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European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2026 May 12;17(1):2661294. doi: 10.1080/20008066.2026.2661294

Latent profiles of PTSD and CPTSD: the importance of emotional hypoactivation

Perfiles latentes de TEPT y TEPTc: la importancia de la hipoactivación emocional

Jordan Teysseyre a,CONTACT, Géraldine Tapia a,#,✉, Camille Raysséguier a,e, Lyna Chami b, Valérie Gissot c, Wissam El-Hage b,c,d,#
PMCID: PMC13169442  PMID: 42117427

ABSTRACT

Background: Complex posttraumatic stress disorder (CPTSD), introduced in ICD-11, is distinguished from posttraumatic stress disorder (PTSD) by persistent disturbances in self-organization (DSO). While categorical approaches are frequently applied, recent research indicates substantial symptom heterogeneity within posttraumatic stress disorders.

Aim: This study aimed to (a) identify latent profiles of posttraumatic symptomatology based on ICD-11 PTSD and DSO dimensions, and (b) examine their associations with a range of trauma exposure and psychological correlates among CPTSD and PTSD patients.

Method: A latent profile analysis was conducted on a mixed sample of clinical and non-clinical participants (N = 235) using the International Trauma Questionnaire. The resulting profiles were compared across indicators of trauma exposure and psychological correlates.

Results: Four latent profiles were identified: three clinical profiles (PTSD, CPTSD with high emotional hypoactivation, and CPTSD with low emotional hypoactivation) and one ‘Low Symptom’ profile. CPTSD profiles were characterised by more severe and varied symptomatology, particularly in the presence of pronounced hypoactivation.

Conclusion: These findings highlight the heterogeneity of posttraumatic symptom profiles and suggest that emotional hypoactivation may contribute to differences between CPTSD presentations. Recognising this dimension alongside psychological functioning may refine diagnostic practices and guide the development of targeted interventions tailored to diverse clinical presentations.

KEYWORDS: CPTSD, PTSD, latent profile analysis, emotional hypoactivation, dissociation, trauma exposure, comorbidities, functioning

HIGHLIGHTS

  • Latent profile analysis identified three clinical subgroups – PTSD, CPTSD with low and high emotional hypoactivation – illustrating the heterogeneity of posttraumatic responses.

  • High levels of hypoactivation characterised the most severe CPTSD profile.

  • By identifying emotional hypoactivation as a hallmark of CPTSD severity, the findings call for refining diagnostic practices and developing targeted interventions that specifically focus on affective and dissociative processes.

1. Background

According to the International Classification of Diseases (ICD-11; World Health Organization, 2019), posttraumatic stress disorder (PTSD) involves three symptom clusters following exposure to one or more extremely threatening or horrific events: (a) intrusive re-experiencing of the trauma; (b) avoidance of trauma-related thoughts, feelings, or reminders; and (c) a persistent sense of threat, reflected in hypervigilance or exaggerated startle responses. CPTSD is additionally marked by ‘Disturbances in Self-Organization’ (DSO), which involves: (a) difficulties in affect regulation (e.g. hyperactivation such as intense anger or overwhelming negative emotions, and hypoactivation such as emotional numbing or detachment); (b) negative self-concept (e.g. feelings of worthlessness, shame, or guilt); and (c) problems sustaining relationships or feeling close to others. Because CPTSD is a relatively recent construct, there is a need to provide empirical evidence supporting its discriminant validity as a distinct disorder. Among the various methods used to support discriminant validity (e.g. confirmatory factor analysis), person-centered approaches such as latent profile or class analyses offer a complementary perspective. They allow examination of whether posttraumatic symptoms form qualitatively distinct configurations rather than reflecting a simple severity gradient. In this line, many studies have used latent profile or class analyses to examine symptom patterns of ICD-11 PTSD and CPTSD (Cloitre et al., 2020) using the International Trauma Questionnaire (ITQ; Cloitre et al., 2018). Most studies suggest that CPTSD is not merely a more severe form of PTSD, but a qualitatively distinct syndrome characterised by DSO. A 2017 narrative review found that 9 out of 10 studies reported at least two distinct symptom profiles representing ICD-11 PTSD and CPTSD (Brewin et al., 2017). Since then, about ten more studies based on ICD-11 have supported this distinction across clinical (e.g. Böttche et al., 2018; Eidhof et al., 2019), non-clinical (e.g. Ben-Ezra et al., 2018; Cyr et al., 2022; Karatzias et al., 2018), and specific populations (e.g. Armstrong et al., 2020; Dhingra et al., 2025; Folke et al., 2019; Frost et al., 2019). Other recent neuroimaging findings also support the existence of partially distinct psychopathological subtypes and neurodevelopmental trajectories within broader clusters of disorders (Fan et al., 2023; Yu et al., 2023). However, some authors challenge this categorical distinction, arguing that differences reflect a severity gradient rather than a distinct symptom structure. For example, two studies identified four-class solutions differing mainly in overall symptom severity, not in the presence of DSO symptoms specific to CPTSD (Bertin et al., 2025; Wolf et al., 2015).

Several authors (e.g. Achterhof et al., 2019; Ford, 2020) have proposed explanations for these discrepancies, such as differences in measurement instruments, sampling variations, or the use of different statistical methods (e.g. Latent Class Analysis vs. Latent Profile Analysis; Achterhof et al., 2019). Moreover, a recent meta-analysis showed that, although the factorial structure of the ITQ based on a six-factor first-order model provides a reasonable fit to the data, this model may be less optimal than a seven-factor first-order correlated model separating affective dysregulation into hyperactivation and hypoactivation (Kindred et al., 2025). Similarly, previous network analyses have shown that these two dimensions related to affective dysregulation are not strongly interrelated, suggesting that they may reflect two types of regulatory problems rather than a completely unified concept (Knefel et al., 2019). Such differentiation is consistent with neurobiological models distinguishing between high arousal (hyperaroused) and low arousal (hypoaroused/shutdown) responses, which are not mutually exclusive and may dynamically alternate (Schauer & Elbert, 2010; Schiavone & Lanius, 2023). Yet, prior research has not consistently reported verifying the underlying factor structure before conducting latent analyses, which may affect the interpretability of the results.

Given these mixed empirical findings, ongoing empirical research is needed to better characterise the psychopathology of CPTSD. Specifically, the use of latent class or profile analyses based on standardised questionnaires, across samples from diverse countries, offers a promising approach to further establish or refine the discriminant validity of CPTSD in the ICD-11 (Cloitre, 2020). Furthermore, most studies have examined the existence of distinct PTSD and CPTSD profiles within non-mixed samples. However, to better reflect the clinical diversity of trauma-related disorders, it is essential to study these profiles within heterogeneous samples, including both exposed individuals from the general population and individuals seeking treatment for PTSD or CPTSD. Additionally, it is crucial to identify clinical correlates, both vulnerability and protective factors, specifically associated with ICD-11 CPTSD and PTSD (Cloitre et al., 2020), including both the type of traumatic event and their subjective experience of it (i.e. peritraumatic dissociation and distress). To date, few studies have investigated how these trauma exposure characteristics are specifically associated with distinct symptom profiles identified through a person-centered approach. Moreover, while some studies have focused on isolated exposure features (e.g. interpersonal nature, serious injury) using latent analyses, none have examined a broader range of exposure characteristics – such as degree of exposure, presence of life threat or serious injury, event type, exposure chronicity, or peritraumatic experiences (e.g. peritraumatic dissociation or distress) – although these are identified as key associated factors in the development of trauma-related disorders (e.g. Frost et al., 2019; Li et al., 2025). In the same way, previous literature reviews suggest that both emotional dysregulation (Jannini et al., 2025) and dissociation (Fung et al., 2023) appear to be central in CPTSD, contributing to its differentiation from PTSD. Further research is therefore needed in clinical samples to specifically explore these two mechanisms (Guzman Torres et al., 2023; Hyland et al., 2024). Beyond these vulnerability factors, some authors have also explored whether individual resources such as psychological well-being (Cloitre et al., 2019) or resilience (Fernández-Fillol et al., 2024) may play a protective role in ICD-11 PTSD or CPTSD. This perspective echoes with the Conservation of Resources theory (Hobfoll, 1989, 2002), which suggests that adaptation to stress depends not only on vulnerability factors but also on the availability and preservation of personal resources. Yet, exploration of their link with latent profiles remains largely unexplored.

