Smoking, Alcohol, and Abdominal Obesity Increase Gastric Cancer Risk after Helicobacter pylori Eradication

Article information

J Korean Cancer Assoc. 2026;.crt.2025.1200

Publication date (electronic) : 2026 January 2

doi : https://doi.org/10.4143/crt.2025.1200

Joo Hyun Lim ¹^,²

, Cheol Min Shin^,¹^,³

, Kyungdo Han ⁴, Jin-Hyung Jung ⁵, Jinju Choi ³, Eun Hyo Jin ¹^,², Seung Joo Kang ¹^,², Dong Ho Lee ¹^,³

¹Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea

²Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea

³Departement of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

⁴Department of Statistics and Actuarial Science, Soongsil University, Seoul, Korea

⁵Samsung Biomedical Research Institute, Sungkyunkwan University School of Medicine, Suwon, Korea

Correspondence: Cheol Min Shin, Department of Internal Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea Tel: 82-31-787-7057 E-mail: scm6md@gmail.com

Received 2025 November 3; Accepted 2025 December 30.

Abstract

Purpose

Helicobacter pylori is the single most important risk factor for gastric cancer (GC). However, H. pylori eradication (HPE) does not eliminate risk of GC. This study aimed to clarify the association of lifestyle factors with GC risk after HPE.

Materials and Methods

Using the Korean National Health Insurance Services database, adult individuals who claimed HPE between 2010 and 2016 were analyzed. The adjusted hazard ratios (aHR) for GC were analyzed according to the lifestyle status including smoking (never, light [< 10 pack-year (PY)], moderate [10-20 PY], and heavy [≥ 20 PY]), alcohol (none, mild [< 30 g/day], and heavy [30 g/day]), and abdominal obesity by using Cox proportional hazard model.

Results

During a median follow-up period of 6.7 years, 9,754 individuals were newly diagnosed with GC among the total of 1,282,702 subjects. Compared with never smokers, moderate (aHR, 1.12 [95% confidence interval (CI), 1.04 to 1.20]) and heavy smokers (1.34 [95% CI, 1.27 to 1.42]) had greater risks of post-HPE GC with dose-response manner. Heavy drinkers had increased risk of GC (1.23 [95% CI, 1.15 to 1.32]) compared with non-drinkers, and those with abdominal obesity had slightly elevated GC risk compared with those without that (1.11 [95% CI, 1.06 to 1.15]). In subgroup analyses, those who had HPE at age ≥ 55 were shown to be more affected by the unhealthy lifestyles (smoking [p-for-interaction < 0.001], alcohol [0.019], and abdominal obesity [0.024]). Also, male showed greater risk increase by smoking habit (p-for-interaction=0.022) than female.

Conclusion

Unhealthy lifestyles like smoking, alcohol, and abdominal obesity were shown as risk factors for post-HPE GC. Those with late HPE were more likely to be affected by unhealthy lifestyles.

Keywords: Stomach neoplasms; Helicobacter pylori; Risk factors; Life style

Introduction

Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide, with 660,000 deaths reported in 2022 [1]. Although its incidence has declined, GC remains one of the most common cancers in East Asia [2]. Helicobacter pylori is a well-established risk factor for GC, and the high incidence of GC in Korea is believed to be related to the widespread prevalence of H. pylori [3]. A Korean nationwide study in 1988 reported 67% of H. pylori prevalence [4]. However, a more recent multicenter study showed a major decrease in prevalence, down to 41.5%, due to the widespread eradication [5]. H. pylori eradication (HPE) has been shown to considerably reduce the incidence of GC [6]. Despite this, GC risk does not completely disappear [7].

As the number of individuals undergoing HPE increases in GC high-risk regions, studies have focused on the factors influencing the risk of GC after HPE. Recent studies have reported that the use of proton pump inhibitors (PPIs) and potassium-competitive acid blockers may influence post-HPE GC risk [8,9].

Additionally, smoking [10], alcohol [11], and obesity [12] have been identified as risk factors for GC. Therefore, these lifestyle factors are believed to increase the risk of GC following HPE. A recent study developed a machine learning model to predict the occurrence of GC after HPE by incorporating clinical factors, including smoking, and alcohol [13]. However, this study did not include information on the amount of alcohol consumption or cigarette smoking. Furthermore, a recent prospective pilot study reported that smoking cessation after HPE reduced the risk of metachronous recurrence in patients who had undergone endoscopic resection for early GC [14]. Despite these findings, large-scale studies evaluating the association of unhealthy lifestyle factors, including smoking, alcohol, and abdominal obesity, with post-HPE GC risk are lacking.

