OBJECTIVE:To investigate the genetic architecture of low-frequency and rare variants of serum urate (SU) in East Asian populations, and to clarify its role as a heritable and modifiable risk factor for gout and cardiometabolic diseases. METHODS:We conducted the largest two-stage, whole-genome sequencing-based, genome-wide scan for SU levels in East Asians, analyzing 9.1 million variants across 7,339 Han Chinese participants from the Healthy Zhejiang One Million People (HOPE) cohort. RESULTS:We verified associations at common and low-frequency loci and identified a novel, replicable male-specific locus at MAN1A2, along with a candidate low-frequency male-specific locus at CPE. Furthermore, using the STAARpipeline framework, we probed rare missense and putative loss-of-function variant aggregates in genes such as SLC22A12, SLC2A9, and G6PC2, which were validated in our replication data set or the UK Biobank. Moreover, we identified and replicated novel associations with rare promoter variants near HDC and SLC22A12, highlighting their potential role in the context of noncoding regulation. Additionally, deep learning-based fine-mapping revealed transcription factors such as HNF1A, RUNX1, and SRF as potential up-regulators of SU-associated genes. CONCLUSION:By resolving East Asian-specific allelic architecture and revealing ancestral diversity in SU genetics, this study advanced translational opportunities for precision urate-lowering therapies and prioritized novel candidates for future research.

Background: Lung cancer continues to be the most frequently diagnosed malignancy globally and a major contributor to cancer-related deaths. Although dietary factors have been increasingly implicated in its development, evidence for the Mediterranean diet (MED) and specific fat subtypes remains limited. Objectives: To examine the associations between MED adherence and dietary fat intakes with lung cancer incidence, mortality, and survival. Methods: We included 191 139 cancer-free participants from the UK Biobank. The validated Oxford WebQ 24-hour dietary questionnaire was used to measure dietary intake. The adherence of MED was assessed by the Alternate Mediterranean diet (AMED) score. Total dietary fat and fat subtype intakes were calculated as a proportion of total energy intake. Associations between dietary factors and lung cancer outcomes were analyzed using adjusted Cox regression. Results: After full adjustment, greater adherence to the MED was associated with a lower lung cancer risk (HRQ4 vs. Q1: 0.66; 95% CI: 0.58-0.77), and lower lung cancer-specific mortality (HRQ4 vs. Q1: 0.61; 95% CI: 0.50-0.74). Higher polyunsaturated fatty acids (PUFAs) intake was linked to lower lung cancer risk (HRQ4 vs. Q1: 0.82; 95% CI: 0.71-0.95) and mortality (HRQ4 vs. Q1: 0.77; 95% CI: 0.63-0.94), whereas higher saturated fatty acids (SFAs) intake was associated with increased lung cancer risk (HRQ4 vs. Q1: 1.25; 95% CI: 1.09-1.45) and mortality (HRQ4 vs. Q1: 1.23; 95% CI: 1.01-1.49). In isocaloric substitution analyses, replacing 1% of energy from SFAs with PUFAs was associated with a 9% and 10% lower risk of lung cancer incidence and mortality. Among participants who developed lung cancer, individuals with high pre-diagnosis AMED scores and PUFAs intake had better post-diagnosis survival than those with low AMED scores and low PUFAs intake (HR = 0.77; 95% CI: 0.61-0.96). Conclusions: Adherence to the MED and higher PUFAs intake were independently related to lower risk of lung cancer and reduced lung cancer-specific mortality. The combination of greater MED adherence and higher PUFAs intake may provide additional benefits for lung cancer post-diagnosis survival. These findings imply that dietary modifications might have a role in both the onset and progression of lung cancer. Further studies are warranted to clarify the mechanistic pathways and inform the development of dietary recommendations.

