TY - JOUR
T1 - Older Tissue Age Derived From Abdominal Computed Tomography Biomarkers of Muscle, Fat, and Bone Is Associated With Chronic Conditions and Higher Mortality
AU - Rule, Andrew D.
AU - Grossardt, Brandon R.
AU - Weston, Alexander D.
AU - Garner, Hillary W.
AU - Kline, Timothy L.
AU - Chamberlain, Alanna M.
AU - Allen, Alina M.
AU - Erickson, Bradley J.
AU - Rocca, Walter A.
AU - St. Sauver, Jennifer L.
N1 - Publisher Copyright:
© 2023 Mayo Foundation for Medical Education and Research
PY - 2024
Y1 - 2024
N2 - Objective: To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age. Methods: We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model. Results: Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions. Conclusion: Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging.
AB - Objective: To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age. Methods: We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model. Results: Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions. Conclusion: Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging.
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U2 - 10.1016/j.mayocp.2023.09.021
DO - 10.1016/j.mayocp.2023.09.021
M3 - Article
C2 - 38310501
AN - SCOPUS:85184067492
SN - 0025-6196
JO - Mayo Clinic proceedings
JF - Mayo Clinic proceedings
ER -