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Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease

BACKGROUND/AIMS: Nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) is paradoxically associated with improved metabolic and pathological features at diagnosis but similar cardiovascular diseases (CVD) prognosis to obese MAFLD. We aimed to utilize the metabolomics to identify the p...

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Autores principales: Shao, Congxiang, Xu, Lishu, Lei, Pingguang, Wang, Wei, Feng, Shiting, Ye, Junzhao, Zhong, Bihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830861/
https://www.ncbi.nlm.nih.gov/pubmed/36624524
http://dx.doi.org/10.1186/s12967-022-03760-6
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author Shao, Congxiang
Xu, Lishu
Lei, Pingguang
Wang, Wei
Feng, Shiting
Ye, Junzhao
Zhong, Bihui
author_facet Shao, Congxiang
Xu, Lishu
Lei, Pingguang
Wang, Wei
Feng, Shiting
Ye, Junzhao
Zhong, Bihui
author_sort Shao, Congxiang
collection PubMed
description BACKGROUND/AIMS: Nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) is paradoxically associated with improved metabolic and pathological features at diagnosis but similar cardiovascular diseases (CVD) prognosis to obese MAFLD. We aimed to utilize the metabolomics to identify the potential metabolite profiles accounting for this phenomenon. METHODS: This prospective multicenter cross-sectional study was conducted in China enrolling derivation and validation cohorts. Liquid chromatography coupled with mass spectrometry and gas chromatography-mass spectrometry were applied to perform a metabolomics measurement. RESULTS: The study involved 120 MAFLD patients and 60 non-MAFLD controls in the derivation cohort. Controls were divided into two groups according to the presence of carotid atherosclerosis (CAS). The MAFLD group was further divided into nonobese MAFLD with/without CAS groups and obese MAFLD with/without CAS groups. Fifty-six metabolites were statistically significant for discriminating the six groups. Among the top 10 metabolites related to CAS in nonobese MAFLD, only phosphatidylethanolamine (PE 20:2/16:0), phosphatidylglycerol (PG 18:0/20:4) and de novo lipogenesis (16:0/18:2n-6) achieved significant areas under the ROC curve (AUCs, 0.67, p = 0.03; 0.79, p = 0.02; 0.63, p = 0.03, respectively). The combination of these three metabolites and liver stiffness achieved a significantly higher AUC (0.92, p < 0.01). In obese MAFLD patients, cystine was found to be significant with an AUC of 0.69 (p = 0.015), followed by sphingomyelin (SM 16:1/18:1) (0.71, p = 0.004) and de novo lipogenesis (16:0/18:2n-6) (0.73, p = 0.004). The combination of these three metabolites, liver fat content and age attained a significantly higher AUC of 0.91 (p < 0.001). The AUCs of these metabolites remained highly significant in the independent validation cohorts involving 200 MAFLD patients and 90 controls. CONCLUSIONS: Diagnostic models combining different metabolites according to BMI categories could raise the accuracy of identifying subclinical CAS. Trial registration The study protocol was approved by the local ethics committee and all the participants have provided written informed consent (Approval number: [2014] No. 112, registered at the Chinese Clinical Trial Registry, ChiCTR-ChiCTR2000034197) GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03760-6.
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spelling pubmed-98308612023-01-11 Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease Shao, Congxiang Xu, Lishu Lei, Pingguang Wang, Wei Feng, Shiting Ye, Junzhao Zhong, Bihui J Transl Med Research BACKGROUND/AIMS: Nonobese metabolic dysfunction-associated fatty liver disease (MAFLD) is paradoxically associated with improved metabolic and pathological features at diagnosis but similar cardiovascular diseases (CVD) prognosis to obese MAFLD. We aimed to utilize the metabolomics to identify the potential metabolite profiles accounting for this phenomenon. METHODS: This prospective multicenter cross-sectional study was conducted in China enrolling derivation and validation cohorts. Liquid chromatography coupled with mass spectrometry and gas chromatography-mass spectrometry were applied to perform a metabolomics measurement. RESULTS: The study involved 120 MAFLD patients and 60 non-MAFLD controls in the derivation cohort. Controls were divided into two groups according to the presence of carotid atherosclerosis (CAS). The MAFLD group was further divided into nonobese MAFLD with/without CAS groups and obese MAFLD with/without CAS groups. Fifty-six metabolites were statistically significant for discriminating the six groups. Among the top 10 metabolites related to CAS in nonobese MAFLD, only phosphatidylethanolamine (PE 20:2/16:0), phosphatidylglycerol (PG 18:0/20:4) and de novo lipogenesis (16:0/18:2n-6) achieved significant areas under the ROC curve (AUCs, 0.67, p = 0.03; 0.79, p = 0.02; 0.63, p = 0.03, respectively). The combination of these three metabolites and liver stiffness achieved a significantly higher AUC (0.92, p < 0.01). In obese MAFLD patients, cystine was found to be significant with an AUC of 0.69 (p = 0.015), followed by sphingomyelin (SM 16:1/18:1) (0.71, p = 0.004) and de novo lipogenesis (16:0/18:2n-6) (0.73, p = 0.004). The combination of these three metabolites, liver fat content and age attained a significantly higher AUC of 0.91 (p < 0.001). The AUCs of these metabolites remained highly significant in the independent validation cohorts involving 200 MAFLD patients and 90 controls. CONCLUSIONS: Diagnostic models combining different metabolites according to BMI categories could raise the accuracy of identifying subclinical CAS. Trial registration The study protocol was approved by the local ethics committee and all the participants have provided written informed consent (Approval number: [2014] No. 112, registered at the Chinese Clinical Trial Registry, ChiCTR-ChiCTR2000034197) GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03760-6. BioMed Central 2023-01-09 /pmc/articles/PMC9830861/ /pubmed/36624524 http://dx.doi.org/10.1186/s12967-022-03760-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Shao, Congxiang
Xu, Lishu
Lei, Pingguang
Wang, Wei
Feng, Shiting
Ye, Junzhao
Zhong, Bihui
Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title_full Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title_fullStr Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title_full_unstemmed Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title_short Metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
title_sort metabolomics to identify fingerprints of carotid atherosclerosis in nonobese metabolic dysfunction-associated fatty liver disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830861/
https://www.ncbi.nlm.nih.gov/pubmed/36624524
http://dx.doi.org/10.1186/s12967-022-03760-6
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