This cross-sectional study compared the gut microbiota between metabolic dysfunction associated steatotic liver disease (MASLD) patients and healthy controls. A total of 1401 participants, including 392 MASLD patients and 1009 healthy controls, were enrolled from one project site of the Healthy Zhejiang One Million People Cohort (HOPE) between January 2022 and June 2023. Shotgun metagenomic sequencing was conducted to compare the composition and functional profiles of the gut microbiome between MASLD patients and healthy controls. Compared to the control group, MASLD patients exhibited significant alterations in both alpha and beta diversity, along with reduced connectivity and robustness of the gut microbial network. We identified significant changes in the abundance of 12 microbial strains between the two groups with two strains (t_SGB4749 and t_SGB4753) enriched and ten strains depleted in MASLD patients. In comparison to the control group, MASLD patients demonstrated distinct differences in the genomic potential related to increased glycolysis, decreased pyruvate metabolism, and elevated lipopolysaccharide (LPS) biosynthesis in both metagenomic functional profiling and single-strain genome analysis. These findings suggest that alterations in specific microbial strains and metabolic pathways may contribute to MASLD pathogenesis.

Background:Suboptimal sleep and diabetes are major contributors to mortality. However, whether sleep patterns differentially affect mortality across glycaemic statuses remains unclear. This study examined associations of sleep patterns (sleep duration and sleep disorders) with all-cause mortality among individuals with normoglycaemia, prediabetes, and diabetes. Methods:Data were obtained from the Taiwan MJ cohort, including 534 238 participants enrolled between 1996 and 2022. Sleep duration ('less than 6 hours', '6-8 hours', 'more than 8 hours') and sleep disorders (yes/no) were assessed via standardised questionnaires. Glycaemic status was classified as normoglycaemia, prediabetes, or diabetes. Mortality data were obtained from the Taiwan Death Registry. Cox proportional hazards regression models were employed to evaluate the association between sleep patterns and the risk of all-cause mortality. Results:The study included 363 863 participants with normoglycaemia, 144 602 with prediabetes, and 25 773 with diabetes. Over a median follow-up period of 19 years, 52 208 deaths were recorded. Compared with those who slept 6-8 hours, normoglycaemia individuals who slept less than 6 hours had a higher risk of all-cause mortality (hazard ratio (HR) = 1.05; 95% confidence interval (CI) = 1.02-1.08) and those who slept more than eight hours had a higher risk of all-cause mortality across all glycaemic groups: normoglycaemia (HR = 1.19; 95% CI = 1.15-1.24), prediabetes (HR = 1.24; 95% CI = 1.19-1.30), and diabetes (HR = 1.29; 95% CI = 1.22-1.36). Sleep disorders were also associated with increased mortality among individuals with prediabetes (HR = 1.04; 95% CI = 1.01-1.07) and diabetes (HR = 1.07; 95% CI = 1.02-1.11). Conclusions:Long sleep durations and sleep disorders were associated with increased mortality, especially among individuals with impaired glucose regulation while short sleep duration was discovered to associate with increased risk of mortality in people with normoglycaemia. These findings highlight the potential role of sleep assessment in risk stratification, although the observational nature of the study limits causal inference.