Based on this background, we conducted a nationwide, population-based cohort study to evaluate the association of smoking, alcohol, and abdominal obesity with GC risk following HPE.

Materials and Methods

1. Data source

This study analyzed nationwide population-based cohort data from the Korean National Health Insurance Service (NHIS). The NHIS is the sole public insurer in Korea, covering approximately 97% of the South Korean population. The NHIS database includes prescription and diagnostic data based on the International Classification of Diseases, 10th revision (ICD-10). The NHIS also provides national health checkups for individuals aged ≥ 20 years.

2. Study population and definitions

In this study, the data of patients who claimed to have HPE between 2010 and 2016 (n=1,971,739) were reviewed (Fig. 1). HPE was defined as a prescription of either of the following regimens for 7-14 days: (1) PPI, clarithromycin, and amoxicillin or (2) PPI, metronidazole, and tetracycline [15]. Those who had undergone national health checkups within 2 years before the HPE were included (n=1,406,571). Individuals with any cancer diagnosis before HPE (n=60,867), those with missing data (n=48,634), and those who developed GC within 1.5 years after HPE (n=14,368) were excluded.

Fig. 1.

Study flow chart. GC, gastric cancer; IR, incidence rate; NHIS, National Health Insurance Service.

The last health checkup data within 2 years before the HPE were reviewed for baseline characteristics. The index date, from which the follow-up duration was calculated, was defined as the date of the HPE prescription.

3. Clinical parameters and biochemical analysis

From the last health checkup data within 2 years before the HPE, the following information was retrieved: age at the HPE, sex, body mass index (BMI, kg/m²), waist circumference (WC, cm), systolic/diastolic blood pressure (mmHg), fasting plasma glucose (FPG) (mg/dL), total cholesterol (mg/dL), smoking amount (none, light [< 10 pack-year (PY)], moderate [10-20 PY], and heavy [≥ 20 PY]), alcohol consumption (none, mild [< 30 g/day], and heavy [≥ 30 g/day]), low income (lowest quartile income among NHIS registrants or income of recipients of the National Basic Livelihood Security Program), regular exercise (high-intensity > 20 min, ≥ 3 times/wk or moderate-intensity > 30 min, ≥ 5 times/wk during the last week), obesity (BMI ≥ 25 kg/m² in accordance with the Asia-Pacific region criteria), abdominal obesity (WC ≥ 90 cm in men and ≥ 85 cm in women) [16], diabetes mellitus (FPG ≥ 126 mg/dL, on oral hypoglycemic agents/insulin, or diagnosed with ICD-10 code E11-14), hypertension (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg [17], on antihypertensive, or diagnosed with ICD-10 code I10-13), dyslipidemia (fasting total cholesterol ≥ 240 mg/dL [18], on lipid-lowering agents, or diagnosis with ICD-10 code E78), and chronic kidney disease (CKD, diagnosed with ICD-10 code N18).

4. Statistical analysis

Baseline characteristics were analyzed with descriptive statistics. For group comparisons, the Student’s t test and chi-square test were applied to continuous and categorical variables, respectively.

The Cox proportional-hazards model was used to compare post-HPE GC risks in groups with unhealthy lifestyle (smoking, alcohol consumption, or abdominal obesity) versus control group. For each group of stratified unhealthy lifestyle habits, the adjusted hazard ratio (aHR) for post-HPE GC risk was presented with a 95% confidence interval (CI) after adjusting for potentially relevant variables (age, sex, BMI, low income, smoking, alcohol, and abdominal obesity).

Subgroup analyses were performed across various characteristic groups with mutual adjustment to identify any interacting factors that influence the association of unhealthy lifestyle with post-HPE GC risk. Sensitivity analyses with lag-periods of 2.5 and 3.5 years were conducted.

All statistical analyses were performed using R ver. 4.3.3 (R Foundation for Statistical Computing). Statistical significance was set at p-value < 0.05. All tests were two-sided.

Results

1. Baseline characteristics

Baseline characteristics are presented in Table 1. Overall, the mean age at HPE was approximately 52 years. Never-smokers and non-drinkers each accounted for approximately half the population. Heavy-smokers accounted for approximately 20%, and heavy-drinkers accounted for approximately 10%. Abdominal obesity was observed in 23% of participants. The median follow-up duration was 6.7 years.

