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ORIGINAL REPORTS
June 23, 2014

Calcium, Vitamin D, Dairy Products, and Mortality Among Colorectal Cancer Survivors: The Cancer Prevention Study-II Nutrition Cohort

Publication: Journal of Clinical Oncology
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Abstract

Purpose

Higher calcium, vitamin D, and dairy product intakes are associated with lower colorectal cancer incidence, but their impacts on colorectal cancer survival are unclear. We evaluated associations of calcium, vitamin D, and dairy product intakes before and after colorectal cancer diagnosis with all-cause and colorectal cancer-specific mortality among colorectal cancer patients.

Patients and Methods

This analysis included 2,284 participants in a prospective cohort who were diagnosed with invasive, nonmetastatic colorectal cancer after baseline (1992 or 1993) and up to 2009. Mortality follow-up was through 2010. Prediagnosis risk factor information was collected on the baseline questionnaire. Postdiagnosis information was collected via questionnaires in 1999 and 2003 and was available for 1,111 patients.

Results

A total of 949 participants with colorectal cancer died during follow-up, including 408 from colorectal cancer. In multivariable-adjusted Cox proportional hazards regression models, postdiagnosis total calcium intake was inversely associated with all-cause mortality (relative risk [RR] for those in the highest relative to the lowest quartiles, 0.72; 95% CI, 0.53-0.98; Ptrend = .02) and associated with marginally statistically significant reduced colorectal cancer-specific mortality (RR, 0.59; 95% CI, 0.33 to 1.05; Ptrend = .01). An inverse association with all-cause mortality was also observed for postdiagnosis milk intake (RR, 0.72; 95% CI, 0.55 to 0.94; Ptrend = .02), but not vitamin D intake. Prediagnosis calcium, vitamin D, and dairy product intakes were not associated with any mortality outcomes.

Conclusion

Higher postdiagnosis intakes of total calcium and milk may be associated with lower risk of death among patients with nonmetastatic colorectal cancer.

Introduction

The overall 5-year relative survival for colorectal cancer is 64% in the United States but decreases to 12% for distant metastatic disease.1 The associations of dietary factors with colorectal cancer incidence have been extensively reported,2 but their roles for colorectal cancer survival are largely unknown.3 Current dietary guidelines for cancer survivors are primarily based on incidence studies.4 Empirical knowledge of modifiable prognostic factors, including diet, for patients with colorectal cancer is needed for the more than 3.5 million colorectal cancer survivors worldwide.5
Higher intakes of calcium, vitamin D, and dairy products are generally associated with lower risk of colorectal cancer incidence in observational studies.6,7 In addition, a major randomized, clinical trial of 1,200 mg of supplemental calcium versus placebo among 930 colorectal adenoma patients reported a 19% reduced risk of adenoma recurrence.8 In contrast, the Women's Health Initiative clinical trial reported no effect of calcium plus vitamin D supplementation on colorectal cancer incidence,911 but suggestive benefits were observed among those not taking personal calcium or vitamin D supplements12 and those not concurrently randomly assigned to estrogen therapies.13 Two studies reported null associations of prediagnostic calcium intake with colorectal cancer survival.14,15 The main circulating biomarker of vitamin D, 25(OH)D, was associated with lower risk of mortality among patients with colorectal cancer.1619 To our knowledge, no study has examined whether total dairy or milk are associated with survival among patients with colorectal cancer. We investigated associations of pre- and postdiagnosis calcium (total, dietary, and supplemental), vitamin D (total and dietary), and dairy product (total and milk only) intakes with all-cause and colorectal cancer–specific mortality in a prospective study of men and women diagnosed with invasive, nonmetastatic colorectal cancer.

Patients and Methods

Study Cohort

Men and women in this study were selected from among the 184,000 participants in the Cancer Prevention Study II (CPS-II) Nutrition Cohort, a prospective study of cancer incidence that began in 1992.20 A 10-page, self-administered questionnaire was used to collect information at baseline regarding demographics, medical history, physical activity, body size, cancer screening and early detection, diet, and other factors. Follow-up questionnaires were sent to participants biennially, beginning in 1997, to update exposure information and to learn of new cancer diagnoses. The CPS-II Nutrition Cohort is approved by the Institutional Review Board of Emory University.
By the end of incidence follow-up on June 30, 2009, 3,832 of the 181,293 participants who had no personal history of the disease at baseline had been diagnosed with invasive colon or rectal cancer. Of these 3,832 patients with colorectal cancer, 2,188 were first self-reported on a follow-up questionnaire and then verified by review of medical records, and 865 patients had their diagnoses confirmed after self-report via linkage with state cancer registries. An additional 779 patients were initially identified as cancer deaths through linkage to the National Death Index (NDI)21; among those 779 patients, 531 colorectal cancer diagnoses were confirmed, either through linkage with state cancer registries (n = 529) or by examination of medical records (n = 2).
Among the 3,832 patients with colorectal cancer, the following exclusions were applied: deaths determined through NDI that were not verified through medical records or cancer registries (n = 248), prevalent cancers (except for nonmelanoma skin cancer) at baseline (n = 387), implausible diagnosis date (n = 11), missing or unknown stage at diagnosis (n = 136), TNM summary stage IV or distant SEER stage at diagnosis (n = 421), nonadenocarcinoma histology (n = 50), implausible death date (n = 2), and poor-quality dietary data at baseline (n = 293). We decided, a priori, to exclude patients with distant metastatic disease, consistent with previous studies from this cohort,2225 because the 5-year relative survival in this group is so poor that it is unlikely that diet would substantially affect long-term mortality.
After exclusions, 2,284 participants (1,274 men and 1,010 women) were included in this analysis. Among them, 1,682 were diagnosed with colon cancer (International Classification of Diseases for Oncology [ICD-O]: C18.0, C18.2–C18.9) and 602 with rectal cancer (ICD-O: C19.9, C20.9). By SEER summary stage, 1,154 participants were diagnosed with localized disease (malignant tumors limited to the colon or rectum) while 1,130 participants had regional disease (tumors that spread to adjacent tissue or regional lymph nodes through the bowel wall).

Study Outcomes

All participants were followed through December 31, 2010 to ascertain their vital status and cause of death (if applicable) through linkage to the NDI. Cause of death was obtained for 99.3% of all known deaths in the cohort. The primary outcome in this study was all-cause mortality. The secondary outcome was mortality specifically resulting from colorectal cancer (International Classification of Diseases Ninth Revision [ICD-9]: 153, 154; International Classification of Diseases Tenth Revision [ICD-10]: C18, C19, C20), defined from the singular underlying cause of death from NDI records. Other major causes of death in this cohort include cardiovascular diseases, neurodegenerative disease, other types of cancer (primarily lung and pancreas cancer), and respiratory system diseases.

Pre- and Postdiagnosis Diet

Prediagnosis diet was assessed at baseline (1992 or 1993) using a modified brief Block Food Frequency Questionnaire (FFQ).20,26,27 Postdiagnosis diet, where available, was assessed in 1999 and 2003, using a modified Willett FFQ.20,2830 Both FFQs used similar questions on usual intake of dairy foods (major sources of dietary calcium and vitamin D, calculated by summing up total servings of milk, yogurt, ice cream, and cheese) and on calcium supplements and multivitamins (the major source of supplemental vitamin D during this time period; Appendix Table A1). For patients diagnosed after baseline and before the date of the 1999 survey completion, the 1999 survey was used for postdiagnosis diet. For patients diagnosed after 1999 and before the date of the 2003 survey completion, the 2003 survey was used for postdiagnosis diet. No postdiagnosis diet data are available from participants who did not return an eligible 1999 or 2003 postdiagnosis survey or from participants who were diagnosed after 2003. Of the 2,284 patients included in the prediagnosis analysis, 1,111 (48.6%) reported postdiagnosis diet.

Statistical Analysis

Sex- and questionnaire-specific quartiles were created for total calcium (ie, diet plus supplements), dietary calcium, total vitamin D (ie, diet plus supplements), dietary vitamin D, dairy, and milk. Questionnaire-specific categories were created for supplemental calcium (three levels) among men and women combined on the basis of visually inspecting the distribution and selecting interpretable cutoff points.
We used multivariable Cox proportional hazards models to calculate relative risks (RRs) and 95% CIs. The underlying time axis for all Cox models was time since diagnosis. For prediagnosis models, person-time began on the date of diagnosis. For postdiagnosis models, we used delayed entry Cox models, wherein person-time started on the date they returned their postdiagnosis FFQ. In all analyses, person-time ended on the date of death or the end of follow-up (December 31, 2010), whichever came first. The proportional hazards assumption was evaluated for the main exposures with a likelihood ratio test by comparing models with and without an interaction term between an exposure and time; no violations were detected.
All analyses were adjusted for age at diagnosis and tumor stage at diagnosis by stratifying within models. For prediagnosis models, we chose a priori to adjust for sex and baseline energy intake, and we in addition adjusted for baseline total folate intake because it changed the RR estimates by approximately 10%. Other demographic, lifestyle, and clinical covariates were evaluated, but none changed the RR estimates by more than 10%. Covariates in the basic postdiagnosis models also included sex and postdiagnosis energy intake, and in addition included postdiagnostic total folate in the multivariable model, to be consistent with the prediagnosis models. Baseline dietary intakes were evaluated as covariates in corresponding postdiagnosis models but did not materially change the RRs, so they were excluded. For each model the linear trend between exposure and mortality risk was assessed using the Wald test and modeling exposure as a continuous variable.
In sensitivity analyses, we excluded participants with a history of diabetes, myocardial infarction, stroke at baseline, and death within 2 years of diagnosis. In addition, because treatment or serious illness may influence diet, we conducted sensitivity analyses excluding participants who completed FFQs within 1 year of diagnosis (1 year before diagnosis for prediagnostic models and 1 year after diagnosis for postdiagnosis models) and deaths within 2 years of the postdiagnosis questionnaire for postdiagnosis models. We tested for statistical interaction of each diet variable with age at diagnosis, sex, tumor stage, tumor sub-site, pre- or postdiagnosis body mass index, physical activity, total energy, and total folate intakes using likelihood ratio tests. All analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC).

