Introduction

Depressive and anxiety disorders are among the leading causes of disability worldwide, both ranked among the top 25 contributors to the global burden of disease in 2019 (1). In Spain, these disorders are also highly prevalent, being among the three most common mental disorders seen in primary care, with anxiety disorders and depressive disorders affecting 10.65% and 4.78% of the population, respectively (2).

Comorbidity between anxiety and depression is common, with rates exceeding 65% (3). Recent studies indicate that over a quarter of individuals diagnosed with an anxiety disorder also meet criteria for major depressive disorder, and nearly half of those with major depressive disorder present with some form of anxiety disorder (4). The presence of high levels of comorbidity suggests the possible existence of common underlying processes, which has led to further study of this topic. In this context, Hong and Cheung (5) propose a transdiagnostic model that helps explain the frequent comorbidity between anxiety and depression. According to this model, both disorders are characterized by high negative affect—a tendency to experience emotions such as fear, guilt, or irritability. They differ, however, in that depression also involves low positive affect, whereas anxiety is associated with heightened physiological hyperarousal. Recent systematic evidence has further reinforced this perspective by identifying common psychological risk factors across emotional disorders (6).

Beyond these common emotional processes, there are several individual and contextual factors that can increase or decrease vulnerability to developing emotional disorders (7). Among these, personality traits such as neuroticism and extraversion play a significant role. Individuals with high levels of neuroticism often experience unpleasant emotions more frequently, have difficulty managing stress and impulses, and tend to exhibit poorer emotional adjustment (8-10). Conversely, higher levels of extraversion are associated with greater optimism, sociability, and a tendency to experience more pleasant emotions (11,12). These traits may function as either vulnerability or protective factors depending on their expression and context (11).

Emotion regulation is another crucial protective factor. It refers to the capacity to modulate the onset, intensity, duration, and expression of emotions in ways that support individual goals and interpersonal relationships (13,14). Even though different dimensions of emotional regulation have been differentially associated with symptomatology depending on the specific strategies employed (15), in general, effective emotion regulation promotes psychological flexibility, impulse control, and adaptive emotional responses. Conversely, emotional dysregulation, characterized by impulsivity and poorly controlled emotional reactions, has been identified as a risk factor for psychopathologies such as anxiety and depression (16).

Finally, social support plays a fundamental buffering role. It positively influences emotional well-being by strengthening self-esteem and self-efficacy, while mitigating the negative impact of anxiety, depression, and stress (17). The presence of supportive networks, such as family, partners, friends, or the broader community, not only contributes to a better quality of life but also facilitates psychosocial adjustment to psychological difficulties (18).

In view of the above, the aim of this study is to analyze how transdiagnostic factors (personality traits, emotional regulation and social support) are associated with the presence of symptoms of anxiety and depression, identifying and classifying these factors into risk or protective profiles through correlations and qualitative comparative analysis (QCA). Based on this objective, the following hypotheses were proposed:

1. H1: Neuroticism will be a negative predictor of mental health, and extraversion will be a positive predictor; specifically, higher levels of neuroticism and lower levels of extraversion will be associated with higher levels of anxiety and depression.

2. H2: Emotional dysregulation will be a negative predictor of mental health, showing positive associations with both anxiety and depression.

3. H3: social support will be a positive predictor of mental health, showing negative associations with anxiety and depression.

Method

Participants

The sample consisted of 61 individuals aged between 20 and 66 years (M = 43.79; SD = 12.75), all experiencing clinically significant emotional distress warranting psychological intervention in either adult mental health services or primary care. The majority were women (73.8%; n = 45), followed by men (24.6%; n = 15), and one participant identified as non-binary (1.6%). At the time of the study, 59.01% of participants were taking psychotropic medication. Additionally, 59.3% had previously received mental health treatment: 11.5% psychiatric care only, 14.8% psychological therapy only, and 32.8% both treatments concurrently.

Instruments

An ad hoc questionnaire was developed to collect socio-demographic variables (gender, age, marital status, employment status, level of education and family socio-economic status) and clinical variables (previous and current psychological, psychiatric, and relevant medical treatments).

Anxiety was assessed using the Spanish version of the State-Trait Anxiety Inventory (STAI) (19,20). The self-report questionnaire includes 40 items rated on a 4-point Likert scale and includes two subscales: (i) State anxiety (a temporary emotional condition characterized by subjective feelings, heightened attention, and conscious perception) and (ii) trait anxiety (a relatively stable predisposition to perceive situations as threatening). Scores are converted to percentiles based on age and gender. In this study, the STAI showed excellent internal consistency, with α =.95 overall,.94 for state anxiety, and.90 for trait anxiety.

