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The exposome of healthy and accelerated aging across 40 countries

Nature Medicine volume 31, pages 3089–3100 (2025)Cite this article

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Abstract

Protective and risk factors can drive healthy or accelerated aging, with distinct environments modulating their effects. The impact of the exposome—the combined physical and social exposures experienced throughout life—on accelerated aging remains unknown. We assessed delayed and accelerated aging in 161,981 participants from 40 countries (45.09% female; mean age, 67.06; s.d., 9.85) by measuring biobehavioral age gaps (BBAGs), defined as the difference between estimated age from protective and risk factors and chronological age, in cross-sectional and longitudinal designs. BBAGs predicted chronological age, followed by regional and exposomal factor analyses, linked to accelerated aging. Europe led in healthy aging, while Egypt and South Africa showed the greatest acceleration; Asia and Latin America fell in between (Cliff’s delta (δd) = 0.15–0.52; all P < 0.0001). Accelerated aging was more evident in eastern and southern Europe; globally, it was also associated with lower income (δd = 0.48–0.56, P < 1 × 10−15). Exposomal factors of accelerated aging include physical (air quality), social (socioeconomic and gender inequality, migration) and sociopolitical (representation, party freedom, suffrage, elections and democracy) determinants (all Cohen’s d (d) > 0.37, P < 0.0001). BBAGs predicted future functional (r (Pearson correlation) = −0.33, P < 1 × 10−15, d = 0.70) and cognitive declines (r = −0.22, P < 1 × 10−15, d = 0.44), and larger BBAGs (P < 0.0001, d = 1.55). Healthy and accelerated aging are influenced by physical, social and sociopolitical exposomes, with considerable disparities across nations.

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Fig. 1: Study design and analysis pipeline.
Fig. 2: Cross-sectional results on BBAGs and multiple associations.
Fig. 3: Longitudinal results on healthy aging outcomes.
Fig. 4: Validation via epidemiological metrics and meta-analysis.

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Data availability

Preprocessed data are freely available via GitHub (https://github.com/euroladbrainlat/Biobehavioral-age-gaps). Additional details can be found in Supplementary Table 11. The datasets come from sources that are either publicly available for direct download after registration or accessible upon request. For Chile and Uruguay, SABE data are available at https://pubmed.ncbi.nlm.nih.gov/16053641/; for Colombia, at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774577/; for Ecuador, at https://www.ecuadorencifras.gob.ec/encuesta-de-salud-bienestar-del-adulto-mayor/; for Brazil, ELSI-Brazil: https://elsi.cpqrr.fiocruz.br; for Costa Rica, CRELES: http://www.creles.berkeley.edu/index.html; for Mexico, ENASEM: https://enasem.org/Home/index_esp.aspx; for China, CLHLS: https://charls.pku.edu.cn/en/; for South Korea, KLoSA: https://survey.keis.or.kr/eng/klosa/klosa01.jsp; for Europe, SHARE: https://www.share-eric.eu; and for Egypt (AL-SEHA), requests should be sent to M. Salama (mohamed-salama@aucegypt.edu). Data may be shared upon approval of a brief research proposal and signing of a data use agreement. The estimated response time is 3–4 weeks. For India (LASI), requests can be directed to S. Bajpai (swati.bajpai@gbhi.org). Access is granted based on a research purpose review and data sharing agreement. Timelines for access may vary depending on the review process, typically within 3–4 weeks. For South Africa (GHS), data requests should be addressed to C. Mostert (cyprian.mostert@aku.edu). Data may be shared upon approval of a brief research proposal and the signing of a data use agreement. The estimated response time is typically 3–4 weeks. Country-level indicators of GNI, GDP, air quality, socioeconomic inequality (Gini index) and migration were obtained from the World Bank (https://databank.worldbank.org/). GII data are available from the WHO (https://www.who.int/data/nutrition/nlis/info/gender-inequality-index-(gii)). Sociopolitical indicators were sourced from the Global State of Democracy Indices (https://www.idea.int/democracytracker/dataset-resources).

Code availability

Analysis codes are freely available via GitHub (https://github.com/euroladbrainlat/Biobehavioral-age-gaps).

References

  1. Fried, L. P. & Rowe, J. W. Health in aging—past, present, and future. N. Engl. J. Med. 383, 1293–1296 (2020).

    PubMed  Google Scholar 

  2. Moguilner, S. et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat. Med. 30, 3646–3657 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023).

