsening MS-related disability, particularly the uncertainty of wheelchair dependence, is among the most distressing aspects of living with MS (Boeije \u0026amp; Janssens, 2004; Pearce \u0026amp; Meyer, 2020). As perceived severity increases and the anticipated timeline for wheelchair use shortens, individuals report heightened rumination, intrusive thoughts, and worsening depressive and anxiety symptoms, regardless of clinical disability status (Janssens et al., 2004; Malivoire et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe current study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heuristic for developing transdiagnostic models of psychopathology (Nolen-Hoeksema \u0026amp; Watson, 2011), together with evidence from non-medically ill populations, supports the proposed relationships between neuroticism, cognitive factors (i.e., anxiety sensitivity, distress tolerance, rumination, intolerance of uncertainty), and depressive and anxiety symptoms depicted in Figure 1. However, these relationships remain largely unexplored in MS and EMS. This omission is significant given the centrality of these factors in cognitive models of depression and anxiety (Beck et al., 1979; Norton \u0026amp; Mehta, 2007; Norton et al., 2005; Paulus et al., 2015; 2016; Sexton et al., 2003) and the high rates of underdiagnosis and undertreatment of these symptoms in MS and EMS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the conceptual model proposed in Figure 1, within MS and EMS samples at two time points (T1: Baseline; T2: 6-months follow-up). Following cross-sectional evaluation of the model, longitudinal analyses examined whether baseline associations prospectively predicted later depressive and anxiety symptoms. Three hypotheses (HYP1-3) were formulated. First, it was hypothesised that neuroticism would predict increases in depressive and anxiety symptoms, mediated by maladaptive levels of cognitive factors (higher rumination, anxiety sensitivity, intolerance of uncertainty, and lower distress tolerance) (HYP1). In the longitudinal model, outcomes at T2 were adjusted for baseline symptom levels, permitting examination of prospective associations between baseline predictors and symptom outcomes. Secondly, it was hypothesised that higher pain severity in EMS and gait-disability in MS may act as moderators of the effects of cognitive factors on depressive and anxiety symptoms (HYP2).\u003c/p\u003e\n\u003cp\u003eConsidering the substantial parallels among these diseases (e.g., Hart et al., 2015; Shigesi et al., 2019), this study explored the plausibility of a common model among disease groups through structural invariance testing. It was hypothesised that models would be identified as structurally invariant (i.e., sharing a similar model structure), reflecting a shared psychological basis for depressive and anxiety symptoms (HYP3). Comparing findings across MS and EMS provides an opportunity understand the utility of treating depressive and anxiety symptoms in similar ways for individuals with one or both conditions. Further, findings contribute to knowledge regarding the unique and shared etiologies of depressive and anxiety symptoms in MS and EMS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants and Procedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were drawn from a larger cohort enrolled in the [INSTITUTION RESEARCH PROGRAM]. Ethical approval for this project was obtained from the [INSTITUTION ETHICS COMMITTEE]\u0026nbsp;(Project Number: XXXXXXXX). Data were collected between August 2021 and 2023 via international advertisements distributed through multiple sclerosis, endometriosis, and cancer society websites, social media platforms, and [LOCAL PUBLIC HOSPITAL] noticeboard and the [INSITUTION UNDERGRADUATE RESEARCH PROGRAM].\u0026nbsp;Participants were invited complete an online survey hosted on the Qualtrics platform hosted at two time points: baseline (T1; Baseline) and time two (T2; 6-month follow-up).\u0026nbsp;Informed consent was obtained from participants at both time points.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following inclusion applied: (1) individual had a self-report of a physician\u0026rsquo;s medical diagnosis of MS or EMS; (2) were at least 18-years old; (3) provided signed consent prior to participating in the study; and (3) could read and complete English language surveys. Individuals of all genders were eligible, acknowledging MS affects men, and recognising the presence of endometriosis among transmasculine individuals (Ferrando, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasured described below demonstrated good-to-excellent\u0026nbsp;reliability and test-retest reliability across disease samples in the current study (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOutcome variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were measured using the 20-item scale Center for Epidemiologic Studies Depression Scale Revised (CESD-R-20; Eaton et al., 2014) and anxiety symptoms by the 7-item Generalised Anxiety Disorder scale (GAD-7;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSpitzer et al., 2006). Respective scale items are summed to reflect global depression and anxiety scores, with higher scores reflecting higher symptomatology. The CESD-R and GAD-7 demonstrate adequate psychometric properties, including excellent concurrent validity when compared to scores to DSM-IV clinical criteria for major depressive disorder (Eaton et al., 2014) and generalised anxiety disorder (Terrill et al., 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExplanatory variables\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuroticism was measured by The Big Factor Inventory (BFI; John \u0026amp; Srivastava, 1999) 8-item neuroticism subscale. The following self-report measures were included to measure cognitive factors: anxiety sensitivity based on The 36-item Anxiety Sensitivity Index Revised (ASI-R; Taylor et al., 1998); intolerance of uncertainty based on the 27-item Intolerance of Uncertainty Scale (IUS; Freeston et al., 1994); distress tolerance based on the 15-item self-report Distress Tolerance Scale (DTS; Simons \u0026amp; Gaher, 2005); and rumination based on The 10-item Ruminative Response Scale (RRS; Treynor et al 2003). Higher scores reflected greater maladaptive cognitive processes, except for distress tolerance, which was scored per scale conventions such that lower scores indicated lower tolerance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDisease-related moderators\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 19-item Pain Outcomes Questionnaire Short-Form (POQ-SF; Clark et al., 2003), established for pain populations, was used to assess pain severity and interference in the endometriosis group. The POQ yields a summed score from 0-190, with higher scores reflecting greater pain severity and interference.\u0026nbsp;The Self-administered Expanded Disability Status Scale (SRDSS; Kaufmann et al., 2020) was used to assess MS-related gait disability. The SRDSS is frequently used as a proxy measure of overall MS-related disability when clinician-rated EDSS scores are not available or feasible, as in large-scale survey (Rodgers et al., 2021; Stanikic et al., 2022).\u0026nbsp;Scores are derived from three mobility-related items and categorised according to established EDSS cut-points (\u0026le;3.5, 4\u0026ndash;6.5, \u0026ge;7), with higher categories reflecting greater physical disability and reduced ambulation.\u0026nbsp;The SRDSS demonstrates excellent accuracy and comparability to clinician-rated EDSS categories (Kaufmann et al., 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analytic strategy\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted in R (R Core Team, 2013). The initial sample compromised 677 individuals. A subgroup of 49 individuals reported comorbid MS and endometriosis and were excluded, yielding a final analytic sample of 628 (MS T1,\u0026nbsp;\u003cem\u003en=\u003c/em\u003e229; T2, \u003cem\u003en=\u003c/em\u003e134; EMS T1, \u003cem\u003en=\u003c/em\u003e399; T2, \u003cem\u003en=\u003c/em\u003e130).