The Association Between Index of Nutritional Quality (INQ) and Gastric Cancer and Evaluation of Nutrient Intakes of Gastric Cancer Patients: A Case-Control Study

AUTHORS

Farhad Vahid 1 , Golaleh Rahmani 2 , Alireza Jafari Naeini 3 , Habib Falahnejad 2 , Sayed Hossein Davoodi 1 , 4 , *

1 Department of Nutrition Research, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Nutrition Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

3 Faculty of Veterinary Medicine, Shahid Chamran University, Ahvaz, Iran

4 Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

How to Cite: Vahid F, Rahmani G, Jafari Naeini A, Falahnejad H, Davoodi S H. The Association Between Index of Nutritional Quality (INQ) and Gastric Cancer and Evaluation of Nutrient Intakes of Gastric Cancer Patients: A Case-Control Study, Int J Cancer Manag. 2018 ; 11(1):e9747. doi: 10.5812/ijcm.9747.

ARTICLE INFORMATION

International Journal of Cancer Management: 11 (1); e9747
Published Online: January 10, 2018
Article Type: Research Article
Received: November 20, 2016
Revised: January 14, 2017
Accepted: December 31, 2017
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Abstract

Background: Gastric cancer (GC) is the fourth major malignancy and the second leading cause of cancer-related deaths worldwide. Northern and northwestern areas of Iran are among the high risk areas for GC. Studies have shown that dietary components are implicated in the etiology of GC. The index of nutritional quality (INQ) is a method of quantitative and qualitative analysis of single foods, meals, and diets. We aimed to assess the association of INQ with GC, and to evaluate the nutrient intake of GC patients.

Methods: The present case-control study included 82 cases and 95 healthy controls attending specialized centers in Tabriz, Iran, from December 2014 to May 2016. INQ scores were computed based on dietary intake assessed using a validated 168-item food frequency questionnaire (FFQ). Logistic regression models were used to estimate multivariable ORs adjusted age, gender, Body Mass Index (BMI), smoking, residency, education, and regular physical activity.

Results: After controlling for several covariates, inverse associations were observed between GC risk and INQs of vitamins A, B6, and D (ORvitA = 0.25 (0.06 - 0.98); ORvitB6 = 0.10 (0.04 - 0.28); and ORvitD = 0.14 (0.02 - 0.84)). Cases had higher intake of total fat, saturated fatty acids, beef, lamb meat, salt, and paprika compared to controls. On the other hand, controls had higher intake of vitamin A, vitamin, vitamin B6, copper, poultry, low fat milk, tea, coffee, turmeric, and saffron compared to cases.

Conclusions: Subjects who follow a more healthy and nutrient-rich diet, especially in terms of vitamins A, B6, and D, are at lower risk of having GC, compared to those who consume a more unhealthy, nutrient-poor diet.

Keywords

Gastric Cancer Index of Nutritional Quality (INQ) Nutritional Assessment Vitamin A Vitamin B6 Vitamin D

Copyright © 2018, Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited

1. Background

Gastric cancer (GC) is the fourth major malignancy and the second leading cause of cancer-related deaths worldwide (1, 2). According to estimates, each year more than 930 thousand new GC cases are being diagnosed, of which at least 700 thousands lose their lives due to this debilitating disease (3). In the Iranian population, GC is the most common cancer in men and the third most common cancer in women (4). Northern and northwestern areas of Iran are among the high risk areas for GC. Ardabil province in the North West has the highest incidence of GC with age standardized rate (ASR) = 49.1 for men and 25.4 for women (5, 6). East Azarbaijan, Golestan, and Semnan provinces are among the areas with high rates of GC (7). GC is one of the most common malignancies in the world with a multi-factorial etiology including infection with H. pylori, smoking, alcohol consumption, unhealthy eating habits, and genetic predisposition (8, 9). On the other hand, there is broad consensus that the vast majority of cancers are preventable (10, 11).

Uneven geographical distribution of GC (12, 13) and the effect of immigration on this disease process (3, 14) represent a significant effect of environmental factors, especially nutritional factors such as quality of diet, in the development of this cancer (15, 16). In addition, geographic and ethnic differences in the incidence of GC and changes in the observed patterns of immigrants show that GC is closely associated with modifiable risk factors like diet (17).

Recently, there has been a growing interest to assess the nutritional quality of the diet and its relationship with chronic diseases (18). Since in developing countries such as Iran, energy intake is the most important indicator of food security, dietary quality assessment procedures should be simple and practical (19). One of these simple methods is the index of nutritional quality or INQ that has important applications in clinical assessment of nutritional problems or situations (20, 21). The INQ is a method of quantitative and qualitative analysis of single foods, meals, and diets. It is a ratio of the nutrient-to-calorie content of foods which may be displayed as bar graphs and tabular data (17).

In the current study, we examined the relationship between INQ scores and the risk of GC. We also evaluated nutrient intakes of GC patients in an Iranian case-control study. Our hypothesis is that a poor diet and unbalanced dietary intakes increase the risk of GC incidence.

2. Methods

2.1. Participants

This hospital based case-control study was conducted at specialized centers in Northwest of Iran from December 2014 to May 2016. The study included 82 patients with GC and 95 healthy controls. The cases were patients with GC who were diagnosed by a gastroenterologist within the previous month. These patients were selected with the random sampling procedure. Controls were randomly selected from other patients’ caregivers attending the same clinics. Controls were frequency matched by age (± 5 year) and sex. Data on cases and controls were collected at the same time and setting. After providing written and verbal explanations about the methodology of the study, informed consent was received from each participant. The study protocol was approved by the local ethics review committee at Shahid Beheshti University of Medical Sciences, Tehran, Iran.

2.2. Inclusion and Exclusion Criteria

Inclusion criteria included the following: a, the absence of any malignancy (except for GC in cases); b, not following special diets such as vegetarian, or the diets resulting in weight changes during the year prior to the interview; c, the absence of conditions such as pregnancy, lactation, or a history of neurological, gastrointestinal, hepatic, endocrine, immunological, renal, or cardiovascular disorders and diseases; d, the age range of 20 - 80 years; and e, willingness to cooperate in the study.

Exclusion criteria included the following: a, not sticking to the study protocol; b, major dietary changes during the study; c, reported energy intake of outside the range of 800 - 5500 kcal.

2.3. Assessment of Dietary Intake

In this study, past year dietary intakes of the subjects were evaluated by a semi-quantitative, valid and reliable food frequency questionnaire (FFQ) (22). This FFQ asks about the average consumption frequency of 168 food items. Participants were asked to report the frequency of consumption of each food item in the last year according to the standard serving size in the questionnaire. Depending on the type of food, subjects indicated their intake of the food items per day, week, month or year, or as never. Then, the information obtained from the questionnaires was analyzed using Nutritionist ІV software (First Databank, Hearst Corp., SanBruno, CA, USA) to calculate the average daily intake of energy and nutrients. The INQ was calculated according to the daily intake of food items.

2.4. Assessment of INQ

The INQ is a method of quantitative and qualitative analysis of single foods, meals, and diets which has special significance in assessing clinical nutritional problems. The INQ is a ratio of the nutrient-to-calorie content of foods. The number of nutrients and the nutrient standards used for analysis are flexible parameters which may be varied for each clinical situation. Illustrative examples include INQ analysis of simple foods, an institutional house diet, the diabetic exchange list, and the diagnostic evaluation of the dietary intake of a hospitalized patient (17).

We calculated the INQ of each nutrient, for which there was a defined recommended dietary allowance (RDA) or adequate intake (AI) in dietary reference intake (DRI) tables, using the following formulae: INQ = consumed amount of a nutrient per 1,000 kcal/RDA or AI of that nutrient per 1,000 kcal (17).

FFQ-derived dietary data were used to calculate INQ scores for all participants. Major food items that were used in the calculation of INQ were as follows: protein, sodium, potassium, vitamin A, vitamin C, iron, vitamin D, vitamin E, thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, biotin, pantothenic acid, vitamin K, magnesium, zinc, manganese, selenium, and fiber.

2.5. Assessment of Other Variables

For all participants the required information about age (year), gender (male, female), education (≤ high school diploma, > high school diploma), smoking (yes, no), H. pylori infection (positive, negative), residency (urban, rural), regular physical activity (yes, no), family history of cancer (yes, no), and alcohol consumption were collected through general information questionnaire during the interviews.

The weight of each participant was measured with light clothing using a SECA digital scale with a100-gram accuracy. The height was measured without shoes in standing position, leaning against the wall and shoulder blades under normal circumstances with an accuracy of 0.5 cm by a tape measure mounted on the wall. Body Mass Index (BMI) was calculated by dividing weight (in kilograms) by the square of height (in square meters).

During several training sessions, the main investigators trained a nutritionist, who was not aware of the study objectives, about how to complete the general information questionnaire and FFQ, and to do the anthropometric measurements.

2.6. Statistical Analyses

In this study we used IBM SPSS software (version 21) for statistical analysis of the data. Chi-square or Fisher’s exact test was used for comparison of categorical variables between groups. In the case of quantitative variables before choosing a statistical test was investigated normality of their distribution using the Kolmogorov-Smirnov test. Then, the independent samples T-test or Mann-Whitney U tests were used for comparison of continuous variables with normal and non-normal distribution between groups, respectively. Crude and multivariable adjusted logistic regression models were used to estimate ORs and 95% CIs of having GC in relation to each nutrient’s INQ. Adjustments were done for age, BMI, gender, education, smoking, residency, and regular physical activity in the adjusted models.

3. Results

Table 1 shows the distribution of socio-demographic, anthropometric, and life-style related characteristics across cases and controls. Cases had higher BMI and H. pylori infection compared to controls. The average BMI was 26.3 in the cases and 24.9 in the controls (P = 0.02). In addition, 74.4% of cases and 51.6% of controls had H. pylori infection (P < 0.01). On the other hand, controls were more active compared to cases. Table 2 shows the distribution of daily dietary intakes across cases and controls. According to Table 3 cases had higher intake of total fat (119.7 ± 42.9 vs. 106.6 ± 32.6), SFA (53.1 ± 38.2 vs. 40.1 ± 26.7), beef (17.7 ± 25.4 vs. 10.3 ± 13.7), lamb meat (17.7 ± 19.0 vs. 12.2 ± 16.0), sunflower seeds (7.1 ± 8.9 vs. 4.1 ± 8.2), salt (2.5 ± 0.8 vs. 1.8 ± 0.6), and paprika (3.2 ± 3.6 vs. 1.1 ± 2.0) compared to controls. On the other hand, controls had higher intake of vitamin A (696.4 ± 377.2 vs. 585.5 ± 203.3), vitamin D (2.4 ± 1.6 vs. 1.9 ± 1.5), vitamin B6 (2.7 ± 1.0 vs. 2.0 ± 0.5), copper (2.8 ± 1.1 vs. 2.4 ± 1.3), poultry (39.4 ± 28.7 vs. 27.4 ± 24.8), low fat milk (76.6 ± 92.2 vs. 52.5 ± 69.8), tea (740.9 ± 662.4 vs. 495.9 ± 499.3), coffee (8.6 ± 19.7 vs. 4.5 ± 6.5), turmeric (1.0 ± 1.0 vs. 0.5 ± 0.8) and saffron (0.4 ± 1.0 vs. 0.2 ± 0.5) compared to cases. There was no significant difference between groups in terms of energy, protein, carbohydrate, MUFA, PUFA, vitamin C, vitamin E, thiamin, riboflavin, folate, vitamin B12, magnesium, zinc, selenium, sugar, and spice intake. Table 3 shows comparison of the INQ of the subjects. Table 3 shows that only the INQ of vitamin A (0.52 ± 0.2 vs. 0.45 ± 0.1) and vitamin B6 (1.4 ± 0.05 vs. 1.0 ± 0.3) are higher in controls compared to cases. To avoid presenting so many statistically insignificant results, only the ORs and 95% CIs for GC risk in relation to INQ of vitamins A, B6, and D are presented in Table 4. After controlling for several covariates, inverse associations were observed between GC risk and INQs of vitamins A, B6, and D (ORvitA = 0.25 (0.06 - 0.98); ORvitB6 = 0.10 (0.04 - 0.28); and ORvitD = 0.14 (0.02 - 0.84)).

Table 1. Distribution of Socio-Demographic, Anthropometric, and Life-Style Related Characteristics Across Cases and Controlsa,b
CharacteristicsCases (N = 82)Controls (N = 95)P Value
Age, y51.3 ± 11.848.3 ± 10.70.07
Body mass index (BMI)26.3 ± 5.124.9 ± 2.70.02
Gender0.98
Females52 (54.74)45 (54.88)
Males43 (45.26)37 (45.12)
Education0.24
≤ High school diploma51 (62.2)67 (70.5)
> High school diploma31 (37.8)28 (29.5)
Smoking0.81
Yes14 (17.1)15 (15.8)
No68 (82.9)80 (84.2)
H. pylori0.00
Positive61 (74.4)49 (51.6)
Negative21 (25.6)46 (48.4)
Residency0.28
Urban60 (73.2)76 (80.0)
Rural22 (26.8)19 (20.0)
Physical activity0.02
Yes14 (17.1)30 (31.6)
No68 (82.9)65 (68.4)
Family history of cancer0.40
Yes13 (15.9)11 (11.6)
No69 (84.1)84 (88.4)
Alcohol consumption0.40
Yes11 (13.4)9 (9.5)
No71 (86.6)86 (90.5)

aIndependent samples T-test or Mann-Whitney U tests and Chi-square or Fisher’s Exact tests were used for comparison of continuous and categorical variables between groups, respectively.

bValues are expressed as mean ± SD or No. (%).

Table 2. Distribution of Daily Dietary Intakes Across Cases and Controlsa,b
VariablesCases (N = 82)Controls (N = 95)P Value
Energy, Kcal3012.9 ± 625.52991.2 ± 549.00.80
Protein, gr101.1 ± 39.2109.4 ± 39.80.16
Carbohydrate, gr308.4 ± 114.1373.5 ± 118.10.69
Total Fat, gr119.7 ± 42.9106.6 ± 32.60.02
Saturated fatty acid, gr53.1 ± 38.240.1 ± 26.7< 0.01
Mono-unsaturated fatty acid, gr29.3 ± 11.330.2 ± 9.60.60
Poly-unsaturated fatty acid, gr28.1 ± 16.631.4 ± 20.50.24
Vitamin A, mcg585.5 ± 203.3696.4 ± 377.20.01
Vitamin C, mg154.4 ± 75.9160.3 ± 55.80.55
Vitamin D, mcg1.9 ± 1.52.4 ± 1.60.02
Vitamin E, mg19.2 ± 9.118.6 ± 6.70.62
Thiamin, mg2.0 ± 0.72.2 ± 0.90.19
Riboflavin, mg2.1 ± 0.62.2 ± 0.80.38
Vitamin B6, mg2.0 ± 0.52.7 ± 1.00.00
Folate, mcg663.5 ± 257.6709 ± 216.20.19
Vitamin B12, mcg5.7 ± 3.95.2 ± 2.50.32
Magnesium, mg507.8 ± 155.4541.7 ± 147.80.14
Zinc, mg15.1 ± 4.415.1 ± 5.70.94
Copper, mcg2.4 ± 1.32.8 ± 1.10.05
Selenium, mcg121.6 ± 48.2128.6 ± 42.00.30
Sugar, gr138.7 ± 50.7127.5 ± 38.00.09
Beef, gr17.7 ± 25.410.3 ± 13.70.01
Lamb Meat, gr17.7 ± 19.012.2 ± 16.00.03
Poultry, gr27.4 ± 24.839.4 ± 28.70.00
Low Fat Milk, gr52.5 ± 69.876.6 ± 92.20.05
Sunflower seeds, gr7.1 ± 8.94.1 ± 8.20.02
Tea, mg495.9 ± 499.3740.9 ± 662.40.00
Coffee, mg4.5 ± 6.58.6 ± 19.70.05
Salt, gr2.5 ± 0.81.8 ± 0.60.00
Paprika, mg3.2 ± 3.61.1 ± 2.00.00
Turmeric, mg0.5 ± 0.81.0 ± 1.00.00
Spice, mg0.6 ± 0.90.9 ± 1.00.09
Saffron, mg0.2 ± 0.50.4 ± 1.00.04

aIndependent samples T-test and Mann-Whitney U tests were used for comparison of continuous variables with normal and non-normal distributions between groups, respectively.

bValues are expressed as mean ± SD.

Table 3. Comparison of the Index of Nutritional Quality (INQ) of the Subjectsa,b
VariablesCases (N = 82)Controls (N = 95)P Value
Protein, gr1.2 ± 0.41.3 ± 0.40.11
Sodiumc89.388.70.93
Potassium0.58 ± 0.20.54 ± 0.20.18
Vitamin A0.45 ± 0.10.52 ± 0.20.03
Vitamin C1.2 ± 0.61.2 ± 0.40.87
Iron1.6 ± 0.61.6 ± .060.60
Vitamin D0.2 ± 0.10.2 ± 0.10.06
Vitamin E0.8 ± 0.40.8 ± 0.30.67
Thiamin1.2 ± 0.51.3 ± 0.60.43
Riboflavin1.1 ± 0.31.1 ± 0.50.37
Niacin1.2 ± 0.51.2 ± 0.50.72
VitaminB61.0 ± 0.31.4 ± .05< 0.01
Folate1.1 ± 0.51.2 ± 0.40.23
VitaminB121.6 ± 1.11.5 ± 0.80.27
Biotinc85.192.30.35
Pantothenic acid1.0 ± 0.41.0 ± 0.40.77
Vitamin K1.5 ± 0.81.7 ± 0.80.18
Magnesium0.8 ± 0.20.9 ± 0.20.15
Zinc1.0 ± 0.31.0 ± 0.40.90
Manganese2.7 ± 1.32.6 ± 1.00.51
Selenium1.5 ± 0.61.6 ± 0.50.26
Fiberc85.192.30.35

aANOVA was used for continuous variables and Chi-square was used for categorical variables.

bValues are expressed as mean ± SD.

cMann-Whitney U test used for the quantitative variables with non-normal distribution.

