We conducted a population-based, case-control study in Connecticut women to test the hypothesis that genetic variations in Th1 and Th2 cytokine genes modify the relationship between body mass index (BMI) and risk of non-Hodgkin lymphoma (NHL). Compared with those with BMI less than 25 kg/m2, women with BMI more than or equal to 25 kg/m2 had 50% to 90% increased risk of NHL among women who carried IFNGR2 (rs9808753) AA, IL5 (rs2069812) CT/TT, IL7R (rs1494555) AA, and TNF (rs1799724) CC genotypes, but no increased risk among women with IFNGR2 AG/GG, IL5 CC, IL7R AG/GG, and TNF CT/TT genotypes. A significant interaction with BMI was only observed for IFNGR2 (rs9808753 Pforinteraction = .034) and IL7R (rs1494555 Pforinteraction = .016) for NHL overall; IL7R (rs1494555 Pforinteraction = .016) and TNF (1799724 Pforinteraction = .031) for B-cell lymphoma; and IL5 (rs2069812 Pforinteraction = .034) for T-cell lymphoma. After stratification by common B-cell lymphoma subtypes, a significant interaction was observed for IFNGR2 (rs9808753 Pforinteraction = .006), IL13 (rs20541 Pforinteraction = .019), and IL7R (rs1494555 Pforinteraction = .012) for marginal zone B-cell lymphoma; IL7R (rs1494555 Pforinteraction = .017) for small lymphocytic lymphoma/chronic lymphocytic leukemia; and IL12A (rs568408 Pforinteraction = .013) and TNF (1799724 Pforinteraction = .04) for follicular lymphoma. The results suggest that common genetic variation in Th1/Th2 pathway genes may modify the association between BMI and NHL risk.

Excessive adipose can impair both humoral and cellular immunity,1  which may be important in the development of non-Hodgkin lymphoma (NHL). Body mass index (BMI), an indirect measure of adiposity, has been linked with NHL risk. Results from epidemiologic studies, however, have been inconsistent.2-18  Some studies have reported a positive association between BMI and risk of NHL,5,9-11,13,14,16-18  whereas others found no association.2-4,6,7,12,15  A recent pooled analysis by the International Lymphoma Epidemiology Consortium, which included 26 000 subjects, reported no association between BMI and overall risk of NHL and most subtypes.19  However, the validity of a pooled odds ratio is questioned because of the significant heterogeneity of the analyzed studies. Genetic variation could explain some of the inconsistent findings.

Cytokines play a crucial role in the regulation of key pathways of the immune system. T helper 1 (Th1) lymphocyte cells produce interleukin-2 (IL-2) and interferon-gamma (IFN-γ), which promote cell-mediated immune responses to fight intracellular pathogens and remove cancerous cells.20,21  T helper 2 (Th2) lymphocyte cells produce IL-4, IL-5, IL-6, IL-10, and IL-13, which favor B-cell activation and production of immunoglobulin, which can protect against extracellular pathogens.20-25  Immune dysfunction resulting from imbalanced regulation and expression of Th1 and Th2 cytokines could play an important role in the etiology of NHL.26,27  Single nucleotide polymorphisms (SNPs) in several cytokine genes (ie, IL4, IL5, IL6, and IL10) have been reported to be associated with the risk of NHL and its major subtypes.28  It is possible that genetic variation in the Th1 and Th2 cytokine genes may modify the relationship between BMI and NHL risk. As such, we conducted a population-based, case-control study in Connecticut women to test the hypothesis.

Study population

The study population has been described in detail elsewhere.29,30  Briefly, all histologically confirmed incident cases of NHL (ICD-O, M-9590-9642, 9690-9701, 9740-9750) diagnosed between 1996 and 2000 in Connecticut were identified through the Yale Cancer Center's Rapid Case Ascertainment Shared Resource. Enrollment criteria included age between 21 and 84 years, residence in Connecticut, female, alive at the time of interview, and without a previous diagnosis of cancer, except for nonmelanoma skin cancer. Of 832 eligible cases, 601 (72%) completed in-person interviews. Pathology slides (or tissue blocks) from all patients were obtained from the original pathology departments and reviewed by 2 independent pathologists. All cases were classified according to the 2001 World Health Organization classification.31 

Female population-based controls from Connecticut were recruited by: (1) random-digit dialing methods for those younger than 65 years; or (2) random selection from the Centers for Medicare and Medicaid Services records for those 65 years of age or older. Controls were frequency matched on age (± 5 years) to cases. The participation rate was 69% among persons identified via the random-digit dialing and 47% among persons identified from the Centers for Medicare and Medicaid Services. Approximately 75% of the study subjects (76.7% of the cases and 74.6% of the controls) provided blood samples, and approximately 10% of the subjects (11.0% of the cases and 10.4% of the controls) provided buccal cell samples for genotyping.

Data collection

The study was approved by the institutional review boards at Yale University, the Connecticut Department of Public Health, and the National Cancer Institute. Participation was voluntary, and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. Those who signed consent were interviewed by trained study nurses at the subject's home or at a convenient location using a standardized and structured questionnaire. Information on anthropometrics, demographics, family history of cancer, smoking and alcohol consumption, occupational exposure, medical conditions and medication use, and diet were collected through in-person interview. Usual adult height and weight were used to calculate BMI.

Genotyping

Genotyping was performed at the National Cancer Institute Core Genotyping Facility (http://cgf.nci.nih.gov). All TaqMan assays (Applied Biosystems) for this study were optimized on the ABI 7900HT detection system with 100% concordance with sequence analysis of 102 persons as listed on the SNP500Cancer website (http://snp500cancer.nci.nih.gov). A total of 39 SNPs in 20 Th1/Th2 immune genes were selected for genotyping based on the following criteria: minor allele frequencies more than 5%, laboratory evidence of function, or prior association with human disease studies.28  Because of a limited amount of DNA available for subjects who provided only buccal cells, we first genotyped subjects who provided a blood sample. If there was suggestive evidence or if we had a relatively high prior that a given SNP was associated with risk of NHL, genotype analysis would include subjects who provided only buccal cell samples.

