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Rationale and Design of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma Subtypes Project

Rationale and Design of the International Lymphoma Epidemiology Consortium (InterLymph)... Abstract Background Non-Hodgkin lymphoma (NHL), the most common hematologic malignancy, consists of numerous subtypes. The etiology of NHL is incompletely understood, and increasing evidence suggests that risk factors may vary by NHL subtype. However, small numbers of cases have made investigation of subtype-specific risks challenging. The International Lymphoma Epidemiology Consortium therefore undertook the NHL Subtypes Project, an international collaborative effort to investigate the etiologies of NHL subtypes. This article describes in detail the project rationale and design. Methods We pooled individual-level data from 20 case-control studies (17471 NHL cases, 23096 controls) from North America, Europe, and Australia. Centralized data harmonization and analysis ensured standardized definitions and approaches, with rigorous quality control. Results The pooled study population included 11 specified NHL subtypes with more than 100 cases: diffuse large B-cell lymphoma (N = 4667), follicular lymphoma (N = 3530), chronic lymphocytic leukemia/small lymphocytic lymphoma (N = 2440), marginal zone lymphoma (N = 1052), peripheral T-cell lymphoma (N = 584), mantle cell lymphoma (N = 557), lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (N = 374), mycosis fungoides/Sézary syndrome (N = 324), Burkitt/Burkitt-like lymphoma/leukemia (N = 295), hairy cell leukemia (N = 154), and acute lymphoblastic leukemia/lymphoma (N = 152). Associations with medical history, family history, lifestyle factors, and occupation for each of these 11 subtypes are presented in separate articles in this issue, with a final article quantitatively comparing risk factor patterns among subtypes. Conclusions The International Lymphoma Epidemiology Consortium NHL Subtypes Project provides the largest and most comprehensive investigation of potential risk factors for a broad range of common and rare NHL subtypes to date. The analyses contribute to our understanding of the multifactorial nature of NHL subtype etiologies, motivate hypothesis-driven prospective investigations, provide clues for prevention, and exemplify the benefits of international consortial collaboration in cancer epidemiology. Each year, more than 500000 individuals worldwide are diagnosed with non-Hodgkin lymphoma (NHL), making it the most common hematologic malignancy (1). NHL is composed of numerous closely related yet heterogeneous diseases with distinctive morphologic, immunophenotypic, genetic, and clinical features (2,3). The strongest known risk factor for some NHLs is severe immunodeficiency, but this accounts for relatively few cases (4). Incidence of NHL rose dramatically in most Western countries throughout the second half of the 20th century, independently of the AIDS epidemic, and appears to have plateaued in the last decade (5–11). A number of epidemiological studies were launched in the 1980s–1990s to identify potential causes of these long-standin g increases and to understand NHL etiology more broadly, yet the “epidemic” of NHL remains poorly understood. In 2001, the World Health Organization (WHO) introduced an international consensus-based classification for hematologic malignancies (2,3). This classification provided the first biologically based, integrated framework for consistently defining the subtypes of NHL, thereby greatly facilitating research on this heterogeneous group of diseases. Subsequent analyses of population-based registry data revealed striking differences in incidence among NHL subtypes by age, sex, race/ethnicity, and calendar year (11–14). Additionally, studies have reported that certain infectious agents are associated with risk of specific NHL subtypes, such as human T-cell lymphotropic virus, type I (HTLV-I) with adult T-cell leukemia/lymphoma (15), and Helicobacter pylori with gastric mucosa-associated lymphoid tissue NHL (16), whereas infection with the HIV (17–19) and hepatitis C virus (20,21) are associated with multiple NHL subtypes. Variation in risk among NHL subtypes also is clearly evident for associations with autoimmune conditions (22), iatrogenic immunodeficiency associated with solid organ transplantation (23–25), and certain common genetic variants (26–31). In contrast, cumulative sun exposure appears to affect the risk of all NHL subtypes (32). The International Lymphoma Epidemiology Consortium (InterLymph) is an open scientific forum for epidemiological research in NHL (http://epi.grants.cancer.gov/InterLymph/) (33). Formed in 2001, InterLymph’s primary goal was to facilitate pooled analyses of individual-level data from lymphoid malignancy case-control studies with the purpose of increasing statistical power for examining associations with rare exposures and less common NHL subtypes. Collaborations among epidemiologists in Europe, North America, and Australia were initiated in the 1990s through formal (34) and informal meetings, where investigators shared draft protocols and questionnaires for recent and planned epidemiological studies. Since its official inception, InterLymph has expanded to become an interdisciplinary group of epidemiologists, pathologists, clinicians, geneticists, immunologists, and biostatisticians who have worked together to publish pooled analyses on a range of individual risk factors among NHL subtypes (20,22,27,28,32,35–42). Despite advances in our understanding of NHL etiology, broad evaluation of risk factor profiles for specific NHL subtypes across a range of exposures is lacking, and little is known about risk factors for many of the less common NHL subtypes. We therefore undertook the “InterLymph NHL Subtypes Project,” a consortium-wide initiative with the aims of 1) evaluating associations for medical history, family history of hematologic malignancy, lifestyle factors, and occupation with specified NHL subtypes, and 2) quantitatively assessing etiologic heterogeneity among NHL subtypes. The project expands previous InterLymph pooled analyses (20,22,27,28,32,35–42) by examining a range of exposures in the same analysis for each NHL subtype, quantitatively assessing differences and commonalities in risk factor associations across a broader range of NHL subtypes, and including new studies that recently joined the consortium. In this article, we describe in detail the design and methods of the project. Methods Project Structure and Coordination The InterLymph NHL Subtypes Project was governed by a Project Coordinating Committee, with representation from each contributing study and InterLymph working group (Immunity & Infection, Lifestyle & Environment, and Pathology). The Committee was led by an interdisciplinary group of epidemiologists (LMM, MSL, JRC), pathologists (DDW, JJT), and biostatisticians (SLS, JNS) who initiated and/or led the project. Additional oversight of the analyses was provided by an analytic working group with biostatisticians from three participating studies (JNS, SLS, YB). Project coordinators corresponded regularly by e-mail and teleconference, and met in-person at four annual InterLymph meetings during 2010–2013. Working groups for each NHL subtype included in the project were formed with representation from participating studies and with other InterLymph members with expertise and interest in that particular subtype. Each group included at least one pathologist, clinician, and biostatistician, in addition to epidemiologists. Communication was facilitated by use of a password-protected web portal for posting study documents and results. Decisions were made by consensus or voting. Study Population Studies eligible for inclusion in these pooled analyses fulfilled the following criteria: 1) case-control design, with incident cases of NHL and information on NHL subtype, 2) availability of individual-level data by December 31, 2011, and 3) participation in InterLymph. A total of 20 studies fulfilled these criteria (Table 1). As described below, studies were included in specific analyses where cases were available with the subtype of interest and data were collected on the particular risk factor under evaluation. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview. Table 1. Characteristics of studies included in the InterLymph NHL Subtypes Project* Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † Participation was typically computed as number participating divided by the number contacted. ‡ Studies were designed to investigate other malignancies in addition to NHL: Engela: Hodgkin lymphoma, multiple myeloma, and lymphoproliferative syndromes; EpiLymph: Hodgkin lymphoma and multiple myeloma; Iowa/Minnesota: leukemia; Italy: Hodgkin lymphoma, multiple myeloma, and soft-tissue sarcoma; Mayo: Hodgkin lymphoma; Nebraska (older): Hodgkin lymphoma and multiple myeloma; SCALE: Hodgkin lymphoma; United Kingdom: Hodgkin lymphoma. § Studies individually matched controls to cases. The remaining studies frequency matched controls to cases, typically by age (5-y groups), sex, race/ethnicity, and geographic location where appropriate. || This recently completed study has not yet published results but had similar methodology to a previous study conducted in the same region (52). View Large Individuals with a known history of solid organ transplantation or HIV/AIDS were ineligible for most studies; any individual who reported a history of these conditions during patient interviews or via questionnaires was excluded from these pooled analyses. All studies conducted direct interviews with study participants, except Iowa/Minnesota and Italy multicenter, which used proxy respondents for deceased cases. In the six studies that used hospital- or clinic-based controls, analyses included all controls used in the original study, regardless of reason for hospital admission. NHL Subtype Ascertainment and Harmonization Eligible cases were diagnosed with incident, histologically confirmed NHL. Most studies had centralized review of pathology reports and diagnostic slides by at least one expert hematopathologist to confirm the NHL diagnoses and assign an NHL subtype, except for the National Cancer Institute-Surveillance, Epidemiology, and End Results Program and Italy multicenter studies, in which cases were diagnosed by local pathologists. Each participating study’s pathology review procedures, rules for NHL subtype classification, and NHL subtype distribution were reviewed by an interdisciplinary team of hematopathologists and epidemiologists. We grouped cases into NHL subtypes according to the WHO classification (2,3) using guidelines from the InterLymph Pathology Working Group (64,65). NHL subtypes with more than 100 cases in the pooled dataset were eligible for inclusion in this project. Because of the potential for risk factors to differ by primary site of disease for certain NHL subtypes (e.g., Sjögren’s syndrome and salivary gland marginal zone lymphoma) (22), we further classified cases according to primary site of NHL for secondary analyses. In most studies (N = 14), primary site of NHL was recorded if known, irrespective of disease stage. In some NHL subtypes, site is a diagnostic criterion. NHL site was categorized as nodal, extranodal lymphatic (Waldeyer’s ring, thymus, or spleen), or extranodal extralymphatic (66). Specific primary sites (e.g., skin, gastrointestinal tract) also were recorded for further analysis in some NHL subtypes. Leukemias were classified as systemic by definition. Cases with widespread disease or primary site listed as bone marrow, blood, or cerebrospinal fluid, and in which subtype was not site-specific by definition, also were classified as systemic. Risk Factor Ascertainment and Harmonization Each study collected data on putative NHL risk factors in a standardized, structured format by in-person or telephone interviews and/or self-administered questionnaires. In some studies, participants also provided a venous blood sample. Risk factors selected for inclusion in this analysis were the available medical history, family history of hematologic malignancy, lifestyle factors, and occupations with data from at least four studies. Each study contributed de-identified, individual-level data for the risk factors of interest. Data harmonization was conducted centrally at the Mayo Clinic under the leadership of SLS. Each exposure variable was harmonized individually and data were then reviewed for consistency among related exposure variables. A small subcommittee reviewed harmonization rules for each exposure, ensured consistency with previously published InterLymph pooled analyses, and advised the analytic approaches for that exposure. Statistical Analysis We defined a single analytical plan and applied it to each of the 11 NHL subtypes, with all analyses conducted centrally at the National Cancer Institute under the leadership of JNS and LMM. Here, we describe this plan in terms of a generic NHL subtype. Each analysis defined individuals with a specific NHL subtype as cases, individuals without any type of NHL as controls, and excluded cases with NHL subtypes other than the one of interest. For analyses of a particular subtype, we only included controls from studies that also contributed cases of that subtype. Analyses were conducted using SAS software, version 9.2 (SAS Institute, Inc, Cary, NC). We examined the relationship between case/control status and each exposure variable individually using unconditional logistic regression models adjusted for age (<30, 30–39, 40–49, 50–59, 60–69, 70–79, ≥80 years), race/ethnicity (white, black, Asian, Hispanic, or other/missing), sex, and study. Confidence intervals (CIs) for the odds ratios (ORs) were estimated by asymptotic theory for maximum likelihood estimation. Statistical significance of each relationship was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than .05 identifying putatively influential factors. We excluded individuals who had missing data for the exposure variable of interest, and we excluded both cases and controls from studies where only a single category of the exposure variable of interest was represented (e.g., no cases and no controls reported history of a particular autoimmune condition). To evaluate effect heterogeneity among the studies included in each analysis, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic. Adapting the definition by Higgins and Thompson to categorical variables (67), we defined H as follows. Let β^ be the vector of size n = number of studies × (number of categories – 1) containing the estimated coefficients from all studies. Let S be the estimated block diagonal variance matrix for β^. Subtract the mean of each coefficient from β^ to obtain Β^. Then, we defined Q=Β^TΣ−1Β^, which has a χ2 with n' = (n − number of categories − 1) degrees of freedom and H=Q/n'. The lower and upper bounds for the 95% CI of H is Hexp(−A) and Hexp(A), where A=1.96(1−(1/(3k2))/(2k) and k=n'−1. In the absence of heterogeneity, H is expected to equal 1. Statistically significant heterogeneity among studies was identified by 95% CI for H that excluded 1. If a category within a particular study included no cases, suggesting that the disease risk was 0 or the log(OR) = −∞, we set the log(OR) to −2 and estimated the standard deviation by rerunning the logistic regression with an additional case added into that category. 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 above logistic regression analyses, but now stratified individuals by age, sex, race/ethnicity, region (North America, Northern Europe, Southern Europe, and Australia), study, study design (i.e., population-based versus. hospital- or clinic-based studies), or putative risk factors identified in the analysis. Forest plots illustrated the results from the stratified analyses to identify possible effect modifiers for each exposure variable. To account for potential confounding, 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 at a time (as well as age, race/ethnicity, sex, and study). Second, we conducted a single logistic regression analysis including all putative risk factors, age, sex, race, and study, this time including a separate missing category for each variable to ensure that the whole study population was included in the analysis (i.e., not excluded due to missing data). Note, however, that the likelihood ratio P values for a putative risk factor excluded the effect of this newly created missing category. Finally, we conducted a forward step-wise logistic regression with all putative risk factors, adjusting for age, sex, race/ethnicity, and study. In most of the original studies, controls were frequency-matched to cases by age and sex. However, because some studies included cases with Hodgkin lymphoma, myeloma, leukemia, and/or soft-tissue sarcoma in addition to NHL (Table 1), each analysis included only a subset of cases causing the matching to be broken. Therefore, as a sensitivity analysis, we repeated the analyses described above using a subset of controls that were frequency-matched by age and sex to cases with that NHL subtype. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls, thus we retained the full set of controls for our main analyses to increase statistical power. The power to detect an association between a dichotomous characteristic and NHL subtype depends on the number of available cases, the prevalence of the characteristic, and the strength of the association. To understand the potential types of associations detectable for each subtype, we calculated the power for varying combinations of those three parameters (Figure 1). The calculations assumed a 1:5 case:control ratio, an α level of 0.05, and that the estimated proportions of exposed cases and controls were normally distributed. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Results Study Population Twenty studies from North America (N = 12), Europe (N = 7), and Australia (N = 1) were included in the InterLymph NHL Subtypes Project, representing most of the large-scale case-control studies of NHL conducted over the last three decades in these regions (Table 1). The pooled dataset included a total of 17471 NHL cases and 23096 controls. More than 75% of participants were derived from the 14 population-based studies, with the remaining participants from hospital- or clinic-based studies. The pooled study controls were 58% male and 93% non-Hispanic white, and had a median age at interview of 59 years (range 16–98 years); 41% were classified in the lowest tertile of socioeconomic status (Table 2). Table 2. Demographic characteristics of controls from participating studies in the InterLymph NHL Subtypes Project* Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † The Yale study was restricted to women, whereas the Kansas and Iowa/Minnesota studies were restricted to men. ‡ Includes individuals with other specified races as well as unknown race. For studies with predominantly white, non-Hispanic populations, individuals with unknown race were assumed to be white (Iowa/Minnesota, Kansas, Mayo Clinic, NCI-SEER [Seattle, Iowa study centers], Nebraska [older], Nebraska [newer], UCSF2, University of Rochester, Yale, Engela, SCALE, United Kingdom). § Age at diagnosis for cases and interview for controls. All studies excluded children (with varying minimum ages), and most studies excluded the elderly (with varying maximum ages) population, except for the Iowa/Minnesota, Kansas, Mayo Clinic, Nebraska (older), and University of Rochester studies, which did not have an upper age limit. || 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 NHL Subtypes Of the 17471 NHL cases in the pooled analyses, 11976 (69%) were derived from studies that classified NHL subtypes according to the WHO classification (2,3) or its closely related predecessor, the Revised European-American Lymphoma classification (68) (Table 3). The remaining 5495 (31%) cases were classified according to the Working Formulation (69). Among cases originally defined by the WHO classification, 11023 (92%) were assigned to one of the 11 subtypes included in this project (i.e., with >100 cases), with the remaining cases assigned to a rare (N = 50 total, <1%) or unspecified (N = 900, 8%) NHL subtype. Where the Working Formulation was the original coding scheme, 3106 (57%) cases were assigned to one of the 11 subtypes included in this project, with the remaining 2389 (43%) considered poorly specified because certain Working Formulation subtypes do not reliably correspond to subtypes defined by the WHO (e.g., chronic lymphocytic leukemia/small lymphocytic lymphoma) or were not recognized in the Working Formulation (e.g., marginal zone lymphoma, mantle cell lymphoma) (64). Table 3. Distribution of the InterLymph NHL Subtypes Project study population by NHL subtype and study* NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866  112  195  244  0||  0||  0||  0||  0||  18  0§  6  0  291   Italy multicenter  1911  407  159  214  0||  55  0||  0§  25  23  30  12  0  986   Italy (Aviano-Milan)  429  47  55  0||  0||  0  0||  0§  0||  9  0§  10  0  308   Kansas  170  27  34  0||  0||  0||  0||  0||  0||  2  0§  1  0  106   Los Angeles**  376  151  41  0||  0||  1  0||  0§  0||  50  0§  16  0  117   Nebraska (older)  441  94  111  0||  0||  0||  0||  0||  0||  6  0§  3  0  227   UCSF1  1302  509  352  0||  0||  0||  0||  0§  47  28  0§  12  0  354  Total  17471  4667  3530  2440  1052  584  557  374  324  295  154  152  50  3292  NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866  http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Endocrine Society Oxford University Press

Rationale and Design of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma Subtypes Project

Journal of the Endocrine Society , Volume 2014 (48) – Aug 30, 2014

Abstract

Abstract Background Non-Hodgkin lymphoma (NHL), the most common hematologic malignancy, consists of numerous subtypes. The etiology of NHL is incompletely understood, and increasing evidence suggests that risk factors may vary by NHL subtype. However, small numbers of cases have made investigation of subtype-specific risks challenging. The International Lymphoma Epidemiology Consortium therefore undertook the NHL Subtypes Project, an international collaborative effort to investigate the etiologies of NHL subtypes. This article describes in detail the project rationale and design. Methods We pooled individual-level data from 20 case-control studies (17471 NHL cases, 23096 controls) from North America, Europe, and Australia. Centralized data harmonization and analysis ensured standardized definitions and approaches, with rigorous quality control. Results The pooled study population included 11 specified NHL subtypes with more than 100 cases: diffuse large B-cell lymphoma (N = 4667), follicular lymphoma (N = 3530), chronic lymphocytic leukemia/small lymphocytic lymphoma (N = 2440), marginal zone lymphoma (N = 1052), peripheral T-cell lymphoma (N = 584), mantle cell lymphoma (N = 557), lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (N = 374), mycosis fungoides/Sézary syndrome (N = 324), Burkitt/Burkitt-like lymphoma/leukemia (N = 295), hairy cell leukemia (N = 154), and acute lymphoblastic leukemia/lymphoma (N = 152). Associations with medical history, family history, lifestyle factors, and occupation for each of these 11 subtypes are presented in separate articles in this issue, with a final article quantitatively comparing risk factor patterns among subtypes. Conclusions The International Lymphoma Epidemiology Consortium NHL Subtypes Project provides the largest and most comprehensive investigation of potential risk factors for a broad range of common and rare NHL subtypes to date. The analyses contribute to our understanding of the multifactorial nature of NHL subtype etiologies, motivate hypothesis-driven prospective investigations, provide clues for prevention, and exemplify the benefits of international consortial collaboration in cancer epidemiology. Each year, more than 500000 individuals worldwide are diagnosed with non-Hodgkin lymphoma (NHL), making it the most common hematologic malignancy (1). NHL is composed of numerous closely related yet heterogeneous diseases with distinctive morphologic, immunophenotypic, genetic, and clinical features (2,3). The strongest known risk factor for some NHLs is severe immunodeficiency, but this accounts for relatively few cases (4). Incidence of NHL rose dramatically in most Western countries throughout the second half of the 20th century, independently of the AIDS epidemic, and appears to have plateaued in the last decade (5–11). A number of epidemiological studies were launched in the 1980s–1990s to identify potential causes of these long-standin g increases and to understand NHL etiology more broadly, yet the “epidemic” of NHL remains poorly understood. In 2001, the World Health Organization (WHO) introduced an international consensus-based classification for hematologic malignancies (2,3). This classification provided the first biologically based, integrated framework for consistently defining the subtypes of NHL, thereby greatly facilitating research on this heterogeneous group of diseases. Subsequent analyses of population-based registry data revealed striking differences in incidence among NHL subtypes by age, sex, race/ethnicity, and calendar year (11–14). Additionally, studies have reported that certain infectious agents are associated with risk of specific NHL subtypes, such as human T-cell lymphotropic virus, type I (HTLV-I) with adult T-cell leukemia/lymphoma (15), and Helicobacter pylori with gastric mucosa-associated lymphoid tissue NHL (16), whereas infection with the HIV (17–19) and hepatitis C virus (20,21) are associated with multiple NHL subtypes. Variation in risk among NHL subtypes also is clearly evident for associations with autoimmune conditions (22), iatrogenic immunodeficiency associated with solid organ transplantation (23–25), and certain common genetic variants (26–31). In contrast, cumulative sun exposure appears to affect the risk of all NHL subtypes (32). The International Lymphoma Epidemiology Consortium (InterLymph) is an open scientific forum for epidemiological research in NHL (http://epi.grants.cancer.gov/InterLymph/) (33). Formed in 2001, InterLymph’s primary goal was to facilitate pooled analyses of individual-level data from lymphoid malignancy case-control studies with the purpose of increasing statistical power for examining associations with rare exposures and less common NHL subtypes. Collaborations among epidemiologists in Europe, North America, and Australia were initiated in the 1990s through formal (34) and informal meetings, where investigators shared draft