Accordingly, the present study aimed to: (a) identify distinct and varied latent symptom profiles based on PTSD and DSO dimensions of the ITQ; (b) examine how these profiles differ according to trauma exposure characteristics and psychological correlates theoretically associated with PTSD and CPTSD. It was expected that profiles characterised by higher symptom severity would be associated with greater trauma exposure, higher dissociation and emotional dysregulation, and lower resilience and well-being.

2. Method

2.1. Population and procedure

Patients were recruited from a Trauma Center (Tours, France), while control participants were recruited from a Clinical Investigation Center (CIC INSERM 1415, Tours, France). Recruitment occurred between January 2023 and January 2025. Patients (N = 146) were recruited during their initial trauma centre consultation due to difficulties following exposure to one or more ICD-11-defined traumatic events. Non-clinical sample (N = 89) was not composed of clinical patients and had experienced at least one potentially traumatic event, but none had a current or past history of psychological or psychiatric treatment. For both groups, exclusion criteria included legal protection status (e.g. guardianship or curatorship), current or past psychotic disorders, neurological disorders, or a history of traumatic brain injury with loss of consciousness exceeding 10 minutes. All eligible participants completed self-report questionnaires after providing written informed consent.

The study received ethical approval from a French ethics committee (approval number: 00112561). Sociodemographic characteristics of both samples are available in Supplementary Material (Table S1).

2.2. Measures

Demographic characteristics – including age, sex, marital status, parental status, professional status, and educational level – were collected from all participants.

2.2.1. ICD-11 CPTSD and PTSD

The International Trauma Questionnaire (ITQ; Cloitre et al., 2018) was used to assess trauma exposure and symptoms of PTSD and CPTSD, based on ICD-11 (WHO, 2019). The ITQ has a two-factor structure comprising six symptom indicators for PTSD (re-experiencing, avoidance, and sense of threat) and six for DSO (affective dysregulation, negative self-concept, and disturbed relationships). Three additional items assess functional impairments related to both PTSD and CPTSD clusters. Each item is rated on a 5-point scale ranging from 0 (‘Not at all’) to 4 (‘Extremely’), referring to the past month. The French version has demonstrated good psychometric properties, including high internal consistency (α = .89; Peraud et al., 2022). In the present study, internal consistency within the original ITQ structure (Cloitre et al., 2018) ranged from acceptable to excellent for most ITQ dimensions (α = .72–.95). Only the affective dysregulation dimension showed low internal consistency when treated as a single dimension (α = .35), suggesting that this dimension may not function as a unitary construct.

2.2.2. Trauma-related experience

Trauma exposure characteristics were assessed using items from a structured trauma questionnaire (see Supplementary Material, Appendix 3). Participants were asked to identify the event they personally considered the most distressing or impactful. Then, they provided a brief description and answered single items evaluating the degree of exposure (direct, witnessed, or experienced by a close other) and several characteristics of the reported event. These included the presence of life threat or serious injury (to self, to other, or none), the event type (sexual interpersonal, non-sexual interpersonal, or non-interpersonal), and the occurrence of exposure (single or repeated). Each response option was operationalised as a dichotomous yes/no variable for subsequent analyses.

The Peritraumatic Dissociative Experience Questionnaire (PDEQ; Marmar et al., 1997) consists of 10 items assessing dissociation during or immediately after traumatic events on a 5-point scale from 0 (‘Not true at all’) to 4 (‘Extremely true’). The French version has shown good psychometric properties and satisfactory internal consistency (Birmes et al., 2005).

The Peritraumatic Distress Inventory (PDI; Brunet et al., 2001) is a 13-item measure assessing distress experienced during or shortly after trauma, rated on a 5-point scale from 0 (‘Not true at all’) to 4 (‘Extremely true’). The French version demonstrates satisfactory psychometric properties for its two-factor structure, covering emotional components and life threat perception (Jehel et al., 2005).

2.2.3. Psychological correlates

The Dissociation Questionnaire (DIS-Q; Vanderlinden et al., 1993) is a 63-item questionnaire evaluating four types of dissociative experiences in daily life: depersonalization-derealization, absorption, amnesia, and loss of control over behaviours, thoughts, or emotions. Items are rated on a 5-point scale ranging from 1 (‘Not at all’) to 5 (‘Extremely’). The French version (Mihaescu et al., 1998) has excellent psychometric properties with very high internal consistency (α = .96).

The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) is a 36-item self-report questionnaire assessing six dimensions of emotion regulation difficulties: nonacceptance of emotions, difficulties in goal-directed behaviour, impulse control, lack of emotional awareness, limited access to emotion regulation strategies, and lack of emotional clarity. Items are rated on a 5-point scale ranging from 1 (‘Almost never’) to 5 (‘Almost always’). The French version demonstrates good psychometric properties and high internal consistency (α = .92 for the total scale) (Dan-Glauser & Scherer, 2013).

The Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001) is a 9-item self-report questionnaire assessing depressive symptoms over the past two weeks according to DSM-5 criteria for major depression. Items are rated on a 4-point scale ranging from 0 (‘Not at all’) to 3 (‘Nearly every day’). The PHQ-9 has demonstrated good psychometric properties (Gauvin et al., 2024; Manea et al., 2012).

The Connor-Davidson Resilience Scale 10 (CD-RISC 10; Campbell-Sills & Stein, 2007) measures psychological resilience (ability to cope with adversity) with 10 items rated on a 5-point scale from 0 (‘Not at all’) to 4 (‘True nearly all the time’) over the past month. The French version has demonstrated good psychometric properties (Hébert et al., 2018; Jean-Thorn et al., 2020).

The World Health Organization-Five Well-Being Index (WHO-5; World Health Organization, 1998) is a 5-item questionnaire assessing subjective well-being. Items are rated on a 6-point frequency scale ranging from 0 (‘Never’) to 5 (‘Always’) for the last 14 days. The French translation is proposed by the WHO, but no validation study is yet available.

2.3. Data analysis

Between-group differences in categorical variables were tested using Pearson’s chi-square. To examine associations between variables used in this study, bivariate Spearman correlations were calculated using the psych package in R (see Supplementary Material, Table S2). To validate the ITQ factorial structure, nine Confirmatory Factor Analysis (CFA) models from a previous meta-analysis (Kindred et al., 2025) were compared using standard fit indices (CFI, TLI, RMSEA, SRMR, AIC, BIC). Models with estimation issues, such as non-positive definite latent variable covariance matrices, were excluded (Wothke, 1993). Latent profile analyses (LPA) were conducted using the tidyLPA package in R on the seven first-order ITQ dimensions from the retained CFA model. The study’s sample size (N = 235) is consistent with previous recommendations and statistical simulations suggesting that a sample size of 200–250 participants is generally sufficient for estimating reliable latent profiles (Collins & Lanza, 2023; Tein et al., 2013). Moreover, all identified profiles accounted for more than 5% of the sample and included over 25 participants, in line with recommended guidelines (Lubke & Neale, 2006; Spurk et al., 2020). Missing data represented 0.21% of responses. Additional analyses supported a missing-at-random (MAR) assumption. Given the extremely low proportion of missingness, missing values were imputed using item-level mean substitution. Models with one to eight profiles were estimated with maximum likelihood and robust standard errors. The optimal solution was selected based on statistical fit indices (AIC, BIC, SABIC, CAIC, ICL), classification quality (entropy, posterior probabilities, smallest class proportion), and parsimony (Masyn, 2013; Nylund et al., 2007). The Bootstrap Likelihood Ratio Test (BLRT) was used to evaluate model improvements. Lower values of the statistical fit indices indicate better relative model fit (Morgan et al., 2016), and a significant BLRT suggests improved fit compared to a model with k-1 profile. Entropy values and posterior class membership probabilities greater than .80 were considered indicative of good classification accuracy (Bauer, 2022; Weller et al., 2020). Solutions including very small classes (<5% of the sample or N < 25) should not be retained (Mathew & Doorenbos, 2022; Spurk et al., 2020). Finally, associations between profiles and clinical features were examined via multinomial logistic regressions using the nnet package, consistent with the exploratory aim of identifying which clinical factors independently contributed to the probability of membership in each latent profile. Given the number of analyses conducted, a Benjamini–Hochberg false discovery rate correction was applied for regression analyses. All analyses were performed using R (Version 4.4.3) within the RStudio environment (Version 2024.12.1 + 563).

