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Temporal relationship between chronic inflammation and insulin resistance and their combined cumulative effect on cancer risk: a longitudinal cohort study

BMC Public Health volume 25, Article number: 1501 (2025) Cite this article

Abstract

Background

Cancer, a chronic and dangerous disease, poses a major public health burden. Inflammation and insulin resistance promote tumorigenesis. However, the temporal relationship between the two and their relationship with cancer risk must be elucidated.

Objective

This study aimed to investigate the association between chronic inflammation, insulin resistance, and the propensity for cancer incidence.

Methods

We explored the temporal relationship between triglyceride index (TyG) and high-sensitivity C-reactive protein (hsCRP) levels using cross-lagged modeling. We used COX proportional risk regression modeling to explore the association between high cumulative triglyceride and glucose index (CumTyG) and high cumulative high-sensitivity C-reactive Protein (CumhsCRP) and cancer risk. We further stratified CumTyG according to tertiles to explore the association of CumhsCRP with cancer risk at different insulin resistance levels and vice versa. We analyzed the association of combined chronic inflammation with insulin resistance, risk of different cancer types, and all-cause mortality. Finally, we performed two sensitivity analyses, excluding patients who developed cancer within the first year of follow-up and those with hsCRP levels > 10 mg/L.

Results

The results of the study showed that the standardized correlation coefficient (β1) between hsCRP_2006/2007 and TyG_2010/2011 was 0.02306, which was significantly higher than the correlation (β2) between TyG_2006/2007 and hsCRP_2010/2011, suggesting that inflammation played a more prominent role in future changes in insulin resistance. Chronic inflammation and insulin resistance are positively and synergistically associated with cancer risk, with high chronic inflammation and high insulin levels increasing the risk of carcinogenesis by 71%. Although CumTyG in different CumhsCRP strata and CumhsCRP in different CumTyG strata promoted carcinogenesis, there were differences in the extent of carcinogenesis. High inflammation and insulin resistance, which promote cancer onset, are closely associated with digestive system cancers. The sensitivity analysis was consistent with the primary results and verified their reliability.

Conclusions

This study revealed the potential impact of inflammation on future changes in insulin resistance. There is a synergy and interaction between chronic inflammation and insulin resistance, which promotes the risk of cancer.

Trial registration number

ChiCTR2000029767 (https://www.chictr.org.cn/showproj.html?proj=48316).

Trial registration date

February 13, 2020.

Peer Review reports

Introduction

Cancer is a chronic and detrimental disease. With the expansion and aging of the global population, the incidence and mortality rates of cancer are progressively rising, imposing a substantial burden on public health [1, 2]. Cancer remains the major barrier to life expectancy and the major public health burden in every country [1, 2].

The innate and adaptive immune systems generate inflammation to eliminate detrimental stimuli, including irritants, sterile lesions, pathogens, and damaged cells, thereby maintaining tissue homeostasis. Therefore, inflammation is an immune response [3, 4]. However, the inflammatory dynamics during cancer progression deviate from this norm. Specifically, acute inflammation triggers an antitumor immune reaction, whereas chronic inflammation fosters immunosuppression, culminating in the emergence of an immunosuppressive tumor microenvironment conducive to tumor initiation, advancement, and metastasis [3, 5]. Notably, persistent and dysregulated chronic inflammation is closely linked to an elevated susceptibility to cancer development [3, 5]. This association underscores why cancer-related inflammation is one of the 14 distinct hallmarks of cancer [6].

Moreover, numerous epidemiological studies have consistently underscored the association between insulin resistance and an elevated risk of diverse cancers [7]. When circulating glucose levels increase, insulin-sensitive cells in the muscle, fat, and liver respond by taking up glucose to maintain insulin sensitivity coordination [7,8,9,10]. Disruptions in any component of the insulin receptor or pathway can precipitate insulin resistance. This condition is characterized by impaired glucose uptake and inhibition of hepatic glucose production [7].