protocols and questionnaires for recent and planned epidemiological studies. Since its official inception, InterLymph has expanded to become an interdisciplinary group of epidemiologists, pathologists, clinicians, geneticists, immunologists, and biostatisticians who have worked together to publish pooled analyses on a range of individual risk factors among NHL subtypes (20,22,27,28,32,35–42). Despite advances in our understanding of NHL etiology, broad evaluation of risk factor profiles for specific NHL subtypes across a range of exposures is lacking, and little is known about risk factors for many of the less common NHL subtypes. We therefore undertook the “InterLymph NHL Subtypes Project,” a consortium-wide initiative with the aims of 1) evaluating associations for medical history, family history of hematologic malignancy, lifestyle factors, and occupation with specified NHL subtypes, and 2) quantitatively assessing etiologic heterogeneity among NHL subtypes. The project expands previous InterLymph pooled analyses (20,22,27,28,32,35–42) by examining a range of exposures in the same analysis for each NHL subtype, quantitatively assessing differences and commonalities in risk factor associations across a broader range of NHL subtypes, and including new studies that recently joined the consortium. In this article, we describe in detail the design and methods of the project. Methods Project Structure and Coordination The InterLymph NHL Subtypes Project was governed by a Project Coordinating Committee, with representation from each contributing study and InterLymph working group (Immunity & Infection, Lifestyle & Environment, and Pathology). The Committee was led by an interdisciplinary group of epidemiologists (LMM, MSL, JRC), pathologists (DDW, JJT), and biostatisticians (SLS, JNS) who initiated and/or led the project. Additional oversight of the analyses was provided by an analytic working group with biostatisticians from three participating studies (JNS, SLS, YB). Project coordinators corresponded regularly by e-mail and teleconference, and met in-person at four annual InterLymph meetings during 2010–2013. Working groups for each NHL subtype included in the project were formed with representation from participating studies and with other InterLymph members with expertise and interest in that particular subtype. Each group included at least one pathologist, clinician, and biostatistician, in addition to epidemiologists. Communication was facilitated by use of a password-protected web portal for posting study documents and results. Decisions were made by consensus or voting. Study Population Studies eligible for inclusion in these pooled analyses fulfilled the following criteria: 1) case-control design, with incident cases of NHL and information on NHL subtype, 2) availability of individual-level data by December 31, 2011, and 3) participation in InterLymph. A total of 20 studies fulfilled these criteria (Table 1). As described below, studies were included in specific analyses where cases were available with the subtype of interest and data were collected on the particular risk factor under evaluation. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview. Table 1. Characteristics of studies included in the InterLymph NHL Subtypes Project* Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † Participation was typically computed as number participating divided by the number contacted. ‡ Studies were designed to investigate other malignancies in addition to NHL: Engela: Hodgkin lymphoma, multiple myeloma, and lymphoproliferative syndromes; EpiLymph: Hodgkin lymphoma and multiple myeloma; Iowa/Minnesota: leukemia; Italy: Hodgkin lymphoma, multiple myeloma, and soft-tissue sarcoma; Mayo: Hodgkin lymphoma; Nebraska (older): Hodgkin lymphoma and multiple myeloma; SCALE: Hodgkin lymphoma; United Kingdom: Hodgkin lymphoma. § Studies individually matched controls to cases. The remaining studies frequency matched controls to cases, typically by age (5-y groups), sex, race/ethnicity, and geographic location where appropriate. || This recently completed study has not yet published results but had similar methodology to a previous study conducted in the same region (52). View Large Individuals with a known history of solid organ transplantation or HIV/AIDS were ineligible for most studies; any individual who reported a history of these conditions during patient interviews or via questionnaires was excluded from these pooled analyses. All studies conducted direct interviews with study participants, except Iowa/Minnesota and Italy multicenter, which used proxy respondents for deceased cases. In the six studies that used hospital- or clinic-based controls, analyses included all controls used in the original study, regardless of reason for hospital admission. NHL Subtype Ascertainment and Harmonization Eligible cases were diagnosed with incident, histologically confirmed NHL. Most studies had centralized review of pathology reports and diagnostic slides by at least one expert hematopathologist to confirm the NHL diagnoses and assign an NHL subtype, except for the National Cancer Institute-Surveillance, Epidemiology, and End Results Program and Italy multicenter studies, in which cases were diagnosed by local pathologists. Each participating study’s pathology review procedures, rules for NHL subtype classification, and NHL subtype distribution were reviewed by an interdisciplinary team of hematopathologists and epidemiologists. We grouped cases into NHL subtypes according to the WHO classification (2,3) using guidelines from the InterLymph Pathology Working Group (64,65). NHL subtypes with more than 100 cases in the pooled dataset were eligible for inclusion in this project. Because of the potential for risk factors to differ by primary site of disease for certain NHL subtypes (e.g., Sjögren’s syndrome and salivary gland marginal zone lymphoma) (22), we further classified cases according to primary site of NHL for secondary analyses. In most studies (N = 14), primary site of NHL was recorded if known, irrespective of disease stage. In some NHL subtypes, site is a diagnostic criterion. NHL site was categorized as nodal, extranodal lymphatic (Waldeyer’s ring, thymus, or spleen), or extranodal extralymphatic (66). Specific primary sites (e.g., skin, gastrointestinal tract) also were recorded for further analysis in some NHL subtypes. Leukemias were classified as systemic by definition. Cases with widespread disease or primary site listed as bone marrow, blood, or cerebrospinal fluid, and in which subtype was not site-specific by definition, also were classified as systemic. Risk Factor Ascertainment and Harmonization Each study collected data on putative NHL risk factors in a standardized, structured format by in-person or telephone interviews and/or self-administered questionnaires. In some studies, participants also provided a venous blood sample. Risk factors selected for inclusion in this analysis were the available medical history, family history of hematologic malignancy, lifestyle factors, and occupations with data from at least four studies. Each study contributed de-identified, individual-level data for the risk factors of interest. Data harmonization was conducted centrally at the Mayo Clinic under the leadership of SLS. Each exposure variable was harmonized individually and data were then reviewed for consistency among related exposure variables. A small subcommittee reviewed harmonization rules for each exposure, ensured consistency with previously published InterLymph pooled analyses, and advised the analytic approaches for that exposure. Statistical Analysis We defined a single analytical plan and applied it to each of the 11 NHL subtypes, with all analyses conducted centrally at the National Cancer Institute under the leadership of JNS and LMM. Here, we describe this plan in terms of a generic NHL subtype. Each analysis defined individuals with a specific NHL subtype as cases, individuals without any type of NHL as controls, and excluded cases with NHL subtypes other than the one of interest. For analyses of a particular subtype, we only included controls from studies that also contributed cases of that subtype. Analyses were conducted using SAS software, version 9.2 (SAS Institute, Inc, Cary, NC). We examined the relationship between case/control status and each exposure variable individually using unconditional logistic regression models adjusted for age (<30, 30–39, 40–49, 50–59, 60–69, 70–79, ≥80 years), race/ethnicity (white, black, Asian, Hispanic, or other/missing), sex, and study. Confidence intervals (CIs) for the odds ratios (ORs) were estimated by asymptotic theory for maximum likelihood estimation. Statistical significance of each relationship was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than .05 identifying putatively influential factors. We excluded individuals who had missing data for the exposure variable