Duplicate samples from 100 study subjects and 40 replicate samples from each of 2 blood donors were interspersed throughout the plates used for genotype analysis. The concordance rates for quality control samples were between 99% and 100% for all assays. The genotype frequencies for 4 SNPs (rs1059293, rs231775, rs2243250, and rs2070874) were not consistent with Hardy-Weinberg equilibrium among non-Hispanic white controls using a χ2 test (P < .05) and were excluded from the final analysis. Another 5 SNPs (rs2069822, rs2069818, rs2069807, 3024509, and rs361525) with minor allele frequency less than 10% were also excluded from the final analysis. A total of 30 SNPs in 17 Th1/Th2 genes, IFNG (rs1861494, rs2069705), IFNGR1 (rs3799488), IFNGR2 (rs9808753), IL10RA (rs9610), IL12A (rs568408, rs582054), IL13 (rs20541, rs1800925, rs1295686), IL15 (rs10833), IL15RA (rs2296135), IL2 (rs2069762), IL4 (rs2243248, rs2243290, rs2243268), IL4R (rs2107356), IL5 (rs2069812), IL6 (rs1800795, rs1800797), IL7R (rs1494555), JAK3 (rs3008), IL10 (rs1800871, rs1800872, rs1800896, rs3024496, rs3024491, rs1800890), and TNF (rs1800629, rs1799724), were included in the final analysis.

Statistical analysis

BMI was calculated as weight (kilograms) divided by the square of height (meters squared), using self-reported usual adult height and weight. We defined persons as normal weight if their BMI was less than 25 kg/m2 and overweight/obese if their BMI was more than or equal to 25 kg/m2 as defined by the World Health Organization. Unconditional logistic regression model was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for associations between BMI, and risk of NHL and its subtypes in different genotype strata. To increase statistical power, heterozygous and homozygous variant genotypes were combined for all genes. Potential confounding variables included in the final models were age (< 50 years, 50-70 years, > 70 years), race (white, black, other), and total energy intake (< 1385 kcal, 1385-1800 kcal, > 1800 kcal). Adjustments for other variables, such as cigarette smoking, alcohol consumption, and family history, did not result in material changes in the observed associations, and these variables were not included in the final models reported here. Significance of gene-BMI interaction was assessed by adding an interaction term in the logistic models. The false discovery rate (FDR) method set at 0.2 was used to control for multiple comparisons. All P values presented are 2-sided, and all analyses were performed using SAS Software Version 9.2 (SAS Institute).

The association between BMI and risk of NHL overall and NHL subtypes are presented in Tables 1 and 2. Compared with women with normal weight, those with a BMI more than or equal to 25.0 kg/m2 had an increased risk of NHL overall (OR = 1.3; 95% CI, 1.0-1.7), B-cell lymphoma (OR = 1.3; 95% CI, 1.0-1.7), and T-cell lymphoma (OR = 2.2; 95% CI, 1.1-4.4). However, statistical significance was only achieved among those with BMI between 25 and 30 kg/m2, but not among those with BMI more than 30 kg/m2. After stratification by common B-cell lymphoma subtypes, an increased risk was observed for diffuse large B-cell lymphoma (OR = 1.3; 95% CI, 0.9-1.9), marginal zone B-cell lymphoma (MZBCL, OR = 1.6; 95% CI, 0.8-3.3), and follicular lymphoma (OR = 1.4; 95% CI, 0.9-2.1), although none of them was statistically significant.

Table 1

Associations between BMI and risk of NHL and common NHL subtypes

BMI, kg/m2Overall
B-cell lymphoma
T-cell lymphoma
CasesControlsOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)P
< 25 251 326 1.0  199 1.0  14 1.0  
25-30 167 167 1.3 (1.0-1.7) .048 132 1.3 (1.0-1.7) .075 17 2.4 (1.2-5.0) .019 
> 30 100 104 1.3 (0.9-1.8) .147 80 1.3 (0.9-1.9) .129 1.8 (0.7-4.6) 1.187 
≥ 25 267 271 1.3 (1.0-1.7) .032 212 1.3 (1.0-1.7) .041 25 2.2 (1.1-4.4) .023 
BMI, kg/m2Overall
B-cell lymphoma
T-cell lymphoma
CasesControlsOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)P
< 25 251 326 1.0  199 1.0  14 1.0  
25-30 167 167 1.3 (1.0-1.7) .048 132 1.3 (1.0-1.7) .075 17 2.4 (1.2-5.0) .019 
> 30 100 104 1.3 (0.9-1.8) .147 80 1.3 (0.9-1.9) .129 1.8 (0.7-4.6) 1.187 
≥ 25 267 271 1.3 (1.0-1.7) .032 212 1.3 (1.0-1.7) .041 25 2.2 (1.1-4.4) .023 

ORs were adjusted for age, race, and total energy intake.

