Abstract
Background and aim
Nutrient pattern approach is an appropriate way to compare nutrient intakes across different populations due to the universality of nutrients’ nature. The current study was purposed to examine the association between patterns of nutrient intakes and risk of ulcerative colitis (UC) among Iranian adults.
Methods
In this case-control study, we enrolled 109 UC patients and 218 age- and sex-matched controls. Dietary intakes were assessed using a validated self-administered 106-item dish-based Food Frequency Questionnaire (FFQ). We also used a pre-tested questionnaire to collect data on potential confounders. A gastroenterology specialist made the diagnosis of UC according to international criteria.
Results
In total, 2 nutrient patterns were identified using factor analysis. We found the first nutrient pattern (NP1), characterized by the high intakes of macronutrients, B-vitamins, selenium, iron, zinc, sodium, phosphorus, manganese, magnesium, copper, calcium, fiber, and vitamins E and D, was inversely associated with odds of UC. This association remained significant after taking potential confounders into account; individuals in the top tertile of NP1 score had 93% lower odds of UC compared with those in the bottom tertile (OR: 0.07, 95% CI, 0.01-0.32). Regarding NP2, containing a high amount of beta-carotene, vitamins A, K, and C, potassium, and folate, a significant inverse association was also found (OR: 0.19, 95% CI, 0.09-0.38); such that in the fully adjusted model, individuals in the third tertile of NP2 score were 64% less likely to have UC compared with those in the first tertile (OR: 0.36, 95% CI, 0.15-0.82).
Conclusion
We found that a dietary pattern rich in antioxidants, B-vitamins, macronutrients, zinc, iron, copper, calcium, potassium, fat-soluble vitamins, and fiber is inversely associated with UC.
Keywords: Nutrient pattern, ulcerative colitis, inflammatory bowel disease, nutrition epidemiology, case-control
Introduction
Diet plays an important role in the etiology of inflammatory bowel disease (IBD).1 Adherence to low FODMAPs (fermentable oligo-, di-, monosaccharides, and polyols) diet has been shown to beneficially affect symptoms and severity of IBD.2 In addition, dietary intake of antioxidants might improve severity of symptoms in IBD patients.3,4 Adherence to a low-fiber diet for 6 months has also resulted in better control of IBD symptoms.5 Overall, earlier studies have mainly focused on the management of IBD symptoms using dietary interventions; however, the preventive effects of dietary factors on the incidence of this condition received limited attention. In a case-control study, adequate intake of vitamin C and folate was associated with a lower risk of ulcerative colitis (UC).6 Findings from a meta-analysis revealed a significant positive association between meat consumption and IBD.7 In addition, lack of a significant association between dietary fiber intake and risk of UC was reported in a prospective cohort study.8
Although consumption of individual nutrients has been examined in relation to IBD, no evidence is available focusing on patterns of nutrients intake. People do not consume nutrients separately; in reality, they are taking several nutrients together, which might interact with each other.9 Nutrients can synergistically affect the absorption of others.10 Furthermore, examining patterns of nutrients intake has several advantages over dietary patterns. Given the nature of nutrients, there are no non-consumers of nutrients. In addition, compared to foods, nutrients cannot be functionally exchanged. Nutrient pattern analysis is better than dietary pattern analysis when comparing dietary intakes across different nations. Although food patterns might provide general information on diet-disease associations, the underlying mechanisms should be explained based on patterns of nutrients intake.
Several previous studies have examined dietary patterns in relation to IBD. In a case-control study, an inverse association was reported between adherence to healthy dietary pattern high in low-fat dairy products, nuts, fish, vegetable oil, olive oil, fruits, and vegetables and odds of UC.11 Among participants of the EPIC cohort, higher UC incidence was reported in those who consumed a diet with high amounts of sugar and soft drinks and low amounts of vegetables.12 However, no information is available linking patterns of nutrients intake to UC so far. The current study was therefore done to examine the relationship between patterns of nutrients intake and risk of UC among Iranian adults.
