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Summary: The development of targeted drugs for the early prevention and management of chronic kidney disease (CKD) is of great importance. However, the success rates and cost-effectiveness of traditional drug development approaches are extremely low. Utilizing large sample genome-wide association study data for drug repurposing has shown promise in many diseases but has not yet been explored in CKD. Herein, we investigated actionable druggable targets to improve renal function using large-scale Mendelian randomization and colocalization analyses. We combined two population-scale independent genetic datasets and validated findings with cell-type-dependent eQTL data of kidney tubular and glomerular samples. We ultimately prioritized two drug targets, opioid receptor-like 1 and F12, with potential genetic support for restoring renal function and subsequent treatment of CKD. Our findings explore the potential pathological mechanisms of CKD, bridge the gap between the molecular mechanisms of pathogenesis and clinical intervention, and provide new strategies in future clinical trials of CKD.

Abstract Objectives We aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). Material and methods In total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regression (LR) and simplified eXtreme gradient boosting (XGBoost) were applied for model constructions. We chose the final models based on their matching degree with data. To simplify the model and increase its usefulness, finally simplified models were built based on the most important 8 variables. Discrimination and calibration were exploited to evaluate the prediction ability. We stratified the risk groups based on risk estimate deciles. Results The simplified XGB model performed better in model discrimination, which AUC were 0.82 (95% CI: 0.78–0.87) in the validation cohort, compared with the AUC of simplified LR model (0.75 [95% CI: 0.69—0.80]). And XGB performed better than sPESI in the validation cohort. A new risk-classification based on XGB could accurately predict low-risk of mortality, and had high consistency with acknowledged risk scores. Conclusions ML models can accurately predict the 30-day mortality of critical PE patients, which could further be used to reduce the burden of ICU stay, decrease the mortality and improve the quality of life for critical PE patients.

Abstract Background Venous thromboembolism (VTE) is a substantial contributor to the global burden of disease. Observational studies have suggested that leisure sedentary behaviours (LSB) are related to the risk of VTE; however, the causal role of LSB in VTE remains unclear. Methods Using data obtained from genome-wide association studies in the UK Biobank (N = 422,218), we identified 84, 21, and 4 single nucleotide polymorphisms (SNPs) related to sedentary television (TV) watching, computer use, and driving, respectively. These SNPs were employed as instrumental variables. Summary statistics for SNP-VTE associations was obtained from the FinnGen study (5,403 cases and 130,235 controls). Two-sample Mendelian randomisation (MR) analyses were performed using inverse-variance weighted (IVW), MR-Egger,weighted median, and weighted mode approaches. Sensitivity analyses were conducted to ensure robustness of the results. Results The main IVW approach demonstrated a positive association between the genetically predicted sedentary TV watching and the risk of VTE [odds ratio (OR):1.35, 95% confidence interval (CI):1.02—1.80, P = 0.039]. However, no significant association was observed for genetically predicted sedentary computer use or driving and VTE risk. The results from our series of sensitivity analyses, including Cochran’s Q test, MR-Egger intercept test, and MR-Pleiotropy RESidual Sum and Outlier method, further supported these findings. Conclusion This study provides evidence of an association between genetically predicted sedentary TV watching and the risk of VTE. Further studies are required to elucidate the underlying causal mechanisms.

BackgroundPrevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months.Materials and methodsThe clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold.ResultsIn total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points.ConclusionMachine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.