Published OnlineFirst July 21, 2014; DOI: 10.1158/1055-9965.EPI-14-0136
Cancer Epidemiology, Biomarkers & Prevention
Research Article
Dietary One-Carbon Nutrient Intake and Risk of Lymphoid and Myeloid Neoplasms: Results of the Netherlands Cohort Study Mirjam M. Heinen1, Piet A. van den Brandt1, Leo J. Schouten1, R. Alexandra Goldbohm2, Harry C. Schouten3, and Bas A.J. Verhage1
Abstract Background: Previous epidemiologic research suggests a protective role of one-carbon nutrients in carcinogenesis. Folate, however, may play a dual role in neoplasms development: protect early in carcinogenesis and promote carcinogenesis at a later stage. We prospectively examined associations between intake of total folate, methionine, riboflavin, vitamin B6, and risk of lymphoid and myeloid neoplasms (including subtypes) and investigated whether alcohol modified the effects of folate. Methods: The Netherlands Cohort Study consists of 120,852 individuals who completed a baseline questionnaire in 1986, including a 150-item food-frequency questionnaire. After 17.3 years of follow-up, 1,280 cases of lymphoid and 222 cases of myeloid neoplasms were available for analysis. Results: Intakes of folate, methionine, and riboflavin were not associated with lymphoid or myeloid neoplasms. For vitamin B6, a statistically significantly increased myeloid neoplasms risk was observed (highest vs. lowest quintile: HR ¼ 1.87; 95% confidence intervals, 1.08–3.25). When analyzing by lymphoid and myeloid neoplasms subtypes, no clear associations were observed for most subtypes, with just a few increased risks for some subtypes and nutrients. Some risks became nonsignificant after excluding early cases. No interaction between alcohol and folate was observed. Conclusions: We observed a few significant positive associations; however, some of these would be expected to arise due to chance alone. Furthermore, some risks became nonsignificant after excluding early cases. Therefore, we conclude that there is no association between one-carbon nutrient intake and risk of lymphoid and myeloid neoplasms. Impact: This study contributes substantially to the limited and inconclusive evidence on the association with one-carbon nutrients. Cancer Epidemiol Biomarkers Prev; 23(10); 2153–64. 2014 AACR.
Introduction Hematologic malignancies are a heterogeneous group of neoplasms that originate from lymphoid and myeloid cells. They account for 7.4% of cancers in males and 6.4% in females worldwide (1). So far, little is known about the causes and only few factors have been linked to lymphoid neoplasms, including age, gender, primary/inherited
1 Department of Epidemiology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centreþ, Maastricht, the Netherlands. 2Department of Prevention and Health, TNO Quality of Life, Leiden, the Netherlands. 3Division of Hematology, Department of Internal Medicine, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centreþ, Maastricht, the Netherlands.
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/). Corresponding Author: Bas A.J. Verhage, Department of Epidemiology, Maastricht University Medical Centreþ, P.O. Box 616, 6200 MD Maastricht, the Netherlands. Phone: 31-43-3882371; Fax: 31-43-388 4128; E-mail: [email protected] doi: 10.1158/1055-9965.EPI-14-0136 2014 American Association for Cancer Research.
immune deficiencies, a number of viruses including the human immunodeficiency virus, and several autoimmune diseases (2). Risk factors for myeloid neoplasms include age, gender, genetic abnormalities, family history, and exposures to radiation and benzene (3). Stratification for subtypes of lymphoid and myeloid neoplasms in previous studies suggested etiologic heterogeneity (4, 5) and, therefore, further research is warranted in which these subtypes with sufficient numbers can be examined. Previous epidemiologic research suggests a protective role of dietary one-carbon nutrients in colorectal carcinogenesis (6, 7) and perhaps other cancers, such as breast, ovarian, and gastric (7–10). Furthermore, research has suggested that alcohol influences cancer risk by antagonizing the one-carbon metabolism (11). However, research also shows that folate may play a dual role in cancer development: supplementation may provide protection early in carcinogenesis in individuals with a low folate status, yet supplementation late in carcinogenesis and potentially at very high intakes may promote carcinogenesis (12–14). Dietary folate consists of monoglutamate and polyglutamate folate species. Many studies have
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most common subtypes, in a large prospective cohort study of men and women in the Netherlands. Furthermore, we examined whether the effect of dietary folate was modified by alcohol intake.
determined that the relative bioavailability of polyglutamates versus corresponding monoglutamates ranges from 50% to 100% (15), therefore, beneficial effects of folate intake might be stronger for folate monoglutamates than for folate polyglutamates. So far, data on the association between one-carbon nutrients and the risk of lymphoid neoplasms are limited to two cohort (16, 17) and four case–control studies (18–21). One cohort study among U.S. women observed no association between folate and non-Hodgkin lymphoma (NHL) risk (17). A cohort study among male smokers observed a borderline significantly decreased risk between vitamin B12 and NHL risk, but no associations with other dietary one-carbon nutrients and no associations among lymphoma subtypes and multiple myeloma (16). As for the case–control studies, significantly decreased risks were observed for folate intake with NHL overall (18, 19), with the subtype diffuse large B-cell lymphoma (DLBCL; refs. 19, 20) and with marginal zone lymphoma (19). Decreased risks were also observed for higher vitamin B6 intake with NHL overall (20), with DLBCL (20) and with marginal zone lymphoma (19). For methionine intake, decreased risks were observed with NHL overall (19, 20), with DLBCL (20) and with follicular lymphoma (19, 20). One hospital-based case–control study did only observe associations between one-carbon nutrients and NHL risk when the analyses were stratified by current drinking status (21). Significant decreased risks were observed for folate intake among the abstainers and former drinkers, whereas no associations were observed among current drinkers (21). Another case–control study did, however, not observe any modifying effects of alcohol on NHL risk (20). As far as the researchers know, no epidemiologic studies have been conducted on the relation between onecarbon nutrients and risk of myeloid neoplasms. We investigated the relations between dietary one-carbon nutrients [i.e., total folate, poly- and monoglutamates, riboflavin (vitamin B2), vitamin B6, and methionine] and risk of lymphoid and myeloid neoplasms, including the
Materials and Methods Study population and cancer follow-up The study design of the Netherlands Cohort Study (NLCS) has been reported in detail elsewhere (22). Briefly, the NLCS was initiated in September 1986 and included 58,279 men and 62,573 women ages 55 to 69 years at the beginning of the study, originating from 204 municipalities with computerized population registries. A selfadministered questionnaire on daily dietary habits, lifestyle factors, and other potential risk factors for cancer was completed at baseline. For reasons of efficiency in questionnaire processing and follow-up, the case-cohort approach (23) was used. Incident cases were derived from the entire cohort, whereas the person-years at risk were estimated from a random sample of 5,000 subjects. This subcohort was chosen immediately after baseline and followed up for vital status information. The entire cohort is being monitored for cancer occurrence by annual record linkage to the Netherlands Cancer Registry and the Netherlands Pathology Registry (24, 25). A total of 17.3 years of follow-up (baseline to December 2003; mean follow-up ¼ 13.7 years) were used for the current analysis. Only one subcohort member was lost to follow-up. The completeness of cancer follow-up was estimated to be >96% (26). All prevalent cancer cases at baseline other than skin cancer were excluded (n ¼ 226), leaving 4,774 subcohort members (Fig. 1). Cases were defined as participants with an incident, histologically verified diagnosis of lymphoid neoplasms (n ¼ 1,280) or myeloid neoplasms (n ¼ 222). Histology is coded by the Netherlands Cancer Registry using the International Classification of Diseases for Oncology (27). Using the histology codes provided by the cancer registries, we
Netherlands Cohort Study on diet and cancer 102,852 men + women Subcohort randomly drawn from total cohort
Annual record linkage with NCR and PALGA Follow-up
5,000
Total lymphoid neoplasms
DLBCL
FL
LL/WM
CLL/SLL
PCN
Total myeloid neoplasms
AML
253
405
270
194
343
222
157
17.3 years
Exclusion of prevalent cancer cases at baseline
4,774 (incl 79 cases)
1,512
295
110
99
Exclusion of subjects with incomplete or inconsistent dietary data
4,438 (incl 74 cases)
1,280
248
96
89
213
Figure 1. Flow diagram of subcohort members and cases of lymphoid and myeloid neoplasms whose data were used in the analysis, NLCS on Diet and Cancer, 1986–2003. FL, follicular lymphoma; NCR, Netherlands Cancer Registry.
