Abstract
This study aims to characterise the distribution and co-prevalence patterns of 11 modifiable gastric cancer (GC) risk factors and to examine their association with gastric health. The research utilised baseline data from the upper gastrointestinal cancer (UGC) screening, early detection and treatment project, as well as the oesophageal cancer prospective cohort in Yangzhong City, spanning 2017–2023. The study sample comprised 12,071 adults aged 40–69 who had completed the screening process, had all relevant study variables, and had no prior history of cancer or upper gastrointestinal surgery. The 11 modifiable GC risk factors (regular smoking, drinking, inadequate intake of vegetables and fruit, intake of red meat and its products, pickled food consumption, irregular diet, fried food consumption, leftover food consumption, eating too quickly, high-salt diet, and Helicobacter pylori infection), and pathologically diagnosed gastric diseases were assessed. Co-prevalence of risk factors was observed in 97.90% of participants. The most prevalent risk factor in both genders was inadequate intake of vegetables and fruit, followed by consumption of red meat and its products. The most common combinations of risk factors include inadequate intake of vegetables and fruit, consumption of red meat and its products, pickled food, and leftover food. An incremental increase in the number of these risk factors corresponded to a significant elevation in the risk of gastric disease, ranging from 8.3 to 9.3% (men: adjusted odds ratio [AOR] 1.093, 95% confidence interval [CI] 1.053–1.134; women: AOR 1.083, 95% CI 1.041–1.128). Participants with more risk factors were more likely to have gastric disease than those with 0–2 risk factors (men with 3–5 risk factors: AOR 1.576, 95% CI 1.217–2.042; men with ≥ 6 risk factors: AOR 1.756, 95% CI 1.344–2.295; women with ≥ 6 risk factors: AOR 1.449, 95% CI 1.162–1.806). Our study reveals significant differences in the prevalence of 11 modifiable GC risk factors among individuals at high risk for UGC in Yangzhong City, and these risk factors exhibit significant co-prevalence. Individuals with more risk factors typically experience poorer gastric health. Therefore, it is crucial to implement comprehensive interventions to enhance public awareness of GC-related knowledge, beliefs, and behaviours. Simultaneously, effectively identifying high-risk groups and providing tailored interventions during screening are essential for improving gastric health.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-17822-4.
Subject terms: Risk factors, Health care, Disease prevention
Introduction
Gastric cancer (GC) is a prevalent and lethal form of cancer, with its incidence and mortality ranking among the top five worldwide in 2022 (new cases and deaths accounting for 4.9% and 6.8% of all cancers, respectively)1. It is estimated that approximately 50% of new cancer cases and over 50% of cancer deaths worldwide occur in Asia, with China being a notable contributor to this statistic1. The latest cancer burden report from the National Cancer Centre of China demonstrates that, although the burden of GC declined between 2000 and 2018, it remains a significant health concern. In 2022, it remained the fifth most prevalent cancer type (358,700 cases) in terms of incidence and the third most prevalent (260,400 cases) in terms of mortality, underscoring its significant domestic health burden2.
Previous studies have shown that approximately 30–50% of cancers can be prevented by avoiding risk factors3,4. Risk factors associated with GC include age, gender, ethnicity, family history, history of gastric disease, smoking, alcohol consumption, unhealthy diets, eating habits, gastrointestinal microbiota, radiation, viral exposures (e.g., Epstein-Barr virus), and Helicobacter pylori infection5,6. The risk of GC can be significantly reduced if some of these modifiable risk factors are addressed. For instance, eradicating Helicobacter pylori has been demonstrated to reduce the risk of developing GC by 14–19%7. Similarly, individuals who have completely quit smoking have a 14% lower risk of GC compared to those who continue to smoke8.
Most extant studies have focused solely on the association between a single variable and GC or have explored the independent role of multiple variables of interest8–11. However, it is important to note that these risk factors do not exist in isolation; they might co-occur or even cluster together, significantly increasing the risk of illness or death. For instance, according to Gaddy and colleagues, a high-salt diet increases the cancer-causing effects of Helicobacter pylori strains that are cagA-positive12. Inoue et al. found that alcohol consumption elevated the risk of GC in habitual smokers, particularly for cardia cancer13. There is a heterogeneity in the ability of family history of upper gastrointestinal cancer (UGC) to predict the risk of death from oesophageal squamous cell carcinoma across gender, age, and lifestyle in a 30-year follow-up study from Lin County, China14. Thus, the combination of risk factors is complex and not merely additive. It is exceptionally important to understand how these risk factors occur together or converge in a population15, as this will effectively aid in identifying at-risk groups.
In response to the high incidence of GC, China launched a cancer screening and early diagnosis and treatment programme targeting high-risk groups in rural areas in 2005. Additionally, the Huaihe River Basin, urban cancer and opportunistic screening pilots focusing on UGCs were launched in 2007, 2012, and 2019, respectively. This series of cancer screening programmes aims to promote secondary prevention of a wide range of highly prevalent cancers, including GC16–18. However, few studies have focused on the number of recognised GC risk factors that co-prevalence, their combination patterns, and their associations with gastric health in rural Chinese populations at high risk of UGC. Therefore, this study aimed to analyse the co-prevalence characteristics of 11 modifiable GC risk factors identified by screening guidelines among residents aged 40–69 years in Yangzhong City, a high-UGC-prevalence region, and evaluate the association between the number of risk factors and gastric health.
