Abstract
INTRODUCTION:
The aim of this study was to explore the clinical characteristics and related factors of centrally mediated abdominal pain syndrome (CAPS).
METHODS:
Our study included 73 patients with CAPS and 132 age-matched and gender-matched healthy controls. The general information of the participants was collected, and the questionnaires were completed including the 7-item Generalized Anxiety Disorder Scale, 9-item Patient Health Questionnaire, Hamilton Anxiety Scale, Hamilton Depression Scale Pittsburgh Sleep Quality Index, Visual Analog Scale, and Short-Form 36. Univariate and forward stepwise regression analyses were performed to explore the influencing factors of CAPS.
RESULTS:
Nonexercise (adjusted odds ration [AOR] 4.53; confidence interval [CI] 1.602-12.809), mild-to-moderate depression (AOR 7.931; CI 3.236-19.438), married status (AOR 3.656; CI 1.317-10.418), and drinking coffee (AOR 0.199; CI 0.051-0.775) were found to be related with centrally mediated abdominal syndrome. The Hamilton Anxiety Scale score (7–13) was significantly related to moderate-to-severe abdominal pain (AOR 7.043; CI 1.319–37.593). Higher Hamilton Depression Scale score was related to lower mental component scale score (β = −0.726, P < 0.01) and physical component scale score (β = −0.706, P < 0.01).
DISCUSSION:
Depression, married status, and nonexercise were the independent risk factors of CAPS. Conversely, coffee intake was an independent protective factor of CAPS. Anxiety was related to the severity of abdominal pain, while depression was related to low health-related quality of life.
KEYWORDS: centrally mediated abdominal syndrome, disorders of gut-brain interaction, anxiety, depression, health-related quality of life
INTRODUCTION
Centrally mediated abdominal syndrome (CAPS) is defined by constant, nearly constant, or repeated episodes of abdominal pain, which lasts for more than 6 months according to Rome IV criteria (1). Although CAPS is not the most prevalent disorder of gut-brain interaction (DGBI) with the prevalence 0.5%–2.1% (2), recurrent unknown abdominal pain still negatively affects patients' lives and generates a heavy burden of disease across the globe (3). A large number of patients sought medical care in outpatient department and unnecessary invasive examinations repeatedly, such as endoscopy and abdominal ultrasound, but the clear etiology remains poorly elucidated (4).
The pathogenesis of CAPS may be associated with gut-brain abnormalities. Dysfunction of gut-brain axis can cause gastrointestinal (GI) symptoms such as abdominal pain, dyspepsia, and constipation (5). The current theory holds that the central sensory processing plays a dominant role in the pathophysiology of CAPS, rather than peripheral stimulation. As long as central sensitization is established, abdominal pain persists even in the lack of peripheral stimulation. Some researchers surmised that dysregulation of descending pain pathway was the main cause of CAPS, and the mental disorder seemed to be a risk factor (1). For example, anxiety could affect pain coping and impaired the endogenous opioid release, which was involved in pain control (6). However previous studies merely showed that CAPS and mental disorders were commonly comorbid in children (7). CAPS is known to be one of the DGBI, which is also called functional gastrointestinal disorder (FGID). It has been suggested that the prevalence of mental disorders are higher among patients with FGIDs, particularly depression, anxiety, and somatization disorder (8). A retrospective study demonstrated that the diagnosis of anxiety or other mental disorders was 3 years before FGID diagnosis; the researchers of that study supposed that we could prevent FGID through intervening mental disorders (9).
A 1-year follow-up study found that if patients had anxious or depressive symptoms at baseline, they were more likely to experience irritable bowel syndrome (IBS) or other DGBI after 1 year (10). In addition, numerous studies indicated that psychological factors strongly linked to IBS, which was the most common DGBI (11). Thus, we assume that there seems to be a similar relationship between CAPS and psychological factors.
Sleep disturbance is likely to be another contributed factor for CAPS. Koh et al (12) found that sleep disturbance was important for the etiology of functional abdominal pain in nurses with shift work. Sleep disturbance causes circadian rhythm disruptions. Normal motility and immune barrier of gastrointestinal tract were dependent on normal circadian rhythms. Furthermore, abnormal circadian rhythms were critical for gut-brain signaling pathway (13). In addition, lifestyle and habits, gender, education level, marital status, and physical activity were also significantly related to functional symptoms (14).
To our knowledge, detailed prospective studies for clinical characteristics of CAPS are lacking. Clinicians have not comprehended adult CAPS deeply. Given this, the aim of our study was to explore the clinical characteristics and the related factors of CAPS.
METHODS
Study design
This study was conducted between October 1, 2022, and January 31, 2023, which prospectively included 73 patients with CAPS and 132 age-matched and gender-matched healthy controls in Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. All participants filled paper questionnaires. Ethical approval was gained from Ethics Committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine before the start of the study (Number: KY2022-150). All enrolled participants signed their informed consent before the inclusion. All information collected was kept confidential strictly.