    CAS  PubMed  Google Scholar 

  4. Nowogrodzki, J. What accelerates brain ageing? This AI ‘brain clock’ points to answers. Nature 633, 20 (2024).

    CAS  PubMed  Google Scholar 

  5. Allada, R. & Bass, J. Circadian mechanisms in medicine. N. Engl. J. Med. 384, 550–561 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Rutledge, J., Oh, H. & Wyss-Coray, T. Measuring biological age using omics data. Nat. Rev. Genet. 23, 715–727 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Nativio, R. et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease. Nat. Genet. 52, 1024–1035 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Thuault, S. Reflections on aging research from within the National Institute on Aging. Nat. Aging 1, 14–18 (2021).

    PubMed  Google Scholar 

  9. Tran Van Hoi, E. et al. Biomarkers of the ageing immune system and their association with frailty—a systematic review. Exp. Gerontol. 176, 112163 (2023).

    CAS  PubMed  Google Scholar 

  10. Mallapaty, S. What is ageing? Even the field’s researchers can’t agree. Nature 636, 283–284 (2024).

    CAS  PubMed  Google Scholar 

  11. Keshavarz, M., Xie, K., Schaaf, K., Bano, D. & Ehninger, D. Targeting the “hallmarks of aging” to slow aging and treat age-related disease: fact or fiction? Mol. Psychiatry 28, 242–255 (2023).

    PubMed  Google Scholar 

  12. Zampino, M. et al. Biomarkers of aging in real life: three questions on aging and the comprehensive geriatric assessment. Geroscience 44, 2611–2622 (2022).

    PubMed  PubMed Central  Google Scholar 

  13. Livingston, G. et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet 404, 572–628 (2024).

    PubMed  Google Scholar 

  14. Paradela, R. S. et al. Population attributable fractions for risk factors for dementia in seven Latin American countries: an analysis using cross-sectional survey data. Lancet Glob. Health 12, e1600–e1610 (2024).

    CAS  PubMed  Google Scholar 

  15. Mukadam, N., Sommerlad, A., Huntley, J. & Livingston, G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. Lancet Glob. Health 7, e596–e603 (2019).

    PubMed  PubMed Central  Google Scholar 

  16. Gonzalez-Gomez, R. et al. Qualitative and quantitative educational disparities and brain signatures in healthy aging and dementia across global settings. eClinicalMedicine 82, 103187 (2025).

    PubMed  PubMed Central  Google Scholar 

  17. Santamaria-Garcia, H. et al. Factors associated with healthy aging in Latin American populations. Nat. Med. 29, 2248–2258 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Ibanez, A. et al. Healthy aging meta-analyses and scoping review of risk factors across Latin America reveal large heterogeneity and weak predictive models. Nat. Aging 4, 1153–1165 (2024).

    PubMed  PubMed Central  Google Scholar 

  19. Gonzalez-Gomez, R. et al. Educational disparities in brain health and dementia across Latin America and the United States. Alzheimers Dement. 20, 5912–5925 (2024).

    PubMed  PubMed Central  Google Scholar 

  20. Legaz, A. et al. Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas. Nat. Aging 5, 259–274 (2025).

    PubMed  Google Scholar 

  21. Miranda, J. J. et al. Understanding the rise of cardiometabolic diseases in low- and middle-income countries. Nat. Med. 25, 1667–1679 (2019).

    CAS  PubMed  Google Scholar 

  22. Ibanez, A. et al. Neuroecological links of the exposome and One Health. Neuron 112, 1905–1910 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ibanez, A. & Eyre, H. Brain capital, ecological development and sustainable environments. BMJ Ment. Health 26, e300803 (2023).

    PubMed  PubMed Central  Google Scholar 

  24. Abolhasani, E. et al. Air pollution and incidence of dementia: a systematic review and meta-analysis. Neurology 100, e242–e254 (2023).

    PubMed  Google Scholar 

  25. Fuller, R. et al. Pollution and health: a progress update. Lancet Planet. Health 6, e535–e547 (2022).

    PubMed  PubMed Central  Google Scholar 

  26. Sheridan, M. A. Measuring the impact of structural inequality on the structure of the brain. Proc. Natl Acad. Sci. USA 120, e2306076120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Zugman, A. et al. Country-level gender inequality is associated with structural differences in the brains of women and men. Proc. Natl Acad. Sci. USA 120, e2218782120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Lorenz-Spreen, P., Oswald, L., Lewandowsky, S. & Hertwig, R. A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat. Hum. Behav. 7, 74–101 (2023).

    PubMed  Google Scholar 

  29. Van Bavel, J. J., Gadarian, S. K., Knowles, E. & Ruggeri, K. Political polarization and health. Nat. Med. 30, 3085–3093 (2024).

