Abstract
Background
The relationship between overall dietary antioxidant potential and risk of irritable bowel syndrome (IBS) is unclear and needs further epidemiologic evidence.
Methods
In this study, we included a total of 157,900 participants from the UK Biobank without irritable bowel syndrome at baseline between 2006 and 2010. Dietary intake was assessed using the online 24-hour dietary recall questionnaire, Oxford WebQ. IBS was identified using the ICD-10 code (K58). The composite dietary antioxidant index (CDAI) was the sum of Z scores for mean intake of individual vitamins A, C, E, manganese, selenium, and zinc from five 24-hour dietary recalls.
Results
During a mean follow-up time of 13.41 years, 2,007 incident cases of IBS were identified among the 157,900 participants. Restricted cubic spline (RCS) showed a nonlinear association (p for nonlinear < 0.001) between CDAI and the risk of IBS, and two-piecewise linear regression identified an inflection point at -0.9. Segmented Cox regression demonstrated that increasing CDAI significantly reduced the risk of IBS when CDAI < -0.9 (HR: 0.900, 95% CI: 0.820–0.988, P = 0.027); however, when CDAI ≥ -0.9, there was a 3% increased risk of IBS (HR: 1.030, 95% CI: 1.001–1.060, P = 0.046). Furthermore, compared to participants in Quintile 2 of CDAI, individuals in Quintile 1 had a 23% increased risk of IBS (HR: 1.228, 95% CI: 1.071–1.408, P = 0.003), while no significant differences were observed for participants in the other quintiles. Nonlinear analysis of independent dietary nutrients suggested a significant U-shaped association only between vitamin C and the risk of IBS, with an inflection point at Z score = -0.1. Sensitivity analyses with multiple imputation for missing covariates supported a negative association between antioxidant intake and IBS risk before the inflection point of CDAI. In contrast, after the inflection point, CDAI seemed to be unrelated to IBS risk.
Conclusion
Moderate supplementation of antioxidants, particularly vitamin C, is beneficial in reducing the risk of IBS. However, excessive intake of dietary antioxidants tends to have no protective effect on IBS risk.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27996-9.
Keywords: Composite dietary antioxidant index, Irritable bowel syndrome, Vitamin C, UK Biobank
Introduction
Irritable bowel syndrome (IBS) is a chronic functional disorder characterized by recurrent abdominal pain and changes in bowel habits. It is estimated that about 12.1% of the global population suffers from IBS [1, 2]. IBS seriously affects people’s quality of life and imposes a significant burden on the global healthcare system [3]. Despite the high incidence of IBS, its pathophysiological mechanisms are not fully understood, and factors such as gastrointestinal motility, visceral hypersensitivity, and dietary habits may be involved in the development of IBS [4]. Recently, with the emergence of various healthy dietary patterns, the impact of diet on IBS seems to be a new focus [5].
Oxidative stress is defined as an imbalance between antioxidants and oxidants. Under certain pathophysiological conditions, the level of reactive oxygen species (ROS) in the body increases, while the antioxidant capacity decreases, and the balance between the two is broken, leading to tissue damage and inflammatory reactions [6]. Previous studies have shown that antioxidant capacity against ROS is impaired in IBS patients, indicating that oxidative stress may be involved in the pathogenesis and symptom progression of IBS [7]. A previous prospective cohort study based on the UK Biobank found that oxidative balance score (OBS) was significantly negatively correlated with inflammatory bowel disease (IBD), especially in female patients with Crohn’s disease [8]. This study confirmed the importance of antioxidant diet and lifestyle in the intestinal inflammatory response. However, this article focuses on IBD, and the OBS generally refers to the antioxidant capacity of diet and lifestyle. Given the crucial role of diet in counteracting oxidative stress, modulating dietary structure to reduce systemic oxidative stress may represent an effective strategy for alleviating IBS [9].
Currently, some epidemiological studies suggest an association between individual antioxidant components, such as vitamins and minerals, and IBS. Existing evidence indicates that individuals with IBS typically exhibit deficiencies in vitamin A, zinc, and selenium, alongside relatively higher dietary intakes of vitamin E and vitamin C [10–12]. Roth et al. found that extraintestinal symptoms and fatigue in IBS patients were negatively correlated with magnesium intake, and increased dietary selenium intake may be associated with significant symptom relief in IBS patients [10]. Furthermore, a cross-sectional study indicated a negative correlation between serum zinc levels and the incidence of IBS-D [13]. However, a Swedish study did not find a correlation between nutrient intake and IBS subtypes or symptom severity [12]. Considering the potential interactions between different antioxidants, examining individual nutrients alone cannot fully explain the risk effects of antioxidant intake on IBS. Unfortunately, currently only one cross-sectional study from Iran has investigated the relationship between dietary total antioxidant capacity (dTAC) and IBS [14]. However, since the calculation of dTAC is based on the ferric reducing antioxidant power (FRAP) value, it may only reflect one aspect of the antioxidant activity in the body, and the inherent limitations of cross-sectional studies limit the generalizability of its conclusions.
Composite Dietary Antioxidant Index (CDAI) is a comprehensive score that assesses the overall antioxidant characteristics of the diet, mainly six dietary antioxidants (including vitamins A, C, E, manganese, selenium, and zinc) [15]. Previous studies have shown a negative correlation between CDAI and various diseases such as hypertension, chronic kidney disease, and colorectal cancer, but the relationship between CDAI and IBS is still unclear [15–17]. Therefore, we conducted a prospective cohort study to investigate the impact of an antioxidant-rich diet assessed by CDAI on the incidence of IBS in a large-scale long-term follow-up population.
Methods
Data source
This is a large-scale, long-term prospective cohort study conducted using the UK Biobank, which recruited approximately 500,000 people from England, Wales, and Scotland from 2006 to 2010 [18]. Basic information about participants such as gender, age, and information obtained through other assessment methods such as touch screen questionnaires, verbal interviews, and physical measurements are recorded. UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (Ref: 11/NW/0382). Information about the UK Biobank protocol can be found on the website (https://www.ukbiobank.ac.uk/). In the present study, the UK Biobank application number is 99,732.
Out of a total of 502,370 participants in the UK Biobank cohort, we excluded participants with no dietary recall (n = 291,420), people with mean energy intake outside the 5-95th percentiles (n = 21,096), baseline IBS (n = 10,544), self-reported IBS (n = 275), IBS before the last dietary recall (n = 502), baseline IBD or other enteritis (n = 5,112), baseline tumor (n = 14,106), and lack of covariates(n = 1,415). Finally, there were 157,900 participants with complete data included in the analysis (Fig. 1).
Fig. 1.