Results

Participants were, on average, age 64 years at baseline and 73 years at diagnosis. Fifty-six percent of participants were men, and most reported their race as white. There were no differences across quartiles of prediagnostic total calcium intake in the distributions of year of diagnosis, sex, tumor stage, grade, sub-site, treatment, or history of hypertension, myocardial infarction, diabetes, and stroke (Table 1). High calcium consumers were slightly older, better educated, more physically active, leaner, more likely to use nonsteroidal anti-inflammatory drugs and postmenopausal hormones (women only), less likely to smoke, and more likely to have a healthier overall diet.
Table 1. Baseline Characteristics of Patients With Colorectal Cancer by Quartiles of Prediagnostic Total Calcium Intake in the CPS-II Nutrition Cohort
Characteristic Quartile of Total Calcium Intake, mg/d* P
Q1 Q2 Q3 Q4
No. % No. % No. % No. %
No. 570 572 570 572  
Age at colorectal cancer diagnosis, years                 < .01
    < 65 93 16.3 69 12.1 61 10.7 47 8.2  
    65 to < 70 141 24.7 117 20.5 112 19.6 97 17.0  
    70 to < 75 134 23.5 169 29.5 148 26.0 172 30.1  
    75 to < 80 131 23.0 141 24.7 151 26.5 147 25.7  
    ≥ 80 71 12.5 76 13.3 98 17.2 109 19.1  
Year of colorectal cancer diagnosis                 .95
    1992-1996 121 21.2 142 24.8 129 22.6 134 23.4  
    1997-2000 173 30.4 165 28.8 167 29.3 175 30.6  
    2001-2004 148 26.0 151 26.4 151 26.5 144 25.2  
    2005-2009 128 22.5 114 19.9 123 21.6 119 20.8  
Sex                 1.00
    Male 318 55.8 319 55.8 318 55.8 319 55.8  
    Female 252 44.2 253 44.2 252 44.2 253 44.2  
Race/ethnicity                 .08
    White/white-Hispanic 551 96.7 563 98.4 561 98.4 562 98.3  
    Black/black-Hispanic 11 1.9 6 1.0 6 1.1 2 0.3  
    Other/missing 8 1.4 3 0.5 3 0.5 8 1.4  
Education                 < .01
    Less than high school 60 10.5 35 6.1 29 5.1 34 5.9  
    High school degree 189 33.2 173 30.2 149 26.1 131 22.9  
    Some college/trade school 153 26.8 168 29.4 176 30.9 176 30.8  
    College graduate 164 28.8 193 33.7 215 37.7 228 39.9  
SEER summary stage                 .29
    Localized 294 51.6 302 52.8 270 47.4 288 50.3  
    Regional 276 48.4 270 47.2 300 52.6 284 49.7  
Tumor grade at diagnosis                 .43
    Well differentiated 68 11.9 68 11.9 73 12.8 69 12.1  
    Moderately differentiated 364 63.9 338 59.1 353 61.9 340 59.4  
    Poorly differentiated 74 13.0 108 18.9 92 16.1 107 18.7  
    Undifferentiated 7 1.2 7 1.2 9 1.6 6 1.0  
Colorectal cancer diagnosis site                 .50
    Colon 407 71.4 431 75.3 421 73.9 423 74.0  
    Rectum 163 28.6 141 24.7 149 26.1 149 26.0  
First course of cancer treatment                  
    Surgery                 .97
        No 12 2.1 12 2.1 13 2.3 11 1.9  
        Yes 415 72.8 411 71.9 409 71.8 425 74.3  
    Chemotherapy                 .83
        No 258 45.3 239 41.8 243 42.6 261 45.6  
        Yes 169 29.6 184 32.2 179 31.4 175 30.6  
    Radiation                 .79
        No 386 67.7 386 67.5 373 65.4 395 69.1  
        Yes 41 7.2 37 6.5 49 8.6 41 7.2  
Family history of colorectal cancer in 1982                 .96
    No 533 93.5 536 93.7 537 94.2 538 94.1  
    Yes 37 6.5 36 6.3 33 5.8 34 5.9  
History of diabetes                 .84
    No 520 91.2 513 89.7 517 90.7 519 90.7  
    Yes 50 8.8 59 10.3 53 9.3 53 9.3  
History of stroke                 .58
    No 558 97.9 557 97.4 561 98.4 557 97.4  
    Yes 12 2.1 15 2.6 9 1.6 15 2.6  
History of myocardial infarction                 .93
    No 529 92.8 525 91.8 526 92.3 529 92.5  
    Yes 41 7.2 47 8.2 44 7.7 43 7.5  
History of hypertension                 .26
    No 342 60.0 345 60.3 366 64.2 336 58.7  
    Yes 228 40.0 227 39.7 204 35.8 236 41.3  
Physical activity, MET-h/wk                 < .01
    Q1 90 15.8 61 10.7 60 10.5 49 8.6  
    Q2 204 35.8 192 33.6 180 31.6 180 31.5  
    Q3 144 25.3 173 30.2 172 30.2 172 30.1  
    Q4 123 21.6 138 24.1 148 26.0 166 29.0  
BMI (kg/m2)                 < .01
    < 18.5 6 1.1 5 0.9 3 0.5 9 1.6  
    18.5 to < 25 187 32.8 190 33.2 218 38.2 253 44.2  
    25 to < 30 259 45.4 258 45.1 237 41.6 218 38.1  
    ≥ 30 112 19.6 108 18.9 103 18.1 84 14.7  
Cigarette-smoking status                 < .01
    Never 228 40 210 36.7 222 38.9 230 40.2  
    Current 74 13 43 7.5 45 7.9 26 4.5  
    Former 268 47 315 55.1 302 53.0 309 54.0  
NSAID use, No. of pills/mo                 < .01
    0 279 48.9 258 45.1 241 42.3 246 43.0  
    1 to < 15 96 16.8 92 16.1 79 13.9 61 10.7  
    15 to < 30 32 5.6 47 8.2 67 11.8 69 12.1  
    30 to < 60 96 16.8 99 17.3 112 19.6 127 22.2  
    ≥ 60 46 8.1 54 9.4 50 8.8 51 8.9  
HRT use among post-menopausal women                 .03
    None 119 49.0 124 50.4 110 44.5 94 38.5  
    Current 50 20.6 64 26.0 78 31.6 78 32.0  
    Former 65 26.8 52 21.1 50 20.2 58 23.8  
Dietary characteristics                  
    Alcohol intake, drinks/d                 < .01
        Nondrinker 207 36.3 229 40.0 227 39.8 255 44.6  
        < 1 178 31.2 212 37.1 234 41.1 209 36.5  
        ≥ 1 173 30.4 124 21.7 98 17.2 99 17.3  
  Mean SD Mean SD Mean SD Mean SD P
Energy intake, kcal/d 1,543.2 605.6 1,617.8 606.5 1,611.1 583.0 1,577.6 594.3 .01
Dietary folate intake, μg/d 214.8 81.3 254.5 90.4 268.0 88.1 288.0 95.4 < .01
Total folate intake, μg/d 257.2 161.2 361.1 210.4 424.7 238.9 563.7 389.3 < .01
Fruit/vegetable intake, servings/d 2.8 1.5 3.2 1.6 3.5 1.8 3.5 1.7 < .01
Red/processed meat intake, servings/wk 6.4 4.1 5.7 3.6 5.2 3.9 4.4 3.4 < .01
Whole grain intake, g/d 44.7 52.6 59.8 63.2 65.7 63.8 69.4 60.5 < .01
NOTE. Some percentages do not add up to 100% because of missing data or rounding.
Abbreviations: BMI, body mass index; CPS-II, Cancer Prevention Study-II; HRT, hormone replacement therapy; MET, metabolic equivalent; NSAID, nonsteroidal anti-inflammatory drug; Q, quartile; SD, standard deviation.
*
Quartiles in men:< 578, 578 to< 776, 776 to < 1,044, ≥ 1,044; quartiles in women:< 553, 553 to < 776, 776 to < 1,156, ≥ 1,156.
P values derived from χ2 test for differences in frequencies across total calcium strata for categorical predictors, and t test for continuous predictors with continuous total calcium intake.
Among the 2,284 patients included in the prediagnosis analyses, 949 deaths occurred (408 from colon or rectal cancer) during a mean follow-up of 7.5 years (standard deviation, 4.6 years; range, 2 days to 18.1 years). No statistically significant associations were observed for any of the prediagnosis diet variables with any of the mortality outcomes (Table 2 and Appendix Table A2). The results were not meaningfully different after additional adjusting for other covariates or after additional sensitivity analyses (data not shown). The results were also null after we included patients with metastatic or unknown tumor stage (Appendix Table A3). In analyses restricted to the 1,111 participants who were included in the postdiagnosis analyses, prediagnosis use of supplemental calcium ≥ 250 mg/d was statistically significantly associated with higher risk of all-cause mortality (RR, 1.65; 95% CI, 1.16 to 2.35; Appendix Table A4); this risk was primarily because of an increased RR for cardiovascular disease mortality (RR, 1.83; 95% CI, 0.82 to 4.09).
Table 2. Associations of 1992 Prediagnostic Calcium, Vitamin D, and Dairy Intakes With All-Cause Mortality Among Patients With Nonmetastatic Colorectal Cancer in the CPS-II Nutrition Cohort
Exposure Range* Total No. of Deaths Person-Years Base Model MV Model
Men Women RR 95% CI RR 95% CI
Total calcium, mg/d
    Q1§ < 578 < 553 227 4,288 1.00 1.00
    Q2 578 to < 776 553 to < 776 227 4,460 0.91 0.75 to 1.10 0.93 0.77 to 1.13
