A nested case-control study of kidney cancer among refinery/petrochemical workers

Affiliation.

  • 1 Exxon Biomedical Sciences, Inc., East Millstone, NJ 08875-2350, USA.
  • PMID: 8793353
  • PMCID: PMC1469371
  • DOI: 10.1289/ehp.96104642

A nested case-control study was designed to evaluate whether a nearly twofold excess of kidney cancer among workers at a refinery/petrochemical plant was associated with cumulative exposure to C2-C5 saturated, C2-C5 unsaturated, C6-C10 aliphatic saturated, C6-C10 aliphatic unsaturated, and C6-C10 aromatic process streams. Nonoccupational risk factors were body mass index (BMI), blood pressure (both measured at about age 28), and smoking. There was no significant association with cumulative exposure or tenure as estimated by conditional logistic regression and adjusted for nonoccupational risk factors. Categorical analysis showed increased odds ratios only in the second (low) and fourth (high) quartiles compared to the first quartile reference group of lowest exposed workers, and a three-quarter-fold increased odds ratio for > 32 years' tenure compared to the < 25-year reference group. The number of cases was small with wide confidence intervals around estimate of risk, so the possibility of an exposure-response trend cannot be ruled out. Multivariate analysis identified overweight (high BMI; p < 0.01) as the most important risk factor in this data set, followed by tenure and increased blood pressure. There was a weak association with current smoking, but not with pack-years smoked. The risk of kidney cancer for a nonsmoker with normal blood pressure but 25% overweight was increased about 2.6-fold (95% CI = 1.2-5.4). The risk of kidney cancer for a nonsmoker of normal weight with high blood pressure (e.g., 150/110), was increased about 4.5 (95% CI, 0.8-26).

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Blood Pressure
  • Body Mass Index
  • Case-Control Studies
  • Chemical Industry
  • Kidney Neoplasms / epidemiology*
  • Logistic Models
  • Middle Aged
  • Occupational Diseases / epidemiology*
  • Petroleum / adverse effects*
  • United States

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  • World J Gastroenterol
  • v.9(1); 2003 Jan 15

Nested case-control study on the risk factors of colorectal cancer

Correspondence to: Kun Chen, Department of Epidemiology, Zhejiang University School of Public Health, 353 Yan-an Road, Hangzhou 310006, Zhejiang Province, China. nc.ude.ujz.meujz@kc

Telephone: +86-571-87217190

AIM: To investigate the risk factors of colon cancer and rectal cancer.

METHODS: A nested case-control study was conducted in a cohort of 64693 subjects who participated in a colorectal cancer screening program from 1989 to 1998 in Jiashan county, Zhejiang, China. 196 cases of colorectal cancer were detected from 1990 to 1998 as the case group and 980 non-colorectal cancer subjects, matched with factors of age, gender, resident location, were randomly selected from the 64693 cohort as controls. By using univariate analysis and mutivariate conditional logistic regression analysis, the odds ratio (OR) and its 95% confidence interval (95%CI) were calculated between colorectal cancer and personal habits, dietary factors, as well as intestinal related symptoms.

RESULTS: The mutivariate analysis results showed that after matched with age, sex and resident location, mucous blood stool history and mixed sources of drinking water were closely associated with colon cancer and rectal cancer, OR values for the mucous blood stool history were 3.508 (95%CI: 1.370-8.985) and 2.139 (95%CI: 1.040-4.402) respectively; for the mixed drinking water sources, 2.387 (95%CI: 1.243-4.587) and 1.951 (95%CI: 1.086-3.506) respectively. All reached the significant level with a P -value less than 0.05.

CONCLUSION: The study suggested that mucous blood stool history and mixed sources of drinking water were the risk factors of colon cancer and rectal cancer. There was no any significant association between dietary habits and the incidence of colorectal cancer.

INTRODUCTION

Colorectal cancer is one of the most common malignant tumors[ 1 - 6 ]. During the past decades, the incidence of colorectal cancer was increased all around the world, more than 500000 cases were diagnosed as colorectal cancer per year. In the east of China, there has been a higher incidence of colorectal cancer. In Jiashan County, the mortality rate of colorectal cancer was the highest among China, which is about 20/100000 per year[ 7 ].

The causes of colorectal cancer are generally regarded as two aspects: heredity and environment[ 8 - 10 ]. The former includes family history of cancer, intestinal polyp history, and so on. The later includes particularly dietary habit and physical activity.

Nested Case-Control Study (NCCS), an analytical epidemiological study method, was first presented by Mantel N, an American epidemiologist, in 1973[ 11 ], and it was widely used after 1980’s[ 12 - 21 ]. All the subjects in such study are selected from a whole cohort, which is generally called cohort set. Compared with the cohort study, NCCS has the privilege of time-saving, money-saving, and trouble-saving; while compared to case-control study, since the exposure data are collected before the incident of disease, it is certain of the causes and time consequences relationship, and observational bias could be controlled efficiently. All of these characteristics of NCCS are suitable to the study of chronic disease, such as cancer.

There are many reports on the risk factors of colorectal cancer using classical case-control study method, however, few studies used nested case-control study method[ 22 - 29 ]. The purpose of this study is to explore the risk factors of colorectal cancer, providing evidence for the prevention of colorectal cancer.

MATERIALS AND METHODS

Selection of cases and controls.

A colorectal cancer screening program beginning on 1 st May 1989 and ending on 30 th April 1990 was conducted in ten countries which belonged to Jiashan county, Zhejiang province, China, including Weitan town, Yangmiao country, Xiadianmiao country, et al From 75842 eligible subjects aged 30 years and over, 64693 subjects were enrolled as the base cohort set, the respondence rate was 85.3%. Moreover, Jiashan county has founded cancer registration system and colorectal cancer report system, monitoring new cancer cases, including colorectal cancer. The cases in this study, who had participated in the 1989-1990 screening program, were the new colorectal cancer patients reported by Institute of Cancer Research and Prevention of Jiashan county. Up to 1998, the total number was 196 cases. Of which, 151 cases were pathological diagnosed, account for 77.1%, 20 cases were diagnosed in the operation, 10.2%, 23 cases were diagnosed by endoscopy, 11.7%. Under the principle of same-country or town, same-sex, and no more than 3 years age disparity, 980 non-colorectal cancer subjects in the cohort set were selected as controls, resulting the final study subjects of 196 cases and 980 controls.

Contents of the study

The study contents composed of three parts as follow: (1) General characteristics: including age, sex, job types, educational levels, address et al; (2). Personal habits: including dietary habits, drinking water sources, alcohol consumption and cigarette smoking, et al; (3). Symptoms and disease history related with colorectal cancer: including changes of stool status, abdominal operation history, intestinal disease history, asthma history and allergy history, family cancer history, ancylostomiasis history, drug using history, psychic stimulation history, and so on.

Investigation methods and quality control

In the 1989-1990 screening investigation, a well-built Investigation Manual as the uniform criteria for inquiring the subjects and filling up the constructed questionnaire was used. All interviewers were trained focusing on the skills of inquiring. No subjects refused to be interviewed except that they were out of towns. For building the database, the questionnaires were coded and put into computer twice to control bias. The data used in this study were taken from this database.

Statistical analysis

Classical analysis methods of case-control study can be used to NCCS data, usually calculating OR value. In this study, Chi-square test was used in the univariate analysis of the data; conditional logistic regression was used in the multivariate analysis. The SPSS 10.0 for windows and the SAS system for windows, version 6.12, were used for completing all the statistical analyses.

General information

In this study, there were 196 cases (84 colon cancer, 112 rectal cancer) and 980 controls. The distribution of age between cases and controls for male and female was shown in Table ​ Table1 1 .

The distribution of subjects by age and sex

The average age of case group was 54.5 ± 10.6 years, while that of the control group was 54.3 ± 10.6 year, there was no statistically significant difference ( t = 0.127, P = 0.899). For sex, there was also no difference statistically ( χ 2 = 0.001, P = 0.979).

Univariate analysis

In order to control the possible confounding bias the age, sex and resident location were matched in the study design. Given that the risk factors of colon cancer might be different from that of rectal cancer, the analysis of the risk factors were separated into colon cancer and rectal cancer, instead of colorectal cancer. The OR and its 95% confidence intervals (95%CI), χ 2 and P values in the univariate analysis were showed respectively in Table ​ Table2 2 and Table ​ Table3 3 (the variables showed P > 0.20 were excluded).

Results of univariate analysis for colon cancer (cases n = 84, controls n = 420)

*drinking mixed water source refers to the subjects drinking different type of water sources through his/her lifetime, mostly drinking river water and gutter water. So is Table ​ Table2, 2 , Table ​ Table3 3 and Table ​ Table4 4

Results of univariate analysis for rectal cancer (cases n = 112, controls n = 560)

In Table ​ Table2, 2 , it was showed that four variables, well water drinking, mixed water source drinking, chronic diarrhea history and intestinal polyp history, were significantly associated with colon cancer ( P < 0.05). The factor of appendicitis history showed an OR value close to significant level ( P = 0.066). For rectal cancer in Table ​ Table3, 3 , there were two variables reached the significant level of P = 0.05, which were mixed water source drinking (OR = 2.02) and mucous blood stool history (OR = 2.14).

Mutivariate analysis

The variables showing associations with the risk of colon cancer and rectal cancer at P < 0.15 level were further tested in forward stepwise conditional logistic regression models. The final model consisted of those variables showing a significant association with the risk of colorectal cancer at P < 0.05 level. Results were showed in Table ​ Table4 4 and Table ​ Table5 5 for colon and rectal cancers, respectively.

Results of multivariate analysis for colon cancer

Table ​ Table4 4 and Table ​ Table5 5 illustrated that, at P = 0.05 level, both for colon cancer and rectal cancer, the final logistic regression model comprised two factors: mixed water source drinking and mucous blood stool history.

Results of multivariate analysis for rectal cancer

It is generally believed that colorectal cancer is the combined outcome of heredity and environment[ 8 - 10 , 30 ]. Despite uncertainties regarding the underlying association between heredity and colorectal cancer, the genetic factors may affect the individual sensitivity to cancer[ 3 , 30 - 37 ]; many documents had reported that the environmental factors might also influence colorectal cancer[ 37 - 46 ]. On the secondary prevention for colorectal cancer, symptoms and/or disorders of pre-cancer, such as intestinal polyps, ulcerative colitis, should be considered[ 48 , 49 ].

Univariate analysis results of this study showed that drinking well water is a protective factor for colon cancer, OR value was 0.542, ( P < 0.05); drinking mixed water, mostly drinking river water and gutter water, was a risk factor both for colon cancer and rectal cancer, OR values were 2.387 (95%CI: 1.247-4.587) and 1.951 (95%CI: 1.086-3.506) in multivariate conditional logistic model, respectively. The association between drinking mixed water and colorectal cancer is consistent with former study. In this study, country subjects account for about 75%, most of the mixed water drinking aggregated in country. In the local country, people usually were drinking river water and well water. It reflects that uncertainty of drinking water source, especially mixed drinking river water and gutting water would increase the incidence of colon cancer and rectal cancer. The findings in this study resembled other study reports[ 45 , 50 ].