\u0026nbsp;Although MS and endometriosis frequently co-occur (Shigesi et al., 2019), the elevated prevalence observed may also reflect the broader recruitment context of the parent study.\u0026nbsp;Exploring how this multimorbidity impacts model parameters was not feasible due to limited sample size and was beyond the scope of this study.\u0026nbsp;All other medical comorbidities were present at prevalence rates below 5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData inspection for missingness revealed \u0026lt;10% missing values at baseline across all groups (range: 0-9.98%). Little\u0026rsquo;s test (1988) confirmed that data were missing completely at random within all-time points and between groups for model variables (\u003cem\u003ep\u003c/em\u003e=.03). Missing data were handled using the Multiple Imputation by Chained Equations (MICE) technique with predictive mean matching, applied within subgroups across time points to preserve temporal dependencies and ensure precise imputation tailored to group characteristics\u0026nbsp;(van Buuren, 2018). Convergence and density plots, and diagnostics (SMD\u0026lt;.02, Kolmogorov-Smirnov Test \u003cem\u003ep\u003c/em\u003e\u0026gt;.05) indicated stable estimates, strong observed-imputed agreement, and minimal bias. Although skewness and kurtosis were within acceptable limits, visual inspection suggested non-normal distributions, supporting the use of non-parametric methods.\u003c/p\u003e\n\u003cp\u003eTwo-tailed Spearman\u0026rsquo;s rho correlations were used to examine bivariate correlations between\u0026nbsp;model variables, adjusted by gender, age, gait-disability (MS group), and pain (EMS group). Between-group differences on sociodemographic, clinical, and model variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables, with effect sizes reported as Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e. Categorical variables were analysed using Pearson\u0026rsquo;s chi-square tests, with effect sizes reported as Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe path model (Figure 1) was analysed using Full Information Maximum Likelihood Estimation, both cross-sectionally at T1 and longitudinally from T1-T2 to examine stable patterns and temporal dynamics. Gender and age were included as covariates to account for group differences. Covariate adjustment performs well compared against matching methods (Elze et al., 2017), which have been criticised for artificially constraining samples and reducing power (King \u0026amp; Nielsen, 2019). Gait-disability (MS group) and pain severity (EMS group) were evaluated as moderators of the relationships between cognitive factors and dependent variables.\u0026nbsp;Standardised regression coefficients were reported to index effect sizes, and the proportion mediation statistic was\u0026nbsp;included as a supplementary index.\u0026nbsp;Robust standard error-based 95% confidence intervals were computed for direct and total effects, while indirect effects were evaluated using 95% Monte Carlo confidence intervals based on 20,000 draws (Preacher \u0026amp; Selig, 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMulti-group analyses were conducted to assess structural non-invariance across groups. A sequential model comparison approach was employed, where path constraints were progressively added to the initially unconstrained model until a significant worsening of fit was observed. Model fit was evaluated against established criteria for global fit indices: non-normed Tucker-Lewis Index (N[NFI] TLI; \u0026gt;.90 acceptable), Comparative Fit Index (CFI; \u0026gt;.90 acceptable), the Root Mean Square Error of Approximation (RMSEA; \u0026lt;.06), and the Standardised Root Mean Square Residual (SRMR; \u0026lt;.08) (Byrne, 2016; Hu \u0026amp; Bentler, 1999). Model non-invariance was determined by a significant Chi-square difference test (\u0026Delta;\u003csub\u003eX\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05) and changes in fit indices, specifically \u0026Delta;RMSEA \u0026ge;.15 or SRMR \u0026ge; .025, supplemented by a change of \u0026ge;.01 in \u0026Delta;CFI/\u0026Delta;TLI (Chen, 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn a-priori power analysis using the pwr \u003cem\u003eR\u003c/em\u003e package indicated that a minimum sample size of \u003cem\u003en\u003c/em\u003e= 123 was required to detect a medium effect (.30) in the planned moderated mediation outcome equation, assuming a two-tailed alpha of .05 and 80% power. Sample size recommendations for detecting moderated mediation effects vary widely, ranging from 50-900 (e.g., Bauer et al., 2006; Xu et al., 2024).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics and clinical characteristics for the final analytic samples are summarised in Table 1. Summary statistics for explanatory and outcome variables are provided in Table 2. Attrition rates were high at 6-month follow-up (EMS drop-out rate: 67.42%; MS: 41.48%), though consistent with previous longitudinal research in these populations (Gete et al., 2023; Weiland et al., 2018). Attrition analyses indicated largely comparable baseline characteristics between retained and non-retained participants, with only a small set of differences (Table S2). Notably, depressive and anxiety symptoms did not differ by retention in either group.\u003c/p\u003e\n\u003cp\u003eThe baseline MS sample comprised 229 neurologist-confirmed cases, predominantly relapsing\u0026ndash;remitting phenotype (70.74%) with moderate gait disability (48.91%). The EMS sample included 399 gynaecologist-confirmed cases, most commonly presenting with cystic lesions within the ovary (endometriomas) (37.84%). Aside from one participant identifying outside the gender binary, all reported concordant sex and gender identity. Men were under-represented, consistent with rates of female predominance in MS (The Multiple Sclerosis International Federation, 2020) and EMS (Parazzini et al., 2020). The majority identified as Caucasian (T1: MS 80.79%; EMS 75.69%) and resided in Australia (47.16%; 88.63%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup differences on study variables\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral between-group differences were observed across sociodemographic, clinical, and explanatory variables, predominantly at T1 rather than T2 (Table 2). At T1, the EMS group reported significantly higher depressive and anxiety symptoms, with the largest effect observed for anxiety (hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e= .43, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001). These differences were not observed at T2, although the incidence of probable MDE (cut-off \u0026ge;16 CESD-R; [Eaton et al., 2014]) and GAD (cut-off \u0026ge;8 GAD score; [Terrill et al., 2015]) was elevated with small effect (\u003cem\u003eg\u003c/em\u003e= .14 and.15, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05, respectively). Further, the EMS group demonstrated greater maladaptive scores on explanatory variables at T1 (e.g., higher neuroticism, anxiety sensitivity, intolerance of uncertainty, rumination, and lower distress tolerance). Similar trends were observed at T2 but not reach significance. Attrition analyses suggested baseline differences between retained and non-retained participants (Table S2). In the EMS group, retained participants reported lower baseline intolerance of uncertainty, anxiety sensitivity, and pain severity. In the MS group, retained participants reported lower baseline intolerance of uncertainty, but no other differences were noted among model variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePartial correlations among study variables\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExplanatory variables were moderately-to-strongly correlated with depressive and anxiety symptoms in expected directions, controlling for the effects of age, gender, pain (EMS group) and gait disability (MS group) (Figures S1-2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel invariance across disease groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural non-invariance was observed in both cross-sectional and longitudinal modelling, justifying the separate reporting of path analyses for each group (Table S3). The unconstrained cross-sectional model (MC1), inclusive of interactions effects, produced fit indices converging on adequate fit [\u003cem\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e(40)= 62.84, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05, CFI= .98, RMSEA= .06, SRMR= .06,\u0026nbsp;(N[NFI] TLI)= 1.00]. The chi-square test was significant\u0026nbsp;[\u003cem\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e(40)= 62.84, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05], as is expected with smaller sample sizes. The unconstrained longitudinal model demonstrated adequate fit [ML1\u0026nbsp;\u003cem\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e(60)= 175.99, \u003cem\u003ep\u003c/em\u003e\u0026lt;.01, CFI= .85, RMSEA= .10, SRMR= .09\u0026nbsp;(N[NFI] TLI)= 1.00]. The exclusion of interaction effects in an unconstrained model (ML2) did not result in any consensus on significant differences in fit,\u0026nbsp;suggesting they may not enhance the explanatory power of the hypothesised model.\u0026nbsp;The partially unconstrained longitudinal model (ML3) demonstrated superior fit indices compared to an unconstrained model (ML2), however, the Chi-square difference test and most changes in fit indices were non-significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen fully constrained models (MC4, ML4) were compared to partially constrained models where select paths were freely estimated to loosen the invariance assumption (MC3, ML3), models differed significantly across groups (MC4 vs MC3:\u0026nbsp;\u0026Delta;\u003csub\u003eX\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e= 62.81, \u003cem\u003ep\u003c/em\u003e\u0026lt;.0001, \u0026Delta;CFI= -.03, \u0026Delta;SRMR= .02; ML4 vs ML3: \u0026Delta;\u003csub\u003eX\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e= 19.19, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05, \u0026Delta;CFI= -.04). Findings suggest that forcing equality constraints leads to worsening fit when attempting a single model for MS and EMS. Inspection of modification indices revealed significant differences in specific direct cross-sectional and longitudinal paths (see MC3: Figure 2; ML3; Figure 3). Initial unconstrained models (MC1, ML1) were reported due to alignment with the hypothesised model (Figure 1), however\u0026nbsp;differences in the magnitude of direct effects between groups are also reported (MC3: Figure 2; ML3; Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePath Analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel pathways are illustrated in Figures 2-3. Complete regression information is provided in Tables S4-5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCross-sectional model pathways across diseases.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eControlling for age and gender, neuroticism had a large total effect on both depression (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.36, 95% CI: .24, .47) and anxiety in the MS group (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.56, 95% CI: \u0026nbsp;.47, .65). Following the inclusion of cognitive mediators, the direct effect on depressive symptoms remained significant but attenuated (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.12, 95% CI: .01, .23). Comparable total effects were observed in the EMS group for depression (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.33, 95% CI: .23, .42) anxiety (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.51, 95% CI: .41, .60), with smaller reductions in directs effects (depression \u003cem\u003e\u0026szlig;\u003c/em\u003e=.22, 95% CI: .13, .33; anxiety \u003cem\u003e\u0026szlig;\u003c/em\u003e=.39, 95% CI: .29, .48).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding hypothesis one, model\u0026nbsp;findings provided partial support for mediation in the MS group, demonstrating that lower distress tolerance (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.17, 95% CI: .02, .33) and higher rumination (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.11, 95% CI: .07, .15) mediated the relationship between neuroticism and depression. Although indirect effects were small, mediators collectively accounted for 46% of the total effect.\u0026nbsp;Lower distress tolerance (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.05, 95% CI: .03, .09) and higher rumination (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.05, 95% CI: .02, .08) mediated the relationship between neuroticism and anxiety symptoms, along with higher anxiety sensitivity (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.05, 95% CI: .02, .08) and intolerance of uncertainty (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.07, 95% CI: .03, .11). Cognitive mediators carried 29% of the total effect of neuroticism on anxiety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the EMS group, higher rumination had a small but significant indirect effect in the relationship between neuroticism and depression (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.05, 95% CI: .02, .09), accounting for 17% of the total effect. Higher intolerance of uncertainty (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.03, 95% CI: .01, .06) and lower distress tolerance (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.04, 95% CI: .02, .08) mediated the anxiety pathway, accounting for 14% of the total effect of neuroticism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContrary to hypothesis two, there was no\u0026nbsp;interaction effects identified for pain in EMS, or gait disability in MS. Visual inspection of interaction plots (Figures S3-18) suggested trends in the effects of increasing gait disability in the on the relationships between distress tolerance, intolerance of uncertainty, rumination, and depression in the MS group (Figures S8-10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLongitudinal model pathways across diseases.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgainst expectations, the total and direct effects of neuroticism on depression and anxiety were non-significant in the MS group. Regarding hypothesis one, no cognitive factors were found to mediate the depression pathway. In contrast, lower distress was found to have an indirect effect in the relationship between neuroticism and anxiety (\u003cem\u003e\u0026szlig;\u003c/em\u003e=.10, 95% CI: .04, .17) and accounted for 76% of the total effect. This pattern suggests that neuroticism primarily exerts its influence on anxiety through distress tolerance, though the overall effect is negligible.