Flowchart of participants included in the final analysis (N=157,900), UK Biobank, 2006-2010. Figure legend: IBS, irritable bowel syndrome; IBD, inflammatory bowel disease
CDAI
We assessed dietary intake using the Oxford WebQ, a 24-hour online dietary recall questionnaire, within the UK Biobank cohort (https://biobank.ctsu.ox.ac.uk/crystal/ukb/docs/DietWebQ.pdf ) [19]. The Composite Dietary Antioxidant Index (CDAI) comprised six antioxidants: vitamins A, C, E, zinc, selenium, and manganese [16]. Dietary intakes of these antioxidants were calculated by multiplying reported food consumption with nutrient values from the Food Composition Table (FCT). Each participant completed the Oxford WebQ five times, and mean intake values were used for analyses. To derive CDAI, each antioxidant was standardized (individual intake minus population mean, divided by standard deviation) and then summed. To minimize recall bias, participants with energy intake outside the 5th to 95th percentiles were excluded.
Determination of IBS
The primary endpoint was new-onset IBS identified by ICD-10 code (K58). IBS diagnosis was determined by linkage to primary care and/or hospital admission data. The follow-up period was from the date of the baseline survey until the date of diagnosis of IBS. For participants who did not develop IBS during the follow-up period, the endpoints of the survey were death, loss to follow-up, or the study endpoint, whichever occurred first. To ensure the accuracy of the results, newly diagnosed IBS patients who self-reported or developed symptoms before the last dietary recall were excluded from the analysis.
Covariates
Based on previous studies, the covariates included in this study were age (continuous), sex (male or female), ethnicity (white and others), educational attainment (less than a college degree, college degrees or higher), Townsend Deprivation Index (TDI), smoking status (never, occasional, and regular), alcohol use (never, ever, and current), body mass index (BMI) (< 25 kg/m2, ≥ 25 kg/m2 ), physical activity (low, moderate, high, and unknown), mental health (yes or no), hypertension (yes or no), diabetes mellitus (yes or no), dietary supplement use (yes or no) [20, 21]. TDI was categorized as low/high economic level based on median values [22]. BMI was defined as weight in kilograms divided by the square of height in meters (kg/m2). International Physical Activity Questionnaire (IPAQ) was primarily used to assess the physical activity levels of adults and categorize the population into low, moderate, and high levels [23]. Mental health status (seen a doctor for nerves, anxiety, tension, or depression) was obtained through questionnaires. History of hypertension and diabetes were determined according to ICD-10. Dietary supplement use was self-reported by participants.
Statistical analysis
Continuous variables were presented as mean (SD) or median (IQR), while categorical variables were presented as sample size (N%). For continuous variables, Student’s t-test or Mann-Whitney U test was used, and for categorical variables, the chi-square (χ2) test was used to compare the differences in baseline characteristics between the IBS and non-IBS groups.
Previous studies have shown that there may be a non-linear relationship between various antioxidant dietary intakes (such as trace elements and vitamins) and the risks of chronic respiratory diseases, strokes, etc [20, 21]. Therefore, we first constructed a restricted cubic spline (RCS) regression model with 3 knots (the 25th, 50th, and 75th percentiles) to determine the dose-response relationship between CDAI and the risk of newly diagnosed IBS. This non-linear relationship was determined by Wald test. Two-piecewise linear regression was used to determine the inflection point in the association between CDAI and IBS. Based on the inflection point, Cox regression was performed to estimate HRs and CIs before and after the inflection point. Participants were then grouped into CDAI quintiles, with the interval containing the inflection point serving as a reference group for regression analysis. Schoenfeld residual test was used to verify whether the model conformed to the proportional hazards assumption. The R package “car” was used to diagnose multicollinearity, and a variance inflation factor (VIF) greater than 10 was considered potential multicollinearity. In this study, all VIFs were less than 4, thus excluding collinearity. Multiple models were constructed to evaluate the relationship between CDAI and the risk of IBS: 1) an unadjusted model; 2) Model 1 adjusted for demographic factors (age, sex, race, and education level); 3) Model 2 further adjusted for TDI, BMI, IPAQ, smoking, and alcohol consumption; 4) Model 3 further adjusted for diabetes, hypertension, mental health status, and dietary supplement use.
Subgroup analysis was performed to determine potential effect modifiers, with the specific method of including the multiplicative term between CDAI and stratification variables in the model and conducting interaction tests through likelihood ratio tests. Several sensitivity analyses were also performed to evaluate the robustness of the results: 1) excluding participants diagnosed with IBS within 3 years after recruitment to prevent potential reverse causality; 2) multiple imputation for missing covariates; 3) newly constructed CDAI calculated based on 7 dietary components containing carotenoids (vitamins A, C, E, zinc, selenium, manganese, and carotenoids) [24]; 4) excluding participants whose energy intake was outside the range of 1-99th percentiles; 5) excluding the group with energy intake levels lower than the basal metabolic rate (BMR); 6) conducting only on participants with ≥ 2–3 dietary recalls.
All statistical analyses were conducted using R software (version 4.2.3). The “survival” package was used to construct Cox proportional hazards regression models, the “rms” package was used to build restricted cubic spline regression models, the “mice” package was used for multiple imputation, and the “segmented” package was used to fit a two-piecewise linear regression to estimate the inflection point. Statistical significance was defined as a two-sided P-value < 0.05.
Results
Study population and baseline characteristics
Table 1 shows the baseline characteristics of participants. Especially, among 157,900 people, 54% were female, with a mean (SD) age of 55.91 (7.93) at enrollment. During a mean follow-up time of 13.41 years, a total of 2,007 (1.27%) were newly diagnosed with IBS.
Table 1.
