Subtype Comparison of Psychosocial Associations With Quality of Life in Functional Dyspepsia

Article information

Psychiatry Investig. 2026;23(4):468-478

Publication date (electronic) : 2026 April 6

doi : https://doi.org/10.30773/pi.2025.0343

Su-Woo Lee ¹

, Seung-Ho Jang ²

, Sung-Hoon Yoon ¹

, Suck-Chei Choi ³

, Sang-Yeol Lee^,¹

¹Department of Psychiatry, School of Medicine, Wonkwang University, Iksan, Republic of Korea

²Jeil Psychiatric Clinic, Gimje, Republic of Korea

³Department of Internal Medicine, School of Medicine, Wonkwang University, Iksan, Republic of Korea

Correspondence: Sang-Yeol Lee, MD, PhD Department of Psychiatry, School of Medicine, Wonkwang University, 895 Muwang-ro, Iksan 54538, Republic of Korea Tel: +82-63-859-1044, Fax: +82-63-857-1043 E-mail: psysangyeol@hanmail.net

Received 2025 October 1; Revised 2025 November 13; Accepted 2025 December 22.

Abstract

Objective

This study aimed to examine the associations of childhood trauma, psychosocial resources (social support and resilience), and emotional distress with quality of life (QoL) in patients with functional dyspepsia (FD), and to explore potential differences between the epigastric pain syndrome (EPS) and postprandial distress syndrome (PDS) subtypes.

Methods

This cross-sectional study included 152 FD patients and 79 healthy controls who completed the Korean versions of the following instruments: Beck Depression Inventory-II (K-BDI-II), Beck Anxiety Inventory (K-BAI), Childhood Trauma Questionnaire (KCTQ), Multidimensional Scale of Perceived Social Support (MSPSS), Connor-Davidson Resilience Scale (CD-RISC-K), and World Health Organization Quality of Life Assessment Instrument Brief Form (WHOQOL-BREF). Negative affect was computed as a principal component analysis-derived composite from K-BDI-II emotional/cognitive items and K-BAI; QoL was a composite of WHOQOLBREF domains. Group differences were assessed using analysis of variance, and hierarchical regression analysis identified predictors of QoL within the patient group. Finally, multivariate regression models compared subtype-specific psychosocial associations.

Results

Compared to controls, patients with FD reported higher emotional distress and childhood trauma, lower psychosocial resources and QoL. Negative affect was consistently associated with lower QoL in both subtypes (EPS: β=-0.360; PDS: β=-0.300). Although direct statistical comparison of pathways between subtypes showed no significant differences, the most prominent protective factor within each group appeared to differ: in the EPS group, higher social support was the most significant predictor of better QoL (β=0.381, p=0.001), whereas in the PDS group, resilience was the key protective factor (β=0.360, p=0.001).

Conclusion

Reducing emotional distress remains a central target for improving QoL in FD. Subtype-tailored emphases may be helpful— enhancing social support in EPS and bolstering resilience in PDS—while noting that between-subtype differences were limited and require confirmation in larger samples.

Keywords: Functional dyspepsia; Postprandial distress syndrome; Epigastric pain syndrome; Social support; Resilience

INTRODUCTION

Functional dyspepsia (FD) is a representative functional gastrointestinal disorder (FGID), characterized by recurrent symptoms such as epigastric pain, bloating, and early satiety, without definite organic abnormalities on endoscopic or imaging examinations based on the Rome IV criteria [1]. FD is further classified into postprandial distress syndrome (PDS) and epieISSN 1976-3026 OPEN ACCESS gastric pain syndrome (EPS) by these criteria, with this subtyping providing a critical rationale for symptom-based diagnostic and therapeutic approaches [1,2].

Despite numerous physiological factors, including delayed gastric emptying, impaired gastric accommodation, and visceral hypersensitivity, that have been investigated to explain the pathophysiology of FD, these mechanisms alone are insufficient to account for its complex and heterogeneous symptoms [2-4]. This limitation has generated increasing interest in the brain-gut interaction, which posits that psychological factors, such as emotion, cognition, and stress, influence gastrointestinal function through the central nervous system. This interaction highlights that FD symptoms result not only from biological mechanisms but also from the interplay with psychological and social factors, underscoring the relevance of the biopsychosocial model in understanding FD [5,6].

Consistent with this perspective, multiple studies have demonstrated that patients with FD experience significantly higher levels of depression and anxiety compared with healthy controls, with symptom severity repeatedly shown to be closely related to psychological distress [7,8]. Furthermore, childhood trauma, such as sexual or physical abuse and neglect, is recognized as an important risk factor for FGIDs, including FD, potentially contributing to long-term effects on stress reactivity, emotion regulation, and coping strategies in adulthood [9,10]. Conversely, studies indicate that social support and resilience, as perceived by an individual, may function as protective factors that buffer the negative impact of psychosocial stress, thereby mitigating symptom burden and decline in quality of life (QoL) [11-13].

