de la O Víctor, Fernández-Cruz Edwin, Matía Matin Pilar, Larrad-Sainz Angélica, Espadas Gil José Luis, Barabash Ana, Fernández-Díaz Cristina M, Calle-Pascual Alfonso L, Rubio-Herrera Miguel A, Martínez J Alfredo
Cardiometabolic Nutrition Group, Precision Nutrition Program, Research Institute on Food and Health Sciences IMDEA Food, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), 28049 Madrid, Spain.
Faculty of Health Sciences, International University of La Rioja (UNIR), 26004 Logroño, Spain.
Nutrients. 2024 Nov 7;16(22):3817. doi: 10.3390/nu16223817.
Recent advances in machine learning technologies and omics methodologies are revolutionizing dietary assessment by integrating phenotypical, clinical, and metabolic biomarkers, which are crucial for personalized precision nutrition. This investigation aims to evaluate the feasibility and efficacy of artificial intelligence tools, particularly machine learning (ML) methods, in analyzing these biomarkers to characterize food and nutrient intake and to predict dietary patterns.
We analyzed data from 138 subjects from the European Dietary Deal project through comprehensive examinations, lifestyle questionnaires, and fasting blood samples. Clustering was based on 72 h dietary recall, considering sex, age, and BMI. Exploratory factor analysis (EFA) assigned nomenclature to clusters based on food consumption patterns and nutritional indices from food frequency questionnaires. Elastic net regression identified biomarkers linked to these patterns, helping construct algorithms.
Clustering and EFA identified two dietary patterns linked to biochemical markers, distinguishing pro-Mediterranean (pro-MP) and pro-Western (pro-WP) patterns. Analysis revealed differences between pro-MP and pro-WP clusters, such as vegetables, pulses, cereals, drinks, meats, dairy, fish, and sweets. Markers related to lipid metabolism, liver function, blood coagulation, and metabolic factors were pivotal in discriminating clusters. Three computational algorithms were created to predict the probabilities of being classified into the pro-WP pattern. The first is the main algorithm, followed by a supervised algorithm, which is a simplified version of the main model that focuses on clinically feasible biochemical parameters and practical scientific criteria, demonstrating good predictive capabilities (ROC curve = 0.91, precision-recall curve = 0.80). Lastly, a reduced biochemical-based algorithm is presented, derived from the supervised algorithm.
This study highlights the potential of biochemical markers in predicting nutritional patterns and the development of algorithms for classifying dietary clusters, advancing dietary intake assessment technologies.
机器学习技术和组学方法的最新进展正在通过整合表型、临床和代谢生物标志物来彻底改变饮食评估，这些生物标志物对于个性化精准营养至关重要。本研究旨在评估人工智能工具，特别是机器学习（ML）方法，在分析这些生物标志物以表征食物和营养摄入以及预测饮食模式方面的可行性和有效性。
我们通过全面检查、生活方式问卷和空腹血样分析了来自欧洲饮食协议项目的138名受试者的数据。聚类基于72小时饮食回忆，考虑性别、年龄和BMI。探索性因子分析（EFA）根据食物频率问卷中的食物消费模式和营养指标为聚类命名。弹性网络回归确定与这些模式相关的生物标志物，有助于构建算法。
聚类和EFA确定了两种与生化标志物相关的饮食模式，区分了亲地中海（pro-MP）和亲西方（pro-WP）模式。分析揭示了pro-MP和pro-WP聚类之间的差异，如蔬菜、豆类、谷物、饮料、肉类、乳制品、鱼类和甜食。与脂质代谢、肝功能、凝血和代谢因子相关的标志物在区分聚类中起关键作用。创建了三种计算算法来预测被分类为pro-WP模式的概率。第一种是主要算法，其次是监督算法，它是主要模型的简化版本，侧重于临床可行的生化参数和实际科学标准，具有良好的预测能力（ROC曲线=0.91，精确召回曲线=0.80）。最后，提出了一种基于生化的简化算法，它源自监督算法。
本研究强调了生化标志物在预测营养模式和开发饮食聚类分类算法方面的潜力，推动了饮食摄入评估技术的发展。
