The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate “biomarker - disease” causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

DEAD-box RNA helicases belong to a large group of RNA-processing factors and play vital roles unwinding RNA helices and in ribosomal RNA biogenesis. Emerging evidence indicates that RNA helicases are associated with genome stability, yet the mechanisms behind this association remain poorly understood. In this study, we performed a comprehensive analysis of RNA helicases using multiplatform proteogenomic databases. More than 50% (28/49) of detected RNA helicases were highly expressed in multiple tumor tissues, and more than 60% (17/28) of tumor-associated members were directly involved in DNA damage repair (DDR). Analysis of repair dynamics revealed that these RNA helicases are engaged in an extensively broad range of DDR pathways. Among these factors is DDX21, which was prominently upregulated in colorectal cancer. The high expression of DDX21 gave rise to frequent chromosome exchange and increased genome fragmentation. Mechanistically, aberrantly high expression of DDX21 triggered inappropriate repair processes by delaying homologous recombination repair and increasing replication stress, leading to genome instability and tumorigenesis. Treatment with distinct chemotherapeutic drugs caused higher lethality to cancer cells with genome fragility induced by DDX21, providing a perspective for treatment of tumors with high DDX21 expression. This study revealed the role of RNA helicases in DNA damage and their associations with cancer, which could expand therapeutic strategies and improve precision treatments for cancer patients with high expression of RNA helicases. Significance: The involvement of the majority of tumor-associated RNA helicases in the DNA damage repair process suggests a new mechanism of tumorigenesis and offers potential alternative therapeutic strategies for cancer.

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481–0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984–0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837–0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766–0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Blood and urine biomarkers are commonly used to diagnose and monitor chronic diseases. We initially screened 67 biomarkers, including 4 urine biomarkers and 63 blood biomarkers, and identified 13 blood biomarkers significantly associated with Parkinson’s disease (PD). Among these, we discovered three novel markers demonstrating strong associations: phosphate (P = 1.81 × 10⁻³), AST/ALT ratio (P = 8.53 × 10⁻⁶), and immature reticulocyte fraction (IRF) (P = 3.49 × 10⁻²⁰). We also substantiated eight well-studied biomarkers and elucidated the roles of two previously ambiguous biomarkers. Our analyses confirmed IGF-1 (P = 7.46 × 10⁻²⁹) as a risk factor, and C-reactive protein (CRP) (P = 1.43 × 10⁻³) as protective against PD. Genetic analysis highlighted that IRF, CRP, and IGF-1 share significant genetic loci with PD, notably at MAPT, SETD1A, HLA-DRB1, and HLA-DQA1. Furthermore, Mendelian randomization (MR) analysis suggested potential causal associations between IGF-1, CRP, and PD. We identified several blood biomarkers that may be associated with the risk of developing PD, providing valuable insights for further exploration of PD-related biomarkers.

Substantial evidence shown that the age at onset (AAO) of Parkinson’s disease (PD) is a major determinant of clinical heterogeneity. However, the mechanisms underlying heterogeneity in the AAO remain unclear. To investigate the risk factors with the AAO of PD, a total of 3156 patients with PD from the UK Biobank were included in this study. We evaluated the effects of polygenic risk scores (PRS), nongenetic risk factors, and their interaction on the AAO using Mann–Whitney U tests and regression analyses. We further identified the genes interacting with nongenetic risk factors for the AAO using genome-wide environment interaction studies. We newly found physical activity (P < 0.0001) was positively associated with AAO and excessive daytime sleepiness (P < 0.0001) was negatively associated with AAO, and reproduced the positive associations of smoking and non-steroidal anti-inflammatory drug intake and the negative association of family history with AAO. In the dose-dependent analyses, smoking duration (P = 1.95 × 10⁻⁶), coffee consumption (P = 0.0150), and tea consumption (P = 0.0008) were positively associated with AAO. Individuals with higher PRS had younger AAO (P = 3.91 × 10⁻⁵). In addition, we observed a significant interaction between the PRS and smoking for AAO (P = 0.0316). Specifically, several genes, including ANGPT1 (P = 7.17 × 10⁻⁷) and PLEKHA6 (P = 4.87 × 10⁻⁶), may influence the positive relationship between smoking and AAO. Our data suggests that genetic and nongenetic risk factors are associated with the AAO of PD and that there is an interaction between the two.