Genome-wide association studies have predominantly implicated common variants in lung cancer susceptibility, whereas the contribution of rare non-coding variation remains incompletely defined. This study comprised 52,550 cases and 1,617,173 controls across three whole-genome sequencing (WGS) cohorts (UK Biobank, the 100,000 Genomes Project, and All of Us) and five imputed genotype-array datasets aimed to identify rare non-coding determinants of lung cancer risk. Two complementary approaches were applied: position-based single-variant testing for variants with adequate allele counts, and gene-based testing aggregating rare and ultra-rare ncRNA variants to increase power. Single-variant meta-analysis identified two novel rare non-coding variants reaching sequencing-based genome-wide significance: rs763076863C22orf46 at 22q13.2 [OR (95% CI): 5.94 (3.46-10.19), P = 4.55 × 10-9] and rs1014871851WWOX at 16q23.1 [OR (95% CI): 7.55 (4.50-12.67), P = 3.71 × 10-9]. Functional annotation-informed gene-based analyses prioritized 37 candidate ncRNAs using eQTL, expression, proteomics, prognosis, cis-Mendelian randomization drug-target evidence, and external replication. CLEC12A-AS1 ranked highest; multi-omics association and chain mediation analyses supported an indirect pathway linking CLEC12A-AS1 burden to lung cancer risk via modulation of CLEC12A. Collectively, results indicate contributions from both individually detectable rare variants and cumulative ultra-rare functional burden within ncRNA regions, highlighting the CLEC12A-AS1/CLEC12A axis as a potential biomarker and translational target.

Polygenic risk scores (PRSs) quantify genetic susceptibilities, yet ancestry imbalance in genome-wide association studies (GWASs) limits the accuracy of monoracial PRSs in non-European populations. Here, we perform a multiancestry GWAS meta-analysis for lung cancer (76,953 cases and 1,886,372 controls), identifying 87 conditionally independent genome-wide significant loci, including two unreported cytobands. We use a PRS construction method, PRS-CSx, to develop a multiancestry PRS ( PRS MA ) which outperforms 32 published PRSs. To enhance predictive power, we construct a multitrait PRS ( PRS MT ) using CatBoost, integrating 32 cross-trait PRSs across three ancestries. Combining PRS MA and PRS MT , we generate PRS MAMT and validate it in independent cohorts (OncoArray, TRICL and All of Us). PRS MAMT demonstrates superior discriminability in European, Asian, and African populations, improves risk stratification, and identifies approximately 10% additional lung cancer cases in the UK Biobank. Individuals with elevated PLCOm2012 scores and high genetic risk exhibit a 12.64-fold higher cumulative risk than those with low scores and low genetic risk, supporting precision prevention strategies.

Background:Immune-mediated inflammatory disease (IMID) and cancer share underlying mechanisms. We aimed to comprehensively evaluate the associations between IMIDs and cancers from global, population and genetic perspectives. Methods:A triangulation framework was employed to assess the association between IMIDs and cancers, using the Global Burden of Disease Study (2012-2021) to analyse six IMIDs and 33 cancers. The UK Biobank (UKBB) prospective cohort was subsequently used to validate these associations, with hazard ratios (HRs) and 95% confidence intervals (CIs) estimated by Cox proportional hazards models. Causal inference based on genetic instruments was performed in the FinnGen and UKBB to assess the potential causal effects between IMIDs and cancers. Results:IMIDs were positively associated with the occurrence of cancers from a global perspective. Moreover, 170 specific IMID-cancer pairs revealed statistically significant associations. A total of 20 pairs of specific IMID-cancer associations were further confirmed in the UKBB cohort. Among these, the five most pronounced associations included atopic dermatitis with Hodgkin lymphoma (HR = 12.56, 95% CI: 1.76-89.59), with ovarian cancer (HR = 5.65, 95% CI: 1.41-22.65) and with non-Hodgkin lymphoma (HR = 5.11, 95% CI: 1.91-13.63); rheumatoid arthritis with Hodgkin lymphoma (HR = 3.85, 95% CI: 1.11-13.32); and psoriasis with Hodgkin lymphoma (HR = 3.43, 95% CI: 1.69-6.96). Additionally, a positive causal association between rheumatoid arthritis and Hodgkin lymphoma (inverse variance weighted OR = 1.31, 95% CI: 1.10-1.57) was observed. Conclusions:This study provides comprehensive evidence of the relationships between IMIDs and cancers from global, population and genetic perspectives and identifies 20 pairs of specific IMID-cancer associations, thereby contributing to advancements in cancer prevention and control.