Depression: Was assessed using the Spanish version of the Beck Depression Inventory-II (BDI-II) (21,22). This self-report questionnaire includes 21 items rated on a 4-point Likert scale (0–3), measuring the presence and severity of depressive symptoms. Total scores range from 0 to 63, with categories for minimal (0–13), mild (14–19), moderate (20–28), and severe depression (29–63). In this study, the BDI-II demonstrated excellent internal consistency (α =.93).

Neuroticism and extraversion were assessed using the Spanish version of the reduced NEO Five Factor Inventory (NEO-FFI) (23,24). This 60-item questionnaire measures five personality factors: extraversion, agreeableness, conscientiousness, neuroticism, and openness. In this study, only neuroticism and extraversion were analyzed. Internal consistency in this sample was good for the overall scale (α =.80) as well as for the two subscales (α =.82 for neuroticism; α =.85 for extraversion).

Emotional dysregulation: Was measured using the Spanish adaptation of the Difficulties in Emotion Regulation Scale (DERS) (25,26). This 28-item instrument evaluates five key dimensions: (i) Lack of control (challenges in managing impulsivity and limited access to regulation strategies), (ii) rejection (denial of both personal and others’ emotional experiences), (iii) interference (obstacles to goal-directed behavior during emotional agitation), (iv) inattention (difficulty recognizing one’s own emotions, reflecting low emotional awareness), and (v) confusion (trouble distinguishing emotions, indicating poor emotional clarity). In the current sample, the scale demonstrated good reliability, with internal consistency coefficients ranging from α =.73 to.92 across subscales and α =.89 for the overall scale.

Social support: Was evaluated with the Spanish adaptation of the Duke Functional Social Support Questionnaire (27,28). This 11-item instrument measures perceived emotional and confidential support using a 5-point Likert scale. Total scores can range from 11 to 55, where higher scores reflect stronger perceived support. In this study, the scale demonstrated good internal consistency (α =.85).

Procedure

The study used a cross-sectional correlational approach. Participants were adults referred by their clinicians after an initial consultation and were evaluated in person at adult mental health or primary care facilities. Eligibility criteria included being over 18 and meeting Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnostic criteria for anxiety, depressive, obsessive-compulsive, trauma-related, or somatic symptom disorders. Individuals were excluded if they were minors, had cognitive impairments, severe physical illnesses, or were diagnosed with substance-induced disorders, bipolar disorder, schizophrenia, addictions, neurodevelopmental, neurocognitive, personality, or eating disorders.

The research protocol was approved by the ethics committees of the participating institutions. All participants received detailed verbal and written information, provided informed consent, and were guaranteed confidentiality and the option to withdraw at any point. To reduce potential bias from fatigue or demotivation, the order of the questionnaires was varied across participants.

Data analysis

Descriptive statistics and correlation analyses were conducted using IBM SPSS Statistics 28. Additionally, fuzzy-set qualitative comparative analyses (fsQCA) were performed using fsQCA 2.5 software (29).

For the fsQCA analyses, the outcome conditions were anxiety and depression. The causal conditions included personality traits (neuroticism and extraversion), emotional dysregulation dimensions (lack of control, rejection, interference, inattention, and confusion), and social support.

Prior to the analyses, the raw composite scores of all variables were calibrated into fuzzy-set membership scores ranging from 0 to 1 using the fsQCA software. Following Ragin (29), the calibration was based on three qualitative thresholds corresponding to full non-membership, the crossover point, and full membership, established at the 10th, 50th, and 90th percentiles of each variable, respectively. This calibration procedure transforms the original scores into fuzzy-set membership values and therefore the calibrated values differ from the original score ranges of the instruments.

Two fsQCA analyses were conducted. First, a necessity analysis was performed to identify conditions that were necessary for the occurrence of the outcome, considering consistency values ≥.90 together with coverage indices (29). Second, a sufficiency analysis was conducted to identify combinations of causal conditions (pathways) associated with the outcomes. Solutions with consistency values above.74 were considered acceptable (30), and raw coverage was used to estimate the proportion of the outcome explained by each pathway (29).

Results

Descriptive analysis

The results of the descriptive analyses are shown in Table 1.

In terms of anxiety symptoms, male participants had notably high levels: 98% were classified within the extreme range and 2% within the high range for state anxiety; regarding trait anxiety, 95% were in the extreme range, 3% in the high range, and 2% in the medium range. Among female participants, similarly elevated levels were identified: 73% scored in the extreme range, 23% in the high range, and 4% in the medium range for state anxiety; and 58% in the extreme range, 23% in the high range, 17% in the medium range, and 2% in the normal range for trait anxiety. Regarding depressive symptomatology, 49% of the sample exhibited severe depressive symptoms, 21% moderate symptoms, 21% mild symptoms, and 14% minimal symptoms.

