Abstract
Background
While Helicobacter pylori (H. pylori) infection is associated with insulin resistance and higher mortality, research on insulin resistance indices and outcomes in H. pylori-infected patients is scarce. This study examines the association between the triglyceride-glucose (TyG) index, an insulin resistance marker, and all-cause and cardiovascular mortality in these patients.
Methods
This study analyzed NHANES 1999–2000 data to assess the association between the TyG index and all-cause and cardiovascular mortality in H. pylori-infected patients using weighted Cox models and restricted cubic spline analysis.
Results
Among 627 participants with a median follow-up of 20.8 years, 108 all-cause and 28 cardiovascular deaths occurred. Cox models showed that TyG was linked to a hazard ratio (HR) of 1.70 for all-cause mortality (95% CI: 1.23–2.34, P < 0.01) and an HR of 2.90 for cardiovascular mortality (95% CI: 1.91–4.42, P < 0.001). Restricted cubic spline analysis confirmed a linear relationship between the TyG index and both mortality risks. Stratified analyses showed that this relationship was significantly associated in most subgroups, but there was no significant interaction.
Conclusion
Higher TyG index is strongly linked to increased risks of both all-cause and cardiovascular mortality in H. pylori-infected patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-025-10556-8.
Keywords: Helicobacter pylori, Cardiovascular diseases, Mortality, Insulin resistance
Background
Helicobacter pylori (H. pylori) is a common gastrointestinal pathogen affecting people globally [1]. This bacterium is linked with several gastrointestinal disorders, including chronic gastritis, peptic ulcers, and gastric cancer [2]. As a leading cause of gastric cancer, H. pylori represents a considerable public health issue worldwide [3]. In addition to its impact on gastrointestinal health, H. pylori infection is associated with cardiovascular disease development and progression [4]. Potential mechanisms for increased cardiovascular risk include systemic inflammation, atherosclerosis promotion, and disturbances in lipid metabolism [4, 5]. Epidemiological studies have found notable associations between H. pylori infection and cardiovascular conditions such as coronary heart disease, hypertension, and cerebrovascular accidents [6–8], which may contribute to elevated cardiovascular mortality.
The triglyceride-glucose (TyG) index, calculated as the product of triglycerides and fasting glucose, serves as a useful and accessible marker for insulin resistance [9]. It is considered a practical and dependable measure compared to more complex insulin resistance assessments [10]. Although the TyG index has been widely demonstrated to be associated with cardiovascular diseases, diabetes, and adverse outcomes in the general population [11–13], it remains unclear whether these associations hold true in individuals with H. pylori infection. H. pylori infection may exacerbate insulin resistance through mechanisms such as chronic inflammation and impaired glucolipid metabolism [14–16], leading to an elevated TyG index. This unique pathological impact of H. pylori infection may alter the relationship between the TyG index and adverse outcomes.
Therefore, this study aims to explore the link between the TyG index, a marker of insulin resistance, and mortality outcomes in H. pylori-infected individuals.
Methods
Study design and cases
This study used data from the National Health and Nutrition Examination Survey (NHANES), which employs a complex sampling methodology to assess the health and nutritional status of the U.S. population. NHANES gathers representative data from Americans biennially through clinical measurements, laboratory tests, and questionnaires. The survey was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board [17].
For this analysis, data from the NHANES 1999–2000 cycle were reviewed, encompassing 9,965 participants. Exclusions were made based on the availability of H. pylori infection status, follow-up information, TyG index data, H. pylori infection negativity, weighting information, and age criteria. Ultimately, 627 participants aged 18–65 years were included for analysis (Fig. 1).
Fig. 1.
Flow chart for inclusion and exclusion of the study population
Assessment of H. pylori infection status
H. pylori infection status was determined by measuring serum IgG antibodies using enzyme-linked immunosorbent assay (ELISA). The ELISA method is comparable to other serologic tests for antibodies [18]. A positive result was defined as an optical density (OD) value of ≥ 1.1 [19].
Mortality assessment
Mortality data from NHANES were classified by cause using the International Classification of Diseases, Tenth Edition (ICD-10). Cardiovascular deaths were categorized into heart disease (ICD codes I00-I09, I11, I13, I20-I51) and cerebrovascular disease (ICD codes I60-I69) [20].
TyG index assessment
The TyG index, used as the exposure variable, was calculated from fasting triglyceride and fasting glucose levels using the formula: TyG = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2] [21].
