Home Life Sciences Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
Article Open Access

Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study

  • , , , , , , , and ORCID logo EMAIL logo
Published/Copyright: November 7, 2024

Abstract

The consumption of beverages, fast foods, fats, and oils has been recognized as key risk factors for the development of gastric cancer (GC) and pancreatic cancer (PC). The aim of this study is to examine the potential association between the risk of developing GC and PC and the consumption of beverages, fast foods, sweets, fats, and oils. Dietary information was collected from 588 participants, including 173 cases of GC, 101 cases of PC, and 314 controls, matched based on age, gender, employment, and marital status. Structured questionnaires were employed to collect data on dietary intake, physical activity, and socio-demographic factors. The case–control study spanned from March 2015 to August 2017. Multinomial logistic regression was utilized to calculate odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). Significance was determined at a level of P < 0.05. The findings revealed that high sugar consumption, particularly the intake of sweets such as candies and biscuits, was significantly associated with an increased risk of GC (OR = 1.87, 95% CI = 1.01–3.45, P-value of trend = 0.035) and (OR = 8.52, 95% CI = 3.38–21.43, P-value of trend < 0.001), respectively. Similarly, the intake of candies and Arabic sweets was associated with a higher risk of PC (OR = 2.51, 95% CI = 1.22–5.17, P-value of trend = 0.019) and (OR = 2.11, 95% CI = 1.07–4.15, P-value of trend = 0.002), respectively. Notably, weekly consumption of chicken sandwiches exhibited a positive association with an increased risk of GC (OR = 3.98, 95% CI = 2.20–7.19, P-value of trend < 0.001) and PC (OR = 4.21, 95% CI = 2.19–8.09, P-value of trend < 0.001). Furthermore, the consumption of specific dietary fats, including margarine, processed nuts, pickled olives, and mayonnaise, was higher among PC and GC cases as compared to control. Weekly consumption of processed nuts was associated with a higher likelihood of developing GC (OR = 2.58, 95% CI = 1.29–5.17, P-value of trend = 0.011) and PC (OR = 2.75, 95% CI = 1.20–6.28, P-value of trend = 0.044). We found significant associations between consumptions of candies, biscuits, Arabic sweets, chicken sandwiches, and specific fats and oils with increased risk of PC and GC in Jordanian adults.

Graphical abstract

Foods associated with increased risk of gastric cancer. Source: Corresponding Author.

1 Introduction

Pancreatic cancer (PC) is a highly lethal malignancy originating in the pancreas, a gland situated behind the stomach that plays a crucial role in digestive enzyme and hormone production, including insulin. With a dismal 5-year survival rate of only 10%, PC stands as one of the deadliest forms of cancer, ranking as the fifth leading cause of cancer-related deaths in developed nations and the eighth globally [1,2]. Similarly, gastric cancer (GC) is a prevalent and life-threatening cancer, ranking as the fourth most common cause of cancer mortality worldwide [3].

The intricate interplay among genetic factors, obesity, and smoking, coupled with diverse dietary patterns, contributes to the complex development of PC [4] and GC [5]. Individual risk is often influenced by a combination of these elements. Thus, regular screenings, adopting healthy lifestyle choices, and promoting early detection are paramount in effectively managing risk and enhancing outcomes for both pancreatic and GCs [6].

Emerging evidence suggests that diets rich in fast food, sugary beverages, fats, oils, and sweets may heighten the risk of both PC and GC [3]. These dietary choices are often characterized by high caloric content, saturated fats, trans fats, and added sugars, can contribute to obesity, insulin resistance, and inflammation, all recognized as risk factors for PC [7]. Salt intake has been identified as a significant risk factor for stomach cancer, as excessive salt consumption may stimulate gastric mucosa, leading to atrophic gastritis, increased DNA synthesis, cell proliferation, and the onset of GC [3]. Numerous studies have emphasized the link between various diets and the development of PC and GC, including the Western diet, the “prudent” diet, and the Mediterranean diet [7,8]. These studies have also highlighted the role of specific food groups like meat, dairy products, dietary fiber, specific fats, and nuts in this association [7,8].

Diets high in red meat and starchy foods/sweets are linked to an increased risk of both PC and GC [3,9,10,11], while insufficient consumption of fresh fruits and vegetables can elevate the risk of GC [3]. Obesity, defined by a body mass index (BMI) of 30 kg/m2 or higher, is another risk factor for GC, with higher BMIs associated with increased susceptibility to the malignancy [7]. Consumption of artificially sweetened drinks and sugar-sweetened beverages (SSB) containing sweeteners like aspartame is associated with elevated PC risk [12]. A recent study found that sugar consumption and specific candy types were linked to a higher PC risk in men, with less pronounced or absent correlation in women [12].

Hyperinsulinemia and hyperglycemia have been implicated in PC formation, primarily due to insulin’s stimulating effect on cell growth [7,8]. Carcinogens in food may interact with gastrointestinal cells, altering gene expression and potentially contributing to malignancy [13]. Excessive sodium chloride consumption can damage gastrointestinal mucosa, while N-nitroso compounds dramatically increase GC risk [3]. Additionally, fried foods may also be carcinogenic, as they can lead to increased insulin resistance, contain saturated fats, and produce carcinogenic by-products during cooking [14,15].

The Gram-negative bacterium Helicobacter pylori has been classified as a class I carcinogen for GC by the World Health Organization [3]. H. pylori can influence gastric epithelial cells directly through epigenetic mechanisms or indirectly by triggering inflammation in the gastric mucosa, increasing the risk of GC development [3].

Despite extensive research, limited studies have investigated the association between fast food, sweets, fats, oils, sugary beverages, and the risk of PC and GC in Arab nations. Therefore, this study aims to fill this knowledge gap by conducting a case–control study to explore potential links between dietary habits and the development of PC and GC in the adult Jordanians.

2 Methods

2.1 Study design and participants

The King Hussein Cancer Center, King Abdullah University Hospital, Jordan University Hospital, and Al-Bashir Hospital in Jordan were the primary sites of the current case–control research, which was conducted between March 2015 and August 2017.

Inclusion criteria comprised Jordanian individuals aged 18 years or above, possessing verbal capability, and being free of chronic disorders associated with dietary modifications, such as renal diseases, liver diseases, and celiac disease. Cases must have a verified diagnosis of GC or PC within the last 6 months, while controls must not have diagnosed with any type of cancer. A total of 588 participants including 173 GC cases, 101 PC cases, and 314 controls (selected from the community) participated in this study. The selection of controls was based on factors such as age, gender, employment, and marital status, ensuring a matched comparison to the cases.

  1. Informed consent: Written informed consent was obtained from all participants.

  2. Ethical approval: The study protocol received approval from the Institutional Review Board Ethics Committee of the King Hussein Cancer Center (IRB No. 15 KHCC 03, Amman, Jordan), King Abdullah University Hospital, Jordan University Hospital, and Al-Bashir Hospital, The protocol strictly adhered to the ethical standards outlined in the 1978 Declaration of Helsinki.

2.2 Data collection

Structured questionnaires were employed to gather information on socio-demographic and health variables, anthropometric measurements, physical activity, and food consumption. Trained dietitians administered and recorded responses to these questions for both cases and controls.

2.3 Anthropometric measurements

Trained dietitians were instructed to collect anthropometric measurements. Participants were instructed to wear only the bare minimum of clothing and shoes. Weight was measured using a scale (SECA GmbH & Co. KG, Hamburg, Germany) to the nearest 0.1 kg. Participants were asked to stand straight and without shoes while having their height measured with a stadiometer (SECA GmbH & Co. KG, Hamburg, Germany) to the nearest 0.1 cm. BMI was then determined by dividing the weight (kg) by the square of the height (m) [16].

2.4 Physical activity questionnaire

A face-to-face interview method was used to collect data about the physical activity level of participants. The 7-day physical activity recall (PAR) is a structured questionnaire that asks participants to recollect how much time they spend exercising each day for a week [17]. The PAR asks questions regarding the quantity, degree, and duration of physical exercise. The total amount of physical activity was calculated by taking the number of hours spent at various levels of physical activity intensity and converting it into metabolic equivalents (METs) per minute per week [18].