An intricate interplay exists between inflammation and insulin resistance. Previous studies have investigated the relationship between heightened baseline insulin resistance, inflammation, and prognosis in cancer patients [11,12,13]. However, it is essential to recognize that inflammation and insulin resistance are subject to dynamic changes. While concurrent escalation in inflammation and insulin resistance has been noted, only a limited number of studies have comprehensively analyzed the temporal relationship between these factors and the effect of their combination on cancer development. To address this research gap, this study investigated the association between chronic inflammation, insulin resistance, and propensity for cancer incidence. Over a span of approximately 4 years prior to the follow-up period, we used the Cumulative Triglyceride and Glucose Index (CumTyG) and cumulative high-sensitivity C-reactive protein (CumhsCRP) as exposures. Subsequently, a cross-lagged model was constructed to dissect the temporal causality between inflammation and insulin resistance and elucidate their combined influence on the occurrence of cancer.

Methods

Recruitment

The Kailuan Study is a prospective cohort study conducted in Tangshan, China and was initiated in 2006. The trial registration is ChiCTR2000029767 (https://www.chictr.org.cn/showproj.html?proj=48316). The participants enrolled in this cohort underwent comprehensive questionnaires (supplementary material 1), whole-body examinations, and updates on their health status every 2 years. The study design and procedural framework have been described in previous reports [14, 15]. Initially, 92,967 participants were included in this study. However, 45, 227 participants were excluded based on the inclusion criteria. Among them, 2,684 participants had missing values for high-sensitivity C-reactive protein (hsCRP) from visit_2006/2007 to visit_2010/2011. Additionally, 35,205 participants had incomplete follow-up data, 7,238 lacked covariate values, and 430 had a history of cancer. Consequently, 47,310 participants were included in the final analysis (Fig. 1).

Fig. 1
figure 1

Strategies and design of the current study

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Outcome assessment

The primary objective of this study was to assess cancer risk during the follow-up period. The determination of cancer occurrence relied predominantly on participant interviews and hospital diagnostic records. The categorization and nomenclature of the cancer types were aligned using the International Classification of Diseases-10 codes. The various cancer types were categorized into distinct groups: respiratory cancers (C33, C34 and C3), digestive cancers (C15-C26), and other systemic cancers (all codes commencing with “C”) [16]. This approach enabled us to explore potential variations in the impact of the exposure variables on the risk of different cancers.

Exposure assessment

Figure 2 illustrates the research strategy used in this study. The exposure period spanned from 2006/2007 to 2010/2011. Cumulative indicators were computed as outlined below, using CumhsCRP as an exemplar: (hsCRP_2006/2007 + hsCRP_2008/2009)/2 × (Visit 2 1) + (hsCRP_2008/2009 + hsCRP_2010/2011)/2 × (Visit 3 2), where “Visit 2 1” and “Visit 3 2” denote the time interval between two successive health surveys. The TyG index was determined using the following equation: ln (triglyceride (TG) [mg/dL] × fasting blood glucose (FBG) [mg/dL]/2). As specific threshold criteria for CumhsCRP are absent, we employed the clinically recommended threshold values for transient hsCRP (1 and 3 mg/L) as thresholds for CumhsCRP [17]. The CumTyG values were dichotomized based on the median value (8.616). To facilitate comparison, the mean, standard deviation, and quartile values of CumhsCRP and CumTyG are displayed in Table S1.

Fig. 2
figure 2

Flow chart

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Covariates

Demographic variables, including age, sex, education, lifestyle factors (such as smoking habits, alcohol, and tea consumption), and occupation were obtained using standardized questionnaires. Furthermore, comprehensive records of medical history and medication use were collected, including data on fatty liver, hypertension, diabetes mellitus, hyperlipidemia, and the use of antihyperlipidemic, hypoglycemic, and antihypertensive medications. Laboratory analyses of lipid composition (high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), total cholesterol (TC), and TG), FBG, and hsCRP levels were conducted using automated analyzers (Hitachi 747; Hitachi, Tokyo, Japan) [18]. Anthropometric data, including height and weight, were collected and body mass index (BMI) was computed according to the established formula: BMI = weight/height (kg/m²) [19]. Current smokers were defined as those who smoked at least one cigarette per day in the past year. Alcohol consumption status was categorized based on the average volume consumed in the preceding year. Educational attainment was stratified into middle school and below or high school and above, whereas occupational roles were differentiated as either mental or physical.

