Abstract
No previous study has examined the association between coffee and caffeine intake and odds of functional dyspepsia (FD). The aim of this study was to investigate the association between coffee and caffeine intake and odds of FD and its components in a large sample of Iranian adults. In this cross-sectional study on 3362 adults aged 18–55 years, a validated food frequency questionnaire (DS-FFQ) was used to assess dietary intakes. A validated modified Persian version of the Rome III questionnaire was used for assessment of FD. Logistic regression was applied to compute odds ratios for FD and its components considering a wide range of covariates. Mean age of study population was 36.2 ± 7.8 years, of them 58.3% were females. The prevalence of FD among study participants was 14.5%. The prevalence of post-prandial fullness, early satiation, and epigastric pain was 7.6, 5.8, and 7.6%, respectively. After controlling for potential confounders, no significant association was observed between coffee (OR: 1.27; 95% CI: 0.86–1.87), and caffeine (OR: 1.00; 95% CI: 0.99–1.02) consumption and odds of FD. In addition, coffee and caffeine consumption was not significantly associated with odds of FD symptoms such as early satiation, post-prandial fullness, and epigastric pain. This was also the case when the analysis was done stratified by gender and BMI status. In conclusion, we found no significant association between coffee and caffeine consumption with odds of FD and its symptoms. Further research in other populations with high coffee consumption is needed.
Keywords: Coffee, Caffeine, Functional dyspepsia
Subject terms: Health care, Gastrointestinal diseases
Introduction
Dyspepsia, as an upper gastrointestinal tract syndrome, is characterized by the epigastric pain or burning, early satiety, postprandial fullness, nausea, vomiting, bloating and even weight loss1. Earlier studies have shown that the majority of dyspepsia cases are functional2. It has been shown that functional dyspepsia (FD) is highly prevalent worldwide. In Iran, it has been estimated that 2.2 to 29.9% of Iranian adult population are affected3.
The pathophysiology of FD remains unknown. Dietary factors including energy intake, some nutrients, food chemicals, food allergens, fiber, carbonated drinks, and some fruits and vegetables have been reported to play a role4. Although coffee is a popular drink in the world, few studies have examined the relationship between coffee consumption and FD5. Coffee stimulates the secretion and production of gastrin and hydrochloric acid through caffeine and other its components6. There are also studies confirmed that coffee consumption reduces the half-time of gastric emptying7, so development of epigastric pain and/or post-prandial fullness following the consumption of coffee could be occurred. Previous studies reported that coffee intake was associated with epigastric burning and heartburn in more than 50% of FD patients5,8,9. Coffee-induced heartburn was also reported in 53% of FD patients, compared with 22% in the control group, in another study8. In a case–control study, pattern of coffee and tea consumption in 113 dyspeptic patients was not significantly different with 113 controls9.
Although FD patients need ongoing support from their physicians, few treatment options have been effective in the management of their symptoms10. It seems that providing nutritional advice can alleviate FD symptoms and improve quality of life. Tea and coffee are common beverages in developing countries, however, there is no study examining the association between coffee and caffeine intake and FD in these countries. Given the high prevalence of FD in developing countries, assessment of the contribution of coffee and caffeine intake to FD is of high priority. Therefore, the aim of the present study was to determine the association of coffee and caffeine intake with FD in Iranian adult population.
Methods and materials
Participants: The present cross-sectional study was conducted in the framework of the SEPAHAN (the Study on the Epidemiology of Psychological, Alimentary Health and Nutrition) project, a cross-sectional study aimed to evaluate the epidemiological concepts of functional gastrointestinal disorders and their association with lifestyle and psychological factors. Detailed information about study design, sampling method, participants’ characteristics, and data collection procedures have been published elsewhere11. Briefly, participants were the general population of Isfahan province who are working in health centers affiliated with Isfahan University of Medical Sciences (IUMS). Data were collected in two separate phases. During the first phase, a detailed self-administered questionnaire on sociodemographic and lifestyle factors, including dietary habits and dietary intakes, was distributed among 10,087 individuals, and 8691 individuals returned the completed questionnaire (response rate: 86.16%). To collect information on gastrointestinal health, in the second phase, the questionnaires were sent to the participants and 6239 completed questionnaires were returned (response rate: 64.6%). After merging data from both phases, we had information on 4763 subjects, who provided complete information on diet and functional gastrointestinal disorders. In the present study, we excluded individuals with total daily energy intake outside the range of 800–4200 kcal per day, as under- and over-reporting of energy intake. Individuals with missing data on any relevant variables were also excluded. Therefore, data from 3362 subjects were included in the current analysis. Informed written consents were obtained from all participants.
