Chinese and American scientists collaborate to develop a model to predict the risk of coronary heart disease

The research group of Wang Minxian, researcher of the Beijing Institute of Genomics (National Bioinformatics Center), Chinese Academy of Sciences, cooperated with the Amit V. Khera research group of Dr. Amit V. Khera of the Broad Institute and Massachusetts General Hospital to develop a new genome-wide polygenic risk scoring model – GPSmult, which integrates the background of different ethnic groups and multiple clinical risk factors for coronary heart disease. The study was published July 7 in Nature Medicine. This achievement is expected to play a role in the early identification and precise stratification of high-risk groups of coronary heart disease, and promote the precise prevention and treatment of coronary heart disease.

Coronary heart disease is one of the most important diseases leading to human death, which is affected by individual genetics, metabolism and poor lifestyle, of which the influence of genetic factors is about 40%~60%. Since individual genetic information remains largely unchanged throughout life and can be collected through non-invasive methods such as blood or saliva as early as infancy, the GPSmult model can predict the risk of future coronary heart disease based on individual genetic information at the earliest stage of life, thus gaining a broad time window for early prevention and intervention of diseases.

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“The prediction accuracy of our model exceeds the ‘gold standard’ used in the field of clinical preventive medicine in the United States to assess an individual’s risk of atherosclerotic cardiovascular disease, greatly improving the accuracy of using genomic genetic information to predict an individual’s future risk of coronary heart disease, and further improving the risk prediction accuracy of about 40% of individuals.” Wang Min first told China Science News.

The model analyzes the correlation between genetic variants and diseases, and models and integrates all genetic variants associated with disease risk on a genome-wide scale based on the results of genome-wide comparative association studies in nearly 270,000 patients with coronary heart disease and 1.18 million healthy people in multiple ethnic populations around the world. In order to improve the accuracy of model prediction, the researchers further integrated the association between genetic variants and ten clinical risk factors and associated comorbidities of coronary heart disease, using more than 13.46 million samples. The researchers comprehensively evaluated the accuracy of the new model in an independently validated population of more than 510,000 people from multi-ethnic backgrounds, and found that the accuracy of the new model was significantly improved compared with the 27 currently published models based on genome-wide information for predicting coronary heart disease risk.

Relationship between coronary heart disease risk and polygenic risk score distribution Courtesy of the author

The researchers also divided the 308,000 European population in the UK Biobank into 100 groups based on the GPS mult score from small to large, and calculated the proportion of people who actually developed coronary heart disease in each group. The results showed that there was a significant correlation between the polygenic risk score calculated by the GPSmult model and the risk of disease, with the incidence of coronary heart disease in the group with the lowest score being less than 0.6%, while the incidence of coronary heart disease in the group with the highest score was as high as 16.3%, and the actual incidence of disease between the two groups was nearly 27 times different, further demonstrating the quasi-certainty of the prediction of the new model.

Current cardiovascular disease prevention guidelines recommend statin therapy for individuals with a history of coronary heart disease, peripheral artery disease, ischaemic stroke, diabetes, or severe hypercholesterolemia to help reduce the risk of recurrence of cardiovascular disease and mortality. The researchers reviewed and analyzed 12-year follow-up data from 308,000 European populations in the UK Biobank and found that individuals in the top 3% of the GPSmult score distribution had a risk of coronary heart disease almost as much as individuals with a pre-existing history despite not having the above past medical history.

“The combined American Heart Association/American College of Cardiology cohort equation primarily uses traditional cardiovascular disease risk factors such as lipids, blood pressure, age, and sex to predict an individual’s atherosclerotic cardiovascular disease risk in the next 10 years. GPSmult predicts the risk of congenital diseases based on individual genetic information, and the combination of the two models is more effective. Wang Min said first. Through the analysis of data on 326,000 multi-ethnic groups in the UK Biobank, the researchers showed that in multiple disease risk stratifications of traditional risk models, GPSmult can further enhance the prediction of individual disease risk, and is widely applicable to individuals with different genetic backgrounds, especially for South Asian populations or individuals with traditional risk stratification at high risk (risk greater than 20%).

Model architecture and training and validation diagram Courtesy of the author

(Source: China Science News Feng Lifei)

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