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<p>Microcirculatory deterioration in diabetes mellitus causes severe organ-specific complications, yet a systemic understanding of its cross-organ pathophysiology remains elusive due to a lack of comprehensive data. To address this gap, we present a high-dimensional dataset mapping microhemodynamic and oxygenation profiles across six organs in murine models of health, pre-diabetes, and type 1 and 2 diabetes. Structured as a third-order tensor, the dataset comprises 10-parameter physio-signatures for each condition, documenting responses to insulin and the GLP-1 receptor agonist liraglutide at one- and two-week endpoints. Our resource enables direct deconvolution of disease- and organ-specific signatures and provides a quantitative platform for comparing therapeutic pharmacodynamics. We propose a vectorial and tensorial analytical framework to dissect systemic patterns, quantify disease perturbation, and identify significant drug-organ interactions. Our foundational dataset is intended to catalyze the development of system-level computational models for managing diabetic microvascular disease.</p>

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A multi-organ atlas of microcirculatory signatures for systemic profiling of diabetic and therapeutic states

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Yuan Li,
  • Weiqi Liu,
  • Yinguyu Wang,
  • Bing Wang,
  • Xiang Xu,
  • Bingwei Li,
  • Xu Zhang,
  • Mingming Liu

摘要

Microcirculatory deterioration in diabetes mellitus causes severe organ-specific complications, yet a systemic understanding of its cross-organ pathophysiology remains elusive due to a lack of comprehensive data. To address this gap, we present a high-dimensional dataset mapping microhemodynamic and oxygenation profiles across six organs in murine models of health, pre-diabetes, and type 1 and 2 diabetes. Structured as a third-order tensor, the dataset comprises 10-parameter physio-signatures for each condition, documenting responses to insulin and the GLP-1 receptor agonist liraglutide at one- and two-week endpoints. Our resource enables direct deconvolution of disease- and organ-specific signatures and provides a quantitative platform for comparing therapeutic pharmacodynamics. We propose a vectorial and tensorial analytical framework to dissect systemic patterns, quantify disease perturbation, and identify significant drug-organ interactions. Our foundational dataset is intended to catalyze the development of system-level computational models for managing diabetic microvascular disease.