Abstract
Background
Mycosis fungoides and Sézary syndrome (MF/SS) are rare cutaneous T-cell lymphomas. Their etiology is poorly understood.
Methods
A pooled analysis of 324 MF/SS cases and 17217 controls from 14 case–control studies from Europe, North America, and Australia, as part of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma (NHL) Subtypes Project, was carried out to investigate associations with lifestyle, medical history, family history, and occupational risk factors. Multivariate logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (CI).
Results
We found an increased risk of MF/SS associated with body mass index equal to or larger than 30kg/m2 (OR = 1.57, 95% CI = 1.03 to 2.40), cigarette smoking for 40 years or more (OR = 1.55, 95% CI = 1.04 to 2.31), eczema (OR = 2.38, 95% CI = 1.73 to 3.29), family history of multiple myeloma (OR = 8.49, 95% CI = 3.31 to 21.80), and occupation as crop and vegetable farmers (OR = 2.37, 95% CI = 1.14 to 4.92), painters (OR = 3.71, 95% CI = 1.94 to 7.07), woodworkers (OR = 2.20, 95% CI = 1.18 to 4.08), and general carpenters (OR = 4.07, 95% CI = 1.54 to 10.75). We also found a reduced risk of MF/SS associated with moderate leisure time physical activity (OR = 0.46, 95% CI = 0.22 to 0.97).
Conclusions
Our study provided the first detailed analysis of risk factors for MF/SS and further investigation is needed to confirm these findings in prospective data and in other populations.
Mycosis fungoides and Sézary syndrome (MF/SS) are mature T-cell lymphomas that originate in the skin. The age-adjusted incidence rate per 100000 person-years in the United States in 2005–2008 was 0.01 for SS and 0.55 for MF (1), with the latter showing a slight increase compared with the rate of 0.41 per 100000 person-years in 2001–05 (1,2). While the age-adjusted incidence rates vary between countries, a slightly increased incidence of MF has also been reported in Norway (3) and Japan (4) over the past decades. The incidence of MF/SS is around 1.5 times higher in males than females (2,3). In the United States, the highest incidence rate of MF is observed among African Americans, with a black-to-white incidence rate ratio of 1.55 (2).
MF presents in the skin with patches/plaques and is characterized by epidermal and dermal infiltration of small to medium-sized T cells with cerebriform nuclei (5). MF generally has a long natural history and is likely to be diagnosed at an early stage, resulting in a generally good prognosis with a median survival of more than 25 years (6,7). SS is characterized by the presence of erythroderma, lymphadenopathy, and neoplastic T lymphocytes in the blood and its behavior is much more aggressive with a median survival of about 5 years (6,7).
Because of the rarity of these diseases, very few epidemiologic studies on MF/SS risk factors have been conducted thus far, and the only identified risk factors are male gender, advanced age, and African American descent (8). Smoking and alcohol consumption (9), several occupations and the related exposures (9–12), atopic diseases (9,13), sun exposure (14,15), and several infectious agents such as human herpesvirus 8, hepatitis C virus (HCV), Borrelia burgdorferi, and cytomegalovirus (16–20) have been studied, but their roles in the etiology of MF/SS remain unestablished.
To advance our understanding of MF/SS etiology, we investigated associations with lifestyle, medical history, family history, and occupational risk factors in a pooled analysis of 324 cases and 17217 controls from 14 case-control studies from Europe, North America, and Australia as part of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma (NHL) Subtypes Project.
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 fulfilled the following criteria: 1) case–control design, with incident, histologically confirmed cases of MF/SS, and 2) availability of individual-level data for at least several risk factors of interest 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 before interview.
NHL Subtype Ascertainment and Harmonization
Cases were classified according to the World Health Organization classification (5,21) using guidelines from the InterLymph Pathology Working Group (22,23). Most studies had some form of centralized pathology review by at least one expert hematopathologist to confirm the diagnoses. Each participating study’s pathology review procedures, rules for NHL subtype classification, and NHL subtype distribution were reviewed by an interdisciplinary team of pathologists and epidemiologists.
Risk Factor Ascertainment and Harmonization
Each study collected data on putative NHL risk factors in a standardized, structured format by in-person or telephone interviews and/or self-reported questionnaires. Risk factors selected for inclusion in this analysis were lifestyle, medical history, family history, and occupational risk factors with data from at least four studies. Centralized harmonization of de-identified individual-level data from each study was a key element of the project. Each exposure variable was harmonized separately, before being reviewed for consistency among related exposure variables. Details of the data harmonization rules are provided elsewhere in this issue.
