根据老龄生物科学的概念，器官衰老和与年龄相关的疾病共享相同的基本分子机制，有效分类人们的生物年龄（比如和日历年龄相比较）较老（或较年轻）的生物标志物的鉴定变得至关重要。这些人实际上将在许多不同的与年龄相关的疾病（包括心血管疾病、神经退行性疾病、癌症等）中有较高（或较低）风险。反过来，患有这些疾病的患者在生物学上比健康的年龄相符个体年龄更大。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（特别是可溶性循环标志物）的有用性，以便在临床症状出现之前识别虚弱患者，以及有患年龄相关疾病风险的患者。在此方面，将讨论所选生物标志物的概述，特别是与代谢应激反应、炎症和细胞死亡（尤其是神经退行性疾病）相关的生物标志物，这些现象都与炎症老化（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，例如细胞外囊泡及其载体、表观遗传标记和肠道微生物组成。由于组学技术的最新进展使得年龄标志物领域的实验室数据大幅增加，从可获得的大量数据中提取生物学意义变得困难，因此将讨论人工智能（AI）方法作为一种越来越重要的策略，用于从原始数据中提取知识，并为医生提供可操作信息来治疗患者。版权所有 © 2023 作者。由 Elsevier B.V. 发布，保留所有权利。

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.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.