ObjectivesAbout 20% of renal masses are proved to be benign after surgery. We aimed to develop a strategy based on radiomics to differentially diagnose renal masses.Methods: In this retrospective study, 726 renal masses (54 benign and 672 malignant) were collected from two hospitals in China and two public databases. Two diagnostic models, kidney mass diagnosis model (KMDM) and kidney cancer subtypes diagnosis model (KCSDM), were developed for differential diagnosis of renal cell carcinoma and their subtypes. Each model was divided into three units (radiomics model, clinical model, and integrated model) and evaluated in the training set and two independent validation sets.MethodsIn this retrospective study, 726 renal masses (54 benign and 672 malignant) were collected from two hospitals in China and two public databases. Two diagnostic models, kidney mass diagnosis model (KMDM) and kidney cancer subtypes diagnosis model (KCSDM), were developed for differential diagnosis of renal cell carcinoma and their subtypes. Each model was divided into three units (radiomics model, clinical model, and integrated model) and evaluated in the training set and two independent validation sets.ResultsA total of 39 radiomic variables were used for the KMDM, and 78 variables for the KCSDM. The radiomics model performed better than the clinical model in both KMDM and KCSDM. The integrated model of KMDM showed the best performance (AUC of 0.937 in training set, 0.942 in both two validation sets), when compared to the other two models and two experts. A cutoff value of risk of malignant index (RMI=1.661) was designated to distinguish the benign and malignant renal masses with a sensitivity of 0.929 and a specificity of 0.833 in validation sets. Similar results were found in the integrated model of KCSDM.ConclusionThe integrated model can noninvasively distinguish benign from malignant renal masses and renal cell carcinoma subtypes with satisfactory sensitivity and specificity.

BACKGROUND & AIMS:Serum lipids, including lipoproteins, cholesterol, and triglycerides, are important modifiable factors influencing human health. However, the associations among different serum lipid profiles and mortality remain insufficiently understood, particularly regarding potential causality and population heterogeneity. This prospective study aims to systematically investigate the relationships between serum lipid concentrations of different densities and sizes with all-cause and cause-specific mortality. METHODS:Cox proportional and Fine-Gray subdistribution hazard models were applied to investigate the associations of 54 lipid concentrations with all-cause and cause-specific mortality (including cardiovascular disease (CVD), cancer, and respiratory disease) in the UK Biobank cohort of 441,448 individuals with 17-year follow-up. Cohorts of 120,967 and 44,168 individuals from the Women's Health Initiative (WHI) with 16-year follow-up and a large-scale meta-analysis were utilized for external replication. We further assessed the underlying causality using Mendelian randomization (MR) and possible modifiers using multiple subgroup analyses. RESULTS:During a median follow-up of 13.8 years, 39,290 deaths occurred, including 7399 from CVD, 18,928 from cancer, and 2707 from respiratory disease. We identified 160 significant associations between lipid concentrations and all-cause and cause-specific mortality. Importantly, most were inverse, with decreased lipid levels linked to increased risk of premature death [hazard ratios (HRs): 0.70-0.98 per standard deviation (SD)]. In contrast, positives were observed for HDL (large/very large) and triglyceride concentrations [HRs: 1.02-1.25 per SD], indicating increased mortality risk with higher levels. Most lipoproteins and cholesterol exhibited nonlinearly correlations with mortality, especially the significant U-shaped in total/HDL. However, MR showed that elevations in several lipids were associated with increased all-cause and CVD-specific mortality risk. Multiple subgroup analyses revealed that age, sex, and lipid-modifying drugs modified the lipid-mortality relationship; specifically, higher lipid concentrations increased mortality risk in younger adults not taking lipid-modifying drugs, but decreased mortality in older adults taking lipid-modifying drugs. The majority of associations were replicated in the WHI and external cohorts. CONCLUSION:Our study systematically reported a large number of associations between serum lipid concentrations and mortality. Subgroup-based population heterogeneity analysis suggests that age, sex, and lipid-modifying drugs could be modifiers for the lipid-mortality relationship. These findings provide more guidance for lipid management and individualized prevention.

