ArticleCardiovascular diabetology2023
Early vascular aging as an index of cardiovascular risk in healthy adults: confirmatory factor analysis from the EVasCu study.
Article in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between body shape index and arterial stiffness: results of the EVasCu study and a meta-analysis.International journal of obesity (2005) · 2025Pooled it
- Association Between Early Vascular Aging and Cardiometabolic Diseases: A Two-Year Longitudinal Study of the EVasCu Cohort.Journal of clinical medicine · 2026Article
- Cellular Heterogeneity During Arterial Aging.Aging cell · 2026Review
- Association of vascular aging and insulin resistance with cardiovascular outcomes in China: PURE cohort study.International journal of surgery (London, England) · 2026Article
- Article
- Adropin and Endothelin-1 as Complementary Signals Associated with Early Vascular Aging in Middle-Aged Type 2 Diabetes.Diseases (Basel, Switzerland) · 2026Article
- Association Between Increased Central and Peripheral Arterial 2 Stiffness and Vitamin Intake in Healthy Adults: EVA Follow-Up 3 Study.Nutrients · 2026Article
- Association between glycometabolic indicators and risk of early vascular aging and their predictive value.Frontiers in endocrinology · 2026Article
- Association Between the Visceral Adiposity Index and Arterial Stiffness: Results of the EVasCu Study and a Meta-Analysis Including EVasCu Data and Prior Studies.Metabolites · 2025Article
- Association between skin autofluorescence and all-cause mortality, cardiovascular mortality and cardiovascular disease: a systematic review and meta-analysis.Clinical research in cardiology : official journal of the German Cardiac Society · 2025Review
- Cellular and molecular mechanisms underlying cardiovascular aging.Cellular & molecular biology letters · 2025Review
- Impacts of Radiation on Metabolism and Vascular Cell Senescence.Antioxidants & redox signaling · 2025Review
- Sex-Specific Patterns in Blood Pressure and Vascular Parameters: The MUJER-EVA Project.Journal of cardiovascular development and disease · 2025Article
- Reference Values of Skin Autofluorescence by Age Groups in Healthy Spanish Adults: Results from the EVasCu Study, a Systematic Review, and a Meta-Analysis.Journal of clinical medicine · 2025Review
- Role of Advanced Glycation End Products in Mediating Glycated Haemoglobin and Pulse Wave Velocity in Healthy Adults.Biomedicines · 2025Article
- Glycated Hemoglobin and Cardiovascular Disease in Patients Without Diabetes.Journal of clinical medicine · 2024Review
- Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.Health information science and systems · 2024Article
- SARS-CoV-2 S Protein Reduces Cytoprotective Defenses and Promotes Human Endothelial Cell Senescence.Aging and disease · 2024Article
- Article
- The Effect of Diet on Vascular Aging: A Narrative Review of the Available Literature.Life (Basel, Switzerland) · 2024Review
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Authors and funding
9 authors.
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Abstract
backgroundThe concept of early vascular aging (EVA) represents a potentially beneficial model for future research into the pathophysiological mechanisms underlying the early manifestations of cardiovascular disease. For this reason, the aims of this study were to verify by confirmatory factor analysis the concept of EVA on a single factor based on vascular, clinical and biochemical parameters in a healthy adult population and to develop a statistical model to estimate the EVA index from variables collected in a dataset to classify patients into different cardiovascular risk groups: healthy vascular aging (HVA) and EVA.
methodsThe EVasCu study, a cross-sectional study, was based on data obtained from 390 healthy adults. To examine the construct validity of a single-factor model to measure accelerated vascular aging, different models including vascular, clinical and biochemical parameters were examined. In addition, unsupervised clustering techniques (using both K-means and hierarchical methods) were used to identify groups of patients sharing similar characteristics in terms of the analysed variables to classify patients into different cardiovascular risk groups: HVA and EVA.
resultsOur data show that a single-factor model including pulse pressure, glycated hemoglobin A1c, pulse wave velocity and advanced glycation end products shows the best construct validity for the EVA index. The optimal value of the risk groups to separate patients is K = 2 (HVA and EVA).
conclusionsThe EVA index proved to be an adequate model to classify patients into different cardiovascular risk groups, which could be valuable in guiding future preventive and therapeutic interventions.
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