\u003c/p\u003e\n\u003cp\u003eIn the EMS group, neuroticism had significant total\u0026nbsp;(\u003cem\u003e\u0026szlig;\u003c/em\u003e= .20, 95% CI: .05, .35)\u0026nbsp;and direct effects on anxiety symptoms\u0026nbsp;(\u003cem\u003e\u0026szlig;\u003c/em\u003e= .20, 95% CI: .04, .36). Neuroticism did not show a significant total effect on depressive symptoms, yet significant indirect effects were observed via distress tolerance (ML3: \u003cem\u003e\u0026szlig;\u003c/em\u003e=-.05, 95% CI: -.12, -.03) and anxiety sensitivity (ML3: \u003cem\u003e\u0026szlig;\u003c/em\u003e= -.04, 95% CI: -.08, -.01), though effects were small. Findings suggest inconsistent mediation (VanderWeele, 2015), with indirect effects opposing the non-significant direct effect of neuroticism on depressive symptoms (ML1: \u003cem\u003e\u0026szlig;\u003c/em\u003e= .01, 95% CI: -.14, .16). Modification indices revealed that the direct effect between anxiety sensitivity and depressive symptoms in the EMS group was significantly different from that in the MS group, both in direction and magnitude. No cognitive factors significantly mediated the relationship between neuroticism and anxiety in the EMS group, also indicated by significant but highly similar direct and total effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo interactions effects for pain (EMS group) or gait disability (MS group) were observed (Figures S19-34), providing no support for hypothesis two, which predicted moderated mediation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sociodemographic and clinical characteristics of participants with MS and EMS at T1 and T2, with between-group comparisons.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"907\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 36.4939%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 35.3914%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003eMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003eEMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003eTest statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003eMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003eEMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003eTest statistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eAge (M\u0026plusmn;SD), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e44.83 (12.97), 43 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e32.70 (7.56), 32 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;-14.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e-1.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e46.88 (12.35), 47.50 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e33.82 (7.46), 32 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e-10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eGender (females, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e178 (77.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e391 (97.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e119.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e109 (81.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e125 (96.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eEmployment (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e138 (60.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e342 (85.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e55.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e82 (61.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e96 (73.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eHighest level of education (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e29 (12.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e51 (12.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eCertificate level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e53 (23.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e75 (18.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e147 (64.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e273 (68.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eYears since formal diagnosis (M\u0026plusmn;SD), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e9.71 (8.34), 6.83 (9.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e4.34 (4.66), 3.25 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e-8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e-.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e11.60 (8.94), 8.50 (12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e6.22 (7.14), 3.29 (6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;-5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e-.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eMS relapse (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e41 (17.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e20 (14.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003ePreceding 12mths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e125 (54.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e72 (53.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eSRDSS categories (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003e\u0026le;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e82 (35.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e30 (22.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003e4-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e112 (48.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e85 (63.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003e\u0026ge;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e35 (15.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e19 (14.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003ePain severity (M\u0026plusmn;SD), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e88.30 (29.48), 87.80 (45.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e96.32 (26.87), 94.00 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e80.16 (30.93), 75.70 (44.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e81.09 (24.98), 77.50 (31.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u003cem\u003e.\u003c/em\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eMS phenotype (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eRRMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e162 (70.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e93 (69.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eProgressive MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e59 (25.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e39 (29.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eUnsure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e8 (3.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e2 (1.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eEndometriosis phenotype (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eDIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e135 (33.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e50 (38.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eEndometrioma(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e151 (37.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e39 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eSuperficial Peritoneal Endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e70 (17.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e31 (23.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eAbdominal wall endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e74 (18.54%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e34 (26.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eNot sure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e127 (31.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e52 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e44 (11.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e23 (17.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eDisease-modifying therapies for MS (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e156 (68.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e114 (85.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eReceiving treatment for Endo (yes, %)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e345 (86.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e110 (84.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eDepressive disorder (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e121 (52.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e217 (54.