BACKGROUND: Identifying the mechanistic pathways potentially associated with incident heart failure (HF) may provide a basis for novel preventive strategies.METHODS AND RESULTS: To identify... Show moreBACKGROUND: Identifying the mechanistic pathways potentially associated with incident heart failure (HF) may provide a basis for novel preventive strategies.METHODS AND RESULTS: To identify proteomic biomarkers and the potential underlying mechanistic pathways that may be associated with incident HF defined as the first hospitalization for HF, a nested-matched case-control design was used with cases (incident HF) and controls (without HF) selected from 3 cohorts (> 20 000 individuals). Controls were matched on cohort, follow-up time, age, and sex. Two independent sample sets (a discovery set with 286 cases and 591 controls and a replication set with 276 cases and 280 controls) were used to discover and replicate the findings. Two hundred fifty-two circulating proteins in the plasma were studied. Adjusting for the matching variables age, sex, and follow-up time (and correcting for multiplicity of tests), 89 proteins were found to be associated with incident HF in the discovery phase, of which 38 were also associated with incident HF in the replication phase. These 38 proteins pointed to 4 main network clusters underlying incident HF: (1) inflammation and apoptosis, indicated by the expression of the TNF (tumor necrosis factor)-family members; (2) extracellular matrix remodeling, angiogenesis and growth, indicated by the expression of proteins associated with collagen metabolism, endothelial function, and vascular homeostasis; (3) blood pressure regulation, indicated by the expression of natriuretic peptides and proteins related to the reninangiotensin- aldosterone system; and (4) metabolism, associated with cholesterol and atherosclerosis.CONCLUSIONS: Clusters of biomarkers associated with mechanistic pathways leading to HF were identified linking inflammation, apoptosis, vascular function, matrix remodeling, blood pressure control, and metabolism. These findings provide important insight on the pathophysiological mechanisms leading to HF. Show less
There are only a limited number of studies that have developed appropriate models which incorporate bioavailability to estimate mixture toxicity. Here, we explored the applicability of the extended... Show moreThere are only a limited number of studies that have developed appropriate models which incorporate bioavailability to estimate mixture toxicity. Here, we explored the applicability of the extended biotic ligand model (BLM) and the WHAM-F tox approach for predicting and interpreting mixture toxicity, with the assumption that interactions between metal ions obey the BLM theory. Seedlings of lettuce Lactuca sativa were exposed to metal mixtures (Cu-Ni, Cu-Cd, and Ni-Cd) contained in hydroponic solutions for 4 days. Inhibition to root elongation was the endpoint used to quantify the toxic response. Assuming that metal ions compete with each other for binding at a single biotic ligand, the extended BLM succeeded in predicting toxicity of three mixtures to lettuce, with more than 82 % of toxicity variation explained. There were no significant differences in the values of f mix50 (i.e., the overall amounts of metal ions bound to the biotic ligand inducing 50 % effect) for the three mixture combinations, showing the possibility of extrapolating these values to other binary metal combinations. The WHAM-F tox approach showed a similar level of precision in estimating mixture toxicity while requiring fewer parameters than the BLM-f mix model. External validation of the WHAM-F tox approach using literature data showed its applicability for other species and other mixtures. The WHAM-F tox model is suitable for delineating mixture effects where the extended BLM also applies. Therefore, in case of lower data availability, we recommend the lower parameterized WHAM-F tox as an effective approach to incorporate bioavailability in quantifying mixture toxicity. Show less
