Bernal-Casas David, Serrano-Marín Joan, Sánchez-Navés Juan, Oller Josep M, Franco Rafael
Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
Metabolites. 2024 Feb 29;14(3):149. doi: 10.3390/metabo14030149.
This paper aimed at devising an intelligence-based method to select compounds that can distinguish between open-angle glaucoma patients, type 2 diabetes patients, and healthy controls. Taking the concentration of 188 compounds measured in the aqueous humour (AH) of patients and controls, linear discriminant analysis (LDA) was used to identify the right combination of compounds that could lead to accurate diagnosis. All possibilities, using the leave-one-out approach, were considered through ad hoc programming and in silico massive data production and statistical analysis. Our proof of concept led to the selection of four molecules: acetyl-ornithine (Ac-Orn), C3 acyl-carnitine (C3), diacyl C42:6 phosphatidylcholine (PC aa C42:6), and C3-DC (C4-OH) acyl-carnitine (C3-DC (C4-OH)) that, taken in combination, would lead to a 95% discriminative success. 100% success was obtained with a non-linear combination of the concentration of three of these four compounds. By discarding younger controls to adjust by age, results were similar although one control was misclassified as a diabetes patient. Methods based on the consideration of individual clinical chemical parameters have limitations in the ability to make a reliable diagnosis, stratify patients, and assess disease progression. Leveraging human AH metabolomic data, we developed a procedure that selects a minimal number of metabolites (3-5) and designs algorithms that maximize the overall accuracy evaluating both positive predictive (PPV) and negative predictive (NPV) values. Our approach of simultaneously considering the levels of a few metabolites can be extended to any other body fluid and has potential to advance precision medicine. Artificial intelligence is expected to use algorithms that use the concentration of three to five molecules to correctly diagnose diseases, also allowing stratification of patients and evaluation of disease progression. In addition, this significant advance shifts focus from a single-molecule biomarker approach to that of an appropriate combination of metabolites.
本文旨在设计一种基于智能的方法来筛选能够区分开角型青光眼患者、2型糖尿病患者和健康对照者的化合物。利用在患者和对照者房水（AH）中测得的188种化合物的浓度，采用线性判别分析（LDA）来确定能够实现准确诊断的化合物的正确组合。通过专门编程以及计算机模拟海量数据生成和统计分析，采用留一法考虑了所有可能性。我们的概念验证导致选择了四种分子：乙酰鸟氨酸（Ac-Orn）、C3酰基肉碱（C3）、二酰基C42:6磷脂酰胆碱（PC aa C42:6）和C3-DC（C4-OH）酰基肉碱（C3-DC（C4-OH）），将它们组合使用可实现95%的判别成功率。使用这四种化合物中三种化合物浓度的非线性组合可获得100%的成功率。通过剔除年轻对照者以进行年龄调整，结果相似，尽管有一名对照者被误分类为糖尿病患者。基于考虑个体临床化学参数的方法在进行可靠诊断、对患者进行分层以及评估疾病进展的能力方面存在局限性。利用人类房水代谢组学数据，我们开发了一种程序，该程序选择最少数量的代谢物（3 - 5种）并设计算法，以最大化评估阳性预测值（PPV）和阴性预测值（NPV）的整体准确性。我们同时考虑少数代谢物水平的方法可扩展到任何其他体液，并具有推动精准医学发展的潜力。预计人工智能将使用利用三到五种分子浓度来正确诊断疾病的算法，还能对患者进行分层并评估疾病进展。此外，这一重大进展将重点从单分子生物标志物方法转移到代谢物的适当组合方法上。
