Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes
Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovasc...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27254-z |
| _version_ | 1797973687680368640 |
|---|---|
| author | Julian Libiseller-Egger Jody E. Phelan Zachi I. Attia Ernest Diez Benavente Susana Campino Paul A. Friedman Francisco Lopez-Jimenez David A. Leon Taane G. Clark |
| author_facet | Julian Libiseller-Egger Jody E. Phelan Zachi I. Attia Ernest Diez Benavente Susana Campino Paul A. Friedman Francisco Lopez-Jimenez David A. Leon Taane G. Clark |
| author_sort | Julian Libiseller-Egger |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ( $$p\le 5 \times 10^{-8}$$ p ≤ 5 × 10 - 8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine. |
| first_indexed | 2024-04-11T04:07:11Z |
| format | Article |
| id | doaj.art-379ddc9b3fe843058aa78cf23f1306e2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T04:07:11Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-379ddc9b3fe843058aa78cf23f1306e22023-01-01T12:16:56ZengNature PortfolioScientific Reports2045-23222022-12-0112111110.1038/s41598-022-27254-zDeep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypesJulian Libiseller-Egger0Jody E. Phelan1Zachi I. Attia2Ernest Diez Benavente3Susana Campino4Paul A. Friedman5Francisco Lopez-Jimenez6David A. Leon7Taane G. Clark8Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical MedicineFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical MedicineDepartment of Cardiovascular Medicine, Mayo Clinic College of MedicineFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical MedicineFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical MedicineDepartment of Cardiovascular Medicine, Mayo Clinic College of MedicineDepartment of Cardiovascular Medicine, Mayo Clinic College of MedicineFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical MedicineFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical MedicineAbstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ( $$p\le 5 \times 10^{-8}$$ p ≤ 5 × 10 - 8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.https://doi.org/10.1038/s41598-022-27254-z |
| spellingShingle | Julian Libiseller-Egger Jody E. Phelan Zachi I. Attia Ernest Diez Benavente Susana Campino Paul A. Friedman Francisco Lopez-Jimenez David A. Leon Taane G. Clark Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes Scientific Reports |
| title | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| title_full | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| title_fullStr | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| title_full_unstemmed | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| title_short | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| title_sort | deep learning derived cardiovascular age shares a genetic basis with other cardiac phenotypes |
| url | https://doi.org/10.1038/s41598-022-27254-z |
| work_keys_str_mv | AT julianlibiselleregger deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT jodyephelan deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT zachiiattia deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT ernestdiezbenavente deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT susanacampino deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT paulafriedman deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT franciscolopezjimenez deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT davidaleon deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes AT taanegclark deeplearningderivedcardiovascularagesharesageneticbasiswithothercardiacphenotypes |