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Cancer Epidemiology, Biomarkers & Prevention
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Published OnlineFirst July 21, 2014; DOI: 10.1158/1055-9965.EPI-14-0136
One-Carbon Nutrients and Hematologic Malignancies
Table 1. Number of lymphoid and myeloid neoplasms in the NLCS on diet and cancer, 1986–2003 Label Lymphoid neoplasms Hodgkin lymphoma NHL NHL, B cell Precursor NHL, B cell Mature NHL, B cell CLL/SLLc Prolymphocytic leukemia, B cell Mantle-cell lymphoma LL/WMc Burkitt lymphoma/leukemia Follicular lymphomac Marginal-zone lymphoma Hairy-cell leukemia DLBCLc PCNc Heavy chain disease NHL, B cell, NOS NHL, T cell Precursor NHL, T cell Mature NHL, T cell Mycosis fungoides/Sezary syndrome Adult T-cell leukemia/lymphoma Peripheral T-cell lymphoma NK/T-cell lymphoma, nasal-type/ aggressive NK-cell leukemia T-cell large granular lymphocytic leukemia Prolymphocytic leukemia, T cell NHL, T cell, NOS NHL, unknown lineage Composite Hodgkin lymphoma and NHL Lymphoid neoplasms, NOS Myeloid neoplasms AMLc Chronic myeloid leukemia Myeloid leukemia, NOS Leukemia, NOS
All cases, number 1,512 40 1,446 1,355 5
Cases available for analysis, number b 1,280 34 1,225 1,149 5
1,284 253
1,089 213
0
0
66 99
53 89
8 110 37 11 295
6 96 31 10 248
405 0 66 73 0
343 0 55 59 0
T, N, U T, N, U T, N, U
62 20 0 38
50 15 0 31
T, N, U
3
3
9831 9832 9834 9590–9591, 9675 9591, 9675, 9727, 9832, 9835 9596 9590–9591, 9820
T, N, U T, N T, N, U T U
0 1
0 1
11 18
9 17
B, U T, B, N, U
1 25
0 21
9840, 9861, 9866–9874, 9891–9931 9863, 9875, 9945–9946 9860 9800–9801, 9805
NA
270 194
222 157
NA NA NA
61 4 11
51 4 10
ICD-O-3 morphology codes
Cell-type
9650–9667
NA
9727–9728, 9835–9836
B, U B
9670, 9823
B, U
9832 9833 9673 9671, 9761
B B, U B, U B, U
9687, 9826 9690–9691, 9695, 9698 9689, 9699, 9760, 9764 9940 9678–9680 9684 9731–9734 9762 9590–9591, 9675
B, U B, U B, U B, U B, U B B, U B, U B
9729, 9837 9727, 9835
T, U T, N
9700–9701 9827 9702, 9705, 9708–9709, 9714–9718 9719, 9948
a
Abbreviations: ICD-O-3, International Classification of Diseases for Oncology, 3rd edition; NOS, not otherwise specified. a Cell type: T, T cell; B, B cell; N, Null/NK cell; U, unknown. b Exclusion of cases with incomplete or inconsistent dietary data. c Subtypes used for analyses.
subdivided the lymphoid neoplasms into categories based on the hierarchical groupings of the International Lymphoma Epidemiology Consortium (InterLymph) nested classification (Table 1; ref. 28). This classification is based on the World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues (29) and the International Classification of Diseases for Oncology, Third Edition (27). About the myeloid neoplasms, we grouped these malignancies into categories defined by the WHO classification (Table 1; ref. 29). For cases that could not be assigned to a specific category (e.g., the "not other-
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wise specified" categories), the summary of the pathology report (received from the Netherlands Pathology Registry PALGA; ref. 24) was inspected and, if possible, an appropriate category was assigned. This was the case for 66.4% (n ¼ 75) for NHL, B cell, NOS; 55.6% (n ¼ 10) for NHL, T cell, NOS; 81.1% (n ¼ 73) for NHL, unknown lineage; 77.8% (n ¼ 35) for lymphoid neoplasms, NOS; and 100% (n ¼ 2) for leukemia, NOS. Subjects with incomplete or inconsistent dietary data were excluded from analysis. Details are given elsewhere (30). Figure 1 shows the selection and exclusion steps that
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resulted in the final numbers of cases and subcohort members that were available for analysis. The NLCS has been approved by the Institutional Review Boards of the TNO Nutrition and Food Research Institute (Zeist, the Netherlands) and Maastricht University (Maastricht, the Netherlands). Questionnaire The dietary section of the questionnaire was a 150-item semiquantitative food-frequency questionnaire (FFQ), which concentrated on the habitual consumption during the year preceding the start of the study. Questionnaire data were key-entered and processed for all incident cases in the cohort and subcohort members in a standardized manner blinded with respect to case/subcohort status. This was done to minimize observer bias in coding and interpretation of the data. Folate intake was calculated using data from a validated liquid chromatography trienzyme method used to analyze the 125 most important Dutch food items contributing to folate intake (31). Daily mean intakes of all other relevant nutrients were calculated by summing the multiplied frequencies and portion sizes of all food items with their tabulated nutrient contents from the Dutch Food Composition Table (32). Data on dietary supplement use were also obtained via the FFQ. However, the use of B-vitamin and multivitamin supplements was low (7.3% and 4.6% in subcohort members, respectively). Moreover, due to legislative restrictions in the Netherlands, it was not allowed until 1994 to use folic acid in vitamin supplements (33). Therefore, folic acid and vitamin B supplement use most likely plays a very minor role in our study population, and supplement use was not further accounted for in the analyses. The FFQ has been validated and tested for reproducibility (30, 34). Pearson correlation coefficients for nutrient intake evaluated by a 9-day diet record and the questionnaire ranged from 0.6 to 0.8 for most nutrients. Statistical analysis Cox proportional hazards models were used to estimate incidence HRs and corresponding 95% confidence intervals (CI). The total person-years at risk estimated from the subcohort were used in the analyses (35). SEs were estimated using a robust covariance matrix estimator to account for increased variance due to sampling from the cohort (36). The proportional hazards assumption was tested using the scaled Schoenfeld residuals (37). All analyses were conducted for both sexes combined and separately for men and women. Furthermore, interactions on a multiplicative scale between sex and the exposure variables were tested for lymphoid and myeloid neoplasms. Because of low case numbers (n < 100), we did not conduct stratified analyses on sex for the subtypes follicular lymphoma and lymphoplasmacytic lymphoma/Waldenstr€ om (LL/WM). HRs were estimated for quintiles of intake (with the lowest quintile of intake regarded as the reference group) based on the genderspecific distribution in the subcohort.
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Variables examined as potential confounders included body mass index, height, energy intake, intake of vegetables, fruit, red meat, dairy, alcohol, fat, and protein, level of education, nonoccupational physical activity, smoking, family history of hematologic malignancies, history of chronic bowel irritation, and rheumatoid arthritis. These potential confounding variables were regarded as confounders if they (i) were associated with lymphoid and myeloid neoplasms and with the intake of the exposure variables and (ii) changed the age- and sex-adjusted risk estimates by at least 10% (using a backwards stepwise procedure). This resulted in a multivariable-adjusted model including age at baseline (years), gender, cigarette smoking (current smoking: yes/no; number of cigarettes smoked per day; number of years of smoking), height (cm), family history of hematologic malignancies (yes/no), level of education (primary school or lower vocational school/intermediate vocational school or high school/higher vocational school or college), and intake of energy (kcal/day) and alcohol (abstainer, 0.1–4, 5–14, 15–29, and 30 g/day). For the analyses of monoglutamates, polyglutamates were included simultaneously and vice versa. For each analysis, trends were evaluated with the Wald test by assigning participants the median value for each level of the categorical exposure variable among the subcohort members, and this variable was entered as a continuous term in the Cox regression model. To evaluate whether early symptoms of disease before diagnosis could have influenced the results, early cases (diagnosed within 2 years after baseline) were excluded in additional analyses. To investigate whether alcohol modified the effect of folate on the risk of lymphoid and myeloid neoplasms, multiplicative interaction terms were used in the regression models and stratum-specific HR estimates were examined. Folate intake was analyzed in tertiles, and three categories of alcohol consumption (abstainer, 0.1-60—90 >90 Family history of hematologic malignancies Level of education Primary school or lower vocational school Intermediate vocational school or high school Higher vocational school or college a
Lymphoid neoplasms (n ¼ 1,280) %
Mean (SD)
49.1
%
DLBCL (n ¼ 248) Mean (SD)
58.6
%
Follicular lymphoma (n ¼ 96) Mean (SD)
58.9
61.9 (4.1)
61.8 (4.2)
61.2 (4.2)
1,989 (513) 214.6 (70.0) 63.9 (36.9) 126.4 (40.5) 1.54 (0.44) 1.48 (0.37) 1,640 (4187) 86.0 (15.6) 72.0 (12.3) 10.3 (13.8) 192.4 (78.0) 180.0 (120.9) 89.3 (41.2) 332.8 (204.1)
1,927 (488) 210.7 (66.6) 63.4 (41.2) 123.6 (37.2) 1.48 (0.38) 1.44 (0.36) 1,563 (372) 86.4 (14.2) 70.5 (11.3) 9.8 (14.8) 195.6 (75.9) 165.2 (117.6) 85.8 (41.1) 305.9 (173.9)
1,966 (529) 222.9 (70.6) 67.9 (38.5) 131.3 (38.5) 1.58 (0.41) 1.50 (0.37) 1,669 (471) 82.4 (17.4) 72.2 (13.3) 9.3 (12.3) 196.1 (67.4) 183.3 (122.4) 89.5 (44.3) 346.5 (192.1)
27.2
25.4 31.9 (12.3) 172.6 (8.5) 25.0 (3.0)
27.8 32.8 (12.1) 172.9 (9.2) 24.8 (2.9)
% 47.9
36.5 32.6 (11.2) 171.8 (8.6) 25.0 (3.1)
20.4 31.1 21.4 27.1 2.7
21.9 30.1 19.8 28.2 4.1
25.7 29.8 16.7 27.8 4.8
22.9 26.0 15.6 35.4 7.3
49.8 35.8
46.6 38.1
51.2 32.7
47.9 40.6
14.4
15.3
16.1
11.5
Energy-adjusted intake. Number of smoking years in ever smokers only.
