Abstract
Background
The role of dietary factors in non-Hodgkin lymphoma (NHL) risk is not yet well understood. Dietary flavonoids are polyphenolic compounds proposed to be anticarcinogenic. Flavonoids are well-characterized antioxidants and metal chelators, and certain flavonoids exhibit antiproliferative and antiestrogenic effects.
Objective
We aimed to evaluate the hypothesis that higher flavonoid intake is associated with lower NHL risk.
Design
During 1998–2000, we identified incident NHL cases aged 20–74 y from 4 US Surveillance, Epidemiology, and End Results cancer registries. Controls without history of NHL were selected by random-digit dialing or from Medicare files and frequency-matched to cases by age, center, race, and sex. Using 3 recently developed US Department of Agriculture nutrient-specific databases, flavonoid intake was estimated from participant responses to a 117-item food-frequency questionnaire (n = 466 cases and 390 controls). NHL risk in relation to flavonoid intake in quartiles was evaluated after adjustment for age, sex, registry, education, NHL family history, and energy intake.
Results
Higher total flavonoid intake was significantly associated with lower risk of NHL (P for trend < 0.01): a 47% lower risk in the highest quartile of intake than in the lowest (95% CI: 31%, 73%). Higher intakes of flavonols, epicatechins, anthocyanidins, and proanthocyanidins were each significantly associated with decreased NHL risk. Similar patterns of risk were observed for the major NHL subtypes—diffuse large B-cell lymphoma (n = 167) and follicular lymphoma (n = 146).
Conclusion
A higher intake of flavonoids, dietary components with several putative anticarcinogenic activities, may be associated with lower NHL risk.
INTRODUCTION
Non-Hodgkin lymphoma (NHL) is the fifth most common cancer in the United States among both men and women, and the lifetime cumulative risk of developing NHL is 1 in 50 (1). NHL is a heterogenous group of malignancies that arise primarily from lymphoid tissue throughout the body. Greater risks have been associated with immunosuppression and infections, but the causes of NHL are not yet clearly established (2, 3).
Flavonoids are polyphenolic compounds, found primarily in fruit and vegetables, that have been proposed to be anticarcinogenic, particularly through 3 mechanisms: prevention of carcinogenic metabolite formation, prevention of tumor cell proliferation, and induction of tumor cell apoptosis (4). Flavonoids are well-characterized antioxidants and metal chelators, and certain flavonoids have been observed to exhibit antiestrogenic and antiproliferative effects (5). Several subclasses of flavonoids are based on chemical structure: flavanols (examples of rich sources: teas and red wine), flavanones (citrus foods), flavones (fruit skins, peppers, and leafy vegetables), isoflavones (soyfoods), flavonols (leeks, onions, leafy vegetables, and tomatoes), anthocyanidins (berries), and proanthocyanidins (apples, chocolate, and nuts). In vitro, flavanols, flavonols, and anthocyandins have been observed to have greater antioxidant capacity than do flavones, flavanones, and isoflavones (6). These mechanistic differences suggest that flavonoid subclasses may be associated differentially with NHL.
The primary objective of this study was to evaluate the risk of NHL in relation to the intakes of total flavonoids and particular flavonoid subclasses in a case-control study of adults from 4 regions of the United States. A secondary objective was to explore associations of flavonoid intake in relation to the risk of common NHL subtypes and to the risk of NHL stratified by smoking.
SUBJECTS AND METHODS
Subjects and study design
Between 1998 and 2000, of 2248 identified incident cases of NHL aged 20–74 y, 1321 cases (59% response) were enrolled from Surveillance, Epidemiology, and End Results (SEER) registries in the state of Iowa; Seattle, WA; Los Angeles, CA; and Detroit, MI. Population-based controls aged <65 y were selected from households contacted by random-digit dialing, and those aged 65–74 y were selected from Centers for Medicare and Medicaid Services files. Controls were frequency-matched to cases by age (in 5-y age groups), center, race, and sex. Of 2409 eligible controls without a history of NHL, 1057 (44%) were enrolled. A split-sample design was used to avoid overburdening participants with a large number of questions. Study participants were placed in either group A (all of the African American participants and 50% of the non-African American participants), which provided a detailed self and family medical history, or in group B (50% of the non-African American participants), which provided a detailed diet and lifestyle history. Both groups answered a core set of questions about demographic characteristics, hair coloring, occupational history, and pesticide use. Each participant in group B received a mailed questionnaire focused on on lifestyle characteristics and detailed diet history.
The study design was described in detail elsewhere (7-10). Among participants assigned to Group B, 482 of 552 cases and 417 of 462 controls returned the dietary questionnaire. In addition, 2 cases and 2 controls from group A inadvertently received and returned the dietary questionnaire; they are included in the analysis, for a total of 484 cases and 419 controls. Eighteen cases and 28 controls were excluded from the analysis because they reported consuming <4 foods/d (men), <3 foods/d (women), or >30 foods (both sexes) or because they left >20% of food items blank. Another control was excluded because of missing information on education. These inclusions left 466 NHL cases and 390 controls in the analysis.
The study was reviewed and approved by institutional review boards for human subjects research at the National Cancer Institute and at each study center (ie, the authors’ institutions in Los Angeles, Detroit, and Seattle and the University of Iowa, Iowa City, IA).
Dietary flavonoid calculations
The FFQ contained 117 line items. For each line item, participants were asked to indicate their usual portion from a selection of 3 serving sizes and to specify how often, on average, they had consumed the food before the previous year, excluding any recent dietary changes. The 9 responses ranged from “never or less than once per month” to “every day.”
