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

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. MethodsIn 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 (∆Concordance 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 (∆C-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 (∆C-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.

Metabolomic profiles predict individual multidisease outcomes

“…We extracted clinical predictors and disease endpoints for 117,981 individuals with serum NMR profiling at the time of cohort recruitment (Methods and Supplementary Tables 1-3). The study population had a median age of 58 years (interquartile range (IQR) 50,63), of whom 54.2% were female, 11% current smokers and 5.2% diagnosed with T2D (Table 1). Median body mass index (BMI) was 26.8 (IQR 24.2, 29.9), systolic blood pressure was 136 mmHg (IQR 124, 149), total cholesterol was 5.65 mmol l -1 (IQR 4.90, 6.42) and glucose was 4.93 mmol l -1 (IQR 4.…”

“…The Rotterdam Study is a prospective, population-based cohort study among individuals living in the Ommoord district in the city of Rotterdam (the Netherlands) 25 , offering metabolomics for 2,949 participants with a median age of 74 years (IQR [70][71][72][73][74][75][76][77][78][79]. The Leiden Longevity PAROFF Study (LLS) 26 comprises offspring and spouses of long-lived individuals, with metabolomics available for 1,655 individuals with a mean age of 59 years (IQR [54][55][56][57][58][59][60][61][62][63]. Finally, the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) is a clinical trial investigating pravastatin effects 27 , of which 960 samples with a median age of 76 years (IQR [73][74][75][76][77][78] are included in the BBMRI-NL platform.…”

“…Dataset partitions and imputation. For model development and testing, we split the dataset into 22 spatially separated partitions based on the location of the assessment center at recruitment as previously established 63 . We analyzed the data in 22-fold nested cross-validation, setting aside one of the spatially separated partitions as a test set, aggregating the remaining partitions and randomly selecting 10% of the aggregated data for the validation set.…”

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

“…DeepVariant was launched by Google, which uses deep neural networks to fast and accurately identify variation sites from DNA sequencing data ( 36 ). The NeuralCVD-based deep survival machine algorithm can estimate the cardiovascular risk for coronary heart disease prevention ( 37 ). DeepCOMBI uses AI for analysis and discovery in genome-wide association studies ( 38 ).…”

ESC Interview: Nico Bruining

“…Causal AI substantially improves the validity of estimating cardiovascular risk and benefit, 4 as presented by Brian Ference, University of Cambridge, UK. The outcome of this study was to train AI algorithms to estimate the causal effects of modifiable targets of intervention in a way that reflects the underlying biology of how disease develops.…”