PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of jnciLink to Publisher's site
 
J Natl Cancer Inst. Nov 21, 2012; 104(22): 1702–1711.
Published online Nov 20, 2012. doi:  10.1093/jnci/djs399
PMCID: PMC3502194
Dietary Glycemic Load and Cancer Recurrence and Survival in Patients with Stage III Colon Cancer: Findings From CALGB 89803
Jeffrey A. Meyerhardt,corresponding author Kaori Sato, Donna Niedzwiecki, Cynthia Ye, Leonard B. Saltz, Robert J. Mayer, Rex B. Mowat, Renaud Whittom, Alexander Hantel, Al Benson, Devin S. Wigler, Alan Venook, and Charles S. Fuchs
Affiliations of authors:Dana-Farber Cancer Institute, Boston, MA (JAM, KS, RJM, DSW, CSF); Cancer and Leukemia Group B Statistical Center, Duke University Medical Center, Durham, NC (DN, CY); Memorial Sloan-Kettering Cancer Center, New York, NY (LBS); Toledo Community Hospital Oncology Program, Toledo, OH (RBM); Hôpital du Sacré-Coeur de Montréal, Montreal, QC, Canada (RW); Loyola University Stritch School of Medicine, Maywood, IL (AH); Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL (AB); University of California at San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, CA (AV).
corresponding authorCorresponding author.
Correspondence to: Jeffrey A. Meyerhardt, MD, MPH, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215 (e-mail: jmeyerhardt/at/partners.org).
Received April 6, 2012; Revised August 6, 2012; Accepted August 13, 2012.
Background
The influence of glycemic load and related measures on survival among colon cancer patients remains largely unknown.
Methods
We conducted a prospective, observational study of 1011 stage III colon cancer patients reporting dietary intake during and 6 months after participation in an adjuvant chemotherapy trial. We examined the influence of glycemic load, glycemic index, fructose, and carbohydrate intakes on cancer recurrence and mortality using Cox proportional hazards regression; all tests of statistical significance were two-sided.
Results
Stage III colon cancer patients in the highest quintile of dietary glycemic load experienced an adjusted hazard ratio (HR) for disease-free survival of 1.79 (95% confidence interval [CI] = 1.29 to 2.48), compared with those in the lowest quintile (P trend across quintiles <.001). Increased glycemic load was associated with similar detriments in recurrence-free (P trend across quintiles <.001) and overall survival (P trend across quintiles <.001). These associations differed statistically significant by body mass index (BMI) (P interaction =.01). Whereas glycemic load was not associated with disease-free survival in patients with BMI < 25kg/m2, higher glycemic load was statistically significant associated with worse disease-free survival among overweight or obese participants (BMI ≥ 25kg/m2; HR = 2.26; 95% CI = 1.53 to 3.32; P trend across quintiles <.001). Increasing total carbohydrate intake was similarly associated with inferior disease-free, recurrence-free, and overall survival (P trend across quintiles <.001).
Conclusion
Higher dietary glycemic load and total carbohydrate intake were statistically significant associated with an increased risk of recurrence and mortality in stage III colon cancer patients. These findings support the role of energy balance factors in colon cancer progression and may offer potential opportunities to improve patient survival.
Preclinical and clinical evidence indicates that hyperinsulinemia may play an important role in the development of colorectal cancer (1,2). Many of the established risk factors of colorectal cancer, including obesity (3,4) and physical inactivity (5), directly influence insulin levels. Other studies have observed elevated risks of colon cancer among those with a history of type 2 diabetes mellitus (6) or elevated blood insulin or plasma C-peptide levels (7–10).
Diet influences systemic insulin levels. Foods with low glucose indexes have reduced serum insulin and glucose responses, compared with foods with high indexes. The physiological response to carbohydrates can be quantified by the glycemic index, a qualitative assessment of foods calculated as a percentage of the body’s plasma glucose response to specific foods compared with the response induced by the same amount of carbohydrate from a standard carbohydrate source, usually white bread or pure glucose (11,12). The glycemic load is calculated by multiplying the carbohydrate index of each food by its glycemic index and the frequency of consumption, providing a qualitative and quantitative measurement (13). Glycemic load and other carbohydrate measures have been associated with the risk of developing colorectal cancer in some (14–17), but not all (18–21), studies.
Recent studies have found a direct association between host factors leading to hyperinsulemia and cancer recurrences and mortality in colorectal cancer survivors (22–28). In a study of stage III colon cancer patients, the highest quintile of consumption of a Western pattern diet (characterized by high intakes of meat, fat, refined grains, and sugar desserts) was associated with a three-fold increase in cancer recurrence and deaths compared with the lowest quintile (27). To further understand which component of a Western pattern diet is associated with poorer outcomes and define the impact of dietary glycemic measures on colon cancer survival, we prospectively examined the association between dietary glycemic load and index, total fructose intake, and total carbohydrates intake on cancer recurrences and survival in a cohort of stage III colon cancer patients enrolled in an adjuvant chemotherapy trial in which extensive data on dietary intake, height, weight, and physical activity were collected at the study onset prior to any subsequent events of cancer recurrence.
