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Published on: July 27, 2018
Association Between Social Isolation With Age-Gap Determined by Artificial Intelligence-Enabled Electrocardiography
Nazanin Rajai1, Jose R Medina-Inojosa1, Bradley R Lewis2
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Background:
Loneliness and social isolation are associated with poor health outcomes such as an increased risk of cardiovascular diseases.
Objectives:
The authors aimed to explore the association between social isolation with biological aging which was determined by artificial intelligence-enabled electrocardiography (AI-ECG) as well as the risk of all-cause mortality.
Methods:
The study included adults aged ≥18 years seen at Mayo Clinic from 2019 to 2022 who respond to a survey for social isolation assessment and had a 12-lead ECG within 1 year of completing the questionnaire. Biological age was determined from ECGs using a previously developed and validated convolutional neural network (AI-ECG age). Age-Gap was defined as AI-ECG age minus chronological age, where positive values reflect an older-than-expected age. The status of social isolation was measured by the previously validated multiple-choice questions based on Social Network Index (SNI) with score ranges between 0 (most isolated) and 4 (least isolated).
Results:
A total of 280,324 subjects were included (chronological age 59.8 ± 16.4 years, 50.9% female). The mean Age-Gap was -0.2 ± 9.16 years. A higher SNI was associated with a lower Age-Gap (β of SNI = 4 was -0.11; 95% CI: -0.22 to -0.01; P < 0.001, adjusted to covariates). Cox proportional hazard analysis revealed the association between social connection and all-cause mortality (HR for SNI = 4, 0.47; 95% CI: 0.43-0.5; P < 0.001).
Conclusions:
Social isolation is associated with accelerating biological aging and all-cause mortality independent of conventional cardiovascular risk factors. This observation underscores the need to address social connection as a health care determinant.

