Abstract
Background
Follicular lymphoma (FL) has been linked with cigarette smoking and, inconsistently, with other risk factors.
Methods
We assessed associations of medical, hormonal, family history, lifestyle, and occupational factors with FL risk in 3530 cases and 22639 controls from 19 case–control studies in the InterLymph consortium. Age-, race/ethnicity-, sex- and study-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression.
Results
Most risk factors that were evaluated showed no association, except for a few modest or sex-specific relationships. FL risk was increased in persons: with a first-degree relative with non-Hodgkin lymphoma (OR = 1.99; 95% CI = 1.55 to 2.54); with greater body mass index as a young adult (OR = 1.15; 95% CI = 1.04 to 1.27 per 5kg/m2 increase); who worked as spray painters (OR = 2.66; 95% CI = 1.36 to 5.24); and among women with Sjögren syndrome (OR = 3.37; 95% CI = 1.23 to 9.19). Lower FL risks were observed in persons: with asthma, hay fever, and food allergy (ORs = 0.79–0.85); blood transfusions (OR = 0.78; 95% CI = 0.68 to 0.89); high recreational sun exposure (OR = 0.74; 95% CI = 0.65 to 0.86, fourth vs first quartile); who worked as bakers or millers (OR = 0.51; 95% CI = 0.28 to 0.93) or university/higher education teachers (OR = 0.58; 95% CI = 0.41 to 0.83). Elevated risks specific to women included current and longer duration of cigarette use, whereas reduced risks included current alcohol use, hay fever, and food allergies. Other factors, including other autoimmune diseases, eczema, hepatitis C virus seropositivity, hormonal drugs, hair dye use, sun exposure, and farming, were not associated with FL risk.
Conclusions
The few relationships observed provide clues suggesting a multifactorial etiology of FL but are limited in the extent to which they explain FL occurrence.
Follicular lymphoma (FL), the second most common form of lymphoma in the United States and western Europe, is a lymphoproliferative disorder of germinal center B cells (1). The US age-adjusted incidence rate for FL during 1992–2001 was 3.18 per 100000, with a 3.6-fold variation between the highest and lowest rates (in white males and American Indian/Alaska Native males, respectively) (2) and a 2.8-fold variation in rates among Asian Americans of different origins (3). Age-adjusted FL rates are slightly higher in males than in females. Most patients present with indolent disease, although 2%–3% of FL cases transform annually to diffuse large B-cell lymphoma (4).
Few epidemiologic studies published before 2004 evaluated risk factors separately for subtypes of non-Hodgkin lymphoma (NHL) based on the Revised European-American Lymphoma (REAL)/ World Health Organization (WHO) classification (5–10). Subsequently, an expanding literature has examined risk factors for the common NHL subtypes, although most of these studies have assessed specific or related categories of exposure but did not evaluate risks across a broad range of exposures. Cigarette smoking has repeatedly been associated with a higher risk of FL (11–13), and some reports, including previous InterLymph pooled analyses, have linked excess risk of FL with Sjögren syndrome (14), blood transfusions (15), family history of hematopoietic malignancies (16,17), hair dyes (18,19), and greater height (20). A few reports have linked occupational exposure to benzene, oils/greases, and other solvents such as styrene and trichloroethylene with increased risks of FL (9,21–23). Reduced risks of FL have been linked with atopic disorders (24), oral contraceptive use (25), alcohol consumption (26,27), and sun exposure (28–30).
We have pooled data from 19 case–control studies conducted in Europe, North America, and Australia to examine associations between medical and family history, lifestyle, hormonal drugs, and occupation. The broad range of risk factors available provided an opportunity to assess multivariate associations, and the large study size, 3530 FL cases and 22639 controls, provided an opportunity to examine relatively rare exposures and weak associations overall and in subgroups defined by sex, race/ethnicity, region, and source of controls.
Methods
Study Population
Detailed methodology for the InterLymph NHL Subtypes Project is provided elsewhere in this issue. Studies eligible for inclusion in this pooled analysis were case–control studies, with incident, histologically confirmed cases of FL defined according to the WHO classification (31,32); each study collected individual-level data for at least several risk factors of interest and these were submitted to the pooling project by December 31, 2011. Most studies excluded individuals with a known history of solid organ transplantation or HIV/AIDS. Contributing studies were approved by local ethics review committees, and all participants provided written, informed consent prior to participation.
Risk Factors and NHL Subtype Ascertainment and Harmonization
Each study collected data on potential NHL risk factors in a standardized, structured format by in-person or telephone interviews, and/or self-administered questionnaires. Risk factors included were those where data were available from at least four studies. Each variable was harmonized individually, then related exposure variables were reviewed for consistency as detailed elsewhere (33).
Cases were classified according to the WHO classification (31,32) using guidelines from the InterLymph Pathology Working Group (34,35).
Statistical Analysis
Risk of FL associated with each exposure variable was evaluated using logistic regression models, adjusting for age, race/ethnicity, sex, and study in a basic adjusted model. The significance of each association was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than 0.05 identifying putatively influential factors. To quantify the magnitude of the association, we estimated the relative risk using odds ratios (ORs) and 95% confidence intervals (CIs) derived from the logistic regression models. Individuals with missing data for a variable of interest were excluded. To evaluate effect heterogeneity among the studies, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic, adapting the definition by Higgins and Thompson to categorical variables (36).
To consider possible effect modification, we repeated the above logistic regression analyses but stratified individuals by age, sex, race/ethnicity, region (ie, North America vs Europe vs Australia), study design (ie, population-based vs hospital-based), or other putative risk factors identified in the analysis. To assess confounding, we first evaluated the risk estimate for each putative risk factor in a series of models that adjusted for one other putative risk factor individually in addition to the basic adjusted model. Next, we conducted a single logistic regression model including all putative risk factors, this time including a separate missing category for each variable to ensure that the entire study population was included in the analysis. Finally, we conducted a forward step-wise logistic regression with all putative risk factors, adjusting for age, sex, race/ethnicity, and study. Results from this series of multivariate adjusted logistic regression models showed little difference from the findings based on the basic adjusted models (ie, adjusted for age, sex, race/ethnicity, and study). We therefore report the results for the basic adjusted models only.
Because controls for most original studies were frequency matched by age and sex to all cases, we conducted sensitivity analyses using a subset of controls individually matched by age and sex to the FL cases. The results were similar to those using the full set of controls and, thus, we retained all controls for the main analyses to increase statistical power.
Results
The median age was similar for FL cases at diagnosis (median: 58 years, range: 18–91 years) and controls at the time of interview (median: 59 years, range: 16–98 years). FL cases were more likely to be female, but FL cases and controls were similar by race/ethnicity (with >90% non-Hispanic whites) and socioeconomic status (Table 1). Most cases and controls were from North America or northern Europe and from population-based studies.
Table 1.
Descriptive characteristics of follicular lymphoma cases and controls
| Controls | Cases | ||
|---|---|---|---|
| No. (%) | No. (%) | ||
| Total | 22639 (86.5) | 3530 (13.5) | |
| Age at diagnosis/interview, y | |||
| <30 | 1356 (6.0) | 38 (1.1) | |
| 30–39 | 2143 (9.5) | 253 (7.2) | |
| 40–49 | 3090 (13.6) | 655 (18.6) | |
| 50–59 | 4870 (21.5) | 1003 (28.4) | |
| 60–69 | 6277 (27.7) | 1011 (28.6) | |
| 70–79 | 4048 (17.9) | 508 (14.4) | |
| ≥80 | 839 (3.7) | 58 (1.6) | |
| Missing | 16 (0.1) | 4 (0.1) | |
| Sex | |||
| Male | 13228 (58.4) | 1785 (50.6) | |
| Female | 9411 (41.6) | 1745 (49.4) | |
| Race | |||
| White non-Hispanic | 21145 (93.4) | 3231 (91.5) | |
| Black | 351 (1.6) | 37 (1.0) | |
| Asian | 321 (1.4) | 70 (2.0) | |
| Hispanic | 334 (1.5) | 59 (1.7) | |
| Other/unknown/missing | 488 (2.2) | 133 (3.8) | |
| Socioeconomic status | |||
| Low | 9266 (40.9) | 1384 (39.2) | |
| Medium | 6577 (29.1) | 1061 (30.1) | |
| High | 6386 (28.2) | 1019 (28.9) | |
| Other/missing | 410 (1.8) | 66 (1.9) | |
| Region | |||
| North America | 11005 (48.6) | 1854 (52.5) | |
| Northern Europe | 6542 (28.9) | 1100 (31.2) | |
| Southern Europe | 4398 (19.4) | 324 (9.2) | |
| Australia | 694 (3.1) | 252 (7.1) | |
| Study design | |||
| Population-based | 17389 (76.8) | 2908 (82.4) | |
| Hospital-based | 5250 (23.2) | 622 (17.6) | |
Medical Conditions and Treatments
Overall, participants with a history of autoimmune diseases involving B-cell or T-cell activation were not at an increased risk of FL, except for those with Sjögren syndrome (Table 2). History of any atopic condition (OR = 0.87; 95% CI = 0.80 to 0.94) or any specific allergies (ORs ranged from 0.82 to 0.88), but not eczema, was associated with a reduced risk of FL (Table 2). Hay fever and food allergy were associated with significantly reduced FL risk in females but not males (P heterogeneity = .01 and .04, respectively; Table 3).
Table 2.
