Allostatic load, genetic susceptibility, and lung cancer risk: Evidence from a prospective cohort and integrative...

Allostatic load (AL), a composite measure of multisystem dysregulation associated with chronic stress, has been linked to lung cancer risk. However, its joint effects with genetic susceptibility and the biological pathways underlying this relationship remain unclear. We conducted a prospective cohort study of 336,945 UK Biobank participants to assess associations between AL and lung cancer incidence and its histological subtypes. A polygenic risk score was constructed to evaluate the joint associations of AL and genetic susceptibility. We further assessed whether AL provided incremental risk stratification beyond established low-dose computed tomography (LDCT) screening eligibility frameworks and integrated plasma proteomic data using machine learning, enrichment, and mediation analyses. We found that higher AL was associated with an increased risk of lung cancer (per-unit HR = 1.073, 95% CI: 1.053-1.094; Low vs. Normal HR = 1.284, 95% CI: 1.065-1.547; High vs. Normal HR = 1.629, 95% CI: 1.347-1.971). Subtype analyses revealed the strongest effect for small-cell lung cancer (SCLC), with significant associations also observed for lung adenocarcinoma and squamous cell carcinoma. High AL combined with high genetic risk was associated with a 136.9% increased risk (HR = 2.369, 95% CI: 1.565-3.583), without evidence of interaction. AL provided additional risk discrimination beyond established LDCT screening eligibility criteria. Proteomic analyses identified 111 AL-associated proteins and 34 proteins with potential mediation signal, including HGF, RARRES2, and GDF15, implicating inflammatory, metabolic, and growth factor-related processes as biological correlates of the AL-lung cancer association. In conclusion, higher AL was independently associated with lung cancer risk, particularly SCLC. Integrative proteomic analyses provided exploratory evidence for biological pathways that may link cumulative physiological dysregulation to lung carcinogenesis. Incorporating AL, as a composite biomarker reflecting cumulative physiological burden, into risk models may improve risk stratification and help identify individuals at elevated risk.