Abstract
Background
Experimental evidence suggests that an overexpression of insulin-like growth factor-I (IGF-I) is implicated in human pancreatic tumors. Increased IGF-II and decreased insulin-like growth factor binding protein-3 (IGFBP-3) serum concentrations have been linked to a number of other cancers.
Methods
We conducted a nested case-control study in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort of men and women 55–74 years of age at baseline, to test whether pre-diagnostic circulating IGF-I, IGF-II, IGFBP-3, andIGF-I/IGFBP-3 molar ratio concentrations were associated with exocrine pancreatic cancer risk. Between 1994 and 2006, 187 incident cases of pancreatic adenocarcinoma occurred (follow-up to 11.7 years). Two controls (n=374), who were alive at the time the case was diagnosed, were selected for each case and matched by age, race, sex and date of blood draw. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using conditional logistic regression, adjusting for smoking.
Results
IGF-I, IGF-II, and IGFBP-3 concentrations were not significantly associated with pancreatic cancer (highest compared with lowest quartile, OR=1.58, 95% CI 0.91–2.76, p-trend=0.25; OR=0.86, 95% CI 0.49–1.50, p-trend=0.31; and OR=0.88, 95% CI 0.51–1.51, p-trend=0.47, respectively). However, a significant positive trend was observed with high IGF-I/IGFBP-3 molar ratio levels (highest compared with lowest quartile OR=1.54, 95% CI 0.89–2.66, p-trend=0.04).
Conclusion
A higher IGF-I/IGFBP-3 molar ratio represents increased free IGF-I, which maybe a risk factor for pancreatic cancer.
Impact
Our results highlight the importance of this biomarker for further investigation in large prospective cohort studies and pooled analysis with other prospective cohorts.
Keywords: IGF-I, IGF-II, IGFBP-3, IGF-I/IGFBP-3, Pancreatic
Introduction
Insulin-like growth factors (IGFs) are peptide hormones that constitute a system important in regulating proliferation and apoptosis in many cell types, including epithelial cells in vitro. IGFs delay apoptosis through either an intrinsic or extrinsic pathway. Intrinsically, it is via the Ras/Raf/mitogen-activated protein kinase or phosphoinositide 3-kinase pathways. Extrinsically, IGFs antagonize cytokine signaling activation(1–5). The IGF axis includes IGF-I and IGF-II, their receptors, and six binding proteins (4). IGF-I is principally synthesized in the liver and regulated by growth hormone, although it can be produced locally(6). Moreover, IGF-II is primarily synthesized by the liver (7, 8). Approximately 80% of IGF-I binds to IGF-binding protein(IGFBP)-3, which forms a complex and limits the bioavailability of IGF-I to bind to the IGF-I receptor (9). IGF-I signaling is important in both cellular mutagenesis and human malignancies, and an overexpression of IGF-I is implicated in human pancreatic tumors(5, 10). Moreover, increased IGF-II and decreased IGFBP-3 concentrations are found in a number of cancers (10–13). Thus far, results from past prospective studies pertaining to the association between IGF-I, IGFBP-3, IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer have been inconsistent(8, 14–15). Moreover, one past study has found no association for IGF-II and pancreatic cancer (8).
Given the conflicting results from recent epidemiologic studies examining the IGF axis and pancreatic cancer, we conducted a nested case-control study in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, to test whether prediagnostic serum IGF-I, IGF-II, and IGFBP-3 concentrations and the IGF-I/IGFBP-3 molar ratio are associated with pancreatic cancer. We hypothesized that a higher IGF-I and IGF-II and lower IGFBP-3 concentration will be associated with increased pancreatic cancer risk. We also hypothesized that higher molar ratios of IGF-I/IGFBP-3, an estimator of bioactive IGF-I (16), would be found in pancreatic cancer cases in comparison to the controls.
Materials and Methods
Study design and population
The PLCO Cancer 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 (17). Briefly, it aimed to test the effectiveness of early detection procedures for prostate, lung, colorectal, and ovarian cancer on disease-specific mortality. The study recruitment and randomization began in November 1993 and was completed in July 2001. The cohort was comprised of 152,810 men and women aged 55 to 74 years old at baseline. Exclusion criteria included subjects that had a personal history of one of the four PLCO cancers or those currently undergoing treatment for any cancer, except for nonmelanoma of the skin, as well as those who had been screened for prostate or colorectal cancer during the past3 years. Participants were randomized to 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.
