Lingwen Ying1, Yun Shen1, Yang Zhang2, Yikun Wang3, Yong Liu4, Jun Yin1, Yufei Wang1, Jingrong Yin1, Wei Zhu1, Yuqian Bao1, Jian Zhou5. 1. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China. 2. Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China; University of Science and Technology of China, Hefei, 230026, China. 3. Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China. Electronic address: wyk@aiofm.ac.cn. 4. Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China. 5. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China. Electronic address: zhoujian@sjtu.edu.cn.
Abstract
BACKGROUND AND AIMS: Advanced glycation end products (AGEs) are reported to be correlated with diabetic vascular complications. This study aimed to investigate the association between AGEs and carotid atherosclerosis (CAS) as a surrogate marker of cardiovascular disease (CVD). METHODS AND RESULTS: A total of 1006 patients with type 2 diabetes were included. CAS was defined as the presence of carotid arterial atherosclerotic plaque in any of bilateral carotid artery segments measured by ultrasonography. AGEs were measured by the noninvasive skin autofluorescence method. AGEage index was calculated as AGEs × age/100. Patients with CAS showed a significantly higher AGEage (P < 0.01), and the prevalence of CAS increased with ascending AGEage levels (P for trend < 0.001). Logistic regression analysis revealed that AGEage was significantly positively associated with odds of CAS, and the odds ratios of the presence of CAS across quartiles of AGEage were 1.00, 3.00 [95% confidence interval (CI) 1.90-4.74], 4.04 (95%CI 2.50-6.53) and 4.99 (95%CI 2.97-8.40) for the multivariable-adjusted model (P for trend <0.001), respectively. In the fully adjusted model, each 5.0 increase in AGEage was associated with a 0.019 mm increment in carotid intima-media thickness. Furthermore, AGEage presented an acceptable predictive value for CAS, with an optimal cutoff point of 43.2, and the sensitivity, specificity and area under the curve (AUC) were 74.5% (95%CI 70.7-78.1%), 61.9% (95%CI 57.2-66.4%) and 0.735 (0.706-0.762), respectively. CONCLUSION: AGEage, the noninvasive measurement of AGEs combined with age is a promising approach for triaging patients at high risk of CVDs.
BACKGROUND AND AIMS: Advanced glycation end products (AGEs) are reported to be correlated with diabetic vascular complications. This study aimed to investigate the association between AGEs and carotid atherosclerosis (CAS) as a surrogate marker of cardiovascular disease (CVD). METHODS AND RESULTS: A total of 1006 patients with type 2 diabetes were included. CAS was defined as the presence of carotid arterial atherosclerotic plaque in any of bilateral carotid artery segments measured by ultrasonography. AGEs were measured by the noninvasive skin autofluorescence method. AGEage index was calculated as AGEs × age/100. Patients with CAS showed a significantly higher AGEage (P < 0.01), and the prevalence of CAS increased with ascending AGEage levels (P for trend < 0.001). Logistic regression analysis revealed that AGEage was significantly positively associated with odds of CAS, and the odds ratios of the presence of CAS across quartiles of AGEage were 1.00, 3.00 [95% confidence interval (CI) 1.90-4.74], 4.04 (95%CI 2.50-6.53) and 4.99 (95%CI 2.97-8.40) for the multivariable-adjusted model (P for trend <0.001), respectively. In the fully adjusted model, each 5.0 increase in AGEage was associated with a 0.019 mm increment in carotid intima-media thickness. Furthermore, AGEage presented an acceptable predictive value for CAS, with an optimal cutoff point of 43.2, and the sensitivity, specificity and area under the curve (AUC) were 74.5% (95%CI 70.7-78.1%), 61.9% (95%CI 57.2-66.4%) and 0.735 (0.706-0.762), respectively. CONCLUSION: AGEage, the noninvasive measurement of AGEs combined with age is a promising approach for triaging patients at high risk of CVDs.
Authors: Michal Schnaider Beeri; Roni Lotan; Jaime Uribarri; Sue Leurgans; David A Bennett; Aron S Buchman Journal: Nutrients Date: 2022-03-31 Impact factor: 6.706