Autoimmune and allergic disorders and risk of follicular lymphoma*
| Controls† | Cases† | |||
|---|---|---|---|---|
| No. (%) | No. (%) | OR (95% CI)‡ | P | |
| History of autoimmune conditions | ||||
| History of autoimmune disease | ||||
| No autoimmune disease | 19423 (95.9) | 3242 (95.8) | 1.00 (referent) | .358 |
| B-cell activation | 157 (0.8) | 39 (1.2) | 1.26 (0.88 to 1.81) | |
| T-cell activation | 664 (3.3) | 100 (3.0) | 0.88 (0.71 to 1.10) | |
| Both | 15 (0.1) | 4 (0.1) | 1.40 (0.45 to 4.32) | |
| Sjögren syndrome | ||||
| No | 6917 (97.2) | 1487 (96.6) | 1.00 (referent) | .024 |
| Yes | 9 (0.1) | 7 (0.5) | 3.37 (1.23 to 9.19) | |
| Systematic lupus erythematosus | ||||
| No | 15987 (98.6) | 2807 (98.0) | 1.00 (referent) | .104 |
| Yes | 33 (0.2) | 12 (0.4) | 1.81 (0.91 to 3.60) | |
| Inflammatory bowel disorder | ||||
| No | 16231 (97.6) | 2620 (97.2) | 1.00 (referent) | .349 |
| Yes | 199 (1.2) | 29 (1.1) | 0.83 (0.56 to 1.24) | |
| Celiac disease | ||||
| No | 8907 (99.4) | 1459 (98.8) | 1.00 (referent) | .632 |
| Yes | 25 (0.3) | 7 (0.5) | 1.24 (0.52 to 2.96) | |
| Type I diabetes | ||||
| No | 13185 (95.9) | 1861 (92.0) | 1.00 (referent) | .982 |
| Yes | 84 (0.6) | 14 (0.7) | 0.99 (0.55 to 1.80) | |
| Atopic disorders | ||||
| Any atopic disorder§ | ||||
| No | 15601 (68.9) | 2345 (66.4) | 1.00 (referent) | <.001 |
| Yes | 6442 (28.5) | 1107 (31.4) | 0.87 (0.80 to 0.94) | |
| Allergy∥ | ||||
| No | 10790 (72.1) | 1903 (70.0) | 1.00 (referent) | .018 |
| Yes | 3309 (22.1) | 590 (21.7) | 0.88 (0.79 to 0.98) | |
| Food allergy | ||||
| No | 12757 (85.2) | 2180 (80.1) | 1.00 (referent) | .007 |
| Yes | 988 (6.6) | 171 (6.3) | 0.79 (0.67 to 0.94) | |
| Asthma | ||||
| No | 18448 (85.6) | 2894 (83.9) | 1.00 (referent) | .018 |
| Yes | 1698 (7.9) | 260 (7.5) | 0.85 (0.74 to 0.97) | |
| Hay fever | ||||
| No | 12467 (71.3) | 2086 (69.3) | 1.00 (referent) | <.001 |
| Yes | 2958 (16.9) | 521 (17.3) | 0.82 (0.73 to 0.91) | |
| Eczema | ||||
| No | 14766 (86.1) | 2452 (82.4) | 1.00 (referent) | .283 |
| Yes | 1605 (9.4) | 318 (10.7) | 1.08 (0.94 to 1.23) | |
* CI = confidence interval; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
§ Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies.
∥ History of allergy excludes drug allergies, asthma, eczema, and hay fever.
Table 3.
Sex-specific medical, lifestyle, family history, and occupational exposures and follicular lymphoma*
| Male | Female | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Controls† | Cases† | Controls† | Cases† | ||||||
| No. (%) | No. (%) | OR (95% CI)‡ | P | No. (%) | No. (%) | OR (95% CI)‡ | P | P heterogeneity | |
| Medical conditions | |||||||||
| Sjögren’s syndrome | |||||||||
| No | — | — | — | — | 3555 (97.5) | 829 (96.4) | 1.00 (referent) | .028 | .0030 |
| Yes | 0 | 0 | — | — | 9 (0.2) | 7 (0.8) | 3.24 (1.19 to 8.80) | — | — |
| Any atopic disorder§ | |||||||||
| No | 9660 (73.0) | 1247 (69.9) | 1.00 (referent) | .180 | 5941 (63.1) | 1098 (62.9) | 1.00 (referent) | <.001 | .0587 |
| Yes | 3187 (24.1) | 497 (27.8) | 0.92 (0.82 to 1.04) | 3255 (34.6) | 610 (35.0) | 0.82 (0.73 to 0.92) | — | — | |
| Allergy|| | |||||||||
| No | 6682 (76.8) | 1049 (73.9) | 1.00 (referent) | .596 | 4108 (65.5) | 854 (65.7) | 1.00 (referent) | .006 | .0947 |
| Yes | 1556 (17.9) | 255 (18.0) | 0.96 (0.82 to 1.12) | — | 1753 (28.0) | 335 (25.8) | 0.82 (0.70 to 0.94) | — | — |
| Food allergy | |||||||||
| No | 6704 (86.1) | 1078 (79.7) | 1.00 (referent) | .744 | 5141 (82.0) | 1034 (79.5) | 1.00 (referent) | .002 | .0409 |
| Yes | 405 (5.2) | 71 (5.3) | 0.96 (0.73 to 1.25) | — | 583 (9.3) | 100 (7.7) | 0.70 (0.56 to 0.88) | — | — |
| Asthma | |||||||||
| No | 10548 (86.3) | 1468 (85.1) | 1.00 (referent) | .112 | 7900 (84.6) | 1426 (82.7) | 1.00 (referent) | .079 | .8354 |
| Yes | 909 (7.4) | 120 (7.0) | 0.85 (0.69 to 1.04) | — | 789 (8.5) | 140 (8.1) | 0.84 (0.69 to 1.02) | — | — |
| Hay fever | |||||||||
| No | 7000 (72.9) | 1031 (69.1) | 1.00 (referent) | .256 | 5467 (69.3) | 1055 (69.6) | 1.00 (referent) | <.001 | .0124 |
| Yes | 1475 (15.4) | 249 (16.7) | 0.91 (0.78 to 1.07) | — | 1483 (18.8) | 272 (18.0) | 0.74 (0.63 to 0.86) | — | — |
| Eczema | |||||||||
| No | 8722 (88.2) | 1272 (85.0) | 1.00 (referent) | .583 | 6044 (83.3) | 1180 (79.7) | 1.00 (referent) | .321 | .8818 |
| Yes | 737 (7.5) | 125 (8.4) | 1.06 (0.86 to 1.30) | — | 868 (12.0) | 193 (13.0) | 1.09 (0.92 to 1.30) | — | — |
| Blood transfusion | |||||||||
| No | 6085 (75.1) | 996 (77.3) | 1.00 (referent) | .005 | 4657 (78.9) | 1020 (81.5) | 1.00 (referent) | .011 | .4838 |
| Yes | 878 (10.8) | 101 (7.8) | 0.74 (0.59 to 0.92) | — | 1088 (18.4) | 196 (15.7) | 0.80 (0.68 to 0.95) | — | — |
| Family history | |||||||||
| NHL | |||||||||
| No | 8544 (86.2) | 1095 (85.5) | 1.00 (referent) | <.001 | 5572 (80.0) | 1050 (83.6) | 1.00 (referent) | .022 | .385 |
| Yes | 132 (1.3) | 52 (4.1) | 2.54 (1.81 to 3.58) | — | 146 (2.1) | 44 (3.5) | 1.54 (1.08 to 2.20) | — | — |
| NHL in male relatives | |||||||||
| No | 7003 (84.5) | 923 (85.8) | 1.00 (referent) | <.001 | 4756 (78.5) | 889 (83.6) | 1.00 (referent) | .777 | .0263 |
| Yes | 54 (0.7) | 23 (1.9) | 2.73 (1.63 to 4.60) | — | 59 (1.0) | 12 (1.1) | 1.10 (0.58 to 2.08) | — | — |
| NHL in female relatives | |||||||||
| No | 6661 (83.9) | 906 (85.8) | 1.00 (referent) | .008 | 4744 (78.3) | 877 (82.5) | 1.00 (referent) | .043 | .5794 |
| Yes | 55 (0.7) | 20 (1.9) | 2.14 (1.26 to 3.65) | — | 71 (1.2) | 24 (2.3) | 1.69 (1.04 to 2.75) | — | — |
| Lifestyle factors | |||||||||
| BMI as a young adult (kg/m2) | |||||||||
| Continuous | 13228 (100.0) | 1785 (100.0) | 1.09 (0.94 to 1.27) | .275 | 9411 (100.0) | 1745 (100.0) | 1.25 (1.09 to 1.44) | .948 | .0606 |
| Physical activity | |||||||||
| None | 319 (9.5) | 41 (6.8) | 1.00 (referent) | .153 | 397 (10.7) | 57 (7.4) | 1.00 (referent) | .190 | .7835 |
| Mild | 210 (6.2) | 52 (8.6) | 1.30 (0.81 to 2.07) | — | 264 (7.1) | 82 (10.7) | 1.53 (1.02 to 2.30) | — | — |
| Moderate | 424 (12.6) | 84 (13.9) | 1.06 (0.69 to 1.62) | — | 510 (13.8) | 125 (16.3) | 1.16 (0.79 to 1.69) | — | — |
| Vigorous | 1380 (41.0) | 255 (42.1) | 1.37 (0.95 to 1.98) | — | 1657 (44.8) | 330 (43.0) | 1.19 (0.86 to 1.66) | — | — |
| History of cigarette smoking¶ | |||||||||
| No | 3934 (30.8) | 520 (31.0) | 1.00 (referent) | .695 | 4945 (54.9) | 744 (45.8) | 1.00 (referent) | <.001 | .0038 |
| Yes | 8047 (63.0) | 987 (58.8) | 0.98 (0.87 to 1.10) | — | 3643 (40.4) | 762 (46.9) | 1.22 (1.09 to 1.37) | — | — |
| History of alcohol consumption | |||||||||
| Nondrinker | 1995 (17.1) | 239 (16.1) | 1.00 (referent) | .551 | 2282 (28.5) | 404 (27.7) | 1.00 (referent) | .002 | .1969 |
| Drinker (at least 1 drink per month) | 7245 (62.2) | 833 (56.3) | 0.95 (0.80 to 1.12) | — | 3749 (46.8) | 630 (43.2) | 0.79 (0.68 to 0.91) | — | — |
| Recreational sun exposure (h/wk) | |||||||||
| Quartile 1 (low) | 1003 (18.1) | 195 (20.4) | 1.00 (referent) | .034 | 1231 (23.3) | 327 (28.4) | 1.00 (referent) | .002 | .8752 |
| Quartile 2 | 1112 (20.1) | 176 (18.4) | 0.77 (0.61 to 0.96) | — | 1220 (23.1) | 245 (21.3) | 0.77 (0.64 to 0.93) | — | — |
| Quartile 3 | 1121 (20.2) | 177 (18.5) | 0.74 (0.58 to 0.93) | — | 1038 (19.7) | 216 (18.8) | 0.78 (0.64 to 0.95) | — | — |
| Quartile 4 (high) | 1745 (31.5) | 277 (29.0) | 0.77 (0.62 to 0.95) | — | 1238 (23.5) | 228 (19.8) | 0.70 (0.58 to 0.85) | — | — |
| Occupational exposures | |||||||||
| Bakers and millers | |||||||||
| No | 6124 (93.7) | 970 (98.1) | 1.00 (referent) | .143 | 5222 (98.7) | 1001 (99.5) | 1.00 (referent) | .040 | .6802 |
| Yes | 97 (1.5) | 7 (0.7) | 0.58 (0.27 to 1.27) | — | 61 (1.2) | 5 (0.5) | 0.42 (0.17 to 1.06) | — | — |
| Chemists and chemical workers | |||||||||
| No | 5804 (92.8) | 884 (95.7) | 1.00 (referent) | .090 | 5090 (99.3) | 989 (99.4) | 1.00 (referent) | .968 | .5551 |
| Yes | 136 (2.2) | 28 (3.0) | 1.46 (0.96 to 2.23) | — | 31 (0.6) | 6 (0.6) | 1.02 (0.42 to 2.49) | — | — |
| Medical worker | |||||||||
| No | 5996 (91.7) | 934 (94.4) | 1.00 (referent) | .489 | 5124 (88.3) | 1023 (87.7) | 1.00 (referent) | .489 | .3100 |
| Yes | 225 (3.4) | 43 (4.3) | 1.13 (0.80 to 1.59) | — | 670 (11.6) | 144 (12.3) | 0.93 (0.77 to 1.14) | — | — |
| Medical doctor | |||||||||
| No | 5709 (93.7) | 912 (97.1) | 1.00 (referent) | .480 | 5515 (99.4) | 1103 (99.5) | 1.00 (referent) | .765 | .9556 |
| Yes | 63 (1.0) | 13 (1.4) | 1.25 (0.68 to 2.32) | — | 19 (0.3) | 5 (0.5) | 1.17 (0.42 to 3.24) | — | — |
| Spray-painter (except construction) | |||||||||
| No | 5465 (94.1) | 790 (96.7) | 1.00 (referent) | <.001 | 4854 (99.6) | 936 (99.9) | 1.00 (referent) | .111 | .8588 |
| Yes | 22 (0.4) | 13 (1.6) | 3.83 (1.87 to 7.84) | — | 7 (0.1) | 0 (0.0) | — | — | |
| University and higher education teachers | |||||||||
| No | 6063 (92.7) | 959 (97.0) | 1.00 (referent) | .011 | 5666 (97.7) | 1145 (98.1) | 1.00 (referent) | .066 | .6104 |
| Yes | 154 (2.4) | 16 (1.6) | 0.53 (0.31 to 0.90) | — | 120 (2.1) | 21 (1.8) | 0.65 (0.40 to 1.05) | — | — |
* BMI = body mass index; CI = confidence interval; NHL = non-Hodgkin lymphoma; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
§ Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies.
