Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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; Brain Lawlor; Ian H Robertson; Kenneth S Kosik; Claudia Duran-Aniotz; Victor Valcour; Jennifer S Yokoyama; Bruce L Miller; Agustin Ibanez

doi:10.21203/rs.3.rs-4150225/v1

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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, Brain Lawlor, Ian H Robertson, Kenneth S Kosik, Claudia Duran-Aniotz, Victor Valcour, Jennifer S Yokoyama, Bruce L Miller, Agustin Ibanez^* (Corresponding Author)

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

86 Citations (Scopus)

28 Downloads (Pure)

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 (R² = 0.37, F² = 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.

Original language	English
Pages (from-to)	3646-3657
Number of pages	23
Journal	Nature Medicine
Volume	30
Issue number	12
Early online date	26 Aug 2024
DOIs	https://doi.org/10.21203/rs.3.rs-4150225/v1 https://doi.org/10.1038/s41591-024-03209-x
Publication status	Published - Dec 2024

Bibliographical note

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
Correction to: Nature Medicine https://doi.org/10.1038/s41591-024-03209-x, published online 26 August 2024. In the version of the article initially published, Brian Lawlor’s name appeared incorrectly (as Brain) and has now been amended in the HTML and PDF versions of the article.

Data Availability Statement

Data availability
All preprocessed data are openly available at: https://osf.io/8zjf4/. The fMRI and EEG datasets comprise sources: (1) currently publicly available for direct download after registration and access application, (2) available after contacting the researcher or (3) accessible after IRB approval of formal data-sharing agreement in a process that can last up to 12 weeks. The fMRI sources that are publicly available for direct download are the following: Alzheimer’s Disease Neuroimaging Initiative (ADNI) (USA) (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp), Chinese Human Connectome Project (CHCP) (China) (https://scidb.cn/en/detail?dataSetId=f512d085f3d3452a9b14689e9997ca94#p2), The Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) (USA) (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp) and the Japanese Strategic Research Program for the Promotion of Brain Science (SRPBS) (Japan) (https://bicr-resource.atr.jp/srpbsopen/). The fMRI sources available after contacting the researcher include ReDLat USA by contacting Bruce Miller at UCSF through [email protected]. The fMRI sources that require IRB approval and a formal data-sharing agreement include: ReDLat pros (Argentina, Chile, Colombia, Mexico, Peru) by contacting Agustín Ibañez at [email protected], Centro de Gerociencia Salud Mental y Metabolismo (GERO) (Chile) by contacting Andrea Slachevsky at [email protected], ReDLat pre (Argentina) by contacting Agustín Ibañez at [email protected], ReDLat pre (Peru) by contacting Nilton Custodio at [email protected], ReDLat pre (Colombia) by contacting Diana Matallana at [email protected], ReDLat pre (Colombia-II) by contacting Felipe Cardona at [email protected], ReDLat pre (Mexico) by contacting Ana Luisa Sosa at [email protected], ReDLat pre (Chile) by contacting María Isabel Behrens at [email protected] and ReDLat pre (Chile) by contacting Andrea Slachevsky at [email protected]. The EEG sources that are publicly available for direct download are Centro de Neurociencias de Cuba (CHBMP) (Cuba) (https://www.synapse.org/Synapse:syn22324937). The EEG sources that are available after contacting the researcher include BrainLat (Argentina) by contacting Agustina Legaz at [email protected], BrainLat (Chile) by contacting Agustina Legaz at [email protected], Izmir University of Economics (Turkey) by contacting Gorsev Gener at [email protected], Trinity College Dublin (Ireland) by contacting Francesca Farina at [email protected], Universidad de Antioquia (Colombia) by contacting Francisco Lopera at [email protected], Universidad de Sao Paulo (Brazil) by contacting Mario Parra at [email protected], Universidad de Roma La Sapienza (Italy) by contacting Susana Lopez at [email protected], University of Strathclyde (UK) by contacting Mario Parra at [email protected], Istanbul Medipol University (Turkey) by contacting Tuba Aktürk at [email protected] and Takeda (Chile) by contacting Daniela Olivares at [email protected]. Indicators of air pollution, socioeconomic inequality (the Gini index), the burden of communicable, maternal, prenatal and nutritional conditions, and the burden of noncommunicable diseases were sourced from the updated country-specific data provided on the World Bank’s platform (https://databank.worldbank.org/). Country-level GII are available on the World Health Organization’s website (https://www.who.int/data/nutrition/nlis/info/gender-inequality-index-(gii)). For additional details, see Supplementary Data 1.

Extended data is available for this paper at https://doi.org/10.1038/s41591-024-03209-x.

Supplementary information
The online version contains supplementary material available at https://doi.org/10.1038/s41591-024-03209-x.

