Association between C-reactive protein variability and cancer incidence: a longitudinal prospective cohort study

Association between C-reactive protein variability and cancer incidence: a longitudinal prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between C-reactive protein variability and cancer incidence: a longitudinal prospective cohort study Yue Chen, Yiming Wang, Xin Zheng, Tong Liu, Chenan Liu, Shiqi Lin, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4894293/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The association between inflammation and cancer has been widely studied, but research on the relationship between the magnitude of inflammatory fluctuations and cancer remains limited. Therefore, this study aims to investigate the association between C-reactive protein (CRP) variability and the occurrence of cancer. Methods A total of 42,514 participants were included, and their CRP levels were measured over a 4-year period. We used the coefficient of variation (CV) of CRP to quantify the variability in inflammation. Cox regression analysis was used to assess the association between CRP variability and cancer risk after adjusting for potential confounding factors. Results High CV of CRP significantly associated with reduced cancer risk (hazard ratio (HR) = 0.84; 95% CI: 0.75–0.94, P = 0.003). Participants with high CRP and low CV had a significantly increased risk of cancer (HR = 1.42; 95% CI: 1.18–1.70, P < 0.001). In the population with long-term stable CRP levels, there is a significant association between the CV and the risk of cancer (HR = 0.81; 95% CI: 0.72–0.92, P = 0.001). Furthermore, the association between the CV and lung cancer was most pronounced. Sensitivity analyses confirmed the stability of the association between CRP variability and cancer. Conclusion High CRP variability is significantly associated with reduced risk of cancer, particularly in the case of lung cancer. This provides a new perspective and evidence for exploring the relationship between inflammation and cancer. Biological sciences/Cancer Health sciences/Risk factors C-reactive protein cancer variability prospective risk Figures Figure 1 Figure 2 Figure 3 Introduction Cancer stands as the second leading cause of global mortality( 1 ), with its incidence showing a concerning annual increase( 2 ). It is estimated that there will be 290,000 new cancer diagnoses and 50,000 cancer-related deaths in 2040, with over 16% of deaths occurring in low- and middle-income countries( 3 ). Furthermore, cancer treatment brings tremendous physical and mental pain and heavy financial burden to patients at the same time( 4 , 5 ). This multifaceted issue transcends individual experiences, affecting families, careers, and the broader social fabric( 6 ). As a result, many studies are actively exploring the risk factors associated with cancer, and a comprehensive understanding of the causes of cancer and effective interventions are urgent and necessary. Inflammation, especially long-term chronic inflammation, is one of the factors contributing to tumor development( 7 , 8 ). This phenomenon is underpinned by various mechanisms, wherein inflammation impacts key processes involved in tumorigenesis—ranging from genomic instability, DNA damage, and oncogene activation to compromised tumor suppressor activity( 9 ). Beyond fostering the initial stages of cancer formation, long-term chronic inflammation plays a pivotal role in cancer progression( 10 , 11 ); approximately 25% of all cancers are associated with chronic inflammation( 12 ). Among the plethora of peripheral inflammatory markers, C reactive protein (CRP) has been widely studied because it has a long plasma half-life, minimal diurnal variation, and is not dependent on age or sex( 13 ). Notably, a prospective cohort study showed a significant association between elevated CRP levels and cancer risk( 14 ). Our previous investigations have further substantiated this connection, revealing an association between elevated CRP levels and unfavorable survival outcomes across various cancer types( 15 , 16 ). These studies demonstrated a strong association between CRP and cancer, but their limitation was that CRP levels were measured only once at baseline. Nonetheless, cancer occurrence is often associated with the long-term effects of risk factors. CRP, as an acute phase response protein, is susceptible to fluctuations in response to both exogenous and exogenous stimuli, especially in the presence of inflammation, autoimmune disease or cancer( 13 ). Relying solely on CRP levels measured at baseline as a predictor of cancer development imposes limitations. In contrast, employing multiple measurements of CRP over a period of time, encompassing both high and low CRP levels and their fluctuations, provides a comprehensive reflection of peripheral inflammation and the corresponding inflammatory response. Variability, denoting the extent of fluctuation in an indicator over time, serves as a crucial metric. Commonly utilized indicators of variability include the coefficient of variation (CV) and the variation independent of the mean (VIM). In this study, our approach involved scrutinizing changes in CRP over time, expressing variability through the CV, and incorporating VIM as a complementary indicator to explore the intricate association between CRP variability and the occurrence of cancer. Methods 1. Study design and population Data for this study were obtained from the Kailuan cohort, a prospective cohort located in northern China. In 2006, employees of the Kailuan Group, including retired employees, were invited to participate, and a total of 101,510 employees agreed to take part and provided signed consent forms. The participants underwent questionnaire interviews, health examinations, and laboratory assessments at Kailuan Hospital. Subsequent medical examinations and questionnaires were administered every 2 years. Participants who underwent fewer than three medical examinations between 2006 and 2010 were excluded. All personal information of the participants included in the final analysis was kept confidential. The study protocol adhered to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Kailuan Medical Group, Kailuan Company. 2. Variable evaluation All participants underwent fasting blood tests, including measurements of CRP, fasting blood glucose, and lipids. Participants with missing blood test results for any of the three medical examinations were excluded. To ensure stability and scientific rigor, participants with CRP levels >15 mg/L were also excluded. Blood measurements were performed using automated analyzers following standard operating procedures. Height and weight were measured by experienced nurses, and the body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters) squared. The demographic information included sex, age, educational attainment, and income. Educational attainment above high school level was classified as “high,” and anything below that was considered “low.” Family monthly income exceeding 1000 yuan per person was classified as “high income,” whereas incomes below this threshold were considered “low income.” Lifestyle data, including smoking, alcohol consumption, and physical exercise, were obtained from the patients’ responses to the lifestyle questionnaire. The primary outcome of this study was cancer incidence, and the secondary outcome was mortality. A prospective follow-up design was employed, tracking all participants from the baseline assessment conducted in 2010-2011 until the occurrence of cancer or death. Death data were obtained from the death certificates provided by the National Vital Statistics Bureau. The follow-up period spanned from the baseline assessment until 2021, during which all outcome events were carefully recorded. 3. Calculation of CRP variability CRP was measured during three distinct periods: 2006-2007, 2008-2009, and 2010-2011. To assess the CRP variability, we used the CV, a widely accepted measure of variability. The CV was calculated by dividing the mean by the standard deviation (SD). To strengthen the robustness and reliability of our findings, we also included the variability independent of the mean (VIM) as a supplementary measure. The VIM provides additional insights into the variability of CRP independent of its mean value. The VIM is a statistical transformation of the SD that is specifically designed to be uncorrelated with the mean levels. It was calculated by fitting a curve of the form y = kx p to a scatter plot of the SD CRP (y-axis) against the mean CRP (x-axis) for all individuals in the cohort. The parameter p is estimated from the data, and k is a constant that can be chosen to ensure that the VIM is on the same scale as the SD. Specifically, if M represents the average value of the mean CRP in the cohort, then k is determined as M p , and the VIM CRP value for any individual is calculated as VIM CRP = (k SD/x p ). This statistical approach allows the assessment of CRP variability independent of its mean, providing additional insights into the relationship between CRP fluctuations and cancer occurrence. The CV and VIM were categorized into three groups based on tertiles: low, moderate, and high. VIM = k × SD(CRP)/Mean(CRP) x SD(CRP) = constant × Mean(CRP) k = Mean (Mean(CRP)) x 4. Statistical analysis Normally distributed continuous variables were summarized using the mean and SD and analyzed using t-tests. Non-normally distributed continuous variables were analyzed using the Wilcoxon rank-sum test. Categorical variables were analyzed using the chi-squared test. This approach ensured the appropriate analysis of different variable types. We employed Cox regression analysis to investigate the relationship between CRP variability and the risk of cancer. Model A was unadjusted, whereas Model B was adjusted for sex, age, education, and income. In addition to the adjustments made in Model B, Model C was further adjusted for BMI, smoking, alcohol consumption, and physical exercise. We created Restricted Cubic Spline (RCS) plots to visually depict the intricate relationship between continuous CV of CRP and the risk of cancer. Furthermore, we fitted the model using the "TRAJ" program in SAS version 9.4 (SAS Institute) to group individuals with similar patterns of CRP change between 2006 and 2010. We then delved into examining the association between CRP variability and cancer occurrence, specifically within subgroups characterized by distinct patterns of change in CRP trajectories. We counted the incidence of different site-specific cancers during the follow-up period and explored the association between CV of CRP and site-specific cancers. We conducted stratified analyses to examine whether this association persists across populations with different characteristics. Several sensitivity analyses were conducted to ensure the robustness of the results and eliminate confounding factors. Owing to the potential lag in cancer incidence, participants who developed cancer within the initial 2-year follow-up period were excluded from the study. To address the impact of extreme values in CRP measurements on the results, participants with CRP levels exceeding 10 mg/L were also excluded. Acknowledging the potential impact of metabolic factors, including blood glucose and lipid levels, on cancer incidence, we included additional adjustments in our analysis. Furthermore, to emphasize that the identified association was specifically attributed to CRP variability during the follow-up period, we controlled for baseline CRP levels. Additionally, our analyses considered aspirin use as a relevant factor. VIM as a complementary indicator of variability, we additionally used COX regression analyses and RCS plots to explore the association between VIM of CRP and cancer incidence. In addition, we used Cox regression analysis to explore the association between the CV and death, as well as cancer-specific mortality. The competing risk plots illustrated the relationships between different levels of CRP variability and the occurrence of cancer and death. All statistical tests were two-sided and were considered significant at P < 0.05. Statistical analysis was performed using SAS (version 9.4) and R (version 4.2.3). Results This study included 42,514 participants from Kailuan cohort. The detailed process of participant inclusion and exclusion is shown in Fig. 1 ( Figure. 