Statistical Analysis
Risk of MF/SS associated with each exposure variable was examined using logistic regression models adjusted for age, race/ethnicity, and gender. Statistical significance of each relationship was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than .05 identifying putatively influential factors. To evaluate effect heterogeneity among the 14 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 (24).
We then examined the relationship between case/control status and each putative risk factor considering possible effect modification and accounting for other potential confounders. To consider possible effect modification, we stratified the above logistic regression analyses by age, gender, race/ethnicity, region (ie, Northern Europe, Southern Europe, North America, and Australia) study, study design (ie population-based vs. hospital-based), or other putative risk factors identified in the analysis. Also, we set multivariate regression models adjusting each risk estimate for the other putative risk factor included one at the time, and a forward step-wise single regression model including all putative risk factors. Because the results did not change substantially with use of the multivariate models, ORs are presented from the minimally adjusted models only.
Because controls for most original studies were frequency-matched to the age and gender distribution of all NHL cases, rather than just MF/SS, we conducted sensitivity analyses using a subset of controls that were frequency-matched by age and gender to cases of MF/SS. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls; thus, we retained the full set of controls for our main analyses to increase statistical power.
Results
This pooled analysis included the largest number of subjects from North America (61% cases and 43% controls), followed by Northern Europe (20% cases and 34% controls), Southern Europe (17% cases and 19% controls), and Australia (1% cases and 4% controls). Most of the study population came from population-based studies (86% cases and 80% controls), with the remainder coming from hospital-based studies (14% cases and 20% controls). Of the 324 cases, 271 (84%) were MF, 13 (4%) were SS, and 40 (12%) were unclassified MF/SS; the majority (78%) of MF/SS cases were histologically classified based on the WHO Classification. Cases and controls showed similar distributions of age, gender, and socioeconomic status (Table 1). MF/SS cases had higher percentages of African Americans and Asians compared with controls (due to the distribution in US studies).
Table 1.
Characteristics of studies included in the InterLymph NHL Subtypes Project*
| Controls | Cases | Total | |
|---|---|---|---|
| No. (%) | No. (%) | No. (%) | |
| Total | 17217 (98.2) | 324 (1.8) | 17541 |
| Study | |||
| North America | |||
| British Columbia | 845 (4.9) | 42 (13.0) | 887 |
| Mayo Clinic | 1314 (7.6) | 9 (2.8) | 1323 |
| NCI-SEER | 1055 (6.1) | 26 (8.0) | 1081 |
| Nebraska (newer) | 533 (3.1) | 7 (2.2) | 540 |
| UCSF1 | 2402 (14.0) | 47 (14.5) | 2449 |
| UCSF2 | 457 (2.7) | 54 (16.7) | 511 |
| University of Rochester | 139 (0.8) | 2 (0.6) | 141 |
| Yale | 717 (4.2) | 12 (3.7) | 729 |
| Europe | |||
| Epilymph | 2460 (14.3) | 38 (11.7) | 2498 |
| Italy multi-center | 1771 (10.3) | 25 (7.7) | 1796 |
| Italy (Aviano-Naples) | 504 (2.9) | 2 (0.6) | 506 |
| SCALE | 3187 (18.5) | 41 (12.7) | 3228 |
| United Kingdom | 1139 (6.6) | 15 (4.6) | 1154 |
| Australia | |||
| New South Wales | 694 (4.0) | 4 (1.2) | 698 |
| Region | |||
| North America | 7462 (43.3) | 199 (61.4) | 7661 |
| Northern Europe | 5820 (33.8) | 65 (20.1) | 5885 |
| Southern Europe | 3241 (18.8) | 56 (17.3) | 3297 |
| Australia | 694 (4.0) | 4 (1.2) | 698 |
| Design | |||
| Population-based | 13846 (80.4) | 280 (86.4) | 14126 |
| Hospital-based | 3371 (19.6) | 44 (13.6) | 3415 |
| Age, y | |||
| <30 | 993 (5.8) | 8 (2.5) | 1001 |
| 30–39 | 1686 (9.8) | 33 (10.2) | 1719 |
| 40–49 | 2543 (14.8) | 57 (17.6) | 2600 |
| 50–59 | 3940 (22.9) | 90 (27.8) | 4030 |
| 60–69 | 4848 (28.2) | 83 (25.6) | 4931 |
| 70–79 | 2949 (17.1) | 47 (14.5) | 2996 |
| ≥80 | 258 (1.5) | 6 (1.9) | 264 |
| Sex | |||
| Male | 9240 (53.7) | 184 (56.8) | 9424 |
| Female | 7977 (46.3) | 140 (43.2) | 8117 |
| Race | |||
| White, non-Hispanic | 15849 (92.1) | 271 (83.6) | 16120 |
| Black | 329 (1.9) | 19 (5.9) | 348 |
| Asian | 308 (1.8) | 22 (6.8) | 330 |
| Hispanic | 289 (1.7) | 7 (2.2) | 296 |
| Other/unknown/missing | 442 (2.6) | 5 (1.5) | 447 |
| Socioeconomic status | 6170 (35.8) | 117 (36.1) | 6287 |
| Medium | 5292 (30.7) | 94 (29.0) | 5386 |
| High | 5507 (32.0) | 109 (33.6) | 5616 |
| Other/missing | 248 (1.4) | 4 (1.2) | 252 |
| NHL classification | |||
| World Health Organization | 13044 (75.8) | 252 (77.8) | 13296 |
| Working Formulation | 4173 (24.2) | 72 (22.2) | 4245 |
* NHL = non-Hodgkin Lymphoma; NCI-SEER = National Cancer Institute Surveillance, Epidemiology, and End Results; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California San Francisco.