    Q3 776 to < 1,044 776 to < 1,156 234 4,255 0.85 0.70 to 1.03 0.88 0.72 to 1.08
    Q4 ≥ 1,044 ≥ 1,156 261 4,235 0.96 0.80 to 1.15 0.99 0.81 to 1.21
    Ptrend         0.99 0.68
Dietary calcium, mg/d                
    Q1§ < 548 < 486 243 4,146 1.00 1.00
    Q2 548 to < 729 486 to < 629 222 4,496 0.84 0.70 to 1.02 0.86 0.71 to 1.04
    Q3 729 to < 949 629 to < 849 237 4,272 0.84 0.69 to 1.01 0.86 0.71 to 1.04
    Q4 ≥ 949 ≥ 849 247 4,325 0.85 0.70 to 1.02 0.86 0.71 to 1.04
    Ptrend         0.13 0.21
Supplemental calcium, mg/d                
    C1§ 0 575 10,733 1.00 1.00
    C2 0.1 to < 250 229 3,891 1.01 0.86, 1.19 1.13 0.90, 1.42
    C3 ≥ 250 145 2,615 1.12 0.92, 1.36 1.22 0.96, 1.54
    Ptrend         0.34 0.10
Total vitamin D, IU/d                
    Q1§ < 122 < 111 229 4,284 1.00 1.00
    Q2 122 to < 191 111 to < 201 218 4,370 0.87 0.71 to 1.05 0.90 0.74 to 1.10
    Q3 191 to < 425 201 to < 467 244 4,278 0.92 0.76 to 1.11 0.97 0.78 to 1.19
    Q4 ≥ 425 ≥ 467 258 4,306 0.95 0.79 to 1.14 1.09 0.82 to 1.47
    Ptrend         0.92 0.32
Dietary vitamin D, IU/d                
    Q1§ < 105 < 90 232 4,283 1.00 1.00
    Q2 105 to < 157 90 to < 136 238 4,347 0.97 0.81 to 1.18 1.00 0.82 to 1.21
    Q3 157 to < 226 136 to < 202 225 4,295 0.89 0.73 to 1.07 0.91 0.75 to 1.11
    Q4 ≥ 226 ≥ 202 254 4,313 0.94 0.78 to 1.13 0.97 0.80 to 1.17
    Ptrend         0.43 0.63
Total dairy, servings/wk                
    Q1§ < 5.5 < 5.0 243 4,044 1.00 1.00
    Q2 5.5 to < 9.6 5.0 to < 8.9 213 4,610 0.78 0.64 to 0.95 0.79 0.65 to 0.96
    Q3 9.6 to < 14.5 8.9 to < 13.4 244 4,351 0.85 0.70 to 1.03 0.87 0.72 to 1.05
    Q4 ≥ 14.5 ≥ 13.4 249 4,233 0.86 0.71 to 1.06 0.88 0.72 to 1.09
    Ptrend         0.47 0.62
Milk, servings/wk                
    Q1§ 0 0 262 4,895 1.00 1.00
    Q2 0.1 to < 5.7 0.1 to < 5.1 204 3,771 1.01 0.84 to 1.23 1.01 0.84 to 1.23
    Q3 5.7 to < 10.5 5.1 to < 10.1 237 4,293 0.97 0.81 to 1.17 0.99 0.82 to 1.19
    Q4 ≥ 10.5 ≥ 10.1 246 4,280 0.94 0.78 to 1.13 0.95 0.79 to 1.15
    Ptrend         0.36 0.46
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy and total folate intakes.
§
Reference group.
Ptrend was calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Among the 1,111 patients included in the postdiagnosis analyses, 429 deaths occurred (143 from colon or rectal cancer) during a mean follow-up of 7.6 years (standard deviation, 3.4 years; range, 20 days to 11.3 years). The mean time between diagnosis and completion of the postdiagnosis questionnaire was 2.6 years. As shown in Table 3, comparing the highest to the lowest quartiles, total calcium (RR, 0.72; 95% CI, 0.53 to 0.98; Ptrend = .02) and milk (RR, 0.72; 95% CI, 0.55 to 0.94; Ptrend = .02) intakes were associated with lower all-cause mortality. Additional adjustment for prediagnostic total calcium and milk had no discernible effect on the results. A marginally statistically significant inverse association with all-cause mortality was observed for total dairy (RR, 0.75; 95% CI, 0.56 to 1.01; Ptrend = .05). Total calcium was also inversely associated with colorectal cancer–specific mortality (highest v lowest quartile RR, 0.59; 95% CI, 0.33 to 1.05; Ptrend = .01; Appendix Table A5).
Table 3. Associations of Postdiagnosis Calcium, Vitamin D, and Dairy Intakes With All-Cause Mortality Among Patients With Nonmetastatic Colorectal Cancer in the CPS-II Nutrition Cohort
Exposure 1999 Range* 2003 Range* Total No. of Deaths Person-Years Base Model MV Model
Men Women Men Women RR 95% CI RR 95% CI
Total calcium, mg/d                    
    Q1§ < 581 < 713 < 683 < 773 112 1,903 1.00 1.00
    Q2 581 to < 775 713 to < 1,170 683 to < 882 773 to < 1,131 118 2,098 0.89 0.67 to 1.18 0.89 0.67, 1.18
    Q3 775 to < 1,105 1,170 to < 1,598 882 to < 1,162 1,131 to < 1,591 100 2,078 0.72 0.54 to 0.96 0.72 0.53, 0.98
    Q4 ≥ 1,105 ≥ 1,598 ≥ 1,162 ≥ 1,591 99 2,325 0.72 0.54 to 0.97 0.72 0.53, 0.98
    Ptrend             0.01 0.02
Dietary calcium, mg/d                    
    Q1§ < 532 < 525 < 613 < 609 105 1,804 1.00 1.00
    Q2 532 to < 683 525 to < 671 613 to < 765 609 to < 766 118 2,277 0.84 0.63 to 1.12 0.84 0.63, 1.11
    Q3 683 to < 885 671 to < 892 765 to < 968 766 to < 990 90 2,100 0.69 0.51 to 0.92 0.69 0.51, 0.93
    Q4 ≥ 885 ≥ 892 ≥ 968 ≥ 990 116 2,223 0.85 0.64 to 1.12 0.86 0.65, 1.14
    Ptrend             0.17 0.21
Supplemental calcium, mg/d                    
    C1§ 0 0 221 3,966 1.00 1.00
    C2 0.1 to < 500 0.1 to < 500 108 2,001 0.90 0.70, 1.16 0.95 0.72 to 1.27
    C3 ≥ 500 ≥ 500 100 2,437 0.94 0.72, 1.23 0.98 0.73 to 1.31
    Ptrend             0.55 0.88
Total vitamin D, IU/d                    
    Q1§ < 164 < 151 < 194 < 219 105 1,894 1.00 1.00
    Q2 164 to < 302 151 to < 379 194 to < 389 219 to < 509 101 2,087 0.79 0.59 to 1.07 0.81 0.59, 1.10
    Q3 302 to < 559 379 to < 588 389 to < 603 509 to < 685 108 2,167 0.90 0.67 to 1.21 0.97 0.67, 1.40
    Q4 ≥ 559 ≥ 588 ≥ 603 ≥ 685 115 2,256 0.80 0.60 to 1.07 0.88 0.57, 1.35
    Ptrend             0.16 0.35
Dietary vitamin D, IU/d                    
    Q1§ < 122 < 100 < 132 < 103 103 2,065 1.00 1.00
    Q2 122 to < 178 100 to < 155 132 to < 188 103 to < 178 115 2,144 0.99 0.75 to 1.31 0.99 0.75, 1.31
    Q3 178 to < 245 155 to < 229 188 to < 267 178 to < 257 105 2,109 0.94 0.70 to 1.25 0.95 0.71, 1.27
    Q4 ≥ 245 ≥ 229 ≥ 267 ≥ 257 106 2,085 0.89 0.67 to 1.19 0.90 0.67, 1.21
    Ptrend             0.29 0.33
Total dairy, servings/wk                    
    Q1§ < 4.7 < 3.9 < 5.1 < 4.8 115 1,931 1.00 1.00
    Q2 4.7 to < 8.2 3.9 to < 7.7 5.1 to < 8.8 4.8 to < 8.1 109 2,106 0.91 0.69 to 1.20 0.91 0.69, 1.21
    Q3 8.2 to < 11.7 7.7 to < 11.6 8.8 to < 12.3 8.1 to < 12.4 98 2,068 0.73 0.54 to 0.98 0.73 0.54, 0.98
    Q4 ≥ 11.7 ≥ 11.6 ≥ 12.3 ≥ 12.4 107 2,299 0.75 0.55 to 1.00 0.75 0.56, 1.01
    Ptrend             0.05 0.05
Milk, servings/wk                    
    Q1§ < 1.1 < 1.0 < 1.0 < 1.0 106 1,844 1.00 1.00
    Q2 1.1 to < 5.6 1.0 to < 3.5 1.0 to < 5.6 1.0 to < 3.3 109 2,135 0.84 0.64 to 1.12 0.85 0.64, 1.13
    Q3 5.6 to < 7.0 3.5 to < 7.0 5.6 to < 7.0 3.3 to < 7.0 41 881 0.76 0.52 to 1.11 0.76 0.52, 1.12
    Q4 ≥ 7.0 ≥ 7.0 ≥ 7.0 ≥ 7.0 173 3,543 0.71 0.55 to 0.93 0.72 0.55, 0.94
    Ptrend             0.01 0.02
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each questionnaire- and sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and postdiagnosis total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and postdiagnosis total energy and total folate intakes.
§
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Because postdiagnosis diet and supplement use may be influenced by serious illness preceding death (reverse causation), we conducted a sensitivity analysis excluding deaths within the first 2 years of follow-up after completion of the postdiagnosis questionnaire. The results after this exclusion seemed similar to the original results. The RRs for the highest compared with the lowest quartile of total calcium and milk, respectively, were 0.69 (95% CI, 0.48 to 0.98; Ptrend = .03) and 0.68 (95% CI, 0.50 to 0.93; Ptrend = .02) for all-cause mortality; and 0.53 (95% CI, 0.24 to 1.19; Ptrend = .02) for total calcium and colorectal cancer–specific mortality.
There was no evidence that the inverse associations of postdiagnosis total calcium and milk intakes with all-cause mortality were modified by age at diagnosis (< 70 years v ≥ 70 years), sex, tumor stage (localized v regional), tumor sub-site (colon v rectum), postdiagnosis body mass index (obese v not obese), physical activity (< median v ≥ median), total energy (< median v ≥ median), or total folate (< median v ≥ median) intakes (results stratified by stage shown in Appendix Table A6; other data not shown).

