Jakob Steinfeldt1, Thore Buergel2, Lukas Loock2, Paul Kittner2, Greg Ruyoga2, Julius Upmeier Zu Belzen2, Simon Sasse2, Henrik Strangalies2, Lara Christmann2, Noah Hollmann2, Benedict Wolf2, Brian Ference3, John Deanfield4, Ulf Landmesser1, Roland Eils5. 1. Department of Cardiology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 2. Center for Digital Health, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany. 3. Centre for Naturally Randomized Trials, University of Cambridge, Cambridge, UK. 4. Institute of Cardiovascular Science, University College London, London, UK. 5. Center for Digital Health, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany. Electronic address: roland.eils@bih-charite.de.
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. 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. 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. 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. FUNDING: Charité-Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative.
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. 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. 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. 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. FUNDING: Charité-Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative.
Authors: Thore Buergel; Jakob Steinfeldt; Greg Ruyoga; Maik Pietzner; Daniele Bizzarri; Dina Vojinovic; Julius Upmeier Zu Belzen; Lukas Loock; Paul Kittner; Lara Christmann; Noah Hollmann; Henrik Strangalies; Jana M Braunger; Benjamin Wild; Scott T Chiesa; Joachim Spranger; Fabian Klostermann; Erik B van den Akker; Stella Trompet; Simon P Mooijaart; Naveed Sattar; J Wouter Jukema; Birgit Lavrijssen; Maryam Kavousi; Mohsen Ghanbari; Mohammad A Ikram; Eline Slagboom; Mika Kivimaki; Claudia Langenberg; John Deanfield; Roland Eils; Ulf Landmesser Journal: Nat Med Date: 2022-09-22 Impact factor: 87.241