Study Population
This prospective cohort was derived from participants in the National Cancer Institute–sponsored Cancer and Leukemia Group B (CALGB) 89803 adjuvant therapy trial for stage III colon cancer (29) (ClinicalTrials.gov identifier NCT00003835; http://clinicaltrials.gov/ct2/show/NCT00003835; accessed August 2, 2012) comparing therapy with weekly 5-fluorouracil and leucovorin to weekly irinotecan, 5-fluorouracil, and leucovorin (30). Between May 1999 and May 2001, 1264 patients were enrolled in the trial. After 87 patients were enrolled, an amendment required participants to complete a questionnaire capturing diet and lifestyle habits midway through adjuvant therapy (Q1) and 6 months after completion of adjuvant therapy (Q2). Figure 1 illustrates the derivation of the final sample size of 1011 patients for this study. Supplementary Table 1 (available online) shows that there were no appreciable differences in baseline characteristics between the 1011 patients who were eligible for these analyses and the other 253 patients treated on CALGB 89803 but not included in this study.
Figure 1.
Figure 1.
Derivation of cohort. Q1 = questionnaire 1 (midway through adjuvant therapy); Q2 = questionnaire 2 (6 months after completion of adjuvant therapy). Caloric intake exclusion = Less than 600 calories or greater than 4200 calories per day for men and less (more ...)
Patients were eligible if they underwent a complete surgical resection of the primary tumor within 56 days of trial entry, had regional lymph node metastases but no evidence of distant metastases, had baseline Eastern Cooperative Oncology Group performance status of 0–2 (31), and had adequate bone marrow, renal, and hepatic function. All patients signed informed consent, which was approved by each site’s institutional review board.
Dietary Assessment
Patients completed semiquantitative food frequency questionnaires (FFQs) that included 131 food items, vitamin and mineral supplements, and open-ended sections for other supplements and foods not specifically listed (32,33). Participants were asked how often, on average over the previous 3 months, they consumed a specific food portion size, with up to nine possible responses, which ranged from never to six or more times per day. We computed nutrient intakes by multiplying the frequency of consumption of each food by the nutrient content of the specified portions using composition values from Department of Agriculture sources supplemented with other data (34). All nutrient values were energy-adjusted using the residuals methods (35).
The glycemic index value was calculated as follows: ∑ incremental blood glucose area under the curve of test food × 100% ∑ incremental blood glucose area under the curve of reference food. Using these glycemic index values, the mean dietary glycemic load was calculated by multiplying the carbohydrate content for each food by its glycemic index value, multiplying that product by the servings of that food per day, and summing values for all food items reported. Each glycemic load unit represents the equivalent of 1 gram of carbohydrate from white bread (13). The overall dietary glycemic index was calculated by dividing glycemic load by the total amount of carbohydrate. Total fructose intake was calculated as free fructose plus fructose from sucrose intake.
In a validity study of 173 women, correlation coefficients between the average intake assessed by two 1-week diet records and the FFQs were as follows: 0.71 for white bread, 0.77 for dark bread, 0.66 for potatoes, 0.84 for orange or grapefruit juice, and 0.56 for noncarbonated fruit drinks (36). In a study specific to cancer patients on chemotherapy, dietary glycemic load as measured by the questionnaire was inversely associated with plasma high-density lipoprotein (P =.007) (37).
Patients who completed the first FFQ (Q1) and whose cancer had not recurred prior to its completion were included in these analyses. The median time from study entry to Q1 was 3.5 months (95% range = 2.5–5.0 months; full range = 0.2–9.9 months). To avoid potential biases due to declining health immediately before recurrence or death, we excluded from analyses patients who experienced either event within 90 days following completion of Q1 (Figure 1). We updated dietary exposures based on the results of the second FFQ (Q2) using cumulative averaging as previously described (17,27,38,39) but weighted proportional to times between Q1 and Q2 and then between Q2 and disease-free survival time. For example, if a patient completed Q1 at 4 months, completed Q2 at 14 months, and had a cancer recurrence at 30 months, the total time between Q1 and cancer recurrence was 26 months and 38% of that time was between Q1 and Q2 and 62% of that time was between Q2 and the recurrence. We therefore calculated the glycemic load as follows: cumulative averaging glycemic load = (glycemic load at Q1×0.38) + {[(glycemic load at Q1 + glycemic load at Q2) / 2] × 0.62}.
Endpoints
The primary endpoint was disease-free survival, defined as time from the completion of Q1 to tumor recurrence, occurrence of a new colon primary tumor, or death from any cause. Recurrence-free survival was defined as time from the completion of Q1 to tumor recurrence or occurrence of a new colon primary tumor, censoring patients who died without known recurrence at last recorded evaluation. Overall survival was defined as time from the completion of Q1 to death from any cause.