Nevertheless, because existing studies have focused primarily on identifying associations between individual key factors and FD, a comprehensive understanding of how these factors interact to affect patients’ QoL remains limited [2,5]. In particular, the structural relationships through which childhood trauma, emotional distress such as depression and anxiety, and protective factors like social support and resilience influence QoL have not been clearly elucidated. Moreover, the two FD subtypes (EPS, PDS) differ in both symptom profiles and pathophysiology; however, few studies have compared the role of psychosocial factors across subtypes [2]. These gaps restrict understanding of psychosocial characteristics in patients with FD and hinder the development of effective intervention strategies in clinical settings.

Therefore, this study aimed to comprehensively investigate the relationships among psychosocial factors affecting QoL in patients with FD within the biopsychosocial model framework. Specifically, it examined how childhood trauma, negative affect (NA), social support, and resilience are structurally related to QoL and explored how these associations operate across the two subtypes (EPS, PDS) of FD. Based on these findings, we anticipate contributing to understanding the psychosocial characteristics of each FD subtype, thereby providing a rationale for further development of targeted intervention strategies.

METHODS

Subjects

This study was conducted from May 2020 to June 2023 on patients diagnosed with FD (EPS or PDS) by a gastroenterologist who attended the brain-gut mental health clinic at a university hospital. The inclusion criteria comprised adults aged 19 to 80 years diagnosed with FD based on the Rome IV criteria, experiencing symptoms within the last 3 months, with symptom duration exceeding 6 months, and showing no organic abnormalities on upper gastrointestinal endoscopy. The healthy control group consisted of individuals without FD symptoms in the past 6 months, meeting the same age criteria as the study participants. The exclusion criteria were: presence of alarm symptoms (e.g., weight loss and hematemesis/melena); organic gastrointestinal diseases (e.g., peptic ulcer, tumors, inflammatory bowel disease, complicated GERD, and history of gastric or esophageal surgery); Helicobacter pylori eradication within the past 4 weeks; initiation or dosage change of antidepressants within the past 8 weeks; pregnancy or lactation; severe systemic illness; and inability to complete questionnaires due to severe psychiatric disorders or cognitive impairment. The study purpose was explained to all participants, and written informed consent was obtained. A total of 152 patients with FD and 79 healthy controls were included. The study was approved by the Institutional Review Board (IRB) of Wonkwang University Hospital (IRB number WKUH 2020-04-006).

Assessment

Depression

Depression severity was measured using the Korean version of the Beck Depression Inventory Second Edition (KBDI-II), originally developed by Beck et al. [14] and later adapted and validated for the Korean population by Sung et al. [15] The K-BDI-II includes 21 self-report items assessing an individual’s condition over the past 2 weeks. Each item is rated on a 4-point Likert scale (0 to 3), with total scores ranging from 0 to 63. According to Sung et al. [15], the internal consistency reliability (Cronbach’s α) was excellent at 0.91. Depression severity was classified as “normal” (≤9), “mild” (10–15), “moderate” (16–23), and “severe” (≥24).

Anxiety

Anxiety severity was measured using the Korean version of the Beck Anxiety Inventory (K-BAI), whose reliability was established by Yook and Kim [16]. The K-BAI comprises 21 selfreport items evaluating anxiety symptoms over the past week across cognitive, behavioral, and somatic domains. Each item is scored on a 4-point Likert scale (0 to 3), with higher scores reflecting more severe anxiety. A score of 22 or higher indicates clinically significant anxiety, with 22–26 classified as “anxiety state,” 27–31 as “severe anxiety,” and 32 or higher as “extreme anxiety.”

Childhood trauma

Childhood trauma was assessed using the Korean version of the Childhood Trauma Questionnaire (K-CTQ), originally developed by Bernstein and Fink [17] and validated for the Korean population by Kim et al. [18] The K-CTQ consists of 28 selfreport items rated on a 5-point Likert scale (1 to 5). Higher total and subscale scores reflect greater trauma severity. According to the Korean validation study, the internal consistency reliability (Cronbach’s α) was 0.88. The K-CTQ includes 5 subscales (emotional neglect, physical neglect, sexual abuse, emotional abuse, physical abuse) and a Minimization-Denial scale to assess response bias.

Social support

Social support perceived by participants was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS), originally developed by Zimet et al. [19] and validated for the Korean population by Shin and Lee [20]. This tool includes 12 items measuring perceived support from 3 subscales: family, friends, and significant others. All items are rated on a 5-point Likert scale, with higher scores indicating greater perceived social support. For analysis, total scores were calculated by summing the 12 items (range 12–60).

Resilience

Resilience was measured using the Korean version of the Connor-Davidson Resilience Scale (CD-RISC-K), originally developed by Connor and Davidson21 and later validated by Baek et al. [22] This tool contains 25 items assessing 5 sub-factors: hardiness, persistence/tolerance, optimism, support, and spirituality. All items are rated on a 5-point Likert scale (0 to 4), with higher scores indicating greater resilience.

