The Aging Imageomics Study: rationale, design and baseline characteristics of the study population

Puig Alcántara, Josep
Biarnés, Carles
Pamplona, Reinald
Serena, Joaquín
Gich Fullà, Jordi
Gallart, Lluis
Portero Otin, Manuel
Alberich Bayarri, Ángel
Jiménez Pastor, Ana
Camacho-Ramos, Eduardo
Mayneris Perxachs, Jordi
Pineda, Victor
Prats Puig, Anna
Gacto Sánchez, Mariano
Deco, Gustavo
Escrichs, Anira
Clotet, Bonaventura
Paredes, Roger
Negredo, Eugenia
Triaire, Bruno
Rodríguez Barrena, Manuel
Heredia Escámez, Alberto
Coronado, Rafael
Graaf, Walter de
Prevost, Valentin
Mitulescu, Anca
Miralles, Felip
Ribas Ripoll, Vicent
Puig Domingo, Manel
Essig, Marco
Figley, Chase R.
Figley, Teresa D.
Albensi, Benedict
Ashraf, Ahmed
Reiber, Johan H.C.
Schifitto, Giovanni
Uddin, Md Nasir
Leiva Salinas, Carlos
Wintermark, Max
Nael, Kambiz
Vilalta Franch, Joan
Barretina, Jordi
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Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging's (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged ≥50 years (mean 67, range 50-96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging ​
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