Abstract
Adipokines are adipocyte-secreted hormones that may mediate the etiologic link between obesity and colorectal cancer; however, the evidence from large prospective studies is limited. We prospectively evaluated the association of plasma adiponectin and soluble leptin receptor (sOB-R) with colorectal cancer risk within the Nurses' Health Study (1990–2008) and the Health Professionals Follow-up Study (1994–2008) among 616 incident colorectal cancer cases and 1,205 controls selected using risk-set sampling and matched on age and date of blood draw. In unconditional logistic regression with adjustment for matching factors and multiple risk factors, plasma adiponectin was significantly associated with reduced risk of colorectal cancer among men, but not among women. Compared with men in the lowest quartile of adiponectin, men in the highest quartile had a relative risk (RR) for colorectal cancer of 0.55 [95% confidence interval (CI), 0.35–0.86; Ptrend = 0.02]. The corresponding RR in women was 0.96 (95% CI, 0.67–1.39; Ptrend = 0.74). Plasma sOB-R was not associated with overall colorectal cancer risk in either men or women. A significant heterogeneity was noted in the association between sOB-R and colorectal cancer by subsite in women (Pheterogeneity = 0.004); sOB-R was significantly associated with increased risk of rectal cancer but not colon cancer. These findings support a role for adiponectin in colorectal carcinogenesis in men. Further studies are warranted to confirm these associations and elucidate potential underlying mechanisms. Cancer Prev Res; 6(9); 875–85. ©2013 AACR.
Introduction
Obesity, particularly central adiposity, is an acknowledged risk factor for colorectal cancer (1). However, the etiologic mechanisms underlying this link have not been fully elucidated. Adipose tissue is an active endocrine organ that produces a range of hormones, collectively termed adipokines. Accumulating evidence suggests that some adipokines, namely adiponectin and leptin, might mediate the association between adiposity and colorectal cancer (2).
Adiponectin, a 30-kDa protein hormone predominantly secreted by white adipose tissues (3), circulates in humans as a trimer, hexamer, and high-molecular weight (HMW) form. Circulating adiponectin is negatively correlated with obesity (4). The mechanisms underlying the reduced levels of circulating adiponectin among obese individuals may involve the abnormal hormonal milieu, enhanced oxidative stress, and the proinflammatory state that prevails in obesity (5). Adiponectin also has significant anti-inflammatory and insulin-sensitizing effects (6). Both inflammation and insulin resistance have been suggested as potential mechanisms that underlie the association between obesity and colorectal cancer (7). In addition, adiponectin has direct anticarcinogenic effects including inhibition of cell growth and induction of apoptosis (8). Although a few epidemiologic studies have examined the association between circulating adiponectin and colorectal cancer, evidence remains inconclusive (9).
Another adipokine, leptin, is a pivotal regulator of energy balance with shown effects on neuroendocrine and immune function, and possibly carcinogenesis (10). Although leptin seems to have both mitogenic and antiapoptotic properties in colon cancer cell lines (11), it does not promote in vivo growth of colon tumors (12). These results suggest that other regulating factors might modulate the activity of leptin. Soluble leptin receptor (sOB-R, also known as LEPR), a principal circulating binding protein in humans, has been shown to regulate the bioavailability of free leptin (13). In humans, plasma sOB-R has been inversely associated with obesity, insulin resistance, and diabetes (14), each of which has been implicated in the etiology of colorectal cancer (15). The only prospective study examining sOB-R and colorectal cancer reported a significantly inverse association between circulating sOB-R and colorectal cancer risk, whereas no association was seen for leptin (16).
To extend these findings, we conducted a nested case–control study within two prospective cohort studies, the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS), and investigated the association of plasma adiponectin and sOB-R with risk of colorectal cancer. An earlier examination of plasma adiponectin in the HPFS observed a significant inverse association with colorectal cancer; however that analysis was limited by the number of cases (n = 179), short follow-up (n = 8 years), and lack of women. In the present study, we offer results that encompass both men and women over 13 to 18 years of follow-up and 616 documented colorectal cancer cases.
Materials and Methods
Study population
We drew participants from two prospective cohort studies: the NHS (started in 1976; n = 121,700 women of ages 30–55 years) and the HPFS (initiated in 1986; n = 51,529 men of ages 40–75 years). Detailed descriptions of the cohorts are provided elsewhere (17, 18). Briefly, in both cohorts, follow-up questionnaires were administered biennially to collect and update medical, lifestyle, and other health-related information; validated food frequency questionnaires (FFQ) were completed every 4 years to update dietary information. The follow-up rates exceeded 90% for both cohorts. We requested written permission to acquire medical records and pathology reports from participants who reported colorectal cancer. A study physician, blinded to exposure information, reviewed records to extract information on histologic type, anatomic location, and stage of the cancer.
Between 1989 and 1990, 32,826 women from the NHS and between 1993 and 1995, 18,225 men from the HPFS returned a blood specimen on ice packs by overnight courier. Our procedures for blood collection, handling, and storage have been previously summarized (19, 20). Among participants who provided plasma samples, we confirmed 360 colorectal cancer cases in the NHS after blood collection through October 1, 2008, and 287 incident colorectal cancer cases in the HPFS after blood draw through January 1, 2008. Using risk-set sampling, we randomly selected up to two controls for each case matched on age (within 2 years) and month/year of blood donation from eligible participants who were alive and free of cancer (except for nonmelanoma skin cancer) at the time of diagnosis of the colorectal cancer case. The study protocol was approved by the Institutional Review Board of the Brigham and Women's Hospital (Boston, MA).
Laboratory assays
Although this study was an extension to the previous study (19), we remeasured plasma adiponectin for all participants using ELISA from ALPCO Diagnostics (21). For sOB-R, we used the ELISA from R&D Systems, as previously described (14). Samples from case patients and their matched control participants were analyzed in the same batch. Quality controls samples were randomly interspersed among the case–control samples. Personnel blinded to quality control and case–control status conducted all assays. The interbatch coefficients of variation from quality control samples were 8.6% for adiponectin and 11.5% for sOB-R. In our previous study, we measured total adiponectin for a subset of participants using another ELISA (Linco Research; ref. 19), which had a correlation of 0.79 with the measurements using the present assay. Biomarkers were assayed in a single run in the HPFS, and measured in two runs in the NHS. To account for possible laboratory variation over time, we used the run-specific cutoff points for association analysis in the NHS.
Other biomarkers previously measured and described in detail elsewhere were used in the analysis of this study, including C-peptide, insulin-like growth factor (IGF)-I, IGF-binding protein (IGFBP)-3, high-sensitivity C-reactive protein (CRP), interleukin (IL)-6, the soluble TNF receptor 2 (sTNFR-2), and 25-hydroxyvitamin D [25(OH)D; refs. 22, 23].
Assessment of dietary and lifestyle factors
As in previous analyses (22), we used information collected from biennial questionnaires on major lifestyle factors for colorectal cancer, such as body weight, physical activity, smoking, family history of colorectal cancer, endoscopic screening, multivitamin use, and aspirin and nonsteroidal anti-inflammatory drug (NSAID) use. Body mass index (BMI) defined as weight in kilograms divided by the square of height in meters (kg/m2) was calculated to assess overall adiposity. In optional questions completed by 69% of the NHS women in 1986 and 65% of the HPFS men in 1987, we instructed participants to measure their waist at the umbilicus and their hips at the largest circumference between the waist and thighs while standing and without measuring over bulky clothing. We have previously shown that these self-reported measurements compared with technician measurements are reasonably accurate (24).