1 ). Among the included participants, 24% were women and 76% were men, with an average age of 53.24 (11.69) years and a median follow-up time of 10.98 years. Participants with higher CRP variability tended to be men, younger, with lower BMIs ( Table. 1 ). In Supplementary Table 1, we categorized mean CRP as high ( ≧ 3 mg/L) or low (< 3 mg/L) based on clinical experience and stratified participants accordingly to present baseline information. The results showed that participants with higher CRP were more likely to be women, older, with higher BMIs ( Table. S1 ). We conducted Cox regression analysis to explore the relationship between CRP variability and cancer occurrence. The high CRP group had a 20% higher risk of cancer than the low CRP group (hazard ratio (HR) = 1.20, 95% CI: 1.07–1.33, P < 0.001). We used RCS to observe the association between continuous CV of CRP and cancer risk, and found that as CV increased, HR decreased ( Figure. 2 (a) ). Subsequently, we stratified CRP variability into three groups based on the tertile intervals of CV of CRP: low, medium, and high. Moderate CRP variability did not show a significant association with cancer risk compared to low variability (adjusted HR = 0.93, 95% CI: 0.84–1.04, P = 0.205), but high variability exhibited a notable protective effect against cancer (adjusted HR = 0.84, 95% CI: 0.75–0.94, P = 0.003). To further investigate the influence of CRP levels on the relationship between variability and cancer risk, we performed an additional Cox regression analysis by cross-classifying CRP levels and variability. The reference group consisted of individuals with low CRP levels and high variability, which had a significant protective effect against cancer. Compared to the reference group, the group with low CRP level and low variability had a 20% increased risk of cancer (adjusted HR = 1.20, 95% CI: 1.06–1.37, P = 0.05), and the group with low CRP level and moderate variability did not have a significantly different cancer risk (adjusted HR = 1.12, 95% CI: 0.99–1.28, P = 0.079). High CRP levels were associated with a higher risk of cancer occurrence regardless of variability, with the group exhibiting the lowest variability showing the highest risk (adjusted HR = 1.42, 95% CI: 1.18–1.70, P < 0.001) ( Table. 2 ). Next, we categorized participants into three distinct long-term CRP change patterns: stable, increasing, and decreasing ( Figure. S1 ). We investigated the association between CRP variability and cancer risk in participants with each of these three different patterns. The results indicated that only in the stable group, CRP variability was significantly associated with the risk of cancer (High variability in stable-pattern: HR = 0.81, 95% CI = 0.72–0.92, P = 0.001) ( Table. 3 ). Furthermore, we explored the association between CRP variability and cancer at different specific sites. During the follow-up period, the incidence of cancer at various specific sites was presented in Supplementary Table 2 ( Table. S2 ). The results indicated a significant association between CV of CRP and the incidence of lung cancer (HR = 0.77, 95% CI = 0.63–0.96, P = 0.018) ( Table. 4 ). To explore whether the association between CRP variability and cancer exists in populations with different characteristics, we performed subgroup analyses. Forest plots showed that significant associations between high variability and cancer were present in most subgroups compared to low variability. Moreover, high variability was more protective against cancer than moderate variability ( Figure. 3 ). We conducted sensitivity analyses to ensure the stability of the findings and to rule out other factors. First, to rule out potential reverse causality, we excluded participants who developed cancer within initial two years of follow-up. Second, we excluded participants with extremely large values of CRP. In addition, we corrected for blood glucose and lipids, CRP at baseline, and aspirin use separately from Model C. The results suggested that these conditions did not affect the association between CRP variability and cancer risk ( Table. 5 ). In addition, we used VIM as a complementary measure of variability. The RCS plot showed a decrease in HR for cancer risk with increasing VIM of CRP ( Figure. 2 (b) ). We used COX regression analysis to explore the association between VIM of CRP and cancer risk. The results showed that VIM demonstrated a similar association with cancer as CV. There was a significant association between higher VIM of CRP and reduced cancer risk ( Figure. S2 ). We further examined the association between CRP variability and the risk of mortality. The results showed no significant association between CRP variability and death and cancer-specific death after correction for confounders ( Table. S3 ). Nonetheless, competing risk plots suggest that high CRP variability has a lower risk of cancer development and death than low variability ( Figure. S3 ). Discussion Our findings suggest a significant association between high variability in CRP and a decreased risk of cancer occurrence. Maintaining a low inflammatory state in the body holds crucial importance for the prevention of cancer. Participants with high levels of CRP accompanied by low variability had a significantly increased risk of developing cancer. Previous studies have shown that CRP serves not only as a prognostic factor for cancer patients but is also associated with an increased risk of future cancer in seemingly healthy participants( 17 ). Suthathar et al. suggested that baseline CRP was associated with a 17% increase in cancer risk, and elevated CRP over time was associated with an 8% increase in cancer risk( 14 ). A study on the trajectory of CRP changes suggests that maintaining a long-term, stable, and low level of CRP is positively meaningful for cancer prevention( 18 , 19 ). Our results keep in line with this conclusion. Participants in a low inflammatory state (CRP < 3 mg/L, or with a trajectory of low-stable) exhibited a significantly lower risk of developing cancer compared to participants in a high inflammatory state (CRP ≥ 3 mg/L). Chronic hyperinflammatory states leads to cellular damage and DNA injury on the one hand. On the other hand, the activation of pro-inflammatory pathways, such as the NF-κB pathway, may excessively inhibit the immune system's surveillance and clearance of malignant cells( 20 ). These factors could potentially contribute to the eventual presence and growth of cancer. Previous studies exploring the association between CRP and cancer have focused on differences in the amount of CRP, neglecting the fluctuating changes in CRP. When the body is exposed to inflammation or infection, the immune system is stimulated, and the liver releases more CRP to respond to the inflammation or infection( 21 ). Even in relatively healthy populations, CRP fluctuates to some extent. Additionally, unhealthy lifestyle factors such as smoking, obesity, chronic psychological stress, and chronic stress are likely to contribute to elevated CRP levels( 22 , 23 ). Evaluating the future risk of cancer based solely on the quantity of CRP is highly susceptible to interference from other factors. In this study, we focused on exploring the association between CRP variability and cancer occurrence. Our study revealed a particularly intriguing finding: the higher the variability of CRP, the lower the risk of cancer occurrence. We cross-combined high and low levels of CRP with the size of variability. It was found that the risk of developing cancer was 42% higher in those with high CRP level-low CRP variability than in those with low CRP level-high CRP variability. Among populations with different CRP trajectories, statistical results revealed that significant protective effects of high CRP variability are observed only in individuals with low-stable CRP patterns. In contrast, no significant protective effect was found in populations with CRP decreasing or increasing patterns, possibly due to sample size limitations, as the sample size for decreasing or increasing CRP patterns were much lower than that of populations with low stable CRP pattern. Furthermore, we observed a significant association between CRP variability and the occurrence of lung cancer. Compared to individuals with low CRP variability, those with high CRP variability had a 23% reduced risk of developing lung cancer. Prior research has demonstrated the relevance of CRP to the prognosis of non-small cell lung cancer patients( 24 , 25 ). Xu et al.'s study established that CRP plays a role in promoting lung cancer development in the tumor microenvironment( 26 ). The Kailuan Cohort Study also previously found a significant association between high levels of baseline CRP and the occurrence of lung cancer( 27 ). Our study further found a significant association between CRP variability and lung cancer. In subgroup analyses, we observed an interesting phenomenon. The protective effect of high CRP variability against cancer incidence was more significant in older individuals compared to middle-aged individuals, in smokers than in non-smokers, and in obese than in non-obese. Both smoking and obesity put the body in a state of chronic inflammation, and CRP is not only a marker of chronic inflammation, but also serves as an internal exposure marker, reflecting the aging state of the organism( 28 ). A prospective cohort study has shown that organismal aging is often accompanied by low-grade inflammation, and that aging of the immune system is accompanied by elevation of the pro-inflammatory marker CRP( 29 ). CRP is also one of the most commonly used biomarkers of immune senescence( 30 ). Cancer, as an age-related disease, is strongly associated with factors such as aging, smoking, and obesity. Our results indicate that, in these cancer risk groups, high CRP variability paradoxically exhibits a protective effect. This suggests that high CRP variability might imply a healthy immune mechanisms opposite to immune aging, hence playing a significant protective role in the mentioned