Sample size
The sample size in this study was determined based on the reported proportions of anxiety and depressive symptoms in patients with CAPS (15) and in the Chinese general population (16). We assumed a proportion of 38.9% of anxiety in patients with CAPS and a proportion of 14.15% of anxiety in the control group, a statistical power of 95%, a 2-sided significance level of 0.05, and a 1:2 ratio for numbers of participants in the case and control groups. The calculation was conducted using the PASS software, and a sample size of 64 cases and 128 controls was needed for this study. We oversampled an additional 15% cases and recruited a total of 73 patients with CAPS. However, we were not able to match each case with 2 controls and recruited a total of 132 controls in this study.
Participants
Between October 1, 2022, and January 31, 2023, a total of 82 outpatients were diagnosed with CAPS according to Rome IV criteria by professional gastroenterologists. Finally, 73 patients were successfully included in the CAPS group according to the inclusion and exclusion criteria. During the same period, 132 age-matched and gender-matched participants were included in the healthy control group. All the healthy controls were enrolled from the subjects who underwent screening gastroscopy and colonoscopy for routine checkups in the digestive endoscopy center and had negative findings. Furthermore, the controls had no history of digestive tract disorder and surgery.
Inclusion and exclusion criteria.
The inclusion criteria for patients with CAPS were as follows: (i) aged 18–85 years, (ii) met the Rome IV diagnostic criteria and diagnosed by professional gastroenterologists, (iii) a signed informed consent, and (iv) all the questionnaires were completed. Inclusion criteria for healthy controls: (i) aged 18–85 years, (ii) no abdominal pain and other gastrointestinal symptoms, (iii) no organic lesions were detected by gastroscopy and colonoscopy within the past 6 months, (iv) a signed informed consent, and (v) all the questionnaires were completed.
The exclusion criteria were as follows: (i) participants complicated with organic lesions, (ii) with a history of abdominal surgery, (iii) pregnant and lactating women, and (iv) participants who failed to complete all the questionnaires.
Data collection
Baseline data.
Baseline data including age, gender, BMI, medication history, educational attainment, marital status, smoking status, coffee intake, and physical activity engagement were obtained from questionnaire.
Psychological questionnaires.
To generally evaluate the severity of depression and anxiety, 4 questionnaires were applied including the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), 9-item Patient Health Questionnaire (PHQ-9), and 7-item Generalized Anxiety Disorder (GAD-7) scale.
HAMA.
HAMA is a clinician-rated instrument, which consists of 14 items and designed to measure severity of anxiety (17). HAMA score criteria are as follows: 0–6 = no anxiety; 7–13 = mild to moderate anxiety; and 14 or over = severe to extreme anxiety (18).
HAMD.
HAMD is the most commonly used clinician-rated scale to categorized patients into different severity depression groups (19). The HAMA score criteria are as follows: 0–6 = no depression; 7–23 = mild to moderate depression; 24 or over = severe to extreme depression (18).
GAD-7.
The GAD-7 is a self-reported anxiety rating scale with satisfied reliability (Cronbach ɑ = 0.89) and validity. The GAD-7 asks how frequently patients have experienced symptoms that GAD-7 mentions, within the past 2 weeks. The total scores are 21 (0–4 score = no anxiety, 5 or over = anxiety present). Higher scores present more severe anxious symptoms (20).
PHQ-9.
This measurement is widely used to quantify the severity of depression within the past 2 weeks in both busy clinical settings and researches. This questionnaire has higher reliability (Cronbach ɑ = 0.86–0.89) in various clinical settings. The total scores are 27 (0–4 score = no depression, 5 or over = depression present). Higher scores present more severe depressive symptoms (21).
Sleep quality questionnaire.
The PSQI is a self-reported sleep quality within the previous 1 month. This scale assesses 7 dimensions: sleep latency, sleep quality, sleep duration, use of sleep medication, daytime dysfunction, habitual sleep efficiency, and sleep disturbances. The final score is calculated as the sum of 7 dimensions. 0–4 score = good sleepers, 5 or higher score = poor sleepers (22). The PSQI is a reliable tool for evaluating all subjects' sleep quality because of Cronbach ɑ = 0.82–0.83 (23).
Visual analog scale (VAS).
The VAS is a self-described scale made up of a 10-cm long horizontal line and widely used in the clinical settings to reflect pain intensity (24). The score of VAS ranged from 0 to 10. Patients were asked to describe abdominal pain intensity by circling the number. The scores were categorized as follows: 0 = no pain, 1–3 = mild pain, 4–6 = moderate pain, and 7–10 = severe pain.
