J Cancer 2020; 11(5):1231-1239. doi:10.7150/jca.39023

Research Paper

Preventable lifestyle and eating habits associated with gastric adenocarcinoma: A case-control study

Lei Huang1 Corresponding address*, Lei Chen2*, Zhong-Xuan Gui2*, Shun Liu2*, Zhi-Jian Wei1 Corresponding address, A-Man Xu1 Corresponding address

1. Department of General Surgery, the First Affiliated Hospital of Anhui Medical University;
2. Second Clinical Medicine College of Anhui Medical University.
*Lei Huang, Lei Chen, Zhong-Xuan Gui, and Shun Liu contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Citation:
Huang L, Chen L, Gui ZX, Liu S, Wei ZJ, Xu AM. Preventable lifestyle and eating habits associated with gastric adenocarcinoma: A case-control study. J Cancer 2020; 11(5):1231-1239. doi:10.7150/jca.39023. Available from http://www.jcancer.org/v11p1231.htm

File import instruction

Abstract

Background: Besides the well-established risk factors for gastric adenocarcinoma (GaC), many other etiological factors remain largely unexplored. This large comprehensive case-control study aimed to investigate the preventable lifestyle and eating habits associated with GaC.

Methods: Consecutive patients with primary microscopically-confirmed GaC diagnosed in 2016-2018 were matched by sex, age, height, and socioeconomic status at a 1:1 ratio with healthy controls. Association of GaC versus control with investigated factors was assessed using the multivariable-adjusted conditional logistic regression for paired samples.

Results: Together 302 GaC patients and 302 healthy controls were investigated. Participants receiving higher education and those eating majorly vegetables had less frequently GaC. The majorly frying cooking habit was associated with a higher incidence of GaC. People complaining about poor sleep quality had more often GaC. The more often one smoked, the more often he/she had GaC. A higher frequency for having pickled food was associated with more frequent GaC, while having more frequently vegetables/fruit, beans, or kelps was associated with less often GaC. A greater preference for sour or bitter taste was associated with less frequent GaC. The frequencies of thin liquid intake after meal, swallowing hot food without adequate cooling, doing other things while eating, eating overnight food, and eating midnight snack were all positively associated with GaC, while going to bed regularly was associated with less often GaC.

Conclusions: Education level, sleep quality, smoking, the frequencies of use of several foods and seasonings, the preference for specific tastes, and various eating and living habits were associated with GaC. The findings offer important hints for further prospective investigations and for easy effective GaC-preventative strategy-making.

Keywords: Gastric adenocarcinoma, lifestyle, eating habits, etiology, case-control study.

Introduction

Gastric cancer, the majority of which is gastric adenocarcinoma (GaC), is the 5th most commonly diagnosed malignancy and the 3rd leading cause of cancer mortality in both sexes combined worldwide, with ~1,034,000 new cases and ~783,000 deaths in 2018 [1]. Its incidence is highest in Eastern Asia [2]. In China, gastric cancer was estimated to affect ~679,000 patients and to cause 498,000 deaths in 2015, and was both the 2nd most commonly diagnosed cancer and the 2nd leading cause of cancer death in both sexes combined [3].

Helicobacter pylori (Hp) is the major risk factor for GaC, contributing to ~90% of new cases of non-cardia GaC [4, 5]. Some unhealthy dietary habits (e.g., food preservation by salting and low fruit/vegetable intake), alcohol consumption, and tobacco smoking have also been shown to be associated with a higher risk of GaC [1, 6-13]. Notably, results on the associations of GaC with some factors (e.g., drinking, smoking, and red meat intake) remain controversial [1, 6-16], and many other preventable risk factors have not yet been well established.

This study aimed to comprehensively investigate the easily-modifiable lifestyle and eating habits associated with GaC and to offer evidence for disease prevention. Our findings can potentially aid to identify people at high risk of GaC and be used for risk-adapted screening.

Methods

Participants

Consecutive patients with first primary microscopically-confirmed GaC diagnosed in the First Affiliated Hospital of Anhui Medical University (FAHAMU) between July 2016 and August 2018 were included in this case-control study. Patients with previous malignancies, with cancers other than GaC, with other benign gastric diseases, with diseases impairing memory (e.g., dementia), with severe dysfunction of important organs, or with severe systematic unfitness were excluded. They were matched by sex, age, height, and socioeconomic status at a 1:1 ratio with healthy controls confirmed not to have any gastric disorders except superficial gastritis. Since many patients with GaC are diagnosed at an advanced stage and are usually significantly thinner compared to the healthy controls and their pre-disease conditions, weight was not included as a matching factor. All participants did not have previous symptomatic reflux, and had fridges for food preservation. Individuals with any first-degree relative having GaC were excluded. Informed consents were obtained from all participants. This study was approved by the Internal Review Board of FAHAMU.

Collected information

Participants were requested to carefully respond to a valid, uniform, and standardized questionnaire and to report their regular, habitual, customary, long-lasting conditions (before having obvious digestive symptoms in GaC patients). To ensure the validity and completeness of the responses, the completion of each questionnaire was supervised by one of the trained authors, who only explained items neutrally when necessary but did not offer any directive or indicative clues.

Information on participant characteristics (sex, age, height, weight, education level, marital status, alcohol drinking, smoking, and passive smoking) and comorbidities (hepatitis, diabetes, hypertension, and allergy) were first collected. Tumor location and differentiation were retrieved for patients. All participants were further requested to report the following: Number of people living and eating together with; eating and cooking habits; drinking water source; frequency score (FS) for intake of pork, chicken, beef, fish, processed meat, pickled food, dried food, smoked/baked food, vegetables/fruit, beans, stewed food, fried food, cereals, tuber crops, kelps, and dairy products; FS for use of yellow rice wine, soy sauce, vinegar, monosodium glutamate, chicken essence, onion/garlic, pepper, and ginger; FS for intake of thick (e.g., thick soup and milk) and thin liquid (e.g., water and juice) before, during, and after meal; FS for several eating habits (swallowing hot food without adequate cooling, not sufficiently chewing, overeating, doing other things while eating, eating deteriorated food, eating overnight food, eating within 0.5 hours after sports, eating midnight snack, and having milk before sleep); FS for eating at home, eating at canteen, and eating box lunch; FS for several sleeping habits (going to bed regularly, dreaming, and afternoon nap); FS for and time of housework and exercise per day; preference score (PS) for sour, sweet, bitter, spicy, and salty tastes; regularity score (RS) for having breakfast, lunch, and supper; degree of satiety and food intake per meal; rest hours after meal; nighttime and noontime sleeping hours; and quality of sleep.