The flavonoid values for each FFQ line item were determined by using the US Department of Agriculture collaborative databases for flavonoids (11), proanthocyanidins (12), and isoflavones (13). The flavonoid database contains information for flavonols (ie, quercetin, kaempferol, myricetin, and isorhamnetin), flavones (ie, luteolin and apigenin), flavanones (ie, herperetin, naringenin, and eriodictyol), flavan-3-ols [ie, (+)-catechin, (+)-gallocatechin, (−)-epicatechin, (−)-epigallocatechin, (−)-epicatechin 3-gallate, (−)-epigallocatechin 3-gallate, theaflavin, theaflavin 3-gallate, theaflavin 3′-gallate, theaflavin 3,3′ digallate, and thearubigins], and anthocyanidins (ie, cyanidin, delphinidin, malvidin, pelargonidin, peonidin, and petunidin). The development of the flavonoid database consists of 2 phases: 1) a survey of the scientific literature for articles containing data on the flavonoid content of foods and 2) an analysis of approximately 60 foods at the Food Composition Laboratory of the US Department of Agriculture. At the time of the present study, the first phase of the project was completed, and the flavonoid data are based on values obtained from scientific articles published in peer-reviewed journals. The proanthocyanidin database contains data for monomers, dimers, trimers, 4–6mers, 7–10mers, and polymers from published sources and analytic data generated at the Arkansas Children's Nutrition Center. Similarly, the isoflavone database contains data on the isoflavone content of foods collected from scientific articles published in peer-reviewed journals and also obtained by sampling of soy-containing foods and subsequent analysis at Iowa State University.
For each line item on the FFQ, contributing foods were identified in the databases. The median flavonoid value of these contributing foods was used as the summary flavonoid value for that line item. For example, to calculate the summary value of quercetin for the line item “apples, applesauce,” the contributing foods identified from the flavonoid database were “apples, raw, with skin” (quercetin = 4.42 mg/100 g edible portion); “apples, raw, without skin” (quercetin = 0.02 mg/100 g edible portion); and “applesauce” (quercetin = 2.00 mg/100 g edible portion). The median value of 2.00 mg/100 g edible portion was used as the summary quercetin intake for “apples, applesauce.”
Daily flavonoid intakes were calculated by summing across all line items the product of the frequency of consumption, the serving size, and the flavonoid content of the food. Flavonoid intakes were grouped into categories—flavonols, flavones, flavanones, catechins, epicatechins, anthocyanidins, proanthocyanidins, and isoflavones.
Statistical analysis
Pearson correlations were calculated for subclasses of flavonoid intake on the continuous scale. Flavonoid and flavonoid subclass intakes were divided into quartiles on the basis of their distribution among controls. The risk of NHL by quartile of total flavonoid intake and intake of each of the flavonoid subclasses was estimated by using odds ratios (OR) (and 95% CIs) derived from unconditional logistic regression models. Because isoflavones and proanthocyanidin intakes were estimated by using different databases than those used for the other flavonoids, total flavonoid intake was evaluated with and without isoflavone and proanthocyanidin intakes. Adjustment variables in logistic regression models include age at reference (20–34, 35–44, 45–54, 55–64, or 65–74 y old), sex, study center, years of education (<12, 12–16, or >16 y), family history of NHL, and total calorie intake as a continuous variable. P for trend was calculated by using quartiles of flavonoid intake as a grouped linear variable. Adjustment for antioxidant micronutrients that were inversely associated with NHL risk (9) did not substantially change risk estimates for total flavonoids and flavonoid subclasses (data not shown).
Because antioxidants can have differential effects between smokers and nonsmokers, exploratory analyses were conducted by stratifying on smoking status (never and former or current) at 1 y before diagnosis. Polytomous regression was used to estimate the risk of the most common NHL subtypes—diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma. Analyses were conducted by using STATA software (version 8.2; Stata Corporation, College Station, TX). Response rates were highest among women, the participants in Iowa, and those with more years of education; therefore, we evaluated quartile intakes of flavonoid and flavonoid subclasses separately among these groups as a means of evaluating nonresponse bias. Results in each group were similar to those for all participants (data not shown).
RESULTS
Descriptive characteristics of the cases and controls included in the dietary analyses are given in Table 1. Most of the participants were white and were >45 y old. Cases for whom we had dietary data were significantly (P < 0.001) younger than controls. Among both cases and controls, most participants had a minimum of 12 y of education. Few participants had a family history of NHL. Average calorie intake was significantly higher in cases (x ± SD: 1921 ± 753) than in controls (1806 ± 712; P = 0.02, 2-sided t test).
TABLE 1.
Selected characteristics of 466 non-Hodgkin lymphoma cases and 390 population controls1
| Characteristic | Controls | Cases | P1 |
|---|---|---|---|
| Study center | 0.70 | ||
| Detroit | 47 (12)2 | 52(11) | |
| Iowa | 136 (35) | 176 (38) | |
| Los Angeles | 82 (21) | 103 (22) | |
| Seattle | 125 (32) | 135 (29) | |
| Education | 0.19 | ||
| <12 y | 36 (9) | 34 (7) | |
| 12–16 y | 217 (56) | 287 (62) | |
| >16 y | 137 (35) | 145 (31) | |
| Age at reference | <0.001 | ||
| 20–34 y | 12 (3) | 26 (6) | |
| 35–14 y | 33 (8) | 59 (13) | |
| 45–54 y | 61 (16) | 99 (21) | |
| 55–64 y | 97 (25) | 130 (28) | |
| 65–74 y | 187 (48) | 152 (33) | |
| Family history of NHL | 0.59 | ||
| No | 373 (96) | 442 (95) | |
| Yes | 17 (4) | 24 (5) | |
| Sex | 0.26 | ||
| Male | 195 (50) | 251 (54) | |
| Female | 195 (50) | 215 (46) | |
| Race | 0.39 | ||
| White | 374 (96) | 441 (95) | |
| Nonwhite | 16 (4) | 25 (5) |
Chi-square test comparing cases and controls.
n; % in parentheses (all such values).
