Abstract
Purpose
Epilepsy is a chronic neurological disorder with complex etiology and high recurrence rates, often leading to substantial impairment in patients’ quality of life (QoL). Within the biopsychosocial (BPS) framework, multiple biological, psychological, and social factors interact to influence QoL. Cognitive dysfunction is common in epilepsy and may play a critical role in this process. This study aimed to investigate the mediating role of cognitive function in the relationship between BPS factors and QoL in patients with epilepsy.
Patients and Methods
A total of 252 patients with epilepsy treated at the First Affiliated Hospital of Anhui Medical University between 2022 and 2024 were included. Participants aged 14–66 years were included, representing late adolescents and adults with epilepsy treated in a tertiary clinical setting. Demographic and clinical characteristics were collected using a self-designed questionnaire. Anxiety, depression, stigma, and social functioning were assessed using the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), Kilifi Stigma Scale for Epilepsy (KSSE), and Social Disability Screening Schedule (SDSS), respectively. Pearson correlation analysis was used to examine associations among BPS factors, cognitive function, and QoL. Mediation analysis was performed to evaluate the mediating effect of cognitive function.
Results
Disease duration, number of anti-seizure medications, anxiety, depression, perceived stigma, and social functioning impairment were all significantly negatively correlated with both cognitive function and QoL (all p < 0.001). Mediation analysis demonstrated that cognitive function partially mediated the associations between each BPS factor and QoL, with all indirect effects reaching statistical significance.
Conclusion
Cognitive function mediates the impact of various biopsychosocial (BPS) factors on the quality of life of patients with epilepsy. This finding suggests that clinical management should place greater emphasis on improving patients’ cognitive function to enhance their overall quality of life.
Keywords: quality of life, cognitive function, biopsychosocial model, psychosocial factors, mediation analysis
Introduction
Epilepsy is a chronic neurological disorder arising from abnormal neuronal discharges in the brain, with its core feature being recurrent epileptic seizures. According to the latest definition by the International League Against Epilepsy (ILAE), the diagnosis of epilepsy can be established based on either two unprovoked seizures or one seizure accompanied by a high risk of recurrence.1 The etiology of epilepsy is highly complex and can be categorized into structural (eg, traumatic brain injury, stroke, brain tumors), genetic, infectious, metabolic, and immune-related causes.2 However, recent studies have shown that approximately 30–40% of patients with epilepsy are classified as having idiopathic or epilepsy of unknown etiology, for which the specific underlying causes remain unelucidated.3 Particularly for subtypes such as temporal lobe epilepsy (TLE) and focal epilepsy, their complex etiologies and high refractoriness significantly impair patients’ health-related quality of life (HRQoL).4 According to research by Thomas et al5 the high recurrence rate and chronic disease course of epilepsy not only lead to neurological dysfunction but may also trigger psychological and social adjustment disorders. In summary, epilepsy is a chronic brain disorder characterized by diverse etiologies, complex mechanisms, and a high tendency for relapse. Accumulating evidence indicates that recurrent epileptic seizures trigger sustained neuroinflammatory and oxidative stress cascades. The excessive production of reactive oxygen species and redox dysregulation interact bidirectionally with glia-mediated inflammation, forming a vicious cycle that promotes neuronal dysfunction and network hyperexcitability in epilepsy,6,7 ultimately impairing patients’ cognitive function. It involves not only neurological dysfunction but also profoundly affects patients’ mental health and social adaptive functioning.
Quality of Life (QoL), a crucial health measurement indicator proposed by the World Health Organization, refers to an individual’s comprehensive reflection of their subjective perception and objective functional status within the physical, psychological, and social domains. For patients with epilepsy, QoL is influenced not only by seizure frequency and medication side effects but also profoundly by the combined effects of multiple factors such as psychological state and social functioning. Quality of life assessment has become a key indicator for comprehensive health management and intervention outcomes in patients with epilepsy.8 Studies have shown that the quality of life (QoL) of patients with epilepsy is significantly lower than that of the general population, and further declines in QoL are observed particularly in the presence of comorbidities such as anxiety and depression.9 Frequent seizures and the side effects of antiseizure medications significantly reduce the quality of life in patients with epilepsy.10 The longitudinal study by Cramer et al11 further indicated that during the treatment of seizure clusters, patients’ scores on the social functioning domain increased alongside improvements in treatment, suggesting that social functioning status is malleable and closely related to disease management. Additionally, a study by Yan et al12 based on a Chinese population found that patients’ self-blame, paranoid ideation, psychological resilience, and social support play significant roles in modulating quality of life. This further confirms that quality of life is influenced by the multidimensional interplay of biological, psychological, and social factors. Therefore, quality of life assessment has become a core indicator for evaluating the comprehensive health status and intervention outcomes in patients with epilepsy. Focusing on its multidimensional influencing factors is of significant importance for improving lifelong health in this patient population.