Table 2

Associations between BMI and risk of NHL and common NHL subtypes

BMI, kg/m2DLBCL
FL
SLL/CLL
MZBCL
CasesControlsOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)P
< 25 77 326 1.0  57 1.0  32 1.0  15 1.0  
25-30 56 167 1.5 (1.0-2.2) .066 35 1.2 (0.8-2.0) .402 18 1.1 (0.6-2.1) .710 14 1.8 (0.8-3.7) .146 
> 30 28 104 1.1 (0.7-1.8) .672 27 1.7 (1.0-2.8) .060 0.9 (0.4-2.0) .836 1.4 (0.5-3.8) .490 
≥ 25 84 271 1.3 (0.9-1.9) .123 62 1.4 (0.9-2.1) .119 27 1.1 (0.6-1.8) .865 20 1.6 (0.8-3.3) .164 
BMI, kg/m2DLBCL
FL
SLL/CLL
MZBCL
CasesControlsOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)PCasesOR (95% CI)P
< 25 77 326 1.0  57 1.0  32 1.0  15 1.0  
25-30 56 167 1.5 (1.0-2.2) .066 35 1.2 (0.8-2.0) .402 18 1.1 (0.6-2.1) .710 14 1.8 (0.8-3.7) .146 
> 30 28 104 1.1 (0.7-1.8) .672 27 1.7 (1.0-2.8) .060 0.9 (0.4-2.0) .836 1.4 (0.5-3.8) .490 
≥ 25 84 271 1.3 (0.9-1.9) .123 62 1.4 (0.9-2.1) .119 27 1.1 (0.6-1.8) .865 20 1.6 (0.8-3.3) .164 

ORs were adjusted for age, race, and total energy intakes.

FL indicates follicular lymphoma; SLL/CLL, small lymphocytic lymphoma/chronic lymphocytic leukemia; and MZBCL, marginal zone B-cell lymphoma.

As shown in Table 3, a significantly increased risk of NHL was associated with BMI among women who carried certain cytokine polymorphisms. Compared with women whose BMI was less than 25 kg/m2, women with a BMI more than or equal to 25.0 kg/m2 had a significantly increased risk of NHL if they carried IFNGR2 (rs9808753) AA genotype (OR = 1.5; 95% CI, 1.1-1.9), IL5 (rs2069812) CT/TT genotypes (OR = 1.7; 95% CI, 1.2-2.4), IL7R (rs1494555) AA genotype (OR = 1.9; 95% CI, 1.3-2.8), and TNF (rs1799724) CC genotype (OR = 1.5; 95% CI, 1.1-2.0), but not among women who carried IFNGR2 AG/GG, IL5 CC, IL7R AG/GG, and TNF CT/TT genotypes. A similar pattern was also observed for B-cell lymphoma and T-cell lymphoma. A statistically significant interaction was only observed for IFNGR2 (rs9808753 Pforinteraction = .034) and IL7R (rs1494555 Pforinteraction = .016) for NHL overall; IL7R (rs1494555 Pforinteraction = .016) and TNF (1799724 Pforinteraction = .031) for B-cell lymphoma; and IL5 (rs2069812 Pforinteraction = .034) for T-cell lymphoma. However, none of the interactions remained statistically significant after FDR adjustment.

Table 3

Associations between Th1/Th2 cytokine polymorphisms, BMI, and risk of NHL

SNPsOverall
B-cell lymphoma
T-cell lymphoma
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
ControlsCasesOR (95% CI)ControlsCasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)
IFNGR2_01 (rs9808753)               
    AA 247 180 1.0 189 202 1.5 (1.1-1.9) 146 1.0 161 1.4 (1.1-1.9) 1.0 18 2.9 (1.2-7.0) 
    AG or GG 62 54 1.0 69 49 0.9 (0.5-1.5) 41 1.0 41 1.0 (0.5-1.7) 1.0 0.8 (0.2-3.1) 
    Pforinteraction      .034    .108    .057 
IL5_02 (rs2069812)               
    CC 145 114 1.0 128 105 1.0 (0.7-1.4) 87 1.0 85 1.0 (0.7-1.5) 1.0 1.0 (0.4-2.6) 
    CT or TT 160 116 1.0 129 145 1.7 (1.2-2.4) 96 1.0 114 1.6 (1.1-2.3) 1.0 14 5.9 (1.6-21.5) 
    Pforinteraction      .081    .236    .034 
IL7R_01 (rs1494555)               
    AA 141 89 1.0 104 116 1.9 (1.3-2.8) 73 1.0 98 1.9 (1.3-2.9) 1.0 5.8 (1.1-31.6) 
    AG or GG 153 127 1.0 134 120 1.1 (0.8-1.6) 103 1.0 89 1.0 (0.7-1.4) 1.0 15 2.2 (0.9-5.5) 
    Pforinteraction      .016    .016    .120 
TNF_07 (rs1799724)               
    CC 243 168 1.0 188 195 1.5 (1.1-2.0) 134 1.0 161 1.6 (1.2-2.1) 1.0 18 2.9 (1.2-6.8) 
    CT or TT 59 57 1.0 66 57 0.9 (0.5-1.6) 46 1.0 41 0.8 (0.5-1.4) 1.0 1.6 (0.3-8.1) 
    Pforinteraction      .056    .031    .147 
SNPsOverall
B-cell lymphoma
T-cell lymphoma
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
BMI < 25, kg/m2
BMI ≥ 25, kg/m2
ControlsCasesOR (95% CI)ControlsCasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)
IFNGR2_01 (rs9808753)               
    AA 247 180 1.0 189 202 1.5 (1.1-1.9) 146 1.0 161 1.4 (1.1-1.9) 1.0 18 2.9 (1.2-7.0) 
    AG or GG 62 54 1.0 69 49 0.9 (0.5-1.5) 41 1.0 41 1.0 (0.5-1.7) 1.0 0.8 (0.2-3.1) 
    Pforinteraction      .034    .108    .057 
IL5_02 (rs2069812)               
    CC 145 114 1.0 128 105 1.0 (0.7-1.4) 87 1.0 85 1.0 (0.7-1.5) 1.0 1.0 (0.4-2.6) 
    CT or TT 160 116 1.0 129 145 1.7 (1.2-2.4) 96 1.0 114 1.6 (1.1-2.3) 1.0 14 5.9 (1.6-21.5) 
    Pforinteraction      .081    .236    .034 
IL7R_01 (rs1494555)               
    AA 141 89 1.0 104 116 1.9 (1.3-2.8) 73 1.0 98 1.9 (1.3-2.9) 1.0 5.8 (1.1-31.6) 
    AG or GG 153 127 1.0 134 120 1.1 (0.8-1.6) 103 1.0 89 1.0 (0.7-1.4) 1.0 15 2.2 (0.9-5.5) 
    Pforinteraction      .016    .016    .120 
TNF_07 (rs1799724)               
    CC 243 168 1.0 188 195 1.5 (1.1-2.0) 134 1.0 161 1.6 (1.2-2.1) 1.0 18 2.9 (1.2-6.8) 
    CT or TT 59 57 1.0 66 57 0.9 (0.5-1.6) 46 1.0 41 0.8 (0.5-1.4) 1.0 1.6 (0.3-8.1) 
    Pforinteraction      .056    .031    .147 