of interest, and we excluded both cases and controls from studies where only a single category of the exposure variable of interest was represented (e.g., no cases and no controls reported history of a particular autoimmune condition). To evaluate effect heterogeneity among the studies included in each analysis, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic. Adapting the definition by Higgins and Thompson to categorical variables (67), we defined H as follows. Let β^ be the vector of size n = number of studies × (number of categories – 1) containing the estimated coefficients from all studies. Let S be the estimated block diagonal variance matrix for β^. Subtract the mean of each coefficient from β^ to obtain Β^. Then, we defined Q=Β^TΣ−1Β^, which has a χ2 with n' = (n − number of categories − 1) degrees of freedom and H=Q/n'. The lower and upper bounds for the 95% CI of H is Hexp(−A) and Hexp(A), where A=1.96(1−(1/(3k2))/(2k) and k=n'−1. In the absence of heterogeneity, H is expected to equal 1. Statistically significant heterogeneity among studies was identified by 95% CI for H that excluded 1. If a category within a particular study included no cases, suggesting that the disease risk was 0 or the log(OR) = −∞, we set the log(OR) to −2 and estimated the standard deviation by rerunning the logistic regression with an additional case added into that category. 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 above logistic regression analyses, but now stratified individuals by age, sex, race/ethnicity, region (North America, Northern Europe, Southern Europe, and Australia), study, study design (i.e., population-based versus. hospital- or clinic-based studies), or putative risk factors identified in the analysis. Forest plots illustrated the results from the stratified analyses to identify possible effect modifiers for each exposure variable. To account for potential confounding, 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 at a time (as well as age, race/ethnicity, sex, and study). Second, we conducted a single logistic regression analysis including all putative risk factors, age, sex, race, and study, this time including a separate missing category for each variable to ensure that the whole study population was included in the analysis (i.e., not excluded due to missing data). Note, however, that the likelihood ratio P values for a putative risk factor excluded the effect of this newly created missing category. Finally, we conducted a forward step-wise logistic regression with all putative risk factors, adjusting for age, sex, race/ethnicity, and study. In most of the original studies, controls were frequency-matched to cases by age and sex. However, because some studies included cases with Hodgkin lymphoma, myeloma, leukemia, and/or soft-tissue sarcoma in addition to NHL (Table 1), each analysis included only a subset of cases causing the matching to be broken. Therefore, as a sensitivity analysis, we repeated the analyses described above using a subset of controls that were frequency-matched by age and sex to cases with that NHL subtype. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls, thus we retained the full set of controls for our main analyses to increase statistical power. The power to detect an association between a dichotomous characteristic and NHL subtype depends on the number of available cases, the prevalence of the characteristic, and the strength of the association. To understand the potential types of associations detectable for each subtype, we calculated the power for varying combinations of those three parameters (Figure 1). The calculations assumed a 1:5 case:control ratio, an α level of 0.05, and that the estimated proportions of exposed cases and controls were normally distributed. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Results Study Population Twenty studies from North America (N = 12), Europe (N = 7), and Australia (N = 1) were included in the InterLymph NHL Subtypes Project, representing most of the large-scale case-control studies of NHL conducted over the last three decades in these regions (Table 1). The pooled dataset included a total of 17471 NHL cases and 23096 controls. More than 75% of participants were derived from the 14 population-based studies, with the remaining participants from hospital- or clinic-based studies. The pooled study controls were 58% male and 93% non-Hispanic white, and had a median age at interview of 59 years (range 16–98 years); 41% were classified in the lowest tertile of socioeconomic status (Table 2). Table 2. Demographic characteristics of controls from participating studies in the InterLymph NHL Subtypes Project* Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † The Yale study was restricted to women, whereas the Kansas and Iowa/Minnesota studies were restricted to men. ‡ Includes individuals with other specified races as well as unknown race. For studies with predominantly white, non-Hispanic populations, individuals with unknown race were assumed to be white (Iowa/Minnesota, Kansas, Mayo Clinic, NCI-SEER [Seattle, Iowa study centers], Nebraska [older], Nebraska [newer], UCSF2, University of Rochester, Yale, Engela, SCALE, United Kingdom). § Age at diagnosis for cases and interview for controls. All studies excluded children (with varying minimum ages), and most studies excluded the elderly (with varying maximum ages) population, except for the Iowa/Minnesota, Kansas, Mayo Clinic, Nebraska (older), and University of Rochester studies, which did not have an upper age limit. || 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 NHL Subtypes Of the 17471 NHL cases in the pooled analyses, 11976 (69%) were derived from studies that classified NHL subtypes according to the WHO classification (2,3) or its closely related predecessor, the Revised European-American Lymphoma classification (68) (Table 3). The remaining 5495 (31%) cases were classified according to the Working Formulation (69). Among cases originally defined by the WHO classification, 11023 (92%) were assigned to one of the 11 subtypes included in this project (i.e., with >100 cases), with the remaining cases assigned to a rare (N = 50 total, <1%) or unspecified (N = 900, 8%) NHL subtype. Where the Working Formulation was the original coding scheme, 3106 (57%) cases were assigned to one of the 11 subtypes included in this project, with the remaining 2389 (43%) considered poorly specified because certain Working Formulation subtypes do not reliably correspond to subtypes defined by the WHO (e.g., chronic lymphocytic leukemia/small lymphocytic lymphoma) or were not recognized in the Working Formulation (e.g., marginal zone lymphoma, mantle cell lymphoma) (64). Table 3. Distribution of the InterLymph NHL Subtypes Project study population by NHL subtype and study* NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866  112  195  244  0||  0||  0||  0||  0||  18  0§  6  0  291   Italy multicenter  1911  407  159  214  0||  55  0||  0§  25  23  30  12  0  986   Italy (Aviano-Milan)  429  47  55  0||  0||  0  0||  0§  0||  9  0§  10  0  308   Kansas  170  27  34  0||  0||  0||  0||  0||  0||  2  0§  1  0  106   Los Angeles**  376  151  41  0||  0||  1  0||  0§  0||  50  0§  16  0  117   Nebraska (older)  441  94  111  0||  0||  0||  0||  0||  0||  6  0§  3  0  227   UCSF1  1302  509  352  0||  0||  0||  0||  0§  47  28  0§  12  0  354  Total  17471  4667  3530  2440  1052  584  557  374  324  295  154  152  50  3292  NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866 

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References (100)

Publisher
Oxford University Press
eISSN
2472-1972
DOI
10.1093/jncimonographs/lgu005
pmid
25174022
Publisher site
See Article on Publisher Site

Abstract

