Scholarlink

<p>Microvascular aging impairs tissue perfusion and contributes to age-related organ dysfunction; however, direct approaches to assess microvascular aging remain limited. Here, we developed machine learning models to estimate chronological age from retinal blood flow data acquired by laser speckle flowgraphy (LSFG) in 1,008 adults undergoing health checkups. Using 18 predefined parameters and 3,253 automatically extracted time-series features, the best model for all participants achieved a mean absolute percentage error (MAPE) of 10.3% between predicted and chronological age. In addition, sex-specific models for women and men outperformed the model for all participants (MAPE 8.6% in women and 9.3% in men), allowing us to explore sex differences in microvascular aging patterns. Based on bias-corrected residuals, we defined a novel biomarker, the relative microvascular aging index (rmVAI). Higher rmVAI was significantly associated with elevated blood pressure, diabetes, metabolic syndrome, and metabolic dysfunction–associated fatty liver disease (MAFLD). Individuals classified as “model-predicted older” (rmVAI &gt; 10%) had higher fatty liver index values (mean 38.2 vs 26.7) and an approximately twofold higher prevalence of hepatic steatosis (26% vs 12%) than those classified as “model-predicted younger” (rmVAI &lt; − 10%). Our LSFG-based machine learning framework provides a scalable and non-invasive tool for quantifying microvascular aging from retinal blood flow and suggests that MAFLD is a potent systemic contributor to accelerated microvascular aging, supporting a putative “liver–microvascular axis” that warrants further investigation for early risk stratification in clinical practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning assessment of retinal blood flow links metabolic dysfunction and accelerated microvascular aging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Shigeyuki Magi,
  • Takahiro Maruyama,
  • Seiji Takagi,
  • Atsuhiko T. Naito,
  • Yuichi Hori

摘要

Microvascular aging impairs tissue perfusion and contributes to age-related organ dysfunction; however, direct approaches to assess microvascular aging remain limited. Here, we developed machine learning models to estimate chronological age from retinal blood flow data acquired by laser speckle flowgraphy (LSFG) in 1,008 adults undergoing health checkups. Using 18 predefined parameters and 3,253 automatically extracted time-series features, the best model for all participants achieved a mean absolute percentage error (MAPE) of 10.3% between predicted and chronological age. In addition, sex-specific models for women and men outperformed the model for all participants (MAPE 8.6% in women and 9.3% in men), allowing us to explore sex differences in microvascular aging patterns. Based on bias-corrected residuals, we defined a novel biomarker, the relative microvascular aging index (rmVAI). Higher rmVAI was significantly associated with elevated blood pressure, diabetes, metabolic syndrome, and metabolic dysfunction–associated fatty liver disease (MAFLD). Individuals classified as “model-predicted older” (rmVAI > 10%) had higher fatty liver index values (mean 38.2 vs 26.7) and an approximately twofold higher prevalence of hepatic steatosis (26% vs 12%) than those classified as “model-predicted younger” (rmVAI < − 10%). Our LSFG-based machine learning framework provides a scalable and non-invasive tool for quantifying microvascular aging from retinal blood flow and suggests that MAFLD is a potent systemic contributor to accelerated microvascular aging, supporting a putative “liver–microvascular axis” that warrants further investigation for early risk stratification in clinical practice.