Nutrients and Genetic Variation Involved in One-Carbon Metabolism and Hodgkin Lymphoma Risk: A Population-based Case-Control Study

Abstract

Nutritional and genetic determinants of the one-carbon metabolism pathway have been related to risk of malignant lymphomas, but little is known about their associations with Hodgkin lymphoma risk specifically. The authors examined nutrient intake (folate, vitamin B₂, vitamin B₆, vitamin B₁₂, methionine) and multivitamin use among 497 Hodgkin lymphoma patients and 638 population-based controls (Massachusetts and Connecticut, 1997–2000), and genetic variation (MTHFR 677C>T, MTHFR 1298A>C, MTR 2756A>G, SHMT1 1420C>T, TYMS 1494del6) and gene-diet interactions in a subset. Unconditional logistic regression was used to calculate multivariable odds ratios and 95% confidence intervals. Hodgkin lymphoma risk was not associated with total nutrient intake or intake from food alone (excluding supplements). Multivitamin use (odds ratio (OR) = 1.46, 95% CI: 1.09, 1.96), total vitamin B₆ (OR_{quartile 4 vs. 1} = 1.62) (P_trend = 0.03), and total vitamin B₁₂ (OR_{quartile 4 vs. 1} = 1.75) (P_trend = 0.02) intakes were positively associated with risk of Epstein-Barr virus-negative, but not -positive, disease. The 5 genetic variants were not significantly associated with Hodgkin lymphoma risk; no significant gene-diet interactions were observed after Bonferroni correction. Study findings do not support a strong role for nutrients and genetic variation in the one-carbon metabolism pathway in susceptibility to Hodgkin lymphoma. Associations between diet and risk of Epstein-Barr virus-negative disease require confirmation in other populations.

Folate, a water-soluble B vitamin, acts as a methyl-group donor in the one-carbon metabolism pathway and is critical for DNA synthesis, repair, and methylation (1). Vitamins B₂, B₆, and B₁₂, which function as enzymatic cofactors, and methionine, which enters the pathway directly, are also required for efficient one-carbon metabolism. Because Hodgkin lymphoma tumors are chromosomally unstable (2) and epigenetic mechanisms have been observed in tumor cell lines (3–6), augmenting dietary intake of folate and related nutrients could offer a modifiable means of reducing Hodgkin lymphoma risk. Previous studies of diet and Hodgkin lymphoma risk have been limited in number and power (7–16), and none have investigated the nutrients involved in one-carbon metabolism.

Hodgkin lymphoma may also be influenced by genetic variation in the one-carbon metabolism pathway. Four prior case-control studies have investigated 2 nonsynonymous polymorphisms in the gene that codes for methylenetetrahydrofolate reductase (MTHFR), a key enzyme of the one-carbon metabolism pathway (17–20). One found an elevated risk of Hodgkin lymphoma associated with the CC versus CT/TT genotype of the MTHFR 677C>T polymorphism (18), whereas 3 other studies were null for MTHFR 677C>T (17, 19, 20) and MTHFR 1298A>C (20). Furthermore, a recent genome-wide association study of classical Hodgkin lymphoma (589 cases and 5,199 controls) identified 3 new susceptibility loci and confirmed the strong association of human leukocyte antigen class II variation with Hodgkin lymphoma risk, but no significant risk variants were found in one-carbon metabolism genes (21). These inconsistent findings may be due to a small number of cases or the inability to consider joint associations between genotype and folate status. Thus, larger studies of Hodgkin lymphoma, particularly with information on both genotype and diet, are needed.

In this population-based case-control study, we assessed the associations of diet (folate, vitamin B₂, vitamin B₆, vitamin B₁₂, and methionine intake, as well as multivitamin use), polymorphisms in folate-metabolizing genes, and gene-diet interactions with Hodgkin lymphoma risk. Interactions of diet with sex, age, and alcohol intake were also considered. We previously showed that older age, male sex, smoking, and lower education were associated with risk of Epstein-Barr virus (EBV)-positive vs. -negative disease in this population (22). Therefore, we also examined dietary and genetic associations according to tumor EBV status. To our knowledge, this is the first study to examine how gene-diet interactions and nutrients involved in one-carbon metabolism are related to Hodgkin lymphoma risk.

MATERIALS AND METHODS

Study population

Cases.

From August 1, 1997, to December 31, 2000, incident Hodgkin lymphoma patients were recruited for a population-based case-control study in the greater Boston, Massachusetts, metropolitan area and in the state of Connecticut. Eligible patients were 15–79 years of age at diagnosis, living within the described geographic area, and without human immunodeficiency virus infection. Of 677 cases invited to participate, 567 (84%) consented.

Pathology material was reviewed by study pathologists (M. Borowitz, R. B. Mann, and E. G. Weir at Johns Hopkins University) to confirm the diagnosis of Hodgkin lymphoma (23). Of the 463 cases with information on pathologic subtype, 447 (97%) were classical Hodgkin lymphoma and 16 were nodular lymphocyte-predominant subtype. We excluded the latter from our analyses, because the nodular lymphocyte-predominant subtype is considered biologically and clinically separate from classical subtypes (24).

