Abstract
Background
Many epidemiologic studies have examined the association between CRP and risk of cancer with inconsistent results.
Methods
We conducted two nested, case-control studies in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial to test whether pre-diagnostic circulating CRP concentrations were associated with pancreatic adenocarcinoma. Between 1985 and 2004, 311 cases occurred in ATBC and between 1994 and 2006, 182 cases occurred in PLCO. Controls (n=510 in ATBC, n=374 in PLCO) were alive at the time the case was diagnosed and were matched by age, date of blood draw, sex, and race. We used conditional logistic regression adjusted for smoking to calculate odds ratios (OR) and 95% confidence intervals (CI) for pancreatic cancer.
Results
CRP concentrations (ng/ml) tended to be inversely or not associated with pancreatic cancer risk in ATBC, PLCO, and combined analyses (per standardized quintile increase in CRP, continuous OR= 0.94, 95% CI 0.89, 0.99; OR=0.99, 95% CI 0.95, 1.04; OR=0.98, 95% CI 0.95, 1.01, respectively). In combined analyses, we observed a significant interaction (p-interaction=0.02) such that inverse associations were suggestive in younger (OR=0.95; 95% CI, 0.90–1.01), but not older participants.
Conclusion
Our results do not support the hypothesis that higher CRP concentrations are associated with incident pancreatic cancer.
Impact
Our results highlight the importance of investigating more specific biomarkers for inflammation that may reflect the biological mechanisms underlying pancreatic cancer in prospective cohort studies.
Keywords: CRP, ATBC, PLCO, Pancreatic, Case-Control
INTRODUCTION
Since Rudolf Virchow suggested in 1863 that cancer originated at sites of chronic inflammation, there has been increasing evidence that inflammation plays a key role in the pathogenesis of a number of cancers (1–7). Though acute inflammatory responses create a protective tissue microenvironment to recognize and repair cell damage, persistent inflammation may promote tumor formation (1, 2). However, epidemiological data linking inflammation to cancer risk are relatively sparse.
Cytokines, inflammatory cells, and chemokines have an intricate involvement in carcinogenesis as they stimulate the proliferation and apoptosis of cancer cells (8). C-reactive protein (CRP) is an acute-phase protein produced in the liver and induced by interleukin-6 (IL-6), IL-1, and tumor necrosis factor alpha (TNF-α) (3–4, 9). Production of CRP increases within 4 to 6 hours of inflammation, doubling every 8 hours thereafter, and peaks between 36 and 50 hours (9). A few epidemiologic studies have reported associations between CRP and risk of cancer, particularly colorectal cancer (3, 10–12). One relatively small study (n=14 cases) reported a non-significant positive association between CRP and pancreatic cancer (13). In addition, previous pancreatic cancer research suggests that CRP correlates inversely with patient survival (14, 15).
We investigated whether serum CRP concentration is associated with pancreatic cancer risk. In accordance with the results from other cancer sites, we hypothesized that higher CRP concentration is associated with increased pancreatic cancer risk. We examined this hypothesis in two case-control studies nested within two cohorts, the Alpha-Tocopherol, Beta-Carotene (ATBC) Cancer Prevention Study cohort and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). To our knowledge, this is among the first studies evaluating the association between CRP and incident pancreatic cancer risk based on cohort data.
MATERIALS AND METHODS
ATBC study population
The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study tested whether α-tocopherol or β-carotene reduced cancer incidence in Finnish male smokers. The study was a double-blind, placebo-controlled, 2 × 2 factorial-design primary intervention trial. The methods have been described previously (16). Briefly, between 1985 and 1988, 29,133 eligible men aged 50–69 years in southwestern Finland who smoked at least 5 cigarettes per day were randomized to receive active supplements or placebo. Subjects were excluded from the study if they had a history of malignancy other than nonmelanoma skin cancer or carcinoma in situ, severe angina on exertion, chronic renal insufficiency, liver cirrhosis, chronic alcoholism, or another medical condition that might limit long-term participation. Also, subjects were excluded who were using supplements containing vitamin E (>20 mg/day), vitamin A (>20,000 IU/day), or β-carotene (>6 mg/day). Written consent was provided by all study participants prior to randomization. The study protocol was approved by the institutional review boards of both the National Public Health Institute in Finland and the National Cancer Institute in the United States.
During their pre-randomization baseline visit, the men completed questionnaires on medical history, smoking habits, dietary intake, and physical activity (16). Trained study staff measured height and weight at baseline using standard methods. Validated self-administered dietary history questionnaires determined the frequency of consumption and usual portion size of 276 food items eaten during the past year, using a color picture booklet as a guide for portion size (17).
PLCO study population
The Prostate, Lunge, Colorectal, and Ovarian Cancer (PLCO) Screening Trial is a randomized multicenter trial in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, DC) that has been previously described in detail (18). It sought to determine the effectiveness of early detection procedures for prostate, lung, colorectal, and ovarian cancer on disease-specific mortality. Study recruitment and randomization began in November 1993 and was completed in July 2001. The study cohort had 152,810 men and women aged 55 to 74 years old at baseline. Exclusion criteria included subjects with a history of one of the four PLCO cancers or those currently undergoing treatment for any cancer, except nonmelanoma of the skin, as well as those screened for prostate or colorectal cancer during the past 3 years. Participants were randomized to either an intervention arm or the control arm. The intervention arm participants had periodic cancer screening tests, which included PSA and digital rectal exams (men), chest X-ray, flexible sigmoidoscopy, or cancer antigen 125 and transvaginal ultrasound (women). Those in the control arm followed their usual medical care. Informed consent was obtained by all participants. The study was approved by the institutional review boards of all 10 screening centers as well as that of the U.S. National Cancer Institute.
The study participants completed self-administered questionnaires that queried information on dietary intake, medical history, family history of cancer, tobacco use, height, weight, physical activity, and other exposures. Diet was assessed using a food frequency questionnaire, which used a grid format to determine the frequency of 137 food items over the past 12 months, 77 of which inquired about usual portion size (19).
