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Abstract
Cardiovascular diseases are a public health concern; they remain the leading cause of morbidity and mortality in patients with type 2 diabetes. Phenotypic information available from retinal fundus images and clinical measurements, in addition to genomic data, can identify relevant biomarkers of cardiovascular health. In this study, we assessed whether such biomarkers stratified risks of major adverse cardiac events (MACE). A retrospective analysis was carried out on an extract from the Tayside GoDARTS bioresource of participants with type 2 diabetes (n = 3,891). A total of 519 features were incorporated, summarising morphometric properties of the retinal vasculature, various single nucleotide polymorphisms (SNPs), as well as routine clinical measurements. After imputing missing features, a predictive model was developed on a randomly sampled set (n = 2,918) using L1-regularised logistic regression (lasso). The model was evaluated on an independent set (n = 973) and its performance associated with overall hazard rate after censoring (log-rank p < 0.0001), suggesting that multimodal features were able to capture important knowledge for MACE risk assessment. We further showed through a bootstrap analysis that all three sources of information (retinal, genetic, routine clinical) offer robust signal. Particularly robust features included: tortuousity, width gradient, and branching point retinal groupings; SNPs known to be associated with blood pressure and cardiovascular phenotypic traits; age at imaging; clinical measurements such as blood pressure and high density lipoprotein. This novel approach could be used for fast and sensitive determination of future risks associated with MACE.
Original language | English |
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Article number | 3591 |
Number of pages | 10 |
Journal | Scientific Reports |
Volume | 9 |
DOIs | |
Publication status | Published - 5 Mar 2019 |
Keywords
- Biomedical engineering
- Cardiovascular diseases
- Computational science
- Predictive markers
ASJC Scopus subject areas
- General
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Dive into the research topics of 'A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features'. Together they form a unique fingerprint.Projects
- 1 Finished
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Multi-modal Retinal Biomarkers for Vascular Dementia; Developing and Enabling Image Analysis Tools (Joint with University of Edinburgh)
Doney, A. (Investigator), McKenna, S. (Investigator) & Trucco, M. (Investigator)
Engineering and Physical Sciences Research Council
30/04/15 → 29/08/18
Project: Research