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Medical History, Lifestyle, Family History, and Occupational Risk Factors for Lymphoplasmacytic Lymphoma/Waldenström’s Macroglobulinemia: The InterLymph Non-Hodgkin Lymphoma Subtypes Project

Medical History, Lifestyle, Family History, and Occupational Risk Factors for Lymphoplasmacytic... Abstract Background Lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia (LPL/WM), a rare non-Hodgkin lymphoma subtype, shows strong familial aggregation and a positive association with chronic immune stimulation, but evidence regarding other risk factors is very limited. Methods The International Lymphoma Epidemiology Consortium (InterLymph) pooled data from 11 predominantly population-based case–control studies from North America, Europe, and Australia to examine medical history, lifestyle, family history, and occupational risk factors for LPL/WM. Age-, sex-, race/ethnicity-, and study-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression for a total of 374 LPL/WM cases and 23 096 controls. Results In multivariate analysis including all putative risk factors, LPL/WM risk was associated with history of Sjögren’s syndrome (OR = 14.0, 95% CI = 3.60 to 54.6), systemic lupus erythematosus (OR = 8.23, 95% CI = 2.69 to 25.2), hay fever (OR = 0.73, 95% CI = 0.54 to 0.99), positive hepatitis C serology (OR = 2.51, 95% CI = 1.03 to 6.17), hematologic malignancy in a first-degree relative (OR = 1.64, 95% CI = 1.02 to 2.64), adult weight (OR = 0.61, 95% CI = 0.44 to 0.85 for highest vs. lowest quartile), duration of cigarette smoking (OR = 1.46, 95% CI = 1.04 to 2.05 for ≥ 40 years vs. nonsmokers), and occupation as a medical doctor (OR = 5.54, 95% CI = 2.19 to 14.0). There was no association with other medical conditions, lifestyle factors, or occupations. Conclusions This pooled analysis confirmed associations with immune conditions and family history of hematologic malignancy, and identified new associations with hay fever, weight, smoking, and occupation, and no association with other lifestyle factors. These findings offer clues to LPL/WM biology and prevention. Lymphoplasmacytic lymphoma (LPL) is a non-Hodgkin lymphoma (NHL) subtype characterized by the proliferation of small B lymphocytes, plasmacytoid lymphocytes, and plasma cells. Most patients with LPL have IgM paraproteins and a minority have both IgM and IgG or other paraproteins, although this is not diagnostic (1). Waldenström’s macroglobulinemia (WM) is a clinicopathological subset of LPL defined as LPL with bone marrow involvement and monoclonal IgM gammopathy (2). LPL/WM is rare, with incidence estimates ranging from 0.031 to 0.043/100 000 person-years in Japan and Taiwan (3) to 0.63/100 000 person-years in the United States (4). Established risk factors for LPL/WM are older age, male gender, white race/ethnicity (4), family history of LPL/WM or another B-cell malignancy (5–7), and history of the precursor condition monoclonal gammopathy of undetermined significance of IgM class (8,9). Other putative risk factors are a history of infectious disease (10–14), autoimmune disease (11,13–15), allergies (13,14), and certain genetic characteristics (16–19). In one study, increased risk of familial LPL/WM (n = 103) was associated with farming and exposure to pesticides, wood dust, and organic solvents (14), whereas another identified no lifestyle or occupational risk factors based on 65 WM cases (20). Together these findings support a role for germline susceptibility genes, antigenic drive, chronic immune stimulation, and possibly occupational factors in LPL/WM carcinogenesis. The largest prior studies have used health record linkage (7,13); thus, there has been no comprehensive evaluation of risk factors for LPL/WM, particularly lifestyle and occupational exposures, and assessment in a multivariate setting. We investigated LPL/WM associations with medical and family history, lifestyle, and occupational risk factors in a pooled analysis of 374 cases and 23 096 controls from 11 case–control studies from Europe, North America, and Australia as part of the International Lymphoma Epidemiology Consortium (InterLymph) NHL Subtypes Project. Methods Study Design and Population Detailed methodology is provided elsewhere in this issue. Studies eligible for inclusion in this pooled analysis of 11 studies met the following criteria: 1) case–control design, with incident, histologically confirmed cases of LPL/WM, and 2) availability of individual-level data for at least several risk factors of interest by December 31, 2011. Seven studies were population based (21–27), one was a combination of population based and hospital based (28), and three were hospital based (29–31). Most studies excluded individuals with a history of solid organ transplantation or HIV/AIDS. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview. NHL Subtype Ascertainment and Harmonization All LPL/WM cases met the criteria for LPL/WM described by the World Health Organization classification (1,32) and satisfied the classification suggested subsequently by the InterLymph Pathology Working Group (33,34). Most studies confirmed diagnoses by centralized pathology review by at least one expert hematopathologist. Risk Factor Ascertainment and Harmonization Each study collected data on putative NHL risk factors in a standardized, structured format using self-administered questionnaires. Risk factor categories included medical, lifestyle, family, and occupations. A requirement for inclusion was that a factor was ascertained in a minimum of two studies that enrolled at least one case of LPL/WM. Centralized harmonization of individual-level, de-identified data from each study was performed. Each variable was harmonized across studies and then data were reviewed for consistency among related exposure variables. Statistical Analysis Risk of LPL/WM associated with each exposure variable was examined using unconditional logistic regression models adjusted for age, race/ethnicity, sex, and study (“basic adjusted model”). Individuals with missing data for the exposure variable were excluded. Statistical significance was evaluated by a likelihood ratio test, comparing models with and without the exposure variable, with P values less than .05 used to identify putatively influential factors to be considered for the multivariate model. When two highly correlated factors or exposures were significant, only one exposure was taken forward. In such instances, the most clinically meaningful or specific exposure was selected, for example, a specific autoimmune disease rather than the composite autoimmune disease variable. Further, if the P value for only one exposure from several related exposures was less than.05, it was not automatically examined in the multivariate model. In these cases, the balance of evidence was taken into account, including whether there was evidence of a dose–response. To evaluate potential effect of heterogeneity among the 11 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 (35) to categorical variables. 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 repeated the basic adjusted models and stratified individuals by age (<30, 30–39, 40–49, 50–59, 60–69, 70–79, and ≥80 years), sex, race/ethnicity, region (North America, Northern Europe, Southern Europe, and Australia), study, study design, and other putative risk factors identified in the analysis. To account for other potential confounders, we conducted two analyses. First, we evaluated the risk estimate for each putative risk factor in a series of models that adjusted for one other putative risk factor as well as age, sex, race/ethnicity, and study. Second, we assessed all putative risk factors in a multivariate logistic regression model, this time including a separate missing category for each variable to ensure that the whole study population was included in the analysis. Lastly, we conducted a forward stepwise logistic regression adding a single putative risk factor at a time, adjusting for age, sex, race/ethnicity, and study (“final model”). As controls for most original studies were chosen to frequency match the age and sex of all eligible lymphoma cases, rather than just LPL/WM, we conducted sensitivity analyses using a subset of controls that were frequency matched by age and sex to cases of LPL/WM. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls. We thus retained the full set of controls for our main analyses to maximize statistical power. Analyses were conducted using SAS software, version 9.2 (SAS Institute Inc, Cary, NC). Results A total of 374 LPL/WM cases (371 LPL and 3 WM; diagnosed 1995–2008) and 23 096 controls were included in this study. Most cases were identified in case–control studies in North America or Northern Europe (Table 1). Sixty-one percent of cases were men and the median age at diagnosis was 64 years (range 27–89). Compared with controls, cases were older and more likely to be men, but there was no difference by race/ethnicity or socioeconomic status (Table 1). Table 1. Characteristics of the pooled lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia case and control participants Characteristic  Cases, No. (%) (n = 374)  Controls, No. (%) (n = 23 096)  Age, y   <30  2 (0.5)  1306 (5.9)   30–39  7 (1.9)  2180 (9.4)   40–49  33 (8.8)  3159 (13.7)   50–59  90 (24.1)  4992 (21.6)   60–69  128 (34.2)  6380 (27.6)   70–79  96 (25.7)  4136 (17.9)   ≥80  17 (4.5)  873 (3.8)   Unknown  1 (0.3)  16 (0.1)  Sex   Male  227 (60.7)  13 495 (58.4)   Female  147 (39.3)  9601 (41.6)  Race/ethnicity   White non-Hispanic  352 (94.1)  21 576 (93.4)   Black  4 (1.1)  351 (1.5)   Asian  3 (0.8)  321 (1.4)   Hispanic  1 (0.3)  360 (1.6)   Other/unknown  14 (3.7)  488 (2.1)  Socioeconomic status*   Low  142 (38.0)  9335 (40.4)   Medium  108 (28.9)  6709 (29.0)   High  118 (31.6)  6642 (28.8)   Other/unknown  6 (1.6)  410 (1.8)  Region   North America  156 (41.7)  11 462 (49.6)   Northern Europe  156 (41.7)  6542 (28.3)   Southern Europe  35 (9.4)  4398 (19.0)   Australia  27 (7.2)  694 (3.0)  Study design   Population based  291 (77.8)  17 846 (77.3)   Hospital based  83 (22.2)  5250 (22.7)  Characteristic  Cases, No. (%) (n = 374)  Controls, No. (%) (n = 23 096)  Age, y   <30  2 (0.5)  1306 (5.9)   30–39  7 (1.9)  2180 (9.4)   40–49  33 (8.8)  3159 (13.7)   50–59  90 (24.1)  4992 (21.6)   60–69  128 (34.2)  6380 (27.6)   70–79  96 (25.7)  4136 (17.9)   ≥80  17 (4.5)  873 (3.8)   Unknown  1 (0.3)  16 (0.1)  Sex   Male  227 (60.7)  13 495 (58.4)   Female  147 (39.3)  9601 (41.6)  Race/ethnicity   White non-Hispanic  352 (94.1)  21 576 (93.4)   Black  4 (1.1)  351 (1.5)   Asian  3 (0.8)  321 (1.4)   Hispanic  1 (0.3)  360 (1.6)   Other/unknown  14 (3.7)  488 (2.1)  Socioeconomic status*   Low  142 (38.0)  9335 (40.4)   Medium  108 (28.9)  6709 (29.0)   High  118 (31.6)  6642 (28.8)   Other/unknown  6 (1.6)  410 (1.8)  Region   North America  156 (41.7)  11 462 (49.6)   Northern Europe  156 (41.7)  6542 (28.3)   Southern Europe  35 (9.4)  4398 (19.0)   Australia  27 (7.2)  694 (3.0)  Study design   Population based  291 (77.8)  17 846 (77.3)   Hospital based  83 (22.2)  5250 (22.7)  *Socioeconomic status was measured by years of education for studies in North America or by dividing measures of education or socioeconomic status into tertiles for studies in Europe or Australia. View Large There was no statistically significant between-study heterogeneity for any of the risk factors examined (data not shown), and there was no evidence of effect modification by study, study design factors, or the other putative risk factors examined (data not shown). Basic Adjusted Model Medical History. Twenty-five LPL/WM cases (7.2%) and 577 controls (4.6%) had a history of autoimmune disease (Table 