Tumor tissue was previously analyzed for the presence of EBV by using in situ hybridization for EBV-encoded RNA transcripts and/or by immunohistochemical assay for the viral latency membrane protein 1 in the Hodgkin and Reed-Sternberg cells (22, 25). A tumor was considered EBV positive if results were positive for either of the assays and EBV negative if both assays were negative or only a single assay was performed and its result was negative (26).

Controls.

Population controls were frequency matched to the age (within 5 years), sex, and state of residency distribution of the cases. Eligible controls were living residents of the study area and without prior history of Hodgkin lymphoma.

In the Boston metropolitan area (132 cities and towns), controls were randomly selected from current “Town Books.” The Town Books are annual records that include the name, sex, street address, and birth year of all residents aged ≥17 years and are >90% complete (27). If a selected control could not be contacted or refused to participate, the next listed eligible person was selected as a replacement control. Of 720 invited controls, 367 (51%) consented.

Connecticut controls aged 18–65 years were identified by random digit dialing; approximately 98.9% of Connecticut residents had home telephone service at the time of the study (28). To avoid overlap of participant responses and clustering by social class, only one participant was recruited per household. To prevent geographic clustering, a maximum of 8 households were screened within a block of 100 telephone numbers. Of the 450 eligible controls arising from 5,632 telephone numbers attempted, 276 (61%) consented to participate in the study. Connecticut controls aged 66–79 years were randomly selected from the Health Care Financing Administration (Medicare) files; 36 (52%) of 69 eligible controls consented to participate.

Participants or their guardians, if minors, gave written informed consent at enrollment. This research protocol was approved by the institutional review boards of the Harvard School of Public Health, Yale University School of Medicine, and Johns Hopkins Medical School, as well as all participating hospitals, the Massachusetts Cancer Registry, and the Connecticut Tumor Registry in the Connecticut Department of Public Health.

Collection of lifestyle and dietary information

Of the 551 cases and 679 controls who consented, 97% completed a structured telephone interview, and 3% completed an abbreviated mailed questionnaire assessing known and potential risk factors for Hodgkin lymphoma (23, 29). Participants were also invited to complete a validated, semiquantitative food frequency questionnaire containing 61 food and beverage items (including alcohol), plus vitamin and mineral supplements (30). Participants recorded how often (9 options ranging from “never” to “6+ per day”) they consumed each food item in the past year according to a commonly used unit or portion size; multivitamin dose options ranged from “2 or less” to “10 or more” tablets/week. Nutrient intake was calculated by multiplying the frequency response by the nutrient content for the chosen portion size of a particular item. All measures of nutrient intake were adjusted for total energy, separately for men and women, by using the residual method to account for the correlation of nutrient intake with energy intake (31). Of the 511 cases (93%) and 648 controls (95%) who completed the food frequency questionnaire, 24 were excluded for leaving ≥10 items blank or for reporting a sex-specific energy intake >3 standard deviations from the mean on the natural log scale (males: <524 or >4,953 kcal; females: <406 or >4,584 kcal). These exclusions left 497 cases and 638 controls for the dietary analyses.

DNA collection and genotyping

Blood specimens were collected from 466 (85%) of 551 interviewed cases, and buccal cell specimens were collected by using a mailed kit with commercial mouthwash from 372 (55%) of 679 interviewed controls (32). DNA extraction and genotyping were performed at the Dana-Farber/Harvard Cancer Center High Throughput Polymorphism Detection Core. DNA was initially extracted from buccal cells by using the Puregene kit (Gentra Systems, Inc., Minneapolis, Minnesota) and, subsequently, DNA from both the buccal and buffy coat specimens was extracted by using the QIAamp DNA blood kit (Qiagen GmbH, Hilden, Germany).

We measured the genetic variation of 4 nonsynonymous single nucleotide polymorphisms and 1 deletion mutation in 4 candidate genes: methylenetetrahydrofolate reductase gene (MTHFR) 677C>T, MTHFR 1298A>C, methionine synthase gene (MTR) 2756A>G, cytoplasmic serine hydroxymethyl transferase gene (SHMT1) 1420C>T, and thymidylate synthase gene (TYMS) 1494del6. These particular variants were chosen on the basis of impaired function of the gene and/or associations detected in prior studies of genetic variation in one-carbon metabolism and risk of lymphoma (33). DNA primer sets are listed by Skibola et al. (34). Genotyping was performed by using the TaqMan Gene Expression Assay and the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, California). Quality control DNA samples (5–6 sets of duplicates per genotyping assay) yielded 100% concordance in each of the 5 assays.

Twenty-three samples were excluded because of genotyping failure for ≥2 of the 5 TaqMan assays, resulting in 454 cases and 361 controls for the genetic analyses. Information on both diet and genotype was available for 418 cases and 353 controls.