Methods
Study design and participants
This case-control study was carried out among a group of adults in Isfahan, Iran. Cases with UC were adults (aged >18 years) with a confirmed diagnosis of UC by a gastroenterologist, who were registered in the IBD registry database of Isfahan. All registered patients in the database (n = 140) were invited to participate in an educational class on lifestyle. During this class, information about study design and aims was explained, and patients were requested to participate in this study. Out of these 140 patients, 119 agreed to take part in the current study. For each patient with UC, 2 apparently healthy controls were randomly selected after matching for age and sex from our previous large population-based study, namely SEPAHAN, on 8,000 apparently healthy individuals. Before this selection process, first, we excluded all individuals with gastrointestinal disorders (including Crohn’s disease, UC, irritable bowel syndrome, functional dyspepsia, and gastroesophageal reflux disorder) from the whole dataset. Then, age- (±2 years) and sex-matched controls were randomly chosen for each UC patient. After the exclusion of UC patients with incomplete data (n = 30), 327 participants including 109 UC patients and 218 healthy individuals remained for the final analysis. Informed written consents were signed by all participants, including those from the IBD registry and the SEPAHAN project.
Assessment of dietary intakes
The usual dietary intakes of study participants were assessed using a validated self-administered 106-item dish-based Food Frequency Questionnaire (FFQ). As our goal was to evaluate the pre-UC diagnosis diet of patients, individuals with UC were specifically asked to report their usual dietary intake during the year before UC diagnosis. Detailed information about the questionnaire, as well as its design and validity, has been published elsewhere.13 In brief, the questionnaire was consisted of 106 food items with commonly used portion sizes among Iranians. The questionnaire consisted of five categories: (1) mixed dishes, including cooked or canned foods (n = 29); (2) potatoes and grain-based foods (n = 10); (3) dairy products, including milk and other dairy foods, for example, butter and cream (n = 9); (4) fruit and vegetables (n = 22); and (5) miscellaneous foods and beverages, such as sweets, fast foods, prepared meals, nuts, desserts, and beverages (n = 36). Participants were able to report their dietary intakes through nine multiple-choice frequency categories, ranging from “never or less than once a month” to “12 or more times per day.” Daily intakes of each food item were then converted to grams per day using household measures. Total daily energy and nutrient intakes were estimated for each participant by using the US Department of Agriculture food consumption database, modified for Iranian foods.
The validity and reliability of the FFQ were examined in a validation study on a subgroup of 200 randomly selected participants of the SEPAHAN project.13,14 In that study, the FFQ was completed by all participants at baseline and 6 months later. During these 6 months, participants provided 3 detailed dietary records that were used as the gold standard. Based on findings from this study, the FFQ could provide reasonably valid and reliable measures of long-term dietary intakes in the Iranian population; for instance, dietary carbohydrate intake obtained from FFQ was correlated with the one obtained from the average of three dietary records (r = 0.81). Also, previous studies revealed that the FFQ provides valid and reliable information on dietary intakes.15–17
Assessment of UC
The diagnosis of UC was made according to the international criteria18 by a gastroenterology specialist. In addition, the medical records of all cases were reviewed to confirm the diagnosis.
Assessment of other variables
Information about participants’ age, sex (male/female), marital status (married/single/divorced), education (under-university/university educated), smoking (nonsmoker/ex-smoker/current smoker), house ownership (owner/non-owner), the presence of diabetes (yes/no), and hypertension (yes/no) was gathered using pre-tested questionnaires. Assessment of anthropometric measures, including weight, and height was performed using a validated self-administered questionnaire. Then, participants’ body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.
Statistical analysis
We applied principal component analysis (PCA) to identify major nutrient patterns based on 28 nutrients. To reduce errors, this analysis was done only on controls. To examine if the distribution of the different nutrients allows the use of principal components, Kaiser-Meyer-Olkin (KMO) test was used. We used eigenvalues of ≥3 in conjunction with considering scree plot to extract major patterns of nutrients. To obtain independent nutrient patterns, we used the varimax rotation. Then, factor scores for cases were calculated using the following formula: ∑ [(factor loading/Eigenvalue) × standardized mean intake]. In this formula, factor loading values and eigenvalues of each dietary pattern were considered as those obtained for controls. The factor scores for all participants were then considered for further analysis. We used the independent sample t-test and one-way analysis of variance (ANOVA) to determine differences in continuous variables between cases and controls, as well as across tertiles of nutrient patterns’ scores. To examine the distribution of categorical variables between cases and controls and also across tertiles of nutrient patterns’ scores, we applied the Chi-square test. Binary logistic regression was used to assess the association between major nutrients patterns and odds of UC. In the first model, we adjusted for age, sex, and energy intake. Additional adjustment was made for education, marital status, smoking, house possession, diabetes, and hypertension in the second model. In the last model, we did further control for BMI to obtain an obesity-independent association. In these analyses, participants in the first tertile of nutrients patterns’ scores were considered as the reference group. To obtain trend of odds ratios across increasing tertiles of nutrients patterns’ scores, we considered these tertiles as an ordinal variable. All statistical analyses were conducted using the SPSS software (version 19.0; SPSS Inc., Chicago, IL). P values less than .05 were considered as significant level.