BACKGROUND:Age-related structural and functional remodeling of the heart and vessels increases cardiovascular disease (CVD) risk, yet comprehensive assessments using multimodal imaging and genetic characterization remains limited. We aimed to quantify cardiovascular aging using multimodal biomarkers and evaluate its genetic architecture, lifestyle determinants, and prognostic relevance. METHODS:From the UK biobank, cardiovascular magnetic resonance (CMR), electrocardiogram, arterial stiffness, and carotid ultrasound biomarkers were integrated to establish a cardiovascular aging measure using machine learning (ML) models. Seven ML models were evaluated to predict cardiovascular age, and cardiovascular age gap (CardioAG) was calculated using the best-performing model. Associations between CardioAG and incident CVD outcomes were assessed, alongside modality-specific analyses to evaluate the incremental value of multimodal integration. Whole-genome sequencing (WGS) analyses were conducted to identify genetic variants associated with CardioAG, and linear regression models were applied to examine relationships between CardioAG and key lifestyle factors. RESULTS:Among 22,452 participants with complete multimodal data, 13,694 individuals free of baseline CVDs (median age 62.6 years [IQR 56.5-68.3], median follow-up 4.8 years [IQR 3.7-6.3]) were selected for model development. With 57 cardiovascular aging biomarkers, the CatBoost model performed best on the test dataset (Pearson r = 0.75; mean absolute error = 3.88 years). CardioAG was independently associated with hypertension, stroke, atrial fibrillation, coronary artery disease, and composite major adverse cardiovascular event (MACE, hazard ratio = 1.08, 95% CI, 1.06-1.10). Incremental and ablation analyses demonstrated complementary contributions across imaging and functional modalities, with multimodal integration enhancing predictive accuracy and providing independent prognostic value for MACE. WGS analysis identified novel common genetic variants associated with interindividual variability in CardioAG, including RN7SKP155, SVIL, and CBFA2T3, highlighting genetic contributions to vascular remodeling, electrophysiological and hemodynamic regulation, and cardiovascular functional reserve. Dietary factors, sleep duration, physical activity level, smoking, and alcohol intake were found to be significantly associated with CardioAG. CONCLUSIONS:This study developed a unified multimodal cardiovascular aging metric that integrates cardiac structure and function, vascular remodeling and stiffness, and electrophysiological features into a single biologically grounded, cumulative aging signal, providing incremental prognostic utility for CVD, and broadening the biological and genetic landscape underlying cardiovascular aging.

Objectives Previous literature has observed the association between residential street connectivity and weight status. However, whether street connectivity near the workplace influences overweight and obesity among adults is unclear. Exploring these associations is essential for understanding the role of built environment in maintaining health for working adults and providing evidence-based guidelines for urban planning. Methods Based on data from 42,469 participants aged between 18 and 60 in urban Hangzhou, eight street connectivity indicators within a 15-min walking circle surrounding their workplaces were assessed. We used multinomial logistic regressions to examine the cross-sectional associations of street connectivity indicators with overweight and obesity. Additionally, we performed stratified analyses by age and sex to evaluate the potential effect modifications. Results Circle area, shape ratio, walk-score, road density, real node density, and end node density were inversely associated with both overweight and obesity, while average road width was positively associated with overweight and obesity. For example, compared to participants with the lowest quartile, those with the highest quartile of average road width were more likely to be overweight (OR = 1.19, 95% CI: 1.12–1.26) and obesity (OR = 1.31, 95% CI: 1.19–1.43). Conversely, those with the highest quartile of road density had a 15% (95% CI: 0.80–0.90) lower odds of overweight and a 30% (95% CI: 0.64–0.77) of obesity. The association for proportion of intersections was not statistically significant. Conclusions We observed generally beneficial associations of better street connectivity and narrower road width with overweight and obesity. Our findings provide evidence-based guides for urban road planning to improve the pedestrian environment in business areas to address the obesity challenge for adults.

Objective Existing biological age (BA) models often oversimplify aging’s complexity, offering single-dimensional metrics. However, these fail to capture the critical heterogeneity of aging across organs.. This study aims to develop a machine learning-based unified framework to assess and interpret multi-organ biological aging comprehensively. Method Using data from UK Biobank participants, we trained and integrated organ-specific BA estimates to assess multidimensional BA within an ensemble learning framework, and uncover distinct aging patterns. Result Our Fusion BA (an overall estimation of BA) was significantly correlated with chronological age (CA) (mean absolute error (MAE): 4.473 years; Pearson correlation: 0.718, P<0.01). Accelerated Fusion BA derived from the ensemble model (contrast between Fusion BA and CA) predicted 10-year mortality (HR=1.504, 95% CI: 1.438-1.574). Organ-specific BA correlated with organ disease risk and effectively captured distinct aging patterns. Conclusion This framework enables systemic and organ-specific aging assessment, provides actionable tools and insights for clinical risk.

