Prognostic study of mild cognitive impairment progressing to Alzheimer′s disease based on polygenic risk score and machine learning modeling strategy

WANG Rong; CHEN Shuai; ZHAO Caili; LI Zimeng; CUI Jing; WANG Xiaocong; ZHAO Chunni; LIU Long

doi:10.16462/j.cnki.zhjbkz.2023.06.012

Volume 27 Issue 6

Jun. 2023

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WANG Rong, CHEN Shuai, ZHAO Caili, LI Zimeng, CUI Jing, WANG Xiaocong, ZHAO Chunni, LIU Long. Prognostic study of mild cognitive impairment progressing to Alzheimer′s disease based on polygenic risk score and machine learning modeling strategy[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(6): 684-690. doi: 10.16462/j.cnki.zhjbkz.2023.06.012

Citation:

WANG Rong, CHEN Shuai, ZHAO Caili, LI Zimeng, CUI Jing, WANG Xiaocong, ZHAO Chunni, LIU Long. Prognostic study of mild cognitive impairment progressing to Alzheimer′s disease based on polygenic risk score and machine learning modeling strategy[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(6): 684-690. doi: 10.16462/j.cnki.zhjbkz.2023.06.012

Citation:

WANG Rong, CHEN Shuai, ZHAO Caili, LI Zimeng, CUI Jing, WANG Xiaocong, ZHAO Chunni, LIU Long. Prognostic study of mild cognitive impairment progressing to Alzheimer′s disease based on polygenic risk score and machine learning modeling strategy[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(6): 684-690. doi: 10.16462/j.cnki.zhjbkz.2023.06.012

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Prognostic study of mild cognitive impairment progressing to Alzheimer′s disease based on polygenic risk score and machine learning modeling strategy

doi: 10.16462/j.cnki.zhjbkz.2023.06.012

WANG Rong¹,
CHEN Shuai¹,
ZHAO Caili¹,
LI Zimeng¹,
CUI Jing¹,
WANG Xiaocong²,
ZHAO Chunni^{1
,
,},
LIU Long^{1, 3
,
,}

1.
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2.
School of Public Health and Preventive Medicine, MONASH University, Victoria 3800, Australia
3.
Shanxi Key Laboratory of Risk Assessment for Serious Diseases, Taiyuan 030001, China

Funds:

National Natural Science Foundation of China 81903418

National Natural Science Foundation of China 82173632

More Information

Corresponding author: ZHAO Chunni, E-mail: 1964340853@qq.com; LIU Long, E-mail: biostat-ll@sxmu.edu.cn
Received Date: 2022-08-08
Rev Recd Date: 2022-10-27

Available Online: 2023-07-10

Publish Date: 2023-06-10

Abstract

Abstract

Objective To provide the theoretical basis for modeling the fifth-year prognostic prediction of the conversion from mild cognitive impairment (MCI) to alzheimer′s disease (AD), this study explored the prognostic prediction performance of polygenic risk score and machine learning methods on the progression from MCI to AD from the perspective of whole genome and candidate genome. Methods Using clumping and thresholding (C+T), polygenic risk scores-continuous shrinkage (PRS-CS), random survival forest (RSF), and survival support vector machine (SSVM) to predict the fifth-year prognostic prediction of patients who progressed from MCI to AD.The polygenic risk score of AD obtained by C+T and PRS-CS were included as independent predictors in Cox proportional hazards regression model, while RSF and SSVM were directly included in all single nucleotide polymorphisms (SNPs) related to AD from the perspective of candidate genome for statistical modeling.Finally, C-index was used as the evaluation index of the prediction effect of the model. Results The difference in C-index between the whole genome and candidate genome was less than 0.01 for both C+T and PRS-CS methods, while the maximum difference of C-index between the two methods was 0.04, and there was no statistical difference between them.The machine learning methods significantly outperformed the PRS methods.The C-index of RSF and SSVM reached 0.76, indicating significance increases of 0.07 and 0.11 over C+T and PRS-CS, respectively (all P < 0.05). Conclusions Machine learning methods perform well and provide a more feasible statistical modeling scheme for the prognostic prediction of the progression of MCI to AD.
- Mild cognitive impairment,
- Alzheimer's disease,
- Polygenic risk score,
- Machine learning,
- Prognostic prediction

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References(14)

References

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