Results
Comparing cases and controls in terms of general characteristics is shown in Table 1. Compared with controls, UC patients were less likely to be university-educated. No other significant difference was seen between cases and controls.
Table 1.
General characteristics of cases and controls.
| Variables | Controls | Cases | P-valuea |
|---|---|---|---|
| n | 218 | 109 | |
| Age (years) | 39.5 ± 10.0 | 41.5 ± 11.8 | .12 |
| Male (%) | 47.7 | 48.1 | .94 |
| BMI (kg/m2) | 25.3 ± 3.8 | 25.1 ± 3.4 | .72 |
| Overweight or obese (%) | 48.1 | 46.9 | .84 |
| Married (%) | 80.3 | 77.9 | .61 |
| University graduated (%) | 54.6 | 38.0 | .005 |
| House possession (owner) (%) | 71.1 | 80.0 | .09 |
| Current smoker (%) | 4.1 | 6.0 | .12 |
| Hypertension (%) | 6.0 | 8.3 | .46 |
| Diabetes (%) | 2.3 | 3.7 | .48 |
Data are presented as mean ± SD or percent.
BMI, body mass index; SD, standard deviation.
Obtained from the independent sample t-test or Chi-square test, where appropriate.
Based on visual inspection of scree plot and Eigenvalues of ≥3, we extracted two major patterns of nutrients (Table 2). The KMO value was 0.83, indicating good sampling adequacy. The first nutrient pattern (NP1) was characterized by high intakes of carbohydrates, protein, fat, thiamin, riboflavin, pantothenic acid, niacin, pyridoxine, vitamin B12, selenium, iron, zinc, sodium, phosphorus, manganese, magnesium, copper, calcium, dietary fiber, and vitamins E and D. The second nutrient pattern (NP2) was associated with greater intakes of beta-carotene, vitamins A, K and C, potassium, and folate. In total, these 2 nutrient patterns explained 71.92% of the total variance in nutrient intakes.
Table 2.
Components of the nutrient patterns.
| NP1 | NP2 | |
|---|---|---|
| Carbohydrate | 0.833 | - |
| Fat | 0.696 | 0.400 |
| Protein | 0.866 | 0.345 |
| Total dietary fiber | 0.688 | 0.548 |
| Selenium | 0.945 | - |
| Niacin | 0.924 | - |
| Iron | 0.908 | - |
| Zinc | 0.873 | 0.381 |
| Thiamin | 0.867 | - |
| Phosphorus | 0.829 | 0.39 |
| Pantothenic acid | 0.866 | 0.39 |
| Sodium | 0.847 | - |
| Riboflavin | 0.812 | 0.314 |
| Manganese | 0.808 | - |
| Magnesium | 0.801 | 0.471 |
| Copper | 0.721 | 0.548 |
| Calcium | 0.691 | - |
| Vitamin B6 | 0.688 | 0.597 |
| Vitamin B12 | 0.594 | 0.353 |
| Vitamin E | 0.542 | 0.410 |
| Vitamin D | 0.349 | - |
| Beta-carotene | - | 0.895 |
| Vitamin A | - | 0.867 |
| Vitamin K | - | 0.865 |
| Vitamin C | - | 0.841 |
| Potassium | 0.569 | 0.733 |
| Folate | 0.618 | 0.664 |
| Caffeine | - | - |
Factor loadings of <0.3 were omitted due to simplicity.
NP, nutrient pattern
General characteristics of participants across tertiles of nutrient patterns’ scores are presented in Table 3. We found no overall significant difference in terms of these variables across tertiles of both nutrient patterns.
Table 3.