2); two cases and 26 controls reported more than one. These two cases reported rheumatoid arthritis and Sjögren’s syndrome, as did two of the controls. Individually, Sjögren’s syndrome and systemic lupus erythematosus were very strongly associated with LPL/WM risk, but the case numbers were too small to examine the relationship with disease latency (n = 3 and n = 4, respectively; Table 2). There was no association between LPL/WM risk and history of other selected autoimmune conditions (Table 2). Risk was strongly increased in association with autoimmune disease characterized by B-cell activation but not T-cell activation (Table 2). LPL/WM risk was inversely associated with a history of hay fever (odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.51 to 0.96) but was unrelated to history of asthma, eczema, any specific allergy, or any atopic condition (Table 2). Table 2. Basic adjusted association between personal history of autoimmune or allergic disease and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia (LPL/WM) risk* Disease†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Autoimmune diseases   Sjögren syndrome   No  174  6079  Referent  .003   Yes  3  9  13.2 (3.42 to 50.9)     Systemic lupus erythematosus   No  270  10 470  Referent  .002   Yes  4  24  8.73 (2.91 to 26.2)     Myasthenia gravis   No  228  7506  Referent  .171   Yes  1  4  6.60 (0.70 to 62.3)     Sarcoidosis   No  318  9379  Referent  .362   Yes  2  27  2.11 (0.49 to 9.01)     Rheumatoid arthritis||   No  208  6368  Referent  .474   Yes  4  82  1.48 (0.53 to 4.13)     Celiac disease   No  268  7593  Referent  .790   Yes  1  25  1.33 (0.18 to 10.1)     Crohn’s disease   No  315  9787  Referent  .994   Yes  1  29  0.99 (0.13 to 7.48)     Type 1 diabetes   No  207  7402  Referent  .967   Yes  1  43  0.96 (0.13 to 7.09)     Psoriasis   No  199  7333  Referent  .819   Yes  7  228  0.92 (0.42 to 1.98)     Inflammatory bowel disorder   No  343  10 932  Referent  .825   Yes  4  111  0.89 (0.32 to 2.47)     Ulcerative colitis   No  279  7777  Referent  .849   Yes  3  81  0.89 (0.28 to 2.89)     Polymyositis or dermatomyositis   No  61  3580  Referent  .419   Yes  0  18  —     Multiple sclerosis   No  314  9365  Referent  .332   Yes  0  13  —     Pernicious anemia   No  71  3146  Referent  .401   Yes  0  8  —     Hemolytic anemia   No  94  3793  Referent  .562   Yes  0  7  —     Systemic sclerosis or scleroderma   No  142  3848  Referent  .576   Yes  0  4  —     Any autoimmune disease¶   None  349  11 911  Referent  .054   B-cell activation  10  121  2.78 (1.43 to 5.43)     T-cell activation  15  444  1.02 (0.60 to 1.74)     Both B- and T-cell activation  0  12  —    Atopic disorders   Hay fever   No  218  7235  Referent  .022   Yes  64  2511  0.70 (0.51 to 0.96)     Asthma   No  295  9923  Referent  .932   Yes  33  1075  0.98 (0.68 to 1.42)     Eczema   No  309  9941  Referent  .891   Yes  36  1291  0.98 (0.68 to 1.39)     Any specific allergy#   No  226  7233  Referent  .409   Yes  92  3168  0.90 (0.69 to 1.16)     Food allergy   No  274  8939  Referent  .157   Yes  20  908  0.72 (0.45 to 1.16)     Any atopic disorder**   No  225  7392  Referent  .229   Yes  140  4868  0.87 (0.70 to 1.09)    Disease†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Autoimmune diseases   Sjögren syndrome   No  174  6079  Referent  .003   Yes  3  9  13.2 (3.42 to 50.9)     Systemic lupus erythematosus   No  270  10 470  Referent  .002   Yes  4  24  8.73 (2.91 to 26.2)     Myasthenia gravis   No  228  7506  Referent  .171   Yes  1  4  6.60 (0.70 to 62.3)     Sarcoidosis   No  318  9379  Referent  .362   Yes  2  27  2.11 (0.49 to 9.01)     Rheumatoid arthritis||   No  208  6368  Referent  .474   Yes  4  82  1.48 (0.53 to 4.13)     Celiac disease   No  268  7593  Referent  .790   Yes  1  25  1.33 (0.18 to 10.1)     Crohn’s disease   No  315  9787  Referent  .994   Yes  1  29  0.99 (0.13 to 7.48)     Type 1 diabetes   No  207  7402  Referent  .967   Yes  1  43  0.96 (0.13 to 7.09)     Psoriasis   No  199  7333  Referent  .819   Yes  7  228  0.92 (0.42 to 1.98)     Inflammatory bowel disorder   No  343  10 932  Referent  .825   Yes  4  111  0.89 (0.32 to 2.47)     Ulcerative colitis   No  279  7777  Referent  .849   Yes  3  81  0.89 (0.28 to 2.89)     Polymyositis or dermatomyositis   No  61  3580  Referent  .419   Yes  0  18  —     Multiple sclerosis   No  314  9365  Referent  .332   Yes  0  13  —     Pernicious anemia   No  71  3146  Referent  .401   Yes  0  8  —     Hemolytic anemia   No  94  3793  Referent  .562   Yes  0  7  —     Systemic sclerosis or scleroderma   No  142  3848  Referent  .576   Yes  0  4  —     Any autoimmune disease¶   None  349  11 911  Referent  .054   B-cell activation  10  121  2.78 (1.43 to 5.43)     T-cell activation  15  444  1.02 (0.60 to 1.74)     Both B- and T-cell activation  0  12  —    Atopic disorders   Hay fever   No  218  7235  Referent  .022   Yes  64  2511  0.70 (0.51 to 0.96)     Asthma   No  295  9923  Referent  .932   Yes  33  1075  0.98 (0.68 to 1.42)     Eczema   No  309  9941  Referent  .891   Yes  36  1291  0.98 (0.68 to 1.39)     Any specific allergy#   No  226  7233  Referent  .409   Yes  92  3168  0.90 (0.69 to 1.16)     Food allergy   No  274  8939  Referent  .157   Yes  20  908  0.72 (0.45 to 1.16)     Any atopic disorder**   No  225  7392  Referent  .229   Yes  140  4868  0.87 (0.70 to 1.09)    * CI = confidence interval; OR = odds ratio. † Self-reported condition diagnosed at least 2 years before LPL/WM diagnosis/interview. ‡ The counts do not add up to the total number of cases/controls due to data missing by design or report. § Adjusted for age, sex, race/ethnicity, and study. || Only those who also reported receiving corticosteroid or immunosuppressive treatment for rheumatoid arthritis. ¶ Includes self-reported history of specific autoimmune diseases occurring ≥2 years before diagnosis/interview (except the New South Wales study, which did not ascertain date of onset). Autoimmune diseases were classified according to whether they are primarily mediated by B-cell or T-cell responses. 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. # Any specific allergy included plant, food, animal, dust, insect, or mold and excluded drug allergies, asthma, eczema, and hay fever. **Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies. View Large LPL/WM risk was strongly increased in association with positive serology for hepatitis C virus (HCV) infection (OR = 2.70, 95% CI = 1.11 to 6.56; n = 6). No other infectious diseases were examined. Risk was not associated with the receipt of one or more blood transfusions (OR = 1.06, 95% CI = 0.74 to 1.53), transfusion age, transfusion number, or year of transfusion (data not shown). Neither history of gastric ulcer nor peptic ulcer predicted LPL/WM risk (data not shown). LPL/WM risk was associated with the number of children (P = .023); relative to women with a single child, risk was decreased for women with no children (OR = 0.32, 95% CI = 0.12 to 0.87) and two children (OR = 0.34, 95% CI = 0.15 to 0.77) but was attenuated and not statistically significant for three or more children (OR = 0.63, 95% CI = 0.32 to 1.22). As LPL/WM risk was not associated with time since the last birth, oral contraceptive use, the age contraceptives were first used, hormone replacement therapy, or the age hormone replacement therapy was first used (data not shown), this variable was not taken forward to multivariable analysis. Family History. Hematological malignancy in one or more first-degree relatives was reported by 21 cases (10.6%) and 452 controls (5.8%). Risk was moderately increased for having a family member with history of any hematological malignancy (OR = 1.65, 95% CI = 1.03 to 2.65) or with leukemia (OR = 2.19, 95% CI = 1.21 to 3.96). There was no association between LPL/WM risk and family history of Hodgkin lymphoma, NHL, or multiple myeloma (Table 3). Table 3. Basic adjusted association between first-degree family history of hematological malignancy and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Family member malignancy type and relationship to case/control†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Any hematological malignancy   No  177  7303  Referent  .050   Yes  21  452  1.65 (1.03 to 2.65)    Hodgkin lymphoma   No  166  6496  Referent  .369   Yes  2  34  2.10 (0.48 to 9.10)    Non-Hodgkin lymphoma   No  172  6837  Referent  .683   Yes  6  197  1.20 (0.52 to 2.76)    Leukemia||   No  165  6819  Referent  .018   Yes  13  215  2.19 (1.21 to 3.96)    Multiple myeloma   No  168  6496  Referent  .143   Yes  0  34  -    Family member malignancy type and relationship to case/control†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Any hematological malignancy   No  177  7303  Referent  .050   Yes  21  452  1.65 (1.03 to 2.65)    Hodgkin lymphoma   No  166  6496  Referent  .369   Yes  2  34  2.10 (0.48 to 9.10)    Non-Hodgkin lymphoma   No  172  6837  Referent  .683   Yes  6  197  1.20 (0.52 to 2.76)    Leukemia||   No  165  6819  Referent  .018   Yes  13  215  2.19 (1.21 to 3.96)    Multiple myeloma   No  168  6496  Referent  .143   Yes  0  34  -    * CI = confidence interval; OR = odds ratio. † Self-reported family history; some participants had more than one affected relative. ‡ The counts do not add up to the total number of cases/controls due to data missing by design or report. § Adjusted for age, sex, race/ethnicity, and study. || Leukemia includes chronic lymphocytic leukemia/small lymphocytic lymphoma. View Large Lifestyle Factors. Usual adult weight was inversely associated with risk of LPL/WM (P = .015); relative to the first quartile, LPL/WM risk was 0.68 (95% CI = 0.49 to 0.95) for the second quartile, 0.70 (95% CI = 0.52 to 0.95) for the third quartile, and 0.60 (95% CI = 0.44 to 0.83) for the fourth quartile. The same association was observed for body mass index as an adult (P = .034), but not body mass index as a young adult (P = .63; Table 4). Neither usual adult height nor physical activity predicted LPL/WM risk (Table 4). Table 4. Basic adjusted association between lifestyle factors and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Lifestyle factor  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Adult height        .731   Quartile 1 (low)  82  2787  Referent     Quartile 2  85  2816  0.97 (0.70 to 1.33)     Quartile 3  75  2747  0.82 (0.59 to 1.14)     Quartile 4 (high)  82  2873  0.93 (0.67 to 1.29)    Usual adult weight§   Quartile 1 (low)  88  2432  Referent  .015   Quartile 2  66  2377  0.68 (0.49 to 0.95)     Quartile 3  91  3115  0.70 (0.52 to 0.95)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    Usual adult BMI| (kg/m2)   15–<18.5  4  178  0.74 (0.26 to 2.10)     18.5–<22.5  67  2058  Referent  .034   22.5–<25  90  2659  0.86 (0.62 to 1.20)     25–<30  124  4335  0.69 (0.50 to 0.94)     30–<35  31  1454  0.56 (0.36 to 0.87)     35–50  8  539  0.43 (0.20 to 0.91)    BMI as a young adult (kg/m2)   15–<18.5  9  226  1.71 (0.79 to 3.70)     18.5–<22.5  36  1432  Referent  .624   22.5–<25  21  667  1.32 (0.74 to 2.35)     25–<30  8  362  0.93 (0.41 to 2.08)     30–50  1  82  0.86 (0.11 to 6.61)    Physical activity   None  18  686  Referent  .617   Mild  15  426  1.08 (0.49 to 2.37)     Moderate  30  883  0.95 (0.47 to 1.90)     Vigorous  55  3027  0.68 (0.38 to 1.22)    History of cigarette smoking||   No  118  4935  Referent  .076   Yes  188  5782  1.25 (0.98 to 1.59)    Cigarette smoking status   Nonsmoker  118  4935  Referent  .354   Former smoker  126  3616  1.22 (0.94 to 1.59)     Current smoker  59  2090  1.31 (0.95 to 1.82)     Smoker, status unknown  3  76  1.18 (0.28 to 4.93)    Age started smoking cigarettes   Nonsmoker  118  4935  Referent  .386   <14 years  15  514  1.16 (0.67 to 2.03)     14–<18 years  