ORs were adjusted for age, race, and total energy intakes.

After stratification by common B-cell lymphoma subtypes (Table 4), a significant interaction was observed for IFNGR2 (rs9808753 Pforinteraction = .006), IL13 (rs20541 Pforinteraction = .019), and IL7R (rs1494555 Pforinteraction = .012) among MZBCL; IL7R (rs1494555 Pforinteraction = .017) among small lymphocytic lymphoma/chronic lymphocytic leukemia; and IL12A (rs568408 Pforinteraction = .013) and TNF (1799724 Pforinteraction = .04) among follicular lymphoma. After adjustment for FDR, 3 interactions, IFNGR2 (rs9808753), IL13 (rs20541), and IL7R (rs1494555) with MZBCL, remained statistically significant. Although a significantly increased risk of NHL was observed for several other cytokine polymorphisms, none of them showed a significant interaction with BMI and risk of NHL and its subtypes (supplemental Tables 1-2, available on the Blood Web site; see the Supplemental Materials link at the top of the online article).

Table 4

Associations between Th1/Th2 cytokine polymorphisms, BMI, and risk of common B-cell lymphoma subtypes

SNPsDLBCL
MZBCL
SLL/CLL
FL
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)
IFNGR2_01 (rs9808753)                 
    AA 58 1.0 61 1.3 (0.9-2.0) 1.0 17 2.8 (1.1-6.6) 27 1.0 21 1.1 (0.6-2.0) 39 1.0 47 1.6 (1.0-2.7) 
    AG or GG 15 1.0 21 1.3 (0.6-2.9) 1.0 0.3 (0.1-1.5) 1.0 1.6 (0.3-7.0) 13 1.0 11 0.8 (0.3-1.9) 
    Pforinteraction    .834    .006    .529    .134 
IL12A_01 (rs568408)                 
    GG 51 1.0 56 1.3 (0.8-2.0) 10 1.0 14 1.7 (0.7-3.9) 23 1.0 19 1.0 (0.5-2.0) 47 1.0 40 1.1 (0.7-1.7) 
    AG or AA 23 1.0 26 1.6 (0.8-3.1) 1.0 1.7 (0.4-6.2) 1.0 1.1 (0.3-3.3) 1.0 20 5.5 (1.9-15.5) 
    Pforinteraction    .770    .788    .646    .013 
IL13_01 (rs20541)                 
    GG 43 1.0 46 1.2 (0.8-2.0) 14 1.0 0.7 (0.3-1.8) 18 1.0 18 1.2 (0.6-2.4) 31 1.0 33 1.3 (0.7-2.2) 
    AG or AA 23 1.0 34 2.1 (1.1-3.9) 1.0 12.5 (1.5-106) 11 1.0 0.9 (0.3-2.4) 17 1.0 23 1.9 (0.9-3.9) 
    Pforinteraction    .157    .019    .680    .817 
IL7R_01 (rs1494555)                 
    AA 31 1.0 36 1.6 (0.9-2.8) 1.0 10 5.2 (1.3-19.7) 1.0 16 2.9 (1.2-7.3) 23 1.0 29 1.9 (1.0-3.5) 
    AG or GG 36 1.0 41 1.3 (0.8-2.2) 12 1.0 0.5 (0.2-1.4) 20 1.0 0.5 (0.2-1.1) 26 1.0 26 1.2 (0.7-2.2) 
    Pforinteraction    .381    .012    .017    .348 
TNF_07 (rs1799724)                 
    CC 52 1.0 66 1.6 (1.1-2.5) 12 1.0 14 1.5 (0.7-3.4) 21 1.0 21 1.3 (0.7-2.6) 38 1.0 49 1.8 (1.1-3.0) 
    CT or TT 17 1.0 16 0.9 (0.4-1.9) 1.0 1.4 (0.3-7.0) 1.0 0.7 (0.2-2.1) 13 1.0 0.6 (0.2-1.6) 
    Pforinteraction    .141    .882    .325    .040 
SNPsDLBCL
MZBCL
SLL/CLL
FL
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
BMI < 25 kg/m2
BMI ≥ 25 kg/m2
CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)CasesOR (95% CI)
IFNGR2_01 (rs9808753)                 
    AA 58 1.0 61 1.3 (0.9-2.0) 1.0 17 2.8 (1.1-6.6) 27 1.0 21 1.1 (0.6-2.0) 39 1.0 47 1.6 (1.0-2.7) 
    AG or GG 15 1.0 21 1.3 (0.6-2.9) 1.0 0.3 (0.1-1.5) 1.0 1.6 (0.3-7.0) 13 1.0 11 0.8 (0.3-1.9) 
    Pforinteraction    .834    .006    .529    .134 
IL12A_01 (rs568408)                 
    GG 51 1.0 56 1.3 (0.8-2.0) 10 1.0 14 1.7 (0.7-3.9) 23 1.0 19 1.0 (0.5-2.0) 47 1.0 40 1.1 (0.7-1.7) 
    AG or AA 23 1.0 26 1.6 (0.8-3.1) 1.0 1.7 (0.4-6.2) 1.0 1.1 (0.3-3.3) 1.0 20 5.5 (1.9-15.5) 
    Pforinteraction    .770    .788    .646    .013 
IL13_01 (rs20541)                 
    GG 43 1.0 46 1.2 (0.8-2.0) 14 1.0 0.7 (0.3-1.8) 18 1.0 18 1.2 (0.6-2.4) 31 1.0 33 1.3 (0.7-2.2) 
    AG or AA 23 1.0 34 2.1 (1.1-3.9) 1.0 12.5 (1.5-106) 11 1.0 0.9 (0.3-2.4) 17 1.0 23 1.9 (0.9-3.9) 
    Pforinteraction    .157    .019    .680    .817 
IL7R_01 (rs1494555)                 
    AA 31 1.0 36 1.6 (0.9-2.8) 1.0 10 5.2 (1.3-19.7) 1.0 16 2.9 (1.2-7.3) 23 1.0 29 1.9 (1.0-3.5) 
    AG or GG 36 1.0 41 1.3 (0.8-2.2) 12 1.0 0.5 (0.2-1.4) 20 1.0 0.5 (0.2-1.1) 26 1.0 26 1.2 (0.7-2.2) 
    Pforinteraction    .381    .012    .017    .348 
TNF_07 (rs1799724)                 
    CC 52 1.0 66 1.6 (1.1-2.5) 12 1.0 14 1.5 (0.7-3.4) 21 1.0 21 1.3 (0.7-2.6) 38 1.0 49 1.8 (1.1-3.0) 
    CT or TT 17 1.0 16 0.9 (0.4-1.9) 1.0 1.4 (0.3-7.0) 1.0 0.7 (0.2-2.1) 13 1.0 0.6 (0.2-1.6) 
    Pforinteraction    .141    .882    .325    .040 

