Prognostic Significance and Biologic Associations of Senescence-Associated Secretory Phenotype Biomarkers in Heart Failure

Abstract

Background: The role of cellular senescence in human heart failure (HF) remains unclear. The senescence-associated secretory phenotype (SASP) is composed of proteins released by senescent cells. We assessed the prognostic significance and biologic pathways associated with the SASP in human HF using a plasma proteomics approach.

Methods and results: We measured 25 known SASP proteins among 2248 PHFS (Penn HF Study) participants using the SOMAScan V4 assay. We extracted the common variance in these proteins to generate SASP factor scores and assessed the relationship between these SASP factor scores and (1) all-cause death and (2) the composite of death or HF hospital admission. We also assessed the relationship of each SASP factor to 4746 other proteins, correcting for multiple comparisons, followed by pathway analyses. Two SASP factors were identified. Both factors were associated with older age, lower estimated glomerular filtration rate, and more advanced New York Heart Association class, among other clinical variables. Both SASP factors exhibited a significant positive association with the risk of death independent of the Meta-Analysis of Global-Group in Chronic HF score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels. The 2 identified SASP factors were associated with 1201 and 1554 proteins, respectively, belonging to various pathways including the coagulation system, complement system, acute phase response signaling, and retinoid X receptor-related pathways that regulate cell metabolism.

Conclusions: Increased SASP components are independently associated with adverse outcomes in HF. Biologic pathways associated with SASP are predominantly related to coagulation, inflammation, and cell metabolism.

Keywords: aging; cell senescence; heart failure; pathways; proteomics.

Figure 1

Heat map correlation matrix of senescence‐associated secretory phenotype (SASP) biomarkers, organized into hierarchical clusters (A) and factor loadings map showing the association of each factor with different SASP biomarkers (B).

Figure 2. Box and whisker charts of Cox proportional hazards regression models for death and DHFA of (1) nonadjusted factors of senescence (model 1) and (2) factors of senescence adjusted for MAGGIC risk score and NT‐proBNP (model 2).

In model 1, the association of each factor with risk of adverse outcomes was assessed separately; factors were not included within the same model. In model 2, adjustments were also done on each factor separately. DHFA indicates death or heart failure–related hospital admission; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; and NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.

Figure 3. Volcano plots showing proteins that are significantly associated with (A) first factor of senescence (B) second factor of senescence.

The lower faint solid line represents a nominal alpha level of 0.05. The upper solid line represents an alpha level of 0.05 corrected for multiple comparisons. Only the top 100 aptamers are shown.

Figure 4. Pathways significantly correlated with (top) first factor of senescence factor scores and (bottom) second factor of senescence factor scores.

Pathways in gray indicate a significant association with indeterminate direction. Blue bars indicate a significant positive association. Red bars indicate a significant negative association. Numbers at the end of the bars indicate the Z score corresponding to direction and strength of association. ABRA indicates Actin‐Binding Rho‐activating Protein; DHCR24, 24‐Dehydrocholesterol Reductase; FXR, Farnesoid X receptor; G2/M, growth₂/mitosis; GP6, glycoprotein 6; LXR, liver X receptor; RHOGDI, rho‐GDP‐dissociation inhibitor; RXR, retinoid X receptor; and Th1/2, T‐helper cell 1/2.