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<p>The aim of this study was to identify and validate distinct patterns of vascular aging, focusing on a novel high-risk vascular aging (HRVA) cluster. Key biomarkers such as aortic pulse wave velocity, glycated hemoglobin, pulse pressure, and advanced glycation end-products were used to enhance cardiovascular risk stratification and explore implications for targeted interventions. Data from multiple studies were integrated, and K-means clustering identified three vascular aging patterns: healthy vascular aging (HVA), early vascular aging (EVA), and high-risk vascular aging (HRVA). ROC analysis determined optimal thresholds for key biomarkers. ANOVA and Chi-square tests evaluated differences and associations across clusters, supported by contingency tables and residual analysis. The HRVA cluster exhibited significantly elevated biomarker levels compared to the HVA and EVA clusters. Statistically significant differences were observed across clusters (p <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\le \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≤</mo> </math></EquationSource> </InlineEquation> 0.001), confirmed by ANOVA. Chi-square tests revealed strong associations between cluster membership and categorical variables, further validating the distinct profiles. The HRVA group demonstrated a particularly high risk of adverse cardiovascular events, emphasizing the clinical relevance. The identification of the HRVA cluster provides new insights into vascular aging, suggesting the need for intensive, personalized interventions. Future research should focus on validating these clusters longitudinally and exploring genetic, environmental, and lifestyle factors to improve cardiovascular outcomes.</p>

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A leap forward in early vascular aging research: uncovering the high-risk vascular aging patterns

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Arturo Martinez-Rodrigo,
  • Alicia Saz-Lara,
  • João Pedrosa,
  • Iris Otero-Luis,
  • Nerea Moreno-Herraiz,
  • Carla Geovanna Lever-Megina,
  • Isabel Antonia Martínez-Ortega,
  • Jose Manuel Pastor,
  • Iván Cavero-Redondo

摘要

The aim of this study was to identify and validate distinct patterns of vascular aging, focusing on a novel high-risk vascular aging (HRVA) cluster. Key biomarkers such as aortic pulse wave velocity, glycated hemoglobin, pulse pressure, and advanced glycation end-products were used to enhance cardiovascular risk stratification and explore implications for targeted interventions. Data from multiple studies were integrated, and K-means clustering identified three vascular aging patterns: healthy vascular aging (HVA), early vascular aging (EVA), and high-risk vascular aging (HRVA). ROC analysis determined optimal thresholds for key biomarkers. ANOVA and Chi-square tests evaluated differences and associations across clusters, supported by contingency tables and residual analysis. The HRVA cluster exhibited significantly elevated biomarker levels compared to the HVA and EVA clusters. Statistically significant differences were observed across clusters (p \(\le \) ≤ 0.001), confirmed by ANOVA. Chi-square tests revealed strong associations between cluster membership and categorical variables, further validating the distinct profiles. The HRVA group demonstrated a particularly high risk of adverse cardiovascular events, emphasizing the clinical relevance. The identification of the HRVA cluster provides new insights into vascular aging, suggesting the need for intensive, personalized interventions. Future research should focus on validating these clusters longitudinally and exploring genetic, environmental, and lifestyle factors to improve cardiovascular outcomes.