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The genetic architecture of biological age in nine human organ systems.
Wen, Junhao; Tian, Ye Ella; Skampardoni, Ioanna; Yang, Zhijian; Cui, Yuhan; Anagnostakis, Filippos; Mamourian, Elizabeth; Zhao, Bingxin; Toga, Arthur W; Zalesky, Andrew; Davatzikos, Christos.
Afiliación
  • Wen J; Laboratory of AI and Biomedical Science (LABS), University of Southern California, Los Angeles, CA, USA. junhaowe@usc.edu.
  • Tian YE; Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
  • Skampardoni I; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Yang Z; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Cui Y; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Anagnostakis F; Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
  • Mamourian E; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Zhao B; Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Toga AW; Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
  • Zalesky A; Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
  • Davatzikos C; Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nat Aging ; 4(9): 1290-1307, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38942983
ABSTRACT
Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Envejecimiento Límite: Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Nat Aging Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Envejecimiento Límite: Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Nat Aging Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos