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OBJECTIVE This study aimed to examine the associations of longitudinal obesity trajectories and polygenic risk with sex-specific brain aging. METHODS We analyzed 35,092 UK Biobank participants (16,484 males and 18,608 females). Sex-specific XGBoost models estimated multimodal brain age. We derived 16-year longitudinal obesity trajectories from repeatedly collected anthropometric measurements. Polygenic risk scores were constructed based on 55 independent genetic loci. Multivariable logistic regression examined associations of obesity trajectories and genetic risk with brain age acceleration. RESULTS A total of 8198 (49.73%) males and 9089 (48.84%) females had accelerated brain aging. High genetic risk significantly increased brain age acceleration odds (males: OR = 1.39; females: OR = 1.34). Crucially, the high-stable obesity trajectory exerted a stronger effect in males (OR = 1.90, 95% CI: 1.64-2.21) than in females (OR = 1.25, 95% CI: 1.12-1.40), with the joint presence of high genetic risk and high-stable obesity amplifying risk to an OR of 2.78 in males and 1.57 in females. Conversely, shifting from obesity to non-obesity reduced risk by 30% in males and 18% in females. CONCLUSIONS These findings underscore long-term obesity as a critical, sex-dimorphic driver of accelerated brain aging, and midlife weight management offers robust neuroprotection even in genetically susceptible individuals.
Multimodal diagnostic strategies and risk stratification are essential for improving early lung cancer detection, clinical outcomes, and personalized interventions. To develop a predictive model for lung cancer incidence by integrating plasma proteomics, genetic factors, and demographic data. We analyzed data from the UK Biobank, which included 47,655 adults without a history of lung cancer at baseline, with 641 incident cases and a median follow-up duration of 13.87 years. COX proportional hazards modeling with SHapley Additive exPlanations (SHAP) sequential feature selection was used to identify significant plasma proteins associated with lung cancer. An XGBoost-based survival model was constructed and evaluated to assess its predictive utility. Proteomic risk score (ProRS), demographic, and Combined risk scores were developed to evaluate their predictive capabilities across genetic risk strata. Among 2911 plasma proteins, we identified 15 significant plasma proteins associated with the development of lung cancer. The XGBoost survival model demonstrated promising predictive performance, achieving a C-statistics of 0.803. Risk stratification revealed that high-risk groups had up to a 101-fold increased risk of lung cancer compared to low-risk groups. We analyzed expression patterns of 15 carcinogenesis-associated proteins during the 15-year period preceding diagnosis. TNR and CLEC3B were identified as the earliest dysregulated proteins, with expression levels consistently downregulated beginning as early as 14 years before diagnosis. GDF15 exhibited sustained and pronounced upregulation throughout the entire 14-year observation window. Integrating plasma proteomics, polygenic risk scores, and demographic factors improves personalized lung cancer risk stratification, offering potential for refined screening strategies.
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