With the exception of flavonols and epicatechins (r = 0.94), intakes of flavonoid subclasses were not strongly correlated with each other. Other correlations were between r = −0.06 and r = 0.38. The correlation between the 2 measures of total flavonoids (total flavonoids excluding isoflavone and proanthocyanidin or total flavonoids) was r = 0.80 (P < 0.001).
The intakes of flavonols, epicatechins, anthocyanidins, proanthocyanidins, and total flavonoids were inversely associated with NHL risk; the ORs for the highest quartile compared with the lowest ranged from 0.47 to 0.67 (Table 2). The association with total flavonoid intake, including isoflavone and proanthocyanidin intakes, was stronger than the association excluding these intakes. Risk estimates for follicular lymphoma and DLBCL, the major subtypes of NHL, in relation to flavonoid intakes were similar to the association we observed for NHL overall (Table 3).
TABLE 2.
Risk of non-Hodgkin lymphoma in relation to the intakes of total flavonoids and specific flavonoid subclasses1
| Flavonoid and quartile (Q) | Min | Max | Controls | Cases | OR (95% CI) | P for trend |
|---|---|---|---|---|---|---|
| mg/d | n (%) | |||||
| Flavonols | 0.01 | |||||
| Q1 | 0.284 | 7.05 | 98 (25) | 153 (33) | 1.00 Reference | |
| Q2 | 7.06 | 11.9 | 97 (25) | 132 (28) | 0.93 (0.64, 1.36) | |
| Q3 | 12.0 | 17.4 | 98 (25) | 83 (18) | 0.55 (0.37, 0.83) | |
| Q4 | 17.5 | 40.6 | 97 (25) | 98 (21) | 0.64 (0.43, 0.96) | |
| Flavones | 0.19 | |||||
| Q1 | 0.00 | 0.129 | 99 (25) | 134 (29) | 1.00 Reference | |
| Q2 | 0.130 | 0.310 | 97 (25) | 139 (30) | 1.15 (0.79, 1.68) | |
| Q3 | 0.311 | 0.585 | 97 (25) | 97 (21) | 0.81 (0.54, 1.21) | |
| Q4 | 0.586 | 5.79 | 97 (25) | 96 (21) | 0.85 (0.56, 1.28) | |
| Flavanones | 0.57 | |||||
| Q1 | 0.00 | 4.13 | 104 (27) | 128 (27) | 1.00 Reference | |
| Q2 | 4.14 | 15.9 | 91 (23) | 106 (23) | 0.97 (0.66, 1.44) | |
| Q3 | 16.0 | 33.5 | 98 (25) | 118 (25) | 1.08 (0.73, 1.60) | |
| Q4 | 33.6 | 219 | 97 (25) | 114 (24) | 1.09 (0.74, 1.63) | |
| Catechins | 0.15 | |||||
| Q1 | 0.00 | 1.13 | 97 (25) | 82 (18) | 1.00 Reference | |
| Q2 | 1.14 | 2.40 | 98 (25) | 133 (29) | 1.62 (1.08, 2.43) | |
| Q3 | 2.41 | 4.36 | 98 (25) | 119 (26) | 1.39 (0.92, 2.11) | |
| Q4 | 4.37 | 30.8 | 97 (25) | 132 (28) | 1.51 (0.98, 2.33) | |
| Epicatechins | 0.01 | |||||
| Q1 | 0.00 | 4.41 | 102 (26) | 155 (33) | 1.00 Reference | |
| Q2 | 4.42 | 12.3 | 109 (28) | 151 (32) | 1.03 (0.71, 1.48) | |
| Q3 | 12.4 | 14.9 | 89 (23) | 71 (15) | 0.53 (0.35, 0.80) | |
| Q4 | 15.0 | 36.8 | 90 (23) | 89 (19) | 0.67 (0.45, 1.00) | |
| Anthocyanidins | 0.01 | |||||
| Q1, Q2 | 0.00 | 0.00 | 236 (60) | 322 (69) | 1.00 Reference | |
| Q3 | 0.001 | 0.152 | 57 (15) | 49 (11) | 0.67 (0.43, 1.04) | |
| Q4 | 0.153 | 18.4 | 97 (25) | 95 (20) | 0.66 (0.46, 0.94) | |
| Theaflavins | 0.71 | |||||
| Q1 | 0.00 | 0.102 | 97 (25) | 114 (24) | 1.00 Reference | |
| Q2 | 0.103 | 0.218 | 98 (25) | 113 (24) | 0.95 (0.64, 1.41) | |
| Q3 | 0.219 | 0.416 | 98 (25) | 100 (21) | 0.81 (0.54, 1.22) | |
| Q4 | 0.417 | 3.14 | 97 (25) | 139 (30) | 1.14 (0.76, 1.71) | |
| Isoflavones | 0.97 | |||||
| Q1 | 0.00 | 0.262 | 98 (25) | 100 (21) | 1.00 Reference | |
