Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
Authors: Sebastian Moguilner, Sandra Baez, Hernan Hernandez, Joaquín Migeot, Agustina Legaz, Raul Gonzalez-Gomez, Francesca R. Farina, Pavel Prado, Jhosmary Cuadros, Enzo Tagliazucchi, Florencia Altschuler, Marcelo Adrián Maito, María E. Godoy, Josephine Cruzat, Pedro A. Valdes-Sosa, Francisco Lopera, John Fredy Ochoa-Gómez, Alfredis Gonzalez Hernandez, Jasmin Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Renato Anghinah, Luís E. d’Almeida Manfrinati, Sol Fittipaldi, Vicente Medel, Daniela Olivares, Görsev G. Yener, Javier Escudero, Claudio Babiloni, Robert Whelan, Bahar Güntekin, Harun Yırıkoğulları, Hernando Santamaria-Garcia, Alberto Fernández Lucas, David Huepe, Gaetano Di Caterina, Marcio Soto-Añari, Agustina Birba, Agustin Sainz-Ballesteros, Carlos Coronel-Oliveros, Amanuel Yigezu, Eduar Herrera, Daniel Abasolo, Kerry Kilborn, Nicolás Rubido, Ruaridh A. Clark, Ruben Herzog, Deniz Yerlikaya, Kun Hu, Mario A. Parra, Pablo Reyes, Adolfo M. García, Diana L. Matallana, José Alberto Avila-Funes, Andrea Slachevsky, María I. Behrens, Nilton Custodio, Juan F. Cardona, Pablo Barttfeld, Ignacio L. Brusco, Martín A. Bruno, Ana L. Sosa Ortiz, Stefanie D. Pina-Escudero, Leonel T. Takada, Elisa Resende, Katherine L. Possin, Maira Okada de Oliveira, Alejandro Lopez-Valdes, Brian Lawlor, Ian H. Robertson, Kenneth S. Kosik, Claudia Duran-Aniotz, Victor Valcour, Jennifer S. Yokoyama, Bruce Miller, Agustin Ibanez
Journal: Nature Medicine (2024)

Abstract Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC ( R ² = 0.37, F ² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.