Prognosis Analysis of Rat Liver Transplantation Under Direct Vision of Single Operator.

OBJECTIVE: This study aims to investigate the impact of surgical experimental variables on the prognosis of orthotopic liver transplantation (OLT) in rats, with the goal of enhancing the efficacy of modeling techniques. METHODS: Using Kamada's "two-cuff method" of rat orthotopic liver transplantation, 76 pairs of SD-Wistar rats were performed orthotopic liver transplantation from March to September 2023. Thirteen experimental factors during the perioperative period and the survival time of recipient rats were collected and recorded. To explore the surgical factors affecting the prognosis of rat liver transplantation and summarize the surgical techniques. RESULTS: The success rate of orthotopic liver transplantation in SD-Wistar rats was 68.4%, with 24 recipients surviving within 3-7 days and 28 recipients surviving more than 1 week. Donor liver perfusion, recipient blood loss, recipient liver blood expulsion, anhepatic phase, suprahepatic inferior vena cava anastomosis time and anesthesia recovery time are related to the survival of recipient rats after liver transplantation. Donor liver perfusion, eliminating blood in recipient liver and intraoperative blood loss of recipient are surgical factors affecting the prognosis of liver transplantation in rats. The survival time of recipient rats with liver perfusion through abdominal aorta, eliminating blood in recipient liver was relatively prolonged after operation. CONCLUSION: Under the condition of reasonable control of the anhepatic phase, the perfusion method of the donor liver, whether to eliminate blood in recipient liver, and intraoperative blood loss of recipient are important surgical factors affecting the prognosis of liver transplantation in rats.

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.

Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

The Causal Effect of Intra-Pancreatic Fat Deposition on Acute and Chronic Pancreatitis: A Mendelian Randomization Study.

INTRODUCTION: Recent associative studies have linked intra-pancreatic fat deposition (IPFD) with risk of pancreatitis, but the causal relationship remains unclear. METHODS: Utilizing Mendelian randomization, we evaluated the causal association between genetically predicted IPFD and pancreatitis. This approach utilized genetic variants from genome-wide association studies of IPFD (n=25,617), acute pancreatitis (n=6,787 cases/361,641 controls), and chronic pancreatitis (n=3,875 cases/361,641 controls). RESULTS: Genetically predicted IPFD was significantly associated with acute pancreatitis (OR per 1-SD increase: 1.40[95%CI:1.12-1.76], p=0.0032) and chronic pancreatitis (OR:1.64[95%CI:1.13-2.39], p=0.0097). DISCUSSION: Our findings support a causal role of IPFD in pancreatitis, suggesting that reducing IPFD could lower the risk of pancreatitis.

Benchmarking DNA Foundation Models for Genomic Sequence Classification.

The rapid advancement of DNA foundation language models has revolutionized the field of genomics, enabling the decoding of complex patterns and regulatory mechanisms within DNA sequences. However, the current evaluation of these models often relies on fine-tuning and limited datasets, which introduces biases and limits the assessment of their true potential. Here, we present a benchmarking study of three recent DNA foundation language models, including DNABERT-2, Nucleotide Transformer version-2 (NT-v2), and HyenaDNA, focusing on the quality of their zero-shot embeddings across a diverse range of genomic tasks and species through analyses of 57 real datasets. We found that DNABERT-2 exhibits the most consistent performance across human genome-related tasks, while NT-v2 excels in epigenetic modification detection. HyenaDNA stands out for its exceptional runtime scalability and ability to handle long input sequences. Importantly, we demonstrate that using mean token embedding consistently improves the performance of all three models compared to the default setting of sentence-level summary token embedding, with average AUC improvements ranging from 4.3% to 9.7% for different DNA foundation models. Furthermore, the performance differences between these models are significantly reduced when using mean token embedding. Our findings provide a framework for selecting and optimizing DNA language models, guiding researchers in applying these tools effectively in genomic studies.

An atlas of genetic effects on the monocyte methylome across European and African populations.

