Association Between Social Isolation With Age-Gap Determined by Artificial Intelligence-Enabled Electrocardiography.

Area of Science:

• Cardiology
• Gerontology
• Public Health

Background:

• Loneliness and social isolation are linked to adverse health outcomes, including cardiovascular disease.
• Understanding the impact of social isolation on biological aging is critical for public health.

Purpose of the Study:

• To investigate the association between social isolation and biological aging, as assessed by artificial intelligence-enabled electrocardiography (AI-ECG).
• To examine the relationship between social isolation, biological aging, and the risk of all-cause mortality.

Main Methods:

• Adults (≥18 years) from Mayo Clinic (2019-2022) with survey data on social isolation (Social Network Index, SNI) and 12-lead ECG were included.
• Biological age was determined using a validated AI-ECG convolutional neural network, calculating Age-Gap (AI-ECG age - chronological age).
• Cox proportional hazard analysis assessed the association between SNI, Age-Gap, and all-cause mortality.

Main Results:

• The study included 280,324 subjects (mean age 59.8 years, 50.9% female).
• A higher SNI score (indicating less isolation) was associated with a lower Age-Gap (β = -0.11, P < 0.001), suggesting less accelerated biological aging.
• Social connection (higher SNI) was significantly associated with reduced all-cause mortality (HR for SNI=4: 0.47, P < 0.001).

Conclusions:

• Social isolation is independently associated with accelerated biological aging and increased all-cause mortality.
• AI-ECG can quantify biological aging and its association with social isolation.
• Integrating social connection assessment into healthcare is essential for mitigating health risks.