Quality of life questionnaire.
The 36-Item Short-Form Survey questionnaire is the most commonly used generic instrument to measure patients' health-related quality of life by most of clinicians and researchers. The questionnaire mainly measures physical health (physical component scale [PCS]) and mental health (mental component scale [MCS]) by means of 8 domains including physical functioning, role-physical, bodily pain, general health, vitality, social functioning, role-emotional, and mental health. Higher scores indicate better quality of life (25).
Physical activity.
We defined the duration of exercise or walking ≥3 hr/wk as physical activity/exercise and the duration of exercise or walking <3 hr/wk as physical inactivity/nonexercise regardless of the occupations. This standard was established based on the General Practice Physical Activity Questionnaire (GPPAQ) (26) and GPPAQ-WALK (27) and combined with the actual situation of the participants in our research. Considering that some participants of our study had retired, some worked part-time, some were freelancers, and the participants took part in various physical activities, such as walking, gym, cycling, and swimming, GPPAQ and GPPAQ-WALK were not fully applicable to these people. Thus, we finally defined the abovementioned criteria.
Statistical analysis
Normally distributed continuous data were described as the mean ± SD, and non-normally distributed continuous data were presented as median (P25, P75). Categorical data were expressed as frequency (percentage). The Student t test, Wilcoxon rank-sum tests, χ2 test, and Fisher's exact test were used to analyze differences between groups. Univariate and forward stepwise multivariate logistic regression (LR), Spearman correlation analysis, and multivariable linear regressions were used for exploring risk or protective factors. Two-sided P < 0.05 was considered to be statistically significant. All statistical analyses were conducted using IBM SPSS Statistics version 26.0, and figures were made with GaphPad Prism 9.
RESULTS
The demographic and clinical features of 2 groups are summarized in Table 1. In the CAPS group, 47 (64.4%) of the patients were women; the mean age was 49.11 ± 14.75 years, and the BMI was 21.38 (19.93–24.50) kg/m2. In the healthy control group, 93 (70.5%) of the participants were women; the mean age was 46.37 ± 5.58 years, and the BMI was 21.93 ± 3.05 kg/m2. There were no significant differences in age, gender, and BMI between 2 groups. All patients with CAPS had a history of medication to improve symptoms before enrollment. Of the 73 patients with CAPS, 37 took pinaverium bromide, 12 took trimebutine, 8 took PPI, 9 took tricyclic antidepressant, 5 took duloxetine, and 2 took flupentixol-melitracen. All healthy controls had no related medication history. Among the participants, nonexercise, noncoffee, and nontea intake were more common in the CAPS group than in the healthy control group (nonexercise: 65 [89%] vs 68 [51.5%] P < 0.001; noncoffee: 69 [94.4%] vs 78 [59.1%] P < 0.001; nontea 65 [89%] vs 91 [68.9%] P < 0.05). This suggested that lifestyle and habits may be related to the prevalence of CAPS. In addition, education attainment and marital status were significantly different between patients with CAPS and controls. There were more higher-degree participants in the control group than in the CAPS group (university: 60 [45.5%] vs 18 [24.7%]; postcollege: 34 [25.8%] vs 2 [2.7%]). And there were more married participants in the CAPS group than in the control group (married: 62 [84.9%] vs 62 [47%]). The difference in smoke condition was not statistically significant.
Table 1.
Baseline characteristics of the participants
| Variables | Patients with CAPS N = 73 |
Healthy controls N = 132 |
P |
| Gender, n (%) | 0.23 | ||
| Male | 26 (35.6) | 39 (29.5) | |
| Female | 47 (64.4) | 93 (70.5) | |
| Age (y) | 49.11 ± 14.75 | 46.37 ± 5.58 | 0.06 |
| BMI (kg/m2) | |||
| Medication history (%) | 21.38 (19.93–24.50) | 21.93 ± 3.05 | 0.929 |
| Pinaverium bromide | 37 (50.7) | 0 | NA |
| Trimebutine | 12 (16.4) | 0 | NA |