3. Results

3.1. Confirmatory factorial analysis

Fit indices are presented in Table 1. Among the nine tested models as in previous meta-analysis (Kindred et al., 2025), four models were not retained because they produced inadmissible solutions, including negative latent variances (Models 3, 4, and 5) or a non-positive definite latent covariance matrix (Model 2), and were therefore not interpretable (Wothke, 1993). Five yielded valid estimations and were retained for comparison: an unidimensional CPTSD factor model (Model 1), a model with two second-order factors, featuring three first-order PTSD factors and six DSO items (Model 6), a model with two correlated second-order PTSD and DSO factors, each with six items (Model 7), a model with seven correlated first-order factors (Model 8), and a model with two-factor second-order factors and seven first-order factors (Model 9).

Table 1.

Fit indices for Confirmatory Factorial Analysis of the ITQ.

Model CFI TLI RMSEA SRMR AIC BIC
1 .743 .686 .181 .079 8871.398 8953.912
6 .893 .859 .122 .077 8637.132 8733.399
7 .845 .807 .142 .084 8710.039 8795.991
8 .981 .964 .061 .025 8512.314 8660.152
9 .975 .966 .060 .036 8508.546 8611.689
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Note: CFI = Comparative fit index; TLI = Tucker-Lewis Index; RMSEA = Root mean square error of approximation; SRMR = Standardised root mean square residual; AIC = Akaike Information Criterion; BIC = Bayesian information criterion. The model with the best fit is shown in bold.

In the present study, the most representative ICD-11 model (i.e. Model 4) failed to produce a valid estimation in the present analysis. Models 8 and 9 demonstrated excellent and comparable fit, and both clearly outperformed the other models. While Model 8 showed slightly better absolute fit indices (CFI = .981, TLI = .964, RMSEA = .061, SRMR = .025, AIC = 8512, BIC = 8660), Model 9 demonstrated greater parsimony (CFI = .975, TLI = .966, RMSEA = .060, SRMR = .036, AIC = 8508, BIC = 8611). Based on its strong fit, parsimony, and theoretical interpretability, the first-order factors of model 9 were retained for subsequent analyses.

3.2. Latent profile analysis and symptoms between-group comparison

Table 2 presents model fit indices for latent profile solutions ranging from one to eight classes. The Bootstrap Likelihood Ratio Test (BLRT) was significant for each successive model (p = .009), indicating improved fit with additional classes. The AIC, BIC, and SABIC reached their lowest values for the 7-class solution (AIC = 3908; BIC = 4123). Entropy values exceeded .80 across all models, with the highest observed in the 8-class solution (entropy = .91).

Table 2.

Fit indices for latent profile analysis for 1–8 classes.

Classes AIC BIC CAIC SABIC ICL Entropy prob_min prob_max n_min n_max BLRT
1 4696 4744 4758 4700 -4744 1 1 1 1 1 NA
2 4132 4208 4230 4138 -4219 0.912 0.977 0.981 0.443 0.557 .009
3 4044 4148 4178 4053 -4185 0.848 0.899 0.968 0.319 0.353 .009
4 3993 4124 4162 4004 -4165 0.857 0.815 0.969 0.153 0.315 .009
5 3970 4129 4175 3983 -4169 0.877 0.887 0.956 0.068 0.272 .009
6 3954 4141 4195 3969 -4180 0.882 0.793 0.956 0.043 0.272 .009
7 3908 4123 4185 3926 -4157 0.909 0.870 0.987 0.047 0.268 .009
8 3910 4152 4222 3931 -4186 0.915 0.792 0.975 0.026 0.268 .297
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Note: AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; CAIC = Consistent Akaike Information Criterion; SABIC = Sample-Size Adjusted BIC; ICL = Integrated Completed Likelihood; Entropy = Classification accuracy index (range 0–1); prob_min = Minimum posterior class membership probability; prob_max = Maximum posterior class membership probability; n_min = Proportion of individuals in the smallest class; n_max = Proportion of individuals in the largest class; BLRT = Bootstrap Likelihood Ratio Test. The selected model is shown in bold.

Following methodological recommendations (Masyn, 2013; Nylund et al., 2007; Tofighi & Enders, 2007), model selection considered statistical fit, parsimony, interpretability, and class size. Although the 7-class solution yielded the lowest BIC (BIC = 4123), the improvement over the 4-class model (BIC = 4124) was negligible and lacked theoretical or practical advantages. Additionally, the 7-class model included a very small class (4.7%), raising concerns about stability (Lubke & Neale, 2006). The 4-class solution provided the best balance between fit and interpretability, showing good fit (BIC = 4124; entropy = .86), high classification precision (.81 to .97), and well-balanced class sizes (15.3% to 31.5%), supporting its empirical and clinical relevance.

The 4-Classes model revealed the following profiles (Figure 1):

  • - Class 1 (blue) PTSD profile: High levels of re-experiencing (RE) symptoms and sense of threat (THR), with moderate avoidance (AV).

  • - Class 2 (green) CPTSD with low hypoactivation: Elevated PTSD and DSO symptoms except for emotional hypoactivation (i.e. emotional numbing).

  • - Class 3 (red) CPTSD with high hypoactivation: High levels of all PTSD and DSO symptoms, including emotional hypoactivation.

  • - Class 4 (grey) Low-symptom profile: Very low symptom levels across each PTSD and DSO dimension.

Figure 1.

A line graph comparing standardized mean scores with error bars for PTSD, CPTSD subgroups, and Low Symptoms across ITQ dimensions. The figure shows a line graph with 4 symptom groups plotted across several dimensions of the International Trauma Questionnaire. The x axis label reads Dimensions of the International Trauma Questionnaire, with categories from left to right: RE, AV, THR, HYPER, HYPO, NSC, and DR. The y axis label reads Standardized mean scores, with a numeric scale from approximately -2 to 2 in intervals of 1. Four lines represent groups listed in the legend at the top: PTSD with sample size 36 and 15.3 percent, CPTSD with low hypoactivation with sample size 54 and 23.0 percent, CPTSD with high hypoactivation with sample size 71 and 30.2 percent, and Low Symptoms with sample size 74 and 31.5 percent. Each point on every line has vertical error bars indicating 95 percent confidence intervals. The CPTSD with high hypoactivation line stays above 0 on most dimensions and peaks near HYPER and DR. The CPTSD with low hypoactivation line stays slightly above 0 except for a dip at HYPO. The PTSD line hovers around 0, with a clear drop at AV and HYPO. The Low Symptoms dashed line remains below 0 across all dimensions. All data are approximate.

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Mean standardised values on CPTSD and PTSD symptoms dimensions across latent profiles with 95% confidence intervals (N = 235).

Note: RE = Re-Experiencing; AV = Avoidance; THR = persistent Sense of Threat; HYPER = Hyperactivation; HYPO = Hypoactivation; NSC = Negative Self-Concept; DR = Disturbances in Relationships.

The distribution of ITQ latent profiles by recruitment group is presented in Table 3. Between-profile ANOVA and post hoc tests examined differences in PTSD and DSO symptoms across profiles. Detailed results are provided in the Supplementary Material (see Appendix 2).

Table 3.

Distribution of ITQ latent profiles by recruitment source.

ITQ latent profile Clinical (N, %) Non-clinical (N, %) χ2 p
Low symptoms 10 (6.8%) 64 (71.9%) 113.47 <.001
PTSD 25 (17.1%) 11 (12.4%)    
CPTSD with low hypoactivation 46 (31.5%) 8 (9.0%)    
CPTSD with high hypoactivation 65 (44.5%) 6 (6.7%)    
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Note: Values are n (%), with percentages calculated within recruitment group.

3.3. Trauma-related experiences

Table 4 shows the associations between the most severe potentially traumatic exposure reported by individuals and each latent profile. Both CPTSD profiles were strongly associated with direct exposure (ORs = 8.44–11.44, p < .001), sexual interpersonal trauma (ORs = 8.82–10.85, p < .001), and repeated exposure (ORs = 5.42–5.91, p < .001). The high hypoactivation profile was additionally associated by serious injury to self (OR = 3.70, 95% CI [1.27–10.80], p = .040). Regarding the subjective experience of trauma exposure, peritraumatic symptoms were significantly associated with all three clinical profiles, with increasing odds from PTSD to CPTSD for both peritraumatic dissociation (PDEQ; CPTSD with high hypoactivation: OR = 3.76, 95% CI [2.20–6.43], p < .001, see Table 4) and peritraumatic distress (PDI; CPTSD with high hypoactivation: OR = 3.00, CI [1.78–5.06], p < .001, see Table 4).