Statistical methods

Two-sample t tests and chi-square tests were used to compare descriptive characteristics between cases and controls. Unconditional logistic regression was used to assess the associations of nutrient intake and genetic polymorphisms with risk of Hodgkin lymphoma. Quartiles of nutrient and alcohol intake were constructed according to sex-specific distributions among controls and modeled as indicator variables. The association of nutrient intake with Hodgkin lymphoma risk was estimated as the odds ratio and its 95% confidence interval, with the lowest quartile assigned as the referent group. We tested for linear trends by modeling the median values of the quartiles as a continuous variable and evaluated statistical significance using the Wald test.

The minimally adjusted model included total energy intake (continuous) and the matching factors: age (5-year age groups), sex (male, female), and state of residence (Massachusetts, Connecticut). The fully adjusted model included the following variables that were significantly associated with overall Hodgkin lymphoma risk when added to the minimally adjusted model: race/ethnicity (non-Hispanic white, nonwhite/missing (n = 2 missing)); history of smoking ≥10 lifetime packs of cigarettes (yes, no/missing (n = 1 missing)); aspirin use during the past 5 years (≥2, <2 tablets per week, missing (n = 46)); acetaminophen use during the past 5 years (≥2, <2 tablets per week, missing (n = 51)); education (less than high school, high school, college/missing (n = 2 missing), advanced degree); nursery school/daycare attendance for ≥1 year (yes, no, missing/don't know (n = 32)); and current alcohol intake (drinker, nondrinker).

We examined whether associations between diet and Hodgkin lymphoma differed by sex (male, female), age (<50, ≥50 years), or current alcohol intake (drinker, nondrinker) by constructing an interaction term between nutrient intake (median value of each quartile) or multivitamin use (yes/no) and the stratification variable. Interaction terms were evaluated by using likelihood ratio tests, adjusting for energy intake and the matching factors. We also examined the risk of EBV-positive and EBV-negative Hodgkin lymphoma compared with controls in separate multiple logistic regression models, adjusting for the matching factors, energy intake, smoking history, and education, on the basis of previously observed associations (22).

To assess whether genetic variation in MTHFR 677C>T, MTHFR 1298A>C, MTR 2756A>G, SHMT1 1420C > T, and TYMS 1494del6 was associated with Hodgkin lymphoma risk, we constructed indicator variables for genotype and assigned homozygous wildtype as the referent. Global P values were calculated by performing likelihood ratio tests (2 df) comparing the intercept-only model (adjusted for the matching factors and race/ethnicity) with the model including the indicator variables for genotype.

We used likelihood ratio tests to evaluate 30 gene-diet interactions between the 5 genetic variants and nutrient intake (total folate, total vitamin B₂, total vitamin B₆, total vitamin B₁₂, methionine) or multivitamin use. Interaction terms were constructed by multiplying genotype (presence of at least 1 variant allele vs. homozygous wildtype) by the median values of the nutrient quartiles. Dominant inheritance models were used for the interaction terms because of sparse data among the homozygous variant genotypes.

Analyses were conducted by using SAS, version 9.1, software (SAS Institute, Inc., Cary, North Carolina). All statistical tests were 2 sided, and P < 0.05 was considered statistically significant, unless otherwise noted.

RESULTS

The mean age at diagnosis among Hodgkin lymphoma cases was 38 years (standard deviation = 15 years); the mean age at study recruitment among controls was 40 years (standard deviation = 16 years). Compared with controls, cases were less likely to be regular aspirin users, to have attended nursery school for ≥1 years in childhood, and to possess a higher education degree, and they were more likely to be regular acetaminophen users (Table 1). This study population comprised approximately 53% males and 47% females, and the greater majority of participants (∼90 %) reported non-Hispanic white race/ethnicity.

Table 1.

Distribution of Select Characteristics Among Hodgkin Lymphoma Cases and Controls, Boston, Massachusetts, Metropolitan Area and State of Connecticut, 1997–2000^a

	Cases (n = 497)		Controls (n = 638)		P Valueb
	%	Mean	%	Mean
Age, years
<20	6.0		4.6
20–29	26.0		25.4
30–39	31.0		28.2
40–49	18.0		18.2
50–59	8.5		8.8
60–69	5.6		7.5
≥70	4.8		7.4		0.38
Sex
Male	51.7		54.1
Female	48.3		45.9		0.43
State of residence
Massachusetts	56.9		54.9
Connecticut	43.1		45.1		0.48
Race/ethnicity
White	92.3		88.2
Black	2.8		4.4
Hispanic	2.2		3.1
Other	2.8		4.3		0.18
Body mass index, kg/m2		25.9		26.4	0.11
Regular aspirin user	13.0		18.2		0.02
Regular acetaminophen user	26.8		17.8		<0.01
Multivitamin use	45.7		41.8		0.19
Smoked ≥10 packs of cigarettes in lifetime	50.5		46.3		0.16
Attended nursery school for ≥1 years	11.5		16.2		0.03
Participant's education
Less than high school	8.9		6.1
High school	26.0		24.5
College	51.3		51.3
Advanced degree	13.9		18.1		0.11
Alcohol intake, g/day		6.3		7.7	0.06
Energy intake, kcal/day		1,605		1,608	0.94
Tumor Epstein-Barr virus status
Positive	16.3
Negative	57.6
Missing	26.2

^aRestricted to cases and controls who completed the food frequency questionnaire.