| PPI | 8 (11) | 0 | NA |
| TCA | 9 (12.3) | 0 | NA |
| Duloxetine | 5 (6.8) | 0 | NA |
| Flupentixol-melitracen | 2 (2.8) | 0 | NA |
| Education, n (%) | <0.001*** | ||
| Primary school or below | 12 (16.4) | 6 (4.5) | |
| Junior school | 29 (39.7) | 11 (8.3) | |
| High school | 12 (16.4) | 21 (15.9) | |
| University studies | 18 (24.7) | 60 (45.5) | |
| Postcollege | 2 (2.7) | 34 (25.8) | |
| Marital status, n (%) | <0.001*** | ||
| Married | 62 (84.9) | 62 (47) | |
| Single | 10 (13.7) | 68 (51.5) | |
| Divorced | 1 (1.4) | 1 (0.8) | |
| Widowed | 0 | 1 (0.8) | |
| Smoke, n (%) | 0.464 | ||
| Smoke | 9 (12.3) | 12 (9.1) | |
| Nonsmoke | 64 (87.7) | 120 (90.9) | |
| Coffee, n (%) | <0.001*** | ||
| Coffee | 4 (5.5) | 54 (40.9) | |
| Noncoffee | 69 (94.4) | 78 (59.1) | |
| Tea, n (%) | 0.001** | ||
| Tea | 8 (11) | 41 (31.1) | |
| Nontea | 65 (89) | 91 (68.9) | |
| Exercise, n (%) | <0.001*** | ||
| Exercise | 8 (11) | 64 (48.5) | |
| Nonexercise | 65 (89) | 68 (51.5) | |
| GAD-7 score M (P25–P75) | 5 (3–7.5) | 2 (0–5) | <0.001*** |
| GAD-7 severity, n (%) | <0.001*** | ||
| GAD-7 <5 | 35 (47.9) | 98 (74.2) | |
| GAD-7 ≥5 | 38 (52.1) | 34 (25.8) | |
| PHQ-9 score M (P25–P75) | 4 (3–6) | 2 (1–5.75) | 0.001*** |
| PHQ-9 severity, n (%) | 0.006** | ||
| PHQ-9<5 | 38 (52.1) | 94 (71.2) | |
| PHQ-9≥5 | 35 (47.9) | 38 (28.8) | |
| PSQI score M (P25–P75) | 6 (5–9) | 5 (3–7) | <0.001*** |
| PSQI severity, n (%) | 0.005** | ||
| PSQI≤5 | 27 (37) | 76 (57.6) | |
| PSQI>5 | 46 (63) | 56 (42.4) | |
| HAMA score M (P25–P75) | 8 (5.5–10) | 4 (2–7) | <0.001*** |
| HAMA severity, n (%) | <0.001*** | ||
| No anxiety (0–6) | 27 (37) | 97 (73.5) | |
| Mild-to-moderate anxiety (7–13) | 37 (50.7) | 30 (22.7) | |
| Severe-to-extreme anxiety (≥14) | 9 (12.3) | 5 (3.8) | |
| HAMD score M (P25–P75) | 7 (5–10) | 3 (1–5) | <0.001*** |
| HAMD severity, n (%) | <0.001*** | ||
| No depression (0–6) | 28 (38.4) | 115 (87.1) | |
| Mild-to-moderate depression (7–23) | 44 (60.3) | 17 (12.9) | |
| Severe-to-extreme depression (≥24) | 1 (1.4) | 0 |
BMI, body mass index; CPAS, centrally mediated abdominal pain syndrome; GAD-7, 7-item Generalized Anxiety Disorder; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; PHQ-9, 9-item Patient Health Questionnaire; PPIs, proton pump inhibitors; PSQI, Pittsburgh Sleep Quality Inventory; TCA, tricyclic antidepressants.
**P < 0.01; ***P < 0.001.
The questionnaire results of psychological status and sleep quality between 2 groups are summarized in Table 1. As expected, patients have higher GAD-7 score (5 [3–7.5] vs 2 [0–5] P < 0.001), PHQ-9 score (4 [3–6] vs 2 [1–5.75] P < 0.05), HAMA score (8 [5.5–10] vs 4 [2–7] P < 0.001), HAMD score (7 [5–10] vs 3 [1–5] P < 0.001), and PSQI score (6 [5–9] vs 5 [3–7] P < 0.001) than healthy controls. Furthermore, 50.7% of patients with CAPS showed mild-to-moderate anxiety, and 12.3% showed severe-to-extreme anxiety, and the corresponding proportions of healthy controls were 22.7% and 3.8%. Approximately 60.3% of patients with CAPS showed mild-to-moderate depression, and 1.4% showed severe-to-extreme depression. The different severity of anxiety (P < 0.001) and depression (P < 0.001) between the 2 groups were statistically significant. In summary, compared with healthy controls, more patients had complication of depression or anxiety. Meanwhile, poor sleep quality (PSQI>5) was more frequently noted in the CAPS group than in the healthy control group (63% vs 42.4%, P < 0.05).
The univariate LR model for CPAS are summarized in Table 2. According to the results of model, exercise condition, educational attainment, marital status, coffee consumption, tea consumption, HAMD/HAMA/PSQI severity, GAD-7, and PHQ-9 were included in the forward stepwise regression model (LR). Ultimately, nonexercise (adjusted odds ratio [AOR] 4.53; CI [1.602–12.809]), mild-to-moderate depression (AOR 7.931; CI [3.236–19.438]), and married status (AOR 3.656; CI [1.317–10.418]) are risk factors of CAPS after adjusting for age and gender. By contrast, drinking coffee is a protective factors of CAPS (AOR 0.199; CI 0.051–0.775) (Figure 1).
