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December 31, 2022Scientific ReportsOpen Access

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

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Why the study?

Although AI ECG-derived cardiovascular age deviation from chronological age (delta age) is linked to mortality and co-morbidities, its genetic underpinning remains unknown.

Population

34,432 individuals in the UK Biobank

Design

Genome-wide association study

Authors

JLJulian Libiseller-EggerUniversity of ViennaJPJody PhelanUniversity of LondonZAZachi I. AttiaElectrophysiology

Discussion

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Implication

Supports AI-derived cardiovascular age as a biomarker; leaves open clinical utility pending prospective outcome trials.

Structured PICO

P
Population
34,432 participants from the UK Biobank (36,349 for initial age prediction), mean age 64.25, 48.4% male. Predominantly healthy cohort (<6% had diagnosed cardiovascular conditions more severe than hypertension, 62.5% never/rarely smoked).
I
Intervention
Deep learning-derived cardiovascular age (delta age) calculated from 12-lead ECGs
O
Outcome
Genetic loci associated with delta age (the difference between chronological age and ECG-predicted age)surrogate

The genetic basis of deep learning-derived cardiovascular age is predominantly determined by genes directly involved with the cardiovascular system, validating its utility as a biomarker for cardiovascular aging.

Limitations

  • More statistical power (e.g. through larger sample size) will be needed to confirm suggestive associations with delta age

Cite This Study

Libiseller-Egger et al. (2022) studied this question.

synapsesocial.com/papers/6a1010dd64e8141cd26000ebhttps://doi.org/10.1038/s41598-022-27254-z

Topics

Artificial intelligence in cardiology
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