Statistical analysis

Given the large sample size of this study, we use skewness and kurtosis tests to assess whether the continuous variables follow a normal distribution. Continuous variables are represented as mean (standard deviation [SD]) or median (interquartile range [IQR]), and categorical variables are presented as numbers (percentages). Normally distributed continuous variables were assessed using analysis of variance (ANOVA), whereas skewed continuous variables were assessed using the Kruskal–Wallis test. Categorical variables were analyzed using the chi-square test to ascertain differences between the groups at baseline. Cross-lagged panel modeling was used to evaluate the temporal relationship and lagged effects of hsCRP and TyG levels across different stages. In this context, the exogenous variables included hsCRP_2006/2007 and TyG_2006/2007, and the endogenous variables included hsCRP_2010/2011 and TyG_2010/2011. This model comprises of cross-lagged and autoregressive paths. Path coefficients (β1) were calculated for hsCRP_2006/2007 versus TyG_2010/2011 and TyG_2006/2007 versus hsCRP_2010/2011 (β2) while accounting for autoregressive influences. Statistical differences in β1 and β2 were examined using a t test. To investigate the association between the combination of CumhsCRP and CumTyG and cancer risk, a Cox risk regression model was employed (this study met the proportional hazards assumption), and risk ratios (HR) and corresponding 95% confidence intervals (CI) were calculated. Model 1 was adjusted for age and sex; Model 2 further incorporated education, occupation, smoking status, alcohol consumption, tea consumption, physical activity, and BMI; and Model 3 was additionally adjusted for HDL-C, LDL-C, TC, antihypertensive, antihyperlipidemic, antihyperglycemic medications, hypertension, diabetes mellitus, hyperlipidemia, fatty liver, liver cirrhosis, hepatitis B, gallstones, and gallbladder polyps. We performed two sensitivity analyses to ensure robustness and consistency. First, participants who developed cancer within the first year were excluded to mitigate the potential reverse causality. Second, individuals with hsCRP levels ≥ 10 mg/L during the exposure period were excluded. All statistical analyses were conducted using SAS software (version 9.4; SAS Institute, Cary, NC, USA) and R software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). The SAS Proc Calis program was used for the cross-lag analysis. A significance threshold of two-sided P values < 0.05 was adopted.

Results

Temporal relationship between TyG and HsCRP

The results of the temporal analyses of hsCRP and TyG levels are shown in Fig. 3 and Table S2. After adjusting for confounding factors, the standardized correlation coefficient (β1) between hsCRP_2006/2007 and TyG_2010/2011 was 0.02306 (95% CI, 0.01442–0.03170; P < 0.001). In parallel, the correlation between TyG_2006/2007 and hsCRP_2010/2011 (β2) was 0.01555 (95% CI, 0.00559–0.02551; P < 0.001), significantly smaller compared to β1 (P < 0.001). These findings suggested an intricate interplay between hsCRP and TyG, with inflammation emerging as a more pronounced contributor to future alterations in insulin resistance.

Fig. 3
figure 3

Cross-lagged standard regression coefficient of hsCRP and TyG (n = 47,310). Note: *P < 0.001. The cross-lagged model was adjusted for age, sex, education, smoking status, drinking status, physical activities, BMI, antihypertensives, lipid-lowering drugs, and hypoglycemic drug measured in 2006/2007. Abbreviations: TyG, triglyceride glucose index; BMI, body mass index; HsCRP, high-sensitivity C-reactive protein

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Baseline characteristics

The Baseline was defined as the initiation of the follow-up period and comparative baseline data are presented in Table 1. The study cohort primarily comprised 36, 570 men (77.30%). At the start of the follow-up, the mean age (standard deviation) was 53.05 (11.89) years. A discernible pattern emerged: as chronic inflammation and insulin resistance escalated, participants exhibited progressively older age, elevated BMI, and increased concentrations of serum TC. Conversely, there was a progressive decrease in HDL-C levels. Additionally, participants in the high inflammation and high insulin resistance groups demonstrated a higher prevalence of conditions, such as fatty liver, cirrhosis, gallstones, hypertension, diabetes, and hyperlipidemia. They also reported greater utilization of hypolipidemic and hypoglycemic medications.