Statistical Analysis
In the clinical trial, there was no statistical difference in either disease-free or overall survival between the treatment arms (30). Therefore, data for patients in both arms were combined and analyzed according to quintiles of each exposure. Cox proportional hazards regression (40) was used to adjust for potential confounders. We used time-varying covariates to adjust for physical activity and body mass index (BMI) with updating from Q2. Other covariates were entered into the model as fixed covariates. Covariates with missing variables were coded with indicator variables. We tested for linear trend across quintiles by assigning each participant the median value for the quintile and modeling this value as a continuous variable, consistent with prior studies (41–43). The Cox regression models met the assumption of proportionality by both time-dependent covariate and Schoenfeld residuals methods. All P values are two-sided, with P less than.05 considered statistically significant; P values were not adjusted for multiple comparisons. Tests for effect modification by sex and other potential interactions were performed and reported.
Patient registration and clinical data collection were managed and analyses were conducted by the CALGB Statistical Center. All analyses were based on the study database frozen on March 31, 2009.
Baseline Characteristics
Study participants were drawn from a multicenter study of adjuvant chemotherapy after surgery in patients with stage III colon cancer. We calculated participant’s dietary glycemic load, glycemic index, total fructose intake, and total carbohydrate intake using cumulative updating to reflect average intake reported during and after adjuvant chemotherapy. Baseline characteristics by quintiles of dietary glycemic load and index are shown in Table 1. Similar baseline characteristic distributions were seen for total fructose and total carbohydrate intakes (data not shown).
Table 1.
Table 1.
Baseline characteristics of 1011 patients by quintile for dietary glycemic load and glycemic index*
Colon Cancer Recurrences and Survival
The median follow-up from the time of completion of Q1 was 7.3 years. In total, 343 of the 1011 patients included in this analysis had cancer recurrence; 262 of these 343 patients died. An additional 43 patients died without documented cancer recurrence.
The primary endpoint of this analysis was disease-free survival. Higher dietary glycemic load was associated with statistically significant worse disease-free, recurrence-free, and overall survival (Table 2). Compared with patients with the lowest glycemic load quintile, those in the highest quintile experienced an adjusted hazard ratio (HR) for disease-free survival of 1.79 (95 % confidence interval [CI] = 1.29 to 2.48; P trend across quintiles <.001). Although the association does not appear linear when reviewing the point estimates by quintile, the P trend across quintiles does reflect data across all quintiles, thereby demonstrating an association with increasing level of glycemic load. To isolate the influence of glycemic load on cancer recurrence, we used the endpoint recurrence-free survival and confirmed that higher glycemic load was associated with a stastistically significantly increased risk in cancer recurrence (P trend across quintiles <.001).
Table 2.
Table 2.
Associations between colon cancer recurrence and mortality and dietary glycemic load, glycemic index, total fructose intake, and total carbohydrate intake
To address the possibility that changes in dietary habits could reflect occult cancer or impending death, we excluded patients who developed cancer recurrence or died within 90 days of completing Q1 in our primary analyses. To further address this issue, we repeated the Cox proportional hazard models after excluding patients who developed cancer recurrence or died within 180 days of completing Q1 (n = 967), and our results remained largely unchanged. Patients in the highest quintile of dietary glycemic load had an adjusted hazard ratio for cancer recurrence or death of 1.67 (95% CI = 1.18 to 2.35; P trend across quintiles <.001). Conversely, because Q1 was not uniformly completed at 3 months from study entry as recommended in the protocol, we modeled our analyses using survival times from study entry [the same time variable used in the treatment trial analysis (30)]. Patients in the highest quintile of dietary glycemic load had an adjusted hazard ratio for cancer recurrence or death of 1.67 (95% CI = 1.21 to 2.32; P trend across quintiles <.001).
We similarly examined the association of other dietary markers associated with insulin resistance on cancer recurrence and mortality (Table 2). Dietary glycemic index was not associated with disease-free, recurrence-free, or overall survival. Total fructose intake was statistically significant associated with recurrence-free survival (HR = 1.42; 95% CI = 1.02 to 1.97, comparing extreme quintiles). In contrast, the relation between total fructose intake and disease-free or overall survival did not reach statistical significance.
Increasing total carbohydrate intake was statistically significant associated with disease-free, recurrence-free, and overall survival. Compared with patients in the lowest quintile of total carbohydrate intake, those in the highest quintile of total carbohydrate intake experienced a hazard ratio for cancer recurrence or death from any cause of 1.80 (95% CI = 1.61 to 2.48; P trend across quintiles <.001). Increasing total carbohydrate intake appeared to confer similar deleterious associations for both recurrence-free and overall survival.
Stratified Analyses
We examined the influence of dietary glycemic load on disease-free survival across strata of other potential predictors of patient outcome (Table 3). We found that the influence of dietary glycemic load on disease-free survival was statistically significant modified by BMI (P interaction =.01). Whereas glycemic load was not associated with disease-free survival in those with BMI < 25kg/m2, higher glycemic load was statistically significant associated with worse disease-free survival among overweight or obese participants (BMI ≥ 25kg/m2; adjusted HR = 2.26; 95% CI = 1.53 to 3.32, comparing extreme quintiles; P trend across quintiles <.001). Of note, we observed a similar interaction between increasing quintiles of carbohydrate and BMI (P interaction =.006). No statistically significant interactions were demonstrated by age, sex, performance status, number of positive lymph nodes, treatment group, or level of physical activity (data not shown).