    PubMed  Google Scholar 

  30. Riley, M. W., Matarazzo, J. D. & Baum, A. The Aging Dimension: Perspectives in Behavioral Medicine (Routledge, 2024).

  31. Kabacik, S. et al. The relationship between epigenetic age and the hallmarks of aging in human cells. Nat. Aging 2, 484–493 (2022).

    PubMed  PubMed Central  Google Scholar 

  32. Farina, F. R. et al. Next generation brain health: transforming global research and public health to promote prevention of dementia and reduce its risk in young adult populations. Lancet Healthy Longev. 5, 100665 (2024).

    PubMed  Google Scholar 

  33. Abubakar, I. et al. The UCL-Lancet Commission on Migration and Health: the health of a world on the move. Lancet 392, 2606–2654 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Baez, S. et al. Structural inequality and temporal brain dynamics across diverse samples. Clin. Transl. Med. 14, e70032 (2024).

    PubMed  PubMed Central  Google Scholar 

  35. Wu, Y. T. et al. Education and wealth inequalities in healthy ageing in eight harmonised cohorts in the ATHLOS consortium: a population-based study. Lancet Public Health 5, e386–e394 (2020).

    PubMed  PubMed Central  Google Scholar 

  36. Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446 (2020).

    PubMed  PubMed Central  Google Scholar 

  37. Livingston, G. et al. Dementia prevention, intervention, and care. Lancet 390, 2673–2734 (2017).

    PubMed  Google Scholar 

  38. Ibanez, A., Kringelbach, M. & Deco, G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn. Sci. 28, 319–338 (2023).

    Google Scholar 

  39. Ibanez, A. & Zimmer, E. Time to synergize mental health with brain health. Nat. Ment. Health 1, 441–443 (2023).

    PubMed  PubMed Central  Google Scholar 

  40. Jeste, D. V., Depp, C. A. & Vahia, I. V. Successful cognitive and emotional aging. World Psychiatry 9, 78–84 (2010).

    PubMed  PubMed Central  Google Scholar 

  41. Moffitt, T. E. Behavioral and social research to accelerate the geroscience translation agenda. Ageing Res. Rev. 63, 101146 (2020).

    PubMed  PubMed Central  Google Scholar 

  42. Ibanez, A. The mind’s golden cage and cognition in the wild. Trends Cogn. Sci. 26, 1031–1034 (2022).

    PubMed  PubMed Central  Google Scholar 

  43. Local Burden of Disease Educational Attainment CollaboratorsMapping disparities in education across low- and middle-income countries. Nature 577, 235–238 (2020).

    Google Scholar 

  44. Ibanez, A., Legaz, A. & Ruiz-Adame, M. Addressing the gaps between socioeconomic disparities and biological models of dementia. Brain 146, 3561–3564 (2023).

    PubMed  PubMed Central  Google Scholar 

  45. Baez, S., Alladi, S. & Ibanez, A. Global South research is critical for understanding brain health, ageing and dementia. Clin. Transl. Med. 13, e1486 (2023).

    PubMed  PubMed Central  Google Scholar 

  46. Stevens, G. et al. Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries. Eur. J. Public Health 23, 146–152 (2013).

    PubMed  Google Scholar 

  47. Legaz, A., Baez, S. & Ibanez, A. Unequal burdens: how structural socioeconomic inequality shapes brain health in aging and dementia. Neuroscience 569, 245–247 (2025).

    CAS  PubMed  Google Scholar 

  48. Artero, S. et al. Risk profiles for mild cognitive impairment and progression to dementia are gender specific. J. Neurol. Neurosurg. Psychiatry 79, 979–984 (2008).

    CAS  PubMed  Google Scholar 

  49. Martin, J. et al. Investigating sex differences in risk and protective factors in the progression of mild cognitive impairment to dementia: a systematic review. J. Alzheimers Dis. 97, 101–119 (2024).

    PubMed  Google Scholar 

  50. Hagg, S. & Jylhava, J. Sex differences in biological aging with a focus on human studies. eLife 10, e63425 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Huang, B. et al. Gender differences in the association between hearing loss and cognitive function. Am. J. Alzheimers Dis. Other Demen. 35, 1533317519871167 (2020).

    PubMed  Google Scholar 

  52. Baez, S., Castro-Aldrete, L., Britton, G. B., Ibañez, A. & Santuccione-Chadha, A. Enhancing brain health in the Global South through a sex and gender lens. Nat. Ment. Health 2, 1308–1317 (2024).