The biopsychosocial model, initially proposed by Engel,13 posits that health and illness are not only influenced by physiological factors but are also profoundly modulated by psychological and social contexts. Elliott and Richardson systematically applied this model to the study of quality of life in epilepsy, positing that the side effects of anti-seizure medications (ASMs), seizure control levels (biological dimension), anxiety and depression (psychological dimension), and social factors (social dimension) collectively determine the quality of life in patients with epilepsy.14 A recent systematic review further demonstrates that biological factors (eg, medications), psychological state, and social context interact and influence each other, with changes in QoL often arising from their combined effects.1 While substantial evidence confirms that biological, psychological, and social dimensions collectively influence the quality of life in patients with epilepsy, other underlying factors affecting their quality of life remain to be explored.
Decline in cognitive function is an important determinant of reduced quality of life in patients with epilepsy, and is particularly pronounced in those with temporal lobe epilepsy and drug-resistant epilepsy.2 Research indicates that long-term seizure frequency and the use of anti-seizure medications, particularly polytherapy, can significantly impair patients’ attention, memory, and executive function.15 Through mediation analysis, Lozano-García et al2 found that the number of ASMs used indirectly affects QoL via cognitive function, with cognitive impairment serving as a significant mediating factor in the decline of quality of life. Giovannetti et al16 also noted that cognitive and psychological factors exert joint effects, collectively influencing the social functioning and QoL of patients with epilepsy. This highlights that improving cognitive function may serve as an important target for QoL interventions. Therefore, focusing on cognitive function in epilepsy represents another important direction for clinical research aimed at exploring how biopsychosocial factors influence QoL.
Mediation analysis is an effective statistical method for elucidating the pathways through which variables interact. It is commonly used to examine the underlying mechanism by which an independent variable influences a dependent variable through a mediator variable.17 Mediation analysis has been widely applied in neuroscience research. Zhao et al18 applied mediation analysis to brain functional connectomics, elucidating the mediating pathways through which brain functional networks link therapeutic interventions to behavioral outcomes. In epilepsy research, similar studies have been reported. Lozano-García et al2 employed a multiple mediation model, verifying that the number of anti-seizure medications used indirectly influences quality of life through cognitive function. Previous studies have identified numerous biological, psychological, and social factors associated with quality of life in epilepsy. At the biological level, seizure frequency and antiseizure medication use have been consistently linked to poorer quality of life.19,20 At the psychological level, depression and anxiety have been recognized as major independent predictors of reduced quality of life.21 At the social level, factors such as perceived stigma and diminished social support have also been shown to negatively affect patient outcomes.22,23 However, these factors have typically been investigated in isolation or within single-domain frameworks, with limited consideration of their interrelationships. As a result, it remains unclear how multiple biopsychosocial factors jointly influence quality of life and whether cognitive function serves as a common pathway linking these factors to patient outcomes. Addressing this gap may provide a more integrated understanding of quality of life in epilepsy and help identify potential targets for intervention. Therefore, the present study employed a biopsychosocial framework and mediation analysis to examine the direct and indirect pathways through which biological, psychological, and social factors influence quality of life via cognitive function.
Research Methods
Participants
A total of 252 patients diagnosed with epilepsy and treated at our hospital between 2022 and 2024 were included in this study.
The inclusion criteria were as follows: (1) Diagnosis of epilepsy meeting the International League Against Epilepsy (ILAE) criteria; (2) Age between 14 and 66 years, regardless of gender; (3) Use of at least one anti-seizure medication; (4) No restrictions regarding the etiology or type of epilepsy; (5) Ability to read, comprehend, and complete a series of self-reported scales; (6) Voluntary participation with signed informed consent.
The exclusion criteria were as follows: (1) Individuals who lacked self-assessment capacity or declined to participate; (2) Patients with progressive central nervous system diseases such as acute stroke, malignant or metastatic brain tumors, or various forms of encephalitis in the acute phase; (3) Individuals diagnosed with dementia or severe psychiatric disorders, such as schizophrenia; (4) Patients with a history of severe systemic diseases affecting the heart, lungs, liver, kidneys, or hematopoietic system; (5) Patients who declined to participate in the study.
The lower age limit of 14 years was selected because patients at this stage are typically managed within adult neurology services in our clinical setting and are capable of completing standardized neuropsychological assessments reliably.