Table 2.
Univariate logistic regression analysis models of CAPS
| β | Odds ratio (95% CI) | P | |
| Education | |||
| Primary school | Reference | ||
| Junior school | 0.276 | 1.318 (0.397–4.380) | 0.652 |
| High school | −1.253 | 0.286 (0.085–0.958) | 0.042* |
| University studies | −1.897 | 0.150 (0.049–0.456) | 0.001** |
| Postcollege | −3.526 | 0.029 (0.005–0.166) | <0.001*** |
| Marital status | |||
| Single | Reference | ||
| Married | 1.917 | 6.80 (3.046–13.747) | <0.001*** |
| Coffee | |||
| Noncoffee intake | Reference | ||
| Coffee intake | −2.480 | 0.086 (0.029–0.243) | <0.001*** |
| Tea | |||
| Nontea intake | Reference | ||
| Tea intake | −1.298 | 0.273 (0.12–0.621) | 0.002** |
| Exercise engagement | |||
| Exercise | Reference | ||
| Nonexercise | 2.034 | 7.647 (3.402–17.187) | <0.001*** |
| GAD-7 | |||
| GAD-7<5 | Reference | ||
| GAD-7 ≥5 | 1.141 | 3.129 (1.713–5.717) | <0.001*** |
| PHQ-9 | |||
| PHQ-9 <5 = 0 | Reference | ||
| PHQ-9 ≥5 | 0.823 | 2.278 (1.258–4.127) | 0.007** |
| HAMD | |||
| No depression (0–6) | Reference | ||
| Mild-to-moderate depression (7–23) | 2.364 | 10.63 (5.302–21.313) | <0.001*** |
| HAMA | |||
| No anxiety (0–6) | Reference | ||
| Mild-to-moderate anxiety (7–13) | 1.489 | 4.431 (2.329–8.430) | <0.001*** |
| Severe-to-extreme anxiety (≥14) | 1.867 | 6.467 (2.329–20.908) | 0.002** |
| PSQI | |||
| PSQI≤5 | Reference | ||
| PSQI>5 | 0.838 | 2.312 (1.285–4.160) | 0.005** |
CAPS, centrally mediated abdominal pain syndrome; GAD-7, 7-item Generalized Anxiety Disorder; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; PHQ-9, 9-item Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality Inventory.
*P < 0.05; **P < 0.01; ***P < 0.001.
Figure 1.
Forest plot showed independent risk and protective factors of CAPS according to forward stepwise regression model. Adjusted odds ratio (AOR) for age and gender; mild-to-moderate depression: HAMD score = 7–23; no depression: HAMD score = 0–6. CPAS, centrally mediated abdominal pain syndrome; HAMD, Hamilton Depression Rating Scale.
To further assess the effect of different pain severity on a patient's health-related quality of life (HRQOL) and clinical figures, patients were asked for completing the VAS and 36-Item Short-Form Survey questionnaires. Seventy-three patients with CAPS were divided into 2 subgroups regarding the VAS score, mild pain group (VAS score 0–3) and moderate-to-severe pain group (VAS score 4–10). BMI, age, gender, exercise condition, severity of HAMD/PSQI, and GAD-7/PHQ-9/HAMA/HAMD/PSQI score were not statistically significant in 2 subgroups except for HAMA severity, which showed statistically significant differences between subgroups. Mild-to-moderate anxious patients were more common in the moderate-to-severe pain group than in the mild pain group (34 [58.6%] vs 3 [20%], P < 0.05) (Table 3). Based on the result of bivariate LR after adjusting for age and gender, patients with CAPS with mild-to-moderate anxiety had a significantly higher risk of experiencing moderate-to-severe abdominal pain when compared with patients without anxiety (odds ratio = 7.043 CI [1.319–37.593]) (Figure 2).
Table 3.