Abstract Background Non-Hodgkin lymphoma (NHL), the most common hematologic malignancy, consists of numerous subtypes. The etiology of NHL is incompletely understood, and increasing evidence suggests that risk factors may vary by NHL subtype. However, small numbers of cases have made investigation of subtype-specific risks challenging. The International Lymphoma Epidemiology Consortium therefore undertook the NHL Subtypes Project, an international collaborative effort to investigate the etiologies of NHL subtypes. This article describes in detail the project rationale and design. Methods We pooled individual-level data from 20 case-control studies (17471 NHL cases, 23096 controls) from North America, Europe, and Australia. Centralized data harmonization and analysis ensured standardized definitions and approaches, with rigorous quality control. Results The pooled study population included 11 specified NHL subtypes with more than 100 cases: diffuse large B-cell lymphoma (N = 4667), follicular lymphoma (N = 3530), chronic lymphocytic leukemia/small lymphocytic lymphoma (N = 2440), marginal zone lymphoma (N = 1052), peripheral T-cell lymphoma (N = 584), mantle cell lymphoma (N = 557), lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (N = 374), mycosis fungoides/Sézary syndrome (N = 324), Burkitt/Burkitt-like lymphoma/leukemia (N = 295), hairy cell leukemia (N = 154), and acute lymphoblastic leukemia/lymphoma (N = 152). Associations with medical history, family history, lifestyle factors, and occupation for each of these 11 subtypes are presented in separate articles in this issue, with a final article quantitatively comparing risk factor patterns among subtypes. Conclusions The International Lymphoma Epidemiology Consortium NHL Subtypes Project provides the largest and most comprehensive investigation of potential risk factors for a broad range of common and rare NHL subtypes to date. The analyses contribute to our understanding of the multifactorial nature of NHL subtype etiologies, motivate hypothesis-driven prospective investigations, provide clues for prevention, and exemplify the benefits of international consortial collaboration in cancer epidemiology. Each year, more than 500000 individuals worldwide are diagnosed with non-Hodgkin lymphoma (NHL), making it the most common hematologic malignancy (1). NHL is composed of numerous closely related yet heterogeneous diseases with distinctive morphologic, immunophenotypic, genetic, and clinical features (2,3). The strongest known risk factor for some NHLs is severe immunodeficiency, but this accounts for relatively few cases (4). Incidence of NHL rose dramatically in most Western countries throughout the second half of the 20th century, independently of the AIDS epidemic, and appears to have plateaued in the last decade (5–11). A number of epidemiological studies were launched in the 1980s–1990s to identify potential causes of these long-standin g increases and to understand NHL etiology more broadly, yet the “epidemic” of NHL remains poorly understood. In 2001, the World Health Organization (WHO) introduced an international consensus-based classification for hematologic malignancies (2,3). This classification provided the first biologically based, integrated framework for consistently defining the subtypes of NHL, thereby greatly facilitating research on this heterogeneous group of diseases. Subsequent analyses of population-based registry data revealed striking differences in incidence among NHL subtypes by age, sex, race/ethnicity, and calendar year (11–14). Additionally, studies have reported that certain infectious agents are associated with risk of specific NHL subtypes, such as human T-cell lymphotropic virus, type I (HTLV-I) with adult T-cell leukemia/lymphoma (15), and Helicobacter pylori with gastric mucosa-associated lymphoid tissue NHL (16), whereas infection with the HIV (17–19) and hepatitis C virus (20,21) are associated with multiple NHL subtypes. Variation in risk among NHL subtypes also is clearly evident for associations with autoimmune conditions (22), iatrogenic immunodeficiency associated with solid organ transplantation (23–25), and certain common genetic variants (26–31). In contrast, cumulative sun exposure appears to affect the risk of all NHL subtypes (32). The International Lymphoma Epidemiology Consortium (InterLymph) is an open scientific forum for epidemiological research in NHL (http://epi.grants.cancer.gov/InterLymph/) (33). Formed in 2001, InterLymph’s primary goal was to facilitate pooled analyses of individual-level data from lymphoid malignancy case-control studies with the purpose of increasing statistical power for examining associations with rare exposures and less common NHL subtypes. Collaborations among epidemiologists in Europe, North America, and Australia were initiated in the 1990s through formal (34) and informal meetings, where investigators shared draft protocols and questionnaires for recent and planned epidemiological studies. Since its official inception, InterLymph has expanded to become an interdisciplinary group of epidemiologists, pathologists, clinicians, geneticists, immunologists, and biostatisticians who have worked together to publish pooled analyses on a range of individual risk factors among NHL subtypes (20,22,27,28,32,35–42). Despite advances in our understanding of NHL etiology, broad evaluation of risk factor profiles for specific NHL subtypes across a range of exposures is lacking, and little is known about risk factors for many of the less common NHL subtypes. We therefore undertook the “InterLymph NHL Subtypes Project,” a consortium-wide initiative with the aims of 1) evaluating associations for medical history, family history of hematologic malignancy, lifestyle factors, and occupation with specified NHL subtypes, and 2) quantitatively assessing etiologic heterogeneity among NHL subtypes. The project expands previous InterLymph pooled analyses (20,22,27,28,32,35–42) by examining a range of exposures in the same analysis for each NHL subtype, quantitatively assessing differences and commonalities in risk factor associations across a broader range of NHL subtypes, and including new studies that recently joined the consortium. In this article, we describe in detail the design and methods of the project. Methods Project Structure and Coordination The InterLymph NHL Subtypes Project was governed by a Project Coordinating Committee, with representation from each contributing study and InterLymph working group (Immunity & Infection, Lifestyle & Environment, and Pathology). The Committee was led by an interdisciplinary group of epidemiologists (LMM, MSL, JRC), pathologists (DDW, JJT), and biostatisticians (SLS, JNS) who initiated and/or led the project. Additional oversight of the analyses was provided by an analytic working group with biostatisticians from three participating studies (JNS, SLS, YB). Project coordinators corresponded regularly by e-mail and teleconference, and met in-person at four annual InterLymph meetings during 2010–2013. Working groups for each NHL subtype included in the project were formed with representation from participating studies and with other InterLymph members with expertise and interest in that particular subtype. Each group included at least one pathologist, clinician, and biostatistician, in addition to epidemiologists. Communication was facilitated by use of a password-protected web portal for posting study documents and results. Decisions were made by consensus or voting. Study Population Studies eligible for inclusion in these pooled analyses fulfilled the following criteria: 1) case-control design, with incident cases of NHL and information on NHL subtype, 2) availability of individual-level data by December 31, 2011, and 3) participation in InterLymph. A total of 20 studies fulfilled these criteria (Table 1). As described below, studies were included in specific analyses where cases were available with the subtype of interest and data were collected on the particular risk factor under evaluation. Contributing studies were approved by local ethics review committees, and all participants provided informed consent before interview. Table 1. Characteristics of studies included in the InterLymph NHL Subtypes Project* Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  Region  Location  Years of diagnosis  Design  Participation, %†  Total No.  Study (reference)  Cases  Controls  Cases  Controls  North America   British Columbia (43)  Vancouver, Victoria, British Columbia (Canada)  2000–2004  Population-based  79  46  833  845   Iowa/Minnesota (44)‡  Iowa, Minnesota (US)  1981–1983  Population-based  87  81  866  1245   Kansas (45)§  Kansas (US)  1976–1982  Population-based  96  94  170  948   Los Angeles (46)§  Los Angeles, California (US)  1989–1992  Population-based  45  69  376  375   Mayo Clinic (47,48)‡  Iowa, Minnesota, Wisconsin (US)  2002–2008  Clinic-based  69  69  1120  1314   NCI-SEER (49)  Detroit, Michigan; Iowa; Los Angeles, California; Seattle, Washington (US)  1998–2000  Population-based  76  52  1316  1055   Nebraska (older)‡ (50)  Nebraska (US)  1983–1986  Population-based  91  87  441  1432   Nebraska (newer) (51)  Nebraska (US)  1999–2002  Population-based  73  77  387  533   UCSF1 (52)  San Francisco, California (US)  1988–1995  Population-based  72  78  1302  2402   UCSF2||  San Francisco, California (US)  2001–2006  Population-based  70  68  487  457   University of Rochester (53)  Rochester, New York (US)  2005–2007  Hospital-based  96  78  129  139   Yale (54)  Connecticut (US)  1995–2001  Population-based  72  47–69  600  717  Europe   Engela (55)‡,§  Bordeaux, Brest, Caen, Lille, Nantes, Toulouse (France)  2000–2004  Hospital-based  97  93  567  722   EpiLymph (56)‡,§  Spain; France; Germany; Italy; Ireland; Czech Republic  1998–2004  Population-based (Italy, Germany), otherwise hospital-based  82–93  44–96  1735  2460   Italy multicenter (57)‡  Firenze, Forli, Imperia, Latina, Novara, Ragusa, Siena, Torino, Varese, Vercelli, Verona (Italy)  1990–1993  Population-based  82  74  1911  1771   Italy (Aviano-Milan) (58,59)  