    PubMed  PubMed Central  Google Scholar 

  53. Caldwell, J. Z. K. & Isenberg, N. The aging brain: risk factors and interventions for long term brain health in women. Curr. Opin. Obstet. Gynecol. 35, 169–175 (2023).

    PubMed  PubMed Central  Google Scholar 

  54. Klomp, J. & de Haan, J. Is the political system really related to health? Soc. Sci. Med. 69, 36–46 (2009).

    PubMed  Google Scholar 

  55. Bollyky, T. J. et al. The relationships between democratic experience, adult health, and cause-specific mortality in 170 countries between 1980 and 2016: an observational analysis. Lancet 393, 1628–1640 (2019).

    PubMed  PubMed Central  Google Scholar 

  56. Fraser, T., Aldrich, D. P., Panagopoulos, C., Hummel, D. & Kim, D. The harmful effects of partisan polarization on health. PNAS Nexus 1, pgac011 (2022).

    PubMed  PubMed Central  Google Scholar 

  57. Santamaría-García, H. et al. Allostatic interoceÿptive overload across psychiatric and neurological conditions. Biol. Psychiatry 97, 8–40 (2024).

    Google Scholar 

  58. Migeot, J. A., Duran-Aniotz, C. A., Signorelli, C. M., Piguet, O. & Ibanez, A. A predictive coding framework of allostatic–interoceptive overload in frontotemporal dementia. Trends Neurosci. 45, 838–853 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ibanez, A. & Slachevsky, A. Environmental–genetic interactions in ageing and dementia across Latin America. Nat. Rev. Neurol. 20, 571–572 (2024).

    PubMed  PubMed Central  Google Scholar 

  60. Allison, P. Multiple imputation for missing data: a cautionary tale. Sociol. Methods Res. 28, 301–309 (1999).

    Google Scholar 

  61. Friedman, J. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1139–1232 (2000).

    Google Scholar 

  62. Muller, A. C. & Guido, S. Introduction to Machine Learning with Python: A Guide for Data Scientists (O’Reilly Media, 2018).

  63. Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D. & Mermelstein, R. J. A practical guide to calculating Cohen’s f2, a measure of local effect size, from PROC MIXED. Front. Psychol. 3, 111 (2012).

    PubMed  PubMed Central  Google Scholar 

  64. Breiman, L. Classification and Regression Trees (Routledge, 2017).

  65. Feurer, M. & Hutter, F. in Automated Machine Learning: Methods, Systems, Challenges (eds Hutter, F. et al.) 3–33 (Springer, 2019).

  66. Bzdok, D., Altman, N. & Krzywinski, M. Statistics versus machine learning. Nat. Methods 15, 233–234 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).

    PubMed  Google Scholar 

  68. Chan, J. Y.-L. et al. Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics 10, 1283 (2022).

    Google Scholar 

  69. Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E. & Miller, K. L. Estimation of brain age delta from brain imaging. Neuroimage 200, 528–539 (2019).

    PubMed  Google Scholar 

  70. World Bank. Economy and Growth Indicators (World Bank, accessed September 2024); https://data.worldbank.org/indicator

  71. Meissel, K. & Yao, E. S. Using Cliff’s delta as a non-parametric effect size measure: an accessible web app and R tutorial. Pract. Assess. Res. Eval. 29, 2 (2024).

    Google Scholar 

  72. Funder, D. C. & Ozer, D. J. Evaluating effect size in psychological research: sense and nonsense. Adv. Methods Pract. Psychol. Sci. 2, 156–168 (2019).

    Google Scholar 

  73. Steptoe, A., Deaton, A. & Stone, A. A. Subjective wellbeing, health, and ageing. Lancet 385, 640–648 (2015).

    PubMed  Google Scholar 

  74. Andrade, C. Understanding relative risk, odds ratio, and related terms: as simple as it can get. J. Clin. Psychiatry 76, e857–e861 (2015).

    PubMed  Google Scholar 

  75. Paul, P., Pennell, M. L. & Lemeshow, S. Standardizing the power of the Hosmer–Lemeshow goodness of fit test in large data sets. Stat. Med. 32, 67–80 (2013).