Table 4. Odds Ratios (OR) and 95% Confidence Intervals for Gastric Cancer Risk in Relation to Index of Nutritional Quality (INQ) of Vitamins A, B6, and D
INQORsLower BoundUpper BoundP Value
Vitamin Aa0.170.021.000.05
Vitamin B6a0.060.020.220.00
Vitamin Da0.210.041.100.06
Vitamin Ab0.250.060.980.04
Vitamin B6b0.100.040.280.00
Vitamin Db0.140.020.840.03

acrud model.

bAdjusted model. Adjustments were done for age, body mass index, gender, education, smoking, residency, and regular physical activity.

4. Discussion

The present study is the first one to investigate the relationship between INQs and GC risk in Iran. In this study, we observed inverse associations between GC risk and INQs of vitamins A, B6, and these results supported our hypothesis that following a healthier and nutrient-rich diet is associated with a reduced risk of GC. Also, in this study we observed that GC patients’ intake of total fat, SFA, beef, lamb meat and salt were significantly higher compared to controls. In line with our study, several studies (17), including meta-analysis studies (23, 24), have shown that high intake of total fat and SFA are associated with increased risk of GC. However, it should be noted that some studies (25) have not observed a significant association between total fat and SFA intake and GC risk. Furthermore, consistent with our findings, several studies (26) have observed significant positive association between the consumption of beef, lamb meat, and salt and GC.

In the present study, it was observed that the controls had higher intakes of vitamin A, vitamin D, vitamin B6, poultry, low fat milk, turmeric, and saffron compared to cases. Similar previous studies have shown that there is inverse association between GC risk and intakes of vitamin A (24, 27, 28) D (27, 29, 30) and B6 (31, 32) a finding which is in line with our results. Moreover, studies investigating the association of GC risk and intakes of turmeric (curcumin) (33, 34) and saffron (35, 36) have reported similar inverse relationships. However, regarding the relationship between GC risk and intakes of white meat (37, 38) and low-fat milk (28, 39) the finding of previous studies are conflicting, which could be due to a host of different reasons such as difference in methodology, and residual confounding.

We observed fewer differences in dietary intakes between groups when using INQs instead of absolute intakes. This indicates that the application of standard tools and indexes such as INQ might result in more precise and functional comparisons when assessing the association of dietary exposures with different health outcomes, compared to the traditional evaluation of absolute dietary intakes.

In a similar study by Lim et al. in Korea (17), as in our study, a higher INQ of vitamin A was observed in GC patients compared to the controls. In contrast, the opposite was observed in case of vitamin B6 (17). Despite these differences, our findings regarding the inverse association of GC risk and INQs of vitamins A, B6, and D is generally supported by those obtained from previous studies (17) in which a protective role for each of these vitamins has been postulated against GC.

The inverse association between INQs of some nutrients and GC risk in this study is very encouraging. Although the exact mechanisms of the potential protective effects of vitamins A, B6, and D against GC have not yet been clarified, a few mechanisms have been proposed.

One of the proposed mechanisms is the crucial role of vitamin A and D in combating the chronic inflammation, an important contributor in developing GC, via their effects in inhibition of inflammatory markers’ gene expression (17, 29). Another proposed mechanism involves the roles of these vitamins in decreasing systemic inflammation, and subsequently the GC incidence, by reducing insulin resistance (40-43). In case of Vitamin B6, as this vitamin has a crucial role in amino acid and amines metabolism, it is logical to assume an essential part for this vitamin in reducing the chronic inflammation. In fact, some studies have shown a direct association between vitamin B6 deficiency and increased levels of inflammatory markers, such as C-reactive protein, interleukin 6, and tumor necrosis factor alpha, all of which have been postulated to play a role in gastric carcinogenesis (43-46). However, future comprehensive studies are necessary to investigate the exact mechanisms of protective effects of vitamin A, B6, and D against GC.

An important strength of this study is the fact that it is the first one in Iran to examine the association of INQ and GC. Since the INQ is based on standards and adjusts energy intake, it assesses the nutritional status of subjects more accurately than the usual and routine evaluation procedures. Another important strength is the use of a validated and reproducible FFQ (22), which allowed for a comprehensive assessment of major nutrient sources in diet, although some measurement errors inherent in the FFQ may be present. Also, controls were selected carefully by ensuring that none of them had any condition related to diet or other major risk factors associated with GC. However, there are a few limitations inherent in our study which needs to be considered. As with other case-control studies the probability of recall and selection biases could not be entirely ruled out.

However, administering validated FFQs by trained interviewers in a hospital setting might have, to some extent, reduced the recall bias and improved comparability of information of cases and controls. Another limitation of the study is related to the use of INQ. Since INQ is calculated based on the DRI, it cannot be calculated for nutrients or food items for which there is no defined DRI. Therefore, it is possible that the potential effects of these nutrients or food items on GC have been ignored in the present study. However, it should be noted that we did our best to compensate for this limitation by comparing the intakes of these nutrients or food items between cases and controls by using conventional methods.