|| History of allergy excludes drug allergies, asthma, eczema, and hay fever.
¶ Smoked longer than 6 months or more than 100 cigarettes in lifetime.
History of a blood transfusion was associated with a 22% lower risk of FL (Table 4). Reductions in FL risk were most notable for those who received a transfusion after age 55 years and within 40 years before the diagnosis of FL/interview. Positive hepatitis C virus serology was not linked with FL risk (OR = 1.28; 95% CI = 0.64 to 2.57, based on 11 exposed cases and 34 exposed controls). Neither use of oral contraceptives nor use of hormonal replacement therapy was linked with FL risk (data not shown).
Table 4.
History of blood transfusions and risk of follicular lymphoma*
| Controls† | Cases† | |||
|---|---|---|---|---|
| No. (%) | No. (%) | OR (95% CI)‡ | P | |
| Blood transfusion | ||||
| No | 10742 (76.7) | 2016 (79.4) | 1.00 (referent) | <.001 |
| Yes | 1966 (14.0) | 297 (11.7) | 0.78 (0.68 to 0.89) | |
| Age at first transfusion | ||||
| No transfusion | 10742 (76.7) | 2016 (79.4) | 1.00 (referent) | .003 |
| <25 y | 483 (3.4) | 82 (3.2) | 0.83 (0.65 to 1.06) | |
| 25–39 y | 579 (4.1) | 99 (3.9) | 0.80 (0.64 to 1.00) | |
| 40–54 y | 449 (3.2) | 71 (2.8) | 0.82 (0.63 to 1.06) | |
| 55 or older | 455 (3.2) | 45 (1.8) | 0.62 (0.45 to 0.85) | |
| Total number of blood transfusions | ||||
| No transfusion | 10742 (76.7) | 2016 (79.4) | 1.00 (referent) | .003 |
| 1 transfusion | 1306 (9.3) | 202 (8.0) | 0.83 (0.71 to 0.97) | |
| 2 transfusions | 361 (2.6) | 47 (1.9) | 0.63 (0.46 to 0.86) | |
| 3 or more transfusions | 229 (1.6) | 35 (1.4) | 0.73 (0.50 to 1.05) | |
| Transfusion, but number unknown | 70 (0.5) | 13 (0.5) | 0.88 (0.48 to 1.62) | |
| Number of years from 1st transfusion to date of diagnosis/interview | ||||
| No transfusion | 10742 (76.7) | 2016 (79.4) | 1.00 (referent) | .003 |
| <20 y | 878 (6.3) | 121 (4.8) | 0.77 (0.63 to 0.94) | |
| 20–39 y | 748 (5.3) | 118 (4.6) | 0.76 (0.62 to 0.93) | |
| ≥40 y | 340 (2.4) | 58 (2.3) | 0.86 (0.64 to 1.14) | |
| Blood transfusion before 1990 | ||||
| No transfusion | 10742 (76.7) | 2016 (79.4) | 1.00 (referent) | <.001 |
| Before 1990 | 1457 (10.4) | 235 (9.3) | 0.83 (0.71 to 0.96) | |
| After 1990 | 404 (2.9) | 44 (1.7) | 0.62 (0.45 to 0.86) | |
| Transfusion year unknown | 105 (0.7) | 18 (0.7) | 0.68 (0.39 to 1.17) | |
* CI = confidence interval; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
Family History of Hematopoietic Malignancies
Participants with a first-degree relative with a history of NHL experienced approximately a twofold greater risk of FL than participants without such a history (Table 5), and risks were elevated in both male and female participants except females with affected male relatives (Table 3). Male and female participants with first-degree male, but not female, relatives with multiple myeloma had an increased risk of FL (Table 5). FL was not increased in participants with first-degree relatives with leukemia or Hodgkin lymphoma (Table 5).
Table 5.
Family history of hematopoietic malignancies and risk of follicular lymphoma*
| Controls† | Cases† | |||
|---|---|---|---|---|
| First-degree family history | No. (%) | No. (%) | OR (95% CI)‡ | P |
| Any hematologic malignancy | ||||
| No | 14346 (81.5) | 2144 (81.3) | 1.00 (referent) | <.001 |
| Yes | 769 (4.4) | 198 (7.5) | 1.48 (1.25 to 1.75) | |
| Any hematologic malignancy in male relatives | ||||
| No | 11543 (80.5) | 1759 (82.2) | 1.00 (referent) | <.001 |
| Yes | 329 (2.3) | 88 (4.1) | 1.56 (1.22 to 2.00) | |
| Any hematologic malignancy in female relatives | ||||
| No | 11560 (80.6) | 1764 (82.5) | 1.00 (referent) | .011 |
| Yes | 312 (2.2) | 83 (3.9) | 1.41 (1.09 to 1.82) | |
| NHL | ||||
| No | 14116 (83.6) | 2145 (84.6) | 1.00 (referent) | <.001 |
| Yes | 278 (1.6) | 96 (3.8) | 1.99 (1.55 to 2.54) | |
| NHL in male relatives | ||||
| No | 11759 (82.0) | 1812 (84.7) | 1.00 (referent) | .004 |
| Yes | 113 (0.8) | 35 (1.6) | 1.84 (1.24 to 2.73) | |
| NHL in female relatives | ||||
| No | 11746 (81.9) | 1803 (84.3) | 1.00 (referent) | <.001 |
| Yes | 126 (0.9) | 44 (2.1) | 1.93 (1.35 to 2.75) | |
| Multiple myeloma | ||||
| No | 11327 (81.9) | 1795 (85.4) | 1.00 (referent) | .040 |
| Yes | 41 (0.3) | 16 (0.8) | 1.93 (1.06 to 3.51) | |
| Multiple myeloma in male relatives | ||||
| No | 8048 (76.6) | 1338 (81.7) | 1.00 (referent) | .003 |
| Yes | 16 (0.2) | 11 (0.7) | 3.64 (1.65 to 8.05) | |
| Multiple myeloma in female relatives | ||||
| No | 8842 (92.1) | 1550 (91.6) | 1.00 (referent) | .870 |
| Yes | 25 (0.3) | 5 (0.3) | 0.92 (0.35 to 2.46) | |
| Leukemia | ||||
| No | 13831 (92.4) | 2129 (91.3) | 1.00 (referent) | .853 |
| Yes | 402 (2.7) | 65 (2.8) | 0.98 (0.74 to 1.28) | |
| Leukemia in male relatives | ||||
| No | 11634 (92.6) | 1811 (91.5) | 1.00 (referent) | .873 |
| Yes | 197 (1.6) | 31 (1.6) | 0.97 (0.66 to 1.43) | |
| Leukemia in female relatives | ||||
| No | 11680 (92.9) | 1815 (91.7) | 1.00 (referent) | .933 |
| Yes | 151 (1.2) | 27 (1.4) | 1.02 (0.67 to 1.55) | |
| HL | ||||
| No | 14149 (94.5) | 2173 (93.2) | 1.00 (referent) | .133 |
| Yes | 84 (0.6) | 21 (0.9) | 1.47 (0.90 to 2.40) | |
| HL in male relatives | ||||
| No | 11288 (93.6) | 1795 (92.3) | 1.00 (referent) | .239 |
| Yes | 39 (0.3) | 11 (0.6) | 1.53 (0.77 to 3.04) | |
| HL in female relatives | ||||
| No | 10316 (94.6) | 1694 (93.6) | 1.00 (referent) | .456 |
| Yes | 29 (0.3) | 7 (0.4) | 1.39 (0.60 to 3.23) | |
* CI = confidence interval; HL = Hodgkin lymphoma; NHL = non-Hodgkin lymphoma; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
Lifestyle Factors
Risks for FL were increased in those who were overweight (OR = 1.49; 95% CI = 1.21 to 1.83) or obese (OR = 1.46; 95% CI = 0.98 to 2.17) as young adults and rose 15% with each five kg/m2 increase in young adult body mass index (BMI) (Table 6). No significant relationship was observed for usual adult BMI or weight. Greater adult height in males, but not females, was associated with increased risk of FL (data not shown).
