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Research Articles

Physical Activity in Relation to Risk of Hematologic Cancers: A Systematic Review and Meta-analysis

Carmen Jochem, Michael F. Leitzmann, Marlen Keimling, Daniela Schmid and Gundula Behrens
DOI: 10.1158/1055-9965.EPI-13-0699 Published May 2014

Abstract

Background: Despite the existence of numerous biologic pathways potentially linking increased physical activity to decreased risk of hematologic cancers, the associations between physical activity and subtype-specific hematologic cancers have not been comprehensively quantified.

Methods: We conducted a systematic review and meta-analysis of physical activity in relation to subtype-specific hematologic cancers. We summarized the data from 23 eligible studies (15 cohort and eight case–control studies) and estimated summary relative risks (RR) and 95% confidence intervals (CI) using random-effects models.

Results: When comparing high versus low physical activity levels, the RR for non-Hodgkin lymphoma was 0.91 (95% CI, 0.82–1.00), for Hodgkin lymphoma it was 0.86 (95% CI, 0.58–1.26), for leukemia it was 0.97 (95% CI, 0.84–1.13), and for multiple myeloma it was 0.86 (95% CI, 0.68–1.09). When focusing on subtypes of non-Hodgkin lymphoma, the RR for diffuse large B-cell lymphoma was 0.95 (95% CI, 0.80–1.14) and for follicular lymphoma it was 1.01 (95% CI, 0.83–1.22). In an exploratory analysis combining all hematologic cancers, high versus low physical activity levels yielded a statistically significant RR of 0.93 (95% CI, 0.88–0.99).

Conclusions: Physical activity showed statistically nonsignificant associations with risks of non-Hodgkin lymphoma, Hodgkin lymphoma, multiple myeloma, and leukemia. These findings may not represent a true lack of associations given the variation in high versus low physical activity definitions, the quality of physical activity assessments, and the variability in hematologic cancer classification schemes in individual studies.

Impact: Physical activity is unrelated to risks of subtype-specific hematologic cancers. Cancer Epidemiol Biomarkers Prev; 23(5); 833–46. ©2014 AACR.

Introduction

Hematologic cancers represent a heterogeneous group of malignant neoplasms of the hematopoietic and lymphoid tissues and they comprise non-Hodgkin lymphoma, Hodgkin lymphoma, leukemia, and multiple myeloma. In 2008, 6.9% of cancer incidence and 7.3% of cancer mortality worldwide were due to hematologic cancers (1). Non-Hodgkin lymphoma and leukemia rank among the top 10 cancer sites globally, and in developed countries, non-Hodgkin lymphoma is the seventh most frequently diagnosed cancer (1). Among non-Hodgkin lymphoma, diffuse large B-cell lymphoma is the most common subtype.

Obesity has been suggested to be a risk factor for non-Hodgkin lymphoma, Hodgkin lymphoma, leukemia, and multiple myeloma (2–4). Similarly, a deficient or suppressed immune system is a recognized risk factor common to non-Hodgkin lymphoma, Hodgkin lymphoma, multiple myeloma, and leukemia (5–8). Physical activity is inversely linked to adiposity (9) and is positively associated with immune function (10) and thus, may play a role in the prevention of hematologic cancer. Physical activity may also prevent the development of hematologic cancer independently of body weight control through enhancements of insulin sensitivity, anti-inflammatory mechanisms, and antioxidant defense systems (11–16).

Despite the existence of numerous biologic pathways potentially linking increased physical activity to decreased risk of hematologic cancer, the epidemiologic evidence relating physical activity to hematologic cancer has been mixed, and the results of a recent meta-analysis on physical activity and lymphoma were not statistically significant (17). Thus, the relation between physical activity and hematologic cancer remains inconclusive.

The purpose of the current systematic review and meta-analysis was to quantify the association between physical activity and hematologic cancer subtypes. To our knowledge, the current investigation is the first meta-analysis to include a comprehensive investigation of physical activity in relation to hematologic cancer subtypes.

Materials and Methods

Systematic search strategy and study selection

Studies were identified through a systematic search of PubMed from inception to June 2013 using the following search terms for physical activity: physical activity, exercise, cardiorespiratory fitness, cardiovascular fitness, resistance training, endurance training, aerobic, sport, athletes, players, lifestyle, and anthropometric. Those terms were combined with the following (truncated) terms for hematologic cancers using an AND operator: hematologic malignancy, hematologic neoplasm, hematopoietic malignancy, hematopoietic neoplasm, lymphoid, leukemia, lymphoma, multiple myeloma, and cancer. Cancer outcomes other than hematologic cancers were excluded. The search was limited to studies in humans and no language restrictions were imposed.

Using that search, 949 citations were identified (Supplementary Fig. S1). After screening titles and abstracts, 911 citations were excluded; the remaining 38 full articles were given a more detailed assessment. Studies that met the following criteria were considered further: observational cohort or case–control studies of physical activity and hematologic cancers that provided an effect estimate [relative risk (RR), HR, OR, or standardized incidence ratio (SIR)], with 95% confidence intervals (CI) or sufficient information to calculate those values. Fifteen citations were excluded after full-text screening and two additional articles were found by a manual search. Of the 25 studies (18–42) on physical activity and hematologic cancers, two studies (41, 42) were removed because they did not provide a risk estimate for a hematologic cancer subtype. The remaining 23 studies (18–40) were included in the meta-analysis.