| Q2 | 0.263 | 0.495 | 97 (25) | 126 (27) | 1.25 (0.84, 1.86) | |
| Q3 | 0.496 | 0.910 | 98 (25) | 114 (24) | 0.98 (0.65, 1.48) | |
| Q4 | 0.911 | 53.5 | 97 (25) | 126 (27) | 1.08 (0.71, 1.63) | |
| Proanthocyanidins | 0.01 | |||||
| Q1 | 2.48 | 62.5 | 97 (25) | 147 (32) | 1.00 Reference | |
| Q2 | 62.6 | 91.1 | 98 (25) | 118 (25) | 0.80 (0.55, 1.18) | |
| Q3 | 91.2 | 127 | 98 (25) | 99 (21) | 0.60 (0.40, 0.90) | |
| Q4 | 128 | 357 | 97 (25) | 102 (22) | 0.62 (0.41, 0.94) | |
| Total flavonoids excluding isoflavone and proanthocyanidin | 0.06 | |||||
| Q1 | 0.771 | 30.4 | 97 (25) | 153 (33) | 1.00 Reference | |
| Q2 | 30.5 | 47.6 | 98 (25) | 107 (23) | 0.74 (0.50, 1.08) | |
| Q3 | 47.7 | 69.0 | 98 (25) | 107 (23) | 0.71 (0.48, 1.05) | |
| Q4 | 69.1 | 288 | 97 (25) | 99 (21) | 0.67 (0.45, 1.02) | |
| Total flavonoids | <0.01 | |||||
| Q1 | 4.82 | 102 | 97 (25) | 161 (35) | 1.00 Reference | |
| Q2 | 103 | 144 | 98 (25) | 107 (23) | 0.65 (0.44, 0.95) | |
| Q3 | 145 | 201 | 98 (25) | 113 (24) | 0.64 (0.44, 0.95) | |
| Q4 | 202 | 536 | 97 (25) | 85 (18) | 0.47 (0.31, 0.73) | |
Min, minimum; Max. maximum; OR, odds ratio. ORs were calculated by using unconditional logistic regression, adjusted for study center, years of education (<12, 12–16, or >16 y). age at reference (20–34, 35–44, 45–54, 55–64, or 65–74 y), family history of non-Hodgkin lymphoma, sex, and total kcal. Because of skewedness of the distribution of intake values, categories for flavanones, epicatechins, and proanthocyanidins are not exact quartiles.
TABLE 3.
Risk of follicular and diffuse subtypes of non-Hodgkin lymphoma in relation to the intakes of total flavonoids and specific flavonoid subclasses1
| Diffuse large B-cell lymphoma (n = 167) |
Follicular (n = 146) |
|||||
|---|---|---|---|---|---|---|
| Flavonoids and quartile (Q) | Cases | OR (95% CI) | P for trend | Cases | OR (95% CI) | P for trend |
| n | mg/d | n | mg/d | |||
| Flavanols | 0.01 | 0.02 | ||||
| Q1 | 68 | 1.00 Reference | 42 | 1.00 Reference | ||
| Q2 | 38 | 0.58 (0.35, 0.96) | 53 | 1.30 (0.79, 2.16) | ||
| Q3 | 26 | 0.36 (0.21, 0.63) | 26 | 0.61 (0.34, 1.09) | ||
| Q4 | 35 | 0.47 (0.28, 0.80) | 25 | 0.59 (0.33, 1.07) | ||
| Flavones | 0.34 | 0.58 | ||||
| Q1 | 49 | 1.00 Reference | 37 | 1.00 Reference | ||
| Q2 | 47 | 1.05 (0.63, 1.73) | 47 | 1.35 (0.79, 2.29) | ||
| Q3 | 38 | 0.86 (0.50, 1.46) | 29 | 0.83 (0.47, 1.48) | ||
| Q4 | 33 | 0.80 (0.45, 1.40) | 33 | 1.01 (0.56, 1.80) | ||
| Flavanones | 0.78 | 0.47 | ||||
| Q1 | 49 | 1.00 Reference | 38 | 1.00 Reference | ||
| Q2 | 42 | 1.04 (0.62, 1.73) | 34 | 1.06 (0.61, 1.82) | ||
| Q3 | 36 | 0.86 (0.50, 1.46) | 34 | 1.03 (0.59, 1.79) | ||
| Q4 | 40 | 0.98 (0.58, 1.67) | 40 | 1.25 (0.72, 2.14) | ||
| Catechins | 0.11 | 0.55 | ||||
| Q1 | 25 | 1.00 Reference | 26 | 1.00 Reference | ||
| Q2 | 42 | 1.63 (0.91, 2.92) | 49 | 1.93 (1.11, 3.35) | ||
| Q3 | 49 | 1.79 (1.00, 3.19) | 31 | 1.14 (0.63, 2.10) | ||
| Q4 | 51 | 1.67 (0.91, 3.06) | 40 | 1.51 (0.82, 2.77) | ||
| Epicatechins | <0.01 | 0.01 | ||||
| Q1 | 69 | 1.00 Reference | 43 | 1.00 Reference | ||
| Q2 | 49 | 0.76 (0.48, 1.22) | 59 | 1.38 (0.84, 2.23) | ||
| Q3 | 19 | 0.29 (0.16, 0.54) | 22 | 0.56 (0.31, 1.03) | ||
