Mediating effect of soluble B-cell activation immune markers on the association between anthropometric and lifestyle factors and lymphoma development

Abstract

Sustained B-cell activation is an important mechanism contributing to B-cell lymphoma (BCL). We aimed to validate four previously reported B-cell activation markers predictive of BCL risk (sCD23, sCD27, sCD30, and CXCL13) and to examine their possible mediating effects on the association between anthropometric and lifestyle factors and major BCL subtypes. Pre-diagnostic serum levels were measured for 517 BCL cases and 525 controls in a nested case-control study. The odds ratios of BCL were 6.2 in the highest versus lowest quartile for sCD23, 2.6 for sCD30, 4.2 for sCD27, and 2.6 for CXCL13. Higher levels of all markers were associated with increased risk of chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and diffuse large B-cell lymphoma (DLBCL). Following mutual adjustment for the other immune markers, sCD23 remained associated with all subtypes and CXCL13 with FL and DLBCL. The associations of sCD23 with CLL and DLBCL and CXCL13 with DLBCL persisted among cases sampled > 9 years before diagnosis. sCD23 showed a good predictive ability (area under the curve = 0.80) for CLL, in particular among older, male participants. sCD23 and CXCL13 showed a mediating effect between body mass index (positive) and DLBCL risk, while CXCL13 contributed to the association between physical activity (inverse) and DLBCL. Our data suggest a role of B-cell activation in BCL development and a mediating role of the immune system for lifestyle factors.

Figure 1

Correlation between serum level of immune markers and time to diagnosis (TTD) for different BCL subtype and controls; TTD for controls was calculated based on diagnosis date of the index cases.

Figure 2

ROC curve and test accuracy parameters of sCD23 for cutoff at 30th (≥ 1832 pg/mL), 60th (≥ 2,453 pg/mL), and 90th (≥ 3,608 pg/mL) deciles for CLL (A) and stratified by gender (B, C); ROC receiver operating characteristic, PPV positive predictive value, NPV negative predictive value.