Table 4.

Multinomial logistic regression analysis for trauma-exposure related features of the worst event as predictors of ITQ latent profiles.

Worst trauma exposure features PTSD
(n = 36; 15.3%)		 CPTSD with low hypoactivation
(n = 54; 23.0%)		 CPTSD with high hypoactivation
(n = 71; 30.2)
n (%) OR [95% CI] p n (%) OR [95% CI] p n (%) OR [95% CI] p
Degree of exposure                  
Direct exposure 28 (77.8%) 3.69 [1.49–9.16] .013 48 (88.9%) 8.44 [3.22–22.13] <.001 65 (91.5%) 11.44 [4.41–29.64] <.001
Witnessed exposure 8 (22.2%) 0.50 [0.20–1.24] .222 4 (7.4%) 0.14 [0.05–0.43] .002 6 (8.4%) 0.16 [0.06–0.42] <.001
Experienced by a close other 2 (5.5%) 0.67 [0.13–3.48] .811 4 (7.4%) 0.91 [0.24–3.38] .951 6 (8.4%) 1.05 [0.32–3.41] .951
Exposure characteristics                  
Life threat to self 18 (50.0%) 2.70 [1.18–6.20] .043 24 (44.4%) 2.16 [1.03–4.54] .084 37 (52.1%) 2.94 [1.47–5.87] .008
Life threat to other 18 (50.0%) 1.11 [0.50–2.47] .946 14 (25.9%) 0.39 [0.18–0.83] .039 17 (23.9%) 0.35 [0.17–0.71] .012
Serious injury to self 6 (16.7%) 2.76 [0.78–9.75] .197 9 (16.7%) 2.76 [0.87–8.77] .153 15 (21.1%) 3.70 [1.27–10.80] .040
Serious injury or death of other 10 (27.7%) 1.11 [0.45–2.73] .946 10 (18.5%) 0.66 [0.28–1.56] .472 13 (18.3%) 0.65 [0.29–1.44] .413
Non-sexual interpersonal event (ref = Non-interpersonal event) 9 (25.0%) 0.67 [0.26–1.77] .564 18 (33.3%) 3.03 [1.12–8.18] .060 21 (29.6%) 2.18 [0.91–5.23] .153
Sexual interpersonal event (ref = Non-interpersonal event) 7 (19.4%) 1.21 [0.39–3.72] .925 28 (51.8%) 10.85 [3.76–31.34] <.001 37 (52.1%) 8.82 [3.40–22.87] <.001
Repeated exposure 17 (47.2%) 1.68 [0.74–3.81] .339 41 (75.9%) 5.91 [2.67–13.12] <.001 52 (74.3%) 5.42 [2.61–11.24] <.001
Trauma-related experiences                  
Peritraumatic Dissociation (PDEQ)   2.24 [1.29–3.88] .005   2.81 [1.64–4.81] <.001   3.76 [2.20–6.43] <.001
Peritraumatic Distress (PDI)   1.76 [1.03–3.01] .039   3.11 [1.82–5.32] <.001   3.00 [1.78–5.06] <.001
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Note: OR = Odds Ratio; CI = Confidence Interval; ITQ = International Trauma Questionnaire. Values represent multinomial logistic regressions (Univariate models for degree of exposure and exposure characteristics, and multivariate model for trauma-related experiences) comparing each latent profile to the reference group (Low Symptom profile). P-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR). Significant ORs are displayed in bold.

3.4. Psychological correlates with ICD-11 posttraumatic latent profiles

Table 5 presents the results of multivariate multinomial logistic regressions for each latent profile based on the ITQ, with the ‘Low symptoms’ profile used as the reference.

Table 5.

Multivariate multinomial logistic regression analyses for psychological correlates as predictors of ITQ latent profiles.

  PTSD
(n = 36; 15.3%)		 CPTSD with low hypoactivation
(n = 54; 23.0%)		 CPTSD with high hypoactivation
(n = 71; 30.2%)
  OR [95% CI] p OR [95% CI] p OR [95% CI] p
Model 1 – Vulnerabilities            
Current Dissociation (DIS-Q) 2.90 [1.17–7.18] .032 4.11 [1.60–10.57] .008 7.65 [2.98–19.66] <.001
Emotional Dysregulation (DERS) 0.90 [0.45–1.81] .763 2.35 [1.10–5.03] .036 1.69 [0.79–3.64] .198
Depression (PHQ-9) 2.97 [1.38–6.39] .01 7.28 [3.15–16.79] <.001 8.32 [3.59–19.25] <.001
Model 2 – Resources            
Resilience (CD-RISC 10) 0.97 [0.51–1.84] .914 0.34 [0.18–0.64] .001 0.46 [0.25–0.84] .013
Well-Being (WHO-5) 0.23 [0.12–0.46] <.001 0.19 [0.10–0.37] <.001 0.17 [0.09–0.32] <.001
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Note: OR = Odds Ratio; CI = Confidence Interval; DIS-Q = Dissociation Questionnaire; DERS = Difficulties in Emotion Regulation Scale; PHQ-9 = Patient Health Questionnaire 9; CD-RISC 10 = Connor-Davidson Resilience Scale 10; WHO-5 = World Health Organization – 5 Well-Being Index. Values represent multivariate multinomial logistic regressions comparing each profile to the reference group (Low Symptoms profile). P-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR). Significant ORs are displayed in bold.

Current dissociation (DIS-Q) was significantly associated with all profiles compared to the low-symptom profile. The strongest association was observed for the CPTSD with high hypoactivation profile (OR = 7.65, 95% CI [2.98–19.66], p < .001), followed by CPTSD with low hypoactivation (OR = 4.11, 95% CI [1.60–10.57], p < .01), and PTSD (OR = 2.90, 95% CI [1.17–7.18], p = .032). Emotional dysregulation (DERS) was only associated with CPTSD with low hypoactivation (OR = 2.35, CI [1.10–5.03], p = .036). Depressive symptoms (PHQ-9) were significantly and gradually associated with PTSD (OR = 2.97, 95%CI [1.38–6.39], p = .01), CPTSD with low hypoactivation (OR = 7.28, 95% CI [3.15–16.79], p < .001) and CPTSD with high hypoactivation (OR = 8.32, 95% CI [3.59–19.25], p < .001). Psychological resources were negatively associated with each CPTSD profile. Resilience (CD-RISC 10) was significantly associated with both CPTSD with high hypoactivation (OR = 0.46, [0.25–0.84], p = .013) and CPTSD with low hypoactivation (OR = 0.34, [0.18–0.64], p = .001), but not with PTSD (OR = 0.97, [0.51–1.84], p = .914). Well-being (WHO-5) was significantly associated with PTSD (OR = 0.23, [0.12–0.46], p < .001) and both CPTSD profiles (ORs = 0.17–0.19, p < .001).

4. Discussion

The primary aim of this study was to identify latent profiles of posttraumatic symptoms using the ITQ in a clinical and non-clinical sample. A secondary aim was to examine whether these profiles were associated with (a) characteristics of the most distressing trauma exposure, and (b) a range of psychological correlates.

CFA partially supported the ITQ structure. Consistent with previous validations (Cloitre et al., 2018; Peraud et al., 2022), two second-order factors were identified: ‘PTSD core symptoms’ and ‘DSO’. However, divergence emerged in the first-order factors: the ‘Affective dysregulation’ factor split into two distinct factors, ‘hyperactivation’ and ‘hypoactivation’, resulting in a final model with seven first-order factors. This aligns with findings from a systematic review (Redican et al., 2021) and a recent meta-analysis suggesting it provides a better model fit (Kindred et al., 2025). This is also consistent with previous theoretical and empirical studies suggesting that hyperactivation and hypoactivation are not mutually exclusive and may dynamically alternate (Schauer & Elbert, 2010; Schiavone & Lanius, 2023), and may therefore reflect related but partially distinct forms of affective dysregulation rather than a fully unified construct (Knefel et al., 2019).

