Abstract
Few epidemiological studies have examined the relationship between dietary fat, which may affect immune function, and risk of Hodgkin lymphoma (HL). The aim of this study was to test the hypothesis that high dietary intake of fat and specific subtypes of fat is associated with the risk of HL among 486 HL cases and 630 population-based controls recruited between 1997–2000 in Connecticut and Massachusetts. Unconditional logistic regression was used to calculate odds ratios (OR) and 95 % confidence intervals (CIs) stratified by age and gender. Among younger adults, HL risk was significantly and positively associated with higher intake of saturated fat (ORs for increasing quartiles= 1.3, 1.8, and 2.1; p trend = 0.04), and negatively associated with higher intake of monounsaturated fat (ORs for increasing quartiles= 0.5, 0.5, and 0.4; p trend = 0.03), after adjustment for potential confounders including lifestyle and other dietary factors. The associations with saturated fat (ORs for increasing quartile = 2.4, 3.2, and 4.4; p trend < 0.01) and monounsaturated fat (ORs for increasing quartile= 0.3, 0.6, and 0.3; p trend = 0.04) were most apparent in younger women, whereas there was no significant association between intake of total fat or any type of fat and risk of HL in older females or younger or older males. These findings show that the associations between dietary fat and risk of HL may vary by gender and age, and require confirmation in other populations.
Keywords: Hodgkin lymphoma, dietary fat, saturated fat, monounsaturated fat
Introduction
Hodgkin lymphoma (HL) is a relatively rare malignancy, the etiology of which is complex and poorly understood. From a public health perspective, it is worthwhile to study modifiable factors, such as diet, that may reduce the occurrence of HL. Little is currently known about the association between dietary factors and risk of Hodgkin lymphoma (HL). Previous evidence has indicated that higher intake of dietary fat can alter immune function in humans (1), and influence the progression of lymphomas in mice (2, 3). Moreover, some dietary fatty acids can influence the production of type 1 cytokines (4). Saturated fat, in particular, may induce anti-apoptotic behavior in T cells and increase expression of pro-inflammatory agents (5), and a decrease in total fat intake to 25% of energy has been shown to improve some measures of immune status in humans (6). These collective findings indicating an effect of dietary fat on immune status provide biological plausibility for an influence of dietary fat intake on the risk of immune system disorders in humans.
Previous studies have investigated the association of fat and fat subtypes with the risk of non-Hodgkin lymphoma (NHL) (7–13), and have suggested that there is a significant positive association between NHL and fat intake, mainly for animal fat and saturated fat (8–12). However, few epidemiological studies have examined the relationship between dietary fat and risk of HL, which has a different set of risk factors than NHL. Specifically, only one previous study, a case-control study of lymphoma in Northern Italy that included 158 histologically confirmed cases of HL, has investigated the associations between specific fat sources and HL risk, and observed that dietary intake of liver and ham, but not other major dietary sources of fat, was associated with an increased risk of HL (14). In order to further explore this relationship, given the suggestive but limited data concerning dietary fat intake and HL risk, we tested the hypotheses that high dietary intakes of fat and specific subtypes of fat are associated with the risk of HL in a population-based case-control study of 486 HL cases and 630 controls conducted in Connecticut and Massachusetts. Because risk factors differ between Epstein-Barr virus (EBV)-positive vs. –negative disease (15), we also examined dietary associations according to tumor EBV status.
Materials and Methods
Study population
Cases were patients diagnosed with HL from August 1, 1997, to December 31, 2000. Incident HL patients were recruited through a rapid case ascertainment system of hospitals in the study area which included the greater Boston, MA, metropolitan area and the state of Connecticut, as well as from state cancer registries. 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.
Population controls were frequency matched to the age (within 5 years), gender, and state of residency of the cases. Eligible controls were living residents of the study area and without a prior history of HL. Controls from the Boston metropolitan area (132 cities and towns) were randomly selected from current “Town Books.” The Town Books are annual records that include the name, gender, street address, and birth year of all residents aged ≥ 17 years and are > 90% complete (16). 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 (17). 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 potential 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 Health Care Financing Administration (Medicare) files, of which 36 (52%) of 69 eligible controls consented to participate. In total, 679 controls from Massachusetts and Connecticut participated by completing the study interview.
All study participants provided written informed consent (or, if younger than 18 years, assent) at the time of enrollment in the study. 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 the 68 participating hospitals, the Massachusetts Cancer Registry, and the Connecticut Tumor Registry associated with the Connecticut Department of Public Health.
Histopathology
Pathology material was reviewed by study pathologists (M. Borowitz, R. B. Mann, and E. G. Weir at the Johns Hopkins University) to confirm the diagnosis of HL (18, 19). Of the 463 cases with information on pathologic subtype, 447 (97%) were classical HL 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 (20).
