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Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification

Crossref DOI link: https://doi.org/10.1186/s43556-026-00438-z

Published Online: 2026-04-10

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Extremera-García, Mª Jesús

Rojas-Torres, Marta

Priego-Torres, Blanca

Beltrán-Camacho, Lucía

Eslava-Alcón, Sara

Rodríguez-Martín, Francisco

Benítez-Camacho, Josefa

Ballesteros-Ribelles, Antonio

del Val, Ana Martínez

Olsen, Jesper

Lozano-Loaiza, Eva

González-García, Mª Ángela

Sanchez-Morillo, Daniel

Fernández-Vega, Alejandro

Montaner, Joan

Doiz, Esther

Rodriguez-Piñero, Manuel

Durán-Ruiz, Mª Carmen https://orcid.org/0000-0003-3954-3734
Funding

Funding for this research was provided by:

Instituto de Salud Carlos III (PI16-00784)

Instituto de Salud Carlos III (PI20-00716)

Instituto de Salud Carlos III (PI24-01161)

Novo Nordisk Fonden (NNF14CC0001)

H2020 Excellent Science (EPIC-XS project no. 823839)

Universidad de Cadiz
License Information

Text and Data Mining valid from 2026-04-10

Version of Record valid from 2026-04-10
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Article History

Received: 9 June 2025

Revised: 6 March 2026

Accepted: 13 March 2026

First Online: 10 April 2026

Declarations

:

: This study was approved by the Ethics Committee of HUPM (Registration number CEI 92.16-FIS-CD-2016, July 16th 2016). Written informed consent was given from all participants prior sample and data collection, in accordance to the principles of the Declaration of Helsinki. Personal data were anonymized from the beginning, according to the Spanish Data Protection Law Ley Orgánica 3/2018, December 5th.

: Not applicable.

: The authors declare that they have no competing interests.

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