FS was defined as: 0, never; 1, ≤1 time per month; 2, 2-3 times per month; 3, 1-2 times per week; 4, 3-4 times per week; 5, 5-6 times per week; 6, 1 time per day; 7, 2 times per day; 8, 3 times per day; 9, ≥4 times per day. The frequency was modified from the Food Frequency Questionnaire [17]. PS ranged from 1 (extremely dislike) to 7 (extremely like) with an increment of 1. RS ranged from 1 (very regular) to 5 (very irregular) with an increment of 1.

Statistical analyses

The paired t and χ2 tests were used for comparing continuous and categorical variables between groups, respectively. Associations of GaC versus control with the investigated factors were first computed in basic models using the multivariable conditional logistic regression for paired samples adjusting for sex, age, and height, and the significant factors were then all incorporated into a final multivariable logistic model also adjusting for sex, age, and height. Subgroup analyses were further performed for cardia and non-cardia cancers, respectively. Statistical significance was defined by 2-sided P<0.05. Data analyses were performed using R 3.5.1 (https://www.r-project.org/).

Results

Participant characteristics

Initially 628 questionnaires were collected. After excluding the unqualified ones and their pairs, finally 604 cases (nPatients=302; nControls=302) were analyzed. Male proportions in both groups were 69%. The mean ages in the patient and control groups were 60 ± 11 and 59 ± 11 years, respectively. Mean heights were 165 ± 7 and 164 ± 7 cm for patients and controls, respectively. For patients, the proportions of tumors located at the gastric cardia, fundus/body, antrum/pylorus, and whole stomach were 46%, 23%, 29%, and 2%, respectively, and the proportions of well-differentiated, moderately-differentiated, and poorly-differentiated/undifferentiated cancers were 5%, 25%, and 70%, respectively.

Basic models

Compared to uneducated patients, patients going to primary school (OR=0.55), middle school (OR=0.41), high school (OR=0.45), and college/university (OR=0.25) were significantly associated with less often GaC (Table 1). Alcohol drinking was significantly associated with more frequent GaC (OR=2.46), and per 1 higher FS the odds for GaC increased by 0.10. Smoking was associated with more often GaC (OR=2.26), and the odds for GaC increased by 0.17 per 1 higher FS. The FS for passive smoking was also significantly associated with GaC (OR=1.21). No significant associations of GaC with marital status, or histories of hepatitis, diabetes, hypertension, or allergy were observed.

Regarding food and liquid intake (Table 2), the FS for intake of pork (OR=0.89), beef (OR=0.84), fish (OR=0.82), and egg (OR=0.84) was significantly inversely associated with GaC. Particularly, the number of eggs eaten per day was significantly negatively associated with GaC (OR=0.74), and not eating any eggs per day was associated with a higher proportion of GaC compared to eating 1 egg per day (OR=2.17). The FS for having processed meat (OR=1.23), pickled food (OR=1.21), dried food (OR=1.26), and smoked/baked food (OR=1.19) was significantly positively associated with GaC. Having vegetables/fruit, beans, cereals, tuber crops, and kelps decreased the odds for GaC by 0.34, 0.25, 0.18, 0.17, and 0.24 per every additional PS, respectively. The FS for use of yellow rice wine (OR=0.81), soy sauce (OR=0.89), vinegar (OR=0.84), onion/garlic (OR=0.81), pepper (OR=0.85), and ginger (OR=0.79) was significantly inversely associated with GaC, while the FS for use of monosodium glutamate (OR=1.09) and chicken essence (OR=1.07) was positively associated with GaC. The PS for sour (OR=0.86), bitter (OR=0.70), and spicy tastes (OR=0.82) was significantly less frequently associated with GaC.

 Table 1 

Basic participant characteristics

VariableValue/comment1ControlsPatientsOR (95% CI)2Ptrend
EducationUneducated64 (21)91 (32)1.00 (ref.)0.001
Primary school84 (28)82 (29)0.55 (0.34-0.88)
Middle school85 (29)69 (24)0.41 (0.25-0.67)
High school32 (11)28 (10)0.45 (0.24-0.85)
College/university33 (11)17 (6)0.25 (0.12-0.52)
Marital statusMarried269 (89)259 (87)1.00 (ref.)0.322
Single33 (11)38 (13)1.29 (0.78-2.15)
MigrantNo258 (85)167 (62)1.00 (ref.)<0.001
Yes44 (15)102 (38)3.66 (2.37-5.66)
History of hepatitisNo296 (98)260 (96)1.00 (ref.)0.310
Yes6 (2)10 (4)1.72 (0.60-4.90)
History of diabetesNo273 (90)247 (94)1.00 (ref.)0.164
Yes29 (10)16 (6)0.63 (0.33-1.21)
History of hypertensionNo224 (74)212 (79)1.00 (ref.)0.112
Yes78 (26)55 (21)0.72 (0.48-1.08)
History of allergyNo293 (97)258 (96)1.00 (ref.)0.260
Yes9 (3)12 (4)1.68 (0.68-4.15)
Alcohol drinkingNo172 (57)94 (32)1.00 (ref.)<0.001
Yes129 (43)199 (68)2.46 (1.70-3.57)
Frequency score2 ± 3; 0 (0-3)3 ± 3; 2 (0-5)1.10 (1.03-1.19)0.008
SmokingNo106 (35)49 (17)1.00 (ref.)<0.001
Yes195 (65)242 (83)2.26 (1.51-3.39)
Frequency score2 ± 3; 0 (0-1)4 ± 4; 3 (0-9)1.17 (1.10-1.23)<0.001
Passive smokingFrequency score1 ± 2; 0 (0-2)3 ± 3; 2 (0-5)1.21 (1.13-1.30)<0.001

Categorical variables are shown as count (percentage [%]), and continuous variables as mean ± standard deviation.