Materials and methods
Data sources and population
This study is cross-sectional and utilises data from the Rural UGC Screening and Early Diagnosis and Treatment Project, and the Oesophageal Cancer Prospective Cohort in Yangzhong City, with a focus on enhancing secondary prevention of UGC19,20. The study population was determined by cluster sampling, based on the local UGC incidence. The two projects complement each other in their implementation. Details of the specific study designs, target populations, screening methods, and follow-up protocols of the two projects can be found in previous literature16,17,19–21. In total, 13,475 residents from 24 neighbourhoods/administrative villages across five secondary districts (accounting for 5/6 of Yangzhong City) were registered at Yangzhong People’s Hospital (the sole screening facility) from November 2017 to December 2023. This analysis included 12,071 household registered residents aged between 40 and 69 years, after excluding those with incomplete screenings, missing variables, or a history of cancer or upper gastrointestinal surgery (Fig. 1). This study was conducted by the Declaration of Helsinki and was approved by the Ethics Committees of the Yangzhong People’s Hospital (KY202402) and the Cancer Hospital of the Chinese Academy of Medical Sciences (NO. 16-171/1250). We obtained informed consent from all participating subjects.
Fig. 1.
Flowchart of participants.
Study procedure
After each resident who volunteered to participate signed an informed consent form, screening registration, physical measurements, epidemiological investigations, laboratory tests for biochemical indices, endoscopy, and pathological diagnosis were conducted sequentially. Only those subjects who had completed all processes were considered eligible. Epidemiological investigations were conducted by doctors, nurses, and epidemiological investigators who had been trained and certified by the UGC Screening Expert Group using the project’s structured questionnaire (with full authorisation). The survey included sociodemographic characteristics (gender, age, residence, marital status, and so on), lifestyle (tea consumption, regular smoking, and drinking), food intake (intake of hot food, spicy food, vegetables and fruit, red meat and its products, pickled food, and fried food), dietary habits (irregular diet, consumption of leftovers, eating too quickly, and a high-salt diet), frequency of exercise, history of gastric disease, and family history of cancer and common chronic diseases.
All eligible subjects underwent painless endoscopy and biopsy pathology. The former included endoscopic preparation and sedation with anaesthesia, followed by examination of the oesophagus, cardia, stomach, and duodenum, supplemented by endoscopic-assisted diagnostic techniques for early-stage UGC to improve the detection rate of early-stage lesions. During this period, biopsies were taken from suspicious lesions in the oesophagus, cardia, and gastric mucosa, or from high-incidence sites. Specimens were standardised and processed for pathological diagnosis. Of note, specimens taken from the stomach initially underwent a rapid urease test to determine whether an individual was infected with Helicobacter pylori. Details of endoscopic and diagnostic pathology techniques can be found in previous literature19,21.
Measures and definitions
GC risk factors may vary by ethnicity, and this study incorporates 11 modifiable risk factors associated with GC based on the Chinese Guidelines (2022)22. They include regular smoking, alcohol consumption, an unhealthy diet (inadequate intake of vegetables and fruit, intake of red meat and its products, pickled food, and fried food), unhealthy dietary habits (irregular diet, consumption of leftovers, eating too quickly, and a high-salt diet), and Helicobacter pylori infection.
Regular smoking
The survey asked, “How often do you currently smoke? (no smoking; occasional smoking; most days; every day)” and inquired, “Has this behaviour lasted or accumulated for at least 6 months to date?” The aim was to determine the frequency and duration of smoking among respondents. A regular smoker is defined as an individual who smokes every day for at least 6 months23.
Drinking
Participants responded to items about alcohol consumption over the past year, which included options such as “never or almost never drinking; drinking only occasionally on special occasions (e.g., celebrations or holidays); drinking only during specific months of the year (e.g., harvest periods or summer) and generally not drinking in other seasons; drinking every month of the year regardless of season but with a frequency of less than weekly; drinking at least once a week, regardless of season”. Alcohol consumption was defined as a frequency of at least once per week24.
Inadequate vegetable and fruit intake
Participants were asked to report their frequency of vegetable and fruit consumption (daily; 4–6 days/week; 1–3 days/week; 1–3 days/month; none/rarely) and their typical daily intake in grams over the past year. The Dietary Guidelines for Chinese Residents (2016) recommend that Chinese adults consume at least 300–500 g of vegetables and 200–400 g of fruit daily24,25. As such, consuming less than 500 g of vegetables and fruit per day was considered inadequate24.
Other unhealthy diet
The consumption of red meat and its products, as well as pickled and fried foods, was assessed by inquiring about the frequency of their intake over the past year. The options provided were daily; 4–6 days per week; 1–3 days per week; 1–3 days per month; and none or rarely. An intake frequency of at least once a week for these foods was categorised as “yes”17.