b
cases than among subcohort members. In addition, there were more subjects with a family history of hematologic malignancies among cases than among subcohort members, especially among follicular lymphoma and AML cases (2.7% among subcohort members, 7.3% among follicular lymphoma, and 6.4% among AML cases). Table 4 shows the multivariable-adjusted HRs and 95% CIs for the association between one-carbon nutrients and lymphoid and myeloid neoplasms, including subtypes. Intakes of folate, methionine, and riboflavin were not associated with lymphoid and myeloid neoplasms. For vitamin B6, a statistically significantly increased myeloid neoplasms risk was observed (HR ¼ 1.87; 95% CI, 1.08–3.25
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for highest vs. lowest quintile of intake, Ptrend ¼ 0.02; Table 4). Analysis by lymphoid and myeloid subtypes indicated significantly increased PCN risks for folate (HR ¼ 1.73; 95% CI, 1.14–2.61 for highest vs. lowest quintile of intake) and vitamin B6 intake (HR ¼ 1.66; 95% CI, 1.03–2.67) and a significantly lower DLBCL risk for methionine intake (HR ¼ 0.56; 95% CI, 0.33–0.97). No clear associations were observed for other subtypes. When examining the associations between the risk for lymphoid and myeloid neoplasms and intake of monoand polyglutamates, no clear associations were observed for lymphoid and myeloid neoplasms overall and for most of the subtypes (Supplementary Table S1). For
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b
Energy-adjusted intake. Number of smoking years in ever smokers only.
a
Male sex Age, y Daily dietary intake Energy, kcal Total folate, mg Monoglutamates, mg Polyglutamates, mg Vitamin B2, mg Vitamin B6, mg Methionine, mg Total fata, g Total proteina, g Alcohol, g Vegetables, g Fruit, g Red meat, g Dairy, g Other characteristics Current smoker Years of smokingb Height, cm Body mass index, kg/m2 Physical activity (nonoccupational), min/d 30 >30–60 >60–90 >90 Family history of hematologic malignancies Level of education Primary school or lower vocational school Intermediate vocational school or high school Higher vocational school or college
%
30.1 (12.5) 172.2 (8.0) 25.3 (2.7)
47.9 37.6 14.6
39.3 37.1 23.6
22.5
22.8 28.9 22.8 25.6 4.7
32.5 (12.1) 173.2 (8.4) 24.8 (3.1)
% 66.2
25.8 29.2 19.1 25.8 3.4
22.5
2,065 (536) 221.9 (78.6) 69.6 (41.8) 127.4 (46.0) 1.60 (0.46) 1.52 (0.39) 1,702 (439) 88.2 (16.8) 73.5 (12.6) 11.8 (14.8) 194.0 (84.1) 176.6 (124.2) 92.2 (43.5) 357.2 (213.0)
2,014 (511) 206.8 (58.4) 61.3 (31.0) 121.5 (33.7) 1.52 (0.47) 1.49 (0.33) 1,608 (369) 83.3 (13.8) 70.8 (11.4) 14.3 (19.2) 185.7 (63.4) 184.3 (113.4) 84.4 (41.3) 333.7 (236.7)
Mean (SD) 62.2 (4.0)
61.8
CLL/SLL (n ¼ 213)
61.9 (4.1)
Mean (SD)
LL/WM (n ¼ 89)
30.4 (12.5) 172.1 (8.4) 25.2 (3.1)
1,987 (518) 221.0 (76.7) 65.3 (38.4) 130.5 (43.3) 1.58 (0.48) 1.50 (0.38) 1,655 (424) 84.6 (15.7) 71.9 (13.0) 8.9 (11.7) 195.5 (82.6) 193.5 (121.2) 88.0 (37.5) 341.6 (215.0)
62.0 (4.0)
Mean (SD)
50.2 36.4 13.4
19.8 31.0 20.7 28.6 3.5
20.7
52.5
%
PCN (n ¼ 343)
32.2 (13.5) 172.4 (8.9) 25.2 (3.1)
1,973 (512) 211.1 (61.1) 60.4 (30.0) 126.7 (39.2) 1.53 (0.40) 1.48 (0.37) 1,645 (400) 84.5 (15.3) 72.8 (11.6) 11.6 (15.8) 198.4 (80.8) 185.6 (118.2) 89.4 (37.1) 322.4 (202.0)
61.8 (4.3)
Mean (SD)
46.7 34.2 17.1
17.2 29.4 26.2 27.2 5.9
32.0
62.2
%
Myeloid neoplasms (n ¼ 222)
32.1 (13.5) 172.2 (8.8) 25.5 (3.4)
1,972 (537) 213.0 (67.2) 60.8 (32.8) 128.1 (42.4) 1.55 (0.40) 1.47 (0.37) 1,629 (425) 84.2 (14.9) 72.1 (11.8) 10.6 (14.8) 201.2 (85.0) 188.2 (118.7) 85.9 (35.5) 337.5 (197.6)
61.7 (4.3)
Mean (SD)
48.4 35.7 15.9
17.3 32.1 26.9 23.7 6.4
31.9
61.2
%
AML (n ¼ 157)
Table 3. Baseline characteristics (mean or percent) of LL/WM, CLL/SLL, PCN, myeloid neoplasms overall, and AML cases; NLCS on diet and cancer, 1986–2003
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Range of intakea Men Women Number of person-years Lymphoid neoplasms Number of cases HR (95% CI)b DLBCL Number of cases HR (95% CI)b FL Number of cases HR (95% CI)b LL/WM Number of cases HR (95% CI)b CLL/SLL Number of cases HR (95% CI)b PCN Number of cases HR (95% CI)b Myeloid neoplasms Number of cases HR (95% CI)b AML Number of cases HR (95% CI)b
Nutrient 167.7–197.4 147.0–174.4 11,862 288 1.20 (0.97–1.48) 59 1.44 (0.94–2.20) 22 1.90 (0.89–4.06) 20 0.98 (0.51–1.88) 44 0.95 (0.60–1.51) 78 1.47 (1.00–2.15) 44 1.04 (0.66–1.63) 30 0.94 (0.56–1.59)
223 1.00c
42 1.00c
11 1.00c
18 1.00c
40 1.00c
49 1.00c