Background:The role of postoperative carcinoembryonic antigen (CEA) levels in non-small lung cancer (NSCLC) prognostic evaluation remains unclear. Additionally, the dynamic changes in CEA levels during the perioperative period have not been fully characterized. Methods:We retrospectively reviewed stage I-IIIA NSCLC patients who underwent curative resection. A latent class growth mixed model was employed to categorize patients into distinct CEA trajectory groups. The Kaplan-Meier method assessed the relationship between CEA trajectory groups and recurrence-free survival (RFS) and overall survival (OS). Multivariate analysis of perioperative CEA levels in relation to RFS and overall survival OS was performed using Cox proportional hazards regression. Results:A total of 5733 patients were included in our study. Elevated postoperative CEA levels were associated with higher risks of recurrence (HR = 2.64, 95% CI: 1.65-4.23) and mortality (HR = 3.34, 95% CI: 2.09-5.80) compared to normal CEA levels. Furthermore, patients with normal preoperative CEA but elevated postoperative levels also had higher risks of recurrence (HR = 3.00, 95% CI: 1.77-5.10) and mortality (HR = 3.30, 95% CI: 1.79-6.07). Three CEA trajectory categories were identified: low-stable, early-rising, and later-rising. Compared to the low-stable group, the early-rising group had significantly higher risks of recurrence (HR = 10.84, 95% CI: 5.57-21.10) and mortality (HR = 13.37, 95% CI: 5.45-32.81). The later-rising group had lower, but still significant, risks of recurrence (HR = 3.56, 95% CI: 1.62-7.81). Conclusion:Continuous postoperative monitoring of CEA levels in NSCLC patients is essential, especially for those with elevated postoperative CEA levels.

BACKGROUND:Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung cancer risk based on epidemiological questionnaires, comparing them with traditional logistic regression models. METHODS:A retrospective case-control study was conducted using data from 5421 lung cancer cases and 10,831 matched controls. The dataset included a wide range of demographic, clinical, and behavioral risk factors from epidemiological questionnaires. We developed and compared multiple machine learning algorithms, including LightGBM and stacking ensemble models, alongside logistic regression for predicting lung cancer risk. Model performance was evaluated using accuracy, area under the curve (AUC), and recall. RESULTS:The stacking model outperformed traditional logistic regression, achieving an AUC of 0.887 (0.870-0.903) compared to 0.858 (0.839-0.878) for logistic regression. LightGBM also performed well, with an AUC of 0.884 (0.867-0.901). The stacking model achieved an accuracy of 81.2%, with a recall of 0.755, higher than the logistic regression model's accuracy of 79.4%. Compared to classical lung cancer prediction models (LLP and PLCO), the logistic regression and ML models improved AUC by 12% to 27%. CONCLUSIONS:Integrating machine learning models into lung cancer screening programs can significantly enhance early detection efforts. Machine learning approaches, such as LightGBM and stacking, offer improved accuracy and predictive power over traditional models. However, efforts to enhance model interpretability through explainable AI techniques are necessary for broader clinical adoption.