2.5 Dietary intake assessment

A validated Arabic quantitative food frequency questionnaire (FFQ) was used to estimate the intake of dairy products, meats, fish, and eggs [19]. Over the previous 12 months, participants were questioned about their consumption of selected beverages, fast foods, sweets, processed nuts (salted or smoked), pickled olives, fats, and oils. The participants were asked to recollect how frequently, on average, over the previous year they had consumed one standard serving of a certain food item in 10 classes (1–6 times/year, 7–11 times/year, 1 time/month, 2–3 times/month, 1 time/week, 2 times/week, 3–4 times/week, 5–6 times/week, 1 day, 2 or more/day). The portion sizes of each food item were determined based on commonly used portion sizes into three categories (small, medium, or large). Food models and standard measuring tools (e.g., cup, tablespoon, teaspoon, and glass) were used to estimate the consumed portion size of food items of dairy products, meats, fish, and eggs precisely.

2.6 Statistical analysis

The Statistical Package for the Social Sciences version 29 (IBM Corporation, Armonk, NY, USA) was used for the statistical analysis, and a value of P < 0.05 was regarded as statistically significant. To look for variations among participants depending on their consumption frequencies, descriptive analyses were conducted. Mean and standard error of the mean (SEM) were used to represent normally distributed continuous data, while frequency and percentage were used to report normally distributed categorical variables. To compare variations in the means of continuous variables, one-way ANOVA was used. The Pearson Chi-square test was used to see if categorical variables differed. The consumption of beverages, fast foods, sweets, fats, and oils was categorized into four categories based on how frequently they are consumed: daily, weekly, monthly, and rarely.

The median (25th–75th percentile) was calculated to represent the weekly consumption of beverages, fast foods, sweets, fats, and oils. The Kruskal–Wallis one-way ANOVA test was performed to find variations in the consumption of food items among PC cases, GC cases, and controls.

The odds ratios (ORs) and accompanying 95% confidence intervals (CIs) for various categories of intake of beverages, fast foods, sweets, fats, and oils were calculated using a multinomial logistic regression. The category with the lowest intake of consumption (rarely) served as the reference group. Based on the previously indicated risk variables for PC, prospective confounders such as age, gender, marital status, education level, BMI, smoking, smoking duration, family history of cancer, physical activity (MET-min/week), and daily caloric intake were chosen. P-value for trend was calculated using linear logistic regression.

3 Results

Socio-demographic and health characteristics of the study population are shown in Table 1 and were described by Al-Awwad [20]. The ages and heights of participants with PC and GC were found to be similar to those of the control group. However, there was a significant difference among PC and GC patients compared to controls in previous weight (P-value = 0.009), current weight (P-value < 0.001), previous BMI (P-value = 0.008), current BMI (P-value = 0.001), total calorie intake (P-value = 0.002), and physical activity (P-value < 0.001). Additionally, a significant difference between the three groups in a family history of cancer was detected (P-value = 0.001). The family history of cancer in controls was 31.8%, while in PC and GC cases it was 48.8 and 48.6%, respectively.

Table 1

Socio-demographic and health characteristics of the study participants [20]

Health variables Controls (n = 314) mean ± SEM GC cases (n = 173) mean ± SEM PC cases (n = 101) mean ± SEM P-value*
Age (years) 54.0 ± 0.7 54.1 ± 1.0 56.97 ± 1.2 0.093
Height (cm) 168.0 ± 0.49 167.9 ± 0.72 166.2 ± 0.9 0.212
Previous weight (kg) 79.4 ± 1.2b 85.3 ± 1.6a 83.4 ± 2.0ab 0.009
Current weight (kg) 80.9 ± 0.94a 70.6 ± 1.3b 69.4 ± 1.4b <0.001
Previous BMI (kg/m2) 28.3 ± 0.53b 30.1 ± 0.50a 30.2 ± 0.73a 0.008
Current BMI (kg/m2) 28.7 ± 0.33a 25.0 ± 0.49b 25.1 ± 0.57b 0.001
Total caloric intake (kcal/day) 2600.2 ± 150.5b 3239.5 ± 80.2a 3135.1 ± 88.3a 0.002
Physical activity (MET-min/week)** 1314.7 ± 45.6a 1031.5 ± 42.7b 952.9 ± 47.2b <0.001
Socio-demographic variables n (%)
Gender
Male 191 (60.8%) 107 (61.8%) 59 (58.4%) 0.853
Female 123 (39.2%) 66 (38.2%) 42 (41.6%)
Marital status
Married 273 (86.9%) 148 (85.5%) 87 (86.1%) 0.714
Single 20 (6.4%) 8 (4.6%) 5 (5.0%)
Divorced 7 (2.2%) 3 (1.7%) 3 (3.0%)
Widowed 14 (4.5%) 14 (8.1%) 6 (5.9%)
Educational level
Less than high school diploma 98 (31.2%) 64 (34.0%) 31 (30.7%) 0.348
High school diploma and above 216 (68.8%) 109 (66.0%) 70 (69.3%)
Working
Yes 153 (48.7%) 82 (47.4%) 45 (44.6%) 0.778
No 161 (51.3%) 91 (52.6%) 56 (55.4%)
Smoking
Yes 99 (31.5%) 56 (32.4%) 38 (37.6%) 0.552
No 215 (68.5%) 117 (67.6%) 63 (62.4%)
Family history of cancer
Yes 100 (31.8%) 84 (48.6%) 49 (48.8%) 0.001
No 214 (68.2%) 89 (51.4%) 52 (51.5%)

*P values calculated by one-way ANOVA for continuous variables and Pearson X 2 for categorical variables. P value <0.05 was considered statistically significant.

**MET = metabolic equivalent.

Table 2 shows the median intake of beverages, fast foods, sweets, fats, and oils of the study participants. There was a significant difference among controls and both GC and PC cases who consume sugar-sweetened fruit drinks and coffee (P-value = 0.03 and <0.001, respectively). PC cases showed the highest median intake of sugar-sweetened fruit drinks with 270 mL/day and coffee with 187.5 mL/day. Regarding fast food consumption, the GC cases had the highest intake of French fries ([145.0 (38.6–388.5 g/week)]; P-value <0.001), while controls had the highest intake of burger ([4.4 (0.0–66.2 g/week)]; P-value = 0.013). The consumption of chicken (fried or grilled) sandwiches was lower in control group ([41.6 (0.0–104.6 g/week)]; P-value <0.001). There were significant differences in consumption of added sugar, candies, biscuits, and Ma’amoul (date-stuffed cookies and nuts-stuffed cookies). The controls reported lower intake of both added sugar (25 [5.0–45.0 g/day]; P-value = 0.008) and Ma’amoul (10.5 [4.1–14.0 g/week]; P-value = 0.002). On the other hand, the consumption of candies (33.5 [12.8–115.5 g/week]; P-value = 0.003) was higher in PC cases, while biscuit consumption (33.5 [12.8–115.5 g/week]; P-value = 0.004) was higher among GC patients. The consumption of margarine, processed nuts (salted or smoked), pickled olives, olive oil, corn oil, and mayonnaise was significantly higher in the GC and PC groups compared to the control group (P < 0.05).

Table 2

Median intake of beverages, fast foods, sweets, fats, and oils of the study participants

Food item Median (25th–75th percentile) P-value*
Controls (n = 314) GC cases (n = 173) PC cases (n = 101)
Beverages
Sugar-sweetened fruit drinks (mL/day) 21.6 (0.0–75.6) 56.7 (5.4–270.0) 270.0 (56.7–360.0) 0.030
Soda (mL/day) 17.6 (0.0–99.6) 22.3 (0.0–151.8) 17.6 (0.0–113.8)) 0.567
Coffee (mL/day) 75.0 (10.7–187.5) 75.0 (32.1–187.5) 187.5 (75.0–337.5) <0.001
Tea (mL/day) 300.0 (120.0–540.0) 300.0 (120.0–540.0) 300.0 (120.0–540.0) 0.310
Fast foods (g/week)
French fries (g/week) 108.8 (25.6–225.3) 145.0 (38.6–388.5) 108.8 (64.5–300.4) <0.001
Falafel sandwich (g/week) 164.6 (35.3–341.0) 164.6 (35.3–341.0) 164.6 (67.6–426.3) 0.070
Chicken sandwiches (g/week) 41.6 (0.0–104.6) 104.6 (12.6–176.4) 104.6 (12.6–176.4) <0.001
Burger (g/week) 4.4 (0.0–66.2) 0.0 (0.0–52.9) 0.0 (0.0–79.4) 0.013
Pizza (g/week) 30.5 (0.0–36.5) 30.5 (0.0–36.5) 30.5 (0.0–36.5) 0.354
Sweets
Added sugar (g/day) 25.0 (5.0–45.0) 25.0 (10.0–60.0) 25.0 (10.0–60.0) 0.008
Chocolate (g/week) 44.1 (7.6–157.5) 44.1 (6.9–183.8) 44.1 (4.1–210.0) 0.936
Candies (g/week) 2.3 (0.0–11.8) 2.8 (0.0–32.3) 33.5 (12.8–115.5) 0.003
Biscuits (g/week) 16.2 (3.5–44.6) 33.5 (12.8–115.5) 16.2 (4.6–5.8) 0.004
Cake (g/week) 8.4 (7.0–23.2) 8.4 (7.0–39.2) 8.4 (4.9–29.1) 0.485
Ma’amoul (g/week) 10.5 (4.2–14.0) 12.6 (10.5–14.0) 12.6 (10.5–14.0) 0.002
Arabic sweets (g/week) 38.8 (29.4–97.6) 38.8 (25.9–164.6) 38.8 (29.4–164.6) 0.694
Fats and oils
Margarine (g/week) 3.5 (0.0–10.4) 10.4 (3.5–44.1) 10.4 (3.5–44.1) <0.001
Butter (g/week) 3.5 (0.0–10.4) 3.5 (0.0–26.1) 3.2 (0–10.4) 0.102
Processed nuts (g/week) 24.4 (8.8–54.9) 41.2 (24.4–85.3) 41.2 (12.5–85.3) <0.001
Pickled olives (g/week) 77.0 (16–121.7) 115.5 (57.8–154.0) 115.5 (44.7–154.0) 0.002
Olive oil (g/week) 140.0 (70.0–175.0) 175.0 (140.0–175.0) 140.0 (70–175.0) <0001
Sunflower oil (g/week) 5.0 (5.0–17.5) 5.0 (5.0–5.0) 5.0 (5.0–5.0) 0.756
Corn oil (g/week) 17.5 (7.7–70.0) 140.0 (87.5–175.0) 140.0 (105.0–175.0) <0.001
Mayonnaise (g/week) 3.5 (0.0–10.4) 10.4 (3.5–44.1) 10.4 (3.5–44.1) <0.001

*P values were calculated by Mann–Whitney U-test and P value < 0.05 was considered statistically significant.

Regarding coffee consumption, although none of the consumption frequencies showed any association, a significant (P-value of trend = 0.004) increase in the trend of developing PC was detected, as shown in Table 3.

Table 3

Adjusted ORs and corresponding 95% CIs of consumption of selected beverages among 101 PC cases and 173 GC cases

OR (95% CI)* P-trend
Food item Rarely** Monthly Weekly Daily
GC Sugar-sweetened fruit drinks 1 0.84 (0.46–1.53) 0.65 (0.36–1.16) 2.61 (1.31–5.18) 0.066
Cases/control 42/92 34/79 47/108 50/35
Soda 1 0.63 (0.35–1.12) 0.83 (0.47–1.47) 1.18 (0.61–2.30) 0.326
Cases/control 53/88 35/93 47/88 38/45
Coffee 1 0.50 (0.17–1.52) 0.44 (0.20–0.98) 0.82 (0.44–1.54) 0.194
Cases/control 29/41 6/22 18/62 120/189
Tea 1 0.28 (0.07–1.09) 0.59 (0.20–1.74) 0.59 (.25–1.42) 0.428
Cases/control 12/17 5/19 16/31 140/247
PC Sugar-sweetened fruit drinks 1 0.51 (0.24–1.09) 0.80 (0.41–1.53) 2.38 (1.10–5.16) 0.083
Cases/control 29/92 15/79 31/108 26/35
Soda 1 0.65 (0.33–1.26) 0.96 (0.49–1.88) 1.46 (0.67–3.18) 0.427
Cases/control 30/88 26/93 26/88 19/45
Coffee 1 0.20 (0.02–1.77) 0.87 (0.31–2.46) 1.78 (0.75–4.23) 0.004
Cases/control 8/41 1/22 12/62 80/189
Tea 1 0.33 (0.03–3.54) 1.02 (0.22–4.68) 1.58 (0.45–5.55) 0.395
Cases/control 5/17 1/19 7/31 88/247

*Adjusted for caloric intake, age, gender, marital status, education level, body weight status, smoking, period of smoking, family history of cancer, and physical activity level. The control group was considered the reference group for analysis.

**Reference group.

The findings of Table 4 reveal that the consumption of chicken sandwiches on a weekly (OR = 4.21, 95% CI = 2.19–8.09, P-value of trend < 0.001) basis was significantly associated with PC, while the risk of developing GC is associated with the consumption of chicken sandwiches on a monthly (OR = 1.78, 95% CI = 1.07–2.96, P-value of trend < 0.001) and weekly basis only (OR = 3.98, 95% CI = 2.20–7.19, P-value of trend < 0.001).

Table 4

Adjusted ORs and corresponding 95% CIs of consumption of some fast foods in 101 PC and 173 GC cases

OR (95% CI)* P-trend
Food item Rarely** Monthly Weekly Daily
GC French fries 1 0.86 (0.42–1.75) 1.12 (0.58–2.17) 1.42 (0.48–4.18) 0.521
Cases/control 22/40 100/95 37/164 22/15
Falafel sandwich 1 1.04 (0.51–2.11) 0.68 (0.36–1.28) 1.01 (0.35–2.85) 0.839
Cases/control 24/42 41/65 91/193 17/14
Chicken sandwiches 1 1.78 (1.07–2.96) 3.98 (2.20–7.19) <0.001
Cases/control 48/146 64/116 60/51 1/1
Burger 1 0.84 (0.50–1.41) 1.54 (0.65–3.63) 0.293
Cases/control 120/218 36/79 15/17 2/0
Pizza 1 1.10 (0.71–1.73) 0.91 (0.29–2.91) 0.647
Cases/control 97/194 71/107 5/13 0/0
PC French fries 1 0.95 (0.41–2.22) 1.39 (0.64–3.05) 0.94 (0.20–4.35) 0.236
Cases/control 11/40 27/95 59/164 4/15
Falafel sandwich 1 0.73 (0.30–1.79) 0.96 (.46–2.02) 1.33 (0.36–4.88) 0.823
Cases/control 14/42 16/65 66/193 5/14
Chicken sandwiches 1 1.55 (0.84–2.87) 4.21 (2.19–8.09) <0.001
Cases/control 27/146 33/116 40/51 1/1
Burger 1 0.52 (0.27–1.03) 0.95 (0.32–2.77) 0.900
Cases/control 80/218 15/79 6/17 0/0
Pizza 1 1.54 (0.92–2.58) 1.45 (0.40–5.26) 0.404
Cases/control 53/194 44/107 4/13 0/0

*Adjusted for caloric intake, age, gender, marital status, education level, body weight status, smoking, period of smoking, family history of cancer, and physical activity level. The control group was considered the reference group for analysis.

**Reference group.

In Table 5, the findings showed that biscuit consumption was significantly associated with the risk of GC (ORs: 2.73; monthly, 2.95; weekly, and 8.52; daily, P-value of trend < 0.001). Additionally, candies’ consumption on a monthly and weekly basis was positively associated with an increased risk of PC (ORs: 2.51; monthly, and 2.0; weekly, P-value of trend = 0.019), while monthly consumption of chocolate had an inverse relationship (ORs: 0.43, P-value of trend = 0.929). Arabic sweets were significantly associated with the likelihood of a higher risk of PC (ORs: 2.11; monthly, 3.68; weekly, and 1.75, P-value of trend = 0.002).

Table 5

Adjusted ORs and corresponding 95% CIs of consumption of some sweets among 101 PC cases and 173 GC cases

OR (95% CI)* P-trend
Food item Rarely** Monthly Weekly Daily
GC Added sugar 1 0.30 (0.05–1.75) 0.55 (0.21–1.46) 0.83 (0.45–1.50) 0.994
Cases/control 25/45 3/7 9/32 136/230
Candies 1 1.87 (1.01–3.45) 1.56 (0.91–2.69) 1.30 (0.60–2.81) 0.035
Cases/control 80/188 29/37 44/65 20/24
Chocolate 1 0.86 (0.45–1.65) 0.70 (0.38–1.29) 1.09 (0.54–2.20) 0.832
Cases/control 37/58 38/74 55/125 43/57
Biscuits 1 2.73 (1.14–6.54) 2.95 (1.31–6.62) 8.52 (3.38–21.43) <0.001
Cases/control 11/54 38/79 77/152 47/29
Cake 1 0.95 (0.57–1.60) 1.34 (0.72–2.50) 0.39 (0.04–3.42) 0.120
Cases/control 45/82 83/162 43/67 2/3
Ma’amoul 1 0.94 (0.56–1.57) 0.92 (0.47–1.82) 0.46 (0.05–4.19) 0.770
Cases/control 112/201 40/72 20/36 1/5
Arabic sweets 1 1.09 (0.65–1.82) 1.52 (0.77–2.99) 1.87 (0.25–14.20) 0.076
Cases/control 37/83 91/175 42/54 3/2
PC Added sugar 1 0.52 (0.054–5.03) 0.18 (0.02–1.51) 1.71 (0.78–3.73) 0.077
Cases/control 10/45 1/7 1/32 89/230
Candies 1 2.51 (1.22–5.17) 2.0 (1.03–3.78) 1.83 (0.77–4.32) 0.019
Cases/control 46/188 19/37 24/65 12/24
Chocolate 1 0.43 (0.20–0.94) 0.57 (0.29–1.11) 1.00 (0.47–0.12) 0.929
Cases/control 28/58 15/74 32/125 26/57
Biscuits 1 2.10 (0.90–4.93) 2.52 (1.01–6.31) 3.90 (1.39–10.92) 0.073
Cases/control 8/54 29/79 48/152 16/29
Cake 1 0.94 (0.53–1.68) 1.07 (0.52–2.22) 0.931
Cases/control 27/82 54/162 20/67 0/3
Ma’amoul 1 1.29 (0.73–2.28) 1.15 (0.51–2.58) 0.937
Cases/control 62/201 29/72 10/36 0/5
Arabic sweets 1 2.11 (1.07–4.15) 3.68 (1.64–8.22) 0.002
Cases/control 15/83 58/175 27/54 0/2

*Adjusted for caloric intake, age, gender, marital status, education level, body weight status, smoking, period of smoking, family history of cancer, and physical activity level. The control group was considered the reference group for analysis.

**Reference group.

Table 6 shows that among the GC group, margarine consumption showed a significant risk increase, with ORs of 2.80, 3.86, and 5.12 for monthly, weekly, and daily consumption, respectively (P-value of trend = 0.001). Weekly consumption of processed nuts (OR = 2.58, 95% CI = 1.29–5.17, P-value of trend = 0.011) and butter (OR = 2.07, 95% CI = 1.12–3.84, P-value of trend = 0.021) was also significantly associated with higher risk of GC. Additionally, corn oil displayed a significant trend in risk (P-value of trend < 0.001). Regarding PC, this study also revealed that margarine consumption had a significant risk increase, with ORs of 8.15 for daily consumption (P-value of trend = 0.002). Processed nuts consumption was linked to higher risk for weekly consumption (OR = 2.75, 95% CI = 1.20–6.28, P-value of trend = 0.044). Corn oil showed a significant protective effect when consumed on weekly basis (OR = 0.13, 95% CI = 0.03–0.60, P-value of trend < 0.001), and mayonnaise potentially had a protective effect for monthly consumption (P-value of trend = 0.299).

Table 6

Adjusted ORs and corresponding 95% CIs of consumption of fats and oils in 101 PC cases and 173 GC cases

OR (95% CI)* P-trend
Food item Rarely** Monthly Weekly Daily
GC Margarine 1 2.80 (1.68–4.67) 3.86 (2.07–7.208) 5.12 (1.18–22.26) <0.001
Cases/control 40/145 79/73 48/36 6/4
Butter 1 1.21 (0.72–2.01) 2.07 (1.12–3.84) 1.54 (0.38–6.29) 0.021
Cases/control 88/200 43/73 37/36 5/5
Processed nuts 1 1.05 (0.50–2.19) 2.58 (1.29–5.17) 2.49 (0.98–6.28) 0.011
Cases/control 17/52 37/113 97/128 22/21
Pickled olives 1 1.02 (0.33–3.11) 1.22 (0.49–3.08) 1.95 (0.79–4.81) 0.145
Cases/control 9/27 12/36 48/118 104/133
Olive oil 1 0.34 (0.02–6.56) 4.31 (0.49–38.18) 0.011
Cases/control 0/0 1/7 2/29 170/278
Corn oil 1 2.68 (0.49–14.6) 7.64 (1.52–38.27) <0.001
Cases/control 2/14 16/51 1/118 154/131
Sunflower oil 1 3.90 (1.89–8.05) 0.61 (0.19–1.99) 3.01 (1.32–6.86) 0.914
Cases/control 18/51 118/156 5/52 32/55
Mayonnaise 1 0.61 (0.35–1.04) 0.98 (0.47–2.04) 0.95 (0.14–6.60) 0.282
Cases/control 126/211 27/71 17/29 3/3
PC Margarine 1 2.0 (1.10–3.64) 3.71 (0.85–7.44) 8.15 (1.52–43.73) 0.002
Cases/control 27/145 43/73 28/36 3/4
Butter 1 1.28 (0.71–2.29) 1.55 (0.71–3.36) 1.46 (0.30–7.13) 0.478
Cases/control 57/200 26/73 15/36 3/5
Processed nuts 1 1.37 (0.56–3.34) 2.75 (1.20–6.28) 1.32 (0.37–4.66) 0.044
Cases/control 11/52 24/113 61/128 5/21
Pickled olives 1 1.26 (0.30–5.39) 2.04 (0.62–6.67) 3.07 (0.95–9.90) 0.030
Cases/control 4/27 7/36 35/118 55/133
Olive oil 1 4.49 (0.98–20.27)
Cases/control 0/0 0/7 7/29 94/278
Corn oil 1 0.83 (0.21–3.24) 0.13 (0.03–0.60) 2.55 (0.77–8.47) <0.001
Cases/control 4/14 11/51 4/118 82/131
Sunflower oil 1 2.10 (0.99–4.45) 0.20 (0.04–0.98) 1.82 (0.76–4.36) 0.971
Cases/control 12/51 66/156 2/52 21/55
Mayonnaise 1 0.30 (0.13–0.69) 1.02 (0.44–2.36) 1.08 (0.10–12.31) 0.299
Cases/control 79/211 10/71 11/29 1/3

*Adjusted for caloric intake, age, gender, marital status, education level, body weight status, smoking, period of smoking, family history of cancer, and physical activity level. The control group was considered the reference group for analysis.

**Reference group.

4 Discussion

This study aimed to investigate the potential association between the consumption of fast food, sweets, fats, oils, and SSB, and the likelihood of development of PC and GC among Jordanian adults. The study considered several factors, including socio-demographic characteristics, physical activity, and dietary habits, to facilitate a better understanding of these potential associations. A prospective data from a study involving up to 98,265 American adults revealed a positive association between the consumption of ultra-processed foods such as fast food, sweets, and SSB and the risk of PC [21]. This finding expands upon the existing knowledge linking ultra-processed foods with PC highlighting their potential role as carcinogens [21]. Similarly, a study investigating the association between ultra-processed foods and GC showed that there is strong evidence linking the consumption of processed meat to an increased risk of colorectal cancer [22]. Additionally, a study conducted by Kliemann et al. showed a strong evidence that consuming more salt-preserved foods and Cantonese-style salted fish raises the risk of GC [22].

The finding of a positive association between the consumption of sweets, such as candies and Arabic sweets, and an increased risk of PC aligns with existing literature [23,24]. Similarly, this association is particularly notable for those who consume candies and biscuits on a monthly or weekly basis. It is important to highlight that these findings emphasize the potential role of high sugar intake in promoting PC development. High sugar consumption can lead to elevated blood sugar levels and insulin response, which are known to be associated with PC risk. The potential mechanisms linking sugar consumption to an increased risk of PC and GC are multifaceted and complex. High sugar intake may lead to chronic hyperglycemia, insulin resistance, and elevated insulin and insulin-like growth factor levels, all of which can promote cell growth and inflammation, contributing to carcinogenesis [23]. This association was also demonstrated in few studies that have examined the relationship between added sugar consumption and the risk of PC. Lyon et al. [24] found that women who consume high amount of added sugar had a 3.7-fold higher risk of developing PC compared to women with low added sugar intakes. However, the risk was only 1.3 times in men with low added sugar intakes [24]. Nevertheless, it is crucial to note that a case–control study reported 50% increase in the risk of developing PC, although this increase did not reach statistical significance [25].

Similarly, this study reported an association between sweets consumption (candies and biscuits) and an increased risk of GC, particularly for those who showed frequent consumption. These findings suggest that the consumption of sugary sweets may have adverse impact on gastric health. It is important to note that this association could be influenced by factors such as obesity and insulin resistance, which are common risk factors for both PC and GC. As the consumption of sweeteners continues to increase globally, there have been multiple reports suggesting a potential link between sweeteners and the promotion of GC [26]. This study reported that the excessive consumption of sweets is associated with increased likelihood of developing GC. A study conducted by Castelló in 2019 found that frequent consumption of sweets was linked to a 75% higher risk of developing GC. Additionally, the study observed a slight increase in the risk of GC in individuals who consume large amounts of sweets, particularly cakes and candy [27].

