神经退行性变
细胞外小泡
生物年龄
疾病
诊断生物标志物
生物信息学
医学
组学
老年学
癌症
神经科学
生物
病理
内科学
细胞生物学
作者
Stefano Salvioli,Maria Sofia Basile,Leonardo Bencivenga,Sara Carrino,Maria Conte,Sarah Damanti,Rebecca De Lorenzo,Eleonora Fiorenzato,Alessandro Gialluisi,Assunta Ingannato,Angelo Antonini,Nicola Baldini,Miriam Capri,Simone Cenci,Licia Iacoviello,Benedetta Nacmias,Fabiola Olivieri,Giuseppe Rengo,Patrizia Rovere‐Querini,Fabrizia Lattanzio
标识
DOI:10.1016/j.arr.2023.102044
摘要
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.
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