CT Biomarkers for Phenotypic Biological Aging: Emerging Concepts and Advantages.

Abstract

Aging is a complex phenomenon reflecting the time-dependent accumulation of damage that results in progressive structural and functional decline, disease risk, and death. Chronological age (CA) is an imperfect measure of health but remains an important driver of health care decisions. Biological age (BA) is a construct that attempts to provide a more holistic evaluation of the cumulative effects of aging and aging-related disease. The emergence of "omics"-based aging clocks (eg, epigenomics) has improved BA estimation, but imaging remains underutilized. CT biomarkers of muscle, fat, aortic calcification, and bone are examples of biomarkers of aging that can be used to construct a BA model (ie, CT-based biological age). As opposed to cellular and subcellular "frailomics" used in existing BA models, CT biomarkers are accessible and reproducible and reflect big-picture net phenotypic effects of aging at the tissue level (eg, using tissue segmentation). Recent technological advancements and improvements in artificial intelligence (AI) technologies have transformed our understanding of aging, and rapid automated AI tools enable scaling of image-based approaches for population-level impact. The understandable nature of explainable AI imaging tools instills trust in a model's prediction compared with opaque black box methodologies. Automated imaging-based body composition tools also can be applied opportunistically in either a retrospective or prospective fashion without the need for additional imaging, specialized testing, or patient time. Using a CT-based phenotypic approach to BA estimation is a practical example of opportunistic imaging that could be used to improve existing medical decision making and risk prediction for individual patient and societal benefit in ways that existing frailomics have failed.