Unhealthy dietary habits
Eating irregularities, consumption of leftovers, and eating speed were assessed using a closed-ended questionnaire. Specifically, the questionnaire asked: (1) In the past year, how often did you experience eating irregularities (such as skipping breakfast, and the like) or consuming leftovers? (daily; 4–6 days/week; 1–3 days/week; 1–3 days/month; none/rarely); (2) How quickly do you chew your food? (slowly; medium; somewhat fast; very fast [wolfing down food]). This study defined an irregular diet and leftover intake occurring at least once a week as “yes”17. Self-reported eating speeds of “somewhat fast” and “very fast” were categorised as “eating too quickly”. Furthermore, an assessment of high-salt diets was conducted via a closed-ended inquiry: “How would you describe the taste of your family meals—salty or light? (salty; medium; light)”. The categories of medium and light were combined to indicate a non-high-salt diet, while salty was categorised as a high-salt diet.
Helicobacter pylori infection
If the rapid urease test yields a positive result, it indicates an infection. Conversely, a negative result indicates no infection.
Gastric diseases
These include pathologically diagnosed atrophic gastritis, intestinal metaplasia, low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and GC.
Risk factor co-prevalence
The presence of two or more risk factors in an individual was considered to indicate risk factor co-prevalence.
Covariant variable
Sociodemographic variables such as sex, age, residence, marital status, education, household size, and average annual household income were also collected. Other health-related characteristics included self-reported tea consumption (at least once a week), consumption of hot food (including tea; at least once a week), consumption of spicy food (at least once a week), physical activity levels (never, 1–3 times/month, 1–2 times/week, or at least 3 times/week), history of gastric disorders (atrophic gastritis, hypertrophic gastritis, gastric ulcers, gastric polyps), and family history of cancer and common chronic diseases (hypertension, hyperlipidaemia, coronary heart disease, stroke, diabetes mellitus, psychiatric disorders, and others). In addition, oesophageal lesions diagnosed by this screening (reflux oesophagitis, low-grade/high-grade intraepithelial neoplasia, and oesophageal cancer) were included.
Statistical analysis
SPSS 27.0 and R version 4.2.2 were utilised for statistical analyses. Initially, descriptive analyses were conducted to describe the distribution of participants’ sociodemographic characteristics, other health-related characteristics, and 11 selected GC-related risk factors across gender groups, respectively. The mean ± standard deviation and frequencies (proportions) were used as appropriate, alongside t-tests and chi-squared tests for between-group comparisons. Multiple Poisson regression was employed to evaluate the independent factors influencing the number of risk factors in the total population, as well as in men and women (adjusted for all possible covariates, which were tested to be free of multicollinearity). We also describe the top 5 combination patterns in both sexes. Participants were scored 1 point for each present risk factor and 0 points for each absent one. Total scores ranged from 0 to 1126,27. We set a score of 0 to 2, 3 to 5, and 6 or more as a three-categorical variable (based on data distribution and clinical interpretation). We fitted multivariable binary logistic regression models with the diagnosis of GC and precancerous lesions (0 = no; 1 = yes) as the dependent variable and the number of risk factors (categorical or continuous variables) as the exposure variable (adjusted for all possible covariates). We present the adjusted odds ratio (AOR), 95% confidence interval (CI), and results of the linear trend test. Ultimately, we applied a restricted cubic spline (RCS) with 4 knots, using the minimum value as the reference group, and conducted subgroup analyses to assess heterogeneity in the association between the number of risk factors and GC and precancerous lesions. All statistical tests were two-sided, with a P value of less than 0.05 regarded as statistically significant.
Results
Sociodemographic and other health-related characteristics of participants
Table 1 presents the sociodemographic and other health-related characteristics of the participants. A total of 12,071 participants were included in the study, of whom 5242 (43.43%) were men and 6829 (56.57%) were women. Participants living in rural areas accounted for 82.36%, those who were married for 93.68%, those with a junior high school education for 49.50%, those with 1–3 family members for 58.66%, and those with an annual household income of at least CNY 110,000 for 39.40%. The differences in age, residence, marital status, education, household size, average annual household income, tea consumption, hot food consumption, spicy food consumption, physical activity, history of gastric disease, oesophageal diagnosis, family history of common chronic diseases, and gastric diagnosis were statistically significant between genders (P < 0.05). In contrast, the difference in family history of cancer was not statistically significant between genders (P > 0.05). In addition, basic information on participants categorised by stomach health status is provided in Supplementary Table 1.
Table 1.