Tumor tissue was previously analyzed for the presence of EBV by using in situ hybridization for EBV-encoded RNA transcripts and/or by an immunohistochemical assay for the viral latency membrane protein-1 in the malignant Hodgkin’s and Reed-Sternberg cells (15, 21). A tumor was considered EBV positive if results were positive for either of the assays and was considered EBV negative if both assays were negative or if only a single assay was done and its result was negative (22). Interpretation of EBV assays was done by consensus of all three study pathologists.
Collection of lifestyle and dietary information
Of the 551 cases and 679 controls that consented, 97% completed a structured telephone interview, and 3% completed an abbreviated mailed questionnaire assessing known and potential risk factors for HL (18, 19). The median time between HL diagnosis and case interview was 7.2 months (range: 2.6–44.6 months). Participants were also invited to complete a validated, semi-quantitative food frequency questionnaire containing 61 food and beverage items (including alcohol). Total dietary fat and fat subtypes (saturated, monounsaturated, and polyunsaturated) were previously shown to have moderate reproducibility in repeated questionnaires (23). 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. Nutrient intake was calculated by multiplying the frequency response by the nutrient content for the chosen portion size of a particular item. Of the 511 cases (93%) and 648 controls (95%) who completed the food frequency questionnaire, 43 were excluded for leaving >=10 items blank or for reporting implausible total caloric intakes (<800 or >6000 kcal/day for men, and <600 or >5000 kcal/day for women). These exclusions left 486 cases and 630 controls in the final analytic dataset.
Statistical analysis
Unconditional logistic regression was used to estimate the association between the risk of HL and intake of dietary fat (total, vegetable, and animal fat) and fat subtypes (saturated, monounsaturated, and polyunsaturated) in multivariate adjusted models. For each nutrient index, quartiles of consumption were produced by dividing the frequency distribution of the control group at the 25th, 50th, and 75th percentiles among gender- and age-specific groups. All nutrient data were adjusted for total energy intake by means of the multivariate nutrient density method, where each nutrient density (i.e., macronutrients divided by total energy) is entered together with total energy into a multivariate logistic regression model (24).
Relationships between fat and intake of fat subtypes and HL risk were estimated using odds ratio (ORs) and 95% confidence intervals, 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 the statistical significance of this predictor using the Wald test. The fully adjusted model included the following variables: age (5-year categories), gender, state of residence (Connecticut, Massachusetts), race/ethnicity (non-Hispanic white, nonwhite/missing), smoking history (ever, never), education (less than high school, high school, college/missing, advanced degree), body mass index (weight (kg)/height (m)2, <25, 25–30, >=30), total energy intake (quartiles), and intake of other types of fat (quartiles). Adjustment for other variables (such as aspirin use, other nonsteroidal anti-inflammatory drug use, and acetaminophen use) was also evaluated, but these variables did not result in material changes in the observed associations, and therefore they were not included in the final model. Potential confounders were selected based on prior knowledge as well as the 10 % change-in-estimate criteria (25), and likelihood ratio tests comparing models with and without the additional variables. These analyses were stratified by age at diagnosis and included young adults (< 45 years) and older adults (>= 45 years), on the basis of regional age-specific incidence patterns (26–30) and differences in risk factor patterns according to age (31, 32). Within each of these two age groups, data were further examined by gender. Nutrient-gender and nutrient-age interactions were assessed in the regression model. Estimates were also stratified by tumor EBV status or tumor histology among cases.
Analyses were conducted using SAS version 9.3 (SAS Institute, Inc., Cary, North Carolina). All statistical tests were 2 sided, and p < 0.05 was considered statistically significant, unless otherwise noted.
RESULTS
Table 1 presents selected characteristics of the cases and controls. Cases and controls were well balanced on age, gender, state of residence, and ethnicity, except that the HL cases had lower educational attainment and a slightly higher percentage of ever smokers compared to the controls.
Table 1.