The associations between lifestyle factors and risk of MF/SS based on basic adjusted models are presented in Table 2. An increased risk was observed for people who had smoked for 40 years or longer (OR = 1.60, 95% CI = 1.08 to 2.38), and for obesity [body mass index (BMI) ≥ 30kg/m2: OR = 1.58, 95% CI = 1.04 to 2.41] with reference to BMI between 18.5 and 22.4kg/m2. However, no evidence of an increasing trend was observed with increasing years of smoking or BMI. On the other hand, compared with people who were not engaged in leisure-time physical activity, those who reported moderate (OR = 0.44, 95% CI = 0.21 to 0.91) or vigorous (OR = 0.50, 95% CI = 0.28 to 0.90) physical activity was inversely associated with a reduced risk of MF/SS. Again, no trend was detected with level of physical activity. We did not observe an association with alcohol consumption, hair dye use or sun exposure.
Table 2.
Associations between lifestyle factors and risk of Mycosis fungoides and Sézary syndrome*
| Controls | Cases | OR (95% CI)† | |
|---|---|---|---|
| No. (%) | No. (%) | ||
| History of alcohol consumption | |||
| Non-drinker | 3003 (19.2) | 73 (26.3) | 1.00 (referent) |
| Drinker (at least one drink per month) | 8289 (52.9) | 146 (52.5) | 0.80 (0.58 to 1.09) |
| History of cigarette smoking‡ | |||
| No | 6997 (42.7) | 121 (42.9) | 1.00 (referent) |
| Yes | 8451 (51.6) | 139 (49.3) | 0.97 (0.75 to 1.25) |
| 0–20, y | 3090 (18.9) | 47 (16.7) | 0.86 (0.61 to 1.23) |
| 21–<30, y | 1783 (10.9) | 27 (9.6) | 0.83 (0.54 to 1.28) |
| 30–<40, y | 1737 (10.6) | 24 (8.5) | 0.79 (0.50 to 1.24) |
| 40≥, y | 1742 (10.6) | 40 (14.2) | 1.60 (1.08 to 2.38) |
| Missing | 1023 (6.2) | 23 (8.2) | |
| Physical activity | |||
| None | 716 (10.1) | 20 (14.7) | 1.00 (referent) |
| Mild | 474 (6.7) | 14 (10.3) | 0.64 (0.29 to 1.40) |
| Moderate | 934 (13.2) | 20 (14.7) | 0.44 (0.21 to 0.91) |
| Vigorous | 3037 (43.0) | 45 (33.1) | 0.50 (0.28 to 0.90) |
| Usual adult BMI, kg/m2 | |||
| 15–<18.5 | 209 (1.4) | 5 (1.7) | 1.30 (0.50 to 3.39) |
| 18.5–<22.5 | 2943 (19.9) | 47 (15.9) | 1.00 (referent) |
| 22.5–<25 | 3601 (24.4) | 59 (20.0) | 1.03 (0.69 to 1.52) |
| 25–<30 | 5220 (35.4) | 107 (36.3) | 1.25 (0.87 to 1.80) |
| 35–50 | 2175 (14.7) | 55 (18.6) | 1.58 (1.04 to 2.41) |
| Used hair dyes before 1980 | |||
| Never hair dye | 1406 (14.4) | 27 (13.7) | 1.00 (referent) |
| Ever hair dye use <1980 | 1101 (11.3) | 21 (10.7) | 1.08 (0.55 to 2.10) |
| Hair dye use only 1980≥ | 966 (9.9) | 12 (6.1) | 0.78 (0.36 to 1.71) |
| Hair dye use, time period unknown | 986 (10.1) | 25 (12.7) | 0.99 (0.48 to 2.05) |
| Male | 5071 (51.8) | 108 (54.8) | |
| Total sun exposure (h/wk) | |||
| Q1 | 1508 (18.7) | 24 (21.2) | 1.00 (referent) |
| Q2 | 1594 (19.8) | 21 (18.6) | 0.83 (0.46 to 1.50) |
| Q3 | 1633 (20.3) | 21 (18.6) | 0.83 (0.45 to 1.51) |
| Q4 | 1714 (21.3) | 21 (18.6) | 0.75 (0.41 to 1.40) |
* CI = confidence interval; OR = odds ratio.
