Bosco Alice, Arru Francesca, Abis Alessandra, Fanos Vassilios, Dessì Angelica
Neonatal Intensive Care Unit, Department of Surgical Sciences, University of Cagliari, AOU Cagliari, 09124 Cagliari, Italy.
Int J Mol Sci. 2025 Apr 27;26(9):4164. doi: 10.3390/ijms26094164.
Precision medicine stems from a new approach to the prevention, diagnosis and treatment of patients, due to the shift in focus away from pathology and towards the uniqueness of the individual, personalising the diagnostic-therapeutic pathway. This paradigm shift has been made possible by the emergence of new high-throughput technologies capable of generating large amounts of data on multiple levels of a biological system, identifying pathology-related genes, transcripts, proteins and metabolites. Metabolomics plays a primary role in this context, providing, through non-invasive sampling, a very close image of the phenotype of the organism being studied by detecting metabolites, end products downstream of gene transcription, present in cells, tissues, organs and biological fluids. The enormous amount of data that these modern technologies make available, together with the need to elucidate the complex interplay of the various biological levels by combining data from distinct omics, has led to the need to employ advanced informatics techniques, among which artificial intelligence has recently emerged. These innovations are of great interest in the field of perinatology, representing an attempt to optimise the diagnostic timeline for the most critical newborns. In addition, they may contribute to the improvement of prevention strategies available to date. All these contributions prove to be crucial at very vulnerable life stages, allowing crucial intervention opportunities. In this review, we have analysed studies that have integrated metabolomics with at least one other omics in the perinatal field, attempting to highlight the usefulness of multiomics integration and the different methods employed.
精准医学源于一种针对患者预防、诊断和治疗的新方法，这是由于关注点从病理学转向个体独特性，从而使诊断-治疗途径个性化。新的高通量技术的出现使这种范式转变成为可能，这些技术能够在生物系统的多个层面上生成大量数据，识别与病理学相关的基因、转录本、蛋白质和代谢物。在这种背景下，代谢组学发挥着主要作用，通过非侵入性采样，通过检测存在于细胞、组织、器官和生物体液中的代谢物（基因转录下游的终产物），提供所研究生物体表型的非常接近的图像。这些现代技术提供的大量数据，以及通过整合来自不同组学的数据来阐明各种生物水平之间复杂相互作用的需求，导致需要采用先进的信息学技术，其中人工智能最近崭露头角。这些创新在围产医学领域引起了极大兴趣，代表了一种为最关键的新生儿优化诊断时间表的尝试。此外，它们可能有助于改进现有的预防策略。所有这些贡献在非常脆弱的生命阶段被证明是至关重要的，提供了关键的干预机会。在这篇综述中，我们分析了在围产期领域将代谢组学与至少一种其他组学相结合的研究，试图突出多组学整合的有用性以及所采用的不同方法。