Identification of covariates
Covariates from the NHANES database included age, gender, race, education, BMI, smoking status, alcohol consumption, hypertension, and diabetes. Smoking was defined as consuming at least 100 cigarettes in a lifetime [22], and alcohol consumption as having at least 12 drinks per year [23]. Hypertension, diabetes and lipid-lowering drugs were self-reported.
Statistical analysis
Data were weighted for national representativeness, and missing covariates were imputed. Continuous variables are presented as means with weighted standard errors, and categorical variables as frequencies with weighted percentages. We employed three weighted Cox proportional hazards models to assess the TyG index’s impact on all-cause and cardiovascular mortality, both continuously and by tertiles. Model 1 was unadjusted; Model 2 adjusted for age, sex, race, education, and BMI; Model 3 further adjusted for smoking, alcohol consumption, hypertension, diabetes and lipid-lowering drugs. Discrimination was assessed using the concordance index (C-index). Stratified and interaction analyses considered gender, BMI, smoking, alcohol use, and hypertension. Nonlinear relationships were explored using restricted cubic spline analysis, and sensitivity analyses excluded covariates with missing data. Statistical significance was set at P < 0.05, with analyses conducted using R software (version 4.2.2).
Results
Baseline characteristics
A total of 627 participants with H. pylori infection were assessed. Table 1 details the baseline characteristics across TyG tertiles. Higher TyG levels were associated with older age, increased prevalence of diabetes, elevated blood pressure, and higher levels of metabolic markers (total cholesterol, triglycerides, fasting glucose, HbA1c, fasting LDL). HDL levels were lower in those with higher TyG. No significant differences were observed in BMI, PIR, and CRP.
Table 1.
Weighted baseline characteristics of included subjects
| Tertile 1 (7.27–8.39, N = 209) |
Tertile 2 (8.39–8.95, N = 209) |
Tertile 3 (8.95–12.48, N = 209) |
P value | |
|---|---|---|---|---|
| TyG | 8.04(0.03) | 8.68(0.02) | 9.42(0.04) | < 0.001 |
| Age (years), mean(SE) | 36.62(1.20) | 42.32(0.93) | 46.94(1.32) | < 0.001 |
| BMI, mean(SE) | 26.70(0.50) | 28.07(0.54) | 28.66(0.59) | 0.10 |
| PIR, mean(SE) | 2.55(0.17) | 2.50(0.20) | 2.54(0.16) | 0.96 |
| Gender, n(%) | 0.15 | |||
| Male | 92(44.08) | 114(59.50) | 101(52.97) | |
| Female | 117(55.92) | 95(40.50) | 108(47.03) | |
| Race, n(%) | 0.02 | |||
| White | 22(30.26) | 42(43.53) | 36(48.99) | |
| Black | 81(32.68) | 39(14.37) | 24( 8.88) | |
| Mexican American | 77(12.14) | 100(15.37) | 126(19.34) | |
| Other Race | 29(24.92) | 28(26.72) | 23(22.79) | |
| Education, n(%) | 0.45 | |||
| Less Than High School | 101(36.26) | 112(41.26) | 128(48.01) | |
| High School Diploma | 47(26.20) | 40(21.49) | 30(16.08) | |
| More Than High School | 61(37.53) | 57(37.25) | 51(35.91) | |
| Smoking, n(%) | 0.09 | |||
| No | 132(57.04) | 104(43.74) | 101(38.39) | |
| Yes | 77(42.96) | 105(56.26) | 108(61.61) | |
| Alcohol Drinking, n(%) | 0.03 | |||
| No | 76(31.61) | 55(19.94) | 66(27.77) | |
| Yes | 133(68.39) | 154(80.06) | 143(72.23) | |
| Hypertension, n(%) | 0.08 | |||
| No | 176(85.09) | 159(77.53) | 146(68.87) | |
| Yes | 33(14.91) | 50(22.47) | 63(31.13) | |
| Diabetes, n(%) | < 0.001 | |||
| No | 3( 1.64) | 6( 1.83) | 31(14.00) | |
| Yes | 206(98.36) | 203(98.17) | 178(86.00) | |
| lipid-lowering drugs | 0.35 | |||
| No | 135(67.90) | 141(73.57) | 125(59.90) | |
| Yes | 74(32.10) | 68(26.43) | 84(40.10) | |
| All-Cause Death, n(%) | < 0.01 | |||
| No | 189(93.09) | 180(82.91) | 150(72.92) | |
| Yes | 20( 6.91) | 29(17.09) | 59(27.08) | |
| Cardiovascular Death, n(%) | < 0.01 | |||
| No | 204(98.38) | 206(97.78) | 189(90.92) | |