Dietary intakes assessment: Dietary intakes were collected using a Willett-format Dish-based 106-item Semi-quantitative Food Frequency Questionnaire (DS-FFQ), which was designed and validated specifically for Iranian adults. Additional information about the design, foods included as well as the validity of this questionnaire has been reported elsewhere12. Briefly, we provided a comprehensive list of foods and dishes commonly consumed by this population. Then, those foods that were nutrient-rich, often consumed or contributed to between-person variation in dietary intakes were selected. This time-consuming process led to the remaining of 106 food items in 5 various categories in the questionnaire: (1) mixed dishes (cooked or canned, 29 items); (2) grains (different types of bread, biscuits, cakes and potato, 10 items); (3) dairy products (dairy, butter and cream, 9 items); (4) fruits and vegetables (22 items); and (5) miscellaneous food items and beverages (including fast foods, nuts, sweets, desserts and beverages, 36 items). In order to provide precise and accurate estimates, the portion size of foods and mixed dishes were added to the questionnaire. Nine multiple choice frequency response categories ranging from “never or < 1/month” to “ ≥ 12/day” were provided for each food items in the questionnaire for reporting dietary intakes of participants. Finally, daily intake of all food items was computed and then converted to grams per day using a booklet about household measures13.
The validity of DS-FFQ was examined in a subgroup of 200 randomly selected participants of the SEPAHAN project. All participants in the validation study completed the DS-FFQ at study baseline and 6 months later. During this validation study, participants provided three detailed dietary records that were used as the gold standard. As shown in earlier studies, it seems that this questionnaire provides reasonably valid measures of long-term dietary intakes12.
Calculation of coffee and caffeine intake: To assess coffee intake, we requested participants to report the average number of glasses of coffee they usually consume in the preceding year considering the time scale of day, week or month. They were able to choose one of these frequency response categories: “never or < 1 glass/month”, “1–3 glasses/month”, “1 glass/week”, “2–4 glasses/week”, “5–6 glasses/week”, “1 glass/day”, “2–3 glasses/day”, “4–5 glasses/day” and “ ≥ 6 glasses of coffee in a day”. Total caffeine intake was estimated by summing up the caffeine intake took from all caffeine-containing foods and beverages.
Assessment of functional dyspepsia: To examine gastrointestinal health, we applied a modified Persian version of the Rome III questionnaire. Our earlier publications revealed that this questionnaire reasonably identifies people with improper function of gastrointestinal tract in this population11. The validity and reliability of Rome III questionnaire in an Iranian population has also been demonstrated previously14. In the present study, individuals with one or more features of the following characteristics were defined as having FD: bothersome postprandial fullness (defined as feeling uncomfortably full after a regular-sized meal, often or always), early satiation (defined as being unable to finish a regular-sized meal, often or always), and/or epigastric pain or epigastric burning (defined as feeling pain or burning in the middle of abdomen, often or always). Additionally, we asked participants about the severity of each FD symptom using a four-item rating scale (mild, moderate, severe, and very severe).
Assessment of other variables: Required information on other variables including age, sex, smoking status, and disease history (diabetes and colitis) was obtained from demographic and medical history questionnaires. The General Practice Physical Activity Questionnaire (GPPAQ) was used to assess physical activity levels of study participants. The GPPAQ is a simple validated screening tool for ranking adults’ physical activity with a focus on current general activities15. Patients were asked to report their activities based on GPPAQ. Current physical activity has been used as a reliable contributor for objective assessment of overall physical activity levels. Based on their responses, participants were classified into 4 categories; (1) Inactive (sedentary job and no physical exercise or cycling), (2) Moderately inactive (sedentary job and some but < 1 h physical exercise and/or cycling per week OR standing job and no physical exercise or cycling), (3) Moderately active (sedentary job and 1–2.9 h physical exercise and/or cycling per week OR standing job and some but < 1 h physical exercise and/or cycling per week OR physical job and no physical exercise or cycling), (4) Active (sedentary job and > 3 h physical exercise and/or cycling per week OR standing job and 1–2.9 h physical exercise and/or cycling per week OR physical job and some but < 1 h physical exercise and/or cycling per week OR heavy manual job). However, in the current study due to low number of subjects in some of the above-mentioned categories, individuals in the “inactive” and “moderately inactive” groups were combined and were defined as those with “sedentary physical activity”. Similarly, individuals in the “moderately active” and “active” categories were combined and then defined as “physically active”.
Data on diet-related practices including meal regularity (often or always/never or occasionally), chewing efficiency (a lot/not a lot), breakfast skipping (skipper/non-skipper), and intra-meal fluid intake (< 3 glasses/ ≥ 3 glasses) were also assessed through the use of a pretested questionnaire. Anthropometric measures including weight, height, and waist circumference were assessed using a self-administered questionnaire. The validity of self-reported values of weight, height, and waist circumferences (WC) was examined in a pilot study on 200 participants from the same population. In the validation study, self-reported values of anthropometric indices were compared with actually measured values. The correlation coefficients for self-reported weight, height, and WC versus corresponding measured values were 0.95 (P < 0.001), 0.83 (P < 0.001), and 0.60 (P < 0.001), respectively. Body Mass Index (BMI) was calculated by dividing weight (kg) to height (m2). The correlation coefficient for computed BMI from self-reported values, and the one from measured values was 0.70 (P < 0.001)16.
