In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform

Abstract The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through...

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Main Authors: Shurong Ma, Lu Yang, Jinwen Lai, Shan Cheng, Yunshu Zhang, Zeming Wu, Anliang Huang, Tianfu Wei, Qiuying Luo, Mimi Wang, Jianling Du, Peiyuan Yin
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04652-2
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author Shurong Ma
Lu Yang
Jinwen Lai
Shan Cheng
Yunshu Zhang
Zeming Wu
Anliang Huang
Tianfu Wei
Qiuying Luo
Mimi Wang
Jianling Du
Peiyuan Yin
author_facet Shurong Ma
Lu Yang
Jinwen Lai
Shan Cheng
Yunshu Zhang
Zeming Wu
Anliang Huang
Tianfu Wei
Qiuying Luo
Mimi Wang
Jianling Du
Peiyuan Yin
author_sort Shurong Ma
collection DOAJ
description Abstract The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through an integration of six distinct conditions, including a normal phase, a pre-column chemical derivatization and four reversed phase separation methods for the quantification of a total of 1609 small molecules (32 sub-classes) in serum after normalization using isotope-labeled internal standards. After undergoing rigorous methodological validation and comprehensive comparison with untargeted strategies, we present a new dataset of metabolomic profile encompassing a cohort of 200 healthy individuals and 100 newly diagnosed Type 2 diabetes mellitus (T2DM) patients from the northern region of China. The overall differential analysis results indicated obvious metabolic disturbance of amino acid, fatty acids, lysophosphatidyl-choline and triacylglycerol in T2DM. We hereby make these technical validation results and the profiling dataset publicly available to the scientific community, showcasing its exceptional sensitivity and robustness as an invaluable tool for the comprehensive targeted metabolome analysis.
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spelling doaj-art-8314f3260ec049629fa8f4f47df7f73d2025-08-20T01:57:44ZengNature PortfolioScientific Data2052-44632025-03-0112111010.1038/s41597-025-04652-2In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platformShurong Ma0Lu Yang1Jinwen Lai2Shan Cheng3Yunshu Zhang4Zeming Wu5Anliang Huang6Tianfu Wei7Qiuying Luo8Mimi Wang9Jianling Du10Peiyuan Yin11Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityDepartment of Endocrinology, The First Affiliated Hospital of Dalian Medical UniversityLaboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityLaboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityLaboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityIPhenome Biotechnology Inc.Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityLaboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityIPhenome Biotechnology Inc.Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityDepartment of Endocrinology, The First Affiliated Hospital of Dalian Medical UniversityLaboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical UniversityAbstract The profiling of metabolites provides an immediate snapshot that depicts crucial physiological information, holding immense potential for the early diagnosis and prognosis of diseases, including diabetes. Herein, we proposed an optimized and in-depth target-based metabolome platform through an integration of six distinct conditions, including a normal phase, a pre-column chemical derivatization and four reversed phase separation methods for the quantification of a total of 1609 small molecules (32 sub-classes) in serum after normalization using isotope-labeled internal standards. After undergoing rigorous methodological validation and comprehensive comparison with untargeted strategies, we present a new dataset of metabolomic profile encompassing a cohort of 200 healthy individuals and 100 newly diagnosed Type 2 diabetes mellitus (T2DM) patients from the northern region of China. The overall differential analysis results indicated obvious metabolic disturbance of amino acid, fatty acids, lysophosphatidyl-choline and triacylglycerol in T2DM. We hereby make these technical validation results and the profiling dataset publicly available to the scientific community, showcasing its exceptional sensitivity and robustness as an invaluable tool for the comprehensive targeted metabolome analysis.https://doi.org/10.1038/s41597-025-04652-2
spellingShingle Shurong Ma
Lu Yang
Jinwen Lai
Shan Cheng
Yunshu Zhang
Zeming Wu
Anliang Huang
Tianfu Wei
Qiuying Luo
Mimi Wang
Jianling Du
Peiyuan Yin
In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
Scientific Data
title In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
title_full In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
title_fullStr In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
title_full_unstemmed In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
title_short In-depth profile of biosignatures for T2DM cohort utilizing an integrated targeted LC-MS platform
title_sort in depth profile of biosignatures for t2dm cohort utilizing an integrated targeted lc ms platform
url https://doi.org/10.1038/s41597-025-04652-2
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