Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application
Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable e...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2016/3898502 |
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| _version_ | 1832555039887982592 |
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| author | Matthias S. Klein Jane Shearer |
| author_facet | Matthias S. Klein Jane Shearer |
| author_sort | Matthias S. Klein |
| collection | DOAJ |
| description | Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine. |
| format | Article |
| id | doaj-art-43badeb2f4cc4b90bf0a0807b10a9c7c |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-43badeb2f4cc4b90bf0a0807b10a9c7c2025-02-03T05:49:41ZengWileyJournal of Diabetes Research2314-67452314-67532016-01-01201610.1155/2016/38985023898502Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical ApplicationMatthias S. Klein0Jane Shearer1Faculty of Kinesiology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, CanadaFaculty of Kinesiology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, CanadaType 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.http://dx.doi.org/10.1155/2016/3898502 |
| spellingShingle | Matthias S. Klein Jane Shearer Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application Journal of Diabetes Research |
| title | Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application |
| title_full | Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application |
| title_fullStr | Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application |
| title_full_unstemmed | Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application |
| title_short | Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application |
| title_sort | metabolomics and type 2 diabetes translating basic research into clinical application |
| url | http://dx.doi.org/10.1155/2016/3898502 |
| work_keys_str_mv | AT matthiassklein metabolomicsandtype2diabetestranslatingbasicresearchintoclinicalapplication AT janeshearer metabolomicsandtype2diabetestranslatingbasicresearchintoclinicalapplication |