1 Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
2 Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN.
3 Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA.
4 Division of Epidemiology, University of Minnesota School of Public Health, Minneapolis, MN.
Received for publication January 9, 2002; accepted for publication May 2, 2002.
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ABSTRACT |
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anthropometry; cohort studies; leukemia, lymphocytic, chronic; lymphoma, non-Hodgkin
Abbreviations: Abbreviations: B-CLL, B-cell chronic lymphocytic leukemia; CI, confidence interval; ICD-O-2, International Classification of Diseases for Oncology, Second Edition; RR, relative risk.
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INTRODUCTION |
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Diets high in fat (6, 7) or meat products (69) have been associated with risk of non-Hodgkins lymphoma. Several anthropometric measures, including body mass (10, 11) and height (7, 12), have also been suggested as non-Hodgkins lymphoma risk factors. Data from epidemiologic, clinical, and animal studies, albeit limited, provide some support for the hypothesis that obesity is associated with impairments of both humoral and cellular immunity (13). Obesity is also associated with insulin resistance and the development of adult-onset diabetes, and adult-onset diabetes has been shown in some studies to be a risk factor for non-Hodgkins lymphoma (1417). Physical inactivity is an important determinant of obesity and appears to be a risk factor for several types of cancer, particularly colon cancer and female reproductive cancers (18, 19). The underlying mechanisms responsible for these associations with physical inactivity are not known but are thought to involve body weight, changes in steroid hormone levels (perhaps caused by changes in menstrual function for women), or modulation of immune function (1820). The extremely limited data available suggest that there is no association between the level of physical activity and risk of non-Hodgkins lymphoma or lymphatic leukemias (2123). There are no data on either anthropometric characteristics or physical activity and risk of B-CLL specifically.
We therefore evaluated whether anthropometric factors and physical activity were associated with non-Hodgkins lymphoma or B-CLL in a large prospective cohort of Iowa women. In secondary analyses, we evaluated heterogeneity of the results by the most common subtypes of non-Hodgkins lymphoma.
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MATERIALS AND METHODS |
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Self-reported items on the 1986 baseline questionnaire included demographic data, medical history, and dietary and other lifestyle factors. Participants reported current height and weight and weight at ages 18, 30, 40, and 50 years. The body mass index at baseline and at each respective age was calculated as weight (in kilograms) at the given age divided by baseline height squared (in meters). A paper tape measure and written instructions for having a friend measure circumferences of the waist (1 inch (2.54 cm) above the umbilicus) and hips (maximal protrusion) were enclosed with the questionnaire. The waist/hip ratio was calculated from these measures. This protocol has been shown to be valid (correlation coefficient with measures by trained technician, 0.84) and reliable (correlation of measures at two different time periods,
0.85) (25). Relative weight at age 12 was ascertained using the following question: "Think back to when you were in sixth gradeor about the age of 12. Would you say at that time, compared to other girls your age, your weight was below average for your age and height, about average for your age and height, or above average for your age and height?"
Physical activity was assessed by asking participants three questions about whether they participated in any leisure exercise and, if so, the frequency of moderate and vigorous activities. We did not have estimates of occupational physical activity. Responses to these questions were combined to create a three-level activity score (low, medium, high). This scale is strongly associated with total mortality and cardiovascular disease mortality in this cohort (26).
Cohort follow-up
Vital status and cancer incidence in the cohort were ascertained through 13 years of continuous follow-up (1986 through 1998). Follow-up questionnaires were mailed in 1987, 1989, 1992, and 1997 to ascertain vital status and address changes. Deaths were also ascertained by annual linkage to a database of Iowa death certificates, supplemented by linkage to the National Death Index for survey nonrespondents and emigrants from Iowa. Outmigration has been estimated at 1 percent per year.
Cancer incidence except for nonmelanoma skin cancer was ascertained by annual linkage to the Iowa Cancer Registry, part of the National Cancer Institutes Surveillance, Epidemiology, and End Results program (2). All participants were linked by a combination of Social Security number, first, last, and maiden name, birth date, and ZIP code. The Iowa Cancer Registry collects cancer data including identifying information, tumor site, morphology, histologic grade, and extent of disease on all persons who were Iowa residents at the time of their diagnosis. All tumor site and morphology data were derived from pathology reports of the diagnosing hematopathologist, and there was no centralized pathology review.