Sociodemographic and other health-related characteristics of participants.
| Variable | Total (n = 12,071) | Men (n = 5242) | Women (n = 6829) | P |
|---|---|---|---|---|
| Age (year), Mean ± SD | 56.24 ± 7.27 | 56.40 ± 7.35 | 56.12 ± 7.20 | 0.035 |
| Residence, n (%) | < 0.001 | |||
| Rural | 9942 (82.36) | 4419 (84.30) | 5523 (80.88) | |
| Urban | 2129 (17.64) | 823 (15.70) | 1306 (19.12) | |
| Marital status, n (%) | < 0.001 | |||
| Never married/divorced/widowed/separated | 763 (6.32) | 205 (3.91) | 558 (8.17) | |
| Married | 11,308 (93.68) | 5037 (96.09) | 6271 (91.83) | |
| Education, n (%) | < 0.001 | |||
| No formal schooling | 921 (7.63) | 105 (2.00) | 816 (11.95) | |
| Primary school | 3114 (25.80) | 987 (18.83) | 2127 (31.15) | |
| Junior high school | 5975 (49.50) | 2766 (52.77) | 3209 (46.99) | |
| Senior high school and above | 2061 (17.07) | 1384 (26.40) | 677 (9.91) | |
| Household size, n (%) | < 0.001 | |||
| 1–3 | 7081 (58.66) | 3205 (61.14) | 3876 (56.76) | |
| 4–6 | 4790 (39.68) | 1955 (37.29) | 2835 (41.51) | |
| ≥ 7 | 200 (1.66) | 82 (1.56) | 118 (1.73) | |
| Average annual household income (CNY), n (%) | < 0.001 | |||
| 0–29,999 | 966 (8.00) | 357 (6.81) | 609 (8.92) | |
| 30,000–69,999 | 3190 (26.43) | 1369 (26.12) | 1821 (26.67) | |
| 70,000–109,999 | 3159 (26.17) | 1406 (26.82) | 1753 (25.67) | |
| ≥ 110,000 | 4756 (39.40) | 2110 (40.25) | 2646 (38.75) | |
| Tea consumption, n (%) | < 0.001 | |||
| No | 9808 (81.25) | 3199 (61.03) | 6609 (96.78) | |
| Yes | 2263 (18.75) | 2043 (38.97) | 220 (3.22) | |
| Hot food, n (%) | < 0.001 | |||
| No | 10,243 (84.86) | 4036 (76.99) | 6207 (90.89) | |
| Yes | 1828 (15.14) | 1206 (23.01) | 622 (9.11) | |
| Spicy food, n (%) | < 0.001 | |||
| No | 10,458 (86.64) | 4365 (83.27) | 6093 (89.22) | |
| Yes | 1613 (13.36) | 877 (16.73) | 736 (10.78) | |
| Physical activity, n (%) | 0.005 | |||
| Never | 7630 (63.21) | 3242 (61.85) | 4388 (64.26) | |
| 1–3/month or 1–2/week | 1167 (9.67) | 499 (9.52) | 668 (9.78) | |
| ≥ 3/week | 3274 (27.12) | 1501 (28.63) | 1773 (25.96) | |
| History of gastric disordera, n (%) | < 0.001 | |||
| No | 11,233 (93.06) | 4831 (92.16) | 6402 (93.75) | |
| Yes | 838 (6.94) | 411 (7.84) | 427 (6.25) | |
| Oesophageal lesionb, n (%) | < 0.001 | |||
| No | 11,290 (93.53) | 4737 (90.37) | 6553 (95.96) | |
| Yes | 781 (6.47) | 505 (9.63) | 276 (4.04) | |
| Family history of cancer, n (%) | 0.181 | |||
| No | 4444 (36.82) | 1965 (37.49) | 2479 (36.30) | |
| Yes | 7627 (63.18) | 3277 (62.51) | 4350 (63.70) | |
| Family history of common chronic diseasesc, n (%) | 0.025 | |||
| No | 4179 (34.62) | 1873 (35.73) | 2306 (33.77) | |
| Yes | 7892 (65.38) | 3369 (64.27) | 4523 (66.23) | |
| Gastric disease, n (%) | < 0.001 | |||
| Otherd | 7230 (59.90) | 2926 (55.82) | 4304 (63.03) | |
| Precancerous diseasee | 4558 (37.76) | 2128 (40.60) | 2430 (35.58) | |
| Precancerous lesionf | 220 (1.82) | 145 (2.77) | 75 (1.10) | |
| Cancerg | 63 (0.52) | 43 (0.82) | 20 (0.29) | |
| Gastric disease, n (%) | < 0.001 | |||
| Other | 7230 (59.90) | 2926 (55.82) | 4304 (63.03) | |
| Precancerous disease and aboveh | 4841 (40.10) | 2316 (44.18) | 2525 (36.97) |
Comparisons between groups were made using t-tests and chi-square tests.
SD standard deviation, CNY Chinese Yuan.
aHistory of gastric disorder includes atrophic gastritis, hypertrophic gastritis, gastric ulcers, and gastric polyps.
bOesophageal lesion includes reflux oesophagitis, low-grade and high-grade intraepithelial neoplasia, and cancer.
cFamily history of common chronic diseases involves hypertension, hyperlipidaemia, coronary heart disease, stroke, diabetes mellitus, psychiatric disorders, and so on.
dPathologically diagnosed as normal, non-atrophic inflammation, and other conditions.
ePathologically diagnosed as atrophic gastritis and intestinal metaplasia.
fPathologically diagnosed as low-grade and high-grade intraepithelial neoplasia.
gPathologically diagnosed as cancer.
hPathologically diagnosed as atrophic gastritis, intestinal metaplasia, low-grade and high-grade intraepithelial neoplasia, and cancer.