76  2401  1.21 (0.89 to 1.63)     18–<20 years  36  1224  1.11 (0.76 to 1.63)     ≥20 years  60  1583  1.45 (1.05 to 2.00)     Smoker, age started unknown  1  60  0.79 (0.11 to 5.82)    Years since quitting smoking   Nonsmoker  118  4935  Referent  .304   >25 years ago  38  1160  1.01 (0.69 to 1.48)     16–25 years ago  38  947  1.35 (0.92 to 1.97)     5–15 years ago  30  963  1.22 (0.81 to 1.84)     <5 years ago  16  496  1.38 (0.81 to 2.37)     Former smoker, unknown when quit  4  50  3.01 (1.05 to 8.67)     Current smoker  59  2090  1.32 (0.95 to 1.83)    Smoking frequency   Nonsmoker  118  4935  Referent  .086   ≤10 cigarettes/day  76  2125  1.37 (1.02 to 1.85)     11–20 cigarettes/day  71  2387  1.12 (0.83 to 1.53)     21–30 cigarettes/day  23  548  1.72 (1.07 to 2.77)     >30 cigarettes/day  11  529  0.76 (0.40 to 1.44)     Smoker, frequency unknown  7  193  1.45 (0.65 to 3.22)     Continuous (per-year)      1.00 (1.00 to 1.00)  .390  Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .324   1–20 years  44  2007  1.01 (0.71 to 1.44)     21–30 years  39  1258  1.23 (0.84 to 1.79)   30–39 years  46  1235  1.32 (0.93 to 1.89)     ≥40 years  57  1194  1.49 (1.06 to 2.09)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)     Continuous (per-year)      1.01 (1.00 to 1.02)  .022  Lifetime cigarette exposure   Nonsmoker  118  4935  Referent  .612   1–10 pack-years  52  1827  1.27 (0.91 to 1.78)     11–20 pack-years  35  1206  1.13 (0.76 to 1.66)     21–35 pack-years  44  1274  1.25 (0.87 to 1.78)     ≥36 pack-years  50  1247  1.33 (0.93 to 1.89)     Smoker, pack-years unknown  7  228  1.24 (0.56 to 2.75)     Continuous (per-year)      1.00 (1.00 to 1.01)  .376  History of alcohol consumption   Nondrinker  42  1960  Referent  .808   Drinker¶  135  4621  1.04 (0.71 to 1.53)    Alcohol consumption status   Nondrinker  42  1960  Referent  .812   Former drinker  16  583  1.45 (0.76 to 2.78)     Current drinker  78  3200  1.01 (0.62 to 1.65)     Drinker, status unknown  41  838  0.96 (0.51 to 1.82)    Lifestyle factor  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Adult height        .731   Quartile 1 (low)  82  2787  Referent     Quartile 2  85  2816  0.97 (0.70 to 1.33)     Quartile 3  75  2747  0.82 (0.59 to 1.14)     Quartile 4 (high)  82  2873  0.93 (0.67 to 1.29)    Usual adult weight§   Quartile 1 (low)  88  2432  Referent  .015   Quartile 2  66  2377  0.68 (0.49 to 0.95)     Quartile 3  91  3115  0.70 (0.52 to 0.95)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    Usual adult BMI| (kg/m2)   15–<18.5  4  178  0.74 (0.26 to 2.10)     18.5–<22.5  67  2058  Referent  .034   22.5–<25  90  2659  0.86 (0.62 to 1.20)     25–<30  124  4335  0.69 (0.50 to 0.94)     30–<35  31  1454  0.56 (0.36 to 0.87)     35–50  8  539  0.43 (0.20 to 0.91)    BMI as a young adult (kg/m2)   15–<18.5  9  226  1.71 (0.79 to 3.70)     18.5–<22.5  36  1432  Referent  .624   22.5–<25  21  667  1.32 (0.74 to 2.35)     25–<30  8  362  0.93 (0.41 to 2.08)     30–50  1  82  0.86 (0.11 to 6.61)    Physical activity   None  18  686  Referent  .617   Mild  15  426  1.08 (0.49 to 2.37)     Moderate  30  883  0.95 (0.47 to 1.90)     Vigorous  55  3027  0.68 (0.38 to 1.22)    History of cigarette smoking||   No  118  4935  Referent  .076   Yes  188  5782  1.25 (0.98 to 1.59)    Cigarette smoking status   Nonsmoker  118  4935  Referent  .354   Former smoker  126  3616  1.22 (0.94 to 1.59)     Current smoker  59  2090  1.31 (0.95 to 1.82)     Smoker, status unknown  3  76  1.18 (0.28 to 4.93)    Age started smoking cigarettes   Nonsmoker  118  4935  Referent  .386   <14 years  15  514  1.16 (0.67 to 2.03)     14–<18 years  76  2401  1.21 (0.89 to 1.63)     18–<20 years  36  1224  1.11 (0.76 to 1.63)     ≥20 years  60  1583  1.45 (1.05 to 2.00)     Smoker, age started unknown  1  60  0.79 (0.11 to 5.82)    Years since quitting smoking   Nonsmoker  118  4935  Referent  .304   >25 years ago  38  1160  1.01 (0.69 to 1.48)     16–25 years ago  38  947  1.35 (0.92 to 1.97)     5–15 years ago  30  963  1.22 (0.81 to 1.84)     <5 years ago  16  496  1.38 (0.81 to 2.37)     Former smoker, unknown when quit  4  50  3.01 (1.05 to 8.67)     Current smoker  59  2090  1.32 (0.95 to 1.83)    Smoking frequency   Nonsmoker  118  4935  Referent  .086   ≤10 cigarettes/day  76  2125  1.37 (1.02 to 1.85)     11–20 cigarettes/day  71  2387  1.12 (0.83 to 1.53)     21–30 cigarettes/day  23  548  1.72 (1.07 to 2.77)     >30 cigarettes/day  11  529  0.76 (0.40 to 1.44)     Smoker, frequency unknown  7  193  1.45 (0.65 to 3.22)     Continuous (per-year)      1.00 (1.00 to 1.00)  .390  Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .324   1–20 years  44  2007  1.01 (0.71 to 1.44)     21–30 years  39  1258  1.23 (0.84 to 1.79)   30–39 years  46  1235  1.32 (0.93 to 1.89)     ≥40 years  57  1194  1.49 (1.06 to 2.09)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)     Continuous (per-year)      1.01 (1.00 to 1.02)  .022  Lifetime cigarette exposure   Nonsmoker  118  4935  Referent  .612   1–10 pack-years  52  1827  1.27 (0.91 to 1.78)     11–20 pack-years  35  1206  1.13 (0.76 to 1.66)     21–35 pack-years  44  1274  1.25 (0.87 to 1.78)     ≥36 pack-years  50  1247  1.33 (0.93 to 1.89)     Smoker, pack-years unknown  7  228  1.24 (0.56 to 2.75)     Continuous (per-year)      1.00 (1.00 to 1.01)  .376  History of alcohol consumption   Nondrinker  42  1960  Referent  .808   Drinker¶  135  4621  1.04 (0.71 to 1.53)    Alcohol consumption status   Nondrinker  42  1960  Referent  .812   Former drinker  16  583  1.45 (0.76 to 2.78)     Current drinker  78  3200  1.01 (0.62 to 1.65)     Drinker, status unknown  41  838  0.96 (0.51 to 1.82)    * BMI = body mass index; CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Quartile 1 (<72.6kg males, <58.1kg females), quartile 2 (72.6–79.9kg males, 58.1–64.9kg females), quartile 3 (80.0–88.9kg males, 65.0–74.7kg females), and quartile 4 (≥89.0kg males, ≥74.8kg females). || Smoked longer than 6 months or more than 100 cigarettes in a lifetime. ¶ At least one drink per month. View Large Ever smoking cigarettes was unrelated to LPL/WM risk (OR = 1.25, 95% CI = 0.98 to 1.59). There was also no association with smoking status, years since quitting, pack-years, or frequency of smoking (Table 4). LPL/WM risk was elevated for those who started smoking when they were at least 20 years of age (OR = 1.45, 95% CI = 1.05 to 2.00, relative to nonsmokers). LPL/WM risk was also positively associated with duration of cigarette smoking, both when examined as a continuous variable (P = .022), and for the highest category of smoking duration (OR = 1.49, 95% CI = 1.06 to 2.09, for ≥ 40 years relative to nonsmokers; Table 4). LPL/WM risk was not associated with any measure of alcohol consumption, including ever drinking alcohol (OR = 1.04, 95% CI = 0.71 to 1.53), consumption 2 years before diagnosis/interview (Table 4), or age started, duration, servings per week as an adult, lifetime consumption, or beer, liquor, or wine consumption (data not shown). Sun exposure history was unrelated to LPL/WM risk, either total (OR = 0.86, 95% CI = 0.49 to 1.52 for highest vs. lowest quartile) or recreational (OR = 0.83, 95% CI = 0.59 to 1.18 for highest vs. lowest quartile). No measure of hair dye use in women predicted risk of LPL/WM (data not shown). Occupations. On the basis of four exposed cases (2.5%), LPL/WM risk was positively associated with occupation as a forestry worker (OR = 3.17, 95% CI = 1.08 to 9.34). There was no association between risk of LPL/WM and any other farming or animal-related occupation or farm residence (Table 5). Risk was increased for medical doctors (n = 6 exposed cases, OR = 5.23, 95% CI = 2.11 to 12.9), specifically those working in this occupation for more than 10 years (n = 5 exposed cases, OR = 12.7, 95% CI = 4.41 to 36.8). There was no association between LPL/WM risk and all medical occupations combined (OR = 1.42, 95% CI = 0.84 to 2.41). Table 5. Basic adjusted association between farm residence or farm/animal-related occupation and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Personal farm-related history  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Ever lived or worked on a farm   No  143  5424  Ref  .744   Yes  65  3170  0.95 (0.67 to 1.33)     Ever lived on a farm   No  66  3230  Ref  .262   Yes  45  2617  0.79 (0.53 to 1.19)     Ever worked on a farm   No  166  6727  Ref  .651   Yes  27  1210  0.90 (0.58 to 1.40)    Any farming occupation§   No  156  6189  Ref  .563   Yes  27  835  1.14 (0.73 to 1.78)     Animal farming   No  178  6847  Ref  .929   Yes  5  177  0.96 (0.38 to 2.41)     Crop farming   No  174  6757  Ref  .736   Yes  9  267  1.14 (0.55 to 2.37)     Field crop and vegetables   No  131  5520  Ref  .588   Yes  5  115  1.35 (0.48 to 3.78)     Mixed animal and crop   No  163  6036  Ref  .697   Yes  15  455  1.12 (0.64 to 1.96)     General farmer   No  152  5491  Ref  .519   Yes  10  272  1.26 (0.64 to 2.47)    Forestry worker   No  158  5740  Ref  .066||   Yes  4  34  3.17 (1.08 to 9.34)    Meat worker   No  181  6958  Ref  .960   Yes  2  66  1.03 (0.25 to 4.35)    Personal farm-related history  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Ever lived or worked on a farm   No  143  5424  Ref  .744   Yes  65  3170  0.95 (0.67 to 1.33)     Ever lived on a farm   No  66  3230  Ref  .262   Yes  45  2617  0.79 (0.53 to 1.19)     Ever worked on a farm   No  166  6727  Ref  .651   Yes  27  1210  0.90 (0.58 to 1.40)    Any farming occupation§   No  156  6189  Ref  .563   Yes  27  835  1.14 (0.73 to 1.78)     Animal farming   No  178  6847  Ref  .929   Yes  5  177  0.96 (0.38 to 2.41)     Crop farming   No  174  6757  Ref  .736   Yes  9  267  1.14 (0.55 to 2.37)     Field crop and vegetables   No  131  5520  Ref  .588   Yes  5  115  1.35 (0.48 to 3.78)     Mixed animal and crop   No  163  6036  Ref  .697   Yes  15  455  1.12 (0.64 to 1.96)     General farmer   No  152  5491  Ref  .519   Yes  10  272  1.26 (0.64 to 2.47)    Forestry worker   No  158  5740  Ref  .066||   Yes  4  34  3.17 (1.08 to 9.34)    Meat worker   No  181  6958  Ref  .960   Yes  2  66  1.03 (0.25 to 4.35)    * CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Occupation at any time in life (eight studies) or the longest held occupation (two studies); occupations coded according to the International Standard Classification of Occupations (ISCO), Revised Edition 1968 (53). || P value using likelihood ratio test; P value using Wald test .036. View Large Final Model We observed no strong evidence of confounding; factors that were statistically significantly associated with LPL/WM risk in basic adjusted models remained statistically significant in multivariate analysis and the point estimates were largely unattenuated after inclusion of the covariates (Table 6). Table 6. Factors associated with lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk, basic adjusted, and final stepwise model risk estimates* Factor  Cases, No.†  Controls, No.