Table 6.
Lifestyle factors and risk of follicular lymphoma*
| Controls† | Cases† | |||
|---|---|---|---|---|
| No. (%) | No. (%) | OR (95% CI)‡ | P | |
| BMI, weight, and height | ||||
| BMI as a young adult, kg/m2 | ||||
| 15–<18.5 | 382 (2.5) | 66 (2.5) | 0.90 (0.67 to 1.19) | |
| 18.5–<22.5 | 2800 (18.1) | 464 (17.8) | 1.00 (referent) | .001 |
| 22.5–<25 | 1391 (9.0) | 201 (7.7) | 1.03 (0.85 to 1.24) | |
| 25–<30 | 838 (5.4) | 164 (6.3) | 1.49 (1.21 to 1.83) | |
| 30–50 | 172 (1.1) | 34 (1.3) | 1.46 (0.98 to 2.17) | |
| Continuous (5kg/m2 increase in BMI) | 5583 | 929 | 1.21 (1.09 to 1.35) | <.001 |
| Usual adult BMI, kg/m2 | ||||
| 15–<18.5 | 267 (1.6) | 25 (0.9) | 0.67 (0.44 to 1.03) | |
| 18.5–<22.5 | 3481 (20.3) | 538 (19.4) | 1.00 (referent) | .143 |
| 22.5–<25 | 4276 (25.0) | 706 (25.5) | 1.09 (0.96 to 1.23) | |
| 25–<30 | 6112 (35.7) | 959 (34.6) | 1.01 (0.89 to 1.14) | |
| 30–<35 | 1760 (10.3) | 325 (11.7) | 1.07 (0.91 to 1.25) | |
| 35–50 | 608 (3.6) | 109 (3.9) | 0.93 (0.73 to 1.17) | |
| Continuous (5kg/m2 increase in BMI) | 16504 | 2662 | 0.99 (0.95 to 1.04) | .735 |
| Usual adult height | ||||
| Quartile 1 (low) | 4131 (24.1) | 584 (21.1) | 1.00 (referent) | .124 |
| Quartile 2 | 3852 (22.5) | 603 (21.8) | 1.04 (0.92 to 1.18) | |
| Quartile 3 | 4169 (24.3) | 695 (25.1) | 1.05 (0.93 to 1.19) | |
| Quartile 4 (high) | 4352 (25.4) | 780 (28.1) | 1.15 (1.02 to 1.30) | |
| Usual adult weight | ||||
| Quartile 1 (low) | 4115 (24.0) | 583 (21.0) | 1.00 (referent) | .263 |
| Quartile 2 | 3953 (23.1) | 627 (22.6) | 1.01 (0.89 to 1.14) | |
| Quartile 3 | 4335 (25.3) | 680 (24.5) | 0.94 (0.83 to 1.07) | |
| Quartile 4 (high) | 4101 (24.0) | 772 (27.8) | 1.06 (0.94 to 1.20) | |
| Physical activity | ||||
| No | 716 (10.1) | 98 (7.1) | 1.00 (referent) | . 055 |
| Mild | 474 (6.7) | 134 (9.8) | 1.41 (1.04 to 1.91) | |
| Moderate | 934 (13.2) | 209 (15.2) | 1.09 (0.83 to 1.45) | |
| Vigorous | 3037 (43.0) | 585 (42.6) | 1.26 (0.99 to 1.60) | |
| Cigarette smoking | ||||
| History of cigarette smoking§ | ||||
| No | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .046 |
| Yes | 11690 (53.6) | 1749 (53.0) | 1.09 (1.00 to 1.18) | |
| Smoking status | ||||
| Nonsmoker | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .009 |
| Former smoker | 6327 (29.0) | 956 (29.0) | 1.02 (0.93 to 1.12) | |
| Current smoker | 4829 (22.2) | 743 (22.5) | 1.19 (1.07 to 1.32) | |
| Smoker, status unknown | 534 (2.5) | 50 (1.5) | 1.05 (0.76 to 1.45) | |
| Age started smoking cigarettes regularly | ||||
| Nonsmoker | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .051 |
| <14 y | 1068 (4.9) | 125 (3.8) | 0.88 (0.72 to 1.07) | |
| 14–17 y | 4348 (20.0) | 710 (21.5) | 1.12 (1.01 to 1.25) | |
| 18–19 y | 2352 (10.8) | 384 (11.6) | 1.11 (0.97 to 1.26) | |
| ≥20 y | 3251 (14.9) | 475 (14.4) | 1.11 (0.99 to 1.25) | |
| Smoker, age start unknown | 671 (3.1) | 55 (1.7) | 0.94 (0.69 to 1.27) | |
| Frequency of cigarette smoking | ||||
| Nonsmoker | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .088 |
| Smoker, 1–10 cigarettes/d | 3910 (17.9) | 603 (18.3) | 1.09 (0.98 to 1.21) | |
| Smoker, 11–20 cigarettes/d | 4766 (21.9) | 731 (22.1) | 1.13 (1.02 to 1.25) | |
| Smoker, 21–30 cigarettes/d | 1248 (5.7) | 189 (5.7) | 1.09 (0.92 to 1.29) | |
| Smoker, >30 cigarettes/d | 1339 (6.1) | 155 (4.7) | 0.90 (0.75 to 1.09) | |
| Smoker, cigarettes/day unknown | 427 (2.0) | 71 (2.2) | 1.11 (0.84 to 1.45) | |
| Continuous | 20173 | 2946 | 1.00 (1.00 to 1.00) | .948 |
| Duration of cigarette smoking | ||||
| Nonsmoker | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .013 |
| 1–20 y | 3917 (18.0) | 534 (16.2) | 1.02 (0.91 to 1.14) | |
| 21–30 y | 2341 (10.7) | 388 (11.8) | 1.10 (0.97 to 1.25) | |
| 30–39 y | 2392 (11.0) | 417 (12.6) | 1.13 (1.00 to 1.28) | |
| ≥40 y | 2749 (12.6) | 391 (11.8) | 1.18 (1.04 to 1.35) | |
| Smoker, duration unknown | 291 (1.3) | 19 (0.6) | 0.63 (0.39 to 1.01) | |
| Continuous | 20278 | 2994 | 1.00 (1.00 to 1.01) | .006 |
| Lifetime cigarette exposure | ||||
| Nonsmoker | 8879 (40.7) | 1264 (38.3) | 1.00 (referent) | .150 |
| 1–10 pack-years | 3473 (15.9) | 501 (15.2) | 1.06 (0.95 to 1.19) | |
| 11–20 pack-years | 2272 (10.4) | 341 (10.3) | 1.06 (0.93 to 1.21) | |
| 21–35 pack-years | 2369 (10.9) | 409 (12.4) | 1.18 (1.04 to 1.34) | |
| ≥36 pack-years | 3038 (13.9) | 425 (12.9) | 1.10 (0.97 to 1.25) | |
| Smoker, pack-years unknown | 538 (2.5) | 73 (2.2) | 0.93 (0.72 to 1.22) | |
| Alcohol consumption | ||||
| History of alcohol consumption | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .009 |
| Drinker (at least 1 drink per month) | 10994 (55.9) | 1463 (49.8) | 0.86 (0.77 to 0.96) | |
| Alcohol consumption status | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .003 |
| Former drinker | 609 (3.1) | 125 (4.3) | 1.16 (0.91 to 1.49) | |
| Current drinker | 5010 (25.5) | 723 (24.6) | 0.88 (0.76 to 1.03) | |
| Drinker, status unknown | 5375 (27.3) | 615 (20.9) | 0.81 (0.69 to 0.95) | |
| Age at first alcohol consumption | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .023 |
| <20 y | 2281 (11.6) | 316 (10.8) | 0.92 (0.76 to 1.12) | |
| 20–29 y | 2908 (14.8) | 349 (11.9) | 0.88 (0.74 to 1.05) | |
| ≥30 y | 768 (3.9) | 117 (4.0) | 1.07 (0.85 to 1.35) | |
| Drinker, age start unknown | 5037 (25.6) | 681 (23.2) | 0.80 (0.68 to 0.93) | |
| Duration of alcohol consumption | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .078 |
| 1–20 y | 1307 (6.6) | 128 (4.4) | 0.87 (0.69 to 1.11) | |
| 21–30 y | 1085 (5.5) | 164 (5.6) | 0.90 (0.72 to 1.13) | |
| 30–39 y | 1247 (6.3) | 182 (6.2) | 0.95 (0.76 to 1.17) | |
| ≥40 y | 1900 (9.7) | 243 (8.3) | 1.00 (0.81 to 1.22) | |
| Drinker, duration unknown | 5455 (27.7) | 746 (25.4) | 0.80 (0.69 to 0.93) | |
| Servings of alcohol per week as an adult | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .017 |
| <1 drink/wk | 955 (4.9) | 182 (6.2) | 0.89 (0.74 to 1.08) | |
| 1–6 drinks/wk | 3738 (19.0) | 571 (19.4) | 0.85 (0.75 to 0.97) | |
| 7–13 drinks/wk | 2216 (11.3) | 288 (9.8) | 0.84 (0.72 to 0.99) | |
| 14–27 drinks/wk | 2137 (10.9) | 258 (8.8) | 0.91 (0.77 to 1.08) | |
| ≥28 drinks/wk or binge drinkers | 1918 (9.8) | 157 (5.3) | 0.78 (0.64 to 0.96) | |
| Drinker, drinks/week unknown | 30 (0.2) | 7 (0.2) | 3.00 (1.25 to 7.23) | |
| Grams of ethanol per week as an adult, consumed from any type of alcoholic beverage | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .005 |
| Quartile 1 (low) | 2421 (12.3) | 347 (11.8) | 0.79 (0.68 to 0.92) | |
| Quartile 2 | 2471 (12.6) | 325 (11.1) | 0.83 (0.71 to 0.97) | |
| Quartile 3 | 2488 (12.6) | 315 (10.7) | 0.87 (0.75 to 1.02) | |
| Quartile 4 (high) | 2534 (12.9) | 230 (7.8) | 0.79 (0.66 to 0.94) | |
| Drinker, grams consumed unknown | 1080 (5.5) | 246 (8.4) | 1.33 (0.97 to 1.83) | |
| Lifetime alcohol consumption | ||||
| Nondrinker | 4277 (21.7) | 643 (21.9) | 1.00 (referent) | .007 |
| 1–100 kg | 1444 (7.3) | 182 (6.2) | 0.75 (0.60 to 0.93) | |
| 101–200 kg | 641 (3.3) | 67 (2.3) | 0.68 (0.51 to 0.91) | |
| 201–400 kg | 651 (3.3) | 96 (3.3) | 1.05 (0.81 to 1.37) | |
| >400 kg | 759 (3.9) | 75 (2.6) | 0.85 (0.64 to 1.14) | |
| Drinker, lifetime consumption unknown | 7499 (38.1) | 1043 (35.5) | 0.90 (0.79 to 1.03) | |
| Continuous | 7886 | 1106 | 1.00 (1.00 to 1.00) | .893 |
| Sun exposure | ||||
| Total sun exposure (h/wk) | ||||
| Quartile 1 (low) | 1508 (18.7) | 337 (20.6) | 1.00 (referent) | .116 |
| Quartile 2 | 1594 (19.8) | 293 (18.0) | 0.83 (0.69 to 0.99) | |
| Quartile 3 | 1633 (20.3) | 307 (18.8) | 0.88 (0.73 to 1.05) | |
| Quartile 4 (high) | 1714 (21.3) | 299 (18.3) | 0.82 (0.69 to 0.99) | |
| Recreational sun exposure (h/wk) | ||||
| Quartile 1 (low) | 2234 (20.6) | 522 (24.8) | 1.00 (referent) | <.001 |
| Quartile 2 | 2332 (21.6) | 421 (20.0) | 0.77 (0.67 to 0.90) | |
| Quartile 3 | 2159 (20.0) | 393 (18.6) | 0.77 (0.66 to 0.89) | |
| Quartile 4 (high) | 2983 (27.6) | 505 (24.0) | 0.74 (0.65 to 0.86) | |
* BMI = body mass index; CI = confidence interval; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
§ Smoked longer than 6 months or more than 100 cigarettes in lifetime.