† OR (95% CI) adjusted for age, sex, and race.
‡ Smoked longer than 6 months or more than 100 cigarettes in lifetime.
Among previous medical conditions, eczema was significantly associated with an increased risk of MF/SS (Table 3). Although the association was stronger for those who were diagnosed within 10 years of MF/SS diagnosis (OR = 4.12, 95% CI = 1.54 to 11.04 for 2–<5 years before diagnosis; OR = 4.87, 95% CI = 2.15 to 11.02 for 5–<10 years before diagnosis), suggesting possible misdiagnosis of eczema as MF/SS, risk was statisticalltly elevated also for history of eczema beyond 10 years (OR = 1.90, 95% CI = 1.27 to 2.85). An evaluation of individual autoimmune diseases was not permissible due to small numbers. We observed two cases of autoimmune diseases that activate both B and T cells, resulting in a significant increase in MF/SS risk. Other medical conditions, including atopic disorders other than eczema, psoriasis, inflammatory bowel disorders, blood transfusion, HCV infection, oral contraceptive use, and hormone replacement therapy showed weak associations (Table 3). A family history of multiple myeloma, but not family history of hematologic malignancies overall, or history of other specific lymphohemopoietic cancer, showed an excess risk (OR = 6.17, 95% CI = 2.39 to 15.91, based on six cases).
Table 3.
Associations between medical history and risk of Mycosis fungoides and Sézary syndrome*
| Controls | Cases | OR (95% CI)† | |
|---|---|---|---|
| No. (%) | No. (%) | ||
| Blood transfusion | |||
| No | 9591 (82.2) | 159 (78.7) | 1.00 (referent) |
| Yes | 1715 (14.7) | 34 (16.8) | 1.19 (0.81 to 1.74) |
| Ever used OC | |||
| No | 2164 (28.0) | 32 (21.6) | 1.00 (referent) |
| Yes | 1392 (18.0) | 34 (23.0) | 1.65 (0.91 to 3.01) |
| Male | 4044 (52.3) | 80 (54.1) | |
| Ever used HRT | |||
| No | 1716 (26.7) | 37 (29.6) | 1.00 (referent) |
| Yes | 1072 (16.7) | 22 (17.6) | 1.17 (0.65 to 2.13) |
| Male | 3351 (52.2) | 64 (51.2) | |
| Infection of HCV | |||
| No | 6746 (66.8) | 128 (73.6) | 1.00 (referent) |
| Yes | 152 (1.5) | 1 (0.6) | 0.47 (0.07 to 3.47) |
| Missing | 3194 (31.6) | 45 (25.9) | |
| Ulcer | |||
| No | 11639 (82.8) | 240 (86.3) | 1.00 (referent) |
| Yes | 1020 (7.3) | 17 (6.1) | 0.93 (0.56 to 1.54) |
| Psoriasis | |||
| No | 10756 (97.4) | 158 (95.8) | 1.00 (referent) |
| Yes | 256 (2.3) | 7 (4.2) | 1.94 (0.89 to 4.23) |
| Inflammatory bowel disorder | |||
| No | 14454 (97.5) | 276 (95.2) | 1.00 (referent) |
| Yes | 179 (1.2) | 7 (2.4) | 1.82 (0.83 to 3.99) |
| Ulcerative colitis | |||
| No | 11886 (97.2) | 238 (95.2) | 1.00 (referent) |
| Yes | 145 (1.2) | 5 (2.0) | 1.68 (0.67 to 4.21) |
| History of autoimmune disease | |||
| No autoimmune disease | 16500 (95.8) | 305 (94.1) | 1.00 (referent) |
| B-cell activation | 127 (0.7) | 2 (0.6) | 1.02 (0.25 to 4.17) |
| T-cell activation | 577 (3.4) | 15 (4.6) | 1.49 (0.87 to 2.55) |
| Both | 13 (0.1) | 2 (0.6) | 9.82 (2.05 to 47.03) |
| Any atopic disorder‡ | |||
| No | 11285 (65.5) | 185 (57.1) | 1.00 (referent) |
| Yes | 5690 (33.0) | 129 (39.8) | 1.25 (0.98 to 1.61) |
| Allergy§ | |||
| No | 9254 (68.8) | 161 (61.7) | 1.00 (referent) |
| Yes | 3338 (24.8) | 73 (28.0) | 0.91 (0.67 to 1.24) |
| Food allergy | |||
| No | 11065 (82.2) | 178 (68.2) | 1.00 (referent) |
| Yes | 972 (7.2) | 21 (8.0) | 1.04 (0.64 to 1.69) |
| Asthma | |||
| No | 14140 (82.8) | 260 (80.7) | 1.00 (referent) |