Table 1.

Baseline characteristics

Regarding smoking, the heavier smoking groups tended to be older, while the average age of non-smokers was similar to that of the heavy-smokers. The proportion of men was lowest among non-smokers and higher in the heavier smoking groups. BMI, WC, and FPG levels were greater in the heavier smoking groups. Abdominal obesity, diabetes mellitus, hypertension, dyslipidemia, and metabolic syndrome were most prevalent among heavy-smokers, while CKD was most prevalent among non-smokers. Low income was more common among non-smokers. Regular exercise rates were the lowest among heavy-smokers. Heavy drinking proportion was higher among the heavier smoking groups.

According to alcohol consumption, non-drinkers were older than drinkers, and male proportion was higher in heavier drinking groups. Heavier drinking groups were more likely to have higher BMI, WC, and FPG. Abdominal obesity, diabetes mellitus, hypertension, and metabolic syndrome were most prevalent among heavy-drinkers, while dyslipidemia and CKD were more prevalent among non-drinkers. Low income was most common among non-drinkers, and regular exercise rate was lowest among non-drinkers.

2. Post-HPE GC risk according to lifestyle

Among the 1,282,702 individuals enrolled, 9,754 developed GC (incidence rate, 1.12 per 1000 person-years). HRs for post-HPE GC according to lifestyle factors are presented in Table 2.

Table 2.

Risk of gastric cancer after Helicobacter pylori eradication according to lifestyle factors

Regarding smoking amount, moderate- and heavy-smokers showed higher post-HPE GC risks than non-smokers (aHR [95% CI], 1.12 [1.04 to 1.20] for moderate-smokers; 1.34 [1.27 to 1.42] for heavy-smokers), after adjusting for potentially related factors like age at HPE, sex, low income, alcohol, and abdominal obesity. However, light-smokers did not show a risk increase (aHR [95% CI], 1.01 [0.93 to 1.08]). As for alcohol, heavy-drinkers exhibited an increase in risk compared to non-drinkers (aHR [95% CI], 1.23 [1.15 to 1.32]), while mild-drinkers did not. Individuals with abdominal obesity showed a slightly increased post-HPE GC risk compared to those without abdominal obesity (aHR [95% CI], 1.11 [1.06 to 1.15]).

3. Subgroup analyses according to age, sex, abdominal obesity, alcohol, smoking, regular exercise, and income

Subgroup analyses were conducted based on potential interacting factors, including age at HPE (< 55/≥ 55 years), sex (male/female), abdominal obesity (yes/no), alcohol consumption (non-mild/heavy), smoking (never/ex/current), regular exercise (yes/no), and income (low/mid-high), to identify any subgroup that might be more susceptible to the association of lifestyle with post-HPE GC risk.

Fig. 2A shows the aHRs for the association of smoking with post-HPE GC risk in each subgroup, along with p-for-interaction. Those who had HPE at age ≥ 55 years and males were more affected by smoking in developing post-HPE GC compared to their counterparts (p-for-interaction < 0.001 and 0.022, respectively).

Fig. 2.

Association of lifestyle factors with gastric cancer risk after Helicobacter pylori eradication across population subgroups. Forest plots of hazard ratios (with middle square markings on the bars) and 95% confidence intervals (represented by error bars) under adjustment for age at H. pylori eradication, sex, income (low/mid-high), smoking status (never/former/current), alcohol consumption (none/mild [< 30 g/day]/heavy [≥ 30 g/day]), and abdominal obesity (no/yes). (A) Smoking. (B) Alcohol consumption. (C) Abdominal obesity.

Subgroup analyses for the association of alcohol with post-HPE GC risk are presented in Fig. 2B. Only age at HPE ≥ 55 years showed a positive interaction with alcohol (p-for-interaction=0.019).

Similarly, regarding the association of abdominal obesity with post-HPE GC risk, HPE at age ≥ 55 years showed a stronger association compared to the counterpart (p-for-interaction=0.024) (Fig. 2C).