    PubMed  Google Scholar 

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Acknowledgements

H.H. is supported by Davos Alzheimer’s collaborative. A.I. is supported by grants from the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat, supported by Fogarty International Center (FIC), National Institutes of Health, National Institutes of Aging (R01 AG057234, R01 AG075775, R01 AG21051, R01 AG083799, CARDS-NIH), Alzheimer’s Association (SG-20-725707), Rainwater Charitable Foundation—The Bluefield Project to Cure FTD, and Global Brain Health Institute), ANID/FONDECYT Regular (1210195 and 1210176 and 1220995), ANID/PIA/ANILLOS ACT210096, FONDEF ID20I10152 and ANID/FONDAP 15150012. F.R.F. is supported by Alzheimer’s Association Fellowship (AARF-21-848281). C.D.-A. is supported by ANID/FONDECYT Regular 1210622, ANID/PIA/ANILLOS ACT210096, Alzheimer’s Association (AARGD-24- 1310017), ANID/FOVI240065 and ANID/Proyecto Exploración 13240170. J. Cruzat is supported by ANID (FONDECYT Postdoctorado number 3240042). H.S.-G. is supported by NIH R01 (Social Epigenetics of Alzheimer’s Disease and Related Dementias in Latin American Countries, number: 1R01AG082056-01A1), Global Brain Health Institute and Alzheimer Association (‘Brain Health in Individuals with Exposition to High Violence in Colombia’, number: GBHI ALZ UK-23-971135). In addition, research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TW012455. J.J.M. acknowledges having received support from the Alliance for Health Policy and Systems Research (2009/32034, 2012/253750), Bloomberg Philanthropies (grant 46129, via University of North Carolina at Chapel Hill School of Public Health), FONDECYT via CIENCIACTIVA/CONCYTEC, British Council, British Embassy and the Newton–Paulet Fund (223-2018, 224-2018), DFID/MRC/Wellcome Global Health Trials (MR/M007405/1), Fogarty International Center (R21TW009982, D71TW010877, R21TW011740, K01TW011478), Grand Challenges Canada (GMH-POC-0335-04), International Development Research Center Canada (IDRC 106887, 108167), Inter-American Institute for Global Change Research (IAI CRN3036), National Cancer Institute (NCI 1P20CA217231), National Council for Scientific and Technological Development (CNPq Brasil 408523/2023-9), National Health and Medical Research Council (NHMRC 2022566), National Heart, Lung and Blood Institute (NHLBI HHSN268200900033C, 5U01HL114180, 1UM1HL134590), National Institute for Health and Care Research (NIHR 150261, NIHR 150287), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK K23DK135798), National Institute of Mental Health (NIMH 1U19MH098780), NSW Health, Cardiovascular Elite Postdoctoral Researcher Grants (H23/37663), Swiss National Science Foundation (40P740-160366), UKRI BBSRC (BB/T009004/1), UKRI EPSRC (EP/V043102/1), UKRI MRC (MR/P008984/1, MR/P024408/1, MR/P02386X/1, MR/X004163/1, MR/X020851/1), Wellcome (074833/Z/04/Z, 093541/Z/10/Z, 103994/Z/14/Z, 107435/Z/15/Z, 205177/Z/16/Z, 214185/Z/18/Z, 218743/Z/19/Z), World Diabetes Foundation (WDF15-1224) and the World Health Organization (2021/1189041, 2022/1249357). J.M. is supported by postdoctoral fellowships granted by the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) and the Atlantic Fellows for Equity in Brain Health program. Although not directly funding this work, CU receives research funding support from The UKRI Medical Research Council (MR/Y019822/1), Alzheimer’s Association (SAGA23-1141999), The Wellcome Leap Dynamic resilience program (co-funded by Temasek Trust), National Institute of Health (NIH) (RO1-AG074562), Office for Veterans’ Affairs UK Defense and Security Accelerator (DASA) Fund (G2-SCH-2022-11-12245), Global Brain Health Institute (UFRA-424|CA-0241758), Davos Alzheimer’s Collaborative Global Cohorts Fund and RoseTrees Foundation (Seedcorn2021\100220). S.B. is supported by Global Brain Health Institute, Alzheimer’s Association, Alzheimer’s Society UK, Pilot Awards for Global Brain Health Leaders (Grant Number: GBHI ALZ UK- 25-1289623). The contents of this publication are solely the responsibility of the authors and do not represent the official views of these institutions. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Hernan Hernandez, Hernando Santamaria-Garcia.