Data extraction

For each study, the following data were extracted by the principal reviewer (C. Jochem): first author's last name, year of publication, sample size, number of cases, study geographic region, type of physical activity assessment, domains and intensities of physical activity, timing in life of physical activity, definition of the highest and the lowest categories of physical activity, reported physical activity effect estimates with corresponding 95% CI, adjustment variables, and information needed to assess the methodological quality of each study. All data extraction was double-checked by a second reviewer (G. Behrens).

Quality assessment

Quality assessment was independently conducted by all reviewers and any disagreement was resolved by consensus. A quality score developed by Monninkhof and colleagues (43) and previously used in several meta-analyses of physical activity and specific cancers (17, 43–46) was applied to assess the methodological quality of the selected studies (supplementary Materials and Methods).

Statistical analysis

Effect estimates were interpreted as estimates of the RRi. The natural logarithms of those risk estimates [log(RRi)] with their corresponding standard errors si = [log(upper 95% CI bound or RR) − log(RR)]/1.96 were calculated using a random-effects model to determine the weighted average of those log(RRi)s while allowing for effect measure heterogeneity. The log(RRi)s were weighted by wi = 1/(si2 + t2), where si denoted the standard error of log(RRi) and t2 denoted the restricted maximum likelihood estimate of the overall variance (47).

The main meta-analysis included one risk estimate per hematologic cancer subtype (non-Hodgkin lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, Hodgkin lymphoma, leukemia, and multiple myeloma), per gender, and per physical activity domain for each study. If risk estimates were available separately for each sex as well as for men and women combined, the latter were not included in the main meta-analysis. If risk estimates were provided for different numbers of adjustment factors, the maximally adjusted risk estimate was used. If a study presented several risk estimates for physical activity at various time periods in life, the risk estimate for the most recent time period was chosen (21, 22, 34) as most studies provided risk estimates for recent physical activity.

The 23 underlying studies used varying definitions of hematologic cancer subtypes, sometimes without providing information about the specific classification scheme used. In our meta-analysis, the hematologic cancer groups of non-Hodgkin lymphoma, Hodgkin lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, chronic lymphocytic lymphoma/small lymphocytic lymphoma, and multiple myeloma included all risk estimates that were available for those specific subtypes. Our group of non-Hodgkin lymphoma did not include histologic subtypes of non-Hodgkin lymphoma. No study provided information on the specific subtype of Hodgkin lymphoma. Our group of leukemia included risk estimates that were either classified as all leukemia or specific leukemia subtypes, such as acute or chronic myeloid leukemia. Our groupings are consistent to the extent possible across studies included in the meta-analyses.

Publication bias was assessed using a funnel plot and Begg's rank correlation test (48). Statistical heterogeneity between risk estimates was estimated using the Q- and I2 statistics (47).

For each subsite-specific hematologic cancer, a priori determined subanalyses were performed that were stratified by study design, gender, study quality score, physical activity domain, type of physical activity assessment, timing in life of physical activity, number of adjustment factors, adjustment for smoking, adjustment for alcohol consumption, adjustment for adiposity, and study geographic region. Potential heterogeneity of the physical activity and hematologic cancer subtype associations was assessed according to those factors using random-effects meta-regression, where the model that included the current factor of interest as a single explanatory variable was compared with the null model that included no explanatory variable.

An exploratory dose–response meta-analysis was conducted using fractional polynomials based on a modified version (Supplementary Materials and Methods) of the method proposed by Rota and colleagues (49). For that analysis, we considered seven studies (19, 20, 22, 23, 29, 39, 40) that investigated recreational physical activity expressed as metabolic equivalent task (MET)-hours or MET-minutes per week in relation to hematologic cancer subtypes. The midpoint of each physical activity category was used as the dose associated with the RR for that category. Because the highest categories of physical activity in the underlying studies were open-ended, the dose corresponding to the highest category was defined as 1.5 times the value of the lower bound of that category. The reference level (lowest category) was set to 0 MET-hours. On the basis of the gender-specific RR estimates from the seven included studies, a total of 27 individual dose–response relationships were summarized using multivariate random-effects meta-analysis.

All statistical analyses were performed with R version 2.12.2 (50) using the packages “metafor” (51) and “mvmeta” (52). All risk estimates are reported with 95% CI and P < 0.05 were considered statistically significant.

Results

The current meta-analysis comprised a total of 1,648,601 subjects and 19,334 hematologic cancer cases. Table 1 presents the main characteristics and results of the 15 cohort studies (26–40) and eight case–control studies (18–25) included. Three studies investigated more than one physical activity domain (19, 24, 40) and nine studies provided results stratified by gender (20, 22, 23, 25, 26, 29, 32, 39, 40).

View this table:
Table 1.

Characteristics of the 15 cohort studies and eight case–control studies of physical activity and hematologic cancer risk included in the meta-analysis

Comparing the highest versus the lowest level of physical activity, the 23 risk estimates for non-Hodgkin lymphoma were consistent with a borderline statistically significant inverse association (RR, 0.91; 95% CI, 0.82–1.00; I2 = 39%; Fig. 1). When focusing on subtypes of non-Hodgkin lymphoma, the nine risk estimates for diffuse large B-cell lymphoma yielded a statistically nonsignificant relation with high versus low physical activity (RR, 0.95; 95% CI, 0.80–1.14; I2 = 0%; Fig. 2). Similarly, the summary risk estimate of the nine available risk estimates for physical activity and follicular lymphoma was null at 1.01 (95% CI, 0.83–1.22; I2 = 2%; Fig. 2). The six risk estimates available for Hodgkin lymphoma yielded a statistically nonsignificant risk reduction with high versus low physical activity levels (RR, 0.86; 95% CI, 0.58–1.26; I2 = 35%; Fig. 1). An analysis of all risk estimates available for non-Hodgkin lymphoma and Hodgkin lymphoma combined (N = 29) showed a statistically significant reduction in lymphoma risk with high versus low physical activity level (RR, 0.90; 95% CI, 0.81–0.99; Fig. 1).