Discussion

This study suggests that higher intakes of total calcium and milk after colorectal cancer diagnosis are associated with lower risk of mortality. These associations persisted after adjusting for important covariates, such as sex and tumor stage, and after several sensitivity analyses. We found no evidence that calcium, vitamin D, or dairy product intakes before colorectal cancer diagnosis were associated with mortality. To our knowledge, this is the first study to report associations of dairy and milk (both pre- and postdiagnosis) with colorectal cancer survival, and also the first to assess the role of postdiagnosis calcium and vitamin D intakes.
Calcium, 25(OH)D (the major circulating form of vitamin D), and dairy products are associated with lower risk of incident colorectal cancer based on several meta-analyses.6,7,31 An earlier CPS-II Nutrition Cohort study reported inverse associations of colorectal cancer incidence with total calcium and total vitamin D intakes.32 The World Cancer Research Fund and American Institute for Cancer Research Continuous Update Project33 concluded in 2011 that calcium and milk were both probable factors associated with lower colorectal cancer risk.
In contrast to the substantial evidence of a role for calcium, vitamin D, and dairy products in colorectal cancer primary prevention, the role of these factors in colorectal cancer survival is less studied.3 In two cohort studies, prediagnosis dietary calcium intake was not associated with all-cause mortality among patients with colorectal cancer, consistent with our findings.14,15 25(OH)D, either pre- or postdiagnosis, was associated with longer colorectal cancer survival in four previous studies1619; in the current study, we observed no association with dietary vitamin D intake, which may not optimally reflect serum vitamin D status. In a large, pooled analysis, vitamin D intake was positively associated with serum 25(OH)D level, but the associations were relatively weak (Spearman correlation was 0.22 for dietary vitamin D and 0.29 for total vitamin D).34
In the current study, we found a statistically significant lower risk of death among patients with higher postdiagnostic intakes of total calcium and milk. Although not completely understood, several possible biologic mechanisms might underlie these associations. Clinical trials conducted among patients with previous colorectal adenoma suggested that daily treatment with calcium, compared with placebo, was associated with lower risk of colorectal adenoma recurrence.8,35,36 Potential mechanisms include calcium's ability to bind to bile and fatty acids and prevent or lower toxicity3739; direct effects on colonocyte proliferation,40,41 differentiation,42 and apoptosis43; and, alterations in K-ras mutations.44 Although these mechanisms were originally proposed in the primary prevention context, it is reasonable to hypothesize that calcium may also act through these mechanisms after diagnosis to reduce the risk of cancer recurrence, thus ultimately improving chance of survival. Direct clinical or epidemiologic evidence of calcium in colorectal cancer progression is limited, but in vitro evidence suggests that calcium may promote E-cadherin expression and suppress β-catenin/T-cell factor activation through the calcium sensing receptor, and restrain their malignant behaviors.45 Thus, calcium may be capable of limiting growth and distant metastasis from cancer cells that escaped the colon at the time of treatment. In our data, the strong inverse association of postdiagnosis total calcium intake with colorectal cancer–specific mortality was consistent with these mechanisms.
Whereas postdiagnosis calcium intake was associated with lower risk of all-cause and colorectal cancer–specific mortality, there were no such associations with prediagnosis diet. Reasons for these discrepant findings are unclear. It is possible that calcium may have short-term rather than long-term effects on colorectal cancer progression and survival, and therefore only postdiagnosis diet is relevant in this context. It is also important to note that different FFQs were used to assess pre- and postdiagnosis diet. The correlation coefficient (Pearson) between pre- and postdiagnosis total calcium intake was 0.37: this moderate correlation might suggest that participants changed their calcium intake after cancer diagnosis or, alternatively, this could reflect differences in the dietary assessment instruments. The FFQs used in this study included the major food and beverage sources of calcium and vitamin D, and validation studies have shown good agreements between estimates from diet recall and these FFQs (eg, Pearson correlation coefficients ranged from 0.57 to 0.66 for calcium and from 0.52 to 0.88 for dairy products).27,29,30 Therefore, we believe that the low correlations between pre- and postdiagnosis diet are more likely resulting from real changes in diet after cancer diagnosis.
We observed a potential higher risk of all-cause mortality from prediagnosis supplemental calcium intake, especially when restricting the analysis to the 1,111 participants who were included in the postdiagnosis analyses. Additional research should address whether this is a real potential harm to colorectal cancer patients.
Milk may be associated with improved survival among patients with colorectal cancer because it is a rich source of dietary calcium and vitamin D. In addition, milk is a primary dietary source of conjugated linoleic acid, which was found to inhibit colorectal cancer cell growth in vitro.46,47 Other potentially beneficial components in dairy products include butyric acid, lactoferrin, and fermentation products.46
The strengths of our study include its large sample size, prospective design, and detailed pre- and postdiagnosis questionnaire information. We were also able to examine cause-specific mortality. Limitations include the lack of information on adverse effects from treatment and tumor recurrence. FFQs may underestimate diet-disease associations compared with more objective biomarker measurements because of nondifferential misclassification. For large cohort studies, however, FFQs offer a feasible method to detect potential associations (especially when using energy-adjusted nutrients) in the absence of biomarker measurements.48 As in most studies of this type, estimates of the effects of prediagnosis exposures are potentially biased because of selecting patients who survived until the occurrence of colorectal cancer or the first postdiagnosis questionnaire.4951
In conclusion, higher intakes of total calcium and milk after, but not before, colorectal cancer diagnosis may be associated with lower overall mortality. Our findings, if replicated in future observational studies and randomized trials, will provide important guidance for cancer survivors who are actively seeking diet and lifestyle changes to improve their prognosis.

Acknowledgment

The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. We thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors also acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries and cancer registries supported by the National Cancer Institute SEER program.
See accompanying editorial on page 2290

Authors' Disclosures of Potential Conflicts of Interest

The authors indicated no potential conflicts of interest.