Aviano, Milan (Italy)  1983–1992  Hospital-based  >97  >97  429  1157   Italy (Aviano-Naples) (60)  Aviano, Naples (Italy)  1999–2002  Hospital-based  97  91  225  504   SCALE (61)‡  Denmark; Sweden  1999–2002  Population-based  81  71  3055  3187   United Kingdom (62)‡,§  Lancashire/South Lakeland, South England, Yorkshire (United Kingdom)  1998–2001  Population-based  75  71  828  1139  Australia   New South Wales (63)  New South Wales, Australian Capital Territory (Australia)  2000–2001  Population-based  85  61  694  694  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † Participation was typically computed as number participating divided by the number contacted. ‡ Studies were designed to investigate other malignancies in addition to NHL: Engela: Hodgkin lymphoma, multiple myeloma, and lymphoproliferative syndromes; EpiLymph: Hodgkin lymphoma and multiple myeloma; Iowa/Minnesota: leukemia; Italy: Hodgkin lymphoma, multiple myeloma, and soft-tissue sarcoma; Mayo: Hodgkin lymphoma; Nebraska (older): Hodgkin lymphoma and multiple myeloma; SCALE: Hodgkin lymphoma; United Kingdom: Hodgkin lymphoma. § Studies individually matched controls to cases. The remaining studies frequency matched controls to cases, typically by age (5-y groups), sex, race/ethnicity, and geographic location where appropriate. || This recently completed study has not yet published results but had similar methodology to a previous study conducted in the same region (52). View Large Individuals with a known history of solid organ transplantation or HIV/AIDS were ineligible for most studies; any individual who reported a history of these conditions during patient interviews or via questionnaires was excluded from these pooled analyses. All studies conducted direct interviews with study participants, except Iowa/Minnesota and Italy multicenter, which used proxy respondents for deceased cases. In the six studies that used hospital- or clinic-based controls, analyses included all controls used in the original study, regardless of reason for hospital admission. NHL Subtype Ascertainment and Harmonization Eligible cases were diagnosed with incident, histologically confirmed NHL. Most studies had centralized review of pathology reports and diagnostic slides by at least one expert hematopathologist to confirm the NHL diagnoses and assign an NHL subtype, except for the National Cancer Institute-Surveillance, Epidemiology, and End Results Program and Italy multicenter studies, in which cases were diagnosed by local pathologists. Each participating study’s pathology review procedures, rules for NHL subtype classification, and NHL subtype distribution were reviewed by an interdisciplinary team of hematopathologists and epidemiologists. We grouped cases into NHL subtypes according to the WHO classification (2,3) using guidelines from the InterLymph Pathology Working Group (64,65). NHL subtypes with more than 100 cases in the pooled dataset were eligible for inclusion in this project. Because of the potential for risk factors to differ by primary site of disease for certain NHL subtypes (e.g., Sjögren’s syndrome and salivary gland marginal zone lymphoma) (22), we further classified cases according to primary site of NHL for secondary analyses. In most studies (N = 14), primary site of NHL was recorded if known, irrespective of disease stage. In some NHL subtypes, site is a diagnostic criterion. NHL site was categorized as nodal, extranodal lymphatic (Waldeyer’s ring, thymus, or spleen), or extranodal extralymphatic (66). Specific primary sites (e.g., skin, gastrointestinal tract) also were recorded for further analysis in some NHL subtypes. Leukemias were classified as systemic by definition. Cases with widespread disease or primary site listed as bone marrow, blood, or cerebrospinal fluid, and in which subtype was not site-specific by definition, also were classified as systemic. Risk Factor Ascertainment and Harmonization Each study collected data on putative NHL risk factors in a standardized, structured format by in-person or telephone interviews and/or self-administered questionnaires. In some studies, participants also provided a venous blood sample. Risk factors selected for inclusion in this analysis were the available medical history, family history of hematologic malignancy, lifestyle factors, and occupations with data from at least four studies. Each study contributed de-identified, individual-level data for the risk factors of interest. Data harmonization was conducted centrally at the Mayo Clinic under the leadership of SLS. Each exposure variable was harmonized individually and data were then reviewed for consistency among related exposure variables. A small subcommittee reviewed harmonization rules for each exposure, ensured consistency with previously published InterLymph pooled analyses, and advised the analytic approaches for that exposure. Statistical Analysis We defined a single analytical plan and applied it to each of the 11 NHL subtypes, with all analyses conducted centrally at the National Cancer Institute under the leadership of JNS and LMM. Here, we describe this plan in terms of a generic NHL subtype. Each analysis defined individuals with a specific NHL subtype as cases, individuals without any type of NHL as controls, and excluded cases with NHL subtypes other than the one of interest. For analyses of a particular subtype, we only included controls from studies that also contributed cases of that subtype. Analyses were conducted using SAS software, version 9.2 (SAS Institute, Inc, Cary, NC). We examined the relationship between case/control status and each exposure variable individually using unconditional logistic regression models adjusted for age (<30, 30–39, 40–49, 50–59, 60–69, 70–79, ≥80 years), race/ethnicity (white, black, Asian, Hispanic, or other/missing), sex, and study. Confidence intervals (CIs) for the odds ratios (ORs) were estimated by asymptotic theory for maximum likelihood estimation. Statistical significance of each relationship was evaluated by a likelihood ratio test, comparing models with and without the exposure variable of interest, with P values less than .05 identifying putatively influential factors. We excluded individuals who had missing data for the exposure variable of interest, and we excluded both cases and controls from studies where only a single category of the exposure variable of interest was represented (e.g., no cases and no controls reported history of a particular autoimmune condition). To evaluate effect heterogeneity among the studies included in each analysis, we performed a separate logistic regression within each study and then quantified the variability of the coefficients by the H statistic. Adapting the definition by Higgins and Thompson to categorical variables (67), we defined H as follows. Let β^ be the vector of size n = number of studies × (number of categories – 1) containing the estimated coefficients from all studies. Let S be the estimated block diagonal variance matrix for β^. Subtract the mean of each coefficient from β^ to obtain Β^. Then, we defined Q=Β^TΣ−1Β^, which has a χ2 with n' = (n − number of categories − 1) degrees of freedom and H=Q/n'. The lower and upper bounds for the 95% CI of H is Hexp(−A) and Hexp(A), where A=1.96(1−(1/(3k2))/(2k) and k=n'−1. In the absence of heterogeneity, H is expected to equal 1. Statistically significant heterogeneity among studies was identified by 95% CI for H that excluded 1. If a category within a particular study included no cases, suggesting that the disease risk was 0 or the log(OR) = −∞, we set the log(OR) to −2 and estimated the standard deviation by rerunning the logistic regression with an additional case added into that category. 