LPA revealed four subgroups, including three distinct symptomatic profiles, highlighting considerable heterogeneity within posttraumatic stress disorders. The identified four-class structure reflects patterns previously documented in CPTSD literature (Armstrong et al., 2020; Ben-Ezra et al., 2018; Böttche et al., 2018; Dhingra et al., 2025; Knefel et al., 2015; Perkonigg et al., 2016). The first profile signified elevated core PTSD symptoms with minimal DSO. The second showed high severity across PTSD and DSO symptoms except emotional hypoactivation. The third represented a more severe CPTSD subtype, characterised by heightened emotional hypoactivation. The fourth included individuals with low symptom endorsement, serving as a comparison group.

Aligned with the ICD-11 conceptualisation (WHO, 2019), the PTSD profile differed from the low-symptom group only on core PTSD clusters. In contrast, the two CPTSD profiles exhibited broader symptomatology, including DSO. Emotional hypoactivation distinguished the two CPTSD profiles, suggesting that this dimension may contribute to differences in symptom experience. These findings not only support the categorical distinction between ICD-11 CPTSD and PTSD (Ben-Ezra et al., 2018; Redican et al., 2021) but also suggest the existence of CPTSD subtypes that differ according to levels of emotional hypoactivation. Given the established links between emotional numbing and dissociation (Feeny et al., 2000; Shin et al., 2019), this distinction is particularly relevant in light of the growing interest in dissociative subtypes of trauma-related disorders (see Hyland et al., 2024; Lanius et al., 2012; Misitano et al., 2024). To date, several studies have highlighted associations between ICD-11 affective dysregulation and dissociative states (Hyland et al., 2020; Møller et al., 2021) while other authors consider hypoactivation as a form of dissociative state (Frewen & Lanius, 2006; Lanius et al., 2012). Our findings thus point to a severity gradient primarily in affective hypoactivation, which may intersect with dissociative mechanisms, supporting efforts to refine symptom classification in CPTSD.

Regarding trauma-exposure-related characteristics, all pathological profiles shared the life-threatening nature of the event. The PTSD profile was associated chiefly with direct exposures and life threat to self, whereas CPTSD profiles were linked primarily to direct, repeated, and interpersonal traumas, particularly sexual violence. These findings are consistent with previous research (Böttche et al., 2018) and align with the ICD-11 conceptualisation (WHO, 2019). The high hypoactivation CPTSD profile was unassociated with non-sexual interpersonal trauma and uniquely associated with serious physical injuries. This specific association may be understood in light of converging evidence indicating that painful experiences, whether through pain stimulation, pain perception, and both neural and psychophysiological pain markers, are consistently associated with heightened subsequent dissociative states (Danböck et al., 2023).

Regarding psychological correlates, as expected, dissociation during or after trauma was linked to all clinical profiles, with the strongest association observed in the more severe CPTSD profile, exhibiting high emotional hypoactivation. This seems particularly relevant given that the DSO symptoms (Jowett et al., 2022), and especially the affective dysregulation cluster (Hyland et al., 2020), are the posttraumatic dimension most strongly associated with dissociative symptoms. These findings are consistent with prior studies (Hyland et al., 2024) and support a continuum from hyperactivation to hypoactivation (Lanius et al., 2010). Furthermore, emotional dysregulation and reduced individual resources (resilience and well-being) were evident clinical profiles, especially pronounced within CPTSD groups. Emotional dysregulation – notably the hypoactivation dimension identified in our profiles – appears to play an important role in differentiating symptom profiles, warranting further investigation. By extending our consideration to the emotional dysregulation difficulties, our findings indicated only CPTSD with low hypoactivation profile was associated with these difficulties, following the transdiagnostic model of Gratz and Roemer (2004). Although unexpected at first glance, these findings may be understood in light of models distinguishing hyperactivation and hypoactivation responses (e.g. Lanius et al., 2010). Emotional hypoactivation, as described in the ICD-11, represents a form of emotional overmodulation characterised by reduced emotional experience (i.e. feeling emotionally numb or empty), which may limit the subjective perception of emotion regulation difficulties in the CPTSD profile with high hypoactivation. This invites further research to help clarify how these processes contribute to CPTSD symptomatology, particularly regarding emotional hyperactivation and hypoactivation symptoms. Consistent with previous studies on ICD-11 PTSD and CPTSD (Barbano et al., 2019; Hyland et al., 2018), depressive symptoms were significantly associated with the PTSD profile and with both CPTSD profiles. These results suggest that the distinction observed in CPTSD profiles regarding emotional hypoactivation cannot be fully explained by comorbid depression alone. Finally, our results regarding internal resources, such as resilience and psychological well-being, align with findings that CPTSD correlated with lower quality of life (Beckord et al., 2025) and reduced levels of both hedonic and eudaimonic well-being (Li et al., 2023). They also extend findings suggesting that resilience is negatively associated with DSO but not PTSD symptoms (Fernández -Fillol et al., 2021). These underline the importance of internal resources as protective and differentiating factors between CPTSD and PTSD (Cloitre et al., 2019).

4.1. Limitations

This study has several limitations. Its cross-sectional design precludes establishing developmental pathways linking mental health measures with posttraumatic disorders. The observed associations should therefore not be interpreted as consequences, but rather as associations that may potentially reflect bidirectional relationships. The selection of the latent profile number includes inherent subjectivity, and measurement dependencies between items and potential overfitting may have influenced the latent profile structure. Independent replication would strengthen the robustness of these profiles. Exclusive reliance on self-report questionnaires may introduce biases related to social desirability or self-perception, limiting measurement accuracy (e.g. emotion dysregulation, dissociation). Another limitation concerns the small number of items per dimension of the ITQ, which restricts the assessment of each symptom and indicator’s reliability. This issue is particularly salient when CFA results require splitting a dimension, as in the present study. In addition, the modest sample size may have limited statistical power and affected the stability of the identified profiles. Additionally, sampling biases might affect generalizability despite a relatively large mixed clinical/non-clinical sample; small effects could remain undetected. Sociodemographic differences were also observed between our clinical and non-clinical samples, which may reflect either risk factors for trauma exposure or consequences of traumatic experiences.

4.2. Implications and future research directions

CFA and LPA findings partially support the ICD-11 framework, yet further research is needed to clarify emotional processes underlying CPTSD and PTSD and delineate shared versus distinct features. Our findings indicate that a more nuanced assessment of affective dysregulation in CPTSD is warranted. Future revisions of the ITQ could benefit from capturing these dimensions separately to better discriminate CPTSD patients’ functioning. Additionally, our results suggest that dissociation may represent not only an acute trauma response but also a prolonged emotional regulation strategy, potentially defining a distinct symptomatic profile. Therefore, investigating the mechanistic links between emotional dysregulation and dissociation may provide valuable insights into therapeutic targets, allowing interventions to more precisely address the underlying processes driving dysfunction in CPTSD. Future research should also examine the developmental period of trauma exposure (e.g. childhood versus adulthood), as this factor may contribute to differences in CPTSD symptom profiles. It may also be particularly relevant for understanding variations in emotional experience and dissociative symptoms.

Clinically, these findings underscore the importance of comprehensive assessments evaluating dissociation, emotional regulation, and resources to guide personalised treatment. Trauma-focused interventions should address symptom heterogeneity within CPTSD, particularly profiles marked by emotional hypoactivation and dissociation, which may benefit most from tailored therapies.

Supplementary Material

R1 Supplementary material Posttraumatic profiles.docx
ZEPT_A_2661294_SM5645.docx (43.4KB, docx)

Acknowledgments

We extend our gratitude to the participants for their time and their involvement in the study.

Funding Statement

This work was supported by the ‘Fondation de France’ under grant number 00112561 and by the ‘Association Francophone du Trauma et de la Dissociation (AFTD)’.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author (JT or GT). The data are not publicly available due to their containing information that could compromise the privacy of research participants.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2026.2661294.