| Yes | 5(1.62) | 3(2.22) | 20(9.08) | |
| SBP, mmHg, mean(SE) | 117.25(1.35) | 122.14(2.50) | 125.03(1.09) | < 0.01 |
| DBP, mmHg, mean(SE) | 71.50(0.70) | 73.16(0.86) | 75.26(1.00) | 0.04 |
| TC, mg/dL, mean(SE) | 178.54(4.02) | 204.51(4.38) | 215.51(4.17) | < 0.001 |
| HDL, mg/dL, mean(SE) | 53.99(1.44) | 46.70(1.52) | 40.41(0.96) | < 0.001 |
| LDL, mg/dL, mean(SE) | 110.59(3.50) | 132.75(4.10) | 127.08(3.01) | < 0.001 |
| TG, mg/dL, mean(SE) | 70.16(2.07) | 125.26(2.23) | 240.95(9.41) | < 0.001 |
| GLU, mg/dL, mean(SE) | 91.05(0.72) | 96.46(1.24) | 119.98(4.98) | < 0.001 |
| SCR, mmol/L, mean(SE) | 60.11(1.72) | 65.45(2.65) | 60.67(2.12) | 0.29 |
| ALT, IU/L, mean(SE) | 44.63(0.23) | 44.83(0.32) | 44.63(0.47) | 0.72 |
| AST, IU/L, mean(SE) | 22.76(0.80) | 27.32(2.03) | 27.37(2.41) | 0.03 |
| HBA1C, %, mean(SE) | 5.17(0.03) | 5.33(0.07) | 5.94(0.13) | < 0.001 |
| CRP, mg/dL, mean(SE) | 0.34(0.04) | 0.38(0.04) | 0.51(0.07) | 0.13 |
SE: standard error; BMI, body mass index; PIR, poverty income ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein; SCR, serum creatinine; GLU, bloodglucose; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CRP, C-reactive protein
Survival analysis
Over a median surveillance of 20.8 years, there were 108 all-cause deaths and 28 cardiovascular deaths. Additional follow-up information, including the average follow-up period, total person-years, and incidence rates per 1000 person-years, is provided in Supplementary Table 1. Weighted Kaplan-Meier survival curves showed that higher TyG levels were associated with both higher all-cause and cardiovascular mortality (both P < 0.01, Fig. 2). Weighted Cox proportional hazards models evaluated the association between the TyG index and both all-cause and cardiovascular mortality (Table 2). Higher TyG levels, analyzed as a continuous variable, were significantly associated with increased mortality risks. In the fully adjusted Model 3, TyG was linked to a hazard ratio (HR) of 1.70 for all-cause mortality (95% CI: 1.23–2.34, P < 0.01) and an HR of 2.90 for cardiovascular mortality (95% CI: 1.91–4.42, P < 0.001). When divided into tertiles, the highest TyG tertile had significantly higher risks for all-cause (HR: 2.09, 95% CI: 1.14–3.81, P = 0.02) and cardiovascular mortality (HR: 2.99, 95% CI: 1.11–8.02, P = 0.03).
Fig. 2.
Kaplan-Meier Curves for the Association Between TyG and All-Cause Mortality (A) and Cardiovascular Mortality (B)
Table 2.
The association between the TyG index and mortality
| Model 1 HR(95% CI) P value |
Model 2 HR(95% CI) P value |
Model 3 HR(95% CI) P value |
|
|---|---|---|---|
| All-cause mortality | |||
| TyG (continuous) | 2.22(1.75,2.80) < 0.001 | 1.89(1.42,2.51) < 0.001 | 1.70(1.23,2.34) < 0.01 |
| Tertile 1 | ref | ref | ref |
| Tertile 2 | 2.62(1.50,4.55) < 0.001 | 1.78(1.17,2.7 ) < 0.01 | 1.73(1.11,2.71) 0.02 |
| Tertile 3 | 4.31(2.11,8.79) < 0.001 | 2.27(1.22,4.24) < 0.01 | 2.09(1.14,3.81) 0.02 |
| P for trend | < 0.001 | 0.03 | 0.04 |
| CVD mortality | |||
| TyG (continuous) | 3.12(1.91,5.10) < 0.001 | 2.98(1.72,5.16) < 0.001 | 2.90(1.91, 4.42) < 0.001 |
| Tertile 1 | ref | ref | ref |
| Tertile 2 | 1.46(0.29,7.30) 0.64 | 1.00(0.26,4.39) 0.01 | 0.99(0.24,4.07) 0.99 |
| Tertile 3 | 6.16(2.16,17.59) < 0.001 | 3.14(1.29,7.62) < 0.01 | 2.99(1.11,8.02) 0.03 |
| P for trend | < 0.001 | 0.01 | 0.01 |
Model 1: Non-adjusted
Model 2: Adjusted for age, gender, race, education, BMI
Model 3: Further adjusted for smoking, alcohol drinking, hypertension, diabetes and lipid-lowering drugs based on model 2
HR: Hazard ratio; CI: Confidence interval
RCS analysis
A linear relationship was observed between TyG and all-cause (Pfor nonlinear = 0.19) and cardiovascular mortality (Pfor nonlinear = 0.45) via RCS analysis, with a notable increase in risk when TyG exceeded 8.63 (Fig. 3).