41 1.00c
31 1.00c
Q2
68.4–167.5 46.9–147.0 11,226
Q1
40 1.21 (0.74–1.99)
54 1.25 (0.81–1.94)
70 1.28 (0.86–1.89)
38 0.80 (0.50–1.28)
18 0.80 (0.40–1.59)
15 1.27 (0.56–2.86)
61 1.48 (0.98–2.23)
259 1.04 (0.84–1.29)
197.5–225.3 174.5–202.3 12,349
Q3
Q4
31 1.03 (0.59–1.81)
40 1.03 (0.62–1.71)
89 1.73 (1.14–2.61)
54 1.16 (0.71–1.91)
16 0.73 (0.33–1.58)
24 2.16 (0.95–4.89)
48 1.39 (0.87–2.23)
279 1.21 (0.96–1.52)
267.9–740.7 239.6–781.1 11,997
Q5
0.95
0.99
0.06
0.53
0.34
0.09
0.61
0.45
P for trend
33 1.00c
42 1.00c
60 1.00c
36 1.00c
17 1.00c
19 1.00c
60 1.00c
246 1.00c
472–1,370 161–1,191 11,493
Q1
(Continued on the following page)
25 0.79 (0.45–1.39)
43 1.05 (0.65–1.69)
57 1.02 (0.67–1.56)
37 0.76 (0.47–1.23)
17 0.75 (0.39–1.47)
24 2.05 (0.93–4.52)
38 0.96 (0.59–1.55)
231 0.93 (0.74–1.16)
225.3–267.7 202.3–239.4 12,215
Folate (mg/d)
29 0.88 (0.52–1.49)
38 0.93 (0.59–1.49)
57 0.89 (0.60–1.31)
35 0.95 (0.59–1.55)
18 0.99 (0.50–1.97)
17 0.83 (0.43–1.60)
50 0.81 (0.54–1.22)
230 0.90 (0.73–1.12)
1,370–1,589 1,192–1,383 11,904
Q2
32 0.97 (0.57–1.65)
48 1.21 (0.76–1.91)
85 1.23 (0.84–1.80)
41 1.08 (0.65–1.77)
20 1.04 (0.54–2.03)
14 0.67 (0.32–1.39)
48 0.77 (0.51–1.18)
263 1.00 (0.81–1.25)
1,590–1,776 1.384–1,557 12,112
Q3
36 1.11 (0.64–1.93)
54 1.43 (0.88–2.32)
68 0.97 (0.64–1.47)
42 1.16 (0.69–1.94)
18 0.91 (0.43–1.95)
26 1.21 (0.61–2.42)
56 0.93 (0.59–1.43)
275 1.07 (0.85–1.34)
1,776–2,022 1,557–1,771 12,050
Q4
Methionine (mg/day)
27 0.86 (0.45–1.62)
40 1.14 (0.66–1.97)
73 0.97 (0.61–1.56)
59 1.63 (0.94–2.80)
16 0.75 (0.33–1.75)
20 0.90 (0.42–1.91)
34 0.56 (0.33–0.97)
266 1.02 (0.79–1.32)
2,023–3,829 1,773–3,247 12,089
Q5
Table 4. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to quintiles of dietary folate, methionine, riboflavin, and vitamin B6 intake (men and women); NLCS on diet and cancer, 1986–2003
0.88
0.32
0.95
0.05
0.49
0.85
0.10
0.54
P for trend
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One-Carbon Nutrients and Hematologic Malignancies
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2160
Cancer Epidemiol Biomarkers Prev; 23(10) October 2014 1.22–1.42 1.11–1.32 11,975 246 0.94 (0.76–1.16) 50 0.95 (0.63–1.43) 21 1.57 (0.77–3.18) 16 0.70 (0.36–1.35) 44 1.28 (0.79–2.05) 60 0.85 (0.58–1.24) 49 1.34 (0.86–2.10) 36 1.71 (0.99–2.94)
0.45–1.22 0.42–1.11 11,431
246 1.00c
52 1.00c
13 1.00c
22 1.00c
32 1.00c
64 1.00c
37 1.00c
22 1.00c
Q2
32 1.56 (0.90–2.71)
46 1.29 (0.82–2.03)
64 0.89 (0.61–1.31)
37 1.08 (0.65–1.78)
13 0.55 (0.28–1.11)
19 1.45 (0.70–3.01)
55 1.08 (0.71–1.63)
239 0.92 (0.74–1.15)
1.42–1.62 1.32–1.51 11,928
Q3
39 1.91 (1.07–3.40)
49 1.36 (0.83–2.24)
77 1.00 (0.67–1.48)
45 1.24 (0.74–2.05)
18 0.73 (0.38–1.39)
17 1.24 (0.57–2.68)
50 0.95 (0.61–1.48)
267 0.98 (0.78–1.22)
1.62–1.91 1.52–1.75 12,258
Q4
28 1.44 (0.78–2.66)
41 1.21 (0.71–2.05)
78 0.99 (0.64–1.53)
55 1.54 (0.91–2.62)
20 0.83 (0.40–1.70)
26 1.90 (0.91–3.97)
41 0.83 (0.50–1.37)
282 1.05 (0.83–1.34)
1.91–4.51 1.76–3.66 12,057
Q5
0.31
0.58
0.79
0.15
0.74
0.17
0.49
0.55
Ptrend
27 1.00c
36 1.00c
46 1.00c
44 1.00c
12 1.00c
14 1.00c
47 1.00c
218 1.00c
0.35–1.23 0.27–1.07 11,187
Q1
30 1.10 (0.64–1.90)
42 1.19 (0.74–1.90)
74 1.45 (0.98–2.15)
35 0.68 (0.43–1.09)
18 1.42 (0.66–3.02)
17 1.16 (0.56–2.38)
63 1.31 (0.87–1.95)
257 1.09 (0.88–1.35)
1.23–1.42 1.07–1.24 11,937
Q2
32 1.26 (0.73–2.18)
46 1.42 (0.88–2.28)
65 1.28 (0.84–1.94)
31 0.60 (0.37–0.98)
16 1.27 (0.57–2.80)
22 1.52 (0.75–3.09)
42 0.92 (0.58–1.44)
242 1.04 (0.83–1.30)
1.42–1.61 1.24–1.38 12,308
Q3
36 1.46 (0.83–2.58)
49 1.58 (0.97–2.58)
72 1.32 (0.86–2.01)
50 0.88 (0.55–1.40)
27 2.16 (1.05–4.47)
20 1.41 (0.68–2.94)
54 1.15 (0.73–1.81)
289 1.18 (0.94–1.48)
1.61–1.82 1.39–1.58 12,042
Q4
Vitamin B6 (mg/day)
32 1.45 (0.75–2.78)
49 1.87 (1.08–3.25)
86 1.66 (1.03–2.67)
53 0.92 (0.54–1.57)
16 1.34 (0.51–3.49)
23 1.65 (0.71–3.83)
42 1.00 (0.59–1.71)
274 1.18 (0.91–1.54)
1.83–3.93 1.58–2.77 12,175
Q5
0.17
0.02
0.12
0.76
0.34
0.22
0.79
0.17
P for trend
a
Abbreviation: FL, follicular lymphoma. Range of intake in subcohort. b Adjusted for age (y), sex, energy (kcal/d), level of education (primary school or lower vocational school/intermediate vocational school or high school/higher vocational school or college), smoking (current smoking: yes/no; number of cigarettes smoked per day; number of years of smoking), height (cm), alcohol consumption (abstainer, 0.1–4, 5–14, 15–29, 30 g/d), and family history of hematologic malignancies (yes/no). c Reference category.