1Frequency score assignment was as follows: 0, never; 1, ≤1 time per month; 2, 2-3 times per month; 3, 1-2 times per week; 4, 3-4 times per week; 5, 5-6 times per week; 6, 1 time per day; 7, 2 times per day; 8, 3 times per day; 9, ≥4 times per day.

2Odds ratio (OR) with 95% confidence interval (CI) for the association of each variable with gastric cancer versus control was calculated using multivariable logistic regression with adjustment for sex and age. Significant ORs are marked in bold. ref., reference.

 Table 2 

Food and liquid intake

VariableValue/comment1ControlsPatientsOR (95% CI)2Ptrend
PorkFrequency score4 ± 2; 4 (3-6)4 ± 2; 4 (3-6)0.89 (0.81-0.98)0.019
ChickenFrequency score3 ± 1; 3 (2-3)2 ± 2; 2 (1-3)0.95 (0.85-1.07)0.401
BeefFrequency score2 ± 1; 1 (1-2)1 ± 1; 1 (1-2)0.84 (0.72-0.97)0.021
FishFrequency score3 ± 1; 3 (2-3)2 ± 1; 2 (1-3)0.82 (0.72-0.92)0.001
EggFrequency score4 ± 2; 5 (3-6)4 ± 2; 4 (2-5)0.84 (0.76-0.92)<0.001
Eggs per dayAs continuous1 ± 1; 1 (1-1)1 ± 1; 1 (1-1)0.74 (0.55-0.98)0.037
040 (13)65 (23)2.17 (1.38-3.40)0.003
1229 (76)181 (63)1.00 (ref.)
≥233 (11)41 (14)1.35 (0.81-2.25)
Processed meatFrequency score1 ± 1; 0 (0-1)1 ± 1; 1 (0-2)1.23 (1.08-1.40)0.002
Pickled foodFrequency score3 ± 2; 2 (1-5)4 ± 2; 4 (2-6)1.21 (1.12-1.31)<0.001
Dried foodFrequency score1 ± 1; 1 (0-2)2 ± 2; 2 (1-3)1.26 (1.12-1.41)<0.001
Smoked/baked foodFrequency score1 ± 1; 0 (0-1)1 ± 1; 0 (0-1)1.19 (1.02-1.38)0.028
Vegetables and fruitFrequency score6 ± 1; 6 (6-7)5 ± 2; 6 (4-7)0.66 (0.59-0.73)<0.001
BeansFrequency score4 ± 2; 4 (3-5)3 ± 2; 3 (2-4)0.75 (0.67-0.84)<0.001
Stewed foodFrequency score2 ± 1; 1 (1-2)2 ± 1; 1 (1-2)1.03 (0.91-1.18)0.638
Fried foodFrequency score1 ± 1; 1 (1-2)2 ± 1; 1 (1-2)1.07 (0.95-1.21)0.265
CerealsFrequency score3 ± 2; 3 (2-4)2 ± 2; 2 (1-3)0.82 (0.75-0.91)<0.001
Tuber cropsFrequency score3 ± 2; 3 (2-4)3 ± 2; 3 (2-3)0.83 (0.75-0.93)0.001
KelpsFrequency score2 ± 1; 2 (1-3)2 ± 1; 2 (1-2)0.76 (0.67-0.86)<0.001
Yellow rice wineFrequency score4 ± 2; 4 (2-6)3 ± 2; 2 (0-4)0.81 (0.75-0.87)<0.001
Soy sauceFrequency score5 ± 2; 6 (4-6)5 ± 2; 5 (4-6)0.89 (0.81-0.97)0.009
VinegarFrequency score4 ± 2; 5 (3-6)3 ± 2; 3 (2-6)0.84 (0.78-0.91)<0.001
Monosodium glutamateFrequency score3 ± 3; 1 (0-6)3 ± 3; 3 (0-6)1.09 (1.02-1.16)0.006
Chicken essenceFrequency score3 ± 3; 2 (0-6)3 ± 3; 4 (0-6)1.07 (1.01-1.14)0.033
Onion and garlicFrequency score6 ± 2; 6 (5-6)5 ± 2; 5 (3-7)0.81 (0.74-0.89)<0.001
PepperFrequency score5 ± 2; 5 (3-6)4 ± 2; 4 (2-6)0.85 (0.79-0.92)<0.001
GingerFrequency score5 ± 2; 6 (5-6)5 ± 2; 5 (3-6)0.79 (0.72-0.86)<0.001
Dairy productFrequency score1 ± 2; 0 (0-3)1 ± 2; 1 (0-2)1.01 (0.92-1.09)0.999
Sour tastePreference score3 ± 2; 2 (2-4)3 ± 1; 2 (2-4)0.86 (0.77-0.97)0.010
Sweet tastePreference score4 ± 2; 4 (2-5)4 ± 2; 4 (2-5)0.91 (0.82-1.01)0.077
Bitter tastePreference score3 ± 1; 2 (2-4)2 ± 1; 2 (1-3)0.70 (0.61-0.80)<0.001
Spicy tastePreference score4 ± 2; 4 (3-5)4 ± 2; 4 (2-5)0.82 (0.74-0.91)<0.001
Salty tastePreference score4 ± 1; 4 (4-5)4 ± 2; 4 (3-5)0.98 (0.88-1.10)0.747

Categorical variables are shown as count (percentage [%]), and continuous variables as mean ± standard deviation.

