| Title: | Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort |
| Journal: | The Lancet Digital Health |
| Published: | 1 Feb 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35090679/ |
| DOI: | https://doi.org/10.1016/s2589-7500(21)00249-1 |
| URL: | http://www.thelancet.com/article/S2589750021002491/pdf |
Publication 13860
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Abstract
BACKGROUND: In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, towards clinical application, the added value over clinical predictors later in life is crucial. Currently, this genotype-phenotype relationship and implications for overall cardiovascular risk are unclear.</p>
METHODS: In this study, we developed and validated a neural network-based risk model (NeuralCVD) integrating polygenic and clinical predictors in 395 713 cardiovascular disease-free participants from the UK Biobank cohort. The primary outcome was the first record of a major adverse cardiac event (MACE) within 10 years. We compared the NeuralCVD model with both established clinical scores (SCORE, ASCVD, and QRISK3 recalibrated to the UK Biobank cohort) and a linear Cox-Model, assessing risk discrimination, net reclassification, and calibration over 22 spatially distinct recruitment centres.</p>
FINDINGS: The NeuralCVD score was well calibrated and improved on the best clinical baseline, QRISK3 (ΔConcordance index [C-index] 0·01, 95% CI 0·009-0·011; net reclassification improvement (NRI) 0·0488, 95% CI 0·0442-0·0534) and a Cox model (ΔC-index 0·003, 95% CI 0·002-0·004; NRI 0·0469, 95% CI 0·0429-0·0511) in risk discrimination and net reclassification. After adding polygenic scores we found further improvements on population level (ΔC-index 0·006, 95% CI 0·005-0·007; NRI 0·0116, 95% CI 0·0066-0·0159). Additionally, we identified an interaction of genetic information with the pre-existing clinical phenotype, not captured by conventional models. Additional high polygenic risk increased overall risk most in individuals with low to intermediate clinical risk, and age younger than 50 years.</p>
INTERPRETATION: Our results demonstrated that the NeuralCVD score can estimate cardiovascular risk trajectories for primary prevention. NeuralCVD learns the transition of predictive information from genotype to phenotype and identifies individuals with high genetic predisposition before developing a severe clinical phenotype. This finding could improve the reprioritisation of otherwise low-risk individuals with a high genetic cardiovascular predisposition for preventive interventions.</p>
FUNDING: Charité-Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative.</p>
9 Keywords
- Cardiovascular Diseases
- Genotype
- Heart Disease Risk Factors
- Humans
- Neural Networks, Computer
- Phenotype
- Predictive Value of Tests
- Risk Assessment
- United Kingdom
15 Authors
- Jakob Steinfeldt
- Thore Buergel
- Lukas Loock
- Paul Kittner
- Greg Ruyoga
- Julius Upmeier Zu Belzen
- Simon Sasse
- Henrik Strangalies
- Lara Christmann
- Noah Hollmann
- Benedict Wolf
- Brian Ference
- John Deanfield
- Ulf Landmesser
- Roland Eils
1 Application
| Application ID | Title |
|---|---|
| 51157 | Stratification and modelling of competing risks in cardiovascular disease events based on multi-level patient characteristics and genetics |
Enabling scientific discoveries that improve human health