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@ARTICLE{Bartolomucci:271286,
      author       = {Bartolomucci, Alessandro and Kane, Alice E and Gaydosh,
                      Lauren and Razzoli, Maria and McCoy, Brianah M and Ehninger,
                      Dan and Chen, Brian H and Howlett, Susan E and
                      Snyder-Mackler, Noah},
      title        = {{A}nimal {M}odels {R}elevant for {G}eroscience: {C}urrent
                      {T}rends and {F}uture {P}erspectives in {B}iomarkers, and
                      {M}easures of {B}iological {A}ging.},
      journal      = {The journals of gerontology / Series A},
      volume       = {79},
      number       = {9},
      issn         = {1079-5006},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford Univ. Pr.},
      reportid     = {DZNE-2024-01025},
      pages        = {glae135},
      year         = {2024},
      abstract     = {For centuries, aging was considered inevitable and
                      immutable. Geroscience provides the conceptual framework to
                      shift this focus toward a new view that regards aging as an
                      active biological process, and the biological age of an
                      individual as a modifiable entity. Significant steps forward
                      have been made toward the identification of biomarkers for
                      and measures of biological age, yet knowledge gaps in
                      geroscience are still numerous. Animal models of aging are
                      the focus of this perspective, which discusses how
                      experimental design can be optimized to inform and refine
                      the development of translationally relevant measures and
                      biomarkers of biological age. We provide recommendations to
                      the field, including: the design of longitudinal studies in
                      which subjects are deeply phenotyped via repeated multilevel
                      behavioral/social/molecular assays; the need to consider
                      sociobehavioral variables relevant for the species studied;
                      and finally, the importance of assessing age of onset,
                      severity of pathologies, and age-at-death. We highlight
                      approaches to integrate biomarkers and measures of
                      functional impairment using machine learning approaches
                      designed to estimate biological age as well as to predict
                      future health declines and mortality. We expect that
                      advances in animal models of aging will be crucial for the
                      future of translational geroscience but also for the next
                      chapter of medicine.},
      subtyp        = {Review Article},
      keywords     = {Biomarkers / Animals / Aging: physiology / Models, Animal /
                      Geroscience / Humans / Animal model (Other) / Biomarkers
                      (Other) / Frailty (Other) / Senescence (Other) / Biomarkers
                      (NLM Chemicals)},
      cin          = {AG Ehninger},
      ddc          = {570},
      cid          = {I:(DE-2719)1013005},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39126297},
      pmc          = {pmc:PMC11316208},
      doi          = {10.1093/gerona/glae135},
      url          = {https://pub.dzne.de/record/271286},
}