ATBC and PLCO case and control selection
Details about the nested case-control sets used in the present study have been published previously (20–22). Cases included incident primary pancreatic adenocarcinomas (International Classification of DiseasesNinth Revision ICD-9code 157 or ICD-O-3 code C250-C259 or C25.0-C25.3, C25.7-C25.9) for the ATBC and PLCO Studies, respectively. Endocrine pancreatic tumors (157.7 or C25.4, histology type, 8150, 8151, 8153, 8155, 8240) were excluded because the etiology of these cancers is thought to be different. ATBC Study cases were identified through the linkage to the Finnish Cancer Registry, which provides complete case ascertainment in Finland (23), while PLCO pancreatic cancer cases were identified by self-report in the annual mail-in survey, state cancer registries, death certificates, physician referrals, and reports of next of kin for deceased individuals. In the ATBC cohort, we identified 311 exocrine pancreatic cancer cases for which serum samples had been collected at baseline. The interval between serum collection and diagnosis extended to 19.1 years (median 9.4 years). For the PLCO cohort, we identified 182 exocrine pancreatic cancer cases between 1994 and 2006 (follow-up to 11.7 years; median, 5.4 years). In total, 74.5% of the ATBC cases and 92.5% of the PLCO cases were confirmed through medical review. In sensitivity analyses, results were similar when the non-confirmed cases were excluded; therefore, all cases were included to increase statistical power.
Controls were selected with a control to case ratio of 2:1 (ATBC, PLCO) and 1:1 for the ATBC cases identified during later follow-up (21). All controls were alive and free from pancreatic cancer on the date the matched case was diagnosed. Controls were matched to cases on age (±5 years), date of blood draw (± 30 days for ATBC and within 2 month blocks for PLCO), sex (PLCO), and race (PLCO).
Measurement of serum C-reactive protein
At the pre-randomization visit, overnight fasting (ATBC) or nonfasting (PLCO) serum samples were obtained from study participants and stored at −70°C. The ATBC and PLCO frozen serum samples were collected at different times (December 2006 and February 2009, respectively). CRP concentrations were measured by Dr. Michael Pollak’s laboratory (The Lady Davis Institute for Medical Research in Montreal Quebec, Canada) using enzyme-linked immunosorbent assay (ELISA) with reagents from Beckman Coulter, Diagnostic Systems Laboratory (Webster, TX) in 2007 and 2009 for ATBC and PLCO, respectively. Case and control samples within their respective cohorts were handled in a similar manner and were laboratory-blinded to case-control status. Matched samples were analyzed consecutively as triplets within batches and blinded replicate pooled quality control samples were placed in triplicate toward the beginning and end of each batch and comprised 10% of each batch. Using a variance components estimation procedure, with logarithmically-transformed quality control measurements across all batches (24), the estimated overall (intrabatch and interbatch) coefficient of variation were 9.6% for ATBC and 7.9% for PLCO.
Statistical analysis
The distributions of selected characteristics of cases and controls for each cohort were compared using the Wilcoxon rank sum test for the continuous variables and χ2 tests for categorical variables (Tables 1 and 2). Body mass index (BMI: weight (kg)/height (m)2) was calculated from measured weight and height. Potential confounders were also identified by calculating means using generalized linear models and proportions using frequencies of baseline characteristics among the controls across CRP quintiles (Table 3). Potential confounders examined in the analyses were age; education; baseline height, weight, body mass index (BMI); history of diabetes and family history of pancreatic cancer; dietary nutrients from foods (energy, carbohydrate, fat, saturated fat, protein); red meat; alcohol intake; and physical activity. BMI was categorized to be consistent with the WHO obesity classifications as <25 (normal), 25 to <30 (overweight), and ≥30 kg/m2 (obese) (25). Foods were energy adjusted using the residual method described by Willett and Stampfer (26). In ATBC, we examined cigarette smoking habits (number of years smoked, and number of cigarettes smoked per day) and occupational and leisure activity. In PLCO, we investigated smoking status (never, former, current), history (number of cigarettes smoked per day, number of years smoked, pack-years, smoking cessation), and leisure physical activity.
Table 1.
Characteristics | Cases (n = 311) | Controls (n = 510) | P* |
---|---|---|---|
C-reactive protein, ng/ml | 4.5 (2.1–8.9) | 5.4 (2.4–12.3) | 0.03 |
Age, years | 58.0 (55.0–62.0) | 58.0 (55.0–62.0) | 0.73 |
Smoking history | |||
Total cigarettes per day | 20.0 (15.0–25.0) | 20.0 (15.0–25.0) | 0.56 |
Years smoked, years | 39.0 (32.0–43.0) | 39.0 (34.0–43.0) | 0.84 |
Height, cm | 173 (166–182) | 173 (165–180) | 0.04 |
Body Mass Index, kg/m2 | 25.8 (23.7–28.0) | 26.1 (23.8–28.7) | 0.25 |
BMI - WHO cut points, n (%) | |||
<25.0 (normal weight) | 121 (38.9) | 189 (37.1) | 0.87 |
≥25.0 and <30.0 (overweight) | 139 (44.7) | 236 (46.3) | |
≥30.0 (obese) | 51 (16.4) | 85 (16.6) | |
Self-reported diabetes mellitus, n (%) | 22 (7.1) | 31 (6.1) | 0.57 |
Education, n (%) | |||
Less than elementary school | 12 (3.9) | 31 (6.1) | 0.02 |
Elementary school | 228 (73.3) | 385 (75.5) | |
Some junior high school | 15 (4.8) | 27 (5.3) | |
Junior high school graduate | 19 (6.1) | 40 (7.8) | |
Some senior high school | 9 (2.9) | 6 (1.2) | |
Senior high school graduate | 28 (9.0) | 21 (4.1) | |
Living in city, n (%) | 139 (44.7) | 220 (43.1) | 0.66 |
Dietary intake per day† | |||
Red meat (g) | 23.1 (7.3–49.5) | 19.1 (6.0–46.0) | 0.01 |
Alcohol (g) | 9.1 (2.1–24.2) | 10.7 (2.8–26.2) | 0.40 |
Energy (kcal) | 2,704 (2,228–3,200) | 2,753 (2,230–3,220) | 0.65 |
Total fat (g) | 102.4 (93.3–112.1) | 100.6 (90.2–110.0) | 0.04 |
Saturated fat (g) | 50.5 (38.4–64.2) | 49.4 (35.3–65.0) | 0.34 |
Carbohydrate (g) | 287.6 (232.9–356.3) | 299.3 (228.6–354.2) | 0.51 |
Protein (g) | 102.1 (86.8–119.0) | 103.2 (85.3–120.5) | 0.11 |
Physical activity, n (%) | |||
Occupational | |||
Sedentary | 50 (16.1) | 50 (9.8) | 0.10 |
Moderate | 91 (29.3) | 141 (27.7) | |
Heavy | 19 (6.1) | 41 (8.0) | |
Non-working | 151 (48.5) | 278 (54.5) | |
Leisure‡ | |||
Sedentary | 128 (41.3) | 231 (45.3) | 0.66 |
Light, moderate | 164 (52.9) | 245 (48.0) | |
Exercise to keep fit | 18 (5.8) | 34 (6.7) |
P values for categorical variables based on χ2 or Fisher’s exact test and P values for continuous variables based on Wilcoxon rank sum test.