1Frequency score assignment was as follows: 0, never; 1, ≤1 time per month; 2, 2-3 times per month; 3, 1-2 times per week; 4, 3-4 times per week; 5, 5-6 times per week; 6, 1 time per day; 7, 2 times per day; 8, 3 times per day; 9, ≥4 times per day. Preference score ranged from 1 (extremely dislike) to 7 (extremely like).

2Odds ratio (OR) with 95% confidence interval (CI) for the association of each variable with gastric cancer versus control was calculated using multivariable logistic regression with adjustment for sex and age. Significant ORs are marked in bold.

ref., reference.

Concerning eating and living habits (Table 3), the number of people living or eating together with was not significantly associated with GaC. Compared to people having majorly vegetables for food, those keeping a balanced diet (OR=2.17) and having majorly meat (OR=3.77) were significantly more likely to have GaC. The majorly frying cooking habit was significantly associated with a higher possibility of GaC compared to majorly steaming/boiling (OR=2.67). Drinking well water was significantly associated with GaC compared to tap water (OR=2.36). The RS for breakfast (OR=1.54), lunch (OR=1.77), and supper (OR=1.78) was significantly positively associated with GaC, while no significant association was shown for degree of satiety. Higher FS for thin liquid intake before meal (OR=1.13), both thick (OR=1.13) and thin liquid intake during meal (OR=1.12), and thin liquid intake after meal (OR=1.19) was associated with increased odds for GaC, while higher FS for thick liquid intake after meal was associated with less frequent GaC (OR=0.89). The FS for overeating (OR=1.47), not sufficiently chewing (OR=1.16), doing other things while eating (OR=1.13), swallowing hot food without adequate cooling (OR=1.25), eating deteriorated food (OR=1.87), eating overnight food (OR=1.16), eating within 0.5 hours after sports (OR=1.13), and eating midnight snack (OR=1.54) was all significantly positively associated with GaC, while there was no significantly association between GaC and having milk before sleep. While eating at home was significantly associated with less frequent GaC (OR=0.88 per 1 FS), eating at canteen was significantly associated with more often GaC (OR=1.12 per 1 FS). The FS for eating box lunch was not significantly associated with GaC. No significant associations were observed for rest hours after meal, or nighttime or noontime sleep hours. Compared to good sleep quality, moderate (OR=1.88) and poor quality (OR=2.81) were associated with increased odds for GaC. The more often one goes to bed regularly and has afternoon nap, the decreased odds for GaC (OR=0.80 and 0.93 per 1 FS, respectively). No significant associations of GaC with housework or exercise were observed.

 Table 3 

Eating and living habits

VariableValue/comment1ControlsPatientsOR (95% CI)2Ptrend
No. of people living together withAs continuous3 ± 2; 2 (1-4)3 ± 2; 2 (1-4)1.10 (1.00-1.21)0.060
No. of people eating together withAs continuous2 ± 2; 2 (1-4)3 ± 2; 2 (1-4)1.05 (0.96-1.15)0.295
Eating habitMajorly vegetables139 (46)70 (25)1.00 (ref.)<0.001
Balanced142 (47)165 (59)2.17 (1.49-3.15)
Majorly meat19 (6)43 (15)3.77 (2.00-7.10)
Cooking habitMajorly steaming/boiling280 (93)241 (83)1.00 (ref.)<0.001
Majorly frying22 (7)51 (17)2.67 (1.55-4.59)
Drinking waterWell water47 (16)88 (30)2.36 (1.57-3.55)<0.001
Tap water253 (84)201 (70)1.00 (ref.)
BreakfastRegularity score2 ± 1; 1 (1-1)2 ± 1; 2 (1-3)1.54 (1.33-1.79)<0.001
LunchRegularity score1 ± 1; 1 (1-1)2 ± 1; 2 (1-3)1.77 (1.48-2.11)<0.001
SupperRegularity score1 ± 1; 1 (1-1)2 ± 1; 2 (1-3)1.78 (1.50-2.12)<0.001
Degree of satietyAs continuous8 ± 48 ± 20.99 (0.93-1.06)0.800
Food intakeOn diet42 (14)30 (11)0.78 (0.46-1.32)0.593
Normal195 (66)182 (68)1.00 (ref.)
Overeating59 (20)56 (21)1.05 (0.69-1.61)
OvereatingFrequency score0 ± 1; 0 (0-1)1 ± 2; 0 (0-2)1.47 (1.28-1.68)<0.001
Thick liquid intake before mealFrequency score1 ± 2; 0 (0-1)1 ± 2; 0 (0-2)1.05 (0.95-1.15)0.342
Thin liquid intake before mealFrequency score1 ± 2; 0 (0-1)2 ± 3; 1 (0-3)1.13 (1.05-1.21)0.001
Thick liquid intake during mealFrequency score1 ± 2; 0 (0-1)2 ± 2; 1 (0-3)1.13 (1.03-1.22)0.006
Thin liquid intake during mealFrequency score1 ± 2; 0 (0-1)2 ± 2; 1 (0-3)1.12 (1.04-1.21)0.004
Thick liquid intake after mealFrequency score2 ± 3; 1 (0-5)2 ± 2; 1 (0-3)0.89 (0.83-0.96)0.002
Thin liquid intake after mealFrequency score1 ± 3; 0 (0-2)3 ± 3; 2 (0-5)1.19 (1.11-1.27)<0.001
Not sufficiently chewingFrequency score2 ± 2; 1 (0-4)3 ± 3; 2 (0-5)1.16 (1.08-1.23)<0.001
Doing other things while eatingFrequency score1 ± 2; 0 (0-1)2 ± 2; 0 (0-2)1.13 (1.04-1.22)0.005
Swallowing hot food without adequate coolingFrequency score1 ± 2; 0 (0-2)3 ± 3; 2 (0-5)1.25 (1.16-1.35)<0.001
Eating deteriorated foodFrequency score0 ± 1; 0 (0-0)1 ± 1; 0 (0-1)1.87 (1.48-2.35)<0.001
Eating overnight foodFrequency score2 ± 2; 2 (0-4)3 ± 3; 3 (1-5)1.16 (1.06-1.26)0.001
Eating within 0.5 h after sportsFrequency score1 ± 2; 0 (0-2)2 ± 2; 1 (0-3)1.13 (1.03-1.23)0.008
Rest hours after mealAs continuous1 ± 1; 1 (0-1)1 ± 1; 1 (1-1)1.05 (0.82-1.34)0.723
Eating midnight snackFrequency score0 ± 1; 0 (0-0)1 ± 2; 0 (0-1)1.54 (1.29-1.83)<0.001
Eating at homeFrequency score7 ± 2; 8 (7-8)7 ± 2; 8 (6-8)0.88 (0.81-0.96)0.004
Eating at canteenFrequency score1 ± 2; 0 (0-0)1 ± 2; 0 (0-1)1.12 (1.02-1.22)0.017
Eating box lunchFrequency score1 ± 2; 0 (0-1)1 ± 2; 1 (0-2)1.06 (0.96-1.17)0.256
Milk before sleepFrequency score1 ± 2; 0 (0-1)1 ± 1; 0 (0-1)0.93 (0.84-1.03)0.183
Nighttime sleep hoursAs continuous8 ± 1; 8 (7-8)8 ± 1; 8 (7-8)0.94 (0.84-1.06)0.341
Noontime sleep hoursAs continuous1 ± 1; 1 (1-2)1 ± 1; 1 (0-2)0.99 (0.81-1.23)0.957
Sleep qualityGood94 (31)46 (17)1.00 (ref.)<0.001
Moderate90 (30)82 (31)1.88 (1.17-3.02)
Poor115 (38)140 (52)2.81 (1.80-4.39)
Going to bed regularlyFrequency score5 ± 2; 6 (4-6)4 ± 2; 4 (1-6)0.80 (0.74-0.87)<0.001
DreamingFrequency score3 ± 2; 3 (1-5)3 ± 2; 3 (2-5)1.02 (0.94-1.11)0.608
Afternoon napFrequency score4 ± 2; 4 (1-6)3 ± 2; 4 (1-6)0.93 (0.87-1.00)0.048
Heavy houseworkFrequency score3 ± 3; 3 (1-6)3 ± 3; 2 (1-6)1.00 (0.94-1.07)0.925
Light houseworkFrequency score5 ± 2; 5 (3-6)4 ± 3; 5 (2-6)1.01 (0.94-1.09)0.724
Housework hours per dayAs continuous2 ± 2; 2 (1-4)3 ± 2; 2 (1-3)1.06 (0.98-1.15)0.158
ExerciseFrequency score3 ± 3; 3 (0-6)3 ± 3; 3 (0-5)0.95 (0.89-1.01)0.081
Exercise hours per dayAs continuous2 ± 3; 1 (0-3)2 ± 3; 1 (0-3)1.02 (0.96-1.09)0.491