ORs were adjusted for age, race, and total energy intake.

SLL/CLL indicates small lymphocytic lymphoma/chronic lymphocytic leukemia; and FL, follicular lymphoma.

Our study provided the first comprehensive analysis of interaction between BMI, genetic polymorphisms in Th1/Th2 pathway genes, and risk of NHL and its subtypes. Significant interactions were observed for IFNGR2 (rs9808753), IL12A (rs568408), IL13 (rs20541), IL5 (rs2069812), IL7R (rs1494555), and TNF (rs1799724) for NHL overall and/or various NHL subtypes.

Consistent with previous studies,5,9-11,13,14,16-18  our study suggested that overweight/obesity was associated with an increased risk of NHL overall, B-cell lymphoma, and T-cell lymphoma. Macrophages and different types of T lymphocytes accumulating in expanding adipose tissue can interfere with both the innate and adaptive immune system.32  In addition, cytokine polymorphisms, a germline mutation that may affect quality or quantity of cytokines, may enhance or deteriorate adipose inflammation and subsequently modify the association between BMI and risk of NHL.

The study suggested that IFNGR2 polymorphism (rs9808753) may have modified the relationship between BMI and NHL risk for both B-cell lymphoma and T-cell lymphoma. The gene IFNGR2 encodes the non–ligand-binding β-chain of the IFN-γ located on chromosome 21.33  Initiation of the IFN-γ signal transduction cascade serves to directly inhibit viral replication and both stimulates and modulates the immune system. Recent evidence suggested that IFN-γ regulates fat inflammation and enhances adaptive immunity, which may contribute to the complications of obesity.34  Although SNP rs9808753 leads to an amino acid substitution of arginine for glutamine (Gln64Arg) within IFNGR2, it is currently unclear whether this change would interfere with the binding ability with IFN-γ or alter other biologic functions. Effect modification was observed for T-cell and several B-cell lymphomas, suggesting that the IFN-γ transduction pathway could play a role in the relationship between BMI and risk of NHL.

We also observed a potential interaction between interleukin 12A (IL12A) and BMI and NHL overall, follicular lymphoma, and T-cell lymphoma risk. IL12A encodes the p35 subunit of IL12 that is a heterodimeric cytokine consisting of 2 subunits, p35 (α-chain) and p40 (β-chain). IL12 is a key cytokine inducing the production of IFN-γ, eliciting the differentiation of Th1 cells and forming a link between innate resistance and adaptive immunity.35,36  The observed effect modification could be the result of the disruption of the IFN-γ transduction pathway or some other unknown mechanism(s).

The gene IL13 encodes an immunoregulatory cytokine produced primarily by activated Th2 cells. The IL13 cytokine exerts antiapoptotic functions and is linked to leukemogenesis.37  Overexpression of IL13 has been confirmed in HTLV-1-positive and Tax-transformed cells,37  and a recent study reported that IL13 (rs20541) AG/AA genotypes were associated with a reduced risk of T-cell lymphoma.38  The current study found suggestive effect modification by IL13 (rs20541) on the association between BMI and risk of T-cell lymphoma, diffuse large B-cell lymphoma, and MZBCL. Although the underlying mechanism is currently unclear, it is possible that the role of IL13 on NHL risk is the result of unbalanced regulation and expression of Th1 and Th2 cytokines.

