Diabetes is now understood to be a systemic metabolic disorder, involving disruptions across multiple organs rather than a single glycemic pathway. Yet, in clinical practice, metabolic health is still assessed mainly through blood glucose and HbA1c, markers that capture only global outcomes, not the underlying coordination among organs. 

What remains largely invisible is how the brain, liver, pancreas, muscle, adipose tissue, and other organs interact as an integrated metabolic network. There is therefore a pressing need for a method that can map inter-organ metabolic coordination in vivo, quantify how this coordination differs across glycemic states, and identify early, network-level signatures of metabolic imbalance.

In a study published in European Journal of Nuclear Medicine and Molecular Imaging, a research team led by Prof. HU Zhanli from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, along with Prof. DONG Mengjie from Peking University Shenzhen Hospital, uncovered how metabolic coordination among major organs shifts across normoglycemia, prediabetes, and diabetes.

The researchers analyzed whole-body [¹⁸F]FDG PET/CT scans from 1,149 adults classified into normoglycemic, pre-diabetic, and diabetic groups, and examined how metabolic coordination among organs, the "inter-organ metabolic network," differs across glycemic states. They quantified how strongly organs such as the brain, liver, pancreas, and muscle relate to one another in their metabolic activity, and how each individual’s pattern deviates from a healthy reference.

The analysis showed that as glycemic status shifts from normal to prediabetes and diabetes, the coordination between organs becomes weaker, with fewer and less stable metabolic links. Early changes were most evident in brain-centered connections, especially those linking the brain with the liver, pancreas, and skeletal muscle, suggesting that the neuro-peripheral metabolic axis is particularly sensitive to early metabolic imbalance. 

At the individual level, the analysis revealed that people with prediabetes showed more diverse metabolic patterns, while those with diabetes exhibited more concentrated but deeper disruptions in specific organ-to-organ relationships. Machine-learning interpretation further confirmed that these brain-peripheral connections were the strongest indicators of metabolic state, highlighting their central role in systemic metabolic regulation.

This study introduces a systems-level view of metabolic health by focusing on how organs coordinate with one another, and shows that disruptions in this coordination appear earlier than traditional clinical markers, suggesting that whole-body [¹⁸F]FDG PET/CT could help identify metabolic risk at a much earlier stage. 

Future research will focus on validate these findings in longitudinal cohorts and explore whether early interventions can help restore disturbed organ-to-organ coordination.