Categorical variables are shown as count (percentage [%]), and continuous variables as mean ± standard deviation.

1Regularity score ranged from 1 (very regular) to 5 (very irregular). Frequency score assignment was as follows: 0, never; 1, ≤1 time per month; 2, 2-3 times per month; 3, 1-2 times per week; 4, 3-4 times per week; 5, 5-6 times per week; 6, 1 time per day; 7, 2 times per day; 8, 3 times per day; 9, ≥4 times per day.

2Odds ratio (OR) with 95% confidence interval (CI) for the association of each variable with gastric cancer versus control was calculated using multivariable logistic regression with adjustment for sex and age. Significant ORs are marked in bold. ref., reference.

Final multivariable model

In the final multivariable model (Table 4), participants receiving primary school (OR=0.27) or middle school education (OR=0.21) had significantly less often GaC compared to those uneducated. Compared to people keeping a balanced diet, those having majorly vegetables had significantly less frequently GaC (OR=0.23). The majorly frying cooking habit was significantly associated with a higher incidence of GaC compared to the majorly steaming/boiling habit (OR=10.23). Compared to people having good sleep quality, those complaining about poor sleep quality had significantly more often GaC (OR=3.18). The more often one smoked, the more often he/she had GaC (OR=1.28 per 1 FS). Higher FS for having pickled food was significantly associated with more frequent GaC (OR=1.41), while having more frequently vegetables/fruit (OR=0.60), beans (OR=0.73), or kelps (OR=0.72) was significantly associated with less often GaC. A greater PS for sour (OR=0.77) or bitter taste (OR=0.50) was significantly associated with less frequent GaC. The FS for thin liquid intake after meal (OR=1.27), swallowing hot food without adequate cooling (OR=1.21), doing other things while eating (OR=1.23), eating overnight food (OR=1.25), and eating midnight snack (OR=1.49) was all significantly positively associated with GaC, while going to bed regularly was significantly associated with less often GaC (OR=0.81 per 1 FS).

The association patterns for cardia and non-cardia cancers were mostly similar with the whole cases, only with few exceptions. For cardia cancers, The FS for having eggs was significantly associated with less often GaC (OR=0.32). The FS for eating vegetables/fruit was more strongly associated with reduced cardia carcinoma frequency (OR=0.16) compared to overall and non-cardia carcinomas. The FS for vinegar use (OR=0.65) and the PS for spicy taste (OR=0.57) were only significantly negatively associated with non-cardia cancers. More frequent pepper use was only significantly associated with less often cardia cancers (OR=0.47), and more often thin liquid intake during meal (OR=2.27), more often chewing insufficiently (OR=2.69), more frequently eating deteriorated food (OR=7.84), and more irregular supper intake (OR=6.37) were only significantly associated with more frequent cardia cancers.

Discussion

This study comprehensively reported eating and living habits associated with GaC in a large Chinese population, offering further insights into potentially modifiable factors and providing important evidence for making GaC-preventive strategies. Furthermore, some differences in association patterns and/or strengths between cardia and non-cardia cancers were found for some factors.

