Recent research has illuminated a significant relationship between social isolation and accelerated biological aging, highlighting that individuals with limited social connections face an elevated risk of premature death.
By leveraging AI-driven electrocardiograms (AI-ECG) to gauge the biological age of over 280,000 adults, the study unveiled that those with robust social networks displayed narrower discrepancies between their biological and chronological ages, indicating a slower rate of biological aging.
The Social Network Index served as a crucial tool in gauging levels of social isolation, revealing that lower scores, indicative of heightened social isolation, were linked to increased mortality risks across all demographic groups. This study underscores the critical role of social connectivity as a fundamental element of health and longevity.
Key Findings:
- Social Connectivity and Health: Individuals with stronger social networks, as measured by the Social Network Index, demonstrated smaller disparities between biological and chronological age, signifying a protective effect against accelerated aging.
- Mortality Risk: Participants with lower Social Network Index scores faced heightened mortality risks during a two-year follow-up period, emphasizing the health consequences of social isolation.
- Aging Disparities: The research noted that non-white participants generally exhibited larger average age disparities compared to their white counterparts, particularly among those with weaker social connections, indicating broader health inequities.
This study highlights the significance of social connections in influencing biological aging and mortality risks across diverse demographic groups. It underscores the necessity of addressing social connectivity as a vital component of the social determinants of health.
To explore the impact of social interactions on biological aging, researchers analyzed data from over 280,000 adults who received outpatient care between June 2019 and March 2022. Participants completed a questionnaire on social determinants of health and had AI-ECG records available within one year.
An AI-ECG model, developed at Mayo Clinic, was utilized to estimate biological age, which was then compared to chronological age. Previous studies have established that AI-ECG age predictions reflect the biological age of the heart. A positive age gap indicates accelerated biological aging, while a negative value suggests a slower rate of biological aging.
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