Case and control selection
Vital status of cohort participants and pancreatic cancer cases were identified by self-report in the annual mail-in survey, state cancer registries, death certificates, physician referrals, and reports from next of kin for deceased individuals. All medical and pathologic records related to pancreatic cancer diagnosis and supporting documentation was obtained and abstracted by trained medical record specialists for cancer confirmation. Incident primary adenocarcinoma of the exocrine pancreas (International Classification of Disease for Oncology, 3rd edition code C250–C259) was included in the statistical analysis. Endocrine pancreatic tumors (histology type, 8150, 8151, 8153, 8155, 8240) are rare and excluded because the etiology of these cancers is thought to be different from that of exocrine pancreas. There were one hundred eighty-seven incident cases of pancreatic adenocarcinomas between 1994 and 2006 (follow-up to 11.7 years; median, 5.4 years). One hundred seventy-three pancreatic cancer cases (92.5%) were confirmed through medical review. In sensitivity analyses, results were similar when the nonconfirmed cases were excluded; therefore, all cases were included in order to increase statistical power.
Controls, alive at the time the case was diagnosed, were randomly selected from all PLCO participants. Two controls were matched to each case by age (±5 years), race, sex, and calendar date of blood draw in 2-monthblocks.
Biomarkers
Nonfasting blood samples were collected in 10 mL red top blood tube at study year T0 from the intervention arm subjects and processed within 2 hours of collection, either on site or at a central processing laboratory, into fractions stored at −70°C. We sent our samples to Michael Pollak’s laboratory at Lady Davis Institute for Medical Research in Montreal Quebec, Canada. At the laboratory, serum concentrations of IGF-I, IGF-II, and IGFBP-3 were assayed by enzyme-linked immunosorbent assay (ELISA) with reagents from Diagnostic Systems Laboratory, part of Beckman Coulter (Brea, CA). Case and control samples were treated identically and were laboratory-blinded to case-control status. Matched serum case and control samples were analyzed consecutively as triplets within batches and blinded replicate “phantom” samples from two pooled 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 (18), the estimated overall (intrabatch and interbatch) coefficient of variation percents of the IGF-I, IGF-II, and IGFBP-3assays were 10.3%, 5.1%, and 5.1%, respectively.
Assessment of diet and baseline characteristics
Study subjects completed a self-administered baseline questionnaire that included medical history, family history of cancer, reproductive history, hormone and oral contraceptive use, tobacco use, use of selected drugs, height, weight, physical activity, and exposure to other risk factors at the initial screening exam. We assessed dietary intake using a self-administered 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).
Statistical analysis
The distribution of selected characteristics of cases and controls were compared using the Wilcoxon rank sum test for the continuous variables and χ2 tests for categorical variables (Table 1). Potential confounders were also identified by calculating means and proportions of baseline characteristics among the controls across IGF-I, IGF-II, and IGFBP-3 quartiles(Table 2). Potential confounders that were examined in analyses included age; smoking status (never, former, current) and history (number of cigarettes smoked per day, years smoked, pack-years, smoking cessation); education; baseline height, weight, body mass index (BMI); history of diabetes; 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)(20). Foods highly correlated with energy and dietary nutrients were energy adjusted using the residual method described by Willett and Stampfer (21).
Table 1.