| Yes | 1441 (8.4) | 28 (8.7) | 0.99 (0.67 to 1.48) |
| Hay fever | |||
| No | 9198 (64.9) | 132 (48.4) | 1.00 (referent) |
| Yes | 2740 (19.3) | 55 (20.1) | 0.90 (0.63 to 1.30) |
| History of eczema | |||
| No or <2 y before diagnosis | 12157 (84.9) | 205 (74.5) | 1.00 (referent) |
| Yes, 2–<5 y before diagnosis | 74 (0.5) | 5 (1.8) | 4.12 (1.54 to 11.04) |
| Yes, 5–<10 y before diagnosis | 92 (0.6) | 7 (2.5) | 4.87 (2.15 to 11.02) |
| Yes, 10 y or more before diagnosis | 894 (6.2) | 30 (10.9) | 1.90 (1.27 to 2.85) |
| Yes, unknown age | 343 (2.4) | 10 (3.6) | 2.04 (1.03 to 4.04) |
| Missing | 751 (5.2) | 18 (6.5) | |
| First degree family history | |||
| Any hematologic malignancy | |||
| No | 10152 (76.1) | 202 (72.4) | 1.00 (referent) |
| Yes | 581 (4.4) | 16 (5.7) | 1.16 (0.68 to 1.98) |
| Multiple myeloma | |||
| No | 7671 (74.5) | 162 (71.1) | 1.00 (referent) |
| Yes | 36 (0.3) | 6 (2.6) | 6.17 (2.39 to 15.91) |
* CI = confidence interval; HCV = hepatitis C virus; HRT = hormone replacement therapy; OC = oral contraceptives; OR = odds ratio.
† OR (95% CI) adjusted for age, sex, and race.
‡ Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies.
§ History of allergy excludes drug allergies, asthma, eczema, and hay fever.
An elevated risk of MF/SS was associated with several occupations (Table 4), including crop and vegetable farm workers (OR = 2.76, 95% CI = 1.35 to 5.61), painters (OR = 3.42, 95% CI = 1.81 to 6.47), woodworkers (OR = 2.19, 95% CI = 1.19 to 4.03), and general carpenters (OR = 4.50, 95% CI = 1.74 to 11.62). A significant linear trend was observed with years of employment for woodworkers (P for trend = .025) but not others (data not shown). None of the other occupations evaluated showed a significant association with MF/SS risk.
Table 4.
Associations between occupation and risk of Mycosis fungoides and Sézary syndrome*
| Controls | Cases | OR (95% CI)† | |
|---|---|---|---|
| No. (%) | No. (%) | ||
| Baker and miller | |||
| No | 11152 (96.0) | 209 (96.8) | 1.00 (referent) |
| Yes | 141 (1.2) | 4 (1.9) | 1.80 (0.65 to 4.95) |
| Cleaner | |||
| No | 10775 (92.8) | 207 (95.8) | 1.00 (referent) |
| Yes | 518 (4.5) | 6 (2.8) | 0.64 (0.28 to 1.46) |
| Driver | |||
| No | 10467 (90.1) | 200 (92.6) | 1.00 (referent) |
| Yes | 826 (7.1) | 13 (6.0) | 0.83 (0.46 to 1.48) |
| Electrical and electronics worker | |||
| No | 10589 (91.2) | 202 (93.5) | 1.00 (referent) |
| Yes | 704 (6.1) | 11 (5.1) | 0.83 (0.44 to 1.54) |
| Engine mechanic | |||
| No | 10231 (93.9) | 196 (96.1) | 1.00 (referent) |
| Yes | 345 (3.2) | 5 (2.5) | 0.74 (0.30 to 1.83) |
| Ever worked in farming and farm workers any type | |||
| No | 9980 (85.9) | 185 (85.6) | 1.00 (referent) |
| Yes | 1313 (11.3) | 28 13.0) | 1.45 (0.94 to 2.22) |
| Crop and vegetable farm worker | |||
| No | 9677 (94.5) | 156 (93.4) | 1.00 (referent) |
| Yes | 226 (2.2) | 9 (5.4) | 2.76 (1.35 to 5.61) |
| Hair dresser | |||
| No | 11144 (95.9) | 210 (97.2) | 1.00 (referent) |
| Yes | 149 (1.3) | 3 (1.4) | 1.24 (0.39 to 3.95) |
| General unspecified laborer | |||
| No | 10667 (91.8) | 206 (95.4) | 1.00 (referent) |
| Yes | 626 (5.4) | 7 (3.2) | 0.60 (0.28 to 1.29) |
| Leather worker | |||
| No | 8841 (95.0) | 164 (95.9) | 1.00 (referent) |
| Yes | 147 (1.6) | 4 (2.3) | 1.93 (0.69 to 5.37) |
| Meat worker | |||
| No | 11195 (96.4) | 210 (97.2) | 1.00 (referent) |