high-risk populations. However, this is only our speculation, and the lack of indicators for the diagnosis of immune aging in our cohort precludes further exploration of the association between CRP variability and immune aging. Through this study, we observed an association between CRP variability and the occurrence of cancer. Although our research cannot precisely explain the mechanistic aspects of this association, it still holds instructive significance for cancer prevention. Firstly, maintaining low levels of CRP is crucial for cancer prevention. Secondly, long-term monitoring of CRP fluctuations, coupled with early intervention and management, is advantageous in preventing the occurrence of cancer or other immune-related diseases. One of the strengths of our study is its pioneering exploration of the relationship between the amplitude of inflammation fluctuations and cancer. To the best of our knowledge, no similar study has been conducted to date. This provides new insights into understanding the longitudinal association between CRP changes and cancer risk. Additionally, we adjusted for many confounding factors, such as obesity and lifestyle, in the risk model, enhancing the reliability of the results. Our study has certain limitations. First, there is a significant imbalance in sex distribution in our cohort, with a larger number of men than women. Despite conducting stratified analyses based on the sex, the results may still be prone to bias. Second, factors influencing cancer occurrence are multifaceted, including metabolic factors. Although we adjusted for TC and TG in sensitivity analyses, we couldn't completely eliminate the impact in this regard. Third, while we hypothesize an association between low CRP variability and immune aging, the lack of specific data for diagnosing immune aging in our cohort prevents us from confirming this hypothesis. In future research, it is essential to delve deeper into the specific mechanistic connections among CRP variability, immune aging, and cancer occurrence. Conclusion Through this study, we discovered the significant role of CRP variability in the occurrence of cancer, especially in lung cancer. High levels of CRP concentration coupled with low variability were strongly associated with a substantial increase in cancer risk. Furthermore, the protective mechanisms of high CRP variability in cancer need to be better elucidated in future experimental studies. Declarations Conflict of Interest The authors declare that there is no conflict of interest. Ethics approval and consent to participate This study followed the Helsinki declaration. All participants signed an informed consent form. Trial registration: Kailuan study, ChiCTR‐TNRC‐11001489. Registered 24 August, 2011‐Retrospectively registered, http://www.chictr.org.cn/showprojen.aspx?proj=8050. Consent for Publication Obtained Availability of data and materials The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Funding This work was supported by National Key Research and Development Program (grant number 2022YFC2010101); Laboratory for Clinical Medicine, Capital Medical University (grant number 2023-SYJCLC01); National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Project for Major Diseases: Comprehensive Treatment and Management of Critically Ill Elderly Inpatients (grant number 2019.YLFW). Authors’ contributions CY wrote the manuscript. CY, WYM, ZX, LCA, LT, and LSQ analyzed and interpreted the patient data, DL, ZQS and SHP made substantial contributions to the conception, design, and intellectual content of the studies. All authors read and approved the final manuscript. Acknowledgments We would like to thank Editage (www.editage.cn) for English language editing. We are grateful to all the participants and staffs who have been part of the kailuan cohort which has enabled this research. 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Tables Table. 1 Baseline characteristics of participants, stratified by the coefficient of variation of CRP (n=42514) CV of CRP Lowest tertile (<53.07) Middle tertile (53.07~84.05) Highest tertile ( ≧ 84.05) P-value N 14173 14172 14169 Sex, n (%) Woman 3622 (25.6) 3504 (24.7) 3093 (21.8) <0.001 Man 10551 (74.4) 10668 (75.3) 11076 (78.2) Age 54.56 (11.82) 53.55 (11.77) 51.62 (11.47) <0.001 Education, n (%) <0.001 Low 10336 (72.9) 11040 (77.8) 10991 (77.6) High 3837 (27.1) 3132 (22.1) 3178 (22.4) Income, n (%) <0.001 Low 12832 (90.5) 13207 (93.2) 13355 (94.3) High 1341 (9.5) 965 (6.8) 814 (5.7) Smoke, n (%) <0.001 No 8880 (62.7) 9223 (65.1) 9088 (64.1) Current and past 5293 (37.3) 4949 (34.9) 5081 (35.9) Alcohol intake, n (%) <0.001 No 7951 (56.1) 8606 (60.7) 8458 (59.7) Yes 6222 (43.9) 5566 (39.3) 5711 (40.3) Physical exercise, n (%) <0.001 No 1204 (8.5) 1301 (9.2) 1465 (10.3) Yes 12969 (91.5) 12871 (90.8) 12704 (89.7) BMI 25.47 (3.39) 25.23 (3.38) 24.79 (3.29) <0.001 FBG 5.75 (1.67) 5.68 (1.76) 5.58 (1.59) <0.001 TG 1.36[0.93,2.06] 1.28 [0.88,1.91] 1.26[0.90,1.85] <0.001 CRP (2010) 1.27[0.70,2.50] 1.21[0.68,2.43] 1.00[0.50,2.70] <0.001 CRP (2008) 1.33[0.80,2.70] 1.60[0.81,3.03] 1.60[0.70,3.20] <0.001 CRP (2006) 1.10[0.60,2.30] 0.70[0.29,1.80] 0.33[0.11,1.08] <0.001 Mean CRP 1.30[0.75,2.52] 1.40[0.77,2.50] 1.57[0.95,2.60] <0.001 Notes: Continuous variables were presented as mean ± standard deviation (SD). Continuous variables that were not normally distributed were expressed as the median (interquartile range). Categorical variables were presented as numbers and percentages. Differences in normally and non-normally distributed baseline characteristics were compared using the chi-square test or t-test and Wilcoxon rank sum test, respectively. CV, coefficient of variation; CRP, C reactive protein; BMI, body mass index; FBG, fasting blood-glucose; TG, triglyceride. Table. 2 Association between serum C reactive protein levels and variability and cancer incidence Model A Model B Model C Case/N HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value CRP levels Low (<3mg/L) 1445/34118 ref. ref. ref. ref. ref. ref. High (≧3mg/L) 457/8396 1.33(1.19,1.47) <0.001 1.22(1.09.1.35) <0.001 1.20(1.07,1.33) <0.001 CRP variability* Low 689/14173 ref. ref. ref. ref. ref. ref. Moderate 645/14172 0.93(0.83,1.03) 0.181 0.93(0.83,1.04) 0.183 0.93(0.84,1.04) 0.205 High 568/14169 0.81(0.72,0.90) <0.001 0.83(0.75,0.93) 0.002 0.84(0.75,0.94) 0.003 P for trend <0.001 0.001 0.003 CRP levels and variability Low CRP with high variability 428/11322 ref. ref. ref. ref. ref. ref. Low CRP with moderate variability 497/11495 1.16(1.02,1.32) 0.026 1.13(0.99,1.29) 0.062 1.12(0.99,1.28) 0.079 Low CRP with low variability 520/11301 1.24(1.09,1.41) <0.001 1.22(1.07,1.38) 0.003 1.20(1.06,1.37) 0.005 High CRP with high variability 140/2847 1.33(1.1,1.61) 0.003 1.27(1.05,1.53) 0.015 1.25(1.03,1.51) 0.025 High CRP with moderate variability 148/2677 1.52(1.26,1.84) <0.001 1.36(1.13,1.64) <0.001 1.33(1.1,1.61) 0.003 High CRP with low variability 169/2872 1.65(1.38,1.97) <0.001 1.45(1.21,1.73) <0.001 1.42(1.18,1.70) <0.001 P for trend <0.001 <0.001 <0.001 Notes: Data are presented as hazard ratios (95% confidence intervals). Model A: unadjusted; Model B: adjusted for sex, age, education, income; Model C: additionally adjusted for BMI, smoke, alcohol intake, physical exercise based on Model B. *We categorized the variability into three groups based on the tertile intervals of CV of CRP: low, medium, and high. CRP, C reactive protein; HR, hazard ratio; CI, confidence interval; P-value , probability. Table. 3 Association between CRP variability and cancer incidence in different CRP trajectories (2006-2010) Case/N HR (95%CI) p-value Continuous CV of CRP (per_SD) Overall 1902/42514 0.95 (0.90,0.99) 0.013 Track 1 (stable) 1607/36579 0.95 (0.90,1.00) 0.032 Track 2 (increase) 138/2792 0.93 (0.79,1.08) 0.335 Track 3 (decrease) 157/3143 0.89 (0.76,1.03) 0.122 CV tertile interval of CRP Track 1 (stable-pattern) Lowest tertile 585/12394 ref. Middle tertile 555/12255 0.95 (0.85,1.07) 0.426 Highest tertile 467/11930 0.81 (0.72,0.92) 0.001 Track 2 (increase-pattern) Lowest tertile 45/701 ref. Middle tertile 35/889 0.59 (0.38,0.93) 0.021 Highest tertile 58/1202 0.71 (0.48,1.05) 0.083 Track 3 (decrease-pattern) Lowest tertile 59/1078 ref. Middle tertile 55/1028 0.96(0.67,1.39) 0.848 Highest tertile 43/1037 0.74 (0.51,1.09) 0.127 Notes: COX regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake and physical exercise. Data are presented as hazard ratios (95% confidence intervals). CV, coefficient of variation; CRP, C reactive protein; HR, hazard ratio; CI, confidence interval; P-value , probability. Table. 4 Association between CRP coefficient of variation and site-specific cancer incidence Lowest tertile Middle tertile Highest tertile HR (95%CI) P-value HR (95%CI) P-value Head and neck ref. 1.68 (1.09,2.59) 0.018 1.32 (0.84,2.08) 0.236 Esophagus ref. 1.98 (1.04,3.77) 0.037 1.30 (0.65,2.60) 0.463 Gastrointestinal ref. 1.22 (0.82,1.82) 0.318 1.13 (0.75,1.71) 0.546 Colorectal ref. 0.81 (0.61,1.09) 0.161 0.78 (0.58,1.05) 0.100 Liver and gallbladder ref. 1.36 (0.93,1.96) 0.109 0.81 (0.53,1.24) 0.328 Pancreatic ref. 1.63 (0.67,3.93) 0.280 1.36 (0.72,2.48) 0.245 Lung ref. 0.74 (0.59,0.91) 0.005 0.77 (0.63,0.96) 0.018 Breast ref. 0.90 (0.62,1.32) 0.587 0.71 (0.47,1.09) 0.117 Uterus and ovaries ref. 1.08 (0.56,2.06) 0.825 1.18 (0.61,2.28) 0.626 Prostate ref. 0.58 (0.32,1.03) 0.063 0.81 (0.47,1.40) 0.448 Kidney ref. 1.14 (0.67,1.95) 0.631 0.78 (0.43,1.42) 0.422 Urinary bladder ref. 0.97 (0.53,1.80) 0.935 0.79 (0.40,1.54) 0.488 Hematology* ref. 0.81 (0.44,1.51) 0.511 0.63 (0.32,1.26) 0.191 Notes: Data are presented as hazard ratios (95% confidence intervals). Cox regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake, physical activity. HR, hazard ratio; CI, confidence interval; P -value, probability. Bolded text indicates statistical significance. * Tumors of the hematology include lymphoma and leukemia. Table. 5 Sensitivity Analyses CV of CRP HR(95%CI) P-value Exclude cancer occurrence within first 2 years (n=41948) Lowest tertile ref. Middle tertile 0.89 (0.80-1.00) 0.059 Highest tertile 0.79 (0.70-0.90) 10 mg/L) (n=40412) Lowest tertile ref. Middle tertile 0.94 (0.84-1.05) 0.283 Highest tertile 0.86 (0.77-0.97) 0.014 Additionally adjusted for blood glucose and lipid Lowest tertile ref. Middle tertile 0.93 (0.84-1.04) 0.201 Highest tertile 0.84 (0.75-0.94) 0.003 Additionally adjusted for basic CRP Lowest tertile ref. Middle tertile 0.93 (0.84-1.04) 0.209 Highest tertile 0.84 (0.75-0.94) 0.002 Additionally adjusted for taking aspirin Lowest tertile ref. Middle tertile 0.93 (0.84-1.04) 0.212 Highest tertile 0.85 (0.76-0.95) 0.003 Notes: Data are presented as hazard ratios (95% confidence intervals). Cox regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake, physical activity. CV, coefficient of variation; HR, hazard ratio; CI, confidence interval; P -value, probability. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4894293","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354836433,"identity":"76e370e9-af73-423a-a947-ba557aff8e0b","order_by":0,"name":"Yue Chen","email":"","orcid":"","institution":"The Second Clinical Medical College of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":354836434,"identity":"e41bad9f-c1d3-45ee-a8c1-8145c2741cd1","order_by":1,"name":"Yiming Wang","email":"","orcid":"","institution":"Kailuan General 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Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACCcY2BoYKKIeHeC1nSNPCwMYA0kW8FvnZzW2PC+fZyevOSGB88LaNQd6ckBaDOwfbjWduSzbcdiOB2XBuG4PhzgZCWiQS26R5tx1IMLuRwCbN28aQYHCAkMNmgLTMAWth/02UFoYbIC0NEFuYidJiANIy4xjQL2ceNkvOOSdhuIGww9KfSRfU2MmbHU8++OFNmY08YYcBATOEYmxgAEUTUYCZOGWjYBSMglEwYgEA2NM9fqQigNMAAAAASUVORK5CYII=","orcid":"","institution":"The Second Clinical Medical College of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hanping","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-08-11 08:12:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4894293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4894293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66655415,"identity":"d3e9676b-593f-44d4-a2b0-5ac3f8973c94","added_by":"auto","created_at":"2024-10-15 08:12:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35484,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart\u003c/p\u003e\n\u003cp\u003eNotes: CRP, C reactive protein\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-4894293/v1/bf4a973edb28de8834ba68ee.png"},{"id":66655417,"identity":"9e38407c-cd6e-4008-b5e8-240c97dd37c5","added_by":"auto","created_at":"2024-10-15 08:12:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36423,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between coefficient of variability and variation independent of the mean of CRP and risk of cancer occurrence\u003c/p\u003e\n\u003cp\u003e(a) Association between coefficient of variability of CRP and risk of cancer occurrence\u003c/p\u003e\n\u003cp\u003e(b) Association between variation independent of the mean of CRP and risk of cancer occurrence\u003c/p\u003e\n\u003cp\u003eNotes: HR, hazard ratio; CV, coefficient of variability; VIM, variation independent of the mean.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-4894293/v1/6c3d64a52d89675573020f11.png"},{"id":66655416,"identity":"edda22d6-47fb-4a02-aa85-0788989619ad","added_by":"auto","created_at":"2024-10-15 08:12:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69353,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of CRP coefficient of variation and cancer occurrence in participants with different characteristics\u003c/p\u003e\n\u003cp\u003eNotes: BMI, body mass index; HR, hazard ratio; 95%CI, 95% confidence interval.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-4894293/v1/6d795600f9a7e9a2e2d23ce6.png"},{"id":66656417,"identity":"7a6c8c98-deff-4f71-8e27-e72ff653d402","added_by":"auto","created_at":"2024-10-15 08:20:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4894293/v1/2310a134-1759-40b5-9f9c-d85f47d7969f.pdf"},{"id":66655418,"identity":"2f031caf-de1c-449a-85d6-75ba4b9fbccd","added_by":"auto","created_at":"2024-10-15 08:12:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":219556,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4894293/v1/154dabfa400ebde7ac355b3d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between C-reactive protein variability and cancer incidence: a longitudinal prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer stands as the second leading cause of global mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), with its incidence showing a concerning annual increase(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is estimated that there will be 290,000 new cancer diagnoses and 50,000 cancer-related deaths in 2040, with over 16% of deaths occurring in low- and middle-income countries(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, cancer treatment brings tremendous physical and mental pain and heavy financial burden to patients at the same time(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This multifaceted issue transcends individual experiences, affecting families, careers, and the broader social fabric(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). As a result, many studies are actively exploring the risk factors associated with cancer, and a comprehensive understanding of the causes of cancer and effective interventions are urgent and necessary.\u003c/p\u003e \u003cp\u003eInflammation, especially long-term chronic inflammation, is one of the factors contributing to tumor development(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This phenomenon is underpinned by various mechanisms, wherein inflammation impacts key processes involved in tumorigenesis\u0026mdash;ranging from genomic instability, DNA damage, and oncogene activation to compromised tumor suppressor activity(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Beyond fostering the initial stages of cancer formation, long-term chronic inflammation plays a pivotal role in cancer progression(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); approximately 25% of all cancers are associated with chronic inflammation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Among the plethora of peripheral inflammatory markers, C reactive protein (CRP) has been widely studied because it has a long plasma half-life, minimal diurnal variation, and is not dependent on age or sex(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Notably, a prospective cohort study showed a significant association between elevated CRP levels and cancer risk(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Our previous investigations have further substantiated this connection, revealing an association between elevated CRP levels and unfavorable survival outcomes across various cancer types(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These studies demonstrated a strong association between CRP and cancer, but their limitation was that CRP levels were measured only once at baseline.\u003c/p\u003e \u003cp\u003eNonetheless, cancer occurrence is often associated with the long-term effects of risk factors. CRP, as an acute phase response protein, is susceptible to fluctuations in response to both exogenous and exogenous stimuli, especially in the presence of inflammation, autoimmune disease or cancer(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Relying solely on CRP levels measured at baseline as a predictor of cancer development imposes limitations. In contrast, employing multiple measurements of CRP over a period of time, encompassing both high and low CRP levels and their fluctuations, provides a comprehensive reflection of peripheral inflammation and the corresponding inflammatory response. Variability, denoting the extent of fluctuation in an indicator over time, serves as a crucial metric. Commonly utilized indicators of variability include the coefficient of variation (CV) and the variation independent of the mean (VIM). In this study, our approach involved scrutinizing changes in CRP over time, expressing variability through the CV, and incorporating VIM as a complementary indicator to explore the intricate association between CRP variability and the occurrence of cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cem\u003e1. Study design and population\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eData for this study were obtained from the Kailuan cohort, a prospective cohort located in northern China. In 2006, employees of the Kailuan Group, including retired employees, were invited to participate, and a total of 101,510 employees agreed to take part and provided signed consent forms. The participants underwent questionnaire interviews, health examinations, and laboratory assessments at\u0026nbsp;Kailuan Hospital. Subsequent medical examinations and questionnaires were administered every 2 years. Participants who underwent fewer than three medical examinations between 2006 and 2010 were excluded. All personal information of the participants included in the final analysis was kept confidential. The study protocol adhered to the guidelines of the Declaration\u0026nbsp;of Helsinki and was approved by the Ethics Committee of\u0026nbsp;Kailuan Medical Group, Kailuan Company.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2. Variable evaluation\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAll participants underwent fasting blood tests, including measurements of\u0026nbsp;CRP, fasting blood glucose, and lipids. Participants with missing blood test results\u0026nbsp;for any of the three medical examinations were excluded. To ensure stability and scientific rigor, participants with CRP levels \u0026gt;15 mg/L were also excluded. Blood measurements were performed using automated analyzers following standard operating procedures. Height and weight were measured by experienced nurses, and\u0026nbsp;the body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters)\u0026nbsp;squared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe demographic information included sex, age, educational attainment, and income. Educational attainment above high school level was classified as \u0026ldquo;high,\u0026rdquo; and anything below that was considered \u0026ldquo;low.\u0026rdquo; Family monthly income exceeding 1000 yuan per person was classified as \u0026ldquo;high income,\u0026rdquo; whereas incomes below this threshold were considered \u0026ldquo;low income.\u0026rdquo; Lifestyle data, including smoking, alcohol consumption, and physical exercise, were obtained from the patients\u0026rsquo; responses to the lifestyle questionnaire.\u003c/p\u003e\n\u003cp\u003eThe primary outcome of this study was cancer incidence, and the secondary outcome was mortality. A prospective follow-up design was employed, tracking all participants from the baseline assessment conducted in 2010-2011 until the occurrence of cancer or death. Death data were obtained from the death certificates provided by the National Vital Statistics Bureau. The follow-up period spanned from the baseline assessment until 2021, during which all outcome events were carefully recorded.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3. Calculation of CRP variability\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eCRP was measured during three distinct periods: 2006-2007, 2008-2009, and 2010-2011. To assess the\u0026nbsp;CRP variability, we used the CV, a widely accepted measure of variability. The CV was calculated by dividing the mean by the standard deviation (SD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo strengthen the robustness and reliability of our findings, we also included the variability independent of the mean (VIM) as a supplementary measure. The VIM provides additional insights into the variability of CRP independent of its mean value. The VIM is a statistical transformation of the SD that is specifically designed to be uncorrelated with\u0026nbsp;the mean levels. It\u0026nbsp;was calculated by fitting a curve of the form y = kx\u003csup\u003ep\u003c/sup\u003e to a scatter plot of\u0026nbsp;the SD CRP (y-axis) against the mean CRP (x-axis) for all individuals in the cohort.