Baseline characteristics of the included participants
| Total (n = 157,900) |
Non-IBS (n = 155,893) |
IBS (n = 2,007) |
P | |
|---|---|---|---|---|
| Follow-up (years), mean (SD) | 13.41 (1.07) | 13.48 (0.82) | 8.16 (3.07) | < 0.001 |
| Age, mean (SD) | 55.91 (7.93) | 55.91 (7.93) | 56.04 (7.73) | 0.477 |
| Sex, n (%) | < 0.001 | |||
| Female | 84,805 (54) | 83,388 (53) | 1417 (71) | |
| Male | 73,095 (46) | 72,505 (47) | 590 (29) | |
| Ethnicity, n (%) | 0.254 | |||
| Others | 6915 (4) | 6838 (4) | 77 (4) | |
| The white | 150,985 (96) | 149,055 (96) | 1930 (96) | |
| Education attainment, n (%) | 0.013 | |||
| Less than a college degree | 150,142 (95) | 148,258 (95) | 1884 (94) | |
| College degrees or higher | 7758 (5) | 7635 (5) | 123 (6) | |
| Smoke, n (%) | 0.594 | |||
| Never smoked | 145,996 (92) | 144,142 (92) | 1854 (92) | |
| Smoke regularly | 8111 (5) | 8001 (5) | 110 (5) | |
| Smoke occasionally | 3793 (2) | 3750 (2) | 43 (2) | |
| Alcohol, n (%) | < 0.001 | |||
| Never | 4731 (3) | 4663 (3) | 68 (3) | |
| Previous | 4323 (3) | 4215 (3) | 108 (5) | |
| Current | 148,846 (94) | 147,015 (94) | 1831 (91) | |
| Townsend deprivation index, n (%) | 0.033 | |||
| Low economic level | 78,987 (50) | 77,935 (50) | 1052 (52) | |
| High economic level | 78,913 (50) | 77,958 (50) | 955 (48) | |
| IPAQ1, n (%) | < 0.001 | |||
| Low | 24,380 (15) | 24,032 (15) | 348 (17) | |
| Moderate | 57,461 (36) | 56,775 (36) | 686 (34) | |
| High | 52,765 (33) | 52,151 (33) | 614 (31) | |
| Unknown | 23,294 (15) | 22,935 (15) | 359 (18) | |
| Mental health status2, n (%) | < 0.001 | |||
| No | 142,294 (90) | 140,631 (90) | 1663 (83) | |
| Yes | 15,606 (10) | 15,262 (10) | 344 (17) | |
| Dietary supplement, n (%) | < 0.001 | |||
| No | 80,298 (51) | 79,380 (51) | 918 (46) | |
| Yes | 77,602 (49) | 76,513 (49) | 1089 (54) | |
| Type II Diabetes, n (%) | 0.003 | |||
| No | 154,960 (98) | 153,009 (98) | 1951 (97) | |
| Yes | 2940 (2) | 2884 (2) | 56 (3) | |
| Hypertension, n (%) | 0.271 | |||
| No | 120,554 (76) | 119,043 (76) | 1511 (75) | |
| Yes | 37,346 (24) | 36,850 (24) | 496 (25) | |
| Body mass index, n (%) | 0.204 | |||
| < 25 kg/m2 | 59,565 (38) | 58,780 (38) | 785 (39) | |
| ≥ 25 kg/m2 | 98,335 (62) | 97,113 (62) | 1222 (61) | |
| Z Score- CDAI, mean (SD) | -0.01 (2.07) | -0.01 (2.07) | -0.02 (2.22) | 0.851 |
| Z Score-Vitamin A, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | 0.00 (1.05) | 0.988 |
| Z Score-Vitamin C, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | -0.02 (1.06) | 0.276 |
| Z Score-Vitamin E, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | 0.01 (1.03) | 0.474 |
| Z Score-Manganese, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | -0.02 (1.01) | 0.277 |
| Z Score-Selenium, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | -0.03 (0.99) | 0.203 |
| Z Score-Zinc, mean (SD) | 0.00 (1.00) | 0.00 (1.00) | -0.02 (1.03) | 0.344 |
Data was expressed as mean (standard deviation) or numbers (percentage %)
Abbreviation: IBS irritable bowel syndrome
1, Physical activity was categorized as low, moderate and high based on the International Physical Activity Questionnaire (IPAQ)
2, Mental disorders were assessed according to the touchscreen question (“Have you ever seen a psychiatrist for nerves, anxiety, tension or depression?”)
Compared to non-IBS individuals, participants with IBS were found to have a higher proportion of females, lower socioeconomic status, less physical activity, higher prevalence of psychological health issues, higher alcohol consumption, and a greater tendency to use dietary supplements. Additionally, they had lower dietary intake of copper and zinc and a higher prevalence of diabetes (Table 1). Additional baseline characteristic analysis was conducted for participants with different CDAI index levels (Table S1).
Association between CDAI and the risk of IBS
After fully adjusting for potential confounders, the restricted cubic spline (RCS) analysis showed a nonlinear association (p for nonlinear < 0.001), and a two-piecewise linear regression identified an inflection point at -0.9 (Fig. 2A and B). After full adjustment, segmented Cox regression analysis showed that an increase in CDAI significantly reduced the risk of IBS when CDAI < -0.9 (HR: 0.900, 95% CI: 0.820–0.988, P = 0.027); while above − 0.9, CDAI was associated with a 3% increase in the risk of IBS (HR: 1.030, 95% CI: 1.001–1.060, P = 0.046). Furthermore, compared to participants in Quintile 2 of CDAI, individuals in Quintile 1 had a 23% increased risk of IBS (HR: 1.228, 95% CI: 1.071–1.408, P = 0.003), while no significant differences were observed for participants in the other quintiles (all P > 0.05) (Table 2).
Fig. 2.
Dose-response relationship and the two-piecewise linear regression of CDAI and vitamin C with the risk of IBS. Figure legend: The RCS curves of CDAI (A), vitamin C (C) and IBS, as well as the two-piecewise linear regression analysis of CDAI (B), vitamin C (D) and IBS. The solid lines and shaded areas in RCS curves represent the central risk estimates and 95% CIs. All models were adjusted for age, sex, ethnicity, education, smoking and drinking status, BMI, TDI, IPAQ, mental health status, type II Diabetes, hypertension, and dietary supplement. For vitamin C, there was additionally adjusted for the Z score of the other five nutrients. Abbreviation: BMI, Body mass index; CDAI, Composite Dietary Antioxidant Index; CI, confidence interval; HR, hazard ratio; IBS, irritable bowel syndrome; IPAQ, International Physical Activity Questionnaire; RCS: Restrictive cubic spline
Table 2.