Prevalence of the 11 risk factors
Table 2 presents the prevalence of 11 risk factors associated with GC. Inadequate intake of vegetables and fruit, as well as consumption of red meat and its products, were the most common risk factors among both sexes, respectively. The prevalence rates of regular smoking, drinking, inadequate vegetable and fruit intake, intake of red meat and its products, irregular diet, fried food consumption, eating too quickly, high-salt diet, and Helicobacter pylori infection were higher in men than in women, while the prevalence rates of pickled food and leftover food consumption were lower in men than in women (P < 0.05). Additionally, we observed that risk factor co-prevalence was present in most participants (97.90%), with women being more likely to have 2–4 risk factors and men having a higher likelihood of having five or more risk factors.
Table 2.
Prevalence of 11 risk factors for gastric cancer.
| Variable | Total (n = 12,071) | Men (n = 5242) | Women (n = 6829) | P |
|---|---|---|---|---|
| Regular smoking, n (%) | < 0.001 | |||
| No | 9406 (77.92) | 2587 (49.35) | 6819 (99.85) | |
| Yes | 2665 (22.08) | 2655 (50.65) | 10 (0.15) | |
| Drinking, n (%) | < 0.001 | |||
| No | 9696 (80.32) | 3060 (58.37) | 6636 (97.17) | |
| Yes | 2375 (19.68) | 2182 (41.63) | 193 (2.83) | |
| Inadequate vegetable and fruit intake, n (%) | < 0.001 | |||
| No | 957 (7.93) | 338 (6.45) | 619 (9.06) | |
| Yes | 11,114 (92.07) | 4904 (93.55) | 6210 (90.94) | |
| Red meat and its products, n (%) | < 0.001 | |||
| No | 2261 (18.73) | 809 (15.43) | 1452 (21.26) | |
| Yes | 9810 (81.27) | 4433 (84.57) | 5377 (78.74) | |
| Pickled food, n (%) | 0.001 | |||
| No | 5380 (44.57) | 2424 (46.24) | 2956 (43.29) | |
| Yes | 6691 (55.43) | 2818 (53.76) | 3873 (56.71) | |
| Irregular diet, n (%) | < 0.001 | |||
| No | 11,522 (95.45) | 4966 (94.73) | 6556 (96.00) | |
| Yes | 549 (4.55) | 276 (5.27) | 273 (4.00) | |
| Fried food, n (%) | 0.003 | |||
| No | 11,673 (96.70) | 5040 (96.15) | 6633 (97.13) | |
| Yes | 398 (3.30) | 202 (3.85) | 196 (2.87) | |
| Leftover food, n (%) | < 0.001 | |||
| No | 5238 (43.39) | 2470 (47.12) | 2768 (40.53) | |
| Yes | 6833 (56.61) | 2772 (52.88) | 4061 (59.47) | |
| Eating too quickly, n (%) | < 0.001 | |||
| No | 8800 (72.90) | 3443 (65.68) | 5357 (78.44) | |
| Yes | 3271 (27.10) | 1799 (34.32) | 1472 (21.56) | |
| High-salt diet, n (%) | < 0.001 | |||
| No | 9339 (77.37) | 3701 (70.60) | 5638 (82.56) | |
| Yes | 2732 (22.63) | 1541 (29.40) | 1191 (17.44) | |
| Helicobacter pylori infection, n (%) | < 0.001 | |||
| No | 6820 (56.50) | 2798 (53.38) | 4022 (58.90) | |
| Yes | 5251 (43.50) | 2444 (46.62) | 2807 (41.10) | |
| Number of risk factors, Mean ± SD | 4.28 ± 1.53 | 4.96 ± 1.57 | 3.76 ± 1.26 | < 0.001 |
| Number of risk factors, n (%) | < 0.001 | |||
| 0 | 12 (0.10) | 3 (0.06) | 9 (0.13) | |
| 1 | 241 (2.00) | 36 (0.69) | 205 (3.00) | |
| 2 | 1118 (9.26) | 253 (4.83) | 865 (12.67) | |
| 3 | 2420 (20.05) | 634 (12.09) | 1786 (26.15) | |
| 4 | 3167 (26.24) | 1074 (20.49) | 2093 (30.65) | |
| 5 | 2712 (22.47) | 1360 (25.94) | 1352 (19.80) | |
| 6 | 1456 (12.06) | 1046 (19.95) | 410 (6.00) | |
| 7 | 645 (5.34) | 550 (10.49) | 95 (1.39) | |
| 8 | 231 (1.91) | 218 (4.16) | 13 (0.19) | |
| 9 | 65 (0.54) | 64 (1.22) | 1 (0.01) | |
| 10 | 4 (0.03) | 4 (0.08) | 0 (0.00) | |
| 11 | 0 (0.00) | 0 (0.00) | 0 (0.00) |
SD standard deviation.