Using a previously validated assessment (25, 26), physical activity was calculated by summing the products of time spent at each recreational or leisure-time activity with the average metabolic equivalent (MET) for that activity. Dietary information was obtained from the validated FFQs administered in 1980, 1984, 1986, and 1990 in the NHS, and from the FFQs in 1986, 1990, and 1994 in the HPFS (27). To represent the overall dietary pattern, we calculated a summary score based on individual food intake for each participant according to the Dietary Approaches to Stop Hypertension (DASH) diet, which features high intakes of fruit, vegetables, legumes, and nuts; moderate amounts of low-fat dairy products; and low amounts of animal protein and sweets (28). Adherence to the DASH diet has been associated with reduced risk of colorectal cancer in the two cohorts (29).
Statistical analysis
We used the extreme Studentized deviate Many-Outlier procedure to identify statistical outliers in biomarker measurements (30). After excluding outliers and participants whose plasma failed in laboratory assays, we included 346 cases and 686 controls for the adiponectin analysis; 340 cases and 371 controls for the sOB-R analysis in the NHS, as we initially matched only one control for each case for sOB-R measurements. In the HPFS, a total of 270 cases and 519 controls were included for analyses of both adiponectin and sOB-R.
We compared mean (SD) and medians (interquartile ranges, IQR) of continuous variables for case and control participants using paired t test and Wilcoxon signed-rank test, respectively. We used conditional logistic regression to compare categorical variables. We calculated the age- and sex-adjusted Spearman partial correlation coefficients to assess the relationships of plasma adiponectin and sOB-R with lifestyle factors and other biomarkers among control participants.
We categorized the plasma markers into quartiles within each cohort on the basis of the distribution in the controls, and estimated relative risks (RR) and 95% confidence intervals (CI) for colorectal cancer using logistic regression. Tests for trend were conducted using the median value for each quartile as a continuous variable in the regression models. We obtained similar results using conditional logistic regression models or unconditional logistic regression models with adjustment for matching factors; we thus present the results from unconditional logistic regression because for subgroup analyses unconditional regression allows us to use all the controls and has enhanced power.
We then conducted multivariable analyses in men and women separately with adjustment for potential confounders, including family history of colorectal cancer, endoscopic screening, history of polyp, multivitamin use, smoking, alcohol consumption, physical activity, regular aspirin/NSAID use, plasma 25(OH)D, and DASH score. In women, we additionally adjusted for menopausal status and current postmenopausal hormone use. In sensitivity analyses, we also controlled for individual food or nutrient intake, including red meat, and energy-adjusted intake of folate, calcium, and total fiber, instead of DASH score. Because the results using DASH score or individual items were essentially the same, we used DASH score in our multivariate models to maximize statistical power. To better approximate long-term lifestyle and nutritional status, we used cumulative averages through the time of blood collection in our analyses. Missing information was carried forward from available information from prior questionnaires.
We conducted stratified analyses to evaluate whether observed associations varied by lifestyle factors or other markers. To test for multiplicative interaction, we included cross-product terms for stratification factors and biomarkers to our models. We also examined possible heterogeneity in the relationship between biomarkers and colorectal cancer according to cancer subsite using a polytomous logistic regression model. To calculate Pheterogeneity between case groups, we conducted a likelihood ratio test comparing the model in which the association with biomarkers was allowed to vary between the case groups to a model in which all the associations were held constant.
We used SAS version 9.2 (SAS Institute, Inc.) for all analyses with the exception of the polytomous logistic regression model, for which we used STATA version 11.0 (StataCorp). All statistical tests were two-sided and P < 0.05 was considered statistically significant.
Results
In both cohorts, colorectal cancer cases had a significantly higher waist circumference, and were less likely to use aspirin and consume folate than controls (Table 1). In women, compared with colorectal cancer patients, control participants tended to use postmenopausal hormone, had higher calcium intake and DASH score. In men, patients with colorectal cancer were more likely to have family history of colorectal cancer and to be obese than controls. Plasma concentrations of adiponectin and sOB-R significantly differed between cases and controls in men (P = 0.001), but not in women (P = 0.18 and 0.82, respectively). With respect to other plasma biomarkers, as compared with controls, colorectal cancer cases had significantly lower 25(OH)D levels.
Baseline characteristics of study participants in the NHS (1990) and the HPFS (1994)
. | Women . | Men . | ||||
---|---|---|---|---|---|---|
Baseline characteristics . | Cases (n = 346) . | Controls (n = 686) . | P . | Cases (n = 270) . | Controls (n = 519) . | P . |
Mean age at blood draw (SD), y | 59.0 (6.7) | 59.0 (6.7) | 0.57 | 65.8(8.3) | 65.7(8.3) | 0.28 |
Colorectal cancer in a parent or sibling, % | 13.9 | 12.0 | 0.43 | 19.6 | 13.9 | 0.03 |
History of previous endoscopy, % | 12.1 | 15.2 | 0.20 | 56. 7 | 67.1 | 0.004 |
History of polyp, % | 7.51 | 4.96 | 0.11 | 14.1 | 14.8 | 0.75 |
Postmenopausal, % | 85.8 | 86.9 | 0.51 | — | — | — |
Current use of hormones, %a | 38.1 | 46.3 | 0.03 | — | — | — |
Current multivitamin use, % | 34.8 | 38.9 | 0.22 | 47.4 | 52.0 | 0.26 |
Regular aspirin use (≥2 tablets/wk), %b | 38.4 | 46.1 | 0.02 | 41.9 | 48.6 | 0.05 |
