The Pooled Cohort Equations (PCE) were created in 2013 to assess ASCVD risk in primary prevention. In 2023 the American Heart Association published the PREVENT equations to assess the risk of cardiovascular disease in primary prevention. The comparative performance of PCE and PREVENT for predicting 10-year ASCVD risk has not been evaluated in an external large-scale epidemiologic cohort. The study population includes participants of the UK Biobank who were free of clinical cardiovascular disease. 10-year ASCVD risk was calculated using the PCE and PREVENT equations. Harrel’s C-Statistics and delta C-Statistics were calculated for males and females to evaluate risk discrimination. Predicted 10-year risks were divided into deciles as well as risk groups for each equation and stratified by sex to compare predicted risk versus observed risk within each risk group, with calibration slopes calculated by decile. Sensitivity and specificity were also analyzed to assess statin eligibility. The final cohort was 368,125 individuals ages 40–73 (mean age 56.2, 54.7 % female, 94.0 % white). The C-statistics for PCE were 0.729 (0.722–0.736) for females and 0.688 (0.683–0.693) for males; C-Statistics for PREVENT were 0.728 (0.721–0.735) for females and 0.687 (0.682–0.692) for males, with delta C-Statistics being 0.001 (p = 0.87) for females and 0.001 (p = 0.82) for males. Predicted risks were closer to observed risks for PREVENT as compared to PCE, and PREVENT tended to estimate lower risk (mean risk of 4.6 % compared to 8.3 % for PCE). PREVENT demonstrated higher specificity but lower sensitivity than PCE using the current 7.5 % risk threshold for statin eligibility, and a Youden index of 4.5 % risk was found for PREVENT. There is no significant difference in 10-year ASCVD risk discrimination between PCE and PREVENT equations. However, the PREVENT equations demonstrate better calibration in the UK Biobank.

Background: Liver biopsy and hepatic venous pressure gradient (HVPG), the gold standard for assessing advanced fibrosis (AF) and clinically significant portal hypertension (CSPH), are invasive, costly, and time-consuming. Goal: We investigated if the combination of fibrosis index based on 4 factors (FIB-4) and liver stiffness measure (LSM) can identify AF and more importantly, CSPH. Patients and Methods: Patients with chronic liver disease referred for transjugular liver biopsy were analyzed retrospectively. FIB-4 and LSM were compared with liver histology for diagnosing AF. FIB-4, LSM, and platelet count were compared with HVPG for diagnosing CSPH. Optimal cutoffs for predicting CSPH were determined by grid search. A composite log-odds to predict CSPH was derived from logistic regression using LSM, FIB-4, and gender. Internal bootstrap validation and external validation were performed. Results: A total of 142 patients were included in the derivation; 42.3% had AF, and 11.3% had CSPH using the current gold standards. The area under the receiver operating characteristic curve (AUROC) for LSM, FIB-4, and their combination to predict AF were 0.7550, 0.7049, and 0.7768, respectively. LSM, FIB-4, and platelet count predicted CSPH with AUROC 0.6818, 0.7532, and 0.7240, respectively. LSM plus FIB-4 showed the best performance in predicting CSPH with AUROC 0.8155. Based on LSM, FIB-4, and gender, a novel model—the Portal Hypertension Assessment Tool (PHAT)—was developed to predict CSPH. PHAT score ≥−2.76 predicted CSPH with sensitivity 94%, specificity 67%, positive predictive value 27%, negative predictive value 99%, and accuracy 70%. In internal and external validation, AUROCs for the model were 0.8293 and 0.7899, respectively. Conclusion: A model consisting of FIB-4, LSM, and gender can identify CSPH among patients with chronic liver disease.

Neutrophil elastase (NE) has been reported to be a pro-inflammatory stimulus for macrophages. The aim of the present study was to determine the impact of NE exposure on the human macrophage proteome and evaluate its impact on pro-inflammatory signals. Human blood monocytes from healthy volunteers were differentiated to macrophages and then exposed to either 500 nM of NE or control vehicle for 2 h in triplicate. Label-free quantitative proteomics analysis identified 41 differentially expressed proteins in the NE versus control vehicle datasets. A total of 26 proteins were downregulated and of those, 21 were cell surface proteins. Importantly, four of the cell surface proteins were proteoglycans: neuropilin 1 (NRP1), syndecan 2 (SDC2), glypican 4 (GPC4), and CD99 antigen-like protein 2 (CD99L2) along with neuropilin 2 (NRP2), CD99 antigen (CD99), and endoglin (ENG) which are known interactors. Additional NE-targeted proteins related to macrophage function were also measured including CD40, CD48, SPINT1, ST14, and MSR1. Collectively, this study provides a comprehensive unbiased view of selective NE-targeted cell surface proteins in chronically inflamed lungs.