General characteristics of the study participants across tertiles of nutrient patterns’ scores.
| Variables | Tertiles of NP1 |
Tertiles of NP2 |
||||||
|---|---|---|---|---|---|---|---|---|
| T1 | T2 | T3 | P-valuea | T1 | T2 | T3 | P-valuea | |
| n | 109 | 109 | 109 | 109 | 109 | 109 | ||
| Age (years) | 40.9 ± 11.3 | 40 ± 9.4 | 39.4 ± 11.1 | .60 | 39.6 ± 10.1 | 40.3 ± 10.4 | 40.5 ± 11.5 | .82 |
| Male (%) | 52.5 | 50.5 | 40.2 | .17 | 51 | 44.8 | 47.6 | .67 |
| BMI (kg/m2) | 25.5 ± 3.7 | 25.4 ± 4.2 | 24.8 ± 3.1 | .38 | 25.4 ± 3.5 | 25.1 ± 4 | 25.3 ± 3.5 | .25 |
| Overweight or obese (%) | 50 | 39.4 | 54.1 | .08 | 53.7 | 41.3 | 48.6 | .18 |
| Married (%) | 84.3 | 74.5 | 79.6 | .21 | 81.3 | 78.7 | 78.5 | .85 |
| University graduated (%) | 53.7 | 50.5 | 48.6 | .75 | 45.4 | 56 | 45.9 | .21 |
| House possession (owner) (%) | 72.8 | 77.9 | 72.4 | .63 | 70.8 | 75.6 | 76.8 | .6 |
| Current smoker (%) | 4.1 | 5.1 | 5 | .41 | 6.2 | 4.1 | 4 | .64 |
| Hypertension (%) | 8.3 | 3.7 | 8.3 | .29 | 4.6 | 10.1 | 5.5 | .22 |
| Diabetes (%) | 4.6 | - | 3.7 | .08 | 3.7 | 1.8 | 2.8 | .7 |
| Ulcerative colitis (%) | 26.6 | 34.9 | 38.5 | .16 | 43.1 | 12.8 | 44 | <0.001 |
Data are presented as mean ± SD or percent.
BMI, body mass index; NP, nutrient pattern; SD, standard deviation.
Obtained from the analysis of variance (ANOVA) or Chi-square test, where appropriate.
Dietary intakes of selected food groups and nutrients are illustrated in Table 4. UC patients had higher intakes of energy and vitamin C, and lower intakes of refined grains, dairy, fruits, protein, fiber, vitamin E, vitamin B6, iron, and caffeine compared to controls. Individuals with the highest tertile of NP1 score had greater intakes of whole and refined grains, dairy, fruits, vegetables, nuts, energy, protein, carbohydrate, fat, fiber, vitamins E, D, B6, and C, iron, and caffeine compared with those in the lowest tertile. Also, NP2 was associated with higher intakes of whole grains, dairy, fruits, vegetables, nuts, energy, protein, carbohydrates, fat, fiber, vitamins E, D, B6, and C, and iron.
Table 4.
Dietary intakes of study participants across tertiles of nutrient patterns’ scores.
| Ulcerative colitisa
|
Tertiles of NP1c
|
Tertiles of NP2c
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Yes | No | P-valueb | T1 | T2 | T3 | P-valued | T1 | T2 | T3 | P-valued | |
| n | 109 | 218 | 109 | 109 | 109 | 109 | 109 | 109 | |||
| Energy (Kcal/d) | 3014 ± 101 | 2328 ± 69 | <.001 | 1623 ± 40 | 2457 ± 38 | 3659 ± 107 | <.001 | 2075 ± 78 | 2347 ± 66 | 3316 ± 125 | <.001 |
| Food groups | |||||||||||
