Artificial Intelligence-Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure.

OBJECTIVE:To ascertain whether heart failure (HF) itself is a senescent phenomenon independent of age, and how this is reflected at a molecular level in the circulating progenitor cell niche, and at a substrate level using a novel electrocardiogram (ECG)-based artificial intelligence platform. PATIENTS AND METHODS:Between October 14, 2016, and October 29, 2020, CD34+ progenitor cells were analyzed by flow cytometry and isolated by magnetic-activated cell sorting from patients of similar age with New York Heart Association functional classes IV (n = 17) and I-II (n = 10) heart failure with reduced ejection fraction and healthy controls (n = 10). CD34+ cellular senescence was quantitated by human telomerase reverse transcriptase expression and telomerase expression by quantitative polymerase chain reaction, and senescence-associated secretory phenotype (SASP) protein expression assayed in plasma. An ECG-based artificial intelligence (AI) algorithm was used to determine cardiac age and difference from chronological age (AI ECG age gap). RESULTS:CD34+ counts and telomerase expression were significantly reduced and AI ECG age gap and SASP expression increased in all HF groups compared with healthy controls. Expression of SASP protein was closely associated with telomerase activity and severity of HF phenotype and inflammation. Telomerase activity was more closely associated with CD34+ cell counts and AI ECG age gap. CONCLUSION:We conclude from this pilot study that HF may promote a senescent phenotype independent of chronological age. We show for the first time that the AI ECG in HF shows a phenotype of cardiac aging beyond chronological age, and appears to be associated with cellular and molecular evidence of senescence.