Statistical analysis: General characteristics of study participants across categories of coffee and caffeine intake were presented as means ± SDs for continuous variables and percentages for categorical variables. To examine the differences across categories, we used ANOVA for continuous variables and chi-square test for categorical variables. Dietary intakes of study participants across categories of coffee and caffeine intake were presented as mean ± SEs using ANCOVA. We used binary logistic regression to estimate ORs and 95% CIs for the presence of FD and its components across categories of coffee and caffeine intake in crude and multivariable-adjusted models. The trend of ORs across categories of coffee and caffeine intake was determined by considering categories of coffee and caffeine intake as continuous variables in the logistic regression analysis. In these analyses, age, sex (male/female), and energy intake (continuous) were adjusted in the first model. Physical activity (< 1 h/week/ ≥ 1 h/week), smoking status (non-smoker/former smokers and current smokers), and self-reported diabetes (yes/no) and colitis (yes/no) were adjusted for in the second model. Meal regularity (often or always/never or occasionally), chewing sufficiency (a lot/not a lot), intra-meal fluid consumption (< 3 glasses/ ≥ 3 glasses), and breakfast skipping (skipper/non-skipper), were adjusted for in the third model. Dietary intakes including fat intake, dairy products, processed meats, fruits, and vegetables were additionally adjusted in model IV. Further adjustment for BMI was performed in the last model. All statistical analyses were done using the Statistical Package for Social Sciences (version 20; SPSS Inc.). P < 0.05 was considered as statistically significant.
Results
Mean age and BMI of study population was 36.2 ± 7.8 years and 24.9 ± 3.82 kg/m2, respectively. Median (IQR) of caffeine intake was 71.6 (77.6) mg. The prevalence of FD among study participants was 14.5%. General characteristics of study participants across categories of coffee and caffeine intake are shown in Table 1. Participants who consumed coffee weekly or more were more likely to be physically active and current smoker compared with those who did not consume coffee. In terms of caffeine intake, those in the top tertile of caffeine intake were more likely to be older, current smoker, had regular meal pattern, and were less likely to be female compared with those in the bottom tertile. No other significant differences were found in terms of other variables.
Table 1.
General characteristics of study participants across categories of coffee and caffeine intakea.
| Coffee intake | P-valueb | Caffeine intake | P-valueb | |||||
|---|---|---|---|---|---|---|---|---|
| None (n = 2308) |
Monthly (n = 491) |
Weekly or more (n = 287) |
T1 (n = 1107) |
T2 (n = 1133) |
T3 (n = 1122) |
|||
| Age (y) | 36.02 ± 7.68 | 36.51 ± 8.30 | 35.53 ± 7.21 | 0.26 | 35.70 ± 8.16 | 35.90 ± 7.78 | 37.17 ± 7.67 | < 0.001 |
| BMI (kg/m2) | 24.87 ± 3.84 | 24.59 ± 3.78 | 24.75 ± 3.48 | 0.32 | 24.82 ± 3.95 | 24.81 ± 3.72 | 25.07 ± 3.78 | 0.21 |
| Female (%) | 58.8 | 57.6 | 64.8 | 0.11 | 60.40 | 60.6 | 53.7 | 0.001 |
| Married (%) | 82.0 | 78.7 | 79.2 | 0.19 | 80.70 | 81.2 | 83.1 | 0.65 |
| Physically active (≥ 1 h/week) (%) | 12.0 | 16.9 | 12.5 | 0.01 | 13.6 | 11.3 | 14.7 | 0.05 |
| Current smokers (%) | 12.2 | 15.3 | 16.7 | 0.02 | 12.7 | 12.5 | 16.1 | 0.02 |
| Disease history (diabetes, colitis) (%) | 3.2 | 1.4 | 2.8 | 0.10 | 3.6 | 2.6 | 2.7 | 0.31 |
| Medication use (%) | 6.0 | 5.1 | 7.0 | 0.54 | 6.6 | 5.6 | 6.1 | 0.60 |
| Breakfast skipping (%) | 7.3 | 5.7 | 9.8 | 0.11 | 7.5 | 7.0 | 7.5 | 0.88 |
| Regular meal pattern (%) | 0.01 | |||||||
| Often or always | 61.7 | 61.3 | 57.1 | 0.32 | 57.0 | 62.8 | 61.1 | |
| Never or occasionally | 38.3 | 38.7 | 42.9 | 43.0 | 37. 2 | 38.9 | ||
| Chewing sufficiency (%) | 0.39 | |||||||
| A lot | 13/0 | 11.8 | 12.7 | 0.77 | 13.40 | 13.7 | 11.9 | |
| Not a lot | 87.0 | 88.2 | 87.3 | 86.6 | 86.3 | 88.1 | ||
| Fluid consumption (%) | 0.18 | |||||||
| < 3 glasses | 3/0 | 4.2 | 2.5 | 0.31 | 4.0 | 2.6 | 3.1 | |
| ≥ 3 glasses | 97.0 | 95.8 | 97.5 | 96.0 | 97.4 | 96.0 | ||
aAll values are mean ± SD, unless indicated; bANOVA for continuous variables and chi-squared test for categorical variables.