We found that people receiving primary or middle school education had significantly less often GaC compared to uneducated people, which is consistent with some previous studies showing that better education was associated with reduced GaC risk [18, 19].

 Table 4 

Factors associated with gastric cancer using full multivariable-adjusted model, overall and by location

VariableValue/comment1Overall gastric cancerCardia cancerNon-cardia cancer
OR (95% CI)2PtrendOR (95% CI)2PtrendOR (95% CI)2Ptrend
EducationUneducated1.00 (ref.)0.0141.00 (ref.)0.0791.00 (ref.)0.012
Primary school0.27 (0.09-0.78)0.03 (<0.01-0.83)0.11 (0.02-0.64)
Middle school0.21 (0.06-0.72)<0.01 (<0.01-0.12)0.21 (0.03-1.34)
High school1.51 (0.38-6.03)0.09 (<0.01-4.27)1.81 (0.24-14.00)
College/university0.46 (0.10-2.12)0.20 (<0.01-15.73)0.05 (<0.01-0.66)
Eating habitMajorly vegetables0.23 (0.09-0.56)0.0030.03 (<0.01-0.44)0.0370.11 (0.02-0.46)0.011
Balanced1.00 (ref.)1.00 (ref.)1.00 (ref.)
Majorly meat0.34 (0.10-1.19)0.32 (0.01-9.57)0.43 (0.07-2.62)
Cooking habitMajorly steaming/boiling1.00 (ref.)0.001
Majorly frying10.23 (2.70-38.80)
Sleep qualityGood1.00 (ref.)0.015
Moderate1.01 (0.38-2.70)
Poor3.18 (1.23-8.23)
SmokingFrequency score1.28 (1.12-1.46)<0.0011.64 (1.12-2.41)0.0121.32 (1.07-1.61)0.009
EggFrequency score0.32 (0.12-0.83)0.019
Pickled foodFrequency score1.41 (1.16-1.71)<0.0012.00 (1.10-3.63)0.0231.55 (1.12-2.14)0.008
Vegetables and fruitFrequency score0.60 (0.46-0.79)<0.0010.16 (0.06-0.45)0.0010.73 (0.51-1.05)0.094
BeansFrequency score0.73 (0.55-0.97)0.0320.54 (0.35-0.86)0.009
KelpsFrequency score0.72 (0.54-0.96)0.0260.24 (0.09-0.61)0.003
VinegarFrequency score0.65 (0.46-0.92)0.015
PepperFrequency score0.84 (0.68-1.03)0.0900.47 (0.26-0.83)0.010
Sour tastePreference score0.77 (0.59-1.00)0.047
Bitter tastePreference score0.50 (0.36-0.69)<0.0010.14 (0.05-0.41)<0.0010.38 (0.22-0.65)0.001
Spicy tastePreference score0.57 (0.38-0.86)0.008
Thin liquid intake during mealFrequency score2.27 (1.12-4.61)0.024
Thin liquid intake after mealFrequency score1.27 (1.06-1.52)0.0081.45 (1.12-1.87)0.005
Swallowing hot food without adequate coolingFrequency score1.21 (1.00-1.47)0.0451.44 (1.07-1.95)0.017
SupperRegularity score6.37 (1.20-33.88)0.030
Not sufficiently chewingFrequency score1.18 (1.00-1.40)0.0582.69 (1.48-4.86)0.001
Doing other things while eatingFrequency score1.23 (1.01-1.50)0.0411.50 (1.10-2.05)0.011
Eating deteriorated foodFrequency score1.37 (0.95-1.96)0.0937.84 (2.32-26.49)0.001
Eating overnight foodFrequency score1.25 (1.03-1.52)0.0251.57 (1.13-2.18)0.007
Eating midnight snackFrequency score1.49 (1.09-2.03)0.0133.57 (1.04-12.30)0.0442.04 (1.22-3.41)0.007
Going to bed regularlyFrequency score0.81 (0.68-0.96)0.0180.74 (0.56-0.96)0.025

1Frequency score assignment was as follows: 0, never; 1, ≤1 time per month; 2, 2-3 times per month; 3, 1-2 times per week; 4, 3-4 times per week; 5, 5-6 times per week; 6, 1 time per day; 7, 2 times per day; 8, 3 times per day; 9, ≥4 times per day. Preference score ranged from 1 (extremely dislike) to 7 (extremely like). Regularity score ranged from 1 (very regular) to 5 (very irregular).

2Odds ratio (OR) with 95% confidence interval (CI) for the association of each variable with cancer (overall, cardia, and non-cardia) versus control was calculated using multivariable logistic regression with adjustment for sex, age, and all significant variables identified in the preliminary models adjusting for sex and age only. Results with P < 0.10 are shown, and significant ORs with P < 0.05 are marked in bold. ref., reference.

Better education could help to form and keep healthy eating and living habits, while well-educated people might face greater pressure in this competitive modern era. The associations for those receiving high school and college/university education were insignificant in overall patients and most subgroups, which could be partly explained by the small case numbers in these groups. People having majorly vegetables for food had significantly less frequent GaC, which is also supported by previous studies [20, 21]. Furthermore, we found that more frequent vegetable/fruit intake was significantly associated with a reduced frequency of cardia cancer but not of non-cardia cancer. Previous evidence remains controversial regarding the association between red meat intake and GaC risk [16], and The Netherlands Cohort Study did not show a significant association [15]. The insignificance for the habit of majorly meat intake in our study might be partly explained by the paucity of participants in that group. The majorly frying cooking habit, which could generate various carcinogenic substances in a temperature-dependent manner, was associated with a higher overall GaC incidence compared to majorly steaming/boiling. We found that poor sleep quality was significantly associated with a higher GaC incidence compared to good quality. Notably, sleep quality could be influenced by various factors like time to go to bed and psychiatric status. We further found that the frequency of going to bed regularly was significantly associated with a reduced risk of GaC especially non-cardia cancer. Our finding that smoking was associated with GaC in a dose-dependent manner was well supported by previous literature [22]; however, we did not observe a significant association for alcohol drinking frequency, on which previous evidence remains controversial [14].