References

  1. Achterhof, R., Huntjens, R. J. C., Meewisse, M.-L., & Kiers, H. A. L. (2019). Assessing the application of latent class and latent profile analysis for evaluating the construct validity of complex posttraumatic stress disorder: Cautions and limitations. European Journal of Psychotraumatology, 10(1), 1698223. 10.1080/20008198.2019.1698223 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Armstrong, R., Phillips, L., Alkemade, N., Louise, O., & Donnell, M. (2020). Using latent class analysis to support the ICD-11 complex posttraumatic stress disorder diagnosis in a sample of homeless adults. Journal of Traumatic Stress, 33(5), 677–687. 10.1002/jts.22554 [DOI] [PubMed] [Google Scholar]
  3. Barbano, A. C., van der Mei, W. F., deRoon-Cassini, T. A., Grauer, E., Lowe, S. R., Matsuoka, Y. J., O’Donnell, M., Olff, M., Qi, W., Ratanatharathorn, A., Schnyder, U., Seedat, S., Kessler, R. C., Koenen, K. C., Shalev, A. Y., & Ptsd, the I. C. to P . (2019). Differentiating PTSD from anxiety and depression: Lessons from the ICD-11 PTSD diagnostic criteria. Depression and Anxiety, 36(6), 490–498. 10.1002/da.22881 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bauer, J. (2022). A primer to latent profile and latent class analysis. In Goller M., Kyndt E., Paloniemi S., & Damşa C. (Eds.), Methods for researching professional learning and development: Challenges, applications and empirical illustrations (pp. 243–268). Springer International Publishing. 10.1007/978-3-031-08518-5_11 [DOI] [Google Scholar]
  5. Beckord, J., Birke, J., Krakowczyk, J. B., Hesse, S., Hinney, A., Dörrie, N., Schneider, J. S., Komlenac, N., Bäuerle, A., & Teufel, M. (2025). PTSD and CPTSD in the new ICD-11 – A latent profile analysis. Psychiatry Research, 344, 116350. 10.1016/j.psychres.2024.116350 [DOI] [PubMed] [Google Scholar]
  6. Ben-Ezra, M., Karatzias, T., Hyland, P., Brewin, C. R., Cloitre, M., Bisson, J. I., Roberts, N. P., Lueger-Schuster, B., & Shevlin, M. (2018). Posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) as per ICD-11 proposals: A population study in Israel. Depression and Anxiety, 35(3), 264–274. 10.1002/da.22723 [DOI] [PubMed] [Google Scholar]
  7. Bertin, F., Clarys, D., Tapia, G., Chami, L., El-Hage, W., & Vancappel, A. (2025). Are all PTSD cases complex PTSD? Results from a latent profile analysis. European Journal of Trauma & Dissociation, 9(1), 100494. 10.1016/j.ejtd.2024.100494 [DOI] [Google Scholar]
  8. Birmes, P., Brunet, A., Benoit, M., Defer, S., Hatton, L., Sztulman, H., & Schmitt, L. (2005). Validation of the peritraumatic dissociative experiences questionnaire self-report version in two samples of French-speaking individuals exposed to trauma. European Psychiatry, 20(2), 145–151. 10.1016/j.eurpsy.2004.06.033 [DOI] [PubMed] [Google Scholar]
  9. Böttche, M., Ehring, T., Krüger-Gottschalk, A., Rau, H., Schäfer, I., Schellong, J., Dyer, A., & Knaevelsrud, C. (2018). Testing the ICD-11 proposal for complex PTSD in trauma-exposed adults: Factor structure and symptom profiles. European Journal of Psychotraumatology, 9(1). 10.1080/20008198.2018.1512264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brewin, C. R., Cloitre, M., Hyland, P., Shevlin, M., Maercker, A., Bryant, R. A., Humayun, A., Jones, L. M., Kagee, A., Rousseau, C., Somasundaram, D., Suzuki, Y., Wessely, S., Van Ommeren, M., & Reed, G. M. (2017). A review of current evidence regarding the ICD-11 proposals for diagnosing PTSD and complex PTSD. Clinical Psychology Review, 58, 1–15. 10.1016/j.cpr.2017.09.001 [DOI] [PubMed] [Google Scholar]
  11. Brunet, A., Weiss, D. S., Metzler, T. J., Best, S. R., Neylan, T. C., Rogers, C., Fagan, J., & Marmar, C. R. (2001). The Peritraumatic Distress Inventory: A proposed measure of PTSD criterion A2. American Journal of Psychiatry, 158(9), 1480–1485. 10.1176/appi.ajp.158.9.1480 [DOI] [PubMed] [Google Scholar]
  12. Campbell-Sills, L., & Stein, M. B. (2007). Psychometric analysis and refinement of the Connor–Davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress, 20(6), 1019–1028. 10.1002/jts.20271 [DOI] [PubMed] [Google Scholar]
  13. Cloitre, M. (2020). ICD-11 complex post-traumatic stress disorder: Simplifying diagnosis in trauma populations. The British Journal of Psychiatry, 216(3), 129–131. 10.1192/bjp.2020.43 [DOI] [PubMed] [Google Scholar]
  14. Cloitre, M., Brewin, C. R., Bisson, J. I., Hyland, P., Karatzias, T., Lueger-Schuster, B., Maercker, A., Roberts, N. P., & Shevlin, M. (2020). Evidence for the coherence and integrity of the complex PTSD (CPTSD) diagnosis: Response to Achterhof et al., (2019) and Ford (2020). European Journal of Psychotraumatology, 11(1), 1739873. 10.1080/20008198.2020.1739873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cloitre, M., Hyland, P., Bisson, J. I., Brewin, C. R., Roberts, N. P., Karatzias, T., & Shevlin, M. (2019). ICD-11 posttraumatic stress disorder and complex posttraumatic stress disorder in the United States: A population-based study. Journal of Traumatic Stress, 32(6), 833–842. 10.1002/jts.22454 [DOI] [PubMed] [Google Scholar]
  16. Cloitre, M., Shevlin, M., Brewin, C. R., Bisson, J. I., Roberts, N. P., Maercker, A., Karatzias, T., & Hyland, P. (2018). The International Trauma Questionnaire: Development of a self-report measure of ICD-11 PTSD and complex PTSD. Acta Psychiatrica Scandinavica, 138(6), 536–546. 10.1111/acps.12956 [DOI] [PubMed] [Google Scholar]
  17. Collins, L. M., & Lanza, S. T. (2023). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. John Wiley & Sons.
  18. Cyr, G., Godbout, N., Cloitre, M., & Bélanger, C. (2022). Distinguishing among symptoms of posttraumatic stress disorder, complex posttraumatic stress disorder, and borderline personality disorder in a community sample of women. Journal of Traumatic Stress, 35(1), 186–196. 10.1002/jts.22719 [DOI] [PubMed] [Google Scholar]
  19. Dan-Glauser, E. S., & Scherer, K. R. (2013). The Difficulties in Emotion Regulation Scale (DERS). Swiss Journal of Psychology, 72(1), 5–11. 10.1024/1421-0185/a000093 [DOI] [Google Scholar]
  20. Danböck, S. K., Franke, L. K., Miedl, S. F., Liedlgruber, M., Bürkner, P.-C., & Wilhelm, F. H. (2023). Experimental induction of peritraumatic dissociation: The role of negative affect and pain and their psychophysiological and neural correlates. Behaviour Research and Therapy, 164, 104289. 10.1016/j.brat.2023.104289 [DOI] [PubMed] [Google Scholar]
  21. Dhingra, K., Boyda, D., Mitchell, S. M., & Taylor, P. J. (2025). ICD-11 posttraumatic stress disorder (PTSD) and complex PTSD in a sample of prison staff: A latent profile approach. Journal of Traumatic Stress, 38(2), 305–316. 10.1002/jts.23128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Eidhof, M. B., Djelantik, A. A. A. M. J., Klaassens, E. R., Kantor, V., Rittmansberger, D., Sleijpen, M., Steenbakkers, A., Weindl, D., & ter Heide, F. J. J. (2019). Complex posttraumatic stress disorder in patients exposed to emotional neglect and traumatic events: Latent class analysis. Journal of Traumatic Stress, 32(1), 23–31. 10.1002/jts.22363 [DOI] [PubMed] [Google Scholar]
  23. Fan, H., Liu, Z., Wu, X., Yu, G., Gu, X., Kuang, N., Zhang, K., Liu, Y., Jia, T., Sahakian, B. J., Robbins, T. W., Schumann, G., Cheng, W., Feng, J., Becker, B., & Zhang, J. (2023). Decoding anxiety–impulsivity subtypes in preadolescent internalising disorders: Findings from the Adolescent Brain Cognitive Development study. The British Journal of Psychiatry, 223(6), 542–554. 10.1192/bjp.2023.107 [DOI] [PubMed] [Google Scholar]