References

1.
Cancer Facts & Figures 2014 2014 American Cancer Society Atlanta American Cancer Society
2.
AT Chan, EL Giovannucci: Primary prevention of colorectal cancer Gastroenterology 138: 2029– 2043,2010 e10
3.
A Vrieling, E Kampman: 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 92: 471– 490,2010
4.
CL Rock, C Doyle, W Demark-Wahnefried, etal: Nutrition and physical activity guidelines for cancer survivors CA Cancer J Clin 62: 243– 274,2012
5.
F Bray, JS Ren, E Masuyer, etal: Global estimates of cancer prevalence for 27 sites in the adult population in 2008 Int J Cancer 132: 1133– 1145,2013
6.
E Cho, SA Smith-Warner, D Spiegelman, etal: Dairy foods, calcium, and colorectal cancer: A pooled analysis of 10 cohort studies J Natl Cancer Inst 96: 1015– 1022,2004
7.
M Touvier, DS Chan, R Lau, etal: Meta-analyses of vitamin D intake, 25-hydroxyvitamin D status, vitamin D receptor polymorphisms, and colorectal cancer risk Cancer Epidemiol Biomarkers Prev 20: 1003– 1016,2011
8.
JA Baron, M Beach, JS Mandel, etal: Calcium supplements for the prevention of colorectal adenomas. Calcium Polyp Prevention Study Group N Engl J Med 340: 101– 107,1999
9.
J Wactawski-Wende, JM Kotchen, GL Anderson, etal: Calcium plus vitamin D supplementation and the risk of colorectal cancer N Engl J Med 354: 684– 696,2006
10.
JA Cauley, RT Chlebowski, J Wactawski-Wende, etal: Calcium plus vitamin D supplementation and health outcomes five years after active intervention ended: The Women's Health Initiative J Womens Health (Larchmt) 22: 915– 929,2013
11.
RL Prentice, MB Pettinger, RD Jackson, etal: Health risks and benefits from calcium and vitamin D supplementation: Women's Health Initiative clinical trial and cohort study Osteoporos Int 24: 567– 580,2013
12.
MJ Bolland, A Grey, GD Gamble, etal: Calcium and vitamin D supplements and health outcomes: A reanalysis of the Women's Health Initiative (WHI) limited-access data set Am J Clin Nutr 94: 1144– 1149,2011
13.
EL Ding, S Mehta, WW Fawzi, etal: Interaction of estrogen therapy with calcium and vitamin D supplementation on colorectal cancer risk: Reanalysis of Women's Health Initiative randomized trial Int J Cancer 122: 1690– 1694,2008
14.
ML Slattery, TK French, MJ Egger, etal: Diet and survival of patients with colon cancer in Utah: Is there an association? Int J Epidemiol 18: 792– 797,1989
15.
JA Zell, AJ McEligot, A Ziogas, etal: Differential effects of wine consumption on colorectal cancer outcomes based on family history of the disease Nutr Cancer 59: 36– 45,2007
16.
V Fedirko, E Riboli, A Tjønneland, etal: Prediagnostic 25-hydroxyvitamin D, VDR and CASR polymorphisms, and survival in patients with colorectal cancer in western European ppulations Cancer Epidemiol Biomarkers Prev 21: 582– 593,2012
17.
K Ng, JA Meyerhardt, K Wu, etal: Circulating 25-hydroxyvitamin d levels and survival in patients with colorectal cancer J Clin Oncol 26: 2984– 2991,2008
18.
K Ng, BM Wolpin, JA Meyerhardt, etal: Prospective study of predictors of vitamin D status and survival in patients with colorectal cancer Br J Cancer 101: 916– 923,2009
19.
H Mezawa, T Sugiura, M Watanabe, etal: Serum vitamin D levels and survival of patients with colorectal cancer: Post-hoc analysis of a prospective cohort study BMC Cancer 10: 347,2010
20.
EE Calle, C Rodriguez, EJ Jacobs, etal: The American Cancer Society Cancer Prevention Study II Nutrition Cohort: Rationale, study design, and baseline characteristics Cancer 94: 2490– 2501,2002
21.
EE Calle, DD Terrell: Utility of the National Death Index for ascertainment of mortality among cancer prevention study II participants Am J Epidemiol 137: 235– 241,1993
22.
ML McCullough, SM Gapstur, R Shah, etal: Association between red and processed meat intake and mortality among colorectal cancer survivors J Clin Oncol 31: 2773– 2782,2013
23.
AN Dehal, CC Newton, EJ Jacobs, etal: Impact of diabetes mellitus and insulin use on survival after colorectal cancer diagnosis: The Cancer Prevention Study-II Nutrition Cohort J Clin Oncol 30: 53– 59,2012
24.
PT Campbell, AV Patel, CC Newton, etal: Associations of recreational physical activity and leisure time spent sitting with colorectal cancer survival J Clin Oncol 31: 876– 885,2013
25.
PT Campbell, CC Newton, AN Dehal, etal: Impact of body mass index on survival after colorectal cancer diagnosis: The Cancer Prevention Study-II Nutrition Cohort J Clin Oncol 30: 42– 52,2012
26.
G Block, AM Hartman, D Naughton: A reduced dietary questionnaire: Development and validation Epidemiology 1: 58– 64,1990
27.
EW Flagg, RJ Coates, EE Calle, etal: Validation of the American Cancer Society Cancer Prevention Study II Nutrition Survey Cohort Food Frequency Questionnaire Epidemiology 11: 462– 468,2000
28.
WC Willett, L Sampson, MJ Stampfer, etal: Reproducibility and validity of a semiquantitative food frequency questionnaire Am J Epidemiol 122: 51– 65,1985
29.
EB Rimm, EL Giovannucci, MJ Stampfer, etal: Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals Am J Epidemiol 135: 1114– 1126,1992
30.
D Feskanich, EB Rimm, EL Giovannucci, etal: Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire J Am Diet Assoc 93: 790– 796,1993
31.
D Aune, R Lau, DS Chan, etal: Dairy products and colorectal cancer risk: A systematic review and meta-analysis of cohort studies Ann Oncol 23: 37– 45,2012
32.
ML McCullough, AS Robertson, C Rodriguez, etal: Calcium, vitamin D, dairy products, and risk of colorectal cancer in the Cancer Prevention Study II Nutrition Cohort (United States) Cancer Causes Control 14: 1– 12,2003
33.
Colorectal cancer 2011 report: Food, nutrition, physical activity, and the prevention of colorectal cancer World Cancer Research Fund/American Institute for Cancer Research http://www.aicr.org/continuous-update-project/reports/Colorectal-Cancer-2011-Report.pdf
34.
ML McCullough, SJ Weinstein, DM Freedman, etal: Correlates of circulating 25-hydroxyvitamin D: Cohort Consortium Vitamin D Pooling Project of Rarer Cancers Am J Epidemiol 172: 21– 35,2010
35.
C Bonithon-Kopp, O Kronborg, A Giacosa, etal: Calcium and fibre supplementation in prevention of colorectal adenoma recurrence: A randomised intervention trial. European Cancer Prevention Organisation Study Group Lancet 356: 1300– 1306,2000
36.
B Hofstad, K Almendingen, M Vatn, etal: Growth and recurrence of colorectal polyps: A double-blind 3-year intervention with calcium and antioxidants Digestion 59: 148– 156,1998
37.
HL Newmark, MJ Wargovich, WR Bruce: Colon cancer and dietary fat, phosphate, and calcium: A hypothesis J Natl Cancer Inst 72: 1323– 1325,1984
38.
MJ Wargovich, VW Eng, HL Newmark: Calcium inhibits the damaging and compensatory proliferative effects of fatty acids on mouse colon epithelium Cancer Lett 23: 253– 258,1984
39.
MJ Govers, DS Termont, JA Lapré, etal: Calcium in milk products precipitates intestinal fatty acids and secondary bile acids and thus inhibits colonic cytotoxicity in humans Cancer Res 56: 3270– 3275,1996
40.
M Buset, M Lipkin, S Winawer, etal: Inhibition of human colonic epithelial cell proliferation in vivo and in vitro by calcium Cancer Res 46: 5426– 5430,1986
41.
RM Bostick, L Fosdick, JR Wood, etal: Calcium and colorectal epithelial cell proliferation in sporadic adenoma patients: A randomized, double-blinded, placebo-controlled clinical trial J Natl Cancer Inst 87: 1307– 1315,1995
42.
V Fedirko, RM Bostick, WD Flanders, etal: Effects of vitamin D and calcium on proliferation and differentiation in normal colon mucosa: A randomized clinical trial Cancer Epidemiol Biomarkers Prev 18: 2933– 2941,2009
43.
V Fedirko, RM Bostick, WD Flanders, etal: Effects of vitamin D and calcium supplementation on markers of apoptosis in normal colon mucosa: A randomized, double-blind, placebo-controlled clinical trial Cancer Prev Res (Phila) 2: 213– 223,2009
44.
X Llor, RF Jacoby, BB Teng, etal: K-ras mutations in 1,2-dimethylhydrazine-induced colonic tumors: Effects of supplemental dietary calcium and vitamin D deficiency Cancer Res 51: 4305– 4309,1991
45.
S Chakrabarty, V Radjendirane, H Appelman, etal: Extracellular calcium and calcium sensing receptor function in human colon carcinomas: Promotion of E-cadherin expression and suppression of beta-catenin/TCF activation Cancer Res 63: 67– 71,2003
46.
T Norat, E Riboli: Dairy products and colorectal cancer. A review of possible mechanisms and epidemiological evidence Eur J Clin Nutr 57: 1– 17,2003
47.
TD Shultz, BP Chew, WR Seaman, etal: Inhibitory effect of conjugated dienoic derivatives of linoleic acid and beta-carotene on the in vitro growth of human cancer cells Cancer Lett 63: 125– 133,1992
48.
V Kipnis, AF Subar, D Midthune, etal: Structure of dietary measurement error: Results of the OPEN biomarker study Am J Epidemiol 158: 14– 21,2003 discussion 22-26
49.
WD Flanders, M Klein: Properties of 2 counterfactual effect definitions of a point exposure Epidemiology 18: 453– 460,2007
50.
IJ Dahabreh, DM Kent: Index event bias as an explanation for the paradoxes of recurrence risk research JAMA 305: 822– 823,2011
51.
WD Flanders, RC Eldridge, W McClellan: A nearly unavoidable mechanism for collider bias with index-event studies Epidemiology (in press)