Table 3.
Table 3.
Subgroup analyses of multivariable-adjusted disease-free survival by quintile of dietary glycemic load*
Given our prior data associating Western pattern diet with disease-free survival in this cohort (27), we examined whether the glycemic load findings persisted across strata of Western pattern diet (Figure 2). Regardless of the level of Western pattern diet, increasing glycemic load was associated with worse disease-free survival.
Figure 2.
Figure 2.
Hazard ratio (HR) for disease-free survival according to combinations of the dietary glycemic load by quintile and median levels of Western pattern diet.
In this cohort of stage III colon cancer patients enrolled in a clinical trial of postoperative adjuvant chemotherapy, increasing dietary glycemic load and total carbohydrate intake were each associated with an increased risk of cancer recurrence or death. Moreover, the deleterious association of dietary glycemic load and total carbohydrate intake on survival was principally observed in patients who were overweight or obese.
Dietary glycemic load and glycemic index have been extensively studied as potential risk factors for the development of colorectal cancer, with mixed results (14–21,44–47). In a meta-analysis of case–control and cohort studies, both glycemic load (relative risk [RR] = 1.26; 95% CI = 1.11 to 1.44) and glycemic index (RR = 1.18; 95% CI = 1.05 to 1.34) were statistically significant, albeit modestly, associated with a greater risk of colorectal cancer. In studies reporting positive associations, dietary glycemic load and total carbohydrate or fructose intake were often stronger predictors of the risk than dietary glycemic index (14,17).
Weight, physical activity, and diet are well-established risk factors for colorectal cancer (48). In contrast, fewer studies have examined the influence of these behaviors on survival among patients with colorectal cancer. In recent observations, obesity, sedentary lifestyle, and Western dietary pattern have each been associated with an increased risk for cancer recurrence and death among patients who have undergone curative surgical resection for colorectal cancer (49,50). Although the mediators for this increased risk of recurrence and death are poorly defined, hyperinsulinemia and perturbations in the insulinlike growth factor axis have been proposed as underlying biologic mechanisms for these observations. Regular consumption of high-glycemic meals results in increased insulin levels, decreased leptin, and increased BMI (51–54). In preclinical studies of intestinal epithelial cells and colon cancer cell lines, insulin binds to the insulin receptor on the cell surface and stimulates cell proliferation while inhibiting apoptosis (55–58). Studies have demonstrated a correlation between plasma C-peptide, a marker of longer-term insulin production, and dietary glycemic load, fructose, and carbohydrates (59,60). In a cohort of nonmetastatic colorectal cancer patients, subjects with higher levels of baseline circulating C-peptide experienced statistically significantly increased colorectal cancer-specific mortality when compared with patients with the lowest levels (61). In light of the current findings in stage III patients, we hypothesize that excess energy balance, including higher dietary glycemic load, may stimulate systemic insulin production, which may, in turn, promote cell proliferation and inhibit apoptosis of micrometastases. In CALGB 89803, blood samples were not collected from participants to further study potential correlations between glycemic load and carbohydrate intake and hormonal markers, although an ongoing adjuvant colon cancer study (CALGB 80702) is collecting both FFQs and blood samples to allow such studies in the future.
There are several advantages to a cohort of patients treated within a National Cancer Institute–sponsored clinical trial. First, all patients had lymph node–positive cancer, reducing the impact of heterogeneity by disease stage. Second, treatment and follow-up care were standardized, and the date and nature of recurrence were prospectively recorded. Detailed information on other prognostic variables was prospectively collected at study entry. Finally, we updated dietary data to reflect changes in diet that may occur after patients have completed adjuvant therapy and recovered from treatment effects.
Our study is not without limitations. Patients who enroll in randomized trials may differ from the population at large. To participate, patients must meet eligibility criteria, be selected as an appropriate candidate, and be motivated to participate. However, we did observe reasonable variability in dietary intake among patients enrolled from community and academic centers across North America.
Also, we cannot completely exclude the possibility that diets associated with increased dietary glycemic load and total carbohydrates may be reflective of other cancer-specific predictors of poor prognosis. However, we did not observe any statistically significant association between these exposures and other patient or tumor-associated predictors of disease-free survival. Moreover, the detrimental association of these dietary exposures remained largely unchanged across the number of positive lymph nodes and performance status. We adjusted for other energy balance factors, including BMI and physical activity, and the associations remained statistically significant.
We considered the possibility that patients with either occult cancer recurrences or other statistically significant poor prognostic characteristics may have increased their dietary glycemic load or carbohydrate intake as an alternative source of needed calories. To minimize this bias, we excluded recurrences or deaths within 90 days of FFQ completion. When we extended this restriction to 6 months, we continued to observe a deleterious influence of dietary glycemic load on patient outcome. In addition, the association between dietary glycemic load and total carbohydrate intake and cancer recurrence or death appeared greatest among patients who were either overweight or obese. Finally, because patients on this clinical trial underwent comprehensive staging at study entry and were followed with prescribed follow-up visits and testing, we would expect few patients to have undetected recurrences over extended periods.