QoL

QoL was evaluated using the Korean version of the World Health Organization Quality of Life Assessment Instrument Brief Form (WHOQOL-BREF), developed by the WHO Quality of Life Group and validated by Min et al. [23] This scale measures QoL across 24 items in 4 domains: physical, psychological, social health, and living environment, plus two items on overall QoL. All items are rated on a 5-point Likert scale. Domain scores are calculated by averaging item scores, with higher scores reflecting better QoL.

Composite variable construction

NA was calculated using one-factor principal component analysis after z-standardizing scores from the BDI-II emotional and cognitive subscales (BDI E+C) and the total BAI score in the FD group. Because the somatic domain of the BDI-II overlaps with gastrointestinal symptoms and general physical complaints, including it could inflate physical burden in NA and bias associations with the physical QoL domain. Therefore, somatic items were excluded [24]. The correlation between two indicators (BDI (E+C) and BAI) was r=0.64. The first principal component accounted for 83.0% of the variance, with loadings of 0.91 for both BDI (E+C) and BAI. The internal consistency (α) was 0.80, and the First Principal Component Score (PC1) was used as NA. QoL scores for the four domains were converted to a 0–100 scale per the WHOQOL-BREF manual, and calculated only when domain response exceeded 80%. The mean of the 4 domain scores was then used to construct the composite QoL score [25].

Statistics

All analyses were conducted using two-sided tests, with significance defined as p<0.05. Continuous variables were presented as mean±standard deviation, while categorical variables were expressed as frequencies (%). To compare the three groups (EPS, PDS, and healthy controls), one-way analysis of variance with post hoc pairwise tests adjusted by the Holm step-down procedure was applied for continuous variables, and χ² tests for categorical variables. Pearson correlation analyses were performed to examine relationships among major variables within the FD group. A hierarchical regression was conducted with QoL (Composite QoL) as the dependent variable, with variables entered sequentially to reflect theoretical and temporal precedence. Sociodemographic factors were entered in Step 1, childhood trauma (CTQ) in Step 2, protective factors (MSPSS, CD-RISC) in Step 3, and NA in Step 4. HC3 robust standard errors were applied, and results were reported with standardized regression coefficients (β) and 95% confidence intervals (CIs). Subtype interaction analyses estimated path coefficients separately for EPS and PDS, with between-group differences tested in a single model including group-predictor interaction terms. In subtype-specific multivariable regressions, Composite QoL was regressed on NA, MSPSS, CD-RISC, and CTQ. P-values from pairwise correlations and subtype-specific regressions were adjusted using the Holm step-down procedure to control the family-wise error rate at α=0.05. Data were analyzed using Python 3.11 (pandas, NumPy, SciPy, scikit-learn, statsmodels).

RESULTS

Demographic and clinical characteristics of the participants

There were no significant differences between groups in key sociodemographic factors, including age, sex, marital status, education, income, smoking, alcohol, and chronic disease. Thus, comparability for subtype comparisons, regression, and pathway analyses was maintained (Table 1).

Table 1.

Demographic and clinical characteristics of the participants

Descriptive statistics, reliability, and correlations among key variables in patients with FD

Table 2 presents descriptive statistics and internal consistency for key variables in the FD group. All key variables of NA (BDI E+C), BDI total, BAI total, MSPSS total, and CDRISC total demonstrated excellent reliability (α=0.90, 0.90, 0.93, 0.92, and 0.92, respectively); CTQ total showed acceptable reliability (α=0.69), while the BDI somatic subscale demonstrated relatively lower reliability (α=0.61). Zero-order correlations among all study variables are illustrated in Figure 1, while pairwise correlations among the core variables are summarized in Supplementary Table 1. QoL (WHOQOL total, 0–100) showed a significant negative correlation with depression (r=-0.544, Holm-adjusted p<0.001) and a moderate negative correlation with anxiety (r=-0.389, Holm-adjusted p< 0.001). Conversely, social support and resilience showed a moderate positive correlation with QoL (r=0.564, r=0.531, both Holm-adjusted p<0.001). Although childhood trauma correlated positively with depression and anxiety (r=0.264, r=0.211, both Holm-adjusted p<0.01), no significant simple correlation was observed with QoL (r=0.100, Holm-adjusted p>0.05). Additionally, a structure emerged in which anxiety negatively impacted QoL, whereas social support and resilience, as protective factors, were positively associated with QoL (Figure 1).

Table 2.

Descriptive statistics and reliability of all study variables

Figure 1.

Heat map of pairwise Pearson correlations among all study variables. Cell colors encode r (direction and magnitude); corresponding p-values are provided in Supplementary Table 1. BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory; CTQ, Childhood Trauma Questionnaire; MSPSS, Multidimensional Scale of Perceived Social Support; CD-RISC, Connor-Davidson Resilience Scale; WHOQOL, World Health Organization Quality of Life.

