A Potential Ratio for Detecting Subclinical Atherosclerosis: Insight into Advanced NMR Lipid Profiles in Severe Obesity

Individuals with severe obesity (SO) are at a high risk of developing cardiovascular disease, but traditional lipid parameters are insufficient for accurately assessing the risk. This study aims to investigate the advanced characteristics of lipoproteins that may contribute to subclinical atherosclerosis in SO and the impact of bariatric surgery (BS). The study included 37 patients with SO with a one-year follow-up post-BS, and 40 control subjects. Advanced lipoprotein profiles were assessed using nuclear magnetic resonance. BS normalized proatherogenic lipoprotein alterations in SO. Small LDL and medium HDL particle numbers differed between plaque and non-plaque groups. A ratio of these particles showed an AUC of 83%, suggesting it could effectively predict subclinical atherosclerosis. Advanced NMR analysis offers more specific information on lipid profiles in SO. The small LDL-P to medium HDL-P ratio could be a valuable tool for detecting and managing subclinical atherosclerosis in this population.

Authors

Institutions

Publication Details

Journal
Journal of Cardiovascular Translational Research
Published
2025-10-29
DOI
https://doi.org/10.1007/s12265-025-10696-x
Citations
1
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
1.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Potential Ratio for Detecting Subclinical Atherosclerosis: Insight into Advanced NMR Lipid Profiles in Severe Obesity

Núria Amigó, M. Dolores López‐Tejero, David Ricart-Jané, Eva Pardina et al.
1 citations
Journal of Cardiovascular Translational Research
Diabetes, Cardiovascular Risks, and Lipoproteins
1.29
article

A Potential Ratio for Detecting Subclinical Atherosclerosis: Insight into Advanced NMR Lipid Profiles in Severe Obesity

Núria Amigó, M. Dolores López‐Tejero, David Ricart-Jané, Eva Pardina, Juan Antonio Baena‐Fustegueras, Marcelino Bermúdez-López, Julia Peinado‐Onsurbe, Júlia Carmona‐Maurici, Iratxe Eskubi-Turró, Anna Viñas
article en
1 citations

Abstract

Individuals with severe obesity (SO) are at a high risk of developing cardiovascular disease, but traditional lipid parameters are insufficient for accurately assessing the risk. This study aims to investigate the advanced characteristics of lipoproteins that may contribute to subclinical atherosclerosis in SO and the impact of bariatric surgery (BS). The study included 37 patients with SO with a one-year follow-up post-BS, and 40 control subjects. Advanced lipoprotein profiles were assessed using nuclear magnetic resonance. BS normalized proatherogenic lipoprotein alterations in SO. Small LDL and medium HDL particle numbers differed between plaque and non-plaque groups. A ratio of these particles showed an AUC of 83%, suggesting it could effectively predict subclinical atherosclerosis. Advanced NMR analysis offers more specific information on lipid profiles in SO. The small LDL-P to medium HDL-P ratio could be a valuable tool for detecting and managing subclinical atherosclerosis in this population.

Journal of Cardiovascular Translational ResearchVol. 18(6)
Hospital Universitari Arnau de Vilanova (ES), Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (ES), Institut de Recerca Biomèdica Catalunya Sud (ES), Spanish Clinical Research Network (ES), Instituto de Investigación Biomédica de Lleida (ES), Universitat de Barcelona (ES)
Universitat de Barcelona
Openalex Percentile: Top 14%
Diabetes, Cardiovascular Risks, and Lipoproteins
1.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.