Given that patients who consume high glycemic loads or carbohydrates after cancer diagnosis may have consumed a similar diet before diagnosis, we cannot exclude the possibility that individuals with these dietary exposures acquire tumors that are biologically more aggressive. Nonetheless, as stated above, we did not observe any statistically significant association between these dietary habits and tumor-related characteristics associated with cancer recurrence.
Diet was self-reported in this study. The FFQ from this study has been extensively validated in healthy populations (32,33) as well as in a population of patients receiving cytotoxic chemotherapy (37). Diet was recorded prior to any knowledge of colon cancer-related outcomes, thus reducing the likelihood of reporting biases.
Following a curative surgical resection of stage III colon cancer, clinical trials have demonstrated adjuvant chemotherapy improves disease-free and overall survival. The current prospective analysis, imbedded in such a clinical trial, suggests that a specific dietary behavior, in the form of higher dietary glycemic load and total carbohydrate intake, is statistically significant associated with impaired disease-free survival among patients treated with adjuvant therapy. Although our observational study does not provide conclusive evidence for causality, these findings support the potential role of energy balance factors in colon cancer progression and may offer opportunities to further improve patient survival. Further analyses are underway to confirm these associations in other cohorts of colon cancer survivors.
Funding
National Cancer Institute (CA31946 to the Cancer and Leukemia Group B, Monica M. Bertagnolli, MD, chairman; CA33601 to the CALGB Statistical Center, Daniel J. Sargent, PhD; CA 118553 and CA149222 to JAM and CSF); Pharmacia & Upjohn Company, now Pfizer Oncology; GI SPORE grant (P50CA127003 to JAM and CSF).
Supplementary Material
Supplementary Data
Notes
The sponsors did not participate in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation and writing of the manuscript; or the decision to submit the manuscript for publication. The sponsors did approve the final manuscript. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute.
The authors have no conflicts of interest issues to report related to this manuscript and findings.
The following institutions participated in this study: Baptist Cancer Institute CCOP, Memphis, TN, Lee S. Schwartzberg, MD, supported by CA71323; Cancer Centers of the Carolinas, Greenville, SC, Jeffrey K. Giguere, MD, supported by CA29165; Christiana Care Health Services, Inc CCOP, Wilmington, DE, Stephen Grubbs, MD, supported by CA45418; Dana-Farber Cancer Institute, Boston, MA, Harold J. Burstein, MD, PhD, supported by CA32291; Dartmouth Medical School–Norris Cotton Cancer Center, Lebanon, NH, Konstantin Dragnev, MD, supported by CA04326; Duke University Medical Center, Durham, NC, Jeffrey Crawford, MD, supported by CA47577; Georgetown University Medical Center, Washington, DC, Minetta C. Liu, MD, supported by CA77597; Hematology-Oncology Associates of Central New York CCOP, Syracuse, NY, Jeffrey Kirshner, MD, supported by CA45389; Long Island Jewish Medical Center, Lake Success, NY, Kanti R. Rai, MD, supported by CA35279; Massachusetts General Hospital, Boston, MA, Jeffrey W. Clark, MD, supported by CA32291; Memorial Sloan-Kettering Cancer Center, New York, NY, Clifford A. Hudis, MD, supported by CA77651; Missouri Baptist Medical Center, St. Louis, MO, Alan P. Lyss, MD, supported by CA114558-02; Mount Sinai Medical Center, Miami, FL, Michael A. Schwartz, MD, supported by CA45564; Mount Sinai School of Medicine, New York, NY, Lewis R. Silverman, MD, supported by CA04457; Nevada Cancer Research Foundation CCOP, Las Vegas, NV, John A. Ellerton, MD, supported by CA35421; North Shore–Long Island Jewish Health System, New Hyde Park, NY, Daniel Budman, MD, supported by CA35279; Rhode Island Hospital, Providence, RI, William Sikov, MD, supported by CA08025; Roswell Park Cancer Institute, Buffalo, NY, Ellis Levine, MD, supported by CA59518; Southeast Cancer Control Consortium Inc CCOP, Goldsboro, NC, James N. Atkins, MD, supported by CA45808; State University of New York Upstate Medical University, Syracuse, NY, Stephen L. Graziano, MD, supported by CA21060; The Ohio State University Medical Center, Columbus, OH, Clara D. Bloomfield, MD, supported by CA77658; University of California at San Diego, San Diego, CA, Barbara A. Parker, MD, supported by CA11789; University of California at San Francisco, San Francisco, CA, Charles J. Ryan, MD, supported by CA60138; University of Chicago, Chicago, IL, Hedy L. Kindler, MD, supported by CA41287; University of Illinois MBCCOP, Chicago, IL, David J. Peace, MD, supported by CA74811; University of Iowa, Iowa City, IA, Daniel A. Vaena, MD, supported by CA47642; University of Maryland Greenebaum Cancer Center, Baltimore, MD, Martin Edelman, MD, supported by CA31983; University of Massachusetts Medical School, Worcester, MA, William V. Walsh, MD, supported by CA37135; University of Minnesota, Minneapolis, MN, Bruce A Peterson, MD, supported by CA16450; University of Missouri/Ellis Fischel Cancer Center, Columbia, MO, Carl E. Freter, MD, supported by CA12046; University of Nebraska Medical Center, Omaha, NE, Anne Kessinger, MD, supported by CA77298; University of North Carolina at Chapel Hill, Chapel Hill, NC, Thomas C. Shea, MD, supported by CA47559; University of Tennessee Memphis, Memphis, TN, Harvey B. Niell, MD, supported by CA47555; University of Vermont, Burlington, VT, Steven M. Grunberg, MD, supported by CA77406; Wake Forest University School of Medicine, Winston-Salem, NC, David D. Hurd, MD, supported by CA03927; Walter Reed Army Medical Center, Washington, DC, Brendan M. Weiss, MD, supported by CA26806; Washington University School of Medicine, St. Louis, MO, Nancy Bartlett, MD, supported by CA77440; Weill Medical College of Cornell University, New York, NY, John Leonard, MD, supported by CA07968.