Comparison of psychosocial characteristics and QoL among EPS, PDS, and healthy controls

The comparison among the three groups showed significant differences in psychosocial factors and overall WHOQOLBREF domains. Emotional and cognitive depression (BDI-II E+C) demonstrated a clear group effect (F=33.85, η²=0.229, p<0.001), with all pairwise comparisons reaching significance (EPS>PDS>control group). Anxiety (BAI) showed a similar pattern (F=33.44, η²=0.227, p<0.001), with both patient groups scoring higher than the control group, though no difference was observed between PDS and EPS. Childhood trauma was higher in patient groups than in controls, indicated by both total score (CTQ; F=14.85, η²=0.115, p<0.001) and physical neglect (CTQ-PN; F=24.32, η²=0.176, p<0.001), with no significant difference between PDS and EPS. Social support (MSPSS) followed the pattern control group >PDS >EPS (F=19.06, η²=0.143, p<0.001), with PDS scoring higher than EPS. Resilience (CD-RISC) also showed significant group differences (F=9.85, η²=0.080, p<0.001), with the EPS group scoring lowest, while PDS and controls were similar. Group effects on QoL appeared in all four domains (F range=16.81–34.37, all p<0.001). Both patient groups scored lower than controls. Overall, patient groups, especially EPS, showed higher depression, anxiety, and trauma, alongside lower social support and resilience, and reported lower QoL (Table 3).

Table 3.

Group differences in psychosocial measures

Hierarchical regression analysis of QoL among patients with FD

Hierarchical regression analysis was conducted with WHOQOL-BREF of the FD patient group as the dependent variable. The explanatory power was R²=0.13 in Step 1 (sociodemographic covariates). It increased to R²=0.17 in Step 2 (adding the CTQ total score) (ΔR²=0.046). When social support (MSPSS) and resilience (CD-RISC) were added in Step 3, explanatory power increased substantially to R²=0.48 (ΔR²=0.307), and further rose to R²=0.53 in Step 4 after adding the NA factor (BDI E+C and 1 factor of BAI) (ΔR²=0.048). In the final model, MSPSS (β=+0.34, 95% CI [0.15, 0.53], p<0.001) and CDRISC (β=+0.24, [0.06, 0.42], p=0.008) showed a significant positive association with QoL, while NA showed a significant negative association (β=-0.25, [-0.42, -0.09], p=0.003). Although the direct effect of CTQ total score was weak, it remained statistically significant (β=+0.16, [0.02, 0.31], p=0.024). None of the covariates, including age, sex, education, income, smoking, alcohol, and chronic disease, was significant (Table 4).

Table 4.

Hierarchical (blockwise) regression predicting QoL

Comparison of psychosocial pathways to QoL between EPS and PDS groups

Exploring multi-group path analysis, NA was significantly associated with decreased QoL in both groups (EPS: β=-0.360, p=0.008; PDS: β=-0.300, p=0.007). Childhood trauma was associated with increased NA in both groups (EPS: β=0.424, p=0.001; PDS: β=0.273, p=0.001). Regarding direct effects on QoL, descriptively different patterns emerged. In the EPS group, social support was significantly associated with improved QoL (β=0.381, p=0.001), while in the PDS group, resilience was significantly associated with improved QoL (β=0.360, p=0.001). Social support was also significant in the PDS group, although its effect size was smaller (β=0.240, p=0.022). Social support and resilience were associated with decreased NA in EPS and PDS, respectively (EPS: β=-0.311, p=0.014; PDS: β=-0.253, p=0.030). Conversely, the direct effect of CTQ on QoL and its influence on social support and resilience were generally insignificant. None of the interaction effects between groups and predictors were statistically significant, indicating that the differences in path coefficients between the two subtypes were not pronounced (Table 5).

Table 5.

Comparison of psychosocial pathways to quality of life between EPS and PDS groups

Comparison of psychological and psychosocial predictors of QoL between EPS and PDS

In the multivariate regression analysis with QoL total score as the dependent variable, subtype-specific patterns were observed. In the EPS group, social support (MSPSS) showed a significant positive association with improved QoL (Δ/1SD≈ +4.05, p=0.010), while CD-RISC and CTQ were statistically insignificant. In the PDS group, CD-RISC demonstrated a strong positive association with improved QoL (Δ/1SD≈+4.67, p=0.005). A positive direction was observed for MSPSS, although it was not statistically significant, while CTQ showed only a weak positive tendency. NA was negatively associated with QoL in both groups (Δ/1SD≈-3.86, p=0.035; Δ/1SD≈ -4.93, p=0.033). It was observed that the overall slope difference among subtypes did not reach statistical significance (Table 6). Figure 2 presents mini–forest plots summarizing the adjusted associations with QoL for each predictor (Δ/1SD, 95% CI, Holm-adjusted p), enabling side-by-side comparison of EPS and PDS.

Table 6.

Multivariate predictors of QoL in functional dyspepsia by subtype

Figure 2.