4. Sensitivity analyses with lag-periods of 2.5 and 3.5 years

In the main analysis presented in Table 2, the lag-period was set to 1.5 years. Although a 1-year lag-period is commonly used in cohort studies regarding cancer incidence, this study included additional 6 months to account for the time required for the effect of HPE. To validate the results, sensitivity analyses with 2.5-year and 3.5-year lag-periods were performed (Table 3). The aHRs with a 2.5-year lag-period were nearly identical to those with a 1.5-year lag-period. However, a risk increase in the mild-drinkers was detected (aHR [95% CI], 1.06 [1.003 to 1.11]), unlike with a 1.5-year lag-period, though the risk was minimal. In the analysis with a 3.5-year lag-period, the results showed a similar pattern but with slightly elevated risks, especially in the heavy-smoker, heavy-drinker, and abdominal obesity groups (aHR [95% CI], 1.36 [1.27 to 1.46] in heavy-smokers; 1.28 [1.18 to 1.39] in heavy-drinkers; 1.13 [1.07 to 1.19] in the abdominal obesity group).

Table 3.

Sensitivity analyses with lag periods of 2.5 years and 3.5 years

Discussion

Among this large cohort, including approximately 1,300,000 individuals who had HPE, 0.49% developed GC during a median follow-up of 6.7 years after eradication. This study showed that unhealthy lifestyle habits, including smoking, alcohol consumption, and abdominal obesity, were major risk factors for post-HPE GC in the Korean adult population. Notably, heavy-smoking and heavy-drinking were considerable risk factors, increasing the risk by approximately 30% and 20%, respectively, whereas abdominal obesity increased the risk by approximately 10%. Individuals who had HPE after the age of 55 were more likely to be affected by these lifestyle factors.

Although the incidence of GC has been decreasing recently due to the aging population, its prevalence rate is expected to remain for a while [19]. To date, H. pylori is known as the strongest risk factor for GC [20]. However, HPE does not completely eliminate GC risk. HPE has been reported to decrease GC risk by approximately 50% [21]. Meanwhile, the prevalence of H. pylori is decreasing due to hygiene improvement and the spread of HPE [2]. Therefore, the importance of risk factors other than H. pylori is becoming more evident. In addition to H. pylori, age [22], family history of GC [23], salt intake [24], smoking [10], alcohol [11], and obesity [12] have been reported as risk factors for GC. To prevent GC development, attention must be given to modifiable risk factors. However, previous studies addressing such factors did not fully adjust for H. pylori, making it difficult to determine the accurate impact. Therefore, identifying modifiable risk factors for post-HPE GC has become important. Nevertheless, to date, only one study has investigated lifestyle factors as risk factors for post-HPE GC [13]. This study used machine learning to create a post-HPE GC prediction model incorporating 26 clinical variables, including smoking, drinking, and obesity. Although smoking and drinking were identified as risk factors, their relative significance was < 1%, considerably lower than those of other risk factors like age (49.5%) or intestinal metaplasia (17.9%). Obesity, on the other hand, was not found as a risk factor. This study treated lifestyle factors simply as yes or no and did not analyze the degree of smoking or drinking. In contrast, the current study stratified smoking and drinking amount. Furthermore, we selected abdominal obesity rather than BMI to assess obesity, as abdominal obesity has been shown as a better predictor for clinical diseases, than BMI [25]. This is because abdominal obesity is a more accurate measure of body fat accumulation. In our study, the risk associated with unhealthy lifestyle tended to increase proportionally with the amount of smoking and drinking. Additionally, the magnitude of the risk increased modestly with increasing lag-period, reflecting the fact that lifestyle factors take time to influence health outcomes.

To our knowledge, this is the first study to evaluate the association of unhealthy lifestyle with post-HPE GC risk using a nationwide population-based cohort. Most studies predicting post-HPE GC risk have focused on unmodifiable clinicopathological features, including endoscopic findings, pathology, or ulcer histology [26,27]. Although the impact of lifestyle factors may be relatively small compared to that of clinicopathological factors, they are important because they can be addressed through individual efforts. The results of this study are promising as they may pave the way for primary prevention beyond the secondary prevention typically emphasized in previous studies.