Regular NSAID use (≥2 tablets/wk), % | 17.2 | 18.3 | 0.73 | 11.5 | 12.1 | 0.87 |
Current smoker, % | 14.5 | 12.3 | 0.31 | 5.00 | 4.87 | 0.95 |
Mean BMI (SD), kg/m2 | 26.0 (4.93) | 25.5 (4.72) | 0.13 | 26.2 (3.05) | 25.4 (2.71) | <0.001 |
Mean waist circumference (SD), inch | 31.9 (4.71) | 31.3 (4.35) | 0.05 | 38.6 (3.51) | 37.5 (3.32) | <0.001 |
Mean waist-to-hip ratio (SD) | 0.79 (0.09) | 0.78 (0.08) | 0.24 | 0.96 (0.05) | 0.94 (0.05) | <0.001 |
Mean physical activity (SD), MET-h/wk | 16.6 (19.0) | 16.9 (21.3) | 0.78 | 31.9 (27.0) | 31.0 (25.1) | 0.65 |
Mean daily intakes (SD) | ||||||
Alcohol, g | 5.67 (9.63) | 5.48 (9.44) | 0.75 | 12.3 (14.9) | 12.0 (14.8) | 0.71 |
Folate, μg | 416 (204) | 453 (239) | 0.01 | 494 (210) | 521 (228) | 0.09 |
Calcium, mg | 995 (553) | 1075 (565) | 0.04 | 920 (387) | 926 (342) | 0.67 |
Total fiber, g | 18.6 (5.80) | 18.9 (5.72) | 0.37 | 22.0 (6.33) | 22.6 (6.47) | 0.18 |
Red meat as main dish, servings | 0.30 (0.18) | 0.29 (0.17) | 0.48 | 0.27 (0.22) | 0.26 (0.18) | 0.52 |
Mean DASH score (SD) | 23.7 (4.14) | 24.4 (4.37) | 0.02 | 24.2 (4.62) | 24.7 (4.55) | 0.10 |
Median adiponectin (IQR), μg/mL | 8.04 (5.31–11.08) | 8.19 (5.85–10.64) | 0.18 | 4.99 (3.30–6.89) | 5.32 (3.71–7.70) | 0.001 |
Median sOB-R (IQR), ng/mLc | 32.6 (26.8–39.0) | 32.0 (27.2–39.7) | 0.82 | 25.1 (20.8–29.3) | 26.3 (21.9–31.5) | 0.001 |
Median CRP (IQR), mg/Lc | 1.52 (0.65–3.28) | 1.67 (0.71–3.61) | 0.004 | 1.34 (0.67–2.62) | 1.13 (0.60–2.21) | 0.96 |
Median IL-6 (IQR), pg/mLc | 1.16 (0.81–1.90) | 1.15 (0.78–1.79) | 0.27 | 1.60 (0.99–2.65) | 1.40 (0.94–2.26) | 0.54 |
Median sTNFR-2 (IQR), ng/mLc | 2.65 (2.24–3.14) | 2.58 (2.17–3.08) | 0.80 | 2.73 (2.34–3.22) | 2.73 (2.35–3.32) | 0.18 |
Median C-peptide (IQR), ng/mlc | 1.93 (1.34–2.84) | 1.82 (1.27–2.77) | 0.98 | 2.32 (1.60–3.49) | 2.09 (1.40–3.25) | 0.36 |
Median 25(OH)D (IQR), ng/mLc | 24.0 (17.5–30.0) | 26.1 (19.6–32.1) | <0.001 | 27.5 (22.4–33.4) | 28.8 (23.1–34.2) | 0.09 |
Median IGF-I/IGFBP-3 ratio (IQR)c | 0.15 (0.11–0.18) | 0.14 (0.11–0.18) | 0.08 | 0.13 (0.07–0.16) | 0.12 (0.07–1.16) | 0.19 |
. | Women . | Men . | ||||
---|---|---|---|---|---|---|
Baseline characteristics . | Cases (n = 346) . | Controls (n = 686) . | P . | Cases (n = 270) . | Controls (n = 519) . | P . |
Mean age at blood draw (SD), y | 59.0 (6.7) | 59.0 (6.7) | 0.57 | 65.8(8.3) | 65.7(8.3) | 0.28 |
Colorectal cancer in a parent or sibling, % | 13.9 | 12.0 | 0.43 | 19.6 | 13.9 | 0.03 |
History of previous endoscopy, % | 12.1 | 15.2 | 0.20 | 56. 7 | 67.1 | 0.004 |
History of polyp, % | 7.51 | 4.96 | 0.11 | 14.1 | 14.8 | 0.75 |
Postmenopausal, % | 85.8 | 86.9 | 0.51 | — | — | — |
Current use of hormones, %a | 38.1 | 46.3 | 0.03 | — | — | — |
Current multivitamin use, % | 34.8 | 38.9 | 0.22 | 47.4 | 52.0 | 0.26 |
Regular aspirin use (≥2 tablets/wk), %b | 38.4 | 46.1 | 0.02 | 41.9 | 48.6 | 0.05 |
Regular NSAID use (≥2 tablets/wk), % | 17.2 | 18.3 | 0.73 | 11.5 | 12.1 | 0.87 |
Current smoker, % | 14.5 | 12.3 | 0.31 | 5.00 | 4.87 | 0.95 |
Mean BMI (SD), kg/m2 | 26.0 (4.93) | 25.5 (4.72) | 0.13 | 26.2 (3.05) | 25.4 (2.71) | <0.001 |
Mean waist circumference (SD), inch | 31.9 (4.71) | 31.3 (4.35) | 0.05 | 38.6 (3.51) | 37.5 (3.32) | <0.001 |
Mean waist-to-hip ratio (SD) | 0.79 (0.09) | 0.78 (0.08) | 0.24 | 0.96 (0.05) | 0.94 (0.05) | <0.001 |
Mean physical activity (SD), MET-h/wk | 16.6 (19.0) | 16.9 (21.3) | 0.78 | 31.9 (27.0) | 31.0 (25.1) | 0.65 |
Mean daily intakes (SD) | ||||||
Alcohol, g | 5.67 (9.63) | 5.48 (9.44) | 0.75 | 12.3 (14.9) | 12.0 (14.8) | 0.71 |
Folate, μg | 416 (204) | 453 (239) | 0.01 | 494 (210) | 521 (228) | 0.09 |
Calcium, mg | 995 (553) | 1075 (565) | 0.04 | 920 (387) | 926 (342) | 0.67 |
Total fiber, g | 18.6 (5.80) | 18.9 (5.72) | 0.37 | 22.0 (6.33) | 22.6 (6.47) | 0.18 |
Red meat as main dish, servings | 0.30 (0.18) | 0.29 (0.17) | 0.48 | 0.27 (0.22) | 0.26 (0.18) | 0.52 |
Mean DASH score (SD) | 23.7 (4.14) | 24.4 (4.37) | 0.02 | 24.2 (4.62) | 24.7 (4.55) | 0.10 |
Median adiponectin (IQR), μg/mL | 8.04 (5.31–11.08) | 8.19 (5.85–10.64) | 0.18 | 4.99 (3.30–6.89) | 5.32 (3.71–7.70) | 0.001 |
Median sOB-R (IQR), ng/mLc | 32.6 (26.8–39.0) | 32.0 (27.2–39.7) | 0.82 | 25.1 (20.8–29.3) | 26.3 (21.9–31.5) | 0.001 |
Median CRP (IQR), mg/Lc | 1.52 (0.65–3.28) | 1.67 (0.71–3.61) | 0.004 | 1.34 (0.67–2.62) | 1.13 (0.60–2.21) | 0.96 |
Median IL-6 (IQR), pg/mLc | 1.16 (0.81–1.90) | 1.15 (0.78–1.79) | 0.27 | 1.60 (0.99–2.65) | 1.40 (0.94–2.26) | 0.54 |
Median sTNFR-2 (IQR), ng/mLc | 2.65 (2.24–3.14) | 2.58 (2.17–3.08) | 0.80 | 2.73 (2.34–3.22) | 2.73 (2.35–3.32) | 0.18 |
Median C-peptide (IQR), ng/mlc | 1.93 (1.34–2.84) | 1.82 (1.27–2.77) | 0.98 | 2.32 (1.60–3.49) | 2.09 (1.40–3.25) | 0.36 |
Median 25(OH)D (IQR), ng/mLc | 24.0 (17.5–30.0) | 26.1 (19.6–32.1) | <0.001 | 27.5 (22.4–33.4) | 28.8 (23.1–34.2) | 0.09 |
Median IGF-I/IGFBP-3 ratio (IQR)c | 0.15 (0.11–0.18) | 0.14 (0.11–0.18) | 0.08 | 0.13 (0.07–0.16) | 0.12 (0.07–1.16) | 0.19 |
aPercentage is among postmenopausal women.
bA standard tablet contains 325-mg aspirin.
cIn the NHS, 340 cases and 371 controls were available for sOB-R analysis, and some participants had missing values on the measurements of other biomarkers [3 women for IL-6, 3 women for sTNFR-2, 15 women for C-peptide, 22 women for 25(OH)D, and 21 women for IGF-I/IGFBP-3 ratio]. In the HPFS, there were 1, 3, and 1 men without measurements of CRP, C-peptide, and IGF-I/IGFBP-3 ratio, respectively.
As shown in Table 2, plasma levels of total adiponectin and sOB-R had a significantly positive correlation (r = 0.29 and 0.37 in women and men, respectively; P < 0.001). Both markers showed significantly inverse correlations with BMI, waist circumference, waist-to-hip ratio, inflammatory markers, and C-peptide. In contrast, physical activity, DASH score, and plasma 25(OH)D seemed to be positively correlated with adiponectin and sOB-R levels. Alcohol consumption was positively correlated with plasma adiponectin levels, whereas pack-years of smoking and IGF-I/IGFBP-3 ratio displayed inverse correlations with sOB-R. These correlations did not seem to differ by gender.
