Abstract
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.
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Acknowledgements
We thank Xi’nan Duan for his technical support in decoding algorithms. This work was supported by the Beijing Municipal Natural Science Foundation (Grant No. 7252093).
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Li, Y., Liu, W., Wang, Y. et al. A multi-organ atlas of microcirculatory signatures for systemic profiling of diabetic and therapeutic states. Sci Data (2026). https://doi.org/10.1038/s41597-026-07430-w
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DOI: https://doi.org/10.1038/s41597-026-07430-w
