Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese
Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum met...
Ausführliche Beschreibung
Autor*in: |
Jiarui Chen [verfasserIn] Ronald Siyi Lu [verfasserIn] Candela Diaz-Canestro [verfasserIn] Erfei Song [verfasserIn] Xi Jia [verfasserIn] Yan Liu [verfasserIn] Cunchuan Wang [verfasserIn] Cynthia K.Y. Cheung [verfasserIn] Gianni Panagiotou [verfasserIn] Aimin Xu [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2024 |
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In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 23(2024), Seite 791-800 |
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Übergeordnetes Werk: |
volume:23 ; year:2024 ; pages:791-800 |
Links: |
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DOI / URN: |
10.1016/j.csbj.2024.01.007 |
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Katalog-ID: |
DOAJ096249269 |
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245 | 1 | 0 | |a Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese |
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520 | |a Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. | ||
650 | 4 | |a Non-alcoholic fatty liver disease | |
650 | 4 | |a Simple steatosis | |
650 | 4 | |a Nonalcoholic steatohepatitis | |
650 | 4 | |a Metabolomics | |
650 | 4 | |a Lipidomics | |
650 | 4 | |a Biomarkers | |
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700 | 0 | |a Ronald Siyi Lu |e verfasserin |4 aut | |
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700 | 0 | |a Gianni Panagiotou |e verfasserin |4 aut | |
700 | 0 | |a Aimin Xu |e verfasserin |4 aut | |
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10.1016/j.csbj.2024.01.007 doi (DE-627)DOAJ096249269 (DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Jiarui Chen verfasserin aut Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. Non-alcoholic fatty liver disease Simple steatosis Nonalcoholic steatohepatitis Metabolomics Lipidomics Biomarkers Biotechnology Ronald Siyi Lu verfasserin aut Candela Diaz-Canestro verfasserin aut Erfei Song verfasserin aut Xi Jia verfasserin aut Yan Liu verfasserin aut Cunchuan Wang verfasserin aut Cynthia K.Y. Cheung verfasserin aut Gianni Panagiotou verfasserin aut Aimin Xu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 791-800 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:791-800 https://doi.org/10.1016/j.csbj.2024.01.007 kostenfrei https://doaj.org/article/8daa620be9e24b74a5135fab902f6fe4 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000072 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 791-800 |
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10.1016/j.csbj.2024.01.007 doi (DE-627)DOAJ096249269 (DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Jiarui Chen verfasserin aut Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. Non-alcoholic fatty liver disease Simple steatosis Nonalcoholic steatohepatitis Metabolomics Lipidomics Biomarkers Biotechnology Ronald Siyi Lu verfasserin aut Candela Diaz-Canestro verfasserin aut Erfei Song verfasserin aut Xi Jia verfasserin aut Yan Liu verfasserin aut Cunchuan Wang verfasserin aut Cynthia K.Y. Cheung verfasserin aut Gianni Panagiotou verfasserin aut Aimin Xu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 791-800 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:791-800 https://doi.org/10.1016/j.csbj.2024.01.007 kostenfrei https://doaj.org/article/8daa620be9e24b74a5135fab902f6fe4 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000072 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 791-800 |
allfields_unstemmed |
10.1016/j.csbj.2024.01.007 doi (DE-627)DOAJ096249269 (DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Jiarui Chen verfasserin aut Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. Non-alcoholic fatty liver disease Simple steatosis Nonalcoholic steatohepatitis Metabolomics Lipidomics Biomarkers Biotechnology Ronald Siyi Lu verfasserin aut Candela Diaz-Canestro verfasserin aut Erfei Song verfasserin aut Xi Jia verfasserin aut Yan Liu verfasserin aut Cunchuan Wang verfasserin aut Cynthia K.Y. Cheung verfasserin aut Gianni Panagiotou verfasserin aut Aimin Xu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 791-800 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:791-800 https://doi.org/10.1016/j.csbj.2024.01.007 kostenfrei https://doaj.org/article/8daa620be9e24b74a5135fab902f6fe4 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000072 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 791-800 |
allfieldsGer |
10.1016/j.csbj.2024.01.007 doi (DE-627)DOAJ096249269 (DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Jiarui Chen verfasserin aut Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. Non-alcoholic fatty liver disease Simple steatosis Nonalcoholic steatohepatitis Metabolomics Lipidomics Biomarkers Biotechnology Ronald Siyi Lu verfasserin aut Candela Diaz-Canestro verfasserin aut Erfei Song verfasserin aut Xi Jia verfasserin aut Yan Liu verfasserin aut Cunchuan Wang verfasserin aut Cynthia K.Y. Cheung verfasserin aut Gianni Panagiotou verfasserin aut Aimin Xu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 791-800 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:791-800 https://doi.org/10.1016/j.csbj.2024.01.007 kostenfrei https://doaj.org/article/8daa620be9e24b74a5135fab902f6fe4 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000072 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 791-800 |
allfieldsSound |
10.1016/j.csbj.2024.01.007 doi (DE-627)DOAJ096249269 (DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Jiarui Chen verfasserin aut Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. Non-alcoholic fatty liver disease Simple steatosis Nonalcoholic steatohepatitis Metabolomics Lipidomics Biomarkers Biotechnology Ronald Siyi Lu verfasserin aut Candela Diaz-Canestro verfasserin aut Erfei Song verfasserin aut Xi Jia verfasserin aut Yan Liu verfasserin aut Cunchuan Wang verfasserin aut Cynthia K.Y. Cheung verfasserin aut Gianni Panagiotou verfasserin aut Aimin Xu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 23(2024), Seite 791-800 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:23 year:2024 pages:791-800 https://doi.org/10.1016/j.csbj.2024.01.007 kostenfrei https://doaj.org/article/8daa620be9e24b74a5135fab902f6fe4 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037024000072 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2024 791-800 |
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Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese |
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Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. |
abstractGer |
Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. |
abstract_unstemmed |
Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ096249269</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413144927.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.csbj.2024.01.007</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ096249269</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8daa620be9e24b74a5135fab902f6fe4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TP248.13-248.65</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jiarui Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstracts: Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-alcoholic fatty liver disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simple steatosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonalcoholic steatohepatitis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metabolomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lipidomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomarkers</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biotechnology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ronald Siyi Lu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Candela Diaz-Canestro</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Erfei Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xi Jia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yan Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cunchuan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cynthia K.Y. Cheung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gianni Panagiotou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Aimin Xu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Computational and Structural Biotechnology Journal</subfield><subfield code="d">Elsevier, 2013</subfield><subfield code="g">23(2024), Seite 791-800</subfield><subfield code="w">(DE-627)731890086</subfield><subfield code="w">(DE-600)2694435-2</subfield><subfield code="x">20010370</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2024</subfield><subfield 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