Figure 1.
Figure 1.

Forest plot of a random-effects meta-analysis including risk estimates of non-Hodgkin lymphoma and Hodgkin lymphoma. RRs quantify the association between physical activity (PA) and hematologic cancer subtype risk for the highest versus lowest levels of physical activity, grouped by men, women, and men and women combined for non-Hodgkin lymphoma and Hodgkin lymphoma. The size of each box is proportional to the weight the risk estimate contributed to the summary risk estimate. OPA, occupational physical activity; RPA, recreational physical activity; TPA, total physical activity.

Figure 2.
Figure 2.

Forest plot of a random-effects meta-analysis including risk estimates of diffuse large B-cell lymphoma and follicular lymphoma. RRs quantify the association between physical activity (PA) and hematologic cancer subtype risk for the highest versus lowest levels of physical activity, grouped by men, women, and men and women combined for diffuse large B-cell lymphoma and follicular lymphoma. The size of each box is proportional to the weight the risk estimate contributed to the summary risk estimate. OPA, occupational physical activity; RPA, recreational physical activity; TPA, total physical activity.

Nine risk estimates were available for high versus low physical activity and leukemia and resulted in a summary risk estimate of 0.97 (95% CI, 0.84–1.13; I2 = 23%; Fig. 3). Similarly, seven risk estimates for physical activity and chronic lymphocytic lymphoma/small lymphocytic lymphoma yielded a null summary risk estimate of 0.99 (95% CI, 0.75–1.29; I2 = 54%; Fig. 3). By comparison, the 12 available risk estimates of high versus low physical activity and multiple myeloma showed a statistically nonsignificant inverse relation (RR, 0.86; 95% CI, 0.68–1.09; I2 = 38%; Fig. 3).

Figure 3.
Figure 3.

Forest plot of a random-effects meta-analysis including risk estimates of leukemia, chronic lymphocytic lymphoma/small lymphocytic lymphoma, and multiple myeloma. RRs quantify the association between physical activity (PA) and hematologic cancer subtype risk for the highest versus lowest levels of physical activity, grouped by men, women, and men and women combined for leukemia and chronic lymphocytic lymphoma/small lymphocytic lymphoma. The size of each box is proportional to the weight the risk estimate contributed to the summary risk estimate. OPA, occupational physical activity; RPA, recreational physical activity; TPA, total physical activity.

We next investigated potential effect modification of the associations between physical activity and hematologic cancer subtypes (Supplementary Tables S1A–G). No statistically significant effect modification was observed for any of the variables studied for any of the hematologic cancer subtypes, apart from apparent effect modification of the physical activity and diffuse large B-cell lymphoma relation by study geographic region. Specifically, physical activity was positively associated with diffuse large B-cell lymphoma in studies from Europe (RR, 3.41; 95% CI, 1.14–10.18), whereas it was unrelated to diffuse large B-cell lymphoma in studies from North America (RR, 0.92; 95% CI, 0.77–1.10; Pdifference = 0.02).

In a subanalysis, we combined 75 risk estimates and noted a statistically significant 7% reduced risk of total hematologic cancer when comparing the highest versus the lowest level of physical activity (RR, 0.93; 95% CI, 0.88–0.99; Supplementary Table S2). No evidence for publication bias was found in a funnel plot (Supplementary Fig. S2). Begg's rank correlation test also indicated no publication bias (P = 0.82). In contrast, the Q-statistic indicated heterogeneity of study results (P = 0.04; I2 = 24%).

In further subanalyses, we found that the summary risk estimate for total hematologic cancer did not differ by study design (Pdifference = 0.72). In contrast, the relation of physical activity to total hematologic cancer varied according to gender, showing an inverse relation with physical activity in women (RR, 0.88; 95% CI, 0.79–0.98), but not in men (RR, 1.03; 95% CI, 0.94–1.01; Pdifference = 0.03). Furthermore, studies with a large number of adjustment factors yielded a more pronounced inverse relation of physical activity to total hematologic cancer (RR, 0.85; 95% CI, 0.76–0.95) than studies with an intermediate (RR, 1.03; 95% CI, 0.94–1.12) or low number of adjustment factors (RR, 0.92; 95% CI, 0.82–1.04; Pdifference = 0.04). Also, studies that adjusted for smoking showed a stronger inverse association between physical activity and total hematologic cancer (RR, 0.86; 95% CI, 0.78–0.95) than studies that did not adjust for smoking (RR, 1.00; 95% CI, 0.93–1.07; Pdifference = 0.02).

We conducted an exploratory dose–response meta-analysis of recreational physical activity expressed as energy-expenditure in MET-hours/week in relation to total hematologic cancer risk. The following dose–response curve was identified as the one with the best model fit: RR = exp(a1 dose2 + a2 dose3), where a1 = −0.000271, a2 = −0.000004, var(a1) = 1 × 10−8, cov(a1, a2) = −2 × 10−10, and var(a2) = 3 × 10−12 (Fig. 4). That model did not indicate heterogeneity between studies (I2 = 9%; Pheterogeneity = 0.29). It showed an inverse association between physical activity and total hematologic cancer (Fig. 4), suggesting that a risk reduction of 10% was reached at 26 MET-hours/week (RR, 0.90; 95% CI, 0.83–0.97). The maximum risk reduction was detected at 41 MET-hours/week (RR, 0.86; 95% CI, 0.76–0.97). A linear dose–response meta-analysis including all physical activity doses up to 41 MET-hours indicated that each 12 MET-hour/week increase in recreational physical activity was associated with a 5% reduced total hematologic cancer risk (RR, 0.95; 95% CI, 0.91–0.99). Energy expenditure values exceeding 48 MET-hours/week were no longer related to a statistically significant risk reduction.