All foods and nutrients energy adjusted except alcohol and based on n = 300 cases and n = 473 controls
Leisure activity variables based on n= 507 controls and n= 310 cases
Table 2.
Characteristics | Cases (n = 182) | Controls (n = 364) | P* |
---|---|---|---|
C-reactive protein, ng/ml | 9.3 (1.1–18.6) | 8.8 (0.8–16.7) | 0.11 |
Age, years | 66.0 (61.0–69.0) | 66.0 (61.0–69.0) | 0.83 |
Sex, male, n (%) | 121 (64.7) | 242 (64.7) | 1.00 |
Race, n (%) | |||
White | 169 (90.4) | 338 (90.4) | 1.00 |
Black | 6 (3.2) | 12 (3.2) | |
Hispanic | 3 (1.6) | 6 (1.6) | |
Asian | 9 (4.8) | 18 (4.8) | |
Smoking status, n (%) | |||
Never | 72 (38.5) | 174 (46.5) | <0.0001 |
Former quit ≥15 years | 48 (25.7) | 112 (30.0) | |
Former quit <15 years | 31 (16.6) | 62 (16.6) | |
Current | 36 (19.2) | 26 (6.9) | |
Height (cm) | |||
Male | 177.1 (167.6–185.4) | 177.1 (170.2–185.4) | 0.74 |
Female | 162.0 (152.4–170.2) | 162.5 (154.9–170.2) | 0.69 |
BMI (kg/m2) | 26.7 (24.3–29.9) | 26.5 (23.9–29.1) | 0.18 |
BMI - WHO cut points, n (%) | |||
<25.0 (normal weight) | 57 (30.5) | 133 (35.6) | 0.29 |
≥25.0 and <30 (overweight) | 84 (44.9) | 168 (44.9) | |
≥30 (obese) | 46 (24.6) | 73 (19.5) | |
Self-reported diabetes mellitus, n (%) | 22 (12.2) | 36 (9.9) | 0.40 |
Family history of pancreatic cancer, n (%) | 7 (3.8) | 8 (2.2) | 0.27 |
Education, n (%) | |||
Less than high school | 14 (7.5) | 39 (10.4) | 0.50 |
High school graduate | 48 (25.7) | 86 (23.0) | |
Post-high school, vocational training | 22 (11.8) | 41 (11.0) | |
Some college | 38 (20.2) | 67 (17.9) | |
College graduate | 37 (19.8) | 65 (17.4) | |
Post-college graduate | 28 (15.0) | 76 (20.3) | |
Dietary intake per day† | |||
Red meat (g) | 55.8 (31.7–96.5) | 61.3 (33.1–102.9 | 0.38 |
Alcohol (g) | 1.4 (0.3–9.4) | 0.9 (0.3–9.0) | 0.60 |
Energy (kcal) | 1,819 (1,495–2,278) | 1,976 (1,480–2,626) | 0.05 |
Total fat (g) | 61.3 (41.0–77.9) | 63.1 (45.2–90.8) | 0.07 |
Saturated fat (g) | 20.4 (13.4–26.4) | 21.0 (14.8–30.4) | 0.07 |
Carbohydrate (g) | 247.5 (186.5–308.2) | 265.8 (204.7–346.0) | 0.02 |
Protein (g) | 72.9 (55.1–90.0) | 77.3 (57.3–101.1) | 0.05 |
Vigorous physical activity, hours per week,‡n (%) | |||
None or <1 h | 66 (38.1) | 113 (32.1) | 0.23 |
1–3 h | 38 (22.0) | 98 (27.8) | |
>4 h | 69 (39.9) | 141 (40.1) |
P values for categorical variables based on χ2 or Fisher’s exact test and P values for continuous variables based on Wilcoxon rank sum test.
All foods and nutrients energy adjusted except alcohol and based on n = 181 cases and n = 358 controls
Vigorous activity variables based on n = 173 cases and n = 352 controls
Table 3.