The frequency of pickled food intake, a well-recognized risk factor for GaC [23], was associated with increased risks of both cardia and non-cardia cancers. Interestingly, more frequent egg intake was significantly associated with a reduced risk of cardia cancer but not of non-cardia cancer. While higher frequencies of intake of beans and kelps were both significantly associated with a decreased overall GaC risk, beans intake was significantly associated with non-cardia cancer and kelps intake with cardia cancer. More often vinegar use was significantly associated with a reduced non-cardia cancer risk, and a greater preference for sour taste was significantly associated with a lower overall GaC risk. We previously reported that distal GaC was mostly associated with hypoacidity [24], and adequate acidification of inner-stomach environment might be protective against malignancy, possibly by inhibiting growth and proliferation of organisms. More frequent pepper use was associated with a lower incidence of cardia cancer, and a greater preference of spicy taste was significantly associated with a lower cardia cancer risk. However, an early study [25] reported that Chili pepper consumption was positively associated with GaC risk. The discrepancies from our findings could be possibly due to the different strains between Asia and South America. Notably, a greater preference for bitter taste was significantly associated with reduced incidences of both cardia and non-cardia cancers. The associations with various food and seasoning intake and flavor preference offer important clues for easy GaC-preventative strategy making, which should be further validated by prospective studies.

Some specific eating habits were further found to be associated with GaC risk through multivariable analysis. The more often one had thin liquid during meal, the more probably he/she had cardia adenocarcinoma, while more frequent thin liquid intake after meal was significantly associated with an increased risk of non-cardia cancer. Thin liquid intake during/after meal could dilute the gastric liquid, thus increasing the burden of stomach. Notably, we did not observe a significant association for thick liquid intake. Swallowing hot food without adequate cooling was associated with an increased GaC risk, which might be due to the damaging effect of heat to gastric mucosa. Insufficiently chewing was associated with an increased GaC risk, which could be attributed to the increased digestive burden for the stomach. Doing other things while eating could reduce the blood flow to the stomach, potentially causing the organ to be more vulnerable. The frequencies of eating deteriorated and overnight food, which might contain increased carcinogenic microorganisms and chemical compounds, were both associated with an increased GaC risk. Among 3 meals in a day, only the irregularity degree of supper was significantly associated with cardia cancer. A short interval between having supper and going to bed could induce and accelerate reflux, a risk factor for cardia cancer. Accordingly, eating midnight snack was significantly associated with an increased GaC risk. Nearly all of these potential GaC risk factors could be modifiable. If prospectively validated, GaC-preventative strategies could be accordingly made.

This case-control study was limited by its retrospective observational nature. The associations observed do not suggest causality, and should be validated in prospective cohorts. Recall bias could affect the accuracy of the results. There could be other risk factors that have not been accounted for in this study (e.g., depression). Hp infection status was not adjusted for in this study, considering that the measure for cancer patients might not reflect the real pre-cancer status. Some originally Hp-infected patients may have the infection status turn negative during the development of cancer. Furthermore, it would be difficult to know the exact duration of infection which might differ largely between the patient and control groups. The case numbers in some subgroups were not large enough to obtain statistical significance, and larger relevant studies are encouraged. Notably, the risk factors for GaC in Western people could be different from those in Asian people. Molecular and genetic risk factors could potentially further help to identify people at risk.

Our study is a large comprehensive investigation on various easily modifiable factors potentially causing GaC in Asian people. Further subgroup analyses according to tumor location were conducted. While some identified GaC-associated factors have been reported previously, there are various newly detected modifiable and preventable eating and living habits, which provide important informative clues for future investigations and which will contribute greatly to GaC prevention if validated prospectively. Once validated, the findings can serve as references for making effective population-based strategies to prevent GaC. Through health education campaigns to raise the public awareness of the modifiable and preventable factors associated with GaC, it can be expected that a significant proportion of GaC cases can be avoided in a cost-effective manner, especially for individuals without Hp infection who may have poorer prognosis if developing GaC [26]. Our evidence-based findings provide novel clues to help to identify people at a high risk of GaC which can be potentially used for risk-adapted screening and which may contribute to early diagnosis. Modifying the validated factors may even help to prolong survival and improve quality of life for patients with GaC, and further studies in these aspects are needed.

In conclusion, education level, sleep quality, smoking, the frequencies of use of several foods and seasonings, the preference for specific tastes, and various eating and living habits were significantly associated with GaC, with some location-specific differences. Our findings offer important hints for further prospective investigations and for easy effective GaC-preventative strategy making.

Acknowledgements

The authors would most sincerely thank the reviewers and editors for critically reviewing this paper and for the constructive and thoughtful comments and suggestions.

Funding

This work was supported by National College Students' Innovation and Entrepreneurship Training Program (9021446101) and National Natural Science Foundation of China (81572350). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' contributions

Huang L, Chen L, Gui ZX, Liu S, Wei ZJ, and Xu AM designed the research; Huang L, Chen L, Gui ZX, Liu S, and Wei ZJ performed the research; Huang L analyzed and interpreted the data, and wrote the manuscript; Chen L, Gui ZX, Liu S, Wei ZJ, and Xu AM critically reviewed the paper.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from each investigated individual. No individual patient data were reported.

Availability of data and materials

The data that support the findings of this study are available from our center but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Grant support

This work was supported by National College Students' Innovation and Entrepreneurship Training Program (9021446101) and National Natural Science Foundation of China (81572350). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests

The authors have declared that no competing interest exists.