Distribution of Select Characteristics among Hodgkin Lymphoma Cases and Controls, in Massachusetts and State of Connecticut, 1997–2000a
| Cases (n=486) | Controls (n=630) | |||
|---|---|---|---|---|
|
| ||||
| No. | % | No. | % | |
| Age, years | ||||
| <20 | 35 | 7.2 | 36 | 5.7 |
| 20–29 | 129 | 26.5 | 160 | 25.4 |
| 30–39 | 149 | 30.7 | 177 | 28.1 |
| 40–49 | 83 | 17.1 | 113 | 17.9 |
| 50–59 | 41 | 8.4 | 51 | 8.1 |
| 60–69 | 29 | 6.0 | 50 | 7.9 |
| >=70 | 20 | 4.1 | 43 | 6.8 |
| Sex | ||||
| Male | 251 | 51.6 | 342 | 54.3 |
| Female | 235 | 48.4 | 288 | 45.7 |
| State of residence | ||||
| Massachusetts | 275 | 56.6 | 344 | 54.6 |
| Connecticut | 211 | 43.4 | 286 | 45.4 |
| Race/ethnicity | ||||
| White | 447 | 92.0 | 554 | 87.9 |
| Black | 14 | 2.9 | 28 | 4.4 |
| Hispanic | 11 | 2.3 | 20 | 3.2 |
| Other | 14 | 2.9 | 28 | 4.4 |
| Participant’s education | ||||
| Less than high school | 43 | 8.8 | 39 | 6.2 |
| High school | 126 | 25.9 | 155 | 24.6 |
| College | 249 | 51.2 | 321 | 51.0 |
| Advanced degree | 68 | 14.0 | 115 | 18.3 |
| Cigarette smoking (ever) | ||||
| Smoker b | 244 | 50.2 | 290 | 46.0 |
| Nonsmoker | 242 | 49.8 | 340 | 54.0 |
| Alcohol intake (mean) | 6.4 | 7.8 | ||
| Body mass index, kg/m2 (mean) | 25.8 | 26.4 | ||
| Energy intake, kcal/day (mean) | 1620 | 1613 | ||
| Tumor Epstein-Barr virus status | ||||
| Positive | 78 | |||
| Negative | 281 | |||
| Missing | 128 | |||
Restricted to cases and controls who completed the food frequency questionnaire.
More than 10 packs of cigarettes in lifetime.
Cases and controls did not differ in terms of the means of total energy intake, fat intake (total, vegetable, and animal fat), and intake of specific subtypes of fat (saturated, monounsaturated, and polyunsaturated fat; data not shown). After adjusting for age, gender, state of residence, race, education, smoking history, BMI, total energy intake, and intake of other types of fat, we observed a positive trend that was of borderline significance between HL risk and increasing intake of saturated fat (OR for subsequent quartiles = 1.5 (95 % CI = 1.0–2.2); 1.6 (95 % CI = 1.0–2.6); 1.8 (95 % CI = 1.1–3.1); p trend = 0.08). An inverse association between monounsaturated fat intake and HL risk was found (OR = 0.6, 95 % CI = 0.3–1.1) compared to the lowest quartile, but the trend was statistically nonsignificant (p trend = 0.14). There was no association with HL risk for intake of total fat, vegetable fat, animal fat, or polyunsaturated fat (data not shown).
Results for stratified analyses by age (< 45 and >=45 years) are shown in Table 2. The associations with intake of monounsaturated fat were found to differ significantly across age groups (p heterogeneity = 0.04). Among younger adults, the HL risk was significantly and negatively associated with higher intake of monounsaturated fat (ORs for increasing quartiles= 0.5 (95 % CI = 0.3–0.8), 0.5 (95 % CI = 0.3–0.9), and 0.4 (95% CI = 0.2–0.8); p trend = 0.03) after the adjustment for lifestyle and dietary confounding factors. HL risk was also positively associated with higher intake of saturated fat (ORs for increasing quartiles= 1.3 (95% CI = 0.8–2.1), 1.8 (95 % CI = 1.0–3.2), and 2.1 (95 % CI = 1.1–4.1); p trend = 0.04) among younger adults, although the interaction between age and the intake of saturated fat was not significant (P heterogeneity = 0.69). There were no significant associations with HL risk for intake of total fat or for any fat subtype in older adults (Table 2).
Table 2.