Chronic diarrhea, mucous blood stool and constipation history are the pre-clinical symptoms of colorectal cancer[ 51 - 54 ]. This study has found the positive association between mucous blood stool history and colorectal cancer. In final colon cancer logistic model and rectal cancer logistic model, the OR values of mucous blood stool history were 3.508 (95%CI: 1.370-8.985) and 2.139 (95%CI: 1.04-4.402) respectively, both reached statistical significant level. Univariate analysis also showed that, for colon cancer, the OR value of chronic diarrhea history was 2.018 (95%CI: 1.023-4.06), P < 0.05, but did not enter the final logistic regression model.

Intestinal polyp history commonly regarded as a high risk factor for colorectal cancer[ 55 - 63 ]. Although the univariate analysis result showed a positive association between intestinal polyp history and colon cancer, OR = 6.503 (95%CI: 2.009-21.049), P < 0.05, after being matched with age, sex and location, the factor did not enter the final logistic regression model. However, the association between intestinal polyp history and rectal cancer could not reach the significant level even in the univariate analysis.

Although the association between dietary habits and colorectal cancer has been reported[ 35 , 38 , 39 , 41 , 43 , 47 , 64 , 65 ], this study was not able to confirm such a positive association. It was reported that increasing fat while reducing fibrous in diet would increase the incidence of colorectal cancer[ 66 - 68 ]. In this study, after merging the two variables, pork eating and vegetable eating, into one variable by cross-difference method, we got a negative association. Red meat eating, such as fish cooked with soy souse, was reported to be the risk factor of colorectal cancer[ 69 ], but the result of this did not agree with that. Moreover, recent reports were not consistent with each other about the association between cigarette smoking and colorectal cancer, nor does the alcohol consumption[ 69 - 74 ]. This study did not find any statistical association between cigarette smoking, nor was alcohol consumption and colorectal cancer.

Nested case control study, namely case control study within cohort, is based on a cohort set. After baseline investigation for the cohort set, including population structure, exposure factors and pertinent factors, the study subjects are divided into two groups: the disease individuals form the case group and the individuals of control group need to be randomly selected from the non-disease subjects. This kind of study can be analyzed statistically as a case-control study. The risk factors found in nested one are certain in cause and time consequence. In addition, the number of case in this study was abundance after ten years of follow-up; the controls were randomly selected from the whole disease-free cohort set and can represent the normal public population well. All of these endue the results with persuasion.

It should be noted that, after ten years of follow-up, some exposure factors may have changed, factors such as dietary habits, drinking water sources, intestinal disease history may be different from the primate investigation. All the changes may discount the preciseness of the conclusion. That the association between dietary habits and colorectal cancer could not be put forward any positive evidence might be explained by such changes.

Supported by the National Natural Science Foundation of China, No. 30170828

Edited by Zhao M

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  • Nested case-control study of lung cancer in four Chinese tin mines
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  • Department of Labor Health and Occupational Diseases, Tongji Medical College, 13 Hang Kong Lu, Wuhan, Hubei, People's Republic of China
  • Correspondence to:
 Dr W Chen, Institute of Hygiene and Occupational Medicine, Medical School, University of Essen, Hufeland Strasse 55, D-45122 Essen, Germany;
 weihong.chen{at}uni-essen.de

Objectives: To evaluate the relation between occupational dust exposure and lung cancer in tin mines. This is an update of a previous study of miners with high exposure to dust at four tin mines in southern China.

Methods: A nested case-control study of 130 male lung cancer cases and 627 controls was initiated from a cohort study of 7855 subjects employed at least 1 year between 1972 and 1974 in four tin mines in China. Three of the tin mines were in Dachang and one was in Limu. Cumulative total exposure to dust and cumulative exposure to arsenic were calculated for each person based on industrial hygiene records. Measurements of arsenic, polycyclic aromatic hydrocarbons (PAHs), and radon in the work sites were also evaluated. Odds ratios (ORs), standard statistic analysis and logistic regression were used for analyses.

Results: Increased risk of lung cancer was related to cumulative exposure to dust, duration of exposure, cumulative exposure to arsenic, and tobacco smoking. The risk ratios for low, medium, and high cumulative exposure to dust were 2.1 (95% confidence interval (95% CI) 1.1 to 3.8), 1.7 (95% CI 0.9 to 3.1), and 2.8 (95% CI 1.6 to 5.0) respectively after adjustment for smoking. The risk for lung cancer among workers with short, medium, and long exposure to dust were 1.9 (95% CI 1.0 to 3.5), 2.3 (95% CI 1.3 to 4.1), and 2.3 (95% CI 1.2 to 4.2) respectively after adjusting for smoking. Several sets of risk factors for lung cancer were compared, and the best predictive model included tobacco smoking (OR=1.6, 95% CI 1.1 to 2.4) and cumulative exposure to arsenic (ORs for different groups from low to high exposure were 2.1 (95% CI 1.1 to 3.9); 2.1 (95% CI 1.1 to 3.9); 1.8 (95% CI 1.0 to 3.6); and 3.6 (95% CI 1.8 5 to 7.3)). No excess of lung cancer was found among silicotic subjects in the Limu tin mine although there was a high prevelance of silicosis. Exposures to radon were low in the four tin mines and no carcinogenic PAHs were detected.

Conclusions: These findings provide little support for the hypothesis that respirable crystalline silica induces lung cancer. Ore dust in work sites acts as a carrier, the exposure to arsenic and tobacco smoking play a more important part in carcinogenesis of lung cancer in tin miners. Silicosis seems not to be related to the increased risk of lung cancer.

  • lung cancer
  • dust exposure
  • PAHs, polycyclic aromatic hydrocarbons
  • SMR, standardised mortality ratio
  • OR, odds ratio
  • CTD, cumulative total dust

http://dx.doi.org/10.1136/oem.59.2.113

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The association between crystalline silica and lung cancer has been the subject of extensive discussion in recent years. In 1997, an International Agency for Research on Cancer (IARC) working group reviewed this subject and concluded that there was sufficient evidence in humans for the carcinogenicity of inhaled crystalline silica in the form of quartz or cristobalite from occupational sources. 1 However, in their overall evaluation, the working group also noted that carcinogenicity in humans was not detected in all industrial circumstances. For the studies among ore miners who were potentially exposed to silica dust, consistent evidence for a relation between silica and lung cancer was not found. Also, confounders—such as other known occupational respiratory carcinogens—were not taken into account in most of these studies.

As ore mines, tin mines were the subject in several studies. Hodgson and Jones 2 followed up a cohort of 3010 miners who worked between 1941 and 1984 in two tin ore mines in the United Kingdom. They found that mortality from lung cancer was significantly increased (105 observed, standardised mortality ratio (SMR) 1.58, 95% CI 1.29 to 1.91) and that there was a strong dose-response relation with duration of exposure underground. Smoking and radon daughters (about 10 working level months) were considered to be the main risk factors for lung cancer in their study. High mortality of lung cancer was also reported among southern Chinese tin miners. 3, 4 Fu et al 5 conducted a case-control study in one Chinese tin mine in the Dachang area and showed a significant correlation between lung cancer and years of underground exposure to dust. The smoking adjusted odds ratio (OR) for lung cancer was 1.05 (95% CI 1.03 to 1.07), but they did not consider concomitant exposure to arsenic in that mine.

In 1992, one cohort study (1972–89) conducted by Chen et al 6 including 7855 miners in four tin mines in southern China confirmed that the mortality from lung cancer was 189.25×10 -5 , higher than the national mortality from lung cancer among city residents in China (89 observed, SMR 1.98, 95% CI 1.59 to 2.43). The mortality from lung cancer in miners with high exposure to dust was 2.2 (95% CI 1.3 to 3.6) compared with miners with low or no exposure. From this cohort, Mclaughlin et al 7 developed one nested case-control study which included tin mines, tungsten mines, copper/iron mines, and pottery factories. Their results supported an OR from lung cancer significantly related to exposure to dust and showed that exposure to arsenic confounded the dose-response relation between exposure to crystalline silica and risk of lung cancer in tin mines. In their study, the mean age of the tin miners was only 50.4 at the end of 1989. Since then new subjects with lung cancer have been diagnosed. The present study followed up the previous cohort in four tin mines to the end of 1994 and initiated a nested case-control study of lung cancer for analysis. Besides detailed cumulative total dust, exposure concentrations for individual miners were calculated, and historical estimates of exposure to arsenic were supplied in this study. All four tin mines are underground, with known occupational carcinogens including crystalline silica, arsenic, and radon. The objectives of this study are to verify previous findings and to clarify the role of crystalline silica and arsenic in the high mortality of lung cancer in these tin mines.

Main messages

There was a positive exposure-response relation between exposure to dust and risk for lung cancer.

There was no excess of lung cancer among silicotic patientss.

There was a strong exposure-response relatjon between exposure to arsenic and risk for lung cancer.

Policy implications

An exposure limit for exposure to dust with high arsenic contents should be considered.

More information for evaluating risk of lung cancer and exposure to crystalline silica is needed.

MATERIALS AND METHODS

Research subjects.

This study compared four tin mines in Guangxi province in southern China, three tin mines in Dachang and one in Limu. The Dachang tin mines here are not the same as the Dachang tin mine in the study be Fu et al . 5 All 7855 employees who had worked for at least 1 year between 1 January 1972 and 31 December 1974 in any of these four tin mines were selected to the cohort. The cohort was followed up to the end of 1994. All subjects who died of primary lung cancer were selected as cases. Two women cases were excluded from the analysis to avoid the influence of sex. These cases were matched to about five controls, based on age (decade of birth), sex, and mine. Controls who died at an age younger than the age at diagnosis of corresponding cases were excluded from analysis. Exposure and medical data were obtained from personal and medical examination records in each mine. A questionnaire was also administered to the study subject or a member of his family to obtain information on demographic factors, including medical history and tobacco smoking. Amount of tobacco smoking was expressed as packs of cigarettes/day×years (pack-years) smoked. One pack was considered to consist of 20 cigarettes and the smoking of 50 g tobacco to be equivalent to three packs of cigarette.

Ascertainment of lung cancer

All subjects in the cohort were traced for vital status and cause of death to the end of 1994. For the cases who died of primary lung cancer, their diagnostic information such as biopsy results were reconfirmed through medical records in local or regional hospitals. A panel of professional radiologists reviewed all chest x ray films of the cases of lung cancer for this study.

