Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults. Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models. Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (P trend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS. Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations. Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.
Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults. Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models. Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (P trend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS. Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations. Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.
Authors: Suveen Angraal; Bobak J Mortazavi; Aakriti Gupta; Rohan Khera; Tariq Ahmad; Nihar R Desai; Daniel L Jacoby; Frederick A Masoudi; John A Spertus; Harlan M Krumholz Journal: JACC Heart Fail Date: 2019-10-09 Impact factor: 12.035
Authors: Tyler M Seibert; Chun Chieh Fan; Yunpeng Wang; Verena Zuber; Roshan Karunamuni; J Kellogg Parsons; Rosalind A Eeles; Douglas F Easton; ZSofia Kote-Jarai; Ali Amin Al Olama; Sara Benlloch Garcia; Kenneth Muir; Henrik Grönberg; Fredrik Wiklund; Markus Aly; Johanna Schleutker; Csilla Sipeky; Teuvo Lj Tammela; Børge G Nordestgaard; Sune F Nielsen; Maren Weischer; Rasmus Bisbjerg; M Andreas Røder; Peter Iversen; Tim J Key; Ruth C Travis; David E Neal; Jenny L Donovan; Freddie C Hamdy; Paul Pharoah; Nora Pashayan; Kay-Tee Khaw; Christiane Maier; Walther Vogel; Manuel Luedeke; Kathleen Herkommer; Adam S Kibel; Cezary Cybulski; Dominika Wokolorczyk; Wojciech Kluzniak; Lisa Cannon-Albright; Hermann Brenner; Katarina Cuk; Kai-Uwe Saum; Jong Y Park; Thomas A Sellers; Chavdar Slavov; Radka Kaneva; Vanio Mitev; Jyotsna Batra; Judith A Clements; Amanda Spurdle; Manuel R Teixeira; Paula Paulo; Sofia Maia; Hardev Pandha; Agnieszka Michael; Andrzej Kierzek; David S Karow; Ian G Mills; Ole A Andreassen; Anders M Dale Journal: BMJ Date: 2018-01-10
Authors: Amit V Khera; Mark Chaffin; Krishna G Aragam; Mary E Haas; Carolina Roselli; Seung Hoan Choi; Pradeep Natarajan; Eric S Lander; Steven A Lubitz; Patrick T Ellinor; Sekar Kathiresan Journal: Nat Genet Date: 2018-08-13 Impact factor: 38.330