1. Giovannucci E. Insulin, insulin-like growth factors and colon cancer: a review of the evidence J Nutr 2001. 131(11)(suppl):3109S–3120S. [PubMed]
2. Kaaks R, Lukanova A. Energy balance and cancer: the role of insulin and insulin-like growth factor-I Proc Nutr Soc. 2001. 60(1):91–106. [PubMed]
3. Giovannucci E. Diet, body weight, and colorectal cancer: a summary of the epidemiologic evidence J Womens Health (Larchmt). 2003. 12(2):173–182. [PubMed]
4. Moghaddam AA, Woodward M, Huxley R. Obesity and risk of colorectal cancer: a meta-analysis of 31 studies with 70,000 events Cancer Epidemiol Biomarkers Prev. 2007. 16(12):2533–2547. [PubMed]
5. Samad AK, Taylor RS, Marshall T, et al. A meta-analysis of the association of physical activity with reduced risk of colorectal cancer Colorectal Dis. 2005. 7(3):204–13. [PubMed]
6. Larsson SC, Rafter J, Holmberg L, et al. Red meat consumption and risk of cancers of the proximal colon, distal colon and rectum: the Swedish Mammography Cohort Int J Cancer. 2005. 113(5):829–834. [PubMed]
7. Kaaks R, Toniolo P, Akhmedkhanov A, et al. Serum C-peptide, insulin-like growth factor (IGF)-I, IGF-binding proteins, and colorectal cancer risk in women J Natl Cancer Inst. 2000. 92(19):1592–1600. [PubMed]
8. Wei EK, Ma J, Pollak MN, et al. A prospective study of C-peptide, insulin-like growth factor-I, insulin-like growth factor binding protein-1, and the risk of colorectal cancer in women Cancer Epidemiol Biomarkers Prev. 2005. 14(4):850–855. [PubMed]
9. Schoen RE, Tangen CM, Kuller LH, et al. Increased blood glucose and insulin, body size, and incident colorectal cancer J Natl Cancer Inst. 1999. 91(13):1147–1154. [PubMed]
10. Ma J, Giovannucci E, Pollak M, et al. A prospective study of plasma C-peptide and colorectal cancer risk in men . J Natl Cancer Inst. 2004. 96(7):546–553. [PubMed]
11. Wolever TM, Jenkins DJ. The use of the glycemic index in predicting the blood glucose response to mixed meals Am J Clin Nutr. 1986. 43(1):167–172. [PubMed]
12. Wolever TM, Jenkins DJ, Jenkins AL, et al. The glycemic index: methodology and clinical implications Am J Clin Nutr. 1991. 54(5):846–854. [PubMed]
13. Liu S, Manson JE, Stampfer MJ, et al. Dietary glycemic load assessed by food-frequency questionnaire in relation to plasma high-density-lipoprotein cholesterol and fasting plasma triacylglycerols in postmenopausal women Am J Clin Nutr. 2001. 73(3):560–566. [PubMed]
14. Higginbotham S, Zhang ZF, Lee IM, et al. Dietary glycemic load and risk of colorectal cancer in the Women’s Health Study J Natl Cancer Inst 2004. 96(3):229–233. [PubMed]
15. Franceschi S, Dal Maso L, Augustin L, et al. Dietary glycemic load and colorectal cancer risk. Ann Oncol. 2001. 12(2):173–178. [PubMed]
16. Gnagnarella P, Gandini S, La Vecchia C, et al. Glycemic index, glycemic load, and cancer risk: a meta-analysis Am J Clin Nutr. 2008. 87(6):1793–1801. [PubMed]
17. Michaud DS, Fuchs CS, Liu S, et al. Dietary glycemic load, carbohydrate, sugar, and colorectal cancer risk in men and women Cancer Epidemiol Biomarkers Prev. 2005. 14(1):138–147. [PubMed]
18. Larsson SC, Giovannucci E, Wolk A. Dietary carbohydrate, glycemic index, and glycemic load in relation to risk of colorectal cancer in women Am J Epidemiol. 2007. 165(3):256–261. [PubMed]
19. Strayer L, Jacobs DR, Jr., Schairer C, et al. Dietary carbohydrate, glycemic index, and glycemic load and the risk of colorectal cancer in the BCDDP cohort Cancer Causes Control. 2007. 18(8):853–863. [PubMed]
20. Weijenberg MP, Mullie PF, Brants HA, et al. Dietary glycemic load, glycemic index and colorectal cancer risk: results from the Netherlands Cohort Study Int J Cancer. 2008. 122(3):620–629. [PubMed]