The findings of this study revealed no significant difference in the risk of GC and PC associated with the intake of sugar-sweetened fruit drinks. When considering the impact of dietary choices on the risk of PC and GC, the associations with the consumption of sweetened beverages exhibited varying associations. Chan et al. [12] reported that higher consumption of sweet beverages was linked to an elevated risk of PC among men. However, these associations were inconsistent or non-existent among women. In a separate prospective study, involving US nurses and other health workers, women who consumed more than three sugar-sweetened soft drinks per week had a significantly higher risk (57% increase) of developing PC compared to women who consumed less than one sugar-sweetened soft drink per month [28]. It is worth noting that caloric sweetener content in soft drinks may differ between countries [28]. Another cohort study indicated that the consumption of SSB was found to be significantly associated with an increased risk of PC [29]. The highest risks were observed among individuals under the age of 40 [29]. Furthermore, the study revealed a clear dose–response relationship, indicating that the risk of PC increased with the quantity of SSB consumed, even as low as one drink per day [29]. The results of this study highlighted the potential link between sugar and soft drink consumption and the risk of PC, especially in the context of factors such as hyperglycemia, insulin resistance, and a high BMI. Nevertheless, based on this study, daily consumption of sugar-sweetened drinks was linked to an increased likelihood of developing both PC and GC. Additionally, type 2 diabetes and obesity, both characterized by abnormal glucose tolerance and insulin resistance, are well-established risk factors for PC [9,10,11]. High sugar consumption raises blood sugar and insulin levels, creating a favorable environment for PC cell growth [10].

In this study, the frequency of coffee consumption, specifically daily intake, was found to be significantly different between control group and GC and PC cases with OR of 1.78 (0.75–4.23) and P-value of trend = 0.004. Contrary to our findings, two cohort studies conducted as part of the International Stop Project consortium, reported no significant associations between the consumption of caffeinated and decaffeinated drinks. While considering the OR for coffee drinkers was 1.03 (95% CI: 0.94–1.13), indicating a slight increase in the risk of developing GC [30]. These studies did not provide evidence of a protective effect even for individuals with low to moderate coffee consumption. However, a non-significant 20% increased risk was reported among those with the highest coffee consumption levels [30].

Regarding fast food and its potential association with PC and GC, this study revealed that the consumption of French fries was particularly higher among individual in the GC group, with a median weekly intake of 145.0 g/week. This suggests that individuals in GC group may be more likely to consume French fries frequently. In contrast, the control group participants reported lower consumption of chicken sandwiches and burgers with a median weekly intake of 41.6 g/week for chicken sandwich and 4.4 g/week for burgers. In this study, we found an association between the consumption of chicken sandwiches and the risk of developing GC and PC. Data from a case–control study in residents of the Huaihe River Basin in China also found that the fast food pattern could increase the risk of GC, potentially due to a higher consumption of barbecue food, fried food, red meat, and seafood [31].

In this study, a positive association was observed between margarine consumption and GC with OR of 2.80 (95% CI: 1.68–4.67) for monthly consumption, 3.86 (95% CI: 2.07–7.208) for weekly consumption, and 5.12 (95% CI: 1.18–22.26) for daily consumption, with a significant P-value of trend < 0.001. Similarly, the intake of processed nuts, olive oil, and corn oil showed increasing risks with higher consumption frequencies (P-value of trend = 0.011, 0.01, and <0.001, respectively). In PC, both margarine and corn oil showed significant associations, with margarine having a significant P-value of trend of 0.002. Corn oil, when consumed daily, showed an OR of 2.55 (95% CI: 0.77–8.47) with a highly significant P-value of trend < 0.001. These findings indicate that the frequent consumption of margarine, processed nuts, and corn oil may be risk factors for both GC and PC.

There are statistically significant associations between total, saturated, and monounsaturated fat consumption, and PC, according to large prospective research [32]. The exocrine function of the pancreas, which secretes lipase, an enzyme that breaks down fat, may be a general mechanism for the association between fat consumption and PC. When fats and fatty acids from chyme enter the duodenum, they cause the release of cholecystokinin, which increases the production of pancreatic enzymes and causes pancreatic hypertrophy and hyperplasia. This might make the pancreas susceptible to other carcinogens [32,33].

In a population-based case–control research, higher intakes of both dietary saturated fatty acids and monounsaturated fatty acids were shown to be significantly associated with the development of GC [34]. Additionally, in agreement with our findings, a case–control study revealed an association between fat intake and PC, this aligning with other studies that found a link between total fat intake and PC death rates among various countries [35]. An association was identified with the consumption of margarine, while no such association was found with butter intake [35]. A study conducted in North America showed that consuming a significant amount of total fat was associated with an increased risk of developing GC [36].

High-fat diet combined with high sugar intake has been shown to significantly contribute to the development of cancer through mechanisms such as insulin resistance, inflammation, and oxidative stress [37]. These elements are typical of the Western diet, which is characterized by high consumption of red and processed meats, refined sugars, and unhealthy fats. Such dietary patterns are linked to numerous adverse health effects, including obesity, type 2 diabetes, and increased risk of certain cancers [38,39]. In contrast, the Mediterranean diet, which emphasizes the intake of fruits, vegetables, whole grains, legumes, nuts, and healthy fats like olive oil, offers protective benefits. This diet is associated with lower risk of chronic diseases [39], including PC [40] and GC [41]. Therefore, shifting from a Western diet to a Mediterranean diet can significantly improve overall health and reduce the risk of developing these serious conditions.

In GC cases, we found a positive association between processed nuts consumption on weekly or daily basis, showing higher ORs of 2.58 (95% CI: 1.29–5.17) and 2.49 (95% CI: 0.98–6.28), respectively, compared to those who rarely consume processed nuts. Similarly, for PC cases, weekly processed nuts consumption is associated with elevated ORs of 2.75 (95% CI: 1.20–6.28) compared to the control group. The prevalent variety of processed nuts typically eaten in Jordan comprises those that are salted or smoked. A recent meta-analysis of case–control and cohort studies has indicated that a high intake of salt dramatically increases the risk of GC dramatically (OR = 2.05, 95% CI, 1.60–2.62) [42]. A thorough meta-analysis of longitudinal research revealed a significant adverse impact of both total salt intake and the consumption of salt-rich foods on the occurrence of GC in the general population [43]. Numerous epidemiological studies have highlighted a connection between salt consumption and the risk of stomach cancer [44,45]. Sodium chloride, a component of salt, has been demonstrated to enhance the development of stomach cancer both in rat experiments using N-methyl-N-nitro-N-nitrosoguanidine and in human studies [46]. This detrimental effect is attributed to the damaging impact of high salt doses on the protective mucin layer that covers the stomach epithelium. Additionally, excessive salt consumption results in elevated osmotic pressure, further harming the epithelial cells. Prolonged damage to the mucous membrane eventually leads to conditions such as chronic atrophic gastritis and intestinal metaplasia, which are precursors to the development of stomach cancer [47]. A study of Eom et al. [48] indicates that the possible mechanism of AFB1 carcinogenicity may be associated to an imbalance between the capacity for bioactivation and detoxification in gastric tissue. Considering that aflatoxin B1 (AFB1) is frequently exposed to the human stomach through diets, including the consumption of nuts, and knowing that AFB1 metabolic enzymes, including CYP1A2, EPHX1, and GSTs, are expressed in human stomach tissue [48]. It was reported that 9-exo-epoxide, a reac-tive metabolite of AFB1, accumulated in the glandular sto-mach in a state of glutathione depletion [48,49]. Long-term consumption of high nitrate concentrations has been asso-ciated to an increased risk of GC. These nitrates are transformed into carcinogenic nitrites by the H. pylori bacteria found in salted, dried, and smoked foods [50].