BACKGROUND:Intestinal infectious disease is a common infectious disease that is closely related to meteorological conditions and air pollution factors. We aim to construct a short-term prediction model for the daily incidence of common intestinal infectious diseases in Changzhou city. METHODS:The daily incidence data of hand, foot, and mouth disease and other infectious diarrhea in Changzhou and the daily meteorological data and air pollutant data in the same period were collected from May 13, 2014 to December 31, 2024. The meteorological data consisted of temperature, humidity, wind speed, air pressure, etc. Air pollutant data included PM2.5, PM10, SO2, NO2, O3, and AQI indicators. Three models, Long Short-Term Memory (LSTM), Transformer, and a hybrid model combining seasonal trend decomposition with Transformer, were constructed and compared. Additionally, an advanced STL-T-L hybrid model (Seasonal-Trend decomposition using Loess, Transformer, and LSTM) was proposed for further analysis. Bayesian optimization was used to determine the hyperparameters of the deep learning model. The model integrated historical incidence data, environmental factors, and engineered time characteristics, lag terms, and rolling statistics. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scale error (MASE) were calculated on the independent test set to evaluate the prediction performance of the model. RESULTS:Among all the evaluated models, the STL-T-L hybrid model showed the best prediction performance on the test set, with RMSE of 6.337, MAE of 4.524, MAPE of 58.482%, MASE of 0.638. The prediction model built in this study considered the historical incidence of the disease and incorporated various meteorological and air pollution factors. The results showed that the STL-T-L model incorporating these features achieved the best prediction results. CONCLUSION:The STL-T-L model can effectively predict the common intestinal infectious diseases and can be used as a tool for monitoring and early warning of intestinal infectious diseases in Changzhou.

Background: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. Methods: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. Results: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1–2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3–5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6–7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( P trend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( P trend = 0.011) and MCEs ( P trend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( P interaction = 0.037). Conclusions: Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

With the emergence of population-scale whole-genome sequencing (WGS), rare variants can be captured precisely. Studying rare variants explains part of the heritability of complex traits that is overlooked by conventional genome-wide association studies (GWASs). However, the extent to which imputed data can approximate or improve upon the power of WGS data in rare variant association studies remains unclear. Using the UK Biobank WGS data (n = 150,119) as the ground truth, we first evaluated the consistency of rare variants in the single-nucleotide polymorphism (SNP) array data imputed using TOPMed or HRC+UK10K reference panel. Imputation quality (average R2) of the TOPMed-imputed data reached 0.6 even for extremely rare variants with minor allele count ≤ 5. TOPMed-imputed data were closer to WGS data across three ethnic groups, with average Cramer's V > 0.75. Furthermore, association tests were performed on 45 traits. At the same sample size (n = 150,119), neither imputed dataset outperformed WGS data, but the results of the TOPMed-imputed data were more consistent with those of WGS data. When the sample size was increased to 488,377, the number of significant rare variants identified from the TOPMed-imputed data increased by 27.71% for quantitative traits and by approximately 10-fold for binary traits. Finally, we meta-analyzed the association results of SNP array and WGS for lung cancer and epithelial ovarian cancer, respectively. Compared to WGS-based results, more significant variants and genes were identified. Our findings highlight that incorporating rare variants imputed using large-scale sequencing populations can boost the power of rare variant association studies when WGS has limited sample sizes.

C-reactive protein (CRP) serves as a pivotal marker of systemic inflammation, yet its genetic architecture has predominantly been explored within European populations. Our multi-ancestry sequencing-based genome-wide association study (seqGWAS) meta-analysis encompasses 447,369 Europeans, 10,389 Africans, 9685 Asians, and 9200 Hispanics in the discovery set, and 23,521 Europeans, 7160 Africans, 771 Asians, and 5178 Hispanics in the replication set. We identify 113 independent association signals (Pdiscovery ≤ 5 × 10-9 and Preplication ≤ 0.05), including 21 loci that passed the conditional analysis, among which 3 are European-specific. Cross ancestry fine-mapping pinpoints 19 of 113 independent signals within the 95% credible set. Functional annotation reveals significant enrichment in blood tissue, H3K27me3 histone marks, and exonic regions. Leveraging the Polygenic Priority Score (PoPS) and gene-based analyses, we implicate 151 genes as potential regulators of CRP levels, 55 of which have not been previously reported. Among these, 17 genes and four proteins show causal evidence or strong colocalization with CRP-related pathologies.