\u0026nbsp;The parameter p is estimated from the data, and k is a constant that can be chosen to ensure that the VIM is on the same scale as\u0026nbsp;the SD. Specifically, if M represents the average value of the mean CRP in the cohort, then k is determined as\u0026nbsp;M\u003csup\u003ep\u003c/sup\u003e, and the VIM CRP value for any individual is calculated as VIM CRP = (k SD/x\u003csup\u003ep\u003c/sup\u003e). This statistical approach allows the assessment of CRP variability independent of its mean, providing additional insights into the relationship between CRP fluctuations and cancer occurrence. The CV and VIM were categorized into three groups based on tertiles: low, moderate, and high.\u003c/p\u003e\n\u003cp\u003eVIM = k \u0026times; SD(CRP)/Mean(CRP)\u003csup\u003ex\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSD(CRP) = constant \u0026times; Mean(CRP)\u003c/p\u003e\n\u003cp\u003ek = Mean (Mean(CRP))\u003csup\u003ex\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4. Statistical analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eNormally distributed continuous variables were summarized using\u0026nbsp;the mean and SD\u0026nbsp;and analyzed using t-tests. Non-normally distributed continuous variables were analyzed using the Wilcoxon rank-sum test. Categorical variables were analyzed using the chi-squared test. This approach ensured the appropriate analysis of different variable types.\u003c/p\u003e\n\u003cp\u003eWe employed Cox regression analysis to investigate the relationship between CRP variability and the risk of cancer.\u0026nbsp;Model A was\u0026nbsp;unadjusted, whereas Model B was adjusted for sex, age, education, and income. In addition to the adjustments made in Model B,\u0026nbsp;Model C was further adjusted for BMI, smoking, alcohol consumption, and physical exercise.\u0026nbsp;We created Restricted Cubic Spline (RCS) plots to visually depict the intricate relationship between continuous CV of CRP and the risk of cancer.\u0026nbsp;Furthermore, we fitted the model using the \u0026quot;TRAJ\u0026quot; program in SAS version 9.4 (SAS Institute) to group individuals with similar patterns of CRP change between 2006 and 2010. We then delved into examining the association between CRP variability and cancer occurrence, specifically within subgroups characterized by distinct patterns of change in CRP trajectories. We counted the incidence of different site-specific cancers during the follow-up period and explored the association between CV of CRP and site-specific cancers. We conducted stratified analyses to examine whether this association persists across populations with different characteristics.\u0026nbsp;Several sensitivity analyses were conducted to ensure\u0026nbsp;the robustness of the results and eliminate confounding factors. Owing to the potential lag in cancer incidence, participants who developed cancer within the initial 2-year follow-up period were excluded\u0026nbsp;from the study.\u0026nbsp;To address the impact of extreme values in CRP measurements on the results, participants with CRP levels exceeding 10 mg/L were also excluded. Acknowledging the potential impact of metabolic factors, including blood glucose and lipid levels, on cancer incidence, we included additional adjustments in our analysis. Furthermore, to emphasize that the identified association was specifically attributed to CRP variability during the follow-up period, we controlled for baseline CRP levels. Additionally, our analyses considered aspirin use as a relevant factor.\u003c/p\u003e\n\u003cp\u003eVIM as a complementary indicator of variability, we additionally used COX regression analyses and RCS plots to explore the association between VIM of CRP and cancer incidence. In addition, we used Cox regression analysis to explore the association between the CV and death, as well as cancer-specific mortality. The competing risk plots illustrated the relationships between different levels of CRP variability and the occurrence of cancer and death.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided and were considered significant at P \u0026lt; 0.05. Statistical analysis was performed using SAS (version 9.4) and R (version 4.2.3).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 42,514 participants from Kailuan cohort. The detailed process of participant inclusion and exclusion is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (\u003cb\u003eFigure. 1\u003c/b\u003e). Among the included participants, 24% were women and 76% were men, with an average age of 53.24 (11.69) years and a median follow-up time of 10.98 years. Participants with higher CRP variability tended to be men, younger, with lower BMIs (\u003cb\u003eTable. 1\u003c/b\u003e). In Supplementary Table\u0026nbsp;1, we categorized mean CRP as high ( ≧ 3 mg/L) or low (\u0026lt; 3 mg/L) based on clinical experience and stratified participants accordingly to present baseline information. The results showed that participants with higher CRP were more likely to be women, older, with higher BMIs (\u003cb\u003eTable. S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted Cox regression analysis to explore the relationship between CRP variability and cancer occurrence. The high CRP group had a 20% higher risk of cancer than the low CRP group (hazard ratio (HR) = 1.20, 95% CI: 1.07–1.33, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). We used RCS to observe the association between continuous CV of CRP and cancer risk, and found that as CV increased, HR decreased (\u003cb\u003eFigure. 2 (a)\u003c/b\u003e). Subsequently, we stratified CRP variability into three groups based on the tertile intervals of CV of CRP: low, medium, and high. Moderate CRP variability did not show a significant association with cancer risk compared to low variability (adjusted HR = 0.93, 95% CI: 0.84–1.04, \u003cem\u003eP\u003c/em\u003e = 0.205), but high variability exhibited a notable protective effect against cancer (adjusted HR = 0.84, 95% CI: 0.75–0.94, \u003cem\u003eP\u003c/em\u003e = 0.003). To further investigate the influence of CRP levels on the relationship between variability and cancer risk, we performed an additional Cox regression analysis by cross-classifying CRP levels and variability. The reference group consisted of individuals with low CRP levels and high variability, which had a significant protective effect against cancer. Compared to the reference group, the group with low CRP level and low variability had a 20% increased risk of cancer (adjusted HR = 1.20, 95% CI: 1.06–1.37, \u003cem\u003eP\u003c/em\u003e = 0.05), and the group with low CRP level and moderate variability did not have a significantly different cancer risk (adjusted HR = 1.12, 95% CI: 0.99–1.28, \u003cem\u003eP\u003c/em\u003e = 0.079). High CRP levels were associated with a higher risk of cancer occurrence regardless of variability, with the group exhibiting the lowest variability showing the highest risk (adjusted HR = 1.42, 95% CI: 1.18–1.70, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (\u003cb\u003eTable. 2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eNext, we categorized participants into three distinct long-term CRP change patterns: stable, increasing, and decreasing (\u003cb\u003eFigure. S1\u003c/b\u003e). We investigated the association between CRP variability and cancer risk in participants with each of these three different patterns. The results indicated that only in the stable group, CRP variability was significantly associated with the risk of cancer (High variability in stable-pattern: HR = 0.81, 95% CI = 0.72–0.92, \u003cem\u003eP\u003c/em\u003e = 0.001) (\u003cb\u003eTable. 3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, we explored the association between CRP variability and cancer at different specific sites. During the follow-up period, the incidence of cancer at various specific sites was presented in Supplementary Table\u0026nbsp;2 (\u003cb\u003eTable. S2\u003c/b\u003e). The results indicated a significant association between CV of CRP and the incidence of lung cancer (HR = 0.77, 95% CI = 0.63–0.96, \u003cem\u003eP\u003c/em\u003e = 0.018) (\u003cb\u003eTable. 4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo explore whether the association between CRP variability and cancer exists in populations with different characteristics, we performed subgroup analyses. Forest plots showed that significant associations between high variability and cancer were present in most subgroups compared to low variability. Moreover, high variability was more protective against cancer than moderate variability (\u003cb\u003eFigure. 3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe conducted sensitivity analyses to ensure the stability of the findings and to rule out other factors. First, to rule out potential reverse causality, we excluded participants who developed cancer within initial two years of follow-up. Second, we excluded participants with extremely large values of CRP. In addition, we corrected for blood glucose and lipids, CRP at baseline, and aspirin use separately from Model C. The results suggested that these conditions did not affect the association between CRP variability and cancer risk (\u003cb\u003eTable. 5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn addition, we used VIM as a complementary measure of variability. The RCS plot showed a decrease in HR for cancer risk with increasing VIM of CRP (\u003cb\u003eFigure. 2 (b)\u003c/b\u003e). We used COX regression analysis to explore the association between VIM of CRP and cancer risk. The results showed that VIM demonstrated a similar association with cancer as CV. There was a significant association between higher VIM of CRP and reduced cancer risk (\u003cb\u003eFigure. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe further examined the association between CRP variability and the risk of mortality. The results showed no significant association between CRP variability and death and cancer-specific death after correction for confounders (\u003cb\u003eTable. S3\u003c/b\u003e). Nonetheless, competing risk plots suggest that high CRP variability has a lower risk of cancer development and death than low variability (\u003cb\u003eFigure. S3\u003c/b\u003e).\u003c/p\u003e\n\n "},{"header":"Discussion","content":"\u003cp\u003eOur findings suggest a significant association between high variability in CRP and a decreased risk of cancer occurrence. Maintaining a low inflammatory state in the body holds crucial importance for the prevention of cancer. Participants with high levels of CRP accompanied by low variability had a significantly increased risk of developing cancer.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that CRP serves not only as a prognostic factor for cancer patients but is also associated with an increased risk of future cancer in seemingly healthy participants(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Suthathar et al. suggested that baseline CRP was associated with a 17% increase in cancer risk, and elevated CRP over time was associated with an 8% increase in cancer risk(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A study on the trajectory of CRP changes suggests that maintaining a long-term, stable, and low level of CRP is positively meaningful for cancer prevention(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Our results keep in line with this conclusion. Participants in a low inflammatory state (CRP \u0026lt; 3 mg/L, or with a trajectory of low-stable) exhibited a significantly lower risk of developing cancer compared to participants in a high inflammatory state (CRP ≥ 3 mg/L). Chronic hyperinflammatory states leads to cellular damage and DNA injury on the one hand. On the other hand, the activation of pro-inflammatory pathways, such as the NF-κB pathway, may excessively inhibit the immune system's surveillance and clearance of malignant cells(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These factors could potentially contribute to the eventual presence and growth of cancer.