Salvioli Stefano, Basile Maria Sofia, Bencivenga Leonardo, Carrino Sara, Conte Maria, Damanti Sarah, De Lorenzo Rebecca, Fiorenzato Eleonora, Gialluisi Alessandro, Ingannato Assunta, Antonini Angelo, Baldini Nicola, Capri Miriam, Cenci Simone, Iacoviello Licia, Nacmias Benedetta, Olivieri Fabiola, Rengo Giuseppe, Querini Patrizia Rovere, Lattanzio Fabrizia
Department of Medical and Surgical Science, University of Bologna, Bologna, Italy; IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
Ageing Res Rev. 2023 Nov;91:102044. doi: 10.1016/j.arr.2023.102044. Epub 2023 Aug 28.
According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.
根据老年科学概念，即机体衰老和与年龄相关的疾病具有相同的基本分子机制，识别能够有效将人群分类为生物学年龄比其实际（即日历）年龄更大（或更小）的衰老生物标志物变得至关重要。事实上，这些人患许多不同的与年龄相关疾病的风险更高（或更低），包括心血管疾病、神经退行性变、癌症等。反过来，患有这些疾病的患者在生物学上比年龄匹配的健康个体更老。到目前为止，已经描述了许多与年龄相关的生物标志物。本综述的目的是讨论其中一些生物标志物（特别是可溶性循环生物标志物）的用途，以便在临床症状出现之前识别体弱患者以及有患与年龄相关疾病风险的患者。将在这方面讨论所选生物标志物的概述，特别是我们将关注与代谢应激反应、炎症和细胞死亡（特别是在神经退行性变中）相关的生物标志物，所有这些现象都与炎症衰老（慢性、低度、与年龄相关的炎症）有关。在综述的第二部分，将讨论下一代标志物，如细胞外囊泡及其货物、表观遗传标志物和肠道微生物群组成。由于组学技术的最新进展使得年龄生物标志物领域的实验室数据产量呈指数级增长，难以从大量可用数据中提取生物学意义，因此将讨论人工智能（AI）方法，作为从原始数据中提取知识并为从业者提供可操作信息以治疗患者的日益重要的策略。