Multivariate Cox regression analysis for the risk of IBS with CDAI and vitamin C
| Crude model | Model 1 | Model 2 | Model 3 | Model 4 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | ||
| CDAI (Z Score) | |||||||||||
| Segmented | CDAI< -0.9 | 0.901(0.821,0.987) | 0.026 | 0.893(0.814,0.980) | 0.017 | 0.901(0.821,0.989) | 0.029 | 0.900(0.820,0.988) | 0.027 | / | / |
| CDAI≥ -0.9 | 1.034(1.005,1.064) | 0.022 | 1.032(1.002,1.062) | 0.034 | 1.032(1.003,1.062) | 0.032 | 1.030(1.001,1.060) | 0.046 | / | / | |
| Quintiles | Q2(-1.664,-0.713) | ref | ref | ref | ref | ||||||
| Q1(-5.197,-1.664) | 1.238(1.080,1.420) | 0.002 | 1.243(1.085,1.426) | 0.002 | 1.230(1.073,1.410) | 0.003 | 1.228(1.071,1.408) | 0.003 | / | / | |
| Q3(-0.713,0.203) | 1.081(0.939,1.244) | 0.281 | 1.082(0.940,1.246) | 0.271 | 1.089(0.946,1.254) | 0.236 | 1.090(0.947,1.255) | 0.231 | / | / | |
| Q4(0.203,1.432) | 0.968(0.837,1.118) | 0.655 | 0.975(0.844,1.127) | 0.734 | 0.985(0.852,1.138) | 0.834 | 0.985(0.852,1.139) | 0.839 | / | / | |
| Q5(1.432,37.248) | 1.104(0.959,1.270) | 0.168 | 1.102(0.958,1.268) | 0.174 | 1.111(0.966,1.279) | 0.141 | 1.106(0.961,1.273) | 0.159 | / | / | |
| Vitamin C (Z Score) | |||||||||||
| Segmented | Vitamin C < − 0.1 | 0.828(0.714,0.961) | 0.013 | 0.809(0.696,0.939) | 0.005 | 0.831(0.714,0.966) | 0.016 | 0.828(0.712,0.963) | 0.014 | 0.847(0.725,0.990) | 0.037 |
| Vitamin C≥ -0.1 | 1.103(1.029,1.183) | 0.006 | 1.108(1.033,1.188) | 0.004 | 1.107(1.032,1.188) | 0.004 | 1.103(1.029,1.183) | 0.006 | 1.089(1.011,1.172) | 0.024 | |
| Quintiles | Q3(-0.375,0.084] | ref | ref | ref | ref | ref | |||||
| Q1[-1.764,-0.831] | 1.251(1.088,1.438) | 0.002 | 1.281(1.113,1.473) | < 0.001 | 1.253(1.089,1.441) | 0.002 | 1.244(1.081,1.431) | 0.002 | 1.245(1.080,1.435) | 0.003 | |
| Q2(-0.831,-0.375] | 1.190(1.033,1.370) | 0.016 | 1.189(1.033,1.369) | 0.016 | 1.183(1.027,1.362) | 0.020 | 1.178(1.023,1.356) | 0.023 | 1.178(1.023,1.357) | 0.023 | |
| Q4(0.084,0.707] | 1.103(0.956,1.273) | 0.180 | 1.104(0.956,1.274) | 0.178 | 1.111(0.963,1.283) | 0.149 | 1.114(0.965,1.286) | 0.139 | 1.112(0.963,1.284) | 0.148 | |
| Q5(0.707,16.270] | 1.101(0.954,1.271) | 0.189 | 1.106(0.958,1.277) | 0.168 | 1.112(0.963,1.284) | 0.147 | 1.109(0.960,1.280) | 0.160 | 1.101(0.951,1.274) | 0.197 | |
Crude model: Not adjusted
Abbreviations: CI confidence interval, HR hazard ratio, IBS irritable bowel syndrome, IPAQ International Physical Activity Questionnaire, ref reference
Model 1: Adjusted for age, gender, education, and ethnicity
Model 2: Further adjusted for smoking and drinking status, body mass index, Townsend deprivation index, and IPAQ based on crude Model 1
Model 3: Further adjusted for mental health status, type II Diabetes, hypertension, and dietary supplement based on Model 2
Model 4: Further adjusted for the Z score of vitamin A, vitamin E, Manganese, Selenium, and Zinc based on Model 3
Association of individual diet of CDAI with the risk of IBS
As shown in Fig. S1, only vitamin C was independently and nonlinearly associated with IBS (Nonlinear P < 0.001, P for overall < 0.001). Figure 3 showed that the Pearson correlation coefficients of the six antioxidant dietary components were all less than 0.5. Two-piecewise linear regression suggested an inflection point at -0.1. Further segmented regression analysis indicated that after adjusting for potential confounders and the other five nutrients, when the Z score <-0.1, vitamin C intake was associated with a 15% reduction in IBS risk (HR: 0.847, 95% CI: 0.725–0.990, P = 0.037); while when the Z score ≥ -0.1, vitamin C intake was associated with an 8.9% increase in IBS risk (HR: 1.089, 95% CI: 1.011–1.172, P = 0.024) (Fig. 2C and D). After categorizing participants based on quintiles, compared to those in Quintile 3 where the inflection point was located, the HRs for participants in Quintile 1 and Quintile 2 were 1.245 (95% CI: 1.080, 1.435, P = 0.003) and 1.178 (95% CI: 1.023, 1.357, P = 0.023), respectively. However, there was no significant difference in the risk between participants in Quintile 4 and Quintile 5 compared to those in Quintile 3 (P > 0.05, respectively) (Table 2).
Fig. 3.
Pearson correlation coefficients for six components of CDAI. Abbreviations: CDAI, Composite Dietary Antioxidant Index
Stratified analysis and sensitivity analysis
Stratified analysis revealed that among individuals with a CDAI Z-score≥ -0.9, compared to the population aged ≥ 60 years, the positive association between CDAI intake and the risk of IBS was stronger in the population aged < 60 years. Compared to other racial groups, the positive association between CDAI intake and the risk of IBS was stronger in the white population. When the Z-score of vitamin C intake was ≥ -0.1, there was a significant multiplicative interaction between gender, race, and vitamin C. Compared with females, the positive association between vitamin C intake and IBS risk was more pronounced in males (Fig. 4). Sensitivity analysis, using multiple imputation for missing covariates, showed a robust association between increased intake of antioxidants and reduced IBS risk when CDAI < -0.9. However, when CDAI ≥ -0.9, increasing antioxidant intake did not alter the risk of IBS (Table 3). In addition, we performed sensitivity analyses by expanding the energy intake inclusion range to the 1–99th percentiles (Table S2), excluding the group with energy intake levels lower than the BMR (Table S3), the results were consistent with the primary findings. Considering variations in the number of dietary recalls among participants, we categorized participants according to the number of valid 24-hour dietary recalls and compared the incidence of IBS and CDAI scores across groups (Table S4). Statistically significant differences were detected (P = 0.03 and P < 0.001, respectively). To further evaluate the potential impact of the number of dietary recalls on our results, we restricted the analysis to participants with ≥ 2–3 dietary recall records (n = 99296; Table S5). The results were consistent with the primary findings, further confirming the robustness of our research outcomes.
Fig. 4.
Subgroup analysis of the association between dietary CDAI, vitamin C intakes and the risk of IBS divided by -0.9 and -0.1, respectively. Figure legend: Each stratification was adjusted for age, sex, ethnicity, education, smoking and drinking status, BMI, TDI, IPAQ, mental health status, type II Diabetes, hypertension, and dietary supplement, and the stratified analysis for vitamin C was additionally adjusted for the Z score of the other five nutrients. When the association between each stratified variable and IBS was evaluated, this variable was excluded from the adjustment. Abbreviations: BMI, body mass index; CDAI, Composite Dietary Antioxidant Index; CI, confidence interval; HR, hazard ratio; IBS, irritable bowel syndrome; IPAQ, International Physical Activity Questionnaire; P int, P for interaction; TDI, townsend deprivation index
Table 3.