Dietary intakes of participants across categories of coffee and caffeine intake are shown in Table 2. Compared with those in the lowest category of coffee intake, those in the highest category had higher intakes of energy, fats, and processed meats and lower intakes of carbohydrates, proteins, red meats, vegetables, nuts and legumes, and dairy products. In terms of caffeine intake, participants in the highest tertile had higher intakes of energy, fats, carbohydrates, processed meats, and tea and lower intakes of proteins, red meats, and dairy products.
Table 2.
Dietary intakes of study participants across categories of coffee and caffeine intakea.
| Coffee intake | P-value | Caffeine intake | P-valueb | |||||
|---|---|---|---|---|---|---|---|---|
| None | Monthly | Weekly or more | T1 | T2 | T3 | |||
| Energy (kcal/d) | 2309.4 ± 17.3 | 2531.2 ± 38.0 | 2874.5 ± 50.3 | < 0.001 | 2160.4 ± 25.6 | 2375.4 ± 24.9 | 2604.2 ± 25.2 | < 0.001 |
| Fats (g/d) | 97.8 ± 0.39 | 101.37 ± 0.86 | 103.97 ± 1.16 | < 0.001 | 99.09 ± 0.59 | 97.25 ± 0.57 | 99.47 ± 0.59 | 0.01 |
| Carbohydrates (g/d) | 297.65 ± 1.04 | 290.33 ± 2.27 | 293.87 ± 3.05 | 0.01 | 289.81 ± 1.55 | 296.65 ± 1.49 | 295.49 ± 1.53 | 0.004 |
| Proteins (g/d) | 89.35 ± 0.29 | 89.29 ± 0.64 | 79.96 ± 0.86 | < 0.001 | 91.30 ± 0.43 | 88.48 ± 0.42 | 85.10 ± 0.43 | < 0.001 |
| Red meats (g/d) | 67.90 ± 0.76 | 69.51 ± 1.66 | 58.91 ± 2.23 | < 0.001 | 71.60 ± 1.16 | 66.61 ± 1.11 | 64.44 ± 1.14 | < 0.001 |
| Processed meats (g/d) | 4.92 ± 0.20 | 8.26 ± 0.44 | 8.38 ± 0.59 | < 0.001 | 5.56 ± 0.30 | 5.32 ± 0.29 | 6.49 ± 0.30 | 0.01 |
| Fruits (g/d) | 186.4 ± 3.61 | 197.75 ± 7.89 | 186.79 ± 10.59 | 0.42 | 190.49 ± 5.32 | 186.32 ± 5.11 | 183.87 ± 5.24 | 0.67 |
| Vegetables (g/d) | 216.24 ± 2.36 | 210.64 ± 5.16 | 190.13 ± 6.92 | 0.002 | 219.36 ± 3.40 | 211.18 ± 3.34 | 208.9 ± 3.42 | 0.08 |
| Nuts and legumes (g/d) | 58.41 ± 0.79 | 56.59 ± 1.73 | 48.71 ± 2.32 | < 0.001 | 59.36 ± 1.17 | 56.83 ± 1.12 | 55.60 ± 1.15 | 0.07 |
| Dairy products (g/d) | 345.11 ± 5.67 | 330.87 ± 12.39 | 265.45 ± 16.63 | < 0.001 | 358.54 ± 8.37 | 333.99 ± 8.04 | 310.59 ± 8.24 | < 0.001 |
| Tea (ml/d) | 378.2 ± 6.1 | 382.9 ± 13.4 | 364.2 ± 18.1 | 0.92 | 124.0 ± 6.2 | 338.4 ± 5.9 | 650.8 ± 6.1 | < 0.001 |
aAll values are means ± standard error (SE); energy intake is adjusted for age and gender, all other values are adjusted for age, gender, and energy intake.
bObtained from ANCOVA.
Multivariable-adjusted ORs and 95% CIs for FD and its components across categories of coffee and caffeine intake are shown in Table 3. No significant association was seen between coffee consumption and odds of FD and its components. This association remained unchanged even after controlling for a wide range of confounders; such that the association between coffee intake and FD (OR: 1.27; 95% CI: 0.86–1.87), early satiation (OR: 1.37; 95% CI: 0.77–2.43), post-prandial fullness (OR: 1.22; 95% CI: 0.73–2.06), and epigastric pain (OR: 0.91; 95% CI: 0.53–1.58) was not significant.
Table 3.