General Information and Clinical Data
General and clinical data were collected using a self-designed questionnaire.Demographic information included age, gender, years of education, place of residence, occupation, household economic status, history of smoking and alcohol use, and marital status. Clinical data encompassed epilepsy duration (in months), number of anti-seizure medications used, specific medication names, seizure type, current disease status (remission, relapse, or drug-resistant), age at first seizure, seizure frequency, and epilepsy-related medical history.
Biopsychosocial Model Assessment
In the present study, the Hamilton Anxiety Rating Scale (HAMA) and the Hamilton Depression Rating Scale (HAMD) were employed as psychological assessment tools to evaluate the severity of anxiety and depressive symptoms in participants.24
According to the Hamilton Anxiety Rating Scale (HAMA), a total score greater than 29 suggests severe anxiety in the participant; a score greater than 21 indicates marked anxiety; a score greater than 14 reflects definite anxiety; a score greater than 7 denotes possible anxiety; and a score below 7 is interpreted as no significant anxiety.
Based on the Hamilton Depression Rating Scale (HAMD), a total score exceeding 24 suggests that the participant may be experiencing severe depression. A score between 18 and 24 indicates the likelihood of moderate depression, while a score ranging from 7 to 17 reflects mild depression. A score below 7 is generally interpreted as no clinically significant depression.
Stigma is recognized as an important psychosocial variable affecting quality of life.25 To assess the level of perceived stigma experienced by participants, the Kilifi Stigma Scale for Epilepsy (KSSE) was employed as the measurement tool.26 The scale comprises 15 items, each rated on a 3-point scale and scored from 0 to 2. A higher total score indicates a greater level of perceived stigma experienced by the participant.
To assess the social functioning of participants, the Social Disability Screening Schedule (SDSS) was employed as the assessment tool.27 The scale consists of 10 items. A total score of 2 or higher indicates the presence of social functioning deficits in the participant.
In summary, this study classified disease duration, number of medications, and other similar variables as biological factors; anxiety, depression, and stigma as psychological factors; and social functioning as a social factor, thereby forming a complete biopsychosocial (BPS) model framework.
Cognitive Function
To assess the cognitive function of participants, the Montreal Cognitive Assessment (MoCA) was employed as the measurement tool in this study.28 The MoCA provides a brief and multidomain evaluation of cognitive functioning. In epilepsy research, the MoCA has been widely applied as a clinically feasible cognitive screening instrument, particularly in large clinical samples where comprehensive neuropsychological batteries are not practical. Since the aim of the present study was to examine overall cognitive functioning as a global mediator within the biopsychosocial framework rather than domain-specific cognitive deficits, a global cognitive screening measure was considered appropriate. The MoCA has a maximum total score of 30 points. It comprises eight subdomains: visuospatial/executive functions, naming, memory, attention, language, abstraction, delayed recall, and orientation, with a total possible score of 30. A score below 26 points indicates potential cognitive impairment in the participant. For patients with a lower level of education (educational attainment of 12 years or less), one point may be added to the total score to reduce the false positive rate.
Quality of Life
To assess the quality of life of participants, the Quality of Life in Epilepsy Inventory-31 (QOLIE-31) was employed as the measurement tool in this study.29 The QOLIE-31 consists of 31 items organized into seven subscales: Seizure Worry, Overall Quality of Life, Emotional Well-being, Energy/Fatigue, Cognitive Functioning, Medication Effects, and Social Functioning. The scale employs multiple scoring methods. After standardization, the score of each subscale is converted to a unified scale ranging from 0 to 100. The total score is calculated as a weighted sum of the subscale scores, with higher scores indicating better quality of life.
Statistical Analysis
Statistical analyses were performed using R software. All continuous variables were mean-centered, and missing values were handled via complete-case analysis. Pearson correlation analysis was employed to examine the relationships among biopsychosocial variables, cognitive function and quality of life.
