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Tang Q, Liu S, Tao C, Wang J, Zhao H, Wang G, Zhao X, Ren Q, Zhang L, Su B, Xu J, An H. A new method for vascular age estimation based on relative risk difference in vascular aging. Comput Biol Med 2024; 171:108155. [PMID: 38430740 DOI: 10.1016/j.compbiomed.2024.108155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/26/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.
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Affiliation(s)
- Qingfeng Tang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China; Anhui Engineering Research Center of Intelligent Perception and Elderly Care, Chuzhou University, Chuzhou 239000, China.
| | - Shiping Liu
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Chao Tao
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Jue Wang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Huanhuan Zhao
- Anhui Engineering Research Center of Intelligent Perception and Elderly Care, Chuzhou University, Chuzhou 239000, China; School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China.
| | - Guangjun Wang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Xu Zhao
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
| | - Qun Ren
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
| | - Liangliang Zhang
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China.
| | - Benyue Su
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China; School of Mathematics and Computer Science, Tongling University, Tongling 244061, China.
| | - Jiatuo Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Hui An
- Health Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, China.
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