Selected baseline characteristics of case and cohort control subjects (median and interdecile range or number and proportion)
| Characteristics | Cases (n = 187) | Controls (n = 374) | P* |
|---|---|---|---|
| Age (y) | 66 (59.0–72.0) | 66 (58.0–72.0) | 0.82 |
| Sex, male, n (%) | 121 (64.7) | 242 (64.7) | 0.96 |
| Serum IGF-I (ng/mL) | 184.7 (105.4–274.0) | 175.8 (95.2–271.4) | 0.5 |
| Serum IGF-II (ng/mL) | 1618.8 (1151.0–2228.0) | 1641.9 (1154.7–2179.9) | 0.43 |
| Serum IGFBP-3 (ng/mL) | 3564.3 (2644.4–4993.4) | 3700.0 (2544.8–4883.5) | 0.43 |
| Race, n (%) | |||
| White | 169 (90.4) | 338 (90.4) | 0.99 |
| 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) | 0.0002 |
| Former | 79 (42.3) | 174 (46.5) | |
| Former quit ≥15 y | 48 (25.7) | 112 (29.9) | |
| Former quit <15 y | 31 (16.6) | 62 (16.6) | |
| Current | 36 (19.3) | 26 (7.0) | |
| Height (cm) | |||
| Male | 177.8 (167.6–185.4) | 177.8 (170.2–185.4) | 0.74 |
| Female | 162.6 (152.4–170.2) | 162.6 (154.9–170.2) | 0.69 |
| BMI (kg/m2) | 26.7 (22.3–32.7) | 26.5 (22.0–32.9) | 0.18 |
| WHO cut points, n (%) | |||
| <25.0 | 57 (30.5) | 133 (35.6) | 0.29 |
| >25.0 and <30 | 84 (44.9) | 168 (44.9) | |
| ≥30 | 46 (24.6) | 73 (19.5) | |
| Medical history, n (%) | |||
| Self-reported diabetes mellitus | 22 (12.2) | 36 (9.9) | 0.41 |
| Family history of pancreatic cancer, n (%) | 7 (3.76) | 8 (2.15) | 0.27 |
| Education, n (%) | |||
| Less than high school | 14 (7.5) | 39 (10.4) | 0.64 |
| High school graduate | 48 (25.7) | 86 (23.0) | |
| Post-high school, vocational training | 22 (11.8) | 41 (11.0) | |
| Some college | 38 (20.3) | 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 (16.3–149.2) | 61.3 (19.4–165.7) | 0.79 |
| Alcohol (g) | |||
| 0–2 drinks | 163 (90.05) | 326 (91.06) | 0.87 |
| 3–5 drinks | 14 (7.74) | 25 (6.98) | |
| ≥6 drinks | 4 (2.21) | 7 (1.96) | |
| Nutrients | |||
| Energy (kcal) | 1,819 (1,074–2,957) | 1,976 (1,130–3,298) | 0.16 |
| Total fat (g) | 61.3 (28.1–100.3) | 73.1 (33.1–116.2) | 0.22 |
| Saturated fat (g) | 20.4 (9.8–33.8) | 21.0 (10.8–40.9) | 0.2 |
| Carbohydrate (g) | 247.5 (131.2–389.4) | 265.8 (157.4–423.6) | 0.07 |
| Protein (g) | 72.9 (42.0–126.4) | 77.3 (45.0–128.9) | 0.16 |
| Vigorous physical activity, hours per week,‡ n (%) | |||
| None or <1 h | 66 (38.2) | 113 (32.1) | |
| 1–3 h | 70 (40.5) | 146 (41.5) | |
| >4 h | 37 (21.4) | 93 (26.4) | 0.18 |
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 supplements and alcohol and based on n = 181 cases and 358 controls
Vigorous activity variables based on n = 173 cases and n = 352 controls
Table 2.