Table 1 Baseline characteristics
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Prospective study of CumhsCRP combined with CumTyG and risk of cancer incidence

Throughout the follow-up period, 2,104 instances of cancer were documented among 47,310 participants. This study showed a positive association between chronic inflammation, insulin resistance, and the risk of cancer incidence. Specifically, the second, third, and highest quartiles of CumTyG were linked to 19%, 34%, and 39% increase in the risk of cancer incidence, respectively. Similarly, moderate and high levels of chronic inflammation increased cancer risk by 17% and 42%, respectively (Table 2). By employing the low CumhsCRP and CumTyG group (group 1) as reference variables, we found that the risk of cancer increased as both chronic inflammation and insulin resistance intensified. For instance, in groups 2, 3, 4, 5, and 6, the risk of cancer increased by 18%, 45%, 29%, 45%, and 71%, respectively, illustrating the synergistic effect of severe inflammation and chronic insulin resistance on the risk of cancer.

Table 2 Cancer risk upon co-exposure stratified by CumhsCRP thresholds (1, 3 mg/L) and CumTyG (quartile)
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Furthermore, although CumTyG tended to promote cancer occurrence in various CumhsCRP strata, the extent differed (Table 3). Particularly, the risk associated with the highest CumTyG quartile was significantly elevated in the CumCRP ≥ 3 mg/L group (HR: 1.45, 95% CI: 1.15–1.86, P < 0.001, P for interaction = 0.021). There was a dose-response association between the CumTyG quartiles and cancer risk in the moderate- and high-inflammation groups. The trend of increasing tumor risk was significant in the high-inflammation group, whereas the trend was more repressed in the moderate-inflammation group. However, in the low-inflammation group, CumTyG increased the risk of cancer but showed an increasing and then a decreasing trend. Notably, the association between the second quartile of CumTyG levels and cancer risk was insignificant in the mid-inflammatory group.

Table 3 CumTyG-associated cancer risks in the entire cohort and across the cumulative CRP thresholds (1, 3 mg/L)
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Next, we explored the relationship between CumCRP and the risk of tumorigenesis in the different CumTyG strata (Table 4). The association between moderate inflammation and cancer risk was not statistically significant in the moderate insulin resistance group (Q3 and Q4 group). These findings underscore the interactive effect of insulin resistance and inflammation levels on cancer incidence. Moreover, it’s worth highlighting that moderate levels of inflammation may be pivotal for the body, especially in participants with moderate insulin resistance.

Table 4 CumhsCRP-associated cancer risks in the entire cohort and across the CumTyG (quartile)
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Sensitivity analyses

We conducted two sensitivity analyses to ascertain the reliability of the findings (Table S3). First, after excluding individuals who developed cancer within the initial year, an elevated risk of tumor development emerged across various groups. Specifically, Groups 2, 3, 4, 5, and 6 experienced increased risks of 19%, 35%, 31%, 43%, and 58%, respectively. The second sensitivity analysis excluded participants with hsCRP ≥ 10 mg/L during the exposure period [17, 20]. The outcomes of both sensitivity analyses consistently underscored the positive association between elevated inflammation, high insulin resistance, and an increased risk of cancer.

Subgroup analysis

Finally, we analyzed the association between inflammation levels, insulin resistance, and cancer risk in different subgroups. Our analysis revealed that elevated inflammation levels and heightened insulin resistance were significantly and positively associated with the risk of different cancer types. Specifically, Groups 2, 3, 4, 5, and 6 exhibited increased risk by 1.29-, 1.72-, 1.77-, 1.76-, and 2.24-fold for digestive system cancers, respectively. Regarding the risk of developing respiratory system cancer, only the risks in Group 3, Group 5 and Group 6 increased, by 34%, 48% and 74% respectively (Table S4). The risks of different subgroup analyses are shown in Figure S1.

