Contrast-induced acute kidney injury: from pathophysiology to risk prediction and prevention strategies
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Pooja Anjanappa
Abstract
Concerns regarding contrast-induced acute kidney injury (CI-AKI) remain significant due to the increasing use of iodinated contrast agents (ICM) in diagnostic and medical procedures. Although the frequency of CI-AKI has decreased over the decades, it continues to be a major cause of hospital-acquired acute kidney damage, thereby elevating the risk of mortality, morbidity, and prolonged hospital stays. The complex pathophysiology of CI-AKI involves vasoconstriction, oxidative stress, renal medullary hypoxia, inflammatory responses, and direct tubular toxicity. Recent discoveries have identified ferroptosis and neutrophil extracellular traps (NETs) as additional mechanisms contributing to endothelial and tubular damage. Furthermore, microRNAs such as miR-30c, miR-21, and miR-141-3p have emerged as preliminary biomarkers and therapeutic targets due to their regulatory effects on cellular apoptosis and inflammatory pathways. Procedural controversies persist regarding the risk differences between intravenous and intra-arterial contrast administration; however, evidence suggests that patient comorbidities and procedural complexity, rather than the route of administration alone, determine the risk of CI-AKI. Risk stratification tools, such as the Mehran and ACEF scores, provide frameworks for identifying high-risk patients and guiding preventive strategies. This review integrates an understanding of the molecular pathogenesis of CI-AKI, clarifies procedural debates, and highlights emerging biomarkers and risk models.
Acknowledgments
The authors used Paperpal® (Cactus Communications), an AI-assisted language editing tool, solely for improving grammar, clarity, and readability. All scientific content and interpretations are the authors’ own.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: PA contributed to the conceptualization, literature search, and initial drafting of the manuscript. SK contributed to the literature search, critical revision of the manuscript for important intellectual content and provided subject-matter expertise. YS contributed to the literature search, supervision, manuscript editing, and final approval of the version to be published. All authors reviewed and approved the final manuscript and agree to be accountable for all aspects of the work.
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Use of Large Language Models, AI and Machine Learning Tools: Used Paperpal® (Cactus Communications), an AI-assisted language editing tool, solely for improving grammar, clarity, and readability.
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Conflict of interest: All authors affirm that they have no competing interests that could potentially interfere with the impartiality of the research, analysis, or conclusions presented in this manuscript.
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Research funding: None declared.
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Data availability: Not applicable.
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