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e34 (25.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e44 (33.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eAnxiety disorder (yes, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e92 (40.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e234 (58.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e39 (29.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e68 (50.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e14.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7508%;\"\u003e\n \u003cp\u003eComorbid conditions (yes, %)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4741%;\"\u003e\n \u003cp\u003e119 (51.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e253 (63.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e57 (42.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4664%;\"\u003e\n \u003cp\u003e72 (55.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.37155%;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e\u0026lt;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.18192%;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e (-) variable is not relevant to characterising the disease group or was not assessed at T2.\u0026nbsp;In all inferential analyses, the MS group were used as the reference group for comparisons by disease type.\u0026nbsp;One participant reported \u0026lsquo;Other\u0026rsquo; gender, with no descriptor of gender identity in the accompanying open-ended question. Variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables, with effect sizes reported as Hedges\u0026rsquo; \u003cem\u003eg\u0026nbsp;\u003c/em\u003efor significant effects. Categorical variables were analysed using Pearson\u0026rsquo;s chi-square tests, with effect sizes reported as Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e. Abbreviations: MS=\u003cem\u003e\u0026nbsp;\u003c/em\u003eMultiple sclerosis; SRDSS= Self-Administered Expanded Disability Scale; RRMS= relapsing-remitting multiple sclerosis; DIE= deep-infiltrating endometriosis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eTreatment refers to any medical or allied health intervention for the management of endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eSee Table S6. for full list of physical and psychiatric comorbidities (excluding depressive or anxiety disorders).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSummary statistics for explanatory and outcome variables among participants with MS and EMS at T1 and T2, with between-group comparisons.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"888\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 38.8514%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 26.0135%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003eMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003eEMS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003eTest statistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003cp\u003e(n= 134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003eEMS\u003c/p\u003e\n \u003cp\u003e(n= 130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003eTest statistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eDepressive symptoms (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e27.84 (16.30), 25 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e33 (17.50), 34 (28.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e19.60 (14.70), 16 (23.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e27.80 (17.40), 22 (24.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eCESD-R Probable MDE \u0026gt;16 (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e158 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e298 (35.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e82 (61.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e96 (71.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026lt;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eAnxiety symptoms (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e7.66 (5.76), 7 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e10.10 (5.61), 10 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e6.82 (5.26), 6 (8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e10.10 (5.59), 10 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eGAD-7 severity categories (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eProbable GAD \u0026gt;8\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e101 (44.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e226 (56.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e59 (44.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e78 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026lt;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eMinimal \u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e80 (34.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e63 (15.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e49 (36.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e20 (15.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eMild \u0026gt;5 to \u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e49 (21.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e90 (22.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e23 (17.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e31 (23.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eModerate \u0026ge;10 to \u0026lt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e56 (24.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e112 (28.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e36 (26.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e37 (28.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u0026ge;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e29 (12.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e91 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e15 (11.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e34 (26.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eNeuroticism (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e25 (7.03), 25 (9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e28.80 (5.80), 29 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eIntolerance of uncertainty (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e68.40 (22.50), 69.50 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e76.40 (20), 79 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e58.40 (22.70), 55 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e71.80 (21), 72 (29.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eDistress tolerance (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e48.50 (14.40), 47 (22.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e43.50 (12.10), 44 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e-4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e-.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e49.70 (14.30), 50 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e44.10 (12.80), 45 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e-2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eRumination (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e21.70 (6.40), 22 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e24.20 (5.85), 24 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e21 (6.49), 20 (10)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e23.40 (5.54), 23 (7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0045%;\"\u003e\n \u003cp\u003eAnxiety sensitivity (M\u0026plusmn;SD), median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7117%;\"\u003e\n \u003cp\u003e49.40 (31.60), 53 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7252%;\"\u003e\n \u003cp\u003e61.10 (30.60), 65 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.57207%;\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.29279%;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10.5856%;\"\u003e\n \u003cp\u003e42 (29.10), 41 (44.