Elucidating the genetic architecture of DNA methylation (DNAm) is crucial for decoding the etiology of complex diseases. However, current epigenomic studies often suffer from incomplete coverage of methylation sites and the use of tissues containing heterogeneous cell populations. To address these challenges, we present a comprehensive human methylome atlas based on deep whole-genome bisulfite sequencing (WGBS) and whole-genome sequencing (WGS) of purified monocytes from 298 European Americans (EA) and 160 African Americans (AA) in the Louisiana Osteoporosis Study. Our atlas enables the analysis of over 25 million DNAm sites. We identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis -regions for EA and AA populations, respectively, with 880,108 sites shared between ancestries. While cis -meQTLs exhibited population-specific patterns, primarily due to differences in minor allele frequencies, shared cis -meQTLs showed high concordance across ancestries. Notably, cis -heritability estimates revealed significantly higher mean values in the AA population (0.09) compared to the EA population (0.04). Furthermore, we developed population-specific DNAm imputation models using Elastic Net, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. The performance of our MWAS models was validated through a systematic multi-ancestry analysis of 41 complex traits from the Million Veteran Program. Our findings bridge the gap between genomics and the monocyte methylome, uncovering novel methylation-phenotype associations and their transferability across diverse ancestries. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .

Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study.

BACKGROUND: Multiple novel protein biomarkers have been shown to be associated with prostate cancer risk using genetic instruments. This study aimed to externally validate the associations of 30 genetically predicted candidate proteins with prostate cancer risk using aptamer-based levels in US Black and White men in the Atherosclerosis Risk in Communities (ARIC) study. Plasma protein levels were previously measured by SomaScan® using the blood collected in 1990-1992. METHODS: Among 4864 eligible participants, we ascertained 667 first primary prostate cancer cases through 2015. Hazard ratios (HRs) of prostate cancer and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression for tertiles of each protein. We adjusted for age, race, and other risk factors. RESULTS: Of the 30 proteins and considering a nominal p trend < 0.05, two were positively associated with prostate cancer risk-RF1ML (tertile 3 vs. 1: HR = 1.23; 95% CI 1.02-1.48; p trend = 0.037) and TPST1 (1.28, 95% CI 1.06-1.55; p trend = 0.0087); two were inversely associated-ATF6A (HR = 0.80, 95% CI 0.65-0.98; p trend = 0.028) and SPINT2 (HR = 0.74, 95% CI 0.61-0.90; p trend = 0.0025). One protein, KDEL2, which was nonlinearly associated (test-for-linearity: p < 0.01) showed a statistically significant lower risk in the second tertile (HR = 0.79, 95% CI 0.65-0.95). Of these five, four proteins-ATF6A, KDEL2, RF1ML, and TPST1-were consistent in the direction of association with the discovery studies. CONCLUSION: This study validated some pre-diagnostic protein biomarkers of the risk of prostate cancer.

Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells.

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Association between pre- and post-diagnosis healthy eating index 2020 and ovarian cancer survival: evidence from a prospective cohort study.

Background: Previous studies on the association between diet quality and ovarian cancer (OC) survival are limited and inconsistent. We evaluated the relationship between pre- and post-diagnosis diet quality based on the Healthy Eating Index-2020 (HEI-2020), as well as their changes and OC survival. Methods: This prospective cohort study involved 1082 patients with OC aged 18-79 years, enrolled between 2015 and 2022. Detailed dietary intake before and after diagnosis was recorded using a validated food frequency questionnaire. Deaths were ascertained until February 16th, 2023 via medical records and active follow-up. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI). Results: We included 549 OC cases with a median follow-up of 44.9 months, representing 206 total deaths. Higher HEI scores were associated with better OS (pre-diagnosis: HRT3 vs. T1 0.66, 95%CI: 0.46-0.93, HR1-SD 0.84, 95%CI: 0.73-0.96; post-diagnosis: HRT3 vs. T1 0.68, 95%CI: 0.49-0.96, HR1-SD 0.80, 95%CI: 0.69-0.92). Compared to the stable group, the group with decreased HEI scores (>3%) from pre- to post-diagnosis had worse OS (HR 1.93, 95%CI: 1.26-2.97). Conclusion: High pre- and post-diagnosis diet quality was associated with improved OC survival, whereas deterioration in diet quality after diagnosis was associated with decreased OC survival.

Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism.

BACKGROUND: Two important questions regarding the genetics of pancreatic adenocarcinoma (PDAC) are 1. Which germline genetic variants influence the incidence of this cancer; and 2. Whether PDAC causally predisposes to associated non-malignant phenotypes, such as type 2 diabetes (T2D) and venous thromboembolism (VTE). METHODS: In this study of 8803 patients with PDAC and 67,523 controls, we first performed a large-scale transcriptome-wide association study to investigate the association between genetically determined gene expression in normal pancreas tissue and PDAC risk. Secondly, we used Mendelian Randomization (MR) to analyse the causal relationships among PDAC, T2D (74,124 cases and 824,006 controls) and VTE (30,234 cases and 172,122 controls). FINDINGS: Sixteen genes showed an association with PDAC risk (FDR <0.10), including six genes not yet reported for PDAC risk (PPIP5K2, TFR2, HNF4G, LRRC10B, PRC1 and FBXL20) and ten previously reported genes (INHBA, SMC2, ABO, PDX1, MTMR6, ACOT2, PGAP3, STARD3, GSDMB, ADAM33). MR provided support for a causal effect of PDAC on T2D using genetic instruments in the HNF4G and PDX1 loci, and unidirectional causality of VTE on PDAC involving the ABO locus (OR 2.12, P < 1e-7). No evidence of a causal effect of PDAC on VTE was found. INTERPRETATION: These analyses identified candidate susceptibility genes and disease relationships for PDAC that warrant further investigation. HNF4G and PDX1 may induce PDAC-associated diabetes, whereas ABO may induce the causative effect of VTE on PDAC. FUNDING: National Institutes of Health (USA).

Enhancement of Cu2ZnSn(S, Se)4 device performance using an IPA/MOE hybrid solvent system in ambient air.

In this work, a mixed precursor solvent system comprising isopropyl alcohol (IPA) and 2-methoxy ethanol (MOE) is introduced for the fabrication of Cu2ZnSn(S, Se)4 (CZTSSe) thin films under ambient conditions. The effects of different IPA/(MOE + IPA) ratios on the characteristics of CZTSSe films and the corresponding devices were investigated. Our research results indicate that the addition of IPA enhances the wettability of Cu-Zn-Sn-S precursor solution on the substrate, reduces Sn loss in the film during high-temperature annealing, and diminishes band tail states. Additionally, adding IPA leads to effective enlargement of grain size, improved crystallinity, and enhanced light absorption. However, excessive content of IPA negatively impacts CZTSSe film properties and the device's performance. Notably, when substituting 20% of MOE with IPA, the short-circuit current density (JSC) increased from 30.84 mA cm-2 to 35.55 mA cm-2 in the resulting CZTSSe device, and the efficiency improved from 9.19% to 10.63%. This work provides a new method of a solvent system for preparing efficient kesterite-based solar cells.

Binder-Responsive DNA Strand Displacement Enables High-Throughput and High-Fidelity Discovering of Small Molecular DNA Binders.

High-throughput screening (HTS) is pivotal in the discovery of small molecules that bind to DNA, yet there are limited sensing mechanisms available for designing HTS assays for DNA binders. Herein, we introduce a binder-responsive toehold-mediated DNA strand displacement (BR-TMSD) technique featuring programmable reaction kinetics in response to DNA-binder interactions. When two DNA binders are used, BR-TMSD is initiated through a rapid binder displacement, followed by the DNA strand displacement. The orthogonal displacement reactions of BR-TMSD enables a high-fidelity, dual-channel HTS assay, returning 19 new DNA binders from a library of 1,170 compounds.

Genetic Evidence for a Causal Link between Intra-Pancreatic Fat Deposition and Pancreatitis: a Mendelian Randomization Study.