BACKGROUND:Precise assessment of brain aging is crucial for early detection of neurodegenerative disorders and aiding clinical practice. Existing magnetic resonance imaging (MRI)-based methods excel in this task, but they still have room for improvement in capturing local morphological variations across brain regions and preserving the inherent neurobiological topological structures. OBJECTIVE:To develop and validate a deep learning framework incorporating both connectivity and complexity for accurate brain aging estimation, facilitating early identification of neurodegenerative diseases. METHODS:We used 5889 T1-weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative dataset. We proposed a novel brain vision graph neural network (BVGN), incorporating neurobiologically informed feature extraction modules and global association mechanisms to provide a sensitive deep learning-based imaging biomarker. Model performance was evaluated using mean absolute error (MAE) against benchmark models, while generalization capability was further validated on an external UK Biobank dataset. We calculated the brain age gap across distinct cognitive states and conducted multiple logistic regressions to compare its discriminative capacity against conventional cognitive-related variables in distinguishing cognitively normal (CN) and mild cognitive impairment (MCI) states. Longitudinal track, Cox regression, and Kaplan-Meier plots were used to investigate the longitudinal performance of the brain age gap. RESULTS:The BVGN model achieved an MAE of 2.39 years, surpassing current state-of-the-art approaches while obtaining an interpretable saliency map and graph theory supported by medical evidence. Furthermore, its performance was validated on the UK Biobank cohort (N=34,352) with an MAE of 2.49 years. The brain age gap derived from BVGN exhibited significant difference across cognitive states (CN vs MCI vs Alzheimer disease; P<.001), and demonstrated the highest discriminative capacity between CN and MCI than general cognitive assessments, brain volume features, and apolipoprotein E4 carriage (area under the receiver operating characteristic curve [AUC] of 0.885 vs AUC ranging from 0.646 to 0.815). Brain age gap exhibited clinical feasibility combined with Functional Activities Questionnaire, with improved discriminative capacity in models achieving lower MAEs (AUC of 0.945 vs 0.923 and 0.911; AUC of 0.935 vs 0.900 and 0.881). An increasing brain age gap identified by BVGN may indicate underlying pathological changes in the CN to MCI progression, with each unit increase linked to a 55% (hazard ratio=1.55, 95% CI 1.13-2.13; P=.006) higher risk of cognitive decline in individuals who are CN and a 29% (hazard ratio=1.29, 95% CI 1.09-1.51; P=.002) increase in individuals with MCI. CONCLUSIONS:BVGN offers a precise framework for brain aging assessment, demonstrates strong generalization on an external large-scale dataset, and proposes novel interpretability strategies to elucidate multiregional cooperative aging patterns. The brain age gap derived from BVGN is validated as a sensitive biomarker for early identification of MCI and predicting cognitive decline, offering substantial potential for clinical applications.

Metabolic syndrome (MetS) encompasses a collection of metabolic abnormalities. This study aims to determine which combination of MetS components has the highest mortality risk, and to investigate the causal relationships between MetS components and longevity. Prospective analyses were conducted on 340,196 participants from the MJ cohort at baseline, and 121,936 participants had follow-up MetS information. We defined MetS according to the NCEP ATP III criteria. The study’s outcomes included mortality from cardiovascular disease (CVD), cancer, and all causes combined. We employed Cox proportional hazard models to calculate hazard ratios (HRs) and 95