Figure 4.
Figure 4.

Dose–response analysis of MET-hours/week spent in moderate to vigorous physical activity in relation to total hematologic cancer risk.

Discussion

This is the first meta-analysis to comprehensively examine physical activity in relation to hematologic cancer subtypes. We found that high versus low physical activity showed statistically nonsignificant relations to non-Hodgkin lymphoma, Hodgkin lymphoma, multiple myeloma, and leukemia. The relations of physical activity to hematologic cancer subtypes were not modified by study design, gender, study quality score, physical activity domain, type of physical activity assessment, timing in life of physical activity, number of adjustment factors, adjustment for smoking, adjustment for alcohol consumption, and adjustment for adiposity.

A recent meta-analysis (17) that focused on non-Hodgkin lymphoma and Hodgkin lymphoma pooled the data from 12 studies (16 risk estimates for non-Hodgkin lymphoma; four risk estimates for Hodgkin lymphoma) and reported a statistically nonsignificant reduction in lymphoma risk with high versus low levels of physical activity (RR, 0.90; 95% CI, 0.79–1.02), with observed heterogeneity by study design (Pdifference = 0.04). Our meta-analysis included nine additional risk estimates for lymphoma and yielded an identical point estimate that was statistically significant (RR, 0.90; 95% CI, 0.81–0.99) and showed no effect modification by study design (Pdifference = 0.72).

In contrast to the previous meta-analysis (17), the present meta-analysis considered all main hematologic cancer subtypes (non-Hodgkin lymphoma, Hodgkin lymphoma, multiple myeloma, and leukemia). In addition, it incorporated one additional study by Paffenbarger and colleagues (36) that provided risk estimates for physical activity and both non-Hodgkin lymphoma and Hodgkin lymphoma, in addition to leukemia. The current meta-analysis also examined the potential for publication bias.

Our exploratory dose–response meta-analysis indicated that a 5% risk reduction of total hematologic cancer could be achieved for each 12 MET-hour/week increase in recreational physical activity (equivalent to walking at a moderate pace for 30 minutes daily; ref. 53). Our observation of an upward trend of the dose–response curve for values exceeding 41 MET-hours/week indicates that there may be an upper level of physical activity that no longer provides protection against total hematologic cancer risk. In this context, it is interesting to note that extremely high levels of physical activity are thought to impair the immune system (54). Particular caution is needed in interpreting our findings for total hematologic cancer because of etiologic heterogeneity for individual hematologic cancer subtypes.

Two case–control studies on physical activity and hematologic cancer (20, 23) presented results stratified by body mass index (BMI). In one study (20), physical activity showed a more pronounced inverse association with adult leukemia among obese than overweight or normal weight men and women, although the test for interaction was not statistically significant. In the other study (23), an inverse relation of physical activity to non-Hodgkin lymphoma was seen only among obese or normal weight men, whereas no association with physical activity was noted among overweight men. In contrast, physical activity displayed inverse associations with non-Hodgkin lymphoma among nonobese women, whereas the relation of physical activity to non-Hodgkin lymphoma among obese women was null (23). Those inconsistent gradients in hematologic cancer risk associated with physical activity across categories of BMI suggest that physical activity and adiposity affect hematologic cancer risk through distinct etiologic mechanisms. In support of the notion that the inverse association between physical activity and hematologic cancer is not mediated by healthy body mass among physically active persons, in our meta-analysis the magnitude of the summary risk estimates of physical activity and hematologic cancer subtypes that were adjusted for BMI were similar to the risk estimates that were unadjusted for BMI.

In our study, the associations between physical activity and hematologic cancer subtypes were not modified by gender. However, in a subanalysis of total hematologic cancer, we noted that the relation with physical activity was statistically significantly inverse in women but null in men. Possible explanations for the observed difference between women and men include reproductive factors and exogenous estrogens, but relevant biologic pathways of hematologic cancer risk as they interact with physical activity are not well investigated.

Although the relations of physical activity to hematologic cancer subtypes did not vary according to smoking, in a subanalysis of total hematologic cancer, we found that studies that adjusted for smoking showed a stronger inverse association with physical activity than studies that did not adjust for smoking. Smoking is inversely related to physical activity (55) and positively associated with all investigated hematologic cancer subtypes (56–58) except multiple myeloma (59). Thus, adjustment for smoking would be expected to produce a less pronounced inverse association between physical activity and total hematologic cancer than not adjusting for smoking. However, because studies that adjusted for smoking also tended to include a large number of additional adjustment factors, we were unable to discern the effects of controlling for smoking from those controlling for a large number of other adjustment factors, the latter variable of which also represented an effect modifier of the physical activity and total hematologic cancer relation.

Our observation that European studies yielded a positive association between physical activity and diffuse large B-cell lymphoma was based on only two RR estimates. Multiple comparisons could have yielded such a finding by chance because we evaluated a large number of potential effect modifiers.