Correlation analysis

Significant correlations were observed among the variables analyzed. First, depression was positively correlated with anxiety, both with state (r =.766, p <.01) and trait (r =.766, p <.01). Regarding personality traits, neuroticism was positively correlated with state anxiety (r =.663, p <.01), trait anxiety (r =.811, p <.01) and depression (r =.705, p <.01). Conversely, extraversion was negatively correlated with state anxiety (r = -.512, p <.01), trait anxiety (r = −.567, p <.01); and depression (r = −.567, p <.01). In terms of emotional regulation, higher emotional dysregulation score (total scale) was positively related to state anxiety (r =.686, p <.01), trait anxiety (r =.689, p <.01); and depression (r =.725, p <.01). Finally, social support was negatively correlated with anxiety state (r = −.320, p <.05), anxiety trait (r = −.269, p <.05); and depression (r = −.277, p <.05).

Fuzzy-set comparative qualitative analysis

First, to carry out the QCA models, the calculated calibration values are presented (Table 2). Then, necessity analyses were performed, followed by those of sufficiency, as suggested in the literature. Anxiety and depression were established as the criterion variable (outcome condition according to QCA terminology).

Necessity analysis

Based on the results obtained in the Necessity analysis, there was no necessary condition for the high or low levels of anxiety or depression based on the studied variables, since all consistency values were below.90 (29).

Sufficiency analysis

Regarding the sufficiency analysis, resulting models for anxiety and depression were shown in Table 3, based on the premise that in QCA a model is informative when the consistency is above.74 (30).

Regarding anxiety models, for high levels of anxiety, nine pathways explained 71% of cases (overall consistency =.91; overall coverage =.71). The three most relevant pathways were: (a) high neuroticism, low extraversion, high interference and rejection, and low social support (consistency =.97; coverage =.47); (b) high interference and rejection, and low confusion (consistency =.95; coverage =.43); and (c) high neuroticism, low extraversion, high lack of control, rejection and inattention and low social support (consistency =.97; coverage =.32). These combinations explained 47%, 43% and 32% of high anxiety cases, respectively. For low levels of anxiety, eight pathways explained 85% of cases (overall consistency =.77; overall coverage =.85). The three most relevant were: (a) low confusion and interference (consistency =.81; coverage =.65); (b) low neuroticism and low lack of control, rejection, inattention and confusion (consistency =.92; coverage =.55); and (c) low interference and low social support (consistency =.87; coverage =.52). These explained 65%, 55% and 52% of the low anxiety cases, respectively.

Regarding depression models, for high levels of depression, seven pathways explained 69% of cases (overall consistency =.96; overall coverage =.69). The three most relevant pathways were: (a) high inattention and confusion, and low social support (consistency =.96; coverage =.38); (b) high neuroticism, low extraversion, and high rejection and inattention (consistency = 1; coverage =.36); and (c) high neuroticism, low extraversion, high rejection and interference, low confusion and low social support (consistency =.97; coverage =.35). These combinations explained 36%, 43% and 35% of high anxiety cases, respectively. For low levels of depression, seven pathways explained 86% of cases (overall consistency =.83; overall coverage =.86). The three most relevant were: (a) low neuroticism, high extraversion and low inattention (consistency =.90; coverage =.57); (b) low neuroticism, high extraversion, low lack of control, rejection, inattention and confusion (consistency =.93; coverage =.57); and (c) high extraversion and low rejection, inattention and confusion (consistency =.93; coverage =.56). These explained 57%, 57% and 56% of the low depression cases, respectively.

Discussion

The aim of the present study was to analyze how transdiagnostic factors (personality traits, emotional regulation and social support) are associated with the presence of anxiety and depression symptomatology, identifying and classifying these factors into risk or protective profiles through correlations and QCA analysis in a clinical adult population in Spain.

As expected, the clinical sample presented markedly elevated levels of depression and anxiety, with anxiety state being particularly pronounced. This finding aligns with prior evidence highlighting the high prevalence and comorbidity of depressive and anxiety disorders, which rank among the leading contributors to the global health burden and affect a significant proportion of the population in Spain (1-3).

Our first hypothesis proposed that neuroticism would be a negative predictor and extraversion a positive predictor of mental health; specifically, that higher levels of neuroticism and lower levels of extraversion would be associated with higher levels of anxiety and depression. Our findings supported this hypothesis and align with previous research. Based on our correlational analyses, our analyses showed strong positive correlations between neuroticism and both anxiety and depression, indicating that higher neuroticism is linked to worse mental health outcome (8-10). In contrast, extraversion demonstrated moderate, negative correlations with these symptoms, supporting its role as a protective factor: individuals reporting higher extraversion tend to experience lower levels of anxiety and depression (11,12). Furthermore, the QCA models revealed that high neuroticism combined with low extraversion constituted sufficient conditions for elevated anxiety and depression, underscoring their relevance as vulnerability factors. Conversely, low neuroticism together with high extraversion emerged among the sufficient conditions associated with lower depression levels, reinforcing their protective influence on mental health.