| Whole grains (g/d) | 38 ± 7.9 | 48 ± 55 | .28 | 54.9 ± 3.8 | 84.8 ± 6.2 | 136.3 ± 12.6 | <.001 | 76 ± 9.4 | 71.7 ± 5.9 | 128 ± 10 | <.001 |
| Refined grains (g/d) | 267 ± 17 | 354 ± 12 | <.001 | 187.7 ± 9.7 | 323.9 ± 14.7 | 495.8 ± 26.1 | <.001 | 327 ± 26.9 | 325 ± 18.7 | 352.2 ± 19.6 | .62 |
| Dairy (g/d) | 248 ± 28 | 366 ± 18 | .002 | 230.2 ± 20.4 | 314.4 ± 24.1 | 406.2 ± 29.3 | <.001 | 232.4 ± 17.8 | 344.5 ± 28.9 | 372.4 ± 27.1 | <.001 |
| Fruits (g/d) | 256 ± 19 | 266 ± 13 | <.001 | 308 ± 23.5 | 385.5 ± 26.9 | 544.2 ± 49.4 | <.001 | 246 ± 20.4 | 322.3 ± 18 | 666.7 ± 47.7 | <.001 |
| Vegetables (g/d) | 197 ± 13 | 206 ± 8.7 | .60 | 154.2 ± 8.3 | 204.3 ± 9.9 | 272.2 ± 15.6 | <.001 | 126.3 ± 5.2 | 192.8 ± 7.3 | 310.7 ± 15 | <.001 |
| Nuts (g/d) | 9.7 ± 1.4 | 8.4 0.9 | .46 | 5.6 ± 0.7 | 7.9 ± 0.9 | 12.3 ± 2 | .02 | 5.7 ± 0.7 | 9.1 ± 1.8 | 11 ± 1.2 | .02 |
| Nutrients | |||||||||||
| Protein (g/d) | 89 ± 1.8 | 95 ± 1.2 | .005 | 58.5 ± 1.4 | 89.2 ± 1.6 | 135.4 ± 4.1 | <.001 | 74.4 ± 2.8 | 86.9 ± 2.4 | 121.9 ± 4.9 | <.001 |
| Carbohydrate (g/d) | 315 ± 6.36 | 318 ± 4.45 | .73 | 198.6 ± 6.5 | 299.8 ± 7.4 | 456.2 ± 17.1 | <.001 | 261.6 ± 13.4 | 286.4 ± 10 | 406.4 ± 17.6 | <.001 |
| Fat (g/d) | 109 ± 2.48 | 104 1.66 | .07 | 69.6 ± 2 | 104.3 ± 2.2 | 149.3 ± 5.6 | <.001 | 83.6 ± 2.7 | 98.7 ± 2.9 | 140.9 ± 6.1 | <.001 |
| Total fiber (g/d) | 18 ± 0.67 | 24 ± 0.45 | <.001 | 14.8 ± 0.5 | 20.5 ± 0.6 | 30.2 ± 1 | <.001 | 15.4 ± 0.7 | 20.7 ± 0.6 | 29.4 ± 0.9 | <.001 |
| Vitamin E (mg/d) | 13.2 ± 0.64 | 22.4 ± 0.43 | <.001 | 13.4 ± 0.5 | 19.2 ± 0.6 | 26.2 ± 0.9 | <.001 | 14.2 ± 0.6 | 20 ± 0.7 | 24.6 ± 0.9 | <.001 |
| Vitamin D (mcg/d) | 0.86 ± 0.08 | 1.02 ± 0.05 | .13 | 0.69 ± 0.43 | 0.95 ± 0.09 | 1.31 ± 0.98 | <.001 | 0.69 ± 0.05 | 0.95 ± 0.05 | 1.31 ± 0.11 | <.001 |
| Vitamin B6 (mg/d) | 1.63 ± 0.04 | 2.10 ± 0.03 | <.001 | 1.3 ± 0.04 | 1.8 ± 0.04 | 2.6 ± 0.07 | <.001 | 1.3 ± 0.04 | 1.9 ± 0.04 | 2.5 ± 0.07 | <.001 |
| Vitamin C (mg/d) | 162 ± 8.18 | 111 ± 5.49 | <.001 | 96.6 ± 6.3 | 117.7 ± 6.3 | 166.9 ± 12.1 | <.001 | 74.2 ± 4.3 | 101.9 ± 3.1 | 205.1 ± 11.6 | <.001 |
| Iron (mg/d) | 15.8 ± 0.33 | 18.8 ± 37 | <.001 | 10.5 ± 0.2 | 17 ± 0.3 | 26.6 ± 0.7 | <.001 | 14.7 ± 0.7 | 16.9 ± 0.6 | 22.4 ± 0.8 | <.001 |
| Caffeine (mg/d) | 64 ± 7.7 | 95 ± 5.2 | .002 | 70.8 ± 5.6 | 93.6 ± 8.5 | 91.3 ± 7 | .05 | 78.2 ± 6 | 90.9 ± 7.6 | 86.5 ± 8 | .45 |
NP, nutrient pattern; SD, standard deviation; SE, standard error; T, tertile
Data are presented as mean ± SE.
Adopted from Khademi et al.50
Data are presented as mean ± SD.
Obtained from the analysis of variance (ANOVA).