ATBC | PLCO | |||||
---|---|---|---|---|---|---|
Characteristics | Q1 | Q3 | Q5 | Q1 | Q3 | Q5 |
C-reactive protein, ng/ml | 1.1 | 5.5 | 38.3 | 0.9 | 4.9 | 28.3 |
Age, years | 58.7 | 58.1 | 58.6 | 64.7 | 65.8 | 64.9 |
Smoking history | ||||||
Total cigarettes per day | 18.3 | 20.8 | 21.3 | 7.6 | 11.5 | 14.3 |
Years smoked, years | 35.9 | 37.6 | 38.3 | 12.0 | 15.6 | 21.1 |
Smoking status (%) | ||||||
Never | - | - | - | 55.7 | 49.3 | 40.8 |
Former quit ≥15 years | - | - | - | 30.4 | 22.7 | 29.6 |
Former quit <15 years | - | - | - | 11.4 | 20.0 | 16.9 |
Current | 100 | 100 | 100 | 2.5 | 8.0 | 12.7 |
Height (cm) | 172.0 | 173.4 | 172.3 | 173.1 | 172.8 | 170.5 |
BMI (kg/m2) | 24.9 | 27.0 | 27.1 | 25.0 | 27.2 | 29.3 |
BMI - WHO cut points (%) | ||||||
<25.0 (normal weight) | 53.5 | 27.9 | 32.7 | 51.9 | 30.7 | 22.5 |
≥25.0 and <30 (overweight) | 39.6 | 51.2 | 47.8 | 44.3 | 53.3 | 36.6 |
≥30 (obese) | 6.9 | 20.9 | 19.5 | 3.8 | 16.0 | 40.9 |
Medical history (%) | ||||||
Diabetes mellitus | 5.0 | 6.0 | 6.2 | 3.9 | 13.5 | 18.6 |
Dietary intake per day† | ||||||
Red meat (g) | 25.6 | 22.8 | 26.1 | 67.5 | 78.8 | 81.6 |
Alcohol (g) | 18.2 | 19.0 | 21.7 | 12.0 | 15.8 | 15.1 |
Energy (kcal) | 2,781 | 2,696 | 2,893 | 2,000 | 2,139 | 2,117 |
Total fat (g) | 100.5 | 100.6 | 106.3 | 63.8 | 69.8 | 70.5 |
Saturated fat (g) | 48.6 | 51.9 | 54 | 21.1 | 23.3 | 24.4 |
Carbohydrate (g) | 313.6 | 300.3 | 296.8 | 271.4 | 280.2 | 273.1 |
Protein (g) | 104.1 | 103.0 | 102.9 | 77.0 | 82.7 | 83.4 |
Vigorous physical activity, hours per week (%) | ||||||
None or <1 h | - | - | - | 21.1 | 28.8 | 41.8 |
1–3 h | - | - | - | 36.6 | 30.1 | 26.9 |
>4 h | - | - | - | 42.3 | 41.1 | 31.3 |
Physical activity (%) | ||||||
Occupational | ||||||
Sedentary | 12.9 | 15.8 | 7.1 | - | - | - |
Moderate | 26.7 | 26.2 | 29.2 | - | - | - |
Heavy | 8.9 | 7.1 | 6.2 | - | - | - |
Non-working | 51.5 | 50.8 | 57.5 | - | - | - |
Leisure | ||||||
Sedentary | 35.6 | 45.4 | 59.3 | - | - | - |
Light, moderate | 54.5 | 48.6 | 36.3 | - | - | - |
Exercise to keep fit | 9.9 | 6.0 | 4.4 | - | - | - |
Blank cells due to lack of available data
All dietary variables energy-adjusted except alcohol based on controls with complete dietary data
Conditional logistic regression was used to estimate odds ratios for pancreatic cancer, with the lowest quintile serving as the reference category. Linear tests for trend were based on a continuous variable, and the continuous CRP odds ratios were standardized to the average size of the three central quintiles. Multivariable models were developed by individually entering potentially confounding variables into the model using both forward and backward methods. Confounders were defined as variables which changed the risk estimates by more than 10%. No variables met these criteria, however, because smoking is the primary risk factor for pancreatic cancer (27), we adjusted for smoking (duration and intensity) for ATBC and never, former quit ≥15 years ago, former quit <15 years ago, and current for PLCO. We present data separately for each cohort, and as there was no significant interaction by cohort (P=0.16); we combined data from both cohorts by creating quintiles based on the controls from both studies. We also created cohort-specific cut-points based on the control cut-points from each study. For the cohort-specific cut-point analyses we used a score variable to calculate the trend.
Effect modification by age, BMI, and smoking intensity and duration was evaluated with cross-product terms composed of continuous CRP and dichotomized (median split) effect modifier variables in multivariable models, and in stratified analyses. We used unconditional logistic regression in stratified analyses adjusting for the matching variables and confounders. We also stratified our analyses a priori by follow-up year of case diagnosis (e.g. <5 years, ≥5 years, ≥10 years after baseline) to assess the potential impact of reverse causation.
All statistical analyses were performed using SAS software (SAS Institute Inc., Cary, North Carolina), and statistical tests were two-sided.
RESULTS
Tables 1 and 2 show the baseline characteristics of the cases and controls for the ATBC and PLCO cohorts, respectively. Cases and controls tended to have similar baseline characteristics (Tables 1–2). In ATBC, compared to controls, cases had a significantly lower CRP concentrations (P=0.03), and greater height (P=0.04), total fat (P=0.04) and red meat intake (P=0.01); and tended to be more educated (P=0.02). In PLCO, compared to controls, cases were more likely to be current smokers (P<0.0001) and had lower carbohydrate (P=0.02), energy (P=0.05), and protein intake (P=0.05).
Table 3 shows the means or proportions of selected characteristics among control participants according to quintile of CRP concentration in the ATBC and PLCO studies, respectively. Higher CRP concentrations were directly associated with increasing BMI, obesity, and less vigorous (PLCO) or more sedentary (ATBC) leisure activity, and inversely associated with normal BMI and exercising to keep fit (ATBC) or more vigorous activity (PLCO). In the ATBC controls, higher CRP concentrations were positively associated with not working (P=0.04). In PLCO, higher CRP concentrations were associated with current smoking (P=0.01) and a medical history of diabetes (P=0.001).
