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Predicting accelerated and delayed aging in global settings: biobehavioral age gaps and the role of global exposomes

  • Hernan Hernandez
  • , Hernando Santamaria-Garcia
  • , Sebastian Moguilner
  • , Agustina Legaz
  • , Pavel Prado
  • , Jhosmary Cuadros
  • , Liset Gonzalez
  • , Raul Gonzalez-Gomez
  • , Joaquín Migeot
  • , Carlos Coronel-Oliveros
  • , Enzo Tagliazucchi
  • , Marcelo Adrian Maito
  • , Maria Eugenia Godoy
  • , Josephine Cruzat
  • , Ahmed Shaheen
  • , Temitope Hannah Farombi
  • , Daniel Salazar
  • , Lucas Uglione Da Ros
  • , Wyllians Vendramini Borelli
  • , Eduardo R. Zimmer
  • Alfred Kongnyu Njamnshi, Swati Bajpai, A. B. Dey, Cyprian M. Mostert, Zul Merali, Mohamed Salama, Sara Ayman Moustafa, Francesca R. Farina, Sol Fittipaldi, Florencia Altschuler, Vicente Medel, David Huepe, Kristine Yaffe, Chinedu T. Udeh-Momoh, Harris A. Eyre, Pawel Swieboda, Brian Lawlor, Jaime Miranda, Claudia Duran-Aniotz, Sandra Baez, Agustin Ibanez
  • Universidad Adolfo Ibáñez
  • Harvard University
  • Universidad de San Andrés
  • Universidad San Sebastián
  • Universidad de Concepción
  • Facultad de Ciencias, Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso
  • Alexandria University
  • University College Hospital, Ibadan
  • The London School of Economics and Political Science
  • Universidade Federal do Rio Grande do Sul
  • Pontifícia Universidade Católica do Rio Grande do Sul
  • Brain Research Africa Initiative
  • All India Institute of Medical Sciences, New Delhi
  • Aga Khan University
  • American University in Cairo
  • Trinity College Dublin
  • University of California at San Francisco
  • Rice University
  • International Center for Future Generations
  • University of Sydney

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Global health challenges like aging and dementia are shaped by socioeconomic disparities, environmental factors, and social determinants of health. We developed a behavioral age gap (BAG), measuring the difference between expected behavioral age and chronological age METHOD: We utilized a cross-sectional sample (n = 161,981) comprising countries from Latin America, Europe, Asia, and Africa. Behavioral age was estimated using a Gradient Boosting Regressor with 10-fold cross-validation, incorporating multiple risk factors (hypertension, diabetes, heart disease, female sex, visual impairment and hearing impairment) and protective factors (cognition, functional ability, education) associated with healthy aging. BAG was calculated as the difference between predicted and chronological age, and adjusted gaps were derived from the residuals of regressing BAG on chronological age. RESULT: Chronological age was accurately estimated using biobehavioral predictors. Key protective predictors were functional ability, education, and cognition, while main risks were hearing impairment, heart disease, and hypertension. Participants were categorized into delayed or accelerated aging groups to explore biobehavioral factors in aging. Both models demonstrated high predictive accuracy, especially for accelerated aging. BAGs varied significantly across regions and income levels, increasing from Europe to Asia, LA, and Africa. Participants from LIC displayed accelerated aging compared to HIC. Adverse exposomes were linked to accelerated aging with large effect sizes. CONCLUSION: This work positions BAGs as markers of aging disparities, emphasizing the influence of inequalities and exposomes, while providing measures for targeted interventions and research.

Original languageEnglish
Pages (from-to)e099534
JournalAlzheimer's and Dementia
Volume21
Issue numberS6
DOIs
StatePublished - 01 Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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