Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China.
The University of Glasgow, Glasgow, G12 8QQ, Scotland.
Sci Rep. 2021 Nov 2;11(1):21430. doi: 10.1038/s41598-021-00938-8.
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.
人工神经网络（ANN）是挖掘数据的主要工具，其灵感来自于人类大脑和神经系统。有几项研究阐明了其在医学中的应用。然而，还没有人将 ANN 应用于预测叶酸治疗高同型半胱氨酸血症（HHcy）的疗效。疗效已被证明与遗传和环境因素有关，而以前的研究只关注后者。解释方差遗传风险评分（EV-GRS）具有更好的效能，可以代表遗传结构的影响。我们的目的是将 EV-GRS 添加到环境因素中，以建立 ANN 来预测叶酸治疗 HHcy 的疗效。我们进行了前瞻性队列研究，纳入了 638 例 HHcy 患者。多层感知算法被应用于构建 ANN。为了评估 ANN 的效果，我们还建立了逻辑回归（LR）模型进行比较。根据我们的结果，EV-GRS 与疗效呈统计学相关，无论是作为连续变量（OR=3.301，95%CI 1.954-5.576，P<0.001）还是分类变量（OR=3.870，95%CI 2.092-7.159，P<0.001）进行分析。在我们的 ANN 模型中，准确率为 84.78%，约登指数为 0.7073，AUC 为 0.938。这些指标表明效能更高。与 LR 相比，ANN 模型的 AUC、准确率和约登指数（84.78%、0.938、0.7073）略高于 LR 模型（83.33%、0.910、0.6687）。因此，与传统的 LR 模型相比，ANN 模型在预测叶酸治疗 HHcy 的疗效方面可能具有更高的临床应用价值。在验证集中测试两个模型时，我们得到了相同的结论。本研究似乎是第一个建立 ANN 模型的研究，该模型将 EV-GRS 添加到环境因素中以预测叶酸治疗 HHcy 的疗效。该模型将为临床医生提供一种新的决策和个体化治疗方案制定方法。