Appendix

Table A1. Comparison of Questions on Each Food Frequency Questionnaire on Usual Intake of Dairy Foods, Calcium Supplements, and Multivitamins: Cancer Prevention Study II Nutrition Cohort
Food 1992* 1999 2003
Milk Whole milk and beverages with whole milk Whole milk Whole milk
  2% milk and beverages with 2% milk 2% milk 2% milk
  Skim milk, 1%, or buttermilk Skim or 1% milk Skim or 1% milk
Other dairy products Cheeses and cheese spreads (regular and low fat) Cheese (cottage or ricotta, and other) Cheese (cottage or ricotta, and other)
  Ice cream (regular and low fat) Ice cream (regular and nonfat/sherbet) Ice cream (regular and nonfat/sherbet)
  Yogurt (regular and low fat, including frozen) Yogurt (plain or artificially sweetened, frozen, and other) Yogurt (plain or artificially sweetened, frozen, and other)
  Restaurant pizza Pizza Pizza
Calcium supplements Calcium or dolomite Calcium Calcium
  Frequency per week or per day Regular use: yes/no Regular use: yes/no
  Amount in each tablet (250 mg, 500 mg, 600 mg, or 750 mg) Amount per day (≤ 900 mg [calculated as 500 mg], ≥ 901 mg [calculated as 1,000 mg], unknown) Pills per week
Amount in each pill (≤ 350 mg [calculated as 250 mg], ≥ 400 mg [calculated as 500 mg], unknown)
Multivitamins Use at least once per week yes/no Currently yes/no Currently yes/no
  Type Frequency per week Frequency per week
      Stress-tabs type Brand (write-in) Brand (write-in)
      Therapeutic, Theragran type    
      One-a-day type or Centrum    
  No. of tablets per day or per week    
*
Dairy in 1992 was calculated as all types of milk (8-ounce glass serving) plus cheese and cheese spreads (2-ounce serving) plus ice cream (1½-cup serving) plus yogurt (1-cup serving) plus cheese on pizza (1½-ounce serving).
Dairy in 1999 and 2003 was calculated as all types of milk (8-ounce glass serving) plus ice cream (1½-cup serving) plus yogurt (1-cup serving) plus cottage cheese (2-cup serving) plus processed cheese (2-ounce serving) plus hard cheese (1½-ounce serving) plus cheese on pizza (1½-ounce serving).
Table A2. Associations of 1992 Prediagnostic Calcium, Vitamin D, and Dairy Intakes With Colorectal Cancer Mortality Among Patients With Nonmetastatic Colorectal Cancer in the CPS-II Nutrition Cohort
Exposure Range* Total No. of Deaths Person-Years Base Model MV Model
Men Women RR 95% CI RR 95% CI
Total calcium, mg/d                
    Q1§ < 578 < 553 101 4,288 1.00 1.00
    Q2 578 to < 776 553 to < 776 103 4,460 0.99 0.75 to 1.32 1.04 0.77 to 1.39
    Q3 776 to < 1,044 776 to < 1,156 102 4,255 0.88 0.66 to 1.17 0.94 0.69 to 1.26
    Q4 ≥ 1,044 ≥ 1,156 102 4,235 0.95 0.72 to 1.26 1.01 0.74 to 1.38
    Ptrend         0.78 0.91
Dietary calcium, mg/d                
    Q1§ < 548 < 486 106 4,146 1.00 1.00
    Q2 548 to < 729 486 to < 629 99 4,496 0.93 0.70 to 1.24 0.96 0.72 to 1.28
    Q3 729 to < 949 629 to < 849 107 4,272 0.95 0.72 to 1.25 0.99 0.74 to 1.31
    Q4 ≥ 949 ≥ 849 96 4,325 0.82 0.62 to 1.10 0.86 0.64 to 1.16
    Ptrend         0.18 0.30
Supplemental calcium, mg/d                
    C1§ 0 250 10,733 1.00 1.00
    C2 0.1 to < 250 95 3,891 0.98 0.77, 1.25 1.11 0.79, 1.57
    C3 ≥ 250 63 2,615 1.07 0.79, 1.43 1.18 0.83, 1.66
    Ptrend         0.76 0.36
Total vitamin D, IU/d                
    Q1§ < 122 < 111 96 4,284 1.00 1.00
    Q2 122 to < 191 111 to < 201 108 4,370 1.06 0.80 to 1.40 1.12 0.83 to 1.49
    Q3 191 to < 425 201 to < 467 99 4,278 0.90 0.67 to 1.21 1.00 0.72 to 1.38
    Q4 ≥ 425 ≥ 467 105 4,306 0.98 0.73 to 1.30 1.14 0.73 to 1.78
    Ptrend         0.77 0.61
Dietary vitamin D, IU/d                
    Q1§ < 105 < 90 103 4,283 1.00 1.00
    Q2 105 to < 157 90 to < 136 109 4,347 1.01 0.76 to 1.33 1.03 0.78 to 1.36
    Q3 157 to < 226 136 to < 202 93 4,295 0.87 0.65 to 1.16 0.90 0.67 to 1.22
    Q4 ≥ 226 ≥ 202 103 4,313 0.91 0.69 to 1.21 0.96 0.72 to 1.28
    Ptrend         0.39 0.61
Total dairy, servings/wk                
    Q1§ < 5.5 < 5.0 110 4,044 1.00 1.00
    Q2 5.5 to < 9.6 5.0 to < 8.9 91 4,610 0.83 0.62 to 1.10 0.84 0.63 to 1.13
    Q3 9.6 to < 14.5 8.9 to < 13.4 107 4,351 0.89 0.67 to 1.18 0.92 0.69 to 1.23
    Q4 ≥ 14.5 ≥ 13.4 100 4,233 0.86 0.63 to 1.17 0.89 0.65 to 1.22
    Ptrend         0.55 0.73
Milk, servings/wk                
    Q1§ 0 0 110 4,895 1.00 1.00
    Q2 0.1 to < 5.7 0.1 to < 5.1 88 3,771 1.07 0.80 to 1.43 1.06 0.79 to 1.42
    Q3 5.7 to < 10.5 5.1 to < 10.1 114 4,293 1.07 0.81 to 1.40 1.08 0.82 to 1.42
    Q4 ≥ 10.5 ≥ 10.1 96 4,280 0.95 0.71 to 1.28 0.98 0.73 to 1.32
    Ptrend         0.62 0.80
Abbreviations: C, category; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy and total folate intakes.
§
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Table A3. Associations of 1992 Prediagnostic Calcium, Vitamin D, and Dairy Intakes With All-Cause Mortality Among Patients With Colorectal Cancer of All Stages in the CPS-II Nutrition Cohort
Exposure Range* Total No. of Deaths Person- Years Base Model MV Model
Men Women RR 95% CI RR 95% CI
Total calcium, mg/d                
    Q1§ < 579 < 545 337 4,562 1.00 1.00
    Q2 579 to < 775 545 to < 773 335 4,874 0.93 0.79 to 1.09 0.96 0.81 to 1.13
    Q3 775 to < 1,033 773 to < 1,152 344 4,563 0.89 0.76 to 1.05 0.94 0.79 to 1.11
    Q4 ≥ 1,033 ≥ 1,152 366 4,628 0.96 0.82 to 1.12 1.00 0.84 to 1.19
    Ptrend         0.85 0.75
Dietary calcium, mg/d                
    Q1§ < 548 < 481 350 4,427 1.00 1.00
    Q2 548 to < 724 481 to < 620 337 4,859 0.86 0.73 to 1.01 0.88 0.75 to 1.04
    Q3 724 to < 944 620 to < 848 342 4,640 0.92 0.78 to 1.07 0.95 0.81 to 1.12
    Q4 ≥ 944 ≥ 848 353 4,701 0.87 0.75 to 1.03 0.91 0.77 to 1.07
    Ptrend         0.22 0.45
Supplemental calcium, mg/d                
    C1§ 0 851 11,618 1.00 1.00
    C2 0.1 to < 250 321 4,219 1.01 0.88 to 1.16 1.11 0.91 to 1.35
    C3 ≥ 250 210 2,790 1.06 0.90 to 1.26 1.14 0.94 to 1.40
    Ptrend         0.52 0.19
Total vitamin D, IU/d                
    Q1§ < 123 < 108 343 4,534 1.00 1.00
    Q2 123 to < 191 108 to < 193 324 4,728 0.87 0.74 to 1.03 0.92 0.78 to 1.09
    Q3 191 to < 420 193 to < 461 350 4,660 0.94 0.80 to 1.11 1.02 0.85 to 1.22
    Q4 ≥ 420 ≥ 461 365 4,704 0.95 0.81 to 1.11 1.08 0.84 to 1.40
    Ptrend         0.99 0.33
Dietary vitamin D, IU/d                
    Q1§ < 106 < 90 345 4,562 1.00 1.00
    Q2 106 to < 158 90 to < 135 338 4,758 0.88 0.75 to 1.03 0.90 0.77 to 1.06
    Q3 158 to < 227 135 to < 203 331 4,737 0.88 0.75 to 1.03 0.92 0.78 to 1.09
    Q4 ≥ 227 ≥ 203 368 4,571 0.97 0.83 to 1.14 1.02 0.86 to 1.20
    Ptrend         0.98 0.52
Total dairy, servings/wk                
    Q1§ < 5.5 < 4.9 350 4,394 1.00 1.00
    Q2 5.5 to < 9.6 4.9 to < 8.8 321 4,886 0.81 0.68 to 0.95 0.82 0.70 to 0.97
    Q3 9.6 to < 14.6 8.8 to < 13.3 352 4,761 0.89 0.76 to 1.05 0.92 0.78 to 1.09
    Q4 ≥ 14.6 ≥ 13.3 359 4,585 0.92 0.77 to 1.09 0.95 0.80 to 1.14
    Ptrend         0.88 0.79
Milk, servings/wk                
    Q1§ 0 0 440 5,365 1.00 1.00
    Q2 0.1 to < 5.5 0.1 to < 4.8 286 3,847 0.97 0.82 to 1.14 0.96 0.82 to 1.14
    Q3 5.5 to < 10.4 4.8 to < 10.0 344 4,859 0.92 0.78 to 1.08 0.93 0.80 to 1.09
    Q4 ≥ 10.4 ≥ 10.0 368 4,677 0.95 0.81 to 1.12 0.98 0.83 to 1.15
    Ptrend         0.48 0.72
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy and total folate intakes.
§
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Table A4. Associations of 1992 Prediagnostic Calcium, Vitamin D, and Dairy Intakes With All-Cause Mortality Among Patients With Colorectal Cancer With Postdiagnosis Data (n = 1,111) in the CPS-II Nutrition Cohort
Exposure Range* Total No. of Deaths Person-Years Base Model MV Model
Men Women RR 95% CI RR 95% CI
Total calcium, mg/d                
    Q1§ < 578 < 553 100 2,733 1.00 1.00
    Q2 578 to < 776 553 to < 776 101 2,909 0.84 0.63 to 1.12 0.87 0.64 to 1.17
    Q3 776 to < 1,044 776 to < 1,156 107 2,765 0.88 0.65 to 1.17 0.91 0.67 to 1.23
    Q4 ≥ 1,044 ≥ 1,156 121 2,913 0.91 0.68 to 1.20 0.96 0.70 to 1.30
    Ptrend         0.87 0.79
Dietary calcium, mg/d                
    Q1§ < 548 < 486 109 2,556 1.00 1.00
    Q2 548 to < 729 486 to < 629 98 3,031 0.69 0.52 to 0.92 0.69 0.51 to 0.92
    Q3 729 to < 949 629 to < 849 106 2,676 0.81 0.61 to 1.08 0.82 0.61 to 1.10
    Q4 ≥ 949 ≥ 849 116 3,058 0.74 0.56 to 0.97 0.75 0.56 to 1.00