†  Basic adjusted model‡  Final model§  OR (95% CI)  P  OR (95% CI)  P  Sjögren syndrome   No  174  6079  Referent  .003  Referent§  .002   Yes  3  9  13.2 (3.42 to 50.9)    14.0 (3.60 to 54.6)    Systemic lupus erythematosus   No  270  10 470  Referent  .002  Referent  .003   Yes  4  24  8.73 (2.91 to 26.2)    8.23 (2.69 to 25.2)    Serology hepatitis C virus infection   Negative  201  5259  Referent  .050  Referent  .075||   Positive  6  95  2.70 (1.11 to 6.56)    2.51 (1.03 to 6.17)    Hay fever   No  218  7235  Referent  .022  Referent  .017   Yes  64  2511  0.70 (0.51 to 0.96)    0.73 (0.54 to 0.99)    Usual adult weight¶   Quartile 1 (low)  88  2432  Referent  .015  Referent  .024   Quartile 2  66  2377  0.68 (0.49 to 0.95)    0.71 (0.51 to 0.99)     Quartile 3  91  3115  0.70 (0.52 to 0.95)    0.72 (0.53 to 0.98)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    0.61 (0.44 to 0.85)    Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .145  Referent  .148   1–20 years  44  2007  1.01 (0.71 to 1.44)    1.01 (0.71 to 1.45)     21–30 years  39  1258  1.23 (0.84 to 1.79)    1.26 (0.86 to 1.84)     30–39 years  46  1235  1.32 (0.93 to 1.89)    1.35 (0.95 to 1.94)     ≥40 years  57  1194  1.49 (1.06 to 2.09)    1.46 (1.04 to 2.05)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)    1.10 (0.26 to 4.66)    Family history hematological malignancy   No  177  7303  Referent  .050  Referent  .060#   Yes  21  452  1.65 (1.03 to 2.65)    1.64 (1.02 to 2.64)    Occupation: medical doctor**   No  177  6970  Referent  .003  Referent  .002   Yes  6  43  5.23 (2.11 to 12.9)    5.54 (2.19 to 14.0)    Factor  Cases, No.†  Controls, No.†  Basic adjusted model‡  Final model§  OR (95% CI)  P  OR (95% CI)  P  Sjögren syndrome   No  174  6079  Referent  .003  Referent§  .002   Yes  3  9  13.2 (3.42 to 50.9)    14.0 (3.60 to 54.6)    Systemic lupus erythematosus   No  270  10 470  Referent  .002  Referent  .003   Yes  4  24  8.73 (2.91 to 26.2)    8.23 (2.69 to 25.2)    Serology hepatitis C virus infection   Negative  201  5259  Referent  .050  Referent  .075||   Positive  6  95  2.70 (1.11 to 6.56)    2.51 (1.03 to 6.17)    Hay fever   No  218  7235  Referent  .022  Referent  .017   Yes  64  2511  0.70 (0.51 to 0.96)    0.73 (0.54 to 0.99)    Usual adult weight¶   Quartile 1 (low)  88  2432  Referent  .015  Referent  .024   Quartile 2  66  2377  0.68 (0.49 to 0.95)    0.71 (0.51 to 0.99)     Quartile 3  91  3115  0.70 (0.52 to 0.95)    0.72 (0.53 to 0.98)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    0.61 (0.44 to 0.85)    Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .145  Referent  .148   1–20 years  44  2007  1.01 (0.71 to 1.44)    1.01 (0.71 to 1.45)     21–30 years  39  1258  1.23 (0.84 to 1.79)    1.26 (0.86 to 1.84)     30–39 years  46  1235  1.32 (0.93 to 1.89)    1.35 (0.95 to 1.94)     ≥40 years  57  1194  1.49 (1.06 to 2.09)    1.46 (1.04 to 2.05)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)    1.10 (0.26 to 4.66)    Family history hematological malignancy   No  177  7303  Referent  .050  Referent  .060#   Yes  21  452  1.65 (1.03 to 2.65)    1.64 (1.02 to 2.64)    Occupation: medical doctor**   No  177  6970  Referent  .003  Referent  .002   Yes  6  43  5.23 (2.11 to 12.9)    5.54 (2.19 to 14.0)    * CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Adjusted for age, sex, race/ethnicity, study, Sjögren syndrome, systemic lupus erythematosus, serology hepatitis C virus infection, hay fever, usual adult weight, smoking duration, family history of hematological malignancy, and medical occupation. || P value using likelihood ratio test; P value using Wald test .021. ¶ Quartile 1 (<72.6 kg males, <58.1 kg females), quartile 2 (72.6–79.9 kg males, 58.1–64.9 kg females), quartile 3 (80.0–88.9 kg males, 65.0–74.7 kg females), quartile 4 (≥89.0 kg males, ≥74.8 kg females). # Occupation at any time in life (eight studies) or the longest held occupation (two studies); occupations coded according to the International Standard Classification of Occupations (ISCO), Revised Edition 1968 (53); code 061 = medical doctor. View Large Discussion In a large-scale, international pooled case–control analysis of predominantly nonfamilial LPL/WM, risk was increased in those with a history of Sjögren’s syndrome, systemic lupus erythematosus, HCV infection, and a family history of hematologic malignancy. These findings support and extend prior studies suggesting a role for conditions characterized by chronic immune stimulation and genetic factors in LPL/WM pathogenesis. Novel findings were a decreased risk for history of hay fever and high usual adult weight, an increased risk for smoking cigarettes for 40 or more years, and occupation as a medical doctor, and no evidence of an association for other lifestyle factors and occupations. We observed a very strong association between LPL/WM risk and Sjögren’s syndrome, in agreement with previous large-scale medical record–based studies in the United States and Sweden (11,13). A marked increased risk was also observed for systemic lupus erythematosus, consistent with a report from one study in our pooled analysis (15), and a medical record study (13). Unlike the medical record studies, we did not find an increased risk for rheumatoid arthritis (11), Crohn’s disease (11), or autoimmune hemolytic anemia (13). However, our composite autoimmune variable indicated an increased risk for autoimmune conditions with activated B cells, but not activated T cells. This association is consistent with the finding that familial LPL/WM patients are twice as likely as unaffected relatives to report a history of autoimmune disorders (14). The pathogenic mechanism is believed to involve chronic antigen-driven inflammation and activated proliferating lymphocytes (36,37). We confirmed an association between LPL/WM risk and family history of hematologic malignancy (5–7). Our data favored an association with leukemia but not NHL, Hodgkin lymphoma, or multiple myeloma, in partial agreement with a case–control study (7) that reported coaggregation with chronic lymphocytic leukemia and NHL, but not Hodgkin lymphoma or multiple myeloma. This association is thought to suggest a role for common susceptibility genes, as supported by an increased risk of LPL/WM among first-degree relatives of people with monoclonal gammopathy of undetermined significance (9,38). Further, recent evidence of an association of both personal and family history of Sjögren’s syndrome and autoimmune hemolytic anemia with LPL/WM risk (13) supports a role for shared susceptibility for LPL/WM and certain autoimmune conditions. At this time, information on specific genes or genomic regions in LPL/WM susceptibility is limited (15,16,23). The five studies in our analysis with HCV serology data formed the basis of an earlier InterLymph report showing a statistically significant association between HCV and LPL (12), consistent with US (10,11), but not Swedish (13), medical record studies. The known association of HCV infection with type II mixed cryoglobulinemia and monoclonal gammopathy of undetermined significance (10), both of which increase LPL/WM risk (39,40), supports a true association between HCV and LPL/WM. In addition, antiviral treatment can be an effective first-line therapy for HCV-positive LPL (40). HCV infection is believed to promote lymphomagenesis via chronic immune stimulation and elevated IgM levels (10,41–43). Our finding of an inverse association between LPL/WM risk and personal history of hay fever is not consistent with prior studies that observed a positive (13,14) or no association (11,20) with individual allergic conditions or any allergy. Large-scale cohort study data are needed to clarify the relationship with this exposure (44). Even though the relationship has not been previously examined, our observation that LPL/WM risk appears lower for individuals with higher adult weight is unexpected. Not only is there evidence of an increased risk of NHL and some other NHL subtypes for those of higher adiposity (45–47), but obesity is a chronic low-grade inflammatory state characterized by lymphocyte proliferation (48). It is possible the association we observed is due to chance or selection bias. Uniquely to LPL/WM, however, IgM-producing B1 B cells are found in milky spots on the omentum and fat-associated lymphoid clusters on the mesentery [reviewed in (49)]. It is possible their physical proximity to adipose tissue uniquely affects their physiology and progression to LPL/WM. Although not entirely consistent across all of the smoking variables we examined, we found evidence of a weak positive association between LPL/WM risk and cigarette smoking. LPL/WM risk was increased 1.4-fold among those who had smoked for 40 or more years. Smoking history was not associated with LPL/WM risk in two previous studies, one based on 65 cases (20) and the other 103 cases (14). Although this finding requires confirmation in larger studies, a history of smoking has been weakly positively associated with other NHL subtypes (50), and an association is biologically plausible given the immunosuppressive effects of chronic cigarette smoke (51,52). We observed an elevated risk of LPL/WM for medical doctors but not health-care workers more generally. There is no prior evidence of such a relationship. We did not confirm the previously reported increased risk of familial LPL/WM and exposure to farming, pesticides, wood dust, and organic solvents (14); however, our analyses were limited to a small number of job titles rather than exposure to specific chemical compounds. This is the first pooled analysis of medical history, lifestyle, family history, and occupational risk factors for LPL/WM using the 2001 and 2008 WHO classification for LPL/WM and pathology report review. It is the only observational study of LPL/WM to examine the role of potential effect modification and confounding and, thus, determine the independence of these putative risk factors. The exposure data are of high quality and the findings are generalizable to predominantly white populations because most studies were population based and were conducted in Europe, North America, and Australia. Some limitations need to be considered. The study populations were predominantly Caucasian and the number of LPL/WM cases was relatively small, although this is one of the largest case–control interview-based studies of this rare lymphoma subtype to date. Given the rarity of LPL/WM, we undertook an exploratory analytical approach, without adjustment for multiple statistical tests. Lack of data on Ig levels, therapy for autoimmune diseases, duration and therapy for HCV, and history of infections other than HCV restricted our interpretation of some findings. We are also unable to exclude recall bias or reverse causality, with underlying LPL/WM misdiagnosed as Sjögren’s syndrome or systemic lupus erythematosus. Further, misdiagnosis of MALT or splenic marginal zone lymphoma as LPL/WM is an alternative explanation for the associations we observed with autoimmune disease and HCV infection. Finally, our occupational analyses are based on job titles, not exposure to specific agents. We have confirmed an association between LPL/WM risk and history of specific immune-stimulatory medical conditions and a family history of hematologic malignancy, and we have shown for the first time that these risk factors appear to be independent. These findings have future translational potential both biologically and clinically. Other novel findings, specifically the associations with hay fever, adult weight, smoking for 40 or more years, and occupation as a medical doctor, require confirmation in large-scale case–control studies of LPL/WM and long-term cohort studies of monoclonal gammopathy of undetermined significance. 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. Pooling of the occupation data was supported by the National Cancer Institute/National Institutes of Health (R03CA125831). 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, 2009SGR1465), 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 (IG 10068) (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 Institute, 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). 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Medical History, Lifestyle, Family History, and Occupational Risk Factors for Lymphoplasmacytic Lymphoma/Waldenström’s Macroglobulinemia: The InterLymph Non-Hodgkin Lymphoma Subtypes Project

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Published by Oxford University Press 2014.