Topographic and morphologic data were coded using the International Classification of Diseases for Oncology, Second Edition (ICD-O-2) (27), which for lymphomas was derived from the Working Formulation (28). The histologic subtypes of non-Hodgkins lymphoma were grouped according to the method of Groves et al. (29) into the following subtypes based on ICD-O-2 codes: small lymphocytic, follicular, diffuse, high grade, and peripheral T cell. This system is a modification of the Working Formulation for use in epidemiologic studies. Chronic lymphoctyic leukemia was also analyzed but as a distinct endpoint; that is, it was not aggregated with small lymphocytic lymphoma.
Data analysis
Before data analysis, we excluded women with a self-reported history of cancer or cancer chemotherapy on the 1986 baseline questionnaire (n = 3,902) to provide a cancer-free at-risk cohort. Each woman accumulated person-years of follow-up from the date of receipt of the 1986 baseline questionnaire until the date of non-Hodgkins lymphoma (or B-CLL) diagnosis, date of emigration from Iowa, or date of death; if none of these events occurred, person-years were accumulated through December 31, 1998. Two women who were censored at their baseline date were further excluded, leaving a final cohort of 37,932 for analysis. All continuous anthropometric variables were classified into quartiles based on the distribution in the at-risk cohort. Physical activity was grouped in low, moderate, and high levels (26). Relative weight at age 12 years was categorized according to possible responses (below average, average, above average), with the average group serving as the reference category. Age-adjusted and multivariate-adjusted relative risks, along with 95 percent confidence intervals, were calculated as measures of association of the risk factors with non-Hodgkins lymphoma incidence and were estimated using Cox proportional hazards regression (30), with the survival modeled as a function of age (31). Other risk factors for non-Hodgkins lymphoma in this data set included age, marital status, farm residence (32), transfusion history (33), adult-onset diabetes (33), alcohol use (34), cigarette smoking (35), red meat and fruit intake (6), and use of hormone replacement therapy, and they were evaluated in multivariate models as potential confounders.
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RESULTS |
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The age-adjusted associations of baseline anthropometric characteristics and of physical activity with risk of non-Hodgkins lymphoma overall and with selected non-Hodgkins lymphoma subtypes are presented in table 1. There was no evidence of an association between height and non-Hodgkins lymphoma, B-CLL, or any of the non-Hodgkins lymphoma subtypes, with the possible exception of small lymphocytic lymphoma. For small lymphocytic lymphoma, there was an inverse association with height, but none of the point estimates was statistically significant, and there was no evidence of a dose-response relation. There was no evidence of a dose-response relation between baseline weight, body mass index, and waist/hip ratio and risk of all non-Hodgkins lymphoma or of diffuse or follicular lymphoma. For small lymphocytic lymphoma and B-CLL, there were also no statistically significant associations although, for weight and body mass index, relative risks were below one for small lymphocytic lymphoma and above one for B-CLL.
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The age-adjusted association of historical anthropometric characteristics and lymphoma risk is presented in table 2. For all non-Hodgkins lymphoma and for diffuse and follicular non-Hodgkins lymphoma, there was little evidence for associations with relative weight at age 12 years and body mass index at ages 18, 30, 40, or 50 years of age. These results did not change after multivariate adjustment (data not shown). For relative weight at age 12 years, there were associations for both small lymphocytic lymphoma and B-CLL. For small lymphocytic lymphoma, women who reported above average weight at age 12 years were at increased risk relative to women who were average weight at age 12 (RR = 2.4, 95 percent CI: 1.0, 5.8), as were women who were below average weight at age 12 (RR = 1.7, 95 percent CI: 0.8, 3.9). In contrast, for B-CLL, compared with women who were of average weight at age 12 years, women who were of below average weight were at a slightly lower risk (RR = 0.7, 95 percent CI: 0.3, 1.3) while women who were of above average weight were at slightly elevated risk (RR = 1.5, 95 percent CI: 0.8, 3.0), and the trend across these categories was statistically significant (p = 0.05). These associations remained unchanged after multivariate adjustment (table 3).
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DISCUSSION |
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There are also limitations. Most importantly, the sample size (and therefore the precision of our estimates) was limited for the rarer subtypes, particularly small lymphocytic lymphoma. Moreover, multiple comparisons were made, and the few results at p < 0.05 may be due to chance. The initial response to the baseline questionnaire was only 43 percent. However, although this may limit the generalizability of the results, the virtually complete follow-up of the cohort ensures a high internal validity. Furthermore, nonresponders to the baseline survey have also been followed, and although they had slightly higher rates of smoking-related diseases, the association of body mass index with cancer occurrence (all sites and breast, endometrial, colon, and lung individually) was not appreciably affected by nonresponse bias (36). In addition, the anthropometric (37) and physical activity (26) variables have been associated with other morbidity or mortality endpoints in this study, consistent with a majority of the literature. The results of this study, however, may not generalize to males or non-Caucasian populations.