\u003c/p\u003e\u003cp\u003ePrevious studies exploring the association between CRP and cancer have focused on differences in the amount of CRP, neglecting the fluctuating changes in CRP. When the body is exposed to inflammation or infection, the immune system is stimulated, and the liver releases more CRP to respond to the inflammation or infection(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Even in relatively healthy populations, CRP fluctuates to some extent. Additionally, unhealthy lifestyle factors such as smoking, obesity, chronic psychological stress, and chronic stress are likely to contribute to elevated CRP levels(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Evaluating the future risk of cancer based solely on the quantity of CRP is highly susceptible to interference from other factors. In this study, we focused on exploring the association between CRP variability and cancer occurrence. Our study revealed a particularly intriguing finding: the higher the variability of CRP, the lower the risk of cancer occurrence. We cross-combined high and low levels of CRP with the size of variability. It was found that the risk of developing cancer was 42% higher in those with high CRP level-low CRP variability than in those with low CRP level-high CRP variability. Among populations with different CRP trajectories, statistical results revealed that significant protective effects of high CRP variability are observed only in individuals with low-stable CRP patterns. In contrast, no significant protective effect was found in populations with CRP decreasing or increasing patterns, possibly due to sample size limitations, as the sample size for decreasing or increasing CRP patterns were much lower than that of populations with low stable CRP pattern. Furthermore, we observed a significant association between CRP variability and the occurrence of lung cancer. Compared to individuals with low CRP variability, those with high CRP variability had a 23% reduced risk of developing lung cancer. Prior research has demonstrated the relevance of CRP to the prognosis of non-small cell lung cancer patients(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Xu et al.'s study established that CRP plays a role in promoting lung cancer development in the tumor microenvironment(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The Kailuan Cohort Study also previously found a significant association between high levels of baseline CRP and the occurrence of lung cancer(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Our study further found a significant association between CRP variability and lung cancer.\u003c/p\u003e\u003cp\u003eIn subgroup analyses, we observed an interesting phenomenon. The protective effect of high CRP variability against cancer incidence was more significant in older individuals compared to middle-aged individuals, in smokers than in non-smokers, and in obese than in non-obese. Both smoking and obesity put the body in a state of chronic inflammation, and CRP is not only a marker of chronic inflammation, but also serves as an internal exposure marker, reflecting the aging state of the organism(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). A prospective cohort study has shown that organismal aging is often accompanied by low-grade inflammation, and that aging of the immune system is accompanied by elevation of the pro-inflammatory marker CRP(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). CRP is also one of the most commonly used biomarkers of immune senescence(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Cancer, as an age-related disease, is strongly associated with factors such as aging, smoking, and obesity. Our results indicate that, in these cancer risk groups, high CRP variability paradoxically exhibits a protective effect. This suggests that high CRP variability might imply a healthy immune mechanisms opposite to immune aging, hence playing a significant protective role in the mentioned high-risk populations. However, this is only our speculation, and the lack of indicators for the diagnosis of immune aging in our cohort precludes further exploration of the association between CRP variability and immune aging.\u003c/p\u003e\u003cp\u003eThrough this study, we observed an association between CRP variability and the occurrence of cancer. Although our research cannot precisely explain the mechanistic aspects of this association, it still holds instructive significance for cancer prevention. Firstly, maintaining low levels of CRP is crucial for cancer prevention. Secondly, long-term monitoring of CRP fluctuations, coupled with early intervention and management, is advantageous in preventing the occurrence of cancer or other immune-related diseases. One of the strengths of our study is its pioneering exploration of the relationship between the amplitude of inflammation fluctuations and cancer. To the best of our knowledge, no similar study has been conducted to date. This provides new insights into understanding the longitudinal association between CRP changes and cancer risk. Additionally, we adjusted for many confounding factors, such as obesity and lifestyle, in the risk model, enhancing the reliability of the results.\u003c/p\u003e\u003cp\u003eOur study has certain limitations. First, there is a significant imbalance in sex distribution in our cohort, with a larger number of men than women. Despite conducting stratified analyses based on the sex, the results may still be prone to bias. Second, factors influencing cancer occurrence are multifaceted, including metabolic factors. Although we adjusted for TC and TG in sensitivity analyses, we couldn't completely eliminate the impact in this regard. Third, while we hypothesize an association between low CRP variability and immune aging, the lack of specific data for diagnosing immune aging in our cohort prevents us from confirming this hypothesis. In future research, it is essential to delve deeper into the specific mechanistic connections among CRP variability, immune aging, and cancer occurrence.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough this study, we discovered the significant role of CRP variability in the occurrence of cancer, especially in lung cancer. High levels of CRP concentration coupled with low variability were strongly associated with a substantial increase in cancer risk. Furthermore, the protective mechanisms of high CRP variability in cancer need to be better elucidated in future experimental studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the Helsinki declaration. All participants signed an informed consent form. Trial registration: Kailuan study, ChiCTR‐TNRC‐11001489. Registered 24 August, 2011‐Retrospectively registered, http://www.chictr.org.cn/showprojen.aspx?proj=8050.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObtained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key Research and Development Program (grant number 2022YFC2010101); Laboratory for Clinical Medicine, Capital Medical University (grant number 2023-SYJCLC01); National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Project for Major Diseases: Comprehensive Treatment and Management of Critically Ill Elderly Inpatients (grant number 2019.YLFW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCY wrote the manuscript. CY, WYM, ZX, LCA, LT, and LSQ analyzed and interpreted the patient data, DL, ZQS and SHP made substantial contributions to the conception, design, and intellectual content of the studies. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.cn) for English language editing. We are grateful to all the participants and staffs who have been part of the kailuan cohort which has enabled this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis and writing of this study, AI was used only for language polishing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed, F., Samantasinghar, A., Soomro, A. M., Kim, S. \u0026amp; Choi, K. H. 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Association of Initial and Longitudinal Changes in C-reactive Protein With the Risk of Cardiovascular Disease, Cancer, and Mortality. Mayo Clin Proc. ;98(4):549\u0026thinsp;\u0026ndash;\u0026thinsp;58. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, M. et al. Association between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: From the investigation on nutrition status and clinical outcome of common cancers study. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1131496 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, H. et al. The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non-small cell lung cancer. \u003cem\u003eJ. Cachexia Sarcopenia Muscle\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (2), 869\u0026ndash;878 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllin, K. H., Bojesen, S. E. \u0026amp; Nordestgaard, B. G. Baseline C-reactive protein is associated with incident cancer and survival in patients with cancer. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (13), 2217\u0026ndash;2224 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, T. et al. C-reactive protein trajectories and the risk of all cancer types: A prospective cohort study. \u003cem\u003eInt. J. Cancer\u003c/em\u003e. \u003cb\u003e151\u003c/b\u003e (2), 297\u0026ndash;307 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, J. et al. Evidence-based guideline on immunonutrition in patients with cancer. \u003cem\u003ePrecision Nutr.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (1), e00031 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes, Q. et al. Chronic inflammation and cancer; the two sides of a coin. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e338\u003c/b\u003e, 122390 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson, M. E. et al. A biofunctional review of C-reactive protein (CRP) as a mediator of inflammatory and immune responses: differentiating pentameric and modified CRP isoform effects. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1264383 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristofaro, D. G. D. et al. Are C-reactive protein concentrations affected by smoking status and physical activity levels? A longitudinal study. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (11), e0293453 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaufenbiel, I. et al. Influence of Nutrition and Physical Activity on Local and Systemic Inflammatory Signs in Experimentally Induced Gingivitis. \u003cem\u003eNutrients\u003c/em\u003e ;\u003cb\u003e15\u003c/b\u003e(15). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNassar, Y. M. et al. C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework. \u003cem\u003eCancers (Basel)\u003c/em\u003e ;\u003cb\u003e15\u003c/b\u003e(22). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, B. P. \u0026amp; Shi, H. Precision nutrition: concept, evolution, and future vision. \u003cem\u003ePrecision Nutr.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PN9.0000000000000002\u003c/span\u003e\u003cspan address=\"10.1097/PN9.0000000000000002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu, W-L. et al. High-fat diet induces C-reactive protein secretion, promoting lung adenocarcinoma via immune microenvironment modulation. \u003cem\u003eDis. Model. Mech.\u003c/em\u003e ;\u003cb\u003e16\u003c/b\u003e(11). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, J. et al. Association Between Baseline C-Reactive Protein and the Risk of Lung Cancer: A Prospective Population-Based Cohort Study. \u003cem\u003eCancer Prev. Res. (Phila)\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e (11), 747\u0026ndash;754 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, M. et al. C-reactive protein and cancer risk: a pan-cancer study of prospective cohort and Mendelian randomization analysis. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 301 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Sleen, Y. et al. Frailty is related to serum inflammageing markers: results from the VITAL study. \u003cem\u003eImmun. Ageing\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e (1), 68 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Tran, E. et al. Biomarkers of the ageing immune system and their association with frailty - A systematic review. \u003cem\u003eExp. Gerontol.\u003c/em\u003e \u003cb\u003e176\u003c/b\u003e, 112163 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable. 1 Baseline characteristics of participants, stratified by the coefficient of variation of CRP (n=42514)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.258064516129032%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.74193548387096%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV of CRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLowest tertile (\u0026lt;53.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle tertile (53.07~84.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest tertile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e≧\u003c/strong\u003e\u003cstrong\u003e84.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e14173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e14172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e14169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eWoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e3622 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e3504 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e3093 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e10551 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e10668 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e11076 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e54.56 (11.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e53.55 (11.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e51.62 (11.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e10336 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e11040 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e10991 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e3837 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e3132 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e3178 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eIncome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e12832 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e13207 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e13355 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1341 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e965 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e814 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e8880 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e9223 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e9088 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eCurrent and past\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e5293 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e4949 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e5081 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eAlcohol intake, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e7951 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e8606 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e8458 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e6222 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e5566 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e5711 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003ePhysical exercise, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1204 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e1301 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e1465 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e12969 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e12871 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e12704 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e25.47 (3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e25.23 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e24.79 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e5.75 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e5.68 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e5.58 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1.36[0.93,2.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e1.28 [0.88,1.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e1.26[0.90,1.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eCRP (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1.27[0.70,2.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e1.21[0.68,2.43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e1.00[0.50,2.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eCRP (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1.33[0.80,2.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e1.60[0.81,3.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e1.60[0.70,3.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eCRP (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1.10[0.60,2.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e0.70[0.29,1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e0.33[0.11,1.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eMean CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.486268174474958%\"\u003e\n \u003cp\u003e1.30[0.75,2.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.909531502423263%\"\u003e\n \u003cp\u003e1.40[0.77,2.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.809369951534734%\"\u003e\n \u003cp\u003e1.57[0.95,2.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eContinuous variables were presented as mean \u0026plusmn; standard deviation (SD). Continuous variables that were not normally distributed were expressed as the median (interquartile range). Categorical variables were presented as numbers and percentages. Differences in normally and non-normally distributed baseline characteristics were compared using the chi-square test or t-test and Wilcoxon rank sum test, respectively. CV, coefficient of variation; CRP, C reactive protein; BMI, body mass index; FBG, fasting blood-glucose; TG, triglyceride.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 2 Association between serum C reactive protein levels and variability and cancer incidence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"107%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase/N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLow (\u0026lt;3mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1445/34118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eHigh (≧3mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e457/8396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.33(1.19,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.22(1.09.1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.20(1.07,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP variability*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e689/14173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e645/14172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.93(0.83,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.93(0.83,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.93(0.84,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e568/14169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.81(0.72,0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.83(0.75,0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e0.84(0.75,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP levels and variability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLow CRP with high variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e428/11322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLow CRP with moderate variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e497/11495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.16(1.02,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.13(0.99,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.12(0.99,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eLow CRP with low variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e520/11301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.24(1.09,1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.22(1.07,1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.20(1.06,1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eHigh CRP with high variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e140/2847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.33(1.1,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.27(1.05,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.25(1.03,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eHigh CRP with moderate variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e148/2677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.52(1.26,1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.36(1.13,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.33(1.1,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eHigh CRP with low variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e169/2872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.65(1.38,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.45(1.21,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e1.42(1.18,1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Data are presented as hazard ratios (95% confidence intervals).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel A: unadjusted; Model B: adjusted for sex, age, education, income; Model C: additionally adjusted for BMI, smoke, alcohol intake, physical exercise based on Model B.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*We categorized the variability into three groups based on the tertile intervals of CV of CRP: low, medium, and high.