Combining Genetic Risk Score with Artificial Neural Network to Predict the Efficacy of Folic Acid Therapy to Hyperhomocysteinemia

Overview

Journal Sci Rep

Specialty Science

Date 2021 Nov 3

PMID 34728708

Authors

Xiaorui Chen

Xiaowen Huang

Diao Jie

Caifang Zheng

Xiliang Wang

Bowen Zhang

Weihao Shao

Gaili Wang

Weidong Zhang

Affiliations

Soon will be listed here.

Abstract

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954-5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092-7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

Citing Articles

[Ethical aspects of the development, authorization and implementation of applications in ophthalmology based on artificial intelligence : Statement of the German Ophthalmological Society (DOG) and the Professional Association of German...].

Ophthalmologie. 2025; .

PMID: 39964395 DOI: 10.1007/s00347-025-02189-8.

References

Hiramatsu N, Kurosaki M, Sakamoto N, Iwasaki M, Sakamoto M, Suzuki Y . Pretreatment prediction of anemia progression by pegylated interferon alpha-2b plus ribavirin combination therapy in chronic hepatitis C infection: decision-tree analysis. J Gastroenterol. 2011; 46(9):1111-9. DOI: 10.1007/s00535-011-0412-z. View

Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N . The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health. 2018; 164:1-6. DOI: 10.1016/j.puhe.2018.07.012. View

Yazdani Charati J, Janbabaei G, Alipour N, Mohammadi S, Ghorbani Gholiabad S, Fendereski A . Survival prediction of gastric cancer patients by Artificial Neural Network model. Gastroenterol Hepatol Bed Bench. 2018; 11(2):110-117. PMC: 5990918. View

Lafaro R, Pothula S, Kubal K, Inchiosa M, Pothula V, Yuan S . Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables. PLoS One. 2015; 10(12):e0145395. PMC: 4692524. DOI: 10.1371/journal.pone.0145395. View

Imai S, Takekuma Y, Kashiwagi H, Miyai T, Kobayashi M, Iseki K . Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice. PLoS One. 2020; 15(7):e0236789. PMC: 7390378. DOI: 10.1371/journal.pone.0236789. View

Li H, Luo M, Zheng J, Luo J, Zeng R, Feng N . An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study. Medicine (Baltimore). 2017; 96(6):e6090. PMC: 5313026. DOI: 10.1097/MD.0000000000006090. View

Ohishi T, Fujita T, Suzuki D, Nishida T, Asukai M, Matsuyama Y . Serum homocysteine levels are affected by renal function during a 3-year period of minodronate therapy in female osteoporotic patients. J Bone Miner Metab. 2018; 37(2):319-326. DOI: 10.1007/s00774-018-0920-5. View

Messedi M, Frigui M, Chaabouni K, Turki M, Neifer M, Lahiyani A . Methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and variations of homocysteine concentrations in patients with Behcet's disease. Gene. 2013; 527(1):306-10. DOI: 10.1016/j.gene.2013.06.041. View

Peng H, Man C, Xu J, Fan Y . Elevated homocysteine levels and risk of cardiovascular and all-cause mortality: a meta-analysis of prospective studies. J Zhejiang Univ Sci B. 2015; 16(1):78-86. PMC: 4288948. DOI: 10.1631/jzus.B1400183. View

10.

Dhonukshe-Rutten R, de Vries J, de Bree A, van der Put N, van Staveren W, de Groot L . Dietary intake and status of folate and vitamin B12 and their association with homocysteine and cardiovascular disease in European populations. Eur J Clin Nutr. 2007; 63(1):18-30. DOI: 10.1038/sj.ejcn.1602897. View

11.

Schaffer A, Verdoia M, Cassetti E, Marino P, Suryapranata H, De Luca G . Relationship between homocysteine and coronary artery disease. Results from a large prospective cohort study. Thromb Res. 2014; 134(2):288-93. DOI: 10.1016/j.thromres.2014.05.025. View

12.

Shah H, Jan M, Altaf A, Salahudin M . Correlation of hyper-homocysteinemia with coronary artery disease in absence of conventional risk factors among young adults. J Saudi Heart Assoc. 2018; 30(4):305-310. PMC: 6067060. DOI: 10.1016/j.jsha.2018.04.002. View

13.

Kong X, Huang X, Zhao M, Xu B, Xu R, Song Y . Platelet Count Affects Efficacy of Folic Acid in Preventing First Stroke. J Am Coll Cardiol. 2018; 71(19):2136-2146. DOI: 10.1016/j.jacc.2018.02.072. View

14.

Tian H, Tian D, Zhang C, Wang W, Wang L, Ge M . Efficacy of Folic Acid Therapy in Patients with Hyperhomocysteinemia. J Am Coll Nutr. 2017; 36(7):528-532. DOI: 10.1080/07315724.2017.1330162. View

15.

Huang X, Li D, Zhao Q, Zhang C, Ren B, Yue L . Association between BHMT and CBS gene promoter methylation with the efficacy of folic acid therapy in patients with hyperhomocysteinemia. J Hum Genet. 2019; 64(12):1227-1235. DOI: 10.1038/s10038-019-0672-7. View

16.

Huang X, Zhao Q, Li D, Ren B, Yue L, Shi F . Association between gene promoter methylation of the one-carbon metabolism pathway and serum folate among patients with hyperhomocysteinemia. Eur J Clin Nutr. 2020; 74(12):1677-1684. DOI: 10.1038/s41430-020-0657-9. View

17.

Du B, Tian H, Tian D, Zhang C, Wang W, Wang L . Genetic polymorphisms of key enzymes in folate metabolism affect the efficacy of folate therapy in patients with hyperhomocysteinaemia. Br J Nutr. 2018; 119(8):887-895. DOI: 10.1017/S0007114518000508. View

18.

Vickram A, Kamini A, Das R, Pathy M, Parameswari R, Archana K . Validation of artificial neural network models for predicting biochemical markers associated with male infertility. Syst Biol Reprod Med. 2016; 62(4):258-65. DOI: 10.1080/19396368.2016.1185654. View

19.

Loftus T, Brakenridge S, Croft C, Smith R, Efron P, Moore F . Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention. J Surg Res. 2017; 212:42-47. PMC: 5644023. DOI: 10.1016/j.jss.2016.12.032. View

20.

Pasini A . Artificial neural networks for small dataset analysis. J Thorac Dis. 2015; 7(5):953-60. PMC: 4454870. DOI: 10.3978/j.issn.2072-1439.2015.04.61. View