Fig. 3.
The nonlinear relationship between TyG and all-cause mortality (A) and cardiovascular mortality (B)
Stratified analyses
Stratified analyses indicated a significant positive association between TyG and both all-cause and cardiovascular mortality in most subgroups (Fig. 4). Interaction analyses did not reveal significant effects between TyG and mortality risks across the subgroups.
Fig. 4.
Stratified analyses of the associations between TyG and all-cause mortality (A) and cardiovascular mortality (B)
Predictive efficiency of TyG and its components
After adjusting for all covariates, the TyG index showed better prognostic discrimination for all-cause and cardiovascular mortality than its components, fasting blood glucose and fasting triglycerides, based on C-index analysis (Table 3).
Table 3.
C-index for Mortality Prediction using TyG and its components
| All-cause mortality | CVD mortality | |
|---|---|---|
| TyG | 0.799(0.758,0.841) | 0.840(0.771,0.909) |
| Fasting blood glucose | 0.797(0.755,0.839) | 0.826(0.758,0.893) |
| Fasting triglycerides | 0.795(0.753,0.837) | 0.833(0.768,0.898) |
Adjusted for age, gender, race, education, BMI, smoking, alcohol drinking, hypertension, diabetes and lipid-lowering drugs
Sensitivity analysis
E-values for TyG in predicting all-cause and cardiovascular mortality were calculated as 2.79 and 5.25, respectively (Table 4). These values indicate that the observed associations between TyG and mortality remain robust even in the presence of potential unmeasured confounding factors.
Table 4.
E-values for the Association between TyG and all-cause Mortality and Cardiovascular Mortality
| E-value | |
|---|---|
| All-cause mortality | 2.79 |
| CVD mortality | 5.25 |
Analysis for H. pylori-negative population
TyG levels were significantly higher in the H. pylori-positive population (N = 627) compared to the H. pylori-negative population (N = 949) (Supplementary Fig. 1). In the H. pylori-negative population, analysis using weighted Cox proportional hazards models showed no statistically significant association between TyG and all-cause or cardiovascular mortality after adjusting for all covariates (Supplementary Table 2).
Discussion
This study identifies a significant association between higher TyG index levels and increased all-cause and cardiovascular mortality in H. pylori-infected patients. Individuals in the highest TyG tertile had a 109% higher risk of all-cause mortality and a 199% higher risk of cardiovascular mortality. A linear relationship was found between the TyG index and mortality risks, with a critical threshold of 8.63 for both outcomes.
H. pylori infection can lead to insulin resistance and systemic inflammation, which may elevate cardiovascular risk and mortality [24, 25]. Our recent meta-analysis, including 41 cohort studies with 230,288 participants, demonstrated a slight increase in CVD risk associated with H. pylori infection [4]. Additionally, another meta-analysis of 41 studies involving 9,559 participants found that H. pylori infection was more strongly associated with type 2 diabetes or general diabetes than type 1 diabetes, indicating a closer link to insulin resistance-related conditions [26]. While the link between the dietary inflammation index and H. pylori infection-related mortality has been noted [27], the role of insulin resistance markers, such as TyG, has not been extensively reported. Our findings, which align with previous research [28–30], highlight the TyG index as a strong predictor of adverse health outcomes in this population. Furthermore, as seen in prior studies [31], this study observed significantly higher TyG index levels in H. pylori-positive individuals compared to those without the infection. However, no statistically significant association was found between the TyG index and all-cause or cardiovascular mortality in H. pylori-negative individuals. These results suggest that the predictive value of the TyG index is particularly pronounced in H. pylori-positive populations, offering greater clinical relevance in this subgroup.