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10.1093/jncimonographs/lgu002
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Abstract

Abstract Background Lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia (LPL/WM), a rare non-Hodgkin lymphoma subtype, shows strong familial aggregation and a positive association with chronic immune stimulation, but evidence regarding other risk factors is very limited. Methods The International Lymphoma Epidemiology Consortium (InterLymph) pooled data from 11 predominantly population-based case–control studies from North America, Europe, and Australia to examine medical history, lifestyle, family history, and occupational risk factors for LPL/WM. Age-, sex-, race/ethnicity-, and study-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression for a total of 374 LPL/WM cases and 23 096 controls. Results In multivariate analysis including all putative risk factors, LPL/WM risk was associated with history of Sjögren’s syndrome (OR = 14.0, 95% CI = 3.60 to 54.6), systemic lupus erythematosus (OR = 8.23, 95% CI = 2.69 to 25.2), hay fever (OR = 0.73, 95% CI = 0.54 to 0.99), positive hepatitis C serology (OR = 2.51, 95% CI = 1.03 to 6.17), hematologic malignancy in a first-degree relative (OR = 1.64, 95% CI = 1.02 to 2.64), adult weight (OR = 0.61, 95% CI = 0.44 to 0.85 for highest vs. lowest quartile), duration of cigarette smoking (OR = 1.46, 95% CI = 1.04 to 2.05 for ≥ 40 years vs. nonsmokers), and occupation as a medical doctor (OR = 5.54, 95% CI = 2.19 to 14.0). There was no association with other medical conditions, lifestyle factors, or occupations. Conclusions This pooled analysis confirmed associations with immune conditions and family history of hematologic malignancy, and identified new associations with hay fever, weight, smoking, and occupation, and no association with other lifestyle factors. These findings offer clues to LPL/WM biology and prevention. Lymphoplasmacytic lymphoma (LPL) is a non-Hodgkin lymphoma (NHL) subtype characterized by the proliferation of small B lymphocytes, plasmacytoid lymphocytes, and plasma cells. Most patients with LPL have IgM paraproteins and a minority have both IgM and IgG or other paraproteins, although this is not diagnostic (1). Waldenström’s macroglobulinemia (WM) is a clinicopathological subset of LPL defined as LPL with bone marrow involvement and monoclonal IgM gammopathy (2). LPL/WM is rare, with incidence estimates ranging from 0.031 to 0.043/100 000 person-years in Japan and Taiwan (3) to 0.63/100 000 person-years in the United States (4). Established risk factors for LPL/WM are older age, male gender, white race/ethnicity (4), family history of LPL/WM or another B-cell malignancy (5–7), and history of the precursor condition monoclonal gammopathy of undetermined significance of IgM class (8,9). Other putative risk factors are a history of infectious disease (10–14), autoimmune disease (11,13–15), allergies (13,14), and certain genetic characteristics (16–19). In one study, increased risk of familial LPL/WM (n = 103) was associated with farming and exposure to pesticides, wood dust, and organic solvents (14), whereas another identified no lifestyle or occupational risk factors based on 65 WM cases (20). Together these findings support a role for germline susceptibility genes, antigenic drive, chronic immune stimulation, and possibly occupational factors in LPL/WM carcinogenesis. The largest prior studies have used health record linkage (7,13); thus, there has been no comprehensive evaluation of risk factors for LPL/WM, particularly lifestyle and occupational exposures, and assessment in a multivariate setting. We investigated LPL/WM associations with medical and family history, lifestyle, and occupational risk factors in a pooled analysis of 374 cases and 23 096 controls from 11 case–control studies from Europe, North America, and Australia as part of the International Lymphoma Epidemiology Consortium (InterLymph) NHL Subtypes Project. Methods Study Design and Population Detailed methodology is provided elsewhere in this issue. Studies eligible for inclusion in this pooled analysis of 11 studies met the following criteria: 1) case–control design, with incident, histologically confirmed cases of LPL/WM, and 2) availability of individual-level data for at least several risk factors of interest by December 31, 2011. Seven studies were population based (21–27), one was a combination of population based and hospital based (28), and three were hospital based (29–31). Most studies excluded individuals with a history of solid organ transplantation or HIV/AIDS. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview. NHL Subtype Ascertainment and Harmonization All LPL/WM cases met the criteria for LPL/WM described by the World Health Organization classification (1,32) and satisfied the classification suggested subsequently by the InterLymph Pathology Working Group (33,34). Most studies confirmed diagnoses by centralized pathology review by at least one expert hematopathologist. Risk Factor Ascertainment and Harmonization Each study collected data on putative NHL risk factors in a standardized, structured format using self-administered questionnaires. Risk factor categories included medical, lifestyle, family, and occupations. A requirement for inclusion was that a factor was ascertained in a minimum of two studies that enrolled at least one case of LPL/WM. Centralized harmonization of individual-level, de-identified data from each study was performed. Each variable was harmonized across studies and then data were reviewed for consistency among related exposure variables. Statistical Analysis Risk of LPL/WM associated with each exposure variable was examined using unconditional logistic regression models adjusted for age, race/ethnicity, sex, and study (“basic adjusted model”). Individuals with missing data for the exposure variable were excluded. Statistical significance was evaluated by a likelihood ratio test, comparing models with and without the exposure variable, with P values less than .05 used to identify putatively influential factors to be considered for the multivariate model. When two highly correlated factors or exposures were significant, only one exposure was taken forward. In such instances, the most clinically meaningful or specific exposure was selected, for example, a specific autoimmune disease rather than the composite autoimmune disease variable. Further, if the P value for only one exposure from several related exposures was less than.05, it was not automatically examined in the multivariate model. In these cases, the balance of evidence was taken into account, including whether there was evidence of a dose–response. To evaluate potential effect of heterogeneity among the 11 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 (35) to categorical variables. 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 repeated the basic adjusted models and stratified individuals by age (<30, 30–39, 40–49, 50–59, 60–69, 70–79, and ≥80 years), sex, race/ethnicity, region (North America, Northern Europe, Southern Europe, and Australia), study, study design, and other putative risk factors identified in the analysis. To account for other potential confounders, we conducted two analyses. First, we evaluated the risk estimate for each putative risk factor in a series of models that adjusted for one other putative risk factor as well as age, sex, race/ethnicity, and study. Second, we assessed all putative risk factors in a multivariate logistic regression model, this time including a separate missing category for each variable to ensure that the whole study population was included in the analysis. Lastly, we conducted a forward stepwise logistic regression adding a single putative risk factor at a time, adjusting for age, sex, race/ethnicity, and study (“final model”). As controls for most original studies were chosen to frequency match the age and sex of all eligible lymphoma cases, rather than just LPL/WM, we conducted sensitivity analyses using a subset of controls that were frequency matched by age and sex to cases of LPL/WM. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls. We thus retained the full set of controls for our main analyses to maximize statistical power. Analyses were conducted using SAS software, version 9.2 (SAS Institute Inc, Cary, NC). Results A total of 374 LPL/WM cases (371 LPL and 3 WM; diagnosed 1995–2008) and 23 096 controls were included in this study. Most cases were identified in case–control studies in North America or Northern Europe (Table 1). Sixty-one percent of cases were men and the median age at diagnosis was 64 years (range 27–89). Compared with controls, cases were older and more likely to be men, but there was no difference by race/ethnicity or socioeconomic status (Table 1). Table 1. Characteristics of the pooled lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia case and control participants Characteristic  Cases, No. (%) (n = 374)  Controls, No. (%) (n = 23 096)  Age, y   <30  2 (0.5)  1306 (5.9)   30–39  7 (1.9)  2180 (9.4)   40–49  33 (8.8)  3159 (13.7)   50–59  90 (24.1)  4992 (21.6)   60–69  128 (34.2)  6380 (27.6)   70–79  96 (25.7)  4136 (17.9)   ≥80  17 (4.5)  873 (3.8)   Unknown  1 (0.3)  16 (0.1)  Sex   Male  227 (60.7)  13 495 (58.4)   Female  147 (39.3)  9601 (41.6)  Race/ethnicity   White non-Hispanic  352 (94.1)  21 576 (93.4)   Black  4 (1.1)  351 (1.5)   Asian  3 (0.8)  321 (1.4)   Hispanic  1 (0.3)  360 (1.6)   Other/unknown  14 (3.7)  488 (2.1)  Socioeconomic status*   Low  142 (38.0)  9335 (40.4)   Medium  108 (28.9)  6709 (29.0)   High  118 (31.6)  6642 (28.8)   Other/unknown  6 (1.6)  410 (1.8)  Region   North America  156 (41.7)  11 462 (49.6)   Northern Europe  156 (41.7)  6542 (28.3)   Southern Europe  35 (9.4)  4398 (19.0)   Australia  27 (7.2)  694 (3.0)  Study design   Population based  291 (77.8)  17 846 (77.3)   Hospital based  83 (22.2)  5250 (22.7)  Characteristic  Cases, No. (%) (n = 374)  Controls, No. (%) (n = 23 096)  Age, y   <30  2 (0.5)  1306 (5.9)   30–39  7 (1.9)  2180 (9.4)   40–49  33 (8.8)  3159 (13.7)   50–59  90 (24.1)  4992 (21.6)   60–69  128 (34.2)  6380 (27.6)   70–79  96 (25.7)  4136 (17.9)   ≥80  17 (4.5)  873 (3.8)   Unknown  1 (0.3)  16 (0.1)  Sex   Male  227 (60.7)  13 495 (58.4)   Female  147 (39.3)  9601 (41.6)  Race/ethnicity   White non-Hispanic  352 (94.1)  21 576 (93.4)   Black  4 (1.1)  351 (1.5)   Asian  3 (0.8)  321 (1.4)   Hispanic  1 (0.3)  360 (1.6)   Other/unknown  14 (3.7)  488 (2.1)  Socioeconomic status*   Low  142 (38.0)  9335 (40.4)   Medium  108 (28.9)  6709 (29.0)   High  118 (31.6)  6642 (28.8)   Other/unknown  6 (1.6)  410 (1.8)  Region   North America  156 (41.7)  11 462 (49.6)   Northern Europe  156 (41.7)  6542 (28.3)   Southern Europe  35 (9.4)  4398 (19.0)   Australia  27 (7.2)  694 (3.0)  Study design   Population based  291 (77.8)  17 846 (77.3)   Hospital based  83 (22.2)  5250 (22.7)  *Socioeconomic status was measured by years of education for studies in North America or by dividing measures of education or socioeconomic status into tertiles for studies in Europe or Australia. View Large There was no statistically significant between-study heterogeneity for any of the risk factors examined (data not shown), and there was no evidence of effect modification by study, study design factors, or the other putative risk factors examined (data not shown). Basic Adjusted Model Medical History. Twenty-five LPL/WM cases (7.2%) and 577 controls (4.6%) had a history of autoimmune disease (Table 2); two cases and 26 controls reported more than one. These two cases reported rheumatoid arthritis and Sjögren’s syndrome, as did two of the controls. Individually, Sjögren’s