| Yes | 98 (0.8) | 3 (1.4) | 1.76 (0.55 to 5.67) |
| Medical worker | |||
| No | 10423 (89.7) | 198 (91.7) | 1.00 (referent) |
| Yes | 870 (7.5) | 15 (6.9) | 0.89 (0.52 to 1.53) |
| Metal worker | |||
| No | 10612 (91.4) | 206 (95.4) | 1.00 (referent) |
| Yes | 681 (5.9) | 7 (3.2) | 0.58 (0.27 to 1.24) |
| Painter | |||
| No | 11099 (95.5) | 202 (93.5) | 1.00 (referent) |
| Yes | 194 (1.7) | 11 (5.1) | 3.42 (1.81 to 6.47) |
| Printer | |||
| No | 11075 (95.3) | 208 (96.3) | 1.00 (referent) |
| Yes | 218 (1.9) | 5 (2.3) | 1.33 (0.54 to 3.29) |
| Teacher | |||
| No | 10142 (87.3) | 190 (88.0) | 1.00 (referent) |
| Yes | 1151 (9.9) | 23 (10.6) | 1.12 (0.71 to 1.75) |
| Textile worker | |||
| No | 10552 (90.8) | 199 (92.1) | 1.00 (referent) |
| Yes | 741(6.4) | 14 (6.5) | 1.30 (0.73 to 2.32) |
| Woodworker | |||
| No | 10969 (94.4) | 201 (93.1) | 1.00 (referent) |
| Yes | 324 (2.8) | 12 (5.6) | 2.19 (1.19 to 4.03) |
| General carpenter | |||
| No | 10493 (96.3) | 196 (96.1) | 1.00 (referent) |
| Yes | 71 (0.7) | 5 (2.5) | 4.50 (1.74 to 11.62) |
* CI = confidence interval; OR = odds ratio.
† OR(95% CI) adjusted for age, sex, and race.
Results from multivariate analysis are presented in Table 5. All statistically significant associations remained except for vigorous leisure time physical activity.
Table 5.
Significant assocaitions from multivariate model*
| Controls | Cases | OR (95% CI)† | |
|---|---|---|---|
| No. (%) | No. (%) | ||
| History of cigarette smoking‡ | |||
| No | 6997 (42.7) | 121 (42.9) | 1.00 (reference) |
| 1–20, y | 3090 (18.9) | 47 (16.7) | 0.85 (0.60 to 1.21) |
| 21–30, y | 1783 (10.9) | 27 (9.6) | 0.77 (0.50 to 1.19) |
| 30–39, y | 1737 (10.6) | 24 (8.5) | 0.81 (0.51 to 1.28) |
| 40≥, y | 1742 (10.6) | 40 (14.2) | 1.55 (1.04 to 2.31) |
| Physical activity | |||
| None | 716 (10.1) | 20 (14.7) | 1.00 (reference) |
| Mild | 474 (6.7) | 14 (10.3) | 0.74 (0.33 to 1.64) |
| Moderate | 934 (13.2) | 20 (14.7) | 0.46 (0.22 to 0.97) |
| Vigorous | 3037 (43.0) | 45 (33.1) | 0.58 (0.32 to 1.08) |
| Usual adult BMI, kg/m2 | |||
| 18.5–<22.5 | 2943 (19.9) | 47 (15.9) | 1.00 (reference) |
| 15–<18.5 | 209 (1.4) | 5 (1.7) | 1.39 (0.53 to 3.70) |
| 22.5–<25 | 3601 (24.4) | 59 (20.0) | 1.02 (0.69 to 1.52) |
| 25–<30 | 5220 (35.4) | 107 (36.3) | 1.24 (0.86 to 1.78) |
| 30–50 | 2175 (14.7) | 55 (18.6) | 1.57 (1.03 to 2.40) |
| History of autoimmune disease | |||
| No autoimmune disease | 16500 (95.8) | 305 (94.1) | 1.00 (reference) |
| B-cell activation | 127 (0.7) | 2 (0.6) | 1.00 (0.24 to 4.13) |
| T-cell activation | 577 (3.4) | 15 (4.6) | 1.48 (0.86 to 2.53) |
| Both | 13 (0.1) | 2 (0.6) | 9.45 (1.80 to 49.60) |
| History of eczema | |||
| No | 12100 (84.6) | 202 (73.5) | 1.00 (reference) |
| Yes | 1460 (10.2) | 55 (20.0) | 2.38 (1.73 to 3.29) |
| Family history of multiple myeloma | |||
| No | 7671 (74.5) | 162 (71.1) | 1.00 (reference) |
| Yes | 36 (0.3) | 6 (2.6) | 8.49 (3.31 to 21.80) |
| Crop and vegetable farm workers | |||
| No | 9677 (94.5) | 156 (93.4) | 1.00 (reference) |
| Yes | 226 (2.2) | 9 (5.4) | 2.37 (1.14 to 4.92) |
| Painter | |||
| No | 11099 (95.5) | 202 (93.5) | 1.00 (reference) |
| Yes | 194 (1.7) | 11 (5.1) | 3.71 (1.94 to 7.07) |
| Woodworkers | |||
| No | 10969 (94.4) | 201 (93.1) | 1.00 (reference) |
| Yes | 324 (2.8) | 12 (5.6) | 2.20 (1.18 to 4.08) |
| General carpenter | |||
