Extracellular vesicles with biomarkers for metabolic diseases and chronic inflammation associated diseases

Many metabolic disorders are chronic diseases that require continuous biomarker monitoring to ensure the patient is stable and responding to treatment. Likewise, diseases associated with chronic inflammation require close monitoring to prevent damage to organic systems such as the cardiovascular and renal systems. The detection of extracellular vesicles (EVs) with specific biomarkers has prognostic value regarding the organs that are being affected and the patient’s metabolic condition. This monitoring is not possible due to the complexity of the methods that are not available for routine analysis or that involving sending samples to specialized laboratories. This project aims to develop new technologies for easy monitoring of patients and prognosis of severe progression of diseases associated with chronic inflammation or metabolism.

Part of this project has been developed in Fabry disease (FD). FD a is multi-systemic, X-linked lysosomal storage disease caused by a range of mutations in the GLA gene that encodes for alpha-galactosidase A (α-gal A). Mutations leading to a deficient or absent activity of the enzyme α-gal A result in a progressive accumulation of lysosomal glycosphingolipids, such as globotriaosylceramide (Gb-3). As a consequence of the anomalous accumulation of Gb-3, different cellular mechanisms are triggered, leading to a dysfunctional endosomal–lysosomal system, activation of chronic inflammation and apoptosis, contributing to the progression of FD. The most affected systems are the cardiovascular system (cardiomyocytes, conduction system cells, vascular endothelial, and smooth muscle cells and fibroblasts), the renal system (podocytes, tubular, glomerular, mesangial, and interstitial cells), and the nervous system (neurons in autonomic and posterior root ganglia). Within this project we are aiming to uncover novel biomarkers for FD prognosis.

Funding Agency

PRR – Health from Portugal (HfPT) – PPS A1.05


Project Reference

Project Members

Main Project Outcomes

S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.

“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.