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6982%;\"\u003e\n \u003cp\u003e53.40 (29.50), 54 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.27928%;\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.41892%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e (-) variable is not relevant to characterising the disease group. Variables were examined using pooled linear models with estimated marginal means and contrasts for continuous variables with Hedges\u0026rsquo; \u003cem\u003eg\u0026nbsp;\u003c/em\u003eeffect sizes. Categorical variables were analysed using Pearson\u0026rsquo;s chi-square tests Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u0026nbsp;\u003c/em\u003eeffect sizes. Abbreviations: MS= Multiple sclerosis; EMS= Endometriosis; CESD-R=\u0026nbsp;Center for Epidemiologic Studies Depression Scale Revised;\u0026nbsp;GAD-7=\u0026nbsp;Generalised Anxiety Disorder scale.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u0026lsquo;Probable GAD\u0026rsquo; category is not mutually exclusive as it is defined as \u0026gt;8, overlapping with mild-to-severe categories.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to examine the mediating roles of specific cognitive factors (rumination, anxiety sensitivity, intolerance of uncertainty, distress tolerance) in the relationships between neuroticism and depressive and anxiety symptoms in individuals with EMS or MS. Pathways were examined both cross-sectionally and prospectively within an integrated model, enabling differentiation between cognitive factors associated with symptoms at a single time point and those predictive of symptom trajectories over time. Additionally, clinical indicators of disease severity \u0026ndash; pain severity in EMS and gait disability levels in MS \u0026ndash; were explored as moderators of the effects of cognitive factors on depression and anxiety. Overall, findings provide partial support for the model outlined in Figure 1, and the heuristic for transdiagnostic cognitive models of psychopathology more broadly (Nolen-Hoeksema \u0026amp; Watson, 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcerning mediation hypotheses, the most robust evidence was for distress tolerance as a potential transdiagnostic cognitive factor underlying the relationships between neuroticism and depression and anxiety. Rumination, anxiety sensitivity, and intolerance of uncertainty were also implicated, though with notable symptom-specific variations within and across disease groups and time points.\u0026nbsp;Path findings are discussed in detail below. Contrary to the moderated mediation hypotheses, neither pain nor gait disability moderated factor-symptoms relationships. Structural invariance testing indicated that increased model complexity was necessary to account for differences in the effects of explanatory variables on dependent variables. However, aside from a distinct pathway in the EMS group \u0026ndash; where the effects of neuroticism on high depressive symptoms were partially transmitted through lower baseline anxiety sensitivity levels \u0026ndash; groups exhibited shared trait-factor and factor-symptom relationships, supporting the robustness of the hypothesised model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCognitive mediators at a single time-point \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower distress tolerance mediated the cross-sectional relationships between neuroticism and anxiety in both groups, and between neuroticism and depressive symptoms in the MS group. Findings extend existing evidence for distress tolerance as a transdiagnostic cognitive factor in depression and anxiety beyond in non-medically ill populations (e.g., Allan et al., 2014; Li et al., 2023) to chronic inflammatory disease contexts. Further, higher rumination mediated the relationship between neuroticism and anxiety in MS and was a common cognitive mediator in depression pathways in both groups though with a more pronounced effect in the MS group. These findings are consistent with meta-analytic evidence linking rumination with depression in both MS (Davenport et al., 2024b) and EMS (Davenport et al., 2024a). Rumination is frequently observed in MS samples (e.g., Malivoire et al., 2018; Sauder et al., 2021) and is thought to partly arise from neurobiological changes affecting emotion regulation (e.g., lesion load in the amygdala-prefrontal tracts), especially in individuals with clinical levels of depressive symptoms (Meyer-Arndt, 2022).\u003c/p\u003e\n\u003cp\u003eIntolerance of uncertainty was a common mediator in anxiety pathways across both groups, alongside higher anxiety sensitivity in the MS group. These findings replicate prior work identifying anxiety sensitivity as a predictor of anxiety in MS (Yazar et al., 2021) and support broader evidence linking intolerance associations and anxiety sensitivity with depression in MS (Alschuler et al., 2021; Khatibi et al., 2020; Fahy, 2023). Moreover, the observed mediation patterns are consistent with literature in non-medically ill populations, demonstrating that the relationships between neuroticism and depressive and anxiety symptoms are partly explained by intolerance of uncertainty, anxiety sensitivity, and rumination (Clarke \u0026amp; Kiropoulos, 2021; Paulus et al., 2015; Norton \u0026amp; Mehta, 2007; Merino et al., 2016).\u003c/p\u003e\n\u003cp\u003eThe direct effects of several cognitive factors on depressive and anxiety symptoms remained significant after controlling for neuroticism, suggesting the influence unexamined distal factors. Adverse childhood experiences (ACEs) represent one such candidate. A body of evidence suggests that ACEs may shape maladaptive cognitive processes implicated in depression and anxiety, including intolerance of uncertainty, rumination, and emotion regulation difficulties in non-medically ill populations (Mares et al., 2023; Sarin et al., 2010; Watkins, 2009), as well as negatively framed illness appraisals among individuals with asthma (Traino et al., 2023). Given the high prevalence of ACEs in individuals with MS (Polick et al., 2022) and EMS (Harris et al., 2018), and their associations with disease onset and severity (Liebermann et al., 2018; Pust et al., 2020), examining ACEs as distal determinants of cognitive pathways represents an important direction for future research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe predictive value of distress tolerance as a transdiagnostic process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal longitudinal findings implicated distress tolerance in prospective symptom trajectories. The longitudinal association between lower distress tolerance and increased anxiety symptoms in the MS group is consistent with findings from non-medically ill populations (e.g., Hashoul Andary et al., 2016; Lin et al., 2018). Similarly, the mediating role of distress tolerance in the association between neuroticism and anxiety symptoms is supported by prior research in non-medically ill populations (Ranney et al., 2022). Against expectations, the direct effects of neuroticism on depressive symptoms in both groups and on anxiety symptoms in MS were non-significant, suggesting that distress tolerance may enhance the predictive validity of neuroticism on symptoms (MacKinnon et al., 2000). Attrition analyses provide support for the stability of longitudinal findings for distress tolerance in MS, with no significant baseline differences between retained and non-retained participants on this cognitive factor. By contrast, the absence of a prospective indirect effect via intolerance of uncertainty may reflect selective retention, with lower baseline intolerance of uncertainty in the retained MS sample potentially attenuating this pathway.