This study has some limitations worth noting. First, it was performed retrospectively. A prospective study on GC risk with this design would be fundamentally unrealistic due to the low incidence of post-HPE GC. The incidence rate in this study was 1.12 per 1,000 person-years. We believe the current research hypotheses can only be verified through a large-scale cohort study. Second, when defining the eradication population, eradication success was not considered as this information was unobtainable from the NHIS database. The study population likely included some cases of eradication failure, which may have introduced noise in predicting post-HPE GC risk. Third, this study did not account for GC family history owing to the lack of relevant data. Fourth, alcohol, smoking, and obesity were assessed based on data obtained immediately before HPE. However, potential lifestyle changes following HPE could not be evaluated. However, gastric carcinogenesis is a long-term process, and assessing the impact of lifestyle changes would require ≥ 10 years of follow-up. Previous meta-analyses have shown that GC risk decreased by 13% 10 years after smoking cessation [28], and those who quit smoking for up to 15 years had a 65% greater risk of intestinal metaplasia, a well-known precancerous lesion, than never-smokers [29]. Another meta-analysis showed that alcohol abstainers for up to 5 years had a two-fold greater GC risk than non-drinkers, but abstinence for > 5 years reduced the risk to that of non-drinkers [30]. Given the design of our study, it is challenging to assess the effect of lifestyle changes on post-HPE GC risk.

In conclusion, this study demonstrated that unhealthy lifestyle factors including smoking, alcohol, and abdominal obesity are associated with an increased risk of GC after HPE. Furthermore, the association was more pronounced in individuals aged ≥ 55 at the time of eradication. For high-risk individuals, maintaining healthy lifestyle is necessary to prevent GC even after HPE, and this should be emphasized in cases of late eradication therapy.

Notes

Ethical Statement

This study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB No. X-2305-828-902). The study was conducted in compliance with the 1964 Declaration of Helsinki. The requirement for informed consent was waived due to the retrospective study design, and the Institutional Review Board approved the waiver of informed consent as no identifying information was involved.

Author Contributions

Conceived and designed the analysis: Lim JH, Shin CM, Han K, Jung JH.

Collected the data: Han K, Jung JH.

Contributed data or analysis tools: Lim JH, Shin CM, Han K, Jung JH.

Performed the analysis: Lim JH, Shin CM, Han K, Jung JH.

Wrote the paper: Lim JH, Shin CM.

Critical revision: Choi J, Jin EH, Kang SJ, Lee DH.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This work was supported by grant no 06-2023-0153 from the Seoul National University Bundang Hospital Research Fund.

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Table 1.

Baseline characteristics

	Total (n=1,282,702)	Smoking amount
	Total (n=1,282,702)	None (n=676,602)	Light (< 10 PY) (n=175,223)	Moderate (10-20 PY) (n=175,144)	Heavy (≥ 20 PY) (n=255,733)	p-value
Age at HPE (cont, yr)	52.1±11.2	53.5±11.3	46.5±11.3	48.7±10.1	54.3±9.4	< 0.001
Male sex	755,798 (58.9)	187,436 (27.7)	148,472 (84.7)	168,088 (96.0)	251,802 (98.5)	< 0.001
BMI (cont, kg/m²)	24.0±3.2	23.9±3.2	24.1±3.2	24.3±3.1	24.3±3.1	< 0.001
WC (cont, cm)	81.6±9.0	79.5±9.0	82.7±8.5	84.0±8.1	84.9±8.1	< 0.001
Systolic BP (mmHg)	123.1±14.6	122.3±15.0	122.7±13.8	123.9±13.7	125.0±14.3	< 0.001
Diastolic BP (mmHg)	76.7±10.0	75.8±10.0	77.1±9.8	77.9±9.8	78.2±9.9	< 0.001
FPG (cont, mg/dL)	99.7±24.1	98.1±22.4	98.3±22.4	100.3±24.2	104.3±28.6	< 0.001
Total cholesterol	197.7±37.4	198.3±37.5	195.7±36.4	198.0±36.8	197.4±38.1	< 0.001
Obesity	462,077 (36.0)	226,173 (33.4)	64,744 (37.0)	69,178 (39.5)	101,982 (39.9)	< 0.001
Abdominal obesity	296,648 (23.1)	146,625 (21.7)	37,827 (21.6)	55,384 (31.6)	100,610 (39.3)	< 0.001
Diabetes mellitus	147,867 (11.5)	70,614 (10.4)	14,998 (8.6)	18,889 (10.8)	43,366 (17.0)	< 0.001
Hypertension	395,436 (30.8)	208,352 (30.8)	43,552 (24.9)	49,972 (28.5)	93,560 (36.6)	< 0.001
Dyslipidemia	322,613 (25.2)	180,683 (26.7)	34,190 (19.5)	38,821 (22.2)	68,919 (27.0)	< 0.001
Chronic kidney disease	56,101 (4.4)	34,988 (5.2)	5,412 (3.1)	5,434 (3.1)	10,267 (4.0)	< 0.001
Metabolic syndrome	403,285 (31.4)	200,844 (29.7)	46,447 (26.5)	55,384 (31.6)	100,610 (39.3)	< 0.001
Low income	246,996 (19.3)	146,443 (21.6)	26,449 (15.1)	25,896 (14.8)	48,208 (18.9)	< 0.001
Regular exercise	258,432 (20.2)	136,451 (20.2)	37,692 (21.5)	35,741 (20.4)	48,548 (19.0)	< 0.001
Smoking status
Never	676,602 (52.8)	676,602 (100)	0	0	0	NA
Former	229,908 (17.9)	0	84,977 (48.5)	64,773 (37.0)	80,158 (31.3)
Current	376,192 (29.3)	0	90,246 (51.5)	110,371 (63.0)	175,575 (68.7)
Smoking amount
None	676,602 (52.8)	NA	NA	NA	NA	NA
Light (< 10 PY)	175,223 (13.7)	NA	NA	NA	NA
Moderate (10-20 PY)	175,144 (13.7)	NA	NA	NA	NA
Heavy (≥ 20 PY)	255,733 (19.9)	NA	NA	NA	NA
Drinking amount
None	638,072 (49.7)	471,829 (69.7)	48,342 (27.6)	44,446 (25.4)	73,455 (28.7)	< 0.001
Mild (< 30 g/day)	523,323 (40.8)	187,200 (27.7)	107,952 (61.6)	104,050 (59.4)	124,121 (48.5)
Heavy (≥ 30 g/day)	121,307 (9.5)	17,573 (2.6)	18,929 (10.8)	26,648 (15.2)	58,157 (22.7)
F/U duration, median (Q1-Q3)	6.7 (5.1-8.5)	6.7 (5.1-8.5)	6.7 (5.1-8.5)	6.6 (5.0-8.5)	6.7 (5.0-8.5)	< 0.001

