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Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study

OBJECTIVE: We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. METHODS: This study considered...

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Autores principales: Villasanta-Gonzalez, Alejandro, Mora-Ortiz, Marina, Alcala-Diaz, Juan F., Rivas-Garcia, Lorenzo, Torres-Peña, Jose D., Lopez-Bascon, Asuncion, Calderon-Santiago, Monica, Arenas-Larriva, Antonio P., Priego‑Capote, Feliciano, Malagon, Maria M., Eichelmann, Fabian, Perez-Martinez, Pablo, Delgado-Lista, Javier, Schulze, Matthias B., Camargo, Antonio, Lopez-Miranda, Jose
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401778/
https://www.ncbi.nlm.nih.gov/pubmed/37537576
http://dx.doi.org/10.1186/s12933-023-01933-1
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author Villasanta-Gonzalez, Alejandro
Mora-Ortiz, Marina
Alcala-Diaz, Juan F.
Rivas-Garcia, Lorenzo
Torres-Peña, Jose D.
Lopez-Bascon, Asuncion
Calderon-Santiago, Monica
Arenas-Larriva, Antonio P.
Priego‑Capote, Feliciano
Malagon, Maria M.
Eichelmann, Fabian
Perez-Martinez, Pablo
Delgado-Lista, Javier
Schulze, Matthias B.
Camargo, Antonio
Lopez-Miranda, Jose
author_facet Villasanta-Gonzalez, Alejandro
Mora-Ortiz, Marina
Alcala-Diaz, Juan F.
Rivas-Garcia, Lorenzo
Torres-Peña, Jose D.
Lopez-Bascon, Asuncion
Calderon-Santiago, Monica
Arenas-Larriva, Antonio P.
Priego‑Capote, Feliciano
Malagon, Maria M.
Eichelmann, Fabian
Perez-Martinez, Pablo
Delgado-Lista, Javier
Schulze, Matthias B.
Camargo, Antonio
Lopez-Miranda, Jose
author_sort Villasanta-Gonzalez, Alejandro
collection PubMed
description OBJECTIVE: We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. METHODS: This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60–40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out. RESULTS: LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%. CONCLUSION: Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-01933-1.
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spelling pubmed-104017782023-08-05 Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study Villasanta-Gonzalez, Alejandro Mora-Ortiz, Marina Alcala-Diaz, Juan F. Rivas-Garcia, Lorenzo Torres-Peña, Jose D. Lopez-Bascon, Asuncion Calderon-Santiago, Monica Arenas-Larriva, Antonio P. Priego‑Capote, Feliciano Malagon, Maria M. Eichelmann, Fabian Perez-Martinez, Pablo Delgado-Lista, Javier Schulze, Matthias B. Camargo, Antonio Lopez-Miranda, Jose Cardiovasc Diabetol Research OBJECTIVE: We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. METHODS: This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60–40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out. RESULTS: LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%. CONCLUSION: Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-01933-1. BioMed Central 2023-08-03 /pmc/articles/PMC10401778/ /pubmed/37537576 http://dx.doi.org/10.1186/s12933-023-01933-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Villasanta-Gonzalez, Alejandro
Mora-Ortiz, Marina
Alcala-Diaz, Juan F.
Rivas-Garcia, Lorenzo
Torres-Peña, Jose D.
Lopez-Bascon, Asuncion
Calderon-Santiago, Monica
Arenas-Larriva, Antonio P.
Priego‑Capote, Feliciano
Malagon, Maria M.
Eichelmann, Fabian
Perez-Martinez, Pablo
Delgado-Lista, Javier
Schulze, Matthias B.
Camargo, Antonio
Lopez-Miranda, Jose
Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title_full Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title_fullStr Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title_full_unstemmed Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title_short Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study
title_sort plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: cordioprev study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401778/
https://www.ncbi.nlm.nih.gov/pubmed/37537576
http://dx.doi.org/10.1186/s12933-023-01933-1
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