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

2020; Elsevier BV; Volume: 189; Linguagem: Inglês

10.1016/j.mad.2020.111257

1872-6216

Josep Puig, Carles Biarnés, Salvador Pedraza, Joan C. Vilanova, Reinald Pamplona, José Manuel Fernández‐Real, Ramón Brugada, Rafel Ramos, Gabriel Coll de Tuero, Laia Calvó‐Perxas, Joaquı́n Serena, Lluís Ramió‐Torrentà, Jordi Gich, L. Gallart, Manuel Portero‐Otín, Ángel Alberich‐Bayarri, Ana Jiménez-Pastor, Eduardo Camacho-Ramos, Jordi Mayneris‐Perxachs, Víctor Pineda, Raquel Font‐Lladó, Anna Prats‐Puig, Mariano-Luis Gacto, Gustavo Deco, Anira Escrichs, Bonaventura Clotet, Roger Paredes, Eugènia Negredo, B. Triaire, Manuel Rodríguez, Alberto Heredia-Escámez, Rafael Jaraba Coronado, Wolter de Graaf, Valentin Prévost, Anca Mitulescu, Pepus Daunis‐i‐Estadella, Santiago Thió‐Henestrosa, Felip Miralles, Vicent Ribas, Manel Puig‐Domingo, Marco Essig, Chase R. Figley, Teresa D. Figley, Benedict C. Albensi, Ahmed Ashraf, Johan H. C. Reiber, Giovanni Schifitto, Md Nasir Uddin, Carlos Leiva‐Salinas, Max Wintermark, Kambiz Nael, Joan Vilalta‐Franch, Jordi Barretina, Josep Garre‐Olmo,

Cardiovascular Disease and Adiposity

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.