Potential effect modification by IL7R (rs1494555) was observed for MZBCL and small lymphocytic lymphoma/chronic lymphocytic leukemia in the current study. The IL7R encodes a receptor for IL-7. Proper functioning of IL7R requires the IL-2 receptor, a γ-chain, which is shared by various cytokines. It has been shown that IL7R plays a critical role in V(D)J recombination during lymphocyte development.39-41  Mutations in the IL7R gene have been associated with severe combined immunodeficiency.42  Further knowledge of genetic polymorphisms and the relationship to the cytokine network would help elucidate the association between BMI and NHL.

To date, TNF is one of the most promising candidate genes leading to increased susceptibility of NHL.43-48  The TNF gene regulates the expression of the cytokine TNF-α, which gives rise to the inflammatory response to infection. Furthermore, high levels of TNF-α promote activation of the transcription factor nuclear factor-κB, which has antiapoptotic effects on B cells.49  As obesity promotes increased production of TNF-α,1  it is possible that genetic variations in TNF may modify the relationship between BMI and risk of NHL. In the current study, we found a potential interaction between TNF (rs1799724) SNP, BMI, and risk of follicular lymphoma.

The study has several strengths. First, it is a population-based, case-control study with histologically confirmed incident NHL cases, which minimized potential disease misclassification. Second, our study, for the first time, reported the effect of modification by Th1/Th2 genes and the association between BMI and NHL. The major limitation of our study is the modest sample size, particularly for NHL subtype analysis. As such, chance cannot be ruled out for some of the significant findings. However, we adjusted for multiple comparisons using the FDR approach because of the number of SNPs examined in the study. In addition, as the study to assess the interaction between genetic polymorphisms and BMI and risk of NHL, the topic warrants further investigation. Finally, the study was designed to investigate the role of hair dye use in relation to NHL risk. Only women were included in the study because of the higher prevalence of hair dye use among women. As such, the results may not be generalizable to men.

In conclusion, our study suggests that common genetic variations in the Th1/Th2 pathways genes may modify the association between BMI and risk of NHL. The positive results in our study need to be replicated in larger population studies with greater power.

The online version of this article contains a data supplement.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

This work was supported by the National Institutes of Health (grant CA62006), the Intramural Research Program of the National Institutes of Health, National Cancer Institute, and the National Institutes of Health Fogarty (training grants 1D43TW008323-01 and 1D43TW007864-01).

This publication was made possible by a Clinical and Translational Science Award (grant UL1 RR024139) from the National Center for Research Resources, a component of the National Institutes of Health, and NHL roadmap for medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of National Center for Research Resources.

National Institutes of Health

Contribution: Y.C., T.Z., F.F., X.C., M.D., Y.L., P.B., and Y.Z. conceived and led the study; T.Z., T.H., B.L., and Y.Z. carried out the population-based, case-control study; Q.L., N.R., and S.J.C. performed the genotyping at NCI; Y.C. and Y.Z. carried out the statistical analysis; Y.C., C.K., and Y.Z. drafted and revised the manuscript; and all authors reviewed and approved the manuscript.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Yawei Zhang, Yale University School of Public Health, 60 College St, LEPH 440, New Haven, CT 06520; e-mail: yawei.zhang@yale.edu.

1
Stallone
 
D
The influence of obesity and its treatment on the immune system.
Nutr Rev
1994
, vol. 
52
 
2
(pg. 
37
-
50
)
2
Cerhan
 
JR
Bernstein
 
L
Severson
 
RK
, et al. 
Anthropometrics, physical activity, related medical conditions, and the risk of non-Hodgkin lymphoma.
Cancer Causes Control
2005
, vol. 
16
 
10
(pg. 
1203
-
1214
)
3
Chang
 
ET
Hjalgrim
 
H
Smedby
 
KE
, et al. 
Body mass index and risk of malignant lymphoma in Scandinavian men and women.
J Natl Cancer Inst
2005
, vol. 
97
 
3
(pg. 
210
-
218
)
4
Fernberg
 
P
Odenbro
 
A
Bellocco
 
R
Boffetta
 
P
Pawitan
 
Y
Adami
 
J
Tobacco use, body mass index and the risk of malignant lymphomas: a nationwide cohort study in Sweden.
Int J Cancer
2006
, vol. 
118
 
9
(pg. 
2298
-
2302
)
5
Holly
 
EA
Lele
 
C
Bracci
 
PM
McGrath
 
MS
Case-control study of non-Hodgkin's lymphoma among women and heterosexual men in the San Francisco Bay Area, California.
Am J Epidemiol
1999
, vol. 
150
 
4
(pg. 
375
-
389
)
6
MacInnis
 
RJ
English
 
DR
Hopper
 
JL
Giles
 
GG
Body size and composition and the risk of lymphohematopoietic malignancies.
J Natl Cancer Inst
2005
, vol. 
97
 
15
(pg. 
1154
-
1157
)
7
Moller
 
H
Mellemgaard
 
A
Lindvig
 
K
Olsen
 
JH
Obesity and cancer risk: a Danish record-linkage study.
Eur J Cancer
1994
, vol. 
30A
 
3
(pg. 
344
-
350
)
8
Oh
 
SW
Yoon
 
YS
Shin
 
SA
Effects of excess weight on cancer incidences depending on cancer sites and histologic findings among men: Korea National Health Insurance Corporation Study.
J Clin Oncol
2005
, vol. 
23
 
21
(pg. 
4742
-
4754
)
9
Pan
 
SY
Johnson
 
KC
Ugnat
 
AM
Wen
 
SW
Mao
 
Y
Association of obesity and cancer risk in Canada.
Am J Epidemiol
2004
, vol. 
159
 
3
(pg. 
259
-
268
)
10
Pan
 
SY
Mao
 
Y
Ugnat
 
AM
Physical activity, obesity, energy intake, and the risk of non-Hodgkin's lymphoma: a population-based case-control study.
Am J Epidemiol
2005
, vol. 
162
 