Selected characteristics of control subjects (means or proportions) by quartile of serum IGF-I concentration*
| Characteristics | Men | Women | ||||||
|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 | |
| Age (y) | 66.8 | 64.6 | 65.3 | 64.8 | 64.7 | 64.3 | 65.2 | 64.7 |
| Serum IGF-I (ng/mL) | 115.8 | 174.1 | 215.8 | 286 | 83.4 | 128.4 | 164.8 | 236.7 |
| Serum IGF-II (ng/mL) | 1248.6 | 1627.6 | 1725.8 | 1884.8 | 1409.8 | 1637.4 | 1866.9 | 2180.2 |
| Serum IGFBP-3 (ng/mL) | 2647.8 | 3530.8 | 3890.3 | 4408.3 | 2994.7 | 3657.6 | 4185.9 | 4974.5 |
| Serum IGF-I/IGFBP-3 Molar Ratio | 0.16 | 0.18 | 0.21 | 0.24 | 0.1 | 0.13 | 0.14 | 0.18 |
| Race (%) | ||||||||
| White | 89.2 | 86.7 | 91.4 | 89.8 | 82.9 | 100 | 94.4 | 93.6 |
| Smoking status (%) | ||||||||
| Never | 47.7 | 36.7 | 37.9 | 39 | 54.3 | 60 | 66.7 | 48.4 |
| Former | 46.1 | 56.6 | 48.3 | 57.6 | 40 | 33.3 | 27.8 | 45.1 |
| Former quit ≥15 y | 29.2 | 31.6 | 27.6 | 45.7 | 28.6 | 16.7 | 19.5 | 29 |
| Former quit <15 y | 16.9 | 25 | 20.7 | 11.9 | 11.4 | 16.6 | 8.3 | 16.1 |
| Current | 6.2 | 6.7 | 13.8 | 3.4 | 5.7 | 6.7 | 5.5 | 6.5 |
| Height (cm) | 178.2 | 176.3 | 178.2 | 178.4 | 161.7 | 164.2 | 162.3 | 162.2 |
| BMI (kg/m2) | 26.3 | 27.5 | 26.9 | 27.5 | 27.3 | 26.7 | 27.1 | 26.4 |
| WHO cut points (%) | ||||||||
| <25.0 | 38.5 | 28.3 | 34.5 | 23.7 | 37.2 | 50 | 41.7 | 45.2 |
| >25.0 and <30 | 46.1 | 48.3 | 43.1 | 61 | 37.1 | 30 | 41.7 | 35.5 |
| ≥30 | 15.4 | 23.4 | 22.4 | 15.3 | 25.7 | 20 | 16.6 | 19.3 |
| Medical history (%) | ||||||||
| Self-reported diabetes mellitus | 14.8 | 11.9 | 8.6 | 6.9 | 6.1 | 3.3 | 11.1 | 13.3 |
| Education (%) | ||||||||
| Less than high school | 9.2 | 11.7 | 10.3 | 18.6 | 0 | 13.3 | 11.1 | 3.3 |
| High school graduate | 15.4 | 16.7 | 13.8 | 25.4 | 31.4 | 23.4 | 44.4 | 29 |
| Post-high school, vocational training | 7.7 | 10 | 6.9 | 10.2 | 17.1 | 13.3 | 13.9 | 16.1 |
| Some college | 23.1 | 18.3 | 19 | 11.9 | 20 | 20 | 16.7 | 12.9 |
| College graduate | 23.1 | 23.3 | 15.5 | 16.9 | 14.3 | 16.7 | 5.6 | 16.1 |
| Post-college graduate | 21.5 | 20 | 34.5 | 17 | 17.1 | 13.3 | 8.3 | 22.6 |
| Dietary intake per day† | ||||||||
| Red meat (g) | 95.5 | 103.3 | 92.2 | 89.6 | 52.8 | 56.3 | 58.1 | 52 |
| Alcohol (g) | ||||||||
| 0–2 drinks | 80.7 | 76.3 | 89.7 | 94.8 | 97 | 96.6 | 100 | 100 |
| 3–5 drinks | 16.1 | 13.5 | 8.6 | 5.2 | 3 | 3.4 | 0 | 0 |
| ≥6 drinks | 3.2 | 10.2 | 1.7 | 0 | 0 | 0 | 0 | 0 |
| Nutrients | ||||||||
| Energy (kcal) | 2,277 | 2,583 | 2,266 | 2,202 | 1,722 | 1,673 | 1,844 | 1,677 |
| Total fat (g) | 78.2 | 86.2 | 75.7 | 77.6 | 53.1 | 50.9 | 59.5 | 52.6 |
| Saturated fat (g) | 27.2 | 30.1 | 25.6 | 27 | 18 | 16.8 | 19.8 | 17.42 |
| Carbohydrate (g) | 287.9 | 329 | 301.6 | 287.1 | 243.8 | 236.3 | 265.7 | 238.7 |
| Protein (g) | 89 | 96.6 | 88.8 | 89.3 | 71 | 67.8 | 71.8 | 68.1 |
| Vigorous physical activity, hours per week (%) | ||||||||
| None or <1 h | 27.9 | 33.9 | 29.8 | 37.5 | 21.9 | 51.8 | 21.9 | 34.6 |
| 1–3 h | 31.1 | 23.7 | 22.8 | 26.8 | 37.5 | 24.1 | 34.4 | 26.9 |
| >4 h | 41 | 42.4 | 47.4 | 35.7 | 40.6 | 24.1 | 43.7 | 38.5 |
Sex-specific quartiles based on IGF-I concentration cut-points observed separately for men and women in the study population at baseline
All dietary variables energy-adjusted except alcohol based on 348 controls with complete diet data