Mediation analysis was conducted using the lavaan package in R software. A model incorporating the following variables was constructed: one independent variable, one dependent variable, and one mediator variable. In this model, factors from the biopsychosocial (BPS) model were specified as the independent variable, quality of life as the dependent variable, and cognitive function as the proposed pathway linking BPS factors to quality of life. This procedure was used to assess both direct and indirect (mediated) effects. The direct effect represents the association between BPS factors and quality of life. The indirect (mediated) effect represents the association between BPS factors and quality of life that is transmitted through the mediator, cognitive function. To eliminate the potential confounding effects of demographic and clinical characteristics on the mediation pathways, age, years of education and seizure frequency were statistically controlled for as covariates in all mediation models. The significance of the regression coefficients for the hypothesized indirect effects was evaluated using the bias-corrected Bootstrap method with 5000 resamples. A 95% confidence interval (95% CI) that did not include zero and a p-value < 0.05 were considered indicative of a statistically significant effect. Multicollinearity among predictor variables was assessed using Variance Inflation Factors (VIF), with VIF values <5 considered indicative of acceptable independence between variables. Missing data were handled using full information maximum likelihood (FIML), which allows estimation under the assumption of missing at random. Because the models were fully saturated (df = 0), global model fit indices (eg, CFI, RMSEA, SRMR) were not computed, as they are mathematically undefined for saturated models that perfectly reproduce the observed covariance structure by definition. Model adequacy was instead evaluated through the statistical significance and direction of individual path coefficients, the magnitude and confidence intervals of indirect effects, and variance explained (R2) for each outcome variable. All analyses were performed using R (version 4.4.3, http://www.R-project.org, The R Foundation).
Results
General Characteristics of the Participants
The sociodemographic and clinical characteristics of the 252 participants are summarized in Table 1.
Table 1.
Sociodemographic Characteristics, Clinical Features, and Scale Scores of the Patients
| Variable | Mean ± SD / n (%) |
|---|---|
| Age, years, Mean ± SD | 31.7 ± 10.4 |
| Gender, n (%) | |
| Male | 51% |
| Female | 49% |
| Body Mass Index (BMI), Mean ± SD | 23.3 ± 2.6 |
| Years of Education, Mean ± SD | 10.5 ± 2.7 |
| Unemployed or not in education, n(%) | 17% |
| Household Monthly Income, n (%) | |
| ≤ 3000 CNY | 38% |
| 3000–5000 CNY | 27% |
| 5000–10000 CNY | 19% |
| >10000 CNY | 15% |
| Disease Duration (DD), months, Mean ± SD | 80.1 ± 91.1 |
| Age at Seizure Onset, years, Mean ± SD | 24.7 ± 11.5 |
| Current Disease Status, n (%) | |
| Controlled | 69% |
| Drug-resistant | 31% |
| Seizure Type, n (%) | |
| Generalized onset | 56% |
| Focal onset | 35% |
| Unknown onset | 9% |
| Etiology of Epilepsy, n (%) | |
| Structural causes | 32% |
| Genetic causes | 5% |
| Infectious causes | 8% |
| Metabolic causes | 3% |
| Immune causes | 8% |
| Unknown etiology | 45% |
| Number of Anti-seizure Medications, Mean ± SD | 1.8 ± 0.9 |
| Mood Disorder Scores, Mean ± SD | |
| Depression (HAMD) | 7.2 ± 5.6 |
| Anxiety (HAMA) | 5.6 ± 5.0 |
| QOLIE-31 Scores, Mean ± SD | |
| Total Score | 55.9 ± 9.6 |
| Seizure Worry | 4.0 ± 1.7 |
| Overall Quality of Life | 8.0 ± 1.6 |
| Emotional Well-being | 8.7 ± 1.9 |
| Energy/Fatigue | 7.0 ± 1.6 |
| Cognitive Functioning | 14.9 ± 5.3 |
| Medication Effects | 1.4 ± 0.6 |
| Social Functioning | 11.8 ± 2.9 |
| MoCA Score, Mean ± SD | 24.0 ± 3.5 |
| SDSS Score, Mean ± SD | 6.4 ± 3.9 |
| KSSE Score, Mean ± SD | 8.3 ± 5.9 |
Results of Correlation Analysis
The correlation matrix of all study variables is presented in Figure 1. The analysis revealed a significant positive correlation between disease duration and the number of anti-seizure medications used (r = 0.736, p < 0.001), suggesting that a longer disease duration is associated with a greater number of prescribed medications. Disease duration was also positively correlated with HAMA (r = 0.236, p < 0.001) and HAMD (r = 0.231, p < 0.001) scores, indicating that prolonged illness may be accompanied by an increase in anxiety and depressive symptoms. Furthermore, disease duration showed significant positive correlations with stigma (r = 0.275, p < 0.001) and Social Disability Screening Schedule (SDSS) scores (r = 0.175, p < 0.01), implying that longer disease duration is linked to greater perceived stigma and poorer social functioning. The number of medications was positively correlated with HAMA (r = 0.285, p < 0.001) and HAMD (r = 0.335, p < 0.001) scores. Concurrently, the number of medications was correlated with stigma (r = 0.316, p < 0.001) and SDSS scores (r = 0.253, p < 0.001), suggesting that polytherapy may exert negative effects on both psychological and social factors.