Authors and Affiliations

  1. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile

    Hernan Hernandez, Sebastian Moguilner, Agustina Legaz, Jhosmary Cuadros, Raul Gonzalez-Gomez, Joaquín Migeot, Carlos Coronel-Oliveros, Enzo Tagliazucchi, Marcelo Adrián Maito, María E. Godoy, Josephine Cruzat, Sol Fittipaldi, Vicente Medel, Claudia Duran-Aniotz & Agustin Ibanez

  2. PhD Program in Neuroscience, Pontificia Universidad Javeriana, Bogotá, Colombia

    Hernando Santamaria-Garcia

  3. Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia

    Hernando Santamaria-Garcia

  4. Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina

    Sebastian Moguilner, Agustina Legaz, Marcelo Adrián Maito, María E. Godoy, Florencia Altschuler & Agustin Ibanez

  5. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Sebastian Moguilner

  6. Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland

    Francesca R. Farina, Joaquín Migeot, Carlos Coronel-Oliveros, Swati Bajpai, Mohamed Salama, Sara A. Moustafa, Sol Fittipaldi, Brian Lawlor, Sandra Baez & Agustin Ibanez

  7. Pritzker School of Medicine, The University of Chicago, Chicago, IL, USA

    Francesca R. Farina

  8. Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago de Chile, Chile

    Pavel Prado

  9. Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal, Venezuela

    Jhosmary Cuadros

  10. Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Jhosmary Cuadros

  11. Facultad de Ingeniería, Universidad de Concepción, Concepción, Chile

    Liset Gonzalez

  12. Trinity College Dublin, The University of Dublin, Dublin, Ireland

    Carlos Coronel-Oliveros & Agustin Ibanez

  13. University of Buenos Aires, Buenos Aires, Argentina

    Enzo Tagliazucchi

  14. Alexandria Faculty of Medicine, Alexandria, Egypt

    Ahmed Shaheen

  15. Department of Medicine, University College Hospital, Ibadan, Nigeria

    Temitope Farombi

  16. London School of Economics and Political Science, London, UK

    Daniel Salazar

  17. Graduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Lucas Uglione Da Ros & Wyllians V. Borelli

  18. Department of Morphological Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Wyllians V. Borelli

  19. Graduate Program in Biological Sciences: Biochemistry (PPGBioq), UFRGS, Porto Alegre, Brazil

    Wyllians V. Borelli & Eduardo R. Zimmer

  20. Department of Pharmacology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Eduardo R. Zimmer

  21. Brain Research Africa Initiative (BRAIN), Yaounde, Cameroon

    Alfred K. Njamnshi

  22. Neuroscience Lab, Faculty of Medicine and Biomedical Sciences, The University of Yaounde I, Yoaunde, Cameroon

    Alfred K. Njamnshi

  23. Neurology Department, Central Hospital Yaounde, Yaounde, Cameroon

    Alfred K. Njamnshi

  24. All India Institute of Medical Sciences, New Delhi, India

    Swati Bajpai & A. B. Dey

  25. Brain and Mind Institute, Aga Khan University, Nairobi, Kenya

    Cyprian Mostert, Zul Merali & Chinedu Momoh

  26. Department of Population Health, Aga Khan University, Nairobi, Kenya

    Cyprian Mostert

  27. University of Ottawa, Ottawa, Ontario, Canada

    Zul Merali

  28. Department of Neuroscience, Carleton University, Ottawa, Ontario, Canada

    Zul Merali

  29. Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt

    Mohamed Salama & Sara A. Moustafa

  30. Faculty of Medicine, Mansoura University, Mansoura, Egypt

    Mohamed Salama & Sara A. Moustafa

  31. School of Psychology, Trinity College Dublin, Dublin, Ireland

    Sol Fittipaldi & Brian Lawlor

  32. Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile

    David Huepe

  33. Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Kristine Yaffe

  34. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Kristine Yaffe

  35. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Kristine Yaffe

  36. Neuro-Policy Program, Center for Health and Biosciences, Baker Institute for Public Policy, Rice University, Houston, TX, USA