Risk of Hodgkin lymphoma associated with type of dietary fat, stratified by age, in Massachusetts and Connecticut, 1997–2000
| Nutrients | < 45 years
|
≥ 45 years
|
P-value for heterogeneity | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Median (g/1,000 kcal) | Ca/Co | ORa | 95% | CI | Median (g/1,000 kcal) | Ca/Co | ORa | 95% | CI | ||
| Total fat | |||||||||||
| Q1 | 24.5 | 106/110 | 1.0 | 23.7 | 25/48 | 1.0 | 0.38 | ||||
| Q2 | 31.7 | 72/110 | 0.7 | 0.4 | 1.0 | 32.0 | 29/47 | 1.2 | 0.6 | 2.7 | |
| Q3 | 36.0 | 85/110 | 0.8 | 0.5 | 1.2 | 38.3 | 35/47 | 1.7 | 0.8 | 3.5 | |
| Q4 | 43.5 | 100/110 | 0.9 | 0.6 | 1.4 | 46.9 | 34/48 | 1.3 | 0.7 | 2.5 | |
| P for trend | 0.76 | 0.41 | |||||||||
| Animal fat b | |||||||||||
| Q1 | 12.8 | 84/110 | 1.0 | 12.1 | 27/47 | 1.0 | 0.60 | ||||
| Q2 | 18.7 | 94/110 | 1.1 | 0.7 | 1.7 | 17.8 | 28/48 | 1.2 | 0.6 | 2.4 | |
| Q3 | 23.2 | 91/110 | 1.1 | 0.7 | 1.6 | 23.6 | 46/47 | 2.1 | 1.0 | 4.3 | |
| Q4 | 30.0 | 94/110 | 1.0 | 0.7 | 1.6 | 31.9 | 22/48 | 1.1 | 0.6 | 2.2 | |
| P for trend | 0.84 | 0.47 | |||||||||
| Vegetable fat b | |||||||||||
| Q1 | 7.5 | 92/110 | 1.0 | 7.9 | 26/48 | 1.0 | 0.23 | ||||
| Q2 | 11.1 | 96/110 | 1.0 | 0.7 | 1.5 | 11.5 | 24/47 | 1.0 | 0.5 | 2.1 | |
| Q3 | 13.8 | 103/110 | 1.1 | 0.7 | 1.6 | 14.6 | 34/47 | 1.1 | 0.5 | 2.3 | |
| Q4 | 18.7 | 72/110 | 0.7 | 0.5 | 1.2 | 21.4 | 39/48 | 1.3 | 0.7 | 2.6 | |
| P for trend | 0.34 | 0.26 | |||||||||
| Saturated fat c | |||||||||||
| Q1 | 8.7 | 86/110 | 1.0 | 7.9 | 27/48 | 1.0 | 0.69 | ||||
| Q2 | 11.7 | 81/110 | 1.3 | 0.8 | 2.1 | 11.2 | 35/47 | 1.7 | 0.7 | 4.0 | |
| Q3 | 13.7 | 93/110 | 1.8 | 1.0 | 3.2 | 14.1 | 29/48 | 0.9 | 0.3 | 2.5 | |
| Q4 | 17.2 | 103/110 | 2.1 | 1.1 | 4.1 | 17.8 | 32/47 | 0.9 | 0.3 | 2.8 | |
| P for trend | 0.04 | 0.78 | |||||||||
| Monounsaturated fat c | |||||||||||
| Q1 | 8.7 | 110/110 | 1.0 | 8.0 | 24/48 | 1.0 | 0.04 | ||||
| Q2 | 11.6 | 69/110 | 0.5 | 0.3 | 0.8 | 11.5 | 30/47 | 1.0 | 0.4 | 2.6 | |
| Q3 | 13.6 | 98/110 | 0.5 | 0.3 | 0.9 | 14.0 | 36/47 | 1.1 | 0.4 | 3.0 | |
| Q4 | 16.7 | 86/110 | 0.4 | 0.2 | 0.8 | 17.6 | 33/48 | 1.5 | 0.4 | 4.9 | |
| P for trend | 0.03 | 0.64 | |||||||||
| Polyunsaturated fat c | |||||||||||
| Q1 | 3.5 | 96/110 | 1.0 | 3.5 | 26/47 | 1.0 | 0.93 | ||||
| Q2 | 4.6 | 103/110 | 1.2 | 0.8 | 1.8 | 4.7 | 25/48 | 1.0 | 0.4 | 2.2 | |
| Q3 | 5.5 | 86/110 | 1.1 | 0.7 | 1.8 | 5.6 | 26/48 | 1.1 | 0.5 | 2.4 | |
| Q4 | 6.9 | 76/110 | 1.1 | 0.7 | 1.7 | 7.5 | 46/47 | 1.6 | 0.7 | 3.6 | |
| P for trend | 0.97 | 0.14 | |||||||||
Abbreviations: ca/co, numbers of cases and controls; CI, confidence interval; OR, odds ratio; Q, quartile.
Adjusted for age (5-year categories), gender (male, female), state of residence (Connecticut, Massachusetts), race (white, nonwhite), education (less than high school, high school, college, advanced degree), smoking history (ever, never), body mass index (< 25, 25 to < 30, ≥ 30 kg/m2),and total energy intake (quartiles).
Estimates for animal fat, and vegetable fat intakes are mutually adjusted.
Estimates for saturated fat, monounsaturated fat, and polyunsaturated fat intakes are mutually adjusted.
Stratified results for these associations by gender and age are shown in Table 3, while the median intakes of each type of fat according to age and gender are shown in Supplemental Table 1. Among younger women, a significant positive association between HL risk and intake of saturated fat was observed (ORs for increasing quartile = 2.4 (95 % CI = 1.1–5.1), 3.2 (95 % CI = 1.3–7.5), and 4.4 (95 % CI = 1.6–11.9); P trend < 0.01), and the trend associated with an inverse association with HL risk was also significant for intake of monounsaturated fat (ORs for increasing quartile= 0.3 (95% CI = 0.2–0.7), 0.6 (95% CI = 0.2–1.3), and 0.3 (95% CI = 0.1–0.8); P trend = 0.04). HL risk was not associated with greater intake of total fat or other subtypes of fat. Moreover, we did not detect any significant association between intake of total fat and each type of fat and risk of HL in older females, or in younger or older males (Table 3). There were no significant interactions for gender and any subtypes of fat intake, due largely to the limited sample sizes (data not shown).
