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Peer-reviewedCC BY-NC-NDAnimal models and experimental medicine

Animal models of anemia of inflammation: Methodological advances, limitations, and translational applications

  • Jiayi Cheng1,2,3
  • Xiaodi Zhou1,2,3
  • Qian Liu1,2,3
  • Zixian Liu4,5,6
  • Hao Wu1,2,3
6 affiliations
  1. 1College of Veterinary Medicine Huazhong Agricultural University Wuhan China
  2. 2Hubei Hongshan Laboratory Wuhan China
  3. 3Frontiers Science Center for Animal Breeding and Sustainable Production Huazhong Agricultural University Wuhan China
  4. 4Cancer Center, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China
  5. 5Institute of Radiation Oncology, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China
  6. 6Hubei Key Laboratory of Precision Radiation Oncology Wuhan China
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Abstract

Anemia of inflammation (AI) represents one of the most prevalent secondary anemias in clinical practice, frequently complicating a wide spectrum of chronic inflammatory diseases. Given its highly heterogeneous etiology and complex pathophysiological mechanisms, the establishment of rational and high‐fidelity animal models is vital for elucidating the core mechanisms of AI and optimizing clinical diagnostic and therapeutic strategies. This narrative review provides a structured categorization of current AI animal models into three major paradigms: sterile inflammation models, disease‐specific models, and genetically engineered models. We provide an in‐depth comparative analysis of their underlying modeling principles, methodological protocols, and characteristic phenotypic profiles, while comprehensively evaluating their respective translational advantages and intrinsic limitations. Furthermore, we discuss the prospective applications of these models in deciphering pathomechanisms, validating therapeutic targets, and developing interventional strategies. To enhance reproducibility and translational relevance, we propose a streamlined phenotyping panel and a model selection framework. The precise selection and rational application of these tailored models will contribute to improved reliability of fundamental AI research and accelerate its translational progression from bench to bedside.

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Cite

BibTeXRIS
BibTeX
@article{cheng2026animal,
  title = {{Animal models of anemia of inflammation: Methodological advances, limitations, and translational applications}},
  author = {Jiayi Cheng and Xiaodi Zhou and Qian Liu and Zixian Liu and Hao Wu},
  journal = {Animal models and experimental medicine},
  year = {2026},
  month = oct,
  doi = {10.1002/ame2.70304},
  url = {https://europepmc.org/article/PMC/PMC13670329},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Animal models of anemia of inflammation: Methodological advances, limitations, and translational applications
AU  - Jiayi Cheng
AU  - Xiaodi Zhou
AU  - Qian Liu
AU  - Zixian Liu
AU  - Hao Wu
PY  - 2026
DA  - 2026/10/15
JO  - Animal models and experimental medicine
DO  - 10.1002/ame2.70304
UR  - https://europepmc.org/article/PMC/PMC13670329
AB  - Anemia of inflammation (AI) represents one of the most prevalent secondary anemias in clinical practice, frequently complicating a wide spectrum of chronic inflammatory diseases. Given its highly heterogeneous etiology and complex pathophysiological mechanisms, the establishment of rational and high‐fidelity animal models is vital for elucidating the core mechanisms of AI and optimizing clinical diagnostic and therapeutic strategies. This narrative review provides a structured categorization of current AI animal models into three major paradigms: sterile inflammation models, disease‐specific models, and genetically engineered models. We provide an in‐depth comparative analysis of their underlying modeling principles, methodological protocols, and characteristic phenotypic profiles, while comprehensively evaluating their respective translational advantages and intrinsic limitations. Furthermore, we discuss the prospective applications of these models in deciphering pathomechanisms, validating therapeutic targets, and developing interventional strategies. To enhance reproducibility and translational relevance, we propose a streamlined phenotyping panel and a model selection framework. The precise selection and rational application of these tailored models will contribute to improved reliability of fundamental AI research and accelerate its translational progression from bench to bedside.
SN  - 2096-5451
SN  - 2576-2095
ER  -