\u003c/p\u003e\n\u003cp\u003eThe direct effect of neuroticism on anxiety symptoms in the EMS group remained significant and comparable in magnitude to the total effect, highlighting the need for exploration of this pathway and its underlying processes. The longitudinal model showed a negative indirect effect of neuroticism on later depressive symptoms through distress tolerance, alongside a positive prospective path from baseline distress tolerance to follow-up depression. The directionality of this effect diverges from the cross-sectional pattern observed in the EMS group and is inconsistent with prior longitudinal mediation findings in non-medically ill populations (Ranney et al., 2022). Considering the adjustment for baseline depressive symptoms, this pattern is unlikely to reflect simple symptom continuity and is more consistent with inconsistent mediation, suggesting that distress tolerance may capture a more complex process once shared variance with other cognitive factors is accounted for.\u003c/p\u003e\n\u003cp\u003eOne explanation for the prospective association between higher distress tolerance and depressive symptoms in the EMS group is that distress tolerance may reflect an acquired capacity to endure distress, rather than adaptive emotional functioning. This aligns the concept of \u003cem\u003edistress over-tolerance\u003c/em\u003e (Lynch \u0026amp; Mizon, 2011), which is increasingly conceptualised as a mechanism underpinning experience avoidance strategies, including non-suicidal self-injury (NSSI) (Chung et al., 2025). While such strategies are often enacted to alleviate negative affect in the short term (Zvolensky et al., 2010), they confer longer term risk for depressive symptoms (Chapman et al., 2006; Anestis \u0026amp; Joiner, 2012; Faura Garcia et al., 2023). This interpretation may be particularly relevant in EMS, where the chronicity of pain, diagnostic delay, and ongoing symptom management demands may reinforce avoidant coping (Williams et al., 2024). Consistent with this, women with EMS, particularly those experiencing chronic pain, demonstrate greater reliance on avoidant coping strategies that facilitate emotional suppression relative to non-medically ill populations (Thomas et al., 2006). Moreover, rates of NSSI are elevated in this population and are strongly associated with depressive symptoms (Estes et al., 2021).\u0026nbsp;Future research should distinguish between adaptive distress tolerance and endurance-based forms of distress persistence and examine whether coping strategies moderate associations with depressive symptoms over time in the EMS population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMediation effects independent of pain and gait disability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeither pain in the EMS group nor gait disability levels in the MS group moderated the effects of cognitive factors on depressive or anxiety symptoms. Cognitive factors were moderately-to-strongly associated with depressive and anxiety symptoms, while gait disability and pain levels were weakly associated or non-significant. This aligns with broader findings that psychological and cognitive factors account for unique variance in depressive and anxiety symptoms beyond the effects of over and above the pain intensity in EMS (Facchin et al., 2017) or disability in MS (Podda et al., 2020). Cognitive-affective theories of pain (Eccleston \u0026amp; Crombez, 1999; Chapman, 1978) suggest that the novelty of a threat, such as pain, compared to one\u0026rsquo;s prior experiences and knowledge of its onset, determines its impact on cognitive biases and distress. Thus, in the contexts where pain in EMS and increasing gait disability in MS are expected, the impact of these variables on cognitive factors and depressive and anxiety symptoms may be reduced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. Firstly, substantial attrition reduced the samples available for longitudinal modelling, constraining power and likely limiting sensitivity to detect smaller indirect and moderated mediation effects, while also increasing uncertainty around parameter estimates.\u0026nbsp;This is particularly relevant to the EMS group in which retained and non-retained participants varied on several characteristics. Notably, attrition rates are comparable to prior research in these populations\u0026nbsp;(Gete et al., 2023; Weiland et al., 2018) and should be considered in the context of the coronavirus pandemic \u0026ndash; a period associated with\u0026nbsp;increased respondent burden and\u0026nbsp;lowered response rates (De Koning et al., 2021).\u0026nbsp;Secondly, although a subgroup of participants reported comorbid MS and EMS, the sample size was insufficient to examine multimorbidity as a distinct analytic subgroup.\u0026nbsp;Future research should consider the cumulative allostatic load of these two chronic conditions, including the effects of multimorbidity on cognitive factors, and depressive and anxiety symptoms.\u0026nbsp;Finally, while a two-wave design was appropriate for examining prospective associations, future research would benefit from multi-wave designs to test bidirectional influences between cognitive factors and symptoms, including autoregressive and cross-lagged paths.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical and theoretical implications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresent findings highlight the potential clinical value of targeting distress tolerance to reduce depressive and anxiety symptoms in individuals with MS and EMS.\u0026nbsp;Interest in the role of distress tolerance in depression and anxiety is paralleled by the dissemination of cognitive therapies designed to promote it, namely dialectical behaviour therapy (DBT; Linehan, 1993).\u0026nbsp;Meta-analytic evidence confirms the efficacy of DBT for reducing depression and anxiety in non-medically ill samples (Delaquis et al., 2022). In MS, however, evidence remains limited; a scoping review identified only two randomised controlled trials, both demonstrating improvements in depressive and anxiety symptoms (Blair et al., 2017; Sepehri et al., 2017; Zarotti et al., 2023). Although DBT has not yet been evaluated in EMS, qualitative evidence suggests it may offer benefits for both the physical and psychological sequelae of the condition (Dowding et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFindings also suggest potential utility in targeting rumination, anxiety sensitivity, and intolerance of uncertainty to reduce depressive and anxiety symptoms, particularly for short-term symptom reduction. This aligns with interventional evidence demonstrating that cognitive therapies focused on anxiety sensitivity (Fitzgerald et al., 2021) and intolerance of uncertainty (Wilson et al., 2023) produce reductions in depression and anxiety, though these effects are rarely maintained long-term. Similarly, cognitive behavioural approaches targeting rumination (e.g., Spinhoven et al., 2018) may reduce depressive symptoms in EMS and confer transdiagnostic benefits across both depressive and anxiety symptoms in MS.\u003c/p\u003e\n\u003cp\u003eTo the authors\u0026rsquo; knowledge, this study is among the first to translate transdiagnostic frameworks to MS and EMS, examining neuroticism and core cognitive factors implicated in depression and anxiety. Findings partially support Nolen-Hoeksema and Watson\u0026rsquo;s (2011) heuristic, with the model performing well in capturing the underlying patterns and relationships across both groups. While a distinct pathway involving anxiety sensitivity and depressive symptoms was observed in the EMS group, and some effects varied in magnitude, groups exhibited substantial shared trait-factor and factor-symptom relationships. This suggests a shared psychological background underlying depressive and anxiety symptoms, extending an emerging evidence-base of biological parallels between these conditions into the psychological domain.