The etiologic pathways linking increased physical activity to decreased risk of hematologic cancer are not established, but several plausible mechanisms exist. Immunodeficiency is a risk factor for non-Hodgkin lymphoma and Hodgkin lymphoma (5, 8), and physical activity enhances the immune system by increasing the number and activity of natural killer cells, macrophages, lymphokine-activated killer cells, and regulating cytokines (16). Physical activity may also decrease hematologic cancer risk by reducing insulin resistance (12) and decreasing levels of insulin and insulin-like growth factors (11, 60, 61). A further potential mechanism is an exercise-induced reduction of proinflammatory mediators such as interleukin-6 (IL-6), C-reactive protein (CRP), and TNF-α (16), factors that have been positively associated with hematologic cancer (62, 63). Physical activity may also increase the production of antioxidant enzymes, thereby improving DNA repair capacity (15, 16). Finally, physical activity may indirectly decrease hematologic cancer risk by preventing adiposity and reducing body fat (13) because obesity represents a risk factor for non-Hodgkin lymphoma, Hodgkin lymphoma, leukemia, and multiple myeloma (2–4).

Strengths of the present meta-analysis are its large sample size, which provided substantial statistical power and permitted extensive subanalyses. A major asset is the heterogeneity of hematologic cancer subtypes included in the analysis, which enabled us to examine whether physical activity differentially affects specific hematologic cancer subtypes. Also, we comprehensively assessed effect modification of the relation of physical activity to subtype-specific hematologic cancers. In addition, we applied a study quality score that addressed potential selection bias, misclassification, and confounding. Reassuringly, we found no heterogeneity of the risk estimates between higher and lower quality studies.

Limitations of the current meta-analysis are the heterogeneous study designs and the variability in the definitions of high and low levels of physical activity in the underlying studies. However, we addressed the heterogeneity in high and low levels of physical activity in an exploratory dose–response meta-analysis in which we combined RRs of hematologic cancer associated with comparable doses of physical activity. It is possible that preclinical symptoms of hematologic cancer influenced physical activity habits at baseline. Our analyses were limited in that we were unable to remove cases diagnosed in the time period proximal to physical activity assessment. Thus, we may have overestimated any possible inverse association between physical activity and hematologic cancers in our study.

A further potential shortcoming of our study is measurement error in physical activity assessment because studies used self-reports or interview methods to assess physical activity rather than objective instruments such as accelerometers. However, nondifferential misclassification of physical activity levels would have tended to underestimate but not overstate risk estimates. Notwithstanding, the common use of accelerometers might have increased comparability between studies.

Comparability of studies would also have been enhanced if all studies had used a uniform hematologic cancer classification scheme. Some studies did not specify the classification scheme used, whereas other studies provided detailed information about the use of common classification schemes such as the second or third editions of the International Classification of Diseases for Oncology (ICD-O-2/-3) or the InterLymph classification scheme (64). We were unable to distinguish between classical and nonclassical histologic types of Hodgkin lymphoma or between younger adult and older adult Hodgkin lymphoma because of lack of such data.

In summary, our meta-analysis indicates that physical activity is unrelated to risk of subtypes of hematologic cancer. Future studies should include standardized, high-quality physical activity assessments to refine our knowledge regarding whether specific intensities, durations, and frequencies of physical activity may be potentially relevant for protection from hematologic cancers. In addition, mechanistic research is needed to delineate the biologic mechanisms underlying the possible relations of physical activity to hematologic cancer subtypes.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors' Contributions

Conception and design: C. Jochem, M.F. Leitzmann, G. Behrens

Development of methodology: C. Jochem, M.F. Leitzmann, G. Behrens

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C. Jochem, M.F. Leitzmann, G. Behrens

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): C. Jochem, M.F. Leitzmann, G. Behrens

Writing, review, and/or revision of the manuscript: C. Jochem, M.F. Leitzmann, M. Keimling, D. Schmid, G. Behrens

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): C. Jochem

Study supervision: M.F. Leitzmann, G. Behrens

Acknowledgments

The authors are indebted to Sylvia Pietsch for expert help.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

  • Received July 12, 2013.
  • Revision received January 27, 2014.
  • Accepted February 10, 2014.