Age-adjusted Spearman partial correlation coefficients of plasma adiponectin and sOB-R with lifestyle factors and biomarkers among control participants in the NHS (1990) and the HPFS (1994)a
. | Adiponectin, μg/mL . | sOB-R, ng/mL . | ||
---|---|---|---|---|
Variable . | Women . | Men . | Women . | Men . |
sOB-R, ng/mL | 0.29b | 0.37b | — | — |
BMI, kg/m2 | −0.31b | −0.24b | −0.37b | −0.40b |
Waist circumference, inch | −0.32b | −0.21b | −0.33b | −0.38b |
Waist-to-hip ratio | −0.32b | −0.26b | −0.34b | −0.26b |
Physical activity, MET-h/wk | 0.05d | 0.06d | 0.09d | 0.15b |
Pack-years of smoking | −0.02d | −0.01d | −0.15c | −0.15b |
Alcohol consumption, g/d | 0.11c | 0.08 | −0.03d | <0.001d |
DASH score | 0.03d | 0.09d | 0.09d | 0.20b |
CRP, mg/L | −0.25b | −0.17b | −0.16b | −0.18b |
IL-6, pg/mL | −0.22b | −0.11c | −0.23b | −0.21b |
sTNFR-2, ng/mL | −0.14b | −0.11c | −0.19b | −0.11c |
25(OH)D, ng/mL | 0.09c | 0.08d | 0.03d | 0.14b |
C-peptide, ng/mL | −0.38b | −0.30b | −0.44b | −0.30b |
IGF-I/IGFBP-3 ratio | 0.01d | 0.02d | −0.25b | −0.08d |
. | Adiponectin, μg/mL . | sOB-R, ng/mL . | ||
---|---|---|---|---|
Variable . | Women . | Men . | Women . | Men . |
sOB-R, ng/mL | 0.29b | 0.37b | — | — |
BMI, kg/m2 | −0.31b | −0.24b | −0.37b | −0.40b |
Waist circumference, inch | −0.32b | −0.21b | −0.33b | −0.38b |
Waist-to-hip ratio | −0.32b | −0.26b | −0.34b | −0.26b |
Physical activity, MET-h/wk | 0.05d | 0.06d | 0.09d | 0.15b |
Pack-years of smoking | −0.02d | −0.01d | −0.15c | −0.15b |
Alcohol consumption, g/d | 0.11c | 0.08 | −0.03d | <0.001d |
DASH score | 0.03d | 0.09d | 0.09d | 0.20b |
CRP, mg/L | −0.25b | −0.17b | −0.16b | −0.18b |
IL-6, pg/mL | −0.22b | −0.11c | −0.23b | −0.21b |
sTNFR-2, ng/mL | −0.14b | −0.11c | −0.19b | −0.11c |
25(OH)D, ng/mL | 0.09c | 0.08d | 0.03d | 0.14b |
C-peptide, ng/mL | −0.38b | −0.30b | −0.44b | −0.30b |
IGF-I/IGFBP-3 ratio | 0.01d | 0.02d | −0.25b | −0.08d |
NOTE: MET = (caloric need/kilogram body weight per hour activity)/(caloric need/kilogram body weight per hour at rest).
aCorrelation analysis for biomarkers was restricted to the participants with measurement information available, as described in the footnote of Table 1.
bP < 0.001.
cP < 0.05.
dP ≥ 0.05.
Table 3 shows the associations of plasma adiponectin and sOB-R with risk of colorectal cancer in both cohorts. Adiponectin was not associated with colorectal cancer risk in women, but was significantly associated with reduced risk of colorectal cancer in men. After adjusting for matching factors and multiple risk factors for colorectal cancer, men in the highest quartile (Q4) of adiponectin had a 45% lower risk of colorectal cancer than those in the lowest quartile (Q1; 95% CI, 0.35–0.86; Ptrend = 0.02). Further adjustment for BMI did not essentially alter the results. However, adding waist circumference instead of BMI to the multivariate model attenuated the inverse association between adiponectin and colorectal cancer (for Q4 vs. Q1: multivariable RR, 0.78; 95% CI, 0.46–1.32; Ptrend = 0.39). For sOB-R, no association was observed with colorectal cancer risk in either women or men after adjustment for major risk factors of colorectal cancer including BMI (for Q4 vs. Q1: RR, 1.23, 95% CI, 0.77–1.97, Ptrend = 0.53 in women; RR, 0.77, 95% CI, 0.48–1.24, Ptrend = 0.33 in men).
RR of colorectal cancer according to plasma adiponectin and sOB-R in the NHS (1990–2008) and HPFS (1994–2008)
. | . | . | . | Model 1c . | Model 2d . | Model 3e . |
---|---|---|---|---|---|---|
Qa . | Medianb . | No. of cases . | No. of controls . | RR (95% CI) . | RR (95% CI) . | RR (95% CI) . |
Adiponectin, μg/mL | ||||||
Women | ||||||
Q1 | 4.56, 4.01 | 103 | 172 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 7.05, 7.28 | 77 | 172 | 0.75 (0.52–1.08) | 0.80 (0.55–1.17) | 0.82 (0.56–1.20) |
Q3 | 9.50, 9.07 | 74 | 171 | 0.72 (0.50–1.05) | 0.71 (0.49–1.04) | 0.74 (0.51–1.10) |
Q4 | 12.7, 12.5 | 92 | 171 | 0.90 (0.63–1.29) | 0.96 (0.67–1.39) | 1.01 (0.69–1.49) |
Ptrendf | 0.59 | 0.74 | 0.99 | |||
Men | ||||||
Q1 | 3.00 | 88 | 129 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 4.50 | 61 | 130 | 0.68 (0.46–1.03) | 0.69 (0.46–1.05) | 0.71 (0.47–1.08) |
Q3 | 6.18 | 75 | 130 | 0.83 (0.56–1.24) | 0.88 (0.59–1.33) | 0.92 (0.61–1.39) |
Q4 | 9.95 | 46 | 130 | 0.51 (0.33–0.79) | 0.55 (0.35–0.86) | 0.61 (0.38–0.96) |
Ptrendf | 0.007 | 0.02 | 0.07 | |||
sOB-R, ng/mL | ||||||
Women | ||||||
Q1 | 23.7, 25.7 | 84 | 94 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 29.0, 30.7 | 72 | 92 | 0.88 (0.57–1.35) | 0.89 (0.58–1.39) | 0.93 (0.60–1.45) |
Q3 | 34.6, 37.1 | 102 | 93 | 1.24 (0.82–1.87) | 1.32 (0.86–2.03) | 1.40 (0.91–2.18) |
Q4 | 44.8, 46.0 | 82 | 92 | 0.99 (0.65–1.51) | 1.11 (0.71–1.75) | 1.23 (0.77–1.97) |
Ptrendf | 0.79 | 0.81 | 0.53 | |||
Men | ||||||
Q1 | 19.4 | 83 | 129 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 24.2 | 70 | 130 | 0.83 (0.56–1.25) | 0.80 (0.53–1.21) | 0.85 (0.56–1.29) |
Q3 | 28.2 | 66 | 130 | 0.79 (0.52–1.18) | 0.80 (0.53–1.23) | 0.89 (0.58–1.38) |
Q4 | 36.2 | 51 | 130 | 0.61 (0.40–0.93) | 0.66 (0.42–1.04) | 0.77 (0.48–1.24) |
Ptrendf | 0.02 | 0.09 | 0.33 |
. | . | . | . | Model 1c . | Model 2d . | Model 3e . |
---|---|---|---|---|---|---|
Qa . | Medianb . | No. of cases . | No. of controls . | RR (95% CI) . | RR (95% CI) . | RR (95% CI) . |
Adiponectin, μg/mL | ||||||
Women | ||||||
Q1 | 4.56, 4.01 | 103 | 172 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 7.05, 7.28 | 77 | 172 | 0.75 (0.52–1.08) | 0.80 (0.55–1.17) | 0.82 (0.56–1.20) |
Q3 | 9.50, 9.07 | 74 | 171 | 0.72 (0.50–1.05) | 0.71 (0.49–1.04) | 0.74 (0.51–1.10) |
Q4 | 12.7, 12.5 | 92 | 171 | 0.90 (0.63–1.29) | 0.96 (0.67–1.39) | 1.01 (0.69–1.49) |
Ptrendf | 0.59 | 0.74 | 0.99 | |||
Men | ||||||
Q1 | 3.00 | 88 | 129 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 4.50 | 61 | 130 | 0.68 (0.46–1.03) | 0.69 (0.46–1.05) | 0.71 (0.47–1.08) |
Q3 | 6.18 | 75 | 130 | 0.83 (0.56–1.24) | 0.88 (0.59–1.33) | 0.92 (0.61–1.39) |
Q4 | 9.95 | 46 | 130 | 0.51 (0.33–0.79) | 0.55 (0.35–0.86) | 0.61 (0.38–0.96) |
Ptrendf | 0.007 | 0.02 | 0.07 | |||
sOB-R, ng/mL | ||||||
Women | ||||||
Q1 | 23.7, 25.7 | 84 | 94 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 29.0, 30.7 | 72 | 92 | 0.88 (0.57–1.35) | 0.89 (0.58–1.39) | 0.93 (0.60–1.45) |
Q3 | 34.6, 37.1 | 102 | 93 | 1.24 (0.82–1.87) | 1.32 (0.86–2.03) | 1.40 (0.91–2.18) |
Q4 | 44.8, 46.0 | 82 | 92 | 0.99 (0.65–1.51) | 1.11 (0.71–1.75) | 1.23 (0.77–1.97) |
Ptrendf | 0.79 | 0.81 | 0.53 | |||
Men | ||||||
Q1 | 19.4 | 83 | 129 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
Q2 | 24.2 | 70 | 130 | 0.83 (0.56–1.25) | 0.80 (0.53–1.21) | 0.85 (0.56–1.29) |
Q3 | 28.2 | 66 | 130 | 0.79 (0.52–1.18) | 0.80 (0.53–1.23) | 0.89 (0.58–1.38) |
Q4 | 36.2 | 51 | 130 | 0.61 (0.40–0.93) | 0.66 (0.42–1.04) | 0.77 (0.48–1.24) |
Ptrendf | 0.02 | 0.09 | 0.33 |
Abbreviation: Q, quartile.
