Abstract Background Unhealthy sleep health has become a rising public health epidemic, and environmental issues have become a burgeoning orientation to probe into the factors affecting sleep health. Volatile organic compounds (VOCs), common organic pollutants in the air, were connected with some somatic diseases, while whether VOCs exposure or which VOCs lead to sleep abnormality was yet explored so far. Methods We analyzed blood content data of five VOCs (LBXVBF, blood bromoform; LBXVBM, blood bromodichloromethane; LBXVCF, blood chloroform; LBXVCM, blood dibromochloromethane; LBXVME, blood MTBE) reported for 5740 adults in NHANES 2007–2012. Sleep health categories, including trouble sleeping, sleep disorder, and insufficient (<6 h/day) or excessive (>9 h/day) sleep, were examined. Weighted multiple linear/logistic regression models were constructed to estimate the linear associations of VOCs exposure with sleep health. As for survival analysis of different VOCs related with individual survival outcomes, we selected the weighted multivariate COX regression model to assess. To explore the non-linear association between variables, restricted cubic spline (RCS) regression analysis was carried out. Moreover, three adjustment regression modeling strategies were utilized to evaluate the VOCs effect on sleep health. Results According to the adjusted linear RCS regression model, it is reported that the U-shaped nonlinear relationship between blood VOCs (LBXVBM: P-overall = 0.0284; LBXVCM: P-overall = 0.0321) and sleep duration. The fully adjusted logistic RCS model revealed that LBXVBM, LBXVCM and LBXVCF all displayed statistically significant U-shaped curves in trouble sleep and insufficient sleep groups (P-overall < 0.05). The adjusted COX-RCS analysis results revealed that the LBXVME (p-overall = 0.0359) was risk factor for all-cause mortality. Conclusions There was a negative non-linear association between specific blood VOCs and sleep health among U.S. adults, and this adverse effect was mainly manifested in trouble sleeping and prolonged sleep duration. Moreover, the results of survival analysis showed that environmental VOCs exposure could induce adverse survival outcomes. Future prospective longitudinal studies should be conducted to further investigate and determine the degree of the association between VOCs and sleep health.

Abstract Aims We aimed to investigate the independent associations between growth differentiation factor 15 (GDF‐15) level at admission and cardiovascular (CV) death, thrombotic events, heart failure (HF), and bleeding outcomes in patients with coronary artery disease (CAD). Methods and results We measured the plasma concentrations of GDF‐15 centrally in patients from the BIomarker‐based Prognostic Assessment for patients with Stable angina and acute coronary Syndrome (BIPass) registry, which consecutively enrolled patients with CAD from November 2017 to September 2019 at five tertiary hospitals in China. The outcomes included CV death, thrombotic events [myocardial infarction (MI) and ischaemic stroke], HF events [acute HF during hospitalization and hospitalization for HF post‐discharge (A/H HF) and cardiogenic shock], and bleeding outcomes [non‐coronary artery bypass grafting‐related major bleeding and clinically significant bleeding (CSB)] during the 12 month follow‐up period after hospitalization. Among 6322 patients with CAD {65.4% male, median age 63.7 [inter‐quartile range (IQR)] 56.0–70.1 years}, the median concentration of plasma GDF‐15 at admission was 1091 (IQR 790.5–1635.0) ng/L. Higher concentrations of GDF‐15 were associated with an increased risk of CV death [hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.35–2.88, P < 0.001], A/H HF (HR 2.69, 95% CI 1.92–3.77, P < 0.001), cardiogenic shock (HR 1.46, 95% CI 1.04–2.05, P = 0.029), and CSB (HR 1.48, 95% CI 1.22–1.79, P < 0.001), but not for MI or stroke, after adjusting for clinical risk factors and prognostic biomarkers. Adding GDF‐15 to the model with risk factors and biomarkers improved the net reclassification for CV death, A/H HF, cardiogenic shock, and CSB. Conclusions In patients with CAD, admission levels of GDF‐15 were associated with an increased 1 year risk of CV death, HF, and bleeding outcomes, but not with thrombotic events. GDF‐15 may be a prognostic biomarker for CV death, HF, and bleeding outcomes and could be used to refine the risk assessment of these specific clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT04044066

Background Risk models integrating new biomarkers to predict cardiovascular events in acute coronary syndromes (ACS) are lacking. Therefore, we evaluated the prognostic value of biomarkers in addition to clinical predictors and developed a biomarker-based risk model for major adverse cardiovascular events (MACE) within 12 months after hospital admission with ACS. Methods Patients (n = 4407) consecutively enrolled from November, 2017 to October, 2019 in three hospitals of a prospective Chinese registry (BIomarker-based Prognostic Assessment for Patients with Stable Angina and Acute Coronary Syndromes, BIPass) were designated as the risk model development cohort. Validation was performed in 1409 patients enrolled in two independent hospitals. Cox proportional hazards regression analysis was used to generate a risk prediction model and evaluate the incremental prognostic value of each biomarker. Findings Over 12 months, 196 patients experienced MACE (5.1%/year). Among twelve candidate biomarkers, N -terminal pro-B-type natriuretic peptide (NT-proBNP) measured at baseline showed the most prognostic capability independent of clinical predictors. The developed BIPass risk model included age, hypertension, previous myocardial infarction, stroke, Killip class, heart rate, and NT-proBNP. It displayed improved discrimination (C-statistic 0.79, 95% CI 0.73-0.85), calibration (GOF = 9.82, p = 0.28) and clinical decision curve in the validation cohort, outperforming the GRACE and TIMI risk scores. Cumulative rates for MACE demonstrated good separation in the BIPass predicted low, intermediate, and high-risk groups. Interpretation The BIPass risk model, integrating clinical variables and NT-proBNP, is useful for predicting 12 month MACE in ACS. It effectively identifies a gradient risk of cardiovascular events to aid personalized care. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.