Code availability
The code used to preprocess and analyze the data of this work is available in an Open Science Foundation repository at the following address: https://osf.io/8zjf4/

Funding

This work was supported by Latin American Brain Health Institute (BrainLat) # BL-SRGP2020-02 awarded to MAP and AI. AI is supported by grants from ReDLat [National Institutes of Health and the Fogarty International Center (FIC), National Institutes of Aging (R01 AG057234, R01AG075775, AG021051, R01 AG083799, CARDS-NIH 75N95022C00031), Alzheimer's Association (SG-20-725707), Rainwater Charitable Foundation, The Bluefield project to cure FTD, and Global Brain Health Institute)], ANID/FONDECYT Regular (1210195, 1210176 and 1220995); and ANID/FONDAP/15150012. AMG is partially supported by the National Institute on Aging of the National Institutes of Health (R01AG075775, R01AG083799, 2P01AG019724); ANID (FONDECYT Regular 1210176, 1210195); and DICYT-USACH (032351G_DAS). The contents of this publication are solely the author's responsibility and do not represent the official views of these institutions.

Funders	Funder number
Latin American Brain Health Institute	BL-SRGP2020-02
National Institutes of Health	R01 AG057234, R01AG075775, AG021051, R01 AG083799, CARDS-NIH 75N95022C00031, SG-20-725707, 2P01AG019724

Keywords

Cognitive aging
Dementia
Developing World

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Moguilner, S., Baez, S., Hernandez, H., Migeot, J., Legaz, A., Gonzalez-Gomez, R., Farina, F. R., Prado, P., Cuadros, J., Tagliazucchi, E., Altschuler, F., Maito, M. A., Godoy, M. E., Cruzat, J., Valdes-Sosa, P. A., Lopera, F., Ochoa-Gómez, J. F., Hernandez, A. G., Bonilla-Santos, J., ... Ibanez, A. (2024). Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nature Medicine, 30(12), 3646-3657. https://doi.org/10.21203/rs.3.rs-4150225/v1, https://doi.org/10.1038/s41591-024-03209-x

Moguilner, S, Baez, S, Hernandez, H, Migeot, J, Legaz, A, Gonzalez-Gomez, R, Farina, FR, Prado, P, Cuadros, J, Tagliazucchi, E, Altschuler, F, Maito, MA, Godoy, ME, Cruzat, J, Valdes-Sosa, PA, Lopera, F, Ochoa-Gómez, JF, Hernandez, AG, Bonilla-Santos, J, Gonzalez-Montealegre, RA, Anghinah, R, d'Almeida Manfrinati, LE, Fittipaldi, S, Medel, V, Olivares, D, Yener, GG, Escudero, J, Babiloni, C, Whelan, R, Güntekin, B, Yırıkoğulları, H, Santamaria-Garcia, H, Lucas, AF, Huepe, D, Di Caterina, G, Soto-Añari, M, Birba, A, Sainz-Ballesteros, A, Coronel-Oliveros, C, Yigezu, A, Herrera, E, Abasolo, D, Kilborn, K, Rubido, N, Clark, RA, Herzog, R, Yerlikaya, D, Hu, K, Parra, MA, Reyes, P, García, AM, Matallana, DL, Avila-Funes, JA, Slachevsky, A, Behrens, MI, Custodio, N, Cardona, JF, Barttfeld, P, Brusco, IL, Bruno, MA, Sosa Ortiz, AL, Pina-Escudero, SD, Takada, LT, Resende, E, Possin, KL, de Oliveira, MO, Lopez-Valdes, A, Lawlor, B, Robertson, IH, Kosik, KS, Duran-Aniotz, C, Valcour, V, Yokoyama, JS, Miller, BL & Ibanez, A 2024, 'Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations', Nature Medicine, vol. 30, no. 12, pp. 3646-3657. https://doi.org/10.21203/rs.3.rs-4150225/v1, https://doi.org/10.1038/s41591-024-03209-x

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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 (R² = 0.37, F² = 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.",

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AU - Baez, Sandra

AU - Hernandez, Hernan

AU - Migeot, Joaquín

AU - Legaz, Agustina

AU - Gonzalez-Gomez, Raul

AU - Farina, Francesca R

AU - Prado, Pavel

AU - Cuadros, Jhosmary

AU - Tagliazucchi, Enzo

AU - Altschuler, Florencia

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AU - Valdes-Sosa, Pedro A

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AU - Ochoa-Gómez, John Fredy

AU - Hernandez, Alfredis Gonzalez

AU - Bonilla-Santos, Jasmin

AU - Gonzalez-Montealegre, Rodrigo A

AU - Anghinah, Renato

AU - d'Almeida Manfrinati, Luís E

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AU - Medel, Vicente

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AU - Yener, Görsev G

AU - Escudero, Javier

AU - Babiloni, Claudio

AU - Whelan, Robert

AU - Güntekin, Bahar

AU - Yırıkoğulları, Harun

AU - Santamaria-Garcia, Hernando

AU - Lucas, Alberto Fernández

AU - Huepe, David

AU - Di Caterina, Gaetano

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AU - Birba, Agustina

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AU - Coronel-Oliveros, Carlos

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AU - Abasolo, Daniel

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AU - Lopez-Valdes, Alejandro

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N2 - 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 (R² = 0.37, F² = 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.

AB - 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 (R² = 0.37, F² = 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.

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JO - Nature Medicine

JF - Nature Medicine

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Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Abstract

Bibliographical note

Data Availability Statement

Funding

Keywords

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