The results of sensitivity analyses of the association between dietary CDAI intakes and the risk of IBS
| Crude model | Model 1 | Model 2 | Model 3 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | ||
| New IBS within three years of follow-up was excluded | CDAI< -0.9 | 0.904(0.823,0.993) | 0.034 | 0.897(0.816,0.985) | 0.023 | 0.905(0.824,0.995) | 0.039 | 0.903(0.822,0.993) | 0.035 |
| CDAI≥ -0.9 | 1.035(1.006,1.066) | 0.019 | 1.033(1.003,1.063) | 0.030 | 1.033(1.004,1.064) | 0.027 | 1.031(1.001,1.061) | 0.039 | |
| Q2(-1.664,-0.714] | ref | ref | ref | ref | |||||
| Q1[-5.197,-1.664] | 1.237(1.077,1.420) | 0.003 | 1.242(1.082,1.426) | 0.002 | 1.228(1.070,1.410) | 0.004 | 1.234(1.075,1.417) | 0.003 | |
| Q3(-0.714,0.203] | 1.066(0.924,1.230) | 0.379 | 1.068(0.926,1.231) | 0.369 | 1.074(0.931,1.239) | 0.324 | 1.073(0.930,1.238) | 0.333 | |
| Q4(0.203,1.432] | 0.963(0.832,1.115) | 0.613 | 0.970(0.838,1.123) | 0.687 | 0.980(0.847,1.135) | 0.791 | 0.977(0.844,1.131) | 0.752 | |
| Q5(1.432,37.248] | 1.105(0.959,1.273) | 0.169 | 1.102(0.957,1.270) | 0.178 | 1.112(0.965,1.282) | 0.141 | 1.102(0.956,1.270) | 0.182 | |
| Multivariate interpolation with covariates | CDAI< -0.9 | 0.899(0.820,0.985) | 0.023 | 0.892(0.813,0.978) | 0.015 | 0.901(0.822,0.988) | 0.027 | 0.900(0.821,0.987) | 0.026 |
| CDAI≥ -0.9 | 1.032(1.002,1.062) | 0.034 | 1.029(1.000,1.059) | 0.053 | 1.029(1.000,1.059) | 0.051 | 1.027(0.998,1.057) | 0.072 | |
| Q2(-1.666,-0.715] | ref | ref | ref | ref | |||||
| Q1[-5.197,-1.666] | 1.219(1.065,1.396) | 0.004 | 1.224(1.069,1.402) | 0.003 | 1.212(1.058,1.388) | 0.006 | 1.217(1.062,1.393) | 0.005 | |
| Q3(-0.715,0.202] | 1.073(0.933,1.234) | 0.321 | 1.075(0.935,1.236) | 0.308 | 1.081(0.941,1.243) | 0.271 | 1.080(0.940,1.242) | 0.278 | |
| Q4(0.202,1.431] | 0.950(0.823,1.097) | 0.485 | 0.958(0.830,1.106) | 0.559 | 0.967(0.838,1.117) | 0.648 | 0.963(0.834,1.112) | 0.611 | |
| Q5(1.431,37.248] | 1.088(0.947,1.250) | 0.236 | 1.086(0.945,1.248) | 0.244 | 1.095(0.952,1.258) | 0.204 | 1.084(0.943,1.246) | 0.256 | |
| Newly constructed CDAI containing carotenoids | CDAI< -0.9 | 0.950(0.919,0.982) | 0.003 | 0.947(0.916,0.979) | 0.001 | 0.953(0.921,0.985) | 0.005 | 0.953(0.921,0.985) | 0.005 |
| CDAI≥ -0.9 | 1.025(1.007,1.042) | 0.005 | 1.024(1.007,1.041) | 0.007 | 1.024(1.006,1.041) | 0.008 | 1.022(1.005,1.039) | 0.013 | |
| Q2(-3.43,-1.351) | ref | ref | ref | ref | |||||
| Q1(-11.996,-3.43) | 1.232(1.075,1.411) | 0.003 | 1.249(1.090,1.431) | 0.001 | 1.235(1.078,1.415) | 0.002 | 1.237(1.079,1.417) | 0.002 | |
| Q3(-1.351,0.594) | 1.001(0.868,1.155) | 0.985 | 1.003(0.870,1.157) | 0.965 | 1.013(0.878,1.168) | 0.860 | 1.009(0.875,1.163) | 0.903 | |
| Q4(0.594,3.105) | 0.985(0.854,1.137) | 0.839 | 0.996(0.863,1.149) | 0.952 | 1.008(0.874,1.164) | 0.910 | 1.002(0.868,1.156) | 0.983 | |
| Q5(3.105,46.286) | 1.097(0.954,1.261) | 0.195 | 1.101(0.958,1.266) | 0.177 | 1.114(0.969,1.282) | 0.129 | 1.097(0.954,1.262) | 0.193 | |
Crude model: Not adjusted
Abbreviations: CDAI Composite Dietary Antioxidant Index, IBS irritable bowel syndrome, IPAQ International Physical Activity Questionnaire
Model 1: Adjusted for age, gender, education, and ethnicity
Model 2: Further adjusted for smoking and drinking status, body mass index, Townsend deprivation index, and IPAQ based on crude Model 1
Model 3: Further adjusted for mental health status, type II Diabetes, hypertension, and dietary supplement based on Model 2
Discussion
In this large, prospective cohort study with a follow-up of 13.41 years, we found a non-linear exposure-response relationship between the composite dietary antioxidant index (CDAI) and IBS, with a threshold effect analysis suggesting a turning point at -0.9. When CDAI was < -0.9, higher CDAI was associated with a decreased risk of IBS, while no protective effect on IBS risk was observed when CDAI was ≥ -0.9. After incorporating the five-category CDAI into the model, the results showed that participants in the first quintile (Q1) had a higher risk of IBS compared to those in the second quintile (Q2), while no significant differences were observed for other quintile groups. In the independent dietary analysis, only vitamin C showed a robust association with IBS risk, with a two-piecewise linear regression indicating a turning point at -0.1. Further stratified analysis revealed that when CDAI ≥ -0.9, the association between CDAI and the risk of IBS was stronger among individuals aged < 60 years and white people. When dietary intake of vitamin C was ≥ -0.1, there was a significant multiplicative interaction between gender, race, and vitamin C. Sensitivity analysis using multiple imputation for missing covariates showed a robust association between increased intake of antioxidants and reduced IBS risk when CDAI < -0.9, whereas increasing antioxidant intake did not alter the incidence of IBS when CDAI was ≥ -0.9. Overall, our findings indicate that moderate increases in dietary antioxidant intake—particularly vitamin C—may contribute to reducing the risk of IBS. In contrast, higher antioxidant intake not only offers no additional protective benefit but may also be weakly associated with an elevated risk of IBS, though this latter association lacks clinical significance.
To our knowledge, this is the first prospective cohort study to investigate the association between overall dietary antioxidant capacity and IBS risk. Previous clinical randomized controlled studies have suggested that diets high in antioxidants, such as the Mediterranean diet and Tritordeum-based foods, may alleviate abdominal symptoms in IBS patients [25–27]. Only one study has explored the relationship between an antioxidant-rich diet and IBS, and found no association between dietary total antioxidant capacity (dTAC) and the incidence or severity of IBS [14]. However, it is well-established that numerous dietary components exert a biphasic effect, wherein both excessive and insufficient intake can be detrimental to normal physiological function. This critical consideration was not incorporated into the analyses of these prior studies. In our study, we prospectively investigated the relationship between CDAI and IBS incidence using the latest assessment of dietary antioxidant capacity. By using RCS regression and two-piecewise linear regression, we found a non-linear relationship between CDAI and IBS risk, with a turning point at -0.9. Sensitivity analysis confirmed that a low CDAI does increase IBS risk; however, contrary to expectations, there was no protective effect of CDAI beyond − 0.9. Additionally, we identified vitamin C as the primary antioxidant associated with IBS risk.