Range of intakea Men Women Number of person-years Lymphoid neoplasms Number of cases HR (95% CI)b DLBCL Number of cases HR (95% CI)b FL Number of cases HR (95% CI)b LL/WM Number of cases HR (95% CI)b CLL/SLL Number of cases HR (95% CI)b PCN Number of cases HR (95% CI)b Myeloid neoplasms Number of cases HR (95% CI)b AML Number of cases HR (95% CI)b
Q1
Riboflavin (mg/d)
Table 4. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to quintiles of dietary folate, methionine, riboflavin, and vitamin B6 intake (men and women); NLCS on diet and cancer, 1986–2003 (Cont'd)
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Table 5. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to tertiles of folate intake, by categories of alcohol consumption (men and women); NLCS on diet and cancer, 1986–2003 Tertiles of folate intake T1 Alcohol consumption, Number of g/d cases HR (95% CI)a Lymphoid neoplasms Abstainerb 0.1– 0.05; data not shown). For folate intake, we observed statistically significantly increased lymphoid neoplasms risks among women (HR ¼ 1.38, 95% CI, 1.00–1.92; HR ¼ 1.45, 95% CI, 1.05–2.00; HR ¼ 1.14, 95% CI, 0.81–1.62; and HR ¼ 1.45, 95% CI, 1.01–2.07 for quintiles 2–5 vs. the first quintile, respectively). Only for the lymphoma subtype PCN, similar increased risks were observed (data not shown). No associations with folate intake were observed among men. After excluding the first 2 years of follow-up (85 lymphoid neoplasm cases and 15 myeloid neoplasm cases), some of the statistically significantly increased and decreased risks became nonsignificant. This included the decreased DLBCL risk for methionine (HR ¼ 0.59; 95% CI, 0.34–1.02 for highest vs. lowest quintile) and the increased PCN risk for vitamin B6 (HR ¼ 1.58; 95% CI, 0.97–2.59). To investigate this into more detail, a test for interaction
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between the exposure of interest and time was calculated and the remaining follow-up period was stratified into three periods (2–
Cancer Epidemiology, Biomarkers & Prevention
Research Article
Dietary One-Carbon Nutrient Intake and Risk of Lymphoid and Myeloid Neoplasms: Results of the Netherlands Cohort Study Mirjam M. Heinen1, Piet A. van den Brandt1, Leo J. Schouten1, R. Alexandra Goldbohm2, Harry C. Schouten3, and Bas A.J. Verhage1
Abstract Background: Previous epidemiologic research suggests a protective role of one-carbon nutrients in carcinogenesis. Folate, however, may play a dual role in neoplasms development: protect early in carcinogenesis and promote carcinogenesis at a later stage. We prospectively examined associations between intake of total folate, methionine, riboflavin, vitamin B6, and risk of lymphoid and myeloid neoplasms (including subtypes) and investigated whether alcohol modified the effects of folate. Methods: The Netherlands Cohort Study consists of 120,852 individuals who completed a baseline questionnaire in 1986, including a 150-item food-frequency questionnaire. After 17.3 years of follow-up, 1,280 cases of lymphoid and 222 cases of myeloid neoplasms were available for analysis. Results: Intakes of folate, methionine, and riboflavin were not associated with lymphoid or myeloid neoplasms. For vitamin B6, a statistically significantly increased myeloid neoplasms risk was observed (highest vs. lowest quintile: HR ¼ 1.87; 95% confidence intervals, 1.08–3.25). When analyzing by lymphoid and myeloid neoplasms subtypes, no clear associations were observed for most subtypes, with just a few increased risks for some subtypes and nutrients. Some risks became nonsignificant after excluding early cases. No interaction between alcohol and folate was observed. Conclusions: We observed a few significant positive associations; however, some of these would be expected to arise due to chance alone. Furthermore, some risks became nonsignificant after excluding early cases. Therefore, we conclude that there is no association between one-carbon nutrient intake and risk of lymphoid and myeloid neoplasms. Impact: This study contributes substantially to the limited and inconclusive evidence on the association with one-carbon nutrients. Cancer Epidemiol Biomarkers Prev; 23(10); 2153–64. 2014 AACR.
Introduction Hematologic malignancies are a heterogeneous group of neoplasms that originate from lymphoid and myeloid cells. They account for 7.4% of cancers in males and 6.4% in females worldwide (1). So far, little is known about the causes and only few factors have been linked to lymphoid neoplasms, including age, gender, primary/inherited
1 Department of Epidemiology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centreþ, Maastricht, the Netherlands. 2Department of Prevention and Health, TNO Quality of Life, Leiden, the Netherlands. 3Division of Hematology, Department of Internal Medicine, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centreþ, Maastricht, the Netherlands.
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/). Corresponding Author: Bas A.J. Verhage, Department of Epidemiology, Maastricht University Medical Centreþ, P.O. Box 616, 6200 MD Maastricht, the Netherlands. Phone: 31-43-3882371; Fax: 31-43-388 4128; E-mail: [email protected] doi: 10.1158/1055-9965.EPI-14-0136 2014 American Association for Cancer Research.
immune deficiencies, a number of viruses including the human immunodeficiency virus, and several autoimmune diseases (2). Risk factors for myeloid neoplasms include age, gender, genetic abnormalities, family history, and exposures to radiation and benzene (3). Stratification for subtypes of lymphoid and myeloid neoplasms in previous studies suggested etiologic heterogeneity (4, 5) and, therefore, further research is warranted in which these subtypes with sufficient numbers can be examined. Previous epidemiologic research suggests a protective role of dietary one-carbon nutrients in colorectal carcinogenesis (6, 7) and perhaps other cancers, such as breast, ovarian, and gastric (7–10). Furthermore, research has suggested that alcohol influences cancer risk by antagonizing the one-carbon metabolism (11). However, research also shows that folate may play a dual role in cancer development: supplementation may provide protection early in carcinogenesis in individuals with a low folate status, yet supplementation late in carcinogenesis and potentially at very high intakes may promote carcinogenesis (12–14). Dietary folate consists of monoglutamate and polyglutamate folate species. Many studies have
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most common subtypes, in a large prospective cohort study of men and women in the Netherlands. Furthermore, we examined whether the effect of dietary folate was modified by alcohol intake.
determined that the relative bioavailability of polyglutamates versus corresponding monoglutamates ranges from 50% to 100% (15), therefore, beneficial effects of folate intake might be stronger for folate monoglutamates than for folate polyglutamates. So far, data on the association between one-carbon nutrients and the risk of lymphoid neoplasms are limited to two cohort (16, 17) and four case–control studies (18–21). One cohort study among U.S. women observed no association between folate and non-Hodgkin lymphoma (NHL) risk (17). A cohort study among male smokers observed a borderline significantly decreased risk between vitamin B12 and NHL risk, but no associations with other dietary one-carbon nutrients and no associations among lymphoma subtypes and multiple myeloma (16). As for the case–control studies, significantly decreased risks were observed for folate intake with NHL overall (18, 19), with the subtype diffuse large B-cell lymphoma (DLBCL; refs. 19, 20) and with marginal zone lymphoma (19). Decreased risks were also observed for higher vitamin B6 intake with NHL overall (20), with DLBCL (20) and with marginal zone lymphoma (19). For methionine intake, decreased risks were observed with NHL overall (19, 20), with DLBCL (20) and with follicular lymphoma (19, 20). One hospital-based case–control study did only observe associations between one-carbon nutrients and NHL risk when the analyses were stratified by current drinking status (21). Significant decreased risks were observed for folate intake among the abstainers and former drinkers, whereas no associations were observed among current drinkers (21). Another case–control study did, however, not observe any modifying effects of alcohol on NHL risk (20). As far as the researchers know, no epidemiologic studies have been conducted on the relation between onecarbon nutrients and risk of myeloid neoplasms. We investigated the relations between dietary one-carbon nutrients [i.e., total folate, poly- and monoglutamates, riboflavin (vitamin B2), vitamin B6, and methionine] and risk of lymphoid and myeloid neoplasms, including the
Materials and Methods Study population and cancer follow-up The study design of the Netherlands Cohort Study (NLCS) has been reported in detail elsewhere (22). Briefly, the NLCS was initiated in September 1986 and included 58,279 men and 62,573 women ages 55 to 69 years at the beginning of the study, originating from 204 municipalities with computerized population registries. A selfadministered questionnaire on daily dietary habits, lifestyle factors, and other potential risk factors for cancer was completed at baseline. For reasons of efficiency in questionnaire processing and follow-up, the case-cohort approach (23) was used. Incident cases were derived from the entire cohort, whereas the person-years at risk were estimated from a random sample of 5,000 subjects. This subcohort was chosen immediately after baseline and followed up for vital status information. The entire cohort is being monitored for cancer occurrence by annual record linkage to the Netherlands Cancer Registry and the Netherlands Pathology Registry (24, 25). A total of 17.3 years of follow-up (baseline to December 2003; mean follow-up ¼ 13.7 years) were used for the current analysis. Only one subcohort member was lost to follow-up. The completeness of cancer follow-up was estimated to be >96% (26). All prevalent cancer cases at baseline other than skin cancer were excluded (n ¼ 226), leaving 4,774 subcohort members (Fig. 1). Cases were defined as participants with an incident, histologically verified diagnosis of lymphoid neoplasms (n ¼ 1,280) or myeloid neoplasms (n ¼ 222). Histology is coded by the Netherlands Cancer Registry using the International Classification of Diseases for Oncology (27). Using the histology codes provided by the cancer registries, we
Netherlands Cohort Study on diet and cancer 102,852 men + women Subcohort randomly drawn from total cohort
Annual record linkage with NCR and PALGA Follow-up
5,000
Total lymphoid neoplasms
DLBCL
FL
LL/WM
CLL/SLL
PCN
Total myeloid neoplasms
AML
253
405
270
194
343
222
157
17.3 years
Exclusion of prevalent cancer cases at baseline
4,774 (incl 79 cases)
1,512
295
110
99
Exclusion of subjects with incomplete or inconsistent dietary data
4,438 (incl 74 cases)
1,280
248
96
89
213
Figure 1. Flow diagram of subcohort members and cases of lymphoid and myeloid neoplasms whose data were used in the analysis, NLCS on Diet and Cancer, 1986–2003. FL, follicular lymphoma; NCR, Netherlands Cancer Registry.
