Frequent longitudinal blood microsampling and proteome monitoring identify disease markers and enable timely intervention in a mouse model of type 1 diabetes – Published online 04/08/2025

Anirudra Parajuli, Annika Bendes, Fabian Byvald, Virginia M. Stone, Emma E. Ringqvist, Marta Butrym, Emmanouil Angelis, Sophie Kipper, Stefan Bauer, Niclas Roxhed, Jochen M. Schwenk & Malin Flodström-Tullberg

Monitoring dynamic molecular changes has the potential to transform our ability to capture and understand early events that drive disease onset, enabling timely detection and intervention. In this issue, Parajuli and Bendes et al (https://doi.org/10.1007/s00125-025-06502-7) use an experimental model to demonstrate that frequent microsampling of dried blood spots enables high-resolution proteomic analysis, which can capture temporal molecular patterns that precede the onset of type 1 diabetes—changes that would have been missed with conventional, less frequent sampling. By leveraging machine learning to interpret longitudinal proteome data, the study identifies early biomarkers associated with disease-triggering events, allowing timely therapeutic intervention. This minimally invasive strategy offers a scalable, cost-effective framework for monitoring at-risk individuals in home settings, with potential to transform early intervention paradigms in type 1 diabetes. The authors suggest integrating temporal proteomic signatures with predictive modelling may pave the way for precision diagnostics and targeted treatment during the presymptomatic phase of the disease.

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