CDAI was developed and evaluated based on circulating inflammatory markers, IL-1β and TNF-α levels [28]. These markers reflect the biological effects of dietary antioxidants on oxidative-induced systemic inflammation. Studies have shown that IBS patients have a prolonged low-grade inflammation in the gut, and antioxidants can reduce the production of pro-inflammatory cytokines, thus inhibiting the inflammatory response [29–31]. In addition, oxidative stress increases the permeability of the intestinal mucosa, allowing inflammatory signals to pass through the intestinal membrane and causing the aggregation of inflammatory cells in the muscle and submucosal layers [32]. Furthermore, one study showed that plasma xanthine oxidase activity was significantly increased in IBS patients, and xanthine oxidase-derived ROS mediated the upregulation of interleukin-1β (IL-1b) and tumor necrosis factor-a (TNF-a) expression [7, 33]. However, we found that when CDAI≥-0.9, there is a weak positive correlation between CDAI and the incidence of IBS. A reasonable explanation for this finding is that due to processes such as fermentation, the antioxidant activity of milk, grains, fruits and vegetables, meat, and fish can be enhanced [34]. Therefore, a higher intake of antioxidants typically reflects a higher intake of Fermentable oligo-, di-, monosaccharides and polyols (FODMAPs) diets. FODMAP refers to short-chain carbohydrates that are difficult to absorb and are fermented in the large intestine, producing gas and increasing intestinal osmotic pressure, causing bloating and abdominal pain [35]. At the same time, fructans can cause changes in the activities of the central nervous system through visceral transmission signals, and interact with the brain-gut axis to play a role in symptom perception [36]. Moreover, these highly fermented foods may affect the intestinal flora, neural-gut peptide signals, mucosal inflammation and other mechanisms, exacerbating the symptoms of various types of IBS [35].
In the present study, we found that vitamin C played a key role. The intake of vitamin C is associated with a U-shaped relationship with the risk of IBS, with a turning point at -0.1. Vitamin C is well-known as a potent antioxidant that neutralizes reactive oxygen species produced by phagocytes, maintaining important substances such as LDL, proteins, and DNA in a reduced state [37, 38]. Since humans lack L-gulono-1,4-lactone oxidase, they cannot synthesize vitamin C internally and rely entirely on dietary intake [39]. Studies have also shown that vitamin C promotes the production of short-chain fatty acids (SCFA), particularly butyrate, by gut microbiota [40]. By activating peroxisome proliferator-activated receptor gamma (PPARγ), butyrate stimulates intestinal β-oxidation and oxygen consumption, maintaining an anaerobic environment in the gut lumen [41]. Importantly, vitamin C intake increases the abundance and diversity of intestinal microbiota, improves intestinal morphology, restores villus length and goblet cell numbers, reduces permeability and fibrosis, thereby alleviating intestinal inflammation [42]. Interestingly, we found that high vitamin C intake is associated with a weak association with the risk of IBS. As an important component of CDAI, several studies have shown that high-dose vitamin C is a pro-oxidant rather than an antioxidant, and high concentrations of vitamin C may enhance ROS production through the p66Shc/Rac1 GTPase pathway, exerting pro-oxidant activity [43, 44]. However, it is noteworthy that the intake of vitamin C in this study is within the normal dietary range (mean (SD) of vitamin C: 127.5(74.5) mg/day, the 95th percentile of vitamin C: 263.0 mg/day). The existing evidence does not support that vitamin C has an oxidative stimulating activity at this dose [45]. The weak association observed between high dietary vitamin C intake and IBS is more plausibly attributed to confounding factors (e.g., dietary pattern-related confounders), measurement biases, or other extraneous variables rather than a direct causal relationship.
In stratified analysis, we found that the association of CDAI with the risk of IBS was stronger in the population under 60 years old when CDAI was ≥-0.9. Elderly individuals have weakened gastrointestinal motility and digestive capacity, so even with the same intake of antioxidants, younger adults have a higher proportion of absorption and utilization, making the effect of dietary factors more significant [46]. When the Z score of vitamin C was ≥-0.1, the association of vitamin C intake with the risk of IBS was stronger in males. This may be related to a stronger responsiveness of males to vitamin C, although the specific mechanism remains to be explored. Additionally, due to the smaller proportion of other ethnic groups, the interaction between ethnicity and CDAI or vitamin C may not have practical significance.
Advantages and limitations
To our knowledge, this is the first prospective cohort study investigating the risk relationship between six antioxidant components and IBS in the general population. In this study, we evaluated the nonlinear dose-response relationship between CDAI and the risk of IBS. To validate the robustness of the results, we excluded patients diagnosed with IBS within three years after recruitment to minimise the risk of reverse causality and used multiple imputation to reduce bias between estimated and true effects due to missing covariates. Additionally, the antioxidant capacity of carotenoids was considered, and a new CDAI was constructed to validate the results.
However, some limitations need to be considered. Firstly, although known confounding factors were taken into account, the possibility of unmeasured or residual confounding factors (such as fiber subclasses, FODMAP intake and cooking methods) still exists, which may have led to the “dilution” or “amplification” of the association between antioxidants and IBS. Secondly, the UK Biobank only recorded the date of the first onset of IBS. This limitation restricts further research on the association between dietary intake and different subtypes and symptoms of IBS. Thirdly, the influence of recall bias on determining individual dietary intake through 24-hour recall is inevitable. To address this issue, we used the average of five 24-hour dietary data; however, less than 20% of participants in the UK Biobank had data with two or more dietary records. Fourth, using only ICD-10 code (K58) to identify IBS in a cohort with a mean age of 55.9 years results in underestimated true incidence of the IBS. Finally, our study was conducted only in the UK, which may limit the applicability of our findings to all populations globally.
Conclusion
In summary, our study provides new insights into the relationship between antioxidant diet and the risk of IBS. The association between CDAI and IBS risk exhibits a significant threshold effect. Combining sensitivity analysis, we identified that lower antioxidant dietary intake is associated with a higher risk of IBS, and excessive intake tends to have no protective effect on IBS risk. Furthermore, we identified an independent and robust association between dietary vitamin C intake and incident IBS, which offers valuable insights for the dietary management of patients with IBS in clinical practice.
Supplementary Information
Supplementary Material 1: Fig S1. Nonlinear analysis of six independent nutrients of CDAI and the risk of IBS. Figure legend: The RCS curves of vitamin A (A), vitamin C (B), vitamin E (C), Manganese (D), Selenium (E), Zinc (F) and IBS. The solid lines and shaded areas in RCS curves represent the central risk estimates and 95% CIs. All models were adjusted for age, sex, ethnicity, education, smoking and drinking status, BMI, TDI, IPAQ, mental health status, type II Diabetes, hypertension, and dietary supplement. When the association between each nutrient and IBS was evaluated, the Z scores of the other five nutrients were additionally adjusted. Abbreviation: BMI, Body mass index; CI, confidence interval; HR, hazard ratio; IBS, irritable bowel syndrome; IPAQ, International Physical Activity Questionnaire; RCS: Restrictive cubic spline.
Acknowledgements
The authors thank the UK Biobank and participants.