Combination pattern of 11 risk factors
Table 3 provides the combination patterns of 11 GC-related risk factors (only the top 5 combinations are listed). Among men, the top two combinations were inadequate intake of vegetables and fruit, red meat and its products, pickled food, and leftover food (2.21%), and inadequate intake of vegetables and fruit combined with red meat and its products (2.04%). For women, the most common combinations were inadequate intake of vegetables and fruit, red meat and its products, pickled food, and leftover food (10.65%), followed by these same factors plus Helicobacter pylori infection (6.82%). Additionally, the most prevalent combination patterns in the overall population mirrored those observed in women.
Table 3.
Combined pattern of 11 risk factors for gastric cancer.
| Rank | Risk number | Regular smoking | Drinking | Inadequate vegetable and fruit intake | Red meat and its products | Pickled food | Irregular diet | Fried food | Leftover food | Eating too quickly | High-salt diet | Helicobacter pylori infection | N (%) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall | 1 | 4 | √ | √ | √ | √ | 843 (6.98) | |||||||
| 2 | 5 | √ | √ | √ | √ | √ | 544 (4.51) | |||||||
| 3 | 3 | √ | √ | √ | 496 (4.11) | |||||||||
| 4 | 2 | √ | √ | 487 (4.03) | ||||||||||
| 5 | 3 | √ | √ | √ | 412 (3.41) | |||||||||
| Men | 1 | 4 | √ | √ | √ | √ | 116 (2.21) | |||||||
| 2 | 2 | √ | √ | 107 (2.04) | ||||||||||
| 3 | 3 | √ | √ | √ | 97 (1.85) | |||||||||
| 4 | 3 | √ | √ | √ | 94 (1.79) | |||||||||
| 5 | 3 | √ | √ | √ | 78 (1.49) | |||||||||
| 5* | 5 | √ | √ | √ | √ | √ | 78 (1.49) | |||||||
| Women | 1 | 4 | √ | √ | √ | √ | 727 (10.65) | |||||||
| 2 | 5 | √ | √ | √ | √ | √ | 466 (6.82) | |||||||
| 3 | 3 | √ | √ | √ | 402 (5.89) | |||||||||
| 4 | 2 | √ | √ | 380 (5.56) | ||||||||||
| 5 | 3 | √ | √ | √ | 334 (4.89) |
Only the top five combination patterns were listed.
*Tied for fifth place.
In multivariable Poisson regression analyses, having an education level of senior high school and above, engaging in some physical activity, and having a family history of cancer were associated with fewer GC risk factors in men. Men with annual household incomes of CNY 70,000 or more, as well as those who consumed tea, hot food, and spicy food, and those with oesophageal lesions, had a higher likelihood of having more GC risk factors. Women with a senior high school education and above, as well as those engaging in physical activity three or more times per week, were identified as protective factors against the co-prevalence of GC risk factors. In contrast, factors such as ageing, having only a primary school education, living in households with 4–6 family members, an annual household income of CNY 70,000 or more, and consuming a hot and spicy diet in women were associated with risk factor co-prevalence (all P < 0.05, Supplementary Table 2).
Association between co-prevalent risk factors for gastric cancer and gastric health
The analyses of the association between co-prevalent GC risk factors and gastric disease using multivariate logistic regression are presented in Table 4. The results indicated that for each unit increase in risk factors, the likelihood of developing gastric disease increased by 9.3% (95% CI 1.053–1.134) in men and 8.3% (95% CI 1.041–1.128) in women. Additionally, a dose–response relationship was observed in the number of risk factors associated with gastric disease, with all P values for the trend being less than 0.01. Specifically, men with 3–5 and 6 or more risk factors were 57.6% (95% CI 1.217–2.042) and 75.6% (95% CI 1.344–2.295) more likely to have gastric disease, respectively, compared with men who had 0–2 risk factors. Similarly, women with six or more risk factors were 44.9% (95% CI 1.162–1.806) more likely to have gastric disease compared with those with 0–2 risk factors.
Table 4.
Multivariable analysis of the association between the number of risk factors for gastric cancer and gastric health.
| Variable | Total | Men | Women | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Non-gastric diseasea, n (%) | Gastric diseaseb, n (%) | AOR (95% CI)c | Non-gastric diseasea, n (%) | Gastric diseaseb, n (%) | AOR (95% CI)d | Non-gastric diseasea n (%) | Gastric diseaseb, n (%) | AOR (95% CI)d | |
| Number of risk factors (continuous) | 1.089 (1.059–1.118)*** | 1.093 (1.053–1.134)*** | 1.083 (1.041–1.128)*** | ||||||
| Number of risk factors (categorical) | |||||||||
| 0–2 | 915 (12.66) | 456 (9.42) | 1.000 (reference) | 194 (6.63) | 98 (4.23) | 1.000 (reference) | 721 (16.75) | 358 (14.18) | 1.000 (reference) |
| 3–5 | 5004 (69.21) | 3295 (68.06) | 1.238 (1.094–1.401)*** | 1722 (58.85) | 1346 (58.12) | 1.576 (1.217–2.042)*** | 3282 (76.25) | 1949 (77.19) | 1.141 (0.991–1.314) |
| 6+ | 1311 (18.13) | 1090 (22.52) | 1.442 (1.242–1.674)*** | 1010 (34.52) | 872 (37.65) | 1.756 (1.344–2.295)*** | 301 (6.99) | 218 (8.63) | 1.449 (1.162–1.806)*** |
| P for trend | 1.194 (1.110–1.284)*** | 1.200 (1.085–1.327)*** | 1.188 (1.068–1.321)** | ||||||
AOR adjusted odds ratio.
