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Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank
Riyaz S Patel1,2, Spiros Denaxas3,4, Laurence J Howe1,2
1Institute of Cardiovascular Sciences, University College London, London, United Kingdom.
Insights
A new algorithm identifies six coronary artery disease (CAD) phenotypes using electronic health records, aiding reproducible research. This approach helps standardize disease phenotyping for better clinical insights and outcomes.
Area of Science:
- Cardiovascular Disease Research
- Biomedical Informatics
- Genetics and Genomics
Background:
- Inconsistent disease phenotyping due to a lack of standardized data combination methods hinders research reproducibility.
- Developing standardized approaches for large-scale data analysis is crucial for advancing medical research.
Purpose of the Study:
- To develop and evaluate a rules-based algorithm for identifying coronary artery disease (CAD) sub-phenotypes.
- To utilize electronic health records (EHR) and questionnaire data from the UK Biobank (UKB) for phenotyping.
Main Methods:
- A prospective cohort study involving 502,000 individuals aged 40-69 from the UK Biobank (2006-2010).
- Phenotyping into six predefined CAD phenotypes using hospital admission codes, mortality records, and baseline survey data.
- Estimation of a polygenic risk score (PRS) for CAD in 408,470 unrelated individuals of European descent.
Main Results:
- The algorithm identified 37,476 individuals with various CAD phenotypes, including myocardial infarction (MI) and non-MI related CAD, both prevalent and incident.
- Mortality rates varied significantly across phenotypes, with incident MI showing the highest and self-reported non-MI CAD the lowest.
- Polygenic risk score (PRS) showed graded associations across phenotypes, strongest for prevalent MI and weakest for prevalent self-reported non-MI CAD.
Conclusions:
- An algorithmic, EHR-based approach successfully distinguished six CAD phenotypes with distinct survival and PRS associations.
- The study supports the adoption of open, standardized phenotyping approaches for improving research reproducibility in CAD and other conditions.
- The developed algorithm is available in the HDR UK phenotype library, facilitating wider use and validation.
Importance:
A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility.
Objective:
To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB).
Design:
Case-control and cohort study.
Setting:
Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping.
Participants:
We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated.
Exposure:
CAD Phenotypes.
Main Outcomes And Measures:
Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD.
Results:
The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/).
Conclusions:
An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.
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