Clinical figures and questionnaire results of CAPS subgroups
| Mild pain group n = 15 | Moderate-to-severe pain group n = 58 | P | |
| BMI (kg/cm2) | 22.26 ± 2.79 | 21.88 (20.31–24.51) | 0.544 |
| Age(y) | 45.46 ± 12.85 | 50.64 ± 15.04 | 0.255 |
| Gender, n (%) | 0.527 | ||
| Female | 8 (53.3) | 39 (67.2) | |
| Male | 7 (46.7) | 19 (32.8) | |
| Exercise status, n (%) | 0.853 | ||
| Exercise | 2 (13.4) | 6 (10.3) | |
| Nonexercise | 13 (86.6) | 52 (89.7) | |
| PSQI score | 6.23 ± 2.92 | 6.5 (5–9) | 0.418 |
| PSQI | 0.565 | ||
| PSQI≤5 | 6 (46.2) | 21 (36.2) | |
| PSQI>5 | 8 (53.8) | 38 (63.8) | |
| GAD-7 score | 4 (3–8) | 5 (2–7.75) | 0.787 |
| GAD-7, n (%) | 0.714 | ||
| GAD-7<5 | 7 (46.7) | 28 (48.2) | |
| GAD-7≥5 | 8 (53.3) | 30 (51.8) | |
| PHQ-9 score | 4 (2.5–7.5) | 4 (2.5–6) | 0.757 |
| PHQ-9, n (%) | 0.921 | ||
| PHQ-9<5 | 7 (46.7) | 31 (53.4) | |
| PHQ-9≥5 | 8 (53.3) | 27 (46.6) | |
| HAMD score | 6 (3.5–8) | 7 (5–10) | 0.238 |
| HAMD severity, n (%) | 0.469 | ||
| No depression (0–6) | 7 (46.7) | 21 (36.2) | |
| Mild-to-moderate depression (7–23) | 8 (53.3) | 36 (62.1) | |
| Severe-to-extreme depression (≥24) | 0 | 1 (1.7) | |
| HAMA, n (%) | 6 (4.5–13.5) | 8 (5.25–10) | 0.507 |
| HAMA severity | 0.012* | ||
| No anxiety (0–6) | 8 (53.3) | 19 (32.8) | |
| Mild-to-moderate anxiety (7–13) | 3 (20) | 34 (58.6) | |
| Severe-to-extreme anxiety (≥14) | 4 (26.7) | 5 (8.6) | |
| SF-36 | |||
| PF | 100 (95–100) | 100 (91.25–100) | 0.843 |
| RP | 100 (75–100) | 100 (75–100) | 0.320 |
| BP | 62 (62–74) | 62 (52–62) | 0.016 |
| GH | 51.08 ± 17.50 | 49.50 ± 16.00 | 0.754 |
| VT | 80 (65–92.5) | 80 (70–85) | 0.902 |
| SF | 87.5 (81.25–100) | 75 (75–87.5) | 0.078 |
| RE | 100 (100–100) | 100 (66.67–100) | 0.135 |
| MH | 69.54 ± 15.01 | 80 (68–92) | 0.137 |
| PCS | 78 (60.25–82) | 72.38 (64.06–78.94) | 0.282 |
| MCS | 80.38 (68.96–91.07) | 79.09 (70.14–87.38) | 0.825 |
Mild pain group: the visual analog scale score 0–3; moderate-to-severe pain group: the visual analog scale score 4–10.
BMI, body mass index; BP, bodily pain; CAPS, centrally mediated abdominal pain syndrome; GAD-7, 7-item Generalized Anxiety Disorder; GH, general health; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; MCS, mental component scale; MH, mental health; PCS, physical component scale; PF, physical functioning; PHQ-9, 9-item Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality Inventory; RE, role-emotional; RP, role-physical; SF, social functioning; SF-36, 36-Item Short-Form Survey; VT, vitality.
*P < 0.05.
Figure 2.
Forest plot showed independent risk factors of abdominal pain intensity according to bivariate logistic regression model. Severe-to-extreme anxiety: HAMA score ≥14; mild-to-moderate anxiety: HAMA score 7–13; no anxiety: HAMA score = 0–6. HAMA, Hamilton Anxiety Rating Scale.
On the contrary, we found no statistically significant difference in HRQOL between the 2 subgroups (PCS: 75.5 [60.25–80.75] vs 73 [66.75–79.25]) P = 0.715; MCS (80.38 [68.96–91.07] vs 79.09 [70.14–87.38] P = 0.825) (Table 3). Of interest, we found that patients' mental disorders were associated with PCS and MCS. As summarized in Table 4, PCS score was significantly negatively related to PHQ-9 score (rs = −0.372, P < 0.01), GAD-7 score (rs = −0.325, P < 0.01), HAMA score (rs = −0.414, P < 0.01), and HAMD score (rs = −0.473, P < 0.01), and MCS score was significantly negatively associated with PHQ-9 score (rs = −0.409, P < 0.01), GAD-7 score (rs = −0.366, P < 0.01), HAMA score (rs = −0.390, P < 0.01), and HAMD score (rs = −0.467, P < 0.01). The abovementioned results were further analyzed by multivariate linear regression (Table 5). Both in the MCS and PCS regression models, higher HAMD scores were related to lower MCS score (β = −0.753, P < 0.01) and PCS score (β = −0.716, P < 0.01) after adjusting for HAMA, GAD-7, and PHQ-9 scores.
Table 4.