aPathologically diagnosed as normal, non-atrophic inflammation, and other conditions.
bPathologically diagnosed as atrophic gastritis, intestinal metaplasia, low-grade and high-grade intraepithelial neoplasia, and cancer.
cAOR estimated by using multivariate logistic regression, adjusted for all other variables.
dAOR estimated by using multivariate logistic regression, adjusted for all other variables except for gender.
*P < 0.05, **P < 0.01, ***P < 0.001.
Furthermore, the RCS analysis confirmed a positive linear relationship between the two variables in all study populations, with all P values for nonlinearity > 0.05 (see Supplementary Figs. 1–3). Subgroup analyses revealed a consistent positive association across all subgroups in all study populations (P values for interaction > 0.05, Supplementary Table 3).
Discussion
Our study assessed the prevalence and co-prevalence of 11 modifiable GC risk factors in a high-risk population for UGC in Yangzhong City, southeastern China. We also explored the association between the co-prevalence of these risk factors and gastric health. Among all risk factors, the most prevalent in both genders were inadequate intake of vegetables and fruit, along with consumption of red meat and its products. Risk factor co-prevalence was widespread, with men showing a higher tendency for such co-prevalence than women. The most common risk factor combinations in both sexes were inadequate intake of vegetables and fruit, intake of red meat and its products, pickled food, and leftover food. Our study also found that a greater number of risk factors (i.e., risk factor co-prevalence) was associated with an increased risk of gastric disease, and this relationship remained consistent across all subgroups. Our study underscores the potential value of identifying the number and co-prevalence patterns of GC risk factors in preventing gastric disease.
Previous studies have analysed the clustering of risk factors for non-communicable diseases (NCDs)27,28. The majority of these risk factors include tobacco use, insufficient physical activity, inadequate consumption of vegetables and fruit, overweight and obesity, central obesity, and so on. Other metabolic factors, such as blood glucose and blood pressure, have also been considered, as these factors are essential for the prevention and treatment of NCDs27,28. However, few studies have been conducted to characterise the co-prevalence of risk factors for GC. To our knowledge, a study investigating clusters of lifestyle risk factors and screening behaviours for GC concluded that physical inactivity was most common in both sexes. This finding contradicts the results of the present study29. A cross-sectional study from Bhutan indicated that high salt intake was the most common modifiable risk factor in both genders30. However, Sarveswaran et al. and Hossain et al. found that inadequate consumption of vegetables and fruit was the most common risk factor among target participants27,28, which is consistent with the present study’s findings. A cross-sectional study conducted in Wuwei, a region with a high incidence of GC in China, showed that the prevalence rate of Helicobacter pylori in the general population was 35.6%31. A study from Taiwan showed that the prevalence rate of Helicobacter pylori was 26.6%32. The results of both the above-mentioned studies were lower than those of the present study. In addition, some scholars have found that the prevalence rates of unhealthy behaviours such as an unhealthy diet (89.5%)33, sedentary behaviour during leisure time (80.9%), and consumption of carbonated drinks (60.4%)34, are also relatively high. This study also analysed patterns of risk factor co-prevalence. It revealed that inadequate consumption of vegetables and fruit, intake of red meat and its products, pickled food, and leftover food were the most common risk factor combinations in both sexes, which is inconsistent with the studies mentioned above27–30. A study focusing on rural adolescents in Nigeria showed that a diet high in animal lipids and salt was the most common clustering pattern (19.6%)35. In addition, some scholars have explored potential clustering categories of risk factors through professional analytical methods to conduct subsequent correlation studies. For example, a study on health risk behaviours among pregnant women in Brazil identified three clustering patterns (none, moderate, and high) using latent class analysis36. Clark et al. combined stratification and the k-means algorithm to identify several clustering patterns of risk behaviours37. It is essential to acknowledge that variations in study context, study design, target population, covariates, and analytical methods can lead to inconsistencies in comparisons. Consequently, we must approach the results of these comparisons with caution. It is suggested that these high-prevalence and high-risk factor combinations can be targeted for intervention in daily screenings to improve the efficiency of comprehensive GC prevention.