aQuartiles and their medians of plasma markers were based on the distribution among the control participants. For the NHS in women, the run-specific cutoff points were used.
bFor the NHS in women, the median values of each quartile separately for the two runs (1990–2004, 2006–2008) are given.
cAdjusted for matching factors (age at blood draw and date of blood draw).
dAdjusted for matching factors (age at blood draw and date of blood draw), fasting status of blood collection, colorectal cancer in parent or sibling, prior lower gastrointestinal endoscopy, history of polyp, regular use of multivitamins, pack-years of smoking (never smoking, <10, 10–24, 25–49, ≥50 pack-years), alcohol consumption (0, 0–5, 5–15, >15g/d), physical activity (tertile, MET-h/wk), regular aspirin/NSAID use (≥2 tablets/wk), plasma 25(OH)D (tertile, ng/mL), and DASH score (quartile). In NHS, menopausal status and current postmenopausal hormone use were additionally adjusted.
eAdditionally adjusted for BMI (kg/m2).
fTests for trend were conducted using the median values for each quartile of analyte.
We also examined whether adjustment for other biomarkers may influence the associations of adiponectin and sOB-R with colorectal cancer risk in both cohorts. Among participants with available biomarker measurements, the associations for adiponectin and sOB-R remained essentially unchanged when plasma C-peptide, IGF-I/IGFBP3 ratio, CRP, IL-6, and sTNFR-2 were included in the multivariable model (model 3 in Table 3) individually or in combination. For example, in men the multivariable RRs of colorectal cancer comparing extreme quartiles of adiponectin were 0.64 (95% CI, 0.40–1.03; Ptrend = 0.13) after adjusting for C-peptide, and 0.63 (95% CI, 0.40–1.00; Ptrend = 0.11) after adjusting for IGF-I/IGFBP3 ratio.
We also investigated the joint associations for adiponectin and sOB-R, and did not observe any significant interaction between the two markers in either women or men (Pinteraction = 0.14 and 0.80, respectively; Supplementary Table S1). Compared with individuals in the lowest tertiles of both markers, those in the highest tertile of adiponectin and lowest tertile of sOB-R showed a substantially decreased risk of colorectal cancer. However, no further decrease was seen with increasing levels of sOB-R among those in the highest tertile of adiponectin.
We further conducted analyses by cancer site (Table 4). For adiponectin, no association was found with risk of either colon or rectal cancer in women (Pheterogeneity = 0.36). In contrast, in men the highest quartile of plasma adiponectin was significantly associated with reduced risk of colon cancer and nonsignificantly with rectal cancer. For sOB-R, we observed a significant difference in its association with cancer risk by subsite in women (Pheterogeneity = 0.004). A significantly increased risk of for Q4 compared with Q1 was found for rectal cancer but not for colon cancer. In contrast, sOB-R had no significant association with either colon or rectal cancer among men (Pheterogeneity = 0.72).
RR of colorectal cancer according to plasma adiponectin and sOB-R by cancer subsite, in the NHS (1990–2008) and HPFS (1994–2008)
. | Q1a . | Q2 . | Q3 . | Q4 . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
. | No. of cases . | No. of cases . | RR (95% CI)b . | No. of cases . | RR (95% CI)b . | No. of cases . | RR (95% CI)b . | Ptrendc . | Pheterogeneityd . |
Adiponectin | |||||||||
Women | 0.36 | ||||||||
Colon cancer | 86 | 56 | 0.72 (0.47–1.09) | 56 | 0.67 (0.44–1.02) | 75 | 0.95 (0.63–1.43) | 0.77 | |
Rectal cancer | 17 | 21 | 1.31 (0.65–2.65) | 18 | 1.18 (0.57–2.49) | 17 | 1.26 (0.59–2.71) | 0.59 | |
Men | 0.95 | ||||||||
Colon cancer | 68 | 48 | 0.72 (0.46–1.14) | 56 | 0.89 (0.57–1.39) | 35 | 0.60 (0.36–0.98) | 0.08 | |
Rectal cancer | 20 | 13 | 0.67 (0.32–1.41) | 19 | 1.03 (0.52–2.03) | 11 | 0.64 (0.29–1.40) | 0.41 | |
sOB-R | |||||||||
Women | 0.004 | ||||||||
Colon cancer | 72 | 61 | 0.88 (0.55–1.40) | 76 | 1.14 (0.71–1.81) | 58 | 0.91 (0.55–1.51) | 0.54 | |
Rectal cancer | 12 | 11 | 1.08 (0.44–2.66) | 26 | 3.22 (1.44–7.21) | 24 | 3.45 (1.47–8.07) | 0.005 | |
Men | 0.72 | ||||||||
Colon cancer | 67 | 53 | 0.79 (0.50–1.25) | 50 | 0.84 (0.52–1.34) | 37 | 0.69 (0.42–1.16) | 0.19 | |
Rectal cancer | 16 | 17 | 1.07 (0.51–2.23) | 16 | 1.12 (0.53–2.38) | 14 | 1.10 (0.50–2.41) | 0.80 |
. | Q1a . | Q2 . | Q3 . | Q4 . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
. | No. of cases . | No. of cases . | RR (95% CI)b . | No. of cases . | RR (95% CI)b . | No. of cases . | RR (95% CI)b . | Ptrendc . | Pheterogeneityd . |
Adiponectin | |||||||||
Women | 0.36 | ||||||||
Colon cancer | 86 | 56 | 0.72 (0.47–1.09) | 56 | 0.67 (0.44–1.02) | 75 | 0.95 (0.63–1.43) | 0.77 | |
Rectal cancer | 17 | 21 | 1.31 (0.65–2.65) | 18 | 1.18 (0.57–2.49) | 17 | 1.26 (0.59–2.71) | 0.59 | |
Men | 0.95 | ||||||||
Colon cancer | 68 | 48 | 0.72 (0.46–1.14) | 56 | 0.89 (0.57–1.39) | 35 | 0.60 (0.36–0.98) | 0.08 | |
Rectal cancer | 20 | 13 | 0.67 (0.32–1.41) | 19 | 1.03 (0.52–2.03) | 11 | 0.64 (0.29–1.40) | 0.41 | |
sOB-R | |||||||||
Women | 0.004 | ||||||||
Colon cancer | 72 | 61 | 0.88 (0.55–1.40) | 76 | 1.14 (0.71–1.81) | 58 | 0.91 (0.55–1.51) | 0.54 | |
Rectal cancer | 12 | 11 | 1.08 (0.44–2.66) | 26 | 3.22 (1.44–7.21) | 24 | 3.45 (1.47–8.07) | 0.005 | |
Men | 0.72 | ||||||||
Colon cancer | 67 | 53 | 0.79 (0.50–1.25) | 50 | 0.84 (0.52–1.34) | 37 | 0.69 (0.42–1.16) | 0.19 | |
Rectal cancer | 16 | 17 | 1.07 (0.51–2.23) | 16 | 1.12 (0.53–2.38) | 14 | 1.10 (0.50–2.41) | 0.80 |
Abbreviation: Q, quartile.