Table 3.
Risk of Hodgkin lymphoma associated with the type of dietary fat, stratified by age and gender, in Massachusetts and Connecticut, 1997–2000
| Nutrients | < 45 years
|
>= 45 years
|
||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Male
|
Female
|
Male
|
Female
|
|||||||||||||
| Ca/Co | ORa | 95% | CI | Ca/Co | ORa | 95% | CI | Ca/Co | ORa | 95% | CI | Ca/Co | ORa | 95% | CI | |
| Total fat | ||||||||||||||||
| Q1 | 49/58 | 1.0 | 46/52 | 1.0 | 12/28 | 1.0 | 14/19 | 1.0 | ||||||||
| Q2 | 37/57 | 0.6 | 0.3 | 1.0 | 38/53 | 0.9 | 0.5 | 1.7 | 12/27 | 1.0 | 0.4 | 2.9 | 14/21 | 1.2 | 0.4 | 3.9 |
| Q3 | 46/59 | 0.7 | 0.4 | 1.2 | 45/51 | 1.0 | 0.6 | 1.9 | 26/29 | 1.9 | 0.7 | 5.2 | 12/20 | 1.2 | 0.4 | 3.8 |
| Q4 | 55/57 | 0.8 | 0.4 | 1.4 | 47/53 | 1.1 | 0.6 | 2.1 | 14/27 | 1.2 | 0.4 | 3.3 | 19/19 | 1.9 | 0.6 | 6.0 |
| P for trend | 0.53 | 0.68 | 0.50 | 0.32 | ||||||||||||
| P for heterogeneity by gender | 0.84 | 0.69 | ||||||||||||||
| Animal fat b | ||||||||||||||||
| Q1 | 41/58 | 1.0 | 47/52 | 1.0 | 11/27 | 1.0 | 16/19 | 1.0 | ||||||||
| Q2 | 41/57 | 1.0 | 0.5 | 1.8 | 43/52 | 1.5 | 0.8 | 2.6 | 15/28 | 2.0 | 0.7 | 5.8 | 15/20 | 0.6 | 0.2 | 1.9 |
| Q3 | 57/58 | 1.0 | 0.5 | 1.9 | 41/53 | 1.3 | 0.7 | 2.5 | 25/28 | 3.7 | 1.3 | 10.5 | 19/21 | 1.1 | 0.3 | 3.6 |
| Q4 | 48/58 | 0.7 | 0.4 | 1.5 | 45/52 | 1.5 | 0.8 | 3.1 | 13/28 | 1.7 | 0.5 | 5.5 | 9/19 | 0.5 | 0.1 | 1.8 |
| P for trend | 0.48 | 0.24 | 0.25 | 0.53 | ||||||||||||
| P for heterogeneity by gender | 0.75 | 0.76 | ||||||||||||||
| Vegetable fat b | ||||||||||||||||
| Q1 | 49/57 | 1.0 | 45/52 | 1.0 | 18/27 | 1.0 | 9/19 | 1.0 | ||||||||
| Q2 | 49/59 | 1.2 | 0.7 | 2.2 | 46/52 | 0.9 | 0.5 | 1.7 | 13/29 | 0.4 | 0.1 | 1.3 | 12/21 | 1.7 | 0.5 | 5.5 |
| Q3 | 53/57 | 1.4 | 0.8 | 2.5 | 48/52 | 1.0 | 0.5 | 1.8 | 12/27 | 1.0 | 0.4 | 2.7 | 16/20 | 1.5 | 0.4 | 5.0 |
| Q4 | 36/58 | 0.9 | 0.5 | 1.8 | 37/53 | 0.7 | 0.4 | 1.3 | 21/28 | 0.8 | 0.3 | 2.3 | 22/19 | 2.5 | 0.7 | 8.8 |
| P for trend | 0.99 | 0.42 | 0.80 | 0.13 | ||||||||||||
| P for heterogeneity by gender | 0.60 | 0.11 | ||||||||||||||
| Saturated fatc | ||||||||||||||||
| Q1 | 50/58 | 1.0 | 39/53 | 1.0 | 13/28 | 1.0 | 13/19 | 1.0 | ||||||||
| Q2 | 36/58 | 0.8 | 0.4 | 1.7 | 37/51 | 2.4 | 1.1 | 5.1 | 15/28 | 1.1 | 0.3 | 4.1 | 16/21 | 0.7 | 0.1 | 3.1 |
| Q3 | 47/58 | 1.0 | 0.4 | 2.5 | 50/53 | 3.2 | 1.3 | 7.5 | 18/28 | 0.5 | 0.1 | 2.6 | 16/20 | 0.6 | 0.1 | 3.8 |
| Q4 | 54/57 | 1.0 | 0.4 | 2.7 | 50/52 | 4.4 | 1.6 | 11.9 | 18/27 | 0.9 | 0.2 | 5.0 | 14/19 | 0.3 | 0.0 | 2.2 |
| P for trend | 0.91 | <0.01 | 0.78 | 0.26 | ||||||||||||
| P for heterogeneity by gender | 0.24 | 0.40 | ||||||||||||||
| Monounsaturated fatc | ||||||||||||||||
| Q1 | 45/57 | 1.0 | 47/53 | 1.0 | 7/27 | 1.0 | 14/20 | 1.0 | ||||||||
| Q2 | 46/59 | 0.6 | 0.3 | 1.3 | 43/52 | 0.3 | 0.2 | 0.7 | 18/28 | 1.4 | 0.3 | 5.4 | 15/20 | 1.2 | 0.2 | 5.8 |
