Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

Zhang, Liying; Wang, Yikang; Niu, Miaomiao; Wang, Chongjian; Wang, Zhenfei

doi:10.1038/s41598-020-61123-x

Cited by 93 publications

(81 citation statements)

References 46 publications

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“…Notably, the three serum lipid parameters showed no collinearity. The findings indicated that the GBM classifier could predict the incidence of dyslipidaemia better, which was confirmed in our previous study [ 35 ]. This might be because the GBM classifier could address the intricate relationship between predictors and dyslipidaemia.…”

Section: Discussionsupporting

confidence: 87%

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

et al. 2021

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Background Few studies have developed risk models for dyslipidaemia, especially for rural populations. Furthermore, the performance of genetic factors in predicting dyslipidaemia has not been explored. The purpose of this study is to develop and evaluate prediction models with and without genetic factors for dyslipidaemia in rural populations. Methods A total of 3596 individuals from the Henan Rural Cohort Study were included in this study. According to the ratio of 7:3, all individuals were divided into a training set and a testing set. The conventional models and conventional+GRS (genetic risk score) models were developed with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in the training set. The 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 the models, and the calibration curve was used to show calibration ability in the testing set. Results Compared to the lowest quartile of GRS, the hazard ratio (HR) (95% confidence interval (CI)) of individuals in the highest quartile of GRS was 1.23(1.07, 1.41) in the total population. Age, family history of diabetes, physical activity, body mass index (BMI), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were used to develop the conventional models, and the AUCs of the 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 AUCs increased by 0.005, 0.018, 0.023, and 0.015 with the Cox, ANN, RF, and GBM classifiers, respectively. The corresponding NRI and IDI were 25.6, 7.8, 14.1, and 18.1% and 2.3, 1.0, 2.5, and 1.8%, respectively. Conclusion Genetic factors could improve the predictive ability of the dyslipidaemia risk model, suggesting that genetic information could be provided as a potential predictor to screen for clinical dyslipidaemia. Trial registration The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register. (Trial registration: ChiCTR-OOC-15006699. Registered 6 July 2015 - Retrospectively registered).

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Section: Discussionsupporting

confidence: 87%

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

et al. 2021

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“…Notably, the three serum lipid parameters showed no collinearity. The ndings indicated that the GBM classi er could predict the incidence of dyslipidaemia better, which was con rmed in our previous study [35]. This might be because the GBM classi er could address the intricate relationship between predictors and dyslipidaemia.…”

Section: Discussionsupporting

confidence: 81%

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

Niu

Zhang

Wang

et al. 2021

Preprint

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Background: Few studies have developed risk models for dyslipidaemia, especially for rural populations. Furthermore, the performance of genetic factors in predicting dyslipidaemia has not been explored. The purpose of this study is to develop and evaluate prediction models with and without genetic factors for dyslipidaemia in rural populations. Methods: A total of 3596 individuals from the Henan Rural Cohort Study were included in this study. According to the ratio of 7:3, all individuals were divided into a training set and a testing set. The conventional models and conventional+GRS (genetic risk score) models were developed with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in the training set. The 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 the models, and the calibration curve was used to show calibration ability in the testing set. Results: Compared to the lowest quartile of GRS, the hazard ratio ( HR ) (95% confidence interval ( CI )) of individuals in the highest quartile of GRS was 1.23(1.07, 1.41) in the total population. Age, family history of diabetes, physical activity, body mass index (BMI), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were used to develop the conventional models, and the AUCs of the 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 AUCs increased by 0.005, 0.018, 0.023, and 0.015 with the Cox, ANN, RF, and GBM classifiers, respectively. The corresponding NRI and IDI were 25.6%, 7.8%, 14.1%, and 18.1% and 2.3%, 1.0%, 2.5%, and 1.8%, respectively. Conclusion: Genetic factors could improve the predictive ability of the dyslipidaemia risk model, suggesting that genetic information could be provided as a potential predictor to screen for clinical dyslipidaemia.

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“…What is noteworthy is that the three serum lipid parameters showed no collinearity. The ndings pointed out that GBM classi er could predict the incidence of dyslipidemia better, which had been con rmed in the previous study [30]. This might be due to the GBM classi er could deal with the intricate relationship between predictors and dyslipidemia.…”

Section: Discussionsupporting

confidence: 70%

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

Niu

Zhang

Wang

et al. 2020

Preprint

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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

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Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

Cited by 93 publications

References 46 publications

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

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

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