Mini forest plots of adjusted direct effects on QoL within FD subtypes. (A) EPS and (B) PDS. Points indicate Δ/1SD (0–100 QoL scale); horizontal lines show 95% CIs (Table 6). Filled markers denote Holm-adjusted p<0.05; open markers denote non-significant. The vertical dashed line indicates no effect (0). Estimates are associational; between-subtype interactions were not significant. NA, negative affect; MSPSS, Multidimensional Scale of Perceived Social Support; CD-RISC, Connor-Davidson Resilience Scale; CTQ, Childhood Trauma Questionnaire; QoL, quality of life; FD, functional dyspepsia; EPS, epigastric pain syndrome; PDS, postprandial distress syndrome; CI, confidence interval.

DISCUSSION

This study aimed to compare and identify heterogeneity in the effects of subtype-specific psychosocial factors on QoL in patients with FD [26], a disease with high prevalence worldwide. The results showed that NA was consistently associated with a decline in QoL, whereas protective factors functioned differently by subtype (social support in EPS and resilience in PDS). In addition, childhood trauma appeared to affect QoL mainly by mediating NA rather than exerting a direct effect.

Negative affect

One of the most consistent findings in this study is that NA occupies a key predictor for reducing QoL among patients with FD. This aligns with a prior systematic review showing that depression and anxiety decrease QoL in FD [7]. A metaanalysis also reported that the prevalence and severity of anxiety and depression are significantly higher in FD [8]. Recent studies further suggest that NA, such as depression and anxiety, is not only associated with co-occurring FD symptoms but also with triggering symptoms and reducing treatment response in some patients [7.8]. Effective management of emotional distress is therefore crucial for symptom relief and improved outcomes. This study found that emotional and cognitive depression scores (excluding somatic symptoms) were higher in the EPS group, where pain is the main symptom, compared to PDS. This finding is consistent with neuroimaging evidence that pain perception and emotional processing are integrated within shared brain regions of the central nervous system, particularly the anterior cingulate cortex and insula [6,27]. Since cognitive distortions in depression, including rumination and catastrophizing, may intensify attention to pain signals and exaggerate their meaning, activation of these emotion–pain circuits is thought to exacerbate the pain experience [27,28].

Childhood trauma

Exploring the model combining the entire FD group, childhood trauma showed minor direct effects on QoL. However, in the path model separating subtypes, these direct effects weakened or disappeared, while indirect pathways, primarily mediated by NA, became prominent. The weak positive direct effect of CTQ on QoL in the final regression model likely reflects statistical suppression arising when variance shared with other covariates, particularly NA, was controlled for, rather than CTQ improving QoL. In addition, the influence of CTQ on social support and resilience was not consistently observed. This finding aligns with epidemiological evidence indicating that childhood trauma elevates FD risk mainly by increasing emotional vulnerability rather than directly weakening protective factors [9]. A recent large-scale study also reported that adverse childhood experiences are significantly associated with elevated FD risk in adulthood [10], suggesting long-term health effects. This aligns with the “latent vulnerability” model [10,29], which posits that chronic childhood stress induces long-term changes in the stress response system, such as the hypothalamic– pituitary–adrenal axis, leading to stress hypersensitivity or emotional dysregulation in adulthood [30,31]. From this perspective, although assessing trauma history in patients with FD remains clinically important, interventions may be better focused on stabilizing current emotional states and correcting maladaptive coping [32]. An affect-focused therapeutic strategy grounded in an understanding of trauma is therefore recommended.

Social support

Social support is recognized as a crucial protective factor that mitigates the NA of stress [12,33]. When patients feel understood by those around them, their sense of disease control improves, leading to greater adherence and more favorable outcomes [34,35]. Independent studies have likewise shown that higher perceived social support relates to higher QoL in patients with FD [36-38], though this study suggests subtype-specific variation. While social support was positively associated with QoL in both subtypes in the subtype-specific models before multipletesting correction, only the EPS group retained statistical significance after Holm adjustment (Table 6). Although betweengroup differences in path coefficients were not significant, the within-EPS association appeared more robust, supporting EPS-attuned clinical emphasis as a hypothesis-generating implication. This indicates protective factors may function differently depending on subtype, reflecting symptom characteristics. In EPS, epigastric pain occurs intermittently and unpredictably, inducing anxiety and helplessness [39]. In such acute stress contexts, social support offering empathy and practical help can mitigate psychological distress. This finding can be contextualized by considering a potential link between EPS and visceral hypersensitivity, and the possibility that this hypersensitivity can be amplified by emotional distress via brain–gut axis processes. Within this context, emotional distress could compromise QoL in EPS chiefly by augmenting hypersensitivity, and social support might play a role in interrupting this maladaptive feedback loop by reducing initial distress. Conversely, social isolation may worsen symptoms, underscoring the need for clinicians to evaluate social environments and proactively link patients with resources [40,41]. Moreover, prior studies highlight that QoL in FD is shaped by broad interactions across psychological, relational, and symptomatic domains, underscoring the value of supportive treatment contexts [5,13].