ABSTRACT Objective To identify genetic loci that exhibit potential interactions with smoking status on insulin sensitivity and islet β‐cell function within normal glucose tolerance (NGT) populations. Methods All participants underwent an OGTT to confirm NGT status, followed by assessments of insulin sensitivity and β‐cell function. Analyses were performed in NGT participants from Nanjing (N = 4808) and Jurong (N = 508) for discovery and validation, respectively. Smoking status was categorized into nonsmokers and smokers. After excluding ineligible individuals, a two‐stage genome‐wide interaction association analysis (GWIS) was conducted in NGT individuals, with the discovery phase (N = 1377) identifying gene–environment interactions and the validation phase (N = 485) confirming significant loci. Subsequent analyses included stratified analysis and expression quantitative trait locus (eQTL) colocalization. Results GWIS identified ten SNPs in three loci, including rs4713207 (OR14J1, Pmeta = 3.95 × 10−8) for insulin resistance, rs17708475 (NKAIN2, Pmeta = 4.83 × 10−8) for insulin sensitivity, and rs201613 (MYH3, Pmeta = 1.05 × 10−8) for disposition index. Stratified analyses revealed differential effects of smoking across genotypes at these loci. Specifically, smoking was associated with increased insulin resistance in rs4713207 homozygotes (p = 2.15 × 10−5), while an opposite effect was observed in wild‐type individuals (p = 0.022). Colocalization analysis indicated that the smoking‐related interaction near rs4713207 is driven by a shared causal variant influencing HCG4 (PP.H4 = 0.70) and ZNF311 (PP.H4 = 0.74) expression in the pancreas. Conclusions Our findings reveal gene‐smoking interactions that affect insulin sensitivity and β‐cell function, providing new insights into the heterogeneity of metabolic phenotypes and advancing personalized risk assessment.

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

BACKGROUND:The co-occurrence of metabolic dysfunction and neurodegenerative diseases suggests a genetic link, yet the shared genetic architecture and causality remain unclear. We aimed to comprehensively characterise these genetic relationships. METHODS:We investigated genetic correlations among four neurodegenerative diseases and seven metabolic dysfunctions, followed by bidirectional Mendelian randomisation (MR) to assess potential causal relationships. Pleiotropy analysis (PLACO) was used to detect the pleiotropic effects of genetic variants. Significant pleiotropic loci were refined and annotated using functional mapping and annotation (FUMA) and Bayesian colocalisation analysis. We further explored mapped genes with tissue-specific expression and gene set enrichment analyses. RESULTS:We identified significant genetic correlations in nine out of 28 trait pairs. MR suggested causal relationships between specific trait pairs. Pleiotropy analysis revealed 25 931 significant single-nucleotide polymorphisms, with 246 pleiotropic loci identified via FUMA and 55 causal loci through Bayesian colocalisation. These loci are involved in neurotransmitter transport and immune response mechanisms, notably the missense variant rs41286192 in SLC18B1. The tissue-specific analysis highlighted the pancreas, left ventricle, amygdala, and liver as critical organs in disease progression. Drug target analysis linked 74 unique genes to existing therapeutic agents, while gene set enrichment identified 189 pathways related to lipid metabolism, cell differentiation and immune responses. CONCLUSION:Our findings reveal a shared genetic basis, pleiotropic loci, and potential causal relationships between metabolic dysfunction and neurodegenerative diseases. These insights highlight the biological connections underlying their phenotypic association and offer implications for future research to reduce the risk of neurodegenerative diseases.

Alcohol consumption has complex effects on diabetes and metabolic disease, but there is widespread heterogeneity within populations and the specific reasons are unclear. Genetic factors may play a role and warrant exploration. The aim of this study was to elucidate genetic variants modulating the impact of alcohol consumption on insulin sensitivity and pancreatic beta cell function within populations presenting normal glucose tolerance (NGT). We recruited 4194 volunteers in Nanjing, 854 in Jurong and an additional 5833 in Nanjing for Discovery cohorts 1 and 2 and a Validation cohort, respectively. We performed an OGTT on all participants, establishing a stringent NGT group, and then assessed insulin sensitivity and beta cell function. Alcohol consumption was categorised as abstinent, light-to-moderate (<210 g per week) or heavy (≥210 g per week). After excluding ineligible individuals, an exploratory genome-wide association study identified potential variants interacting with alcohol consumption in 1862 NGT individuals. These findings were validated in an additional cohort of 2169 NGT individuals. Cox proportional hazard regression was further employed to evaluate the effect of the interaction between the potential variants and alcohol consumption on the risk of type 2 diabetes within the UK Biobank cohort. A significant correlation was observed between drinking levels and insulin sensitivity, accompanied by a consequent inverse relationship with insulin resistance and beta cell insulin secretion after adjusting for confounding factors in NGT individuals. However, no significant associations were noted in the disposition indexes. The interaction of variant rs56221195 with alcohol intake exhibited a pronounced effect on the liver insulin resistance index (LIRI) in the discovery set, corroborated in the validation set (combined p=1.32 × 10−11). Alcohol consumption did not significantly affect LIRI in rs56221195 wild-type (TT) carriers, but a strong negative association emerged in heterozygous (TA) and homozygous (AA) individuals. The rs56221195 variant also significantly interacts with alcohol consumption, influencing the total insulin secretion index INSR120 (the ratio of the AUC of insulin to glucose from 0 to 120 min) (p=2.06 × 10−9) but not disposition index. In the UK Biobank, we found a significant interaction between rs56221195 and alcohol consumption, which was linked to the risk of type 2 diabetes (HR 0.897, p=0.008). Our findings reveal the effects of the interaction of alcohol and rs56221195 on hepatic insulin sensitivity in NGT individuals. It is imperative to weigh potential benefits and detriments thoughtfully when considering alcohol consumption across diverse genetic backgrounds.