Crude and multivariable-adjusted ORs and 95% CIs for functional dyspepsia (FD) and its components across categories of coffee and caffeine intakea.
| Coffee intake | P-trend | Caffeine intake | P-trend | |||||
|---|---|---|---|---|---|---|---|---|
| None | Monthly | Weekly or more | T1 (< 69.4 mg/d) |
T2 (69.4–106.4 mg/d) |
T3 (≥ 106.4 mg/d) |
|||
| Functional dyspepsia | ||||||||
| Crude | 1.00 | 1.07 (0.81–1.41) | 1.23 (0.88–1.71) | 0.20 | 1.00 | 0.90 (0.71–1.14) | 0.98 (0.78–1.24) | 0.89 |
| Model I | 1.00 | 1.01 (0.75–1.37) | 1.32 (0.92–1.89) | 0.18 | 1.00 | 0.94 (0.73–1.21) | 1.03 (0.79–1.33) | 0.81 |
| Model II | 1.00 | 1.02 (0.76–1.39) | 1.32 (0.92–1.89) | 0.18 | 1.00 | 0.94 (0.73–1.21) | 1.03 (0.80–1.34) | 0.79 |
| Model III | 1.00 | 1.09 (0.79–1.48) | 1.29 (0.89–1.88) | 0.17 | 1.00 | 0.99 (0.76–1.29) | 1.07 (0.82–1.40) | 0.60 |
| Model IV | 1.00 | 1.07 (0.79–1.47) | 1.25 (0.86–1.84) | 0.23 | 1.00 | 0.99 (0.76–1.29) | 1.05 (0.80–1.38) | 0.67 |
| Model V | 1.00 | 1.06 (0.77–1.46) | 1.27 (0.86–1.87) | 0.24 | 1.00 | 0.99 (0.75–1.29) | 1.00 (0.99–1.02) | 0.99 |
| Early satiation | ||||||||
| Crude | 1.00 | 1.30 (0.87–1.93) | 1.35 (0.83–2.21) | 0.11 | 1.00 | 0.86 (0.60–1.24) | 1.08 (0.76–1.53) | 0.64 |
| Model I | 1.00 | 1.15 (0.74–1.80) | 1.45 (0.86–2.44) | 0.14 | 1.00 | 0.86 (0.59–1.28) | 1.03 (0.70–1.52) | 0.86 |
| Model II | 1.00 | 1.18 (0.75–1.84) | 1.43 (0.84–2.41) | 0.15 | 1.00 | 0.86 (0.58–1.28) | 1.03 (0.70–1.53) | 0.85 |
| Model III | 1.00 | 1.26 (0.80–1.98) | 1.36 (0.79–2.34) | 0.17 | 1.00 | 0.86 (0.57–1.28) | 1.04 (0.70–1.54) | 0.84 |
| Model IV | 1.00 | 1.22 (0.77–1.92) | 1.31 (0.75–2.27) | 0.24 | 1.00 | 0.85 (0.57–1.28) | 1.02 (0.69–1.53) | 0.89 |
| Model V | 1.00 | 1.14 (0.70–1.83) | 1.37 (0.77–2.43) | 0.25 | 1.00 | 0.86 (0.57–1.29) | 0.94 (0.62–1.43) | 0.77 |
| Post-prandial fullness | ||||||||
| Crude | 1.00 | 0.85 (0.58–1.26) | 1.11 (0.71–1.74) | 0.94 | 1.00 | 1.27 (0.87–1.73) | 1.05 (0.83–1.46) | 0.75 |
| Model I | 1.00 | 0.79 (0.51–1.21) | 1.13 (0.69–1.85) | 0.96 | 1.00 | 1.43 (0.74–2.01) | 1.26 (0.86–1.81) | 0.20 |
| Model II | 1.00 | 0.80 (0.52–1.23) | 1.14 (0.70–1.86) | 0.99 | 1.00 | 1.44 (0.75–2.02) | 1.27 (0.84–1.82) | 0.18 |
| Model III | 1.00 | 0.82 (0.53–1.29) | 1.18 (0.72–1.95) | 0.83 | 1.00 | 1.50 (0.80–2.13) | 1.26 (0.79–1.84) | 0.22 |
| Model IV | 1.00 | 0.87 (0.55–1.36) | 1.24 (0.24–2.06) | 0.65 | 1.00 | 1.47 (0.77–2.09) | 1.26 (0.75–1.84) | 0.22 |
| Model V | 1.00 | 0.91 (0.58–1.44) | 1.22 (0.73–2.06) | 0.62 | 1.00 | 1.47 (0.70–2.10) | 1.24 (0.85–1.82) | 0.26 |
| Epigastric pain | ||||||||
| Crude | 1.00 | 0.77 (0.52–1.14) | 0.95 (0.60–1.50) | 0.45 | 1.00 | 0.74 (0.54–1.02) | 0.85 (0.63–1.15) | 0.29 |
| Model I | 1.00 | 0.77 (0.50–1.17) | 1.03 (0.63–1.69) | 0.65 | 1.00 | 0.69 (0.49–0.97) | 0.87 (0.62–1.21) | 0.39 |
| Model II | 1.00 | 0.78 (0.51–1.19) | 1.02 (0.62–1.68) | 0.66 | 1.00 | 0.69 (0.49–0.97) | 0.87 (0.62–1.22) | 0.39 |
| Model III | 1.00 | 0.80 (0.52–1.25) | 0.98 (0.58–1.66) | 0.63 | 1.00 | 0.76 (0.53–1.08) | 0.91 (0.64–1.29) | 0.60 |
| Model IV | 1.00 | 0.80 (0.51–1.26) | 0.97 (0.56–1.65) | 0.61 | 1.00 | 0.75 (0.53–1.07) | 0.90 (0.64–1.28) | 0.56 |
| Model V | 1.00 | 0.79 (0.50–1.25) | 0.91 (0.53–1.58) | 0.48 | 1.00 | 0.78 (0.54–1.11) | 0.88 (0.61–1.26) | 0.47 |
aValues are OR (95% CIs).