syndrome and systemic lupus erythematosus were very strongly associated with LPL/WM risk, but the case numbers were too small to examine the relationship with disease latency (n = 3 and n = 4, respectively; Table 2). There was no association between LPL/WM risk and history of other selected autoimmune conditions (Table 2). Risk was strongly increased in association with autoimmune disease characterized by B-cell activation but not T-cell activation (Table 2). LPL/WM risk was inversely associated with a history of hay fever (odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.51 to 0.96) but was unrelated to history of asthma, eczema, any specific allergy, or any atopic condition (Table 2). Table 2. Basic adjusted association between personal history of autoimmune or allergic disease and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia (LPL/WM) risk* Disease†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Autoimmune diseases   Sjögren syndrome   No  174  6079  Referent  .003   Yes  3  9  13.2 (3.42 to 50.9)     Systemic lupus erythematosus   No  270  10 470  Referent  .002   Yes  4  24  8.73 (2.91 to 26.2)     Myasthenia gravis   No  228  7506  Referent  .171   Yes  1  4  6.60 (0.70 to 62.3)     Sarcoidosis   No  318  9379  Referent  .362   Yes  2  27  2.11 (0.49 to 9.01)     Rheumatoid arthritis||   No  208  6368  Referent  .474   Yes  4  82  1.48 (0.53 to 4.13)     Celiac disease   No  268  7593  Referent  .790   Yes  1  25  1.33 (0.18 to 10.1)     Crohn’s disease   No  315  9787  Referent  .994   Yes  1  29  0.99 (0.13 to 7.48)     Type 1 diabetes   No  207  7402  Referent  .967   Yes  1  43  0.96 (0.13 to 7.09)     Psoriasis   No  199  7333  Referent  .819   Yes  7  228  0.92 (0.42 to 1.98)     Inflammatory bowel disorder   No  343  10 932  Referent  .825   Yes  4  111  0.89 (0.32 to 2.47)     Ulcerative colitis   No  279  7777  Referent  .849   Yes  3  81  0.89 (0.28 to 2.89)     Polymyositis or dermatomyositis   No  61  3580  Referent  .419   Yes  0  18  —     Multiple sclerosis   No  314  9365  Referent  .332   Yes  0  13  —     Pernicious anemia   No  71  3146  Referent  .401   Yes  0  8  —     Hemolytic anemia   No  94  3793  Referent  .562   Yes  0  7  —     Systemic sclerosis or scleroderma   No  142  3848  Referent  .576   Yes  0  4  —     Any autoimmune disease¶   None  349  11 911  Referent  .054   B-cell activation  10  121  2.78 (1.43 to 5.43)     T-cell activation  15  444  1.02 (0.60 to 1.74)     Both B- and T-cell activation  0  12  —    Atopic disorders   Hay fever   No  218  7235  Referent  .022   Yes  64  2511  0.70 (0.51 to 0.96)     Asthma   No  295  9923  Referent  .932   Yes  33  1075  0.98 (0.68 to 1.42)     Eczema   No  309  9941  Referent  .891   Yes  36  1291  0.98 (0.68 to 1.39)     Any specific allergy#   No  226  7233  Referent  .409   Yes  92  3168  0.90 (0.69 to 1.16)     Food allergy   No  274  8939  Referent  .157   Yes  20  908  0.72 (0.45 to 1.16)     Any atopic disorder**   No  225  7392  Referent  .229   Yes  140  4868  0.87 (0.70 to 1.09)    Disease†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Autoimmune diseases   Sjögren syndrome   No  174  6079  Referent  .003   Yes  3  9  13.2 (3.42 to 50.9)     Systemic lupus erythematosus   No  270  10 470  Referent  .002   Yes  4  24  8.73 (2.91 to 26.2)     Myasthenia gravis   No  228  7506  Referent  .171   Yes  1  4  6.60 (0.70 to 62.3)     Sarcoidosis   No  318  9379  Referent  .362   Yes  2  27  2.11 (0.49 to 9.01)     Rheumatoid arthritis||   No  208  6368  Referent  .474   Yes  4  82  1.48 (0.53 to 4.13)     Celiac disease   No  268  7593  Referent  .790   Yes  1  25  1.33 (0.18 to 10.1)     Crohn’s disease   No  315  9787  Referent  .994   Yes  1  29  0.99 (0.13 to 7.48)     Type 1 diabetes   No  207  7402  Referent  .967   Yes  1  43  0.96 (0.13 to 7.09)     Psoriasis   No  199  7333  Referent  .819   Yes  7  228  0.92 (0.42 to 1.98)     Inflammatory bowel disorder   No  343  10 932  Referent  .825   Yes  4  111  0.89 (0.32 to 2.47)     Ulcerative colitis   No  279  7777  Referent  .849   Yes  3  81  0.89 (0.28 to 2.89)     Polymyositis or dermatomyositis   No  61  3580  Referent  .419   Yes  0  18  —     Multiple sclerosis   No  314  9365  Referent  .332   Yes  0  13  —     Pernicious anemia   No  71  3146  Referent  .401   Yes  0  8  —     Hemolytic anemia   No  94  3793  Referent  .562   Yes  0  7  —     Systemic sclerosis or scleroderma   No  142  3848  Referent  .576   Yes  0  4  —     Any autoimmune disease¶   None  349  11 911  Referent  .054   B-cell activation  10  121  2.78 (1.43 to 5.43)     T-cell activation  15  444  1.02 (0.60 to 1.74)     Both B- and T-cell activation  0  12  —    Atopic disorders   Hay fever   No  218  7235  Referent  .022   Yes  64  2511  0.70 (0.51 to 0.96)     Asthma   No  295  9923  Referent  .932   Yes  33  1075  0.98 (0.68 to 1.42)     Eczema   No  309  9941  Referent  .891   Yes  36  1291  0.98 (0.68 to 1.39)     Any specific allergy#   No  226  7233  Referent  .409   Yes  92  3168  0.90 (0.69 to 1.16)     Food allergy   No  274  8939  Referent  .157   Yes  20  908  0.72 (0.45 to 1.16)     Any atopic disorder**   No  225  7392  Referent  .229   Yes  140  4868  0.87 (0.70 to 1.09)    * CI = confidence interval; OR = odds ratio. † Self-reported condition diagnosed at least 2 years before LPL/WM diagnosis/interview. ‡ The counts do not add up to the total number of cases/controls due to data missing by design or report. § Adjusted for age, sex, race/ethnicity, and study. || Only those who also reported receiving corticosteroid or immunosuppressive treatment for rheumatoid arthritis. ¶ Includes self-reported history of specific autoimmune diseases occurring ≥2 years before diagnosis/interview (except the New South Wales study, which did not ascertain date of onset). Autoimmune diseases were classified according to whether they are primarily mediated by B-cell or T-cell responses. 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. # Any specific allergy included plant, food, animal, dust, insect, or mold and excluded drug allergies, asthma, eczema, and hay fever. **Atopic disorders include asthma, eczema, hay fever, or other allergies, excluding drug allergies. View Large LPL/WM risk was strongly increased in association with positive serology for hepatitis C virus (HCV) infection (OR = 2.70, 95% CI = 1.11 to 6.56; n = 6). No other infectious diseases were examined. Risk was not associated with the receipt of one or more blood transfusions (OR = 1.06, 95% CI = 0.74 to 1.53), transfusion age, transfusion number, or year of transfusion (data not shown). Neither history of gastric ulcer nor peptic ulcer predicted LPL/WM risk (data not shown). LPL/WM risk was associated with the number of children (P = .023); relative to women with a single child, risk was decreased for women with no children (OR = 0.32, 95% CI = 0.12 to 0.87) and two children (OR = 0.34, 95% CI = 0.15 to 0.77) but was attenuated and not statistically significant for three or more children (OR = 0.63, 95% CI = 0.32 to 1.22). As LPL/WM risk was not associated with time since the last birth, oral contraceptive use, the age contraceptives were first used, hormone replacement therapy, or the age hormone replacement therapy was first used (data not shown), this variable was not taken forward to multivariable analysis. Family History. Hematological malignancy in one or more first-degree relatives was reported by 21 cases (10.6%) and 452 controls (5.8%). Risk was moderately increased for having a family member with history of any hematological malignancy (OR = 1.65, 95% CI = 1.03 to 2.65) or with leukemia (OR = 2.19, 95% CI = 1.21 to 3.96). There was no association between LPL/WM risk and family history of Hodgkin lymphoma, NHL, or multiple myeloma (Table 3). Table 3. Basic adjusted association between first-degree family history of hematological malignancy and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Family member malignancy type and relationship to case/control†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Any hematological malignancy   No  177  7303  Referent  .050   Yes  21  452  1.65 (1.03 to 2.65)    Hodgkin lymphoma   No  166  6496  Referent  .369   Yes  2  34  2.10 (0.48 to 9.10)    Non-Hodgkin lymphoma   No  172  6837  Referent  .683   Yes  6  197  1.20 (0.52 to 2.76)    Leukemia||   No  165  6819  Referent  .018   Yes  13  215  2.19 (1.21 to 3.96)    Multiple myeloma   No  168  6496  Referent  .143   Yes  0  34  -    Family member malignancy type and relationship to case/control†  Cases, No.‡  Controls, No.‡  OR (95% CI)§  P  Any hematological malignancy   No  177  7303  Referent  .050   Yes  21  452  1.65 (1.03 to 2.65)    Hodgkin lymphoma   No  166  6496  Referent  .369   Yes  2  34  2.10 (0.48 to 9.10)    Non-Hodgkin lymphoma   No  172  6837  Referent  .683   Yes  6  197  1.20 (0.52 to 2.76)    Leukemia||   No  165  6819  Referent  .018   Yes  13  215  2.19 (1.21 to 3.96)    Multiple myeloma   No  168  6496  Referent  .143   Yes  0  34  -    * CI = confidence interval; OR = odds ratio. † Self-reported family history; some participants had more than one affected relative. ‡ The counts do not add up to the total number of cases/controls due to data missing by design or report. § Adjusted for age, sex, race/ethnicity, and study. || Leukemia includes chronic lymphocytic leukemia/small lymphocytic lymphoma. View Large Lifestyle Factors. Usual adult weight was inversely associated with risk of LPL/WM (P = .015); relative to the first quartile, LPL/WM risk was 0.68 (95% CI = 0.49 to 0.95) for the second quartile, 0.70 (95% CI = 0.52 to 0.95) for the third quartile, and 0.60 (95% CI = 0.44 to 0.83) for the fourth quartile. The same association was observed for body mass index as an adult (P = .034), but not body mass index as a young adult (P = .63; Table 4). Neither usual adult height nor physical activity predicted LPL/WM risk (Table 4). Table 4. Basic adjusted association between lifestyle factors and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Lifestyle factor  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Adult height        .731   Quartile 1 (low)  82  2787  Referent     Quartile 2  85  2816  0.97 (0.70 to 1.33)     Quartile 3  75  2747  0.82 (0.59 to 1.14)     Quartile 4 (high)  82  2873  0.93 (0.67 to 1.29)    Usual adult weight§   Quartile 1 (low)  88  2432  Referent  .015   Quartile 2  66  2377  0.68 (0.49 to 0.95)     Quartile 3  91  3115  0.70 (0.52 to 0.95)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    Usual adult BMI| (kg/m2)   15–<18.5  4  178  0.74 (0.26 to 2.10)     18.5–<22.5  67  2058  Referent  .034   22.5–<25  90  2659  0.86 (0.62 to 1.20)     25–<30  124  4335  0.69 (0.50 to 0.94)     30–<35  31  1454  0.56 (0.36 to 0.87)     35–50  8  539  0.43 (0.20 to 0.91)    BMI as a young adult (kg/m2)   15–<18.5  9  226  1.71 (0.79 to 3.70)     18.5–<22.5  36  1432  Referent  .624   22.5–<25  21  667  1.32 (0.74 to 2.35)     25–<30  8  362  0.93 (0.41 to 2.08)     30–50  1  82  0.86 (0.11 to 6.61)    Physical activity   None  18  686  Referent  .617   Mild  15  426  1.08 (0.49 to 2.37)     Moderate  30  883  0.95 (0.47 to 1.90)     Vigorous  55  3027  0.68 (0.38 to 1.22)    History of cigarette smoking||   No  118  4935  Referent  .076   Yes  188  5782  1.25 (0.98 to 1.59)    Cigarette smoking status   Nonsmoker  118  4935  Referent  .354   Former smoker  126  3616  1.22 (0.94 to 1.59)     Current smoker  59  2090  1.31 (0.95 to 1.82)     Smoker, status unknown  3  76  1.18 (0.28 to 4.93)    Age started smoking cigarettes   Nonsmoker  118  4935  Referent  .386   <14 years  15  514  1.16 (0.67 to 2.03)     14–<18 years  76  2401  1.21 (0.89 to 1.63)     18–<20 years  36  1224  1.11 (0.76 to 1.63)     ≥20 years  60  1583  1.45 (1.05 to 2.00)     Smoker, age started unknown  1  60  0.79 (0.11 to 5.82)    Years since quitting smoking   Nonsmoker  118  4935  Referent  .304   >25 years ago  38  1160  1.01 (0.69 to 1.48)     16–25 years ago  38  947  1.35 (0.92 to 1.97)     5–15 years ago  30  963  1.22 (0.81 to 1.84)     <5 years ago  16  496  1.38 (0.81 to 2.37)     Former smoker, unknown when quit  4  50  3.01 (1.05 to 8.67)     Current smoker  59  2090  1.32 (0.95 to 1.83)    Smoking frequency   Nonsmoker  118  4935  Referent  .086   ≤10 cigarettes/day  76  2125  1.37 (1.02 to 1.85)     11–20 cigarettes/day  71  2387  1.12 (0.83 to 1.53)     21–30 cigarettes/day  23  548  1.72 (1.07 to 2.77)     >30 cigarettes/day  11  529  0.76 (0.40 to 1.44)     Smoker, frequency unknown  7  193  1.45 (0.65 to 3.22)     Continuous (per-year)      1.00 (1.00 to 1.00)  .390  Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .324   1–20 years  44  2007  1.01 (0.71 to 1.44)     21–30 years  39  1258  1.23 (0.84 to 1.79)   30–39 years  46  1235  1.32 (0.93 to 1.89)     ≥40 years  57  1194  1.49 (1.06 to 2.09)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)     Continuous (per-year)      1.01 (1.00 to 1.02)  .022  Lifetime cigarette exposure   Nonsmoker  118  4935  Referent  .612   1–10 pack-years  52  1827  1.27 (0.91 to 1.78)     11–20 pack-years  35  1206  1.13 (0.76 to 1.66)     21–35 pack-years  44  1274  1.25 (0.87 to 1.78)     ≥36 pack-years  50  1247  1.33 (0.93 to 1.89)     Smoker, pack-years unknown  7  228  1.24 (0.56 to 2.75)     Continuous (per-year)      1.00 (1.00 to 1.01)  .376  History of alcohol consumption   Nondrinker  42  1960  Referent  .808   Drinker¶  135  4621  1.04 (0.71 to 1.53)    Alcohol consumption status   Nondrinker  42  1960  Referent  .812   Former drinker  16  583  1.45 (0.76 to 2.78)     Current drinker  78  3200  1.01 (0.62 to 1.65)     Drinker, status unknown  41  838  0.96 (0.51 to 1.82)    Lifestyle factor  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Adult height        .731   Quartile 1 (low)  82  2787  Referent     Quartile 2  85  2816  0.97 (0.70 to 1.33)     Quartile 3  75  2747  0.82 (0.59 to 1.14)     Quartile 4 (high)  82  2873  0.93 (0.67 to 1.29)    Usual adult weight§   Quartile 1 (low)  88  2432  Referent  .015   Quartile 2  66  2377  0.68 (0.49 to 0.95)     Quartile 3  91  3115  0.70 (0.52 to 0.95)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    Usual adult BMI| (kg/m2)   15–<18.5  4  178  0.74 (0.26 to 2.10)     18.5–<22.5  67  2058  Referent  .034   22.5–<25  90  2659  0.86 (0.62 to 1.20)     25–<30  124  4335  0.69 (0.50 to 0.94)     30–<35  31  1454  0.56 (0.36 to 0.87)     35–50  8  539  0.43 (0.20 to 0.91)    BMI as a young adult (kg/m2)   15–<18.5  9  226  1.71 (0.79 to 3.70)     18.5–<22.5  36  1432  Referent  .624   22.5–<25  21  667  1.32 (0.74 to 2.35)     25–<30  8  362  0.93 (0.41 to 2.08)     30–50  1  82  0.86 (0.11 to 6.61)    Physical activity   None  18  686  Referent  .617   Mild  15  426  1.08 (0.49 to 2.37)     Moderate  30  883  0.95 (0.47 to 1.90)     Vigorous  55  3027  0.68 (0.38 to 1.22)    History of cigarette smoking||   No  118  4935  Referent  .076   Yes  188  5782  1.25 (0.98 to 1.59)    Cigarette smoking status   Nonsmoker  118  4935  Referent  .354   Former smoker  126  3616  1.22 (0.94 to 1.59)     Current smoker  59  2090  1.31 (0.95 to 1.82)     Smoker, status unknown  3  76  1.18 (0.28 to 4.93)    Age started smoking cigarettes   Nonsmoker  118  4935  Referent  .386   <14 years  15  514  1.16 (0.67 to 2.03)     14–<18 years  76  2401  1.21 (0.89 to 1.63)     18–<20 years  36  1224  1.11 (0.76 to 1.63)     ≥20 years  60  1583  1.45 (1.05 to 2.00)     Smoker, age started unknown  1  60  0.79 (0.11 to 5.82)    Years since quitting smoking   Nonsmoker  118  4935  Referent  .304   >25 years ago  38  1160  1.01 (0.69 to 1.48)     16–25 years ago  38  947  1.35 (0.92 to 1.97)     5–15 years ago  30  963  1.22 (0.81 to 1.84)     <5 years ago  16  496  1.38 (0.81 to 2.37)     Former smoker, unknown when quit  4  50  3.01 (1.05 to 8.67)     Current smoker  59  2090  1.32 (0.95 to 1.83)    Smoking frequency   Nonsmoker  118  4935  Referent  .086   ≤10 cigarettes/day  76  2125  1.37 (1.02 to 1.85)     11–20 cigarettes/day  71  2387  1.12 (0.83 to 1.53)     21–30 cigarettes/day  23  548  1.72 (1.07 to 2.77)     >30 cigarettes/day  11  529  0.76 (0.40 to 1.44)     Smoker, frequency unknown  7  193  1.45 (0.65 to 3.22)     Continuous (per-year)      1.00 (1.00 to 1.00)  .390  Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .324   1–20 years  44  2007  1.01 (0.71 to 1.44)     21–30 years  39  1258  1.23 (0.84 to 1.79)   30–39 years  46  1235  1.32 (0.93 to 1.89)     ≥40 years  57  1194  1.49 (1.06 to 2.09)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)     Continuous (per-year)      1.01 (1.00 to 1.02)  .022  Lifetime cigarette exposure   Nonsmoker  118  4935  Referent  .612   1–10 pack-years  52  1827  1.27 (0.91 to 1.78)     11–20 pack-years  35  1206  1.13 (0.76 to 1.66)     21–35 pack-years  44  1274  1.25 (0.87 to 1.78)     ≥36 pack-years  50  1247  1.33 (0.93 to 1.89)     Smoker, pack-years unknown  7  228  1.24 (0.56 to 2.75)     Continuous (per-year)      1.00 (1.00 to 1.01)  .376  History of alcohol consumption   Nondrinker  42  1960  Referent  .808   Drinker¶  135  4621  1.04 (0.71 to 1.53)    Alcohol consumption status   Nondrinker  42  1960  Referent  .812   Former drinker  16  583  1.45 (0.76 to 2.78)     Current drinker  78  3200  1.01 (0.62 to 1.65)     Drinker, status unknown  41  838  0.96 (0.51 to 1.82)    * BMI = body mass index; CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Quartile 1 (<72.6kg males, <58.1kg females), quartile 2 (72.6–79.9kg males, 58.1–64.9kg females), quartile 3 (80.0–88.9kg males, 65.0–74.7kg females), and quartile 4 (≥89.0kg males, ≥74.8kg females). || Smoked longer than 6 months or more than 100 cigarettes in a lifetime. ¶ At least one drink per month. View Large Ever smoking cigarettes was unrelated to LPL/WM risk (OR = 1.25, 95% CI = 0.98 to 1.59). There was also no association with smoking status, years since quitting, pack-years, or frequency of smoking (Table 4). LPL/WM risk was elevated for those who started smoking when they were at least 20 years of age (OR = 1.45, 95% CI = 1.05 to 2.00, relative to nonsmokers). LPL/WM risk was also positively associated with duration of cigarette smoking, both when examined as a continuous variable (P = .022), and for the highest category of smoking duration (OR = 1.49, 95% CI = 1.06 to 2.09, for ≥ 40 years relative to nonsmokers; Table 4). LPL/WM risk was not associated with any measure of alcohol consumption, including ever drinking alcohol (OR = 1.04, 95% CI = 0.71 to 1.53), consumption 2 years before diagnosis/interview (Table 4), or age started, duration, servings per week as an adult, lifetime consumption, or beer, liquor, or wine consumption (data not shown). Sun exposure history was unrelated to LPL/WM risk, either total (OR = 0.86, 95% CI = 0.49 to 1.52 for highest vs. lowest quartile) or recreational (OR = 0.83, 95% CI = 0.59 to 1.18 for highest vs. lowest quartile). No measure of hair dye use in women predicted risk of LPL/WM (data not shown). Occupations. On the basis of four exposed cases (2.5%), LPL/WM risk was positively associated with occupation as a forestry worker (OR = 3.17, 95% CI = 1.08 to 9.34). There was no association between risk of LPL/WM and any other farming or animal-related occupation or farm residence (Table 5). Risk was increased for medical doctors (n = 6 exposed cases, OR = 5.23, 95% CI = 2.11 to 12.9), specifically those working in this occupation for more than 10 years (n = 5 exposed cases, OR = 12.7, 95% CI = 4.41 to 36.8). There was no association between LPL/WM risk and all medical occupations combined (OR = 1.42, 95% CI = 0.84 to 2.41). Table 5. Basic adjusted association between farm residence or farm/animal-related occupation and lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk* Personal farm-related history  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Ever lived or worked on a farm   No  143  5424  Ref  .744   Yes  65  3170  0.95 (0.67 to 1.33)     Ever lived on a farm   No  66  3230  Ref  .262   Yes  45  2617  0.79 (0.53 to 1.19)     Ever worked on a farm   No  166  6727  Ref  .651   Yes  27  1210  0.90 (0.58 to 1.40)    Any farming occupation§   No  156  6189  Ref  .563   Yes  27  835  1.14 (0.73 to 1.78)     Animal farming   No  178  6847  Ref  .929   Yes  5  177  0.96 (0.38 to 2.41)     Crop farming   No  174  6757  Ref  .736   Yes  9  267  1.14 (0.55 to 2.37)     Field crop and vegetables   No  131  5520  Ref  .588   Yes  5  115  1.35 (0.48 to 3.78)     Mixed animal and crop   No  163  6036  Ref  .697   Yes  15  455  1.12 (0.64 to 1.96)     General farmer   No  152  5491  Ref  .519   Yes  10  272  1.26 (0.64 to 2.47)    Forestry worker   No  158  5740  Ref  .066||   Yes  4  34  3.17 (1.08 to 9.34)    Meat worker   No  181  6958  Ref  .960   Yes  2  66  1.03 (0.25 to 4.35)    Personal farm-related history  Cases, No.†  Controls, No.†  OR (95% CI)‡  P  Ever lived or worked on a farm   No  143  5424  Ref  .744   Yes  65  3170  0.95 (0.67 to 1.33)     Ever lived on a farm   No  66  3230  Ref  .262   Yes  45  2617  0.79 (0.53 to 1.19)     Ever worked on a farm   No  166  6727  Ref  .651   Yes  27  1210  0.90 (0.58 to 1.40)    Any farming occupation§   No  156  6189  Ref  .563   Yes  27  835  1.14 (0.73 to 1.78)     Animal farming   No  178  6847  Ref  .929   Yes  5  177  0.96 (0.38 to 2.41)     Crop farming   No  174  6757  Ref  .736   Yes  9  267  1.14 (0.55 to 2.37)     Field crop and vegetables   No  131  5520  Ref  .588   Yes  5  115  1.35 (0.48 to 3.78)     Mixed animal and crop   No  163  6036  Ref  .697   Yes  15  455  1.12 (0.64 to 1.96)     General farmer   No  152  5491  Ref  .519   Yes  10  272  1.26 (0.64 to 2.47)    Forestry worker   No  158  5740  Ref  .066||   Yes  4  34  3.17 (1.08 to 9.34)    Meat worker   No  181  6958  Ref  .960   Yes  2  66  1.03 (0.25 to 4.35)    * CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Occupation at any time in life (eight studies) or the longest held occupation (two studies); occupations coded according to the International Standard Classification of Occupations (ISCO), Revised Edition 1968 (53). || P value using likelihood ratio test; P value using Wald test .036. View Large Final Model We observed no strong evidence of confounding; factors that were statistically significantly associated with LPL/WM risk in basic adjusted models remained statistically significant in multivariate analysis and the point estimates were largely unattenuated after inclusion of the covariates (Table 6). Table 6. Factors associated with lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia risk, basic adjusted, and final stepwise model risk estimates* Factor  Cases, No.†  Controls, No.†  Basic adjusted model‡  Final model§  OR (95% CI)  P  OR (95% CI)  P  Sjögren syndrome   No  174  6079  Referent  .003  Referent§  .002   Yes  3  9  13.2 (3.42 to 50.9)    14.0 (3.60 to 54.6)    Systemic lupus erythematosus   No  270  10 470  Referent  .002  Referent  .003   Yes  4  24  8.73 (2.91 to 26.2)    8.23 (2.69 to 25.2)    Serology hepatitis C virus infection   Negative  201  5259  Referent  .050  Referent  .075||   Positive  6  95  2.70 (1.11 to 6.56)    2.51 (1.03 to 6.17)    Hay fever   No  218  7235  Referent  .022  Referent  .017   Yes  64  2511  0.70 (0.51 to 0.96)    0.73 (0.54 to 0.99)    Usual adult weight¶   Quartile 1 (low)  88  2432  Referent  .015  Referent  .024   Quartile 2  66  2377  0.68 (0.49 to 0.95)    0.71 (0.51 to 0.99)     Quartile 3  91  3115  0.70 (0.52 to 0.95)    0.72 (0.53 to 0.98)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    0.61 (0.44 to 0.85)    Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .145  Referent  .148   1–20 years  44  2007  1.01 (0.71 to 1.44)    1.01 (0.71 to 1.45)     21–30 years  39  1258  1.23 (0.84 to 1.79)    1.26 (0.86 to 1.84)     30–39 years  46  1235  1.32 (0.93 to 1.89)    1.35 (0.95 to 1.94)     ≥40 years  57  1194  1.49 (1.06 to 2.09)    1.46 (1.04 to 2.05)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)    1.10 (0.26 to 4.66)    Family history hematological malignancy   No  177  7303  Referent  .050  Referent  .060#   Yes  21  452  1.65 (1.03 to 2.65)    1.64 (1.02 to 2.64)    Occupation: medical doctor**   No  177  6970  Referent  .003  Referent  .002   Yes  6  43  5.23 (2.11 to 12.9)    5.54 (2.19 to 14.0)    Factor  Cases, No.