  24. Feeny, N. C., Zoellner, L. A., Fitzgibbons, L. A., & Foa, E. B. (2000). Exploring the roles of emotional numbing, depression, and dissociation in PTSD. Journal of Traumatic Stress, 13(3), 489–498. 10.1023/A:1007789409330 [DOI] [PubMed] [Google Scholar]
  25. Fernández -Fillol, C., Pitsiakou, C., Perez -Garcia, M., Teva, I., & Hidalgo- Ruzzante, N. (2021). Complex PTSD in survivors of intimate partner violence: Risk factors related to symptoms and diagnoses. European Journal of Psychotraumatology, 12(1), 2003616. 10.1080/20008198.2021.2003616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fernández-Fillol, C., Hidalgo-Ruzzante, N., Perez-Garcia, M., Hyland, P., Shevlin, M., & Karatzias, T. (2024). The role of resilience in the relationship between intimate partner violence severity and ICD-11 CPTSD severity. European Journal of Psychotraumatology, 15(1), 2285671. 10.1080/20008066.2023.2285671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Folke, S., Nielsen, A. B. S., Andersen, S. B., Karatzias, T., & Karstoft, K.-I. (2019). ICD-11 PTSD and complex PTSD in treatment-seeking Danish veterans: A latent profile analysis. European Journal of Psychotraumatology, 10(1), 1686806. 10.1080/20008198.2019.1686806 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ford, J. D. (2020). New findings questioning the construct validity of complex posttraumatic stress disorder (cPTSD): Let’s take a closer look. European Journal of Psychotraumatology, 11(1), 1708145. 10.1080/20008198.2019.1708145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Frewen, P. A., & Lanius, R. A. (2006). Neurobiology of dissociation: Unity and disunity in mind–body–brain. Psychiatric Clinics, 29(1), 113–128. 10.1016/j.psc.2005.10.016 [DOI] [PubMed] [Google Scholar]
  30. Frost, R., Hyland, P., McCarthy, A., Halpin, R., Shevlin, M., & Murphy, J. (2019). The complexity of trauma exposure and response: Profiling PTSD and CPTSD among a refugee sample. Psychological Trauma: Theory, Research, Practice, and Policy, 11(2), 165–175. 10.1037/tra0000408 [DOI] [PubMed] [Google Scholar]
  31. Fung, H. W., Chien, W. T., Lam, S. K. K., & Ross, C. A. (2023). The relationship between dissociation and complex post-traumatic stress disorder: A scoping review. Trauma, Violence, & Abuse, 24(5), 2966–2982. 10.1177/15248380221120835 [DOI] [PubMed] [Google Scholar]
  32. Gauvin, G., Côté, L.-P., & Bardon, C. (2024). Validation of the Kessler-10, Kessler-6 and the PHQ-9 among the French-speaking Quebec general population. Revue Québécoise de Psychologie, 44(3), 132–152. 10.7202/1114903ar [DOI] [Google Scholar]
  33. Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of The Difficulties in Emotion Regulation Scale. Journal of Psychopathology and Behavioral Assessment, 26(1), 41–54. 10.1023/B:JOBA.0000007455.08539.94 [DOI] [Google Scholar]
  34. Guzman Torres, E., Krause-Utz, A., & Sack, M. (2023). Predictors of complex PTSD: The role of trauma characteristics, dissociation, and comorbid psychopathology. Borderline Personality Disorder and Emotion Dysregulation, 10(1), 1. 10.1186/s40479-022-00208-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hébert, M., Parent, N., Simard, C., & Laverdière, A. (2018). Validation of the French Canadian version of the brief Connor–Davidson Resilience Scale (CD-RISC 10). Canadian Journal of Behavioural Science/Revue Canadienne Des Sciences Du Comportement, 50(1), 9–16. 10.1037/cbs0000092 [DOI] [Google Scholar]
  36. Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. 10.1037/0003-066X.44.3.513 [DOI] [PubMed] [Google Scholar]
  37. Hobfoll, S. E. (2002). Social and psychological resources and adaptation. Review of General Psychology, 6(4), 307–324. 10.1037/1089-2680.6.4.307 [DOI] [Google Scholar]
  38. Hyland, P., Hamer, R., Fox, R., Vallières, F., Karatzias, T., Shevlin, M., & Cloitre, M. (2024). Is dissociation a fundamental component of ICD-11 complex posttraumatic stress disorder? Journal of Trauma & Dissociation, 25(1), 45–61. 10.1080/15299732.2023.2231928 [DOI] [PubMed] [Google Scholar]
  39. Hyland, P., Shevlin, M., Fyvie, C., Cloitre, M., & Karatzias, T. (2020). The relationship between ICD-11 PTSD, complex PTSD and dissociative experiences. Journal of Trauma & Dissociation, 21(1), 62–72. 10.1080/15299732.2019.1675113 [DOI] [PubMed] [Google Scholar]
  40. Hyland, P., Shevlin, M., Fyvie, C., & Karatzias, T. (2018). Posttraumatic stress disorder and complex posttraumatic stress disorder in DSM-5 and ICD-11: Clinical and behavioral correlates. Journal of Traumatic Stress, 31(2), 174–180. 10.1002/jts.22272 [DOI] [PubMed] [Google Scholar]
  41. Jannini, T. B., Daniele, G., Rossi, R., Niolu, C., & Lorenzo, G. D. (2025). Emotional dysregulation in complex post-traumatic stress disorder: A narrative review. Journal of Psychopathology, 31. 10.36148/2284-0249-N810 [DOI]
  42. Jean-Thorn, A., Amédée, L. M., Paradis, A., & Hébert, M. (2020). Validation d’une échelle de résilience (CD-RISC 10) auprès de mères d’enfants victimes d’agression sexuelle. International Journal of Child and Adolescent Resilience, 7(1), 39–48. 10.7202/1072587ar [DOI] [Google Scholar]
  43. Jehel, L., Brunet, A., Paterniti, S., & Guelfi, J. D. (2005). Validation de la version française de l’Inventaire de Détresse Péritraumatique. The Canadian Journal of Psychiatry, 50(1), 67–71. 10.1177/070674370505000112 [DOI] [PubMed] [Google Scholar]
  44. Jowett, S., Karatzias, T., Shevlin, M., & Hyland, P. (2022). Psychological trauma at different developmental stages and ICD-11 CPTSD: The role of dissociation. Journal of Trauma & Dissociation, 23(1), 52–67. 10.1080/15299732.2021.1934936 [DOI] [PubMed] [Google Scholar]
  45. Karatzias, T., Hyland, P., Ben-Ezra, M., & Shevlin, M. (2018). Hyperactivation and hypoactivation affective dysregulation symptoms are integral in complex posttraumatic stress disorder: Results from a nonclinical Israeli sample. International Journal of Methods in Psychiatric Research, 27(4), e1745. 10.1002/mpr.1745 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kindred, R., Jak, S., Hamer, R., Nedeljkovic, M., & Bates, G. W. (2025). Evaluating the ICD-11 PTSD and complex PTSD constructs: A meta-analytic confirmatory factor analysis of the International Trauma Questionnaire. Assessment, 10731911251340837. 10.1177/10731911251340837 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Knefel, M., Garvert, D. W., Cloitre, M., & Lueger-Schuster, B. (2015). Update to an evaluation of ICD-11 PTSD and complex PTSD criteria in a sample of adult survivors of childhood institutional abuse by Knefel & Lueger-Schuster (2013): A latent profile analysis. European Journal of Psychotraumatology, 6(1), 25290. 10.3402/ejpt.v6.25290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Knefel, M., Karatzias, T., Ben-Ezra, M., Cloitre, M., Lueger-Schuster, B., & Maercker, A. (2019). The replicability of ICD-11 complex post-traumatic stress disorder symptom networks in adults. The British Journal of Psychiatry, 214(6), 361–368. 10.1192/bjp.2018.286 [DOI] [PubMed] [Google Scholar]
  49. Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Lanius, R. A., Brand, B., Vermetten, E., Frewen, P. A., & Spiegel, D. (2012). The dissociative subtype of posttraumatic stress disorder: Rationale, clinical and neurobiological evidence, and implications. Depression and Anxiety, 29(8), 701–708. 10.1002/da.21889 [DOI] [PubMed] [Google Scholar]