Lung cancer is a malignant tumor with a high morbidity and mortality rate worldwide, causing an increasing disease burden. Of these, the most common type is non-small cell lung cancer (NSCLC), which accounts for 80-85% of all lung cancer cases. Genetic research is crucial for continuously discovering susceptibility genes related to lung cancer for in-depth study. The role of genetic predisposition in the development of NSCLC, particularly within circadian rhythm pathways known to govern various physiological processes, is increasingly acknowledged. Yet, the association between genetic variants of circadian rhythm-related genes and NSCLC susceptibility among Chinese populations is not fully understood. This study carried out a two-phase (discovery and validation stages) research design to identify genetic variants associated with NSCLC risk within the circadian rhythm pathway. We employed extensive whole-genome sequencing (WGS) for 1,104 NSCLC cases and 9,635 controls. FastGWA-GLMM was used for single-locus risk association analysis of NSCLC, and we screened candidate SNPs in the validation set that comprised 4,444 cases and 174,282 controls from the Biobank Japan Project (BBJ). Furthermore, GCTA-COJO conditional analysis was utilized to confirm SNPs related to NSCLC risk. Finally, potential genetic variations that may regulate gene expression were explored in GTEx and QTLbase. RNA sequencing data were utilized for transcriptomic verification. Our study identified eight candidate SNPs associated with NSCLC susceptibility within the circadian rhythm pathway that met the requirement with P < 0.05 in both the discovery and validation populations. After conditional analysis, five of these SNPs remained. The A allele of CUL1 rs78524436 (ORmeta = 1.18, 95%CI: 1.09-1.29, Pmeta = 7.99e-5) and the A allele of TEF rs9611588 (ORmeta = 1.06, 95%CI: 1.02-1.10, Pmeta = 1.28e-3) were associated with an increased risk of NSCLC. The A allele of FBXL21 rs2069868 (ORmeta = 0.86, 95%CI: 0.80-0.96, Pmeta = 4.78e-4), the T allele of CSNK1D rs147316973 (ORmeta = 0.76, 95%CI: 0.65-0.88, Pmeta = 5.93e-4), and the A allele of RORA rs1589701 (ORmeta = 0.94, 95%CI: 0.91-0.98, Pmeta = 3.40e-3) were associated with a lower risk of NSCLC, separately. The eQTL results revealed an association between RORA rs1589701 and TEF rs9611588 with the expression levels of RORA and TEF, respectively. Transcriptome data indicated that RORA and TEF showed lower expression levels in tumor tissues compared to normal tissues (P < 0.001). Moreover, poorer survival was observed in patients with lower RORA and TEF expressions (log-rank P < 0.05). Our findings spotlight potential susceptibility loci within circadian rhythm pathway genes that modulate NSCLC carcinogenesis, which enriches the understanding of the genetic susceptibility of NSCLC in the Chinese population and provides a more solid basis for exploring the biological mechanism of circadian rhythm genes in NSCLC.

Previous studies on serum iron levels and mortality risk have yielded inconsistent findings based on single-point measurements. How serum iron levels and their longitudinal changes influence all-cause and cause-specific mortality remains unknown. This study investigated associations between baseline serum iron levels, their longitudinal changes, and all-cause and cause-specific mortality in a prospective cohort. Participants were recruited from the Taiwan MJ cohort (1997–2007) and followed until December 31, 2022. Baseline serum iron was categorized as low, normal, or high. Based on changes at a second visit, participants were further classified as persistent normal, progression to abnormal, reversion to normal, or persistent abnormal. Cox proportional hazard models were used for analysis. Over a median follow-up of 19.0 years, 33,005 deaths occurred. Fully adjusted models demonstrated J-shaped associations between serum iron and all-cause and cause-specific mortality (all P < 0.001), with higher all-cause mortality risks in low (HR 1.27, 95

Diet plays a crucial role in the management and recovery of cancer patients. Making appropriate dietary choices can help provide necessary nutrients, enhance the immune system, maintain energy levels, and promote recovery. To examine the associations of dietary scores for 9 healthy eating patterns with risk of all-cause and cancer mortality in cancer survivors. A prospective cohort of cancer survivors (n = 4001; weighted number, 17548466) from the US National Health and Nutrition Examination Survey from 1999 to 2018. Participants were linked to mortality data from their interview and physical examination date through December 31, 2019. The exposures were Alternative Healthy Eating Index-2010 (AHEI2010), Alternate Mediterranean Diet score (AMED), Dietary Approaches to Stop Hypertension score (DASH), Dietary inflammation index (DII), Healthy dietary score (HDS), Healthy eating index-2020 (HEI2020), Plant-based diet index (PDI), Healthful plant-based diet index (hPDI), and World Cancer Research Fund/American Institute for Cancer Research dietary score (WADS). Main Outcomes were All-cause and cancer mortality. The final study sample included 4001 cancer survivors (mean [SD] baseline age, 61.86[14.79] years; 2408 (66.37%) females; 2519(82.33) white individuals). During a total of 33130 person-years of follow-up, 1385 Participants died. Of note, when comparing the highest with the lowest tertiles, only AMED (HR (95% CI): 0.80 (0.65-0.98), P = 0.03), HEI2020 (HR (95% CI): 0.81 (0.68-0.96), P = 0.02), and hPDI (HR (95% CI): 0.84 (0.71-0.98), P = 0.03) were significantly associated with all-cause mortality. Also, higher levels of HEI2020 were associated with a lower risk of cancer mortality in cancer survivors (HR (95% CI): 0.70 (0.55-0.88), P = 0.003). All dietary scores had no nonlinear association with all-cause mortality. The inverse associations between these scores and risk of mortality were consistent in subgroups. In this cohort study of a nationally representative sample of US cancer survivors, embracing a comprehensive and nutritionally balanced dietary pattern, as exemplified by HEI2020, holds tremendous value in promoting the well-being and recovery of cancer survivors. Zilong Bian, Xiaohang Xu, Siyun Zhou, Xue Li, Xifeng Wu. Healthy dietary patterns and cancer survival: A cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 808.