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 above logistic regression analyses, but now stratified individuals by age, sex, race/ethnicity, region (North America, Northern Europe, Southern Europe, and Australia), study, study design (i.e., population-based versus. hospital- or clinic-based studies), or putative risk factors identified in the analysis. Forest plots illustrated the results from the stratified analyses to identify possible effect modifiers for each exposure variable. To account for potential confounding, 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 at a time (as well as age, race/ethnicity, sex, and study). Second, we conducted a single logistic regression analysis including all putative risk factors, age, sex, race, and study, this time including a separate missing category for each variable to ensure that the whole study population was included in the analysis (i.e., not excluded due to missing data). Note, however, that the likelihood ratio P values for a putative risk factor excluded the effect of this newly created missing category. Finally, we conducted a forward step-wise logistic regression with all putative risk factors, adjusting for age, sex, race/ethnicity, and study. In most of the original studies, controls were frequency-matched to cases by age and sex. However, because some studies included cases with Hodgkin lymphoma, myeloma, leukemia, and/or soft-tissue sarcoma in addition to NHL (Table 1), each analysis included only a subset of cases causing the matching to be broken. Therefore, as a sensitivity analysis, we repeated the analyses described above using a subset of controls that were frequency-matched by age and sex to cases with that NHL subtype. The results from these sensitivity analyses were very similar to the results obtained using the full set of controls, thus we retained the full set of controls for our main analyses to increase statistical power. The power to detect an association between a dichotomous characteristic and NHL subtype depends on the number of available cases, the prevalence of the characteristic, and the strength of the association. To understand the potential types of associations detectable for each subtype, we calculated the power for varying combinations of those three parameters (Figure 1). The calculations assumed a 1:5 case:control ratio, an α level of 0.05, and that the estimated proportions of exposed cases and controls were normally distributed. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Figure 1. View largeDownload slide The power to detect an association between a characteristic (e.g., autoimmune disease) and a non-Hodgkin lymphoma subtype depends on the number of cases, the prevalence of that characteristic in the control group, and the odds ratio [OR] indicative of effect size. Each figure shows the power as a function of the number of cases in the study for different effect sizes (1.25 ≤ OR ≤ 4.0), given the prevalence of the dichotomous characteristic among controls (0.02 ≤ prevalence ≤ 0.2). The top row includes up to 3000 cases, whereas the bottom row focuses on a smaller sample size (up to 500 cases). Calculations assume five controls:one case and α = 0.05. Results Study Population Twenty studies from North America (N = 12), Europe (N = 7), and Australia (N = 1) were included in the InterLymph NHL Subtypes Project, representing most of the large-scale case-control studies of NHL conducted over the last three decades in these regions (Table 1). The pooled dataset included a total of 17471 NHL cases and 23096 controls. More than 75% of participants were derived from the 14 population-based studies, with the remaining participants from hospital- or clinic-based studies. The pooled study controls were 58% male and 93% non-Hispanic white, and had a median age at interview of 59 years (range 16–98 years); 41% were classified in the lowest tertile of socioeconomic status (Table 2). Table 2. Demographic characteristics of controls from participating studies in the InterLymph NHL Subtypes Project* Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  Region  Controls, No.  Male, %†  Race/ethnicity, %  Age, y§  Socioeconomic status, %||  Study  White  Black  Asian  Hispanic  Other ‡  Median (range)  Low  Medium  High  Missing  North America   British Columbia  845  53  77  0  17  0  7  60 (20–80)  38  32  29  1   Iowa/Minnesota  1245  100  100  0  0  0  0  68 (30–97)  71  18  11  0   Kansas  948  100  96  0  0  1  3  63 (18–98)  36  32  24  8   Los Angeles  375  49  75  5  3  16  0  54 (17–79)  33  35  32  0   Mayo Clinic  1314  54  98  0  0  1  1  63 (21–94)  23  24  40  13   NCI-SEER  1055  52  78  14  2  5  1  61 (20–74)  37  31  31  0   Nebraska (older)  1432  51  99  0  0  0  1  68 (19–98)  66  17  12  6   Nebraska (newer)  533  53  96  2  1  1  1  59 (20–76)  45  26  29  0   UCSF1  2402  65  81  6  5  7  2  53 (21–74)  29  25  46  0   UCSF2  457  59  94  0  0  6  0  59 (20–84)  15  29  56  0   University of Rochester  139  44  88  6  1  3  1  52 (21–85)  40  25  32  4   Yale  717  0  92  3  0  4  1  64 (23–86)  37  31  32  1  Europe   Engela  722  62  100  0  0  0  0  55 (18–76)  32  37  31  0   EpiLymph  2460  54  97  0  0  0  3  59 (17–76)  46  41  14  0   Italy multicenter  1771  52  100  0  0  0  0  58 (19–75)  53  24  23  0   Italy (Aviano-Milan)  1157  61  100  0  0  0  0  57 (17–85)  57  21  22  1   Italy (Aviano-Naples)  504  68  100  0  0  0  0  63 (18–83)  47  20  33  0   SCALE  3187  55  95  0  0  0  5  59 (18–76)  29  34  36  2   United Kingdom  1139  55  100  0  0  0  0  53 (16–69)  33  35  32  0  Australia   New South Wales  694  57  87  0  2  0  11  57 (21–74)  34  35  31  0  Total  23096  58  93  2  1  2  2  59 (16–98)  41  29  29  2  * InterLymph = International Lymphoma Epidemiology Consortium; NCI-SEER = National Cancer Institute-Surveillance, Epidemiology, and End Results Program; NHL = non-Hodgkin lymphoma; SCALE = Scandinavian Lymphoma Etiology Study; UCSF = University of California, San Francisco. † The Yale study was restricted to women, whereas the Kansas and Iowa/Minnesota studies were restricted to men. ‡ Includes individuals with other specified races as well as unknown race. For studies with predominantly white, non-Hispanic populations, individuals with unknown race were assumed to be white (Iowa/Minnesota, Kansas, Mayo Clinic, NCI-SEER [Seattle, Iowa study centers], Nebraska [older], Nebraska [newer], UCSF2, University of Rochester, Yale, Engela, SCALE, United Kingdom). § Age at diagnosis for cases and interview for controls. All studies excluded children (with varying minimum ages), and most studies excluded the elderly (with varying maximum ages) population, except for the Iowa/Minnesota, Kansas, Mayo Clinic, Nebraska (older), and University of Rochester studies, which did not have an upper age limit. || 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 NHL Subtypes Of the 17471 NHL cases in the pooled analyses, 11976 (69%) were derived from studies that classified NHL subtypes according to the WHO classification (2,3) or its closely related predecessor, the Revised European-American Lymphoma classification (68) (Table 3). The remaining 5495 (31%) cases were classified according to the Working Formulation (69). Among cases originally defined by the WHO classification, 11023 (92%) were assigned to one of the 11 subtypes included in this project (i.e., with >100 cases), with the remaining cases assigned to a rare (N = 50 total, <1%) or unspecified (N = 900, 8%) NHL subtype. Where the Working Formulation was the original coding scheme, 3106 (57%) cases were assigned to one of the 11 subtypes included in this project, with the remaining 2389 (43%) considered poorly specified because certain Working Formulation subtypes do not reliably correspond to subtypes defined by the WHO (e.g., chronic lymphocytic leukemia/small lymphocytic lymphoma) or were not recognized in the Working Formulation (e.g., marginal zone lymphoma, mantle cell lymphoma) (64). Table 3. Distribution of the InterLymph NHL Subtypes Project study population by NHL subtype and study* NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866  112  195  244  0||  0||  0||  0||  0||  18  0§  6  0  291   Italy multicenter  1911  407  159  214  0||  55  0||  0§  25  23  30  12  0  986   Italy (Aviano-Milan)  429  47  55  0||  0||  0  0||  0§  0||  9  0§  10  0  308   Kansas  170  27  34  0||  0||  0||  0||  0||  0||  2  0§  1  0  106   Los Angeles**  376  151  41  0||  0||  1  0||  0§  0||  50  0§  16  0  117   Nebraska (older)  441  94  111  0||  0||  0||  0||  0||  0||  6  0§  3  0  227   UCSF1  1302  509  352  0||  0||  0||  0||  0§  47  28  0§  12  0  354  Total  17471  4667  3530  2440  1052  584  557  374  324  295  154  152  50  3292  NHL subtype classification†  Cases  Study  Total  DLBCL  FL  CLL/SLL  MZL  PTCL  MCL  LPL  MF/SS  BL  HCL  ALL  Other  NOS  WHO (total)  11976  3320  2583  1982  1052  528  557  374  252  159  124  92  50  903   British Columbia  833  218  228  42‡  101  33  50  42  42  10  0§  6  3  58   Engela  567  174  101  132  20  18  25  21  0  11  36  8  0  21   EpiLymph  1735  516  251  414  138  78  67  44  38  24  15  46  13  91   Italy (Aviano-Naples)  225  112  36  18‡  14  9  2  10  2  9  0§  0§  2  11   Mayo Clinic  1120  210  271  376  68  32  56  22  9  7  0§  1  6  62   NCI-SEER  1316  413  318  133‡  106  55  50  28  26  12  0§  0§  1  174   Nebraska (newer)  387  103  123  29‡  35  12  16  5  7  12  0§  1  0  44   New South Wales  694  231  252  29‡  61  16  22  27  4  4  10  5  4  29   SCALE  3055  796  586  752  117  121  148  116  41  31  63  15  16  253   UCSF2  487  0¶  0¶  0¶  187  77  58  43  54  35  0§  7  3  23   University of Rochester**  129  32  45  7‡  24  8  7  0§  2  0  0§  0  0  4   United Kingdom  828  326  236  0‡  141  50  40  0§  15  0§  0§  0§  2  18   Yale  600  189  136  50‡  40  19  16  16  12  4  0§  3  0  115  Working Formulation (total)  5495  1347  947  458  0  56  0  0  72  136  30  60  0  2389   Iowa/Minnesota  866 

Journal

Journal of the Endocrine SocietyOxford University Press

Published: Aug 30, 2014

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