Model I: adjusted for age, sex, and energy intake.
Model II: additionally, adjusted for physical activity, smoking status, and disease history (diabetes, colitis).
Model III: additionally, adjusted for regular meal pattern, chewing sufficiency, breakfast skipping, and fluid consumption.
Model IV: additionally, adjusted for fats, dairy products, fruits, vegetables, and processed meats.
Model V: additionally, adjusted for BMI.
With regards to caffeine intake, we observed no significant association between caffeine intake and odds of FD either before (data presented in Table 3) or after controlling for potential confounders (OR: 1.00; 95% CI: 0.99–1.02), early satiation (OR: 0.94; 95% CI: 0.62–1.43), post-prandial fullness (OR: 1.24; 95% CI: 0.85–1.82), and epigastric pain (OR: 0.88; 95% CI: 0.61–1.26).
When we examined the association separately by gender and BMI status (Table 4), we observed no significant association between coffee consumption and odds of FD in either gender (for men: OR: 1.59; 95% CI: 0.77–3.27, and for women: OR: 1.17; 95% CI: 0.74–1.87). In addition, no significant relationship was observed between coffee consumption and odds of FD in normal-weight (OR: 1.05; 95% CI: 0.63–1.75) and overweight subjects (OR: 1.58; 95% CI: 0.86–2.90). Furthermore, we failed to find any significant association between caffeine intake and odds of FD (for men: OR: 0.71; 95% CI: 0.43–1.16, and for women: OR: 1.18; 95% CI: 0.84–1.65). The same associations were also seen by BMI status (for normal-weight subjects: OR: 1.09; 95% CI: 0.76–1.59, and for overweight subjects: OR: 0.87; 95% CI: 0.57–1.32).
Table 4.
Gender- and BMI-stratified crude and multivariable-adjusted ORs and 95% CIs for functional dyspepsia (FD) across categories of coffee and caffeine intakea.
| Coffee intake | P-trend | Caffeine intake | P-trend | |||||
|---|---|---|---|---|---|---|---|---|
| None | Monthly | Weekly or more | T1 (< 69.4 mg/d) |
T2 (69.4–106.4 mg/d) |
T3 (≥ 106.4 mg/d) |
|||
| Men | ||||||||
| Crude | 1.00 | 1.32 (0.85–2.05) | 1.31 (0.72–3.36) | 0.19 | 1.00 | 0.77 (0.51–1.16) | 0.80 (0.55–1.19) | 0.29 |
| Model Ib | 1.00 | 1.21 (0.73–2.01) | 1.75 (0.91–3.34) | 0.08 | 1.00 | 0.74 (0.70–1.18) | 0.78 (0.50–1.22) | 0.29 |
| Model II | 1.00 | 1.21 (0.73–2.02) | 1.73 (0.90–3.32) | 0.09 | 1.00 | 0.74 (0.46–1.17) | 0.77 (0.49–1.21) | 0.27 |
| Model III | 1.00 | 1.24 (0.73–2.10) | 1.86 (0.96–3.62) | 0.06 | 1.00 | 0.82 (0.51–1.33) | 0.79 (0.49–1.27) | 0.35 |
| Model IV | 1.00 | 1.06 (0.62–1.84) | 1.64 (0.82–3.26) | 0.21 | 1.00 | 0.86 (0.53–1.40) | 0.78 (0.49–1.26) | 0.31 |
| Model V | 1.00 | 0.97 (0.54–1.71) | 1.59 (0.77–3.27) | 0.33 | 1.00 | 0.85 (0.51–1.39) | 0.71 (0.43–1.16) | 0.17 |
| Women | ||||||||
| Crude | 1.00 | 0.95 (0.67–1.36) | 1.16 (0.78–1.73) | 0.60 | 1.00 | 0.98 (0.73–1.31) | 1.14 (0.85–1.53) | 0.36 |
| Model Ib | 1.00 | 0.92 (0.63–1.34) | 1.18 (0.77–1.82) | 0.63 | 1.00 | 1.03 (0.76–140) | 1.17 (0.86–1.61) | 0.31 |
| Model II | 1.00 | 0.95 (0.65–1.38) | 1.19 (0.78–1.84) | 0.56 | 1.00 | 1.05 (0.77–1.42) | 1.20 (0.87–1.64) | 0.26 |
| Model III | 1.00 | 1.02 (0.70–1.51) | 1.12 (0.71–1.77) | 0.62 | 1.00 | 1.08 (0.79–1.48) | 1.24 (0.89–1.73) | 0.19 |