Discussion

This study used cross-lagged modelling to investigate the temporal relationship between TyG and hsCRP and revealed an interaction between inflammation and insulin resistance. Notably, inflammation has emerged as the dominant factor driving changes in insulin resistance. By categorizing the participants into distinct groups, we unearthed a tangible association between elevated CumTyG and CumhsCRP levels and an elevated risk of cancer. We further combined cumulative TyG and cumulative hsCRP levels and found a synergistic effect between high inflammation levels and high insulin resistance, which increased cancer risk. These findings clarify the strong association among high levels of inflammation, insulin resistance, and cancer risk.

In this study, in order to explore the temporal relationships and causal directions among variables, we selected the cross - lagged panel model. This model is a statistical method for longitudinal research. It measures multiple variables at different time points and uses cross - lagged path coefficients to analyze the sequential order of causal relationships among variables.However, this model has key assumptions and potential limitations. The model assumes that the relationships between variables are stationary within the research period, that is, the intensity and nature of the influence of independent variables on dependent variables are stable, and there are no unmeasured time - invariant confounding factors. But in reality, the relationships between variables are easily affected by time factors, and it is difficult to ensure that all confounding factors are measured. Its limitations are mainly reflected in residual confounding, where unmeasured confounding variables can bias the results; and bidirectional causal interpretation bias. In fact, there may be bidirectional causal relationships between variables. If not fully considered, it is easy to lead to over - interpretation or misinterpretation of the results.

To our knowledge, this is the first study to explore the relationship between chronic inflammation combined with insulin resistance and cancer risk [11,12,13, 21,22,23]. A previous study has employed CRP to reflect the inflammation level and low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (LHR) ratio to reflect the level of insulin resistance, and has found that high inflammation (CRP ≥ 10 mg/L) and high insulin resistance (LHR ≥ 3.56) are associated with a poor cancer prognosis [24]. This study combined CRP and LHR to categorize participants into three groups: Group 1 (low CRP and low LHR), Group 2 (low CRP and high LHR or high CRP and low LHR), and Group 3 (high CRP and high LHR); Groups 2 and 3 had an increased risk of cancer death by 37% and 75%, respectively. Another study aimed to develop C-reactive protein- triglyceride glucose index that reflects inflammation and insulin [11]. This index demonstrated its potential as a reliable predictor of cancer patient prognosis, with a substantial 46% elevated mortality risk in individuals with elevated CTI levels. However, no study has yet reported the interplay between chronic inflammation and insulin resistance and the relationship between the two and cancer risk, and our study fills this gap.

Our study yielded several interesting findings. First, our results show a bidirectional relationship between chronic inflammation and insulin resistance. Cross-lagged modeling showed a more pronounced effect of inflammation on future changes in insulin levels. This temporal interaction suggests the existence of a dynamic feedback loop in which chronic inflammation and insulin resistance may exacerbate each other over time. Second, our study revealed how changes in inflammation levels modulate the association between insulin resistance and cancer risk and vice versa. This interplay highlights the intricate relationship between these factors and cancer risk. Inflammation may amplify the impact of insulin resistance on cancer risk, and insulin resistance may modify the relationship between inflammation and cancer. These findings emphasize the importance of considering inflammation and insulin resistance as factors related to cancer risk rather than as isolated contributors. In addition, this study reveals a close relationship among inflammation, insulin resistance, and digestive system cancers. Due to the long - term exposure of the digestive system to external substances, inflammation is likely to be triggered. In a chronic inflammatory state, inflammatory cells in the inflammatory microenvironment, along with inflammatory factors, activate signaling pathways like nuclear factor - κB (NF - κB). This, in turn, promotes the abnormal proliferation of epithelial cells and inhibits apoptosis, creating conditions for tumorigenesis. Finally, our study finds that moderate levels of inflammation and insulin resistance do not increase the risk of tumor occurrence. This may be because moderate inflammation can activate the immune system, enabling macrophages to more efficiently eliminate abnormal cells, enhancing the activity of natural killer (NK) cells, and promoting the secretion of cytokines such as interferons, which further strengthen the immune surveillance function and thus inhibit the growth and spread of tumor cells.