    Ptrend         0.13 0.19
Supplemental calcium, mg/d                
    C1§ 0 251 6,987 1.00 1.00
    C2 0.1 to < 250 110 2,682 1.00 0.79 to 1.27 1.31 0.94 to 1.83
    C3 ≥ 250 68 1,652 1.34 0.99 to 1.80 1.65 1.16 to 2.35
    Ptrend         0.12 0.01
Total vitamin D, IU/d                
    Q1§ < 122 < 111 109 2,718 1.00 1.00
    Q2 122 to < 191 111 to < 201 88 2,843 0.69 0.51 to 0.93 0.70 0.52 to 0.95
    Q3 191 to < 425 201 to < 467 114 2,792 0.84 0.64 to 1.12 0.85 0.63 to 1.16
    Q4 ≥ 425 ≥ 467 118 2,968 0.79 0.60 to 1.05 0.91 0.58 to 1.43
    Ptrend         0.49 0.85
Dietary vitamin D, IU/d                
    Q1§ < 105 < 90 105 2,679 1.00 1.00
    Q2 105 to < 157 90 to < 136 102 2,826 0.88 0.66 to 1.18 0.89 0.66 to 1.19
    Q3 157 to < 226 136 to < 202 107 2,901 0.85 0.64 to 1.12 0.86 0.65 to 1.15
    Q4 ≥ 226 ≥ 202 115 2,915 0.84 0.63 to 1.11 0.85 0.63 to 1.13
    Ptrend         0.22 0.27
Total dairy, servings/wk                
    Q1§ < 5.5 < 5.0 105 2,490 1.00 1.00
    Q2 5.5 to < 9.6 5.0 to < 8.9 92 3,028 0.72 0.54 to 0.98 0.73 0.54 to 0.99
    Q3 9.6 to < 14.5 8.9 to < 13.4 116 2,941 0.83 0.63 to 1.10 0.84 0.63 to 1.12
    Q4 ≥ 14.5 ≥ 13.4 116 2,862 0.80 0.59 to 1.09 0.82 0.60 to 1.13
    Ptrend         0.42 0.52
Milk, servings/wk                
    Q1§ 0 0 118 3,259 1.00 1.00
    Q2 0.1 to < 5.7 0.1 to < 5.1 99 2,304 1.23 0.93 to 1.64 1.23 0.93 to 1.64
    Q3 5.7 to < 10.5 5.1 to < 10.1 100 2,883 0.89 0.67 to 1.19 0.91 0.69 to 1.21
    Q4 ≥ 10.5 ≥ 10.1 112 2,874 0.89 0.67 to 1.19 0.90 0.68 to 1.20
    Ptrend         0.14 0.17
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and 1992 total energy and total folate intakes.
§
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Table A5. Associations of Postdiagnosis Calcium, Vitamin D, and Dairy Intakes With Colorectal Cancer Mortality Among Patients With Nonmetastatic Colorectal Cancer in the CPS-II Nutrition Cohort
Exposure 1999 Range* 2003 Range* Total No. of Deaths Person-Years Base Model MV Model
Men Women Men Women RR 95% CI RR 95% CI
Total calcium, mg/d                    
    Q1§ < 581 < 713 < 683 < 773 37 1,903 1.00 1.00
    Q2 581 to < 775 713 to < 1,170 683 to < 882 773 to < 1,131 49 2,098 1.11 0.69 to 1.78 1.15 0.71 to 1.86
    Q3 775 to <1,105 1,170 to < 1,598 882 to < 1,162 1,131 to < 1,591 33 2,078 0.75 0.46 to 1.24 0.81 0.48 to 1.38
    Q4 ≥ 1,105 ≥ 1,598 ≥ 1,162 ≥ 1,591 24 2,325 0.54 0.31 to 0.94 0.59 0.33 to 1.05
    Ptrend             < 0.01 0.01
Dietary calcium, mg/d                    
    Q1§ < 532 < 525 < 613 < 609 35 1,804 1.00 1.00
    Q2 532 to < 683 525 to < 671 613 to < 765 609 to < 766 36 2,277 0.86 0.52 to 1.42 0.85 0.51 to 1.41
    Q3 683 to < 885 671 to < 892 765 to < 968 766 to < 990 35 2,100 0.90 0.55 to 1.48 0.98 0.59 to 1.62
    Q4 ≥ 885 ≥ 892 ≥ 968 ≥ 990 37 2,223 0.91 0.56 to 1.47 1.00 0.61 to 1.63
    Ptrend             0.53 0.83
Supplemental calcium, mg/d                    
    C1§ 0 0 74 3,966 1.00 1.00
    C2 0.1 to < 500 0.1 to < 500 42 2,001 0.92 0.61 to 1.38 1.04 0.65 to 1.69
    C3 ≥ 500 ≥ 500 27 2,437 0.58 0.35 to 0.95 0.65 0.38 to 1.11
    Ptrend             0.04 0.13
Total vitamin D, IU/d                    
    Q1§ < 164 < 151 < 194 < 219 33 1,894 1.00 1.00
    Q2 164 to < 302 151 to < 379 194 to < 389 219 to < 509 38 2,087 0.84 0.51 to 1.38 0.99 0.59 to 1.66
    Q3 302 to < 559 379 to < 588 389 to < 603 509 to < 685 34 2,167 0.85 0.51 to 1.42 1.31 0.66 to 2.58
    Q4 ≥ 559 ≥ 588 ≥ 603 ≥ 685 38 2,256 0.90 0.54 to 1.49 1.74 0.80 to 3.77
    Ptrend             0.45 0.52
Dietary vitamin D, IU/d                    
    Q1§ < 122 < 100 < 132 < 103 32 2,065 1.00 1.00
    Q2 122 to < 178 100 to < 155 132 to < 188 103 to < 178 34 2,144 0.76 0.45 to 1.28 0.78 0.46 to 1.32
    Q3 178 to < 245 155 to < 229 188 to < 267 178 to < 257 37 2,109 1.01 0.61 to 1.68 1.11 0.67 to 1.85
    Q4 ≥ 245 ≥ 229 ≥ 267 ≥ 257 40 2,085 1.18 0.72 to 1.93 1.28 0.77 to 2.10
    Ptrend             0.31 0.19
Total dairy, servings/wk                    
    Q1§ < 4.7 < 3.9 < 5.1 < 4.8 37 1,931 1.00 1.00
    Q2 4.7 to < 8.2 3.9 to < 7.7 5.1 to < 8.8 4.8 to < 8.1 31 2,106 0.73 0.44 to 1.22 0.73 0.44 to 1.23
    Q3 8.2 to < 11.7 7.7 to < 11.6 8.8 to < 12.3 8.1 to < 12.4 41 2,068 0.87 0.53 to 1.44 0.92 0.56 to 1.52
    Q4 ≥ 11.7 ≥ 11.6 ≥ 12.3 ≥ 12.4 34 2,299 0.71 0.42 to 1.19 0.73 0.44 to 1.23
    Ptrend             0.26 0.32
Milk, servings/wk                    
    Q1§ < 1.1 < 1.0 < 1.0 < 1.0 33 1,844 1.00 1.00
    Q2 1.1 to < 5.6 1.0 to < 3.5 1.0 to < 5.6 1.0 to < 3.3 33 2,135 0.88 0.53 to 1.45 0.90 0.54 to 1.49
    Q3 5.6 to < 7.0 3.5 to < 7.0 5.6 to < 7.0 3.3 to < 7.0 14 881 0.85 0.43 to 1.65 0.85 0.44 to 1.67
    Q4 ≥ 7.0 ≥ 7.0 ≥ 7.0 ≥ 7.0 63 3,543 0.87 0.55 to 1.38 0.93 0.59 to 1.49
    Ptrend             0.61 0.81
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Range obtained from each questionnaire- and sex-specific quartile of all exposures, except for supplemental calcium, which was obtained from each category for both sexes combined.
Base model adjusted for age at diagnosis, sex, tumor stage, and post-diagnosis total energy intake.
Multivariable model adjusted for age at diagnosis, sex, tumor stage, and post-diagnosis total energy and total folate intakes.
§
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.
Table A6. Associations of Postdiagnosis Calcium, Vitamin D, and Dairy Intakes With All-Cause Mortality Among Patients With Colorectal Cancer in the CPS-II Nutrition Cohort Stratified by Tumor Stage at Diagnosis
Exposure Localized Stage Regional Stage Pinteraction
Total No. of Deaths Person-Years RR 95% CI Total No. of Deaths Person-Years RR 95% CI
Total calcium, mg/d                  
    Q1* 55 1,150 1.00 57 753 1.00  
    Q2 60 1,237 1.00 0.67 to 1.49 58 861 0.79 0.52 to 1.20  
    Q3 60 1,190 0.97 0.64 to 1.46 40 888 0.52 0.33 to 0.82  
    Q4 46 1,332 0.69 0.45 to 1.07 53 993 0.74 0.47 to 1.16  
    Ptrend       0.12       0.09 .08
Dietary calcium, mg/d                  
    Q1* 50 1,031 1.00 55 773 1.00  
    Q2 65 1,351 0.91 0.61 to 1.35 53 926 0.75 0.50 to 1.14  
    Q3 47 1,211 0.72 0.47 to 1.11 43 889 0.67 0.43 to 1.04  
    Q4 59 1,316 0.83 0.55 to 1.25 57 907 0.90 0.60 to 1.34  
    Ptrend       0.30       0.49 .75
Supplemental calcium, mg/d                  
    C1* 122 2,404 1.00 99 1,562 1.00  
    C2 48 1,179 0.87 0.59 to 1.29 60 822 1.05 0.69 to 1.61  
    C3 51 1,326 1.00 0.67 to 1.48 49 1,111 0.99 0.64 to 1.54  
    Ptrend       0.92       0.96 .68
Total vitamin D, IU/d                  
    Q1* 57 1,186 1.00 48 708 1.00  
    Q2 52 1,096 0.88 0.57 to 1.34 49 991 0.76 0.48 to 1.19  
    Q3 60 1,270 0.98 0.60 to 1.60 48 897 0.99 0.55 to 1.77  
    Q4 52 1,358 0.64 0.36 to 1.13 63 897 1.33 0.68 to 2.59  
    Ptrend       0.13       0.79 .23
Dietary vitamin D, IU/d                  
    Q1* 61 1,254 1.00 42 811 1.00  
    Q2 58 1,241 0.93 0.64 to 1.36 57 903 1.13 0.73 to 1.74  
    Q3 53 1,149 0.86 0.58 to 1.27 52 960 1.07 0.69 to 1.67  
    Q4 49 1,266 0.71 0.47 to 1.08 57 820 1.18 0.77 to 1.82  
    Ptrend       0.07       0.57 .53
Total dairy, servings/wk                  
    Q1* 62 1,196 1.00 53 735 1.00  
    Q2 54 1,208 0.93 0.63 to 1.38 55 898 0.91 0.61 to 1.38  
    Q3 49 1,096 0.85 0.57 to 1.28 49 971 0.63 0.40 to 0.98  
    Q4 56 1,409 0.69 0.46 to 1.05 51 889 0.82 0.53 to 1.27  
    Ptrend       0.06       0.37 .42
Milk, servings/wk                  
    Q1* 58 1,100 1.00 48 744 1.00  
    Q2 55 1,282 0.76 0.51 to 1.13 54 853 1.00 0.66 to 1.52  
    Q3 21 464 0.75 0.43 to 1.28 20 417 0.75 0.43 to 1.31  
    Q4 87 2,063 0.67 0.46 to 0.98 86 1,480 0.80 0.54 to 1.18  
    Ptrend       0.05       0.17 .84
NOTE. Only showing multivariable model results adjusted for age at diagnosis, sex, tumor stage, and postdiagnosis total energy and total folate intakes.
Abbreviations: C, category; CPS-II, Cancer Prevention Study-II; MV, multivariable; Q, quartile; RR, relative risk.
*
Reference group.
Ptrend calculated by using the median exposure in each quartile, specific to sex, for all exposures except for supplement calcium, which was calculated using the actual categories (ie, 1, 2, and 3) for both sexes.
Supplemental calcium was categorized based on visually inspecting the distribution of the variable.