Table 4 shows the main effects for the association between CRP and pancreatic cancer in the ATBC, PLCO, and combined nested case-control set. In ATBC, higher concentrations of CRP tended to be inversely associated with pancreatic cancer (high compared to low quintile, OR, 0.71; 95% CI, 0.44–1.16, p-trend=0.03, continuous OR, 0.94; 95% CI, 0.89–0.99). The inverse association remained when we excluded cases that occurred earlier during follow-up (i.e., high compared to low quintile CRP, cases occurring ≥ 5 years OR, 0.71; 95% CI, 0.41–1.23; Ptrend= 0.05, continuous OR, 0.92; 95% CI, 0.86–0.99; and ≥ 10 years OR, 0.57; 95% CI, 0.26–1.22; Ptrend= 0.03, continuous OR, 0.93; 95% CI, 0.85–1.01). In PLCO, higher concentrations of CRP were not significantly associated with pancreatic cancer risk (high compared to low quintile, OR, 0.84; 95% CI, 0.41–1.74; Ptrend=0.79, continuous OR=0.99, 95% CI 0.95–1.04). There was no association when early cases were excluded (high compared to low quintile, ≥5 years smoking-adjusted OR, 0.97; 95% CI, 0.90–1.05; Ptrend=0.41). However, we observed a non-significant positive association for longer follow-up (high compared to low quintile, ≥8 years smoking-adjusted OR, 1.24; 95% CI, 0.92–1.66; Ptrend=0.33). There was no association between CRP concentrations and pancreatic cancer in the combined analyses based on overall pooled cut-points (high compared to low quintile, OR, 1.00; 95% CI, 0.69–1.46; Ptrend=0.12, continuous OR, 0.98; 95% CI, 0.95–1.01) and cohort-specific cut-points (OR, 1.01; 95% CI, 0.70–1.46; Ptrend=0.76). In the pooled analyses we observed suggestive inverse associations among cases with longer follow-up (≥5 years smoking-adjusted OR, 0.87; 95% CI, 0.55–1.37; Ptrend=0.48, continuous OR, 0.95; 95% CI, 0.86–1.05).
Table 4.
Quintile of Serum CRP Concentration, ng/ml | |||||||
---|---|---|---|---|---|---|---|
ATBC | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.97 | >1.97 and ≤4.06 | >4.06 and ≤7.42 | >7.42 and ≤14.05 | >14.05 | ||
Cases/Controls, n | 64/102 | 71/102 | 81/102 | 50/103 | 45/101 | ||
Crude OR (95% CI)* | 1.00 (reference) | 1.05 (0.67–1.64) | 1.26 (0.82–1.93) | 0.74 (0.47–1.18) | 0.72 (0.44–1.17) | 0.03 | 0.94 (0.89–0.99) |
Smoking-adjusted OR (95% CI)† | 1.00 (reference) | 1.05 (0.67–1.65) | 1.24 (0.81–1.91) | 0.73 (0.46–1.16) | 0.71 (0.44–1.16) | 0.03 | 0.94 (0.89–0.99) |
Quintile of Serum CRP Concentration, ng/ml | |||||||
PLCO | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.52 | >1.52 and ≤2.96 | >2.96 and ≤5.98 | >5.98 and ≤10.48 | >10.48 | ||
Cases/Controls, n | 33/73 | 25/72 | 39/73 | 45/72 | 30/72 | ||
Crude OR (95% CI)‡ | 1.00 (reference) | 0.70 (0.31–1.63) | 1.27 (0.65–2.50) | 1.55 (0.78–3.06) | 1.12 (0.56–2.23) | 0.88 | 1.00 (0.96–1.04) |
Smoking-adjusted OR (95% CI)§ | 1.00 (reference) | 0.53 (0.22–1.28) | 1.34 (0.67–2.65) | 1.46 (0.73–2.95) | 0.84 (0.41–1.74) | 0.79 | 0.99 (0.95–1.04) |
Quintile of Serum CRP Concentration, ng/ml | |||||||
Combined | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.67 | >1.67 and ≤3.64 | >3.64 and ≤6.79 | >6.79 and ≤12.59 | >12.59 | ||
Cases/Controls, n | 86/175 | 107/174 | 124/175 | 87/174 | 89/174 | ||
Crude OR (95% CI)‡ | 1.00 (reference) | 1.12 (0.78–1.60) | 1.25 (0.89–1.76) | 1.00 (0.70–1.43) | 0.88 (0.61–1.26) | 0.06 | 0.97 (0.94–1.00) |
Smoking-adjusted OR (95% CI)§ | 1.00 (reference) | 1.18 (0.82–1.71) | 1.32 (0.94–1.87) | 1.05 (0.73–1.52) | 1.00 (0.69–1.46) | 0.12 | 0.98 (0.95–1.01) |
Quintile of Serum CRP Concentration, ng/ml | |||||||
Combined cohort specific cut-points‖ | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | |
C-Reactive Protein | |||||||
Cases/Controls, n | 97/175 | 96/174 | 120/175 | 95/175 | 85/173 | ||
Crude OR (95% CI)‡ | 1.00 (reference) | 0.97 (0.68–1.38) | 1.22 (0.87–1.69) | 0.98 (0.70–1.38) | 0.95 (0.67–1.37) | 0.62 | |
Smoking-adjusted OR (95% CI)§ | 1.00 (reference) | 1.02 (0.71–1.47) | 1.24 (0.89–1.74) | 0.99 (0.70–1.41) | 1.01 (0.70–1.46) | 0.76 |
Crude OR adjusted for matching variables (age, date of blood draw, follow-up time)
Multivariable model additionally adjusted for smoking intensity and smoking duration
Crude OR adjusted for matching variables (age, race, sex, date of blood draw)
Adjusted for smoking (never, former quit ≥ 15 years ago, former quit < 15 years ago, current)
Cohort specific cut-points based on CRP concentrations observed separately in the ATBC and PLCO cohorts as defined above
Table 5 shows the association between CRP concentrations and pancreatic cancer stratified by age. Although no significant associations were observed in either the ATBC or PLCO datasets separately (p-interaction=0.91 and 0.05, respectively), in the combined analyses we observed a significant interaction by age (p-interaction=0.02) such that younger participants had a non-significant inverse association and trend (high compared to low quintile, OR=0.81, 95% CI 0.45–1.47, p-trend=0.17), while older participants tended to have a pattern of non-significant elevated risk with increasing CRP concentrations. We did not observe a significant interaction of the CRP and pancreatic cancer association by BMI or smoking status in either the ATBC or PLCO studies.
Table 5.