^bP values were calculated by using t tests to compare means or chi-square tests with percentages between cases and controls.

In the dietary analysis adjusting for the matching factors and energy intake, nutrient intakes (total and from food alone) and current multivitamin use were not associated with Hodgkin lymphoma risk (Table 2). Odds ratio estimates were similar between the fully and minimally adjusted models. Because there was no evidence of significant interactions between age groups, sex, or alcohol consumption and nutrient intake or multivitamin use on Hodgkin lymphoma risk (data not shown), all odds ratios are presented without stratification by these factors. Associations between nutrient intake and risk of Hodgkin lymphoma according to tumor EBV status are presented in Table 3. Total vitamin B₂ intake was associated with a slightly increased risk (P_trend = 0.04) of EBV-positive disease. Risk of EBV-negative disease was positively associated with total vitamin B₆ (P_trend = 0.03) and B₁₂ (P_trend = 0.02) intakes and multivitamin use (P_trend = 0.01). The associations for total vitamin B₆ and B₁₂ intakes with risk of EBV-negative disease were attenuated and statistically nonsignificant after adjustment for multivitamin use in the same model (data not shown). A suggestion of a dose-response trend was seen for number of multivitamin tablets taken per week and risk of EBV-negative disease (odds ratio (OR)_1–2vs.0 = 0.97, 95% confidence interval (CI): 0.52, 1.83; OR_3–5vs.0 = 1.73, 95% CI: 1.08, 2.76; OR_6–9vs.0 = 1.41, 95% CI: 0.98, 2.02; OR_≥10vs.0 = 2.22, 95% CI: 1.06, 4.66) (P_trend = 0.007). Nutrients from food alone were not differentially associated with Hodgkin lymphoma risk according to tumor EBV status.

Table 2.

Odds Ratios and 95% Confidence Intervals for the Association of Nutrients Involved in One-Carbon Metabolism With Risk of Hodgkin Lymphoma, Boston, Massachusetts, Metropolitan Area and State of Connecticut, 1997–2000

Quartile of Intake	Median Nutrient Intake in Controls		No. of Cases	No. of Controls	Model 1a		Model 2b
	Men	Women			OR	95% CI	OR	95% CI
Folate, μg/day
Total
Q1	230	203	131	159	1.00	Referent	1.00	Referent
Q2	325	308	107	160	0.82	0.59, 1.16	0.89	0.63, 1.27
Q3	458	557	122	161	0.94	0.67, 1.33	1.06	0.75, 1.51
Q4	790	812	137	158	1.13	0.81, 1.57	1.23	0.87, 1.75
Ptrend					0.20		0.10
From food
Q1	211	189	121	160	1.00	Referent	1.00	Referent
Q2	279	253	128	159	1.09	0.78, 1.53	1.09	0.78, 1.55
Q3	362	316	130	161	1.11	0.79, 1.54	1.20	0.84, 1.70
Q4	459	436	118	158	1.02	0.72, 1.43	1.15	0.80, 1.64
Ptrend					0.94		0.41
Vitamin B2, mg/day
Total
Q1	1.3	1.1	126	160	1.00	Referent	1.00	Referent
Q2	1.7	1.6	101	160	0.78	0.55, 1.11	0.77	0.54, 1.11
Q3	2.2	2.8	130	160	1.05	0.74, 1.47	1.08	0.76, 1.54
Q4	4.0	4.3	140	158	1.18	0.85, 1.65	1.17	0.83, 1.65
Ptrend					0.08		0.08
From food
Q1	1.2	1.0	114	160	1.00	Referent	1.00	Referent
Q2	1.5	1.3	121	159	1.07	0.76, 1.50	0.98	0.69, 1.39
Q3	1.8	1.6	143	162	1.23	0.88, 1.72	1.17	0.82, 1.65
Q4	2.3	2.1	119	157	1.07	0.76, 1.50	0.98	0.69, 1.41
Ptrend					0.70		0.96
Vitamin B6, mg/day
Total
Q1	1.5	1.3	125	159	1.00	Referent	1.00	Referent
Q2	1.9	1.9	116	159	0.97	0.69, 1.37	1.04	0.73, 1.49
Q3	2.6	3.4	110	162	0.91	0.64, 1.29	1.03	0.72, 1.49
Q4	5.3	6.2	146	158	1.29	0.92, 1.80	1.38	0.98, 1.97
Ptrend					0.07		0.05
From food
Q1	1.4	1.2	125	160	1.00	Referent	1.00	Referent
Q2	1.7	1.5	119	159	0.96	0.69, 1.34	0.96	0.78, 1.36
Q3	2.0	1.8	130	160	1.05	0.75, 1.47	1.18	0.83, 1.67
Q4	2.5	2.2	123	159	1.05	0.75, 1.48	1.13	0.79, 1.61
Ptrend					0.70		0.38
Vitamin B12, μg/day
Total
Q1	3.3	2.7	107	161	1.00	Referent	1.00	Referent
Q2	4.7	4.6	127	158	1.22	0.86, 1.72	1.26	0.88, 1.79
Q3	7.7	8.9	124	162	1.21	0.86, 1.71	1.18	0.82, 1.68
Q4	17.4	16.1	139	157	1.42	1.00, 2.00	1.52	1.06, 2.17
Ptrend					0.11		0.05
From food
Q1	2.9	2.5	106	161	1.00	Referent	1.00	Referent
Q2	4.0	3.4	119	157	1.17	0.83, 1.65	1.18	0.83, 1.69
Q3	5.4	4.5	148	163	1.39	0.99, 1.95	1.37	0.97, 1.94
Q4	8.5	6.8	124	157	1.25	0.88, 1.77	1.29	0.90, 1.84
Ptrend					0.22		0.17
Methionine, g/day
From food
Q1	1.3	1.1	130	160	1.00	Referent	1.00	Referent
Q2	1.6	1.5	139	159	1.07	0.77, 1.49	1.15	0.82, 1.61
Q3	1.9	1.8	119	161	0.87	0.62, 1.22	0.97	0.68, 1.37
Q4	2.3	2.2	109	158	0.85	0.60, 1.19	0.94	0.66, 1.34
Ptrend					0.23		0.57
Current multivitamin usec
No			266	368	1.00	Referent	1.00	Referent
Yes			224	264	1.21	0.95, 1.55	1.25	0.97, 1.61
Ptrend					0.12		0.08

