Genetic factor increased identification efficiency of predictive models for dyslipidemia: a prospective cohort study

Abstract

Background: Few studies have developed risk models for dyslipidemia, especially for rural population. Further, the performance of genetic factors in predicting dyslipidemia was not explored. The purpose is to develop and evaluate the prediction models with and without genetic factor for dyslipidemia in Chinese rural population.Methods: A total of 3596 individuals from the Henan Rural Cohort study were included in this study. All subjects were divided into training set and testing set in a ratio of 7:3. The conventional models and conventional+GRS models were developed with COX regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in training set. Area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to assess the discrimination ability of models and the calibration curve was used to show calibration ability in testing set.Results: Compared to the lowest GRS quartile, HR (95%CI) of individuals in the highest GRS quartile was 1.23(1.07, 1.41) in total population. Age, family history of diabetes, physical activity, BMI, TG, HDL-C, and LDLC-C were included and developed the conventional models, and the AUC of COX, ANN, RF, and GBM classifiers were 0.702(0.673, 0.729), 0.736(0.708, 0.762), 0.787 (0.762, 0.811), and 0.816(0.792, 0.839), respectively. After adding GRS, the AUC increased by 0.005, 0.018, 0.023, and 0.015 with COX, ANN, RF, and GBM classifiers, respectively. The corresponding NRI and IDI were 25.6%, 7.8%, 14.1%, 18.1% and 2.3%, 1.0%, 2.5%, 1.8%, respectively.Conclusion: Genetic factors could improve the predictive ability for dyslipidemia risk model, suggesting genetic information could be provided as a potential predictor to screen clinical dyslipidemia.Trial RegistrationThe Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register. (Trial registration: ChiCTR-OOC-15006699. Registered 6 July 2015 - Retrospectively registered) http://www.chictr.org.cn/showproj.aspx?proj=11375