| No | 10493 (96.3) | 196 (96.1) | 1.00 (reference) |
| Yes | 71 (0.7) | 5 (2.5) | 4.07 (1.54 to 10.75) |
* CI = confidence interval; OR = odds ratio.
† OR (95% CI) adjusted for age, sex, race, and all other variables listed in the table.
‡ Smoked longer than 6 months or more than 100 cigarettes in lifetime.
Limiting our analysis to MF cases (n = 271) did not change the results (data not shown). No meaningful inter-study heterogeneity was detected.
Discussion
The results of our pooled analysis of 324 cases and 17217 controls from 14 case-control studies from Europe, North America, and Australia, suggest that subjects with a positive family history of multiple myeloma and subjects working in crop and vegetable farms, or as painters, carpenters or woodworkers, might be at an increased risk of MF/SS. A history of eczema for more than 10 years before MF/SS diagnosis also increased risk. Among personal and lifestyle risk factors, only obesity and prolonged cigarette smoking seem to convey an increased risk, while a moderate/vigorous leisure time physical activity might be protective. As both MF and SS are rare, few results have previously been published and are available for comparison with our findings.
Chronic exposure to cigarette smoke has been associated with decreased immune responsiveness, particularly for T cells, in both human and animal studies (25), which would suggest a potential link to decreased immune surveillance and increased lymphoma risk. In a previous pooled InterLymph study, heavy smoking was associated with an increased risk of follicular lymphoma but not other NHL subtypes, including MF (26). In the European multicenter study of MF, a linear increase in MF risk with increasing pack-years of smoking was observed, although the trend was not statistically significant (27). In the analyses presented here, a significant association was observed among individuals who had smoked cigarettes for 40 years or more, but no dose–response was observed with increasing duration.
Obesity promotes a state of low-grade chronic inflammation and increased production of proinflammatory cytokines such as interleukin (IL)-6, tumor necrosis factor-α, IL-1b, and leptin (28). These cytokines can deregulate T- and B-cell responses and enhance B-cell proliferation and survival, factors that may provide a milieu that favors lymphomagenesis (29). In our analysis, a BMI greater than or equal to 30kg/m2 was associated with an increased risk of MF/SS, although we were unable to support with statistical significance the observed linear increase in risk by increasing BMI. A similar finding was reported for diffuse large B-cell lymphoma in a previous InterLymph study of NHL overall and common NHL subtypes; however, MF/SS was not analyzed as a separate outcome in that study (30).
Moderate physical activity may improve immune function and it may therefore protect against NHL and possibly MF/SS (31). In our study we found that, compared with people who were not engaged in leisure-time physical activity, those who engaged in moderate and vigorous physical activity experienced a reduced risk of MF/SS. However, the decrease in MF/SS risk by increasing level of physical activity was not linear, and the multivariate analysis partially weakened the inverse association. Previous reports suggest that moderate exercise may reduce NHL risk (32,33). More research in this area is warranted.
