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Public Health

  • 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 Ibañez
  • Pontificia Universidad Javeriana
  • Harvard Medical School
  • Universidad de San Andrés
  • Universidad San Sebastian
  • Universidad de Concepción
  • Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)
  • Faculty of Medicine
  • University College Hospital, Ibadan
  • London School of Economics and Political Science
  • Universidade Federal do Rio Grande do Sul
  • Museu de Ciência e Tecnología - PUCRS
  • Brain Research Africa Initiative
  • All India Institute of Medical Sciences, New Delhi
  • Aga Khan University
  • Institute of Global Health and Human Ecology at the American University in Cairo
  • American University in Cairo
  • Trinity College Dublin
  • University of California San Francisco Center for Tuberculosis
  • Rice University
  • International Center for Future Generations
  • University of Sydney

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)e099534
PublicaciónAlzheimer's & dementia : the journal of the Alzheimer's Association
Volumen21
DOI
EstadoPublicada - 1 dic. 2025
Publicado de forma externa

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