Data on silicosis

Chest radiographs for each cohort member were kept by hygienists in all four tin mines. Silicosis was defined as diagnosed by at least two of three radiologists in a panel who were using the 1986 Chinese pneumoconiosis radiographic diagnostic criteria as previously described. 8 The Chinese stage I, II, and III were found to agree closely with ILO profusion category 1, 2, and 3, respectively.

Occupational exposure data

Industrial hygienists have regularly been measuring and recording environmental exposure to airborne total dust and percentage of silica for miners in the four tin mines since 1950s. The Chinese total dust monitoring scheme is based on a gravimetric method and uses a battery operated sampler which collects total airborne dust directly onto an exposed preweighed filter. The unit was usually placed near the workers when at work. Sampling at a flow rate of 25 l/min, the sampler was typically operated for 15 to 20 minutes when the observed task was in progress. After sampling, the filters were placed in glass tubes and returned to the laboratory where they were weighed to determine the total airborne dust concentration. 9

All available monitoring data were used to create a job title/calendar year exposure matrix. For missing data on years or job titles, consensus estimations were made by industrial hygiene experts, public health doctors, safety engineers, samplers, and local supervisors based on the history of control measures and major changes in technical processes in the mines, and on comparisons with previous and subsequent years at this job title or the same year of other job titles. The previous job title/calendar year exposure matrix 10 was modified for this study.

Work history (job titles and years) for every subject was abstracted from employment records in files of the mining companies. These records include job titles and calendar working year for the full duration of employment of the miners. The cumulative exposure to total dust was calculated for every subject by combining the exposure matrix and work history, with the following equation:

Cumulative exposure to total dust (mg/m 3 -year)=Σni=1 (Ci×Ti)

Where: Ci=total dust concentration for the job and employment period obtained from the job-exposure matrix; Ti=duration of employment (years) of subject for the job (i) from work history, it was adjusted by the number of hours worked/day, one year in dust is defined as 8 hours/day and 270 days/year.

Respirable fraction of total dust was estimated to be 25% ±4% and respirable crystalline silica concentration was estimated to be 3.6% ±0.8% of the total dust concentration. 11, 12 The conversion factors among different job titles in tin mines were not significantly different.

Airborne arsenic concentration before 1988 was estimated as the product of arsenic content of dust multiplied by the total dust concentration in the work sites. Direct airborne arsenic concentration was measured after 1988. Cumulative exposure to arsenic for individual miners was estimated by combined arsenic concentration and work history, such as cumulative exposure to total dust.

Confounding exposures including arsenic, polycyclic aromatic hydrocarbons (PAHs), radon, cadmium, etc, were only measured on work sites after 1988. 9

Statistical analysis

The Mantel-Haenszel OR was used to measure the association between lung cancer and various hazards or risk factors in this analysis. A 95% confidence interval (95% CI) for the OR was calculated by Miettinen's test based method. The stratified analysis method was used to adjust for the effect of smoking. Statistical analyses were performed with the statistical analysis software (SAS). The χ 2 values were calculated by SAS program PROC FREQ. The SAS program PROC CATMOD procedure was used to perform unconditional logistic regression models and to estimate the OR as a surrogate for the relative risk.

A total of 7855 miners were identified in our cohort. There were 5322 miners (male 4443 and female 879) from Dachang and 2533 (male 2101 and female 432) from Limu. The mean age was 34.9 years when miners entered the cohort in 1974 and 54.1 years for survivors at the end of 1994 in the cohort (table 1). Among the cohort, 3082 miners were still working, 1067 had left tin mines, 2672 had retired, and 1034 miners had died. There were 91 miners (1.2% of whole cohort) considered lost to follow up after they left the tin mines. The mortality of the cohort was 603.8×10 -5 and cancer was the leading cause of death (38.2%). Among various cancers, lung cancer (33.4%) was the top cause for deaths. The SMR of lung cancer was 2.39 times greater than the Chinese national mortality.

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Age distribution of miners in the cohort to the end of 1994

One hundred and thirty men with lung cancer and 640 controls were identified. Thirteen controls were excluded because of a lack of complete work history. So in the final analysis, the number of controls were 627. One hundred and one cases of lung cancer and 489 controls who came from Dachang, and 29 cases of lung cancer and 138 control miners came from Limu. A detailed comparison of general characteristics and dust exposure between cases and controls is provided in table 2. Among 112 cases of lung cancer who had worked with dust exposure, 26 cases started exposure before 1950, 74 cases started exposed work in 1950–60, and only 11 cases started exposure after 1960. High percentages of tobacco smokers were found among cases (88.5%) and controls (82.5%) in the tin mines. Smoking more than 20 cigarettes a day was associated with a 1.6 (95% CI 1.1 to 2.4) increased risk of lung cancer.

Characteristics of workers with lung cancer and controls

The mean concentration of total dust in four tin mines was about 25 mg/m 3 before the 1950s. The dust concentration has been progressively decreasing since 1960 because work practices with increased protection have been installed and used. The mean concentration of total dust gradually decreased to 4–8 mg/m 3 in the 1960s, 3–6 mg/m 3 in the 1970s and 1–4 mg/m 3 after 1980. The dust concentration in the Limu tin mine was slightly higher than in the Dachang mines, but not significantly so. Crystalline silica dust concentration ranged between 20% and 40% in the four mines, the mean % of crystalline silica was 34.8% in Dachang and 34.6% in Limu. Other metals in the dust included inorganic arsenic, aluminum, lead, and cadmium. The mean arsenic content of dust measured in 1988 was 6.03% in the Dachang tin mines and 0.46% in Limu. The respirable arsenic concentrations at different concentrations of dust are summarised in table 3. High respirable arsenic concentrations were found in Dachang, but not in the Limu tin mine. The mean concentration of underground PAHs was 372.9 μg/m 3 in Dachang and 7.6 μg/m 3 in Limu, but carcinogenic PAHs were not detected in all mines. The exposure concentration of radon was low in all mines, the mean exposure was 0.02 working level months/year in Dachang and 0.01 working level months/year in the Limu tin mine, both of them are below the occupational exposure limit in China. Asbestos was not detected.

Respirable concentration of arsenic by exposure (μg/m 3 ) in tin mines

The risks of lung cancer associated with cumulative exposure to total dust and duration of exposure after adjusting for tobacco smoking are given in table 4. The mean values from low exposure to high exposure were 25.0 mg/m 3 -y, 82.5 mg/m 3 -y, and 186.5 mg/m 3 -y. The mean values from short to long duration of exposure were 5.8 years, 14.3 years, and 26.4 years. Compared with no exposure to dust, risk of lung cancer showed a significant increased trend with rising cumulative exposure to dust and extending duration of exposure. The risk of lung cancer among miners with exposure to dust was 2.2 (95% CI 1.3 to 3.7) times higher than in those without exposure to dust. However, exposure to dust did not increase the risk of lung cancer in the Limu tin mine (OR 2.2; 95% CI 0.5 to 9.6) when the Limu and Dachang mines were analyzed separately, although similar increased trends for the risk of lung cancer in both types of mines were found in three groups of exposure to dust.

Odds ratios (95% CI) for lung cancer among tin miners by cumulative dust exposure and duration of exposure*

Table 5 shows the risk ratios (95% CIs) for lung cancer adjusted for smoking by cross categories of cumulative exposure to dust and cumulative exposure to arsenic. Significantly increased trends were found in the risk of lung cancer with increasing exposures to arsenic. The relative risk for subjects with low exposure to arsenic and high exposure to dust was 2.2, close to the risk for subjects with low exposure to dust and medium exposure to arsenic (OR 2.0). High correlation ( r =0.82, p=0.0001) was found between exposure to dust and exposure to arsenic, which prevented any adjustment for arsenic in the estimate of risk related to crystalline silica.

Smoking adjusted odds ratio for lung cancer by cross categories of cumulative exposure to dust and cumulative exposure to arsenic

The percentage of silicosis in Dachang tin mine was 31% (185/590) and in Limu it was 35% (58/167). Table 6 shows the risk for lung cancer by the categories of silicosis after controlling smoking. Few subjects were found in silicosis category 3 because most miners died for other complications before they developed to this stage. Significant excess of lung cancer among silicotic workers was found only in Dachang tin miners (OR 2.4, 95% CI 1.6 to 3.8), and not in Limu tin miners (OR 0.8, 95% CI 0.3 to 1.9).

Odds ratios for lung cancer among tin miners by stages of silicosis*

Table 7 shows the relative risk (95% CI) for lung cancer estimated from multivariate logistic regression models with different sets of risk factors. Tobacco smoking (≥20 cigarette/day v <20 cigarette/day) combined with cumulative exposure to dust, duration of exposure to dust, and cumulative exposure to arsenic were associated with the risk of lung cancer (models 1, 3, and 5). The likelihood ratio was used to test the goodness of fit for these models. When silicosis was included in the model, the goodness of fit of model 2 was higher than model 1 and model 7, but silicosis did not significantly contribute to the risk of lung cancer (p=0.08) in model 2. The goodness of fit of model 4 and model 7 was lower than that of model 3, and the goodness of fit of model 6 and model 7 was lower than that of model 5 after silicosis was included. The duration of exposure did not show a significant trend in model 4 and silicosis did not significantly contribute to the risk of lung cancer in model 6 (p=0.08). Thus, the most significantly fitting model is model 5, then model 3, and models 1 and 7.

Relative risk (95% CI) for lung cancer from the logistic regression model

Historically, the dust concentrations in the four tin mines in our study have been high and the percentage of crystalline silica in bulk dust was about 20%–40%. The findings of this study confirm the results of previous study—strong increasing trends in risk of lung cancer with exposure to dust. This study also showed an increasing correlation between risk of lung cancer and duration of exposure to dust and cumulative exposure to arsenic. Tobacco smoking was another main factor related to the risk for lung cancer.

The carcinogenic risk of crystalline silica on humans in ore mines was assessed in several studies, but consistent evidence for a relation between crystalline silica and lung cancer was not found. The cohort studies conducted by McDonald et al , 13 Steenland and Brown, 14 Lawler et al , 15 and Kinlena and Willows 16 provided limited support for the hypothesis that lung cancer is induced by crystalline silica. But contrary conclusions were drawn from the studies of Hnizdo and Sluis-Cremer, 17 Reid and Sluis-Cremer, 18 Kusiak et al , 19 Pham et al , 20 and Amandus and Costello. 21 They found increased standardised mortality ratios for lung cancer among miners exposed to crystalline silica dust. The results of case-control studies also provided different conclusion. Hnizdo et al supported the idea that the risk of lung cancer was associated with cumulative exposure to dust. 22 Mastangelo et al suggested an increased risk of lung cancer among silicotic workers. 23 However, the studies by Samet et al 24 and Heesel et al 25 did not find significant dose-response effects for exposure to silica and lung cancer.