In conclusion, findings of the present study suggest that subjects who follow a more healthy and nutrient-rich diet, especially in terms of vitamins A, B6, and D, are at lower risk of having GC, compared to those who consume a more unhealthy, nutrient-poor diet. Thus, encouraging higher intake of these nutrients and recommendations regarding following a more nutrient-rich diet could be a potentially effective strategy in prevention of GC. However, future studies of high methodological quality are warranted to gain a clear insight into the relationship between diet and GC, and to further deepen our understanding about the role of dietary components in gastric carcinogenesis.

Acknowledgements

Footnotes

References

  • 1.

    Crew KD, Neugut AI. Epidemiology of gastric cancer. World J Gastroenterol. 2006;12(3):354-62. doi: 10.3748/wjg.v12.i3.354. [PubMed: 16489633].

  • 2.

    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. doi: 10.3322/caac.21262. [PubMed: 25651787].

  • 3.

    Dendup T, Richter JM, Yamaoka Y, Wangchuk K, Malaty HM. Geographical distribution of the incidence of gastric cancer in Bhutan. World J Gastroenterol. 2015;21(38):10883-9. doi: 10.3748/wjg.v21.i38.10883. [PubMed: 26478679].

  • 4.

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

  • 5.

    Malekzadeh R, Derakhshan MH, Malekzadeh Z. Gastric cancer in Iran: epidemiology and risk factors. Arch Iran Med. 2009;12(6):576-83. [PubMed: 19877751].

  • 6.

    Mousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z. Cancer incidence and mortality in Iran. Ann Oncol. 2009;20(3):556-63. doi: 10.1093/annonc/mdn642. [PubMed: 19073863].

  • 7.

    Sadjadi A, Malekzadeh R, Derakhshan MH, Sepehr A, Nouraie M, Sotoudeh M, et al. Cancer occurrence in Ardabil: results of a population-based cancer registry from Iran. Int J Cancer. 2003;107(1):113-8. doi: 10.1002/ijc.11359. [PubMed: 12925965].

  • 8.

    Babaei M, Mousavi S, Malek M, Tosi G, Masoumeh Z, Danaei N, et al. Cancer occurrence in Semnan Province, Iran: results of a population-based cancer registry. Asian Pac J Cancer Prev. 2005;6(2):159-64. [PubMed: 16101326].

  • 9.

    Somi MH, Farhang S, Mirinezhad SK, Naghashi S, Seif-Farshad M, Golzari M. Cancer in East Azerbaijan, Iran: results of a population-based cancer registry. Asian Pac J Cancer Prev. 2008;9(2):327-30. [PubMed: 18712985].

  • 10.

    Fock KM, Talley N, Moayyedi P, Hunt R, Azuma T, Sugano K, et al. Asia-Pacific consensus guidelines on gastric cancer prevention. J Gastroenterol Hepatol. 2008;23(3):351-65. doi: 10.1111/j.1440-1746.2008.05314.x. [PubMed: 18318820].

  • 11.

    Fei SJ, Xiao SD. Diet and gastric cancer: a case-control study in Shanghai urban districts. Chin J Dig Dis. 2006;7(2):83-8. doi: 10.1111/j.1443-9573.2006.00252.x. [PubMed: 16643335].

  • 12.

    Vahid F, Zand H, Nosrat-Mirshekarlou E, Najafi R, Hekmatdoost A. The role dietary of bioactive compounds on the regulation of histone acetylases and deacetylases: a review. Gene. 2015;562(1):8-15. doi: 10.1016/j.gene.2015.02.045. [PubMed: 25701602].

  • 13.

    Amin AR, Kucuk O, Khuri FR, Shin DM. Perspectives for cancer prevention with natural compounds. J Clin Oncol. 2009;27(16):2712-25. doi: 10.1200/JCO.2008.20.6235. [PubMed: 19414669].

  • 14.

    Yoneoka D, Saito E, Nakaoka S. New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening. Sci Rep. 2016;6:26582. doi: 10.1038/srep26582. [PubMed: 27215347].

  • 15.

    Mousavi SM, Sundquist J, Hemminki K. Does immigration play a role in the risk of gastric cancer by site and by histological type? A study of first-generation immigrants in Sweden. Gastric Cancer. 2011;14(3):285-9. doi: 10.1007/s10120-011-0033-5. [PubMed: 21431296].

  • 16.

    Nguyen DK, Maggard-Gibbons M. Age, poverty, acculturation, and gastric cancer. Surgery. 2013;154(3):444-52. doi: 10.1016/j.surg.2013.05.017. [PubMed: 23972650].

  • 17.

