BACKGROUND: Abnormal immune function is a key factor in predisposition to non-Hodgkin lymphoma (NHL). We evaluated the association of 30 cytokines individually and as a profile with diffuse large B-cell (DLBCL) and follicular (FL) lymphomas. METHODS: We used a multiplexed assay to measure 30 cytokine concentrations in pre-treatment serum in a case-control study of 234 FL, 188 DLBCL, and 400 control participants. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) adjusted for age and sex, and polytomous regression was used to evaluate heterogeneity between FL and DLBCL. Principal components analysis (PCA) was used to assess cytokine profiles associated with FL and DLBCL. RESULTS: In single cytokine modeling, we found that 12 of the 30 circulating serum cytokines were significantly (P<0.05) associated with FL and/or DLBCL after accounting for multiple testing (q<0.05). Soluble IL-2R (sIL-2R) had the strongest association with both FL (OR=6.0 for highest versus lowest tertile, 95% CI 3.8-9.5; p-trend=1.8 × 10(-21)) and DLBCL (OR=7.6, 95% CI 4.5-13.1; p-trend=7.2 × 10(-20)). IL1RA and IL-12p40 also showed similar associations for DLBCL and FL. In contrast, HGF, MIG, and MIP-1α had a stronger association with DLBCL compared to FL, and IL-6, IL-8, IL-10, IFN-γ, IP-10, and VEGF were only statistically significantly associated with DLBCL after accounting for multiple testing. However, in PCA modeling, a cytokine profile based on sIL-2R, IL-1RA, MIG, IP-10, IL-8, and IL-12p40 explained most of the variability between controls and both FL and DLBCL. CONCLUSIONS: We identified some cytokines unique to DLBCL, but overall cytokine associations were more similar than distinct for DLBCL and FL. While these data are limited by concerns of reverse causality, they do suggest cytokines and cytokine profiles that can be prioritized in future studies.
BACKGROUND: Abnormal immune function is a key factor in predisposition to non-Hodgkin lymphoma (NHL). We evaluated the association of 30 cytokines individually and as a profile with diffuse large B-cell (DLBCL) and follicular (FL) lymphomas. METHODS: We used a multiplexed assay to measure 30 cytokine concentrations in pre-treatment serum in a case-control study of 234 FL, 188 DLBCL, and 400 control participants. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) adjusted for age and sex, and polytomous regression was used to evaluate heterogeneity between FL and DLBCL. Principal components analysis (PCA) was used to assess cytokine profiles associated with FL and DLBCL. RESULTS: In single cytokine modeling, we found that 12 of the 30 circulating serum cytokines were significantly (P<0.05) associated with FL and/or DLBCL after accounting for multiple testing (q<0.05). Soluble IL-2R (sIL-2R) had the strongest association with both FL (OR=6.0 for highest versus lowest tertile, 95% CI 3.8-9.5; p-trend=1.8 × 10(-21)) and DLBCL (OR=7.6, 95% CI 4.5-13.1; p-trend=7.2 × 10(-20)). IL1RA and IL-12p40 also showed similar associations for DLBCL and FL. In contrast, HGF, MIG, and MIP-1α had a stronger association with DLBCL compared to FL, and IL-6, IL-8, IL-10, IFN-γ, IP-10, and VEGF were only statistically significantly associated with DLBCL after accounting for multiple testing. However, in PCA modeling, a cytokine profile based on sIL-2R, IL-1RA, MIG, IP-10, IL-8, and IL-12p40 explained most of the variability between controls and both FL and DLBCL. CONCLUSIONS: We identified some cytokines unique to DLBCL, but overall cytokine associations were more similar than distinct for DLBCL and FL. While these data are limited by concerns of reverse causality, they do suggest cytokines and cytokine profiles that can be prioritized in future studies.
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