ATBC | Quintile of Serum CRP Concentration, ng/ml* | ||||||
---|---|---|---|---|---|---|---|
<58 years old | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.97 | >1.97 and ≤4.06 | >4.06 and ≤7.42 | >7.42 and ≤14.05 | >14.05 | ||
Cases/Controls, n | 35/46 | 29/49 | 32/52 | 28/43 | 17/46 | ||
Crude OR (95% CI)† | 1.00 (reference) | 0.77 (0.41–1.46) | 0.80 (0.43–1.49) | 0.85 (0.44–1.62) | 0.49 (0.24–0.99) | 0.04 | 0.90 (0.82–1.00) |
Smoking-adjusted OR (95% CI)‡ | 1.00 (reference) | 0.76 (0.40–1.44) | 0.76 (0.40–1.43) | 0.82 (0.43–1.59) | 0.48 (0.24–0.99) | 0.05 | 0.90 (0.82–1.00) |
≥58 years old | |||||||
Cases/Controls, n | 29/56 | 42/53 | 49/50 | 22/60 | 28/55 | ||
Crude OR (95% CI)† | 1.00 (reference) | 1.53 (0.83–2.79) | 1.91 (1.05–3.48) | 0.72 (0.37–1.41) | 0.99 (0.52–1.87) | 0.12 | 0.95 (0.90–1.01) |
Smoking-adjusted OR (95% CI)‡ | 1.00 (reference) | 1.46 (0.79–2.68) | 1.80 (0.98–3.30) | 0.70 (0.36–1.36) | 0.93 (0.49–1.78) | 0.09 | 0.95 (0.89–1.01) |
PLCO | Quintile of Serum CRP Concentration, ng/ml* | ||||||
<66 years old | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.52 | >1.52 and ≤2.96 | >2.96 and ≤5.98 | >5.98 and ≤10.48 | >10.48 | ||
Cases/Controls, n | 24/38 | 13/40 | 16/30 | 17/31 | 20/35 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 0.66 (0.21–2.11) | 0.98 (0.36–2.68) | 1.00 (0.35–2.85) | 0.95 (0.37–2.45) | 0.39 | 0.97 (0.90–1.04) |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 0.53 (0.15–1.80) | 1.03 (0.34–3.17) | 0.93 (0.31–2.80) | 0.65 (0.23–1.84) | 0.26 | 0.95 (0.86–1.04) |
≥66 years old | |||||||
Cases/Controls, n | 9/35 | 12/32 | 23/43 | 28/41 | 20/37 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 0.93 (0.25–3.49) | 1.71 (0.62–4.69) | 2.85 (1.01–8.05) | 2.06 (0.68–6.24) | 0.46 | 1.02 (0.97–1.08) |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 0.73 (0.18–3.01) | 1.68 (0.60–4.67) | 2.44 (0.81–7.33) | 1.43 (0.43–4.78) | 0.77 | 1.01 (0.95–1.08) |
Combined | Quintile of Serum CRP Concentration, ng/ml* | ||||||
Younger individuals** | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | Continuous |
C-Reactive Protein | ≤1.67 | >1.67 and ≤3.64 | >3.64 and ≤6.79 | >6.79 and ≤12.59 | >12.59 | ||
Cases/Controls, n | 59/84 | 42/89 | 48/82 | 45/74 | 37/81 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 0.67 (0.41–1.10) | 0.82 (0.51–1.34) | 0.86 (0.52–1.42) | 0.65 (0.39–1.09) | 0.13 | 0.96 (0.92–1.01) |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 0.66 (0.40–1.09) | 0.83 (0.51–1.36) | 0.81 (0.49–1.35) | 0.60 (0.36–1.02) | 0.08 | 0.95 (0.90–1.01) |
Older individuals†† | |||||||
Cases/Controls, n | 38/91 | 54/85 | 72/93 | 50/101 | 48/92 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 1.51 (0.91–2.52) | 1.91 (1.17–3.12) | 1.25 (0.75–2.08) | 1.29 (0.77–2.16) | 0.82 | 0.98 (0.95–1.02) |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 1.49 (0.89–2.49) | 1.89 (1.15–3.10) | 1.19 (0.71–2.00) | 1.19 (0.71–2.02) | 0.24 | 0.98 (0.94–1.02) |
Combined cohort-specific cutpoints‡‡ | Quintile of Serum CRP Concentration, ng/ml* | ||||||
Younger individuals** | Q1 | Q2 | Q3 | Q4 | Q5 | Ptrend | |
Cases/Controls, n | 59/84 | 42/89 | 48/82 | 45/74 | 37/81 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 0.68 (0.38–1.22) | 0.77 (0.45–1.31) | 0.87 (0.51–1.49) | 0.79 (0.44–1.40) | 0.28 | |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 0.72 (0.40–1.31) | 0.79 (0.46–1.37) | 0.86 (0.50–1.48) | 0.81 (0.45–1.47) | 0.17 | |
Older individuals†† | |||||||
Cases/Controls, n | 38/91 | 54/85 | 72/93 | 50/101 | 48/92 | ||
Crude OR (95% CI)§ | 1.00 (reference) | 1.32 (0.78–2.26) | 1.98 (1.20–3.28) | 1.32 (0.79–2.23) | 1.25 (0.72–2.17) | 0.68 | |
Smoking-adjusted OR (95% CI)‖ | 1.00 (reference) | 1.39 (0.80–2.41) | 1.89 (1.13–3.15) | 1.19 (0.69–2.04) | 1.28 (0.73–2.27) | 0.92 |
Interaction by age not significant for ATBC and PLCO, but significant in combined analyses (P=0.91, P=0.05, and P=0.02, respectively)
Crude OR adjusted for matching variables (age, date of blood draw, follow-up time)
Multivariable model additionally adjusted for smoking intensity and smoking duration
Crude OR adjusted for matching variables (age, race, sex, date of blood draw)
Adjusted for smoking (never, former quit ≥ 15 years ago, former quit < 15 years ago, current)
Combined subjects <58 years old from ATBC and those <66 years old from PLCO
Combined subjects ≥58 years old from ATBC and those ≥66 years old from PLCO
Cohort specific cut-points based on CRP concentrations based on ATBC and PLCO controls separately as defined above
DISCUSSION
Overall we observed inverse associations between CRP and risk of pancreatic cancer among men in the ATBC Study cohort but not participants in the PLCO Study. The association was significantly modified by age such that in analyses of both cohorts separately and in study combined analyses, non-significant inverse associations were most evident among younger individuals, while non-significant positive associations were observed among older cohort participants.