References

  1. 1.↵
    1. Ferlay J,
    2. Shin HR,
    3. Bray F,
    4. Forman D,
    5. Mathers C,
    6. Parkin DM
    . Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010;127:2893–917.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Larsson SC,
    2. Wolk A
    . Body mass index and risk of multiple myeloma: a meta-analysis. Int J Cancer 2007;121:2512–6.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Larsson SC,
    2. Wolk A
    . Overweight and obesity and incidence of leukemia: a meta-analysis of cohort studies. Int J Cancer 2008;122:1418–21.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Larsson SC,
    2. Wolk A
    . Body mass index and risk of non-Hodgkin's and Hodgkin's lymphoma: a meta-analysis of prospective studies. Eur J Cancer 2011;47:2422–30.
    OpenUrlCrossRefPubMed
  5. 5.↵
    1. Schottenfeld D,
    2. Fraumeni JFJ
    1. Hartge P,
    2. Wang SP,
    3. Bracci PM,
    4. Devesa SS,
    5. Holly EA
    . Non-Hodgkin lymphoma. In: Schottenfeld D, Fraumeni JFJ , editors. Cancer epidemiology and prevention. 3rd ed. New York: Oxford University Press; 2006. p. 898–918.
  6. 6.↵
    1. Schottenfeld D,
    2. Fraumeni JFJ
    1. De Roos AJ,
    2. Baris D,
    3. Weiss NS,
    4. Herrinton LJ
    . Multiple myeloma. In: Schottenfeld D, Fraumeni JFJ , editors. Cancer epidemiology and prevention. 3rd ed. New York: Oxford University Press; 2006. p. 919–45.
  7. 7.↵
    1. Schottenfeld D,
    2. Fraumeni JFJ
    1. Linet MS,
    2. Devesa SS,
    3. Morgan GJ
    . The leukemias. In: Schottenfeld D, Fraumeni JFJ , editors. Cancer epidemiology and prevention. 3rd ed. New York: Oxford University Press; 2006. p. 841–71.
  8. 8.↵
    1. Schottenfeld D,
    2. Fraumeni JFJ
    1. Mueller NE,
    2. Grufferman S
    . Hodgkin lymphoma. In: Schottenfeld D, Fraumeni JFJ , editors. Cancer epidemiology and prevention. 3rd ed. New York: Oxford University Press; 2006. p. 872–97.
  9. 9.↵
    1. Wing RR
    . Physical activity in the treatment of the adulthood overweight and obesity: current evidence and research issues. Med Sci Sports Exerc 1999;3:547–52.
    OpenUrl
  10. 10.↵
    1. Woods JA
    . Physical activity, exercise, and immune function. Brain Behav Immun 2005;19:369–70.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. McTiernan A
    . Mechanisms linking physical activity with cancer. Nat Rev Cancer 2008;8:205–11.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Hawley JA,
    2. Lessard SJ
    . Exercise training-induced improvements in insulin action. Acta Physiol 2008;192:127–35.
    OpenUrl
  13. 13.↵
    1. McTiernan A,
    2. Sorensen B,
    3. Irwin ML,
    4. Morgan A,
    5. Yasui Y,
    6. Rudolph RE,
    7. et al.
    Exercise effect on weight and body fat in men and women. Obesity 2007;15:1496–512.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Shephard RJ,
    2. Shek PN
    . Cancer, immune function, and physical activity. Can J Appl Physiol 1995;20:1–25.
    OpenUrlPubMed
  15. 15.↵
    1. Gomez-Cabrera MC,
    2. Domenech E,
    3. Vina J
    . Moderate exercise is an antioxidant: upregulation of antioxidant genes by training. Free Radic Biol Med 2008;44:126–31.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Pan SY,
    2. Morrison H
    . Physical activity and hematologic cancer prevention. Recent Results Cancer Res 2011;186:135–58.
    OpenUrlPubMed
  17. 17.↵
    1. Vermaete NV,
    2. Wolter P,
    3. Verhoef GE,
    4. Kollen BJ,
    5. Kwakkel G,
    6. Schepers L,
    7. et al.
    Physical activity and risk of lymphoma: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2013;22:1173–84.
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Brownson RC,
    2. Chang JC,
    3. Davis JR,
    4. Smith CA
    . Physical activity on the job and cancer in Missouri. Am J Public Health 1991;81:639–42.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Cerhan JR,
    2. Bernstein L,
    3. Severson RK,
    4. Davis S,
    5. Colt JS,
    6. Blair A,
    7. et al.
    Anthropometrics, physical activity, related medical conditions, and the risk of non-hodgkin lymphoma. Cancer Causes Control 2005;16:1203–14.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Kasim K,
    2. Johnson KC,
    3. Levallois P,
    4. Abdous B,
    5. Auger P,
    6. Canadian Cancer Registries Epidemiology Research G
    . Recreational physical activity and the risk of adult leukemia in Canada. Cancer Causes Control 2009;20:1377–86.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Keegan TH,
    2. Glaser SL,
    3. Clarke CA,
    4. Dorfman RF,
    5. Mann RB,
    6. DiGiuseppe JA,
    7. et al.
    Body size, physical activity, and risk of Hodgkin's lymphoma in women. Cancer Epidemiol Biomarkers Prev 2006;15:1095–101.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Kelly JL,
    2. Fredericksen ZS,
    3. Liebow M,
    4. Shanafelt TD,
    5. Thompson CA,
    6. Call TG,
    7. et al.
    The association between early life and adult body mass index and physical activity with risk of non-Hodgkin lymphoma: impact of gender. Ann Epidemiol 2012;22:855–62.
    OpenUrlPubMed
  23. 23.↵
    1. Pan SY,
    2. Mao Y,
    3. Ugnat AM,
    4. Canadian Cancer Registries Epidemiology Research G
    . Physical activity, obesity, energy intake, and the risk of non-Hodgkin's lymphoma: a population-based case-control study. Am J Epidemiol 2005;162:1162–73.