Authors’ contributions
LLF and ZY wrote the main part of the paper. YR and WLL make a critical revision of the manuscript for important intellectual content. CJM, LSW, SYT, and SZY responsible for proper layout of images. ML, NY, and YFC revised the manuscript appropriately. FD conceived and supervised this manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
The study was funded by the Key Research and Development Projects of Shaanxi Province (S2023-YF-YBSF-1670).
Data availability
All data used in this study are publicly accessible from UK Biobank via their standard data access procedure at https://www.ukbiobank.ac.uk/.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki. UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (Ref: 11/NW/0382). All participants in the UK Biobank gave written, informed consent before the baseline visit. This approval means that researchers do not require separate ethical clearance. In the present study, the UK Biobank application number is 99732.
Consent for publication
All participants provided written informed consent before participation.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Laifu Li and Yan Zhuang contributed equally to this work and shared the first authorship.
References
- 1.Ford AC, Sperber AD, Corsetti M, Camilleri M. Irritable bowel syndrome. Lancet (London England). 2020;396(10263):1675–88. [DOI] [PubMed] [Google Scholar]
- 2.El-Salhy M, Hatlebakk JG, Hausken T. Diet in irritable bowel syndrome (IBS): interaction with gut microbiota and gut hormones. Nutrients. 2019;11(8):1824. [DOI] [PMC free article] [PubMed]
- 3.Lacy BE, Pimentel M, Brenner DM, Chey WD, Keefer LA, Long MD, Moshiree B. ACG Clinical Guideline: Management of Irritable Bowel Syndrome. Am J Gastroenterol. 2021;116(1):17–44. [DOI] [PubMed] [Google Scholar]
- 4.El-Salhy M. Recent developments in the pathophysiology of irritable bowel syndrome. World J Gastroenterol. 2015;21(25):7621–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Di Rosa C, Altomare A, Terrigno V, Carbone F, Tack J, Cicala M, Guarino MPL. constipation-predominant irritable bowel syndrome (IBS-C): effects of different nutritional patterns on intestinal dysbiosis and symptoms. Nutrients. 2023;15(7):1647. [DOI] [PMC free article] [PubMed]
- 6.Marreiro DD, Cruz KJ, Morais JB, Beserra JB, Severo JS, de Oliveira AR. Zinc and Oxidative Stress: Current Mechanisms. Antioxid (Basel Switzerland). 2017;6(2):24. [DOI] [PMC free article] [PubMed]
- 7.Mete R, Tulubas F, Oran M, Yılmaz A, Avci BA, Yildiz K, Turan CB, Gurel A. The role of oxidants and reactive nitrogen species in irritable bowel syndrome: a potential etiological explanation. Med Sci monitor: Int Med J experimental Clin Res. 2013;19:762–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Li F, Chang Y, Wang Z, Wang Z, Zhao Q, Han X, Xu Z, Yu C, Liu Y, Chang S, et al. Antioxidant insights: investigating the protective role of oxidative balance in inflammatory bowel disease. Front Endocrinol. 2024;15:1386142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.He H, Chen X, Ding Y, Chen X, He X. Composite dietary antioxidant index associated with delayed biological aging: a population-based study. Aging (Albany NY). 2024;16(1):15–27. [DOI] [PMC free article] [PubMed]
- 10.Roth B, Larsson E, Ohlsson B. Poor intake of vitamins and minerals is associated with symptoms among patients with irritable bowel syndrome. J Gastroenterol Hepatol. 2022;37(7):1253–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bahrami A, Gonoodi K, Khayyatzadeh SS, Tayefi M, Darroudi S, Bahrami-Taghanaki H, Eslami S, Jaberi N, Ferns GA, Farahmand K, et al. The association of trace elements with premenstrual syndrome, dysmenorrhea and irritable bowel syndrome in adolescents. Eur J Obstet Gynecol Reprod Biol. 2019;233:114–9. [DOI] [PubMed] [Google Scholar]
- 12.Böhn L, Störsrud S, Simrén M. Nutrient intake in patients with irritable bowel syndrome compared with the general population. Neurogastroenterol Motil. 2013;25(1):23–e3021. [DOI] [PubMed] [Google Scholar]
- 13.Rezazadegan M, Soheilipour M, Tarrahi MJ, Amani R. Correlation Between Zinc Nutritional Status with Serum Zonulin and Gastrointestinal Symptoms in Diarrhea-Predominant Irritable Bowel Syndrome: A Case-Control Study. Dig Dis Sci. 2022;67(8):3632–8. [DOI] [PubMed] [Google Scholar]
- 14.Saneie S, Aminianfar A, Shidfar F, Keshteli AH, Esmaillzadeh A, Adibi P. The association between dietary total antioxidant capacity and odds and severity of irritable bowel syndrome among Iranian adults: a cross-sectional study. BMC Gastroenterol. 2022;22(1):472. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Yu YC, Paragomi P, Wang R, Jin A, Schoen RE, Sheng LT, Pan A, Koh WP, Yuan JM, Luu HN. Composite dietary antioxidant index and the risk of colorectal cancer: Findings from the Singapore Chinese Health Study. Int J Cancer. 2022;150(10):1599–608. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wu M, Si J, Liu Y, Kang L, Xu B. Association between composite dietary antioxidant index and hypertension: insights from NHANES. Clin Exp Hypertens. 2023;45(1):2233712. [DOI] [PubMed] [Google Scholar]
- 17.Wang M, Huang ZH, Zhu YH, He P, Fan QL. Association between the composite dietary antioxidant index and chronic kidney disease: evidence from NHANES 2011–2018. Food Funct. 2023;14(20):9279–86. [DOI] [PubMed] [Google Scholar]
- 18.Palmer LJ. UK Biobank: bank on it. Lancet. 2007;369(9578):1980–2. [DOI] [PubMed] [Google Scholar]
- 19.Liu B, Young H, Crowe FL, Benson VS, Spencer EA, Key TJ, Appleby PN, Beral V. Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr. 2011;14(11):1998–2005. [DOI] [PubMed] [Google Scholar]
- 20.Wang S, Teng H, Zhang L, Wu L. Association between dietary antioxidant intakes and chronic respiratory diseases in adults. World Allergy Organ J. 2024;17(1):100851. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mao J, Zhao Y, Hu H, Zhou M, Yang X. An L-shaped association between composite dietary antioxidant index and stroke: Evidence from NHANES 2011–2020. J Stroke Cerebrovasc Dis. 2024;33(3):107578. [DOI] [PubMed] [Google Scholar]
