Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective

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

doi:10.1016/j.arr.2023.102044

Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective

Ageing Res Rev. 2023 Nov:91:102044. doi: 10.1016/j.arr.2023.102044. Epub 2023 Aug 28.

Authors

Stefano Salvioli¹, Maria Sofia Basile², Leonardo Bencivenga³, Sara Carrino⁴, Maria Conte⁴, Sarah Damanti⁵, Rebecca De Lorenzo⁵, Eleonora Fiorenzato⁶, Alessandro Gialluisi⁷, Assunta Ingannato⁸, Angelo Antonini⁹, Nicola Baldini¹⁰, Miriam Capri⁴, Simone Cenci⁵, Licia Iacoviello⁷, Benedetta Nacmias⁸, Fabiola Olivieri¹¹, Giuseppe Rengo¹², Patrizia Rovere Querini⁵, Fabrizia Lattanzio¹³

Affiliations

¹ Department of Medical and Surgical Science, University of Bologna, Bologna, Italy; IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy. Electronic address: stefano.salvioli@unibo.it.
² IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
³ Department of Translational Medical Sciences, University of Naples Federico II, Napoli, Italy.
⁴ Department of Medical and Surgical Science, University of Bologna, Bologna, Italy.
⁵ IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, Milano, Italy.
⁶ Parkinson's Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), Department of Neurosciences, University of Padova, Padova, Italy.
⁷ Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy; EPIMED Research Center, Department of Medicine and Surgery, University of Insubria, Varese, Italy.
⁸ Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy.
⁹ Parkinson's Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), Department of Neurosciences, University of Padova, Padova, Italy; Center for Neurodegenerative Disease Research (CESNE), Department of Neurosciences, University of Padova, Padova, Italy.
¹⁰ IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
¹¹ Department of Clinical and Molecular Sciences, Università Politecnica Delle Marche, Ancona, Italy; Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona, Italy.
¹² Department of Translational Medical Sciences, University of Naples Federico II, Napoli, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Scientific Institute of Telese Terme, Telese Terme, Italy.
¹³ Scientific Direction, IRCCS INRCA, Ancona, Italy.

PMID: 37647997
DOI: 10.1016/j.arr.2023.102044

Abstract

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.

Keywords: Artificial Intelligence; Biomarkers of aging; Frailty; Inflammaging; Multimorbidity.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Aging / metabolism
Artificial Intelligence
Biomarkers / metabolism
Frailty* / diagnosis
Humans
Inflammation / metabolism

Substances

Biomarkers