Abbreviations: CI, confidence interval; OR, odds ratio; Q, quartile.

^aAdjusted for age (5-year age groups), sex (male, female), state of residence (Connecticut, Massachusetts), and total energy intake (continuous).

^bAdditionally adjusted for race/ethnicity (white, nonwhite), smoking history (ever, never), regular aspirin use (yes, no, missing), regular acetaminophen use (yes, no, missing), education (less than high school, high school, college, advanced degree), nursery school attendance for more than 1 year (yes, no, missing/don't know), and alcohol intake (nondrinker, drinker).

^cExcludes 13 participants missing current multivitamin use.

Table 3.

Odds Ratios and 95% Confidence Intervals for the Association of Nutrients Involved in One-Carbon Metabolism With Risk of EBV-positive or -negative Hodgkin Lymphoma, Boston, Massachusetts, Metropolitan Area and State of Connecticut, 1997–2000

Quartile of Intake	No. of Controls	EBV-positive Diseasea			EBV-negative Diseasea
		No. of Cases	OR	95% CI	No. of Cases	OR	95% CI
Folate, μg/day
Total
Q1	159	20	1.00	Referent	73	1.00	Referent
Q2	160	19	1.08	0.54, 2.14	59	0.82	0.54, 1.25
Q3	161	16	0.93	0.45, 1.92	80	1.20	0.80, 1.79
Q4	158	26	1.61	0.83, 3.11	74	1.21	0.80, 1.83
Ptrend			0.16			0.10
From food
Q1	160	17	1.00	Referent	64	1.00	Referent
Q2	159	23	1.41	0.72, 2.79	75	1.24	0.83, 1.86
Q3	161	22	1.57	0.78, 3.17	81	1.35	0.90, 2.02
Q4	158	19	1.32	0.64, 2.70	66	1.16	0.76, 1.77
Ptrend			0.44			0.56
Vitamin B2, mg/day
Total
Q1	160	17	1.00	Referent	72	1.00	Referent
Q2	160	17	1.05	0.50, 2.19	53	0.73	0.47, 1.12
Q3	160	19	1.19	0.57, 2.48	86	1.31	0.87, 1.97
Q4	158	28	1.94	0.99, 3.80	75	1.19	0.79, 1.79
Ptrend			0.04			0.07
From food
Q1	160	17	1.00	Referent	67	1.00	Referent
Q2	159	20	1.25	0.62, 2.52	73	1.09	0.72, 1.63
Q3	162	17	1.04	0.51, 2.16	80	1.17	0.78, 1.74
Q4	157	27	1.74	0.90, 3.40	66	1.05	0.69, 1.58
Ptrend			0.10			0.89
Vitamin B6, mg/day
Total
Q1	159	23	1.00	Referent	62	1.00	Referent
Q2	159	17	0.78	0.39, 1.56	74	1.31	0.86, 2.00
Q3	162	15	0.73	0.35, 1.51	70	1.29	0.84, 1.99
Q4	158	26	1.33	0.70, 2.53	80	1.62	1.06, 2.47
Ptrend			0.25			0.03
From food
Q1	160	20	1.00	Referent	67	1.00	Referent
Q2	159	19	1.05	0.53, 2.08	78	1.24	0.83, 1.86
Q3	160	18	1.03	0.52, 2.07	79	1.26	0.84, 1.89
Q4	159	24	1.45	0.74, 2.83	62	1.11	0.72, 1.69
Ptrend			0.28			0.75
Vitamin B12, μg/day
Total
Q1	161	18	1.00	Referent	57	1.00	Referent
Q2	158	19	1.13	0.56, 2.28	71	1.33	0.87, 2.04
Q3	162	24	1.41	0.72, 2.78	77	1.48	0.97, 2.26
Q4	157	20	1.20	0.59, 2.44	81	1.75	1.15, 2.68
Ptrend			0.86			0.02
From food
Q1	161	18	1.00	Referent	60	1.00	Referent
Q2	157	21	1.27	0.64, 2.53	70	1.22	0.81, 1.86
Q3	163	20	1.24	0.62, 2.48	84	1.42	0.95, 2.14
Q4	157	22	1.35	0.68, 2.67	72	1.37	0.90, 2.09
Ptrend			0.45			0.15
Methionine, g/day
From food
Q1	160	25	1.00	Referent	66	1.00	Referent
Q2	159	23	0.98	0.52, 1.84	77	1.20	0.80, 1.81
Q3	161	21	0.96	0.51, 1.83	72	1.08	0.71, 1.63
Q4	158	12	0.53	0.25, 1.12	71	1.13	0.75, 1.71
Ptrend			0.12			0.66
Current multivitamin useb
No	368	49	1.00	Referent	144	1.00	Referent
Yes	264	30	0.98	0.59, 1.62	139	1.46	1.09, 1.96
Ptrend			0.92			0.01