As a marker of insulin resistance, the TyG index correlates with cardiovascular risk factors, including hyperglycemia, hypertension, and dyslipidemia [11, 12]. Insulin resistance contributes to endothelial dysfunction, atherosclerosis, and myocardial metabolic disorders [32], increasing cardiovascular disease and mortality risk. Our findings align with these mechanisms: higher TyG tertiles are associated with elevated blood pressure, glucose levels, HbA1c, triglycerides, total cholesterol, and LDL, and decreased HDL. Additionally, a higher TyG index indicates disturbances in glucose and lipid metabolism, notably elevated triglycerides and glucose. These abnormalities can drive systemic inflammation and oxidative stress, exacerbating cardiovascular disease [12]. Previous research has also linked oxidative balance with H. pylori infection and mortality [33]. Overall, our results validate the TyG index as a valuable tool for stratifying risks.
This study uses nationally representative NHANES data, enhancing the generalizability of the findings, and robustness is further supported by multiple subgroup and sensitivity analyses. There are several limitations in our study. First, as an observational study, it cannot establish causality. Second, serologic Helicobacter pylori IgG as a diagnostic tool may not fully capture the current infection status, as it cannot differentiate between active and past infections. Third, the NHANES data used in this study were limited to 1999–2000 due to the availability of H. pylori serologic data in these two years, and the relatively small sample size may limit the generalizability of the findings. Additionally, potential confounding factors such as dietary habits and physical activity were not accounted for.
Conclusion
A higher TyG index, indicative of insulin resistance, is linked to increased all-cause and cardiovascular mortality in H. pylori-infected patients. Evaluating the TyG index could enhance risk assessment for this group.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1: Supplementary Fig. 1. Comparison of TyG Index Levels Between H. pylori-Positive and H. pylori-Negative Groups
Supplementary Material 2: Supplementary Table 1. Mortality Rates of the H. pylori-Infected Population. Supplementary Table 2. The association between the TyG index and mortality in the H. pylori-negative population (N = 949).
Acknowledgements
Not applicable.
Abbreviations
- TyG
Triglyceride-Glucose
- H. pylori
Helicobacter pylori
- ELISA
Enzyme-Linked Immunosorbent Assay
- BMI
Body Mass Index
- NHANES
National Health and Nutrition Examination Survey
- RCS
Restricted Cubic Splines
- HR
Hazard Ratio
- CI
Confidence Interval
- NCHS
National Centre for Health Statistics
- SE
Standard Error
Author contributions
X.Y. Cai, and Y.L. Huang share the role of corresponding authors. Y. Cao, L.X. Li, F.P. Qiu W.X. Wen, Y.X. Chen and conducted a literature search and collected information. The data was analyzed by Y. Cao, L.X. Li. The manuscript was drafted by Y. Cao, L.X. Li, H. Zhang, X.Y. Cai, and Y.L. Huang, and then critically revised by all authors for important intellectual content. The study was overseen by Y.L. Huang. The final manuscript has been reviewed and approved by all authors. Y.L. Huang is responsible for the guarantee. Corresponding authors confirm that all authors listed satisfy the criteria for authorship.
Funding
This work was supported by the Guangzhou Science and Technology Plan Project (2023B01J1011), Guangdong Medical Research Foundation Project (B2023061), Guangdong Medical Research Foundation (A2024382), Guangdong Province Basic and Applied Basic Research Fund (2023B1151120088), GuangDong Basic and Applied Basic Research Foundation (2019B1515120044), Guangdong Provincial Bureau of Traditional Chinese Medicine Research Project (20231321), Scientific Research Start Plan of Shunde Hospital, Southern Medical University (SRSP2022016, SRSP2022012, SRSP2023017).
Data availability
All data were sourced from the publicly available NHANES. All statistical code are available by contrating the corresponding authors with proper reason.
Declarations
Ethics approval and consent to participate
All data were sourced from the publicly available NHANES, which received informed consent from all participants and was ethically approved by the NCHS Research Ethics Review Board.
Consent for publication
Not applicable.
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.
Yue Cao and Lingxiao Li share the first authorship.
Contributor Information
Xiaoyan Cai, Email: 454479384@smu.edu.cn.
Yuli Huang, Email: hyuli821@smu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 1: Supplementary Fig. 1. Comparison of TyG Index Levels Between H. pylori-Positive and H. pylori-Negative Groups
Supplementary Material 2: Supplementary Table 1. Mortality Rates of the H. pylori-Infected Population. Supplementary Table 2. The association between the TyG index and mortality in the H. pylori-negative population (N = 949).
Data Availability Statement
All data were sourced from the publicly available NHANES. All statistical code are available by contrating the corresponding authors with proper reason.