| Q4 | 30 | 0.49 (0.29, 0.84) | 22 | 0.59 (0.32, 1.06) | ||
| Anthocyanidins | 0.21 | 0.10 | ||||
| Q1, Q2 | 115 | 1.00 Reference | 100 | 1.00 Reference | ||
| Q3 | 15 | 0.61 (0.32, 1.15) | 17 | 0.70 (0.38, 1.30) | ||
| Q4 | 37 | 0.77 (0.48, 1.25) | 29 | 0.67 (0.40, 1.12) | ||
| Theaflavins | 0.74 | 0.99 | ||||
| Q1 | 40 | 1.00 Reference | 35 | 1.00 Reference | ||
| Q2 | 43 | 1.00 (0.59, 1.70) | 38 | 1.06 (0.62, 1.84) | ||
| Q3 | 34 | 0.73 (0.42, 1.28) | 35 | 0.96 (0.55, 1.68) | ||
| Q4 | 50 | 0.99 (0.58, 1.72) | 38 | 1.03 (0.58, 1.84) | ||
| Isoflavones | 0.70 | 0.94 | ||||
| Q1 | 37 | 1.00 Reference | 34 | 1.00 Reference | ||
| Q2 | 46 | 1.19 (0.70, 2.01) | 40 | 1.19 (0.68, 2.03) | ||
| Q3 | 56 | 0.77 (0.44, 1.36) | 37 | 1.04 (0.59, 1.84) | ||
| Q4 | 48 | 1.01 (0.58, 1.77) | 35 | 1.02 (0.57, 1.82) | ||
| Proanthocyanidins | 0.01 | 0.02 | ||||
| Q1 | 55 | 1.00 Reference | 41 | 1.00 Reference | ||
| Q2 | 37 | 0.64 (0.38, 1.07) | 50 | 1.20 (0.72, 1.99) | ||
| Q3 | 39 | 0.57 (0.33, 0.97) | 31 | 0.69 (0.39, 1.23) | ||
| Q4 | 36 | 0.49 (0.28, 0.86) | 24 | 0.53 (0.29, 0.99) | ||
| Total flavonoids excluding isoflavone and proanthocyanidin | 0.02 | 0.13 | ||||
| Q1 | 62 | 1.00 Reference | 46 | 1.00 Reference | ||
| Q2 | 34 | 0.54 (0.32, 0.91) | 35 | 0.78 (0.46, 1.32) | ||
| Q3 | 36 | 0.54 (0.32, 0.90) | 37 | 0.80 (0.47, 1.36) | ||
| Q4 | 35 | 0.53 (0.30, 0.91) | 28 | 0.61 (0.34, 1.09) | ||
| Total flavonoids | <0.01 | <0.01 | ||||
| Q1 | 62 | 1.00 Reference | 47 | 1.00 Reference | ||
| Q2 | 35 | 0.50 (0.30, 0.84) | 44 | 0.89 (0.53, 1.49) | ||
| Q3 | 37 | 0.49 (0.29, 0.84) | 38 | 0.75 (0.44, 1.28) | ||
| Q4 | 33 | 0.39 (0.22, 0.71) | 17 | 0.32 (0.16, 0.63) | ||
OR, odds ratio. ORs were calculated by using unconditional logistic regression, adjusted for study center, years of education (<12, 12–16, or >16 y), age at reference (20–34, 35–44, 45–54, 55–64, or 65–74 y), family history of non-Hodgkin lymphoma, sex, and total kcal. Because of skewedness of the distribution of intake values, the categories for flavanones, epicatechins, and proanthocyanidins are not exact quartiles.
Total flavonoid intake was significantly inversely associated with NHL risk in never and former smokers (P < 0.01) but not in current smokers (P = 0.79; Table 4). For total flavonoids without isoflavone and proanthocyanidin intakes, the test for interaction was statistically significant (P = 0.01). Among never and former smokers, increasing intakes of flavonols, epicatechins, anthocyanidins, and proanthocyanidins were associated with significant (P < 0.05) inverse trends in risk of NHL. In addition, among never and former smokers, there was a suggestion of a positive association of catechins with NHL risk (highest quartile OR: 1.79; 95% CI: 1.10, 2.90). Among current smokers, none of these flavonoid subclasses was associated with a significantly lower NHL risk, whereas a greater intake of flavanones was associated with a significantly greater NHL risk (highest quartile OR: 4.52; 95% CI: 1.33, 15.3; P for interaction = 0.03). The associations with flavonoid intake among former (n = 308) and never (n = 384) smokers were mostly inverse; however, trends were stronger in never smokers (data not shown).
TABLE 4.