\u003c/p\u003e\n\u003cp\u003eCRP, C reactive protein; HR, hazard ratio; CI, confidence interval; \u003cem\u003eP-value\u003c/em\u003e, probability.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 3 Association between CRP variability and cancer incidence in different CRP trajectories (2006-2010)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase/N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous CV of CRP (per_SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e1902/42514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.95 (0.90,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 1 (stable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e1607/36579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.95 (0.90,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 2 (increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e138/2792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.93 (0.79,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 3 (decrease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e157/3143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.89 (0.76,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV tertile interval of CRP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 1 (stable-pattern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e585/12394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e555/12255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.95 (0.85,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e467/11930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.81 (0.72,0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 2 (increase-pattern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e45/701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e35/889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.59 (0.38,0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e58/1202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.71 (0.48,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eTrack 3 (decrease-pattern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e59/1078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e55/1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.96(0.67,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.29757785467128%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82006920415225%\"\u003e\n \u003cp\u003e43/1037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.61937716262976%\"\u003e\n \u003cp\u003e0.74 (0.51,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.262975778546714%\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e COX regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake and physical exercise. Data are presented as hazard ratios (95% confidence intervals). CV, coefficient of variation; CRP, C reactive protein; HR, hazard ratio; CI, confidence interval; \u003cem\u003eP-value\u003c/em\u003e, probability.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 4 Association between CRP coefficient of variation and site-specific cancer incidence\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLowest tertile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.579964850615113%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle tertile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.646748681898067%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest tertile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eHead and neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.68 (1.09,2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.32 (0.84,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eEsophagus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.98 (1.04,3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.30 (0.65,2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eGastrointestinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.22 (0.82,1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.13 (0.75,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eColorectal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e0.81 (0.61,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.78 (0.58,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eLiver and gallbladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.36 (0.93,1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.81 (0.53,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003ePancreatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.63 (0.67,3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.36 (0.72,2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74 (0.59,0.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.77 (0.63,0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e0.90 (0.62,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.71 (0.47,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eUterus and ovaries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.08 (0.56,2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e1.18 (0.61,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eProstate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e0.58 (0.32,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.81 (0.47,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eKidney\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e1.14 (0.67,1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.78 (0.43,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eUrinary bladder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e0.97 (0.53,1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.79 (0.40,1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.374340949033392%\"\u003e\n \u003cp\u003eHematology*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.398945518453427%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\"\u003e\n \u003cp\u003e0.81 (0.44,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.423550087873462%\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.804920913884008%\"\u003e\n \u003cp\u003e0.63 (0.32,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.84182776801406%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eData are presented as hazard ratios (95% confidence intervals). Cox regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake, physical activity. HR, hazard ratio; CI, confidence interval; \u003cem\u003eP\u003c/em\u003e-value, probability. Bolded text indicates statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eTumors of the hematology\u0026nbsp;include lymphoma and leukemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 5 Sensitivity Analyses\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"486\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV of CRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclude cancer occurrence within first 2 years (n=41948)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.89 (0.80-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.79 (0.70-0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclude CRP maxima (CRP\u0026gt;10 mg/L) (n=40412)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.94 (0.84-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.86 (0.77-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditionally adjusted for blood glucose and lipid\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.93 (0.84-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.84 (0.75-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditionally adjusted for basic CRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.93 (0.84-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.84 (0.75-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditionally adjusted for taking aspirin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eLowest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eMiddle tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.93 (0.84-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36213991769547%\"\u003e\n \u003cp\u003eHighest tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.592592592592595%\"\u003e\n \u003cp\u003e0.85 (0.76-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.045267489711936%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eNotes:\u003c/strong\u003e Data are presented as hazard ratios (95% confidence intervals). Cox regression model adjusted for sex, age, education, income, BMI, smoke, alcohol intake, physical activity. CV, coefficient of variation; HR, hazard ratio; CI, confidence interval; \u003cem\u003eP\u003c/em\u003e-value, probability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein, cancer, variability, prospective, risk","lastPublishedDoi":"10.21203/rs.3.rs-4894293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4894293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association between inflammation and cancer has been widely studied, but research on the relationship between the magnitude of inflammatory fluctuations and cancer remains limited. Therefore, this study aims to investigate the association between C-reactive protein (CRP) variability and the occurrence of cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 42,514 participants were included, and their CRP levels were measured over a 4-year period. We used the coefficient of variation (CV) of CRP to quantify the variability in inflammation. Cox regression analysis was used to assess the association between CRP variability and cancer risk after adjusting for potential confounding factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh CV of CRP significantly associated with reduced cancer risk (hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.84; 95% CI: 0.75\u0026ndash;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Participants with high CRP and low CV had a significantly increased risk of cancer (HR\u0026thinsp;=\u0026thinsp;1.42; 95% CI: 1.18\u0026ndash;1.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the population with long-term stable CRP levels, there is a significant association between the CV and the risk of cancer (HR\u0026thinsp;=\u0026thinsp;0.81; 95% CI: 0.72\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Furthermore, the association between the CV and lung cancer was most pronounced. Sensitivity analyses confirmed the stability of the association between CRP variability and cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigh CRP variability is significantly associated with reduced risk of cancer, particularly in the case of lung cancer. This provides a new perspective and evidence for exploring the relationship between inflammation and cancer.\u003c/p\u003e","manuscriptTitle":"Association between C-reactive protein variability and cancer incidence: a longitudinal prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 08:12:44","doi":"10.21203/rs.3.rs-4894293/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37716239,"name":"Biological sciences/Cancer"},{"id":37716240,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-01-09T10:56:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 08:12:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4894293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4894293","identity":"rs-4894293","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}