    OpenUrlAbstract/FREE Full Text
  24. 24.↵
    1. Parent ME,
    2. Rousseau MC,
    3. El-Zein M,
    4. Latreille B,
    5. Desy M,
    6. Siemiatycki J
    . Occupational and recreational physical activity during adult life and the risk of cancer among men. Cancer Epidemiol 2011;35:151–9.
    OpenUrlPubMed
  25. 25.↵
    1. Zahm SH,
    2. Hoffman-Goetz L,
    3. Dosemeci M,
    4. Cantor K,
    5. Blair A
    . Occupational physical activity and non-Hodgkin's lymphoma. Med Sci Sports Exerc 1999;31:566–71.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Birmann BM,
    2. Giovannucci E,
    3. Rosner B,
    4. Anderson KC,
    5. Colditz GA
    . Body mass index, physical activity, and risk of multiple myeloma. Cancer Epidemiol Biomarkers Prev 2007;16:1474–8.
    OpenUrlAbstract/FREE Full Text
  27. 27.↵
    1. Blair CK,
    2. Cerhan JR,
    3. Folsom AR,
    4. Ross JA
    . Anthropometric characteristics and risk of multiple myeloma. Epidemiology 2005;16:691–4.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Cerhan JR
    . Anthropometric characteristics, physical activity, and risk of non-Hodgkin's lymphoma subtypes and B-cell chronic lymphocytic leukemia: a prospective study. Am J Epidemiol 2002;156:527–35.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Hofmann JN,
    2. Moore SC,
    3. Lim U,
    4. Park Y,
    5. Baris D,
    6. Hollenbeck AR,
    7. et al.
    Body mass index and physical activity at different ages and risk of multiple myeloma in the NIH-AARP diet and health study. Am J Epidemiol 2013;177:776–86.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Kabat GC,
    2. Wu JW,
    3. Moore SC,
    4. Morton LM,
    5. Park Y,
    6. Hollenbeck AR,
    7. et al.
    Lifestyle and dietary factors in relation to risk of chronic myeloid leukemia in the NIH-AARP diet and health study. Cancer Epidemiol Biomarkers Prev 2013;22:848–54.
    OpenUrlAbstract/FREE Full Text
  31. 31.↵
    1. Kabat GC,
    2. Kim MY,
    3. Jean Wactawski W,
    4. Bea JW,
    5. Edlefsen KL,
    6. Adams-Campbell LL,
    7. et al.
    Anthropometric factors, physical activity, and risk of non-Hodgkin's lymphoma in the Women's Health Initiative. Cancer Epidemiol 2012;36:52–9.
    OpenUrlPubMed
  32. 32.↵
    1. Khan MMH,
    2. Mori M,
    3. Sakauchi F,
    4. Matsuo K,
    5. Ozasa K,
    6. Tamakoshi A
    . Risk factors for multiple myeloma: evidence from the Japan Collaborative Cohort (JACC) study. Asian Pac J Cancer Prev 2006;7:575–81.
    OpenUrlPubMed
  33. 33.↵
    1. Lim U,
    2. Morton LM,
    3. Subar AF,
    4. Baris D,
    5. Stolzenberg-Solomon R,
    6. Leitzmann M,
    7. et al.
    Alcohol, smoking, and body size in relation to incident Hodgkin's and non-Hodgkin's lymphoma risk. Am J Epidemiol 2007;166:697–708.
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Lu Y,
    2. Prescott J,
    3. Sullivan-Halley J,
    4. Henderson KD,
    5. Ma H,
    6. Chang ET,
    7. et al.
    Body size, recreational physical activity, and B-cell non-Hodgkin lymphoma risk among women in the California teachers study. Am J Epidemiol 2009;170:1231–40.
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    1. Ma X,
    2. Park Y,
    3. Mayne ST,
    4. Wang R,
    5. Sinha R,
    6. Hollenbeck AR,
    7. et al.
    Diet, lifestyle, and acute myeloid leukemia in the NIH-AARP cohort. Am J Epidemiol 2010;171:312–22.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Paffenbarger RS,
    2. Lee I-M,
    3. Wing AL
    . The influence of physical activity on the incidence of site-specific cancers in college alumni. Adv Exp Med Biol 1992;322:7–15.
    OpenUrlPubMed
  37. 37.↵
    1. Pukkala E,
    2. Kaprio J,
    3. Koskenvuo M,
    4. Kujala U,
    5. Sarna S
    . Cancer incidence among FinnishWorld class male athletes. Int J Sports Med 2000;21:216–20.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Soll-Johanning H,
    2. Bach E
    . Occupational exposure to air pollution and cancer risk among Danish urban mail carriers. Int Arch Occup Environ Health 2004;77:351–6.
    OpenUrlPubMed
  39. 39.↵
    1. Teras LR,
    2. Gapstur SM,
    3. Diver WR,
    4. Birmann BM,
    5. Patel AV
    . Recreational physical activity, leisure sitting time and risk of non-Hodgkin lymphoid neoplasms in the American Cancer Society Cancer Prevention Study II Cohort. Int J Cancer 2012;131:1912–20.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. van Veldhoven CM,
    2. Khan AE,
    3. Teucher B,
    4. Rohrmann S,
    5. Raaschou-Nielsen O,
    6. Tjonneland A,
    7. et al.
    Physical activity and lymphoid neoplasms in the European Prospective Investigation into Cancer and nutrition (EPIC). Eur J Cancer 2011;47:748–60.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Robsahm TE,
    2. Hestvik UE,
    3. Veierod MB,
    4. Fagerlie A,
    5. Nystad W,
    6. Engebretsen L,
    7. et al.
    Cancer risk in Norwegian world class athletes. Cancer Causes Control 2010;21:1711–9.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Wannamethee SG,
    2. Shaper AG,
    3. Walker M
    . Physical activity and risk of cancer in middle-aged men. Br J Cancer 2001;85:1311–6.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Monninkhof EM,
    2. Elias SG,
    3. Vlems FA,
    4. van der Tweel I,
    5. Schuit AJ,
    6. Voskuil DW,
    7. et al.