| Q3 | 42/57 | 0.7 | 0.3 | 1.7 | 50/52 | 0.6 | 0.2 | 1.3 | 23/28 | 2.5 | 0.5 | 11.8 | 12/19 | 2.2 | 0.3 | 15.4 |
| Q4 | 54/58 | 0.6 | 0.2 | 2.0 | 36/52 | 0.3 | 0.1 | 0.8 | 16/28 | 1.4 | 0.2 | 10.1 | 18/20 | 3.2 | 0.3 | 30.9 |
| P for trend | 0.65 | 0.04 | 0.73 | 0.27 | ||||||||||||
| P for heterogeneity by gender | 0.18 | 0.93 | ||||||||||||||
| Polyunsaturated fatc | ||||||||||||||||
| Q1 | 49/58 | 1.0 | 47/52 | 1.0 | 13/27 | 1.0 | 10/20 | 1.0 | ||||||||
| Q2 | 45/58 | 0.8 | 0.5 | 1.6 | 63/52 | 1.8 | 1.0 | 3.4 | 12/28 | 0.4 | 0.1 | 1.2 | 18/20 | 1.3 | 0.4 | 4.7 |
| Q3 | 53/58 | 1.3 | 0.7 | 2.5 | 32/52 | 0.8 | 0.4 | 1.5 | 12/28 | 0.9 | 0.3 | 2.6 | 12/19 | 1.2 | 0.3 | 4.3 |
| Q4 | 40/57 | 1.1 | 0.5 | 2.2 | 34/53 | 1.1 | 0.5 | 2.2 | 27/28 | 1.7 | 0.5 | 5.1 | 19/20 | 1.8 | 0.4 | 7.2 |
| P for trend | 0.66 | 0.71 | 0.21 | 0.37 | ||||||||||||
| P for heterogeneity by gender | 0.89 | 0.47 | ||||||||||||||
Abbreviations: ca/co, numbers of cases and controls; CI, confidence interval; OR, odds ratio; Q, quartile.
Adjusted for age (5-year categories), gender (male, female), state of residence (Connecticut, Massachusetts), race (white, nonwhite), education (less than high school, high school, college, advanced degree), smoking history (ever, never), body mass index (< 25, 25 to < 30, ≥ 30 kg/m2),and total energy intake (quartiles).
Estimates for animal fat, and vegetable fat intakes are mutually adjusted.
Estimates for saturated fat, monounsaturated fat, and polyunsaturated fat intakes are mutually adjusted.
To examine whether the association between HL risk and fat intake varied by the presence of EBV in tumor cells, case-control comparisons were stratified by tumor EBV status among cases. As shown in Table 4, the risk of EBV-negative HL was significantly and positively associated with higher intake of saturated fat (ORs for increasing quartiles= 1.7 (95 % CI =1.0–2.9), 2.3 (95 % CI = 1.2–4.2), and 2.5 (95 % CI = 1.2–4.9); p trend = 0.01), and negatively associated with higher intake of monounsaturated fat (ORs for increasing quartiles= 0.5 (95 % CI = 0.3–0.9), 0.5 (95 % CI = 0.3–1.0), and 0.4 (95 % CI = 0.2–0.9); p trend = 0.07). Further stratified by age and gender, these significant associations were found among younger women (ORs for increasing quartiles of saturated fat = 2.7 (95 % CI = 1.1–6.2), 5.1 (95 % CI = 1.9–13.9), and 4.4 (95 % CI = 1.4–14.0); p trend = 0.01; ORs for increasing quartiles of monounsaturated fat = 0.4 (95 % CI = 0.2–0.9), 0.4 (95 % CI = 0.1–1.1), and 0.2 (95 % CI = 0.1–0.7); p trend = 0.02), but not other age-gender groups (data not shown), although the results were statistically unstable due to the further reduced sample size (109 cases/209 controls among < 45-year-old women; 30 cases/79 controls among >= 45-year-old women). There were no significant associations with EBV-positive HL risk for intake of total fat or any fat subtype.
