[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].

B I Yalaev, A V Novikov, I R Minniakhmetov, R I Khusainova
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Abstract

Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease. Currently, the predictive power of neural network models is insufficient for their incorporation into modern osteoporosis diagnostic protocols. Studies in this area are sporadic, but are widely demanded, as their results are of great importance for preventive medicine. This leads to the need to search for the most effective machine learning approaches and optimise the selection of genetic markers as input parameters to neural network models.

Aim: to evaluate the effectiveness of machine learning and neural network analysis to develop predictive risk models for osteoporosis based on clinical predictors and genetic markers of osteoporetic fractures.

Materials and methods: The predictive models were trained using a database of genotyping and clinical characteristics of 701 women and 501 men living in the Volga-Ural region of Russia. Anthropometric parameters, data on gender, bone mineral density level, and the results of genotyping of 152 polymorphic loci of candidate genes and replication loci of the GEFOS consortium's full genome-wide association search were included as input parameters.

Results: It was found that the model for predicting low bone mineral density, including 6 polymorphic variants of the OPG gene (rs2073618, rs2073617, rs7844539, rs3102735, rs3134069) and 5 polymorphic variants of microRNA binding sites in the mRNA of genes involved in bone metabolism (COL11A1 - rs1031820, FGF2 - rs6854081, miR-146 - rs2910164, ZNF239 - rs10793442, SPARC - rs1054204 and VDR - rs11540149) (AUC=0.81 for men and AUC=0.82 for women).

Conclusion: The results confirm the promising application of machine learning to predict the risk of osteoporosis at the preclinical stage of the disease based on the analysis of clinical and genetic factors.

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Abstract Image

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[利用神经网络训练建立低骨矿物质密度风险的预后临床和遗传模型]。
背景:骨质疏松症是一种常见的年龄相关性致残疾病,由于其病程长且隐蔽,早期诊断困难,往往在骨折后才诊断出来。在这方面,人们对旨在早期预测骨质疏松症的机器学习技术的先进发展寄予厚望,包括使用包含该疾病遗传和临床预测因素信息的大型数据集。然而,由于疾病的复杂多基因和异质性,在预测模型中包含DNA标记物充满了许多困难。目前,神经网络模型的预测能力不足以将其纳入现代骨质疏松症诊断方案。在这方面的研究是零星的,但广泛的需求,因为他们的结果对预防医学具有重要意义。这导致需要寻找最有效的机器学习方法,并优化遗传标记作为神经网络模型输入参数的选择。目的:评价基于骨质疏松性骨折的临床预测因子和遗传标记,利用机器学习和神经网络分析建立骨质疏松症预测风险模型的有效性。材料和方法:使用俄罗斯伏尔加-乌拉尔地区701名女性和501名男性的基因分型和临床特征数据库对预测模型进行训练。输入参数包括人体测量参数、性别数据、骨密度水平以及候选基因的152个多态性位点的基因分型结果和GEFOS联盟全基因组关联搜索的复制位点。结果:发现低骨密度预测模型包括6个OPG基因多态性变异(rs2073618、rs2073617、rs7844539、rs3102735、rs3134069)和5个骨代谢相关基因mRNA microRNA结合位点多态性变异(COL11A1 - rs1031820、FGF2 - rs6854081、miR-146 - rs2910164、ZNF239 - rs10793442、SPARC - rs1054204和VDR - rs11540149)(男性AUC=0.81,女性AUC=0.82)。结论:该结果证实了机器学习在基于临床和遗传因素分析的疾病临床前阶段预测骨质疏松症风险的应用前景。
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