12
(pg. 
1162
-
1173
)
11
Rapp
 
K
Schroeder
 
J
Klenk
 
J
, et al. 
Obesity and incidence of cancer: a large cohort study of over 145 000 adults in Austria.
Br J Cancer
2005
, vol. 
93
 
9
(pg. 
1062
-
1067
)
12
Samanic
 
C
Chow
 
WH
Gridley
 
G
Jarvholm
 
B
Fraumeni
 
JF
Relation of body mass index to cancer risk in 362 552 Swedish men.
Cancer Causes Control
2006
, vol. 
17
 
7
(pg. 
901
-
909
)
13
Skibola
 
CF
Holly
 
EA
Forrest
 
MS
, et al. 
Body mass index, leptin and leptin receptor polymorphisms, and non-Hodgkin lymphoma.
Cancer Epidemiol Biomarkers Prev
2004
, vol. 
13
 
5
(pg. 
779
-
786
)
14
Willett
 
EV
Skibola
 
CF
Adamson
 
P
, et al. 
Non-Hodgkin's lymphoma, obesity and energy homeostasis polymorphisms.
Br J Cancer
2005
, vol. 
93
 
7
(pg. 
811
-
816
)
15
Wolk
 
A
Gridley
 
G
Svensson
 
M
, et al. 
A prospective study of obesity and cancer risk (Sweden).
Cancer Causes Control
2001
, vol. 
12
 
1
(pg. 
13
-
21
)
16
Bahl
 
S
Cotterchio
 
M
Kreiger
 
N
Klar
 
N
Antidepressant medication use and non-Hodgkin's lymphoma risk: no association.
Am J Epidemiol
2004
, vol. 
160
 
6
(pg. 
566
-
575
)
17
Chiu
 
BC
Gapstur
 
SM
Greenland
 
P
Wang
 
R
Dyer
 
A
Body mass index, abnormal glucose metabolism, and mortality from hematopoietic cancer.
Cancer Epidemiol Biomarkers Prev
2006
, vol. 
15
 
12
(pg. 
2348
-
2354
)
18
Calle
 
EE
Rodriguez
 
C
Walker-Thurmond
 
K
Thun
 
MJ
Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U. S. adults.
N Engl J Med
2003
, vol. 
348
 
17
(pg. 
1625
-
1638
)
19
Willett
 
EV
Morton
 
LM
Hartge
 
P
, et al. 
Non-Hodgkin lymphoma and obesity: a pooled analysis from the InterLymph Consortium.
Int J Cancer
2008
, vol. 
122
 
9
(pg. 
2062
-
2070
)
20
Spilianakis
 
CG
Lalioti
 
MD
Town
 
T
Lee
 
GR
Flavell
 
RA
Interchromosomal associations between alternatively expressed loci.
Nature
2005
, vol. 
435
 
7042
(pg. 
637
-
645
)
21
Lucey
 
DR
Clerici
 
M
Shearer
 
GM
Type 1 and type 2 cytokine dysregulation in human infectious, neoplastic, and inflammatory diseases.
Clin Microbiol Rev
1996
, vol. 
9
 
4
(pg. 
532
-
562
)
22
Foster
 
CB
Chanock
 
SJ
Mining variations in genes of innate and phagocytic immunity: current status and future prospects.
Curr Opin Hematol
2000
, vol. 
7
 
1
(pg. 
9
-
15
)
23
Gergely
 
L
Aleksza
 
M
Varoczy
 
L
, et al. 
Intracellular IL-4/IFN-gamma producing peripheral T lymphocyte subsets in B cell non-Hodgkin's lymphoma patients.
Eur J Haematol
2004
, vol. 
72
 
5
(pg. 
336
-
341
)
24
Keen
 
LJ
The extent and analysis of cytokine and cytokine receptor gene polymorphism.
Transpl Immunol
2002
, vol. 
10
 
2–3
(pg. 
143
-
146
)
25
Hofmann
 
SR
Ettinger
 
R
Zhou
 
YJ
, et al. 
Cytokines and their role in lymphoid development, differentiation and homeostasis.
Curr Opin Allergy Clin Immunol
2002
, vol. 
2
 
6
(pg. 
495
-
506
)
26
Chiu
 
BC
Weisenburger
 
DD
An update of the epidemiology of non-Hodgkin's lymphoma.
Clin Lymphoma
2003
, vol. 
4
 
3
(pg. 
161
-
168
)
27
Mori
 
T
Takada
 
R
Watanabe
 
R
Okamoto
 
S
Ikeda
 
Y
T-helper (Th)1/Th2 imbalance in patients with previously untreated B-cell diffuse large cell lymphoma.
Cancer Immunol Immunother
2001
, vol. 
50
 
10
(pg. 
566
-
568
)
28
Lan
 
Q
Zheng
 
T
Rothman
 
N
, et al. 
Cytokine polymorphisms in the Th1/Th2 pathway and susceptibility to non-Hodgkin lymphoma.
Blood
2006
, vol. 
107
 
10
(pg. 
4101
-
4108
)
29
Zhang
 
Y
Holford
 
TR
Leaderer
 
B
, et al. 
Hair-coloring product use and risk of non-Hodgkin's lymphoma: a population-based case-control study in Connecticut.
Am J Epidemiol
2004
, vol. 
159
 
2
(pg. 
148
-
154
)
30
Zheng
 
T
Holford
 
TR
Leaderer
 
B
, et al. 
Diet and nutrient intakes and risk of non-Hodgkin's lymphoma in Connecticut women.
Am J Epidemiol
2004
, vol. 
159
 