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFindings provide partial support for the model outlined, and the heuristic for transdiagnostic cognitive models of psychopathology more broadly (Nolen-Hoeksema \u0026amp; Watson, 2011). The most robust evidence was for distress tolerance as a potential transdiagnostic cognitive factor underlying the relationships between neuroticism and depression and anxiety, highlighting potential clinical utility in examining therapeutic modalities targeting distress tolerance. Rumination, anxiety sensitivity, and intolerance of uncertainty were also implicated, with notable symptom-specific variations within and across disease groups and time points. Pain severity in the EMS group and gait disability levels in the MS group were not found to moderate the factor-symptom relationships. Structural non-invariance was detected between groups at each time-point, though groups exhibited substantial shared trait-factor and factor-symptom relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLEAD AUTHOR NAME (Conceptualization-Lead, Data curation- Lead, Formal analysis- Lead, Investigation- Lead, Methodology- Lead, Visualization- Lead, Funding acquisition-Supporting, Writing–original draft- Lead, Project administration- Lead, Writing–review \u0026amp; editing- Lead).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCO-AUTHOR NAME (Conceptualisation-Equal, Investigation-Supporting, Methodology-Supporting, Visualization-Supporting, Validation-Equal, Writing–review \u0026amp; editing-Supporting, Supervision-Supporting).\u003c/p\u003e\n\u003cp\u003eCO-AUTHOR NAME (Conceptualisation-Equal, Investigation-Supporting, Methodology- Supporting, Supervision-Lead, Visualization-Supporting, Validation-Equal, Writing–original draft-Supporting, Writing–review \u0026amp; editing-Supporting, Funding Acquisition-Lead).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author was supported by [RESEARCH FUNDING INSTITUTION]; and the [GOVERNMENT FUNDING BODY] while undertaking this work. Funding sources had no direct involvement in the conduct of the research and/or preparation of the article. This research was partly funded by [UNIVERSITY DEPARTMENT NAME] research incentives grant (recipient CO-AUTHOR 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified data related to the results of this study can be requested from the corresponding author upon reasonable request. A data sharing agreement will be developed with the corresponding author and researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompliance with Ethical Standards\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the [UNIVERSITY ETHICS COMMITTEE] (Project No.XXX). Data were collected between August 2021 and 2023 via international advertisements distributed through multiple sclerosis, endometriosis, and cancer society websites, social media platforms, and [LOCAL PUBLIC HOSPITAL] noticeboard and the [UNIVERSITY RESEARCH PROGRAM]. Participants completed an online survey hosted on the Qualtrics platform. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllan, N. P., Macatee, R. J., Norr, A. M., \u0026amp; Schmidt, N. B. (2014). Direct Agresti and interactive effects of distress tolerance and anxiety sensitivity on generalized anxiety and depression. \u003cem\u003eCognitive Therapy and Research\u003c/em\u003e, 38, 530-540. https://doi.org/10.1007/s10608-014-9623-y \u003c/li\u003e\n\u003cli\u003eAlschuler, K. N., \u0026amp; Beier, M. L. (2015). 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Longitudinal relations between non-suicidal self-injury and both depression and anxiety among senior high school adolescents: a cross-lagged panel network analysis. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, e18134. https://doi.org/10.7717/peerj.18134\u003c/li\u003e\n\u003cli\u003eZvolensky, M. J., Vujanovic, A. A., Bernstein, A., \u0026amp; Leyro, T. (2010). Distress tolerance: Theory, measurement, and relations to psychopathology. \u003cem\u003eCurrent Directions in Psychological Science, 19\u003c/em\u003e(6), 406-410. https://doi.org/10.1177/0963721410388642 \u003c/li\u003e\n\u003c/ol\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":"Multiple sclerosis, Endometriosis, Transdiagnostic science, Personality, Cognitive mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-9195106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9195106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eClinical levels of depressive and anxiety symptoms are prevalent in Multiple Sclerosis (MS) and Endometriosis (EMS), yet their underlying mechanisms remain poorly understood. Guided by a transdiagnostic cognitive framework (Nolen-Hoeksema \u0026amp; Watkins, 2011), this study examined whether anxiety sensitivity, intolerance of uncertainty, distress tolerance, and rumination mediate the pathways between neuroticism and depressive and anxiety symptoms. Further, pain severity in EMS and gait-disability may represent contextual moderators of factor-symptom relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePopulation-based cohorts of individuals with MS (T1, baseline: \u003cem\u003en=\u003c/em\u003e229; T2, 6-month follow-up: \u003cem\u003en=\u003c/em\u003e134) and EMS (T1, \u003cem\u003en=\u003c/em\u003e399; T2, \u003cem\u003en=\u003c/em\u003e130) completed online surveys at two-time points. Adopting a path-analytic approach, the relationships between neuroticism, cognitive factors, and depressive and anxiety symptoms were evaluated cross-sectionally and prospectively within an integrated model. Gender and age were included as covariates. Pain severity in EMS, and gait disability in MS, were examined as moderating factors. Structural invariance testing explored the plausibility of a common model among diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCross-sectionally, distress tolerance and rumination emerged as the most consistent mediators, with the largest effect for distress tolerance in the relationship between neuroticism and depressive symptoms in the MS group (\u003cem\u003eß\u003c/em\u003e=.17, 95% CI: .02, .33). Anxiety sensitivity and intolerance of uncertainty demonstrated symptom- and disease-specific associations. Longitudinally, lower distress tolerance mediated the association between neuroticism and later anxiety symptoms in MS (\u003cem\u003eß\u003c/em\u003e=.10, 95% CI: .04, .17), whereas in EMS, distress tolerance and anxiety sensitivity showed small indirect effects in the depressive pathway. No interaction effects were detected. Although structural non-invariance was observed, substantial convergence in trait–factor and factor–symptom associations was evident across diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe principal finding identified distress tolerance as a transdiagnostic cognitive factor linking neuroticism with affective symptoms, underscoring its therapeutic relevance and suggesting shared psychological mechanisms across MS and EMS. Findings extend an emerging evidence-base of cross-disease parallels into the psychological domain.\u003c/p\u003e","manuscriptTitle":"A Longitudinal Transdiagnostic Cognitive Model of Depression and Anxiety in Chronic Inflammatory Disease: Evidence from Multiple Sclerosis and Endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 04:43:55","doi":"10.21203/rs.3.rs-9195106/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":"4da0eab9-2e25-4e3a-8185-5205e4df9b77","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T04:43:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 04:43:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9195106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9195106","identity":"rs-9195106","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}