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Table 1. Number of lymphoid and myeloid neoplasms in the NLCS on diet and cancer, 1986–2003 Label Lymphoid neoplasms Hodgkin lymphoma NHL NHL, B cell Precursor NHL, B cell Mature NHL, B cell CLL/SLLc Prolymphocytic leukemia, B cell Mantle-cell lymphoma LL/WMc Burkitt lymphoma/leukemia Follicular lymphomac Marginal-zone lymphoma Hairy-cell leukemia DLBCLc PCNc Heavy chain disease NHL, B cell, NOS NHL, T cell Precursor NHL, T cell Mature NHL, T cell Mycosis fungoides/Sezary syndrome Adult T-cell leukemia/lymphoma Peripheral T-cell lymphoma NK/T-cell lymphoma, nasal-type/ aggressive NK-cell leukemia T-cell large granular lymphocytic leukemia Prolymphocytic leukemia, T cell NHL, T cell, NOS NHL, unknown lineage Composite Hodgkin lymphoma and NHL Lymphoid neoplasms, NOS Myeloid neoplasms AMLc Chronic myeloid leukemia Myeloid leukemia, NOS Leukemia, NOS
All cases, number 1,512 40 1,446 1,355 5
Cases available for analysis, number b 1,280 34 1,225 1,149 5
1,284 253
1,089 213
0
0
66 99
53 89
8 110 37 11 295
6 96 31 10 248
405 0 66 73 0
343 0 55 59 0
T, N, U T, N, U T, N, U
62 20 0 38
50 15 0 31
T, N, U
3
3
9831 9832 9834 9590–9591, 9675 9591, 9675, 9727, 9832, 9835 9596 9590–9591, 9820
T, N, U T, N T, N, U T U
0 1
0 1
11 18
9 17
B, U T, B, N, U
1 25
0 21
9840, 9861, 9866–9874, 9891–9931 9863, 9875, 9945–9946 9860 9800–9801, 9805
NA
270 194
222 157
NA NA NA
61 4 11
51 4 10
ICD-O-3 morphology codes
Cell-type
9650–9667
NA
9727–9728, 9835–9836
B, U B
9670, 9823
B, U
9832 9833 9673 9671, 9761
B B, U B, U B, U
9687, 9826 9690–9691, 9695, 9698 9689, 9699, 9760, 9764 9940 9678–9680 9684 9731–9734 9762 9590–9591, 9675
B, U B, U B, U B, U B, U B B, U B, U B
9729, 9837 9727, 9835
T, U T, N
9700–9701 9827 9702, 9705, 9708–9709, 9714–9718 9719, 9948
a
Abbreviations: ICD-O-3, International Classification of Diseases for Oncology, 3rd edition; NOS, not otherwise specified. a Cell type: T, T cell; B, B cell; N, Null/NK cell; U, unknown. b Exclusion of cases with incomplete or inconsistent dietary data. c Subtypes used for analyses.
subdivided the lymphoid neoplasms into categories based on the hierarchical groupings of the International Lymphoma Epidemiology Consortium (InterLymph) nested classification (Table 1; ref. 28). This classification is based on the World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues (29) and the International Classification of Diseases for Oncology, Third Edition (27). About the myeloid neoplasms, we grouped these malignancies into categories defined by the WHO classification (Table 1; ref. 29). For cases that could not be assigned to a specific category (e.g., the "not other-
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wise specified" categories), the summary of the pathology report (received from the Netherlands Pathology Registry PALGA; ref. 24) was inspected and, if possible, an appropriate category was assigned. This was the case for 66.4% (n ¼ 75) for NHL, B cell, NOS; 55.6% (n ¼ 10) for NHL, T cell, NOS; 81.1% (n ¼ 73) for NHL, unknown lineage; 77.8% (n ¼ 35) for lymphoid neoplasms, NOS; and 100% (n ¼ 2) for leukemia, NOS. Subjects with incomplete or inconsistent dietary data were excluded from analysis. Details are given elsewhere (30). Figure 1 shows the selection and exclusion steps that
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resulted in the final numbers of cases and subcohort members that were available for analysis. The NLCS has been approved by the Institutional Review Boards of the TNO Nutrition and Food Research Institute (Zeist, the Netherlands) and Maastricht University (Maastricht, the Netherlands). Questionnaire The dietary section of the questionnaire was a 150-item semiquantitative food-frequency questionnaire (FFQ), which concentrated on the habitual consumption during the year preceding the start of the study. Questionnaire data were key-entered and processed for all incident cases in the cohort and subcohort members in a standardized manner blinded with respect to case/subcohort status. This was done to minimize observer bias in coding and interpretation of the data. Folate intake was calculated using data from a validated liquid chromatography trienzyme method used to analyze the 125 most important Dutch food items contributing to folate intake (31). Daily mean intakes of all other relevant nutrients were calculated by summing the multiplied frequencies and portion sizes of all food items with their tabulated nutrient contents from the Dutch Food Composition Table (32). Data on dietary supplement use were also obtained via the FFQ. However, the use of B-vitamin and multivitamin supplements was low (7.3% and 4.6% in subcohort members, respectively). Moreover, due to legislative restrictions in the Netherlands, it was not allowed until 1994 to use folic acid in vitamin supplements (33). Therefore, folic acid and vitamin B supplement use most likely plays a very minor role in our study population, and supplement use was not further accounted for in the analyses. The FFQ has been validated and tested for reproducibility (30, 34). Pearson correlation coefficients for nutrient intake evaluated by a 9-day diet record and the questionnaire ranged from 0.6 to 0.8 for most nutrients. Statistical analysis Cox proportional hazards models were used to estimate incidence HRs and corresponding 95% confidence intervals (CI). The total person-years at risk estimated from the subcohort were used in the analyses (35). SEs were estimated using a robust covariance matrix estimator to account for increased variance due to sampling from the cohort (36). The proportional hazards assumption was tested using the scaled Schoenfeld residuals (37). All analyses were conducted for both sexes combined and separately for men and women. Furthermore, interactions on a multiplicative scale between sex and the exposure variables were tested for lymphoid and myeloid neoplasms. Because of low case numbers (n < 100), we did not conduct stratified analyses on sex for the subtypes follicular lymphoma and lymphoplasmacytic lymphoma/Waldenstr€ om (LL/WM). HRs were estimated for quintiles of intake (with the lowest quintile of intake regarded as the reference group) based on the genderspecific distribution in the subcohort.
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Variables examined as potential confounders included body mass index, height, energy intake, intake of vegetables, fruit, red meat, dairy, alcohol, fat, and protein, level of education, nonoccupational physical activity, smoking, family history of hematologic malignancies, history of chronic bowel irritation, and rheumatoid arthritis. These potential confounding variables were regarded as confounders if they (i) were associated with lymphoid and myeloid neoplasms and with the intake of the exposure variables and (ii) changed the age- and sex-adjusted risk estimates by at least 10% (using a backwards stepwise procedure). This resulted in a multivariable-adjusted model including age at baseline (years), gender, cigarette smoking (current smoking: yes/no; number of cigarettes smoked per day; number of years of smoking), height (cm), family history of hematologic malignancies (yes/no), level of education (primary school or lower vocational school/intermediate vocational school or high school/higher vocational school or college), and intake of energy (kcal/day) and alcohol (abstainer, 0.1–4, 5–14, 15–29, and 30 g/day). For the analyses of monoglutamates, polyglutamates were included simultaneously and vice versa. For each analysis, trends were evaluated with the Wald test by assigning participants the median value for each level of the categorical exposure variable among the subcohort members, and this variable was entered as a continuous term in the Cox regression model. To evaluate whether early symptoms of disease before diagnosis could have influenced the results, early cases (diagnosed within 2 years after baseline) were excluded in additional analyses. To investigate whether alcohol modified the effect of folate on the risk of lymphoid and myeloid neoplasms, multiplicative interaction terms were used in the regression models and stratum-specific HR estimates were examined. Folate intake was analyzed in tertiles, and three categories of alcohol consumption (abstainer, 0.1-60—90 >90 Family history of hematologic malignancies Level of education Primary school or lower vocational school Intermediate vocational school or high school Higher vocational school or college a
Lymphoid neoplasms (n ¼ 1,280) %
Mean (SD)
49.1
%
DLBCL (n ¼ 248) Mean (SD)
58.6
%
Follicular lymphoma (n ¼ 96) Mean (SD)
58.9
61.9 (4.1)
61.8 (4.2)
61.2 (4.2)
1,989 (513) 214.6 (70.0) 63.9 (36.9) 126.4 (40.5) 1.54 (0.44) 1.48 (0.37) 1,640 (4187) 86.0 (15.6) 72.0 (12.3) 10.3 (13.8) 192.4 (78.0) 180.0 (120.9) 89.3 (41.2) 332.8 (204.1)
1,927 (488) 210.7 (66.6) 63.4 (41.2) 123.6 (37.2) 1.48 (0.38) 1.44 (0.36) 1,563 (372) 86.4 (14.2) 70.5 (11.3) 9.8 (14.8) 195.6 (75.9) 165.2 (117.6) 85.8 (41.1) 305.9 (173.9)
1,966 (529) 222.9 (70.6) 67.9 (38.5) 131.3 (38.5) 1.58 (0.41) 1.50 (0.37) 1,669 (471) 82.4 (17.4) 72.2 (13.3) 9.3 (12.3) 196.1 (67.4) 183.3 (122.4) 89.5 (44.3) 346.5 (192.1)
27.2
25.4 31.9 (12.3) 172.6 (8.5) 25.0 (3.0)
27.8 32.8 (12.1) 172.9 (9.2) 24.8 (2.9)
% 47.9
36.5 32.6 (11.2) 171.8 (8.6) 25.0 (3.1)
20.4 31.1 21.4 27.1 2.7
21.9 30.1 19.8 28.2 4.1
25.7 29.8 16.7 27.8 4.8
22.9 26.0 15.6 35.4 7.3
49.8 35.8
46.6 38.1
51.2 32.7
47.9 40.6
14.4
15.3
16.1
11.5
Energy-adjusted intake. Number of smoking years in ever smokers only.