Figure 1.
Correlation heatmap of study variables. Correlation matrix. The matrix presents Pearson correlation coefficients among the primary study variables (DD, ASMs, HAMA, HAMD, stigma, SDSS, MoCA, and QoL). Values represent correlation coefficients, with the diagonal (1.0) indicating perfect self-correlation. ***p < 0.001; **p < 0.01. NA, not applicable (p-values are not reported for diagonal self-correlation elements).
Abbreviations: DD, disease duration; ASMs, Anti-Seizure Medications; HAMA, Hamilton Anxiety; HAMD, Hamilton Depression; SDSS, Social Disability Screening Schedule; MoCA, Montreal Cognitive Assessment; QoL, quality of life.
A significant positive correlation was observed between HAMA and HAMD scores (r = 0.673, p < 0.001), indicating a strong co-occurrence of anxiety and depressive symptoms. SDSS scores were positively correlated with both HAMA (r = 0.228, p < 0.001) and HAMD (r = 0.283, p < 0.001). Similarly, stigma scores showed positive correlations with HAMA (r = 0.307, p < 0.001) and HAMD (r = 0.343, p < 0.001). A significant positive correlation was observed between stigma and SDSS scores (r = 0.487, p < 0.001).
Furthermore, MoCA scores showed a positive correlation with the total quality of life score (r = 0.263, p < 0.001), suggesting that better cognitive function is associated with higher quality of life. Conversely, the total quality of life score demonstrated significant negative correlations with disease duration (r = −0.309, p < 0.001), number of anti-seizure medications (r = −0.375, p < 0.001), stigma (r = −0.523, p < 0.001), HAMA scores (r = −0.349, p < 0.001), HAMD scores (r = −0.341, p < 0.001), and SDSS scores (r = −0.337, p < 0.001). These findings indicate that longer disease duration, a greater number of medications, more severe anxiety and depressive symptoms, and higher levels of social functioning impairment are all associated with lower quality of life.
Mediation Analysis Results
The mediation pathway diagrams for all six models are illustrated in Figure 2. VIF values for all predictor variables ranged from 1.050 to 2.462 (DD = 2.256, ASMs = 2.462, HAMA = 1.891, HAMD = 1.985, stigma = 1.457, SDSS = 1.364, age = 1.050, education = 1.068), all below the accepted threshold of 5.0, confirming the absence of problematic multicollinearity. Post-hoc Monte Carlo power analyses indicated that statistical power ranged from 0.991 to 0.999 across all six models, confirming the robustness of the present findings.
Figure 2.
Mediation model of cognitive function between BPS factors and QoL. Mediation pathway diagrams illustrating the mediating role of MoCA between various influencing factors and quality of life (QoL) in patients with epilepsy. (A) Mediation pathway of disease duration (DD) on QoL through MoCA. (B) Mediation pathway of the number of anti-seizure medications (ASMs) on QoL through MoCA. (C) Mediation pathway of anxiety symptoms (HAMA score) on QoL through MoCA. (D) Mediation pathway of depressive symptoms (HAMD score) on QoL through MoCA. (E) Mediation pathway of stigma on QoL through MoCA. (F) Mediation pathway of social dysfunction (SDSS score) on QoL through MoCA. In all models, each factor negatively affected MoCA, which in turn positively predicted QoL, with both direct and indirect effects reaching statistical significance. β values represent standardized regression coefficients. All indirect effects were tested using the Bootstrap method.
Abbreviations: MoCA, Montreal Cognitive Assessment; QoL, quality of life; DD, disease duration; ASMs, number of anti-seizure medications; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; SDSS, Social Disability Screening Schedule.
According to the path analysis results, disease duration, number of medications, stigma, anxiety, depression, and social functioning deficits all showed negative effects on quality of life (disease duration: β = −0.027, p < 0.001; number of medications: β = −3.599, p < 0.001; anxiety: β = −0.498, p < 0.001; depression: β = −0.550, p < 0.001; stigma: β = −0.798, p < 0.001; social functioning deficits: β = −0.698, p < 0.001). Concurrently, disease duration, number of medications, stigma, anxiety, depression, and social functioning deficits also exerted negative effects on MoCA scores, ie, cognitive function (disease duration: β = −0.008, p = 0.010; number of medications: β = −0.956, p < 0.001; anxiety: β = −0.173, p < 0.001; depression: β = −0.208, p < 0.001; stigma: β = −0.162, p < 0.001; social functioning deficits: β = −0.350, p < 0.001).