A number of studies have investigated CRP in relation to other cancers, particularly colorectal cancer with positive (3, 12) and inverse (10–11) associations reported. Previous prospective studies conducted in Washington County, Maryland and another in the ATBC study both showed 2–3 fold elevated colorectal cancer risk with increasing CRP concentrations (3, 12). In contrast, similar to our results, others have reported inverse associations between CRP and adenoma and colorectal cancer (10, 11). One multiethnic trial from Honolulu examined whether IL-6 and CRP concentrations and single nucleotide polymorphisms (SNPs) in the IL-6 or CRP genes were associated with colorectal adenoma risk (10). In the main effects model, there was no association between CRP concentration and colorectal cancer. However, two different SNPs in the CRP gene (rs1205 and rs1130864) demonstrated that alleles that were associated with higher CRP concentrations were also associated with a reduced risk of adenoma. The Women’s Health Study showed an inverse association between CRP and risk of proximal colon cancer (11) and no significant associations by tumor stage at diagnosis. Potential limitations of these as well as our study are that CRP is measured at only one point in time and it is possible that other factors correlated with higher CRP concentrations could explain the observed associations.
The inverse associations that we observe between CRP concentrations and pancreatic cancer are unexpected and mechanisms that may explain our association are speculative. Inflammation is important for tissue homeostasis and closely associates with immune response (10–11, 28–29). CRP recognizes damaged cells in the body and aids in their removal by binding to them as well as other viable apoptotic cells (10, 29). Therefore, higher CRP levels may augment phagocytosis of apoptotic cells and contribute to their removal. This, in turn, could prevent tumor development as high CRP levels are associated with tissue repair and exclusion of cells, which may be more apparent in smokers because they may have more inflammation. This could potentially explain the observed inverse association in ATBC but not PLCO, due to the ATBC population being composed of solely male smokers (30). The interaction of CRP and pancreatic cancer risk by age is also difficult to explain. There is evidence that the production of pro-inflammatory cytokines increase with age (31). Why younger individuals with high CRP would have lower pancreatic cancer risk than those with low levels is not clear, although it could possibly be due to chance.
Strengths of this study include the prospective design with CRP measured in blood samples collected several years before cancer diagnosis. The associations became stronger when cases occurring early during follow-up were excluded decreasing the likelihood of reverse causation, though there was some attenuation in ATBC during longer follow-up. The association between CRP and pancreatic cancer risk also switched from protective to positive after cases from the first 8 years were excluded in the PLCO study. By pooling data from two nested case-control studies, we increased the study size and our power to observe associations if they exist.
Our study also has limitations. A single measurement of CRP in peripheral blood may not represent lifetime inflammatory exposure or tissue specific inflammation, such as that in the pancreas. CRP is known to be a nonspecific marker of inflammation and it is also possible that other underlying diseases and cigarette smoking could influence CRP concentrations in the serum masking any contribution from pancreatic inflammation. There may be unknown correlates to serum CRP in our study that are not controlled and may explain the associations that we observe. All these factors could contribute to inaccurate risk estimates between CRP and pancreatic cancer.
In conclusion, we observed an inverse association between increasing CRP concentrations and risk of pancreatic cancer in the ATBC Study; however, there was no significant association in the PLCO study or in cohort combined analyses. In combined analyses we observed a significant interaction such that inverse associations were apparent in younger but not older participants. Additional prospective studies are necessary to evaluate associations between CRP and perhaps more specific markers of pancreatic inflammation and risk of pancreatic cancer.
Acknowledgments
Acknowledgement of financial support: This research was supported by the Intramural Research Program of the National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services.
REFERENCES
- 1.Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420:860–867. doi: 10.1038/nature01322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature. 2008;454:436–444. doi: 10.1038/nature07205. [DOI] [PubMed] [Google Scholar]
- 3.Erlinger TP, Platz EA, Rifai N, Helzlsouer KJ. C-reactive protein and the risk of incident colorectal cancer. JAMA. 2004;291:585–590. doi: 10.1001/jama.291.5.585. [DOI] [PubMed] [Google Scholar]
- 4.Pine JK, Fusai KG, Young R, et al. Serum C-reactive protein concentration and the prognosis of ductal adenocarcinoma of the head of pancreas. EJSO. 2009;35:605–610. doi: 10.1016/j.ejso.2008.12.002. [DOI] [PubMed] [Google Scholar]
- 5.Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001;357:539–545. doi: 10.1016/S0140-6736(00)04046-0. [DOI] [PubMed] [Google Scholar]
- 6.Rifai N, Buring JE, Lee IM, et al. Is C-reactive protein specific for vascular disease in women? Ann Intern Med. 2002;136:529–533. doi: 10.7326/0003-4819-136-7-200204020-00010. [DOI] [PubMed] [Google Scholar]
