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Life sciences · Journal article
Journal of Translational Medicine · October 21, 2026
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Obesity is increasingly recognized as a systemic immunometabolic disorder that influences lymphoma risk, tumor biology, treatment response, and survivorship. Epidemiological evidence links obesity to an increased incidence of several lymphoid malignancies, particularly diffuse large B-cell lymphoma and follicular lymphoma, although these associations vary across lymphoma subtypes, sex, age, and metabolic phenotype. Mechanistically, obesity promotes chronic inflammation, adipokine imbalance, and metabolic stress, leading to activation of oncogenic pathways including IL-6/STAT3, NF-κB, insulin/IGF-1, and PI3K/AKT/mTOR signaling, as well as lipid metabolic rewiring and stromal-myeloid remodeling within the lymphoma microenvironment. Importantly, recent advances in single-cell and spatial transcriptomics, multi-omics profiling, and integrative bioinformatic analyses have further refined our understanding of these immunometabolic interactions. Clinically, obesity exerts a complex and context-dependent influence. Several studies describe a controversial “obesity paradox” in selected DLBCL cohorts treated with rituximab-based chemoimmunotherapy, although these findings may reflect reverse causality, collider bias, and limitations of BMI-based analyses. In contrast, emerging, although heterogeneous, evidence suggests that obesity and obesity-associated metabolic alterations may influence CAR-T-cell kinetics and inflammatory toxicity, while their impact on therapeutic efficacy remains uncertain. Importantly, body mass index remains an imperfect surrogate for biologically relevant adiposity, as it does not adequately capture visceral fat accumulation, insulin resistance, inflammatory activation, muscle depletion, or sarcopenic obesity. Collectively, this review highlights the need to move beyond BMI-based classifications by integrating body composition assessment, metabolic profiling, and emerging multi-omics approaches to improve lymphoma risk stratification, biomarker discovery, and precision therapeutic strategies.