Artificial intelligence-based chest computed tomography assessment of bone, vascular, and epicardial adipose changes in type 2 diabetes

doi:10.4239/wjd.121422

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World Journal of Diabetes

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1948-9358

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Diabetes. Aug 15, 2026; 17(8): 121422
Published online Aug 15, 2026. doi: 10.4239/wjd.121422

Table 7 Threshold effect analysis of the relationship between bone mineral density and epicardial adipose tissue density

Model	Threshold	β (95%CI)	P value
Model 1: Standard linear regression	-	-0.06 (-0.07 to -0.06)	< 0.001
Model 2: Piecewise linear regression	-	-	-
Threshold	174.91	-	-
< 174.91	-	-0.12 (-0.13 to -0.10)	< 0.001
≥ 174.91	-	0.02 (-0.00 to 0.04)	0.060
Likelihood ratio test P	-	-	< 0.001

Model 1: Simple linear regression. Model 2: Piecewise linear regression fitted after threshold identification using the maximum likelihood method. The likelihood ratio test compares model 2 with model 1, P value < 0.05 indicates a significant threshold effect.

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Citation: Liu YK, Wu FY, Jiang CP, Xia YX, Li Y, Zeng WW, Zhang SY, Fan DN, Zhang MX, Liu TC, Wu XP, Dong Y, Chen W, Zhao YH, Li T, Li Z, Xu XY. Artificial intelligence-based chest computed tomography assessment of bone, vascular, and epicardial adipose changes in type 2 diabetes. World J Diabetes 2026; 17(8): 121422
URL: https://www.wjgnet.com/1948-9358/full/v17/i8/121422.htm
DOI: https://dx.doi.org/10.4239/wjd.121422

Liu YK, Wu FY, Jiang CP, Xia YX, Li Y, Zeng WW, Zhang SY, Fan DN, Zhang MX, Liu TC, Wu XP, Dong Y, Chen W, Zhao YH, Li T, Li Z, Xu XY. Artificial intelligence-based chest computed tomography assessment of bone, vascular, and epicardial adipose changes in type 2 diabetes. World J Diabetes 2026; 17(8): 121422 [DOI: 10.4239/wjd.121422]

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