In our study, it is interesting that the relation between risk of lung cancer and cumulative exposure to dust or duration of exposure to dust was found only in the Dachang tin mines, not in Limu, despite the fact that dust concentrations and percentages of crystalline silica in dust were similar in all the tin mines. The fewer cases of lung cancer in the Limu tin mine may at least partly account for this difference because the trend of increasing ORs in three exposed groups of the Limu tin mine is not much different from that in the Dachang tin mines. However, the evidence of no excess of lung cancer among silicotic workers in the Limu tin mine cannot be explained only by smaller numbers. The number and prevalence of silicosis in the Limu tin mine (516 silicotic workers, 20.4%) are higher than that in Dahang tin mines (418 silicotic workers, 7.9%) in the cohort. That means that there are enough silicotic workers in the Limu tin mine, and the exposure concentration of dust in this tin mine is not lower than that in Dachang, because a clear exposure-response relation for silicosis and cumulative exposure to dust was reported in our previous study in the same tin mines. 26 Thus, the results from the Limu tin mine strongly suggest that silicosis is not a risk factor for lung cancer. This suggestion is also confirmed from the logistic model. When silicosis was included, it did not significantly contribute to the risk for lung cancer. As silicosis means high exposure to dust, the excess of lung cancer among silicotic workers should be attributed to the high cumulative exposure to dust.

The effects from other lung carcinogens, arsenic, PAHs, radon, and tobacco smoking were also evaluated in our study. Firstly, the carcinogenic PAHs were not detected in four tin mines, and radon exposures in all work sites were very low. Secondly, tobacco smoking is related to risk of lung cancer. It should be pointed that the percentages of smokers in both cases and controls were high (88.5% in lung cancer cases and 82.5% in controls). The relation between exposure to dust or arsenic and risk of lung cancer was not changed after adjusting for smoking. Thirdly, arsenic as a positive carcinogenic agent was found to be associated with the risk for lung cancer. The positive dose-response trend was shown between cumulative exposure to arsenic and risk for lung cancer. Also, arsenic concentrations were high in Dachang and low in Limu. This would be another reason for lack of lung cancer among silicotic miners exposed to dust in the Limu tin mine. The study conducted by Taylor et al 3 in tin mines in Yunnan province in China (high mortality from lung cancer was also found in this area) provided a conclusion consistent with ours. The concentrations of arsenic exposure in their study were close to (a littler higher than) those in our study, and their results suggested that the incidence of lung cancer is related to high arsenic concentration. They also suggested that duration of exposure to arsenic may be more important than intensity in the aetiology of lung cancer. Several other studies found high exposure to arsenic induced high incidence of lung cancer in smelter workers. 27– 30 But in these studies, airborne arsenic concentrations were higher than 50 μg/m 3 and cumulative exposure to arsenic reached 750 μg/m 3 -year or more, higher than that in this study. Therefore, the carcinogenesis of crystalline silica cannot be excluded in our study, because a significant excess of lung cancer was found even in the lowest category of exposure to arsenic. The mean arsenic concentration was estimated to be about 3.7 μg/m 3 and mean cumulative exposure to arsenic was 46.6 μg/m 3 -year in this category, too low to cause lung cancer because no excess of lung cancer was noted at the 10 μg/m 3 concentration in the study by Enterline et al . 31 High correlations between exposure to arsenic and exposure to dust or silica prevented us from going on to adjust any of these values during the analyses. Thus, crystalline silica was not the only carcinogenic factor in this study, ore particles work like carriers, arsenic stuck to ore particles seems to be more important for risk of lung cancer. Also, it should be noted that exposure assessment for confounding agents including arsenic, PAHs, and radon began in the 1980s. Total dust concentration greatly decreased from the 1950s to the 1980s, therefore the cumulative exposure to arsenic may have been underestimated or overestimated in the earlier years.

In summary, this study has shown some evidence to support the view that high exposure to dust may induce a high risk of lung cancer, and silicosis is not a direct risk for increased lung cancer. A strong dose-response relation was found between the risk of lung cancer and cumulative exposure to dust, cumulative exposure to arsenic, and duration of exposure to dust. High arsenic concentration in dust and smoking seem to play a more important part than crystalline silica in causing high mortality from lung cancer.

Acknowledgments

We thank Dr Paul J A Borm (Medical Institute for Environmental Hygiene in Duesseldorf) and Professor Joachim Bruch (Institute for Hygiene and Occupational Medicine in University of Essen) for reviewing the manuscript and providing many valuable comments. We also thank Professor Chen Rongan at Tongji Medical College and industrial hygienists in the four tin mines for participating in this survey.

  • ↵ International Agency for Research on Cancer . IARC monographs on the evaluation of carcinogenic risks to humans, Vol 68. Silica, some silicates, coal dust and para-aramide fibrils . Lyon: IARC; 1997:209–11.
  • ↵ Hodgson JT , Jones RD. Mortality of a cohort of tin miners 1941–86. Br J Ind Med 1990 ; 47 : 665 –76. OpenUrl PubMed Web of Science
  • ↵ Taylor PR , Qiao YL, Schatzkin A, et al . Relation of arsenic exposure to lung cancer among tin miners in Yunnan Province, China. Br J Ind Med 1989 ; 46 : 881 –6. OpenUrl PubMed Web of Science
  • ↵ Yu Y , Huang M, Xu G. The occupational causes for high incidence of lung cancer among underground miners in Yunan tin mines. Chinese Journal of Industrial Hygiene and Occupational Diseases 1993 ; 11 : 73 –5.
  • ↵ Fu H , Gu X, Jin X, et al . Lung cancer among tin miners in southeast China: silica exposure, silicosis, and cigarette smoking. Am J Ind Med 1994 ; 26 : 373 –81. OpenUrl PubMed Web of Science
  • ↵ Chen J , McLaughlin JK, Zhang JY, et al . Mortality among dust-exposed Chinese mine and pottery workers. J Occup Med 1992 ; 34 : 311 –6. OpenUrl CrossRef PubMed Web of Science
  • ↵ McLaughlin JK , Chen JQ, Dosemeci M, et al . A nested case-control study of lung cancer among silica exposed workers in China. Br J Ind Med 1992 ; 49 : 167 –71. OpenUrl PubMed Web of Science
  • ↵ Hodous TK , Chen R-A, Kinaley KB, et al . A comparison of pneumoconiosis interpretation between Chinese and American readers and classifications. Journal of Tongji Medical University 1991 ; 11 : 225 –9.
  • ↵ Wu Z , Hearl F, Peng K, et al . Current occupational exposure in Chinese iron and copper mines. Appl Occup Environ Hyg 1992 ; 7 : 735 –43. OpenUrl CrossRef
  • ↵ Dosemeci M , Chen JQ, Hearl F, et al . Estimating historical exposure to silica among mine and pottery workers in the People's Republic of China. Am J Ind Med 1993 ; 24 : 55 –66. OpenUrl PubMed Web of Science
  • ↵ Gao P , Chen BT, Hearl FJ, et al . Estimating factors to convert Chinese “total dust” measurements to ACGIH respirable concentrations in metal mines and pottery industries. Ann Occup Hyg 2000 ; 44 : 251 –7. OpenUrl Abstract / FREE Full Text
  • ↵ Zhuang Z , Hearl F, Chen W, et al . Estimating historical respirable crystalline silica exposure for Chinese pottery workers and iron/copper, tin, and tungsten miners. Ann Occup Hyg 2001 ; 45 : 631 –42. OpenUrl Abstract / FREE Full Text
  • ↵ McDonald JC , Gibbs GW, Liddell FD, et al . Mortality after long exposure to cummingtonite-grunerite. Am Rev Respir Dis 1978 ; 118 : 271 –7. OpenUrl PubMed Web of Science
  • ↵ Steenland K , Brown D. Mortality study of gold miners exposed to silica and non-asbestiform amphibole minerals: an update with 14 more years of follow up. Am J Ind Med 1995 ; 27 : 217 –29. OpenUrl CrossRef PubMed Web of Science
  • ↵ Lawler A , Mandel J, Schuman L. Mortality study of Minnesota iron ore miners: premliminary results. In: Wagner WI, Rom WN, Merchant JA, eds. Health issue related to metal and nonmetallic mining . Boston: Butterworths; 1983.
  • ↵ Kinlen LJ , Willows AN. Decline in the lung cancer hazard: a prospective study of the mortality of iron ore miners in Cumbria. Br J Ind Med 1988 ; 45 : 219 –24. OpenUrl PubMed Web of Science
  • ↵ Hnizdo E , Sluis-Cremer GK. Silica exposure, silicosis, and lung cancer: a mortality study of South African gold miners. Br J Ind Med 1991 ; 48 : 53 –60. OpenUrl PubMed Web of Science
  • ↵ Reid PJ , Sluis-Cremer GK. Mortality of white South African gold miners. Occup Environ Med 1996 ; 53 : 11 –6. OpenUrl Abstract / FREE Full Text
  • ↵ Kusiak RA , Springer J, Ritchie AC, et al . Carcinoma of the lung in Ontario gold miners: possible aetiological factors. Br J Ind Med 1991 ; 48 : 808 –17. OpenUrl PubMed Web of Science
  • ↵ Pham QT , Gaertner M, Mur JM, et al . Incidence of lung cancer among iron miners. Eur J Respir Dis 1983 ; 64 : 534 –40. OpenUrl PubMed Web of Science
  • ↵ Amandus H , Costello J. Silicosis and lung cancer in US metal miners. Arch Environ Health 1991 ; 46 : 82 –9. OpenUrl PubMed Web of Science
  • ↵ Hnizdo E , Murray J, Klempman S. Lung cancer in relation to exposure to silica dust, silicosis and uranium production in South African gold miners. Thorax 1997 ; 52 : 271 –5. OpenUrl Abstract
  • ↵ Mastrangelo G , Zambon P, Simonato L, et al . A case-referent study investigating the relationship between exposure to silica dust and lung cancer. Int Arch Occup Environ Health 1988 ; 60 : 299 –302. OpenUrl CrossRef PubMed Web of Science
  • ↵ Samet JM , Pathak DR, Morgan MV, et al . Silicosis and lung cancer risk in underground uranium miners. Health Phys 1994 ; 66 : 450 –3. OpenUrl PubMed Web of Science
  • ↵ Hessel PA , Sluis-Cremer GK, Hnizdo E. Silica exposure, silicosis, and lung cancer: a necropsy study. Br J Ind Med 1990 ; 47 : 4 –9. OpenUrl PubMed Web of Science
  • ↵ Chen W , Zhuang Z, Attfield MD, et al . Exposure to silica and silicosis among tin miners in China: exposure-response analyses and risk assessment. Occup Environ Med 2001 ; 58 : 31 –7. OpenUrl Abstract / FREE Full Text
  • ↵ Jarup L , Pershagen G. Arsenic exposure, smoking, and lung cancer in smelter workers: a case-control study. Am J Epidemiol 1991 ; 134 : 545 –51. OpenUrl Abstract / FREE Full Text
  • Hertz-Picciotto I , Smith AH. Observations on the dose-response curve for arsenic exposure and lung cancer. Scand J Work Environ Health 1993 ; 19 (4): 217 –26. OpenUrl CrossRef PubMed Web of Science
  • Enterline PE , Marsh GM, Henderson V, et al . Mortality update of a cohort of US man made mineral fibre workers. Ann Occup Hyg 1987 ; 31 : 625 –56. OpenUrl Abstract / FREE Full Text
  • ↵ Enterline PE , Day R, Marsh GM. Cancers related to exposure to arsenic at a copper smelter. Occup Environ Med 1995 ; 52 : 28 –32. OpenUrl Abstract / FREE Full Text
  • ↵ Enterline PE , Marsh GM, Esmen NA, et al . Some effects of cigarette smoking, arsenic, and SO 2 on mortality among US copper smelter workers. J Occup Med 1987 ; 29 : 831 –8. OpenUrl PubMed Web of Science

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nested case control study cancer

EP717 Module 5 - Epidemiologic Study Designs – Part 2:

Case-control studies.