Adult weight and body mass
We found no evidence of an association of weight or body mass, either at study baseline or during adulthood, with the risk of non-Hodgkins lymphoma overall. This is consistent with three other incidence studies (7, 38, 39), a mortality study (40), and a hospital-based case-control study (41). In contrast, a Swedish cohort study (11) found that women with a hospital diagnosis of obesity were at excess risk of developing non-Hodgkins lymphoma (43 observed cases; RR = 1.6, 95 percent CI: 1.2, 2.1), although there was a weak inverse association for men (nine observed cases; standardized incidence ratio = 0.7, 95 percent CI: 0.3, 1.3). The only other study reporting an association with obesity was a large population-based case-control study conducted in San Francisco, California (10). Among heterosexuals, there was a strong, graded, positive association between body mass index and the risk of non-Hodgkins lymphoma for both men (odds ratio = 2.6 for a body mass index of 30 vs. <20, 95 percent CI: 1.2, 5.7) and women (odds ratio = 3.1 for a body mass index of
30 vs. <20, 95 percent CI: 1.4, 6.8). The latter associations remained after adjustment for several medical history and lifestyle factors. Associations by non-Hodgkins lymphoma subtype were not reported. When we repeated our analyses using the same cutpoints as the latter study, we still found no association (data not reported). In the San Francisco case-control study, 21 percent of the eligible patients died before they could be contacted, and another 10 percent were too ill to be interviewed. Since survival (required to get into the study) could be related to body mass, we excluded all cases in our study who died within 9 months of diagnosis to see if this might explain the discrepancy between our studies. After this exclusion, however, we still found no association between body mass index and the risk of non-Hodgkins lymphoma. We clearly had sufficient power to detect an association of the magnitude reported in the San Francisco study. Furthermore, it is unlikely that we were unable to detect an association because of exposure misclassification, as body mass index has been strongly correlated with breast and endometrial cancer incidence (42, 43) and coronary heart disease mortality (37) in this cohort. Although discrepancies remain, the balance of evidence published to date suggests no association of body mass with non-Hodgkins lymphoma.
To our knowledge, no data have been reported on the association of adult obesity with the risk of non-Hodgkins lymphoma subtypes or of B-CLL. There are some published data on the association of body mass index with the risk of all leukemias, with studies showing both weak positive associations (3840) and no association (11, 44). We found no evidence of an association of weight or body mass index with the risk of diffuse or follicular lymphoma. We did find suggestive inverse associations of weight, body mass index, and body mass index at ages 40 and 50 years with the risk of small lymphocytic lymphoma. In addition, we found suggestive positive associations of body mass index with the risk of B-CLL, particularly body mass indexes in later adulthood. However, most of these results were not statistically significant, and there was generally no clear dose-response trend, although the multivariate results for body mass index at age 50 years were statistically significant for both small lymphocytic lymphoma (inverse association, p trend = 0.04) and B-CLL (positive association, p trend = 0.03). It is somewhat surprising that the associations for small lymphocytic lymphoma and B-CLL are in opposite directions. This is based on the prediction that these two low-grade malignacies of B cells would be predicted to have similar risk factor profiles, because they may represent different clinical spectrums of closely related B-cell subpopulations. Indeed, their biologic and pathologic similarities have led to their classification together in the most recent World Health Organization classification system (5). Because of the small sample sizes in these analyses, however, our findings from this secondary analysis may be due to chance, and thus replication is needed.
Height and adolescent obesity
Adult height and adolescent obesity in part reflect the influence of nutritional factors during early childhood and adolescence on growth, with taller adult height and greater obesity reflecting more adequate (or excess) availability of food and energy and greater exposure to growth-related hormones. There are few data on the association of adolescent obesity with the risk of non-Hodgkins lymphoma and no data for non-Hodgkins lymphoma subtypes or B-CLL. The cohort studies of former students of Harvard University and the University of Pennsylvania found no association of obesity in college with the risk of either non-Hodgkins lymphoma or lymphatic leukemia (45). In our data, we also found little evidence of an important role for either relative weight at age 12 years or body mass index at age 18 and the risk of non-Hodgkins lymphoma overall or for diffuse or follicular non-Hodgkins lymphoma. There were, however, positive associations for B-CLL, consistent with the positive associations seen with obesity measures at other ages in this cohort. Relative weight at age 12 years was also associated with small lymphocytic lymphoma, but the increased risk was observed for both being underweight and being overweight relative to peers, although this was statistically significant only for being overweight. The subjective nature of the assessment of weight at age 12 years may have a higher potential for misclassification (both differential and nondifferential), which could bias our results.