Further mediation analysis revealed that cognitive function played a significant partial mediating role in the relationship between these variables and quality of life. Specifically, within the biological dimension, cognitive function partially mediated the effect of disease duration on QoL, with a significant indirect effect (p = 0.025, 95% CI [−0.009, −0.001]). This indicates that as disease duration increases, patients’ cognitive function declines, which in turn further affects their quality of life. The total effect of the number of anti-seizure medications on quality of life was partially mediated by cognitive function (p = 0.012, 95% CI [−0.907, −0.163]), suggesting that polytherapy leads to cognitive decline, thereby negatively impacting QoL.
Within the psychological dimension, stigma, anxiety, and depression not only directly reduced quality of life but also exerted their influence partially through cognitive function (anxiety: p = 0.014, 95% CI [−0.110, −0.012]; depression: p = 0.014, 95% CI [−0.158, −0.028]; stigma: p = 0.031, 95% CI [−0.185, −0.033]). This indicates that psychological distress factors such as anxiety, depression, and stigma may impair quality of life by exacerbating cognitive dysfunction. In the social dimension, cognitive function also partially mediated the relationship between social functioning deficits and quality of life (p = 0.022, 95% CI [−0.263, −0.034]), demonstrating that impaired social functioning not only directly affects subjective quality of life but also amplifies its negative effect through cognitive function.
Discussion
Grounded in the biopsychosocial model, this study explored the influence of multiple factors on the decline in quality of life among patients with epilepsy, as well as the mediating role played by cognitive function. The results indicated that disease duration, number of anti-seizure medications, stigma, anxiety, depression, and social functioning all significantly affected quality of life. Furthermore, cognitive impairment was involved in this process and served a mediating role. All identified mediation pathways reached statistical significance (p < 0.05), and the overall trend supports cognitive function as a key mediating variable, though the partial nature of this mediation suggests that additional pathways beyond cognitive function are also operating. These findings are consistent with our initial hypothesis.
Biological factors constitute a key component of the biopsychosocial model. Factors such as disease duration, seizure frequency, lesion location, and the number of anti-seizure medications directly determine the neurological function and quality of life in patients with epilepsy. Prior studies have indicated that these factors, including disease duration, seizure frequency, lesion site, and anti-seizure medications, can affect patients’ cognitive and behavioral functions, thereby further influencing their quality of life.30,31 Our findings demonstrate that among biological factors, disease duration showed a significant negative correlation with quality of life, indicating that longer disease duration is associated with poorer quality of life, and a greater number of anti-seizure medications was likewise linked to reduced quality of life. Additionally, disease duration was negatively correlated with cognitive function, meaning that longer disease duration corresponded to worse cognitive performance. Further path analysis revealed that cognitive function was associated with the relationship between disease duration and QoL as a significant mediating variable, indicating that it serves as an important pathway through which biological factors may be linked to quality of life.
Long-term recurrent seizures in epilepsy may lead to impairment of neural network function, affecting cognitive domains such as memory and attention. At a mechanistic level, cumulative seizure activity and repetitive intermittent neuronal discharges can result in dysfunction of neural networks, thereby contributing to declines in memory, attention, and executive functions.32 Neuroimaging studies have shown that patients with long-term epilepsy exhibit reduced gray matter volume and impaired functional connectivity in the frontal and temporal lobes.33 While effectively controlling seizures, antiepileptic drugs are often associated with various adverse effects, such as drowsiness, impaired attention, and cognitive slowing. Recent studies in patients with epilepsy have found, through event-related potentials (N-200/P-300) and neuropsychological tests, that anti-seizure medications have been associated with slowed cognitive processing speed or attention deficits, which has been associated with reduced efficiency in daily activities and diminished life satisfaction.34 Thus, disease duration and medication burden may affect cognitive function by impairing neural plasticity and disrupting normal brain network connectivity, thereby contributing to reduced quality of life in patients. Emerging evidence suggests that neuroinflammatory pathways and novel therapeutic targets may represent important mechanisms through which seizure-related biological burden affects epilepsy-associated comorbidities, including cognitive decline.35,36 Furthermore, the specific biochemical mechanisms of individual ASMs—including modulation of ion channels, GABAergic signalling, and glutamate transmission—may contribute directly to cognitive side effects that vary in profile and severity across different agents.37 These neurobiological considerations further support the present finding that medication burden was associated with cognitive impairment as a significant indirect pathway to reduced QoL. Thus, longer disease duration and greater medication burden were associated with poorer cognitive performance, and cognitive function in turn was associated with reduced quality of life in this sample. Targeting interventions to address cognitive dysfunction may represent an important future approach to improving quality of life, pending confirmation in longitudinal studies.