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A Nested Case-Control Study

Interpretation of the odds ratio, test yourself, recap of case-control design.

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Now consider a hypothetical prospective cohort study among 89,949 women in whom the investigators took blood samples and froze them at baseline for possible future use. After following the cohort for 12 years the investigators wanted to investigate a possible association between the pesticide DDT and breast cancer. Since they had frozen blood samples collected at baseline, they had the option of having the samples tested for DDT levels. If they had done this, the table below shows what they would have found.

If they had had this data, they could have calculated the risk ratio:

RR = (360/13,636) / (1,079/76,313) = 1.87

However, the cost of analyzing each sample for DDT was $20, and to analyze all of them would have cost close to $1.8 million. So, like the previous study, the exposure data was very costly.

Although this was a prospective cohort study, we could regard the cohort as a source population and conduct a case-control study drawing samples from the cohort . We could, for example, analyze the blood samples on all of the women who had developed breast cancer during the 12 year follow up and on 2,878 randomly selected samples from the women without breast cancer (i.e., twice as many controls as cases). This would be described as a nested case-control study , i.e., nested within a cohort study.

The results might have looked like this:

Odds Ratio = (a/c) / (b/d) = (360/1,079) / (432/2,446)

= 1.89 during the 12 year follow up study

So, they could achieve an odds ratio that is very close to what the risk ratio would have been at a much lower cost: (1,439+2,878) x $20 = $86,340.

The odds ratio is a legitimate measure of association, and, when the outcome of interest is uncommon, it provides a good estimate of what the risk ratio would have been if a cohort study had been possible. When looking at increasingly common outcomes, the odds ratio gives estimates that are more extreme than the risk ratio, i.e., further away from the null value. 

Not surprisingly, the interpretation of an odds is therefore similar to the interpretation of a risk ratio.

  • The null value (no difference) is 1.0.
  • Odds ratios > 1 suggest an increase in risk
  • Odds ratios < 1 suggest a decrease in risk

The odds ratio above would be interpreted as follows:

"Women with high DDT blood levels at baseline had 1.89 times the odds of developing breast cancer compared to women with low blood levels of DDT during the 12 year observation period."

Calculate the odds ratio for the association between playing video games and development of hypertension. Interpret the odds ratio you calculate in a sentence. See if you can do both of these correctly before looking at the answer.

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Inflammation marker and risk of pancreatic cancer: a nested case–control study within the EPIC cohort

British Journal of Cancer volume  106 ,  pages 1866–1874 ( 2012 ) Cite this article

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Background:

Established risk factors for pancreatic cancer include smoking, long-standing diabetes, high body fatness, and chronic pancreatitis, all of which can be characterised by aspects of inflammatory processes. However, prospective studies investigating the relation between inflammatory markers and pancreatic cancer risk are scarce.

We conducted a nested case–control study within the European Prospective Investigation into Cancer and Nutrition, measuring prediagnostic blood levels of C-reactive protein (CRP), interleukin-6 (IL-6), and soluble receptors of tumour necrosis factor- α (sTNF-R1, R2) in 455 pancreatic cancer cases and 455 matched controls. Odds ratios (ORs) were estimated using conditional logistic regression models.

None of the inflammatory markers were significantly associated with risk of pancreatic cancer overall, although a borderline significant association was observed for higher circulating sTNF-R2 (crude OR=1.52 (95% confidence interval (CI) 0.97–2.39), highest vs lowest quartile). In women, however, higher sTNF-R1 levels were significantly associated with risk of pancreatic cancer (crude OR=1.97 (95% CI 1.02–3.79)). For sTNF-R2, risk associations seemed to be stronger for diabetic individuals and those with a higher BMI.

Conclusion:

Prospectively, CRP and IL-6 do not seem to have a role in our study with respect to risk of pancreatic cancer, whereas sTNF-R1 seemed to be a risk factor in women and sTNF-R2 might be a mediator in the risk relationship between overweight and diabetes with pancreatic cancer. Further large prospective studies are needed to clarify the role of proinflammatory proteins and cytokines in the pathogenesis of exocrine pancreatic cancer.

Evidence is accumulating that systemic low-grade chronic inflammation in addition to local inflammation in the pancreas is involved in the pathogenesis of pancreatic cancer ( Farrow and Evers, 2002 ; Whitcomb, 2004 ; McKay et al, 2008 ). Research findings pointing to this direction include the documented relationship of pancreatic cancer risk with chronic pancreatitis ( Raimondi et al, 2010 ), as well as with smoking ( Lynch et al, 2009 ; Vrieling et al, 2010 ), pre-existing and long-standing diabetes mellitus ( Huxley et al, 2005 ), and excess weight ( Genkinger et al, 2010 ), all of which are known or suggestive determinants of low-grade inflammatory states ( Whitcomb, 2004 ; Kolb and Mandrup-Poulsen, 2005 ; Hotamisligil, 2006 ; Goncalves et al, 2011 ).

Even though the mechanisms by which chronic inflammation leads to carcinogenesis are not fully understood, it is generally accepted that inflammation results in repeated DNA damage and in the accumulation of genetic defects ( McKay et al, 2008 ). However, proinflammatory cytokines and growth factors are also released in response to the tumour, making it difficult to distinguish between cause and effect in the inflammatory processes ( McKay et al, 2008 ).

Circulating C-reactive protein (CRP) concentration, an acute-phase protein produced in the liver, is increased in pancreatic cancer patients ( Barber et al, 1999 ; Moses et al, 2009 ; Mroczko et al, 2010 ), most likely as part of the systemic inflammatory response to the tumour. Interleukin-6 (IL-6) and tumour necrosis factor- α (TNF- α ) are upregulating factors of CRP and have also been shown to be increased in pancreatic cancer patients ( Barber et al, 1999 ; Ebrahimi et al, 2004 ; Moses et al, 2009 ; Talar-Wojnarowska et al, 2009 ; Mroczko et al, 2010 ). Prospectively, increased levels of CRP have inconsistently been associated with pancreatic cancer risk. To our knowledge, prospective studies on the association of IL-6, TNF- α , or its receptors with risk of pancreatic cancer are lacking.

We measured prediagnostic concentrations of CRP, IL-6, and soluble TNF receptors (sTNF-R1 and R2) in blood samples of 455 primary exocrine pancreatic cancer cases and 455 individually matched controls within the Prospective Investigation into Cancer and Nutrition (EPIC) as possible reflections of either pancreatic cancer or a metabolic risk factor potentially increasing pancreatic cancer risk by aggravating pancreatic inflammatory disease.

Materials and Methods

Study population.

The European Prospective Investigation into Cancer and Nutrition (EPIC) is a large cohort study conducted in 23 centres in ten European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Spain, Sweden, and the United Kingdom). Detailed descriptions of study design, population, and baseline data collection of the cohort can be found elsewhere ( Haftenberger et al, 2002 ; Riboli et al, 2002 ). Briefly, about 370 000 women and 150 000 men were enroled between 1992 and 2000. Participants provided information on dietary habits and lifestyle factors, and in addition, weight, height, and waist and hip circumferences were measured at baseline. Each participant provided informed consent, and the local ethical review committees approved the EPIC cohort study as well as the current project.

Blood sample collection and storage

In the seven EPIC core countries (France, Germany, Greece, Italy, the Netherlands, Spain, and the United Kingdom), blood samples were collected at baseline, based on a standardized protocol and aliquoted in plastic straws (plasma, serum, erythrocytes, and buffy coat for DNA). The aliquoted specimens were then stored in a central biorepository in liquid nitrogen (−196 °C). In Sweden, all samples were stored locally in freezers at −70 °C and in Denmark in nitrogen vapour (−150 °C). In this study, Norway was excluded because blood samples were only recently collected and very few pancreatic cancer cases have been diagnosed after blood donation.

Follow-up for cancer incidence and vital status

In six of the participating countries (Denmark, Italy, the Netherlands, Spain, Sweden, and the United Kingdom), follow-up of cancer cases was based on population registries. In the other three countries (France, Germany, and Greece), a combination of methods was used including health insurance records, cancer and pathology registries, and active follow-up through study subjects and their next-of-kin. In all EPIC centres, data on vital status are collected from mortality registries at the regional or national level, which is combined with health insurance data (France) or data collected by active follow-up (Greece). Cases reported in this study were all diagnosed up to the latest dates of complete follow-up, which was between December 2002 and 2005, depending on the study centre. For Germany, Greece, and France, the end of follow-up was the last known contact, date of diagnosis, or date of death, whichever came first.

Selection of case and control subjects

Up to December 2006, follow-up has led to the identification of 578 incident cases of non-endocrine pancreas cancer that were coded according to ICD-10 (C25.0–25.3, 25.7–25.9), and for 455 of these cases blood specimens were available. Exclusion criteria were occurrence of other malignant tumours preceding the diagnosis of pancreatic cancer, except for non-melanoma skin cancer. Of the 455 cases, 334 (76%) were microscopically confirmed and the remaining 24% were diagnosed by imaging results, physical examination, or clinical symptoms. Most tumours occurred in the head of the pancreas (42%), followed by body (7%) and tail (5%), while the rest of the tumours were of unknown localisation. For each case, one control subject was selected, that was alive and free of cancer at the time the index case was diagnosed, using an incidence density sampling procedure. All identified cases were matched with one control by centre, sex, age at blood collection (±3 years), date of blood donation (±3 months), time of blood donation (±2 h), fasting status (<3 h, 3–6 h, >6 h after last meal), and use of hormones (oral contraceptive pill, hormone, or oestrogen replacement therapy).