- 7.Kwon KA, Kim SH, Oh SY, et al. Clinical significance of preoperative serum vascular endothelial growth factor, interleukin-6, and C-reactive protein level in colorectal cancer. BMC Cancer. 2010;10:203. doi: 10.1186/1471-2407-10-203. [Epub ahead of print] [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Miyake K, Imura S, Nishioka M, et al. Serum evaluation of soluble interferon-α/β receptor and high-sensitivity C-reactive protein for diagnosis of the patients with gastrointestinal and hepatobiliary-pancreatic cancer. Cytokine. 2010;49:251–255. doi: 10.1016/j.cyto.2009.09.010. [DOI] [PubMed] [Google Scholar]
- 9.Mahmoud FA, Rivera N. The role of C-reactive protein as a prognostic indicator in advanced cancer. Curr Oncol Rep. 2002;4:250–255. doi: 10.1007/s11912-002-0023-1. [DOI] [PubMed] [Google Scholar]
- 10.Ognjanovic S, Yamamoto J, Saltzman B, et al. Serum CRP and IL-6, genetic variants and risk of colorectal adenoma in a multiethnic population. Cancer Causes Control. 2010;21:1131–1138. doi: 10.1007/s10552-010-9540-7. [DOI] [PubMed] [Google Scholar]
- 11.Zhang SM, Buring JE, Lee I, Cook NR, Ridker PM. C-reactive protein levels are not associated with increased risk for colorectal cancer in women. Ann Med. 2005;142:425–432. doi: 10.7326/0003-4819-142-6-200503150-00008. [DOI] [PubMed] [Google Scholar]
- 12.Gunter MJ, Stolzenberg-Solomon R, Cross AJ, et al. A prospective study of serum C-reactive protein and colorectal cancer risk in men. Cancer Res. 2006;66:2483–2487. doi: 10.1158/0008-5472.CAN-05-3631. [DOI] [PubMed] [Google Scholar]
- 13.Trichopoulos D, Psaltopoulou T, Orfanos P, et al. Plasma C-reactive protein and risk of cancer: a prospective study from Greece. Cancer Epidemiol Biomarkers Prev. 2006;15:381–384. doi: 10.1158/1055-9965.EPI-05-0626. [DOI] [PubMed] [Google Scholar]
- 14.Jamieson NB, Glen P, McMillan DC, et al. Systematic inflammatory response predicts outcome in patients undergoing resection for ductal adenocarcinoma head of pancreas. Br J Cancer. 2005;92:21–23. doi: 10.1038/sj.bjc.6602305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Tingstedt B, Johansson P, Andersson B, Andersson R. Predictive factors in pancreatic ductal adenocarcinoma: role of inflammatory response. Scand J Gastroenterol. 2007;42:754–759. doi: 10.1080/00365520601058452. [DOI] [PubMed] [Google Scholar]
- 16.ATBC Cancer Prevention Study Group. The Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention Study: design, methods, participant characteristics, and compliance. Ann Epidemiol. 1994;4:1–10. doi: 10.1016/1047-2797(94)90036-1. [DOI] [PubMed] [Google Scholar]
- 17.Pietinen P, Hartman AM, Haapa E, et al. Reproducibility and validity of dietary assessment instruments. I. A self-administered food use questionnaire with a portion size picture booklet. Am J Epidemiol. 1988;128:655–666. doi: 10.1093/oxfordjournals.aje.a115013. [DOI] [PubMed] [Google Scholar]
- 18.Prorok PC, Andriole GL, Bresalier RS, et al. Design of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials. 2000;21:273S–309S. doi: 10.1016/s0197-2456(00)00098-2. [DOI] [PubMed] [Google Scholar]
- 19.Thompson FE, Kipnis V, Midthune D, et al. Performance of a food-frequency questionnaire in the US NIH-AARP (National Institutes of Health-American Association of Retired Persons) Diet and Health Study. Public Health Nutr. 2008;11:183–195. doi: 10.1017/S1368980007000419. [DOI] [PubMed] [Google Scholar]
- 20.Stolzenberg-Solomon RZ, Vieth R, Azad A, et al. A prospective nested case-control study of vitamin D status and pancreatic cancer risk in male smokers. Cancer Res. 2006;66:10213–10219. doi: 10.1158/0008-5472.CAN-06-1876. [DOI] [PubMed] [Google Scholar]
- 21.Stolzenberg-Solomon RZ, Weinstein S, Pollak M, et al. Prediagnostic adiponectin concentrations and pancreatic cancer risk in male smokers. Am J Epidemiol. 2008;168:1047–1055. doi: 10.1093/aje/kwn221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Stolzenberg-Solomon RZ, Hayes RB, Horst RL, Anderson KE, Hollis BW, Silverman DT. Serum vitamin D and risk of pancreatic cancer in the prostate, lung, colorectal, and ovarian cancer screening trial. Cancer Res. 2009;69:1439–1447. doi: 10.1158/0008-5472.CAN-08-2694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Korhonen P, Malila N, Pukkala E, et al. The Finnish Cancer Registry as follow-up source of a large trial cohort—accuracy and delay. Acta Oncol. 2002;41:381–388. doi: 10.1080/028418602760169442. [DOI] [PubMed] [Google Scholar]
- 24.Fears TR, Ziegler RG, Donaldson JL, et al. Reproducibility studies and interlaboratory concordance for androgen assays in female plasma. Cancer Epidemiol Biomarkers Prev. 2000;9:403–412. [PubMed] [Google Scholar]
- 25.De Onis M, Habicht JP. Anthropometric reference data for international use: recommendations from a World Health Organization Expert Committee. Am J Clin Nutr. 1996:650–658. doi: 10.1093/ajcn/64.4.650. [DOI] [PubMed] [Google Scholar]
- 26.Willett W, Stampfer MJ. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol. 1986;124:17–27. doi: 10.1093/oxfordjournals.aje.a114366. [DOI] [PubMed] [Google Scholar]
- 27.Anderson KE, Mack TM, Silverman DT. Cancer of the pancreas. In: Schottenfeld D, Fraumeni JF Jr, editors. Cancer epidemiology and prevention. New York: Oxford University Press: 2006. pp. 721–762. [Google Scholar]
- 28.Du Clos TW. Function of C-reactive protein. Ann Med. 2000;32:274–278. doi: 10.3109/07853890009011772. [DOI] [PubMed] [Google Scholar]
- 29.Gershov D, Kim S, Brot N, Elkon KB. C-reactive protein binds to apoptotic cells, protects the cells from assembly of the terminal complement components, and sustains and anti-inflammatory innate immune response: implications for systemic autoimmunity. J Exp Med. 2000;192:1353–1364. doi: 10.1084/jem.192.9.1353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.van der Vaart H, Postma DS, Timens W, et al. Acute effects of cigarette smoking on inflammation in healthy intermittent smokers. Respir Res. 2005;6:1–11. doi: 10.1186/1465-9921-6-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Chung HY, Kim HJ, Kim JW, Yu BP. The inflammation hypothesis and aging. Ann N Y Acad Sci. 2006;928:327–335. [PubMed] [Google Scholar]