References

1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394-424

2. Huang L, Xu AM. Adenocarcinoma of esophagogastric junction: controversial classification, surgical management, and clinicopathology. Chin J Cancer Res. 2014;26:226-30

3. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F. et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66:115-32

4. Plummer M, Franceschi S, Vignat J, Forman D, de Martel C. Global burden of gastric cancer attributable to Helicobacter pylori. Int J Cancer. 2015;136:487-90

5. Islami F, Chen W, Yu XQ, Lortet-Tieulent J, Zheng R, Flanders WD. et al. Cancer deaths and cases attributable to lifestyle factors and infections in China, 2013. Ann Oncol. 2017;28:2567-74

6. Wang Z, Koh WP, Jin A, Wang R, Yuan JM. Composite protective lifestyle factors and risk of developing gastric adenocarcinoma: the Singapore Chinese Health Study. Br J Cancer. 2017;116:679-87

7. Buckland G, Travier N, Huerta JM, Bueno-de-Mesquita HB, Siersema PD, Skeie G. et al. Healthy lifestyle index and risk of gastric adenocarcinoma in the EPIC cohort study. Int J Cancer. 2015;137:598-606

8. Navarro Silvera SA, Mayne ST, Gammon MD, Vaughan TL, Chow WH, Dubin JA. et al. Diet and lifestyle factors and risk of subtypes of esophageal and gastric cancers: classification tree analysis. Ann Epidemiol. 2014;24:50-7

9. Lee YY, Derakhshan MH. Environmental and lifestyle risk factors of gastric cancer. Arch Iran Med. 2013;16:358-65

10. Navarro Silvera SA, Mayne ST, Risch HA, Gammon MD, Vaughan T, Chow WH. et al. Principal component analysis of dietary and lifestyle patterns in relation to risk of subtypes of esophageal and gastric cancer. Ann Epidemiol. 2011;21:543-50

11. Kim RH, Chang MS, Kim HJ, Song KS, Kim YS, Choi BY. et al. Medical history and lifestyle factors contributing to Epstein-Barr virus-associated gastric carcinoma and conventional gastric carcinoma in Korea. Anticancer Res. 2010;30:2469-75

12. Yamaji Y, Watabe H, Yoshida H, Kawabe T, Wada R, Mitsushima T. et al. High-risk population for gastric cancer development based on serum pepsinogen status and lifestyle factors. Helicobacter. 2009;14:81-6

13. Chen MJ, Chiou YY, Wu DC, Wu SL. Lifestyle habits and gastric cancer in a hospital-based case-control study in Taiwan. Am J Gastroenterol. 2000;95:3242-9

14. Tramacere I, Negri E, Pelucchi C, Bagnardi V, Rota M, Scotti L. et al. A meta-analysis on alcohol drinking and gastric cancer risk. Ann Oncol. 2012;23:28-36

15. Keszei AP, Schouten LJ, Goldbohm RA, van den Brandt PA. Red and processed meat consumption and the risk of esophageal and gastric cancer subtypes in The Netherlands Cohort Study. Ann Oncol. 2012;23:2319-26

16. Song P, Lu M, Yin Q, Wu L, Zhang D, Fu B. et al. Red meat consumption and stomach cancer risk: a meta-analysis. J Cancer Res Clin Oncol. 2014;140:979-92

17. Subar AF, Thompson FE, Kipnis V, Midthune D, Hurwitz P, McNutt S. et al. Comparative validation of the Block, Willett, and National Cancer Institute food frequency questionnaires: the Eating at America's Table Study. Am J Epidemiol. 2001;154:1089-99

18. Lagergren J, Andersson G, Talback M, Drefahl S, Bihagen E, Harkonen J. et al. Marital status, education, and income in relation to the risk of esophageal and gastric cancer by histological type and site. Cancer. 2016;122:207-12

19. Trujillo Rivera A, Sampieri CL, Morales Romero J, Montero H, Acosta Mesa HG, Cruz Ramirez N. et al. Risk factors associated with gastric cancer in Mexico: education, breakfast and chili. Rev Esp Enferm Dig. 2018;110:372-9

20. Wang T, Cai H, Sasazuki S, Tsugane S, Zheng W, Cho ER. et al. Fruit and vegetable consumption, Helicobacter pylori antibodies, and gastric cancer risk: A pooled analysis of prospective studies in China, Japan, and Korea. Int J Cancer. 2017;140:591-9

21. Peleteiro B, Padrao P, Castro C, Ferro A, Morais S, Lunet N. Worldwide burden of gastric cancer in 2012 that could have been prevented by increasing fruit and vegetable intake and predictions for 2025. Br J Nutr. 2016;115:851-9

22. Nomura AM, Wilkens LR, Henderson BE, Epplein M, Kolonel LN. The association of cigarette smoking with gastric cancer: the multiethnic cohort study. Cancer Causes Control. 2012;23:51-8

23. Ren JS, Kamangar F, Forman D, Islami F. Pickled food and risk of gastric cancer-a systematic review and meta-analysis of English and Chinese literature. Cancer Epidemiol Biomarkers Prev. 2012;21:905-15

24. Huang L, Xu AM, Li TJ, Han WX, Xu J. Should peri-gastrectomy gastric acidity be our focus among gastric cancer patients?. World J Gastroenterol. 2014;20:6981-8

25. Lopez-Carrillo L, Hernandez Avila M, Dubrow R. Chili pepper consumption and gastric cancer in Mexico: a case-control study. Am J Epidemiol. 1994;139:263-71

26. Marrelli D, Pedrazzani C, Berardi A, Corso G, Neri A, Garosi L. et al. Negative Helicobacter pylori status is associated with poor prognosis in patients with gastric cancer. Cancer. 2009;115:2071-80

Author contact

Corresponding address Corresponding author: Prof. A-Man Xu (amanxucom), Dr. Zhi-Jian Wei (305801533com), and Dr. Lei Huang (huangleizhentingcom), Department of General Surgery, the First Affiliated Hospital of Anhui Medical University, 218 JiXi Avenue, Hefei 230022, Anhui, China. Phone: +86-551-62922114; Fax: +86-551-63633742.


Received 2019-8-5
Accepted 2019-11-14
Published 2020-1-1