- 22.Dolan SA, Jarman B, Bajekal M, Davies PM, Hart D. Measuring disadvantage: changes in the underprivileged area, Townsend, and Carstairs scores 1981-91. J Epidemiol Commun Health. 1995;49(Suppl 2):S30–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8:115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhang Y, Duan Z, Lu H, Lu G, Fu Y, Li G, Wang S. Physical activity modifies the association of the composite dietary antioxidant index with all-cause mortality in the US osteoarthritis population. Front Public Health. 2023;11:1297245. Published 2023 Dec 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Al-Biltagi M, El Amrousy D, El Ashry H, Maher S, Mohammed MA, Hasan S. Effects of adherence to the Mediterranean diet in children and adolescents with irritable bowel syndrome. World J Clin Pediatr. 2022;11(4):330–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Staudacher HM, Mahoney S, Canale K, Opie RS, Loughman A, So D, Beswick L, Hair C, Jacka FN. Clinical trial: A Mediterranean diet is feasible and improves gastrointestinal and psychological symptoms in irritable bowel syndrome. Aliment Pharmacol Ther. 2024;59(4):492–503. [DOI] [PubMed]
- 27.Russo F, Riezzo G, Linsalata M, Orlando A, Tutino V, Prospero L, D’Attoma B, Giannelli G. Managing Symptom Profile of IBS-D Patients With Tritordeum-Based Foods: Results From a Pilot Study. Front Nutr. 2022;9:797192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Luu HN, Wen W, Li H, Dai Q, Yang G, Cai Q, Xiang YB, Gao YT, Zheng W, Shu XO. Are dietary antioxidant intake indices correlated to oxidative stress and inflammatory marker levels? Antioxid Redox Signal. 2015;22(11):951–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ng QX, Soh AYS, Loke W, Lim DY, Yeo WS. The role of inflammation in irritable bowel syndrome (IBS). J Inflamm Res. 2018;11:345–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Neha K, Haider MR, Pathak A, Yar MS. Medicinal prospects of antioxidants: A review. Eur J Med Chem. 2019;178:687–704. [DOI] [PubMed] [Google Scholar]
- 31.Arulselvan P, Fard MT, Tan WS, Gothai S, Fakurazi S, Norhaizan ME, Kumar SS. Role of Antioxidants and Natural Products in Inflammation. Oxid Med Cell Longev. 2016;2016:5276130. [DOI] [PMC free article] [PubMed]
- 32.Balmus IM, Ciobica A, Cojocariu R, Luca AC, Gorgan L. Irritable Bowel Syndrome and Neurological Deficiencies: Is There A Relationship? The Possible Relevance of the Oxidative Stress Status. Medicina (Kaunas). 2020;56(4):175. [DOI] [PMC free article] [PubMed]
- 33.Schwartz MD, Repine JE, Abraham E. Xanthine oxidase-derived oxygen radicals increase lung cytokine expression in mice subjected to hemorrhagic shock. Am J Respir Cell Mol Biol. 1995;12(4):434–40. [DOI] [PubMed] [Google Scholar]
- 34.Melini F, Melini V, Luziatelli F, Ficca AG, Ruzzi M. Health-Promoting Components in Fermented Foods: An Up-to-Date Systematic Review. Nutrients. 2019;11(5). [DOI] [PMC free article] [PubMed]
- 35.Van den Houte K. Dietary approaches in functional bowel disorders. Best Pract Res Clin Gastroenterol. 2025;79:102077. [DOI] [PubMed] [Google Scholar]
- 36.Murray K, Wilkinson-Smith V, Hoad C, Costigan C, Cox E, Lam C, Marciani L, Gowland P, Spiller RC. Differential effects of FODMAPs (fermentable oligo-, di-, mono-saccharides and polyols) on small and large intestinal contents in healthy subjects shown by MRI. Am J Gastroenterol. 2014;109(1):110–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Jacob RA, Sotoudeh G. Vitamin C function and status in chronic disease. Nutr Clin care: official publication Tufts Univ. 2002;5(2):66–74. [DOI] [PubMed] [Google Scholar]
- 38.Padayatty SJ, Katz A, Wang Y, Eck P, Kwon O, Lee JH, Chen S, Corpe C, Dutta A, Dutta SK, et al. Vitamin C as an antioxidant: evaluation of its role in disease prevention. J Am Coll Nutr. 2003;22(1):18–35. [DOI] [PubMed] [Google Scholar]
- 39.Doseděl M, Jirkovský E, Macáková K, Krčmová LK, Javorská L, Pourová J, Mercolini L, Remião F, Nováková L, Mladěnka P et al. Vitamin C-Sources, Physiological Role, Kinetics, Deficiency, Use, Toxicity, and Determination. Nutrients. 2021;13(2):615. [DOI] [PMC free article] [PubMed]
- 40.Pham VT, Dold S, Rehman A, Bird JK, Steinert RE. Vitamins, the gut microbiome and gastrointestinal health in humans. Nutr Res (New York NY). 2021;95:35–53. [DOI] [PubMed] [Google Scholar]
- 41.Li XY, Meng L, Shen L, Ji HF. Regulation of gut microbiota by vitamin C, vitamin E and β-carotene. Food Res Int (Ottawa Ont). 2023;169:112749. [DOI] [PubMed] [Google Scholar]
- 42.Li Y, Salih Ibrahim RM, Chi HL, Xiao T, Xia WJ, Li HB, Kang YM. Altered Gut Microbiota is Involved in the Anti-Hypertensive Effects of Vitamin C in Spontaneously Hypertensive Rat. Mol Nutr Food Res. 2021;65(7):e2000885. [DOI] [PubMed] [Google Scholar]
- 43.Podmore ID, Griffiths HR, Herbert KE, Mistry N, Mistry P, Lunec J. Vitamin C exhibits pro-oxidant properties. Nature. 1998;392(6676):559. [DOI] [PubMed] [Google Scholar]
- 44.Mir HA, Ali R, Wani ZA, Khanday FA. Pro-oxidant vitamin C mechanistically exploits p66Shc/Rac1 GTPase pathway in inducing cytotoxicity. Int J Biol Macromol. 2022;205:154–68. [DOI] [PubMed] [Google Scholar]
- 45.Compounds IoMUPoDAaR. In. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids. edn. Washington (DC): National Academies Press (US); 2000. [PubMed] [Google Scholar]
- 46.Soenen S, Rayner CK, Jones KL, Horowitz M. The ageing gastrointestinal tract. Curr Opin Clin Nutr Metab Care. 2016;19(1):12–8. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Fig S1. Nonlinear analysis of six independent nutrients of CDAI and the risk of IBS. Figure legend: The RCS curves of vitamin A (A), vitamin C (B), vitamin E (C), Manganese (D), Selenium (E), Zinc (F) and IBS. The solid lines and shaded areas in RCS curves represent the central risk estimates and 95% CIs. All models were adjusted for age, sex, ethnicity, education, smoking and drinking status, BMI, TDI, IPAQ, mental health status, type II Diabetes, hypertension, and dietary supplement. When the association between each nutrient and IBS was evaluated, the Z scores of the other five nutrients were additionally adjusted. Abbreviation: BMI, Body mass index; CI, confidence interval; HR, hazard ratio; IBS, irritable bowel syndrome; IPAQ, International Physical Activity Questionnaire; RCS: Restrictive cubic spline.
Data Availability Statement
All data used in this study are publicly accessible from UK Biobank via their standard data access procedure at https://www.ukbiobank.ac.uk/.