Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease
Background Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| Subjects: | |
| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.123.030934 |
| _version_ | 1797540995332571136 |
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| author | Robert Philibert Timur K. Dogan Stacey Knight Ferhaan Ahmad Stanley Lau George Miles Kirk U. Knowlton Meeshanthini V. Dogan |
| author_facet | Robert Philibert Timur K. Dogan Stacey Knight Ferhaan Ahmad Stanley Lau George Miles Kirk U. Knowlton Meeshanthini V. Dogan |
| author_sort | Robert Philibert |
| collection | DOAJ |
| description | Background Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic–epigenetic tools may provide a new avenue for assessing CHD. Methods and Results In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence–guided, clinically implementable method that uses input from 6 methylation‐sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine‐phospho‐guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches. Conclusions We conclude that this scalable integrated genetic–epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation‐sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response. |
| first_indexed | 2024-03-10T13:08:58Z |
| format | Article |
| id | doaj.art-bfe7e7f53a5a4220be172f7367450a9f |
| institution | Directory Open Access Journal |
| issn | 2047-9980 |
| language | English |
| last_indexed | 2024-03-10T13:08:58Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj.art-bfe7e7f53a5a4220be172f7367450a9f2023-11-21T10:53:12ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802023-11-01122210.1161/JAHA.123.030934Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart DiseaseRobert Philibert0Timur K. Dogan1Stacey Knight2Ferhaan Ahmad3Stanley Lau4George Miles5Kirk U. Knowlton6Meeshanthini V. Dogan7Cardio Diagnostics Inc Chicago IL USACardio Diagnostics Inc Chicago IL USAIntermountain Heart Institute, Intermountain Healthcare Salt Lake City UT USADivision of Cardiovascular Medicine, Department of Internal Medicine University of Iowa Iowa City IA USASouthern California Heart Centers San Gabriel CA USADepartment of Molecular and Human Genetics Baylor College of Medicine Houston TX USAIntermountain Heart Institute, Intermountain Healthcare Salt Lake City UT USACardio Diagnostics Inc Chicago IL USABackground Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic–epigenetic tools may provide a new avenue for assessing CHD. Methods and Results In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence–guided, clinically implementable method that uses input from 6 methylation‐sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine‐phospho‐guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches. Conclusions We conclude that this scalable integrated genetic–epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation‐sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.https://www.ahajournals.org/doi/10.1161/JAHA.123.030934artificial intelligencecoronary heart diseasediagnosisepigeneticsgeneticsmachine learning |
| spellingShingle | Robert Philibert Timur K. Dogan Stacey Knight Ferhaan Ahmad Stanley Lau George Miles Kirk U. Knowlton Meeshanthini V. Dogan Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease artificial intelligence coronary heart disease diagnosis epigenetics genetics machine learning |
| title | Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease |
| title_full | Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease |
| title_fullStr | Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease |
| title_full_unstemmed | Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease |
| title_short | Validation of an Integrated Genetic‐Epigenetic Test for the Assessment of Coronary Heart Disease |
| title_sort | validation of an integrated genetic epigenetic test for the assessment of coronary heart disease |
| topic | artificial intelligence coronary heart disease diagnosis epigenetics genetics machine learning |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.123.030934 |
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