21. Terry PD, Jain M, Miller AB, et al. Glycemic load, carbohydrate intake, and risk of colorectal cancer in women: a prospective cohort study J Natl Cancer Inst. 2003. 95(12):914–916. [PubMed]
22. Dignam JJ, Polite BN, Yothers G, et al. Body mass index and outcomes in patients who receive adjuvant chemotherapy for colon cancer J Natl Cancer Inst. 2006. 98(22):1647–1654. [PubMed]
23. Haydon AM, Macinnis RJ, English DR, et al. Effect of physical activity and body size on survival after diagnosis with colorectal cancer Gut. 2006. 55(1):62–67. [PMC free article] [PubMed]
24. Meyerhardt JA, Heseltine D, Niedzwiecki D, et al. Impact of physical activity on cancer recurrence and survival in patients with stage III colon cancer: findings from CALGB 89803 J Clin Oncol. 2006. 24(22):3535–3541. [PubMed]
25. Meyerhardt JA, Giovannucci EL, Ogino S, et al. Physical activity and male colorectal cancer survival Arch Intern Med. 2009. 169(22):2102–2108. [PMC free article] [PubMed]
26. Meyerhardt JA, Giovannucci EL, Holmes MD, et al. Physical activity and survival after colorectal cancer diagnosis J Clin Oncol 2006. 24(22):3527–3534. [PubMed]
27. Meyerhardt JA, Niedzwiecki D, Hollis D, et al. Association of dietary patterns with cancer recurrence and survival in patients with stage III colon cancer JAMA. 2007. 298(7):754–764. [PubMed]
28. Meyerhardt JA, Niedzwiecki D, Hollis D, et al. Impact of body mass index and weight change after treatment on cancer recurrence and survival in patients with stage III colon cancer: findings from Cancer and Leukemia Group B 89803 J Clin Oncol. 2008. 26(25):4109–4115. [PMC free article] [PubMed]
29. Greene FL. TNM staging for malignancies of the digestive tract: 2003 changes and beyond Semin Surg Oncol. 2003. 21(1):23–29. [PubMed]
30. Saltz LB, Niedzwiecki D, Hollis D, et al. Irinotecan fluorouracil plus leucovorin is not superior to fluorouracil plus leucovorin alone as adjuvant treatment for stage III colon cancer: results of CALGB 89803 J Clin Oncol. 2007. 25(23):3456–3461. [PubMed]
31. Zubrod C, Scheiderman M, Frei E, et al. Appraisal of methods for the study of chemotherapy in man: comparative therapeutic trial of nitrogen mustard and triethylene thiophosphoramide J Chron Dis. 1960. 11 7–33 .
32. Willett WC, Reynolds RD, Cottrell-Hoehner S, et al. Validation of a semi-quantitative food frequency questionnaire: comparison with a 1-year diet record J Am Diet Assoc 1987. 87(1):43–47. [PubMed]
33. Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire Am J Epidemiol. 1985. 122(1):51–65. [PubMed]
34. U.S. Department of Agriculture ARS USDA Nutrient Database for Standard Reference, release 10. Nutrient Data Laboratory home page. http://www.nal.usda.gov/fnic/foodcomp Accessed December 22, 2008.
35. Willett W, Stampfer MJ. Total energy intake: implications for epidemiologic analyses Am J Epidemiol. 1986. 124(1):17–27. [PubMed]
36. Salvini S, Hunter DJ, Sampson L, et al. Food-based validation of a dietary questionnaire: the effects of week-to-week variation in food consumption Int J Epidemiol. 1989. 18(4):858–867. [PubMed]
37. Meyerhardt JA, Heseltine D, Campos H, et al. Assessment of a dietary questionnaire in cancer patients receiving cytotoxic chemotherapy J Clin Oncol. 2005. 23(33):8453–8460. [PubMed]
38. Oh K, Willett WC, Fuchs CS, et al. Glycemic index, glycemic load, and carbohydrate intake in relation to risk of distal colorectal adenoma in women Cancer Epidemiol Biomarkers Prev. 2004. 13(7):1192–1198. [PubMed]
39. Hu FB, Stampfer MJ, Rimm E, et al. Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am J Epidemiol. 1999. 149(6): 531–540. [PubMed]