5 Study limitations and strengths

The present study comes with certain limitations. First, recall bias is a common concern in case–control studies, particularly when gathering dietary information. Additionally, the research assistants were not blinded to the participants’ diagnoses, meaning they were aware of who had the condition and who did not. However, research assistants were extensively trained and interacted with participants in a professional and uniform manner, irrespective of their case or control status. Another limitation is the lack of biochemical levels of these micronutrients. On the other hand, this case–control study presents several strengths. These strengths encompass adjusting statistical analyses for a variety of important confounding factors, especially smoking, which is strongly associated with GC and PC, enhancing the robustness of our findings by mitigating the impact of these variables on GC risk. Other important strengths include using an ethnically validated FFQ, enrolling newly diagnosed GC cases and cancer-free controls from major hospitals to represent diverse Jordanian dietary patterns, and achieving a high compliance rate with the questionnaire, resulting in an impressive response rate exceeding 95%. In addition, the used FFQ includes cultural foods, commonly used portion sizes for each food item, and accounted for seasonal variations for a more precise estimation of participants’ dietary history.

6 Conclusion

In conclusion, this study showed a significant association between dietary choices and the risk of PC and GC in Jordanian adults. High sugar consumption and certain dietary fats increase the risk of both PC and GC. High sugar consumption, particularly from sweets like candies, was significantly associated with an increased risk of both PC and GC. Daily consumption of Arabic desserts was also linked to higher risk of PC, and consumption of biscuits was associated with risk of GC. Additionally, weekly consumption of chicken sandwiches showed associations with elevated GC and PC risks. Moreover, the study revealed that the consumption of specific fats and oils, including margarine, nuts, pickled olives, corn oil, and mayonnaise, was notably higher among PC and GC cases, highlighting the potential influence of dietary fats on cancer development. It is recommended to address the dietary factors contributing to PC and GC by highlighting the impact of diet on their development. Shifting from a Western diet to a Mediterranean diet is advised to improve overall health and lower the risk of these cancers. Promoting healthier eating habits can play a crucial role in decreasing the prevalence of PC and GC not only in Jordan but also worldwide.

Acknowledgements

The authors would like to thank Dr Narmeen Al-Awwad for her support in the administrative work at The Hashemite University.

  1. Funding information: The authors would like to express their thanks to the Deanship of Scientific Research at The Hashemite University for funding this research project. Open Access funding was provided by the Qatar National Library.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal. All authors were involved in critical revisions and approved the final manuscript. RT and TA were responsible for the study conception, design, and methodology development. SSA, TA, AH, and YR handled data acquisition. RT and SSA performed data analysis and interpretation. RA, SAS, SAJ, GA, YR, and RT contributed to drafting the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Ethical approval and consent to participate: The study protocol was approved by the Institutional Review Board Ethics Committee of the King Hussein Cancer Center (IRB No. 15 KHCC 03, Amman, Jordan), King Abdullah University Hospital, Jordan University Hospital, and Al-Bashir Hospital.

  5. Informed consent: Written informed consent was obtained from all participants.

  6. Data availability statement: The datasets are available from the corresponding author on reasonable request.

References

[1] McWilliams RR, Maisonneuve P, Bamlet WR, Petersen GM, Li D, Risch HA, et al. Risk factors for early-onset and very-early-onset pancreatic adenocarcinoma: a pancreatic cancer case–control consortium (PanC4) analysis. Pancreas. 2016;45(2):311–6. 10.1097/MPA.0000000000000392, PubMed PMID: 26646264; PubMed Central PMCID: PMCPMC4710562.Search in Google Scholar PubMed PubMed Central

[2] Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55(2):74–108. 10.3322/canjclin.55.2.74, PubMed PMID: 15761078.Search in Google Scholar

[3] Machlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies. Int J Mol Sci. 2020;21(11):4012. 10.3390/ijms21114012, PubMed PMID: 32512697; PubMed Central PMCID: PMCPMC7312039 Epub 20200604.Search in Google Scholar

[4] Zeng L, Wu Z, Yang J, Zhou Y, Chen R. Association of genetic risk and lifestyle with pancreatic cancer and their age dependency: a large prospective cohort study in the UK Biobank. BMC Med. 2023;21(1):489. 10.1186/s12916-023-03202-0.Search in Google Scholar PubMed PubMed Central

[5] Jardim SR, de Souza LMP, de Souza HSP. The rise of gastrointestinal cancers as a global phenomenon: unhealthy behavior or progress? Int J Environ Res Public Health. 2023;20(4):3640. 10.3390/ijerph20043640, PubMed PMID: 36834334; PubMed Central PMCID: PMCPMC9962127 Epub 20230218.Search in Google Scholar

[6] Lippman SM, Abate-Shen C, Colbert Maresso KL, Colditz GA, Dannenberg AJ, Davidson NE, et al. AACR white paper: shaping the future of cancer prevention – a roadmap for advancing science and public health. Cancer Prev Res. 2018;11(12):735–78. 10.1158/1940-6207.Capr-18-0421.Search in Google Scholar

[7] Stolzenberg-Solomon RZ, Schairer C, Moore S, Hollenbeck A, Silverman DT. Lifetime adiposity and risk of pancreatic cancer in the NIH-AARP Diet and Health Study cohort. Am J Clin Nutr. 2013;98(4):1057–65. Epub 20130828. 10.3945/ajcn.113.058123, PubMed PMID: 23985810; PubMed Central PMCID: PMCPMC3778860.Search in Google Scholar PubMed PubMed Central

[8] Gapstur SM, Gann PH, Lowe W, Liu K, Colangelo L, Dyer A. Abnormal glucose metabolism and pancreatic cancer mortality. JAMA. 2000;283(19):2552–8. 10.1001/jama.283.19.2552, PubMed PMID: 10815119.Search in Google Scholar

[9] La Vecchia C, Bosetti C, Lucchini F, Bertuccio P, Negri E, Boyle P, et al. Cancer mortality in Europe, 2000–2004, and an overview of trends since 1975. Ann Oncol. 2010;21(6):1323–60. 10.1093/annonc/mdp530, PubMed PMID: 19948741 Epub 20091130.Search in Google Scholar

[10] Michaud DS, Giovannucci E, Willett WC, Colditz GA, Stampfer MJ, Fuchs CS. Physical activity, obesity, height, and the risk of pancreatic cancer. JAMA. 2001;286(8):921–9. 10.1001/jama.286.8.921, PubMed PMID: 11509056.Search in Google Scholar

[11] Silverman DT, Schiffman M, Everhart J, Goldstein A, Lillemoe KD, Swanson GM, et al. Diabetes mellitus, other medical conditions and familial history of cancer as risk factors for pancreatic cancer. Br J Cancer. 1999;80(11):1830–7. 10.1038/sj.bjc.6690607, PubMed PMID: 10468306; PubMed Central PMCID: PMCPMC2363127.Search in Google Scholar PubMed PubMed Central

[12] Chan JM, Wang F, Holly EA. Sweetened beverages, and risk of pancreatic cancer in a large population-based case–control study. Cancer Causes Control. 2009;20(6):835–46Epub 20090311. 10.1007/s10552-009-9323-1, PubMed PMID: 19277880; PubMed Central PMCID: PMCPMC2694313.Search in Google Scholar

[13] Zhong GC, Zhu Q, Gong JP, Cai D, Hu JJ, Dai X, et al. Fried food consumption and the risk of pancreatic cancer: a large prospective multicenter study. Front Nutr. 2022;9:889303. 10.3389/fnut.2022.889303, PubMed PMID: 35958255; PubMed Central PMCID: PMCPMC9362838 Epub 20220722.Search in Google Scholar

[14] Uzcudun AE, Retolaza IR, Fernandez PB, Sanchez Hernandez JJ, Grande AG, Garcia AG, et al. Nutrition and pharyngeal cancer: results from a case–control study in Spain. Head Neck. 2002;24(9):830–40. 10.1002/hed.10142, PubMed PMID: 12211047.Search in Google Scholar PubMed

[15] Conroy T, Bachet JB, Ayav A, Huguet F, Lambert A, Caramella C, et al. Current standards and new innovative approaches for treatment of pancreatic cancer. Eur J Cancer 2016 57:10–22. 10.1016/j.ejca.2015.12.026, PubMed PMID: 26851397 Epub 20160204.Search in Google Scholar

[16] Lee RD, Nieman DC. Nutritional assessment. 5th ed. New York: McGraw-Hill; 2007.Search in Google Scholar

[17] Sallis JF, Haskell WL, Wood PD, Fortmann SP, Rogers T, Blair SN, et al. Physical activity assessment methodology in the Five-City Project. Am J Epidemiol. 1985;121(1):91–106. 10.1093/oxfordjournals.aje.a113987, PubMed PMID: 3964995.Search in Google Scholar PubMed

[18] Washburn RA, Jacobsen DJ, Sonko BJ, Hill JO, Donnelly JE. The validity of the stanford seven-day physical activity recall in young adults. Med Sci Sports Exerc. 2003;35(8):1374–80. 10.1249/01.Mss.0000079081.08476.Ea, PubMed PMID: 12900693.Search in Google Scholar