| Model IV | 1.00 | 1.05 (0.71–1.55) | 1.12 (0.70–1.78) | 0.60 | 1.00 | 1.07 (0.78–1.48) | 1.23 (0.88–1.71) | 0.22 |
| Model V | 1.00 | 1.08 (0.73–1.60) | 1.17 (0.74–1.87) | 0.46 | 1.00 | 1.07 (0.77–1.48) | 1.18 (0.84–1.65) | 0.34 |
| BMI < 25 kg/m2 | ||||||||
| Crude | 1.00 | 1.15 (0.80–1.65) | 1.10 (0.70–1.73) | 0.48 | 1.00 | 0.88 (0.64–1.21) | 1.04 (0.76–1.43) | 0.81 |
| Model Ic | 1.00 | 1.12 (0.75–1.65) | 1.20 (0.75–1.94) | 0.37 | 1.00 | 0.96 (0.68–1.35) | 1.14 (0.80–1.62) | 0.46 |
| Model II | 1.00 | 1.11 (0.75–1.65) | 1.19 (0.74–1.92) | 0.40 | 1.00 | 0.96 (0.69–1.36) | 1.13 (0.79–1.61) | 0.49 |
| Model III | 1.00 | 1.18 (0.78–1.77) | 1.10 (0.66–1.82) | 0.51 | 1.00 | 0.97 (0.68–1.39) | 1.12 (0.77–1.62) | 0.54 |
| Model IV | 1.00 | 1.18 (0.78–1.78) | 1.05 (0.63–1.75) | 0.62 | 1.00 | 0.97 (0.68–1.39) | 1.09 (0.76–1.59) | 0.62 |
| BMI ≥ 25 kg/m2 | ||||||||
| Crude | 1.00 | 0.98 (0.63–1.53) | 1.38 (0.83–2.31) | 0.31 | 1.00 | 0.88 (0.62–1.26) | 0.81 (0.56–1.15) | 0.24 |
| Model Ic | 1.00 | 0.92 (0.57–1.48) | 1.53 (0.87–2.68) | 0.29 | 1.00 | 0.87 (0.59–1.29) | 0.83 (0.56–1.22) | 0.33 |
| Model II | 1.00 | 0.95 (0.58–1.54) | 1.52 (0.87–2.68) | 0.26 | 1.00 | 0.87 (0.59–1.29) | 0.83 (0.56–1.22) | 0.35 |
| Model III | 1.00 | 0.95 (0.58–1.57) | 1.57 (0.87–2.83) | 0.25 | 1.00 | 0.97 (0.65–1.46) | 0.88 (0.58–1.32) | 0.54 |
| Model IV | 1.00 | 0.94 (0.56–1.57) | 1.58 (0.86–2.90) | 0.27 | 1.00 | 0.97 (0.64–1.46) | 0.87 (0.57–1.32) | 0.51 |
aValues are OR (95% CIs).
bModel I: adjusted for age and energy intake.
cModel I: adjusted for age, sex, and energy intake.
Model II: additionally, adjusted for physical activity, smoking status, and disease history (diabetes, colitis).
Model III: additionally, adjusted for regular meal pattern, chewing sufficiency, breakfast skipping, and fluid consumption.
Model IV: additionally, adjusted for fats, dairy products, fruits, vegetables, and processed meats.
Model V: additionally, adjusted for BMI.
We also examined the association between tea consumption and FD. After adjustment for potential confounders, no significant association was found between tea consumption and odds of FD (OR: 0.96; 95% CI: 0.69–1.32) (data not shown).
Discussion
In this cross-sectional study, no significant association was observed between coffee and caffeine consumption and odds of FD. In addition, coffee and caffeine consumption was not significantly associated with odds of FD symptoms including early satiation, post-prandial fullness, and epigastric pain.
Numerous observational studies have been performed to elucidate the role of coffee consumption in the development of digestive symptoms, however, the results are contradictory5,8,9. These studies are either limited to comparing coffee consumption between FD patients or healthy individuals5,8,9, or just introduced coffee as an effective factor in developing FD symptoms5,8. To our knowledge, this is the first study that examined the association between coffee and caffeine intake and odds of FD in the context of a comprehensive study in which the contribution of other factors was taken into account.