Risk of non-Hodgkin lymphoma in relation to intakes of total flavonoids and specific flavonoid subclasses stratified by smoking status1
| Never and former smokers (n = 692) |
Current smokers (n = 142) |
||||||
|---|---|---|---|---|---|---|---|
| Flavonoids and quartile (Q) | Cases and controls | OR (95% CI) | P for trend | Cases and controls | OR (95% CI) | P for trend | P for interaction2 |
| n | mg/d | n | mg/d | ||||
| Flavonols | <0.01 | 0.88 | 0.18 | ||||
| Q1 | 207 | 1.00 Reference | 38 | 1.00 Reference | |||
| Q2 | 144 | 0.86 (0.57, 1.31) | 32 | 1.71 (0.58, 5.06) | |||
| Q3 | 157 | 0.55 (0.36, 0.86) | 21 | 0.96 (0.28, 3.35) | |||
| Q4 | 134 | 0.58 (0.36, 0.92) | 51 | 1.24 (0.46, 3.40) | |||
| Flavones | 0.23 | 0.94 | 0.63 | ||||
| Q1 | 178 | 1.00 Reference | 53 | 1.00 Reference | |||
| Q2 | 190 | 1.25 (0.82, 1.92) | 39 | 0.72 (0.27, 1.90) | |||
| Q3 | 159 | 0.85 (0.54, 1.32) | 30 | 0.68 (0.24, 1.96) | |||
| Q4 | 165 | 0.86 (0.55, 1.35) | 20 | 1.27 (0.38, 4.26) | |||
| Flavanones | 0.73 | 0.01 | 0.03 | ||||
| Q1 | 164 | 1.00 Reference | 61 | 1.00 Reference | |||
| Q2 | 158 | 0.85 (0.54, 1.34) | 33 | 2.45 (0.88, 6.76) | |||
| Q3 | 189 | 0.88 (0.57, 1.36) | 22 | 3.11 (0,94, 10.3) | |||
| Q4 | 181 | 0.91 (0.58, 1.42) | 26 | 4.52 (1.33, 15.3) | |||
| Catechins | 0.06 | 0.44 | 0.40 | ||||
| Q1 | 139 | 1.00 Reference | 36 | 1.00 Reference | |||
| Q2 | 187 | 1.81 (1.15, 2.86) | 39 | 1.16 (0.42, 3.20) | |||
| Q3 | 180 | 1.58 (0.99, 2.52) | 31 | 1.18 (0.39, 3.63) | |||
| Q4 | 186 | 1.79 (1.10, 2.90) | 36 | 0.60 (0.19, 1.92) | |||
| Epicatechins | <0.01 | 0.90 | 0.32 | ||||
| Q1 | 220 | 1.00 Reference | 31 | 1.00 Reference | |||
| Q2 | 226 | 1.00 (0.68, 1.48) | 30 | 1.39 (0,43, 4.46) | |||
| Q3 | 131 | 0.54 (0.34, 0.86) | 28 | 0.63 (0.18, 2.26) | |||
| Q4 | 115 | 0.61 (0.38, 0.97) | 53 | 1.15 (0.40, 3.36) | |||
| Anthocyanidins | 0.02 | 0.64 | 0.26 | ||||
| Q1, Q2 | 437 | 1.00 Reference | 107 | 1.00 Reference | |||
| Q3 | 87 | 0.63 (0.39, 1.02) | 14 | 1.28 (0.35, 4.71) | |||
| Q4 | 168 | 0.65 (0.44, 0.96) | 21 | 0.68 (0.24, 1.98) | |||
| Theaflavins | 0.80 | 0.20 | 0.79 | ||||
| Q1 | 182 | 1.00 Reference | 25 | 1.00 Reference | |||
| Q2 | 174 | 0.88 (0.57, 1.35) | 32 | 1.59 (0.51, 5.01) | |||
| Q1 | 151 | 0.79 (0.51, 1.24) | 38 | 1.19 (0.37, 3.83) | |||
| Q4 | 185 | 0.98 (0.63, 1.53) | 47 | 2.28 (0.72, 7.15) | |||
| Isoflavones | 0.83 | 0.16 | 0.47 | ||||
| Q1 | 171 | 1.00 Reference | 24 | 1.00 Reference | |||
| Q2 | 186 | 1.30 (0.84, 2.00) | 32 | 1.15 (0.36, 3.68) | |||
| Q3 | 152 | 0.98 (0.62, 1.57) | 49 | 1.34 (0.45, 4.02) | |||
| Q4 | 183 | 1.04 (0.67, 1.63) | 37 | 2.35 (0.70, 7.82) | |||
| Proanthocyanidins | 0.02 | 0.72 | 0.94 | ||||
| Q1 | 195 | 1.00 Reference | 46 | 1.00 Reference | |||
| Q2 | 179 | 0.86 (0.56, 1.31) | 29 | 0.40 (0.13, 1.19) | |||
| Q3 | 162 | 0.56 (0.36, 0.87) | 32 | 0.98 (0.34, 2.86) | |||
| Q4 | 156 | 0.64 (0.40, 1.02) | 35 | 0.59 (0.18, 1.98) | |||
| Total flavonoids excluding isoflavone and proanthocyanidin | 0.02 | 0.20 | 0.01 | ||||
| Q1 | 199 | 1.00 Reference | 46 | 1.00 Reference | |||
| Q2 | 177 | 0.73 (0.48, 1.12) | 23 | 0.87 (0.29, 2.64) | |||
| Q3 | 160 | 0.67 (0.43, 1.04) | 39 | 1.37 (0.50, 3.77) | |||
| Q4 | 156 | 0.59 (0.38, 0.94) | 34 | 1.99 (0.64, 6.21) | |||
| Total flavonoids | <0.01 | 0.79 | 0.25 | ||||
| Q1 | 201 | 1.00 Reference | 50 | 1.00 Reference | |||
| Q2 | 175 | 0.68 (0.44, 1.04) | 26 | 0.80 (0.27, 2.41) | |||
| Q3 | 176 | 0.63 (0.41, 0.98) | 32 | 1.10 (0.38, 3.20) | |||
| Q4 | 140 | 0.44 (0.27, 0.72) | 34 | 1.11 (0.36, 3.37) | |||
OR, odds ratio. ORs were calculated by using unconditional logistic regression, adjusted for study center, years of education (<12, 12–16, or >16 y), age at reference (20–34, 35–44, 45–54, 55–64, or 65–74 y), family history of non-Hodgkin lymphoma, sex, and total kcal. Because of skewedness of the distribution of intake values, categories for flavanones, epicatechins, and proanthocyanidins are not exact quartiles.
Interaction tested whether the association of flavonoid subgroup with non-Hodgkin lymphoma differed between never and former smokers and current smokers.