Laboratory assays

Plasma (in Scandinavian samples) and serum concentrations of CRP were measured by multiplex immunoassays using the Fluorokine MAP Obesity Base Kit (R&D Systems Inc., Minneapolis, MN, USA). Interleukin-6 and sTNF receptors were measured by enzyme linked immune sorbent assays using the Quantikine kit (R&D Systems Inc.). The total amount of free receptor plus the total amount of receptor bound to TNF is measured using this method. All measurements were performed in our specialised immunoassay laboratory of the Division of Cancer Epidemiology (German Cancer Research Center, Heidelberg, Germany). Samples of cases and matched controls were analysed within the same analytical batch. Intra-batch and inter-batch coefficients of variation were 6.6 and 10.8% for IL-6, 3.6 and 4.1% for sTNF-R1, 5.5 and 11.0% for sTNF-R2, and 10.3 and 11.6% for CRP. Units of IL-6 are expressed as pg per ml, of sTNF receptors as ng per ml, and of CRP as mg per litre. One batch during the sTNF-R2 measurements did not perform well and, therefore, 70 subjects were excluded due to technically invalid results (all from Malmo, Sweden).

Statistical analysis

Case and control differences across baseline characteristics were assessed by paired t -tests (continuous variables) or by generalised McNemar’s Test (categorical variables). Spearman’s partial rank correlation coefficients ( r ) adjusted for age, sex, and EPIC recruitment centre were used to assess the strength of associations between waist circumference, waist–hip ratio, BMI, glycated haemoglobin (HbA1c), and inflammatory markers, as well as for the correlation between the inflammatory markers.

Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for pancreatic cancer at different serum levels of IL-6, sTNF receptors, and CRP were calculated by conditional logistic regression models, using the exposure assessments of the matched case–control sets. Continuous measurements of the inflammatory markers were log2 transformed to achieve approximate normality. In this scale, a unit increase corresponds to a doubling of concentration. Quartile cut-points were based on the distribution of biomarkers among controls. Sex-specific quartile cut-points had a negligible effect on risk estimates and were, therefore, not applied. Modelling the median within each quartile as a continuous variable was used to assess linear trends in ORs. Testing the model fit for categorical vs continuous models resulted in very similar AICs, with a slightly better fit for the latter model.

Inflammatory markers may be downstream in the causal chain of excess body weight, smoking, or diabetes and pancreatic cancer. Alternatively, other pathways might explain associations of these conditions with risk of pancreatic cancer and, hence, inflammatory markers may be independently related to cancer or not at all. We tried to elucidate these rather complex and yet unknown relationships in our study by applying different adjustment models and by performing several subgroups analyses. All these models and methods are of exploratory nature in our study.

Potential confounding of factors other than those controlled for by matching were examined by assessing the association of these factors with pancreatic cancer risk using unconditional logistic regression models adjusted for matching factors, by correlation analyses, and by including these as additional factors in conditional logistic regression models. Body mass index, waist–hip ratio, waist circumference, alcohol consumption, current and past tobacco smoking, and diabetes were considered as potential confounders. Variables remained in the models if they were associated with pancreatic cancer, correlated with the inflammatory markers, or changed the β -estimate by more than 10%. On the basis of these conditions, BMI as a continuous variable and smoking as a categorical variable (never smoking, former smoking (quitting smoking <10 years ago, ⩾ 10 years ago), current smoking (<10, 10–20, ⩾ 20 cigarettes a day), missing) were considered as confounding factors and remained in the multivariate adjusted model. To assess a possible confounding effect of diabetes on the risk associations, we controlled for diabetes in further exploratory analyses. Subjects were defined as diabetics if they self-reported the condition in the baseline questionnaire at recruitment ( n =52) and/or had HbA1c levels ⩾ 6.5% in the current study ( n =93). This percentage is used as a cut-off for diabetes diagnosis ( ADA, 2009 ). Glycated haemoglobin has been measured previously in the same study population ( Grote et al, 2011 ). Physical activity and socioeconomic status did not markedly change the risk estimates and were, therefore, not included in the final model.

Subgroup analyses were performed to assess possible effect modifications by sex, diabetes and smoking status, by median age (62 years), waist circumference (96 cm for men, 80 for women), waist–hip ratio (0.95 for men, 0.80 for women), and median BMI (26.2 kg m −2 for men, 24.6 for women), or by lag-time (time between blood collection and diagnosis of pancreatic cancer, ⩽ vs >5 years). Cross-product terms were added in logistic regression models and Wald tests were performed to examine whether any apparent heterogeneity of effect was significant. To limit reverse causation bias, which could occur when the advanced tumour causes changes in inflammatory marker levels, we performed subgroup analyses with 2 years of follow-up as a cut-point ( ⩽ vs >2 years).

All statistical analyses were conducted using the Statistical Analysis System (SAS) software package, Version 9.2 (SAS Institute Inc., Cary, NC, USA). All statistical tests were two-tailed and significant at the 5% level.

Baseline characteristics of pancreatic cancer cases and matched control subjects are shown in Table 1 . Mean age at recruitment into the initial cohort was 58 years and mean age of cases at pancreatic cancer diagnosis was 63 years, resulting in mean follow-up time of 5.3 years for cases (range 0–13). Female pancreatic cancer cases had a significantly higher BMI and waist circumference than corresponding controls, but no difference in waist–hip ratio was observed. For men, however, no significant difference for any of the anthropometric measures comparing cases and controls was seen. A higher percentage of cases currently smoked compared with controls (31% vs 22%). At baseline, cases also reported more often to be diabetic and/or had HbA1c levels ⩾ 6.5% compared with controls (14% vs 8%). However, these results are not mutually adjusted and serve descriptive purposes only.

Among controls, sTNF-R1 and sTNF-R2 showed a high degree of correlation. The correlation of circulating CRP levels with IL-6, sTNF-R1, and sTNF-R2 concentrations was relatively high with Spearman’s rank correlation coefficients up to 0.44. Waist circumference, BMI, and waist–hip ratio correlated moderately with CRP and IL-6, and to lesser extent with sTNF-R1 but not with sTNF-R2 concentrations ( Table 2 ). Participants with diabetes (self-reported at baseline and/or HbA1c ⩾ 6.5%) and those who smoked had higher levels of CRP and IL-6 than non-diabetics ( Table 2 ). Mutual adjustments for smoking categories and/or BMI resulted in unaltered (diabetes) or stronger associations (smoking, data not shown).

The potential confounders or effect modifiers overweight (OR=1.05 (95% CI 1.01–1.08), per 5 BMI units), smoking (OR=1.84 (95% CI 1.30–2.60), current vs never), and diabetes (OR=1.74 (95% CI 1.12–2.71)) were associated with risk of pancreatic cancer in our study.

Pancreatic cancer risk tended to be increased with higher levels of sTNF-R2 (crude OR=1.52 (95% CI 0.97–2.39) comparing highest with lowest quartiles, P -trend over quartiles=0.07), but these associations were not significant at the 5% level, and BMI and smoking adjustments attenuated the risks of pancreatic cancer ( Table 3 ). Elevated CRP (crude OR=1.36 (95% CI 0.92–2.01), P -trend=0.26), IL-6 (OR=1.30 (95% CI 0.84–2.00), P -trend=0.61), and sTNF-R1 levels (OR=1.23 (95% CI 0.78–1.94), P -trend=0.23) showed no significant association with risk of pancreatic cancer. Adjustments for HbA1c levels and mutually for the other inflammatory markers in addition to BMI and smoking categories attenuated risk estimates for elevated levels of inflammatory markers closer to 1.0 (data not shown). Exclusion of subjects with CRP levels above 10 mg l −1 (as this is more likely an indication for an acute rather than a chronic inflammatory state) had no effect on the association between CRP levels and pancreatic cancer risk (data not shown). Women tended to be at increased pancreatic cancer risks for higher CRP or sTNF receptor levels, and specifically so for sTNF-R1, although risk estimates were inconsistently significant between categorical and continuous analyses and between crude and BMI and smoking-adjusted models ( Table 3 ).

Tests for heterogeneity of continuous sTNF receptors, adjusted for matching factors, resulted in statistically significant differences in pancreatic cancer risk by median BMI, diabetes, and smoking status, but not by median waist circumference, waist–hip ratio or median age. Compared with never smokers, risks in former and current smokers were elevated, albeit not statistical significant. Diabetics ( P- interaction=0.001) and subjects with a BMI above the median ( P- interaction=0.04) had a significantly higher risk of pancreatic cancer with elevated levels of sTNF-R2 than non-diabetics or subjects with lower than median BMI, respectively ( Figure 1B ). Adjusting subgroup analyses for BMI, smoking categories, HbA1c levels, and/or mutually for inflammatory markers attenuated the risk estimates to non-significance (data not shown). Interestingly, higher circulating CRP and IL-6 levels tended to be related to increased pancreatic cancer risk in leaner subjects, although ORs and tests for interaction were not statistically significant ( Figure 1C and D ).

figure 1

Crude relative risks (OR (95% CI)) of pancreatic cancer for a doubling in sTNF receptor concentrations ( A and B ), CRP ( C ), and IL-6 ( D ), all and stratified by median BMI (26.2 for men, 24.6 for women), diabetes, smoking status, and length of follow-up ( ⩽ 2 vs >2 years). Note: Stratified analysis using unconditional logistic regression was adjusted for matching factors (EPIC recruitment centre, sex, age at blood collection, date of blood donation, time of blood donation, fasting status, and use of hormones). Ca/Co=number of cases/controls. Size of squares is proportional to number of participants in the respective subgroup; squares represent ORs, with error bars indicating 95% CIs. a P for interaction was based on the Wald statistics, adjusted for matching factors. b Median BMI for male controls was 26.20 kg m −2 , for female controls 24.61 kg m −2 . c Diabetics included subjects with self-reported diabetes status at baseline and subjects with glycated haemoglobin (HbA1c) levels ⩾ 6.5% or both. d FUP=follow-up time (years), using conditional logistic regression.

In our nested case–control study of 455 pancreatic cancer subjects and 455 individually matched controls, higher circulating levels of sTNF-R2, but not of sTNF-R1, CRP, and IL-6 levels, tended to be positively associated with the risk of pancreatic cancer. Stratification by sex revealed significantly increased pancreatic cancer risks in women for higher sTNF-R1 levels. Positive associations between sTNF-R2 and pancreatic cancer seemed to be likely for diabetic subjects, those with a higher BMI, and possibly also for smokers.