Resilience

Resilience showed a significant direct association with QoL in PDS. In contrast to EPS, which presents with intermittent and unpredictable severe pain, PDS is characterized by persistent discomfort, including chronic postprandial bloating and early satiety [5,42]. Such chronic discomfort may reflect impaired gastric accommodation and motility rather than acute hypersensitivity. From this physiological perspective, psychosocial factors may exert their influence not by amplifying pain, but by shaping how patients cope with ongoing discomfort. To cope with this chronic discomfort, external assistance or temporary interventions are insufficient. Hence, it may be believed that the role of internal psychological resources enabling patients to reinterpret situations and endure and manage daily discomfort positively will become relatively important. This finding aligns with previous studies suggesting that resilience plays a crucial role as a psychological resource in stress adaptation [12]. According to the authors’ previous study, patients with PDS demonstrated higher levels of resilience compared to those with EPS, identifying resilience as the most influential factor in explaining QoL in patients with FD, which aligns with the present results [43].

Clinical implications for each subtype

These observed subtype-specific psychosocial patterns within the same disease suggest potential directions for patient-tailored interventions, pending further confirmation. Reducing NA remains a primary cross-subtype target, while supportive and resilience-building strategies tailored to symptom profiles may provide useful emphases for clinical care. For example, in EPS, CBT or emotion regulation training may be relevant to address pain-related distress and anxiety, alongside strengthening social support. For PDS, resilience- and coping-focused interventions may help prevent helplessness and restore self-efficacy in symptom management. Integrating pharmacological care with such psychosocial strategies may optimize FD management. In addition, incorporating psychosocial profiles alongside Rome IV subtypes could enhance their clinical utility, which warrants validation in larger studies [44-46].

Limitations

This study has certain limitations. First, the cross-sectional design limits the ability to make causal inferences. Specifically, it cannot determine whether identified psychosocial factors are the cause of the decline in QoL or a consequence of FD symptoms. Thus, future longitudinal research is required to clarify this causal relationship. Second, due to self-reported survey data from a single institution, this study is limited in sample representativeness and generalizability, with potential reporting bias from memory errors. Third, although relative differences in social support in EPS and resilience in PDS showed clinically meaningful trends, between-group differences were not statistically significant. This does not necessarily indicate the absence of group differences, but may instead reflect insufficient statistical power given the sample size. Therefore, interpretations based on subtype should be understood as trend-level findings, requiring further studies with larger samples. Fourth, classification of FD subtypes was based on primary symptoms; however, overlap between PDS and EPS is common, and subtype boundaries may not be clearly defined. Thus, results reflect pure subtype trends and cannot address overlap. Fifth, the social domain demonstrated relatively low reliability among WHOQOL subscales, requiring caution when interpreting related results. Sixth, the potential confounding effects of medications prescribed for FD symptoms or coexisting psychiatric conditions were not controlled for in this study. This medication use may have independently influenced the observed QoL and psychosocial variables. Lastly, this observational study did not directly verify the effects of psychosocial interventions. Thus, whether targeting these factors improves QoL in FD cannot be determined. Meta-analyses suggest consistent improvements in gastrointestinal symptoms, depression, and anxiety, but effects on QoL remain inconsistent [47].

Strengths

Despite its limitations, this study has several clear strengths. First, the key variable NA was constructed using the total score of the BAI and only the emotional and cognitive domains of the BDI-II to minimize overlap between depression scores and physical symptoms. Measurement validity was also ensured by using validated Korean versions of the questionnaires, which have demonstrated reliability and validity. Second, from a statistical perspective, heteroskedasticity and multicollinearity were addressed to improve the stability of estimates. Specifically, robust standard errors with the Huber-White method were applied to address heteroskedasticity. Multicollinearity was also managed by confirming the variance inflation factor when high correlations were observed. Third, this study directly compared psychosocial pathways between FD subtypes within the same structural model, providing preliminary evidence that may inform subtype-specific treatment considerations. Furthermore, it not only reported psychosocial correlations for all patients with FD but also identified pathway factors that were particularly prominent by subtype, increasing clinical applicability. This integrated biopsychosocial treatment model may contribute to improved outcomes compared to traditional single approaches [6], and its effectiveness should be validated in further studies [27-29].

Conclusion

In within-subtype analyses, social support in EPS and resilience in PDS were identified as relatively prominent protective signals for QoL, suggesting possible psychosocial differentiation between FD subtypes. However, further research is necessary to definitively confirm these distinctions. In contrast, NA demonstrated a consistent and statistically significant correlation with poorer QoL across both subtypes. Taken together, these findings indicate that a subtype-attuned approach may represent a preliminary yet clinically relevant direction: in EPS, prioritizing relief of emotional distress and strengthening support networks, and in PDS, focusing on enhancing resilience and coping skills, while cross-subtype reduction of NA remains the primary target [48].

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0343.

Supplementary Table 1.

Pairwise Pearson correlations among core study variables

pi-2025-0343-Supplementary-Table-1.pdf

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Seung-Ho Jang, Sang-Yeol Lee, Su-Woo Lee. Data curation: Su-Woo Lee, Seung-Ho Jang. Formal analysis: Su-Woo Lee, Seung-Ho Jang. Funding acquisition: Sang-Yeol Lee. Investigation: Su-Woo Lee, Seung-Ho Jang. Methodology: Seung-Ho Jang, Suck-Chei Choi, Sang-Yeol Lee. Software: Su-Woo Lee. Validation: Sung-Hoon Yoon. Writing—original draft: Su-Woo Lee, Seung-Ho Jang. Writing—review & editing: Seung-Ho Jang, Sang-Yeol Lee, Su-Woo Lee.