    Harris A. Eyre

  37. Euro-Mediterranean Economists Association, Barcelona, Spain

    Harris A. Eyre

  38. Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA

    Harris A. Eyre

  39. International Center for Future Generations (ICFG), Brussels, Belgium

    Pawel Swieboda

  40. Brain Capital Alliance, San Francisco, CA, USA

    Pawel Swieboda

  41. Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia

    J. Jaime Miranda

  42. CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru

    J. Jaime Miranda

  43. Universidad de los Andes, Bogota, Colombia

    Sandra Baez

Authors
  1. Hernan Hernandez
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  2. Hernando Santamaria-Garcia
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  3. Sebastian Moguilner
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  8. Liset Gonzalez
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  9. Raul Gonzalez-Gomez
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  10. Joaquín Migeot
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  11. Carlos Coronel-Oliveros
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  12. Enzo Tagliazucchi
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  13. Marcelo Adrián Maito
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  14. María E. Godoy
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  15. Josephine Cruzat
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  16. Ahmed Shaheen
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  17. Temitope Farombi
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  18. Daniel Salazar
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  19. Lucas Uglione Da Ros
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  20. Wyllians V. Borelli
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  21. Eduardo R. Zimmer
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  22. Alfred K. Njamnshi
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  23. Swati Bajpai
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  24. A. B. Dey
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  25. Cyprian Mostert
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  26. Zul Merali
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  27. Mohamed Salama
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  28. Sara A. Moustafa
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  29. Sol Fittipaldi
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  30. Florencia Altschuler
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  31. Vicente Medel
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  32. David Huepe
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  33. Kristine Yaffe
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  34. Chinedu Momoh
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  35. Harris A. Eyre
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  36. Pawel Swieboda
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  37. Brian Lawlor
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  38. J. Jaime Miranda
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  39. Claudia Duran-Aniotz
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  40. Sandra Baez
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  41. Agustin Ibanez
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Contributions

A.I., H.H., H.S.-G. and S.B. were responsible for conceptualization and formal analysis, and wrote the original draft. All authors contributed to the review and editing. H.H., H.S.-G., L.U.D.R., W.V.B., E.R.Z., S.B., C. Mostert, M.S. and S.M. had access to the raw data. All authors had access to all data in the study, and the corresponding authors had final responsibility for the decision to submit for publication.

Corresponding authors

Correspondence to Sandra Baez or Agustin Ibanez.

Ethics declarations

Competing interests

E.R.Z. has served on scientific advisory boards for Nintx, Novo Nordisk and Masima. He is also a cofounder and a minority shareholder at Masima. The other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Yao Yao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ming Yang, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Age distribution by countries.

The figure illustrates the age distribution across countries for three different datasets. a) Age distribution for the cross-sectional dataset, which includes a total of 161,981 participants. b) Age distribution for the cross-sectional subsample consisting of 102,725 participants, with additional predictors available for analysis. c) Age distribution for the longitudinal dataset, where the same group of 21,631 participants is observed across two waves (Wave 1 and Wave 2), highlighting the distribution of age by country in each wave.

Extended Data Fig. 2 Supplementary analysis on the cross-sectional subsample.

(a) Feature importance, assessed via mean decrease in impurity (MDI), enabled the prediction of chronological age using biobehavioral factors. The sample size for the analyses reported in this figure included 102,725 individuals. Goodness-of-fit and feature importance metrics are provided. (b) MDI facilitated the characterization of groups with more delayed (left panel) and more accelerated (right panel) aging. Goodness-of-fit and feature importance metrics are provided. (c) Average BBAGs distribution by continent. The color bar indicates younger (blue) and older (red) BBAGs. (d) BBAGs comparisons by continent (left panel) and by European regions (right panel). (e) BBAGs comparisons between low- and high-income countries, based on gross national income (GNI) and gross domestic product (GDP) indicators. (f) Linear regression models were used to assess the relationship between BBAGs and all exposomes, as well as combined social, physical, and sociopolitical exposomes. (g) Linear regression models were also used to assess the associations between BBAGs and individual social (gender equality, migration, and structural equality) and physical exposomes (air quality). (h) Linear regression models were used to examine the association between BBAGs and individual sociopolitical exposomes (democracy indicators). All P-values reported in Panels f, g, and h were < 1e-15. The maps were created in Python using the Plotly library (https://plotly.com/python/maps/). All other illustrations and icons were designed using GIMP (https://www.gimp.org).

Extended Data Fig. 3 Sensitivity analyses of exposome effects.

The 3D plots display the density of exposomes and BBAGs. The sample size for the analyses reported in this figure included 161,981 individuals. (a) Linear regression models tracked the relationship between BBAGs and combined social exposomes, controlling for physical and sociopolitical exposomes. (b) Linear regression models assessed the role of individual social exposomes (gender equality, migration, and structural equality) and physical exposomes (air quality) on BBAGs, controlling for sociopolitical exposomes. (c) Linear regression models examined the association between BBAGs and individual sociopolitical exposomes (democracy indicators), controlling for social and physical exposomes. We used a one-sided F-test to evaluate the statistical significance of the linear regression model, testing whether the model explains a substantially greater proportion of variance than expected by chance. (d) Linear fit between BBAGs and all exposomes, excluding zones with sparse exposome density, and combined social, physical, and sociopolitical exposomes. (e) Linear regression models assessing the relationship between BAGs and individual social (gender equality, migration, and structural equality) and physical exposomes (air quality), excluding zones with sparse exposome density. (f) Linear regression models examining the association between BBAGs and individual sociopolitical exposomes (democracy indicators), excluding zones with sparse exposome density. All P-values reported in Panels (a) to (f) were < 1e-15.

