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Gialluisi A, Di Castelnuovo A, Costanzo S, Bonaccio M, Persichillo M, Magnacca S, De Curtis A, Cerletti C, Donati MB, de Gaetano G, Capobianco E, Iacoviello L. Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. Eur J Epidemiol 2021; 37:35-48. [PMID: 34453631 DOI: 10.1007/s10654-021-00797-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 08/07/2021] [Indexed: 01/05/2023]
Abstract
Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.
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Affiliation(s)
- Alessandro Gialluisi
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy.
| | | | - Simona Costanzo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Marialaura Bonaccio
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Mariarosaria Persichillo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | | | - Amalia De Curtis
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Chiara Cerletti
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Maria Benedetta Donati
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Giovanni de Gaetano
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, USA
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
- Department of Medicine and Surgery, Research Center in Epidemiology and Preventive Medicine (EPIMED), University of Insubria, Varese, Italy
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