b
cases than among subcohort members. In addition, there were more subjects with a family history of hematologic malignancies among cases than among subcohort members, especially among follicular lymphoma and AML cases (2.7% among subcohort members, 7.3% among follicular lymphoma, and 6.4% among AML cases). Table 4 shows the multivariable-adjusted HRs and 95% CIs for the association between one-carbon nutrients and lymphoid and myeloid neoplasms, including subtypes. Intakes of folate, methionine, and riboflavin were not associated with lymphoid and myeloid neoplasms. For vitamin B6, a statistically significantly increased myeloid neoplasms risk was observed (HR ¼ 1.87; 95% CI, 1.08–3.25
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for highest vs. lowest quintile of intake, Ptrend ¼ 0.02; Table 4). Analysis by lymphoid and myeloid subtypes indicated significantly increased PCN risks for folate (HR ¼ 1.73; 95% CI, 1.14–2.61 for highest vs. lowest quintile of intake) and vitamin B6 intake (HR ¼ 1.66; 95% CI, 1.03–2.67) and a significantly lower DLBCL risk for methionine intake (HR ¼ 0.56; 95% CI, 0.33–0.97). No clear associations were observed for other subtypes. When examining the associations between the risk for lymphoid and myeloid neoplasms and intake of monoand polyglutamates, no clear associations were observed for lymphoid and myeloid neoplasms overall and for most of the subtypes (Supplementary Table S1). For
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b
Energy-adjusted intake. Number of smoking years in ever smokers only.
a
Male sex Age, y Daily dietary intake Energy, kcal Total folate, mg Monoglutamates, mg Polyglutamates, mg Vitamin B2, mg Vitamin B6, mg Methionine, mg Total fata, g Total proteina, g Alcohol, g Vegetables, g Fruit, g Red meat, g Dairy, g Other characteristics Current smoker Years of smokingb Height, cm Body mass index, kg/m2 Physical activity (nonoccupational), min/d 30 >30–60 >60–90 >90 Family history of hematologic malignancies Level of education Primary school or lower vocational school Intermediate vocational school or high school Higher vocational school or college
%
30.1 (12.5) 172.2 (8.0) 25.3 (2.7)
47.9 37.6 14.6
39.3 37.1 23.6
22.5
22.8 28.9 22.8 25.6 4.7
32.5 (12.1) 173.2 (8.4) 24.8 (3.1)
% 66.2
25.8 29.2 19.1 25.8 3.4
22.5
2,065 (536) 221.9 (78.6) 69.6 (41.8) 127.4 (46.0) 1.60 (0.46) 1.52 (0.39) 1,702 (439) 88.2 (16.8) 73.5 (12.6) 11.8 (14.8) 194.0 (84.1) 176.6 (124.2) 92.2 (43.5) 357.2 (213.0)
2,014 (511) 206.8 (58.4) 61.3 (31.0) 121.5 (33.7) 1.52 (0.47) 1.49 (0.33) 1,608 (369) 83.3 (13.8) 70.8 (11.4) 14.3 (19.2) 185.7 (63.4) 184.3 (113.4) 84.4 (41.3) 333.7 (236.7)
Mean (SD) 62.2 (4.0)
61.8
CLL/SLL (n ¼ 213)
61.9 (4.1)
Mean (SD)
LL/WM (n ¼ 89)
30.4 (12.5) 172.1 (8.4) 25.2 (3.1)
1,987 (518) 221.0 (76.7) 65.3 (38.4) 130.5 (43.3) 1.58 (0.48) 1.50 (0.38) 1,655 (424) 84.6 (15.7) 71.9 (13.0) 8.9 (11.7) 195.5 (82.6) 193.5 (121.2) 88.0 (37.5) 341.6 (215.0)
62.0 (4.0)
Mean (SD)
50.2 36.4 13.4
19.8 31.0 20.7 28.6 3.5
20.7
52.5
%
PCN (n ¼ 343)
32.2 (13.5) 172.4 (8.9) 25.2 (3.1)
1,973 (512) 211.1 (61.1) 60.4 (30.0) 126.7 (39.2) 1.53 (0.40) 1.48 (0.37) 1,645 (400) 84.5 (15.3) 72.8 (11.6) 11.6 (15.8) 198.4 (80.8) 185.6 (118.2) 89.4 (37.1) 322.4 (202.0)
61.8 (4.3)
Mean (SD)
46.7 34.2 17.1
17.2 29.4 26.2 27.2 5.9
32.0
62.2
%
Myeloid neoplasms (n ¼ 222)
32.1 (13.5) 172.2 (8.8) 25.5 (3.4)
1,972 (537) 213.0 (67.2) 60.8 (32.8) 128.1 (42.4) 1.55 (0.40) 1.47 (0.37) 1,629 (425) 84.2 (14.9) 72.1 (11.8) 10.6 (14.8) 201.2 (85.0) 188.2 (118.7) 85.9 (35.5) 337.5 (197.6)
61.7 (4.3)
Mean (SD)
48.4 35.7 15.9
17.3 32.1 26.9 23.7 6.4
31.9
61.2
%
AML (n ¼ 157)
Table 3. Baseline characteristics (mean or percent) of LL/WM, CLL/SLL, PCN, myeloid neoplasms overall, and AML cases; NLCS on diet and cancer, 1986–2003
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Range of intakea Men Women Number of person-years Lymphoid neoplasms Number of cases HR (95% CI)b DLBCL Number of cases HR (95% CI)b FL Number of cases HR (95% CI)b LL/WM Number of cases HR (95% CI)b CLL/SLL Number of cases HR (95% CI)b PCN Number of cases HR (95% CI)b Myeloid neoplasms Number of cases HR (95% CI)b AML Number of cases HR (95% CI)b
Nutrient 167.7–197.4 147.0–174.4 11,862 288 1.20 (0.97–1.48) 59 1.44 (0.94–2.20) 22 1.90 (0.89–4.06) 20 0.98 (0.51–1.88) 44 0.95 (0.60–1.51) 78 1.47 (1.00–2.15) 44 1.04 (0.66–1.63) 30 0.94 (0.56–1.59)
223 1.00c
42 1.00c
11 1.00c
18 1.00c
40 1.00c
49 1.00c
41 1.00c
31 1.00c
Q2
68.4–167.5 46.9–147.0 11,226
Q1
40 1.21 (0.74–1.99)
54 1.25 (0.81–1.94)
70 1.28 (0.86–1.89)
38 0.80 (0.50–1.28)
18 0.80 (0.40–1.59)
15 1.27 (0.56–2.86)
61 1.48 (0.98–2.23)
259 1.04 (0.84–1.29)
197.5–225.3 174.5–202.3 12,349
Q3
Q4
31 1.03 (0.59–1.81)
40 1.03 (0.62–1.71)
89 1.73 (1.14–2.61)
54 1.16 (0.71–1.91)
16 0.73 (0.33–1.58)
24 2.16 (0.95–4.89)
48 1.39 (0.87–2.23)
279 1.21 (0.96–1.52)
267.9–740.7 239.6–781.1 11,997
Q5
0.95
0.99
0.06
0.53
0.34
0.09
0.61
0.45
P for trend
33 1.00c
42 1.00c
60 1.00c
36 1.00c
17 1.00c
19 1.00c
60 1.00c
246 1.00c
472–1,370 161–1,191 11,493
Q1
(Continued on the following page)
25 0.79 (0.45–1.39)
43 1.05 (0.65–1.69)
57 1.02 (0.67–1.56)
37 0.76 (0.47–1.23)
17 0.75 (0.39–1.47)
24 2.05 (0.93–4.52)
38 0.96 (0.59–1.55)
231 0.93 (0.74–1.16)
225.3–267.7 202.3–239.4 12,215
Folate (mg/d)
29 0.88 (0.52–1.49)
38 0.93 (0.59–1.49)
57 0.89 (0.60–1.31)
35 0.95 (0.59–1.55)
18 0.99 (0.50–1.97)
17 0.83 (0.43–1.60)
50 0.81 (0.54–1.22)