aReference category (RR = 1).
bAdjusted for the same variables as in model 3 in Table 3, but using polytomous logistic regression.
cTests for trend were conducted using the median values for each quartile of analyte.
dPheterogeneity for associations with colon versus rectal cancer was estimated using likelihood ratio test by comparing the polytomous logistic regression model constraining common effects for all variables on both outcomes to the model allowing the effect for analyte to vary by outcome.
We subsequently conducted analyses according to selected subgroups (Table 5). The relationship between adiponectin and colorectal cancer significantly differed by CRP levels among women (Pinteraction = 0.02), with a positive association in low CRP group and inverse association in high CRP group, although neither was statistically significant. In men, the inverse association between adiponectin and colorectal cancer seemed stronger among the subgroups that had lower physical activity levels, did not regularly use aspirin/NSAID, and had higher CRP or C-peptide levels (all Ptrend < 0.05). However, formal tests for interaction did not attain significance for any of these factors. Similarly, sOB-R was significantly associated with reduced colorectal cancer risk among men who did not regularly use aspirin/NSAID or had higher sTNFR-2 levels (all Ptrend < 0.05), although a test of interaction was not statistically significant.
RR of colorectal cancer associated with continuous log-transformed concentrations of plasma adiponectin and plasma sOB-R, by subgroups, in the NHS (1990–2008) and the HPFS (1994–2008)
. | Adiponectin . | sOB-R . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Women . | Men . | Women . | Men . | ||||||||
. | No. of cases . | No. of controls . | RR (95% CI)a . | Ptrendb . | No. of cases . | No. of controls . | RR (95% CI)a . | Ptrendb . | RR (95% CI)a . | Ptrendb . | RR (95% CI)a . | Ptrendb . |
BMIc,d | ||||||||||||
<25 | 182 | 389 | 1.08 (0.69–1.68) | 0.74 | 102 | 254 | 0.74 (0.44–1.24) | 0.25 | 1.05 (0.45–2.49) | 0.91 | 0.57 (0.20–1.64) | 0.30 |
≥25 | 164 | 297 | 0.99 (0.64–1.55) | 0.97 | 168 | 265 | 0.77 (0.50–1.20) | 0.25 | 1.71 (0.59–4.93) | 0.32 | 0.93 (0.40–2.15) | 0.86 |
Pinteractione | 0.60 | 0.69 | 0.89 | 0.38 | ||||||||
Physical activityd,f | ||||||||||||
<15 in women, <25 in men | 221 | 438 | 1.07 (0.73–1.57) | 0.72 | 132 | 257 | 0.62 (0.39–0.97) | 0.04 | 1.65 (0.75–3.65) | 0.21 | 0.44 (0.18–1.10) | 0.08 |
≥15 in women, ≥25 in men | 125 | 248 | 0.82 (0.49–1.39) | 0.47 | 138 | 262 | 0.83 (0.51–1.33) | 0.43 | 0.81 (0.27–2.43) | 0.71 | 0.72 (0.29–1.76) | 0.47 |
Pinteractione | 0.37 | 0.20 | 0.37 | 0.22 | ||||||||
Aspirin/NSAID use | ||||||||||||
Nonregular user | 187 | 321 | 1.00 (0.65–1.54) | 0.99 | 138 | 237 | 0.64 (0.41–1.00) | 0.05 | 1.41 (0.59–3.39) | 0.44 | 0.39 (0.16–0.95) | 0.04 |
Regular user | 159 | 365 | 1.02 (0.66–1.58) | 0.93 | 132 | 282 | 0.73 (0.46–1.16) | 0.18 | 1.11 (0.43–2.91) | 0.82 | 0.84 (0.34–2.08) | 0.71 |
Pinteractione | 0.95 | 0.90 | 0.78 | 0.43 | ||||||||
Median CRP, mg/L | ||||||||||||
<1.67 in women, <1.13 in men | 183 | 343 | 1.34 (0.85–2.10) | 0.20 | 117 | 259 | 0.94 (0.58–1.55) | 0.82 | 1.41 (0.59–3.35) | 0.44 | 0.66 (0.26–1.67) | 0.37 |
≥1.67 in women, ≥1.13 in men | 163 | 343 | 0.74 (0.47–1.15) | 0.18 | 153 | 259 | 0.60 (0.38–0.95) | 0.03 | 0.99 (0.37–2.66) | 0.99 | 0.53 (0.22–1.26) | 0.15 |
Pinteractione | 0.02 | 0.13 | 0.52 | 0.52 | ||||||||
Median IL-6, pg/mL | ||||||||||||
<1.15 in women, <1.40 in men | 169 | 342 | 0.90 (0.57–1.44) | 0.67 | 110 | 259 | 0.67 (0.40–1.12) | 0.13 | 1.07 (0.44–2.60) | 0.88 | 0.68 (0.25–1.83) | 0.44 |
≥1.15 in women, ≥1.40 in men | 176 | 342 | 1.07 (0.70–1.63) | 0.75 | 160 | 260 | 0.70 (0.45–1.07) | 0.10 | 1.40 (0.53–3.69) | 0.50 | 0.58 (0.25–1.37) | 0.22 |
Pinteractione | 0.61 | 0.83 | 0.80 | 0.81 | ||||||||
Median sTNFR-2, ng/mL | ||||||||||||
<2.58 in women, <2.73 in men | 152 | 343 | 1.25 (0.76–2.05) | 0.37 | 134 | 259 | 0.68 (0.42–1.11) | 0.12 | 0.97 (0.36–2.63) | 0.95 | 1.02 (0.40–2.61) | 0.97 |
≥2.58 in women, ≥2.73 in men | 191 | 343 | 0.96 (0.64–1.44) | 0.84 | 136 | 260 | 0.72 (0.46–1.12) | 0.14 | 1.66 (0.70–3.93) | 0.25 | 0.31 (0.13–0.75) | 0.009 |
Pinteractione | 0.44 | 0.99 | 0.82 | 0.17 | ||||||||
Median C-peptide, ng/mL | ||||||||||||
<1.82 in women, <2.09 in men | 153 | 337 | 1.07 (0.62–1.83) | 0.81 | 110 | 258 | 0.87 (0.52–1.44) | 0.59 | 0.86 (0.30–2.48) | 0.78 | 0.43 (0.15–1.20) | 0.11 |
≥1.82 in women, ≥2.09 in men | 190 | 337 | 1.04 (0.70–1.55) | 0.85 | 160 | 258 | 0.59 (0.37–0.94) | 0.03 | 1.77 (0.74–4.23) | 0.20 | 0.59 (0.25–1.39) | 0.23 |
Pinteractione | 0.78 | 0.15 | 0.20 | 0.87 |
. | Adiponectin . | sOB-R . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
. | Women . | Men . | Women . | Men . | ||||||||
. | No. of cases . | No. of controls . | RR (95% CI)a . | Ptrendb . | No. of cases . | No. of controls . | RR (95% CI)a . | Ptrendb . | RR (95% CI)a . | Ptrendb . | RR (95% CI)a . | Ptrendb . |
BMIc,d | ||||||||||||
<25 | 182 | 389 | 1.08 (0.69–1.68) | 0.74 | 102 | 254 | 0.74 (0.44–1.24) | 0.25 | 1.05 (0.45–2.49) | 0.91 | 0.57 (0.20–1.64) | 0.30 |