40. Cox D. Regression models and life tables J R Stat Soc B. 1972. 34 187–220 .
41. Hu FB, Rimm EB, Stampfer MJ, et al. Prospective study of major dietary patterns and risk of coronary heart disease in men Am J Clin Nutr. 2000. 72(4):912–921. [PubMed]
42. Michaud DS, Skinner HG, Wu K, et al. Dietary patterns and pancreatic cancer risk in men and women J Natl Cancer Inst. 2005. 97(7):518–524. [PubMed]
43. van Dam RM, Rimm EB, Willett WC, et al. Dietary patterns and risk for type 2 diabetes mellitus in U.S. men Ann Intern Med. 2002. 136(3):201–209. [PubMed]
44. Kabat GC, Shikany JM, Beresford SA, et al. Dietary carbohydrate, glycemic index, and glycemic load in relation to colorectal cancer risk in the Women’s Health Initiative Cancer Causes Control. 2008. 19(10): 1291–1298. [PubMed]
45. Li HL, Yang G, Shu XO, et al. Dietary glycemic load and risk of colorectal cancer in Chinese women Am J Clin Nutr. 2011. 93(1):101–107. [PubMed]
46. McCarl M, Harnack L, Limburg PJ, et al. Incidence of colorectal cancer in relation to glycemic index and load in a cohort of women Cancer Epidemiol Biomarkers Prev. 2006. 15(5):892–896. [PubMed]
47. Mulholland HG, Murray LJ, Cardwell CR, et al. Glycemic index, glycemic load, and risk of digestive tract neoplasms: a systematic review and meta-analysis Am J Clin Nutr. 2009. 89(2):568–576. [PubMed]
48. Giovannucci E. Modifiable risk factors for colon cancer Gastroenterol Clin North Am. 2002. 31(4):925–943. [PubMed]
49. Meyerhardt JA, Ma J, Courneya KS. Energetics in colorectal and prostate cancer J Clin Oncol. 2010. 28(26):4066–4073. [PMC free article] [PubMed]
50. Vrieling A, Kampman E. The role of body mass index, physical activity, and diet in colorectal cancer recurrence and survival: a review of the literature Am J Clin Nutr. 2010. 92(3):471–490. [PubMed]
51. Mendez MA, Covas MI, Marrugat J, et al. Glycemic load, glycemic index, and body mass index in Spanish adults Am J Clin Nutr 2009. 89(1):316–322. [PubMed]
52. Mirza NM, Klein CJ, Palmer MG, et al. Effects of high and low glycemic load meals on energy intake, satiety and hunger in obese Hispanic-American youth Int J Pediatr Obes. 2011. 6(2–2):e523–e531. [PMC free article] [PubMed]
53. Micha R, Nelson M. Glycemic index and glycemic load used in combination to characterize metabolic responses of mixed meals in healthy lean young adults J Am Coll Nutr. 2011. 30(2):113–125. [PubMed]
54. Barkoukis H, Marchetti CM, Nolan B, et al. A high glycemic meal suppresses the postprandial leptin response in normal healthy adults Ann Nutr Metab. 2007. 51(6):512–518. [PubMed]
55. Pollak MN, Perdue JF, Margolese RG, et al. Presence of somatomedin receptors on primary human breast and colon carcinomas Cancer Lett. 1987. 38(1–2):223–30. [PubMed]
56. Watkins L, Lewis L, Levine A. Characterization of the synergistic effect of insulin and transferrin and the regulation of their receptors on a human colon carcinoma cell line . Int J Cancer. 1990. 45 372–375. [PubMed]
57. Koenuma M, Yamori T, Tsuruo T. Insulin and insulin-like growth factor 1 stimulate proliferation of metastatic variants of colon carcinoma 26 Jpn J Cancer Res. 1989. 80(1):51–58. [PubMed]
58. Bjork J, Nilsson J, Hultcrantz R, et al. Growth-regulatory effects of sensory neuropeptides, epidermal growth factor, insulin, and somatostatin on the non-transformed intestinal epithelial cell line IEC-6 and the colon cancer cell line HT 29 Scand J Gastroenterol. 1993. 28(10):879–884. [PubMed]
59. Brito JO, Ponciano K, Figueroa D, et al. Parasympathetic dysfunction is associated with insulin resistance in fructose-fed female rats Braz J Med Biol Res. 2008. 41(9):804–808. [PubMed]
60. Wu T, Giovannucci E, Pischon T, et al. Fructose, glycemic load, and quantity and quality of carbohydrate in relation to plasma C-peptide concentrations in US women Am J Clin Nutr. 2004. 80(4):1043–1049. [PubMed]
61. Wolpin BM, Meyerhardt JA, Chan AT, et al. Insulin, the insulin-like growth factor axis, and mortality in patients with nonmetastatic colorectal cancer J Clin Oncol. 2009. 27(2):176–185. [PMC free article] [PubMed]
Articles from JNCI Journal of the National Cancer Institute are provided here courtesy of
Oxford University Press