History of cigarette smoking was positively associated with FL risk in females but not males (P heterogeneity = .004; Table 3). Among women, a modest increase in risk of FL in those who ever smoked cigarettes was limited to current smokers, along with a significant positive trend for duration of smoking (data not shown). The trend in pack-years of smoking in women was more clearly related to duration than frequency of cigarettes smoked (data not shown). FL risks were reduced modestly in women, but not men, who ever drank alcohol, particularly current drinkers (Table 3), but there was no clear pattern with duration, number of drinks per week, or cumulative alcohol consumption (data not shown); we note, however, that many studies did not collect these data.
A lower risk of FL was associated with increasing number of hours per week of recreational sun exposure for both men and women (Table 3) but was attenuated for total sun exposure hours (Table 6). FL risk, examined in females only, was not linked with hair dye use overall or by type, duration, or frequency (data not shown), except for a modest increase in those who used hair dyes before 1980 (OR = 1.40, 95% CI = 1.10 to 1.78).
Occupational Factors
Working or living on a farm was not associated with risk of FL (Table 7). Bakers and millers, and those working as university or higher education teachers, experienced reduced risks, whereas spray painters had increased risks of FL. A modest, nonsignificant increase in risk of FL was seen for those ever working as medical doctors, but those working more than 10 years in this occupation had a significantly elevated risk (OR = 2.06, 95% CI = 1.08 to 3.92, based on 38 cases vs 13 controls). Employment in other occupations was not associated with risk of FL (Table 7).
Table 7.
Occupational factors and risk of follicular lymphoma*
| Controls† | Cases† | |||
|---|---|---|---|---|
| No. (%) | No. (%) | OR (95% CI)‡ | P | |
| Farm residence and/or farming & related occupation | ||||
| Ever lived on a farm | ||||
| No | 4779 (56.3) | 822 (58.1) | 1.00 (referent) | .592 |
| Yes | 3470 (40.9) | 554 (39.1) | 0.97 (0.85 to 1.09) | |
| Ever worked on a farm | ||||
| No | 11675 (80.0) | 1978 (83.0) | 1.00 (referent) | .177 |
| Yes | 2717 (18.6) | 371 (15.6) | 0.92 (0.81 to 1.04) | |
| Animal farm workers | ||||
| No | 11699 (94.8) | 2092 (97.0) | 1.00 (referent) | .985 |
| Yes | 316 (2.6) | 52 (2.4) | 1.00 (0.73 to 1.36) | |
| Crop farm workers | ||||
| No | 11442 (92.7) | 2069 (96.0) | 1.00 (referent) | .994 |
| Yes | 573 (4.6) | 75 (3.5) | 1.00 (0.78 to 1.29) | |
| Farm workers, any type | ||||
| No | 10583 (85.8) | 1935 (89.7) | 1.00 (referent) | .839 |
| Yes | 1432 (11.6) | 209 (9.7) | 0.98 (0.84 to 1.16) | |
| Forestry worker | ||||
| No | 11227 (96.6) | 2001 (99.1) | 1.00 (referent) | .580 |
| Yes | 71 (0.6) | 7 (0.3) | 0.81 (0.37 to 1.77) | |
| Meat worker | ||||
| No | 11907 (96.5) | 2121 (98.4) | 1.00 (referent) | .491 |
| Yes | 108 (0.9) | 23 (1.1) | 1.18 (0.74 to 1.88) | |
| Other selected occupations | ||||
| Bakers and millers | ||||
| No/never | 11857 (96.1) | 2132 (98.9) | 1.00 (referent) | .017 |
| Yes | 158 (1.3) | 12 (0.6) | 0.51 (0.28 to 0.93) | |
| Chemists and chemical workers | ||||
| No/never | 11848 (96.0) | 2110 (97.9) | 1.00 (referent) | .156 |
| Yes | 167 (1.4) | 34 (1.6) | 1.33 (0.91 to 1.94) | |
| Petroleum worker | ||||
| No/never | 10558 (96.9) | 1905 (99.3) | 1.00 (referent) | .518 |
| Yes | 18 (0.2) | 2 (0.1) | 0.63 (0.14 to 2.78) | |
| Medical worker | ||||
| No/never | 11120 (90.1) | 1957 (90.8) | 1.00 (referent) | .752 |
| Yes | 895 (7.3) | 187 (8.7) | 0.97 (0.82 to 1.16) | |
| Medical doctor | ||||
| No/never | 11921 (96.6) | 2123 (98.5) | 1.00 (referent) | .425 |
| Yes | 82 (0.7) | 18 (0.8) | 1.24 (0.74 to 2.10) | |
| Spray-painter (except construction) | ||||
| No/never | 10596 (96.7) | 1777 (98.4) | 1.00 (referent) | .008 |
| Yes | 29 (0.3) | 13 (0.7) | 2.66 (1.36 to 5.24) | |
| University and higher education teachers | ||||
| No/never | 11729 (95.1) | 2104 (97.6) | 1.00 (referent) | .001 |
| Yes | 274 (2.2) | 37 (1.7) | 0.58 (0.41 to 0.83) | |
* CI = confidence interval; OR = odds ratio.
† The counts do not add up to the total # of cases/controls due to data missing by design or report.
‡ OR (95% CI) adjusted for age, sex, race/ethnicity, and study.
Risks According to Race/Ethnicity, Source of Population, and Region
For the associations observed in all FL patients, patterns were generally similar by race/ethnicity, except for increased risks for any atopic disorder and hay fever, and a trend in recreational sun exposure in Asians (Supplementary Table 1, available online). Comparison of FL risks in population-based versus hospital-based studies revealed that the findings were mainly driven by the former (Supplementary Table 2, available online). Risks according to region were mostly consistent, although risks for combined and individual atopic allergic disorders showed greater reductions in risk in Australia than in Europe or North America (Supplementary Table 3, available online).
Discussion
In the largest pooled analysis of case–control studies focused on FL and the first to assess a broad range of exposures simultaneously, most risk factors examined were not associated with risk of FL specifically, except for a few mostly modest or sex-specific relationships. We found novel associations with FL risk for certain occupations, including reduced risk in bakers/millers, and university/higher education teachers, and increased risk in spray painters. With our larger number of studies and strong evidence for independence in multivariate models, this analysis extends earlier InterLymph observations of an increased risk of FL for those with Sjögren syndrome (37), a first-degree relative with a history of NHL (17), and reduced risks among those with allergic diseases (24) and greater recreational hours spent per week in the sun (30). Increased FL risks among current cigarette smokers, in conjunction with a positive trend with duration of smoking, were restricted to women, as were the significantly reduced risks of FL in those with a history of hay fever or food allergy. We also found a reduced risk of FL with a history of blood transfusion.
Although meta-analyses of all NHL (5,38,39) found a modest up to 10% increase in risk among those who had worked in farming, few studies have evaluated occupational risks for FL specifically. A pooled analysis of studies in Kansas and Nebraska found increases in FL risk in association with employment in agriculture or farming that were not statistically significant (40). Two studies with stratification of cases by chromosomal translocation in t(14;18) found associations with certain agricultural pesticides (but not farming per se) for t(14;18)-positive NHL, but not for t(14;18)-negative cases (41,42). Although t(14;18) is not specific to FL, this molecular feature occurs in 70%–90% of FL cases compared with 20%–30% of diffuse large B-cell lymphoma cases and 5%–10% of other NHL subtypes (31). It is possible that FL may be associated with specific exposures in farming, such as poultry (43), which were not characterized in our assessment of farming as an occupation. Unfortunately, molecular characterization according to t(14;18) status was not available for the majority of the FL cases in this pooled study. Our finding of an increased risk of FL in spray painters is consistent with previous reports finding that NHL risk is elevated among those working in the occupation of painter and those working with solvents used in paint product formulations (44,45). The decreased risk of FL in university/higher education teachers is not consistent with the results from a meta-analysis showing increased risk among teachers (38), but the meta-analysis included teachers at all levels, whereas our finding was restricted to university/higher education teachers. The meta-analysis did not provide separate estimates of risk for university/higher education teachers. This, and our findings for medical doctors and for bakers/millers, should be further investigated in occupational epidemiologic studies to evaluate specific exposures.