Spearman correlation analysis of HRQOL of patients with CAPS
| PCS rs | MCS rs | |
| PHQ-9 score | −0.372** | −0.409** |
| GAD-7 score | −0.325** | −0.366** |
| HAMA score | −0.414** | −0.390** |
| HAMD score | −0.473** | −0.467** |
| PSQI score | −0.211 | −0.169 |
**P < 0.01.
GAD-7, 7-item Generalized Anxiety Disorder; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; HRQOL, health-related quality of life; MCS, mental component scale; PCS, physical component scale; PHQ-9, 9-item Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality inventory.
Table 5.
Multivariate linear regression of HRQOL of patients with CAPS
| MCS | PCS | ||
| β | β | ||
| R2 = 0.303** | R2 = 0.286** | ||
| GAD-7 score | −0.232 | GAD7 | −0.261 |
| PHQ-9 score | −0.128 | PHQ-9 | −0.009 |
| HAMA score | 0.478 | HAMA | −0.363 |
| HAMD score | −0.753** | HAMD | −0.716** |
GAD-7, 7-item Generalized Anxiety Disorder; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale; HRQOL, health-related quality of life; MCS, mental component scale; PCS, physical component scale; PHQ-9, 9-item Patient Health Questionnaire.
**P < 0.01.
DISCUSSION
A previous study found that psychosocial factors were the major risk factors of CAPS because CAPS was independent with daily functioning including diet, defecation habit, etc. Even with a lack of peripheral stimulation, abdominal pain still presented (28). Another retrospective observational study showed that 16/18 (88.9%) and 7/18 (38.9%) patients with CAPS were diagnosed with depression and anxiety, respectively (15). In line with previous findings, our study found that psychological questionnaire scores, whether evaluated by clinicians or patients, were significantly higher in the CAPS group. A higher proportion of patients with CAPS experienced various intensities of depression (mild to moderate: 60.3%; severe to extreme: 1.4%) and anxiety (mild to moderate: 50.7%; severe to extreme: 12.3%) compared with controls. According to the result of forward stepwise LR model, mild-to-moderate depression was a strong risk factor of CAPS. However, regarding the analysis of HRQOL, our study showed that PCS scores and MCS scores were not significantly different between CAPS subgroups. Depression status had a negative impact on HRQOL including mental health and physical health, which matched the expectation and previous findings. In accordance with the result of correlation analysis and multivariate linear regression, higher HAMD scores related to worse CAPS patients' HRQOL. This result seemed to be similar with previous findings. We reviewed some studies on HRQOL of patients with IBS because there were fewer available resources on HRQOL of patients with CAPS. Lackner et al (29) noted that the primary predictors of IBS HRROL were psychosocial and extraintestinal factors rather than GI symptom severity and frequency. In parallel, our study found that mild-to-moderate anxiety significantly improve the risk of severe abdominal pain. The evaluation of anxiety included gastrointestinal-specific anxiety and general anxiety in our study. Labus et al (30) demonstrated that gastrointestinal symptom-specific anxiety was a mediated factor of general anxiety and GI symptom severity. Another study also presented that anxious affect was more related to symptom severity in IBS and other DGIB (31). Overall, anxious or depressive affect played an important role in DGIB. Unfortunately, there were few researches on the relations of adult CAPS and depression or anxiety, most of published studies targeted at children or adolescents. Thus, we referred to published literature on children CAPS. A study in children demonstrated that functional abdominal pain syndrome was an internalizing disorder, for instance, depression and anxiety. Children with depression or anxiety had a higher risk of CAPS compared with healthy controls (32). Psychological intervention also had been proven as one of the most effective therapies for CAPS in children, including cognitive behavioral therapy and relaxation skills (33). Therefore, we speculated that children CAPS and adult CAPS had similarities, and depression played an important role throughout the course of adult CAPS. A large of literature had shown that psychological therapies were crucial for the prognosis of functional abdominal pain, especially depression or anxiety coexisted (29). Thus, it was essential to figure out psychiatric comorbidities and provide early psychiatric intervention or prescribe psychotropic medication for the management of CAPS.
In addition to that, our study showed that physical inactivity was more frequent in the CAPS group (65 [89%]) compared with that in the healthy control group (68 [51.5%]). Forward stepwise regression illustrated that physical inactivity might be another risk factor of CAPS (AOR 4.53). Our finding was consistent with published studies. A prospective cohort study in Swedish demonstrated that physical inactivity was an independent risk factor for increasing complaints of functional gastrointestinal symptoms, especially functional abdominal pain. Their study also showed that 30 minutes walking a day significantly decreases the risk of abdominal pain (P = 0.001) (34). Another cross-sectional observational study presented that participants who engaged physical activity ≤1 time per week had 1.47 times higher odds of suffering various FGIDs (P = 0.02) and physical activity ≥3 times per week might be protective against FGIDs (35). FGIDs had been redefined as DGBI based on Rome IV criteria. Several theories explained that physical activity had an impact on neuronal generation and adaption of brain (36). Altogether, we surmised that appropriate physical activity frequency might lower the prevalence of CAPS. Existing evidences on the relationship with CAPS and physical activity were limited. Nonetheless, the positive effect of physical activity on CAPS cannot be denied.