As a crucial component of the biopsychosocial model, the emotional state of patients with epilepsy significantly affects their quality of life. Anxiety, depression, and perceived stigma—the subjective experience of being labeled or excluded—are among the most common psychiatric comorbidities in epilepsy, exerting negative effects on disease management, social adaptation, and treatment adherence. Scores on the Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) quantify the severity of anxiety and depressive symptoms, serving as important indicators for assessing patients’ mental health and its impact on their quality of life. Recent studies have indicated that mental health status is closely linked to the quality of life in patients with epilepsy, and in some cases, its impact may even surpass that of the biological characteristics of the disease itself.38 Anxiety and depressive symptoms are significantly negatively correlated with quality of life. Furthermore, these conditions frequently co-occur, and their negative impact on quality of life exhibits an additive effect.39 Previous research has shown that perceived stigma is an important independent predictor of reduced health-related quality of life in patients with epilepsy.40 Our results revealed a significant negative correlation between perceived stigma and quality of life, as well as between both HAMA and HAMD scores and quality of life, indicating a direct detrimental effect of emotional disorders on patients’ quality of life. Furthermore, path analysis suggested that stigma, anxiety, and depression may further reduce quality of life by impairing cognitive function, implying that cognitive function plays a mediating role between emotional disorders and quality of life.
Under conditions of chronic stress and depression, an imbalance between excitatory and inhibitory neurotransmitter systems, such as GABA and glutamate, can lead to functional abnormalities in the prefrontal cortex and related brain networks. These neurobiological disruptions subsequently trigger cognitive impairments through several interrelated pathways.41 Studies using structural and functional magnetic resonance imaging have revealed that patients with temporal lobe epilepsy comorbid with depression exhibit structural and functional abnormalities in brain regions such as the prefrontal cortex and hippocampus, which are involved in both emotional regulation and cognitive functioning.42 Therefore, anxiety and depression in patients with epilepsy may be associated with cognitive impairment through disruption of relevant brain networks, leading to dysfunction in networks such as the attentional and executive networks. Perceived stigma, as a psychosocial burden, undermines patients’ social identity, self-esteem, and willingness to engage in activities, thereby directly affecting their quality of life. A large-sample study conducted in southwestern China revealed that approximately 49% of adult patients with epilepsy experienced stigma, which was associated with significantly lower total scores on the QOLIE-31 and poorer performance across multiple subdomains, such as cognitive impairment and social functioning.43 In summary, a lack of social functioning and the presence of perceived stigma can weaken the positive effects of the social environment, leading to cognitive impairment and limiting social participation, thereby indirectly reducing the quality of life in patients with epilepsy. Future efforts should prioritize the assessment and intervention of mental health in this population, particularly through the integrated management of cognitive dysfunction, which may contribute to improving patients’ quality of life, though this requires verification through longitudinal intervention studies.
In the social dimension of the biopsychosocial model, social functioning represents an individual’s ability to maintain social roles, interpersonal relationships, and participation in everyday activities. In the present study, social functioning was operationalized using the Social Disability Screening Schedule (SDSS), which captures functional limitations. Our findings demonstrated that greater social functioning impairment was significantly associated with reduced quality of life in patients with epilepsy. This observation is consistent with recent evidence indicating that social functioning impairment is one of the strongest determinants of health-related quality of life in epilepsy populations. A recent systematic review reported that deficits in social functioning, social participation, and daily role engagement consistently predict poorer quality of life outcomes across different epilepsy subtypes.44 Similarly, large-scale meta-analytic evidence has highlighted social functioning as a central domain contributing to overall well-being beyond seizure control alone.45 Importantly, our mediation analysis demonstrated that cognitive function partially mediated the relationship between social functioning deficits and quality of life. Reduced social functioning may limit cognitive stimulation and environmental interaction, both of which are essential for maintaining cognitive performance. Conversely, cognitive impairment may further be associated with reduced social functioning, forming a reciprocal cycle that negatively affects overall functioning. Good social functioning may enhance the sense of social participation, self-efficacy, and cognitive resources, thereby being associated with improving daily living self-care abilities and social interactions, which in turn may contribute to elevated quality of life. Therefore, improving social functioning in patients with epilepsy, reducing social discrimination, and improving public health education may serve as important social intervention directions for enhancing their overall quality of life, pending confirmation from longitudinal and interventional research.