	Alcohol consumption
	None (n=638,072)	Mild (< 30 g/day) (n=523,323)	Heavy (≥ 30 g/day) (n=121,307)	p-value
Age at HPE (cont, yr)	54.7±11.2	49.4±10.6	49.6±10.1	< 0.001
Male sex	242,892 (38.1)	398,259 (76.1)	114,647 (94.5)	< 0.001
BMI (cont, kg/m²)	23.9±3.2	24.1±3.1	24.5±3.1	< 0.001
WC (cont, cm)	80.3±9.1	82.4±8.7	85.1±8.2	< 0.001
Systolic BP (mmHg)	122.2±14.9	123.3±14.2	127.0±14.3	< 0.001
Diastolic BP (mmHg)	75.6±9.8	77.4±9.9	80.0±10.0	< 0.001
FPG (cont, mg/dL)	98.8±24.0	99.6±23.3	104.4±27.7	< 0.001
Total cholesterol	198.0±38.2	197.2±36.4	198.5±37.3	< 0.001
Obesity	218,617 (34.3)	190,914 (36.5)	52,546 (43.3)	< 0.001
Abdominal obesity	145,070 (22.7)	116,431 (22.3)	35,147 (29.0)	< 0.001
Diabetes mellitus	77,787 (12.2)	53,141 (10.2)	16,939 (14.0)	< 0.001
Hypertension	199,363 (31.2)	151,028 (28.9)	45,045 (37.1)	< 0.001
Dyslipidemia	180,955 (28.4)	113,824 (21.8)	27,834 (23.0)	< 0.001
Chronic kidney disease	36,874 (5.8)	16,198 (3.1)	3,029 (2.5)	< 0.001
Metabolic syndrome	205,830 (32.3)	151,843 (29.0)	45,612 (37.6)	< 0.001
Low income	139,104 (21.8)	87,779 (16.8)	20,113 (16.6)	< 0.001
Regular exercise	124,468 (19.5)	109,744 (21.0)	24,220 (20.0)	< 0.001
Smoking status
Never	471,829 (74.0)	187,200 (35.8)	17,573 (14.5)	< 0.001
Former	70,572 (11.1)	127,477 (24.4)	31,859 (26.3)
Current	95,671 (15.0)	208,646 (39.9)	71,875 (59.3)
Smoking amount
None	471,829 (74.0)	187,200 (35.8)	17,573 (14.5)	< 0.001
Light (< 10 PY)	48,342 (7.6)	107,952 (20.6)	18,929 (15.6)
Moderate (10-20 PY)	44,446 (7.0)	104,050 (19.9)	26,648 (22.0)
Heavy (≥ 20 PY)	73,445 (11.5)	124,121 (23.7)	58,157 (47.9)
Drinking amount
None	NA	NA	NA	NA
Mild (< 30 g/day)	NA	NA	NA
Heavy (≥ 30 g/day)	NA	NA	NA
F/U duration, median (Q1-Q3)	6.7 (5.1-8.5)	6.7 (5.0-8.5)	6.7 (5.0-8.5)	< 0.001