230 0.90 (0.73–1.12)
1,370–1,589 1,192–1,383 11,904
Q2
32 0.97 (0.57–1.65)
48 1.21 (0.76–1.91)
85 1.23 (0.84–1.80)
41 1.08 (0.65–1.77)
20 1.04 (0.54–2.03)
14 0.67 (0.32–1.39)
48 0.77 (0.51–1.18)
263 1.00 (0.81–1.25)
1,590–1,776 1.384–1,557 12,112
Q3
36 1.11 (0.64–1.93)
54 1.43 (0.88–2.32)
68 0.97 (0.64–1.47)
42 1.16 (0.69–1.94)
18 0.91 (0.43–1.95)
26 1.21 (0.61–2.42)
56 0.93 (0.59–1.43)
275 1.07 (0.85–1.34)
1,776–2,022 1,557–1,771 12,050
Q4
Methionine (mg/day)
27 0.86 (0.45–1.62)
40 1.14 (0.66–1.97)
73 0.97 (0.61–1.56)
59 1.63 (0.94–2.80)
16 0.75 (0.33–1.75)
20 0.90 (0.42–1.91)
34 0.56 (0.33–0.97)
266 1.02 (0.79–1.32)
2,023–3,829 1,773–3,247 12,089
Q5
Table 4. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to quintiles of dietary folate, methionine, riboflavin, and vitamin B6 intake (men and women); NLCS on diet and cancer, 1986–2003
0.88
0.32
0.95
0.05
0.49
0.85
0.10
0.54
P for trend
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Cancer Epidemiol Biomarkers Prev; 23(10) October 2014 1.22–1.42 1.11–1.32 11,975 246 0.94 (0.76–1.16) 50 0.95 (0.63–1.43) 21 1.57 (0.77–3.18) 16 0.70 (0.36–1.35) 44 1.28 (0.79–2.05) 60 0.85 (0.58–1.24) 49 1.34 (0.86–2.10) 36 1.71 (0.99–2.94)
0.45–1.22 0.42–1.11 11,431
246 1.00c
52 1.00c
13 1.00c
22 1.00c
32 1.00c
64 1.00c
37 1.00c
22 1.00c
Q2
32 1.56 (0.90–2.71)
46 1.29 (0.82–2.03)
64 0.89 (0.61–1.31)
37 1.08 (0.65–1.78)
13 0.55 (0.28–1.11)
19 1.45 (0.70–3.01)
55 1.08 (0.71–1.63)
239 0.92 (0.74–1.15)
1.42–1.62 1.32–1.51 11,928
Q3
39 1.91 (1.07–3.40)
49 1.36 (0.83–2.24)
77 1.00 (0.67–1.48)
45 1.24 (0.74–2.05)
18 0.73 (0.38–1.39)
17 1.24 (0.57–2.68)
50 0.95 (0.61–1.48)
267 0.98 (0.78–1.22)
1.62–1.91 1.52–1.75 12,258
Q4
28 1.44 (0.78–2.66)
41 1.21 (0.71–2.05)
78 0.99 (0.64–1.53)
55 1.54 (0.91–2.62)
20 0.83 (0.40–1.70)
26 1.90 (0.91–3.97)
41 0.83 (0.50–1.37)
282 1.05 (0.83–1.34)
1.91–4.51 1.76–3.66 12,057
Q5
0.31
0.58
0.79
0.15
0.74
0.17
0.49
0.55
Ptrend
27 1.00c
36 1.00c
46 1.00c
44 1.00c
12 1.00c
14 1.00c
47 1.00c
218 1.00c
0.35–1.23 0.27–1.07 11,187
Q1
30 1.10 (0.64–1.90)
42 1.19 (0.74–1.90)
74 1.45 (0.98–2.15)
35 0.68 (0.43–1.09)
18 1.42 (0.66–3.02)
17 1.16 (0.56–2.38)
63 1.31 (0.87–1.95)
257 1.09 (0.88–1.35)
1.23–1.42 1.07–1.24 11,937
Q2
32 1.26 (0.73–2.18)
46 1.42 (0.88–2.28)
65 1.28 (0.84–1.94)
31 0.60 (0.37–0.98)
16 1.27 (0.57–2.80)
22 1.52 (0.75–3.09)
42 0.92 (0.58–1.44)
242 1.04 (0.83–1.30)
1.42–1.61 1.24–1.38 12,308
Q3
36 1.46 (0.83–2.58)
49 1.58 (0.97–2.58)
72 1.32 (0.86–2.01)
50 0.88 (0.55–1.40)
27 2.16 (1.05–4.47)
20 1.41 (0.68–2.94)
54 1.15 (0.73–1.81)
289 1.18 (0.94–1.48)
1.61–1.82 1.39–1.58 12,042
Q4
Vitamin B6 (mg/day)
32 1.45 (0.75–2.78)
49 1.87 (1.08–3.25)
86 1.66 (1.03–2.67)
53 0.92 (0.54–1.57)
16 1.34 (0.51–3.49)
23 1.65 (0.71–3.83)
42 1.00 (0.59–1.71)
274 1.18 (0.91–1.54)
1.83–3.93 1.58–2.77 12,175
Q5
0.17
0.02
0.12
0.76
0.34
0.22
0.79
0.17
P for trend
a
Abbreviation: FL, follicular lymphoma. Range of intake in subcohort. b Adjusted for age (y), sex, energy (kcal/d), level of education (primary school or lower vocational school/intermediate vocational school or high school/higher vocational school or college), smoking (current smoking: yes/no; number of cigarettes smoked per day; number of years of smoking), height (cm), alcohol consumption (abstainer, 0.1–4, 5–14, 15–29, 30 g/d), and family history of hematologic malignancies (yes/no). c Reference category.
Range of intakea Men Women Number of person-years Lymphoid neoplasms Number of cases HR (95% CI)b DLBCL Number of cases HR (95% CI)b FL Number of cases HR (95% CI)b LL/WM Number of cases HR (95% CI)b CLL/SLL Number of cases HR (95% CI)b PCN Number of cases HR (95% CI)b Myeloid neoplasms Number of cases HR (95% CI)b AML Number of cases HR (95% CI)b
Q1
Riboflavin (mg/d)
Table 4. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to quintiles of dietary folate, methionine, riboflavin, and vitamin B6 intake (men and women); NLCS on diet and cancer, 1986–2003 (Cont'd)
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Table 5. Multivariable-adjusted HRs and 95% CI for lymphoid and myeloid neoplasms according to tertiles of folate intake, by categories of alcohol consumption (men and women); NLCS on diet and cancer, 1986–2003 Tertiles of folate intake T1 Alcohol consumption, Number of g/d cases HR (95% CI)a Lymphoid neoplasms Abstainerb 0.1– 0.05; data not shown). For folate intake, we observed statistically significantly increased lymphoid neoplasms risks among women (HR ¼ 1.38, 95% CI, 1.00–1.92; HR ¼ 1.45, 95% CI, 1.05–2.00; HR ¼ 1.14, 95% CI, 0.81–1.62; and HR ¼ 1.45, 95% CI, 1.01–2.07 for quintiles 2–5 vs. the first quintile, respectively). Only for the lymphoma subtype PCN, similar increased risks were observed (data not shown). No associations with folate intake were observed among men. After excluding the first 2 years of follow-up (85 lymphoid neoplasm cases and 15 myeloid neoplasm cases), some of the statistically significantly increased and decreased risks became nonsignificant. This included the decreased DLBCL risk for methionine (HR ¼ 0.59; 95% CI, 0.34–1.02 for highest vs. lowest quintile) and the increased PCN risk for vitamin B6 (HR ¼ 1.58; 95% CI, 0.97–2.59). To investigate this into more detail, a test for interaction
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between the exposure of interest and time was calculated and the remaining follow-up period was stratified into three periods (2–