Cancer development is a complex process involving multifactorial interactions in which chronic inflammation and insulin resistance play prominent roles in cancer development and progression [25]. Long-standing chronic inflammation not only directly affects the tumor microenvironment but also negatively affects immune function, resulting in immune tolerance and escape, which contributes to tumor formation, progression, and metastasis. Chronic inflammation may also trigger local and systemic inflammatory responses, leading to cell damage, DNA mutations, and accumulation of precancerous lesions, ultimately creating a favorable environment for tumorigenesis [26]. This complex relationship is further exemplified during tumor growth, invasion and metastasis [27]. Chronic inflammation may affect the biology of tumor cells by regulating several signaling pathways, including cell proliferation, apoptosis, and angiogenesis. Insulin resistance also plays a key role in the mechanism of tumorigenesis [26, 28]. Insulin resistance may lead to excessive release of insulin growth factor, which interferes with cellular signaling pathways and causes alterations in cellular responses to external signals, affecting the function and behavior of tumor cells [29]. In addition, insulin resistance may interfere with metabolic homeostasis, leading to an intracellular energy imbalance that promotes tumor cell growth and proliferation [30]. In this complex relationship, the degree of chronic inflammation and insulin resistance combine to influence tumor development [31].

Based on the above findings, we can apply the research results to cancer prevention strategies from two key aspects: anti - inflammation and anti - insulin resistance. In terms of anti - inflammation, for people with a high cancer risk and persistently abnormal inflammatory markers, the rational use of anti - inflammatory drugs may reduce the likelihood of cancer occurrence. For example, non - steroidal anti - inflammatory drugs have been proven by research that long - term low - dose use can reduce the incidence of some cancers such as gastric cancer [32]. Insulin - sensitizing therapy also has great potential for cancer prevention. For individuals with insulin resistance, improving insulin sensitivity through lifestyle interventions (such as a reasonable diet and increased exercise) and the use of insulin - sensitizing agents (such as metformin) not only helps control blood glucose levels but may also have a positive impact on cancer prevention [33].

Based on a sample of nearly 50,000 individuals, this study explored the association among chronic inflammation, insulin resistance, and cancer risk. Moreover, we used a prospective study design, which provides strong support for observing the dynamics of inflammation and insulin resistance from a temporal perspective and links them to cancer risk. We included different types of cancers, which allowed us to gain a more comprehensive understanding of their inherent associations.

Despite the strengths of this study, the following limitations remain. First, although we minimized the effect of bias by adequately adjusting for potential confounders, we could not rule out unconsidered confounders. In addition, the measurement of biomarkers (e.g., hsCRP and TyG) may be affected by a variety of factors, such as the time point and measurement error, which may also affect the accuracy of the results. Finally, although we observed an interaction between inflammation and insulin resistance, this study did not delve into the underlying molecular mechanisms or biological pathways. Further experimental studies are required to explain these associations. In conclusion, the strengths of this study were its long-term follow-up, prospective design, and large sample size, which provided important insights into our deeper understanding of the relationship between inflammation, insulin resistance, and cancer risk.

Conclusions

This study revealed a temporal interaction between inflammation and insulin resistance, emphasizing the potential impact of inflammation on future changes in insulin resistance. It was further found that inflammation and insulin resistance synergize with each other to promote the risk of cancer occurrence.

Data availability

Data and programming code is available upon request. Further enquiries can be directed to the corresponding author.

