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Kale D, Fatangare A, Phapale P, Sickmann A. Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update. Cells 2023; 12:2796. [PMID: 38132115 PMCID: PMC10741540 DOI: 10.3390/cells12242796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.
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Affiliation(s)
- Dipali Kale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44139 Dortmund, Germany; (A.F.); (P.P.)
| | | | | | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44139 Dortmund, Germany; (A.F.); (P.P.)
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Su Y, Lai X, Guo K, Wang X, Chen S, Liang K, Pu K, Wang Y, Hu J, Wei X, Chen Y, Wang H, Lin W, Ni W, Lin Y, Zhu J, Ng KM. Covalent Bonding and Coulomb Repulsion-Guided AuNP Array: A Tunable and Reusable Substrate for Metabolomic Characterization of Lung Cancer Patient Sera. Anal Chem 2022; 94:16910-16918. [PMID: 36417775 DOI: 10.1021/acs.analchem.2c04319] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has gained increased attention in the metabolic characterization of human biofluids. However, the stability and reproducibility of nanoparticle-based substrates remain two of the biggest challenges in high-salt environments. Here, by controlling the extent of Coulomb repulsion of 26 nm positively charged AuNPs, a homogeneous layer of covalently bonded AuNPs on a coverslip with tunable interparticle distances down to 16 nm has been successfully fabricated to analyze small biomolecules in human serum. Compared with the self-assembled AuNP array, the covalently bonded AuNP array showed superior performances on stability, reproducibility, and sensitivity in high-salt environments. The stable attachment of AuNPs maintained a detection reproducibility with a RSD less than 12% and enabled the reusability of the array for 10 experiments without significant signal deterioration (<15%) and carryover effects. Moreover, the closely positioned AuNPs allowed the coupling of photoinduced plasmons to generate an enhanced electric field, which promotes the generation of excited electrons to facilitate the desorption/ionization processes instead of the heat dissipation, thus enhancing the detection sensitivity with detection limits down to the femtomole level. Combined with machine learning methods, the AuNP array has been successfully applied to discover seven biomarkers for differentiating early-stage lung cancer patients from healthy controls. It is anticipated that this simple approach of developing robust AuNP arrays can also be extended to other types of NP arrays for wider applications of SALDI-MS technology.
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Affiliation(s)
- Yang Su
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Xiaopin Lai
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Kunbin Guo
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Xin Wang
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Siyu Chen
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Kaiqing Liang
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Keyuan Pu
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Yue Wang
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Jun Hu
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
| | - Xiaolong Wei
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Yuping Chen
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Hongbiao Wang
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Wen Lin
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Wenxiu Ni
- Department of Medicinal Chemistry, Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Yan Lin
- The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P. R. China
| | - Janshon Zhu
- Guangdong RangerBio Technologies Company Limited, Dongguan 523000, P. R. China
| | - Kwan-Ming Ng
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, Guangdong 515063, P. R. China
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