Marriage seemed to be another risk factor of CAPS according to our study. Our finding was in accord with a previous research, which reported the Odd Ratio for the prevalence of functional dyspepsia in married individuals was 1.35 compared with single individuals (P = 0.004) (37). Conversely, Turki et al (38) found that marital status was not a predictor of IBS prevalence in their cross-sectional study. The relationship between marital status and FGIDs prevalence remained unclear. We preferred to agree with married status as a risk factor because one study showed that distressed marriage could induce depression and leaky gut (39). Unfortunately, we did not identify whether married participants were satisfied with their marriage in our study. Nevertheless, we still believed that a happy relationship was good for the mood of patients with CAPS at least. A good mood had a beneficial effect on improvement of patients' symptoms theoretically.
Of interest, our LR model showed that coffee intake was related to reduce the risk of CAPS prevalence (AOR 0.199; CI 0.051–0.775, P < 0.05). Our result was in line with other researchers' findings. Magnus et al (40) presented that coffee intake ≥3 times/d could lower the risk of functional abdominal pain (P = 0.002). Another study showed that appropriate coffee intake was beneficial for patients with IBS (41). The underlying mechanism and causal association have to be defined in depth.
In our study, sleep quality was not an independent predictor of CAPS prevalence and patients' health-related quality of life, even if both PSQI scores and proportion of poor sleepers were higher in the CAPS group. Our results were needed to confirm by further studies because previous study indicated that DGIBs increased nocturnal autonomic arousal risk and broke the normal circadian rhythm, which lead to poor sleep (42). Numerous previous studies indicated that sleep disturbance had robust relationship with functional dyspepsia and IBS. Previous population-based study reported that 21.3% patients with FD and 33.3% patients with IBS complained of sleep disturbance (43). Another explanation was also possible. Psychological factors may be an intermediate factor between CAPS prevalence and sleep disturbance. One research in patients with FD showed that depression or anxiety was closely related to sleep disturbance (5). Psychological factors and sleep disturbance were reciprocal causation. However, available studies on link between CAPS and sleep disturbance were limited. Thus, specific relationship remains obscure.
Some limitations of this study should be considered. First, selection bias may be present in our study. The healthy controls in our study were recruited from the digestive endoscopy center, which may not be representative of the general population. To reduce selection bias, we enrolled healthy controls who just underwent screening endoscopy as their annual health checkup program and had negative endoscopic findings. Second, recall bias and social bias may be present in our study by using self-reported measures such as PHQ-9, GAD-7, and PSQI. However, for the purpose of reducing bias, we also used clinician-rated instrument such as HAMA and HAMD in this study. Third, marital satisfaction was not investigated among the married participants in our study.
Our study showed that married status and nonexercise were the independent risk factors of CAPS. Conversely, coffee intake was an independent protective factor of CAPS. Anxiety was significantly related to abdominal pain severity, while depression was strongly associated with low health-related quality of life.
CONFLICTS OF INTEREST
Guarantor of the article: Zou Duowu, PhD.
Specific author contributions: Z.D. and Y.W.: study concept and design; project administration; writing—review and editing. T.Y.: study concept and design; acquisition and compilation of data; data interpretation; drafting of the article. S.J.: acquisition and compilation of data; statistical analysis; data interpretation; drafting of the article. C.H.: acquisition and compilation of data; data interpretation; drafting of the article. Z.Y.: acquisition and compilation of data; data interpretation. All authors approved the final version of the article.
Finanical support: None to report.
Potential competing interests: None to report.
Study Highlights.
WHAT IS KNOWN
✓ Centrally mediated abdominal pain syndrome (CAPS) is an updated definition according to Rome IV criteria.
✓ Chronic recurrent abdominal pain bothers patients and gastroenterology clinicians.
✓ There are few data on the clinical characteristics and related factors of CAPS.
WHAT IS NEW HERE
✓ Depression, married status, and nonexercise were the independent risk factors of CAPS.
✓ Coffee intake decreased the risk of CAPS.
✓ Depression impaired health-related quality of life of CAPS.
✓ Anxiety was related with the pain severity of CAPS.
Footnotes
Tang Yuming, Song Jiani and Chen Hefeng contributed equally to this article.
Contributor Information
Tang Yuming, Email: windwindy2000@126.com.
Song Jiani, Email: like_sushi@sina.com.
Chen Hefeng, Email: charlottechf@139.com.
Zhu Ying, Email: zy10871@rjh.com.cn.
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