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Automated comprehensive CT assessment of the risk of diabetes and associated cardiometabolic conditions

Automated comprehensive CT assessment of the risk of diabetes and associated cardiometabolic conditions
Automated comprehensive CT assessment of the risk of diabetes and associated cardiometabolic conditions
Background: CT, performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored.

Purpose: to evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities.

Materials and Methods: this retrospective study included Korean adults (age ≥25 years) who underwent health screening with 18F-fluorodeoxyglucose (18F-FDG) PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral/subcutaneous fat, muscle, bone density, liver fat, all normalized to height (m2) and aortic calcification. Predictive performance was assessed using area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively.

Results: the cross-sectional and cohort analyses included 32166 (mean age, 44.6 years ±5.7 [SD], 28833 men) and 27298 adults (mean age, 43.8 years ±4.8 [SD], 24820 men), respectively. Diabetes prevalence and incidence were 6% at baseline and 9% during the 7.3-year median follow-up, respectively. The visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUCs of 0.70 (95%CI: 0.68, 0.71) in men and 0.82 (95%CI: 0.78, 0.85) in women, and Harrell C-indices of 0.68 (95%CI: 0.67, 0.69) in men and 0.82 (95%CI: 0.77, 0.86) in women, respectively. Combining the visceral fat, muscle area indices, liver fat fraction, and aortic calcification improved the predictive performance, yielding Harrell C-indices of 0.69 (95%CI: 0.68, 0.71) in men and 0.83 (95%CI: 0.78, 0.87) in women. Visceral fat index AUCs for identifying metabolic syndrome were 0.81 (95%CI: 3 0.80, 0.81) in men and 0.90 (95%CI: 0.88, 0.91) in women. Automated CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores >100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95.

Conclusion: automated comprehensive multiorgan CT analysis identified individuals at current and future high risk of diabetes and other cardiometabolic comorbidities

0033-8419
e233410
Chang, Yoosoo
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Yoon, Soon Ho
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Kwon, Ria
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Kang, Jeonggyu
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Kim, Young Hwan
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Kim, Jong-Min
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Chung, Han-Jae
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Choi, JunHyeok
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Jung, Hyun-Suk
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Lim, Ga-Young
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Ahn, Jiin
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Wild, Sarah H.
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Byrne, Chris
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Ryu, Seungho
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Chang, Yoosoo
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Yoon, Soon Ho
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Kwon, Ria
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Kang, Jeonggyu
e28e70de-ac56-43a9-a325-a7ec5666405b
Kim, Young Hwan
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Kim, Jong-Min
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Chung, Han-Jae
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Choi, JunHyeok
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Jung, Hyun-Suk
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Lim, Ga-Young
1f84833f-ade4-4ba6-a0e0-4718664f9efc
Ahn, Jiin
e9338e27-1238-4ae5-b005-5a675684c556
Wild, Sarah H.
4f73fc0e-6cb2-4c1a-92af-a6276c05fbb9
Byrne, Chris
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Ryu, Seungho
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Chang, Yoosoo, Yoon, Soon Ho, Kwon, Ria, Kang, Jeonggyu, Kim, Young Hwan, Kim, Jong-Min, Chung, Han-Jae, Choi, JunHyeok, Jung, Hyun-Suk, Lim, Ga-Young, Ahn, Jiin, Wild, Sarah H., Byrne, Chris and Ryu, Seungho (2024) Automated comprehensive CT assessment of the risk of diabetes and associated cardiometabolic conditions. Radiology, 312 (2), e233410, [e233410]. (doi:10.1148/radiol.233410).

Record type: Article

Abstract

Background: CT, performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored.

Purpose: to evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities.

Materials and Methods: this retrospective study included Korean adults (age ≥25 years) who underwent health screening with 18F-fluorodeoxyglucose (18F-FDG) PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral/subcutaneous fat, muscle, bone density, liver fat, all normalized to height (m2) and aortic calcification. Predictive performance was assessed using area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively.

Results: the cross-sectional and cohort analyses included 32166 (mean age, 44.6 years ±5.7 [SD], 28833 men) and 27298 adults (mean age, 43.8 years ±4.8 [SD], 24820 men), respectively. Diabetes prevalence and incidence were 6% at baseline and 9% during the 7.3-year median follow-up, respectively. The visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUCs of 0.70 (95%CI: 0.68, 0.71) in men and 0.82 (95%CI: 0.78, 0.85) in women, and Harrell C-indices of 0.68 (95%CI: 0.67, 0.69) in men and 0.82 (95%CI: 0.77, 0.86) in women, respectively. Combining the visceral fat, muscle area indices, liver fat fraction, and aortic calcification improved the predictive performance, yielding Harrell C-indices of 0.69 (95%CI: 0.68, 0.71) in men and 0.83 (95%CI: 0.78, 0.87) in women. Visceral fat index AUCs for identifying metabolic syndrome were 0.81 (95%CI: 3 0.80, 0.81) in men and 0.90 (95%CI: 0.88, 0.91) in women. Automated CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores >100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95.

Conclusion: automated comprehensive multiorgan CT analysis identified individuals at current and future high risk of diabetes and other cardiometabolic comorbidities

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More information

Accepted/In Press date: 22 May 2024
e-pub ahead of print date: 6 August 2024

Identifiers

Local EPrints ID: 490315
URI: http://eprints.soton.ac.uk/id/eprint/490315
ISSN: 0033-8419
PURE UUID: faad872b-9f3b-412a-9bfe-257f253583de
ORCID for Chris Byrne: ORCID iD orcid.org/0000-0001-6322-7753

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Date deposited: 23 May 2024 16:47
Last modified: 21 Aug 2024 01:36

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Contributors

Author: Yoosoo Chang
Author: Soon Ho Yoon
Author: Ria Kwon
Author: Jeonggyu Kang
Author: Young Hwan Kim
Author: Jong-Min Kim
Author: Han-Jae Chung
Author: JunHyeok Choi
Author: Hyun-Suk Jung
Author: Ga-Young Lim
Author: Jiin Ahn
Author: Sarah H. Wild
Author: Chris Byrne ORCID iD
Author: Seungho Ryu

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