RT Journal Article
T1 Urinary metabolic signatures reflect cardiovascular risk in the young, middle-aged, and elderly populations
A1 Martínez, Paula J.
A1 Agudiez, Marta
A1 Molero, Dolores
A1 Martín Lorenzo, Marta
A1 Baldán Martín, Montserrat
A1 Santiago Hernández, Aránzazu
A1 García Segura, Juan Manuel
A1 Madruga, Felipe
A1 Cabrera Sierra, Martha
A1 Calvo, Eva
A1 Ruiz Hurtado, Gema
A1 Barderas, María G.
A1 Vivanco, Fernando
A1 Ruilope, Luis M.
A1 Álvarez Llamas, Gloria
AB The predictive value of traditional cardiovascular risk estimators is limited, and young and elderly populations are particularly underrepresented. We aimed to investigate the urine metabolome and its association with cardiovascular risk to identify novel markers that might complement current estimators based on age. Urine samples were collected from 234 subjects categorized into three age-grouped cohorts: 30–50 years (cohort I, young), 50–70 years (cohort II, middle-aged), and > 70 years (cohort III, elderly). Each cohort was further classified into three groups: (a) control, (b) individuals with cardiovascular risk factors, and (c) those who had a previous cardiovascular event. Novel urinary metabolites linked to cardiovascular risk were identified by nuclear magnetic resonance in cohort I and then evaluated by target mass spectrometry quantification in all cohorts. A previously identified metabolic fingerprint associated with atherosclerosis was also analyzed and its potential risk estimation investigated in the three aged cohorts. Three different metabolic signatures were identified according to age: 2-hydroxybutyrate, gamma-aminobutyric acid, hypoxanthine, guanidoacetate, oxaloacetate, and serine in young adults; citrate, cyclohexanol, glutamine, lysine, pantothenate, pipecolate, threonine, and tyramine shared by middle-aged and elderly adults; and trimethylamine N-oxide and glucuronate associated with cardiovascular risk in all three cohorts. The urinary metabolome contains a metabolic signature of cardiovascular risk that differs across age groups. These signatures might serve to complement existing algorithms and improve the accuracy of cardiovascular risk prediction for personalized prevention.
PB Springer
SN 1432-1440
YR 2020
FD 2020-09-11
LK https://hdl.handle.net/20.500.14352/8015
UL https://hdl.handle.net/20.500.14352/8015
LA eng
NO Instituto de Salud Carlos III (ISCIII) / FEDER
NO Red de Investigación Renal (REDinREN)
NO Comunidad de Madrid
NO Sociedad Española de Cardiología para la Investigación Básica 2017
NO Fundación SENEFRO
NO Fundación Renal Íñigo Álvarez de Toledo
NO Fundación Conchita Rábago de Jiménez Díaz
DS Docta Complutense
RD 10 abr 2025