한빛사논문
- 조회78
Yoosoo Chang*, Soon Ho Yoon*, Ria Kwon, Jeonggyu Kang, Young Hwan Kim, Jong-Min Kim, Han-Jae Chung, JunHyeok Choi, Hyun-Suk Jung, Ga-Young Lim, Jiin Ahn, Sarah H. Wild, Christopher D. Byrne, Seungho Ryu
From the Center for Cohort Studies (Y.C., R.K., J.K., J.H.C., H.S.J., G.Y.L., J.A., S.R.) and Department of Occupational and Environmental Medicine (Y.C., S.R.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea (Y.C., S.R.); Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Republic of Korea (S.H.Y.); Research & Science Division, MEDICAL IP, Seoul, Republic of Korea (J.M.K., H.J.C.); Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea (R.K., G.Y.L.); Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (Y.H.K.); Usher Institute, University of Edinburgh, Edinburgh, United Kingdom (S.H.W.); Department of Nutrition and Metabolism, University of Southampton Faculty of Medicine, Southampton, United Kingdom (C.D.B.); and National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom (C.D.B.).
*Y.C. and S.H.Y. contributed equally to this work.
Address correspondence to S.R.
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 fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with 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, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95.
Conclusion
Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities.
논문정보
- Research article
- 2024년 08월 (BRIC 등록일 2024-08-07)
- 국내 연구진
- 의약학 > 응용의학
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