Multivariable-adjusted odds ratios and 95% CIs for UC across tertiles of nutrient patterns’ scores are presented in Table 5. We found no significant association between NP1 and the odds of UC in the crude model. However, after taking sex, age, and energy intake into account, a significant inverse association was seen between NP1 and UC. Further adjustments for demographic variables, hypertension, diabetes, and BMI resulted in no change in this association (OR: 0.07, 95% CI, 0.01-0.32). With respect to NP2, we found a significant inverse association for subjects in the second tertile of NP2 score compared with those in the first tertile (OR: 0.19, 95% CI, 0.09-0.38). Nevertheless, in the fully adjusted model, individuals in the third tertile of NP2 score were 64% less likely to have UC compared with those in the first tertile (OR: 0.36, 95% CI, 0.15-0.82).
Table 5.
Multivariable-adjusted odds ratios for ulcerative colitis across tertiles of nutrient patterns’ scores.
| Tertiles of NP1 score |
Tertiles of NP2 score |
|||||||
|---|---|---|---|---|---|---|---|---|
| T1 | T2 | T3 | P-trenda | T1 | T2 | T3 | P-trenda | |
| Crude | 1.00 | 1.47 (0.82-2.63) | 1.72 (0.97-3.07) | .06 | 1.00 | 0.19 (0.09-0.38) | 1.03 (0.60-1.77) | .88 |
| Model 1 | 1.00 | 0.44 (0.20-0.94) | 0.06 (0.01-0.22) | <.001 | 1.00 | 0.14 (0.06-0.29) | 0.34 (0.16-0.69) | .001 |
| Model 2 | 1.00 | 0.44 (0.18-1.10) | 0.07 (0.01-0.30) | <.001 | 1.00 | 0.16 (0.07-0.38) | 0.38 (0.17-0.84) | .01 |
| Model 3 | 1.00 | 0.44 (0.17-1.10) | 0.07 (0.01-0.32) | <.001 | 1.00 | 0.15 (0.06-0.36) | 0.36 (0.15-0.82) | .01 |
Data are presented as OR (95% CI).
Model 1: adjusted for age, sex, and energy intake.
Model 2: further adjustments were made for marital status, education, house possession, hypertension, diabetes, and smoking.
Model 3: additionally adjusted for BMI.
BMI, body mass index; CI, confidence interval; NP, nutrient pattern; OR, odds ratio; T, tertile.
Obtained from binary logistic regression.
Discussion
In the current study, we found that a NP characterized by high intakes of carbohydrate, protein, fat, thiamin, riboflavin, pantothenic acid, niacin, pyridoxine, vitamin B12, selenium, iron, zinc, sodium, phosphorus, manganese, magnesium, copper, calcium, dietary fiber, and vitamins E and D was inversely associated with odds of UC. With regard to the NP2, which was rich in beta-carotene, vitamins A, K, and C, potassium, and folate, a protective association was seen against UC. To the best of our knowledge, this is the first study investigating the relation between major nutrient patterns and risk of UC.
Previous studies on the association between nutrient intakes and UC have mainly focused on individual nutrient intake such as vitamin D, zinc, and iron.19–21 Nutrient pattern analysis is a well-known approach in nutrition epidemiology that considers the intakes of all nutrients and their interactions.22 The link between patterns of nutrient intakes and several chronic conditions, including metabolic syndrome, diabetes, fractures, and different types of cancers, has been examined previously22–24; however, no study is available investigating the nutrient patterns in relation to UC. In the current study, a pattern rich in carbohydrate, protein, fat, thiamin, riboflavin, pantothenic acid, niacin, pyridoxine, vitamin B12, selenium, iron, zinc, sodium, phosphorus, manganese, magnesium, copper, calcium, dietary fiber, and vitamins E and D was inversely associated with UC. In earlier studies on individual nutrients, dietary intakes of vitamin D,25 zinc,26 and vitamin E27 were associated with reduced risk of IBD. Evidence of zinc deficiency in IBD patients was also available.28 A pilot study revealed that the administration of 600-1500 mg thiamin alleviated the symptoms of fatigue in IBD patients.29 Other studies illustrated reduced levels of B-vitamins in IBD patients compared with healthy controls.30,31 The beneficial effect of selenium intake on inflammatory biomarkers in IBD was reported by Kudva et al.32 The protective effect of dietary fiber against the risk of IBD has also frequently been reported.33,34 Given the anti-inflammatory properties of magnesium and vitamin E, these nutrients have also been reported to beneficially affect the incidence of UC.35,36 In contrast, consumption of high-fat diets was associated with an increased risk of UC. This might happen through impairing the gut immune system and increasing the rate of intestinal epithelial damage.37 In addition, iron and copper-rich dietary patterns might induce inflammatory responses and adversely affect the risk of UC.38,39 Altogether, considering the interaction between nutrients and their synergistic effects, it seems that the overall nutrient pattern is much more important than individual nutrients in terms of its effect on chronic conditions like UC.
