Are Advanced Glycation End Products in Skin Associated with Vascular Dysfunction Markers? A Meta-Analysis.
Alicia Del Saz LaraCelia Alvarez-BuenoVicente Martínez-VizcainoBlanca Notario-PachecoIrene Sequí-DominguezIván Cavero-RedondoPublished in: International journal of environmental research and public health (2020)
Evidence exists regarding the association between advanced glycation end products and different cardiovascular disease subclinical processes, such as arterial stiffness and atherosclerosis. With this systematic review and meta-analysis, we aimed to provide a synthesis of the evidence regarding the association of arterial stiffness measured by pulse wave velocity and atherosclerosis measured by carotid intima media thickness with skin autofluorescence. A systematic search was performed using: MEDLINE (PubMed), SCOPUS, and Web of Science, until 30 March 2020. Cross-sectional studies or baseline data from prospective longitudinal studies were considered. The DerSimonian and Laird method was used to calculate the pooled estimates of correlation coefficients and the corresponding 95% confidence intervals (CI) for the association of pulse wave velocity and carotid intima media thickness with skin autofluorescence. Twenty-five studies were included in the systematic review and meta-analysis, including 6306 subjects. The pooled correlation coefficient was 0.25 (95% CI: 0.18, 0.31) for pulse wave velocity and skin autofluorescence, and 0.31 (95% CI: 0.25, 0.38) for carotid intima media thickness and skin autofluorescence. This systematic review and meta-analysis provide a synthesis of the evidence showing a positive weak association of pulse wave velocity and carotid intima media thickness with skin autofluorescence.
Keyphrases
- blood pressure
- cardiovascular disease
- soft tissue
- wound healing
- cross sectional
- cardiovascular risk factors
- optical coherence tomography
- blood flow
- public health
- type diabetes
- oxidative stress
- computed tomography
- magnetic resonance imaging
- metabolic syndrome
- machine learning
- electronic health record
- magnetic resonance
- cardiovascular events
- coronary artery disease