As the distribution for IGF-I was observed to be different for men and women in our data and that of others (9), we used sex-specific cut points in sex-stratified analyses and in the both sexes combined models (Tables 2–3). Conditional logistic regression was used to calculate odds ratios (ORs) for pancreatic cancer with subjects in the lowest quartile as the referent. Linear trends were tested using the p-value based on treating the median values within each quartile of the controls as a continuous variable. Continuous ORs were determined by standardizing to the average size of the two central quartiles. We also examined the molar ratio of IGF-I to IGFBP-3, a surrogate estimate of free IGF-I (1 ng/ml IGF-I=0.130 nM, 1 ng/ml IGFBP-3=0.036 nM). We individually added or subtracted covariates to the model in order to develop multivariable models using forward and backward approaches. Variables were retained in the model if they are associated with both the disease and the IGF-axis biomarker and changed the ORs>10%. Though no covariates changed the point estimate of the ORs>10%, smoking (never, former quit >15 years ago, former quit <15 years ago, current), an established risk factor for pancreatic cancer in our study, was included in our final model(22). To evaluate whether preclinical disease influenced the association between the IGF-I/IGFBP-3 molar ratio and pancreatic cancer, we conducted an analysis to compare risk for cases that occurred during the first 5 years and more than 5 years of follow-up.
Table 3.
Age- and variable-adjusted ORs and 95% Confidence Intervals of baseline serum IGF-I, IGF-II, IGFBP-3 and IGF-I/IGFBP-3 molar ratio concentrations and pancreatic cancer among 187 cases and 374 matched control samples
| Quartile of Serum IGF-I Concentration, ng/mL*§ | ||||||
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Men | <151.4 | >151.4 and <191.9 | >191.9 and <237.8 | >237.8 | ||
| Case/Controls (n) | 25/65 | 31/60 | 33/58 | 32/59 | ||
| Crude OR (95% CI)† | 1.00 (reference) | 1.41 (0.73–2.74) | 1.59 (0.81–3.12) | 1.51 (0.77–2.97) | 0.24 | 1.09 (0.94–1.26) |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 1.34 (0.68–2.65) | 1.39 (0.69–2.82) | 1.56 (0.78–3.14) | 0.22 | 1.09 (0.94–1.27) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Women | <106.4 | >106.4 and <144.1 | >144.1 and <186.8 | >186.8 | ||
| Case/Controls (n) | 14/35 | 20/30 | 14/36 | 18/31 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.82 (0.74–4.49) | 0.99 (0.41–2.36) | 1.58 (0.64–3.92) | 0.66 | 1.04 (0.84–1.27) |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 2.12 (0.82–5.45) | 0.95 (0.39–2.34) | 1.74 (0.67–4.51) | 0.68 | 1.03 (0.83–1.27) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Combined Characteristics | ||||||
| Case/Controls (n) | 39/100 | 51/89 | 46/95 | 51/90 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.53 (0.90–2.60) | 1.29 (0.76–2.19) | 1.53 (0.89–2.62) | 0.23 | |
| Smoking-adjusted OR (95%CI) ‡ | 1.00 (reference) | 1.57 (0.91–2.71) | 1.19 (0.69–2.06) | 1.58 (0.91–2.76) | 0.25 | |
| Quartile of Serum IGFBP-3 Concentration, ng/mL§ | ||||||
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Men | <2977.6 | >2977.6 and <3526.6 | >3526.6 and <4181.2 | >4181.2 | ||
| Case/Controls (n) | 28/62 | 36/55 | 29/62 | 28/63 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.44 (0.77–2.69) | 1.04 (0.55–1.96) | 0.98 (0.50–1.95) | 0.7 | 1.01 (0.86–1.18_ |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 1.55 (0.81–2.96) | 0.96 (0.50–1.86) | 1.03 (0.51–2.09) | 0.73 | 1.01 (0.86–1.19) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Women | <3236.8 | >3236.8 and <3922.7 | >3922.7 and <4481.0 | >4481.0 | ||
