Scholarlink

<p>The retina provides a unique window into systemic health, yet molecular mechanisms linking retinal features (oculomics) to clinical traits in aging remain unclear. In this study, we leveraged the homogeneous Canton 70 s Alumni Cohort (<i>N</i> = 258 females aged ~70 years) to minimize socio-demographic confounders and extracted oculomic features from fundus images using AutoMorph. Linear mixed-effects models identified 129 significant associations between oculomic and clinical features (<i>p</i> &lt; 0.05). Sparse canonical correlation analysis indicated a key phenotypic retina-body axis (<i>r</i> = 0.538, <i>p</i> = 0.047) primarily driven by central retinal venular equivalent (Hubbard, zone b) and mean corpuscular hemoglobin concentration. Permutational Multivariate Analysis of Variance revealed that oculomic categories were significantly associated with systemic conditions like chest pain, dyslipidemia, and stroke. Age differentially impacted retinal features across clinical condition status (adjusted <i>p</i> for interaction &lt; 0.2), with pronounced trends in individuals with health problems but relative stability in healthy controls. Plasma proteomics was integrated to explore potential molecular mechanisms. Weighted gene co-expression network analysis identified shared proteomic modules associated with both oculomic and clinical features. These modules were enriched in pathways including complement and coagulation cascades, cholesterol metabolism, and cytokine-cytokine receptor interaction. This study establishes both phenotypic and molecular connections underlying the retina-body axis in a female aging cohort.</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

The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

查看全文
  • Selena Wei Zhang,
  • Shunming Liu,
  • Yanxian Chen,
  • Jing Liu,
  • Ying Yao,
  • Xianwen Shang,
  • Honghua Yu,
  • Yu Huang,
  • Mingguang He

摘要

The retina provides a unique window into systemic health, yet molecular mechanisms linking retinal features (oculomics) to clinical traits in aging remain unclear. In this study, we leveraged the homogeneous Canton 70 s Alumni Cohort (N = 258 females aged ~70 years) to minimize socio-demographic confounders and extracted oculomic features from fundus images using AutoMorph. Linear mixed-effects models identified 129 significant associations between oculomic and clinical features (p < 0.05). Sparse canonical correlation analysis indicated a key phenotypic retina-body axis (r = 0.538, p = 0.047) primarily driven by central retinal venular equivalent (Hubbard, zone b) and mean corpuscular hemoglobin concentration. Permutational Multivariate Analysis of Variance revealed that oculomic categories were significantly associated with systemic conditions like chest pain, dyslipidemia, and stroke. Age differentially impacted retinal features across clinical condition status (adjusted p for interaction < 0.2), with pronounced trends in individuals with health problems but relative stability in healthy controls. Plasma proteomics was integrated to explore potential molecular mechanisms. Weighted gene co-expression network analysis identified shared proteomic modules associated with both oculomic and clinical features. These modules were enriched in pathways including complement and coagulation cascades, cholesterol metabolism, and cytokine-cytokine receptor interaction. This study establishes both phenotypic and molecular connections underlying the retina-body axis in a female aging cohort.