Abbreviations: CI, confidence interval; EBV, Epstein-Barr virus; OR, odds ratio; Q, quartile.

^aAdjusted for age (5-year age groups), sex (male, female), state of residence (Connecticut, Massachusetts), total energy intake (continuous), smoking history (ever, never), and education (less than high school, high school, college, advanced degree).

^bExcludes 13 participants missing current multivitamin use.

The 5 genetic variants had genotyping success rates between 94% and 99%, and all were in Hardy-Weinberg equilibrium among the controls (Table 4). No significant associations were detected between any of the genotypes and risk of Hodgkin lymphoma in the co-dominant (P value range: 0.20–0.99; Table 4) or dominant (P value range: 0.15–0.87; data not shown) model, adjusting for the matching factors and race/ethnicity. We also did not detect any significant associations between genotype and risk of EBV-negative or -positive Hodgkin lymphoma (data not shown).

Table 4.

Odds Ratios and 95% Confidence Intervals for the Association of Genetic Variation in One-Carbon Metabolism Genes With Risk of Hodgkin Lymphoma, Boston, Massachusetts, Metropolitan Area and State of Connecticut, 1997–2000

Genea and Single Nucleotide Polymorphism or Deletion	rs No.	Genotype	Hardy-Weinberg P value, Controls	Cases		Controls		ORb	95% CI	Global P Valuec
				No.	%	No.	%
MTHFR 677C>Td	1801133	CCe	0.17	197	44	134	38	1.00	Referent
		CT		195	43	174	50	0.76	0.56, 1.04
		TT		58	13	41	12	0.99	0.62, 1.59	0.20
MTHFR 1298A>C	1801131	AA	0.42	213	47	171	48	1.00	Referent
		AC		199	44	156	43	1.03	0.76, 1.38
		CC		40	9	31	9	1.01	0.60, 1.71	0.99
MTR 2756A>G	1805087	AA	0.32	305	68	234	65	1.00	Referent
		AG		131	29	108	30	0.94	0.69, 1.29
		GG		14	3	18	5	0.59	0.28, 1.22	0.35
SHMT1 1420C>T	1979277	CC	0.44	213	48	150	44	1.00	Referent
		CT		189	43	159	47	0.84	0.62, 1.14
		TT		41	9	29	9	0.95	0.56, 1.62	0.56
TYMS 1494del6f	16430	del −/−	0.43	185	43	165	47	1.00	Referent
		del −/+		185	42	144	42	1.12	0.82, 1.53
		del +/+		65	15	39	11	1.45	0.91, 2.31	0.28

Abbreviations: CI, confidence interval; del, deletion; rs, reference SNP (single nucleotide polymorphism) identification; OR, odds ratio.

^aMTHFR, methylenetetrahydrofolate reductase gene; MTR, methionine synthase gene; SHMT1, cytoplasmic serine hydroxymethyl transferase gene; TYMS, thymidylate synthase gene.

^bAdjusted for age (5-year age groups), sex (male, female), state of residence (Connecticut, Massachusetts), and race/ethnicity (white, nonwhite).

^cLikelihood ratio test comparing the intercept-only model with the model including the indicator variables for genotype, 2 df.

^dC-to-T substitution at nucleotide 677.

