ArticleCardiovascular diabetology2024
Validation of an early vascular aging construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multidomain Arterial Afterload and Left Atrial-Left Ventricular Coupling in Hypertensive Phenotypes.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- Association Between Early Vascular Aging and Cardiometabolic Diseases: A Two-Year Longitudinal Study of the EVasCu Cohort.Journal of clinical medicine · 2026Article
- Protocol for a Radial Pulse-Based Vascular Age Index Integrated with Traditional Korean Medicine Pulse Diagnosis.Journal of pharmacopuncture · 2026Article
- Associations of Lipid and Adiposity Indices with Vascular Aging Stages in a Multicenter Chinese Population.Current medical science · 2026Article
- Under-recognized cardiovascular risk enhancers in women: A call to rethink clinical assessment on risk stratification.American journal of preventive cardiology · 2025Article
- Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.Health information science and systems · 2024Article
- Correlation between obesity and early vascular aging in middle-aged and young adult health check-up populations.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
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Authors and funding
5 authors.
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Abstract
backgroundCardiovascular diseases (CVDs) remain a major global health concern, necessitating advanced risk assessment beyond traditional factors. Early vascular aging (EVA), characterized by accelerated vascular changes, has gained importance in cardiovascular risk assessment.
methodsThe EVasCu study in Spain examined 390 healthy participants using noninvasive measurements. A construct of four variables (Pulse Pressure, Pulse Wave Velocity, Glycated Hemoglobin, Advanced Glycation End Products) was used for clustering. K-means clustering with principal component analysis revealed two clusters, healthy vascular aging (HVA) and early vascular aging (EVA). External validation variables included sociodemographic, adiposity, glycemic, inflammatory, lipid profile, vascular, and blood pressure factors.
resultsEVA cluster participants were older and exhibited higher adiposity, poorer glycemic control, dyslipidemia, altered vascular properties, and higher blood pressure. Significant differences were observed for age, smoking status, body mass index, waist circumference, fat percentage, glucose, insulin, C-reactive protein, diabetes prevalence, lipid profiles, arterial stiffness, and blood pressure levels. These findings demonstrate the association between traditional cardiovascular risk factors and EVA.
conclusionsThis study validates a clustering model for EVA and highlights its association with established risk factors. EVA assessment can be integrated into clinical practice, allowing early intervention and personalized cardiovascular risk management.
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