†  Controls, No.†  Basic adjusted model‡  Final model§  OR (95% CI)  P  OR (95% CI)  P  Sjögren syndrome   No  174  6079  Referent  .003  Referent§  .002   Yes  3  9  13.2 (3.42 to 50.9)    14.0 (3.60 to 54.6)    Systemic lupus erythematosus   No  270  10 470  Referent  .002  Referent  .003   Yes  4  24  8.73 (2.91 to 26.2)    8.23 (2.69 to 25.2)    Serology hepatitis C virus infection   Negative  201  5259  Referent  .050  Referent  .075||   Positive  6  95  2.70 (1.11 to 6.56)    2.51 (1.03 to 6.17)    Hay fever   No  218  7235  Referent  .022  Referent  .017   Yes  64  2511  0.70 (0.51 to 0.96)    0.73 (0.54 to 0.99)    Usual adult weight¶   Quartile 1 (low)  88  2432  Referent  .015  Referent  .024   Quartile 2  66  2377  0.68 (0.49 to 0.95)    0.71 (0.51 to 0.99)     Quartile 3  91  3115  0.70 (0.52 to 0.95)    0.72 (0.53 to 0.98)     Quartile 4 (high)  79  3299  0.60 (0.44 to 0.83)    0.61 (0.44 to 0.85)    Duration of cigarette smoking   Nonsmoker  118  4935  Referent  .145  Referent  .148   1–20 years  44  2007  1.01 (0.71 to 1.44)    1.01 (0.71 to 1.45)     21–30 years  39  1258  1.23 (0.84 to 1.79)    1.26 (0.86 to 1.84)     30–39 years  46  1235  1.32 (0.93 to 1.89)    1.35 (0.95 to 1.94)     ≥40 years  57  1194  1.49 (1.06 to 2.09)    1.46 (1.04 to 2.05)     Smoker, duration unknown  2  88  1.14 (0.27 to 4.75)    1.10 (0.26 to 4.66)    Family history hematological malignancy   No  177  7303  Referent  .050  Referent  .060#   Yes  21  452  1.65 (1.03 to 2.65)    1.64 (1.02 to 2.64)    Occupation: medical doctor**   No  177  6970  Referent  .003  Referent  .002   Yes  6  43  5.23 (2.11 to 12.9)    5.54 (2.19 to 14.0)    * CI = confidence interval; OR = odds ratio. † The counts do not add up to the total number of cases/controls due to data missing by design or report. ‡ Adjusted for age, sex, race/ethnicity, and study. § Adjusted for age, sex, race/ethnicity, study, Sjögren syndrome, systemic lupus erythematosus, serology hepatitis C virus infection, hay fever, usual adult weight, smoking duration, family history of hematological malignancy, and medical occupation. || P value using likelihood ratio test; P value using Wald test .021. ¶ Quartile 1 (<72.6 kg males, <58.1 kg females), quartile 2 (72.6–79.9 kg males, 58.1–64.9 kg females), quartile 3 (80.0–88.9 kg males, 65.0–74.7 kg females), quartile 4 (≥89.0 kg males, ≥74.8 kg females). # Occupation at any time in life (eight studies) or the longest held occupation (two studies); occupations coded according to the International Standard Classification of Occupations (ISCO), Revised Edition 1968 (53); code 061 = medical doctor. View Large Discussion In a large-scale, international pooled case–control analysis of predominantly nonfamilial LPL/WM, risk was increased in those with a history of Sjögren’s syndrome, systemic lupus erythematosus, HCV infection, and a family history of hematologic malignancy. These findings support and extend prior studies suggesting a role for conditions characterized by chronic immune stimulation and genetic factors in LPL/WM pathogenesis. Novel findings were a decreased risk for history of hay fever and high usual adult weight, an increased risk for smoking cigarettes for 40 or more years, and occupation as a medical doctor, and no evidence of an association for other lifestyle factors and occupations. We observed a very strong association between LPL/WM risk and Sjögren’s syndrome, in agreement with previous large-scale medical record–based studies in the United States and Sweden (11,13). A marked increased risk was also observed for systemic lupus erythematosus, consistent with a report from one study in our pooled analysis (15), and a medical record study (13). Unlike the medical record studies, we did not find an increased risk for rheumatoid arthritis (11), Crohn’s disease (11), or autoimmune hemolytic anemia (13). However, our composite autoimmune variable indicated an increased risk for autoimmune conditions with activated B cells, but not activated T cells. This association is consistent with the finding that familial LPL/WM patients are twice as likely as unaffected relatives to report a history of autoimmune disorders (14). The pathogenic mechanism is believed to involve chronic antigen-driven inflammation and activated proliferating lymphocytes (36,37). We confirmed an association between LPL/WM risk and family history of hematologic malignancy (5–7). Our data favored an association with leukemia but not NHL, Hodgkin lymphoma, or multiple myeloma, in partial agreement with a case–control study (7) that reported coaggregation with chronic lymphocytic leukemia and NHL, but not Hodgkin lymphoma or multiple myeloma. This association is thought to suggest a role for common susceptibility genes, as supported by an increased risk of LPL/WM among first-degree relatives of people with monoclonal gammopathy of undetermined significance (9,38). Further, recent evidence of an association of both personal and family history of Sjögren’s syndrome and autoimmune hemolytic anemia with LPL/WM risk (13) supports a role for shared susceptibility for LPL/WM and certain autoimmune conditions. At this time, information on specific genes or genomic regions in LPL/WM susceptibility is limited (15,16,23). The five studies in our analysis with HCV serology data formed the basis of an earlier InterLymph report showing a statistically significant association between HCV and LPL (12), consistent with US (10,11), but not Swedish (13), medical record studies. The known association of HCV infection with type II mixed cryoglobulinemia and monoclonal gammopathy of undetermined significance (10), both of which increase LPL/WM risk (39,40), supports a true association between HCV and LPL/WM. In addition, antiviral treatment can be an effective first-line therapy for HCV-positive LPL (40). HCV infection is believed to promote lymphomagenesis via chronic immune stimulation and elevated IgM levels (10,41–43). Our finding of an inverse association between LPL/WM risk and personal history of hay fever is not consistent with prior studies that observed a positive (13,14) or no association (11,20) with individual allergic conditions or any allergy. Large-scale cohort study data are needed to clarify the relationship with this exposure (44). Even though the relationship has not been previously examined, our observation that LPL/WM risk appears lower for individuals with higher adult weight is unexpected. Not only is there evidence of an increased risk of NHL and some other NHL subtypes for those of higher adiposity (45–47), but obesity is a chronic low-grade inflammatory state characterized by lymphocyte proliferation (48). It is possible the association we observed is due to chance or selection bias. Uniquely to LPL/WM, however, IgM-producing B1 B cells are found in milky spots on the omentum and fat-associated lymphoid clusters on the mesentery [reviewed in (49)]. It is possible their physical proximity to adipose tissue uniquely affects their physiology and progression to LPL/WM. Although not entirely consistent across all of the smoking variables we examined, we found evidence of a weak positive association between LPL/WM risk and cigarette smoking. LPL/WM risk was increased 1.4-fold among those who had smoked for 40 or more years. Smoking history was not associated with LPL/WM risk in two previous studies, one based on 65 cases (20) and the other 103 cases (14). Although this finding requires confirmation in larger studies, a history of smoking has been weakly positively associated with other NHL subtypes (50), and an association is biologically plausible given the immunosuppressive effects of chronic cigarette smoke (51,52). We observed an elevated risk of LPL/WM for medical doctors but not health-care workers more generally. There is no prior evidence of such a relationship. We did not confirm the previously reported increased risk of familial LPL/WM and exposure to farming, pesticides, wood dust, and organic solvents (14); however, our analyses were limited to a small number of job titles rather than exposure to specific chemical compounds. This is the first pooled analysis of medical history, lifestyle, family history, and occupational risk factors for LPL/WM using the 2001 and 2008 WHO classification for LPL/WM and pathology report review. It is the only observational study of LPL/WM to examine the role of potential effect modification and confounding and, thus, determine the independence of these putative risk factors. The exposure data are of high quality and the findings are generalizable to predominantly white populations because most studies were population based and were conducted in Europe, North America, and Australia. Some limitations need to be considered. The study populations were predominantly Caucasian and the number of LPL/WM cases was relatively small, although this is one of the largest case–control interview-based studies of this rare lymphoma subtype to date. Given the rarity of LPL/WM, we undertook an exploratory analytical approach, without adjustment for multiple statistical tests. Lack of data on Ig levels, therapy for autoimmune diseases, duration and therapy for HCV, and history of infections other than HCV restricted our interpretation of some findings. We are also unable to exclude recall bias or reverse causality, with underlying LPL/WM misdiagnosed as Sjögren’s syndrome or systemic lupus erythematosus. Further, misdiagnosis of MALT or splenic marginal zone lymphoma as LPL/WM is an alternative explanation for the associations we observed with autoimmune disease and HCV infection. Finally, our occupational analyses are based on job titles, not exposure to specific agents. We have confirmed an association between LPL/WM risk and history of specific immune-stimulatory medical conditions and a family history of hematologic malignancy, and we have shown for the first time that these risk factors appear to be independent. These findings have future translational potential both biologically and clinically. Other novel findings, specifically the associations with hay fever, adult weight, smoking for 40 or more years, and occupation as a medical doctor, require confirmation in large-scale case–control studies of LPL/WM and long-term cohort studies of monoclonal gammopathy of undetermined significance. 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. Pooling of the occupation data was supported by the National Cancer Institute/National Institutes of Health (R03CA125831). 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, 2009SGR1465), 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 (IG 10068) (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 Institute, 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). 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JNCI Monographs – Oxford University Press

Published: Aug 30, 2014

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