DISCUSSION
We observed that the intakes of flavonols, epicatechins, anthocyanidins, proanthocyanidins, and total flavonoids were inversely associated with NHL risk. None of the flavonoid subclasses were positively associated with NHL risk. With the exception of flavonols and epicatechins, correlations between flavonoid subclasses were not strong, which suggested that the associations were not collinear. Many studies have evaluated fruit and vegetable intakes in relation to NHL risk; however, no previous studies evaluated NHL risk and flavonoids. Generally, fruit and vegetables were inversely associated with NHL risk, and stronger associations were often observed for specific types of vegetables or fruit (14, 15). These previous observations and our current findings suggest that associations with NHL risk may be limited to or stronger with particular plant compounds, including subclasses of flavonoids.
In previous reports from the same study, our group observed significant inverse associations between NHL risk and higher intakes of vegetables and specific micronutrients (9, 10). Intakes of the flavonoid subclasses inversely associated with NHL risk in these analyses were not strongly correlated (Pearson's r < 0.35) with intakes of vegetables in the study population; therefore, the findings cannot be explained solely by vegetable intake. Besides fruit and vegetables, rich sources of flavonoid intake in the study population included tea, wine, nuts, and chocolate. In previous analyses, some differences in associations of fruit, vegetables, and particular micronutrients were observed for follicular and DLBCL NHL risk (9, 10). However, in the present study, we did not observe differences in the associations of flavonoids with respect to subtype risk. If flavonoids exert a broad action in the carcinogenesis process, such as inhibition of promotion, we would not expect to observe different associations between the tumor subtypes.
Smoking is a source of oxidative stress. Lower concentrations of plasma antioxidants have been observed in smokers than in nonsmokers, and smoking cessation has been associated with increasing concentrations of plasma antioxidants (16-18). Because smokers appear to metabolize antioxidants differently from nonsmokers, we analyzed the associations between flavonoids and NHL risk stratified by smoking. The lower risk of NHL in relation to flavonoid intake was limited to former smokers and never smokers, and we observed somewhat stronger associations for never smokers (data not shown). Although our analyses were limited by small numbers of current smokers, we observed a positive association for flavanones among smokers. Flavanones are the primary flavonoids in citrus fruit. In our analysis, “oranges (not including juice),” “orange or grapefruit juice,” and “grapefruit (not including juice)” were the line items contributing to flavanone intake. The different associations between current smokers and former and never smokers may be due to different flavonoid metabolism or neutralization of reactive oxygen species between nonsmokers and smokers. Alternatively, the difference we observed may be due to chance or to differences in the recall of dietary intakes between current smokers and nonsmokers.
The present study had several potential limitations. Response rates were low, particularly for controls, and participation by cases, controls, or both may have been associated with flavonoid consumption. To assess the potential for response bias, we previously assessed factors associated with nonresponse in this study, which included lower socioeconomic status, older age, Hispanic ethnicity, and nonwhite race, and found that they were similar for cases and controls (19). Risk estimates for education and household income based on all eligible cases and controls were similar to risk estimates among participating cases and controls, which indicated that the extent of bias due to nonresponse was small. Likewise, flavonoid analyses limited to subgroups with the highest responses (ie, women, participants in Iowa, and those with more years of education) showed similar associations to the study population overall.
Another limitation was the assumptions made in the calculation of flavonoid values for each food item. Flavonoid concentrations varied across foods included in some line items; when this occurred, we assigned the median flavonoid value of the contributing foods. Moreover, the database did not contain flavonoid information on all of the food items. These limitations would be likely to reduce the precision and variability of our estimated intake values but would not be likely to markedly alter the ranking of cases or controls by intake. These measurement errors would represent a source of nondifferential bias and would have reduced the ability to detect an association between dietary intake and NHL risk. Thus, our results may be conservative. An additional limitation is that the FFQ was not designed for the evaluation of flavonoid intake. Limited information was collected about tea, coffee, and alcohol, which are rich sources of flavonoids. This limited information also reduced variability in intake and, in particular, limited our ability to evaluate particular classes of flavonoids, such as proanthocyandins and theaflavins. Another limitation is that this is a case-control study, and bias in recalling dietary intake between cases and controls is a concern. A strength of using nutrient classifications, such as total flavonoids, is that, whereas they allow a finer stratification than do fruit and vegetables, there is adequate variation in intake. In addition, associations between flavonoids and NHL may provide information on the biology of NHL. A limitation of using nutrient classifications is that it may be more difficult to provide public health recommendations regarding the consumption of high-nutrient groupings than regarding the consumption of fruit and vegetables.
We observed that total flavonoid intake and the intakes of particular flavonoid subclasses were inversely associated with NHL risk. Our sample size of current smokers was somewhat small, but we observed some differences in these associations between smokers and nonsmokers, which suggested some interactions between smoking and flavonoid intake for NHL risk. These results support the associations seen in other studies of lower NHL risk in relation to plant food intake, and they suggest that specific flavonoid compounds within plants may be partly responsible. Because quantifying flavonoid intake is challenging, further study of NHL risk and of these compounds, with the use of more precise quantification, is warranted.
Acknowledgments
We gratefully acknowledge Westat Inc for study coordination; International Management Services, Inc, for data management; and BBI-Biotech for specimen handling.
The authors’ responsibilities were as follows—PH, MHW, JRC, WC, SD, and MS: study design; PH, JRC, WC, SD, MS, and MHW: data collection; CLF and MHW: data analysis; and CLF, MHW, LMM, JRC, WC, SD, PH, and MS: writing and review of the manuscript.