≥25 | 164 | 297 | 0.99 (0.64–1.55) | 0.97 | 168 | 265 | 0.77 (0.50–1.20) | 0.25 | 1.71 (0.59–4.93) | 0.32 | 0.93 (0.40–2.15) | 0.86 |
Pinteractione | 0.60 | 0.69 | 0.89 | 0.38 | ||||||||
Physical activityd,f | ||||||||||||
<15 in women, <25 in men | 221 | 438 | 1.07 (0.73–1.57) | 0.72 | 132 | 257 | 0.62 (0.39–0.97) | 0.04 | 1.65 (0.75–3.65) | 0.21 | 0.44 (0.18–1.10) | 0.08 |
≥15 in women, ≥25 in men | 125 | 248 | 0.82 (0.49–1.39) | 0.47 | 138 | 262 | 0.83 (0.51–1.33) | 0.43 | 0.81 (0.27–2.43) | 0.71 | 0.72 (0.29–1.76) | 0.47 |
Pinteractione | 0.37 | 0.20 | 0.37 | 0.22 | ||||||||
Aspirin/NSAID use | ||||||||||||
Nonregular user | 187 | 321 | 1.00 (0.65–1.54) | 0.99 | 138 | 237 | 0.64 (0.41–1.00) | 0.05 | 1.41 (0.59–3.39) | 0.44 | 0.39 (0.16–0.95) | 0.04 |
Regular user | 159 | 365 | 1.02 (0.66–1.58) | 0.93 | 132 | 282 | 0.73 (0.46–1.16) | 0.18 | 1.11 (0.43–2.91) | 0.82 | 0.84 (0.34–2.08) | 0.71 |
Pinteractione | 0.95 | 0.90 | 0.78 | 0.43 | ||||||||
Median CRP, mg/L | ||||||||||||
<1.67 in women, <1.13 in men | 183 | 343 | 1.34 (0.85–2.10) | 0.20 | 117 | 259 | 0.94 (0.58–1.55) | 0.82 | 1.41 (0.59–3.35) | 0.44 | 0.66 (0.26–1.67) | 0.37 |
≥1.67 in women, ≥1.13 in men | 163 | 343 | 0.74 (0.47–1.15) | 0.18 | 153 | 259 | 0.60 (0.38–0.95) | 0.03 | 0.99 (0.37–2.66) | 0.99 | 0.53 (0.22–1.26) | 0.15 |
Pinteractione | 0.02 | 0.13 | 0.52 | 0.52 | ||||||||
Median IL-6, pg/mL | ||||||||||||
<1.15 in women, <1.40 in men | 169 | 342 | 0.90 (0.57–1.44) | 0.67 | 110 | 259 | 0.67 (0.40–1.12) | 0.13 | 1.07 (0.44–2.60) | 0.88 | 0.68 (0.25–1.83) | 0.44 |
≥1.15 in women, ≥1.40 in men | 176 | 342 | 1.07 (0.70–1.63) | 0.75 | 160 | 260 | 0.70 (0.45–1.07) | 0.10 | 1.40 (0.53–3.69) | 0.50 | 0.58 (0.25–1.37) | 0.22 |
Pinteractione | 0.61 | 0.83 | 0.80 | 0.81 | ||||||||
Median sTNFR-2, ng/mL | ||||||||||||
<2.58 in women, <2.73 in men | 152 | 343 | 1.25 (0.76–2.05) | 0.37 | 134 | 259 | 0.68 (0.42–1.11) | 0.12 | 0.97 (0.36–2.63) | 0.95 | 1.02 (0.40–2.61) | 0.97 |
≥2.58 in women, ≥2.73 in men | 191 | 343 | 0.96 (0.64–1.44) | 0.84 | 136 | 260 | 0.72 (0.46–1.12) | 0.14 | 1.66 (0.70–3.93) | 0.25 | 0.31 (0.13–0.75) | 0.009 |
Pinteractione | 0.44 | 0.99 | 0.82 | 0.17 | ||||||||
Median C-peptide, ng/mL | ||||||||||||
<1.82 in women, <2.09 in men | 153 | 337 | 1.07 (0.62–1.83) | 0.81 | 110 | 258 | 0.87 (0.52–1.44) | 0.59 | 0.86 (0.30–2.48) | 0.78 | 0.43 (0.15–1.20) | 0.11 |
≥1.82 in women, ≥2.09 in men | 190 | 337 | 1.04 (0.70–1.55) | 0.85 | 160 | 258 | 0.59 (0.37–0.94) | 0.03 | 1.77 (0.74–4.23) | 0.20 | 0.59 (0.25–1.39) | 0.23 |
Pinteractione | 0.78 | 0.15 | 0.20 | 0.87 |
NOTE: MET = (caloric need/kilogram body weight per hour activity)/(caloric need/kilogram body weight per hour at rest).
aStratum-specific RRs and 95% CIs were estimated for the one-unit increase of log-transformed concentrations of adiponectin and sOB-R using logistic regression, with adjustment for the same covariates as in model 3 in Table 3. When stratified by regular aspirin/NSAID use, this variable was excluded from the multivariate model.
bTest for trend was conducted using the continuous log-transformed concentrations of adiponectin and sOB-R.
cWeight (kg)/height (m)2.
dWe further adjusted for the interacting variables in the continuous form to control for residual confounding in the stratified analysis.
eMultiplicative interaction was evaluated between log-transformed total adiponectin and sOB-R concentrations and the stratified variables in a multivariable-adjusted logistic regression.
fMET-h/wk.
In sensitivity analyses, excluding colorectal cancer cases diagnosed within the first 2 years after blood draw yielded similar results. For example, the multivariable-adjusted RRs (as in model 3 of Table 3) of colorectal cancer comparing extreme quartiles of adiponectin were 1.07 (95% CI, 0.72–1.59) in women and 0.59 (95% CI, 0.35–0.98) in men, respectively. Further exclusion of patients with colorectal cancer diagnosed within 4 years of blood collection did not essentially change the associations. The RRs of colorectal cancer comparing extreme quartiles of adiponectin were 1.02 (95% CI, 0.67–1.54) in women and 0.57 (95% CI, 0.33–1.00) in men, respectively. To assess the potential confounding effect of colorectal cancer screening, we restricted our analyses to participants without prior endoscopy, and the results remained unchanged (for Q4 vs. Q1 of adiponectin: RR, 0.89, 95% CI, 0.60–1.33 in women; RR, 0.43, 95% CI, 0.19–0.94 in men). Excluding those with prior polyps also did not affect our associations (for Q4 vs. Q1 of adiponectin: RR, 0.93, 95% CI, 0.63–1.36 in women; RR, 0.56, 95% CI, 0.34–0.92 in men). We also excluded participants with history of diabetes mellitus at baseline and the results did not materially change (data not shown).
Discussion
In this prospective case–control study nested within two large cohorts, we found that high plasma adiponectin was associated with reduced risk of colorectal cancer among men, but not among women. Plasma sOB-R had no association with overall colorectal cancer risk in either men or women, but was positively associated with female rectal cancer.