[19] Tayyem RF, Abu-Mweis SS, Bawadi HA, Agraib L, Bani-Hani K. Validation of a food frequency questionnaire to assess macronutrient and micronutrient intake among Jordanians. J Acad Nutr Diet. 2014;114(7):1046–52. Epub 20131112 10.1016/j.jand.2013.08.019, PubMed PMID: 24231366.Search in Google Scholar PubMed

[20] Al-Awwad N, Allehdan S, Al-Jaberi T, Hushki A, Albtoush Y, Bani-Hani K, et al. Dietary and lifestyle factors associated with gastric and pancreatic cancers: a case–control study. Preventive Nutr Food Sci. 2021;26(1):30–9. 10.3746/PNF.2021.26.1.30.Search in Google Scholar

[21] Zhong GC, Zhu Q, Cai D, Hu JJ, Dai X, Gong JP, et al. Ultra-processed food consumption and the risk of pancreatic cancer in the prostate, lung, colorectal and ovarian cancer screening trial. Int J Cancer. 2023;152(5):835–44. 10.1002/ijc.34290, PubMed PMID: 36094042 Epub 20221007.Search in Google Scholar

[22] Kliemann N, Al Nahas A, Vamos EP, Touvier M, Kesse-Guyot E, Gunter MJ, et al. Ultra-processed foods and cancer risk: from global food systems to individual exposures and mechanisms. Br J Cancer. 2022;127(1):14–20. 10.1038/s41416-022-01749-y, PubMed PMID: 35236935; PubMed Central PMCID: PMCPMC9276654 Epub 20220302.Search in Google Scholar

[23] Giovannucci E. Nutrition, insulin, insulin-like growth factors and cancer. Hormone Metab Res. 2003;35(11/12):694–704.10.1055/s-2004-814147Search in Google Scholar PubMed

[24] Lyon JL, Slattery ML, Mahoney A, Robison LM. Dietary intake as a risk factor for cancer of the exocrine pancreas. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive. Oncology. 1993;2(6):513–8.Search in Google Scholar

[25] Bueno de Mesquita HB, Maisonneuve P, Runia S, Moerman CJ. Intake of foods and nutrients and cancer of the exocrine pancreas: a population-based case–control study in The Netherlands. Int J Cancer. 1991;48(4):540–9. 10.1002/ijc.2910480411, PubMed PMID: 1646177.Search in Google Scholar PubMed

[26] Pan B, Ge L, Lai H, Wang Q, Wang Q, Zhang Q, et al. Association of soft drink and 100% fruit juice consumption with all-cause mortality, cardiovascular diseases mortality, and cancer mortality: a systematic review and dose–response meta-analysis of prospective cohort studies. Crit Rev food Sci Nutr. 2022;62(32):8908–19.10.1080/10408398.2021.1937040Search in Google Scholar PubMed

[27] Castelló A, Amiano P, Fernández de Larrea N, Martín V, Alonso MH, Castaño-Vinyals G, et al. Low adherence to the western and high adherence to the mediterranean dietary patterns could prevent colorectal cancer. Eur J Nutr. 2019;58(4):1495–505. 10.1007/s00394-018-1674-5.Search in Google Scholar PubMed

[28] Llaha F, Gil-Lespinard M, Unal P, de Villasante I, Castaneda J, Zamora-Ros R. Consumption of sweet beverages and cancer risk. a systematic review and meta-analysis of observational studies. Nutrients. 2021;13(2):516. 10.3390/nu13020516, PubMed PMID: 33557387; PubMed Central PMCID: PMCPMC7915548 Epub 20210204.Search in Google Scholar

[29] Chen CH, Tsai MK, Lee JH, Lin RT, Hsu CY, Wen C, et al. “Sugar-sweetened Bbeverages” is an independent risk from pancreatic cancer: based on half a million asian cohort followed for 25 years. Front Oncol. 2022;12:835901. 10.3389/fonc.2022.835901, PubMed PMID: 35463371; PubMed Central PMCID: PMCPMC9022008 Epub 20220407.Search in Google Scholar

[30] Martimianaki G, Bertuccio P, Alicandro G, Pelucchi C, Bravi F, Carioli G, et al. Coffee consumption and gastric cancer: a pooled analysis from the stomach cancer pooling project consortium. Eur J Cancer Prev. 2022;31(2):117–27. 10.1097/CEJ.0000000000000680, PubMed PMID: 34545022; PubMed Central PMCID: PMCPMC8972971.Search in Google Scholar

[31] Wu X, Zhang Q, Guo H, Wang N, Fan X, Zhang B, et al. Dietary patterns and risk for gastric cancer: a case–control study in residents of the Huaihe River Basin, China. Front Nutr. 2023;10:1118113. 10.3389/fnut.2023.1118113, PubMed PMID: 36755993; PubMed Central PMCID: PMCPMC9899829 Epub 20230123.Search in Google Scholar

[32] Thiébaut R, Kotti S, Jung C, Merlin F, Colombel JF, Lemann M, et al. TNFSF15 polymorphisms are associated with susceptibility to inflammatory bowel disease in a new European cohort. Am J Gastroenterol. 2009;104(2):384–91. 10.1038/ajg.2008.36, PubMed PMID: 19174806 Epub 20090113.Search in Google Scholar

[33] Woutersen RA, Appel MJ, van Garderen-Hoetmer A, Wijnands MV. Dietary fat and carcinogenesis. Mutat Res. 1999;443(1–2):111–27. 10.1016/s1383-5742(99)00014-9, PubMed PMID: 10415435.Search in Google Scholar

[34] Zhu Z, Cheng Y, Qi Q, Lu Y, Ma S, Li S, et al. Association of infant and young child feeding practices with cognitive development at 10–12 years: a birth cohort in rural Western China. Br J Nutr. 2020;123(7):768–79. 10.1017/s0007114519003271, PubMed PMID: 31831094 Epub 20191213.Search in Google Scholar

[35] Norell SE, Ahlbom A, Erwald R, Jacobson G, Lindberg-Navier I, Olin R, et al. Diet and pancreatic cancer: a case–control study. Am J Epidemiol. 1986;124(6):894–902. 10.1093/oxfordjournals.aje.a114479, PubMed PMID: 3776972.Search in Google Scholar PubMed

[36] Bao Y, Hu FB, Giovannucci EL, Wolpin BM, Stampfer MJ, Willett WC, et al. Nut consumption and risk of pancreatic cancer in women. Br J Cancer. 2013;109(11):2911–6. 10.1038/bjc.2013.665, PubMed PMID: 24149179; PubMed Central PMCID: PMCPMC3844914 Epub 20131022.Search in Google Scholar

[37] Goncalves MD, Hopkins BD, Cantley LC. Dietary fat and sugar in promoting cancer development and progression. Annu Rev Cancer Biol. 2019;3:255–73. 10.1146/annurev-cancerbio-030518-055855.Search in Google Scholar

[38] Hu JX, Zhao CF, Chen WB, Liu QC, Li QW, Lin YY, et al. Pancreatic cancer: a review of epidemiology, trend, and risk factors. World J Gastroenterol. 2021;27(27):4298–321. 10.3748/wjg.v27.i27.4298, PubMed PMID: 34366606; PubMed Central PMCID: PMCPMC8316912.Search in Google Scholar PubMed PubMed Central

[39] Mentella MC, Scaldaferri F, Ricci C, Gasbarrini A, Miggiano GAD. Cancer and Mediterranean diet: a review. Nutrients. 2019;11(9):2059. 10.3390/nu11092059, PubMed PMID: 31480794; PubMed Central PMCID: PMCPMC6770822 Epub 20190902.Search in Google Scholar

[40] Tayyem R, Hammad S, Allehdan S, Al-Jaberi T, Hushki A, Rayyan Y, et al. Dietary patterns associated with the risk of pancreatic cancer: case–control study findings. Medicine. 2022;101(48):e31886. 10.1097/md.0000000000031886, PubMed PMID: 36482566; PubMed Central PMCID: PMCPMC9726302.Search in Google Scholar

[41] Tayyem R, Al-Awwad N, Allehdan S, Ajeen R, Al-Jaberi T, Rayyan Y, et al. Mediterranean dietary pattern is associated with lower odds of gastric cancer: a case–control study. Cancer Manag Res. 2022;14:2017–29. 10.2147/cmar.S360468, PubMed PMID: 35747711; PubMed Central PMCID: PMCPMC9211070 Epub 20220617.Search in Google Scholar

