Temporal evolution of liver function parameters predicts clinical outcome in chronic heart failure patients (Bio-SHiFT study).

BACKGROUND: Liver dysfunction contributes to worse clinical outcomes in heart failure (HF) patients. However, studies exploring temporal evolutions of liver function parameters in chronic HF (CHF) pa- tients, and their associations with clinical outcome, are scarce. Detailed temporal patterns of alkaline phosphatase (ALP), gamma glutamyl transpeptidase (GGTP), total bilirubin (TBIL) and albumin (ALB) were investigated, and their relation with clinical outcome, in patients with stable CHF with reduced ejection fraction. METHODS: Tri-monthly plasma samples were collected from 250 patients during 2.2 (1.4-2.5) years of follow-up. ALP, GGTP, ALB, and TBIL were measured in 749 selected samples and the relationship between repeatedly measured biomarker levels and the primary endpoint (PEP; composite of cardiovas- cular death, heart transplantation, left ventricular assist device implantation, and hospitalization for worsened HF) was evaluated by joint models. RESULTS: Mean age was 66 ± 13 years; 74% were men, 25% in New York Heart Association class III-IV. 66 (26%) patients reached the PEP. Repeatedly measured levels of TBIL, ALP, GGTP, and ALB were associated with the PEP after adjustment for N-terminal prohormone B-type natriuretic peptide and high sensitivity troponin T (hazard ratio [95% confidence interval] per doubling of biomarker level: 1.98 [1.32; 2.95], p = 0.002; 1.84 [1.09; 3.05], p = 0.018, 1.33 [1.08; 1.63], p = 0.006 and 1.14 [1.09; 1.20], p < 0.001, respectively). Serial levels of ALP and GGTP, and slopes of the temporal evolutions of ALB and TBIL, adjusted for clinical variables, were also significantly associated with the PEP. CONCLUSIONS: Changes in serum levels of TBIL, ALP, GGTP, and ALB precede adverse cardiovascular events in patients with CHF. These routine liver function parameters may provide additional prognostic information in heart failure with reduced ejection fraction patients in clinical practice.

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum.

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions.

Proteomic biomarkers related to obesity in heart failure with reduced ejection fraction and their associations with outcome.

OBJECTIVE: Heart failure (HF) pathophysiology in patients with obesity may be distinct. To study these features, we identified obesity-related biomarkers from 4210 circulating proteins in patients with HF with reduced ejection fraction (HFrEF) and examined associations of these proteins with HF prognosis and biological mechanisms. METHODS: In 373 patients with trimonthly blood sampling during a median follow-up of 2.1 (25th-75th percentile: 1.1-2.6) years, we applied an aptamer-based multiplex approach measuring 4210 proteins in baseline samples and the last two samples before study end. Associations between obesity (BMI > 30 kg/m2) and baseline protein levels were analyzed. Subsequently, associations of serially measured obesity-related proteins with biological mechanisms and the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, left ventricular assist device implantation, and heart transplantation) were examined. RESULTS: Obesity was identified in 26% (96/373) of patients. A total of 30% (112/373) experienced a PEP (with obesity: 26% [25/96] vs. without obesity: 31% [87/277]). A total of 141/4210 proteins were linked to obesity, reflecting mechanisms of neuron projection development, cell adhesion, and muscle cell migration. A total of 50/141 proteins were associated with the PEP, of which 12 proteins related to atherosclerosis or hypertrophy provided prognostic information beyond clinical characteristics, N-terminal pro-B-type natriuretic peptide, and high-sensitivity troponin T. CONCLUSIONS: Patients with HFrEF and obesity show distinct proteomic profiles compared to patients with HFrEF without obesity. Obesity-related proteins are independently associated with HF outcome. These proteins carry potential to improve management of obesity-related HF and could be leads for future research.

Targeted proteomics and metabolomics for biomarker discovery in abdominal aortic aneurysm and post-EVAR sac volume.

BACKGROUND AND AIMS: Abdominal aortic aneurysm (AAA) patients undergo uniform surveillance programs both leading up to, and following surgery. Circulating biomarkers could play a pivotal role in individualizing surveillance. We applied a multi-omics approach to identify relevant biomarkers and gain pathophysiological insights. MATERIALS AND METHODS: In this cross-sectional study, 108 AAA patients and 200 post-endovascular aneurysm repair (post-EVAR) patients were separately investigated. We performed partial least squares regression and ingenuity pathway analysis on circulating concentrations of 96 proteins (92 Olink Cardiovascular-III panel, 4 ELISA-assays) and 199 metabolites (measured by LC-TQMS), and their associations with CT-based AAA/sac volume. RESULTS: The median (25th-75th percentile) maximal diameter was 50.0 mm (46.0, 53.0) in the AAA group, and 55.4 mm (45.0, 64.2) in the post-EVAR group. Correcting for clinical characteristics in AAA patients, the aneurysm volume Z-score differed 0.068 (95 %CI: (0.042, 0.093)), 0.066 (0.047, 0.085) and -0.051 (-0.064, -0.038) per Z-score valine, leucine and uPA, respectively. After correcting for clinical characteristics and orthogonalization in the post-EVAR group, the sac volume Z-score differed 0.049 (0.034, 0.063) per Z-score TIMP-4, -0.050 (-0.064, -0.037) per Z-score LDL-receptor, -0.051 (-0.062, -0.040) per Z-score 1-OG/2-OG and -0.056 (-0.066, -0.045) per Z-score 1-LG/2-LG. CONCLUSIONS: The branched-chain amino acids and uPA were related to AAA volume. For post-EVAR patients, LDL-receptor, monoacylglycerols and TIMP-4 are potential biomarkers for sac volume. Additionally, distinct markers for sac change were identified.

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk.

BACKGROUND: We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk. METHODS AND RESULTS: BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS. CONCLUSIONS: Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.