Funding Statement

This study was supported by Wonkwang University in 2025.

Acknowledgments

None

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	EPS (N=82)	PDS (N=70)	Control (N=79)	F/χ²	p
Age (yr)	57.47±12.89	60.47±12.47	61.52±6.26	2.958	0.054
Sex				0.231	0.891
Male	24 (29.3)	22 (31.4)	22 (27.8)
Female	58 (70.7)	48 (68.6)	57 (72.2)
Marital status				7.462	0.280
Unmarried	6 (7.3)	3 (4.3)	11 (13.9)
Married	61 (74.4)	58 (82.9)	59 (74.7)
Divorced	10 (12.2)	4 (5.7)	6 (7.6)
Widowed	5 (6.1)	5 (7.1)	3 (3.8)
Education (yr)				13.222	0.104
None	1 (1.2)	1 (1.4)	2 (2.5)
<7	14 (17.1)	9 (12.9)	14 (17.7)
7–9	10 (12.2)	10 (14.3)	23 (29.1)
10–12	32 (39.0)	28 (40.0)	17 (21.5)
>12	24 (29.3)	19 (27.1)	23 (29.1)
Income (dollars/month)				17.898	0.057
<1,000	19 (23.2)	20 (28.6)	22 (27.8)
1,000–2,000	17 (20.7)	9 (12.9)	19 (24.1)
2,000–3,000	15 (18.3)	8 (11.4)	18 (22.8)
3,000–4,000	11 (13.4)	11 (15.7)	8 (10.1)
4,000–5,000	5 (6.1)	4 (5.7)	10 (12.7)
>5,000	3 (3.7)	10 (14.3)	2 (2.5)
Smoking				1.000	0.606
No	68 (82.9)	56 (80.0)	72 (91.1)
Yes	10 (12.2)	9 (12.9)	7 (8.9)
Alcohol				3.337	0.188
No	66 (80.5)	55 (78.6)	61 (77.2)
Yes	9 (11.0)	10 (14.3)	18 (22.8)
Chronic disease				0.810	0.667
No	43 (52.4)	40 (57.1)	48 (60.8)
Yes	37 (45.1)	29 (41.4)	31 (39.2)

Variable	Mean±SD	Alpha (α)
BDI emotional	8.55±5.14	0.84
BDI cognitive	4.43±4.01	0.85
BDI somatic	6.63±2.71	0.61
BDI (E+C)	12.99±8.52	0.90
BDI total	19.62±10.15	0.90
BAI total	15.19±11.14	0.93
CTQ total	54.57±9.75	0.69
MSPSS total	32.59±9.06	0.92
CD-RISC total	59.32±19.19	0.92
WHOQOL physical (0–100)	45.70±13.85	0.64
WHOQOL psychological (0–100)	52.03±15.09	0.75
WHOQOL social (0–100)	46.22±17.18	0.64
WHOQOL environmental (0–100)	56.31±16.46	0.86
WHOQOL total (0–100)	50.06±12.80	-

Variable	PDS	EPS	Control	F	p	η²	Post-hoc (Holm-Bonferroni)
BDI-II non-somatic (E+C)	10.19±6.72	15.38±9.19	5.80±5.69	33.85	<0.001	0.229	1<2^*; 1>3^; 2>3^**
BAI total	14.14±9.56	16.09±12.32	4.41±5.68	33.44	<0.001	0.227	1>3^*; 2>3^*
CTQ total	54.47±9.64	54.65±9.90	46.62±11.79	14.85	<0.001	0.115	1>3^*; 2>3^*
CTQ physical neglect	11.76±2.93	11.66±2.80	8.75±3.41	24.32	<0.001	0.176	1>3^*; 2>3^*
MSPSS total	34.54±8.42	30.93±9.30	39.06±7.22	19.06	<0.001	0.143	1>2^; 1<3^; 2<3^**
CD-RISC total	64.07±19.87	55.27±17.73	67.92±18.15	9.85	<0.001	0.080	1>2^; 2<3^*
WHOQOL physical (0–100)	48.37±14.66	43.42±12.77	59.90±11.21	34.37	<0.001	0.232	1>2^; 1<3^; 2<3^*
WHOQOL psychological (0–100)	55.06±16.13	49.44±13.71	66.03±15.85	24.60	<0.001	0.178	1>2^; 1<3^; 2<3^*
WHOQOL social (0–100)	48.57±16.42	44.21±17.65	59.49±16.86	17.06	<0.001	0.130	1<3^*; 2<3^*
WHOQOL environmental (0–100)	59.55±17.98	53.54±14.58	67.84±14.58	16.81	<0.001	0.128	1>2^; 1<3^; 2<3^**