We found no association of height with the risk of all non-Hodgkins lymphoma, non-Hodgkins lymphoma subtypes, or B-CLL. There was no association of height with the risk of non-Hodgkins lymphoma or leukemia (all) in a cohort study of Icelanders (39), although other cohort studies have reported a positive association with non-Hodgkins lymphoma incidence (7), lymphoma mortality (44), and hematopoietic cancer mortality (12). The latter two studies were based on mortality data, had small numbers of events, and lacked adjustment for dietary factors, but the former study using incidence data from the Nurses Health Study (7) did not have these limitations and is comparable in design to this study. In the Nurses Health Study, women who were 68 inches (1.73 m) were at a 2.4-fold increased risk of non-Hodgkins lymphoma (95 percent CI: 1.2, 4.7) compared with women who were <62 inches (1.57 m), and there was a clear gradient in risk with increasing height. Neither our study nor the Nurses Health Study evaluated the association of height with non-Hodgkins lymphoma mortality, but the magnitude of associations found in cohort studies of lymphoma or hematopoietic mortality (12, 44) is compatible with the magnitude of the effect seen in the incidence data from the Nurses Health Study. Our incidence data, in contrast, are not compatible with these other studies, and more data based on incidence will be required to clarify the validity of this association. No prior study has published data on the association of height with non-Hodgkins lymphoma subtypes or with B-CLL specifically.
Body fat distribution
To our knowledge, there are no data on body fat distribution and the risk of non-Hodgkins lymphoma or B-CLL; we found that this is unlikely to be an important risk factor in the etiology of these malignancies overall or for non-Hodgkins lymphoma subtypes.
Physical activity
Overall, we found no association between the level of leisure-time physical activity and the risk of non-Hodgkins lymphoma or B-CLL. A limitation of these data is the lack of occupational physical activity. Although we did observe a suggestive positive association of physical inactivity with the risk of follicular non-Hodgkins lymphoma, this association attenuated after multivariate adjustment. A larger study is needed to better characterize this association. Occupational physical activity was not associated with either non-Hodgkins lymphoma or leukemia in a registry-based case-control study (other cancers served as controls) (22), and physical activity in college was not associated with the risk of non-Hodgkins lymphoma or lymphatic leukemia in two cohort studies of college alumni (21, 23). Physical activity, even modest levels, has been most strongly associated with a reduced risk of colon cancer and cancers of the female reproductive tract (18, 20), and the mechanistic basis for these observations includes control of body size, changes in steroid hormone metabolism, changes in menstrual function (females), or modulation of immune function. As reviewed above, a majority of studies have not found an association of non-Hodgkins lymphoma with obesity, suggesting that weight control is unlikely to be an important mechanism. Menstrual characteristics do not appear to be important risk factors for non-Hodgkins lymphoma, at least in this cohort (46). Although there are some animal and human data showing immunologic effects of both moderate and strenuous physical activity (18, 19), persons with primary or acquired immunodeficiency do not develop colon and female reproductive tract cancers but, rather, skin cancer, lymphomas, Kaposi's sarcoma, and other rare or unusual tumors (47). Thus, the overall lack of association of physical activity with the risk of non-Hodgkins lymphoma (particularly diffuse lymphoma), a cancer whose development is thought to be correlated with the host immune status, suggests that immunomodulation may not be a major mechanistic basis for the protective association of physical activity with the development of cancer in general.
Conclusions
We found no evidence that height, weight, body mass, or physical activity plays an important role in the development of non-Hodgkins lymphoma overall or of diffuse or follicular non-Hodgkins lymphoma in particular. The inverse associations of several body mass index measures with small lymphocytic non-Hodgkins lymphoma, but positive associations with B-CLL, are novel. However, the latter associations must be interpreted with caution because of the small sample size, and they clearly require replication. Given the generally null findings from the literature and in this study, as well as weak data for a major role of obesity or physical activity in immune modulation, these factors are unlikely to have a critical role in the etiology of non-Hodgkins lymphoma. In addition, the more strongly documented correlates between diet and the risk of non-Hodgkins lymphoma (6, 7, 48, 49) indicate that future research will likely be more productive in evaluating the role of specific dietary factors in the etiology of these malignancies.
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ACKNOWLEDGMENTS |
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The authors thank Ching-Ping Hong for technical assistance and Mary Jo Janisch for assistance in preparing this manuscript.
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NOTES |
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REFERENCES |
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