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Journal of Clinical Oncology
Pages: 2335 - 2343
PubMed: 24958826

History

Published online: June 23, 2014
Published in print: August 01, 2014

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Baiyu Yang
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Marjorie L. McCullough
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Susan M. Gapstur
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Eric J. Jacobs
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Roberd M. Bostick
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Veronika Fedirko
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
W. Dana Flanders
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.
Peter T. Campbell [email protected]
Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, and Peter T. Campbell, American Cancer Society; Roberd M. Bostick, Veronika Fedirko, and W. Dana Flanders, Winship Cancer Institute, Emory University; Baiyu Yang, Emory University, Atlanta, GA.

Notes

Corresponding author: Peter T. Campbell, PhD, Epidemiology Research Program, American Cancer Society National Home Office, 250 Williams St NW, Atlanta, GA 30303; e-mail: [email protected]

Author Contributions

Conception and design: Baiyu Yang, Marjorie L. McCullough, Peter T. Campbell
Financial support: Susan M. Gapstur, Peter T. Campbell
Administrative support: Susan M. Gapstur, Peter T. Campbell
Provision of study materials or patients: Marjorie L. McCullough, Susan M. Gapstur, Peter T. Campbell
Collection and assembly of data: Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, Peter T. Campbell
Data analysis and interpretation: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors

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Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Funding Information

Supported by the American Cancer Society.

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Baiyu Yang, Marjorie L. McCullough, Susan M. Gapstur, Eric J. Jacobs, Roberd M. Bostick, Veronika Fedirko, W. Dana Flanders, Peter T. Campbell
Journal of Clinical Oncology 2014 32:22, 2335-2343

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