    Physical activity and breast cancer: a systematic review. Epidemiology 2007;18:137–57.
    OpenUrlCrossRefPubMed
  44. 44.↵
    1. Behrens G,
    2. Leitzmann MF
    . The association between physical activity and renal cancer: systematic review and meta-analysis. Br J Cancer 2013;108:798–811.
    OpenUrlCrossRefPubMed
  45. 45.↵
    1. Liu Y,
    2. Hu F,
    3. Li D,
    4. Wang F,
    5. Zhu L,
    6. Chen W,
    7. et al.
    Does physical activity reduce the risk of prostate cancer? A systematic review and meta-analysis. Eur Urol 2011;60:1029–44.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. Voskuil DW,
    2. Monninkhof EM,
    3. Elias SG,
    4. Vlems FA,
    5. van Leeuwen FE,
    6. Task Force Physical A
    . Physical activity and endometrial cancer risk, a systematic review of current evidence. Cancer Epidemiol Biomarkers Prev 2007;16:639–48.
    OpenUrlAbstract/FREE Full Text
  47. 47.↵
    1. Higgins JP,
    2. Thompson SG
    . Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539–58.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Begg CB,
    2. Mazumdar M
    . Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088–101.
    OpenUrlCrossRefPubMed
  49. 49.↵
    1. Rota M,
    2. Bellocco R,
    3. Scotti L,
    4. Tramacere I,
    5. Jenab M,
    6. Corrao G,
    7. et al.
    Random-effects meta-regression models for studying nonlinear dose-response relationship, with an application to alcohol and esophageal squamous cell carcinoma. Stat Med 2010;29:2679–87.
    OpenUrlCrossRefPubMed
  50. 50.↵
    R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Core Team; 2013.
  51. 51.↵
    1. Viechtbauer W
    . Conducting meta-analyses in R with the metafor package. J Stat Softw 2010;36:1–48.
    OpenUrl
  52. 52.↵
    1. Gasparrini A,
    2. Armstrong B,
    3. Kenward MG
    . Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat Med 2012;31:3821–39.
    OpenUrlCrossRefPubMed
  53. 53.↵
    1. Ainsworth BE,
    2. Haskell WL,
    3. Herrmann SD,
    4. Meckes N,
    5. Bassett DR Jr.,
    6. Tudor-Locke C,
    7. et al.
    2011 Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 2011;43:1575–81.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Walsh NP,
    2. Gleeson M,
    3. Pyne DB,
    4. Nieman DC,
    5. Dhabhar FS,
    6. Shephard RJ,
    7. et al.
    Position statement. Part two: maintaining immune health. Exerc Immunol Rev 2011;17:64–103.
    OpenUrlPubMed
  55. 55.↵
    1. Leitzmann MF,
    2. Koebnick C,
    3. Abnet CC,
    4. Freedman ND,
    5. Park Y,
    6. Hollenbeck A,
    7. et al.
    Prospective study of physical activity and lung cancer by histologic type in current, former, and never smokers. Am J Epidemiol 2009;169:542–53.
    OpenUrlAbstract/FREE Full Text
  56. 56.↵
    1. Castillo JJ,
    2. Dalia S
    . Cigarette smoking is associated with a small increase in the incidence of non-Hodgkin lymphoma: a meta-analysis of 24 observational studies. Leuk Lymphoma 2012;53:1911–9.
    OpenUrlCrossRefPubMed
  57. 57.↵
    1. Castillo JJ,
    2. Dalia S,
    3. Shum H
    . Meta-analysis of the association between cigarette smoking and incidence of Hodgkin's Lymphoma. J Clin Oncol 2011;29:3900–6.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    1. Brownson RC,
    2. Novotny TE,
    3. Perry MC
    . Cigarette smoking and adult leukemia. A meta-analysis. Arch Intern Med 1993;153:469–75.
    OpenUrlCrossRefPubMed
  59. 59.↵
    1. Psaltopoulou T,
    2. Sergentanis TN,
    3. Kanellias N,
    4. Kanavidis P,
    5. Terpos E,
    6. Dimopoulos MA
    . Tobacco smoking and risk of multiple myeloma: a meta-analysis of 40 observational studies. Int J Cancer 2013;132:2413–31.
    OpenUrlCrossRefPubMed
  60. 60.↵
    1. Khandwala HM,
    2. McCutcheon IE,
    3. Flyvbjerg A,
    4. Friend KE
    . The effects of insulin-like growth factors on tumorigenesis and neoplastic growth. Endocr Rev 2000;21:215–44.
    OpenUrlCrossRefPubMed
  61. 61.↵
    1. Gupta K,
    2. Krishnaswamy G,
    3. Karnad A,
    4. Peiris AN
    . Insulin: a novel factor in carcinogenesis. Am J Med Sci 2002;323:140–5.
    OpenUrlCrossRefPubMed
  62. 62.↵
    1. Sugiyama H,
    2. Inoue K,
    3. Ogawa H,
    4. Yamagami T,
    5. Soma T,
    6. Miyake S,
    7. et al.
    The expression of IL-6 and its related genes in acute leukemia. Leuk Lymphoma 1996;21:49–52.
    OpenUrlPubMed
  63. 63.↵
    1. Mischke R,
    2. Waterston M,
    3. Eckersall PD
    . Changes in C-reactive protein and haptoglobin in dogs with lymphatic neoplasia. Vet J 2007;174:188–92.
    OpenUrlCrossRefPubMed
  64. 64.↵
    1. Turner JJ,
    2. Morton LM,
    3. Linet MS,
    4. Clarke CA,
    5. Kadin ME,
    6. Vajdic CM,
    7. et al.
    InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions. Blood 2010;116:e90–8.
    OpenUrlAbstract/FREE Full Text
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Cancer Epidemiology Biomarkers & Prevention: 23 (5)
May 2014
Volume 23, Issue 5

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Physical Activity in Relation to Risk of Hematologic Cancers: A Systematic Review and Meta-analysis
Carmen Jochem, Michael F. Leitzmann, Marlen Keimling, Daniela Schmid and Gundula Behrens
Cancer Epidemiol Biomarkers Prev May 1 2014 (23) (5) 833-846; DOI: 10.1158/1055-9965.EPI-13-0699
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