As anticipated, cognitive impairment was identified as a significant underlying mechanism associated with the relationship between BPS factors and QoL in patients with epilepsy. In other words, whether biological, psychological, or social in nature, these factors may ultimately contribute to a decline in patients’ quality of life by initiating or exacerbating cognitive dysfunction. Patients with epilepsy frequently experience cognitive impairments across multiple domains, including memory, attention, executive function, and language. These deficits not only directly affect their daily living abilities but also indirectly diminish their quality of life by exacerbating psychological issues such as anxiety and depression.15,46 Treatment with anti-seizure medications (ASMs), particularly polytherapy, increases the risk of cognitive impairment. Although some medications have a relatively minor impact on cognition, excessive medication burden or the use of high-risk agents may lead to declines in attention, memory, and executive function.47 Epilepsy-related abnormal neuronal discharges and remodeling of brain networks can disrupt the speed and synchrony of information processing, thereby contributing to impairments in memory and executive functions.48 Additionally, impairments in social cognition, such as deficits in emotion recognition and reduced empathy, further compromise patients’ social support and social functioning, thereby exacerbating the decline in their quality of life.49 In summary, epilepsy-related cognitive impairment results from the combined effects of multiple biological, psychological, and social factors, involving pathways such as neuronal abnormalities, disrupted brain networks, medication side effects, and psychological stress. It is important to note, however, that the mediation observed in the present study was partial rather than complete across all BPS pathways. This finding indicates that cognitive function, while a significant mediating variable, does not fully account for the statistical associations between BPS factors and QoL. Other mechanisms are likely to contribute to these pathways, including neuroinflammatory markers, illness perception, self-efficacy, and social identity processes. Future studies should incorporate these variables into more comprehensive mediation models to provide a more complete account of the mechanisms through which biopsychosocial factors affect quality of life in epilepsy. The innovation of this study lies in its systematic validation of the mediating role of cognitive function within the BPS model, suggesting that the decline in quality of life among patients with epilepsy is not attributable to a single-dimensional factor but rather emerges from the interplay of multiple contributing elements.
However, this study has several limitations. First, the sample included both late adolescents and adults, and developmental differences in cognitive maturation may have influenced the observed mediation pathways. In addition, the cross-sectional observational design precludes determination of temporal ordering or causal interpretation of the identified associations. Future longitudinal studies focusing on strictly adult or pediatric cohorts are warranted to further validate the developmental specificity and directionality of these pathways. Second, cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), which is primarily a global screening instrument rather than a comprehensive neuropsychological battery. Therefore, potential ceiling effects may exist in higher-functioning patients, and subtle or domain-specific cognitive differences may have been underestimated. Future studies employing more comprehensive neuropsychological assessments are needed to clarify the mediating roles of specific cognitive domains, such as attention, memory, and executive function. Third, although the sample size was adequate for detecting medium effects, it may still have limited the statistical power to identify small or more complex mediation effects. Moreover, using only global cognitive function as a mediator may oversimplify the biopsychosocial mechanisms underlying quality of life in patients with epilepsy. Future research with larger samples and multidimensional mediation models should examine cognitive, psychological, clinical, and social pathways together to better clarify the mechanisms influencing quality of life.
Conclusion
Taken together, from an integrated biopsychosocial perspective, this study highlights the pivotal mediating role of cognitive function in the decline of quality of life among patients with epilepsy. Cognitive impairment is not only a direct consequence of the disease itself but also reflects the combined effect of psychological and socio-environmental factors. Future efforts should explore the integrated optimization of cognitive rehabilitation, psychological intervention, and social functioning support systems as potential avenues for improving the quality of life for patients with epilepsy; however, the present cross-sectional findings require confirmation through prospective longitudinal and randomized intervention studies before firm clinical recommendations can be made.
Funding Statement
This work was supported by the Research Fund of the Anhui Institute of Translational Medicine (No. 2023zhyx-C34), the project of Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, The First Affiliated Hospital of Anhui Medical University (No. 2025KLCND03), and the Natural Science Foundation of China (No. 82090034).
Abbreviations
MoCA, Montreal Cognitive Assessment; DD, disease duration; ASMs, Anti-Seizure Medications; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; SDSS, Social Disability Screening Schedule; KSSE, Kilifi Stigma Scale for Epilepsy; QoL, quality of life.
Ethics Approval and Consent to Participate
The Ethics Committee of Anhui Medical University approved all study protocols (Ethical approval number: 2019H022). All participants provided informed consent before participating in the study. The experiment is in accordance with the ethical principles of the Declaration of Helsinki.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The author(s) report no conflicts of interest in this work.
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