| Case/Controls (n) | 17/32 | 16/34 | 17/33 | 16/33 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 0.89 (0.38–2.08) | 0.97 (0.41–2.31) | 0.92 (0.41–2.07) | 0.88 | 0.96 (0.79–1.17) |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 0.78 (0.33–1.87) | 0.92 (0.38–2.28) | 0.77 (0.32–1.83) | 0.63 | 0.94 (0.76–1.15) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Combined Characteristics | ||||||
| Case/Controls (n) | 45/92 | 52/91 | 46/94 | 44/97 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.17 (0.71–1.93) | 1.00 (0.60–1.68) | 0.92 (0.55–1.56) | 0.62 | |
| Smoking-adjusted OR (95%CI) ‡ | 1.00 (reference) | 1.16 (0.69–1.95) | 0.95 (0.55–1.61) | 0.88 (0.51–1.51) | 0.47 | |
| Quartile of Serum IGF-II Concentration, ng/mL§ | ||||||
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Men | <1339.3 | >1339.3 and <1576.6 | >1576.6 and <1831.8 | >1831.8 | ||
| Case/Controls (n) | 29/61 | 37/54 | 31/60 | 24/67 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.40 (0.76–2.60) | 1.07 (0.56–2.06) | 0.70 (0.34–1.42) | 0.2 | 0.99 (0.85–1.15) |
| Smoking-adjusted OR (95% CI)‡ | 1.00 (reference) | 1.40 (0.74–2.65) | 1.01 (0.51–1.99) | 0.75 (0.36–1.56) | 0.29 | 0.99 (0.86–1.15) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Women | <1452.5 | >1452.5 and <1741.8 | >1741.8 and <1999.7 | >1999.7 | ||
| Case/Controls (n) | 14/35 | 21/29 | 15/35 | 16/33 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 1.79 (0.77–4.16) | 1.08 (0.44–2.63) | 1.21 (0.51–2.86) | 0.32 | |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 1.75 (0.73–4.17) | 1.05 (0.43–2.60) | 0.86 (0.49–1.50) | 0.31 | |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Combined Characteristics | ||||||
| Case/Controls (n) | 43/95 | 58/83 | 46/96 | 40/100 | ||
| Crude OR (95% CI) | 1.00 (reference) | 1.52 (0.92–2.51) | 1.05 (0.62–1.77) | 0.87 (0.50–1.49) | 0.32 | |
| Smoking-adjusted OR (95%CI) ‡ | 1.00 (reference) | 1.49 (0.89–2.49) | 1.01 (0.59–1.72) | 0.86 (0.49–1.50) | 0.31 | |
| Quartile of Serum IGF-I/IGFBP-3 Concentration, ng/mL§ | ||||||
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Men | ≤0.17 | >0.17 and ≤0.20 | >0.20 and ≤0.23 | >0.23 | ||
| Case/Controls (n) | 27/63 | 23/68 | 38/53 | 33/58 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 0.79 (0.39–1.58) | 1.68 (0.89–3.19) | 1.41 (0.72–2.77) | 0.1 | |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 0.66 (0.32–1.36) | 1.52 (0.78–2.96) | 1.39 (0.69–2.80) | 0.1 | |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Women | ≤0.12 | >0.12 and ≤0.13 | >0.13 and ≤0.16 | >0.16 | ||
| Case/Controls (n) | 16/33 | 13/37 | 18/32 | 19/30 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 0.72 (0.29–1.79) | 1.23 (0.49–3.11) | 1.42 (0.57–3.53) | 0.33 | 1.03 (0.98–1.09) |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 0.60 (0.23–1.52) | 1.15 (0.45–2.94) | 1.47 (0.58–3.75) | 0.29 | 1.04 (0.98–1.10) |
| Q1 | Q2 | Q3 | Q4 | Ptrend | Continuous | |
| Combined Characteristics | ||||||
| Case/Controls (n) | 44/106 | 30/82 | 61/97 | 52/89 | ||
| Crude OR (95% CI) † | 1.00 (reference) | 0.89 (0.50–1.59) | 1.56 (0.50–1.59) | 1.51 (0.88–2.57) | 0.04 | |
| Smoking-adjusted OR (95% CI) ‡ | 1.00 (reference) | 0.79 (0.43–1.44) | 1.44 (0.86–2.41) | 1.54 (0.89–2.66) | 0.04 | |
Sex-specific quartiles based on IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ratio concentration cut-points observed separately for men and women in the study population at baseline
Crude OR adjusted for matching variables (age, race, sex, and date of blood draw based on 2-month blocks).