Our study revealed that risk factor co-prevalence is widespread among the population, with ageing, male sex, lower educational level, and higher average annual household income as significant predictors of co-prevalence. These findings align with those from other regional studies28,30,38,39. Moreover, individuals consuming hot and spicy food, engaging in lower levels of physical activity, and those with oesophageal lesions were more prone to having multiple risk factors. To the best of our knowledge, consuming hot or spicy food, as well as engaging in lower levels of physical activity, adversely affects overweight and obesity17,40,41. Deficiencies in health literacy among obese individuals may lead to poor lifestyle habits42–44. Some of the 11 risk factors included in this study (regular smoking, alcohol consumption, and pickled food consumption, among others) are also associated with oesophageal cancer45. Therefore, the presence of oesophageal lesions may be one of the manifestations of the coexistence of these risk factors. We also discovered that tea consumption was among the risk factors for co-prevalence in men. Generally, drinking tea is considered a healthy behaviour46,47, due to its high content of antioxidants and anti-inflammatory substances. This phenomenon may result from the general awareness of the beneficial effects of tea consumption among the population, leading them to drink tea after realising they are obese or in poor health. Moreover, among women, the risk factors co-prevalence was more prevalent among those with 4–6 family members compared to those with 1–3 family members, which may be associated with poorer lifestyle habits within the family48. Finally, men with a family history of cancer were less likely to experience co-prevalence, consistent with the conclusion of Hwang et al.49.
We also observed a stable, independent, positive linear association with gastric disorders, irrespective of whether the number of risk factors was treated as a continuous or categorical variable. Although fewer studies have been conducted on gastric diseases, previous research has shown that cardiovascular disease (CVD) risk factor clustering significantly increases the odds of prehypertension, regardless of gender and age, and that a dose–response relationship exists50. Studies have found that in diabetic populations, the presence of three or more CVD risk factors is more predictive of kidney disease in African Americans and of CVD in white individuals51. The clustering of CVD risk factors was also found to significantly increase the risk of type 2 diabetes mellitus among adults in Inner Mongolia, China. For individuals with two risk factors, the adjusted hazard ratio was 2.257 (95% CI 1.448–3.518); for those with three or more risk factors, the adjusted hazard ratio was 3.316 (95% CI 2.119–5.188)52. These findings suggest that a significant elevation in the risk of corresponding diseases accompanies increased risk factors. Here are a few possible explanations. Firstly, synergies between risk factors may amplify their hazardous effects53. Secondly, a lack of awareness regarding GC risk factors and preventive measures can lead to the accumulation of these risk factors, which may potentially impair gastric health54. Finally, studies have shown that individuals with multiple risk factors are less likely to adhere to screening guidelines, resulting in delayed detection of gastric disorders29. Therefore, to improve population-level gastric health, comprehensive interventions should be implemented based on the distribution and co-prevalence characteristics of risk factors. Additionally, health education should be actively promoted to enhance public awareness of GC risk factors and preventive measures.
This study is based on a well-designed cohort study and a national cancer screening program implemented in Yangzhong City, covering approximately five-sixths of the districts in Yangzhong. Furthermore, this study may be the first to explore the co-prevalence of GC high-risk factors and their association with gastric diseases in a high UGC prevalence region in China. However, this study has several limitations. Firstly, as a cross-sectional study, it has inherent limitations. Secondly, since most risk factors were collected via self-reporting, this may have introduced recall and reporting biases. Thirdly, high-frequency risk factor combination patterns or potential risk categories have not been identified using professional cluster analysis methods, thus overlooking the inherent connections between risk factors. Fourthly, in this study, each present risk factor was assigned a value of 1, which may ignore the specificity of how risk factors affect gastric health. Additionally, H. pylori infection was not detected using the gold standard test (e.g., breath test), potentially leading to an underestimation of its prevalence. Finally, due to constraints in the study design, the definitions of some risk factors did not align with those in previous studies, limiting comparisons with other research.
In conclusion, this study reveals differences in the prevalence of 11 modifiable GC risk factors among individuals at high risk for UGC in Yangzhong City; however, these risk factors exhibit a clear tendency toward co-prevalence. Furthermore, individuals with more risk factors tended to have poorer gastric health. Therefore, comprehensive interventions are urgently needed to improve public awareness regarding knowledge, beliefs, and preventive behaviours related to GC. Health education should be actively promoted during the screening process to enhance public understanding of GC, effectively identify high-risk groups, and thereby facilitate targeted interventions.
Supplementary Information
Author contributions
Conceptualisation: X.F., J.H.Z., C.D., Z.L.H., and J.S.Z. Data curation: X.F., Z.L.H., and C.D. Formal analysis: X.F. Funding acquisition: X.F., J.H.Z., C.D., and Z.L.H. Methodology: X.F., J.H.Z., C.D., and J.S.Z. Project administration: X.F., and C.D. Visualisation: X.F. Writing–original draft: X.F., and C.D. Writing–review & editing: X.F., J.H.Z., C.D., Z.L.H., and J.S.Z.
Funding
This study was supported by the Zhenjiang City key research and development plan (Grant NO. SH2022051) and Jiangsu Province Preventive Medicine general Project (Ym2023031).
Data availability
The datasets supporting the results of this study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets supporting the results of this study are available from the corresponding author upon reasonable request.