Aging is an important risk factor of cancer, and a healthy lifestyle is one possible measure to delay aging. Limited research has investigated the relationship between a healthy lifestyle, biological aging, and cancer incidence. This prospective study enrolled 122, 023 individuals from the Taiwan MJ cohort. The multivariable logistic regression model was utilized to examine the associations between the healthy lifestyle scores (HLS, integrating never or quitted smoking, non-harmful drinking, active physical activity, healthy body fat percentage, and healthy diet) and biological aging acceleration alteration, which was measured by changes in multi-dimensional aging measure acceleration (ΔMDAgeAccel). The Cox regression model was used to evaluated the joint association of HLS and ΔMDAgeAccel with cancer risk. Compared to individuals with HLS of 0, increasing HLS was associated with decreased odds of MDAgeAccel increase, with the adjusted odds ratio (aOR) of 0.684 (95% CI 0.557 to 0.841), 0.589 (0.483 to 0.718), 0.500 (0.411 to 0.610), 0.426 (0.349 to 0.519), and 0.379 (0.310 to 0.464) for HLS of 1, 2, 3, 4, and 5, respectively. These results remained consistent across stratifications by baseline aging acceleration status, age group, and sex. Individuals with accelerated aging at baseline and higher HLS exhibited lower odds ratio of persistent aging acceleration. The hazard ratio (HR) for cancer incidence was 0.774 (0.665 to 0.900) for individuals with a higher HLS and decreased MDAgeAccel, 0.932 (0.779 to 1.116) for individuals with a higher HLS and increased MDAgeAccel, compared with those with a lower HLS and increased MDAgeAccel. Adherence to healthy lifestyle may lower aging acceleration, and reducing cancer risk. Junlin Jia, Chi Pang Wen, Huakang Tu, Junlong Pan, Wanzhu Lu, Min Yang, Andi Xu, Sicong Wang, Wenyuan Li, Xifeng Wu. Longitudinal evidence of healthy lifestyles slowing biological aging and reducing cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2321.

Biological age is an important measure of aging that reflects an individual's physical health and is linked to various diseases. Current prediction models are still limited in precision, and the risk factors for accelerated aging remain underexplored. Therefore, we aimed to develop a precise biological age and assess the impact of socio-demographic and behavioral patterns on the aging process.We utilized Deep Neural Networks (DNN) to construct biological age from participants with physical examinations, blood samples, and questionnaires data from the China Kadoorie Biobank (CKB) between June 2004 and December 2016. △age, calculated as the residuals between biological age and chronological age, was used to investigate the associations of age acceleration with diseases. Socio-demographics (gender, education attainment, marital status, household income) and lifestyle characteristics (body mass index [BMI], smoking, drinking, physical activity, and sleep) were also assessed to explore their impact on age acceleration. 18,261 participants aged 57 ± 10 years were included in this study. The DNN-based biological age model has demonstrated accurate predictive performance, achieving a mean absolute error of 3.655 years. △age was associated with increased risks of various morbidity and mortality, with the highest associations found for circulatory and respiratory diseases, with hazard ratios of 1.033 (95% CI: 1.023, 1.042) and 1.078 (95% CI: 1.027, 1.130), respectively. Socio-demographics, including being female, lower education, widowed or divorced, and low household income, along with behavioral patterns, such as being underweight, insufficient physical activity, and poor sleep, were associated with accelerated aging. Our DNN model is capable of constructing a precise biological age using commonly collected data. Socio-demographics and lifestyle factors were associated with accelerated aging, highlighting that addressing modifiable risk factors can effectively slow age acceleration and reduce disease risk, providing valuable insights for interventions to promote healthy aging.

