Skin Autofluorescence as a Potential Adjunctive Marker for Cardiovascular Risk Assessment in Type 2 Diabetes: A Systematic Review.
Delia Reurean-PintileiAnca Mihaela Pantea StoianClaudia-Gabriela PotcovaruTeodor SalmenDelia CintezaRoxana-Adriana StoicaSandra LazărBogdan TimarPublished in: International journal of molecular sciences (2024)
Diabetes mellitus (DM), due to its long-term hyperglycemia, leads to the accumulation of advanced glycation end-products (AGEs), especially in the vessel walls. Skin autofluorescence (SAF) is a non-invasive tool that measures AGEs. DM patients have a rich dietary source in AGEs, associated with high oxidative stress and long-term inflammation. AGEs represent a cardiovascular (CV) risk factor, and they are linked with CV events. Our objective was to assess whether SAF predicts future CV events (CVE) by examining its association with other CV risk factors in patients with type 2 DM (T2DM). Additionally, we assessed the strengths and limitations of SAF as a predictive tool for CVE. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, we conducted a systematic review with CRD42024507397 protocol, focused on AGEs, T2DM, SAF, and CV risk. We identified seven studies from 2014 to 2024 that predominantly used the AGE Reader Diagnostic Optic tool. The collective number of patients involved is 8934, with an average age of 63. So, SAF is a valuable, non-invasive marker for evaluating CV risk in T2DM patients. It stands out as a CV risk factor associated independently with CVE. SAF levels are influenced by prolonged hyperglycemia, lifestyle, aging, and other chronic diseases such as depression, and it can be used as a predictive tool for CVE.
Keyphrases
- risk factors
- type diabetes
- oxidative stress
- end stage renal disease
- risk assessment
- ejection fraction
- newly diagnosed
- cardiovascular disease
- systematic review
- glycemic control
- randomized controlled trial
- prognostic factors
- peritoneal dialysis
- patient reported outcomes
- meta analyses
- depressive symptoms
- metabolic syndrome
- physical activity
- dna damage
- heavy metals
- adipose tissue
- optical coherence tomography
- soft tissue