^eAmino acids: A, alanine; C, cysteine; G, glycine; T, threonine.

^fSix-base pair deletion at nucleotide 1494.

Five of the 30 gene-diet interactions were statistically significant (Table 5). Most notably, multivitamin use was positively associated with Hodgkin lymphoma risk (OR = 1.67, 95% CI: 1.09, 2.65) (P = 0.02) among participants homozygous for the MTHFR 1298 wildtype allele (AA), whereas no association was found among those with 1 or 2 copies of the variant allele (P_interaction = 0.01). No significant interactions were observed for the MTHFR 677C>T, MTR 2756A>G, and SHMT1 1420C>T genotypes (data not shown). After applying a Bonferroni correction (α = 0.05/30 = 0.0017) to account for multiple comparisons, none of the 5 interactions shown in Table 5 remained statistically significant (35).

Table 5.

Odds Ratios and 95% Confidence Intervals for the Association of Select Nutrients Involved in One-Carbon Metabolism With Risk of Hodgkin Lymphoma, by Genotype, Boston, Massachusetts, Metropolitan Area and State of Connecticut, 1997–2000

Nutrient, Gene,a and Single Nucleotide Polymorphism or Deletion, With Genotype	Quartile of Nutrient Intake															Ptrend orP Value	Pinteraction
	1			2				3				4
	Cases,no.	Controls, no.	ORb	Cases, no.	Controls, no.	ORb	95% CI	Cases, no.	Controls, no.	ORb	95% CI	Cases, no.	Controls, no.	ORb	95% CI
Total folate
MTHFR 1298A>Cc
AAd	43	34	1.00	37	45	0.64	0.33, 1.24	46	42	0.96	0.51, 1.83	69	46	1.19	0.65, 2.19	0.21	0.02
AC, CC	62	42	1.00	48	30	1.27	0.67, 2.40	60	52	0.84	0.47, 1.48	51	59	0.78	0.44, 1.39	0.26
Total vitamin B2
MTHFR 1298A>C
AA	37	41	1.00	40	41	0.99	0.51, 1.89	46	39	1.33	0.70, 2.54	72	46	1.71	0.93, 3.13	0.06	0.01
AC, CC	64	36	1.00	40	41	0.51	0.27, 0.97	65	53	0.68	0.38, 1.20	52	53	0.62	0.34, 1.12	0.40
TYMS 1494del6e
−/−	40	37	1.00	24	42	0.41	0.20, 0.86	52	38	1.23	0.64, 2.38	60	45	1.31	0.70, 2.47	0.04	0.04
−/+, +/+	57	37	1.00	54	35	1.11	0.60, 2.07	54	52	0.74	0.41, 1.33	59	54	0.84	0.47, 1.50	0.39
Total vitamin B6
TYMS 1494del6
−/−	34	35	1.00	41	39	1.21	0.61, 2.39	37	44	0.97	0.49, 1.93	64	44	2.00	1.04, 3.86	0.02	0.03
−/+, +/+	59	31	1.00	53	44	0.71	0.38, 1.32	52	46	0.69	0.37, 1.29	60	57	0.66	0.36, 1.19	0.30
	Current Multivitamin Nonuse			Current Multivitamin Usef
	Cases,no.	Controls, no.	OR	Cases, no.	Controls, no.	OR	95% CI
MTHFR 1298A>C
AA	82	93	1.00	107	72	1.67	1.09, 2.65									0.02	0.01
AC, CC	130	94	1.00	90	87	0.85	0.56, 1.29									0.45

Abbreviations: CI, confidence interval; del, deletion; OR, odds ratio.

^aMTHFR, methylenetetrahydrofolate reductase gene; TYMS, thymidylate synthase gene.

^bAdjusted for age (5-year age groups), sex (male, female), state of residence (Connecticut, Massachusetts), and race/ethnicity (white, nonwhite).

^cA-to-C substitution at nucleotide 1298.

^dAmino acids: A, alanine; C, cysteine.

^eSix-base pair deletion at nucleotide 1494.

^fExcludes 13 participants missing current multivitamin use.

DISCUSSION

In this population-based case-control study, we found no association of folate, vitamin B₂, vitamin B₆, vitamin B₁₂, or methionine intake with overall classical Hodgkin lymphoma risk. Although no prior studies of these factors in relation to Hodgkin lymphoma exist, 3 case-control (36–38) and 2 prospective (39, 40) studies have examined these nutrients in relation to non-Hodgkin lymphoma risk with mixed findings. Overall, the current evidence does not support a strong association between non-Hodgkin lymphoma and one-carbon nutrients, which is in agreement with our largely null findings for Hodgkin lymphoma risk.

Because the etiology and risk factor profile of Hodgkin lymphoma may differ by EBV status, we examined the risk of each subtype separately (22, 41). Total vitamin B₆ and B₁₂ intakes and multivitamin use were positively associated with EBV-negative disease, but not EBV-positive disease, in our study population. These results may, however, be explained through multivitamins, because there was no association for vitamin B₆ or B₁₂ from food sources alone, and adjustment of total vitamin B₆ or B₁₂ intake for multivitamin use attenuated the associations. We also observed a slight positive association between total vitamin B₂ and risk of EBV-positive disease, although this finding is based on a small number of cases and, like other observed associations, may be due to chance.