Table 4.
Association of the type of dietary fat with risk of EBV-positive or -negative Hodgkin lymphoma, in Massachusetts and State of Connecticut, 1997–2000
| Nutrients | No. of Controls | EBV-negative
|
EBV-positive
|
||||||
|---|---|---|---|---|---|---|---|---|---|
| No. of cases | ORa | 95% | CI | No. of cases | ORa | 95% | CI | ||
| Total fat | |||||||||
| Q1 | 158 | 68 | 1.0 | 23 | 1.0 | ||||
| Q2 | 161 | 64 | 0.9 | 0.6 | 1.4 | 14 | 0.6 | 0.3 | 1.3 |
| Q3 | 157 | 83 | 1.2 | 0.8 | 1.9 | 21 | 0.9 | 0.5 | 1.8 |
| Q4 | 154 | 66 | 1.0 | 0.7 | 1.6 | 20 | 0.9 | 0.4 | 1.8 |
| P for trend | 0.53 | 0.97 | |||||||
| Animal fatb | |||||||||
| Q1 | 156 | 55 | 1.0 | 20 | 1.0 | ||||
| Q2 | 159 | 73 | 1.3 | 0.8 | 2.0 | 22 | 1.3 | 0.7 | 2.6 |
| Q3 | 159 | 88 | 1.5 | 1.0 | 2.3 | 18 | 1.0 | 0.5 | 2.1 |
| Q4 | 156 | 65 | 1.2 | 0.7 | 2.0 | 18 | 1.1 | 0.5 | 2.4 |
| P for trend | 0.24 | 0.98 | |||||||
| Vegetable fatb | |||||||||
| Q1 | 158 | 60 | 1.0 | 24 | 1.0 | ||||
| Q2 | 159 | 76 | 1.2 | 0.8 | 1.9 | 17 | 0.6 | 0.3 | 1.3 |
| Q3 | 159 | 91 | 1.4 | 1.0 | 2.2 | 15 | 0.6 | 0.3 | 1.1 |
| Q4 | 154 | 54 | 0.9 | 0.6 | 1.5 | 22 | 0.7 | 0.4 | 1.4 |
| P for trend | 0.89 | 0.33 | |||||||
| Saturated fatc | |||||||||
| Q1 | 158 | 54 | 1.0 | 21 | 1.0 | ||||
| Q2 | 157 | 70 | 1.7 | 1.0 | 2.9 | 20 | 1.4 | 0.6 | 3.1 |
| Q3 | 159 | 78 | 2.3 | 1.2 | 4.2 | 14 | 1.0 | 0.4 | 3.1 |
| Q4 | 156 | 79 | 2.5 | 1.2 | 4.9 | 23 | 1.7 | 0.5 | 5.4 |
| P for trend | 0.01 | 0.51 | |||||||
| Monounsaturated fatc | |||||||||
| Q1 | 158 | 73 | 1.0 | 24 | 1.0 | ||||
| Q2 | 158 | 62 | 0.5 | 0.3 | 0.9 | 16 | 0.6 | 0.3 | 1.5 |
| Q3 | 159 | 78 | 0.5 | 0.3 | 1.0 | 18 | 0.6 | 0.2 | 1.8 |
| Q4 | 155 | 68 | 0.4 | 0.2 | 0.9 | 20 | 0.6 | 0.2 | 2.3 |
| P for trend | 0.07 | 0.38 | |||||||
| Polyunsaturated fatc | |||||||||
| Q1 | 157 | 61 | 1.0 | 26 | 1.0 | ||||
| Q2 | 161 | 85 | 1.5 | 1.0 | 2.4 | 16 | 0.6 | 0.3 | 1.2 |
| Q3 | 158 | 72 | 1.4 | 0.9 | 2.2 | 16 | 0.8 | 0.4 | 1.8 |
| Q4 | 154 | 63 | 1.4 | 0.8 | 2.3 | 20 | 1.1 | 0.5 | 2.5 |
| P for trend | 0.28 | 0.61 | |||||||
Abbreviations: CI, confidence interval; OR, odds ratio; Q, quartile.
Adjusted for age (5-year categories), gender (male, female), state of residence (Connecticut, Massachusetts), race (white, nonwhite), education (less than high school, high school, college, advanced degree), smoking history (ever, never), body mass index (< 25, 25 to < 30, ≥ 30 kg/m2), and total energy intake (quartiles)
Estimates for animal fat, and vegetable fat intakes are mutually adjusted.
Estimates for saturated fat, monounsaturated fat, and polyunsaturated fat intakes are mutually adjusted.