In our study, drinking 6 glasses or more of coffee was not associated with odds of FD or its components in a large group of adult population. Our findings are somewhat against to previous studies that have suggested that dyspepsia and heartburn are precipitated by drinking the same amount of coffee per day5,8. However, it must be kept in mind that earlier studies did not take into account the role of other covariates. The effect of coffee in the development of FD symptoms has been attributed to its effect on stimulating gastric reflux, enhancing gastric acid secretion, and releasing gastrin5,6. However, findings from some meta-analyses revealed that coffee did not seem to be a causal factor for chronic gastroesophageal reflux disease (GERD)17. It seems that discrepancy in participants’ lifestyle, race, and methods for processing coffee might provide some reasons for different findings across studies. Other than caffeine, coffee contains other bioactive components such as potassium, magnesium, and antioxidant substances (tocopherols and phenol chlorogenic acid) that may reduce the risk of FD18,19. These constituents may offset the adverse effects of caffeine or may dilute any adverse effects. This could be a possible explanation for the lack of an association between coffee intake and FD. In addition, physiologic effects of caffeine depend on several factors including average amount of consumption per day, coffee origin and method of preparation. The amount of coffee responsible for the effect on functional dyspepsia were not reported in previous studies5,8,9, however, the average daily intake of caffeine in Iran (146.67 mg)20 is lower than the global average intake (200 mg)21. Among methods of preparation of coffee, roasting will partly destroy chlorogenic acid and may oxidize some other compounds to form new compounds22–24. Caffeine may show its harmful potential only in interacting with a particular genetic predisposition or other environmental factors, and some people may be genetically more sensitive to the adverse effects of caffeine25. In addition, coffee consumption may be related to unhealthy behaviors, such as smoking, excessive consumption of alcohol and sugar, and a poorer diet which would distort the independent association of coffee intake and FD26. Given the observational nature of available studies in this regard, it is suggested to design clinical trials to find the probable effects of coffee intake on GI symptoms. In order to further explain the reason for the lack of a significant relationship between coffee and caffeine intake and FD in this study, the following points must be noted. First, dietary triggers can differ from person to person27. So that, some not experiencing worsened symptoms from coffee or caffeine. In addition, caffeine may stimulate gastric motility and enhance digestion28, potentially alleviating dyspeptic symptoms for some patients. Additionally, the comforting perception of coffee may help manage symptoms, as the enjoyment of coffee could reduce discomfort associated with functional dyspepsia29.
It must be noted that several factors could confound the relationship between coffee consumption and functional dyspepsia. One of these factors is people’s attitude towards the effect of caffeinated drinks on functional dyspepsia. People with FD may have minimized coffee and caffeinated drinks intake due to the disease or the doctor’s advice30.
This study has some strengths as well as some limitations. The strengths of our study include large sample size, controlling for a wide range of confounders in the analyses, such as dietary habits and examining the associations stratified by gender and BMI. In addition, the use of a validated FFQ for assessment of coffee intake should also be considered. However, there are some limitations in our study that should be considered when interpreting our findings. First, due to cross-sectional nature of the study, causal relationships between coffee and caffeine consumption and FD cannot be established. In addition, because coffee and caffeine consumption was assessed by FFQ, some misclassification of study subjects in terms of exposure is inevitable, as is with all epidemiologic studies. Low number of FD patients in our study may have limited our ability to detect modest association between coffee and caffeine consumption and odds of FD. Furthermore, height and weight have been measured by self-report in this study. Finally, we did not have information on type of the coffee used by the participants as well as its preparation methods, which may affect our findings. Finally, it must be taken into account that FD and its symptoms in the current study has been investigated by a questionnaire. Although the questionnaire we used for this purpose was validated, some sort of misclassification in terms of outcome might have occurred.
In conclusion, we found no association between coffee and caffeine consumption and odds of FD and its components. Additional studies that focus on this issue, in particular in a population with frequent coffee consumption, is necessary. Investigating the impact of discontinuing coffee and caffeine consumption on FD symptoms through an interventional study could elucidate the effects of caffeine on FD. In the absence of a significant association, we can only offer general dietary guidance in daily clinical practice and make dietary recommendations tailored to individual caffeine sensitivity.
Author contributions
GK and ASM contributed in conception, design, research, statistical analyses, data interpretation, and manuscript drafting. AHK and PA contributed in conception, design, and data interpretation. AE supervised the study. All authors contributed to the article and approved the submitted version and final manuscript for submission.
Funding
The authors received no specific funding for this study.
Data availability
The dataset used and analyzed during the current study is available from the corresponding author on a reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval and consent participants
The study protocol was ethically approved by the Regional Bioethics Committee of Isfahan University of Medical Sciences. All participants provided written informed consent forms. All methods were carried out in accordance with relevant guideline and regulations.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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 dataset used and analyzed during the current study is available from the corresponding author on a reasonable request.