TY - JOUR
T1 - The Aging Imageomics Study
T2 - rationale, design and baseline characteristics of the study population
AU - Puig, Josep
AU - Biarnes, Carles
AU - Pedraza, Salvador
AU - Vilanova, Joan C.
AU - Pamplona, Reinald
AU - Fernández-Real, José Manuel
AU - Brugada, Ramon
AU - Ramos, Rafel
AU - Coll-de-Tuero, Gabriel
AU - Calvo-Perxas, Laia
AU - Serena, Joaquin
AU - Ramió-Torrentà, Lluís
AU - Gich, Jordi
AU - Gallart, Lluis
AU - Portero-Otin, Manel
AU - Alberich-Bayarri, Angel
AU - Jimenez-Pastor, Ana
AU - Camacho-Ramos, Eduardo
AU - Mayneris-Perxachs, Jordi
AU - Pineda, Victor
AU - Font, Raquel
AU - Prats-Puig, Anna
AU - Gacto, Mariano Luis
AU - Deco, Gustavo
AU - Escrichs, Anira
AU - Clotet, Bonaventura
AU - Paredes, Roger
AU - Negredo, Eugenia
AU - Triaire, Bruno
AU - Rodríguez, Manuel
AU - Heredia-Escámez, Alberto
AU - Coronado, Rafael
AU - de Graaf, Wolter
AU - Prevost, Valentin
AU - Mitulescu, Anca
AU - Daunis-i-Estadella, Pepus
AU - Thió-Henestrosa, Santiago
AU - Miralles, Felip
AU - Ribas-Ripoll, Vicent
AU - Puig-Domingo, Manel
AU - Essig, Marco
AU - Figley, Chase R.
AU - Figley, Teresa D.
AU - Albensi, Benedict
AU - Ashraf, Ahmed
AU - Reiber, Johan H.C.
AU - Schifitto, Giovanni
AU - Md Nasir, Uddin
AU - Leiva-Salinas, Carlos
AU - Wintermark, Max
AU - Nael, Kambiz
AU - Vilalta-Franch, Joan
AU - Barretina, Jordi
AU - Garre-Olmo, Josep
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - aging
KW - big data analyses
KW - biomarkers
KW - population-based study
KW - radiomics
KW - whole-body magnetic resonance imaging
UR - https://www.scopus.com/pages/publications/85085759717
UR - https://www.scopus.com/inward/citedby.url?scp=85085759717&partnerID=8YFLogxK
U2 - 10.1016/j.mad.2020.111257
DO - 10.1016/j.mad.2020.111257
M3 - Article
C2 - 32437737
AN - SCOPUS:85085759717
SN - 0047-6374
VL - 189
JO - Mechanisms of Ageing and Development
JF - Mechanisms of Ageing and Development
M1 - 111257
ER -