In agreement with a previous InterLymph study (13), we found that a previous medical history of eczema was associated with an increased risk of MF/SS, which appeared to be stronger for those who were diagnosed within 10 years of MF/SS diagnosis. Such a pattern might suggest the possibility that early MF may be mistaken for eczema in some cases. Alternatively, the association with eczema may be an indicator of eczema as an early disease rather than a risk factor as it often goes undiagnosed for years. However, the risk remained significantly elevated for those who were diagnosed more than 10 years before MF/SS diagnosis. Eczema is a form of chronic dermatitis which is known to have a pathogenetic association with early stages of MF (34). Specific autoimmune diseases were rare and no analysis of their associations with MF/SS risk was feasible; after categorizing autoimmune diseases by whether B or T cells were activated, no association was observed. However, two cases of autoimmune diseases that activate both B and T cells were observed, resulting in an elevated MF/SS risk. B cell activating diseases included Hashimoto thyroiditis, hemolytic anemia, myasthenia gravis, pernicious anemia, rheumatoid arthritis, Sjögren’s syndrome, and systemic lupus erythematosus. T cell activating diseases included celiac disease, immune thrombocytopenic purpura, inflammatory bowel disorder (Crohn’s disease, ulcerative colitis), multiple sclerosis, polymyositis or dermatomyositis, psoriasis, sarcoidosis, systemic sclerosis or scleroderma, and type 1 diabetes.
A role for genetic susceptibility in MF/SS is supported by the accumulating evidence of common genetic variations altering MF risk (35,36). In our study, persons with a family history of multiple myeloma had an excess risk of MF/SS, but no association was found with family history of any hematologic malignancy. In a previous pooled InterLymph analysis, risk of specific NHL subtypes, including T-cell lymphomas (MF/SS were not separately evaluated), were elevated among subjects who reported a family history of hematologic malignancies in first-degree relatives, particularly multiple myeloma in males (37). However, it is also possible that since multiple myeloma and MF are increased in blacks (37), this may confound the association.
The evaluation of occupational risk factors showed that crop and vegetable farm workers, painters, woodworkers and carpenters experienced an increased risk of MF/SS. Although we did not examine specific occupational exposures, our findings are consistent with the results of other studies that examined exposures potentially encountered in these occupations. In a European case–control study, occupational exposures to the broad category of aromatic and/or halogenated hydrocarbons, which are widely used as solvents, and to pesticides in general were identified as potential risk factors for MF (10). An excess risk of NHL among farmers and agricultural workers has been repeatedly reported, suggesting a potential link with farming exposures including pesticides (38,39). Among the most commonly used agrochemicals, organophosphate insecticides were associated with an increased risk in the European Epilymph study, limited to the chronic lymphocytic leukemia subtype (40). Other occupations previously associated with an increase in MF risk include different manufacturing industries, such as petrochemical, textile, and various metal industries (41–43). Painters and woodworkers may also be exposed to solvents in paint thinners and paint and grease removers, including benzene and trichloroethylene previously associated with increased risk of other NHL subtypes in prior reports from included studies (44,45). Other exposures possibly related to the excess risk we observed for these occupations include chlorophenols, wood dust, and molds (Cocco P, unpublished data). Our results suggest that these and other potentially harmful exposures should be explored in greater detail in future investigations using advanced occupational exposure assessment methods.
Although this is the largest study to date that examines numerous putative risk factors in relation to MF/SS, the small number of subjects was still a limitation. All cases were histologically confirmed, but centralized review of all cases by a team of study pathologists was not feasible, and thus some misclassification may be present. As multiple hypotheses have been tested and a number of comparisons have been made, chance findings cannot be ruled out. Since only 13 SS cases were included in this study, we were unable to examine associations specific to SS; therefore, the observed associations were predominantly driven by MF and may not apply to SS. Another limitation is related to the number of comparisons we made, which might have generated several positive findings as the sole result of chance. However, negative findings might have been missed as the study size is insufficient to detect weaker associations. In conclusion, our pooled analysis of lifestyle factors, medical history, and occupation and MF/SS suggests potential positive associations with elevated BMI, long-term cigarette smoking, eczema, and family history of multiple myeloma, and a potential negative association with moderate leisure time physical activity. Our findings for farming and other occupations point to avenues for additional research to identify specific occupational exposures that may be responsible for these associations. Future research is warranted to confirm these findings in prospectively collected data and in other populations.
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 2010–13 were supported by the Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute/National Institutes of Health (2010–13); Lymphoma Coalition (2010–13); 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, multi-center); 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).
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.
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