[42] Ge S, Feng X, Shen L, Wei Z, Zhu Q, Sun J. Association between habitual dietary salt intake and risk of gastric cancer: a systematic review of observational studies. Gastroenterol Res Pract. 2012;2012:808120. 10.1155/2012/808120, PubMed PMID: 23125851; PubMed Central PMCID: PMCPMC3485508 Epub 20121022.Search in Google Scholar

[43] Wu B, Yang D, Yang S, Zhang G. Dietary salt intake and gastric cancer risk: A systematic review and meta-analysis. Front Nut. 2021;8:801228. 10.3389/fnut.2021.801228.Search in Google Scholar PubMed PubMed Central

[44] Fang X, Wei J, He X, An P, Wang H, Jiang L, et al. Landscape of dietary factors associated with risk of gastric cancer: a systematic review and dose–response meta-analysis of prospective cohort studies. Eur J Cancer. 2015;51(18):2820–32. 10.1016/j.ejca.2015.09.010, PubMed PMID: 26589974 Epub 20151114.Search in Google Scholar

[45] Wang XQ, Terry PD, Yan H. Review of salt consumption and stomach cancer risk: epidemiological and biological evidence. World J Gastroenterol. 2009;15(18):2204–13. 10.3748/wjg.15.2204, PubMed PMID: 19437559; PubMed Central PMCID: PMCPMC2682234.Search in Google Scholar PubMed PubMed Central

[46] Velmurugan B, Bhuvaneswari V, Burra UK, Nagini S. Prevention of N-methyl-N’-nitro-N-nitrosoguanidine and saturated sodium chloride-induced gastric carcinogenesis in Wistar rats by lycopene. Eur J Cancer Prev. 2002;11(1):19–26. 10.1097/00008469-200202000-00004, PubMed PMID: 11917205.Search in Google Scholar PubMed

[47] Thapa S, Fischbach LA, Delongchamp R, Faramawi MF, Orloff M. The association between salt and potential mediators of the gastric precancerous process. Cancers. 2019;11(4):535. 10.3390/cancers11040535, PubMed PMID: 30991669; PubMed Central PMCID: PMCPMC6520685 Epub 20190415.Search in Google Scholar

[48] Eom SY, Yim DH, Zhang Y, Yun JK, Moon SI, Yun HY, et al. Dietary aflatoxin B1 intake, genetic polymorphisms of CYP1A2, CYP2E1, EPHX1, GSTM1, and GSTT1, and gastric cancer risk in Korean. Cancer Causes Control. 2013;24(11):1963–72. 10.1007/s10552-013-0272-3, PubMed PMID: 23949201 Epub 20130815.Search in Google Scholar

[49] Mysuru Shivanna L, Urooj A. A review on dietary and non-dietary risk factors associated with gastrointestinal cancer. J Gastrointest Cancer. 2016;47(3):247–54. 10.1007/s12029-016-9845-1, PubMed PMID: 27270712.Search in Google Scholar PubMed

[50] Zhang FX, Miao Y, Ruan JG, Meng SP, Dong JD, Yin H, et al. Association between nitrite and nitrate intake and risk of gastric cancer: A systematic review and meta-analysis. Med Sci Monit. 2019;25:1788–99. 10.12659/MSM.914621.Search in Google Scholar PubMed PubMed Central

Received: 2024-03-20
Revised: 2024-07-09
Accepted: 2024-09-30
Published Online: 2024-11-07

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L.
  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
  6. Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties
  7. Participatory on-farm evaluation of new orange-fleshed sweetpotato varieties in Southern Ethiopia
  8. Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments
  9. Biology of Spodoptera frugiperda (Lepidoptera: Noctuidae) on different types of plants feeds: Potency as a pest on various agricultural plants
  10. Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice
  11. Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance
  12. Nicotinamide as a biostimulant improves soybean growth and yield
  13. Farmer’s willingness to accept the sustainable zoning-based organic farming development plan: A lesson from Sleman District, Indonesia
  14. Uncovering hidden determinants of millennial farmers’ intentions in running conservation agriculture: An application of the Norm Activation Model
  15. Mediating role of leadership and group capital between human capital component and sustainability of horticultural agribusiness institutions in Indonesia
  16. Biochar technology to increase cassava crop productivity: A study of sustainable agriculture on degraded land
  17. Effect of struvite on the growth of green beans on Mars and Moon regolith simulants
  18. UrbanAgriKG: A knowledge graph on urban agriculture and its embeddings
  19. Provision of loans and credit by cocoa buyers under non-price competition: Cocoa beans market in Ghana
  20. Effectiveness of micro-dosing of lime on selected chemical properties of soil in Banja District, North West, Ethiopia
  21. Effect of weather, nitrogen fertilizer, and biostimulators on the root size and yield components of Hordeum vulgare
  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
  30. Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy
  31. Forage yield, seed, and forage qualitative traits evaluation by determining the optimal forage harvesting stage in dual-purpose cultivation in safflower varieties (Carthamus tinctorius L.)
  32. The influence of tourism on the development of urban space: Comparison in Hanoi, Danang, and Ho Chi Minh City
  33. Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia
  34. The role of organic rice farm income on farmer household welfare: Evidence from Yogyakarta, Indonesia
  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
  36. Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia
  37. Factors influencing green, environmentally-friendly consumer behaviour
  38. Factors affecting coffee farmers’ access to financial institutions: The case of Bandung Regency, Indonesia
  39. Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons
  40. Sustainability analysis and decision-making strategy for swamp buffalo (Bubalus bubalis carabauesis) conservation in Jambi Province, Indonesia
  41. Understanding factors affecting rice purchasing decisions in Indonesia: Does rice brand matter?
  42. An implementation of an extended theory of planned behavior to investigate consumer behavior on hygiene sanitation-certified livestock food products
  43. Information technology adoption in Indonesia’s small-scale dairy farms
  44. Draft genome of a biological control agent against Bipolaris sorokiniana, the causal phytopathogen of spot blotch in wheat (Triticum turgidum L. subsp. durum): Bacillus inaquosorum TSO22
  45. Assessment of the recurrent mutagenesis efficacy of sesame crosses followed by isolation and evaluation of promising genetic resources for use in future breeding programs
  46. Fostering cocoa industry resilience: A collaborative approach to managing farm gate price fluctuations in West Sulawesi, Indonesia
  47. Field investigation of component failures for selected farm machinery used in small rice farming operations
  48. Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans
  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
  50. Weather index-based agricultural insurance for flower farmers: Willingness to pay, sales, and profitability perspectives
  51. Meta-analysis of dietary Bacillus spp. on serum biochemical and antioxidant status and egg quality of laying hens
  52. Biochemical characterization of trypsin from Indonesian skipjack tuna (Katsuwonus pelamis) viscera
  53. Determination of C-factor for conventional cultivation and soil conservation technique used in hop gardens
  54. Empowering farmers: Unveiling the economic impacts of contract farming on red chilli farmers’ income in Magelang District, Indonesia
  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
  56. Labor productivity of lowland rice (Oryza sativa L.) farmers in Central Java Province, Indonesia
  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
  59. Effects of dietary Adansonia digitata L. (baobab) seed meal on growth performance and carcass characteristics of broiler chickens: A systematic review and meta-analysis
  60. Analysis and structural characterization of the vid-pisco market
  61. Pseudomonas fluorescens SP007s enhances defense responses against the soybean bacterial pustule caused by Xanthomonas axonopodis pv. glycines
  62. A brief investigation on the prospective of co-composted biochar as a fertilizer for Zucchini plants cultivated in arid sandy soil
  63. Supply chain efficiency of red chilies in the production center of Sleman Indonesia based on performance measurement system
  64. Investment development path for developed economies: Is agriculture different?
  65. Power relations among actors in laying hen business in Indonesia: A MACTOR analysis
  66. High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought
  67. Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions
  68. Structuration, risk management, and institutional dynamics in resolving palm oil conflicts
  69. Spacing strategies for enhancing drought resilience and yield in maize agriculture
  70. Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types
  71. Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation
  72. Yield stability of biofertilizer treatments to soybean in the rainy season based on the GGE biplot
  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
  85. Physical and textural properties and sensory acceptability of wheat bread partially incorporated with unripe non-commercial banana cultivars
  86. Cereibacter sphaeroides ST16 and ST26 were used to solubilize insoluble P forms to improve P uptake, growth, and yield of rice in acidic and extreme saline soil
  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
Downloaded on 23.3.2026 from https://www.degruyterbrill.com/document/doi/10.1515/opag-2022-0372/html
Scroll to top button