Reasons for the female-specific modest increase in FL risk associated with cigarette smoking are not clear, but these results are consistent with those of a recent meta-analysis of 24 studies which reported a 43% increased risk of FL in female smokers compared with nonsmokers, but no association of smoking status, duration, or intensity in males (46). Findings from cohort studies (11,27,47) also provide some support for this association. An association of cigarette smoking with FL is biologically plausible in that increased rates of t(14;18) translocations have been observed in heavy smokers (48). The current analysis, with more than twice as many studies and threefold more FL cases than our earlier InterLymph consortium analysis (13), provides more precise estimates of FL risk (20% increase in our analysis of “current” smokers vs 31% in our earlier assessment), and identified a similar significant relationship with increasing duration of smoking, establishing that this association is not likely to be confounded by alcohol, BMI, or a variety of other risk factors evaluated here.
Our finding of an excess risk of FL associated with Sjögren’s syndrome is consistent with our earlier InterLymph pooled analysis (12 studies, 12982 NHL cases (14)) in which Sjögren’s syndrome was the only autoimmune disease associated with FL.
We found reductions in risk of FL in relation to history of allergic conditions except for eczema. These findings are consistent with our earlier pooled analysis (2842 FL) (24). However, this first sex-specific assessment revealed that the significant reductions in risks for any allergy, hay fever, and food allergy were restricted to females. Our findings of increased risks for atopic disorders and hay fever in Asian participants require further evaluation.
Our results also mirror those from our previous pooled InterLymph analysis (1703 FL) (17) that found a twofold increased FL risk among participants with a first-degree relative with a history of NHL, with risks elevated in both male and female participants, except females with affected male relatives (17). Our data also support findings from other studies of a stronger familial association of NHL risk in male relatives, which is consistent for FL and other lymphoma subtypes (49–51).
The present study reveals a reduced risk of FL only in current drinkers who were female, but not related to the frequency or duration of their alcohol consumption. In our earlier InterLymph pooled evaluation based on nine studies (1307 FL) (26), we observed a reduced risk of FL associated with alcohol consumption, particularly in current drinkers, but with no evidence of dose–response relationships with frequency or duration of alcohol use. Prospective studies have shown mixed results with moderate (52–56) and heavy (27) alcohol intake associated with reductions in FL risk ranging from 23% to 41% in some studies, but not in others (57–59). One cohort study found an elevated risk of FL among women who were former alcohol drinkers (60). It has been postulated that these findings may be due to effects of alcohol in modulating immune function (61) but because immune deficiency has not been shown to be important for FL risk, chronic inflammation may be a potential biologic mechanism, although alcohol has many other biologic effects. An alternative explanation is that the association is not causal but may reflect differences in other characteristics between alcohol drinkers and nondrinkers.
In our previous InterLymph evaluation of sun exposure (1642 FL) (30), we found a downward trend in risk with increasing total recreational sun exposure, particularly for exposures at 18–40 years of age and in the 10 years before diagnosis for all B-cell lymphomas and for FL, but no association with occupational sun exposure. This inverse association may be due partly to effects on the immune function from sun exposure (62), vitamin D production (63), or chance. A cohort study examining ambient residential ultraviolet radiation among California teachers showed null findings for FL (28).
To our knowledge, the current study is the first to find that persons with a history of blood transfusion experienced a modestly reduced risk of FL. Blood transfusion (which suppresses cellular immunity, includes transfer of allogeneic cells, and may transmit infectious and chemical agents) has been associated with increased risk of all NHL in some (15,64–66), but not all studies (67–72). Limited data on the role of transfusions in FL suggest no risk (73–76) or a modest increase in risk (8,15). Cerhan and colleagues (15) have suggested that transfusions may be a marker for underlying medical conditions rather than directly associated with NHL or its subtypes.
The association of overweight and obesity in early adulthood with FL risk is strengthened by the significant positive dose–response trend of early adult BMI with FL risk. However, most cohort studies (7,56,58,77–82), with one exception (83), found no relationship of early adult weight or BMI with FL, although many of these studies included relatively small numbers of FL cases. Our finding of a relationship between greater adult height in males, but not females, in relation to FL is likely a chance finding since most cohort studies (56,58,78–82) reported no relationship of height in men or women with FL except for three (20,77,83) that found a positive relationship in women.
This pooled analysis is the first and largest multivariate assessment of a broad range of putative risk factors for FL. Other strengths include assessment of effect modification (particularly gender) and confounding. The systematic nature of the subtype evaluation using the WHO classification, exposure assessment by standardized questionnaires, population-based design for most of the studies, along with careful efforts to harmonize the variables included in the pooled analyses, represent additional strengths. Limitations include the self-reported nature of the data collected, the difficulty of using retrospectively collected information, reliance on job titles instead of specific occupational exposures, the limited types of exposures evaluated, and lack of comprehensive assessment of many of the individual putative risk factors, multiple comparisons, and absence of assessment of some variables for all studies. Sex-specific associations could be due to chance or unexplained bias, although it is possible that such associations may reflect genetic variation, hormonal exposure, or occupational exposures. Other limitations include lack of independent evaluation of exposures and the potential for recall bias. Some findings (eg, female-specific associations with alcohol consumption, the inverse association with history of blood transfusions or the relationship with BMI in early adulthood) may be due to chance.
In conclusion, the majority of the factors evaluated were not associated with risk of FL. As noted above, associations with blood transfusion and BMI in early adulthood are inconsistent with prior studies. The sex-specific findings for cigarette smoking and allergic disorders, as well as the associations with some occupations, deserve further evaluation. Although this study does not identify risk factors that explain much of FL occurrence, the few relationships observed do provide clues suggesting a complex multifactorial etiology.
Funding
This pooled analysis was supported by the Intramural Research Program of the National Cancer Institute/National Institutes of Health and National Cancer Institute/National Institutes of Health (R01 CA14690, U01 CA118444, and R01 CA92153-S1).
InterLymph annual meetings during 2010–2013 were supported by the Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute/National Institutes of Health (2010–2013); Lymphoma Coalition (2010–2013); National Institutes of Health Office of Rare Diseases Research (2010); National Cancer Institute/National Institutes of Health (R13 CA159842 01) (2011); University of Cagliari, Provincial Administration of Cagliari, Banca di Credito Sardo, and Consorzio Industriale Sardo, Italy (2011); Intramural Research Program of the National Cancer Institute/National Institutes of Health (2012); and Faculté de Médecine de Dijon, Institut de Veille Sanitaire, Registre des hémopathies malignes de Côte d’Or, INSERM, Institut National du Cancer, Université de Bourgogne, Groupe Ouest Est d’Etude des Leucémies et Autres Maladies du Sang (GOELAMS), l’Institut Bergonié, The Lymphoma Study Association (LYSA), Registre Régional des Hémopathies de Basse Normandie, and the City of Dijon, France (2013). Meeting space at the 2013 Annual Meeting of the American Association for Cancer Research (AACR) was provided by the Molecular Epidemiology Group (MEG) of the AACR.
Individual studies were supported by: the Canadian Institutes for Health Research (CIHR), Canadian Cancer Society, and Michael Smith Foundation for Health Research (British Columbia); Intramural Research Program of the National Cancer Institute/National Institutes of Health (Iowa/Minnesota); National Cancer Institute/National Institutes of Health (N01-CP-ES-11027) (Kansas); National Cancer Institute/National Institutes of Health (R01 CA50850) (Los Angeles); National Cancer Institute/National Institutes of Health (R01 CA92153 and P50 CA97274), Lymphoma Research Foundation (164738), and the Henry J. Predolin Foundation (Mayo Clinic); Intramural Research Program of the National Cancer Institute/National Institutes of Health and Public Health Service (contracts N01-PC-65064, N01-PC-67008, N01-PC-67009, N01-PC-67010, and N02-PC-71105) (NCI-SEER); National Cancer Institute/National Institutes of Health (R01CA100555 and R03CA132153) and American Institute for Cancer Research (99B083) (Nebraska [newer]); National Cancer Institute/National Institutes of Health (N01-CP-95618) and State of Nebraska Department of Health (LB-506) (Nebraska [older]); National Cancer Institute/National Institutes of Health (R01CA45614, RO1CA154643-01A1, and R01CA104682) (UCSF1); National Cancer Institute/National Institutes of Health (CA143947, CA150037, R01CA087014, R01CA104682, RO1CA122663, and RO1CA154643-01A1) (UCSF2); National Heart Lung and Blood Institute/National Institutes of Health (hematology training grant award T32 HL007152), National Center for Research Resources/National Institutes of Health (UL 1 RR024160), and National Cancer Institute/National Institutes of Health (K23 CA102216 and P50 CA130805) (University of Rochester); National Cancer Institute/National Institutes of Health (CA62006 and CA165923) (Yale); Association pour la Recherche contre le Cancer, Fondation de France, AFSSET, and a donation from Faberge employees (Engela); European Commission (QLK4-CT-2000-00422 and FOOD-CT-2006-023103), Spanish Ministry of Health (CIBERESP, PI11/01810, RCESP C03/09, RTICESP C03/10, and RTIC RD06/0020/0095), Rio Hortega (CM13/00232), Agència de Gestió d’Ajuts Universitaris i de Recerca–Generalitat de Catalunya (Catalonian Government, 2009SGR1026), National Institutes of Health (contract NO1-CO-12400), Italian Ministry of Education, University and Research (PRIN 2007 prot. 2007WEJLZB, PRIN 2009 prot. 20092ZELR2), Italian Association for Cancer Research (IG grant 11855/2011); Federal Office for Radiation Protection (StSch4261 and StSch4420), José Carreras Leukemia Foundation (DJCLS-R04/08), German Federal Ministry for Education and Research (BMBF-01-EO-1303), Health Research Board, Ireland and Cancer Research Ireland, and Czech Republic MH CZ – DRO (MMCI, 00209805) (EpiLymph); National Cancer Institute/National Institutes of Health (CA51086), European Community (Europe Against Cancer Programme), and Italian Alliance Against Cancer (Lega Italiana per la Lotta contro i Tumori) (Italy, multicenter); Italian Association for Cancer Research (Italy, Aviano-Milan); Italian Association for Cancer Research (Italy, Aviano-Naples); Swedish Cancer Society (2009/659), Stockholm County Council (20110209), Strategic Research Program in Epidemiology at Karolinska Institut, Swedish Cancer Society (02 6661), Danish Cancer Research Foundation, Lundbeck Foundation (R19-A2364), Danish Cancer Society (DP 08-155), National Cancer Institute/National Institutes of Health (5R01 CA69669-02), and Plan Denmark (SCALE); Leukaemia & Lymphoma Research, UK; and Australian National Health and Medical Research Council (ID990920), Cancer Council NSW, and University of Sydney Faculty of Medicine (New South Wales).
