A Signature of Pre-Operative Biomarkers of Cellular Senescence to Predict Risk of Cardiac and Kidney Adverse Events after Cardiac Surgery

Abstract

Importance: Improved pre-operative risk stratification methods are needed for targeted risk mitigation and optimization of care pathways for cardiac patients. This is the first report demonstrating pre-operative, aging-related biomarkers of cellular senescence and immune system function can predict risk of common and serious cardiac surgery-related adverse events.

Design: Multi-center 331-patient cohort study that enrolled patients undergoing coronary artery bypass grafing (CABG) surgery with 30-day follow-up. Included a quaternary care center and two community-based hospitals. Primary outcome was KDIGO-defined acute kidney injury (AKI). Secondary outcomes: decline in eGFR ≥25% at 30d and a composite of major adverse cardiac and kidney events at 30d (MACKE30). Biomarkers were assessed in blood samples collected prior to surgery.

Results: A multivariate regression model of six senescence biomarkers (p16, p14, LAG3, CD244, CD28 and suPAR) identified patients at risk for AKI (NPV 86.6%, accuracy 78.6%), decline in eGFR (NPV 93.5%, accuracy 85.2%), and MACKE30 (NPV 91.4%, accuracy 79.9%). Patients in the top risk tertile had 7.8 (3.3-18.4) higher odds of developing AKI, 4.5 (1.6-12.6) higher odds of developing renal decline at 30d follow-up, and 5.7 (2.1-15.6) higher odds of developing MACKE30 versus patients in the bottom tertile. All models remained significant when adjusted for clinical variables.

Conclusions: A network of senescence biomarkers, a fundamental mechanism of aging, can identify patients at risk for adverse kidney and cardiac events when measured pre-operatively. These findings lay the foundation to improve pre-surgical risk assessment with measures that capture heterogeneity of aging, thereby improving clinical outcomes and resource utilization in cardiac surgery.

Keywords: Acute kidney injury; Biological aging; CABG; Cellular senescence; P16; Pre-operative risk assessment.

Figure 1:

Cellular senescence network. A. Overall cellular senescence load results from two competing biological processes: induction of senescent cells due to cellular stress (magenta arrows) and clearance of senescent cells by the immune system (teal arrows). B. Network of cellular senescence biomarkers used in this study. Biomarkers with a primary known function in establishing senescence are shown in magenta, and those with immune system function are in shown in teal. C. Scatterplot correlation matrix of pre-operative levels of cellular senescence network biomarkers as well as chronological age. The color of each correlation circle represents the correlation between each pair of variables on a scale from red (+1) to blue (−1). The size of each circle represents the significance test between the variables. A larger circle indicates a more significant relationship, and the Pearson correlation coefficient is shown as a number and a line of linear fit on the corresponding scatterplot. The histograms (diagonal across the matrix) show the distribution of each biomarker in the entire cohort.

Figure 2:

Cellular senescence-based models predict cardiac surgery-associated adverse events. (A) AKI, (B) decline in eGFR between baseline and a 30d post-operative visit, and (C) a composite of major adverse cardiac and kidney events at the 30d post-op (MACKE30). Biomarkers comprising each predictive model are shown. Interactions between biomarkers that are included in the predictive models are shown by connecting lines. The performance of each model was assessed by ROC analysis and performance metrics for each model at a selected threshold are shown (value and 95% CI).

Figure 3:

Predictive ability of senescence-based models is unchanged after adjustment for clinical variables. Odds ratios, with 95% CI, are plotted for cellular senescence-based models (Biomarker) for AKI, 30d decline in eGFR, and MACKE30. Each model was also adjusted for age, CKD, diabetes, and CHF (Biomarker + clinical).