Predictor	Block 1 (Demographics)			Block 2 (+CTQ)			Block 3 (+MSPSS, CD-RISC)			Block 4 (+NA)
Predictor	β (SE)	95% CI	p	β (SE)	95% CI	p	β (SE)	95% CI	p	β (SE)	95% CI	p
Age	0.17 (0.10)	-0.03, 0.36	0.088	0.21 (0.09)	0.03, 0.40	0.025^*	0.09 (0.07)	-0.05, 0.22	0.220	0.06 (0.07)	-0.07, 0.20	0.361
Sex	0.05 (0.12)	-0.19, 0.29	0.695	0.04 (0.13)	-0.21, 0.29	0.750	-0.07 (0.10)	-0.27, 0.13	0.507	-0.08 (0.09)	-0.26, 0.10	0.363
Education	0.20 (0.12)	-0.03, 0.44	0.094	0.20 (0.12)	-0.03, 0.43	0.086	0.12 (0.09)	-0.07, 0.30	0.215	0.08 (0.09)	-0.10, 0.26	0.367
Income	0.22 (0.11)	0.01, 0.44	0.044^*	0.26 (0.11)	0.05, 0.47	0.014^*	0.15 (0.09)	-0.02, 0.32	0.082	0.15 (0.08)	-0.01, 0.32	0.067
Smoking	-0.02 (0.11)	-0.24, 0.21	0.892	-0.00 (0.11)	-0.22, 0.22	0.992	-0.09 (0.10)	-0.27, 0.10	0.369	-0.09 (0.09)	-0.27, 0.09	0.348
Alcohol	0.01 (0.10)	-0.18, 0.20	0.926	-0.01 (0.10)	-0.21, 0.18	0.889	-0.01 (0.11)	-0.22, 0.20	0.918	0.01 (0.10)	-0.19, 0.20	0.948
Chronic disease	0.09 (0.03)	0.03, 0.14	0.004^**	0.08 (0.05)	-0.01, 0.17	0.065	0.01 (0.05)	-0.09, 0.10	0.898	0.03 (0.08)	-0.12, 0.19	0.700
CTQ-SF total				0.22 (0.09)	0.04, 0.40	0.017^*	0.11 (0.08)	-0.04, 0.26	0.149	0.16 (0.07)	0.02, 0.31	0.024^*
MSPSS total							0.40 (0.10)	0.20, 0.61	<0.001^***	0.34 (0.10)	0.15, 0.53	<0.001^***
CD-RISC total							0.31 (0.09)	0.13, 0.49	0.001^***	0.24 (0.09)	0.06, 0.42	0.008^**
Negative affect										-0.25 (0.08)	-0.42, -0.09	0.003^**
Model R²	0.13			0.17			0.48			0.53
Model ΔR²	0.128			0.046			0.307			0.048

Path (predictor → outcome)	EPS			PDS			Group-diff p^* (interaction)
Path (predictor → outcome)	β (SE)	95% CI	p	β (SE)	95% CI	p	Group-diff p^* (interaction)
CTQ → MSPSS	0.179 (0.216)	-0.250, 0.609	0.409	0.211 (0.120)	-0.028, 0.450	0.083	0.939
CTQ → CD-RISC	0.241 (0.140)	-0.038, 0.520	0.089	0.074 (0.126)	-0.177, 0.325	0.557	0.441
CTQ → NA	0.424 (0.123)	0.179, 0.668	0.001	0.273 (0.080)	0.113, 0.433	0.001	0.111
MSPSS → NA	-0.311 (0.123)	-0.556, -0.065	0.014	-0.258 (0.142)	-0.542, 0.026	0.075	0.535
CD-RISC → NA	-0.242 (0.123)	-0.487, 0.003	0.053	-0.253 (0.114)	-0.481, -0.025	0.030	0.591
NA → QoL	-0.360 (0.132)	-0.623, -0.097	0.008	-0.300 (0.108)	-0.516, -0.084	0.007	0.583
MSPSS → QoL	0.381 (0.111)	0.159, 0.603	0.001	0.240 (0.102)	0.036, 0.445	0.022	0.668
CD-RISC → QoL	0.139 (0.091)	-0.043, 0.321	0.133	0.360 (0.104)	0.151, 0.568	0.001	0.099
CTQ → QoL	0.076 (0.142)	-0.208, 0.359	0.596	0.126 (0.078)	-0.029, 0.281	0.109	0.658

Predictor	EPS Δ/1SD (95% CI)	p (Holm)	PDS Δ/1SD (95% CI)	p (Holm)	Group diff p
NA	-3.86 (-6.67, -1.06)	0.035	-4.93 (-8.40, -1.45)	0.033	0.641
MSPSS	4.05 (1.55, 6.54)	0.010	3.50 (0.45, 6.55)	0.098	0.785
CD-RISC	1.66 (-0.73, 4.05)	0.346	4.67 (2.01, 7.32)	0.005	0.099
CTQ	1.12 (-2.16, 4.40)	0.504	2.08 (0.08, 4.07)	0.126	0.626