Extended Data Fig. 4 Longitudinal analysis excluding the healthy aging factor assessed for association.

The sample size for the analyses reported in this figure included 21,631 individuals. (a) Linear regression models tracked the relationships between recalculated BBAGs in wave 1 (excluding the specific healthy aging factor used as the outcome) and healthy aging factors (cognition, functional ability, and well-being) in wave 2. Recalculated BBAGs in wave 1 significantly predicted poorer cognition, functional ability, and well-being in wave 2. All P-values reported were < 1e-15.

Extended Data Fig. 5 Longitudinal analysis on the South African dataset.

The sample size for the analyses reported in this figure included 5,431 individuals. (a) Linear regression models were used to assess the relationships between BBAGs in wave 1 and healthy aging factors (walking and memory) in wave 2. BBAGs in wave 1 significantly predicted poorer cognition, functional ability, and well-being in wave 2. (b) Linear regression models tracked the relationships between BBAGs in wave 1 as significant predictors of BBAGs in wave 2. All P-values reported were < 1e-15.

Extended Data Fig. 6 Validation using epidemiological metrics and meta-analysis on the cross-sectional subsample and the South African dataset.

(a) Odds ratios and attributable risk for the cross-sectional subsample. Results showed that BBAGs are significantly associated with poorer functional ability and cognition. Analyses reported in this panel included 101,870 individuals for cognition, 99,588 for functional ability, and 101,618 individuals for well-being. (b) Relative risk and Attributable risk for the South African dataset. BBAGs in wave 1 were significantly associated with declines in walking and memory, despite the effects on walking ability showing an uncertain confidence interval. Analyses reported in this panel included 3,868 individuals. (c) Meta-analysis on cross-sectional subsample for cognition, functional ability, and wellbeing using common-effects and random-effects models. Larger BBAGs are linked to poorer outcomes in all domains, with high heterogeneity across countries. All P-values reported in this panel were < 0.01. We used Cochran’s Q test to assess heterogeneity across studies, reporting the associated p-value, degrees of freedom, and I2 statistic. (d) Summary of cross-sectional results, showing the average of common- and random-effects models by income level (low or high) based on gross national income (GNI) and gross domestic product (GDP) classifications. Color bar indicates effect sizes (small: blue and large: red). Accelerated aging is more strongly linked to poorer healthy aging outcomes—cognition, functional ability, and well-being—in low-income countries compared to high-income countries for both classifications (GNI: p = 4.05e-9, r = 0.82 and GDP: p = 8.35e-4, r = 0.47). The maps were created in Python using the Plotly library (https://plotly.com/python/maps/). All other illustrations and icons were designed using GIMP (https://www.gimp.org).

Extended Data Fig. 7 Behavioral age and BBAGs calculation without imputation.

(a) Feature importance analyses enabled the prediction of chronological age using biobehavioral factors. The sample size for the analyses reported in this figure included 148,188 individuals. Goodness-of-fit and feature importance metrics are provided. (b) Mean decrease in impurity (MDI) facilitated the characterization of groups with more delayed (left panel) and more accelerated (right panel) aging. Goodness-of-fit and feature importance metrics are provided.

Extended Data Fig. 8 Behavioral age and BBAGs modulation by sex.

The sample size for the analyses reported in this figure was 148,188 individuals. Panels (a) and (c): Feature importance analyses enabled the prediction of chronological age using biobehavioral factors in females and males, respectively. Goodness-of-fit and feature importance metrics are provided. Panels (b) and (d): Feature importance analyses characterized groups with more delayed (left panel) and more accelerated (right panel) aging in females and males, respectively. Goodness-of-fit and feature importance metrics are provided. Panels (e), (f), and (g): Linear regression models tracked the relationship between BBAGs and various social exposomes across sex: Panel (e): Combined social exposomes. Panel (f): Social and physical exposomes. Panel (g): Sociopolitical exposomes. All P-values reported were < 1e-15.

Supplementary information

Supplementary Information

Supplementary Notes 1–6, Tables 1–11 and References.

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Hernandez, H., Santamaria-Garcia, H., Moguilner, S. et al. The exposome of healthy and accelerated aging across 40 countries. Nat Med 31, 3089–3100 (2025). https://doi.org/10.1038/s41591-025-03808-2

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