Background:Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis.Methods:We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases.Results:Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer.Conclusions:The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

(1) Background: Numerous studies have demonstrated that shorter leucocyte telomere length (LTL) is associated with aging. Sleep is an important aging-related lifestyle. However, the causal relationship and direction between sleep traits and LTL remain unclear. (2) Methods: The causal relationship was assessed by bi-directional and non-linear Mendelian randomization in the UK Biobank (UKB) cohort. Further, we combined nap during day and chronotype as circadian rhythm, MR analysis was applied to circadian rhythm as well. (3) Results: MR analysis with LTL as the outcome showed causal effect of nap during the day (β = -0.073, 95%CI [-0.127, -0.020], FDR-corrected P = 0.045) on LTL. No genetic association of other sleep traits and LTL was observed in MR analysis. Meanwhile, a later circadian rhythm was associated with a shorter telomere length (β = -0.132, 95%CI [-0.185, -0.078], P < 0.001). (4) Conclusions: In this study, individuals with frequent daytime naps and late circadian rhythm had shorter LTL. However, both bi-directional and non-linear MR failed to reveal any evidence that sleep duration was associated with telomere shortening.

Digestive disorders are a significant contributor to the global burden of disease and seriously affect human quality of life. Research has already confirmed the presence of pleiotropic genetic loci among digestive disorders, and studies have explored shared genetic factors among pan-cancers, including various malignant digestive disorders. However, most cross-phenotype studies within the digestive tract system have been limited to a few traits, with no systematic coverage of common benign and malignant digestive disorders. Here, we analyzed data from the UK Biobank to investigate 21 digestive disorders, exploring the genetic correlations and causal relationships between diseases, as well as the common genetic factors and potential biological pathways driving these relationships. Our findings confirmed the extensive genetic correlation and causal relationship between digestive disorders, providing important insights into the genetic etiology, causality, disease prevention, and clinical treatment of diseases.

Rationale: Over 40% of lung cancer cases occurred in never-smokers in China. However, high-risk never-smokers were precluded from benefiting from lung cancer screening as most screening guidelines did not consider them. Objectives: We sought to develop and validate prediction models for 3-year lung cancer risks for never- and ever-smokers, named the China National Cancer Center Lung Cancer models (China NCC-LCm2021 models). Methods: 425,626 never-smokers and 128,952 ever-smokers from the National Lung Cancer Screening program were used as the training cohort and analyzed using multivariable Cox models. Models were validated in two independent prospective cohorts: one included 369,650 never-smokers and 107,678 ever-smokers (841 and 421 lung cancers), and the other included 286,327 never-smokers and 78,469 ever-smokers (503 and 127 lung cancers). Measurements and Main Results: The areas under the receiver operating characteristic curves in the two validation cohorts were 0.698 and 0.673 for never-smokers and 0.728 and 0.752 for ever-smokers. Our models had higher areas under the receiver operating characteristic curves than other existing models and were well calibrated in the validation cohort. The China NCC-LCm2021 ⩾0.47% threshold was suggested for never-smokers and ⩾0.51% for ever-smokers. Moreover, we provided a range of threshold options with corresponding expected screening outcomes, screening targets, and screening efficiency. Conclusion: The construction of the China NCC-LCm2021 models can accurately reflect individual risk of lung cancer, regardless of smoking status. Our models can significantly increase the feasibility of conducting centralized lung cancer screening programs because we provide justified thresholds to define the high-risk population of lung cancer and threshold options to adapt different configurations of medical resources.