In the acute-phase response to tissues damage, infection, inflammation, or malignant neoplasia, CRP is increasingly produced by hepatocytes, predominantly under control by IL-6. C-reactive protein binds to damaged cell membranes or apoptotic cells, forming an aggregate that activates the complement pathway, resulting in the phagocytosis of the damaged cells and in increased proinflammatory pathophysiological effects. C-reactive protein, therefore, reflects ongoing inflammation and/or tissue damage and functions as a proinflammatory mediator. In this context, it may not only be a marker of a disease, but it may also contribute to pathogenesis ( Pepys and Hirschfield, 2003 ). In several small hospital-based case–control studies, CRP levels were significantly higher in pancreatic cancer cases compared with chronic pancreatitis patients or controls ( Barber et al, 1999 ; Moses et al, 2009 ; Mroczko et al, 2010 ). In addition, elevated levels of CRP were associated with a poor prognosis in pancreatic cancer patients ( McKay et al, 2008 ). Prospectively, no association was observed in a Greek study with 14 pancreatic cancer cases ( Trichopoulos et al, 2006 ), whereas a weak decrease in pancreatic cancer risk with an OR of 0.94 (95% CI 0.89–0.99) was seen among 311 cases in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC) cohort of male Finish smokers ( Douglas et al, 2010 ). The same authors did not find an association in the Ovarian Cancer Screening Trial (PLCO) or in combined analyses of both cohorts. Our results are in line with the prospective Greek and PLCO study showing no association of CRP with risk of pancreatic cancer.

No prospective study has been conducted so far to assess the association of circulating TNF- α , its soluble receptors, or IL-6 levels with risk of pancreatic cancer, both upregulators of CRP. Tumour necrosis factor- α is a proinflammatory cytokine produced by many cell types, including cancer cells, upon exogenous noxious stimuli. The effects of TNF- α are mediated mainly by two receptors, TNF-R1 and TNF-R2, which also circulate in soluble forms upon shedding. Tumour necrosis factor receptor activation leads to induction of genes involved in inflammation and cell survival, resulting in the activation of nuclear factor- κ B (NF- κ B). However, if NF- κ B activation is inadequate, apoptosis is mediated via accumulation of reactive oxygen species as a late response to TNF- α . This cytokine, thus, is not only involved in maintenance of the immune system, but also in pathological processes such as malignant diseases. The majority of cell types and tissues express both receptor types ( Balkwill, 2006 ), and among colon cancer patients it has been shown that the concentrations of sTNF-Rs correlate with the stage of disease as tumour cells have a greater tendency than non-malignant cells to shed forms of their cell surface proteins ( Aderka, 1996 ). Soluble TNF receptors can serve as TNF antagonists, carrier proteins of TNF, slow release reservoirs for TNF, and stabilisers of TNF bioactivity. It is not known, however, whether the two soluble receptors have distinct or similar functions ( Aderka, 1996 ), and based on this, we cannot explain why we observed a potential increase in pancreatic cancer risk for elevated sTNF-R2 but not for sTNF-R1. It might be, however, that sTNF-R2 has a more prominent role in pancreatic cancer development. This aspect needs to be explored in functional studies. So far, TNF- α and/or the soluble receptors have been assessed in hospital-based case–control studies with pancreatic cancer patients, observing either higher levels of TNF- α /soluble TNF receptors among pancreatic cancer subjects than among controls (healthy volunteers or chronic pancreatitis patients ( Barber et al, 1999 ; Talar-Wojnarowska et al, 2009 )), or no difference in serum levels ( Ebrahimi et al, 2004 ). To our knowledge, our nested case–control study within the prospective EPIC cohort study is the first to address the association of sTNF receptors with risk of pancreatic cancer, and we observed a non-significant increase in risk overall, which was more apparent for sTNF-R2 than sTNF-R1, and which was attenuated after adjustments for smoking status, BMI, and HbA1c levels or diabetes status. It is unclear why we found a difference in risk between men and women with elevated risks for increasing levels of sTNF-R1 in women only.

As with TNF- α , pancreatic cancer patients’ IL-6 concentrations have shown to be higher than in healthy controls in hospital-based case–control studies ( Barber et al, 1999 ; Ebrahimi et al, 2004 ; Moses et al, 2009 ; Mroczko et al, 2010 ). In contrast to these observations, in our prospective study we did not find elevated pre-diagnostic IL-6 concentrations in subjects who became pancreatic cancer cases later in time compared to non-cancer controls at baseline. Interleukin-6 is synthesised by many cell types in response to stimulation from TNF- α and IL-1, and indirectly regulates cell proliferation and apoptosis through its activation of other factors. Therefore, IL-6 has a role in chronic inflammation, which may enhance cancer development ( Hodge et al, 2005 ). However, due to the small number of prospective studies so far investigating the relationship of IL-6 with cancer, a recent published review concluded that it is yet impossible to determine whether IL-6 is causally related to cancer ( Heikkilä et al, 2008 ).

It has been shown in a wide range of studies that CRP, IL-6, TNF- α , and TNF receptor levels vary by body weight, with higher levels among overweight or obese compared with normal weight subjects, and with decreasing levels during weight loss ( Himmerich et al, 2006 ; Forsythe et al, 2008 ). Furthermore, compared with never smokers, cigarette smokers also have significantly higher levels of CRP and IL-6, and possibly also of TNF receptors ( Fernandez-Real et al, 2003 ). Finally, subclinical systemic inflammation has been reported in type 2 diabetes ( Kolb and Mandrup-Poulsen, 2005 ), including elevated levels of the aforementioned and evaluated parameters in our study. In our study, elevated levels of CRP, IL-6 and sTNF-R1 correlated with excess weight and, in addition, higher CRP and IL-6 levels were associated with smoking and diabetes.

Furthermore, overweight, smoking, or diabetic participants at baseline were at increased pancreatic cancer risk. This risk was even stronger if overweight or diabetic participants had elevated levels of sTNF-R2, even though this marker was not correlated with BMI or associated with diabetes in controls. This can be interpreted as sTNF-R2 being a mediator of the relationship between overweight and/or diabetes and pancreatic cancer. A similar scenario is likely for sTNF-R1, but our results do not clearly support this hypothesis ( Figure 1A ). In contrast, stratification by median BMI, diabetes, or smoking status resulted in similar weak risk estimates for elevated CRP and IL-6 concentrations. It seems as if, regardless of the presence of a putative pancreatic cancer risk factor (overweight, diabetes, and smoking), these inflammatory markers are not associated with pancreatic cancer risk themselves. In addition, they also do not appear to be in the causal chain between risk factor and cancer.

Some strengths and limitations of our study should be mentioned. Although a single measurement of a biomarker, as assessed in our study, could result in random misclassification, CRP, IL-6, and sTNF receptors have been shown to be reliably measured over time ( Gu et al, 2009 ; Clendenen et al, 2010 ). A major strength of our study is that questionnaire data and blood samples were collected prospectively around the same time point, prior to pancreatic cancer diagnosis, which reduces the possibility of reverse causation bias to some extent. In addition, pancreatic cancer risk seemed to be stronger for elevated sTNF receptor levels among subjects with longer follow-up times. A limitation of our study is that information on pancreatic or liver disorders, on inflammatory diseases, or on use of anti-inflammatory drugs was not recorded for most of the EPIC centres; therefore, controlling for these potential confounders was not possible. Consequently, we cannot exclude the possibility that the observed suggestive increased pancreatic cancer risk among individuals with elevated sTNF-R2 levels may partly be due to chronic pancreatitis or impaired liver function, for example. Furthermore, number of subjects in specific subgroups were rather small; thus, we cannot rule out that results obtained from these analyses are chance findings. Further large prospective studies are needed to verify our results in the respective subgroups with sufficient power to detect significant risk associations.

Prospectively, CRP and IL-6 do not seem to play a role in our study with respect to risk of pancreatic cancer, whereas sTNF-R1 seemed to be a risk factor in women and sTNF-R2 might be a mediator in the risk relationship between overweight and diabetes with pancreatic cancer. In order to clarify the role of proinflammatory proteins and cytokines in the pathogenesis of exocrine pancreatic cancer, more prospective studies in large settings are needed, controlling for the potential bias of other conditions and stratifying by sex.

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ADA (2009) International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32 (7): 1327–1334

Article   Google Scholar  

Aderka D (1996) The potential biological and clinical significance of the soluble tumor necrosis factor receptors. Cytokine Growth Factor Rev 7 (3): 231–240

Article   CAS   Google Scholar  

Balkwill F (2006) TNF-alpha in promotion and progression of cancer. Cancer Metastasis Rev 25 (3): 409–416

Barber MD, Fearon KC, Ross JA (1999) Relationship of serum levels of interleukin-6, soluble interleukin-6 receptor and tumour necrosis factor receptors to the acute-phase protein response in advanced pancreatic cancer. Clin Sci (Lond) 96 (1): 83–87

Clendenen TV, Arslan AA, Lokshin AE, Idahl A, Hallmans G, Koenig KL, Marrangoni AM, Nolen BM, Ohlson N, Zeleniuch-Jacquotte A, Lundin E (2010) Temporal reliability of cytokines and growth factors in EDTA plasma. BMC Res Notes 3 : 302

Douglas JB, Silverman DT, Weinstein SJ, Graubard BI, Pollak MN, Tao Y, Virtamo J, Albanes D, Stolzenberg-Solomon RZ (2010) Serum C-reactive protein and risk of pancreatic cancer in two nested, case-control studies. Cancer Epidemiol Biomarkers Prev 20 (2): 359–369

Ebrahimi B, Tucker SL, Li D, Abbruzzese JL, Kurzrock R (2004) Cytokines in pancreatic carcinoma: correlation with phenotypic characteristics and prognosis. Cancer 101 (12): 2727–2736

Farrow B, Evers BM (2002) Inflammation and the development of pancreatic cancer. Surg Oncol 10 (4): 153–169

Fernandez-Real JM, Broch M, Vendrell J, Ricart W (2003) Smoking, fat mass and activation of the tumor necrosis factor-alpha pathway. Int J Obes Relat Metab Disord 27 (12): 1552–1556

Forsythe LK, Wallace JM, Livingstone MB (2008) Obesity and inflammation: the effects of weight loss. Nutr Res Rev 21 (2): 117–133

Genkinger JM, Spiegelman D, Anderson KE, Bernstein L, van den Brandt PA, Calle EE, English DR, Folsom AR, Freudenheim JL, Fuchs CS, Giles GG, Giovannucci E, Horn-Ross PL, Larsson SC, Leitzmann M, Mannisto S, Marshall JR, Miller AB, Patel AV, Rohan TE, Stolzenberg-Solomon RZ, Verhage BA, Virtamo J, Willcox BJ, Wolk A, Ziegler RG, Smith-Warner SA (2010) A pooled analysis of 14 cohort studies of anthropometric factors and pancreatic cancer risk. Int J Cancer 29 (7): 1708–1717

Goncalves RB, Coletta RD, Silvério KG, Benevides L, Casati MZ, da Silva JS, Nociti FH (2011) Impact of smoking on inflammation: overview of molecular mechanisms. Inflamm Res 60 (5): 409–424