Supplementary Material
We thank the following individuals for their substantial contributions to this project: Aaron D. Norman, Dennis P. Robinson, and Priya Ramar (Mayo Clinic College of Medicine) for their work at the InterLymph Data Coordinating Center in organizing, collating, harmonizing, and documenting of the data from the participating studies in the InterLymph Consortium; Michael Spriggs, Peter Hui, and Bill Wheeler (Information Management Services, Inc) for their programming support; and Noelle Richa Siegfried and Emily Smith (RTI International) for project coordination.
References
- 1. Freedman A. Follicular lymphoma: 2012 update on diagnosis and management. Am J Hematol. 2012;87(10):988–995 [DOI] [PubMed] [Google Scholar]
- 2. Morton LM, Wang SS, Devesa SS, Hartge P, Weisenburger DD, Linet MS. Lymphoma incidence patterns by WHO subtype in the United States, 1992-2001. Blood. 2006;107(1):265–276 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Carreon JD, Morton LM, Devesa SS, et al. Incidence of lymphoid neoplasms by subtype among six Asian ethnic groups in the United States, 1996-2004. Cancer Causes Control. 2008;19(10):1171–1181 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Link BK, Maurer MJ, Nowakowski GS, et al. Rates and outcomes of follicular lymphoma transformation in the immunochemotherapy era: a report from the University of Iowa/MayoClinic Specialized Program of Research Excellence Molecular Epidemiology Resource. J Clin Oncol. 2013;31(26):3272–3278 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Blair A, Zahm SH, Pearce NE, Heineman EF, Fraumeni JF., Jr Clues to cancer etiology from studies of farmers. Scand J Work Environ Health. 1992;18(4):209–215 [DOI] [PubMed] [Google Scholar]
- 6. Tatham L, Tolbert P, Kjeldsberg C. Occupational risk factors for subgroups of non-Hodgkin’s lymphoma. Epidemiology. 1997;8(5):551–558 [DOI] [PubMed] [Google Scholar]
- 7. Cerhan JR, Janney CA, Vachon CM, et al. Anthropometric characteristics, physical activity, and risk of non-Hodgkin’s lymphoma subtypes and B-cell chronic lymphocytic leukemia: a prospective study. Am J Epidemiol. 2002;156(6):527–535 [DOI] [PubMed] [Google Scholar]
- 8. Cerhan JR, Wallace RB, Dick F, et al. Blood transfusions and risk of non-Hodgkin’s lymphoma subtypes and chronic lymphocytic leukemia. Cancer Epidemiol Biomarkers Prev. 2001;10(4):361–368 [PubMed] [Google Scholar]
- 9. Blair A, Linos A, Stewart PA, et al. Evaluation of risks for non-Hodgkin’s lymphoma by occupation and industry exposures from a case-control study. Am J Ind Med. 1993;23(2):301–312 [DOI] [PubMed] [Google Scholar]
- 10. Waddell BL, Zahm SH, Baris D, et al. Agricultural use of organophosphate pesticides and the risk of non-Hodgkin’s lymphoma among male farmers (United States). Cancer Causes Control. 2001;12(6):509–517 [DOI] [PubMed] [Google Scholar]
- 11. Diver WR, Patel AV, Thun MJ, Teras LR, Gapstur SM. The association between cigarette smoking and non-Hodgkin lymphoid neoplasms in a large US cohort study. Cancer Causes Control. 2012;23(8):1231–1240 [DOI] [PubMed] [Google Scholar]
- 12. Lu Y, Wang SS, Reynolds P, et al. Cigarette smoking, passive smoking, and non-Hodgkin lymphoma risk: evidence from the California Teachers Study. Am J Epidemiol. 2011;174(5):563–573 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Morton LM, Hartge P, Holford TR, et al. Cigarette smoking and risk of non-Hodgkin lymphoma: a pooled analysis from the International Lymphoma Epidemiology Consortium (interlymph). Cancer Epidemiol Biomarkers Prev. 2005;14(4):925–933 [DOI] [PubMed] [Google Scholar]
- 14. Ekström Smedby K, Vajdic CM, Falster M, et al. Autoimmune disorders and risk of non-Hodgkin lymphoma subtypes: a pooled analysis within the InterLymph Consortium. Blood. 2008;111(8):4029–4038 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Cerhan JR, Engels EA, Cozen W, et al. Blood transfusion, anesthesia, surgery and risk of non-Hodgkin lymphoma in a population-based case-control study. Int J Cancer. 2008;123(4):888–894 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Chang ET, Smedby KE, Hjalgrim H, et al. Family history of hematopoietic malignancy and risk of lymphoma. J Natl Cancer Inst. 2005;97(19):1466–1474 [DOI] [PubMed] [Google Scholar]
- 17. Wang SS, Slager SL, Brennan P, et al. Family history of hematopoietic malignancies and risk of non-Hodgkin lymphoma (NHL): a pooled analysis of 10 211 cases and 11 905 controls from the International Lymphoma Epidemiology Consortium (InterLymph). Blood. 2007;109(8):3479–3488 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. de Sanjosé S, Benavente Y, Nieters A, et al. Association between personal use of hair dyes and lymphoid neoplasms in Europe. Am J Epidemiol. 2006;164(1):47–55 [DOI] [PubMed] [Google Scholar]
- 19. Zhang Y, de Sanjosé S, Bracci PM, et al. Personal use of hair dye and the risk of certain subtypes of non-Hodgkin lymphoma. Am J Epidemiol. 2008;167(11):1321–1331 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Murphy F, Kroll ME, Pirie K, Reeves G, Green J, Beral V. Body size in relation to incidence of subtypes of haematological malignancy in the prospective Million Women Study. Br J Cancer. 2013;108(11):2390–2398 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Cocco P, t’Mannetje A, Fadda D, et al. Occupational exposure to solvents and risk of lymphoma subtypes: results from the Epilymph case-control study. Occup Environ Med. 2010;67(5):341–347 [DOI] [PubMed] [Google Scholar]
- 22. Cocco P, Vermeulen R, Flore V, et al. Occupational exposure to trichloroethylene and risk of non-Hodgkin lymphoma and its major subtypes: a pooled InterLymph [correction of IinterLlymph] analysis. Occup Environ Med. 2013;70(11):795–802 [DOI] [PubMed] [Google Scholar]
- 23. Jiao J, Zheng T, Lan Q, et al. Occupational solvent exposure, genetic variation of DNA repair genes, and the risk of non-Hodgkin’s lymphoma. Eur J Cancer Prev. 2012;21(6):580–584 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Vajdic CM, Falster MO, de Sanjose S, et al. Atopic disease and risk of non-Hodgkin lymphoma: an InterLymph pooled analysis. Cancer Res. 2009;69(16):6482–6489 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Kane EV, Bernstein L, Bracci PM, et al. ; InterLymph Consortium. Postmenopausal hormone therapy and non-Hodgkin lymphoma: a pooled analysis of InterLymph case-control studies. Ann Oncol. 2013;24(2):433–441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Morton LM, Zheng T, Holford TR, et al. ; InterLymph Consortium. Alcohol consumption and risk of non-Hodgkin lymphoma: a pooled analysis. Lancet Oncol. 2005;6(7):469–476 [DOI] [PubMed] [Google Scholar]
- 27. Kroll ME, Murphy F, Pirie K, Reeves GK, Green J, Beral V; Million Women Study Collaborators. Alcohol drinking, tobacco smoking and subtypes of haematological malignancy in the UK Million Women Study. Br J Cancer. 2012;107(5):879–887 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Chang ET, Canchola AJ, Cockburn M, et al. Adulthood residential ultraviolet radiation, sun sensitivity, dietary vitamin D, and risk of lymphoid malignancies in the California Teachers Study. Blood. 2011;118(6):1591–1599 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Hartge P, Lim U, Freedman DM, et al. Ultraviolet radiation, dietary vitamin D, and risk of non-Hodgkin lymphoma (United States). Cancer Causes Control. 2006;17(8):1045–1052 [DOI] [PubMed] [Google Scholar]
- 30. Kricker A, Armstrong BK, Hughes AM, et al. ; Interlymph Consortium. Personal sun exposure and risk of non Hodgkin lymphoma: a pooled analysis from the Interlymph Consortium. Int J Cancer. 2008;122(1):144–154 [DOI] [PubMed] [Google Scholar]
- 31. Jaffe ES, Harris NL, Stein H, Vardiman JW, eds. World Health Organization Classification of Tumors. Pathology and Genetics of Tumors of Haematopoietic and Lymphoid Tissues. Lyon, France: IARC Press; 2001 [Google Scholar]
- 32. Swerdlow SH, Campo E. WHO Classification: Pathology and Genetics of Tumors of Haematopoietic and Lymphoid Tissues. Lyon, France: IARC Press; 2008 [Google Scholar]
- 33. Morton LM, Sampson JN, Cerhan JR, et al. Rationale and design of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma Subtypes Project. J Natl Cancer Inst Monogr. 2014;. 48:1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Morton LM, Turner JJ, Cerhan JR, et al. Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph). Blood. 2007;110(2):695–708 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Turner JJ, Morton LM, Linet MS, et al. InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions. Blood. 2010;116(20):e90–e98 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–1558 [DOI] [PubMed] [Google Scholar]
- 37. Smedby KE, Askling J, Mariette X, Baecklund E. Autoimmune and inflammatory disorders and risk of malignant lymphomas–an update. J Intern Med. 2008;264(6):514–527 [DOI] [PubMed] [Google Scholar]
- 38. Boffetta P, de Vocht F. Occupation and the risk of non-Hodgkin lymphoma. Cancer Epidemiol Biomarkers Prev. 2007;16(3):369–372 [DOI] [PubMed] [Google Scholar]
- 39. Khuder SA, Schaub EA, Keller-Byrne JE. Meta-analyses of non-Hodgkin’s lymphoma and farming. Scand J Work Environ Health. 1998;24(4):255–261 [DOI] [PubMed] [Google Scholar]
- 40. Zheng T, Blair A, Zhang Y, Weisenburger DD, Zahm SH. Occupation and risk of non-Hodgkin’s lymphoma and chronic lymphocytic leukemia. J Occup Environ Med. 2002;44(5):469–474 [DOI] [PubMed] [Google Scholar]