Adjusted for smoking (never, former quit > 15 years ago, former quit < 15 years ago, current).
There was no interaction by sex for IGF-I, IGFBP-3, IGF-II, and IGF-I/IGFBP-3 molar ratio (p=0.94, p=0.84, p=0.72, and p=0.84, respectively)
All statistical analyses were performed using Statistical Analytic Systems software(SAS Institute Inc., Cary, North Carolina), and statistical tests were two tailed.
Results
Cases and controls had similar baseline characteristics (Table 1), although more cases than controls reported being a current smoker (P = 0.0002). There was no significant difference between serum IGF-I, IGF-II, and IGFBP-3 concentrations between cases and controls(P >0.4). Spearman’s correlation coefficients demonstrated a significant positive correlation of IGF-I with IGF-II(r=0.52, P<0.0001), IGFBP-3(r=0.63, P<0.0001), and IGF-I/IGFBP-3 molar ratio(r=0.74, P<0.0001). IGF-II was significantly positively correlated toIGFBP-3 (r=0.93, P<0.0001) but significantly inversely correlated toIGF-I/IGFBP-3 molar ratio (r=−0.10, P=0.02).
Table 2 shows the sex-specific means and proportions of selected characteristics among control participants according to quartile of serum IGF-I concentration. Compared to that of women, the distribution for IGF-I was higher for men; however, the distribution of IGF-II and IGFBP-3 was higher among women. For both men and women, increasing IGF-I concentration was directly related to higher IGF-II and IGFBP-3 concentrations and higher IGF-I/IGFBP-3 molar ratios. Higher IGF-I concentrations corresponded to less alcohol use (P <0.05) in men while no significant associations were apparent in women (Table 2). In addition, higher IGF-I/IGFBP-3 molar ratios were directly associated with being overweight (BMI ≥25 and <30) and less alcohol use (P trend <0.05)among men and did not significantly correspond to any characteristic among women. Although not significant in either sex, a higher IGF-I/IGFBP-3 molar ratio tended to be inversely associated with obesity.
Higher IGF-I concentrations tended to be positively but not significantly associated with pancreatic cancer in sex stratified or combined models (Table 4) while higher levels of IGFBP-3 were not associated with pancreatic cancer; however, higher compared to lower IGF-I/IGFBP-3 molar ratio concentrations tended to be positively associated with pancreatic cancer for both men (OR, 1.39; 95%CI, 0.69–2.80) and women (OR, 1.47; 95% CI, 0.58–3.75) in the smoking-adjusted model with the sex combined models showing a significant trend across quartiles in both the crude and smoking-adjusted models (P trend = 0.04and P trend = 0.04, respectively). Insulin-like growth factor II was not associated with pancreatic cancer in any models.
The association betweenIGF-I/IGFBP-3 molar ratio and pancreatic cancer was similar in time stratified analysis(i.e. first 5 years, continuous OR= 1.40, 95% CI, 0.49–3.99; P = 0.10and greater than or equal to 5 year OR=1.43, 95% CI, 0.59–3.44, P = 0.66). Moreover, the association for IGF-II and for IGFBP–3 with pancreatic cancer had no clear difference between short and long follow-up (first 5 years, continuous OR=1.03, 95% CI, 0.38–2.82, P =0.95 and OR=0.79, 95% CI, 0.29–2.15, P = 0.64, respectively; and greater than or equal to 5 year OR=1.00, 95% CI, 0.39–2.57, P = 0.99 and OR=1.14, 95% CI, 0.44–2.95, P = 0.78, respectively). In contrast, the association between IGF-I and pancreatic cancer was stronger among cases with shorter follow-up(first 5 years, continuous OR=2.28, 95% CI, 0.71–7.30; P = 0.17and greater than or equal to 5 year OR=1.51, 95% CI, 0.65–3.54, P = 0.34), although none of these risk estimates reached statistical significance. There was no significant interaction by sex for IGF-I, IGFBP3, IGF-II, and IGF-I/IGFBP-3 molar ratio (p=0.94, p=0.84, p=0.72, and p=0.84, respectively).