Abbreviations

BMI:

Body mass index

CRP:

C-reactive protein

CumCRP:

Cumulative C-reactive protein

CumhsCRP:

Cumulative high-sensitivity C-reactive protein

CumTyG:

Cumulative Triglyceride and Glucose Index

FBG:

Fasting blood glucose

HDL-C:

High-density lipoprotein

HR:

Hazard ratio

hsCRP:

High-sensitivity C-reactive protein

LDL-C:

Low-density lipoproteinTC: Total cholesterol

TG:

Triglyceride

TyG:

Triglyceride and Glucose Index

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Acknowledgements

The authors thank all the staff members of the Kailuan Study Team for their contribution.

Funding

This work was supported by the National Key Research and Development Program (2022YFC2009600, 2022YFC2009601), Laboratory for Clinical Medicine, Capital Medical University(2023-SYJCLC01), National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Project for Major Diseases: Comprehensive Treatment and Management of Critically Ill Elderly Inpatients (No.2019.YLFW) to Dr. Hanping Shi.

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Author notes

  1. Xin Zheng, Yiming Wang and Yue Chen have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China

    Xin Zheng, Yue Chen, Chenan Liu,  TongLiu, Shiqi Lin, Ziwen Wang, Hailun Xie, Xiaoyue Liu, Jinyu Shi, Heyang Zhang, Li Deng & Hanping Shi

  2. Key Laboratory of Cancer, FSMP for State Market Regulation, Beijing, 100038, China

    Xin Zheng, Yue Chen, Chenan Liu,  TongLiu, Shiqi Lin, Ziwen Wang, Hailun Xie, Xiaoyue Liu, Jinyu Shi, Heyang Zhang, Li Deng & Hanping Shi

  3. National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China

    Xin Zheng, Yue Chen, Chenan Liu,  TongLiu, Shiqi Lin, Ziwen Wang, Hailun Xie, Xiaoyue Liu, Jinyu Shi, Heyang Zhang, Li Deng & Hanping Shi

  4. Department of Hepatological Surgery, Kailuan General Hospital, Tangshan, 063000, China

    Yiming Wang & Xiangming Ma

  5. Department of Biology, Faculty of Science, The University of Hong Kong, Hong Kong, China

    Xing Siyu

  6. Department of General Surgery, Kailuan General Hospital, Tangshan, 063000, China

    Qingsong Zhang

  7. Department of Cardiology, Kailuan General Hospital, Tangshan, 063000, China

    Shouling Wu

Authors
  1. Xin Zheng
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  2. Yiming Wang
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  3. Yue Chen
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  4. Chenan Liu
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  5. TongLiu
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  6. Shiqi Lin
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  7. Ziwen Wang
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  8. Hailun Xie
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  9. Xiaoyue Liu
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  10. Jinyu Shi
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  11. Heyang Zhang
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  12. Xiangming Ma
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  13. Xing Siyu
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  14. Li Deng
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  15. Qingsong Zhang
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  16. Shouling Wu
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  17. Hanping Shi
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Contributions

Conceptualization, Xin Zheng, Yue Chen. Tong Liu and Chenan Liu; methodology, Xin Zheng and Yiming Wang; software, Yiming Wang and Xin Zheng; validation, Shiqi Lin., Hailun Xie and Ziwen Wang; formal analysis, Xin Zheng and Yiming Wang; investigation, Ziwen Wang, Hailun Xie and Xiaoyue Liu; writing—original draft preparation, Xin Zheng, Jinyu Shi and Heyang Zhang; writing—review and editing, Li Deng, Qingsong Zhang, Xing Siyu, and Xiangming Ma; supervision, Hanping Shi and Shouling Wu; funding acquisition, Hanping Shi. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Qingsong Zhang, Shouling Wu or Hanping Shi.

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Institutional review board

Our study complied with the Declaration of Helsinki and was approved by the Ethics Committees of the Kailuan Medical Group, Kailuan Group.

Informed consent

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Competing interests

The authors declare no competing interests.

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Zheng, X., Wang, Y., Chen, Y. et al. Temporal relationship between chronic inflammation and insulin resistance and their combined cumulative effect on cancer risk: a longitudinal cohort study. BMC Public Health 25, 1501 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22632-4

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BMC Public Health

ISSN: 1471-2458

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