    Lim H, Cho G, Kim S. Evaluation of nutrient intake and diet quality of gastric cancer patients in Korea. Nutr Res Pract. 2012;6(3):213-20. doi: 10.4162/nrp.2012.6.3.213. [PubMed: 22808345].

  • 18.

    Zhong C, Li KN, Bi JW, Wang BC. Sodium intake, salt taste and gastric cancer risk according to Helicobacter pylori infection, smoking, histological type and tumor site in China. Asian Pac J Cancer Prev. 2012;13(6):2481-4. [PubMed: 22938408].

  • 19.

    Coulston AM. The search continues for a tool to evaluate dietary quality. Am J Clin Nutr. 2001;74(4):417. [PubMed: 11566637].

  • 20.

    Torheim LE, Barikmo I, Parr CL, Hatloy A, Ouattara F, Oshaug A. Validation of food variety as an indicator of diet quality assessed with a food frequency questionnaire for Western Mali. Eur J Clin Nutr. 2003;57(10):1283-91. doi: 10.1038/sj.ejcn.1601686. [PubMed: 14506490].

  • 21.

    Esposito K, Marfella R, Ciotola M, Di Palo C, Giugliano F, Giugliano G, et al. Effect of a mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome: a randomized trial. JAMA. 2004;292(12):1440-6. doi: 10.1001/jama.292.12.1440. [PubMed: 15383514].

  • 22.

    Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654-62. doi: 10.1017/S1368980009991698. [PubMed: 19807937].

  • 23.

    Hu J, La Vecchia C, Negri E, de Groh M, Morrison H, Mery L, et al. Macronutrient intake and stomach cancer. Cancer Causes Control. 2015;26(6):839-47. doi: 10.1007/s10552-015-0557-9. [PubMed: 25791128].

  • 24.

    Pakseresht M, Forman D, Malekzadeh R, Yazdanbod A, West RM, Greenwood DC, et al. Dietary habits and gastric cancer risk in north-west Iran. Cancer Causes Control. 2011;22(5):725-36. doi: 10.1007/s10552-011-9744-5. [PubMed: 21347819].

  • 25.

    Han J, Jiang Y, Liu X, Meng Q, Xi Q, Zhuang Q, et al. Dietary Fat Intake and Risk of Gastric Cancer: A Meta-Analysis of Observational Studies. PLoS One. 2015;10(9). e0138580. doi: 10.1371/journal.pone.0138580. [PubMed: 26402223].

  • 26.

    O'Doherty MG, Freedman ND, Hollenbeck AR, Schatzkin A, Murray LJ, Cantwell MM, et al. Association of dietary fat intakes with risk of esophageal and gastric cancer in the NIH-AARP diet and health study. Int J Cancer. 2012;131(6):1376-87. doi: 10.1002/ijc.27366. [PubMed: 22116732].

  • 27.

    Qiu JL, Chen K, Zheng JN, Wang JY, Zhang LJ, Sui LM. Nutritional factors and gastric cancer in Zhoushan Islands, China. World J Gastroenterol. 2005;11(28):4311-6. [PubMed: 16038026].

  • 28.

    Hu J, La Vecchia C, DesMeules M, Negri E, Mery L, Canadian Cancer Registries Epidemiology Research G. Meat and fish consumption and cancer in Canada. Nutr Cancer. 2008;60(3):313-24. doi: 10.1080/01635580701759724. [PubMed: 18444165].

  • 29.

    Kong P, Cai Q, Geng Q, Wang J, Lan Y, Zhan Y, et al. Vitamin intake reduce the risk of gastric cancer: meta-analysis and systematic review of randomized and observational studies. PLoS One. 2014;9(12). e116060. doi: 10.1371/journal.pone.0116060. [PubMed: 25549091].

  • 30.

    Wu Y, Ye Y, Shi Y, Li P, Xu J, Chen K, et al. Association between vitamin A, retinol intake and blood retinol level and gastric cancer risk: A meta-analysis. Clin Nutr. 2015;34(4):620-6. doi: 10.1016/j.clnu.2014.06.007. [PubMed: 25008141].

  • 31.

    Vyas N, Companioni RC, Tiba M, Alkhawam H, Catalano C, Sogomonian R, et al. Association between serum vitamin D levels and gastric cancer: A retrospective chart analysis. World J Gastrointest Oncol. 2016;8(9):688-94. doi: 10.4251/wjgo.v8.i9.688. [PubMed: 27672427].

  • 32.

    Ren C, Qiu MZ, Wang DS, Luo HY, Zhang DS, Wang ZQ, et al. Prognostic effects of 25-hydroxyvitamin D levels in gastric cancer. J Transl Med. 2012;10:16. doi: 10.1186/1479-5876-10-16. [PubMed: 22284859].

