Background: Many observational epidemiology studies discovered that kidney cancer and impaired kidney function have a bidirectional relationship. However, it remains unclear whether these two kinds of traits are causally linked. In this study, we aimed to investigate the bidirectional causal relation between kidney cancer and kidney function biomarkers (creatinine‐based estimated glomerular filtration rate (eGFRcrea), cystatin C‐based estimated glomerular filtration rate (eGFRcys), blood urea nitrogen (BUN), serum urate, and urinary albumin‐to‐creatinine ratio (UACR)).Methods: For both directions, single‐nucleotide polymorphisms (SNPs), as genetic instruments, for the five kidney function traits were selected from up to 1,004,040 individuals, and SNPs for kidney cancer were from 408,786 participants(1338 cases). In the main analysis, we applied two state‐of‐the‐art MR methods, namely, contamination mixture and Robust Adjusted Profile Score to downweight the effect of weak instrument bias, pleiotropy, and extreme outliers. We additionally conducted traditional MR analyses as sensitivity analyses. Summary‐level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Kaiser Permanente.Results: Based on 99 SNPs, we found that the eGFRcrea had a significant negative causal effect on the risk of kidney cancer (OR = 0.007, 95% CI:2.6 × 10⁻⁴–0.569, p = 0.041). After adjusting for body composition or diabetes, urate had a significant negative causal effect on kidney cancer (OR <1, p < 0.05). For UACR, it showed a strong causal effect on kidney cancer, after adjusting for body composition (OR = 14.503, 95% CI: 2.546–96.001, p = 0.032). Due to lacking significant signals and effect power for the reverse MR, further investigations are warranted.Conclusions: Our study suggested a potential causal effect of damaged kidney function on kidney cancer. EGFRcrea and UACR might be causally associated with kidney cancer, especially when patients were comorbid with obesity or diabetes. We called for larger sample‐size studies to further unravel the underlying causal relationship and the exact mechanism.

Background: The CyberKnife© system combines real-time image guidance and a dynamic tracking system to implement frameless radiotherapy. This umbrella review is aimed to evaluate the effectiveness a... Plain language summary An umbrella review on the effectiveness and safety of the CyberKnife© system was performed by comprehensively searching for all related publications. The CyberKnife system ha...

The West-China Hospital Alliance Longitudinal Epidemiology Wellness (WHALE) Study establishes a robust, multidimensional database to provide comprehensive insights into health-to-disease transitions, advancing proactive healthcare and enhancing understanding of the interplay among genetic, behavioral, and environmental factors in disease. The WHALE Study includes a database and a cohort. The WHALE Database, established in 2010, integrates health check-up data from 11 hospitals, covering sociodemographic, lifestyle, medical history, and clinical data. The WHALE Health Trajectory Cohort, launched in November 2024, recruits adults with at least three health check-ups, featuring biennial active follow-ups and passive linkage with regional healthcare databases. As of January 2025, the WHALE Database includes over 3.4 million health records from 1,526,686 participants, with a mean age of 40.3 years and a balanced gender distribution. Notably, 23.88% of participants had at least three health check-ups, and 3.31% had more than ten, highlighting a significant proportion with repeated measurements. The study provides key insights into health trajectories by examining the associations of biomarker data and their trajectory patterns with aging, pre-disease conditions, and disease diagnoses. The strengths of the WHALE Study include its large sample size, longitudinal design, diverse representation, comprehensive data, and robust quality control. Limitations include potential selection bias, data variability across centers, and reliance on self-reported data for some variables.

Objectives: To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks.Materials and Methods: We use Medical Information Mart for Intensive Care III as our dataset. We employ positive-unlabeled learning to achieve unbiased loss estimation, which is free of misleading training signal. We then utilize reweighting mechanism to compensate for the imbalance between positive and negative samples. To further close the performance gap caused by poor quality annotation, we integrate the supervision provided by the automatic annotation tool Medical Concept Annotation Toolkit which can ease the heavy burden of manual validation.Results: Our benchmarking results show that positive-unlabeled learning with reweighting outperforms competitive baseline methods over a range of missing label ratios. Integrating supervision provided by annotation tool further boosted the performance.Discussion: Considering the annotation noise and severe imbalance, unbiased loss estimation and reweighting mechanism are both important for learning from undercoded clinical records. Unbiased loss requires the estimation of false negative ratios and estimation through trained models is practical and competitive.Conclusions: The combination of positive-unlabeled learning with reweighting and supervision provided by the annotation tool is a promising solution to learn from undercoded clinical records.

IgA nephropathy (IgAN) is the most common primary glomerular disease in China and worldwide. The proliferation of B cells is known to be associated with both risk and prognosis of IgAN, but the epigenetic mechanism underlying this association is unknown. In this study we carried out the first Epigenome-wide Association Study (EWAS) by using the latest Infinium Methylation EPIC BeadChip on 184 B cell-specific samples (92 case/control pairs) for Chinese IgAN population. After rigorous data normalization and residual batch effect correction, linear mixed effect model was used to detect methylation CpG sites associated with IgAN adjusting for age, gender, and smoking. False discovery rate (FDR) less than 10% was used to account for multiple testing. Weighted gene co-methylation networks were generated to identify gene modules highly correlated with IgAN. Permutation test was performed to account for potential effect of overfitting. After adjusting clinical covariates and potential technical batch effects, 3 CpGs corresponding to PCDH17, TERT, WDR82 genes and 3 in the intergenic regions passed genome-wide significant threshold. Methylation network analysis identified an additional IgAN associated gene module, containing 72 significant CpGs including GALNT6, IQSEC1, CDC16 and SYS1, involved in the pathway related to tubular atrophy/interstitial fibrosis of IgAN. These results suggested important DNA methylation and gene targets in CD19 + B cells for the pathogenesis of IgAN.