Continuous variables (mean±standard deviation); categorical variables (number [%]). BMI, body mass index; BP, blood pressure; cont, continuous; F/U, follow-up; FPG, fasting plasma glucose; HPE, Helicobacter pylori eradication; NA, not applicable; PY, pack-year; WC, waist circumference.

	No.	Gastric cancer	Duration	IR	Model 1^a) HR (95% CI)	Model 2^b) aHR (95% CI)	Model 3^c) HR (95% CI)
Smoking amount
None	676,602	4,242	4,594,393.53	0.92	1 (reference)	1 (reference)	1 (reference)
Light (< 10 PY)	175,223	1,010	1,186,527.12	0.85	0.92 (0.86-0.99)	1.02 (0.94-1.10)	1.01 (0.93-1.08)
Moderate (10-20 PY)	175,144	1,297	1,175,930.18	1.10	1.19 (1.12-1.27)	1.14 (1.06-1.22)	1.12 (1.04-1.20)
Heavy (≥ 20 PY)	255,733	3,205	1,715,315.95	1.87	2.02 (1.93-2.12)	1.38 (1.31-1.46)	1.34 (1.27-1.42)
Alcohol consumption
None	638,072	4,719	4,328,392.31	1.09	1 (reference)	1 (reference)	1 (reference)
Mild (< 30 g/day)	523,323	3,859	3,528,468.84	1.09	1.00 (0.96-1.05)	1.08 (1.03-1.13)	1.05 (0.998-1.10)
Heavy (≥ 30 g/day)	121,307	1,176	815,305.63	1.44	1.32 (1.24-1.41)	1.30 (1.22-1.39)	1.23 (1.15-1.32)
Abdominal obesity
No	986,054	6,943	6,698,712.79	1.04	1 (reference)	1 (reference)	1 (reference)
Yes	296,648	2,811	1,973,453.99	1.42	1.37 (1.32-1.44)	1.11 (1.06-1.16)	1.11 (1.06-1.15)

	Lag period 2.5 years					Lag period 3.5 years
	No.	Gastric cancer	Duration	IR	aHR (95% CI)	No.	Gastric cancer	Duration	IR	aHR (95% CI)
Smoking amount
None	674,467	3,462	3,918,839.27	0.88	1 (reference)	672,285	2,942	3,245,434.92	0.91	1 (reference)
Light (< 10 PY)	174,659	818	1,011,581.30	0.81	0.99 (0.91-1.08)	174,102	700	837,200.20	0.84	1.00 (0.92-1.10)
Moderate (10-20 PY)	174,451	1,064	1,001,124.73	1.06	1.12 (1.04-1.21)	173,751	892	827,025.57	1.08	1.11 (1.02-1.21)
Heavy (≥ 20 PY)	253,873	2,613	1,460,464.70	1.79	1.34 (1.26-1.43)	251,988	2,253	1,207,517.18	1.87	1.36 (1.27-1.46)
Alcohol consumption
None	635,288	3,824	3,691,685.82	1.04	1 (reference)	632,373	3,246	3,057,831.33	1.06	1 (reference)
Mild (< 30 g/day)	521,476	3,162	3,006,031.14	1.05	1.06 (1.003-1.11)	519,669	2,698	2,485,452.05	1.09	1.06 (1.002-1.12)
Heavy (≥ 30 g/day)	120,686	971	694,293.04	1.40	1.25 (1.16-1.35)	120,084	843	573,894.49	1.47	1.28 (1.18-1.39)
Abdominal obesity
No	982,312	5,661	5,714,487	0.99	1 (reference)	978,517	4,809	4,734,035.00	1.02	1 (reference)
Yes	295,138	2,296	1,677,522.62	1.37	1.11 (1.06-1.17)	293,609	1,978	1,383,142.88	1.43	1.13 (1.07-1.19)