Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

Area of Science:

• Cardiology
• Biotechnology
• Gerontology

Background:

• Artificial intelligence (AI) algorithms analyzing 12-lead ECGs can estimate "physiologic age."
• Peripheral microvascular endothelial function (PMEF) serves as an index for vascular aging.

Purpose of the Study:

• To investigate the association between PMEF and accelerated physiologic aging estimated by AI-ECG.
• To determine the impact of this association on major adverse cardiovascular events (MACE).

Main Methods:

• 531 patients underwent ECG and noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry (RHPAT).
• Δ age was calculated as (AI-ECG age - chronological age). Abnormal PMEF was defined as RHPAT index ≤2.0.

Main Results:

• Patients with abnormal PMEF exhibited higher Δ age (2.3 years) compared to those with normal PMEF (0.5 years; P=0.01).
• RHPAT index showed a negative association with Δ age (β=-0.08; P=0.048) after risk factor adjustment.
• The highest quartile of Δ age was linked to a 4.72-fold increased MACE risk in patients with abnormal PMEF (P=0.02).

Conclusions:

• Vascular aging, assessed via endothelial function, correlates with accelerated physiologic aging identified by AI-ECG.
• Endothelial dysfunction combined with accelerated physiologic aging significantly elevates cardiovascular event risk.