DISCUSSION
In this population-based case-control study, we found that the risk of HL was positively associated with saturated fat and negatively with monounsaturated fat among younger adults, particularly younger women, but not among older adults. However, no association was observed between HL risk and intake of total fat, vegetable fat, animal fat, or polyunsaturated fat. Therefore, these results provide evidence that the risk of HL may be associated with particular subtypes of dietary fat, and that the associations may be limited to specific subgroups based on age and gender.
Although no previous studies have examined intake of total fat and fat subtypes in relation to HL risk, several case-control and cohort studies have investigated these nutrients in relation to NHL risk, with mixed findings (7–13). Both case-control and cohort studies suggest that there is a positive association between saturated fat intake and risk of NHL (8–11). Positive associations were also reported for monounsaturated fat consumption (9–11) and polyunsaturated fat (13), but a protective effect of high consumption of polyunsaturated fats was found in two other studies (8, 9). It is important to note that the etiologies of HL and NHL, which itself comprises dozens of heterogeneous lymphoma subtypes, are distinct (33), and that an association with one lymphoma type does not imply that other lymphomas are also associated with a given risk factor. In the one previous case-control study that examined associations between specific fat sources and HL risk, liver and ham intake were associated with an increased risk of HL (ORs = 1.8 and 2.8, respectively; P < 0.05) based on 158 HL cases and 1157 controls, whereas no association was reported for other food sources of fat, such as meat, butter, milk, and fish (14).
There is experimental evidence from animal studies that fat may influence immune function either by actions on the cyclooxygenase, lipoxygenase, or cytochrome P-450 pathways or directly on cell function through its effects on cell membrane composition (34). However, the type of fatty acids consumed may be important in modulating the immune response. Changes in dietary fat concentration or level of saturation can alter membrane phospholipid fatty acid composition in a variety of cellular and subcellular membranes, which could result in impaired immune function if these changes occur in lymphocytes (35–37). Immune dysfunction, in turn, can contribute to HL risk, as demonstrated by the elevated risk of HL in patients with HIV (38), autoimmune disease (39), and organ transplantation (40, 41).
Our findings that the association with HL for different subtypes of fat varied by age and gender are notable given observations from several descriptive studies, which have suggested variation in the rising of time trends in the incidence rate of HL by age and/or gender (26–30). In particular, Glaser examined the incidence trends of HL by gender and indicated a rising incidence in young adults, particularly females, based on incidence data from a national cancer survey in the United States (26). Two other studies also examined HL incidence over time and reported diverse trends between younger and older adults (27, 28). Another descriptive study among young adults from the Connecticut Tumor Registry also found that the incidence rate of HL increased dramatically in females between 1970 and 1992, but seemed to slow in males (30).
The observed variation by age and sex in the association between dietary fat intake and HL risk could be explained by the involvement of the insulin and insulin-like growth factor (IGF) axis and/or sex steroids or adipokines in HL pathogenesis. Disruption of these pathways can lead to distortion of the normal balance between cell proliferation, differentiation, and apoptosis, with variation by gender and age (42, 43). For example, circulating levels of total IGF-1 (44) and its main binding protein (45), IGFBP-3, which regulate cell proliferation, differentiation, and apoptosis, are thought to be important in tumor development, although these have not been specifically studied in the context of HL. The potential explanation for differences in associations by age is the known secular trends in fat intake. Although older and younger patients in this study were recruited during the same calendar years, current reported diet may be similar to past diet, which may be very different among older and younger adults base on secular trends in fat intake. The discordant findings between younger and older women and between males and females necessitate stratification by age and gender in future research examining the relationship between dietary subtypes of fat intake and HL risk.
Intake of saturated fat and monounsaturated fat was associated with EBV-negative disease, but not EBV-positive disease, in our study population. The potential biological mechanism underpinning these findings is unclear and needs further investigation. The pathogenesis of EBV-positive HL may be more strongly determined by the oncogenic properties of EBV and other immune-related exposures than by dietary factors.
To our knowledge, this is the first study to assess the relationship between dietary fat intake and specific subtypes and HL risk. The assessment of nutrient intake was by a validated food frequency questionnaire, and the extensive baseline lifestyle questionnaire allowed us to evaluate several potential confounders. Although our study population of HL cases was one of the largest to date, statistical power was limited, especially for analyses within age and gender subgroups. With respect to potential biases, participation rates were rather low in controls, and a particular concern is whether controls are representative of the populations from which cases were drawn. 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. (19) 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 HL in cases.
In summary, the associations between dietary fat and risk of HL varied by gender, age, and tumor EBV status. The risk of HL, especially EBV-negative disease, was positively associated with saturated fat, and negatively with monounsaturated fat in young women. Our findings warrant future investigations of addressing HL risk associated with types of fat in particular subgroups, as well as the potential for gene-environment interactions involving nutrient metabolizing genes.
Supplementary Material
References
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