PMID- 33681127
OWN - NLM
STAT- MEDLINE
DCOM- 20210527
LR  - 20210527
IS  - 2296-2565 (Electronic)
IS  - 2296-2565 (Linking)
VI  - 9
DP  - 2021
TI  - Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for 
      Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study.
PG  - 606711
LID - 10.3389/fpubh.2021.606711 [doi]
LID - 606711
AB  - 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.
CI  - Copyright (c) 2021 Wang, Zhang, Niu, Li, Tu, Liu, Hou, Mao, Wang and Wang.
FAU - Wang, Yikang
AU  - Wang Y
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Zhang, Liying
AU  - Zhang L
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
AD  - School of Information Engineering, Zhengzhou University, Zhengzhou, China.
FAU - Niu, Miaomiao
AU  - Niu M
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Li, Ruiying
AU  - Li R
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Tu, Runqi
AU  - Tu R
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Liu, Xiaotian
AU  - Liu X
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Hou, Jian
AU  - Hou J
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Mao, Zhenxing
AU  - Mao Z
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
FAU - Wang, Zhenfei
AU  - Wang Z
AD  - School of Information Engineering, Zhengzhou University, Zhengzhou, China.
FAU - Wang, Chongjian
AU  - Wang C
AD  - Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou 
      University, Zhengzhou, China.
LA  - eng
SI  - ChiCTR/ChiCTR-OOC-15006699
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210217
PL  - Switzerland
TA  - Front Public Health
JT  - Frontiers in public health
JID - 101616579
SB  - IM
MH  - Adult
MH  - Cohort Studies
MH  - *Diabetes Mellitus, Type 2/epidemiology
MH  - Humans
MH  - Machine Learning
MH  - Prospective Studies
MH  - Risk Assessment
MH  - Risk Factors
PMC - PMC7925839
OTO - NOTNLM
OT  - cohort study
OT  - genetic risk score
OT  - machine learning
OT  - risk prediction
OT  - type 2 diabetes
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/03/09 06:00
MHDA- 2021/05/28 06:00
PMCR- 2021/02/17
CRDT- 2021/03/08 05:55
PHST- 2020/09/15 00:00 [received]
PHST- 2021/01/25 00:00 [accepted]
PHST- 2021/03/08 05:55 [entrez]
PHST- 2021/03/09 06:00 [pubmed]
PHST- 2021/05/28 06:00 [medline]
PHST- 2021/02/17 00:00 [pmc-release]
AID - 10.3389/fpubh.2021.606711 [doi]
PST - epublish
SO  - Front Public Health. 2021 Feb 17;9:606711. doi: 10.3389/fpubh.2021.606711. 
      eCollection 2021.