Lipids are essential for cellular functioning considering their role in membrane composition, signaling, and energy metabolism. The brain is the second most abundant organ in terms of lipid concentration and diversity only after adipose tissue. However, in the central system (CNS) lipid dysregulation has been linked to the etiology, progression, and severity of neurodegenerative diseases such as Alzheimeŕs, Parkinson, and Multiple Sclerosis. Advances in the human genome and subsequent sequencing technologies allowed us the study of lipidomics as a promising approach to diagnosis and treatment of neurodegeneration. Lipidomics advances rapidly increased the amount and quality of data allowing the integration with other omic types as well as implementing novel bioinformatic and quantitative tools such as machine learning (ML). Integration of lipidomics data with ML, as a powerful quantitative predictive approach, led to improvements in diagnostic biomarker prediction, clinical data integration, network, and systems approaches for neural behavior, novel etiology markers for inflammation, and neurodegeneration progression and even Mass Spectrometry image analysis. In this sense, by exploiting lipidomics data with ML is possible to improve the identification of new biomarkers or unveil new molecular mechanisms associated with lipid impairment across neurodegeneration. In this review, we present the lipidomic neurobiology state-of-the-art highlighting its potential applications to study neurodegenerative conditions. Also, we present theoretical background, applications, and advances in the integration of lipidomics with ML. This review opens the door to new approaches in this rising field.

中文翻译：

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脑脂质组学作为神经退行性疾病的新兴领域：机器学习方法的观点

考虑到脂质在膜组成、信号传导和能量代谢中的作用，脂质对细胞功能至关重要。就脂质浓度和多样性而言，大脑是仅次于脂肪组织的第二大器官。然而，在中枢系统 (CNS) 中，脂质失调与阿尔茨海默病、帕金森病和多发性硬化症等神经退行性疾病的病因、进展和严重程度有关。人类基因组的进步和随后的测序技术使我们能够将脂质组学研究作为诊断和治疗神经退行性疾病的一种有前途的方法。脂质组学的进步迅速增加了数据的数量和质量，允许与其他组学类型集成以及实施新的生物信息学和定量工具，如机器学习 (ML)。脂质组学数据与 ML 的整合，作为一种强大的定量预测方法，导致了诊断生物标志物预测、临床数据整合、网络和神经行为系统方法、炎症的新病因标志物和神经变性进展甚至质谱图像分析的改进. 从这个意义上说，通过利用 ML 的脂质组学数据有可能改进新生物标志物的识别或揭示与跨神经变性的脂质损伤相关的新分子机制。在这篇综述中，我们介绍了脂质组学神经生物学的最新技术，重点介绍了其在研究神经退行性疾病方面的潜在应用。此外，我们还介绍了脂质组学与 ML 整合的理论背景、应用和进展。