In the present study, we found that a diet containing high amounts of beta-carotene, vitamins A, K, and C, potassium, and folate was associated with reduced odds of UC. Some reports are available on the prevalent vitamin A and K deficiencies among IBD patients.40,41 Also, it has been shown that folic acid administration has a role in the prevention and management of IBD.42 In a case-control study, UC patients had higher intakes of dietary vitamin C and folate compared with controls.6 A meta-analysis showed that adherence to a healthy dietary pattern rich in beta-carotene, vitamin A, and C was inversely associated with risk of IBD.43
Several mechanisms have been suggested for the role of nutrients in UC. Given that UC is associated with oxidative stress and elevated levels of inflammatory cytokines, nutrients with antioxidant properties might have a favorable impact on this condition by scavenging free radicals, reducing cytokine and pro-oxidative enzyme concentrations, and improving the anti-oxidative capabilities of cells.44,45 The anti-inflammatory properties of some nutrients, including vitamin D, magnesium, vitamin E, fiber, and B-vitamins, might also play a role in this regard.46,47 In addition, dietary fiber can affect the gut microbiome, which has a regulatory influence on the immune response and maintenance of immunological homeostasis.48 In addition, dietary fiber is proposed to exert an anti-inflammatory effect through one of its main products, butyrate. Butyrate is thought to reduce colonic permeability through enhancement of peroxisome proliferator-activated receptor C activation, and a genetically susceptible host to UC may be predisposed because of increased permeability.49
This study has several strengths. As far as we know, this is the first study examining the association between patterns of nutrient intakes and UC. We applied a validated FFQ to collect dietary data. A wide range of potential confounders was adjusted to obtain an independent association; however, the possible effect of some unmeasured confounding variable, such as duration of UC diagnosis, cannot be ignored. This study also has some limitations. As several nutrients (fiber, vitamin B6, vitamin E, and copper) had large factor loadings in two NPs, it seems that the NPs cannot be made completely orthogonal. Given the case-control design of the current study, our findings are subject to selection and recall bias. As with all epidemiological studies, misclassification of study participants due to the use of FFQ is unavoidable. In addition, cases in the present study were selected from hospitals. The generalizability of study findings might be weakened by this methodological choice. Finally, like all observational studies, reverse causation is also possible.
In conclusion, we found that a nutrient pattern rich in antioxidants, B-vitamins, macronutrients, zinc, iron, copper, calcium, potassium, fat-soluble vitamins, and fiber is associated with decreased odds of UC. Further studies, particularly those with prospective cohort design, are needed to confirm our findings.
Contributor Information
Omid Sadeghi, Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
Zeinab Khademi, Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran; Department of Clinical Nutrition, School of Nutrition and Food Science, Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.
Parvane Saneei, Department of Community Nutrition, School of Nutrition and Food Science, Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Ammar Hassanzadeh Keshteli, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Gastroenterology and hepatology research center, Isfahan University of Medical Sciences, Isfahan, Iran.
Hamed Daghaghzadeh, Gastroenterology and hepatology research center, Isfahan University of Medical Sciences, Isfahan, Iran.
Hamid Tavakkoli, Gastroenterology and hepatology research center, Isfahan University of Medical Sciences, Isfahan, Iran.
Peyman Adibi, Gastroenterology and hepatology research center, Isfahan University of Medical Sciences, Isfahan, Iran.
Ahmad Esmaillzadeh, Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran; Obesity and Eating Habits Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Food Security Research Center, Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran.
Funding
None declared.
Conflicts of Interest
None declared.
Data Availability
The datasets analyzed during the current study are available from the corresponding author upon reasonable request.
Ethics
The whole project was ethically approved by the Ethical Committee of the Tehran University of Medical Sciences, Tehran, Iran (IR.TUMS.VCR.REC.1398.497).
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed during the current study are available from the corresponding author upon reasonable request.