- 41. Chiu BC, Dave BJ, Blair A, Gapstur SM, Zahm SH, Weisenburger DD. Agricultural pesticide use and risk of t(14;18)-defined subtypes of non-Hodgkin lymphoma. Blood. 2006;108(4):1363–1369 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Schroeder JC, Olshan AF, Baric R, et al. Agricultural risk factors for t(14;18) subtypes of non-Hodgkin’s lymphoma. Epidemiology. 2001;12(6):701–709 [DOI] [PubMed] [Google Scholar]
- 43. Beane Freeman LE, Deroos AJ, Koutros S, et al. Poultry and livestock exposure and cancer risk among farmers in the agricultural health study. Cancer Causes Control. 2012;23(5):663–670 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Bethwaite PB, Pearce N, Fraser J. Cancer risks in painters: study based on the New Zealand Cancer Registry. Br J Ind Med. 1990;47(11):742–746 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Lundberg I, Milatou-Smith R. Mortality and cancer incidence among Swedish paint industry workers with long-term exposure to organic solvents. Scand J Work Environ Health. 1998;24(4):270–275 [DOI] [PubMed] [Google Scholar]
- 46. Castillo JJ, Dalia S. Cigarette smoking is associated with a small increase in the incidence of non-Hodgkin lymphoma: a meta-analysis of 24 observational studies. Leuk Lymphoma. 2012;53(10):1911–1919 [DOI] [PubMed] [Google Scholar]
- 47. Parker AS, Cerhan JR, Dick F, et al. Smoking and risk of non-Hodgkin lymphoma subtypes in a cohort of older women. Leuk Lymphoma. 2000;37(3-4):341–349 [DOI] [PubMed] [Google Scholar]
- 48. Bell DA, Liu Y, Cortopassi GA. Occurrence of bcl-2 oncogene translocation with increased frequency in the peripheral blood of heavy smokers. J Natl Cancer Inst. 1995;87(3):223–224 [DOI] [PubMed] [Google Scholar]
- 49. 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(9):1415–1421 [PubMed] [Google Scholar]
- 50. Goldgar DE, Easton DF, Cannon-Albright LA, Skolnick MH. Systematic population-based assessment of cancer risk in first-degree relatives of cancer probands. J Natl Cancer Inst. 1994;86(21):1600–1608 [DOI] [PubMed] [Google Scholar]
- 51. Pottern LM, Linet M, Blair A, et al. Familial cancers associated with subtypes of leukemia and non-Hodgkin’s lymphoma. Leuk Res. 1991;15(5):305–314 [DOI] [PubMed] [Google Scholar]
- 52. Allen NE, Beral V, Casabonne D, et al. ; Million Women Study Collaborators. Moderate alcohol intake and cancer incidence in women. J Natl Cancer Inst. 2009;101(5):296–305 [DOI] [PubMed] [Google Scholar]
- 53. Chiu BC, Cerhan JR, Gapstur SM, et al. Alcohol consumption and non-Hodgkin lymphoma in a cohort of older women. Br J Cancer. 1999;80(9):1476–1482 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Gapstur SM, Diver WR, McCullough ML, Teras LR, Thun MJ, Patel AV. Alcohol intake and the incidence of non-Hodgkin lymphoid neoplasms in the cancer prevention study II nutrition cohort. Am J Epidemiol. 2012;176(1):60–69 [DOI] [PubMed] [Google Scholar]
- 55. Kanda J, Matsuo K, Inoue M, et al. ; Japan Public Health Center-based Prospective Study Group. Association of alcohol intake with the risk of malignant lymphoma and plasma cell myeloma in Japanese: a population-based cohort study (Japan Public Health Center-based Prospective Study). Cancer Epidemiol Biomarkers Prev. 2010;19(2):429–434 [DOI] [PubMed] [Google Scholar]
- 56. Lim U, Morton LM, Subar AF, et al. Alcohol, smoking, and body size in relation to incident Hodgkin’s and non-Hodgkin’s lymphoma risk. Am J Epidemiol. 2007;166(6):697–708 [DOI] [PubMed] [Google Scholar]
- 57. Lim U, Weinstein S, Albanes D, et al. Dietary factors of one-carbon metabolism in relation to non-Hodgkin lymphoma and multiple myeloma in a cohort of male smokers. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1109–1114 [DOI] [PubMed] [Google Scholar]
- 58. Troy JD, Hartge P, Weissfeld JL, et al. Associations between anthropometry, cigarette smoking, alcohol consumption, and non-Hodgkin lymphoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Am J Epidemiol. 2010;171(12):1270–1281 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59. Heinen MM, Verhage BA, Schouten LJ, Goldbohm RA, Schouten HC, van den Brandt PA. Alcohol consumption and risk of lymphoid and myeloid neoplasms: results of the Netherlands cohort study. Int J Cancer. 2013;133(7):1701–1712 [DOI] [PubMed] [Google Scholar]
- 60. Chang ET, Clarke CA, Canchola AJ, et al. Alcohol consumption over time and risk of lymphoid malignancies in the California Teachers Study cohort. Am J Epidemiol. 2010;172(12):1373–1383 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61. Mufti SI, Darban HR, Watson RR. Alcohol, cancer, and immunomodulation. Crit Rev Oncol Hematol. 1989;9(3):243–261 [DOI] [PubMed] [Google Scholar]
- 62. Ullrich SE. Does exposure to UV radiation induce a shift to a Th-2-like immune reaction? Photochem Photobiol. 1996;64(2):254–258 [DOI] [PubMed] [Google Scholar]
- 63. Reichrath J, Rappl G. Ultraviolet light (UV)-induced immunosuppression: is vitamin D the missing link? J Cell Biochem. 2003;89(1):6–8 [DOI] [PubMed] [Google Scholar]
- 64. Blomberg J, Möller T, Olsson H, Anderson H, Jonsson M. Cancer morbidity in blood recipients–results of a cohort study. Eur J Cancer. 1993;29A(15):2101–2105 [DOI] [PubMed] [Google Scholar]
- 65. Cerhan JR, Wallace RB, Folsom AR, et al. Medical history risk factors for non-Hodgkin’s lymphoma in older women. J Natl Cancer Inst. 1997;89(4):314–318 [DOI] [PubMed] [Google Scholar]
- 66. Memon A, Doll R. A search for unknown blood-borne oncogenic viruses. Int J Cancer. 1994;58(3):366–368 [DOI] [PubMed] [Google Scholar]
- 67. Adami J, Nyrén O, Bergström R, et al. Blood transfusion and non-Hodgkin lymphoma: lack of association. Ann Intern Med. 1997;127(5):365–371 [DOI] [PubMed] [Google Scholar]
- 68. Chow EJ, Holly EA. Blood transfusions as a risk factor for non-Hodgkin’s lymphoma in the San Francisco Bay Area: a population-based study. Am J Epidemiol. 2002;155(8):725–731 [DOI] [PubMed] [Google Scholar]
- 69. Nelson RA, Levine AM, Bernstein L. Blood transfusions and the risk of intermediate- or high-grade non-Hodgkin’s lymphoma. J Natl Cancer Inst. 1998;90(22):1742–1743 [DOI] [PubMed] [Google Scholar]
- 70. Tavani A, Soler M, La Vecchia C, Franceschi S. Re: Blood transfusions and the risk of intermediate- or high-grade non-Hodgkin’s lymphoma. J Natl Cancer Inst. 1999;91(15):1332–1333 [DOI] [PubMed] [Google Scholar]
- 71. Zhang Y, Holford TR, Leaderer B, et al. Prior medical conditions and medication use and risk of non-Hodgkin lymphoma in Connecticut United States women. Cancer Causes Control. 2004;15(4):419–428 [DOI] [PubMed] [Google Scholar]
- 72. Vajdic CM, Grulich AE, Kaldor JM, et al. Specific infections, infection-related behavior, and risk of non-Hodgkin lymphoma in adults. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1102–1108 [DOI] [PubMed] [Google Scholar]
- 73. Brandt L, Brandt J, Olsson H, Anderson H, Möller T. Blood transfusion as a risk factor for non-Hodgkin lymphoma. Br J Cancer. 1996;73(9):1148–1151 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74. Chang CM, Quinlan SC, Warren JL, Engels EA. Blood transfusions and the subsequent risk of hematologic malignancies. Transfusion. 2010;50(10):2249–2257 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75. Erber E, Lim U, Maskarinec G, Kolonel LN. Common immune-related risk factors and incident non-Hodgkin lymphoma: the multiethnic cohort. Int J Cancer. 2009;125(6):1440–1445 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76. Zhang Y, Holford TR, Leaderer B, et al. Blood transfusion and risk of non-Hodgkin’s lymphoma in Connecticut women. Am J Epidemiol. 2004;160(4):325–330 [DOI] [PubMed] [Google Scholar]
- 77. Britton JA, Khan AE, Rohrmann S, et al. Anthropometric characteristics and non-Hodgkin’s lymphoma and multiple myeloma risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). Haematologica. 2008;93(11):1666–1677 [DOI] [PubMed] [Google Scholar]
- 78. Kabat GC, Kim MY, Jean-Wactawski-Wende, et al. Anthropometric factors, physical activity, and risk of non-Hodgkin’s lymphoma in the Women’s Health Initiative. Cancer Epidemiol. 2012;36(1):52–59 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79. Maskarinec G, Erber E, Gill J, Cozen W, Kolonel LN. Overweight and obesity at different times in life as risk factors for non-Hodgkin’s lymphoma: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2008;17(1):196–203 [DOI] [PubMed] [Google Scholar]
- 80. Patel AV, Diver WR, Teras LR, Birmann BM, Gapstur SM. Body mass index, height and risk of lymphoid neoplasms in a large United States cohort. Leuk Lymphoma. 2013;54(6):1221–1227 [DOI] [PubMed] [Google Scholar]
- 81. Pylypchuk RD, Schouten LJ, Goldbohm RA, Schouten HC, van den Brandt PA. Body mass index, height, and risk of lymphatic malignancies: a prospective cohort study. Am J Epidemiol. 2009;170(3):297–307 [DOI] [PubMed] [Google Scholar]
- 82. Lu Y, Prescott J, Sullivan-Halley J, et al. Body size, recreational physical activity, and B-cell non-Hodgkin lymphoma risk among women in the California teachers study. Am J Epidemiol. 2009;170(10):1231–1240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83. Bertrand KA, Giovannucci E, Zhang SM, Laden F, Rosner B, Birmann BM. A prospective analysis of body size during childhood, adolescence, and adulthood and risk of non-Hodgkin lymphoma. Cancer Prev Res (Phila). 2013;6(8):864–873 [DOI] [PMC free article] [PubMed] [Google Scholar]
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