Following hypothesis two, it was proposed that emotional dysregulation would be a negative predictor of mental health, showing positive associations with both anxiety and depression. Consistent with prior research, our findings confirm this expectation: emotional dysregulation was strongly and positively correlated with anxiety and depressive symptoms. This supports the notion that individuals with high levels of emotional dysregulation tend to be more impulsive and have a reduced capacity for self-regulation, making them more vulnerable to psychopathologies such as anxiety and depression (16). Furthermore, the QCA models showed that several subscales of emotional dysregulation were part of the sufficient combinations predicting high levels of anxiety and depression—specifically, the presence of rejection and interference. Conversely, the absence of interference and inattention appeared among the sufficient conditions for lower levels of these symptoms (13,14). It should be noted that an unexpected result was observed in relation to the confusion subscale: its absence (lower scores indicating less trouble distinguishing emotions and greater emotional clarity) was identified as sufficient for higher levels of anxiety and depression. At first, this seems contrary to what one might expect, since better emotional clarity is typically associated with better mental health outcomes. However, this finding could suggest that, in certain individuals, high emotional clarity coexists with greater awareness of distressing thoughts and feelings, which could amplify psychological symptoms rather than mitigate them. In other words, being highly aware of negative emotional states without having the corresponding ability to regulate them or distance oneself from them could, paradoxically, increase vulnerability to anxiety and depression. This interpretation aligns with literature indicating that emotional clarity, when not accompanied by adequate emotion regulation strategies, may intensify rather than alleviate psychopathological symptoms (15). Therefore, our results suggest that emotional clarity alone may not provide consistent protection; its adaptive value likely depends on how it interacts with other regulatory processes.

Finally, according to our third hypothesis, it was proposed that social support would be a positive predictor of mental health, showing negative associations with anxiety and depression. The results of our study are partially consistent with the existing literature. Regarding correlation analysis, although negative correlations were observed between perceived social support and psychopathological variables, these correlations were weak. On the contrary, the sufficiency models from the QCA analyses indicate that social support does function as a protective factor, as its presence appeared in combinations predicting low levels of anxiety, while its absence was associated with high levels of both anxiety and depression. These findings align with previous research that highlights social support as an important indicator of psychological well-being and a key facilitator of psychosocial adjustment in the context of mental disorders (17-18).

Finally, it is important to highlight that the integration of complementary methodologies enriches our understanding of risk or protective mental health factors. While traditional linear techniques, such as correlational analyses, clarify the direction and strength of associations between individual variables and these symptoms, configurational approaches like QCA reveal how different combinations of factors can lead to similar anxiety and depression outcomes. This dual approach allows for the identification of both common and distinct pathways contributing to these disorders, providing a more comprehensive and nuanced perspective. Such a multidimensional analytic strategy is especially valuable for advancing research on transdiagnostic factors and for guiding the development of more targeted and effective clinical interventions.

Despite the valuable insights offered by this study, several limitations must be considered. Firstly, the cross-sectional and correlational nature of the design restricts our capacity to establish causal relationships; future studies employing longitudinal or clinical designs could offer a deeper understanding of these associations. Furthermore, the use of non-probabilistic sampling, the overrepresentation of women in the sample, and the focus on participants from the Valencian Community constrain the generalizability of the results. Additionally, although self-report questionnaires remain a widely accepted research method and the instruments used demonstrated solid psychometric properties and were administered with counterbalancing, it is still possible that responses were influenced by social desirability bias. We acknowledge these limitations and aim to address them in subsequent research.

In summary, these findings highlight the multifaceted role of transdiagnostic factors in shaping mental health outcomes within clinical populations. Neuroticism and emotional dysregulation act as significant risk factors for anxiety and depression, whereas extraversion and social support serve as protective factors. Importantly, our results underscore that emotional clarity, often assumed to be universally beneficial, may increase vulnerability when not supported by effective regulatory strategies. Together, these insights advance our understanding of the complex interplay among personality, emotion regulation, and social context in mental health and offer directions for tailored prevention and intervention strategies that target these transdiagnostic processes.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Conflicts of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Artificial intelligence declaration

Artificial intelligence tools were used exclusively to improve language clarity, grammar, and readability. All scientific content, interpretation of findings, and final revisions were reviewed and approved by the authors, who take full responsibility for the manuscript.

References

Author notes

^a Correspondence author: selene.valero@uv.es

Additional information

How to cite: Ribera-Asensi O, Giménez-Benavent S, Pérez-Marín M, Valero-Moreno S. Anxiety and depression in a clinical population: transdiagnostic factors in mental health. Univ Med. 2026;67. https://doi.org/10.11144/Javeriana.umed67.adcp

Descargar