Footnotes
None of the authors had any personal or financial conflict of interest.
REFERENCES
- 1.Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Vol. 57. CA Cancer J Clin; 2007. Cancer statistics, 2007. pp. 43–66. [DOI] [PubMed] [Google Scholar]
- 2.Alexander D, Mink P, Adami HO, et al. The non-Hodgkin lymphomas: a review of the epidemiologic literature. Int J Cancer. 2007;120:1–39. doi: 10.1002/ijc.22719. abstr. [DOI] [PubMed] [Google Scholar]
- 3.Hartge P, Bracci PM, Wang SS, Devesa SS, Holly EA. Non-Hodgkin lymphoma. In: Schottenfeld D, Fraumeni JF Jr, editors. Cancer epidemiology and prevention. United Kingdom: Oxford University Press; Oxford: 2006. pp. 898–918. [Google Scholar]
- 4.Messina M, Kucuk O, Lampe J. An overview of the health effects of isoflavones with an emphasis on prostate cancer risk and prostatespecific antigen levels. J AOAC Int. 2006;89:1121–34. abstr. [PubMed] [Google Scholar]
- 5.Miksicek RJ. Commonly occurring plant flavonoids have estrogenic activity. Mol Pharmacol. 1993;44:37–43. [PubMed] [Google Scholar]
- 6.Rao YK, Geethangili M, Fang SH, Tzeng YM. Antioxidant and cytotoxic activities of naturally occurring phenolic and related compounds: a comparative study. Food Chem Toxicol. 2007;114:78–85. doi: 10.1016/j.fct.2007.03.012. Epub 2007 Aug 2. [DOI] [PubMed] [Google Scholar]
- 7.Chatterjee N, Hartge P, Cerhan JR, et al. Risk of non-Hodgkin's lymphoma and family history of lymphatic, hematologic, and other cancers. Cancer Epidemiol Biomarkers Prev. 2004;13:1415–21. [PubMed] [Google Scholar]
- 8.Cross AJ, Ward MH, Schenk M, et al. Meat and meat-mutagen intake and risk of non-Hodgkin lymphoma: results from a NCI-SEER case-control study. Carcinogenesis. 2006;27:293–7. doi: 10.1093/carcin/bgi212. [DOI] [PubMed] [Google Scholar]
- 9.Kelemen LE, Cerhan JR, Lim U, et al. Vegetables, fruit, and antioxidantrelated nutrients and risk of non-Hodgkin lymphoma: a National Cancer Institute–Surveillance, Epidemiology, and End Results populationbased case-control study. Am J Clin Nutr. 2006;83:1401–10. doi: 10.1093/ajcn/83.6.1401. [DOI] [PubMed] [Google Scholar]
- 10.Lim U, Schenk M, Kelemen LE, et al. Dietary determinants of one-carbon metabolism and the risk of non-Hodgkin's lymphoma: NCI-SEER casecontrol study, 1998–2000. Am J Epidemiol. 2005;162:953–64. doi: 10.1093/aje/kwi310. [DOI] [PubMed] [Google Scholar]
- 11.US Department of Agriculture [25 June 2007];Database for the flavonoid content of selected foods. Internet: http://www.nal.usda.gov/fnic/foodcomp/Data/Flav/flav.html.2003.
- 12.US Department of Agriculture [25 June 2007];Database for proanthoycanidin content of selected foods. Internet: http://www.ars.usda.gov/Services/docs.htm?docid_5843.2004.
- 13.US Department of Agriculture [25 June 2007];USDA-Iowa State University database on the isoflavone content of foods, release 1.3. Internet: http://www.nal.usda.gov/fnic/foodcomp/Data/isoflav/isoflav.html.2002.
- 14.Cross AJ, Lim U. The role of dietary factors in the epidemiology of non-Hodgkin's lymphoma. Leuk Lymphoma. 2006;47:2477–87. doi: 10.1080/10428190600932927. [DOI] [PubMed] [Google Scholar]
- 15.Rohrmann S, Becker N, Linseisen J, et al. Fruit and vegetable consumption and lymphoma risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). Cancer Causes Control. 2007;18:537–49. doi: 10.1007/s10552-007-0125-z. [DOI] [PubMed] [Google Scholar]
- 16.Polidori MC, Mecocci P, Stahl W, Sies H. Cigarette smoking cessation increases plasma levels of several antioxidant micronutrients and improves resistance towards oxidative challenge. Br J Nutr. 2003;90:147–50. doi: 10.1079/bjn2003890. [DOI] [PubMed] [Google Scholar]
- 17.Alberg AJ. The influence of cigarette smoking on circulating concentrations of antioxidant micronutrients. Toxicology. 2002;180:121–37. doi: 10.1016/s0300-483x(02)00386-4. [DOI] [PubMed] [Google Scholar]
- 18.Dietrich M, Block G, Norkus EP, et al. Smoking and exposure to environmental tobacco smoke decrease some plasma antioxidants and increase_-tocopherol in vivo after adjustment for dietary antioxidant intakes. Am J Clin Nutr. 2003;77:160–6. doi: 10.1093/ajcn/77.1.160. [DOI] [PubMed] [Google Scholar]
- 19.Shen M, Cozen W, Huang L, et al. Census and geographic differences between respondents and non-respondents in a case-control study of non-Hodgkin lymphoma. Am J Epidemiol. 2008;167:350–61. doi: 10.1093/aje/kwm292. Epub 2007 Nov 6. [DOI] [PubMed] [Google Scholar]