Discussion
We observed a significant trend across quartile sin the association between IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer. The highest compared with lowest quartileIGF-I/IGFBP-3 molar ratio concentrations yielded a non-significant 54% increase in pancreatic cancer risk. We observed a non-significant positive association for IGF-I concentrations but did not observe associations for IGF-II or IGFBP-3 concentrations and pancreatic cancer in this nested case-control study of men and women.
Three previous epidemiologic studies have investigated IGF axis analytes measured in peripheral blood and risk of pancreatic cancer with inconsistent study results. Consistent with our study, another smaller nested case-control study (n = 69 cases) within the Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC) (14) showed a non-significant 131% increased pancreatic cancer risk with IGF-I. In addition, the JACC study found an increased pancreatic cancer risk with increasing levels of serum IGFBP-3, which differs from the non-significant results from our study. One other small prospective case-control study (n=93 cases) from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study with follow-up to 12.7 years examined the incidence of pancreatic cancer among male smokers(15). All participants in the study were alive and free of cancer after 5 years of follow-up. Although the authors found no association between IGF-I, IGFBP-3, IGF-I/IGFBP-3 molar ratio, and risk of pancreatic cancer, IGF-I and IGFBP-3 showed non-significant protective associations, but the confounding effect of smoking could not be taken into account. Finally, a meta-analysis of four combined nested case-control studies from the Nurses’ Health Study, the Health Professionals Follow-up Study, the Physicians’ Health Study, and the Women’s Health Initiative showed no association between IGF-I, IGF-II, IGFBP-3, IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer (8). This study had a similar number of cases (n = 212); however, in contrast to our study, the authors found a non-significant protective association between both IGF-I and the IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer.
The possible mechanism surrounding IGF-I andIGFBP-3 and risk of pancreatic cancer has been reviewed in detail (23). Increases in IGF-I levels enhance IGF-I bioactivity while higher IGFBP-3 levels decrease it. Growth hormone (GH) stimulates total IGF-I but also total IGFBP-3, although the IGF-I/IGFBP-3 molar ratio increases. GH stimulation, in turn, is regulated by insulin, which is itself influenced by other exogenous factors (24–26). For example, obesity as a result of chronic high energy intake is a determinant of insulin resistance and hyperinsulinemia. IGF-I and its receptor, IGF-IR, are abundant in pancreatic cancer cell lines, and signaling through IGF-IR leads to diminished apoptosis and increased proliferation (13, 27–30).
Strengths of this study are that it is prospective with serum IGF-I, IGF-II, and IGFBP-3 being measured in blood samples of participants collected before cancer diagnosis, which decreases the likelihood of reverse causation. In addition, risks seemed to be similar in analyses stratified by time. Our study is larger than two of the previous studies and has internal validity since cases and controls are taken from the same cohort. Residual confounding by smoking is not likely because there were few current smokers in our study. In addition, we found a weak association between former smoking and pancreatic cancer in our study (former compared with never smoker OR, 1.26; 95% CI, 0.52–3.08). Our results may be considered generalizable to men and women as well as never, former, and current smokers that are similar to the population that participated in the PLCO study.
Weaknesses of this study include that the follow-up of the PLCO cohort is relatively short (up to 11.7 years; median, 5.4 years). Moreover, IGF concentrations change during a lifetime (31) and a single measurement of serum IGF-I, IGF-II, and IGFBP-3 may not reflect lifetime IGF status(32). As in the previous studies, our study had relatively small sample sizes, limiting the power to observe an association if one exists. There is also the possibility that unknown correlates to serum IGF status may exist and could explain the association between IGFs and risk of pancreatic cancer.
In conclusion, we observed a positive significant trend among those with higher levels of IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer and confirmed the non-significant associations for IGF-I, IGF-II, IGFBP-3, and risk of pancreatic cancer from previous studies. Larger prospective studies, as well as pooled analysis with other prospective cohorts, are necessary to foster more conclusive evidence for the IGF axis analytes and pancreatic cancer development.
Supplementary Material
Acknowledgments
Acknowledgment 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
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