The positive association between multivitamin use and EBV-negative disease may be biased, because diet was assessed postdiagnostically, and a proportion of cases may have initiated supplement use as a result of their diagnosis (42). However, a small number of prospective studies have also found positive associations between multivitamin use and cancer risk: breast cancer (43), prostate cancer (44), and non-Hodgkin lymphoma (45). Zhang et al. (45) reported a 48% increased risk of non-Hodgkin lymphoma among women who used multivitamins for >10 years but found no association for men. The lack of an association between multivitamins and EBV-positive disease in our study may be due to low power. Alternatively, the pathogenesis of EBV-positive Hodgkin lymphoma may be largely determined by the oncogenic properties of EBV (2), not by dietary factors. Overall, the role of multivitamins in lymphomagenesis merits further investigation.

We did not find a link between genetic variants in key folate-metabolizing genes, including the MTHFR 677C>T and MTHFR 1298A>C polymorphisms, and Hodgkin lymphoma risk. Our null findings for the MTHFR polymorphisms are consistent with results from 3 prior case-control studies of 25–50 cases (17, 19, 20). However, Deligezer et al. (18) reported an increased risk of Hodgkin lymphoma (n = 51 cases) associated with the MTHFR 677 CC versus the CT/TT genotype, which contradicts the hypothesis that reduced enzymatic activity increases Hodgkin lymphoma risk (46).

Inconsistencies across prior genetic studies may be due in part to inadequate statistical power or effect modification by nutrient status. In fact, we found suggestive evidence of gene-diet interactions in our study. In general, high nutrient intake or multivitamin use was associated with increased risk of Hodgkin lymphoma among carriers of the wildtype genotype, whereas the association was null or nonsignificantly inverse among variant carriers. Because the variant alleles in MTHFR and TYMS reduce enzymatic activity (46, 47), these results suggest that individuals with unimpaired enzyme activity and high intakes of certain one-carbon metabolism-related nutrients are at highest risk of Hodgkin lymphoma. This conclusion contradicts the hypothesized mechanism that a diet high in one-carbon metabolism-related nutrients would provide a more stable environment for DNA synthesis and methylation, thereby reducing cancer risk (1). An alternative explanation is that reduced MTHFR activity favors the supply of 5,10-methylenetetrahydrafolate toward DNA synthesis and repair, away from the methylation cycle of the pathway, thereby reducing the likelihood of aberrant hypermethylation of tumor suppressor genes. This pattern has been seen in colorectal cancer, where individuals carrying the MTHFR 677C>T variant allele and with adequate folate status had the lowest cancer risk (48). We cannot, however, rule out the possibility that the gene-diet interactions we observed are chance findings, especially because none remained significant after Bonferroni correction for multiple comparisons.

Although our study population of Hodgkin lymphoma cases was one of the largest to date, statistical power was limited, especially for analyses within subgroups and tests for interactions. Response rates among controls were rather low, which could have led to selection bias, particularly if participation was related to quality of nutrition. We attempted to limit this potential selection bias by replacing nonparticipating Massachusetts controls with individuals drawn from the same residential area, and Chang et al. (29) further showed that the income distribution across census tracts of our consented Massachusetts controls was representative of the source population. However, data to evaluate this potential source of bias were not available among Connecticut controls. Recall bias is another potential limitation, because the estimate of nutrient intake relied on the participant's ability to recall usual dietary habits in the previous year, and diet was assessed after diagnosis of Hodgkin lymphoma. Finally, our study mostly includes young adult cases and may not be representative of older-onset (>55 years) disease etiology.

Our study has several strengths. We are the first to examine the link between Hodgkin lymphoma and dietary intake of nutrients involved in one-carbon metabolism. The assessment of nutrient intake was of high quality, as measured by a validated food frequency questionnaire, and the extensive baseline questionnaire allowed us to evaluate several potential confounders. Another unique aspect of our study was the collection of both dietary and genetic information, allowing for exploratory assessment of gene-diet interactions with respect to Hodgkin lymphoma risk. Consideration of the joint role of folate-metabolizing genes and related nutrients is biologically relevant and can aid in understanding the etiology of lymphomagenesis.

Overall, our study does not support a strong link between Hodgkin lymphoma risk and dietary intake of nutrients involved in one-carbon metabolism or genetic variation in this pathway. The potential increased risk of EBV-negative Hodgkin lymphoma among multivitamin users should be explored in other studies, ideally with prospective data. Additionally, larger studies with sufficient power to detect moderate gene-diet interactions are warranted to clarify the role of one-carbon metabolism in Hodgkin lymphoma risk.

Glossary

Abbreviations

CI

confidence interval

EBV

Epstein-Barr virus

OR

odds ratio