Previously, we reported an inverse association between plasma adiponectin and male colorectal cancer risk in the HPFS after 8 years of follow-up (19). In the present study, we documented an additional 91 cases after 14 years of follow-up, confirming our prior results. Adiponectin has been hypothesized to protect against carcinogenesis by influencing insulin sensitivity and the inflammatory state (2), both of which have been implicated in the etiology of colorectal cancer (7). However, in this study, results for adiponectin were essentially unchanged after adjustment for C-peptide, a marker for insulin secretion, and CRP, IL-6, or sTNF-R2, biomarkers of inflammation. These results suggest alternative mechanisms for adiponectin beyond its insulin-sensitizing and anti-inflammatory actions by which it may influence colorectal cancer risk.
Adiponectin circulates in different forms in plasma. Recent evidence suggests that HMW adiponectin is the active form of this hormone with respect to insulin sensitivity and has a stronger relationship with lower risk of diabetes (21). Thus, HMW adiponectin has been hypothesized to be more closely related to colorectal cancer risk than total adiponectin. Surprisingly, however, the European Prospective Investigation into Cancer and Nutrition Study (EPIC) found that non-HMW adiponectin rather than HMW adiponectin was significantly associated with reduced risk of colorectal cancer (31). These results further suggest that adiponectin may exert its anticarcinogenic effect through mechanisms other than modulation of insulin sensitivity. Experimental studies have reported that adiponectin could directly inhibit colorectal cancer cell growth and proliferation (8, 32), and induce endothelial apoptosis (33). In parallel with these lines of evidence, genetic association studies have shown that variants of adiponectin and its receptor genes are related to altered colorectal cancer risk (34, 35).
We did not find any significant association between total adiponectin and colorectal cancer incidence among women. In the EPIC study, the inverse association between total adiponectin and colorectal cancer risk was slightly stronger among men than among women (RR, 0.72 vs. 0.79), although non-HMW adiponectin was more strongly associated with reduced risk of colorectal cancer among women than among men (RR, 0.47 vs. 0.53; ref. 31). A prospective study on colorectal adenoma, a well-established precursor lesion of colorectal cancer, also found that the inverse association of total adiponectin was restricted to men (36). Given the potential heterogeneity by gender, it is possible that failing to take into account sex-specific associations might partially explain the overall null relationship between adiponectin and colorectal cancer observed in two other prospective studies (37, 38).
Although the exact mechanism responsible for such heterogeneity by sex remains to be elucidated, the generally higher levels of adiponectin in women may contribute to the null association. Studies have found that the gender difference in adiponectin concentrations is independent of fat mass or distribution, and may result from the influence of sex-steroid hormones (39). Adiponectin levels are high in hypogonadal men and reduced by testosterone administration in both men and murine models (40). Gonadectomy increases total circulating adiponectin, and this change is inversely associated with circulating androgen, but not estradiol (41). On the other hand, obesity has been consistently associated with colorectal cancer risk, albeit with a weaker magnitude of association among women compared with men. Endogenous sex hormones have been differentially related to colorectal cancer risk in men and women (42). Thus, the heterogeneity in the association between adiposity and adiponectin and risk of colorectal cancer according to sex may also reflect a distinct influence of altered estrogen and testosterone concentrations related to adiposity (43, 44).
By subsite analysis, we found the association of adiponectin with colon cancer was stronger than with rectal cancer. This finding was expected as metabolic factors such as abdominal fatness, physical inactivity, and hyperlipidemia were almost invariably associated with a larger increased risk for colon cancer than for rectal cancer (45). Consistent with the EPIC study, we also found the association between adiponectin and risk of colorectal cancer varied by CRP levels, such that the inverse relationship was stronger among individuals with high CRP levels than among those with low CRP concentrations. Because circulating CRP levels are an established marker of systematic inflammation and adiponectin possesses anti-inflammatory effect (2), it is possible that these results reflect a role for adiponectin in conferring a benefit against colorectal cancer development among people with chronic inflammatory states, but not in those with normal or low inflammation. Nevertheless, we did not find any interaction between adiponectin and other inflammatory markers than CRP. Thus, further studies are warranted to elucidate the potential influence of additional inflammatory cytokines on the anticarcinogenic effect of adiponectin.
For sOB-R, we did not find any relationship with overall colorectal cancer development in either men or women; however, a positive association was observed with risk of rectal cancer in women. sOB-R, either the product of alternatively spliced mRNA species or proteolytic cleavage products of membrane-bound forms of leptin receptor, contains only extracellular domains that bind to circulating leptin and perhaps regulate the bioavailability of free leptin (46). In addition, some studies suggest that reduced amounts of sOB-R may reflect decreased expression of membrane-anchored leptin receptor and that sOB-R directly block leptin action, thus contributing to leptin resistance in obese individuals (47). Although leptin has been involved in adiposity-induced insulin resistance, its effect on colorectal carcinogensis is unclear and epidemiologic evidence is inconsistent (16, 48, 49). For sOB-R, only one prospective study investigated its relationship to colorectal cancer risk and observed a significantly inverse association (16). In contrast, studies on genetic variations in leptin receptors did not find any association with colorectal cancer risk (50, 51). Given the sparse data and inconsistent findings, more investigations are needed to discern the complex effects of sOB-R and leptin on colorectal cancer development. With regard to obesity and female rectal cancer, a recent meta-analysis found that abdominal adiposity assessed by waist circumference was significantly associated with increased risk of rectal cancer in women, with the magnitude even larger than in men (52). However, this result failed to replicate in either of the two subsequent large studies in the U.S. and Chinese women (53, 54). Thus, further studies on sex-specific rectal cancer risk associated with obesity are warranted.
Our study has several strengths, including prospective blood collection, measurement of multiple biomarkers related to colorectal cancer, high follow-up rate of participants, and detailed information on covariates, which allowed us to adjust for potential confounding and evaluate possible effect modification. One limitation of the current study is the single measurement of plasma markers that may not represent their long-term levels. However, previous studies have shown that these markers are generally stable over time (55, 56). Other limitations include examination of only total adiponectin rather than specific forms of adiponectin, including HMW and non-HMW adiponectin, and sOB-R but not leptin.
In conclusion, in this prospective nested case–control study, plasma adiponectin was inversely associated with colorectal cancer risk in men but not women. This relationship was independent of BMI, inflammatory, and other metabolic biomarkers. Plasma sOB-R did not seem to be related to the overall risk of colorectal cancer.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Disclaimer
Certain data used in this publication were obtained from the Department of Public Health (DPH). The authors assume full responsibility for analyses and interpretation of these data.
Authors' Contributions
Conception and design: M. Song, C.S. Fuchs, A.T. Chan
Development of methodology: M. Song, C.S. Fuchs, E.L. Giovannucci, A.T. Chan
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Ogino, C.S. Fuchs, E.L. Giovannucci, A.T. Chan
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): M. Song, X. Zhang, S. Ogino, C.S. Fuchs, E.L. Giovannucci, A.T. Chan
Writing, review, and/or revision of the manuscript: M. Song, X. Zhang, K. Wu, S. Ogino, C.S. Fuchs, E.L. Giovannucci, A.T. Chan
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.T. Chan
Study supervision: K. Wu, C.S. Fuchs, A.T. Chan
Acknowledgments
The authors thank the participants and staff of the NHS and the HPFS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. In addition, this study was approved by the Connecticut DPH Human Investigations Committee.
Grant Support
This work was supported by U.S. NIH grants [P01 CA87969 (to S.E. Hankinson), P01 CA55075 (to W.C. Willett), UM1 CA167552 (to W.C. Willett), P50 CA127003 (to C.S. Fuchs), R01 CA151993 (to S. Ogino), K24 DK098311 (to A.T. Chan) and R01 CA137178 (to A.T. Chan)]. A.T. Chan is a Damon Runyon Clinical Investigator.
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