  51. Lanius, R. A., Vermetten, E., Loewenstein, R. J., Brand, B., Schmahl, C., Bremner, J. D., & Spiegel, D. (2010). Emotion modulation in PTSD: Clinical and neurobiological evidence for a dissociative subtype. American Journal of Psychiatry, 167(6), 640–647. 10.1176/appi.ajp.2009.09081168 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Li, D., Luo, J., Yan, X., & Liang, Y. (2023). Complex Posttraumatic Stress Disorder (CPTSD) as an Independent diagnosis: Differences in hedonic and eudaimonic well-being between CPTSD and PTSD. Healthcare, 11(8), 1188. 10.3390/healthcare11081188 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Li, K., Liu, Y., Zhong, B., & Tong, J. (2025). Trauma exposure, prevalence and associated factors of complex PTSD in mainland China: A cross-sectional survey. European Journal of Psychotraumatology, 16(1), 2502208. 10.1080/20008066.2025.2502208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Lubke, G., & Neale, M. C. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41(4), 499–532. 10.1207/s15327906mbr4104_4 [DOI] [PubMed] [Google Scholar]
  55. Manea, L., Gilbody, S., & McMillan, D. (2012). Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): A meta-analysis. Canadian Medical Association Journal, 184(3), E191–E196. 10.1503/cmaj.110829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Marmar, C. R., Weiss, D. S., & Metzler, T. J. (1997). The Peritraumatic Dissociative Experiences Questionnaire. In J. P. Wilson, & C. R. Marmar (Eds.), Assessing psychological trauma and PTSD (pp. 412–428). New York: The Guilford Press. [Google Scholar]
  57. Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In Little T. D. (Ed.), The Oxford handbook of quantitative methods in psychology: Vol. 2: Statistical analysis (pp. 551–611). Oxford University Press. 10.1093/oxfordhb/9780199934898.013.0025 [DOI] [Google Scholar]
  58. Mathew, A., & Doorenbos, A. Z. (2022). Latent profile analysis – An emerging advanced statistical approach to subgroup identification. Indian Journal of Continuing Nursing Education, 23(2), 127–133. 10.4103/ijcn.ijcn_24_22 [DOI] [Google Scholar]
  59. Mihaescu, G., Vanderlinden, J., Sechaud, M., Heinze, X., Velardi, A., Finot, S. C., & Baettig, D. (1998). The Dissociation Questionnaire DIS-Q: Preliminary results with a French-speaking Swiss population. L’Encephale, 24(4), 337–346. [PubMed] [Google Scholar]
  60. Misitano, A., Moro, A. S., Ferro, M., & Forresi, B. (2024). The dissociative subtype of post-traumatic stress disorder: A systematic review of the literature using the latent profile analysis. Journal of Trauma & Dissociation, 25(3), 349–365. 10.1080/15299732.2022.2120155 [DOI] [PubMed] [Google Scholar]
  61. Møller, L., Bach, B., Augsburger, M., Elklit, A., Søgaard, U., & Simonsen, E. (2021). Structure of ICD-11 complex PTSD and relationship with psychoform and somatoform dissociation. European Journal of Trauma & Dissociation, 5(3), 100233. 10.1016/j.ejtd.2021.100233 [DOI] [Google Scholar]
  62. Morgan, G. B., Hodge, K. J., & Baggett, A. R. (2016). Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices. Computational Statistics & Data Analysis, 93, 146–161. 10.1016/j.csda.2015.02.019 [DOI] [Google Scholar]
  63. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. 10.1080/10705510701575396 [DOI] [Google Scholar]
  64. Peraud, W., Hebrard, L., Lavandier, A., Brockbanck-Chasey, S., Brennstuhl, M. J., & Quintard, B. (2022). French cross-cultural adaptation and validation of the International Trauma Questionnaire (ITQ) in a French community sample. European Journal of Psychotraumatology, 13(2), 2152109. 10.1080/20008066.2022.2152109 [DOI] [PMC free article] [PubMed]
  65. Perkonigg, A., Höfler, M., Cloitre, M., Wittchen, H.-U., Trautmann, S., & Maercker, A. (2016). Evidence for two different ICD-11 posttraumatic stress disorders in a community sample of adolescents and young adults. European Archives of Psychiatry and Clinical Neuroscience, 266(4), 317–328. 10.1007/s00406-015-0639-4 [DOI] [PubMed] [Google Scholar]
  66. Redican, E., Nolan, E., Hyland, P., Cloitre, M., McBride, O., Karatzias, T., Murphy, J., & Shevlin, M. (2021). A systematic literature review of factor analytic and mixture models of ICD-11 PTSD and CPTSD using the International Trauma Questionnaire. Journal of Anxiety Disorders, 79, 102381. 10.1016/j.janxdis.2021.102381 [DOI] [PubMed] [Google Scholar]
  67. Schauer, M., & Elbert, T. (2010). Dissociation following traumatic stress: Etiology and treatment. Zeitschrift Für Psychologie/Journal of Psychology, 218(2), 109–127. 10.1027/0044-3409/a000018 [DOI] [Google Scholar]
  68. Schiavone, F. L., & Lanius, R. A. (2023). The neurobiology of dissociation in chronic PTSD. In S. N. Gold, M. J. Dorahy, & J. A. O'Neil (Eds.), Dissociation and the dissociative disorders (2nd ed., Vol. 1, pp. 634–642). Routledge. [Google Scholar]
  69. Shin, G. I., Goldstein, L. H., & Pick, S. (2019). Evidence for subjective emotional numbing following induced acute dissociation. Behaviour Research and Therapy, 119, 103407. 10.1016/j.brat.2019.05.004 [DOI] [PubMed] [Google Scholar]
  70. Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. 10.1016/j.jvb.2020.103445 [DOI] [Google Scholar]
  71. Tein, J.-Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. 10.1080/10705511.2013.824781 [DOI] [PMC free article] [PubMed]
  72. Tofighi, D., & Enders, C. (2007). Identifying the correct number of classes in growth mixture models. G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models, (pp. 317–341). Emerald Publishing Limited. Emerald Publishing Limited. 10.1108/978-1-60752-634-620251017. [DOI] [Google Scholar]
  73. Vanderlinden, J., Van Dyck, R., Vandereycken, W., Vertommen, H., & Jan Verkes, R. (1993). The Dissociation Questionnaire (DIS-Q): Development and characteristics of a new self-report questionnaire. Clinical Psychology & Psychotherapy, 1(1), 21–27. 10.1002/cpp.5640010105 [DOI] [Google Scholar]
  74. Weller, B. E., Bowen, N. K., & Faubert, S. J. (2020). Latent class analysis: A guide to best practice. Journal of Black Psychology, 46(4), 287–311. 10.1177/0095798420930932 [DOI] [Google Scholar]
  75. Wolf, E. J., Miller, M. W., Kilpatrick, D., Resnick, H. S., Badour, C. L., Marx, B. P., Keane, T. M., Rosen, R. C., & Friedman, M. J. (2015). ICD-11 complex PTSD in US national and veteran samples: Prevalence and structural associations with PTSD. Clinical Psychological Science: A Journal of the Association for Psychological Science, 3(2), 215–229. 10.1177/2167702614545480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. World Health Organization . (1998). The World Health Organisation-five well-being index (WHO-5). [Google Scholar]
  77. World Health Organization . (2019). International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/ [Google Scholar]
  78. Wothke, W. (1993). Nonpositive definite matrices in structural modeling. In Bollen K. A. & Long J. S. (Eds.), Testing structural equation models (pp. 256–293). Sage. [Google Scholar]
  79. Yu, G., Liu, Z., Wu, X., Becker, B., Zhang, K., Fan, H., Peng, S., Kuang, N., Kang, J., Dong, G., Zhao, X.-M., Schumann, G., Feng, J., Sahakian, B. J., Robbins, T. W., Palaniyappan, L., & Zhang, J. (2023). Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: An adolescent brain and cognitive development study. Journal of Psychiatry and Neuroscience, 48(5), E345–E356. 10.1503/jpn.220202 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

R1 Supplementary material Posttraumatic profiles.docx
ZEPT_A_2661294_SM5645.docx (43.4KB, docx)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author (JT or GT). The data are not publicly available due to their containing information that could compromise the privacy of research participants.

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