Grote VA, Rohrmann S, Nieters A, Dossus L, Tjonneland A, Halkjaer J, Overvad K, Fagherazzi G, Boutron-Ruault MC, Morois S, Teucher B, Becker S, Sluik D, Boeing H, Trichopoulou A, Lagiou P, Trichopoulos D, Palli D, Pala V, Tumino R, Vineis P, Panico S, Rodriguez L, Duell EJ, Molina-Montes E, Dorronsoro M, Huerta JM, Ardanaz E, Jeurnink SM, Beulens JW, Peeters PH, Sund M, Ye W, Lindkvist B, Johansen D, Khaw KT, Wareham N, Allen N, Crowe F, Jenab M, Romieu I, Michaud DS, Riboli E, Romaguera D, Bueno-de-Mesquita HB, Kaaks R (2011) Diabetes mellitus, glycated haemoglobin and C-peptide levels in relation to pancreatic cancer risk: a study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Diabetologia 54 (12): 3037–3046

Gu Y, Zeleniuch-Jacquotte A, Linkov F, Koenig KL, Liu M, Velikokhatnaya L, Shore RE, Marrangoni A, Toniolo P, Lokshin AE, Arslan AA (2009) Reproducibility of serum cytokines and growth factors. Cytokine 45 (1): 44–49

Haftenberger M, Lahmann PH, Panico S, Gonzalez CA, Seidell JC, Boeing H, Giurdanella MC, Krogh V, Bueno-de-Mesquita HB, Peeters PH, Skeie G, Hjartaker A, Rodriguez M, Quiros JR, Berglund G, Janlert U, Khaw KT, Spencer EA, Overvad K, Tjonneland A, Clavel-Chapelon F, Tehard B, Miller AB, Klipstein-Grobusch K, Benetou V, Kiriazi G, Riboli E, Slimani N (2002) Overweight, obesity and fat distribution in 50- to 64-year-old participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (6B): 1147–1162

Heikkilä K, Ebrahim S, Lawlor DA (2008) Systematic review of the association between circulating interleukin-6 (IL-6) and cancer. Eur J Cancer 44 (7): 937–945

Himmerich H, Fulda S, Linseisen J, Seiler H, Wolfram G, Himmerich S, Gedrich K, Pollmacher T (2006) TNF-alpha, soluble TNF receptor and interleukin-6 plasma levels in the general population. Eur Cytokine Netw 17 (3): 196–201

CAS   PubMed   Google Scholar  

Hodge DR, Hurt EM, Farrar WL (2005) The role of IL-6 and STAT3 in inflammation and cancer. Eur J Cancer 41 (16): 2502–2512

Hotamisligil GS (2006) Inflammation and metabolic disorders. Nature 444 (7121): 860–867

Huxley R, Ansary-Moghaddam A, Berrington de González A, Barzi F, Woodward M (2005) Type-II diabetes and pancreatic cancer: a meta-analysis of 36 studies. Br J Cancer 92 (11): 2076–2083

Kolb H, Mandrup-Poulsen T (2005) An immune origin of type 2 diabetes? Diabetologia 48 (6): 1038–1050

Lynch SM, Vrieling A, Lubin JH, Kraft P, Mendelsohn JB, Hartge P, Canzian F, Steplowski E, Arslan AA, Gross M, Helzlsouer K, Jacobs EJ, LaCroix A, Petersen G, Zheng W, Albanes D, Amundadottir L, Bingham SA, Boffetta P, Boutron-Ruault MC, Chanock SJ, Clipp S, Hoover RN, Jacobs K, Johnson KC, Kooperberg C, Luo J, Messina C, Palli D, Patel AV, Riboli E, Shu XO, Rodriguez Suarez L, Thomas G, Tjonneland A, Tobias GS, Tong E, Trichopoulos D, Virtamo J, Ye W, Yu K, Zeleniuch-Jacquette A, Bueno-de-Mesquita HB, Stolzenberg-Solomon RZ (2009) Cigarette smoking and pancreatic cancer: a pooled analysis from the pancreatic cancer cohort consortium. Am J Epidemiol 170 (4): 403–413

McKay CJ, Glen P, McMillan DC (2008) Chronic inflammation and pancreatic cancer. Best Pract Res Clin Gastroenterol 22 (1): 65–73

Moses AG, Maingay J, Sangster K, Fearon KC, Ross JA (2009) Pro-inflammatory cytokine release by peripheral blood mononuclear cells from patients with advanced pancreatic cancer: relationship to acute phase response and survival. Oncol Rep 21 (4): 1091–1095

CAS   Google Scholar  

Mroczko B, Groblewska M, Gryko M, Kedra B, Szmitkowski M (2010) Diagnostic usefulness of serum interleukin 6 (IL-6) and C-reactive protein (CRP) in the differentiation between pancreatic cancer and chronic pancreatitis. J Clin Lab Anal 24 (4): 256–261

Pepys MB, Hirschfield GM (2003) C-reactive protein: a critical update. J Clin Invest 111 (12): 1805–1812

Raimondi S, Lowenfels AB, Morselli-Labate AM, Maisonneuve P, Pezzilli R (2010) Pancreatic cancer in chronic pancreatitis; aetiology, incidence, and early detection. Best Pract Res Clin Gastroenterol 24 (3): 349–358

Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M, Charrondière UR, Hémon B, Casagrande C, Vignat J, Overvad K, Tjønneland A, Clavel-Chapelon F, Thiébaut A, Wahrendorf J, Boeing H, Trichopoulos D, Trichopoulou A, Vineis P, Palli D, Bueno-De-Mesquita HB, Peeters PH, Lund E, Engeset D, González CA, Barricarte A, Berglund G, Hallmans G, Day NE, Key TJ, Kaaks R, Saracci R (2002) European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 5 (6B): 1113–1124

Talar-Wojnarowska R, Gasiorowska A, Smolarz B, Romanowicz-Makowska H, Kulig A, Malecka-Panas E (2009) Tumor necrosis factor alpha and interferon gamma genes polymorphisms and serum levels in pancreatic adenocarcinoma. Neoplasma 56 (1): 56–62

Trichopoulos D, Psaltopoulou T, Orfanos P, Trichopoulou A, Boffetta P (2006) Plasma C-reactive protein and risk of cancer: a prospective study from Greece. Cancer Epidemiol Biomarkers Prev 15 (2): 381–384

Vrieling A, Bueno-de-Mesquita HB, Boshuizen HC, Michaud DS, Severinsen MT, Overvad K, Olsen A, Tjonneland A, Clavel-Chapelon F, Boutron-Ruault MC, Kaaks R, Rohrmann S, Boeing H, Nothlings U, Trichopoulou A, Moutsiou E, Dilis V, Palli D, Krogh V, Panico S, Tumino R, Vineis P, van Gils CH, Peeters PH, Lund E, Gram IT, Rodriguez L, Agudo A, Larranaga N, Sanchez MJ, Navarro C, Barricarte A, Manjer J, Lindkvist B, Sund M, Ye W, Bingham S, Khaw KT, Roddam A, Key T, Boffetta P, Duell EJ, Jenab M, Gallo V, Riboli E (2010) Cigarette smoking, environmental tobacco smoke exposure and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer 126 (10): 2394–2403

Whitcomb DC (2004) Inflammation and Cancer V. Chronic pancreatitis and pancreatic cancer. Am J Physiol Gastrointest Liver Physiol 287 (2): G315–G319

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Acknowledgements

We thank Laure Dossus for greatly appreciated statistical support, and Britta Lederer and Sigrid Henke for their excellent work in performing the immunoassays. We would also like to take the opportunity to thank the anonymous referees for greatly improving our manuscript. VAG was funded by a grant from the German Research Foundation, Graduiertenkolleg 793: Epidemiology of communicable and chronic non-communicable diseases and their interrelationships. This work was supported by WCRF UK and WCRF International, grant no. 2009/39. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum (DKFZ) and Federal Ministry of Education and Research (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020; Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (UK).

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Grote, V., Kaaks, R., Nieters, A. et al. Inflammation marker and risk of pancreatic cancer: a nested case–control study within the EPIC cohort. Br J Cancer 106 , 1866–1874 (2012). https://doi.org/10.1038/bjc.2012.172

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Revised : 10 February 2012

Accepted : 29 March 2012

Published : 26 April 2012

Issue Date : 22 May 2012

DOI : https://doi.org/10.1038/bjc.2012.172

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COMMENTS

  1. What Is a Case Study?

    When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to lear...

  2. Why Are Case Studies Important?

    Case studies are important because they help make something being discussed more realistic for both teachers and learners. Case studies help students to see that what they have learned is not purely theoretical but instead can serve to crea...

  3. What Are Some Examples of Case Studies?

    Examples of a case study could be anything from researching why a single subject has nightmares when they sleep in their new apartment, to why a group of people feel uncomfortable in heavily populated areas. A case study is an in-depth anal...

  4. A nested case-control study of kidney cancer among refinery

    A nested case-control study was designed to evaluate whether a nearly twofold excess of kidney cancer among workers at a refinery/petrochemical plant was

  5. A Nested Case-Control Study

    Suppose a prospective cohort study were conducted among almost 90,000 women for the purpose of studying the determinants of cancer and

  6. Nested case–control study of the effects of non-steroidal anti

    We carried out a nested case–control study to measure the rate ratio (RR) for invasive female breast cancer in relation to non-steroidal anti-inflammatory

  7. Nested case-control study on the risk factors of colorectal cancer

    The risk factors found in nested one are certain in cause and time consequence. In addition, the number of case in this study was abundance after ten years of

  8. Nested case–control study of night shift work and breast cancer risk

    Conclusions The results indicate that frequent night shift work increases the risk for breast cancer and suggest a higher risk with longer duration of intense

  9. Body Weight and Breast Cancer: Nested Case–Control Study in

    For instance, women with greater body size during childhood and puberty were at decreased risk of breast cancer in several developed countries,11, 12, 13, 14

  10. Nested case-control study of lung cancer in four Chinese tin mines

    Methods: A nested case-control study of 130 male lung cancer cases and 627 controls was initiated from a cohort study of 7855 subjects employed at least 1 year

  11. Nested case–control study

    A nested case–control (NCC) study is a variation of a case–control study in which cases and controls are drawn from the population in a fully enumerated

  12. Thyroid Cancer and Psoriasis: A Nested Case–Control Study

    Abstract. Previous researchers have suggested an elevated risk of thyroid cancer (TC) in patients with psoriasis with mixed results. The current

  13. A Nested Case-Control Study

    ... cancer (i.e., twice as many controls as cases). This would be described as a nested case-control study, i.e., nested within a cohort study.

  14. Inflammation marker and risk of pancreatic cancer: a nested case

    Methods: We conducted a nested case–control study within the European Prospective Investigation into Cancer and Nutrition, measuring