Introduction: Recent associative studies have linked intra-pancreatic fat deposition (IPFD) with risk of pancreatitis, but the causal relationship remains unclear. Methods: Utilizing Mendelian randomization, we evaluated the causal association between genetically predicted IPFD and pancreatitis. This approach utilized genetic variants from genome-wide association studies of IPFD (n=25,617), acute pancreatitis (n=6,787 cases/361,641 controls), and chronic pancreatitis (n=3,875 cases/361,641 controls). Results: Genetically predicted IPFD was significantly associated with acute pancreatitis (OR per 1-SD increase: 1.40[95%CI:1.12-1.76], p=0.0032) and chronic pancreatitis (OR:1.64[95%CI:1.13-2.39], p=0.0097). Discussion: Our findings support a causal role of IPFD in pancreatitis, suggesting that reducing IPFD could lower the risk of pancreatitis.

Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. METHODS: A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). RESULTS: Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P < 0.05) according to the random effects model. Based on the AMSTAR tool, 16 (55.2%) meta-analyses were high quality and none was low quality. The certainty of the evidence, assessed by the GRADE framework, showed that 6 (6.3%) associations were supported by moderate-quality evidence. When compared with the lowest category of the TyG index, the risk of contrast-induced nephropathy (CIN) [relative risk (RR) = 2.25, 95%CI 1.82, 2.77], the risk of stroke in patients with diabetes mellitus (RR = 1.26, 95%CI 1.18, 1.33) or with acute coronary syndrome disease (RR = 1.56, 95%CI 1.06, 2.28), the prognosis of coronary artery disease (CAD)-non-fatal MI (RR = 2.02, 95%CI 1.32, 3.10), and the severity of CAD including coronary artery stenosis (RR = 3.49, 95%CI 1.71, 7.12) and multi-vessel CAD (RR = 2.33, 95%CI 1.59, 3.42) increased with high TyG index. CONCLUSION: We found that the TyG index was positively associated with many diseases including the risk of CIN and stroke, the prognosis of CAD, and the severity of CAD which were supported by moderate-quality evidence. TyG index might be useful to identify people at high-risk for developing these diseases.

Analysis of the relationship between shorter sleep duration and wrist fractures: based on NHANES.

BACKGROUND: Wrist fracture is one of the common limb fractures. Its incidence rate increases with age and osteoporosis. Nowadays, Sleep health is increasingly valued, but the relationship between wrist fractures and sleep time is not yet clear. METHODS: Data in this study were collected and screened from the NHANES from 2005 to 2010 and 2013 to 2014. The variables were extracted from interviews and compared between the wrist fractures and the sleep duration. The data was analyzed by weighted multivariate logistic regression. RESULTS: After excluding individuals who were not eligible and had invalid data, we finally identified 1835 participants for inclusion in this study. We found a negative association between the sleep duration and the fractured of the wrist (OR = 1.027,95% CI (1.027, 1.028), P < 0.00001). CONCLUSION: This study demons that the association between the sleep duration and the fractures of the wrist is significant. Our findings provide a better understanding of the relationship between sleep duration and wrist fractures. This study may help us reducing the incidence of wrist fractures in the population based on healthy sleep management in the future, and improve the quality of life of middle-aged and elderly patients. Provide evidence for clinical patients to manage healthy sleep.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking. METHODS: We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8-11), moderate (4-7), and low (0-3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547). RESULTS: The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I2 = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger's test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance (P < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodological quality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate, and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE. CONCLUSIONS: Our findings suggest a potential relationship between ABO and Rh blood groups and adverse health outcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. SIGNIFICANCE: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study.

BACKGROUND: Early-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification. METHODS: We included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes. RESULTS: The 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99-2.78] than LOPC (HR = 1.95, 95% CI 1.89-2.01; I2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98-5.54) but not to LOPC (HR = 0.98, 95% CI 0.96-1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancer Age-based PheWAS; https://mulongdu.shinyapps.io/proap ), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures. CONCLUSIONS: This study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.