BackgroundOsteosarcoma is a rare disease, yet it is the most frequent primary malignant bone tumor, with poor survival in metastatic cases. Current PD-1 and PD-L1 checkpoint inhibitors show limited efficacy in osteosarcoma, necessitating further investigation into other immune checkpoint factors.MethodsWe analyzed immune checkpoint proteins in plasma from 67 osteosarcoma patients and 50 healthy controls, examined their transcriptional levels in tumor tissues, validated the results using public databases, and elucidated potential mechanisms.ResultsCD48, TIMD-4, B7-H6, CD134, B7-H5, CD47, and S100A8/A9 were significantly elevated in osteosarcoma patients, each linked to increased osteosarcoma risk. In patients who developed metastasis, CD48, B7-H2, TIMD-4, B7-H6, CD134, B7-H5, CD47, and S100A8/A9 were also elevated and correlated with higher metastasis risk. Using peripheral blood levels of these eight factors, we identified osteosarcoma immune subtypes and built an excellent predictive model for metastasis (C-index = 0.876, predicting metastasis within one year). The gene expression of these factors in tumor tissues showed an inverse correlation with metastasis compared to peripheral blood. Single-cell analysis revealed differential expression of these factors in non-specific immune cells from metastatic patients.ConclusionSoluble immune checkpoint factors were identified as significantly associated with osteosarcoma metastasis. Using peripheral blood biomarkers, we characterized immune subtypes of osteosarcoma, and developed a predictive model for metastasis. These biomarkers may serve as potential therapeutic targets for future immunotherapy.

BACKGROUND:Lung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. METHODS:PRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. RESULTS:The best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC = 0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI : 0.629,0.653) and odds ratio of 1.71 (95% CI : 1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. CONCLUSIONS:Multi-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Dysregulated RNA splicing is a post-transcriptional molecular feature that significantly influences tumor progression and prognosis. However, the role of alternative splicing in the development of non-small cell lung cancer (NSCLC) within the Chinese population remains poorly understood. In this study, we investigated the genetic regulation of splicing in 245 tumor and 297 normal lung tissue samples from Chinese NSCLC patients. By integrating splicing data with a meta-analyzed genome-wide association study (GWAS) for NSCLC in East Asians (7,035 cases and 185,413 controls), we identified 14 novel NSCLC-associated splicing events (FDR < 0.05) through a splicing transcriptome-wide association study (spTWAS). Additionally, we validated the involvement of the splicing gene FARP1 and the EIF3 family, both of which have been associated with NSCLC risk. By combining the results of differential splicing analysis and spTWAS, followed by colocalization analysis and putative splicing factor predictions, we highlighted the critical roles of splicing events in TP63 (1st exon skipping) and TPM1 (6th exons mutually exclusive) in NSCLC, bridging the missing biology between SNP-NSCLC association. Furthermore, we underscored several splicing events in genes including ILK, which were also associated with NSCLC prognosis. In conclusion, this study elucidated the genetic architecture of splicing in lung tissues and revealed the significant contribution of splicing dysregulation to the carcinogenesis and prognosis of NSCLC.