Eicosanoid and eicosanoid-related inflammatory mediators and exercise intolerance in heart failure with preserved ejection fraction.

Systemic inflammation has been implicated in the pathobiology of heart failure with preserved ejection fraction (HFpEF). Here, we examine the association of upstream mediators of inflammation as ascertained by fatty-acid derived eicosanoid and eicosanoid-related metabolites with HFpEF status and exercise manifestations of HFpEF. Among 510 participants with chronic dyspnea and preserved LVEF who underwent invasive cardiopulmonary exercise testing, we find that 70 of 890 eicosanoid and related metabolites are associated with HFpEF status, including 17 named and 53 putative eicosanoids (FDR q-value < 0.1). Prostaglandin (15R-PGF2α, 11ß-dhk-PGF2α) and linoleic acid derivatives (12,13 EpOME) are associated with greater odds of HFpEF, while epoxides (8(9)-EpETE), docosanoids (13,14-DiHDPA), and oxylipins (12-OPDA) are associated with lower odds of HFpEF. Among 70 metabolites, 18 are associated with future development of heart failure in the community. Pro- and anti-inflammatory eicosanoid and related metabolites may contribute to the pathogenesis of HFpEF and serve as potential targets for intervention.

The association of eicosanoids and eicosanoid-related metabolites with pulmonary hypertension.

BACKGROUND: Eicosanoids are bioactive lipids that regulate systemic inflammation and exert vasoactive effects. Specific eicosanoid metabolites have previously been associated with pulmonary hypertension (PH), yet their role remains incompletely understood. METHODS: We studied 482 participants with chronic dyspnoea who underwent clinically indicated cardiopulmonary exercise testing (CPET) with invasive haemodynamic monitoring. We performed comprehensive profiling of 888 eicosanoids and eicosanoid-related metabolites using directed non-targeted mass spectrometry, and examined associations with PH (mean pulmonary arterial pressure (mPAP) >20 mmHg), PH subtypes and physiological correlates, including transpulmonary metabolite gradients. RESULTS: Among 482 participants (mean±sd age 56±16 years, 62% women), 200 had rest PH. We found 48 eicosanoids and eicosanoid-related metabolites that were associated with PH. Specifically, prostaglandin (11ß-dhk-PGF2α), linoleic acid (12,13-EpOME) and arachidonic acid derivatives (11,12-DiHETrE) were associated with higher odds of PH (false discovery rate q<0.05 for all). By contrast, epoxide (8(9)-EpETE), α-linolenic acid (13(S)-HOTrE(γ)) and lipokine derivatives (12,13-DiHOME) were associated with lower odds. Among PH-related eicosanoids, 14 showed differential transpulmonary metabolite gradients, with directionality suggesting that metabolites associated with lower odds of PH also displayed pulmonary artery uptake. In individuals with exercise PH, eicosanoid profiles were intermediate between no PH and rest PH, with six metabolites that differed between rest and exercise PH. CONCLUSIONS: Our findings highlight the role of specific eicosanoids, including linoleic acid and epoxide derivatives, as potential regulators of inflammation in PH. Of note, physiological correlates, including transpulmonary metabolite gradients, may prioritise future studies focused on eicosanoid-related pathways as important contributors to PH pathogenesis.

Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis.

INTRODUCTION: The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events. METHODS: A hospital-based cohort of patients referred for right heart catheterization between 2005-2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates. RESULTS: Among 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) "young men with obesity", (2) "women with hypertension", (3) "men with overweight", (4) "men with cardiometabolic and cardiovascular disease", and (5) "men with structural heart disease and atrial fibrillation." Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41-2.00 and HR 1.52, 95% CI 1.24-1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04-1.52, relative to phenogroup 1). CONCLUSIONS: Cluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH.

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes.

We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth "permissive" subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.