  • 33.

    Mayne ST, Risch HA, Dubrow R, Chow WH, Gammon MD, Vaughan TL, et al. Nutrient intake and risk of subtypes of esophageal and gastric cancer. Cancer Epidemiol Biomarkers Prev. 2001;10(10):1055-62. [PubMed: 11588131].

  • 34.

    Kaaks R, Tuyns AJ, Haelterman M, Riboli E. Nutrient intake patterns and gastric cancer risk: a case-control study in Belgium. Int J Cancer. 1998;78(4):415-20. doi: 10.1002/(SICI)1097-0215(19981109)78:4<415::AID-IJC4>3.0.CO;2-X. [PubMed: 9797127].

  • 35.

    Sarkar A, De R, Mukhopadhyay AK. Curcumin as a potential therapeutic candidate for Helicobacter pylori associated diseases. World J Gastroenterol. 2016;22(9):2736-48. doi: 10.3748/wjg.v22.i9.2736. [PubMed: 26973412].

  • 36.

    Cai XZ, Huang WY, Qiao Y, Du SY, Chen Y, Chen D, et al. Inhibitory effects of curcumin on gastric cancer cells: a proteomic study of molecular targets. Phytomedicine. 2013;20(6):495-505. doi: 10.1016/j.phymed.2012.12.007. [PubMed: 23351961].

  • 37.

    Bathaie SZ, Hoshyar R, Miri H, Sadeghizadeh M. Anticancer effects of crocetin in both human adenocarcinoma gastric cancer cells and rat model of gastric cancer. Biochem Cell Biol. 2013;91(6):397-403. doi: 10.1139/bcb-2013-0014. [PubMed: 24219281].

  • 38.

    Bolhassani A, Khavari A, Bathaie SZ. Saffron and natural carotenoids: Biochemical activities and anti-tumor effects. Biochim Biophys Acta. 2014;1845(1):20-30. doi: 10.1016/j.bbcan.2013.11.001. [PubMed: 24269582].

  • 39.

    Lippi G, Mattiuzzi C, Cervellin G. Meat consumption and cancer risk: a critical review of published meta-analyses. Crit Rev Oncol Hematol. 2016;97:1-14. doi: 10.1016/j.critrevonc.2015.11.008. [PubMed: 26633248].

  • 40.

    Bao A, Li Y, Tong Y, Zheng H, Wu W, Wei C. 1,25-Dihydroxyvitamin D(3) and cisplatin synergistically induce apoptosis and cell cycle arrest in gastric cancer cells. Int J Mol Med. 2014;33(5):1177-84. doi: 10.3892/ijmm.2014.1664. [PubMed: 24573222].

  • 41.

    Cao Y, Wittert G, Taylor AW, Adams R, Appleton S, Shi Z. Nutrient patterns and chronic inflammation in a cohort of community dwelling middle-aged men. Clin Nutr. 2017;36(4):1040-7. doi: 10.1016/j.clnu.2016.06.018. [PubMed: 27395328].

  • 42.

    Okayasu I, Hana K, Nemoto N, Yoshida T, Saegusa M, Yokota-Nakatsuma A, et al. Vitamin A Inhibits Development of Dextran Sulfate Sodium-Induced Colitis and Colon Cancer in a Mouse Model. Biomed Res Int. 2016;2016:4874809. doi: 10.1155/2016/4874809. [PubMed: 27298823].

  • 43.

    Vahid F, Shivappa N, Karamati M, Naeini AJ, Hebert JR, Davoodi SH. Association between Dietary Inflammatory Index (DII) and risk of prediabetes: a case-control study. Appl Physiol Nutr Metab. 2017;42(4):399-404. doi: 10.1139/apnm-2016-0395. [PubMed: 28177734].

  • 44.

    Noy N. Vitamin A in regulation of insulin responsiveness: mini review. Proc Nutr Soc. 2016;75(2):212-5. doi: 10.1017/S0029665115004322. [PubMed: 26729422].

  • 45.

    Mohammadi SM, Eghbali SA, Soheilikhah S, Ashkezari SJ, Salami M, Afkhami-Ardekani M, et al. The effects of vitamin D supplementation on adiponectin level and insulin resistance in first-degree relatives of subjects with type 2 diabetes: a randomized double-blinded controlled trial. Electron Physician. 2016;8(9):2849-54. doi: 10.19082/2849. [PubMed: 27790335].

  • 46.

    Stettin D, Waldmann A, Strohle A, Hahn A. Association between Helicobacter pylori-infection, C-reactive protein and status of B vitamins. Adv Med Sci. 2008;53(2):205-13. doi: 10.2478/v10039-008-0050-8. [PubMed: 19230307].

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