5
(pg. 
454
-
466
)
31
Jaffe
 
ES
Stein
 
H
Vardiman
 
JW
World Health Organization Classification of Tumors: Pathology and Genetics of Tumors of Haematopoietic and Lymphoid Tissues
2001
Lyon, France
IARC Press
32
Sell
 
H
Eckel
 
J
Adipose tissue inflammation: novel insight into the role of macrophages and lymphocytes.
Curr Opin Clin Nutr Metab Care
2010
, vol. 
13
 
4
(pg. 
366
-
370
)
33
Mogensen
 
KE
Lewerenz
 
M
Reboul
 
J
Lutfalla
 
G
Uze
 
G
The type I interferon receptor: structure, function, and evolution of a family business.
J Interferon Cytokine Res
1999
, vol. 
19
 
10
(pg. 
1069
-
1098
)
34
Rocha
 
VZ
Folco
 
EJ
Sukhova
 
G
, et al. 
Interferon-gamma, a Th1 cytokine, regulates fat inflammation: a role for adaptive immunity in obesity.
Circ Res
2008
, vol. 
103
 
5
(pg. 
467
-
476
)
35
Trinchieri
 
G
Interleukin-12 and the regulation of innate resistance and adaptive immunity.
Nat Rev Immunol
2003
, vol. 
3
 
2
(pg. 
133
-
146
)
36
Hsieh
 
CS
Macatonia
 
SE
Tripp
 
CS
Wolf
 
SF
O'Garra
 
A
Murphy
 
KM
Development of TH1 CD4+ T cells through IL-12 produced by Listeria-induced macrophages.
Science
1993
, vol. 
260
 
5107
(pg. 
547
-
549
)
37
Waldele
 
K
Schneider
 
G
Ruckes
 
T
Grassmann
 
R
Interleukin-13 overexpression by tax transactivation: a potential autocrine stimulus in human T-cell leukemia virus-infected lymphocytes.
J Virol
2004
, vol. 
78
 
12
(pg. 
6081
-
6090
)
38
Wang
 
SS
Carreon
 
JD
Hanchard
 
B
Chanock
 
S
Hisada
 
M
Common genetic variants and risk for non-Hodgkin lymphoma and adult T-cell lymphoma/leukemia in Jamaica.
Int J Cancer
2009
, vol. 
125
 
6
(pg. 
1479
-
1482
)
39
Sudo
 
T
Nishikawa
 
S
Ohno
 
N
, et al. 
Expression and function of the interleukin 7 receptor in murine lymphocytes.
Proc Natl Acad Sci U S A
1993
, vol. 
90
 
19
(pg. 
9125
-
9129
)
40
Rolink
 
A
Haasner
 
D
Nishikawa
 
S
Melchers
 
F
Changes in frequencies of clonable pre B cells during life in different lymphoid organs of mice.
Blood
1993
, vol. 
81
 
9
(pg. 
2290
-
2300
)
41
Van De Wiele
 
CJ
Marino
 
JH
Murray
 
BW
Vo
 
SS
Whetsell
 
ME
Teague
 
TK
Thymocytes between the beta-selection and positive selection checkpoints are nonresponsive to IL-7 as assessed by STAT-5 phosphorylation.
J Immunol
2004
, vol. 
172
 
7
(pg. 
4235
-
4244
)
42
Giliani
 
S
Mori
 
L
de Saint Basile
 
G
, et al. 
Interleukin-7 receptor alpha (IL-7Ralpha) deficiency: cellular and molecular bases: analysis of clinical, immunological, and molecular features in 16 novel patients.
Immunol Rev
2005
, vol. 
203
 
1
(pg. 
110
-
126
)
43
Rothman
 
N
Skibola
 
CF
Wang
 
SS
, et al. 
Genetic variation in TNF and IL10 and risk of non-Hodgkin lymphoma: a report from the InterLymph Consortium.
Lancet Oncol
2006
, vol. 
7
 
1
(pg. 
27
-
38
)
44
Wang
 
SS
Cozen
 
W
Cerhan
 
JR
, et al. 
Immune mechanisms in non-Hodgkin lymphoma: joint effects of the TNF G308A and IL10 T3575A polymorphisms with non-Hodgkin lymphoma risk factors.
Cancer Res
2007
, vol. 
67
 
10
(pg. 
5042
-
5054
)
45
Bel Hadj Jrad
 
B
Chatti
 
A
Laatiri
 
A
, et al. 
Tumor necrosis factor promoter gene polymorphism associated with increased susceptibility to non-Hodgkin's lymphomas.
Eur J Haematol
2007
, vol. 
78
 
2
(pg. 
117
-
122
)
46
Purdue
 
MP
Lan
 
Q
Kricker
 
A
, et al. 
Polymorphisms in immune function genes and risk of non-Hodgkin lymphoma: findings from the New South Wales non-Hodgkin Lymphoma Study.
Carcinogenesis
2007
, vol. 
28
 
3
(pg. 
704
-
712
)
47
Wang
 
SS
Cerhan
 
JR
Hartge
 
P
, et al. 
Common genetic variants in proinflammatory and other immunoregulatory genes and risk for non-Hodgkin lymphoma.
Cancer Res
2006
, vol. 
66
 
19
(pg. 
9771
-
9780
)
48
Spink
 
CF
Keen
 
LJ
Mensah
 
FK
Law
 
GR
Bidwell
 
JL
Morgan
 
GJ
Association between non-Hodgkin lymphoma and haplotypes in the TNF region.
Br J Haematol
2006
, vol. 
133
 
3
(pg. 
293
-
300
)
49
Karin
 
M
Greten
 
FR
NF-kappaB: linking inflammation and immunity to cancer development and progression.
Nat Rev Immunol
2005
, vol. 
5
 
10
(pg. 
749
-
759
)

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